repo_name
stringlengths 7
92
| path
stringlengths 5
129
| copies
stringclasses 201
values | size
stringlengths 4
6
| content
stringlengths 1.03k
375k
| license
stringclasses 15
values |
|---|---|---|---|---|---|
Healthcast/RSV | python/all_year_predict/methods.py | 2 | 3879 | #!/usr/bin/pyhton
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, neighbors, linear_model
from sklearn import svm
from sklearn import metrics
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
def apply_algorithm(paras, X, y):
if paras['clf'] == 'svm':
clf = svm.SVC(kernel=paras['svm'][1], C=paras['svm'][0], probability=True)
elif paras['clf'] == 'knn':
clf = neighbors.KNeighborsClassifier(paras['knn'][0],\
weights=paras['knn'][1])
elif paras['clf'] == 'rf':
clf = RandomForestClassifier(max_depth=paras['rf'][0], \
n_estimators=paras['rf'][1],\
max_features=paras['rf'][2])
else:
print str("unknown classifier")
sys.exit(2)
return clf
def apply_evaluation(paras, X, y, clf, data):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, \
random_state=0)
clf.fit(X_train, y_train)
r = clf.predict(X_test)
d = clf.decision_function(X)
p = clf.predict_proba(X).T[1]*3
h = data["hospital"].T[data["city"].index(paras["city"])]
h1 = h.astype(float)
m = max(h1)
h1=h1/m*4
plt.figure()
# plt.plot(d)
plt.plot(y)
plt.plot(h1)
plt.plot(p)
# height = 4
# bottom = -2
# ss = data["season_start"]
# date=data["date1"]
# c_id = data["city"].index(paras["city"])
# ylabel = data["ylabels"]
# for m in ss:
# plt.plot([m, m],[bottom, height], 'y--', linewidth=1)
#
# for m in range(1, len(ss)-1):
# a = ss[m]
# plt.text(a-5,height, date[a].split('-')[0])
#
# #plot the start week
# up=1
# for j in range(len(ylabel.T[c_id])-1):
# if ylabel.T[c_id,j] == 1 :
# plt.plot([j, j],[bottom, height], 'k-', linewidth=2)
# if up==1:
# plt.text(j-10, height-1, date[j])
# up=0
# else:
# plt.text(j-10, height-2, date[j])
# up=1
#
plt.show()
#plot the results
# x_min, x_max = X_train[:, 0].min() - 1, X_train[:, 0].max() + 1
# y_min, y_max = X_train[:, 1].min() - 1, X_train[:, 1].max() + 1
#
# xx, yy = np.meshgrid(np.arange(x_min, x_max, 1), np.arange(y_min, y_max, 1))
# Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Z = Z.reshape(xx.shape)
#
# plt.figure()
# plt.pcolormesh(xx, yy, Z)
# plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train)
# plt.xlim(xx.min(), xx.max())
# plt.ylim(yy.min(), yy.max())
# plt.title("binary classification classification")
# plt.show()
#
if paras['eva'] == 'accuracy':
print "The accuracy:"
print metrics.accuracy_score(y_test, r)
elif paras['eva'] == 'precision':
print "The precision:"
print metrics.precision_score(y_test, r)
elif paras['eva'] == 'recall':
print "The recall:"
print metrics.recall_score(y_test, r)
elif paras['eva'] == 'confusion':
print "The confusion matrix:"
print metrics.confusion_matrix(y_test, r)
elif paras['eva'] == 'report':
print "The report:"
print metrics.classification_report(y_test, r)
elif paras['eva'] == 'roc' and paras['clf'] == 'svm':
scores = clf.decision_function(X_test)
print "The auc:"
fpr, tpr, thresholds = metrics.roc_curve(y_test, scores)
roc_auc = metrics.auc(fpr, tpr)
print str(roc_auc)
plt.figure()
plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], 'k--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.show()
| gpl-2.0 |
nmayorov/scikit-learn | examples/plot_multilabel.py | 236 | 4157 | # Authors: Vlad Niculae, Mathieu Blondel
# License: BSD 3 clause
"""
=========================
Multilabel classification
=========================
This example simulates a multi-label document classification problem. The
dataset is generated randomly based on the following process:
- pick the number of labels: n ~ Poisson(n_labels)
- n times, choose a class c: c ~ Multinomial(theta)
- pick the document length: k ~ Poisson(length)
- k times, choose a word: w ~ Multinomial(theta_c)
In the above process, rejection sampling is used to make sure that n is more
than 2, and that the document length is never zero. Likewise, we reject classes
which have already been chosen. The documents that are assigned to both
classes are plotted surrounded by two colored circles.
The classification is performed by projecting to the first two principal
components found by PCA and CCA for visualisation purposes, followed by using
the :class:`sklearn.multiclass.OneVsRestClassifier` metaclassifier using two
SVCs with linear kernels to learn a discriminative model for each class.
Note that PCA is used to perform an unsupervised dimensionality reduction,
while CCA is used to perform a supervised one.
Note: in the plot, "unlabeled samples" does not mean that we don't know the
labels (as in semi-supervised learning) but that the samples simply do *not*
have a label.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_multilabel_classification
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import SVC
from sklearn.preprocessing import LabelBinarizer
from sklearn.decomposition import PCA
from sklearn.cross_decomposition import CCA
def plot_hyperplane(clf, min_x, max_x, linestyle, label):
# get the separating hyperplane
w = clf.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(min_x - 5, max_x + 5) # make sure the line is long enough
yy = a * xx - (clf.intercept_[0]) / w[1]
plt.plot(xx, yy, linestyle, label=label)
def plot_subfigure(X, Y, subplot, title, transform):
if transform == "pca":
X = PCA(n_components=2).fit_transform(X)
elif transform == "cca":
X = CCA(n_components=2).fit(X, Y).transform(X)
else:
raise ValueError
min_x = np.min(X[:, 0])
max_x = np.max(X[:, 0])
min_y = np.min(X[:, 1])
max_y = np.max(X[:, 1])
classif = OneVsRestClassifier(SVC(kernel='linear'))
classif.fit(X, Y)
plt.subplot(2, 2, subplot)
plt.title(title)
zero_class = np.where(Y[:, 0])
one_class = np.where(Y[:, 1])
plt.scatter(X[:, 0], X[:, 1], s=40, c='gray')
plt.scatter(X[zero_class, 0], X[zero_class, 1], s=160, edgecolors='b',
facecolors='none', linewidths=2, label='Class 1')
plt.scatter(X[one_class, 0], X[one_class, 1], s=80, edgecolors='orange',
facecolors='none', linewidths=2, label='Class 2')
plot_hyperplane(classif.estimators_[0], min_x, max_x, 'k--',
'Boundary\nfor class 1')
plot_hyperplane(classif.estimators_[1], min_x, max_x, 'k-.',
'Boundary\nfor class 2')
plt.xticks(())
plt.yticks(())
plt.xlim(min_x - .5 * max_x, max_x + .5 * max_x)
plt.ylim(min_y - .5 * max_y, max_y + .5 * max_y)
if subplot == 2:
plt.xlabel('First principal component')
plt.ylabel('Second principal component')
plt.legend(loc="upper left")
plt.figure(figsize=(8, 6))
X, Y = make_multilabel_classification(n_classes=2, n_labels=1,
allow_unlabeled=True,
random_state=1)
plot_subfigure(X, Y, 1, "With unlabeled samples + CCA", "cca")
plot_subfigure(X, Y, 2, "With unlabeled samples + PCA", "pca")
X, Y = make_multilabel_classification(n_classes=2, n_labels=1,
allow_unlabeled=False,
random_state=1)
plot_subfigure(X, Y, 3, "Without unlabeled samples + CCA", "cca")
plot_subfigure(X, Y, 4, "Without unlabeled samples + PCA", "pca")
plt.subplots_adjust(.04, .02, .97, .94, .09, .2)
plt.show()
| bsd-3-clause |
rjenc29/numerical | course/matplotlib/examples/fill_example.py | 1 | 2229 | """
Illustrate different ways of using the various fill functions.
"""
import numpy as np
import matplotlib.pyplot as plt
import example_utils
def main():
fig, axes = example_utils.setup_axes()
fill_example(axes[0])
fill_between_example(axes[1])
stackplot_example(axes[2])
example_utils.title(fig, 'fill/fill_between/stackplot: Filled polygons',
y=0.95)
fig.savefig('fill_example.png', facecolor='none')
plt.show()
def fill_example(ax):
# Use fill when you want a simple filled polygon between vertices
x, y = fill_data()
ax.fill(x, y, color='lightblue')
ax.margins(0.1)
example_utils.label(ax, 'fill')
def fill_between_example(ax):
# Fill between fills between two curves or a curve and a constant value
# It can be used in several ways. We'll illustrate a few below.
x, y1, y2 = sin_data()
# The most basic (and common) use of fill_between
err = np.random.rand(x.size)**2 + 0.1
y = 0.7 * x + 2
ax.fill_between(x, y + err, y - err, color='orange')
# Filling between two curves with different colors when they cross in
# different directions
ax.fill_between(x, y1, y2, where=y1>y2, color='lightblue')
ax.fill_between(x, y1, y2, where=y1<y2, color='forestgreen')
# Note that this is fillbetween*x*!
ax.fill_betweenx(x, -y1, where=y1>0, color='red', alpha=0.5)
ax.fill_betweenx(x, -y1, where=y1<0, color='blue', alpha=0.5)
ax.margins(0.15)
example_utils.label(ax, 'fill_between/x')
def stackplot_example(ax):
# Stackplot is equivalent to a series of ax.fill_between calls
x, y = stackplot_data()
ax.stackplot(x, y.cumsum(axis=0), alpha=0.5)
example_utils.label(ax, 'stackplot')
#-- Data generation ----------------------
def stackplot_data():
x = np.linspace(0, 10, 100)
y = np.random.normal(0, 1, (5, 100))
y = y.cumsum(axis=1)
y -= y.min(axis=0, keepdims=True)
return x, y
def sin_data():
x = np.linspace(0, 10, 100)
y = np.sin(x)
y2 = np.cos(x)
return x, y, y2
def fill_data():
t = np.linspace(0, 2*np.pi, 100)
r = np.random.normal(0, 1, 100).cumsum()
r -= r.min()
return r * np.cos(t), r * np.sin(t)
main()
| mit |
tjhunter/phd-thesis-tjhunter | python/kdd/plot_network.py | 1 | 1065 |
__author__ = 'tjhunter'
import build
import json
import pylab as pl
from matplotlib.collections import LineCollection
# Draws the network as a pdf and SVG file.
def draw_network(ax, fd, link_style):
def decode_line(l):
#print l
dct = json.loads(l)
lats = dct['lats']
lons = dct['lons']
return zip(lons, lats)
lines = [decode_line(l) for l in fd]
#print lines
xmin = min([x for l in lines for x,y in l])
xmax = max([x for l in lines for x,y in l])
ymin = min([y for l in lines for x,y in l])
ymax = max([y for l in lines for x,y in l])
lc = LineCollection(lines, **link_style)
ax.add_collection(lc, autolim=True)
return ((xmin,xmax),(ymin,ymax))
fname = build.data_name('kdd/net_export_6.json')
fig = pl.figure("fig1",figsize=(10,10))
ax = fig.gca()
ax.set_axis_off()
style = {'colors':'k','linewidths':0.5}
with open(fname) as f:
(xlims, ylims) = draw_network(ax, f, style)
ax.set_xlim(*xlims)
ax.set_ylim(*ylims)
# Saving in pdf is a bit slow
build.save_figure(fig, 'figures-kdd/network_export_6',save_svg=True)
| apache-2.0 |
kcompher/thunder | thunder/extraction/source.py | 6 | 31847 | from numpy import asarray, mean, sqrt, ndarray, amin, amax, concatenate, sum, zeros, maximum, \
argmin, newaxis, ones, delete, NaN, inf, isnan, clip, logical_or, unique, where, all
from thunder.utils.serializable import Serializable
from thunder.utils.common import checkParams, aslist
from thunder.rdds.images import Images
from thunder.rdds.series import Series
class Source(Serializable, object):
"""
A single source, represented as a list of coordinates and other optional specifications.
A source also has a set of lazily computed attributes useful for representing and comparing
its geometry, such as center, bounding box, and bounding polygon. These properties
will be computed lazily and made available as attributes when requested.
Parameters
----------
coordinates : array-like
List of 2D or 3D coordinates, can be a list of lists or array of shape (n,2) or (n,3)
values : list or array-like
Value (or weight) associated with each coordiante
id : int or string
Arbitrary specification per source, typically an index or string label
Attributes
----------
center : list or array-like
The coordinates of the center of the source
polygon : list or array-like
The coordinates of a polygon bounding the region (a convex hull)
bbox : list or array-like
Boundaries of the source (with the lowest values for all axes followed by the highest values)
area : scalar
The area of the region
"""
from zope.cachedescriptors import property
def __init__(self, coordinates, values=None, id=None):
self.coordinates = asarray(coordinates)
if self.coordinates.ndim == 1 and len(self.coordinates) > 0:
self.coordinates = asarray([self.coordinates])
if values is not None:
self.values = asarray(values)
if self.values.ndim == 0:
self.values = asarray([self.values])
if not (len(self.coordinates) == len(self.values)):
raise ValueError("Lengths of coordinates %g and values %g do not match"
% (len(self.coordinates), len(self.values)))
if id is not None:
self.id = id
@property.Lazy
def center(self):
"""
Find the region center using a mean.
"""
# TODO Add option to use weights
return mean(self.coordinates, axis=0)
@property.Lazy
def polygon(self):
"""
Find the bounding polygon as a convex hull
"""
# TODO Add option for simplification
from scipy.spatial import ConvexHull
if len(self.coordinates) >= 4:
inds = ConvexHull(self.coordinates).vertices
return self.coordinates[inds]
else:
return self.coordinates
@property.Lazy
def bbox(self):
"""
Find the bounding box.
"""
mn = amin(self.coordinates, axis=0)
mx = amax(self.coordinates, axis=0)
return concatenate((mn, mx))
@property.Lazy
def area(self):
"""
Find the region area.
"""
return len(self.coordinates)
def restore(self, skip=None):
"""
Remove all lazy properties, will force recomputation
"""
if skip is None:
skip = []
elif isinstance(skip, str):
skip = [skip]
for prop in LAZY_ATTRIBUTES:
if prop in self.__dict__.keys() and prop not in skip:
del self.__dict__[prop]
return self
def distance(self, other, method='euclidean'):
"""
Distance between the center of this source and another.
Parameters
----------
other : Source, or array-like
Either another source, or the center coordinates of another source
method : str
Specify a distance measure to used for spatial distance between source
centers. Current options include Euclidean distance ('euclidean') and
L1-norm ('l1').
"""
from numpy.linalg import norm
checkParams(method, ['euclidean', 'l1'])
if method == 'l1':
order = 1
else:
order = 2
if isinstance(other, Source):
return norm(self.center - other.center, ord=order)
elif isinstance(other, list) or isinstance(other, ndarray):
return norm(self.center - asarray(other), ord=order)
def overlap(self, other, method='fraction'):
"""
Compute the overlap between this source and other.
Options are a symmetric measure of overlap based on the fraction
of intersecting pixels relative to the union ('fraction'), an assymmetric
measure of overlap that expresses detected intersecting pixels
(relative to this source) using precision and recall rates ('rates'), or
a correlation coefficient of the weights within the intersection
(not defined for binary weights) ('correlation')
Parameters
----------
other : Source
The source to compute overlap with.
method : str
Which estimate of overlap to compute, options are
'fraction' (symmetric) 'rates' (asymmetric) or 'correlation'
"""
checkParams(method, ['fraction', 'rates', 'correlation'])
coordsSelf = aslist(self.coordinates)
coordsOther = aslist(other.coordinates)
intersection = [a for a in coordsSelf if a in coordsOther]
nhit = float(len(intersection))
ntotal = float(len(set([tuple(x) for x in coordsSelf] + [tuple(x) for x in coordsOther])))
if method == 'rates':
recall = nhit / len(coordsSelf)
precision = nhit / len(coordsOther)
return recall, precision
if method == 'fraction':
return nhit / float(ntotal)
if method == 'correlation':
from scipy.stats import spearmanr
if not (hasattr(self, 'values') and hasattr(other, 'values')):
raise ValueError('Sources must have values to compute correlation')
else:
valuesSelf = aslist(self.values)
valuesOther = aslist(other.values)
if len(intersection) > 0:
left = [v for v, c in zip(valuesSelf, coordsSelf) if c in coordsOther]
right = [v for v, c in zip(valuesOther, coordsOther) if c in coordsSelf]
rho, _ = spearmanr(left, right)
else:
rho = 0.0
return rho
def merge(self, other):
"""
Combine this source with other
"""
self.coordinates = concatenate((self.coordinates, other.coordinates))
if hasattr(self, 'values'):
self.values = concatenate((self.values, other.values))
return self
def tolist(self):
"""
Convert array-like attributes to list
"""
import copy
new = copy.copy(self)
for prop in ["coordinates", "values", "center", "bbox", "polygon"]:
if prop in self.__dict__.keys():
val = new.__getattribute__(prop)
if val is not None and not isinstance(val, list):
setattr(new, prop, val.tolist())
return new
def toarray(self):
"""
Convert array-like attributes to ndarray
"""
import copy
new = copy.copy(self)
for prop in ["coordinates", "values", "center", "bbox", "polygon"]:
if prop in self.__dict__.keys():
val = new.__getattribute__(prop)
if val is not None and not isinstance(val, ndarray):
setattr(new, prop, asarray(val))
return new
def crop(self, minBound, maxBound):
"""
Crop a source by removing coordinates outside bounds.
Follows normal slice indexing conventions.
Parameters
----------
minBound : tuple
Minimum or starting bounds for each axis
maxBound : tuple
Maximum or ending bounds for each axis
"""
coords = self.coordinates
newid = self.id if hasattr(self, 'id') else None
if hasattr(self, 'values') and self.values is not None:
values = self.values
inside = [(c, v) for c, v in zip(coords, values) if c not in coords]
newcoords, newvalues = zip(*inside)
return Source(coordinates=newcoords, values=newvalues, id=newid)
else:
newcoords = [c for c in coords if all(c >= minBound) and all(c < maxBound)]
return Source(coordinates=newcoords, id=newid)
def dilate(self, size):
"""
Dilate a source using morphological operators.
Parameters
----------
size : int
Size of dilation in pixels
"""
if size == 0:
newcoords = self.coordinates
else:
size = (size * 2) + 1
if hasattr(self, 'values') and self.values is not None:
raise AttributeError('Cannot dilate sources with values')
from skimage.morphology import binary_dilation
coords = self.coordinates
extent = self.bbox[len(self.center):] - self.bbox[0:len(self.center)] + 1 + size * 2
m = zeros(extent)
coords = (coords - self.bbox[0:len(self.center)] + size)
m[coords.T.tolist()] = 1
m = binary_dilation(m, ones((size, size)))
newcoords = asarray(where(m)).T + self.bbox[0:len(self.center)] - size
newcoords = [c for c in newcoords if all(c >= 0)]
newid = self.id if hasattr(self, 'id') else None
return Source(coordinates=newcoords, id=newid)
def exclude(self, other):
"""
Remove coordinates derived from another Source or an array.
If other is an array, will remove coordinates of all
non-zero elements from this source. If other is a source,
will remove any matching coordinates.
Parameters
----------
other : ndarray or Source
Source to remove
"""
if isinstance(other, ndarray):
coordsOther = asarray(where(other)).T
else:
coordsOther = aslist(other.coordinates)
coordsSelf = aslist(self.coordinates)
newid = self.id if hasattr(self, 'id') else None
if hasattr(self, 'values') and self.values is not None:
valuesSelf = self.values
complement = [(c, v) for c, v in zip(coordsSelf, valuesSelf) if c not in coordsOther]
newcoords, newvalues = zip(*complement)
return Source(coordinates=newcoords, values=newvalues, id=newid)
else:
complement = [a for a in coordsSelf if a not in coordsOther]
return Source(coordinates=complement, id=newid)
def outline(self, inner, outer):
"""
Compute source outline by differencing two dilations
Parameters
----------
inner : int
Size of inner outline boundary (in pixels)
outer : int
Size of outer outline boundary (in pixels)
"""
return self.dilate(outer).exclude(self.dilate(inner))
def transform(self, data, collect=True):
"""
Extract series from data using a list of sources.
Currently only supports averaging over coordinates.
Params
------
data : Images or Series object
The data from which to extract
collect : boolean, optional, default = True
Whether to collect to local array or keep as a Series
"""
if not (isinstance(data, Images) or isinstance(data, Series)):
raise Exception("Input must either be Images or Series (or a subclass)")
# TODO add support for weighting
if isinstance(data, Images):
output = data.meanByRegions([self.coordinates]).toSeries()
else:
output = data.meanOfRegion(self.coordinates)
if collect:
return output.collectValuesAsArray()
else:
return output
def mask(self, dims=None, binary=True, outline=False, color=None):
"""
Construct a mask from a source, either locally or within a larger image.
Parameters
----------
dims : list or tuple, optional, default = None
Dimensions of large image in which to draw mask. If none, will restrict
to the bounding box of the region.
binary : boolean, optional, deafult = True
Whether to incoporate values or only show a binary mask
outline : boolean, optional, deafult = False
Whether to only show outlines (derived using binary dilation)
color : str or array-like
RGB triplet (from 0 to 1) or named color (e.g. 'red', 'blue')
"""
from thunder import Colorize
coords = self.coordinates
if dims is None:
extent = self.bbox[len(self.center):] - self.bbox[0:len(self.center)] + 1
m = zeros(extent)
coords = (coords - self.bbox[0:len(self.center)])
else:
m = zeros(dims)
if hasattr(self, 'values') and self.values is not None and binary is False:
m[coords.T.tolist()] = self.values
else:
m[coords.T.tolist()] = 1
if outline:
from skimage.morphology import binary_dilation
m = binary_dilation(m, ones((3, 3))) - m
if color is not None:
m = Colorize(cmap='indexed', colors=[color]).transform([m])
return m
def inbounds(self, minBound, maxBound):
"""
Check what fraction of coordinates are inside given bounds
Parameters
----------
minBound : list or tuple
Minimum bounds
maxBounds : list or tuple
Maximum bounds
"""
minCheck = sum(self.coordinates < minBound, axis=1) > 0
maxCheck = sum(self.coordinates > maxBound, axis=1) > 0
fraction = 1 - sum(logical_or(minCheck, maxCheck)) / float(len(self.coordinates))
return fraction
@staticmethod
def fromMask(mask, id=None):
"""
Genearte a source from a mask.
Assumes that the mask is an image where all non-zero
elements are part of the source. If all non-zero
elements are 1, then values will be ignored
as the source is assumed to be binary.
Parameters
----------
mask : array-like
An array (typically 2D or 3D) containing the image mask
id : int or string
Arbitrary identifier for the source, typically an int or string
"""
mask = asarray(mask)
u = unique(mask)
if len(u) == 2 and u[0] == 0 and u[1] == 1:
inds = where(mask)
return Source(coordinates=asarray(zip(*inds)), id=id)
else:
inds = where(mask)
values = mask[inds]
coords = asarray(zip(*inds))
return Source(coordinates=coords, values=values, id=id)
@staticmethod
def fromCoordinates(coordinates, values=None, id=None):
"""
Generate a source from a list of coordinates and values.
Parameters
----------
coordinates : array-like
List coordinates as a list of lists or array of shape (n,2) or (n,3)
values : list or array-like
Value (or weight) associated with each coordiante
id : int or string
Arbitrary specification per source, typically an index or string label
"""
return Source(coordinates, values, id)
def __repr__(self):
s = self.__class__.__name__
for opt in ["id", "center", "bbox"]:
if hasattr(self, opt):
o = self.__getattribute__(opt)
os = o.tolist() if isinstance(o, ndarray) else o
s += '\n%s: %s' % (opt, repr(os))
return s
class SourceModel(Serializable, object):
"""
A source model as a collection of extracted sources.
Parameters
----------
sources : list or Sources or a single Source
The identified sources
See also
--------
Source
"""
def __init__(self, sources):
if isinstance(sources, Source):
self.sources = [sources]
elif isinstance(sources, list) and isinstance(sources[0], Source):
self.sources = sources
elif isinstance(sources, list):
self.sources = []
for ss in sources:
self.sources.append(Source(ss))
else:
raise Exception("Input type not recognized, must be Source, list of Sources, "
"or list of coordinates, got %s" % type(sources))
def __getitem__(self, entry):
if not isinstance(entry, int):
raise IndexError("Selection not recognized, must be Int, got %s" % type(entry))
return self.sources[entry]
def combiner(self, prop, tolist=True):
combined = []
for s in self.sources:
p = getattr(s, prop)
if tolist:
p = p.tolist()
combined.append(p)
return combined
@property
def coordinates(self):
"""
List of coordinates combined across sources
"""
return self.combiner('coordinates')
@property
def values(self):
"""
List of coordinates combined across sources
"""
return self.combiner('values')
@property
def centers(self):
"""
Array of centers combined across sources
"""
return asarray(self.combiner('center'))
@property
def polygons(self):
"""
List of polygons combined across sources
"""
return self.combiner('polygon')
@property
def areas(self):
"""
List of areas combined across sources
"""
return self.combiner('area', tolist=False)
@property
def count(self):
"""
Number of sources
"""
return len(self.sources)
def masks(self, dims=None, binary=True, outline=False, base=None, color=None, inds=None):
"""
Composite masks combined across sources as an image.
Parameters
----------
dims : list or tuple, optional, default = None
Dimensions of image in which to create masks, must either provide
these or provide a base image
binary : boolean, optional, deafult = True
Whether to incoporate values or only show a binary mask
outline : boolean, optional, deafult = False
Whether to only show outlines (derived using binary dilation)
base : SourceModel or array-like, optional, deafult = None
Base background image on which to put masks,
or another set of sources (usually for comparisons).
color : str, optional, deafult = None
Color to assign regions, will assign randomly if 'random'
inds : array-like, optional, deafult = None
List of indices if only showing a subset
"""
from thunder import Colorize
from matplotlib.cm import get_cmap
if inds is None:
inds = range(0, self.count)
if dims is None and base is None:
raise Exception("Must provide image dimensions for composite masks "
"or provide a base image.")
if base is not None and isinstance(base, SourceModel):
outline = True
if dims is None and base is not None:
dims = asarray(base).shape
if isinstance(base, SourceModel):
base = base.masks(dims, color='silver')
elif isinstance(base, ndarray):
base = Colorize(cmap='indexed', colors=['white']).transform([base])
if base is not None and color is None:
color = 'deeppink'
if color == 'random':
combined = zeros(list(dims) + [3])
ncolors = min(self.count, 20)
colors = get_cmap('rainbow', ncolors)(range(0, ncolors, 1))[:, 0:3]
for i in inds:
combined = maximum(self.sources[i].mask(dims, binary, outline, colors[i % len(colors)]), combined)
else:
combined = zeros(dims)
for i in inds:
combined = maximum(self.sources[i].mask(dims, binary, outline), combined)
if color is not None and color != 'random':
combined = Colorize(cmap='indexed', colors=[color]).transform([combined])
if base is not None:
combined = maximum(base, combined)
return combined
def match(self, other, unique=False, minDistance=inf):
"""
For each source in self, find the index of the closest source in other.
Uses euclidean distances between centers to determine distances.
Can select nearest matches with or without enforcing uniqueness;
if unique is False, will return the closest source in other for
each source in self, possibly repeating sources multiple times
if unique is True, will only allow each source in other to be matched
with a single source in self, as determined by a greedy selection procedure.
The minDistance parameter can be used to prevent far-away sources from being
chosen during greedy selection.
Params
------
other : SourceModel
The source model to match sources to
unique : boolean, optional, deafult = True
Whether to only return unique matches
minDistance : scalar, optiona, default = inf
Minimum distance to use when selecting matches
"""
from scipy.spatial.distance import cdist
targets = other.centers
targetInds = range(0, len(targets))
matches = []
for s in self.sources:
update = 1
# skip if no targets left, otherwise update
if len(targets) == 0:
update = 0
else:
dists = cdist(targets, s.center[newaxis])
if dists.min() < minDistance:
ind = argmin(dists)
else:
update = 0
# apply updates, otherwise add a nan
if update == 1:
matches.append(targetInds[ind])
if unique is True:
targets = delete(targets, ind, axis=0)
targetInds = delete(targetInds, ind)
else:
matches.append(NaN)
return matches
def distance(self, other, minDistance=inf):
"""
Compute the distance between each source in self and other.
First estimates a matching source from other for each source
in self, then computes the distance between the two sources.
The matches are unique, using a greedy procedure,
and minDistance can be used to prevent outliers during matching.
Parameters
----------
other : SourceModel
The sources to compute distances to
minDistance : scalar, optiona, default = inf
Minimum distance to use when matching indices
"""
inds = self.match(other, unique=True, minDistance=minDistance)
d = []
for jj, ii in enumerate(inds):
if ii is not NaN:
d.append(self[jj].distance(other[ii]))
else:
d.append(NaN)
return asarray(d)
def overlap(self, other, method='fraction', minDistance=inf):
"""
Estimate overlap between sources in self and other.
Will compute the similarity of sources in self that are found
in other, based on either source pixel overlap or correlation.
Parameters
----------
other : SourceModel
The sources to compare to
method : str, optional, default = 'fraction"
Method to use when computing overlap between sources
('fraction', 'rates', or 'correlation')
minDistance : scalar, optional, default = inf
Minimum distance to use when matching indices
"""
inds = self.match(other, unique=True, minDistance=minDistance)
d = []
for jj, ii in enumerate(inds):
if ii is not NaN:
d.append(self[jj].overlap(other[ii], method=method))
else:
if method == 'rates':
d.append((NaN, NaN))
else:
d.append(NaN)
return asarray(d)
def similarity(self, other, metric='distance', thresh=5, minDistance=inf):
"""
Estimate similarity to another set of sources using recall and precision.
Will compute the number of sources in self that are also
in other, based on a given distance metric and a threshold.
The recall rate is the number of matches divided by the number in self,
and the precision rate is the number of matches divided by the number in other.
Typically self is ground truth and other is an estimate.
The F score is defined as 2 * (recall * precision) / (recall + precision)
Before computing metrics, all sources in self are matched to other,
and a minimum distance can be set to control matching.
Parameters
----------
other : SourceModel
The sources to compare to.
metric : str, optional, default = 'distance'
Metric to use when computing distances,
options include 'distance' and 'overlap'
thresh : scalar, optional, default = 5
The distance below which a source is considered found.
minDistance : scalar, optional, default = inf
Minimum distance to use when matching indices.
"""
checkParams(metric, ['distance', 'overlap'])
if metric == 'distance':
# when evaluating distances,
# minimum distance should be the threshold
if minDistance == inf:
minDistance = thresh
vals = self.distance(other, minDistance=minDistance)
vals[isnan(vals)] = inf
compare = lambda x: x < thresh
elif metric == 'overlap':
vals = self.overlap(other, method='fraction', minDistance=minDistance)
vals[isnan(vals)] = 0
compare = lambda x: x > thresh
else:
raise Exception("Metric not recognized")
recall = sum(map(compare, vals)) / float(self.count)
precision = sum(map(compare, vals)) / float(other.count)
score = 2 * (recall * precision) / (recall + precision)
return recall, precision, score
def transform(self, data, collect=True):
"""
Extract series from data using a list of sources.
Currently only supports simple averaging over coordinates.
Params
------
data : Images or Series object
The data from which to extract signals
collect : boolean, optional, default = True
Whether to collect to local array or keep as a Series
"""
if not (isinstance(data, Images) or isinstance(data, Series)):
raise Exception("Input must either be Images or Series (or a subclass)")
# TODO add support for weighting
if isinstance(data, Images):
output = data.meanByRegions(self.coordinates).toSeries()
else:
output = data.meanByRegions(self.coordinates)
if collect:
return output.collectValuesAsArray()
else:
return output
def clean(self, cleaners=None):
"""
Apply one or more cleaners to sources, returning filtered sources
Parameters
----------
cleaners : Cleaner or list of Cleaners, optional, default = None
Which cleaners to apply, if None, will apply BasicCleaner with defaults
"""
from thunder.extraction.cleaners import Cleaner, BasicCleaner
from copy import copy
if isinstance(cleaners, list):
for c in cleaners:
if not isinstance(c, Cleaner):
raise Exception("List must only contain Cleaners")
elif isinstance(cleaners, Cleaner):
cleaners = [cleaners]
elif cleaners is None:
cleaners = [BasicCleaner()]
else:
raise Exception("Must provide Cleaner or list of Cleaners, got %s" % type(cleaners))
newmodel = copy(self)
for c in cleaners:
newmodel = c.clean(newmodel)
return newmodel
def dilate(self, size):
"""
Dilate all sources using morphological operators
Parameters
----------
size : int
Size of dilation in pixels
"""
return SourceModel([s.dilate(size) for s in self.sources])
def outline(self, inner, outer):
"""
Outline all sources
inner : int
Size of inner outline boundary (in pixels)
outer : int
Size of outer outline boundary (in pixels)
"""
return SourceModel([s.outline(inner, outer) for s in self.sources])
def crop(self, minBound, maxBound):
"""
Crop all sources by removing coordinates outside of bounds
Parameters
----------
minBound : tuple
Minimum or starting bounds for each axis
maxBound : tuple
Maximum or ending bounds for each axis
"""
return SourceModel([s.crop(minBound, maxBound) for s in self.sources])
def save(self, f, include=None, overwrite=False, **kwargs):
"""
Custom save to file with simplified, human-readable output, and selection of lazy attributes.
"""
import copy
output = copy.deepcopy(self)
if isinstance(include, str):
include = [include]
if include is not None:
for prop in include:
map(lambda s: getattr(s, prop), output.sources)
output.sources = map(lambda s: s.restore(include).tolist(), output.sources)
simplify = lambda d: d['sources']['py/homogeneousList']['data']
super(SourceModel, output).save(f, simplify=simplify, overwrite=overwrite, **kwargs)
@classmethod
def load(cls, f, **kwargs):
"""
Custom load from file to handle simplified, human-readable output
"""
unsimplify = lambda d: {'sources': {
'py/homogeneousList': {'data': d, 'module': 'thunder.extraction.source', 'type': 'Source'}}}
output = super(SourceModel, cls).load(f, unsimplify=unsimplify)
output.sources = map(lambda s: s.toarray(), output.sources)
return output
@classmethod
def deserialize(cls, d, **kwargs):
"""
Custom load from JSON to handle simplified, human-readable output
"""
unsimplify = lambda d: {'sources': {
'py/homogeneousList': {'data': d, 'module': 'thunder.extraction.source', 'type': 'Source'}}}
output = super(SourceModel, cls).deserialize(d, unsimplify=unsimplify)
output.sources = map(lambda s: s.toarray(), output.sources)
return output
def __repr__(self):
s = self.__class__.__name__
s += '\n%g sources' % (len(self.sources))
return s
LAZY_ATTRIBUTES = ["center", "polygon", "bbox", "area"]
| apache-2.0 |
amanzi/ats-dev | tools/utils/transect_data.py | 2 | 7741 | """Loads and/or plots 2D, topologlically structured data on quadrilaterals using matplotlib.
"""
import sys,os
import numpy as np
import h5py
import mesh
import colors
def fullname(varname):
fullname = varname
if not '.cell.' in fullname:
fullname = fullname+'.cell.0'
return fullname
def transect_data(varnames, keys='all', directory=".", filename="visdump_data.h5",
mesh_filename="visdump_mesh.h5", coord_order=None, deformable=False, return_map=False):
"""Pulls simulation output into structured 2D arrays for transect-based, (i,j) indexing.
Input:
varnames | A list of variable names to pull, e.g.
| ['saturation_liquid', 'saturation_ice'], or a single variable
| name, e.g. 'saturation_liquid'
keys | Indices of timesteps to pull. Either an int (i.e. 0, -1, etc)
| for the kth timestep, or a list of ints, or 'all'.
directory | Directory of the run. Defaults to '.'
filename | Filename of the run. Defaults to 'visdump_data.h5'
mesh_filename | Filename of the mesh. Defaults to 'visdump_mesh.h5'
coord_order | Order of the transect coordinates. Defaults to ['x','z']. The
| mesh is sorted in this order.
deformable | Is the mesh deforming?
return_map | See return value below.
Output:
Output is an array of shape:
( len(varnames+2), len(keys), n_cells_coord_order[0], n_cells_coord_order[1] )
data[0,0,:,:] is the coord_order[0] centroid
data[1,0,:,:] is the coord_order[1] centroid
data[i+2,k,:,:] is the ith varname data at the kth requested timestep, sorted in
the same way as the centroids.
Note that the data is re-ordered in INCREASING coordinate, i.e. bottom to top in z.
If return_map is True, then returns a tuple, (data, map) where
map is a (NX,NZ) array of integers specifying which global id
corresponds to the (i,j) cell. This is useful for mapping input
data back INTO the unstructured mesh.
Example usage:
Calculate and plot the thaw depth at step 5.
// Pull saturation ice -- TD is where sat ice = 0."
data = transect_data(['saturation_ice', 5)
// x coordinate for plotting
x = data[0,0,:,0]
// for each column, find highest z where sat_ice > 0.
td_i = np.array([np.where(data[2,0,i,:] > 0.)[0][-1] for i in range(data.shape[2])])
// now that we have an index into the highest cell with ice, determine td as the
// mean of the highest cell with ice and the one above that. Note this assumes
// all columns have some thawing.
td_z = np.array( [ (dat[1,0,i,td_i[i]] + dat[1,0,i,td_i[i+1]]) / 2.
for i in range(len(td_i)) ] )
plt.plot(x, td_z)
"""
if coord_order is None:
coord_order = ['x','z']
if type(varnames) is str:
varnames = [varnames,]
# get centroids
xyz = mesh.meshElemCentroids(mesh_filename, directory)
# round to avoid issues
xyz = np.round(xyz, decimals=5)
# get ordering of centroids
dtype = [(coord_order[0], float), (coord_order[1], float)]
num_order = []
for i in coord_order:
if i == 'x':
num_order.append(0)
elif i == 'y':
num_order.append(1)
elif i == 'z':
num_order.append(2)
xyz_sort_order = np.array([tuple([xyz[i,x] for x in num_order]) for i in range(len(xyz))], dtype=dtype)
xyz_sorting = xyz_sort_order.argsort(order=coord_order)
with h5py.File(os.path.join(directory,filename),'r') as dat:
keys_avail = dat[fullname(varnames[0])].keys()
keys_avail.sort(lambda a,b: int.__cmp__(int(a),int(b)))
if keys == 'all':
keys = keys_avail
elif type(keys) is str:
keys = [keys,]
elif type(keys) is int:
keys = [keys_avail[keys],]
elif type(keys) is slice:
keys = keys_avail[keys]
elif type(keys) is list:
if all(type(k) is int for k in keys):
keys = [keys_avail[k] for k in keys]
elif all(type(k) is str for k in keys):
pass
else:
raise RuntimeError("Keys requested cannot be processed -- should be 'all', int, or str key, or list of ints or strs.")
# get data
vals = np.zeros((len(varnames)+2, len(keys), len(xyz)), 'd')
for i,key in enumerate(keys):
if deformable:
xyz = mesh.meshElemCentroids(mesh_filename, directory)
vals[0,i,:] = xyz[xyz_sorting,num_order[0]]
vals[1,i,:] = xyz[xyz_sorting,num_order[1]]
for j,varname in enumerate(varnames):
vals[j+2,i,:] = dat[fullname(varname)][key][:,0][xyz_sorting]
# reshape the data
# determine nx
nx = len(set(vals[0,0,:]))
nz = vals.shape[2] / nx
if (nx * nz != vals.shape[2]):
raise RuntimeError("Assumption about first coordinate being cleanly binnable is falling apart -- ask Ethan to rethink this algorithm!")
shp = vals.shape
if not return_map:
return vals.reshape(shp[0], shp[1], nx, nz)
else:
return vals.reshape(shp[0], shp[1], nx, nz), xyz_sorting.reshape(nx, nz)
def plot(dataset, ax, cax=None, vmin=None, vmax=None, cmap="jet",
label=None, mesh_filename="visdump_mesh.h5", directory=".", y_coord=0.0,
linewidths=1):
"""Draws a dataset on an ax."""
import matplotlib.collections
from matplotlib import pyplot as plt
if vmin is None:
vmin = dataset.min()
if vmax is None:
vmax = dataset.max()
# get the mesh and collapse to 2D
etype, coords, conn = mesh.meshElemXYZ(filename=mesh_filename, directory=directory)
if etype is not 'HEX':
raise RuntimeError("Only works for Hexs")
coords2 = np.array([[coords[i][0::2] for i in c[1:] if abs(coords[i][1] - y_coord) < 1.e-8] for c in conn])
try:
assert coords2.shape[2] == 2
assert coords2.shape[1] == 4
except AssertionError:
print(coords2.shape)
for c in conn:
if len(c) != 9:
print c
raise RuntimeError("what is a conn?")
coords3 = np.array([coords[i][:] for i in c[1:] if abs(coords[i][1] - y_coord) < 1.e-8])
if coords3.shape[0] != 4:
print coords
raise RuntimeError("Unable to squash to 2D")
# reorder anti-clockwise
for i,c in enumerate(coords2):
centroid = c.mean(axis=0)
def angle(p1,p2):
a1 = np.arctan2((p1[1]-centroid[1]),(p1[0]-centroid[0]))
a2 = np.arctan2((p2[1]-centroid[1]),(p2[0]-centroid[0]))
if a1 < a2:
return -1
elif a2 < a1:
return 1
else:
return 0
c2 = np.array(sorted(c,angle))
coords2[i] = c2
polygons = matplotlib.collections.PolyCollection(coords2, edgecolor='k', cmap=cmap, linewidths=linewidths)
polygons.set_array(dataset)
polygons.set_clim(vmin,vmax)
ax.add_collection(polygons)
xmin = min(c[0] for c in coords.itervalues())
xmax = max(c[0] for c in coords.itervalues())
zmin = min(c[2] for c in coords.itervalues())
zmax = max(c[2] for c in coords.itervalues())
ax.set_xlim(xmin,xmax)
ax.set_ylim(zmin,zmax)
if cax is not None:
cb = plt.colorbar(polygons, cax=cax)
if label is not None:
cb.set_label(label)
return ((xmin,xmax),(zmin,zmax))
| bsd-3-clause |
lbishal/scikit-learn | examples/gaussian_process/plot_gpc_isoprobability.py | 45 | 3025 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=================================================================
Iso-probability lines for Gaussian Processes classification (GPC)
=================================================================
A two-dimensional classification example showing iso-probability lines for
the predicted probabilities.
"""
print(__doc__)
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# Adapted to GaussianProcessClassifier:
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Licence: BSD 3 clause
import numpy as np
from matplotlib import pyplot as pl
from matplotlib import cm
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import DotProduct, ConstantKernel as C
# A few constants
lim = 8
def g(x):
"""The function to predict (classification will then consist in predicting
whether g(x) <= 0 or not)"""
return 5. - x[:, 1] - .5 * x[:, 0] ** 2.
# Design of experiments
X = np.array([[-4.61611719, -6.00099547],
[4.10469096, 5.32782448],
[0.00000000, -0.50000000],
[-6.17289014, -4.6984743],
[1.3109306, -6.93271427],
[-5.03823144, 3.10584743],
[-2.87600388, 6.74310541],
[5.21301203, 4.26386883]])
# Observations
y = np.array(g(X) > 0, dtype=int)
# Instanciate and fit Gaussian Process Model
kernel = C(0.1, (1e-5, np.inf)) * DotProduct(sigma_0=0.1) ** 2
gp = GaussianProcessClassifier(kernel=kernel)
gp.fit(X, y)
print("Learned kernel: %s " % gp.kernel_)
# Evaluate real function and the predicted probability
res = 50
x1, x2 = np.meshgrid(np.linspace(- lim, lim, res),
np.linspace(- lim, lim, res))
xx = np.vstack([x1.reshape(x1.size), x2.reshape(x2.size)]).T
y_true = g(xx)
y_prob = gp.predict_proba(xx)[:, 1]
y_true = y_true.reshape((res, res))
y_prob = y_prob.reshape((res, res))
# Plot the probabilistic classification iso-values
fig = pl.figure(1)
ax = fig.gca()
ax.axes.set_aspect('equal')
pl.xticks([])
pl.yticks([])
ax.set_xticklabels([])
ax.set_yticklabels([])
pl.xlabel('$x_1$')
pl.ylabel('$x_2$')
cax = pl.imshow(y_prob, cmap=cm.gray_r, alpha=0.8,
extent=(-lim, lim, -lim, lim))
norm = pl.matplotlib.colors.Normalize(vmin=0., vmax=0.9)
cb = pl.colorbar(cax, ticks=[0., 0.2, 0.4, 0.6, 0.8, 1.], norm=norm)
cb.set_label('${\\rm \mathbb{P}}\left[\widehat{G}(\mathbf{x}) \leq 0\\right]$')
pl.clim(0, 1)
pl.plot(X[y <= 0, 0], X[y <= 0, 1], 'r.', markersize=12)
pl.plot(X[y > 0, 0], X[y > 0, 1], 'b.', markersize=12)
cs = pl.contour(x1, x2, y_true, [0.], colors='k', linestyles='dashdot')
cs = pl.contour(x1, x2, y_prob, [0.666], colors='b',
linestyles='solid')
pl.clabel(cs, fontsize=11)
cs = pl.contour(x1, x2, y_prob, [0.5], colors='k',
linestyles='dashed')
pl.clabel(cs, fontsize=11)
cs = pl.contour(x1, x2, y_prob, [0.334], colors='r',
linestyles='solid')
pl.clabel(cs, fontsize=11)
pl.show()
| bsd-3-clause |
bikong2/scikit-learn | benchmarks/bench_plot_approximate_neighbors.py | 244 | 6011 | """
Benchmark for approximate nearest neighbor search using
locality sensitive hashing forest.
There are two types of benchmarks.
First, accuracy of LSHForest queries are measured for various
hyper-parameters and index sizes.
Second, speed up of LSHForest queries compared to brute force
method in exact nearest neighbors is measures for the
aforementioned settings. In general, speed up is increasing as
the index size grows.
"""
from __future__ import division
import numpy as np
from tempfile import gettempdir
from time import time
from sklearn.neighbors import NearestNeighbors
from sklearn.neighbors.approximate import LSHForest
from sklearn.datasets import make_blobs
from sklearn.externals.joblib import Memory
m = Memory(cachedir=gettempdir())
@m.cache()
def make_data(n_samples, n_features, n_queries, random_state=0):
"""Create index and query data."""
print('Generating random blob-ish data')
X, _ = make_blobs(n_samples=n_samples + n_queries,
n_features=n_features, centers=100,
shuffle=True, random_state=random_state)
# Keep the last samples as held out query vectors: note since we used
# shuffle=True we have ensured that index and query vectors are
# samples from the same distribution (a mixture of 100 gaussians in this
# case)
return X[:n_samples], X[n_samples:]
def calc_exact_neighbors(X, queries, n_queries, n_neighbors):
"""Measures average times for exact neighbor queries."""
print ('Building NearestNeighbors for %d samples in %d dimensions' %
(X.shape[0], X.shape[1]))
nbrs = NearestNeighbors(algorithm='brute', metric='cosine').fit(X)
average_time = 0
t0 = time()
neighbors = nbrs.kneighbors(queries, n_neighbors=n_neighbors,
return_distance=False)
average_time = (time() - t0) / n_queries
return neighbors, average_time
def calc_accuracy(X, queries, n_queries, n_neighbors, exact_neighbors,
average_time_exact, **lshf_params):
"""Calculates accuracy and the speed up of LSHForest."""
print('Building LSHForest for %d samples in %d dimensions' %
(X.shape[0], X.shape[1]))
lshf = LSHForest(**lshf_params)
t0 = time()
lshf.fit(X)
lshf_build_time = time() - t0
print('Done in %0.3fs' % lshf_build_time)
accuracy = 0
t0 = time()
approx_neighbors = lshf.kneighbors(queries, n_neighbors=n_neighbors,
return_distance=False)
average_time_approx = (time() - t0) / n_queries
for i in range(len(queries)):
accuracy += np.in1d(approx_neighbors[i], exact_neighbors[i]).mean()
accuracy /= n_queries
speed_up = average_time_exact / average_time_approx
print('Average time for lshf neighbor queries: %0.3fs' %
average_time_approx)
print ('Average time for exact neighbor queries: %0.3fs' %
average_time_exact)
print ('Average Accuracy : %0.2f' % accuracy)
print ('Speed up: %0.1fx' % speed_up)
return speed_up, accuracy
if __name__ == '__main__':
import matplotlib.pyplot as plt
# Initialize index sizes
n_samples = [int(1e3), int(1e4), int(1e5), int(1e6)]
n_features = int(1e2)
n_queries = 100
n_neighbors = 10
X_index, X_query = make_data(np.max(n_samples), n_features, n_queries,
random_state=0)
params_list = [{'n_estimators': 3, 'n_candidates': 50},
{'n_estimators': 5, 'n_candidates': 70},
{'n_estimators': 10, 'n_candidates': 100}]
accuracies = np.zeros((len(n_samples), len(params_list)), dtype=float)
speed_ups = np.zeros((len(n_samples), len(params_list)), dtype=float)
for i, sample_size in enumerate(n_samples):
print ('==========================================================')
print ('Sample size: %i' % sample_size)
print ('------------------------')
exact_neighbors, average_time_exact = calc_exact_neighbors(
X_index[:sample_size], X_query, n_queries, n_neighbors)
for j, params in enumerate(params_list):
print ('LSHF parameters: n_estimators = %i, n_candidates = %i' %
(params['n_estimators'], params['n_candidates']))
speed_ups[i, j], accuracies[i, j] = calc_accuracy(
X_index[:sample_size], X_query, n_queries, n_neighbors,
exact_neighbors, average_time_exact, random_state=0, **params)
print ('')
print ('==========================================================')
# Set labels for LSHForest parameters
colors = ['c', 'm', 'y']
legend_rects = [plt.Rectangle((0, 0), 0.1, 0.1, fc=color)
for color in colors]
legend_labels = ['n_estimators={n_estimators}, '
'n_candidates={n_candidates}'.format(**p)
for p in params_list]
# Plot precision
plt.figure()
plt.legend(legend_rects, legend_labels,
loc='upper left')
for i in range(len(params_list)):
plt.scatter(n_samples, accuracies[:, i], c=colors[i])
plt.plot(n_samples, accuracies[:, i], c=colors[i])
plt.ylim([0, 1.3])
plt.xlim(np.min(n_samples), np.max(n_samples))
plt.semilogx()
plt.ylabel("Precision@10")
plt.xlabel("Index size")
plt.grid(which='both')
plt.title("Precision of first 10 neighbors with index size")
# Plot speed up
plt.figure()
plt.legend(legend_rects, legend_labels,
loc='upper left')
for i in range(len(params_list)):
plt.scatter(n_samples, speed_ups[:, i], c=colors[i])
plt.plot(n_samples, speed_ups[:, i], c=colors[i])
plt.ylim(0, np.max(speed_ups))
plt.xlim(np.min(n_samples), np.max(n_samples))
plt.semilogx()
plt.ylabel("Speed up")
plt.xlabel("Index size")
plt.grid(which='both')
plt.title("Relationship between Speed up and index size")
plt.show()
| bsd-3-clause |
mtconley/turntable | test/lib/python2.7/site-packages/scipy/stats/tests/test_morestats.py | 7 | 38719 | # Author: Travis Oliphant, 2002
#
# Further enhancements and tests added by numerous SciPy developers.
#
from __future__ import division, print_function, absolute_import
import warnings
import numpy as np
from numpy.random import RandomState
from numpy.testing import (TestCase, run_module_suite, assert_array_equal,
assert_almost_equal, assert_array_less, assert_array_almost_equal,
assert_raises, assert_, assert_allclose, assert_equal, dec, assert_warns)
from scipy import stats
# Matplotlib is not a scipy dependency but is optionally used in probplot, so
# check if it's available
try:
import matplotlib.pyplot as plt
have_matplotlib = True
except:
have_matplotlib = False
g1 = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000]
g2 = [0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988]
g3 = [0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996]
g4 = [1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996]
g5 = [0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996]
g6 = [1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996]
g7 = [0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002]
g8 = [0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006]
g9 = [1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991]
g10 = [0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997]
class TestShapiro(TestCase):
def test_basic(self):
x1 = [0.11,7.87,4.61,10.14,7.95,3.14,0.46,
4.43,0.21,4.75,0.71,1.52,3.24,
0.93,0.42,4.97,9.53,4.55,0.47,6.66]
w,pw = stats.shapiro(x1)
assert_almost_equal(w,0.90047299861907959,6)
assert_almost_equal(pw,0.042089745402336121,6)
x2 = [1.36,1.14,2.92,2.55,1.46,1.06,5.27,-1.11,
3.48,1.10,0.88,-0.51,1.46,0.52,6.20,1.69,
0.08,3.67,2.81,3.49]
w,pw = stats.shapiro(x2)
assert_almost_equal(w,0.9590270,6)
assert_almost_equal(pw,0.52460,3)
def test_bad_arg(self):
# Length of x is less than 3.
x = [1]
assert_raises(ValueError, stats.shapiro, x)
class TestAnderson(TestCase):
def test_normal(self):
rs = RandomState(1234567890)
x1 = rs.standard_exponential(size=50)
x2 = rs.standard_normal(size=50)
A,crit,sig = stats.anderson(x1)
assert_array_less(crit[:-1], A)
A,crit,sig = stats.anderson(x2)
assert_array_less(A, crit[-2:])
def test_expon(self):
rs = RandomState(1234567890)
x1 = rs.standard_exponential(size=50)
x2 = rs.standard_normal(size=50)
A,crit,sig = stats.anderson(x1,'expon')
assert_array_less(A, crit[-2:])
olderr = np.seterr(all='ignore')
try:
A,crit,sig = stats.anderson(x2,'expon')
finally:
np.seterr(**olderr)
assert_(A > crit[-1])
def test_bad_arg(self):
assert_raises(ValueError, stats.anderson, [1], dist='plate_of_shrimp')
class TestAndersonKSamp(TestCase):
def test_example1a(self):
# Example data from Scholz & Stephens (1987), originally
# published in Lehmann (1995, Nonparametrics, Statistical
# Methods Based on Ranks, p. 309)
# Pass a mixture of lists and arrays
t1 = [38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0]
t2 = np.array([39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8])
t3 = np.array([34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0])
t4 = np.array([34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8])
assert_warns(UserWarning, stats.anderson_ksamp, (t1, t2, t3, t4),
midrank=False)
with warnings.catch_warnings():
warnings.filterwarnings('ignore', message='approximate p-value')
Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4), midrank=False)
assert_almost_equal(Tk, 4.449, 3)
assert_array_almost_equal([0.4985, 1.3237, 1.9158, 2.4930, 3.2459],
tm, 4)
assert_almost_equal(p, 0.0021, 4)
def test_example1b(self):
# Example data from Scholz & Stephens (1987), originally
# published in Lehmann (1995, Nonparametrics, Statistical
# Methods Based on Ranks, p. 309)
# Pass arrays
t1 = np.array([38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0])
t2 = np.array([39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8])
t3 = np.array([34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0])
t4 = np.array([34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8])
with warnings.catch_warnings():
warnings.filterwarnings('ignore', message='approximate p-value')
Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4), midrank=True)
assert_almost_equal(Tk, 4.480, 3)
assert_array_almost_equal([0.4985, 1.3237, 1.9158, 2.4930, 3.2459],
tm, 4)
assert_almost_equal(p, 0.0020, 4)
def test_example2a(self):
# Example data taken from an earlier technical report of
# Scholz and Stephens
# Pass lists instead of arrays
t1 = [194, 15, 41, 29, 33, 181]
t2 = [413, 14, 58, 37, 100, 65, 9, 169, 447, 184, 36, 201, 118]
t3 = [34, 31, 18, 18, 67, 57, 62, 7, 22, 34]
t4 = [90, 10, 60, 186, 61, 49, 14, 24, 56, 20, 79, 84, 44, 59, 29,
118, 25, 156, 310, 76, 26, 44, 23, 62]
t5 = [130, 208, 70, 101, 208]
t6 = [74, 57, 48, 29, 502, 12, 70, 21, 29, 386, 59, 27]
t7 = [55, 320, 56, 104, 220, 239, 47, 246, 176, 182, 33]
t8 = [23, 261, 87, 7, 120, 14, 62, 47, 225, 71, 246, 21, 42, 20, 5,
12, 120, 11, 3, 14, 71, 11, 14, 11, 16, 90, 1, 16, 52, 95]
t9 = [97, 51, 11, 4, 141, 18, 142, 68, 77, 80, 1, 16, 106, 206, 82,
54, 31, 216, 46, 111, 39, 63, 18, 191, 18, 163, 24]
t10 = [50, 44, 102, 72, 22, 39, 3, 15, 197, 188, 79, 88, 46, 5, 5, 36,
22, 139, 210, 97, 30, 23, 13, 14]
t11 = [359, 9, 12, 270, 603, 3, 104, 2, 438]
t12 = [50, 254, 5, 283, 35, 12]
t13 = [487, 18, 100, 7, 98, 5, 85, 91, 43, 230, 3, 130]
t14 = [102, 209, 14, 57, 54, 32, 67, 59, 134, 152, 27, 14, 230, 66,
61, 34]
with warnings.catch_warnings():
warnings.filterwarnings('ignore', message='approximate p-value')
Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4, t5, t6, t7, t8,
t9, t10, t11, t12, t13, t14),
midrank=False)
assert_almost_equal(Tk, 3.288, 3)
assert_array_almost_equal([0.5990, 1.3269, 1.8052, 2.2486, 2.8009],
tm, 4)
assert_almost_equal(p, 0.0041, 4)
def test_example2b(self):
# Example data taken from an earlier technical report of
# Scholz and Stephens
t1 = [194, 15, 41, 29, 33, 181]
t2 = [413, 14, 58, 37, 100, 65, 9, 169, 447, 184, 36, 201, 118]
t3 = [34, 31, 18, 18, 67, 57, 62, 7, 22, 34]
t4 = [90, 10, 60, 186, 61, 49, 14, 24, 56, 20, 79, 84, 44, 59, 29,
118, 25, 156, 310, 76, 26, 44, 23, 62]
t5 = [130, 208, 70, 101, 208]
t6 = [74, 57, 48, 29, 502, 12, 70, 21, 29, 386, 59, 27]
t7 = [55, 320, 56, 104, 220, 239, 47, 246, 176, 182, 33]
t8 = [23, 261, 87, 7, 120, 14, 62, 47, 225, 71, 246, 21, 42, 20, 5,
12, 120, 11, 3, 14, 71, 11, 14, 11, 16, 90, 1, 16, 52, 95]
t9 = [97, 51, 11, 4, 141, 18, 142, 68, 77, 80, 1, 16, 106, 206, 82,
54, 31, 216, 46, 111, 39, 63, 18, 191, 18, 163, 24]
t10 = [50, 44, 102, 72, 22, 39, 3, 15, 197, 188, 79, 88, 46, 5, 5, 36,
22, 139, 210, 97, 30, 23, 13, 14]
t11 = [359, 9, 12, 270, 603, 3, 104, 2, 438]
t12 = [50, 254, 5, 283, 35, 12]
t13 = [487, 18, 100, 7, 98, 5, 85, 91, 43, 230, 3, 130]
t14 = [102, 209, 14, 57, 54, 32, 67, 59, 134, 152, 27, 14, 230, 66,
61, 34]
with warnings.catch_warnings():
warnings.filterwarnings('ignore', message='approximate p-value')
Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4, t5, t6, t7, t8,
t9, t10, t11, t12, t13, t14),
midrank=True)
assert_almost_equal(Tk, 3.294, 3)
assert_array_almost_equal([0.5990, 1.3269, 1.8052, 2.2486, 2.8009],
tm, 4)
assert_almost_equal(p, 0.0041, 4)
def test_not_enough_samples(self):
assert_raises(ValueError, stats.anderson_ksamp, np.ones(5))
def test_no_distinct_observations(self):
assert_raises(ValueError, stats.anderson_ksamp,
(np.ones(5), np.ones(5)))
def test_empty_sample(self):
assert_raises(ValueError, stats.anderson_ksamp, (np.ones(5), []))
class TestAnsari(TestCase):
def test_small(self):
x = [1,2,3,3,4]
y = [3,2,6,1,6,1,4,1]
W, pval = stats.ansari(x,y)
assert_almost_equal(W,23.5,11)
assert_almost_equal(pval,0.13499256881897437,11)
def test_approx(self):
ramsay = np.array((111, 107, 100, 99, 102, 106, 109, 108, 104, 99,
101, 96, 97, 102, 107, 113, 116, 113, 110, 98))
parekh = np.array((107, 108, 106, 98, 105, 103, 110, 105, 104,
100, 96, 108, 103, 104, 114, 114, 113, 108, 106, 99))
with warnings.catch_warnings():
warnings.filterwarnings('ignore',
message="Ties preclude use of exact statistic.")
W, pval = stats.ansari(ramsay, parekh)
assert_almost_equal(W,185.5,11)
assert_almost_equal(pval,0.18145819972867083,11)
def test_exact(self):
W,pval = stats.ansari([1,2,3,4],[15,5,20,8,10,12])
assert_almost_equal(W,10.0,11)
assert_almost_equal(pval,0.533333333333333333,7)
def test_bad_arg(self):
assert_raises(ValueError, stats.ansari, [], [1])
assert_raises(ValueError, stats.ansari, [1], [])
class TestBartlett(TestCase):
def test_data(self):
args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
T, pval = stats.bartlett(*args)
assert_almost_equal(T,20.78587342806484,7)
assert_almost_equal(pval,0.0136358632781,7)
def test_bad_arg(self):
# Too few args raises ValueError.
assert_raises(ValueError, stats.bartlett, [1])
class TestLevene(TestCase):
def test_data(self):
args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
W, pval = stats.levene(*args)
assert_almost_equal(W,1.7059176930008939,7)
assert_almost_equal(pval,0.0990829755522,7)
def test_trimmed1(self):
# Test that center='trimmed' gives the same result as center='mean'
# when proportiontocut=0.
W1, pval1 = stats.levene(g1, g2, g3, center='mean')
W2, pval2 = stats.levene(g1, g2, g3, center='trimmed', proportiontocut=0.0)
assert_almost_equal(W1, W2)
assert_almost_equal(pval1, pval2)
def test_trimmed2(self):
x = [1.2, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 100.0]
y = [0.0, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 200.0]
np.random.seed(1234)
x2 = np.random.permutation(x)
# Use center='trimmed'
W0, pval0 = stats.levene(x, y, center='trimmed', proportiontocut=0.125)
W1, pval1 = stats.levene(x2, y, center='trimmed', proportiontocut=0.125)
# Trim the data here, and use center='mean'
W2, pval2 = stats.levene(x[1:-1], y[1:-1], center='mean')
# Result should be the same.
assert_almost_equal(W0, W2)
assert_almost_equal(W1, W2)
assert_almost_equal(pval1, pval2)
def test_equal_mean_median(self):
x = np.linspace(-1,1,21)
np.random.seed(1234)
x2 = np.random.permutation(x)
y = x**3
W1, pval1 = stats.levene(x, y, center='mean')
W2, pval2 = stats.levene(x2, y, center='median')
assert_almost_equal(W1, W2)
assert_almost_equal(pval1, pval2)
def test_bad_keyword(self):
x = np.linspace(-1,1,21)
assert_raises(TypeError, stats.levene, x, x, portiontocut=0.1)
def test_bad_center_value(self):
x = np.linspace(-1,1,21)
assert_raises(ValueError, stats.levene, x, x, center='trim')
def test_too_few_args(self):
assert_raises(ValueError, stats.levene, [1])
class TestBinomP(TestCase):
def test_data(self):
pval = stats.binom_test(100,250)
assert_almost_equal(pval,0.0018833009350757682,11)
pval = stats.binom_test(201,405)
assert_almost_equal(pval,0.92085205962670713,11)
pval = stats.binom_test([682,243],p=3.0/4)
assert_almost_equal(pval,0.38249155957481695,11)
def test_bad_len_x(self):
# Length of x must be 1 or 2.
assert_raises(ValueError, stats.binom_test, [1,2,3])
def test_bad_n(self):
# len(x) is 1, but n is invalid.
# Missing n
assert_raises(ValueError, stats.binom_test, [100])
# n less than x[0]
assert_raises(ValueError, stats.binom_test, [100], n=50)
def test_bad_p(self):
assert_raises(ValueError, stats.binom_test, [50, 50], p=2.0)
class TestFindRepeats(TestCase):
def test_basic(self):
a = [1,2,3,4,1,2,3,4,1,2,5]
res,nums = stats.find_repeats(a)
assert_array_equal(res,[1,2,3,4])
assert_array_equal(nums,[3,3,2,2])
def test_empty_result(self):
# Check that empty arrays are returned when there are no repeats.
a = [10, 20, 50, 30, 40]
repeated, counts = stats.find_repeats(a)
assert_array_equal(repeated, [])
assert_array_equal(counts, [])
class TestFligner(TestCase):
def test_data(self):
# numbers from R: fligner.test in package stats
x1 = np.arange(5)
assert_array_almost_equal(stats.fligner(x1,x1**2),
(3.2282229927203536, 0.072379187848207877), 11)
def test_trimmed1(self):
# Test that center='trimmed' gives the same result as center='mean'
# when proportiontocut=0.
Xsq1, pval1 = stats.fligner(g1, g2, g3, center='mean')
Xsq2, pval2 = stats.fligner(g1, g2, g3, center='trimmed', proportiontocut=0.0)
assert_almost_equal(Xsq1, Xsq2)
assert_almost_equal(pval1, pval2)
def test_trimmed2(self):
x = [1.2, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 100.0]
y = [0.0, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 200.0]
# Use center='trimmed'
Xsq1, pval1 = stats.fligner(x, y, center='trimmed', proportiontocut=0.125)
# Trim the data here, and use center='mean'
Xsq2, pval2 = stats.fligner(x[1:-1], y[1:-1], center='mean')
# Result should be the same.
assert_almost_equal(Xsq1, Xsq2)
assert_almost_equal(pval1, pval2)
# The following test looks reasonable at first, but fligner() uses the
# function stats.rankdata(), and in one of the cases in this test,
# there are ties, while in the other (because of normal rounding
# errors) there are not. This difference leads to differences in the
# third significant digit of W.
#
#def test_equal_mean_median(self):
# x = np.linspace(-1,1,21)
# y = x**3
# W1, pval1 = stats.fligner(x, y, center='mean')
# W2, pval2 = stats.fligner(x, y, center='median')
# assert_almost_equal(W1, W2)
# assert_almost_equal(pval1, pval2)
def test_bad_keyword(self):
x = np.linspace(-1,1,21)
assert_raises(TypeError, stats.fligner, x, x, portiontocut=0.1)
def test_bad_center_value(self):
x = np.linspace(-1,1,21)
assert_raises(ValueError, stats.fligner, x, x, center='trim')
def test_bad_num_args(self):
# Too few args raises ValueError.
assert_raises(ValueError, stats.fligner, [1])
class TestMood(TestCase):
def test_mood(self):
# numbers from R: mood.test in package stats
x1 = np.arange(5)
assert_array_almost_equal(stats.mood(x1, x1**2),
(-1.3830857299399906, 0.16663858066771478), 11)
def test_mood_order_of_args(self):
# z should change sign when the order of arguments changes, pvalue
# should not change
np.random.seed(1234)
x1 = np.random.randn(10, 1)
x2 = np.random.randn(15, 1)
z1, p1 = stats.mood(x1, x2)
z2, p2 = stats.mood(x2, x1)
assert_array_almost_equal([z1, p1], [-z2, p2])
def test_mood_with_axis_none(self):
#Test with axis = None, compare with results from R
x1 = [-0.626453810742332, 0.183643324222082, -0.835628612410047,
1.59528080213779, 0.329507771815361, -0.820468384118015,
0.487429052428485, 0.738324705129217, 0.575781351653492,
-0.305388387156356, 1.51178116845085, 0.389843236411431,
-0.621240580541804, -2.2146998871775, 1.12493091814311,
-0.0449336090152309, -0.0161902630989461, 0.943836210685299,
0.821221195098089, 0.593901321217509]
x2 = [-0.896914546624981, 0.184849184646742, 1.58784533120882,
-1.13037567424629, -0.0802517565509893, 0.132420284381094,
0.707954729271733, -0.23969802417184, 1.98447393665293,
-0.138787012119665, 0.417650750792556, 0.981752777463662,
-0.392695355503813, -1.03966897694891, 1.78222896030858,
-2.31106908460517, 0.878604580921265, 0.035806718015226,
1.01282869212708, 0.432265154539617, 2.09081920524915,
-1.19992581964387, 1.58963820029007, 1.95465164222325,
0.00493777682814261, -2.45170638784613, 0.477237302613617,
-0.596558168631403, 0.792203270299649, 0.289636710177348]
x1 = np.array(x1)
x2 = np.array(x2)
x1.shape = (10, 2)
x2.shape = (15, 2)
assert_array_almost_equal(stats.mood(x1, x2, axis=None),
[-1.31716607555, 0.18778296257])
def test_mood_2d(self):
# Test if the results of mood test in 2-D case are consistent with the
# R result for the same inputs. Numbers from R mood.test().
ny = 5
np.random.seed(1234)
x1 = np.random.randn(10, ny)
x2 = np.random.randn(15, ny)
z_vectest, pval_vectest = stats.mood(x1, x2)
for j in range(ny):
assert_array_almost_equal([z_vectest[j], pval_vectest[j]],
stats.mood(x1[:, j], x2[:, j]))
# inverse order of dimensions
x1 = x1.transpose()
x2 = x2.transpose()
z_vectest, pval_vectest = stats.mood(x1, x2, axis=1)
for i in range(ny):
# check axis handling is self consistent
assert_array_almost_equal([z_vectest[i], pval_vectest[i]],
stats.mood(x1[i, :], x2[i, :]))
def test_mood_3d(self):
shape = (10, 5, 6)
np.random.seed(1234)
x1 = np.random.randn(*shape)
x2 = np.random.randn(*shape)
for axis in range(3):
z_vectest, pval_vectest = stats.mood(x1, x2, axis=axis)
# Tests that result for 3-D arrays is equal to that for the
# same calculation on a set of 1-D arrays taken from the
# 3-D array
axes_idx = ([1, 2], [0, 2], [0, 1]) # the two axes != axis
for i in range(shape[axes_idx[axis][0]]):
for j in range(shape[axes_idx[axis][1]]):
if axis == 0:
slice1 = x1[:, i, j]
slice2 = x2[:, i, j]
elif axis == 1:
slice1 = x1[i, :, j]
slice2 = x2[i, :, j]
else:
slice1 = x1[i, j, :]
slice2 = x2[i, j, :]
assert_array_almost_equal([z_vectest[i, j],
pval_vectest[i, j]],
stats.mood(slice1, slice2))
def test_mood_bad_arg(self):
# Raise ValueError when the sum of the lengths of the args is less than 3
assert_raises(ValueError, stats.mood, [1], [])
class TestProbplot(TestCase):
def test_basic(self):
np.random.seed(12345)
x = stats.norm.rvs(size=20)
osm, osr = stats.probplot(x, fit=False)
osm_expected = [-1.8241636, -1.38768012, -1.11829229, -0.91222575,
-0.73908135, -0.5857176, -0.44506467, -0.31273668,
-0.18568928, -0.06158146, 0.06158146, 0.18568928,
0.31273668, 0.44506467, 0.5857176, 0.73908135,
0.91222575, 1.11829229, 1.38768012, 1.8241636]
assert_allclose(osr, np.sort(x))
assert_allclose(osm, osm_expected)
res, res_fit = stats.probplot(x, fit=True)
res_fit_expected = [1.05361841, 0.31297795, 0.98741609]
assert_allclose(res_fit, res_fit_expected)
def test_sparams_keyword(self):
np.random.seed(123456)
x = stats.norm.rvs(size=100)
# Check that None, () and 0 (loc=0, for normal distribution) all work
# and give the same results
osm1, osr1 = stats.probplot(x, sparams=None, fit=False)
osm2, osr2 = stats.probplot(x, sparams=0, fit=False)
osm3, osr3 = stats.probplot(x, sparams=(), fit=False)
assert_allclose(osm1, osm2)
assert_allclose(osm1, osm3)
assert_allclose(osr1, osr2)
assert_allclose(osr1, osr3)
# Check giving (loc, scale) params for normal distribution
osm, osr = stats.probplot(x, sparams=(), fit=False)
def test_dist_keyword(self):
np.random.seed(12345)
x = stats.norm.rvs(size=20)
osm1, osr1 = stats.probplot(x, fit=False, dist='t', sparams=(3,))
osm2, osr2 = stats.probplot(x, fit=False, dist=stats.t, sparams=(3,))
assert_allclose(osm1, osm2)
assert_allclose(osr1, osr2)
assert_raises(ValueError, stats.probplot, x, dist='wrong-dist-name')
assert_raises(AttributeError, stats.probplot, x, dist=[])
class custom_dist(object):
"""Some class that looks just enough like a distribution."""
def ppf(self, q):
return stats.norm.ppf(q, loc=2)
osm1, osr1 = stats.probplot(x, sparams=(2,), fit=False)
osm2, osr2 = stats.probplot(x, dist=custom_dist(), fit=False)
assert_allclose(osm1, osm2)
assert_allclose(osr1, osr2)
@dec.skipif(not have_matplotlib)
def test_plot_kwarg(self):
np.random.seed(7654321)
fig = plt.figure()
fig.add_subplot(111)
x = stats.t.rvs(3, size=100)
res1, fitres1 = stats.probplot(x, plot=plt)
plt.close()
res2, fitres2 = stats.probplot(x, plot=None)
res3 = stats.probplot(x, fit=False, plot=plt)
plt.close()
res4 = stats.probplot(x, fit=False, plot=None)
# Check that results are consistent between combinations of `fit` and
# `plot` keywords.
assert_(len(res1) == len(res2) == len(res3) == len(res4) == 2)
assert_allclose(res1, res2)
assert_allclose(res1, res3)
assert_allclose(res1, res4)
assert_allclose(fitres1, fitres2)
# Check that a Matplotlib Axes object is accepted
fig = plt.figure()
ax = fig.add_subplot(111)
stats.probplot(x, fit=False, plot=ax)
plt.close()
def test_probplot_bad_args(self):
# Raise ValueError when given an invalid distribution.
assert_raises(ValueError, stats.probplot, [1], dist="plate_of_shrimp")
def test_wilcoxon_bad_arg():
# Raise ValueError when two args of different lengths are given or
# zero_method is unknown.
assert_raises(ValueError, stats.wilcoxon, [1], [1,2])
assert_raises(ValueError, stats.wilcoxon, [1,2], [1,2], "dummy")
def test_mvsdist_bad_arg():
# Raise ValueError if fewer than two data points are given.
data = [1]
assert_raises(ValueError, stats.mvsdist, data)
def test_kstat_bad_arg():
# Raise ValueError if n > 4 or n > 1.
data = [1]
n = 10
assert_raises(ValueError, stats.kstat, data, n=n)
def test_kstatvar_bad_arg():
# Raise ValueError is n is not 1 or 2.
data = [1]
n = 10
assert_raises(ValueError, stats.kstatvar, data, n=n)
def test_ppcc_max_bad_arg():
# Raise ValueError when given an invalid distribution.
data = [1]
assert_raises(ValueError, stats.ppcc_max, data, dist="plate_of_shrimp")
class TestBoxcox_llf(TestCase):
def test_basic(self):
np.random.seed(54321)
x = stats.norm.rvs(size=10000, loc=10)
lmbda = 1
llf = stats.boxcox_llf(lmbda, x)
llf_expected = -x.size / 2. * np.log(np.sum(x.std()**2))
assert_allclose(llf, llf_expected)
def test_array_like(self):
np.random.seed(54321)
x = stats.norm.rvs(size=100, loc=10)
lmbda = 1
llf = stats.boxcox_llf(lmbda, x)
llf2 = stats.boxcox_llf(lmbda, list(x))
assert_allclose(llf, llf2, rtol=1e-12)
def test_2d_input(self):
# Note: boxcox_llf() was already working with 2-D input (sort of), so
# keep it like that. boxcox() doesn't work with 2-D input though, due
# to brent() returning a scalar.
np.random.seed(54321)
x = stats.norm.rvs(size=100, loc=10)
lmbda = 1
llf = stats.boxcox_llf(lmbda, x)
llf2 = stats.boxcox_llf(lmbda, np.vstack([x, x]).T)
assert_allclose([llf, llf], llf2, rtol=1e-12)
def test_empty(self):
assert_(np.isnan(stats.boxcox_llf(1, [])))
class TestBoxcox(TestCase):
def test_fixed_lmbda(self):
np.random.seed(12345)
x = stats.loggamma.rvs(5, size=50) + 5
xt = stats.boxcox(x, lmbda=1)
assert_allclose(xt, x - 1)
xt = stats.boxcox(x, lmbda=-1)
assert_allclose(xt, 1 - 1/x)
xt = stats.boxcox(x, lmbda=0)
assert_allclose(xt, np.log(x))
# Also test that array_like input works
xt = stats.boxcox(list(x), lmbda=0)
assert_allclose(xt, np.log(x))
def test_lmbda_None(self):
np.random.seed(1234567)
# Start from normal rv's, do inverse transform to check that
# optimization function gets close to the right answer.
np.random.seed(1245)
lmbda = 2.5
x = stats.norm.rvs(loc=10, size=50000)
x_inv = (x * lmbda + 1)**(-lmbda)
xt, maxlog = stats.boxcox(x_inv)
assert_almost_equal(maxlog, -1 / lmbda, decimal=2)
def test_alpha(self):
np.random.seed(1234)
x = stats.loggamma.rvs(5, size=50) + 5
# Some regular values for alpha, on a small sample size
_, _, interval = stats.boxcox(x, alpha=0.75)
assert_allclose(interval, [4.004485780226041, 5.138756355035744])
_, _, interval = stats.boxcox(x, alpha=0.05)
assert_allclose(interval, [1.2138178554857557, 8.209033272375663])
# Try some extreme values, see we don't hit the N=500 limit
x = stats.loggamma.rvs(7, size=500) + 15
_, _, interval = stats.boxcox(x, alpha=0.001)
assert_allclose(interval, [0.3988867, 11.40553131])
_, _, interval = stats.boxcox(x, alpha=0.999)
assert_allclose(interval, [5.83316246, 5.83735292])
def test_boxcox_bad_arg(self):
# Raise ValueError if any data value is negative.
x = np.array([-1])
assert_raises(ValueError, stats.boxcox, x)
def test_empty(self):
assert_(stats.boxcox([]).shape == (0,))
class TestBoxcoxNormmax(TestCase):
def setUp(self):
np.random.seed(12345)
self.x = stats.loggamma.rvs(5, size=50) + 5
def test_pearsonr(self):
maxlog = stats.boxcox_normmax(self.x)
assert_allclose(maxlog, 1.804465, rtol=1e-6)
def test_mle(self):
maxlog = stats.boxcox_normmax(self.x, method='mle')
assert_allclose(maxlog, 1.758101, rtol=1e-6)
# Check that boxcox() uses 'mle'
_, maxlog_boxcox = stats.boxcox(self.x)
assert_allclose(maxlog_boxcox, maxlog)
def test_all(self):
maxlog_all = stats.boxcox_normmax(self.x, method='all')
assert_allclose(maxlog_all, [1.804465, 1.758101], rtol=1e-6)
class TestBoxcoxNormplot(TestCase):
def setUp(self):
np.random.seed(7654321)
self.x = stats.loggamma.rvs(5, size=500) + 5
def test_basic(self):
N = 5
lmbdas, ppcc = stats.boxcox_normplot(self.x, -10, 10, N=N)
ppcc_expected = [0.57783375, 0.83610988, 0.97524311, 0.99756057,
0.95843297]
assert_allclose(lmbdas, np.linspace(-10, 10, num=N))
assert_allclose(ppcc, ppcc_expected)
@dec.skipif(not have_matplotlib)
def test_plot_kwarg(self):
# Check with the matplotlib.pyplot module
fig = plt.figure()
fig.add_subplot(111)
stats.boxcox_normplot(self.x, -20, 20, plot=plt)
plt.close()
# Check that a Matplotlib Axes object is accepted
fig.add_subplot(111)
ax = fig.add_subplot(111)
stats.boxcox_normplot(self.x, -20, 20, plot=ax)
plt.close()
def test_invalid_inputs(self):
# `lb` has to be larger than `la`
assert_raises(ValueError, stats.boxcox_normplot, self.x, 1, 0)
# `x` can not contain negative values
assert_raises(ValueError, stats.boxcox_normplot, [-1, 1], 0, 1)
def test_empty(self):
assert_(stats.boxcox_normplot([], 0, 1).size == 0)
class TestCircFuncs(TestCase):
def test_circfuncs(self):
x = np.array([355,5,2,359,10,350])
M = stats.circmean(x, high=360)
Mval = 0.167690146
assert_allclose(M, Mval, rtol=1e-7)
V = stats.circvar(x, high=360)
Vval = 42.51955609
assert_allclose(V, Vval, rtol=1e-7)
S = stats.circstd(x, high=360)
Sval = 6.520702116
assert_allclose(S, Sval, rtol=1e-7)
def test_circfuncs_small(self):
x = np.array([20,21,22,18,19,20.5,19.2])
M1 = x.mean()
M2 = stats.circmean(x, high=360)
assert_allclose(M2, M1, rtol=1e-5)
V1 = x.var()
V2 = stats.circvar(x, high=360)
assert_allclose(V2, V1, rtol=1e-4)
S1 = x.std()
S2 = stats.circstd(x, high=360)
assert_allclose(S2, S1, rtol=1e-4)
def test_circmean_axis(self):
x = np.array([[355,5,2,359,10,350],
[351,7,4,352,9,349],
[357,9,8,358,4,356]])
M1 = stats.circmean(x, high=360)
M2 = stats.circmean(x.ravel(), high=360)
assert_allclose(M1, M2, rtol=1e-14)
M1 = stats.circmean(x, high=360, axis=1)
M2 = [stats.circmean(x[i], high=360) for i in range(x.shape[0])]
assert_allclose(M1, M2, rtol=1e-14)
M1 = stats.circmean(x, high=360, axis=0)
M2 = [stats.circmean(x[:,i], high=360) for i in range(x.shape[1])]
assert_allclose(M1, M2, rtol=1e-14)
def test_circvar_axis(self):
x = np.array([[355,5,2,359,10,350],
[351,7,4,352,9,349],
[357,9,8,358,4,356]])
V1 = stats.circvar(x, high=360)
V2 = stats.circvar(x.ravel(), high=360)
assert_allclose(V1, V2, rtol=1e-11)
V1 = stats.circvar(x, high=360, axis=1)
V2 = [stats.circvar(x[i], high=360) for i in range(x.shape[0])]
assert_allclose(V1, V2, rtol=1e-11)
V1 = stats.circvar(x, high=360, axis=0)
V2 = [stats.circvar(x[:,i], high=360) for i in range(x.shape[1])]
assert_allclose(V1, V2, rtol=1e-11)
def test_circstd_axis(self):
x = np.array([[355,5,2,359,10,350],
[351,7,4,352,9,349],
[357,9,8,358,4,356]])
S1 = stats.circstd(x, high=360)
S2 = stats.circstd(x.ravel(), high=360)
assert_allclose(S1, S2, rtol=1e-11)
S1 = stats.circstd(x, high=360, axis=1)
S2 = [stats.circstd(x[i], high=360) for i in range(x.shape[0])]
assert_allclose(S1, S2, rtol=1e-11)
S1 = stats.circstd(x, high=360, axis=0)
S2 = [stats.circstd(x[:,i], high=360) for i in range(x.shape[1])]
assert_allclose(S1, S2, rtol=1e-11)
def test_circfuncs_array_like(self):
x = [355,5,2,359,10,350]
assert_allclose(stats.circmean(x, high=360), 0.167690146, rtol=1e-7)
assert_allclose(stats.circvar(x, high=360), 42.51955609, rtol=1e-7)
assert_allclose(stats.circstd(x, high=360), 6.520702116, rtol=1e-7)
def test_empty(self):
assert_(np.isnan(stats.circmean([])))
assert_(np.isnan(stats.circstd([])))
assert_(np.isnan(stats.circvar([])))
def test_accuracy_wilcoxon():
freq = [1, 4, 16, 15, 8, 4, 5, 1, 2]
nums = range(-4, 5)
x = np.concatenate([[u] * v for u, v in zip(nums, freq)])
y = np.zeros(x.size)
T, p = stats.wilcoxon(x, y, "pratt")
assert_allclose(T, 423)
assert_allclose(p, 0.00197547303533107)
T, p = stats.wilcoxon(x, y, "zsplit")
assert_allclose(T, 441)
assert_allclose(p, 0.0032145343172473055)
T, p = stats.wilcoxon(x, y, "wilcox")
assert_allclose(T, 327)
assert_allclose(p, 0.00641346115861)
# Test the 'correction' option, using values computed in R with:
# > wilcox.test(x, y, paired=TRUE, exact=FALSE, correct={FALSE,TRUE})
x = np.array([120, 114, 181, 188, 180, 146, 121, 191, 132, 113, 127, 112])
y = np.array([133, 143, 119, 189, 112, 199, 198, 113, 115, 121, 142, 187])
T, p = stats.wilcoxon(x, y, correction=False)
assert_equal(T, 34)
assert_allclose(p, 0.6948866, rtol=1e-6)
T, p = stats.wilcoxon(x, y, correction=True)
assert_equal(T, 34)
assert_allclose(p, 0.7240817, rtol=1e-6)
def test_wilcoxon_tie():
# Regression test for gh-2391.
# Corresponding R code is:
# > result = wilcox.test(rep(0.1, 10), exact=FALSE, correct=FALSE)
# > result$p.value
# [1] 0.001565402
# > result = wilcox.test(rep(0.1, 10), exact=FALSE, correct=TRUE)
# > result$p.value
# [1] 0.001904195
stat, p = stats.wilcoxon([0.1] * 10)
expected_p = 0.001565402
assert_equal(stat, 0)
assert_allclose(p, expected_p, rtol=1e-6)
stat, p = stats.wilcoxon([0.1] * 10, correction=True)
expected_p = 0.001904195
assert_equal(stat, 0)
assert_allclose(p, expected_p, rtol=1e-6)
class TestMedianTest(TestCase):
def test_bad_n_samples(self):
# median_test requires at least two samples.
assert_raises(ValueError, stats.median_test, [1, 2, 3])
def test_empty_sample(self):
# Each sample must contain at least one value.
assert_raises(ValueError, stats.median_test, [], [1, 2, 3])
def test_empty_when_ties_ignored(self):
# The grand median is 1, and all values in the first argument are
# equal to the grand median. With ties="ignore", those values are
# ignored, which results in the first sample being (in effect) empty.
# This should raise a ValueError.
assert_raises(ValueError, stats.median_test,
[1, 1, 1, 1], [2, 0, 1], [2, 0], ties="ignore")
def test_empty_contingency_row(self):
# The grand median is 1, and with the default ties="below", all the
# values in the samples are counted as being below the grand median.
# This would result a row of zeros in the contingency table, which is
# an error.
assert_raises(ValueError, stats.median_test, [1, 1, 1], [1, 1, 1])
# With ties="above", all the values are counted as above the
# grand median.
assert_raises(ValueError, stats.median_test, [1, 1, 1], [1, 1, 1],
ties="above")
def test_bad_ties(self):
assert_raises(ValueError, stats.median_test, [1, 2, 3], [4, 5], ties="foo")
def test_bad_keyword(self):
assert_raises(TypeError, stats.median_test, [1, 2, 3], [4, 5], foo="foo")
def test_simple(self):
x = [1, 2, 3]
y = [1, 2, 3]
stat, p, med, tbl = stats.median_test(x, y)
# The median is floating point, but this equality test should be safe.
assert_equal(med, 2.0)
assert_array_equal(tbl, [[1, 1], [2, 2]])
# The expected values of the contingency table equal the contingency table,
# so the statistic should be 0 and the p-value should be 1.
assert_equal(stat, 0)
assert_equal(p, 1)
def test_ties_options(self):
# Test the contingency table calculation.
x = [1, 2, 3, 4]
y = [5, 6]
z = [7, 8, 9]
# grand median is 5.
# Default 'ties' option is "below".
stat, p, m, tbl = stats.median_test(x, y, z)
assert_equal(m, 5)
assert_equal(tbl, [[0, 1, 3], [4, 1, 0]])
stat, p, m, tbl = stats.median_test(x, y, z, ties="ignore")
assert_equal(m, 5)
assert_equal(tbl, [[0, 1, 3], [4, 0, 0]])
stat, p, m, tbl = stats.median_test(x, y, z, ties="above")
assert_equal(m, 5)
assert_equal(tbl, [[0, 2, 3], [4, 0, 0]])
def test_basic(self):
# median_test calls chi2_contingency to compute the test statistic
# and p-value. Make sure it hasn't screwed up the call...
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8]
stat, p, m, tbl = stats.median_test(x, y)
assert_equal(m, 4)
assert_equal(tbl, [[1, 2], [4, 2]])
exp_stat, exp_p, dof, e = stats.chi2_contingency(tbl)
assert_allclose(stat, exp_stat)
assert_allclose(p, exp_p)
stat, p, m, tbl = stats.median_test(x, y, lambda_=0)
assert_equal(m, 4)
assert_equal(tbl, [[1, 2], [4, 2]])
exp_stat, exp_p, dof, e = stats.chi2_contingency(tbl, lambda_=0)
assert_allclose(stat, exp_stat)
assert_allclose(p, exp_p)
stat, p, m, tbl = stats.median_test(x, y, correction=False)
assert_equal(m, 4)
assert_equal(tbl, [[1, 2], [4, 2]])
exp_stat, exp_p, dof, e = stats.chi2_contingency(tbl, correction=False)
assert_allclose(stat, exp_stat)
assert_allclose(p, exp_p)
if __name__ == "__main__":
run_module_suite()
| mit |
Vimos/scikit-learn | sklearn/utils/testing.py | 29 | 25405 | """Testing utilities."""
# Copyright (c) 2011, 2012
# Authors: Pietro Berkes,
# Andreas Muller
# Mathieu Blondel
# Olivier Grisel
# Arnaud Joly
# Denis Engemann
# Giorgio Patrini
# Thierry Guillemot
# License: BSD 3 clause
import os
import inspect
import pkgutil
import warnings
import sys
import struct
import scipy as sp
import scipy.io
from functools import wraps
from operator import itemgetter
try:
# Python 2
from urllib2 import urlopen
from urllib2 import HTTPError
except ImportError:
# Python 3+
from urllib.request import urlopen
from urllib.error import HTTPError
import tempfile
import shutil
import os.path as op
import atexit
import unittest
# WindowsError only exist on Windows
try:
WindowsError
except NameError:
WindowsError = None
import sklearn
from sklearn.base import BaseEstimator
from sklearn.externals import joblib
from nose.tools import raises
from nose import with_setup
from numpy.testing import assert_almost_equal
from numpy.testing import assert_array_equal
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_less
from numpy.testing import assert_approx_equal
import numpy as np
from sklearn.base import (ClassifierMixin, RegressorMixin, TransformerMixin,
ClusterMixin)
from sklearn.cluster import DBSCAN
__all__ = ["assert_equal", "assert_not_equal", "assert_raises",
"assert_raises_regexp", "raises", "with_setup", "assert_true",
"assert_false", "assert_almost_equal", "assert_array_equal",
"assert_array_almost_equal", "assert_array_less",
"assert_less", "assert_less_equal",
"assert_greater", "assert_greater_equal",
"assert_approx_equal", "SkipTest"]
_dummy = unittest.TestCase('__init__')
assert_equal = _dummy.assertEqual
assert_not_equal = _dummy.assertNotEqual
assert_true = _dummy.assertTrue
assert_false = _dummy.assertFalse
assert_raises = _dummy.assertRaises
SkipTest = unittest.case.SkipTest
assert_dict_equal = _dummy.assertDictEqual
assert_in = _dummy.assertIn
assert_not_in = _dummy.assertNotIn
assert_less = _dummy.assertLess
assert_greater = _dummy.assertGreater
assert_less_equal = _dummy.assertLessEqual
assert_greater_equal = _dummy.assertGreaterEqual
try:
assert_raises_regex = _dummy.assertRaisesRegex
except AttributeError:
# Python 2.7
assert_raises_regex = _dummy.assertRaisesRegexp
# assert_raises_regexp is deprecated in Python 3.4 in favor of
# assert_raises_regex but lets keep the backward compat in scikit-learn with
# the old name for now
assert_raises_regexp = assert_raises_regex
def assert_warns(warning_class, func, *args, **kw):
"""Test that a certain warning occurs.
Parameters
----------
warning_class : the warning class
The class to test for, e.g. UserWarning.
func : callable
Calable object to trigger warnings.
*args : the positional arguments to `func`.
**kw : the keyword arguments to `func`
Returns
-------
result : the return value of `func`
"""
# very important to avoid uncontrolled state propagation
clean_warning_registry()
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
# Trigger a warning.
result = func(*args, **kw)
if hasattr(np, 'VisibleDeprecationWarning'):
# Filter out numpy-specific warnings in numpy >= 1.9
w = [e for e in w
if e.category is not np.VisibleDeprecationWarning]
# Verify some things
if not len(w) > 0:
raise AssertionError("No warning raised when calling %s"
% func.__name__)
found = any(warning.category is warning_class for warning in w)
if not found:
raise AssertionError("%s did not give warning: %s( is %s)"
% (func.__name__, warning_class, w))
return result
def assert_warns_message(warning_class, message, func, *args, **kw):
# very important to avoid uncontrolled state propagation
"""Test that a certain warning occurs and with a certain message.
Parameters
----------
warning_class : the warning class
The class to test for, e.g. UserWarning.
message : str | callable
The entire message or a substring to test for. If callable,
it takes a string as argument and will trigger an assertion error
if it returns `False`.
func : callable
Calable object to trigger warnings.
*args : the positional arguments to `func`.
**kw : the keyword arguments to `func`.
Returns
-------
result : the return value of `func`
"""
clean_warning_registry()
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
if hasattr(np, 'VisibleDeprecationWarning'):
# Let's not catch the numpy internal DeprecationWarnings
warnings.simplefilter('ignore', np.VisibleDeprecationWarning)
# Trigger a warning.
result = func(*args, **kw)
# Verify some things
if not len(w) > 0:
raise AssertionError("No warning raised when calling %s"
% func.__name__)
found = [issubclass(warning.category, warning_class) for warning in w]
if not any(found):
raise AssertionError("No warning raised for %s with class "
"%s"
% (func.__name__, warning_class))
message_found = False
# Checks the message of all warnings belong to warning_class
for index in [i for i, x in enumerate(found) if x]:
# substring will match, the entire message with typo won't
msg = w[index].message # For Python 3 compatibility
msg = str(msg.args[0] if hasattr(msg, 'args') else msg)
if callable(message): # add support for certain tests
check_in_message = message
else:
check_in_message = lambda msg: message in msg
if check_in_message(msg):
message_found = True
break
if not message_found:
raise AssertionError("Did not receive the message you expected "
"('%s') for <%s>, got: '%s'"
% (message, func.__name__, msg))
return result
# To remove when we support numpy 1.7
def assert_no_warnings(func, *args, **kw):
# very important to avoid uncontrolled state propagation
clean_warning_registry()
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
result = func(*args, **kw)
if hasattr(np, 'VisibleDeprecationWarning'):
# Filter out numpy-specific warnings in numpy >= 1.9
w = [e for e in w
if e.category is not np.VisibleDeprecationWarning]
if len(w) > 0:
raise AssertionError("Got warnings when calling %s: [%s]"
% (func.__name__,
', '.join(str(warning) for warning in w)))
return result
def ignore_warnings(obj=None, category=Warning):
"""Context manager and decorator to ignore warnings.
Note. Using this (in both variants) will clear all warnings
from all python modules loaded. In case you need to test
cross-module-warning-logging this is not your tool of choice.
Parameters
----------
category : warning class, defaults to Warning.
The category to filter. If Warning, all categories will be muted.
Examples
--------
>>> with ignore_warnings():
... warnings.warn('buhuhuhu')
>>> def nasty_warn():
... warnings.warn('buhuhuhu')
... print(42)
>>> ignore_warnings(nasty_warn)()
42
"""
if callable(obj):
return _IgnoreWarnings(category=category)(obj)
else:
return _IgnoreWarnings(category=category)
class _IgnoreWarnings(object):
"""Improved and simplified Python warnings context manager and decorator.
This class allows to ignore the warnings raise by a function.
Copied from Python 2.7.5 and modified as required.
Parameters
----------
category : tuple of warning class, defaut to Warning
The category to filter. By default, all the categories will be muted.
"""
def __init__(self, category):
self._record = True
self._module = sys.modules['warnings']
self._entered = False
self.log = []
self.category = category
def __call__(self, fn):
"""Decorator to catch and hide warnings without visual nesting."""
@wraps(fn)
def wrapper(*args, **kwargs):
# very important to avoid uncontrolled state propagation
clean_warning_registry()
with warnings.catch_warnings():
warnings.simplefilter("ignore", self.category)
return fn(*args, **kwargs)
return wrapper
def __repr__(self):
args = []
if self._record:
args.append("record=True")
if self._module is not sys.modules['warnings']:
args.append("module=%r" % self._module)
name = type(self).__name__
return "%s(%s)" % (name, ", ".join(args))
def __enter__(self):
clean_warning_registry() # be safe and not propagate state + chaos
warnings.simplefilter("ignore", self.category)
if self._entered:
raise RuntimeError("Cannot enter %r twice" % self)
self._entered = True
self._filters = self._module.filters
self._module.filters = self._filters[:]
self._showwarning = self._module.showwarning
def __exit__(self, *exc_info):
if not self._entered:
raise RuntimeError("Cannot exit %r without entering first" % self)
self._module.filters = self._filters
self._module.showwarning = self._showwarning
self.log[:] = []
clean_warning_registry() # be safe and not propagate state + chaos
assert_less = _dummy.assertLess
assert_greater = _dummy.assertGreater
def _assert_allclose(actual, desired, rtol=1e-7, atol=0,
err_msg='', verbose=True):
actual, desired = np.asanyarray(actual), np.asanyarray(desired)
if np.allclose(actual, desired, rtol=rtol, atol=atol):
return
msg = ('Array not equal to tolerance rtol=%g, atol=%g: '
'actual %s, desired %s') % (rtol, atol, actual, desired)
raise AssertionError(msg)
if hasattr(np.testing, 'assert_allclose'):
assert_allclose = np.testing.assert_allclose
else:
assert_allclose = _assert_allclose
def assert_raise_message(exceptions, message, function, *args, **kwargs):
"""Helper function to test error messages in exceptions.
Parameters
----------
exceptions : exception or tuple of exception
Name of the estimator
function : callable
Calable object to raise error
*args : the positional arguments to `function`.
**kw : the keyword arguments to `function`
"""
try:
function(*args, **kwargs)
except exceptions as e:
error_message = str(e)
if message not in error_message:
raise AssertionError("Error message does not include the expected"
" string: %r. Observed error message: %r" %
(message, error_message))
else:
# concatenate exception names
if isinstance(exceptions, tuple):
names = " or ".join(e.__name__ for e in exceptions)
else:
names = exceptions.__name__
raise AssertionError("%s not raised by %s" %
(names, function.__name__))
def fake_mldata(columns_dict, dataname, matfile, ordering=None):
"""Create a fake mldata data set.
Parameters
----------
columns_dict : dict, keys=str, values=ndarray
Contains data as columns_dict[column_name] = array of data.
dataname : string
Name of data set.
matfile : string or file object
The file name string or the file-like object of the output file.
ordering : list, default None
List of column_names, determines the ordering in the data set.
Notes
-----
This function transposes all arrays, while fetch_mldata only transposes
'data', keep that into account in the tests.
"""
datasets = dict(columns_dict)
# transpose all variables
for name in datasets:
datasets[name] = datasets[name].T
if ordering is None:
ordering = sorted(list(datasets.keys()))
# NOTE: setting up this array is tricky, because of the way Matlab
# re-packages 1D arrays
datasets['mldata_descr_ordering'] = sp.empty((1, len(ordering)),
dtype='object')
for i, name in enumerate(ordering):
datasets['mldata_descr_ordering'][0, i] = name
scipy.io.savemat(matfile, datasets, oned_as='column')
class mock_mldata_urlopen(object):
def __init__(self, mock_datasets):
"""Object that mocks the urlopen function to fake requests to mldata.
`mock_datasets` is a dictionary of {dataset_name: data_dict}, or
{dataset_name: (data_dict, ordering).
`data_dict` itself is a dictionary of {column_name: data_array},
and `ordering` is a list of column_names to determine the ordering
in the data set (see `fake_mldata` for details).
When requesting a dataset with a name that is in mock_datasets,
this object creates a fake dataset in a StringIO object and
returns it. Otherwise, it raises an HTTPError.
"""
self.mock_datasets = mock_datasets
def __call__(self, urlname):
dataset_name = urlname.split('/')[-1]
if dataset_name in self.mock_datasets:
resource_name = '_' + dataset_name
from io import BytesIO
matfile = BytesIO()
dataset = self.mock_datasets[dataset_name]
ordering = None
if isinstance(dataset, tuple):
dataset, ordering = dataset
fake_mldata(dataset, resource_name, matfile, ordering)
matfile.seek(0)
return matfile
else:
raise HTTPError(urlname, 404, dataset_name + " is not available",
[], None)
def install_mldata_mock(mock_datasets):
# Lazy import to avoid mutually recursive imports
from sklearn import datasets
datasets.mldata.urlopen = mock_mldata_urlopen(mock_datasets)
def uninstall_mldata_mock():
# Lazy import to avoid mutually recursive imports
from sklearn import datasets
datasets.mldata.urlopen = urlopen
# Meta estimators need another estimator to be instantiated.
META_ESTIMATORS = ["OneVsOneClassifier", "MultiOutputEstimator",
"MultiOutputRegressor", "MultiOutputClassifier",
"OutputCodeClassifier", "OneVsRestClassifier",
"RFE", "RFECV", "BaseEnsemble"]
# estimators that there is no way to default-construct sensibly
OTHER = ["Pipeline", "FeatureUnion", "GridSearchCV", "RandomizedSearchCV",
"SelectFromModel"]
# some trange ones
DONT_TEST = ['SparseCoder', 'EllipticEnvelope', 'DictVectorizer',
'LabelBinarizer', 'LabelEncoder',
'MultiLabelBinarizer', 'TfidfTransformer',
'TfidfVectorizer', 'IsotonicRegression',
'OneHotEncoder', 'RandomTreesEmbedding',
'FeatureHasher', 'DummyClassifier', 'DummyRegressor',
'TruncatedSVD', 'PolynomialFeatures',
'GaussianRandomProjectionHash', 'HashingVectorizer',
'CheckingClassifier', 'PatchExtractor', 'CountVectorizer',
# GradientBoosting base estimators, maybe should
# exclude them in another way
'ZeroEstimator', 'ScaledLogOddsEstimator',
'QuantileEstimator', 'MeanEstimator',
'LogOddsEstimator', 'PriorProbabilityEstimator',
'_SigmoidCalibration', 'VotingClassifier']
def all_estimators(include_meta_estimators=False,
include_other=False, type_filter=None,
include_dont_test=False):
"""Get a list of all estimators from sklearn.
