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edxnercel/edx-platform | .pycharm_helpers/pydev/pydev_ipython/inputhook.py | 52 | 18411 | # coding: utf-8
"""
Inputhook management for GUI event loop integration.
"""
#-----------------------------------------------------------------------------
# Copyright (C) 2008-2011 The IPython Development Team
#
# Distributed under the terms of the BSD License. The full license is in
# the file COPYING, distributed as part of this software.
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
import sys
import select
#-----------------------------------------------------------------------------
# Constants
#-----------------------------------------------------------------------------
# Constants for identifying the GUI toolkits.
GUI_WX = 'wx'
GUI_QT = 'qt'
GUI_QT4 = 'qt4'
GUI_GTK = 'gtk'
GUI_TK = 'tk'
GUI_OSX = 'osx'
GUI_GLUT = 'glut'
GUI_PYGLET = 'pyglet'
GUI_GTK3 = 'gtk3'
GUI_NONE = 'none' # i.e. disable
#-----------------------------------------------------------------------------
# Utilities
#-----------------------------------------------------------------------------
def ignore_CTRL_C():
"""Ignore CTRL+C (not implemented)."""
pass
def allow_CTRL_C():
"""Take CTRL+C into account (not implemented)."""
pass
#-----------------------------------------------------------------------------
# Main InputHookManager class
#-----------------------------------------------------------------------------
class InputHookManager(object):
"""Manage PyOS_InputHook for different GUI toolkits.
This class installs various hooks under ``PyOSInputHook`` to handle
GUI event loop integration.
"""
def __init__(self):
self._return_control_callback = None
self._apps = {}
self._reset()
self.pyplot_imported = False
def _reset(self):
self._callback_pyfunctype = None
self._callback = None
self._current_gui = None
def set_return_control_callback(self, return_control_callback):
self._return_control_callback = return_control_callback
def get_return_control_callback(self):
return self._return_control_callback
def return_control(self):
return self._return_control_callback()
def get_inputhook(self):
return self._callback
def set_inputhook(self, callback):
"""Set inputhook to callback."""
# We don't (in the context of PyDev console) actually set PyOS_InputHook, but rather
# while waiting for input on xmlrpc we run this code
self._callback = callback
def clear_inputhook(self, app=None):
"""Clear input hook.
Parameters
----------
app : optional, ignored
This parameter is allowed only so that clear_inputhook() can be
called with a similar interface as all the ``enable_*`` methods. But
the actual value of the parameter is ignored. This uniform interface
makes it easier to have user-level entry points in the main IPython
app like :meth:`enable_gui`."""
self._reset()
def clear_app_refs(self, gui=None):
"""Clear IPython's internal reference to an application instance.
Whenever we create an app for a user on qt4 or wx, we hold a
reference to the app. This is needed because in some cases bad things
can happen if a user doesn't hold a reference themselves. This
method is provided to clear the references we are holding.
Parameters
----------
gui : None or str
If None, clear all app references. If ('wx', 'qt4') clear
the app for that toolkit. References are not held for gtk or tk
as those toolkits don't have the notion of an app.
"""
if gui is None:
self._apps = {}
elif gui in self._apps:
del self._apps[gui]
def enable_wx(self, app=None):
"""Enable event loop integration with wxPython.
Parameters
----------
app : WX Application, optional.
Running application to use. If not given, we probe WX for an
existing application object, and create a new one if none is found.
Notes
-----
This methods sets the ``PyOS_InputHook`` for wxPython, which allows
the wxPython to integrate with terminal based applications like
IPython.
If ``app`` is not given we probe for an existing one, and return it if
found. If no existing app is found, we create an :class:`wx.App` as
follows::
import wx
app = wx.App(redirect=False, clearSigInt=False)
"""
import wx
from distutils.version import LooseVersion as V
wx_version = V(wx.__version__).version
if wx_version < [2, 8]:
raise ValueError("requires wxPython >= 2.8, but you have %s" % wx.__version__)
from pydev_ipython.inputhookwx import inputhook_wx
self.set_inputhook(inputhook_wx)
self._current_gui = GUI_WX
if app is None:
app = wx.GetApp()
if app is None:
app = wx.App(redirect=False, clearSigInt=False)
app._in_event_loop = True
self._apps[GUI_WX] = app
return app
def disable_wx(self):
"""Disable event loop integration with wxPython.
This merely sets PyOS_InputHook to NULL.
"""
if GUI_WX in self._apps:
self._apps[GUI_WX]._in_event_loop = False
self.clear_inputhook()
def enable_qt4(self, app=None):
"""Enable event loop integration with PyQt4.
Parameters
----------
app : Qt Application, optional.
Running application to use. If not given, we probe Qt for an
existing application object, and create a new one if none is found.
Notes
-----
This methods sets the PyOS_InputHook for PyQt4, which allows
the PyQt4 to integrate with terminal based applications like
IPython.
If ``app`` is not given we probe for an existing one, and return it if
found. If no existing app is found, we create an :class:`QApplication`
as follows::
from PyQt4 import QtCore
app = QtGui.QApplication(sys.argv)
"""
from pydev_ipython.inputhookqt4 import create_inputhook_qt4
app, inputhook_qt4 = create_inputhook_qt4(self, app)
self.set_inputhook(inputhook_qt4)
self._current_gui = GUI_QT4
app._in_event_loop = True
self._apps[GUI_QT4] = app
return app
def disable_qt4(self):
"""Disable event loop integration with PyQt4.
This merely sets PyOS_InputHook to NULL.
"""
if GUI_QT4 in self._apps:
self._apps[GUI_QT4]._in_event_loop = False
self.clear_inputhook()
def enable_gtk(self, app=None):
"""Enable event loop integration with PyGTK.
Parameters
----------
app : ignored
Ignored, it's only a placeholder to keep the call signature of all
gui activation methods consistent, which simplifies the logic of
supporting magics.
Notes
-----
This methods sets the PyOS_InputHook for PyGTK, which allows
the PyGTK to integrate with terminal based applications like
IPython.
"""
from pydev_ipython.inputhookgtk import create_inputhook_gtk
self.set_inputhook(create_inputhook_gtk(self._stdin_file))
self._current_gui = GUI_GTK
def disable_gtk(self):
"""Disable event loop integration with PyGTK.
This merely sets PyOS_InputHook to NULL.
"""
self.clear_inputhook()
def enable_tk(self, app=None):
"""Enable event loop integration with Tk.
Parameters
----------
app : toplevel :class:`Tkinter.Tk` widget, optional.
Running toplevel widget to use. If not given, we probe Tk for an
existing one, and create a new one if none is found.
Notes
-----
If you have already created a :class:`Tkinter.Tk` object, the only
thing done by this method is to register with the
:class:`InputHookManager`, since creating that object automatically
sets ``PyOS_InputHook``.
"""
self._current_gui = GUI_TK
if app is None:
try:
import Tkinter as _TK
except:
# Python 3
import tkinter as _TK
app = _TK.Tk()
app.withdraw()
self._apps[GUI_TK] = app
from pydev_ipython.inputhooktk import create_inputhook_tk
self.set_inputhook(create_inputhook_tk(app))
return app
def disable_tk(self):
"""Disable event loop integration with Tkinter.
This merely sets PyOS_InputHook to NULL.
"""
self.clear_inputhook()
def enable_glut(self, app=None):
""" Enable event loop integration with GLUT.
Parameters
----------
app : ignored
Ignored, it's only a placeholder to keep the call signature of all
gui activation methods consistent, which simplifies the logic of
supporting magics.
Notes
-----
This methods sets the PyOS_InputHook for GLUT, which allows the GLUT to
integrate with terminal based applications like IPython. Due to GLUT
limitations, it is currently not possible to start the event loop
without first creating a window. You should thus not create another
window but use instead the created one. See 'gui-glut.py' in the
docs/examples/lib directory.
The default screen mode is set to:
glut.GLUT_DOUBLE | glut.GLUT_RGBA | glut.GLUT_DEPTH
"""
import OpenGL.GLUT as glut
from pydev_ipython.inputhookglut import glut_display_mode, \
glut_close, glut_display, \
glut_idle, inputhook_glut
if GUI_GLUT not in self._apps:
glut.glutInit(sys.argv)
glut.glutInitDisplayMode(glut_display_mode)
# This is specific to freeglut
if bool(glut.glutSetOption):
glut.glutSetOption(glut.GLUT_ACTION_ON_WINDOW_CLOSE,
glut.GLUT_ACTION_GLUTMAINLOOP_RETURNS)
glut.glutCreateWindow(sys.argv[0])
glut.glutReshapeWindow(1, 1)
glut.glutHideWindow()
glut.glutWMCloseFunc(glut_close)
glut.glutDisplayFunc(glut_display)
glut.glutIdleFunc(glut_idle)
else:
glut.glutWMCloseFunc(glut_close)
glut.glutDisplayFunc(glut_display)
glut.glutIdleFunc(glut_idle)
self.set_inputhook(inputhook_glut)
self._current_gui = GUI_GLUT
self._apps[GUI_GLUT] = True
def disable_glut(self):
"""Disable event loop integration with glut.
This sets PyOS_InputHook to NULL and set the display function to a
dummy one and set the timer to a dummy timer that will be triggered
very far in the future.
"""
import OpenGL.GLUT as glut
from glut_support import glutMainLoopEvent # @UnresolvedImport
glut.glutHideWindow() # This is an event to be processed below
glutMainLoopEvent()
self.clear_inputhook()
def enable_pyglet(self, app=None):
"""Enable event loop integration with pyglet.
Parameters
----------
app : ignored
Ignored, it's only a placeholder to keep the call signature of all
gui activation methods consistent, which simplifies the logic of
supporting magics.
Notes
-----
This methods sets the ``PyOS_InputHook`` for pyglet, which allows
pyglet to integrate with terminal based applications like
IPython.
"""
from pydev_ipython.inputhookpyglet import inputhook_pyglet
self.set_inputhook(inputhook_pyglet)
self._current_gui = GUI_PYGLET
return app
def disable_pyglet(self):
"""Disable event loop integration with pyglet.
This merely sets PyOS_InputHook to NULL.
"""
self.clear_inputhook()
def enable_gtk3(self, app=None):
"""Enable event loop integration with Gtk3 (gir bindings).
Parameters
----------
app : ignored
Ignored, it's only a placeholder to keep the call signature of all
gui activation methods consistent, which simplifies the logic of
supporting magics.
Notes
-----
This methods sets the PyOS_InputHook for Gtk3, which allows
the Gtk3 to integrate with terminal based applications like
IPython.
"""
from pydev_ipython.inputhookgtk3 import create_inputhook_gtk3
self.set_inputhook(create_inputhook_gtk3(self._stdin_file))
self._current_gui = GUI_GTK
def disable_gtk3(self):
"""Disable event loop integration with PyGTK.
This merely sets PyOS_InputHook to NULL.
"""
self.clear_inputhook()
def enable_mac(self, app=None):
""" Enable event loop integration with MacOSX.
We call function pyplot.pause, which updates and displays active
figure during pause. It's not MacOSX-specific, but it enables to
avoid inputhooks in native MacOSX backend.
Also we shouldn't import pyplot, until user does it. Cause it's
possible to choose backend before importing pyplot for the first
time only.
"""
def inputhook_mac(app=None):
if self.pyplot_imported:
pyplot = sys.modules['matplotlib.pyplot']
try:
pyplot.pause(0.01)
except:
pass
else:
if 'matplotlib.pyplot' in sys.modules:
self.pyplot_imported = True
self.set_inputhook(inputhook_mac)
self._current_gui = GUI_OSX
def disable_mac(self):
self.clear_inputhook()
def current_gui(self):
"""Return a string indicating the currently active GUI or None."""
return self._current_gui
inputhook_manager = InputHookManager()
enable_wx = inputhook_manager.enable_wx
disable_wx = inputhook_manager.disable_wx
enable_qt4 = inputhook_manager.enable_qt4
disable_qt4 = inputhook_manager.disable_qt4
enable_gtk = inputhook_manager.enable_gtk
disable_gtk = inputhook_manager.disable_gtk
enable_tk = inputhook_manager.enable_tk
disable_tk = inputhook_manager.disable_tk
enable_glut = inputhook_manager.enable_glut
disable_glut = inputhook_manager.disable_glut
enable_pyglet = inputhook_manager.enable_pyglet
disable_pyglet = inputhook_manager.disable_pyglet
enable_gtk3 = inputhook_manager.enable_gtk3
disable_gtk3 = inputhook_manager.disable_gtk3
enable_mac = inputhook_manager.enable_mac
disable_mac = inputhook_manager.disable_mac
clear_inputhook = inputhook_manager.clear_inputhook
set_inputhook = inputhook_manager.set_inputhook
current_gui = inputhook_manager.current_gui
clear_app_refs = inputhook_manager.clear_app_refs
# We maintain this as stdin_ready so that the individual inputhooks
# can diverge as little as possible from their IPython sources
stdin_ready = inputhook_manager.return_control
set_return_control_callback = inputhook_manager.set_return_control_callback
get_return_control_callback = inputhook_manager.get_return_control_callback
get_inputhook = inputhook_manager.get_inputhook
# Convenience function to switch amongst them
def enable_gui(gui=None, app=None):
"""Switch amongst GUI input hooks by name.
This is just a utility wrapper around the methods of the InputHookManager
object.
Parameters
----------
gui : optional, string or None
If None (or 'none'), clears input hook, otherwise it must be one
of the recognized GUI names (see ``GUI_*`` constants in module).
app : optional, existing application object.
For toolkits that have the concept of a global app, you can supply an
existing one. If not given, the toolkit will be probed for one, and if
none is found, a new one will be created. Note that GTK does not have
this concept, and passing an app if ``gui=="GTK"`` will raise an error.
Returns
-------
The output of the underlying gui switch routine, typically the actual
PyOS_InputHook wrapper object or the GUI toolkit app created, if there was
one.
"""
if get_return_control_callback() is None:
raise ValueError("A return_control_callback must be supplied as a reference before a gui can be enabled")
guis = {GUI_NONE: clear_inputhook,
GUI_OSX: enable_mac,
GUI_TK: enable_tk,
GUI_GTK: enable_gtk,
GUI_WX: enable_wx,
GUI_QT: enable_qt4, # qt3 not supported
GUI_QT4: enable_qt4,
GUI_GLUT: enable_glut,
GUI_PYGLET: enable_pyglet,
GUI_GTK3: enable_gtk3,
}
try:
gui_hook = guis[gui]
except KeyError:
if gui is None or gui == '':
gui_hook = clear_inputhook
else:
e = "Invalid GUI request %r, valid ones are:%s" % (gui, guis.keys())
raise ValueError(e)
return gui_hook(app)
__all__ = [
"GUI_WX",
"GUI_QT",
"GUI_QT4",
"GUI_GTK",
"GUI_TK",
"GUI_OSX",
"GUI_GLUT",
"GUI_PYGLET",
"GUI_GTK3",
"GUI_NONE",
"ignore_CTRL_C",
"allow_CTRL_C",
"InputHookManager",
"inputhook_manager",
"enable_wx",
"disable_wx",
"enable_qt4",
"disable_qt4",
"enable_gtk",
"disable_gtk",
"enable_tk",
"disable_tk",
"enable_glut",
"disable_glut",
"enable_pyglet",
"disable_pyglet",
"enable_gtk3",
"disable_gtk3",
"enable_mac",
"disable_mac",
"clear_inputhook",
"set_inputhook",
"current_gui",
"clear_app_refs",
"stdin_ready",
"set_return_control_callback",
"get_return_control_callback",
"get_inputhook",
"enable_gui"]
| agpl-3.0 |
tcarmelveilleux/IcarusAltimeter | Analysis/altitude_analysis.py | 1 | 1202 | # -*- coding: utf-8 -*-
"""
Created on Tue Jul 14 19:34:31 2015
@author: Tennessee
"""
import numpy as np
import matplotlib.pyplot as plt
def altitude(atm_hpa, sea_level_hpa):
return 44330 * (1.0 - np.power(atm_hpa / sea_level_hpa, 0.1903))
def plot_alt():
default_msl = 101300.0
pressure = np.linspace(97772.58 / 100.0, 79495.0 / 100.0, 2000)
alt_nominal = altitude(pressure, default_msl) - altitude(97772.58 / 100.0, default_msl)
alt_too_high = altitude(pressure, default_msl + (1000 / 100.0)) - altitude(97772.58 / 100.0, default_msl + (1000 / 100.0))
alt_too_low = altitude(pressure, default_msl - (1000 / 100.0)) - altitude(97772.58 / 100.0, default_msl - (1000 / 100.0))
f1 = plt.figure()
ax = f1.gca()
ax.plot(pressure, alt_nominal, "b-", label="nom")
ax.plot(pressure, alt_too_high, "r-", label="high")
ax.plot(pressure, alt_too_low, "g-", label="low")
ax.legend()
f1.show()
f2 = plt.figure()
ax = f2.gca()
ax.plot(pressure, alt_too_high - alt_nominal, "r-", label="high")
ax.plot(pressure, alt_too_low - alt_nominal, "g-", label="low")
ax.legend()
f2.show()
| mit |
PatrickOReilly/scikit-learn | examples/plot_johnson_lindenstrauss_bound.py | 67 | 7474 | r"""
=====================================================================
The Johnson-Lindenstrauss bound for embedding with random projections
=====================================================================
The `Johnson-Lindenstrauss lemma`_ states that any high dimensional
dataset can be randomly projected into a lower dimensional Euclidean
space while controlling the distortion in the pairwise distances.
.. _`Johnson-Lindenstrauss lemma`: https://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma
Theoretical bounds
==================
The distortion introduced by a random projection `p` is asserted by
the fact that `p` is defining an eps-embedding with good probability
as defined by:
.. math::
(1 - eps) \|u - v\|^2 < \|p(u) - p(v)\|^2 < (1 + eps) \|u - v\|^2
Where u and v are any rows taken from a dataset of shape [n_samples,
n_features] and p is a projection by a random Gaussian N(0, 1) matrix
with shape [n_components, n_features] (or a sparse Achlioptas matrix).
The minimum number of components to guarantees the eps-embedding is
given by:
.. math::
n\_components >= 4 log(n\_samples) / (eps^2 / 2 - eps^3 / 3)
The first plot shows that with an increasing number of samples ``n_samples``,
the minimal number of dimensions ``n_components`` increased logarithmically
in order to guarantee an ``eps``-embedding.
The second plot shows that an increase of the admissible
distortion ``eps`` allows to reduce drastically the minimal number of
dimensions ``n_components`` for a given number of samples ``n_samples``
Empirical validation
====================
We validate the above bounds on the digits dataset or on the 20 newsgroups
text document (TF-IDF word frequencies) dataset:
- for the digits dataset, some 8x8 gray level pixels data for 500
handwritten digits pictures are randomly projected to spaces for various
larger number of dimensions ``n_components``.
- for the 20 newsgroups dataset some 500 documents with 100k
features in total are projected using a sparse random matrix to smaller
euclidean spaces with various values for the target number of dimensions
``n_components``.
The default dataset is the digits dataset. To run the example on the twenty
newsgroups dataset, pass the --twenty-newsgroups command line argument to this
script.
For each value of ``n_components``, we plot:
- 2D distribution of sample pairs with pairwise distances in original
and projected spaces as x and y axis respectively.
- 1D histogram of the ratio of those distances (projected / original).
We can see that for low values of ``n_components`` the distribution is wide
with many distorted pairs and a skewed distribution (due to the hard
limit of zero ratio on the left as distances are always positives)
while for larger values of n_components the distortion is controlled
and the distances are well preserved by the random projection.
Remarks
=======
According to the JL lemma, projecting 500 samples without too much distortion
will require at least several thousands dimensions, irrespective of the
number of features of the original dataset.
Hence using random projections on the digits dataset which only has 64 features
in the input space does not make sense: it does not allow for dimensionality
reduction in this case.
On the twenty newsgroups on the other hand the dimensionality can be decreased
from 56436 down to 10000 while reasonably preserving pairwise distances.
"""
print(__doc__)
import sys
from time import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn.random_projection import johnson_lindenstrauss_min_dim
from sklearn.random_projection import SparseRandomProjection
from sklearn.datasets import fetch_20newsgroups_vectorized
from sklearn.datasets import load_digits
from sklearn.metrics.pairwise import euclidean_distances
# Part 1: plot the theoretical dependency between n_components_min and
# n_samples
# range of admissible distortions
eps_range = np.linspace(0.1, 0.99, 5)
colors = plt.cm.Blues(np.linspace(0.3, 1.0, len(eps_range)))
# range of number of samples (observation) to embed
n_samples_range = np.logspace(1, 9, 9)
plt.figure()
for eps, color in zip(eps_range, colors):
min_n_components = johnson_lindenstrauss_min_dim(n_samples_range, eps=eps)
plt.loglog(n_samples_range, min_n_components, color=color)
plt.legend(["eps = %0.1f" % eps for eps in eps_range], loc="lower right")
plt.xlabel("Number of observations to eps-embed")
plt.ylabel("Minimum number of dimensions")
plt.title("Johnson-Lindenstrauss bounds:\nn_samples vs n_components")
# range of admissible distortions
eps_range = np.linspace(0.01, 0.99, 100)
# range of number of samples (observation) to embed
n_samples_range = np.logspace(2, 6, 5)
colors = plt.cm.Blues(np.linspace(0.3, 1.0, len(n_samples_range)))
plt.figure()
for n_samples, color in zip(n_samples_range, colors):
min_n_components = johnson_lindenstrauss_min_dim(n_samples, eps=eps_range)
plt.semilogy(eps_range, min_n_components, color=color)
plt.legend(["n_samples = %d" % n for n in n_samples_range], loc="upper right")
plt.xlabel("Distortion eps")
plt.ylabel("Minimum number of dimensions")
plt.title("Johnson-Lindenstrauss bounds:\nn_components vs eps")
# Part 2: perform sparse random projection of some digits images which are
# quite low dimensional and dense or documents of the 20 newsgroups dataset
# which is both high dimensional and sparse
if '--twenty-newsgroups' in sys.argv:
# Need an internet connection hence not enabled by default
data = fetch_20newsgroups_vectorized().data[:500]
else:
data = load_digits().data[:500]
n_samples, n_features = data.shape
print("Embedding %d samples with dim %d using various random projections"
% (n_samples, n_features))
n_components_range = np.array([300, 1000, 10000])
dists = euclidean_distances(data, squared=True).ravel()
# select only non-identical samples pairs
nonzero = dists != 0
dists = dists[nonzero]
for n_components in n_components_range:
t0 = time()
rp = SparseRandomProjection(n_components=n_components)
projected_data = rp.fit_transform(data)
print("Projected %d samples from %d to %d in %0.3fs"
% (n_samples, n_features, n_components, time() - t0))
if hasattr(rp, 'components_'):
n_bytes = rp.components_.data.nbytes
n_bytes += rp.components_.indices.nbytes
print("Random matrix with size: %0.3fMB" % (n_bytes / 1e6))
projected_dists = euclidean_distances(
projected_data, squared=True).ravel()[nonzero]
plt.figure()
plt.hexbin(dists, projected_dists, gridsize=100, cmap=plt.cm.PuBu)
plt.xlabel("Pairwise squared distances in original space")
plt.ylabel("Pairwise squared distances in projected space")
plt.title("Pairwise distances distribution for n_components=%d" %
n_components)
cb = plt.colorbar()
cb.set_label('Sample pairs counts')
rates = projected_dists / dists
print("Mean distances rate: %0.2f (%0.2f)"
% (np.mean(rates), np.std(rates)))
plt.figure()
plt.hist(rates, bins=50, normed=True, range=(0., 2.))
plt.xlabel("Squared distances rate: projected / original")
plt.ylabel("Distribution of samples pairs")
plt.title("Histogram of pairwise distance rates for n_components=%d" %
n_components)
# TODO: compute the expected value of eps and add them to the previous plot
# as vertical lines / region
plt.show()
| bsd-3-clause |
do-mpc/do-mpc | testing/test_oscillating_masses_discrete_dae.py | 1 | 3206 | #
# This file is part of do-mpc
#
# do-mpc: An environment for the easy, modular and efficient implementation of
# robust nonlinear model predictive control
#
# Copyright (c) 2014-2019 Sergio Lucia, Alexandru Tatulea-Codrean
# TU Dortmund. All rights reserved
#
# do-mpc 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.
#
# do-mpc 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 General Public License
# along with do-mpc. If not, see <http://www.gnu.org/licenses/>.
import numpy as np
import matplotlib.pyplot as plt
from casadi import *
from casadi.tools import *
import pdb
import sys
import unittest
sys.path.append('../')
import do_mpc
sys.path.pop(-1)
sys.path.append('../examples/oscillating_masses_discrete_dae/')
from template_model import template_model
from template_mpc import template_mpc
from template_simulator import template_simulator
sys.path.pop(-1)
class TestOscillatingMassesDiscrete(unittest.TestCase):
def test_oscillating_masses_discrete(self):
"""
Get configured do-mpc modules:
"""
model = template_model()
mpc = template_mpc(model)
simulator = template_simulator(model)
estimator = do_mpc.estimator.StateFeedback(model)
"""
Set initial state
"""
np.random.seed(99)
x0 = np.random.rand(model.n_x)-0.5
mpc.x0 = x0
simulator.x0 = x0
estimator.x0 = x0
# Use initial state to set the initial guess.
mpc.set_initial_guess()
# This is only meaningful for DAE systems.
simulator.set_initial_guess()
"""
Run some steps:
"""
for k in range(5):
u0 = mpc.make_step(x0)
y_next = simulator.make_step(u0)
x0 = estimator.make_step(y_next)
"""
Store results (from reference run):
"""
#do_mpc.data.save_results([mpc, simulator, estimator], 'results_oscillatingMasses_dae', overwrite=True)
"""
Compare results to reference run:
"""
ref = do_mpc.data.load_results('./results/results_oscillatingMasses_dae.pkl')
test = ['_x', '_u', '_aux', '_time', '_z']
for test_i in test:
# Check MPC
check = np.allclose(mpc.data.__dict__[test_i], ref['mpc'].__dict__[test_i])
self.assertTrue(check)
# Check Simulator
check = np.allclose(simulator.data.__dict__[test_i], ref['simulator'].__dict__[test_i])
self.assertTrue(check)
# Estimator
check = np.allclose(estimator.data.__dict__[test_i], ref['estimator'].__dict__[test_i])
self.assertTrue(check)
if __name__ == '__main__':
unittest.main()
| lgpl-3.0 |
mdmueller/ascii-profiling | parallel.py | 1 | 4245 | import timeit
import time
from astropy.io import ascii
import pandas
import numpy as np
from astropy.table import Table, Column
from tempfile import NamedTemporaryFile
import random
import string
import matplotlib.pyplot as plt
import webbrowser
def make_table(table, size=10000, n_floats=10, n_ints=0, n_strs=0, float_format=None, str_val=None):
if str_val is None:
str_val = "abcde12345"
cols = []
for i in xrange(n_floats):
dat = np.random.uniform(low=1, high=10, size=size)
cols.append(Column(dat, name='f{}'.format(i)))
for i in xrange(n_ints):
dat = np.random.randint(low=-9999999, high=9999999, size=size)
cols.append(Column(dat, name='i{}'.format(i)))
for i in xrange(n_strs):
if str_val == 'random':
dat = np.array([''.join([random.choice(string.letters) for j in range(10)]) for k in range(size)])
else:
dat = np.repeat(str_val, size)
cols.append(Column(dat, name='s{}'.format(i)))
t = Table(cols)
if float_format is not None:
for col in t.columns.values():
if col.name.startswith('f'):
col.format = float_format
t.write(table.name, format='ascii')
output_text = []
def plot_case(n_floats=10, n_ints=0, n_strs=0, float_format=None, str_val=None):
global table1, output_text
n_rows = (10000, 20000, 50000, 100000, 200000) # include 200000 for publish run
numbers = (1, 1, 1, 1, 1)
repeats = (3, 2, 1, 1, 1)
times_fast = []
times_fast_parallel = []
times_pandas = []
for n_row, number, repeat in zip(n_rows, numbers, repeats):
table1 = NamedTemporaryFile()
make_table(table1, n_row, n_floats, n_ints, n_strs, float_format, str_val)
t = timeit.repeat("ascii.read(table1.name, format='basic', guess=False, use_fast_converter=True)",
setup='from __main__ import ascii, table1', number=number, repeat=repeat)
times_fast.append(min(t) / number)
t = timeit.repeat("ascii.read(table1.name, format='basic', guess=False, parallel=True, use_fast_converter=True)",
setup='from __main__ import ascii, table1', number=number, repeat=repeat)
times_fast_parallel.append(min(t) / number)
t = timeit.repeat("pandas.read_csv(table1.name, sep=' ', header=0)",
setup='from __main__ import table1, pandas', number=number, repeat=repeat)
times_pandas.append(min(t) / number)
plt.loglog(n_rows, times_fast, '-or', label='io.ascii Fast-c')
plt.loglog(n_rows, times_fast_parallel, '-og', label='io.ascii Parallel Fast-c')
plt.loglog(n_rows, times_pandas, '-oc', label='Pandas')
plt.grid()
plt.legend(loc='best')
plt.title('n_floats={} n_ints={} n_strs={} float_format={} str_val={}'.format(
n_floats, n_ints, n_strs, float_format, str_val))
plt.xlabel('Number of rows')
plt.ylabel('Time (sec)')
img_file = 'graph{}.png'.format(len(output_text) + 1)
plt.savefig(img_file)
plt.clf()
text = 'Pandas to io.ascii Fast-C speed ratio: {:.2f} : 1<br/>'.format(times_fast[-1] / times_pandas[-1])
text += 'io.ascii parallel to Pandas speed ratio: {:.2f} : 1'.format(times_pandas[-1] / times_fast_parallel[-1])
output_text.append((img_file, text))
plot_case(n_floats=10, n_ints=0, n_strs=0)
plot_case(n_floats=10, n_ints=10, n_strs=10)
plot_case(n_floats=10, n_ints=10, n_strs=10, float_format='%.4f')
plot_case(n_floats=10, n_ints=0, n_strs=0, float_format='%.4f')
plot_case(n_floats=0, n_ints=0, n_strs=10)
plot_case(n_floats=0, n_ints=0, n_strs=10, str_val="'asdf asdfa'")
plot_case(n_floats=0, n_ints=0, n_strs=10, str_val="random")
plot_case(n_floats=0, n_ints=10, n_strs=0)
html_file = open('out.html', 'w')
html_file.write('<html><head><meta charset="utf-8"/><meta content="text/html;charset=UTF-8" http-equiv="Content-type"/>')
html_file.write('</html><body><h1 style="text-align:center;">Profile of io.ascii</h1>')
for img, descr in output_text:
html_file.write('<img src="{}"><p style="font-weight:bold;">{}</p><hr>'.format(img, descr))
html_file.write('</body></html>')
html_file.close()
webbrowser.open('out.html')
| mit |
gtcasl/eiger | Eiger.py | 1 | 20400 | #!/usr/bin/python
#
# \file Eiger.py
# \author Eric Anger <eanger@gatech.edu>
# \date July 6, 2012
#
# \brief Command line interface into Eiger modeling framework
#
# \changes Added more plot functionality; Benjamin Allan, SNL 5/2013
#
import argparse
import matplotlib.pyplot as plt
import numpy as np
import math
import tempfile
import shutil
import os
from ast import literal_eval
import json
import sys
from collections import namedtuple
from tabulate import tabulate
from sklearn.cluster import KMeans
from eiger import database, PCA, LinearRegression
Model = namedtuple('Model', ['metric_names', 'means', 'stdevs',
'rotation_matrix', 'kmeans', 'models'])
def import_model(args):
database.addModelFromFile(args.database, args.file, args.source_name, args.description)
def export_model(args):
database.dumpModelToFile(args.database, args.file, args.id)
def list_models(args):
all_models = database.getModels(args.database)
print tabulate(all_models, headers=['ID', 'Description', 'Created', 'Source'])
def trainModel(args):
print "Training the model..."
training_DC = database.DataCollection(args.training_dc, args.database)
try:
performance_metric_id = [m[0] for m in training_DC.metrics].index(args.target)
except ValueError:
print "Unable to find target metric '%s', " \
"please specify a valid one: " % (args.target,)
for (my_name,my_desc,my_type) in training_DC.metrics:
print "\t%s" % (my_name,)
return
training_performance = training_DC.profile[:,performance_metric_id]
metric_names = [m[0] for m in training_DC.metrics if m[0] != args.target]
if args.predictor_metrics != None:
metric_names = filter(lambda x: x in args.predictor_metrics, metric_names)
metric_ids = [[m[0] for m in training_DC.metrics].index(n) for n in metric_names]
if not metric_ids:
print "Unable to make model for empty data collection. Aborting..."
return
training_profile = training_DC.profile[:,metric_ids]
#pca
training_pca = PCA.PCA(training_profile)
nonzero_components = training_pca.nonzeroComponents()
rotation_matrix = training_pca.components[:,nonzero_components]
rotated_training_profile = np.dot(training_profile, rotation_matrix)
#kmeans
n_clusters = args.clusters
kmeans = KMeans(n_clusters)
means = np.mean(rotated_training_profile, axis=0)
stdevs = np.std(rotated_training_profile - means, axis=0, ddof=1)
stdevs[stdevs==0.0] = 1.0
clusters = kmeans.fit_predict((rotated_training_profile - means)/stdevs)
# reserve a vector for each model created per cluster
models = [0] * len(clusters)
print "Modeling..."
for i in range(n_clusters):
cluster_profile = rotated_training_profile[clusters==i,:]
cluster_performance = training_performance[clusters==i]
regression = LinearRegression.LinearRegression(cluster_profile,
cluster_performance)
pool = [LinearRegression.identityFunction()]
for col in range(cluster_profile.shape[1]):
if('inv_quadratic' in args.regressor_functions):
pool.append(LinearRegression.powerFunction(col, -2))
if('inv_linear' in args.regressor_functions):
pool.append(LinearRegression.powerFunction(col, -1))
if('inv_sqrt' in args.regressor_functions):
pool.append(LinearRegression.powerFunction(col, -.5))
if('sqrt' in args.regressor_functions):
pool.append(LinearRegression.powerFunction(col, .5))
if('linear' in args.regressor_functions):
pool.append(LinearRegression.powerFunction(col, 1))
if('quadratic' in args.regressor_functions):
pool.append(LinearRegression.powerFunction(col, 2))
if('log' in args.regressor_functions):
pool.append(LinearRegression.logFunction(col))
if('cross' in args.regressor_functions):
for xcol in range(col, cluster_profile.shape[1]):
pool.append(LinearRegression.crossFunction(col, xcol))
if('div' in args.regressor_functions):
for xcol in range(col, cluster_profile.shape[1]):
pool.append(LinearRegression.divFunction(col,xcol))
pool.append(LinearRegression.divFunction(xcol,col))
(models[i], r_squared, r_squared_adj) = regression.select(pool,
threshold=args.threshold,
folds=args.nfolds)
print "Index\tMetric Name"
print '\n'.join("%s\t%s" % metric for metric in enumerate(metric_names))
print "PCA matrix:"
print rotation_matrix
print "Model:\n" + str(models[i])
print "Finished modeling cluster %s:" % (i,)
print "r squared = %s" % (r_squared,)
print "adjusted r squared = %s" % (r_squared_adj,)
model = Model(metric_names, means, stdevs, rotation_matrix, kmeans, models)
# if we want to save the model file, copy it now
outfilename = training_DC.name + '.model' if args.output == None else args.output
if args.json == True:
writeToFileJSON(model, outfilename)
else:
writeToFile(model, outfilename)
if args.test_fit:
args.experiment_dc = args.training_dc
args.model = outfilename
testModel(args)
def dumpCSV(args):
training_DC = database.DataCollection(args.training_dc, args.database)
names = [met[0] for met in training_DC.metrics]
if args.metrics != None:
names = args.metrics
header = ','.join(names)
idxs = training_DC.metricIndexByName(names)
profile = training_DC.profile[:,idxs]
outfile = sys.stdout if args.output == None else args.output
np.savetxt(outfile, profile, delimiter=',',
header=header, comments='')
def testModel(args):
print "Testing the model fit..."
test_DC = database.DataCollection(args.experiment_dc, args.database)
model = readFile(args.model)
_runExperiment(model.kmeans, model.means, model.stdevs, model.models,
model.rotation_matrix, test_DC,
args, model.metric_names)
def readFile(infile):
with open(infile, 'r') as modelfile:
first_char = modelfile.readline()[0]
if first_char == '{':
return readJSONFile(infile)
else:
return readBespokeFile(infile)
def plotModel(args):
print "Plotting model..."
model = readFile(args.model)
if args.plot_pcs_per_metric:
PCA.PlotPCsPerMetric(rotation_matrix, metric_names,
title="PCs Per Metric")
if args.plot_metrics_per_pc:
PCA.PlotMetricsPerPC(rotation_matrix, metric_names,
title="Metrics Per PC")
def _stringToArray(string):
"""
Parse string of form [len](number,number,number,...) to a numpy array.
"""
length = string[:string.find('(')]
values = string[string.find('('):]
arr = np.array(literal_eval(values))
return np.reshape(arr, literal_eval(length))
def _runExperiment(kmeans, means, stdevs, models, rotation_matrix,
experiment_DC, args, metric_names):
unordered_metric_ids = experiment_DC.metricIndexByType('deterministic',
'nondeterministic')
unordered_metric_names = [experiment_DC.metrics[mid][0] for mid in unordered_metric_ids]
# make sure all metric_names are in experiment_DC.metrics[:][0]
have_metrics = [x in unordered_metric_names for x in metric_names]
if not all(have_metrics):
print("Experiment DC does not have matching metrics. Aborting...")
return
# set the correct ordering
expr_metric_ids = [unordered_metric_ids[unordered_metric_names.index(name)]
for name in metric_names]
for idx,metric in enumerate(experiment_DC.metrics):
if(metric[0] == args.target):
performance_metric_id = idx
performance = experiment_DC.profile[:,performance_metric_id]
profile = experiment_DC.profile[:,expr_metric_ids]
rotated_profile = np.dot(profile, rotation_matrix)
means = np.mean(rotated_profile, axis=0)
stdevs = np.std(rotated_profile - means, axis=0, ddof=1)
stdevs = np.nan_to_num(stdevs)
stdevs[stdevs==0.0] = 1.0
clusters = kmeans.predict((rotated_profile - means)/stdevs)
prediction = np.empty_like(performance)
for i in range(len(kmeans.cluster_centers_)):
prediction[clusters==i] = abs(models[i].poll(rotated_profile[clusters==i]))
if args.show_prediction:
print "Actual\t\tPredicted"
print '\n'.join("%s\t%s" % x for x in zip(performance,prediction))
mse = sum([(a-p)**2 for a,p in
zip(performance, prediction)]) / len(performance)
rmse = math.sqrt(mse)
mape = 100 * sum([abs((a-p)/a) for a,p in
zip(performance,prediction)]) / len(performance)
print "Number of experiment trials: %s" % len(performance)
print "Mean Average Percent Error: %s" % mape
print "Mean Squared Error: %s" % mse
print "Root Mean Squared Error: %s" % rmse
def writeToFileJSON(model, outfile):
# Let's assume model has all the attributes we care about
json_root = {}
json_root["metric_names"] = [name for name in model.metric_names]
json_root["means"] = [mean for mean in model.means.tolist()]
json_root["std_devs"] = [stdev for stdev in model.stdevs.tolist()]
json_root["rotation_matrix"] = [[elem for elem in row] for row in model.rotation_matrix.tolist()]
json_root["clusters"] = []
for i in range(len(model.kmeans.cluster_centers_)):
json_cluster = {}
json_cluster["center"] = [center for center in model.kmeans.cluster_centers_[i].tolist()]
# get models in json format
json_cluster["regressors"] = model.models[i].toJSONObject()
json_root["clusters"].append(json_cluster)
with open(outfile, 'w') as out:
json.dump(json_root, out, indent=4)
def readJSONFile(infile):
with open(infile, 'r') as modelfile:
json_root = json.load(modelfile)
metric_names = json_root['metric_names']
means = np.array(json_root['means'])
stdevs = np.array(json_root['std_devs'])
rotation_matrix = np.array(json_root['rotation_matrix'])
empty_kmeans = KMeans(n_clusters=len(json_root['clusters']), n_init=1)
centers = []
models = []
for cluster in json_root['clusters']:
centers.append(np.array(cluster['center']))
models.append(LinearRegression.Model.fromJSONObject(cluster['regressors']))
kmeans = empty_kmeans.fit(centers)
return Model(metric_names, means, stdevs, rotation_matrix, kmeans, models)
def writeToFile(model, outfile):
with open(outfile, 'w') as modelfile:
# For printing the original model file encoding
modelfile.write("%s\n%s\n" % (len(model.metric_names), '\n'.join(model.metric_names)))
modelfile.write("[%s](%s)\n" %
(len(model.means), ','.join([str(mean) for mean in model.means.tolist()])))
modelfile.write("[%s](%s)\n" %
(len(model.stdevs), ','.join([str(stdev) for stdev in model.stdevs.tolist()])))
modelfile.write("[%s,%s]" % model.rotation_matrix.shape)
modelfile.write("(%s)\n" %
','.join(["(%s)" %
','.join([str(elem) for elem in row])
for row in model.rotation_matrix.tolist()]))
for i in range(len(model.kmeans.cluster_centers_)):
modelfile.write('Model %s\n' % i)
modelfile.write("[%s](%s)\n" % (model.rotation_matrix.shape[1],
','.join([str(center) for center in
model.kmeans.cluster_centers_[i].tolist()])))
modelfile.write(repr(model.models[i]))
modelfile.write('\n') # need a trailing newline
def readBespokeFile(infile):
"""Returns a Model namedtuple with all the model parts"""
with open(infile, 'r') as modelfile:
lines = iter(modelfile.read().splitlines())
n_params = int(lines.next())
metric_names = [lines.next() for i in range(n_params)]
means = _stringToArray(lines.next())
stdevs = _stringToArray(lines.next())
rotation_matrix = _stringToArray(lines.next())
models = []
centroids = []
try:
while True:
name = lines.next() # kill a line
centroids.append(_stringToArray(lines.next()))
weights = _stringToArray(lines.next())
functions = [LinearRegression.stringToFunction(lines.next())
for i in range(weights.shape[0])]
models.append(LinearRegression.Model(functions, weights))
except StopIteration:
pass
kmeans = KMeans(len(centroids))
kmeans.cluster_centers_ = np.array(centroids)
return Model(metric_names, means, stdevs, rotation_matrix, kmeans, models)
def convert(args):
print "Converting model..."
with open(args.input, 'r') as modelfile:
first_char = modelfile.readline()[0]
if first_char == '{':
model = readJSONFile(args.input)
writeToFile(model, args.output)
else:
model = readBespokeFile(args.input)
writeToFileJSON(model, args.output)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description = \
'Command line interface into Eiger performance modeling framework \
for all model generation, polling, and serialization tasks.',
argument_default=None,
fromfile_prefix_chars='@')
subparsers = parser.add_subparsers(title='subcommands')
train_parser = subparsers.add_parser('train',
help='train a model with data from the database',
description='Train a model with data from the database')
train_parser.set_defaults(func=trainModel)
dump_parser = subparsers.add_parser('dump',
help='dump data collection to CSV',
description='Dump data collection as CSV')
dump_parser.set_defaults(func=dumpCSV)
test_parser = subparsers.add_parser('test',
help='test how well a model predicts a data collection',
description='Test how well a model predicts a data collection')
test_parser.set_defaults(func=testModel)
plot_parser = subparsers.add_parser('plot',
help='plot the behavior of a model',
description='Plot the behavior of a model')
plot_parser.set_defaults(func=plotModel)
convert_parser = subparsers.add_parser('convert',
help='transform a model into a different file format',
description='Transform a model into a different file format')
convert_parser.set_defaults(func=convert)
list_model_parser = subparsers.add_parser('list',
help='list available models in the Eiger DB',
description='List available models in the Eiger DB')
list_model_parser.set_defaults(func=list_models)
import_model_parser = subparsers.add_parser('import',
help='import model file into the Eiger DB',
description='Import model file into the Eiger DB')
import_model_parser.set_defaults(func=import_model)
export_model_parser = subparsers.add_parser('export',
help='export model from Eiger DB to file',
description='Export model from Eiger DB to file')
export_model_parser.set_defaults(func=export_model)
"""TRAINING ARGUMENTS"""
train_parser.add_argument('database', type=str, help='Name of the database file')
train_parser.add_argument('training_dc', type=str,
help='Name of the training data collection')
train_parser.add_argument('target', type=str,
help='Name of the target metric to predict')
train_parser.add_argument('--test-fit', action='store_true', default=False,
help='If set will test the model fit against the training data.')
train_parser.add_argument('--show-prediction', action='store_true',
default=False,
help='If set, send the actual and predicted values to stdout.')
train_parser.add_argument('--predictor-metrics', nargs='*',
help='Only use these metrics when building a model.')
train_parser.add_argument('--output', type=str,
help='Filename to output file to, otherwise use "<training_dc>.model"')
train_parser.add_argument('--clusters', '-k', type=int, default=1,
help='Number of clusters for kmeans')
train_parser.add_argument('--threshold', type=float,
help='Cutoff threshold of increase in adjusted R-squared value when'
' adding new predictors to the model')
train_parser.add_argument('--nfolds', type=int,
help='Number of folds to use in k-fold cross validation.')
train_parser.add_argument('--regressor-functions', nargs='*',
default=['inv_quadratic', 'inv_linear', 'inv_sqrt', 'sqrt',
'linear', 'quadratic', 'log', 'cross', 'div'],
help='Regressor functions to use. Options are linear, quadratic, '
'sqrt, inv_linear, inv_quadratic, inv_sqrt, log, cross, and div. '
'Defaults to all.')
train_parser.add_argument('--json', action='store_true', default=False,
help='Output model in JSON format, rather than bespoke')
"""DUMP CSV ARGUMENTS"""
dump_parser.add_argument('database', type=str, help='Name of the database file')
dump_parser.add_argument('training_dc', type=str,
help='Name of the data collection to dump')
dump_parser.add_argument('--metrics', nargs='*',
help='Only dump these metrics.')
dump_parser.add_argument('--output', type=str, help='Name of file to dump CSV to')
"""TEST ARGUMENTS"""
test_parser.add_argument('database', type=str, help='Name of the database file')
test_parser.add_argument('experiment_dc', type=str,
help='Name of the data collection to experiment on')
test_parser.add_argument('model', type=str,
help='Name of the model to use')
test_parser.add_argument('target', type=str,
help='Name of the target metric to predict')
test_parser.add_argument('--show-prediction', action='store_true',
default=False,
help='If set, send the actual and predicted values to stdout.')
"""PLOT ARGUMENTS"""
plot_parser.add_argument('model', type=str,
help='Name of the model to use')
plot_parser.add_argument('--plot-pcs-per-metric', action='store_true',
default=False,
help='If set, plots the breakdown of principal components per metric.')
plot_parser.add_argument('--plot-metrics-per-pc',
action='store_true',
default=False,
help='If set, plots the breakdown of metrics per principal component.')
"""CONVERT ARGUMENTS"""
convert_parser.add_argument('input', type=str,
help='Name of input model to convert from')
convert_parser.add_argument('output', type=str,
help='Name of output model to convert to')
"""LIST ARGUMENTS"""
list_model_parser.add_argument('database', type=str, help='Name of the database file')
"""IMPORT ARGUMENTS"""
import_model_parser.add_argument('database', type=str,
help='Name of the database file')
import_model_parser.add_argument('file', type=str,
help='Name of the model file to import')
import_model_parser.add_argument('source_name', type=str,
help='Name of the source of the model (ie Eiger)')
import_model_parser.add_argument('--description', type=str,
default='',
help='String to describe the model')
"""EXPORT ARGUMENTS"""
export_model_parser.add_argument('database', type=str,
help='Name of the database file')
export_model_parser.add_argument('id', type=int,
help='ID number identifying which model in the database to export ')
export_model_parser.add_argument('file', type=str,
help='Name of the file to export into')
args = parser.parse_args()
args.func(args)
print "Done."
| bsd-3-clause |
droundy/deft | talks/colloquium/figs/plot-walls.py | 1 | 3242 | #!/usr/bin/python
# We need the following two lines in order for matplotlib to work
# without access to an X server.
from __future__ import division
import matplotlib
matplotlib.use('Agg')
import pylab, numpy, sys
xmax = 2.5
xmin = -0.4
def plotit(dftdata, mcdata):
dft_len = len(dftdata[:,0])
dft_dr = dftdata[2,0] - dftdata[1,0]
mcdata = numpy.insert(mcdata,0,0,0)
mcdata[0,0]=-10
mcoffset = 10/2
offset = -3/2
n0 = dftdata[:,6]
nA = dftdata[:,8]
nAmc = mcdata[:,11]
n0mc = mcdata[:,10]
pylab.figure(figsize=(6, 6))
pylab.subplots_adjust(hspace=0.001)
n_plt = pylab.subplot(3,1,3)
n_plt.plot(mcdata[:,0]/2+mcoffset,mcdata[:,1]*4*numpy.pi/3,"b-",label='$n$ Monte Carlo')
n_plt.plot(dftdata[:,0]/2+offset,dftdata[:,1]*4*numpy.pi/3,"b--",label='$n$ DFT')
n_plt.legend(loc='best', ncol=1).draw_frame(False) #.get_frame().set_alpha(0.5)
n_plt.yaxis.set_major_locator(pylab.MaxNLocator(6,steps=[1,5,10],prune='upper'))
pylab.ylim(ymin=0)
pylab.xlim(xmin, xmax)
pylab.xlabel("$z/\sigma$")
pylab.ylabel("$n(\mathbf{r})$")
n_plt.axvline(x=0, color='k', linestyle=':')
n = len(mcdata[:,0])
#pylab.twinx()
dftr = dftdata[:,0]/2+offset
thiswork = dftdata[:,5]
gross = dftdata[:,7]
stop_here = int(dft_len - 1/dft_dr)
print stop_here
start_here = int(2.5/dft_dr)
off = 1
me = 40
A_plt = pylab.subplot(3,1,1)
A_plt.axvline(x=0, color='k', linestyle=':')
A_plt.plot(mcdata[:,0]/2+mcoffset,mcdata[:,2+2*off]/nAmc,"r-",label="$g_\sigma^A$ Monte Carlo")
A_plt.plot(dftr[dftr>=0],thiswork[dftr>=0],"ro",markevery=me*.8,label="$g_\sigma^A$ this work")
A_plt.plot(dftr[dftr>=0],gross[dftr>=0],"rx",markevery=me,label="Gross",
markerfacecolor='none',markeredgecolor='red', markeredgewidth=1)
A_plt.legend(loc='best', ncol=1).draw_frame(False) #.get_frame().set_alpha(0.5)
A_plt.yaxis.set_major_locator(pylab.MaxNLocator(integer=True,prune='upper'))
pylab.ylim(ymin=0)
pylab.ylabel("$g_\sigma^A$")
pylab.xlim(xmin, xmax)
n0mc[0]=1
mcdata[0,10]=1
S_plt = pylab.subplot(3,1,2)
S_plt.axvline(x=0, color='k', linestyle=':')
S_plt.plot(mcdata[:,0]/2+mcoffset,mcdata[:,3+2*off]/n0mc,"g-",label="$g_\sigma^S$ Monte Carlo")
S_plt.plot(dftdata[:,0]/2+offset,dftdata[:,4],"gx",markevery=me/2,label="Yu and Wu")
S_plt.legend(loc='best', ncol=1).draw_frame(False) #.get_frame().set_alpha(0.5)
#pylab.ylim(ymax=12)
S_plt.yaxis.set_major_locator(pylab.MaxNLocator(5,integer=True,prune='upper'))
pylab.xlim(xmin, xmax)
pylab.ylim(ymin=0)
pylab.ylabel("$g_\sigma^S$")
xticklabels = A_plt.get_xticklabels() + S_plt.get_xticklabels()
pylab.setp(xticklabels, visible=False)
mcdata10 = numpy.loadtxt('../../papers/contact/figs/mc-walls-20-196.dat')
dftdata10 = numpy.loadtxt('../../papers/contact/figs/wallsWB-0.10.dat')
mcdata40 = numpy.loadtxt('../../papers/contact/figs/mc-walls-20-817.dat')
dftdata40 = numpy.loadtxt('../../papers/contact/figs/wallsWB-0.40.dat')
plotit(dftdata10, mcdata10)
pylab.savefig('figs/walls-10.pdf', transparent=True)
plotit(dftdata40, mcdata40)
pylab.savefig('figs/walls-40.pdf', transparent=True)
| gpl-2.0 |
chintak/scikit-image | skimage/feature/util.py | 1 | 4726 | import numpy as np
from skimage.util import img_as_float
class FeatureDetector(object):
def __init__(self):
self.keypoints_ = np.array([])
def detect(self, image):
"""Detect keypoints in image.
Parameters
----------
image : 2D array
Input image.
"""
raise NotImplementedError()
class DescriptorExtractor(object):
def __init__(self):
self.descriptors_ = np.array([])
def extract(self, image, keypoints):
"""Extract feature descriptors in image for given keypoints.
Parameters
----------
image : 2D array
Input image.
keypoints : (N, 2) array
Keypoint locations as ``(row, col)``.
"""
raise NotImplementedError()
def plot_matches(ax, image1, image2, keypoints1, keypoints2, matches,
keypoints_color='k', matches_color=None, only_matches=False):
"""Plot matched features.
Parameters
----------
ax : matplotlib.axes.Axes
Matches and image are drawn in this ax.
image1 : (N, M [, 3]) array
First grayscale or color image.
image2 : (N, M [, 3]) array
Second grayscale or color image.
keypoints1 : (K1, 2) array
First keypoint coordinates as ``(row, col)``.
keypoints2 : (K2, 2) array
Second keypoint coordinates as ``(row, col)``.
matches : (Q, 2) array
Indices of corresponding matches in first and second set of
descriptors, where ``matches[:, 0]`` denote the indices in the first
and ``matches[:, 1]`` the indices in the second set of descriptors.
keypoints_color : matplotlib color, optional
Color for keypoint locations.
matches_color : matplotlib color, optional
Color for lines which connect keypoint matches. By default the
color is chosen randomly.
only_matches : bool, optional
Whether to only plot matches and not plot the keypoint locations.
"""
image1 = img_as_float(image1)
image2 = img_as_float(image2)
new_shape1 = list(image1.shape)
new_shape2 = list(image2.shape)
if image1.shape[0] < image2.shape[0]:
new_shape1[0] = image2.shape[0]
elif image1.shape[0] > image2.shape[0]:
new_shape2[0] = image1.shape[0]
if image1.shape[1] < image2.shape[1]:
new_shape1[1] = image2.shape[1]
elif image1.shape[1] > image2.shape[1]:
new_shape2[1] = image1.shape[1]
if new_shape1 != image1.shape:
new_image1 = np.zeros(new_shape1, dtype=image1.dtype)
new_image1[:image1.shape[0], :image1.shape[1]] = image1
image1 = new_image1
if new_shape2 != image2.shape:
new_image2 = np.zeros(new_shape2, dtype=image2.dtype)
new_image2[:image2.shape[0], :image2.shape[1]] = image2
image2 = new_image2
image = np.concatenate([image1, image2], axis=1)
offset = image1.shape
if not only_matches:
ax.scatter(keypoints1[:, 1], keypoints1[:, 0],
facecolors='none', edgecolors=keypoints_color)
ax.scatter(keypoints2[:, 1] + offset[1], keypoints2[:, 0],
facecolors='none', edgecolors=keypoints_color)
ax.imshow(image)
ax.axis((0, 2 * offset[1], offset[0], 0))
for i in range(matches.shape[0]):
idx1 = matches[i, 0]
idx2 = matches[i, 1]
if matches_color is None:
color = np.random.rand(3, 1)
else:
color = matches_color
ax.plot((keypoints1[idx1, 1], keypoints2[idx2, 1] + offset[1]),
(keypoints1[idx1, 0], keypoints2[idx2, 0]),
'-', color=color)
def _prepare_grayscale_input_2D(image):
image = np.squeeze(image)
if image.ndim != 2:
raise ValueError("Only 2-D gray-scale images supported.")
return img_as_float(image)
def _mask_border_keypoints(image_shape, keypoints, distance):
"""Mask coordinates that are within certain distance from the image border.
Parameters
----------
image_shape : (2, ) array_like
Shape of the image as ``(rows, cols)``.
keypoints : (N, 2) array
Keypoint coordinates as ``(rows, cols)``.
distance : int
Image border distance.
Returns
-------
mask : (N, ) bool array
Mask indicating if pixels are within the image (``True``) or in the
border region of the image (``False``).
"""
rows = image_shape[0]
cols = image_shape[1]
mask = (((distance - 1) < keypoints[:, 0])
& (keypoints[:, 0] < (rows - distance + 1))
& ((distance - 1) < keypoints[:, 1])
& (keypoints[:, 1] < (cols - distance + 1)))
return mask
| bsd-3-clause |
NicWayand/xray | xarray/plot/utils.py | 1 | 6442 | import pkg_resources
import numpy as np
import pandas as pd
from ..core.pycompat import basestring
def _load_default_cmap(fname='default_colormap.csv'):
"""
Returns viridis color map
"""
from matplotlib.colors import LinearSegmentedColormap
# Not sure what the first arg here should be
f = pkg_resources.resource_stream(__name__, fname)
cm_data = pd.read_csv(f, header=None).values
return LinearSegmentedColormap.from_list('viridis', cm_data)
def _determine_extend(calc_data, vmin, vmax):
extend_min = calc_data.min() < vmin
extend_max = calc_data.max() > vmax
if extend_min and extend_max:
extend = 'both'
elif extend_min:
extend = 'min'
elif extend_max:
extend = 'max'
else:
extend = 'neither'
return extend
def _build_discrete_cmap(cmap, levels, extend, filled):
"""
Build a discrete colormap and normalization of the data.
"""
import matplotlib as mpl
if not filled:
# non-filled contour plots
extend = 'max'
if extend == 'both':
ext_n = 2
elif extend in ['min', 'max']:
ext_n = 1
else:
ext_n = 0
n_colors = len(levels) + ext_n - 1
pal = _color_palette(cmap, n_colors)
new_cmap, cnorm = mpl.colors.from_levels_and_colors(
levels, pal, extend=extend)
# copy the old cmap name, for easier testing
new_cmap.name = getattr(cmap, 'name', cmap)
return new_cmap, cnorm
def _color_palette(cmap, n_colors):
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
colors_i = np.linspace(0, 1., n_colors)
if isinstance(cmap, (list, tuple)):
# we have a list of colors
try:
# first try to turn it into a palette with seaborn
from seaborn.apionly import color_palette
pal = color_palette(cmap, n_colors=n_colors)
except ImportError:
# if that fails, use matplotlib
# in this case, is there any difference between mpl and seaborn?
cmap = ListedColormap(cmap, N=n_colors)
pal = cmap(colors_i)
elif isinstance(cmap, basestring):
# we have some sort of named palette
try:
# first try to turn it into a palette with seaborn
from seaborn.apionly import color_palette
pal = color_palette(cmap, n_colors=n_colors)
except (ImportError, ValueError):
# ValueError is raised when seaborn doesn't like a colormap
# (e.g. jet). If that fails, use matplotlib
try:
# is this a matplotlib cmap?
cmap = plt.get_cmap(cmap)
except ValueError:
# or maybe we just got a single color as a string
cmap = ListedColormap([cmap], N=n_colors)
pal = cmap(colors_i)
else:
# cmap better be a LinearSegmentedColormap (e.g. viridis)
pal = cmap(colors_i)
return pal
def _determine_cmap_params(plot_data, vmin=None, vmax=None, cmap=None,
center=None, robust=False, extend=None,
levels=None, filled=True, cnorm=None):
"""
Use some heuristics to set good defaults for colorbar and range.
Adapted from Seaborn:
https://github.com/mwaskom/seaborn/blob/v0.6/seaborn/matrix.py#L158
Parameters
==========
plot_data: Numpy array
Doesn't handle xarray objects
Returns
=======
cmap_params : dict
Use depends on the type of the plotting function
"""
ROBUST_PERCENTILE = 2.0
import matplotlib as mpl
calc_data = np.ravel(plot_data[~pd.isnull(plot_data)])
# Setting center=False prevents a divergent cmap
possibly_divergent = center is not False
# Set center to 0 so math below makes sense but remember its state
center_is_none = False
if center is None:
center = 0
center_is_none = True
# Setting both vmin and vmax prevents a divergent cmap
if (vmin is not None) and (vmax is not None):
possibly_divergent = False
# vlim might be computed below
vlim = None
if vmin is None:
if robust:
vmin = np.percentile(calc_data, ROBUST_PERCENTILE)
else:
vmin = calc_data.min()
elif possibly_divergent:
vlim = abs(vmin - center)
if vmax is None:
if robust:
vmax = np.percentile(calc_data, 100 - ROBUST_PERCENTILE)
else:
vmax = calc_data.max()
elif possibly_divergent:
vlim = abs(vmax - center)
if possibly_divergent:
# kwargs not specific about divergent or not: infer defaults from data
divergent = ((vmin < 0) and (vmax > 0)) or not center_is_none
else:
divergent = False
# A divergent map should be symmetric around the center value
if divergent:
if vlim is None:
vlim = max(abs(vmin - center), abs(vmax - center))
vmin, vmax = -vlim, vlim
# Now add in the centering value and set the limits
vmin += center
vmax += center
# Choose default colormaps if not provided
if cmap is None:
if divergent:
cmap = "RdBu_r"
else:
cmap = "viridis"
# Allow viridis before matplotlib 1.5
if cmap == "viridis":
cmap = _load_default_cmap()
# Handle discrete levels
if levels is not None:
if isinstance(levels, int):
ticker = mpl.ticker.MaxNLocator(levels)
levels = ticker.tick_values(vmin, vmax)
vmin, vmax = levels[0], levels[-1]
if extend is None:
extend = _determine_extend(calc_data, vmin, vmax)
if levels is not None:
cmap, cnorm = _build_discrete_cmap(cmap, levels, extend, filled)
return dict(vmin=vmin, vmax=vmax, cmap=cmap, extend=extend,
levels=levels, norm=cnorm)
def _infer_xy_labels(darray, x, y):
"""
Determine x and y labels. For use in _plot2d
darray must be a 2 dimensional data array.
"""
if x is None and y is None:
if darray.ndim != 2:
raise ValueError('DataArray must be 2d')
y, x = darray.dims
elif x is None or y is None:
raise ValueError('cannot supply only one of x and y')
elif any(k not in darray.coords for k in (x, y)):
raise ValueError('x and y must be coordinate variables')
return x, y
| apache-2.0 |
sinhrks/seaborn | seaborn/matrix.py | 5 | 40890 | """Functions to visualize matrices of data."""
import itertools
import colorsys
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
import pandas as pd
from scipy.spatial import distance
from scipy.cluster import hierarchy
from .axisgrid import Grid
from .palettes import cubehelix_palette
from .utils import despine, axis_ticklabels_overlap
from .external.six.moves import range
def _index_to_label(index):
"""Convert a pandas index or multiindex to an axis label."""
if isinstance(index, pd.MultiIndex):
return "-".join(map(str, index.names))
else:
return index.name
def _index_to_ticklabels(index):
"""Convert a pandas index or multiindex into ticklabels."""
if isinstance(index, pd.MultiIndex):
return ["-".join(map(str, i)) for i in index.values]
else:
return index.values
def _convert_colors(colors):
"""Convert either a list of colors or nested lists of colors to RGB."""
to_rgb = mpl.colors.colorConverter.to_rgb
try:
to_rgb(colors[0])
# If this works, there is only one level of colors
return list(map(to_rgb, colors))
except ValueError:
# If we get here, we have nested lists
return [list(map(to_rgb, l)) for l in colors]
def _matrix_mask(data, mask):
"""Ensure that data and mask are compatabile and add missing values.
Values will be plotted for cells where ``mask`` is ``False``.
``data`` is expected to be a DataFrame; ``mask`` can be an array or
a DataFrame.
"""
if mask is None:
mask = np.zeros(data.shape, np.bool)
if isinstance(mask, np.ndarray):
# For array masks, ensure that shape matches data then convert
if mask.shape != data.shape:
raise ValueError("Mask must have the same shape as data.")
mask = pd.DataFrame(mask,
index=data.index,
columns=data.columns,
dtype=np.bool)
elif isinstance(mask, pd.DataFrame):
# For DataFrame masks, ensure that semantic labels match data
if not mask.index.equals(data.index) \
and mask.columns.equals(data.columns):
err = "Mask must have the same index and columns as data."
raise ValueError(err)
# Add any cells with missing data to the mask
# This works around an issue where `plt.pcolormesh` doesn't represent
# missing data properly
mask = mask | pd.isnull(data)
return mask
class _HeatMapper(object):
"""Draw a heatmap plot of a matrix with nice labels and colormaps."""
def __init__(self, data, vmin, vmax, cmap, center, robust, annot, fmt,
annot_kws, cbar, cbar_kws,
xticklabels=True, yticklabels=True, mask=None):
"""Initialize the plotting object."""
# We always want to have a DataFrame with semantic information
# and an ndarray to pass to matplotlib
if isinstance(data, pd.DataFrame):
plot_data = data.values
else:
plot_data = np.asarray(data)
data = pd.DataFrame(plot_data)
# Validate the mask and convet to DataFrame
mask = _matrix_mask(data, mask)
# Reverse the rows so the plot looks like the matrix
plot_data = plot_data[::-1]
data = data.ix[::-1]
mask = mask.ix[::-1]
plot_data = np.ma.masked_where(np.asarray(mask), plot_data)
# Get good names for the rows and columns
xtickevery = 1
if isinstance(xticklabels, int) and xticklabels > 1:
xtickevery = xticklabels
xticklabels = _index_to_ticklabels(data.columns)
elif isinstance(xticklabels, bool) and xticklabels:
xticklabels = _index_to_ticklabels(data.columns)
elif isinstance(xticklabels, bool) and not xticklabels:
xticklabels = ['' for _ in range(data.shape[1])]
ytickevery = 1
if isinstance(yticklabels, int) and yticklabels > 1:
ytickevery = yticklabels
yticklabels = _index_to_ticklabels(data.index)
elif isinstance(yticklabels, bool) and yticklabels:
yticklabels = _index_to_ticklabels(data.index)
elif isinstance(yticklabels, bool) and not yticklabels:
yticklabels = ['' for _ in range(data.shape[0])]
else:
yticklabels = yticklabels[::-1]
# Get the positions and used label for the ticks
nx, ny = data.T.shape
xstart, xend, xstep = 0, nx, xtickevery
self.xticks = np.arange(xstart, xend, xstep) + .5
self.xticklabels = xticklabels[xstart:xend:xstep]
ystart, yend, ystep = (ny - 1) % ytickevery, ny, ytickevery
self.yticks = np.arange(ystart, yend, ystep) + .5
self.yticklabels = yticklabels[ystart:yend:ystep]
# Get good names for the axis labels
xlabel = _index_to_label(data.columns)
ylabel = _index_to_label(data.index)
self.xlabel = xlabel if xlabel is not None else ""
self.ylabel = ylabel if ylabel is not None else ""
# Determine good default values for the colormapping
self._determine_cmap_params(plot_data, vmin, vmax,
cmap, center, robust)
# Save other attributes to the object
self.data = data
self.plot_data = plot_data
self.annot = annot
self.fmt = fmt
self.annot_kws = {} if annot_kws is None else annot_kws
self.cbar = cbar
self.cbar_kws = {} if cbar_kws is None else cbar_kws
def _determine_cmap_params(self, plot_data, vmin, vmax,
cmap, center, robust):
"""Use some heuristics to set good defaults for colorbar and range."""
calc_data = plot_data.data[~np.isnan(plot_data.data)]
if vmin is None:
vmin = np.percentile(calc_data, 2) if robust else calc_data.min()
if vmax is None:
vmax = np.percentile(calc_data, 98) if robust else calc_data.max()
# Simple heuristics for whether these data should have a divergent map
divergent = ((vmin < 0) and (vmax > 0)) or center is not None
# Now set center to 0 so math below makes sense
if center is None:
center = 0
# A divergent map should be symmetric around the center value
if divergent:
vlim = max(abs(vmin - center), abs(vmax - center))
vmin, vmax = -vlim, vlim
self.divergent = divergent
# Now add in the centering value and set the limits
vmin += center
vmax += center
self.vmin = vmin
self.vmax = vmax
# Choose default colormaps if not provided
if cmap is None:
if divergent:
self.cmap = "RdBu_r"
else:
self.cmap = cubehelix_palette(light=.95, as_cmap=True)
else:
self.cmap = cmap
def _annotate_heatmap(self, ax, mesh):
"""Add textual labels with the value in each cell."""
xpos, ypos = np.meshgrid(ax.get_xticks(), ax.get_yticks())
for x, y, val, color in zip(xpos.flat, ypos.flat,
mesh.get_array(), mesh.get_facecolors()):
if val is not np.ma.masked:
_, l, _ = colorsys.rgb_to_hls(*color[:3])
text_color = ".15" if l > .5 else "w"
val = ("{:" + self.fmt + "}").format(val)
ax.text(x, y, val, color=text_color,
ha="center", va="center", **self.annot_kws)
def plot(self, ax, cax, kws):
"""Draw the heatmap on the provided Axes."""
# Remove all the Axes spines
despine(ax=ax, left=True, bottom=True)
# Draw the heatmap
mesh = ax.pcolormesh(self.plot_data, vmin=self.vmin, vmax=self.vmax,
cmap=self.cmap, **kws)
# Set the axis limits
ax.set(xlim=(0, self.data.shape[1]), ylim=(0, self.data.shape[0]))
# Add row and column labels
ax.set(xticks=self.xticks, yticks=self.yticks)
xtl = ax.set_xticklabels(self.xticklabels)
ytl = ax.set_yticklabels(self.yticklabels, rotation="vertical")
# Possibly rotate them if they overlap
plt.draw()
if axis_ticklabels_overlap(xtl):
plt.setp(xtl, rotation="vertical")
if axis_ticklabels_overlap(ytl):
plt.setp(ytl, rotation="horizontal")
# Add the axis labels
ax.set(xlabel=self.xlabel, ylabel=self.ylabel)
# Annotate the cells with the formatted values
if self.annot:
self._annotate_heatmap(ax, mesh)
# Possibly add a colorbar
if self.cbar:
ticker = mpl.ticker.MaxNLocator(6)
cb = ax.figure.colorbar(mesh, cax, ax,
ticks=ticker, **self.cbar_kws)
cb.outline.set_linewidth(0)
def heatmap(data, vmin=None, vmax=None, cmap=None, center=None, robust=False,
annot=False, fmt=".2g", annot_kws=None,
linewidths=0, linecolor="white",
cbar=True, cbar_kws=None, cbar_ax=None,
square=False, ax=None, xticklabels=True, yticklabels=True,
mask=None,
**kwargs):
"""Plot rectangular data as a color-encoded matrix.
This function tries to infer a good colormap to use from the data, but
this is not guaranteed to work, so take care to make sure the kind of
colormap (sequential or diverging) and its limits are appropriate.
This is an Axes-level function and will draw the heatmap into the
currently-active Axes if none is provided to the ``ax`` argument. Part of
this Axes space will be taken and used to plot a colormap, unless ``cbar``
is False or a separate Axes is provided to ``cbar_ax``.
Parameters
----------
data : rectangular dataset
2D dataset that can be coerced into an ndarray. If a Pandas DataFrame
is provided, the index/column information will be used to label the
columns and rows.
vmin, vmax : floats, optional
Values to anchor the colormap, otherwise they are inferred from the
data and other keyword arguments. When a diverging dataset is inferred,
one of these values may be ignored.
cmap : matplotlib colormap name or object, optional
The mapping from data values to color space. If not provided, this
will be either a cubehelix map (if the function infers a sequential
dataset) or ``RdBu_r`` (if the function infers a diverging dataset).
center : float, optional
The value at which to center the colormap. Passing this value implies
use of a diverging colormap.
robust : bool, optional
If True and ``vmin`` or ``vmax`` are absent, the colormap range is
computed with robust quantiles instead of the extreme values.
annot : bool, optional
If True, write the data value in each cell.
fmt : string, optional
String formatting code to use when ``annot`` is True.
annot_kws : dict of key, value mappings, optional
Keyword arguments for ``ax.text`` when ``annot`` is True.
linewidths : float, optional
Width of the lines that will divide each cell.
linecolor : color, optional
Color of the lines that will divide each cell.
cbar : boolean, optional
Whether to draw a colorbar.
cbar_kws : dict of key, value mappings, optional
Keyword arguments for `fig.colorbar`.
cbar_ax : matplotlib Axes, optional
Axes in which to draw the colorbar, otherwise take space from the
main Axes.
square : boolean, optional
If True, set the Axes aspect to "equal" so each cell will be
square-shaped.
ax : matplotlib Axes, optional
Axes in which to draw the plot, otherwise use the currently-active
Axes.
xticklabels : list-like, int, or bool, optional
If True, plot the column names of the dataframe. If False, don't plot
the column names. If list-like, plot these alternate labels as the
xticklabels. If an integer, use the column names but plot only every
n label.
yticklabels : list-like, int, or bool, optional
If True, plot the row names of the dataframe. If False, don't plot
the row names. If list-like, plot these alternate labels as the
yticklabels. If an integer, use the index names but plot only every
n label.
mask : boolean array or DataFrame, optional
If passed, data will not be shown in cells where ``mask`` is True.
Cells with missing values are automatically masked.
kwargs : other keyword arguments
All other keyword arguments are passed to ``ax.pcolormesh``.
Returns
-------
ax : matplotlib Axes
Axes object with the heatmap.
Examples
--------
Plot a heatmap for a numpy array:
.. plot::
:context: close-figs
>>> import numpy as np; np.random.seed(0)
>>> import seaborn as sns; sns.set()
>>> uniform_data = np.random.rand(10, 12)
>>> ax = sns.heatmap(uniform_data)
Change the limits of the colormap:
.. plot::
:context: close-figs
>>> ax = sns.heatmap(uniform_data, vmin=0, vmax=1)
Plot a heatmap for data centered on 0:
.. plot::
:context: close-figs
>>> normal_data = np.random.randn(10, 12)
>>> ax = sns.heatmap(normal_data)
Plot a dataframe with meaningful row and column labels:
.. plot::
:context: close-figs
>>> flights = sns.load_dataset("flights")
>>> flights = flights.pivot("month", "year", "passengers")
>>> ax = sns.heatmap(flights)
Annotate each cell with the numeric value using integer formatting:
.. plot::
:context: close-figs
>>> ax = sns.heatmap(flights, annot=True, fmt="d")
Add lines between each cell:
.. plot::
:context: close-figs
>>> ax = sns.heatmap(flights, linewidths=.5)
Use a different colormap:
.. plot::
:context: close-figs
>>> ax = sns.heatmap(flights, cmap="YlGnBu")
Center the colormap at a specific value:
.. plot::
:context: close-figs
>>> ax = sns.heatmap(flights, center=flights.loc["January", 1955])
Plot every other column label and don't plot row labels:
.. plot::
:context: close-figs
>>> data = np.random.randn(50, 20)
>>> ax = sns.heatmap(data, xticklabels=2, yticklabels=False)
Don't draw a colorbar:
.. plot::
:context: close-figs
>>> ax = sns.heatmap(flights, cbar=False)
Use different axes for the colorbar:
.. plot::
:context: close-figs
>>> grid_kws = {"height_ratios": (.9, .05), "hspace": .3}
>>> f, (ax, cbar_ax) = plt.subplots(2, gridspec_kw=grid_kws)
>>> ax = sns.heatmap(flights, ax=ax,
... cbar_ax=cbar_ax,
... cbar_kws={"orientation": "horizontal"})
Use a mask to plot only part of a matrix
.. plot::
:context: close-figs
>>> corr = np.corrcoef(np.random.randn(10, 200))
>>> mask = np.zeros_like(corr)
>>> mask[np.triu_indices_from(mask)] = True
>>> with sns.axes_style("white"):
... ax = sns.heatmap(corr, mask=mask, vmax=.3, square=True)
"""
# Initialize the plotter object
plotter = _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt,
annot_kws, cbar, cbar_kws, xticklabels, yticklabels,
mask)
# Add the pcolormesh kwargs here
kwargs["linewidths"] = linewidths
kwargs["edgecolor"] = linecolor
# Draw the plot and return the Axes
if ax is None:
ax = plt.gca()
if square:
ax.set_aspect("equal")
plotter.plot(ax, cbar_ax, kwargs)
return ax
class _DendrogramPlotter(object):
"""Object for drawing tree of similarities between data rows/columns"""
def __init__(self, data, linkage, metric, method, axis, label, rotate):
"""Plot a dendrogram of the relationships between the columns of data
Parameters
----------
data : pandas.DataFrame
Rectangular data
"""
self.axis = axis
if self.axis == 1:
data = data.T
if isinstance(data, pd.DataFrame):
array = data.values
else:
array = np.asarray(data)
data = pd.DataFrame(array)
self.array = array
self.data = data
self.shape = self.data.shape
self.metric = metric
self.method = method
self.axis = axis
self.label = label
self.rotate = rotate
if linkage is None:
self.linkage = self.calculated_linkage
else:
self.linkage = linkage
self.dendrogram = self.calculate_dendrogram()
# Dendrogram ends are always at multiples of 5, who knows why
ticks = 10 * np.arange(self.data.shape[0]) + 5
if self.label:
ticklabels = _index_to_ticklabels(self.data.index)
ticklabels = [ticklabels[i] for i in self.reordered_ind]
if self.rotate:
self.xticks = []
self.yticks = ticks
self.xticklabels = []
self.yticklabels = ticklabels
self.ylabel = _index_to_label(self.data.index)
self.xlabel = ''
else:
self.xticks = ticks
self.yticks = []
self.xticklabels = ticklabels
self.yticklabels = []
self.ylabel = ''
self.xlabel = _index_to_label(self.data.index)
else:
self.xticks, self.yticks = [], []
self.yticklabels, self.xticklabels = [], []
self.xlabel, self.ylabel = '', ''
if self.rotate:
self.X = self.dendrogram['dcoord']
self.Y = self.dendrogram['icoord']
else:
self.X = self.dendrogram['icoord']
self.Y = self.dendrogram['dcoord']
def _calculate_linkage_scipy(self):
if np.product(self.shape) >= 10000:
UserWarning('This will be slow... (gentle suggestion: '
'"pip install fastcluster")')
pairwise_dists = distance.pdist(self.array, metric=self.metric)
linkage = hierarchy.linkage(pairwise_dists, method=self.method)
del pairwise_dists
return linkage
def _calculate_linkage_fastcluster(self):
import fastcluster
# Fastcluster has a memory-saving vectorized version, but only
# with certain linkage methods, and mostly with euclidean metric
vector_methods = ('single', 'centroid', 'median', 'ward')
euclidean_methods = ('centroid', 'median', 'ward')
euclidean = self.metric == 'euclidean' and self.method in \
euclidean_methods
if euclidean or self.method == 'single':
return fastcluster.linkage_vector(self.array,
method=self.method,
metric=self.metric)
else:
pairwise_dists = distance.pdist(self.array, metric=self.metric)
linkage = fastcluster.linkage(pairwise_dists, method=self.method)
del pairwise_dists
return linkage
@property
def calculated_linkage(self):
try:
return self._calculate_linkage_fastcluster()
except ImportError:
return self._calculate_linkage_scipy()
def calculate_dendrogram(self):
"""Calculates a dendrogram based on the linkage matrix
Made a separate function, not a property because don't want to
recalculate the dendrogram every time it is accessed.