This function crawls the module and gets all classes that inherit
from BaseEstimator. Classes that are defined in test-modules are not
included.
By default meta_estimators such as GridSearchCV are also not included.
Parameters
----------
include_meta_estimators : boolean, default=False
Whether to include meta-estimators that can be constructed using
an estimator as their first argument. These are currently
BaseEnsemble, OneVsOneClassifier, OutputCodeClassifier,
OneVsRestClassifier, RFE, RFECV.
include_other : boolean, default=False
Wether to include meta-estimators that are somehow special and can
not be default-constructed sensibly. These are currently
Pipeline, FeatureUnion and GridSearchCV
include_dont_test : boolean, default=False
Whether to include "special" label estimator or test processors.
type_filter : string, list of string, or None, default=None
Which kind of estimators should be returned. If None, no filter is
applied and all estimators are returned. Possible values are
'classifier', 'regressor', 'cluster' and 'transformer' to get
estimators only of these specific types, or a list of these to
get the estimators that fit at least one of the types.
Returns
-------
estimators : list of tuples
List of (name, class), where ``name`` is the class name as string
and ``class`` is the actuall type of the class.
"""
def is_abstract(c):
if not(hasattr(c, '__abstractmethods__')):
return False
if not len(c.__abstractmethods__):
return False
return True
all_classes = []
# get parent folder
path = sklearn.__path__
for importer, modname, ispkg in pkgutil.walk_packages(
path=path, prefix='sklearn.', onerror=lambda x: None):
if (".tests." in modname):
continue
module = __import__(modname, fromlist="dummy")
classes = inspect.getmembers(module, inspect.isclass)
all_classes.extend(classes)
all_classes = set(all_classes)
estimators = [c for c in all_classes
if (issubclass(c[1], BaseEstimator) and
c[0] != 'BaseEstimator')]
# get rid of abstract base classes
estimators = [c for c in estimators if not is_abstract(c[1])]
if not include_dont_test:
estimators = [c for c in estimators if not c[0] in DONT_TEST]
if not include_other:
estimators = [c for c in estimators if not c[0] in OTHER]
# possibly get rid of meta estimators
if not include_meta_estimators:
estimators = [c for c in estimators if not c[0] in META_ESTIMATORS]
if type_filter is not None:
if not isinstance(type_filter, list):
type_filter = [type_filter]
else:
type_filter = list(type_filter) # copy
filtered_estimators = []
filters = {'classifier': ClassifierMixin,
'regressor': RegressorMixin,
'transformer': TransformerMixin,
'cluster': ClusterMixin}
for name, mixin in filters.items():
if name in type_filter:
type_filter.remove(name)
filtered_estimators.extend([est for est in estimators
if issubclass(est[1], mixin)])
estimators = filtered_estimators
if type_filter:
raise ValueError("Parameter type_filter must be 'classifier', "
"'regressor', 'transformer', 'cluster' or "
"None, got"
" %s." % repr(type_filter))
# drop duplicates, sort for reproducibility
# itemgetter is used to ensure the sort does not extend to the 2nd item of
# the tuple
return sorted(set(estimators), key=itemgetter(0))
def set_random_state(estimator, random_state=0):
"""Set random state of an estimator if it has the `random_state` param.
Classes for whom random_state is deprecated are ignored. Currently DBSCAN
is one such class.
"""
if isinstance(estimator, DBSCAN):
return
if "random_state" in estimator.get_params():
estimator.set_params(random_state=random_state)
def if_matplotlib(func):
"""Test decorator that skips test if matplotlib not installed."""
@wraps(func)
def run_test(*args, **kwargs):
try:
import matplotlib
matplotlib.use('Agg', warn=False)
# this fails if no $DISPLAY specified
import matplotlib.pyplot as plt
plt.figure()
except ImportError:
raise SkipTest('Matplotlib not available.')
else:
return func(*args, **kwargs)
return run_test
def skip_if_32bit(func):
"""Test decorator that skips tests on 32bit platforms."""
@wraps(func)
def run_test(*args, **kwargs):
bits = 8 * struct.calcsize("P")
if bits == 32:
raise SkipTest('Test skipped on 32bit platforms.')
else:
return func(*args, **kwargs)
return run_test
def if_safe_multiprocessing_with_blas(func):
"""Decorator for tests involving both BLAS calls and multiprocessing.
Under POSIX (e.g. Linux or OSX), using multiprocessing in conjunction with
some implementation of BLAS (or other libraries that manage an internal
posix thread pool) can cause a crash or a freeze of the Python process.
In practice all known packaged distributions (from Linux distros or
Anaconda) of BLAS under Linux seems to be safe. So we this problem seems to
only impact OSX users.
This wrapper makes it possible to skip tests that can possibly cause
this crash under OS X with.
Under Python 3.4+ it is possible to use the `forkserver` start method
for multiprocessing to avoid this issue. However it can cause pickling
errors on interactively defined functions. It therefore not enabled by
default.
"""
@wraps(func)
def run_test(*args, **kwargs):
if sys.platform == 'darwin':
raise SkipTest(
"Possible multi-process bug with some BLAS")
return func(*args, **kwargs)
return run_test
def clean_warning_registry():
"""Safe way to reset warnings."""
warnings.resetwarnings()
reg = "__warningregistry__"
for mod_name, mod in list(sys.modules.items()):
if 'six.moves' in mod_name:
continue
if hasattr(mod, reg):
getattr(mod, reg).clear()
def check_skip_network():
if int(os.environ.get('SKLEARN_SKIP_NETWORK_TESTS', 0)):
raise SkipTest("Text tutorial requires large dataset download")
def check_skip_travis():
"""Skip test if being run on Travis."""
if os.environ.get('TRAVIS') == "true":
raise SkipTest("This test needs to be skipped on Travis")
def _delete_folder(folder_path, warn=False):
"""Utility function to cleanup a temporary folder if still existing.
Copy from joblib.pool (for independence).
"""
try:
if os.path.exists(folder_path):
# This can fail under windows,
# but will succeed when called by atexit
shutil.rmtree(folder_path)
except WindowsError:
if warn:
warnings.warn("Could not delete temporary folder %s" % folder_path)
class TempMemmap(object):
def __init__(self, data, mmap_mode='r'):
self.temp_folder = tempfile.mkdtemp(prefix='sklearn_testing_')
self.mmap_mode = mmap_mode
self.data = data
def __enter__(self):
fpath = op.join(self.temp_folder, 'data.pkl')
joblib.dump(self.data, fpath)
data_read_only = joblib.load(fpath, mmap_mode=self.mmap_mode)
atexit.register(lambda: _delete_folder(self.temp_folder, warn=True))
return data_read_only
def __exit__(self, exc_type, exc_val, exc_tb):
_delete_folder(self.temp_folder)
with_network = with_setup(check_skip_network)
with_travis = with_setup(check_skip_travis)
class _named_check(object):
"""Wraps a check to show a useful description
Parameters
----------
check : function
Must have ``__name__`` and ``__call__``
arg_text : str
A summary of arguments to the check
"""
# Setting the description on the function itself can give incorrect results
# in failing tests
def __init__(self, check, arg_text):
self.check = check
self.description = ("{0[1]}.{0[3]}:{1.__name__}({2})".format(
inspect.stack()[1], check, arg_text))
def __call__(self, *args, **kwargs):
return self.check(*args, **kwargs)
| bsd-3-clause |
antoinecarme/pyaf | tests/perf/test_ozone_debug_perf.py | 1 | 1566 | import pandas as pd
import numpy as np
# from memory_profiler import profile
# from memprof import *
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
#get_ipython().magic('matplotlib inline')
# @memprof
def test_ozone_debug_perf():
b1 = tsds.load_ozone()
df = b1.mPastData
# df.tail(10)
# df[:-10].tail()
# df[:-10:-1]
# df.describe()
lEngine = autof.cForecastEngine()
lEngine
H = b1.mHorizon;
lEngine.mOptions.mDebugPerformance = True;
lEngine.mOptions.mEnableCycles = False;
lEngine.mOptions.mEnableTimeBasedTrends = False;
lEngine.mOptions.mEnableARModels = False;
lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H);
lEngine.getModelInfo();
print(lEngine.mSignalDecomposition.mTrPerfDetails.head());
lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
lEngine.standardPlots("outputs/my_ozone");
dfapp_in = df.copy();
dfapp_in.tail()
dfapp_out = lEngine.forecast(dfapp_in, H);
#dfapp_out.to_csv("outputs/ozone_apply_out.csv")
dfapp_out.tail(2 * H)
print("Forecast Columns " , dfapp_out.columns);
Forecast_DF = dfapp_out[[b1.mTimeVar , b1.mSignalVar, b1.mSignalVar + '_Forecast']]
print(Forecast_DF.info())
print("Forecasts\n" , Forecast_DF.tail(H).values);
print("\n\n<ModelInfo>")
print(lEngine.to_json());
print("</ModelInfo>\n\n")
print("\n\n<Forecast>")
print(Forecast_DF.tail(2*H).to_json(date_format='iso'))
print("</Forecast>\n\n")
test_ozone_debug_perf();
| bsd-3-clause |
stevertaylor/NX01 | newcmaps.py | 28 | 50518 | # New matplotlib colormaps by Nathaniel J. Smith, Stefan van der Walt,
# and (in the case of viridis) Eric Firing.
#
# This file and the colormaps in it are released under the CC0 license /
# public domain dedication. We would appreciate credit if you use or
# redistribute these colormaps, but do not impose any legal restrictions.
#
# To the extent possible under law, the persons who associated CC0 with
# mpl-colormaps have waived all copyright and related or neighboring rights
# to mpl-colormaps.
#
# You should have received a copy of the CC0 legalcode along with this
# work. If not, see <http://creativecommons.org/publicdomain/zero/1.0/>.
__all__ = ['magma', 'inferno', 'plasma', 'viridis']
_magma_data = [[0.001462, 0.000466, 0.013866],
[0.002258, 0.001295, 0.018331],
[0.003279, 0.002305, 0.023708],
[0.004512, 0.003490, 0.029965],
[0.005950, 0.004843, 0.037130],
[0.007588, 0.006356, 0.044973],
[0.009426, 0.008022, 0.052844],
[0.011465, 0.009828, 0.060750],
[0.013708, 0.011771, 0.068667],
[0.016156, 0.013840, 0.076603],
[0.018815, 0.016026, 0.084584],
[0.021692, 0.018320, 0.092610],
[0.024792, 0.020715, 0.100676],
[0.028123, 0.023201, 0.108787],
[0.031696, 0.025765, 0.116965],
[0.035520, 0.028397, 0.125209],
[0.039608, 0.031090, 0.133515],
[0.043830, 0.033830, 0.141886],
[0.048062, 0.036607, 0.150327],
[0.052320, 0.039407, 0.158841],
[0.056615, 0.042160, 0.167446],
[0.060949, 0.044794, 0.176129],
[0.065330, 0.047318, 0.184892],
[0.069764, 0.049726, 0.193735],
[0.074257, 0.052017, 0.202660],
[0.078815, 0.054184, 0.211667],
[0.083446, 0.056225, 0.220755],
[0.088155, 0.058133, 0.229922],
[0.092949, 0.059904, 0.239164],
[0.097833, 0.061531, 0.248477],
[0.102815, 0.063010, 0.257854],
[0.107899, 0.064335, 0.267289],
[0.113094, 0.065492, 0.276784],
[0.118405, 0.066479, 0.286321],
[0.123833, 0.067295, 0.295879],
[0.129380, 0.067935, 0.305443],
[0.135053, 0.068391, 0.315000],
[0.140858, 0.068654, 0.324538],
[0.146785, 0.068738, 0.334011],
[0.152839, 0.068637, 0.343404],
[0.159018, 0.068354, 0.352688],
[0.165308, 0.067911, 0.361816],
[0.171713, 0.067305, 0.370771],
[0.178212, 0.066576, 0.379497],
[0.184801, 0.065732, 0.387973],
[0.191460, 0.064818, 0.396152],
[0.198177, 0.063862, 0.404009],
[0.204935, 0.062907, 0.411514],
[0.211718, 0.061992, 0.418647],
[0.218512, 0.061158, 0.425392],
[0.225302, 0.060445, 0.431742],
[0.232077, 0.059889, 0.437695],
[0.238826, 0.059517, 0.443256],
[0.245543, 0.059352, 0.448436],
[0.252220, 0.059415, 0.453248],
[0.258857, 0.059706, 0.457710],
[0.265447, 0.060237, 0.461840],
[0.271994, 0.060994, 0.465660],
[0.278493, 0.061978, 0.469190],
[0.284951, 0.063168, 0.472451],
[0.291366, 0.064553, 0.475462],
[0.297740, 0.066117, 0.478243],
[0.304081, 0.067835, 0.480812],
[0.310382, 0.069702, 0.483186],
[0.316654, 0.071690, 0.485380],
[0.322899, 0.073782, 0.487408],
[0.329114, 0.075972, 0.489287],
[0.335308, 0.078236, 0.491024],
[0.341482, 0.080564, 0.492631],
[0.347636, 0.082946, 0.494121],
[0.353773, 0.085373, 0.495501],
[0.359898, 0.087831, 0.496778],
[0.366012, 0.090314, 0.497960],
[0.372116, 0.092816, 0.499053],
[0.378211, 0.095332, 0.500067],
[0.384299, 0.097855, 0.501002],
[0.390384, 0.100379, 0.501864],
[0.396467, 0.102902, 0.502658],
[0.402548, 0.105420, 0.503386],
[0.408629, 0.107930, 0.504052],
[0.414709, 0.110431, 0.504662],
[0.420791, 0.112920, 0.505215],
[0.426877, 0.115395, 0.505714],
[0.432967, 0.117855, 0.506160],
[0.439062, 0.120298, 0.506555],
[0.445163, 0.122724, 0.506901],
[0.451271, 0.125132, 0.507198],
[0.457386, 0.127522, 0.507448],
[0.463508, 0.129893, 0.507652],
[0.469640, 0.132245, 0.507809],
[0.475780, 0.134577, 0.507921],
[0.481929, 0.136891, 0.507989],
[0.488088, 0.139186, 0.508011],
[0.494258, 0.141462, 0.507988],
[0.500438, 0.143719, 0.507920],
[0.506629, 0.145958, 0.507806],
[0.512831, 0.148179, 0.507648],
[0.519045, 0.150383, 0.507443],
[0.525270, 0.152569, 0.507192],
[0.531507, 0.154739, 0.506895],
[0.537755, 0.156894, 0.506551],
[0.544015, 0.159033, 0.506159],
[0.550287, 0.161158, 0.505719],
[0.556571, 0.163269, 0.505230],
[0.562866, 0.165368, 0.504692],
[0.569172, 0.167454, 0.504105],
[0.575490, 0.169530, 0.503466],
[0.581819, 0.171596, 0.502777],
[0.588158, 0.173652, 0.502035],
[0.594508, 0.175701, 0.501241],
[0.600868, 0.177743, 0.500394],
[0.607238, 0.179779, 0.499492],
[0.613617, 0.181811, 0.498536],
[0.620005, 0.183840, 0.497524],
[0.626401, 0.185867, 0.496456],
[0.632805, 0.187893, 0.495332],
[0.639216, 0.189921, 0.494150],
[0.645633, 0.191952, 0.492910],
[0.652056, 0.193986, 0.491611],
[0.658483, 0.196027, 0.490253],
[0.664915, 0.198075, 0.488836],
[0.671349, 0.200133, 0.487358],
[0.677786, 0.202203, 0.485819],
[0.684224, 0.204286, 0.484219],
[0.690661, 0.206384, 0.482558],
[0.697098, 0.208501, 0.480835],
[0.703532, 0.210638, 0.479049],
[0.709962, 0.212797, 0.477201],
[0.716387, 0.214982, 0.475290],
[0.722805, 0.217194, 0.473316],
[0.729216, 0.219437, 0.471279],
[0.735616, 0.221713, 0.469180],
[0.742004, 0.224025, 0.467018],
[0.748378, 0.226377, 0.464794],
[0.754737, 0.228772, 0.462509],
[0.761077, 0.231214, 0.460162],
[0.767398, 0.233705, 0.457755],
[0.773695, 0.236249, 0.455289],
[0.779968, 0.238851, 0.452765],
[0.786212, 0.241514, 0.450184],
[0.792427, 0.244242, 0.447543],
[0.798608, 0.247040, 0.444848],
[0.804752, 0.249911, 0.442102],
[0.810855, 0.252861, 0.439305],
[0.816914, 0.255895, 0.436461],
[0.822926, 0.259016, 0.433573],
[0.828886, 0.262229, 0.430644],
[0.834791, 0.265540, 0.427671],
[0.840636, 0.268953, 0.424666],
[0.846416, 0.272473, 0.421631],
[0.852126, 0.276106, 0.418573],
[0.857763, 0.279857, 0.415496],
[0.863320, 0.283729, 0.412403],
[0.868793, 0.287728, 0.409303],
[0.874176, 0.291859, 0.406205],
[0.879464, 0.296125, 0.403118],
[0.884651, 0.300530, 0.400047],
[0.889731, 0.305079, 0.397002],
[0.894700, 0.309773, 0.393995],
[0.899552, 0.314616, 0.391037],
[0.904281, 0.319610, 0.388137],
[0.908884, 0.324755, 0.385308],
[0.913354, 0.330052, 0.382563],
[0.917689, 0.335500, 0.379915],
[0.921884, 0.341098, 0.377376],
[0.925937, 0.346844, 0.374959],
[0.929845, 0.352734, 0.372677],
[0.933606, 0.358764, 0.370541],
[0.937221, 0.364929, 0.368567],
[0.940687, 0.371224, 0.366762],
[0.944006, 0.377643, 0.365136],
[0.947180, 0.384178, 0.363701],
[0.950210, 0.390820, 0.362468],
[0.953099, 0.397563, 0.361438],
[0.955849, 0.404400, 0.360619],
[0.958464, 0.411324, 0.360014],
[0.960949, 0.418323, 0.359630],
[0.963310, 0.425390, 0.359469],
[0.965549, 0.432519, 0.359529],
[0.967671, 0.439703, 0.359810],
[0.969680, 0.446936, 0.360311],
[0.971582, 0.454210, 0.361030],
[0.973381, 0.461520, 0.361965],
[0.975082, 0.468861, 0.363111],
[0.976690, 0.476226, 0.364466],
[0.978210, 0.483612, 0.366025],
[0.979645, 0.491014, 0.367783],
[0.981000, 0.498428, 0.369734],
[0.982279, 0.505851, 0.371874],
[0.983485, 0.513280, 0.374198],
[0.984622, 0.520713, 0.376698],
[0.985693, 0.528148, 0.379371],
[0.986700, 0.535582, 0.382210],
[0.987646, 0.543015, 0.385210],
[0.988533, 0.550446, 0.388365],
[0.989363, 0.557873, 0.391671],
[0.990138, 0.565296, 0.395122],
[0.990871, 0.572706, 0.398714],
[0.991558, 0.580107, 0.402441],
[0.992196, 0.587502, 0.406299],
[0.992785, 0.594891, 0.410283],
[0.993326, 0.602275, 0.414390],
[0.993834, 0.609644, 0.418613],
[0.994309, 0.616999, 0.422950],
[0.994738, 0.624350, 0.427397],
[0.995122, 0.631696, 0.431951],
[0.995480, 0.639027, 0.436607],
[0.995810, 0.646344, 0.441361],
[0.996096, 0.653659, 0.446213],
[0.996341, 0.660969, 0.451160],
[0.996580, 0.668256, 0.456192],
[0.996775, 0.675541, 0.461314],
[0.996925, 0.682828, 0.466526],
[0.997077, 0.690088, 0.471811],
[0.997186, 0.697349, 0.477182],
[0.997254, 0.704611, 0.482635],
[0.997325, 0.711848, 0.488154],
[0.997351, 0.719089, 0.493755],
[0.997351, 0.726324, 0.499428],
[0.997341, 0.733545, 0.505167],
[0.997285, 0.740772, 0.510983],
[0.997228, 0.747981, 0.516859],
[0.997138, 0.755190, 0.522806],
[0.997019, 0.762398, 0.528821],
[0.996898, 0.769591, 0.534892],
[0.996727, 0.776795, 0.541039],
[0.996571, 0.783977, 0.547233],
[0.996369, 0.791167, 0.553499],
[0.996162, 0.798348, 0.559820],
[0.995932, 0.805527, 0.566202],
[0.995680, 0.812706, 0.572645],
[0.995424, 0.819875, 0.579140],
[0.995131, 0.827052, 0.585701],
[0.994851, 0.834213, 0.592307],
[0.994524, 0.841387, 0.598983],
[0.994222, 0.848540, 0.605696],
[0.993866, 0.855711, 0.612482],
[0.993545, 0.862859, 0.619299],
[0.993170, 0.870024, 0.626189],
[0.992831, 0.877168, 0.633109],
[0.992440, 0.884330, 0.640099],
[0.992089, 0.891470, 0.647116],
[0.991688, 0.898627, 0.654202],
[0.991332, 0.905763, 0.661309],
[0.990930, 0.912915, 0.668481],
[0.990570, 0.920049, 0.675675],
[0.990175, 0.927196, 0.682926],
[0.989815, 0.934329, 0.690198],
[0.989434, 0.941470, 0.697519],
[0.989077, 0.948604, 0.704863],
[0.988717, 0.955742, 0.712242],
[0.988367, 0.962878, 0.719649],
[0.988033, 0.970012, 0.727077],
[0.987691, 0.977154, 0.734536],
[0.987387, 0.984288, 0.742002],
[0.987053, 0.991438, 0.749504]]
_inferno_data = [[0.001462, 0.000466, 0.013866],
[0.002267, 0.001270, 0.018570],
[0.003299, 0.002249, 0.024239],
[0.004547, 0.003392, 0.030909],
[0.006006, 0.004692, 0.038558],
[0.007676, 0.006136, 0.046836],
[0.009561, 0.007713, 0.055143],
[0.011663, 0.009417, 0.063460],
[0.013995, 0.011225, 0.071862],
[0.016561, 0.013136, 0.080282],
[0.019373, 0.015133, 0.088767],
[0.022447, 0.017199, 0.097327],
[0.025793, 0.019331, 0.105930],
[0.029432, 0.021503, 0.114621],
[0.033385, 0.023702, 0.123397],
[0.037668, 0.025921, 0.132232],
[0.042253, 0.028139, 0.141141],
[0.046915, 0.030324, 0.150164],
[0.051644, 0.032474, 0.159254],
[0.056449, 0.034569, 0.168414],
[0.061340, 0.036590, 0.177642],
[0.066331, 0.038504, 0.186962],
[0.071429, 0.040294, 0.196354],
[0.076637, 0.041905, 0.205799],
[0.081962, 0.043328, 0.215289],
[0.087411, 0.044556, 0.224813],
[0.092990, 0.045583, 0.234358],
[0.098702, 0.046402, 0.243904],
[0.104551, 0.047008, 0.253430],
[0.110536, 0.047399, 0.262912],
[0.116656, 0.047574, 0.272321],
[0.122908, 0.047536, 0.281624],
[0.129285, 0.047293, 0.290788],
[0.135778, 0.046856, 0.299776],
[0.142378, 0.046242, 0.308553],
[0.149073, 0.045468, 0.317085],
[0.155850, 0.044559, 0.325338],
[0.162689, 0.043554, 0.333277],
[0.169575, 0.042489, 0.340874],
[0.176493, 0.041402, 0.348111],
[0.183429, 0.040329, 0.354971],
[0.190367, 0.039309, 0.361447],
[0.197297, 0.038400, 0.367535],
[0.204209, 0.037632, 0.373238],
[0.211095, 0.037030, 0.378563],
[0.217949, 0.036615, 0.383522],
[0.224763, 0.036405, 0.388129],
[0.231538, 0.036405, 0.392400],
[0.238273, 0.036621, 0.396353],
[0.244967, 0.037055, 0.400007],
[0.251620, 0.037705, 0.403378],
[0.258234, 0.038571, 0.406485],
[0.264810, 0.039647, 0.409345],
[0.271347, 0.040922, 0.411976],
[0.277850, 0.042353, 0.414392],
[0.284321, 0.043933, 0.416608],
[0.290763, 0.045644, 0.418637],
[0.297178, 0.047470, 0.420491],
[0.303568, 0.049396, 0.422182],
[0.309935, 0.051407, 0.423721],
[0.316282, 0.053490, 0.425116],
[0.322610, 0.055634, 0.426377],
[0.328921, 0.057827, 0.427511],
[0.335217, 0.060060, 0.428524],
[0.341500, 0.062325, 0.429425],
[0.347771, 0.064616, 0.430217],
[0.354032, 0.066925, 0.430906],
[0.360284, 0.069247, 0.431497],
[0.366529, 0.071579, 0.431994],
[0.372768, 0.073915, 0.432400],
[0.379001, 0.076253, 0.432719],
[0.385228, 0.078591, 0.432955],
[0.391453, 0.080927, 0.433109],
[0.397674, 0.083257, 0.433183],
[0.403894, 0.085580, 0.433179],
[0.410113, 0.087896, 0.433098],
[0.416331, 0.090203, 0.432943],
[0.422549, 0.092501, 0.432714],
[0.428768, 0.094790, 0.432412],
[0.434987, 0.097069, 0.432039],
[0.441207, 0.099338, 0.431594],
[0.447428, 0.101597, 0.431080],
[0.453651, 0.103848, 0.430498],
[0.459875, 0.106089, 0.429846],
[0.466100, 0.108322, 0.429125],
[0.472328, 0.110547, 0.428334],
[0.478558, 0.112764, 0.427475],
[0.484789, 0.114974, 0.426548],
[0.491022, 0.117179, 0.425552],
[0.497257, 0.119379, 0.424488],
[0.503493, 0.121575, 0.423356],
[0.509730, 0.123769, 0.422156],
[0.515967, 0.125960, 0.420887],
[0.522206, 0.128150, 0.419549],
[0.528444, 0.130341, 0.418142],
[0.534683, 0.132534, 0.416667],
[0.540920, 0.134729, 0.415123],
[0.547157, 0.136929, 0.413511],
[0.553392, 0.139134, 0.411829],
[0.559624, 0.141346, 0.410078],
[0.565854, 0.143567, 0.408258],
[0.572081, 0.145797, 0.406369],
[0.578304, 0.148039, 0.404411],
[0.584521, 0.150294, 0.402385],
[0.590734, 0.152563, 0.400290],
[0.596940, 0.154848, 0.398125],
[0.603139, 0.157151, 0.395891],
[0.609330, 0.159474, 0.393589],
[0.615513, 0.161817, 0.391219],
[0.621685, 0.164184, 0.388781],
[0.627847, 0.166575, 0.386276],
[0.633998, 0.168992, 0.383704],
[0.640135, 0.171438, 0.381065],
[0.646260, 0.173914, 0.378359],
[0.652369, 0.176421, 0.375586],
[0.658463, 0.178962, 0.372748],
[0.664540, 0.181539, 0.369846],
[0.670599, 0.184153, 0.366879],
[0.676638, 0.186807, 0.363849],
[0.682656, 0.189501, 0.360757],
[0.688653, 0.192239, 0.357603],
[0.694627, 0.195021, 0.354388],
[0.700576, 0.197851, 0.351113],
[0.706500, 0.200728, 0.347777],
[0.712396, 0.203656, 0.344383],
[0.718264, 0.206636, 0.340931],
[0.724103, 0.209670, 0.337424],
[0.729909, 0.212759, 0.333861],
[0.735683, 0.215906, 0.330245],
[0.741423, 0.219112, 0.326576],
[0.747127, 0.222378, 0.322856],
[0.752794, 0.225706, 0.319085],
[0.758422, 0.229097, 0.315266],
[0.764010, 0.232554, 0.311399],
[0.769556, 0.236077, 0.307485],
[0.775059, 0.239667, 0.303526],
[0.780517, 0.243327, 0.299523],
[0.785929, 0.247056, 0.295477],
[0.791293, 0.250856, 0.291390],
[0.796607, 0.254728, 0.287264],
[0.801871, 0.258674, 0.283099],
[0.807082, 0.262692, 0.278898],
[0.812239, 0.266786, 0.274661],
[0.817341, 0.270954, 0.270390],
[0.822386, 0.275197, 0.266085],
[0.827372, 0.279517, 0.261750],
[0.832299, 0.283913, 0.257383],
[0.837165, 0.288385, 0.252988],
[0.841969, 0.292933, 0.248564],
[0.846709, 0.297559, 0.244113],
[0.851384, 0.302260, 0.239636],
[0.855992, 0.307038, 0.235133],
[0.860533, 0.311892, 0.230606],
[0.865006, 0.316822, 0.226055],
[0.869409, 0.321827, 0.221482],
[0.873741, 0.326906, 0.216886],
[0.878001, 0.332060, 0.212268],
[0.882188, 0.337287, 0.207628],
[0.886302, 0.342586, 0.202968],
[0.890341, 0.347957, 0.198286],
[0.894305, 0.353399, 0.193584],
[0.898192, 0.358911, 0.188860],
[0.902003, 0.364492, 0.184116],
[0.905735, 0.370140, 0.179350],
[0.909390, 0.375856, 0.174563],
[0.912966, 0.381636, 0.169755],
[0.916462, 0.387481, 0.164924],
[0.919879, 0.393389, 0.160070],
[0.923215, 0.399359, 0.155193],
[0.926470, 0.405389, 0.150292],
[0.929644, 0.411479, 0.145367],
[0.932737, 0.417627, 0.140417],
[0.935747, 0.423831, 0.135440],
[0.938675, 0.430091, 0.130438],
[0.941521, 0.436405, 0.125409],
[0.944285, 0.442772, 0.120354],
[0.946965, 0.449191, 0.115272],
[0.949562, 0.455660, 0.110164],
[0.952075, 0.462178, 0.105031],
[0.954506, 0.468744, 0.099874],
[0.956852, 0.475356, 0.094695],
[0.959114, 0.482014, 0.089499],
[0.961293, 0.488716, 0.084289],
[0.963387, 0.495462, 0.079073],
[0.965397, 0.502249, 0.073859],
[0.967322, 0.509078, 0.068659],
[0.969163, 0.515946, 0.063488],
[0.970919, 0.522853, 0.058367],
[0.972590, 0.529798, 0.053324],
[0.974176, 0.536780, 0.048392],
[0.975677, 0.543798, 0.043618],
[0.977092, 0.550850, 0.039050],
[0.978422, 0.557937, 0.034931],
[0.979666, 0.565057, 0.031409],
[0.980824, 0.572209, 0.028508],
[0.981895, 0.579392, 0.026250],
[0.982881, 0.586606, 0.024661],
[0.983779, 0.593849, 0.023770],
[0.984591, 0.601122, 0.023606],
[0.985315, 0.608422, 0.024202],
[0.985952, 0.615750, 0.025592],
[0.986502, 0.623105, 0.027814],
[0.986964, 0.630485, 0.030908],
[0.987337, 0.637890, 0.034916],
[0.987622, 0.645320, 0.039886],
[0.987819, 0.652773, 0.045581],
[0.987926, 0.660250, 0.051750],
[0.987945, 0.667748, 0.058329],
[0.987874, 0.675267, 0.065257],
[0.987714, 0.682807, 0.072489],
[0.987464, 0.690366, 0.079990],
[0.987124, 0.697944, 0.087731],
[0.986694, 0.705540, 0.095694],
[0.986175, 0.713153, 0.103863],
[0.985566, 0.720782, 0.112229],
[0.984865, 0.728427, 0.120785],
[0.984075, 0.736087, 0.129527],
[0.983196, 0.743758, 0.138453],
[0.982228, 0.751442, 0.147565],
[0.981173, 0.759135, 0.156863],
[0.980032, 0.766837, 0.166353],
[0.978806, 0.774545, 0.176037],
[0.977497, 0.782258, 0.185923],
[0.976108, 0.789974, 0.196018],
[0.974638, 0.797692, 0.206332],
[0.973088, 0.805409, 0.216877],
[0.971468, 0.813122, 0.227658],
[0.969783, 0.820825, 0.238686],
[0.968041, 0.828515, 0.249972],
[0.966243, 0.836191, 0.261534],
[0.964394, 0.843848, 0.273391],
[0.962517, 0.851476, 0.285546],
[0.960626, 0.859069, 0.298010],
[0.958720, 0.866624, 0.310820],
[0.956834, 0.874129, 0.323974],
[0.954997, 0.881569, 0.337475],
[0.953215, 0.888942, 0.351369],
[0.951546, 0.896226, 0.365627],
[0.950018, 0.903409, 0.380271],
[0.948683, 0.910473, 0.395289],
[0.947594, 0.917399, 0.410665],
[0.946809, 0.924168, 0.426373],
[0.946392, 0.930761, 0.442367],
[0.946403, 0.937159, 0.458592],
[0.946903, 0.943348, 0.474970],
[0.947937, 0.949318, 0.491426],
[0.949545, 0.955063, 0.507860],
[0.951740, 0.960587, 0.524203],
[0.954529, 0.965896, 0.540361],
[0.957896, 0.971003, 0.556275],
[0.961812, 0.975924, 0.571925],
[0.966249, 0.980678, 0.587206],
[0.971162, 0.985282, 0.602154],
[0.976511, 0.989753, 0.616760],
[0.982257, 0.994109, 0.631017],
[0.988362, 0.998364, 0.644924]]
_plasma_data = [[0.050383, 0.029803, 0.527975],
[0.063536, 0.028426, 0.533124],
[0.075353, 0.027206, 0.538007],
[0.086222, 0.026125, 0.542658],
[0.096379, 0.025165, 0.547103],
[0.105980, 0.024309, 0.551368],
[0.115124, 0.023556, 0.555468],
[0.123903, 0.022878, 0.559423],
[0.132381, 0.022258, 0.563250],
[0.140603, 0.021687, 0.566959],
[0.148607, 0.021154, 0.570562],
[0.156421, 0.020651, 0.574065],
[0.164070, 0.020171, 0.577478],
[0.171574, 0.019706, 0.580806],
[0.178950, 0.019252, 0.584054],
[0.186213, 0.018803, 0.587228],
[0.193374, 0.018354, 0.590330],
[0.200445, 0.017902, 0.593364],
[0.207435, 0.017442, 0.596333],
[0.214350, 0.016973, 0.599239],
[0.221197, 0.016497, 0.602083],
[0.227983, 0.016007, 0.604867],
[0.234715, 0.015502, 0.607592],
[0.241396, 0.014979, 0.610259],
[0.248032, 0.014439, 0.612868],
[0.254627, 0.013882, 0.615419],
[0.261183, 0.013308, 0.617911],
[0.267703, 0.012716, 0.620346],
[0.274191, 0.012109, 0.622722],
[0.280648, 0.011488, 0.625038],
[0.287076, 0.010855, 0.627295],
[0.293478, 0.010213, 0.629490],
[0.299855, 0.009561, 0.631624],
[0.306210, 0.008902, 0.633694],
[0.312543, 0.008239, 0.635700],
[0.318856, 0.007576, 0.637640],
[0.325150, 0.006915, 0.639512],
[0.331426, 0.006261, 0.641316],
[0.337683, 0.005618, 0.643049],
[0.343925, 0.004991, 0.644710],
[0.350150, 0.004382, 0.646298],
[0.356359, 0.003798, 0.647810],
[0.362553, 0.003243, 0.649245],
[0.368733, 0.002724, 0.650601],
[0.374897, 0.002245, 0.651876],
[0.381047, 0.001814, 0.653068],
[0.387183, 0.001434, 0.654177],
[0.393304, 0.001114, 0.655199],
[0.399411, 0.000859, 0.656133],
[0.405503, 0.000678, 0.656977],
[0.411580, 0.000577, 0.657730],
[0.417642, 0.000564, 0.658390],
[0.423689, 0.000646, 0.658956],
[0.429719, 0.000831, 0.659425],
[0.435734, 0.001127, 0.659797],
[0.441732, 0.001540, 0.660069],
[0.447714, 0.002080, 0.660240],
[0.453677, 0.002755, 0.660310],
[0.459623, 0.003574, 0.660277],
[0.465550, 0.004545, 0.660139],
[0.471457, 0.005678, 0.659897],
[0.477344, 0.006980, 0.659549],
[0.483210, 0.008460, 0.659095],
[0.489055, 0.010127, 0.658534],
[0.494877, 0.011990, 0.657865],
[0.500678, 0.014055, 0.657088],
[0.506454, 0.016333, 0.656202],
[0.512206, 0.018833, 0.655209],
[0.517933, 0.021563, 0.654109],
[0.523633, 0.024532, 0.652901],
[0.529306, 0.027747, 0.651586],
[0.534952, 0.031217, 0.650165],
[0.540570, 0.034950, 0.648640],
[0.546157, 0.038954, 0.647010],
[0.551715, 0.043136, 0.645277],
[0.557243, 0.047331, 0.643443],
[0.562738, 0.051545, 0.641509],
[0.568201, 0.055778, 0.639477],
[0.573632, 0.060028, 0.637349],
[0.579029, 0.064296, 0.635126],
[0.584391, 0.068579, 0.632812],
[0.589719, 0.072878, 0.630408],
[0.595011, 0.077190, 0.627917],
[0.600266, 0.081516, 0.625342],
[0.605485, 0.085854, 0.622686],
[0.610667, 0.090204, 0.619951],
[0.615812, 0.094564, 0.617140],
[0.620919, 0.098934, 0.614257],
[0.625987, 0.103312, 0.611305],
[0.631017, 0.107699, 0.608287],
[0.636008, 0.112092, 0.605205],
[0.640959, 0.116492, 0.602065],
[0.645872, 0.120898, 0.598867],
[0.650746, 0.125309, 0.595617],
[0.655580, 0.129725, 0.592317],
[0.660374, 0.134144, 0.588971],
[0.665129, 0.138566, 0.585582],
[0.669845, 0.142992, 0.582154],
[0.674522, 0.147419, 0.578688],
[0.679160, 0.151848, 0.575189],
[0.683758, 0.156278, 0.571660],
[0.688318, 0.160709, 0.568103],
[0.692840, 0.165141, 0.564522],
[0.697324, 0.169573, 0.560919],
[0.701769, 0.174005, 0.557296],
[0.706178, 0.178437, 0.553657],
[0.710549, 0.182868, 0.550004],
[0.714883, 0.187299, 0.546338],
[0.719181, 0.191729, 0.542663],
[0.723444, 0.196158, 0.538981],
[0.727670, 0.200586, 0.535293],
[0.731862, 0.205013, 0.531601],
[0.736019, 0.209439, 0.527908],
[0.740143, 0.213864, 0.524216],
[0.744232, 0.218288, 0.520524],
[0.748289, 0.222711, 0.516834],
[0.752312, 0.227133, 0.513149],
[0.756304, 0.231555, 0.509468],
[0.760264, 0.235976, 0.505794],
[0.764193, 0.240396, 0.502126],
[0.768090, 0.244817, 0.498465],
[0.771958, 0.249237, 0.494813],
[0.775796, 0.253658, 0.491171],
[0.779604, 0.258078, 0.487539],
[0.783383, 0.262500, 0.483918],
[0.787133, 0.266922, 0.480307],
[0.790855, 0.271345, 0.476706],
[0.794549, 0.275770, 0.473117],
[0.798216, 0.280197, 0.469538],
[0.801855, 0.284626, 0.465971],
[0.805467, 0.289057, 0.462415],
[0.809052, 0.293491, 0.458870],
[0.812612, 0.297928, 0.455338],
[0.816144, 0.302368, 0.451816],
[0.819651, 0.306812, 0.448306],
[0.823132, 0.311261, 0.444806],
[0.826588, 0.315714, 0.441316],
[0.830018, 0.320172, 0.437836],
[0.833422, 0.324635, 0.434366],
[0.836801, 0.329105, 0.430905],
[0.840155, 0.333580, 0.427455],
[0.843484, 0.338062, 0.424013],
[0.846788, 0.342551, 0.420579],
[0.850066, 0.347048, 0.417153],
[0.853319, 0.351553, 0.413734],
[0.856547, 0.356066, 0.410322],
[0.859750, 0.360588, 0.406917],
[0.862927, 0.365119, 0.403519],
[0.866078, 0.369660, 0.400126],
[0.869203, 0.374212, 0.396738],
[0.872303, 0.378774, 0.393355],
[0.875376, 0.383347, 0.389976],
[0.878423, 0.387932, 0.386600],
[0.881443, 0.392529, 0.383229],
[0.884436, 0.397139, 0.379860],
[0.887402, 0.401762, 0.376494],
[0.890340, 0.406398, 0.373130],
[0.893250, 0.411048, 0.369768],
[0.896131, 0.415712, 0.366407],
[0.898984, 0.420392, 0.363047],
[0.901807, 0.425087, 0.359688],
[0.904601, 0.429797, 0.356329],
[0.907365, 0.434524, 0.352970],
[0.910098, 0.439268, 0.349610],
[0.912800, 0.444029, 0.346251],
[0.915471, 0.448807, 0.342890],
[0.918109, 0.453603, 0.339529],
[0.920714, 0.458417, 0.336166],
[0.923287, 0.463251, 0.332801],
[0.925825, 0.468103, 0.329435],
[0.928329, 0.472975, 0.326067],
[0.930798, 0.477867, 0.322697],
[0.933232, 0.482780, 0.319325],
[0.935630, 0.487712, 0.315952],
[0.937990, 0.492667, 0.312575],
[0.940313, 0.497642, 0.309197],
[0.942598, 0.502639, 0.305816],
[0.944844, 0.507658, 0.302433],
[0.947051, 0.512699, 0.299049],
[0.949217, 0.517763, 0.295662],
[0.951344, 0.522850, 0.292275],
[0.953428, 0.527960, 0.288883],
[0.955470, 0.533093, 0.285490],
[0.957469, 0.538250, 0.282096],
[0.959424, 0.543431, 0.278701],
[0.961336, 0.548636, 0.275305],
[0.963203, 0.553865, 0.271909],
[0.965024, 0.559118, 0.268513],
[0.966798, 0.564396, 0.265118],
[0.968526, 0.569700, 0.261721],
[0.970205, 0.575028, 0.258325],
[0.971835, 0.580382, 0.254931],
[0.973416, 0.585761, 0.251540],
[0.974947, 0.591165, 0.248151],
[0.976428, 0.596595, 0.244767],
[0.977856, 0.602051, 0.241387],
[0.979233, 0.607532, 0.238013],
[0.980556, 0.613039, 0.234646],
[0.981826, 0.618572, 0.231287],
[0.983041, 0.624131, 0.227937],
[0.984199, 0.629718, 0.224595],
[0.985301, 0.635330, 0.221265],
[0.986345, 0.640969, 0.217948],
[0.987332, 0.646633, 0.214648],
[0.988260, 0.652325, 0.211364],
[0.989128, 0.658043, 0.208100],
[0.989935, 0.663787, 0.204859],
[0.990681, 0.669558, 0.201642],
[0.991365, 0.675355, 0.198453],
[0.991985, 0.681179, 0.195295],
[0.992541, 0.687030, 0.192170],
[0.993032, 0.692907, 0.189084],
[0.993456, 0.698810, 0.186041],
[0.993814, 0.704741, 0.183043],
[0.994103, 0.710698, 0.180097],
[0.994324, 0.716681, 0.177208],
[0.994474, 0.722691, 0.174381],
[0.994553, 0.728728, 0.171622],
[0.994561, 0.734791, 0.168938],
[0.994495, 0.740880, 0.166335],
[0.994355, 0.746995, 0.163821],
[0.994141, 0.753137, 0.161404],
[0.993851, 0.759304, 0.159092],
[0.993482, 0.765499, 0.156891],
[0.993033, 0.771720, 0.154808],
[0.992505, 0.777967, 0.152855],
[0.991897, 0.784239, 0.151042],
[0.991209, 0.790537, 0.149377],
[0.990439, 0.796859, 0.147870],
[0.989587, 0.803205, 0.146529],
[0.988648, 0.809579, 0.145357],
[0.987621, 0.815978, 0.144363],
[0.986509, 0.822401, 0.143557],
[0.985314, 0.828846, 0.142945],
[0.984031, 0.835315, 0.142528],
[0.982653, 0.841812, 0.142303],
[0.981190, 0.848329, 0.142279],
[0.979644, 0.854866, 0.142453],
[0.977995, 0.861432, 0.142808],
[0.976265, 0.868016, 0.143351],
[0.974443, 0.874622, 0.144061],
[0.972530, 0.881250, 0.144923],
[0.970533, 0.887896, 0.145919],
[0.968443, 0.894564, 0.147014],
[0.966271, 0.901249, 0.148180],
[0.964021, 0.907950, 0.149370],
[0.961681, 0.914672, 0.150520],
[0.959276, 0.921407, 0.151566],
[0.956808, 0.928152, 0.152409],
[0.954287, 0.934908, 0.152921],
[0.951726, 0.941671, 0.152925],
[0.949151, 0.948435, 0.152178],
[0.946602, 0.955190, 0.150328],
[0.944152, 0.961916, 0.146861],
[0.941896, 0.968590, 0.140956],
[0.940015, 0.975158, 0.131326]]
_viridis_data = [[0.267004, 0.004874, 0.329415],
[0.268510, 0.009605, 0.335427],
[0.269944, 0.014625, 0.341379],
[0.271305, 0.019942, 0.347269],
[0.272594, 0.025563, 0.353093],
[0.273809, 0.031497, 0.358853],
[0.274952, 0.037752, 0.364543],
[0.276022, 0.044167, 0.370164],
[0.277018, 0.050344, 0.375715],
[0.277941, 0.056324, 0.381191],
[0.278791, 0.062145, 0.386592],
[0.279566, 0.067836, 0.391917],
[0.280267, 0.073417, 0.397163],
[0.280894, 0.078907, 0.402329],
[0.281446, 0.084320, 0.407414],
[0.281924, 0.089666, 0.412415],
[0.282327, 0.094955, 0.417331],
[0.282656, 0.100196, 0.422160],
[0.282910, 0.105393, 0.426902],
[0.283091, 0.110553, 0.431554],
[0.283197, 0.115680, 0.436115],
[0.283229, 0.120777, 0.440584],
[0.283187, 0.125848, 0.444960],
[0.283072, 0.130895, 0.449241],
[0.282884, 0.135920, 0.453427],
[0.282623, 0.140926, 0.457517],
[0.282290, 0.145912, 0.461510],
[0.281887, 0.150881, 0.465405],
[0.281412, 0.155834, 0.469201],
[0.280868, 0.160771, 0.472899],
[0.280255, 0.165693, 0.476498],
[0.279574, 0.170599, 0.479997],
[0.278826, 0.175490, 0.483397],
[0.278012, 0.180367, 0.486697],
[0.277134, 0.185228, 0.489898],
[0.276194, 0.190074, 0.493001],
[0.275191, 0.194905, 0.496005],
[0.274128, 0.199721, 0.498911],
[0.273006, 0.204520, 0.501721],
[0.271828, 0.209303, 0.504434],
[0.270595, 0.214069, 0.507052],
[0.269308, 0.218818, 0.509577],
[0.267968, 0.223549, 0.512008],
[0.266580, 0.228262, 0.514349],
[0.265145, 0.232956, 0.516599],
[0.263663, 0.237631, 0.518762],
[0.262138, 0.242286, 0.520837],
[0.260571, 0.246922, 0.522828],
[0.258965, 0.251537, 0.524736],
[0.257322, 0.256130, 0.526563],
[0.255645, 0.260703, 0.528312],
[0.253935, 0.265254, 0.529983],
[0.252194, 0.269783, 0.531579],
[0.250425, 0.274290, 0.533103],
[0.248629, 0.278775, 0.534556],
[0.246811, 0.283237, 0.535941],
[0.244972, 0.287675, 0.537260],
[0.243113, 0.292092, 0.538516],
[0.241237, 0.296485, 0.539709],
[0.239346, 0.300855, 0.540844],
[0.237441, 0.305202, 0.541921],
[0.235526, 0.309527, 0.542944],
[0.233603, 0.313828, 0.543914],
[0.231674, 0.318106, 0.544834],
[0.229739, 0.322361, 0.545706],
[0.227802, 0.326594, 0.546532],
[0.225863, 0.330805, 0.547314],
[0.223925, 0.334994, 0.548053],
[0.221989, 0.339161, 0.548752],
[0.220057, 0.343307, 0.549413],
[0.218130, 0.347432, 0.550038],
[0.216210, 0.351535, 0.550627],
[0.214298, 0.355619, 0.551184],
[0.212395, 0.359683, 0.551710],
[0.210503, 0.363727, 0.552206],
[0.208623, 0.367752, 0.552675],
[0.206756, 0.371758, 0.553117],
[0.204903, 0.375746, 0.553533],
[0.203063, 0.379716, 0.553925],
[0.201239, 0.383670, 0.554294],
[0.199430, 0.387607, 0.554642],
[0.197636, 0.391528, 0.554969],
[0.195860, 0.395433, 0.555276],
[0.194100, 0.399323, 0.555565],
[0.192357, 0.403199, 0.555836],
[0.190631, 0.407061, 0.556089],
[0.188923, 0.410910, 0.556326],
[0.187231, 0.414746, 0.556547],
[0.185556, 0.418570, 0.556753],
[0.183898, 0.422383, 0.556944],
[0.182256, 0.426184, 0.557120],
[0.180629, 0.429975, 0.557282],
[0.179019, 0.433756, 0.557430],
[0.177423, 0.437527, 0.557565],
[0.175841, 0.441290, 0.557685],
[0.174274, 0.445044, 0.557792],
[0.172719, 0.448791, 0.557885],
[0.171176, 0.452530, 0.557965],
[0.169646, 0.456262, 0.558030],
[0.168126, 0.459988, 0.558082],
[0.166617, 0.463708, 0.558119],
[0.165117, 0.467423, 0.558141],
[0.163625, 0.471133, 0.558148],
[0.162142, 0.474838, 0.558140],
[0.160665, 0.478540, 0.558115],
[0.159194, 0.482237, 0.558073],
[0.157729, 0.485932, 0.558013],
[0.156270, 0.489624, 0.557936],
[0.154815, 0.493313, 0.557840],
[0.153364, 0.497000, 0.557724],
[0.151918, 0.500685, 0.557587],
[0.150476, 0.504369, 0.557430],
[0.149039, 0.508051, 0.557250],
[0.147607, 0.511733, 0.557049],
[0.146180, 0.515413, 0.556823],
[0.144759, 0.519093, 0.556572],
[0.143343, 0.522773, 0.556295],
[0.141935, 0.526453, 0.555991],
[0.140536, 0.530132, 0.555659],
[0.139147, 0.533812, 0.555298],
[0.137770, 0.537492, 0.554906],
[0.136408, 0.541173, 0.554483],
[0.135066, 0.544853, 0.554029],
[0.133743, 0.548535, 0.553541],
[0.132444, 0.552216, 0.553018],
[0.131172, 0.555899, 0.552459],
[0.129933, 0.559582, 0.551864],
[0.128729, 0.563265, 0.551229],
[0.127568, 0.566949, 0.550556],
[0.126453, 0.570633, 0.549841],
[0.125394, 0.574318, 0.549086],
[0.124395, 0.578002, 0.548287],
[0.123463, 0.581687, 0.547445],
[0.122606, 0.585371, 0.546557],
[0.121831, 0.589055, 0.545623],
[0.121148, 0.592739, 0.544641],
[0.120565, 0.596422, 0.543611],
[0.120092, 0.600104, 0.542530],
[0.119738, 0.603785, 0.541400],
[0.119512, 0.607464, 0.540218],
[0.119423, 0.611141, 0.538982],
[0.119483, 0.614817, 0.537692],
[0.119699, 0.618490, 0.536347],
[0.120081, 0.622161, 0.534946],
[0.120638, 0.625828, 0.533488],
[0.121380, 0.629492, 0.531973],
[0.122312, 0.633153, 0.530398],
[0.123444, 0.636809, 0.528763],
[0.124780, 0.640461, 0.527068],
[0.126326, 0.644107, 0.525311],
[0.128087, 0.647749, 0.523491],
[0.130067, 0.651384, 0.521608],
[0.132268, 0.655014, 0.519661],
[0.134692, 0.658636, 0.517649],
[0.137339, 0.662252, 0.515571],
[0.140210, 0.665859, 0.513427],
[0.143303, 0.669459, 0.511215],
[0.146616, 0.673050, 0.508936],
[0.150148, 0.676631, 0.506589],
[0.153894, 0.680203, 0.504172],
[0.157851, 0.683765, 0.501686],
[0.162016, 0.687316, 0.499129],
[0.166383, 0.690856, 0.496502],
[0.170948, 0.694384, 0.493803],
[0.175707, 0.697900, 0.491033],
[0.180653, 0.701402, 0.488189],
[0.185783, 0.704891, 0.485273],
[0.191090, 0.708366, 0.482284],
[0.196571, 0.711827, 0.479221],
[0.202219, 0.715272, 0.476084],
[0.208030, 0.718701, 0.472873],
[0.214000, 0.722114, 0.469588],
[0.220124, 0.725509, 0.466226],
[0.226397, 0.728888, 0.462789],
[0.232815, 0.732247, 0.459277],
[0.239374, 0.735588, 0.455688],
[0.246070, 0.738910, 0.452024],
[0.252899, 0.742211, 0.448284],
[0.259857, 0.745492, 0.444467],
[0.266941, 0.748751, 0.440573],
[0.274149, 0.751988, 0.436601],
[0.281477, 0.755203, 0.432552],
[0.288921, 0.758394, 0.428426],
[0.296479, 0.761561, 0.424223],
[0.304148, 0.764704, 0.419943],
[0.311925, 0.767822, 0.415586],
[0.319809, 0.770914, 0.411152],
[0.327796, 0.773980, 0.406640],
[0.335885, 0.777018, 0.402049],
[0.344074, 0.780029, 0.397381],
[0.352360, 0.783011, 0.392636],
[0.360741, 0.785964, 0.387814],
[0.369214, 0.788888, 0.382914],
[0.377779, 0.791781, 0.377939],
[0.386433, 0.794644, 0.372886],
[0.395174, 0.797475, 0.367757],
[0.404001, 0.800275, 0.362552],
[0.412913, 0.803041, 0.357269],
[0.421908, 0.805774, 0.351910],
[0.430983, 0.808473, 0.346476],
[0.440137, 0.811138, 0.340967],
[0.449368, 0.813768, 0.335384],
[0.458674, 0.816363, 0.329727],
[0.468053, 0.818921, 0.323998],
[0.477504, 0.821444, 0.318195],
[0.487026, 0.823929, 0.312321],
[0.496615, 0.826376, 0.306377],
[0.506271, 0.828786, 0.300362],
[0.515992, 0.831158, 0.294279],
[0.525776, 0.833491, 0.288127],
[0.535621, 0.835785, 0.281908],
[0.545524, 0.838039, 0.275626],
[0.555484, 0.840254, 0.269281],
[0.565498, 0.842430, 0.262877],
[0.575563, 0.844566, 0.256415],
[0.585678, 0.846661, 0.249897],
[0.595839, 0.848717, 0.243329],
[0.606045, 0.850733, 0.236712],
[0.616293, 0.852709, 0.230052],
[0.626579, 0.854645, 0.223353],
[0.636902, 0.856542, 0.216620],
[0.647257, 0.858400, 0.209861],
[0.657642, 0.860219, 0.203082],
[0.668054, 0.861999, 0.196293],
[0.678489, 0.863742, 0.189503],
[0.688944, 0.865448, 0.182725],
[0.699415, 0.867117, 0.175971],
[0.709898, 0.868751, 0.169257],
[0.720391, 0.870350, 0.162603],
[0.730889, 0.871916, 0.156029],
[0.741388, 0.873449, 0.149561],
[0.751884, 0.874951, 0.143228],
[0.762373, 0.876424, 0.137064],
[0.772852, 0.877868, 0.131109],
[0.783315, 0.879285, 0.125405],
[0.793760, 0.880678, 0.120005],
[0.804182, 0.882046, 0.114965],
[0.814576, 0.883393, 0.110347],
[0.824940, 0.884720, 0.106217],
[0.835270, 0.886029, 0.102646],
[0.845561, 0.887322, 0.099702],
[0.855810, 0.888601, 0.097452],
[0.866013, 0.889868, 0.095953],
[0.876168, 0.891125, 0.095250],
[0.886271, 0.892374, 0.095374],
[0.896320, 0.893616, 0.096335],
[0.906311, 0.894855, 0.098125],
[0.916242, 0.896091, 0.100717],
[0.926106, 0.897330, 0.104071],
[0.935904, 0.898570, 0.108131],
[0.945636, 0.899815, 0.112838],
[0.955300, 0.901065, 0.118128],
[0.964894, 0.902323, 0.123941],
[0.974417, 0.903590, 0.130215],
[0.983868, 0.904867, 0.136897],
[0.993248, 0.906157, 0.143936]]
from matplotlib.colors import ListedColormap
cmaps = {}
for (name, data) in (('magma', _magma_data),
('inferno', _inferno_data),
('plasma', _plasma_data),
('viridis', _viridis_data)):
cmaps[name] = ListedColormap(data, name=name)
magma = cmaps['magma']
inferno = cmaps['inferno']
plasma = cmaps['plasma']
viridis = cmaps['viridis']
| mit |
natj/bender | paper/figs/fig9.py | 1 | 4141 | import numpy as np
import math
from pylab import *
from palettable.wesanderson import Zissou_5 as wsZ
import matplotlib.ticker as mtick
from scipy.interpolate import interp1d
from scipy.interpolate import griddata
from scipy.signal import savgol_filter
def smooth(xx, yy):
yy = savgol_filter(yy, 7, 2)
np.clip(yy, 0.0, 1000.0, out=yy)
yy[0] = 0.0
yy[-1] = 0.0
return xx, yy
#Read JN files
def read_lineprof(fname):
da = np.genfromtxt(fname, delimiter=",")
des = np.diff(da[:,0])[2]
norm = np.sum(des*da[:,1])
return da[:,0],da[:,1]/norm
#Read JN files
def read_csv(fname):
da = np.genfromtxt(fname, delimiter=",")
des = np.diff(da[:,0])[2]
norm = np.sum(des*da[:,1])
return da[:,0],da[:,1] #/norm
## Plot
fig = figure(figsize=(5,3), dpi=80)
rc('font', family='serif')
rc('xtick', labelsize='xx-small')
rc('ytick', labelsize='xx-small')
gs = GridSpec(1, 1)
#gs.update(wspace = 0.34)
#gs.update(hspace = 0.4)
lsize = 10.0
xmin = 0.69
xmax = 0.82
#error window limits
eymin = -0.5
eymax = 0.5
#path to files
#path_JN = "../../out3/lines/"
path_JN = "../../out/lines2/"
#labels size
tsize = 10.0
nu = '700'
#fig.text(0.5, 0.92, '$\\theta_s = 18^{\\circ}$', ha='center', va='center', size=tsize)
#fig.text(0.5, 0.72, '$\\theta_s = 45^{\\circ}$', ha='center', va='center', size=tsize)
#fig.text(0.5, 0.52, '$\\theta_s = 90^{\\circ}$', ha='center', va='center', size=tsize)
#fig.text(0.5, 0.32, 'Hopf $\\theta_s = 45^{\circ}$', ha='center', va='center', size=tsize)
#fig.text(0.5, 0.12, 'Phase',ha='center', va='center', size=lsize)
ax1 = subplot(gs[0,0])
ax1.minorticks_on()
ax1.set_xlim(xmin, xmax)
ax1.set_ylim(0.0, 30)
ax1.set_ylabel('Normalized flux',size=lsize)
ax1.set_xlabel('Energy $E/E\'$',size=lsize)
#xx1, yy1 = read_lineprof(path_JN+'lineprof_f700pbbr10m1.4i20.csv')
#ax1.plot(xx1, yy1, "k--")
#xx2, yy2 = read_lineprof(path_JN+'lineprof_obl_HTq0_f700pbbr10m1.4i20.csv')
#ax1.plot(xx2, yy2, "k-")
#lineprof_obl_HTq3_f700pbbr10m1.4i20.csv
#lineprof_obl_HTq5_f700pbbr10m1.4i20.csv
#lineprof_obl_HTq2_f700pbbr10m1.4i20.csv
files_JN = [
"lineprof_f700pbbr10m1.4i20.csv",
"lineprof_obl_f700pbbr10m1.4i20.csv",
#"lineprof_sph2_HTqfix_f700pbbr10m1.4i20.csv"]
#"lineprof_obl_HTq0_f700pbbr10m1.4i20.csv",
"lineprof_obl_HTq1_f700pbbr10m1.4i20.csv"]
#"lineprof_obl_HTq4_f700pbbr10m1.4i20.csv"]
files_JN = ['sch/lineprofile_f700_bb_r10_m1.4_i20.csv',
'obl/lineprofile_f700_bb_r10_m1.4_i20.csv',
'q/lineprofile_f700_bb_r10_m1.4_i20.csv']
cols = ["black",
"blue",
"red",
"magenta"]
i = 0
for file_name in files_JN:
xx, yy = read_lineprof(path_JN+file_name)
xx, yy = smooth(xx, yy)
ax1.plot(xx, yy, color=cols[i], linestyle="solid")
i += 1
#path_JN = "../../out3/lines/"
xx, yy = read_lineprof("../../out3/lines/lineprof_obl_HTq4_f700pbbr10m1.4i20.csv")
ax1.plot(xx, yy, color="red", linestyle="dashed")
#files_Bau = [
#"sch+dopp.csv",
#"sch+dopp+obl.csv",
#"HT.csv",
#"HT_obl.csv"]
files_Bau = ['sch.csv', 'obl.csv', 'ht.csv']
i = 0
for file_name in files_Bau:
xx, yy = read_csv(path_JN+file_name)
#rescale xx for correct scaling
#xx = (xx-0.72)/(0.89-0.72)*(0.8-0.72) + 0.72
#ax1.plot(xx, yy, color=cols[i], linestyle="dashed")
i += 1
############ q's
#xx3, yy3 = read_lineprof(path_JN+'lineprof_obl_HTq1_f700pbbr10m1.4i20.csv')
#ax1.plot(xx3, yy3, "k-", label="$q = -0.268$")
#
#xx4, yy4 = read_lineprof(path_JN+'lineprof_obl_HTq2_f700pbbr10m1.4i20.csv')
#ax1.plot(xx4, yy4, "r-", label="$q \\times 2$")
#
#xx5, yy5 = read_lineprof(path_JN+'lineprof_obl_HTq3_f700pbbr10m1.4i20.csv')
#ax1.plot(xx5, yy5, "g-", label="$q \\times 3$")
#
#xx6, yy6 = read_lineprof(path_JN+'lineprof_obl_HTq4_f700pbbr10m1.4i20.csv')
#ax1.plot(xx6, yy6, "b-", label="$q \\times 4$")
#
#xx7, yy7 = read_lineprof(path_JN+'lineprof_obl_HTq5_f700pbbr10m1.4i20.csv')
#ax1.plot(xx7, yy7, "m-", label="$q \\times 5$")
#
#legend = ax1.legend(loc='upper left', shadow=False, labelspacing=0.1)
#for label in legend.get_texts():
# label.set_fontsize('x-small')
savefig('fig9_testi.pdf', bbox_inches='tight')
| mit |
mhue/scikit-learn | examples/text/document_classification_20newsgroups.py | 222 | 10500 | """
======================================================
Classification of text documents using sparse features
======================================================
This is an example showing how scikit-learn can be used to classify documents
by topics using a bag-of-words approach. This example uses a scipy.sparse
matrix to store the features and demonstrates various classifiers that can
efficiently handle sparse matrices.
The dataset used in this example is the 20 newsgroups dataset. It will be
automatically downloaded, then cached.
The bar plot indicates the accuracy, training time (normalized) and test time
(normalized) of each classifier.
"""
# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Lars Buitinck <L.J.Buitinck@uva.nl>
# License: BSD 3 clause
from __future__ import print_function
import logging
import numpy as np
from optparse import OptionParser
import sys
from time import time
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.linear_model import RidgeClassifier
from sklearn.pipeline import Pipeline
from sklearn.svm import LinearSVC
from sklearn.linear_model import SGDClassifier
from sklearn.linear_model import Perceptron
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.naive_bayes import BernoulliNB, MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import NearestCentroid
from sklearn.ensemble import RandomForestClassifier
from sklearn.utils.extmath import density
from sklearn import metrics
# Display progress logs on stdout
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)s %(message)s')
# parse commandline arguments
op = OptionParser()
op.add_option("--report",
action="store_true", dest="print_report",
help="Print a detailed classification report.")
op.add_option("--chi2_select",
action="store", type="int", dest="select_chi2",
help="Select some number of features using a chi-squared test")
op.add_option("--confusion_matrix",
action="store_true", dest="print_cm",
help="Print the confusion matrix.")
op.add_option("--top10",
action="store_true", dest="print_top10",
help="Print ten most discriminative terms per class"
" for every classifier.")
op.add_option("--all_categories",
action="store_true", dest="all_categories",
help="Whether to use all categories or not.")
op.add_option("--use_hashing",
action="store_true",
help="Use a hashing vectorizer.")
op.add_option("--n_features",
action="store", type=int, default=2 ** 16,
help="n_features when using the hashing vectorizer.")
op.add_option("--filtered",
action="store_true",
help="Remove newsgroup information that is easily overfit: "
"headers, signatures, and quoting.")
(opts, args) = op.parse_args()
if len(args) > 0:
op.error("this script takes no arguments.")
sys.exit(1)
print(__doc__)
op.print_help()
print()
###############################################################################
# Load some categories from the training set
if opts.all_categories:
categories = None
else:
categories = [
'alt.atheism',
'talk.religion.misc',
'comp.graphics',
'sci.space',
]
if opts.filtered:
remove = ('headers', 'footers', 'quotes')
else:
remove = ()
print("Loading 20 newsgroups dataset for categories:")
print(categories if categories else "all")
data_train = fetch_20newsgroups(subset='train', categories=categories,
shuffle=True, random_state=42,
remove=remove)
data_test = fetch_20newsgroups(subset='test', categories=categories,
shuffle=True, random_state=42,
remove=remove)
print('data loaded')
categories = data_train.target_names # for case categories == None
def size_mb(docs):
return sum(len(s.encode('utf-8')) for s in docs) / 1e6
data_train_size_mb = size_mb(data_train.data)
data_test_size_mb = size_mb(data_test.data)
print("%d documents - %0.3fMB (training set)" % (
len(data_train.data), data_train_size_mb))
print("%d documents - %0.3fMB (test set)" % (
len(data_test.data), data_test_size_mb))
print("%d categories" % len(categories))
print()
# split a training set and a test set
y_train, y_test = data_train.target, data_test.target
print("Extracting features from the training data using a sparse vectorizer")
t0 = time()
if opts.use_hashing:
vectorizer = HashingVectorizer(stop_words='english', non_negative=True,
n_features=opts.n_features)
X_train = vectorizer.transform(data_train.data)
else:
vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,
stop_words='english')
X_train = vectorizer.fit_transform(data_train.data)
duration = time() - t0
print("done in %fs at %0.3fMB/s" % (duration, data_train_size_mb / duration))
print("n_samples: %d, n_features: %d" % X_train.shape)
print()
print("Extracting features from the test data using the same vectorizer")
t0 = time()
X_test = vectorizer.transform(data_test.data)
duration = time() - t0
print("done in %fs at %0.3fMB/s" % (duration, data_test_size_mb / duration))
print("n_samples: %d, n_features: %d" % X_test.shape)
print()
# mapping from integer feature name to original token string
if opts.use_hashing:
feature_names = None
else:
feature_names = vectorizer.get_feature_names()
if opts.select_chi2:
print("Extracting %d best features by a chi-squared test" %
opts.select_chi2)
t0 = time()
ch2 = SelectKBest(chi2, k=opts.select_chi2)
X_train = ch2.fit_transform(X_train, y_train)
X_test = ch2.transform(X_test)
if feature_names:
# keep selected feature names
feature_names = [feature_names[i] for i
in ch2.get_support(indices=True)]
print("done in %fs" % (time() - t0))
print()
if feature_names:
feature_names = np.asarray(feature_names)
def trim(s):
"""Trim string to fit on terminal (assuming 80-column display)"""
return s if len(s) <= 80 else s[:77] + "..."
###############################################################################
# Benchmark classifiers
def benchmark(clf):
print('_' * 80)
print("Training: ")
print(clf)
t0 = time()
clf.fit(X_train, y_train)
train_time = time() - t0
print("train time: %0.3fs" % train_time)
t0 = time()
pred = clf.predict(X_test)
test_time = time() - t0
print("test time: %0.3fs" % test_time)
score = metrics.accuracy_score(y_test, pred)
print("accuracy: %0.3f" % score)
if hasattr(clf, 'coef_'):
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
if opts.print_top10 and feature_names is not None:
print("top 10 keywords per class:")
for i, category in enumerate(categories):
top10 = np.argsort(clf.coef_[i])[-10:]
print(trim("%s: %s"
% (category, " ".join(feature_names[top10]))))
print()
if opts.print_report:
print("classification report:")
print(metrics.classification_report(y_test, pred,
target_names=categories))
if opts.print_cm:
print("confusion matrix:")
print(metrics.confusion_matrix(y_test, pred))
print()
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time
results = []
for clf, name in (
(RidgeClassifier(tol=1e-2, solver="lsqr"), "Ridge Classifier"),
(Perceptron(n_iter=50), "Perceptron"),
(PassiveAggressiveClassifier(n_iter=50), "Passive-Aggressive"),
(KNeighborsClassifier(n_neighbors=10), "kNN"),
(RandomForestClassifier(n_estimators=100), "Random forest")):
print('=' * 80)
print(name)
results.append(benchmark(clf))
for penalty in ["l2", "l1"]:
print('=' * 80)
print("%s penalty" % penalty.upper())
# Train Liblinear model
results.append(benchmark(LinearSVC(loss='l2', penalty=penalty,
dual=False, tol=1e-3)))
# Train SGD model
results.append(benchmark(SGDClassifier(alpha=.0001, n_iter=50,
penalty=penalty)))
# Train SGD with Elastic Net penalty
print('=' * 80)
print("Elastic-Net penalty")
results.append(benchmark(SGDClassifier(alpha=.0001, n_iter=50,
penalty="elasticnet")))
# Train NearestCentroid without threshold
print('=' * 80)
print("NearestCentroid (aka Rocchio classifier)")
results.append(benchmark(NearestCentroid()))
# Train sparse Naive Bayes classifiers
print('=' * 80)
print("Naive Bayes")
results.append(benchmark(MultinomialNB(alpha=.01)))
results.append(benchmark(BernoulliNB(alpha=.01)))
print('=' * 80)
print("LinearSVC with L1-based feature selection")
# The smaller C, the stronger the regularization.
# The more regularization, the more sparsity.
results.append(benchmark(Pipeline([
('feature_selection', LinearSVC(penalty="l1", dual=False, tol=1e-3)),
('classification', LinearSVC())
])))
# make some plots
indices = np.arange(len(results))
results = [[x[i] for x in results] for i in range(4)]
clf_names, score, training_time, test_time = results
training_time = np.array(training_time) / np.max(training_time)
test_time = np.array(test_time) / np.max(test_time)
plt.figure(figsize=(12, 8))
plt.title("Score")
plt.barh(indices, score, .2, label="score", color='r')
plt.barh(indices + .3, training_time, .2, label="training time", color='g')
plt.barh(indices + .6, test_time, .2, label="test time", color='b')
plt.yticks(())
plt.legend(loc='best')
plt.subplots_adjust(left=.25)
plt.subplots_adjust(top=.95)
plt.subplots_adjust(bottom=.05)
for i, c in zip(indices, clf_names):
plt.text(-.3, i, c)
plt.show()
| bsd-3-clause |
Shatki/PyIMU | test/magnetosphere.py | 1 | 1580 | from mpl_toolkits.mplot3d import axes3d
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from socket import *
import time
# Объявляем все глобальные переменные
HOST = '192.168.0.76'
PORT = 21566
BUFSIZ = 512
ADDR = (HOST, PORT)
bad_packet = 0
good_packet = 0
# fig, ax = plt.subplots()
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# Socket
# tcpCliSock = socket(AF_INET, SOCK_STREAM)
# tcpCliSock.connect(ADDR)
# Запрет на ожидание
plt.ion()
tstart = time.time()
# real-time plotting loop
X, Y, Z = [], [], []
while True:
try:
# читаем данные из сети
tcpCliSock.c
data = tcpCliSock.recv(BUFSIZ)
if data:
print(len(X), data)
data = data.decode().split(',')
if len(data) == 9:
# print('Data received', data)
# tcpCliSock.send(b'Ok')
good_packet += 1
else:
bad_packet += 1
# читаем данные из сети
data = tcpCliSock.recv(BUFSIZ)
X.append(data[0])
Y.append(data[1])
Z.append(data[2])
frame = ax.scatter(X, Y, Z, c='b', marker='o')
# Remove old line collection before drawing
#if oldcol is not None:
# ax.collections.remove(oldcol)
plt.pause(0.001 / len(X))
except KeyboardInterrupt:
tcpCliSock.close()
print('FPS: %f' % (len(X) / (time.time() - tstart)))
break
| gpl-3.0 |
DamCB/tyssue | tyssue/draw/ipv_draw.py | 2 | 8114 | """3D visualisation inside the notebook.
"""
import warnings
import numpy as np
import pandas as pd
from matplotlib import cm
from ipywidgets import interact
from ..config.draw import sheet_spec
from ..utils.utils import spec_updater, get_sub_eptm
try:
import ipyvolume as ipv
except ImportError:
print(
"""
This module needs ipyvolume to work.
You can install it with:
$ conda install -c conda-forge ipyvolume
"""
)
def browse_history(history, coords=["x", "y", "z"], **draw_specs_kw):
times = history.time_stamps
num_frames = times.size
draw_specs = sheet_spec()
spec_updater(draw_specs, draw_specs_kw)
sheet = history.retrieve(0)
ipv.clear()
fig, meshes = sheet_view(sheet, coords, **draw_specs_kw)
lim_inf = sheet.vert_df[sheet.coords].min().min()
lim_sup = sheet.vert_df[sheet.coords].max().max()
ipv.xyzlim(lim_inf, lim_sup)
def set_frame(i=0):
fig.animation = 0
t = times[i]
meshes = _get_meshes(history.retrieve(t), coords, draw_specs)
update_view(fig, meshes)
ipv.show()
interact(set_frame, i=(0, num_frames - 1))
def update_view(fig, meshes):
for old, new in zip(fig.meshes, meshes):
old.x = new.x
old.y = new.y
old.z = new.z
old.color = new.color
old.triangles = new.triangles
old.lines = new.lines
def sheet_view(sheet, coords=["x", "y", "z"], **draw_specs_kw):
"""
Creates a javascript renderer of the edge lines to be displayed
in Jupyter Notebooks
Returns
-------
fig: a :class:`ipyvolume.widgets.Figure` widget
mesh: a :class:`ipyvolume.widgets.Mesh` mesh widget
"""
# ipv.style.use(["dark", "minimal"])
draw_specs = sheet_spec()
spec_updater(draw_specs, draw_specs_kw)
fig = ipv.gcf()
fig.meshes = fig.meshes + _get_meshes(sheet, coords, draw_specs)
box_size = max(*(np.ptp(sheet.vert_df[u]) for u in sheet.coords))
border = 0.05 * box_size
lim_inf = sheet.vert_df[sheet.coords].min().min() - border
lim_sup = sheet.vert_df[sheet.coords].max().max() + border
ipv.xyzlim(lim_inf, lim_sup)
return fig, fig.meshes
def view_ipv(sheet, coords=["x", "y", "z"], **edge_specs):
"""
Creates a javascript renderer of the edge lines to be displayed
in Jupyter Notebooks
Returns
-------
fig: a :class:`ipyvolume.widgets.Figure` widget
mesh: a :class:`ipyvolume.widgets.Mesh` mesh widget
"""
warnings.warn("`view_ipv` is deprecated, use the more generic `sheet_view`")
mesh = edge_mesh(sheet, coords, **edge_specs)
fig = ipv.gcf()
fig.meshes = fig.meshes + [mesh]
box_size = max(*(np.ptp(sheet.vert_df[u]) for u in sheet.coords))
border = 0.05 * box_size
lim_inf = sheet.vert_df[sheet.coords].min().min() - border
lim_sup = sheet.vert_df[sheet.coords].max().max() + border
ipv.xyzlim(lim_inf, lim_sup)
return fig, mesh
def edge_mesh(sheet, coords, **edge_specs):
"""
Creates a ipyvolume Mesh of the edge lines to be displayed
in Jupyter Notebooks
Returns
-------
mesh: a :class:`ipyvolume.widgets.Mesh` mesh widget
"""
spec = sheet_spec()["edge"]
spec.update(**edge_specs)
if callable(spec["color"]):
spec["color"] = spec["color"](sheet)
if isinstance(spec["color"], str):
color = spec["color"]
elif hasattr(spec["color"], "__len__"):
color = _wire_color_from_sequence(spec, sheet)[:, :3]
u, v, w = coords
mesh = ipv.Mesh(
x=sheet.vert_df[u],
y=sheet.vert_df[v],
z=sheet.vert_df[w],
lines=sheet.edge_df[["srce", "trgt"]].astype(dtype=np.uint32),
color=color,
)
return mesh
def face_mesh(sheet, coords, **face_draw_specs):
"""
Creates a ipyvolume Mesh of the face polygons
"""
Ne, Nf = sheet.Ne, sheet.Nf
if callable(face_draw_specs["color"]):
face_draw_specs["color"] = face_draw_specs["color"](sheet)
if isinstance(face_draw_specs["color"], str):
color = face_draw_specs["color"]
elif hasattr(face_draw_specs["color"], "__len__"):
color = _face_color_from_sequence(face_draw_specs, sheet)[:, :3]
if "visible" in sheet.face_df.columns:
edges = sheet.edge_df[sheet.upcast_face(sheet.face_df["visible"])].index
_sheet = get_sub_eptm(sheet, edges)
if _sheet is not None:
sheet = _sheet
if isinstance(color, np.ndarray):
faces = sheet.face_df["face_o"].values.astype(np.uint32)
edges = edges.values.astype(np.uint32)
indexer = np.concatenate([faces, edges + Nf, edges + Ne + Nf])
color = color.take(indexer, axis=0)
epsilon = face_draw_specs.get("epsilon", 0)
up_srce = sheet.edge_df[["s" + c for c in coords]]
up_trgt = sheet.edge_df[["t" + c for c in coords]]
Ne, Nf = sheet.Ne, sheet.Nf
if epsilon > 0:
up_face = sheet.edge_df[["f" + c for c in coords]].values
up_srce = (up_srce - up_face) * (1 - epsilon) + up_face
up_trgt = (up_trgt - up_face) * (1 - epsilon) + up_face
mesh_ = np.concatenate(
[sheet.face_df[coords].values, up_srce.values, up_trgt.values]
)
triangles = np.vstack(
[sheet.edge_df["face"], np.arange(Ne) + Nf, np.arange(Ne) + Ne + Nf]
).T.astype(dtype=np.uint32)
mesh = ipv.Mesh(
x=mesh_[:, 0], y=mesh_[:, 1], z=mesh_[:, 2], triangles=triangles, color=color
)
return mesh
def _wire_color_from_sequence(edge_spec, sheet):
"""
"""
color_ = edge_spec["color"]
cmap = cm.get_cmap(edge_spec.get("colormap", "viridis"))
if color_.shape in [(sheet.Nv, 3), (sheet.Nv, 4)]:
return np.asarray(color_)
if color_.shape == (sheet.Nv,):
if np.ptp(color_) < 1e-10:
return np.ones((sheet.Nv, 3)) * 0.7
return cmap((color_ - color_.min()) / np.ptp(color_))
if color_.shape in [(sheet.Ne, 3), (sheet.Ne, 4)]:
color_ = pd.DataFrame(color_, index=sheet.edge_df.index)
color_["srce"] = sheet.edge_df["srce"]
color_ = color_.groupby("srce").mean().values
return color_
if color_.shape == (sheet.Ne,):
color_ = pd.DataFrame(color_, index=sheet.edge_df.index)
color_["srce"] = sheet.edge_df["srce"]
color_ = color_.groupby("srce").mean().values.ravel()
if np.ptp(color_) < 1e-10:
warnings.warn("Attempting to draw a colormap " "with a uniform value")
return np.ones((sheet.Nv, 3)) * 0.7
return cmap((color_ - color_.min()) / np.ptp(color_))
else:
raise ValueError("The 'color' value of the spec doesn't have a correct shape.")
def _face_color_from_sequence(face_spec, sheet):
color_ = face_spec["color"]
cmap = cm.get_cmap(face_spec.get("colormap", "viridis"))
Nf, Ne = sheet.Nf, sheet.Ne
color_min, color_max = face_spec.get("color_range", (color_.min(), color_.max()))
face_mesh_shape = Nf + 2 * Ne
if color_.shape in [(sheet.Nf, 3), (sheet.Nf, 4)]:
return np.concatenate([color_, color_, color_])
elif color_.shape == (sheet.Nf,):
if np.ptp(color_) < 1e-10:
# warnings.warn("Attempting to draw a colormap with a uniform value")
return np.ones((face_mesh_shape, 3)) * 0.5
normed = (color_ - color_min) / (color_max - color_min)
up_color = sheet.upcast_face(normed).values
return cmap(np.concatenate([normed, up_color, up_color]))
else:
raise ValueError(
"shape of `face_spec['color']` must be either (Nf, 3), (Nf, 4) or (Nf,)"
)
def _get_meshes(sheet, coords, draw_specs):
meshes = []
edge_spec = draw_specs["edge"]
if edge_spec["visible"]:
edges = edge_mesh(sheet, coords, **edge_spec)
meshes.append(edges)
else:
edges = None
face_spec = draw_specs["face"]
if face_spec["visible"]:
faces = face_mesh(sheet, coords, **face_spec)
meshes.append(faces)
else:
faces = None
return meshes
| gpl-3.0 |
ephes/scikit-learn | examples/decomposition/plot_faces_decomposition.py | 204 | 4452 | """
============================
Faces dataset decompositions
============================
This example applies to :ref:`olivetti_faces` different unsupervised
matrix decomposition (dimension reduction) methods from the module
:py:mod:`sklearn.decomposition` (see the documentation chapter
:ref:`decompositions`) .
"""
print(__doc__)
# Authors: Vlad Niculae, Alexandre Gramfort
# License: BSD 3 clause
import logging
from time import time
from numpy.random import RandomState
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_olivetti_faces
from sklearn.cluster import MiniBatchKMeans
from sklearn import decomposition
# Display progress logs on stdout
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)s %(message)s')
n_row, n_col = 2, 3
n_components = n_row * n_col
image_shape = (64, 64)
rng = RandomState(0)
###############################################################################
# Load faces data
dataset = fetch_olivetti_faces(shuffle=True, random_state=rng)
faces = dataset.data
n_samples, n_features = faces.shape
# global centering
faces_centered = faces - faces.mean(axis=0)
# local centering
faces_centered -= faces_centered.mean(axis=1).reshape(n_samples, -1)
print("Dataset consists of %d faces" % n_samples)
###############################################################################
def plot_gallery(title, images, n_col=n_col, n_row=n_row):
plt.figure(figsize=(2. * n_col, 2.26 * n_row))
plt.suptitle(title, size=16)
for i, comp in enumerate(images):
plt.subplot(n_row, n_col, i + 1)
vmax = max(comp.max(), -comp.min())
plt.imshow(comp.reshape(image_shape), cmap=plt.cm.gray,
interpolation='nearest',
vmin=-vmax, vmax=vmax)
plt.xticks(())
plt.yticks(())
plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)
###############################################################################
# List of the different estimators, whether to center and transpose the
# problem, and whether the transformer uses the clustering API.
estimators = [
('Eigenfaces - RandomizedPCA',
decomposition.RandomizedPCA(n_components=n_components, whiten=True),
True),
('Non-negative components - NMF',
decomposition.NMF(n_components=n_components, init='nndsvda', beta=5.0,
tol=5e-3, sparseness='components'),
False),
('Independent components - FastICA',
decomposition.FastICA(n_components=n_components, whiten=True),
True),
('Sparse comp. - MiniBatchSparsePCA',
decomposition.MiniBatchSparsePCA(n_components=n_components, alpha=0.8,
n_iter=100, batch_size=3,
random_state=rng),
True),
('MiniBatchDictionaryLearning',
decomposition.MiniBatchDictionaryLearning(n_components=15, alpha=0.1,
n_iter=50, batch_size=3,
random_state=rng),
True),
('Cluster centers - MiniBatchKMeans',
MiniBatchKMeans(n_clusters=n_components, tol=1e-3, batch_size=20,
max_iter=50, random_state=rng),
True),
('Factor Analysis components - FA',
decomposition.FactorAnalysis(n_components=n_components, max_iter=2),
True),
]
###############################################################################
# Plot a sample of the input data
plot_gallery("First centered Olivetti faces", faces_centered[:n_components])
###############################################################################
# Do the estimation and plot it
for name, estimator, center in estimators:
print("Extracting the top %d %s..." % (n_components, name))
t0 = time()
data = faces
if center:
data = faces_centered
estimator.fit(data)
train_time = (time() - t0)
print("done in %0.3fs" % train_time)
if hasattr(estimator, 'cluster_centers_'):
components_ = estimator.cluster_centers_
else:
components_ = estimator.components_
if hasattr(estimator, 'noise_variance_'):
plot_gallery("Pixelwise variance",
estimator.noise_variance_.reshape(1, -1), n_col=1,
n_row=1)
plot_gallery('%s - Train time %.1fs' % (name, train_time),
components_[:n_components])
plt.show()
| bsd-3-clause |
devanshdalal/scikit-learn | examples/ensemble/plot_isolation_forest.py | 39 | 2361 | """
==========================================
IsolationForest example
==========================================
An example using IsolationForest for anomaly detection.
The IsolationForest 'isolates' observations by randomly selecting a feature
and then randomly selecting a split value between the maximum and minimum
values of the selected feature.
Since recursive partitioning can be represented by a tree structure, the
number of splittings required to isolate a sample is equivalent to the path
length from the root node to the terminating node.
This path length, averaged over a forest of such random trees, is a measure
of normality and our decision function.
Random partitioning produces noticeable shorter paths for anomalies.
Hence, when a forest of random trees collectively produce shorter path lengths
for particular samples, they are highly likely to be anomalies.
.. [1] Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation forest."
Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationForest
rng = np.random.RandomState(42)
# Generate train data
X = 0.3 * rng.randn(100, 2)
X_train = np.r_[X + 2, X - 2]
# Generate some regular novel observations
X = 0.3 * rng.randn(20, 2)
X_test = np.r_[X + 2, X - 2]
# Generate some abnormal novel observations
X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))
# fit the model
clf = IsolationForest(max_samples=100, random_state=rng)
clf.fit(X_train)
y_pred_train = clf.predict(X_train)
y_pred_test = clf.predict(X_test)
y_pred_outliers = clf.predict(X_outliers)
# plot the line, the samples, and the nearest vectors to the plane
xx, yy = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50))
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.title("IsolationForest")
plt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r)
b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white')
b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='green')
c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red')
plt.axis('tight')
plt.xlim((-5, 5))
plt.ylim((-5, 5))
plt.legend([b1, b2, c],
["training observations",
"new regular observations", "new abnormal observations"],
loc="upper left")
plt.show()
| bsd-3-clause |
thp44/delphin_6_automation | data_process/2d_1d/archieve/moisture_content_comparison.py | 1 | 18274 | __author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules
import pandas as pd
import matplotlib.pyplot as plt
# RiBuild Modules
# -------------------------------------------------------------------------------------------------------------------- #
# RIBuild
out_folder = r'C:\Users\ocni\PycharmProjects\delphin_6_automation\data_process\2d_1d\processed_data'
graphic_folder = r'U:\RIBuild\2D_1D\Processed Results\4A'
hdf_file = out_folder + '/relative_moisture_content.h5'
# Open HDF
# Uninsulated
dresdenzp_highratio_uninsulated_4a = pd.read_hdf(hdf_file, 'dresden_zp_high_ratio_uninsulated_4a')
dresdenzd_highratio_uninsulated_4a = pd.read_hdf(hdf_file, 'dresden_zd_high_ratio_uninsulated_4a')
postdam_highratio_uninsulated_4a = pd.read_hdf(hdf_file, 'potsdam_high_ratio_uninsulated_4a')
dresdenzp_lowratio_uninsulated_4a = pd.read_hdf(hdf_file, 'dresden_zp_low_ratio_uninsulated_4a')
dresdenzd_lowratio_uninsulated_4a = pd.read_hdf(hdf_file, 'dresden_zd_low_ratio_uninsulated_4a')
postdam_lowratio_uninsulated_4a = pd.read_hdf(hdf_file, 'potsdam_low_ratio_uninsulated_4a')
total_uninsulated_4a = pd.concat([dresdenzp_highratio_uninsulated_4a, dresdenzd_highratio_uninsulated_4a,
postdam_highratio_uninsulated_4a, dresdenzp_lowratio_uninsulated_4a,
dresdenzd_lowratio_uninsulated_4a, postdam_lowratio_uninsulated_4a])
# Insulated
dresdenzp_highratio_insulated_4a = pd.read_hdf(hdf_file, 'dresden_zp_high_ratio_insulated_4a')
dresdenzd_highratio_insulated_4a = pd.read_hdf(hdf_file, 'dresden_zd_high_ratio_insulated_4a')
postdam_highratio_insulated_4a = pd.read_hdf(hdf_file, 'potsdam_high_ratio_insulated_4a')
dresdenzp_lowratio_insulated_4a = pd.read_hdf(hdf_file, 'dresden_zp_low_ratio_insulated_4a')
dresdenzd_lowratio_insulated_4a = pd.read_hdf(hdf_file, 'dresden_zd_low_ratio_insulated_4a')
postdam_lowratio_insulated_4a = pd.read_hdf(hdf_file, 'potsdam_low_ratio_insulated_4a')
total_insulated_4a = pd.concat([dresdenzp_highratio_insulated_4a, dresdenzd_highratio_insulated_4a,
postdam_highratio_insulated_4a, dresdenzp_lowratio_insulated_4a,
dresdenzd_lowratio_insulated_4a, postdam_lowratio_insulated_4a])
def plots(plot, save=False):
"""
Creates box plots from all the wall scenarios
"""
if plot == 'uninsulated' or plot == 'all':
plt.figure('dresdenzp_highratio_uninsulated_4a_moisture', figsize=(16, 8), tight_layout=True)
dresdenzp_highratio_uninsulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 1100)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Dresden ZP - Mortar: High Cement Ratio - Insulation: None')
if save:
plt.savefig(f"{graphic_folder}/dresdenzp_highratio_uninsulated_4a_moisture")
plt.figure('dresdenzd_highratio_uninsulated_4a_moisture', figsize=(16, 8), tight_layout=True)
dresdenzd_highratio_uninsulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 1100)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Dresden ZD - Mortar: High Cement Ratio - Insulation: None')
if save:
plt.savefig(f"{graphic_folder}/dresdenzd_highratio_uninsulated_4a_moisture")
plt.figure('postdam_highratio_uninsulated_4a_moisture', figsize=(16, 8), tight_layout=True)
postdam_highratio_uninsulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 1100)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Potsdam - Mortar: High Cement Ratio - Insulation: None')
if save:
plt.savefig(f"{graphic_folder}/postdam_highratio_uninsulated_4a_moisture")
plt.figure('dresdenzp_lowratio_uninsulated_4a_moisture', figsize=(16, 8), tight_layout=True)
dresdenzp_lowratio_uninsulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 1100)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Dresden ZP - Mortar: Low Cement Ratio - Insulation: None')
if save:
plt.savefig(f"{graphic_folder}/dresdenzp_lowratio_uninsulated_4a_moisture")
plt.figure('dresdenzd_lowratio_uninsulated_4a_moisture', figsize=(16, 8), tight_layout=True)
dresdenzd_lowratio_uninsulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 1100)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Dresden ZD - Mortar: Low Cement Ratio - Insulation: None')
if save:
plt.savefig(f"{graphic_folder}/dresdenzd_lowratio_uninsulated_4a_moisture")
plt.figure('postdam_lowratio_uninsulated_4a_moisture', figsize=(16, 8), tight_layout=True)
postdam_lowratio_uninsulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 1100)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Potsdam - Mortar: Low Cement Ratio - Insulation: None')
if save:
plt.savefig(f"{graphic_folder}/postdam_lowratio_uninsulated_4a_moisture")
plt.figure('total_uninsulated_4a_moisture', figsize=(16, 8), tight_layout=True)
total_uninsulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 1100)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: All - Mortar: All - Insulation: None')
if save:
plt.savefig(f"{graphic_folder}/total_uninsulated_4a_moisture")
if plot == 'insulated' or plot == 'all':
plt.figure('dresdenzp_highratio_insulated_4a_moisture', figsize=(16, 8), tight_layout=True)
dresdenzp_highratio_insulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 2000)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Dresden ZP - Mortar: High Cement Ratio - Insulation: Calcium Silicate')
if save:
plt.savefig(f"{graphic_folder}/dresdenzp_highratio_insulated_4a_moisture")
plt.figure('dresdenzd_highratio_insulated_4a_moisture', figsize=(16, 8), tight_layout=True)
dresdenzd_highratio_insulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 2000)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Dresden ZD - Mortar: High Cement Ratio - Insulation: Calcium Silicate')
if save:
plt.savefig(f"{graphic_folder}/dresdenzd_highratio_insulated_4a_moisture")
plt.figure('postdam_highratio_insulated_4a_moisture', figsize=(16, 8), tight_layout=True)
postdam_highratio_insulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 2000)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Potsdam - Mortar: High Cement Ratio - Insulation: Calcium Silicate')
if save:
plt.savefig(f"{graphic_folder}/postdam_highratio_insulated_4a_moisture")
plt.figure('dresdenzp_lowratio_insulated_4a_moisture', figsize=(16, 8), tight_layout=True)
dresdenzp_lowratio_insulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 2000)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Dresden ZP - Mortar: Low Cement Ratio - Insulation: Calcium Silicate')
if save:
plt.savefig(f"{graphic_folder}/dresdenzp_lowratio_insulated_4a_moisture")
plt.figure('dresdenzd_lowratio_insulated_4a_moisture', figsize=(16, 8), tight_layout=True)
dresdenzd_lowratio_insulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 2000)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Dresden ZD - Mortar: Low Cement Ratio - Insulation: Calcium Silicate')
if save:
plt.savefig(f"{graphic_folder}/dresdenzd_lowratio_insulated_4a_moisture")
plt.figure('postdam_lowratio_insulated_4a_moisture', figsize=(16, 8), tight_layout=True)
postdam_lowratio_insulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 2000)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: Potsdam - Mortar: Low Cement Ratio - Insulation: Calcium Silicate')
if save:
plt.savefig(f"{graphic_folder}/postdam_lowratio_insulated_4a_moisture")
plt.figure('total_insulated_4a_moisture', figsize=(16, 8), tight_layout=True)
total_insulated_4a.boxplot(showfliers=False)
plt.ylim(-5, 2000)
plt.ylabel('Relative Difference in %')
plt.title('Weighted Relative Difference between 1D and 2D\n'
'Moisture Content\n'
'Brick: All - Mortar: All - Insulation: Calcium Silicate')
if save:
plt.savefig(f"{graphic_folder}/total_insulated_4a_moisture")
plt.show()
plots('all', False)
def std3_ratio(print_=False, excel=False):
"""Computes ratio of outliers in the data sets. Outliers is here defined as data points deviating with more
the 3 standard deviations from the mean."""
std3_uninsulated_ratio_ = uninsulated()
std3_insulated_ratio_ = insulated()
if print_:
print('Uninsulated')
print(std3_uninsulated_ratio_)
print('')
print('Insulated')
print(std3_insulated_ratio_)
if excel:
writer = pd.ExcelWriter(f'{out_folder}/moisture_std_ratios.xlsx')
std3_uninsulated_ratio_.to_excel(writer, 'Uninsulated')
std3_insulated_ratio_.to_excel(writer, 'Insulated')
writer.save()
def uninsulated():
"""Computes the outliers for the uninsulated cases"""
outliers_total_uninsulated = (total_uninsulated_4a.shape[0] -
total_uninsulated_4a.sub(total_uninsulated_4a.mean())
.div(total_uninsulated_4a.std()).abs().lt(3).sum()) / total_uninsulated_4a.shape[0]
outliers_zd_high_uninsulated = (dresdenzd_highratio_uninsulated_4a.shape[0] -
dresdenzd_highratio_uninsulated_4a.sub(dresdenzd_highratio_uninsulated_4a.mean())
.div(dresdenzd_highratio_uninsulated_4a.std()).abs().lt(3).sum()) \
/ dresdenzd_highratio_uninsulated_4a.shape[0]
outliers_zp_high_uninsulated = (dresdenzp_highratio_uninsulated_4a.shape[0] -
dresdenzp_highratio_uninsulated_4a.sub(dresdenzp_highratio_uninsulated_4a.mean())
.div(dresdenzp_highratio_uninsulated_4a.std()).abs().lt(3).sum()) \
/ dresdenzp_highratio_uninsulated_4a.shape[0]
outliers_pd_high_uninsulated = (postdam_highratio_uninsulated_4a.shape[0] -
postdam_highratio_uninsulated_4a.sub(postdam_highratio_uninsulated_4a.mean())
.div(postdam_highratio_uninsulated_4a.std()).abs().lt(3).sum()) \
/ postdam_highratio_uninsulated_4a.shape[0]
outliers_zd_low_uninsulated = (dresdenzd_lowratio_uninsulated_4a.shape[0] -
dresdenzd_lowratio_uninsulated_4a.sub(dresdenzd_lowratio_uninsulated_4a.mean())
.div(dresdenzd_lowratio_uninsulated_4a.std()).abs().lt(3).sum()) \
/ dresdenzd_lowratio_uninsulated_4a.shape[0]
outliers_zp_low_uninsulated = (dresdenzp_lowratio_uninsulated_4a.shape[0] -
dresdenzp_lowratio_uninsulated_4a.sub(dresdenzp_lowratio_uninsulated_4a.mean())
.div(dresdenzp_lowratio_uninsulated_4a.std()).abs().lt(3).sum()) \
/ dresdenzp_lowratio_uninsulated_4a.shape[0]
outliers_pd_low_uninsulated = (postdam_lowratio_uninsulated_4a.shape[0] -
postdam_lowratio_uninsulated_4a.sub(postdam_lowratio_uninsulated_4a.mean())
.div(postdam_lowratio_uninsulated_4a.std()).abs().lt(3).sum()) \
/ postdam_lowratio_uninsulated_4a.shape[0]
outliers_uninsulated_ratio_ = pd.concat([outliers_total_uninsulated, outliers_zd_high_uninsulated,
outliers_zp_high_uninsulated, outliers_pd_high_uninsulated,
outliers_zd_low_uninsulated, outliers_zp_low_uninsulated,
outliers_pd_low_uninsulated], axis=1)
outliers_uninsulated_ratio_.columns = ["Brick: All - Mortar: All - Insulation: None",
"Brick: Dresden ZD - Mortar: High Cement Ratio - Insulation: None",
"Brick: Dresden ZP - Mortar: High Cement Ratio - Insulation: None",
"Brick: Potsdam - Mortar: High Cement Ratio - Insulation: None",
"Brick: Dresden ZD - Mortar: Low Cement Ratio - Insulation: None",
"Brick: Dresden ZP - Mortar: Low Cement Ratio - Insulation: None",
"Brick: Potsdam - Mortar: Low Cement Ratio - Insulation: None"]
return outliers_uninsulated_ratio_
def insulated():
"""Computes the outliers for the insulated cases"""
outliers_total_insulated = (total_insulated_4a.shape[0] - total_insulated_4a.sub(total_insulated_4a.mean())
.div(total_insulated_4a.std()).abs().lt(3).sum()) / total_insulated_4a.shape[0]
outliers_zd_high_insulated = (dresdenzd_highratio_insulated_4a.shape[0] -
dresdenzd_highratio_insulated_4a.sub(dresdenzd_highratio_insulated_4a.mean())
.div(dresdenzd_highratio_insulated_4a.std()).abs().lt(3).sum()) \
/ dresdenzd_highratio_insulated_4a.shape[0]
outliers_zp_high_insulated = (dresdenzp_highratio_insulated_4a.shape[0] -
dresdenzp_highratio_insulated_4a.sub(dresdenzp_highratio_insulated_4a.mean())
.div(dresdenzp_highratio_insulated_4a.std()).abs().lt(3).sum()) \
/ dresdenzp_highratio_insulated_4a.shape[0]
outliers_pd_high_insulated = (postdam_highratio_insulated_4a.shape[0] -
postdam_highratio_insulated_4a.sub(postdam_highratio_insulated_4a.mean())
.div(postdam_highratio_insulated_4a.std()).abs().lt(3).sum()) \
/ postdam_highratio_insulated_4a.shape[0]
outliers_zd_low_insulated = (dresdenzd_lowratio_insulated_4a.shape[0] -
dresdenzd_lowratio_insulated_4a.sub(dresdenzd_lowratio_insulated_4a.mean())
.div(dresdenzd_lowratio_insulated_4a.std()).abs().lt(3).sum()) \
/ dresdenzd_lowratio_insulated_4a.shape[0]
outliers_zp_low_insulated = (dresdenzp_lowratio_insulated_4a.shape[0] -
dresdenzp_lowratio_insulated_4a.sub(dresdenzp_lowratio_insulated_4a.mean())
.div(dresdenzp_lowratio_insulated_4a.std()).abs().lt(3).sum()) \
/ dresdenzp_lowratio_insulated_4a.shape[0]
outliers_pd_low_insulated = (postdam_lowratio_insulated_4a.shape[0] -
postdam_lowratio_insulated_4a.sub(postdam_lowratio_insulated_4a.mean())
.div(postdam_lowratio_insulated_4a.std()).abs().lt(3).sum()) \
/ postdam_lowratio_insulated_4a.shape[0]
std2_insulated_ratio_ = pd.concat([outliers_total_insulated, outliers_zd_high_insulated,
outliers_zp_high_insulated, outliers_pd_high_insulated,
outliers_zd_low_insulated, outliers_zp_low_insulated,
outliers_pd_low_insulated], axis=1)
std2_insulated_ratio_.columns = ["Brick: All - Mortar: All - Insulation: None",
"Brick: Dresden ZD - Mortar: High Cement Ratio - Insulation: Calcium Silicate",
"Brick: Dresden ZP - Mortar: High Cement Ratio - Insulation: Calcium Silicate",
"Brick: Potsdam - Mortar: High Cement Ratio - Insulation: Calcium Silicate",
"Brick: Dresden ZD - Mortar: Low Cement Ratio - Insulation: Calcium Silicate",
"Brick: Dresden ZP - Mortar: Low Cement Ratio - Insulation: Calcium Silicate",
"Brick: Potsdam - Mortar: Low Cement Ratio - Insulation: Calcium Silicate"]
return std2_insulated_ratio_
#std3_ratio(False, True)
| mit |
miaecle/deepchem | devtools/archive/jenkins/generate_graph.py | 2 | 5220 | import csv
import os
import numpy as np
import matplotlib.pyplot as plt
import time
plt.switch_backend('agg')
TODO = {
('tox21', 'random'): [
'weave', 'graphconv', 'tf', 'tf_robust', 'irv', 'xgb', 'logreg',
'textcnn'
],
('clintox', 'random'): [
'weave', 'graphconv', 'tf', 'tf_robust', 'irv', 'xgb', 'logreg',
'textcnn'
],
('sider', 'random'): [
'weave', 'graphconv', 'tf', 'tf_robust', 'irv', 'xgb', 'logreg',
'textcnn'
],
('bbbp', 'scaffold'):
['weave', 'graphconv', 'tf', 'irv', 'xgb', 'logreg', 'textcnn'],
('bace_c', 'scaffold'):
['weave', 'graphconv', 'tf', 'irv', 'xgb', 'logreg', 'textcnn'],
('hiv', 'scaffold'):
['weave', 'graphconv', 'tf', 'irv', 'xgb', 'logreg', 'textcnn'],
('muv', 'random'): ['graphconv', 'tf', 'tf_robust', 'irv', 'xgb', 'logreg'],
('delaney', 'random'): [
'weave_regression', 'graphconvreg', 'tf_regression', 'xgb_regression',
'krr', 'textcnn_regression', 'dag_regression', 'mpnn'
],
('sampl', 'random'): [
'weave_regression', 'graphconvreg', 'tf_regression', 'xgb_regression',
'krr', 'textcnn_regression', 'dag_regression', 'mpnn'
],
('lipo', 'random'): [
'weave_regression', 'graphconvreg', 'tf_regression', 'xgb_regression',
'krr', 'textcnn_regression', 'dag_regression', 'mpnn'
],
('qm7', 'stratified'): [
'dtnn', 'graphconvreg', 'tf_regression_ft', 'krr_ft'
],
('qm8', 'random'): [
'dtnn', 'graphconvreg', 'weave_regression', 'textcnn_regression',
'mpnn', 'tf_regression', 'tf_regression_ft'
],
}
ORDER = [
'logreg', 'rf', 'rf_regression', 'xgb', 'xgb_regression', 'kernelsvm',
'krr', 'krr_ft', 'tf', 'tf_regression', 'tf_regression_ft', 'tf_robust',
'irv', 'textcnn', 'textcnn_regression', 'graphconv', 'graphconvreg', 'dag',
'dag_regression', 'ani', 'weave', 'weave_regression', 'dtnn', 'mpnn'
]
COLOR = {
'logreg': '#3F3F3F',
'rf': '#67AD4F',
'rf_regression': '#67AD4F',
'xgb': '#0E766C',
'xgb_regression': '#0E766C',
'kernelsvm': '#FC926B',
'krr': '#FC926B',
'krr_ft': '#5A372A',
'tf': '#2B6596',
'tf_regression': '#2B6596',
'tf_regression_ft': '#162939',
'tf_robust': '#775183',
'irv': '#D9D9D9',
'graphconv': '#A4D192',
'graphconvreg': '#A4D192',
'dag': '#D06329',
'dag_regression': '#D06329',
'ani': '#D9D9D9',
'weave': '#8196AE',
'weave_regression': '#8196AE',
'textcnn': '#811B18',
'textcnn_regression': '#811B18',
'dtnn': '#D06329',
'mpnn': '#7B0A48'
}
TODO_list = set()
for key in TODO.keys():
for val in TODO[key]:
TODO_list.add((key[0], key[1], val))
def read_results(path):
Results = set()
with open(path, 'r') as f:
reader = csv.reader(f)
for line in reader:
Results.add((line[0], line[1], line[3]))
return Results
def run_benchmark(path, deepchem_dir):
finished = read_results(path)
os.chdir(deepchem_dir)
os.chdir('./examples')
while len(TODO_list - finished) > 0:
todo = TODO_list - finished
for p in todo:
os.system('python benchmark.py --seed 123 -d ' + p[0] + ' -s ' + p[1] +
' -m ' + p[2])
def plot(dataset, split, path, out_path):
if dataset in [
'bace_c', 'bbbp', 'clintox', 'hiv', 'muv', 'pcba', 'pcba_146',
'pcba_2475', 'sider', 'tox21', 'toxcast'
]:
mode = 'classification'
else:
mode = 'regression'
data = {}
with open(path, 'r') as f:
reader = csv.reader(f)
for line in reader:
if line[0] == dataset and line[1] == split:
data[line[3]] = line[8]
labels = []
values = []
colors = []
for model in ORDER:
if model in data.keys():
labels.append(model)
colors.append(COLOR[model])
values.append(float(data[model]))
y_pos = np.arange(len(labels))
plt.rcdefaults()
fig, ax = plt.subplots()
ax.barh(y_pos, values, align='center', color='green')
ax.set_yticks(y_pos)
ax.set_yticklabels(labels)
ax.invert_yaxis()
if mode == 'regression':
ax.set_xlabel('R square')
ax.set_xlim(left=0., right=1.)
else:
ax.set_xlabel('ROC-AUC')
ax.set_xlim(left=0.4, right=1.)
t = time.localtime(time.time())
ax.set_title("Performance on %s (%s split), %i-%i-%i" %
(dataset, split, t.tm_year, t.tm_mon, t.tm_mday))
plt.tight_layout()
for i in range(len(colors)):
ax.get_children()[i].set_color(colors[i])
ax.text(
values[i] - 0.1, y_pos[i] + 0.1, str("%.3f" % values[i]), color='white')
fig.savefig(os.path.join(out_path, dataset + '_' + split + '.png'))
#plt.show()
if __name__ == '__main__':
current_dir = os.path.dirname(os.path.realpath(__file__))
DEEPCHEM_DIR = os.path.split(os.path.split(current_dir)[0])[0]
FILE = os.path.join(os.path.join(DEEPCHEM_DIR, 'examples'), 'results.csv')
#run_benchmark(FILE, DEEPCHEM_DIR)
save_dir = os.path.join(DEEPCHEM_DIR, 'datasets/MolNet_pic')
if not os.path.exists(save_dir):
os.mkdir(save_dir)
for pair in TODO.keys():
plot(pair[0], pair[1], FILE, save_dir)
os.system('aws s3 sync ' + save_dir +
' s3://deepchem.io/trained_models/MolNet_pic')
| mit |
xiongzhenggang/xiongzhenggang.github.io | AI/data/deeplearning24054/planar_utils.py | 2 | 2271 | import matplotlib.pyplot as plt
import numpy as np
import sklearn
import sklearn.datasets
import sklearn.linear_model
def plot_decision_boundary(model, X, Y):
# Set min and max values and give it some padding
x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1
y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1
h = 0.01
# Generate a grid of points with distance h between them
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# Predict the function value for the whole grid
Z = model(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
# Plot the contour and training examples
plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
plt.ylabel('x2')
plt.xlabel('x1')
plt.scatter(X[0, :], X[1, :], c=Y.reshape(X[0,:].shape), cmap=plt.cm.Spectral)
def sigmoid(x):
"""
Compute the sigmoid of x
Arguments:
x -- A scalar or numpy array of any size.