Returns
-------
dendrogram : dict
Dendrogram dictionary as returned by scipy.cluster.hierarchy
.dendrogram. The important key-value pairing is
"reordered_ind" which indicates the re-ordering of the matrix
"""
return hierarchy.dendrogram(self.linkage, no_plot=True,
color_list=['k'], color_threshold=-np.inf)
@property
def reordered_ind(self):
"""Indices of the matrix, reordered by the dendrogram"""
return self.dendrogram['leaves']
def plot(self, ax):
"""Plots a dendrogram of the similarities between data on the axes
Parameters
----------
ax : matplotlib.axes.Axes
Axes object upon which the dendrogram is plotted
"""
for x, y in zip(self.X, self.Y):
ax.plot(x, y, color='k', linewidth=.5)
if self.rotate and self.axis == 0:
ax.invert_xaxis()
ax.yaxis.set_ticks_position('right')
ymax = min(map(min, self.Y)) + max(map(max, self.Y))
ax.set_ylim(0, ymax)
ax.invert_yaxis()
else:
xmax = min(map(min, self.X)) + max(map(max, self.X))
ax.set_xlim(0, xmax)
despine(ax=ax, bottom=True, left=True)
ax.set(xticks=self.xticks, yticks=self.yticks,
xlabel=self.xlabel, ylabel=self.ylabel)
xtl = ax.set_xticklabels(self.xticklabels)
ytl = ax.set_yticklabels(self.yticklabels, rotation='vertical')
# Force a draw of the plot to avoid matplotlib window error
plt.draw()
if len(ytl) > 0 and axis_ticklabels_overlap(ytl):
plt.setp(ytl, rotation="horizontal")
if len(xtl) > 0 and axis_ticklabels_overlap(xtl):
plt.setp(xtl, rotation="vertical")
return self
def dendrogram(data, linkage=None, axis=1, label=True, metric='euclidean',
method='average', rotate=False, ax=None):
"""Draw a tree diagram of relationships within a matrix
Parameters
----------
data : pandas.DataFrame
Rectangular data
linkage : numpy.array, optional
Linkage matrix
axis : int, optional
Which axis to use to calculate linkage. 0 is rows, 1 is columns.
label : bool, optional
If True, label the dendrogram at leaves with column or row names
metric : str, optional
Distance metric. Anything valid for scipy.spatial.distance.pdist
method : str, optional
Linkage method to use. Anything valid for
scipy.cluster.hierarchy.linkage
rotate : bool, optional
When plotting the matrix, whether to rotate it 90 degrees
counter-clockwise, so the leaves face right
ax : matplotlib axis, optional
Axis to plot on, otherwise uses current axis
Returns
-------
dendrogramplotter : _DendrogramPlotter
A Dendrogram plotter object.
Notes
-----
Access the reordered dendrogram indices with
dendrogramplotter.reordered_ind
"""
plotter = _DendrogramPlotter(data, linkage=linkage, axis=axis,
metric=metric, method=method,
label=label, rotate=rotate)
if ax is None:
ax = plt.gca()
return plotter.plot(ax=ax)
class ClusterGrid(Grid):
def __init__(self, data, pivot_kws=None, z_score=None, standard_scale=None,
figsize=None, row_colors=None, col_colors=None, mask=None):
"""Grid object for organizing clustered heatmap input on to axes"""
if isinstance(data, pd.DataFrame):
self.data = data
else:
self.data = pd.DataFrame(data)
self.data2d = self.format_data(self.data, pivot_kws, z_score,
standard_scale)
self.mask = _matrix_mask(self.data2d, mask)
if figsize is None:
width, height = 10, 10
figsize = (width, height)
self.fig = plt.figure(figsize=figsize)
if row_colors is not None:
row_colors = _convert_colors(row_colors)
self.row_colors = row_colors
if col_colors is not None:
col_colors = _convert_colors(col_colors)
self.col_colors = col_colors
width_ratios = self.dim_ratios(self.row_colors,
figsize=figsize,
axis=1)
height_ratios = self.dim_ratios(self.col_colors,
figsize=figsize,
axis=0)
nrows = 3 if self.col_colors is None else 4
ncols = 3 if self.row_colors is None else 4
self.gs = gridspec.GridSpec(nrows, ncols, wspace=0.01, hspace=0.01,
width_ratios=width_ratios,
height_ratios=height_ratios)
self.ax_row_dendrogram = self.fig.add_subplot(self.gs[nrows - 1, 0:2],
axisbg="white")
self.ax_col_dendrogram = self.fig.add_subplot(self.gs[0:2, ncols - 1],
axisbg="white")
self.ax_row_colors = None
self.ax_col_colors = None
if self.row_colors is not None:
self.ax_row_colors = self.fig.add_subplot(
self.gs[nrows - 1, ncols - 2])
if self.col_colors is not None:
self.ax_col_colors = self.fig.add_subplot(
self.gs[nrows - 2, ncols - 1])
self.ax_heatmap = self.fig.add_subplot(self.gs[nrows - 1, ncols - 1])
# colorbar for scale to left corner
self.cax = self.fig.add_subplot(self.gs[0, 0])
self.dendrogram_row = None
self.dendrogram_col = None
def format_data(self, data, pivot_kws, z_score=None,
standard_scale=None):
"""Extract variables from data or use directly."""
# Either the data is already in 2d matrix format, or need to do a pivot
if pivot_kws is not None:
data2d = data.pivot(**pivot_kws)
else:
data2d = data
if z_score is not None and standard_scale is not None:
raise ValueError(
'Cannot perform both z-scoring and standard-scaling on data')
if z_score is not None:
data2d = self.z_score(data2d, z_score)
if standard_scale is not None:
data2d = self.standard_scale(data2d, standard_scale)
return data2d
@staticmethod
def z_score(data2d, axis=1):
"""Standarize the mean and variance of the data axis
Parameters
----------
data2d : pandas.DataFrame
Data to normalize
axis : int
Which axis to normalize across. If 0, normalize across rows, if 1,
normalize across columns.
Returns
-------
normalized : pandas.DataFrame
Noramlized data with a mean of 0 and variance of 1 across the
specified axis.
"""
if axis == 1:
z_scored = data2d
else:
z_scored = data2d.T
z_scored = (z_scored - z_scored.mean()) / z_scored.std()
if axis == 1:
return z_scored
else:
return z_scored.T
@staticmethod
def standard_scale(data2d, axis=1):
"""Divide the data by the difference between the max and min
Parameters
----------
data2d : pandas.DataFrame
Data to normalize
axis : int
Which axis to normalize across. If 0, normalize across rows, if 1,
normalize across columns.
vmin : int
If 0, then subtract the minimum of the data before dividing by
the range.
Returns
-------
standardized : pandas.DataFrame
Noramlized data with a mean of 0 and variance of 1 across the
specified axis.
>>> import numpy as np
>>> d = np.arange(5, 8, 0.5)
>>> ClusterGrid.standard_scale(d)
array([ 0. , 0.2, 0.4, 0.6, 0.8, 1. ])
"""
# Normalize these values to range from 0 to 1
if axis == 1:
standardized = data2d
else:
standardized = data2d.T
subtract = standardized.min()
standardized = (standardized - subtract) / (
standardized.max() - standardized.min())
if axis == 1:
return standardized
else:
return standardized.T
def dim_ratios(self, side_colors, axis, figsize, side_colors_ratio=0.05):
"""Get the proportions of the figure taken up by each axes
"""
figdim = figsize[axis]
# Get resizing proportion of this figure for the dendrogram and
# colorbar, so only the heatmap gets bigger but the dendrogram stays
# the same size.
dendrogram = min(2. / figdim, .2)
# add the colorbar
colorbar_width = .8 * dendrogram
colorbar_height = .2 * dendrogram
if axis == 0:
ratios = [colorbar_width, colorbar_height]
else:
ratios = [colorbar_height, colorbar_width]
if side_colors is not None:
# Add room for the colors
ratios += [side_colors_ratio]
# Add the ratio for the heatmap itself
ratios += [.8]
return ratios
@staticmethod
def color_list_to_matrix_and_cmap(colors, ind, axis=0):
"""Turns a list of colors into a numpy matrix and matplotlib colormap
These arguments can now be plotted using heatmap(matrix, cmap)
and the provided colors will be plotted.
Parameters
----------
colors : list of matplotlib colors
Colors to label the rows or columns of a dataframe.
ind : list of ints
Ordering of the rows or columns, to reorder the original colors
by the clustered dendrogram order
axis : int
Which axis this is labeling
Returns
-------
matrix : numpy.array
A numpy array of integer values, where each corresponds to a color
from the originally provided list of colors
cmap : matplotlib.colors.ListedColormap
"""
# check for nested lists/color palettes.
# Will fail if matplotlib color is list not tuple
if any(issubclass(type(x), list) for x in colors):
all_colors = set(itertools.chain(*colors))
n = len(colors)
m = len(colors[0])
else:
all_colors = set(colors)
n = 1
m = len(colors)
colors = [colors]
color_to_value = dict((col, i) for i, col in enumerate(all_colors))
matrix = np.array([color_to_value[c]
for color in colors for c in color])
shape = (n, m)
matrix = matrix.reshape(shape)
matrix = matrix[:, ind]
if axis == 0:
# row-side:
matrix = matrix.T
cmap = mpl.colors.ListedColormap(all_colors)
return matrix, cmap
def savefig(self, *args, **kwargs):
if 'bbox_inches' not in kwargs:
kwargs['bbox_inches'] = 'tight'
self.fig.savefig(*args, **kwargs)
def plot_dendrograms(self, row_cluster, col_cluster, metric, method,
row_linkage, col_linkage):
# Plot the row dendrogram
if row_cluster:
self.dendrogram_row = dendrogram(
self.data2d, metric=metric, method=method, label=False, axis=0,
ax=self.ax_row_dendrogram, rotate=True, linkage=row_linkage)
else:
self.ax_row_dendrogram.set_xticks([])
self.ax_row_dendrogram.set_yticks([])
# PLot the column dendrogram
if col_cluster:
self.dendrogram_col = dendrogram(
self.data2d, metric=metric, method=method, label=False,
axis=1, ax=self.ax_col_dendrogram, linkage=col_linkage)
else:
self.ax_col_dendrogram.set_xticks([])
self.ax_col_dendrogram.set_yticks([])
despine(ax=self.ax_row_dendrogram, bottom=True, left=True)
despine(ax=self.ax_col_dendrogram, bottom=True, left=True)
def plot_colors(self, xind, yind, **kws):
"""Plots color labels between the dendrogram and the heatmap
Parameters
----------
heatmap_kws : dict
Keyword arguments heatmap
"""
# Remove any custom colormap and centering
kws = kws.copy()
kws.pop('cmap', None)
kws.pop('center', None)
kws.pop('vmin', None)
kws.pop('vmax', None)
kws.pop('xticklabels', None)
kws.pop('yticklabels', None)
if self.row_colors is not None:
matrix, cmap = self.color_list_to_matrix_and_cmap(
self.row_colors, yind, axis=0)
heatmap(matrix, cmap=cmap, cbar=False, ax=self.ax_row_colors,
xticklabels=False, yticklabels=False,
**kws)
else:
despine(self.ax_row_colors, left=True, bottom=True)
if self.col_colors is not None:
matrix, cmap = self.color_list_to_matrix_and_cmap(
self.col_colors, xind, axis=1)
heatmap(matrix, cmap=cmap, cbar=False, ax=self.ax_col_colors,
xticklabels=False, yticklabels=False,
**kws)
else:
despine(self.ax_col_colors, left=True, bottom=True)
def plot_matrix(self, colorbar_kws, xind, yind, **kws):
self.data2d = self.data2d.iloc[yind, xind]
self.mask = self.mask.iloc[yind, xind]
# Try to reorganize specified tick labels, if provided
xtl = kws.pop("xticklabels", True)
try:
xtl = np.asarray(xtl)[xind]
except (TypeError, IndexError):
pass
ytl = kws.pop("yticklabels", True)
try:
ytl = np.asarray(ytl)[yind]
except (TypeError, IndexError):
pass
heatmap(self.data2d, ax=self.ax_heatmap, cbar_ax=self.cax,
cbar_kws=colorbar_kws, mask=self.mask,
xticklabels=xtl, yticklabels=ytl, **kws)
self.ax_heatmap.yaxis.set_ticks_position('right')
self.ax_heatmap.yaxis.set_label_position('right')
def plot(self, metric, method, colorbar_kws, row_cluster, col_cluster,
row_linkage, col_linkage, **kws):
colorbar_kws = {} if colorbar_kws is None else colorbar_kws
self.plot_dendrograms(row_cluster, col_cluster, metric, method,
row_linkage=row_linkage, col_linkage=col_linkage)
try:
xind = self.dendrogram_col.reordered_ind
except AttributeError:
xind = np.arange(self.data2d.shape[1])
try:
yind = self.dendrogram_row.reordered_ind
except AttributeError:
yind = np.arange(self.data2d.shape[0])
self.plot_colors(xind, yind, **kws)
self.plot_matrix(colorbar_kws, xind, yind, **kws)
return self
def clustermap(data, pivot_kws=None, method='average', metric='euclidean',
z_score=None, standard_scale=None, figsize=None, cbar_kws=None,
row_cluster=True, col_cluster=True,
row_linkage=None, col_linkage=None,
row_colors=None, col_colors=None, mask=None, **kwargs):
"""Plot a hierarchically clustered heatmap of a pandas DataFrame
Parameters
----------
data: pandas.DataFrame
Rectangular data for clustering. Cannot contain NAs.
pivot_kws : dict, optional
If `data` is a tidy dataframe, can provide keyword arguments for
pivot to create a rectangular dataframe.
method : str, optional
Linkage method to use for calculating clusters.
See scipy.cluster.hierarchy.linkage documentation for more information:
http://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html
metric : str, optional
Distance metric to use for the data. See
scipy.spatial.distance.pdist documentation for more options
http://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.pdist.html
z_score : int or None, optional
Either 0 (rows) or 1 (columns). Whether or not to calculate z-scores
for the rows or the columns. Z scores are: z = (x - mean)/std, so
values in each row (column) will get the mean of the row (column)
subtracted, then divided by the standard deviation of the row (column).
This ensures that each row (column) has mean of 0 and variance of 1.
standard_scale : int or None, optional
Either 0 (rows) or 1 (columns). Whether or not to standardize that
dimension, meaning for each row or column, subtract the minimum and
divide each by its maximum.
figsize: tuple of two ints, optional
Size of the figure to create.
cbar_kws : dict, optional
Keyword arguments to pass to ``cbar_kws`` in ``heatmap``, e.g. to
add a label to the colorbar.
{row,col}_cluster : bool, optional
If True, cluster the {rows, columns}.
{row,col}_linkage : numpy.array, optional
Precomputed linkage matrix for the rows or columns. See
scipy.cluster.hierarchy.linkage for specific formats.
{row,col}_colors : list-like, optional
List of colors to label for either the rows or columns. Useful to
evaluate whether samples within a group are clustered together. Can
use nested lists for multiple color levels of labeling.
mask : boolean array or DataFrame, optional
If passed, data will not be shown in cells where ``mask`` is True.
Cells with missing values are automatically masked. Only used for
visualizing, not for calculating.
kwargs : other keyword arguments
All other keyword arguments are passed to ``sns.heatmap``
Returns
-------
clustergrid : ClusterGrid
A ClusterGrid instance.
Notes
-----
The returned object has a ``savefig`` method that should be used if you
want to save the figure object without clipping the dendrograms.
To access the reordered row indices, use:
``clustergrid.dendrogram_row.reordered_ind``
Column indices, use:
``clustergrid.dendrogram_col.reordered_ind``
Examples
--------
Plot a clustered heatmap:
.. plot::
:context: close-figs
>>> import seaborn as sns; sns.set()
>>> flights = sns.load_dataset("flights")
>>> flights = flights.pivot("month", "year", "passengers")
>>> g = sns.clustermap(flights)
Don't cluster one of the axes:
.. plot::
:context: close-figs
>>> g = sns.clustermap(flights, col_cluster=False)
Use a different colormap and add lines to separate the cells:
.. plot::
:context: close-figs
>>> cmap = sns.cubehelix_palette(as_cmap=True, rot=-.3, light=1)
>>> g = sns.clustermap(flights, cmap=cmap, linewidths=.5)
Use a different figure size:
.. plot::
:context: close-figs
>>> g = sns.clustermap(flights, cmap=cmap, figsize=(7, 5))
Standardize the data across the columns:
.. plot::
:context: close-figs
>>> g = sns.clustermap(flights, standard_scale=1)
Normalize the data across the rows:
.. plot::
:context: close-figs
>>> g = sns.clustermap(flights, z_score=0)
Use a different clustering method:
.. plot::
:context: close-figs
>>> g = sns.clustermap(flights, method="single", metric="cosine")
Add colored labels on one of the axes:
.. plot::
:context: close-figs
>>> season_colors = (sns.color_palette("BuPu", 3) +
... sns.color_palette("RdPu", 3) +
... sns.color_palette("YlGn", 3) +
... sns.color_palette("OrRd", 3))
>>> g = sns.clustermap(flights, row_colors=season_colors)
"""
plotter = ClusterGrid(data, pivot_kws=pivot_kws, figsize=figsize,
row_colors=row_colors, col_colors=col_colors,
z_score=z_score, standard_scale=standard_scale,
mask=mask)
return plotter.plot(metric=metric, method=method,
colorbar_kws=cbar_kws,
row_cluster=row_cluster, col_cluster=col_cluster,
row_linkage=row_linkage, col_linkage=col_linkage,
**kwargs)
| bsd-3-clause |
i-namekawa/TopSideMonitor | plotting.py | 1 | 37323 | import os, sys, time
from glob import glob
import cv2
from pylab import *
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.backends.backend_pdf import PdfPages
matplotlib.rcParams['figure.facecolor'] = 'w'
from scipy.signal import argrelextrema
import scipy.stats as stats
import scipy.io as sio
from scipy import signal
from xlwt import Workbook
# specify these in mm to match your behavior chamber.
CHMAMBER_LENGTH=235
WATER_HIGHT=40
# quick plot should also show xy_within and location_one_third etc
# summary PDF: handle exception when a pickle file missing some fish in other pickle file
## these three taken from http://stackoverflow.com/a/18420730/566035
def strided_sliding_std_dev(data, radius=5):
windowed = rolling_window(data, (2*radius, 2*radius))
shape = windowed.shape
windowed = windowed.reshape(shape[0], shape[1], -1)
return windowed.std(axis=-1)
def rolling_window(a, window):
"""Takes a numpy array *a* and a sequence of (or single) *window* lengths
and returns a view of *a* that represents a moving window."""
if not hasattr(window, '__iter__'):
return rolling_window_lastaxis(a, window)
for i, win in enumerate(window):
if win > 1:
a = a.swapaxes(i, -1)
a = rolling_window_lastaxis(a, win)
a = a.swapaxes(-2, i)
return a
def rolling_window_lastaxis(a, window):
"""Directly taken from Erik Rigtorp's post to numpy-discussion.
<http://www.mail-archive.com/numpy-discussion@scipy.org/msg29450.html>"""
if window < 1:
raise ValueError, "`window` must be at least 1."
if window > a.shape[-1]:
raise ValueError, "`window` is too long."
shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
strides = a.strides + (a.strides[-1],)
return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
## stealing ends here... //
def filterheadxy(headx,heady,thrs_denom=10):
b, a = signal.butter(8, 0.125)
dhy = np.abs(np.hstack((0, np.diff(heady,1))))
thrs = np.nanstd(dhy)/thrs_denom
ind2remove = dhy>thrs
headx[ind2remove] = np.nan
heady[ind2remove] = np.nan
headx = interp_nan(headx)
heady = interp_nan(heady)
headx = signal.filtfilt(b, a, headx, padlen=150)
heady = signal.filtfilt(b, a, heady, padlen=150)
return headx,heady
def smoothRad(theta, thrs=np.pi/4*3):
jumps = (np.diff(theta) > thrs).nonzero()[0]
print 'jumps.size', jumps.size
while jumps.size:
# print '%d/%d' % (jumps[0], theta.size)
theta[jumps+1] -= np.pi
jumps = (np.diff(theta) > thrs).nonzero()[0]
return theta
def datadct2array(data, key1, key2):
# put these in a MATLAB CELL
trialN = len(data[key1][key2])
matchedUSnameP = np.zeros((trialN,), dtype=np.object)
fnameP = np.zeros((trialN,), dtype=np.object)
# others to append to a list
eventsP = []
speed3DP = []
movingSTDP = []
d2inflowP = []
xP, yP, zP = [], [], []
XP, YP, ZP = [], [], []
ringpixelsP = []
peaks_withinP = []
swimdir_withinP = []
xy_withinP = []
location_one_thirdP = []
dtheta_shapeP = []
dtheta_velP = []
turns_shapeP = []
turns_velP = []
for n, dct in enumerate(data[key1][key2]):
# MATLAB CELL
matchedUSnameP[n] = dct['matchedUSname']
fnameP[n] = dct['fname']
# 2D array
eventsP.append([ele if type(ele) is not list else ele[0] for ele in dct['events']])
speed3DP.append(dct['speed3D'])
movingSTDP.append(dct['movingSTD'])
d2inflowP.append(dct['d2inflow'])
xP.append(dct['x'])
yP.append(dct['y'])
zP.append(dct['z'])
XP.append(dct['X'])
YP.append(dct['Y'])
ZP.append(dct['Z'])
ringpixelsP.append(dct['ringpixels'])
peaks_withinP.append(dct['peaks_within'])
swimdir_withinP.append(dct['swimdir_within'])
xy_withinP.append(dct['xy_within'])
location_one_thirdP.append(dct['location_one_third'])
dtheta_shapeP.append(dct['dtheta_shape'])
dtheta_velP.append(dct['dtheta_vel'])
turns_shapeP.append(dct['turns_shape'])
turns_velP.append(dct['turns_vel'])
TVroi = np.array(dct['TVroi'])
SVroi = np.array(dct['SVroi'])
return matchedUSnameP, fnameP, np.array(eventsP), np.array(speed3DP), np.array(d2inflowP), \
np.array(xP), np.array(yP), np.array(zP), np.array(XP), np.array(YP), np.array(ZP), \
np.array(ringpixelsP), np.array(peaks_withinP), np.array(swimdir_withinP), \
np.array(xy_withinP), np.array(dtheta_shapeP), np.array(dtheta_velP), \
np.array(turns_shapeP), np.array(turns_velP), TVroi, SVroi
def pickle2mat(fp, data=None):
# fp : full path to pickle file
# data : option to provide data to skip np.load(fp)
if not data:
data = np.load(fp)
for key1 in data.keys():
for key2 in data[key1].keys():
matchedUSname, fname, events, speed3D, d2inflow, x, y, z, X, Y, Z, \
ringpixels, peaks_within, swimdir_within, xy_within, dtheta_shape, dtheta_vel, \
turns_shape, turns_vel, TVroi, SVroi = datadct2array(data, key1, key2)
datadict = {
'matchedUSname' : matchedUSname,
'fname' : fname,
'events' : events,
'speed3D' : speed3D,
'd2inflow' : d2inflow,
'x' : x,
'y' : y,
'z' : z,
'X' : X,
'Y' : Y,
'Z' : Z,
'ringpixels' : ringpixels,
'peaks_within' : peaks_within,
'swimdir_within' : swimdir_within,
'xy_within' : xy_within,
'dtheta_shape' : dtheta_shape,
'dtheta_vel' : dtheta_vel,
'turns_shape' : turns_shape,
'turns_vel' : turns_vel,
'TVroi' : TVroi,
'SVroi' : SVroi,
}
outfp = '%s_%s_%s.mat' % (fp[:-7],key1,key2)
sio.savemat(outfp, datadict, oned_as='row', do_compression=True)
def interp_nan(x):
'''
Replace nan by interporation
http://stackoverflow.com/questions/6518811/interpolate-nan-values-in-a-numpy-array
'''
ok = -np.isnan(x)
if (ok == False).all():
return x
else:
xp = ok.ravel().nonzero()[0]
fp = x[ok]
_x = np.isnan(x).ravel().nonzero()[0]
x[-ok] = np.interp(_x, xp, fp)
return x
def polytest(x,y,rx,ry,rw,rh,rang):
points=cv2.ellipse2Poly(
(rx,ry),
axes=(rw/2,rh/2),
angle=rang,
arcStart=0,
arcEnd=360,
delta=3
)
return cv2.pointPolygonTest(np.array(points), (x,y), measureDist=1)
def depthCorrection(z,x,TVx1,TVx2,SVy1,SVy2,SVy3):
z0 = z - SVy1
x0 = x - TVx1
mid = (SVy2-SVy1)/2
adj = (z0 - mid) / (SVy2-SVy1) * (SVy2-SVy3) * (1-(x0)/float(TVx2-TVx1))
return z0 + adj + SVy1 # back to abs coord
def putNp2xls(array, ws):
for r, row in enumerate(array):
for c, val in enumerate(row):
ws.write(r, c, val)
def drawLines(mi, ma, events, fps=30.0):
CS, USs, preRange = events
plot([CS-preRange, CS-preRange], [mi,ma], '--c') # 2 min prior odor
plot([CS , CS ], [mi,ma], '--g', linewidth=2) # CS onset
if USs:
if len(USs) > 3:
colors = 'r' * len(USs)
else:
colors = [_ for _ in ['r','b','c'][:len(USs)]]
for c,us in zip(colors, USs):
plot([us, us],[mi,ma], linestyle='--', color=c, linewidth=2) # US onset
plot([USs[0]+preRange/2,USs[0]+preRange/2], [mi,ma], linestyle='--', color=c, linewidth=2) # end of US window
xtck = np.arange(0, max(CS+preRange, max(USs)), 0.5*60*fps) # every 0.5 min tick
else:
xtck = np.arange(0, CS+preRange, 0.5*60*fps) # every 0.5 min tick
xticks(xtck, xtck/fps/60)
gca().xaxis.set_minor_locator(MultipleLocator(5*fps)) # 5 s minor ticks
def approachevents(x,y,z, ringpolyTVArray, ringpolySVArray, fishlength=134, thrs=None):
'''
fishlength: some old scrits may call this with fishlength
thrs: multitrack GUI provides this by ringAppearochLevel spin control.
can be an numpy array (to track water level change etc)
'''
smoothedz = np.convolve(np.hanning(10)/np.hanning(10).sum(), z, 'same')
peaks = argrelextrema(smoothedz, np.less)[0] # less because 0 is top in image.
# now filter peaks by height.
ringLevel = ringpolySVArray[:,1]
if thrs is None:
thrs = ringLevel+fishlength/2
if type(thrs) == int: # can be numpy array or int
thrs = ringLevel.mean() + thrs
peaks = peaks[ z[peaks] < thrs ]
else: # numpy array should be ready to use
peaks = peaks[ z[peaks] < thrs[peaks] ]
# now filter out by TVringCenter
peaks_within = get_withinring(ringpolyTVArray, peaks, x, y)
return smoothedz, peaks_within
def get_withinring(ringpolyTVArray, timepoints, x, y):
rx = ringpolyTVArray[:,0].astype(np.int)
ry = ringpolyTVArray[:,1].astype(np.int)
rw = ringpolyTVArray[:,2].astype(np.int)
rh = ringpolyTVArray[:,3].astype(np.int)
rang = ringpolyTVArray[:,4].astype(np.int)
# poly test
peaks_within = []
for p in timepoints:
points=cv2.ellipse2Poly(
(rx[p],ry[p]),
axes=(rw[p]/2,rh[p]/2),
angle=rang[p],
arcStart=0,
arcEnd=360,
delta=3
)
inout = cv2.pointPolygonTest(np.array(points), (x[p],y[p]), measureDist=1)
if inout > 0:
peaks_within.append(p)
return peaks_within
def location_ring(x,y,ringpolyTVArray):
rx = ringpolyTVArray[:,0].astype(np.int)
ry = ringpolyTVArray[:,1].astype(np.int)
rw = ringpolyTVArray[:,2].astype(np.int)
rh = ringpolyTVArray[:,3].astype(np.int)
d2ringcenter = np.sqrt((x-rx)**2 + (y-ry)**2)
# filter by radius 20% buffer in case the ring moves around
indices = (d2ringcenter < 1.2*max(rw.max(), rh.max())).nonzero()[0]
xy_within = get_withinring(ringpolyTVArray, indices, x, y)
return xy_within
def swimdir_analysis(x,y,z,ringpolyTVArray,ringpolySVArray,TVx1,TVy1,TVx2,TVy2,fps=30.0):
# smoothing
# z = np.convolve(np.hanning(16)/np.hanning(16).sum(), z, 'same')
# two cameras have different zoom settings. So, distance per pixel is different. But, for
# swim direction, it does not matter how much x,y are compressed relative to z.
# ring z level from SV
rz = ringpolySVArray[:,1].astype(np.int)
# ring all other params from TV
rx = ringpolyTVArray[:,0].astype(np.int)
ry = ringpolyTVArray[:,1].astype(np.int)
rw = ringpolyTVArray[:,2].astype(np.int)
rh = ringpolyTVArray[:,3].astype(np.int)
rang = ringpolyTVArray[:,4].astype(np.int)
speed3D = np.sqrt( np.diff(x)**2 + np.diff(y)**2 + np.diff(z)**2 )
speed3D = np.hstack(([0], speed3D))
# line in 3D http://tutorial.math.lamar.edu/Classes/CalcIII/EqnsOfLines.aspx
# x-x0 y-y0 z-z0
# ---- = ---- = ----
# a b c
# solve them for z = rz. x0,y0,z0 are tvx, tvy, svy
# x = (a * (rz-z)) / c + x0
dt = 3 # define slope as diff between current and dt frame before
a = np.hstack( (np.ones(dt), x[dt:]-x[:-dt]) )
b = np.hstack( (np.ones(dt), y[dt:]-y[:-dt]) )
c = np.hstack( (np.ones(dt), z[dt:]-z[:-dt]) )
c[c==0] = np.nan # avoid zero division
water_x = (a * (rz-z) / c) + x
water_y = (b * (rz-z) / c) + y
upwards = c<-2/30.0*fps # not accurate when c is small or negative
xok = (TVx1 < water_x) & (water_x < TVx2)
yok = (TVy1 < water_y) & (water_y < TVy2)
filtered = upwards & xok & yok# & -np.isinf(water_x) & -np.isinf(water_y)
water_x[-filtered] = np.nan
water_y[-filtered] = np.nan
# figure()
# ax = subplot(111)
# ax.imshow(npData['TVbg'], cmap=cm.gray) # clip out from TVx1,TVy1
# ax.plot(x-TVx1, y-TVy1, 'c')
# ax.plot(water_x-TVx1, water_y-TVy1, 'r.')
# xlim([0, TVx2-TVx1]); ylim([TVy2-TVy1, 0])
# draw(); show()
SwimDir = []
for n in filtered.nonzero()[0]:
inout = polytest(water_x[n],water_y[n],rx[n],ry[n],rw[n],rh[n],rang[n])
SwimDir.append((n, inout, speed3D[n])) # inout>0 are inside
return SwimDir, water_x, water_y
def plot_eachTr(events, x, y, z, inflowpos, ringpixels, peaks_within, swimdir_within=None,
pp=None, _title=None, fps=30.0, inmm=False):
CS, USs, preRange = events
# preRange = 3600 2 min prior and 1 min after CS. +900 for 0.5 min
if USs:
xmin, xmax = CS-preRange-10*fps, USs[0]+preRange/2+10*fps
else:
xmin, xmax = CS-preRange-10*fps, CS+preRange/2+(23+10)*fps
fig = figure(figsize=(12,8), facecolor='w')
subplot(511) # Swimming speed
speed3D = np.sqrt( np.diff(x)**2 + np.diff(y)**2 + np.diff(z)**2 )
drawLines(np.nanmin(speed3D), np.nanmax(speed3D), events, fps) # go behind
plot(speed3D)
movingSTD = np.append( np.zeros(fps*10), strided_sliding_std_dev(speed3D, fps*10) )
plot(movingSTD, linewidth=2)
plot(np.ones_like(speed3D) * speed3D.std()*6, '-.', color='gray')
ylim([-5, speed3D[xmin:xmax].max()])
xlim([xmin,xmax]); title(_title)
if inmm:
ylabel('Speed 3D (mm),\n6SD thr');
else:
ylabel('Speed 3D, 6SD thr');
ax = subplot(512) # z level
drawLines(z.min(), z.max(), events)
plot(z, 'b')
pkx = peaks_within.nonzero()[0]
if inmm:
plot(pkx, peaks_within[pkx]*z[xmin:xmax].max()*0.97, 'mo')
if swimdir_within is not None:
___x = swimdir_within.nonzero()[0]
plot(___x, swimdir_within[___x]*z[xmin:xmax].max()*0.96, 'g+')
ylim([z[xmin:xmax].min()*0.95, z[xmin:xmax].max()])
xlim([xmin,xmax]); ylabel('Z (mm)')
else:
plot(pkx, peaks_within[pkx]*z[xmin:xmax].min()*0.97, 'mo')
if swimdir_within is not None:
___x = swimdir_within.nonzero()[0]
plot(___x, swimdir_within[___x]*z[xmin:xmax].min()*0.96, 'g+')
ylim([z[xmin:xmax].min()*0.95, z[xmin:xmax].max()])
ax.invert_yaxis(); xlim([xmin,xmax]); ylabel('z')
subplot(513) # x
drawLines(x.min(), x.max(), events)
plot(x, 'b')
plot(y, 'g')
xlim([xmin,xmax]); ylabel('x,y')
subplot(514) # Distance to the inflow tube
xin, yin, zin = inflowpos
d2inflow = np.sqrt((x-xin) ** 2 + (y-yin) ** 2 + (z-zin) ** 2 )
drawLines(d2inflow.min(), d2inflow.max(), events)
plot(d2inflow)
ylim([d2inflow[xmin:xmax].min(), d2inflow[xmin:xmax].max()])
xlim([xmin,xmax]); ylabel('distance to\ninflow tube')
subplot(515) # ringpixels: it seems i never considered TV x,y for this
rpmax, rpmin = np.nanmax(ringpixels[xmin:xmax]), np.nanmin(ringpixels[xmin:xmax])
drawLines(rpmin, rpmax, events)
plot(ringpixels)
plot(pkx, peaks_within[pkx]*rpmax*1.06, 'mo')
if swimdir_within is not None:
plot(___x, swimdir_within[___x]*rpmax*1.15, 'g+')
ylim([-100, rpmax*1.2])
xlim([xmin,xmax]); ylabel('ringpixels')
tight_layout()
if pp:
fig.savefig(pp, format='pdf')
rng = np.arange(CS-preRange, CS+preRange, dtype=np.int)
return speed3D[rng], movingSTD[rng], d2inflow[rng], ringpixels[rng]
def plot_turnrates(events, dthetasum_shape,dthetasum_vel,turns_shape,turns_vel,
pp=None, _title=None, thrs=np.pi/4*(133.33333333333334/120), fps=30.0):
CS, USs, preRange = events
# preRange = 3600 2 min prior and 1 min after CS. +900 for 0.5 min
if USs:
xmin, xmax = CS-preRange-10*fps, USs[0]+preRange/2+10*fps
else:
xmin, xmax = CS-preRange-10*fps, CS+preRange/2+(23+10)*fps
fig = figure(figsize=(12,8), facecolor='w')
subplot(211)
drawLines(dthetasum_shape.min(), dthetasum_shape.max(), events)
plot(np.ones_like(dthetasum_shape)*thrs,'gray',linestyle='--')
plot(-np.ones_like(dthetasum_shape)*thrs,'gray',linestyle='--')
plot(dthetasum_shape)
dmax = dthetasum_shape[xmin:xmax].max()
plot(turns_shape, (0.5+dmax)*np.ones_like(turns_shape), 'o')
temp = np.zeros_like(dthetasum_shape)
temp[turns_shape] = 1
shape_cumsum = np.cumsum(temp)
shape_cumsum -= shape_cumsum[xmin]
plot( shape_cumsum / shape_cumsum[xmax] * (dmax-dthetasum_shape.min()) + dthetasum_shape.min())
xlim([xmin,xmax]); ylabel('Shape based'); title('Orientation change per 4 frames: ' + _title)
ylim([dthetasum_shape[xmin:xmax].min()-1, dmax+1])
subplot(212)
drawLines(dthetasum_vel.min(), dthetasum_vel.max(), events)
plot(np.ones_like(dthetasum_vel)*thrs,'gray',linestyle='--')
plot(-np.ones_like(dthetasum_vel)*thrs,'gray',linestyle='--')
plot(dthetasum_vel)
dmax = dthetasum_vel[xmin:xmax].max()
plot(turns_vel, (0.5+dmax)*np.ones_like(turns_vel), 'o')
temp = np.zeros_like(dthetasum_vel)
temp[turns_vel] = 1
vel_cumsum = np.cumsum(temp)
vel_cumsum -= vel_cumsum[xmin]
plot( vel_cumsum / vel_cumsum[xmax] * (dmax-dthetasum_shape.min()) + dthetasum_shape.min())
ylim([dthetasum_vel[xmin:xmax].min()-1, dmax+1])
xlim([xmin,xmax]); ylabel('Velocity based')
tight_layout()
if pp:
fig.savefig(pp, format='pdf')
def trajectory(x, y, z, rng, ax, _xlim=[0,640], _ylim=[480,480+300], _zlim=[150,340],
color='b', fps=30.0, ringpolygon=None):
ax.plot(x[rng],y[rng],z[rng], color=color)
ax.view_init(azim=-75, elev=-180+15)
if ringpolygon:
rx, ry, rz = ringpolygon
ax.plot(rx, ry, rz, color='gray')
ax.set_xlim(_xlim[0],_xlim[1])
ax.set_ylim(_ylim[0],_ylim[1])
ax.set_zlim(_zlim[0],_zlim[1])
title(("(%2.1f min to %2.1f min)" % (rng[0]/fps/60.0,(rng[-1]+1)/60.0/fps)))
draw()
def plotTrajectory(x, y, z, events, _xlim=None, _ylim=None, _zlim=None, fps=30.0, pp=None, ringpolygon=None):
CS, USs, preRange = events
rng1 = np.arange(CS-preRange, CS-preRange/2, dtype=int)
rng2 = np.arange(CS-preRange/2, CS, dtype=int)
if USs:
rng3 = np.arange(CS, min(USs), dtype=int)
rng4 = np.arange(min(USs), min(USs)+preRange/2, dtype=int)
combined = np.hstack((rng1,rng2,rng3,rng4))
else:
combined = np.hstack((rng1,rng2))
if _xlim is None:
_xlim = map( int, ( x[combined].min(), x[combined].max() ) )
if _ylim is None:
_ylim = map( int, ( y[combined].min(), y[combined].max() ) )
if _zlim is None:
_zlim = map( int, ( z[combined].min(), z[combined].max() ) )
if ringpolygon:
_zlim[0] = min( _zlim[0], int(ringpolygon[2][0]) )
fig3D = plt.figure(figsize=(12,8), facecolor='w')
ax = fig3D.add_subplot(221, projection='3d'); trajectory(x,y,z,rng1,ax,_xlim,_ylim,_zlim,'c',fps,ringpolygon)
ax = fig3D.add_subplot(222, projection='3d'); trajectory(x,y,z,rng2,ax,_xlim,_ylim,_zlim,'c',fps,ringpolygon)
if USs:
ax = fig3D.add_subplot(223, projection='3d'); trajectory(x,y,z,rng3,ax,_xlim,_ylim,_zlim,'g',fps,ringpolygon)
ax = fig3D.add_subplot(224, projection='3d'); trajectory(x,y,z,rng4,ax,_xlim,_ylim,_zlim,'r',fps,ringpolygon)
tight_layout()
if pp:
fig3D.savefig(pp, format='pdf')
def add2DataAndPlot(fp, fish, data, createPDF):
if createPDF:
pp = PdfPages(fp[:-7]+'_'+fish+'.pdf')
else:
pp = None
params = np.load(fp)
fname = os.path.basename(fp).split('.')[0] + '.avi'
dirname = os.path.dirname(fp)
preRange = params[(fname, 'mog')]['preRange']
fps = params[(fname, 'mog')]['fps']
TVx1 = params[(fname, fish)]['TVx1']
TVy1 = params[(fname, fish)]['TVy1']
TVx2 = params[(fname, fish)]['TVx2']
TVy2 = params[(fname, fish)]['TVy2']
SVx1 = params[(fname, fish)]['SVx1']
SVx2 = params[(fname, fish)]['SVx2']
SVx3 = params[(fname, fish)]['SVx3']
SVy1 = params[(fname, fish)]['SVy1']
SVy2 = params[(fname, fish)]['SVy2']
SVy3 = params[(fname, fish)]['SVy3']
ringAppearochLevel = params[(fname, fish)]['ringAppearochLevel']
_npz = os.path.join(dirname, os.path.join('%s_%s.npz' % (fname[:-4], fish)))
# if os.path.exists(_npz):
npData = np.load(_npz)
tvx = npData['TVtracking'][:,0] # x with nan
tvy = npData['TVtracking'][:,1] # y
headx = npData['TVtracking'][:,3] # headx
heady = npData['TVtracking'][:,4] # heady
svy = npData['SVtracking'][:,1] # z
InflowTubeTVArray = npData['InflowTubeTVArray']
InflowTubeSVArray = npData['InflowTubeSVArray']
inflowpos = InflowTubeTVArray[:,0], InflowTubeTVArray[:,1], InflowTubeSVArray[:,1]
ringpixels = npData['ringpixel']
ringpolyTVArray = npData['ringpolyTVArray']
ringpolySVArray = npData['ringpolySVArray']
TVbg = npData['TVbg']
print os.path.basename(_npz), 'loaded.'
x,y,z = map(interp_nan, [tvx,tvy,svy])
# z level correction by depth (x)
z = depthCorrection(z,x,TVx1,TVx2,SVy1,SVy2,SVy3)
smoothedz, peaks_within = approachevents(x, y, z,
ringpolyTVArray, ringpolySVArray, thrs=ringAppearochLevel)
# convert to numpy array from list
temp = np.zeros_like(x)
temp[peaks_within] = 1
peaks_within = temp
# normalize to mm
longaxis = float(max((TVx2-TVx1), (TVy2-TVy1))) # before rotation H is applied they are orthogonal
waterlevel = float(SVy2-SVy1)
X = (x-TVx1) / longaxis * CHMAMBER_LENGTH
Y = (TVy2-y) / longaxis * CHMAMBER_LENGTH
Z = (SVy2-z) / waterlevel * WATER_HIGHT # bottom of chamber = 0, higher more positive
inflowpos_mm = ((inflowpos[0]-TVx1) / longaxis * CHMAMBER_LENGTH,
(TVy2-inflowpos[1]) / longaxis * CHMAMBER_LENGTH,
(SVy2-inflowpos[2]) / waterlevel * WATER_HIGHT )
# do the swim direction analysis here
swimdir, water_x, water_y = swimdir_analysis(x,y,z,
ringpolyTVArray,ringpolySVArray,TVx1,TVy1,TVx2,TVy2,fps)
# all of swimdir are within ROI (frame#, inout, speed) but not necessary within ring
sdir = np.array(swimdir)
withinRing = sdir[:,1]>0 # inout>0 are inside ring
temp = np.zeros_like(x)
temp[ sdir[withinRing,0].astype(int) ] = 1
swimdir_within = temp
# location_ring
xy_within = location_ring(x,y, ringpolyTVArray)
temp = np.zeros_like(x)
temp[xy_within] = 1
xy_within = temp
# location_one_third
if (TVx2-TVx1) > (TVy2-TVy1):
if np.abs(np.arange(TVx1, longaxis+TVx1, longaxis/3) + longaxis/6 - inflowpos[0].mean()).argmin() == 2:
location_one_third = x-TVx1 > longaxis/3*2
else:
location_one_third = x < longaxis/3
else:
if np.abs(np.arange(TVy1, longaxis+TVy1, longaxis/3) + longaxis/6 - inflowpos[1].mean()).argmin() == 2:
location_one_third = y-TVy1 > longaxis/3*2
else:
location_one_third = y < longaxis/3
# turn rate analysis (shape based)
heady, headx = map(interp_nan, [heady, headx])
headx, heady = filterheadxy(headx, heady)
dy = heady - y
dx = headx - x
theta_shape = np.arctan2(dy, dx)
# velocity based
cx, cy = filterheadxy(x.copy(), y.copy()) # centroid x,y
vx = np.append(0, np.diff(cx))
vy = np.append(0, np.diff(cy))
theta_vel = np.arctan2(vy, vx)
# prepare ringpolygon for trajectory plot
rx, ry, rw, rh, rang = ringpolyTVArray.mean(axis=0).astype(int) # use mm ver above
rz = ringpolySVArray.mean(axis=0)[1].astype(int)
RX = (rx-TVx1) / longaxis * CHMAMBER_LENGTH
RY = (TVy2-ry) / longaxis * CHMAMBER_LENGTH
RW = rw / longaxis * CHMAMBER_LENGTH / 2
RH = rh / longaxis * CHMAMBER_LENGTH / 2
RZ = (SVy2-rz) / waterlevel * WATER_HIGHT
points = cv2.ellipse2Poly(
(RX.astype(int),RY.astype(int)),
axes=(RW.astype(int),RH.astype(int)),
angle=rang,
arcStart=0,
arcEnd=360,
delta=3
)
ringpolygon = [points[:,0], points[:,1], np.ones(points.shape[0]) * RZ]
eventTypeKeys = params[(fname, fish)]['EventData'].keys()
CSs = [_ for _ in eventTypeKeys if _.startswith('CS')]
USs = [_ for _ in eventTypeKeys if _.startswith('US')]
# print CSs, USs
# events
for CS in CSs:
CS_Timings = params[(fname, fish)]['EventData'][CS]
CS_Timings.sort()
# initialize when needed
if CS not in data[fish].keys():
data[fish][CS] = []
# now look around for US after it within preRange
for t in CS_Timings:
tr = len(data[fish][CS])+1
rng = np.arange(t-preRange, t+preRange, dtype=np.int)
matchedUSname = None
for us in USs:
us_Timings = params[(fname, fish)]['EventData'][us]
matched = [_ for _ in us_Timings if t-preRange < _ < t+preRange]
if matched:
events = [t, matched, preRange] # ex. CS+
matchedUSname = us
break
else:
continue
_title = '(%s, %s) trial#%02d %s (%s)' % (CS, matchedUSname[0], tr, fname, fish)
print _title, events
_speed3D, _movingSTD, _d2inflow, _ringpixels = plot_eachTr(events, X, Y, Z, inflowpos_mm,
ringpixels, peaks_within, swimdir_within, pp, _title, fps, inmm=True)
# 3d trajectory
_xlim = (0, CHMAMBER_LENGTH)
_zlim = (RZ.max(),0)
plotTrajectory(X, Y, Z, events, _xlim=_xlim, _zlim=_zlim, fps=fps, pp=pp, ringpolygon=ringpolygon)
# turn rate analysis
# shape based
theta_shape[rng] = smoothRad(theta_shape[rng].copy(), thrs=np.pi/2)
dtheta_shape = np.append(0, np.diff(theta_shape)) # full length
kernel = np.ones(4)
dthetasum_shape = np.convolve(dtheta_shape, kernel, 'same')
# 4 frames = 1000/30.0*4 = 133.3 ms
thrs = (np.pi / 2) * (133.33333333333334/120) # Braubach et al 2009 90 degree in 120 ms
peaks_shape = argrelextrema(abs(dthetasum_shape), np.greater)[0]
turns_shape = peaks_shape[ (abs(dthetasum_shape[peaks_shape]) > thrs).nonzero()[0] ]
# velocity based
theta_vel[rng] = smoothRad(theta_vel[rng].copy(), thrs=np.pi/2)
dtheta_vel = np.append(0, np.diff(theta_vel))
dthetasum_vel = np.convolve(dtheta_vel, kernel, 'same')
peaks_vel = argrelextrema(abs(dthetasum_vel), np.greater)[0]
turns_vel = peaks_vel[ (abs(dthetasum_vel[peaks_vel]) > thrs).nonzero()[0] ]
plot_turnrates(events, dthetasum_shape, dthetasum_vel, turns_shape, turns_vel, pp, _title, fps=fps)
_temp = np.zeros_like(dtheta_shape)
_temp[turns_shape] = 1
turns_shape_array = _temp
_temp = np.zeros_like(dtheta_vel)
_temp[turns_vel] = 1
turns_vel_array = _temp
# plot swim direction analysis
fig = figure(figsize=(12,8), facecolor='w')
ax1 = subplot(211)
ax1.imshow(TVbg, cmap=cm.gray) # TVbg is clip out of ROI
ax1.plot(x[rng]-TVx1, y[rng]-TVy1, 'gray')
ax1.plot(water_x[t-preRange:t]-TVx1, water_y[t-preRange:t]-TVy1, 'c.')
if matched:
ax1.plot( water_x[t:matched[0]]-TVx1,
water_y[t:matched[0]]-TVy1, 'g.')
ax1.plot( water_x[matched[0]:matched[0]+preRange/4]-TVx1,
water_y[matched[0]:matched[0]+preRange/4]-TVy1, 'r.')
xlim([0, TVx2-TVx1]); ylim([TVy2-TVy1, 0])
title(_title)
ax2 = subplot(212)
ax2.plot( swimdir_within )
ax2.plot( peaks_within*1.15-0.1, 'mo' )
if matched:
xmin, xmax = t-preRange-10*fps, matched[0]+preRange/4
else:
xmin, xmax = t-preRange-10*fps, t+preRange/2+10*fps
gzcs = np.cumsum(swimdir_within)
gzcs -= gzcs[xmin]
ax2.plot( gzcs/gzcs[xmax] )
drawLines(0,1.2, events)
ylim([0,1.2])
xlim([xmin, xmax])
ylabel('|: SwimDirection\no: approach events')
data[fish][CS].append( {
'fname' : fname,
'x': x[rng], 'y': y[rng], 'z': z[rng],
'X': X[rng], 'Y': Y[rng], 'Z': Z[rng], # calibrate space (mm)
'speed3D': _speed3D, # calibrate space (mm)
'movingSTD' : _movingSTD, # calibrate space (mm)
'd2inflow': _d2inflow, # calibrate space (mm)
'ringpixels': _ringpixels,
'peaks_within': peaks_within[rng],
'xy_within': xy_within[rng],
'location_one_third' : location_one_third[rng],
'swimdir_within' : swimdir_within[rng],
'dtheta_shape': dtheta_shape[rng],
'dtheta_vel': dtheta_vel[rng],
'turns_shape': turns_shape_array[rng], # already +/- preRange
'turns_vel': turns_vel_array[rng],
'events' : events,
'matchedUSname' : matchedUSname,
'TVroi' : (TVx1,TVy1,TVx2,TVy2),
'SVroi' : (SVx1,SVy1,SVx2,SVy2),
} )
if pp:
fig.savefig(pp, format='pdf')
close('all') # release memory ASAP!
if pp:
pp.close()
def getPDFs(pickle_files, fishnames=None, createPDF=True):
# type checking args
if type(pickle_files) is str:
pickle_files = [pickle_files]
# convert to a list or set of fish names
if type(fishnames) is str:
fishnames = [fishnames]
elif not fishnames:
fishnames = set()
# re-organize trials into a dict "data"
data = {}
# figure out trial number (sometime many trials in one files) for each fish
# go through all pickle_files and use timestamps of file to sort events.
timestamps = []
for fp in pickle_files:
# collect ctime of pickled files
fname = os.path.basename(fp).split('.')[0] + '.avi'
timestamps.append( time.strptime(fname, "%b-%d-%Y_%H_%M_%S.avi") )
# look into the pickle and collect fish analyzed
params = np.load(fp) # loading pickled file!
if type(fishnames) is set:
for fish in [fs for fl,fs in params.keys() if fl == fname and fs != 'mog']:
fishnames.add(fish)
timestamps = sorted(range(len(timestamps)), key=timestamps.__getitem__)
# For each fish, go thru all pickled files
for fish in fishnames:
data[fish] = {}
# now go thru the sorted
for ind in timestamps:
fp = pickle_files[ind]
print 'processing #%d\n%s' % (ind, fp)
add2DataAndPlot(fp, fish, data, createPDF)
return data
def plotTrials(data, fish, CSname, key, step, offset=0, pp=None):
fig = figure(figsize=(12,8), facecolor='w')
ax1 = fig.add_subplot(121) # raw trace
ax2 = fig.add_subplot(222) # learning curve
ax3 = fig.add_subplot(224) # bar plot
preP, postP, postP2 = [], [], []
longestUS = 0
for n, measurement in enumerate(data[fish][CSname]):
tr = n+1
CS, USs, preRange = measurement['events']
subplot(ax1)
mi = -step*(tr-1)
ma = mi + step
drawLines(mi, ma, (preRange, [preRange+(USs[0]-CS)], preRange))
longestUS = max([us-CS+preRange*3/2 for us in USs]+[longestUS])
# 'measurement[key]': vector around the CS timing (+/-) preRange. i.e., preRange is the center
ax1.plot(measurement[key]-step*(tr-1)+offset)
title(CSname+': '+key) # cf. preRange = 3600 frames
pre = measurement[key][:preRange].mean()+offset # 2 min window
post = measurement[key][preRange:preRange+(USs[0]-CS)].mean()+offset # 23 s window
post2 = measurement[key][preRange+(USs[0]-CS):preRange*3/2+(USs[0]-CS)].mean()+offset # 1 min window after US
preP.append(pre)
postP.append(post)
postP2.append(post2)
ax3.plot([1, 2, 3], [pre, post, post2],'o-')
ax1.set_xlim([0,longestUS])
ax1.axis('off')
subplot(ax2)
x = range(1, tr+1)
y = np.diff((preP,postP), axis=0).ravel()
ax2.plot( x, y, 'ko-', linewidth=2 )
ax2.plot( x, np.zeros_like(x), '-.', linewidth=1, color='gray' )
# grid()
slope, intercept, rvalue, pval, stderr = stats.stats.linregress(x,y)
title('slope = zero? p-value = %f' % pval)
ax2.set_xlabel("Trial#")
ax2.set_xlim([0.5,tr+0.5])
ax2.set_ylabel('CS - pre')
subplot(ax3)
ax3.bar([0.6, 1.6, 2.6], [np.nanmean(preP), np.nanmean(postP), np.nanmean(postP2)], facecolor='none')
t, pval = stats.ttest_rel(postP, preP)
title('paired t p-value = %f' % pval)
ax3.set_xticks([1,2,3])
ax3.set_xticklabels(['pre', CSname, measurement['matchedUSname']])
ax3.set_xlim([0.5,3.5])
ax3.set_ylabel('Raw mean values')
tight_layout(2, h_pad=1, w_pad=1)
if pp:
fig.savefig(pp, format='pdf')
close('all')
return np.vstack((preP, postP, postP2))
def getSummary(data, dirname=None):
for fish in data.keys():
for CSname in data[fish].keys():
if dirname:
pp = PdfPages(os.path.join(dirname, '%s_for_%s.pdf' % (CSname,fish)))
print 'generating %s_for_%s.pdf' % (CSname,fish)
book = Workbook()
sheet1 = book.add_sheet('speed3D')
avgs = plotTrials(data, fish, CSname, 'speed3D', 30, pp=pp)
putNp2xls(avgs, sheet1)
sheet2 = book.add_sheet('d2inflow')
avgs = plotTrials(data, fish, CSname, 'd2inflow', 200, pp=pp)
putNp2xls(avgs, sheet2)
# sheet3 = book.add_sheet('smoothedz')
sheet3 = book.add_sheet('Z')
# avgs = plotTrials(data, fish, CSname, 'smoothedz', 100, pp=pp)
avgs = plotTrials(data, fish, CSname, 'Z', 30, pp=pp)
putNp2xls(avgs, sheet3)
sheet4 = book.add_sheet('ringpixels')
avgs = plotTrials(data, fish, CSname, 'ringpixels', 1200, pp=pp)
putNp2xls(avgs, sheet4)
sheet5 = book.add_sheet('peaks_within')
avgs = plotTrials(data, fish, CSname, 'peaks_within', 1.5, pp=pp)
putNp2xls(avgs, sheet5)
sheet6 = book.add_sheet('swimdir_within')
avgs = plotTrials(data, fish, CSname, 'swimdir_within', 1.5, pp=pp)
putNp2xls(avgs, sheet6)
sheet7 = book.add_sheet('xy_within')
avgs = plotTrials(data, fish, CSname, 'xy_within', 1.5, pp=pp)
putNp2xls(avgs, sheet7)
sheet8 = book.add_sheet('turns_shape')
avgs = plotTrials(data, fish, CSname, 'turns_shape', 1.5, pp=pp)
putNp2xls(avgs, sheet8)
sheet9 = book.add_sheet('turns_vel')
avgs = plotTrials(data, fish, CSname, 'turns_vel', 1.5, pp=pp)
putNp2xls(avgs, sheet9)
if dirname:
pp.close()
book.save(os.path.join(dirname, '%s_for_%s.xls' % (CSname,fish)))
close('all')
else:
show()
def add2Pickles(dirname, pickle_files):
# dirname : folder to look for pickle files
# pickle_files : output, a list to be concatenated.
pattern = os.path.join(dirname, '*.pickle')
temp = [_ for _ in glob(pattern) if not _.endswith('- Copy.pickle') and
not os.path.basename(_).startswith('Summary')]
pickle_files += temp
if __name__ == '__main__':
pickle_files = []
# small test data
# add2Pickles('R:/Data/itoiori/behav/adult whitlock/conditioning/NeuroD/Aug4/test', pickle_files)
# outputdir = 'R:/Data/itoiori/behav/adult whitlock/conditioning/NeuroD/Aug4/test'
# show me what you got
for pf in pickle_files:
print pf
fp = os.path.join(outputdir, 'Summary.pickle')
createPDF = True # useful when plotting etc code updated
if 1: # refresh analysis
data = getPDFs(pickle_files, createPDF=createPDF)
import cPickle as pickle
with open(os.path.join(outputdir, 'Summary.pickle'), 'wb') as f:
pickle.dump(data, f)
else: # or reuse previous
data = np.load(fp)
getSummary(data, outputdir)
pickle2mat(fp, data)
| bsd-3-clause |
francisco-dlp/hyperspy | hyperspy/drawing/utils.py | 1 | 57321 | # -*- coding: utf-8 -*-
# Copyright 2007-2016 The HyperSpy developers
#
# This file is part of HyperSpy.
#
# HyperSpy 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.
#
# HyperSpy 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 HyperSpy. If not, see <http://www.gnu.org/licenses/>.
import copy
import itertools
import textwrap
from traits import trait_base
import matplotlib.pyplot as plt
import matplotlib as mpl
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.backend_bases import key_press_handler
import warnings
import numpy as np
from distutils.version import LooseVersion
import logging
import hyperspy as hs
_logger = logging.getLogger(__name__)
def contrast_stretching(data, saturated_pixels):
"""Calculate bounds that leaves out a given percentage of the data.
Parameters
----------
data: numpy array
saturated_pixels: scalar, None
The percentage of pixels that are left out of the bounds. For example,
the low and high bounds of a value of 1 are the 0.5% and 99.5%
percentiles. It must be in the [0, 100] range. If None, set the value
to 0.
Returns
-------
vmin, vmax: scalar
The low and high bounds
Raises
------
ValueError if the value of `saturated_pixels` is out of the valid range.
"""
# Sanity check
if saturated_pixels is None:
saturated_pixels = 0
if not 0 <= saturated_pixels <= 100:
raise ValueError(
"saturated_pixels must be a scalar in the range[0, 100]")
vmin = np.nanpercentile(data, saturated_pixels / 2.)
vmax = np.nanpercentile(data, 100 - saturated_pixels / 2.)
return vmin, vmax
MPL_DIVERGING_COLORMAPS = [
"BrBG",
"bwr",
"coolwarm",
"PiYG",
"PRGn",
"PuOr",
"RdBu",
"RdGy",
"RdYIBu",
"RdYIGn",
"seismic",
"Spectral", ]
# Add reversed colormaps
MPL_DIVERGING_COLORMAPS += [cmap + "_r" for cmap in MPL_DIVERGING_COLORMAPS]
def centre_colormap_values(vmin, vmax):
"""Calculate vmin and vmax to set the colormap midpoint to zero.
Parameters
----------
vmin, vmax : scalar
The range of data to display.
Returns
-------
cvmin, cvmax : scalar
The values to obtain a centre colormap.
"""
absmax = max(abs(vmin), abs(vmax))
return -absmax, absmax
def create_figure(window_title=None,
_on_figure_window_close=None,
disable_xyscale_keys=False,
**kwargs):
"""Create a matplotlib figure.
This function adds the possibility to execute another function
when the figure is closed and to easily set the window title. Any
keyword argument is passed to the plt.figure function
Parameters
----------
window_title : string
_on_figure_window_close : function
disable_xyscale_keys : bool, disable the `k`, `l` and `L` shortcuts which
toggle the x or y axis between linear and log scale.
Returns
-------
fig : plt.figure
"""
fig = plt.figure(**kwargs)
if window_title is not None:
# remove non-alphanumeric characters to prevent file saving problems
# This is a workaround for:
# https://github.com/matplotlib/matplotlib/issues/9056
reserved_characters = r'<>"/\|?*'
for c in reserved_characters:
window_title = window_title.replace(c, '')
window_title = window_title.replace('\n', ' ')
window_title = window_title.replace(':', ' -')
fig.canvas.set_window_title(window_title)
if disable_xyscale_keys and hasattr(fig.canvas, 'toolbar'):
# hack the `key_press_handler` to disable the `k`, `l`, `L` shortcuts
manager = fig.canvas.manager
fig.canvas.mpl_disconnect(manager.key_press_handler_id)
manager.key_press_handler_id = manager.canvas.mpl_connect(
'key_press_event',
lambda event: key_press_handler_custom(event, manager.canvas))
if _on_figure_window_close is not None:
on_figure_window_close(fig, _on_figure_window_close)
return fig
def key_press_handler_custom(event, canvas):
if event.key not in ['k', 'l', 'L']:
key_press_handler(event, canvas, canvas.manager.toolbar)
def on_figure_window_close(figure, function):
"""Connects a close figure signal to a given function.
Parameters
----------
figure : mpl figure instance
function : function
"""
def function_wrapper(evt):
function()
figure.canvas.mpl_connect('close_event', function_wrapper)
def plot_RGB_map(im_list, normalization='single', dont_plot=False):
"""Plot 2 or 3 maps in RGB.
Parameters
----------
im_list : list of Signal2D instances
normalization : {'single', 'global'}
dont_plot : bool
Returns
-------
array: RGB matrix
"""
# from widgets import cursors
height, width = im_list[0].data.shape[:2]
rgb = np.zeros((height, width, 3))
rgb[:, :, 0] = im_list[0].data.squeeze()
rgb[:, :, 1] = im_list[1].data.squeeze()
if len(im_list) == 3:
rgb[:, :, 2] = im_list[2].data.squeeze()
if normalization == 'single':
for i in range(len(im_list)):
rgb[:, :, i] /= rgb[:, :, i].max()
elif normalization == 'global':
rgb /= rgb.max()
rgb = rgb.clip(0, rgb.max())
if not dont_plot:
figure = plt.figure()
ax = figure.add_subplot(111)
ax.frameon = False
ax.set_axis_off()
ax.imshow(rgb, interpolation='nearest')
# cursors.set_mpl_ax(ax)
figure.canvas.draw_idle()
else:
return rgb
def subplot_parameters(fig):
"""Returns a list of the subplot parameters of a mpl figure.
Parameters
----------
fig : mpl figure
Returns
-------
tuple : (left, bottom, right, top, wspace, hspace)
"""
wspace = fig.subplotpars.wspace
hspace = fig.subplotpars.hspace
left = fig.subplotpars.left
right = fig.subplotpars.right
top = fig.subplotpars.top
bottom = fig.subplotpars.bottom
return left, bottom, right, top, wspace, hspace
class ColorCycle:
_color_cycle = [mpl.colors.colorConverter.to_rgba(color) for color
in ('b', 'g', 'r', 'c', 'm', 'y', 'k')]
def __init__(self):
self.color_cycle = copy.copy(self._color_cycle)
def __call__(self):
if not self.color_cycle:
self.color_cycle = copy.copy(self._color_cycle)
return self.color_cycle.pop(0)
def plot_signals(signal_list, sync=True, navigator="auto",
navigator_list=None, **kwargs):
"""Plot several signals at the same time.
Parameters
----------
signal_list : list of BaseSignal instances
If sync is set to True, the signals must have the
same navigation shape, but not necessarily the same signal shape.
sync : True or False, default "True"
If True: the signals will share navigation, all the signals
must have the same navigation shape for this to work, but not
necessarily the same signal shape.
navigator : {"auto", None, "spectrum", "slider", BaseSignal}, default "auto"
See signal.plot docstring for full description
navigator_list : {List of navigator arguments, None}, default None
Set different navigator options for the signals. Must use valid
navigator arguments: "auto", None, "spectrum", "slider", or a
hyperspy Signal. The list must have the same size as signal_list.
If None, the argument specified in navigator will be used.
**kwargs
Any extra keyword arguments are passed to each signal `plot` method.
Example
-------
>>> s_cl = hs.load("coreloss.dm3")
>>> s_ll = hs.load("lowloss.dm3")
>>> hs.plot.plot_signals([s_cl, s_ll])
Specifying the navigator:
>>> s_cl = hs.load("coreloss.dm3")
>>> s_ll = hs.load("lowloss.dm3")
>>> hs.plot.plot_signals([s_cl, s_ll], navigator="slider")
Specifying the navigator for each signal:
>>> s_cl = hs.load("coreloss.dm3")
>>> s_ll = hs.load("lowloss.dm3")
>>> s_edx = hs.load("edx.dm3")
>>> s_adf = hs.load("adf.dm3")
>>> hs.plot.plot_signals(
[s_cl, s_ll, s_edx], navigator_list=["slider",None,s_adf])
"""
import hyperspy.signal
if navigator_list:
if not (len(signal_list) == len(navigator_list)):
raise ValueError(
"signal_list and navigator_list must"
" have the same size")
if sync:
axes_manager_list = []
for signal in signal_list:
axes_manager_list.append(signal.axes_manager)
if not navigator_list:
navigator_list = []
if navigator is None:
navigator_list.extend([None] * len(signal_list))
elif isinstance(navigator, hyperspy.signal.BaseSignal):
navigator_list.append(navigator)
navigator_list.extend([None] * (len(signal_list) - 1))
elif navigator == "slider":
navigator_list.append("slider")
navigator_list.extend([None] * (len(signal_list) - 1))
elif navigator == "spectrum":
navigator_list.extend(["spectrum"] * len(signal_list))
elif navigator == "auto":
navigator_list.extend(["auto"] * len(signal_list))
else:
raise ValueError(
"navigator must be one of \"spectrum\",\"auto\","
" \"slider\", None, a Signal instance")
# Check to see if the spectra have the same navigational shapes
temp_shape_first = axes_manager_list[0].navigation_shape
for i, axes_manager in enumerate(axes_manager_list):
temp_shape = axes_manager.navigation_shape
if not (temp_shape_first == temp_shape):
raise ValueError(
"The spectra does not have the same navigation shape")
axes_manager_list[i] = axes_manager.deepcopy()
if i > 0:
for axis0, axisn in zip(axes_manager_list[0].navigation_axes,
axes_manager_list[i].navigation_axes):
axes_manager_list[i]._axes[axisn.index_in_array] = axis0
del axes_manager
for signal, navigator, axes_manager in zip(signal_list,
navigator_list,
axes_manager_list):
signal.plot(axes_manager=axes_manager,
navigator=navigator,
**kwargs)
# If sync is False
else:
if not navigator_list:
navigator_list = []
navigator_list.extend([navigator] * len(signal_list))
for signal, navigator in zip(signal_list, navigator_list):
signal.plot(navigator=navigator,
**kwargs)
def _make_heatmap_subplot(spectra):
from hyperspy._signals.signal2d import Signal2D
im = Signal2D(spectra.data, axes=spectra.axes_manager._get_axes_dicts())
im.metadata.General.title = spectra.metadata.General.title
im.plot()
return im._plot.signal_plot.ax
def set_xaxis_lims(mpl_ax, hs_axis):
"""
Set the matplotlib axis limits to match that of a HyperSpy axis
Parameters
----------
mpl_ax : :class:`matplotlib.axis.Axis`
The ``matplotlib`` axis to change
hs_axis : :class:`~hyperspy.axes.DataAxis`
The data axis that contains the values that control the scaling
"""
x_axis_lower_lim = hs_axis.axis[0]
x_axis_upper_lim = hs_axis.axis[-1]
mpl_ax.set_xlim(x_axis_lower_lim, x_axis_upper_lim)
def _make_overlap_plot(spectra, ax, color="blue", line_style='-'):
if isinstance(color, str):
color = [color] * len(spectra)
if isinstance(line_style, str):
line_style = [line_style] * len(spectra)
for spectrum_index, (spectrum, color, line_style) in enumerate(
zip(spectra, color, line_style)):
x_axis = spectrum.axes_manager.signal_axes[0]
spectrum = _transpose_if_required(spectrum, 1)
ax.plot(x_axis.axis, spectrum.data, color=color, ls=line_style)
set_xaxis_lims(ax, x_axis)
_set_spectrum_xlabel(spectra if isinstance(spectra, hs.signals.BaseSignal)
else spectra[-1], ax)
ax.set_ylabel('Intensity')
ax.autoscale(tight=True)
def _make_cascade_subplot(
spectra, ax, color="blue", line_style='-', padding=1):
max_value = 0
for spectrum in spectra:
spectrum_yrange = (np.nanmax(spectrum.data) -
np.nanmin(spectrum.data))
if spectrum_yrange > max_value:
max_value = spectrum_yrange
if isinstance(color, str):
color = [color] * len(spectra)
if isinstance(line_style, str):
line_style = [line_style] * len(spectra)
for spectrum_index, (spectrum, color, line_style) in enumerate(
zip(spectra, color, line_style)):
x_axis = spectrum.axes_manager.signal_axes[0]
spectrum = _transpose_if_required(spectrum, 1)
data_to_plot = ((spectrum.data - spectrum.data.min()) /
float(max_value) + spectrum_index * padding)
ax.plot(x_axis.axis, data_to_plot, color=color, ls=line_style)
set_xaxis_lims(ax, x_axis)
_set_spectrum_xlabel(spectra if isinstance(spectra, hs.signals.BaseSignal)
else spectra[-1], ax)
ax.set_yticks([])
ax.autoscale(tight=True)
def _plot_spectrum(spectrum, ax, color="blue", line_style='-'):
x_axis = spectrum.axes_manager.signal_axes[0]
ax.plot(x_axis.axis, spectrum.data, color=color, ls=line_style)
set_xaxis_lims(ax, x_axis)
def _set_spectrum_xlabel(spectrum, ax):
x_axis = spectrum.axes_manager.signal_axes[0]
ax.set_xlabel("%s (%s)" % (x_axis.name, x_axis.units))
def _transpose_if_required(signal, expected_dimension):
# EDS profiles or maps have signal dimension = 0 and navigation dimension
# 1 or 2. For convenience transpose the signal if possible
if (signal.axes_manager.signal_dimension == 0 and
signal.axes_manager.navigation_dimension == expected_dimension):
return signal.T
else:
return signal
def plot_images(images,
cmap=None,
no_nans=False,
per_row=3,
label='auto',
labelwrap=30,
suptitle=None,
suptitle_fontsize=18,
colorbar='multi',
centre_colormap="auto",
saturated_pixels=0,
scalebar=None,
scalebar_color='white',
axes_decor='all',
padding=None,
tight_layout=False,
aspect='auto',
min_asp=0.1,
namefrac_thresh=0.4,
fig=None,
vmin=None,
vmax=None,
*args,
**kwargs):
"""Plot multiple images as sub-images in one figure.
Extra keyword arguments are passed to `matplotlib.figure`.
Parameters
----------
images : list of Signal2D or BaseSignal
`images` should be a list of Signals to plot. For `BaseSignal` with
navigation dimensions 2 and signal dimension 0, the signal will be
tranposed to form a `Signal2D`.
Multi-dimensional images will have each plane plotted as a separate
image.
If any signal shape is not suitable, a ValueError will be raised.
cmap : matplotlib colormap, list, or ``'mpl_colors'``, *optional*
The colormap used for the images, by default read from ``pyplot``.
A list of colormaps can also be provided, and the images will
cycle through them. Optionally, the value ``'mpl_colors'`` will
cause the cmap to loop through the default ``matplotlib``
colors (to match with the default output of the
:py:func:`~.drawing.utils.plot_spectra` method.
Note: if using more than one colormap, using the ``'single'``
option for ``colorbar`` is disallowed.
no_nans : bool, optional
If True, set nans to zero for plotting.
per_row : int, optional
The number of plots in each row
label : None, str, or list of str, optional
Control the title labeling of the plotted images.
If None, no titles will be shown.
If 'auto' (default), function will try to determine suitable titles
using Signal2D titles, falling back to the 'titles' option if no good
short titles are detected.
Works best if all images to be plotted have the same beginning
to their titles.
If 'titles', the title from each image's metadata.General.title
will be used.
If any other single str, images will be labeled in sequence using
that str as a prefix.
If a list of str, the list elements will be used to determine the
labels (repeated, if necessary).
labelwrap : int, optional
integer specifying the number of characters that will be used on
one line
If the function returns an unexpected blank figure, lower this
value to reduce overlap of the labels between each figure
suptitle : str, optional
Title to use at the top of the figure. If called with label='auto',
this parameter will override the automatically determined title.
suptitle_fontsize : int, optional
Font size to use for super title at top of figure
colorbar : {'multi', None, 'single'}
Controls the type of colorbars that are plotted.
If None, no colorbar is plotted.
If 'multi' (default), individual colorbars are plotted for each
(non-RGB) image
If 'single', all (non-RGB) images are plotted on the same scale,
and one colorbar is shown for all
centre_colormap : {"auto", True, False}
If True the centre of the color scheme is set to zero. This is
specially useful when using diverging color schemes. If "auto"
(default), diverging color schemes are automatically centred.
saturated_pixels: None, scalar or list of scalar, optional, default: 0
If list of scalar, the length should match the number of images to
show. If provide in the list, set the value to 0.
The percentage of pixels that are left out of the bounds. For
example, the low and high bounds of a value of 1 are the 0.5% and
99.5% percentiles. It must be in the [0, 100] range.
scalebar : {None, 'all', list of ints}, optional
If None (or False), no scalebars will be added to the images.
If 'all', scalebars will be added to all images.
If list of ints, scalebars will be added to each image specified.
scalebar_color : str, optional
A valid MPL color string; will be used as the scalebar color
axes_decor : {'all', 'ticks', 'off', None}, optional
Controls how the axes are displayed on each image; default is 'all'
If 'all', both ticks and axis labels will be shown
If 'ticks', no axis labels will be shown, but ticks/labels will
If 'off', all decorations and frame will be disabled
If None, no axis decorations will be shown, but ticks/frame will
padding : None or dict, optional
This parameter controls the spacing between images.
If None, default options will be used
Otherwise, supply a dictionary with the spacing options as
keywords and desired values as values
Values should be supplied as used in pyplot.subplots_adjust(),
and can be:
'left', 'bottom', 'right', 'top', 'wspace' (width),
and 'hspace' (height)
tight_layout : bool, optional
If true, hyperspy will attempt to improve image placement in
figure using matplotlib's tight_layout
If false, repositioning images inside the figure will be left as
an exercise for the user.
aspect : str or numeric, optional
If 'auto', aspect ratio is auto determined, subject to min_asp.
If 'square', image will be forced onto square display.
If 'equal', aspect ratio of 1 will be enforced.
If float (or int/long), given value will be used.
min_asp : float, optional
Minimum aspect ratio to be used when plotting images
namefrac_thresh : float, optional
Threshold to use for auto-labeling. This parameter controls how
much of the titles must be the same for the auto-shortening of
labels to activate. Can vary from 0 to 1. Smaller values
encourage shortening of titles by auto-labeling, while larger
values will require more overlap in titles before activing the
auto-label code.
fig : mpl figure, optional
If set, the images will be plotted to an existing MPL figure
vmin, vmax : scalar or list of scalar, optional, default: None
If list of scalar, the length should match the number of images to
show.
A list of scalar is not compatible with a single colorbar.
See vmin, vmax of matplotlib.imshow() for more details.
*args, **kwargs, optional
Additional arguments passed to matplotlib.imshow()
Returns
-------
axes_list : list
a list of subplot axes that hold the images
See Also
--------
plot_spectra : Plotting of multiple spectra
plot_signals : Plotting of multiple signals
plot_histograms : Compare signal histograms
Notes
-----
`interpolation` is a useful parameter to provide as a keyword
argument to control how the space between pixels is interpolated. A
value of ``'nearest'`` will cause no interpolation between pixels.
`tight_layout` is known to be quite brittle, so an option is provided
to disable it. Turn this option off if output is not as expected,
or try adjusting `label`, `labelwrap`, or `per_row`
"""
def __check_single_colorbar(cbar):
if cbar == 'single':
raise ValueError('Cannot use a single colorbar with multiple '
'colormaps. Please check for compatible '
'arguments.')
from hyperspy.drawing.widgets import ScaleBar
from hyperspy.misc import rgb_tools
from hyperspy.signal import BaseSignal
# Check that we have a hyperspy signal
im = [images] if not isinstance(images, (list, tuple)) else images
for image in im:
if not isinstance(image, BaseSignal):
raise ValueError("`images` must be a list of image signals or a "
"multi-dimensional signal."
" " + repr(type(images)) + " was given.")
# For list of EDS maps, transpose the BaseSignal
if isinstance(images, (list, tuple)):
images = [_transpose_if_required(image, 2) for image in images]
# If input is >= 1D signal (e.g. for multi-dimensional plotting),
# copy it and put it in a list so labeling works out as (x,y) when plotting
if isinstance(images,
BaseSignal) and images.axes_manager.navigation_dimension > 0:
images = [images._deepcopy_with_new_data(images.data)]
n = 0
for i, sig in enumerate(images):
if sig.axes_manager.signal_dimension != 2:
raise ValueError("This method only plots signals that are images. "
"The signal dimension must be equal to 2. "
"The signal at position " + repr(i) +
" was " + repr(sig) + ".")