Return:
s -- sigmoid(x)
"""
s = 1/(1+np.exp(-x))
return s
def load_planar_dataset():
np.random.seed(1)
m = 400 # number of examples
N = int(m/2) # number of points per class
D = 2 # dimensionality
X = np.zeros((m,D)) # data matrix where each row is a single example
Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue)
a = 4 # maximum ray of the flower
for j in range(2):
ix = range(N*j,N*(j+1))
t = np.linspace(j*3.12,(j+1)*3.12,N) + np.random.randn(N)*0.2 # theta
r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius
X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
Y[ix] = j
X = X.T
Y = Y.T
return X, Y
def load_extra_datasets():
N = 200
noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3)
noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2)
blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6)
gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None)
no_structure = np.random.rand(N, 2), np.random.rand(N, 2)
return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure | gpl-3.0 |
airanmehr/bio | Scripts/TimeSeriesPaper/Plot/topSNPs.py | 1 | 1589 | '''
Copyleft Oct 14, 2016 Arya Iranmehr, PhD Student, Bafna Lab, UC San Diego, Email: airanmehr@gmail.com
'''
import numpy as np;
np.set_printoptions(linewidth=200, precision=5, suppress=True)
import pandas as pd;
pd.options.display.max_rows = 20;
pd.options.display.expand_frame_repr = False
import seaborn as sns
import matplotlib as mpl
import os;
home = os.path.expanduser('~') + '/'
import Utils.Util as utl
import Scripts.TimeSeriesPaper.RealData.Utils as rutl
a = rutl.loadAllScores().groupby(level='h', axis=1).apply(rutl.HstatisticAll)
df = pd.read_pickle(utl.outpath + 'real/scores.df')
i = df.lrd.sort_values().index[-1]
df.loc[i]
cd = pd.read_pickle(utl.outpath + 'real/CD.F59.df')
import Utils.Plots as pplt
import pylab as plt
names = rutl.loadSNPIDs()
sns.set_style("white", {"grid.color": "0.9", 'axes.linewidth': .5, "grid.linewidth": "9.99"})
mpl.rc('font', **{'family': 'serif', 'serif': ['Computer Modern']});
mpl.rc('text', usetex=True)
reload(pplt)
f, ax = plt.subplots(1, 2, sharey=True, dpi=300, figsize=(4, 2))
i = a[0.5].sort_values().index[-1]
sns.set_context("notebook", font_scale=1, rc={"lines.linewidth": 1.2})
pplt.plotSiteReal(cd.loc[i], ax=ax[0], legend=True)
ax[0].set_title('{}:{:.0f} ({})'.format(i[0], i[1], names.loc[i]), fontsize=8)
i = df.lrdiff.sort_values().index[-1]
pplt.plotSiteReal(cd.loc[i], ax=ax[1])
sns.set_context("notebook", font_scale=1, rc={"lines.linewidth": 1.2})
ax[1].set_title('{}:{:.0f} ({})'.format(i[0], i[1], names.loc[i]), fontsize=8)
plt.gcf().subplots_adjust(bottom=0.2)
pplt.savefig('topSNPs', 300)
plt.show()
| mit |
sighingnow/sighingnow.github.io | resource/k_nearest_neighbors/dating.py | 1 | 3622 | #! /usr/bin/env python
# -*- coding: utf-8
'''
Name: dating.py(KNN algorithm)
Training and test dataset: dating.txt
Created on Feb 8, 2015
@author: Tao He
'''
__author__ = 'Tao He'
from numpy import array as nmarray
from matplotlib import pyplot as plt
LABEL_MAP = {
'didntLike': 1,
'smallDoses': 2,
'largeDoses': 3,
}
ATTR_MAP = {
1: 'Number of frequent flyer miles earned per year',
2: 'Percentage of time spent playing video games',
3: 'Liters of ice cream consumed per week',
}
def create_dataset(filename=None):
''' Return data group and labels.
Get the data from file.
If the filename is not specialed, return None.
dataformat: flyerMiles, gameTime, icecream, label.
'''
def normalize_data(data=None):
''' Normalized dataset.
Normalize all data to range 0-1.
'''
if data is None:
return None
for column in range(data[0].__len__()):
max_val, min_val = max(data[:, column]), min(data[:, column])
for row in range(data.__len__()):
data[row][column] = (data[row][column]-min_val)/(max_val-min_val)
return data
if filename == None:
return (None, None)
group = []
labels = []
with open(filename, mode='r') as fp_data:
for line in fp_data:
group.append([float(num) for num in line[:-1].split('\t')[0:3]])
labels.append(LABEL_MAP[line[:-1].split('\t')[3]])
return normalize_data(nmarray(group)), labels
def draw_pic(group=None, labels=None, x=0, y=0):
''' Draw a subplot from data group.
'''
if group is None or labels is None:
return None
name = 'knn-dating'
figure = plt.figure(num=name, dpi=100)
ax_main = figure.add_subplot(1, 1, 1, xlabel=ATTR_MAP[x+1], ylabel=ATTR_MAP[y+1], title=name)
ax_main.scatter(group[:, x], group[:, y],
s=15*nmarray(labels),
c=[[i/LABEL_MAP.__len__()] for i in labels])
plt.show()
## plt.savefig('%s.png'%name, format='png', dpi=100)
def knn_classify(group, labels, attrs, ratio=0.5, item=0, k=3):
''' Return the type of item.
knn classify function.
'''
def get_dist(i, j):
''' Return the distence of group[i] and group[j].
'''
dist = 0.0
for attr in attrs:
dist += (group[i][attr]-group[j][attr])*(group[i][attr]-group[j][attr])
return dist
length = group.__len__()
distence = []
for i in range(int(length*ratio), length):
distence.append((i, get_dist(item, i)))
cnt = {}
distence.sort(key=lambda item: item[1])
for i in range(k):
label = labels[distence[i][0]]
if label in cnt:
cnt[label] += 1
else:
cnt[label] = 1
return sorted(cnt.items(), key=lambda item: item[1], reverse=True)[0][0]
def knn():
''' KNN classify algorithm.
'''
data, labels = create_dataset('dating.txt')
ratio, attr = 0.5, [0, 1, 2]
cnt, cnt_correct = 0, 0
length = data.__len__()
for i in range(0, int(length*ratio)):
cnt += 1
knn_type = knn_classify(data, labels, attr, ratio, i, 3)
# print('case[%d]: real: %d, knn: %d'%(i, labels[i], knn_type))
if knn_type == labels[i]:
cnt_correct += 1
print('total: %d, correct: %d, correct ratio: %f'%(cnt, cnt_correct, cnt_correct/cnt))
if __name__ == '__main__':
knn()
# vim: set sw=4, ts=4, fileencoding=utf-8
| mit |
Obus/scikit-learn | benchmarks/bench_plot_approximate_neighbors.py | 85 | 6377 | """
Benchmark for approximate nearest neighbor search using
locality sensitive hashing forest.
There are two types of benchmarks.
First, accuracy of LSHForest queries are measured for various
hyper-parameters and index sizes.
Second, speed up of LSHForest queries compared to brute force
method in exact nearest neighbors is measures for the
aforementioned settings. In general, speed up is increasing as
the index size grows.
"""
from __future__ import division
import numpy as np
from tempfile import gettempdir
from time import time
from sklearn.neighbors import NearestNeighbors
from sklearn.neighbors.approximate import LSHForest
from sklearn.datasets import make_blobs
from sklearn.externals.joblib import Memory
m = Memory(cachedir=gettempdir())
@m.cache()
def make_data(n_samples, n_features, n_queries, random_state=0):
"""Create index and query data."""
print('Generating random blob-ish data')
X, _ = make_blobs(n_samples=n_samples + n_queries,
n_features=n_features, centers=100,
shuffle=True, random_state=random_state)
# Keep the last samples as held out query vectors: note since we used
# shuffle=True we have ensured that index and query vectors are
# samples from the same distribution (a mixture of 100 gaussians in this
# case)
return X[:n_samples], X[n_samples:]
def calc_exact_neighbors(X, queries, n_queries, n_neighbors):
"""Measures average times for exact neighbor queries."""
print ('Building NearestNeighbors for %d samples in %d dimensions' %
(X.shape[0], X.shape[1]))
nbrs = NearestNeighbors(algorithm='brute', metric='cosine').fit(X)
average_time = 0
t0 = time()
neighbors = nbrs.kneighbors(queries, n_neighbors=n_neighbors,
return_distance=False)
average_time = (time() - t0) / n_queries
return neighbors, average_time
def calc_accuracy(X, queries, n_queries, n_neighbors, exact_neighbors,
average_time_exact, **lshf_params):
"""Calculates accuracy and the speed up of LSHForest."""
print('Building LSHForest for %d samples in %d dimensions' %
(X.shape[0], X.shape[1]))
lshf = LSHForest(**lshf_params)
t0 = time()
lshf.fit(X)
lshf_build_time = time() - t0
print('Done in %0.3fs' % lshf_build_time)
accuracy = 0
t0 = time()
approx_neighbors = lshf.kneighbors(queries, n_neighbors=n_neighbors,
return_distance=False)
average_time_approx = (time() - t0) / n_queries
for i in range(len(queries)):
accuracy += np.in1d(approx_neighbors[i], exact_neighbors[i]).mean()
accuracy /= n_queries
speed_up = average_time_exact / average_time_approx
print('Average time for lshf neighbor queries: %0.3fs' %
average_time_approx)
print ('Average time for exact neighbor queries: %0.3fs' %
average_time_exact)
print ('Average Accuracy : %0.2f' % accuracy)
print ('Speed up: %0.1fx' % speed_up)
return speed_up, accuracy
if __name__ == '__main__':
import matplotlib.pyplot as plt
# Initialize index sizes
n_samples = [int(1e3), int(1e4), int(1e5), int(1e6)]
n_features = int(1e2)
n_queries = 100
n_neighbors = 10
X_index, X_query = make_data(np.max(n_samples), n_features, n_queries,
random_state=0)
params_list = [{'n_estimators': 3, 'n_candidates': 50},
{'n_estimators': 5, 'n_candidates': 70},
{'n_estimators': 10, 'n_candidates': 100}]
accuracies = np.zeros((len(n_samples), len(params_list)), dtype=float)
speed_ups = np.zeros((len(n_samples), len(params_list)), dtype=float)
for i, sample_size in enumerate(n_samples):
print ('==========================================================')
print ('Sample size: %i' % sample_size)
print ('------------------------')
exact_neighbors, average_time_exact = calc_exact_neighbors(
X_index[:sample_size], X_query, n_queries, n_neighbors)
for j, params in enumerate(params_list):
print ('LSHF parameters: n_estimators = %i, n_candidates = %i' %
(params['n_estimators'], params['n_candidates']))
speed_ups[i, j], accuracies[i, j] = calc_accuracy(
X_index[:sample_size], X_query, n_queries, n_neighbors,
exact_neighbors, average_time_exact, random_state=0, **params)
print ('')
print ('==========================================================')
# Set labels for LSHForest parameters
colors = ['c', 'm', 'y']
p1 = plt.Rectangle((0, 0), 0.1, 0.1, fc=colors[0])
p2 = plt.Rectangle((0, 0), 0.1, 0.1, fc=colors[1])
p3 = plt.Rectangle((0, 0), 0.1, 0.1, fc=colors[2])
labels = ['n_estimators=' + str(params_list[0]['n_estimators']) +
', n_candidates=' + str(params_list[0]['n_candidates']),
'n_estimators=' + str(params_list[1]['n_estimators']) +
', n_candidates=' + str(params_list[1]['n_candidates']),
'n_estimators=' + str(params_list[2]['n_estimators']) +
', n_candidates=' + str(params_list[2]['n_candidates'])]
# Plot precision
plt.figure()
plt.legend((p1, p2, p3), (labels[0], labels[1], labels[2]),
loc='upper left')
for i in range(len(params_list)):
plt.scatter(n_samples, accuracies[:, i], c=colors[i])
plt.plot(n_samples, accuracies[:, i], c=colors[i])
plt.ylim([0, 1.3])
plt.xlim(np.min(n_samples), np.max(n_samples))
plt.semilogx()
plt.ylabel("Precision@10")
plt.xlabel("Index size")
plt.grid(which='both')
plt.title("Precision of first 10 neighbors with index size")
# Plot speed up
plt.figure()
plt.legend((p1, p2, p3), (labels[0], labels[1], labels[2]),
loc='upper left')
for i in range(len(params_list)):
plt.scatter(n_samples, speed_ups[:, i], c=colors[i])
plt.plot(n_samples, speed_ups[:, i], c=colors[i])
plt.ylim(0, np.max(speed_ups))
plt.xlim(np.min(n_samples), np.max(n_samples))
plt.semilogx()
plt.ylabel("Speed up")
plt.xlabel("Index size")
plt.grid(which='both')
plt.title("Relationship between Speed up and index size")
plt.show()
| bsd-3-clause |
cswiercz/sympy | sympy/physics/quantum/state.py | 58 | 29186 | """Dirac notation for states."""
from __future__ import print_function, division
from sympy import (cacheit, conjugate, Expr, Function, integrate, oo, sqrt,
Tuple)
from sympy.core.compatibility import u, range
from sympy.printing.pretty.stringpict import stringPict
from sympy.physics.quantum.qexpr import QExpr, dispatch_method
__all__ = [
'KetBase',
'BraBase',
'StateBase',
'State',
'Ket',
'Bra',
'TimeDepState',
'TimeDepBra',
'TimeDepKet',
'Wavefunction'
]
#-----------------------------------------------------------------------------
# States, bras and kets.
#-----------------------------------------------------------------------------
# ASCII brackets
_lbracket = "<"
_rbracket = ">"
_straight_bracket = "|"
# Unicode brackets
# MATHEMATICAL ANGLE BRACKETS
_lbracket_ucode = u("\N{MATHEMATICAL LEFT ANGLE BRACKET}")
_rbracket_ucode = u("\N{MATHEMATICAL RIGHT ANGLE BRACKET}")
# LIGHT VERTICAL BAR
_straight_bracket_ucode = u("\N{LIGHT VERTICAL BAR}")
# Other options for unicode printing of <, > and | for Dirac notation.
# LEFT-POINTING ANGLE BRACKET
# _lbracket = u"\u2329"
# _rbracket = u"\u232A"
# LEFT ANGLE BRACKET
# _lbracket = u"\u3008"
# _rbracket = u"\u3009"
# VERTICAL LINE
# _straight_bracket = u"\u007C"
class StateBase(QExpr):
"""Abstract base class for general abstract states in quantum mechanics.
All other state classes defined will need to inherit from this class. It
carries the basic structure for all other states such as dual, _eval_adjoint
and label.
This is an abstract base class and you should not instantiate it directly,
instead use State.
"""
@classmethod
def _operators_to_state(self, ops, **options):
""" Returns the eigenstate instance for the passed operators.
This method should be overridden in subclasses. It will handle being
passed either an Operator instance or set of Operator instances. It
should return the corresponding state INSTANCE or simply raise a
NotImplementedError. See cartesian.py for an example.
"""
raise NotImplementedError("Cannot map operators to states in this class. Method not implemented!")
def _state_to_operators(self, op_classes, **options):
""" Returns the operators which this state instance is an eigenstate
of.
This method should be overridden in subclasses. It will be called on
state instances and be passed the operator classes that we wish to make
into instances. The state instance will then transform the classes
appropriately, or raise a NotImplementedError if it cannot return
operator instances. See cartesian.py for examples,
"""
raise NotImplementedError(
"Cannot map this state to operators. Method not implemented!")
@property
def operators(self):
"""Return the operator(s) that this state is an eigenstate of"""
from .operatorset import state_to_operators # import internally to avoid circular import errors
return state_to_operators(self)
def _enumerate_state(self, num_states, **options):
raise NotImplementedError("Cannot enumerate this state!")
def _represent_default_basis(self, **options):
return self._represent(basis=self.operators)
#-------------------------------------------------------------------------
# Dagger/dual
#-------------------------------------------------------------------------
@property
def dual(self):
"""Return the dual state of this one."""
return self.dual_class()._new_rawargs(self.hilbert_space, *self.args)
@classmethod
def dual_class(self):
"""Return the class used to construt the dual."""
raise NotImplementedError(
'dual_class must be implemented in a subclass'
)
def _eval_adjoint(self):
"""Compute the dagger of this state using the dual."""
return self.dual
#-------------------------------------------------------------------------
# Printing
#-------------------------------------------------------------------------
def _pretty_brackets(self, height, use_unicode=True):
# Return pretty printed brackets for the state
# Ideally, this could be done by pform.parens but it does not support the angled < and >
# Setup for unicode vs ascii
if use_unicode:
lbracket, rbracket = self.lbracket_ucode, self.rbracket_ucode
slash, bslash, vert = u('\N{BOX DRAWINGS LIGHT DIAGONAL UPPER RIGHT TO LOWER LEFT}'), \
u('\N{BOX DRAWINGS LIGHT DIAGONAL UPPER LEFT TO LOWER RIGHT}'), \
u('\N{BOX DRAWINGS LIGHT VERTICAL}')
else:
lbracket, rbracket = self.lbracket, self.rbracket
slash, bslash, vert = '/', '\\', '|'
# If height is 1, just return brackets
if height == 1:
return stringPict(lbracket), stringPict(rbracket)
# Make height even
height += (height % 2)
brackets = []
for bracket in lbracket, rbracket:
# Create left bracket
if bracket in set([_lbracket, _lbracket_ucode]):
bracket_args = [ ' ' * (height//2 - i - 1) +
slash for i in range(height // 2)]
bracket_args.extend(
[ ' ' * i + bslash for i in range(height // 2)])
# Create right bracket
elif bracket in set([_rbracket, _rbracket_ucode]):
bracket_args = [ ' ' * i + bslash for i in range(height // 2)]
bracket_args.extend([ ' ' * (
height//2 - i - 1) + slash for i in range(height // 2)])
# Create straight bracket
elif bracket in set([_straight_bracket, _straight_bracket_ucode]):
bracket_args = [vert for i in range(height)]
else:
raise ValueError(bracket)
brackets.append(
stringPict('\n'.join(bracket_args), baseline=height//2))
return brackets
def _sympystr(self, printer, *args):
contents = self._print_contents(printer, *args)
return '%s%s%s' % (self.lbracket, contents, self.rbracket)
def _pretty(self, printer, *args):
from sympy.printing.pretty.stringpict import prettyForm
# Get brackets
pform = self._print_contents_pretty(printer, *args)
lbracket, rbracket = self._pretty_brackets(
pform.height(), printer._use_unicode)
# Put together state
pform = prettyForm(*pform.left(lbracket))
pform = prettyForm(*pform.right(rbracket))
return pform
def _latex(self, printer, *args):
contents = self._print_contents_latex(printer, *args)
# The extra {} brackets are needed to get matplotlib's latex
# rendered to render this properly.
return '{%s%s%s}' % (self.lbracket_latex, contents, self.rbracket_latex)
class KetBase(StateBase):
"""Base class for Kets.
This class defines the dual property and the brackets for printing. This is
an abstract base class and you should not instantiate it directly, instead
use Ket.
"""
lbracket = _straight_bracket
rbracket = _rbracket
lbracket_ucode = _straight_bracket_ucode
rbracket_ucode = _rbracket_ucode
lbracket_latex = r'\left|'
rbracket_latex = r'\right\rangle '
@classmethod
def default_args(self):
return ("psi",)
@classmethod
def dual_class(self):
return BraBase
def __mul__(self, other):
"""KetBase*other"""
from sympy.physics.quantum.operator import OuterProduct
if isinstance(other, BraBase):
return OuterProduct(self, other)
else:
return Expr.__mul__(self, other)
def __rmul__(self, other):
"""other*KetBase"""
from sympy.physics.quantum.innerproduct import InnerProduct
if isinstance(other, BraBase):
return InnerProduct(other, self)
else:
return Expr.__rmul__(self, other)
#-------------------------------------------------------------------------
# _eval_* methods
#-------------------------------------------------------------------------
def _eval_innerproduct(self, bra, **hints):
"""Evaluate the inner product betweeen this ket and a bra.
This is called to compute <bra|ket>, where the ket is ``self``.
This method will dispatch to sub-methods having the format::
``def _eval_innerproduct_BraClass(self, **hints):``
Subclasses should define these methods (one for each BraClass) to
teach the ket how to take inner products with bras.
"""
return dispatch_method(self, '_eval_innerproduct', bra, **hints)
def _apply_operator(self, op, **options):
"""Apply an Operator to this Ket.
This method will dispatch to methods having the format::
``def _apply_operator_OperatorName(op, **options):``
Subclasses should define these methods (one for each OperatorName) to
teach the Ket how operators act on it.
Parameters
==========
op : Operator
The Operator that is acting on the Ket.
options : dict
A dict of key/value pairs that control how the operator is applied
to the Ket.
"""
return dispatch_method(self, '_apply_operator', op, **options)
class BraBase(StateBase):
"""Base class for Bras.
This class defines the dual property and the brackets for printing. This
is an abstract base class and you should not instantiate it directly,
instead use Bra.
"""
lbracket = _lbracket
rbracket = _straight_bracket
lbracket_ucode = _lbracket_ucode
rbracket_ucode = _straight_bracket_ucode
lbracket_latex = r'\left\langle '
rbracket_latex = r'\right|'
@classmethod
def _operators_to_state(self, ops, **options):
state = self.dual_class().operators_to_state(ops, **options)
return state.dual
def _state_to_operators(self, op_classes, **options):
return self.dual._state_to_operators(op_classes, **options)
def _enumerate_state(self, num_states, **options):
dual_states = self.dual._enumerate_state(num_states, **options)
return [x.dual for x in dual_states]
@classmethod
def default_args(self):
return self.dual_class().default_args()
@classmethod
def dual_class(self):
return KetBase
def __mul__(self, other):
"""BraBase*other"""
from sympy.physics.quantum.innerproduct import InnerProduct
if isinstance(other, KetBase):
return InnerProduct(self, other)
else:
return Expr.__mul__(self, other)
def __rmul__(self, other):
"""other*BraBase"""
from sympy.physics.quantum.operator import OuterProduct
if isinstance(other, KetBase):
return OuterProduct(other, self)
else:
return Expr.__rmul__(self, other)
def _represent(self, **options):
"""A default represent that uses the Ket's version."""
from sympy.physics.quantum.dagger import Dagger
return Dagger(self.dual._represent(**options))
class State(StateBase):
"""General abstract quantum state used as a base class for Ket and Bra."""
pass
class Ket(State, KetBase):
"""A general time-independent Ket in quantum mechanics.
Inherits from State and KetBase. This class should be used as the base
class for all physical, time-independent Kets in a system. This class
and its subclasses will be the main classes that users will use for
expressing Kets in Dirac notation [1]_.
Parameters
==========
args : tuple
The list of numbers or parameters that uniquely specify the
ket. This will usually be its symbol or its quantum numbers. For
time-dependent state, this will include the time.
Examples
========
Create a simple Ket and looking at its properties::
>>> from sympy.physics.quantum import Ket, Bra
>>> from sympy import symbols, I
>>> k = Ket('psi')
>>> k
|psi>
>>> k.hilbert_space
H
>>> k.is_commutative
False
>>> k.label
(psi,)
Ket's know about their associated bra::
>>> k.dual
<psi|
>>> k.dual_class()
<class 'sympy.physics.quantum.state.Bra'>
Take a linear combination of two kets::
>>> k0 = Ket(0)
>>> k1 = Ket(1)
>>> 2*I*k0 - 4*k1
2*I*|0> - 4*|1>
Compound labels are passed as tuples::
>>> n, m = symbols('n,m')
>>> k = Ket(n,m)
>>> k
|nm>
References
==========
.. [1] http://en.wikipedia.org/wiki/Bra-ket_notation
"""
@classmethod
def dual_class(self):
return Bra
class Bra(State, BraBase):
"""A general time-independent Bra in quantum mechanics.
Inherits from State and BraBase. A Bra is the dual of a Ket [1]_. This
class and its subclasses will be the main classes that users will use for
expressing Bras in Dirac notation.
Parameters
==========
args : tuple
The list of numbers or parameters that uniquely specify the
ket. This will usually be its symbol or its quantum numbers. For
time-dependent state, this will include the time.
Examples
========
Create a simple Bra and look at its properties::
>>> from sympy.physics.quantum import Ket, Bra
>>> from sympy import symbols, I
>>> b = Bra('psi')
>>> b
<psi|
>>> b.hilbert_space
H
>>> b.is_commutative
False
Bra's know about their dual Ket's::
>>> b.dual
|psi>
>>> b.dual_class()
<class 'sympy.physics.quantum.state.Ket'>
Like Kets, Bras can have compound labels and be manipulated in a similar
manner::
>>> n, m = symbols('n,m')
>>> b = Bra(n,m) - I*Bra(m,n)
>>> b
-I*<mn| + <nm|
Symbols in a Bra can be substituted using ``.subs``::
>>> b.subs(n,m)
<mm| - I*<mm|
References
==========
.. [1] http://en.wikipedia.org/wiki/Bra-ket_notation
"""
@classmethod
def dual_class(self):
return Ket
#-----------------------------------------------------------------------------
# Time dependent states, bras and kets.
#-----------------------------------------------------------------------------
class TimeDepState(StateBase):
"""Base class for a general time-dependent quantum state.
This class is used as a base class for any time-dependent state. The main
difference between this class and the time-independent state is that this
class takes a second argument that is the time in addition to the usual
label argument.
Parameters
==========
args : tuple
The list of numbers or parameters that uniquely specify the ket. This
will usually be its symbol or its quantum numbers. For time-dependent
state, this will include the time as the final argument.
"""
#-------------------------------------------------------------------------
# Initialization
#-------------------------------------------------------------------------
@classmethod
def default_args(self):
return ("psi", "t")
#-------------------------------------------------------------------------
# Properties
#-------------------------------------------------------------------------
@property
def label(self):
"""The label of the state."""
return self.args[:-1]
@property
def time(self):
"""The time of the state."""
return self.args[-1]
#-------------------------------------------------------------------------
# Printing
#-------------------------------------------------------------------------
def _print_time(self, printer, *args):
return printer._print(self.time, *args)
_print_time_repr = _print_time
_print_time_latex = _print_time
def _print_time_pretty(self, printer, *args):
pform = printer._print(self.time, *args)
return pform
def _print_contents(self, printer, *args):
label = self._print_label(printer, *args)
time = self._print_time(printer, *args)
return '%s;%s' % (label, time)
def _print_label_repr(self, printer, *args):
label = self._print_sequence(self.label, ',', printer, *args)
time = self._print_time_repr(printer, *args)
return '%s,%s' % (label, time)
def _print_contents_pretty(self, printer, *args):
label = self._print_label_pretty(printer, *args)
time = self._print_time_pretty(printer, *args)
return printer._print_seq((label, time), delimiter=';')
def _print_contents_latex(self, printer, *args):
label = self._print_sequence(
self.label, self._label_separator, printer, *args)
time = self._print_time_latex(printer, *args)
return '%s;%s' % (label, time)
class TimeDepKet(TimeDepState, KetBase):
"""General time-dependent Ket in quantum mechanics.
This inherits from ``TimeDepState`` and ``KetBase`` and is the main class
that should be used for Kets that vary with time. Its dual is a
``TimeDepBra``.
Parameters
==========
args : tuple
The list of numbers or parameters that uniquely specify the ket. This
will usually be its symbol or its quantum numbers. For time-dependent
state, this will include the time as the final argument.
Examples
========
Create a TimeDepKet and look at its attributes::
>>> from sympy.physics.quantum import TimeDepKet
>>> k = TimeDepKet('psi', 't')
>>> k
|psi;t>
>>> k.time
t
>>> k.label
(psi,)
>>> k.hilbert_space
H
TimeDepKets know about their dual bra::
>>> k.dual
<psi;t|
>>> k.dual_class()
<class 'sympy.physics.quantum.state.TimeDepBra'>
"""
@classmethod
def dual_class(self):
return TimeDepBra
class TimeDepBra(TimeDepState, BraBase):
"""General time-dependent Bra in quantum mechanics.
This inherits from TimeDepState and BraBase and is the main class that
should be used for Bras that vary with time. Its dual is a TimeDepBra.
Parameters
==========
args : tuple
The list of numbers or parameters that uniquely specify the ket. This
will usually be its symbol or its quantum numbers. For time-dependent
state, this will include the time as the final argument.
Examples
========
>>> from sympy.physics.quantum import TimeDepBra
>>> from sympy import symbols, I
>>> b = TimeDepBra('psi', 't')
>>> b
<psi;t|
>>> b.time
t
>>> b.label
(psi,)
>>> b.hilbert_space
H
>>> b.dual
|psi;t>
"""
@classmethod
def dual_class(self):
return TimeDepKet
class Wavefunction(Function):
"""Class for representations in continuous bases
This class takes an expression and coordinates in its constructor. It can
be used to easily calculate normalizations and probabilities.
Parameters
==========
expr : Expr
The expression representing the functional form of the w.f.
coords : Symbol or tuple
The coordinates to be integrated over, and their bounds
Examples
========
Particle in a box, specifying bounds in the more primitive way of using
Piecewise:
>>> from sympy import Symbol, Piecewise, pi, N
>>> from sympy.functions import sqrt, sin
>>> from sympy.physics.quantum.state import Wavefunction
>>> x = Symbol('x', real=True)
>>> n = 1
>>> L = 1
>>> g = Piecewise((0, x < 0), (0, x > L), (sqrt(2//L)*sin(n*pi*x/L), True))
>>> f = Wavefunction(g, x)
>>> f.norm
1
>>> f.is_normalized
True
>>> p = f.prob()
>>> p(0)
0
>>> p(L)
0
>>> p(0.5)
2
>>> p(0.85*L)
2*sin(0.85*pi)**2
>>> N(p(0.85*L))
0.412214747707527
Additionally, you can specify the bounds of the function and the indices in
a more compact way:
>>> from sympy import symbols, pi, diff
>>> from sympy.functions import sqrt, sin
>>> from sympy.physics.quantum.state import Wavefunction
>>> x, L = symbols('x,L', positive=True)
>>> n = symbols('n', integer=True, positive=True)
>>> g = sqrt(2/L)*sin(n*pi*x/L)
>>> f = Wavefunction(g, (x, 0, L))
>>> f.norm
1
>>> f(L+1)
0
>>> f(L-1)
sqrt(2)*sin(pi*n*(L - 1)/L)/sqrt(L)
>>> f(-1)
0
>>> f(0.85)
sqrt(2)*sin(0.85*pi*n/L)/sqrt(L)
>>> f(0.85, n=1, L=1)
sqrt(2)*sin(0.85*pi)
>>> f.is_commutative
False
All arguments are automatically sympified, so you can define the variables
as strings rather than symbols:
>>> expr = x**2
>>> f = Wavefunction(expr, 'x')
>>> type(f.variables[0])
<class 'sympy.core.symbol.Symbol'>
Derivatives of Wavefunctions will return Wavefunctions:
>>> diff(f, x)
Wavefunction(2*x, x)
"""
#Any passed tuples for coordinates and their bounds need to be
#converted to Tuples before Function's constructor is called, to
#avoid errors from calling is_Float in the constructor
def __new__(cls, *args, **options):
new_args = [None for i in args]
ct = 0
for arg in args:
if isinstance(arg, tuple):
new_args[ct] = Tuple(*arg)
else:
new_args[ct] = arg
ct += 1
return super(Function, cls).__new__(cls, *new_args, **options)
def __call__(self, *args, **options):
var = self.variables
if len(args) != len(var):
raise NotImplementedError(
"Incorrect number of arguments to function!")
ct = 0
#If the passed value is outside the specified bounds, return 0
for v in var:
lower, upper = self.limits[v]
#Do the comparison to limits only if the passed symbol is actually
#a symbol present in the limits;
#Had problems with a comparison of x > L
if isinstance(args[ct], Expr) and \
not (lower in args[ct].free_symbols
or upper in args[ct].free_symbols):
continue
if (args[ct] < lower) == True or (args[ct] > upper) == True:
return 0
ct += 1
expr = self.expr
#Allows user to make a call like f(2, 4, m=1, n=1)
for symbol in list(expr.free_symbols):
if str(symbol) in options.keys():
val = options[str(symbol)]
expr = expr.subs(symbol, val)
return expr.subs(zip(var, args))
def _eval_derivative(self, symbol):
expr = self.expr
deriv = expr._eval_derivative(symbol)
return Wavefunction(deriv, *self.args[1:])
def _eval_conjugate(self):
return Wavefunction(conjugate(self.expr), *self.args[1:])
def _eval_transpose(self):
return self
@property
def free_symbols(self):
return self.expr.free_symbols
@property
def is_commutative(self):
"""
Override Function's is_commutative so that order is preserved in
represented expressions
"""
return False
@classmethod
def eval(self, *args):
return None
@property
def variables(self):
"""
Return the coordinates which the wavefunction depends on
Examples
========
>>> from sympy.physics.quantum.state import Wavefunction
>>> from sympy import symbols
>>> x,y = symbols('x,y')
>>> f = Wavefunction(x*y, x, y)
>>> f.variables
(x, y)
>>> g = Wavefunction(x*y, x)
>>> g.variables
(x,)
"""
var = [g[0] if isinstance(g, Tuple) else g for g in self._args[1:]]
return tuple(var)
@property
def limits(self):
"""
Return the limits of the coordinates which the w.f. depends on If no
limits are specified, defaults to ``(-oo, oo)``.
Examples
========
>>> from sympy.physics.quantum.state import Wavefunction
>>> from sympy import symbols
>>> x, y = symbols('x, y')
>>> f = Wavefunction(x**2, (x, 0, 1))
>>> f.limits
{x: (0, 1)}
>>> f = Wavefunction(x**2, x)
>>> f.limits
{x: (-oo, oo)}
>>> f = Wavefunction(x**2 + y**2, x, (y, -1, 2))
>>> f.limits
{x: (-oo, oo), y: (-1, 2)}
"""
limits = [(g[1], g[2]) if isinstance(g, Tuple) else (-oo, oo)
for g in self._args[1:]]
return dict(zip(self.variables, tuple(limits)))
@property
def expr(self):
"""
Return the expression which is the functional form of the Wavefunction
Examples
========
>>> from sympy.physics.quantum.state import Wavefunction
>>> from sympy import symbols
>>> x, y = symbols('x, y')
>>> f = Wavefunction(x**2, x)
>>> f.expr
x**2
"""
return self._args[0]
@property
def is_normalized(self):
"""
Returns true if the Wavefunction is properly normalized
Examples
========
>>> from sympy import symbols, pi
>>> from sympy.functions import sqrt, sin
>>> from sympy.physics.quantum.state import Wavefunction
>>> x, L = symbols('x,L', positive=True)
>>> n = symbols('n', integer=True, positive=True)
>>> g = sqrt(2/L)*sin(n*pi*x/L)
>>> f = Wavefunction(g, (x, 0, L))
>>> f.is_normalized
True
"""
return (self.norm == 1.0)
@property
@cacheit
def norm(self):
"""
Return the normalization of the specified functional form.
This function integrates over the coordinates of the Wavefunction, with
the bounds specified.
Examples
========
>>> from sympy import symbols, pi
>>> from sympy.functions import sqrt, sin
>>> from sympy.physics.quantum.state import Wavefunction
>>> x, L = symbols('x,L', positive=True)
>>> n = symbols('n', integer=True, positive=True)
>>> g = sqrt(2/L)*sin(n*pi*x/L)
>>> f = Wavefunction(g, (x, 0, L))
>>> f.norm
1
>>> g = sin(n*pi*x/L)
>>> f = Wavefunction(g, (x, 0, L))
>>> f.norm
sqrt(2)*sqrt(L)/2
"""
exp = self.expr*conjugate(self.expr)
var = self.variables
limits = self.limits
for v in var:
curr_limits = limits[v]
exp = integrate(exp, (v, curr_limits[0], curr_limits[1]))
return sqrt(exp)
def normalize(self):
"""
Return a normalized version of the Wavefunction
Examples
========
>>> from sympy import symbols, pi
>>> from sympy.functions import sqrt, sin
>>> from sympy.physics.quantum.state import Wavefunction
>>> x = symbols('x', real=True)
>>> L = symbols('L', positive=True)
>>> n = symbols('n', integer=True, positive=True)
>>> g = sin(n*pi*x/L)
>>> f = Wavefunction(g, (x, 0, L))
>>> f.normalize()
Wavefunction(sqrt(2)*sin(pi*n*x/L)/sqrt(L), (x, 0, L))
"""
const = self.norm
if const == oo:
raise NotImplementedError("The function is not normalizable!")
else:
return Wavefunction((const)**(-1)*self.expr, *self.args[1:])
def prob(self):
"""
Return the absolute magnitude of the w.f., `|\psi(x)|^2`
Examples
========
>>> from sympy import symbols, pi
>>> from sympy.functions import sqrt, sin
>>> from sympy.physics.quantum.state import Wavefunction
>>> x, L = symbols('x,L', real=True)
>>> n = symbols('n', integer=True)
>>> g = sin(n*pi*x/L)
>>> f = Wavefunction(g, (x, 0, L))
>>> f.prob()
Wavefunction(sin(pi*n*x/L)**2, x)
"""
return Wavefunction(self.expr*conjugate(self.expr), *self.variables)
| bsd-3-clause |
ryandougherty/mwa-capstone | MWA_Tools/build/matplotlib/doc/mpl_examples/pylab_examples/demo_bboximage.py | 12 | 1805 | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.image import BboxImage
from matplotlib.transforms import Bbox, TransformedBbox
if __name__ == "__main__":
fig = plt.figure(1)
ax = plt.subplot(121)
txt = ax.text(0.5, 0.5, "test", size=30, ha="center", color="w")
kwargs = dict()
bbox_image = BboxImage(txt.get_window_extent,
norm = None,
origin=None,
clip_on=False,
**kwargs
)
a = np.arange(256).reshape(1,256)/256.
bbox_image.set_data(a)
ax.add_artist(bbox_image)
ax = plt.subplot(122)
a = np.linspace(0, 1, 256).reshape(1,-1)
a = np.vstack((a,a))
maps = sorted(m for m in plt.cm.datad if not m.endswith("_r"))
#nmaps = len(maps) + 1
#fig.subplots_adjust(top=0.99, bottom=0.01, left=0.2, right=0.99)
ncol = 2
nrow = len(maps)//ncol + 1
xpad_fraction = 0.3
dx = 1./(ncol + xpad_fraction*(ncol-1))
ypad_fraction = 0.3
dy = 1./(nrow + ypad_fraction*(nrow-1))
for i,m in enumerate(maps):
ix, iy = divmod(i, nrow)
#plt.figimage(a, 10, i*10, cmap=plt.get_cmap(m), origin='lower')
bbox0 = Bbox.from_bounds(ix*dx*(1+xpad_fraction),
1.-iy*dy*(1+ypad_fraction)-dy,
dx, dy)
bbox = TransformedBbox(bbox0, ax.transAxes)
bbox_image = BboxImage(bbox,
cmap = plt.get_cmap(m),
norm = None,
origin=None,
**kwargs
)
bbox_image.set_data(a)
ax.add_artist(bbox_image)
plt.draw()
plt.show()
| gpl-2.0 |
cle1109/scot | doc/sphinxext/inheritance_diagram.py | 4 | 13650 | """
Defines a docutils directive for inserting inheritance diagrams.
Provide the directive with one or more classes or modules (separated
by whitespace). For modules, all of the classes in that module will
be used.
Example::
Given the following classes:
class A: pass
class B(A): pass
class C(A): pass
class D(B, C): pass
class E(B): pass
.. inheritance-diagram: D E
Produces a graph like the following:
A
/ \
B C
/ \ /
E D
The graph is inserted as a PNG+image map into HTML and a PDF in
LaTeX.
"""
import inspect
import os
import re
import subprocess
try:
from hashlib import md5
except ImportError:
from md5 import md5
from docutils.nodes import Body, Element
from docutils.parsers.rst import directives
from sphinx.roles import xfileref_role
def my_import(name):
"""Module importer - taken from the python documentation.
This function allows importing names with dots in them."""
mod = __import__(name)
components = name.split('.')
for comp in components[1:]:
mod = getattr(mod, comp)
return mod
class DotException(Exception):
pass
class InheritanceGraph(object):
"""
Given a list of classes, determines the set of classes that
they inherit from all the way to the root "object", and then
is able to generate a graphviz dot graph from them.
"""
def __init__(self, class_names, show_builtins=False):
"""
*class_names* is a list of child classes to show bases from.
If *show_builtins* is True, then Python builtins will be shown
in the graph.
"""
self.class_names = class_names
self.classes = self._import_classes(class_names)
self.all_classes = self._all_classes(self.classes)
if len(self.all_classes) == 0:
raise ValueError("No classes found for inheritance diagram")
self.show_builtins = show_builtins
py_sig_re = re.compile(r'''^([\w.]*\.)? # class names
(\w+) \s* $ # optionally arguments
''', re.VERBOSE)
def _import_class_or_module(self, name):
"""
Import a class using its fully-qualified *name*.
"""
try:
path, base = self.py_sig_re.match(name).groups()
except:
raise ValueError(
"Invalid class or module '%s' specified for inheritance diagram" % name)
fullname = (path or '') + base
path = (path and path.rstrip('.'))
if not path:
path = base
try:
module = __import__(path, None, None, [])
# We must do an import of the fully qualified name. Otherwise if a
# subpackage 'a.b' is requested where 'import a' does NOT provide
# 'a.b' automatically, then 'a.b' will not be found below. This
# second call will force the equivalent of 'import a.b' to happen
# after the top-level import above.
my_import(fullname)
except ImportError:
raise ValueError(
"Could not import class or module '%s' specified for inheritance diagram" % name)
try:
todoc = module
for comp in fullname.split('.')[1:]:
todoc = getattr(todoc, comp)
except AttributeError:
raise ValueError(
"Could not find class or module '%s' specified for inheritance diagram" % name)
# If a class, just return it
if inspect.isclass(todoc):
return [todoc]
elif inspect.ismodule(todoc):
classes = []
for cls in todoc.__dict__.values():
if inspect.isclass(cls) and cls.__module__ == todoc.__name__:
classes.append(cls)
return classes
raise ValueError(
"'%s' does not resolve to a class or module" % name)
def _import_classes(self, class_names):
"""
Import a list of classes.
"""
classes = []
for name in class_names:
classes.extend(self._import_class_or_module(name))
return classes
def _all_classes(self, classes):
"""
Return a list of all classes that are ancestors of *classes*.
"""
all_classes = {}
def recurse(cls):
all_classes[cls] = None
for c in cls.__bases__:
if c not in all_classes:
recurse(c)
for cls in classes:
recurse(cls)
return all_classes.keys()
def class_name(self, cls, parts=0):
"""
Given a class object, return a fully-qualified name. This
works for things I've tested in matplotlib so far, but may not
be completely general.
"""
module = cls.__module__
if module == '__builtin__':
fullname = cls.__name__
else:
fullname = "%s.%s" % (module, cls.__name__)
if parts == 0:
return fullname
name_parts = fullname.split('.')
return '.'.join(name_parts[-parts:])
def get_all_class_names(self):
"""
Get all of the class names involved in the graph.
"""
return [self.class_name(x) for x in self.all_classes]
# These are the default options for graphviz
default_graph_options = {
"rankdir": "LR",
"size": '"8.0, 12.0"'
}
default_node_options = {
"shape": "box",
"fontsize": 10,
"height": 0.25,
"fontname": "Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",
"style": '"setlinewidth(0.5)"'
}
default_edge_options = {
"arrowsize": 0.5,
"style": '"setlinewidth(0.5)"'
}
def _format_node_options(self, options):
return ','.join(["%s=%s" % x for x in options.items()])
def _format_graph_options(self, options):
return ''.join(["%s=%s;\n" % x for x in options.items()])
def generate_dot(self, fd, name, parts=0, urls={},
graph_options={}, node_options={},
edge_options={}):
"""
Generate a graphviz dot graph from the classes that
were passed in to __init__.
*fd* is a Python file-like object to write to.
*name* is the name of the graph
*urls* is a dictionary mapping class names to http urls
*graph_options*, *node_options*, *edge_options* are
dictionaries containing key/value pairs to pass on as graphviz
properties.
"""
g_options = self.default_graph_options.copy()
g_options.update(graph_options)
n_options = self.default_node_options.copy()
n_options.update(node_options)
e_options = self.default_edge_options.copy()
e_options.update(edge_options)
fd.write('digraph %s {\n' % name)
fd.write(self._format_graph_options(g_options))
for cls in self.all_classes:
if not self.show_builtins and cls in __builtins__.values():
continue
name = self.class_name(cls, parts)
# Write the node
this_node_options = n_options.copy()
url = urls.get(self.class_name(cls))
if url is not None:
this_node_options['URL'] = '"%s"' % url
fd.write(' "%s" [%s];\n' %
(name, self._format_node_options(this_node_options)))
# Write the edges
for base in cls.__bases__:
if not self.show_builtins and base in __builtins__.values():
continue
base_name = self.class_name(base, parts)
fd.write(' "%s" -> "%s" [%s];\n' %
(base_name, name,
self._format_node_options(e_options)))
fd.write('}\n')
def run_dot(self, args, name, parts=0, urls={},
graph_options={}, node_options={}, edge_options={}):
"""
Run graphviz 'dot' over this graph, returning whatever 'dot'
writes to stdout.
*args* will be passed along as commandline arguments.
*name* is the name of the graph
*urls* is a dictionary mapping class names to http urls
Raises DotException for any of the many os and
installation-related errors that may occur.
"""
try:
dot = subprocess.Popen(['dot'] + list(args),
stdin=subprocess.PIPE, stdout=subprocess.PIPE,
close_fds=True)
except OSError:
raise DotException("Could not execute 'dot'. Are you sure you have 'graphviz' installed?")
except ValueError:
raise DotException("'dot' called with invalid arguments")
except:
raise DotException("Unexpected error calling 'dot'")
self.generate_dot(dot.stdin, name, parts, urls, graph_options,
node_options, edge_options)
dot.stdin.close()
result = dot.stdout.read()
returncode = dot.wait()
if returncode != 0:
raise DotException("'dot' returned the errorcode %d" % returncode)
return result
class inheritance_diagram(Body, Element):
"""
A docutils node to use as a placeholder for the inheritance
diagram.
"""
pass
def inheritance_diagram_directive(name, arguments, options, content, lineno,
content_offset, block_text, state,
state_machine):
"""
Run when the inheritance_diagram directive is first encountered.
"""
node = inheritance_diagram()
class_names = arguments
# Create a graph starting with the list of classes
graph = InheritanceGraph(class_names)
# Create xref nodes for each target of the graph's image map and
# add them to the doc tree so that Sphinx can resolve the
# references to real URLs later. These nodes will eventually be
# removed from the doctree after we're done with them.
for name in graph.get_all_class_names():
refnodes, x = xfileref_role(
'class', ':class:`%s`' % name, name, 0, state)
node.extend(refnodes)
# Store the graph object so we can use it to generate the
# dot file later
node['graph'] = graph
# Store the original content for use as a hash
node['parts'] = options.get('parts', 0)
node['content'] = " ".join(class_names)
return [node]
def get_graph_hash(node):
return md5(node['content'] + str(node['parts'])).hexdigest()[-10:]
def html_output_graph(self, node):
"""
Output the graph for HTML. This will insert a PNG with clickable
image map.
"""
graph = node['graph']
parts = node['parts']
graph_hash = get_graph_hash(node)
name = "inheritance%s" % graph_hash
path = '_images'
dest_path = os.path.join(setup.app.builder.outdir, path)
if not os.path.exists(dest_path):
os.makedirs(dest_path)
png_path = os.path.join(dest_path, name + ".png")
path = setup.app.builder.imgpath
# Create a mapping from fully-qualified class names to URLs.
urls = {}
for child in node:
if child.get('refuri') is not None:
urls[child['reftitle']] = child.get('refuri')
elif child.get('refid') is not None:
urls[child['reftitle']] = '#' + child.get('refid')
# These arguments to dot will save a PNG file to disk and write
# an HTML image map to stdout.
image_map = graph.run_dot(['-Tpng', '-o%s' % png_path, '-Tcmapx'],
name, parts, urls)
return ('<img src="%s/%s.png" usemap="#%s" class="inheritance"/>%s' %
(path, name, name, image_map))
def latex_output_graph(self, node):
"""
Output the graph for LaTeX. This will insert a PDF.
"""
graph = node['graph']
parts = node['parts']
graph_hash = get_graph_hash(node)
name = "inheritance%s" % graph_hash
dest_path = os.path.abspath(os.path.join(setup.app.builder.outdir, '_images'))
if not os.path.exists(dest_path):
os.makedirs(dest_path)
pdf_path = os.path.abspath(os.path.join(dest_path, name + ".pdf"))
graph.run_dot(['-Tpdf', '-o%s' % pdf_path],
name, parts, graph_options={'size': '"6.0,6.0"'})
return '\n\\includegraphics{%s}\n\n' % pdf_path
def visit_inheritance_diagram(inner_func):
"""
This is just a wrapper around html/latex_output_graph to make it
easier to handle errors and insert warnings.
"""
def visitor(self, node):
try:
content = inner_func(self, node)
except DotException as e:
# Insert the exception as a warning in the document
warning = self.document.reporter.warning(str(e), line=node.line)
warning.parent = node
node.children = [warning]
else:
source = self.document.attributes['source']
self.body.append(content)
node.children = []
return visitor
def do_nothing(self, node):
pass
def setup(app):
setup.app = app
setup.confdir = app.confdir
app.add_node(
inheritance_diagram,
latex=(visit_inheritance_diagram(latex_output_graph), do_nothing),
html=(visit_inheritance_diagram(html_output_graph), do_nothing))
app.add_directive(
'inheritance-diagram', inheritance_diagram_directive,
False, (1, 100, 0), parts = directives.nonnegative_int)
| mit |
davidtrem/ThunderStorm | thunderstorm/lightning/utils.py | 1 | 5027 | # -*- coding: utf-8 -*-
# Copyright (C) 2010-2013 Trémouilles David
#This file is part of Thunderstorm.
#
#ThunderStrom is free software: you can redistribute it and/or modify
#it under the terms of the GNU Lesser General Public License as published by
#the Free Software Foundation, either version 3 of the License, or
#(at your option) any later version.
#
#ThunderStorm is distributed in the hope that it will be useful,
#but WITHOUT ANY WARRANTY; without even the implied warranty of
#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
#GNU Lesser General Public License for more details.
#
#You should have received a copy of the GNU Lesser General Public License
#along with ThunderStorm. If not, see <http://www.gnu.org/licenses/>.
"""
Various utility functions
"""
import matplotlib
from weakref import WeakValueDictionary
from weakref import WeakKeyDictionary
import warnings
class UniversalCursors(object):
def __init__(self):
self.all_cursor_orient = WeakKeyDictionary()
self.all_canvas = WeakValueDictionary()
self.all_axes = WeakValueDictionary()
self.backgrounds = {}
self.visible = True
self.needclear = False
def _onmove(self, event):
for canvas in self.all_canvas.values():
if not canvas.widgetlock.available(self):
return
if event.inaxes is None or not self.visible:
if self.needclear:
self._update(event)
for canvas in self.all_canvas.values():
canvas.draw()
self.needclear = False
return
self._update(event)
def _update(self, event):
# 1/ Reset background
for canvas in self.all_canvas.values():
canvas.restore_region(self.backgrounds[id(canvas)])
# 2/ update cursors
for cursors in self.all_cursor_orient.keys():
orient = self.all_cursor_orient[cursors]
if (event.inaxes in [line.get_axes() for line in cursors]
and self.visible):
visible = True
self.needclear = True
else:
visible = False
for line in cursors:
if orient == 'vertical':
line.set_xdata((event.xdata, event.xdata))
if orient == 'horizontal':
line.set_ydata((event.ydata, event.ydata))
line.set_visible(visible)
ax = line.get_axes()
ax.draw_artist(line)
# 3/ update canvas
for canvas in self.all_canvas.values():
canvas.blit(canvas.figure.bbox)
def _clear(self, event):
"""clear the cursor"""
self.backgrounds = {}
for canvas in self.all_canvas.values():
self.backgrounds[id(canvas)] = (
canvas.copy_from_bbox(canvas.figure.bbox))
for cursor in self.all_cursor_orient.keys():
for line in cursor:
line.set_visible(False)
def add_cursor(self, axes=(), orient='vertical', **lineprops):
class CursorList(list):
def __hash__(self):
return hash(tuple(self))
cursors = CursorList() # Required to keep weakref
for ax in axes:
self.all_axes[id(ax)] = ax
ax_canvas = ax.get_figure().canvas
if ax_canvas not in self.all_canvas.values():
#if not ax_canvas.supports_blit:
# warnings.warn("Must use canvas that support blit")
# return
self.all_canvas[id(ax_canvas)] = ax_canvas
ax_canvas.mpl_connect('motion_notify_event', self._onmove)
ax_canvas.mpl_connect('draw_event', self._clear)
if orient == 'vertical':
line = ax.axvline(ax.get_xbound()[0], visible=False,
animated=True, **lineprops)
if orient == 'horizontal':
line = ax.axhline(ax.get_ybound()[0], visible=False,
animated=True, **lineprops)
cursors.append(line)
self.all_cursor_orient[cursors] = orient
return cursors
def autoscale_visible_lines(axs):
"""
Function to autoscale only on visible lines.
"""
mplt_ver = [int(elem) for elem in matplotlib.__version__.split('.')[0:2]]
ignore = True
for line in (axs.lines):
if not line.get_visible():
continue # jump to next line if this one is not visible
if mplt_ver[0] == 0 and mplt_ver[1] < 98:
axs.dataLim.update_numerix(line.get_xdata(),
line.get_ydata(),
ignore)
else:
axs.dataLim.update_from_data_xy(line.get_xydata(),
ignore)
ignore = False
axs.autoscale_view()
return None
def neg_bool_list(a_list):
return [not elem for elem in a_list]
| gpl-3.0 |
mtconley/turntable | test/lib/python2.7/site-packages/scipy/interpolate/fitpack2.py | 7 | 57978 | """
fitpack --- curve and surface fitting with splines
fitpack is based on a collection of Fortran routines DIERCKX
by P. Dierckx (see http://www.netlib.org/dierckx/) transformed
to double routines by Pearu Peterson.
"""
# Created by Pearu Peterson, June,August 2003
from __future__ import division, print_function, absolute_import
__all__ = [
'UnivariateSpline',
'InterpolatedUnivariateSpline',
'LSQUnivariateSpline',
'BivariateSpline',
'LSQBivariateSpline',
'SmoothBivariateSpline',
'LSQSphereBivariateSpline',
'SmoothSphereBivariateSpline',
'RectBivariateSpline',
'RectSphereBivariateSpline']
import warnings
from numpy import zeros, concatenate, alltrue, ravel, all, diff, array, ones
import numpy as np
from . import fitpack
from . import dfitpack
################ Univariate spline ####################
_curfit_messages = {1:"""
The required storage space exceeds the available storage space, as
specified by the parameter nest: nest too small. If nest is already
large (say nest > m/2), it may also indicate that s is too small.
The approximation returned is the weighted least-squares spline
according to the knots t[0],t[1],...,t[n-1]. (n=nest) the parameter fp
gives the corresponding weighted sum of squared residuals (fp>s).
""",
2:"""
A theoretically impossible result was found during the iteration
proces for finding a smoothing spline with fp = s: s too small.
There is an approximation returned but the corresponding weighted sum
of squared residuals does not satisfy the condition abs(fp-s)/s < tol.""",
3:"""
The maximal number of iterations maxit (set to 20 by the program)
allowed for finding a smoothing spline with fp=s has been reached: s
too small.
There is an approximation returned but the corresponding weighted sum
of squared residuals does not satisfy the condition abs(fp-s)/s < tol.""",
10:"""
Error on entry, no approximation returned. The following conditions
must hold:
xb<=x[0]<x[1]<...<x[m-1]<=xe, w[i]>0, i=0..m-1
if iopt=-1:
xb<t[k+1]<t[k+2]<...<t[n-k-2]<xe"""
}
# UnivariateSpline, ext parameter can be an int or a string
_extrap_modes = {0: 0, 'extrapolate': 0,
1: 1, 'zeros': 1,
2: 2, 'raise': 2,
3: 3, 'const': 3}
class UnivariateSpline(object):
"""
One-dimensional smoothing spline fit to a given set of data points.
Fits a spline y = spl(x) of degree `k` to the provided `x`, `y` data. `s`
specifies the number of knots by specifying a smoothing condition.
Parameters
----------
x : (N,) array_like
1-D array of independent input data. Must be increasing.
y : (N,) array_like
1-D array of dependent input data, of the same length as `x`.
w : (N,) array_like, optional
Weights for spline fitting. Must be positive. If None (default),
weights are all equal.
bbox : (2,) array_like, optional
2-sequence specifying the boundary of the approximation interval. If
None (default), ``bbox=[x[0], x[-1]]``.
k : int, optional
Degree of the smoothing spline. Must be <= 5.
Default is k=3, a cubic spline.
s : float or None, optional
Positive smoothing factor used to choose the number of knots. Number
of knots will be increased until the smoothing condition is satisfied::
sum((w[i] * (y[i]-spl(x[i])))**2, axis=0) <= s
If None (default), ``s = len(w)`` which should be a good value if
``1/w[i]`` is an estimate of the standard deviation of ``y[i]``.
If 0, spline will interpolate through all data points.
ext : int or str, optional
Controls the extrapolation mode for elements
not in the interval defined by the knot sequence.
* if ext=0 or 'extrapolate', return the extrapolated value.
* if ext=1 or 'zeros', return 0
* if ext=2 or 'raise', raise a ValueError
* if ext=3 of 'const', return the boundary value.
The default value is 0.
See Also
--------
InterpolatedUnivariateSpline : Subclass with smoothing forced to 0
LSQUnivariateSpline : Subclass in which knots are user-selected instead of
being set by smoothing condition
splrep : An older, non object-oriented wrapping of FITPACK
splev, sproot, splint, spalde
BivariateSpline : A similar class for two-dimensional spline interpolation
Notes
-----
The number of data points must be larger than the spline degree `k`.
**NaN handling**: If the input arrays contain ``nan`` values, the result
is not useful, since the underlying spline fitting routines cannot deal
with ``nan`` . A workaround is to use zero weights for not-a-number
data points:
>>> w = np.isnan(y)
>>> y[w] = 0.
>>> spl = UnivariateSpline(x, y, w=~w)
Notice the need to replace a ``nan`` by a numerical value (precise value
does not matter as long as the corresponding weight is zero.)
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from scipy.interpolate import UnivariateSpline
>>> x = np.linspace(-3, 3, 50)
>>> y = np.exp(-x**2) + 0.1 * np.random.randn(50)
>>> plt.plot(x, y, 'ro', ms=5)
Use the default value for the smoothing parameter:
>>> spl = UnivariateSpline(x, y)
>>> xs = np.linspace(-3, 3, 1000)
>>> plt.plot(xs, spl(xs), 'g', lw=3)
Manually change the amount of smoothing:
>>> spl.set_smoothing_factor(0.5)
>>> plt.plot(xs, spl(xs), 'b', lw=3)
>>> plt.show()
"""
def __init__(self, x, y, w=None, bbox=[None]*2, k=3, s=None, ext=0):
# _data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
try:
self.ext = _extrap_modes[ext]
except KeyError:
raise ValueError("Unknown extrapolation mode %s." % ext)
data = dfitpack.fpcurf0(x,y,k,w=w,
xb=bbox[0],xe=bbox[1],s=s)
if data[-1] == 1:
# nest too small, setting to maximum bound
data = self._reset_nest(data)
self._data = data
self._reset_class()
@classmethod
def _from_tck(cls, tck, ext=0):
"""Construct a spline object from given tck"""
self = cls.__new__(cls)
t, c, k = tck
self._eval_args = tck
#_data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
self._data = (None,None,None,None,None,k,None,len(t),t,
c,None,None,None,None)
self.ext = ext
return self
def _reset_class(self):
data = self._data
n,t,c,k,ier = data[7],data[8],data[9],data[5],data[-1]
self._eval_args = t[:n],c[:n],k
if ier == 0:
# the spline returned has a residual sum of squares fp
# such that abs(fp-s)/s <= tol with tol a relative
# tolerance set to 0.001 by the program
pass
elif ier == -1:
# the spline returned is an interpolating spline
self._set_class(InterpolatedUnivariateSpline)
elif ier == -2:
# the spline returned is the weighted least-squares
# polynomial of degree k. In this extreme case fp gives
# the upper bound fp0 for the smoothing factor s.
self._set_class(LSQUnivariateSpline)
else:
# error
if ier == 1:
self._set_class(LSQUnivariateSpline)
message = _curfit_messages.get(ier,'ier=%s' % (ier))
warnings.warn(message)
def _set_class(self, cls):
self._spline_class = cls
if self.__class__ in (UnivariateSpline, InterpolatedUnivariateSpline,
LSQUnivariateSpline):
self.__class__ = cls
else:
# It's an unknown subclass -- don't change class. cf. #731
pass
def _reset_nest(self, data, nest=None):
n = data[10]
if nest is None:
k,m = data[5],len(data[0])
nest = m+k+1 # this is the maximum bound for nest
else:
if not n <= nest:
raise ValueError("`nest` can only be increased")
t, c, fpint, nrdata = [np.resize(data[j], nest) for j in [8,9,11,12]]
args = data[:8] + (t,c,n,fpint,nrdata,data[13])
data = dfitpack.fpcurf1(*args)
return data
def set_smoothing_factor(self, s):
""" Continue spline computation with the given smoothing
factor s and with the knots found at the last call.
This routine modifies the spline in place.
"""
data = self._data
if data[6] == -1:
warnings.warn('smoothing factor unchanged for'
'LSQ spline with fixed knots')
return
args = data[:6] + (s,) + data[7:]
data = dfitpack.fpcurf1(*args)
if data[-1] == 1:
# nest too small, setting to maximum bound
data = self._reset_nest(data)
self._data = data
self._reset_class()
def __call__(self, x, nu=0, ext=None):
"""
Evaluate spline (or its nu-th derivative) at positions x.
Parameters
----------
x : array_like
A 1-D array of points at which to return the value of the smoothed
spline or its derivatives. Note: x can be unordered but the
evaluation is more efficient if x is (partially) ordered.
nu : int
The order of derivative of the spline to compute.
ext : int
Controls the value returned for elements of ``x`` not in the
interval defined by the knot sequence.
* if ext=0 or 'extrapolate', return the extrapolated value.
* if ext=1 or 'zeros', return 0
* if ext=2 or 'raise', raise a ValueError
* if ext=3 or 'const', return the boundary value.
The default value is 0, passed from the initialization of
UnivariateSpline.
"""
x = np.asarray(x)
# empty input yields empty output
if x.size == 0:
return array([])
# if nu is None:
# return dfitpack.splev(*(self._eval_args+(x,)))
# return dfitpack.splder(nu=nu,*(self._eval_args+(x,)))
if ext is None:
ext = self.ext
else:
try:
ext = _extrap_modes[ext]
except KeyError:
raise ValueError("Unknown extrapolation mode %s." % ext)
return fitpack.splev(x, self._eval_args, der=nu, ext=ext)
def get_knots(self):
""" Return positions of (boundary and interior) knots of the spline.
"""
data = self._data
k,n = data[5],data[7]
return data[8][k:n-k]
def get_coeffs(self):
"""Return spline coefficients."""
data = self._data
k,n = data[5],data[7]
return data[9][:n-k-1]
def get_residual(self):
"""Return weighted sum of squared residuals of the spline
approximation: ``sum((w[i] * (y[i]-spl(x[i])))**2, axis=0)``.
"""
return self._data[10]
def integral(self, a, b):
""" Return definite integral of the spline between two given points.
"""
return dfitpack.splint(*(self._eval_args+(a,b)))
def derivatives(self, x):
""" Return all derivatives of the spline at the point x."""
d,ier = dfitpack.spalde(*(self._eval_args+(x,)))
if not ier == 0:
raise ValueError("Error code returned by spalde: %s" % ier)
return d
def roots(self):
""" Return the zeros of the spline.
Restriction: only cubic splines are supported by fitpack.
"""
k = self._data[5]
if k == 3:
z,m,ier = dfitpack.sproot(*self._eval_args[:2])
if not ier == 0:
raise ValueError("Error code returned by spalde: %s" % ier)
return z[:m]
raise NotImplementedError('finding roots unsupported for '
'non-cubic splines')
def derivative(self, n=1):
"""
Construct a new spline representing the derivative of this spline.
Parameters
----------
n : int, optional
Order of derivative to evaluate. Default: 1
Returns
-------
spline : UnivariateSpline
Spline of order k2=k-n representing the derivative of this
spline.
See Also
--------
splder, antiderivative
Notes
-----
.. versionadded:: 0.13.0
Examples
--------
This can be used for finding maxima of a curve:
>>> from scipy.interpolate import UnivariateSpline
>>> x = np.linspace(0, 10, 70)
>>> y = np.sin(x)
>>> spl = UnivariateSpline(x, y, k=4, s=0)
Now, differentiate the spline and find the zeros of the
derivative. (NB: `sproot` only works for order 3 splines, so we
fit an order 4 spline):
>>> spl.derivative().roots() / np.pi
array([ 0.50000001, 1.5 , 2.49999998])
This agrees well with roots :math:`\pi/2 + n\pi` of `cos(x) = sin'(x)`.
"""
tck = fitpack.splder(self._eval_args, n)
return UnivariateSpline._from_tck(tck, self.ext)
def antiderivative(self, n=1):
"""
Construct a new spline representing the antiderivative of this spline.
Parameters
----------
n : int, optional
Order of antiderivative to evaluate. Default: 1
Returns
-------
spline : UnivariateSpline
Spline of order k2=k+n representing the antiderivative of this
spline.
Notes
-----
.. versionadded:: 0.13.0
See Also
--------
splantider, derivative
Examples
--------
>>> from scipy.interpolate import UnivariateSpline
>>> x = np.linspace(0, np.pi/2, 70)
>>> y = 1 / np.sqrt(1 - 0.8*np.sin(x)**2)
>>> spl = UnivariateSpline(x, y, s=0)
The derivative is the inverse operation of the antiderivative,
although some floating point error accumulates:
>>> spl(1.7), spl.antiderivative().derivative()(1.7)
(array(2.1565429877197317), array(2.1565429877201865))
Antiderivative can be used to evaluate definite integrals:
>>> ispl = spl.antiderivative()
>>> ispl(np.pi/2) - ispl(0)
2.2572053588768486
This is indeed an approximation to the complete elliptic integral
:math:`K(m) = \\int_0^{\\pi/2} [1 - m\\sin^2 x]^{-1/2} dx`:
>>> from scipy.special import ellipk
>>> ellipk(0.8)
2.2572053268208538
"""
tck = fitpack.splantider(self._eval_args, n)
return UnivariateSpline._from_tck(tck, self.ext)
class InterpolatedUnivariateSpline(UnivariateSpline):
"""
One-dimensional interpolating spline for a given set of data points.
Fits a spline y = spl(x) of degree `k` to the provided `x`, `y` data. Spline
function passes through all provided points. Equivalent to
`UnivariateSpline` with s=0.
Parameters
----------
x : (N,) array_like
Input dimension of data points -- must be increasing
y : (N,) array_like
input dimension of data points
w : (N,) array_like, optional
Weights for spline fitting. Must be positive. If None (default),
weights are all equal.
bbox : (2,) array_like, optional
2-sequence specifying the boundary of the approximation interval. If
None (default), ``bbox=[x[0], x[-1]]``.
k : int, optional
Degree of the smoothing spline. Must be 1 <= `k` <= 5.
ext : int or str, optional
Controls the extrapolation mode for elements
not in the interval defined by the knot sequence.
* if ext=0 or 'extrapolate', return the extrapolated value.
* if ext=1 or 'zeros', return 0
* if ext=2 or 'raise', raise a ValueError
* if ext=3 of 'const', return the boundary value.
The default value is 0.
See Also
--------
UnivariateSpline : Superclass -- allows knots to be selected by a
smoothing condition
LSQUnivariateSpline : spline for which knots are user-selected
splrep : An older, non object-oriented wrapping of FITPACK
splev, sproot, splint, spalde
BivariateSpline : A similar class for two-dimensional spline interpolation
Notes
-----
The number of data points must be larger than the spline degree `k`.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from scipy.interpolate import InterpolatedUnivariateSpline
>>> x = np.linspace(-3, 3, 50)
>>> y = np.exp(-x**2) + 0.1 * np.random.randn(50)
>>> spl = InterpolatedUnivariateSpline(x, y)
>>> plt.plot(x, y, 'ro', ms=5)
>>> xs = np.linspace(-3, 3, 1000)
>>> plt.plot(xs, spl(xs), 'g', lw=3, alpha=0.7)
>>> plt.show()
Notice that the ``spl(x)`` interpolates `y`:
>>> spl.get_residual()
0.0
"""
def __init__(self, x, y, w=None, bbox=[None]*2, k=3, ext=0):
# _data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
self._data = dfitpack.fpcurf0(x,y,k,w=w,
xb=bbox[0],xe=bbox[1],s=0)
self._reset_class()
try:
self.ext = _extrap_modes[ext]
except KeyError:
raise ValueError("Unknown extrapolation mode %s." % ext)
_fpchec_error_string = """The input parameters have been rejected by fpchec. \
This means that at least one of the following conditions is violated:
1) k+1 <= n-k-1 <= m
2) t(1) <= t(2) <= ... <= t(k+1)
t(n-k) <= t(n-k+1) <= ... <= t(n)
3) t(k+1) < t(k+2) < ... < t(n-k)
4) t(k+1) <= x(i) <= t(n-k)
5) The conditions specified by Schoenberg and Whitney must hold
for at least one subset of data points, i.e., there must be a
subset of data points y(j) such that
t(j) < y(j) < t(j+k+1), j=1,2,...,n-k-1
"""
class LSQUnivariateSpline(UnivariateSpline):
"""
One-dimensional spline with explicit internal knots.
Fits a spline y = spl(x) of degree `k` to the provided `x`, `y` data. `t`
specifies the internal knots of the spline
Parameters
----------
x : (N,) array_like
Input dimension of data points -- must be increasing
y : (N,) array_like
Input dimension of data points
t : (M,) array_like
interior knots of the spline. Must be in ascending order and::
bbox[0] < t[0] < ... < t[-1] < bbox[-1]
w : (N,) array_like, optional
weights for spline fitting. Must be positive. If None (default),
weights are all equal.
bbox : (2,) array_like, optional
2-sequence specifying the boundary of the approximation interval. If
None (default), ``bbox = [x[0], x[-1]]``.
k : int, optional
Degree of the smoothing spline. Must be 1 <= `k` <= 5.
Default is k=3, a cubic spline.
ext : int or str, optional
Controls the extrapolation mode for elements
not in the interval defined by the knot sequence.
* if ext=0 or 'extrapolate', return the extrapolated value.
* if ext=1 or 'zeros', return 0
* if ext=2 or 'raise', raise a ValueError
* if ext=3 of 'const', return the boundary value.
The default value is 0.
Raises
------
ValueError
If the interior knots do not satisfy the Schoenberg-Whitney conditions
See Also
--------
UnivariateSpline : Superclass -- knots are specified by setting a
smoothing condition
InterpolatedUnivariateSpline : spline passing through all points
splrep : An older, non object-oriented wrapping of FITPACK
splev, sproot, splint, spalde
BivariateSpline : A similar class for two-dimensional spline interpolation
Notes
-----
The number of data points must be larger than the spline degree `k`.
Knots `t` must satisfy the Schoenberg-Whitney conditions,
i.e., there must be a subset of data points ``x[j]`` such that
``t[j] < x[j] < t[j+k+1]``, for ``j=0, 1,...,n-k-2``.
Examples
--------
>>> from scipy.interpolate import LSQUnivariateSpline
>>> import matplotlib.pyplot as plt
>>> x = np.linspace(-3, 3, 50)
>>> y = np.exp(-x**2) + 0.1 * np.random.randn(50)
Fit a smoothing spline with a pre-defined internal knots:
>>> t = [-1, 0, 1]
>>> spl = LSQUnivariateSpline(x, y, t)
>>> xs = np.linspace(-3, 3, 1000)
>>> plt.plot(x, y, 'ro', ms=5)
>>> plt.plot(xs, spl(xs), 'g-', lw=3)
>>> plt.show()
Check the knot vector:
>>> spl.get_knots()
array([-3., -1., 0., 1., 3.])
"""
def __init__(self, x, y, t, w=None, bbox=[None]*2, k=3, ext=0):
# _data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
xb = bbox[0]
xe = bbox[1]
if xb is None:
xb = x[0]
if xe is None:
xe = x[-1]
t = concatenate(([xb]*(k+1), t, [xe]*(k+1)))
n = len(t)
if not alltrue(t[k+1:n-k]-t[k:n-k-1] > 0, axis=0):
raise ValueError('Interior knots t must satisfy '
'Schoenberg-Whitney conditions')
if not dfitpack.fpchec(x, t, k) == 0:
raise ValueError(_fpchec_error_string)
data = dfitpack.fpcurfm1(x, y, k, t, w=w, xb=xb, xe=xe)
self._data = data[:-3] + (None, None, data[-1])
self._reset_class()
try:
self.ext = _extrap_modes[ext]
except KeyError:
raise ValueError("Unknown extrapolation mode %s." % ext)
################ Bivariate spline ####################
class _BivariateSplineBase(object):
""" Base class for Bivariate spline s(x,y) interpolation on the rectangle
[xb,xe] x [yb, ye] calculated from a given set of data points
(x,y,z).
See Also
--------
bisplrep, bisplev : an older wrapping of FITPACK
BivariateSpline :
implementation of bivariate spline interpolation on a plane grid
SphereBivariateSpline :
implementation of bivariate spline interpolation on a spherical grid
"""
def get_residual(self):
""" Return weighted sum of squared residuals of the spline
approximation: sum ((w[i]*(z[i]-s(x[i],y[i])))**2,axis=0)
"""
return self.fp
def get_knots(self):
""" Return a tuple (tx,ty) where tx,ty contain knots positions
of the spline with respect to x-, y-variable, respectively.
The position of interior and additional knots are given as
t[k+1:-k-1] and t[:k+1]=b, t[-k-1:]=e, respectively.
"""
return self.tck[:2]
def get_coeffs(self):
""" Return spline coefficients."""
return self.tck[2]
def __call__(self, x, y, mth=None, dx=0, dy=0, grid=True):
"""
Evaluate the spline or its derivatives at given positions.
Parameters
----------
x, y : array-like
Input coordinates.
If `grid` is False, evaluate the spline at points ``(x[i],
y[i]), i=0, ..., len(x)-1``. Standard Numpy broadcasting
is obeyed.
If `grid` is True: evaluate spline at the grid points
defined by the coordinate arrays x, y. The arrays must be
sorted to increasing order.
dx : int
Order of x-derivative
.. versionadded:: 0.14.0
dy : int
Order of y-derivative
.. versionadded:: 0.14.0
grid : bool
Whether to evaluate the results on a grid spanned by the
input arrays, or at points specified by the input arrays.
.. versionadded:: 0.14.0
mth : str
Deprecated argument. Has no effect.
"""
x = np.asarray(x)
y = np.asarray(y)
if mth is not None:
warnings.warn("The `mth` argument is deprecated and will be removed",
FutureWarning)
tx, ty, c = self.tck[:3]
kx, ky = self.degrees
if grid:
if x.size == 0 or y.size == 0:
return np.zeros((x.size, y.size), dtype=self.tck[2].dtype)
if dx or dy:
z,ier = dfitpack.parder(tx,ty,c,kx,ky,dx,dy,x,y)
if not ier == 0:
raise ValueError("Error code returned by parder: %s" % ier)
else:
z,ier = dfitpack.bispev(tx,ty,c,kx,ky,x,y)
if not ier == 0:
raise ValueError("Error code returned by bispev: %s" % ier)
else:
# standard Numpy broadcasting
if x.shape != y.shape:
x, y = np.broadcast_arrays(x, y)
shape = x.shape
x = x.ravel()
y = y.ravel()
if x.size == 0 or y.size == 0:
return np.zeros(shape, dtype=self.tck[2].dtype)
if dx or dy:
z,ier = dfitpack.pardeu(tx,ty,c,kx,ky,dx,dy,x,y)
if not ier == 0:
raise ValueError("Error code returned by pardeu: %s" % ier)
else:
z,ier = dfitpack.bispeu(tx,ty,c,kx,ky,x,y)
if not ier == 0:
raise ValueError("Error code returned by bispeu: %s" % ier)
z = z.reshape(shape)
return z
_surfit_messages = {1:"""
The required storage space exceeds the available storage space: nxest
or nyest too small, or s too small.
The weighted least-squares spline corresponds to the current set of
knots.""",
2:"""
A theoretically impossible result was found during the iteration
process for finding a smoothing spline with fp = s: s too small or
badly chosen eps.
Weighted sum of squared residuals does not satisfy abs(fp-s)/s < tol.""",
3:"""
the maximal number of iterations maxit (set to 20 by the program)
allowed for finding a smoothing spline with fp=s has been reached:
s too small.
Weighted sum of squared residuals does not satisfy abs(fp-s)/s < tol.""",
4:"""
No more knots can be added because the number of b-spline coefficients
(nx-kx-1)*(ny-ky-1) already exceeds the number of data points m:
either s or m too small.
The weighted least-squares spline corresponds to the current set of
knots.""",
5:"""
No more knots can be added because the additional knot would (quasi)
coincide with an old one: s too small or too large a weight to an
inaccurate data point.
The weighted least-squares spline corresponds to the current set of
knots.""",
10:"""
Error on entry, no approximation returned. The following conditions
must hold:
xb<=x[i]<=xe, yb<=y[i]<=ye, w[i]>0, i=0..m-1
If iopt==-1, then
xb<tx[kx+1]<tx[kx+2]<...<tx[nx-kx-2]<xe
yb<ty[ky+1]<ty[ky+2]<...<ty[ny-ky-2]<ye""",
-3:"""
The coefficients of the spline returned have been computed as the
minimal norm least-squares solution of a (numerically) rank deficient
system (deficiency=%i). If deficiency is large, the results may be
inaccurate. Deficiency may strongly depend on the value of eps."""
}
class BivariateSpline(_BivariateSplineBase):
"""
Base class for bivariate splines.
This describes a spline ``s(x, y)`` of degrees ``kx`` and ``ky`` on
the rectangle ``[xb, xe] * [yb, ye]`` calculated from a given set
of data points ``(x, y, z)``.
This class is meant to be subclassed, not instantiated directly.
To construct these splines, call either `SmoothBivariateSpline` or
`LSQBivariateSpline`.
See Also
--------
UnivariateSpline : a similar class for univariate spline interpolation
SmoothBivariateSpline :
to create a BivariateSpline through the given points
LSQBivariateSpline :
to create a BivariateSpline using weighted least-squares fitting
SphereBivariateSpline :
bivariate spline interpolation in spherical cooridinates
bisplrep : older wrapping of FITPACK
bisplev : older wrapping of FITPACK
"""
@classmethod
def _from_tck(cls, tck):
"""Construct a spline object from given tck and degree"""
self = cls.__new__(cls)
if len(tck) != 5:
raise ValueError("tck should be a 5 element tuple of tx, ty, c, kx, ky")
self.tck = tck[:3]
self.degrees = tck[3:]
return self
def ev(self, xi, yi, dx=0, dy=0):
"""
Evaluate the spline at points
Returns the interpolated value at ``(xi[i], yi[i]),
i=0,...,len(xi)-1``.
Parameters
----------
xi, yi : array-like
Input coordinates. Standard Numpy broadcasting is obeyed.
dx : int
Order of x-derivative
.. versionadded:: 0.14.0
dy : int
Order of y-derivative
.. versionadded:: 0.14.0
"""
return self.__call__(xi, yi, dx=dx, dy=dy, grid=False)
def integral(self, xa, xb, ya, yb):
"""
Evaluate the integral of the spline over area [xa,xb] x [ya,yb].
Parameters
----------
xa, xb : float
The end-points of the x integration interval.
ya, yb : float
The end-points of the y integration interval.
Returns
-------
integ : float
The value of the resulting integral.
"""
tx,ty,c = self.tck[:3]
kx,ky = self.degrees
return dfitpack.dblint(tx,ty,c,kx,ky,xa,xb,ya,yb)
class SmoothBivariateSpline(BivariateSpline):
"""
Smooth bivariate spline approximation.
Parameters
----------
x, y, z : array_like
1-D sequences of data points (order is not important).
w : array_like, optional
Positive 1-D sequence of weights, of same length as `x`, `y` and `z`.
bbox : array_like, optional
Sequence of length 4 specifying the boundary of the rectangular
approximation domain. By default,
``bbox=[min(x,tx),max(x,tx), min(y,ty),max(y,ty)]``.
kx, ky : ints, optional
Degrees of the bivariate spline. Default is 3.
s : float, optional
Positive smoothing factor defined for estimation condition:
``sum((w[i]*(z[i]-s(x[i], y[i])))**2, axis=0) <= s``
Default ``s=len(w)`` which should be a good value if ``1/w[i]`` is an
estimate of the standard deviation of ``z[i]``.
eps : float, optional
A threshold for determining the effective rank of an over-determined
linear system of equations. `eps` should have a value between 0 and 1,
the default is 1e-16.
See Also
--------
bisplrep : an older wrapping of FITPACK
bisplev : an older wrapping of FITPACK
UnivariateSpline : a similar class for univariate spline interpolation
LSQUnivariateSpline : to create a BivariateSpline using weighted
Notes
-----
The length of `x`, `y` and `z` should be at least ``(kx+1) * (ky+1)``.
"""
def __init__(self, x, y, z, w=None, bbox=[None] * 4, kx=3, ky=3, s=None,
eps=None):
xb,xe,yb,ye = bbox
nx,tx,ny,ty,c,fp,wrk1,ier = dfitpack.surfit_smth(x,y,z,w,
xb,xe,yb,ye,
kx,ky,s=s,
eps=eps,lwrk2=1)
if ier > 10: # lwrk2 was to small, re-run
nx,tx,ny,ty,c,fp,wrk1,ier = dfitpack.surfit_smth(x,y,z,w,
xb,xe,yb,ye,
kx,ky,s=s,
eps=eps,lwrk2=ier)
if ier in [0,-1,-2]: # normal return
pass
else:
message = _surfit_messages.get(ier,'ier=%s' % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx[:nx],ty[:ny],c[:(nx-kx-1)*(ny-ky-1)]
self.degrees = kx,ky
class LSQBivariateSpline(BivariateSpline):
"""
Weighted least-squares bivariate spline approximation.
Parameters
----------
x, y, z : array_like
1-D sequences of data points (order is not important).
tx, ty : array_like
Strictly ordered 1-D sequences of knots coordinates.
w : array_like, optional
Positive 1-D array of weights, of the same length as `x`, `y` and `z`.
bbox : (4,) array_like, optional
Sequence of length 4 specifying the boundary of the rectangular
approximation domain. By default,
``bbox=[min(x,tx),max(x,tx), min(y,ty),max(y,ty)]``.
kx, ky : ints, optional
Degrees of the bivariate spline. Default is 3.
s : float, optional
Positive smoothing factor defined for estimation condition:
``sum((w[i]*(z[i]-s(x[i], y[i])))**2, axis=0) <= s``
Default ``s=len(w)`` which should be a good value if ``1/w[i]`` is an
estimate of the standard deviation of ``z[i]``.
eps : float, optional
A threshold for determining the effective rank of an over-determined
linear system of equations. `eps` should have a value between 0 and 1,
the default is 1e-16.
See Also
--------
bisplrep : an older wrapping of FITPACK
bisplev : an older wrapping of FITPACK
UnivariateSpline : a similar class for univariate spline interpolation
SmoothBivariateSpline : create a smoothing BivariateSpline
Notes
-----
The length of `x`, `y` and `z` should be at least ``(kx+1) * (ky+1)``.
"""
def __init__(self, x, y, z, tx, ty, w=None, bbox=[None]*4, kx=3, ky=3,
eps=None):
nx = 2*kx+2+len(tx)
ny = 2*ky+2+len(ty)
tx1 = zeros((nx,),float)
ty1 = zeros((ny,),float)
tx1[kx+1:nx-kx-1] = tx
ty1[ky+1:ny-ky-1] = ty
xb,xe,yb,ye = bbox
tx1,ty1,c,fp,ier = dfitpack.surfit_lsq(x,y,z,tx1,ty1,w,
xb,xe,yb,ye,
kx,ky,eps,lwrk2=1)
if ier > 10:
tx1,ty1,c,fp,ier = dfitpack.surfit_lsq(x,y,z,tx1,ty1,w,
xb,xe,yb,ye,
kx,ky,eps,lwrk2=ier)
if ier in [0,-1,-2]: # normal return
pass
else:
if ier < -2:
deficiency = (nx-kx-1)*(ny-ky-1)+ier
message = _surfit_messages.get(-3) % (deficiency)
else:
message = _surfit_messages.get(ier, 'ier=%s' % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx1, ty1, c
self.degrees = kx, ky
class RectBivariateSpline(BivariateSpline):
"""
Bivariate spline approximation over a rectangular mesh.
Can be used for both smoothing and interpolating data.
Parameters
----------
x,y : array_like
1-D arrays of coordinates in strictly ascending order.
z : array_like
2-D array of data with shape (x.size,y.size).
bbox : array_like, optional
Sequence of length 4 specifying the boundary of the rectangular
approximation domain. By default,
``bbox=[min(x,tx),max(x,tx), min(y,ty),max(y,ty)]``.
kx, ky : ints, optional
Degrees of the bivariate spline. Default is 3.
s : float, optional
Positive smoothing factor defined for estimation condition:
``sum((w[i]*(z[i]-s(x[i], y[i])))**2, axis=0) <= s``
Default is ``s=0``, which is for interpolation.
See Also
--------
SmoothBivariateSpline : a smoothing bivariate spline for scattered data
bisplrep : an older wrapping of FITPACK
bisplev : an older wrapping of FITPACK
UnivariateSpline : a similar class for univariate spline interpolation
"""
def __init__(self, x, y, z, bbox=[None] * 4, kx=3, ky=3, s=0):
x, y = ravel(x), ravel(y)
if not all(diff(x) > 0.0):
raise TypeError('x must be strictly increasing')
if not all(diff(y) > 0.0):
raise TypeError('y must be strictly increasing')
if not ((x.min() == x[0]) and (x.max() == x[-1])):
raise TypeError('x must be strictly ascending')
if not ((y.min() == y[0]) and (y.max() == y[-1])):
raise TypeError('y must be strictly ascending')
if not x.size == z.shape[0]:
raise TypeError('x dimension of z must have same number of '
'elements as x')
if not y.size == z.shape[1]:
raise TypeError('y dimension of z must have same number of '
'elements as y')
z = ravel(z)
xb, xe, yb, ye = bbox
nx, tx, ny, ty, c, fp, ier = dfitpack.regrid_smth(x, y, z, xb, xe, yb,
ye, kx, ky, s)
if ier not in [0, -1, -2]:
msg = _surfit_messages.get(ier, 'ier=%s' % (ier))
raise ValueError(msg)
self.fp = fp
self.tck = tx[:nx], ty[:ny], c[:(nx - kx - 1) * (ny - ky - 1)]
self.degrees = kx, ky
_spherefit_messages = _surfit_messages.copy()
_spherefit_messages[10] = """
ERROR. On entry, the input data are controlled on validity. The following
restrictions must be satisfied:
-1<=iopt<=1, m>=2, ntest>=8 ,npest >=8, 0<eps<1,
0<=teta(i)<=pi, 0<=phi(i)<=2*pi, w(i)>0, i=1,...,m
lwrk1 >= 185+52*v+10*u+14*u*v+8*(u-1)*v**2+8*m
kwrk >= m+(ntest-7)*(npest-7)
if iopt=-1: 8<=nt<=ntest , 9<=np<=npest
0<tt(5)<tt(6)<...<tt(nt-4)<pi
0<tp(5)<tp(6)<...<tp(np-4)<2*pi
if iopt>=0: s>=0
if one of these conditions is found to be violated,control
is immediately repassed to the calling program. in that
case there is no approximation returned."""
_spherefit_messages[-3] = """
WARNING. The coefficients of the spline returned have been computed as the
minimal norm least-squares solution of a (numerically) rank
deficient system (deficiency=%i, rank=%i). Especially if the rank
deficiency, which is computed by 6+(nt-8)*(np-7)+ier, is large,
the results may be inaccurate. They could also seriously depend on
the value of eps."""
class SphereBivariateSpline(_BivariateSplineBase):
"""
Bivariate spline s(x,y) of degrees 3 on a sphere, calculated from a
given set of data points (theta,phi,r).
.. versionadded:: 0.11.0
See Also
--------
bisplrep, bisplev : an older wrapping of FITPACK
UnivariateSpline : a similar class for univariate spline interpolation
SmoothUnivariateSpline :
to create a BivariateSpline through the given points
LSQUnivariateSpline :
to create a BivariateSpline using weighted least-squares fitting
"""
def __call__(self, theta, phi, dtheta=0, dphi=0, grid=True):
"""
Evaluate the spline or its derivatives at given positions.
Parameters
----------
theta, phi : array-like
Input coordinates.
If `grid` is False, evaluate the spline at points
``(theta[i], phi[i]), i=0, ..., len(x)-1``. Standard
Numpy broadcasting is obeyed.
If `grid` is True: evaluate spline at the grid points
defined by the coordinate arrays theta, phi. The arrays
must be sorted to increasing order.
dtheta : int
Order of theta-derivative
.. versionadded:: 0.14.0
dphi : int
Order of phi-derivative
.. versionadded:: 0.14.0
grid : bool
Whether to evaluate the results on a grid spanned by the
input arrays, or at points specified by the input arrays.
.. versionadded:: 0.14.0
"""
theta = np.asarray(theta)
phi = np.asarray(phi)
if theta.size > 0 and (theta.min() < 0. or theta.max() > np.pi):
raise ValueError("requested theta out of bounds.")
if phi.size > 0 and (phi.min() < 0. or phi.max() > 2. * np.pi):
raise ValueError("requested phi out of bounds.")
return _BivariateSplineBase.__call__(self, theta, phi,
dx=dtheta, dy=dphi, grid=grid)
def ev(self, theta, phi, dtheta=0, dphi=0):
"""
Evaluate the spline at points
Returns the interpolated value at ``(theta[i], phi[i]),
i=0,...,len(theta)-1``.
Parameters
----------
theta, phi : array-like
Input coordinates. Standard Numpy broadcasting is obeyed.
dtheta : int
Order of theta-derivative
.. versionadded:: 0.14.0
dphi : int
Order of phi-derivative
.. versionadded:: 0.14.0
"""
return self.__call__(theta, phi, dtheta=dtheta, dphi=dphi, grid=False)
class SmoothSphereBivariateSpline(SphereBivariateSpline):
"""
Smooth bivariate spline approximation in spherical coordinates.
.. versionadded:: 0.11.0
Parameters
----------
theta, phi, r : array_like
1-D sequences of data points (order is not important). Coordinates
must be given in radians. Theta must lie within the interval (0, pi),
and phi must lie within the interval (0, 2pi).
w : array_like, optional
Positive 1-D sequence of weights.
s : float, optional
Positive smoothing factor defined for estimation condition:
``sum((w(i)*(r(i) - s(theta(i), phi(i))))**2, axis=0) <= s``
Default ``s=len(w)`` which should be a good value if 1/w[i] is an
estimate of the standard deviation of r[i].
eps : float, optional
A threshold for determining the effective rank of an over-determined
linear system of equations. `eps` should have a value between 0 and 1,
the default is 1e-16.
Notes
-----
For more information, see the FITPACK_ site about this function.
.. _FITPACK: http://www.netlib.org/dierckx/sphere.f
Examples
--------
Suppose we have global data on a coarse grid (the input data does not
have to be on a grid):
>>> theta = np.linspace(0., np.pi, 7)
>>> phi = np.linspace(0., 2*np.pi, 9)
>>> data = np.empty((theta.shape[0], phi.shape[0]))
>>> data[:,0], data[0,:], data[-1,:] = 0., 0., 0.
>>> data[1:-1,1], data[1:-1,-1] = 1., 1.
>>> data[1,1:-1], data[-2,1:-1] = 1., 1.
>>> data[2:-2,2], data[2:-2,-2] = 2., 2.
>>> data[2,2:-2], data[-3,2:-2] = 2., 2.
>>> data[3,3:-2] = 3.
>>> data = np.roll(data, 4, 1)
We need to set up the interpolator object
>>> lats, lons = np.meshgrid(theta, phi)
>>> from scipy.interpolate import SmoothSphereBivariateSpline
>>> lut = SmoothSphereBivariateSpline(lats.ravel(), lons.ravel(),
data.T.ravel(),s=3.5)
As a first test, we'll see what the algorithm returns when run on the
input coordinates
>>> data_orig = lut(theta, phi)
Finally we interpolate the data to a finer grid
>>> fine_lats = np.linspace(0., np.pi, 70)
>>> fine_lons = np.linspace(0., 2 * np.pi, 90)
>>> data_smth = lut(fine_lats, fine_lons)
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(131)
>>> ax1.imshow(data, interpolation='nearest')
>>> ax2 = fig.add_subplot(132)
>>> ax2.imshow(data_orig, interpolation='nearest')
>>> ax3 = fig.add_subplot(133)
>>> ax3.imshow(data_smth, interpolation='nearest')
>>> plt.show()
"""
def __init__(self, theta, phi, r, w=None, s=0., eps=1E-16):
if np.issubclass_(w, float):
w = ones(len(theta)) * w
nt_, tt_, np_, tp_, c, fp, ier = dfitpack.spherfit_smth(theta, phi,
r, w=w, s=s,
eps=eps)
if ier not in [0, -1, -2]:
message = _spherefit_messages.get(ier, 'ier=%s' % (ier))
raise ValueError(message)
self.fp = fp
self.tck = tt_[:nt_], tp_[:np_], c[:(nt_ - 4) * (np_ - 4)]
self.degrees = (3, 3)
class LSQSphereBivariateSpline(SphereBivariateSpline):
"""
Weighted least-squares bivariate spline approximation in spherical
coordinates.
.. versionadded:: 0.11.0
Parameters
----------
theta, phi, r : array_like
1-D sequences of data points (order is not important). Coordinates
must be given in radians. Theta must lie within the interval (0, pi),
and phi must lie within the interval (0, 2pi).
tt, tp : array_like
Strictly ordered 1-D sequences of knots coordinates.
Coordinates must satisfy ``0 < tt[i] < pi``, ``0 < tp[i] < 2*pi``.
w : array_like, optional
Positive 1-D sequence of weights, of the same length as `theta`, `phi`
and `r`.
eps : float, optional
A threshold for determining the effective rank of an over-determined
linear system of equations. `eps` should have a value between 0 and 1,
the default is 1e-16.
Notes
-----
For more information, see the FITPACK_ site about this function.
.. _FITPACK: http://www.netlib.org/dierckx/sphere.f
Examples
--------
Suppose we have global data on a coarse grid (the input data does not
have to be on a grid):
>>> theta = np.linspace(0., np.pi, 7)
>>> phi = np.linspace(0., 2*np.pi, 9)
>>> data = np.empty((theta.shape[0], phi.shape[0]))
>>> data[:,0], data[0,:], data[-1,:] = 0., 0., 0.
>>> data[1:-1,1], data[1:-1,-1] = 1., 1.
>>> data[1,1:-1], data[-2,1:-1] = 1., 1.
>>> data[2:-2,2], data[2:-2,-2] = 2., 2.
>>> data[2,2:-2], data[-3,2:-2] = 2., 2.
>>> data[3,3:-2] = 3.
>>> data = np.roll(data, 4, 1)
We need to set up the interpolator object. Here, we must also specify the
coordinates of the knots to use.
>>> lats, lons = np.meshgrid(theta, phi)
>>> knotst, knotsp = theta.copy(), phi.copy()
>>> knotst[0] += .0001
>>> knotst[-1] -= .0001
>>> knotsp[0] += .0001
>>> knotsp[-1] -= .0001
>>> from scipy.interpolate import LSQSphereBivariateSpline
>>> lut = LSQSphereBivariateSpline(lats.ravel(), lons.ravel(),
data.T.ravel(),knotst,knotsp)
As a first test, we'll see what the algorithm returns when run on the
input coordinates
>>> data_orig = lut(theta, phi)
Finally we interpolate the data to a finer grid
>>> fine_lats = np.linspace(0., np.pi, 70)
>>> fine_lons = np.linspace(0., 2*np.pi, 90)
>>> data_lsq = lut(fine_lats, fine_lons)
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(131)
>>> ax1.imshow(data, interpolation='nearest')
>>> ax2 = fig.add_subplot(132)
>>> ax2.imshow(data_orig, interpolation='nearest')
>>> ax3 = fig.add_subplot(133)
>>> ax3.imshow(data_lsq, interpolation='nearest')
>>> plt.show()
"""
def __init__(self, theta, phi, r, tt, tp, w=None, eps=1E-16):
if np.issubclass_(w, float):
w = ones(len(theta)) * w
nt_, np_ = 8 + len(tt), 8 + len(tp)
tt_, tp_ = zeros((nt_,), float), zeros((np_,), float)
tt_[4:-4], tp_[4:-4] = tt, tp
tt_[-4:], tp_[-4:] = np.pi, 2. * np.pi
tt_, tp_, c, fp, ier = dfitpack.spherfit_lsq(theta, phi, r, tt_, tp_,
w=w, eps=eps)
if ier < -2:
deficiency = 6 + (nt_ - 8) * (np_ - 7) + ier
message = _spherefit_messages.get(-3) % (deficiency, -ier)
warnings.warn(message)
elif ier not in [0, -1, -2]:
message = _spherefit_messages.get(ier, 'ier=%s' % (ier))
raise ValueError(message)
self.fp = fp
self.tck = tt_, tp_, c
self.degrees = (3, 3)
_spfit_messages = _surfit_messages.copy()
_spfit_messages[10] = """
ERROR: on entry, the input data are controlled on validity
the following restrictions must be satisfied.
-1<=iopt(1)<=1, 0<=iopt(2)<=1, 0<=iopt(3)<=1,
-1<=ider(1)<=1, 0<=ider(2)<=1, ider(2)=0 if iopt(2)=0.
-1<=ider(3)<=1, 0<=ider(4)<=1, ider(4)=0 if iopt(3)=0.
mu >= mumin (see above), mv >= 4, nuest >=8, nvest >= 8,
kwrk>=5+mu+mv+nuest+nvest,
lwrk >= 12+nuest*(mv+nvest+3)+nvest*24+4*mu+8*mv+max(nuest,mv+nvest)
0< u(i-1)<u(i)< pi,i=2,..,mu,
-pi<=v(1)< pi, v(1)<v(i-1)<v(i)<v(1)+2*pi, i=3,...,mv
if iopt(1)=-1: 8<=nu<=min(nuest,mu+6+iopt(2)+iopt(3))
0<tu(5)<tu(6)<...<tu(nu-4)< pi
8<=nv<=min(nvest,mv+7)
v(1)<tv(5)<tv(6)<...<tv(nv-4)<v(1)+2*pi
the schoenberg-whitney conditions, i.e. there must be
subset of grid co-ordinates uu(p) and vv(q) such that
tu(p) < uu(p) < tu(p+4) ,p=1,...,nu-4
(iopt(2)=1 and iopt(3)=1 also count for a uu-value
tv(q) < vv(q) < tv(q+4) ,q=1,...,nv-4
(vv(q) is either a value v(j) or v(j)+2*pi)
if iopt(1)>=0: s>=0
if s=0: nuest>=mu+6+iopt(2)+iopt(3), nvest>=mv+7
if one of these conditions is found to be violated,control is
immediately repassed to the calling program. in that case there is no
approximation returned."""
class RectSphereBivariateSpline(SphereBivariateSpline):
"""
Bivariate spline approximation over a rectangular mesh on a sphere.
Can be used for smoothing data.
.. versionadded:: 0.11.0
Parameters
----------
u : array_like
1-D array of latitude coordinates in strictly ascending order.
Coordinates must be given in radians and lie within the interval
(0, pi).
v : array_like
1-D array of longitude coordinates in strictly ascending order.
Coordinates must be given in radians, and must lie within (0, 2pi).
r : array_like
2-D array of data with shape ``(u.size, v.size)``.
s : float, optional
Positive smoothing factor defined for estimation condition
(``s=0`` is for interpolation).
pole_continuity : bool or (bool, bool), optional
Order of continuity at the poles ``u=0`` (``pole_continuity[0]``) and
``u=pi`` (``pole_continuity[1]``). The order of continuity at the pole
will be 1 or 0 when this is True or False, respectively.
Defaults to False.
pole_values : float or (float, float), optional
Data values at the poles ``u=0`` and ``u=pi``. Either the whole
parameter or each individual element can be None. Defaults to None.
pole_exact : bool or (bool, bool), optional
Data value exactness at the poles ``u=0`` and ``u=pi``. If True, the
value is considered to be the right function value, and it will be
fitted exactly. If False, the value will be considered to be a data
value just like the other data values. Defaults to False.
pole_flat : bool or (bool, bool), optional
For the poles at ``u=0`` and ``u=pi``, specify whether or not the
approximation has vanishing derivatives. Defaults to False.
See Also
--------
RectBivariateSpline : bivariate spline approximation over a rectangular
mesh
Notes
-----
Currently, only the smoothing spline approximation (``iopt[0] = 0`` and
``iopt[0] = 1`` in the FITPACK routine) is supported. The exact
least-squares spline approximation is not implemented yet.
When actually performing the interpolation, the requested `v` values must
lie within the same length 2pi interval that the original `v` values were
chosen from.
For more information, see the FITPACK_ site about this function.
.. _FITPACK: http://www.netlib.org/dierckx/spgrid.f
Examples
--------
Suppose we have global data on a coarse grid
>>> lats = np.linspace(10, 170, 9) * np.pi / 180.
>>> lons = np.linspace(0, 350, 18) * np.pi / 180.
>>> data = np.dot(np.atleast_2d(90. - np.linspace(-80., 80., 18)).T,
np.atleast_2d(180. - np.abs(np.linspace(0., 350., 9)))).T
We want to interpolate it to a global one-degree grid
>>> new_lats = np.linspace(1, 180, 180) * np.pi / 180
>>> new_lons = np.linspace(1, 360, 360) * np.pi / 180
>>> new_lats, new_lons = np.meshgrid(new_lats, new_lons)
We need to set up the interpolator object
>>> from scipy.interpolate import RectSphereBivariateSpline
>>> lut = RectSphereBivariateSpline(lats, lons, data)
Finally we interpolate the data. The `RectSphereBivariateSpline` object
only takes 1-D arrays as input, therefore we need to do some reshaping.
>>> data_interp = lut.ev(new_lats.ravel(),
... new_lons.ravel()).reshape((360, 180)).T
Looking at the original and the interpolated data, one can see that the
interpolant reproduces the original data very well:
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(211)
>>> ax1.imshow(data, interpolation='nearest')
>>> ax2 = fig.add_subplot(212)
>>> ax2.imshow(data_interp, interpolation='nearest')
>>> plt.show()
Chosing the optimal value of ``s`` can be a delicate task. Recommended
values for ``s`` depend on the accuracy of the data values. If the user
has an idea of the statistical errors on the data, she can also find a
proper estimate for ``s``. By assuming that, if she specifies the
right ``s``, the interpolator will use a spline ``f(u,v)`` which exactly
reproduces the function underlying the data, she can evaluate
``sum((r(i,j)-s(u(i),v(j)))**2)`` to find a good estimate for this ``s``.
For example, if she knows that the statistical errors on her
``r(i,j)``-values are not greater than 0.1, she may expect that a good
``s`` should have a value not larger than ``u.size * v.size * (0.1)**2``.
If nothing is known about the statistical error in ``r(i,j)``, ``s`` must
be determined by trial and error. The best is then to start with a very
large value of ``s`` (to determine the least-squares polynomial and the
corresponding upper bound ``fp0`` for ``s``) and then to progressively
decrease the value of ``s`` (say by a factor 10 in the beginning, i.e.
``s = fp0 / 10, fp0 / 100, ...`` and more carefully as the approximation
shows more detail) to obtain closer fits.
The interpolation results for different values of ``s`` give some insight
into this process:
>>> fig2 = plt.figure()
>>> s = [3e9, 2e9, 1e9, 1e8]
>>> for ii in xrange(len(s)):
>>> lut = RectSphereBivariateSpline(lats, lons, data, s=s[ii])
>>> data_interp = lut.ev(new_lats.ravel(),
... new_lons.ravel()).reshape((360, 180)).T
>>> ax = fig2.add_subplot(2, 2, ii+1)
>>> ax.imshow(data_interp, interpolation='nearest')
>>> ax.set_title("s = %g" % s[ii])
>>> plt.show()
"""
def __init__(self, u, v, r, s=0., pole_continuity=False, pole_values=None,
pole_exact=False, pole_flat=False):
iopt = np.array([0, 0, 0], dtype=int)
ider = np.array([-1, 0, -1, 0], dtype=int)
if pole_values is None:
pole_values = (None, None)
elif isinstance(pole_values, (float, np.float32, np.float64)):
pole_values = (pole_values, pole_values)
if isinstance(pole_continuity, bool):
pole_continuity = (pole_continuity, pole_continuity)
if isinstance(pole_exact, bool):
pole_exact = (pole_exact, pole_exact)
if isinstance(pole_flat, bool):
pole_flat = (pole_flat, pole_flat)
r0, r1 = pole_values
iopt[1:] = pole_continuity
if r0 is None:
ider[0] = -1
else:
ider[0] = pole_exact[0]
if r1 is None:
ider[2] = -1
else:
ider[2] = pole_exact[1]
ider[1], ider[3] = pole_flat
u, v = np.ravel(u), np.ravel(v)
if not np.all(np.diff(u) > 0.0):
raise TypeError('u must be strictly increasing')
if not np.all(np.diff(v) > 0.0):
raise TypeError('v must be strictly increasing')
if not u.size == r.shape[0]:
raise TypeError('u dimension of r must have same number of '
'elements as u')
if not v.size == r.shape[1]:
raise TypeError('v dimension of r must have same number of '
'elements as v')
if pole_continuity[1] is False and pole_flat[1] is True:
raise TypeError('if pole_continuity is False, so must be '
'pole_flat')
if pole_continuity[0] is False and pole_flat[0] is True:
raise TypeError('if pole_continuity is False, so must be '
'pole_flat')
r = np.ravel(r)
nu, tu, nv, tv, c, fp, ier = dfitpack.regrid_smth_spher(iopt, ider,
u.copy(), v.copy(), r.copy(), r0, r1, s)
if ier not in [0, -1, -2]:
msg = _spfit_messages.get(ier, 'ier=%s' % (ier))
raise ValueError(msg)
self.fp = fp
self.tck = tu[:nu], tv[:nv], c[:(nu - 4) * (nv-4)]
self.degrees = (3, 3)
| mit |
imatge-upc/saliency | shallow/train.py | 2 | 3064 | # add to kfkd.py
from lasagne import layers
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet,BatchIterator
import os
import numpy as np
from sklearn.utils import shuffle
import cPickle as pickle
import matplotlib.pyplot as plt
import Image
import ImageOps
from scipy import misc
import scipy.io
import theano
def load():
f = file('data_Salicon_T.cPickle', 'rb')
loaded_obj = pickle.load(f)
f.close()
X, y = loaded_obj
return X, y
def float32(k):
return np.cast['float32'](k)
class AdjustVariable(object):
def __init__(self, name, start=0.03, stop=0.001):
self.name = name
self.start, self.stop = start, stop
self.ls = None
def __call__(self, nn, train_history):
if self.ls is None:
self.ls = np.linspace(self.start, self.stop, nn.max_epochs)
epoch = train_history[-1]['epoch']
new_value = float32(self.ls[epoch - 1])
getattr(nn, self.name).set_value(new_value)
class FlipBatchIterator(BatchIterator):
def transform(self, Xb, yb):
Xb, yb = super(FlipBatchIterator, self).transform(Xb, yb)
# Flip half of the images in this batch at random:
bs = Xb.shape[0]
indices = np.random.choice(bs, bs / 2, replace=False)
Xb[indices] = Xb[indices, :, :, ::-1]
tmp = yb[indices].reshape(bs/2,1,48,48)
mirror = tmp[ :,:,:, ::-1]
yb[indices] = mirror.reshape(bs/2,48*48)
return Xb, yb
net2 = NeuralNet(
layers=[
('input', layers.InputLayer),
('conv1', layers.Conv2DLayer),
('pool1', layers.MaxPool2DLayer),
('conv2', layers.Conv2DLayer),
('pool2', layers.MaxPool2DLayer),
('conv3', layers.Conv2DLayer),
('pool3', layers.MaxPool2DLayer),
('hidden4', layers.DenseLayer),
('maxout6',layers.FeaturePoolLayer),
('output', layers.DenseLayer),
],
input_shape=(None, 3, 96, 96),
conv1_num_filters=32, conv1_filter_size=(5, 5), pool1_pool_size=(2, 2),
conv2_num_filters=64, conv2_filter_size=(3, 3), pool2_pool_size=(2, 2),
conv3_num_filters=64, conv3_filter_size=(3, 3), pool3_pool_size=(2, 2),
hidden4_num_units=48*48*2,
maxout6_pool_size=2,output_num_units=48*48,output_nonlinearity=None,
update_learning_rate=theano.shared(float32(0.05)),
update_momentum=theano.shared(float32(0.9)),
regression=True,
on_epoch_finished=[
AdjustVariable('update_learning_rate', start=0.05, stop=0.0001),
AdjustVariable('update_momentum', start=0.9, stop=0.999),
],
batch_iterator_train=FlipBatchIterator(batch_size=128),
max_epochs=1200,
verbose=1,
)
X, y = load()
print("X.shape == {}; X.min == {:.3f}; X.max == {:.3f}".format(
X.shape, X.min(), X.max()))
print("y.shape == {}; y.min == {:.3f}; y.max == {:.3f}".format(
y.shape, y.min(), y.max()))
X = X.astype(np.float32)
y = y.astype(np.float32)
net.fit(X, y)
with open('JuntingNet_SALICON.pickle', 'wb') as f:
pickle.dump(net2, f, -1) | mit |
MikeLing/shogun | examples/undocumented/python/graphical/interactive_svm_demo.py | 6 | 12586 | """
Shogun demo, based on PyQT Demo by Eli Bendersky
Christian Widmer
Soeren Sonnenburg
License: GPLv3
"""
import numpy
import sys, os, csv
from PyQt4.QtCore import *
from PyQt4.QtGui import *
import matplotlib
from matplotlib.colorbar import make_axes, Colorbar
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
from matplotlib.figure import Figure
from shogun import *
import util
class Form(QMainWindow):
def __init__(self, parent=None):
super(Form, self).__init__(parent)
self.setWindowTitle('SHOGUN interactive demo')
self.data = DataHolder()
self.series_list_model = QStandardItemModel()
self.create_menu()
self.create_main_frame()
self.create_status_bar()
self.on_show()
def load_file(self, filename=None):
filename = QFileDialog.getOpenFileName(self,
'Open a data file', '.', 'CSV files (*.csv);;All Files (*.*)')
if filename:
self.data.load_from_file(filename)
self.fill_series_list(self.data.series_names())
self.status_text.setText("Loaded " + filename)
def on_show(self):
self.axes.clear()
self.axes.grid(True)
self.axes.plot(self.data.x1_pos, self.data.x2_pos, 'ro')
self.axes.plot(self.data.x1_neg, self.data.x2_neg, 'bo')
self.axes.set_xlim((-5,5))
self.axes.set_ylim((-5,5))
self.canvas.draw()
self.fill_series_list(self.data.get_stats())
def on_about(self):
msg = __doc__
QMessageBox.about(self, "About the demo", msg.strip())
def fill_series_list(self, names):
self.series_list_model.clear()
for name in names:
item = QStandardItem(name)
item.setCheckState(Qt.Unchecked)
item.setCheckable(False)
self.series_list_model.appendRow(item)
def onclick(self, event):
print 'button=%d, x=%d, y=%d, xdata=%f, ydata=%f'%(event.button, event.x, event.y, event.xdata, event.ydata)
if event.button==1:
label = 1.0
else:
label = -1.0
self.data.add_example(event.xdata, event.ydata, label)
self.on_show()
def clear(self):
self.data.clear()
self.on_show()
def enable_widgets(self):
kernel_name = self.kernel_combo.currentText()
if kernel_name == "LinearKernel":
self.sigma.setDisabled(True)
self.degree.setDisabled(True)
elif kernel_name == "PolynomialKernel":
self.sigma.setDisabled(True)
self.degree.setEnabled(True)
elif kernel_name == "GaussianKernel":
self.sigma.setEnabled(True)
self.degree.setDisabled(True)
def train_svm(self):
width = float(self.sigma.text())
degree = int(self.degree.text())
self.axes.clear()
self.axes.grid(True)
self.axes.plot(self.data.x1_pos, self.data.x2_pos, 'ro')
self.axes.plot(self.data.x1_neg, self.data.x2_neg, 'bo')
# train svm
labels = self.data.get_labels()
print type(labels)
lab = BinaryLabels(labels)
features = self.data.get_examples()
train = RealFeatures(features)
kernel_name = self.kernel_combo.currentText()
print "current kernel is %s" % (kernel_name)
if kernel_name == "LinearKernel":
gk = LinearKernel(train, train)
gk.set_normalizer(IdentityKernelNormalizer())
elif kernel_name == "PolynomialKernel":
gk = PolyKernel(train, train, degree, True)
gk.set_normalizer(IdentityKernelNormalizer())
elif kernel_name == "GaussianKernel":
gk = GaussianKernel(train, train, width)
cost = float(self.cost.text())
print "cost", cost
svm = LibSVM(cost, gk, lab)
svm.train()
svm.set_epsilon(1e-2)
x, y, z = util.compute_output_plot_isolines(svm, gk, train)
plt=self.axes.pcolor(x, y, z)
CS=self.axes.contour(x, y, z, [-1,0,1], linewidths=1, colors='black', hold=True)
#CS=self.axes.contour(x, y, z, linewidths=1, colors='black', hold=True)
#CS=self.axes.contour(x, y, z, 5, linewidths=1, colors='black', hold=True)
matplotlib.pyplot.clabel(CS, inline=1, fontsize=10)
self.axes.set_xlim((-5,5))
self.axes.set_ylim((-5,5))
cmap = matplotlib.cm.jet
norm = matplotlib.colors.Normalize(numpy.min(z), numpy.max(z))
print CS.get_clim()
if not self.cax:
self.cax, kw = make_axes(self.axes)
# ColorbarBase derives from ScalarMappable and puts a colorbar
# in a specified axes, so it has everything needed for a
# standalone colorbar. There are many more kwargs, but the
# following gives a basic continuous colorbar with ticks
# and labels.
cb1 = matplotlib.colorbar.ColorbarBase(self.cax, cmap=cmap,
norm=norm)
self.canvas.draw()
def create_main_frame(self):
self.main_frame = QWidget()
plot_frame = QWidget()
self.dpi = 100
self.fig = Figure((6.0, 6.0), dpi=self.dpi)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self.main_frame)
cid = self.canvas.mpl_connect('button_press_event', self.onclick)
self.axes = self.fig.add_subplot(111)
self.cax = None
#self.mpl_toolbar = NavigationToolbar(self.canvas, self.main_frame)
log_label = QLabel("Number of examples:")
self.series_list_view = QListView()
self.series_list_view.setModel(self.series_list_model)
cost_label = QLabel('C')
#self.cost = QSpinBox()#QLineEdit()
self.cost = QLineEdit()
self.cost.setText("1.0")
#self.cost.setMinimum(1)
spin_label2 = QLabel('sigma')
self.sigma = QLineEdit()
self.sigma.setText("1.2")
#self.sigma.setMinimum(1)
self.degree = QLineEdit()
self.degree.setText("2")
#self.sigma.setMinimum(1)
spins_hbox = QHBoxLayout()
spins_hbox.addWidget(cost_label)
spins_hbox.addWidget(self.cost)
spins_hbox.addWidget(spin_label2)
spins_hbox.addWidget(self.sigma)
spins_hbox.addWidget(self.degree)
spins_hbox.addStretch(1)
self.legend_cb = QCheckBox("Show Support Vectors")
self.legend_cb.setChecked(False)
self.show_button = QPushButton("&Train SVM")
self.connect(self.show_button, SIGNAL('clicked()'), self.train_svm)
self.clear_button = QPushButton("&Clear")
self.connect(self.clear_button, SIGNAL('clicked()'), self.clear)
self.kernel_combo = QComboBox()
self.kernel_combo.insertItem(-1, "GaussianKernel")
self.kernel_combo.insertItem(-1, "PolynomialKernel")
self.kernel_combo.insertItem(-1, "LinearKernel")
self.kernel_combo.maximumSize = QSize(300, 50)
self.connect(self.kernel_combo, SIGNAL("currentIndexChanged(QString)"), self.enable_widgets)
left_vbox = QVBoxLayout()
left_vbox.addWidget(self.canvas)
#left_vbox.addWidget(self.mpl_toolbar)
right0_vbox = QVBoxLayout()
right0_vbox.addWidget(log_label)
right0_vbox.addWidget(self.series_list_view)
#right0_vbox.addWidget(self.legend_cb)
right0_vbox.addStretch(1)
right2_vbox = QVBoxLayout()
right2_label = QLabel("Settings")
right2_vbox.addWidget(right2_label)
right2_vbox.addWidget(self.show_button)
right2_vbox.addWidget(self.kernel_combo)
right2_vbox.addLayout(spins_hbox)
right2_clearlabel = QLabel("Remove Data")
right2_vbox.addWidget(right2_clearlabel)
right2_vbox.addWidget(self.clear_button)
right2_vbox.addStretch(1)
right_vbox = QHBoxLayout()
right_vbox.addLayout(right0_vbox)
right_vbox.addLayout(right2_vbox)
hbox = QVBoxLayout()
hbox.addLayout(left_vbox)
hbox.addLayout(right_vbox)
self.main_frame.setLayout(hbox)
self.setCentralWidget(self.main_frame)
self.enable_widgets()
def create_status_bar(self):
self.status_text = QLabel("")
self.statusBar().addWidget(self.status_text, 1)
def create_menu(self):
self.file_menu = self.menuBar().addMenu("&File")
load_action = self.create_action("&Load file",
shortcut="Ctrl+L", slot=self.load_file, tip="Load a file")
quit_action = self.create_action("&Quit", slot=self.close,
shortcut="Ctrl+Q", tip="Close the application")
self.add_actions(self.file_menu,
(load_action, None, quit_action))
self.help_menu = self.menuBar().addMenu("&Help")
about_action = self.create_action("&About",
shortcut='F1', slot=self.on_about,
tip='About the demo')
self.add_actions(self.help_menu, (about_action,))
def add_actions(self, target, actions):
for action in actions:
if action is None:
target.addSeparator()
else:
target.addAction(action)
def create_action( self, text, slot=None, shortcut=None,
icon=None, tip=None, checkable=False,
signal="triggered()"):
action = QAction(text, self)
if icon is not None:
action.setIcon(QIcon(":/%s.png" % icon))
if shortcut is not None:
action.setShortcut(shortcut)
if tip is not None:
action.setToolTip(tip)
action.setStatusTip(tip)
if slot is not None:
self.connect(action, SIGNAL(signal), slot)
if checkable:
action.setCheckable(True)
return action
class DataHolder(object):
""" Just a thin wrapper over a dictionary that holds integer
data series. Each series has a name and a list of numbers
as its data. The length of all series is assumed to be
the same.
The series can be read from a CSV file, where each line
is a separate series. In each series, the first item in
the line is the name, and the rest are data numbers.
"""
def __init__(self, filename=None):
self.clear()
self.load_from_file(filename)
def clear(self):
self.x1_pos = []
self.x2_pos = []
self.x1_neg = []
self.x2_neg = []
def get_stats(self):
num_neg = len(self.x1_neg)
num_pos = len(self.x1_pos)
str_neg = "num negative examples: %i" % num_neg
str_pos = "num positive examples: %i" % num_pos
return (str_neg, str_pos)
def get_labels(self):
return numpy.array([1]*len(self.x1_pos) + [-1]*len(self.x1_neg), dtype=numpy.float64)
def get_examples(self):
num_pos = len(self.x1_pos)
num_neg = len(self.x1_neg)
examples = numpy.zeros((2,num_pos+num_neg))
for i in xrange(num_pos):
examples[0,i] = self.x1_pos[i]
examples[1,i] = self.x2_pos[i]
for i in xrange(num_neg):
examples[0,i+num_pos] = self.x1_neg[i]
examples[1,i+num_pos] = self.x2_neg[i]
return examples
def add_example(self, x1, x2, label):
if label==1:
self.x1_pos.append(x1)
self.x2_pos.append(x2)
else:
self.x1_neg.append(x1)
self.x2_neg.append(x2)
def load_from_file(self, filename=None):
self.data = {}
self.names = []
if filename:
for line in csv.reader(open(filename, 'rb')):
self.names.append(line[0])
self.data[line[0]] = map(int, line[1:])
self.datalen = len(line[1:])
def series_names(self):
""" Names of the data series
"""
return self.names
def series_len(self):
""" Length of a data series
"""
return self.datalen
def series_count(self):
return len(self.data)
def get_series_data(self, name):
return self.data[name]
def main():
app = QApplication(sys.argv)
form = Form()
form.show()
app.exec_()
if __name__ == "__main__":
main()
#~ dh = DataHolder('qt_mpl_data.csv')
#~ print dh.data
#~ print dh.get_series_data('1991 Sales')
#~ print dh.series_names()
#~ print dh.series_count()
| gpl-3.0 |
jlcarmic/producthunt_simulator | venv/lib/python2.7/site-packages/scipy/integrate/odepack.py | 62 | 9420 | # Author: Travis Oliphant
from __future__ import division, print_function, absolute_import
__all__ = ['odeint']
from . import _odepack
from copy import copy
import warnings
class ODEintWarning(Warning):
pass
_msgs = {2: "Integration successful.",
1: "Nothing was done; the integration time was 0.",
-1: "Excess work done on this call (perhaps wrong Dfun type).",
-2: "Excess accuracy requested (tolerances too small).",
-3: "Illegal input detected (internal error).",
-4: "Repeated error test failures (internal error).",
-5: "Repeated convergence failures (perhaps bad Jacobian or tolerances).",
-6: "Error weight became zero during problem.",
-7: "Internal workspace insufficient to finish (internal error)."
}
def odeint(func, y0, t, args=(), Dfun=None, col_deriv=0, full_output=0,
ml=None, mu=None, rtol=None, atol=None, tcrit=None, h0=0.0,
hmax=0.0, hmin=0.0, ixpr=0, mxstep=0, mxhnil=0, mxordn=12,
mxords=5, printmessg=0):
"""
Integrate a system of ordinary differential equations.
Solve a system of ordinary differential equations using lsoda from the
FORTRAN library odepack.
Solves the initial value problem for stiff or non-stiff systems
of first order ode-s::
dy/dt = func(y, t0, ...)
where y can be a vector.
*Note*: The first two arguments of ``func(y, t0, ...)`` are in the
opposite order of the arguments in the system definition function used
by the `scipy.integrate.ode` class.
Parameters
----------
func : callable(y, t0, ...)
Computes the derivative of y at t0.
y0 : array
Initial condition on y (can be a vector).
t : array
A sequence of time points for which to solve for y. The initial
value point should be the first element of this sequence.
args : tuple, optional
Extra arguments to pass to function.
Dfun : callable(y, t0, ...)
Gradient (Jacobian) of `func`.
col_deriv : bool, optional
True if `Dfun` defines derivatives down columns (faster),
otherwise `Dfun` should define derivatives across rows.
full_output : bool, optional
True if to return a dictionary of optional outputs as the second output
printmessg : bool, optional
Whether to print the convergence message
Returns
-------
y : array, shape (len(t), len(y0))
Array containing the value of y for each desired time in t,
with the initial value `y0` in the first row.
infodict : dict, only returned if full_output == True
Dictionary containing additional output information
======= ============================================================
key meaning
======= ============================================================
'hu' vector of step sizes successfully used for each time step.
'tcur' vector with the value of t reached for each time step.
(will always be at least as large as the input times).
'tolsf' vector of tolerance scale factors, greater than 1.0,
computed when a request for too much accuracy was detected.
'tsw' value of t at the time of the last method switch
(given for each time step)
'nst' cumulative number of time steps
'nfe' cumulative number of function evaluations for each time step
'nje' cumulative number of jacobian evaluations for each time step
'nqu' a vector of method orders for each successful step.
'imxer' index of the component of largest magnitude in the
weighted local error vector (e / ewt) on an error return, -1
otherwise.
'lenrw' the length of the double work array required.
'leniw' the length of integer work array required.
'mused' a vector of method indicators for each successful time step:
1: adams (nonstiff), 2: bdf (stiff)
======= ============================================================
Other Parameters
----------------
ml, mu : int, optional
If either of these are not None or non-negative, then the
Jacobian is assumed to be banded. These give the number of
lower and upper non-zero diagonals in this banded matrix.
For the banded case, `Dfun` should return a matrix whose
rows contain the non-zero bands (starting with the lowest diagonal).
Thus, the return matrix `jac` from `Dfun` should have shape
``(ml + mu + 1, len(y0))`` when ``ml >=0`` or ``mu >=0``.
The data in `jac` must be stored such that ``jac[i - j + mu, j]``
holds the derivative of the `i`th equation with respect to the `j`th
state variable. If `col_deriv` is True, the transpose of this
`jac` must be returned.
rtol, atol : float, optional
The input parameters `rtol` and `atol` determine the error
control performed by the solver. The solver will control the
vector, e, of estimated local errors in y, according to an
inequality of the form ``max-norm of (e / ewt) <= 1``,
where ewt is a vector of positive error weights computed as
``ewt = rtol * abs(y) + atol``.
rtol and atol can be either vectors the same length as y or scalars.
Defaults to 1.49012e-8.
tcrit : ndarray, optional
Vector of critical points (e.g. singularities) where integration
care should be taken.
h0 : float, (0: solver-determined), optional
The step size to be attempted on the first step.
hmax : float, (0: solver-determined), optional
The maximum absolute step size allowed.
hmin : float, (0: solver-determined), optional
The minimum absolute step size allowed.
ixpr : bool, optional
Whether to generate extra printing at method switches.
mxstep : int, (0: solver-determined), optional
Maximum number of (internally defined) steps allowed for each
integration point in t.
mxhnil : int, (0: solver-determined), optional
Maximum number of messages printed.
mxordn : int, (0: solver-determined), optional
Maximum order to be allowed for the non-stiff (Adams) method.
mxords : int, (0: solver-determined), optional
Maximum order to be allowed for the stiff (BDF) method.
See Also
--------
ode : a more object-oriented integrator based on VODE.
quad : for finding the area under a curve.
Examples
--------
The second order differential equation for the angle `theta` of a
pendulum acted on by gravity with friction can be written::
theta''(t) + b*theta'(t) + c*sin(theta(t)) = 0
where `b` and `c` are positive constants, and a prime (') denotes a
derivative. To solve this equation with `odeint`, we must first convert
it to a system of first order equations. By defining the angular
velocity ``omega(t) = theta'(t)``, we obtain the system::
theta'(t) = omega(t)
omega'(t) = -b*omega(t) - c*sin(theta(t))
Let `y` be the vector [`theta`, `omega`]. We implement this system
in python as:
>>> def pend(y, t, b, c):
... theta, omega = y
... dydt = [omega, -b*omega - c*np.sin(theta)]
... return dydt
...
We assume the constants are `b` = 0.25 and `c` = 5.0:
>>> b = 0.25
>>> c = 5.0
For initial conditions, we assume the pendulum is nearly vertical
with `theta(0)` = `pi` - 0.1, and it initially at rest, so
`omega(0)` = 0. Then the vector of initial conditions is
>>> y0 = [np.pi - 0.1, 0.0]
We generate a solution 101 evenly spaced samples in the interval
0 <= `t` <= 10. So our array of times is:
>>> t = np.linspace(0, 10, 101)
Call `odeint` to generate the solution. To pass the parameters
`b` and `c` to `pend`, we give them to `odeint` using the `args`
argument.
>>> from scipy.integrate import odeint
>>> sol = odeint(pend, y0, t, args=(b, c))
The solution is an array with shape (101, 2). The first column
is `theta(t)`, and the second is `omega(t)`. The following code
plots both components.
>>> import matplotlib.pyplot as plt
>>> plt.plot(t, sol[:, 0], 'b', label='theta(t)')
>>> plt.plot(t, sol[:, 1], 'g', label='omega(t)')
>>> plt.legend(loc='best')
>>> plt.xlabel('t')
>>> plt.grid()
>>> plt.show()
"""
if ml is None:
ml = -1 # changed to zero inside function call
if mu is None:
mu = -1 # changed to zero inside function call
t = copy(t)
y0 = copy(y0)
output = _odepack.odeint(func, y0, t, args, Dfun, col_deriv, ml, mu,
full_output, rtol, atol, tcrit, h0, hmax, hmin,
ixpr, mxstep, mxhnil, mxordn, mxords)
if output[-1] < 0:
warning_msg = _msgs[output[-1]] + " Run with full_output = 1 to get quantitative information."
warnings.warn(warning_msg, ODEintWarning)
elif printmessg:
warning_msg = _msgs[output[-1]]
warnings.warn(warning_msg, ODEintWarning)
if full_output:
output[1]['message'] = _msgs[output[-1]]
output = output[:-1]
if len(output) == 1:
return output[0]
else:
return output
| mit |
blab/antibody-response-pulse | bcell-array/code/Virus_Bcell_IgM_IgG_Infection_OAS_new.py | 1 | 13195 |
# coding: utf-8
# # Antibody Response Pulse
# https://github.com/blab/antibody-response-pulse
#
# ### B-cells evolution --- cross-reactive antibody response after influenza virus infection or vaccination
# ### Adaptive immune response for repeated infection
# In[3]:
'''
author: Alvason Zhenhua Li
date: 04/09/2015
'''
get_ipython().magic(u'matplotlib inline')
import numpy as np
import matplotlib.pyplot as plt
import os
from matplotlib.ticker import FuncFormatter
import alva_machinery_event_OAS_new as alva
AlvaFontSize = 23
AlvaFigSize = (15, 5)
numberingFig = 0
# plotting
dir_path = '/Users/al/Desktop/GitHub/antibody-response-pulse/bcell-array/figure'
file_name = 'Virus-Bcell-IgM-IgG'
figure_name = '-equation'
file_suffix = '.png'
save_figure = os.path.join(dir_path, file_name + figure_name + file_suffix)
numberingFig = numberingFig + 1
plt.figure(numberingFig, figsize=(12, 5))
plt.axis('off')
plt.title(r'$ Virus-Bcell-IgM-IgG \ equations \ (antibody-response \ for \ repeated-infection) $'
, fontsize = AlvaFontSize)
plt.text(0, 7.0/9, r'$ \frac{\partial V_n(t)}{\partial t} = +\mu_{v}V_{n}(t)(1 - \frac{V_n(t)}{V_{max}}) - \phi_{m} M_{n}(t) V_{n}(t) - \phi_{g} G_{n}(t) V_{n}(t) $'
, fontsize = 1.2*AlvaFontSize)
plt.text(0, 5.0/9, r'$ \frac{\partial B_n(t)}{\partial t} = +\mu_{b}V_{n}(t)(1 - \frac{V_n(t)}{V_{max}}) + (\beta_{m} + \beta_{g}) V_{n}(t) B_{n}(t) - \mu_{b} B_{n}(t) + m_b V_{n}(t)\frac{B_{i-1}(t) - 2B_i(t) + B_{i+1}(t)}{(\Delta i)^2} $'
, fontsize = 1.2*AlvaFontSize)
plt.text(0, 3.0/9,r'$ \frac{\partial M_n(t)}{\partial t} = +\xi_{m} B_{n}(t) - \phi_{m} M_{n}(t) V_{n}(t) - \mu_{m} M_{n}(t) $'
, fontsize = 1.2*AlvaFontSize)
plt.text(0, 1.0/9,r'$ \frac{\partial G_n(t)}{\partial t} = +\xi_{g} B_{n}(t) - \phi_{g} G_{n}(t) V_{n}(t) - \mu_{g} G_{n}(t) + m_a V_{n}(t)\frac{G_{i-1}(t) - 2G_i(t) + G_{i+1}(t)}{(\Delta i)^2} $'
, fontsize = 1.2*AlvaFontSize)
plt.savefig(save_figure, dpi = 100)
plt.show()
# define the V-M-G partial differential equations
def dVdt_array(VBMGxt = [], *args):
# naming
V = VBMGxt[0]
B = VBMGxt[1]
M = VBMGxt[2]
G = VBMGxt[3]
x_totalPoint = VBMGxt.shape[1]
# there are n dSdt
dV_dt_array = np.zeros(x_totalPoint)
# each dSdt with the same equation form
dV_dt_array[:] = +inRateV*V[:]*(1 - V[:]/maxV) - killRateVm*M[:]*V[:] - killRateVg*G[:]*V[:]
return(dV_dt_array)
def dBdt_array(VBMGxt = [], *args):
# naming
V = VBMGxt[0]
B = VBMGxt[1]
M = VBMGxt[2]
G = VBMGxt[3]
x_totalPoint = VBMGxt.shape[1]
# there are n dSdt
dB_dt_array = np.zeros(x_totalPoint)
# each dSdt with the same equation form
Bcopy = np.copy(B)
centerX = Bcopy[:]
leftX = np.roll(Bcopy[:], 1)
rightX = np.roll(Bcopy[:], -1)
leftX[0] = centerX[0]
rightX[-1] = centerX[-1]
dB_dt_array[:] = +inRateB*V[:]*(1 - V[:]/maxV) + (actRateBm + alva.event_active + alva.event_OAS_B)*V[:]*B[:] - outRateB*B[:] + mutatRateB*V[:]*(leftX[:] - 2*centerX[:] + rightX[:])/(dx**2)
return(dB_dt_array)
def dMdt_array(VBMGxt = [], *args):
# naming
V = VBMGxt[0]
B = VBMGxt[1]
M = VBMGxt[2]
G = VBMGxt[3]
x_totalPoint = VBMGxt.shape[1]
# there are n dSdt
dM_dt_array = np.zeros(x_totalPoint)
# each dSdt with the same equation form
dM_dt_array[:] = +inRateM*B[:] - consumeRateM*M[:]*V[:] - outRateM*M[:]
return(dM_dt_array)
def dGdt_array(VBMGxt = [], *args):
# naming
V = VBMGxt[0]
B = VBMGxt[1]
M = VBMGxt[2]
G = VBMGxt[3]
x_totalPoint = VBMGxt.shape[1]
# there are n dSdt
dG_dt_array = np.zeros(x_totalPoint)
# each dSdt with the same equation form
Gcopy = np.copy(G)
centerX = Gcopy[:]
leftX = np.roll(Gcopy[:], 1)
rightX = np.roll(Gcopy[:], -1)
leftX[0] = centerX[0]
rightX[-1] = centerX[-1]
dG_dt_array[:] = +(inRateG + alva.event_OAS)*B[:] - consumeRateG*G[:]*V[:] - outRateG*G[:] + mutatRateA*(leftX[:] - 2*centerX[:] + rightX[:])/(dx**2)
return(dG_dt_array)
# In[7]:
# setting parameter
timeUnit = 'day'
if timeUnit == 'hour':
hour = float(1)
day = float(24)
elif timeUnit == 'day':
day = float(1)
hour = float(1)/24
elif timeUnit == 'year':
year = float(1)
day = float(1)/365
hour = float(1)/24/365
maxV = float(50) # max virus/micro-liter
inRateV = 0.2/hour # in-rate of virus
killRateVm = 0.0003/hour # kill-rate of virus by antibody-IgM
killRateVg = killRateVm # kill-rate of virus by antibody-IgG
inRateB = 0.06/hour # in-rate of B-cell
outRateB = inRateB/8 # out-rate of B-cell
actRateBm = killRateVm # activation rate of naive B-cell
inRateM = 0.16/hour # in-rate of antibody-IgM from naive B-cell
outRateM = inRateM/1 # out-rate of antibody-IgM from naive B-cell
consumeRateM = killRateVm # consume-rate of antibody-IgM by cleaning virus
inRateG = inRateM/10 # in-rate of antibody-IgG from memory B-cell
outRateG = outRateM/250 # out-rate of antibody-IgG from memory B-cell
consumeRateG = killRateVg # consume-rate of antibody-IgG by cleaning virus
mutatRateB = 0.00003/hour # B-cell mutation rate
mutatRateA = 0.0001/hour # antibody mutation rate
mutatRateB = 0.0000/hour # B-cell mutation rate
mutatRateA = 0.000/hour # antibody mutation rate
# time boundary and griding condition
minT = float(0)
maxT = float(6*28*day)
totalPoint_T = int(1*10**3 + 1)
gT = np.linspace(minT, maxT, totalPoint_T)
spacingT = np.linspace(minT, maxT, num = totalPoint_T, retstep = True)
gT = spacingT[0]
dt = spacingT[1]
# space boundary and griding condition
minX = float(0)
maxX = float(3)
totalPoint_X = int(maxX - minX + 1)
gX = np.linspace(minX, maxX, totalPoint_X)
gridingX = np.linspace(minX, maxX, num = totalPoint_X, retstep = True)
gX = gridingX[0]
dx = gridingX[1]
gV_array = np.zeros([totalPoint_X, totalPoint_T])
gB_array = np.zeros([totalPoint_X, totalPoint_T])
gM_array = np.zeros([totalPoint_X, totalPoint_T])
gG_array = np.zeros([totalPoint_X, totalPoint_T])
# initial output condition
#gV_array[1, 0] = float(2)
#[pre-parameter, post-parameter, recovered-day, OAS+, OSA-]
actRateBg_1st = 0.0002/hour # activation rate of memory B-cell at 1st time (pre-)
actRateBg_2nd = actRateBg_1st*10 # activation rate of memory B-cell at 2nd time (post-)
origin_virus = int(1)
current_virus = int(2)
event_parameter = np.array([[actRateBg_1st,
actRateBg_2nd,
14*day,
+5/hour,
-actRateBm - actRateBg_1st + (actRateBm + actRateBg_1st)/3,
origin_virus,
current_virus]])
# [viral population, starting time, first]
# [viral population, starting time] ---first
infection_period = 1*28*day
viral_population = np.zeros(int(maxX + 1))
viral_population[origin_virus:current_virus + 1] = 3
infection_starting_time = np.arange(int(maxX + 1))*infection_period
event_1st = np.zeros([int(maxX + 1), 2])
event_1st[:, 0] = viral_population
event_1st[:, 1] = infection_starting_time
print ('event_1st = {:}'.format(event_1st))
# [viral population, starting time] ---2nd]
viral_population = np.zeros(int(maxX + 1))
viral_population[origin_virus:current_virus + 1] = 0
infection_starting_time = np.arange(int(maxX + 1))*0
event_2nd = np.zeros([int(maxX + 1), 2])
event_2nd[:, 0] = viral_population
event_2nd[:, 1] = infection_starting_time
print ('event_2nd = {:}'.format(event_2nd))
event_table = np.array([event_parameter, event_1st, event_2nd])
# Runge Kutta numerical solution
pde_array = np.array([dVdt_array, dBdt_array, dMdt_array, dGdt_array])
initial_Out = np.array([gV_array, gB_array, gM_array, gG_array])
gOut_array = alva.AlvaRungeKutta4XT(pde_array, initial_Out, minX, maxX, totalPoint_X, minT, maxT, totalPoint_T, event_table)
# plotting
gV = gOut_array[0]
gB = gOut_array[1]
gM = gOut_array[2]
gG = gOut_array[3]
numberingFig = numberingFig + 1
for i in range(totalPoint_X):
figure_name = '-response-%i'%(i)
figure_suffix = '.png'
save_figure = os.path.join(dir_path, file_name + figure_name + file_suffix)
plt.figure(numberingFig, figsize = AlvaFigSize)
plt.plot(gT, gV[i], color = 'red', label = r'$ V_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)
plt.plot(gT, gM[i], color = 'blue', label = r'$ IgM_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)
plt.plot(gT, gG[i], color = 'green', label = r'$ IgG_{%i}(t) $'%(i), linewidth = 3.0, alpha = 0.5)
plt.plot(gT, gM[i] + gG[i], color = 'gray', linewidth = 5.0, alpha = 0.5, linestyle = 'dashed'
, label = r'$ IgM_{%i}(t) + IgG_{%i}(t) $'%(i, i))
plt.grid(True, which = 'both')
plt.title(r'$ Antibody \ from \ Virus-{%i} $'%(i), fontsize = AlvaFontSize)
plt.xlabel(r'$time \ (%s)$'%(timeUnit), fontsize = AlvaFontSize)
plt.ylabel(r'$ Neutralization \ \ titer $', fontsize = AlvaFontSize)
plt.xlim([minT, maxT])
plt.xticks(fontsize = AlvaFontSize*0.6)
plt.yticks(fontsize = AlvaFontSize*0.6)
plt.ylim([2**0, 2**14])
plt.yscale('log', basey = 2)
plt.legend(loc = (1,0), fontsize = AlvaFontSize)
plt.savefig(save_figure, dpi = 100)
plt.show()
# In[5]:
# Experimental lab data from OAS paper
gT_lab = np.array([28, 28 + 7, 28 + 14, 28 + 28]) + 28
gPR8_lab = np.array([2**(9 + 1.0/10), 2**(13 - 1.0/5), 2**(13 + 1.0/3), 2**(13 - 1.0/4)])
standard_PR8 = gPR8_lab**(3.0/4)
gFM1_lab = np.array([0, 2**(6 - 1.0/5), 2**(7 - 1.0/4), 2**(8 + 1.0/4)])
standard_FM1 = gFM1_lab**(3.0/4)
bar_width = 2.0
# Sequential immunization graph
numberingFig = numberingFig + 1
plt.figure(numberingFig, figsize = (12, 6))
plt.subplot(111)
plt.plot(gT, (gM[origin_virus] + gG[origin_virus]), linewidth = 5.0, alpha = 0.5, color = 'gray'
, label = r'$ Origin-virus $')
plt.plot(gT, (gM[origin_virus + 1] + gG[origin_virus + 1]), linewidth = 5.0, alpha = 0.5, color = 'red'
, label = r'$ Subsequence-virus $')
plt.bar(gT_lab - bar_width/2, gPR8_lab, bar_width, alpha = 0.6, color = 'gray', yerr = standard_PR8
, error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ PR8-virus $')
plt.bar(gT_lab + bar_width/2, gFM1_lab, bar_width, alpha = 0.6, color = 'red', yerr = standard_FM1
, error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ FM1-virus $')
plt.grid(True, which = 'both')
plt.title(r'$ Original \ Antigenic \ Sin \ (sequential-infection)$', fontsize = AlvaFontSize)
plt.xlabel(r'$time \ (%s)$'%(timeUnit), fontsize = AlvaFontSize)
plt.ylabel(r'$ Neutralization \ \ titer $', fontsize = AlvaFontSize)
plt.xticks(fontsize = AlvaFontSize*0.6)
plt.yticks(fontsize = AlvaFontSize*0.6)
plt.xlim([minT, 6*30*day])
plt.ylim([2**5, 2**14])
plt.yscale('log', basey = 2)
# gca()---GetCurrentAxis and Format the ticklabel to be 2**x
plt.gca().yaxis.set_major_formatter(FuncFormatter(lambda x, pos: int(2**(np.log(x)/np.log(2)))))
#plt.gca().xaxis.set_major_locator(plt.MultipleLocator(7))
plt.legend(loc = (1, 0), fontsize = AlvaFontSize)
plt.show()
# In[6]:
# Experimental lab data from OAS paper
gT_lab = np.array([28, 28 + 7, 28 + 14, 28 + 28]) + 28
gPR8_lab = np.array([2**(9 + 1.0/10), 2**(13 - 1.0/5), 2**(13 + 1.0/3), 2**(13 - 1.0/4)])
standard_PR8 = gPR8_lab**(3.0/4)
gFM1_lab = np.array([0, 2**(6 - 1.0/5), 2**(7 - 1.0/4), 2**(8 + 1.0/4)])
standard_FM1 = gFM1_lab**(3.0/4)
bar_width = 1.0
# Sequential immunization graph
figure_name = '-Original-Antigenic-Sin-infection'
figure_suffix = '.png'
save_figure = os.path.join(dir_path, file_name + figure_name + file_suffix)
numberingFig = numberingFig + 1
plt.figure(numberingFig, figsize = (12, 6))
plt.subplot(111)
plt.plot(gT, (gM[origin_virus] + gG[origin_virus]), linewidth = 5.0, alpha = 0.5, color = 'gray'
, label = r'$ Origin-virus $')
plt.plot(gT, (gM[origin_virus + 1] + gG[origin_virus + 1]), linewidth = 5.0, alpha = 0.5, color = 'red'
, label = r'$ Subsequence-virus $')
plt.bar(gT_lab - bar_width/2, gPR8_lab, bar_width, alpha = 0.6, color = 'gray', yerr = standard_PR8
, error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ PR8-virus $')
plt.bar(gT_lab + bar_width/2, gFM1_lab, bar_width, alpha = 0.6, color = 'red', yerr = standard_FM1
, error_kw = dict(elinewidth = 1, ecolor = 'black'), label = r'$ FM1-virus $')
plt.grid(True, which = 'both')
plt.title(r'$ Original \ Antigenic \ Sin \ (sequential-infection)$', fontsize = AlvaFontSize)
plt.xlabel(r'$time \ (%s)$'%(timeUnit), fontsize = AlvaFontSize)
plt.ylabel(r'$ Neutralization \ \ titer $', fontsize = AlvaFontSize)
plt.xticks(fontsize = AlvaFontSize*0.6)
plt.yticks(fontsize = AlvaFontSize*0.6)
plt.xlim([minT, 3*30*day])
plt.ylim([2**5, 2**14])
plt.yscale('log', basey = 2)
# gca()---GetCurrentAxis and Format the ticklabel to be 2**x
plt.gca().yaxis.set_major_formatter(FuncFormatter(lambda x, pos: int(2**(np.log(x)/np.log(2)))))
plt.gca().xaxis.set_major_locator(plt.MultipleLocator(7))
plt.legend(loc = (1, 0), fontsize = AlvaFontSize)
plt.savefig(save_figure, dpi = 100, bbox_inches='tight')
plt.show()
# In[ ]:
| gpl-2.0 |
adhix11/pmtk3 | python/demos/linregDemo1.py | 26 | 1104 | #!/usr/bin/python2.4
import numpy
import scipy.stats
import matplotlib.pyplot as plt
def main():
# true parameters
w = 2
w0 = 3
sigma = 2
# make data
numpy.random.seed(1)
Ntrain = 20
xtrain = numpy.linspace(0,10,Ntrain)
ytrain = w*xtrain + w0 + numpy.random.random(Ntrain)*sigma
Ntest = 100
xtest = numpy.linspace(0,10,Ntest)
ytest = w*xtest + w0 + numpy.random.random(Ntest)*sigma
# from http://www2.warwick.ac.uk/fac/sci/moac/students/peter_cock/python/lin_reg/
# fit
west, w0est, r_value, p_value, std_err = scipy.stats.linregress(xtrain, ytrain)
# display
print "Param \t True \t Est"
print "w0 \t %5.3f \t %5.3f" % (w0, w0est)
print "w \t %5.3f \t %5.3f" % (w, west)
# plot
plt.close()
plt.plot(xtrain, ytrain, 'ro')
plt.hold(True)
#plt.plot(xtest, ytest, 'ka-')
ytestPred = west*xtest + w0est
#ndx = range(0, Ntest, 10)
#h = plt.plot(xtest[ndx], ytestPred[ndx], 'b*')
h = plt.plot(xtest, ytestPred, 'b-')
plt.setp(h, 'markersize', 12)
if __name__ == '__main__':
main()
| mit |
marcusmueller/gnuradio | gr-filter/examples/fft_filter_ccc.py | 7 | 4367 | #!/usr/bin/env python
#
# Copyright 2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# any later version.
#
# GNU Radio is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with GNU Radio; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street,
# Boston, MA 02110-1301, USA.
#
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
from gnuradio import gr, filter
from gnuradio import analog
from gnuradio import blocks
from gnuradio import eng_notation
from gnuradio.eng_arg import eng_float, intx
from argparse import ArgumentParser
import sys
import numpy
try:
from matplotlib import pyplot
except ImportError:
print("Error: could not from matplotlib import pyplot (http://matplotlib.sourceforge.net/)")
sys.exit(1)
class example_fft_filter_ccc(gr.top_block):
def __init__(self, N, fs, bw0, bw1, tw, atten, D):
gr.top_block.__init__(self)
self._nsamps = N
self._fs = fs
self._bw0 = bw0
self._bw1 = bw1
self._tw = tw
self._at = atten
self._decim = D
taps = filter.firdes.complex_band_pass_2(1, self._fs,
self._bw0, self._bw1,
self._tw, self._at)
print("Num. Taps: ", len(taps))
self.src = analog.noise_source_c(analog.GR_GAUSSIAN, 1)
self.head = blocks.head(gr.sizeof_gr_complex, self._nsamps)
self.filt0 = filter.fft_filter_ccc(self._decim, taps)
self.vsnk_src = blocks.vector_sink_c()
self.vsnk_out = blocks.vector_sink_c()
self.connect(self.src, self.head, self.vsnk_src)
self.connect(self.head, self.filt0, self.vsnk_out)
def main():
parser = ArgumentParser(conflict_handler="resolve")
parser.add_argument("-N", "--nsamples", type=int, default=10000,
help="Number of samples to process [default=%(default)r]")
parser.add_argument("-s", "--samplerate", type=eng_float, default=8000,
help="System sample rate [default=%(default)r]")
parser.add_argument("-S", "--start-pass", type=eng_float, default=1000,
help="Start of Passband [default=%(default)r]")
parser.add_argument("-E", "--end-pass", type=eng_float, default=2000,
help="End of Passband [default=%(default)r]")
parser.add_argument("-T", "--transition", type=eng_float, default=100,
help="Transition band [default=%(default)r]")
parser.add_argument("-A", "--attenuation", type=eng_float, default=80,
help="Stopband attenuation [default=%(default)r]")
parser.add_argument("-D", "--decimation", type=int, default=1,
help="Decmation factor [default=%(default)r]")
args = parser.parse_args()
put = example_fft_filter_ccc(args.nsamples,
args.samplerate,
args.start_pass,
args.end_pass,
args.transition,
args.attenuation,
args.decimation)
put.run()
data_src = numpy.array(put.vsnk_src.data())
data_snk = numpy.array(put.vsnk_out.data())
# Plot the signals PSDs
nfft = 1024
f1 = pyplot.figure(1, figsize=(12,10))
s1 = f1.add_subplot(1,1,1)
s1.psd(data_src, NFFT=nfft, noverlap=nfft / 4,
Fs=args.samplerate)
s1.psd(data_snk, NFFT=nfft, noverlap=nfft / 4,
Fs=args.samplerate)
f2 = pyplot.figure(2, figsize=(12,10))
s2 = f2.add_subplot(1,1,1)
s2.plot(data_src)
s2.plot(data_snk.real, 'g')
pyplot.show()
if __name__ == "__main__":
try:
main()
except KeyboardInterrupt:
pass
| gpl-3.0 |
ttthy1/2017sejongAI | week14/Mnist.py | 1 | 2273 | # Lab 7 Learning rate and Evaluation
import tensorflow as tf
import random
import matplotlib.pyplot as plt
tf.set_random_seed(777) # for reproducibility
from tensorflow.examples.tutorials.mnist import input_data
# Check out https://www.tensorflow.org/get_started/mnist/beginners for
# more information about the mnist dataset
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
nb_classes = 10
# MNIST data image of shape 28 * 28 = 784
X = tf.placeholder(tf.float32, [None, 784])
# 0 - 9 digits recognition = 10 classes
Y = tf.placeholder(tf.float32, [None, nb_classes])
W = tf.Variable(tf.random_normal([784, nb_classes]))
b = tf.Variable(tf.random_normal([nb_classes]))
# Hypothesis (using softmax)
hypothesis = tf.nn.softmax(tf.matmul(X, W) + b)
cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)
# Test model
is_correct = tf.equal(tf.arg_max(hypothesis, 1), tf.arg_max(Y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
# parameters
training_epochs = 15
batch_size = 100
with tf.Session() as sess:
# Initialize TensorFlow variables
sess.run(tf.global_variables_initializer())
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0
total_batch = int(mnist.train.num_examples / batch_size)
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
c, _ = sess.run([cost, optimizer], feed_dict={
X: batch_xs, Y: batch_ys})
avg_cost += c / total_batch
print('Epoch:', '%04d' % (epoch + 1),
'cost =', '{:.9f}'.format(avg_cost))
print("Learning finished")
# Test the model using test sets
print("Accuracy: ", accuracy.eval(session=sess, feed_dict={
X: mnist.test.images, Y: mnist.test.labels}))
# Get one and predict
r = random.randint(0, mnist.test.num_examples - 1)
print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))
print("Prediction: ", sess.run(
tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1]}))
plt.imshow(
mnist.test.images[r:r + 1].reshape(28, 28),
cmap='Greys',
interpolation='nearest')
plt.show()
| gpl-3.0 |
Micket/CCBuilder | make_cc.py | 1 | 8680 | #!/usr/bin/env python3
from __future__ import print_function
from __future__ import division
import argparse
import pickle
import time
import CCBuilder as ccb
import CCBuilder_c as ccb_c
import numpy as np
import scipy.special
def uniform_dist(x):
""" Returns uniform distributions of given range """
return lambda: np.random.uniform(x[0], x[1])
def weibull_dist(a, mu):
""" Returns Weibull distributions for given shape parameter and average """
return lambda: np.random.weibull(a) * mu / scipy.special.gamma(1/a + 1)
def parse_dist(arg):
# Parses input string for given distribution.
# Returns a distribution, and the average
d, params = arg.split(':')
params = [float(x) for x in params.split(',')]
if d == 'U':
return uniform_dist(params), np.mean(params)
elif d == 'W':
a, mu = params
return weibull_dist(a, mu), mu
parser = argparse.ArgumentParser(description='''Generate a WC microstructure.
Grain shape/size supports 2 types of distributions:
Uniform: U:low,high
Weibull: U:a,mu (a=k in some notation, mu=mean)
''',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# parser.add_argument('-V', dest='verbose', action='store_true', help='Verbose mode.')
parser.add_argument('-f', dest='fname', metavar='basename', required=True, help='Output base filename.')
parser.add_argument('-L', dest='L', metavar='length', required=True, type=float, help='Cell length (volume is L^3)')
parser.add_argument('-m', dest='m', metavar='m', required=True, type=int,
help='Grid resolution. Total number of voxels are (m*L)^3')
parser.add_argument('--vol_frac_goal', dest="vol_frac_goal", metavar='v', required=True, type=float,
help='Goal for volume fraction WC (excluding overlap)')
parser.add_argument('-s', dest='seed', metavar='s', default=None, type=int,
help='Seed for RNG. Given identical parameters, ' +
'CCBuilder will generate identical output given a controlled seed.')
parser.add_argument('--stray_cleanup', action='store_true', help='Clean up stray voxels')
group = parser.add_argument_group('WC grain shape')
group.add_argument('-k', dest='k_dist', metavar='type,[params]', default='U:0.4,1.4',
help='k distribution')
group.add_argument('-r', dest='r_dist', metavar='type,[params]', default='U:0.1,0.4',
help='r distribution')
group.add_argument('-d', dest='d_dist', metavar='type,[params]', default='U:0.5,1.5',
help='d distribution')
group = parser.add_argument_group('Packing')
group.add_argument('--use_potential', action='store_true', help='Use repulsive potential.')
group.add_argument('--nr_tries', dest='nr_tries', metavar='n', default=2500, type=int,
help='Number of random translations.')
group.add_argument('--delta', dest='delta', metavar='d', type=float,
help='Maximum distance for randomized translations.')
group.add_argument('--m_coarse', dest="m_coarse", metavar='mc', default=10,
help='Grid resolution during packing.')
group = parser.add_argument_group('Potts simulation')
group.add_argument('--mc_steps', dest="mc_steps", metavar='steps', default=0.05, type=float,
help='Monte-Carlo steps (scales with (m*L)^4. Set to zero to turn off.')
group.add_argument('--tau', dest='tau', metavar='t', default=0.5, type=float,
help='Ficticious temperature in Potts model.')
options = parser.parse_args()
if options.seed is not None:
np.random.seed(options.seed)
# Heuristic mapping from actual to goal volume fraction
# vol_frac_goal = (alpha - 2)/(2 * alpha) + 1/alpha * np.sqrt(1 - alpha * np.log(-2*(vol_frac - 1)))
d_eq, d_0 = parse_dist(options.d_dist)
r, r_0 = parse_dist(options.r_dist)
k, k_0 = parse_dist(options.k_dist)
fname = options.fname
# to avoid confusion with types:
m = np.int(options.m)
m_coarse = np.int(options.m_coarse)
L = np.float(options.L)
mc_steps = np.float(options.mc_steps)
vol_frac_goal = np.double(options.vol_frac_goal)
tau = np.double(options.tau)
nr_tries = np.int(options.nr_tries)
delta_x = d_0/float(m)
M = np.int(m * L / d_0)
M_coarse = np.int(m_coarse * L / d_0)
idelta = M
idelta_coarse = M_coarse
if options.delta:
idelta = np.int(M * options.delta / L)
idelta_coarse = np.int(M_coarse * options.delta / L)
trunc_triangles = ccb.prepare_triangles(vol_frac_goal, L, r, k, d_eq)
# trunc_triangles = trunc_triangles[:1]
# trunc_triangles[0].rot_matrix = np.eye(3)
# trunc_triangles[0].rot_matrix_tr = np.eye(3)
# trunc_triangles[0].midpoint = np.array([2., 2., 2.])
# Sort triangles w.r.t. volume, so that large triangles are added to the box first (better packing)
trunc_triangles.sort(key=lambda x: x.volume, reverse=True)
print('Prepared', len(trunc_triangles), 'triangles')
if options.use_potential:
ccb.optimize_midpoints(L, trunc_triangles)
if m_coarse == m:
grain_ids, overlaps, voxel_indices = ccb_c.populate_voxels(M, L, trunc_triangles, nr_tries, idelta, 1.0)
else:
if nr_tries > 0:
# Optimization: Use coarser grid for packing, then insert packed grains into fine grid
# No need to get the return values, trunc_triangles
ccb_c.populate_voxels(M_coarse, L, trunc_triangles, nr_tries, idelta_coarse, 1.0)
grain_ids, overlaps, voxel_indices = ccb_c.populate_voxels(M, L, trunc_triangles, 1, 0, 1.0)
if mc_steps > 0:
start_time = time.time()
# Do Potts on coarse grid first for an improved initial guess.
M_coarseMC = M//2
grain_ids_coarse, overlaps_coarse, voxel_indices_coarse = ccb_c.populate_voxels(M_coarseMC, L, trunc_triangles, 0, 0, 1.0)
_, gb_voxels_coarse, _ = ccb_c.calc_surface_prop(M_coarseMC, grain_ids_coarse)
ccb_c.make_mcp_bound(M_coarseMC, grain_ids_coarse, gb_voxels_coarse, overlaps_coarse, voxel_indices_coarse,
np.int(mc_steps * M_coarseMC**4), tau)
# Copy over that solution to the overlap regions of the fine grid as a starting point
M2 = M**2
i = np.nonzero(overlaps)[0]
iz = i // M2
iy = (i - iz*M2) // M
ix = i - iz*M2 - iy*M
cix = ix * M_coarseMC // M
ciy = iy * M_coarseMC // M
ciz = iz * M_coarseMC // M
ci = cix + ciy*M_coarseMC + ciz*M_coarseMC**2
gid = grain_ids_coarse[ci]
# Could use a Cython implementation for efficiency.
for ii, g in zip(i, gid):
if g != grain_ids[ii] and np.searchsorted(voxel_indices[g-2], ii) < len(voxel_indices[g-2]):
grain_ids[ii] = g
# This might change a few voxels to a value that they shouldn't obtain, but it's barely noticeable
# grain_ids_1[i] = grain_ids_coarse[ci]
_, gb_voxels, _ = ccb_c.calc_surface_prop(M, grain_ids)
# and run the full resolution MCP:
ccb_c.make_mcp_bound(M, grain_ids, gb_voxels, overlaps, voxel_indices, np.int(mc_steps * M ** 4), tau)
print('Potts model took {} seconds'.format(np.str(time.time() - start_time)))
if options.stray_cleanup:
start_time = time.time()
ccb_c.stray_cleanup(M, grain_ids)
print('Stray voxel cleanup took {} seconds'.format(np.str(time.time() - start_time)))
surface_voxels, gb_voxels, interface_voxels = ccb_c.calc_surface_prop(M, grain_ids)
phases, good_voxels, euler_angles = ccb_c.calc_grain_prop(M, grain_ids, trunc_triangles)
phase_volumes = np.bincount(phases)
vol_frac_WC = phase_volumes[2] / np.float(M ** 3)
vol_frac_Co = 1 - vol_frac_WC
mass_frac_WC = ccb.mass_fraction(vol_frac_WC)
sum_gb_voxels = np.sum(gb_voxels)
contiguity = sum_gb_voxels / np.float(sum_gb_voxels + np.sum(interface_voxels))
print('Contiguity {:5f}, Co volume frac {:.5f}, mass frac {:.5f}'.format(
contiguity, 1 - vol_frac_WC, ccb.mass_fraction(vol_frac_WC)))
ccb.write_dream3d(fname, 3 * [M], 3 * [delta_x], trunc_triangles, grain_ids, phases, good_voxels,
euler_angles, surface_voxels, gb_voxels, interface_voxels, overlaps)
with open(fname + '_trunc_triangles.data', 'wb') as f:
pickle.dump([t.rot_matrix for t in trunc_triangles], f)
# Saving grain volume data
if False:
grain_volumes = np.bincount(grain_ids)
d_eq = ccb.volume_to_eq_d(grain_volumes[2:] * delta_x ** 3)
# np.savetxt(fname + '_d_orig.txt', [t.d_eq for t in trunc_triangles])
np.savetxt(fname + '_d.txt', d_eq)
# Plot initial and final distributions
import matplotlib.pyplot as plt
plt.hist(np.array([t.d_eq for t in trunc_triangles]), alpha=0.5, bins=15, normed=True, label='Initial')
plt.hist(d_eq, alpha=0.5, bins=15, normed=True, label='Final')
plt.legend(loc='upper right')
plt.show()
| gpl-3.0 |
BlueBrain/NEST | testsuite/manualtests/cross_check_test_mip_corrdet.py | 13 | 2594 | # -*- coding: utf-8 -*-
#
# cross_check_test_mip_corrdet.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# NEST is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NEST. If not, see <http://www.gnu.org/licenses/>.
# Script to check correlation_detector.
# Calculates spike cross correlation function of both spike trains in
# spike_detector-0-0-3.gdf. The file is generated after running the
# testscript testsuite/unittests/test_mip_corrdet.sli
#
# Author: Helias
# Date: 08-04-07
#
from scipy import *
from matplotlib.pylab import * # for plot
# Auto- and crosscorrelation functions for spike trains.
#
# A time bin of size tbin is centered around the time difference it
# represents If the correlation function is calculated for tau in
# [-tau_max, tau_max], the pair events contributing to the left-most
# bin are those for which tau in [-tau_max-tbin/2, tau_max+tbin/2) and
# so on.
# correlate two spike trains with each other
# assumes spike times to be ordered in time
# tau > 0 means spike2 is later than spike1
#
# tau_max: maximum time lag in ms correlation function
# tbin: bin size
# spike1: first spike train [tspike...]
# spike2: second spike train [tspike...]
#
def corr_spikes_sorted(spike1, spike2, tbin, tau_max, h):
tau_max_i = int(tau_max/h)
tbin_i = int(tbin/h)
cross = zeros(int(2*tau_max_i/tbin_i+1), 'd')
j0 = 0
for spki in spike1:
j = j0
while j < len(spike2) and spike2[j] - spki < -tau_max_i - tbin_i/2.0:
j += 1
j0 = j
while j < len(spike2) and spike2[j] - spki < tau_max_i + tbin_i/2.0:
cross[int((spike2[j] - spki + tau_max_i + 0.5*tbin_i)/tbin_i)] += 1.0
j += 1
return cross
def main():
# resolution
h = 0.1
tau_max = 100.0 # ms correlation window
t_bin = 10.0 # ms bin size
# read input from spike detector
spikes = load('spike_detector-0-0-3.gdf')
sp1 = spikes[find(spikes[:,0] == 4), 1]
sp2 = spikes[find(spikes[:,0] == 5), 1]
cross = corr_spikes_sorted(sp1, sp2, t_bin, tau_max, h)
print cross
print sum(cross)
main()
| gpl-2.0 |
jcasner/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/path.py | 69 | 20263 | """
Contains a class for managing paths (polylines).
"""
import math
from weakref import WeakValueDictionary
import numpy as np
from numpy import ma
from matplotlib._path import point_in_path, get_path_extents, \
point_in_path_collection, get_path_collection_extents, \
path_in_path, path_intersects_path, convert_path_to_polygons
from matplotlib.cbook import simple_linear_interpolation
class Path(object):
"""
:class:`Path` represents a series of possibly disconnected,
possibly closed, line and curve segments.
The underlying storage is made up of two parallel numpy arrays:
- *vertices*: an Nx2 float array of vertices
- *codes*: an N-length uint8 array of vertex types
These two arrays always have the same length in the first
dimension. For example, to represent a cubic curve, you must
provide three vertices as well as three codes ``CURVE3``.
The code types are:
- ``STOP`` : 1 vertex (ignored)
A marker for the end of the entire path (currently not
required and ignored)
- ``MOVETO`` : 1 vertex
Pick up the pen and move to the given vertex.
- ``LINETO`` : 1 vertex
Draw a line from the current position to the given vertex.
- ``CURVE3`` : 1 control point, 1 endpoint
Draw a quadratic Bezier curve from the current position,
with the given control point, to the given end point.
- ``CURVE4`` : 2 control points, 1 endpoint
Draw a cubic Bezier curve from the current position, with
the given control points, to the given end point.
- ``CLOSEPOLY`` : 1 vertex (ignored)
Draw a line segment to the start point of the current
polyline.
Users of Path objects should not access the vertices and codes
arrays directly. Instead, they should use :meth:`iter_segments`
to get the vertex/code pairs. This is important, since many
:class:`Path` objects, as an optimization, do not store a *codes*
at all, but have a default one provided for them by
:meth:`iter_segments`.
Note also that the vertices and codes arrays should be treated as
immutable -- there are a number of optimizations and assumptions
made up front in the constructor that will not change when the
data changes.
"""
# Path codes
STOP = 0 # 1 vertex
MOVETO = 1 # 1 vertex
LINETO = 2 # 1 vertex
CURVE3 = 3 # 2 vertices
CURVE4 = 4 # 3 vertices
CLOSEPOLY = 5 # 1 vertex
NUM_VERTICES = [1, 1, 1, 2, 3, 1]
code_type = np.uint8
def __init__(self, vertices, codes=None):
"""
Create a new path with the given vertices and codes.
*vertices* is an Nx2 numpy float array, masked array or Python
sequence.
*codes* is an N-length numpy array or Python sequence of type
:attr:`matplotlib.path.Path.code_type`.
These two arrays must have the same length in the first
dimension.
If *codes* is None, *vertices* will be treated as a series of
line segments.
If *vertices* contains masked values, they will be converted
to NaNs which are then handled correctly by the Agg
PathIterator and other consumers of path data, such as
:meth:`iter_segments`.
"""
if ma.isMaskedArray(vertices):
vertices = vertices.astype(np.float_).filled(np.nan)
else:
vertices = np.asarray(vertices, np.float_)
if codes is not None:
codes = np.asarray(codes, self.code_type)
assert codes.ndim == 1
assert len(codes) == len(vertices)
assert vertices.ndim == 2
assert vertices.shape[1] == 2
self.should_simplify = (len(vertices) >= 128 and
(codes is None or np.all(codes <= Path.LINETO)))
self.has_nonfinite = not np.isfinite(vertices).all()
self.codes = codes
self.vertices = vertices
#@staticmethod
def make_compound_path(*args):
"""
(staticmethod) Make a compound path from a list of Path
objects. Only polygons (not curves) are supported.
"""
for p in args:
assert p.codes is None
lengths = [len(x) for x in args]
total_length = sum(lengths)
vertices = np.vstack([x.vertices for x in args])
vertices.reshape((total_length, 2))
codes = Path.LINETO * np.ones(total_length)
i = 0
for length in lengths:
codes[i] = Path.MOVETO
i += length
return Path(vertices, codes)
make_compound_path = staticmethod(make_compound_path)
def __repr__(self):
return "Path(%s, %s)" % (self.vertices, self.codes)
def __len__(self):
return len(self.vertices)
def iter_segments(self, simplify=None):
"""
Iterates over all of the curve segments in the path. Each
iteration returns a 2-tuple (*vertices*, *code*), where
*vertices* is a sequence of 1 - 3 coordinate pairs, and *code* is
one of the :class:`Path` codes.
If *simplify* is provided, it must be a tuple (*width*,
*height*) defining the size of the figure, in native units
(e.g. pixels or points). Simplification implies both removing
adjacent line segments that are very close to parallel, and
removing line segments outside of the figure. The path will
be simplified *only* if :attr:`should_simplify` is True, which
is determined in the constructor by this criteria:
- No curves
- More than 128 vertices
"""
vertices = self.vertices
if not len(vertices):
return
codes = self.codes
len_vertices = len(vertices)
isfinite = np.isfinite
NUM_VERTICES = self.NUM_VERTICES
MOVETO = self.MOVETO
LINETO = self.LINETO
CLOSEPOLY = self.CLOSEPOLY
STOP = self.STOP
if simplify is not None and self.should_simplify:
polygons = self.to_polygons(None, *simplify)
for vertices in polygons:
yield vertices[0], MOVETO
for v in vertices[1:]:
yield v, LINETO
elif codes is None:
if self.has_nonfinite:
next_code = MOVETO
for v in vertices:
if np.isfinite(v).all():
yield v, next_code
next_code = LINETO
else:
next_code = MOVETO
else:
yield vertices[0], MOVETO
for v in vertices[1:]:
yield v, LINETO
else:
i = 0
was_nan = False
while i < len_vertices:
code = codes[i]
if code == CLOSEPOLY:
yield [], code
i += 1
elif code == STOP:
return
else:
num_vertices = NUM_VERTICES[int(code)]
curr_vertices = vertices[i:i+num_vertices].flatten()
if not isfinite(curr_vertices).all():
was_nan = True
elif was_nan:
yield curr_vertices[-2:], MOVETO
was_nan = False
else:
yield curr_vertices, code
i += num_vertices
def transformed(self, transform):
"""
Return a transformed copy of the path.
.. seealso::
:class:`matplotlib.transforms.TransformedPath`:
A specialized path class that will cache the
transformed result and automatically update when the
transform changes.
"""
return Path(transform.transform(self.vertices), self.codes)
def contains_point(self, point, transform=None):
"""
Returns *True* if the path contains the given point.
If *transform* is not *None*, the path will be transformed
before performing the test.
"""
if transform is not None:
transform = transform.frozen()
return point_in_path(point[0], point[1], self, transform)
def contains_path(self, path, transform=None):
"""
Returns *True* if this path completely contains the given path.
If *transform* is not *None*, the path will be transformed
before performing the test.
"""
if transform is not None:
transform = transform.frozen()
return path_in_path(self, None, path, transform)
def get_extents(self, transform=None):
"""
Returns the extents (*xmin*, *ymin*, *xmax*, *ymax*) of the
path.
Unlike computing the extents on the *vertices* alone, this
algorithm will take into account the curves and deal with
control points appropriately.
"""
from transforms import Bbox
if transform is not None:
transform = transform.frozen()
return Bbox(get_path_extents(self, transform))
def intersects_path(self, other, filled=True):
"""
Returns *True* if this path intersects another given path.
*filled*, when True, treats the paths as if they were filled.
That is, if one path completely encloses the other,
:meth:`intersects_path` will return True.
"""
return path_intersects_path(self, other, filled)
def intersects_bbox(self, bbox, filled=True):
"""
Returns *True* if this path intersects a given
:class:`~matplotlib.transforms.Bbox`.
*filled*, when True, treats the path as if it was filled.
That is, if one path completely encloses the other,
:meth:`intersects_path` will return True.
"""
from transforms import BboxTransformTo
rectangle = self.unit_rectangle().transformed(
BboxTransformTo(bbox))
result = self.intersects_path(rectangle, filled)
return result
def interpolated(self, steps):
"""
Returns a new path resampled to length N x steps. Does not
currently handle interpolating curves.
"""
vertices = simple_linear_interpolation(self.vertices, steps)
codes = self.codes
if codes is not None:
new_codes = Path.LINETO * np.ones(((len(codes) - 1) * steps + 1, ))
new_codes[0::steps] = codes
else:
new_codes = None
return Path(vertices, new_codes)
def to_polygons(self, transform=None, width=0, height=0):
"""
Convert this path to a list of polygons. Each polygon is an
Nx2 array of vertices. In other words, each polygon has no
``MOVETO`` instructions or curves. This is useful for
displaying in backends that do not support compound paths or
Bezier curves, such as GDK.
If *width* and *height* are both non-zero then the lines will
be simplified so that vertices outside of (0, 0), (width,
height) will be clipped.
"""
if len(self.vertices) == 0:
return []
if transform is not None:
transform = transform.frozen()
if self.codes is None and (width == 0 or height == 0):
if transform is None:
return [self.vertices]
else:
return [transform.transform(self.vertices)]
# Deal with the case where there are curves and/or multiple
# subpaths (using extension code)
return convert_path_to_polygons(self, transform, width, height)
_unit_rectangle = None
#@classmethod
def unit_rectangle(cls):
"""
(staticmethod) Returns a :class:`Path` of the unit rectangle
from (0, 0) to (1, 1).
"""
if cls._unit_rectangle is None:
cls._unit_rectangle = \
Path([[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0]])
return cls._unit_rectangle
unit_rectangle = classmethod(unit_rectangle)
_unit_regular_polygons = WeakValueDictionary()
#@classmethod
def unit_regular_polygon(cls, numVertices):
"""
(staticmethod) Returns a :class:`Path` for a unit regular
polygon with the given *numVertices* and radius of 1.0,
centered at (0, 0).
"""
if numVertices <= 16:
path = cls._unit_regular_polygons.get(numVertices)
else:
path = None
if path is None:
theta = (2*np.pi/numVertices *
np.arange(numVertices + 1).reshape((numVertices + 1, 1)))
# This initial rotation is to make sure the polygon always
# "points-up"
theta += np.pi / 2.0
verts = np.concatenate((np.cos(theta), np.sin(theta)), 1)
path = Path(verts)
cls._unit_regular_polygons[numVertices] = path
return path
unit_regular_polygon = classmethod(unit_regular_polygon)
_unit_regular_stars = WeakValueDictionary()
#@classmethod
def unit_regular_star(cls, numVertices, innerCircle=0.5):
"""
(staticmethod) Returns a :class:`Path` for a unit regular star
with the given numVertices and radius of 1.0, centered at (0,
0).
"""
if numVertices <= 16:
path = cls._unit_regular_stars.get((numVertices, innerCircle))
else:
path = None
if path is None:
ns2 = numVertices * 2
theta = (2*np.pi/ns2 * np.arange(ns2 + 1))
# This initial rotation is to make sure the polygon always
# "points-up"
theta += np.pi / 2.0
r = np.ones(ns2 + 1)
r[1::2] = innerCircle
verts = np.vstack((r*np.cos(theta), r*np.sin(theta))).transpose()
path = Path(verts)
cls._unit_regular_polygons[(numVertices, innerCircle)] = path
return path
unit_regular_star = classmethod(unit_regular_star)
#@classmethod
def unit_regular_asterisk(cls, numVertices):
"""
(staticmethod) Returns a :class:`Path` for a unit regular
asterisk with the given numVertices and radius of 1.0,
centered at (0, 0).
"""
return cls.unit_regular_star(numVertices, 0.0)
unit_regular_asterisk = classmethod(unit_regular_asterisk)
_unit_circle = None
#@classmethod
def unit_circle(cls):
"""
(staticmethod) Returns a :class:`Path` of the unit circle.
The circle is approximated using cubic Bezier curves. This
uses 8 splines around the circle using the approach presented
here:
Lancaster, Don. `Approximating a Circle or an Ellipse Using Four
Bezier Cubic Splines <http://www.tinaja.com/glib/ellipse4.pdf>`_.
"""
if cls._unit_circle is None:
MAGIC = 0.2652031
SQRTHALF = np.sqrt(0.5)
MAGIC45 = np.sqrt((MAGIC*MAGIC) / 2.0)
vertices = np.array(
[[0.0, -1.0],
[MAGIC, -1.0],
[SQRTHALF-MAGIC45, -SQRTHALF-MAGIC45],
[SQRTHALF, -SQRTHALF],
[SQRTHALF+MAGIC45, -SQRTHALF+MAGIC45],
[1.0, -MAGIC],
[1.0, 0.0],
[1.0, MAGIC],
[SQRTHALF+MAGIC45, SQRTHALF-MAGIC45],
[SQRTHALF, SQRTHALF],
[SQRTHALF-MAGIC45, SQRTHALF+MAGIC45],
[MAGIC, 1.0],
[0.0, 1.0],
[-MAGIC, 1.0],
[-SQRTHALF+MAGIC45, SQRTHALF+MAGIC45],
[-SQRTHALF, SQRTHALF],
[-SQRTHALF-MAGIC45, SQRTHALF-MAGIC45],
[-1.0, MAGIC],
[-1.0, 0.0],
[-1.0, -MAGIC],
[-SQRTHALF-MAGIC45, -SQRTHALF+MAGIC45],
[-SQRTHALF, -SQRTHALF],
[-SQRTHALF+MAGIC45, -SQRTHALF-MAGIC45],
[-MAGIC, -1.0],
[0.0, -1.0],
[0.0, -1.0]],
np.float_)
codes = cls.CURVE4 * np.ones(26)
codes[0] = cls.MOVETO
codes[-1] = cls.CLOSEPOLY
cls._unit_circle = Path(vertices, codes)
return cls._unit_circle
unit_circle = classmethod(unit_circle)
#@classmethod
def arc(cls, theta1, theta2, n=None, is_wedge=False):
"""
(staticmethod) Returns an arc on the unit circle from angle
*theta1* to angle *theta2* (in degrees).
If *n* is provided, it is the number of spline segments to make.
If *n* is not provided, the number of spline segments is
determined based on the delta between *theta1* and *theta2*.
Masionobe, L. 2003. `Drawing an elliptical arc using
polylines, quadratic or cubic Bezier curves
<http://www.spaceroots.org/documents/ellipse/index.html>`_.
"""
# degrees to radians
theta1 *= np.pi / 180.0
theta2 *= np.pi / 180.0
twopi = np.pi * 2.0
halfpi = np.pi * 0.5
eta1 = np.arctan2(np.sin(theta1), np.cos(theta1))
eta2 = np.arctan2(np.sin(theta2), np.cos(theta2))
eta2 -= twopi * np.floor((eta2 - eta1) / twopi)
if (theta2 - theta1 > np.pi) and (eta2 - eta1 < np.pi):
eta2 += twopi
# number of curve segments to make
if n is None:
n = int(2 ** np.ceil((eta2 - eta1) / halfpi))
if n < 1:
raise ValueError("n must be >= 1 or None")
deta = (eta2 - eta1) / n
t = np.tan(0.5 * deta)
alpha = np.sin(deta) * (np.sqrt(4.0 + 3.0 * t * t) - 1) / 3.0
steps = np.linspace(eta1, eta2, n + 1, True)
cos_eta = np.cos(steps)
sin_eta = np.sin(steps)
xA = cos_eta[:-1]
yA = sin_eta[:-1]
xA_dot = -yA
yA_dot = xA
xB = cos_eta[1:]
yB = sin_eta[1:]
xB_dot = -yB
yB_dot = xB
if is_wedge:
length = n * 3 + 4
vertices = np.zeros((length, 2), np.float_)
codes = Path.CURVE4 * np.ones((length, ), Path.code_type)
vertices[1] = [xA[0], yA[0]]
codes[0:2] = [Path.MOVETO, Path.LINETO]
codes[-2:] = [Path.LINETO, Path.CLOSEPOLY]
vertex_offset = 2
end = length - 2
else:
length = n * 3 + 1
vertices = np.zeros((length, 2), np.float_)
codes = Path.CURVE4 * np.ones((length, ), Path.code_type)
vertices[0] = [xA[0], yA[0]]
codes[0] = Path.MOVETO
vertex_offset = 1
end = length
vertices[vertex_offset :end:3, 0] = xA + alpha * xA_dot
vertices[vertex_offset :end:3, 1] = yA + alpha * yA_dot
vertices[vertex_offset+1:end:3, 0] = xB - alpha * xB_dot
vertices[vertex_offset+1:end:3, 1] = yB - alpha * yB_dot
vertices[vertex_offset+2:end:3, 0] = xB
vertices[vertex_offset+2:end:3, 1] = yB
return Path(vertices, codes)
arc = classmethod(arc)
#@classmethod
def wedge(cls, theta1, theta2, n=None):
"""
(staticmethod) Returns a wedge of the unit circle from angle
*theta1* to angle *theta2* (in degrees).
If *n* is provided, it is the number of spline segments to make.
If *n* is not provided, the number of spline segments is
determined based on the delta between *theta1* and *theta2*.
"""
return cls.arc(theta1, theta2, n, True)
wedge = classmethod(wedge)
_get_path_collection_extents = get_path_collection_extents
def get_path_collection_extents(*args):
"""
Given a sequence of :class:`Path` objects, returns the bounding
box that encapsulates all of them.
"""
from transforms import Bbox
if len(args[1]) == 0:
raise ValueError("No paths provided")
return Bbox.from_extents(*_get_path_collection_extents(*args))
| agpl-3.0 |
nik-hil/fastai | deeplearning2/rossman_exp.py | 10 | 5451 | train_ratio=0.9
use_dict=True
use_scaler=False
init_emb=False
split_contins=True
samp_size = 100000
#samp_size = 0
import math, keras, datetime, pandas as pd, numpy as np, keras.backend as K
import matplotlib.pyplot as plt, xgboost, operator, random, pickle, os
from sklearn_pandas import DataFrameMapper
from sklearn.preprocessing import LabelEncoder, Imputer, StandardScaler
from keras.models import Model
from keras.layers import merge, Input
from keras.layers.core import Dense, Activation, Reshape, Flatten, Dropout
from keras.layers.embeddings import Embedding
from keras.optimizers import Adam
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l2
from keras import initializations
np.set_printoptions(4)
cfg = K.tf.ConfigProto()
cfg.gpu_options.allow_growth = True
K.set_session(K.tf.Session(config=cfg))
os.chdir('data/rossman')
cat_var_dict = {'Store': 50, 'DayOfWeek': 6, 'Year': 2, 'Month': 6,
'Day': 10, 'StateHoliday': 3, 'CompetitionMonthsOpen': 2,
'Promo2Weeks': 1, 'StoreType': 2, 'Assortment': 3, 'PromoInterval': 3,
'CompetitionOpenSinceYear': 4, 'Promo2SinceYear': 4, 'State': 6,
'Week': 2, 'Events': 4, 'Promo_fw': 1,
'Promo_bw': 1, 'StateHoliday_fw': 1,
'StateHoliday_bw': 1, 'SchoolHoliday_fw': 1,
'SchoolHoliday_bw': 1}
cats, contins= [o for n,o in np.load('vars.npz').items()]
y = np.load('deps.npz').items()[0][1]
if samp_size != 0:
np.random.seed(42)
idxs = sorted(np.random.choice(len(y), samp_size, replace=False))
cats= cats[idxs]
contins= contins[idxs]
y= y[idxs]
n=len(y)
train_size = int(n*train_ratio)
contins_trn_orig, contins_val_orig = contins[:train_size], contins[train_size:]
cats_trn, cats_val = cats[:train_size], cats[train_size:]
y_trn, y_val = y[:train_size], y[train_size:]
contin_map_fit = pickle.load(open('contin_maps.pickle', 'rb'))
cat_map_fit = pickle.load(open('cat_maps.pickle', 'rb'))
def cat_map_info(feat): return feat[0], len(feat[1].classes_)
co_enc = StandardScaler().fit(contins_trn_orig)
tf_contins_trn = co_enc.transform(contins_trn_orig)
tf_contins_val = co_enc.transform(contins_val_orig)
"""
def rmspe(y_pred, targ = y_valid_orig):
return math.sqrt(np.square((targ - y_pred)/targ).mean())
def log_max_inv(preds, mx = max_log_y): return np.exp(preds * mx)
def normalize_inv(preds): return preds * ystd + ymean
"""
def split_cols(arr): return np.hsplit(arr,arr.shape[1])
def emb_init(shape, name=None):
return initializations.uniform(shape, scale=0.6/shape[1], name=name)
def get_emb(feat):
name, c = cat_map_info(feat)
if use_dict:
c2 = cat_var_dict[name]
else:
c2 = (c+2)//3
if c2>50: c2=50
inp = Input((1,), dtype='int64', name=name+'_in')
if init_emb:
u = Flatten(name=name+'_flt')(Embedding(c, c2, input_length=1)(inp))
else:
u = Flatten(name=name+'_flt')(Embedding(c, c2, input_length=1, init=emb_init)(inp))
return inp,u
def get_contin(feat):
name = feat[0][0]
inp = Input((1,), name=name+'_in')
return inp, Dense(1, name=name+'_d')(inp)
def split_data():
if split_contins:
map_train = split_cols(cats_trn) + split_cols(contins_trn)
map_valid = split_cols(cats_val) + split_cols(contins_val)
else:
map_train = split_cols(cats_trn) + [contins_trn]
map_valid = split_cols(cats_val) + [contins_val]
return (map_train, map_valid)
def get_contin_one():
n_contin = contins_trn.shape[1]
contin_inp = Input((n_contin,), name='contin')
contin_out = BatchNormalization()(contin_inp)
return contin_inp, contin_out
def train(model, map_train, map_valid, bs=128, ne=10):
return model.fit(map_train, y_trn, batch_size=bs, nb_epoch=ne,
verbose=0, validation_data=(map_valid, y_val))
def get_model():
if split_contins:
conts = [get_contin(feat) for feat in contin_map_fit.features]
cont_out = [d for inp,d in conts]
cont_inp = [inp for inp,d in conts]
else:
contin_inp, contin_out = get_contin_one()
cont_out = [contin_out]
cont_inp = [contin_inp]
embs = [get_emb(feat) for feat in cat_map_fit.features]
x = merge([emb for inp,emb in embs] + cont_out, mode='concat')
x = Dropout(0.02)(x)
x = Dense(1000, activation='relu', init='uniform')(x)
x = Dense(500, activation='relu', init='uniform')(x)
x = Dense(1, activation='sigmoid')(x)
model = Model([inp for inp,emb in embs] + cont_inp, x)
model.compile('adam', 'mean_absolute_error')
#model.compile(Adam(), 'mse')
return model
for split_contins in [True, False]:
for use_dict in [True, False]:
for use_scaler in [True, False]:
for init_emb in [True, False]:
print ({'split_contins':split_contins, 'use_dict':use_dict,
'use_scaler':use_scaler, 'init_emb':init_emb})
if use_scaler:
contins_trn = tf_contins_trn
contins_val = tf_contins_val
else:
contins_trn = contins_trn_orig
contins_val = contins_val_orig
map_train, map_valid = split_data()
model = get_model()
hist = np.array(train(model, map_train, map_valid, 128, 10)
.history['val_loss'])
print(hist)
print(hist.min())
| apache-2.0 |
adiIspas/Machine-Learning_A-Z | Machine Learning A-Z/Part 7 - Natural Language Processing/Section 36 - Natural Language Processing/natural_language_processing.py | 3 | 1452 | # Natural Language Processing
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = '\t', quoting = 3)
# Cleaning the texts
import re
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
corpus = []
for i in range(0, 1000):
review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])
review = review.lower()
review = review.split()
ps = PorterStemmer()
review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
review = ' '.join(review)
corpus.append(review)
# Creating the Bag of Words model
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(max_features = 1500)
X = cv.fit_transform(corpus).toarray()
y = dataset.iloc[:, 1].values
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)
# Fitting Naive Bayes to the Training set
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred) | mit |
nomadcube/scikit-learn | examples/bicluster/plot_spectral_coclustering.py | 276 | 1736 | """
==============================================
A demo of the Spectral Co-Clustering algorithm
==============================================
This example demonstrates how to generate a dataset and bicluster it
using the the Spectral Co-Clustering algorithm.
The dataset is generated using the ``make_biclusters`` function, which
creates a matrix of small values and implants bicluster with large
values. The rows and columns are then shuffled and passed to the
Spectral Co-Clustering algorithm. Rearranging the shuffled matrix to
make biclusters contiguous shows how accurately the algorithm found
the biclusters.
"""
print(__doc__)
# Author: Kemal Eren <kemal@kemaleren.com>
# License: BSD 3 clause
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import make_biclusters
from sklearn.datasets import samples_generator as sg
from sklearn.cluster.bicluster import SpectralCoclustering
from sklearn.metrics import consensus_score
data, rows, columns = make_biclusters(
shape=(300, 300), n_clusters=5, noise=5,
shuffle=False, random_state=0)
plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Original dataset")
data, row_idx, col_idx = sg._shuffle(data, random_state=0)
plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Shuffled dataset")
model = SpectralCoclustering(n_clusters=5, random_state=0)
model.fit(data)
score = consensus_score(model.biclusters_,
(rows[:, row_idx], columns[:, col_idx]))
print("consensus score: {:.3f}".format(score))
fit_data = data[np.argsort(model.row_labels_)]
fit_data = fit_data[:, np.argsort(model.column_labels_)]
plt.matshow(fit_data, cmap=plt.cm.Blues)
plt.title("After biclustering; rearranged to show biclusters")
plt.show()
| bsd-3-clause |
tayebzaidi/snova_analysis | Miscellaneous/typ1a_features.py | 1 | 2252 | import matplotlib.pyplot as plt
import scipy.interpolate as scinterp
import numpy as np
import peakfinding
import peak_original
import smoothing
import plotter
import random
import readin
import sys
import os
if __name__== '__main__':
Mbdata = []
delM15data = []
path = "/Users/zaidi/Documents/REU/restframe/"
filenames = os.listdir(path)
random.shuffle(filenames)
for filename in filenames:
current_file = os.path.join(path, filename)
data= readin.readin_SNrest(filename)
indB = np.where((data.band == 'B'))
Bdata = data[indB]
Bdata = np.sort(Bdata)
if len(Bdata.phase) > 3:
spl = scinterp.UnivariateSpline(Bdata.phase, Bdata.mag)
spl.set_smoothing_factor(2./len(Bdata.phase))
phase_new = np.arange(Bdata.phase[0], Bdata.phase[-1], 1)
mag_new = spl(phase_new)
maxp, minp = peak_original.peakdet(mag_new, 0.5, phase_new)
if len(minp) > 0 and minp[0][0] < 5 and minp[0][0] > -5:
Mb = minp[0][1]
delM15 = minp[0][1] - spl(minp[0][0]+15)
Mbdata.append(Mb)
delM15data.append(delM15)
if delM15 > 0 or delM15 < -5:
print minp
print filename
print spl(minp[0][0] + 15)
fig = plt.figure(1)
ax = fig.add_subplot(1,1,1)
ax.plot(phase_new, mag_new)
ax.plot(Bdata.phase, Bdata.mag)
if len(minp) > 0:
ax.scatter(minp[:,0],minp[:,1])
plt.show(fig)
'''
maxp, minp = peakfinding.peakdetect(mag_new, phase_new, 200, 1.5)
if len(minp) > 0:
print minp
print filename
fig = plt.figure(1)
ax = fig.add_subplot(1,1,1)
#ax.scatter(minp[:,0], minp[:,1],'bo')
#ax.plot(Bdata.phase, Bdata.mag)
#plt.show(fig)
'''
#interp = smoothing.Interpolate1D(data.phase
print Mbdata
print delM15data
fig = plt.figure(2)
ax = fig.add_subplot(1,1,1)
ax.scatter(Mbdata, delM15data)
plt.show(fig)
| gpl-3.0 |
gmatteo/pymatgen | pymatgen/io/gaussian.py | 2 | 59623 | # coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
This module implements input and output processing from Gaussian.
"""
import re
import warnings
import numpy as np
import scipy.constants as cst
from monty.io import zopen
from pymatgen.core.composition import Composition
from pymatgen.core.periodic_table import Element
from pymatgen.core.structure import Molecule
from pymatgen.core.operations import SymmOp
from pymatgen.core.units import Ha_to_eV
from pymatgen.electronic_structure.core import Spin
from pymatgen.util.coord import get_angle
__author__ = "Shyue Ping Ong, Germain Salvato-Vallverdu, Xin Chen"
__copyright__ = "Copyright 2013, The Materials Virtual Lab"
__version__ = "0.1"
__maintainer__ = "Shyue Ping Ong"
__email__ = "ongsp@ucsd.edu"
__date__ = "8/1/15"
float_patt = re.compile(r"\s*([+-]?\d+\.\d+)")
def read_route_line(route):
"""
read route line in gaussian input/output and return functional basis_set
and a dictionary of other route parameters
Args:
route (str) : the route line
return
functional (str) : the method (HF, PBE ...)
basis_set (str) : the basis set
route (dict) : dictionary of parameters
"""
scrf_patt = re.compile(r"^([sS][cC][rR][fF])\s*=\s*(.+)")
multi_params_patt = re.compile(r"^([A-z]+[0-9]*)[\s=]+\((.*)\)$")
functional = None
basis_set = None
route_params = {}
dieze_tag = None
if route:
if "/" in route:
tok = route.split("/")
functional = tok[0].split()[-1]
basis_set = tok[1].split()[0]
for tok in [functional, basis_set, "/"]:
route = route.replace(tok, "")
for tok in route.split():
if scrf_patt.match(tok):
m = scrf_patt.match(tok)
route_params[m.group(1)] = m.group(2)
elif tok.upper() in ["#", "#N", "#P", "#T"]:
# does not store # in route to avoid error in input
if tok == "#":
dieze_tag = "#N"
else:
dieze_tag = tok
continue
else:
m = re.match(multi_params_patt, tok.strip("#"))
if m:
pars = {}
for par in m.group(2).split(","):
p = par.split("=")
pars[p[0]] = None if len(p) == 1 else p[1]
route_params[m.group(1)] = pars
else:
d = tok.strip("#").split("=")
route_params[d[0]] = None if len(d) == 1 else d[1]
return functional, basis_set, route_params, dieze_tag
class GaussianInput:
"""
An object representing a Gaussian input file.
"""
# Commonly used regex patterns
_zmat_patt = re.compile(r"^(\w+)*([\s,]+(\w+)[\s,]+(\w+))*[\-\.\s,\w]*$")
_xyz_patt = re.compile(r"^(\w+)[\s,]+([\d\.eE\-]+)[\s,]+([\d\.eE\-]+)[\s,]+" r"([\d\.eE\-]+)[\-\.\s,\w.]*$")
def __init__(
self,
mol,
charge=None,
spin_multiplicity=None,
title=None,
functional="HF",
basis_set="6-31G(d)",
route_parameters=None,
input_parameters=None,
link0_parameters=None,
dieze_tag="#P",
gen_basis=None,
):
"""
Args:
mol: Input molecule. It can either be a Molecule object,
a string giving the geometry in a format supported by Guassian,
or ``None``. If the molecule is ``None``, you will need to use
read it in from a checkpoint. Consider adding ``CHK`` to the
``link0_parameters``.
charge: Charge of the molecule. If None, charge on molecule is used.
Defaults to None. This allows the input file to be set a
charge independently from the molecule itself.
If ``mol`` is not a Molecule object, then you must specify a charge.
spin_multiplicity: Spin multiplicity of molecule. Defaults to None,
which means that the spin multiplicity is set to 1 if the
molecule has no unpaired electrons and to 2 if there are
unpaired electrons. If ``mol`` is not a Molecule object, then you
must specify the multiplicity
title: Title for run. Defaults to formula of molecule if None.
functional: Functional for run.
basis_set: Basis set for run.
route_parameters: Additional route parameters as a dict. For example,
{'SP':"", "SCF":"Tight"}
input_parameters: Additional input parameters for run as a dict. Used
for example, in PCM calculations. E.g., {"EPS":12}
link0_parameters: Link0 parameters as a dict. E.g., {"%mem": "1000MW"}
dieze_tag: # preceding the route line. E.g. "#p"
gen_basis: allows a user-specified basis set to be used in a Gaussian
calculation. If this is not None, the attribute ``basis_set`` will
be set to "Gen".
"""
self._mol = mol
# Determine multiplicity and charge settings
if isinstance(mol, Molecule):
self.charge = charge if charge is not None else mol.charge
nelectrons = mol.charge + mol.nelectrons - self.charge
if spin_multiplicity is not None:
self.spin_multiplicity = spin_multiplicity
if (nelectrons + spin_multiplicity) % 2 != 1:
raise ValueError(
"Charge of {} and spin multiplicity of {} is"
" not possible for this molecule".format(self.charge, spin_multiplicity)
)
else:
self.spin_multiplicity = 1 if nelectrons % 2 == 0 else 2
# Get a title from the molecule name
self.title = title if title else self._mol.composition.formula
else:
self.charge = charge
self.spin_multiplicity = spin_multiplicity
# Set a title
self.title = title if title else "Restart"
# Store the remaining settings
self.functional = functional
self.basis_set = basis_set
self.link0_parameters = link0_parameters if link0_parameters else {}
self.route_parameters = route_parameters if route_parameters else {}
self.input_parameters = input_parameters if input_parameters else {}
self.dieze_tag = dieze_tag if dieze_tag[0] == "#" else "#" + dieze_tag
self.gen_basis = gen_basis
if gen_basis is not None:
self.basis_set = "Gen"
@property
def molecule(self):
"""
Returns molecule associated with this GaussianInput.