# increment n by the navigation size, or by 1 if the navigation size is
# <= 0
n += (sig.axes_manager.navigation_size
if sig.axes_manager.navigation_size > 0
else 1)
# If no cmap given, get default colormap from pyplot:
if cmap is None:
cmap = [plt.get_cmap().name]
elif cmap == 'mpl_colors':
for n_color, c in enumerate(mpl.rcParams['axes.prop_cycle']):
make_cmap(colors=['#000000', c['color']],
name='mpl{}'.format(n_color))
cmap = ['mpl{}'.format(i) for i in
range(len(mpl.rcParams['axes.prop_cycle']))]
__check_single_colorbar(colorbar)
# cmap is list, tuple, or something else iterable (but not string):
elif hasattr(cmap, '__iter__') and not isinstance(cmap, str):
try:
cmap = [c.name for c in cmap] # convert colormap to string
except AttributeError:
cmap = [c for c in cmap] # c should be string if not colormap
__check_single_colorbar(colorbar)
elif isinstance(cmap, mpl.colors.Colormap):
cmap = [cmap.name] # convert single colormap to list with string
elif isinstance(cmap, str):
cmap = [cmap] # cmap is single string, so make it a list
else:
# Didn't understand cmap input, so raise error
raise ValueError('The provided cmap value was not understood. Please '
'check input values.')
# If any of the cmaps given are diverging, and auto-centering, set the
# appropriate flag:
if centre_colormap == "auto":
centre_colormaps = []
for c in cmap:
if c in MPL_DIVERGING_COLORMAPS:
centre_colormaps.append(True)
else:
centre_colormaps.append(False)
# if it was True, just convert to list
elif centre_colormap:
centre_colormaps = [True]
# likewise for false
elif not centre_colormap:
centre_colormaps = [False]
# finally, convert lists to cycle generators for adaptive length:
centre_colormaps = itertools.cycle(centre_colormaps)
cmap = itertools.cycle(cmap)
def _check_arg(arg, default_value, arg_name):
if isinstance(arg, list):
if len(arg) != n:
_logger.warning('The provided {} values are ignored because the '
'length of the list does not match the number of '
'images'.format(arg_name))
arg = [default_value] * n
else:
arg = [arg] * n
return arg
vmin = _check_arg(vmin, None, 'vmin')
vmax = _check_arg(vmax, None, 'vmax')
saturated_pixels = _check_arg(saturated_pixels, 0, 'saturated_pixels')
# Sort out the labeling:
div_num = 0
all_match = False
shared_titles = False
user_labels = False
if label is None:
pass
elif label == 'auto':
# Use some heuristics to try to get base string of similar titles
label_list = [x.metadata.General.title for x in images]
# Find the shortest common string between the image titles
# and pull that out as the base title for the sequence of images
# array in which to store arrays
res = np.zeros((len(label_list), len(label_list[0]) + 1))
res[:, 0] = 1
# j iterates the strings
for j in range(len(label_list)):
# i iterates length of substring test
for i in range(1, len(label_list[0]) + 1):
# stores whether or not characters in title match
res[j, i] = label_list[0][:i] in label_list[j]
# sum up the results (1 is True, 0 is False) and create
# a substring based on the minimum value (this will be
# the "smallest common string" between all the titles
if res.all():
basename = label_list[0]
div_num = len(label_list[0])
all_match = True
else:
div_num = int(min(np.sum(res, 1)))
basename = label_list[0][:div_num - 1]
all_match = False
# trim off any '(' or ' ' characters at end of basename
if div_num > 1:
while True:
if basename[len(basename) - 1] == '(':
basename = basename[:-1]
elif basename[len(basename) - 1] == ' ':
basename = basename[:-1]
else:
break
# namefrac is ratio of length of basename to the image name
# if it is high (e.g. over 0.5), we can assume that all images
# share the same base
if len(label_list[0]) > 0:
namefrac = float(len(basename)) / len(label_list[0])
else:
# If label_list[0] is empty, it means there was probably no
# title set originally, so nothing to share
namefrac = 0
if namefrac > namefrac_thresh:
# there was a significant overlap of label beginnings
shared_titles = True
# only use new suptitle if one isn't specified already
if suptitle is None:
suptitle = basename
else:
# there was not much overlap, so default back to 'titles' mode
shared_titles = False
label = 'titles'
div_num = 0
elif label == 'titles':
# Set label_list to each image's pre-defined title
label_list = [x.metadata.General.title for x in images]
elif isinstance(label, str):
# Set label_list to an indexed list, based off of label
label_list = [label + " " + repr(num) for num in range(n)]
elif isinstance(label, list) and all(
isinstance(x, str) for x in label):
label_list = label
user_labels = True
# If list of labels is longer than the number of images, just use the
# first n elements
if len(label_list) > n:
del label_list[n:]
if len(label_list) < n:
label_list *= (n // len(label_list)) + 1
del label_list[n:]
else:
raise ValueError("Did not understand input of labels.")
# Determine appropriate number of images per row
rows = int(np.ceil(n / float(per_row)))
if n < per_row:
per_row = n
# Set overall figure size and define figure (if not pre-existing)
if fig is None:
k = max(plt.rcParams['figure.figsize']) / max(per_row, rows)
f = plt.figure(figsize=(tuple(k * i for i in (per_row, rows))))
else:
f = fig
# Initialize list to hold subplot axes
axes_list = []
# Initialize list of rgb tags
isrgb = [False] * len(images)
# Check to see if there are any rgb images in list
# and tag them using the isrgb list
for i, img in enumerate(images):
if rgb_tools.is_rgbx(img.data):
isrgb[i] = True
# Determine how many non-rgb Images there are
non_rgb = list(itertools.compress(images, [not j for j in isrgb]))
if len(non_rgb) == 0 and colorbar is not None:
colorbar = None
warnings.warn("Sorry, colorbar is not implemented for RGB images.")
# Find global min and max values of all the non-rgb images for use with
# 'single' scalebar
if colorbar == 'single':
# get a g_saturated_pixels from saturated_pixels
if isinstance(saturated_pixels, list):
g_saturated_pixels = min(np.array([v for v in saturated_pixels]))
else:
g_saturated_pixels = saturated_pixels
# estimate a g_vmin and g_max from saturated_pixels
g_vmin, g_vmax = contrast_stretching(np.concatenate(
[i.data.flatten() for i in non_rgb]), g_saturated_pixels)
# if vmin and vmax are provided, override g_min and g_max
if isinstance(vmin, list):
_logger.warning('vmin have to be a scalar to be compatible with a '
'single colorbar')
else:
g_vmin = vmin if vmin is not None else g_vmin
if isinstance(vmax, list):
_logger.warning('vmax have to be a scalar to be compatible with a '
'single colorbar')
else:
g_vmax = vmax if vmax is not None else g_vmax
if next(centre_colormaps):
g_vmin, g_vmax = centre_colormap_values(g_vmin, g_vmax)
# Check if we need to add a scalebar for some of the images
if isinstance(scalebar, list) and all(isinstance(x, int)
for x in scalebar):
scalelist = True
else:
scalelist = False
idx = 0
ax_im_list = [0] * len(isrgb)
# Replot: create a list to store references to the images
replot_ims = []
# Loop through each image, adding subplot for each one
for i, ims in enumerate(images):
# Get handles for the signal axes and axes_manager
axes_manager = ims.axes_manager
if axes_manager.navigation_dimension > 0:
ims = ims._deepcopy_with_new_data(ims.data)
for j, im in enumerate(ims):
ax = f.add_subplot(rows, per_row, idx + 1)
axes_list.append(ax)
data = im.data
centre = next(centre_colormaps) # get next value for centreing
# Enable RGB plotting
if rgb_tools.is_rgbx(data):
data = rgb_tools.rgbx2regular_array(data, plot_friendly=True)
l_vmin, l_vmax = None, None
else:
data = im.data
# Find min and max for contrast
l_vmin, l_vmax = contrast_stretching(
data, saturated_pixels[idx])
l_vmin = vmin[idx] if vmin[idx] is not None else l_vmin
l_vmax = vmax[idx] if vmax[idx] is not None else l_vmax
if centre:
l_vmin, l_vmax = centre_colormap_values(l_vmin, l_vmax)
# Remove NaNs (if requested)
if no_nans:
data = np.nan_to_num(data)
# Get handles for the signal axes and axes_manager
axes_manager = im.axes_manager
axes = axes_manager.signal_axes
# Set dimensions of images
xaxis = axes[0]
yaxis = axes[1]
extent = (
xaxis.low_value,
xaxis.high_value,
yaxis.high_value,
yaxis.low_value,
)
if not isinstance(aspect, (int, float)) and aspect not in [
'auto', 'square', 'equal']:
_logger.warning("Did not understand aspect ratio input. "
"Using 'auto' as default.")
aspect = 'auto'
if aspect == 'auto':
if float(yaxis.size) / xaxis.size < min_asp:
factor = min_asp * float(xaxis.size) / yaxis.size
elif float(yaxis.size) / xaxis.size > min_asp ** -1:
factor = min_asp ** -1 * float(xaxis.size) / yaxis.size
else:
factor = 1
asp = np.abs(factor * float(xaxis.scale) / yaxis.scale)
elif aspect == 'square':
asp = abs(extent[1] - extent[0]) / abs(extent[3] - extent[2])
elif aspect == 'equal':
asp = 1
elif isinstance(aspect, (int, float)):
asp = aspect
if 'interpolation' not in kwargs.keys():
kwargs['interpolation'] = 'nearest'
# Get colormap for this image:
cm = next(cmap)
# Plot image data, using vmin and vmax to set bounds,
# or allowing them to be set automatically if using individual
# colorbars
if colorbar == 'single' and not isrgb[i]:
axes_im = ax.imshow(data,
cmap=cm,
extent=extent,
vmin=g_vmin, vmax=g_vmax,
aspect=asp,
*args, **kwargs)
ax_im_list[i] = axes_im
else:
axes_im = ax.imshow(data,
cmap=cm,
extent=extent,
vmin=l_vmin,
vmax=l_vmax,
aspect=asp,
*args, **kwargs)
ax_im_list[i] = axes_im
# If an axis trait is undefined, shut off :
if isinstance(xaxis.units, trait_base._Undefined) or \
isinstance(yaxis.units, trait_base._Undefined) or \
isinstance(xaxis.name, trait_base._Undefined) or \
isinstance(yaxis.name, trait_base._Undefined):
if axes_decor == 'all':
_logger.warning(
'Axes labels were requested, but one '
'or both of the '
'axes units and/or name are undefined. '
'Axes decorations have been set to '
'\'ticks\' instead.')
axes_decor = 'ticks'
# If all traits are defined, set labels as appropriate:
else:
ax.set_xlabel(axes[0].name + " axis (" + axes[0].units + ")")
ax.set_ylabel(axes[1].name + " axis (" + axes[1].units + ")")
if label:
if all_match:
title = ''
elif shared_titles:
title = label_list[i][div_num - 1:]
else:
if len(ims) == n:
# This is true if we are plotting just 1
# multi-dimensional Signal2D
title = label_list[idx]
elif user_labels:
title = label_list[idx]
else:
title = label_list[i]
if ims.axes_manager.navigation_size > 1 and not user_labels:
title += " %s" % str(ims.axes_manager.indices)
ax.set_title(textwrap.fill(title, labelwrap))
# Set axes decorations based on user input
set_axes_decor(ax, axes_decor)
# If using independent colorbars, add them
if colorbar == 'multi' and not isrgb[i]:
div = make_axes_locatable(ax)
cax = div.append_axes("right", size="5%", pad=0.05)
plt.colorbar(axes_im, cax=cax)
# Add scalebars as necessary
if (scalelist and idx in scalebar) or scalebar == 'all':
ax.scalebar = ScaleBar(
ax=ax,
units=axes[0].units,
color=scalebar_color,
)
# Replot: store references to the images
replot_ims.append(im)
idx += 1
# If using a single colorbar, add it, and do tight_layout, ensuring that
# a colorbar is only added based off of non-rgb Images:
if colorbar == 'single':
foundim = None
for i in range(len(isrgb)):
if (not isrgb[i]) and foundim is None:
foundim = i
if foundim is not None:
f.subplots_adjust(right=0.8)
cbar_ax = f.add_axes([0.9, 0.1, 0.03, 0.8])
f.colorbar(ax_im_list[foundim], cax=cbar_ax)
if tight_layout:
# tight_layout, leaving room for the colorbar
plt.tight_layout(rect=[0, 0, 0.9, 1])
elif tight_layout:
plt.tight_layout()
elif tight_layout:
plt.tight_layout()
# Set top bounds for shared titles and add suptitle
if suptitle:
f.subplots_adjust(top=0.85)
f.suptitle(suptitle, fontsize=suptitle_fontsize)
# If we want to plot scalebars, loop through the list of axes and add them
if scalebar is None or scalebar is False:
# Do nothing if no scalebars are called for
pass
elif scalebar == 'all':
# scalebars were taken care of in the plotting loop
pass
elif scalelist:
# scalebars were taken care of in the plotting loop
pass
else:
raise ValueError("Did not understand scalebar input. Must be None, "
"\'all\', or list of ints.")
# Adjust subplot spacing according to user's specification
if padding is not None:
plt.subplots_adjust(**padding)
# Replot: connect function
def on_dblclick(event):
# On the event of a double click, replot the selected subplot
if not event.inaxes:
return
if not event.dblclick:
return
subplots = [axi for axi in f.axes if isinstance(axi, mpl.axes.Subplot)]
inx = list(subplots).index(event.inaxes)
im = replot_ims[inx]
# Use some of the info in the subplot
cm = subplots[inx].images[0].get_cmap()
clim = subplots[inx].images[0].get_clim()
sbar = False
if (scalelist and inx in scalebar) or scalebar == 'all':
sbar = True
im.plot(colorbar=bool(colorbar),
vmin=clim[0],
vmax=clim[1],
no_nans=no_nans,
aspect=asp,
scalebar=sbar,
scalebar_color=scalebar_color,
cmap=cm)
f.canvas.mpl_connect('button_press_event', on_dblclick)
return axes_list
def set_axes_decor(ax, axes_decor):
if axes_decor == 'off':
ax.axis('off')
elif axes_decor == 'ticks':
ax.set_xlabel('')
ax.set_ylabel('')
elif axes_decor == 'all':
pass
elif axes_decor is None:
ax.set_xlabel('')
ax.set_ylabel('')
ax.set_xticklabels([])
ax.set_yticklabels([])
def make_cmap(colors, name='my_colormap', position=None,
bit=False, register=True):
"""
Create a matplotlib colormap with customized colors, optionally registering
it with matplotlib for simplified use.
Adapted from Chris Slocum's code at:
https://github.com/CSlocumWX/custom_colormap/blob/master/custom_colormaps.py
and used under the terms of that code's BSD-3 license
Parameters
----------
colors : iterable
list of either tuples containing rgb values, or html strings
Colors should be arranged so that the first color is the lowest
value for the colorbar and the last is the highest.
name : str
name of colormap to use when registering with matplotlib
position : None or iterable
list containing the values (from [0,1]) that dictate the position
of each color within the colormap. If None (default), the colors
will be equally-spaced within the colorbar.
bit : boolean
True if RGB colors are given in 8-bit [0 to 255] or False if given
in arithmetic basis [0 to 1] (default)
register : boolean
switch to control whether or not to register the custom colormap
with matplotlib in order to enable use by just the name string
"""
def _html_color_to_rgb(color_string):
""" convert #RRGGBB to an (R, G, B) tuple """
color_string = color_string.strip()
if color_string[0] == '#':
color_string = color_string[1:]
if len(color_string) != 6:
raise ValueError(
"input #{} is not in #RRGGBB format".format(color_string))
r, g, b = color_string[:2], color_string[2:4], color_string[4:]
r, g, b = [int(n, 16) / 255 for n in (r, g, b)]
return r, g, b
bit_rgb = np.linspace(0, 1, 256)
if position is None:
position = np.linspace(0, 1, len(colors))
else:
if len(position) != len(colors):
raise ValueError("position length must be the same as colors")
elif position[0] != 0 or position[-1] != 1:
raise ValueError("position must start with 0 and end with 1")
cdict = {'red': [], 'green': [], 'blue': []}
for pos, color in zip(position, colors):
if isinstance(color, str):
color = _html_color_to_rgb(color)
elif bit:
color = (bit_rgb[color[0]],
bit_rgb[color[1]],
bit_rgb[color[2]])
cdict['red'].append((pos, color[0], color[0]))
cdict['green'].append((pos, color[1], color[1]))
cdict['blue'].append((pos, color[2], color[2]))
cmap = mpl.colors.LinearSegmentedColormap(name, cdict, 256)
if register:
mpl.cm.register_cmap(name, cmap)
return cmap
def plot_spectra(
spectra,
style='overlap',
color=None,
line_style=None,
padding=1.,
legend=None,
legend_picking=True,
legend_loc='upper right',
fig=None,
ax=None,
**kwargs):
"""Plot several spectra in the same figure.
Extra keyword arguments are passed to `matplotlib.figure`.
Parameters
----------
spectra : list of Signal1D or BaseSignal
Ordered spectra list of signal to plot. If `style` is "cascade" or
"mosaic" the spectra can have different size and axes. For `BaseSignal`
with navigation dimensions 1 and signal dimension 0, the signal will be
tranposed to form a `Signal1D`.
style : {'overlap', 'cascade', 'mosaic', 'heatmap'}
The style of the plot.
color : matplotlib color or a list of them or `None`
Sets the color of the lines of the plots (no action on 'heatmap').
If a list, if its length is less than the number of spectra to plot,
the colors will be cycled. If `None`, use default matplotlib color
cycle.
line_style: matplotlib line style or a list of them or `None`
Sets the line style of the plots (no action on 'heatmap').
The main line style are '-','--','steps','-.',':'.
If a list, if its length is less than the number of
spectra to plot, line_style will be cycled. If
If `None`, use continuous lines, eg: ('-','--','steps','-.',':')
padding : float, optional, default 0.1
Option for "cascade". 1 guarantees that there is not overlapping.
However, in many cases a value between 0 and 1 can produce a tighter
plot without overlapping. Negative values have the same effect but
reverse the order of the spectra without reversing the order of the
colors.
legend: None or list of str or 'auto'
If list of string, legend for "cascade" or title for "mosaic" is
displayed. If 'auto', the title of each spectra (metadata.General.title)
is used.
legend_picking: bool
If true, a spectrum can be toggle on and off by clicking on
the legended line.
legend_loc : str or int
This parameter controls where the legend is placed on the figure;
see the pyplot.legend docstring for valid values
fig : matplotlib figure or None
If None, a default figure will be created. Specifying fig will
not work for the 'heatmap' style.
ax : matplotlib ax (subplot) or None
If None, a default ax will be created. Will not work for 'mosaic'
or 'heatmap' style.
**kwargs
remaining keyword arguments are passed to matplotlib.figure() or
matplotlib.subplots(). Has no effect on 'heatmap' style.
Example
-------
>>> s = hs.load("some_spectra")
>>> hs.plot.plot_spectra(s, style='cascade', color='red', padding=0.5)
To save the plot as a png-file
>>> hs.plot.plot_spectra(s).figure.savefig("test.png")
Returns
-------
ax: matplotlib axes or list of matplotlib axes
An array is returned when `style` is "mosaic".
"""
import hyperspy.signal
def _reverse_legend(ax_, legend_loc_):
"""
Reverse the ordering of a matplotlib legend (to be more consistent
with the default ordering of plots in the 'cascade' and 'overlap'
styles
Parameters
----------
ax_: matplotlib axes
legend_loc_: str or int
This parameter controls where the legend is placed on the
figure; see the pyplot.legend docstring for valid values
"""
l = ax_.get_legend()
labels = [lb.get_text() for lb in list(l.get_texts())]
handles = l.legendHandles
ax_.legend(handles[::-1], labels[::-1], loc=legend_loc_)
# Before v1.3 default would read the value from prefereces.
if style == "default":
style = "overlap"
if color is not None:
if isinstance(color, str):
color = itertools.cycle([color])
elif hasattr(color, "__iter__"):
color = itertools.cycle(color)
else:
raise ValueError("Color must be None, a valid matplotlib color "
"string or a list of valid matplotlib colors.")
else:
if LooseVersion(mpl.__version__) >= "1.5.3":
color = itertools.cycle(
plt.rcParams['axes.prop_cycle'].by_key()["color"])
else:
color = itertools.cycle(plt.rcParams['axes.color_cycle'])
if line_style is not None:
if isinstance(line_style, str):
line_style = itertools.cycle([line_style])
elif hasattr(line_style, "__iter__"):
line_style = itertools.cycle(line_style)
else:
raise ValueError("line_style must be None, a valid matplotlib"
" line_style string or a list of valid matplotlib"
" line_style.")
else:
line_style = ['-'] * len(spectra)
if legend is not None:
if isinstance(legend, str):
if legend == 'auto':
legend = [spec.metadata.General.title for spec in spectra]
else:
raise ValueError("legend must be None, 'auto' or a list of"
" string")
elif hasattr(legend, "__iter__"):
legend = itertools.cycle(legend)
if style == 'overlap':
if fig is None:
fig = plt.figure(**kwargs)
if ax is None:
ax = fig.add_subplot(111)
_make_overlap_plot(spectra,
ax,
color=color,
line_style=line_style,)
if legend is not None:
ax.legend(legend, loc=legend_loc)
_reverse_legend(ax, legend_loc)
if legend_picking is True:
animate_legend(fig=fig, ax=ax)
elif style == 'cascade':
if fig is None:
fig = plt.figure(**kwargs)
if ax is None:
ax = fig.add_subplot(111)
_make_cascade_subplot(spectra,
ax,
color=color,
line_style=line_style,
padding=padding)
if legend is not None:
plt.legend(legend, loc=legend_loc)
_reverse_legend(ax, legend_loc)
elif style == 'mosaic':
default_fsize = plt.rcParams["figure.figsize"]
figsize = (default_fsize[0], default_fsize[1] * len(spectra))
fig, subplots = plt.subplots(
len(spectra), 1, figsize=figsize, **kwargs)
if legend is None:
legend = [legend] * len(spectra)
for spectrum, ax, color, line_style, legend in zip(
spectra, subplots, color, line_style, legend):
spectrum = _transpose_if_required(spectrum, 1)
_plot_spectrum(spectrum, ax, color=color, line_style=line_style)
ax.set_ylabel('Intensity')
if legend is not None:
ax.set_title(legend)
if not isinstance(spectra, hyperspy.signal.BaseSignal):
_set_spectrum_xlabel(spectrum, ax)
if isinstance(spectra, hyperspy.signal.BaseSignal):
_set_spectrum_xlabel(spectrum, ax)
fig.tight_layout()
elif style == 'heatmap':
if not isinstance(spectra, hyperspy.signal.BaseSignal):
import hyperspy.utils
spectra = [_transpose_if_required(spectrum, 1) for spectrum in
spectra]
spectra = hyperspy.utils.stack(spectra)
with spectra.unfolded():
ax = _make_heatmap_subplot(spectra)
ax.set_ylabel('Spectra')
ax = ax if style != "mosaic" else subplots
return ax
def animate_legend(fig=None, ax=None):
"""Animate the legend of a figure.
A spectrum can be toggle on and off by clicking on the legended line.
Parameters
----------
fig: None | matplotlib.figure
If None pick the current figure using "plt.gcf"
ax: None | matplotlib.axes
If None pick the current axes using "plt.gca".
Note
----
Code inspired from legend_picking.py in the matplotlib gallery
"""
if fig is None:
fig = plt.gcf()
if ax is None:
ax = plt.gca()
lines = ax.lines[::-1]
lined = dict()
leg = ax.get_legend()
for legline, origline in zip(leg.get_lines(), lines):
legline.set_picker(5) # 5 pts tolerance
lined[legline] = origline
def onpick(event):
# on the pick event, find the orig line corresponding to the
# legend proxy line, and toggle the visibility
legline = event.artist
if legline.axes == ax:
origline = lined[legline]
vis = not origline.get_visible()
origline.set_visible(vis)
# Change the alpha on the line in the legend so we can see what lines
# have been toggled
if vis:
legline.set_alpha(1.0)
else:
legline.set_alpha(0.2)
fig.canvas.draw_idle()
fig.canvas.mpl_connect('pick_event', onpick)
def plot_histograms(signal_list,
bins='freedman',
range_bins=None,
color=None,
line_style=None,
legend='auto',
fig=None,
**kwargs):
"""Plot the histogram of every signal in the list in the same figure.
This function creates a histogram for each signal and plot the list with
the `utils.plot.plot_spectra` function.
Parameters
----------
signal_list : iterable
Ordered spectra list to plot. If `style` is "cascade" or "mosaic"
the spectra can have different size and axes.
bins : int or list or str, optional
If bins is a string, then it must be one of:
'knuth' : use Knuth's rule to determine bins
'scotts' : use Scott's rule to determine bins
'freedman' : use the Freedman-diaconis rule to determine bins
'blocks' : use bayesian blocks for dynamic bin widths
range_bins : tuple or None, optional.
the minimum and maximum range for the histogram. If not specified,
it will be (x.min(), x.max())
color : valid matplotlib color or a list of them or `None`, optional.
Sets the color of the lines of the plots. If a list, if its length is
less than the number of spectra to plot, the colors will be cycled. If
If `None`, use default matplotlib color cycle.
line_style: valid matplotlib line style or a list of them or `None`,
optional.
The main line style are '-','--','steps','-.',':'.
If a list, if its length is less than the number of
spectra to plot, line_style will be cycled. If
If `None`, use continuous lines, eg: ('-','--','steps','-.',':')
legend: None or list of str or 'auto', optional.
Display a legend. If 'auto', the title of each spectra
(metadata.General.title) is used.
legend_picking: bool, optional.
If true, a spectrum can be toggle on and off by clicking on
the legended line.
fig : matplotlib figure or None, optional.
If None, a default figure will be created.
**kwargs
other keyword arguments (weight and density) are described in
np.histogram().
Example
-------
Histograms of two random chi-square distributions
>>> img = hs.signals.Signal2D(np.random.chisquare(1,[10,10,100]))
>>> img2 = hs.signals.Signal2D(np.random.chisquare(2,[10,10,100]))
>>> hs.plot.plot_histograms([img,img2],legend=['hist1','hist2'])
Returns
-------
ax: matplotlib axes or list of matplotlib axes
An array is returned when `style` is "mosaic".
"""
hists = []
for obj in signal_list:
hists.append(obj.get_histogram(bins=bins,
range_bins=range_bins, **kwargs))
if line_style is None:
line_style = 'steps'
return plot_spectra(hists, style='overlap', color=color,
line_style=line_style, legend=legend, fig=fig)
| gpl-3.0 |
linegpe/FYS3150 | Project4/expect_random_T1.py | 1 | 3161 | import numpy as np
import matplotlib.pyplot as plt
data1 = np.loadtxt("expect_random_T1.00.dat")
data2 = np.loadtxt("expect_ordered_T1.00.dat")
data3 = np.loadtxt("expect_random2_T2.40.dat")
data4 = np.loadtxt("expect_ordered2_T2.40.dat")
values1 = data1[0::1]
values2 = data2[0::1]
values3 = data3[0::1]
values4 = data4[0::1]
N1 = len(values1)
x1 = np.linspace(0,N1,N1)
N2 = len(values3)
x2 = np.linspace(0,N2,N2)
figure1 = plt.figure()
labels = figure1.add_subplot(111)
# Turn off axis lines and ticks of the big subplot
labels.spines['top'].set_color('none')
labels.spines['bottom'].set_color('none')
labels.spines['left'].set_color('none')
labels.spines['right'].set_color('none')
labels.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off')
plt.xlabel("Number of Monte Carlo cycles",fontsize=15)
plt.ylabel("Mean energy per spin",fontsize=15)
#figure1.yaxis.set_ticks_position(right)
#figure1.ylabel.set_ticks_position('left')
#figure1.yaxis.tick_right()
fig1 = figure1.add_subplot(211)
fig1.plot(x1,values1[:,0],label="Random initial spins, T=1")
fig1.plot(x1,values2[:,0],label="Ordered initial spins, T=1")
fig1.tick_params(axis='x', labelsize=15) #HOW TO PUT THIS ON THE RIGHT SIDE?
fig1.tick_params(axis='y', labelsize=15)
fig1.yaxis.tick_right()
#plt.ylabel(r"$\langle E\rangle /L^2$",fontsize=17)
#plt.xlabel("Number of Monte Carlo cycles",fontsize=15)
plt.legend()
plt.axis([0,N1,-3,0])
#plt.show()
fig2 = figure1.add_subplot(212)
fig2.plot(x2,values3[:,0],label="Random initial spins, T=2.4")
fig2.plot(x2,values4[:,0],label="Ordered initial spins, T=2.4")
fig2.tick_params(axis='x', labelsize=15)
fig2.tick_params(axis='y', labelsize=15)
fig2.yaxis.tick_right()
#plt.ylabel(r"$\langle E\rangle /L^2$",fontsize=15)
#plt.xlabel("Number of Monte Carlo cycles",fontsize=15)
plt.legend()
plt.axis([0,50000,-2,-0.4])
plt.show()
figure2 = plt.figure()
labels = figure2.add_subplot(111)
labels.spines['top'].set_color('none')
labels.spines['bottom'].set_color('none')
labels.spines['left'].set_color('none')
labels.spines['right'].set_color('none')
labels.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off')
plt.xlabel("Number of Monte Carlo cycles",fontsize=15)
plt.ylabel("Absolute magnetization per spin",fontsize=15)
fig1 = figure2.add_subplot(211)
fig1.plot(x1,values1[:,1],label="Random initial spins, T=1")
fig1.plot(x1,values2[:,1],label="Ordered initial spins, T=1")
fig1.tick_params(axis='x', labelsize=15)
fig1.tick_params(axis='y', labelsize=15)
fig1.yaxis.tick_right()
#fig2.ylabel(r"$abs(\langle M \rangle /L^2)$",fontsize=15)
#fig2.xlabel("Number of Monte Carlo cycles",fontsize=15)
plt.legend()
plt.axis([0,N1,0.2,1.6])
#plt.show()
fig2 = figure2.add_subplot(212)
fig2.plot(x2,values3[:,1],label="Random initial spins, T=2.4")
fig2.plot(x2,values4[:,1],label="Ordered initial spins, T=2.4")
fig2.tick_params(axis='x', labelsize=15)
fig2.tick_params(axis='y', labelsize=15)
fig2.yaxis.tick_right()
#plt.ylabel(r"$abs(\langle M\rangle / L^2)$",fontsize=15)
#plt.xlabel("Number of Monte Carlo cycles",fontsize=15)
plt.legend()
#plt.axis([0,8e6,-0.1,1.4])
plt.show() | gpl-3.0 |
abimannans/scikit-learn | examples/tree/plot_tree_regression_multioutput.py | 206 | 1800 | """
===================================================================
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
regr_1 = DecisionTreeRegressor(max_depth=2)
regr_2 = DecisionTreeRegressor(max_depth=5)
regr_3 = DecisionTreeRegressor(max_depth=8)
regr_1.fit(X, y)
regr_2.fit(X, y)
regr_3.fit(X, y)
# Predict
X_test = np.arange(-100.0, 100.0, 0.01)[:, np.newaxis]
y_1 = regr_1.predict(X_test)
y_2 = regr_2.predict(X_test)
y_3 = regr_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 |
darshanthaker/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/_cm.py | 70 | 375423 | """
Color data and pre-defined cmap objects.
This is a helper for cm.py, originally part of that file.
Separating the data (this file) from cm.py makes both easier
to deal with.
Objects visible in cm.py are the individual cmap objects ('autumn',
etc.) and a dictionary, 'datad', including all of these objects.
"""
import matplotlib as mpl
import matplotlib.colors as colors
LUTSIZE = mpl.rcParams['image.lut']
_binary_data = {
'red' : ((0., 1., 1.), (1., 0., 0.)),
'green': ((0., 1., 1.), (1., 0., 0.)),
'blue' : ((0., 1., 1.), (1., 0., 0.))
}
_bone_data = {'red': ((0., 0., 0.),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(1.0, 1.0, 1.0)),
'blue': ((0., 0., 0.),(1.0, 1.0, 1.0))}
_autumn_data = {'red': ((0., 1.0, 1.0),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(1.0, 1.0, 1.0)),
'blue': ((0., 0., 0.),(1.0, 0., 0.))}
_bone_data = {'red': ((0., 0., 0.),(0.746032, 0.652778, 0.652778),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(0.365079, 0.319444, 0.319444),
(0.746032, 0.777778, 0.777778),(1.0, 1.0, 1.0)),
'blue': ((0., 0., 0.),(0.365079, 0.444444, 0.444444),(1.0, 1.0, 1.0))}
_cool_data = {'red': ((0., 0., 0.), (1.0, 1.0, 1.0)),
'green': ((0., 1., 1.), (1.0, 0., 0.)),
'blue': ((0., 1., 1.), (1.0, 1., 1.))}
_copper_data = {'red': ((0., 0., 0.),(0.809524, 1.000000, 1.000000),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(1.0, 0.7812, 0.7812)),
'blue': ((0., 0., 0.),(1.0, 0.4975, 0.4975))}
_flag_data = {'red': ((0., 1., 1.),(0.015873, 1.000000, 1.000000),
(0.031746, 0.000000, 0.000000),(0.047619, 0.000000, 0.000000),
(0.063492, 1.000000, 1.000000),(0.079365, 1.000000, 1.000000),
(0.095238, 0.000000, 0.000000),(0.111111, 0.000000, 0.000000),
(0.126984, 1.000000, 1.000000),(0.142857, 1.000000, 1.000000),
(0.158730, 0.000000, 0.000000),(0.174603, 0.000000, 0.000000),
(0.190476, 1.000000, 1.000000),(0.206349, 1.000000, 1.000000),
(0.222222, 0.000000, 0.000000),(0.238095, 0.000000, 0.000000),
(0.253968, 1.000000, 1.000000),(0.269841, 1.000000, 1.000000),
(0.285714, 0.000000, 0.000000),(0.301587, 0.000000, 0.000000),
(0.317460, 1.000000, 1.000000),(0.333333, 1.000000, 1.000000),
(0.349206, 0.000000, 0.000000),(0.365079, 0.000000, 0.000000),
(0.380952, 1.000000, 1.000000),(0.396825, 1.000000, 1.000000),
(0.412698, 0.000000, 0.000000),(0.428571, 0.000000, 0.000000),
(0.444444, 1.000000, 1.000000),(0.460317, 1.000000, 1.000000),
(0.476190, 0.000000, 0.000000),(0.492063, 0.000000, 0.000000),
(0.507937, 1.000000, 1.000000),(0.523810, 1.000000, 1.000000),
(0.539683, 0.000000, 0.000000),(0.555556, 0.000000, 0.000000),
(0.571429, 1.000000, 1.000000),(0.587302, 1.000000, 1.000000),
(0.603175, 0.000000, 0.000000),(0.619048, 0.000000, 0.000000),
(0.634921, 1.000000, 1.000000),(0.650794, 1.000000, 1.000000),
(0.666667, 0.000000, 0.000000),(0.682540, 0.000000, 0.000000),
(0.698413, 1.000000, 1.000000),(0.714286, 1.000000, 1.000000),
(0.730159, 0.000000, 0.000000),(0.746032, 0.000000, 0.000000),
(0.761905, 1.000000, 1.000000),(0.777778, 1.000000, 1.000000),
(0.793651, 0.000000, 0.000000),(0.809524, 0.000000, 0.000000),
(0.825397, 1.000000, 1.000000),(0.841270, 1.000000, 1.000000),
(0.857143, 0.000000, 0.000000),(0.873016, 0.000000, 0.000000),
(0.888889, 1.000000, 1.000000),(0.904762, 1.000000, 1.000000),
(0.920635, 0.000000, 0.000000),(0.936508, 0.000000, 0.000000),
(0.952381, 1.000000, 1.000000),(0.968254, 1.000000, 1.000000),
(0.984127, 0.000000, 0.000000),(1.0, 0., 0.)),
'green': ((0., 0., 0.),(0.015873, 1.000000, 1.000000),
(0.031746, 0.000000, 0.000000),(0.063492, 0.000000, 0.000000),
(0.079365, 1.000000, 1.000000),(0.095238, 0.000000, 0.000000),
(0.126984, 0.000000, 0.000000),(0.142857, 1.000000, 1.000000),
(0.158730, 0.000000, 0.000000),(0.190476, 0.000000, 0.000000),
(0.206349, 1.000000, 1.000000),(0.222222, 0.000000, 0.000000),
(0.253968, 0.000000, 0.000000),(0.269841, 1.000000, 1.000000),
(0.285714, 0.000000, 0.000000),(0.317460, 0.000000, 0.000000),
(0.333333, 1.000000, 1.000000),(0.349206, 0.000000, 0.000000),
(0.380952, 0.000000, 0.000000),(0.396825, 1.000000, 1.000000),
(0.412698, 0.000000, 0.000000),(0.444444, 0.000000, 0.000000),
(0.460317, 1.000000, 1.000000),(0.476190, 0.000000, 0.000000),
(0.507937, 0.000000, 0.000000),(0.523810, 1.000000, 1.000000),
(0.539683, 0.000000, 0.000000),(0.571429, 0.000000, 0.000000),
(0.587302, 1.000000, 1.000000),(0.603175, 0.000000, 0.000000),
(0.634921, 0.000000, 0.000000),(0.650794, 1.000000, 1.000000),
(0.666667, 0.000000, 0.000000),(0.698413, 0.000000, 0.000000),
(0.714286, 1.000000, 1.000000),(0.730159, 0.000000, 0.000000),
(0.761905, 0.000000, 0.000000),(0.777778, 1.000000, 1.000000),
(0.793651, 0.000000, 0.000000),(0.825397, 0.000000, 0.000000),
(0.841270, 1.000000, 1.000000),(0.857143, 0.000000, 0.000000),
(0.888889, 0.000000, 0.000000),(0.904762, 1.000000, 1.000000),
(0.920635, 0.000000, 0.000000),(0.952381, 0.000000, 0.000000),
(0.968254, 1.000000, 1.000000),(0.984127, 0.000000, 0.000000),
(1.0, 0., 0.)),
'blue': ((0., 0., 0.),(0.015873, 1.000000, 1.000000),
(0.031746, 1.000000, 1.000000),(0.047619, 0.000000, 0.000000),
(0.063492, 0.000000, 0.000000),(0.079365, 1.000000, 1.000000),
(0.095238, 1.000000, 1.000000),(0.111111, 0.000000, 0.000000),
(0.126984, 0.000000, 0.000000),(0.142857, 1.000000, 1.000000),
(0.158730, 1.000000, 1.000000),(0.174603, 0.000000, 0.000000),
(0.190476, 0.000000, 0.000000),(0.206349, 1.000000, 1.000000),
(0.222222, 1.000000, 1.000000),(0.238095, 0.000000, 0.000000),
(0.253968, 0.000000, 0.000000),(0.269841, 1.000000, 1.000000),
(0.285714, 1.000000, 1.000000),(0.301587, 0.000000, 0.000000),
(0.317460, 0.000000, 0.000000),(0.333333, 1.000000, 1.000000),
(0.349206, 1.000000, 1.000000),(0.365079, 0.000000, 0.000000),
(0.380952, 0.000000, 0.000000),(0.396825, 1.000000, 1.000000),
(0.412698, 1.000000, 1.000000),(0.428571, 0.000000, 0.000000),
(0.444444, 0.000000, 0.000000),(0.460317, 1.000000, 1.000000),
(0.476190, 1.000000, 1.000000),(0.492063, 0.000000, 0.000000),
(0.507937, 0.000000, 0.000000),(0.523810, 1.000000, 1.000000),
(0.539683, 1.000000, 1.000000),(0.555556, 0.000000, 0.000000),
(0.571429, 0.000000, 0.000000),(0.587302, 1.000000, 1.000000),
(0.603175, 1.000000, 1.000000),(0.619048, 0.000000, 0.000000),
(0.634921, 0.000000, 0.000000),(0.650794, 1.000000, 1.000000),
(0.666667, 1.000000, 1.000000),(0.682540, 0.000000, 0.000000),
(0.698413, 0.000000, 0.000000),(0.714286, 1.000000, 1.000000),
(0.730159, 1.000000, 1.000000),(0.746032, 0.000000, 0.000000),
(0.761905, 0.000000, 0.000000),(0.777778, 1.000000, 1.000000),
(0.793651, 1.000000, 1.000000),(0.809524, 0.000000, 0.000000),
(0.825397, 0.000000, 0.000000),(0.841270, 1.000000, 1.000000),
(0.857143, 1.000000, 1.000000),(0.873016, 0.000000, 0.000000),
(0.888889, 0.000000, 0.000000),(0.904762, 1.000000, 1.000000),
(0.920635, 1.000000, 1.000000),(0.936508, 0.000000, 0.000000),
(0.952381, 0.000000, 0.000000),(0.968254, 1.000000, 1.000000),
(0.984127, 1.000000, 1.000000),(1.0, 0., 0.))}
_gray_data = {'red': ((0., 0, 0), (1., 1, 1)),
'green': ((0., 0, 0), (1., 1, 1)),
'blue': ((0., 0, 0), (1., 1, 1))}
_hot_data = {'red': ((0., 0.0416, 0.0416),(0.365079, 1.000000, 1.000000),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(0.365079, 0.000000, 0.000000),
(0.746032, 1.000000, 1.000000),(1.0, 1.0, 1.0)),
'blue': ((0., 0., 0.),(0.746032, 0.000000, 0.000000),(1.0, 1.0, 1.0))}
_hsv_data = {'red': ((0., 1., 1.),(0.158730, 1.000000, 1.000000),
(0.174603, 0.968750, 0.968750),(0.333333, 0.031250, 0.031250),
(0.349206, 0.000000, 0.000000),(0.666667, 0.000000, 0.000000),
(0.682540, 0.031250, 0.031250),(0.841270, 0.968750, 0.968750),
(0.857143, 1.000000, 1.000000),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(0.158730, 0.937500, 0.937500),
(0.174603, 1.000000, 1.000000),(0.507937, 1.000000, 1.000000),
(0.666667, 0.062500, 0.062500),(0.682540, 0.000000, 0.000000),
(1.0, 0., 0.)),
'blue': ((0., 0., 0.),(0.333333, 0.000000, 0.000000),
(0.349206, 0.062500, 0.062500),(0.507937, 1.000000, 1.000000),
(0.841270, 1.000000, 1.000000),(0.857143, 0.937500, 0.937500),
(1.0, 0.09375, 0.09375))}
_jet_data = {'red': ((0., 0, 0), (0.35, 0, 0), (0.66, 1, 1), (0.89,1, 1),
(1, 0.5, 0.5)),
'green': ((0., 0, 0), (0.125,0, 0), (0.375,1, 1), (0.64,1, 1),
(0.91,0,0), (1, 0, 0)),
'blue': ((0., 0.5, 0.5), (0.11, 1, 1), (0.34, 1, 1), (0.65,0, 0),
(1, 0, 0))}
_pink_data = {'red': ((0., 0.1178, 0.1178),(0.015873, 0.195857, 0.195857),
(0.031746, 0.250661, 0.250661),(0.047619, 0.295468, 0.295468),
(0.063492, 0.334324, 0.334324),(0.079365, 0.369112, 0.369112),
(0.095238, 0.400892, 0.400892),(0.111111, 0.430331, 0.430331),
(0.126984, 0.457882, 0.457882),(0.142857, 0.483867, 0.483867),
(0.158730, 0.508525, 0.508525),(0.174603, 0.532042, 0.532042),
(0.190476, 0.554563, 0.554563),(0.206349, 0.576204, 0.576204),
(0.222222, 0.597061, 0.597061),(0.238095, 0.617213, 0.617213),
(0.253968, 0.636729, 0.636729),(0.269841, 0.655663, 0.655663),
(0.285714, 0.674066, 0.674066),(0.301587, 0.691980, 0.691980),
(0.317460, 0.709441, 0.709441),(0.333333, 0.726483, 0.726483),
(0.349206, 0.743134, 0.743134),(0.365079, 0.759421, 0.759421),
(0.380952, 0.766356, 0.766356),(0.396825, 0.773229, 0.773229),
(0.412698, 0.780042, 0.780042),(0.428571, 0.786796, 0.786796),
(0.444444, 0.793492, 0.793492),(0.460317, 0.800132, 0.800132),
(0.476190, 0.806718, 0.806718),(0.492063, 0.813250, 0.813250),
(0.507937, 0.819730, 0.819730),(0.523810, 0.826160, 0.826160),
(0.539683, 0.832539, 0.832539),(0.555556, 0.838870, 0.838870),
(0.571429, 0.845154, 0.845154),(0.587302, 0.851392, 0.851392),
(0.603175, 0.857584, 0.857584),(0.619048, 0.863731, 0.863731),
(0.634921, 0.869835, 0.869835),(0.650794, 0.875897, 0.875897),
(0.666667, 0.881917, 0.881917),(0.682540, 0.887896, 0.887896),
(0.698413, 0.893835, 0.893835),(0.714286, 0.899735, 0.899735),
(0.730159, 0.905597, 0.905597),(0.746032, 0.911421, 0.911421),
(0.761905, 0.917208, 0.917208),(0.777778, 0.922958, 0.922958),
(0.793651, 0.928673, 0.928673),(0.809524, 0.934353, 0.934353),
(0.825397, 0.939999, 0.939999),(0.841270, 0.945611, 0.945611),
(0.857143, 0.951190, 0.951190),(0.873016, 0.956736, 0.956736),
(0.888889, 0.962250, 0.962250),(0.904762, 0.967733, 0.967733),
(0.920635, 0.973185, 0.973185),(0.936508, 0.978607, 0.978607),
(0.952381, 0.983999, 0.983999),(0.968254, 0.989361, 0.989361),
(0.984127, 0.994695, 0.994695),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(0.015873, 0.102869, 0.102869),
(0.031746, 0.145479, 0.145479),(0.047619, 0.178174, 0.178174),
(0.063492, 0.205738, 0.205738),(0.079365, 0.230022, 0.230022),
(0.095238, 0.251976, 0.251976),(0.111111, 0.272166, 0.272166),
(0.126984, 0.290957, 0.290957),(0.142857, 0.308607, 0.308607),
(0.158730, 0.325300, 0.325300),(0.174603, 0.341178, 0.341178),
(0.190476, 0.356348, 0.356348),(0.206349, 0.370899, 0.370899),
(0.222222, 0.384900, 0.384900),(0.238095, 0.398410, 0.398410),
(0.253968, 0.411476, 0.411476),(0.269841, 0.424139, 0.424139),
(0.285714, 0.436436, 0.436436),(0.301587, 0.448395, 0.448395),
(0.317460, 0.460044, 0.460044),(0.333333, 0.471405, 0.471405),
(0.349206, 0.482498, 0.482498),(0.365079, 0.493342, 0.493342),
(0.380952, 0.517549, 0.517549),(0.396825, 0.540674, 0.540674),
(0.412698, 0.562849, 0.562849),(0.428571, 0.584183, 0.584183),
(0.444444, 0.604765, 0.604765),(0.460317, 0.624669, 0.624669),
(0.476190, 0.643958, 0.643958),(0.492063, 0.662687, 0.662687),
(0.507937, 0.680900, 0.680900),(0.523810, 0.698638, 0.698638),
(0.539683, 0.715937, 0.715937),(0.555556, 0.732828, 0.732828),
(0.571429, 0.749338, 0.749338),(0.587302, 0.765493, 0.765493),
(0.603175, 0.781313, 0.781313),(0.619048, 0.796819, 0.796819),
(0.634921, 0.812029, 0.812029),(0.650794, 0.826960, 0.826960),
(0.666667, 0.841625, 0.841625),(0.682540, 0.856040, 0.856040),
(0.698413, 0.870216, 0.870216),(0.714286, 0.884164, 0.884164),
(0.730159, 0.897896, 0.897896),(0.746032, 0.911421, 0.911421),
(0.761905, 0.917208, 0.917208),(0.777778, 0.922958, 0.922958),
(0.793651, 0.928673, 0.928673),(0.809524, 0.934353, 0.934353),
(0.825397, 0.939999, 0.939999),(0.841270, 0.945611, 0.945611),
(0.857143, 0.951190, 0.951190),(0.873016, 0.956736, 0.956736),
(0.888889, 0.962250, 0.962250),(0.904762, 0.967733, 0.967733),
(0.920635, 0.973185, 0.973185),(0.936508, 0.978607, 0.978607),
(0.952381, 0.983999, 0.983999),(0.968254, 0.989361, 0.989361),
(0.984127, 0.994695, 0.994695),(1.0, 1.0, 1.0)),
'blue': ((0., 0., 0.),(0.015873, 0.102869, 0.102869),
(0.031746, 0.145479, 0.145479),(0.047619, 0.178174, 0.178174),
(0.063492, 0.205738, 0.205738),(0.079365, 0.230022, 0.230022),
(0.095238, 0.251976, 0.251976),(0.111111, 0.272166, 0.272166),
(0.126984, 0.290957, 0.290957),(0.142857, 0.308607, 0.308607),
(0.158730, 0.325300, 0.325300),(0.174603, 0.341178, 0.341178),
(0.190476, 0.356348, 0.356348),(0.206349, 0.370899, 0.370899),
(0.222222, 0.384900, 0.384900),(0.238095, 0.398410, 0.398410),
(0.253968, 0.411476, 0.411476),(0.269841, 0.424139, 0.424139),
(0.285714, 0.436436, 0.436436),(0.301587, 0.448395, 0.448395),
(0.317460, 0.460044, 0.460044),(0.333333, 0.471405, 0.471405),
(0.349206, 0.482498, 0.482498),(0.365079, 0.493342, 0.493342),
(0.380952, 0.503953, 0.503953),(0.396825, 0.514344, 0.514344),
(0.412698, 0.524531, 0.524531),(0.428571, 0.534522, 0.534522),
(0.444444, 0.544331, 0.544331),(0.460317, 0.553966, 0.553966),
(0.476190, 0.563436, 0.563436),(0.492063, 0.572750, 0.572750),
(0.507937, 0.581914, 0.581914),(0.523810, 0.590937, 0.590937),
(0.539683, 0.599824, 0.599824),(0.555556, 0.608581, 0.608581),
(0.571429, 0.617213, 0.617213),(0.587302, 0.625727, 0.625727),
(0.603175, 0.634126, 0.634126),(0.619048, 0.642416, 0.642416),
(0.634921, 0.650600, 0.650600),(0.650794, 0.658682, 0.658682),
(0.666667, 0.666667, 0.666667),(0.682540, 0.674556, 0.674556),
(0.698413, 0.682355, 0.682355),(0.714286, 0.690066, 0.690066),
(0.730159, 0.697691, 0.697691),(0.746032, 0.705234, 0.705234),
(0.761905, 0.727166, 0.727166),(0.777778, 0.748455, 0.748455),
(0.793651, 0.769156, 0.769156),(0.809524, 0.789314, 0.789314),
(0.825397, 0.808969, 0.808969),(0.841270, 0.828159, 0.828159),
(0.857143, 0.846913, 0.846913),(0.873016, 0.865261, 0.865261),
(0.888889, 0.883229, 0.883229),(0.904762, 0.900837, 0.900837),
(0.920635, 0.918109, 0.918109),(0.936508, 0.935061, 0.935061),
(0.952381, 0.951711, 0.951711),(0.968254, 0.968075, 0.968075),
(0.984127, 0.984167, 0.984167),(1.0, 1.0, 1.0))}
_prism_data = {'red': ((0., 1., 1.),(0.031746, 1.000000, 1.000000),
(0.047619, 0.000000, 0.000000),(0.063492, 0.000000, 0.000000),
(0.079365, 0.666667, 0.666667),(0.095238, 1.000000, 1.000000),
(0.126984, 1.000000, 1.000000),(0.142857, 0.000000, 0.000000),
(0.158730, 0.000000, 0.000000),(0.174603, 0.666667, 0.666667),
(0.190476, 1.000000, 1.000000),(0.222222, 1.000000, 1.000000),
(0.238095, 0.000000, 0.000000),(0.253968, 0.000000, 0.000000),
(0.269841, 0.666667, 0.666667),(0.285714, 1.000000, 1.000000),
(0.317460, 1.000000, 1.000000),(0.333333, 0.000000, 0.000000),
(0.349206, 0.000000, 0.000000),(0.365079, 0.666667, 0.666667),
(0.380952, 1.000000, 1.000000),(0.412698, 1.000000, 1.000000),
(0.428571, 0.000000, 0.000000),(0.444444, 0.000000, 0.000000),
(0.460317, 0.666667, 0.666667),(0.476190, 1.000000, 1.000000),
(0.507937, 1.000000, 1.000000),(0.523810, 0.000000, 0.000000),
(0.539683, 0.000000, 0.000000),(0.555556, 0.666667, 0.666667),
(0.571429, 1.000000, 1.000000),(0.603175, 1.000000, 1.000000),
(0.619048, 0.000000, 0.000000),(0.634921, 0.000000, 0.000000),
(0.650794, 0.666667, 0.666667),(0.666667, 1.000000, 1.000000),
(0.698413, 1.000000, 1.000000),(0.714286, 0.000000, 0.000000),
(0.730159, 0.000000, 0.000000),(0.746032, 0.666667, 0.666667),
(0.761905, 1.000000, 1.000000),(0.793651, 1.000000, 1.000000),
(0.809524, 0.000000, 0.000000),(0.825397, 0.000000, 0.000000),
(0.841270, 0.666667, 0.666667),(0.857143, 1.000000, 1.000000),
(0.888889, 1.000000, 1.000000),(0.904762, 0.000000, 0.000000),
(0.920635, 0.000000, 0.000000),(0.936508, 0.666667, 0.666667),
(0.952381, 1.000000, 1.000000),(0.984127, 1.000000, 1.000000),
(1.0, 0.0, 0.0)),
'green': ((0., 0., 0.),(0.031746, 1.000000, 1.000000),
(0.047619, 1.000000, 1.000000),(0.063492, 0.000000, 0.000000),
(0.095238, 0.000000, 0.000000),(0.126984, 1.000000, 1.000000),
(0.142857, 1.000000, 1.000000),(0.158730, 0.000000, 0.000000),
(0.190476, 0.000000, 0.000000),(0.222222, 1.000000, 1.000000),
(0.238095, 1.000000, 1.000000),(0.253968, 0.000000, 0.000000),
(0.285714, 0.000000, 0.000000),(0.317460, 1.000000, 1.000000),
(0.333333, 1.000000, 1.000000),(0.349206, 0.000000, 0.000000),
(0.380952, 0.000000, 0.000000),(0.412698, 1.000000, 1.000000),
(0.428571, 1.000000, 1.000000),(0.444444, 0.000000, 0.000000),
(0.476190, 0.000000, 0.000000),(0.507937, 1.000000, 1.000000),
(0.523810, 1.000000, 1.000000),(0.539683, 0.000000, 0.000000),
(0.571429, 0.000000, 0.000000),(0.603175, 1.000000, 1.000000),
(0.619048, 1.000000, 1.000000),(0.634921, 0.000000, 0.000000),
(0.666667, 0.000000, 0.000000),(0.698413, 1.000000, 1.000000),
(0.714286, 1.000000, 1.000000),(0.730159, 0.000000, 0.000000),
(0.761905, 0.000000, 0.000000),(0.793651, 1.000000, 1.000000),
(0.809524, 1.000000, 1.000000),(0.825397, 0.000000, 0.000000),
(0.857143, 0.000000, 0.000000),(0.888889, 1.000000, 1.000000),
(0.904762, 1.000000, 1.000000),(0.920635, 0.000000, 0.000000),
(0.952381, 0.000000, 0.000000),(0.984127, 1.000000, 1.000000),
(1.0, 1.0, 1.0)),
'blue': ((0., 0., 0.),(0.047619, 0.000000, 0.000000),
(0.063492, 1.000000, 1.000000),(0.079365, 1.000000, 1.000000),
(0.095238, 0.000000, 0.000000),(0.142857, 0.000000, 0.000000),
(0.158730, 1.000000, 1.000000),(0.174603, 1.000000, 1.000000),
(0.190476, 0.000000, 0.000000),(0.238095, 0.000000, 0.000000),
(0.253968, 1.000000, 1.000000),(0.269841, 1.000000, 1.000000),
(0.285714, 0.000000, 0.000000),(0.333333, 0.000000, 0.000000),
(0.349206, 1.000000, 1.000000),(0.365079, 1.000000, 1.000000),
(0.380952, 0.000000, 0.000000),(0.428571, 0.000000, 0.000000),
(0.444444, 1.000000, 1.000000),(0.460317, 1.000000, 1.000000),
(0.476190, 0.000000, 0.000000),(0.523810, 0.000000, 0.000000),
(0.539683, 1.000000, 1.000000),(0.555556, 1.000000, 1.000000),
(0.571429, 0.000000, 0.000000),(0.619048, 0.000000, 0.000000),
(0.634921, 1.000000, 1.000000),(0.650794, 1.000000, 1.000000),
(0.666667, 0.000000, 0.000000),(0.714286, 0.000000, 0.000000),
(0.730159, 1.000000, 1.000000),(0.746032, 1.000000, 1.000000),
(0.761905, 0.000000, 0.000000),(0.809524, 0.000000, 0.000000),
(0.825397, 1.000000, 1.000000),(0.841270, 1.000000, 1.000000),
(0.857143, 0.000000, 0.000000),(0.904762, 0.000000, 0.000000),
(0.920635, 1.000000, 1.000000),(0.936508, 1.000000, 1.000000),
(0.952381, 0.000000, 0.000000),(1.0, 0.0, 0.0))}
_spring_data = {'red': ((0., 1., 1.),(1.0, 1.0, 1.0)),
'green': ((0., 0., 0.),(1.0, 1.0, 1.0)),
'blue': ((0., 1., 1.),(1.0, 0.0, 0.0))}
_summer_data = {'red': ((0., 0., 0.),(1.0, 1.0, 1.0)),
'green': ((0., 0.5, 0.5),(1.0, 1.0, 1.0)),
'blue': ((0., 0.4, 0.4),(1.0, 0.4, 0.4))}
_winter_data = {'red': ((0., 0., 0.),(1.0, 0.0, 0.0)),
'green': ((0., 0., 0.),(1.0, 1.0, 1.0)),
'blue': ((0., 1., 1.),(1.0, 0.5, 0.5))}
_spectral_data = {'red': [(0.0, 0.0, 0.0), (0.05, 0.4667, 0.4667),
(0.10, 0.5333, 0.5333), (0.15, 0.0, 0.0),
(0.20, 0.0, 0.0), (0.25, 0.0, 0.0),
(0.30, 0.0, 0.0), (0.35, 0.0, 0.0),
(0.40, 0.0, 0.0), (0.45, 0.0, 0.0),
(0.50, 0.0, 0.0), (0.55, 0.0, 0.0),
(0.60, 0.0, 0.0), (0.65, 0.7333, 0.7333),
(0.70, 0.9333, 0.9333), (0.75, 1.0, 1.0),
(0.80, 1.0, 1.0), (0.85, 1.0, 1.0),
(0.90, 0.8667, 0.8667), (0.95, 0.80, 0.80),
(1.0, 0.80, 0.80)],
'green': [(0.0, 0.0, 0.0), (0.05, 0.0, 0.0),
(0.10, 0.0, 0.0), (0.15, 0.0, 0.0),
(0.20, 0.0, 0.0), (0.25, 0.4667, 0.4667),
(0.30, 0.6000, 0.6000), (0.35, 0.6667, 0.6667),
(0.40, 0.6667, 0.6667), (0.45, 0.6000, 0.6000),
(0.50, 0.7333, 0.7333), (0.55, 0.8667, 0.8667),
(0.60, 1.0, 1.0), (0.65, 1.0, 1.0),
(0.70, 0.9333, 0.9333), (0.75, 0.8000, 0.8000),
(0.80, 0.6000, 0.6000), (0.85, 0.0, 0.0),
(0.90, 0.0, 0.0), (0.95, 0.0, 0.0),
(1.0, 0.80, 0.80)],
'blue': [(0.0, 0.0, 0.0), (0.05, 0.5333, 0.5333),
(0.10, 0.6000, 0.6000), (0.15, 0.6667, 0.6667),
(0.20, 0.8667, 0.8667), (0.25, 0.8667, 0.8667),
(0.30, 0.8667, 0.8667), (0.35, 0.6667, 0.6667),
(0.40, 0.5333, 0.5333), (0.45, 0.0, 0.0),
(0.5, 0.0, 0.0), (0.55, 0.0, 0.0),
(0.60, 0.0, 0.0), (0.65, 0.0, 0.0),
(0.70, 0.0, 0.0), (0.75, 0.0, 0.0),
(0.80, 0.0, 0.0), (0.85, 0.0, 0.0),
(0.90, 0.0, 0.0), (0.95, 0.0, 0.0),
(1.0, 0.80, 0.80)]}
autumn = colors.LinearSegmentedColormap('autumn', _autumn_data, LUTSIZE)
bone = colors.LinearSegmentedColormap('bone ', _bone_data, LUTSIZE)
binary = colors.LinearSegmentedColormap('binary ', _binary_data, LUTSIZE)
cool = colors.LinearSegmentedColormap('cool', _cool_data, LUTSIZE)
copper = colors.LinearSegmentedColormap('copper', _copper_data, LUTSIZE)
flag = colors.LinearSegmentedColormap('flag', _flag_data, LUTSIZE)
gray = colors.LinearSegmentedColormap('gray', _gray_data, LUTSIZE)
hot = colors.LinearSegmentedColormap('hot', _hot_data, LUTSIZE)
hsv = colors.LinearSegmentedColormap('hsv', _hsv_data, LUTSIZE)
jet = colors.LinearSegmentedColormap('jet', _jet_data, LUTSIZE)
pink = colors.LinearSegmentedColormap('pink', _pink_data, LUTSIZE)
prism = colors.LinearSegmentedColormap('prism', _prism_data, LUTSIZE)
spring = colors.LinearSegmentedColormap('spring', _spring_data, LUTSIZE)
summer = colors.LinearSegmentedColormap('summer', _summer_data, LUTSIZE)
winter = colors.LinearSegmentedColormap('winter', _winter_data, LUTSIZE)
spectral = colors.LinearSegmentedColormap('spectral', _spectral_data, LUTSIZE)