"""
return self._mol
@staticmethod
def _parse_coords(coord_lines):
"""
Helper method to parse coordinates.
"""
paras = {}
var_pattern = re.compile(r"^([A-Za-z]+\S*)[\s=,]+([\d\-\.]+)$")
for l in coord_lines:
m = var_pattern.match(l.strip())
if m:
paras[m.group(1).strip("=")] = float(m.group(2))
species = []
coords = []
# Stores whether a Zmatrix format is detected. Once a zmatrix format
# is detected, it is assumed for the remaining of the parsing.
zmode = False
for l in coord_lines:
l = l.strip()
if not l:
break
if (not zmode) and GaussianInput._xyz_patt.match(l):
m = GaussianInput._xyz_patt.match(l)
species.append(m.group(1))
toks = re.split(r"[,\s]+", l.strip())
if len(toks) > 4:
coords.append([float(i) for i in toks[2:5]])
else:
coords.append([float(i) for i in toks[1:4]])
elif GaussianInput._zmat_patt.match(l):
zmode = True
toks = re.split(r"[,\s]+", l.strip())
species.append(toks[0])
toks.pop(0)
if len(toks) == 0:
coords.append(np.array([0, 0, 0]))
else:
nn = []
parameters = []
while len(toks) > 1:
ind = toks.pop(0)
data = toks.pop(0)
try:
nn.append(int(ind))
except ValueError:
nn.append(species.index(ind) + 1)
try:
val = float(data)
parameters.append(val)
except ValueError:
if data.startswith("-"):
parameters.append(-paras[data[1:]])
else:
parameters.append(paras[data])
if len(nn) == 1:
coords.append(np.array([0, 0, parameters[0]]))
elif len(nn) == 2:
coords1 = coords[nn[0] - 1]
coords2 = coords[nn[1] - 1]
bl = parameters[0]
angle = parameters[1]
axis = [0, 1, 0]
op = SymmOp.from_origin_axis_angle(coords1, axis, angle, False)
coord = op.operate(coords2)
vec = coord - coords1
coord = vec * bl / np.linalg.norm(vec) + coords1
coords.append(coord)
elif len(nn) == 3:
coords1 = coords[nn[0] - 1]
coords2 = coords[nn[1] - 1]
coords3 = coords[nn[2] - 1]
bl = parameters[0]
angle = parameters[1]
dih = parameters[2]
v1 = coords3 - coords2
v2 = coords1 - coords2
axis = np.cross(v1, v2)
op = SymmOp.from_origin_axis_angle(coords1, axis, angle, False)
coord = op.operate(coords2)
v1 = coord - coords1
v2 = coords1 - coords2
v3 = np.cross(v1, v2)
adj = get_angle(v3, axis)
axis = coords1 - coords2
op = SymmOp.from_origin_axis_angle(coords1, axis, dih - adj, False)
coord = op.operate(coord)
vec = coord - coords1
coord = vec * bl / np.linalg.norm(vec) + coords1
coords.append(coord)
def _parse_species(sp_str):
"""
The species specification can take many forms. E.g.,
simple integers representing atomic numbers ("8"),
actual species string ("C") or a labelled species ("C1").
Sometimes, the species string is also not properly capitalized,
e.g, ("c1"). This method should take care of these known formats.
"""
try:
return int(sp_str)
except ValueError:
sp = re.sub(r"\d", "", sp_str)
return sp.capitalize()
species = [_parse_species(sp) for sp in species]
return Molecule(species, coords)
@staticmethod
def from_string(contents):
"""
Creates GaussianInput from a string.
Args:
contents: String representing an Gaussian input file.
Returns:
GaussianInput object
"""
lines = [l.strip() for l in contents.split("\n")]
link0_patt = re.compile(r"^(%.+)\s*=\s*(.+)")
link0_dict = {}
for i, l in enumerate(lines):
if link0_patt.match(l):
m = link0_patt.match(l)
link0_dict[m.group(1).strip("=")] = m.group(2)
route_patt = re.compile(r"^#[sSpPnN]*.*")
route = ""
route_index = None
for i, l in enumerate(lines):
if route_patt.match(l):
route += " " + l
route_index = i
# This condition allows for route cards spanning multiple lines
elif (l == "" or l.isspace()) and route_index:
break
functional, basis_set, route_paras, dieze_tag = read_route_line(route)
ind = 2
title = []
while lines[route_index + ind].strip():
title.append(lines[route_index + ind].strip())
ind += 1
title = " ".join(title)
ind += 1
toks = re.split(r"[,\s]+", lines[route_index + ind])
charge = int(float(toks[0]))
spin_mult = int(toks[1])
coord_lines = []
spaces = 0
input_paras = {}
ind += 1
for i in range(route_index + ind, len(lines)):
if lines[i].strip() == "":
spaces += 1
if spaces >= 2:
d = lines[i].split("=")
if len(d) == 2:
input_paras[d[0]] = d[1]
else:
coord_lines.append(lines[i].strip())
mol = GaussianInput._parse_coords(coord_lines)
mol.set_charge_and_spin(charge, spin_mult)
return GaussianInput(
mol,
charge=charge,
spin_multiplicity=spin_mult,
title=title,
functional=functional,
basis_set=basis_set,
route_parameters=route_paras,
input_parameters=input_paras,
link0_parameters=link0_dict,
dieze_tag=dieze_tag,
)
@staticmethod
def from_file(filename):
"""
Creates GaussianInput from a file.
Args:
filename: Gaussian input filename
Returns:
GaussianInput object
"""
with zopen(filename, "r") as f:
return GaussianInput.from_string(f.read())
def _find_nn_pos_before_site(self, siteindex):
"""
Returns index of nearest neighbor atoms.
"""
alldist = [(self._mol.get_distance(siteindex, i), i) for i in range(siteindex)]
alldist = sorted(alldist, key=lambda x: x[0])
return [d[1] for d in alldist]
def get_zmatrix(self):
"""
Returns a z-matrix representation of the molecule.
"""
output = []
outputvar = []
for i, site in enumerate(self._mol):
if i == 0:
output.append("{}".format(site.specie))
elif i == 1:
nn = self._find_nn_pos_before_site(i)
bondlength = self._mol.get_distance(i, nn[0])
output.append("{} {} B{}".format(self._mol[i].specie, nn[0] + 1, i))
outputvar.append("B{}={:.6f}".format(i, bondlength))
elif i == 2:
nn = self._find_nn_pos_before_site(i)
bondlength = self._mol.get_distance(i, nn[0])
angle = self._mol.get_angle(i, nn[0], nn[1])
output.append("{} {} B{} {} A{}".format(self._mol[i].specie, nn[0] + 1, i, nn[1] + 1, i))
outputvar.append("B{}={:.6f}".format(i, bondlength))
outputvar.append("A{}={:.6f}".format(i, angle))
else:
nn = self._find_nn_pos_before_site(i)
bondlength = self._mol.get_distance(i, nn[0])
angle = self._mol.get_angle(i, nn[0], nn[1])
dih = self._mol.get_dihedral(i, nn[0], nn[1], nn[2])
output.append(
"{} {} B{} {} A{} {} D{}".format(self._mol[i].specie, nn[0] + 1, i, nn[1] + 1, i, nn[2] + 1, i)
)
outputvar.append("B{}={:.6f}".format(i, bondlength))
outputvar.append("A{}={:.6f}".format(i, angle))
outputvar.append("D{}={:.6f}".format(i, dih))
return "\n".join(output) + "\n\n" + "\n".join(outputvar)
def get_cart_coords(self):
"""
Return the cartesian coordinates of the molecule
"""
def to_s(x):
return "%0.6f" % x
outs = []
for i, site in enumerate(self._mol):
outs.append(" ".join([site.species_string, " ".join([to_s(j) for j in site.coords])]))
return "\n".join(outs)
def __str__(self):
return self.to_string()
def to_string(self, cart_coords=False):
"""
Return GaussianInput string
Option: whe cart_coords sets to True return the cartesian coordinates
instead of the z-matrix
"""
def para_dict_to_string(para, joiner=" "):
para_str = []
# sorted is only done to make unittests work reliably
for par, val in sorted(para.items()):
if val is None or val == "":
para_str.append(par)
elif isinstance(val, dict):
val_str = para_dict_to_string(val, joiner=",")
para_str.append("{}=({})".format(par, val_str))
else:
para_str.append("{}={}".format(par, val))
return joiner.join(para_str)
output = []
if self.link0_parameters:
output.append(para_dict_to_string(self.link0_parameters, "\n"))
# Handle functional or basis set set to None, empty string or whitespace
func_str = "" if self.functional is None else self.functional.strip()
bset_str = "" if self.basis_set is None else self.basis_set.strip()
if func_str != "" and bset_str != "":
func_bset_str = " {}/{}".format(func_str, bset_str)
else:
# don't use the slash if either or both are set as empty
func_bset_str = " {}{}".format(func_str, bset_str).rstrip()
output.append(
"{diez}{func_bset} {route}".format(
diez=self.dieze_tag,
func_bset=func_bset_str,
route=para_dict_to_string(self.route_parameters),
)
)
output.append("")
output.append(self.title)
output.append("")
charge_str = "" if self.charge is None else "%d" % self.charge
multip_str = "" if self.spin_multiplicity is None else " %d" % self.spin_multiplicity
output.append("{}{}".format(charge_str, multip_str))
if isinstance(self._mol, Molecule):
if cart_coords is True:
output.append(self.get_cart_coords())
else:
output.append(self.get_zmatrix())
elif self._mol is not None:
output.append(str(self._mol))
output.append("")
if self.gen_basis is not None:
output.append("{:s}\n".format(self.gen_basis))
output.append(para_dict_to_string(self.input_parameters, "\n"))
output.append("\n")
return "\n".join(output)
def write_file(self, filename, cart_coords=False):
"""
Write the input string into a file
Option: see __str__ method
"""
with zopen(filename, "w") as f:
f.write(self.to_string(cart_coords))
def as_dict(self):
"""
:return: MSONable dict
"""
return {
"@module": self.__class__.__module__,
"@class": self.__class__.__name__,
"molecule": self.molecule.as_dict(),
"functional": self.functional,
"basis_set": self.basis_set,
"route_parameters": self.route_parameters,
"title": self.title,
"charge": self.charge,
"spin_multiplicity": self.spin_multiplicity,
"input_parameters": self.input_parameters,
"link0_parameters": self.link0_parameters,
"dieze_tag": self.dieze_tag,
}
@classmethod
def from_dict(cls, d):
"""
:param d: dict
:return: GaussianInput
"""
return GaussianInput(
mol=Molecule.from_dict(d["molecule"]),
functional=d["functional"],
basis_set=d["basis_set"],
route_parameters=d["route_parameters"],
title=d["title"],
charge=d["charge"],
spin_multiplicity=d["spin_multiplicity"],
input_parameters=d["input_parameters"],
link0_parameters=d["link0_parameters"],
)
class GaussianOutput:
"""
Parser for Gaussian output files.
.. note::
Still in early beta.
Attributes:
.. attribute:: structures
All structures from the calculation in the standard orientation. If the
symmetry is not considered, the standard orientation is not printed out
and the input orientation is used instead. Check the `standard_orientation`
attribute.
.. attribute:: structures_input_orientation
All structures from the calculation in the input orientation or the
Z-matrix orientation (if an opt=z-matrix was requested).
.. attribute:: opt_structures
All optimized structures from the calculation in the standard orientation,
if the attribute 'standard_orientation' is True, otherwise in the input
or the Z-matrix orientation.
.. attribute:: energies
All energies from the calculation.
.. attribute:: eigenvalues
List of eigenvalues for the last geometry
.. attribute:: MO_coefficients
Matrix of MO coefficients for the last geometry
.. attribute:: cart_forces
All cartesian forces from the calculation.
.. attribute:: frequencies
A list for each freq calculation and for each mode of a dict with
{
"frequency": freq in cm-1,
"symmetry": symmetry tag
"r_mass": Reduce mass,
"f_constant": force constant,
"IR_intensity": IR Intensity,
"mode": normal mode
}
The normal mode is a 1D vector of dx, dy dz of each atom.
.. attribute:: hessian
Matrix of second derivatives of the energy with respect to cartesian
coordinates in the **input orientation** frame. Need #P in the
route section in order to be in the output.
.. attribute:: properly_terminated
True if run has properly terminated
.. attribute:: is_pcm
True if run is a PCM run.
.. attribute:: is_spin
True if it is an unrestricted run
.. attribute:: stationary_type
If it is a relaxation run, indicates whether it is a minimum (Minimum)
or a saddle point ("Saddle").
.. attribute:: corrections
Thermochemical corrections if this run is a Freq run as a dict. Keys
are "Zero-point", "Thermal", "Enthalpy" and "Gibbs Free Energy"
.. attribute:: functional
Functional used in the run.
.. attribute:: basis_set
Basis set used in the run
.. attribute:: route
Additional route parameters as a dict. For example,
{'SP':"", "SCF":"Tight"}
.. attribute:: dieze_tag
# preceding the route line, e.g. "#P"
.. attribute:: link0
Link0 parameters as a dict. E.g., {"%mem": "1000MW"}
.. attribute:: charge
Charge for structure
.. attribute:: spin_multiplicity
Spin multiplicity for structure
.. attribute:: num_basis_func
Number of basis functions in the run.
.. attribute:: electrons
number of alpha and beta electrons as (N alpha, N beta)
.. attribute:: pcm
PCM parameters and output if available.
.. attribute:: errors
error if not properly terminated (list to be completed in error_defs)
.. attribute:: Mulliken_charges
Mulliken atomic charges
.. attribute:: eigenvectors
Matrix of shape (num_basis_func, num_basis_func). Each column is an
eigenvectors and contains AO coefficients of an MO.
eigenvectors[Spin] = mat(num_basis_func, num_basis_func)
.. attribute:: molecular_orbital
MO development coefficients on AO in a more convenient array dict
for each atom and basis set label.
mo[Spin][OM j][atom i] = {AO_k: coeff, AO_k: coeff ... }
.. attribute:: atom_basis_labels
Labels of AO for each atoms. These labels are those used in the output
of molecular orbital coefficients (POP=Full) and in the
molecular_orbital array dict.
atom_basis_labels[iatom] = [AO_k, AO_k, ...]
.. attribute:: resumes
List of gaussian data resume given at the end of the output file before
the quotation. The resumes are given as string.
.. attribute:: title
Title of the gaussian run.
.. attribute:: standard_orientation
If True, the geometries stored in the structures are in the standard
orientation. Else, the geometries are in the input orientation.
.. attribute:: bond_orders
Dict of bond order values read in the output file such as:
{(0, 1): 0.8709, (1, 6): 1.234, ...}
The keys are the atom indexes and the values are the Wiberg bond indexes
that are printed using `pop=NBOREAD` and `$nbo bndidx $end`.
Methods:
.. method:: to_input()
Return a GaussianInput object using the last geometry and the same
calculation parameters.
.. method:: read_scan()
Read a potential energy surface from a gaussian scan calculation.
.. method:: get_scan_plot()
Get a matplotlib plot of the potential energy surface
.. method:: save_scan_plot()
Save a matplotlib plot of the potential energy surface to a file
"""
def __init__(self, filename):
"""
Args:
filename: Filename of Gaussian output file.
"""
self.filename = filename
self._parse(filename)
@property
def final_energy(self):
"""
:return: Final energy in Gaussian output.
"""
return self.energies[-1]
@property
def final_structure(self):
"""
:return: Final structure in Gaussian output.
"""
return self.structures[-1]
def _parse(self, filename):
start_patt = re.compile(r" \(Enter \S+l101\.exe\)")
route_patt = re.compile(r" #[pPnNtT]*.*")
link0_patt = re.compile(r"^\s(%.+)\s*=\s*(.+)")
charge_mul_patt = re.compile(r"Charge\s+=\s*([-\d]+)\s+" r"Multiplicity\s+=\s*(\d+)")
num_basis_func_patt = re.compile(r"([0-9]+)\s+basis functions")
num_elec_patt = re.compile(r"(\d+)\s+alpha electrons\s+(\d+)\s+beta electrons")
pcm_patt = re.compile(r"Polarizable Continuum Model")
stat_type_patt = re.compile(r"imaginary frequencies")
scf_patt = re.compile(r"E\(.*\)\s*=\s*([-\.\d]+)\s+")
mp2_patt = re.compile(r"EUMP2\s*=\s*(.*)")
oniom_patt = re.compile(r"ONIOM:\s+extrapolated energy\s*=\s*(.*)")
termination_patt = re.compile(r"(Normal|Error) termination")
error_patt = re.compile(r"(! Non-Optimized Parameters !|Convergence failure)")
mulliken_patt = re.compile(r"^\s*(Mulliken charges|Mulliken atomic charges)")
mulliken_charge_patt = re.compile(r"^\s+(\d+)\s+([A-Z][a-z]?)\s*(\S*)")
end_mulliken_patt = re.compile(r"(Sum of Mulliken )(.*)(charges)\s*=\s*(\D)")
std_orientation_patt = re.compile(r"Standard orientation")
input_orientation_patt = re.compile(r"Input orientation|Z-Matrix orientation")
orbital_patt = re.compile(r"(Alpha|Beta)\s*\S+\s*eigenvalues --(.*)")
thermo_patt = re.compile(r"(Zero-point|Thermal) correction(.*)=" r"\s+([\d\.-]+)")
forces_on_patt = re.compile(r"Center\s+Atomic\s+Forces\s+\(Hartrees/Bohr\)")
forces_off_patt = re.compile(r"Cartesian\s+Forces:\s+Max.*RMS.*")
forces_patt = re.compile(r"\s+(\d+)\s+(\d+)\s+([0-9\.-]+)\s+([0-9\.-]+)\s+([0-9\.-]+)")
freq_on_patt = re.compile(r"Harmonic\sfrequencies\s+\(cm\*\*-1\),\sIR\sintensities.*Raman.*")
normal_mode_patt = re.compile(r"\s+(\d+)\s+(\d+)\s+([0-9\.-]{4,5})\s+([0-9\.-]{4,5}).*")
mo_coeff_patt = re.compile(r"Molecular Orbital Coefficients:")
mo_coeff_name_patt = re.compile(r"\d+\s((\d+|\s+)\s+([a-zA-Z]{1,2}|\s+))\s+(\d+\S+)")
hessian_patt = re.compile(r"Force constants in Cartesian coordinates:")
resume_patt = re.compile(r"^\s1\\1\\GINC-\S*")
resume_end_patt = re.compile(r"^\s.*\\\\@")
bond_order_patt = re.compile(r"Wiberg bond index matrix in the NAO basis:")
self.properly_terminated = False
self.is_pcm = False
self.stationary_type = "Minimum"
self.corrections = {}
self.energies = []
self.pcm = None
self.errors = []
self.Mulliken_charges = {}
self.link0 = {}
self.cart_forces = []
self.frequencies = []
self.eigenvalues = []
self.is_spin = False
self.hessian = None
self.resumes = []
self.title = None
self.bond_orders = {}
read_coord = 0
read_mulliken = False
read_eigen = False
eigen_txt = []
parse_stage = 0
num_basis_found = False
terminated = False
parse_forces = False
forces = []
parse_freq = False
frequencies = []
read_mo = False
parse_hessian = False
routeline = ""
standard_orientation = False
parse_bond_order = False
input_structures = list()
std_structures = list()
geom_orientation = None
opt_structures = list()
with zopen(filename) as f:
for line in f:
if parse_stage == 0:
if start_patt.search(line):
parse_stage = 1
elif link0_patt.match(line):
m = link0_patt.match(line)
self.link0[m.group(1)] = m.group(2)
elif route_patt.search(line) or routeline != "":
if set(line.strip()) == {"-"}:
params = read_route_line(routeline)
self.functional = params[0]
self.basis_set = params[1]
self.route_parameters = params[2]
route_lower = {k.lower(): v for k, v in self.route_parameters.items()}
self.dieze_tag = params[3]
parse_stage = 1
else:
routeline += line.strip()
elif parse_stage == 1:
if set(line.strip()) == {"-"} and self.title is None:
self.title = ""
elif self.title == "":
self.title = line.strip()
elif charge_mul_patt.search(line):
m = charge_mul_patt.search(line)
self.charge = int(m.group(1))
self.spin_multiplicity = int(m.group(2))
parse_stage = 2
elif parse_stage == 2:
if self.is_pcm:
self._check_pcm(line)
if "freq" in route_lower and thermo_patt.search(line):
m = thermo_patt.search(line)
if m.group(1) == "Zero-point":
self.corrections["Zero-point"] = float(m.group(3))
else:
key = m.group(2).strip(" to ")
self.corrections[key] = float(m.group(3))
if read_coord:
[f.readline() for i in range(3)]
line = f.readline()
sp = []
coords = []
while set(line.strip()) != {"-"}:
toks = line.split()
sp.append(Element.from_Z(int(toks[1])))
coords.append([float(x) for x in toks[3:6]])
line = f.readline()
read_coord = False
if geom_orientation == "input":
input_structures.append(Molecule(sp, coords))
elif geom_orientation == "standard":
std_structures.append(Molecule(sp, coords))
if parse_forces:
m = forces_patt.search(line)
if m:
forces.extend([float(_v) for _v in m.groups()[2:5]])
elif forces_off_patt.search(line):
self.cart_forces.append(forces)
forces = []
parse_forces = False
# read molecular orbital eigenvalues
if read_eigen:
m = orbital_patt.search(line)
if m:
eigen_txt.append(line)
else:
read_eigen = False
self.eigenvalues = {Spin.up: []}
for eigenline in eigen_txt:
if "Alpha" in eigenline:
self.eigenvalues[Spin.up] += [float(e) for e in float_patt.findall(eigenline)]
elif "Beta" in eigenline:
if Spin.down not in self.eigenvalues:
self.eigenvalues[Spin.down] = []
self.eigenvalues[Spin.down] += [float(e) for e in float_patt.findall(eigenline)]
eigen_txt = []
# read molecular orbital coefficients
if (not num_basis_found) and num_basis_func_patt.search(line):
m = num_basis_func_patt.search(line)
self.num_basis_func = int(m.group(1))
num_basis_found = True
elif read_mo:
# build a matrix with all coefficients
all_spin = [Spin.up]
if self.is_spin:
all_spin.append(Spin.down)
mat_mo = {}
for spin in all_spin:
mat_mo[spin] = np.zeros((self.num_basis_func, self.num_basis_func))
nMO = 0
end_mo = False
while nMO < self.num_basis_func and not end_mo:
f.readline()
f.readline()
self.atom_basis_labels = []
for i in range(self.num_basis_func):
line = f.readline()
# identify atom and OA labels
m = mo_coeff_name_patt.search(line)
if m.group(1).strip() != "":
iat = int(m.group(2)) - 1
# atname = m.group(3)
self.atom_basis_labels.append([m.group(4)])
else:
self.atom_basis_labels[iat].append(m.group(4))
# MO coefficients
coeffs = [float(c) for c in float_patt.findall(line)]
for j, c in enumerate(coeffs):
mat_mo[spin][i, nMO + j] = c
nMO += len(coeffs)
line = f.readline()
# manage pop=regular case (not all MO)
if nMO < self.num_basis_func and (
"Density Matrix:" in line or mo_coeff_patt.search(line)
):
end_mo = True
warnings.warn("POP=regular case, matrix " "coefficients not complete")
f.readline()
self.eigenvectors = mat_mo
read_mo = False
# build a more convenient array dict with MO
# coefficient of each atom in each MO.
# mo[Spin][OM j][atom i] =
# {AO_k: coeff, AO_k: coeff ... }
mo = {}
for spin in all_spin:
mo[spin] = [
[{} for iat in range(len(self.atom_basis_labels))] for j in range(self.num_basis_func)
]
for j in range(self.num_basis_func):
i = 0
for iat in range(len(self.atom_basis_labels)):
for label in self.atom_basis_labels[iat]:
mo[spin][j][iat][label] = self.eigenvectors[spin][i, j]
i += 1
self.molecular_orbital = mo
elif parse_freq:
while line.strip() != "": # blank line
ifreqs = [int(val) - 1 for val in line.split()]
for ifreq in ifreqs:
frequencies.append(
{
"frequency": None,
"r_mass": None,
"f_constant": None,
"IR_intensity": None,
"symmetry": None,
"mode": [],
}
)
# read freq, intensity, masses, symmetry ...
while "Atom AN" not in line:
if "Frequencies --" in line:
freqs = map(float, float_patt.findall(line))
for ifreq, freq in zip(ifreqs, freqs):
frequencies[ifreq]["frequency"] = freq
elif "Red. masses --" in line:
r_masses = map(float, float_patt.findall(line))
for ifreq, r_mass in zip(ifreqs, r_masses):
frequencies[ifreq]["r_mass"] = r_mass
elif "Frc consts --" in line:
f_consts = map(float, float_patt.findall(line))
for ifreq, f_const in zip(ifreqs, f_consts):
frequencies[ifreq]["f_constant"] = f_const
elif "IR Inten --" in line:
IR_intens = map(float, float_patt.findall(line))
for ifreq, intens in zip(ifreqs, IR_intens):
frequencies[ifreq]["IR_intensity"] = intens
else:
syms = line.split()[:3]
for ifreq, sym in zip(ifreqs, syms):
frequencies[ifreq]["symmetry"] = sym
line = f.readline()
# read normal modes
line = f.readline()
while normal_mode_patt.search(line):
values = list(map(float, float_patt.findall(line)))
for i, ifreq in zip(range(0, len(values), 3), ifreqs):
frequencies[ifreq]["mode"].extend(values[i : i + 3])
line = f.readline()
parse_freq = False
self.frequencies.append(frequencies)
frequencies = []
elif parse_hessian:
# read Hessian matrix under "Force constants in Cartesian coordinates"
# Hessian matrix is in the input orientation framework
# WARNING : need #P in the route line
parse_hessian = False
ndf = 3 * len(input_structures[0])
self.hessian = np.zeros((ndf, ndf))
j_indices = range(5)
jndf = 0
while jndf < ndf:
for i in range(jndf, ndf):
line = f.readline()
vals = re.findall(r"\s*([+-]?\d+\.\d+[eEdD]?[+-]\d+)", line)
vals = [float(val.replace("D", "E")) for val in vals]
for jval, val in enumerate(vals):
j = j_indices[jval]
self.hessian[i, j] = val
self.hessian[j, i] = val
jndf += len(vals)
line = f.readline()
j_indices = [j + 5 for j in j_indices]
elif parse_bond_order:
# parse Wiberg bond order
line = f.readline()
line = f.readline()
nat = len(input_structures[0])
matrix = list()
for iat in range(nat):
line = f.readline()
matrix.append([float(v) for v in line.split()[2:]])
self.bond_orders = dict()
for iat in range(nat):
for jat in range(iat + 1, nat):
self.bond_orders[(iat, jat)] = matrix[iat][jat]
parse_bond_order = False
elif termination_patt.search(line):
m = termination_patt.search(line)
if m.group(1) == "Normal":
self.properly_terminated = True
terminated = True
elif error_patt.search(line):
error_defs = {
"! Non-Optimized Parameters !": "Optimization " "error",
"Convergence failure": "SCF convergence error",
}
m = error_patt.search(line)
self.errors.append(error_defs[m.group(1)])
elif num_elec_patt.search(line):
m = num_elec_patt.search(line)
self.electrons = (int(m.group(1)), int(m.group(2)))
elif (not self.is_pcm) and pcm_patt.search(line):
self.is_pcm = True
self.pcm = {}
elif "freq" in route_lower and "opt" in route_lower and stat_type_patt.search(line):
self.stationary_type = "Saddle"
elif mp2_patt.search(line):
m = mp2_patt.search(line)
self.energies.append(float(m.group(1).replace("D", "E")))
elif oniom_patt.search(line):
m = oniom_patt.matcher(line)
self.energies.append(float(m.group(1)))
elif scf_patt.search(line):
m = scf_patt.search(line)
self.energies.append(float(m.group(1)))
elif std_orientation_patt.search(line):
standard_orientation = True
geom_orientation = "standard"
read_coord = True
elif input_orientation_patt.search(line):
geom_orientation = "input"
read_coord = True
elif "Optimization completed." in line:
line = f.readline()
if " -- Stationary point found." not in line:
warnings.warn(
"\n" + self.filename + ": Optimization complete but this is not a stationary point"
)
if standard_orientation:
opt_structures.append(std_structures[-1])
else:
opt_structures.append(input_structures[-1])
elif not read_eigen and orbital_patt.search(line):
eigen_txt.append(line)
read_eigen = True
elif mulliken_patt.search(line):
mulliken_txt = []
read_mulliken = True
elif not parse_forces and forces_on_patt.search(line):
parse_forces = True
elif freq_on_patt.search(line):
parse_freq = True
[f.readline() for i in range(3)]
elif mo_coeff_patt.search(line):
if "Alpha" in line:
self.is_spin = True
read_mo = True
elif hessian_patt.search(line):
parse_hessian = True
elif resume_patt.search(line):
resume = []
while not resume_end_patt.search(line):
resume.append(line)
line = f.readline()
# security if \\@ not in one line !
if line == "\n":
break
resume.append(line)
resume = "".join([r.strip() for r in resume])
self.resumes.append(resume)
elif bond_order_patt.search(line):
parse_bond_order = True
if read_mulliken:
if not end_mulliken_patt.search(line):
mulliken_txt.append(line)
else:
m = end_mulliken_patt.search(line)
mulliken_charges = {}
for line in mulliken_txt:
if mulliken_charge_patt.search(line):
m = mulliken_charge_patt.search(line)
dic = {int(m.group(1)): [m.group(2), float(m.group(3))]}
mulliken_charges.update(dic)
read_mulliken = False
self.Mulliken_charges = mulliken_charges
# store the structures. If symmetry is considered, the standard orientation
# is used. Else the input orientation is used.
if standard_orientation:
self.structures = std_structures
self.structures_input_orientation = input_structures
else:
self.structures = input_structures
self.structures_input_orientation = input_structures
# store optimized structure in input orientation
self.opt_structures = opt_structures
if not terminated:
warnings.warn("\n" + self.filename + ": Termination error or bad Gaussian output file !")
def _check_pcm(self, line):
energy_patt = re.compile(r"(Dispersion|Cavitation|Repulsion) energy" r"\s+\S+\s+=\s+(\S*)")
total_patt = re.compile(r"with all non electrostatic terms\s+\S+\s+" r"=\s+(\S*)")
parameter_patt = re.compile(r"(Eps|Numeral density|RSolv|Eps" r"\(inf[inity]*\))\s+=\s*(\S*)")
if energy_patt.search(line):
m = energy_patt.search(line)
self.pcm["{} energy".format(m.group(1))] = float(m.group(2))
elif total_patt.search(line):
m = total_patt.search(line)
self.pcm["Total energy"] = float(m.group(1))
elif parameter_patt.search(line):
m = parameter_patt.search(line)
self.pcm[m.group(1)] = float(m.group(2))
def as_dict(self):
"""
Json-serializable dict representation.
"""
structure = self.final_structure
d = {
"has_gaussian_completed": self.properly_terminated,
"nsites": len(structure),
}
comp = structure.composition
d["unit_cell_formula"] = comp.as_dict()
d["reduced_cell_formula"] = Composition(comp.reduced_formula).as_dict()
d["pretty_formula"] = comp.reduced_formula
d["is_pcm"] = self.is_pcm
d["errors"] = self.errors
d["Mulliken_charges"] = self.Mulliken_charges
unique_symbols = sorted(list(d["unit_cell_formula"].keys()))
d["elements"] = unique_symbols
d["nelements"] = len(unique_symbols)
d["charge"] = self.charge
d["spin_multiplicity"] = self.spin_multiplicity
vin = {
"route": self.route_parameters,
"functional": self.functional,
"basis_set": self.basis_set,
"nbasisfunctions": self.num_basis_func,
"pcm_parameters": self.pcm,
}
d["input"] = vin
nsites = len(self.final_structure)
vout = {
"energies": self.energies,
"final_energy": self.final_energy,
"final_energy_per_atom": self.final_energy / nsites,
"molecule": structure.as_dict(),
"stationary_type": self.stationary_type,
"corrections": self.corrections,
}
d["output"] = vout
d["@module"] = self.__class__.__module__
d["@class"] = self.__class__.__name__
return d
def read_scan(self):
"""
Read a potential energy surface from a gaussian scan calculation.
Returns:
A dict: {"energies": [ values ],
"coords": {"d1": [ values ], "A2", [ values ], ... }}
"energies" are the energies of all points of the potential energy
surface. "coords" are the internal coordinates used to compute the
potential energy surface and the internal coordinates optimized,
labelled by their name as defined in the calculation.
"""
def floatList(l):
"""return a list of float from a list of string"""
return [float(v) for v in l]
scan_patt = re.compile(r"^\sSummary of the potential surface scan:")
optscan_patt = re.compile(r"^\sSummary of Optimized Potential Surface Scan")
coord_patt = re.compile(r"^\s*(\w+)((\s*[+-]?\d+\.\d+)+)")
# data dict return
data = {"energies": list(), "coords": dict()}
# read in file
with zopen(self.filename, "r") as f:
line = f.readline()
while line != "":
if optscan_patt.match(line):
f.readline()
line = f.readline()
endScan = False
while not endScan:
data["energies"] += floatList(float_patt.findall(line))
line = f.readline()
while coord_patt.match(line):
icname = line.split()[0].strip()
if icname in data["coords"]:
data["coords"][icname] += floatList(float_patt.findall(line))
else:
data["coords"][icname] = floatList(float_patt.findall(line))
line = f.readline()
if not re.search(r"^\s+((\s*\d+)+)", line):
endScan = True
else:
line = f.readline()
elif scan_patt.match(line):
line = f.readline()
data["coords"] = {icname: list() for icname in line.split()[1:-1]}
f.readline()
line = f.readline()
while not re.search(r"^\s-+", line):
values = floatList(line.split())
data["energies"].append(values[-1])
for i, icname in enumerate(data["coords"]):
data["coords"][icname].append(values[i + 1])
line = f.readline()
else:
line = f.readline()
return data
def get_scan_plot(self, coords=None):
"""
Get a matplotlib plot of the potential energy surface.
Args:
coords: internal coordinate name to use as abcissa.
"""
from pymatgen.util.plotting import pretty_plot
plt = pretty_plot(12, 8)
d = self.read_scan()
if coords and coords in d["coords"]:
x = d["coords"][coords]
plt.xlabel(coords)
else:
x = range(len(d["energies"]))
plt.xlabel("points")
plt.ylabel("Energy (eV)")
e_min = min(d["energies"])
y = [(e - e_min) * Ha_to_eV for e in d["energies"]]
plt.plot(x, y, "ro--")
return plt
def save_scan_plot(self, filename="scan.pdf", img_format="pdf", coords=None):
"""
Save matplotlib plot of the potential energy surface to a file.
Args:
filename: Filename to write to.
img_format: Image format to use. Defaults to EPS.
coords: internal coordinate name to use as abcissa.
"""
plt = self.get_scan_plot(coords)
plt.savefig(filename, format=img_format)
def read_excitation_energies(self):
"""
Read a excitation energies after a TD-DFT calculation.
Returns:
A list: A list of tuple for each transition such as
[(energie (eV), lambda (nm), oscillatory strength), ... ]
"""
transitions = list()
# read in file
with zopen(self.filename, "r") as f:
line = f.readline()
td = False
while line != "":
if re.search(r"^\sExcitation energies and oscillator strengths:", line):
td = True
if td:
if re.search(r"^\sExcited State\s*\d", line):
val = [float(v) for v in float_patt.findall(line)]
transitions.append(tuple(val[0:3]))
line = f.readline()
return transitions
def get_spectre_plot(self, sigma=0.05, step=0.01):
"""
Get a matplotlib plot of the UV-visible xas. Transition are plotted
as vertical lines and as a sum of normal functions with sigma with. The
broadening is applied in energy and the xas is plotted as a function
of the wavelength.
Args:
sigma: Full width at half maximum in eV for normal functions.
step: bin interval in eV
Returns:
A dict: {"energies": values, "lambda": values, "xas": values}
where values are lists of abscissa (energies, lamba) and
the sum of gaussian functions (xas).
A matplotlib plot.
"""
from scipy.stats import norm
from pymatgen.util.plotting import pretty_plot
plt = pretty_plot(12, 8)
transitions = self.read_excitation_energies()
minval = min([val[0] for val in transitions]) - 5.0 * sigma
maxval = max([val[0] for val in transitions]) + 5.0 * sigma
npts = int((maxval - minval) / step) + 1
eneval = np.linspace(minval, maxval, npts) # in eV
lambdaval = [cst.h * cst.c / (val * cst.e) * 1.0e9 for val in eneval] # in nm
# sum of gaussian functions
spectre = np.zeros(npts)
for trans in transitions:
spectre += trans[2] * norm(eneval, trans[0], sigma)
spectre /= spectre.max()
plt.plot(lambdaval, spectre, "r-", label="spectre")
data = {"energies": eneval, "lambda": lambdaval, "xas": spectre}
# plot transitions as vlines
plt.vlines(
[val[1] for val in transitions],
0.0,
[val[2] for val in transitions],
color="blue",
label="transitions",
linewidth=2,
)
plt.xlabel("$\\lambda$ (nm)")
plt.ylabel("Arbitrary unit")
plt.legend()
return data, plt
def save_spectre_plot(self, filename="spectre.pdf", img_format="pdf", sigma=0.05, step=0.01):
"""
Save matplotlib plot of the spectre to a file.
Args:
filename: Filename to write to.
img_format: Image format to use. Defaults to EPS.
sigma: Full width at half maximum in eV for normal functions.
step: bin interval in eV
"""
d, plt = self.get_spectre_plot(sigma, step)
plt.savefig(filename, format=img_format)
def to_input(
self,
mol=None,
charge=None,
spin_multiplicity=None,
title=None,
functional=None,
basis_set=None,
route_parameters=None,
input_parameters=None,
link0_parameters=None,
dieze_tag=None,
cart_coords=False,
):
"""
Create a new input object using by default the last geometry read in
the output file and with the same calculation parameters. Arguments
are the same as GaussianInput class.
Returns
gaunip (GaussianInput) : the gaussian input object
"""
if not mol:
mol = self.final_structure
if charge is None:
charge = self.charge
if spin_multiplicity is None:
spin_multiplicity = self.spin_multiplicity
if not title:
title = self.title
if not functional:
functional = self.functional
if not basis_set:
basis_set = self.basis_set
if not route_parameters:
route_parameters = self.route_parameters
if not link0_parameters:
link0_parameters = self.link0
if not dieze_tag:
dieze_tag = self.dieze_tag
return GaussianInput(
mol=mol,
charge=charge,
spin_multiplicity=spin_multiplicity,
title=title,
functional=functional,
basis_set=basis_set,
route_parameters=route_parameters,
input_parameters=input_parameters,
link0_parameters=link0_parameters,
dieze_tag=dieze_tag,
)
| mit |
CalvinNeo/EasyMLPlatform | py/graphic/tree.py | 1 | 4067 | #coding:utf8
import numpy as np
import math
import pylab as pl
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import json
class GraphTree:
def __init__(self):
self.jsonobj = {}
self.leafNode = dict(boxstyle = 'round4',fc = '0.8')
self.branchNode = dict(boxstyle = 'sawtooth',fc = '0.8')
self.arrow = dict(arrowstyle = '<-')
self.depth = 0
self.leafcount = 0
def get_depth_leafcount(self,root):
current_node = root.keys()[0] #name of choice node(string)
branch_dict = root[current_node]
maxdepth, thisdepth, thisleafcount = 0,0,0
for current_node in branch_dict.keys():
# print current_node,type(branch_dict[current_node]).__name__
if type(branch_dict[current_node]).__name__ == 'dict':
temp = self.get_depth_leafcount(branch_dict[current_node])
thisdepth = 1 + temp[0]
thisleafcount += temp[1]
else:
thisdepth = 1
thisleafcount += 1
if thisdepth > maxdepth:
maxdepth = thisdepth
return maxdepth,thisleafcount
def load(self,strjson):
self.jsonobj = dict(strjson)
self.depth,self.leafcount = self.get_depth_leafcount(self.jsonobj)
def plotMidText(self, cntrPt, parentPt, txtString):
xMid = (parentPt[0] - cntrPt[0]) / 2.0 + cntrPt[0]
yMid = (parentPt[1] - cntrPt[1]) / 2.0 + cntrPt[1]
self.ax1.text(xMid, yMid, txtString)
def plotNode(self, nodeTxt, cntrPt, parentPt, nodeType):
self.ax1.annotate(nodeTxt, xy = parentPt, xycoords = 'axes fraction', xytext = cntrPt, \
textcoords = 'axes fraction', va = 'center', ha = 'center', bbox = nodeType, arrowprops = self.arrow)
def plotTree(self, myTree, parentPt, nodeTxt):
depth, leaves = self.get_depth_leafcount(myTree)
current_node = myTree.keys()[0]
cntrPt = (self.xOff + (1.0 + leaves) / 2.0 / self.leafcount, self.yOff)
self.plotMidText(cntrPt, parentPt, nodeTxt)
self.plotNode(current_node, cntrPt, parentPt, self.branchNode)
branch_dict = myTree[current_node]
self.yOff -= 1.0 / self.depth
for current_node in branch_dict.keys():
if type(branch_dict[current_node]).__name__ == 'dict':
self.plotTree(branch_dict[current_node], cntrPt, str(current_node))
else:
self.xOff += 1.0 / self.leafcount
self.plotNode(branch_dict[current_node], (self.xOff, self.yOff), cntrPt, self.leafNode)
self.plotMidText((self.xOff, self.yOff), cntrPt, str(current_node))
self.yOff += 1.0 / self.depth
def createPlot(self, show = True, save = ''):
fig = plt.figure(1, facecolor = 'white')
fig.clf()
axprops = dict(xticks = [], yticks = [])
self.ax1 = plt.subplot(111,frameon = False, **axprops)
self.xOff, self.yOff = -0.5 / self.leafcount, 1.0
self.plotTree(self.jsonobj, (0.5,1.0), '')
import StringIO, urllib, base64
if show:
plt.show()
else:
imgdata = StringIO.StringIO()
fig.savefig(imgdata, format='png')
imgdata.seek(0) # rewind the data
uri = 'data:image/png;base64,' + urllib.quote(base64.b64encode(imgdata.buf))
imgdata.close()
return uri
def showPlot(self):
plt.show()
if __name__ == '__main__':
tr = GraphTree()
# aa = '{"no surfacing":{"0":"no","1":{"flippers":{"0":"no","1":"yes"}}}}'
# tr.load(json.loads(aa))
#JSON can't have non-string key
aa = {"aged":{"0":"no","1":{"male":{"0":"no","1":"yes"}}}}
# aa = {'water': {0: 1, 1: {'foot': {0: "'no'", 1: "'yes'"}}}}
print dict(aa)
# aa = {"no surfacing":{0:"no",1:{"flippers":{0:"no",1:"yes"}}}}
# print dict(aa)
tr.load(aa)
print tr.leafcount,tr.depth
tr.createPlot(show=True)
| apache-2.0 |
khiner/aubio | python/demos/demo_waveform_plot.py | 10 | 2099 | #! /usr/bin/env python
import sys
from aubio import pvoc, source
from numpy import zeros, hstack
def get_waveform_plot(filename, samplerate = 0, block_size = 4096, ax = None, downsample = 2**4):
import matplotlib.pyplot as plt
if not ax:
fig = plt.figure()
ax = fig.add_subplot(111)
hop_s = block_size
allsamples_max = zeros(0,)
downsample = downsample # to plot n samples / hop_s
a = source(filename, samplerate, hop_s) # source file
if samplerate == 0: samplerate = a.samplerate
total_frames = 0
while True:
samples, read = a()
# keep some data to plot it later
new_maxes = (abs(samples.reshape(hop_s/downsample, downsample))).max(axis=0)
allsamples_max = hstack([allsamples_max, new_maxes])
total_frames += read
if read < hop_s: break
allsamples_max = (allsamples_max > 0) * allsamples_max
allsamples_max_times = [ ( float (t) / downsample ) * hop_s for t in range(len(allsamples_max)) ]
ax.plot(allsamples_max_times, allsamples_max, '-b')
ax.plot(allsamples_max_times, -allsamples_max, '-b')
ax.axis(xmin = allsamples_max_times[0], xmax = allsamples_max_times[-1])
set_xlabels_sample2time(ax, allsamples_max_times[-1], samplerate)
return ax
def set_xlabels_sample2time(ax, latest_sample, samplerate):
ax.axis(xmin = 0, xmax = latest_sample)
if latest_sample / float(samplerate) > 60:
ax.set_xlabel('time (mm:ss)')
ax.set_xticklabels([ "%02d:%02d" % (t/float(samplerate)/60, (t/float(samplerate))%60) for t in ax.get_xticks()[:-1]], rotation = 50)
else:
ax.set_xlabel('time (ss.mm)')
ax.set_xticklabels([ "%02d.%02d" % (t/float(samplerate), 100*((t/float(samplerate))%1) ) for t in ax.get_xticks()[:-1]], rotation = 50)
if __name__ == '__main__':
import matplotlib.pyplot as plt
if len(sys.argv) < 2:
print "Usage: %s <filename>" % sys.argv[0]
else:
for soundfile in sys.argv[1:]:
get_waveform_plot(soundfile)
# display graph
plt.show()
| gpl-3.0 |
xunilrj/sandbox | courses/course-edx-dat2031x/Simulation.py | 1 | 2680 | # -*- coding: utf-8 -*-
def sim_normal(nums, mean = 600, sd = 30):
import numpy as np
import numpy.random as nr
for n in nums:
dist = nr.normal(loc = mean, scale = sd, size = n)
titl = 'Normal distribution with ' + str(n) + ' values'
print('Summary for ' + str(n) + ' samples')
print(dist_summary(dist, titl))
print('Emperical 95% CIs')
print(np.percentile(dist, [2.5, 97.5]))
print(' ')
return('Done!')
def sim_poisson(nums, mean = 600):
import numpy as np
import numpy.random as nr
for n in nums:
dist = nr.poisson(lam = mean, size = n)
titl = 'Poisson distribution with ' + str(n) + ' values'
print(dist_summary(dist, titl))
print('Emperical 95% CIs')
print(np.percentile(dist, [2.5, 97.5]))
print(' ')
return('Done!')
def dist_summary(dist, names = 'dist_name'):
import pandas as pd
import matplotlib.pyplot as plt
ser = pd.Series(dist)
fig = plt.figure(1, figsize=(9, 6))
ax = fig.gca()
ser.hist(ax = ax, bins = 120)
ax.set_title('Frequency distribution of ' + names)
ax.set_ylabel('Frequency')
plt.show()
return(ser.describe())
def gen_profits(num):
import numpy.random as nr
unif = nr.uniform(size = num)
out = [5 if x < 0.3 else (3.5 if x < 0.6 else 4) for x in unif]
return(out)
def gen_tips(num):
import numpy.random as nr
unif = nr.uniform(size = num)
out = [0 if x < 0.5 else (0.25 if x < 0.7
else (1.0 if x < 0.9 else 2.0)) for x in unif]
return(out)
def sim_lemonade(num, mean = 600, sd = 30, pois = False):
## Simulate the profits and tips for
## a lemonade stand.
import numpy.random as nr
## number of customer arrivals
if pois:
arrivals = nr.poisson(lam = mean, size = num)
else:
arrivals = nr.normal(loc = mean, scale = sd, size = num)
print(dist_summary(arrivals, 'customer arrivals per day'))
## Compute distibution of average profit per arrival
proft = gen_profits(num)
print(dist_summary(proft, 'profit per arrival'))
## Total profits are profit per arrival
## times number of arrivals.
total_profit = arrivals * proft
print(dist_summary(total_profit, 'total profit per day'))
## Compute distribution of average tips per arrival
tps = gen_tips(num)
print(dist_summary(tps, 'tips per arrival'))
## Compute average tips per day
total_tips = arrivals * tps
print(dist_summary(total_tips, 'total tips per day'))
## Compute total profits plus total tips.
total_take = total_profit + total_tips
return(dist_summary(total_take, 'total net per day'))
| apache-2.0 |
EconForge/Smolyak | doc/sphinxext/docscrape_sphinx.py | 62 | 7703 | import re, inspect, textwrap, pydoc
import sphinx
from docscrape import NumpyDocString, FunctionDoc, ClassDoc
class SphinxDocString(NumpyDocString):
def __init__(self, docstring, config={}):
self.use_plots = config.get('use_plots', False)
NumpyDocString.__init__(self, docstring, config=config)
# string conversion routines
def _str_header(self, name, symbol='`'):
return ['.. rubric:: ' + name, '']
def _str_field_list(self, name):
return [':' + name + ':']
def _str_indent(self, doc, indent=4):
out = []
for line in doc:
out += [' '*indent + line]
return out
def _str_signature(self):
return ['']
if self['Signature']:
return ['``%s``' % self['Signature']] + ['']
else:
return ['']
def _str_summary(self):
return self['Summary'] + ['']
def _str_extended_summary(self):
return self['Extended Summary'] + ['']
def _str_param_list(self, name):
out = []
if self[name]:
out += self._str_field_list(name)
out += ['']
for param,param_type,desc in self[name]:
out += self._str_indent(['**%s** : %s' % (param.strip(),
param_type)])
out += ['']
out += self._str_indent(desc,8)
out += ['']
return out
@property
def _obj(self):
if hasattr(self, '_cls'):
return self._cls
elif hasattr(self, '_f'):
return self._f
return None
def _str_member_list(self, name):
"""
Generate a member listing, autosummary:: table where possible,
and a table where not.
"""
out = []
if self[name]:
out += ['.. rubric:: %s' % name, '']
prefix = getattr(self, '_name', '')
if prefix:
prefix = '~%s.' % prefix
autosum = []
others = []
for param, param_type, desc in self[name]:
param = param.strip()
if not self._obj or hasattr(self._obj, param):
autosum += [" %s%s" % (prefix, param)]
else:
others.append((param, param_type, desc))
if autosum:
out += ['.. autosummary::', ' :toctree:', '']
out += autosum
if others:
maxlen_0 = max([len(x[0]) for x in others])
maxlen_1 = max([len(x[1]) for x in others])
hdr = "="*maxlen_0 + " " + "="*maxlen_1 + " " + "="*10
fmt = '%%%ds %%%ds ' % (maxlen_0, maxlen_1)
n_indent = maxlen_0 + maxlen_1 + 4
out += [hdr]
for param, param_type, desc in others:
out += [fmt % (param.strip(), param_type)]
out += self._str_indent(desc, n_indent)
out += [hdr]
out += ['']
return out
def _str_section(self, name):
out = []
if self[name]:
out += self._str_header(name)
out += ['']
content = textwrap.dedent("\n".join(self[name])).split("\n")
out += content
out += ['']
return out
def _str_see_also(self, func_role):
out = []
if self['See Also']:
see_also = super(SphinxDocString, self)._str_see_also(func_role)
out = ['.. seealso::', '']
out += self._str_indent(see_also[2:])
return out
def _str_warnings(self):
out = []
if self['Warnings']:
out = ['.. warning::', '']
out += self._str_indent(self['Warnings'])
return out
def _str_index(self):
idx = self['index']
out = []
if len(idx) == 0:
return out
out += ['.. index:: %s' % idx.get('default','')]
for section, references in idx.iteritems():
if section == 'default':
continue
elif section == 'refguide':
out += [' single: %s' % (', '.join(references))]
else:
out += [' %s: %s' % (section, ','.join(references))]
return out
def _str_references(self):
out = []
if self['References']:
out += self._str_header('References')
if isinstance(self['References'], str):
self['References'] = [self['References']]
out.extend(self['References'])
out += ['']
# Latex collects all references to a separate bibliography,
# so we need to insert links to it
if sphinx.__version__ >= "0.6":
out += ['.. only:: latex','']
else:
out += ['.. latexonly::','']
items = []
for line in self['References']:
m = re.match(r'.. \[([a-z0-9._-]+)\]', line, re.I)
if m:
items.append(m.group(1))
out += [' ' + ", ".join(["[%s]_" % item for item in items]), '']
return out
def _str_examples(self):
examples_str = "\n".join(self['Examples'])
if (self.use_plots and 'import matplotlib' in examples_str
and 'plot::' not in examples_str):
out = []
out += self._str_header('Examples')
out += ['.. plot::', '']
out += self._str_indent(self['Examples'])
out += ['']
return out
else:
return self._str_section('Examples')
def __str__(self, indent=0, func_role="obj"):
out = []
out += self._str_signature()
out += self._str_index() + ['']
out += self._str_summary()
out += self._str_extended_summary()
for param_list in ('Parameters', 'Returns', 'Raises'):
out += self._str_param_list(param_list)
out += self._str_warnings()
out += self._str_see_also(func_role)
out += self._str_section('Notes')
out += self._str_references()
out += self._str_examples()
for param_list in ('Attributes', 'Methods'):
out += self._str_member_list(param_list)
out = self._str_indent(out,indent)
return '\n'.join(out)
class SphinxFunctionDoc(SphinxDocString, FunctionDoc):
def __init__(self, obj, doc=None, config={}):
self.use_plots = config.get('use_plots', False)
FunctionDoc.__init__(self, obj, doc=doc, config=config)
class SphinxClassDoc(SphinxDocString, ClassDoc):
def __init__(self, obj, doc=None, func_doc=None, config={}):
self.use_plots = config.get('use_plots', False)
ClassDoc.__init__(self, obj, doc=doc, func_doc=None, config=config)
class SphinxObjDoc(SphinxDocString):
def __init__(self, obj, doc=None, config={}):
self._f = obj
SphinxDocString.__init__(self, doc, config=config)
def get_doc_object(obj, what=None, doc=None, config={}):
if what is None:
if inspect.isclass(obj):
what = 'class'
elif inspect.ismodule(obj):
what = 'module'
elif callable(obj):
what = 'function'
else:
what = 'object'
if what == 'class':
return SphinxClassDoc(obj, func_doc=SphinxFunctionDoc, doc=doc,
config=config)
elif what in ('function', 'method'):
return SphinxFunctionDoc(obj, doc=doc, config=config)
else:
if doc is None:
doc = pydoc.getdoc(obj)
return SphinxObjDoc(obj, doc, config=config)
| mit |
joergkappes/opengm | src/interfaces/python/examples/python_visitor_gui.py | 14 | 1377 | """
Usage: python_visitor_gui.py
This script shows how one can implement visitors
in pure python and inject them into OpenGM solver.
( not all OpenGM solvers support this kind of
code injection )
"""
import opengm
import numpy
import matplotlib
from matplotlib import pyplot as plt
shape=[100,100]
numLabels=10
unaries=numpy.random.rand(shape[0], shape[1],numLabels)
potts=opengm.PottsFunction([numLabels,numLabels],0.0,0.4)
gm=opengm.grid2d2Order(unaries=unaries,regularizer=potts)
inf=opengm.inference.BeliefPropagation(gm,parameter=opengm.InfParam(damping=0.5))
class PyCallback(object):
def __init__(self,shape,numLabels):
self.shape=shape
self.numLabels=numLabels
self.cmap = matplotlib.colors.ListedColormap ( numpy.random.rand ( self.numLabels,3))
matplotlib.interactive(True)
def begin(self,inference):
print "begin of inference"
def end(self,inference):
print "end of inference"
def visit(self,inference):
gm=inference.gm()
labelVector=inference.arg()
print "energy ",gm.evaluate(labelVector)
labelVector=labelVector.reshape(self.shape)
plt.imshow(labelVector*255.0, cmap=self.cmap,interpolation="nearest")
plt.draw()
callback=PyCallback(shape,numLabels)
visitor=inf.pythonVisitor(callback,visitNth=1)
inf.infer(visitor)
argmin=inf.arg()
| mit |
UCBerkeleySETI/blimpy | blimpy/plotting/plot_time_series.py | 1 | 1628 | from .config import *
from ..utils import rebin, db
from .plot_utils import calc_extent
def plot_time_series(wf, f_start=None, f_stop=None, if_id=0, logged=True, orientation='h', MJD_time=False, **kwargs):
""" Plot the time series.
Args:
f_start (float): start frequency, in MHz
f_stop (float): stop frequency, in MHz
logged (bool): Plot in linear (False) or dB units (True),
kwargs: keyword args to be passed to matplotlib imshow()
"""
ax = plt.gca()
plot_f, plot_data = wf.grab_data(f_start, f_stop, if_id)
# Since the data has been squeezed, the axis for time goes away if only one bin, causing a bug with axis=1
if len(plot_data.shape) > 1:
plot_data = np.nanmean(plot_data, axis=1)
else:
plot_data = np.nanmean(plot_data)
if logged and wf.header['nbits'] >= 8:
plot_data = db(plot_data)
# Make proper time axis for plotting (but only for plotting!). Note that this makes the values inclusive.
extent = calc_extent(wf, plot_f=plot_f, plot_t=wf.timestamps, MJD_time=MJD_time)
plot_t = np.linspace(extent[2], extent[3], len(wf.timestamps))
if MJD_time:
tlabel = "Time [MJD]"
else:
tlabel = "Time [s]"
if logged:
plabel = "Power [dB]"
else:
plabel = "Power [counts]"
# Reverse oder if vertical orientation.
if 'v' in orientation:
plt.plot(plot_data, plot_t, **kwargs)
plt.xlabel(plabel)
else:
plt.plot(plot_t, plot_data, **kwargs)
plt.xlabel(tlabel)
plt.ylabel(plabel)
ax.autoscale(axis='both', tight=True)
| bsd-3-clause |
pyIMS/pyimzML | pyimzml/ImzMLParser.py | 2 | 24463 | # -*- coding: utf-8 -*-
# Copyright 2015 Dominik Fay
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from bisect import bisect_left, bisect_right
import sys
import re
from pathlib import Path
from warnings import warn
import numpy as np
from pyimzml.metadata import Metadata, SpectrumData
from pyimzml.ontology.ontology import convert_cv_param
PRECISION_DICT = {"32-bit float": 'f', "64-bit float": 'd', "32-bit integer": 'i', "64-bit integer": 'l'}
SIZE_DICT = {'f': 4, 'd': 8, 'i': 4, 'l': 8}
INFER_IBD_FROM_IMZML = object()
XMLNS_PREFIX = "{http://psi.hupo.org/ms/mzml}"
param_group_elname = "referenceableParamGroup"
data_processing_elname = "dataProcessing"
instrument_confid_elname = "instrumentConfiguration"
def choose_iterparse(parse_lib=None):
if parse_lib == 'ElementTree':
from xml.etree.ElementTree import iterparse
elif parse_lib == 'lxml':
from lxml.etree import iterparse
else:
try:
from lxml.etree import iterparse
except ImportError:
from xml.etree.ElementTree import iterparse
return iterparse
def _get_cv_param(elem, accession, deep=False, convert=False):
base = './/' if deep else ''
node = elem.find('%s%scvParam[@accession="%s"]' % (base, XMLNS_PREFIX, accession))
if node is not None:
if convert:
return convert_cv_param(accession, node.get('value'))
return node.get('value')
class ImzMLParser:
"""
Parser for imzML 1.1.0 files (see specification here:
http://imzml.org/download/imzml/specifications_imzML1.1.0_RC1.pdf).
Iteratively reads the .imzML file into memory while pruning the per-spectrum metadata (everything in
<spectrumList> elements) during initialization. Returns a spectrum upon calling getspectrum(i). The binary file
is read in every call of getspectrum(i). Use enumerate(parser.coordinates) to get all coordinates with their
respective index. Coordinates are always 3-dimensional. If the third spatial dimension is not present in
the data, it will be set to zero.
The global metadata fields in the imzML file are stored in parser.metadata.
Spectrum-specific metadata fields are not stored by default due to avoid memory issues,
use the `include_spectra_metadata` parameter if spectrum-specific metadata is needed.
"""
def __init__(
self,
filename,
parse_lib=None,
ibd_file=INFER_IBD_FROM_IMZML,
include_spectra_metadata=None,
):
"""
Opens the two files corresponding to the file name, reads the entire .imzML
file and extracts required attributes. Does not read any binary data, yet.
:param filename:
name of the XML file. Must end with .imzML. Binary data file must be named equally but ending with .ibd
Alternatively an open file or Buffer Protocol object can be supplied, if ibd_file is also supplied
:param parse_lib:
XML-parsing library to use: 'ElementTree' or 'lxml', the later will be used if argument not provided
:param ibd_file:
File or Buffer Protocol object for the .ibd file. Leave blank to infer it from the imzml filename.
Set to None if no data from the .ibd file is needed (getspectrum calls will not work)
:param include_spectra_metadata:
None, 'full', or a list/set of accession IDs.
If 'full' is given, parser.spectrum_full_metadata will be populated with a list of
complex objects containing the full metadata for each spectrum.
If a list or set is given, parser.spectrum_metadata_fields will be populated with a dict mapping
accession IDs to lists. Each list will contain the values for that accession ID for
each spectrum. Note that for performance reasons, this mode only searches the
spectrum itself for the value. It won't check any referenced referenceable param
groups if the accession ID isn't present in the spectrum metadata.
"""
# ElementTree requires the schema location for finding tags (why?) but
# fails to read it from the root element. As this should be identical
# for all imzML files, it is hard-coded here and prepended before every tag
self.sl = "{http://psi.hupo.org/ms/mzml}"
# maps each imzML number format to its struct equivalent
self.precisionDict = dict(PRECISION_DICT)
# maps each number format character to its amount of bytes used
self.sizeDict = dict(SIZE_DICT)
self.filename = filename
self.mzOffsets = []
self.intensityOffsets = []
self.mzLengths = []
self.intensityLengths = []
# list of all (x,y,z) coordinates as tuples.
self.coordinates = []
self.root = None
self.metadata = None
if include_spectra_metadata == 'full':
self.spectrum_full_metadata = []
elif include_spectra_metadata is not None:
include_spectra_metadata = set(include_spectra_metadata)
self.spectrum_metadata_fields = {
k: [] for k in include_spectra_metadata
}
self.mzGroupId = self.intGroupId = self.mzPrecision = self.intensityPrecision = None
self.iterparse = choose_iterparse(parse_lib)
self.__iter_read_spectrum_meta(include_spectra_metadata)
if ibd_file is INFER_IBD_FROM_IMZML:
# name of the binary file
ibd_filename = self._infer_bin_filename(self.filename)
self.m = open(ibd_filename, "rb")
else:
self.m = ibd_file
# Dict for basic imzML metadata other than those required for reading
# spectra. See method __readimzmlmeta()
self.imzmldict = self.__readimzmlmeta()
self.imzmldict['max count of pixels z'] = np.asarray(self.coordinates)[:,2].max()
@staticmethod
def _infer_bin_filename(imzml_path):
imzml_path = Path(imzml_path)
ibd_path = [f for f in imzml_path.parent.glob('*')
if re.match(r'.+\.ibd', str(f), re.IGNORECASE) and f.stem == imzml_path.stem][0]
return str(ibd_path)
# system method for use of 'with ... as'
def __enter__(self):
return self
# system method for use of 'with ... as'
def __exit__(self, exc_t, exc_v, trace):
if self.m is not None:
self.m.close()
def __iter_read_spectrum_meta(self, include_spectra_metadata):
"""
This method should only be called by __init__. Reads the data formats, coordinates and offsets from
the .imzML file and initializes the respective attributes. While traversing the XML tree, the per-spectrum
metadata is pruned, i.e. the <spectrumList> element(s) are left behind empty.
Supported accession values for the number formats: "MS:1000521", "MS:1000523", "IMS:1000141" or
"IMS:1000142". The string values are "32-bit float", "64-bit float", "32-bit integer", "64-bit integer".
"""
mz_group = int_group = None
slist = None
elem_iterator = self.iterparse(self.filename, events=("start", "end"))
if sys.version_info > (3,):
_, self.root = next(elem_iterator)
else:
_, self.root = elem_iterator.next()
for event, elem in elem_iterator:
if elem.tag == self.sl + "spectrumList" and event == "start":
self.__process_metadata()
slist = elem
elif elem.tag == self.sl + "spectrum" and event == "end":
self.__process_spectrum(elem, include_spectra_metadata)
slist.remove(elem)
self.__fix_offsets()
def __fix_offsets(self):
# clean up the mess after morons who use signed 32-bit where unsigned 64-bit is appropriate
def fix(array):
fixed = []
delta = 0
prev_value = float('nan')
for value in array:
if value < 0 and prev_value >= 0:
delta += 2**32
fixed.append(value + delta)
prev_value = value
return fixed
self.mzOffsets = fix(self.mzOffsets)
self.intensityOffsets = fix(self.intensityOffsets)
def __process_metadata(self):
if self.metadata is None:
self.metadata = Metadata(self.root)
for param_id, param_group in self.metadata.referenceable_param_groups.items():
if 'm/z array' in param_group.param_by_name:
self.mzGroupId = param_id
for name, dtype in self.precisionDict.items():
if name in param_group.param_by_name:
self.mzPrecision = dtype
if 'intensity array' in param_group.param_by_name:
self.intGroupId = param_id
for name, dtype in self.precisionDict.items():
if name in param_group.param_by_name:
self.intensityPrecision = dtype
if not hasattr(self, 'mzPrecision'):
raise RuntimeError("Could not determine m/z precision")
if not hasattr(self, 'intensityPrecision'):
raise RuntimeError("Could not determine intensity precision")
def __process_spectrum(self, elem, include_spectra_metadata):
arrlistelem = elem.find('%sbinaryDataArrayList' % self.sl)
mz_group = None
int_group = None
for e in arrlistelem:
ref = e.find('%sreferenceableParamGroupRef' % self.sl).attrib["ref"]
if ref == self.mzGroupId:
mz_group = e
elif ref == self.intGroupId:
int_group = e
self.mzOffsets.append(int(_get_cv_param(mz_group, 'IMS:1000102')))
self.mzLengths.append(int(_get_cv_param(mz_group, 'IMS:1000103')))
self.intensityOffsets.append(int(_get_cv_param(int_group, 'IMS:1000102')))
self.intensityLengths.append(int(_get_cv_param(int_group, 'IMS:1000103')))
scan_elem = elem.find('%sscanList/%sscan' % (self.sl, self.sl))
x = _get_cv_param(scan_elem, 'IMS:1000050')
y = _get_cv_param(scan_elem, 'IMS:1000051')
z = _get_cv_param(scan_elem, 'IMS:1000052')
if z is not None:
self.coordinates.append((int(x), int(y), int(z)))
else:
self.coordinates.append((int(x), int(y), 1))
if include_spectra_metadata == 'full':
self.spectrum_full_metadata.append(
SpectrumData(elem, self.metadata.referenceable_param_groups)
)
elif include_spectra_metadata:
for param in include_spectra_metadata:
value = _get_cv_param(elem, param, deep=True, convert=True)
self.spectrum_metadata_fields[param].append(value)
def __readimzmlmeta(self):
"""
DEPRECATED - use self.metadata instead, as it has much greater detail and allows for
multiple scan settings / instruments.
This method should only be called by __init__. Initializes the imzmldict with frequently used metadata from
the .imzML file.
:return d:
dict containing above mentioned meta data
:rtype:
dict
:raises Warning:
if an xml attribute has a number format different from the imzML specification
"""
d = {}
scan_settings_list_elem = self.root.find('%sscanSettingsList' % self.sl)
instrument_config_list_elem = self.root.find('%sinstrumentConfigurationList' % self.sl)
scan_settings_params = [
("max count of pixels x", "IMS:1000042"),
("max count of pixels y", "IMS:1000043"),
("max dimension x", "IMS:1000044"),
("max dimension y", "IMS:1000045"),
("pixel size x", "IMS:1000046"),
("pixel size y", "IMS:1000047"),
("matrix solution concentration", "MS:1000835"),
]
instrument_config_params = [
("wavelength", "MS:1000843"),
("focus diameter x", "MS:1000844"),
("focus diameter y", "MS:1000845"),
("pulse energy", "MS:1000846"),
("pulse duration", "MS:1000847"),
("attenuation", "MS:1000848"),
]
for name, accession in scan_settings_params:
try:
val = _get_cv_param(scan_settings_list_elem, accession, deep=True, convert=True)
if val is not None:
d[name] = val
except ValueError:
warn(Warning('Wrong data type in XML file. Skipped attribute "%s"' % name))
for name, accession in instrument_config_params:
try:
val = _get_cv_param(instrument_config_list_elem, accession, deep=True, convert=True)
if val is not None:
d[name] = val
except ValueError:
warn(Warning('Wrong data type in XML file. Skipped attribute "%s"' % name))
return d
def get_physical_coordinates(self, i):
"""
For a pixel index i, return the real-world coordinates in nanometers.
This is equivalent to multiplying the image coordinates of the given pixel with the pixel size.
:param i: the pixel index
:return: a tuple of x and y coordinates.
:rtype: Tuple[float]
:raises KeyError: if the .imzML file does not specify the attributes "pixel size x" and "pixel size y"
"""
try:
pixel_size_x = self.imzmldict["pixel size x"]
pixel_size_y = self.imzmldict["pixel size y"]
except KeyError:
raise KeyError("Could not find all pixel size attributes in imzML file")
image_x, image_y = self.coordinates[i][:2]
return image_x * pixel_size_x, image_y * pixel_size_y
def getspectrum(self, index):
"""
Reads the spectrum at specified index from the .ibd file.
:param index:
Index of the desired spectrum in the .imzML file
Output:
mz_array: numpy.ndarray
Sequence of m/z values representing the horizontal axis of the desired mass
spectrum
intensity_array: numpy.ndarray
Sequence of intensity values corresponding to mz_array
"""
mz_bytes, intensity_bytes = self.get_spectrum_as_string(index)
mz_array = np.frombuffer(mz_bytes, dtype=self.mzPrecision)
intensity_array = np.frombuffer(intensity_bytes, dtype=self.intensityPrecision)
return mz_array, intensity_array
def get_spectrum_as_string(self, index):
"""
Reads m/z array and intensity array of the spectrum at specified location
from the binary file as a byte string. The string can be unpacked by the struct
module. To get the arrays as numbers, use getspectrum
:param index:
Index of the desired spectrum in the .imzML file
:rtype: Tuple[str, str]
Output:
mz_string:
string where each character represents a byte of the mz array of the
spectrum
intensity_string:
string where each character represents a byte of the intensity array of
the spectrum
"""
offsets = [self.mzOffsets[index], self.intensityOffsets[index]]
lengths = [self.mzLengths[index], self.intensityLengths[index]]
lengths[0] *= self.sizeDict[self.mzPrecision]
lengths[1] *= self.sizeDict[self.intensityPrecision]
self.m.seek(offsets[0])
mz_string = self.m.read(lengths[0])
self.m.seek(offsets[1])
intensity_string = self.m.read(lengths[1])
return mz_string, intensity_string
def portable_spectrum_reader(self):
"""
Builds a PortableSpectrumReader that holds the coordinates list and spectrum offsets in the .ibd file
so that the .ibd file can be read without opening the .imzML file again.
The PortableSpectrumReader can be safely pickled and unpickled, making it useful for reading the spectra
in a distributed environment such as PySpark or PyWren.
"""
return PortableSpectrumReader(self.coordinates,
self.mzPrecision, self.mzOffsets, self.mzLengths,
self.intensityPrecision, self.intensityOffsets, self.intensityLengths)
def getionimage(p, mz_value, tol=0.1, z=1, reduce_func=sum):
"""
Get an image representation of the intensity distribution
of the ion with specified m/z value.
By default, the intensity values within the tolerance region are summed.
:param p:
the ImzMLParser (or anything else with similar attributes) for the desired dataset
:param mz_value:
m/z value for which the ion image shall be returned
:param tol:
Absolute tolerance for the m/z value, such that all ions with values
mz_value-|tol| <= x <= mz_value+|tol| are included. Defaults to 0.1
:param z:
z Value if spectrogram is 3-dimensional.
:param reduce_func:
the bahaviour for reducing the intensities between mz_value-|tol| and mz_value+|tol| to a single value. Must
be a function that takes a sequence as input and outputs a number. By default, the values are summed.
:return:
numpy matrix with each element representing the ion intensity in this
pixel. Can be easily plotted with matplotlib
"""
tol = abs(tol)
im = np.zeros((p.imzmldict["max count of pixels y"], p.imzmldict["max count of pixels x"]))
for i, (x, y, z_) in enumerate(p.coordinates):
if z_ == 0:
UserWarning("z coordinate = 0 present, if you're getting blank images set getionimage(.., .., z=0)")
if z_ == z:
mzs, ints = map(lambda x: np.asarray(x), p.getspectrum(i))
min_i, max_i = _bisect_spectrum(mzs, mz_value, tol)
im[y - 1, x - 1] = reduce_func(ints[min_i:max_i+1])
return im
def browse(p):
"""
Create a per-spectrum metadata browser for the parser.
Usage::
# get a list of the instrument configurations used in the first pixel
instrument_configurations = browse(p).for_spectrum(0).get_ids("instrumentConfiguration")
Currently, ``instrumentConfiguration``, ``dataProcessing`` and ``referenceableParamGroup`` are supported.
For browsing all spectra iteratively, you should by all means use **ascending** indices. Doing otherwise can result
in quadratic runtime. The following example shows how to retrieve all unique instrumentConfigurations used::
browser = browse(p)
all_config_ids = set()
for i, _ in enumerate(p.coordinates):
all_config_ids.update(browser.for_spectrum(i).get_ids("instrumentConfiguration"))
This is a list of ids with which you can find the corresponding ``<instrumentConfiguration>`` tag in the xml tree.
:param p: the parser
:return: the browser
"""
return _ImzMLMetaDataBrowser(p.root, p.filename, p.sl)
def _bisect_spectrum(mzs, mz_value, tol):
ix_l, ix_u = bisect_left(mzs, mz_value - tol), bisect_right(mzs, mz_value + tol) - 1
if ix_l == len(mzs):
return len(mzs), len(mzs)
if ix_u < 1:
return 0, 0
if ix_u == len(mzs):
ix_u -= 1
if mzs[ix_l] < (mz_value - tol):
ix_l += 1
if mzs[ix_u] > (mz_value + tol):
ix_u -= 1
return ix_l, ix_u
class _ImzMLMetaDataBrowser(object):
def __init__(self, root, fn, sl):
self._root = root
self._sl = sl
self._fn = fn
self._iter, self._previous, self._list_elem = None, None, None
self.iterparse = choose_iterparse()
def for_spectrum(self, i):
if self._previous is None or i <= self._previous:
self._iter = self.iterparse(self._fn, events=("start", "end"))
for event, s in self._iter:
if s.tag == self._sl + "spectrumList" and event == "start":
self._list_elem = s
elif s.tag == self._sl + "spectrum" and event == "end":
self._list_elem.remove(s)
if s.attrib["index"] == str(i):
self._previous = i
return _SpectrumMetaDataBrowser(self._root, self._sl, s)
class _SpectrumMetaDataBrowser(object):
def __init__(self, root, sl, spectrum):
self._root = root
self._sl = sl
self._spectrum = spectrum
def get_ids(self, element):
param_methods = {
param_group_elname: self._find_referenceable_param_groups,
data_processing_elname: self._find_data_processing,
instrument_confid_elname: self._find_instrument_configurations,
}
try:
return param_methods[element]()
except KeyError as e:
raise ValueError("Unsupported element: " + str(element))
def _find_referenceable_param_groups(self):
param_group_refs = self._spectrum.findall("%sreferenceableParamGroupRef" % self._sl)
ids = map(lambda g: g.attrib["ref"], param_group_refs)
return ids
def _find_instrument_configurations(self):
ids = None
scan_list = self._spectrum.find("%sscanList" % self._sl)
if scan_list:
scans = scan_list.findall("%sscan[@instrumentConfigurationRef]" % self._sl)
ids = map(lambda s: s.attrib["instrumentConfigurationRef"], scans)
if not ids:
run = self._root.find("%srun")
try:
return [run.attrib["defaultInstrumentConfigurationRef"]]
except KeyError as _:
return list()
else:
return ids
def _find_data_processing(self):
try:
return self._spectrum.attrib["dataProcessingRef"]
except KeyError as _:
spectrum_list = self._root.find("%srun/%sspectrumList" % tuple(2 * [self._sl]))
try:
return [spectrum_list.attrib["defaultDataProcessingRef"]]
except KeyError as _:
return []
class PortableSpectrumReader(object):
"""
A pickle-able class for holding the minimal set of data required for reading,
without holding any references to open files that wouldn't survive pickling.
"""
def __init__(self, coordinates, mzPrecision, mzOffsets, mzLengths,
intensityPrecision, intensityOffsets, intensityLengths):
self.coordinates = coordinates
self.mzPrecision = mzPrecision
self.mzOffsets = mzOffsets
self.mzLengths = mzLengths
self.intensityPrecision = intensityPrecision
self.intensityOffsets = intensityOffsets
self.intensityLengths = intensityLengths
def read_spectrum_from_file(self, file, index):
"""
Reads the spectrum at specified index from the .ibd file.
:param file:
File or file-like object for the .ibd file
:param index:
Index of the desired spectrum in the .imzML file
Output:
mz_array: numpy.ndarray
Sequence of m/z values representing the horizontal axis of the desired mass
spectrum
intensity_array: numpy.ndarray
Sequence of intensity values corresponding to mz_array
"""
file.seek(self.mzOffsets[index])
mz_bytes = file.read(self.mzLengths[index] * SIZE_DICT[self.mzPrecision])
file.seek(self.intensityOffsets[index])
intensity_bytes = file.read(self.intensityLengths[index] * SIZE_DICT[self.intensityPrecision])
mz_array = np.frombuffer(mz_bytes, dtype=self.mzPrecision)
intensity_array = np.frombuffer(intensity_bytes, dtype=self.intensityPrecision)
return mz_array, intensity_array
| apache-2.0 |
InnovArul/codesmart | Assignments/Jul-Nov-2017/reinforcement_learning_udemy/rl/monte_carlo_soft_epsilon.py | 1 | 3861 | from __future__ import print_function
import numpy as np
from grid import standard_grid, negative_grid
from iterative_policy_evaluation import print_values, print_policy
import matplotlib.pyplot as plt
from monte_carlo_exploring_starts import max_dict
EPS = 1e-4
GAMMA = 0.9
ALL_POSSIBLE_ACTIONS = {'U', 'D', 'L', 'R'}
def random_action(a, eps=0.1):
p = np.random.random()
if(p < 1 - eps):
return a
else:
return np.random.choice(list(ALL_POSSIBLE_ACTIONS))
# monte carlo sampling - finding out optimal policy (policy iteration)
def play_game(grid, policy):
all_states = list(grid.actions.keys())
state = (2, 0)
# instead of taking random action at first step, consider the action which is probabilistic with the policy
a = random_action(policy[state])
grid.set_state(state)
states_actions_rewards = [(state, a, 0)] # action is corresponding to the one which is going to be taken
while True:
r = grid.move(a)
state = grid.current_state()
#print(prev_state)
# if game over, break the loop
if grid.game_over():
states_actions_rewards.append((state, None, r)) # agent has hit the wall and we should not allow it to happen
break
else:
# collect the next action that we are gonna take and insert into the trace
a = random_action(policy[state])
states_actions_rewards.append((state, a, r))
# calculate the returns by working backwards from terminal state
G = 0
states_actions_returns = []
for i, state_action_reward in enumerate(reversed(states_actions_rewards)):
state, action, reward = state_action_reward
if i != 0:
states_actions_returns.append((state, action, G))
G = reward + GAMMA * G
states_actions_returns.reverse()
return states_actions_returns
def max_dict(hash):
max_key = None
max_val = float('-inf')
for k in hash:
if(hash[k] > max_val):
max_key, max_val = k, hash[k]
return max_key, max_val
if __name__ == '__main__':
#grid = standard_grid()
grid = negative_grid(-0.1)
print('grid')
print_values(grid.rewards, grid)
# init random policy
policy = {}
for s in grid.actions:
policy[s] = np.random.choice(list(ALL_POSSIBLE_ACTIONS))
print('policy')
print_policy(policy, grid)
# initialioze Q(s, a)
Q = {}
returns = {} # buffer to hold all the returns for a state during monte-carlo game plays
for s in grid.actions: # if state is non terminal
Q[s] = {}
for a in ALL_POSSIBLE_ACTIONS:
# for all the possible actions, initialize Q(s,a)
Q[s][a] = 0
returns[(s, a)] = []
# deltas
deltas = []
for sample in range(5000):
if sample % 500 == 0:
print(sample)
biggest_change = 0
# generate an episode and adapt Q(s, a)
states_actions_returns = play_game(grid, policy)
seen_states_actions = set()
for s, a, G in states_actions_returns:
key = (s, a)
if s not in seen_states_actions:
old_q = Q[s][a]
returns[key].append(G)
Q[s][a] = np.mean(returns[key])
seen_states_actions.add(key)
biggest_change = max(biggest_change, abs(G - old_q))
deltas.append(biggest_change)
# policy improvement
for s in Q:
policy[s] = max_dict(Q[s])[0]
plt.plot(deltas)
plt.show()
V = {}
# policy improvement
for s in Q:
V[s] = max_dict(Q[s])[1]
print('grid')
print_values(V, grid)
print('policy')
print_policy(policy, grid)
| gpl-2.0 |
khrapovs/datastorage | datastorage/compustat.py | 1 | 2589 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Short interest dynamics
"""
from __future__ import print_function, division
import os
import zipfile
import datetime as dt
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
path = os.getenv("HOME") + '/Dropbox/Research/data/Compustat/data/'
# __location__ = os.path.realpath(os.path.join(os.getcwd(),
# os.path.dirname(__file__)))
# path = os.path.join(__location__, path + 'Compustat/data/')
def date_convert(string):
return dt.datetime.strptime(string, '%d-%m-%Y')
def import_data():
"""Import data and save it to the disk.
"""
zf = zipfile.ZipFile(path + 'short_int.zip', 'r')
name = zf.namelist()[0]
short_int = pd.read_csv(zf.open(name),
converters={'datadate': date_convert})
columns = {'datadate': 'date',
'SHORTINTADJ': 'short_int',
'GVKEY': 'gvkey'}
short_int.rename(columns=columns, inplace=True)
short_int.set_index(['gvkey', 'date'], inplace=True)
short_int.sort_index(inplace=True)
short_int.to_hdf(path + 'short_int.h5', key='short_int')
print(short_int.head())
print(short_int.dtypes)
print('Number of unique companies: ',
short_int.index.get_level_values('gvkey').nunique())
print('Number of unique dates: ',
short_int.index.get_level_values('date').nunique())
print('Min and Max date: ',
short_int.index.get_level_values('date').min().date(), ',',
short_int.index.get_level_values('date').max().date())
def load_data():
"""Load data from disk and check for sanity.
"""
return pd.read_hdf(path + 'short_int.h5', 'short_int')
def count_companies(short_int):
"""Plot number of companies over time.
"""
df = short_int.reset_index().groupby('date')['gvkey'].nunique()
sns.set_context('paper')
df.plot(figsize=(10, 3))
plt.show()
data = df.ix[dt.date(2006, 1, 1):dt.date(2007, 6, 30)]
data.plot(figsize=(10, 3))
plt.show()
def mean_short_int(short_int):
"""Mean short interest on each date.
"""
df = short_int.groupby(level='date')['short_int'].mean()
sns.set_context('paper')
df.plot(figsize=(10, 3))
plt.show()
df.ix[:dt.date(2004, 12, 31)].plot(figsize=(10, 3))
plt.show()
df.ix[dt.date(2006, 1, 1):dt.date(2007, 6, 30)].plot(figsize=(10, 3))
plt.show()
if __name__ == '__main__':
import_data()
short_int = load_data()
count_companies(short_int)
mean_short_int(short_int)
| mit |
SanPen/GridCal | src/GridCal/Engine/Simulations/LinearFactors/linear_analysis_ts_driver.py | 1 | 10126 | # This file is part of GridCal.
#
# GridCal is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# GridCal is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with GridCal. If not, see <http://www.gnu.org/licenses/>.
import json
import pandas as pd
import numpy as np
import scipy.sparse as sp
from scipy.sparse.linalg import spsolve, factorized
import time
from GridCal.Engine.Simulations.result_types import ResultTypes
from GridCal.Engine.Core.multi_circuit import MultiCircuit
from GridCal.Engine.Simulations.PowerFlow.power_flow_options import PowerFlowOptions
from GridCal.Engine.Simulations.LinearFactors.linear_analysis import LinearAnalysis
from GridCal.Engine.Simulations.LinearFactors.linear_analysis_driver import LinearAnalysisOptions
from GridCal.Engine.Simulations.results_model import ResultsModel
from GridCal.Engine.Core.time_series_pf_data import compile_time_circuit
from GridCal.Engine.Simulations.driver_types import SimulationTypes
from GridCal.Engine.Simulations.results_template import ResultsTemplate
from GridCal.Engine.Simulations.driver_template import TSDriverTemplate
class LinearAnalysisTimeSeriesResults(ResultsTemplate):
def __init__(self, n, m, time_array, bus_names, bus_types, branch_names):
"""
TimeSeriesResults constructor
@param n: number of buses
@param m: number of branches
@param nt: number of time steps
"""
ResultsTemplate.__init__(self,
name='Linear Analysis time series',
available_results=[ResultTypes.BusActivePower,
ResultTypes.BranchActivePowerFrom,
ResultTypes.BranchLoading
],
data_variables=['bus_names',
'bus_types',
'time',
'branch_names',
'voltage',
'S',
'Sf',
'loading',
'losses'])
self.nt = len(time_array)
self.m = m
self.n = n
self.time = time_array
self.bus_names = bus_names
self.bus_types = bus_types
self.branch_names = branch_names
self.voltage = np.ones((self.nt, n), dtype=float)
self.S = np.zeros((self.nt, n), dtype=float)
self.Sf = np.zeros((self.nt, m), dtype=float)
self.loading = np.zeros((self.nt, m), dtype=float)
self.losses = np.zeros((self.nt, m), dtype=float)
def apply_new_time_series_rates(self, nc: "TimeCircuit"):
rates = nc.Rates.T
self.loading = self.Sf / (rates + 1e-9)
def get_results_dict(self):
"""
Returns a dictionary with the results sorted in a dictionary
:return: dictionary of 2D numpy arrays (probably of complex numbers)
"""
data = {'V': self.voltage.tolist(),
'P': self.S.real.tolist(),
'Q': self.S.imag.tolist(),
'Sbr_real': self.Sf.real.tolist(),
'Sbr_imag': self.Sf.imag.tolist(),
'loading': np.abs(self.loading).tolist()}
return data
def mdl(self, result_type: ResultTypes) -> "ResultsModel":
"""
Get ResultsModel instance
:param result_type:
:return: ResultsModel instance
"""
if result_type == ResultTypes.BusActivePower:
labels = self.bus_names
data = self.S
y_label = '(MW)'
title = 'Bus active power '
elif result_type == ResultTypes.BranchActivePowerFrom:
labels = self.branch_names
data = self.Sf.real
y_label = '(MW)'
title = 'Branch power '
elif result_type == ResultTypes.BranchLoading:
labels = self.branch_names
data = self.loading * 100
y_label = '(%)'
title = 'Branch loading '
elif result_type == ResultTypes.BranchLosses:
labels = self.branch_names
data = self.losses
y_label = '(MVA)'
title = 'Branch losses'
elif result_type == ResultTypes.BusVoltageModule:
labels = self.bus_names
data = self.voltage
y_label = '(p.u.)'
title = 'Bus voltage'
else:
raise Exception('Result type not understood:' + str(result_type))
if self.time is not None:
index = self.time
else:
index = list(range(data.shape[0]))
# assemble model
return ResultsModel(data=data, index=index, columns=labels, title=title, ylabel=y_label, units=y_label)
class LinearAnalysisTimeSeries(TSDriverTemplate):
name = 'Linear analysis time series'
tpe = SimulationTypes.LinearAnalysis_TS_run
def __init__(self, grid: MultiCircuit, options: LinearAnalysisOptions, start_=0, end_=None):
"""
TimeSeries constructor
@param grid: MultiCircuit instance
@param options: LinearAnalysisOptions instance
"""
TSDriverTemplate.__init__(self, grid=grid, start_=start_, end_=end_)
self.options = options
self.results = LinearAnalysisTimeSeriesResults(n=0,
m=0,
time_array=[],
bus_names=[],
bus_types=[],
branch_names=[])
self.ptdf_driver = LinearAnalysis(grid=self.grid, distributed_slack=self.options.distribute_slack)
def get_steps(self):
"""
Get time steps list of strings
"""
return [l.strftime('%d-%m-%Y %H:%M') for l in self.indices]
def run(self):
"""
Run the time series simulation
@return:
"""
self.__cancel__ = False
a = time.time()
if self.end_ is None:
self.end_ = len(self.grid.time_profile)
time_indices = np.arange(self.start_, self.end_ + 1)
ts_numeric_circuit = compile_time_circuit(self.grid)
self.results = LinearAnalysisTimeSeriesResults(n=ts_numeric_circuit.nbus,
m=ts_numeric_circuit.nbr,
time_array=ts_numeric_circuit.time_array[time_indices],
bus_names=ts_numeric_circuit.bus_names,
bus_types=ts_numeric_circuit.bus_types,
branch_names=ts_numeric_circuit.branch_names)
self.indices = pd.to_datetime(ts_numeric_circuit.time_array[time_indices])
self.progress_text.emit('Computing PTDF...')
linear_analysis = LinearAnalysis(grid=self.grid,
distributed_slack=self.options.distribute_slack,
correct_values=self.options.correct_values
)
linear_analysis.run()
self.progress_text.emit('Computing branch flows...')
Pbus_0 = ts_numeric_circuit.Sbus.real[:, time_indices]
self.results.Sf = linear_analysis.get_flows_time_series(Pbus_0)
# compute post process
self.results.loading = self.results.Sf / (ts_numeric_circuit.Rates[:, time_indices].T + 1e-9)
self.results.S = Pbus_0.T
self.elapsed = time.time() - a
# send the finnish signal
self.progress_signal.emit(0.0)
self.progress_text.emit('Done!')
self.done_signal.emit()
if __name__ == '__main__':
from matplotlib import pyplot as plt
from GridCal.Engine import *
fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/IEEE39_1W.gridcal'
# fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/grid_2_islands.xlsx'
# fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/1354 Pegase.xlsx'
main_circuit = FileOpen(fname).open()
options_ = LinearAnalysisOptions()
ptdf_driver = LinearAnalysisTimeSeries(grid=main_circuit, options=options_)
ptdf_driver.run()
pf_options_ = PowerFlowOptions(solver_type=SolverType.NR)
ts_driver = TimeSeries(grid=main_circuit, options=pf_options_)
ts_driver.run()
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax1.set_title('Newton-Raphson based flow')
ax1.plot(ts_driver.results.Sf.real)
ax2 = fig.add_subplot(222)
ax2.set_title('PTDF based flow')
ax2.plot(ptdf_driver.results.Sf.real)
ax3 = fig.add_subplot(223)
ax3.set_title('Difference')
diff = ts_driver.results.Sf.real - ptdf_driver.results.Sf.real
ax3.plot(diff)
fig2 = plt.figure()
ax1 = fig2.add_subplot(221)
ax1.set_title('Newton-Raphson based voltage')
ax1.plot(np.abs(ts_driver.results.voltage))
ax2 = fig2.add_subplot(222)
ax2.set_title('PTDF based voltage')
ax2.plot(ptdf_driver.results.voltage)
ax3 = fig2.add_subplot(223)
ax3.set_title('Difference')
diff = np.abs(ts_driver.results.voltage) - ptdf_driver.results.voltage
ax3.plot(diff)
plt.show()
| gpl-3.0 |
AllenDowney/HeriReligion | archive/thinkplot.py | 3 | 22756 | """This file contains code for use with "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2014 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function
import math
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import warnings
# customize some matplotlib attributes
#matplotlib.rc('figure', figsize=(4, 3))
#matplotlib.rc('font', size=14.0)
#matplotlib.rc('axes', labelsize=22.0, titlesize=22.0)
#matplotlib.rc('legend', fontsize=20.0)
#matplotlib.rc('xtick.major', size=6.0)
#matplotlib.rc('xtick.minor', size=3.0)
#matplotlib.rc('ytick.major', size=6.0)
#matplotlib.rc('ytick.minor', size=3.0)
class _Brewer(object):
"""Encapsulates a nice sequence of colors.
Shades of blue that look good in color and can be distinguished
in grayscale (up to a point).
Borrowed from http://colorbrewer2.org/
"""
color_iter = None
colors = ['#f7fbff', '#deebf7', '#c6dbef',
'#9ecae1', '#6baed6', '#4292c6',
'#2171b5','#08519c','#08306b'][::-1]
# lists that indicate which colors to use depending on how many are used
which_colors = [[],
[1],
[1, 3],
[0, 2, 4],
[0, 2, 4, 6],
[0, 2, 3, 5, 6],
[0, 2, 3, 4, 5, 6],
[0, 1, 2, 3, 4, 5, 6],
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7, 8],
]
current_figure = None
@classmethod
def Colors(cls):
"""Returns the list of colors.
"""
return cls.colors
@classmethod
def ColorGenerator(cls, num):
"""Returns an iterator of color strings.
n: how many colors will be used
"""
for i in cls.which_colors[num]:
yield cls.colors[i]
raise StopIteration('Ran out of colors in _Brewer.')
@classmethod
def InitIter(cls, num):
"""Initializes the color iterator with the given number of colors."""
cls.color_iter = cls.ColorGenerator(num)
fig = plt.gcf()
cls.current_figure = fig
@classmethod
def ClearIter(cls):
"""Sets the color iterator to None."""
cls.color_iter = None
cls.current_figure = None
@classmethod
def GetIter(cls, num):
"""Gets the color iterator."""
fig = plt.gcf()
if fig != cls.current_figure:
cls.InitIter(num)
cls.current_figure = fig
if cls.color_iter is None:
cls.InitIter(num)
return cls.color_iter
def _UnderrideColor(options):
"""If color is not in the options, chooses a color.
"""
if 'color' in options:
return options
# get the current color iterator; if there is none, init one
color_iter = _Brewer.GetIter(5)
try:
options['color'] = next(color_iter)
except StopIteration:
# if you run out of colors, initialize the color iterator
# and try again
warnings.warn('Ran out of colors. Starting over.')
_Brewer.ClearIter()
_UnderrideColor(options)
return options
def PrePlot(num=None, rows=None, cols=None):
"""Takes hints about what's coming.
num: number of lines that will be plotted
rows: number of rows of subplots
cols: number of columns of subplots
"""
if num:
_Brewer.InitIter(num)
if rows is None and cols is None:
return
if rows is not None and cols is None:
cols = 1
if cols is not None and rows is None:
rows = 1
# resize the image, depending on the number of rows and cols
size_map = {(1, 1): (8, 6),
(1, 2): (12, 6),
(1, 3): (12, 6),
(1, 4): (12, 5),
(1, 5): (12, 4),
(2, 2): (10, 10),
(2, 3): (16, 10),
(3, 1): (8, 10),
(4, 1): (8, 12),
}
if (rows, cols) in size_map:
fig = plt.gcf()
fig.set_size_inches(*size_map[rows, cols])
# create the first subplot
if rows > 1 or cols > 1:
ax = plt.subplot(rows, cols, 1)
global SUBPLOT_ROWS, SUBPLOT_COLS
SUBPLOT_ROWS = rows
SUBPLOT_COLS = cols
else:
ax = plt.gca()
return ax
def SubPlot(plot_number, rows=None, cols=None, **options):
"""Configures the number of subplots and changes the current plot.
rows: int
cols: int
plot_number: int
options: passed to subplot
"""
rows = rows or SUBPLOT_ROWS
cols = cols or SUBPLOT_COLS
return plt.subplot(rows, cols, plot_number, **options)
def _Underride(d, **options):
"""Add key-value pairs to d only if key is not in d.
If d is None, create a new dictionary.
d: dictionary
options: keyword args to add to d
"""
if d is None:
d = {}
for key, val in options.items():
d.setdefault(key, val)
return d
def Clf():
"""Clears the figure and any hints that have been set."""
global LOC
LOC = None
_Brewer.ClearIter()
plt.clf()
fig = plt.gcf()
fig.set_size_inches(8, 6)
def Figure(**options):
"""Sets options for the current figure."""
_Underride(options, figsize=(6, 8))
plt.figure(**options)
def Plot(obj, ys=None, style='', **options):
"""Plots a line.
Args:
obj: sequence of x values, or Series, or anything with Render()
ys: sequence of y values
style: style string passed along to plt.plot
options: keyword args passed to plt.plot
"""
options = _UnderrideColor(options)
label = getattr(obj, 'label', '_nolegend_')
options = _Underride(options, linewidth=3, alpha=0.7, label=label)
xs = obj
if ys is None:
if hasattr(obj, 'Render'):
xs, ys = obj.Render()
if isinstance(obj, pd.Series):
ys = obj.values
xs = obj.index
if ys is None:
plt.plot(xs, style, **options)
else:
plt.plot(xs, ys, style, **options)
def Vlines(xs, y1, y2, **options):
"""Plots a set of vertical lines.
Args:
xs: sequence of x values
y1: sequence of y values
y2: sequence of y values
options: keyword args passed to plt.vlines
"""
options = _UnderrideColor(options)
options = _Underride(options, linewidth=1, alpha=0.5)
plt.vlines(xs, y1, y2, **options)
def Hlines(ys, x1, x2, **options):
"""Plots a set of horizontal lines.
Args:
ys: sequence of y values
x1: sequence of x values
x2: sequence of x values
options: keyword args passed to plt.vlines
"""
options = _UnderrideColor(options)
options = _Underride(options, linewidth=1, alpha=0.5)
plt.hlines(ys, x1, x2, **options)
def axvline(x, **options):
"""Plots a vertical line.
Args:
x: x location
options: keyword args passed to plt.axvline
"""
options = _UnderrideColor(options)
options = _Underride(options, linewidth=1, alpha=0.5)
plt.axvline(x, **options)
def axhline(y, **options):
"""Plots a horizontal line.
Args:
y: y location
options: keyword args passed to plt.axhline
"""
options = _UnderrideColor(options)
options = _Underride(options, linewidth=1, alpha=0.5)
plt.axhline(y, **options)
def tight_layout(**options):
"""Adjust subplots to minimize padding and margins.
"""
options = _Underride(options,
wspace=0.1, hspace=0.1,
left=0, right=1,
bottom=0, top=1)
plt.tight_layout()
plt.subplots_adjust(**options)
def FillBetween(xs, y1, y2=None, where=None, **options):
"""Fills the space between two lines.
Args:
xs: sequence of x values
y1: sequence of y values
y2: sequence of y values
where: sequence of boolean
options: keyword args passed to plt.fill_between
"""
options = _UnderrideColor(options)
options = _Underride(options, linewidth=0, alpha=0.5)
plt.fill_between(xs, y1, y2, where, **options)
def Bar(xs, ys, **options):
"""Plots a line.
Args:
xs: sequence of x values
ys: sequence of y values
options: keyword args passed to plt.bar
"""
options = _UnderrideColor(options)
options = _Underride(options, linewidth=0, alpha=0.6)
plt.bar(xs, ys, **options)
def Scatter(xs, ys=None, **options):
"""Makes a scatter plot.
xs: x values
ys: y values
options: options passed to plt.scatter
"""
options = _Underride(options, color='blue', alpha=0.2,
s=30, edgecolors='none')
if ys is None and isinstance(xs, pd.Series):
ys = xs.values
xs = xs.index
plt.scatter(xs, ys, **options)
def HexBin(xs, ys, **options):
"""Makes a scatter plot.
xs: x values
ys: y values
options: options passed to plt.scatter
"""
options = _Underride(options, cmap=matplotlib.cm.Blues)
plt.hexbin(xs, ys, **options)
def Pdf(pdf, **options):
"""Plots a Pdf, Pmf, or Hist as a line.
Args:
pdf: Pdf, Pmf, or Hist object
options: keyword args passed to plt.plot
"""
low, high = options.pop('low', None), options.pop('high', None)
n = options.pop('n', 101)
xs, ps = pdf.Render(low=low, high=high, n=n)
options = _Underride(options, label=pdf.label)
Plot(xs, ps, **options)
def Pdfs(pdfs, **options):
"""Plots a sequence of PDFs.
Options are passed along for all PDFs. If you want different
options for each pdf, make multiple calls to Pdf.
Args:
pdfs: sequence of PDF objects
options: keyword args passed to plt.plot
"""
for pdf in pdfs:
Pdf(pdf, **options)
def Hist(hist, **options):
"""Plots a Pmf or Hist with a bar plot.
The default width of the bars is based on the minimum difference
between values in the Hist. If that's too small, you can override
it by providing a width keyword argument, in the same units
as the values.
Args:
hist: Hist or Pmf object
options: keyword args passed to plt.bar
"""
# find the minimum distance between adjacent values
xs, ys = hist.Render()
# see if the values support arithmetic
try:
xs[0] - xs[0]
except TypeError:
# if not, replace values with numbers
labels = [str(x) for x in xs]
xs = np.arange(len(xs))
plt.xticks(xs+0.5, labels)
if 'width' not in options:
try:
options['width'] = 0.9 * np.diff(xs).min()
except TypeError:
warnings.warn("Hist: Can't compute bar width automatically."
"Check for non-numeric types in Hist."
"Or try providing width option."
)
options = _Underride(options, label=hist.label)
options = _Underride(options, align='center')
if options['align'] == 'left':
options['align'] = 'edge'
elif options['align'] == 'right':
options['align'] = 'edge'
options['width'] *= -1
Bar(xs, ys, **options)
def Hists(hists, **options):
"""Plots two histograms as interleaved bar plots.
Options are passed along for all PMFs. If you want different
options for each pmf, make multiple calls to Pmf.
Args:
hists: list of two Hist or Pmf objects
options: keyword args passed to plt.plot
"""
for hist in hists:
Hist(hist, **options)
def Pmf(pmf, **options):
"""Plots a Pmf or Hist as a line.
Args:
pmf: Hist or Pmf object
options: keyword args passed to plt.plot
"""
xs, ys = pmf.Render()
low, high = min(xs), max(xs)
width = options.pop('width', None)
if width is None:
try:
width = np.diff(xs).min()
except TypeError:
warnings.warn("Pmf: Can't compute bar width automatically."
"Check for non-numeric types in Pmf."
"Or try providing width option.")
points = []
lastx = np.nan
lasty = 0
for x, y in zip(xs, ys):
if (x - lastx) > 1e-5:
points.append((lastx, 0))
points.append((x, 0))
points.append((x, lasty))
points.append((x, y))
points.append((x+width, y))
lastx = x + width
lasty = y
points.append((lastx, 0))
pxs, pys = zip(*points)
align = options.pop('align', 'center')
if align == 'center':
pxs = np.array(pxs) - width/2.0
if align == 'right':
pxs = np.array(pxs) - width
options = _Underride(options, label=pmf.label)
Plot(pxs, pys, **options)
def Pmfs(pmfs, **options):
"""Plots a sequence of PMFs.
Options are passed along for all PMFs. If you want different
options for each pmf, make multiple calls to Pmf.
Args:
pmfs: sequence of PMF objects
options: keyword args passed to plt.plot
"""
for pmf in pmfs:
Pmf(pmf, **options)
def Diff(t):
"""Compute the differences between adjacent elements in a sequence.
Args:
t: sequence of number
Returns:
sequence of differences (length one less than t)
"""
diffs = [t[i+1] - t[i] for i in range(len(t)-1)]
return diffs
def Cdf(cdf, complement=False, transform=None, **options):
"""Plots a CDF as a line.
Args:
cdf: Cdf object
complement: boolean, whether to plot the complementary CDF
transform: string, one of 'exponential', 'pareto', 'weibull', 'gumbel'
options: keyword args passed to plt.plot
Returns:
dictionary with the scale options that should be passed to
Config, Show or Save.
"""
xs, ps = cdf.Render()
xs = np.asarray(xs)
ps = np.asarray(ps)
scale = dict(xscale='linear', yscale='linear')
for s in ['xscale', 'yscale']:
if s in options:
scale[s] = options.pop(s)
if transform == 'exponential':
complement = True
scale['yscale'] = 'log'
if transform == 'pareto':
complement = True
scale['yscale'] = 'log'
scale['xscale'] = 'log'
if complement:
ps = [1.0-p for p in ps]
if transform == 'weibull':
xs = np.delete(xs, -1)
ps = np.delete(ps, -1)
ps = [-math.log(1.0-p) for p in ps]
scale['xscale'] = 'log'
scale['yscale'] = 'log'
if transform == 'gumbel':
xs = np.delete(xs, 0)
ps = np.delete(ps, 0)
ps = [-math.log(p) for p in ps]
scale['yscale'] = 'log'
options = _Underride(options, label=cdf.label)
Plot(xs, ps, **options)
return scale
def Cdfs(cdfs, complement=False, transform=None, **options):
"""Plots a sequence of CDFs.
cdfs: sequence of CDF objects
complement: boolean, whether to plot the complementary CDF
transform: string, one of 'exponential', 'pareto', 'weibull', 'gumbel'
options: keyword args passed to plt.plot
"""
for cdf in cdfs:
Cdf(cdf, complement, transform, **options)
def Contour(obj, pcolor=False, contour=True, imshow=False, **options):
"""Makes a contour plot.
d: map from (x, y) to z, or object that provides GetDict
pcolor: boolean, whether to make a pseudocolor plot
contour: boolean, whether to make a contour plot
imshow: boolean, whether to use plt.imshow
options: keyword args passed to plt.pcolor and/or plt.contour
"""
try:
d = obj.GetDict()
except AttributeError:
d = obj
_Underride(options, linewidth=3, cmap=matplotlib.cm.Blues)
xs, ys = zip(*d.keys())
xs = sorted(set(xs))
ys = sorted(set(ys))
X, Y = np.meshgrid(xs, ys)
func = lambda x, y: d.get((x, y), 0)
func = np.vectorize(func)
Z = func(X, Y)
x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
axes = plt.gca()
axes.xaxis.set_major_formatter(x_formatter)
if pcolor:
plt.pcolormesh(X, Y, Z, **options)
if contour:
cs = plt.contour(X, Y, Z, **options)
plt.clabel(cs, inline=1, fontsize=10)
if imshow:
extent = xs[0], xs[-1], ys[0], ys[-1]
plt.imshow(Z, extent=extent, **options)
def Pcolor(xs, ys, zs, pcolor=True, contour=False, **options):
"""Makes a pseudocolor plot.
xs:
ys:
zs:
pcolor: boolean, whether to make a pseudocolor plot
contour: boolean, whether to make a contour plot
options: keyword args passed to plt.pcolor and/or plt.contour
"""
_Underride(options, linewidth=3, cmap=matplotlib.cm.Blues)
X, Y = np.meshgrid(xs, ys)
Z = zs
x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
axes = plt.gca()
axes.xaxis.set_major_formatter(x_formatter)
if pcolor:
plt.pcolormesh(X, Y, Z, **options)
if contour:
cs = plt.contour(X, Y, Z, **options)
plt.clabel(cs, inline=1, fontsize=10)
def Text(x, y, s, **options):
"""Puts text in a figure.
x: number
y: number
s: string
options: keyword args passed to plt.text
"""
options = _Underride(options,
fontsize=16,
verticalalignment='top',
horizontalalignment='left')
plt.text(x, y, s, **options)
LEGEND = True
LOC = None
def Config(**options):
"""Configures the plot.
Pulls options out of the option dictionary and passes them to
the corresponding plt functions.
"""
names = ['title', 'xlabel', 'ylabel', 'xscale', 'yscale',
'xticks', 'yticks', 'axis', 'xlim', 'ylim']
for name in names:
if name in options:
getattr(plt, name)(options[name])
global LEGEND
LEGEND = options.get('legend', LEGEND)
# see if there are any elements with labels;
# if not, don't draw a legend
ax = plt.gca()
handles, labels = ax.get_legend_handles_labels()
if LEGEND and len(labels) > 0:
global LOC
LOC = options.get('loc', LOC)
frameon = options.get('frameon', True)
try:
plt.legend(loc=LOC, frameon=frameon)
except UserWarning:
pass
# x and y ticklabels can be made invisible
val = options.get('xticklabels', None)
if val is not None:
if val == 'invisible':
ax = plt.gca()
labels = ax.get_xticklabels()
plt.setp(labels, visible=False)
val = options.get('yticklabels', None)
if val is not None:
if val == 'invisible':
ax = plt.gca()
labels = ax.get_yticklabels()
plt.setp(labels, visible=False)
def set_font_size(title_size=16, label_size=16, ticklabel_size=14, legend_size=14):
"""Set font sizes for the title, labels, ticklabels, and legend.
"""
def set_text_size(texts, size):
for text in texts:
text.set_size(size)
ax = plt.gca()
# TODO: Make this function more robust if any of these elements
# is missing.
# title
ax.title.set_size(title_size)
# x axis
ax.xaxis.label.set_size(label_size)
set_text_size(ax.xaxis.get_ticklabels(), ticklabel_size)
# y axis
ax.yaxis.label.set_size(label_size)
set_text_size(ax.yaxis.get_ticklabels(), ticklabel_size)
# legend
legend = ax.get_legend()
if legend is not None:
set_text_size(legend.texts, legend_size)
def bigger_text():
sizes = dict(title_size=16, label_size=16, ticklabel_size=14, legend_size=14)
set_font_size(**sizes)
def Show(**options):
"""Shows the plot.
For options, see Config.
options: keyword args used to invoke various plt functions
"""
clf = options.pop('clf', True)
Config(**options)
plt.show()
if clf:
Clf()
def Plotly(**options):
"""Shows the plot.
For options, see Config.
options: keyword args used to invoke various plt functions
"""
clf = options.pop('clf', True)
Config(**options)
import plotly.plotly as plotly
url = plotly.plot_mpl(plt.gcf())
if clf:
Clf()
return url
def Save(root=None, formats=None, **options):
"""Saves the plot in the given formats and clears the figure.
For options, see Config.
Note: With a capital S, this is the original save, maintained for
compatibility. New code should use save(), which works better
with my newer code, especially in Jupyter notebooks.
Args:
root: string filename root
formats: list of string formats
options: keyword args used to invoke various plt functions
"""
clf = options.pop('clf', True)
save_options = {}
for option in ['bbox_inches', 'pad_inches']:
if option in options:
save_options[option] = options.pop(option)
# TODO: falling Config inside Save was probably a mistake, but removing
# it will require some work
Config(**options)
if formats is None:
formats = ['pdf', 'png']
try:
formats.remove('plotly')
Plotly(clf=False)
except ValueError:
pass
if root:
for fmt in formats:
SaveFormat(root, fmt, **save_options)
if clf:
Clf()
def save(root, formats=None, **options):
"""Saves the plot in the given formats and clears the figure.
For options, see plt.savefig.
Args:
root: string filename root
formats: list of string formats
options: keyword args passed to plt.savefig
"""
if formats is None:
formats = ['pdf', 'png']
try:
formats.remove('plotly')
Plotly(clf=False)
except ValueError:
pass
for fmt in formats:
SaveFormat(root, fmt, **options)
def SaveFormat(root, fmt='eps', **options):
"""Writes the current figure to a file in the given format.
Args:
root: string filename root
fmt: string format
"""
_Underride(options, dpi=300)
filename = '%s.%s' % (root, fmt)
print('Writing', filename)
plt.savefig(filename, format=fmt, **options)
# provide aliases for calling functions with lower-case names
preplot = PrePlot
subplot = SubPlot
clf = Clf
figure = Figure
plot = Plot
vlines = Vlines
hlines = Hlines
fill_between = FillBetween
text = Text
scatter = Scatter
pmf = Pmf
pmfs = Pmfs
hist = Hist
hists = Hists
diff = Diff
cdf = Cdf
cdfs = Cdfs
contour = Contour
pcolor = Pcolor
config = Config
show = Show
def main():
color_iter = _Brewer.ColorGenerator(7)
for color in color_iter:
print(color)
if __name__ == '__main__':
main()
| mit |
PyQuake/earthquakemodels | code/runExperiments/histogramMagnitude.py | 1 | 1982 | import matplotlib.pyplot as plt
import models.model as model
import earthquake.catalog as catalog
from collections import OrderedDict
def histogramMagnitude(catalog_, region):
"""
Creates the histogram of magnitudes by a given region.
Saves the histogram to the follwing path ./code/Zona2/histograms/'+region+'/Magnitude Histogram of ' + str(year) + " " + region + '.png'
Where region, year are given by the application
From 2000 to 2011
"""
definition = model.loadModelDefinition('../params/' + region + '.txt')
catalogFiltred = catalog.filter(catalog_, definition)
year = 2000
while(year < 2012):
data = dict()
for i in range(len(catalogFiltred)):
if catalogFiltred[i]['year'] == year and catalogFiltred[i]['lat'] > 34.8 and catalogFiltred[i][
'lat'] < 37.05 and catalogFiltred[i]['lon'] > 138.8 and catalogFiltred[i]['lon'] < 141.05:
data[catalogFiltred[i]['mag']] = data.get(catalogFiltred[i]['mag'], 0) + 1
b = OrderedDict(sorted(data.items()))
plt.title('Histogram of ' + str(year) + " " + region)
plt.bar(range(len(data)), b.values(), align='center')
plt.xticks(range(len(data)), b.keys(), rotation=25)
# print(b)
axes = plt.gca()
plt.savefig(
'../Zona2/histograms/'+region+'/Magnitude Histogram of ' +
str(year) +
" " +
region +
'.png')
del data
year += 1
def main():
"""
Calls function to plot a hitogram of magnitudes by region, based on JMA catalog
"""
catalog_ = catalog.readFromFile('../data/jmacat_2000_2013.dat')
region = "Kanto"
histogramMagnitude(catalog_, region)
region = "Kansai"
histogramMagnitude(catalog_, region)
region = "Tohoku"
histogramMagnitude(catalog_, region)
region = "EastJapan"
histogramMagnitude(catalog_, region)
if __name__ == "__main__":
main()
| bsd-3-clause |
nicproulx/mne-python | mne/time_frequency/tests/test_psd.py | 2 | 7360 | import numpy as np
import os.path as op
from numpy.testing import assert_array_almost_equal, assert_raises
from nose.tools import assert_true
from mne import pick_types, Epochs, read_events
from mne.io import RawArray, read_raw_fif
from mne.utils import requires_version, slow_test, run_tests_if_main
from mne.time_frequency import psd_welch, psd_multitaper
base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data')
raw_fname = op.join(base_dir, 'test_raw.fif')
event_fname = op.join(base_dir, 'test-eve.fif')
@requires_version('scipy', '0.12')
def test_psd():
"""Tests the welch and multitaper PSD."""
raw = read_raw_fif(raw_fname)
picks_psd = [0, 1]
# Populate raw with sinusoids
rng = np.random.RandomState(40)
data = 0.1 * rng.randn(len(raw.ch_names), raw.n_times)
freqs_sig = [8., 50.]
for ix, freq in zip(picks_psd, freqs_sig):
data[ix, :] += 2 * np.sin(np.pi * 2. * freq * raw.times)
first_samp = raw._first_samps[0]
raw = RawArray(data, raw.info)
tmin, tmax = 0, 20 # use a few seconds of data
fmin, fmax = 2, 70 # look at frequencies between 2 and 70Hz
n_fft = 128
# -- Raw --
kws_psd = dict(tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax,
picks=picks_psd) # Common to all
kws_welch = dict(n_fft=n_fft)
kws_mt = dict(low_bias=True)
funcs = [(psd_welch, kws_welch),
(psd_multitaper, kws_mt)]
for func, kws in funcs:
kws = kws.copy()
kws.update(kws_psd)
psds, freqs = func(raw, proj=False, **kws)
psds_proj, freqs_proj = func(raw, proj=True, **kws)
assert_true(psds.shape == (len(kws['picks']), len(freqs)))
assert_true(np.sum(freqs < 0) == 0)
assert_true(np.sum(psds < 0) == 0)
# Is power found where it should be
ixs_max = np.argmax(psds, axis=1)
for ixmax, ifreq in zip(ixs_max, freqs_sig):
# Find nearest frequency to the "true" freq
ixtrue = np.argmin(np.abs(ifreq - freqs))
assert_true(np.abs(ixmax - ixtrue) < 2)
# Make sure the projection doesn't change channels it shouldn't
assert_array_almost_equal(psds, psds_proj)
# Array input shouldn't work
assert_raises(ValueError, func, raw[:3, :20][0])
# test n_per_seg in psd_welch (and padding)
psds1, freqs1 = psd_welch(raw, proj=False, n_fft=128, n_per_seg=128,
**kws_psd)
psds2, freqs2 = psd_welch(raw, proj=False, n_fft=256, n_per_seg=128,
**kws_psd)
assert_true(len(freqs1) == np.floor(len(freqs2) / 2.))
assert_true(psds1.shape[-1] == np.floor(psds2.shape[-1] / 2.))
# tests ValueError when n_per_seg=None and n_fft > signal length
kws_psd.update(dict(n_fft=tmax * 1.1 * raw.info['sfreq']))
assert_raises(ValueError, psd_welch, raw, proj=False, n_per_seg=None,
**kws_psd)
# ValueError when n_overlap > n_per_seg
kws_psd.update(dict(n_fft=128, n_per_seg=64, n_overlap=90))
assert_raises(ValueError, psd_welch, raw, proj=False, **kws_psd)
# -- Epochs/Evoked --
events = read_events(event_fname)
events[:, 0] -= first_samp
tmin, tmax, event_id = -0.5, 0.5, 1
epochs = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks_psd,
proj=False, preload=True, baseline=None)
evoked = epochs.average()
tmin_full, tmax_full = -1, 1
epochs_full = Epochs(raw, events[:10], event_id, tmin_full, tmax_full,
picks=picks_psd, proj=False, preload=True,
baseline=None)
kws_psd = dict(tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax,
picks=picks_psd) # Common to all
funcs = [(psd_welch, kws_welch),
(psd_multitaper, kws_mt)]
for func, kws in funcs:
kws = kws.copy()
kws.update(kws_psd)
psds, freqs = func(
epochs[:1], proj=False, **kws)
psds_proj, freqs_proj = func(
epochs[:1], proj=True, **kws)
psds_f, freqs_f = func(
epochs_full[:1], proj=False, **kws)
# this one will fail if you add for example 0.1 to tmin
assert_array_almost_equal(psds, psds_f, 27)
# Make sure the projection doesn't change channels it shouldn't
assert_array_almost_equal(psds, psds_proj, 27)
# Is power found where it should be
ixs_max = np.argmax(psds.mean(0), axis=1)
for ixmax, ifreq in zip(ixs_max, freqs_sig):
# Find nearest frequency to the "true" freq
ixtrue = np.argmin(np.abs(ifreq - freqs))
assert_true(np.abs(ixmax - ixtrue) < 2)
assert_true(psds.shape == (1, len(kws['picks']), len(freqs)))
assert_true(np.sum(freqs < 0) == 0)
assert_true(np.sum(psds < 0) == 0)
# Array input shouldn't work
assert_raises(ValueError, func, epochs.get_data())
# Testing evoked (doesn't work w/ compute_epochs_psd)
psds_ev, freqs_ev = func(
evoked, proj=False, **kws)
psds_ev_proj, freqs_ev_proj = func(
evoked, proj=True, **kws)
# Is power found where it should be
ixs_max = np.argmax(psds_ev, axis=1)
for ixmax, ifreq in zip(ixs_max, freqs_sig):
# Find nearest frequency to the "true" freq
ixtrue = np.argmin(np.abs(ifreq - freqs_ev))
assert_true(np.abs(ixmax - ixtrue) < 2)
# Make sure the projection doesn't change channels it shouldn't
assert_array_almost_equal(psds_ev, psds_ev_proj, 27)
assert_true(psds_ev.shape == (len(kws['picks']), len(freqs)))
@slow_test
@requires_version('scipy', '0.12')
def test_compares_psd():
"""Test PSD estimation on raw for plt.psd and scipy.signal.welch."""
raw = read_raw_fif(raw_fname)
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = pick_types(raw.info, meg='grad', eeg=False, stim=False,
exclude=exclude)[:2]
tmin, tmax = 0, 10 # use the first 60s of data
fmin, fmax = 2, 70 # look at frequencies between 5 and 70Hz
n_fft = 2048
# Compute psds with the new implementation using Welch
psds_welch, freqs_welch = psd_welch(raw, tmin=tmin, tmax=tmax, fmin=fmin,
fmax=fmax, proj=False, picks=picks,
n_fft=n_fft, n_jobs=1)
# Compute psds with plt.psd
start, stop = raw.time_as_index([tmin, tmax])
data, times = raw[picks, start:(stop + 1)]
from matplotlib.pyplot import psd
out = [psd(d, Fs=raw.info['sfreq'], NFFT=n_fft) for d in data]
freqs_mpl = out[0][1]
psds_mpl = np.array([o[0] for o in out])
mask = (freqs_mpl >= fmin) & (freqs_mpl <= fmax)
freqs_mpl = freqs_mpl[mask]
psds_mpl = psds_mpl[:, mask]
assert_array_almost_equal(psds_welch, psds_mpl)
assert_array_almost_equal(freqs_welch, freqs_mpl)
assert_true(psds_welch.shape == (len(picks), len(freqs_welch)))
assert_true(psds_mpl.shape == (len(picks), len(freqs_mpl)))
assert_true(np.sum(freqs_welch < 0) == 0)
assert_true(np.sum(freqs_mpl < 0) == 0)
assert_true(np.sum(psds_welch < 0) == 0)
assert_true(np.sum(psds_mpl < 0) == 0)
run_tests_if_main()
| bsd-3-clause |
jadecastro/LTLMoP | src/lib/handlers/motionControl/RRTController.py | 1 | 37133 | #!/usr/bin/env python
"""
===================================================================
RRTController.py - Rapidly-Exploring Random Trees Motion Controller
===================================================================
Uses Rapidly-exploring Random Tree Algorithm to generate paths given the starting position and the goal point.
"""
from numpy import *
from __is_inside import *
import math
import sys,os
from scipy.linalg import norm
from numpy.matlib import zeros
import __is_inside
import time, sys,os
import scipy as Sci
import scipy.linalg
import Polygon, Polygon.IO
import Polygon.Utils as PolyUtils
import Polygon.Shapes as PolyShapes
from math import sqrt, fabs , pi
import random
import thread
import threading
# importing matplotlib to show the path if possible
try:
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import_matplotlib = True
except:
print "matplotlib is not imported. Plotting is disabled"
import_matplotlib = False
class motionControlHandler:
def __init__(self, proj, shared_data,robot_type,max_angle_goal,max_angle_overlap,plotting):
"""
Rapidly-Exploring Random Trees alogorithm motion planning controller
robot_type (int): Which robot is used for execution. BasicSim is 1, ODE is 2, ROS is 3, Nao is 4, Pioneer is 5(default=1)
max_angle_goal (float): The biggest difference in angle between the new node and the goal point that is acceptable. If it is bigger than the max_angle, the new node will not be connected to the goal point. The value should be within 0 to 6.28 = 2*pi. Default set to 6.28 = 2*pi (default=6.28)
max_angle_overlap (float): difference in angle allowed for two nodes overlapping each other. If you don't want any node overlapping with each other, put in 2*pi = 6.28. Default set to 1.57 = pi/2 (default=1.57)
plotting (bool): Check the box to enable plotting. Uncheck to disable plotting (default=True)
"""
self.system_print = False # for debugging. print on GUI ( a bunch of stuffs)
self.finish_print = False # set to 1 to print the original finished E and V before trimming the tree
self.orientation_print = False # show the orientation information of the robot
# Get references to handlers we'll need to communicate with
self.drive_handler = proj.h_instance['drive']
self.pose_handler = proj.h_instance['pose']
# Get information about regions
self.proj = proj
self.coordmap_map2lab = proj.coordmap_map2lab
self.coordmap_lab2map = proj.coordmap_lab2map
self.last_warning = 0
self.previous_next_reg = None
# Store the Rapidly-Exploring Random Tress Built
self.RRT_V = None # array containing all the points on the RRT Tree
self.RRT_E = None # array specifying the connection of points on the Tree
self.E_current_column = None # the current column on the tree (to find the current heading point)
self.Velocity = None
self.currentRegionPoly = None
self.nextRegionPoly = None
self.map = {}
self.all = Polygon.Polygon()
self.trans_matrix = mat([[0,1],[-1,0]]) # transformation matrix for find the normal to the vector
self.stuck_thres = 20 # threshold for changing the range of sampling omega
# Information about the robot (default set to ODE)
if robot_type not in [1,2,3,4,5]:
robot_type = 1
self.system = robot_type
# Information about maximum turning angle allowed from the latest node to the goal point
if max_angle_goal > 2*pi:
max_angle_goal = 2*pi
if max_angle_goal < 0:
max_angle_goal = 0
self.max_angle_allowed = max_angle_goal
# Information about maximum difference in angle allowed between two overlapping nodes
if max_angle_overlap > 2*pi:
max_angle_overlap = 2*pi
if max_angle_overlap < 0:
max_angle_overlap = 0
self.max_angle_overlap = max_angle_overlap
# Information about whether plotting is enabled.
if plotting is True and import_matplotlib == True:
self.plotting = True
else:
self.plotting = False
# Specify the size of the robot
# 1: basicSim; 2: ODE; 3: ROS 4: Nao; 5: Pioneer
# self.radius: radius of the robot
# self.timestep : number of linear segments to break the curve into for calculation of x, y position
# self.step_size : the length of each step for connection to goal point
# self.velocity : Velocity of the robot in m/s in control space (m/s)
if self.system == 1:
self.radius = 5
self.step_size = 25
self.timeStep = 10
self.velocity = 2 # 1.5
if self.system == 2:
self.radius = 5
self.step_size = 15
self.timeStep = 10
self.velocity = 2 # 1.5
elif self.system == 3:
self.ROSInitHandler = shared_data['ROS_INIT_HANDLER']
self.radius = self.ROSInitHandler.robotPhysicalWidth/2
self.step_size = self.radius*3 #0.2
self.timeStep = 8
self.velocity = self.radius/2 #0.08
elif self.system == 4:
self.radius = 0.15*1.2
self.step_size = 0.2 #set the step_size for points be 1/5 of the norm ORIGINAL = 0.4
self.timeStep = 5
self.velocity = 0.05
elif self.system == 5:
self.radius = 0.15
self.step_size = 0.2 #set the step_size for points be 1/5 of the norm ORIGINAL = 0.4
self.timeStep = 5
self.velocity = 0.05
# Operate_system (int): Which operating system is used for execution.
# Ubuntu and Mac is 1, Windows is 2
if sys.platform in ['win32', 'cygwin']:
self.operate_system = 2
else:
self.operate_system = 1
if self.system_print == True:
print "The operate_system is "+ str(self.operate_system)
# Generate polygon for regions in the map
for region in self.proj.rfi.regions:
self.map[region.name] = self.createRegionPolygon(region)
for n in range(len(region.holeList)): # no of holes
self.map[region.name] -= self.createRegionPolygon(region,n)
# Generate the boundary polygon
for regionName,regionPoly in self.map.iteritems():
self.all += regionPoly
# Start plotting if operating in Windows
if self.operate_system == 2 and self.plotting ==True:
# start using anmination to plot the robot
self.fig = plt.figure()
self.ax = self.fig.add_subplot(111)
self.scope = _Scope(self.ax,self)
thread.start_new_thread(self.jplot,())
def gotoRegion(self, current_reg, next_reg, last=False):
"""
If ``last`` is True, we will move to the center of the destination region.
Returns ``True`` if we've reached the destination region.
"""
if current_reg == next_reg and not last:
# No need to move!
self.drive_handler.setVelocity(0, 0) # So let's stop
return True
# Find our current configuration
pose = self.pose_handler.getPose()
# Check if Vicon has cut out
# TODO: this should probably go in posehandler?
if math.isnan(pose[2]):
print "WARNING: No Vicon data! Pausing."
self.drive_handler.setVelocity(0, 0) # So let's stop
time.sleep(1)
return False
###This part will be run when the robot goes to a new region, otherwise, the original tree will be used.
if not self.previous_next_reg == next_reg:
# Entered a new region. New tree should be formed.
self.nextRegionPoly = self.map[self.proj.rfi.regions[next_reg].name]
self.currentRegionPoly = self.map[self.proj.rfi.regions[current_reg].name]
if self.system_print == True:
print "next Region is " + str(self.proj.rfi.regions[next_reg].name)
print "Current Region is " + str(self.proj.rfi.regions[current_reg].name)
#set to zero velocity before tree is generated
self.drive_handler.setVelocity(0, 0)
if last:
transFace = None
else:
# Determine the mid points on the faces connecting to the next region (one goal point will be picked among all the mid points later in buildTree)
transFace = None
q_gBundle = [[],[]] # list of goal points (midpoints of transition faces)
face_normal = [[],[]] # normal of the trnasition faces
for i in range(len(self.proj.rfi.transitions[current_reg][next_reg])):
pointArray_transface = [x for x in self.proj.rfi.transitions[current_reg][next_reg][i]]
transFace = asarray(map(self.coordmap_map2lab,pointArray_transface))
bundle_x = (transFace[0,0] +transFace[1,0])/2 #mid-point coordinate x
bundle_y = (transFace[0,1] +transFace[1,1])/2 #mid-point coordinate y
q_gBundle = hstack((q_gBundle,vstack((bundle_x,bundle_y))))
#find the normal vector to the face
face = transFace[0,:] - transFace[1,:]
distance_face = norm(face)
normal = face/distance_face * self.trans_matrix
face_normal = hstack((face_normal,vstack((normal[0,0],normal[0,1]))))
if transFace is None:
print "ERROR: Unable to find transition face between regions %s and %s. Please check the decomposition (try viewing projectname_decomposed.regions in RegionEditor or a text editor)." % (self.proj.rfi.regions[current_reg].name, self.proj.rfi.regions[next_reg].name)
# Run algorithm to build the Rapid-Exploring Random Trees
self.RRT_V = None
self.RRT_E = None
# For plotting
if self.operate_system == 2:
if self.plotting == True:
self.ax.cla()
else:
self.ax = None
else:
self.ax = None
if self.operate_system == 1 and self.plotting == True:
plt.cla()
self.plotMap(self.map)
plt.plot(pose[0],pose[1],'ko')
self.RRT_V,self.RRT_E,self.E_current_column = self.buildTree(\
[pose[0], pose[1]],pose[2],self.currentRegionPoly, self.nextRegionPoly,q_gBundle,face_normal)
"""
# map the lab coordinates back to pixels
V_tosend = array(mat(self.RRT_V[1:,:])).T
V_tosend = map(self.coordmap_lab2map, V_tosend)
V_tosend = mat(V_tosend).T
s = 'RRT:E'+"["+str(list(self.RRT_E[0]))+","+str(list(self.RRT_E[1]))+"]"+':V'+"["+str(list(self.RRT_V[0]))+","+str(list(V_tosend[0]))+","+str(list(V_tosend[1]))+"]"+':T'+"["+str(list(q_gBundle[0]))+","+str(list(q_gBundle[1]))+"]"
#print s
"""
# Run algorithm to find a velocity vector (global frame) to take the robot to the next region
self.Velocity = self.getVelocity([pose[0], pose[1]], self.RRT_V,self.RRT_E)
#self.Node = self.getNode([pose[0], pose[1]], self.RRT_V,self.RRT_E)
self.previous_next_reg = next_reg
# Pass this desired velocity on to the drive handler
self.drive_handler.setVelocity(self.Velocity[0,0], self.Velocity[1,0], pose[2])
#self.drive_handler.setVelocity(self.Node[0,0], self.Node[1,0], pose[2])
RobotPoly = Polygon.Shapes.Circle(self.radius,(pose[0],pose[1]))
# check if robot is inside the current region
departed = not self.currentRegionPoly.overlaps(RobotPoly)
arrived = self.nextRegionPoly.covers(RobotPoly)
if departed and (not arrived) and (time.time()-self.last_warning) > 0.5:
# Figure out what region we think we stumbled into
for r in self.proj.rfi.regions:
pointArray = [self.coordmap_map2lab(x) for x in r.getPoints()]
vertices = mat(pointArray).T
if is_inside([pose[0], pose[1]], vertices):
print "I think I'm in " + r.name
print pose
break
self.last_warning = time.time()
#print "arrived:"+str(arrived)
return arrived
def createRegionPolygon(self,region,hole = None):
"""
This function takes in the region points and make it a Polygon.
"""
if hole == None:
pointArray = [x for x in region.getPoints()]
else:
pointArray = [x for x in region.getPoints(hole_id = hole)]
pointArray = map(self.coordmap_map2lab, pointArray)
regionPoints = [(pt[0],pt[1]) for pt in pointArray]
formedPolygon= Polygon.Polygon(regionPoints)
return formedPolygon
def getVelocity(self,p, V, E, last=False):
"""
This function calculates the velocity for the robot with RRT.
The inputs are (given in order):
p = the current x-y position of the robot
E = edges of the tree (2 x No. of nodes on the tree)
V = points of the tree (2 x No. of vertices)
last = True, if the current region is the last region
= False, if the current region is NOT the last region
"""
pose = mat(p).T
#dis_cur = distance between current position and the next point
dis_cur = vstack((V[1,E[1,self.E_current_column]],V[2,E[1,self.E_current_column]]))- pose
heading = E[1,self.E_current_column] # index of the current heading point on the tree
if norm(dis_cur) < 1.5*self.radius: # go to next point
if not heading == shape(V)[1]-1:
self.E_current_column = self.E_current_column + 1
dis_cur = vstack((V[1,E[1,self.E_current_column]],V[2,E[1,self.E_current_column]]))- pose
#else:
# dis_cur = vstack((V[1,E[1,self.E_current_column]],V[2,E[1,self.E_current_column]]))- vstack((V[1,E[0,self.E_current_column]],V[2,E[0,self.E_current_column]]))
Vel = zeros([2,1])
Vel[0:2,0] = dis_cur/norm(dis_cur)*0.5 #TUNE THE SPEED LATER
return Vel
def getNode(self,p, V, E, last=False):
"""
This function calculates the velocity for the robot with RRT.
The inputs are (given in order):
p = the current x-y position of the robot
E = edges of the tree (2 x No. of nodes on the tree)
V = points of the tree (2 x No. of vertices)
last = True, if the current region is the last region
= False, if the current region is NOT the last region
"""
pose = mat(p).T
#dis_cur = distance between current position and the next point
dis_cur = vstack((V[1,E[1,self.E_current_column]],V[2,E[1,self.E_current_column]]))- pose
heading = E[1,self.E_current_column] # index of the current heading point on the tree
if norm(dis_cur) < 1.5*self.radius: # go to next point
if not heading == shape(V)[1]-1:
self.E_current_column = self.E_current_column + 1
dis_cur = vstack((V[1,E[1,self.E_current_column]],V[2,E[1,self.E_current_column]]))- pose
Node = zeros([2,1])
Node[0,0] = V[1,E[1,self.E_current_column]]
Node[1,0] = V[2,E[1,self.E_current_column]]
#Vel[0:2,0] = dis_cur/norm(dis_cur)*0.5 #TUNE THE SPEED LATER
return Node
def buildTree(self,p,theta,regionPoly,nextRegionPoly,q_gBundle,face_normal, last=False):
"""
This function builds the RRT tree.
p : x,y position of the robot
theta : current orientation of the robot
regionPoly : current region polygon
nextRegionPoly : next region polygon
q_gBundle : coordinates of q_goals that the robot can reach
face_normal : the normal vector of each face corresponding to each goal point in q_gBundle
"""
q_init = mat(p).T
V = vstack((0,q_init))
theta = self.orientation_bound(theta)
V_theta = array([theta])
#!!! CONTROL SPACE: generate a list of omega for random sampling
omegaLowerBound = -math.pi/20 # upper bound for the value of omega
omegaUpperBound = math.pi/20 # lower bound for the value of omega
omegaNoOfSteps = 20
self.omega_range = linspace(omegaLowerBound,omegaUpperBound,omegaNoOfSteps)
self.omega_range_escape = linspace(omegaLowerBound*4,omegaUpperBound*4,omegaNoOfSteps*4) # range used when stuck > stuck_thres
regionPolyOld = Polygon.Polygon(regionPoly)
regionPoly += PolyShapes.Circle(self.radius*2.5,(q_init[0,0],q_init[1,0]))
# check faces of the current region for goal points
E = [[],[]] # the tree matrix
Other = [[],[]]
path = False # if path formed then = 1
stuck = 0 # count for changing the range of sampling omega
append_after_latest_node = False # append new nodes to the latest node
if self.system_print == True:
print "plotting in buildTree is " + str(self.plotting)
if self.plotting == True:
if not plt.isinteractive():
plt.ion()
plt.hold(True)
while not path:
#step -1: try connection to q_goal (generate path to goal)
i = 0
if self.system_print == True:
print "Try Connection to the goal points"
# pushing possible q_goals into the current region (ensure path is covered by the current region polygon)
q_pass = [[],[],[]]
q_pass_dist = []
q_gBundle = mat(q_gBundle)
face_normal = mat(face_normal)
while i < q_gBundle.shape[1]:
q_g_original = q_gBundle[:,i]
q_g = q_gBundle[:,i]+face_normal[:,i]*1.5*self.radius ##original 2*self.radius
#q_g = q_gBundle[:,i]+(q_gBundle[:,i]-V[1:,(shape(V)[1]-1)])/norm(q_gBundle[:,i]-V[1:,(shape(V)[1]-1)])*1.5*self.radius ##original 2*self.radius
if not regionPolyOld.isInside(q_g[0],q_g[1]):
#q_g = q_gBundle[:,i]-(q_gBundle[:,i]-V[1:,(shape(V)[1]-1)])/norm(q_gBundle[:,i]-V[1:,(shape(V)[1]-1)])*1.5*self.radius ##original 2*self.radius
q_g = q_gBundle[:,i]-face_normal[:,i]*1.5*self.radius ##original 2*self.radius
#forming polygon for path checking
EdgePolyGoal = PolyShapes.Circle(self.radius,(q_g[0,0],q_g[1,0])) + PolyShapes.Circle(self.radius,(V[1,shape(V)[1]-1],V[2:,shape(V)[1]-1]))
EdgePolyGoal = PolyUtils.convexHull(EdgePolyGoal)
dist = norm(q_g - V[1:,shape(V)[1]-1])
#check connection to goal
connect_goal = regionPoly.covers(EdgePolyGoal) #check coverage of path from new point to goal
# compare orientation difference
thetaPrev = V_theta[shape(V)[1]-1]
theta_orientation = abs(arctan((q_g[1,0]- V[2,shape(V)[1]-1])/(q_g[0,0]- V[1,shape(V)[1]-1])))
if q_g[1,0] > V[2,shape(V)[1]-1]:
if q_g[0,0] < V[1,shape(V)[1]-1]: # second quadrant
theta_orientation = pi - theta_orientation
elif q_g[0,0] > V[1,shape(V)[1]-1]: # first quadrant
theta_orientation = theta_orientation
elif q_g[1,0] < V[2,shape(V)[1]-1]:
if q_g[0,0] < V[1,shape(V)[1]-1]: #third quadrant
theta_orientation = pi + theta_orientation
elif q_g[0,0] > V[1,shape(V)[1]-1]: # foruth quadrant
theta_orientation = 2*pi - theta_orientation
# check the angle between vector(new goal to goal_original ) and vector( latest node to new goal)
Goal_to_GoalOriginal = q_g_original - q_g
LatestNode_to_Goal = q_g - V[1:,shape(V)[1]-1]
Angle_Goal_LatestNode= arccos(vdot(array(Goal_to_GoalOriginal), array(LatestNode_to_Goal))/norm(Goal_to_GoalOriginal)/norm(LatestNode_to_Goal))
# if connection to goal can be established and the max change in orientation of the robot is smaller than max_angle, tree is said to be completed.
if self.orientation_print == True:
print "theta_orientation is " + str(theta_orientation)
print "thetaPrev is " + str(thetaPrev)
print "(theta_orientation - thetaPrev) is " + str(abs(theta_orientation - thetaPrev))
print "self.max_angle_allowed is " + str(self.max_angle_allowed)
print "abs(theta_orientation - thetaPrev) < self.max_angle_allowed" + str(abs(theta_orientation - thetaPrev) < self.max_angle_allowed)
print"Goal_to_GoalOriginal: " + str( array(Goal_to_GoalOriginal)) + "; LatestNode_to_Goal: " + str( array(LatestNode_to_Goal))
print vdot(array(Goal_to_GoalOriginal), array(LatestNode_to_Goal))
print "Angle_Goal_LatestNode" + str(Angle_Goal_LatestNode)
if connect_goal and (abs(theta_orientation - thetaPrev) < self.max_angle_allowed) and (Angle_Goal_LatestNode < self.max_angle_allowed):
path = True
q_pass = hstack((q_pass,vstack((i,q_g))))
q_pass_dist = hstack((q_pass_dist,dist))
i = i + 1
if self.system_print == True:
print "checked goal points"
self.E = E
self.V = V
# connection to goal has established
# Obtain the closest goal point that path can be formed.
if path:
if shape(q_pass_dist)[0] == 1:
cols = 0
else:
(cols,) = nonzero(q_pass_dist == min(q_pass_dist))
cols = asarray(cols)[0]
q_g = q_pass[1:,cols]
"""
q_g = q_g-(q_gBundle[:,q_pass[0,cols]]-V[1:,(shape(V)[1]-1)])/norm(q_gBundle[:,q_pass[0,cols]]-V[1:,(shape(V)[1]-1)])*3*self.radius #org 3
if not nextRegionPoly.isInside(q_g[0],q_g[1]):
q_g = q_g+(q_gBundle[:,q_pass[0,cols]]-V[1:,(shape(V)[1]-1)])/norm(q_gBundle[:,q_pass[0,cols]]-V[1:,(shape(V)[1]-1)])*6*self.radius #org 3
"""
if self.plotting == True :
if self.operate_system == 1:
plt.suptitle('Rapidly-exploring Random Tree', fontsize=12)
plt.xlabel('x')
plt.ylabel('y')
if shape(V)[1] <= 2:
plt.plot(( V[1,shape(V)[1]-1],q_g[0,0]),( V[2,shape(V)[1]-1],q_g[1,0]),'b')
else:
plt.plot(( V[1,E[0,shape(E)[1]-1]], V[1,shape(V)[1]-1],q_g[0,0]),( V[2,E[0,shape(E)[1]-1]], V[2,shape(V)[1]-1],q_g[1,0]),'b')
plt.plot(q_g[0,0],q_g[1,0],'ko')
plt.figure(1).canvas.draw()
else:
BoundPolyPoints = asarray(PolyUtils.pointList(regionPoly))
self.ax.plot(BoundPolyPoints[:,0],BoundPolyPoints[:,1],'k')
if shape(V)[1] <= 2:
self.ax.plot(( V[1,shape(V)[1]-1],q_g[0,0]),( V[2,shape(V)[1]-1],q_g[1,0]),'b')
else:
self.ax.plot(( V[1,E[0,shape(E)[1]-1]], V[1,shape(V)[1]-1],q_g[0,0]),( V[2,E[0,shape(E)[1]-1]], V[2,shape(V)[1]-1],q_g[1,0]),'b')
self.ax.plot(q_g[0,0],q_g[1,0],'ko')
# trim the path connecting current node to goal point into pieces if the path is too long now
numOfPoint = floor(norm(V[1:,shape(V)[1]-1]- q_g)/self.step_size)
if numOfPoint < 3:
numOfPoint = 3
x = linspace( V[1,shape(V)[1]-1], q_g[0,0], numOfPoint )
y = linspace( V[2,shape(V)[1]-1], q_g[1,0], numOfPoint )
for i in range(x.shape[0]):
if i != 0:
V = hstack((V,vstack((shape(V)[1],x[i],y[i]))))
E = hstack((E,vstack((shape(V)[1]-2,shape(V)[1]-1))))
#push the goal point to the next region
q_g = q_g+face_normal[:,q_pass[0,cols]]*3*self.radius ##original 2*self.radius
if not nextRegionPoly.isInside(q_g[0],q_g[1]):
q_g = q_g-face_normal[:,q_pass[0,cols]]*6*self.radius ##original 2*self.radius
V = hstack((V,vstack((shape(V)[1],q_g[0,0],q_g[1,0]))))
E = hstack((E,vstack((shape(V)[1]-2 ,shape(V)[1]-1))))
if self.plotting == True :
if self.operate_system == 1:
plt.plot(q_g[0,0],q_g[1,0],'ko')
plt.plot(( V[1,shape(V)[1]-1],V[1,shape(V)[1]-2]),( V[2,shape(V)[1]-1],V[2,shape(V)[1]-2]),'b')
plt.figure(1).canvas.draw()
else:
self.ax.plot(q_g[0,0],q_g[1,0],'ko')
self.ax.plot(( V[1,shape(V)[1]-1],V[1,shape(V)[1]-2]),( V[2,shape(V)[1]-1],V[2,shape(V)[1]-2]),'b')
# path is not formed, try to append points onto the tree
if not path:
# connection_to_tree : connection to the tree is successful
if append_after_latest_node:
V,V_theta,E,Other,stuck,append_after_latest_node, connection_to_tree = self.generateNewNode(V,V_theta,E,Other,regionPoly,stuck, append_after_latest_node)
else:
connection_to_tree = False
while not connection_to_tree:
V,V_theta,E,Other,stuck,append_after_latest_node, connection_to_tree = self.generateNewNode (V,V_theta,E,Other,regionPoly,stuck)
if self.finish_print:
print 'Here is the V matrix:', V, 'Here is the E matrix:',E
print >>sys.__stdout__, 'Here is the V matrix:\n', V, '\nHere is the E matrix:\n',E
#B: trim to a single path
single = 0
while single == 0:
trim = 0
for j in range(shape(V)[1]-3):
(row,col) = nonzero(E == j+1)
if len(col) == 1:
E = delete(E, col[0], 1)
trim = 1
if trim == 0:
single = 1;
####print with matlib
if self.plotting ==True :
if self.operate_system == 1:
plt.plot(V[1,:],V[2,:],'b')
for i in range(shape(E)[1]):
plt.text(V[1,E[0,i]],V[2,E[0,i]], V[0,E[0,i]], fontsize=12)
plt.text(V[1,E[1,i]],V[2,E[1,i]], V[0,E[1,i]], fontsize=12)
plt.figure(1).canvas.draw()
else:
BoundPolyPoints = asarray(PolyUtils.pointList(regionPoly))
self.ax.plot(BoundPolyPoints[:,0],BoundPolyPoints[:,1],'k')
self.ax.plot(V[1,:],V[2,:],'b')
for i in range(shape(E)[1]):
self.ax.text(V[1,E[0,i]],V[2,E[0,i]], V[0,E[0,i]], fontsize=12)
self.ax.text(V[1,E[1,i]],V[2,E[1,i]], V[0,E[1,i]], fontsize=12)
#return V, E, and the current node number on the tree
V = array(V)
return V, E, 0
def generateNewNode(self,V,V_theta,E,Other,regionPoly,stuck,append_after_latest_node =False):
"""
Generate a new node on the current tree matrix
V : the node matrix
V_theta : the orientation matrix
E : the tree matrix (or edge matrix)
Other : the matrix containing the velocity and angular velocity(omega) information
regionPoly: the polygon of current region
stuck : count on the number of times failed to generate new node
append_after_latest_node : append new nodes to the latest node (True only if the previous node addition is successful)
"""
if self.system_print == True:
print "In control space generating path,stuck = " + str(stuck)
connection_to_tree = False # True when connection to the tree is successful
if stuck > self.stuck_thres:
# increase the range of omega since path cannot ge generated
omega = random.choice(self.omega_range_escape)
else:
#!!!! CONTROL SPACE STEP 1 - generate random omega
omega = random.choice(self.omega_range)
#!!!! CONTROL SPACE STEP 2 - pick a random point on the tree
if append_after_latest_node:
tree_index = shape(V)[1]-1
else:
if random.choice([1,2]) == 1:
tree_index = random.choice(array(V[0])[0])
else:
tree_index = shape(V)[1]-1
xPrev = V[1,tree_index]
yPrev = V[2,tree_index]
thetaPrev = V_theta[tree_index]
j = 1
#!!!! CONTROL SPACE STEP 3 - Check path of the robot
path_robot = PolyShapes.Circle(self.radius,(xPrev,yPrev))
while j <= self.timeStep:
xOrg = xPrev
yOrg = yPrev
xPrev = xPrev + self.velocity/omega*(sin(omega* 1 + thetaPrev)-sin(thetaPrev))
yPrev = yPrev - self.velocity/omega*(cos(omega* 1 + thetaPrev)-cos(thetaPrev))
thetaPrev = omega* 1 + thetaPrev
path_robot = path_robot + PolyShapes.Circle(self.radius,(xPrev,yPrev))
j = j + 1
thetaPrev = self.orientation_bound(thetaPrev)
path_all = PolyUtils.convexHull(path_robot)
in_bound = regionPoly.covers(path_all)
"""
# plotting
if plotting == True:
self.plotPoly(path_all,'r',1)
"""
stuck = stuck + 1
if in_bound:
robot_new_node = PolyShapes.Circle(self.radius,(xPrev,yPrev))
# check how many nodes on the tree does the new node overlaps with
nodes_overlap_count = 0
for k in range(shape(V)[1]-1):
robot_old_node = PolyShapes.Circle(self.radius,(V[1,k],V[2,k]))
if robot_new_node.overlaps(robot_old_node):
if abs(thetaPrev - V_theta[k]) < self.max_angle_overlap:
nodes_overlap_count += 1
if nodes_overlap_count == 0 or (stuck > self.stuck_thres+1 and nodes_overlap_count < 2) or (stuck > self.stuck_thres+500):
if stuck > self.stuck_thres+1:
append_after_latest_node = False
if (stuck > self.stuck_thres+500):
stuck = 0
stuck = stuck - 20
# plotting
if self.plotting == True:
self.plotPoly(path_all,'b',1)
if self.system_print == True:
print "node connected"
V = hstack((V,vstack((shape(V)[1],xPrev,yPrev))))
V_theta = hstack((V_theta,thetaPrev))
E = hstack((E,vstack((tree_index ,shape(V)[1]-1))))
Other = hstack((Other,vstack((self.velocity,omega))))
##################### E should add omega and velocity
connection_to_tree = True
append_after_latest_node = True
else:
append_after_latest_node = False
if self.system_print == True:
print "node not connected. check goal point"
else:
append_after_latest_node = False
return V,V_theta,E,Other,stuck,append_after_latest_node, connection_to_tree
def orientation_bound(self,theta):
"""
make sure the returned angle is between 0 to 2*pi
"""
while theta > 2*pi or theta < 0:
if theta > 2*pi:
theta = theta - 2*pi
else:
theta = theta + 2*pi
return theta
def plotMap(self,mappedRegions):
"""
Plotting regions and obstacles with matplotlib.pyplot
number: figure number (see on top)
"""
#if not plt.isinteractive():
# plt.ion()
#plt.hold(True)
if self.operate_system == 1:
for regionName,regionPoly in mappedRegions.iteritems():
self.plotPoly(regionPoly,'k')
plt.figure(1).canvas.draw()
def plotPoly(self,c,string,w = 1):
"""
Plot polygons inside the boundary
c = polygon to be plotted with matlabplot
string = string that specify color
w = width of the line plotting
"""
if bool(c):
for i in range(len(c)):
#toPlot = Polygon.Polygon(c.contour(i))
toPlot = Polygon.Polygon(c.contour(i)) & self.all
if bool(toPlot):
for j in range(len(toPlot)):
#BoundPolyPoints = asarray(PolyUtils.pointList(toPlot.contour(j)))
BoundPolyPoints = asarray(PolyUtils.pointList(Polygon.Polygon(toPlot.contour(j))))
if self.operate_system == 2:
self.ax.plot(BoundPolyPoints[:,0],BoundPolyPoints[:,1],string,linewidth=w)
self.ax.plot([BoundPolyPoints[-1,0],BoundPolyPoints[0,0]],[BoundPolyPoints[-1,1],BoundPolyPoints[0,1]],string,linewidth=w)
else:
plt.plot(BoundPolyPoints[:,0],BoundPolyPoints[:,1],string,linewidth=w)
plt.plot([BoundPolyPoints[-1,0],BoundPolyPoints[0,0]],[BoundPolyPoints[-1,1],BoundPolyPoints[0,1]],string,linewidth=w)
plt.figure(1).canvas.draw()
def data_gen(self):
#self.ax.cla()
for regionName,regionPoly in self.map.iteritems():
self.plotPoly(regionPoly,'k')
"""
#for i in range(len(self.V)):
if shape(V)[1] <= 2:
plt.plot(( V[1,shape(V)[1]-1],q_g[0,0]),( V[2,shape(V)[1]-1],q_g[1,0]),'b')
else:
plt.plot(( V[1,E[0,shape(E)[1]-1]], V[1,shape(V)[1]-1],q_g[0,0]),( V[2,E[0,shape(E)[1]-1]], V[2,shape(V)[1]-1],q_g[1,0]),'b')
self.plotPoly(self.realRobot, 'r')
self.plotPoly(self.robot, 'b')
"""
pose = self.pose_handler.getPose()
self.ax.plot(pose[0],pose[1],'bo')
"""
self.ax.plot(self.q_g[0],self.q_g[1],'ro')
self.plotPoly(self.overlap,'g')
self.plotPoly(self.m_line,'b')
"""
yield(pose[0],pose[1])
"""
self.ax.plot(self.prev_follow[0],self.prev_follow[1],'ko')
"""
def jplot(self):
ani = animation.FuncAnimation(self.fig, self.scope.update, self.data_gen)
plt.show()
class _Scope:
def __init__(self, ax, motion, maxt=2, dt=0.02):
self.i = 0
self.ax = ax
self.line, = self.ax.plot(1)
self.ax.set_ylim(0, 1)
self.motion = motion
def update(self,data):
(data1) = self.motion.data_gen()
a = data1.next()
self.line.set_data(a)
self.ax.relim()
self.ax.autoscale()
return self.line,
| gpl-3.0 |
rbharvs/mnd-learning | supervised.py | 1 | 8636 | import sys
import parsetags
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn import svm
from sklearn.decomposition import PCA as PCA
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import nltk
from nltk.stem import LancasterStemmer
import re
def get_top_tags(segment_tags, n):
tag_freqs = {}
for tag_list in segment_tags.values():
for tag in tag_list:
if tag not in tag_freqs:
tag_freqs[tag] = 0
tag_freqs[tag] += 1
return ['NULL'] + sorted(tag_freqs.keys(), key=lambda x: tag_freqs[x])[-n:]
def get_common_words(n=100):
try:
file_content = open(sys.argv[3]).read()
common_words = nltk.word_tokenize(file_content)
except IndexError:
return None
return set(common_words[:n])
def get_named_entities():
try:
file_content = open(sys.argv[2]).read()
named_entities = nltk.word_tokenize(file_content)
except IndexError:
return None
return set(named_entities)
def filter_segments(segment_tags, ntags):
filtered_segtags = {}
for segment in segment_tags:
# np.random.shuffle(segment_tags[segment])
for tag in segment_tags[segment]:
if tag not in ntags: continue
filtered_segtags[segment] = ntags.index(tag)
if segment not in filtered_segtags:
filtered_segtags[segment] = 0
return filtered_segtags
def increase_num_segments(segment_tags, n, length=1000):
new_segment_tags = {}
segments = sorted(segment_tags.keys(), key=len)
lengths = np.array([len(seg) for seg in segments])
dist = lengths/np.sum(lengths)
random_segments = np.random.choice(segments, size=n, p=dist)
for segment in random_segments:
new_segment = segment
if len(new_segment) > length:
index = np.random.randint(0, len(new_segment)-length)
new_segment = new_segment[index:index+length]
new_segment_tags[new_segment] = segment_tags[segment]
return new_segment_tags
def named_entity_reduction(segment_tags, named_entities, common_words):
punctuation = [',', '.', "'", '?', ';', ':', '!', '(', ')', '`', '--'
'\xe2', '\x80', '\x94', '\x99']
new_segments = []
segments = list(segment_tags.keys())
for segment in segments:
new_segment = ''
tokens = nltk.word_tokenize(segment)
for token in tokens:
if token in punctuation: continue
if token.lower() in common_words: continue
if token not in named_entities: continue
new_segment += token + ' '
new_segments.append(new_segment)
new_segment_tags = {}
for i in range(len(segments)):
new_segment_tags[new_segments[i]] = segment_tags[segments[i]]
return new_segment_tags
def stemming_reduction(segment_tags):
punctuation = [',', '.', "'", '?', ';', ':', '!', '(', ')', '`', '--'
'\xe2', '\x80', '\x94', '\x99']
new_segments = []
stemmer = LancasterStemmer()
segments = list(segment_tags.keys())
for segment in segments:
new_segment = ''
segment = re.sub(r'[^\x00-\x7f]',r'', segment)
tokens = nltk.word_tokenize(segment)
for token in tokens:
if token in punctuation: continue
try:
new_segment += stemmer.stem(token)+' '
except UnicodeDecodeError:
new_segment += ''
new_segments.append(new_segment)
stemmed_segment_tags = {}
for i in range(len(segments)):
stemmed_segment_tags[new_segments[i]] = segment_tags[segments[i]]
return stemmed_segment_tags
def separate_segments(segment_tags, k):
train = {}
for segment in segment_tags.keys():
if np.random.random() < k:
train[segment] = segment_tags.pop(segment)
return train, segment_tags
def bag_of_words(segment_tags, tfidf=False):
#create matrix of word frequencies
segments = list(segment_tags.keys())
vec = CountVectorizer()
word_freqs = vec.fit_transform(segments).toarray()
if tfidf:
tfidf_transformer = TfidfTransformer()
word_freqs = tfidf_transformer.fit_transform(word_freqs)
labels = np.empty(shape=len(segments))
for i in range(len(segments)):
labels[i] = segment_tags[segments[i]]
return word_freqs, labels, segments
def entity_bow(segment_tags, named_entities, common_words):
punctuation = [',', '.', "'", '?', ';', ':', '!', '(', ')', '`', '--'
'\xe2', '\x80', '\x94', '\x99']
new_segments = []
segments = list(segment_tags.keys())
for segment in segments:
new_segment = ''
tokens = nltk.word_tokenize(segment)
for token in tokens:
if token in punctuation: continue
if token.lower() in common_words: continue
if token not in named_entities: continue
new_segment += token + ' '
new_segments.append(new_segment)
vec = CountVectorizer()
word_freqs = vec.fit_transform(new_segments).toarray()
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(word_freqs)
print(word_freqs.shape, X_train_tfidf.shape)
labels = np.empty(shape=len(segments))
for i in range(len(segments)):
labels[i] = segment_tags[segments[i]]
return X_train_tfidf, labels, segments
def pca_plot(Xtrain, ytrain):
#binary classification case
X_reduced = Xtrain
pca = PCA(3)
X_pca = pca.fit_transform(X_reduced)
ax = plt.axes(projection='3d')
for i in range(X_pca.shape[0]):
if ytrain[i] == 1:
ax.scatter(X_pca[i, 0], X_pca[i, 2], X_pca[i, 1], 'o', color='blue')
else:
ax.scatter(X_pca[i, 0], X_pca[i, 2], X_pca[i,1], 'x', color='red')
plt.show()
def randomize_order(X, y, segments):
shuffled_segments = []
indices = np.arange(len(segments))
np.random.shuffle(indices)
X, y = X[indices], y[indices]
for i in indices:
shuffled_segments.append(segments[i])
return X, y, segments
def naive_bayes(segment_tags, k=0.5, normalize=False):
X, y, segments = randomize_order(*bag_of_words(segment_tags, tfidf=normalize))
num_examples = len(segments)
Xtest, ytest = X[int(k*num_examples):, :], y[int(k*num_examples):]
Xtrain, ytrain = X[:int(k*num_examples), :], y[:int(k*num_examples)]
nb_classifier = MultinomialNB().fit(Xtrain, ytrain)
nb_predicted_tags = nb_classifier.predict(Xtest)
nb_success_rate = np.mean(nb_predicted_tags == ytest)
return 1-nb_success_rate
def support_vector(segment_tags, k=0.5, normalize=False):
X, y, segments = randomize_order(*bag_of_words(segment_tags, tfidf=normalize))
num_examples = len(segments)
Xtest, ytest = X[int(k*num_examples):, :], y[int(k*num_examples):]
Xtrain, ytrain = X[:int(k*num_examples), :], y[:int(k*num_examples)]
svm_classifier = svm.SVC()
svm_classifier.fit(Xtrain, ytrain)
svm_predicted_tags = svm_classifier.predict(Xtest)
svm_success_rate = np.mean(svm_predicted_tags == ytest)
return 1-svm_success_rate
if __name__ == "__main__":
orig_segment_tags = parsetags.parse_tags(sys.argv[1])
common_words = get_common_words()
named_entities = get_named_entities()
for sample_size in ['BIG']:
if sample_size == 'BIG':
segment_tags = increase_num_segments(orig_segment_tags, 3000, length=1000)
orig_segment_tags = segment_tags
for text_features in ['REGULAR', 'STEMMED', 'NAMED']:
if text_features == 'STEMMED':
segment_tags = stemming_reduction(orig_segment_tags)
if text_features == 'NAMED':
segment_tags = named_entity_reduction(orig_segment_tags, named_entities, common_words)
for freq_feature in ['COUNT', 'TFIDF']:
# ntags = get_top_tags(segment_tags, 7)
print(sample_size, text_features, freq_feature)
ntags = ['ETA', 'EHDRH', 'AFR']
filtered_segtags = filter_segments(segment_tags, ntags)
with open('Results/' + sample_size + '_' + text_features + '_' + freq_feature + '.txt', 'w') as f:
for i in range(100):
f.write(str(naive_bayes(filtered_segtags, normalize=(freq_feature is 'TFIDF'))) + '\n')
# segment_tags = parsetags.parse_tags(sys.argv[1])
# big_segment_tags = increase_num_segments(segment_tags, 3000, length=1000)
# ntags = get_top_tags(segment_tags, 7)
# for
# # ntags = ['NULL', 'ETA', 'EHDRH', 'AFR']
# common_words = get_common_words()
# named_entities = get_named_entities()
# filtered_segtags = filter_segments(segment_tags, ntags)
# #entity_bow(filtered_segtags, named_entities, common_words)
# naive_bayes(filtered_segtags, named_entities, common_words, features=entity_bow)
# naive_bayes(filtered_segtags)
# support_vector(filtered_segtags)
# predicted_tags = [ntags[int(np.round(nb_predicted_tags[i]))] for i in range(len(svm_predicted_tags))]
# count = 0
# print(ntags)
# for i in range(len(predicted_tags)):
# if predicted_tags[i] == 'NULL':
# if all(tag not in segment_tags[shuffled_segments[i]] for tag in ntags):
# count += 1
# else:
# if predicted_tags[i] in segment_tags[shuffled_segments[i]]:
# count += 1
# print(count/len(predicted_tags))
| mit |
dariox2/CADL | test/testyida6b.py | 1 | 4901 |
#
# test shuffle_batch - 6b
#
# generates a pair of files (color+bn)
# pending: make the tuple match
#
print("Loading tensorflow...")
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import os
from libs import utils
import datetime
tf.set_random_seed(1)
def create_input_pipeline_yida(files1, files2, batch_size, n_epochs, shape, crop_shape=None,
crop_factor=1.0, n_threads=1, seed=None):
producer1 = tf.train.string_input_producer(
files1, capacity=len(files1), shuffle=False)
producer2 = tf.train.string_input_producer(
files2, capacity=len(files2), shuffle=False)
# We need something which can open the files and read its contents.
reader = tf.WholeFileReader()
# We pass the filenames to this object which can read the file's contents.
# This will create another queue running which dequeues the previous queue.
keys1, vals1 = reader.read(producer1)
keys2, vals2 = reader.read(producer2)
# And then have to decode its contents as we know it is a jpeg image
imgs1 = tf.image.decode_jpeg(vals1, channels=3)
imgs2 = tf.image.decode_jpeg(vals2, channels=3)
# We have to explicitly define the shape of the tensor.
# This is because the decode_jpeg operation is still a node in the graph
# and doesn't yet know the shape of the image. Future operations however
# need explicit knowledge of the image's shape in order to be created.
imgs1.set_shape(shape)
imgs2.set_shape(shape)
# Next we'll centrally crop the image to the size of 100x100.
# This operation required explicit knowledge of the image's shape.
if shape[0] > shape[1]:
rsz_shape = [int(shape[0] / shape[1] * crop_shape[0] / crop_factor),
int(crop_shape[1] / crop_factor)]
else:
rsz_shape = [int(crop_shape[0] / crop_factor),
int(shape[1] / shape[0] * crop_shape[1] / crop_factor)]
rszs1 = tf.image.resize_images(imgs1, rsz_shape[0], rsz_shape[1])
rszs2 = tf.image.resize_images(imgs2, rsz_shape[0], rsz_shape[1])
crops1 = (tf.image.resize_image_with_crop_or_pad(
rszs1, crop_shape[0], crop_shape[1])
if crop_shape is not None
else imgs1)
crops2 = (tf.image.resize_image_with_crop_or_pad(
rszs2, crop_shape[0], crop_shape[1])
if crop_shape is not None
else imgs2)
# Now we'll create a batch generator that will also shuffle our examples.
# We tell it how many it should have in its buffer when it randomly
# permutes the order.
min_after_dequeue = len(files1) // 5
# The capacity should be larger than min_after_dequeue, and determines how
# many examples are prefetched. TF docs recommend setting this value to:
# min_after_dequeue + (num_threads + a small safety margin) * batch_size
capacity = min_after_dequeue + (n_threads + 1) * batch_size
# Randomize the order and output batches of batch_size.
batch = tf.train.shuffle_batch([crops1, crops2],
enqueue_many=False,
batch_size=batch_size,
capacity=capacity,
min_after_dequeue=min_after_dequeue,
num_threads=n_threads,
#seed=seed,
)#shapes=(64,64,3))
# alternatively, we could use shuffle_batch_join to use multiple reader
# instances, or set shuffle_batch's n_threads to higher than 1.
return batch
def CELEByida(path):
fs = [os.path.join(path, f)
for f in os.listdir(path) if f.endswith('.jpg')]
fs=sorted(fs)
return fs
print("Loading celebrities...")
from libs.datasets import CELEB
files1 = CELEByida("../session-1/img_align_celeba/") # only 100
files2 = CELEByida("../session-1/img_align_celeba_n/") # only 100
from libs.dataset_utils import create_input_pipeline
batch_size = 8
n_epochs = 3
input_shape = [218, 178, 3]
crop_shape = [64, 64, 3]
crop_factor = 0.8
seed=15
batch1 = create_input_pipeline_yida(
files1=files1, files2=files2,
batch_size=batch_size,
n_epochs=n_epochs,
crop_shape=crop_shape,
crop_factor=crop_factor,
shape=input_shape,
seed=seed)
mntg=[]
sess = tf.Session()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
batres = sess.run(batch1)
batch_xs1=np.array(batres[0])
batch_xs2=np.array(batres[1])
for i in range(0,len(batch_xs1)):
img=batch_xs1[i] / 255.0
mntg.append(img)
img=batch_xs2[i] / 255.0
mntg.append(img)
TID=datetime.date.today().strftime("%Y%m%d")+"_"+datetime.datetime.now().time().strftime("%H%M%S")
m=utils.montage(mntg, saveto="montage_"+TID+".png")
# mntg[0]=color
# mntg[1]=b/n
plt.figure(figsize=(5, 5))
plt.imshow(m)
plt.show()
# eop
| apache-2.0 |
alphacsc/alphacsc | examples/csc/plot_lfp_data.py | 1 | 3791 | """
==============================
CSC to learn LFP spiking atoms
==============================
Here, we show how CSC can be used to learn spiking
atoms from Local Field Potential (LFP) data [1].
[1] Hitziger, Sebastian, et al.
Adaptive Waveform Learning: A Framework for Modeling Variability in
Neurophysiological Signals. IEEE Transactions on Signal Processing (2017).
"""
###############################################################################
# First, let us fetch the data (~14 MB)
import os
from mne.utils import _fetch_file
url = ('https://github.com/hitziger/AWL/raw/master/Experiments/data/'
'LFP_data_contiguous_1250_Hz.mat')
fname = './LFP_data_contiguous_1250_Hz.mat'
if not os.path.exists(fname):
_fetch_file(url, fname)
###############################################################################
# It is a mat file, so we use scipy to load it
from scipy import io
data = io.loadmat(fname)
X, sfreq = data['X'].T, float(data['sfreq'])
###############################################################################
# And now let us look at the data
import numpy as np
import matplotlib.pyplot as plt
start, stop = 11000, 15000
times = np.arange(start, stop) / sfreq
plt.plot(times, X[0, start:stop], color='b')
plt.xlabel('Time (s)')
plt.ylabel(r'$\mu$ V')
plt.xlim([9., 12.])
###############################################################################
# and filter it using a convenient function from MNE. This will remove low
# frequency drifts, but we keep the high frequencies
from mne.filter import filter_data
X = filter_data(
X.astype(np.float64), sfreq, l_freq=1, h_freq=None, fir_design='firwin')
###############################################################################
# Now, we define the parameters of our model.
reg = 6.0
n_times = 2500
n_times_atom = 350
n_trials = 100
n_atoms = 3
n_iter = 60
###############################################################################
# Let's stick to one random state for now, but if you want to learn how to
# select the random state, consult :ref:`this example
# <sphx_glr_auto_examples_plot_simulate_randomstate.py>`.
random_state = 10
###############################################################################
# Now, we epoch the trials
overlap = 0
starts = np.arange(0, X.shape[1] - n_times, n_times - overlap)
stops = np.arange(n_times, X.shape[1], n_times - overlap)
X_new = []
for idx, (start, stop) in enumerate(zip(starts, stops)):
if idx >= n_trials:
break
X_new.append(X[0, start:stop])
X_new = np.vstack(X_new)
del X
###############################################################################
# We remove the mean and scale to unit variance.
X_new -= np.mean(X_new)
X_new /= np.std(X_new)
###############################################################################
# The convolutions can result in edge artifacts at the edges of the trials.
# Therefore, we discount the contributions from the edges by windowing the
# trials.
from numpy import hamming
X_new *= hamming(n_times)[None, :]
###############################################################################
# Of course, in a data-limited setting we want to use as much of the data as
# possible. If this is the case, you can set `overlap` to non-zero (for example
# half the epoch length).
#
# Now, we run regular CSC since the trials are not too noisy
from alphacsc import learn_d_z
pobj, times, d_hat, z_hat, reg = learn_d_z(X_new, n_atoms, n_times_atom,
reg=reg, n_iter=n_iter,
random_state=random_state, n_jobs=1)
###############################################################################
# Let's look at the atoms now.
plt.figure()
plt.plot(d_hat.T)
plt.show()
| bsd-3-clause |
rbn920/feebb | feebb/test.py | 1 | 1640 | from feebb import *
import matplotlib.pyplot as plt
pre = Preprocessor()
pre.load_json('ex_json/test2.json')
elems = [Element(elem) for elem in pre.elements]
print(pre.supports)
beam = Beam(elems, pre.supports)
post = Postprocessor(beam, 10)
print(max(post.interp('moment')))
print(min(post.interp('moment')))
plt.plot(post.interp('moment'))
plt.show()
print(max(post.interp('shear')))
print(min(post.interp('shear')))
plt.plot(post.interp('shear'))
plt.show()
pre = Preprocessor()
pre.load_json('ex_json/test2m.json')
elems = [Element(elem) for elem in pre.elements]
beam = Beam(elems, pre.supports)
post = Postprocessor(beam, 10)
print(max(post.interp('moment')))
print(min(post.interp('moment')))
plt.plot(post.interp('moment'))
plt.show()
print(max(post.interp('shear')))
print(min(post.interp('shear')))
plt.plot(post.interp('shear'))
plt.show()
pre = Preprocessor()
pre.load_json('ex_json/test2mm.json')
elems = [Element(elem) for elem in pre.elements]
beam = Beam(elems, pre.supports)
post = Postprocessor(beam, 10)
print(max(post.interp('moment')))
print(min(post.interp('moment')))
plt.plot(post.interp('moment'))
plt.show()
print(max(post.interp('shear')))
print(min(post.interp('shear')))
plt.plot(post.interp('shear'))
plt.show()
pre = Preprocessor()
pre.load_json('ex_json/test2mmm.json')
elems = [Element(elem) for elem in pre.elements]
beam = Beam(elems, pre.supports)
post = Postprocessor(beam, 10)
print(max(post.interp('moment')))
print(min(post.interp('moment')))
plt.plot(post.interp('moment'))
plt.show()
print(max(post.interp('shear')))
print(min(post.interp('shear')))
plt.plot(post.interp('shear'))
plt.show()
| mit |
Garrett-R/scikit-learn | examples/decomposition/plot_image_denoising.py | 84 | 5820 | """
=========================================
Image denoising using dictionary learning
=========================================
An example comparing the effect of reconstructing noisy fragments
of the Lena image using firstly online :ref:`DictionaryLearning` and
various transform methods.
The dictionary is fitted on the distorted left half of the image, and
subsequently used to reconstruct the right half. Note that even better
performance could be achieved by fitting to an undistorted (i.e.
noiseless) image, but here we start from the assumption that it is not
available.
A common practice for evaluating the results of image denoising is by looking
at the difference between the reconstruction and the original image. If the
reconstruction is perfect this will look like Gaussian noise.
It can be seen from the plots that the results of :ref:`omp` with two
non-zero coefficients is a bit less biased than when keeping only one
(the edges look less prominent). It is in addition closer from the ground
truth in Frobenius norm.
The result of :ref:`least_angle_regression` is much more strongly biased: the
difference is reminiscent of the local intensity value of the original image.
Thresholding is clearly not useful for denoising, but it is here to show that
it can produce a suggestive output with very high speed, and thus be useful
for other tasks such as object classification, where performance is not
necessarily related to visualisation.
"""
print(__doc__)
from time import time
import matplotlib.pyplot as plt
import numpy as np
from scipy.misc import lena
from sklearn.decomposition import MiniBatchDictionaryLearning
from sklearn.feature_extraction.image import extract_patches_2d
from sklearn.feature_extraction.image import reconstruct_from_patches_2d
###############################################################################
# Load Lena image and extract patches
lena = lena() / 256.0
# downsample for higher speed
lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2]
lena /= 4.0
height, width = lena.shape
# Distort the right half of the image
print('Distorting image...')
distorted = lena.copy()
distorted[:, height // 2:] += 0.075 * np.random.randn(width, height // 2)
# Extract all reference patches from the left half of the image
print('Extracting reference patches...')
t0 = time()
patch_size = (7, 7)
data = extract_patches_2d(distorted[:, :height // 2], patch_size)
data = data.reshape(data.shape[0], -1)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0)
print('done in %.2fs.' % (time() - t0))
###############################################################################
# Learn the dictionary from reference patches
print('Learning the dictionary...')
t0 = time()
dico = MiniBatchDictionaryLearning(n_components=100, alpha=1, n_iter=500)
V = dico.fit(data).components_
dt = time() - t0
print('done in %.2fs.' % dt)
plt.figure(figsize=(4.2, 4))
for i, comp in enumerate(V[:100]):
plt.subplot(10, 10, i + 1)
plt.imshow(comp.reshape(patch_size), cmap=plt.cm.gray_r,
interpolation='nearest')
plt.xticks(())
plt.yticks(())
plt.suptitle('Dictionary learned from Lena patches\n' +
'Train time %.1fs on %d patches' % (dt, len(data)),
fontsize=16)
plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
###############################################################################
# Display the distorted image
def show_with_diff(image, reference, title):
"""Helper function to display denoising"""
plt.figure(figsize=(5, 3.3))
plt.subplot(1, 2, 1)
plt.title('Image')
plt.imshow(image, vmin=0, vmax=1, cmap=plt.cm.gray, interpolation='nearest')
plt.xticks(())
plt.yticks(())
plt.subplot(1, 2, 2)
difference = image - reference
plt.title('Difference (norm: %.2f)' % np.sqrt(np.sum(difference ** 2)))
plt.imshow(difference, vmin=-0.5, vmax=0.5, cmap=plt.cm.PuOr,
interpolation='nearest')
plt.xticks(())
plt.yticks(())
plt.suptitle(title, size=16)
plt.subplots_adjust(0.02, 0.02, 0.98, 0.79, 0.02, 0.2)
show_with_diff(distorted, lena, 'Distorted image')
###############################################################################
# Extract noisy patches and reconstruct them using the dictionary
print('Extracting noisy patches... ')
t0 = time()
data = extract_patches_2d(distorted[:, height // 2:], patch_size)
data = data.reshape(data.shape[0], -1)
intercept = np.mean(data, axis=0)
data -= intercept
print('done in %.2fs.' % (time() - t0))
transform_algorithms = [
('Orthogonal Matching Pursuit\n1 atom', 'omp',
{'transform_n_nonzero_coefs': 1}),
('Orthogonal Matching Pursuit\n2 atoms', 'omp',
{'transform_n_nonzero_coefs': 2}),
('Least-angle regression\n5 atoms', 'lars',
{'transform_n_nonzero_coefs': 5}),
('Thresholding\n alpha=0.1', 'threshold', {'transform_alpha': .1})]
reconstructions = {}
for title, transform_algorithm, kwargs in transform_algorithms:
print(title + '...')
reconstructions[title] = lena.copy()
t0 = time()
dico.set_params(transform_algorithm=transform_algorithm, **kwargs)
code = dico.transform(data)
patches = np.dot(code, V)
if transform_algorithm == 'threshold':
patches -= patches.min()
patches /= patches.max()
patches += intercept
patches = patches.reshape(len(data), *patch_size)
if transform_algorithm == 'threshold':
patches -= patches.min()
patches /= patches.max()
reconstructions[title][:, height // 2:] = reconstruct_from_patches_2d(
patches, (width, height // 2))
dt = time() - t0
print('done in %.2fs.' % dt)
show_with_diff(reconstructions[title], lena,
title + ' (time: %.1fs)' % dt)
plt.show()
| bsd-3-clause |
yunque/sms-tools | lectures/03-Fourier-properties/plots-code/symmetry-real-even.py | 26 | 1150 | import matplotlib.pyplot as plt
import numpy as np
import sys
import math
from scipy.signal import triang
from scipy.fftpack import fft, fftshift
M = 127
N = 128
hM1 = int(math.floor((M+1)/2))
hM2 = int(math.floor(M/2))
x = triang(M)
fftbuffer = np.zeros(N)
fftbuffer[:hM1] = x[hM2:]
fftbuffer[N-hM2:] = x[:hM2]
X = fftshift(fft(fftbuffer))
mX = abs(X)
pX = np.unwrap(np.angle(X))
plt.figure(1, figsize=(9.5, 4))
plt.subplot(311)
plt.title('x[n]')
plt.plot(np.arange(-hM2, hM1, 1.0), x, 'b', lw=1.5)
plt.axis([-hM2, hM1, 0, 1])
plt.subplot(323)
plt.title('real(X)')
plt.plot(np.arange(-N/2, N/2, 1.0), np.real(X), 'r', lw=1.5)
plt.axis([-N/2, N/2, min(np.real(X)), max(np.real(X))])
plt.subplot(324)
plt.title('im(X)')
plt.plot(np.arange(-N/2, N/2, 1.0), np.imag(X), 'c', lw=1.5)
plt.axis([-N/2, N/2, -1, 1])
plt.subplot(325)
plt.title('abs(X)')
plt.plot(np.arange(-N/2, N/2, 1.0), mX, 'r', lw=1.5)
plt.axis([-N/2,N/2,min(mX),max(mX)])
plt.subplot(326)
plt.title('angle(X)')
plt.plot(np.arange(-N/2, N/2, 1.0), pX, 'c', lw=1.5)
plt.axis([-N/2, N/2, -1, 1])
plt.tight_layout()
plt.savefig('symmetry-real-even.png')
plt.show()
| agpl-3.0 |
nickgentoo/LSTM-timepredictionPMdata | code/nick_evaluate_suffix_and_remaining_time_only_time_OHenc.py | 1 | 15048 | '''
this script takes as input the LSTM or RNN weights found by train.py
change the path in line 178 of this script to point to the h5 file
with LSTM or RNN weights generated by train.py
Author: Niek Tax
'''
from __future__ import division
from keras.models import load_model
import csv
import copy
import numpy as np
import distance
from itertools import izip
from jellyfish._jellyfish import damerau_levenshtein_distance
import unicodecsv
from sklearn import metrics
from math import sqrt
import time
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
from collections import Counter
from keras.models import model_from_json
import sys
fileprefix=sys.argv[1]
eventlog = sys.argv[2]
csvfile = open('../data/%s' % eventlog, 'r')
spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
next(spamreader, None) # skip the headers
ascii_offset = 161
lastcase = ''
line = ''
firstLine = True
lines = []
timeseqs = []
timeseqs2 = []
timeseqs3 = []
timeseqs4 = []
y_times = []
times = []
times2 = []
times3 = []
times4 = []
# nick
attributes = []
attributes_dict = []
attributes_sizes = []
numlines = 0
casestarttime = None
lasteventtime = None
csvfile = open('../data/%s' % eventlog, 'r')
spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
next(spamreader, None) # skip the headers
ascii_offset = 161
y = []
for row in spamreader:
#print(row)
t = time.strptime(row[2], "%Y-%m-%d %H:%M:%S")
#test different format
#t = 0#time.strptime(row[2], "%Y/%m/%d %H:%M:%S")
if row[0]!=lastcase:
casestarttime = t
lasteventtime = t
lastcase = row[0]
if not firstLine:
#print (line)
lines.append(line)
timeseqs.append(times)
timeseqs2.append(times2)
#target
y_times.extend([times2[-1]-k for k in times2])
timeseqs3.append(times3)
timeseqs4.append(times4)
for i in xrange(len(attributes)):
#print(attributesvalues[i])
attributes[i].append(attributesvalues[i])
else:
#if firstline. I have to add te elements to attributes
for a in row[3:]:
attributes.append([])
attributes_dict.append({})
attributes_sizes.append(0)
#print(attributes)
n_events_in_trace=0
line = ''
times = []
times2 = []
times3 = []
times4 = []
attributesvalues = [ ]
numlines+=1
n_events_in_trace+=1
line+=unichr(int(row[1])+ascii_offset)
timesincelastevent = datetime.fromtimestamp(time.mktime(t))-datetime.fromtimestamp(time.mktime(lasteventtime))
timesincecasestart = datetime.fromtimestamp(time.mktime(t))-datetime.fromtimestamp(time.mktime(casestarttime))
midnight = datetime.fromtimestamp(time.mktime(t)).replace(hour=0, minute=0, second=0, microsecond=0)
timesincemidnight = datetime.fromtimestamp(time.mktime(t))-midnight
timediff = 86400 * timesincelastevent.days + timesincelastevent.seconds
timediff2 = 86400 * timesincecasestart.days + timesincecasestart.seconds
timediff3 = timesincemidnight.seconds
timediff4 = datetime.fromtimestamp(time.mktime(t)).weekday()
times.append(timediff)
times2.append(timediff2)
times3.append(timediff3)
times4.append(timediff4)
lasteventtime = t
firstLine = False
indexnick=0
for a in row[3:]:
if len(attributesvalues)<=indexnick:
attributesvalues.append([])
a=a.strip('"')
#todo cast a intero se e intero if
if a!="":
try:
attr=float(a)
attributesvalues[indexnick].append(attr)
#print("float attr")
#print(a)
except:
if a not in attributes_dict[indexnick]:
attributes_dict[indexnick][a]=attributes_sizes[indexnick]+1
attributes_sizes[indexnick]=attributes_sizes[indexnick]+1
attributesvalues[indexnick].append(attributes_dict[indexnick][a])
else:
attributesvalues[indexnick].append(-1)
# if a in attributes_dict[indexnick]:
# attributesvalues.append(attributes_dict[indexnick][a])
# else:
# attributes_dict[indexnick][a]=attributes_sizes[indexnick]
# attributes_sizes[indexnick]+=1
# attributesvalues.append(attributes_dict[indexnick][a])
indexnick+=1
# add last case
lines.append(line)
timeseqs.append(times)
timeseqs2.append(times2)
timeseqs3.append(times3)
timeseqs4.append(times4)
y_times.extend([times2[-1] - k for k in times2])
for i in xrange(len(attributes)):
attributes[i].append(attributesvalues[i])
numlines+=1
divisor = np.mean([item for sublist in timeseqs for item in sublist])
print('divisor: {}'.format(divisor))
divisor2 = np.mean([item for sublist in timeseqs2 for item in sublist])
print('divisor2: {}'.format(divisor2))
step = 1
sentences = []
softness = 0
next_chars = []
lines = map(lambda x: x + '!', lines)
maxlen = max(map(lambda x: len(x), lines))
chars = map(lambda x: set(x), lines)
chars = list(set().union(*chars))
chars.sort()
target_chars = copy.copy(chars)
chars.remove('!')
lines = map(lambda x: x[:-2], lines)
print('total chars: {}, target chars: {}'.format(len(chars), len(target_chars)))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))
target_char_indices = dict((c, i) for i, c in enumerate(target_chars))
target_indices_char = dict((i, c) for i, c in enumerate(target_chars))
#print(indices_char)
elems_per_fold = int(round(numlines / 3))
fold1 = lines[:elems_per_fold]
fold1_t = timeseqs[:elems_per_fold]
fold1_t2 = timeseqs2[:elems_per_fold]
fold1_t3 = timeseqs3[:elems_per_fold]
fold1_t4 = timeseqs4[:elems_per_fold]
with open('output_files/folds/' + eventlog + 'fold1.csv', 'wb') as csvfile:
spamwriter = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
for row, timeseq in izip(fold1, fold1_t):
spamwriter.writerow([unicode(s).encode("utf-8") + '#{}'.format(t) for s, t in izip(row, timeseq)])
fold2 = lines[elems_per_fold:2 * elems_per_fold]
fold2_t = timeseqs[elems_per_fold:2 * elems_per_fold]
fold2_t2 = timeseqs2[elems_per_fold:2 * elems_per_fold]
fold2_t3 = timeseqs3[elems_per_fold:2 * elems_per_fold]
fold2_t4 = timeseqs4[elems_per_fold:2 * elems_per_fold]
with open('output_files/folds/' + eventlog + 'fold2.csv', 'wb') as csvfile:
spamwriter = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
for row, timeseq in izip(fold2, fold2_t):
spamwriter.writerow([unicode(s).encode("utf-8") + '#{}'.format(t) for s, t in izip(row, timeseq)])
fold3 = lines[2 * elems_per_fold:]
fold3_t = timeseqs[2 * elems_per_fold:]
fold3_t2 = timeseqs2[2 * elems_per_fold:]
fold3_t3 = timeseqs3[2 * elems_per_fold:]
fold3_t4 = timeseqs4[2 * elems_per_fold:]
fold3_a=[a[2*elems_per_fold:] for a in attributes]
with open('output_files/folds/' + eventlog + 'fold3.csv', 'wb') as csvfile:
spamwriter = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
for row, timeseq in izip(fold3, fold3_t):
spamwriter.writerow([unicode(s).encode("utf-8") + '#{}'.format(t) for s, t in izip(row, timeseq)])
y_t_seq=[]
for line in fold1+fold2:
for i in range(0, len(line), 1):
if i == 0:
continue
y_t_seq.append(y_times[0:i])
divisory = np.mean([item for sublist in y_t_seq for item in sublist])
print('divisory: {}'.format(divisory))
lines = fold3
lines_t = fold3_t
lines_t2 = fold3_t2
lines_t3 = fold3_t3
lines_t4 = fold3_t4
attributes=fold3_a
# set parameters
predict_size = maxlen
# load json and create model
json_file = open('output_files/models/'+fileprefix+'_model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new model
model.load_weights("output_files/models/"+fileprefix+"_weights_best.h5")
print("Loaded model from disk")
y_t_seq=[]
# load model, set this to the model generated by train.py
#model = load_model('output_files/models/200_model_59-1.50.h5')
# define helper functions
def encode(ex, sentence, times,times2, times3,times4, sentences_attributes,maxlen=maxlen):
#num_features = len(chars)+5+len(sentences_attributes)
num_features = len(chars) + 5
for idx in xrange(len(attributes)):
num_features += attributes_sizes[idx] + 1
#print(num_features)
X = np.zeros((1, maxlen, num_features), dtype=np.float32)
leftpad = maxlen-len(sentence)
times2 = np.cumsum(times)
#print "sentence",len(sentence)
for t, char in enumerate(sentence):
#print(t)
#midnight = times3[t].replace(hour=0, minute=0, second=0, microsecond=0)
#timesincemidnight = times3[t]-midnight
multiset_abstraction = Counter(sentence[:t+1])
for c in chars:
if c==char:
X[0, t+leftpad, char_indices[c]] = 1
X[0, t+leftpad, len(chars)] = t+1
X[0, t+leftpad, len(chars)+1] = times[t]/divisor
X[0, t+leftpad, len(chars)+2] = times2[t]/divisor2
X[0, t+leftpad, len(chars)+3] = times3[t]/86400
X[0, t+leftpad, len(chars)+4] = times4[t]/7
# for i in xrange(len(sentences_attributes)):
# #print(str(i)+" "+str(t))
# #print(sentences_attributes[i][t])
# #nick check the zero, it is there because it was a list
# X[0, t + leftpad, len(chars) + 5 + i] = sentences_attributes[i][t]
startoh = 0
for j in xrange(len(attributes)):
# X[i, t + leftpad, len(chars) + 5+j]=sentences_attributes[j][i][t]
if attributes_sizes[j] > 0:
X[0, t + leftpad, len(chars) + 5 + startoh + sentences_attributes[j][t]] = 1
else:
X[0, t + leftpad, len(chars) + 5 + startoh] = sentences_attributes[j][t]
startoh += (attributes_sizes[j] + 1)
return X
# # define helper functions
# def encode(sentence, times, times3, sentences_attributes,maxlen=maxlen):
# num_features = len(chars)+5+len(sentences_attributes)
# X = np.zeros((1, maxlen, num_features), dtype=np.float32)
# leftpad = maxlen-len(sentence)
# times2 = np.cumsum(times)
# print "sentence",len(sentence)
# for t, char in enumerate(sentence):
# midnight = times3[t].replace(hour=0, minute=0, second=0, microsecond=0)
# timesincemidnight = times3[t]-midnight
# multiset_abstraction = Counter(sentence[:t+1])
# for c in chars:
# if c==char:
# X[0, t+leftpad, char_indices[c]] = 1
# X[0, t+leftpad, len(chars)] = t+1
# X[0, t+leftpad, len(chars)+1] = times[t]/divisor
# X[0, t+leftpad, len(chars)+2] = times2[t]/divisor2
# X[0, t+leftpad, len(chars)+3] = timesincemidnight.seconds/86400
# X[0, t+leftpad, len(chars)+4] = times3[t].weekday()/7
# for i in xrange(len(sentences_attributes)):
# print(str(i)+" "+str(t))
# print(sentences_attributes[i][t])
# #nick check the zero, it is there because it was a list
# X[0, t + leftpad, len(chars) + 5+i]=sentences_attributes[i][t]
# return X,y
def getSymbol(predictions):
maxPrediction = 0
symbol = ''
i = 0;
for prediction in predictions:
if(prediction>=maxPrediction):
maxPrediction = prediction
symbol = target_indices_char[i]
i += 1
return symbol
one_ahead_gt = []
one_ahead_pred = []
two_ahead_gt = []
two_ahead_pred = []
three_ahead_gt = []
three_ahead_pred = []
y_t_seq=[]
# make predictions
with open('output_files/results/'+fileprefix+'_suffix_and_remaining_time_%s' % eventlog, 'wb') as csvfile:
spamwriter = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
spamwriter.writerow(["Prefix length", "Groud truth", "Ground truth times", "Predicted times", "RMSE", "MAE", "Median AE"])
#considering also size 1 prefixes
#for prefix_size in range(1,maxlen):
#print(prefix_size)
#print(len(lines),len(attributes[0]))
for ex, (line, times, times2, times3, times4) in enumerate(izip(lines, lines_t, lines_t2, lines_t3, lines_t3)):
for prefix_size in range(1, len(line)):#aggiunto -1 perche non voglio avere 0 nel ground truth
#print(line,ex,len(line), len(attributes[0][ex]))
times.append(0)
cropped_line = ''.join(line[:prefix_size])
cropped_times = times[:prefix_size]
#print "times_len",len(cropped_times)
cropped_times2 = times2[:prefix_size]
cropped_times4 = times4[:prefix_size]
cropped_times3 = times3[:prefix_size]
cropped_attributes = [[] for i in xrange(len(attributes))]
for j in xrange(len(attributes)):
#print(attributes[j][ex])
cropped_attributes[j].extend(attributes[j][ex][0:prefix_size])
#print cropped_attributes
#y_t_seq.append(y_times[0:prefix_size])
#cropped_attributes= [a[:prefix_size] for a in attributes]
#print cropped_attribute
ground_truth = ''.join(line[prefix_size:prefix_size+predict_size])
ground_truth_t = times2[prefix_size-1] # era -1
#print(prefix_size,len(times2)-1)
case_end_time = times2[len(times2)-1]
ground_truth_t = case_end_time-ground_truth_t
predicted = ''
total_predicted_time = 0
#perform single prediction
enc = encode(ex,cropped_line, cropped_times,cropped_times2, cropped_times3,cropped_times4, cropped_attributes)
y = model.predict(enc, verbose=0) # make predictions
# split predictions into seperate activity and time predictions
#print y
y_t = y[0][0]
#prediction = getSymbol(y_char) # undo one-hot encoding
#cropped_line += prediction
if y_t<0:
y_t=0
cropped_times.append(y_t)
y_t = y_t * divisor
#cropped_times3.append(cropped_times3[-1] + timedelta(seconds=y_t))
total_predicted_time = total_predicted_time + y_t
output = []
if len(ground_truth)>0:
output.append(prefix_size)
output.append(unicode(ground_truth).encode("utf-8"))
output.append(ground_truth_t)
output.append(total_predicted_time)
output.append(metrics.mean_squared_error([ground_truth_t], [total_predicted_time]))
output.append(metrics.mean_absolute_error([ground_truth_t], [total_predicted_time]))
output.append(metrics.median_absolute_error([ground_truth_t], [total_predicted_time]))
spamwriter.writerow(output)
| gpl-3.0 |
mph-/lcapy | lcapy/nexpr.py | 1 | 7914 | """This module provides the DiscreteTimeDomainExpression class to
represent discrete-time expressions.
Copyright 2020--2021 Michael Hayes, UCECE
"""
from __future__ import division
from .domains import DiscreteTimeDomain
from .sequence import Sequence
from .functions import exp
from .sym import j, oo, pi, fsym, oo
from .dsym import nsym, ksym, zsym, dt
from .ztransform import ztransform
from .dft import DFT
from .seqexpr import SequenceExpression
from .nseq import DiscreteTimeDomainSequence, nseq
from sympy import Sum, summation, limit, DiracDelta
__all__ = ('nexpr', )
class DiscreteTimeDomainExpression(DiscreteTimeDomain, SequenceExpression):
"""Discrete-time expression or symbol."""
var = nsym
seqcls = DiscreteTimeDomainSequence
def __init__(self, val, **assumptions):
check = assumptions.pop('check', True)
if 'integer' not in assumptions:
assumptions['real'] = True
super(DiscreteTimeDomainExpression, self).__init__(val, **assumptions)
expr = self.expr
if check and expr.has(zsym) and not expr.has(Sum):
raise ValueError(
'n-domain expression %s cannot depend on z' % expr)
if check and expr.has(ksym) and not expr.has(Sum):
raise ValueError(
'n-domain expression %s cannot depend on k' % expr)
def _mul_compatible_domains(self, x):
if self.domain == x.domain:
return True
return x.is_constant_domain
def _div_compatible_domains(self, x):
if self.domain == x.domain:
return True
return x.is_constant_domain
def as_expr(self):
return DiscreteTimeDomainExpression(self)
def differentiate(self):
"""First order difference."""
result = (self.expr - self.subs(n - 1).expr) / dt
return self.__class__(result, **self.assumptions)
def integrate(self):
"""First order integration."""
from .sym import symsymbol
from .utils import factor_const
from .extrafunctions import UnitImpulse
from .functions import u
# TODO, get SymPy to optimize this case.
expr = self.expr
const, expr = factor_const(expr, nsym)
if expr.is_Function and expr.func == UnitImpulse:
return dt * u(expr.args[0]) * const
msym = symsymbol('m', integer=True)
result = dt * summation(self.subs(msym).expr, (msym, -oo, nsym))
return self.__class__(result, **self.assumptions)
def ztransform(self, evaluate=True, **assumptions):
"""Determine one-sided z-transform."""
assumptions = self.assumptions.merge_and_infer(self, **assumptions)
result = ztransform(self.expr, self.var, zsym, evaluate)
return self.change(result, domain='Z', **assumptions)
def ZT(self, **assumptions):
return self.ztransform(**assumptions)
def plot(self, ni=None, **kwargs):
"""Plot the sequence. If `ni` is not specified, it defaults to the
range (-20, 20). `ni` can be a vector of specified sequence
indices, a tuple specifing the range, or a constant specifying
the maximum value with the minimum value set to 0.
kwargs include:
axes - the plot axes to use otherwise a new figure is created
xlabel - the x-axis label
ylabel - the y-axis label
xscale - the x-axis scaling, say for plotting as ms
yscale - the y-axis scaling, say for plotting mV
in addition to those supported by the matplotlib plot command.
The plot axes are returned.
"""
if ni is None:
ni = (-20, 20)
from .plot import plot_sequence
return plot_sequence(self, ni, **kwargs)
def initial_value(self):
"""Determine value at n = 0."""
return self.subs(0)
def final_value(self):
"""Determine value at n = oo."""
return self.__class__(limit(self.expr, self.var, oo))
def DFT(self, N=None, evaluate=True):
if N is None:
from .sym import symsymbol
N = symsymbol('N', integer=True, positive=True)
result = DFT(self.expr, nsym, ksym, N, evaluate=evaluate)
return self.change(result, domain='discrete fourier')
def delay(self,m):
"""Delay signal by m samples."""
return self.subs(n - m)
def extent(self, n1=-100, n2=100):
"""Determine extent of the signal.
For example, nexpr([1, 1]).extent() = 2
nexpr([1, 0, 1]).extent() = 3
nexpr([0, 1, 0, 1]).extent() = 3
This performs a search between n=n1 and n=n2."""
return self.seq((n1, n2)).extent()
def discrete_time_fourier_transform(self, var=None,
images=oo, **assumptions):
"""Convert to Fourier domain using discrete time Fourier transform.
Use `images = 0` to avoid the infinite number of spectral images.
"""
return self.DTFT(var, images, **assumptions)
def DTFT(self, var=None, images=oo, **assumptions):
"""Convert to Fourier domain using discrete time Fourier transform.
By default this returns the DTFT in terms of `f`. Use
`.DTFT(w)` to get the angular frequency form, `.DTFT(F)` to
get the normalised frequency form, or `.DTFT(W)` to get the
normalised angular frequency form.
Use `images = 0` to avoid the infinite number of spectral images.
"""
from .extrafunctions import UnitStep
from .symbols import f, omega, Omega, F
from .fexpr import fexpr
from .dtft import DTFT
if var is None:
var = f
if id(var) not in (id(f), id(F), id(omega), id(Omega)):
raise ValueError('DTFT requires var to be f, F, omega, or Omega`, not %s' % var)
dtft = DTFT(self.expr, self.var, fsym, images=images)
result = fexpr(dtft)(var)
result = result.simplify_dirac_delta()
result = result.simplify_heaviside()
result = result.simplify_rect()
# There is a bug in SymPy when simplifying Sum('X(n - m)', (m, -oo, oo))
# result = result.simplify()
result = result.cancel_terms()
return result
def norm_angular_fourier(self, **assumptions):
from .normomegaexpr import Omega
return self.DTFT()(Omega)
def difference_equation(self, inputsym='x', outputsym='y', form='iir'):
"""Create difference equation from impulse response.
`form` can be 'fir' or 'iir' ('direct form I').
"""
H = self.ZT()
return H.difference_equation(inputsym, outputsym, form)
def remove_condition(self):
"""Remove the piecewise condition from the expression."""
if not self.is_conditional:
return self
expr = self.expr
expr = expr.args[0].args[0]
return self.__class__(expr)
def nexpr(arg, **assumptions):
"""Create nExpr object. If `arg` is nsym return n"""
from .expr import Expr
from .seq import seq
if arg is nsym:
return n
if isinstance(arg, Expr):
if assumptions == {}:
return arg
return arg.__class__(arg, **assumptions)
if isinstance(arg, str) and arg.startswith('{'):
return nseq(arg)
from numpy import ndarray
if isinstance(arg, (list, ndarray)):
return DiscreteTimeDomainSequence(arg, var=n).as_impulses()
return DiscreteTimeDomainExpression(arg, **assumptions)
from .expressionclasses import expressionclasses
expressionclasses.register('discrete time', DiscreteTimeDomainExpression)
n = DiscreteTimeDomainExpression('n', integer=True)
| lgpl-2.1 |
fja05680/pinkfish | examples/310.cryptocurrencies/strategy.py | 1 | 6833 | """
The SMA-ROC-portfolio stategy.
This is SMA-ROC strategy applied to a portfolio.
SMA-ROC is a rate of change calculation smoothed by
a moving average.
This module allows us to examine this strategy and try different
period, stop loss percent, margin, and whether to use a regime filter
or not. We split up the total capital between the symbols in the
portfolio and allocate based on either equal weight or volatility
parity weight (inverse volatility).
"""
import datetime
import matplotlib.pyplot as plt
import pandas as pd
from talib.abstract import *
import pinkfish as pf
# A custom indicator to use in this strategy.
def SMA_ROC(ts, mom_lookback=1, sma_timeperiod=20, price='close'):
""" Returns a series which is an SMA with of a daily MOM. """
mom = pf.MOMENTUM(ts, lookback=mom_lookback, time_frame='daily', price=price)
sma_mom = SMA(mom, timeperiod=sma_timeperiod)
return sma_mom
default_options = {
'use_adj' : False,
'use_cache' : True,
'stock_market_calendar' : False,
'stop_loss_pct' : 1.0,
'margin' : 1,
'lookback' : 1,
'sma_timeperiod': 20,
'sma_pct_band': 0,
'use_regime_filter' : True,
'use_vola_weight' : False
}
class Strategy:
def __init__(self, symbols, capital, start, end, options=default_options):
self.symbols = symbols
self.capital = capital
self.start = start
self.end = end
self.options = options.copy()
self.ts = None
self.rlog = None
self.tlog = None
self.dbal = None
self.stats = None
def _algo(self):
pf.TradeLog.cash = self.capital
pf.TradeLog.margin = self.options['margin']
# Create a stop_loss dict for each symbol.
stop_loss = {symbol:0 for symbol in self.portfolio.symbols}
# stop loss pct should range between 0 and 1, user may have
# expressed this as a percentage 0-100
if self.options['stop_loss_pct'] > 1:
self.options['stop_loss_pct'] /= 100
upper_band = self.options['sma_pct_band']/1000
lower_band = -self.options['sma_pct_band']/1000
# Loop though timeseries.
for i, row in enumerate(self.ts.itertuples()):
date = row.Index.to_pydatetime()
end_flag = pf.is_last_row(self.ts, i)
# Get the prices for this row, put in dict p.
p = self.portfolio.get_prices(row,
fields=['close', 'regime', 'sma_roc', 'vola'])
# Sum the inverse volatility for each row.
inverse_vola_sum = 0
for symbol in self.portfolio.symbols:
inverse_vola_sum += 1 / p[symbol]['vola']
# Loop though each symbol in portfolio.
for symbol in self.portfolio.symbols:
# Use variables to make code cleaner.
close = p[symbol]['close']
regime = p[symbol]['regime']
sma_roc = p[symbol]['sma_roc']
inverse_vola = 1 / p[symbol]['vola']
# Sell Logic
# First we check if an existing position in symbol should be sold
# - sell sma_roc < 0
# - sell if price closes below stop loss
# - sell if end of data by adjusted the percent to zero
if symbol in self.portfolio.positions:
if sma_roc < lower_band or close < stop_loss[symbol] or end_flag:
if close < stop_loss[symbol]: print('STOP LOSS!!!')
self.portfolio.adjust_percent(date, close, 0, symbol, row)
# Buy Logic
# First we check to see if there is an existing position, if so do nothing
# - Buy if (regime > 0 or not use_regime_filter) and sma_roc > 0
else:
if (regime > 0 or not self.options['use_regime_filter']) and sma_roc > upper_band:
# Use volatility weight.
if self.options['use_vola_weight']:
weight = inverse_vola / inverse_vola_sum
# Use equal weight.
else:
weight = 1 / len(self.portfolio.symbols)
self.portfolio.adjust_percent(date, close, weight, symbol, row)
# Set stop loss
stop_loss[symbol] = (1-self.options['stop_loss_pct'])*close
# record daily balance
self.portfolio.record_daily_balance(date, row)
def run(self):
self.portfolio = pf.Portfolio()
self.ts = self.portfolio.fetch_timeseries(self.symbols, self.start, self.end,
fields=['close'], use_cache=self.options['use_cache'],
use_adj=self.options['use_adj'],
dir_name='cryptocurrencies',
stock_market_calendar=self.options['stock_market_calendar'])
# Add technical indicator: 200 sma regime filter for each symbol.
def _crossover(ts, ta_param, input_column):
return pf.CROSSOVER(ts, timeperiod_fast=1, timeperiod_slow=200,
price=input_column, prevday=False)
self.ts = self.portfolio.add_technical_indicator(
self.ts, ta_func=_crossover, ta_param=None,
output_column_suffix='regime', input_column_suffix='close')
# Add technical indicator: volatility.
def _volatility(ts, ta_param, input_column):
return pf.VOLATILITY(ts, price=input_column)
self.ts = self.portfolio.add_technical_indicator(
self.ts, ta_func=_volatility, ta_param=None,
output_column_suffix='vola', input_column_suffix='close')
# Add techincal indicator: X day SMA_ROC.
def _sma_roc(ts, ta_param, input_column):
return SMA_ROC(ts, mom_lookback=self.options['lookback'],
sma_timeperiod=self.options['sma_timeperiod'],
price=input_column)
self.ts = self.portfolio.add_technical_indicator(
self.ts, ta_func=_sma_roc, ta_param=None,
output_column_suffix='sma_roc', input_column_suffix='close')
# Finalize timeseries.
self.ts, self.start = self.portfolio.finalize_timeseries(self.ts, self.start)
# Init trade log objects.
self.portfolio.init_trade_logs(self.ts)
self._algo()
self._get_logs()
self._get_stats()
def _get_logs(self):
self.rlog, self.tlog, self.dbal = self.portfolio.get_logs()
def _get_stats(self):
self.stats = pf.stats(self.ts, self.tlog, self.dbal, self.capital)
| mit |
tasoc/photometry | notes/halo_shift.py | 1 | 2629 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
.. codeauthor:: Rasmus Handberg <rasmush@phys.au.dk>
"""
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
import sqlite3
import os.path
#------------------------------------------------------------------------------
def mag2flux(mag):
"""
Convert from magnitude to flux using scaling relation from
aperture photometry. This is an estimate.