datad = {
'autumn': _autumn_data,
'bone': _bone_data,
'binary': _binary_data,
'cool': _cool_data,
'copper': _copper_data,
'flag': _flag_data,
'gray' : _gray_data,
'hot': _hot_data,
'hsv': _hsv_data,
'jet' : _jet_data,
'pink': _pink_data,
'prism': _prism_data,
'spring': _spring_data,
'summer': _summer_data,
'winter': _winter_data,
'spectral': _spectral_data
}
# 34 colormaps based on color specifications and designs
# developed by Cynthia Brewer (http://colorbrewer.org).
# The ColorBrewer palettes have been included under the terms
# of an Apache-stype license (for details, see the file
# LICENSE_COLORBREWER in the license directory of the matplotlib
# source distribution).
_Accent_data = {'blue': [(0.0, 0.49803921580314636,
0.49803921580314636), (0.14285714285714285, 0.83137255907058716,
0.83137255907058716), (0.2857142857142857, 0.52549022436141968,
0.52549022436141968), (0.42857142857142855, 0.60000002384185791,
0.60000002384185791), (0.5714285714285714, 0.69019609689712524,
0.69019609689712524), (0.7142857142857143, 0.49803921580314636,
0.49803921580314636), (0.8571428571428571, 0.090196080505847931,
0.090196080505847931), (1.0, 0.40000000596046448,
0.40000000596046448)],
'green': [(0.0, 0.78823530673980713, 0.78823530673980713),
(0.14285714285714285, 0.68235296010971069, 0.68235296010971069),
(0.2857142857142857, 0.75294119119644165, 0.75294119119644165),
(0.42857142857142855, 1.0, 1.0), (0.5714285714285714,
0.42352941632270813, 0.42352941632270813), (0.7142857142857143,
0.0078431377187371254, 0.0078431377187371254),
(0.8571428571428571, 0.35686275362968445, 0.35686275362968445),
(1.0, 0.40000000596046448, 0.40000000596046448)],
'red': [(0.0, 0.49803921580314636, 0.49803921580314636),
(0.14285714285714285, 0.7450980544090271, 0.7450980544090271),
(0.2857142857142857, 0.99215686321258545, 0.99215686321258545),
(0.42857142857142855, 1.0, 1.0), (0.5714285714285714,
0.21960784494876862, 0.21960784494876862), (0.7142857142857143,
0.94117647409439087, 0.94117647409439087), (0.8571428571428571,
0.74901962280273438, 0.74901962280273438), (1.0,
0.40000000596046448, 0.40000000596046448)]}
_Blues_data = {'blue': [(0.0, 1.0, 1.0), (0.125, 0.9686274528503418,
0.9686274528503418), (0.25, 0.93725490570068359, 0.93725490570068359),
(0.375, 0.88235294818878174, 0.88235294818878174), (0.5,
0.83921569585800171, 0.83921569585800171), (0.625, 0.7764706015586853,
0.7764706015586853), (0.75, 0.70980393886566162, 0.70980393886566162),
(0.875, 0.61176472902297974, 0.61176472902297974), (1.0,
0.41960784792900085, 0.41960784792900085)],
'green': [(0.0, 0.9843137264251709, 0.9843137264251709), (0.125,
0.92156863212585449, 0.92156863212585449), (0.25,
0.85882353782653809, 0.85882353782653809), (0.375,
0.7921568751335144, 0.7921568751335144), (0.5,
0.68235296010971069, 0.68235296010971069), (0.625,
0.57254904508590698, 0.57254904508590698), (0.75,
0.44313725829124451, 0.44313725829124451), (0.875,
0.31764706969261169, 0.31764706969261169), (1.0,
0.18823529779911041, 0.18823529779911041)],
'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125,
0.87058824300765991, 0.87058824300765991), (0.25,
0.7764706015586853, 0.7764706015586853), (0.375,
0.61960786581039429, 0.61960786581039429), (0.5,
0.41960784792900085, 0.41960784792900085), (0.625,
0.25882354378700256, 0.25882354378700256), (0.75,
0.12941177189350128, 0.12941177189350128), (0.875,
0.031372550874948502, 0.031372550874948502), (1.0,
0.031372550874948502, 0.031372550874948502)]}
_BrBG_data = {'blue': [(0.0, 0.019607843831181526,
0.019607843831181526), (0.10000000000000001, 0.039215687662363052,
0.039215687662363052), (0.20000000000000001, 0.17647059261798859,
0.17647059261798859), (0.29999999999999999, 0.49019607901573181,
0.49019607901573181), (0.40000000000000002, 0.76470589637756348,
0.76470589637756348), (0.5, 0.96078431606292725, 0.96078431606292725),
(0.59999999999999998, 0.89803922176361084, 0.89803922176361084),
(0.69999999999999996, 0.75686275959014893, 0.75686275959014893),
(0.80000000000000004, 0.56078433990478516, 0.56078433990478516),
(0.90000000000000002, 0.36862745881080627, 0.36862745881080627), (1.0,
0.18823529779911041, 0.18823529779911041)],
'green': [(0.0, 0.18823529779911041, 0.18823529779911041),
(0.10000000000000001, 0.31764706969261169, 0.31764706969261169),
(0.20000000000000001, 0.5058823823928833, 0.5058823823928833),
(0.29999999999999999, 0.7607843279838562, 0.7607843279838562),
(0.40000000000000002, 0.90980392694473267, 0.90980392694473267),
(0.5, 0.96078431606292725, 0.96078431606292725),
(0.59999999999999998, 0.91764706373214722, 0.91764706373214722),
(0.69999999999999996, 0.80392158031463623, 0.80392158031463623),
(0.80000000000000004, 0.59215688705444336, 0.59215688705444336),
(0.90000000000000002, 0.40000000596046448, 0.40000000596046448),
(1.0, 0.23529411852359772, 0.23529411852359772)],
'red': [(0.0, 0.32941177487373352, 0.32941177487373352),
(0.10000000000000001, 0.54901963472366333, 0.54901963472366333),
(0.20000000000000001, 0.74901962280273438, 0.74901962280273438),
(0.29999999999999999, 0.87450981140136719, 0.87450981140136719),
(0.40000000000000002, 0.96470588445663452, 0.96470588445663452),
(0.5, 0.96078431606292725, 0.96078431606292725),
(0.59999999999999998, 0.78039216995239258, 0.78039216995239258),
(0.69999999999999996, 0.50196081399917603, 0.50196081399917603),
(0.80000000000000004, 0.20784313976764679, 0.20784313976764679),
(0.90000000000000002, 0.0039215688593685627,
0.0039215688593685627), (1.0, 0.0, 0.0)]}
_BuGn_data = {'blue': [(0.0, 0.99215686321258545,
0.99215686321258545), (0.125, 0.97647058963775635,
0.97647058963775635), (0.25, 0.90196079015731812,
0.90196079015731812), (0.375, 0.78823530673980713,
0.78823530673980713), (0.5, 0.64313727617263794, 0.64313727617263794),
(0.625, 0.46274510025978088, 0.46274510025978088), (0.75,
0.27058824896812439, 0.27058824896812439), (0.875,
0.17254902422428131, 0.17254902422428131), (1.0, 0.10588235408067703,
0.10588235408067703)],
'green': [(0.0, 0.98823529481887817, 0.98823529481887817), (0.125,
0.96078431606292725, 0.96078431606292725), (0.25,
0.92549020051956177, 0.92549020051956177), (0.375,
0.84705883264541626, 0.84705883264541626), (0.5,
0.7607843279838562, 0.7607843279838562), (0.625,
0.68235296010971069, 0.68235296010971069), (0.75,
0.54509806632995605, 0.54509806632995605), (0.875,
0.42745098471641541, 0.42745098471641541), (1.0,
0.26666668057441711, 0.26666668057441711)], 'red': [(0.0,
0.9686274528503418, 0.9686274528503418), (0.125,
0.89803922176361084, 0.89803922176361084), (0.25,
0.80000001192092896, 0.80000001192092896), (0.375,
0.60000002384185791, 0.60000002384185791), (0.5,
0.40000000596046448, 0.40000000596046448), (0.625,
0.25490197539329529, 0.25490197539329529), (0.75,
0.13725490868091583, 0.13725490868091583), (0.875, 0.0, 0.0),
(1.0, 0.0, 0.0)]}
_BuPu_data = {'blue': [(0.0, 0.99215686321258545,
0.99215686321258545), (0.125, 0.95686274766921997,
0.95686274766921997), (0.25, 0.90196079015731812,
0.90196079015731812), (0.375, 0.85490196943283081,
0.85490196943283081), (0.5, 0.7764706015586853, 0.7764706015586853),
(0.625, 0.69411766529083252, 0.69411766529083252), (0.75,
0.61568629741668701, 0.61568629741668701), (0.875,
0.48627451062202454, 0.48627451062202454), (1.0, 0.29411765933036804,
0.29411765933036804)],
'green': [(0.0, 0.98823529481887817, 0.98823529481887817), (0.125,
0.92549020051956177, 0.92549020051956177), (0.25,
0.82745099067687988, 0.82745099067687988), (0.375,
0.73725491762161255, 0.73725491762161255), (0.5,
0.58823531866073608, 0.58823531866073608), (0.625,
0.41960784792900085, 0.41960784792900085), (0.75,
0.25490197539329529, 0.25490197539329529), (0.875,
0.058823529630899429, 0.058823529630899429), (1.0, 0.0, 0.0)],
'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125,
0.87843137979507446, 0.87843137979507446), (0.25,
0.74901962280273438, 0.74901962280273438), (0.375,
0.61960786581039429, 0.61960786581039429), (0.5,
0.54901963472366333, 0.54901963472366333), (0.625,
0.54901963472366333, 0.54901963472366333), (0.75,
0.53333336114883423, 0.53333336114883423), (0.875,
0.5058823823928833, 0.5058823823928833), (1.0,
0.30196079611778259, 0.30196079611778259)]}
_Dark2_data = {'blue': [(0.0, 0.46666666865348816,
0.46666666865348816), (0.14285714285714285, 0.0078431377187371254,
0.0078431377187371254), (0.2857142857142857, 0.70196080207824707,
0.70196080207824707), (0.42857142857142855, 0.54117649793624878,
0.54117649793624878), (0.5714285714285714, 0.11764705926179886,
0.11764705926179886), (0.7142857142857143, 0.0078431377187371254,
0.0078431377187371254), (0.8571428571428571, 0.11372549086809158,
0.11372549086809158), (1.0, 0.40000000596046448,
0.40000000596046448)],
'green': [(0.0, 0.61960786581039429, 0.61960786581039429),
(0.14285714285714285, 0.37254902720451355, 0.37254902720451355),
(0.2857142857142857, 0.43921568989753723, 0.43921568989753723),
(0.42857142857142855, 0.16078431904315948, 0.16078431904315948),
(0.5714285714285714, 0.65098041296005249, 0.65098041296005249),
(0.7142857142857143, 0.67058825492858887, 0.67058825492858887),
(0.8571428571428571, 0.46274510025978088, 0.46274510025978088),
(1.0, 0.40000000596046448, 0.40000000596046448)],
'red': [(0.0, 0.10588235408067703, 0.10588235408067703),
(0.14285714285714285, 0.85098040103912354, 0.85098040103912354),
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_GnBu_data = {'blue': [(0.0, 0.94117647409439087,
0.94117647409439087), (0.125, 0.85882353782653809,
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0.70980393886566162), (0.5, 0.76862746477127075, 0.76862746477127075),
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_Greens_data = {'blue': [(0.0, 0.96078431606292725,
0.96078431606292725), (0.125, 0.87843137979507446,
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_Pastel1_data = {'blue': [(0.0, 0.68235296010971069,
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_Pastel2_data = {'blue': [(0.0, 0.80392158031463623,
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_PiYG_data = {'blue': [(0.0, 0.32156863808631897,
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_PRGn_data = {'blue': [(0.0, 0.29411765933036804,
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0.26666668057441711, 0.26666668057441711)],
'red': [(0.0, 0.25098040699958801, 0.25098040699958801),
(0.10000000000000001, 0.46274510025978088, 0.46274510025978088),
(0.20000000000000001, 0.60000002384185791, 0.60000002384185791),
(0.29999999999999999, 0.7607843279838562, 0.7607843279838562),
(0.40000000000000002, 0.90588235855102539, 0.90588235855102539),
(0.5, 0.9686274528503418, 0.9686274528503418),
(0.59999999999999998, 0.85098040103912354, 0.85098040103912354),
(0.69999999999999996, 0.65098041296005249, 0.65098041296005249),
(0.80000000000000004, 0.35294118523597717, 0.35294118523597717),
(0.90000000000000002, 0.10588235408067703, 0.10588235408067703),
(1.0, 0.0, 0.0)]}
_PuBu_data = {'blue': [(0.0, 0.9843137264251709, 0.9843137264251709),
(0.125, 0.94901961088180542, 0.94901961088180542), (0.25,
0.90196079015731812, 0.90196079015731812), (0.375,
0.85882353782653809, 0.85882353782653809), (0.5, 0.81176471710205078,
0.81176471710205078), (0.625, 0.75294119119644165,
0.75294119119644165), (0.75, 0.69019609689712524,
0.69019609689712524), (0.875, 0.55294120311737061,
0.55294120311737061), (1.0, 0.34509804844856262,
0.34509804844856262)],
'green': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125,
0.90588235855102539, 0.90588235855102539), (0.25,
0.81960785388946533, 0.81960785388946533), (0.375,
0.74117648601531982, 0.74117648601531982), (0.5,
0.66274511814117432, 0.66274511814117432), (0.625,
0.56470590829849243, 0.56470590829849243), (0.75,
0.43921568989753723, 0.43921568989753723), (0.875,
0.35294118523597717, 0.35294118523597717), (1.0,
0.21960784494876862, 0.21960784494876862)],
'red': [(0.0, 1.0, 1.0), (0.125, 0.92549020051956177,
0.92549020051956177), (0.25, 0.81568628549575806,
0.81568628549575806), (0.375, 0.65098041296005249,
0.65098041296005249), (0.5, 0.45490196347236633,
0.45490196347236633), (0.625, 0.21176470816135406,
0.21176470816135406), (0.75, 0.019607843831181526,
0.019607843831181526), (0.875, 0.015686275437474251,
0.015686275437474251), (1.0, 0.0078431377187371254,
0.0078431377187371254)]}
_PuBuGn_data = {'blue': [(0.0, 0.9843137264251709,
0.9843137264251709), (0.125, 0.94117647409439087,
0.94117647409439087), (0.25, 0.90196079015731812,
0.90196079015731812), (0.375, 0.85882353782653809,
0.85882353782653809), (0.5, 0.81176471710205078, 0.81176471710205078),
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0.3490196168422699), (1.0, 0.21176470816135406, 0.21176470816135406)],
'green': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125,
0.88627451658248901, 0.88627451658248901), (0.25,
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0.74117648601531982, 0.74117648601531982), (0.5,
0.66274511814117432, 0.66274511814117432), (0.625,
0.56470590829849243, 0.56470590829849243), (0.75,
0.5058823823928833, 0.5058823823928833), (0.875,
0.42352941632270813, 0.42352941632270813), (1.0,
0.27450981736183167, 0.27450981736183167)],
'red': [(0.0, 1.0, 1.0), (0.125, 0.92549020051956177,
0.92549020051956177), (0.25, 0.81568628549575806,
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0.65098041296005249), (0.5, 0.40392157435417175,
0.40392157435417175), (0.625, 0.21176470816135406,
0.21176470816135406), (0.75, 0.0078431377187371254,
0.0078431377187371254), (0.875, 0.0039215688593685627,
0.0039215688593685627), (1.0, 0.0039215688593685627,
0.0039215688593685627)]}
_PuOr_data = {'blue': [(0.0, 0.031372550874948502,
0.031372550874948502), (0.10000000000000001, 0.023529412224888802,
0.023529412224888802), (0.20000000000000001, 0.078431375324726105,
0.078431375324726105), (0.29999999999999999, 0.38823530077934265,
0.38823530077934265), (0.40000000000000002, 0.7137255072593689,
0.7137255072593689), (0.5, 0.9686274528503418, 0.9686274528503418),
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(0.90000000000000002, 0.53333336114883423, 0.53333336114883423), (1.0,
0.29411765933036804, 0.29411765933036804)],
'green': [(0.0, 0.23137255012989044, 0.23137255012989044),
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(0.5, 0.9686274528503418, 0.9686274528503418),
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'red': [(0.0, 0.49803921580314636, 0.49803921580314636),
(0.10000000000000001, 0.70196080207824707, 0.70196080207824707),
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(0.5, 0.9686274528503418, 0.9686274528503418),
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(0.90000000000000002, 0.32941177487373352, 0.32941177487373352),
(1.0, 0.17647059261798859, 0.17647059261798859)]}
_PuRd_data = {'blue': [(0.0, 0.97647058963775635,
0.97647058963775635), (0.125, 0.93725490570068359,
0.93725490570068359), (0.25, 0.85490196943283081,
0.85490196943283081), (0.375, 0.78039216995239258,
0.78039216995239258), (0.5, 0.69019609689712524, 0.69019609689712524),
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0.33725491166114807, 0.33725491166114807), (0.875,
0.26274511218070984, 0.26274511218070984), (1.0, 0.12156862765550613,
0.12156862765550613)],
'green': [(0.0, 0.95686274766921997, 0.95686274766921997), (0.125,
0.88235294818878174, 0.88235294818878174), (0.25,
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0.58039218187332153, 0.58039218187332153), (0.5,
0.3960784375667572, 0.3960784375667572), (0.625,
0.16078431904315948, 0.16078431904315948), (0.75,
0.070588238537311554, 0.070588238537311554), (0.875, 0.0, 0.0),
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'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125,
0.90588235855102539, 0.90588235855102539), (0.25,
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0.87450981140136719, 0.87450981140136719), (0.625,
0.90588235855102539, 0.90588235855102539), (0.75,
0.80784314870834351, 0.80784314870834351), (0.875,
0.59607845544815063, 0.59607845544815063), (1.0,
0.40392157435417175, 0.40392157435417175)]}
_Purples_data = {'blue': [(0.0, 0.99215686321258545,
0.99215686321258545), (0.125, 0.96078431606292725,
0.96078431606292725), (0.25, 0.92156863212585449,
0.92156863212585449), (0.375, 0.86274510622024536,
0.86274510622024536), (0.5, 0.78431373834609985, 0.78431373834609985),
(0.625, 0.729411780834198, 0.729411780834198), (0.75,
0.63921570777893066, 0.63921570777893066), (0.875,
0.56078433990478516, 0.56078433990478516), (1.0, 0.49019607901573181,
0.49019607901573181)],
'green': [(0.0, 0.9843137264251709, 0.9843137264251709), (0.125,
0.92941176891326904, 0.92941176891326904), (0.25,
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'red': [(0.0, 0.98823529481887817, 0.98823529481887817), (0.125,
0.93725490570068359, 0.93725490570068359), (0.25,
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0.32941177487373352, 0.32941177487373352), (1.0,
0.24705882370471954, 0.24705882370471954)]}
_RdBu_data = {'blue': [(0.0, 0.12156862765550613,
0.12156862765550613), (0.10000000000000001, 0.16862745583057404,
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'red': [(0.0, 0.40392157435417175, 0.40392157435417175),
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_RdGy_data = {'blue': [(0.0, 0.12156862765550613,
0.12156862765550613), (0.10000000000000001, 0.16862745583057404,
0.16862745583057404), (0.20000000000000001, 0.30196079611778259,
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0.87843137979507446, 0.87843137979507446), (0.69999999999999996,
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0.30196079611778259, 0.30196079611778259), (1.0, 0.10196078568696976,
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'green': [(0.0, 0.0, 0.0), (0.10000000000000001,
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0.85882353782653809, 0.85882353782653809), (0.5, 1.0, 1.0),
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'red': [(0.0, 0.40392157435417175, 0.40392157435417175),
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_RdPu_data = {'blue': [(0.0, 0.9529411792755127, 0.9529411792755127),
(0.125, 0.86666667461395264, 0.86666667461395264), (0.25,
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0.46666666865348816), (1.0, 0.41568627953529358,
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'green': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125,
0.87843137979507446, 0.87843137979507446), (0.25,
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'red': [(0.0, 1.0, 1.0), (0.125, 0.99215686321258545,
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_RdYlBu_data = {'blue': [(0.0, 0.14901961386203766,
0.14901961386203766), (0.10000000149011612,
0.15294118225574493, 0.15294118225574493),
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0.26274511218070984), (0.30000001192092896,
0.3803921639919281, 0.3803921639919281),
(0.40000000596046448, 0.56470590829849243,
0.56470590829849243), (0.5, 0.74901962280273438,
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0.91372549533843994), (0.80000001192092896,
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(0.10000000149011612, 0.18823529779911041,
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0.85098040103912354, 0.85098040103912354),
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0.45882353186607361, 0.45882353186607361), (1.0,
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[(0.0, 0.64705884456634521, 0.64705884456634521),
(0.10000000149011612, 0.84313726425170898,
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1.0), (0.60000002384185791, 0.87843137979507446,
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0.67058825492858887, 0.67058825492858887),
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0.27058824896812439, 0.27058824896812439), (1.0,
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_RdYlGn_data = {'blue': [(0.0, 0.14901961386203766,
0.14901961386203766), (0.10000000000000001, 0.15294118225574493,
0.15294118225574493), (0.20000000000000001, 0.26274511218070984,
0.26274511218070984), (0.29999999999999999, 0.3803921639919281,
0.3803921639919281), (0.40000000000000002, 0.54509806632995605,
0.54509806632995605), (0.5, 0.74901962280273438, 0.74901962280273438),
(0.59999999999999998, 0.54509806632995605, 0.54509806632995605),
(0.69999999999999996, 0.41568627953529358, 0.41568627953529358),
(0.80000000000000004, 0.38823530077934265, 0.38823530077934265),
(0.90000000000000002, 0.31372550129890442, 0.31372550129890442), (1.0,
0.21568627655506134, 0.21568627655506134)],
'green': [(0.0, 0.0, 0.0), (0.10000000000000001,
0.18823529779911041, 0.18823529779911041), (0.20000000000000001,
0.42745098471641541, 0.42745098471641541), (0.29999999999999999,
0.68235296010971069, 0.68235296010971069), (0.40000000000000002,
0.87843137979507446, 0.87843137979507446), (0.5, 1.0, 1.0),
(0.59999999999999998, 0.93725490570068359, 0.93725490570068359),
(0.69999999999999996, 0.85098040103912354, 0.85098040103912354),
(0.80000000000000004, 0.74117648601531982, 0.74117648601531982),
(0.90000000000000002, 0.59607845544815063, 0.59607845544815063),
(1.0, 0.40784314274787903, 0.40784314274787903)],
'red': [(0.0, 0.64705884456634521, 0.64705884456634521),
(0.10000000000000001, 0.84313726425170898, 0.84313726425170898),
(0.20000000000000001, 0.95686274766921997, 0.95686274766921997),
(0.29999999999999999, 0.99215686321258545, 0.99215686321258545),
(0.40000000000000002, 0.99607843160629272, 0.99607843160629272),
(0.5, 1.0, 1.0), (0.59999999999999998, 0.85098040103912354,
0.85098040103912354), (0.69999999999999996, 0.65098041296005249,
0.65098041296005249), (0.80000000000000004, 0.40000000596046448,
0.40000000596046448), (0.90000000000000002, 0.10196078568696976,
0.10196078568696976), (1.0, 0.0, 0.0)]}
_Reds_data = {'blue': [(0.0, 0.94117647409439087,
0.94117647409439087), (0.125, 0.82352942228317261,
0.82352942228317261), (0.25, 0.63137257099151611,
0.63137257099151611), (0.375, 0.44705882668495178,
0.44705882668495178), (0.5, 0.29019609093666077, 0.29019609093666077),
(0.625, 0.17254902422428131, 0.17254902422428131), (0.75,
0.11372549086809158, 0.11372549086809158), (0.875,
0.08235294371843338, 0.08235294371843338), (1.0, 0.050980392843484879,
0.050980392843484879)],
'green': [(0.0, 0.96078431606292725, 0.96078431606292725), (0.125,
0.87843137979507446, 0.87843137979507446), (0.25,
0.73333334922790527, 0.73333334922790527), (0.375,
0.57254904508590698, 0.57254904508590698), (0.5,
0.41568627953529358, 0.41568627953529358), (0.625,
0.23137255012989044, 0.23137255012989044), (0.75,
0.094117648899555206, 0.094117648899555206), (0.875,
0.058823529630899429, 0.058823529630899429), (1.0, 0.0, 0.0)],
'red': [(0.0, 1.0, 1.0), (0.125, 0.99607843160629272,
0.99607843160629272), (0.25, 0.98823529481887817,
0.98823529481887817), (0.375, 0.98823529481887817,
0.98823529481887817), (0.5, 0.9843137264251709,
0.9843137264251709), (0.625, 0.93725490570068359,
0.93725490570068359), (0.75, 0.79607844352722168,
0.79607844352722168), (0.875, 0.64705884456634521,
0.64705884456634521), (1.0, 0.40392157435417175,
0.40392157435417175)]}
_Set1_data = {'blue': [(0.0, 0.10980392247438431,
0.10980392247438431), (0.125, 0.72156864404678345,
0.72156864404678345), (0.25, 0.29019609093666077,
0.29019609093666077), (0.375, 0.63921570777893066,
0.63921570777893066), (0.5, 0.0, 0.0), (0.625, 0.20000000298023224,
0.20000000298023224), (0.75, 0.15686275064945221,
0.15686275064945221), (0.875, 0.74901962280273438,
0.74901962280273438), (1.0, 0.60000002384185791,
0.60000002384185791)],
'green': [(0.0, 0.10196078568696976, 0.10196078568696976), (0.125,
0.49411764740943909, 0.49411764740943909), (0.25,
0.68627452850341797, 0.68627452850341797), (0.375,
0.30588236451148987, 0.30588236451148987), (0.5,
0.49803921580314636, 0.49803921580314636), (0.625, 1.0, 1.0),
(0.75, 0.33725491166114807, 0.33725491166114807), (0.875,
0.5058823823928833, 0.5058823823928833), (1.0,
0.60000002384185791, 0.60000002384185791)],
'red': [(0.0, 0.89411765336990356, 0.89411765336990356), (0.125,
0.21568627655506134, 0.21568627655506134), (0.25,
0.30196079611778259, 0.30196079611778259), (0.375,
0.59607845544815063, 0.59607845544815063), (0.5, 1.0, 1.0),
(0.625, 1.0, 1.0), (0.75, 0.65098041296005249,
0.65098041296005249), (0.875, 0.9686274528503418,
0.9686274528503418), (1.0, 0.60000002384185791,
0.60000002384185791)]}
_Set2_data = {'blue': [(0.0, 0.64705884456634521,
0.64705884456634521), (0.14285714285714285, 0.38431373238563538,
0.38431373238563538), (0.2857142857142857, 0.79607844352722168,
0.79607844352722168), (0.42857142857142855, 0.76470589637756348,
0.76470589637756348), (0.5714285714285714, 0.32941177487373352,
0.32941177487373352), (0.7142857142857143, 0.18431372940540314,
0.18431372940540314), (0.8571428571428571, 0.58039218187332153,
0.58039218187332153), (1.0, 0.70196080207824707,
0.70196080207824707)],
'green': [(0.0, 0.7607843279838562, 0.7607843279838562),
(0.14285714285714285, 0.55294120311737061, 0.55294120311737061),
(0.2857142857142857, 0.62745100259780884, 0.62745100259780884),
(0.42857142857142855, 0.54117649793624878, 0.54117649793624878),
(0.5714285714285714, 0.84705883264541626, 0.84705883264541626),
(0.7142857142857143, 0.85098040103912354, 0.85098040103912354),
(0.8571428571428571, 0.76862746477127075, 0.76862746477127075),
(1.0, 0.70196080207824707, 0.70196080207824707)],
'red': [(0.0, 0.40000000596046448, 0.40000000596046448),
(0.14285714285714285, 0.98823529481887817, 0.98823529481887817),
(0.2857142857142857, 0.55294120311737061, 0.55294120311737061),
(0.42857142857142855, 0.90588235855102539, 0.90588235855102539),
(0.5714285714285714, 0.65098041296005249, 0.65098041296005249),
(0.7142857142857143, 1.0, 1.0), (0.8571428571428571,
0.89803922176361084, 0.89803922176361084), (1.0,
0.70196080207824707, 0.70196080207824707)]}
_Set3_data = {'blue': [(0.0, 0.78039216995239258,
0.78039216995239258), (0.090909090909090912, 0.70196080207824707,
0.70196080207824707), (0.18181818181818182, 0.85490196943283081,
0.85490196943283081), (0.27272727272727271, 0.44705882668495178,
0.44705882668495178), (0.36363636363636365, 0.82745099067687988,
0.82745099067687988), (0.45454545454545453, 0.38431373238563538,
0.38431373238563538), (0.54545454545454541, 0.4117647111415863,
0.4117647111415863), (0.63636363636363635, 0.89803922176361084,
0.89803922176361084), (0.72727272727272729, 0.85098040103912354,
0.85098040103912354), (0.81818181818181823, 0.74117648601531982,
0.74117648601531982), (0.90909090909090906, 0.77254903316497803,
0.77254903316497803), (1.0, 0.43529412150382996,
0.43529412150382996)],
'green': [(0.0, 0.82745099067687988, 0.82745099067687988),
(0.090909090909090912, 1.0, 1.0), (0.18181818181818182,
0.729411780834198, 0.729411780834198), (0.27272727272727271,
0.50196081399917603, 0.50196081399917603), (0.36363636363636365,
0.69411766529083252, 0.69411766529083252), (0.45454545454545453,
0.70588237047195435, 0.70588237047195435), (0.54545454545454541,
0.87058824300765991, 0.87058824300765991), (0.63636363636363635,
0.80392158031463623, 0.80392158031463623), (0.72727272727272729,
0.85098040103912354, 0.85098040103912354), (0.81818181818181823,
0.50196081399917603, 0.50196081399917603), (0.90909090909090906,
0.92156863212585449, 0.92156863212585449), (1.0,
0.92941176891326904, 0.92941176891326904)],
'red': [(0.0, 0.55294120311737061, 0.55294120311737061),
(0.090909090909090912, 1.0, 1.0), (0.18181818181818182,
0.7450980544090271, 0.7450980544090271), (0.27272727272727271,
0.9843137264251709, 0.9843137264251709), (0.36363636363636365,
0.50196081399917603, 0.50196081399917603), (0.45454545454545453,
0.99215686321258545, 0.99215686321258545), (0.54545454545454541,
0.70196080207824707, 0.70196080207824707), (0.63636363636363635,
0.98823529481887817, 0.98823529481887817), (0.72727272727272729,
0.85098040103912354, 0.85098040103912354), (0.81818181818181823,
0.73725491762161255, 0.73725491762161255), (0.90909090909090906,
0.80000001192092896, 0.80000001192092896), (1.0, 1.0, 1.0)]}
_Spectral_data = {'blue': [(0.0, 0.25882354378700256,
0.25882354378700256), (0.10000000000000001, 0.30980393290519714,
0.30980393290519714), (0.20000000000000001, 0.26274511218070984,
0.26274511218070984), (0.29999999999999999, 0.3803921639919281,
0.3803921639919281), (0.40000000000000002, 0.54509806632995605,
0.54509806632995605), (0.5, 0.74901962280273438, 0.74901962280273438),
(0.59999999999999998, 0.59607845544815063, 0.59607845544815063),
(0.69999999999999996, 0.64313727617263794, 0.64313727617263794),
(0.80000000000000004, 0.64705884456634521, 0.64705884456634521),
(0.90000000000000002, 0.74117648601531982, 0.74117648601531982), (1.0,
0.63529413938522339, 0.63529413938522339)],
'green': [(0.0, 0.0039215688593685627, 0.0039215688593685627),
(0.10000000000000001, 0.24313725531101227, 0.24313725531101227),
(0.20000000000000001, 0.42745098471641541, 0.42745098471641541),
(0.29999999999999999, 0.68235296010971069, 0.68235296010971069),
(0.40000000000000002, 0.87843137979507446, 0.87843137979507446),
(0.5, 1.0, 1.0), (0.59999999999999998, 0.96078431606292725,
0.96078431606292725), (0.69999999999999996, 0.86666667461395264,
0.86666667461395264), (0.80000000000000004, 0.7607843279838562,
0.7607843279838562), (0.90000000000000002, 0.53333336114883423,
0.53333336114883423), (1.0, 0.30980393290519714,
0.30980393290519714)],
'red': [(0.0, 0.61960786581039429, 0.61960786581039429),
(0.10000000000000001, 0.83529412746429443, 0.83529412746429443),
(0.20000000000000001, 0.95686274766921997, 0.95686274766921997),
(0.29999999999999999, 0.99215686321258545, 0.99215686321258545),
(0.40000000000000002, 0.99607843160629272, 0.99607843160629272),
(0.5, 1.0, 1.0), (0.59999999999999998, 0.90196079015731812,
0.90196079015731812), (0.69999999999999996, 0.67058825492858887,
0.67058825492858887), (0.80000000000000004, 0.40000000596046448,
0.40000000596046448), (0.90000000000000002, 0.19607843458652496,
0.19607843458652496), (1.0, 0.36862745881080627,
0.36862745881080627)]}
_YlGn_data = {'blue': [(0.0, 0.89803922176361084,
0.89803922176361084), (0.125, 0.72549021244049072,
0.72549021244049072), (0.25, 0.63921570777893066,
0.63921570777893066), (0.375, 0.55686277151107788,
0.55686277151107788), (0.5, 0.47450980544090271, 0.47450980544090271),
(0.625, 0.364705890417099, 0.364705890417099), (0.75,
0.26274511218070984, 0.26274511218070984), (0.875,
0.21568627655506134, 0.21568627655506134), (1.0, 0.16078431904315948,
0.16078431904315948)],
'green': [(0.0, 1.0, 1.0), (0.125, 0.98823529481887817,
0.98823529481887817), (0.25, 0.94117647409439087,
0.94117647409439087), (0.375, 0.86666667461395264,
0.86666667461395264), (0.5, 0.7764706015586853,
0.7764706015586853), (0.625, 0.67058825492858887,
0.67058825492858887), (0.75, 0.51764708757400513,
0.51764708757400513), (0.875, 0.40784314274787903,
0.40784314274787903), (1.0, 0.27058824896812439,
0.27058824896812439)],
'red': [(0.0, 1.0, 1.0), (0.125, 0.9686274528503418,
0.9686274528503418), (0.25, 0.85098040103912354,
0.85098040103912354), (0.375, 0.67843139171600342,
0.67843139171600342), (0.5, 0.47058823704719543,
0.47058823704719543), (0.625, 0.25490197539329529,
0.25490197539329529), (0.75, 0.13725490868091583,
0.13725490868091583), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)]}
_YlGnBu_data = {'blue': [(0.0, 0.85098040103912354,
0.85098040103912354), (0.125, 0.69411766529083252,
0.69411766529083252), (0.25, 0.70588237047195435,
0.70588237047195435), (0.375, 0.73333334922790527,
0.73333334922790527), (0.5, 0.76862746477127075, 0.76862746477127075),
(0.625, 0.75294119119644165, 0.75294119119644165), (0.75,
0.65882354974746704, 0.65882354974746704), (0.875,
0.58039218187332153, 0.58039218187332153), (1.0, 0.34509804844856262,
0.34509804844856262)],
'green': [(0.0, 1.0, 1.0), (0.125, 0.97254902124404907,
0.97254902124404907), (0.25, 0.91372549533843994,
0.91372549533843994), (0.375, 0.80392158031463623,
0.80392158031463623), (0.5, 0.7137255072593689,
0.7137255072593689), (0.625, 0.56862747669219971,
0.56862747669219971), (0.75, 0.36862745881080627,
0.36862745881080627), (0.875, 0.20392157137393951,
0.20392157137393951), (1.0, 0.11372549086809158,
0.11372549086809158)],
'red': [(0.0, 1.0, 1.0), (0.125, 0.92941176891326904,
0.92941176891326904), (0.25, 0.78039216995239258,
0.78039216995239258), (0.375, 0.49803921580314636,
0.49803921580314636), (0.5, 0.25490197539329529,
0.25490197539329529), (0.625, 0.11372549086809158,
0.11372549086809158), (0.75, 0.13333334028720856,
0.13333334028720856), (0.875, 0.14509804546833038,
0.14509804546833038), (1.0, 0.031372550874948502,
0.031372550874948502)]}
_YlOrBr_data = {'blue': [(0.0, 0.89803922176361084,
0.89803922176361084), (0.125, 0.73725491762161255,
0.73725491762161255), (0.25, 0.56862747669219971,
0.56862747669219971), (0.375, 0.30980393290519714,
0.30980393290519714), (0.5, 0.16078431904315948, 0.16078431904315948),
(0.625, 0.078431375324726105, 0.078431375324726105), (0.75,
0.0078431377187371254, 0.0078431377187371254), (0.875,
0.015686275437474251, 0.015686275437474251), (1.0,
0.023529412224888802, 0.023529412224888802)],
'green': [(0.0, 1.0, 1.0), (0.125, 0.9686274528503418,
0.9686274528503418), (0.25, 0.89019608497619629,
0.89019608497619629), (0.375, 0.76862746477127075,
0.76862746477127075), (0.5, 0.60000002384185791,
0.60000002384185791), (0.625, 0.43921568989753723,
0.43921568989753723), (0.75, 0.29803922772407532,
0.29803922772407532), (0.875, 0.20392157137393951,
0.20392157137393951), (1.0, 0.14509804546833038,
0.14509804546833038)],
'red': [(0.0, 1.0, 1.0), (0.125, 1.0, 1.0), (0.25,
0.99607843160629272, 0.99607843160629272), (0.375,
0.99607843160629272, 0.99607843160629272), (0.5,
0.99607843160629272, 0.99607843160629272), (0.625,
0.92549020051956177, 0.92549020051956177), (0.75,
0.80000001192092896, 0.80000001192092896), (0.875,
0.60000002384185791, 0.60000002384185791), (1.0,
0.40000000596046448, 0.40000000596046448)]}
_YlOrRd_data = {'blue': [(0.0, 0.80000001192092896,
0.80000001192092896), (0.125, 0.62745100259780884,
0.62745100259780884), (0.25, 0.46274510025978088,
0.46274510025978088), (0.375, 0.29803922772407532,
0.29803922772407532), (0.5, 0.23529411852359772, 0.23529411852359772),
(0.625, 0.16470588743686676, 0.16470588743686676), (0.75,
0.10980392247438431, 0.10980392247438431), (0.875,
0.14901961386203766, 0.14901961386203766), (1.0, 0.14901961386203766,
0.14901961386203766)],
'green': [(0.0, 1.0, 1.0), (0.125, 0.92941176891326904,
0.92941176891326904), (0.25, 0.85098040103912354,
0.85098040103912354), (0.375, 0.69803923368453979,
0.69803923368453979), (0.5, 0.55294120311737061,
0.55294120311737061), (0.625, 0.30588236451148987,
0.30588236451148987), (0.75, 0.10196078568696976,
0.10196078568696976), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)],
'red': [(0.0, 1.0, 1.0), (0.125, 1.0, 1.0), (0.25,
0.99607843160629272, 0.99607843160629272), (0.375,
0.99607843160629272, 0.99607843160629272), (0.5,
0.99215686321258545, 0.99215686321258545), (0.625,
0.98823529481887817, 0.98823529481887817), (0.75,
0.89019608497619629, 0.89019608497619629), (0.875,
0.74117648601531982, 0.74117648601531982), (1.0,
0.50196081399917603, 0.50196081399917603)]}
# The next 7 palettes are from the Yorick scientific visalisation package,
# an evolution of the GIST package, both by David H. Munro.
# They are released under a BSD-like license (see LICENSE_YORICK in
# the license directory of the matplotlib source distribution).
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(0.48319327831268311, 0.51764708757400513, 0.51764708757400513),
(0.48739495873451233, 0.51372551918029785, 0.51372551918029785),
(0.49159663915634155, 0.50980395078659058, 0.50980395078659058),
(0.49579831957817078, 0.5058823823928833, 0.5058823823928833), (0.5,
0.50196081399917603, 0.50196081399917603), (0.50420171022415161,
0.49803921580314636, 0.49803921580314636), (0.50840336084365845,
0.49411764740943909, 0.49411764740943909), (0.51260507106781006,
0.49019607901573181, 0.49019607901573181), (0.51680672168731689,
0.48627451062202454, 0.48627451062202454), (0.52100843191146851,
0.48235294222831726, 0.48235294222831726), (0.52521008253097534,
0.47843137383460999, 0.47843137383460999), (0.52941179275512695,
0.47450980544090271, 0.47450980544090271), (0.53361344337463379,
0.47058823704719543, 0.47058823704719543), (0.5378151535987854,
0.46274510025978088, 0.46274510025978088), (0.54201680421829224,
0.45882353186607361, 0.45882353186607361), (0.54621851444244385,
0.45490196347236633, 0.45490196347236633), (0.55042016506195068,
0.45098039507865906, 0.45098039507865906), (0.55462187528610229,
0.44705882668495178, 0.44705882668495178), (0.55882352590560913,
0.44313725829124451, 0.44313725829124451), (0.56302523612976074,
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0.43529412150382996, 0.43529412150382996), (0.57142859697341919,
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0.42745098471641541, 0.42745098471641541), (0.57983195781707764,
0.42352941632270813, 0.42352941632270813), (0.58403360843658447,
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0.41568627953529358, 0.41568627953529358), (0.59243696928024292,
0.4117647111415863, 0.4117647111415863), (0.59663867950439453,
0.40784314274787903, 0.40784314274787903), (0.60084033012390137,
0.40000000596046448, 0.40000000596046448), (0.60504204034805298,
0.3960784375667572, 0.3960784375667572), (0.60924369096755981,
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0.38431373238563538, 0.38431373238563538), (0.62184876203536987,
0.3803921639919281, 0.3803921639919281), (0.62605041265487671,
0.37647059559822083, 0.37647059559822083), (0.63025212287902832,
0.37254902720451355, 0.37254902720451355), (0.63445377349853516,
0.36862745881080627, 0.36862745881080627), (0.63865548372268677,
0.364705890417099, 0.364705890417099), (0.6428571343421936,
0.36078432202339172, 0.36078432202339172), (0.64705884456634521,
0.35686275362968445, 0.35686275362968445), (0.65126049518585205,
0.35294118523597717, 0.35294118523597717), (0.65546220541000366,
0.3490196168422699, 0.3490196168422699), (0.6596638560295105,
0.34509804844856262, 0.34509804844856262), (0.66386556625366211,
0.33725491166114807, 0.33725491166114807), (0.66806721687316895,
0.3333333432674408, 0.3333333432674408), (0.67226892709732056,
0.32941177487373352, 0.32941177487373352), (0.67647057771682739,
0.32549020648002625, 0.32549020648002625), (0.680672287940979,
0.32156863808631897, 0.32156863808631897), (0.68487393856048584,
0.31764706969261169, 0.31764706969261169), (0.68907564878463745,
0.31372550129890442, 0.31372550129890442), (0.69327729940414429,
0.30980393290519714, 0.30980393290519714), (0.6974790096282959,
0.30588236451148987, 0.30588236451148987), (0.70168066024780273,
0.30196079611778259, 0.30196079611778259), (0.70588237047195435,
0.29803922772407532, 0.29803922772407532), (0.71008402109146118,
0.29411765933036804, 0.29411765933036804), (0.71428573131561279,
0.29019609093666077, 0.29019609093666077), (0.71848738193511963,
0.28627452254295349, 0.28627452254295349), (0.72268909215927124,
0.28235295414924622, 0.28235295414924622), (0.72689074277877808,
0.27450981736183167, 0.27450981736183167), (0.73109245300292969,
0.27058824896812439, 0.27058824896812439), (0.73529410362243652,
0.26666668057441711, 0.26666668057441711), (0.73949581384658813,
0.26274511218070984, 0.26274511218070984), (0.74369746446609497,
0.25882354378700256, 0.25882354378700256), (0.74789917469024658,
0.25490197539329529, 0.25490197539329529), (0.75210082530975342,
0.25098040699958801, 0.25098040699958801), (0.75630253553390503,
0.24705882370471954, 0.24705882370471954), (0.76050418615341187,
0.24313725531101227, 0.24313725531101227), (0.76470589637756348,
0.23921568691730499, 0.23921568691730499), (0.76890754699707031,
0.23529411852359772, 0.23529411852359772), (0.77310925722122192,
0.23137255012989044, 0.23137255012989044), (0.77731090784072876,
0.22745098173618317, 0.22745098173618317), (0.78151261806488037,
0.22352941334247589, 0.22352941334247589), (0.78571426868438721,
0.21960784494876862, 0.21960784494876862), (0.78991597890853882,
0.21176470816135406, 0.21176470816135406), (0.79411762952804565,
0.20784313976764679, 0.20784313976764679), (0.79831933975219727,
0.20392157137393951, 0.20392157137393951), (0.8025209903717041,
0.20000000298023224, 0.20000000298023224), (0.80672270059585571,
0.19607843458652496, 0.19607843458652496), (0.81092435121536255,
0.19215686619281769, 0.19215686619281769), (0.81512606143951416,
0.18823529779911041, 0.18823529779911041), (0.819327712059021,
0.18431372940540314, 0.18431372940540314), (0.82352942228317261,
0.18039216101169586, 0.18039216101169586), (0.82773107290267944,
0.17647059261798859, 0.17647059261798859), (0.83193278312683105,
0.17254902422428131, 0.17254902422428131), (0.83613443374633789,
0.16862745583057404, 0.16862745583057404), (0.8403361439704895,
0.16470588743686676, 0.16470588743686676), (0.84453779458999634,
0.16078431904315948, 0.16078431904315948), (0.84873950481414795,
0.15686275064945221, 0.15686275064945221), (0.85294115543365479,
0.14901961386203766, 0.14901961386203766), (0.8571428656578064,
0.14509804546833038, 0.14509804546833038), (0.86134451627731323,
0.14117647707462311, 0.14117647707462311), (0.86554622650146484,
0.13725490868091583, 0.13725490868091583), (0.86974787712097168,
0.13333334028720856, 0.13333334028720856), (0.87394958734512329,
0.12941177189350128, 0.12941177189350128), (0.87815123796463013,
0.12549020349979401, 0.12549020349979401), (0.88235294818878174,
0.12156862765550613, 0.12156862765550613), (0.88655459880828857,
0.11764705926179886, 0.11764705926179886), (0.89075630903244019,
0.11372549086809158, 0.11372549086809158), (0.89495795965194702,
0.10980392247438431, 0.10980392247438431), (0.89915966987609863,
0.10588235408067703, 0.10588235408067703), (0.90336132049560547,
0.10196078568696976, 0.10196078568696976), (0.90756303071975708,
0.098039217293262482, 0.098039217293262482), (0.91176468133926392,
0.094117648899555206, 0.094117648899555206), (0.91596639156341553,
0.086274512112140656, 0.086274512112140656), (0.92016804218292236,
0.08235294371843338, 0.08235294371843338), (0.92436975240707397,
0.078431375324726105, 0.078431375324726105), (0.92857140302658081,
0.074509806931018829, 0.074509806931018829), (0.93277311325073242,
0.070588238537311554, 0.070588238537311554), (0.93697476387023926,
0.066666670143604279, 0.066666670143604279), (0.94117647409439087,
0.062745101749897003, 0.062745101749897003), (0.94537812471389771,
0.058823529630899429, 0.058823529630899429), (0.94957983493804932,
0.054901961237192154, 0.054901961237192154), (0.95378148555755615,
0.050980392843484879, 0.050980392843484879), (0.95798319578170776,
0.047058824449777603, 0.047058824449777603), (0.9621848464012146,
0.043137256056070328, 0.043137256056070328), (0.96638655662536621,
0.039215687662363052, 0.039215687662363052), (0.97058820724487305,
0.035294119268655777, 0.035294119268655777), (0.97478991746902466,
0.031372550874948502, 0.031372550874948502), (0.97899156808853149,
0.023529412224888802, 0.023529412224888802), (0.98319327831268311,
0.019607843831181526, 0.019607843831181526), (0.98739492893218994,
0.015686275437474251, 0.015686275437474251), (0.99159663915634155,
0.011764706112444401, 0.011764706112444401), (0.99579828977584839,
0.0078431377187371254, 0.0078431377187371254), (1.0,
0.0039215688593685627, 0.0039215688593685627)]}
Accent = colors.LinearSegmentedColormap('Accent', _Accent_data, LUTSIZE)
Blues = colors.LinearSegmentedColormap('Blues', _Blues_data, LUTSIZE)
BrBG = colors.LinearSegmentedColormap('BrBG', _BrBG_data, LUTSIZE)
BuGn = colors.LinearSegmentedColormap('BuGn', _BuGn_data, LUTSIZE)
BuPu = colors.LinearSegmentedColormap('BuPu', _BuPu_data, LUTSIZE)
Dark2 = colors.LinearSegmentedColormap('Dark2', _Dark2_data, LUTSIZE)
GnBu = colors.LinearSegmentedColormap('GnBu', _GnBu_data, LUTSIZE)
Greens = colors.LinearSegmentedColormap('Greens', _Greens_data, LUTSIZE)
Greys = colors.LinearSegmentedColormap('Greys', _Greys_data, LUTSIZE)
Oranges = colors.LinearSegmentedColormap('Oranges', _Oranges_data, LUTSIZE)
OrRd = colors.LinearSegmentedColormap('OrRd', _OrRd_data, LUTSIZE)
Paired = colors.LinearSegmentedColormap('Paired', _Paired_data, LUTSIZE)
Pastel1 = colors.LinearSegmentedColormap('Pastel1', _Pastel1_data, LUTSIZE)
Pastel2 = colors.LinearSegmentedColormap('Pastel2', _Pastel2_data, LUTSIZE)
PiYG = colors.LinearSegmentedColormap('PiYG', _PiYG_data, LUTSIZE)
PRGn = colors.LinearSegmentedColormap('PRGn', _PRGn_data, LUTSIZE)
PuBu = colors.LinearSegmentedColormap('PuBu', _PuBu_data, LUTSIZE)
PuBuGn = colors.LinearSegmentedColormap('PuBuGn', _PuBuGn_data, LUTSIZE)
PuOr = colors.LinearSegmentedColormap('PuOr', _PuOr_data, LUTSIZE)
PuRd = colors.LinearSegmentedColormap('PuRd', _PuRd_data, LUTSIZE)
Purples = colors.LinearSegmentedColormap('Purples', _Purples_data, LUTSIZE)
RdBu = colors.LinearSegmentedColormap('RdBu', _RdBu_data, LUTSIZE)
RdGy = colors.LinearSegmentedColormap('RdGy', _RdGy_data, LUTSIZE)
RdPu = colors.LinearSegmentedColormap('RdPu', _RdPu_data, LUTSIZE)
RdYlBu = colors.LinearSegmentedColormap('RdYlBu', _RdYlBu_data, LUTSIZE)
RdYlGn = colors.LinearSegmentedColormap('RdYlGn', _RdYlGn_data, LUTSIZE)
Reds = colors.LinearSegmentedColormap('Reds', _Reds_data, LUTSIZE)
Set1 = colors.LinearSegmentedColormap('Set1', _Set1_data, LUTSIZE)
Set2 = colors.LinearSegmentedColormap('Set2', _Set2_data, LUTSIZE)
Set3 = colors.LinearSegmentedColormap('Set3', _Set3_data, LUTSIZE)
Spectral = colors.LinearSegmentedColormap('Spectral', _Spectral_data, LUTSIZE)
YlGn = colors.LinearSegmentedColormap('YlGn', _YlGn_data, LUTSIZE)
YlGnBu = colors.LinearSegmentedColormap('YlGnBu', _YlGnBu_data, LUTSIZE)
YlOrBr = colors.LinearSegmentedColormap('YlOrBr', _YlOrBr_data, LUTSIZE)
YlOrRd = colors.LinearSegmentedColormap('YlOrRd', _YlOrRd_data, LUTSIZE)
gist_earth = colors.LinearSegmentedColormap('gist_earth', _gist_earth_data, LUTSIZE)
gist_gray = colors.LinearSegmentedColormap('gist_gray', _gist_gray_data, LUTSIZE)
gist_heat = colors.LinearSegmentedColormap('gist_heat', _gist_heat_data, LUTSIZE)
gist_ncar = colors.LinearSegmentedColormap('gist_ncar', _gist_ncar_data, LUTSIZE)
gist_rainbow = colors.LinearSegmentedColormap('gist_rainbow', _gist_rainbow_data, LUTSIZE)
gist_stern = colors.LinearSegmentedColormap('gist_stern', _gist_stern_data, LUTSIZE)
gist_yarg = colors.LinearSegmentedColormap('gist_yarg', _gist_yarg_data, LUTSIZE)
datad['Accent']=_Accent_data
datad['Blues']=_Blues_data
datad['BrBG']=_BrBG_data
datad['BuGn']=_BuGn_data
datad['BuPu']=_BuPu_data
datad['Dark2']=_Dark2_data
datad['GnBu']=_GnBu_data
datad['Greens']=_Greens_data
datad['Greys']=_Greys_data
datad['Oranges']=_Oranges_data
datad['OrRd']=_OrRd_data
datad['Paired']=_Paired_data
datad['Pastel1']=_Pastel1_data
datad['Pastel2']=_Pastel2_data
datad['PiYG']=_PiYG_data
datad['PRGn']=_PRGn_data
datad['PuBu']=_PuBu_data
datad['PuBuGn']=_PuBuGn_data
datad['PuOr']=_PuOr_data
datad['PuRd']=_PuRd_data
datad['Purples']=_Purples_data
datad['RdBu']=_RdBu_data
datad['RdGy']=_RdGy_data
datad['RdPu']=_RdPu_data
datad['RdYlBu']=_RdYlBu_data
datad['RdYlGn']=_RdYlGn_data
datad['Reds']=_Reds_data
datad['Set1']=_Set1_data
datad['Set2']=_Set2_data
datad['Set3']=_Set3_data
datad['Spectral']=_Spectral_data
datad['YlGn']=_YlGn_data
datad['YlGnBu']=_YlGnBu_data
datad['YlOrBr']=_YlOrBr_data
datad['YlOrRd']=_YlOrRd_data
datad['gist_earth']=_gist_earth_data
datad['gist_gray']=_gist_gray_data
datad['gist_heat']=_gist_heat_data
datad['gist_ncar']=_gist_ncar_data
datad['gist_rainbow']=_gist_rainbow_data
datad['gist_stern']=_gist_stern_data
datad['gist_yarg']=_gist_yarg_data
# reverse all the colormaps.
# reversed colormaps have '_r' appended to the name.
def revcmap(data):
data_r = {}
for key, val in data.iteritems():
valnew = [(1.-a, b, c) for a, b, c in reversed(val)]
data_r[key] = valnew
return data_r
cmapnames = datad.keys()
for cmapname in cmapnames:
cmapname_r = cmapname+'_r'
cmapdat_r = revcmap(datad[cmapname])
datad[cmapname_r] = cmapdat_r
locals()[cmapname_r] = colors.LinearSegmentedColormap(cmapname_r, cmapdat_r, LUTSIZE)
| agpl-3.0 |
kpespinosa/BuildingMachineLearningSystemsWithPython | ch04/blei_lda.py | 21 | 2601 | # This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License
from __future__ import print_function
from wordcloud import create_cloud
try:
from gensim import corpora, models, matutils
except:
print("import gensim failed.")
print()
print("Please install it")
raise
import matplotlib.pyplot as plt
import numpy as np
from os import path
NUM_TOPICS = 100
# Check that data exists
if not path.exists('./data/ap/ap.dat'):
print('Error: Expected data to be present at data/ap/')
print('Please cd into ./data & run ./download_ap.sh')
# Load the data
corpus = corpora.BleiCorpus('./data/ap/ap.dat', './data/ap/vocab.txt')
# Build the topic model
model = models.ldamodel.LdaModel(
corpus, num_topics=NUM_TOPICS, id2word=corpus.id2word, alpha=None)
# Iterate over all the topics in the model
for ti in range(model.num_topics):
words = model.show_topic(ti, 64)
tf = sum(f for f, w in words)
with open('topics.txt', 'w') as output:
output.write('\n'.join('{}:{}'.format(w, int(1000. * f / tf)) for f, w in words))
output.write("\n\n\n")
# We first identify the most discussed topic, i.e., the one with the
# highest total weight
topics = matutils.corpus2dense(model[corpus], num_terms=model.num_topics)
weight = topics.sum(1)
max_topic = weight.argmax()
# Get the top 64 words for this topic
# Without the argument, show_topic would return only 10 words
words = model.show_topic(max_topic, 64)
# This function will actually check for the presence of pytagcloud and is otherwise a no-op
create_cloud('cloud_blei_lda.png', words)
num_topics_used = [len(model[doc]) for doc in corpus]
fig,ax = plt.subplots()
ax.hist(num_topics_used, np.arange(42))
ax.set_ylabel('Nr of documents')
ax.set_xlabel('Nr of topics')
fig.tight_layout()
fig.savefig('Figure_04_01.png')
# Now, repeat the same exercise using alpha=1.0
# You can edit the constant below to play around with this parameter
ALPHA = 1.0
model1 = models.ldamodel.LdaModel(
corpus, num_topics=NUM_TOPICS, id2word=corpus.id2word, alpha=ALPHA)
num_topics_used1 = [len(model1[doc]) for doc in corpus]
fig,ax = plt.subplots()
ax.hist([num_topics_used, num_topics_used1], np.arange(42))
ax.set_ylabel('Nr of documents')
ax.set_xlabel('Nr of topics')
# The coordinates below were fit by trial and error to look good
ax.text(9, 223, r'default alpha')
ax.text(26, 156, 'alpha=1.0')
fig.tight_layout()
fig.savefig('Figure_04_02.png')
| mit |
samchrisinger/osf.io | scripts/analytics/tasks.py | 14 | 1913 | import os
import matplotlib
from framework.celery_tasks import app as celery_app
from scripts import utils as script_utils
from scripts.analytics import settings
from scripts.analytics import utils
from website import models
from website import settings as website_settings
from website.app import init_app
from .logger import logger
@celery_app.task(name='scripts.analytics.tasks')
def analytics():
matplotlib.use('Agg')
init_app(routes=False)
script_utils.add_file_logger(logger, __file__)
from scripts.analytics import (
logs, addons, comments, folders, links, watch, email_invites,
permissions, profile, benchmarks
)
modules = (
logs, addons, comments, folders, links, watch, email_invites,
permissions, profile, benchmarks
)
for module in modules:
logger.info('Starting: {}'.format(module.__name__))
module.main()
logger.info('Finished: {}'.format(module.__name__))
upload_analytics()
def upload_analytics(local_path=None, remote_path='/'):
node = models.Node.load(settings.TABULATE_LOGS_NODE_ID)
user = models.User.load(settings.TABULATE_LOGS_USER_ID)
if not local_path:
local_path = website_settings.ANALYTICS_PATH
for name in os.listdir(local_path):
if not os.path.isfile(os.path.join(local_path, name)):
logger.info('create directory: {}'.format(os.path.join(local_path, name)))
metadata = utils.create_object(name, 'folder-update', node, user, kind='folder', path=remote_path)
upload_analytics(os.path.join(local_path, name), metadata['attributes']['path'])
else:
logger.info('update file: {}'.format(os.path.join(local_path, name)))
with open(os.path.join(local_path, name), 'rb') as fp:
utils.create_object(name, 'file-update', node, user, stream=fp, kind='file', path=remote_path)
| apache-2.0 |
Akson/RemoteConsolePlus3 | RemoteConsolePlus3/RCP3/Backends/Processors/Graphs/Plot1D.py | 1 | 2341 | #Created by Dmytro Konobrytskyi, 2014 (github.com/Akson)
import numpy as np
import matplotlib
import matplotlib.pyplot
from RCP3.Infrastructure import TmpFilesStorage
class Backend(object):
def __init__(self, parentNode):
self._parentNode = parentNode
def Delete(self):
"""
This method is called when a parent node is deleted.
"""
pass
def GetParameters(self):
"""
Returns a dictionary with object parameters, their values,
limits and ways to change them.
"""
return {}
def SetParameters(self, parameters):
"""
Gets a dictionary with parameter values and
update object parameters accordingly
"""
pass
def ProcessMessage(self, message):
"""
This message is called when a new message comes.
If an incoming message should be processed by following nodes, the
'self._parentNode.SendMessage(message)'
should be called with an appropriate message.
"""
dataArray = np.asarray(message["Data"])
fig = matplotlib.pyplot.figure(figsize=(6, 4), dpi=float(96))
ax=fig.add_subplot(111)
#n, bins, patches = ax.hist(dataArray, bins=50)
ax.plot(range(len(dataArray)), dataArray)
processedMessage = {"Stream":message["Stream"], "Info":message["Info"]}
filePath, link = TmpFilesStorage.NewTemporaryFile("png")
fig.savefig(filePath,format='png')
matplotlib.pyplot.close(fig)
html = '<img src="http://{}" alt="Image should come here">'.format(link)
processedMessage["Data"] = html
self._parentNode.SendMessage(processedMessage)
"""
print len(message["Data"])
import numpy as np
import matplotlib.pyplot as plt
x = np.array(message["Data"])
num_bins = 50
# the histogram of the data
n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)
plt.subplots_adjust(left=0.15)
plt.show()
"""
def AppendContextMenuItems(self, menu):
"""
Append backend specific menu items to a context menu that user will see
when he clicks on a node.
"""
pass | lgpl-3.0 |
kevin-intel/scikit-learn | examples/multioutput/plot_classifier_chain_yeast.py | 23 | 4637 | """
============================
Classifier Chain
============================
Example of using classifier chain on a multilabel dataset.
For this example we will use the `yeast
<https://www.openml.org/d/40597>`_ dataset which contains
2417 datapoints each with 103 features and 14 possible labels. Each
data point has at least one label. As a baseline we first train a logistic
regression classifier for each of the 14 labels. To evaluate the performance of
these classifiers we predict on a held-out test set and calculate the
:ref:`jaccard score <jaccard_similarity_score>` for each sample.
Next we create 10 classifier chains. Each classifier chain contains a
logistic regression model for each of the 14 labels. The models in each
chain are ordered randomly. In addition to the 103 features in the dataset,
each model gets the predictions of the preceding models in the chain as
features (note that by default at training time each model gets the true
labels as features). These additional features allow each chain to exploit
correlations among the classes. The Jaccard similarity score for each chain
tends to be greater than that of the set independent logistic models.
Because the models in each chain are arranged randomly there is significant
variation in performance among the chains. Presumably there is an optimal
ordering of the classes in a chain that will yield the best performance.
However we do not know that ordering a priori. Instead we can construct an
voting ensemble of classifier chains by averaging the binary predictions of
the chains and apply a threshold of 0.5. The Jaccard similarity score of the
ensemble is greater than that of the independent models and tends to exceed
the score of each chain in the ensemble (although this is not guaranteed
with randomly ordered chains).
"""
# Author: Adam Kleczewski
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_openml
from sklearn.multioutput import ClassifierChain
from sklearn.model_selection import train_test_split
from sklearn.multiclass import OneVsRestClassifier
from sklearn.metrics import jaccard_score
from sklearn.linear_model import LogisticRegression
print(__doc__)
# Load a multi-label dataset from https://www.openml.org/d/40597
X, Y = fetch_openml('yeast', version=4, return_X_y=True)
Y = Y == 'TRUE'
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.2,
random_state=0)
# Fit an independent logistic regression model for each class using the
# OneVsRestClassifier wrapper.
base_lr = LogisticRegression()
ovr = OneVsRestClassifier(base_lr)
ovr.fit(X_train, Y_train)
Y_pred_ovr = ovr.predict(X_test)
ovr_jaccard_score = jaccard_score(Y_test, Y_pred_ovr, average='samples')
# Fit an ensemble of logistic regression classifier chains and take the
# take the average prediction of all the chains.
chains = [ClassifierChain(base_lr, order='random', random_state=i)
for i in range(10)]
for chain in chains:
chain.fit(X_train, Y_train)
Y_pred_chains = np.array([chain.predict(X_test) for chain in
chains])
chain_jaccard_scores = [jaccard_score(Y_test, Y_pred_chain >= .5,
average='samples')
for Y_pred_chain in Y_pred_chains]
Y_pred_ensemble = Y_pred_chains.mean(axis=0)
ensemble_jaccard_score = jaccard_score(Y_test,
Y_pred_ensemble >= .5,
average='samples')
model_scores = [ovr_jaccard_score] + chain_jaccard_scores
model_scores.append(ensemble_jaccard_score)
model_names = ('Independent',
'Chain 1',
'Chain 2',
'Chain 3',
'Chain 4',
'Chain 5',
'Chain 6',
'Chain 7',
'Chain 8',
'Chain 9',
'Chain 10',
'Ensemble')
x_pos = np.arange(len(model_names))
# Plot the Jaccard similarity scores for the independent model, each of the
# chains, and the ensemble (note that the vertical axis on this plot does
# not begin at 0).
fig, ax = plt.subplots(figsize=(7, 4))
ax.grid(True)
ax.set_title('Classifier Chain Ensemble Performance Comparison')
ax.set_xticks(x_pos)
ax.set_xticklabels(model_names, rotation='vertical')
ax.set_ylabel('Jaccard Similarity Score')
ax.set_ylim([min(model_scores) * .9, max(model_scores) * 1.1])
colors = ['r'] + ['b'] * len(chain_jaccard_scores) + ['g']
ax.bar(x_pos, model_scores, alpha=0.5, color=colors)
plt.tight_layout()
plt.show()
| bsd-3-clause |
trankmichael/scikit-learn | examples/cluster/plot_agglomerative_clustering_metrics.py | 402 | 4492 | """
Agglomerative clustering with different metrics
===============================================
Demonstrates the effect of different metrics on the hierarchical clustering.
The example is engineered to show the effect of the choice of different
metrics. It is applied to waveforms, which can be seen as
high-dimensional vector. Indeed, the difference between metrics is
usually more pronounced in high dimension (in particular for euclidean
and cityblock).
We generate data from three groups of waveforms. Two of the waveforms
(waveform 1 and waveform 2) are proportional one to the other. The cosine
distance is invariant to a scaling of the data, as a result, it cannot
distinguish these two waveforms. Thus even with no noise, clustering
using this distance will not separate out waveform 1 and 2.
We add observation noise to these waveforms. We generate very sparse
noise: only 6% of the time points contain noise. As a result, the
l1 norm of this noise (ie "cityblock" distance) is much smaller than it's
l2 norm ("euclidean" distance). This can be seen on the inter-class
distance matrices: the values on the diagonal, that characterize the
spread of the class, are much bigger for the Euclidean distance than for
the cityblock distance.
When we apply clustering to the data, we find that the clustering
reflects what was in the distance matrices. Indeed, for the Euclidean
distance, the classes are ill-separated because of the noise, and thus
the clustering does not separate the waveforms. For the cityblock
distance, the separation is good and the waveform classes are recovered.
Finally, the cosine distance does not separate at all waveform 1 and 2,
thus the clustering puts them in the same cluster.
"""
# Author: Gael Varoquaux
# License: BSD 3-Clause or CC-0
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics import pairwise_distances
np.random.seed(0)
# Generate waveform data
n_features = 2000
t = np.pi * np.linspace(0, 1, n_features)
def sqr(x):
return np.sign(np.cos(x))
X = list()
y = list()
for i, (phi, a) in enumerate([(.5, .15), (.5, .6), (.3, .2)]):
for _ in range(30):
phase_noise = .01 * np.random.normal()
amplitude_noise = .04 * np.random.normal()
additional_noise = 1 - 2 * np.random.rand(n_features)
# Make the noise sparse
additional_noise[np.abs(additional_noise) < .997] = 0
X.append(12 * ((a + amplitude_noise)
* (sqr(6 * (t + phi + phase_noise)))
+ additional_noise))
y.append(i)
X = np.array(X)
y = np.array(y)
n_clusters = 3
labels = ('Waveform 1', 'Waveform 2', 'Waveform 3')
# Plot the ground-truth labelling
plt.figure()
plt.axes([0, 0, 1, 1])
for l, c, n in zip(range(n_clusters), 'rgb',
labels):
lines = plt.plot(X[y == l].T, c=c, alpha=.5)
lines[0].set_label(n)
plt.legend(loc='best')
plt.axis('tight')
plt.axis('off')
plt.suptitle("Ground truth", size=20)
# Plot the distances
for index, metric in enumerate(["cosine", "euclidean", "cityblock"]):
avg_dist = np.zeros((n_clusters, n_clusters))
plt.figure(figsize=(5, 4.5))
for i in range(n_clusters):
for j in range(n_clusters):
avg_dist[i, j] = pairwise_distances(X[y == i], X[y == j],
metric=metric).mean()
avg_dist /= avg_dist.max()
for i in range(n_clusters):
for j in range(n_clusters):
plt.text(i, j, '%5.3f' % avg_dist[i, j],
verticalalignment='center',
horizontalalignment='center')
plt.imshow(avg_dist, interpolation='nearest', cmap=plt.cm.gnuplot2,
vmin=0)
plt.xticks(range(n_clusters), labels, rotation=45)
plt.yticks(range(n_clusters), labels)
plt.colorbar()
plt.suptitle("Interclass %s distances" % metric, size=18)
plt.tight_layout()
# Plot clustering results
for index, metric in enumerate(["cosine", "euclidean", "cityblock"]):
model = AgglomerativeClustering(n_clusters=n_clusters,
linkage="average", affinity=metric)
model.fit(X)
plt.figure()
plt.axes([0, 0, 1, 1])
for l, c in zip(np.arange(model.n_clusters), 'rgbk'):
plt.plot(X[model.labels_ == l].T, c=c, alpha=.5)
plt.axis('tight')
plt.axis('off')
plt.suptitle("AgglomerativeClustering(affinity=%s)" % metric, size=20)
plt.show()
| bsd-3-clause |
advancedplotting/aplot | python/plotserv/api_annotations.py | 1 | 8009 | # Copyright (c) 2014-2015, Heliosphere Research LLC
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from this
# software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
"""
Handles VIs in "api_annotations".
"""
import numpy as np
from matplotlib import pyplot as plt
from .core import resource
from .terminals import remove_none
from . import filters
from . import errors
@resource('text')
def text(ctx, a):
""" Display text on the plot """
plotid = a.plotid()
x = a.float('x')
y = a.float('y')
s = a.string('s')
relative = a.bool('coordinates')
textprops = a.text()
display = a.display()
ctx.set(plotid)
ax = plt.gca()
# None-finite values here mean we skip the plot
if x is None or y is None:
return
k = textprops._k()
k.update(display._k())
k['clip_on'] = True
if relative:
k['transform'] = ax.transAxes
remove_none(k)
plt.text(x, y, s, **k)
@resource('hline')
def hline(ctx, a):
""" Plot a horizontal line """
plotid = a.plotid()
y = a.float('y')
xmin = a.float('xmin')
xmax = a.float('xmax')
line = a.line()
display = a.display()
ctx.set(plotid)
ctx.fail_if_polar()
# Non-finite value provided
if y is None:
return
k = { 'xmin': xmin,
'xmax': xmax,
'linewidth': line.width,
'linestyle': line.style,
'color': line.color if line.color is not None else 'k', }
k.update(display._k())
remove_none(k)
plt.axhline(y, **k)
@resource('vline')
def vline(ctx, a):
""" Plot a vertical line """
plotid = a.plotid()
x = a.float('x')
ymin = a.float('ymin')
ymax = a.float('ymax')
line = a.line()
display = a.display()
ctx.set(plotid)
ctx.fail_if_polar()
# Non-finite value provided
if x is None:
return
k = { 'ymin': ymin,
'ymax': ymax,
'linewidth': line.width,
'linestyle': line.style,
'color': line.color if line.color is not None else 'k', }
k.update(display._k())
remove_none(k)
plt.axvline(x, **k)
@resource('colorbar')
def colorbar(ctx, a):
""" Display a colorbar """
plotid = a.plotid()
label = a.string('label')
ticks = a.dbl_1d('ticks')
ticklabels = a.string_1d('ticklabels')
ctx.set(plotid)
# If no colormapped object has been plotted, MPL complains.
# We permit this, and simply don't add the colorbar.
if ctx.mappable is None:
return
c = plt.colorbar(ctx.mappable)
# Don't bother setting an empty label
if len(label) > 0:
c.set_label(label)
# Both specified
if len(ticks) > 0 and len(ticklabels) > 0:
ticks, ticklabels = filters.filter_1d(ticks, ticklabels)
c.set_ticks(ticks)
c.set_ticklabels(ticklabels)
# Just ticks specified
elif len(ticks) > 0:
ticks = ticks[np.isfinite(ticks)]
c.set_ticks(ticks)
# Just ticklabels specified
else:
# Providing zero-length "ticks" array invokes auto-ticking, in which
# case any ticklabels are ignored.
pass
@resource('legend')
def legend(ctx, a):
""" Represents Legend.vi.
Note that there is no Positions enum on the Python side; the MPL
values are hard-coded into the LabView control.
"""
POSITIONS = { 0: 0,
1: 1,
2: 9,
3: 2,
4: 6,
5: 3,
6: 8,
7: 4,
8: 7,
9: 10 }
plotid = a.plotid()
position = a.enum('position', POSITIONS)
ctx.set(plotid)
k = {'loc': position, 'fontsize': 'medium'}
remove_none(k)
if len(ctx.legend_entries) > 0:
objects, labels = zip(*ctx.legend_entries)
plt.legend(objects, labels, **k)
@resource('label')
def label(ctx, a):
""" Title, X axis and Y axis labels. """
LOCATIONS = {0: 'title', 1: 'xlabel', 2: 'ylabel'}
plotid = a.plotid()
location = a.enum('kind', LOCATIONS)
label = a.string('label')
text = a.text()
ctx.set(plotid)
k = text._k()
if location == 'title':
plt.title(label, **k)
elif location == 'xlabel':
plt.xlabel(label, **k)
elif location == 'ylabel':
ctx.fail_if_polar()
plt.ylabel(label, **k)
else:
pass
@resource('circle')
def circle(ctx, a):
""" Draw a circle on a rectangular plot """
plotid = a.plotid()
x = a.float('x')
y = a.float('y')
radius = a.float('radius')
color = a.color('color')
line = a.line()
display = a.display()
f = ctx.set(plotid)
ctx.fail_if_polar()
ctx.fail_if_log_symlog()
# Like Text.vi, if any critical input is Nan we do nothing
if x is None or y is None or radius is None:
return
# Catch this before MPL complains
if radius <= 0:
return
k = { 'edgecolor': line.color,
'linestyle': line.style,
'linewidth': line.width,
'facecolor': color if color is not None else '#bbbbbb', }
k.update(display._k())
remove_none(k)
c = plt.Circle((x,y), radius, **k)
f.gca().add_artist(c)
@resource('rectangle')
def rectangle(ctx, a):
""" Draw a rectangle """
plotid = a.plotid()
x = a.float('x')
y = a.float('y')
width = a.float('width')
height = a.float('height')
color = a.color('color')
line = a.line()
display = a.display()
f = ctx.set(plotid)
ctx.fail_if_symlog()
# Like Text.vi, if any critical input is Nan we do nothing
if x is None or y is None or width is None or height is None:
return
if width == 0 or height == 0:
return
k = { 'edgecolor': line.color,
'linestyle': line.style,
'linewidth': line.width,
'facecolor': color if color is not None else '#bbbbbb', }
k.update(display._k())
remove_none(k)
r = plt.Rectangle((x,y), width, height, **k)
f.gca().add_artist(r) | bsd-3-clause |
macioosch/dynamo-hard-spheres-sim | convergence-plot.py | 1 | 6346 | #!/usr/bin/env python2
# encoding=utf-8
from __future__ import division, print_function
from glob import glob
from itertools import izip
from matplotlib import pyplot as plt
import numpy as np
input_files = glob("csv/convergence-256000-0.*.csv")
#input_files = glob("csv/convergence-500000-0.*.csv")
#input_files = glob("csv/convergence-1000188-0.*.csv")
#plotted_parameter = "msds_diffusion"
plotted_parameter = "pressures_collision"
#plotted_parameter = "pressures_virial"
#plotted_parameter = "msds_val"
#plotted_parameter = "times"
legend_names = []
tight_layout = False
show_legend = False
for file_number, file_name in enumerate(sorted(input_files)):
data = np.genfromtxt(file_name, delimiter='\t', names=[
"packings","densities","collisions","n_atoms","pressures_virial",
"pressures_collision","msds_val","msds_diffusion","times",
"std_pressures_virial","std_pressures_collision","std_msds_val",
"std_msds_diffusion","std_times"])
n_atoms = data["n_atoms"][0]
density = data["densities"][0]
equilibrated_collisions = data["collisions"] - 2*data["collisions"][0] \
+ data["collisions"][1]
"""
### 5 graphs: D(CPS) ###
tight_layout = True
skip_points = 0
ax = plt.subplot(3, 2, file_number+1)
plt.fill_between((equilibrated_collisions / n_atoms)[skip_points:],
data[plotted_parameter][skip_points:]
- data["std_" + plotted_parameter][skip_points:],
data[plotted_parameter][skip_points:]
+ data["std_" + plotted_parameter][skip_points:], alpha=0.3)
plt.plot((equilibrated_collisions / n_atoms)[skip_points:],
data[plotted_parameter][skip_points:], lw=2)
if plotted_parameter == "msds_diffusion":
plt.ylim(0.990*data[plotted_parameter][-1],
1.005*data[plotted_parameter][-1])
plt.xlim([0, 1e5])
plt.legend(["Density {}".format(data["densities"][0])], loc="lower right")
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.4f'))
plt.xlabel("Collisions per sphere")
plt.ylabel("D")
"""
### 5 graphs: relative D(CPS) ###
tight_layout = True
skip_points = 0
ax = plt.subplot(3, 2, file_number+1)
plt.fill_between((equilibrated_collisions / n_atoms)[skip_points:],
-1 + (data[plotted_parameter][skip_points:]
- data["std_" + plotted_parameter][skip_points:])/data[plotted_parameter][-1],
-1 + (data[plotted_parameter][skip_points:]
+ data["std_" + plotted_parameter][skip_points:])/data[plotted_parameter][-1], alpha=0.3)
plt.plot((equilibrated_collisions / n_atoms)[skip_points:],
-1 + data[plotted_parameter][skip_points:]/data[plotted_parameter][-1], lw=2)
plt.ylim(data["std_" + plotted_parameter][-1]*20*np.array([-1, 1])/data[plotted_parameter][-1])
#plt.xscale("log")
plt.xlim([0, 1e5])
plt.legend(["$\\rho\\sigma^3=\\ {}$".format(data["densities"][0])], loc="lower right")
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.2e'))
plt.xlabel("$C/N$")
plt.ylabel("$[Z_{MD}(C) / Z_{MD}(C=10^5 N)] - 1$")
"""
### 1 graph: D(t) ###
show_legend = True
skip_points = 0
plt.title("D(t) for 5 densities")
plt.loglog(data["times"][skip_points:],
data[plotted_parameter][skip_points:])
legend_names.append(data["densities"][0])
plt.xlabel("Time")
plt.ylabel("D")
"""
"""
### 1 graph: D(t) / Dinf ###
show_legend = True
skip_points = 0
#plt.fill_between(data["times"][skip_points:],
# (data[plotted_parameter] - data["std_" + plotted_parameter])
# / data[plotted_parameter][-1] - 1,
# (data[plotted_parameter] + data["std_" + plotted_parameter])
# / data[plotted_parameter][-1] - 1, color="grey", alpha=0.4)
plt.plot(data["times"][skip_points:],
data[plotted_parameter] / data[plotted_parameter][-1] - 1, lw=1)
legend_names.append(data["densities"][0])
#plt.xscale("log")
plt.xlabel("Time")
plt.ylabel("D / D(t --> inf)")
"""
"""
### 5 graphs: D(1/CPS) ###
tight_layout = True
skip_points = 40
ax = plt.subplot(3, 2, file_number+1)
plt.fill_between((n_atoms / equilibrated_collisions)[skip_points:],
data[plotted_parameter][skip_points:]
- data["std_" + plotted_parameter][skip_points:],
data[plotted_parameter][skip_points:]
+ data["std_" + plotted_parameter][skip_points:], alpha=0.3)
plt.plot((n_atoms / equilibrated_collisions)[skip_points:],
data[plotted_parameter][skip_points:], lw=2)
plt.title("Density {}:".format(data["densities"][0]))
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.7f'))
plt.xlim(xmin=0)
plt.xlabel("1 / Collisions per sphere")
plt.ylabel("D")
"""
"""
### 1 graph: D(CPS) / Dinf ###
show_legend = True
plt.fill_between(equilibrated_collisions / n_atoms,
(data[plotted_parameter] - data["std_" + plotted_parameter])
/ data[plotted_parameter][-1] - 1,
(data[plotted_parameter] + data["std_" + plotted_parameter])
/ data[plotted_parameter][-1] - 1, color="grey", alpha=0.4)
plt.plot(equilibrated_collisions / n_atoms,
data[plotted_parameter] / data[plotted_parameter][-1] - 1, lw=2)
legend_names.append(data["densities"][0])
plt.xlabel("Collisions per sphere")
plt.ylabel("D / D(t --> inf)")
"""
"""
### 1 graph: D(1/CPS) / Dinf ###
show_legend = True
plt.fill_between(n_atoms / equilibrated_collisions,
(data[plotted_parameter] - data["std_" + plotted_parameter])
/ data[plotted_parameter][-1] - 1,
(data[plotted_parameter] + data["std_" + plotted_parameter])
/ data[plotted_parameter][-1] - 1, color="grey", alpha=0.4)
plt.plot( n_atoms / equilibrated_collisions,
data[plotted_parameter] / data[plotted_parameter][-1] - 1)
legend_names.append(data["densities"][0])
plt.xlabel(" 1 / Collisions per sphere")
plt.ylabel(plotted_parameter)
"""
#if tight_layout:
# plt.tight_layout(pad=0.0, w_pad=0.0, h_pad=0.0)
if show_legend:
plt.legend(legend_names, title="Density:", loc="lower right")
plt.show()
| gpl-3.0 |
srio/shadow3-scripts | transfocator_id30b.py | 1 | 25823 | import numpy
import xraylib
"""
transfocator_id30b : transfocator for id13b:
It can:
1) guess the lens configuration (number of lenses for each type) for a given photon energy
and target image size. Use transfocator_compute_configuration() for this task
2) for a given transfocator configuration, compute the main optical parameters
(image size, focal distance, focal position and divergence).
Use transfocator_compute_parameters() for this task
3) Performs full ray tracing. Use id30b_ray_tracing() for this task
Note that for the optimization and parameters calculations the transfocator configuration is
given in keywords. For ray tracing calculations many parameters of the transfocator are hard coded
with the values of id30b
See main program for examples.
Dependencies:
Numpy
xraylib (to compute refracion indices)
Shadow (for ray tracing only)
matplotlib (for some plots of ray=tracing)
Side effects:
When running ray tracing some files are created.
MODIFICATION HISTORY:
2015-03-25 srio@esrf.eu, written
"""
__author__ = "Manuel Sanchez del Rio"
__contact__ = "srio@esrf.eu"
__copyright__ = "ESRF, 2015"
def transfocator_compute_configuration(photon_energy_ev,s_target,\
symbol=["Be","Be","Be"], density=[1.845,1.845,1.845],\
nlenses_max = [15,3,1], nlenses_radii = [500e-4,1000e-4,1500e-4], lens_diameter=0.05, \
sigmaz=6.46e-4, alpha = 0.55, \
tf_p=5960, tf_q=3800, verbose=1 ):
"""
Computes the optimum transfocator configuration for a given photon energy and target image size.
All length units are cm
:param photon_energy_ev: the photon energy in eV
:param s_target: the target image size in cm.
:param symbol: the chemical symbol of the lens material of each type. Default symbol=["Be","Be","Be"]
:param density: the density of each type of lens. Default: density=[1.845,1.845,1.845]
:param nlenses_max: the maximum allowed number of lenases for each type of lens. nlenses_max = [15,3,1]
:param nlenses_radii: the radii in cm of each type of lens. Default: nlenses_radii = [500e-4,1000e-4,1500e-4]
:param lens_diameter: the physical diameter (acceptance) in cm of the lenses. If different for each type of lens,
consider the smaller one. Default: lens_diameter=0.05
:param sigmaz: the sigma (standard deviation) of the source in cm
:param alpha: an adjustable parameter in [0,1](see doc). Default: 0.55 (it is 0.76 for pure Gaussian beams)
:param tf_p: the distance source-transfocator in cm
:param tf_q: the distance transfocator-image in cm
:param:verbose: set to 1 for verbose text output
:return: a list with the number of lenses of each type.
"""
if s_target < 2.35*sigmaz*tf_q/tf_p:
print("Source size FWHM is: %f um"%(1e4*2.35*sigmaz))
print("Maximum Demagnifications is: %f um"%(tf_p/tf_q))
print("Minimum possible size is: %f um"%(1e4*2.35*sigmaz*tf_q/tf_p))
print("Error: redefine size")
return None
deltas = [(1.0 - xraylib.Refractive_Index_Re(symbol[i],photon_energy_ev*1e-3,density[i])) \
for i in range(len(symbol))]
focal_q_target = _tansfocator_guess_focal_position( s_target, p=tf_p, q=tf_q, sigmaz=sigmaz, alpha=alpha, \
lens_diameter=lens_diameter,method=2)
focal_f_target = 1.0 / (1.0/focal_q_target + 1.0/tf_p)
div_q_target = alpha * lens_diameter / focal_q_target
#corrections for extreme cases
source_demagnified = 2.35*sigmaz*focal_q_target/tf_p
if source_demagnified > lens_diameter: source_demagnified = lens_diameter
s_target_calc = numpy.sqrt( (div_q_target*(tf_q-focal_q_target))**2 + source_demagnified**2)
nlenses_target = _transfocator_guess_configuration(focal_f_target,deltas=deltas,\
nlenses_max=nlenses_max,radii=nlenses_radii, )
if verbose:
print("transfocator_compute_configuration: focal_f_target: %f"%(focal_f_target))
print("transfocator_compute_configuration: focal_q_target: %f cm"%(focal_q_target))
print("transfocator_compute_configuration: s_target: %f um"%(s_target_calc*1e4))
print("transfocator_compute_configuration: nlenses_target: ",nlenses_target)
return nlenses_target
def transfocator_compute_parameters(photon_energy_ev, nlenses_target,\
symbol=["Be","Be","Be"], density=[1.845,1.845,1.845],\
nlenses_max = [15,3,1], nlenses_radii = [500e-4,1000e-4,1500e-4], lens_diameter=0.05, \
sigmaz=6.46e-4, alpha = 0.55, \
tf_p=5960, tf_q=3800 ):
"""
Computes the parameters of the optical performances of a given transgocator configuration.
returns a l
All length units are cm
:param photon_energy_ev:
:param nlenses_target: a list with the lens configuration, i.e. the number of lenses of each type.
:param symbol: the chemical symbol of the lens material of each type. Default symbol=["Be","Be","Be"]
:param density: the density of each type of lens. Default: density=[1.845,1.845,1.845]
:param nlenses_max: the maximum allowed number of lenases for each type of lens. nlenses_max = [15,3,1]
TODO: remove (not used)
:param nlenses_radii: the radii in cm of each type of lens. Default: nlenses_radii = [500e-4,1000e-4,1500e-4]
:param lens_diameter: the physical diameter (acceptance) in cm of the lenses. If different for each type of lens,
consider the smaller one. Default: lens_diameter=0.05
:param sigmaz: the sigma (standard deviation) of the source in cm
:param alpha: an adjustable parameter in [0,1](see doc). Default: 0.55 (it is 0.76 for pure Gaussian beams)
:param tf_p: the distance source-transfocator in cm
:param tf_q: the distance transfocator-image in cm
:return: a list with parameters (image_siza, lens_focal_distance,
focal_position from transfocator center, divergence of beam after the transfocator)
"""
deltas = [(1.0 - xraylib.Refractive_Index_Re(symbol[i],photon_energy_ev*1e-3,density[i])) \
for i in range(len(symbol))]
focal_f = _transfocator_calculate_focal_distance( deltas=deltas,\
nlenses=nlenses_target,radii=nlenses_radii)
focal_q = 1.0 / (1.0/focal_f - 1.0/tf_p)
div_q = alpha * lens_diameter / focal_q
#corrections
source_demagnified = 2.35*sigmaz*focal_q/tf_p
if source_demagnified > lens_diameter: source_demagnified = lens_diameter
s_target = numpy.sqrt( (div_q*(tf_q-focal_q))**2 + (source_demagnified)**2 )
return (s_target,focal_f,focal_q,div_q)
def transfocator_nlenses_to_slots(nlenses,nlenses_max=None):
"""
converts the transfocator configuration from a list of the number of lenses of each type,
into a list of active (1) or inactive (0) actuators for the slots.
:param nlenses: the list with number of lenses (e.g., [5,2,0]
:param nlenses_max: the maximum number of lenses of each type, usually powers of two minus one.