Parameters:
mag (float): Magnitude in TESS band.
Returns:
float: Corresponding flux value
"""
return 10**(-0.4*(mag - 20.54))
if __name__ == '__main__':
pass
folder = r'C:\Users\au195407\Documents\tess_data_local\S01_DR01-2114872'
conn = sqlite3.connect(os.path.join(folder, 'todo.sqlite'))
conn.row_factory = sqlite3.Row
cursor = conn.cursor()
cursor.execute("SELECT todolist.starid,tmag,onedge,edgeflux FROM todolist INNER JOIN diagnostics ON todolist.priority=diagnostics.priority;")
results = cursor.fetchall()
starid = np.array([row['starid'] for row in results], dtype='int64')
tmag = np.array([row['tmag'] for row in results])
OnEdge = np.array([np.NaN if row['onedge'] is None else row['onedge'] for row in results])
EdgeFlux = np.array([np.NaN if row['edgeflux'] is None else row['edgeflux'] for row in results])
cursor.close()
conn.close()
print(tmag)
print(OnEdge)
print(EdgeFlux)
tmag_limit = 3.0
flux_limit = 1e-3
indx = (OnEdge > 0)
indx_halo = (tmag <= tmag_limit) & (OnEdge > 0) & (EdgeFlux/mag2flux(tmag) > flux_limit)
indx_spec = (starid == 382420379)
print(starid[indx_halo])
fig = plt.figure()
ax = fig.add_subplot(111)
plt.scatter(tmag[indx], OnEdge[indx], alpha=0.5)
plt.scatter(tmag[indx_halo], OnEdge[indx_halo], marker='x', c='r')
plt.xlim(xmax=tmag_limit)
plt.ylim(ymin=0)
ax.set_xlabel('Tmag')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(tmag[indx], EdgeFlux[indx], alpha=0.5)
ax.set_xlim(xmax=5.0)
#ax.set_ylim(ymin=0.0)
ax.set_yscale('log')
ax.set_xlabel('Tmag')
fig = plt.figure()
ax = fig.add_subplot(111)
plt.scatter(tmag[indx], EdgeFlux[indx]/mag2flux(tmag[indx]), alpha=0.5)
plt.scatter(tmag[indx_halo], EdgeFlux[indx_halo]/mag2flux(tmag[indx_halo]), alpha=0.3, marker='x', c='r')
plt.scatter(tmag[indx_spec], EdgeFlux[indx_spec]/mag2flux(tmag[indx_spec]), alpha=0.3, marker='o', c='g', lw=2)
plt.plot([2.0, 6.0], [1e-3, 2e-2], 'r--')
plt.axhline(flux_limit, c='r', ls='--')
plt.axvline(tmag_limit, c='r', ls='--')
#plt.xlim(xmax=tmag_limit)
ax.set_ylim(ymin=1e-5, ymax=1)
ax.set_yscale('log')
ax.set_ylabel('Edge Flux / Expected Total Flux')
ax.set_xlabel('Tmag')
plt.show()
| gpl-3.0 |
Barmaley-exe/scikit-learn | examples/tree/plot_tree_regression_multioutput.py | 43 | 1791 | """
===================================================================
Multi-output Decision Tree Regression
===================================================================
An example to illustrate multi-output regression with decision tree.
The :ref:`decision trees <tree>`
is used to predict simultaneously the noisy x and y observations of a circle
given a single underlying feature. As a result, it learns local linear
regressions approximating the circle.
We can see that if the maximum depth of the tree (controlled by the
`max_depth` parameter) is set too high, the decision trees learn too fine
details of the training data and learn from the noise, i.e. they overfit.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeRegressor
# Create a random dataset
rng = np.random.RandomState(1)
X = np.sort(200 * rng.rand(100, 1) - 100, axis=0)
y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T
y[::5, :] += (0.5 - rng.rand(20, 2))
# Fit regression model
clf_1 = DecisionTreeRegressor(max_depth=2)
clf_2 = DecisionTreeRegressor(max_depth=5)
clf_3 = DecisionTreeRegressor(max_depth=8)
clf_1.fit(X, y)
clf_2.fit(X, y)
clf_3.fit(X, y)
# Predict
X_test = np.arange(-100.0, 100.0, 0.01)[:, np.newaxis]
y_1 = clf_1.predict(X_test)
y_2 = clf_2.predict(X_test)
y_3 = clf_3.predict(X_test)
# Plot the results
plt.figure()
plt.scatter(y[:, 0], y[:, 1], c="k", label="data")
plt.scatter(y_1[:, 0], y_1[:, 1], c="g", label="max_depth=2")
plt.scatter(y_2[:, 0], y_2[:, 1], c="r", label="max_depth=5")
plt.scatter(y_3[:, 0], y_3[:, 1], c="b", label="max_depth=8")
plt.xlim([-6, 6])
plt.ylim([-6, 6])
plt.xlabel("data")
plt.ylabel("target")
plt.title("Multi-output Decision Tree Regression")
plt.legend()
plt.show()
| bsd-3-clause |
JosmanPS/scikit-learn | examples/cluster/plot_lena_ward_segmentation.py | 271 | 1998 | """
===============================================================
A demo of structured Ward hierarchical clustering on Lena image
===============================================================
Compute the segmentation of a 2D image with Ward hierarchical
clustering. The clustering is spatially constrained in order
for each segmented region to be in one piece.
"""
# Author : Vincent Michel, 2010
# Alexandre Gramfort, 2011
# License: BSD 3 clause
print(__doc__)
import time as time
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from sklearn.feature_extraction.image import grid_to_graph
from sklearn.cluster import AgglomerativeClustering
###############################################################################
# Generate data
lena = sp.misc.lena()
# Downsample the image by a factor of 4
lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2]
X = np.reshape(lena, (-1, 1))
###############################################################################
# Define the structure A of the data. Pixels connected to their neighbors.
connectivity = grid_to_graph(*lena.shape)
###############################################################################
# Compute clustering
print("Compute structured hierarchical clustering...")
st = time.time()
n_clusters = 15 # number of regions
ward = AgglomerativeClustering(n_clusters=n_clusters,
linkage='ward', connectivity=connectivity).fit(X)
label = np.reshape(ward.labels_, lena.shape)
print("Elapsed time: ", time.time() - st)
print("Number of pixels: ", label.size)
print("Number of clusters: ", np.unique(label).size)
###############################################################################
# Plot the results on an image
plt.figure(figsize=(5, 5))
plt.imshow(lena, cmap=plt.cm.gray)
for l in range(n_clusters):
plt.contour(label == l, contours=1,
colors=[plt.cm.spectral(l / float(n_clusters)), ])
plt.xticks(())
plt.yticks(())
plt.show()
| bsd-3-clause |
Habasari/sms-tools | lectures/08-Sound-transformations/plots-code/stftFiltering-orchestra.py | 18 | 1677 | import numpy as np
import time, os, sys
import matplotlib.pyplot as plt
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/transformations/'))
import utilFunctions as UF
import stftTransformations as STFTT
import stft as STFT
(fs, x) = UF.wavread('../../../sounds/orchestra.wav')
w = np.hamming(2048)
N = 2048
H = 512
# design a band stop filter using a hanning window
startBin = int(N*500.0/fs)
nBins = int(N*2000.0/fs)
bandpass = (np.hanning(nBins) * 65.0) - 60
filt = np.zeros(N/2+1)-60
filt[startBin:startBin+nBins] = bandpass
y = STFTT.stftFiltering(x, fs, w, N, H, filt)
mX,pX = STFT.stftAnal(x, fs, w, N, H)
mY,pY = STFT.stftAnal(y, fs, w, N, H)
plt.figure(1, figsize=(12, 9))
plt.subplot(311)
numFrames = int(mX[:,0].size)
frmTime = H*np.arange(numFrames)/float(fs)
binFreq = np.arange(mX[0,:].size)*float(fs)/N
plt.pcolormesh(frmTime, binFreq, np.transpose(mX))
plt.title('mX (orchestra.wav)')
plt.autoscale(tight=True)
plt.subplot(312)
plt.plot(fs*np.arange(mX[0,:].size)/float(N), filt, 'k', lw=1.3)
plt.axis([0, fs/2, -60, 7])
plt.title('filter shape')
plt.subplot(313)
numFrames = int(mY[:,0].size)
frmTime = H*np.arange(numFrames)/float(fs)
binFreq = np.arange(mY[0,:].size)*float(fs)/N
plt.pcolormesh(frmTime, binFreq, np.transpose(mY))
plt.title('mY')
plt.autoscale(tight=True)
plt.tight_layout()
UF.wavwrite(y, fs, 'orchestra-stft-filtering.wav')
plt.savefig('stftFiltering-orchestra.png')
plt.show()
| agpl-3.0 |
heli522/scikit-learn | examples/neighbors/plot_approximate_nearest_neighbors_scalability.py | 225 | 5719 | """
============================================
Scalability of Approximate Nearest Neighbors
============================================
This example studies the scalability profile of approximate 10-neighbors
queries using the LSHForest with ``n_estimators=20`` and ``n_candidates=200``
when varying the number of samples in the dataset.
The first plot demonstrates the relationship between query time and index size
of LSHForest. Query time is compared with the brute force method in exact
nearest neighbor search for the same index sizes. The brute force queries have a
very predictable linear scalability with the index (full scan). LSHForest index
have sub-linear scalability profile but can be slower for small datasets.
The second plot shows the speedup when using approximate queries vs brute force
exact queries. The speedup tends to increase with the dataset size but should
reach a plateau typically when doing queries on datasets with millions of
samples and a few hundreds of dimensions. Higher dimensional datasets tends to
benefit more from LSHForest indexing.
The break even point (speedup = 1) depends on the dimensionality and structure
of the indexed data and the parameters of the LSHForest index.
The precision of approximate queries should decrease slowly with the dataset
size. The speed of the decrease depends mostly on the LSHForest parameters and
the dimensionality of the data.
"""
from __future__ import division
print(__doc__)
# Authors: Maheshakya Wijewardena <maheshakya.10@cse.mrt.ac.lk>
# Olivier Grisel <olivier.grisel@ensta.org>
#
# License: BSD 3 clause
###############################################################################
import time
import numpy as np
from sklearn.datasets.samples_generator import make_blobs
from sklearn.neighbors import LSHForest
from sklearn.neighbors import NearestNeighbors
import matplotlib.pyplot as plt
# Parameters of the study
n_samples_min = int(1e3)
n_samples_max = int(1e5)
n_features = 100
n_centers = 100
n_queries = 100
n_steps = 6
n_iter = 5
# Initialize the range of `n_samples`
n_samples_values = np.logspace(np.log10(n_samples_min),
np.log10(n_samples_max),
n_steps).astype(np.int)
# Generate some structured data
rng = np.random.RandomState(42)
all_data, _ = make_blobs(n_samples=n_samples_max + n_queries,
n_features=n_features, centers=n_centers, shuffle=True,
random_state=0)
queries = all_data[:n_queries]
index_data = all_data[n_queries:]
# Metrics to collect for the plots
average_times_exact = []
average_times_approx = []
std_times_approx = []
accuracies = []
std_accuracies = []
average_speedups = []
std_speedups = []
# Calculate the average query time
for n_samples in n_samples_values:
X = index_data[:n_samples]
# Initialize LSHForest for queries of a single neighbor
lshf = LSHForest(n_estimators=20, n_candidates=200,
n_neighbors=10).fit(X)
nbrs = NearestNeighbors(algorithm='brute', metric='cosine',
n_neighbors=10).fit(X)
time_approx = []
time_exact = []
accuracy = []
for i in range(n_iter):
# pick one query at random to study query time variability in LSHForest
query = queries[rng.randint(0, n_queries)]
t0 = time.time()
exact_neighbors = nbrs.kneighbors(query, return_distance=False)
time_exact.append(time.time() - t0)
t0 = time.time()
approx_neighbors = lshf.kneighbors(query, return_distance=False)
time_approx.append(time.time() - t0)
accuracy.append(np.in1d(approx_neighbors, exact_neighbors).mean())
average_time_exact = np.mean(time_exact)
average_time_approx = np.mean(time_approx)
speedup = np.array(time_exact) / np.array(time_approx)
average_speedup = np.mean(speedup)
mean_accuracy = np.mean(accuracy)
std_accuracy = np.std(accuracy)
print("Index size: %d, exact: %0.3fs, LSHF: %0.3fs, speedup: %0.1f, "
"accuracy: %0.2f +/-%0.2f" %
(n_samples, average_time_exact, average_time_approx, average_speedup,
mean_accuracy, std_accuracy))
accuracies.append(mean_accuracy)
std_accuracies.append(std_accuracy)
average_times_exact.append(average_time_exact)
average_times_approx.append(average_time_approx)
std_times_approx.append(np.std(time_approx))
average_speedups.append(average_speedup)
std_speedups.append(np.std(speedup))
# Plot average query time against n_samples
plt.figure()
plt.errorbar(n_samples_values, average_times_approx, yerr=std_times_approx,
fmt='o-', c='r', label='LSHForest')
plt.plot(n_samples_values, average_times_exact, c='b',
label="NearestNeighbors(algorithm='brute', metric='cosine')")
plt.legend(loc='upper left', fontsize='small')
plt.ylim(0, None)
plt.ylabel("Average query time in seconds")
plt.xlabel("n_samples")
plt.grid(which='both')
plt.title("Impact of index size on response time for first "
"nearest neighbors queries")
# Plot average query speedup versus index size
plt.figure()
plt.errorbar(n_samples_values, average_speedups, yerr=std_speedups,
fmt='o-', c='r')
plt.ylim(0, None)
plt.ylabel("Average speedup")
plt.xlabel("n_samples")
plt.grid(which='both')
plt.title("Speedup of the approximate NN queries vs brute force")
# Plot average precision versus index size
plt.figure()
plt.errorbar(n_samples_values, accuracies, std_accuracies, fmt='o-', c='c')
plt.ylim(0, 1.1)
plt.ylabel("precision@10")
plt.xlabel("n_samples")
plt.grid(which='both')
plt.title("precision of 10-nearest-neighbors queries with index size")
plt.show()
| bsd-3-clause |
kaku289/paparazzi | sw/airborne/test/ahrs/ahrs_utils.py | 86 | 4923 | #! /usr/bin/env python
# Copyright (C) 2011 Antoine Drouin
#
# This file is part of Paparazzi.
#
# Paparazzi is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2, or (at your option)
# any later version.
#
# Paparazzi is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Paparazzi; see the file COPYING. If not, write to
# the Free Software Foundation, 59 Temple Place - Suite 330,
# Boston, MA 02111-1307, USA.
#
from __future__ import print_function
import subprocess
import numpy as np
import matplotlib.pyplot as plt
def run_simulation(ahrs_type, build_opt, traj_nb):
print("\nBuilding ahrs")
args = ["make", "clean", "run_ahrs_on_synth", "AHRS_TYPE=AHRS_TYPE_" + ahrs_type] + build_opt
#print(args)
p = subprocess.Popen(args=args, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=False)
outputlines = p.stdout.readlines()
p.wait()
for i in outputlines:
print(" # " + i, end=' ')
print()
print("Running simulation")
print(" using traj " + str(traj_nb))
p = subprocess.Popen(args=["./run_ahrs_on_synth", str(traj_nb)], stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
shell=False)
outputlines = p.stdout.readlines()
p.wait()
# for i in outputlines:
# print(" "+i, end=' ')
# print("\n")
ahrs_data_type = [('time', 'float32'),
('phi_true', 'float32'), ('theta_true', 'float32'), ('psi_true', 'float32'),
('p_true', 'float32'), ('q_true', 'float32'), ('r_true', 'float32'),
('bp_true', 'float32'), ('bq_true', 'float32'), ('br_true', 'float32'),
('phi_ahrs', 'float32'), ('theta_ahrs', 'float32'), ('psi_ahrs', 'float32'),
('p_ahrs', 'float32'), ('q_ahrs', 'float32'), ('r_ahrs', 'float32'),
('bp_ahrs', 'float32'), ('bq_ahrs', 'float32'), ('br_ahrs', 'float32')]
mydescr = np.dtype(ahrs_data_type)
data = [[] for dummy in xrange(len(mydescr))]
# import code; code.interact(local=locals())
for line in outputlines:
if line.startswith("#"):
print(" " + line, end=' ')
else:
fields = line.strip().split(' ')
#print(fields)
for i, number in enumerate(fields):
data[i].append(number)
print()
for i in xrange(len(mydescr)):
data[i] = np.cast[mydescr[i]](data[i])
return np.rec.array(data, dtype=mydescr)
def plot_simulation_results(plot_true_state, lsty, label, sim_res):
print("Plotting Results")
# f, (ax1, ax2, ax3) = plt.subplots(3, sharex=True, sharey=True)
plt.subplot(3, 3, 1)
plt.plot(sim_res.time, sim_res.phi_ahrs, lsty, label=label)
plt.ylabel('degres')
plt.title('phi')
plt.legend()
plt.subplot(3, 3, 2)
plt.plot(sim_res.time, sim_res.theta_ahrs, lsty)
plt.title('theta')
plt.subplot(3, 3, 3)
plt.plot(sim_res.time, sim_res.psi_ahrs, lsty)
plt.title('psi')
plt.subplot(3, 3, 4)
plt.plot(sim_res.time, sim_res.p_ahrs, lsty)
plt.ylabel('degres/s')
plt.title('p')
plt.subplot(3, 3, 5)
plt.plot(sim_res.time, sim_res.q_ahrs, lsty)
plt.title('q')
plt.subplot(3, 3, 6)
plt.plot(sim_res.time, sim_res.r_ahrs, lsty)
plt.title('r')
plt.subplot(3, 3, 7)
plt.plot(sim_res.time, sim_res.bp_ahrs, lsty)
plt.ylabel('degres/s')
plt.xlabel('time in s')
plt.title('bp')
plt.subplot(3, 3, 8)
plt.plot(sim_res.time, sim_res.bq_ahrs, lsty)
plt.xlabel('time in s')
plt.title('bq')
plt.subplot(3, 3, 9)
plt.plot(sim_res.time, sim_res.br_ahrs, lsty)
plt.xlabel('time in s')
plt.title('br')
if plot_true_state:
plt.subplot(3, 3, 1)
plt.plot(sim_res.time, sim_res.phi_true, 'r--')
plt.subplot(3, 3, 2)
plt.plot(sim_res.time, sim_res.theta_true, 'r--')
plt.subplot(3, 3, 3)
plt.plot(sim_res.time, sim_res.psi_true, 'r--')
plt.subplot(3, 3, 4)
plt.plot(sim_res.time, sim_res.p_true, 'r--')
plt.subplot(3, 3, 5)
plt.plot(sim_res.time, sim_res.q_true, 'r--')
plt.subplot(3, 3, 6)
plt.plot(sim_res.time, sim_res.r_true, 'r--')
plt.subplot(3, 3, 7)
plt.plot(sim_res.time, sim_res.bp_true, 'r--')
plt.subplot(3, 3, 8)
plt.plot(sim_res.time, sim_res.bq_true, 'r--')
plt.subplot(3, 3, 9)
plt.plot(sim_res.time, sim_res.br_true, 'r--')
def show_plot():
plt.show()
| gpl-2.0 |
df8oe/UHSDR | mchf-eclipse/drivers/ui/lcd/edit-8x8-font.py | 4 | 2343 | # Tool to extract 8x8 font data, save to bitmap file, and apply modifications
# to source code after editing the bitmap.
from __future__ import print_function
from matplotlib.pyplot import imread, imsave, imshow, show
import numpy as np
import sys
# Where to find the font data - may need updated if code has changed.
source_file = 'ui_lcd_hy28_fonts.c'
start_marker = 'const uint8_t GL_ASCII8x8_Table [] ='
start_offset = 2 # Data starts this number of lines after start marker.
end_marker = '};' # Indicates end of font data.
# Image filename used in extract and insert modes.
image_file = 'font-8x8.png'
mode = None
if len(sys.argv) > 1:
mode = sys.argv[1]
if mode not in ('show', 'extract', 'insert'):
print("Usage: %s { show | extract | insert }" % sys.argv[0])
sys.exit(1)
# Get the literals used to populate the font array in the source file
lines = [line.rstrip() for line in open(source_file).readlines()]
start = lines.index(start_marker) + start_offset
end = start + lines[start:].index(end_marker)
data = str.join("", lines[start:end])
# Eval the literals to get the values into a numpy array
packed = eval("np.array([%s], np.uint8)" % data)
# Reorganise into a monochrome image, with the 96 8 x 8 characters
# laid out in 8 rows by 12 columns for easier viewing/editing
unpacked = np.unpackbits(packed)
bitmaps = unpacked.reshape(96, 8, 8)
indices = np.arange(96).reshape(8, 12)
image = np.block([[bitmaps[idx] for idx in row] for row in indices])
if mode == 'show':
# Display font image
imshow(image, cmap='binary')
show()
elif mode == 'extract':
# Save font image
imsave(image_file, image, format='png', cmap='binary')
elif mode == 'insert':
# Read in modified font image
image = imread(image_file)[:,:,0].astype(bool)
# Reorganise back to original order
bitmaps = np.vstack([np.vstack(np.split(row, 12, 1))
for row in np.split(image, 8)])
unpacked = bitmaps.reshape(-1)
packed = ~np.packbits(unpacked)
# Replace lines of file in same format as used before
grouped = packed.reshape(-1, 8)
for i, group in enumerate(grouped):
line = (" " + " 0x%02x," * 8) % tuple(group)
lines[start + i] = line
# Write out modified source file
open(source_file, 'w').writelines([line + "\n" for line in lines])
| gpl-3.0 |
kezilu/pextant | pextant/api.py | 2 | 3350 | import csv
import json
import logging
import re
from pextant.solvers.astarMesh import astarSolver
from pextant.analysis.loadWaypoints import JSONloader
import matplotlib.pyplot as plt
logger = logging.getLogger()
class Pathfinder:
"""
This class performs the A* path finding algorithm and contains the Cost Functions. Also includes
capabilities for analysis of a path.
This class still needs performance testing for maps of larger sizes. I don't believe that
we will be doing anything extremely computationally intensive though.
Current cost functions are Time, Distance, and (Metabolic) Energy. It would be useful to be able to
optimize on other resources like battery power or water sublimated, but those are significantly more
difficult because they depend on shadowing and was not implemented by Aaron.
"""
def __init__(self, explorer_model, environmental_model):
cheating = 1
self.solver = astarSolver(environmental_model, explorer_model,
optimize_on = 'Energy', heuristic_accelerate = cheating)
def aStarCompletePath(self, optimize_on, waypoints, returnType="JSON", dh=None, fileName=None ):
pass
def completeSearch(self, optimize_on, waypoints, filepath=None ):
"""
Returns a tuple representing the path and the total cost of the path.
The path will be a list. All activity points will be duplicated in
the returned path.
waypoints is a list of activityPoint objects, in the correct order. fileName is
used when we would like to write stuff to a file and is currently necessary
for csv return types.
"""
segmentsout, rawpoints, items = self.solver.solvemultipoint(waypoints)
if filepath:
extension = re.search('^(.+\/[^/]+)\.(\w+)$', filepath).group(2)
else:
extension = None
if extension == "json":
json.dump(segmentsout.tojson(), filepath)
elif extension == "csv":
header = [['isStation', 'x', 'y', 'z', 'distanceMeters', 'energyJoules', 'timeSeconds']]
rows = header + segmentsout.tocsv()
with open(filepath, 'wb') as csvfile:
writer = csv.writer(csvfile)
for row in rows:
writer.writerow(row)
return rows
return segmentsout, rawpoints, items
def completeSearchFromJSON(self, optimize_on, jsonInput, filepath=None, algorithm="A*",
numTestPoints=0):
jloader = JSONloader.from_string(jsonInput)
waypoints = jloader.get_waypoints()
#if algorithm == "A*":
segmentsout,_,_ = self.completeSearch(optimize_on, waypoints, filepath)
updatedjson = jloader.add_search_sol(segmentsout.list)
return updatedjson
if __name__ == '__main__':
from pextant.analysis.loadWaypoints import loadPoints
from explorers import Astronaut
from EnvironmentalModel import GDALMesh
hi_low = GDALMesh('maps/HI_lowqual_DEM.tif')
waypoints = loadPoints('waypoints/HI_13Nov16_MD7_A.json')
env_model = hi_low.loadSubSection(waypoints.geoEnvelope())
astronaut = Astronaut(80)
pathfinder = Pathfinder(astronaut, env_model)
out = pathfinder.aStarCompletePath('Energy', waypoints)
print out | mit |
qrsforever/workspace | python/learn/thinkstats/rankit.py | 1 | 1807 | #!/usr/bin/python3
# -*- coding: utf-8 -*-
"""This file contains code for use with "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2010 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
import random
import thinkstats
import myplot
import matplotlib.pyplot as pyplot
def Sample(n=6):
"""Generates a sample from a standard normal variate.
n: sample size
Returns: list of n floats
"""
t = [random.normalvariate(0.0, 1.0) for i in range(n)]
t.sort()
return t
def Samples(n=6, m=1000):
"""Generates m samples with size n each.
n: sample size
m: number of samples
Returns: list of m samples
"""
t = [Sample(n) for i in range(m)]
return t
def EstimateRankits(n=6, m=1000):
"""Estimates the expected values of sorted random samples.
n: sample size
m: number of iterations
Returns: list of n rankits
"""
t = Samples(n, m)
t = zip(*t)
means = [thinkstats.Mean(x) for x in t]
return means
def MakeNormalPlot(ys, root=None, line_options={}, **options):
"""Makes a normal probability plot.
Args:
ys: sequence of values
line_options: dictionary of options for pyplot.plot
options: dictionary of options for myplot.Save
"""
# TODO: when n is small, generate a larger sample and desample
n = len(ys)
xs = [random.normalvariate(0.0, 1.0) for i in range(n)]
pyplot.clf()
pyplot.plot(sorted(xs), sorted(ys), 'b.', markersize=3, **line_options)
myplot.Save(root,
xlabel = 'Standard normal values',
legend=False,
**options)
def main():
means = EstimateRankits(84)
print(means)
if __name__ == "__main__":
main()
| mit |
ralbayaty/KaggleRetina | testing/censureHistCalc.py | 1 | 4517 | from skimage.feature import CENSURE
from skimage.color import rgb2gray
import matplotlib.pyplot as plt
import numpy as np
import cv2
import sys
from PIL import Image, ImageDraw
def draw_keypoints(img, kp, scale):
draw = ImageDraw.Draw(img)
# Draw a maximum of 300 keypoints
for i in range(min(len(scale),300)):
x1 = kp[i,1]
y1 = kp[i,0]
x2 = kp[i,1]+2**scale[i]
y2 = kp[i,0]+2**scale[i]
coords = (x1, y1, x2, y2)
draw.ellipse(coords, fill = None, outline ='white')
if __name__ == '__main__':
try:
file_name = sys.argv[1]
except:
print("Didn't give me a file...")
file_name = "Lenna.png"
def nothing(*arg):
pass
# Create sliderbars to change the values of CENSURE parameters online
# Defaults: min_scale=1, max_scale=7, mode='DoB', non_max_threshold=0.15, line_threshold=10
cv2.namedWindow('censure')
cv2.createTrackbar('min_scale', 'censure', 1, 10, nothing)
cv2.createTrackbar('max_scale', 'censure', 7, 20, nothing)
cv2.createTrackbar('mode', 'censure', 2, 2, nothing)
cv2.createTrackbar('non_max_threshold', 'censure', 6, 1000, nothing)
cv2.createTrackbar('line_threshold', 'censure', 10, 100, nothing)
# Read image from file, then inspect the image dimensions
img = cv2.imread(file_name,1)
height, width, channels = img.shape
# Pull the different color channels from the image
blue = img[:,:,0]
green = img[:,:,1]
red = img[:,:,2]
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Make a PIL image from each channel so we can use PIL.Iamge.thumbnail to resize if needed
blue1 = Image.fromarray(blue)
green1 = Image.fromarray(green)
red1 = Image.fromarray(red)
gray1 = Image.fromarray(gray)
# Check if dimensions are above desired, if so then resize keepig aspect ratio
m, n = 512, 512
if height > m or width > n:
blue1.thumbnail((m,n), Image.ANTIALIAS)
green1.thumbnail((m,n), Image.ANTIALIAS)
red1.thumbnail((m,n), Image.ANTIALIAS)
gray1.thumbnail((m,n), Image.ANTIALIAS)
# CENSURE related
mode_dict = {"0": "DoB", "1": "Octagon", "2": "STAR"}
last_num_kp = 0
while True:
vis = gray.copy()
img = img1.copy()
# Read the values of the sliderbars and save them to variables
min_scale = cv2.getTrackbarPos('min_scale', 'censure')
max_scale = cv2.getTrackbarPos('max_scale', 'censure')
if min_scale is 0:
min_scale = 1
if min_scale + max_scale < 3:
max_scale = min_scale + 2
mode = mode_dict[str(cv2.getTrackbarPos('mode', 'censure'))]
non_max_threshold = float(cv2.getTrackbarPos('non_max_threshold', 'censure'))/1000
line_threshold = cv2.getTrackbarPos('line_threshold', 'censure')
# Create a CENSURE feature detector
censure = CENSURE(min_scale=min_scale, max_scale=max_scale, mode=mode,
non_max_threshold=non_max_threshold, line_threshold=line_threshold)
# Obtain the CENSURE features
censure.detect(blue1)
kp_blue, scale_blue = censure.keypoints, censure.scales
censure.detect(green1)
kp_green, scale_green = censure.keypoints, censure.scales
censure.detect(red1)
kp_red, scale_red = censure.keypoints, censure.scales
censure.detect(gray1)
kp_gray, scale_gray = censure.keypoints, censure.scales
# Print the # of features if it has changed between iterations
num_kp = len(censure.keypoints)
if last_num_kp != num_kp:
print("Number of keypoints: " + str(len(censure.keypoints)))
last_num_kp = num_kp
# Draw the feature points on the images
draw_keypoints(blue1, kp_blue, scale_blue)
draw_keypoints(green1, kp_green, scale_green)
draw_keypoints(red1, kp_red, scale_red)
draw_keypoints(gray1, kp_gray, scale_gray)
# Obtain the histogram of scale values
plt.clf() # clear the figure from any previous plot
scale_hist, bin_edges = np.histogram(censure.scales,max_scale-min_scale, (min_scale,max_scale+1))
plt.bar(bin_edges[:-1]-0.5, scale_hist, width = 1)
plt.show(block=False)
plt.draw()
# Show the image with keypoints drawn over
image = cv2.cvtColor(np.asarray(img),cv2.COLOR_BGR2RGB)
cv2.imshow('censure', image)
if 0xFF & cv2.waitKey(500) == 27:
break
cv2.destroyAllWindows() | gpl-2.0 |
gfyoung/numpy | numpy/lib/twodim_base.py | 2 | 27180 | """ Basic functions for manipulating 2d arrays
"""
from __future__ import division, absolute_import, print_function
import functools
from numpy.core.numeric import (
absolute, asanyarray, arange, zeros, greater_equal, multiply, ones,
asarray, where, int8, int16, int32, int64, empty, promote_types, diagonal,
nonzero
)
from numpy.core import overrides
from numpy.core import iinfo, transpose
__all__ = [
'diag', 'diagflat', 'eye', 'fliplr', 'flipud', 'tri', 'triu',
'tril', 'vander', 'histogram2d', 'mask_indices', 'tril_indices',
'tril_indices_from', 'triu_indices', 'triu_indices_from', ]
array_function_dispatch = functools.partial(
overrides.array_function_dispatch, module='numpy')
i1 = iinfo(int8)
i2 = iinfo(int16)
i4 = iinfo(int32)
def _min_int(low, high):
""" get small int that fits the range """
if high <= i1.max and low >= i1.min:
return int8
if high <= i2.max and low >= i2.min:
return int16
if high <= i4.max and low >= i4.min:
return int32
return int64
def _flip_dispatcher(m):
return (m,)
@array_function_dispatch(_flip_dispatcher)
def fliplr(m):
"""
Flip array in the left/right direction.
Flip the entries in each row in the left/right direction.
Columns are preserved, but appear in a different order than before.
Parameters
----------
m : array_like
Input array, must be at least 2-D.
Returns
-------
f : ndarray
A view of `m` with the columns reversed. Since a view
is returned, this operation is :math:`\\mathcal O(1)`.
See Also
--------
flipud : Flip array in the up/down direction.
rot90 : Rotate array counterclockwise.
Notes
-----
Equivalent to m[:,::-1]. Requires the array to be at least 2-D.
Examples
--------
>>> A = np.diag([1.,2.,3.])
>>> A
array([[ 1., 0., 0.],
[ 0., 2., 0.],
[ 0., 0., 3.]])
>>> np.fliplr(A)
array([[ 0., 0., 1.],
[ 0., 2., 0.],
[ 3., 0., 0.]])
>>> A = np.random.randn(2,3,5)
>>> np.all(np.fliplr(A) == A[:,::-1,...])
True
"""
m = asanyarray(m)
if m.ndim < 2:
raise ValueError("Input must be >= 2-d.")
return m[:, ::-1]
@array_function_dispatch(_flip_dispatcher)
def flipud(m):
"""
Flip array in the up/down direction.
Flip the entries in each column in the up/down direction.
Rows are preserved, but appear in a different order than before.
Parameters
----------
m : array_like
Input array.
Returns
-------
out : array_like
A view of `m` with the rows reversed. Since a view is
returned, this operation is :math:`\\mathcal O(1)`.
See Also
--------
fliplr : Flip array in the left/right direction.
rot90 : Rotate array counterclockwise.
Notes
-----
Equivalent to ``m[::-1,...]``.
Does not require the array to be two-dimensional.
Examples
--------
>>> A = np.diag([1.0, 2, 3])
>>> A
array([[ 1., 0., 0.],
[ 0., 2., 0.],
[ 0., 0., 3.]])
>>> np.flipud(A)
array([[ 0., 0., 3.],
[ 0., 2., 0.],
[ 1., 0., 0.]])
>>> A = np.random.randn(2,3,5)
>>> np.all(np.flipud(A) == A[::-1,...])
True
>>> np.flipud([1,2])
array([2, 1])
"""
m = asanyarray(m)
if m.ndim < 1:
raise ValueError("Input must be >= 1-d.")
return m[::-1, ...]
def eye(N, M=None, k=0, dtype=float, order='C'):
"""
Return a 2-D array with ones on the diagonal and zeros elsewhere.
Parameters
----------
N : int
Number of rows in the output.
M : int, optional
Number of columns in the output. If None, defaults to `N`.
k : int, optional
Index of the diagonal: 0 (the default) refers to the main diagonal,
a positive value refers to an upper diagonal, and a negative value
to a lower diagonal.
dtype : data-type, optional
Data-type of the returned array.
order : {'C', 'F'}, optional
Whether the output should be stored in row-major (C-style) or
column-major (Fortran-style) order in memory.
.. versionadded:: 1.14.0
Returns
-------
I : ndarray of shape (N,M)
An array where all elements are equal to zero, except for the `k`-th
diagonal, whose values are equal to one.
See Also
--------
identity : (almost) equivalent function
diag : diagonal 2-D array from a 1-D array specified by the user.
Examples
--------
>>> np.eye(2, dtype=int)
array([[1, 0],
[0, 1]])
>>> np.eye(3, k=1)
array([[ 0., 1., 0.],
[ 0., 0., 1.],
[ 0., 0., 0.]])
"""
if M is None:
M = N
m = zeros((N, M), dtype=dtype, order=order)
if k >= M:
return m
if k >= 0:
i = k
else:
i = (-k) * M
m[:M-k].flat[i::M+1] = 1
return m
def _diag_dispatcher(v, k=None):
return (v,)
@array_function_dispatch(_diag_dispatcher)
def diag(v, k=0):
"""
Extract a diagonal or construct a diagonal array.
See the more detailed documentation for ``numpy.diagonal`` if you use this
function to extract a diagonal and wish to write to the resulting array;
whether it returns a copy or a view depends on what version of numpy you
are using.
Parameters
----------
v : array_like
If `v` is a 2-D array, return a copy of its `k`-th diagonal.
If `v` is a 1-D array, return a 2-D array with `v` on the `k`-th
diagonal.
k : int, optional
Diagonal in question. The default is 0. Use `k>0` for diagonals
above the main diagonal, and `k<0` for diagonals below the main
diagonal.
Returns
-------
out : ndarray
The extracted diagonal or constructed diagonal array.
See Also
--------
diagonal : Return specified diagonals.
diagflat : Create a 2-D array with the flattened input as a diagonal.
trace : Sum along diagonals.
triu : Upper triangle of an array.
tril : Lower triangle of an array.
Examples
--------
>>> x = np.arange(9).reshape((3,3))
>>> x
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>> np.diag(x)
array([0, 4, 8])
>>> np.diag(x, k=1)
array([1, 5])
>>> np.diag(x, k=-1)
array([3, 7])
>>> np.diag(np.diag(x))
array([[0, 0, 0],
[0, 4, 0],
[0, 0, 8]])
"""
v = asanyarray(v)
s = v.shape
if len(s) == 1:
n = s[0]+abs(k)
res = zeros((n, n), v.dtype)
if k >= 0:
i = k
else:
i = (-k) * n
res[:n-k].flat[i::n+1] = v
return res
elif len(s) == 2:
return diagonal(v, k)
else:
raise ValueError("Input must be 1- or 2-d.")
@array_function_dispatch(_diag_dispatcher)
def diagflat(v, k=0):
"""
Create a two-dimensional array with the flattened input as a diagonal.
Parameters
----------
v : array_like
Input data, which is flattened and set as the `k`-th
diagonal of the output.
k : int, optional
Diagonal to set; 0, the default, corresponds to the "main" diagonal,
a positive (negative) `k` giving the number of the diagonal above
(below) the main.
Returns
-------
out : ndarray
The 2-D output array.
See Also
--------
diag : MATLAB work-alike for 1-D and 2-D arrays.
diagonal : Return specified diagonals.
trace : Sum along diagonals.
Examples
--------
>>> np.diagflat([[1,2], [3,4]])
array([[1, 0, 0, 0],
[0, 2, 0, 0],
[0, 0, 3, 0],
[0, 0, 0, 4]])
>>> np.diagflat([1,2], 1)
array([[0, 1, 0],
[0, 0, 2],
[0, 0, 0]])
"""
try:
wrap = v.__array_wrap__
except AttributeError:
wrap = None
v = asarray(v).ravel()
s = len(v)
n = s + abs(k)
res = zeros((n, n), v.dtype)
if (k >= 0):
i = arange(0, n-k)
fi = i+k+i*n
else:
i = arange(0, n+k)
fi = i+(i-k)*n
res.flat[fi] = v
if not wrap:
return res
return wrap(res)
def tri(N, M=None, k=0, dtype=float):
"""
An array with ones at and below the given diagonal and zeros elsewhere.
Parameters
----------
N : int
Number of rows in the array.
M : int, optional
Number of columns in the array.
By default, `M` is taken equal to `N`.
k : int, optional
The sub-diagonal at and below which the array is filled.
`k` = 0 is the main diagonal, while `k` < 0 is below it,
and `k` > 0 is above. The default is 0.
dtype : dtype, optional
Data type of the returned array. The default is float.
Returns
-------
tri : ndarray of shape (N, M)
Array with its lower triangle filled with ones and zero elsewhere;
in other words ``T[i,j] == 1`` for ``i <= j + k``, 0 otherwise.
Examples
--------
>>> np.tri(3, 5, 2, dtype=int)
array([[1, 1, 1, 0, 0],
[1, 1, 1, 1, 0],
[1, 1, 1, 1, 1]])
>>> np.tri(3, 5, -1)
array([[ 0., 0., 0., 0., 0.],
[ 1., 0., 0., 0., 0.],
[ 1., 1., 0., 0., 0.]])
"""
if M is None:
M = N
m = greater_equal.outer(arange(N, dtype=_min_int(0, N)),
arange(-k, M-k, dtype=_min_int(-k, M - k)))
# Avoid making a copy if the requested type is already bool
m = m.astype(dtype, copy=False)
return m
def _trilu_dispatcher(m, k=None):
return (m,)
@array_function_dispatch(_trilu_dispatcher)
def tril(m, k=0):
"""
Lower triangle of an array.
Return a copy of an array with elements above the `k`-th diagonal zeroed.
Parameters
----------
m : array_like, shape (M, N)
Input array.
k : int, optional
Diagonal above which to zero elements. `k = 0` (the default) is the
main diagonal, `k < 0` is below it and `k > 0` is above.
Returns
-------
tril : ndarray, shape (M, N)
Lower triangle of `m`, of same shape and data-type as `m`.
See Also
--------
triu : same thing, only for the upper triangle
Examples
--------
>>> np.tril([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
array([[ 0, 0, 0],
[ 4, 0, 0],
[ 7, 8, 0],
[10, 11, 12]])
"""
m = asanyarray(m)
mask = tri(*m.shape[-2:], k=k, dtype=bool)
return where(mask, m, zeros(1, m.dtype))
@array_function_dispatch(_trilu_dispatcher)
def triu(m, k=0):
"""
Upper triangle of an array.
Return a copy of a matrix with the elements below the `k`-th diagonal
zeroed.
Please refer to the documentation for `tril` for further details.
See Also
--------
tril : lower triangle of an array
Examples
--------
>>> np.triu([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
array([[ 1, 2, 3],
[ 4, 5, 6],
[ 0, 8, 9],
[ 0, 0, 12]])
"""
m = asanyarray(m)
mask = tri(*m.shape[-2:], k=k-1, dtype=bool)
return where(mask, zeros(1, m.dtype), m)
def _vander_dispatcher(x, N=None, increasing=None):
return (x,)
# Originally borrowed from John Hunter and matplotlib
@array_function_dispatch(_vander_dispatcher)
def vander(x, N=None, increasing=False):
"""
Generate a Vandermonde matrix.
The columns of the output matrix are powers of the input vector. The
order of the powers is determined by the `increasing` boolean argument.
Specifically, when `increasing` is False, the `i`-th output column is
the input vector raised element-wise to the power of ``N - i - 1``. Such
a matrix with a geometric progression in each row is named for Alexandre-
Theophile Vandermonde.
Parameters
----------
x : array_like
1-D input array.
N : int, optional
Number of columns in the output. If `N` is not specified, a square
array is returned (``N = len(x)``).
increasing : bool, optional
Order of the powers of the columns. If True, the powers increase
from left to right, if False (the default) they are reversed.
.. versionadded:: 1.9.0
Returns
-------
out : ndarray
Vandermonde matrix. If `increasing` is False, the first column is
``x^(N-1)``, the second ``x^(N-2)`` and so forth. If `increasing` is
True, the columns are ``x^0, x^1, ..., x^(N-1)``.
See Also
--------
polynomial.polynomial.polyvander
Examples
--------
>>> x = np.array([1, 2, 3, 5])
>>> N = 3
>>> np.vander(x, N)
array([[ 1, 1, 1],
[ 4, 2, 1],
[ 9, 3, 1],
[25, 5, 1]])
>>> np.column_stack([x**(N-1-i) for i in range(N)])
array([[ 1, 1, 1],
[ 4, 2, 1],
[ 9, 3, 1],
[25, 5, 1]])
>>> x = np.array([1, 2, 3, 5])
>>> np.vander(x)
array([[ 1, 1, 1, 1],
[ 8, 4, 2, 1],
[ 27, 9, 3, 1],
[125, 25, 5, 1]])
>>> np.vander(x, increasing=True)
array([[ 1, 1, 1, 1],
[ 1, 2, 4, 8],
[ 1, 3, 9, 27],
[ 1, 5, 25, 125]])
The determinant of a square Vandermonde matrix is the product
of the differences between the values of the input vector:
>>> np.linalg.det(np.vander(x))
48.000000000000043
>>> (5-3)*(5-2)*(5-1)*(3-2)*(3-1)*(2-1)
48
"""
x = asarray(x)
if x.ndim != 1:
raise ValueError("x must be a one-dimensional array or sequence.")
if N is None:
N = len(x)
v = empty((len(x), N), dtype=promote_types(x.dtype, int))
tmp = v[:, ::-1] if not increasing else v
if N > 0:
tmp[:, 0] = 1
if N > 1:
tmp[:, 1:] = x[:, None]
multiply.accumulate(tmp[:, 1:], out=tmp[:, 1:], axis=1)
return v
def _histogram2d_dispatcher(x, y, bins=None, range=None, normed=None,
weights=None, density=None):
return (x, y, bins, weights)
@array_function_dispatch(_histogram2d_dispatcher)
def histogram2d(x, y, bins=10, range=None, normed=None, weights=None,
density=None):
"""
Compute the bi-dimensional histogram of two data samples.
Parameters
----------
x : array_like, shape (N,)
An array containing the x coordinates of the points to be
histogrammed.
y : array_like, shape (N,)
An array containing the y coordinates of the points to be
histogrammed.
bins : int or array_like or [int, int] or [array, array], optional
The bin specification:
* If int, the number of bins for the two dimensions (nx=ny=bins).
* If array_like, the bin edges for the two dimensions
(x_edges=y_edges=bins).
* If [int, int], the number of bins in each dimension
(nx, ny = bins).
* If [array, array], the bin edges in each dimension
(x_edges, y_edges = bins).
* A combination [int, array] or [array, int], where int
is the number of bins and array is the bin edges.
range : array_like, shape(2,2), optional
The leftmost and rightmost edges of the bins along each dimension
(if not specified explicitly in the `bins` parameters):
``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range
will be considered outliers and not tallied in the histogram.
density : bool, optional
If False, the default, returns the number of samples in each bin.
If True, returns the probability *density* function at the bin,
``bin_count / sample_count / bin_area``.
normed : bool, optional
An alias for the density argument that behaves identically. To avoid
confusion with the broken normed argument to `histogram`, `density`
should be preferred.
weights : array_like, shape(N,), optional
An array of values ``w_i`` weighing each sample ``(x_i, y_i)``.
Weights are normalized to 1 if `normed` is True. If `normed` is
False, the values of the returned histogram are equal to the sum of
the weights belonging to the samples falling into each bin.
Returns
-------
H : ndarray, shape(nx, ny)
The bi-dimensional histogram of samples `x` and `y`. Values in `x`
are histogrammed along the first dimension and values in `y` are
histogrammed along the second dimension.
xedges : ndarray, shape(nx+1,)
The bin edges along the first dimension.
yedges : ndarray, shape(ny+1,)
The bin edges along the second dimension.
See Also
--------
histogram : 1D histogram
histogramdd : Multidimensional histogram
Notes
-----
When `normed` is True, then the returned histogram is the sample
density, defined such that the sum over bins of the product
``bin_value * bin_area`` is 1.
Please note that the histogram does not follow the Cartesian convention
where `x` values are on the abscissa and `y` values on the ordinate
axis. Rather, `x` is histogrammed along the first dimension of the
array (vertical), and `y` along the second dimension of the array
(horizontal). This ensures compatibility with `histogramdd`.
Examples
--------
>>> import matplotlib as mpl
>>> import matplotlib.pyplot as plt
Construct a 2-D histogram with variable bin width. First define the bin
edges:
>>> xedges = [0, 1, 3, 5]
>>> yedges = [0, 2, 3, 4, 6]
Next we create a histogram H with random bin content:
>>> x = np.random.normal(2, 1, 100)
>>> y = np.random.normal(1, 1, 100)
>>> H, xedges, yedges = np.histogram2d(x, y, bins=(xedges, yedges))
>>> H = H.T # Let each row list bins with common y range.
:func:`imshow <matplotlib.pyplot.imshow>` can only display square bins:
>>> fig = plt.figure(figsize=(7, 3))
>>> ax = fig.add_subplot(131, title='imshow: square bins')
>>> plt.imshow(H, interpolation='nearest', origin='low',
... extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
:func:`pcolormesh <matplotlib.pyplot.pcolormesh>` can display actual edges:
>>> ax = fig.add_subplot(132, title='pcolormesh: actual edges',
... aspect='equal')
>>> X, Y = np.meshgrid(xedges, yedges)
>>> ax.pcolormesh(X, Y, H)
:class:`NonUniformImage <matplotlib.image.NonUniformImage>` can be used to
display actual bin edges with interpolation:
>>> ax = fig.add_subplot(133, title='NonUniformImage: interpolated',
... aspect='equal', xlim=xedges[[0, -1]], ylim=yedges[[0, -1]])
>>> im = mpl.image.NonUniformImage(ax, interpolation='bilinear')
>>> xcenters = (xedges[:-1] + xedges[1:]) / 2
>>> ycenters = (yedges[:-1] + yedges[1:]) / 2
>>> im.set_data(xcenters, ycenters, H)
>>> ax.images.append(im)
>>> plt.show()
"""
from numpy import histogramdd
try:
N = len(bins)
except TypeError:
N = 1
if N != 1 and N != 2:
xedges = yedges = asarray(bins)
bins = [xedges, yedges]
hist, edges = histogramdd([x, y], bins, range, normed, weights, density)
return hist, edges[0], edges[1]
def mask_indices(n, mask_func, k=0):
"""
Return the indices to access (n, n) arrays, given a masking function.
Assume `mask_func` is a function that, for a square array a of size
``(n, n)`` with a possible offset argument `k`, when called as
``mask_func(a, k)`` returns a new array with zeros in certain locations
(functions like `triu` or `tril` do precisely this). Then this function
returns the indices where the non-zero values would be located.
Parameters
----------
n : int
The returned indices will be valid to access arrays of shape (n, n).
mask_func : callable
A function whose call signature is similar to that of `triu`, `tril`.
That is, ``mask_func(x, k)`` returns a boolean array, shaped like `x`.
`k` is an optional argument to the function.
k : scalar
An optional argument which is passed through to `mask_func`. Functions
like `triu`, `tril` take a second argument that is interpreted as an
offset.
Returns
-------
indices : tuple of arrays.
The `n` arrays of indices corresponding to the locations where
``mask_func(np.ones((n, n)), k)`` is True.
See Also
--------
triu, tril, triu_indices, tril_indices
Notes
-----
.. versionadded:: 1.4.0
Examples
--------
These are the indices that would allow you to access the upper triangular
part of any 3x3 array:
>>> iu = np.mask_indices(3, np.triu)
For example, if `a` is a 3x3 array:
>>> a = np.arange(9).reshape(3, 3)
>>> a
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>> a[iu]
array([0, 1, 2, 4, 5, 8])
An offset can be passed also to the masking function. This gets us the
indices starting on the first diagonal right of the main one:
>>> iu1 = np.mask_indices(3, np.triu, 1)
with which we now extract only three elements:
>>> a[iu1]
array([1, 2, 5])
"""
m = ones((n, n), int)
a = mask_func(m, k)
return nonzero(a != 0)
def tril_indices(n, k=0, m=None):
"""
Return the indices for the lower-triangle of an (n, m) array.
Parameters
----------
n : int
The row dimension of the arrays for which the returned
indices will be valid.
k : int, optional
Diagonal offset (see `tril` for details).
m : int, optional
.. versionadded:: 1.9.0
The column dimension of the arrays for which the returned
arrays will be valid.
By default `m` is taken equal to `n`.
Returns
-------
inds : tuple of arrays
The indices for the triangle. The returned tuple contains two arrays,
each with the indices along one dimension of the array.
See also
--------
triu_indices : similar function, for upper-triangular.
mask_indices : generic function accepting an arbitrary mask function.
tril, triu
Notes
-----
.. versionadded:: 1.4.0
Examples
--------
Compute two different sets of indices to access 4x4 arrays, one for the
lower triangular part starting at the main diagonal, and one starting two
diagonals further right:
>>> il1 = np.tril_indices(4)
>>> il2 = np.tril_indices(4, 2)
Here is how they can be used with a sample array:
>>> a = np.arange(16).reshape(4, 4)
>>> a
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
Both for indexing:
>>> a[il1]
array([ 0, 4, 5, 8, 9, 10, 12, 13, 14, 15])
And for assigning values:
>>> a[il1] = -1
>>> a
array([[-1, 1, 2, 3],
[-1, -1, 6, 7],
[-1, -1, -1, 11],
[-1, -1, -1, -1]])
These cover almost the whole array (two diagonals right of the main one):
>>> a[il2] = -10
>>> a
array([[-10, -10, -10, 3],
[-10, -10, -10, -10],
[-10, -10, -10, -10],
[-10, -10, -10, -10]])
"""
return nonzero(tri(n, m, k=k, dtype=bool))
def _trilu_indices_form_dispatcher(arr, k=None):
return (arr,)
@array_function_dispatch(_trilu_indices_form_dispatcher)
def tril_indices_from(arr, k=0):
"""
Return the indices for the lower-triangle of arr.
See `tril_indices` for full details.
Parameters
----------
arr : array_like
The indices will be valid for square arrays whose dimensions are
the same as arr.
k : int, optional
Diagonal offset (see `tril` for details).
See Also
--------
tril_indices, tril
Notes
-----
.. versionadded:: 1.4.0
"""
if arr.ndim != 2:
raise ValueError("input array must be 2-d")
return tril_indices(arr.shape[-2], k=k, m=arr.shape[-1])
def triu_indices(n, k=0, m=None):
"""
Return the indices for the upper-triangle of an (n, m) array.
Parameters
----------
n : int
The size of the arrays for which the returned indices will
be valid.
k : int, optional
Diagonal offset (see `triu` for details).
m : int, optional
.. versionadded:: 1.9.0
The column dimension of the arrays for which the returned
arrays will be valid.
By default `m` is taken equal to `n`.
Returns
-------
inds : tuple, shape(2) of ndarrays, shape(`n`)
The indices for the triangle. The returned tuple contains two arrays,
each with the indices along one dimension of the array. Can be used
to slice a ndarray of shape(`n`, `n`).
See also
--------
tril_indices : similar function, for lower-triangular.
mask_indices : generic function accepting an arbitrary mask function.
triu, tril
Notes
-----
.. versionadded:: 1.4.0
Examples
--------
Compute two different sets of indices to access 4x4 arrays, one for the
upper triangular part starting at the main diagonal, and one starting two
diagonals further right:
>>> iu1 = np.triu_indices(4)
>>> iu2 = np.triu_indices(4, 2)
Here is how they can be used with a sample array:
>>> a = np.arange(16).reshape(4, 4)
>>> a
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
Both for indexing:
>>> a[iu1]
array([ 0, 1, 2, 3, 5, 6, 7, 10, 11, 15])
And for assigning values:
>>> a[iu1] = -1
>>> a
array([[-1, -1, -1, -1],
[ 4, -1, -1, -1],
[ 8, 9, -1, -1],
[12, 13, 14, -1]])
These cover only a small part of the whole array (two diagonals right
of the main one):
>>> a[iu2] = -10
>>> a
array([[ -1, -1, -10, -10],
[ 4, -1, -1, -10],
[ 8, 9, -1, -1],
[ 12, 13, 14, -1]])
"""
return nonzero(~tri(n, m, k=k-1, dtype=bool))
@array_function_dispatch(_trilu_indices_form_dispatcher)
def triu_indices_from(arr, k=0):
"""
Return the indices for the upper-triangle of arr.
See `triu_indices` for full details.
Parameters
----------
arr : ndarray, shape(N, N)
The indices will be valid for square arrays.
k : int, optional
Diagonal offset (see `triu` for details).
Returns
-------
triu_indices_from : tuple, shape(2) of ndarray, shape(N)
Indices for the upper-triangle of `arr`.
See Also
--------
triu_indices, triu
Notes
-----
.. versionadded:: 1.4.0
"""
if arr.ndim != 2:
raise ValueError("input array must be 2-d")
return triu_indices(arr.shape[-2], k=k, m=arr.shape[-1])
| bsd-3-clause |
deepmind/open_spiel | open_spiel/python/egt/alpharank_visualizer_test.py | 1 | 2447 | # Copyright 2019 DeepMind Technologies Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for open_spiel.python.egt.alpharank_visualizer."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import absltest
# pylint: disable=g-import-not-at-top
import matplotlib
matplotlib.use("agg") # switch backend for testing
import mock
import numpy as np
from open_spiel.python.egt import alpharank
from open_spiel.python.egt import alpharank_visualizer
from open_spiel.python.egt import utils
import pyspiel
class AlpharankVisualizerTest(absltest.TestCase):
@mock.patch("%s.alpharank_visualizer.plt" % __name__)
def test_plot_pi_vs_alpha(self, mock_plt):
# Construct game
game = pyspiel.load_matrix_game("matrix_rps")
payoff_tables = utils.game_payoffs_array(game)
_, payoff_tables = utils.is_symmetric_matrix_game(payoff_tables)
payoffs_are_hpt_format = utils.check_payoffs_are_hpt(payoff_tables)
# Compute alpharank
alpha = 1e2
_, _, pi, num_profiles, num_strats_per_population =\
alpharank.compute(payoff_tables, alpha=alpha)
strat_labels = utils.get_strat_profile_labels(payoff_tables,
payoffs_are_hpt_format)
num_populations = len(payoff_tables)
# Construct synthetic pi-vs-alpha history
pi_list = np.empty((num_profiles, 0))
alpha_list = []
for _ in range(2):
pi_list = np.append(pi_list, np.reshape(pi, (-1, 1)), axis=1)
alpha_list.append(alpha)
# Test plotting code (via pyplot mocking to prevent plot pop-up)
alpharank_visualizer.plot_pi_vs_alpha(
pi_list.T,
alpha_list,
num_populations,
num_strats_per_population,
strat_labels,
num_strats_to_label=0)
self.assertTrue(mock_plt.show.called)
if __name__ == "__main__":
absltest.main()
| apache-2.0 |
BhallaLab/moose-core | tests/core/test_function_example.py | 2 | 3483 | # Modified from function.py ---
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import moose
simtime = 1.0
def test_example():
moose.Neutral('/model')
function = moose.Function('/model/function')
function.c['c0'] = 1.0
function.c['c1'] = 2.0
#function.x.num = 1
function.expr = 'c0 * exp(c1*x0) * cos(y0) + sin(t)'
# mode 0 - evaluate function value, derivative and rate
# mode 1 - just evaluate function value,
# mode 2 - evaluate derivative,
# mode 3 - evaluate rate
function.mode = 0
function.independent = 'y0'
nsteps = 1000
xarr = np.linspace(0.0, 1.0, nsteps)
# Stimulus tables allow you to store sequences of numbers which
# are delivered via the 'output' message at each time step. This
# is a placeholder and in real scenario you will be using any
# sourceFinfo that sends out a double value.
input_x = moose.StimulusTable('/xtab')
input_x.vector = xarr
input_x.startTime = 0.0
input_x.stepPosition = xarr[0]
input_x.stopTime = simtime
moose.connect(input_x, 'output', function.x[0], 'input')
yarr = np.linspace(-np.pi, np.pi, nsteps)
input_y = moose.StimulusTable('/ytab')
input_y.vector = yarr
input_y.startTime = 0.0
input_y.stepPosition = yarr[0]
input_y.stopTime = simtime
moose.connect(function, 'requestOut', input_y, 'getOutputValue')
# data recording
result = moose.Table('/ztab')
moose.connect(result, 'requestOut', function, 'getValue')
derivative = moose.Table('/zprime')
moose.connect(derivative, 'requestOut', function, 'getDerivative')
rate = moose.Table('/dz_by_dt')
moose.connect(rate, 'requestOut', function, 'getRate')
x_rec = moose.Table('/xrec')
moose.connect(x_rec, 'requestOut', input_x, 'getOutputValue')
y_rec = moose.Table('/yrec')
moose.connect(y_rec, 'requestOut', input_y, 'getOutputValue')
dt = simtime/nsteps
for ii in range(32):
moose.setClock(ii, dt)
moose.reinit()
moose.start(simtime)
# Uncomment the following lines and the import matplotlib.pyplot as plt on top
# of this file to display the plot.
plt.subplot(3,1,1)
plt.plot(x_rec.vector, result.vector, 'r-', label='z = {}'.format(function.expr))
z = function.c['c0'] * np.exp(function.c['c1'] * xarr) * np.cos(yarr) + np.sin(np.arange(len(xarr)) * dt)
plt.plot(xarr, z, 'b--', label='numpy computed')
plt.xlabel('x')
plt.ylabel('z')
plt.legend()
plt.subplot(3,1,2)
plt.plot(y_rec.vector, derivative.vector, 'r-', label='dz/dy0')
# derivatives computed by putting x values in the analytical formula
dzdy = function.c['c0'] * np.exp(function.c['c1'] * xarr) * (- np.sin(yarr))
plt.plot(yarr, dzdy, 'b--', label='numpy computed')
plt.xlabel('y')
plt.ylabel('dz/dy')
plt.legend()
plt.subplot(3,1,3)
# *** BEWARE *** The first two entries are spurious. Entry 0 is
# *** from reinit sending out the defaults. Entry 2 is because
# *** there is no lastValue for computing real forward difference.
plt.plot(np.arange(2, len(rate.vector), 1) * dt, rate.vector[2:], 'r-', label='dz/dt')
dzdt = np.diff(z)/dt
plt.plot(np.arange(0, len(dzdt), 1.0) * dt, dzdt, 'b--', label='numpy computed')
plt.xlabel('t')
plt.ylabel('dz/dt')
plt.legend()
plt.tight_layout()
plt.savefig(__file__+'.png')
if __name__ == '__main__':
test_example()
| gpl-3.0 |
nomadcube/scikit-learn | examples/covariance/plot_sparse_cov.py | 300 | 5078 | """
======================================
Sparse inverse covariance estimation
======================================
Using the GraphLasso estimator to learn a covariance and sparse precision
from a small number of samples.
To estimate a probabilistic model (e.g. a Gaussian model), estimating the
precision matrix, that is the inverse covariance matrix, is as important
as estimating the covariance matrix. Indeed a Gaussian model is
parametrized by the precision matrix.
To be in favorable recovery conditions, we sample the data from a model
with a sparse inverse covariance matrix. In addition, we ensure that the
data is not too much correlated (limiting the largest coefficient of the
precision matrix) and that there a no small coefficients in the
precision matrix that cannot be recovered. In addition, with a small
number of observations, it is easier to recover a correlation matrix
rather than a covariance, thus we scale the time series.
Here, the number of samples is slightly larger than the number of
dimensions, thus the empirical covariance is still invertible. However,
as the observations are strongly correlated, the empirical covariance
matrix is ill-conditioned and as a result its inverse --the empirical
precision matrix-- is very far from the ground truth.
If we use l2 shrinkage, as with the Ledoit-Wolf estimator, as the number
of samples is small, we need to shrink a lot. As a result, the
Ledoit-Wolf precision is fairly close to the ground truth precision, that
is not far from being diagonal, but the off-diagonal structure is lost.
The l1-penalized estimator can recover part of this off-diagonal
structure. It learns a sparse precision. It is not able to
recover the exact sparsity pattern: it detects too many non-zero
coefficients. However, the highest non-zero coefficients of the l1
estimated correspond to the non-zero coefficients in the ground truth.
Finally, the coefficients of the l1 precision estimate are biased toward
zero: because of the penalty, they are all smaller than the corresponding
ground truth value, as can be seen on the figure.
Note that, the color range of the precision matrices is tweaked to
improve readability of the figure. The full range of values of the
empirical precision is not displayed.
The alpha parameter of the GraphLasso setting the sparsity of the model is
set by internal cross-validation in the GraphLassoCV. As can be
seen on figure 2, the grid to compute the cross-validation score is
iteratively refined in the neighborhood of the maximum.
"""
print(__doc__)
# author: Gael Varoquaux <gael.varoquaux@inria.fr>
# License: BSD 3 clause
# Copyright: INRIA
import numpy as np
from scipy import linalg
from sklearn.datasets import make_sparse_spd_matrix
from sklearn.covariance import GraphLassoCV, ledoit_wolf
import matplotlib.pyplot as plt
##############################################################################
# Generate the data
n_samples = 60
n_features = 20
prng = np.random.RandomState(1)
prec = make_sparse_spd_matrix(n_features, alpha=.98,
smallest_coef=.4,
largest_coef=.7,
random_state=prng)
cov = linalg.inv(prec)
d = np.sqrt(np.diag(cov))
cov /= d
cov /= d[:, np.newaxis]
prec *= d
prec *= d[:, np.newaxis]
X = prng.multivariate_normal(np.zeros(n_features), cov, size=n_samples)
X -= X.mean(axis=0)
X /= X.std(axis=0)
##############################################################################
# Estimate the covariance
emp_cov = np.dot(X.T, X) / n_samples
model = GraphLassoCV()
model.fit(X)
cov_ = model.covariance_
prec_ = model.precision_
lw_cov_, _ = ledoit_wolf(X)
lw_prec_ = linalg.inv(lw_cov_)
##############################################################################
# Plot the results
plt.figure(figsize=(10, 6))
plt.subplots_adjust(left=0.02, right=0.98)
# plot the covariances
covs = [('Empirical', emp_cov), ('Ledoit-Wolf', lw_cov_),
('GraphLasso', cov_), ('True', cov)]
vmax = cov_.max()
for i, (name, this_cov) in enumerate(covs):
plt.subplot(2, 4, i + 1)
plt.imshow(this_cov, interpolation='nearest', vmin=-vmax, vmax=vmax,
cmap=plt.cm.RdBu_r)
plt.xticks(())
plt.yticks(())
plt.title('%s covariance' % name)
# plot the precisions
precs = [('Empirical', linalg.inv(emp_cov)), ('Ledoit-Wolf', lw_prec_),
('GraphLasso', prec_), ('True', prec)]
vmax = .9 * prec_.max()
for i, (name, this_prec) in enumerate(precs):
ax = plt.subplot(2, 4, i + 5)
plt.imshow(np.ma.masked_equal(this_prec, 0),
interpolation='nearest', vmin=-vmax, vmax=vmax,
cmap=plt.cm.RdBu_r)
plt.xticks(())
plt.yticks(())
plt.title('%s precision' % name)
ax.set_axis_bgcolor('.7')
# plot the model selection metric
plt.figure(figsize=(4, 3))
plt.axes([.2, .15, .75, .7])
plt.plot(model.cv_alphas_, np.mean(model.grid_scores, axis=1), 'o-')
plt.axvline(model.alpha_, color='.5')
plt.title('Model selection')
plt.ylabel('Cross-validation score')
plt.xlabel('alpha')
plt.show()
| bsd-3-clause |
rajat1994/scikit-learn | examples/covariance/plot_sparse_cov.py | 300 | 5078 | """
======================================
Sparse inverse covariance estimation
======================================
Using the GraphLasso estimator to learn a covariance and sparse precision
from a small number of samples.
To estimate a probabilistic model (e.g. a Gaussian model), estimating the
precision matrix, that is the inverse covariance matrix, is as important
as estimating the covariance matrix. Indeed a Gaussian model is
parametrized by the precision matrix.
To be in favorable recovery conditions, we sample the data from a model
with a sparse inverse covariance matrix. In addition, we ensure that the
data is not too much correlated (limiting the largest coefficient of the
precision matrix) and that there a no small coefficients in the
precision matrix that cannot be recovered. In addition, with a small
number of observations, it is easier to recover a correlation matrix
rather than a covariance, thus we scale the time series.
Here, the number of samples is slightly larger than the number of
dimensions, thus the empirical covariance is still invertible. However,
as the observations are strongly correlated, the empirical covariance
matrix is ill-conditioned and as a result its inverse --the empirical
precision matrix-- is very far from the ground truth.
If we use l2 shrinkage, as with the Ledoit-Wolf estimator, as the number
of samples is small, we need to shrink a lot. As a result, the
Ledoit-Wolf precision is fairly close to the ground truth precision, that
is not far from being diagonal, but the off-diagonal structure is lost.
The l1-penalized estimator can recover part of this off-diagonal
structure. It learns a sparse precision. It is not able to
recover the exact sparsity pattern: it detects too many non-zero
coefficients. However, the highest non-zero coefficients of the l1
estimated correspond to the non-zero coefficients in the ground truth.
Finally, the coefficients of the l1 precision estimate are biased toward
zero: because of the penalty, they are all smaller than the corresponding
ground truth value, as can be seen on the figure.
Note that, the color range of the precision matrices is tweaked to
improve readability of the figure. The full range of values of the
empirical precision is not displayed.
The alpha parameter of the GraphLasso setting the sparsity of the model is
set by internal cross-validation in the GraphLassoCV. As can be
seen on figure 2, the grid to compute the cross-validation score is
iteratively refined in the neighborhood of the maximum.
"""
print(__doc__)
# author: Gael Varoquaux <gael.varoquaux@inria.fr>
# License: BSD 3 clause
# Copyright: INRIA
import numpy as np
from scipy import linalg
from sklearn.datasets import make_sparse_spd_matrix
from sklearn.covariance import GraphLassoCV, ledoit_wolf
import matplotlib.pyplot as plt
##############################################################################
# Generate the data
n_samples = 60
n_features = 20
prng = np.random.RandomState(1)
prec = make_sparse_spd_matrix(n_features, alpha=.98,
smallest_coef=.4,
largest_coef=.7,
random_state=prng)
cov = linalg.inv(prec)
d = np.sqrt(np.diag(cov))
cov /= d
cov /= d[:, np.newaxis]
prec *= d
prec *= d[:, np.newaxis]
X = prng.multivariate_normal(np.zeros(n_features), cov, size=n_samples)
X -= X.mean(axis=0)
X /= X.std(axis=0)
##############################################################################
# Estimate the covariance
emp_cov = np.dot(X.T, X) / n_samples
model = GraphLassoCV()
model.fit(X)
cov_ = model.covariance_
prec_ = model.precision_
lw_cov_, _ = ledoit_wolf(X)
lw_prec_ = linalg.inv(lw_cov_)
##############################################################################
# Plot the results
plt.figure(figsize=(10, 6))
plt.subplots_adjust(left=0.02, right=0.98)
# plot the covariances
covs = [('Empirical', emp_cov), ('Ledoit-Wolf', lw_cov_),
('GraphLasso', cov_), ('True', cov)]
vmax = cov_.max()
for i, (name, this_cov) in enumerate(covs):
plt.subplot(2, 4, i + 1)
plt.imshow(this_cov, interpolation='nearest', vmin=-vmax, vmax=vmax,
cmap=plt.cm.RdBu_r)
plt.xticks(())
plt.yticks(())
plt.title('%s covariance' % name)
# plot the precisions
precs = [('Empirical', linalg.inv(emp_cov)), ('Ledoit-Wolf', lw_prec_),
('GraphLasso', prec_), ('True', prec)]
vmax = .9 * prec_.max()
for i, (name, this_prec) in enumerate(precs):
ax = plt.subplot(2, 4, i + 5)
plt.imshow(np.ma.masked_equal(this_prec, 0),
interpolation='nearest', vmin=-vmax, vmax=vmax,
cmap=plt.cm.RdBu_r)
plt.xticks(())
plt.yticks(())
plt.title('%s precision' % name)
ax.set_axis_bgcolor('.7')
# plot the model selection metric
plt.figure(figsize=(4, 3))
plt.axes([.2, .15, .75, .7])
plt.plot(model.cv_alphas_, np.mean(model.grid_scores, axis=1), 'o-')
plt.axvline(model.alpha_, color='.5')
plt.title('Model selection')
plt.ylabel('Cross-validation score')
plt.xlabel('alpha')
plt.show()
| bsd-3-clause |
ifarup/colourlab | tests/test_misc.py | 1 | 1116 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
test_misc: Unittests for all functions in the misc module.
Copyright (C) 2017 Ivar Farup
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or (at
your option) any later version.
This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
import unittest
import matplotlib
import matplotlib.pyplot as plt
from colourlab import misc, space, data
t = data.g_MacAdam()
ell = t.get_ellipses(space.xyY)
_, ax = plt.subplots()
misc.plot_ellipses(ell, ax)
misc.plot_ellipses(ell)
class TestPlot(unittest.TestCase):
def test_plot(self):
self.assertTrue(isinstance(ax, matplotlib.axes.Axes))
| gpl-3.0 |
rlouf/patterns-of-segregation | bin/plot_gini.py | 1 | 2527 | """plot_gini.py
Plot the Gini of the income distribution as a function of the number of
households in cities.
"""
from __future__ import division
import csv
import numpy as np
import itertools
from matplotlib import pylab as plt
#
# Parameters and functions
#
income_bins = [1000,12500,17500,22500,27500,32500,37500,42500,47500,55000,70000,90000,115000,135000,175000,300000]
# Puerto-rican cities are excluded from the analysis
PR_cities = ['7442','0060','6360','4840']
#
# Read data
#
## List of MSA
msa = {}
with open('data/names/msa.csv', 'r') as source:
reader = csv.reader(source, delimiter='\t')
reader.next()
for rows in reader:
if rows[0] not in PR_cities:
msa[rows[0]] = rows[1]
#
# Compute gini for all msa
#
gini = []
households = []
for n, city in enumerate(msa):
print "Compute Gini index for %s (%s/%s)"%(msa[city], n+1, len(msa))
## Import households income
data = {}
with open('data/income/msa/%s/income.csv'%city, 'r') as source:
reader = csv.reader(source, delimiter='\t')
reader.next()
for rows in reader:
num_cat = len(rows[1:])
data[rows[0]] = {c:int(h) for c,h in enumerate(rows[1:])}
# Sum over all areal units
incomes = {cat:sum([data[au][cat] for au in data]) for cat in range(num_cat)}
## Compute the Gini index
# See Dixon, P. M.; Weiner, J.; Mitchell-Olds, T.; and Woodley, R.
# "Bootstrapping the Gini Coefficient of Inequality." Ecology 68, 1548-1551, 1987.
g = 0
pop = 0
for a,b in itertools.permutations(incomes, 2):
g += incomes[a]*incomes[b]*abs(income_bins[a]-income_bins[b])
pop = sum([incomes[a] for a in incomes])
average = sum([incomes[a]*income_bins[a] for a in incomes])/pop
gini.append((1/(2*pop**2*average))*g)
households.append(pop)
#
# Plot
#
fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(111)
ax.plot(households, gini, 'o', color='black', mec='black')
ax.set_xlabel(r'$H$', fontsize=30)
ax.set_ylabel(r'$Gini$', fontsize=30)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_position(('outward', 10)) # outward by 10 points
ax.spines['bottom'].set_position(('outward', 10)) # outward by 10 points
ax.spines['left'].set_smart_bounds(True)
ax.spines['bottom'].set_smart_bounds(True)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
ax.set_xscale('log')
plt.savefig('figures/paper/si/gini_income.pdf', bbox_inches='tight')
plt.show()
| bsd-3-clause |
dhhagan/ACT | ACT/thermo/visualize.py | 1 | 13306 | """
Classes and functions used to visualize data for thermo scientific analyzers
"""
from pandas import Series, DataFrame
import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import dates as d
import os
import math
import glob
import matplotlib
import warnings
import sys
__all__ = ['diurnal_plot','diurnal_plot_single', 'ThermoPlot']
def diurnal_plot(data, dates=[], shaded=False, title="Diurnal Profile of Trace Gases", xlabel="Local Time: East St. Louis, MO"):
'''
If plotting the entire DataFrame (data), choose all_data=True, else choose all_data=False
and declare the date or dates to plot as a list. `data` should be a pandas core DataFrame
with time index and each trace gas concentration as a column
returns a single plot for NOx, SO2, and O3
>>>
'''
# Check to make sure the data is a valid dataframe
if not isinstance(data, pd.DataFrame):
print ("data is not a pandas DataFrame, thus this will not end well for you.")
exit
# If length of dates is zero, plot everything
if len(dates) == 0:
# Plot everything, yo!
pass
elif len(dates) == 1:
# Plot just this date
data = data[dates[0]]
elif len(dates) == 2:
# Plot between these dates
data = data[dates[0]:dates[1]]
else:
sys.exit("Dates are not properly configured.")
# Add columns for time to enable simple diurnal trends to be found
data['Time'] = data.index.map(lambda x: x.strftime("%H:%M"))
# Group the data by time and grab the statistics
grouped = data.groupby('Time').describe().unstack()
# set the index to be a str
grouped.index = pd.to_datetime(grouped.index.astype(str))
# Plot
fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(10,9), sharex=True)
# Set plot titles and labels
ax1.set_title(title, fontsize=14)
ax1.set_ylabel(r'$\ [NO_x] (ppb)$', fontsize=14, weight='bold')
ax2.set_ylabel(r'$\ [SO_2] (ppb)$', fontsize=14)
ax3.set_ylabel(r'$\ [O_3] (ppb)$', fontsize=14)
ax3.set_xlabel(xlabel, fontsize=14)
# Make the ticks invisible on the first and second plots
plt.setp( ax1.get_xticklabels(), visible=False)
plt.setp( ax2.get_xticklabels(), visible=False)
# Set y min to zero just in case:
ax1.set_ylim(0,grouped['nox']['mean'].max()*1.05)
ax2.set_ylim(0,grouped['so2']['mean'].max()*1.05)
ax3.set_ylim(0,grouped['o3']['mean'].max()*1.05)
# Plot means
ax1.plot(grouped.index, grouped['nox']['mean'],'g', linewidth=2.0)
ax2.plot(grouped.index, grouped['so2']['mean'], 'r', linewidth=2.0)
ax3.plot(grouped.index, grouped['o3']['mean'], 'b', linewidth=2.0)
# If shaded=true, plot trends
if shaded == True:
ax1.plot(grouped.index, grouped['nox']['75%'],'g')
ax1.plot(grouped.index, grouped['nox']['25%'],'g')
ax1.set_ylim(0,grouped['nox']['75%'].max()*1.05)
ax1.fill_between(grouped.index, grouped['nox']['mean'], grouped['nox']['75%'], alpha=.5, facecolor='green')
ax1.fill_between(grouped.index, grouped['nox']['mean'], grouped['nox']['25%'], alpha=.5, facecolor='green')
ax2.plot(grouped.index, grouped['so2']['75%'],'r')
ax2.plot(grouped.index, grouped['so2']['25%'],'r')
ax2.set_ylim(0,grouped['so2']['75%'].max()*1.05)
ax2.fill_between(grouped.index, grouped['so2']['mean'], grouped['so2']['75%'], alpha=.5, facecolor='red')
ax2.fill_between(grouped.index, grouped['so2']['mean'], grouped['so2']['25%'], alpha=.5, facecolor='red')
ax3.plot(grouped.index, grouped['o3']['75%'],'b')
ax3.plot(grouped.index, grouped['o3']['25%'],'b')
ax3.set_ylim(0,grouped['o3']['75%'].max()*1.05)
ax3.fill_between(grouped.index, grouped['o3']['mean'], grouped['o3']['75%'], alpha=.5, facecolor='blue')
ax3.fill_between(grouped.index, grouped['o3']['mean'], grouped['o3']['25%'], alpha=.5, facecolor='blue')
# Get/Set xticks
ticks = ax1.get_xticks()
ax3.set_xticks(np.linspace(ticks[0], d.date2num(d.num2date(ticks[-1]) + dt.timedelta(hours=3)), 5))
ax3.set_xticks(np.linspace(ticks[0], d.date2num(d.num2date(ticks[-1]) + dt.timedelta(hours=3)), 25), minor=True)
ax3.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%I:%M %p'))
# Make the layout tight to get rid of some whitespace
plt.tight_layout()
plt.show()
return (fig, (ax1, ax2, ax3))
def diurnal_plot_single(data, model='', dates=[], shaded=False, color1 = 'blue',
title="Diurnal Profile of Trace Gases", xlabel="Local Time: East St. Louis, MO",
ylabel=r'$\ [NO_x] (ppb)$'):
'''
`data` should be a pandas core DataFrame with time index and each trace gas concentration as a column
returns a single plot for one of the three analyzers.
>>>diurnal_plot_single(data,model='o3', ylabel='O3', shaded=True, color1='green')
'''
# Check to make sure the data is a valid dataframe
if not isinstance(data, pd.DataFrame):
sys.exit("data is not a pandas DataFrame, thus this will not end well for you.")
# Check to make sure the model is valid
if model.lower() not in ['nox','so2','o3','sox']:
sys.exit("Model is not defined correctly: options are ['nox','so2','sox','o3']")
# Set model to predefined variable
if model.lower() == 'nox':
instr = 'nox'
elif model.lower() == 'so2' or model.lower() == 'sox':
instr = 'sox'
else:
instr = 'o3'
# If not plotting all the data, truncate the dataframe to include only the needed data
if len(dates) == 0:
# plot everything
pass
elif len(dates) == 1:
# plot just this date
data = data[dates[0]]
elif len(dates) == 2:
# plot between these dates
data = data[dates[0]:dates[1]]
else:
sys.exit("You have an error with how you defined your dates")
# Add columns for time to enable simple diurnal trends to be found
data['Time'] = data.index.map(lambda x: x.strftime("%H:%M"))
# Group the data by time and grab the statistics
grouped = data.groupby('Time').describe().unstack()
# set the index to be a str
grouped.index = pd.to_datetime(grouped.index.astype(str))
# Plot
fig, ax = plt.subplots(1, figsize=(8,4))
# Set plot titles and labels
ax.set_title(title, fontsize=14)
ax.set_ylabel(ylabel, fontsize=14, weight='bold')
ax.set_xlabel(xlabel, fontsize=14)
# Set y min to zero just in case:
ax.set_ylim(0,grouped[instr]['mean'].max()*1.05)
# Plot means
ax.plot(grouped.index, grouped[instr]['mean'], color1,linewidth=2.0)
# If shaded=true, plot trends
if shaded == True:
ax.plot(grouped.index, grouped[instr]['75%'],color1)
ax.plot(grouped.index, grouped[instr]['25%'],color1)
ax.set_ylim(0,grouped[instr]['75%'].max()*1.05)
ax.fill_between(grouped.index, grouped[instr]['mean'], grouped[instr]['75%'], alpha=.5, facecolor=color1)
ax.fill_between(grouped.index, grouped[instr]['mean'], grouped[instr]['25%'], alpha=.5, facecolor=color1)
# Get/Set xticks
ticks = ax.get_xticks()
ax.set_xticks(np.linspace(ticks[0], d.date2num(d.num2date(ticks[-1]) + dt.timedelta(hours=3)), 5))
ax.set_xticks(np.linspace(ticks[0], d.date2num(d.num2date(ticks[-1]) + dt.timedelta(hours=3)), 25), minor=True)
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%I:%M %p'))
# Make the layout tight to get rid of some whitespace
plt.tight_layout()
plt.show()
return (fig, ax)
class ThermoPlot():
'''
Allows for easy plotting of internal instrument data. Currently supports the
following models:
- NO, NO2, NOx (42I)
- O3 (49I)
- SO2 (43I)
'''
def __init__(self, data):
self.data = data
def debug_plot(self, args={}):
'''
Plots thermo scientific instrument data for debugging purposes. The top plot contains internal
instrument data such as flow rates and temperatures. The bottom plot contains trace gas data for the
instrument.
instrument must be set to either nox, so2, sox, or o3
>>> nox = ThermoPlot(data)
>>> f, (a1, a2, a3) = nox.debug_plot()
'''
default_args = {
'xlabel':'Local Time, East St Louis, MO',
'ylabpressure':'Flow (LPM)',
'ylabgas':'Gas Conc. (ppb)',
'ylabtemp':'Temperature (C)',
'title_fontsize':'18',
'labels_fontsize':'14',
'grid':False
}
# Figure out what model we are trying to plot and set instrument specific default args
cols = [i.lower() for i in self.data.columns.values.tolist()]
if 'o3' in cols:
default_args['instrument'] = 'o3'
default_args['title'] = "Debug Plot for " + r'$\ O_{3} $' + ": Model 49I"
default_args['color_o3'] = 'blue'
elif 'sox' in cols or 'so2' in cols:
default_args['instrument'] = 'so2'
default_args['title'] = "Debug Plot for " + r'$\ SO_{2} $' + ": Model 43I"
default_args['color_so2'] = 'green'
elif 'nox' in cols:
default_args['instrument'] = 'nox'
default_args['title'] = "Debug Plot for " + r'$\ NO_{x} $' + ": Model 42I"
default_args['color_no'] = '#FAB923'
default_args['color_nox'] = '#FC5603'
default_args['color_no2'] = '#FAE823'
else:
sys.exit("Could not figure out what isntrument this is for")
# If kwargs are set, replace the default values
for key, val in default_args.iteritems():
if args.has_key(key):
default_args[key] = args[key]
# Set up Plot and all three axes
fig, (ax1, ax3) = plt.subplots(2, figsize=(10,6), sharex=True)
ax2 = ax1.twinx()
# set up axes labels and titles
ax1.set_title(default_args['title'], fontsize=default_args['title_fontsize'])
ax1.set_ylabel(default_args['ylabpressure'], fontsize=default_args['labels_fontsize'])
ax2.set_ylabel(default_args['ylabtemp'], fontsize=default_args['labels_fontsize'])
ax3.set_ylabel(default_args['ylabgas'], fontsize=default_args['labels_fontsize'])
ax3.set_xlabel(default_args['xlabel'], fontsize=default_args['labels_fontsize'])
# Make the ticks invisible on the first and second plots
plt.setp( ax1.get_xticklabels(), visible=False )
# Plot the debug data on the top graph
if default_args['instrument'] == 'o3':
self.data['bncht'].plot(ax=ax2, label=r'$\ T_{bench}$')
self.data['lmpt'].plot(ax=ax2, label=r'$\ T_{lamp}$')
self.data['flowa'].plot(ax=ax1, label=r'$\ Q_{A}$', style='--')
self.data['flowb'].plot(ax=ax1, label=r'$\ Q_{B}$', style='--')
self.data['o3'].plot(ax=ax3, color=default_args['color_o3'], label=r'$\ O_{3}$')
elif default_args['instrument'] == 'so2':
self.data['intt'].plot(ax=ax2, label=r'$\ T_{internal}$')
self.data['rctt'].plot(ax=ax2, label=r'$\ T_{reactor}$')
self.data['smplfl'].plot(ax=ax1, label=r'$\ Q_{sample}$', style='--')
self.data['so2'].plot(ax=ax3, label=r'$\ SO_2 $', color=default_args['color_so2'], ylim=[0,self.data['so2'].max()*1.05])
else:
m = max(self.data['convt'].max(),self.data['intt'].max(),self.data['pmtt'].max())
self.data['convt'].plot(ax=ax2, label=r'$\ T_{converter}$')
self.data['intt'].plot(ax=ax2, label=r'$\ T_{internal}$')
self.data['rctt'].plot(ax=ax2, label=r'$\ T_{reactor}$')
self.data['pmtt'].plot(ax=ax2, label=r'$\ T_{PMT}$')
self.data['smplf'].plot(ax=ax1, label=r'$\ Q_{sample}$', style='--')
self.data['ozonf'].plot(ax=ax1, label=r'$\ Q_{ozone}$', style='--')
self.data['no'].plot(ax=ax3, label=r'$\ NO $', color=default_args['color_no'])
self.data['no2'].plot(ax=ax3, label=r'$\ NO_{2}$', color=default_args['color_no2'])
self.data['nox'].plot(ax=ax3, label=r'$\ NO_{x}$', color=default_args['color_nox'], ylim=(0,math.ceil(self.data.nox.max()*1.05)))
# Legends
lines, labels = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
plt.legend(lines+lines2, labels+labels2, bbox_to_anchor=(1.10, 1), loc=2, borderaxespad=0.)
ax3.legend(bbox_to_anchor=(1.10, 1.), loc=2, borderaxespad=0.)
# Hide grids?
ax1.grid(default_args['grid'])
ax2.grid(default_args['grid'])
ax3.grid(default_args['grid'])
# More of the things..
plt.tight_layout()
plt.show()
return fig, (ax1, ax2, ax3) | mit |
ottermegazord/ottermegazord.github.io | onexi/data_processing/s05_genPlots.py | 1 | 1460 | import pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
import pdb
import sys
plt.style.use("ggplot")
os.chdir("..")
ipath = "./Data/Final_Data/"
ifile = "Final_Data"
opath = "./Data/Final_Data/Neighborhoods/"
imgpath = "./Plots/Neighborhood_TS/"
ext = ".csv"
input_var = raw_input("Run mode (analysis/plot): ")
if input_var == "analysis":
df = pd.read_csv(ipath + ifile + ext, low_memory=False)
df2 = df.groupby(["TIME", "NEIGHBORHOOD"]).mean().unstack()
time = df["TIME"].unique().tolist()
nhood = df["NEIGHBORHOOD"].unique().tolist()
nhood = [x for x in nhood if str(x) != 'nan']
for n in nhood:
mean = []
for t in time:
mean.append(df2.loc[t, ("AV_PER_SQFT", n)])
out_df = pd.DataFrame({'TIME': time, 'MEAN_AV_PER_SQFT': mean})
out_df.to_csv(opath + n + ext, index=False)
elif input_var == "plot":
def makePlot(x, y, xlabel, ylabel, title, filename):
x_pos = [i for i, _ in enumerate(x)]
plt.bar(x_pos, y, color='green')
plt.ylabel(ylabel)
plt.xlabel(xlabel)
plt.title(title)
plt.xticks(x_pos, x, fontsize=8)
plt.savefig(filename, bbox_inches="tight", dpi=300)
plt.close()
nhood_files = os.listdir(opath)
for f in nhood_files:
nhood = f[:-4]
df = pd.read_csv(opath + f, low_memory=False)
makePlot(x=df["TIME"].tolist(), y=df["MEAN_AV_PER_SQFT"].tolist(), ylabel="AVG LAND VALUE ($/sqft)", xlabel="TIME (year)", title=nhood, filename=imgpath + nhood +".png")
| mit |
carlthome/librosa | librosa/feature/utils.py | 1 | 8078 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Feature manipulation utilities"""
from warnings import warn
import numpy as np
import scipy.signal
from .._cache import cache
from ..util.exceptions import ParameterError
__all__ = ['delta', 'stack_memory']
@cache(level=40)
def delta(data, width=9, order=1, axis=-1, mode='interp', **kwargs):
r'''Compute delta features: local estimate of the derivative
of the input data along the selected axis.
Delta features are computed Savitsky-Golay filtering.
Parameters
----------
data : np.ndarray
the input data matrix (eg, spectrogram)
width : int, positive, odd [scalar]
Number of frames over which to compute the delta features.
Cannot exceed the length of `data` along the specified axis.
If `mode='interp'`, then `width` must be at least `data.shape[axis]`.
order : int > 0 [scalar]
the order of the difference operator.
1 for first derivative, 2 for second, etc.
axis : int [scalar]
the axis along which to compute deltas.
Default is -1 (columns).
mode : str, {'interp', 'nearest', 'mirror', 'constant', 'wrap'}
Padding mode for estimating differences at the boundaries.
kwargs : additional keyword arguments
See `scipy.signal.savgol_filter`
Returns
-------
delta_data : np.ndarray [shape=(d, t)]
delta matrix of `data` at specified order
Notes
-----
This function caches at level 40.
See Also
--------
scipy.signal.savgol_filter
Examples
--------
Compute MFCC deltas, delta-deltas
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> mfcc = librosa.feature.mfcc(y=y, sr=sr)
>>> mfcc_delta = librosa.feature.delta(mfcc)
>>> mfcc_delta
array([[ 1.666e+01, 1.666e+01, ..., 1.869e-15, 1.869e-15],
[ 1.784e+01, 1.784e+01, ..., 6.085e-31, 6.085e-31],
...,
[ 7.262e-01, 7.262e-01, ..., 9.259e-31, 9.259e-31],
[ 6.578e-01, 6.578e-01, ..., 7.597e-31, 7.597e-31]])
>>> mfcc_delta2 = librosa.feature.delta(mfcc, order=2)
>>> mfcc_delta2
array([[ -1.703e+01, -1.703e+01, ..., 3.834e-14, 3.834e-14],
[ -1.108e+01, -1.108e+01, ..., -1.068e-30, -1.068e-30],
...,
[ 4.075e-01, 4.075e-01, ..., -1.565e-30, -1.565e-30],
[ 1.676e-01, 1.676e-01, ..., -2.104e-30, -2.104e-30]])
>>> import matplotlib.pyplot as plt
>>> plt.subplot(3, 1, 1)
>>> librosa.display.specshow(mfcc)
>>> plt.title('MFCC')
>>> plt.colorbar()
>>> plt.subplot(3, 1, 2)
>>> librosa.display.specshow(mfcc_delta)
>>> plt.title(r'MFCC-$\Delta$')
>>> plt.colorbar()
>>> plt.subplot(3, 1, 3)
>>> librosa.display.specshow(mfcc_delta2, x_axis='time')
>>> plt.title(r'MFCC-$\Delta^2$')
>>> plt.colorbar()
>>> plt.tight_layout()
>>> plt.show()
'''
data = np.atleast_1d(data)
if mode == 'interp' and width > data.shape[axis]:
raise ParameterError("when mode='interp', width={} "
"cannot exceed data.shape[axis]={}".format(width, data.shape[axis]))
if width < 3 or np.mod(width, 2) != 1:
raise ParameterError('width must be an odd integer >= 3')
if order <= 0 or not isinstance(order, int):
raise ParameterError('order must be a positive integer')
kwargs.pop('deriv', None)
kwargs.setdefault('polyorder', order)
return scipy.signal.savgol_filter(data, width,
deriv=order,
axis=axis,
mode=mode,
**kwargs)
@cache(level=40)
def stack_memory(data, n_steps=2, delay=1, **kwargs):
"""Short-term history embedding: vertically concatenate a data
vector or matrix with delayed copies of itself.
Each column `data[:, i]` is mapped to::
data[:, i] -> [data[:, i],
data[:, i - delay],
...
data[:, i - (n_steps-1)*delay]]
For columns `i < (n_steps - 1) * delay` , the data will be padded.
By default, the data is padded with zeros, but this behavior can be
overridden by supplying additional keyword arguments which are passed
to `np.pad()`.
Parameters
----------
data : np.ndarray [shape=(t,) or (d, t)]
Input data matrix. If `data` is a vector (`data.ndim == 1`),
it will be interpreted as a row matrix and reshaped to `(1, t)`.
n_steps : int > 0 [scalar]
embedding dimension, the number of steps back in time to stack
delay : int != 0 [scalar]
the number of columns to step.
Positive values embed from the past (previous columns).
Negative values embed from the future (subsequent columns).
kwargs : additional keyword arguments
Additional arguments to pass to `np.pad`.
Returns
-------
data_history : np.ndarray [shape=(m * d, t)]
data augmented with lagged copies of itself,
where `m == n_steps - 1`.
Notes
-----
This function caches at level 40.
Examples
--------
Keep two steps (current and previous)
>>> data = np.arange(-3, 3)
>>> librosa.feature.stack_memory(data)
array([[-3, -2, -1, 0, 1, 2],
[ 0, -3, -2, -1, 0, 1]])
Or three steps
>>> librosa.feature.stack_memory(data, n_steps=3)
array([[-3, -2, -1, 0, 1, 2],
[ 0, -3, -2, -1, 0, 1],
[ 0, 0, -3, -2, -1, 0]])
Use reflection padding instead of zero-padding
>>> librosa.feature.stack_memory(data, n_steps=3, mode='reflect')
array([[-3, -2, -1, 0, 1, 2],
[-2, -3, -2, -1, 0, 1],
[-1, -2, -3, -2, -1, 0]])
Or pad with edge-values, and delay by 2
>>> librosa.feature.stack_memory(data, n_steps=3, delay=2, mode='edge')
array([[-3, -2, -1, 0, 1, 2],
[-3, -3, -3, -2, -1, 0],
[-3, -3, -3, -3, -3, -2]])
Stack time-lagged beat-synchronous chroma edge padding
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> chroma = librosa.feature.chroma_stft(y=y, sr=sr)
>>> tempo, beats = librosa.beat.beat_track(y=y, sr=sr, hop_length=512)
>>> beats = librosa.util.fix_frames(beats, x_min=0, x_max=chroma.shape[1])
>>> chroma_sync = librosa.util.sync(chroma, beats)
>>> chroma_lag = librosa.feature.stack_memory(chroma_sync, n_steps=3,
... mode='edge')
Plot the result
>>> import matplotlib.pyplot as plt
>>> beat_times = librosa.frames_to_time(beats, sr=sr, hop_length=512)
>>> librosa.display.specshow(chroma_lag, y_axis='chroma', x_axis='time',
... x_coords=beat_times)
>>> plt.yticks([0, 12, 24], ['Lag=0', 'Lag=1', 'Lag=2'])
>>> plt.title('Time-lagged chroma')
>>> plt.colorbar()
>>> plt.tight_layout()
>>> plt.show()
"""
if n_steps < 1:
raise ParameterError('n_steps must be a positive integer')
if delay == 0:
raise ParameterError('delay must be a non-zero integer')
data = np.atleast_2d(data)
t = data.shape[1]
kwargs.setdefault('mode', 'constant')
if kwargs['mode'] == 'constant':
kwargs.setdefault('constant_values', [0])
# Pad the end with zeros, which will roll to the front below
if delay > 0:
padding = (int((n_steps - 1) * delay), 0)
else:
padding = (0, int((n_steps - 1) * -delay))
data = np.pad(data, [(0, 0), padding], **kwargs)
history = data
# TODO: this could be more efficient
for i in range(1, n_steps):
history = np.vstack([np.roll(data, -i * delay, axis=1), history])
# Trim to original width
if delay > 0:
history = history[:, :t]
else:
history = history[:, -t:]
# Make contiguous
return np.asfortranarray(history)
| isc |
michigraber/scikit-learn | examples/calibration/plot_calibration_multiclass.py | 272 | 6972 | """
==================================================
Probability Calibration for 3-class classification
==================================================
This example illustrates how sigmoid calibration changes predicted
probabilities for a 3-class classification problem. Illustrated is the
standard 2-simplex, where the three corners correspond to the three classes.
Arrows point from the probability vectors predicted by an uncalibrated
classifier to the probability vectors predicted by the same classifier after
sigmoid calibration on a hold-out validation set. Colors indicate the true
class of an instance (red: class 1, green: class 2, blue: class 3).
The base classifier is a random forest classifier with 25 base estimators
(trees). If this classifier is trained on all 800 training datapoints, it is
overly confident in its predictions and thus incurs a large log-loss.
Calibrating an identical classifier, which was trained on 600 datapoints, with
method='sigmoid' on the remaining 200 datapoints reduces the confidence of the
predictions, i.e., moves the probability vectors from the edges of the simplex
towards the center. This calibration results in a lower log-loss. Note that an
alternative would have been to increase the number of base estimators which
would have resulted in a similar decrease in log-loss.
"""
print(__doc__)
# Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: BSD Style.
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import make_blobs
from sklearn.ensemble import RandomForestClassifier
from sklearn.calibration import CalibratedClassifierCV
from sklearn.metrics import log_loss
np.random.seed(0)
# Generate data
X, y = make_blobs(n_samples=1000, n_features=2, random_state=42,
cluster_std=5.0)
X_train, y_train = X[:600], y[:600]
X_valid, y_valid = X[600:800], y[600:800]
X_train_valid, y_train_valid = X[:800], y[:800]
X_test, y_test = X[800:], y[800:]
# Train uncalibrated random forest classifier on whole train and validation
# data and evaluate on test data
clf = RandomForestClassifier(n_estimators=25)
clf.fit(X_train_valid, y_train_valid)
clf_probs = clf.predict_proba(X_test)
score = log_loss(y_test, clf_probs)
# Train random forest classifier, calibrate on validation data and evaluate
# on test data
clf = RandomForestClassifier(n_estimators=25)
clf.fit(X_train, y_train)
clf_probs = clf.predict_proba(X_test)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid", cv="prefit")
sig_clf.fit(X_valid, y_valid)
sig_clf_probs = sig_clf.predict_proba(X_test)
sig_score = log_loss(y_test, sig_clf_probs)
# Plot changes in predicted probabilities via arrows
plt.figure(0)
colors = ["r", "g", "b"]
for i in range(clf_probs.shape[0]):
plt.arrow(clf_probs[i, 0], clf_probs[i, 1],
sig_clf_probs[i, 0] - clf_probs[i, 0],
sig_clf_probs[i, 1] - clf_probs[i, 1],
color=colors[y_test[i]], head_width=1e-2)
# Plot perfect predictions
plt.plot([1.0], [0.0], 'ro', ms=20, label="Class 1")
plt.plot([0.0], [1.0], 'go', ms=20, label="Class 2")
plt.plot([0.0], [0.0], 'bo', ms=20, label="Class 3")
# Plot boundaries of unit simplex
plt.plot([0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], 'k', label="Simplex")
# Annotate points on the simplex
plt.annotate(r'($\frac{1}{3}$, $\frac{1}{3}$, $\frac{1}{3}$)',
xy=(1.0/3, 1.0/3), xytext=(1.0/3, .23), xycoords='data',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='center', verticalalignment='center')
plt.plot([1.0/3], [1.0/3], 'ko', ms=5)
plt.annotate(r'($\frac{1}{2}$, $0$, $\frac{1}{2}$)',
xy=(.5, .0), xytext=(.5, .1), xycoords='data',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='center', verticalalignment='center')
plt.annotate(r'($0$, $\frac{1}{2}$, $\frac{1}{2}$)',
xy=(.0, .5), xytext=(.1, .5), xycoords='data',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='center', verticalalignment='center')
plt.annotate(r'($\frac{1}{2}$, $\frac{1}{2}$, $0$)',
xy=(.5, .5), xytext=(.6, .6), xycoords='data',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='center', verticalalignment='center')
plt.annotate(r'($0$, $0$, $1$)',
xy=(0, 0), xytext=(.1, .1), xycoords='data',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='center', verticalalignment='center')
plt.annotate(r'($1$, $0$, $0$)',
xy=(1, 0), xytext=(1, .1), xycoords='data',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='center', verticalalignment='center')
plt.annotate(r'($0$, $1$, $0$)',
xy=(0, 1), xytext=(.1, 1), xycoords='data',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='center', verticalalignment='center')
# Add grid
plt.grid("off")
for x in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
plt.plot([0, x], [x, 0], 'k', alpha=0.2)
plt.plot([0, 0 + (1-x)/2], [x, x + (1-x)/2], 'k', alpha=0.2)
plt.plot([x, x + (1-x)/2], [0, 0 + (1-x)/2], 'k', alpha=0.2)
plt.title("Change of predicted probabilities after sigmoid calibration")
plt.xlabel("Probability class 1")
plt.ylabel("Probability class 2")
plt.xlim(-0.05, 1.05)
plt.ylim(-0.05, 1.05)
plt.legend(loc="best")
print("Log-loss of")
print(" * uncalibrated classifier trained on 800 datapoints: %.3f "
% score)
print(" * classifier trained on 600 datapoints and calibrated on "
"200 datapoint: %.3f" % sig_score)
# Illustrate calibrator
plt.figure(1)
# generate grid over 2-simplex
p1d = np.linspace(0, 1, 20)
p0, p1 = np.meshgrid(p1d, p1d)
p2 = 1 - p0 - p1
p = np.c_[p0.ravel(), p1.ravel(), p2.ravel()]
p = p[p[:, 2] >= 0]
calibrated_classifier = sig_clf.calibrated_classifiers_[0]
prediction = np.vstack([calibrator.predict(this_p)
for calibrator, this_p in
zip(calibrated_classifier.calibrators_, p.T)]).T
prediction /= prediction.sum(axis=1)[:, None]
# Ploit modifications of calibrator
for i in range(prediction.shape[0]):
plt.arrow(p[i, 0], p[i, 1],
prediction[i, 0] - p[i, 0], prediction[i, 1] - p[i, 1],
head_width=1e-2, color=colors[np.argmax(p[i])])
# Plot boundaries of unit simplex
plt.plot([0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], 'k', label="Simplex")
plt.grid("off")
for x in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
plt.plot([0, x], [x, 0], 'k', alpha=0.2)
plt.plot([0, 0 + (1-x)/2], [x, x + (1-x)/2], 'k', alpha=0.2)
plt.plot([x, x + (1-x)/2], [0, 0 + (1-x)/2], 'k', alpha=0.2)
plt.title("Illustration of sigmoid calibrator")
plt.xlabel("Probability class 1")
plt.ylabel("Probability class 2")
plt.xlim(-0.05, 1.05)
plt.ylim(-0.05, 1.05)
plt.show()
| bsd-3-clause |
cavestruz/L500analysis | plotting/profiles/T_Vcirc_evolution/Vcirc_evolution/plot_Vcirc2_nu_binned_Vc500c.py | 1 | 3175 | from L500analysis.data_io.get_cluster_data import GetClusterData
from L500analysis.utils.utils import aexp2redshift
from L500analysis.plotting.tools.figure_formatting import *
from L500analysis.plotting.profiles.tools.profiles_percentile \
import *
from L500analysis.plotting.profiles.tools.select_profiles \
import nu_cut, prune_dict
from L500analysis.utils.constants import rbins
from derived_field_functions import *
color = matplotlib.cm.afmhot_r
aexps = [1.0,0.9,0.8,0.7,0.6,0.5,0.45,0.4,0.35]
nu_threshold = [2.3,2.7]
nu_label = r"%0.1f$\leq\nu_{500c}\leq$%0.1f"%(nu_threshold[0],nu_threshold[1])
db_name = 'L500_NR_0'
db_dir = '/home/babyostrich/Documents/Repos/L500analysis/'
profiles_list = ['r_mid',
'Vcirc2_Vc500c',
'M_dark', 'M_star', 'M_gas',
'R/R500c']
halo_properties_list=['r500c','M_total_500c','nu_500c']
Vcirc2ratioVc500c=r"$\tilde{V}=V^2_{c}/V^2_{c,500c}$"
fVcz1=r"$\tilde{V}/\tilde{V}(z=1)$"
pa = PlotAxes(figname='Vcirc2_Vc500c_nu%0.1f'%nu_threshold[0],
axes=[[0.15,0.4,0.80,0.55],[0.15,0.15,0.80,0.24]],
axes_labels=[Vcirc2ratioVc500c,fVcz1],
xlabel=r"$R/R_{500c}$",
xlim=(0.2,5),
ylims=[(0.6,1.4),(0.6,1.4)])
Vcirc2={}
clkeys = ['Vcirc2_Vc500c']
plots = [Vcirc2]
linestyles = ['-']
for aexp in aexps :
cldata = GetClusterData(aexp=aexp,db_name=db_name,
db_dir=db_dir,
profiles_list=profiles_list,
halo_properties_list=halo_properties_list)
nu_cut_hids = nu_cut(nu=cldata['nu_500c'], threshold=nu_threshold)
for plot, key in zip(plots,clkeys) :
pruned_profiles = prune_dict(d=cldata[key],k=nu_cut_hids)
plot[aexp] = calculate_profiles_mean_variance(pruned_profiles)
pa.axes[Vcirc2ratioVc500c].plot( rbins, Vcirc2[aexp]['mean'],color=color(aexp),
ls='-',label="$z=%3.1f$" % aexp2redshift(aexp))
pa.axes[Vcirc2ratioVc500c].fill_between(rbins, Vcirc2[0.5]['down'], Vcirc2[0.5]['up'],
color=color(0.5), zorder=0)
for aexp in aexps :
for V,ls in zip(plots,linestyles) :
fractional_evolution = get_profiles_division_mean_variance(
mean_profile1=V[aexp]['mean'],
var_profile1=V[aexp]['var'],
mean_profile2=V[0.5]['mean'],
var_profile2=V[0.5]['var'],
)
pa.axes[fVcz1].plot( rbins, fractional_evolution['mean'],
color=color(aexp),ls=ls)
pa.axes[Vcirc2ratioVc500c].annotate(nu_label, xy=(.75, .75), xytext=(.3, 1.3))
pa.axes[Vcirc2ratioVc500c].tick_params(labelsize=12)
pa.axes[Vcirc2ratioVc500c].tick_params(labelsize=12)
pa.axes[fVcz1].set_yticks(arange(0.6,1.4,0.2))
matplotlib.rcParams['legend.handlelength'] = 0
matplotlib.rcParams['legend.numpoints'] = 1
matplotlib.rcParams['legend.fontsize'] = 12
pa.set_legend(axes_label=Vcirc2ratioVc500c,ncol=3,loc='upper right', frameon=False)
pa.color_legend_texts(axes_label=Vcirc2ratioVc500c)
pa.savefig()
| mit |
soleneulmer/atmos | indicators_molec.py | 1 | 4324 | # ===================================
# CALCULATES Ioff and Ires
# Indicators described in Molecfit II
#
# Solene 20.09.2016
# ===================================
#
import numpy as np
from astropy.io import fits
import matplotlib.pyplot as plt
# from PyAstronomy import pyasl
from scipy.interpolate import interp1d
from scipy.interpolate import InterpolatedUnivariateSpline
from scipy import stats
# from sklearn.metrics import mean_squared_error
# from math import sqrt
# from numpy import linalg as LA
# MOLECFIT
#
file_molecfit = '/home/solene/atmos/For_Solene/1203nm/output/molecfit_crires_solene_tac.fits'
hdu_molecfit = fits.open(file_molecfit)
data_molecfit = hdu_molecfit[1].data
cols_molecfit = hdu_molecfit[1].columns
# cols_molecfit.info()
rawwl_molecfit = data_molecfit.field('mlambda')
wl_molecfit = rawwl_molecfit*10e2
trans_molecfit = data_molecfit.field('mtrans')
cflux_molecfit = data_molecfit.field('cflux')
# TELFIT
#
file_telfit = '/home/solene/atmos/trans_telfit.txt'
wl_telfit, trans_telfit, wl_datatelfit, flux_datatelfit = np.loadtxt(
file_telfit, unpack=True)
# Interpolation
f_molecfit = interp1d(wl_molecfit, cflux_molecfit, kind='cubic')
ftrans_molecfit = interp1d(wl_molecfit, trans_molecfit, kind='cubic')
# f_tapas = interp1d(wlcorr_tapas, trans_tapas)
# **1** BINNED DATA
# 3 delta-lambda = 0.036
# Mean and std deviation of bins on the telluric CORRECTED spectrum
fluxmean_bin_means, bin_edges, binnumber = stats.binned_statistic(
wl_datatelfit, f_molecfit(wl_datatelfit), statistic='mean',
bins=np.floor((wl_datatelfit[-1]-wl_datatelfit[0])/0.036))
fluxstd_bin_means, _, _ = stats.binned_statistic(
wl_datatelfit, f_molecfit(wl_datatelfit), statistic=np.std,
bins=np.floor((wl_datatelfit[-1]-wl_datatelfit[0])/0.036))
bin_width = (bin_edges[1] - bin_edges[0])
bin_centers = bin_edges[1:] - bin_width/2
# **2** Bins where average TRANSMISSION is > 0.99
flux_trans_mean_bin_means, _, _ = stats.binned_statistic(
wl_datatelfit, ftrans_molecfit(wl_datatelfit), statistic='mean',
bins=np.floor((wl_datatelfit[-1]-wl_datatelfit[0])/0.036))
# cont_bin_means = flux_trans_mean_bin_means[flux_trans_mean_bin_means > 0.99]
ind_cont = np.where(flux_trans_mean_bin_means > 0.99)
ind_out = np.where((flux_trans_mean_bin_means < 0.95) &
(flux_trans_mean_bin_means > 0.1))
# plt.plot(bin_centers[ind_cont], flux_trans_mean_bin_means[ind_cont], 'kx')
# **3** Interpolation of the continuum cubic
# f_cont = interp1d(bin_centers[ind_cont], flux_trans_mean_bin_means[ind_cont], kind='cubic')
# Extrapolation with constant value spline
f_cont = InterpolatedUnivariateSpline(
bin_centers[ind_cont], flux_trans_mean_bin_means[ind_cont], ext=3)
# bbox=[bin_centers[ind_cont][0], bin_centers[ind_cont][-1]],
# **5** Subtract cont to mean flux
# and Divide offset and std by interpolated continuum mean value
sys_offset = (fluxmean_bin_means - f_cont(bin_centers)) / f_cont(bin_centers)
flux_std = fluxstd_bin_means / f_cont(bin_centers)
# **6** independant WL = Divide by average absorption
absorp_molecfit = 1 - flux_trans_mean_bin_means
sys_offset_final = sys_offset / absorp_molecfit
flux_std_final = flux_std / absorp_molecfit
plt.figure(1)
plt.plot(wl_datatelfit, flux_datatelfit, 'b.-', label='Raw data')
# plt.hlines(flux_bin_means, bin_edges[:-1],
# bin_edges[1:], colors='g', lw=5, label='binned statistic of data')
plt.plot(bin_centers, fluxmean_bin_means, 'rx-', label='Mean binned data')
plt.plot(bin_centers, fluxstd_bin_means, 'kx-', label='Standard deviation binned data')
plt.legend()
plt.figure(2)
plt.plot(wl_datatelfit, flux_datatelfit, 'g.-', label='Data 2nd detector')
plt.plot(wl_molecfit, trans_molecfit, 'r-', label='Molecfit')
plt.plot(wl_datatelfit, f_molecfit(wl_datatelfit),
'b-', label='Corrected data - Molecfit')
plt.plot(wl_datatelfit, f_cont(wl_datatelfit),
'k-', label='Interpolated Continuum')
plt.plot(sys_offset_final[ind_out], flux_std_final[ind_out], 'kx')
plt.plot(flux_trans_mean_bin_means[ind_out],
sys_offset_final[ind_out], 'kx', label='Ioff vs Transmission')
plt.plot(flux_trans_mean_bin_means[ind_out],
flux_std_final[ind_out], 'r.', label='Ires vs Transmission')
plt.xlabel('Wavelength (nm)')
plt.ylabel('Transmission')
plt.legend(loc=3.)
plt.show()
| mit |
ryandougherty/mwa-capstone | MWA_Tools/build/matplotlib/examples/misc/rasterization_demo.py | 6 | 1257 | import numpy as np
import matplotlib.pyplot as plt
d = np.arange(100).reshape(10, 10)
x, y = np.meshgrid(np.arange(11), np.arange(11))
theta = 0.25*np.pi
xx = x*np.cos(theta) - y*np.sin(theta)
yy = x*np.sin(theta) + y*np.cos(theta)
ax1 = plt.subplot(221)
ax1.set_aspect(1)
ax1.pcolormesh(xx, yy, d)
ax1.set_title("No Rasterization")
ax2 = plt.subplot(222)
ax2.set_aspect(1)
ax2.set_title("Rasterization")
m = ax2.pcolormesh(xx, yy, d)
m.set_rasterized(True)
ax3 = plt.subplot(223)
ax3.set_aspect(1)
ax3.pcolormesh(xx, yy, d)
ax3.text(0.5, 0.5, "Text", alpha=0.2,
va="center", ha="center", size=50, transform=ax3.transAxes)
ax3.set_title("No Rasterization")
ax4 = plt.subplot(224)
ax4.set_aspect(1)
m = ax4.pcolormesh(xx, yy, d)
m.set_zorder(-20)
ax4.text(0.5, 0.5, "Text", alpha=0.2,
zorder=-15,
va="center", ha="center", size=50, transform=ax4.transAxes)
ax4.set_rasterization_zorder(-10)
ax4.set_title("Rasterization z$<-10$")
# ax2.title.set_rasterized(True) # should display a warning
plt.savefig("test_rasterization.pdf", dpi=150)
plt.savefig("test_rasterization.eps", dpi=150)
if not plt.rcParams["text.usetex"]:
plt.savefig("test_rasterization.svg", dpi=150)
# svg backend currently ignores the dpi
| gpl-2.0 |
fzalkow/scikit-learn | examples/plot_kernel_approximation.py | 262 | 8004 | """
==================================================
Explicit feature map approximation for RBF kernels
==================================================
An example illustrating the approximation of the feature map
of an RBF kernel.
.. currentmodule:: sklearn.kernel_approximation
It shows how to use :class:`RBFSampler` and :class:`Nystroem` to
approximate the feature map of an RBF kernel for classification with an SVM on
the digits dataset. Results using a linear SVM in the original space, a linear
SVM using the approximate mappings and using a kernelized SVM are compared.
Timings and accuracy for varying amounts of Monte Carlo samplings (in the case
of :class:`RBFSampler`, which uses random Fourier features) and different sized
subsets of the training set (for :class:`Nystroem`) for the approximate mapping
are shown.
Please note that the dataset here is not large enough to show the benefits
of kernel approximation, as the exact SVM is still reasonably fast.
Sampling more dimensions clearly leads to better classification results, but
comes at a greater cost. This means there is a tradeoff between runtime and
accuracy, given by the parameter n_components. Note that solving the Linear
SVM and also the approximate kernel SVM could be greatly accelerated by using
stochastic gradient descent via :class:`sklearn.linear_model.SGDClassifier`.
This is not easily possible for the case of the kernelized SVM.
The second plot visualized the decision surfaces of the RBF kernel SVM and
the linear SVM with approximate kernel maps.
The plot shows decision surfaces of the classifiers projected onto
the first two principal components of the data. This visualization should
be taken with a grain of salt since it is just an interesting slice through
the decision surface in 64 dimensions. In particular note that
a datapoint (represented as a dot) does not necessarily be classified
into the region it is lying in, since it will not lie on the plane
that the first two principal components span.
The usage of :class:`RBFSampler` and :class:`Nystroem` is described in detail
in :ref:`kernel_approximation`.
"""
print(__doc__)
# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# License: BSD 3 clause
# Standard scientific Python imports
import matplotlib.pyplot as plt
import numpy as np
from time import time
# Import datasets, classifiers and performance metrics
from sklearn import datasets, svm, pipeline
from sklearn.kernel_approximation import (RBFSampler,
Nystroem)
from sklearn.decomposition import PCA
# The digits dataset
digits = datasets.load_digits(n_class=9)
# To apply an classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(digits.data)
data = digits.data / 16.
data -= data.mean(axis=0)
# We learn the digits on the first half of the digits
data_train, targets_train = data[:n_samples / 2], digits.target[:n_samples / 2]
# Now predict the value of the digit on the second half:
data_test, targets_test = data[n_samples / 2:], digits.target[n_samples / 2:]
#data_test = scaler.transform(data_test)
# Create a classifier: a support vector classifier
kernel_svm = svm.SVC(gamma=.2)
linear_svm = svm.LinearSVC()
# create pipeline from kernel approximation
# and linear svm
feature_map_fourier = RBFSampler(gamma=.2, random_state=1)
feature_map_nystroem = Nystroem(gamma=.2, random_state=1)
fourier_approx_svm = pipeline.Pipeline([("feature_map", feature_map_fourier),
("svm", svm.LinearSVC())])
nystroem_approx_svm = pipeline.Pipeline([("feature_map", feature_map_nystroem),
("svm", svm.LinearSVC())])
# fit and predict using linear and kernel svm:
kernel_svm_time = time()
kernel_svm.fit(data_train, targets_train)
kernel_svm_score = kernel_svm.score(data_test, targets_test)
kernel_svm_time = time() - kernel_svm_time
linear_svm_time = time()
linear_svm.fit(data_train, targets_train)
linear_svm_score = linear_svm.score(data_test, targets_test)
linear_svm_time = time() - linear_svm_time
sample_sizes = 30 * np.arange(1, 10)
fourier_scores = []
nystroem_scores = []
fourier_times = []
nystroem_times = []
for D in sample_sizes:
fourier_approx_svm.set_params(feature_map__n_components=D)
nystroem_approx_svm.set_params(feature_map__n_components=D)
start = time()
nystroem_approx_svm.fit(data_train, targets_train)
nystroem_times.append(time() - start)
start = time()
fourier_approx_svm.fit(data_train, targets_train)
fourier_times.append(time() - start)
fourier_score = fourier_approx_svm.score(data_test, targets_test)
nystroem_score = nystroem_approx_svm.score(data_test, targets_test)
nystroem_scores.append(nystroem_score)
fourier_scores.append(fourier_score)
# plot the results:
plt.figure(figsize=(8, 8))
accuracy = plt.subplot(211)
# second y axis for timeings
timescale = plt.subplot(212)
accuracy.plot(sample_sizes, nystroem_scores, label="Nystroem approx. kernel")
timescale.plot(sample_sizes, nystroem_times, '--',
label='Nystroem approx. kernel')
accuracy.plot(sample_sizes, fourier_scores, label="Fourier approx. kernel")
timescale.plot(sample_sizes, fourier_times, '--',
label='Fourier approx. kernel')
# horizontal lines for exact rbf and linear kernels:
accuracy.plot([sample_sizes[0], sample_sizes[-1]],
[linear_svm_score, linear_svm_score], label="linear svm")
timescale.plot([sample_sizes[0], sample_sizes[-1]],
[linear_svm_time, linear_svm_time], '--', label='linear svm')
accuracy.plot([sample_sizes[0], sample_sizes[-1]],
[kernel_svm_score, kernel_svm_score], label="rbf svm")
timescale.plot([sample_sizes[0], sample_sizes[-1]],
[kernel_svm_time, kernel_svm_time], '--', label='rbf svm')
# vertical line for dataset dimensionality = 64
accuracy.plot([64, 64], [0.7, 1], label="n_features")
# legends and labels
accuracy.set_title("Classification accuracy")
timescale.set_title("Training times")
accuracy.set_xlim(sample_sizes[0], sample_sizes[-1])
accuracy.set_xticks(())
accuracy.set_ylim(np.min(fourier_scores), 1)
timescale.set_xlabel("Sampling steps = transformed feature dimension")
accuracy.set_ylabel("Classification accuracy")
timescale.set_ylabel("Training time in seconds")
accuracy.legend(loc='best')
timescale.legend(loc='best')
# visualize the decision surface, projected down to the first
# two principal components of the dataset
pca = PCA(n_components=8).fit(data_train)
X = pca.transform(data_train)
# Gemerate grid along first two principal components
multiples = np.arange(-2, 2, 0.1)
# steps along first component
first = multiples[:, np.newaxis] * pca.components_[0, :]
# steps along second component
second = multiples[:, np.newaxis] * pca.components_[1, :]
# combine
grid = first[np.newaxis, :, :] + second[:, np.newaxis, :]
flat_grid = grid.reshape(-1, data.shape[1])
# title for the plots
titles = ['SVC with rbf kernel',
'SVC (linear kernel)\n with Fourier rbf feature map\n'
'n_components=100',
'SVC (linear kernel)\n with Nystroem rbf feature map\n'
'n_components=100']
plt.tight_layout()
plt.figure(figsize=(12, 5))
# predict and plot
for i, clf in enumerate((kernel_svm, nystroem_approx_svm,
fourier_approx_svm)):
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
plt.subplot(1, 3, i + 1)
Z = clf.predict(flat_grid)
# Put the result into a color plot
Z = Z.reshape(grid.shape[:-1])
plt.contourf(multiples, multiples, Z, cmap=plt.cm.Paired)
plt.axis('off')
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=targets_train, cmap=plt.cm.Paired)
plt.title(titles[i])
plt.tight_layout()
plt.show()
| bsd-3-clause |
hugohmk/Epidemic-Emulator | main.py | 1 | 7208 | from epidemic_emulator import node
from datetime import datetime
import platform
import argparse
import time
import os
import matplotlib.pyplot as plt
import random
def parse_network(f, node_id, topology = "clique"):
neighbors = []
nd = None
t = datetime.now()
t = t-t
net = []
index = -1
cnt = 0
for i in f:
i = i.rstrip("\n").split("|")
if len(i)<4:
continue
u = (i[0],(i[1],int(i[2])),[(i[3],t)])
if i[0]==node_id:
nd = u
index = cnt
net.append(u)
cnt+=1
f.close()
# clique
if topology == "clique":
neighbors = [i for i in net if i[0] != node_id]
# star
elif topology == "star":
if index > 0:
neighbors = [net[0]]
else:
neighbors = net[1:]
return neighbors,nd
def simulation_controller(args,nd,network):
# Example nd value:
#('9', ('127.0.0.1', 9179), [('S', datetime.timedelta(0))])
#
# network is a tuple containing every node identifier constructed from
# args.network (default=network.txt) file
r = args.recovery_rate
e = args.endogenous_rate
x = args.exogenous_rate
if nd is not None:
with node.Node(r,e,x) as a:
a.start(nd, network)
if args.interaction == 1:
try:
help_text = """>> Commands:
0 (help) -> print this
1 (print current) -> print current network state
2 (print history) -> print network history
3 (end) -> send shutdown message to all nodes
4 (display state) -> display current network state
5 (display history) -> display network history
"""
print help_text
while True:
opt = raw_input(">> Insert command: ")
if opt == "0":
print help_text
elif opt == "1":
#print a.network_state(),"\n"
a.print_state()
elif opt == "2":
#print a.network_history(),"\n"
a.print_history()
elif opt == "3":
a.display_history()
a.network_shutdown()
a.stop()
break
elif opt == "4":
a.display_state()
elif opt == "5":
a.display_history()
else:
print "Invalid input\n"
except:
a.network_shutdown()
a.stop()
finally:
a.network_shutdown()
a.stop()
elif args.interaction > 1:
print("Running simulation for %d seconds." % args.interaction)
time.sleep(args.interaction)
#a.display_history()
simdata = a.save_simulation_data()
a.network_shutdown()
a.stop()
return simdata
else:
try:
while not a.stopped():
time.sleep(2)
except:
a.stop()
finally:
a.stop()
def process_data(simdata,repetitions,simulation_time):
simresults = [[-1 for t in range(simulation_time+1)] for x in range(repetitions)]
print_stuff = 1
for k in range(repetitions):
if print_stuff:
print("")
print("Run #%d" % (k+1))
print("time\tinfected count")
t = 0
for event in simdata[k]:
if print_stuff: print("%.2f\t%d" % (event[0],event[1]))
time = int(event[0])
infected_count = event[1]
if time < t:
continue
elif t < simulation_time+1:
if print_stuff: print("* %.2f" % event[0])
while t <= time:
simresults[k][t] = infected_count
t = t+1
while t < simulation_time+1:
simresults[k][t] = infected_count
t = t+1
if print_stuff:
print("")
print("Processed output:")
print("time\tinfected count")
for t in range(simulation_time+1):
print("%d\t%d" % (t,simresults[k][t]))
average_results = [0.0 for t in range(simulation_time+1)]
for t in range(simulation_time+1):
for k in range(repetitions):
average_results[t] = average_results[t] + simresults[k][t]
average_results[t] = float(average_results[t]) / repetitions
print(average_results)
plt.plot(list(range(0,simulation_time+1)),average_results,'-o')
axes = plt.gca()
axes.set_xlim([0,simulation_time])
#axes.set_ylim([0,10])
plt.xlabel("Seconds")
plt.ylabel("Infected nodes")
plt.savefig("average_simulation.pdf")
if __name__ == "__main__":
dir_path = os.path.dirname(os.path.realpath(__file__))
dir_path_unix = dir_path.replace("\\","/")
if (platform.system()!="Windows"): dir_path = dir_path_unix
parser = argparse.ArgumentParser()
parser.add_argument("-id","--identifier",required=True,
help="Node identifier")
parser.add_argument("-n","--network",type=argparse.FileType('r'), default = dir_path_unix+"/network.txt",
help="File that contains the network's description; each line presents node_id|node_ip|port_number|initial_state")
# parser.add_argument("-i","--interactive",type=int,default=0,
# help="Interactive mode")
parser.add_argument("-i","--interaction",type=int,default=0,
help="Interaction mode: default (0), interactive (1), simulation (2)")
parser.add_argument("-r","--recovery_rate",type=float,#default=1.0,
help="Simulation parameter: recovery_rate")
parser.add_argument("-e","--endogenous_rate",type=float,#default=1.0,
help="Simulation parameter: endogenous_infection_rate")
parser.add_argument("-x","--exogenous_rate",type=float,#default=1e-6,
help="Simulation parameter: exogenous_infection_rate")
parser.add_argument("-t","--topology",choices=["clique","star"],default="clique",
help="Network topology: clique or star")
args = parser.parse_args()
network = {}
if args.network is not None:
network,nd = parse_network(args.network, args.identifier, args.topology)
simulation_time = args.interaction
repetitions = 1
simdata = []
for i in range(repetitions):
simdata.append(simulation_controller(args,nd,network))
if args.identifier == '0':
process_data(simdata,repetitions,simulation_time)
| mit |
vascotenner/holoviews | holoviews/plotting/mpl/annotation.py | 1 | 3913 | import matplotlib
from matplotlib import patches as patches
from ...core.util import match_spec
from ...core.options import abbreviated_exception
from .element import ElementPlot
class AnnotationPlot(ElementPlot):
"""
AnnotationPlot handles the display of all annotation elements.
"""
def __init__(self, annotation, **params):
self._annotation = annotation
super(AnnotationPlot, self).__init__(annotation, **params)
self.handles['annotations'] = []
def initialize_plot(self, ranges=None):
annotation = self.hmap.last
key = self.keys[-1]
ranges = self.compute_ranges(self.hmap, key, ranges)
ranges = match_spec(annotation, ranges)
axis = self.handles['axis']
opts = self.style[self.cyclic_index]
with abbreviated_exception():
handles = self.draw_annotation(axis, annotation.data, opts)
self.handles['annotations'] = handles
return self._finalize_axis(key, ranges=ranges)
def update_handles(self, key, axis, annotation, ranges, style):
# Clear all existing annotations
for element in self.handles['annotations']:
element.remove()
with abbreviated_exception():
self.handles['annotations'] = self.draw_annotation(axis, annotation.data, style)
class VLinePlot(AnnotationPlot):
"Draw a vertical line on the axis"
style_opts = ['alpha', 'color', 'linewidth', 'linestyle', 'visible']
def draw_annotation(self, axis, position, opts):
return [axis.axvline(position, **opts)]
class HLinePlot(AnnotationPlot):
"Draw a horizontal line on the axis"
style_opts = ['alpha', 'color', 'linewidth', 'linestyle', 'visible']
def draw_annotation(self, axis, position, opts):
"Draw a horizontal line on the axis"
return [axis.axhline(position, **opts)]
class TextPlot(AnnotationPlot):
"Draw the Text annotation object"
style_opts = ['alpha', 'color', 'family', 'weight', 'rotation', 'fontsize', 'visible']
def draw_annotation(self, axis, data, opts):
(x,y, text, fontsize,
horizontalalignment, verticalalignment, rotation) = data
opts['fontsize'] = fontsize
return [axis.text(x,y, text,
horizontalalignment = horizontalalignment,
verticalalignment = verticalalignment,
rotation=rotation, **opts)]
class ArrowPlot(AnnotationPlot):
"Draw an arrow using the information supplied to the Arrow annotation"
_arrow_style_opts = ['alpha', 'color', 'lw', 'linewidth', 'visible']
_text_style_opts = TextPlot.style_opts
style_opts = sorted(set(_arrow_style_opts + _text_style_opts))
def draw_annotation(self, axis, data, opts):
direction, text, xy, points, arrowstyle = data
arrowprops = dict({'arrowstyle':arrowstyle},
**{k: opts[k] for k in self._arrow_style_opts if k in opts})
textopts = {k: opts[k] for k in self._text_style_opts if k in opts}
if direction in ['v', '^']:
xytext = (0, points if direction=='v' else -points)
elif direction in ['>', '<']:
xytext = (points if direction=='<' else -points, 0)
return [axis.annotate(text, xy=xy, textcoords='offset points',
xytext=xytext, ha="center", va="center",
arrowprops=arrowprops, **textopts)]
class SplinePlot(AnnotationPlot):
"Draw the supplied Spline annotation (see Spline docstring)"
style_opts = ['alpha', 'edgecolor', 'linewidth', 'linestyle', 'visible']
def draw_annotation(self, axis, data, opts):
verts, codes = data
patch = patches.PathPatch(matplotlib.path.Path(verts, codes),
facecolor='none', **opts)
axis.add_patch(patch)
return [patch]
| bsd-3-clause |
GkAntonius/feynman | examples/Solid_State_Physics/plot_eph.py | 2 | 1265 | """
Electron-phonon coupling self-energy
====================================
A diagram containing loopy lines.
"""
from feynman import Diagram
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8,2))
ax = fig.add_axes([0,0,1,1], frameon=False)
ax.set_xlim(0, fig.get_size_inches()[0])
ax.set_ylim(0, fig.get_size_inches()[1])
# Init D and ax
D = Diagram(ax)
D.x0 = 0.2
D.y0 = sum(D.ax.get_ylim()) * .35
# Various size
opwidth = 1.
linlen = 2.
txtpad = .8
wiggle_amplitude=.1
# Line styles
Ph_style = dict(style='elliptic loopy', ellipse_spread=.6, xamp=.10, yamp=-.15, nloops=15)
DW_style = dict(style='circular loopy', circle_radius=.7, xamp=.10, yamp=.15, nloops=18)
G_style = dict(style='simple', arrow=True, arrow_param={'width':0.15, 'length': .3})
# Item 1
v11 = D.vertex([D.x0, D.y0])
v12 = D.vertex(v11.xy, dx=opwidth)
Sigma = D.operator([v11, v12])
Sigma.text("$\Sigma^{ep}$")
# Symbol
D.text(v12.x + txtpad, D.y0, "=")
# Item 3
v21 = D.vertex([v12.x + 2 * txtpad, D.y0 - 0.3])
v22 = D.vertex(v21.xy, dx=linlen)
G = D.line(v21, v22, **G_style)
Ph = D.line(v21, v22, **Ph_style)
# Symbol
D.text(v22.x + txtpad, D.y0, "+")
# Item 3
v31 = D.vertex([v22.x + 3 * txtpad, D.y0 - 0.3])
DW = D.line(v31, v31, **DW_style)
D.plot()
plt.show()
| gpl-3.0 |
LaRiffle/axa_challenge | fonction_py/train.py | 1 | 12400 | from fonction_py.tools import *
from fonction_py.preprocess import *
from sklearn import linear_model
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cross_validation
from sklearn.linear_model import LogisticRegression
from sklearn import tree
from sklearn import svm
from sklearn import decomposition
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.grid_search import GridSearchCV
from sklearn.grid_search import RandomizedSearchCV
from scipy.stats import uniform as sp_randint
from sklearn import datasets
from sklearn.linear_model import Ridge
from fonction_py.tim import *
import time
def faireTout():
fields = ['DATE', 'DAY_OFF', 'WEEK_END', 'DAY_WE_DS', 'ASS_ASSIGNMENT', 'CSPL_RECEIVED_CALLS' ] # selectionne les colonnes à lire
c = pd.DataFrame()
<<<<<<< HEAD
listmodel = faireListModel()#recupere le nom et les modeles de chaque truc
data=pd.read_csv("data/trainPure.csv", sep=";", usecols=fields) # LECTURE du fichier de train,
resultat = pd.read_csv("data/submission.txt", sep="\t") # LECTURE dufichier de test
res=[]
model = listmodel[0]
for model in listmodel:
print(model[0]) #affiche le ass assignment
(xTest, x, souvenir, y)=preprocessTOTAL(model[0]) # ajuste le nombre et le nom de feature pour que xTest et x aient les memes
mod= GradientBoostingRegressor(loss='huber', alpha=0.9,n_estimators=100, max_depth=3,learning_rate=.1, min_samples_leaf=9,min_samples_split=9)
mod.fit(x, y) #s'entraine
pred = mod.predict(xTest) # predit
pred[pred>max(y)*1.05]=max(y)*1.05 # pour pas predire trop grand
pred[pred<0]=0 # pas de negatif
pred =np.round(pred).astype(int) # to int
souvenir['prediction']=pred # on l'ajoute a souvenir qui garde le format standard et la date pour qu'on remette tout a la bonne place a la fin
resultat=pd.merge(resultat, souvenir, how='left',on=['DATE', 'ASS_ASSIGNMENT']) # on remet chaque prediction à la bonne ligne -> il cree prediction_x et prediction_y car l'ancienne prediction et la nouvelle colonne de prediction
resultat=resultat.fillna(0) # on remplit les endroits ou on a pas predit avec des 0
resultat['prediction'] = resultat['prediction_x']+resultat['prediction_y'] # merge les deux colonnes
del resultat['prediction_x']
del resultat['prediction_y']
=======
listmodel = faireListModel()
#'Evenements', 'Gestion Amex'
#setFields = set(pd.read_csv("data/fields.txt", sep=";")['0'].values)
# resultat = pd.read_csv("data/submission.txt", sep="\t")
i=0
# res = []
start_time = time.time()
model = listmodel[24]
data=pd.read_csv("data/trainPure.csv", sep=";", usecols=fields) # LECTURE
resultat = pd.read_csv("data/submission.txt", sep="\t") # LECTURE
res=[]
for model in listmodel:
i = i+1
print(model[0])
x,y = preprocess(data.copy(), model[0]) # rajoute les features
model[1].fit(x, y)
#model.score(xTrain, yTrain)
(xTest, souvenir)=preprocessFINAL(x,model[0])
pred = model[1].predict(xTest)
pred[pred>max(y)*1.05]=max(y)*1.05
pred[pred<0]=0
pred =np.round(pred)
souvenir['prediction']=int(pred)
resultat=pd.merge(resultat, souvenir, how='left',on=['DATE', 'ASS_ASSIGNMENT'])
resultat=resultat.fillna(0)
resultat['prediction'] = resultat['prediction_x']+resultat['prediction_y']
del resultat['prediction_x']
del resultat['prediction_y']
x,y = preprocess(data.copy(), 'Téléphonie') # rajoute les features
#model.score(xTrain, yTrain)
(xTest, souvenir)=preprocessFINAL(x,'Téléphonie')
pred=telephoniePred(x,y,xTest)
pred[pred>max(y)*1.05]=max(y)*1.05
pred[pred<0]=0
pred =np.round(pred)
souvenir['prediction']=int(pred)
resultat=pd.merge(resultat, souvenir, how='left',on=['DATE', 'ASS_ASSIGNMENT'])
resultat=resultat.fillna(0)
resultat['prediction'] = resultat['prediction_x']+resultat['prediction_y']
del resultat['prediction_x']
del resultat['prediction_y']
<<<<<<< HEAD
pd.DataFrame(res).to_csv("reslist.csv", sep=";", decimal=",")
resultat.to_csv("vraipred.txt", sep="\t", index =False)
=======
>>>>>>> origin/master
resultat['prediction']=resultat['prediction'].astype(int)
resultat.to_csv("pouranalyse.txt", sep="\t", index =False, encoding='utf-8')
>>>>>>> origin/master
return resultat
def faireListModel():
return [('CAT', linear_model.LinearRegression()),
('CMS', RandomForestRegressor(bootstrap=False, criterion='mse', max_depth=5,
max_features=30, max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Crises',linear_model.LinearRegression()),
('Domicile', RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=30,
max_features=30, max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=90, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Gestion',RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=30,
max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=100, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Gestion - Accueil Telephonique',RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=20,
max_features=30, max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=70, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Gestion Assurances',RandomForestRegressor(bootstrap=False, criterion='mse', max_depth=20,
max_features=30, max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=20, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Gestion Clients', RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,
max_features=90, max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=50, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Gestion DZ', RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,
max_features=30, max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=30, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Gestion Relation Clienteles',RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,
max_features=90, max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=110, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Gestion Renault', RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=30,
max_features=50, max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=30, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Japon',RandomForestRegressor(bootstrap=False, criterion='mse', max_depth=10,
max_features=30, max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=30, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Manager',RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,
max_features=30, max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=30, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Mécanicien',RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=20,
max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=100, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Médical',RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=30,
max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=100, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Nuit', RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=20,
max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=100, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Prestataires',RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=20,
max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=100, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('RENAULT',RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=80,
max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=100, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('RTC',RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=20,
max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=100, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Regulation Medicale',linear_model.LinearRegression()),
('SAP',RandomForestRegressor(bootstrap=False, criterion='mse', max_depth=20,
max_features=30, max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=30, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Services',RandomForestRegressor(bootstrap=False, criterion='mse', max_depth=30,
max_features=30, max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=30, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Tech. Axa',RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=20,
max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=100, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Tech. Inter',RandomForestRegressor(bootstrap=False, criterion='mse', max_depth=30,
max_features=30, max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=30, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Tech. Total',RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=70,
max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=100, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)),
('Téléphonie',GradientBoostingRegressor(loss='huber', alpha=0.9,n_estimators=100, max_depth=3,learning_rate=.1, min_samples_leaf=9,min_samples_split=9) )] | mit |
dhhjx880713/GPy | GPy/plotting/matplot_dep/variational_plots.py | 6 | 4094 | from matplotlib import pyplot as pb, numpy as np
def plot(parameterized, fignum=None, ax=None, colors=None, figsize=(12, 6)):
"""
Plot latent space X in 1D:
- if fig is given, create input_dim subplots in fig and plot in these
- if ax is given plot input_dim 1D latent space plots of X into each `axis`
- if neither fig nor ax is given create a figure with fignum and plot in there
colors:
colors of different latent space dimensions input_dim
"""
if ax is None:
fig = pb.figure(num=fignum, figsize=figsize)
if colors is None:
from ..Tango import mediumList
from itertools import cycle
colors = cycle(mediumList)
pb.clf()
else:
colors = iter(colors)
lines = []
fills = []
bg_lines = []
means, variances = parameterized.mean.values, parameterized.variance.values
x = np.arange(means.shape[0])
for i in range(means.shape[1]):
if ax is None:
a = fig.add_subplot(means.shape[1], 1, i + 1)
elif isinstance(ax, (tuple, list)):
a = ax[i]
else:
raise ValueError("Need one ax per latent dimension input_dim")
bg_lines.append(a.plot(means, c='k', alpha=.3))
lines.extend(a.plot(x, means.T[i], c=next(colors), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
fills.append(a.fill_between(x,
means.T[i] - 2 * np.sqrt(variances.T[i]),
means.T[i] + 2 * np.sqrt(variances.T[i]),
facecolor=lines[-1].get_color(),
alpha=.3))
a.legend(borderaxespad=0.)
a.set_xlim(x.min(), x.max())
if i < means.shape[1] - 1:
a.set_xticklabels('')
pb.draw()
a.figure.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
return dict(lines=lines, fills=fills, bg_lines=bg_lines)
def plot_SpikeSlab(parameterized, fignum=None, ax=None, colors=None, side_by_side=True):
"""
Plot latent space X in 1D:
- if fig is given, create input_dim subplots in fig and plot in these
- if ax is given plot input_dim 1D latent space plots of X into each `axis`
- if neither fig nor ax is given create a figure with fignum and plot in there
colors:
colors of different latent space dimensions input_dim
"""
if ax is None:
if side_by_side:
fig = pb.figure(num=fignum, figsize=(16, min(12, (2 * parameterized.mean.shape[1]))))
else:
fig = pb.figure(num=fignum, figsize=(8, min(12, (2 * parameterized.mean.shape[1]))))
if colors is None:
from ..Tango import mediumList
from itertools import cycle
colors = cycle(mediumList)
pb.clf()
else:
colors = iter(colors)
plots = []
means, variances, gamma = parameterized.mean, parameterized.variance, parameterized.binary_prob
x = np.arange(means.shape[0])
for i in range(means.shape[1]):
if side_by_side:
sub1 = (means.shape[1],2,2*i+1)
sub2 = (means.shape[1],2,2*i+2)
else:
sub1 = (means.shape[1]*2,1,2*i+1)
sub2 = (means.shape[1]*2,1,2*i+2)
# mean and variance plot
a = fig.add_subplot(*sub1)
a.plot(means, c='k', alpha=.3)
plots.extend(a.plot(x, means.T[i], c=next(colors), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
a.fill_between(x,
means.T[i] - 2 * np.sqrt(variances.T[i]),
means.T[i] + 2 * np.sqrt(variances.T[i]),
facecolor=plots[-1].get_color(),
alpha=.3)
a.legend(borderaxespad=0.)
a.set_xlim(x.min(), x.max())
if i < means.shape[1] - 1:
a.set_xticklabels('')
# binary prob plot
a = fig.add_subplot(*sub2)
a.bar(x,gamma[:,i],bottom=0.,linewidth=1.,width=1.0,align='center')
a.set_xlim(x.min(), x.max())
a.set_ylim([0.,1.])
pb.draw()
fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
return fig
| bsd-3-clause |
rbalda/neural_ocr | env/lib/python2.7/site-packages/numpy/lib/npyio.py | 42 | 71218 | from __future__ import division, absolute_import, print_function
import sys
import os
import re
import itertools
import warnings
import weakref
from operator import itemgetter
import numpy as np
from . import format
from ._datasource import DataSource
from numpy.core.multiarray import packbits, unpackbits
from ._iotools import (
LineSplitter, NameValidator, StringConverter, ConverterError,
ConverterLockError, ConversionWarning, _is_string_like, has_nested_fields,
flatten_dtype, easy_dtype, _bytes_to_name
)
from numpy.compat import (
asbytes, asstr, asbytes_nested, bytes, basestring, unicode
)
if sys.version_info[0] >= 3:
import pickle
else:
import cPickle as pickle
from future_builtins import map
loads = pickle.loads
__all__ = [
'savetxt', 'loadtxt', 'genfromtxt', 'ndfromtxt', 'mafromtxt',
'recfromtxt', 'recfromcsv', 'load', 'loads', 'save', 'savez',
'savez_compressed', 'packbits', 'unpackbits', 'fromregex', 'DataSource'
]
class BagObj(object):
"""
BagObj(obj)
Convert attribute look-ups to getitems on the object passed in.
Parameters
----------
obj : class instance
Object on which attribute look-up is performed.
Examples
--------
>>> from numpy.lib.npyio import BagObj as BO
>>> class BagDemo(object):
... def __getitem__(self, key): # An instance of BagObj(BagDemo)
... # will call this method when any
... # attribute look-up is required
... result = "Doesn't matter what you want, "
... return result + "you're gonna get this"
...
>>> demo_obj = BagDemo()
>>> bagobj = BO(demo_obj)
>>> bagobj.hello_there
"Doesn't matter what you want, you're gonna get this"
>>> bagobj.I_can_be_anything
"Doesn't matter what you want, you're gonna get this"
"""
def __init__(self, obj):
# Use weakref to make NpzFile objects collectable by refcount
self._obj = weakref.proxy(obj)
def __getattribute__(self, key):
try:
return object.__getattribute__(self, '_obj')[key]
except KeyError:
raise AttributeError(key)
def __dir__(self):
"""
Enables dir(bagobj) to list the files in an NpzFile.
This also enables tab-completion in an interpreter or IPython.
"""
return object.__getattribute__(self, '_obj').keys()
def zipfile_factory(*args, **kwargs):
import zipfile
kwargs['allowZip64'] = True
return zipfile.ZipFile(*args, **kwargs)
class NpzFile(object):
"""
NpzFile(fid)
A dictionary-like object with lazy-loading of files in the zipped
archive provided on construction.
`NpzFile` is used to load files in the NumPy ``.npz`` data archive
format. It assumes that files in the archive have a ``.npy`` extension,
other files are ignored.
The arrays and file strings are lazily loaded on either
getitem access using ``obj['key']`` or attribute lookup using
``obj.f.key``. A list of all files (without ``.npy`` extensions) can
be obtained with ``obj.files`` and the ZipFile object itself using
``obj.zip``.
Attributes
----------
files : list of str
List of all files in the archive with a ``.npy`` extension.
zip : ZipFile instance
The ZipFile object initialized with the zipped archive.
f : BagObj instance
An object on which attribute can be performed as an alternative
to getitem access on the `NpzFile` instance itself.
allow_pickle : bool, optional
Allow loading pickled data. Default: True
pickle_kwargs : dict, optional
Additional keyword arguments to pass on to pickle.load.
These are only useful when loading object arrays saved on
Python 2 when using Python 3.
Parameters
----------
fid : file or str
The zipped archive to open. This is either a file-like object
or a string containing the path to the archive.
own_fid : bool, optional
Whether NpzFile should close the file handle.
Requires that `fid` is a file-like object.
Examples
--------
>>> from tempfile import TemporaryFile
>>> outfile = TemporaryFile()
>>> x = np.arange(10)
>>> y = np.sin(x)
>>> np.savez(outfile, x=x, y=y)
>>> outfile.seek(0)
>>> npz = np.load(outfile)
>>> isinstance(npz, np.lib.io.NpzFile)
True
>>> npz.files
['y', 'x']
>>> npz['x'] # getitem access
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> npz.f.x # attribute lookup
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
"""
def __init__(self, fid, own_fid=False, allow_pickle=True,
pickle_kwargs=None):
# Import is postponed to here since zipfile depends on gzip, an
# optional component of the so-called standard library.
_zip = zipfile_factory(fid)
self._files = _zip.namelist()
self.files = []
self.allow_pickle = allow_pickle
self.pickle_kwargs = pickle_kwargs
for x in self._files:
if x.endswith('.npy'):
self.files.append(x[:-4])
else:
self.files.append(x)
self.zip = _zip
self.f = BagObj(self)
if own_fid:
self.fid = fid
else:
self.fid = None
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
def close(self):
"""
Close the file.
"""
if self.zip is not None:
self.zip.close()
self.zip = None
if self.fid is not None:
self.fid.close()
self.fid = None
self.f = None # break reference cycle
def __del__(self):
self.close()
def __getitem__(self, key):
# FIXME: This seems like it will copy strings around
# more than is strictly necessary. The zipfile
# will read the string and then
# the format.read_array will copy the string
# to another place in memory.
# It would be better if the zipfile could read
# (or at least uncompress) the data
# directly into the array memory.
member = 0
if key in self._files:
member = 1
elif key in self.files:
member = 1
key += '.npy'
if member:
bytes = self.zip.open(key)
magic = bytes.read(len(format.MAGIC_PREFIX))
bytes.close()
if magic == format.MAGIC_PREFIX:
bytes = self.zip.open(key)
return format.read_array(bytes,
allow_pickle=self.allow_pickle,
pickle_kwargs=self.pickle_kwargs)
else:
return self.zip.read(key)
else:
raise KeyError("%s is not a file in the archive" % key)
def __iter__(self):
return iter(self.files)
def items(self):
"""
Return a list of tuples, with each tuple (filename, array in file).
"""
return [(f, self[f]) for f in self.files]
def iteritems(self):
"""Generator that returns tuples (filename, array in file)."""
for f in self.files:
yield (f, self[f])
def keys(self):
"""Return files in the archive with a ``.npy`` extension."""
return self.files
def iterkeys(self):
"""Return an iterator over the files in the archive."""
return self.__iter__()
def __contains__(self, key):
return self.files.__contains__(key)
def load(file, mmap_mode=None, allow_pickle=True, fix_imports=True,
encoding='ASCII'):
"""
Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.
Parameters
----------
file : file-like object or string
The file to read. File-like objects must support the
``seek()`` and ``read()`` methods. Pickled files require that the
file-like object support the ``readline()`` method as well.
mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional
If not None, then memory-map the file, using the given mode (see
`numpy.memmap` for a detailed description of the modes). A
memory-mapped array is kept on disk. However, it can be accessed
and sliced like any ndarray. Memory mapping is especially useful
for accessing small fragments of large files without reading the
entire file into memory.
allow_pickle : bool, optional
Allow loading pickled object arrays stored in npy files. Reasons for
disallowing pickles include security, as loading pickled data can
execute arbitrary code. If pickles are disallowed, loading object
arrays will fail.
Default: True
fix_imports : bool, optional
Only useful when loading Python 2 generated pickled files on Python 3,
which includes npy/npz files containing object arrays. If `fix_imports`
is True, pickle will try to map the old Python 2 names to the new names
used in Python 3.
encoding : str, optional
What encoding to use when reading Python 2 strings. Only useful when
loading Python 2 generated pickled files on Python 3, which includes
npy/npz files containing object arrays. Values other than 'latin1',
'ASCII', and 'bytes' are not allowed, as they can corrupt numerical
data. Default: 'ASCII'
Returns
-------
result : array, tuple, dict, etc.
Data stored in the file. For ``.npz`` files, the returned instance
of NpzFile class must be closed to avoid leaking file descriptors.
Raises
------
IOError
If the input file does not exist or cannot be read.
ValueError
The file contains an object array, but allow_pickle=False given.
See Also
--------
save, savez, savez_compressed, loadtxt
memmap : Create a memory-map to an array stored in a file on disk.
Notes
-----
- If the file contains pickle data, then whatever object is stored
in the pickle is returned.
- If the file is a ``.npy`` file, then a single array is returned.
- If the file is a ``.npz`` file, then a dictionary-like object is
returned, containing ``{filename: array}`` key-value pairs, one for
each file in the archive.
- If the file is a ``.npz`` file, the returned value supports the
context manager protocol in a similar fashion to the open function::
with load('foo.npz') as data:
a = data['a']
The underlying file descriptor is closed when exiting the 'with'
block.
Examples
--------
Store data to disk, and load it again:
>>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]]))
>>> np.load('/tmp/123.npy')
array([[1, 2, 3],
[4, 5, 6]])
Store compressed data to disk, and load it again:
>>> a=np.array([[1, 2, 3], [4, 5, 6]])
>>> b=np.array([1, 2])
>>> np.savez('/tmp/123.npz', a=a, b=b)
>>> data = np.load('/tmp/123.npz')
>>> data['a']
array([[1, 2, 3],
[4, 5, 6]])
>>> data['b']
array([1, 2])
>>> data.close()
Mem-map the stored array, and then access the second row
directly from disk:
>>> X = np.load('/tmp/123.npy', mmap_mode='r')
>>> X[1, :]
memmap([4, 5, 6])
"""
import gzip
own_fid = False
if isinstance(file, basestring):
fid = open(file, "rb")
own_fid = True
else:
fid = file
if encoding not in ('ASCII', 'latin1', 'bytes'):
# The 'encoding' value for pickle also affects what encoding
# the serialized binary data of Numpy arrays is loaded
# in. Pickle does not pass on the encoding information to
# Numpy. The unpickling code in numpy.core.multiarray is
# written to assume that unicode data appearing where binary
# should be is in 'latin1'. 'bytes' is also safe, as is 'ASCII'.
#
# Other encoding values can corrupt binary data, and we
# purposefully disallow them. For the same reason, the errors=
# argument is not exposed, as values other than 'strict'
# result can similarly silently corrupt numerical data.
raise ValueError("encoding must be 'ASCII', 'latin1', or 'bytes'")
if sys.version_info[0] >= 3:
pickle_kwargs = dict(encoding=encoding, fix_imports=fix_imports)
else:
# Nothing to do on Python 2
pickle_kwargs = {}
try:
# Code to distinguish from NumPy binary files and pickles.
_ZIP_PREFIX = asbytes('PK\x03\x04')
N = len(format.MAGIC_PREFIX)
magic = fid.read(N)
fid.seek(-N, 1) # back-up
if magic.startswith(_ZIP_PREFIX):
# zip-file (assume .npz)
# Transfer file ownership to NpzFile
tmp = own_fid
own_fid = False
return NpzFile(fid, own_fid=tmp, allow_pickle=allow_pickle,
pickle_kwargs=pickle_kwargs)
elif magic == format.MAGIC_PREFIX:
# .npy file
if mmap_mode:
return format.open_memmap(file, mode=mmap_mode)
else:
return format.read_array(fid, allow_pickle=allow_pickle,
pickle_kwargs=pickle_kwargs)
else:
# Try a pickle
if not allow_pickle:
raise ValueError("allow_pickle=False, but file does not contain "
"non-pickled data")
try:
return pickle.load(fid, **pickle_kwargs)
except:
raise IOError(
"Failed to interpret file %s as a pickle" % repr(file))
finally:
if own_fid:
fid.close()
def save(file, arr, allow_pickle=True, fix_imports=True):
"""
Save an array to a binary file in NumPy ``.npy`` format.
Parameters
----------
file : file or str
File or filename to which the data is saved. If file is a file-object,
then the filename is unchanged. If file is a string, a ``.npy``
extension will be appended to the file name if it does not already
have one.
allow_pickle : bool, optional
Allow saving object arrays using Python pickles. Reasons for disallowing
pickles include security (loading pickled data can execute arbitrary
code) and portability (pickled objects may not be loadable on different
Python installations, for example if the stored objects require libraries
that are not available, and not all pickled data is compatible between
Python 2 and Python 3).
Default: True
fix_imports : bool, optional
Only useful in forcing objects in object arrays on Python 3 to be
pickled in a Python 2 compatible way. If `fix_imports` is True, pickle
will try to map the new Python 3 names to the old module names used in
Python 2, so that the pickle data stream is readable with Python 2.
arr : array_like
Array data to be saved.
See Also
--------
savez : Save several arrays into a ``.npz`` archive
savetxt, load
Notes
-----
For a description of the ``.npy`` format, see the module docstring
of `numpy.lib.format` or the Numpy Enhancement Proposal
http://docs.scipy.org/doc/numpy/neps/npy-format.html
Examples
--------
>>> from tempfile import TemporaryFile
>>> outfile = TemporaryFile()
>>> x = np.arange(10)
>>> np.save(outfile, x)
>>> outfile.seek(0) # Only needed here to simulate closing & reopening file
>>> np.load(outfile)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
"""
own_fid = False
if isinstance(file, basestring):
if not file.endswith('.npy'):
file = file + '.npy'
fid = open(file, "wb")
own_fid = True
else:
fid = file
if sys.version_info[0] >= 3:
pickle_kwargs = dict(fix_imports=fix_imports)
else:
# Nothing to do on Python 2
pickle_kwargs = None
try:
arr = np.asanyarray(arr)
format.write_array(fid, arr, allow_pickle=allow_pickle,
pickle_kwargs=pickle_kwargs)
finally:
if own_fid:
fid.close()
def savez(file, *args, **kwds):
"""
Save several arrays into a single file in uncompressed ``.npz`` format.
If arguments are passed in with no keywords, the corresponding variable
names, in the ``.npz`` file, are 'arr_0', 'arr_1', etc. If keyword
arguments are given, the corresponding variable names, in the ``.npz``
file will match the keyword names.
Parameters
----------
file : str or file
Either the file name (string) or an open file (file-like object)
where the data will be saved. If file is a string, the ``.npz``
extension will be appended to the file name if it is not already there.
args : Arguments, optional
Arrays to save to the file. Since it is not possible for Python to
know the names of the arrays outside `savez`, the arrays will be saved
with names "arr_0", "arr_1", and so on. These arguments can be any
expression.
kwds : Keyword arguments, optional
Arrays to save to the file. Arrays will be saved in the file with the
keyword names.
Returns
-------
None
See Also
--------
save : Save a single array to a binary file in NumPy format.
savetxt : Save an array to a file as plain text.
savez_compressed : Save several arrays into a compressed ``.npz`` archive
Notes
-----
The ``.npz`` file format is a zipped archive of files named after the
variables they contain. The archive is not compressed and each file
in the archive contains one variable in ``.npy`` format. For a
description of the ``.npy`` format, see `numpy.lib.format` or the
Numpy Enhancement Proposal
http://docs.scipy.org/doc/numpy/neps/npy-format.html
When opening the saved ``.npz`` file with `load` a `NpzFile` object is
returned. This is a dictionary-like object which can be queried for
its list of arrays (with the ``.files`` attribute), and for the arrays
themselves.
Examples
--------
>>> from tempfile import TemporaryFile
>>> outfile = TemporaryFile()
>>> x = np.arange(10)
>>> y = np.sin(x)
Using `savez` with \\*args, the arrays are saved with default names.
>>> np.savez(outfile, x, y)
>>> outfile.seek(0) # Only needed here to simulate closing & reopening file
>>> npzfile = np.load(outfile)
>>> npzfile.files
['arr_1', 'arr_0']
>>> npzfile['arr_0']
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
Using `savez` with \\**kwds, the arrays are saved with the keyword names.
>>> outfile = TemporaryFile()
>>> np.savez(outfile, x=x, y=y)
>>> outfile.seek(0)
>>> npzfile = np.load(outfile)
>>> npzfile.files
['y', 'x']
>>> npzfile['x']
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
"""
_savez(file, args, kwds, False)
def savez_compressed(file, *args, **kwds):
"""
Save several arrays into a single file in compressed ``.npz`` format.
If keyword arguments are given, then filenames are taken from the keywords.
If arguments are passed in with no keywords, then stored file names are
arr_0, arr_1, etc.
Parameters
----------
file : str
File name of ``.npz`` file.
args : Arguments
Function arguments.
kwds : Keyword arguments
Keywords.
See Also
--------
numpy.savez : Save several arrays into an uncompressed ``.npz`` file format
numpy.load : Load the files created by savez_compressed.
"""
_savez(file, args, kwds, True)
def _savez(file, args, kwds, compress, allow_pickle=True, pickle_kwargs=None):
# Import is postponed to here since zipfile depends on gzip, an optional
# component of the so-called standard library.
import zipfile
# Import deferred for startup time improvement
import tempfile
if isinstance(file, basestring):
if not file.endswith('.npz'):
file = file + '.npz'
namedict = kwds
for i, val in enumerate(args):
key = 'arr_%d' % i
if key in namedict.keys():
raise ValueError(
"Cannot use un-named variables and keyword %s" % key)
namedict[key] = val
if compress:
compression = zipfile.ZIP_DEFLATED
else:
compression = zipfile.ZIP_STORED
zipf = zipfile_factory(file, mode="w", compression=compression)
# Stage arrays in a temporary file on disk, before writing to zip.
fd, tmpfile = tempfile.mkstemp(suffix='-numpy.npy')
os.close(fd)
try:
for key, val in namedict.items():
fname = key + '.npy'
fid = open(tmpfile, 'wb')
try:
format.write_array(fid, np.asanyarray(val),
allow_pickle=allow_pickle,
pickle_kwargs=pickle_kwargs)
fid.close()
fid = None
zipf.write(tmpfile, arcname=fname)
finally:
if fid:
fid.close()
finally:
os.remove(tmpfile)
zipf.close()
def _getconv(dtype):
""" Find the correct dtype converter. Adapted from matplotlib """
def floatconv(x):
x.lower()
if b'0x' in x:
return float.fromhex(asstr(x))
return float(x)
typ = dtype.type
if issubclass(typ, np.bool_):
return lambda x: bool(int(x))
if issubclass(typ, np.uint64):
return np.uint64
if issubclass(typ, np.int64):
return np.int64
if issubclass(typ, np.integer):
return lambda x: int(float(x))
elif issubclass(typ, np.floating):
return floatconv
elif issubclass(typ, np.complex):
return lambda x: complex(asstr(x))
elif issubclass(typ, np.bytes_):
return bytes
else:
return str
def loadtxt(fname, dtype=float, comments='#', delimiter=None,
converters=None, skiprows=0, usecols=None, unpack=False,
ndmin=0):
"""
Load data from a text file.
Each row in the text file must have the same number of values.
Parameters
----------
fname : file or str
File, filename, or generator to read. If the filename extension is
``.gz`` or ``.bz2``, the file is first decompressed. Note that
generators should return byte strings for Python 3k.
dtype : data-type, optional
Data-type of the resulting array; default: float. If this is a
structured data-type, the resulting array will be 1-dimensional, and
each row will be interpreted as an element of the array. In this
case, the number of columns used must match the number of fields in
the data-type.
comments : str or sequence, optional
The characters or list of characters used to indicate the start of a
comment;
default: '#'.
delimiter : str, optional
The string used to separate values. By default, this is any
whitespace.
converters : dict, optional
A dictionary mapping column number to a function that will convert
that column to a float. E.g., if column 0 is a date string:
``converters = {0: datestr2num}``. Converters can also be used to
provide a default value for missing data (but see also `genfromtxt`):
``converters = {3: lambda s: float(s.strip() or 0)}``. Default: None.
skiprows : int, optional
Skip the first `skiprows` lines; default: 0.
usecols : sequence, optional
Which columns to read, with 0 being the first. For example,
``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.
The default, None, results in all columns being read.
unpack : bool, optional
If True, the returned array is transposed, so that arguments may be
unpacked using ``x, y, z = loadtxt(...)``. When used with a structured
data-type, arrays are returned for each field. Default is False.
ndmin : int, optional
The returned array will have at least `ndmin` dimensions.
Otherwise mono-dimensional axes will be squeezed.
Legal values: 0 (default), 1 or 2.
.. versionadded:: 1.6.0
Returns
-------
out : ndarray
Data read from the text file.
See Also
--------
load, fromstring, fromregex
genfromtxt : Load data with missing values handled as specified.
scipy.io.loadmat : reads MATLAB data files
Notes
-----
This function aims to be a fast reader for simply formatted files. The
`genfromtxt` function provides more sophisticated handling of, e.g.,
lines with missing values.
.. versionadded:: 1.10.0
The strings produced by the Python float.hex method can be used as
input for floats.
Examples
--------
>>> from io import StringIO # StringIO behaves like a file object
>>> c = StringIO("0 1\\n2 3")
>>> np.loadtxt(c)
array([[ 0., 1.],
[ 2., 3.]])
>>> d = StringIO("M 21 72\\nF 35 58")
>>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),
... 'formats': ('S1', 'i4', 'f4')})
array([('M', 21, 72.0), ('F', 35, 58.0)],
dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])
>>> c = StringIO("1,0,2\\n3,0,4")
>>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)
>>> x
array([ 1., 3.])
>>> y
array([ 2., 4.])
"""
# Type conversions for Py3 convenience
if comments is not None:
if isinstance(comments, (basestring, bytes)):
comments = [asbytes(comments)]
else:
comments = [asbytes(comment) for comment in comments]
# Compile regex for comments beforehand
comments = (re.escape(comment) for comment in comments)
regex_comments = re.compile(asbytes('|').join(comments))
user_converters = converters
if delimiter is not None:
delimiter = asbytes(delimiter)
if usecols is not None:
usecols = list(usecols)
fown = False
try:
if _is_string_like(fname):
fown = True
if fname.endswith('.gz'):
import gzip
fh = iter(gzip.GzipFile(fname))
elif fname.endswith('.bz2'):
import bz2
fh = iter(bz2.BZ2File(fname))
elif sys.version_info[0] == 2:
fh = iter(open(fname, 'U'))
else:
fh = iter(open(fname))
else:
fh = iter(fname)
except TypeError:
raise ValueError('fname must be a string, file handle, or generator')
X = []
def flatten_dtype(dt):
"""Unpack a structured data-type, and produce re-packing info."""
if dt.names is None:
# If the dtype is flattened, return.
# If the dtype has a shape, the dtype occurs
# in the list more than once.
shape = dt.shape
if len(shape) == 0:
return ([dt.base], None)
else:
packing = [(shape[-1], list)]
if len(shape) > 1:
for dim in dt.shape[-2::-1]:
packing = [(dim*packing[0][0], packing*dim)]
return ([dt.base] * int(np.prod(dt.shape)), packing)
else:
types = []
packing = []
for field in dt.names:
tp, bytes = dt.fields[field]
flat_dt, flat_packing = flatten_dtype(tp)
types.extend(flat_dt)
# Avoid extra nesting for subarrays
if len(tp.shape) > 0:
packing.extend(flat_packing)
else:
packing.append((len(flat_dt), flat_packing))
return (types, packing)
def pack_items(items, packing):
"""Pack items into nested lists based on re-packing info."""
if packing is None:
return items[0]
elif packing is tuple:
return tuple(items)
elif packing is list:
return list(items)
else:
start = 0
ret = []
for length, subpacking in packing:
ret.append(pack_items(items[start:start+length], subpacking))
start += length
return tuple(ret)
def split_line(line):
"""Chop off comments, strip, and split at delimiter.
Note that although the file is opened as text, this function
returns bytes.
"""
line = asbytes(line)
if comments is not None:
line = regex_comments.split(asbytes(line), maxsplit=1)[0]
line = line.strip(asbytes('\r\n'))
if line:
return line.split(delimiter)
else:
return []
try:
# Make sure we're dealing with a proper dtype
dtype = np.dtype(dtype)
defconv = _getconv(dtype)
# Skip the first `skiprows` lines
for i in range(skiprows):
next(fh)
# Read until we find a line with some values, and use
# it to estimate the number of columns, N.
first_vals = None
try:
while not first_vals:
first_line = next(fh)
first_vals = split_line(first_line)
except StopIteration:
# End of lines reached
first_line = ''
first_vals = []
warnings.warn('loadtxt: Empty input file: "%s"' % fname)
N = len(usecols or first_vals)
dtype_types, packing = flatten_dtype(dtype)
if len(dtype_types) > 1:
# We're dealing with a structured array, each field of
# the dtype matches a column
converters = [_getconv(dt) for dt in dtype_types]
else:
# All fields have the same dtype
converters = [defconv for i in range(N)]
if N > 1:
packing = [(N, tuple)]
# By preference, use the converters specified by the user
for i, conv in (user_converters or {}).items():
if usecols:
try:
i = usecols.index(i)
except ValueError:
# Unused converter specified
continue
converters[i] = conv
# Parse each line, including the first
for i, line in enumerate(itertools.chain([first_line], fh)):
vals = split_line(line)
if len(vals) == 0:
continue
if usecols:
vals = [vals[i] for i in usecols]
if len(vals) != N:
line_num = i + skiprows + 1
raise ValueError("Wrong number of columns at line %d"
% line_num)
# Convert each value according to its column and store
items = [conv(val) for (conv, val) in zip(converters, vals)]
# Then pack it according to the dtype's nesting
items = pack_items(items, packing)
X.append(items)
finally:
if fown:
fh.close()
X = np.array(X, dtype)
# Multicolumn data are returned with shape (1, N, M), i.e.
# (1, 1, M) for a single row - remove the singleton dimension there
if X.ndim == 3 and X.shape[:2] == (1, 1):
X.shape = (1, -1)
# Verify that the array has at least dimensions `ndmin`.
# Check correctness of the values of `ndmin`
if ndmin not in [0, 1, 2]:
raise ValueError('Illegal value of ndmin keyword: %s' % ndmin)
# Tweak the size and shape of the arrays - remove extraneous dimensions
if X.ndim > ndmin:
X = np.squeeze(X)
# and ensure we have the minimum number of dimensions asked for
# - has to be in this order for the odd case ndmin=1, X.squeeze().ndim=0
if X.ndim < ndmin:
if ndmin == 1:
X = np.atleast_1d(X)
elif ndmin == 2:
X = np.atleast_2d(X).T
if unpack:
if len(dtype_types) > 1:
# For structured arrays, return an array for each field.
return [X[field] for field in dtype.names]
else:
return X.T
else:
return X
def savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='',
footer='', comments='# '):
"""
Save an array to a text file.
Parameters
----------
fname : filename or file handle
If the filename ends in ``.gz``, the file is automatically saved in
compressed gzip format. `loadtxt` understands gzipped files
transparently.
X : array_like
Data to be saved to a text file.
fmt : str or sequence of strs, optional
A single format (%10.5f), a sequence of formats, or a
multi-format string, e.g. 'Iteration %d -- %10.5f', in which
case `delimiter` is ignored. For complex `X`, the legal options
for `fmt` are:
a) a single specifier, `fmt='%.4e'`, resulting in numbers formatted
like `' (%s+%sj)' % (fmt, fmt)`
b) a full string specifying every real and imaginary part, e.g.
`' %.4e %+.4j %.4e %+.4j %.4e %+.4j'` for 3 columns
c) a list of specifiers, one per column - in this case, the real
and imaginary part must have separate specifiers,
e.g. `['%.3e + %.3ej', '(%.15e%+.15ej)']` for 2 columns
delimiter : str, optional
String or character separating columns.
newline : str, optional
String or character separating lines.
.. versionadded:: 1.5.0
header : str, optional
String that will be written at the beginning of the file.
.. versionadded:: 1.7.0
footer : str, optional
String that will be written at the end of the file.
.. versionadded:: 1.7.0
comments : str, optional
String that will be prepended to the ``header`` and ``footer`` strings,
to mark them as comments. Default: '# ', as expected by e.g.
``numpy.loadtxt``.
.. versionadded:: 1.7.0
See Also
--------
save : Save an array to a binary file in NumPy ``.npy`` format
savez : Save several arrays into an uncompressed ``.npz`` archive
savez_compressed : Save several arrays into a compressed ``.npz`` archive
Notes
-----
Further explanation of the `fmt` parameter
(``%[flag]width[.precision]specifier``):
flags:
``-`` : left justify
``+`` : Forces to precede result with + or -.
``0`` : Left pad the number with zeros instead of space (see width).
width:
Minimum number of characters to be printed. The value is not truncated
if it has more characters.
precision:
- For integer specifiers (eg. ``d,i,o,x``), the minimum number of
digits.
- For ``e, E`` and ``f`` specifiers, the number of digits to print
after the decimal point.
- For ``g`` and ``G``, the maximum number of significant digits.
- For ``s``, the maximum number of characters.
specifiers:
``c`` : character
``d`` or ``i`` : signed decimal integer
``e`` or ``E`` : scientific notation with ``e`` or ``E``.
``f`` : decimal floating point
``g,G`` : use the shorter of ``e,E`` or ``f``
``o`` : signed octal
``s`` : string of characters
``u`` : unsigned decimal integer
``x,X`` : unsigned hexadecimal integer
This explanation of ``fmt`` is not complete, for an exhaustive
specification see [1]_.
References
----------
.. [1] `Format Specification Mini-Language
<http://docs.python.org/library/string.html#
format-specification-mini-language>`_, Python Documentation.
Examples
--------
>>> x = y = z = np.arange(0.0,5.0,1.0)
>>> np.savetxt('test.out', x, delimiter=',') # X is an array
>>> np.savetxt('test.out', (x,y,z)) # x,y,z equal sized 1D arrays
>>> np.savetxt('test.out', x, fmt='%1.4e') # use exponential notation
"""
# Py3 conversions first
if isinstance(fmt, bytes):
fmt = asstr(fmt)
delimiter = asstr(delimiter)
own_fh = False
if _is_string_like(fname):
own_fh = True
if fname.endswith('.gz'):
import gzip
fh = gzip.open(fname, 'wb')
else:
if sys.version_info[0] >= 3:
fh = open(fname, 'wb')
else:
fh = open(fname, 'w')
elif hasattr(fname, 'write'):
fh = fname
else:
raise ValueError('fname must be a string or file handle')
try:
X = np.asarray(X)
# Handle 1-dimensional arrays
if X.ndim == 1:
# Common case -- 1d array of numbers
if X.dtype.names is None:
X = np.atleast_2d(X).T
ncol = 1
# Complex dtype -- each field indicates a separate column
else:
ncol = len(X.dtype.descr)
else:
ncol = X.shape[1]
iscomplex_X = np.iscomplexobj(X)
# `fmt` can be a string with multiple insertion points or a
# list of formats. E.g. '%10.5f\t%10d' or ('%10.5f', '$10d')
if type(fmt) in (list, tuple):
if len(fmt) != ncol:
raise AttributeError('fmt has wrong shape. %s' % str(fmt))
format = asstr(delimiter).join(map(asstr, fmt))
elif isinstance(fmt, str):
n_fmt_chars = fmt.count('%')
error = ValueError('fmt has wrong number of %% formats: %s' % fmt)
if n_fmt_chars == 1:
if iscomplex_X:
fmt = [' (%s+%sj)' % (fmt, fmt), ] * ncol
else:
fmt = [fmt, ] * ncol
format = delimiter.join(fmt)
elif iscomplex_X and n_fmt_chars != (2 * ncol):
raise error
elif ((not iscomplex_X) and n_fmt_chars != ncol):
raise error
else:
format = fmt
else:
raise ValueError('invalid fmt: %r' % (fmt,))
if len(header) > 0:
header = header.replace('\n', '\n' + comments)
fh.write(asbytes(comments + header + newline))
if iscomplex_X:
for row in X:
row2 = []
for number in row:
row2.append(number.real)
row2.append(number.imag)
fh.write(asbytes(format % tuple(row2) + newline))
else:
for row in X:
try:
fh.write(asbytes(format % tuple(row) + newline))
except TypeError:
raise TypeError("Mismatch between array dtype ('%s') and "
"format specifier ('%s')"
% (str(X.dtype), format))
if len(footer) > 0:
footer = footer.replace('\n', '\n' + comments)
fh.write(asbytes(comments + footer + newline))
finally:
if own_fh:
fh.close()
def fromregex(file, regexp, dtype):
"""
Construct an array from a text file, using regular expression parsing.
The returned array is always a structured array, and is constructed from
all matches of the regular expression in the file. Groups in the regular
expression are converted to fields of the structured array.
Parameters
----------
file : str or file
File name or file object to read.
regexp : str or regexp
Regular expression used to parse the file.
Groups in the regular expression correspond to fields in the dtype.
dtype : dtype or list of dtypes
Dtype for the structured array.
Returns
-------
output : ndarray
The output array, containing the part of the content of `file` that
was matched by `regexp`. `output` is always a structured array.
Raises
------
TypeError
When `dtype` is not a valid dtype for a structured array.
See Also
--------
fromstring, loadtxt
Notes
-----
Dtypes for structured arrays can be specified in several forms, but all
forms specify at least the data type and field name. For details see
`doc.structured_arrays`.
Examples
--------
>>> f = open('test.dat', 'w')
>>> f.write("1312 foo\\n1534 bar\\n444 qux")
>>> f.close()
>>> regexp = r"(\\d+)\\s+(...)" # match [digits, whitespace, anything]
>>> output = np.fromregex('test.dat', regexp,
... [('num', np.int64), ('key', 'S3')])
>>> output
array([(1312L, 'foo'), (1534L, 'bar'), (444L, 'qux')],
dtype=[('num', '<i8'), ('key', '|S3')])
>>> output['num']
array([1312, 1534, 444], dtype=int64)
"""
own_fh = False
if not hasattr(file, "read"):
file = open(file, 'rb')
own_fh = True
try:
if not hasattr(regexp, 'match'):
regexp = re.compile(asbytes(regexp))
if not isinstance(dtype, np.dtype):
dtype = np.dtype(dtype)
seq = regexp.findall(file.read())
if seq and not isinstance(seq[0], tuple):
# Only one group is in the regexp.
# Create the new array as a single data-type and then
# re-interpret as a single-field structured array.
newdtype = np.dtype(dtype[dtype.names[0]])
output = np.array(seq, dtype=newdtype)
output.dtype = dtype
else:
output = np.array(seq, dtype=dtype)
return output
finally:
if own_fh:
file.close()
#####--------------------------------------------------------------------------
#---- --- ASCII functions ---
#####--------------------------------------------------------------------------
def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
skip_header=0, skip_footer=0, converters=None,
missing_values=None, filling_values=None, usecols=None,
names=None, excludelist=None, deletechars=None,
replace_space='_', autostrip=False, case_sensitive=True,
defaultfmt="f%i", unpack=None, usemask=False, loose=True,
invalid_raise=True, max_rows=None):
"""
Load data from a text file, with missing values handled as specified.
Each line past the first `skip_header` lines is split at the `delimiter`
character, and characters following the `comments` character are discarded.
Parameters
----------
fname : file or str
File, filename, or generator to read. If the filename extension is
`.gz` or `.bz2`, the file is first decompressed. Note that
generators must return byte strings in Python 3k.
dtype : dtype, optional
Data type of the resulting array.
If None, the dtypes will be determined by the contents of each
column, individually.
comments : str, optional
The character used to indicate the start of a comment.
All the characters occurring on a line after a comment are discarded
delimiter : str, int, or sequence, optional
The string used to separate values. By default, any consecutive
whitespaces act as delimiter. An integer or sequence of integers
can also be provided as width(s) of each field.
skiprows : int, optional
`skiprows` was removed in numpy 1.10. Please use `skip_header` instead.
skip_header : int, optional
The number of lines to skip at the beginning of the file.
skip_footer : int, optional
The number of lines to skip at the end of the file.
converters : variable, optional
The set of functions that convert the data of a column to a value.
The converters can also be used to provide a default value
for missing data: ``converters = {3: lambda s: float(s or 0)}``.
missing : variable, optional
`missing` was removed in numpy 1.10. Please use `missing_values`
instead.
missing_values : variable, optional
The set of strings corresponding to missing data.
filling_values : variable, optional
The set of values to be used as default when the data are missing.
usecols : sequence, optional
Which columns to read, with 0 being the first. For example,
``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns.
names : {None, True, str, sequence}, optional
If `names` is True, the field names are read from the first valid line
after the first `skip_header` lines.
If `names` is a sequence or a single-string of comma-separated names,
the names will be used to define the field names in a structured dtype.
If `names` is None, the names of the dtype fields will be used, if any.
excludelist : sequence, optional
A list of names to exclude. This list is appended to the default list
['return','file','print']. Excluded names are appended an underscore:
for example, `file` would become `file_`.
deletechars : str, optional
A string combining invalid characters that must be deleted from the
names.
defaultfmt : str, optional
A format used to define default field names, such as "f%i" or "f_%02i".
autostrip : bool, optional
Whether to automatically strip white spaces from the variables.
replace_space : char, optional
Character(s) used in replacement of white spaces in the variables
names. By default, use a '_'.
case_sensitive : {True, False, 'upper', 'lower'}, optional
If True, field names are case sensitive.
If False or 'upper', field names are converted to upper case.
If 'lower', field names are converted to lower case.
unpack : bool, optional
If True, the returned array is transposed, so that arguments may be
unpacked using ``x, y, z = loadtxt(...)``
usemask : bool, optional
If True, return a masked array.
If False, return a regular array.
loose : bool, optional
If True, do not raise errors for invalid values.
invalid_raise : bool, optional
If True, an exception is raised if an inconsistency is detected in the
number of columns.
If False, a warning is emitted and the offending lines are skipped.
max_rows : int, optional
The maximum number of rows to read. Must not be used with skip_footer
at the same time. If given, the value must be at least 1. Default is
to read the entire file.
.. versionadded:: 1.10.0
Returns
-------
out : ndarray
Data read from the text file. If `usemask` is True, this is a
masked array.
See Also
--------
numpy.loadtxt : equivalent function when no data is missing.
Notes
-----
* When spaces are used as delimiters, or when no delimiter has been given
as input, there should not be any missing data between two fields.
* When the variables are named (either by a flexible dtype or with `names`,
there must not be any header in the file (else a ValueError
exception is raised).
* Individual values are not stripped of spaces by default.
When using a custom converter, make sure the function does remove spaces.
References
----------
.. [1] Numpy User Guide, section `I/O with Numpy
<http://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html>`_.
Examples
---------
>>> from io import StringIO
>>> import numpy as np
Comma delimited file with mixed dtype
>>> s = StringIO("1,1.3,abcde")
>>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'),
... ('mystring','S5')], delimiter=",")
>>> data
array((1, 1.3, 'abcde'),
dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
Using dtype = None
>>> s.seek(0) # needed for StringIO example only
>>> data = np.genfromtxt(s, dtype=None,
... names = ['myint','myfloat','mystring'], delimiter=",")
>>> data
array((1, 1.3, 'abcde'),
dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
Specifying dtype and names
>>> s.seek(0)
>>> data = np.genfromtxt(s, dtype="i8,f8,S5",
... names=['myint','myfloat','mystring'], delimiter=",")
>>> data
array((1, 1.3, 'abcde'),
dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
An example with fixed-width columns
>>> s = StringIO("11.3abcde")
>>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],
... delimiter=[1,3,5])
>>> data
array((1, 1.3, 'abcde'),
dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', '|S5')])
"""
if max_rows is not None:
if skip_footer:
raise ValueError(
"The keywords 'skip_footer' and 'max_rows' can not be "
"specified at the same time.")
if max_rows < 1:
raise ValueError("'max_rows' must be at least 1.")
# Py3 data conversions to bytes, for convenience
if comments is not None:
comments = asbytes(comments)
if isinstance(delimiter, unicode):
delimiter = asbytes(delimiter)
if isinstance(missing_values, (unicode, list, tuple)):
missing_values = asbytes_nested(missing_values)
#
if usemask:
from numpy.ma import MaskedArray, make_mask_descr
# Check the input dictionary of converters
user_converters = converters or {}
if not isinstance(user_converters, dict):
raise TypeError(
"The input argument 'converter' should be a valid dictionary "
"(got '%s' instead)" % type(user_converters))
# Initialize the filehandle, the LineSplitter and the NameValidator
own_fhd = False
try:
if isinstance(fname, basestring):
if sys.version_info[0] == 2:
fhd = iter(np.lib._datasource.open(fname, 'rbU'))
else:
fhd = iter(np.lib._datasource.open(fname, 'rb'))
own_fhd = True
else:
fhd = iter(fname)
except TypeError:
raise TypeError(
"fname must be a string, filehandle, or generator. "
"(got %s instead)" % type(fname))
split_line = LineSplitter(delimiter=delimiter, comments=comments,
autostrip=autostrip)._handyman
validate_names = NameValidator(excludelist=excludelist,
deletechars=deletechars,
case_sensitive=case_sensitive,
replace_space=replace_space)
# Skip the first `skip_header` rows
for i in range(skip_header):
next(fhd)
# Keep on until we find the first valid values
first_values = None
try:
while not first_values:
first_line = next(fhd)
if names is True:
if comments in first_line:
first_line = (
asbytes('').join(first_line.split(comments)[1:]))
first_values = split_line(first_line)
except StopIteration:
# return an empty array if the datafile is empty
first_line = asbytes('')
first_values = []
warnings.warn('genfromtxt: Empty input file: "%s"' % fname)
# Should we take the first values as names ?
if names is True:
fval = first_values[0].strip()
if fval in comments:
del first_values[0]
# Check the columns to use: make sure `usecols` is a list
if usecols is not None:
try:
usecols = [_.strip() for _ in usecols.split(",")]
except AttributeError:
try:
usecols = list(usecols)
except TypeError:
usecols = [usecols, ]
nbcols = len(usecols or first_values)
# Check the names and overwrite the dtype.names if needed
if names is True:
names = validate_names([_bytes_to_name(_.strip())
for _ in first_values])
first_line = asbytes('')
elif _is_string_like(names):
names = validate_names([_.strip() for _ in names.split(',')])
elif names:
names = validate_names(names)
# Get the dtype
if dtype is not None:
dtype = easy_dtype(dtype, defaultfmt=defaultfmt, names=names,
excludelist=excludelist,
deletechars=deletechars,
case_sensitive=case_sensitive,
replace_space=replace_space)
# Make sure the names is a list (for 2.5)
if names is not None:
names = list(names)
if usecols:
for (i, current) in enumerate(usecols):
# if usecols is a list of names, convert to a list of indices
if _is_string_like(current):
usecols[i] = names.index(current)
elif current < 0:
usecols[i] = current + len(first_values)
# If the dtype is not None, make sure we update it
if (dtype is not None) and (len(dtype) > nbcols):
descr = dtype.descr
dtype = np.dtype([descr[_] for _ in usecols])
names = list(dtype.names)
# If `names` is not None, update the names
elif (names is not None) and (len(names) > nbcols):
names = [names[_] for _ in usecols]
elif (names is not None) and (dtype is not None):
names = list(dtype.names)
# Process the missing values ...............................
# Rename missing_values for convenience
user_missing_values = missing_values or ()
# Define the list of missing_values (one column: one list)
missing_values = [list([asbytes('')]) for _ in range(nbcols)]
# We have a dictionary: process it field by field
if isinstance(user_missing_values, dict):
# Loop on the items
for (key, val) in user_missing_values.items():
# Is the key a string ?
if _is_string_like(key):
try:
# Transform it into an integer
key = names.index(key)
except ValueError:
# We couldn't find it: the name must have been dropped
continue
# Redefine the key as needed if it's a column number
if usecols:
try:
key = usecols.index(key)
except ValueError:
pass
# Transform the value as a list of string
if isinstance(val, (list, tuple)):
val = [str(_) for _ in val]
else:
val = [str(val), ]
# Add the value(s) to the current list of missing
if key is None:
# None acts as default
for miss in missing_values:
miss.extend(val)
else:
missing_values[key].extend(val)
# We have a sequence : each item matches a column
elif isinstance(user_missing_values, (list, tuple)):
for (value, entry) in zip(user_missing_values, missing_values):
value = str(value)
if value not in entry:
entry.append(value)
# We have a string : apply it to all entries
elif isinstance(user_missing_values, bytes):
user_value = user_missing_values.split(asbytes(","))
for entry in missing_values:
entry.extend(user_value)
# We have something else: apply it to all entries
else:
for entry in missing_values:
entry.extend([str(user_missing_values)])
# Process the filling_values ...............................
# Rename the input for convenience
user_filling_values = filling_values
if user_filling_values is None:
user_filling_values = []
# Define the default
filling_values = [None] * nbcols
# We have a dictionary : update each entry individually
if isinstance(user_filling_values, dict):
for (key, val) in user_filling_values.items():
if _is_string_like(key):
try:
# Transform it into an integer
key = names.index(key)
except ValueError:
# We couldn't find it: the name must have been dropped,
continue
# Redefine the key if it's a column number and usecols is defined
if usecols:
try:
key = usecols.index(key)
except ValueError:
pass
# Add the value to the list
filling_values[key] = val
# We have a sequence : update on a one-to-one basis
elif isinstance(user_filling_values, (list, tuple)):
n = len(user_filling_values)
if (n <= nbcols):
filling_values[:n] = user_filling_values
else:
filling_values = user_filling_values[:nbcols]
# We have something else : use it for all entries
else:
filling_values = [user_filling_values] * nbcols
# Initialize the converters ................................
if dtype is None:
# Note: we can't use a [...]*nbcols, as we would have 3 times the same
# ... converter, instead of 3 different converters.
converters = [StringConverter(None, missing_values=miss, default=fill)
for (miss, fill) in zip(missing_values, filling_values)]
else:
dtype_flat = flatten_dtype(dtype, flatten_base=True)
# Initialize the converters
if len(dtype_flat) > 1:
# Flexible type : get a converter from each dtype
zipit = zip(dtype_flat, missing_values, filling_values)
converters = [StringConverter(dt, locked=True,
missing_values=miss, default=fill)
for (dt, miss, fill) in zipit]
else:
# Set to a default converter (but w/ different missing values)
zipit = zip(missing_values, filling_values)
converters = [StringConverter(dtype, locked=True,
missing_values=miss, default=fill)
for (miss, fill) in zipit]
# Update the converters to use the user-defined ones
uc_update = []
for (j, conv) in user_converters.items():
# If the converter is specified by column names, use the index instead
if _is_string_like(j):
try:
j = names.index(j)
i = j
except ValueError:
continue
elif usecols:
try:
i = usecols.index(j)
except ValueError:
# Unused converter specified
continue
else:
i = j
# Find the value to test - first_line is not filtered by usecols:
if len(first_line):
testing_value = first_values[j]
else:
testing_value = None
converters[i].update(conv, locked=True,
testing_value=testing_value,
default=filling_values[i],
missing_values=missing_values[i],)
uc_update.append((i, conv))
# Make sure we have the corrected keys in user_converters...
user_converters.update(uc_update)
# Fixme: possible error as following variable never used.
#miss_chars = [_.missing_values for _ in converters]
# Initialize the output lists ...
# ... rows
rows = []
append_to_rows = rows.append
# ... masks
if usemask:
masks = []
append_to_masks = masks.append
# ... invalid
invalid = []
append_to_invalid = invalid.append
# Parse each line
for (i, line) in enumerate(itertools.chain([first_line, ], fhd)):
values = split_line(line)
nbvalues = len(values)
# Skip an empty line
if nbvalues == 0:
continue
if usecols:
# Select only the columns we need
try:
values = [values[_] for _ in usecols]
except IndexError:
append_to_invalid((i + skip_header + 1, nbvalues))
continue
elif nbvalues != nbcols:
append_to_invalid((i + skip_header + 1, nbvalues))
continue
# Store the values
append_to_rows(tuple(values))
if usemask:
append_to_masks(tuple([v.strip() in m
for (v, m) in zip(values,
missing_values)]))
if len(rows) == max_rows:
break
if own_fhd:
fhd.close()
# Upgrade the converters (if needed)
if dtype is None:
for (i, converter) in enumerate(converters):
current_column = [itemgetter(i)(_m) for _m in rows]
try:
converter.iterupgrade(current_column)
except ConverterLockError:
errmsg = "Converter #%i is locked and cannot be upgraded: " % i
current_column = map(itemgetter(i), rows)
for (j, value) in enumerate(current_column):
try:
converter.upgrade(value)
except (ConverterError, ValueError):
errmsg += "(occurred line #%i for value '%s')"
errmsg %= (j + 1 + skip_header, value)
raise ConverterError(errmsg)
# Check that we don't have invalid values
nbinvalid = len(invalid)
if nbinvalid > 0:
nbrows = len(rows) + nbinvalid - skip_footer
# Construct the error message
template = " Line #%%i (got %%i columns instead of %i)" % nbcols
if skip_footer > 0:
nbinvalid_skipped = len([_ for _ in invalid
if _[0] > nbrows + skip_header])
invalid = invalid[:nbinvalid - nbinvalid_skipped]
skip_footer -= nbinvalid_skipped
#
# nbrows -= skip_footer
# errmsg = [template % (i, nb)
# for (i, nb) in invalid if i < nbrows]
# else:
errmsg = [template % (i, nb)
for (i, nb) in invalid]
if len(errmsg):
errmsg.insert(0, "Some errors were detected !")
errmsg = "\n".join(errmsg)
# Raise an exception ?
if invalid_raise:
raise ValueError(errmsg)
# Issue a warning ?
else:
warnings.warn(errmsg, ConversionWarning)
# Strip the last skip_footer data
if skip_footer > 0:
rows = rows[:-skip_footer]
if usemask:
masks = masks[:-skip_footer]
# Convert each value according to the converter:
# We want to modify the list in place to avoid creating a new one...
if loose:
rows = list(
zip(*[[conv._loose_call(_r) for _r in map(itemgetter(i), rows)]
for (i, conv) in enumerate(converters)]))
else:
rows = list(
zip(*[[conv._strict_call(_r) for _r in map(itemgetter(i), rows)]
for (i, conv) in enumerate(converters)]))
# Reset the dtype
data = rows
if dtype is None:
# Get the dtypes from the types of the converters
column_types = [conv.type for conv in converters]
# Find the columns with strings...
strcolidx = [i for (i, v) in enumerate(column_types)
if v in (type('S'), np.string_)]
# ... and take the largest number of chars.
for i in strcolidx:
column_types[i] = "|S%i" % max(len(row[i]) for row in data)
#
if names is None:
# If the dtype is uniform, don't define names, else use ''
base = set([c.type for c in converters if c._checked])
if len(base) == 1:
(ddtype, mdtype) = (list(base)[0], np.bool)
else:
ddtype = [(defaultfmt % i, dt)
for (i, dt) in enumerate(column_types)]
if usemask:
mdtype = [(defaultfmt % i, np.bool)
for (i, dt) in enumerate(column_types)]
else:
ddtype = list(zip(names, column_types))
mdtype = list(zip(names, [np.bool] * len(column_types)))
output = np.array(data, dtype=ddtype)
if usemask:
outputmask = np.array(masks, dtype=mdtype)
else:
# Overwrite the initial dtype names if needed
if names and dtype.names:
dtype.names = names
# Case 1. We have a structured type
if len(dtype_flat) > 1:
# Nested dtype, eg [('a', int), ('b', [('b0', int), ('b1', 'f4')])]
# First, create the array using a flattened dtype:
# [('a', int), ('b1', int), ('b2', float)]
# Then, view the array using the specified dtype.
if 'O' in (_.char for _ in dtype_flat):
if has_nested_fields(dtype):
raise NotImplementedError(
"Nested fields involving objects are not supported...")
else:
output = np.array(data, dtype=dtype)
else:
rows = np.array(data, dtype=[('', _) for _ in dtype_flat])
output = rows.view(dtype)
# Now, process the rowmasks the same way
if usemask:
rowmasks = np.array(
masks, dtype=np.dtype([('', np.bool) for t in dtype_flat]))
# Construct the new dtype
mdtype = make_mask_descr(dtype)
outputmask = rowmasks.view(mdtype)
# Case #2. We have a basic dtype
else:
# We used some user-defined converters
if user_converters:
ishomogeneous = True
descr = []
for i, ttype in enumerate([conv.type for conv in converters]):
# Keep the dtype of the current converter
if i in user_converters:
ishomogeneous &= (ttype == dtype.type)
if ttype == np.string_:
ttype = "|S%i" % max(len(row[i]) for row in data)
descr.append(('', ttype))
else:
descr.append(('', dtype))
# So we changed the dtype ?
if not ishomogeneous:
# We have more than one field
if len(descr) > 1:
dtype = np.dtype(descr)
# We have only one field: drop the name if not needed.
else:
dtype = np.dtype(ttype)
#
output = np.array(data, dtype)
if usemask:
if dtype.names:
mdtype = [(_, np.bool) for _ in dtype.names]
else:
mdtype = np.bool
outputmask = np.array(masks, dtype=mdtype)
# Try to take care of the missing data we missed
names = output.dtype.names
if usemask and names:
for (name, conv) in zip(names or (), converters):
missing_values = [conv(_) for _ in conv.missing_values
if _ != asbytes('')]
for mval in missing_values:
outputmask[name] |= (output[name] == mval)
# Construct the final array
if usemask:
output = output.view(MaskedArray)
output._mask = outputmask
if unpack:
return output.squeeze().T
return output.squeeze()
def ndfromtxt(fname, **kwargs):
"""
Load ASCII data stored in a file and return it as a single array.
Parameters
----------
fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
--------
numpy.genfromtxt : generic function.
"""
kwargs['usemask'] = False
return genfromtxt(fname, **kwargs)
def mafromtxt(fname, **kwargs):
"""
Load ASCII data stored in a text file and return a masked array.
Parameters
----------
fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
--------
numpy.genfromtxt : generic function to load ASCII data.
"""
kwargs['usemask'] = True
return genfromtxt(fname, **kwargs)
def recfromtxt(fname, **kwargs):
"""
Load ASCII data from a file and return it in a record array.
If ``usemask=False`` a standard `recarray` is returned,
if ``usemask=True`` a MaskedRecords array is returned.
Parameters
----------
fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
--------
numpy.genfromtxt : generic function
Notes
-----
By default, `dtype` is None, which means that the data-type of the output
array will be determined from the data.
"""
kwargs.setdefault("dtype", None)
usemask = kwargs.get('usemask', False)
output = genfromtxt(fname, **kwargs)
if usemask:
from numpy.ma.mrecords import MaskedRecords
output = output.view(MaskedRecords)
else:
output = output.view(np.recarray)
return output
def recfromcsv(fname, **kwargs):
"""
Load ASCII data stored in a comma-separated file.
The returned array is a record array (if ``usemask=False``, see
`recarray`) or a masked record array (if ``usemask=True``,
see `ma.mrecords.MaskedRecords`).
Parameters
----------
fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
--------
numpy.genfromtxt : generic function to load ASCII data.
Notes
-----
By default, `dtype` is None, which means that the data-type of the output
array will be determined from the data.
"""
# Set default kwargs for genfromtxt as relevant to csv import.
kwargs.setdefault("case_sensitive", "lower")
kwargs.setdefault("names", True)
kwargs.setdefault("delimiter", ",")
kwargs.setdefault("dtype", None)
output = genfromtxt(fname, **kwargs)
usemask = kwargs.get("usemask", False)
if usemask:
from numpy.ma.mrecords import MaskedRecords
output = output.view(MaskedRecords)
else:
output = output.view(np.recarray)
return output
| mit |
DrSkippy/Gravitational-Three-Body-Symmetric | sim_pendulum.py | 1 | 1975 | #!/usr/bin/env python
import csv
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
# arg 1 = w init
# arg 2 = n periods
# arg 3 = n ratio
# time step
dt = np.float64(0.00010)
# constants
L_0 = np.float64(1.0) # unstretched length
g = np.float64(9.81) # gravitation
n = np.float64(sys.argv[3])
K_over_M = (n*n - 1)*g/L_0
# initial conditions
theta = np.float64(0)
L = L_0 + g/K_over_M # equilibrium length with gravity
# 2mgl = 1/2 m l^2 w^2
w_sep = np.sqrt(4.*g/L)
w_0 = np.float64(sys.argv[1])
w = w_0
#
v_l_0 = 0
v_l = v_l_0
# periods
T_p = 2.*np.pi/np.sqrt(g/L)
T_k = 2.*np.pi/np.sqrt(K_over_M)
# record some stuff
print "Tp = {} T/dt = {}".format(T_p, T_p/dt)
print "Tk = {} T/dt = {}".format(T_k, T_k/dt)
print "Tk/Tp = {}".format(T_k/T_p)
print "w_esc = {}".format(w_sep)
t = np.float64(0.0)
theta_last = theta
# keep some records
data = []
t_s = []
theta += w*dt/2.
L += v_l*dt/2.
for i in range(int(sys.argv[2])*int(T_p/dt)):
w += -dt*g*np.sin(theta)/L
v_l += -K_over_M*(L-L_0) + g*np.cos(theta) + w*w*L
theta += w*dt
theta = np.fmod(theta, 2.*np.pi)
L += v_l*dt
t += dt
data.append([t, theta, w, L, v_l])
if theta_last < 0 and theta > 0:
t_s.append(t)
theta_last = theta
# periods by measure
t_s = [t_s[i] - t_s[i-1] for i in range(1,len(t_s)) ]
print "avg period = {} std periods = {}".format(np.average(t_s), np.std(t_s))
# plots
df = pd.DataFrame().from_records(data)
df.columns = ["t", "theta", "omega", "l", "v_l"]
df.set_index("t")
ax = df.plot(kind="scatter", x="theta", y="omega", marker=".")
fig = ax.get_figure()
fig.savefig("phase1.png")
ax = df.plot(kind="scatter", x="l", y="v_l", marker=".")
fig = ax.get_figure()
fig.savefig("phase2.png")
# config space
df["y_c"] = -df["l"]
df["x_c"] = df["l"] * np.sin(df["theta"])
ax = df.plot(kind="scatter", x="x_c", y="y_c", marker=".")
fig = ax.get_figure()
fig.savefig("config.png")
| cc0-1.0 |
brian-o/CS-CourseWork | CS491/Program2/testForks.py | 1 | 2677 | ############################################################
'''
testForks.py
Written by: Brian O'Dell, Spetember 2017
A program to run each program a 500 times per thread count.
Then uses the data collected to make graphs and tables that
are useful to evaluate the programs running time.
'''
############################################################
from subprocess import *
from numba import jit
import numpy as np
import csv as csv
import pandas as pd
from pandas.plotting import table
import matplotlib.pyplot as plt
'''
Call the C program multiple times with variable arguments to gather data
The name of the executable should exist before running
'''
@jit
def doCount(name):
j = 0
while (j < 1025):
for i in range(0,501):
call([name,"-t",str(j), "-w"])
if (j == 0):
j = 1
else:
j = 2*j;
'''
Turn the data into something meaningful.
Takes all the data gets the average and standard deviation for each
number of threads. Then plots a graph based on it. Also, makes
a csv with the avg and stddev
'''
@jit
def exportData(name):
DF = pd.read_csv("data/"+name+".csv")
f = {'ExecTime':['mean','std']}
#group by the number of threads in the csv and
#apply the mean and standard deviation functions to the groups
avgDF = DF.groupby('NumThreads').agg(f)
avgTable = DF.groupby('NumThreads', as_index=False).agg(f)
#When the data csv was saved we used 0 to indicate serial execution
#this was so the rows would be in numerical order instead of Alphabetical
#Now rename index 0 to Serial to be an accurate representation
indexList = avgDF.index.tolist()
indexList[0] = 'Serial'
avgDF.index = indexList
#make the bar chart and set the axes
avgPlot = avgDF.plot(kind='bar',
title=('Run Times Using '+ name), legend='False', figsize=(15,8))
avgPlot.set_xlabel("Number of Forks")
avgPlot.set_ylabel("Run Time (seconds)")
#put the data values on top of the bars for clarity
avgPlot.legend(['mean','std deviation'])
for p in avgPlot.patches:
avgPlot.annotate((str(p.get_height())[:6]),
(p.get_x()-.01, p.get_height()), fontsize=9)
#save the files we need
plt.savefig('data/'+name+'Graph.png')
avgTable.to_csv('data/'+name+'Table.csv', index=False, encoding='utf-8')
def main():
doCount("./forkedSemaphor")
doCount("./forkedPrivateCount")
doCount("./forkedPrivateCount32")
exportData("forkedSemaphor")
exportData("forkedPrivateCount")
exportData("forkedPrivateCount32")
if __name__ == '__main__':
main()
| gpl-3.0 |