E.g. [15,3,1]
:return: a list of on (1) and off (0) slots, e.g., [1, 0, 1, 0, 0, 1, 0]
(first type: 1*1+0*2+1*4+0*8=5, second type: 0*1+1*2=2, third type: 0*1=0)
"""
if nlenses_max == None:
nlenses_max = nlenses
ss = []
for i,iopt in enumerate(nlenses):
if iopt > nlenses_max[i]:
print("Error: i:%d, nlenses: %d, nlenses_max: %d"%(i,iopt,nlenses_max[i]))
ncharacters = len("{0:b}".format(nlenses_max[i]))
si = list( ("{0:0%db}"%(ncharacters)).format(int(iopt)) )
si.reverse()
ss += si
on_off = [int(i) for i in ss]
#print("transfocator_nlenses_to_slots: nlenses_max: ",nlenses_max," nlenses: ",nlenses," slots: ",on_off)
return on_off
def _transfocator_calculate_focal_distance(deltas=[0.999998],nlenses=[1],radii=[500e-4]):
inverse_focal_distance = 0.0
for i,nlensesi in enumerate(nlenses):
if nlensesi > 0:
focal_distance_i = radii[i] / (2.*nlensesi*deltas[i])
inverse_focal_distance += 1.0/focal_distance_i
if inverse_focal_distance == 0:
return 99999999999999999999999999.
else:
return 1.0/inverse_focal_distance
def _tansfocator_guess_focal_position( s_target, p=5960., q=3800.0, sigmaz=6.46e-4, \
alpha=0.66, lens_diameter=0.05, method=2):
x = 1e15
if method == 1: # simple sum
AA = 2.35*sigmaz/p
BB = -(s_target + alpha * lens_diameter)
CC = alpha*lens_diameter*q
cc = numpy.roots([AA,BB,CC])
x = cc[1]
return x
if method == 2: # sum in quadrature
AA = ( (2.35*sigmaz)**2)/(p**2)
BB = 0.0
CC = alpha**2 * lens_diameter**2 - s_target**2
DD = - 2.0 * alpha**2 * lens_diameter**2 * q
EE = alpha**2 * lens_diameter**2 * q**2
cc = numpy.roots([AA,BB,CC,DD,EE])
for i,cci in enumerate(cc):
if numpy.imag(cci) == 0:
return numpy.real(cci)
return x
def _transfocator_guess_configuration(focal_f_target,deltas=[0.999998],nlenses_max=[15],radii=[500e-4]):
nn = len(nlenses_max)
ncombinations = (1+nlenses_max[0]) * (1+nlenses_max[1]) * (1+nlenses_max[2])
icombinations = 0
aa = numpy.zeros((3,ncombinations),dtype=int)
bb = numpy.zeros(ncombinations)
for i0 in range(1+nlenses_max[0]):
for i1 in range(1+nlenses_max[1]):
for i2 in range(1+nlenses_max[2]):
aa[0,icombinations] = i0
aa[1,icombinations] = i1
aa[2,icombinations] = i2
bb[icombinations] = focal_f_target - _transfocator_calculate_focal_distance(deltas=deltas,nlenses=[i0,i1,i2],radii=radii)
icombinations += 1
bb1 = numpy.abs(bb)
ibest = bb1.argmin()
return (aa[:,ibest]).tolist()
#
#
#
def id30b_ray_tracing(emittH=4e-9,emittV=1e-11,betaH=35.6,betaV=3.0,number_of_rays=50000,\
density=1.845,symbol="Be",tf_p=1000.0,tf_q=1000.0,lens_diameter=0.05,\
slots_max=None,slots_on_off=None,photon_energy_ev=14000.0,\
slots_lens_thickness=None,slots_steps=None,slots_radii=None,\
s_target=10e-4,focal_f=10.0,focal_q=10.0,div_q=1e-6):
#=======================================================================================================================
# Gaussian undulator source
#=======================================================================================================================
import Shadow
#import Shadow.ShadowPreprocessorsXraylib as sx
sigmaXp = numpy.sqrt(emittH/betaH)
sigmaZp = numpy.sqrt(emittV/betaV)
sigmaX = emittH/sigmaXp
sigmaZ = emittV/sigmaZp
print("\n\nElectron sizes H:%f um, V:%fu m;\nelectron divergences: H:%f urad, V:%f urad"%\
(sigmaX*1e6, sigmaZ*1e6, sigmaXp*1e6, sigmaZp*1e6))
# set Gaussian source
src = Shadow.Source()
src.set_energy_monochromatic(photon_energy_ev)
src.set_gauss(sigmaX*1e2,sigmaZ*1e2,sigmaXp,sigmaZp)
print("\n\nElectron sizes stored H:%f um, V:%f um;\nelectron divergences: H:%f urad, V:%f urad"%\
(src.SIGMAX*1e4,src.SIGMAZ*1e4,src.SIGDIX*1e6,src.SIGDIZ*1e6))
src.apply_gaussian_undulator(undulator_length_in_m=2.8, user_unit_to_m=1e-2, verbose=1)
print("\n\nElectron sizes stored (undulator) H:%f um, V:%f um;\nelectron divergences: H:%f urad, V:%f urad"%\
(src.SIGMAX*1e4,src.SIGMAZ*1e4,src.SIGDIX*1e6,src.SIGDIZ*1e6))
print("\n\nSource size in vertical FWHM: %f um\n"%\
(2.35*src.SIGMAZ*1e4))
src.NPOINT = number_of_rays
src.ISTAR1 = 0 # 677543155
src.write("start.00")
# create source
beam = Shadow.Beam()
beam.genSource(src)
beam.write("begin.dat")
src.write("end.00")
#=======================================================================================================================
# complete the (detailed) transfocator description
#=======================================================================================================================
print("\nSetting detailed Transfocator for ID30B")
slots_nlenses = numpy.array(slots_max)*numpy.array(slots_on_off)
slots_empty = (numpy.array(slots_max)-slots_nlenses)
#
####interactive=True, SYMBOL="SiC",DENSITY=3.217,FILE="prerefl.dat",E_MIN=100.0,E_MAX=20000.0,E_STEP=100.0
Shadow.ShadowPreprocessorsXraylib.prerefl(interactive=False,E_MIN=2000.0,E_MAX=55000.0,E_STEP=100.0,\
DENSITY=density,SYMBOL=symbol,FILE="Be2_55.dat" )
nslots = len(slots_max)
prerefl_file = ["Be2_55.dat" for i in range(nslots)]
print("slots_max: ",slots_max)
#print("slots_target: ",slots_target)
print("slots_on_off: ",slots_on_off)
print("slots_steps: ",slots_steps)
print("slots_radii: ",slots_radii)
print("slots_nlenses: ",slots_nlenses)
print("slots_empty: ",slots_empty)
#calculate distances, nlenses and slots_empty
# these are distances p and q with TF length removed
tf_length = numpy.array(slots_steps).sum() #tf length in cm
tf_fs_before = tf_p - 0.5*tf_length #distance from source to center of transfocator
tf_fs_after = tf_q - 0.5*tf_length # distance from center of transfocator to image
# for each slot, these are the empty distances before and after the lenses
tf_p0 = numpy.zeros(nslots)
tf_q0 = numpy.array(slots_steps) - (numpy.array(slots_max) * slots_lens_thickness)
# add now the p q distances
tf_p0[0] += tf_fs_before
tf_q0[-1] += tf_fs_after
print("tf_p0: ",tf_p0)
print("tf_q0: ",tf_q0)
print("tf_length: %f cm"%(tf_length))
# build transfocator
tf = Shadow.CompoundOE(name='TF ID30B')
tf.append_transfocator(tf_p0.tolist(), tf_q0.tolist(), \
nlenses=slots_nlenses.tolist(), radius=slots_radii, slots_empty=slots_empty.tolist(),\
thickness=slots_lens_thickness, prerefl_file=prerefl_file,\
surface_shape=4, convex_to_the_beam=0, diameter=lens_diameter,\
cylinder_angle=0.0,interthickness=50e-4,use_ccc=0)
itmp = input("SHADOW Source complete. Do you want to run SHADOR trace? [1=Yes,0=No]: ")
if str(itmp) != "1":
return
#trace system
tf.dump_systemfile()
beam.traceCompoundOE(tf,write_start_files=0,write_end_files=0,write_star_files=0, write_mirr_files=0)
#write only last result file
beam.write("star_tf.dat")
print("\nFile written to disk: star_tf.dat")
#
# #ideal calculations
#
print("\n\n\n")
print("=============================================== TRANSFOCATOR OUTPUTS ==========================================")
print("\nTHEORETICAL results: ")
print("REMIND-----With these lenses we obtained (analytically): ")
print("REMIND----- focal_f: %f cm"%(focal_f))
print("REMIND----- focal_q: %f cm"%(focal_q))
print("REMIND----- s_target: %f um"%(s_target*1e4))
demagnification_factor = tf_p/focal_q
theoretical_focal_size = src.SIGMAZ*2.35/demagnification_factor
# analyze shadow results
print("\nSHADOW results: ")
st1 = beam.get_standard_deviation(3,ref=0)
st2 = beam.get_standard_deviation(3,ref=1)
print(" stDev*2.35: unweighted: %f um, weighted: %f um "%(st1*2.35*1e4,st2*2.35*1e4))
tk = beam.histo1(3, nbins=75, ref=1, nolost=1, write="HISTO1")
print(" Histogram FWHM: %f um "%(1e4*tk["fwhm"]))
print(" Transmitted intensity: %f (source was: %d) (transmission is %f %%) "%(beam.intensity(nolost=1), src.NPOINT, beam.intensity(nolost=1)/src.NPOINT*100))
#scan around image
xx1 = numpy.linspace(0.0,1.1*tf_fs_after,11) # position from TF exit plane
#xx0 = focal_q - tf_length*0.5
xx0 = focal_q - tf_length*0.5 # position of focus from TF exit plane
xx2 = numpy.linspace(xx0-100.0,xx0+100,21) # position from TF exit plane
xx3 = numpy.array([tf_fs_after])
xx = numpy.concatenate(([-0.5*tf_length],xx1,xx2,[tf_fs_after]))
xx.sort()
f = open("id30b.spec","w")
f.write("#F id30b.spec\n")
f.write("\n#S 1 calculations for id30b transfocator\n")
f.write("#N 8\n")
labels = " %18s %18s %18s %18s %18s %18s %18s %18s"%\
("pos from source","pos from image","[pos from TF]", "pos from TF center", "pos from focus",\
"fwhm shadow(stdev)","fwhm shadow(histo)","fwhm theoretical")
f.write("#L "+labels+"\n")
out = numpy.zeros((8,xx.size))
for i,pos in enumerate(xx):
beam2 = beam.duplicate()
beam2.retrace(-tf_fs_after+pos)
fwhm1 = 2.35*1e4*beam2.get_standard_deviation(3,ref=1,nolost=1)
tk = beam2.histo1(3, nbins=75, ref=1, nolost=1)
fwhm2 = 1e4*tk["fwhm"]
#fwhm_th = 1e4*transfocator_calculate_estimated_size(pos,diameter=diameter,focal_distance=focal_q)
fwhm_th2 = 1e4*numpy.sqrt( (div_q*(pos+0.5*tf_length-focal_q))**2 + theoretical_focal_size**2 )
#fwhm_th2 = 1e4*( numpy.abs(div_q*(pos-focal_q+0.5*tf_length)) + theoretical_focal_size )
out[0,i] = tf_fs_before+tf_length+pos
out[1,i] = -tf_fs_after+pos
out[2,i] = pos
out[3,i] = pos+0.5*tf_length
out[4,i] = pos+0.5*tf_length-focal_q
out[5,i] = fwhm1
out[6,i] = fwhm2
out[7,i] = fwhm_th2
f.write(" %18.3f %18.3f %18.3f %18.3f %18.3f %18.3f %18.3f %18.3f \n"%\
(tf_fs_before+tf_length+pos,\
-tf_fs_after+pos,\
pos,\
pos+0.5*tf_length,\
pos+0.5*tf_length-focal_q,\
fwhm1,fwhm2,fwhm_th2))
f.close()
print("File with beam evolution written to disk: id30b.spec")
#
# plots
#
itmp = input("Do you want to plot the intensity distribution and beam evolution? [1=yes,0=No]")
if str(itmp) != "1":
return
import matplotlib.pylab as plt
plt.figure(1)
plt.plot(out[1,:],out[5,:],'blue',label="fwhm shadow(stdev)")
plt.plot(out[1,:],out[6,:],'green',label="fwhm shadow(histo1)")
plt.plot(out[1,:],out[7,:],'red',label="fwhm theoretical")
plt.xlabel("Distance from image plane [cm]")
plt.ylabel("spot size [um] ")
ax = plt.subplot(111)
ax.legend(bbox_to_anchor=(1.1, 1.05))
print("Kill graphic to continue.")
plt.show()
Shadow.ShadowTools.histo1(beam,3,nbins=75,ref=1,nolost=1,calfwhm=1)
input("<Enter> to finish.")
return None
def id30b_full_simulation(photon_energy_ev=14000.0,s_target=20.0e-4,nlenses_target=None):
if nlenses_target == None:
force_nlenses = 0
else:
force_nlenses = 1
#
# define lens setup (general)
#
xrl_symbol = ["Be","Be","Be"]
xrl_density = [1.845,1.845,1.845]
lens_diameter = 0.05
nlenses_max = [15,3,1]
nlenses_radii = [500e-4,1000e-4,1500e-4]
sigmaz=6.46e-4
alpha = 0.55
tf_p = 5960 # position of the TF measured from the center of the transfocator
tf_q = 9760 - tf_p # position of the image plane measured from the center of the transfocator
if s_target < 2.35*sigmaz*tf_q/tf_p:
print("Source size FWHM is: %f um"%(1e4*2.35*sigmaz))
print("Maximum Demagnifications is: %f um"%(tf_p/tf_q))
print("Minimum possible size is: %f um"%(1e4*2.35*sigmaz*tf_q/tf_p))
print("Error: redefine size")
return
print("================================== TRANSFOCATOR INPUTS ")
print("Photon energy: %f eV"%(photon_energy_ev))
if force_nlenses:
print("Forced_nlenses: ",nlenses_target)
else:
print("target size: %f cm"%(s_target))
print("materials: ",xrl_symbol)
print("densities: ",xrl_density)
print("Lens diameter: %f cm"%(lens_diameter))
print("nlenses_max:",nlenses_max,"nlenses_radii: ",nlenses_radii)
print("Source size (sigma): %f um, FWHM: %f um"%(1e4*sigmaz,2.35*1e4*sigmaz))
print("Distances: tf_p: %f cm, tf_q: %f cm"%(tf_p,tf_q))
print("alpha: %f"%(alpha))
print("========================================================")
if force_nlenses != 1:
nlenses_target = transfocator_compute_configuration(photon_energy_ev,s_target,\
symbol=xrl_symbol,density=xrl_density,\
nlenses_max=nlenses_max, nlenses_radii=nlenses_radii, lens_diameter=lens_diameter, \
sigmaz=sigmaz, alpha=alpha, \
tf_p=tf_p,tf_q=tf_q, verbose=1)
(s_target,focal_f,focal_q,div_q) = \
transfocator_compute_parameters(photon_energy_ev, nlenses_target,\
symbol=xrl_symbol,density=xrl_density,\
nlenses_max=nlenses_max, nlenses_radii=nlenses_radii, \
lens_diameter=lens_diameter,\
sigmaz=sigmaz, alpha=alpha,\
tf_p=tf_p,tf_q=tf_q)
slots_max = [ 1, 2, 4, 8, 1, 2, 1] # slots
slots_on_off = transfocator_nlenses_to_slots(nlenses_target,nlenses_max=nlenses_max)
print("=============================== TRANSFOCATOR SET")
#print("deltas: ",deltas)
if force_nlenses != 1:
print("nlenses_target (optimized): ",nlenses_target)
else:
print("nlenses_target (forced): ",nlenses_target)
print("With these lenses we obtain: ")
print(" focal_f: %f cm"%(focal_f))
print(" focal_q: %f cm"%(focal_q))
print(" s_target: %f um"%(s_target*1e4))
print(" slots_max: ",slots_max)
print(" slots_on_off: ",slots_on_off)
print("==================================================")
# for theoretical calculations use the focal position and distances given by the target nlenses
itmp = input("Start SHADOW simulation? [1=yes,0=No]: ")
if str(itmp) != "1":
return
#=======================================================================================================================
# Inputs
#=======================================================================================================================
emittH = 3.9e-9
emittV = 10e-12
betaH = 35.6
betaV = 3.0
number_of_rays = 50000
nslots = len(slots_max)
slots_lens_thickness = [0.3 for i in range(nslots)] #total thickness of a single lens in cm
# for each slot, positional gap of the first lens in cm
slots_steps = [ 4, 4, 1.9, 6.1, 4, 4, slots_lens_thickness[-1]]
slots_radii = [.05, .05, .05, .05, 0.1, 0.1, 0.15] # radii of the lenses in cm
AAA= 333
id30b_ray_tracing(emittH=emittH,emittV=emittV,betaH=betaH,betaV=betaV,number_of_rays=number_of_rays,\
density=xrl_density[0],symbol=xrl_symbol[0],tf_p=tf_p,tf_q=tf_q,lens_diameter=lens_diameter,\
slots_max=slots_max,slots_on_off=slots_on_off,photon_energy_ev=photon_energy_ev,\
slots_lens_thickness=slots_lens_thickness,slots_steps=slots_steps,slots_radii=slots_radii,\
s_target=s_target,focal_f=focal_f,focal_q=focal_q,div_q=div_q)
def main():
# this performs the full simulation: calculates the optimum configuration and do the ray-tracing
itmp = input("Enter: \n 0 = optimization calculation only \n 1 = full simulation (ray tracing) \n?> ")
photon_energy_kev = float(input("Enter photon energy in keV: "))
s_target_um = float(input("Enter target focal dimension in microns: "))
if str(itmp) == "1":
id30b_full_simulation(photon_energy_ev=photon_energy_kev*1e3,s_target=s_target_um*1e-4,nlenses_target=None)
#id30b_full_simulation(photon_energy_ev=14000.0,s_target=20.0e-4,nlenses_target=[3,1,1])
else:
#this performs the calculation of the optimizad configuration
nlenses_optimum = transfocator_compute_configuration(photon_energy_kev*1e3,s_target_um*1e-4,\
symbol=["Be","Be","Be"], density=[1.845,1.845,1.845],\
nlenses_max = [15,3,1], nlenses_radii = [500e-4,1000e-4,1500e-4], lens_diameter=0.05, \
sigmaz=6.46e-4, alpha = 0.55, \
tf_p=5960, tf_q=3800, verbose=0 )
print("Optimum lens configuration is: ",nlenses_optimum)
if nlenses_optimum == None:
return
print("Activate slots: ",transfocator_nlenses_to_slots(nlenses_optimum,nlenses_max=[15,3,1]))
# this calculates the parameters (image size, etc) for a given lens configuration
(size, f, q_f, div) = transfocator_compute_parameters(photon_energy_kev*1e3, nlenses_optimum,\
symbol=["Be","Be","Be"], density=[1.845,1.845,1.845],\
nlenses_max = [15,3,1], nlenses_radii = [500e-4,1000e-4,1500e-4], lens_diameter=0.05, \
sigmaz=6.46e-4, alpha = 0.55, \
tf_p=5960, tf_q=3800 )
print("For given configuration ",nlenses_optimum," we get: ")
print(" size: %f cm, focal length: %f cm, focal distance: %f cm, divergence: %f rad: "%(size, f, q_f, div))
if __name__ == "__main__":
main() | mit |
RAJSD2610/SDNopenflowSwitchAnalysis | TotalFlowPlot.py | 1 | 2742 | import os
import pandas as pd
import matplotlib.pyplot as plt
import seaborn
seaborn.set()
path= os.path.expanduser("~/Desktop/ece671/udpt8")
num_files = len([f for f in os.listdir(path)if os.path.isfile(os.path.join(path, f))])
print(num_files)
u8=[]
i=0
def file_len(fname):
with open(fname) as f:
for i, l in enumerate(f):
pass
return i + 1
while i<(num_files/2) :
# df+=[]
j=i+1
path ="/home/vetri/Desktop/ece671/udpt8/ftotal."+str(j)+".csv"
y = file_len(path)
# except: pass
#df.append(pd.read_csv(path,header=None))
# a+=[]
#y=len(df[i].index)-1 #1 row added by default so that table has a entry
if y<0:
y=0
u8.append(y)
i+=1
print(u8)
path= os.path.expanduser("~/Desktop/ece671/udpnone")
num_files = len([f for f in os.listdir(path)if os.path.isfile(os.path.join(path, f))])
print(num_files)
i=0
j=0
u=[]
while i<(num_files/2):
j=i+1
path ="/home/vetri/Desktop/ece671/udpnone/ftotal."+str(j)+".csv"
y = file_len(path)
# except: pass
#df.append(pd.read_csv(path,header=None))
# a+=[]
#y=len(df[i].index)-1 #1 row added by default so that table has a entry
if y<0:
y=0
u.append(y)
i+=1
print(u)
path= os.path.expanduser("~/Desktop/ece671/tcpnone")
num_files = len([f for f in os.listdir(path)if os.path.isfile(os.path.join(path, f))])
print(num_files)
i=0
j=0
t=[]
while i<(num_files/2):
j=i+1
path ="/home/vetri/Desktop/ece671/tcpnone/ftotal."+str(j)+".csv"
y = file_len(path)
# except: pass
#df.append(pd.read_csv(path,header=None))
# a+=[]
#y=len(df[i].index)-1 #1 row added by default so that table has a entry
if y<0:
y=0
t.append(y)
i+=1
print(t)
path= os.path.expanduser("~/Desktop/ece671/tcpt8")
num_files = len([f for f in os.listdir(path)if os.path.isfile(os.path.join(path, f))])
print(num_files)
i=0
j=0
t8=[]
while i<(num_files/2):
j=i+1
path ="/home/vetri/Desktop/ece671/tcpt8/ftotal."+str(j)+".csv"
y = file_len(path)
# except: pass
#df.append(pd.read_csv(path,header=None))
# a+=[]
#y=len(df[i].index)-1 #1 row added by default so that table has a entry
if y<0:
y=0
t8.append(y)
i+=1
print(t8)
#plt.figure(figsize=(4, 5))
plt.plot(list(range(1,len(u8)+1)),u8, '.-',label="udpt8")
plt.plot(list(range(1,len(u)+1)),u, '.-',label="udpnone")
plt.plot(list(range(1,len(t)+1)),t, '.-',label="tcpnone")
plt.plot(list(range(1,len(t8)+1)),t8, '.-',label="tcpt8")
plt.title("Total Flows Present after 1st flow")
plt.xlabel("time(s)")
plt.ylabel("flows")
#plt.frameon=True
plt.legend()
plt.show()
| gpl-3.0 |
JackKelly/neuralnilm_prototype | scripts/e307.py | 2 | 6092 | from __future__ import print_function, division
import matplotlib
import logging
from sys import stdout
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
from neuralnilm import (Net, RealApplianceSource,
BLSTMLayer, DimshuffleLayer,
BidirectionalRecurrentLayer)
from neuralnilm.source import standardise, discretize, fdiff, power_and_fdiff
from neuralnilm.experiment import run_experiment, init_experiment
from neuralnilm.net import TrainingError
from neuralnilm.layers import MixtureDensityLayer
from neuralnilm.objectives import scaled_cost, mdn_nll, scaled_cost_ignore_inactive, ignore_inactive
from neuralnilm.plot import MDNPlotter
from lasagne.nonlinearities import sigmoid, rectify, tanh
from lasagne.objectives import mse
from lasagne.init import Uniform, Normal
from lasagne.layers import (LSTMLayer, DenseLayer, Conv1DLayer,
ReshapeLayer, FeaturePoolLayer, RecurrentLayer)
from lasagne.updates import nesterov_momentum, momentum
from functools import partial
import os
import __main__
from copy import deepcopy
from math import sqrt
import numpy as np
import theano.tensor as T
NAME = os.path.splitext(os.path.split(__main__.__file__)[1])[0]
PATH = "/homes/dk3810/workspace/python/neuralnilm/figures"
SAVE_PLOT_INTERVAL = 250
GRADIENT_STEPS = 100
SEQ_LENGTH = 512
source_dict = dict(
filename='/data/dk3810/ukdale.h5',
appliances=[
['fridge freezer', 'fridge', 'freezer'],
'hair straighteners',
'television'
# 'dish washer',
# ['washer dryer', 'washing machine']
],
max_appliance_powers=[300, 500, 200, 2500, 2400],
on_power_thresholds=[5] * 5,
max_input_power=5900,
min_on_durations=[60, 60, 60, 1800, 1800],
min_off_durations=[12, 12, 12, 1800, 600],
window=("2013-06-01", "2014-07-01"),
seq_length=SEQ_LENGTH,
output_one_appliance=False,
boolean_targets=False,
train_buildings=[1],
validation_buildings=[1],
skip_probability=0.0,
n_seq_per_batch=16,
subsample_target=4,
include_diff=False,
clip_appliance_power=True,
target_is_prediction=False,
independently_center_inputs = True,
standardise_input=True,
standardise_targets=True,
input_padding=0,
lag=0,
reshape_target_to_2D=False,
input_stats={'mean': np.array([ 0.05526326], dtype=np.float32),
'std': np.array([ 0.12636775], dtype=np.float32)},
target_stats={
'mean': np.array([ 0.04066789, 0.01881946,
0.24639061, 0.17608672, 0.10273963],
dtype=np.float32),
'std': np.array([ 0.11449792, 0.07338708,
0.26608968, 0.33463112, 0.21250485],
dtype=np.float32)}
)
N = 50
net_dict = dict(
save_plot_interval=SAVE_PLOT_INTERVAL,
# loss_function=partial(ignore_inactive, loss_func=mdn_nll, seq_length=SEQ_LENGTH),
# loss_function=lambda x, t: mdn_nll(x, t).mean(),
loss_function=lambda x, t: mse(x, t).mean(),
# loss_function=partial(scaled_cost, loss_func=mse),
updates_func=momentum,
learning_rate=1e-02,
learning_rate_changes_by_iteration={
500: 5e-03
# 4000: 1e-03,
# 6000: 5e-06,
# 7000: 1e-06
# 2000: 5e-06
# 3000: 1e-05
# 7000: 5e-06,
# 10000: 1e-06,
# 15000: 5e-07,
# 50000: 1e-07
},
do_save_activations=True
)
def callback(net, epoch):
net.source.reshape_target_to_2D = True
net.plotter = MDNPlotter(net)
net.generate_validation_data_and_set_shapes()
net.loss_function = lambda x, t: mdn_nll(x, t).mean()
net.learning_rate.set_value(1e-05)
def exp_a(name):
# 3 appliances
global source
source_dict_copy = deepcopy(source_dict)
source_dict_copy['reshape_target_to_2D'] = False
source = RealApplianceSource(**source_dict_copy)
source.reshape_target_to_2D = False
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source
))
N = 50
net_dict_copy['layers_config'] = [
{
'type': BidirectionalRecurrentLayer,
'num_units': N,
'gradient_steps': GRADIENT_STEPS,
'W_in_to_hid': Normal(std=1.),
'nonlinearity': tanh
},
{
'type': FeaturePoolLayer,
'ds': 4, # number of feature maps to be pooled together
'axis': 1, # pool over the time axis
'pool_function': T.max
},
{
'type': BidirectionalRecurrentLayer,
'num_units': N,
'gradient_steps': GRADIENT_STEPS,
'W_in_to_hid': Normal(std=1/sqrt(N)),
'nonlinearity': tanh
},
{
'type': DenseLayer,
'W': Normal(std=1/sqrt(N)),
'num_units': source.n_outputs,
'nonlinearity': None
}
]
net_dict_copy['layer_changes'] = {
1001: {
'remove_from': -2,
'callback': callback,
'new_layers': [
{
'type': MixtureDensityLayer,
'num_units': source.n_outputs,
'num_components': 2
}
]
}
}
net = Net(**net_dict_copy)
return net
def main():
# EXPERIMENTS = list('abcdefghijklmnopqrstuvwxyz')
EXPERIMENTS = list('a')
for experiment in EXPERIMENTS:
full_exp_name = NAME + experiment
func_call = init_experiment(PATH, experiment, full_exp_name)
logger = logging.getLogger(full_exp_name)
try:
net = eval(func_call)
run_experiment(net, epochs=None)
except KeyboardInterrupt:
logger.info("KeyboardInterrupt")
break
except Exception as exception:
logger.exception("Exception")
raise
finally:
logging.shutdown()
if __name__ == "__main__":
main()
| mit |
ARudiuk/mne-python | examples/inverse/plot_label_from_stc.py | 31 | 3963 | """
=================================================
Generate a functional label from source estimates
=================================================
Threshold source estimates and produce a functional label. The label
is typically the region of interest that contains high values.
Here we compare the average time course in the anatomical label obtained
by FreeSurfer segmentation and the average time course from the
functional label. As expected the time course in the functional
label yields higher values.
"""
# Author: Luke Bloy <luke.bloy@gmail.com>
# Alex Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.minimum_norm import read_inverse_operator, apply_inverse
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif'
subjects_dir = data_path + '/subjects'
subject = 'sample'
snr = 3.0
lambda2 = 1.0 / snr ** 2
method = "dSPM" # use dSPM method (could also be MNE or sLORETA)
# Compute a label/ROI based on the peak power between 80 and 120 ms.
# The label bankssts-lh is used for the comparison.
aparc_label_name = 'bankssts-lh'
tmin, tmax = 0.080, 0.120
# Load data
evoked = mne.read_evokeds(fname_evoked, condition=0, baseline=(None, 0))
inverse_operator = read_inverse_operator(fname_inv)
src = inverse_operator['src'] # get the source space
# Compute inverse solution
stc = apply_inverse(evoked, inverse_operator, lambda2, method,
pick_ori='normal')
# Make an STC in the time interval of interest and take the mean
stc_mean = stc.copy().crop(tmin, tmax).mean()
# use the stc_mean to generate a functional label
# region growing is halted at 60% of the peak value within the
# anatomical label / ROI specified by aparc_label_name
label = mne.read_labels_from_annot(subject, parc='aparc',
subjects_dir=subjects_dir,
regexp=aparc_label_name)[0]
stc_mean_label = stc_mean.in_label(label)
data = np.abs(stc_mean_label.data)
stc_mean_label.data[data < 0.6 * np.max(data)] = 0.
func_labels, _ = mne.stc_to_label(stc_mean_label, src=src, smooth=True,
subjects_dir=subjects_dir, connected=True)
# take first as func_labels are ordered based on maximum values in stc
func_label = func_labels[0]
# load the anatomical ROI for comparison
anat_label = mne.read_labels_from_annot(subject, parc='aparc',
subjects_dir=subjects_dir,
regexp=aparc_label_name)[0]
# extract the anatomical time course for each label
stc_anat_label = stc.in_label(anat_label)
pca_anat = stc.extract_label_time_course(anat_label, src, mode='pca_flip')[0]
stc_func_label = stc.in_label(func_label)
pca_func = stc.extract_label_time_course(func_label, src, mode='pca_flip')[0]
# flip the pca so that the max power between tmin and tmax is positive
pca_anat *= np.sign(pca_anat[np.argmax(np.abs(pca_anat))])
pca_func *= np.sign(pca_func[np.argmax(np.abs(pca_anat))])
###############################################################################
# plot the time courses....
plt.figure()
plt.plot(1e3 * stc_anat_label.times, pca_anat, 'k',
label='Anatomical %s' % aparc_label_name)
plt.plot(1e3 * stc_func_label.times, pca_func, 'b',
label='Functional %s' % aparc_label_name)
plt.legend()
plt.show()
###############################################################################
# plot brain in 3D with PySurfer if available
brain = stc_mean.plot(hemi='lh', subjects_dir=subjects_dir)
brain.show_view('lateral')
# show both labels
brain.add_label(anat_label, borders=True, color='k')
brain.add_label(func_label, borders=True, color='b')
| bsd-3-clause |
SU-ECE-17-7/hotspotter | hsviz/draw_func2.py | 1 | 54605 | ''' Lots of functions for drawing and plotting visiony things '''
# TODO: New naming scheme
# viz_<func_name> will clear everything. The current axes and fig: clf, cla. # Will add annotations
# interact_<func_name> will clear everything and start user interactions.
# show_<func_name> will always clear the current axes, but not fig: cla # Might # add annotates?
# plot_<func_name> will not clear the axes or figure. More useful for graphs
# draw_<func_name> same as plot for now. More useful for images
from __future__ import division, print_function
from hscom import __common__
(print, print_, print_on, print_off, rrr, profile,
printDBG) = __common__.init(__name__, '[df2]', DEBUG=False, initmpl=True)
# Python
from itertools import izip
from os.path import splitext, split, join, normpath, exists
import colorsys
import itertools
import pylab
import sys
import textwrap
import time
import warnings
# Matplotlib / Qt
import matplotlib
import matplotlib as mpl # NOQA
from matplotlib.collections import PatchCollection, LineCollection
from matplotlib.font_manager import FontProperties
from matplotlib.patches import Rectangle, Circle, FancyArrow
from matplotlib.transforms import Affine2D
from matplotlib.backends import backend_qt4
import matplotlib.pyplot as plt
# Qt
from PyQt4 import QtCore, QtGui
from PyQt4.QtCore import Qt
# Scientific
import numpy as np
import scipy.stats
import cv2
# HotSpotter
from hscom import helpers
from hscom import tools
from hscom.Printable import DynStruct
#================
# GLOBALS
#================
TMP_mevent = None
QT4_WINS = []
plotWidget = None
# GENERAL FONTS
SMALLER = 8
SMALL = 10
MED = 12
LARGE = 14
#fpargs = dict(family=None, style=None, variant=None, stretch=None, fname=None)
FONTS = DynStruct()
FONTS.small = FontProperties(weight='light', size=SMALL)
FONTS.smaller = FontProperties(weight='light', size=SMALLER)
FONTS.med = FontProperties(weight='light', size=MED)
FONTS.large = FontProperties(weight='light', size=LARGE)
FONTS.medbold = FontProperties(weight='bold', size=MED)
FONTS.largebold = FontProperties(weight='bold', size=LARGE)
# SPECIFIC FONTS
FONTS.legend = FONTS.small
FONTS.figtitle = FONTS.med
FONTS.axtitle = FONTS.med
FONTS.subtitle = FONTS.med
FONTS.xlabel = FONTS.smaller
FONTS.ylabel = FONTS.small
FONTS.relative = FONTS.smaller
# COLORS
ORANGE = np.array((255, 127, 0, 255)) / 255.0
RED = np.array((255, 0, 0, 255)) / 255.0
GREEN = np.array(( 0, 255, 0, 255)) / 255.0
BLUE = np.array(( 0, 0, 255, 255)) / 255.0
YELLOW = np.array((255, 255, 0, 255)) / 255.0
BLACK = np.array(( 0, 0, 0, 255)) / 255.0
WHITE = np.array((255, 255, 255, 255)) / 255.0
GRAY = np.array((127, 127, 127, 255)) / 255.0
DEEP_PINK = np.array((255, 20, 147, 255)) / 255.0
PINK = np.array((255, 100, 100, 255)) / 255.0
FALSE_RED = np.array((255, 51, 0, 255)) / 255.0
TRUE_GREEN = np.array(( 0, 255, 0, 255)) / 255.0
DARK_ORANGE = np.array((127, 63, 0, 255)) / 255.0
DARK_YELLOW = np.array((127, 127, 0, 255)) / 255.0
PURPLE = np.array((102, 0, 153, 255)) / 255.0
UNKNOWN_PURP = PURPLE
# FIGURE GEOMETRY
DPI = 80
#DPI = 160
#FIGSIZE = (24) # default windows fullscreen
FIGSIZE_MED = (12, 6)
FIGSIZE_SQUARE = (12, 12)
FIGSIZE_BIGGER = (24, 12)
FIGSIZE_HUGE = (32, 16)
FIGSIZE = FIGSIZE_MED
# Quality drawings
#FIGSIZE = FIGSIZE_SQUARE
#DPI = 120
tile_within = (-1, 30, 969, 1041)
if helpers.get_computer_name() == 'Ooo':
TILE_WITHIN = (-1912, 30, -969, 1071)
# DEFAULTS. (TODO: Can these be cleaned up?)
DISTINCT_COLORS = True # and False
DARKEN = None
ELL_LINEWIDTH = 1.5
if DISTINCT_COLORS:
ELL_ALPHA = .6
LINE_ALPHA = .35
else:
ELL_ALPHA = .4
LINE_ALPHA = .4
LINE_ALPHA_OVERRIDE = helpers.get_arg('--line-alpha-override', type_=float, default=None)
ELL_ALPHA_OVERRIDE = helpers.get_arg('--ell-alpha-override', type_=float, default=None)
#LINE_ALPHA_OVERRIDE = None
#ELL_ALPHA_OVERRIDE = None
ELL_COLOR = BLUE
LINE_COLOR = RED
LINE_WIDTH = 1.4
SHOW_LINES = True # True
SHOW_ELLS = True
POINT_SIZE = 2
base_fnum = 9001
def next_fnum():
global base_fnum
base_fnum += 1
return base_fnum
def my_prefs():
global LINE_COLOR
global ELL_COLOR
global ELL_LINEWIDTH
global ELL_ALPHA
LINE_COLOR = (1, 0, 0)
ELL_COLOR = (0, 0, 1)
ELL_LINEWIDTH = 2
ELL_ALPHA = .5
def execstr_global():
execstr = ['global' + key for key in globals().keys()]
return execstr
def register_matplotlib_widget(plotWidget_):
'talks to PyQt4 guis'
global plotWidget
plotWidget = plotWidget_
#fig = plotWidget.figure
#axes_list = fig.get_axes()
#ax = axes_list[0]
#plt.sca(ax)
def unregister_qt4_win(win):
global QT4_WINS
if win == 'all':
QT4_WINS = []
def register_qt4_win(win):
global QT4_WINS
QT4_WINS.append(win)
def OooScreen2():
nRows = 1
nCols = 1
x_off = 30 * 4
y_off = 30 * 4
x_0 = -1920
y_0 = 30
w = (1912 - x_off) / nRows
h = (1080 - y_off) / nCols
return dict(num_rc=(1, 1), wh=(w, h), xy_off=(x_0, y_0), wh_off=(0, 10),
row_first=True, no_tile=False)
def deterministic_shuffle(list_):
randS = int(np.random.rand() * np.uint(0 - 2) / 2)
np.random.seed(len(list_))
np.random.shuffle(list_)
np.random.seed(randS)
def distinct_colors(N, brightness=.878):
# http://blog.jianhuashao.com/2011/09/generate-n-distinct-colors.html
sat = brightness
val = brightness
HSV_tuples = [(x * 1.0 / N, sat, val) for x in xrange(N)]
RGB_tuples = map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples)
deterministic_shuffle(RGB_tuples)
return RGB_tuples
def add_alpha(colors):
return [list(color) + [1] for color in colors]
def _axis_xy_width_height(ax, xaug=0, yaug=0, waug=0, haug=0):
'gets geometry of a subplot'
autoAxis = ax.axis()
xy = (autoAxis[0] + xaug, autoAxis[2] + yaug)
width = (autoAxis[1] - autoAxis[0]) + waug
height = (autoAxis[3] - autoAxis[2]) + haug
return xy, width, height
def draw_border(ax, color=GREEN, lw=2, offset=None):
'draws rectangle border around a subplot'
xy, width, height = _axis_xy_width_height(ax, -.7, -.2, 1, .4)
if offset is not None:
xoff, yoff = offset
xy = [xoff, yoff]
height = - height - yoff
width = width - xoff
rect = matplotlib.patches.Rectangle(xy, width, height, lw=lw)
rect = ax.add_patch(rect)
rect.set_clip_on(False)
rect.set_fill(False)
rect.set_edgecolor(color)
def draw_roi(roi, label=None, bbox_color=(1, 0, 0),
lbl_bgcolor=(0, 0, 0), lbl_txtcolor=(1, 1, 1), theta=0, ax=None):
if ax is None:
ax = gca()
(rx, ry, rw, rh) = roi
#cos_ = np.cos(theta)
#sin_ = np.sin(theta)
#rot_t = Affine2D([( cos_, -sin_, 0),
#( sin_, cos_, 0),
#( 0, 0, 1)])
#scale_t = Affine2D([( rw, 0, 0),
#( 0, rh, 0),
#( 0, 0, 1)])
#trans_t = Affine2D([( 1, 0, rx + rw / 2),
#( 0, 1, ry + rh / 2),
#( 0, 0, 1)])
#t_end = scale_t + rot_t + trans_t + t_start
# Transformations are specified in backwards order.
trans_roi = Affine2D()
trans_roi.scale(rw, rh)
trans_roi.rotate(theta)
trans_roi.translate(rx + rw / 2, ry + rh / 2)
t_end = trans_roi + ax.transData
bbox = matplotlib.patches.Rectangle((-.5, -.5), 1, 1, lw=2, transform=t_end)
arw_x, arw_y, arw_dx, arw_dy = (-0.5, -0.5, 1.0, 0.0)
arrowargs = dict(head_width=.1, transform=t_end, length_includes_head=True)
arrow = FancyArrow(arw_x, arw_y, arw_dx, arw_dy, **arrowargs)
bbox.set_fill(False)
#bbox.set_transform(trans)
bbox.set_edgecolor(bbox_color)
arrow.set_edgecolor(bbox_color)
arrow.set_facecolor(bbox_color)
ax.add_patch(bbox)
ax.add_patch(arrow)
#ax.add_patch(arrow2)
if label is not None:
ax_absolute_text(rx, ry, label, ax=ax,
horizontalalignment='center',
verticalalignment='center',
color=lbl_txtcolor,
backgroundcolor=lbl_bgcolor)
# ---- GENERAL FIGURE COMMANDS ----
def sanatize_img_fname(fname):
fname_clean = fname
search_replace_list = [(' ', '_'), ('\n', '--'), ('\\', ''), ('/', '')]
for old, new in search_replace_list:
fname_clean = fname_clean.replace(old, new)
fname_noext, ext = splitext(fname_clean)
fname_clean = fname_noext + ext.lower()
# Check for correct extensions
if not ext.lower() in helpers.IMG_EXTENSIONS:
fname_clean += '.png'
return fname_clean
def sanatize_img_fpath(fpath):
[dpath, fname] = split(fpath)
fname_clean = sanatize_img_fname(fname)
fpath_clean = join(dpath, fname_clean)
fpath_clean = normpath(fpath_clean)
return fpath_clean
def set_geometry(fnum, x, y, w, h):
fig = get_fig(fnum)
qtwin = fig.canvas.manager.window
qtwin.setGeometry(x, y, w, h)
def get_geometry(fnum):
fig = get_fig(fnum)
qtwin = fig.canvas.manager.window
(x1, y1, x2, y2) = qtwin.geometry().getCoords()
(x, y, w, h) = (x1, y1, x2 - x1, y2 - y1)
return (x, y, w, h)
def get_screen_info():
from PyQt4 import Qt, QtGui # NOQA
desktop = QtGui.QDesktopWidget()
mask = desktop.mask() # NOQA
layout_direction = desktop.layoutDirection() # NOQA
screen_number = desktop.screenNumber() # NOQA
normal_geometry = desktop.normalGeometry() # NOQA
num_screens = desktop.screenCount() # NOQA
avail_rect = desktop.availableGeometry() # NOQA
screen_rect = desktop.screenGeometry() # NOQA
QtGui.QDesktopWidget().availableGeometry().center() # NOQA
normal_geometry = desktop.normalGeometry() # NOQA
def get_all_figures():
all_figures_ = [manager.canvas.figure for manager in
matplotlib._pylab_helpers.Gcf.get_all_fig_managers()]
all_figures = []
# Make sure you dont show figures that this module closed
for fig in iter(all_figures_):
if not 'df2_closed' in fig.__dict__.keys() or not fig.df2_closed:
all_figures.append(fig)
# Return all the figures sorted by their number
all_figures = sorted(all_figures, key=lambda fig: fig.number)
return all_figures
def get_all_qt4_wins():
return QT4_WINS
def all_figures_show():
if plotWidget is not None:
plotWidget.figure.show()
plotWidget.figure.canvas.draw()
for fig in iter(get_all_figures()):
time.sleep(.1)
fig.show()
fig.canvas.draw()
def all_figures_tight_layout():
for fig in iter(get_all_figures()):
fig.tight_layout()
#adjust_subplots()
time.sleep(.1)
def get_monitor_geom(monitor_num=0):
from PyQt4 import QtGui # NOQA
desktop = QtGui.QDesktopWidget()
rect = desktop.availableGeometry()
geom = (rect.x(), rect.y(), rect.width(), rect.height())
return geom
def golden_wh(x):
'returns a width / height with a golden aspect ratio'
return map(int, map(round, (x * .618, x * .312)))
def all_figures_tile(num_rc=(3, 4), wh=1000, xy_off=(0, 0), wh_off=(0, 10),
row_first=True, no_tile=False, override1=False):
'Lays out all figures in a grid. if wh is a scalar, a golden ratio is used'
# RCOS TODO:
# I want this function to layout all the figures and qt windows within the
# bounds of a rectangle. (taken from the get_monitor_geom, or specified by
# the user i.e. left half of monitor 0). It should lay them out
# rectangularly and choose figure sizes such that all of them will fit.
if no_tile:
return
if not np.iterable(wh):
wh = golden_wh(wh)
all_figures = get_all_figures()
all_qt4wins = get_all_qt4_wins()
if override1:
if len(all_figures) == 1:
fig = all_figures[0]
win = fig.canvas.manager.window
win.setGeometry(0, 0, 900, 900)
update()
return
#nFigs = len(all_figures) + len(all_qt4_wins)
num_rows, num_cols = num_rc
w, h = wh
x_off, y_off = xy_off
w_off, h_off = wh_off
x_pad, y_pad = (0, 0)
printDBG('[df2] Tile all figures: ')
printDBG('[df2] wh = %r' % ((w, h),))
printDBG('[df2] xy_offsets = %r' % ((x_off, y_off),))
printDBG('[df2] wh_offsets = %r' % ((w_off, h_off),))
printDBG('[df2] xy_pads = %r' % ((x_pad, y_pad),))
if sys.platform == 'win32':
h_off += 0
w_off += 40
x_off += 40
y_off += 40
x_pad += 0
y_pad += 100
def position_window(i, win):
isqt4_mpl = isinstance(win, backend_qt4.MainWindow)
isqt4_back = isinstance(win, QtGui.QMainWindow)
if not isqt4_mpl and not isqt4_back:
raise NotImplementedError('%r-th Backend %r is not a Qt Window' % (i, win))
if row_first:
y = (i % num_rows) * (h + h_off) + 40
x = (int(i / num_rows)) * (w + w_off) + x_pad
else:
x = (i % num_cols) * (w + w_off) + 40
y = (int(i / num_cols)) * (h + h_off) + y_pad
x += x_off
y += y_off
win.setGeometry(x, y, w, h)
ioff = 0
for i, win in enumerate(all_qt4wins):
position_window(i, win)
ioff += 1
for i, fig in enumerate(all_figures):
win = fig.canvas.manager.window
position_window(i + ioff, win)
def all_figures_bring_to_front():
all_figures = get_all_figures()
for fig in iter(all_figures):
bring_to_front(fig)
def close_all_figures():
all_figures = get_all_figures()
for fig in iter(all_figures):
close_figure(fig)
def close_figure(fig):
fig.clf()
fig.df2_closed = True
qtwin = fig.canvas.manager.window
qtwin.close()
def bring_to_front(fig):
#what is difference between show and show normal?
qtwin = fig.canvas.manager.window
qtwin.raise_()
qtwin.activateWindow()
qtwin.setWindowFlags(Qt.WindowStaysOnTopHint)
qtwin.setWindowFlags(Qt.WindowFlags(0))
qtwin.show()
def show():
all_figures_show()
all_figures_bring_to_front()
plt.show()
def reset():
close_all_figures()
def draw():
all_figures_show()
def update():
draw()
all_figures_bring_to_front()
def present(*args, **kwargs):
'execing present should cause IPython magic'
print('[df2] Presenting figures...')
with warnings.catch_warnings():
warnings.simplefilter("ignore")
all_figures_tile(*args, **kwargs)
all_figures_show()
all_figures_bring_to_front()
# Return an exec string
execstr = helpers.ipython_execstr()
execstr += textwrap.dedent('''
if not embedded:
print('[df2] Presenting in normal shell.')
print('[df2] ... plt.show()')
plt.show()
''')
return execstr
def save_figure(fnum=None, fpath=None, usetitle=False, overwrite=True):
#import warnings
#warnings.simplefilter("error")
# Find the figure
if fnum is None:
fig = gcf()
else:
fig = plt.figure(fnum, figsize=FIGSIZE, dpi=DPI)
# Enforce inches and DPI
fig.set_size_inches(FIGSIZE[0], FIGSIZE[1])
fnum = fig.number
if fpath is None:
# Find the title
fpath = sanatize_img_fname(fig.canvas.get_window_title())
if usetitle:
title = sanatize_img_fname(fig.canvas.get_window_title())
fpath = join(fpath, title)
# Add in DPI information
fpath_noext, ext = splitext(fpath)
size_suffix = '_DPI=%r_FIGSIZE=%d,%d' % (DPI, FIGSIZE[0], FIGSIZE[1])
fpath = fpath_noext + size_suffix + ext
# Sanatize the filename
fpath_clean = sanatize_img_fpath(fpath)
#fname_clean = split(fpath_clean)[1]
print('[df2] save_figure() %r' % (fpath_clean,))
#adjust_subplots()
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
if not exists(fpath_clean) or overwrite:
fig.savefig(fpath_clean, dpi=DPI)
def set_ticks(xticks, yticks):
ax = gca()
ax.set_xticks(xticks)
ax.set_yticks(yticks)
def set_xticks(tick_set):
ax = gca()
ax.set_xticks(tick_set)
def set_yticks(tick_set):
ax = gca()
ax.set_yticks(tick_set)
def set_xlabel(lbl, ax=None):
if ax is None:
ax = gca()
ax.set_xlabel(lbl, fontproperties=FONTS.xlabel)
def set_title(title, ax=None):
if ax is None:
ax = gca()
ax.set_title(title, fontproperties=FONTS.axtitle)
def set_ylabel(lbl):
ax = gca()
ax.set_ylabel(lbl, fontproperties=FONTS.xlabel)
def plot(*args, **kwargs):
return plt.plot(*args, **kwargs)
def plot2(x_data, y_data, marker='o', title_pref='', x_label='x', y_label='y', *args,
**kwargs):
do_plot = True
ax = gca()
if len(x_data) != len(y_data):
warnstr = '[df2] ! Warning: len(x_data) != len(y_data). Cannot plot2'
warnings.warn(warnstr)
draw_text(warnstr)
do_plot = False
if len(x_data) == 0:
warnstr = '[df2] ! Warning: len(x_data) == 0. Cannot plot2'
warnings.warn(warnstr)
draw_text(warnstr)
do_plot = False
if do_plot:
ax.plot(x_data, y_data, marker, *args, **kwargs)
min_ = min(x_data.min(), y_data.min())
max_ = max(x_data.max(), y_data.max())
# Equal aspect ratio
ax.set_xlim(min_, max_)
ax.set_ylim(min_, max_)
ax.set_aspect('equal')
ax.set_xlabel(x_label, fontproperties=FONTS.xlabel)
ax.set_ylabel(y_label, fontproperties=FONTS.xlabel)
ax.set_title(title_pref + ' ' + x_label + ' vs ' + y_label,
fontproperties=FONTS.axtitle)
def adjust_subplots_xlabels():
adjust_subplots(left=.03, right=.97, bottom=.2, top=.9, hspace=.15)
def adjust_subplots_xylabels():
adjust_subplots(left=.03, right=1, bottom=.1, top=.9, hspace=.15)
def adjust_subplots_safe(left=.1, right=.9, bottom=.1, top=.9, wspace=.3, hspace=.5):
adjust_subplots(left, bottom, right, top, wspace, hspace)
def adjust_subplots(left=0.02, bottom=0.02,
right=0.98, top=0.90,
wspace=0.1, hspace=0.15):
'''
left = 0.125 # the left side of the subplots of the figure
right = 0.9 # the right side of the subplots of the figure
bottom = 0.1 # the bottom of the subplots of the figure
top = 0.9 # the top of the subplots of the figure
wspace = 0.2 # the amount of width reserved for blank space between subplots
hspace = 0.2
'''
#print('[df2] adjust_subplots(%r)' % locals())
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
#=======================
# TEXT FUNCTIONS
# TODO: I have too many of these. Need to consolidate
#=======================
def upperleft_text(txt):
txtargs = dict(horizontalalignment='left',
verticalalignment='top',
#fontsize='smaller',
#fontweight='ultralight',
backgroundcolor=(0, 0, 0, .5),
color=ORANGE)
ax_relative_text(.02, .02, txt, **txtargs)
def upperright_text(txt, offset=None):
txtargs = dict(horizontalalignment='right',
verticalalignment='top',
#fontsize='smaller',
#fontweight='ultralight',
backgroundcolor=(0, 0, 0, .5),
color=ORANGE,
offset=offset)
ax_relative_text(.98, .02, txt, **txtargs)
def lowerright_text(txt):
txtargs = dict(horizontalalignment='right',
verticalalignment='top',
#fontsize='smaller',
#fontweight='ultralight',
backgroundcolor=(0, 0, 0, .5),
color=ORANGE)
ax_relative_text(.98, .92, txt, **txtargs)
def absolute_lbl(x_, y_, txt, roffset=(-.02, -.02), **kwargs):
txtargs = dict(horizontalalignment='right',
verticalalignment='top',
backgroundcolor=(0, 0, 0, .5),
color=ORANGE,
**kwargs)
ax_absolute_text(x_, y_, txt, roffset=roffset, **txtargs)
def ax_relative_text(x, y, txt, ax=None, offset=None, **kwargs):
if ax is None:
ax = gca()
xy, width, height = _axis_xy_width_height(ax)
x_, y_ = ((xy[0]) + x * width, (xy[1] + height) - y * height)
if offset is not None:
xoff, yoff = offset
x_ += xoff
y_ += yoff
ax_absolute_text(x_, y_, txt, ax=ax, **kwargs)
def ax_absolute_text(x_, y_, txt, ax=None, roffset=None, **kwargs):
if ax is None:
ax = gca()
if 'fontproperties' in kwargs:
kwargs['fontproperties'] = FONTS.relative
if roffset is not None:
xroff, yroff = roffset
xy, width, height = _axis_xy_width_height(ax)
x_ += xroff * width
y_ += yroff * height
ax.text(x_, y_, txt, **kwargs)
def fig_relative_text(x, y, txt, **kwargs):
kwargs['horizontalalignment'] = 'center'
kwargs['verticalalignment'] = 'center'
fig = gcf()
#xy, width, height = _axis_xy_width_height(ax)
#x_, y_ = ((xy[0]+width)+x*width, (xy[1]+height)-y*height)
fig.text(x, y, txt, **kwargs)
def draw_text(text_str, rgb_textFG=(0, 0, 0), rgb_textBG=(1, 1, 1)):
ax = gca()
xy, width, height = _axis_xy_width_height(ax)
text_x = xy[0] + (width / 2)
text_y = xy[1] + (height / 2)
ax.text(text_x, text_y, text_str,
horizontalalignment='center',
verticalalignment='center',
color=rgb_textFG,
backgroundcolor=rgb_textBG)
def set_figtitle(figtitle, subtitle='', forcefignum=True, incanvas=True):
if figtitle is None:
figtitle = ''
fig = gcf()
if incanvas:
if subtitle != '':
subtitle = '\n' + subtitle
fig.suptitle(figtitle + subtitle, fontsize=14, fontweight='bold')
#fig.suptitle(figtitle, x=.5, y=.98, fontproperties=FONTS.figtitle)
#fig_relative_text(.5, .96, subtitle, fontproperties=FONTS.subtitle)
else:
fig.suptitle('')
window_figtitle = ('fig(%d) ' % fig.number) + figtitle
fig.canvas.set_window_title(window_figtitle)
def convert_keypress_event_mpl_to_qt4(mevent):
global TMP_mevent
TMP_mevent = mevent
# Grab the key from the mpl.KeyPressEvent
key = mevent.key
print('[df2] convert event mpl -> qt4')
print('[df2] key=%r' % key)
# dicts modified from backend_qt4.py
mpl2qtkey = {'control': Qt.Key_Control, 'shift': Qt.Key_Shift,
'alt': Qt.Key_Alt, 'super': Qt.Key_Meta,
'enter': Qt.Key_Return, 'left': Qt.Key_Left, 'up': Qt.Key_Up,
'right': Qt.Key_Right, 'down': Qt.Key_Down,
'escape': Qt.Key_Escape, 'f1': Qt.Key_F1, 'f2': Qt.Key_F2,
'f3': Qt.Key_F3, 'f4': Qt.Key_F4, 'f5': Qt.Key_F5,
'f6': Qt.Key_F6, 'f7': Qt.Key_F7, 'f8': Qt.Key_F8,
'f9': Qt.Key_F9, 'f10': Qt.Key_F10, 'f11': Qt.Key_F11,
'f12': Qt.Key_F12, 'home': Qt.Key_Home, 'end': Qt.Key_End,
'pageup': Qt.Key_PageUp, 'pagedown': Qt.Key_PageDown}
# Reverse the control and super (aka cmd/apple) keys on OSX
if sys.platform == 'darwin':
mpl2qtkey.update({'super': Qt.Key_Control, 'control': Qt.Key_Meta, })
# Try to reconstruct QtGui.KeyEvent
type_ = QtCore.QEvent.Type(QtCore.QEvent.KeyPress) # The type should always be KeyPress
text = ''
# Try to extract the original modifiers
modifiers = QtCore.Qt.NoModifier # initialize to no modifiers
if key.find(u'ctrl+') >= 0:
modifiers = modifiers | QtCore.Qt.ControlModifier
key = key.replace(u'ctrl+', u'')
print('[df2] has ctrl modifier')
text += 'Ctrl+'
if key.find(u'alt+') >= 0:
modifiers = modifiers | QtCore.Qt.AltModifier
key = key.replace(u'alt+', u'')
print('[df2] has alt modifier')
text += 'Alt+'
if key.find(u'super+') >= 0:
modifiers = modifiers | QtCore.Qt.MetaModifier
key = key.replace(u'super+', u'')
print('[df2] has super modifier')
text += 'Super+'
if key.isupper():
modifiers = modifiers | QtCore.Qt.ShiftModifier
print('[df2] has shift modifier')
text += 'Shift+'
# Try to extract the original key
try:
if key in mpl2qtkey:
key_ = mpl2qtkey[key]
else:
key_ = ord(key.upper()) # Qt works with uppercase keys
text += key.upper()
except Exception as ex:
print('[df2] ERROR key=%r' % key)
print('[df2] ERROR %r' % ex)
raise
autorep = False # default false
count = 1 # default 1
text = QtCore.QString(text) # The text is somewhat arbitrary
# Create the QEvent
print('----------------')
print('[df2] Create event')
print('[df2] type_ = %r' % type_)
print('[df2] text = %r' % text)
print('[df2] modifiers = %r' % modifiers)
print('[df2] autorep = %r' % autorep)
print('[df2] count = %r ' % count)
print('----------------')
qevent = QtGui.QKeyEvent(type_, key_, modifiers, text, autorep, count)
return qevent
def test_build_qkeyevent():
import draw_func2 as df2
qtwin = df2.QT4_WINS[0]
# This reconstructs an test mplevent
canvas = df2.figure(1).canvas
mevent = matplotlib.backend_bases.KeyEvent('key_press_event', canvas, u'ctrl+p', x=672, y=230.0)
qevent = df2.convert_keypress_event_mpl_to_qt4(mevent)
app = qtwin.backend.app
app.sendEvent(qtwin.ui, mevent)
#type_ = QtCore.QEvent.Type(QtCore.QEvent.KeyPress) # The type should always be KeyPress
#text = QtCore.QString('A') # The text is somewhat arbitrary
#modifiers = QtCore.Qt.NoModifier # initialize to no modifiers
#modifiers = modifiers | QtCore.Qt.ControlModifier
#modifiers = modifiers | QtCore.Qt.AltModifier
#key_ = ord('A') # Qt works with uppercase keys
#autorep = False # default false
#count = 1 # default 1
#qevent = QtGui.QKeyEvent(type_, key_, modifiers, text, autorep, count)
return qevent
# This actually doesn't matter
def on_key_press_event(event):
'redirects keypress events to main window'
global QT4_WINS
print('[df2] %r' % event)
print('[df2] %r' % str(event.__dict__))
for qtwin in QT4_WINS:
qevent = convert_keypress_event_mpl_to_qt4(event)
app = qtwin.backend.app
print('[df2] attempting to send qevent to qtwin')
app.sendEvent(qtwin, qevent)
# TODO: FINISH ME
#PyQt4.QtGui.QKeyEvent
#qtwin.keyPressEvent(event)
#fig.canvas.manager.window.keyPressEvent()
def customize_figure(fig, docla):
if not 'user_stat_list' in fig.__dict__.keys() or docla:
fig.user_stat_list = []
fig.user_notes = []
# We dont need to catch keypress events because you just need to set it as
# an application level shortcut
# Catch key press events
#key_event_cbid = fig.__dict__.get('key_event_cbid', None)
#if key_event_cbid is not None:
#fig.canvas.mpl_disconnect(key_event_cbid)
#fig.key_event_cbid = fig.canvas.mpl_connect('key_press_event', on_key_press_event)
fig.df2_closed = False
def gcf():
if plotWidget is not None:
#print('is plotwidget visible = %r' % plotWidget.isVisible())
fig = plotWidget.figure
return fig
return plt.gcf()
def gca():
if plotWidget is not None:
#print('is plotwidget visible = %r' % plotWidget.isVisible())
axes_list = plotWidget.figure.get_axes()
current = 0
ax = axes_list[current]
return ax
return plt.gca()
def cla():
return plt.cla()
def clf():
return plt.clf()
def get_fig(fnum=None):
printDBG('[df2] get_fig(fnum=%r)' % fnum)
fig_kwargs = dict(figsize=FIGSIZE, dpi=DPI)
if plotWidget is not None:
return gcf()
if fnum is None:
try:
fig = gcf()
except Exception as ex:
printDBG('[df2] get_fig(): ex=%r' % ex)
fig = plt.figure(**fig_kwargs)
fnum = fig.number
else:
try:
fig = plt.figure(fnum, **fig_kwargs)
except Exception as ex:
print(repr(ex))
warnings.warn(repr(ex))
fig = gcf()
return fig
def get_ax(fnum=None, pnum=None):
figure(fnum=fnum, pnum=pnum)
ax = gca()
return ax
def figure(fnum=None, docla=False, title=None, pnum=(1, 1, 1), figtitle=None,
doclf=False, **kwargs):
'''
fnum = fignum = figure number
pnum = plotnum = plot tuple
'''
#matplotlib.pyplot.xkcd()
fig = get_fig(fnum)
axes_list = fig.get_axes()
# Ensure my customized settings
customize_figure(fig, docla)
# Convert pnum to tuple format
if tools.is_int(pnum):
nr = pnum // 100
nc = pnum // 10 - (nr * 10)
px = pnum - (nr * 100) - (nc * 10)
pnum = (nr, nc, px)
if doclf: # a bit hacky. Need to rectify docla and doclf
fig.clf()
# Get the subplot
if docla or len(axes_list) == 0:
printDBG('[df2] *** NEW FIGURE %r.%r ***' % (fnum, pnum))
if not pnum is None:
#ax = plt.subplot(*pnum)
ax = fig.add_subplot(*pnum)
ax.cla()
else:
ax = gca()
else:
printDBG('[df2] *** OLD FIGURE %r.%r ***' % (fnum, pnum))
if not pnum is None:
ax = plt.subplot(*pnum) # fig.add_subplot fails here
#ax = fig.add_subplot(*pnum)
else:
ax = gca()
#ax = axes_list[0]
# Set the title
if not title is None:
ax = gca()
ax.set_title(title, fontproperties=FONTS.axtitle)
# Add title to figure
if figtitle is None and pnum == (1, 1, 1):
figtitle = title
if not figtitle is None:
set_figtitle(figtitle, incanvas=False)
return fig
def plot_pdf(data, draw_support=True, scale_to=None, label=None, color=0,
nYTicks=3):
fig = gcf()
ax = gca()
data = np.array(data)
if len(data) == 0:
warnstr = '[df2] ! Warning: len(data) = 0. Cannot visualize pdf'
warnings.warn(warnstr)
draw_text(warnstr)
return
bw_factor = .05
if isinstance(color, (int, float)):
colorx = color
line_color = plt.get_cmap('gist_rainbow')(colorx)
else:
line_color = color
# Estimate a pdf
data_pdf = estimate_pdf(data, bw_factor)
# Get probability of seen data
prob_x = data_pdf(data)
# Get probability of unseen data data
x_data = np.linspace(0, data.max(), 500)
y_data = data_pdf(x_data)
# Scale if requested
if not scale_to is None:
scale_factor = scale_to / y_data.max()
y_data *= scale_factor
prob_x *= scale_factor
#Plot the actual datas on near the bottom perterbed in Y
if draw_support:
pdfrange = prob_x.max() - prob_x.min()
perb = (np.random.randn(len(data))) * pdfrange / 30.
preb_y_data = np.abs([pdfrange / 50. for _ in data] + perb)
ax.plot(data, preb_y_data, 'o', color=line_color, figure=fig, alpha=.1)
# Plot the pdf (unseen data)
ax.plot(x_data, y_data, color=line_color, label=label)
if nYTicks is not None:
yticks = np.linspace(min(y_data), max(y_data), nYTicks)
ax.set_yticks(yticks)
def estimate_pdf(data, bw_factor):
try:
data_pdf = scipy.stats.gaussian_kde(data, bw_factor)
data_pdf.covariance_factor = bw_factor
except Exception as ex:
print('[df2] ! Exception while estimating kernel density')
print('[df2] data=%r' % (data,))
print('[df2] ex=%r' % (ex,))
raise
return data_pdf
def show_histogram(data, bins=None, **kwargs):
print('[df2] show_histogram()')
dmin = int(np.floor(data.min()))
dmax = int(np.ceil(data.max()))
if bins is None:
bins = dmax - dmin
fig = figure(**kwargs)
ax = gca()
ax.hist(data, bins=bins, range=(dmin, dmax))
#help(np.bincount)
fig.show()
def show_signature(sig, **kwargs):
fig = figure(**kwargs)
plt.plot(sig)
fig.show()
def plot_stems(x_data=None, y_data=None):
if y_data is not None and x_data is None:
x_data = np.arange(len(y_data))
pass
if len(x_data) != len(y_data):
print('[df2] WARNING plot_stems(): len(x_data)!=len(y_data)')
if len(x_data) == 0:
print('[df2] WARNING plot_stems(): len(x_data)=len(y_data)=0')
x_data_ = np.array(x_data)
y_data_ = np.array(y_data)
x_data_sort = x_data_[y_data_.argsort()[::-1]]
y_data_sort = y_data_[y_data_.argsort()[::-1]]
markerline, stemlines, baseline = pylab.stem(x_data_sort, y_data_sort, linefmt='-')
pylab.setp(markerline, 'markerfacecolor', 'b')
pylab.setp(baseline, 'linewidth', 0)
ax = gca()
ax.set_xlim(min(x_data) - 1, max(x_data) + 1)
ax.set_ylim(min(y_data) - 1, max(max(y_data), max(x_data)) + 1)
def plot_sift_signature(sift, title='', fnum=None, pnum=None):
figure(fnum=fnum, pnum=pnum)
ax = gca()
plot_bars(sift, 16)
ax.set_xlim(0, 128)
ax.set_ylim(0, 256)
space_xticks(9, 16)
space_yticks(5, 64)
ax.set_title(title)
dark_background(ax)
return ax
def dark_background(ax=None, doubleit=False):
if ax is None:
ax = gca()
xy, width, height = _axis_xy_width_height(ax)
if doubleit:
halfw = (doubleit) * (width / 2)
halfh = (doubleit) * (height / 2)
xy = (xy[0] - halfw, xy[1] - halfh)
width *= (doubleit + 1)
height *= (doubleit + 1)
rect = matplotlib.patches.Rectangle(xy, width, height, lw=0, zorder=0)
rect.set_clip_on(True)
rect.set_fill(True)
rect.set_color(BLACK * .9)
rect = ax.add_patch(rect)
def space_xticks(nTicks=9, spacing=16, ax=None):
if ax is None:
ax = gca()
ax.set_xticks(np.arange(nTicks) * spacing)
small_xticks(ax)
def space_yticks(nTicks=9, spacing=32, ax=None):
if ax is None:
ax = gca()
ax.set_yticks(np.arange(nTicks) * spacing)
small_yticks(ax)
def small_xticks(ax=None):
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(8)
def small_yticks(ax=None):
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(8)
def plot_bars(y_data, nColorSplits=1):
width = 1
nDims = len(y_data)
nGroup = nDims // nColorSplits
ori_colors = distinct_colors(nColorSplits)
x_data = np.arange(nDims)
ax = gca()
for ix in xrange(nColorSplits):
xs = np.arange(nGroup) + (nGroup * ix)
color = ori_colors[ix]
x_dat = x_data[xs]
y_dat = y_data[xs]
ax.bar(x_dat, y_dat, width, color=color, edgecolor=np.array(color) * .8)
def phantom_legend_label(label, color, loc='upper right'):
'adds a legend label without displaying an actor'
pass
#phantom_actor = plt.Circle((0, 0), 1, fc=color, prop=FONTS.legend, loc=loc)
#plt.legend(phant_actor, label, framealpha=.2)
#plt.legend(*zip(*legend_tups), framealpha=.2)
#legend_tups = []
#legend_tups.append((phantom_actor, label))
def legend(loc='upper right'):
ax = gca()
ax.legend(prop=FONTS.legend, loc=loc)
def plot_histpdf(data, label=None, draw_support=False, nbins=10):
freq, _ = plot_hist(data, nbins=nbins)
plot_pdf(data, draw_support=draw_support, scale_to=freq.max(), label=label)
def plot_hist(data, bins=None, nbins=10, weights=None):
if isinstance(data, list):
data = np.array(data)
if bins is None:
dmin = data.min()
dmax = data.max()
bins = dmax - dmin
ax = gca()
freq, bins_, patches = ax.hist(data, bins=nbins, weights=weights, range=(dmin, dmax))
return freq, bins_
def variation_trunctate(data):
ax = gca()
data = np.array(data)
if len(data) == 0:
warnstr = '[df2] ! Warning: len(data) = 0. Cannot variation_truncate'
warnings.warn(warnstr)
return
trunc_max = data.mean() + data.std() * 2
trunc_min = np.floor(data.min())
ax.set_xlim(trunc_min, trunc_max)
#trunc_xticks = np.linspace(0, int(trunc_max),11)
#trunc_xticks = trunc_xticks[trunc_xticks >= trunc_min]
#trunc_xticks = np.append([int(trunc_min)], trunc_xticks)
#no_zero_yticks = ax.get_yticks()[ax.get_yticks() > 0]
#ax.set_xticks(trunc_xticks)
#ax.set_yticks(no_zero_yticks)
#_----------------- HELPERS ^^^ ---------
# ---- IMAGE CREATION FUNCTIONS ----
@tools.debug_exception
def draw_sift(desc, kp=None):
# TODO: There might be a divide by zero warning in here.
''' desc = np.random.rand(128)
desc = desc / np.sqrt((desc**2).sum())
desc = np.round(desc * 255) '''
# This is draw, because it is an overlay
ax = gca()
tau = 2 * np.pi
DSCALE = .25
XYSCALE = .5
XYSHIFT = -.75
ORI_SHIFT = 0 # -tau #1/8 * tau
# SIFT CONSTANTS
NORIENTS = 8
NX = 4
NY = 4
NBINS = NX * NY
def cirlce_rad2xy(radians, mag):
return np.cos(radians) * mag, np.sin(radians) * mag
discrete_ori = (np.arange(0, NORIENTS) * (tau / NORIENTS) + ORI_SHIFT)
# Build list of plot positions
# Build an "arm" for each sift measurement
arm_mag = desc / 255.0
arm_ori = np.tile(discrete_ori, (NBINS, 1)).flatten()
# The offset x,y's for each sift measurment
arm_dxy = np.array(zip(*cirlce_rad2xy(arm_ori, arm_mag)))
yxt_gen = itertools.product(xrange(NY), xrange(NX), xrange(NORIENTS))
yx_gen = itertools.product(xrange(NY), xrange(NX))
# Transform the drawing of the SIFT descriptor to the its elliptical patch
axTrans = ax.transData
kpTrans = None
if kp is None:
kp = [0, 0, 1, 0, 1]
kp = np.array(kp)
kpT = kp.T
x, y, a, c, d = kpT[:, 0]
kpTrans = Affine2D([( a, 0, x),
( c, d, y),
( 0, 0, 1)])
axTrans = ax.transData
# Draw 8 directional arms in each of the 4x4 grid cells
arrow_patches = []
arrow_patches2 = []
for y, x, t in yxt_gen:
index = y * NX * NORIENTS + x * NORIENTS + t
(dx, dy) = arm_dxy[index]
arw_x = x * XYSCALE + XYSHIFT
arw_y = y * XYSCALE + XYSHIFT
arw_dy = dy * DSCALE * 1.5 # scale for viz Hack
arw_dx = dx * DSCALE * 1.5
#posA = (arw_x, arw_y)
#posB = (arw_x+arw_dx, arw_y+arw_dy)
_args = [arw_x, arw_y, arw_dx, arw_dy]
_kwargs = dict(head_width=.0001, transform=kpTrans, length_includes_head=False)
arrow_patches += [FancyArrow(*_args, **_kwargs)]
arrow_patches2 += [FancyArrow(*_args, **_kwargs)]
# Draw circles around each of the 4x4 grid cells
circle_patches = []
for y, x in yx_gen:
circ_xy = (x * XYSCALE + XYSHIFT, y * XYSCALE + XYSHIFT)
circ_radius = DSCALE
circle_patches += [Circle(circ_xy, circ_radius, transform=kpTrans)]
# Efficiently draw many patches with PatchCollections
circ_collection = PatchCollection(circle_patches)
circ_collection.set_facecolor('none')
circ_collection.set_transform(axTrans)
circ_collection.set_edgecolor(BLACK)
circ_collection.set_alpha(.5)
# Body of arrows
arw_collection = PatchCollection(arrow_patches)
arw_collection.set_transform(axTrans)
arw_collection.set_linewidth(.5)
arw_collection.set_color(RED)
arw_collection.set_alpha(1)
# Border of arrows
arw_collection2 = matplotlib.collections.PatchCollection(arrow_patches2)
arw_collection2.set_transform(axTrans)
arw_collection2.set_linewidth(1)
arw_collection2.set_color(BLACK)
arw_collection2.set_alpha(1)
# Add artists to axes
ax.add_collection(circ_collection)
ax.add_collection(arw_collection2)
ax.add_collection(arw_collection)
def feat_scores_to_color(fs, cmap_='hot'):
assert len(fs.shape) == 1, 'score must be 1d'
cmap = plt.get_cmap(cmap_)
mins = fs.min()
rnge = fs.max() - mins
if rnge == 0:
return [cmap(.5) for fx in xrange(len(fs))]
score2_01 = lambda score: .1 + .9 * (float(score) - mins) / (rnge)
colors = [cmap(score2_01(score)) for score in fs]
return colors
def colorbar(scalars, colors):
'adds a color bar next to the axes'
orientation = ['vertical', 'horizontal'][0]
TICK_FONTSIZE = 8
# Put colors and scalars in correct order
sorted_scalars = sorted(scalars)
sorted_colors = [x for (y, x) in sorted(zip(scalars, colors))]
# Make a listed colormap and mappable object
listed_cmap = mpl.colors.ListedColormap(sorted_colors)
sm = plt.cm.ScalarMappable(cmap=listed_cmap)
sm.set_array(sorted_scalars)
# Use mapable object to create the colorbar
cb = plt.colorbar(sm, orientation=orientation)
# Add the colorbar to the correct label
axis = cb.ax.xaxis if orientation == 'horizontal' else cb.ax.yaxis
position = 'bottom' if orientation == 'horizontal' else 'right'
axis.set_ticks_position(position)
axis.set_ticks([0, .5, 1])
cb.ax.tick_params(labelsize=TICK_FONTSIZE)
def draw_lines2(kpts1, kpts2, fm=None, fs=None, kpts2_offset=(0, 0),
color_list=None, **kwargs):
if not DISTINCT_COLORS:
color_list = None
# input data
if not SHOW_LINES:
return
if fm is None: # assume kpts are in director correspondence
assert kpts1.shape == kpts2.shape
if len(fm) == 0:
return
ax = gca()
woff, hoff = kpts2_offset
# Draw line collection
kpts1_m = kpts1[fm[:, 0]].T
kpts2_m = kpts2[fm[:, 1]].T
xxyy_iter = iter(zip(kpts1_m[0],
kpts2_m[0] + woff,
kpts1_m[1],
kpts2_m[1] + hoff))
if color_list is None:
if fs is None: # Draw with solid color
color_list = [ LINE_COLOR for fx in xrange(len(fm))]
else: # Draw with colors proportional to score difference
color_list = feat_scores_to_color(fs)
segments = [((x1, y1), (x2, y2)) for (x1, x2, y1, y2) in xxyy_iter]
linewidth = [LINE_WIDTH for fx in xrange(len(fm))]
line_alpha = LINE_ALPHA
if LINE_ALPHA_OVERRIDE is not None:
line_alpha = LINE_ALPHA_OVERRIDE
line_group = LineCollection(segments, linewidth, color_list, alpha=line_alpha)
#plt.colorbar(line_group, ax=ax)
ax.add_collection(line_group)
#figure(100)
#plt.hexbin(x,y, cmap=plt.cm.YlOrRd_r)
def draw_kpts(kpts, *args, **kwargs):
draw_kpts2(kpts, *args, **kwargs)
def draw_kpts2(kpts, offset=(0, 0), ell=SHOW_ELLS, pts=False, pts_color=ORANGE,
pts_size=POINT_SIZE, ell_alpha=ELL_ALPHA,
ell_linewidth=ELL_LINEWIDTH, ell_color=ELL_COLOR,
color_list=None, rect=None, arrow=False, **kwargs):
if not DISTINCT_COLORS:
color_list = None
printDBG('drawkpts2: Drawing Keypoints! ell=%r pts=%r' % (ell, pts))
# get matplotlib info
ax = gca()
pltTrans = ax.transData
ell_actors = []
# data
kpts = np.array(kpts)
kptsT = kpts.T
x = kptsT[0, :] + offset[0]
y = kptsT[1, :] + offset[1]
printDBG('[df2] draw_kpts()----------')
printDBG('[df2] draw_kpts() ell=%r pts=%r' % (ell, pts))
printDBG('[df2] draw_kpts() drawing kpts.shape=%r' % (kpts.shape,))
if rect is None:
rect = ell
rect = False
if pts is True:
rect = False
if ell or rect:
printDBG('[df2] draw_kpts() drawing ell kptsT.shape=%r' % (kptsT.shape,))
# We have the transformation from unit circle to ellipse here. (inv(A))
a = kptsT[2]
b = np.zeros(len(a))
c = kptsT[3]
d = kptsT[4]
kpts_iter = izip(x, y, a, b, c, d)
aff_list = [Affine2D([( a_, b_, x_),
( c_, d_, y_),
( 0, 0, 1)])
for (x_, y_, a_, b_, c_, d_) in kpts_iter]
patch_list = []
ell_actors = [Circle( (0, 0), 1, transform=aff) for aff in aff_list]
if ell:
patch_list += ell_actors
if rect:
rect_actors = [Rectangle( (-1, -1), 2, 2, transform=aff) for aff in aff_list]
patch_list += rect_actors
if arrow:
_kwargs = dict(head_width=.01, length_includes_head=False)
arrow_actors1 = [FancyArrow(0, 0, 0, 1, transform=aff, **_kwargs) for aff in aff_list]
arrow_actors2 = [FancyArrow(0, 0, 1, 0, transform=aff, **_kwargs) for aff in aff_list]
patch_list += arrow_actors1
patch_list += arrow_actors2
ellipse_collection = matplotlib.collections.PatchCollection(patch_list)
ellipse_collection.set_facecolor('none')
ellipse_collection.set_transform(pltTrans)
if ELL_ALPHA_OVERRIDE is not None:
ell_alpha = ELL_ALPHA_OVERRIDE
ellipse_collection.set_alpha(ell_alpha)
ellipse_collection.set_linewidth(ell_linewidth)
if not color_list is None:
ell_color = color_list
if ell_color == 'distinct':
ell_color = distinct_colors(len(kpts))
ellipse_collection.set_edgecolor(ell_color)
ax.add_collection(ellipse_collection)
if pts:
printDBG('[df2] draw_kpts() drawing pts x.shape=%r y.shape=%r' % (x.shape, y.shape))
if color_list is None:
color_list = [pts_color for _ in xrange(len(x))]
ax.autoscale(enable=False)
ax.scatter(x, y, c=color_list, s=2 * pts_size, marker='o', edgecolor='none')
#ax.autoscale(enable=False)
#ax.plot(x, y, linestyle='None', marker='o', markerfacecolor=pts_color, markersize=pts_size, markeredgewidth=0)
# ---- CHIP DISPLAY COMMANDS ----
def imshow(img, fnum=None, title=None, figtitle=None, pnum=None,
interpolation='nearest', **kwargs):
'other interpolations = nearest, bicubic, bilinear'
#printDBG('[df2] ----- IMSHOW ------ ')
#printDBG('[***df2.imshow] fnum=%r pnum=%r title=%r *** ' % (fnum, pnum, title))
#printDBG('[***df2.imshow] img.shape = %r ' % (img.shape,))
#printDBG('[***df2.imshow] img.stats = %r ' % (helpers.printable_mystats(img),))
fig = figure(fnum=fnum, pnum=pnum, title=title, figtitle=figtitle, **kwargs)
ax = gca()
if not DARKEN is None:
imgdtype = img.dtype
img = np.array(img, dtype=float) * DARKEN
img = np.array(img, dtype=imgdtype)
plt_imshow_kwargs = {
'interpolation': interpolation,
#'cmap': plt.get_cmap('gray'),
'vmin': 0,
'vmax': 255,
}
try:
if len(img.shape) == 3 and img.shape[2] == 3:
# img is in a color format
imgBGR = img
if imgBGR.dtype == np.float64:
if imgBGR.max() <= 1:
imgBGR = np.array(imgBGR, dtype=np.float32)
else:
imgBGR = np.array(imgBGR, dtype=np.uint8)
imgRGB = cv2.cvtColor(imgBGR, cv2.COLOR_BGR2RGB)
ax.imshow(imgRGB, **plt_imshow_kwargs)
elif len(img.shape) == 2:
# img is in grayscale
imgGRAY = img
ax.imshow(imgGRAY, cmap=plt.get_cmap('gray'), **plt_imshow_kwargs)
else:
raise Exception('unknown image format')
except TypeError as te:
print('[df2] imshow ERROR %r' % te)
raise
except Exception as ex:
print('[df2] img.dtype = %r' % (img.dtype,))
print('[df2] type(img) = %r' % (type(img),))
print('[df2] img.shape = %r' % (img.shape,))
print('[df2] imshow ERROR %r' % ex)
raise
#plt.set_cmap('gray')
ax.set_xticks([])
ax.set_yticks([])
#ax.set_autoscale(False)
#try:
#if pnum == 111:
#fig.tight_layout()
#except Exception as ex:
#print('[df2] !! Exception durring fig.tight_layout: '+repr(ex))
#raise
return fig, ax
def get_num_channels(img):
ndims = len(img.shape)
if ndims == 2:
nChannels = 1
elif ndims == 3 and img.shape[2] == 3:
nChannels = 3
elif ndims == 3 and img.shape[2] == 1:
nChannels = 1
else:
raise Exception('Cannot determine number of channels')
return nChannels
def stack_images(img1, img2, vert=None):
nChannels = get_num_channels(img1)
nChannels2 = get_num_channels(img2)
assert nChannels == nChannels2
(h1, w1) = img1.shape[0: 2] # get chip dimensions
(h2, w2) = img2.shape[0: 2]
woff, hoff = 0, 0
vert_wh = max(w1, w2), h1 + h2
horiz_wh = w1 + w2, max(h1, h2)
if vert is None:
# Display the orientation with the better (closer to 1) aspect ratio
vert_ar = max(vert_wh) / min(vert_wh)
horiz_ar = max(horiz_wh) / min(horiz_wh)
vert = vert_ar < horiz_ar
if vert:
wB, hB = vert_wh
hoff = h1
else:
wB, hB = horiz_wh
woff = w1
# concatentate images
if nChannels == 3:
imgB = np.zeros((hB, wB, 3), np.uint8)
imgB[0:h1, 0:w1, :] = img1
imgB[hoff:(hoff + h2), woff:(woff + w2), :] = img2
elif nChannels == 1:
imgB = np.zeros((hB, wB), np.uint8)
imgB[0:h1, 0:w1] = img1
imgB[hoff:(hoff + h2), woff:(woff + w2)] = img2
return imgB, woff, hoff
def show_chipmatch2(rchip1, rchip2, kpts1, kpts2, fm=None, fs=None, title=None,
vert=None, fnum=None, pnum=None, **kwargs):
'''Draws two chips and the feature matches between them. feature matches
kpts1 and kpts2 use the (x,y,a,c,d)
'''
printDBG('[df2] draw_matches2() fnum=%r, pnum=%r' % (fnum, pnum))
# get matching keypoints + offset
(h1, w1) = rchip1.shape[0:2] # get chip (h, w) dimensions
(h2, w2) = rchip2.shape[0:2]
# Stack the compared chips
match_img, woff, hoff = stack_images(rchip1, rchip2, vert)
xywh1 = (0, 0, w1, h1)
xywh2 = (woff, hoff, w2, h2)
# Show the stacked chips
fig, ax = imshow(match_img, title=title, fnum=fnum, pnum=pnum)
# Overlay feature match nnotations
draw_fmatch(xywh1, xywh2, kpts1, kpts2, fm, fs, **kwargs)
return ax, xywh1, xywh2
# draw feature match
def draw_fmatch(xywh1, xywh2, kpts1, kpts2, fm, fs=None, lbl1=None, lbl2=None,
fnum=None, pnum=None, rect=False, colorbar_=True, **kwargs):
'''Draws the matching features. This is draw because it is an overlay
xywh1 - location of rchip1 in the axes
xywh2 - location or rchip2 in the axes
'''
if fm is None:
assert kpts1.shape == kpts2.shape, 'shapes different or fm not none'
fm = np.tile(np.arange(0, len(kpts1)), (2, 1)).T
pts = kwargs.get('draw_pts', False)
ell = kwargs.get('draw_ell', True)
lines = kwargs.get('draw_lines', True)
ell_alpha = kwargs.get('ell_alpha', .4)
nMatch = len(fm)
#printDBG('[df2.draw_fnmatch] nMatch=%r' % nMatch)
x1, y1, w1, h1 = xywh1
x2, y2, w2, h2 = xywh2
offset2 = (x2, y2)
# Custom user label for chips 1 and 2
if lbl1 is not None:
absolute_lbl(x1 + w1, y1, lbl1)
if lbl2 is not None:
absolute_lbl(x2 + w2, y2, lbl2)
# Plot the number of matches
if kwargs.get('show_nMatches', False):
upperleft_text('#match=%d' % nMatch)
# Draw all keypoints in both chips as points
if kwargs.get('all_kpts', False):
all_args = dict(ell=False, pts=pts, pts_color=GREEN, pts_size=2,
ell_alpha=ell_alpha, rect=rect)
all_args.update(kwargs)
draw_kpts2(kpts1, **all_args)
draw_kpts2(kpts2, offset=offset2, **all_args)
# Draw Lines and Ellipses and Points oh my
if nMatch > 0:
colors = [kwargs['colors']] * nMatch if 'colors' in kwargs else distinct_colors(nMatch)
if fs is not None:
colors = feat_scores_to_color(fs, 'hot')
acols = add_alpha(colors)
# Helper functions
def _drawkpts(**_kwargs):
_kwargs.update(kwargs)
fxs1 = fm[:, 0]
fxs2 = fm[:, 1]
draw_kpts2(kpts1[fxs1], rect=rect, **_kwargs)
draw_kpts2(kpts2[fxs2], offset=offset2, rect=rect, **_kwargs)
def _drawlines(**_kwargs):
_kwargs.update(kwargs)
draw_lines2(kpts1, kpts2, fm, fs, kpts2_offset=offset2, **_kwargs)
# User helpers
if ell:
_drawkpts(pts=False, ell=True, color_list=colors)
if pts:
_drawkpts(pts_size=8, pts=True, ell=False, pts_color=BLACK)
_drawkpts(pts_size=6, pts=True, ell=False, color_list=acols)
if lines:
_drawlines(color_list=colors)
else:
draw_boxedX(xywh2)
if fs is not None and colorbar_ and 'colors' in vars() and colors is not None:
colorbar(fs, colors)
#legend()
return None
def draw_boxedX(xywh, color=RED, lw=2, alpha=.5, theta=0):
'draws a big red x. redx'
ax = gca()
x1, y1, w, h = xywh
x2, y2 = x1 + w, y1 + h
segments = [((x1, y1), (x2, y2)),
((x1, y2), (x2, y1))]
trans = Affine2D()
trans.rotate(theta)
trans = trans + ax.transData
width_list = [lw] * len(segments)
color_list = [color] * len(segments)
line_group = LineCollection(segments, width_list, color_list, alpha=alpha,
transOffset=trans)
ax.add_collection(line_group)
def disconnect_callback(fig, callback_type, **kwargs):
#print('[df2] disconnect %r callback' % callback_type)
axes = kwargs.get('axes', [])
for ax in axes:
ax._hs_viewtype = ''
cbid_type = callback_type + '_cbid'
cbfn_type = callback_type + '_func'
cbid = fig.__dict__.get(cbid_type, None)
cbfn = fig.__dict__.get(cbfn_type, None)
if cbid is not None:
fig.canvas.mpl_disconnect(cbid)
else:
cbfn = None
fig.__dict__[cbid_type] = None
return cbid, cbfn
def connect_callback(fig, callback_type, callback_fn):
#print('[df2] register %r callback' % callback_type)
if callback_fn is None:
return
cbid_type = callback_type + '_cbid'
cbfn_type = callback_type + '_func'
fig.__dict__[cbid_type] = fig.canvas.mpl_connect(callback_type, callback_fn)
fig.__dict__[cbfn_type] = callback_fn
| apache-2.0 |
kevin-coder/tensorflow-fork | tensorflow/lite/experimental/micro/examples/micro_speech/apollo3/captured_data_to_wav.py | 11 | 1442 | # Copyright 2018 The TensorFlow Authors. 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.
# ==============================================================================
"""Converts values pulled from the microcontroller into audio files."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import struct
# import matplotlib.pyplot as plt
import numpy as np
import soundfile as sf
def new_data_to_array(fn):
vals = []
with open(fn) as f:
for n, line in enumerate(f):
if n != 0:
vals.extend([int(v, 16) for v in line.split()])
b = ''.join(map(chr, vals))
y = struct.unpack('<' + 'h' * int(len(b) / 2), b)
return y
data = 'captured_data.txt'
values = np.array(new_data_to_array(data)).astype(float)
# plt.plot(values, 'o-')
# plt.show(block=False)
wav = values / np.max(np.abs(values))
sf.write('captured_data.wav', wav, 16000)
| apache-2.0 |
waynenilsen/statsmodels | statsmodels/examples/ex_kde_confint.py | 34 | 1973 | # -*- coding: utf-8 -*-
"""
Created on Mon Dec 16 11:02:59 2013
Author: Josef Perktold
"""
from __future__ import print_function
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import statsmodels.nonparametric.api as npar
from statsmodels.sandbox.nonparametric import kernels
from statsmodels.distributions.mixture_rvs import mixture_rvs
# example from test_kde.py mixture of two normal distributions
np.random.seed(12345)
x = mixture_rvs([.25,.75], size=200, dist=[stats.norm, stats.norm],
kwargs = (dict(loc=-1, scale=.5),dict(loc=1, scale=.5)))
x.sort() # not needed
kde = npar.KDEUnivariate(x)
kde.fit('gau')
ci = kde.kernel.density_confint(kde.density, len(x))
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.hist(x, bins=15, normed=True, alpha=0.25)
ax.plot(kde.support, kde.density, lw=2, color='red')
ax.fill_between(kde.support, ci[:,0], ci[:,1],
color='grey', alpha='0.7')
ax.set_title('Kernel Density Gaussian (bw = %4.2f)' % kde.bw)
# use all kernels directly
x_grid = np.linspace(np.min(x), np.max(x), 51)
x_grid = np.linspace(-3, 3, 51)
kernel_names = ['Biweight', 'Cosine', 'Epanechnikov', 'Gaussian',
'Triangular', 'Triweight', #'Uniform',
]
fig = plt.figure()
for ii, kn in enumerate(kernel_names):
ax = fig.add_subplot(2, 3, ii+1) # without uniform
ax.hist(x, bins=10, normed=True, alpha=0.25)
#reduce bandwidth for Gaussian and Uniform which are to large in example
if kn in ['Gaussian', 'Uniform']:
args = (0.5,)
else:
args = ()
kernel = getattr(kernels, kn)(*args)
kde_grid = [kernel.density(x, xi) for xi in x_grid]
confint_grid = kernel.density_confint(kde_grid, len(x))
ax.plot(x_grid, kde_grid, lw=2, color='red', label=kn)
ax.fill_between(x_grid, confint_grid[:,0], confint_grid[:,1],
color='grey', alpha='0.7')
ax.legend(loc='upper left')
plt.show()
| bsd-3-clause |
pratapvardhan/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 |
zachcp/qiime | qiime/quality_scores_plot.py | 9 | 6918 | #!/usr/bin/env python
# File created Sept 29, 2010
from __future__ import division
__author__ = "William Walters"
__copyright__ = "Copyright 2011, The QIIME Project"
__credits__ = ["William Walters", "Greg Caporaso"]
__license__ = "GPL"
__version__ = "1.9.1-dev"
__maintainer__ = "William Walters"
__email__ = "William.A.Walters@colorado.edu"
from matplotlib import use
use('Agg', warn=False)
from skbio.parse.sequences import parse_fasta
from numpy import arange, std, average
from pylab import plot, savefig, xlabel, ylabel, text, \
hist, figure, legend, title, show, xlim, ylim, xticks, yticks,\
scatter, subplot
from matplotlib.font_manager import fontManager, FontProperties
from qiime.util import gzip_open
from qiime.parse import parse_qual_score
def bin_qual_scores(qual_scores):
""" Bins qual score according to nucleotide position
qual_scores: Dict of label: numpy array of base scores
"""
qual_bins = []
qual_lens = []
for l in qual_scores.values():
qual_lens.append(len(l))
max_seq_size = max(qual_lens)
for base_position in range(max_seq_size):
qual_bins.append([])
for scores in qual_scores.values():
# Add score if exists in base position, otherwise skip
try:
qual_bins[base_position].append(scores[base_position])
except IndexError:
continue
return qual_bins
def get_qual_stats(qual_bins, score_min):
""" Generates bins of averages, std devs, total NT from quality bins"""
ave_bins = []
std_dev_bins = []
total_bases_bins = []
found_first_poor_qual_pos = False
suggested_trunc_pos = None
for base_position in qual_bins:
total_bases_bins.append(len(base_position))
std_dev_bins.append(std(base_position))
ave_bins.append(average(base_position))
if not found_first_poor_qual_pos:
if average(base_position) < score_min:
suggested_trunc_pos = qual_bins.index(base_position)
found_first_poor_qual_pos = True
return ave_bins, std_dev_bins, total_bases_bins, suggested_trunc_pos
def plot_qual_report(ave_bins,
std_dev_bins,
total_bases_bins,
score_min,
output_dir):
""" Plots, saves graph showing quality score averages, stddev.
Additionally, the total nucleotide count for each position is shown on
a second subplot
ave_bins: list with average quality score for each base position
std_dev_bins: list with standard deviation for each base position
total_bases_bins: list with total counts of bases for each position
score_min: lowest value that a given base call can be and still be
acceptable. Used to generate a dotted line on the graph for easy assay
of the poor scoring positions.
output_dir: output directory
"""
t = arange(0, len(ave_bins), 1)
std_dev_plus = []
std_dev_minus = []
for n in range(len(ave_bins)):
std_dev_plus.append(ave_bins[n] + std_dev_bins[n])
std_dev_minus.append(ave_bins[n] - std_dev_bins[n])
figure_num = 0
f = figure(figure_num, figsize=(8, 10))
figure_title = "Quality Scores Report"
f.text(.5, .93, figure_title, horizontalalignment='center', size="large")
subplot(2, 1, 1)
plot(t, ave_bins, linewidth=2.0, color="black")
plot(t, std_dev_plus, linewidth=0.5, color="red")
dashed_line = [score_min] * len(ave_bins)
l, = plot(dashed_line, '--', color='gray')
plot(t, std_dev_minus, linewidth=0.5, color="red")
legend(
('Quality Score Average',
'Std Dev',
'Score Threshold'),
loc='lower left')
xlabel("Nucleotide Position")
ylabel("Quality Score")
subplot(2, 1, 2)
plot(t, total_bases_bins, linewidth=2.0, color="blue")
xlabel("Nucleotide Position")
ylabel("Nucleotide Counts")
outfile_name = output_dir + "/quality_scores_plot.pdf"
savefig(outfile_name)
def write_qual_report(ave_bins,
std_dev_bins,
total_bases_bins,
output_dir,
suggested_trunc_pos):
""" Writes data in bins to output text file
ave_bins: list with average quality score for each base position
std_dev_bins: list with standard deviation for each base position
total_bases_bins: list with total counts of bases for each position
output_dir: output directory
suggested_trunc_pos: Position where average quality score dropped below
the score minimum (25 by default)
"""
outfile_name = output_dir + "/quality_bins.txt"
outfile = open(outfile_name, "w")
outfile.write("# Suggested nucleotide truncation position (None if " +
"quality score average did not drop below the score minimum threshold)" +
": %s\n" % suggested_trunc_pos)
outfile.write("# Average quality score bins\n")
outfile.write(",".join(str("%2.3f" % ave) for ave in ave_bins) + "\n")
outfile.write("# Standard deviation bins\n")
outfile.write(",".join(str("%2.3f" % std) for std in std_dev_bins) + "\n")
outfile.write("# Total bases per nucleotide position bins\n")
outfile.write(",".join(str("%d" %
total_bases) for total_bases in total_bases_bins))
def generate_histogram(qual_fp,
output_dir,
score_min=25,
verbose=True,
qual_parser=parse_qual_score):
""" Main program function for generating quality score histogram
qual_fp: quality score filepath
output_dir: output directory
score_min: minimum score to be considered a reliable base call, used
to generate dotted line on histogram for easy visualization of poor
quality scores.
qual_parser : function to apply to extract quality scores
"""
if qual_fp.endswith('.gz'):
qual_lines = gzip_open(qual_fp)
else:
qual_lines = open(qual_fp, "U")
qual_scores = qual_parser(qual_lines)
# Sort bins according to base position
qual_bins = bin_qual_scores(qual_scores)
# Get average, std dev, and total nucleotide counts for each base position
ave_bins, std_dev_bins, total_bases_bins, suggested_trunc_pos =\
get_qual_stats(qual_bins, score_min)
plot_qual_report(ave_bins, std_dev_bins, total_bases_bins, score_min,
output_dir)
# Save values to output text file
write_qual_report(ave_bins, std_dev_bins, total_bases_bins, output_dir,
suggested_trunc_pos)
if verbose:
print "Suggested nucleotide truncation position (None if quality " +\
"score average did not fall below the minimum score parameter): %s\n" %\
suggested_trunc_pos
| gpl-2.0 |
barbagroup/PetIBM | examples/ibpm/cylinder2dRe40/scripts/plotVorticity.py | 4 | 1401 | """
Computes, plots, and saves the 2D vorticity field from a PetIBM simulation
after 2000 time steps (20 non-dimensional time-units).
"""
import pathlib
import h5py
import numpy
from matplotlib import pyplot
simu_dir = pathlib.Path(__file__).absolute().parents[1]
data_dir = simu_dir / 'output'
# Read vorticity field and its grid from files.
name = 'wz'
filepath = data_dir / 'grid.h5'
f = h5py.File(filepath, 'r')
x, y = f[name]['x'][:], f[name]['y'][:]
X, Y = numpy.meshgrid(x, y)
timestep = 2000
filepath = data_dir / '{:0>7}.h5'.format(timestep)
f = h5py.File(filepath, 'r')
wz = f[name][:]
# Read body coordinates from file.
filepath = simu_dir / 'circle.body'
with open(filepath, 'r') as infile:
xb, yb = numpy.loadtxt(infile, dtype=numpy.float64,
unpack=True, skiprows=1)
pyplot.rc('font', family='serif', size=16)
# Plot the filled contour of the vorticity.
fig, ax = pyplot.subplots(figsize=(6.0, 6.0))
ax.grid()
ax.set_xlabel('x')
ax.set_ylabel('y')
levels = numpy.linspace(-3.0, 3.0, 16)
ax.contour(X, Y, wz, levels=levels, colors='black')
ax.plot(xb, yb, color='red')
ax.set_xlim(-1.0, 4.0)
ax.set_ylim(-2.0, 2.0)
ax.set_aspect('equal')
fig.tight_layout()
pyplot.show()
# Save figure.
fig_dir = simu_dir / 'figures'
fig_dir.mkdir(parents=True, exist_ok=True)
filepath = fig_dir / 'wz{:0>7}.png'.format(timestep)
fig.savefig(str(filepath), dpi=300)
| bsd-3-clause |
jonyroda97/redbot-amigosprovaveis | lib/matplotlib/units.py | 2 | 6084 | """
The classes here provide support for using custom classes with
matplotlib, e.g., those that do not expose the array interface but know
how to convert themselves to arrays. It also supports classes with
units and units conversion. Use cases include converters for custom
objects, e.g., a list of datetime objects, as well as for objects that
are unit aware. We don't assume any particular units implementation;
rather a units implementation must provide the register with the Registry
converter dictionary and a ConversionInterface. For example,
here is a complete implementation which supports plotting with native
datetime objects::
import matplotlib.units as units
import matplotlib.dates as dates
import matplotlib.ticker as ticker
import datetime
class DateConverter(units.ConversionInterface):
@staticmethod
def convert(value, unit, axis):
'convert value to a scalar or array'
return dates.date2num(value)
@staticmethod
def axisinfo(unit, axis):
'return major and minor tick locators and formatters'
if unit!='date': return None
majloc = dates.AutoDateLocator()
majfmt = dates.AutoDateFormatter(majloc)
return AxisInfo(majloc=majloc,
majfmt=majfmt,
label='date')
@staticmethod
def default_units(x, axis):
'return the default unit for x or None'
return 'date'
# finally we register our object type with a converter
units.registry[datetime.date] = DateConverter()
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from matplotlib.cbook import iterable, is_numlike, safe_first_element
import numpy as np
class AxisInfo(object):
"""information to support default axis labeling and tick labeling, and
default limits"""
def __init__(self, majloc=None, minloc=None,
majfmt=None, minfmt=None, label=None,
default_limits=None):
"""
majloc and minloc: TickLocators for the major and minor ticks
majfmt and minfmt: TickFormatters for the major and minor ticks
label: the default axis label
default_limits: the default min, max of the axis if no data is present
If any of the above are None, the axis will simply use the default
"""
self.majloc = majloc
self.minloc = minloc
self.majfmt = majfmt
self.minfmt = minfmt
self.label = label
self.default_limits = default_limits
class ConversionInterface(object):
"""
The minimal interface for a converter to take custom instances (or
sequences) and convert them to values mpl can use
"""
@staticmethod
def axisinfo(unit, axis):
'return an units.AxisInfo instance for axis with the specified units'
return None
@staticmethod
def default_units(x, axis):
'return the default unit for x or None for the given axis'
return None
@staticmethod
def convert(obj, unit, axis):
"""
convert obj using unit for the specified axis. If obj is a sequence,
return the converted sequence. The output must be a sequence of
scalars that can be used by the numpy array layer
"""
return obj
@staticmethod
def is_numlike(x):
"""
The matplotlib datalim, autoscaling, locators etc work with
scalars which are the units converted to floats given the
current unit. The converter may be passed these floats, or
arrays of them, even when units are set. Derived conversion
interfaces may opt to pass plain-ol unitless numbers through
the conversion interface and this is a helper function for
them.
"""
if iterable(x):
for thisx in x:
return is_numlike(thisx)
else:
return is_numlike(x)
class Registry(dict):
"""
register types with conversion interface
"""
def __init__(self):
dict.__init__(self)
self._cached = {}
def get_converter(self, x):
'get the converter interface instance for x, or None'
if not len(self):
return None # nothing registered
# DISABLED idx = id(x)
# DISABLED cached = self._cached.get(idx)
# DISABLED if cached is not None: return cached
converter = None
classx = getattr(x, '__class__', None)
if classx is not None:
converter = self.get(classx)
if isinstance(x, np.ndarray) and x.size:
xravel = x.ravel()
try:
# pass the first value of x that is not masked back to
# get_converter
if not np.all(xravel.mask):
# some elements are not masked
converter = self.get_converter(
xravel[np.argmin(xravel.mask)])
return converter
except AttributeError:
# not a masked_array
# Make sure we don't recurse forever -- it's possible for
# ndarray subclasses to continue to return subclasses and
# not ever return a non-subclass for a single element.
next_item = xravel[0]
if (not isinstance(next_item, np.ndarray) or
next_item.shape != x.shape):
converter = self.get_converter(next_item)
return converter
if converter is None:
try:
thisx = safe_first_element(x)
except (TypeError, StopIteration):
pass
else:
if classx and classx != getattr(thisx, '__class__', None):
converter = self.get_converter(thisx)
return converter
# DISABLED self._cached[idx] = converter
return converter
registry = Registry()
| gpl-3.0 |
xiaoxiamii/scikit-learn | benchmarks/bench_plot_svd.py | 325 | 2899 | """Benchmarks of Singular Value Decomposition (Exact and Approximate)
The data is mostly low rank but is a fat infinite tail.
"""
import gc
from time import time
import numpy as np
from collections import defaultdict
from scipy.linalg import svd
from sklearn.utils.extmath import randomized_svd
from sklearn.datasets.samples_generator import make_low_rank_matrix
def compute_bench(samples_range, features_range, n_iter=3, rank=50):
it = 0
results = defaultdict(lambda: [])
max_it = len(samples_range) * len(features_range)
for n_samples in samples_range:
for n_features in features_range:
it += 1
print('====================')
print('Iteration %03d of %03d' % (it, max_it))
print('====================')
X = make_low_rank_matrix(n_samples, n_features,
effective_rank=rank,
tail_strength=0.2)
gc.collect()
print("benchmarking scipy svd: ")
tstart = time()
svd(X, full_matrices=False)
results['scipy svd'].append(time() - tstart)
gc.collect()
print("benchmarking scikit-learn randomized_svd: n_iter=0")
tstart = time()
randomized_svd(X, rank, n_iter=0)
results['scikit-learn randomized_svd (n_iter=0)'].append(
time() - tstart)
gc.collect()
print("benchmarking scikit-learn randomized_svd: n_iter=%d "
% n_iter)
tstart = time()
randomized_svd(X, rank, n_iter=n_iter)
results['scikit-learn randomized_svd (n_iter=%d)'
% n_iter].append(time() - tstart)
return results
if __name__ == '__main__':
from mpl_toolkits.mplot3d import axes3d # register the 3d projection
import matplotlib.pyplot as plt
samples_range = np.linspace(2, 1000, 4).astype(np.int)
features_range = np.linspace(2, 1000, 4).astype(np.int)
results = compute_bench(samples_range, features_range)
label = 'scikit-learn singular value decomposition benchmark results'
fig = plt.figure(label)
ax = fig.gca(projection='3d')
for c, (label, timings) in zip('rbg', sorted(results.iteritems())):
X, Y = np.meshgrid(samples_range, features_range)
Z = np.asarray(timings).reshape(samples_range.shape[0],
features_range.shape[0])
# plot the actual surface
ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3,
color=c)
# dummy point plot to stick the legend to since surface plot do not
# support legends (yet?)
ax.plot([1], [1], [1], color=c, label=label)
ax.set_xlabel('n_samples')
ax.set_ylabel('n_features')
ax.set_zlabel('Time (s)')
ax.legend()
plt.show()
| bsd-3-clause |
mcocdawc/chemopt | src/chemopt/utilities/_print_versions.py | 2 | 4591 | # The following code was taken from the pandas project and modified.
# http://pandas.pydata.org/
import codecs
import importlib
import locale
import os
import platform
import struct
import sys
def get_sys_info():
"Returns system information as a dict"
blob = []
# commit = cc._git_hash
# blob.append(('commit', commit))
try:
(sysname, nodename, release, version,
machine, processor) = platform.uname()
blob.extend([
("python", "%d.%d.%d.%s.%s" % sys.version_info[:]),
("python-bits", struct.calcsize("P") * 8),
("OS", "%s" % (sysname)),
("OS-release", "%s" % (release)),
# ("Version", "%s" % (version)),
("machine", "%s" % (machine)),
("processor", "%s" % (processor)),
# ("byteorder", "%s" % sys.byteorder),
("LC_ALL", "%s" % os.environ.get('LC_ALL', "None")),
("LANG", "%s" % os.environ.get('LANG', "None")),
("LOCALE", "%s.%s" % locale.getlocale()),
])
except Exception:
pass
return blob
def show_versions(as_json=False):
sys_info = get_sys_info()
deps = [
# (MODULE_NAME, f(mod) -> mod version)
("chemcoord", lambda mod: mod.__version__),
("numpy", lambda mod: mod.version.version),
("scipy", lambda mod: mod.version.version),
("pandas", lambda mod: mod.__version__),
("numba", lambda mod: mod.__version__),
("sortedcontainers", lambda mod: mod.__version__),
("sympy", lambda mod: mod.__version__),
("pytest", lambda mod: mod.__version__),
("pip", lambda mod: mod.__version__),
("setuptools", lambda mod: mod.__version__),
("IPython", lambda mod: mod.__version__),
("sphinx", lambda mod: mod.__version__),
# ("tables", lambda mod: mod.__version__),
# ("matplotlib", lambda mod: mod.__version__),
# ("Cython", lambda mod: mod.__version__),
# ("xarray", lambda mod: mod.__version__),
# ("patsy", lambda mod: mod.__version__),
# ("dateutil", lambda mod: mod.__version__),
# ("pytz", lambda mod: mod.VERSION),
# ("blosc", lambda mod: mod.__version__),
# ("bottleneck", lambda mod: mod.__version__),
# ("numexpr", lambda mod: mod.__version__),
# ("feather", lambda mod: mod.__version__),
# ("openpyxl", lambda mod: mod.__version__),
# ("xlrd", lambda mod: mod.__VERSION__),
# ("xlwt", lambda mod: mod.__VERSION__),
# ("xlsxwriter", lambda mod: mod.__version__),
# ("lxml", lambda mod: mod.etree.__version__),
# ("bs4", lambda mod: mod.__version__),
# ("html5lib", lambda mod: mod.__version__),
# ("sqlalchemy", lambda mod: mod.__version__),
# ("pymysql", lambda mod: mod.__version__),
# ("psycopg2", lambda mod: mod.__version__),
# ("jinja2", lambda mod: mod.__version__),
# ("s3fs", lambda mod: mod.__version__),
# ("pandas_gbq", lambda mod: mod.__version__),
# ("pandas_datareader", lambda mod: mod.__version__)
]
deps_blob = list()
for (modname, ver_f) in deps:
try:
if modname in sys.modules:
mod = sys.modules[modname]
else:
mod = importlib.import_module(modname)
ver = ver_f(mod)
deps_blob.append((modname, ver))
except Exception:
deps_blob.append((modname, None))
if (as_json):
try:
import json
except Exception:
import simplejson as json
j = dict(system=dict(sys_info), dependencies=dict(deps_blob))
if as_json is True:
print(j)
else:
with codecs.open(as_json, "wb", encoding='utf8') as f:
json.dump(j, f, indent=2)
else:
print("\nINSTALLED VERSIONS")
print("------------------")
for k, stat in sys_info:
print("%s: %s" % (k, stat))
print("")
for k, stat in deps_blob:
print("%s: %s" % (k, stat))
def main():
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-j", "--json", metavar="FILE", nargs=1,
help="Save output as JSON into file, pass in "
"'-' to output to stdout")
options = parser.parse_args()[0]
if options.json == "-":
options.json = True
show_versions(as_json=options.json)
return 0
if __name__ == "__main__":
sys.exit(main())
| lgpl-3.0 |
lscheinkman/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/units.py | 70 | 4810 | """
The classes here provide support for using custom classes with
matplotlib, eg those that do not expose the array interface but know
how to converter themselves to arrays. It also supoprts classes with
units and units conversion. Use cases include converters for custom
objects, eg a list of datetime objects, as well as for objects that
are unit aware. We don't assume any particular units implementation,
rather a units implementation must provide a ConversionInterface, and
the register with the Registry converter dictionary. For example,
here is a complete implementation which support plotting with native
datetime objects
import matplotlib.units as units
import matplotlib.dates as dates
import matplotlib.ticker as ticker
import datetime
class DateConverter(units.ConversionInterface):
def convert(value, unit):
'convert value to a scalar or array'
return dates.date2num(value)
convert = staticmethod(convert)
def axisinfo(unit):
'return major and minor tick locators and formatters'
if unit!='date': return None
majloc = dates.AutoDateLocator()
majfmt = dates.AutoDateFormatter(majloc)
return AxisInfo(majloc=majloc,
majfmt=majfmt,
label='date')
axisinfo = staticmethod(axisinfo)
def default_units(x):
'return the default unit for x or None'
return 'date'
default_units = staticmethod(default_units)
# finally we register our object type with a converter
units.registry[datetime.date] = DateConverter()
"""
import numpy as np
from matplotlib.cbook import iterable, is_numlike
class AxisInfo:
'information to support default axis labeling and tick labeling'
def __init__(self, majloc=None, minloc=None,
majfmt=None, minfmt=None, label=None):
"""
majloc and minloc: TickLocators for the major and minor ticks
majfmt and minfmt: TickFormatters for the major and minor ticks
label: the default axis label
If any of the above are None, the axis will simply use the default
"""
self.majloc = majloc
self.minloc = minloc
self.majfmt = majfmt
self.minfmt = minfmt
self.label = label
class ConversionInterface:
"""
The minimal interface for a converter to take custom instances (or
sequences) and convert them to values mpl can use
"""
def axisinfo(unit):
'return an units.AxisInfo instance for unit'
return None
axisinfo = staticmethod(axisinfo)
def default_units(x):
'return the default unit for x or None'
return None
default_units = staticmethod(default_units)
def convert(obj, unit):
"""
convert obj using unit. If obj is a sequence, return the
converted sequence. The ouput must be a sequence of scalars
that can be used by the numpy array layer
"""
return obj
convert = staticmethod(convert)
def is_numlike(x):
"""
The matplotlib datalim, autoscaling, locators etc work with
scalars which are the units converted to floats given the
current unit. The converter may be passed these floats, or
arrays of them, even when units are set. Derived conversion
interfaces may opt to pass plain-ol unitless numbers through
the conversion interface and this is a helper function for
them.
"""
if iterable(x):
for thisx in x:
return is_numlike(thisx)
else:
return is_numlike(x)
is_numlike = staticmethod(is_numlike)
class Registry(dict):
"""
register types with conversion interface
"""
def __init__(self):
dict.__init__(self)
self._cached = {}
def get_converter(self, x):
'get the converter interface instance for x, or None'
if not len(self): return None # nothing registered
#DISABLED idx = id(x)
#DISABLED cached = self._cached.get(idx)
#DISABLED if cached is not None: return cached
converter = None
classx = getattr(x, '__class__', None)
if classx is not None:
converter = self.get(classx)
if converter is None and iterable(x):
# if this is anything but an object array, we'll assume
# there are no custom units
if isinstance(x, np.ndarray) and x.dtype != np.object:
return None
for thisx in x:
converter = self.get_converter( thisx )
return converter
#DISABLED self._cached[idx] = converter
return converter
registry = Registry()
| agpl-3.0 |
plaes/numpy | doc/source/conf.py | 6 | 8773 | # -*- coding: utf-8 -*-
import sys, os, re
# Check Sphinx version
import sphinx
if sphinx.__version__ < "0.5":
raise RuntimeError("Sphinx 0.5.dev or newer required")
# -----------------------------------------------------------------------------
# General configuration
# -----------------------------------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
sys.path.insert(0, os.path.abspath('../sphinxext'))
extensions = ['sphinx.ext.autodoc', 'sphinx.ext.pngmath', 'numpydoc',
'sphinx.ext.intersphinx', 'sphinx.ext.coverage',
'sphinx.ext.doctest',
'plot_directive']
if sphinx.__version__ >= "0.7":
extensions.append('sphinx.ext.autosummary')
else:
extensions.append('autosummary')
extensions.append('only_directives')
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix of source filenames.
source_suffix = '.rst'
# The master toctree document.
#master_doc = 'index'
# General substitutions.
project = 'NumPy'
copyright = '2008-2009, The Scipy community'
# The default replacements for |version| and |release|, also used in various
# other places throughout the built documents.
#
import numpy
# The short X.Y version (including .devXXXX, rcX, b1 suffixes if present)
version = re.sub(r'(\d+\.\d+)\.\d+(.*)', r'\1\2', numpy.__version__)
version = re.sub(r'(\.dev\d+).*?$', r'\1', version)
# The full version, including alpha/beta/rc tags.
release = numpy.__version__
print version, release
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
today_fmt = '%B %d, %Y'
# List of documents that shouldn't be included in the build.
#unused_docs = []
# The reST default role (used for this markup: `text`) to use for all documents.
default_role = "autolink"
# List of directories, relative to source directories, that shouldn't be searched
# for source files.
exclude_dirs = []
# If true, '()' will be appended to :func: etc. cross-reference text.
add_function_parentheses = False
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# -----------------------------------------------------------------------------
# HTML output
# -----------------------------------------------------------------------------
# The style sheet to use for HTML and HTML Help pages. A file of that name
# must exist either in Sphinx' static/ path, or in one of the custom paths
# given in html_static_path.
html_style = 'scipy.css'
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
html_title = "%s v%s Manual (DRAFT)" % (project, version)
# The name of an image file (within the static path) to place at the top of
# the sidebar.
html_logo = 'scipyshiny_small.png'
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
html_sidebars = {
'index': 'indexsidebar.html'
}
# Additional templates that should be rendered to pages, maps page names to
# template names.
html_additional_pages = {
'index': 'indexcontent.html',
}
# If false, no module index is generated.
html_use_modindex = True
# If true, the reST sources are included in the HTML build as _sources/<name>.
#html_copy_source = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
# If nonempty, this is the file name suffix for HTML files (e.g. ".html").
#html_file_suffix = '.html'
# Output file base name for HTML help builder.
htmlhelp_basename = 'numpy'
# Pngmath should try to align formulas properly
pngmath_use_preview = True
# -----------------------------------------------------------------------------
# LaTeX output
# -----------------------------------------------------------------------------
# The paper size ('letter' or 'a4').
#latex_paper_size = 'letter'
# The font size ('10pt', '11pt' or '12pt').
#latex_font_size = '10pt'
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, document class [howto/manual]).
_stdauthor = 'Written by the NumPy community'
latex_documents = [
('reference/index', 'numpy-ref.tex', 'NumPy Reference',
_stdauthor, 'manual'),
('user/index', 'numpy-user.tex', 'NumPy User Guide',
_stdauthor, 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# Additional stuff for the LaTeX preamble.
latex_preamble = r'''
\usepackage{amsmath}
\DeclareUnicodeCharacter{00A0}{\nobreakspace}
% In the parameters section, place a newline after the Parameters
% header
\usepackage{expdlist}
\let\latexdescription=\description
\def\description{\latexdescription{}{} \breaklabel}
% Make Examples/etc section headers smaller and more compact
\makeatletter
\titleformat{\paragraph}{\normalsize\py@HeaderFamily}%
{\py@TitleColor}{0em}{\py@TitleColor}{\py@NormalColor}
\titlespacing*{\paragraph}{0pt}{1ex}{0pt}
\makeatother
% Fix footer/header
\renewcommand{\chaptermark}[1]{\markboth{\MakeUppercase{\thechapter.\ #1}}{}}
\renewcommand{\sectionmark}[1]{\markright{\MakeUppercase{\thesection.\ #1}}}
'''
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
latex_use_modindex = False
# -----------------------------------------------------------------------------
# Intersphinx configuration
# -----------------------------------------------------------------------------
intersphinx_mapping = {'http://docs.python.org/dev': None}
# -----------------------------------------------------------------------------
# Numpy extensions
# -----------------------------------------------------------------------------
# If we want to do a phantom import from an XML file for all autodocs
phantom_import_file = 'dump.xml'
# Make numpydoc to generate plots for example sections
numpydoc_use_plots = True
# -----------------------------------------------------------------------------
# Autosummary
# -----------------------------------------------------------------------------
if sphinx.__version__ >= "0.7":
import glob
autosummary_generate = glob.glob("reference/*.rst")
# -----------------------------------------------------------------------------
# Coverage checker
# -----------------------------------------------------------------------------
coverage_ignore_modules = r"""
""".split()
coverage_ignore_functions = r"""
test($|_) (some|all)true bitwise_not cumproduct pkgload
generic\.
""".split()
coverage_ignore_classes = r"""
""".split()
coverage_c_path = []
coverage_c_regexes = {}
coverage_ignore_c_items = {}
# -----------------------------------------------------------------------------
# Plots
# -----------------------------------------------------------------------------
plot_pre_code = """
import numpy as np
np.random.seed(0)
"""
plot_include_source = True
plot_formats = [('png', 100), 'pdf']
import math
phi = (math.sqrt(5) + 1)/2
import matplotlib
matplotlib.rcParams.update({
'font.size': 8,
'axes.titlesize': 8,
'axes.labelsize': 8,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'legend.fontsize': 8,
'figure.figsize': (3*phi, 3),
'figure.subplot.bottom': 0.2,
'figure.subplot.left': 0.2,
'figure.subplot.right': 0.9,
'figure.subplot.top': 0.85,
'figure.subplot.wspace': 0.4,
'text.usetex': False,
})
| bsd-3-clause |
tequa/ammisoft | ammimain/WinPython-64bit-2.7.13.1Zero/python-2.7.13.amd64/Lib/site-packages/matplotlib/axis.py | 4 | 85084 | """
Classes for the ticks and x and y axis
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from matplotlib import rcParams
import matplotlib.artist as artist
from matplotlib.artist import allow_rasterization
import matplotlib.cbook as cbook
import matplotlib.font_manager as font_manager
import matplotlib.lines as mlines
import matplotlib.patches as mpatches
import matplotlib.scale as mscale
import matplotlib.text as mtext
import matplotlib.ticker as mticker
import matplotlib.transforms as mtransforms
import matplotlib.units as munits
import numpy as np
import warnings
GRIDLINE_INTERPOLATION_STEPS = 180
class Tick(artist.Artist):
"""
Abstract base class for the axis ticks, grid lines and labels
1 refers to the bottom of the plot for xticks and the left for yticks
2 refers to the top of the plot for xticks and the right for yticks
Publicly accessible attributes:
:attr:`tick1line`
a Line2D instance
:attr:`tick2line`
a Line2D instance
:attr:`gridline`
a Line2D instance
:attr:`label1`
a Text instance
:attr:`label2`
a Text instance
:attr:`gridOn`
a boolean which determines whether to draw the tickline
:attr:`tick1On`
a boolean which determines whether to draw the 1st tickline
:attr:`tick2On`
a boolean which determines whether to draw the 2nd tickline
:attr:`label1On`
a boolean which determines whether to draw tick label
:attr:`label2On`
a boolean which determines whether to draw tick label
"""
def __init__(self, axes, loc, label,
size=None, # points
width=None,
color=None,
tickdir=None,
pad=None,
labelsize=None,
labelcolor=None,
zorder=None,
gridOn=None, # defaults to axes.grid depending on
# axes.grid.which
tick1On=True,
tick2On=True,
label1On=True,
label2On=False,
major=True,
):
"""
bbox is the Bound2D bounding box in display coords of the Axes
loc is the tick location in data coords
size is the tick size in points
"""
artist.Artist.__init__(self)
if gridOn is None:
if major and (rcParams['axes.grid.which'] in ('both', 'major')):
gridOn = rcParams['axes.grid']
elif (not major) and (rcParams['axes.grid.which']
in ('both', 'minor')):
gridOn = rcParams['axes.grid']
else:
gridOn = False
self.set_figure(axes.figure)
self.axes = axes
name = self.__name__.lower()
self._name = name
self._loc = loc
if size is None:
if major:
size = rcParams['%s.major.size' % name]
else:
size = rcParams['%s.minor.size' % name]
self._size = size
if width is None:
if major:
width = rcParams['%s.major.width' % name]
else:
width = rcParams['%s.minor.width' % name]
self._width = width
if color is None:
color = rcParams['%s.color' % name]
self._color = color
if pad is None:
if major:
pad = rcParams['%s.major.pad' % name]
else:
pad = rcParams['%s.minor.pad' % name]
self._base_pad = pad
if labelcolor is None:
labelcolor = rcParams['%s.color' % name]
self._labelcolor = labelcolor
if labelsize is None:
labelsize = rcParams['%s.labelsize' % name]
self._labelsize = labelsize
if zorder is None:
if major:
zorder = mlines.Line2D.zorder + 0.01
else:
zorder = mlines.Line2D.zorder
self._zorder = zorder
self.apply_tickdir(tickdir)
self.tick1line = self._get_tick1line()
self.tick2line = self._get_tick2line()
self.gridline = self._get_gridline()
self.label1 = self._get_text1()
self.label = self.label1 # legacy name
self.label2 = self._get_text2()
self.gridOn = gridOn
self.tick1On = tick1On
self.tick2On = tick2On
self.label1On = label1On
self.label2On = label2On
self.update_position(loc)
def apply_tickdir(self, tickdir):
"""
Calculate self._pad and self._tickmarkers
"""
pass
def get_tickdir(self):
return self._tickdir
def get_tick_padding(self):
"""
Get the length of the tick outside of the axes.
"""
padding = {
'in': 0.0,
'inout': 0.5,
'out': 1.0
}
return self._size * padding[self._tickdir]
def get_children(self):
children = [self.tick1line, self.tick2line,
self.gridline, self.label1, self.label2]
return children
def set_clip_path(self, clippath, transform=None):
artist.Artist.set_clip_path(self, clippath, transform)
self.gridline.set_clip_path(clippath, transform)
self.stale = True
set_clip_path.__doc__ = artist.Artist.set_clip_path.__doc__
def get_pad_pixels(self):
return self.figure.dpi * self._base_pad / 72.0
def contains(self, mouseevent):
"""
Test whether the mouse event occurred in the Tick marks.
This function always returns false. It is more useful to test if the
axis as a whole contains the mouse rather than the set of tick marks.
"""
if six.callable(self._contains):
return self._contains(self, mouseevent)
return False, {}
def set_pad(self, val):
"""
Set the tick label pad in points
ACCEPTS: float
"""
self._apply_params(pad=val)
self.stale = True
def get_pad(self):
'Get the value of the tick label pad in points'
return self._base_pad
def _get_text1(self):
'Get the default Text 1 instance'
pass
def _get_text2(self):
'Get the default Text 2 instance'
pass
def _get_tick1line(self):
'Get the default line2D instance for tick1'
pass
def _get_tick2line(self):
'Get the default line2D instance for tick2'
pass
def _get_gridline(self):
'Get the default grid Line2d instance for this tick'
pass
def get_loc(self):
'Return the tick location (data coords) as a scalar'
return self._loc
@allow_rasterization
def draw(self, renderer):
if not self.get_visible():
self.stale = False
return
renderer.open_group(self.__name__)
if self.gridOn:
self.gridline.draw(renderer)
if self.tick1On:
self.tick1line.draw(renderer)
if self.tick2On:
self.tick2line.draw(renderer)
if self.label1On:
self.label1.draw(renderer)
if self.label2On:
self.label2.draw(renderer)
renderer.close_group(self.__name__)
self.stale = False
def set_label1(self, s):
"""
Set the text of ticklabel
ACCEPTS: str
"""
self.label1.set_text(s)
self.stale = True
set_label = set_label1
def set_label2(self, s):
"""
Set the text of ticklabel2
ACCEPTS: str
"""
self.label2.set_text(s)
self.stale = True
def _set_artist_props(self, a):
a.set_figure(self.figure)
def get_view_interval(self):
'return the view Interval instance for the axis this tick is ticking'
raise NotImplementedError('Derived must override')
def _apply_params(self, **kw):
switchkw = ['gridOn', 'tick1On', 'tick2On', 'label1On', 'label2On']
switches = [k for k in kw if k in switchkw]
for k in switches:
setattr(self, k, kw.pop(k))
newmarker = [k for k in kw if k in ['size', 'width', 'pad', 'tickdir']]
if newmarker:
self._size = kw.pop('size', self._size)
# Width could be handled outside this block, but it is
# convenient to leave it here.
self._width = kw.pop('width', self._width)
self._base_pad = kw.pop('pad', self._base_pad)
# apply_tickdir uses _size and _base_pad to make _pad,
# and also makes _tickmarkers.
self.apply_tickdir(kw.pop('tickdir', self._tickdir))
self.tick1line.set_marker(self._tickmarkers[0])
self.tick2line.set_marker(self._tickmarkers[1])
for line in (self.tick1line, self.tick2line):
line.set_markersize(self._size)
line.set_markeredgewidth(self._width)
# _get_text1_transform uses _pad from apply_tickdir.
trans = self._get_text1_transform()[0]
self.label1.set_transform(trans)
trans = self._get_text2_transform()[0]
self.label2.set_transform(trans)
tick_kw = dict([kv for kv in six.iteritems(kw)
if kv[0] in ['color', 'zorder']])
if tick_kw:
self.tick1line.set(**tick_kw)
self.tick2line.set(**tick_kw)
for k, v in six.iteritems(tick_kw):
setattr(self, '_' + k, v)
label_list = [k for k in six.iteritems(kw)
if k[0] in ['labelsize', 'labelcolor']]
if label_list:
label_kw = dict([(k[5:], v) for (k, v) in label_list])
self.label1.set(**label_kw)
self.label2.set(**label_kw)
for k, v in six.iteritems(label_kw):
# for labelsize the text objects covert str ('small')
# -> points. grab the integer from the `Text` object
# instead of saving the string representation
v = getattr(self.label1, 'get_' + k)()
setattr(self, '_label' + k, v)
def update_position(self, loc):
'Set the location of tick in data coords with scalar *loc*'
raise NotImplementedError('Derived must override')
def _get_text1_transform(self):
raise NotImplementedError('Derived must override')
def _get_text2_transform(self):
raise NotImplementedError('Derived must override')
class XTick(Tick):
"""
Contains all the Artists needed to make an x tick - the tick line,
the label text and the grid line
"""
__name__ = 'xtick'
def _get_text1_transform(self):
return self.axes.get_xaxis_text1_transform(self._pad)
def _get_text2_transform(self):
return self.axes.get_xaxis_text2_transform(self._pad)
def apply_tickdir(self, tickdir):
if tickdir is None:
tickdir = rcParams['%s.direction' % self._name]
self._tickdir = tickdir
if self._tickdir == 'in':
self._tickmarkers = (mlines.TICKUP, mlines.TICKDOWN)
elif self._tickdir == 'inout':
self._tickmarkers = ('|', '|')
else:
self._tickmarkers = (mlines.TICKDOWN, mlines.TICKUP)
self._pad = self._base_pad + self.get_tick_padding()
self.stale = True
def _get_text1(self):
'Get the default Text instance'
# the y loc is 3 points below the min of y axis
# get the affine as an a,b,c,d,tx,ty list
# x in data coords, y in axes coords
trans, vert, horiz = self._get_text1_transform()
t = mtext.Text(
x=0, y=0,
fontproperties=font_manager.FontProperties(size=self._labelsize),
color=self._labelcolor,
verticalalignment=vert,
horizontalalignment=horiz,
)
t.set_transform(trans)
self._set_artist_props(t)
return t
def _get_text2(self):
'Get the default Text 2 instance'
# x in data coords, y in axes coords
trans, vert, horiz = self._get_text2_transform()
t = mtext.Text(
x=0, y=1,
fontproperties=font_manager.FontProperties(size=self._labelsize),
color=self._labelcolor,
verticalalignment=vert,
horizontalalignment=horiz,
)
t.set_transform(trans)
self._set_artist_props(t)
return t
def _get_tick1line(self):
'Get the default line2D instance'
# x in data coords, y in axes coords
l = mlines.Line2D(xdata=(0,), ydata=(0,), color=self._color,
linestyle='None', marker=self._tickmarkers[0],
markersize=self._size,
markeredgewidth=self._width, zorder=self._zorder)
l.set_transform(self.axes.get_xaxis_transform(which='tick1'))
self._set_artist_props(l)
return l
def _get_tick2line(self):
'Get the default line2D instance'
# x in data coords, y in axes coords
l = mlines.Line2D(xdata=(0,), ydata=(1,),
color=self._color,
linestyle='None',
marker=self._tickmarkers[1],
markersize=self._size,
markeredgewidth=self._width,
zorder=self._zorder)
l.set_transform(self.axes.get_xaxis_transform(which='tick2'))
self._set_artist_props(l)
return l
def _get_gridline(self):
'Get the default line2D instance'
# x in data coords, y in axes coords
l = mlines.Line2D(xdata=(0.0, 0.0), ydata=(0, 1.0),
color=rcParams['grid.color'],
linestyle=rcParams['grid.linestyle'],
linewidth=rcParams['grid.linewidth'],
alpha=rcParams['grid.alpha'],
markersize=0)
l.set_transform(self.axes.get_xaxis_transform(which='grid'))
l.get_path()._interpolation_steps = GRIDLINE_INTERPOLATION_STEPS
self._set_artist_props(l)
return l
def update_position(self, loc):
'Set the location of tick in data coords with scalar *loc*'
x = loc
nonlinear = (hasattr(self.axes, 'yaxis') and
self.axes.yaxis.get_scale() != 'linear' or
hasattr(self.axes, 'xaxis') and
self.axes.xaxis.get_scale() != 'linear')
if self.tick1On:
self.tick1line.set_xdata((x,))
if self.tick2On:
self.tick2line.set_xdata((x,))
if self.gridOn:
self.gridline.set_xdata((x,))
if self.label1On:
self.label1.set_x(x)
if self.label2On:
self.label2.set_x(x)
if nonlinear:
self.tick1line._invalid = True
self.tick2line._invalid = True
self.gridline._invalid = True
self._loc = loc
self.stale = True
def get_view_interval(self):
'return the Interval instance for this axis view limits'
return self.axes.viewLim.intervalx
class YTick(Tick):
"""
Contains all the Artists needed to make a Y tick - the tick line,
the label text and the grid line
"""
__name__ = 'ytick'
def _get_text1_transform(self):
return self.axes.get_yaxis_text1_transform(self._pad)
def _get_text2_transform(self):
return self.axes.get_yaxis_text2_transform(self._pad)
def apply_tickdir(self, tickdir):
if tickdir is None:
tickdir = rcParams['%s.direction' % self._name]
self._tickdir = tickdir
if self._tickdir == 'in':
self._tickmarkers = (mlines.TICKRIGHT, mlines.TICKLEFT)
elif self._tickdir == 'inout':
self._tickmarkers = ('_', '_')
else:
self._tickmarkers = (mlines.TICKLEFT, mlines.TICKRIGHT)
self._pad = self._base_pad + self.get_tick_padding()
self.stale = True
# how far from the y axis line the right of the ticklabel are
def _get_text1(self):
'Get the default Text instance'
# x in axes coords, y in data coords
trans, vert, horiz = self._get_text1_transform()
t = mtext.Text(
x=0, y=0,
fontproperties=font_manager.FontProperties(size=self._labelsize),
color=self._labelcolor,
verticalalignment=vert,
horizontalalignment=horiz,
)
t.set_transform(trans)
self._set_artist_props(t)
return t
def _get_text2(self):
'Get the default Text instance'
# x in axes coords, y in data coords
trans, vert, horiz = self._get_text2_transform()
t = mtext.Text(
x=1, y=0,
fontproperties=font_manager.FontProperties(size=self._labelsize),
color=self._labelcolor,
verticalalignment=vert,
horizontalalignment=horiz,
)
t.set_transform(trans)
self._set_artist_props(t)
return t
def _get_tick1line(self):
'Get the default line2D instance'
# x in axes coords, y in data coords
l = mlines.Line2D((0,), (0,),
color=self._color,
marker=self._tickmarkers[0],
linestyle='None',
markersize=self._size,
markeredgewidth=self._width,
zorder=self._zorder)
l.set_transform(self.axes.get_yaxis_transform(which='tick1'))
self._set_artist_props(l)
return l
def _get_tick2line(self):
'Get the default line2D instance'
# x in axes coords, y in data coords
l = mlines.Line2D((1,), (0,),
color=self._color,
marker=self._tickmarkers[1],
linestyle='None',
markersize=self._size,
markeredgewidth=self._width,
zorder=self._zorder)
l.set_transform(self.axes.get_yaxis_transform(which='tick2'))
self._set_artist_props(l)
return l
def _get_gridline(self):
'Get the default line2D instance'
# x in axes coords, y in data coords
l = mlines.Line2D(xdata=(0, 1), ydata=(0, 0),
color=rcParams['grid.color'],
linestyle=rcParams['grid.linestyle'],
linewidth=rcParams['grid.linewidth'],
alpha=rcParams['grid.alpha'],
markersize=0)
l.set_transform(self.axes.get_yaxis_transform(which='grid'))
l.get_path()._interpolation_steps = GRIDLINE_INTERPOLATION_STEPS
self._set_artist_props(l)
return l
def update_position(self, loc):
'Set the location of tick in data coords with scalar loc'
y = loc
nonlinear = (hasattr(self.axes, 'yaxis') and
self.axes.yaxis.get_scale() != 'linear' or
hasattr(self.axes, 'xaxis') and
self.axes.xaxis.get_scale() != 'linear')
if self.tick1On:
self.tick1line.set_ydata((y,))
if self.tick2On:
self.tick2line.set_ydata((y,))
if self.gridOn:
self.gridline.set_ydata((y, ))
if self.label1On:
self.label1.set_y(y)
if self.label2On:
self.label2.set_y(y)
if nonlinear:
self.tick1line._invalid = True
self.tick2line._invalid = True
self.gridline._invalid = True
self._loc = loc
self.stale = True
def get_view_interval(self):
'return the Interval instance for this axis view limits'
return self.axes.viewLim.intervaly
class Ticker(object):
locator = None
formatter = None
class Axis(artist.Artist):
"""
Public attributes
* :attr:`axes.transData` - transform data coords to display coords
* :attr:`axes.transAxes` - transform axis coords to display coords
* :attr:`labelpad` - number of points between the axis and its label
"""
OFFSETTEXTPAD = 3
def __str__(self):
return self.__class__.__name__ \
+ "(%f,%f)" % tuple(self.axes.transAxes.transform_point((0, 0)))
def __init__(self, axes, pickradius=15):
"""
Init the axis with the parent Axes instance
"""
artist.Artist.__init__(self)
self.set_figure(axes.figure)
# Keep track of setting to the default value, this allows use to know
# if any of the following values is explicitly set by the user, so as
# to not overwrite their settings with any of our 'auto' settings.
self.isDefault_majloc = True
self.isDefault_minloc = True
self.isDefault_majfmt = True
self.isDefault_minfmt = True
self.isDefault_label = True
self.axes = axes
self.major = Ticker()
self.minor = Ticker()
self.callbacks = cbook.CallbackRegistry()
self._autolabelpos = True
self._smart_bounds = False
self.label = self._get_label()
self.labelpad = rcParams['axes.labelpad']
self.offsetText = self._get_offset_text()
self.majorTicks = []
self.minorTicks = []
self.pickradius = pickradius
# Initialize here for testing; later add API
self._major_tick_kw = dict()
self._minor_tick_kw = dict()
self.cla()
self._set_scale('linear')
def set_label_coords(self, x, y, transform=None):
"""
Set the coordinates of the label. By default, the x
coordinate of the y label is determined by the tick label
bounding boxes, but this can lead to poor alignment of
multiple ylabels if there are multiple axes. Ditto for the y
coodinate of the x label.
You can also specify the coordinate system of the label with
the transform. If None, the default coordinate system will be
the axes coordinate system (0,0) is (left,bottom), (0.5, 0.5)
is middle, etc
"""
self._autolabelpos = False
if transform is None:
transform = self.axes.transAxes
self.label.set_transform(transform)
self.label.set_position((x, y))
self.stale = True
def get_transform(self):
return self._scale.get_transform()
def get_scale(self):
return self._scale.name
def _set_scale(self, value, **kwargs):
self._scale = mscale.scale_factory(value, self, **kwargs)
self._scale.set_default_locators_and_formatters(self)
self.isDefault_majloc = True
self.isDefault_minloc = True
self.isDefault_majfmt = True
self.isDefault_minfmt = True
def limit_range_for_scale(self, vmin, vmax):
return self._scale.limit_range_for_scale(vmin, vmax, self.get_minpos())
def get_children(self):
children = [self.label, self.offsetText]
majorticks = self.get_major_ticks()
minorticks = self.get_minor_ticks()
children.extend(majorticks)
children.extend(minorticks)
return children
def cla(self):
'clear the current axis'
self.set_major_locator(mticker.AutoLocator())
self.set_major_formatter(mticker.ScalarFormatter())
self.set_minor_locator(mticker.NullLocator())
self.set_minor_formatter(mticker.NullFormatter())
self.set_label_text('')
self._set_artist_props(self.label)
# Keep track of setting to the default value, this allows use to know
# if any of the following values is explicitly set by the user, so as
# to not overwrite their settings with any of our 'auto' settings.
self.isDefault_majloc = True
self.isDefault_minloc = True
self.isDefault_majfmt = True
self.isDefault_minfmt = True
self.isDefault_label = True
# Clear the callback registry for this axis, or it may "leak"
self.callbacks = cbook.CallbackRegistry()
# whether the grids are on
self._gridOnMajor = (rcParams['axes.grid'] and
rcParams['axes.grid.which'] in ('both', 'major'))
self._gridOnMinor = (rcParams['axes.grid'] and
rcParams['axes.grid.which'] in ('both', 'minor'))
self.label.set_text('')
self._set_artist_props(self.label)
self.reset_ticks()
self.converter = None
self.units = None
self.set_units(None)
self.stale = True
def reset_ticks(self):
# build a few default ticks; grow as necessary later; only
# define 1 so properties set on ticks will be copied as they
# grow
cbook.popall(self.majorTicks)
cbook.popall(self.minorTicks)
self.majorTicks.extend([self._get_tick(major=True)])
self.minorTicks.extend([self._get_tick(major=False)])
self._lastNumMajorTicks = 1
self._lastNumMinorTicks = 1
def set_tick_params(self, which='major', reset=False, **kw):
"""
Set appearance parameters for ticks and ticklabels.
For documentation of keyword arguments, see
:meth:`matplotlib.axes.Axes.tick_params`.
"""
dicts = []
if which == 'major' or which == 'both':
dicts.append(self._major_tick_kw)
if which == 'minor' or which == 'both':
dicts.append(self._minor_tick_kw)
kwtrans = self._translate_tick_kw(kw, to_init_kw=True)
for d in dicts:
if reset:
d.clear()
d.update(kwtrans)
if reset:
self.reset_ticks()
else:
if which == 'major' or which == 'both':
for tick in self.majorTicks:
tick._apply_params(**self._major_tick_kw)
if which == 'minor' or which == 'both':
for tick in self.minorTicks:
tick._apply_params(**self._minor_tick_kw)
if 'labelcolor' in kwtrans:
self.offsetText.set_color(kwtrans['labelcolor'])
self.stale = True
@staticmethod
def _translate_tick_kw(kw, to_init_kw=True):
# We may want to move the following function to
# a more visible location; or maybe there already
# is something like this.
def _bool(arg):
if cbook.is_string_like(arg):
if arg.lower() == 'on':
return True
if arg.lower() == 'off':
return False
raise ValueError('String "%s" should be "on" or "off"' % arg)
return bool(arg)
# The following lists may be moved to a more
# accessible location.
kwkeys0 = ['size', 'width', 'color', 'tickdir', 'pad',
'labelsize', 'labelcolor', 'zorder', 'gridOn',
'tick1On', 'tick2On', 'label1On', 'label2On']
kwkeys1 = ['length', 'direction', 'left', 'bottom', 'right', 'top',
'labelleft', 'labelbottom', 'labelright', 'labeltop']
kwkeys = kwkeys0 + kwkeys1
kwtrans = dict()
if to_init_kw:
if 'length' in kw:
kwtrans['size'] = kw.pop('length')
if 'direction' in kw:
kwtrans['tickdir'] = kw.pop('direction')
if 'left' in kw:
kwtrans['tick1On'] = _bool(kw.pop('left'))
if 'bottom' in kw:
kwtrans['tick1On'] = _bool(kw.pop('bottom'))
if 'right' in kw:
kwtrans['tick2On'] = _bool(kw.pop('right'))
if 'top' in kw:
kwtrans['tick2On'] = _bool(kw.pop('top'))
if 'labelleft' in kw:
kwtrans['label1On'] = _bool(kw.pop('labelleft'))
if 'labelbottom' in kw:
kwtrans['label1On'] = _bool(kw.pop('labelbottom'))
if 'labelright' in kw:
kwtrans['label2On'] = _bool(kw.pop('labelright'))
if 'labeltop' in kw:
kwtrans['label2On'] = _bool(kw.pop('labeltop'))
if 'colors' in kw:
c = kw.pop('colors')
kwtrans['color'] = c
kwtrans['labelcolor'] = c
# Maybe move the checking up to the caller of this method.
for key in kw:
if key not in kwkeys:
raise ValueError(
"keyword %s is not recognized; valid keywords are %s"
% (key, kwkeys))
kwtrans.update(kw)
else:
raise NotImplementedError("Inverse translation is deferred")
return kwtrans
def set_clip_path(self, clippath, transform=None):
artist.Artist.set_clip_path(self, clippath, transform)
for child in self.majorTicks + self.minorTicks:
child.set_clip_path(clippath, transform)
self.stale = True
def get_view_interval(self):
'return the Interval instance for this axis view limits'
raise NotImplementedError('Derived must override')
def set_view_interval(self, vmin, vmax, ignore=False):
raise NotImplementedError('Derived must override')
def get_data_interval(self):
'return the Interval instance for this axis data limits'
raise NotImplementedError('Derived must override')
def set_data_interval(self):
'''set the axis data limits'''
raise NotImplementedError('Derived must override')
def set_default_intervals(self):
'''set the default limits for the axis data and view interval if they
are not mutated'''
# this is mainly in support of custom object plotting. For
# example, if someone passes in a datetime object, we do not
# know automagically how to set the default min/max of the
# data and view limits. The unit conversion AxisInfo
# interface provides a hook for custom types to register
# default limits through the AxisInfo.default_limits
# attribute, and the derived code below will check for that
# and use it if is available (else just use 0..1)
pass
def _set_artist_props(self, a):
if a is None:
return
a.set_figure(self.figure)
def iter_ticks(self):
"""
Iterate through all of the major and minor ticks.
"""
majorLocs = self.major.locator()
majorTicks = self.get_major_ticks(len(majorLocs))
self.major.formatter.set_locs(majorLocs)
majorLabels = [self.major.formatter(val, i)
for i, val in enumerate(majorLocs)]
minorLocs = self.minor.locator()
minorTicks = self.get_minor_ticks(len(minorLocs))
self.minor.formatter.set_locs(minorLocs)
minorLabels = [self.minor.formatter(val, i)
for i, val in enumerate(minorLocs)]
major_minor = [
(majorTicks, majorLocs, majorLabels),
(minorTicks, minorLocs, minorLabels)]
for group in major_minor:
for tick in zip(*group):
yield tick
def get_ticklabel_extents(self, renderer):
"""
Get the extents of the tick labels on either side
of the axes.
"""
ticks_to_draw = self._update_ticks(renderer)
ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,
renderer)
if len(ticklabelBoxes):
bbox = mtransforms.Bbox.union(ticklabelBoxes)
else:
bbox = mtransforms.Bbox.from_extents(0, 0, 0, 0)
if len(ticklabelBoxes2):
bbox2 = mtransforms.Bbox.union(ticklabelBoxes2)
else:
bbox2 = mtransforms.Bbox.from_extents(0, 0, 0, 0)
return bbox, bbox2
def set_smart_bounds(self, value):
"""set the axis to have smart bounds"""
self._smart_bounds = value
self.stale = True
def get_smart_bounds(self):
"""get whether the axis has smart bounds"""
return self._smart_bounds
def _update_ticks(self, renderer):
"""
Update ticks (position and labels) using the current data
interval of the axes. Returns a list of ticks that will be
drawn.
"""
interval = self.get_view_interval()
tick_tups = [t for t in self.iter_ticks()]
if self._smart_bounds:
# handle inverted limits
view_low, view_high = min(*interval), max(*interval)
data_low, data_high = self.get_data_interval()
if data_low > data_high:
data_low, data_high = data_high, data_low
locs = [ti[1] for ti in tick_tups]
locs.sort()
locs = np.array(locs)
if len(locs):
if data_low <= view_low:
# data extends beyond view, take view as limit
ilow = view_low
else:
# data stops within view, take best tick
cond = locs <= data_low
good_locs = locs[cond]
if len(good_locs) > 0:
# last tick prior or equal to first data point
ilow = good_locs[-1]
else:
# No ticks (why not?), take first tick
ilow = locs[0]
if data_high >= view_high:
# data extends beyond view, take view as limit
ihigh = view_high
else:
# data stops within view, take best tick
cond = locs >= data_high
good_locs = locs[cond]
if len(good_locs) > 0:
# first tick after or equal to last data point
ihigh = good_locs[0]
else:
# No ticks (why not?), take last tick
ihigh = locs[-1]
tick_tups = [ti for ti in tick_tups
if (ti[1] >= ilow) and (ti[1] <= ihigh)]
# so that we don't lose ticks on the end, expand out the interval ever
# so slightly. The "ever so slightly" is defined to be the width of a
# half of a pixel. We don't want to draw a tick that even one pixel
# outside of the defined axis interval.
if interval[0] <= interval[1]:
interval_expanded = interval
else:
interval_expanded = interval[1], interval[0]
if hasattr(self, '_get_pixel_distance_along_axis'):
# normally, one does not want to catch all exceptions that
# could possibly happen, but it is not clear exactly what
# exceptions might arise from a user's projection (their
# rendition of the Axis object). So, we catch all, with
# the idea that one would rather potentially lose a tick
# from one side of the axis or another, rather than see a
# stack trace.
# We also catch users warnings here. These are the result of
# invalid numpy calculations that may be the result of out of
# bounds on axis with finite allowed intervals such as geo
# projections i.e. Mollweide.
with np.errstate(invalid='ignore'):
try:
ds1 = self._get_pixel_distance_along_axis(
interval_expanded[0], -0.5)
except:
warnings.warn("Unable to find pixel distance along axis "
"for interval padding of ticks; assuming no "
"interval padding needed.")
ds1 = 0.0
if np.isnan(ds1):
ds1 = 0.0
try:
ds2 = self._get_pixel_distance_along_axis(
interval_expanded[1], +0.5)
except:
warnings.warn("Unable to find pixel distance along axis "
"for interval padding of ticks; assuming no "
"interval padding needed.")
ds2 = 0.0
if np.isnan(ds2):
ds2 = 0.0
interval_expanded = (interval_expanded[0] - ds1,
interval_expanded[1] + ds2)
ticks_to_draw = []
for tick, loc, label in tick_tups:
if tick is None:
continue
if not mtransforms.interval_contains(interval_expanded, loc):
continue
tick.update_position(loc)
tick.set_label1(label)
tick.set_label2(label)
ticks_to_draw.append(tick)
return ticks_to_draw
def _get_tick_bboxes(self, ticks, renderer):
"""
Given the list of ticks, return two lists of bboxes. One for
tick lable1's and another for tick label2's.
"""
ticklabelBoxes = []
ticklabelBoxes2 = []
for tick in ticks:
if tick.label1On and tick.label1.get_visible():
extent = tick.label1.get_window_extent(renderer)
ticklabelBoxes.append(extent)
if tick.label2On and tick.label2.get_visible():
extent = tick.label2.get_window_extent(renderer)
ticklabelBoxes2.append(extent)
return ticklabelBoxes, ticklabelBoxes2
def get_tightbbox(self, renderer):
"""
Return a bounding box that encloses the axis. It only accounts
tick labels, axis label, and offsetText.
"""
if not self.get_visible():
return
ticks_to_draw = self._update_ticks(renderer)
ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,
renderer)
self._update_label_position(ticklabelBoxes, ticklabelBoxes2)
self._update_offset_text_position(ticklabelBoxes, ticklabelBoxes2)
self.offsetText.set_text(self.major.formatter.get_offset())
bb = []
for a in [self.label, self.offsetText]:
if a.get_visible():
bb.append(a.get_window_extent(renderer))
bb.extend(ticklabelBoxes)
bb.extend(ticklabelBoxes2)
bb = [b for b in bb if b.width != 0 or b.height != 0]
if bb:
_bbox = mtransforms.Bbox.union(bb)
return _bbox
else:
return None
def get_tick_padding(self):
values = []
if len(self.majorTicks):
values.append(self.majorTicks[0].get_tick_padding())
if len(self.minorTicks):
values.append(self.minorTicks[0].get_tick_padding())
if len(values):
return max(values)
return 0.0
@allow_rasterization
def draw(self, renderer, *args, **kwargs):
'Draw the axis lines, grid lines, tick lines and labels'
if not self.get_visible():
return
renderer.open_group(__name__)
ticks_to_draw = self._update_ticks(renderer)
ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,
renderer)
for tick in ticks_to_draw:
tick.draw(renderer)
# scale up the axis label box to also find the neighbors, not
# just the tick labels that actually overlap note we need a
# *copy* of the axis label box because we don't wan't to scale
# the actual bbox
self._update_label_position(ticklabelBoxes, ticklabelBoxes2)
self.label.draw(renderer)
self._update_offset_text_position(ticklabelBoxes, ticklabelBoxes2)
self.offsetText.set_text(self.major.formatter.get_offset())
self.offsetText.draw(renderer)
if 0: # draw the bounding boxes around the text for debug
for tick in self.majorTicks:
label = tick.label1
mpatches.bbox_artist(label, renderer)
mpatches.bbox_artist(self.label, renderer)
renderer.close_group(__name__)
self.stale = False
def _get_label(self):
raise NotImplementedError('Derived must override')
def _get_offset_text(self):
raise NotImplementedError('Derived must override')
def get_gridlines(self):
'Return the grid lines as a list of Line2D instance'
ticks = self.get_major_ticks()
return cbook.silent_list('Line2D gridline',
[tick.gridline for tick in ticks])
def get_label(self):
'Return the axis label as a Text instance'
return self.label
def get_offset_text(self):
'Return the axis offsetText as a Text instance'
return self.offsetText
def get_pickradius(self):
'Return the depth of the axis used by the picker'
return self.pickradius
def get_majorticklabels(self):
'Return a list of Text instances for the major ticklabels'
ticks = self.get_major_ticks()
labels1 = [tick.label1 for tick in ticks if tick.label1On]
labels2 = [tick.label2 for tick in ticks if tick.label2On]
return cbook.silent_list('Text major ticklabel', labels1 + labels2)
def get_minorticklabels(self):
'Return a list of Text instances for the minor ticklabels'
ticks = self.get_minor_ticks()
labels1 = [tick.label1 for tick in ticks if tick.label1On]
labels2 = [tick.label2 for tick in ticks if tick.label2On]
return cbook.silent_list('Text minor ticklabel', labels1 + labels2)
def get_ticklabels(self, minor=False, which=None):
"""
Get the x tick labels as a list of :class:`~matplotlib.text.Text`
instances.
Parameters
----------
minor : bool
If True return the minor ticklabels,
else return the major ticklabels
which : None, ('minor', 'major', 'both')
Overrides `minor`.
Selects which ticklabels to return
Returns
-------
ret : list
List of :class:`~matplotlib.text.Text` instances.
"""
if which is not None:
if which == 'minor':
return self.get_minorticklabels()
elif which == 'major':
return self.get_majorticklabels()
elif which == 'both':
return self.get_majorticklabels() + self.get_minorticklabels()
else:
raise ValueError("`which` must be one of ('minor', 'major', "
"'both') not " + str(which))
if minor:
return self.get_minorticklabels()
return self.get_majorticklabels()
def get_majorticklines(self):
'Return the major tick lines as a list of Line2D instances'
lines = []
ticks = self.get_major_ticks()
for tick in ticks:
lines.append(tick.tick1line)
lines.append(tick.tick2line)
return cbook.silent_list('Line2D ticklines', lines)
def get_minorticklines(self):
'Return the minor tick lines as a list of Line2D instances'
lines = []
ticks = self.get_minor_ticks()
for tick in ticks:
lines.append(tick.tick1line)
lines.append(tick.tick2line)
return cbook.silent_list('Line2D ticklines', lines)
def get_ticklines(self, minor=False):
'Return the tick lines as a list of Line2D instances'
if minor:
return self.get_minorticklines()
return self.get_majorticklines()
def get_majorticklocs(self):
"Get the major tick locations in data coordinates as a numpy array"
return self.major.locator()
def get_minorticklocs(self):
"Get the minor tick locations in data coordinates as a numpy array"
return self.minor.locator()
def get_ticklocs(self, minor=False):
"Get the tick locations in data coordinates as a numpy array"
if minor:
return self.minor.locator()
return self.major.locator()
def _get_tick(self, major):
'return the default tick instance'
raise NotImplementedError('derived must override')
def _copy_tick_props(self, src, dest):
'Copy the props from src tick to dest tick'
if src is None or dest is None:
return
dest.label1.update_from(src.label1)
dest.label2.update_from(src.label2)
dest.tick1line.update_from(src.tick1line)
dest.tick2line.update_from(src.tick2line)
dest.gridline.update_from(src.gridline)
dest.tick1On = src.tick1On
dest.tick2On = src.tick2On
dest.label1On = src.label1On
dest.label2On = src.label2On
def get_label_text(self):
'Get the text of the label'
return self.label.get_text()
def get_major_locator(self):
'Get the locator of the major ticker'
return self.major.locator
def get_minor_locator(self):
'Get the locator of the minor ticker'
return self.minor.locator
def get_major_formatter(self):
'Get the formatter of the major ticker'
return self.major.formatter
def get_minor_formatter(self):
'Get the formatter of the minor ticker'
return self.minor.formatter
def get_major_ticks(self, numticks=None):
'get the tick instances; grow as necessary'
if numticks is None:
numticks = len(self.get_major_locator()())
if len(self.majorTicks) < numticks:
# update the new tick label properties from the old
for i in range(numticks - len(self.majorTicks)):
tick = self._get_tick(major=True)
self.majorTicks.append(tick)
if self._lastNumMajorTicks < numticks:
protoTick = self.majorTicks[0]
for i in range(self._lastNumMajorTicks, len(self.majorTicks)):
tick = self.majorTicks[i]
if self._gridOnMajor:
tick.gridOn = True
self._copy_tick_props(protoTick, tick)
self._lastNumMajorTicks = numticks
ticks = self.majorTicks[:numticks]
return ticks
def get_minor_ticks(self, numticks=None):
'get the minor tick instances; grow as necessary'
if numticks is None:
numticks = len(self.get_minor_locator()())
if len(self.minorTicks) < numticks:
# update the new tick label properties from the old
for i in range(numticks - len(self.minorTicks)):
tick = self._get_tick(major=False)
self.minorTicks.append(tick)
if self._lastNumMinorTicks < numticks:
protoTick = self.minorTicks[0]
for i in range(self._lastNumMinorTicks, len(self.minorTicks)):
tick = self.minorTicks[i]
if self._gridOnMinor:
tick.gridOn = True
self._copy_tick_props(protoTick, tick)
self._lastNumMinorTicks = numticks
ticks = self.minorTicks[:numticks]
return ticks
def grid(self, b=None, which='major', **kwargs):
"""
Set the axis grid on or off; b is a boolean. Use *which* =
'major' | 'minor' | 'both' to set the grid for major or minor ticks.
If *b* is *None* and len(kwargs)==0, toggle the grid state. If
*kwargs* are supplied, it is assumed you want the grid on and *b*
will be set to True.
*kwargs* are used to set the line properties of the grids, e.g.,
xax.grid(color='r', linestyle='-', linewidth=2)
"""
if len(kwargs):
b = True
which = which.lower()
if which in ['minor', 'both']:
if b is None:
self._gridOnMinor = not self._gridOnMinor
else:
self._gridOnMinor = b
for tick in self.minorTicks: # don't use get_ticks here!
if tick is None:
continue
tick.gridOn = self._gridOnMinor
if len(kwargs):
tick.gridline.update(kwargs)
self._minor_tick_kw['gridOn'] = self._gridOnMinor
if which in ['major', 'both']:
if b is None:
self._gridOnMajor = not self._gridOnMajor
else:
self._gridOnMajor = b
for tick in self.majorTicks: # don't use get_ticks here!
if tick is None:
continue
tick.gridOn = self._gridOnMajor
if len(kwargs):
tick.gridline.update(kwargs)
self._major_tick_kw['gridOn'] = self._gridOnMajor
self.stale = True
def update_units(self, data):
"""
introspect *data* for units converter and update the
axis.converter instance if necessary. Return *True*
if *data* is registered for unit conversion.
"""
converter = munits.registry.get_converter(data)
if converter is None:
return False
neednew = self.converter != converter
self.converter = converter
default = self.converter.default_units(data, self)
if default is not None and self.units is None:
self.set_units(default)
if neednew:
self._update_axisinfo()
self.stale = True
return True
def _update_axisinfo(self):
"""
check the axis converter for the stored units to see if the
axis info needs to be updated
"""
if self.converter is None:
return
info = self.converter.axisinfo(self.units, self)
if info is None:
return
if info.majloc is not None and \
self.major.locator != info.majloc and self.isDefault_majloc:
self.set_major_locator(info.majloc)
self.isDefault_majloc = True
if info.minloc is not None and \
self.minor.locator != info.minloc and self.isDefault_minloc:
self.set_minor_locator(info.minloc)
self.isDefault_minloc = True
if info.majfmt is not None and \
self.major.formatter != info.majfmt and self.isDefault_majfmt:
self.set_major_formatter(info.majfmt)
self.isDefault_majfmt = True
if info.minfmt is not None and \
self.minor.formatter != info.minfmt and self.isDefault_minfmt:
self.set_minor_formatter(info.minfmt)
self.isDefault_minfmt = True
if info.label is not None and self.isDefault_label:
self.set_label_text(info.label)
self.isDefault_label = True
self.set_default_intervals()
def have_units(self):
return self.converter is not None or self.units is not None
def convert_units(self, x):
if self.converter is None:
self.converter = munits.registry.get_converter(x)
if self.converter is None:
return x
ret = self.converter.convert(x, self.units, self)
return ret
def set_units(self, u):
"""
set the units for axis
ACCEPTS: a units tag
"""
pchanged = False
if u is None:
self.units = None
pchanged = True
else:
if u != self.units:
self.units = u
pchanged = True
if pchanged:
self._update_axisinfo()
self.callbacks.process('units')
self.callbacks.process('units finalize')
self.stale = True
def get_units(self):
'return the units for axis'
return self.units
def set_label_text(self, label, fontdict=None, **kwargs):
""" Sets the text value of the axis label
ACCEPTS: A string value for the label
"""
self.isDefault_label = False
self.label.set_text(label)
if fontdict is not None:
self.label.update(fontdict)
self.label.update(kwargs)
self.stale = True
return self.label
def set_major_formatter(self, formatter):
"""
Set the formatter of the major ticker
ACCEPTS: A :class:`~matplotlib.ticker.Formatter` instance
"""
self.isDefault_majfmt = False
self.major.formatter = formatter
formatter.set_axis(self)
self.stale = True
def set_minor_formatter(self, formatter):
"""
Set the formatter of the minor ticker
ACCEPTS: A :class:`~matplotlib.ticker.Formatter` instance
"""
self.isDefault_minfmt = False
self.minor.formatter = formatter
formatter.set_axis(self)
self.stale = True
def set_major_locator(self, locator):
"""
Set the locator of the major ticker
ACCEPTS: a :class:`~matplotlib.ticker.Locator` instance
"""
self.isDefault_majloc = False
self.major.locator = locator
locator.set_axis(self)
self.stale = True
def set_minor_locator(self, locator):
"""
Set the locator of the minor ticker
ACCEPTS: a :class:`~matplotlib.ticker.Locator` instance
"""
self.isDefault_minloc = False
self.minor.locator = locator
locator.set_axis(self)
self.stale = True
def set_pickradius(self, pickradius):
"""
Set the depth of the axis used by the picker
ACCEPTS: a distance in points
"""
self.pickradius = pickradius
def set_ticklabels(self, ticklabels, *args, **kwargs):
"""
Set the text values of the tick labels. Return a list of Text
instances. Use *kwarg* *minor=True* to select minor ticks.
All other kwargs are used to update the text object properties.
As for get_ticklabels, label1 (left or bottom) is
affected for a given tick only if its label1On attribute
is True, and similarly for label2. The list of returned
label text objects consists of all such label1 objects followed
by all such label2 objects.
The input *ticklabels* is assumed to match the set of
tick locations, regardless of the state of label1On and
label2On.
ACCEPTS: sequence of strings or Text objects
"""
get_labels = []
for t in ticklabels:
# try calling get_text() to check whether it is Text object
# if it is Text, get label content
try:
get_labels.append(t.get_text())
# otherwise add the label to the list directly
except AttributeError:
get_labels.append(t)
# replace the ticklabels list with the processed one
ticklabels = get_labels
minor = kwargs.pop('minor', False)
if minor:
self.set_minor_formatter(mticker.FixedFormatter(ticklabels))
ticks = self.get_minor_ticks()
else:
self.set_major_formatter(mticker.FixedFormatter(ticklabels))
ticks = self.get_major_ticks()
ret = []
for tick_label, tick in zip(ticklabels, ticks):
# deal with label1
tick.label1.set_text(tick_label)
tick.label1.update(kwargs)
# deal with label2
tick.label2.set_text(tick_label)
tick.label2.update(kwargs)
# only return visible tick labels
if tick.label1On:
ret.append(tick.label1)
if tick.label2On:
ret.append(tick.label2)
self.stale = True
return ret
def set_ticks(self, ticks, minor=False):
"""
Set the locations of the tick marks from sequence ticks
ACCEPTS: sequence of floats
"""
# XXX if the user changes units, the information will be lost here
ticks = self.convert_units(ticks)
if len(ticks) > 1:
xleft, xright = self.get_view_interval()
if xright > xleft:
self.set_view_interval(min(ticks), max(ticks))
else:
self.set_view_interval(max(ticks), min(ticks))
if minor:
self.set_minor_locator(mticker.FixedLocator(ticks))
return self.get_minor_ticks(len(ticks))
else:
self.set_major_locator(mticker.FixedLocator(ticks))
return self.get_major_ticks(len(ticks))
def _update_label_position(self, bboxes, bboxes2):
"""
Update the label position based on the bounding box enclosing
all the ticklabels and axis spine
"""
raise NotImplementedError('Derived must override')
def _update_offset_text_postion(self, bboxes, bboxes2):
"""
Update the label position based on the sequence of bounding
boxes of all the ticklabels
"""
raise NotImplementedError('Derived must override')
def pan(self, numsteps):
'Pan *numsteps* (can be positive or negative)'
self.major.locator.pan(numsteps)
def zoom(self, direction):
"Zoom in/out on axis; if *direction* is >0 zoom in, else zoom out"
self.major.locator.zoom(direction)
def axis_date(self, tz=None):
"""
Sets up x-axis ticks and labels that treat the x data as dates.
*tz* is a :class:`tzinfo` instance or a timezone string.
This timezone is used to create date labels.
"""
# By providing a sample datetime instance with the desired
# timezone, the registered converter can be selected,
# and the "units" attribute, which is the timezone, can
# be set.
import datetime
if isinstance(tz, six.string_types):
import pytz
tz = pytz.timezone(tz)
self.update_units(datetime.datetime(2009, 1, 1, 0, 0, 0, 0, tz))
def get_tick_space(self):
"""
Return the estimated number of ticks that can fit on the axis.
"""
# Must be overridden in the subclass
raise NotImplementedError()
def get_label_position(self):
"""
Return the label position (top or bottom)
"""
return self.label_position
def set_label_position(self, position):
"""
Set the label position (top or bottom)
ACCEPTS: [ 'top' | 'bottom' ]
"""
raise NotImplementedError()
def get_minpos(self):
raise NotImplementedError()
class XAxis(Axis):
__name__ = 'xaxis'
axis_name = 'x'
def contains(self, mouseevent):
"""Test whether the mouse event occured in the x axis.
"""
if six.callable(self._contains):
return self._contains(self, mouseevent)
x, y = mouseevent.x, mouseevent.y
try:
trans = self.axes.transAxes.inverted()
xaxes, yaxes = trans.transform_point((x, y))
except ValueError:
return False, {}
l, b = self.axes.transAxes.transform_point((0, 0))
r, t = self.axes.transAxes.transform_point((1, 1))
inaxis = xaxes >= 0 and xaxes <= 1 and (
(y < b and y > b - self.pickradius) or
(y > t and y < t + self.pickradius))
return inaxis, {}
def _get_tick(self, major):
if major:
tick_kw = self._major_tick_kw
else:
tick_kw = self._minor_tick_kw
return XTick(self.axes, 0, '', major=major, **tick_kw)
def _get_label(self):
# x in axes coords, y in display coords (to be updated at draw
# time by _update_label_positions)
label = mtext.Text(x=0.5, y=0,
fontproperties=font_manager.FontProperties(
size=rcParams['axes.labelsize'],
weight=rcParams['axes.labelweight']),
color=rcParams['axes.labelcolor'],
verticalalignment='top',
horizontalalignment='center')
label.set_transform(mtransforms.blended_transform_factory(
self.axes.transAxes, mtransforms.IdentityTransform()))
self._set_artist_props(label)
self.label_position = 'bottom'
return label
def _get_offset_text(self):
# x in axes coords, y in display coords (to be updated at draw time)
offsetText = mtext.Text(x=1, y=0,
fontproperties=font_manager.FontProperties(
size=rcParams['xtick.labelsize']),
color=rcParams['xtick.color'],
verticalalignment='top',
horizontalalignment='right')
offsetText.set_transform(mtransforms.blended_transform_factory(
self.axes.transAxes, mtransforms.IdentityTransform())
)
self._set_artist_props(offsetText)
self.offset_text_position = 'bottom'
return offsetText
def _get_pixel_distance_along_axis(self, where, perturb):
"""
Returns the amount, in data coordinates, that a single pixel
corresponds to in the locality given by "where", which is also given
in data coordinates, and is an x coordinate. "perturb" is the amount
to perturb the pixel. Usually +0.5 or -0.5.
Implementing this routine for an axis is optional; if present, it will
ensure that no ticks are lost due to round-off at the extreme ends of
an axis.
"""
# Note that this routine does not work for a polar axis, because of
# the 1e-10 below. To do things correctly, we need to use rmax
# instead of 1e-10 for a polar axis. But since we do not have that
# kind of information at this point, we just don't try to pad anything
# for the theta axis of a polar plot.
if self.axes.name == 'polar':
return 0.0
#
# first figure out the pixel location of the "where" point. We use
# 1e-10 for the y point, so that we remain compatible with log axes.
# transformation from data coords to display coords
trans = self.axes.transData
# transformation from display coords to data coords
transinv = trans.inverted()
pix = trans.transform_point((where, 1e-10))
# perturb the pixel
ptp = transinv.transform_point((pix[0] + perturb, pix[1]))
dx = abs(ptp[0] - where)
return dx
def set_label_position(self, position):
"""
Set the label position (top or bottom)
ACCEPTS: [ 'top' | 'bottom' ]
"""
if position == 'top':
self.label.set_verticalalignment('baseline')
elif position == 'bottom':
self.label.set_verticalalignment('top')
else:
msg = "Position accepts only [ 'top' | 'bottom' ]"
raise ValueError(msg)
self.label_position = position
self.stale = True
def _update_label_position(self, bboxes, bboxes2):
"""
Update the label position based on the bounding box enclosing
all the ticklabels and axis spine
"""
if not self._autolabelpos:
return
x, y = self.label.get_position()
if self.label_position == 'bottom':
try:
spine = self.axes.spines['bottom']
spinebbox = spine.get_transform().transform_path(
spine.get_path()).get_extents()
except KeyError:
# use axes if spine doesn't exist
spinebbox = self.axes.bbox
bbox = mtransforms.Bbox.union(bboxes + [spinebbox])
bottom = bbox.y0
self.label.set_position(
(x, bottom - self.labelpad * self.figure.dpi / 72.0)
)
else:
try:
spine = self.axes.spines['top']
spinebbox = spine.get_transform().transform_path(
spine.get_path()).get_extents()
except KeyError:
# use axes if spine doesn't exist
spinebbox = self.axes.bbox
bbox = mtransforms.Bbox.union(bboxes2 + [spinebbox])
top = bbox.y1
self.label.set_position(
(x, top + self.labelpad * self.figure.dpi / 72.0)
)
def _update_offset_text_position(self, bboxes, bboxes2):
"""
Update the offset_text position based on the sequence of bounding
boxes of all the ticklabels
"""
x, y = self.offsetText.get_position()
if not len(bboxes):
bottom = self.axes.bbox.ymin
else:
bbox = mtransforms.Bbox.union(bboxes)
bottom = bbox.y0
self.offsetText.set_position(
(x, bottom - self.OFFSETTEXTPAD * self.figure.dpi / 72.0)
)
def get_text_heights(self, renderer):
"""
Returns the amount of space one should reserve for text
above and below the axes. Returns a tuple (above, below)
"""
bbox, bbox2 = self.get_ticklabel_extents(renderer)
# MGDTODO: Need a better way to get the pad
padPixels = self.majorTicks[0].get_pad_pixels()
above = 0.0
if bbox2.height:
above += bbox2.height + padPixels
below = 0.0
if bbox.height:
below += bbox.height + padPixels
if self.get_label_position() == 'top':
above += self.label.get_window_extent(renderer).height + padPixels
else:
below += self.label.get_window_extent(renderer).height + padPixels
return above, below
def set_ticks_position(self, position):
"""
Set the ticks position (top, bottom, both, default or none)
both sets the ticks to appear on both positions, but does not
change the tick labels. 'default' resets the tick positions to
the default: ticks on both positions, labels at bottom. 'none'
can be used if you don't want any ticks. 'none' and 'both'
affect only the ticks, not the labels.
ACCEPTS: [ 'top' | 'bottom' | 'both' | 'default' | 'none' ]
"""
if position == 'top':
self.set_tick_params(which='both', top=True, labeltop=True,
bottom=False, labelbottom=False)
elif position == 'bottom':
self.set_tick_params(which='both', top=False, labeltop=False,
bottom=True, labelbottom=True)
elif position == 'both':
self.set_tick_params(which='both', top=True,
bottom=True)
elif position == 'none':
self.set_tick_params(which='both', top=False,
bottom=False)
elif position == 'default':
self.set_tick_params(which='both', top=True, labeltop=False,
bottom=True, labelbottom=True)
else:
raise ValueError("invalid position: %s" % position)
self.stale = True
def tick_top(self):
'use ticks only on top'
self.set_ticks_position('top')
def tick_bottom(self):
'use ticks only on bottom'
self.set_ticks_position('bottom')
def get_ticks_position(self):
"""
Return the ticks position (top, bottom, default or unknown)
"""
majt = self.majorTicks[0]
mT = self.minorTicks[0]
majorTop = ((not majt.tick1On) and majt.tick2On and
(not majt.label1On) and majt.label2On)
minorTop = ((not mT.tick1On) and mT.tick2On and
(not mT.label1On) and mT.label2On)
if majorTop and minorTop:
return 'top'
MajorBottom = (majt.tick1On and (not majt.tick2On) and
majt.label1On and (not majt.label2On))
MinorBottom = (mT.tick1On and (not mT.tick2On) and
mT.label1On and (not mT.label2On))
if MajorBottom and MinorBottom:
return 'bottom'
majorDefault = (majt.tick1On and majt.tick2On and
majt.label1On and (not majt.label2On))
minorDefault = (mT.tick1On and mT.tick2On and
mT.label1On and (not mT.label2On))
if majorDefault and minorDefault:
return 'default'
return 'unknown'
def get_view_interval(self):
'return the Interval instance for this axis view limits'
return self.axes.viewLim.intervalx
def set_view_interval(self, vmin, vmax, ignore=False):
"""
If *ignore* is *False*, the order of vmin, vmax
does not matter; the original axis orientation will
be preserved. In addition, the view limits can be
expanded, but will not be reduced. This method is
for mpl internal use; for normal use, see
:meth:`~matplotlib.axes.Axes.set_xlim`.
"""
if ignore:
self.axes.viewLim.intervalx = vmin, vmax
else:
Vmin, Vmax = self.get_view_interval()
if Vmin < Vmax:
self.axes.viewLim.intervalx = (min(vmin, vmax, Vmin),
max(vmin, vmax, Vmax))
else:
self.axes.viewLim.intervalx = (max(vmin, vmax, Vmin),
min(vmin, vmax, Vmax))
def get_minpos(self):
return self.axes.dataLim.minposx
def get_data_interval(self):
'return the Interval instance for this axis data limits'
return self.axes.dataLim.intervalx
def set_data_interval(self, vmin, vmax, ignore=False):
'set the axis data limits'
if ignore:
self.axes.dataLim.intervalx = vmin, vmax
else:
Vmin, Vmax = self.get_data_interval()
self.axes.dataLim.intervalx = min(vmin, Vmin), max(vmax, Vmax)
self.stale = True
def set_default_intervals(self):
'set the default limits for the axis interval if they are not mutated'
xmin, xmax = 0., 1.
dataMutated = self.axes.dataLim.mutatedx()
viewMutated = self.axes.viewLim.mutatedx()
if not dataMutated or not viewMutated:
if self.converter is not None:
info = self.converter.axisinfo(self.units, self)
if info.default_limits is not None:
valmin, valmax = info.default_limits
xmin = self.converter.convert(valmin, self.units, self)
xmax = self.converter.convert(valmax, self.units, self)
if not dataMutated:
self.axes.dataLim.intervalx = xmin, xmax
if not viewMutated:
self.axes.viewLim.intervalx = xmin, xmax
self.stale = True
def get_tick_space(self):
ends = self.axes.transAxes.transform([[0, 0], [1, 0]])
length = ((ends[1][0] - ends[0][0]) / self.axes.figure.dpi) * 72.0
tick = self._get_tick(True)
# There is a heuristic here that the aspect ratio of tick text
# is no more than 3:1
size = tick.label1.get_size() * 3
if size > 0:
return int(np.floor(length / size))
else:
return 2**31 - 1
class YAxis(Axis):
__name__ = 'yaxis'
axis_name = 'y'
def contains(self, mouseevent):
"""Test whether the mouse event occurred in the y axis.
Returns *True* | *False*
"""
if six.callable(self._contains):
return self._contains(self, mouseevent)
x, y = mouseevent.x, mouseevent.y
try:
trans = self.axes.transAxes.inverted()
xaxes, yaxes = trans.transform_point((x, y))
except ValueError:
return False, {}
l, b = self.axes.transAxes.transform_point((0, 0))
r, t = self.axes.transAxes.transform_point((1, 1))
inaxis = yaxes >= 0 and yaxes <= 1 and (
(x < l and x > l - self.pickradius) or
(x > r and x < r + self.pickradius))
return inaxis, {}
def _get_tick(self, major):
if major:
tick_kw = self._major_tick_kw
else:
tick_kw = self._minor_tick_kw
return YTick(self.axes, 0, '', major=major, **tick_kw)
def _get_label(self):
# x in display coords (updated by _update_label_position)
# y in axes coords
label = mtext.Text(x=0, y=0.5,
# todo: get the label position
fontproperties=font_manager.FontProperties(
size=rcParams['axes.labelsize'],
weight=rcParams['axes.labelweight']),
color=rcParams['axes.labelcolor'],
verticalalignment='bottom',
horizontalalignment='center',
rotation='vertical',
rotation_mode='anchor')
label.set_transform(mtransforms.blended_transform_factory(
mtransforms.IdentityTransform(), self.axes.transAxes))
self._set_artist_props(label)
self.label_position = 'left'
return label
def _get_offset_text(self):
# x in display coords, y in axes coords (to be updated at draw time)
offsetText = mtext.Text(x=0, y=0.5,
fontproperties=font_manager.FontProperties(
size=rcParams['ytick.labelsize']
),
color=rcParams['ytick.color'],
verticalalignment='baseline',
horizontalalignment='left')
offsetText.set_transform(mtransforms.blended_transform_factory(
self.axes.transAxes, mtransforms.IdentityTransform())
)
self._set_artist_props(offsetText)
self.offset_text_position = 'left'
return offsetText
def _get_pixel_distance_along_axis(self, where, perturb):
"""
Returns the amount, in data coordinates, that a single pixel
corresponds to in the locality given by *where*, which is also given
in data coordinates, and is a y coordinate.
*perturb* is the amount to perturb the pixel. Usually +0.5 or -0.5.
Implementing this routine for an axis is optional; if present, it will
ensure that no ticks are lost due to round-off at the extreme ends of
an axis.
"""
#
# first figure out the pixel location of the "where" point. We use
# 1e-10 for the x point, so that we remain compatible with log axes.
# transformation from data coords to display coords
trans = self.axes.transData
# transformation from display coords to data coords
transinv = trans.inverted()
pix = trans.transform_point((1e-10, where))
# perturb the pixel
ptp = transinv.transform_point((pix[0], pix[1] + perturb))
dy = abs(ptp[1] - where)
return dy
def set_label_position(self, position):
"""
Set the label position (left or right)
ACCEPTS: [ 'left' | 'right' ]
"""
self.label.set_rotation_mode('anchor')
self.label.set_horizontalalignment('center')
if position == 'left':
self.label.set_verticalalignment('bottom')
elif position == 'right':
self.label.set_verticalalignment('top')
else:
msg = "Position accepts only [ 'left' | 'right' ]"
raise ValueError(msg)
self.label_position = position
self.stale = True
def _update_label_position(self, bboxes, bboxes2):
"""
Update the label position based on the bounding box enclosing
all the ticklabels and axis spine
"""
if not self._autolabelpos:
return
x, y = self.label.get_position()
if self.label_position == 'left':
try:
spine = self.axes.spines['left']
spinebbox = spine.get_transform().transform_path(
spine.get_path()).get_extents()
except KeyError:
# use axes if spine doesn't exist
spinebbox = self.axes.bbox
bbox = mtransforms.Bbox.union(bboxes + [spinebbox])
left = bbox.x0
self.label.set_position(
(left - self.labelpad * self.figure.dpi / 72.0, y)
)
else:
try:
spine = self.axes.spines['right']
spinebbox = spine.get_transform().transform_path(
spine.get_path()).get_extents()
except KeyError:
# use axes if spine doesn't exist
spinebbox = self.axes.bbox
bbox = mtransforms.Bbox.union(bboxes2 + [spinebbox])
right = bbox.x1
self.label.set_position(
(right + self.labelpad * self.figure.dpi / 72.0, y)
)
def _update_offset_text_position(self, bboxes, bboxes2):
"""
Update the offset_text position based on the sequence of bounding
boxes of all the ticklabels
"""
x, y = self.offsetText.get_position()
top = self.axes.bbox.ymax
self.offsetText.set_position(
(x, top + self.OFFSETTEXTPAD * self.figure.dpi / 72.0)
)
def set_offset_position(self, position):
x, y = self.offsetText.get_position()
if position == 'left':
x = 0
elif position == 'right':
x = 1
else:
msg = "Position accepts only [ 'left' | 'right' ]"
raise ValueError(msg)
self.offsetText.set_ha(position)
self.offsetText.set_position((x, y))
self.stale = True
def get_text_widths(self, renderer):
bbox, bbox2 = self.get_ticklabel_extents(renderer)
# MGDTODO: Need a better way to get the pad
padPixels = self.majorTicks[0].get_pad_pixels()
left = 0.0
if bbox.width:
left += bbox.width + padPixels
right = 0.0
if bbox2.width:
right += bbox2.width + padPixels
if self.get_label_position() == 'left':
left += self.label.get_window_extent(renderer).width + padPixels
else:
right += self.label.get_window_extent(renderer).width + padPixels
return left, right
def set_ticks_position(self, position):
"""
Set the ticks position (left, right, both, default or none)
'both' sets the ticks to appear on both positions, but does not
change the tick labels. 'default' resets the tick positions to
the default: ticks on both positions, labels at left. 'none'
can be used if you don't want any ticks. 'none' and 'both'
affect only the ticks, not the labels.
ACCEPTS: [ 'left' | 'right' | 'both' | 'default' | 'none' ]
"""
if position == 'right':
self.set_tick_params(which='both', right=True, labelright=True,
left=False, labelleft=False)
self.set_offset_position(position)
elif position == 'left':
self.set_tick_params(which='both', right=False, labelright=False,
left=True, labelleft=True)
self.set_offset_position(position)
elif position == 'both':
self.set_tick_params(which='both', right=True,
left=True)
elif position == 'none':
self.set_tick_params(which='both', right=False,
left=False)
elif position == 'default':
self.set_tick_params(which='both', right=True, labelright=False,
left=True, labelleft=True)
else:
raise ValueError("invalid position: %s" % position)
self.stale = True
def tick_right(self):
'use ticks only on right'
self.set_ticks_position('right')
def tick_left(self):
'use ticks only on left'
self.set_ticks_position('left')
def get_ticks_position(self):
"""
Return the ticks position (left, right, both or unknown)
"""
majt = self.majorTicks[0]
mT = self.minorTicks[0]
majorRight = ((not majt.tick1On) and majt.tick2On and
(not majt.label1On) and majt.label2On)
minorRight = ((not mT.tick1On) and mT.tick2On and
(not mT.label1On) and mT.label2On)
if majorRight and minorRight:
return 'right'
majorLeft = (majt.tick1On and (not majt.tick2On) and
majt.label1On and (not majt.label2On))
minorLeft = (mT.tick1On and (not mT.tick2On) and
mT.label1On and (not mT.label2On))
if majorLeft and minorLeft:
return 'left'
majorDefault = (majt.tick1On and majt.tick2On and
majt.label1On and (not majt.label2On))
minorDefault = (mT.tick1On and mT.tick2On and
mT.label1On and (not mT.label2On))
if majorDefault and minorDefault:
return 'default'
return 'unknown'
def get_view_interval(self):
'return the Interval instance for this axis view limits'
return self.axes.viewLim.intervaly
def set_view_interval(self, vmin, vmax, ignore=False):
"""
If *ignore* is *False*, the order of vmin, vmax
does not matter; the original axis orientation will
be preserved. In addition, the view limits can be
expanded, but will not be reduced. This method is
for mpl internal use; for normal use, see
:meth:`~matplotlib.axes.Axes.set_ylim`.
"""
if ignore:
self.axes.viewLim.intervaly = vmin, vmax
else:
Vmin, Vmax = self.get_view_interval()
if Vmin < Vmax:
self.axes.viewLim.intervaly = (min(vmin, vmax, Vmin),
max(vmin, vmax, Vmax))
else:
self.axes.viewLim.intervaly = (max(vmin, vmax, Vmin),
min(vmin, vmax, Vmax))
self.stale = True
def get_minpos(self):
return self.axes.dataLim.minposy
def get_data_interval(self):
'return the Interval instance for this axis data limits'
return self.axes.dataLim.intervaly
def set_data_interval(self, vmin, vmax, ignore=False):
'set the axis data limits'
if ignore:
self.axes.dataLim.intervaly = vmin, vmax
else:
Vmin, Vmax = self.get_data_interval()
self.axes.dataLim.intervaly = min(vmin, Vmin), max(vmax, Vmax)
self.stale = True
def set_default_intervals(self):
'set the default limits for the axis interval if they are not mutated'
ymin, ymax = 0., 1.
dataMutated = self.axes.dataLim.mutatedy()
viewMutated = self.axes.viewLim.mutatedy()
if not dataMutated or not viewMutated:
if self.converter is not None:
info = self.converter.axisinfo(self.units, self)
if info.default_limits is not None:
valmin, valmax = info.default_limits
ymin = self.converter.convert(valmin, self.units, self)
ymax = self.converter.convert(valmax, self.units, self)
if not dataMutated:
self.axes.dataLim.intervaly = ymin, ymax
if not viewMutated:
self.axes.viewLim.intervaly = ymin, ymax
self.stale = True
def get_tick_space(self):
ends = self.axes.transAxes.transform([[0, 0], [0, 1]])
length = ((ends[1][1] - ends[0][1]) / self.axes.figure.dpi) * 72.0
tick = self._get_tick(True)
# Having a spacing of at least 2 just looks good.
size = tick.label1.get_size() * 2.0
if size > 0:
return int(np.floor(length / size))
else:
return 2**31 - 1
| bsd-3-clause |
ryfeus/lambda-packs | Tensorflow_LightGBM_Scipy_nightly/source/scipy/stats/_binned_statistic.py | 10 | 25912 | from __future__ import division, print_function, absolute_import
import numpy as np
from scipy._lib.six import callable, xrange
from scipy._lib._numpy_compat import suppress_warnings
from collections import namedtuple
__all__ = ['binned_statistic',
'binned_statistic_2d',
'binned_statistic_dd']
BinnedStatisticResult = namedtuple('BinnedStatisticResult',
('statistic', 'bin_edges', 'binnumber'))
def binned_statistic(x, values, statistic='mean',
bins=10, range=None):
"""
Compute a binned statistic for one or more sets of data.
This is a generalization of a histogram function. A histogram divides
the space into bins, and returns the count of the number of points in
each bin. This function allows the computation of the sum, mean, median,
or other statistic of the values (or set of values) within each bin.
Parameters
----------
x : (N,) array_like
A sequence of values to be binned.
values : (N,) array_like or list of (N,) array_like
The data on which the statistic will be computed. This must be
the same shape as `x`, or a set of sequences - each the same shape as
`x`. If `values` is a set of sequences, the statistic will be computed
on each independently.
statistic : string or callable, optional
The statistic to compute (default is 'mean').
The following statistics are available:
* 'mean' : compute the mean of values for points within each bin.
Empty bins will be represented by NaN.
* 'median' : compute the median of values for points within each
bin. Empty bins will be represented by NaN.
* 'count' : compute the count of points within each bin. This is
identical to an unweighted histogram. `values` array is not
referenced.
* 'sum' : compute the sum of values for points within each bin.
This is identical to a weighted histogram.
* 'min' : compute the minimum of values for points within each bin.
Empty bins will be represented by NaN.
* 'max' : compute the maximum of values for point within each bin.
Empty bins will be represented by NaN.
* function : a user-defined function which takes a 1D array of
values, and outputs a single numerical statistic. This function
will be called on the values in each bin. Empty bins will be
represented by function([]), or NaN if this returns an error.
bins : int or sequence of scalars, optional
If `bins` is an int, it defines the number of equal-width bins in the
given range (10 by default). If `bins` is a sequence, it defines the
bin edges, including the rightmost edge, allowing for non-uniform bin
widths. Values in `x` that are smaller than lowest bin edge are
assigned to bin number 0, values beyond the highest bin are assigned to
``bins[-1]``. If the bin edges are specified, the number of bins will
be, (nx = len(bins)-1).
range : (float, float) or [(float, float)], optional
The lower and upper range of the bins. If not provided, range
is simply ``(x.min(), x.max())``. Values outside the range are
ignored.
Returns
-------
statistic : array
The values of the selected statistic in each bin.
bin_edges : array of dtype float
Return the bin edges ``(length(statistic)+1)``.
binnumber: 1-D ndarray of ints
Indices of the bins (corresponding to `bin_edges`) in which each value
of `x` belongs. Same length as `values`. A binnumber of `i` means the
corresponding value is between (bin_edges[i-1], bin_edges[i]).
See Also
--------
numpy.digitize, numpy.histogram, binned_statistic_2d, binned_statistic_dd
Notes
-----
All but the last (righthand-most) bin is half-open. In other words, if
`bins` is ``[1, 2, 3, 4]``, then the first bin is ``[1, 2)`` (including 1,
but excluding 2) and the second ``[2, 3)``. The last bin, however, is
``[3, 4]``, which *includes* 4.
.. versionadded:: 0.11.0
Examples
--------
>>> from scipy import stats
>>> import matplotlib.pyplot as plt
First some basic examples:
Create two evenly spaced bins in the range of the given sample, and sum the
corresponding values in each of those bins:
>>> values = [1.0, 1.0, 2.0, 1.5, 3.0]
>>> stats.binned_statistic([1, 1, 2, 5, 7], values, 'sum', bins=2)
(array([ 4. , 4.5]), array([ 1., 4., 7.]), array([1, 1, 1, 2, 2]))
Multiple arrays of values can also be passed. The statistic is calculated
on each set independently:
>>> values = [[1.0, 1.0, 2.0, 1.5, 3.0], [2.0, 2.0, 4.0, 3.0, 6.0]]
>>> stats.binned_statistic([1, 1, 2, 5, 7], values, 'sum', bins=2)
(array([[ 4. , 4.5], [ 8. , 9. ]]), array([ 1., 4., 7.]),
array([1, 1, 1, 2, 2]))
>>> stats.binned_statistic([1, 2, 1, 2, 4], np.arange(5), statistic='mean',
... bins=3)
(array([ 1., 2., 4.]), array([ 1., 2., 3., 4.]),
array([1, 2, 1, 2, 3]))
As a second example, we now generate some random data of sailing boat speed
as a function of wind speed, and then determine how fast our boat is for
certain wind speeds:
>>> windspeed = 8 * np.random.rand(500)
>>> boatspeed = .3 * windspeed**.5 + .2 * np.random.rand(500)
>>> bin_means, bin_edges, binnumber = stats.binned_statistic(windspeed,
... boatspeed, statistic='median', bins=[1,2,3,4,5,6,7])
>>> plt.figure()
>>> plt.plot(windspeed, boatspeed, 'b.', label='raw data')
>>> plt.hlines(bin_means, bin_edges[:-1], bin_edges[1:], colors='g', lw=5,
... label='binned statistic of data')
>>> plt.legend()
Now we can use ``binnumber`` to select all datapoints with a windspeed
below 1:
>>> low_boatspeed = boatspeed[binnumber == 0]
As a final example, we will use ``bin_edges`` and ``binnumber`` to make a
plot of a distribution that shows the mean and distribution around that
mean per bin, on top of a regular histogram and the probability
distribution function:
>>> x = np.linspace(0, 5, num=500)
>>> x_pdf = stats.maxwell.pdf(x)
>>> samples = stats.maxwell.rvs(size=10000)
>>> bin_means, bin_edges, binnumber = stats.binned_statistic(x, x_pdf,
... statistic='mean', bins=25)
>>> bin_width = (bin_edges[1] - bin_edges[0])
>>> bin_centers = bin_edges[1:] - bin_width/2
>>> plt.figure()
>>> plt.hist(samples, bins=50, normed=True, histtype='stepfilled',
... alpha=0.2, label='histogram of data')
>>> plt.plot(x, x_pdf, 'r-', label='analytical pdf')
>>> plt.hlines(bin_means, bin_edges[:-1], bin_edges[1:], colors='g', lw=2,
... label='binned statistic of data')
>>> plt.plot((binnumber - 0.5) * bin_width, x_pdf, 'g.', alpha=0.5)
>>> plt.legend(fontsize=10)
>>> plt.show()
"""
try:
N = len(bins)
except TypeError:
N = 1
if N != 1:
bins = [np.asarray(bins, float)]
if range is not None:
if len(range) == 2:
range = [range]
medians, edges, binnumbers = binned_statistic_dd(
[x], values, statistic, bins, range)
return BinnedStatisticResult(medians, edges[0], binnumbers)
BinnedStatistic2dResult = namedtuple('BinnedStatistic2dResult',
('statistic', 'x_edge', 'y_edge',
'binnumber'))
def binned_statistic_2d(x, y, values, statistic='mean',
bins=10, range=None, expand_binnumbers=False):
"""
Compute a bidimensional binned statistic for one or more sets of data.
This is a generalization of a histogram2d function. A histogram divides
the space into bins, and returns the count of the number of points in
each bin. This function allows the computation of the sum, mean, median,
or other statistic of the values (or set of values) within each bin.
Parameters
----------
x : (N,) array_like
A sequence of values to be binned along the first dimension.
y : (N,) array_like
A sequence of values to be binned along the second dimension.
values : (N,) array_like or list of (N,) array_like
The data on which the statistic will be computed. This must be
the same shape as `x`, or a list of sequences - each with the same
shape as `x`. If `values` is such a list, the statistic will be
computed on each independently.
statistic : string or callable, optional
The statistic to compute (default is 'mean').
The following statistics are available:
* 'mean' : compute the mean of values for points within each bin.
Empty bins will be represented by NaN.
* 'median' : compute the median of values for points within each
bin. Empty bins will be represented by NaN.
* 'count' : compute the count of points within each bin. This is
identical to an unweighted histogram. `values` array is not
referenced.
* 'sum' : compute the sum of values for points within each bin.
This is identical to a weighted histogram.
* 'min' : compute the minimum of values for points within each bin.
Empty bins will be represented by NaN.
* 'max' : compute the maximum of values for point within each bin.
Empty bins will be represented by NaN.
* function : a user-defined function which takes a 1D array of
values, and outputs a single numerical statistic. This function
will be called on the values in each bin. Empty bins will be
represented by function([]), or NaN if this returns an error.
bins : int or [int, int] or array_like or [array, array], optional
The bin specification:
* the number of bins for the two dimensions (nx = ny = bins),
* the number of bins in each dimension (nx, ny = bins),
* the bin edges for the two dimensions (x_edge = y_edge = bins),
* the bin edges in each dimension (x_edge, y_edge = bins).
If the bin edges are specified, the number of bins will be,
(nx = len(x_edge)-1, ny = len(y_edge)-1).
range : (2,2) array_like, 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.
expand_binnumbers : bool, optional
'False' (default): the returned `binnumber` is a shape (N,) array of
linearized bin indices.
'True': the returned `binnumber` is 'unraveled' into a shape (2,N)
ndarray, where each row gives the bin numbers in the corresponding
dimension.
See the `binnumber` returned value, and the `Examples` section.
.. versionadded:: 0.17.0
Returns
-------
statistic : (nx, ny) ndarray
The values of the selected statistic in each two-dimensional bin.
x_edge : (nx + 1) ndarray
The bin edges along the first dimension.
y_edge : (ny + 1) ndarray
The bin edges along the second dimension.
binnumber : (N,) array of ints or (2,N) ndarray of ints
This assigns to each element of `sample` an integer that represents the
bin in which this observation falls. The representation depends on the
`expand_binnumbers` argument. See `Notes` for details.
See Also
--------
numpy.digitize, numpy.histogram2d, binned_statistic, binned_statistic_dd
Notes
-----
Binedges:
All but the last (righthand-most) bin is half-open. In other words, if
`bins` is ``[1, 2, 3, 4]``, then the first bin is ``[1, 2)`` (including 1,
but excluding 2) and the second ``[2, 3)``. The last bin, however, is
``[3, 4]``, which *includes* 4.
`binnumber`:
This returned argument assigns to each element of `sample` an integer that
represents the bin in which it belongs. The representation depends on the
`expand_binnumbers` argument. If 'False' (default): The returned
`binnumber` is a shape (N,) array of linearized indices mapping each
element of `sample` to its corresponding bin (using row-major ordering).
If 'True': The returned `binnumber` is a shape (2,N) ndarray where
each row indicates bin placements for each dimension respectively. In each
dimension, a binnumber of `i` means the corresponding value is between
(D_edge[i-1], D_edge[i]), where 'D' is either 'x' or 'y'.
.. versionadded:: 0.11.0
Examples
--------
>>> from scipy import stats
Calculate the counts with explicit bin-edges:
>>> x = [0.1, 0.1, 0.1, 0.6]
>>> y = [2.1, 2.6, 2.1, 2.1]
>>> binx = [0.0, 0.5, 1.0]
>>> biny = [2.0, 2.5, 3.0]
>>> ret = stats.binned_statistic_2d(x, y, None, 'count', bins=[binx,biny])
>>> ret.statistic
array([[ 2., 1.],
[ 1., 0.]])
The bin in which each sample is placed is given by the `binnumber`
returned parameter. By default, these are the linearized bin indices:
>>> ret.binnumber
array([5, 6, 5, 9])
The bin indices can also be expanded into separate entries for each
dimension using the `expand_binnumbers` parameter:
>>> ret = stats.binned_statistic_2d(x, y, None, 'count', bins=[binx,biny],
... expand_binnumbers=True)
>>> ret.binnumber
array([[1, 1, 1, 2],
[1, 2, 1, 1]])
Which shows that the first three elements belong in the xbin 1, and the
fourth into xbin 2; and so on for y.
"""
# This code is based on np.histogram2d
try:
N = len(bins)
except TypeError:
N = 1
if N != 1 and N != 2:
xedges = yedges = np.asarray(bins, float)
bins = [xedges, yedges]
medians, edges, binnumbers = binned_statistic_dd(
[x, y], values, statistic, bins, range,
expand_binnumbers=expand_binnumbers)
return BinnedStatistic2dResult(medians, edges[0], edges[1], binnumbers)
BinnedStatisticddResult = namedtuple('BinnedStatisticddResult',
('statistic', 'bin_edges',
'binnumber'))
def binned_statistic_dd(sample, values, statistic='mean',
bins=10, range=None, expand_binnumbers=False):
"""
Compute a multidimensional binned statistic for a set of data.
This is a generalization of a histogramdd function. A histogram divides
the space into bins, and returns the count of the number of points in
each bin. This function allows the computation of the sum, mean, median,
or other statistic of the values within each bin.
Parameters
----------
sample : array_like
Data to histogram passed as a sequence of D arrays of length N, or
as an (N,D) array.
values : (N,) array_like or list of (N,) array_like
The data on which the statistic will be computed. This must be
the same shape as `x`, or a list of sequences - each with the same
shape as `x`. If `values` is such a list, the statistic will be
computed on each independently.
statistic : string or callable, optional
The statistic to compute (default is 'mean').
The following statistics are available:
* 'mean' : compute the mean of values for points within each bin.
Empty bins will be represented by NaN.
* 'median' : compute the median of values for points within each
bin. Empty bins will be represented by NaN.
* 'count' : compute the count of points within each bin. This is
identical to an unweighted histogram. `values` array is not
referenced.
* 'sum' : compute the sum of values for points within each bin.
This is identical to a weighted histogram.
* 'min' : compute the minimum of values for points within each bin.
Empty bins will be represented by NaN.
* 'max' : compute the maximum of values for point within each bin.
Empty bins will be represented by NaN.
* function : a user-defined function which takes a 1D array of
values, and outputs a single numerical statistic. This function
will be called on the values in each bin. Empty bins will be
represented by function([]), or NaN if this returns an error.
bins : sequence or int, optional
The bin specification must be in one of the following forms:
* A sequence of arrays describing the bin edges along each dimension.
* The number of bins for each dimension (nx, ny, ... = bins).
* The number of bins for all dimensions (nx = ny = ... = bins).
range : sequence, optional
A sequence of lower and upper bin edges to be used if the edges are
not given explicitely in `bins`. Defaults to the minimum and maximum
values along each dimension.
expand_binnumbers : bool, optional
'False' (default): the returned `binnumber` is a shape (N,) array of
linearized bin indices.
'True': the returned `binnumber` is 'unraveled' into a shape (D,N)
ndarray, where each row gives the bin numbers in the corresponding
dimension.
See the `binnumber` returned value, and the `Examples` section of
`binned_statistic_2d`.
.. versionadded:: 0.17.0
Returns
-------
statistic : ndarray, shape(nx1, nx2, nx3,...)
The values of the selected statistic in each two-dimensional bin.
bin_edges : list of ndarrays
A list of D arrays describing the (nxi + 1) bin edges for each
dimension.
binnumber : (N,) array of ints or (D,N) ndarray of ints
This assigns to each element of `sample` an integer that represents the
bin in which this observation falls. The representation depends on the
`expand_binnumbers` argument. See `Notes` for details.
See Also
--------
numpy.digitize, numpy.histogramdd, binned_statistic, binned_statistic_2d
Notes
-----
Binedges:
All but the last (righthand-most) bin is half-open in each dimension. In
other words, if `bins` is ``[1, 2, 3, 4]``, then the first bin is
``[1, 2)`` (including 1, but excluding 2) and the second ``[2, 3)``. The
last bin, however, is ``[3, 4]``, which *includes* 4.
`binnumber`:
This returned argument assigns to each element of `sample` an integer that
represents the bin in which it belongs. The representation depends on the
`expand_binnumbers` argument. If 'False' (default): The returned
`binnumber` is a shape (N,) array of linearized indices mapping each
element of `sample` to its corresponding bin (using row-major ordering).
If 'True': The returned `binnumber` is a shape (D,N) ndarray where
each row indicates bin placements for each dimension respectively. In each
dimension, a binnumber of `i` means the corresponding value is between
(bin_edges[D][i-1], bin_edges[D][i]), for each dimension 'D'.
.. versionadded:: 0.11.0
"""
known_stats = ['mean', 'median', 'count', 'sum', 'std','min','max']
if not callable(statistic) and statistic not in known_stats:
raise ValueError('invalid statistic %r' % (statistic,))
# `Ndim` is the number of dimensions (e.g. `2` for `binned_statistic_2d`)
# `Dlen` is the length of elements along each dimension.
# This code is based on np.histogramdd
try:
# `sample` is an ND-array.
Dlen, Ndim = sample.shape
except (AttributeError, ValueError):
# `sample` is a sequence of 1D arrays.
sample = np.atleast_2d(sample).T
Dlen, Ndim = sample.shape
# Store initial shape of `values` to preserve it in the output
values = np.asarray(values)
input_shape = list(values.shape)
# Make sure that `values` is 2D to iterate over rows
values = np.atleast_2d(values)
Vdim, Vlen = values.shape
# Make sure `values` match `sample`
if(statistic != 'count' and Vlen != Dlen):
raise AttributeError('The number of `values` elements must match the '
'length of each `sample` dimension.')
nbin = np.empty(Ndim, int) # Number of bins in each dimension
edges = Ndim * [None] # Bin edges for each dim (will be 2D array)
dedges = Ndim * [None] # Spacing between edges (will be 2D array)
try:
M = len(bins)
if M != Ndim:
raise AttributeError('The dimension of bins must be equal '
'to the dimension of the sample x.')
except TypeError:
bins = Ndim * [bins]
# Select range for each dimension
# Used only if number of bins is given.
if range is None:
smin = np.atleast_1d(np.array(sample.min(axis=0), float))
smax = np.atleast_1d(np.array(sample.max(axis=0), float))
else:
smin = np.zeros(Ndim)
smax = np.zeros(Ndim)
for i in xrange(Ndim):
smin[i], smax[i] = range[i]
# Make sure the bins have a finite width.
for i in xrange(len(smin)):
if smin[i] == smax[i]:
smin[i] = smin[i] - .5
smax[i] = smax[i] + .5
# Create edge arrays
for i in xrange(Ndim):
if np.isscalar(bins[i]):
nbin[i] = bins[i] + 2 # +2 for outlier bins
edges[i] = np.linspace(smin[i], smax[i], nbin[i] - 1)
else:
edges[i] = np.asarray(bins[i], float)
nbin[i] = len(edges[i]) + 1 # +1 for outlier bins
dedges[i] = np.diff(edges[i])
nbin = np.asarray(nbin)
# Compute the bin number each sample falls into, in each dimension
sampBin = [
np.digitize(sample[:, i], edges[i])
for i in xrange(Ndim)
]
# Using `digitize`, values that fall on an edge are put in the right bin.
# For the rightmost bin, we want values equal to the right
# edge to be counted in the last bin, and not as an outlier.
for i in xrange(Ndim):
# Find the rounding precision
decimal = int(-np.log10(dedges[i].min())) + 6
# Find which points are on the rightmost edge.
on_edge = np.where(np.around(sample[:, i], decimal) ==
np.around(edges[i][-1], decimal))[0]
# Shift these points one bin to the left.
sampBin[i][on_edge] -= 1
# Compute the sample indices in the flattened statistic matrix.
binnumbers = np.ravel_multi_index(sampBin, nbin)
result = np.empty([Vdim, nbin.prod()], float)
if statistic == 'mean':
result.fill(np.nan)
flatcount = np.bincount(binnumbers, None)
a = flatcount.nonzero()
for vv in xrange(Vdim):
flatsum = np.bincount(binnumbers, values[vv])
result[vv, a] = flatsum[a] / flatcount[a]
elif statistic == 'std':
result.fill(0)
flatcount = np.bincount(binnumbers, None)
a = flatcount.nonzero()
for vv in xrange(Vdim):
flatsum = np.bincount(binnumbers, values[vv])
flatsum2 = np.bincount(binnumbers, values[vv] ** 2)
result[vv, a] = np.sqrt(flatsum2[a] / flatcount[a] -
(flatsum[a] / flatcount[a]) ** 2)
elif statistic == 'count':
result.fill(0)
flatcount = np.bincount(binnumbers, None)
a = np.arange(len(flatcount))
result[:, a] = flatcount[np.newaxis, :]
elif statistic == 'sum':
result.fill(0)
for vv in xrange(Vdim):
flatsum = np.bincount(binnumbers, values[vv])
a = np.arange(len(flatsum))
result[vv, a] = flatsum
elif statistic == 'median':
result.fill(np.nan)
for i in np.unique(binnumbers):
for vv in xrange(Vdim):
result[vv, i] = np.median(values[vv, binnumbers == i])
elif statistic == 'min':
result.fill(np.nan)
for i in np.unique(binnumbers):
for vv in xrange(Vdim):
result[vv, i] = np.min(values[vv, binnumbers == i])
elif statistic == 'max':
result.fill(np.nan)
for i in np.unique(binnumbers):
for vv in xrange(Vdim):
result[vv, i] = np.max(values[vv, binnumbers == i])
elif callable(statistic):
with np.errstate(invalid='ignore'), suppress_warnings() as sup:
sup.filter(RuntimeWarning)
try:
null = statistic([])
except:
null = np.nan
result.fill(null)
for i in np.unique(binnumbers):
for vv in xrange(Vdim):
result[vv, i] = statistic(values[vv, binnumbers == i])
# Shape into a proper matrix
result = result.reshape(np.append(Vdim, nbin))
# Remove outliers (indices 0 and -1 for each bin-dimension).
core = [slice(None)] + Ndim * [slice(1, -1)]
result = result[core]
# Unravel binnumbers into an ndarray, each row the bins for each dimension
if(expand_binnumbers and Ndim > 1):
binnumbers = np.asarray(np.unravel_index(binnumbers, nbin))
if np.any(result.shape[1:] != nbin - 2):
raise RuntimeError('Internal Shape Error')
# Reshape to have output (`reulst`) match input (`values`) shape
result = result.reshape(input_shape[:-1] + list(nbin-2))
return BinnedStatisticddResult(result, edges, binnumbers)
| mit |
jeffery-do/Vizdoombot | doom/lib/python3.5/site-packages/scipy/stats/_stats_mstats_common.py | 12 | 8157 | from collections import namedtuple
import numpy as np
from . import distributions
__all__ = ['_find_repeats', 'linregress', 'theilslopes']
def linregress(x, y=None):
"""
Calculate a linear least-squares regression for two sets of measurements.
Parameters
----------
x, y : array_like
Two sets of measurements. Both arrays should have the same length.
If only x is given (and y=None), then it must be a two-dimensional
array where one dimension has length 2. The two sets of measurements
are then found by splitting the array along the length-2 dimension.
Returns
-------
slope : float
slope of the regression line
intercept : float
intercept of the regression line
rvalue : float
correlation coefficient
pvalue : float
two-sided p-value for a hypothesis test whose null hypothesis is
that the slope is zero.
stderr : float
Standard error of the estimated gradient.
See also
--------
optimize.curve_fit : Use non-linear least squares to fit a function to data.
optimize.leastsq : Minimize the sum of squares of a set of equations.
Examples
--------
>>> from scipy import stats
>>> np.random.seed(12345678)
>>> x = np.random.random(10)
>>> y = np.random.random(10)
>>> slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
# To get coefficient of determination (r_squared)
>>> print("r-squared:", r_value**2)
('r-squared:', 0.080402268539028335)
"""
TINY = 1.0e-20
if y is None: # x is a (2, N) or (N, 2) shaped array_like
x = np.asarray(x)
if x.shape[0] == 2:
x, y = x
elif x.shape[1] == 2:
x, y = x.T
else:
msg = ("If only `x` is given as input, it has to be of shape "
"(2, N) or (N, 2), provided shape was %s" % str(x.shape))
raise ValueError(msg)
else:
x = np.asarray(x)
y = np.asarray(y)
if x.size == 0 or y.size == 0:
raise ValueError("Inputs must not be empty.")
n = len(x)
xmean = np.mean(x, None)
ymean = np.mean(y, None)
# average sum of squares:
ssxm, ssxym, ssyxm, ssym = np.cov(x, y, bias=1).flat
r_num = ssxym
r_den = np.sqrt(ssxm * ssym)
if r_den == 0.0:
r = 0.0
else:
r = r_num / r_den
# test for numerical error propagation
if r > 1.0:
r = 1.0
elif r < -1.0:
r = -1.0
df = n - 2
t = r * np.sqrt(df / ((1.0 - r + TINY)*(1.0 + r + TINY)))
prob = 2 * distributions.t.sf(np.abs(t), df)
slope = r_num / ssxm
intercept = ymean - slope*xmean
sterrest = np.sqrt((1 - r**2) * ssym / ssxm / df)
LinregressResult = namedtuple('LinregressResult', ('slope', 'intercept',
'rvalue', 'pvalue',
'stderr'))
return LinregressResult(slope, intercept, r, prob, sterrest)
def theilslopes(y, x=None, alpha=0.95):
r"""
Computes the Theil-Sen estimator for a set of points (x, y).
`theilslopes` implements a method for robust linear regression. It
computes the slope as the median of all slopes between paired values.
Parameters
----------
y : array_like
Dependent variable.
x : array_like or None, optional
Independent variable. If None, use ``arange(len(y))`` instead.
alpha : float, optional
Confidence degree between 0 and 1. Default is 95% confidence.
Note that `alpha` is symmetric around 0.5, i.e. both 0.1 and 0.9 are
interpreted as "find the 90% confidence interval".
Returns
-------
medslope : float
Theil slope.
medintercept : float
Intercept of the Theil line, as ``median(y) - medslope*median(x)``.
lo_slope : float
Lower bound of the confidence interval on `medslope`.
up_slope : float
Upper bound of the confidence interval on `medslope`.
Notes
-----
The implementation of `theilslopes` follows [1]_. The intercept is
not defined in [1]_, and here it is defined as ``median(y) -
medslope*median(x)``, which is given in [3]_. Other definitions of
the intercept exist in the literature. A confidence interval for
the intercept is not given as this question is not addressed in
[1]_.
References
----------
.. [1] P.K. Sen, "Estimates of the regression coefficient based on Kendall's tau",
J. Am. Stat. Assoc., Vol. 63, pp. 1379-1389, 1968.
.. [2] H. Theil, "A rank-invariant method of linear and polynomial
regression analysis I, II and III", Nederl. Akad. Wetensch., Proc.
53:, pp. 386-392, pp. 521-525, pp. 1397-1412, 1950.
.. [3] W.L. Conover, "Practical nonparametric statistics", 2nd ed.,
John Wiley and Sons, New York, pp. 493.
Examples
--------
>>> from scipy import stats
>>> import matplotlib.pyplot as plt
>>> x = np.linspace(-5, 5, num=150)
>>> y = x + np.random.normal(size=x.size)
>>> y[11:15] += 10 # add outliers
>>> y[-5:] -= 7
Compute the slope, intercept and 90% confidence interval. For comparison,
also compute the least-squares fit with `linregress`:
>>> res = stats.theilslopes(y, x, 0.90)
>>> lsq_res = stats.linregress(x, y)
Plot the results. The Theil-Sen regression line is shown in red, with the
dashed red lines illustrating the confidence interval of the slope (note
that the dashed red lines are not the confidence interval of the regression
as the confidence interval of the intercept is not included). The green
line shows the least-squares fit for comparison.
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> ax.plot(x, y, 'b.')
>>> ax.plot(x, res[1] + res[0] * x, 'r-')
>>> ax.plot(x, res[1] + res[2] * x, 'r--')
>>> ax.plot(x, res[1] + res[3] * x, 'r--')
>>> ax.plot(x, lsq_res[1] + lsq_res[0] * x, 'g-')
>>> plt.show()
"""
# We copy both x and y so we can use _find_repeats.
y = np.array(y).flatten()
if x is None:
x = np.arange(len(y), dtype=float)
else:
x = np.array(x, dtype=float).flatten()
if len(x) != len(y):
raise ValueError("Incompatible lengths ! (%s<>%s)" % (len(y), len(x)))
# Compute sorted slopes only when deltax > 0
deltax = x[:, np.newaxis] - x
deltay = y[:, np.newaxis] - y
slopes = deltay[deltax > 0] / deltax[deltax > 0]
slopes.sort()
medslope = np.median(slopes)
medinter = np.median(y) - medslope * np.median(x)
# Now compute confidence intervals
if alpha > 0.5:
alpha = 1. - alpha
z = distributions.norm.ppf(alpha / 2.)
# This implements (2.6) from Sen (1968)
_, nxreps = _find_repeats(x)
_, nyreps = _find_repeats(y)
nt = len(slopes) # N in Sen (1968)
ny = len(y) # n in Sen (1968)
# Equation 2.6 in Sen (1968):
sigsq = 1/18. * (ny * (ny-1) * (2*ny+5) -
np.sum(k * (k-1) * (2*k + 5) for k in nxreps) -
np.sum(k * (k-1) * (2*k + 5) for k in nyreps))
# Find the confidence interval indices in `slopes`
sigma = np.sqrt(sigsq)
Ru = min(int(np.round((nt - z*sigma)/2.)), len(slopes)-1)
Rl = max(int(np.round((nt + z*sigma)/2.)) - 1, 0)
delta = slopes[[Rl, Ru]]
return medslope, medinter, delta[0], delta[1]
def _find_repeats(arr):
# This function assumes it may clobber its input.
if len(arr) == 0:
return np.array(0, np.float64), np.array(0, np.intp)
# XXX This cast was previously needed for the Fortran implementation,
# should we ditch it?
arr = np.asarray(arr, np.float64).ravel()
arr.sort()
# Taken from NumPy 1.9's np.unique.
change = np.concatenate(([True], arr[1:] != arr[:-1]))
unique = arr[change]
change_idx = np.concatenate(np.nonzero(change) + ([arr.size],))
freq = np.diff(change_idx)
atleast2 = freq > 1
return unique[atleast2], freq[atleast2]
| mit |
vshtanko/scikit-learn | examples/cluster/plot_agglomerative_clustering_metrics.py | 402 | 4492 | """
Agglomerative clustering with different metrics
===============================================
Demonstrates the effect of different metrics on the hierarchical clustering.
The example is engineered to show the effect of the choice of different
metrics. It is applied to waveforms, which can be seen as
high-dimensional vector. Indeed, the difference between metrics is
usually more pronounced in high dimension (in particular for euclidean
and cityblock).
We generate data from three groups of waveforms. Two of the waveforms
(waveform 1 and waveform 2) are proportional one to the other. The cosine
distance is invariant to a scaling of the data, as a result, it cannot
distinguish these two waveforms. Thus even with no noise, clustering
using this distance will not separate out waveform 1 and 2.
We add observation noise to these waveforms. We generate very sparse
noise: only 6% of the time points contain noise. As a result, the
l1 norm of this noise (ie "cityblock" distance) is much smaller than it's
l2 norm ("euclidean" distance). This can be seen on the inter-class
distance matrices: the values on the diagonal, that characterize the
spread of the class, are much bigger for the Euclidean distance than for
the cityblock distance.
When we apply clustering to the data, we find that the clustering
reflects what was in the distance matrices. Indeed, for the Euclidean
distance, the classes are ill-separated because of the noise, and thus
the clustering does not separate the waveforms. For the cityblock
distance, the separation is good and the waveform classes are recovered.
Finally, the cosine distance does not separate at all waveform 1 and 2,
thus the clustering puts them in the same cluster.
"""
# Author: Gael Varoquaux
# License: BSD 3-Clause or CC-0
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics import pairwise_distances
np.random.seed(0)
# Generate waveform data
n_features = 2000
t = np.pi * np.linspace(0, 1, n_features)
def sqr(x):
return np.sign(np.cos(x))
X = list()
y = list()
for i, (phi, a) in enumerate([(.5, .15), (.5, .6), (.3, .2)]):
for _ in range(30):
phase_noise = .01 * np.random.normal()
amplitude_noise = .04 * np.random.normal()
additional_noise = 1 - 2 * np.random.rand(n_features)
# Make the noise sparse
additional_noise[np.abs(additional_noise) < .997] = 0
X.append(12 * ((a + amplitude_noise)
* (sqr(6 * (t + phi + phase_noise)))
+ additional_noise))
y.append(i)
X = np.array(X)
y = np.array(y)
n_clusters = 3
labels = ('Waveform 1', 'Waveform 2', 'Waveform 3')
# Plot the ground-truth labelling
plt.figure()
plt.axes([0, 0, 1, 1])
for l, c, n in zip(range(n_clusters), 'rgb',
labels):
lines = plt.plot(X[y == l].T, c=c, alpha=.5)
lines[0].set_label(n)
plt.legend(loc='best')
plt.axis('tight')
plt.axis('off')
plt.suptitle("Ground truth", size=20)
# Plot the distances
for index, metric in enumerate(["cosine", "euclidean", "cityblock"]):
avg_dist = np.zeros((n_clusters, n_clusters))
plt.figure(figsize=(5, 4.5))
for i in range(n_clusters):
for j in range(n_clusters):
avg_dist[i, j] = pairwise_distances(X[y == i], X[y == j],
metric=metric).mean()
avg_dist /= avg_dist.max()
for i in range(n_clusters):
for j in range(n_clusters):
plt.text(i, j, '%5.3f' % avg_dist[i, j],
verticalalignment='center',
horizontalalignment='center')
plt.imshow(avg_dist, interpolation='nearest', cmap=plt.cm.gnuplot2,
vmin=0)
plt.xticks(range(n_clusters), labels, rotation=45)
plt.yticks(range(n_clusters), labels)
plt.colorbar()
plt.suptitle("Interclass %s distances" % metric, size=18)
plt.tight_layout()
# Plot clustering results
for index, metric in enumerate(["cosine", "euclidean", "cityblock"]):
model = AgglomerativeClustering(n_clusters=n_clusters,
linkage="average", affinity=metric)
model.fit(X)
plt.figure()
plt.axes([0, 0, 1, 1])
for l, c in zip(np.arange(model.n_clusters), 'rgbk'):
plt.plot(X[model.labels_ == l].T, c=c, alpha=.5)
plt.axis('tight')
plt.axis('off')
plt.suptitle("AgglomerativeClustering(affinity=%s)" % metric, size=20)
plt.show()
| bsd-3-clause |
bmazin/ARCONS-pipeline | examples/Pal2014-J0337/hTestLimit.py | 1 | 8356 | #Filename: hTestLimit.py
#Author: Matt Strader
#
#This script opens a list of observed photon phases,
import numpy as np
import tables
import numexpr
import matplotlib.pyplot as plt
import multiprocessing
import functools
import time
from kuiper.kuiper import kuiper,kuiper_FPP
from kuiper.htest import h_test,h_fpp,h_test2
from pulsarUtils import nSigma,plotPulseProfile
from histMetrics import kuiperFpp,hTestFpp
from inverseTransformSampling import inverseTransformSampler
def hTestTrial(iTrial,nPhotons,photonPulseFraction,pulseModel,pulseModelQueryPoints):
np.random.seed(int((time.time()+iTrial)*1e6))
modelSampler = inverseTransformSampler(pdf=pulseModel,queryPoints=pulseModelQueryPoints)
nPulsePhotons = int(np.floor(photonPulseFraction*nPhotons))
nBackgroundPhotons = int(np.ceil((1.-photonPulseFraction) * nPhotons))
simPulsePhotons = modelSampler(nPulsePhotons)
#background photons come from a uniform distribution
simBackgroundPhotons = np.random.random(nBackgroundPhotons)
simPhases = np.append(simPulsePhotons,simBackgroundPhotons)
simHDict = h_test2(simPhases)
simH,simM,simPval,simFourierCoeffs = simHDict['H'],simHDict['M'],simHDict['fpp'],simHDict['cs']
print '{} - H,M,fpp,sig:'.format(iTrial),simH,simM,simPval
return {'H':simH,'M':simM,'fpp':simPval}
if __name__=='__main__':
path = '/Scratch/dataProcessing/J0337/masterPhotons3.h5'
wvlStart = 4000.
wvlEnd = 5500.
bLoadFromPl = True
nPhaseBins = 20
hTestPath = '/Scratch/dataProcessing/J0337/hTestResults_withProfiles_{}-{}.npz'.format(wvlStart,wvlEnd)
phaseBinEdges = np.linspace(0.,1.,nPhaseBins+1)
if bLoadFromPl:
photFile = tables.openFile(path,'r')
photTable = photFile.root.photons.photTable
phases = photTable.readWhere('(wvlStart < wavelength) & (wavelength < wvlEnd)')['phase']
photFile.close()
print 'cut wavelengths to range ({},{})'.format(wvlStart,wvlEnd)
nPhotons = len(phases)
print nPhotons,'real photons read'
observedProfile,_ = np.histogram(phases,bins=phaseBinEdges)
observedProfile = 1.0*observedProfile
observedProfileErrors = np.sqrt(observedProfile)
#Do H-test
hDict = h_test2(phases)
H,M,pval,fourierCoeffs = hDict['H'],hDict['M'],hDict['fpp'],hDict['cs']
print 'h-test on real data'
print 'H,M,fpp:',H,M,pval
print nSigma(1-pval),'sigmas'
#h_test2 calculates all fourierCoeffs out to 20, but for the fourier model, we only want the ones out to order M, which optimizes the Zm^2 metric
truncatedFourierCoeffs = fourierCoeffs[0:M]
print 'fourier coeffs:',truncatedFourierCoeffs
#for the model, we want the negative modes as well as positve, so add them
modelFourierCoeffs = np.concatenate([truncatedFourierCoeffs[::-1],[1.],np.conj(truncatedFourierCoeffs)])
#make array of mode numbers
modes = np.arange(-len(truncatedFourierCoeffs),len(truncatedFourierCoeffs)+1)
#save so next time we can set bLoadFromPl=False
np.savez(hTestPath,H=H,M=M,pval=pval,fourierCoeffs=fourierCoeffs,nPhotons=nPhotons,wvlRange=(wvlStart,wvlEnd),modelFourierCoeffs=modelFourierCoeffs,modes=modes,observedProfile=observedProfile,observedProfileErrors=observedProfileErrors,phaseBinEdges=phaseBinEdges)
else:
#Load values from previous run, when we had bLoadFromPl=True
hTestDict = np.load(hTestPath)
H,M,pval,fourierCoeffs,nPhotons,modelFourierCoeffs,modes = hTestDict['H'],hTestDict['M'],hTestDict['pval'],hTestDict['fourierCoeffs'],hTestDict['nPhotons'],hTestDict['modelFourierCoeffs'],hTestDict['modes']
observedProfile,observedProfileErrors,phaseBinEdges = hTestDict['observedProfile'],hTestDict['observedProfileErrors'],hTestDict['phaseBinEdges']
print 'h-test on real data'
print 'H,M,fpp:',H,M,pval
print nSigma(1-pval),'sigmas'
#Plot the observed profile
fig,ax = plt.subplots(1,1)
plotPulseProfile(phaseBinEdges,observedProfile,profileErrors=observedProfileErrors,color='k',plotDoublePulse=False,label='observed',ax=ax)
ax.set_ylabel('counts')
ax.set_xlabel('phase')
ax.set_title('Observed Folded Light Curve {}-{} nm'.format(wvlStart/10.,wvlEnd/10.))
#make as set of x points for the pulse model we'll make
#Do NOT include x=0, or the inverted function will have a jump that causes an excess of samples
#at phase=0
nSmoothPlotPoints=1000
pulseModelQueryPoints = np.linspace(1./nSmoothPlotPoints,1,nSmoothPlotPoints)
def modelProfile(thetas):
return np.sum( modelFourierCoeffs * np.exp(2.j*np.pi*modes*thetas[:,np.newaxis]),axis=1)
lightCurveModel = np.abs(modelProfile(pulseModelQueryPoints))
#for this test we only want the model to be the pulsed component. We will add a DC offset later
pulseModel = lightCurveModel - np.min(lightCurveModel)
#initialPhotonPulseFraction = 1.*np.sum(pulseModel) / np.sum(lightCurveModel)
photonPulseFraction=15400./nPhotons #skip to previously determined answer
print 'photon fraction',photonPulseFraction
#get samples with distribution of the modelProfile
#modelSampler = inverseTransformSampler(pdf=lightCurveModel,queryPoints=pulseModelQueryPoints)
modelSampler = inverseTransformSampler(pdf=pulseModel,queryPoints=pulseModelQueryPoints)
nTrials = 1
#for each trial run the h test on a set of photon phases with our model profile, and with the pulse fraction specified
#we want to make a distribution of H values for this pulse fraction, model, and number of photons
#make a function that only takes the trial number (as an identifier)
mappableHTestTrial = functools.partial(hTestTrial,pulseModel=pulseModel,
pulseModelQueryPoints=pulseModelQueryPoints,nPhotons=nPhotons,
photonPulseFraction=photonPulseFraction)
pool = multiprocessing.Pool(processes=multiprocessing.cpu_count()-3)#leave a few processors for other people
outDicts = pool.map(mappableHTestTrial,np.arange(nTrials))
simHs = np.array([out['H'] for out in outDicts])
simPvals = np.array([out['fpp'] for out in outDicts])
#save the resulting list of H vals
np.savez('sim3-h-{}.npz'.format(nTrials),simHs=simHs,simPvals=simPvals,pval=pval,H=H,photonPulseFraction=photonPulseFraction,nPhotons=nPhotons)
#make a model profile once more for a plot
modelSampler = inverseTransformSampler(pdf=pulseModel,queryPoints=pulseModelQueryPoints)
nPulsePhotons = int(np.floor(photonPulseFraction*nPhotons))
nBackgroundPhotons = int(np.ceil((1.-photonPulseFraction) * nPhotons))
simPulsePhotons = modelSampler(nPulsePhotons)
#background photons come from a uniform distribution
simBackgroundPhotons = np.random.random(nBackgroundPhotons)
#put them together for the full profile
simPhases = np.append(simPulsePhotons,simBackgroundPhotons)
#make a binned phase profile to plot
simProfile,_ = np.histogram(simPhases,bins=phaseBinEdges)
simProfileErrors = np.sqrt(simProfile)#assume Poisson errors
meanLevel = np.mean(simProfile)
fig,ax = plt.subplots(1,1)
ax.plot(pulseModelQueryPoints,meanLevel*lightCurveModel,color='r')
plotPulseProfile(phaseBinEdges,simProfile,profileErrors=simProfileErrors,color='b',plotDoublePulse=False,label='sim',ax=ax)
ax.set_title('Simulated profile')
#
#plt.show()
print '{} trials'.format(len(simHs))
print 'observed fpp:',pval
frac = 1.*np.sum(simPvals<pval)/len(simPvals)
print 'fraction of trials with H below observed fpp:',frac
#hHist,hBinEdges = np.histogram(simHs,bins=100,density=True)
fppHist,fppBinEdges = np.histogram(simPvals,bins=100,density=True)
if nTrials > 1:
fig,ax = plt.subplots(1,1)
ax.plot(fppBinEdges[0:-1],fppHist,drawstyle='steps-post',color='k')
ax.axvline(pval,color='r')
ax.set_xlabel('fpp')
ax.set_ylabel('frequency')
ax.set_title('Distribution of H for model profile')
magG = 17.93
sineMagDiff = -2.5*np.log10(photonPulseFraction)
print 'SDSS magnitude g: {:.2f}'.format(magG)
print 'magnitude difference: {:.2f}'.format(sineMagDiff)
print 'limiting g mag: {:.2f}'.format(magG+sineMagDiff)
plt.show()
| gpl-2.0 |
stefco/geco_data | geco_irig_plot.py | 1 | 5662 | #!/usr/bin/env python
# (c) Stefan Countryman, 2016-2017
DESC="""Plot an IRIG-B signal read from stdin. Assumes that the timeseries
is a sequence of newline-delimited float literals."""
FAST_CHANNEL_BITRATE = 16384 # for IRIG-B, DuoTone, etc.
# THE REST OF THE IMPORTS ARE AFTER THIS IF STATEMENT.
# Quits immediately on --help or -h flags to skip slow imports when you just
# want to read the help documentation.
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description=DESC)
# TODO: make this -i and --ifo instead of detector.
parser.add_argument("--detector",
help=("the detector; used in the title of the output "
"plot"))
parser.add_argument("-O", "--outfile",
help="the filename of the generated plot")
parser.add_argument("-T", "--timeseries",
help="copy from stdin to stdout while reading",
action="store_true")
parser.add_argument("-A", "--actualtime",
help=("actual time signal was recorded "
"(appears in title)"))
args = parser.parse_args()
# Force matplotlib to not use any Xwindows backend. NECESSARY ON CLUSTER.
import matplotlib
matplotlib.use('Agg')
import sys
import time
import numpy as np
import matplotlib.pyplot as plt
import geco_irig_decode
def read_timeseries_stdin(num_lines, cat_to_stdout=False):
"""Read in newline-delimited numerical data from stdin; don't read more
than a second worth of data. If cat_to_stdout is True, print data that
has been read in back to stdout (useful for piped commands)."""
timeseries = np.zeros(num_lines)
line = ""
i = 0
while i < num_lines:
line = float(sys.stdin.readline())
timeseries[i] = line
if cat_to_stdout:
print(line)
i += 1
return timeseries
def irigb_decoded_title(timeseries, IFO=None, actual_time=None):
"""Get a title for an IRIG-B timeseries plot that includes the decoded
time in the timeseries itself."""
# get the detector name
if IFO is None:
detector_suffix = ""
else:
detector_suffix = " at " + IFO
# get the actual time of recording, if provided
if actual_time is None:
actual_time_str = ""
else:
actual_time_str = "\nActual Time: {}".format(actual_time)
# add title and so on
try:
decoded_time = geco_irig_decode.get_date_from_timeseries(timeseries)
decoded_time_str = decoded_time.strftime('%a %b %d %X %Y')
except ValueError as e:
decoded_time_str = "COULD NOT DECODE TIME"
fmt = "One Second of IRIG-B Signal{}\nDecoded Time: {}{}"
return fmt.format(detector_suffix, decoded_time_str, actual_time_str)
def irigb_output_filename(outfile=None):
"""Get the output filename for an IRIG-B plot."""
if outfile is None:
output_filename = "irigb-plot-made-at-" + str(time.time()) + ".png"
else:
output_filename = outfile
# append .png if not already there
if output_filename.split(".")[-1] != "png":
output_filename += ".png"
return output_filename
def plot_with_zoomed_views(timeseries, title, num_subdivs=5, dt=1.,
output_filename=None, overlay=False, linewidth=1):
"""Plot a timeseries and produce num_subdivs subplots that show equal-sized
subdivisions of the full timeseries data to show details (good for
high-bitrate timeseries). If you want to keep plotting data to the same
figure, set 'overlay=True', and the current figure will be plotted to."""
bitrate = int(len(timeseries) / float(dt))
times = np.linspace(0, 1, num=bitrate, endpoint=False)
# find max and min values in timeseries; use these to set plot boundaries
yrange = timeseries.max() - timeseries.min()
ymax = timeseries.max() + 0.1*yrange
ymin = timeseries.min() - 0.1*yrange
if not overlay:
plt.figure()
# print("making plot")
plt.gcf().set_figwidth(7)
plt.gcf().set_figheight(4+1.2*num_subdivs) # ~1.2in height per zoomed plot
# plot the full second on the first row; lines should be black ('k' option).
plt.subplot(num_subdivs + 1, 1, 1)
plt.ylim(ymin, ymax)
plt.plot(times, timeseries, 'k', linewidth=linewidth)
plt.tick_params(axis='y', labelsize='small')
# make num_subdivs subplots to better show the full second
for i in range(num_subdivs):
# print("making plot " + str(i))
plt.subplot(num_subdivs+1, 1, i+2)
plt.ylim(ymin, ymax)
plt.xlim(float(i)/num_subdivs, (float(i)+1)/num_subdivs)
start = bitrate*i // num_subdivs
end = bitrate*(i+1) // num_subdivs
plt.plot(times[start:end], timeseries[start:end], 'k',
linewidth=linewidth)
plt.tick_params(axis='y', labelsize='small')
plt.suptitle(title)
plt.xlabel("Time since start of second [$s$]")
# print("saving plot")
plt.subplots_adjust(left=0.125, right=0.9, bottom=0.1, top=0.9, wspace=0.2,
hspace=0.5)
if not (output_filename is None):
plt.savefig(output_filename)
return plt
if __name__ == '__main__':
timeseries = read_timeseries_stdin(FAST_CHANNEL_BITRATE,
cat_to_stdout=args.timeseries)
title = irigb_decoded_title(timeseries, args.detector, args.actualtime)
output_filename = irigb_output_filename(args.outfile)
plot_with_zoomed_views(timeseries, title, num_subdivs=5, dt=1.,
output_filename=output_filename)
| mit |
olologin/scikit-learn | examples/svm/plot_iris.py | 225 | 3252 | """
==================================================
Plot different SVM classifiers in the iris dataset
==================================================
Comparison of different linear SVM classifiers on a 2D projection of the iris
dataset. We only consider the first 2 features of this dataset:
- Sepal length
- Sepal width
This example shows how to plot the decision surface for four SVM classifiers
with different kernels.
The linear models ``LinearSVC()`` and ``SVC(kernel='linear')`` yield slightly
different decision boundaries. This can be a consequence of the following
differences:
- ``LinearSVC`` minimizes the squared hinge loss while ``SVC`` minimizes the
regular hinge loss.
- ``LinearSVC`` uses the One-vs-All (also known as One-vs-Rest) multiclass
reduction while ``SVC`` uses the One-vs-One multiclass reduction.
Both linear models have linear decision boundaries (intersecting hyperplanes)
while the non-linear kernel models (polynomial or Gaussian RBF) have more
flexible non-linear decision boundaries with shapes that depend on the kind of
kernel and its parameters.
.. NOTE:: while plotting the decision function of classifiers for toy 2D
datasets can help get an intuitive understanding of their respective
expressive power, be aware that those intuitions don't always generalize to
more realistic high-dimensional problems.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
y = iris.target
h = .02 # step size in the mesh
# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
C = 1.0 # SVM regularization parameter
svc = svm.SVC(kernel='linear', C=C).fit(X, y)
rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X, y)
poly_svc = svm.SVC(kernel='poly', degree=3, C=C).fit(X, y)
lin_svc = svm.LinearSVC(C=C).fit(X, y)
# create a mesh to plot in
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
# title for the plots
titles = ['SVC with linear kernel',
'LinearSVC (linear kernel)',
'SVC with RBF kernel',
'SVC with polynomial (degree 3) kernel']
for i, clf in enumerate((svc, lin_svc, rbf_svc, poly_svc)):
# 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(2, 2, i + 1)
plt.subplots_adjust(wspace=0.4, hspace=0.4)
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.title(titles[i])
plt.show()
| bsd-3-clause |
mnip91/proactive-component-monitoring | dev/scripts/perf/perf_graph.py | 12 | 2516 | #!/usr/bin/env python
import sys
import os
import string
import numpy as np
import matplotlib.pyplot as plt
import re
def main():
dir = sys.argv[1]
if len(sys.argv) == 1:
dict = create_dict(dir)
draw_graph(dict)
else:
for i in range(2, len(sys.argv)):
dict = create_dict(dir, sys.argv[i])
draw_graph(dict, sys.argv[i])
def create_dict(rootdir, match='.*'):
pattern = re.compile(match)
dict = {}
for branch in os.listdir(rootdir):
branch_dict = {}
for test in os.listdir(os.path.join(rootdir, branch)):
if pattern.match(test):
file = open(os.path.join(rootdir, branch, test))
str = file.readline()
str = str.strip()
start = str.find("=")
if start != -1:
branch_dict[test] = round(string.atof(str[start+1:]),2)
else:
branch_dict[test] = -1.
dict[branch] = branch_dict
return dict
def get_all_test_name(dict):
for branch in dict:
return dict[branch].keys()
def get_branches(dict):
return dict.keys()
def compare_by_branch(dict):
def local_print(test, d):
print test
for t in d:
print "\t" + t + "\t" + str(d[t])
print
for test in get_all_test_name(dict):
local_dict = {}
for branch in dict:
local_dict[branch] = dict[branch][test]
local_print(test, local_dict)
### Unused ###
def short_test_name(long_name):
return long_name[long_name.rfind('.Test')+5:]
def draw_graph(dict, title):
def autolabel(rects):
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x()+rect.get_width()/2., 1.05*height, '%d'%int(height),
ha='center', va='bottom')
def set_legend(bars, branches):
bs = ()
for bar in bars:
bs = bs + (bar[0],)
ax.legend( bs, branches)
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'b']
branches = get_branches(dict)
all_tests = get_all_test_name(dict)
N = len(all_tests)
ind = np.arange(N)
width = 0.35
fig = plt.figure()
ax = fig.add_subplot(111)
data_sets = []
for branch in branches:
data =()
for test in all_tests:
data = data + (dict[branch].get(test, 0),)
data_sets.append(data)
bars = []
counter = 0
for data in data_sets:
bar = ax.bar(ind + (counter*width), data, width, color=colors[counter])
bars.append(bar)
counter += 1
# add some
ax.set_ylabel('Perf')
ax.set_title('Branch perf comparison for ' + title)
ax.set_xticks(ind+width)
ax.set_xticklabels(map(short_test_name, all_tests))
set_legend(bars, branches)
for bar in bars:
autolabel(bar)
plt.savefig(title + ".png")
if __name__ == "__main__":
main()
| agpl-3.0 |
rgerkin/pyNeuroML | pyneuroml/tune/NeuroMLSimulation.py | 1 | 5357 | '''
A class for running a single instance of a NeuroML model by generating a
LEMS file and using pyNeuroML to run in a chosen simulator
'''
import sys
import time
from pyneuroml import pynml
from pyneuroml.lems import generate_lems_file_for_neuroml
try:
import pyelectro # Not used here, just for checking installation
except:
print('>> Note: pyelectro from https://github.com/pgleeson/pyelectro is required!')
exit()
try:
import neurotune # Not used here, just for checking installation
except:
print('>> Note: neurotune from https://github.com/pgleeson/neurotune is required!')
exit()
class NeuroMLSimulation(object):
def __init__(self,
reference,
neuroml_file,
target,
sim_time=1000,
dt=0.05,
simulator='jNeuroML',
generate_dir = './',
cleanup = True,
nml_doc = None):
self.sim_time = sim_time
self.dt = dt
self.simulator = simulator
self.generate_dir = generate_dir if generate_dir.endswith('/') else generate_dir+'/'
self.reference = reference
self.target = target
self.neuroml_file = neuroml_file
self.nml_doc = nml_doc
self.cleanup = cleanup
self.already_run = False
def show(self):
"""
Plot the result of the simulation once it's been intialized
"""
from matplotlib import pyplot as plt
if self.already_run:
for ref in self.volts.keys():
plt.plot(self.t, self.volts[ref], label=ref)
plt.title("Simulation voltage vs time")
plt.legend()
plt.xlabel("Time [ms]")
plt.ylabel("Voltage [mV]")
else:
pynml.print_comment("First you have to 'go()' the simulation.", True)
plt.show()
def go(self):
lems_file_name = 'LEMS_%s.xml'%(self.reference)
generate_lems_file_for_neuroml(self.reference,
self.neuroml_file,
self.target,
self.sim_time,
self.dt,
lems_file_name = lems_file_name,
target_dir = self.generate_dir,
nml_doc = self.nml_doc)
pynml.print_comment_v("Running a simulation of %s ms with timestep %s ms: %s"%(self.sim_time, self.dt, lems_file_name))
self.already_run = True
start = time.time()
if self.simulator == 'jNeuroML':
results = pynml.run_lems_with_jneuroml(lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
exec_in_dir = self.generate_dir,
verbose=False,
cleanup=self.cleanup)
elif self.simulator == 'jNeuroML_NEURON':
results = pynml.run_lems_with_jneuroml_neuron(lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
exec_in_dir = self.generate_dir,
verbose=False,
cleanup=self.cleanup)
else:
pynml.print_comment_v('Unsupported simulator: %s'%self.simulator)
exit()
secs = time.time()-start
pynml.print_comment_v("Ran simulation in %s in %f seconds (%f mins)\n\n"%(self.simulator, secs, secs/60.0))
self.t = [t*1000 for t in results['t']]
self.volts = {}
for key in results.keys():
if key != 't':
self.volts[key] = [v*1000 for v in results[key]]
if __name__ == '__main__':
sim_time = 700
dt = 0.05
if len(sys.argv) == 2 and sys.argv[1] == '-net':
sim = NeuroMLSimulation('TestNet',
'../../examples/test_data/simplenet.nml',
'simplenet',
sim_time,
dt,
'jNeuroML',
'temp/')
sim.go()
sim.show()
else:
sim = NeuroMLSimulation('TestHH',
'../../examples/test_data/HHCellNetwork.net.nml',
'HHCellNetwork',
sim_time,
dt,
'jNeuroML',
'temp')
sim.go()
sim.show()
| lgpl-3.0 |
0x0all/scikit-learn | examples/plot_multioutput_face_completion.py | 330 | 3019 | """
==============================================
Face completion with a multi-output estimators
==============================================
This example shows the use of multi-output estimator to complete images.
The goal is to predict the lower half of a face given its upper half.
The first column of images shows true faces. The next columns illustrate
how extremely randomized trees, k nearest neighbors, linear
regression and ridge regression complete the lower half of those faces.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_olivetti_faces
from sklearn.utils.validation import check_random_state
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import RidgeCV
# Load the faces datasets
data = fetch_olivetti_faces()
targets = data.target
data = data.images.reshape((len(data.images), -1))
train = data[targets < 30]
test = data[targets >= 30] # Test on independent people
# Test on a subset of people
n_faces = 5
rng = check_random_state(4)
face_ids = rng.randint(test.shape[0], size=(n_faces, ))
test = test[face_ids, :]
n_pixels = data.shape[1]
X_train = train[:, :np.ceil(0.5 * n_pixels)] # Upper half of the faces
y_train = train[:, np.floor(0.5 * n_pixels):] # Lower half of the faces
X_test = test[:, :np.ceil(0.5 * n_pixels)]
y_test = test[:, np.floor(0.5 * n_pixels):]
# Fit estimators
ESTIMATORS = {
"Extra trees": ExtraTreesRegressor(n_estimators=10, max_features=32,
random_state=0),
"K-nn": KNeighborsRegressor(),
"Linear regression": LinearRegression(),
"Ridge": RidgeCV(),
}
y_test_predict = dict()
for name, estimator in ESTIMATORS.items():
estimator.fit(X_train, y_train)
y_test_predict[name] = estimator.predict(X_test)
# Plot the completed faces
image_shape = (64, 64)
n_cols = 1 + len(ESTIMATORS)
plt.figure(figsize=(2. * n_cols, 2.26 * n_faces))
plt.suptitle("Face completion with multi-output estimators", size=16)
for i in range(n_faces):
true_face = np.hstack((X_test[i], y_test[i]))
if i:
sub = plt.subplot(n_faces, n_cols, i * n_cols + 1)
else:
sub = plt.subplot(n_faces, n_cols, i * n_cols + 1,
title="true faces")
sub.axis("off")
sub.imshow(true_face.reshape(image_shape),
cmap=plt.cm.gray,
interpolation="nearest")
for j, est in enumerate(sorted(ESTIMATORS)):
completed_face = np.hstack((X_test[i], y_test_predict[est][i]))
if i:
sub = plt.subplot(n_faces, n_cols, i * n_cols + 2 + j)
else:
sub = plt.subplot(n_faces, n_cols, i * n_cols + 2 + j,
title=est)
sub.axis("off")
sub.imshow(completed_face.reshape(image_shape),
cmap=plt.cm.gray,
interpolation="nearest")
plt.show()
| bsd-3-clause |
offbye/paparazzi | sw/tools/calibration/calibrate_gyro.py | 87 | 4686 | #! /usr/bin/env python
# Copyright (C) 2010 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.
#
#
# calibrate gyrometers using turntable measurements
#
from __future__ import print_function, division
from optparse import OptionParser
import os
import sys
from scipy import linspace, polyval, stats
import matplotlib.pyplot as plt
import calibration_utils
#
# lisa 3
# p : a=-4511.16 b=31948.34, std error= 0.603
# q : a=-4598.46 b=31834.48, std error= 0.734
# r : a=-4525.63 b=32687.95, std error= 0.624
#
# lisa 4
# p : a=-4492.05 b=32684.94, std error= 0.600
# q : a=-4369.63 b=33260.96, std error= 0.710
# r : a=-4577.13 b=32707.72, std error= 0.730
#
# crista
# p : a= 3864.82 b=31288.09, std error= 0.866
# q : a= 3793.71 b=32593.89, std error= 3.070
# r : a= 3817.11 b=32709.70, std error= 3.296
#
def main():
usage = "usage: %prog --id <ac_id> --tt_id <tt_id> --axis <axis> [options] log_filename.data" + "\n" + "Run %prog --help to list the options."
parser = OptionParser(usage)
parser.add_option("-i", "--id", dest="ac_id",
action="store", type=int, default=-1,
help="aircraft id to use")
parser.add_option("-t", "--tt_id", dest="tt_id",
action="store", type=int, default=-1,
help="turntable id to use")
parser.add_option("-a", "--axis", dest="axis",
type="choice", choices=['p', 'q', 'r'],
help="axis to calibrate (p, q, r)",
action="store")
parser.add_option("-v", "--verbose",
action="store_true", dest="verbose")
(options, args) = parser.parse_args()
if len(args) != 1:
parser.error("incorrect number of arguments")
else:
if os.path.isfile(args[0]):
filename = args[0]
else:
print(args[0] + " not found")
sys.exit(1)
if not filename.endswith(".data"):
parser.error("Please specify a *.data log file")
if options.ac_id < 0 or options.ac_id > 255:
parser.error("Specify a valid aircraft id number!")
if options.tt_id < 0 or options.tt_id > 255:
parser.error("Specify a valid turntable id number!")
if options.verbose:
print("reading file "+filename+" for aircraft "+str(options.ac_id)+" and turntable "+str(options.tt_id))
samples = calibration_utils.read_turntable_log(options.ac_id, options.tt_id, filename, 1, 7)
if len(samples) == 0:
print("Error: found zero matching messages in log file!")
print("Was looking for IMU_TURNTABLE from id: "+str(options.tt_id)+" and IMU_GYRO_RAW from id: "+str(options.ac_id)+" in file "+filename)
sys.exit(1)
if options.verbose:
print("found "+str(len(samples))+" records")
if options.axis == 'p':
axis_idx = 1
elif options.axis == 'q':
axis_idx = 2
elif options.axis == 'r':
axis_idx = 3
else:
parser.error("Specify a valid axis!")
#Linear regression using stats.linregress
t = samples[:, 0]
xn = samples[:, axis_idx]
(a_s, b_s, r, tt, stderr) = stats.linregress(t, xn)
print('Linear regression using stats.linregress')
print(('regression: a=%.2f b=%.2f, std error= %.3f' % (a_s, b_s, stderr)))
print(('<define name="GYRO_X_NEUTRAL" value="%d"/>' % (b_s)))
print(('<define name="GYRO_X_SENS" value="%f" integer="16"/>' % (pow(2, 12)/a_s)))
#
# overlay fited value
#
ovl_omega = linspace(1, 7.5, 10)
ovl_adc = polyval([a_s, b_s], ovl_omega)
plt.title('Linear Regression Example')
plt.subplot(3, 1, 1)
plt.plot(samples[:, 1])
plt.plot(samples[:, 2])
plt.plot(samples[:, 3])
plt.legend(['p', 'q', 'r'])
plt.subplot(3, 1, 2)
plt.plot(samples[:, 0])
plt.subplot(3, 1, 3)
plt.plot(samples[:, 0], samples[:, axis_idx], 'b.')
plt.plot(ovl_omega, ovl_adc, 'r')
plt.show()
if __name__ == "__main__":
main()
| gpl-2.0 |
stevenwudi/Kernelized_Correlation_Filter | CNN_training.py | 1 | 3640 | import numpy as np
from keras.optimizers import SGD
from models.CNN_CIFAR import cnn_cifar_batchnormalisation, cnn_cifar_small, cnn_cifar_nodropout, \
cnn_cifar_small_batchnormalisation
from models.DataLoader import DataLoader
from scripts.progress_bar import printProgress
from time import time, localtime
# this is a predefined dataloader
loader = DataLoader(batch_size=32)
# construct the model here (pre-defined model)
model = cnn_cifar_small_batchnormalisation(loader.image_shape)
print(model.name)
nb_epoch = 200
early_stopping = True
early_stopping_count = 0
early_stopping_wait = 3
train_loss = []
valid_loss = []
learning_rate = [0.0001, 0.001, 0.01]
# let's train the model using SGD + momentum (how original).
sgd = SGD(lr=learning_rate[-1], decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer=sgd)
# load validation data from the h5py file (heavy lifting here)
x_valid, y_valid = loader.get_valid()
best_valid = np.inf
for e in range(nb_epoch):
print("epoch %d" % e)
loss_list = []
time_list = []
time_start = time()
for i in range(loader.n_iter_train):
time_start_batch = time()
X_batch, Y_batch = loader.next_train_batch()
loss_list.append(model.train_on_batch(X_batch, Y_batch))
# calculate some time information
time_list.append(time() - time_start_batch)
eta = (loader.n_iter_train - i) * np.array(time_list).mean()
printProgress(i, loader.n_iter_train-1, prefix='Progress:', suffix='batch error: %0.5f, ETA: %0.2f sec.'%(np.array(loss_list).mean(), eta), barLength=50)
printProgress(i, loader.n_iter_train - 1, prefix='Progress:', suffix='batch error: %0.5f' % (np.array(loss_list).mean()), barLength=50)
train_loss.append(np.asarray(loss_list).mean())
print('training loss is %f, one epoch uses: %0.2f sec' % (train_loss[-1], time() - time_start))
valid_loss.append(model.evaluate(x_valid, y_valid))
print('valid loss is %f' % valid_loss[-1])
if best_valid > valid_loss[-1]:
early_stopping_count = 0
print('saving best valid result...')
best_valid = valid_loss[-1]
model.save('./models/CNN_Model_OBT100_multi_cnn_best_valid_'+model.name+'.h5')
else:
# we wait for early stopping loop until a certain time
early_stopping_count += 1
if early_stopping_count > early_stopping_wait:
early_stopping_count = 0
if len(learning_rate) > 1:
learning_rate.pop()
print('decreasing the learning rate to: %f'%learning_rate[-1])
model.optimizer.lr.set_value(learning_rate[-1])
else:
break
lt = localtime()
lt_str = str(lt.tm_year)+"."+str(lt.tm_mon).zfill(2)+"." \
+str(lt.tm_mday).zfill(2)+"."+str(lt.tm_hour).zfill(2)+"."\
+str(lt.tm_min).zfill(2)+"."+str(lt.tm_sec).zfill(2)
np.savetxt('./models/train_loss_'+model.name+'_'+lt_str+'.txt', train_loss)
np.savetxt('./models/valid_loss_'+model.name+'_'+lt_str+'.txt', valid_loss)
model.save('./models/CNN_Model_OBT100_multi_cnn_'+model.name+'_final.h5')
print("done")
#### we show some visualisation here
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
train_loss = np.loadtxt('./models/train_loss_'+model.name+'_'+lt_str+'.txt')
valid_loss = np.loadtxt('./models/valid_loss_'+model.name+'_'+lt_str+'.txt')
plt.plot(train_loss, 'b')
plt.plot(valid_loss, 'r')
blue_label = mpatches.Patch(color='blue', label='train_loss')
red_label = mpatches.Patch(color='red', label='valid_loss')
plt.legend(handles=[blue_label, red_label])
| gpl-3.0 |
dhhagan/PAM | Python/PAM.py | 1 | 5037 | #PAM.py
import re
import glob, os, time
from numpy import *
from pylab import *
def analyzeFile(fileName,delim):
cols = {}
indexToName = {}
lineNum = 0
goodLines = 0
shortLines = 0
FILE = open(fileName,'r')
for line in FILE:
line = line.strip()
if lineNum < 1:
lineNum += 1
continue
elif lineNum == 1:
headings = line.split(delim)
i = 0
for heading in headings:
heading = heading.strip()
cols[heading] = []
indexToName[i] = heading
i += 1
lineNum += 1
lineLength = len(cols)
else:
data = line.split(delim)
if len(data) == lineLength:
goodLines += 1
i = 0
for point in data:
point = point.strip()
cols[indexToName[i]] += [point]
i += 1
lineNum += 1
else:
shortLines += 1
lineNum += 1
continue
FILE.close
return cols, indexToName, lineNum, shortLines
def numericalSort(value):
numbers = re.compile(r'(\d+)')
parts = numbers.split(value)
parts[1::2] = map(int, parts[1::2])
return parts
def popDate(fileName):
run = fileName.split('.')[0]
runNo = run.split('_')[-1]
return runNo
def getFile(date,regex):#Works
files = []
files = sorted((glob.glob('*'+regex+'*')),key=numericalSort,reverse=False)
if date.lower() == 'last':
files = files.pop()
else:
files = [item for item in files if re.search(date,item)]
return files
def plotConc(data,ozone,times):
# This function plots data versus time
import datetime as dt
from matplotlib import pyplot as plt
from matplotlib.dates import date2num
#time = [dt.datetime.strptime(time,"%m/%d/%Y %I:%M:%S %p") for time in times]
time = [dt.datetime.strptime(time,"%m/%d/%Y %I:%M:%S %p") for time in times]
x = date2num(time)
legend1 = []
legend2 = []
fig = plt.figure('Gas Concentration Readings for East St.Louis')
ax1 = fig.add_subplot(111)
ax2 = twinx()
for key,value in data.items():
ax1.plot_date(x,data[key],'-',xdate=True)
legend1.append(key)
for key, value in ozone.items():
ax2.plot_date(x,ozone[key],'-.',xdate=True)
legend2.append(key)
title('Gas Concentrations for East St. Louis', fontsize = 12)
ax1.set_ylabel(r'$Concentration(ppb)$', fontsize = 12)
ax2.set_ylabel(r'$Concentration(ppb)$', fontsize = 12)
xlabel(r"$Time \, Stamp$", fontsize = 12)
ax1.legend(legend1,loc='upper right')
ax2.legend(legend2,loc='lower right')
grid(True)
return
def plotBankRelays(data,relays,times):
# This function plots data versus time
import datetime as dt
from matplotlib import pyplot as plt
from matplotlib.dates import date2num
time = [dt.datetime.strptime(time,"%m/%d/%Y %I:%M:%S %p") for time in times]
x = date2num(time)
#x1 = [date.strftime("%m-%d %H:%M:%S") for date in time]
legend1 = []
legend2 = []
#plt.locator_params(axis='x', nbins=4)
fig = plt.figure('VAPS Thermocouple Readings: Chart 2')
ax1 = fig.add_subplot(111)
ax2 = twinx()
for key,value in data.items():
ax1.plot_date(x,data[key],'-',xdate=True)
legend1.append(key)
for key,value in relays.items():
ax2.plot_date(x,relays[key],'--',xdate=True)
legend2.append(key)
title('VAPS Temperatures: Chart 2', fontsize = 12)
ax1.set_ylabel(r'$Temperature(^oC)$', fontsize = 12)
ax2.set_ylabel(r'$Relay \, States$', fontsize = 12)
ax1.set_xlabel(r"$Time \, Stamp$", fontsize = 12)
#print [num2date(item) for item in ax1.get_xticks()]
#ax1.set_xticks(x)
#ax1.set_xticklabels([date.strftime("%m-%d %H:%M %p") for date in time])
#ax1.legend(bbox_to_anchor=(0.,1.02,1.,.102),loc=3,ncol=2,mode="expand",borderaxespad=0.)
ax1.legend(legend1,loc='upper right')
ax2.legend(legend2,loc='lower right')
#ax1.xaxis.set_major_formatter(FormatStrFormatter(date.strftime("%m-%d %H:%M:%S")))
plt.subplots_adjust(bottom=0.15)
grid(True)
return
def goodFiles(files,goodHeaders,delim): # Good
irregFiles = 0
goodFiles = []
for file in files:
lineNo = 0
falseCount = 0
FILE = open(file,'r')
for line in FILE:
line = line.strip()
if lineNo == 5:
# Check all the headings to make sure the file is good
head = line.split(delim)
for item in head:
if item in goodHeaders:
continue
else:
falseCount += 1
if falseCount == 0:
goodFiles.append(file)
else:
irregFiles += 1
lineNo += 1
else:
lineNo += 1
continue
FILE.close
return goodFiles, irregFiles
| mit |
shadowk29/cusumtools | legacy/minimal_psd.py | 1 | 12009 | ## COPYRIGHT
## Copyright (C) 2015 Kyle Briggs (kbrig035<at>uottawa.ca)
##
## This file is part of cusumtools.
##
## 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 matplotlib
matplotlib.use('TkAgg')
import numpy as np
import tkinter.filedialog
import tkinter as tk
from matplotlib.figure import Figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
import scipy.io as sio
from scipy.signal import bessel, filtfilt, welch
from scikits.samplerate import resample
import pylab as pl
import glob
import os
import time
import pandas as pd
from pandasql import sqldf
import re
def make_format(current, other):
# current and other are axes
def format_coord(x, y):
# x, y are data coordinates
# convert to display coords
display_coord = current.transData.transform((x,y))
inv = other.transData.inverted()
# convert back to data coords with respect to ax
ax_coord = inv.transform(display_coord)
coords = [ax_coord, (x, y)]
return ('Left: {:<40} Right: {:<}'
.format(*['({:.3f}, {:.3f})'.format(x, y) for x,y in coords]))
return format_coord
class App(tk.Frame):
def __init__(self, parent,file_path):
tk.Frame.__init__(self, parent)
parent.deiconify()
self.events_flag = False
self.baseline_flag = False
self.file_path = file_path
##### Trace plotting widgets #####
self.trace_frame = tk.LabelFrame(parent,text='Current Trace')
self.trace_fig = Figure(figsize=(7,5), dpi=100)
self.trace_canvas = FigureCanvasTkAgg(self.trace_fig, master=self.trace_frame)
self.trace_toolbar_frame = tk.Frame(self.trace_frame)
self.trace_toolbar = NavigationToolbar2TkAgg(self.trace_canvas, self.trace_toolbar_frame)
self.trace_toolbar.update()
self.trace_frame.grid(row=0,column=0,columnspan=6,sticky=tk.N+tk.S)
self.trace_toolbar_frame.grid(row=1,column=0,columnspan=6)
self.trace_canvas.get_tk_widget().grid(row=0,column=0,columnspan=6)
##### PSD plotting widgets #####
self.psd_frame = tk.LabelFrame(parent,text='Power Spectrum')
self.psd_fig = Figure(figsize=(7,5), dpi=100)
self.psd_canvas = FigureCanvasTkAgg(self.psd_fig, master=self.psd_frame)
self.psd_toolbar_frame = tk.Frame(self.psd_frame)
self.psd_toolbar = NavigationToolbar2TkAgg(self.psd_canvas, self.psd_toolbar_frame)
self.psd_toolbar.update()
self.psd_frame.grid(row=0,column=6,columnspan=6,sticky=tk.N+tk.S)
self.psd_toolbar_frame.grid(row=1,column=6,columnspan=6)
self.psd_canvas.get_tk_widget().grid(row=0,column=6,columnspan=6)
##### Control widgets #####
self.control_frame = tk.LabelFrame(parent, text='Controls')
self.control_frame.grid(row=2,column=0,columnspan=6,sticky=tk.N+tk.S+tk.E+tk.W)
self.start_entry = tk.Entry(self.control_frame)
self.start_entry.insert(0,'0')
self.start_label = tk.Label(self.control_frame, text='Start Time (s)')
self.start_label.grid(row=0,column=0,sticky=tk.E+tk.W)
self.start_entry.grid(row=0,column=1,sticky=tk.E+tk.W)
self.end_entry = tk.Entry(self.control_frame)
self.end_entry.insert(0,'10')
self.end_label = tk.Label(self.control_frame, text='End Time (s)')
self.end_label.grid(row=0,column=2,sticky=tk.E+tk.W)
self.end_entry.grid(row=0,column=3,sticky=tk.E+tk.W)
self.cutoff_entry = tk.Entry(self.control_frame)
self.cutoff_entry.insert(0,'')
self.cutoff_label = tk.Label(self.control_frame, text='Cutoff (Hz)')
self.cutoff_label.grid(row=1,column=0,sticky=tk.E+tk.W)
self.cutoff_entry.grid(row=1,column=1,sticky=tk.E+tk.W)
self.order_entry = tk.Entry(self.control_frame)
self.order_entry.insert(0,'')
self.order_label = tk.Label(self.control_frame, text='Filter Order')
self.order_label.grid(row=1,column=2,sticky=tk.E+tk.W)
self.order_entry.grid(row=1,column=3,sticky=tk.E+tk.W)
self.samplerate_entry = tk.Entry(self.control_frame)
self.samplerate_entry.insert(0,'250000')
self.samplerate_label = tk.Label(self.control_frame, text='Sampling Frequency (Hz)')
self.samplerate_label.grid(row=1,column=4,sticky=tk.E+tk.W)
self.samplerate_entry.grid(row=1,column=5,sticky=tk.E+tk.W)
self.savegain_entry = tk.Entry(self.control_frame)
self.savegain_entry.insert(0,'1')
self.savegain_label = tk.Label(self.control_frame, text='Sampling Frequency (Hz)')
self.savegain_label.grid(row=0,column=4,sticky=tk.E+tk.W)
self.savegain_entry.grid(row=0,column=5,sticky=tk.E+tk.W)
self.plot_trace = tk.Button(self.control_frame, text='Update Trace', command=self.update_trace)
self.plot_trace.grid(row=2,column=0,columnspan=2,sticky=tk.E+tk.W)
self.normalize = tk.IntVar()
self.normalize.set(0)
self.normalize_check = tk.Checkbutton(self.control_frame, text='Normalize', variable = self.normalize)
self.normalize_check.grid(row=2,column=2,sticky=tk.E+tk.W)
self.plot_psd = tk.Button(self.control_frame, text='Update PSD', command=self.update_psd)
self.plot_psd.grid(row=2,column=3,sticky=tk.E+tk.W)
##### Feedback Widgets #####
self.feedback_frame = tk.LabelFrame(parent, text='Status')
self.feedback_frame.grid(row=2,column=6,columnspan=6,sticky=tk.N+tk.S+tk.E+tk.W)
self.export_psd = tk.Button(self.feedback_frame, text='Export PSD',command=self.export_psd)
self.export_psd.grid(row=1,column=0,columnspan=6,sticky=tk.E+tk.W)
self.export_trace = tk.Button(self.feedback_frame, text='Export Trace',command=self.export_trace)
self.export_trace.grid(row=2,column=0,columnspan=6,sticky=tk.E+tk.W)
self.load_memmap()
self.initialize_samplerate()
def export_psd(self):
try:
data_path = tkinter.filedialog.asksaveasfilename(defaultextension='.csv',initialdir='G:\PSDs for Sam')
np.savetxt(data_path,np.c_[self.f, self.Pxx, self.rms],delimiter=',')
except AttributeError:
self.wildcard.set('Plot the PSD first')
def export_trace(self):
try:
data_path = tkinter.filedialog.asksaveasfilename(defaultextension='.csv',initialdir='G:\Analysis\Pores\NPN\PSDs')
np.savetxt(data_path,self.plot_data,delimiter=',')
except AttributeError:
self.wildcard.set('Plot the trace first')
def load_mapped_data(self):
self.total_samples = len(self.map)
self.samplerate = int(self.samplerate_entry.get())
if self.start_entry.get()!='':
self.start_time = float(self.start_entry.get())
start_index = int((float(self.start_entry.get())*self.samplerate))
else:
self.start_time = 0
start_index = 0
if self.end_entry.get()!='':
self.end_time = float(self.end_entry.get())
end_index = int((float(self.end_entry.get())*self.samplerate))
if end_index > self.total_samples:
end_index = self.total_samples
self.data = self.map[start_index:end_index]
self.data = float(self.savegain_entry.get()) * self.data
def load_memmap(self):
columntypes = np.dtype([('current', '>i2'), ('voltage', '>i2')])
self.map = np.memmap(self.file_path, dtype=columntypes, mode='r')['current']
def integrate_noise(self, f, Pxx):
df = f[1]-f[0]
return np.sqrt(np.cumsum(Pxx * df))
def filter_data(self):
cutoff = float(self.cutoff_entry.get())
order = int(self.order_entry.get())
Wn = 2.0 * cutoff/float(self.samplerate)
b, a = bessel(order,Wn,'low')
padding = 1000
padded = np.pad(self.data, pad_width=padding, mode='median')
self.filtered_data = filtfilt(b, a, padded, padtype=None)[padding:-padding]
def initialize_samplerate(self):
self.samplerate = float(self.samplerate_entry.get())
##### Plot Updating functions #####
def update_trace(self):
self.initialize_samplerate()
self.load_mapped_data()
self.filtered_data = self.data
self.plot_data = self.filtered_data
plot_samplerate = self.samplerate
if self.cutoff_entry.get()!='' and self.order_entry!='':
self.filter_data()
self.plot_data = self.filtered_data
self.trace_fig.clf()
a = self.trace_fig.add_subplot(111)
time = np.linspace(1.0/self.samplerate,len(self.plot_data)/float(self.samplerate),len(self.plot_data))+self.start_time
a.set_xlabel(r'Time ($\mu s$)')
a.set_ylabel('Current (pA)')
self.trace_fig.subplots_adjust(bottom=0.14,left=0.21)
a.plot(time*1e6,self.plot_data,'.',markersize=1)
self.trace_canvas.show()
def update_psd(self):
self.initialize_samplerate()
self.load_mapped_data()
self.filtered_data = self.data
self.plot_data = self.filtered_data
plot_samplerate = self.samplerate
if self.cutoff_entry.get()!='' and self.order_entry!='':
self.filter_data()
self.plot_data = self.filtered_data
maxf = 2*float(self.cutoff_entry.get())
else:
maxf = 2*float(self.samplerate_entry.get())
length = np.minimum(2**18,len(self.filtered_data))
end_index = int(np.floor(len(self.filtered_data)/length)*length)
current = np.average(self.filtered_data[:end_index])
f, Pxx = welch(self.filtered_data, plot_samplerate,nperseg=length)
self.rms = self.integrate_noise(f, Pxx)
if self.normalize.get():
Pxx /= current**2
Pxx *= maxf/2.0
self.rms /= np.absolute(current)
self.f = f
self.Pxx = Pxx
minf = 1
BW_index = np.searchsorted(f, maxf/2)
logPxx = np.log10(Pxx[1:BW_index])
minP = 10**np.floor(np.amin(logPxx))
maxP = 10**np.ceil(np.amax(logPxx))
self.psd_fig.clf()
a = self.psd_fig.add_subplot(111)
a.set_xlabel('Frequency (Hz)')
a.set_ylabel(r'Spectral Power ($\mathrm{pA}^2/\mathrm{Hz}$)')
a.set_xlim(minf, maxf)
a.set_ylim(minP, maxP)
self.psd_fig.subplots_adjust(bottom=0.14,left=0.21)
a.loglog(f[1:],Pxx[1:],'b-')
for tick in a.get_yticklabels():
tick.set_color('b')
a2 = a.twinx()
a2.semilogx(f, self.rms, 'r-')
a2.set_ylabel('RMS Noise (pA)')
a2.set_xlim(minf, maxf)
for tick in a2.get_yticklabels():
tick.set_color('r')
a2.format_coord = make_format(a2, a)
self.psd_canvas.show()
def main():
root=tk.Tk()
root.withdraw()
file_path = tkinter.filedialog.askopenfilename(initialdir='C:/Data/')
App(root,file_path).grid(row=0,column=0)
root.mainloop()
if __name__=="__main__":
main()
| gpl-3.0 |
tienjunhsu/trading-with-python | lib/widgets.py | 78 | 3012 | # -*- coding: utf-8 -*-
"""
A collection of widgets for gui building
Copyright: Jev Kuznetsov
License: BSD
"""
from __future__ import division
import sys
from PyQt4.QtCore import *
from PyQt4.QtGui import *
import numpy as np
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QTAgg as NavigationToolbar
from matplotlib.figure import Figure
import matplotlib.pyplot as plt
class MatplotlibWidget(QWidget):
def __init__(self,parent=None,grid=True):
QWidget.__init__(self,parent)
self.grid = grid
self.fig = Figure()
self.canvas =FigureCanvas(self.fig)
self.canvas.setParent(self)
self.canvas.mpl_connect('button_press_event', self.onPick) # bind pick event
#self.axes = self.fig.add_subplot(111)
margins = [0.05,0.1,0.9,0.8]
self.axes = self.fig.add_axes(margins)
self.toolbar = NavigationToolbar(self.canvas,self)
#self.initFigure()
layout = QVBoxLayout()
layout.addWidget(self.toolbar)
layout.addWidget(self.canvas)
self.setLayout(layout)
def onPick(self,event):
print 'Pick event'
print 'you pressed', event.button, event.xdata, event.ydata
def update(self):
self.canvas.draw()
def plot(self,*args,**kwargs):
self.axes.plot(*args,**kwargs)
self.axes.grid(self.grid)
self.update()
def clear(self):
self.axes.clear()
def initFigure(self):
self.axes.grid(True)
x = np.linspace(-1,1)
y = x**2
self.axes.plot(x,y,'o-')
class PlotWindow(QMainWindow):
''' a stand-alone window with embedded matplotlib widget '''
def __init__(self,parent=None):
super(PlotWindow,self).__init__(parent)
self.setAttribute(Qt.WA_DeleteOnClose)
self.mplWidget = MatplotlibWidget()
self.setCentralWidget(self.mplWidget)
def plot(self,dataFrame):
''' plot dataframe '''
dataFrame.plot(ax=self.mplWidget.axes)
def getAxes(self):
return self.mplWidget.axes
def getFigure(self):
return self.mplWidget.fig
def update(self):
self.mplWidget.update()
class MainForm(QMainWindow):
def __init__(self, parent=None):
QMainWindow.__init__(self, parent)
self.setWindowTitle('Demo: PyQt with matplotlib')
self.plot = MatplotlibWidget()
self.setCentralWidget(self.plot)
self.plot.clear()
self.plot.plot(np.random.rand(10),'x-')
#---------------------
if __name__=='__main__':
app = QApplication(sys.argv)
form = MainForm()
form.show()
app.exec_() | bsd-3-clause |
daniel20162016/my-first | read_xml_all/calcul_matrix_compare_je_good_192matrix.py | 1 | 6357 | # -*- coding: utf-8 -*-
"""
Created on Mon Oct 31 15:45:22 2016
@author: wang
"""
#from matplotlib import pylab as plt
#from numpy import fft, fromstring, int16, linspace
#import wave
from read_wav_xml_good_1 import*
from matrix_24_2 import*
from max_matrix_norm import*
import numpy as np
# open a wave file
filename = 'francois_filon_pure_3.wav'
filename_1 ='francois_filon_pure_3.xml'
word ='je'
wave_signal_float,framerate, word_start_point, word_length_point, word_end_point= read_wav_xml_good_1(filename,filename_1,word)
#print 'word_start_point=',word_start_point
#print 'word_length_point=',word_length_point
#print 'word_end_point=',word_end_point
XJ_1 =wave_signal_float
t_step=1920;
t_entre_step=1440;
t_du_1_1 = int(word_start_point[0]);
t_du_1_2 = int(word_end_point[0]);
t_du_2_1 = int(word_start_point[1]);
t_du_2_2 = int(word_end_point[1]);
t_du_3_1 = int(word_start_point[2]);
t_du_3_2 = int(word_end_point[2]);
t_du_4_1 = int(word_start_point[3]);
t_du_4_2 = int(word_end_point[3]);
t_du_5_1 = int(word_start_point[4]);
t_du_5_2 = int(word_end_point[4]);
fs=framerate
#XJ_du_1 = wave_signal_float[(t_du_1_1-1):t_du_1_2];
#length_XJ_du_1 = int(word_length_point[0]+1);
#x1,y1,z1=matrix_24_2(XJ_du_1,fs)
#x1=max_matrix_norm(x1)
#==============================================================================
# this part is to calcul the first matrix
#==============================================================================
XJ_du_1_2 = XJ_1[(t_du_1_1-1):(t_du_1_1+t_step)];
x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs)
x1_1=max_matrix_norm(x1_1)
matrix_all_step_new_1 = np.zeros([192])
for i in range(0,24):
matrix_all_step_new_1[i]=x1_1[i]
#==============================================================================
# the other colonne is the all fft
#==============================================================================
for i in range(1,8):
XJ_du_1_total = XJ_1[(t_du_1_1+t_entre_step*(i)-1):(t_du_1_1+t_step+t_entre_step*(i) )];
x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs)
x1_all=max_matrix_norm(x1_all)
for j in range(0,24):
matrix_all_step_new_1[24*i+j]=x1_all[j]
#==============================================================================
# this part is to calcul the second matrix
#==============================================================================
for k in range (1,2):
t_start=t_du_2_1
XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)];
x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs)
x1_1=max_matrix_norm(x1_1)
matrix_all_step_new_2 = np.zeros([192])
for i in range(0,24):
matrix_all_step_new_2[i]=x1_1[i]
#==============================================================================
# the other colonne is the all fft
#==============================================================================
for i in range(1,8):
XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )];
x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs)
x1_all=max_matrix_norm(x1_all)
for j in range(0,24):
matrix_all_step_new_2[24*i+j]=x1_all[j]
#==============================================================================
# this part is to calcul the 3 matrix
#==============================================================================
for k in range (1,2):
t_start=t_du_3_1
XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)];
x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs)
x1_1=max_matrix_norm(x1_1)
matrix_all_step_new_3 = np.zeros([192])
for i in range(0,24):
matrix_all_step_new_3[i]=x1_1[i]
#==============================================================================
# the other colonne is the all fft
#==============================================================================
for i in range(1,8):
XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )];
x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs)
x1_all=max_matrix_norm(x1_all)
for j in range(0,24):
matrix_all_step_new_3[24*i+j]=x1_all[j]
#==============================================================================
# this part is to calcul the 4 matrix
#==============================================================================
for k in range (1,2):
t_start=t_du_4_1
XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)];
x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs)
x1_1=max_matrix_norm(x1_1)
matrix_all_step_new_4 = np.zeros([192])
for i in range(0,24):
matrix_all_step_new_4[i]=x1_1[i]
#==============================================================================
# the other colonne is the all fft
#==============================================================================
for i in range(1,8):
# print i
XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )];
x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs)
x1_all=max_matrix_norm(x1_all)
for j in range(0,24):
matrix_all_step_new_4[24*i+j]=x1_all[j]
#print 'matrix_all_step_4=',matrix_all_step_4
#==============================================================================
# this part is to calcul the 5 matrix
#==============================================================================
for k in range (1,2):
t_start=t_du_5_1
XJ_du_1_2 = XJ_1[(t_start-1):(t_start+t_step)];
x1_1,y1_1,z1_1=matrix_24_2(XJ_du_1_2 ,fs)
x1_1=max_matrix_norm(x1_1)
matrix_all_step_new_5 = np.zeros([192])
for i in range(0,24):
matrix_all_step_new_5[i]=x1_1[i]
#==============================================================================
# the other colonne is the all fft
#==============================================================================
for i in range(1,8):
# print i
XJ_du_1_total = XJ_1[(t_start+t_entre_step*(i)-1):(t_start+t_step+t_entre_step*(i) )];
x1_all,y1_all,z1_all=matrix_24_2(XJ_du_1_total,fs)
x1_all=max_matrix_norm(x1_all)
for j in range(0,24):
matrix_all_step_new_5[24*i+j]=x1_all[j]
#print 'matrix_all_step_5=',matrix_all_step_5
np.savez('je_compare_192_matrix.npz',matrix_all_step_new_1,matrix_all_step_new_2,matrix_all_step_new_3,matrix_all_step_new_4,matrix_all_step_new_5)
| mit |
mringel/ThinkStats2 | code/timeseries.py | 66 | 18035 | """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 pandas
import numpy as np
import statsmodels.formula.api as smf
import statsmodels.tsa.stattools as smtsa
import matplotlib.pyplot as pyplot
import thinkplot
import thinkstats2
FORMATS = ['png']
def ReadData():
"""Reads data about cannabis transactions.
http://zmjones.com/static/data/mj-clean.csv
returns: DataFrame
"""
transactions = pandas.read_csv('mj-clean.csv', parse_dates=[5])
return transactions
def tmean(series):
"""Computes a trimmed mean.
series: Series
returns: float
"""
t = series.values
n = len(t)
if n <= 3:
return t.mean()
trim = max(1, n/10)
return np.mean(sorted(t)[trim:n-trim])
def GroupByDay(transactions, func=np.mean):
"""Groups transactions by day and compute the daily mean ppg.
transactions: DataFrame of transactions
returns: DataFrame of daily prices
"""
groups = transactions[['date', 'ppg']].groupby('date')
daily = groups.aggregate(func)
daily['date'] = daily.index
start = daily.date[0]
one_year = np.timedelta64(1, 'Y')
daily['years'] = (daily.date - start) / one_year
return daily
def GroupByQualityAndDay(transactions):
"""Divides transactions by quality and computes mean daily price.
transaction: DataFrame of transactions
returns: map from quality to time series of ppg
"""
groups = transactions.groupby('quality')
dailies = {}
for name, group in groups:
dailies[name] = GroupByDay(group)
return dailies
def PlotDailies(dailies):
"""Makes a plot with daily prices for different qualities.
dailies: map from name to DataFrame
"""
thinkplot.PrePlot(rows=3)
for i, (name, daily) in enumerate(dailies.items()):
thinkplot.SubPlot(i+1)
title = 'price per gram ($)' if i == 0 else ''
thinkplot.Config(ylim=[0, 20], title=title)
thinkplot.Scatter(daily.ppg, s=10, label=name)
if i == 2:
pyplot.xticks(rotation=30)
else:
thinkplot.Config(xticks=[])
thinkplot.Save(root='timeseries1',
formats=FORMATS)
def RunLinearModel(daily):
"""Runs a linear model of prices versus years.
daily: DataFrame of daily prices
returns: model, results
"""
model = smf.ols('ppg ~ years', data=daily)
results = model.fit()
return model, results
def PlotFittedValues(model, results, label=''):
"""Plots original data and fitted values.
model: StatsModel model object
results: StatsModel results object
"""
years = model.exog[:, 1]
values = model.endog
thinkplot.Scatter(years, values, s=15, label=label)
thinkplot.Plot(years, results.fittedvalues, label='model')
def PlotResiduals(model, results):
"""Plots the residuals of a model.
model: StatsModel model object
results: StatsModel results object
"""
years = model.exog[:, 1]
thinkplot.Plot(years, results.resid, linewidth=0.5, alpha=0.5)
def PlotResidualPercentiles(model, results, index=1, num_bins=20):
"""Plots percentiles of the residuals.
model: StatsModel model object
results: StatsModel results object
index: which exogenous variable to use
num_bins: how many bins to divide the x-axis into
"""
exog = model.exog[:, index]
resid = results.resid.values
df = pandas.DataFrame(dict(exog=exog, resid=resid))
bins = np.linspace(np.min(exog), np.max(exog), num_bins)
indices = np.digitize(exog, bins)
groups = df.groupby(indices)
means = [group.exog.mean() for _, group in groups][1:-1]
cdfs = [thinkstats2.Cdf(group.resid) for _, group in groups][1:-1]
thinkplot.PrePlot(3)
for percent in [75, 50, 25]:
percentiles = [cdf.Percentile(percent) for cdf in cdfs]
label = '%dth' % percent
thinkplot.Plot(means, percentiles, label=label)
def SimulateResults(daily, iters=101, func=RunLinearModel):
"""Run simulations based on resampling residuals.
daily: DataFrame of daily prices
iters: number of simulations
func: function that fits a model to the data
returns: list of result objects
"""
_, results = func(daily)
fake = daily.copy()
result_seq = []
for _ in range(iters):
fake.ppg = results.fittedvalues + thinkstats2.Resample(results.resid)
_, fake_results = func(fake)
result_seq.append(fake_results)
return result_seq
def SimulateIntervals(daily, iters=101, func=RunLinearModel):
"""Run simulations based on different subsets of the data.
daily: DataFrame of daily prices
iters: number of simulations
func: function that fits a model to the data
returns: list of result objects
"""
result_seq = []
starts = np.linspace(0, len(daily), iters).astype(int)
for start in starts[:-2]:
subset = daily[start:]
_, results = func(subset)
fake = subset.copy()
for _ in range(iters):
fake.ppg = (results.fittedvalues +
thinkstats2.Resample(results.resid))
_, fake_results = func(fake)
result_seq.append(fake_results)
return result_seq
def GeneratePredictions(result_seq, years, add_resid=False):
"""Generates an array of predicted values from a list of model results.
When add_resid is False, predictions represent sampling error only.
When add_resid is True, they also include residual error (which is
more relevant to prediction).
result_seq: list of model results
years: sequence of times (in years) to make predictions for
add_resid: boolean, whether to add in resampled residuals
returns: sequence of predictions
"""
n = len(years)
d = dict(Intercept=np.ones(n), years=years, years2=years**2)
predict_df = pandas.DataFrame(d)
predict_seq = []
for fake_results in result_seq:
predict = fake_results.predict(predict_df)
if add_resid:
predict += thinkstats2.Resample(fake_results.resid, n)
predict_seq.append(predict)
return predict_seq
def GenerateSimplePrediction(results, years):
"""Generates a simple prediction.
results: results object
years: sequence of times (in years) to make predictions for
returns: sequence of predicted values
"""
n = len(years)
inter = np.ones(n)
d = dict(Intercept=inter, years=years, years2=years**2)
predict_df = pandas.DataFrame(d)
predict = results.predict(predict_df)
return predict
def PlotPredictions(daily, years, iters=101, percent=90, func=RunLinearModel):
"""Plots predictions.
daily: DataFrame of daily prices
years: sequence of times (in years) to make predictions for
iters: number of simulations
percent: what percentile range to show
func: function that fits a model to the data
"""
result_seq = SimulateResults(daily, iters=iters, func=func)
p = (100 - percent) / 2
percents = p, 100-p
predict_seq = GeneratePredictions(result_seq, years, add_resid=True)
low, high = thinkstats2.PercentileRows(predict_seq, percents)
thinkplot.FillBetween(years, low, high, alpha=0.3, color='gray')
predict_seq = GeneratePredictions(result_seq, years, add_resid=False)
low, high = thinkstats2.PercentileRows(predict_seq, percents)
thinkplot.FillBetween(years, low, high, alpha=0.5, color='gray')
def PlotIntervals(daily, years, iters=101, percent=90, func=RunLinearModel):
"""Plots predictions based on different intervals.
daily: DataFrame of daily prices
years: sequence of times (in years) to make predictions for
iters: number of simulations
percent: what percentile range to show
func: function that fits a model to the data
"""
result_seq = SimulateIntervals(daily, iters=iters, func=func)
p = (100 - percent) / 2
percents = p, 100-p
predict_seq = GeneratePredictions(result_seq, years, add_resid=True)
low, high = thinkstats2.PercentileRows(predict_seq, percents)
thinkplot.FillBetween(years, low, high, alpha=0.2, color='gray')
def Correlate(dailies):
"""Compute the correlation matrix between prices for difference qualities.
dailies: map from quality to time series of ppg
returns: correlation matrix
"""
df = pandas.DataFrame()
for name, daily in dailies.items():
df[name] = daily.ppg
return df.corr()
def CorrelateResid(dailies):
"""Compute the correlation matrix between residuals.
dailies: map from quality to time series of ppg
returns: correlation matrix
"""
df = pandas.DataFrame()
for name, daily in dailies.items():
_, results = RunLinearModel(daily)
df[name] = results.resid
return df.corr()
def TestCorrelateResid(dailies, iters=101):
"""Tests observed correlations.
dailies: map from quality to time series of ppg
iters: number of simulations
"""
t = []
names = ['high', 'medium', 'low']
for name in names:
daily = dailies[name]
t.append(SimulateResults(daily, iters=iters))
corr = CorrelateResid(dailies)
arrays = []
for result_seq in zip(*t):
df = pandas.DataFrame()
for name, results in zip(names, result_seq):
df[name] = results.resid
opp_sign = corr * df.corr() < 0
arrays.append((opp_sign.astype(int)))
print(np.sum(arrays))
def RunModels(dailies):
"""Runs linear regression for each group in dailies.
dailies: map from group name to DataFrame
"""
rows = []
for daily in dailies.values():
_, results = RunLinearModel(daily)
intercept, slope = results.params
p1, p2 = results.pvalues
r2 = results.rsquared
s = r'%0.3f (%0.2g) & %0.3f (%0.2g) & %0.3f \\'
row = s % (intercept, p1, slope, p2, r2)
rows.append(row)
# print results in a LaTeX table
print(r'\begin{tabular}{|c|c|c|}')
print(r'\hline')
print(r'intercept & slope & $R^2$ \\ \hline')
for row in rows:
print(row)
print(r'\hline')
print(r'\end{tabular}')
def FillMissing(daily, span=30):
"""Fills missing values with an exponentially weighted moving average.
Resulting DataFrame has new columns 'ewma' and 'resid'.
daily: DataFrame of daily prices
span: window size (sort of) passed to ewma
returns: new DataFrame of daily prices
"""
dates = pandas.date_range(daily.index.min(), daily.index.max())
reindexed = daily.reindex(dates)
ewma = pandas.ewma(reindexed.ppg, span=span)
resid = (reindexed.ppg - ewma).dropna()
fake_data = ewma + thinkstats2.Resample(resid, len(reindexed))
reindexed.ppg.fillna(fake_data, inplace=True)
reindexed['ewma'] = ewma
reindexed['resid'] = reindexed.ppg - ewma
return reindexed
def AddWeeklySeasonality(daily):
"""Adds a weekly pattern.
daily: DataFrame of daily prices
returns: new DataFrame of daily prices
"""
frisat = (daily.index.dayofweek==4) | (daily.index.dayofweek==5)
fake = daily.copy()
fake.ppg[frisat] += np.random.uniform(0, 2, frisat.sum())
return fake
def PrintSerialCorrelations(dailies):
"""Prints a table of correlations with different lags.
dailies: map from category name to DataFrame of daily prices
"""
filled_dailies = {}
for name, daily in dailies.items():
filled_dailies[name] = FillMissing(daily, span=30)
# print serial correlations for raw price data
for name, filled in filled_dailies.items():
corr = thinkstats2.SerialCorr(filled.ppg, lag=1)
print(name, corr)
rows = []
for lag in [1, 7, 30, 365]:
row = [str(lag)]
for name, filled in filled_dailies.items():
corr = thinkstats2.SerialCorr(filled.resid, lag)
row.append('%.2g' % corr)
rows.append(row)
print(r'\begin{tabular}{|c|c|c|c|}')
print(r'\hline')
print(r'lag & high & medium & low \\ \hline')
for row in rows:
print(' & '.join(row) + r' \\')
print(r'\hline')
print(r'\end{tabular}')
filled = filled_dailies['high']
acf = smtsa.acf(filled.resid, nlags=365, unbiased=True)
print('%0.3f, %0.3f, %0.3f, %0.3f, %0.3f' %
(acf[0], acf[1], acf[7], acf[30], acf[365]))
def SimulateAutocorrelation(daily, iters=1001, nlags=40):
"""Resample residuals, compute autocorrelation, and plot percentiles.
daily: DataFrame
iters: number of simulations to run
nlags: maximum lags to compute autocorrelation
"""
# run simulations
t = []
for _ in range(iters):
filled = FillMissing(daily, span=30)
resid = thinkstats2.Resample(filled.resid)
acf = smtsa.acf(resid, nlags=nlags, unbiased=True)[1:]
t.append(np.abs(acf))
high = thinkstats2.PercentileRows(t, [97.5])[0]
low = -high
lags = range(1, nlags+1)
thinkplot.FillBetween(lags, low, high, alpha=0.2, color='gray')
def PlotAutoCorrelation(dailies, nlags=40, add_weekly=False):
"""Plots autocorrelation functions.
dailies: map from category name to DataFrame of daily prices
nlags: number of lags to compute
add_weekly: boolean, whether to add a simulated weekly pattern
"""
thinkplot.PrePlot(3)
daily = dailies['high']
SimulateAutocorrelation(daily)
for name, daily in dailies.items():
if add_weekly:
daily = AddWeeklySeasonality(daily)
filled = FillMissing(daily, span=30)
acf = smtsa.acf(filled.resid, nlags=nlags, unbiased=True)
lags = np.arange(len(acf))
thinkplot.Plot(lags[1:], acf[1:], label=name)
def MakeAcfPlot(dailies):
"""Makes a figure showing autocorrelation functions.
dailies: map from category name to DataFrame of daily prices
"""
axis = [0, 41, -0.2, 0.2]
thinkplot.PrePlot(cols=2)
PlotAutoCorrelation(dailies, add_weekly=False)
thinkplot.Config(axis=axis,
loc='lower right',
ylabel='correlation',
xlabel='lag (day)')
thinkplot.SubPlot(2)
PlotAutoCorrelation(dailies, add_weekly=True)
thinkplot.Save(root='timeseries9',
axis=axis,
loc='lower right',
xlabel='lag (days)',
formats=FORMATS)
def PlotRollingMean(daily, name):
"""Plots rolling mean and EWMA.
daily: DataFrame of daily prices
"""
dates = pandas.date_range(daily.index.min(), daily.index.max())
reindexed = daily.reindex(dates)
thinkplot.PrePlot(cols=2)
thinkplot.Scatter(reindexed.ppg, s=15, alpha=0.1, label=name)
roll_mean = pandas.rolling_mean(reindexed.ppg, 30)
thinkplot.Plot(roll_mean, label='rolling mean')
pyplot.xticks(rotation=30)
thinkplot.Config(ylabel='price per gram ($)')
thinkplot.SubPlot(2)
thinkplot.Scatter(reindexed.ppg, s=15, alpha=0.1, label=name)
ewma = pandas.ewma(reindexed.ppg, span=30)
thinkplot.Plot(ewma, label='EWMA')
pyplot.xticks(rotation=30)
thinkplot.Save(root='timeseries10',
formats=FORMATS)
def PlotFilled(daily, name):
"""Plots the EWMA and filled data.
daily: DataFrame of daily prices
"""
filled = FillMissing(daily, span=30)
thinkplot.Scatter(filled.ppg, s=15, alpha=0.3, label=name)
thinkplot.Plot(filled.ewma, label='EWMA', alpha=0.4)
pyplot.xticks(rotation=30)
thinkplot.Save(root='timeseries8',
ylabel='price per gram ($)',
formats=FORMATS)
def PlotLinearModel(daily, name):
"""Plots a linear fit to a sequence of prices, and the residuals.
daily: DataFrame of daily prices
name: string
"""
model, results = RunLinearModel(daily)
PlotFittedValues(model, results, label=name)
thinkplot.Save(root='timeseries2',
title='fitted values',
xlabel='years',
xlim=[-0.1, 3.8],
ylabel='price per gram ($)',
formats=FORMATS)
PlotResidualPercentiles(model, results)
thinkplot.Save(root='timeseries3',
title='residuals',
xlabel='years',
ylabel='price per gram ($)',
formats=FORMATS)
#years = np.linspace(0, 5, 101)
#predict = GenerateSimplePrediction(results, years)
def main(name):
thinkstats2.RandomSeed(18)
transactions = ReadData()
dailies = GroupByQualityAndDay(transactions)
PlotDailies(dailies)
RunModels(dailies)
PrintSerialCorrelations(dailies)
MakeAcfPlot(dailies)
name = 'high'
daily = dailies[name]
PlotLinearModel(daily, name)
PlotRollingMean(daily, name)
PlotFilled(daily, name)
years = np.linspace(0, 5, 101)
thinkplot.Scatter(daily.years, daily.ppg, alpha=0.1, label=name)
PlotPredictions(daily, years)
xlim = years[0]-0.1, years[-1]+0.1
thinkplot.Save(root='timeseries4',
title='predictions',
xlabel='years',
xlim=xlim,
ylabel='price per gram ($)',
formats=FORMATS)
name = 'medium'
daily = dailies[name]
thinkplot.Scatter(daily.years, daily.ppg, alpha=0.1, label=name)
PlotIntervals(daily, years)
PlotPredictions(daily, years)
xlim = years[0]-0.1, years[-1]+0.1
thinkplot.Save(root='timeseries5',
title='predictions',
xlabel='years',
xlim=xlim,
ylabel='price per gram ($)',
formats=FORMATS)
if __name__ == '__main__':
import sys
main(*sys.argv)
| gpl-3.0 |
JamiiTech/mplh5canvas | examples/multi_plot.py | 4 | 1357 | #!/usr/bin/python
"""Testbed for the animation functionality of the backend, with multiple figures.
It basically produces an long series of frames that get animated on the client
browser side, this time with two figures.
"""
import matplotlib
matplotlib.use('module://mplh5canvas.backend_h5canvas')
from pylab import *
import time
def refresh_data(ax):
t = arange(0.0 + count, 2.0 + count, 0.01)
s = sin(2*pi*t)
ax.lines[0].set_xdata(t)
ax.lines[0].set_ydata(s)
ax.set_xlim(t[0],t[-1])
t = arange(0.0, 2.0, 0.01)
s = sin(2*pi*t)
plot(t, s, linewidth=1.0)
xlabel('time (s)')
ylabel('voltage (mV)')
title('Frist Post')
f = gcf()
ax = f.gca()
count = 0
f2 = figure()
ax2 = f2.gca()
ax2.set_xlabel('IMDB rating')
ax2.set_ylabel('South African Connections')
ax2.set_title('Luds chart...')
ax2.plot(arange(0.0, 5 + count, 0.01), arange(0.0, 5 + count, 0.01))
show(block=False, layout=2)
# show the figure manager but don't block script execution so animation works..
# layout=2 overrides the default layout manager which only shows a single plot in the browser window
while True:
refresh_data(ax)
d = arange(0.0, 5 + count, 0.01)
ax2.lines[0].set_xdata(d)
ax2.lines[0].set_ydata(d)
ax2.set_xlim(d[0],d[-1])
ax2.set_ylim(d[0],d[-1])
f.canvas.draw()
f2.canvas.draw()
count += 0.01
time.sleep(1)
| bsd-3-clause |
xiawei0000/Kinectforactiondetect | ChalearnLAPSample.py | 1 | 41779 | # coding=gbk
#-------------------------------------------------------------------------------
# Name: Chalearn LAP sample
# Purpose: Provide easy access to Chalearn LAP challenge data samples
#
# Author: Xavier Baro
#
# Created: 21/01/2014
# Copyright: (c) Xavier Baro 2014
# Licence: <your licence>
#-------------------------------------------------------------------------------
import os
import zipfile
import shutil
import cv2
import numpy
import csv
from PIL import Image, ImageDraw
from scipy.misc import imresize
class Skeleton(object):
""" Class that represents the skeleton information """
"""¹Ç¼ÜÀ࣬ÊäÈë¹Ç¼ÜÊý¾Ý£¬½¨Á¢Àà"""
#define a class to encode skeleton data
def __init__(self,data):
""" Constructor. Reads skeleton information from given raw data """
# Create an object from raw data
self.joins=dict();
pos=0
self.joins['HipCenter']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['Spine']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['ShoulderCenter']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['Head']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['ShoulderLeft']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['ElbowLeft']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['WristLeft']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['HandLeft']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['ShoulderRight']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['ElbowRight']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['WristRight']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['HandRight']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['HipLeft']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['KneeLeft']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['AnkleLeft']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['FootLeft']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['HipRight']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['KneeRight']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['AnkleRight']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
pos=pos+9
self.joins['FootRight']=(map(float,data[pos:pos+3]),map(float,data[pos+3:pos+7]),map(int,data[pos+7:pos+9]))
def getAllData(self):
""" Return a dictionary with all the information for each skeleton node """
return self.joins
def getWorldCoordinates(self):
""" Get World coordinates for each skeleton node """
skel=dict()
for key in self.joins.keys():
skel[key]=self.joins[key][0]
return skel
def getJoinOrientations(self):
""" Get orientations of all skeleton nodes """
skel=dict()
for key in self.joins.keys():
skel[key]=self.joins[key][1]
return skel
def getPixelCoordinates(self):
""" Get Pixel coordinates for each skeleton node """
skel=dict()
for key in self.joins.keys():
skel[key]=self.joins[key][2]
return skel
def toImage(self,width,height,bgColor):
""" Create an image for the skeleton information """
SkeletonConnectionMap = (['HipCenter','Spine'],['Spine','ShoulderCenter'],['ShoulderCenter','Head'],['ShoulderCenter','ShoulderLeft'], \
['ShoulderLeft','ElbowLeft'],['ElbowLeft','WristLeft'],['WristLeft','HandLeft'],['ShoulderCenter','ShoulderRight'], \
['ShoulderRight','ElbowRight'],['ElbowRight','WristRight'],['WristRight','HandRight'],['HipCenter','HipRight'], \
['HipRight','KneeRight'],['KneeRight','AnkleRight'],['AnkleRight','FootRight'],['HipCenter','HipLeft'], \
['HipLeft','KneeLeft'],['KneeLeft','AnkleLeft'],['AnkleLeft','FootLeft'])
im = Image.new('RGB', (width, height), bgColor)
draw = ImageDraw.Draw(im)
for link in SkeletonConnectionMap:
p=self.getPixelCoordinates()[link[1]]
p.extend(self.getPixelCoordinates()[link[0]])
draw.line(p, fill=(255,0,0), width=5)
for node in self.getPixelCoordinates().keys():
p=self.getPixelCoordinates()[node]
r=5
draw.ellipse((p[0]-r,p[1]-r,p[0]+r,p[1]+r),fill=(0,0,255))
del draw
image = numpy.array(im)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
return image
##ÊÖÊÆÊý¾ÝµÄÀ࣬ÊäÈë·¾¶£¬½¨Á¢ÊÖÊÆÊý¾ÝÀà
class GestureSample(object):
""" Class that allows to access all the information for a certain gesture database sample """
#define class to access gesture data samples
#³õʼ»¯£¬¶ÁÈ¡Îļþ
def __init__ (self,fileName):
""" Constructor. Read the sample file and unzip it if it is necessary. All the data is loaded.
sample=GestureSample('Sample0001.zip')
"""
# Check the given file
if not os.path.exists(fileName): #or not os.path.isfile(fileName):
raise Exception("Sample path does not exist: " + fileName)
# Prepare sample information
self.fullFile = fileName
self.dataPath = os.path.split(fileName)[0]
self.file=os.path.split(fileName)[1]
self.seqID=os.path.splitext(self.file)[0]
self.samplePath=self.dataPath + os.path.sep + self.seqID;
#ÅжÏÊÇzip»¹ÊÇĿ¼
# Unzip sample if it is necessary
if os.path.isdir(self.samplePath) :
self.unzip = False
else:
self.unzip = True
zipFile=zipfile.ZipFile(self.fullFile,"r")
zipFile.extractall(self.samplePath)
# Open video access for RGB information
rgbVideoPath=self.samplePath + os.path.sep + self.seqID + '_color.mp4'
if not os.path.exists(rgbVideoPath):
raise Exception("Invalid sample file. RGB data is not available")
self.rgb = cv2.VideoCapture(rgbVideoPath)
while not self.rgb.isOpened():
self.rgb = cv2.VideoCapture(rgbVideoPath)
cv2.waitKey(500)
# Open video access for Depth information
depthVideoPath=self.samplePath + os.path.sep + self.seqID + '_depth.mp4'
if not os.path.exists(depthVideoPath):
raise Exception("Invalid sample file. Depth data is not available")
self.depth = cv2.VideoCapture(depthVideoPath)
while not self.depth.isOpened():
self.depth = cv2.VideoCapture(depthVideoPath)
cv2.waitKey(500)
# Open video access for User segmentation information
userVideoPath=self.samplePath + os.path.sep + self.seqID + '_user.mp4'
if not os.path.exists(userVideoPath):
raise Exception("Invalid sample file. User segmentation data is not available")
self.user = cv2.VideoCapture(userVideoPath)
while not self.user.isOpened():
self.user = cv2.VideoCapture(userVideoPath)
cv2.waitKey(500)
# Read skeleton data
skeletonPath=self.samplePath + os.path.sep + self.seqID + '_skeleton.csv'
if not os.path.exists(skeletonPath):
raise Exception("Invalid sample file. Skeleton data is not available")
self.skeletons=[]
with open(skeletonPath, 'rb') as csvfile:
filereader = csv.reader(csvfile, delimiter=',')
for row in filereader:
self.skeletons.append(Skeleton(row))
del filereader
# Read sample data
sampleDataPath=self.samplePath + os.path.sep + self.seqID + '_data.csv'
if not os.path.exists(sampleDataPath):
raise Exception("Invalid sample file. Sample data is not available")
self.data=dict()
with open(sampleDataPath, 'rb') as csvfile:
filereader = csv.reader(csvfile, delimiter=',')
for row in filereader:
self.data['numFrames']=int(row[0])
self.data['fps']=int(row[1])
self.data['maxDepth']=int(row[2])
del filereader
# Read labels data
labelsPath=self.samplePath + os.path.sep + self.seqID + '_labels.csv'
if not os.path.exists(labelsPath):
#warnings.warn("Labels are not available", Warning)
self.labels=[]
else:
self.labels=[]
with open(labelsPath, 'rb') as csvfile:
filereader = csv.reader(csvfile, delimiter=',')
for row in filereader:
self.labels.append(map(int,row))
del filereader
#Îö¹¹º¯Êý
def __del__(self):
""" Destructor. If the object unziped the sample, it remove the temporal data """
if self.unzip:
self.clean()
def clean(self):
""" Clean temporal unziped data """
del self.rgb;
del self.depth;
del self.user;
shutil.rmtree(self.samplePath)
#´ÓvideoÖжÁÈ¡Ò»Ö¡·µ»Ø
def getFrame(self,video, frameNum):
""" Get a single frame from given video object """
# Check frame number
# Get total number of frames
numFrames = video.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT)
# Check the given file
if frameNum<1 or frameNum>numFrames:
raise Exception("Invalid frame number <" + str(frameNum) + ">. Valid frames are values between 1 and " + str(int(numFrames)))
# Set the frame index
video.set(cv2.cv.CV_CAP_PROP_POS_FRAMES,frameNum-1)
ret,frame=video.read()
if ret==False:
raise Exception("Cannot read the frame")
return frame
#ÏÂÃæµÄº¯Êý¶¼ÊÇÕë¶ÔÊý¾Ý³ÉÔ±£¬µÄÌض¨Ö¡²Ù×÷µÄ
def getRGB(self, frameNum):
""" Get the RGB color image for the given frame """
#get RGB frame
return self.getFrame(self.rgb,frameNum)
#·µ»ØÉî¶Èͼ£¬Ê¹ÓÃ16int±£´æµÄ
def getDepth(self, frameNum):
""" Get the depth image for the given frame """
#get Depth frame
depthData=self.getFrame(self.depth,frameNum)
# Convert to grayscale
depthGray=cv2.cvtColor(depthData,cv2.cv.CV_RGB2GRAY)
# Convert to float point
depth=depthGray.astype(numpy.float32)
# Convert to depth values
depth=depth/255.0*float(self.data['maxDepth'])
depth=depth.round()
depth=depth.astype(numpy.uint16)
return depth
def getUser(self, frameNum):
""" Get user segmentation image for the given frame """
#get user segmentation frame
return self.getFrame(self.user,frameNum)
def getSkeleton(self, frameNum):
""" Get the skeleton information for a given frame. It returns a Skeleton object """
#get user skeleton for a given frame
# Check frame number
# Get total number of frames
numFrames = len(self.skeletons)
# Check the given file
if frameNum<1 or frameNum>numFrames:
raise Exception("Invalid frame number <" + str(frameNum) + ">. Valid frames are values between 1 and " + str(int(numFrames)))
return self.skeletons[frameNum-1]
def getSkeletonImage(self, frameNum):
""" Create an image with the skeleton image for a given frame """
return self.getSkeleton(frameNum).toImage(640,480,(255,255,255))
def getNumFrames(self):
""" Get the number of frames for this sample """
return self.data['numFrames']
#½«ËùÓеÄÒ»Ö¡Êý¾Ý ´ò°üµ½Ò»¸ö´óµÄ¾ØÕóÀï
def getComposedFrame(self, frameNum):
""" Get a composition of all the modalities for a given frame """
# get sample modalities
rgb=self.getRGB(frameNum)
depthValues=self.getDepth(frameNum)
user=self.getUser(frameNum)
skel=self.getSkeletonImage(frameNum)
# Build depth image
depth = depthValues.astype(numpy.float32)
depth = depth*255.0/float(self.data['maxDepth'])
depth = depth.round()
depth = depth.astype(numpy.uint8)
depth = cv2.applyColorMap(depth,cv2.COLORMAP_JET)
# Build final image
compSize1=(max(rgb.shape[0],depth.shape[0]),rgb.shape[1]+depth.shape[1])
compSize2=(max(user.shape[0],skel.shape[0]),user.shape[1]+skel.shape[1])
comp = numpy.zeros((compSize1[0]+ compSize2[0],max(compSize1[1],compSize2[1]),3), numpy.uint8)
# Create composition
comp[:rgb.shape[0],:rgb.shape[1],:]=rgb
comp[:depth.shape[0],rgb.shape[1]:rgb.shape[1]+depth.shape[1],:]=depth
comp[compSize1[0]:compSize1[0]+user.shape[0],:user.shape[1],:]=user
comp[compSize1[0]:compSize1[0]+skel.shape[0],user.shape[1]:user.shape[1]+skel.shape[1],:]=skel
return comp
def getComposedFrameOverlapUser(self, frameNum):
""" Get a composition of all the modalities for a given frame """
# get sample modalities
rgb=self.getRGB(frameNum)
depthValues=self.getDepth(frameNum)
user=self.getUser(frameNum)
mask = numpy.mean(user, axis=2) > 150
mask = numpy.tile(mask, (3,1,1))
mask = mask.transpose((1,2,0))
# Build depth image
depth = depthValues.astype(numpy.float32)
depth = depth*255.0/float(self.data['maxDepth'])
depth = depth.round()
depth = depth.astype(numpy.uint8)
depth = cv2.applyColorMap(depth,cv2.COLORMAP_JET)
# Build final image
compSize=(max(rgb.shape[0],depth.shape[0]),rgb.shape[1]+depth.shape[1])
comp = numpy.zeros((compSize[0]+ compSize[0],max(compSize[1],compSize[1]),3), numpy.uint8)
# Create composition
comp[:rgb.shape[0],:rgb.shape[1],:]=rgb
comp[:depth.shape[0],rgb.shape[1]:rgb.shape[1]+depth.shape[1],:]= depth
comp[compSize[0]:compSize[0]+user.shape[0],:user.shape[1],:]= mask * rgb
comp[compSize[0]:compSize[0]+user.shape[0],user.shape[1]:user.shape[1]+user.shape[1],:]= mask * depth
return comp
def getComposedFrame_480(self, frameNum, ratio=0.5, topCut=60, botCut=140):
""" Get a composition of all the modalities for a given frame """
# get sample modalities
rgb=self.getRGB(frameNum)
rgb = rgb[topCut:-topCut,botCut:-botCut,:]
rgb = imresize(rgb, ratio, interp='bilinear')
depthValues=self.getDepth(frameNum)
user=self.getUser(frameNum)
user = user[topCut:-topCut,botCut:-botCut,:]
user = imresize(user, ratio, interp='bilinear')
mask = numpy.mean(user, axis=2) > 150
mask = numpy.tile(mask, (3,1,1))
mask = mask.transpose((1,2,0))
# Build depth image
depth = depthValues.astype(numpy.float32)
depth = depth*255.0/float(self.data['maxDepth'])
depth = depth.round()
depth = depth[topCut:-topCut,botCut:-botCut]
depth = imresize(depth, ratio, interp='bilinear')
depth = depth.astype(numpy.uint8)
depth = cv2.applyColorMap(depth,cv2.COLORMAP_JET)
# Build final image
compSize=(max(rgb.shape[0],depth.shape[0]),rgb.shape[1]+depth.shape[1])
comp = numpy.zeros((compSize[0]+ compSize[0],max(compSize[1],compSize[1]),3), numpy.uint8)
# Create composition
comp[:rgb.shape[0],:rgb.shape[1],:]=rgb
comp[:depth.shape[0],rgb.shape[1]:rgb.shape[1]+depth.shape[1],:]= depth
comp[compSize[0]:compSize[0]+user.shape[0],:user.shape[1],:]= mask * rgb
comp[compSize[0]:compSize[0]+user.shape[0],user.shape[1]:user.shape[1]+user.shape[1],:]= mask * depth
return comp
def getDepth3DCNN(self, frameNum, ratio=0.5, topCut=60, botCut=140):
""" Get a composition of all the modalities for a given frame """
# get sample modalities
depthValues=self.getDepth(frameNum)
user=self.getUser(frameNum)
user = user[topCut:-topCut,botCut:-botCut,:]
user = imresize(user, ratio, interp='bilinear')
mask = numpy.mean(user, axis=2) > 150
# Build depth image
depth = depthValues.astype(numpy.float32)
depth = depth*255.0/float(self.data['maxDepth'])
depth = depth.round()
depth = depth[topCut:-topCut,botCut:-botCut]
depth = imresize(depth, ratio, interp='bilinear')
depth = depth.astype(numpy.uint8)
return mask * depth
def getDepthOverlapUser(self, frameNum, x_centre, y_centre, pixel_value, extractedFrameSize=224, upshift = 0):
""" Get a composition of all the modalities for a given frame """
halfFrameSize = extractedFrameSize/2
user=self.getUser(frameNum)
mask = numpy.mean(user, axis=2) > 150
ratio = pixel_value/ 3000
# Build depth image
# get sample modalities
depthValues=self.getDepth(frameNum)
depth = depthValues.astype(numpy.float32)
depth = depth*255.0/float(self.data['maxDepth'])
mask = imresize(mask, ratio, interp='nearest')
depth = imresize(depth, ratio, interp='bilinear')
depth_temp = depth * mask
depth_extracted = depth_temp[x_centre-halfFrameSize-upshift:x_centre+halfFrameSize-upshift, y_centre-halfFrameSize: y_centre+halfFrameSize]
depth = depth.round()
depth = depth.astype(numpy.uint8)
depth = cv2.applyColorMap(depth,cv2.COLORMAP_JET)
depth_extracted = depth_extracted.round()
depth_extracted = depth_extracted.astype(numpy.uint8)
depth_extracted = cv2.applyColorMap(depth_extracted,cv2.COLORMAP_JET)
# Build final image
compSize=(depth.shape[0],depth.shape[1])
comp = numpy.zeros((compSize[0] + extractedFrameSize,compSize[1]+compSize[1],3), numpy.uint8)
# Create composition
comp[:depth.shape[0],:depth.shape[1],:]=depth
mask_new = numpy.tile(mask, (3,1,1))
mask_new = mask_new.transpose((1,2,0))
comp[:depth.shape[0],depth.shape[1]:depth.shape[1]+depth.shape[1],:]= mask_new * depth
comp[compSize[0]:,:extractedFrameSize,:]= depth_extracted
return comp
def getDepthCentroid(self, startFrame, endFrame):
""" Get a composition of all the modalities for a given frame """
x_centre = []
y_centre = []
pixel_value = []
for frameNum in range(startFrame, endFrame):
user=self.getUser(frameNum)
depthValues=self.getDepth(frameNum)
depth = depthValues.astype(numpy.float32)
#depth = depth*255.0/float(self.data['maxDepth'])
mask = numpy.mean(user, axis=2) > 150
width, height = mask.shape
XX, YY, count, pixel_sum = 0, 0, 0, 0
for x in range(width):
for y in range(height):
if mask[x, y]:
XX += x
YY += y
count += 1
pixel_sum += depth[x, y]
if count>0:
x_centre.append(XX/count)
y_centre.append(YY/count)
pixel_value.append(pixel_sum/count)
return [numpy.mean(x_centre), numpy.mean(y_centre), numpy.mean(pixel_value)]
def getGestures(self):
""" Get the list of gesture for this sample. Each row is a gesture, with the format (gestureID,startFrame,endFrame) """
return self.labels
def getGestureName(self,gestureID):
""" Get the gesture label from a given gesture ID """
names=('vattene','vieniqui','perfetto','furbo','cheduepalle','chevuoi','daccordo','seipazzo', \
'combinato','freganiente','ok','cosatifarei','basta','prendere','noncenepiu','fame','tantotempo', \
'buonissimo','messidaccordo','sonostufo')
# Check the given file
if gestureID<1 or gestureID>20:
raise Exception("Invalid gesture ID <" + str(gestureID) + ">. Valid IDs are values between 1 and 20")
return names[gestureID-1]
def exportPredictions(self, prediction,predPath):
""" Export the given prediction to the correct file in the given predictions path """
if not os.path.exists(predPath):
os.makedirs(predPath)
output_filename = os.path.join(predPath, self.seqID + '_prediction.csv')
output_file = open(output_filename, 'wb')
for row in prediction:
output_file.write(repr(int(row[0])) + "," + repr(int(row[1])) + "," + repr(int(row[2])) + "\n")
output_file.close()
def play_video(self):
"""
play the video, Wudi adds this
"""
# Open video access for RGB information
rgbVideoPath=self.samplePath + os.path.sep + self.seqID + '_color.mp4'
if not os.path.exists(rgbVideoPath):
raise Exception("Invalid sample file. RGB data is not available")
self.rgb = cv2.VideoCapture(rgbVideoPath)
while (self.rgb.isOpened()):
ret, frame = self.rgb.read()
cv2.imshow('frame',frame)
if cv2.waitKey(5) & 0xFF == ord('q'):
break
self.rgb.release()
cv2.destroyAllWindows()
def evaluate(self,csvpathpred):
""" Evaluate this sample agains the ground truth file """
maxGestures=11
seqLength=self.getNumFrames()
# Get the list of gestures from the ground truth and frame activation
predGestures = []
binvec_pred = numpy.zeros((maxGestures, seqLength))
gtGestures = []
binvec_gt = numpy.zeros((maxGestures, seqLength))
with open(csvpathpred, 'rb') as csvfilegt:
csvgt = csv.reader(csvfilegt)
for row in csvgt:
binvec_pred[int(row[0])-1, int(row[1])-1:int(row[2])-1] = 1
predGestures.append(int(row[0]))
# Get the list of gestures from prediction and frame activation
for row in self.getActions():
binvec_gt[int(row[0])-1, int(row[1])-1:int(row[2])-1] = 1
gtGestures.append(int(row[0]))
# Get the list of gestures without repetitions for ground truth and predicton
gtGestures = numpy.unique(gtGestures)
predGestures = numpy.unique(predGestures)
# Find false positives
falsePos=numpy.setdiff1d(gtGestures, numpy.union1d(gtGestures,predGestures))
# Get overlaps for each gesture
overlaps = []
for idx in gtGestures:
intersec = sum(binvec_gt[idx-1] * binvec_pred[idx-1])
aux = binvec_gt[idx-1] + binvec_pred[idx-1]
union = sum(aux > 0)
overlaps.append(intersec/union)
# Use real gestures and false positive gestures to calculate the final score
return sum(overlaps)/(len(overlaps)+len(falsePos))
def get_shift_scale(self, template, ref_depth, start_frame=10, end_frame=20, debug_show=False):
"""
Wudi add this method for extracting normalizing depth wrt Sample0003
"""
from skimage.feature import match_template
Feature_all = numpy.zeros(shape=(480, 640, end_frame-start_frame), dtype=numpy.uint16 )
count = 0
for frame_num in range(start_frame,end_frame):
depth_original = self.getDepth(frame_num)
mask = numpy.mean(self.getUser(frame_num), axis=2) > 150
Feature_all[:, :, count] = depth_original * mask
count += 1
depth_image = Feature_all.mean(axis = 2)
depth_image_normalized = depth_image * 1.0 / float(self.data['maxDepth'])
depth_image_normalized /= depth_image_normalized.max()
result = match_template(depth_image_normalized, template, pad_input=True)
#############plot
x, y = numpy.unravel_index(numpy.argmax(result), result.shape)
shift = [depth_image.shape[0]/2-x, depth_image.shape[1]/2-y]
subsize = 25 # we use 25 by 25 region as a measurement for median of distance
minX = max(x - subsize,0)
minY = max(y - subsize,0)
maxX = min(x + subsize,depth_image.shape[0])
maxY = min(y + subsize,depth_image.shape[1])
subregion = depth_image[minX:maxX, minY:maxY]
distance = numpy.median(subregion[subregion>0])
scaling = distance*1.0 / ref_depth
from matplotlib import pyplot as plt
print "[x, y, shift, distance, scaling]"
print str([x, y, shift, distance, scaling])
if debug_show:
fig, (ax1, ax2, ax3, ax4) = plt.subplots(ncols=4, figsize=(8, 4))
ax1.imshow(template)
ax1.set_axis_off()
ax1.set_title('template')
ax2.imshow(depth_image_normalized)
ax2.set_axis_off()
ax2.set_title('image')
# highlight matched region
hcoin, wcoin = template.shape
rect = plt.Rectangle((y-hcoin/2, x-wcoin/2), wcoin, hcoin, edgecolor='r', facecolor='none')
ax2.add_patch(rect)
import cv2
from scipy.misc import imresize
rows,cols = depth_image_normalized.shape
M = numpy.float32([[1,0, shift[1]],[0,1, shift[0]]])
affine_image = cv2.warpAffine(depth_image_normalized, M, (cols, rows))
resize_image = imresize(affine_image, scaling)
resize_image_median = cv2.medianBlur(resize_image,5)
ax3.imshow(resize_image_median)
ax3.set_axis_off()
ax3.set_title('image_transformed')
# highlight matched region
hcoin, wcoin = resize_image_median.shape
rect = plt.Rectangle((wcoin/2-160, hcoin/2-160), 320, 320, edgecolor='r', facecolor='none')
ax3.add_patch(rect)
ax4.imshow(result)
ax4.set_axis_off()
ax4.set_title('`match_template`\nresult')
# highlight matched region
ax4.autoscale(False)
ax4.plot(x, y, 'o', markeredgecolor='r', markerfacecolor='none', markersize=10)
plt.show()
return [shift, scaling]
def get_shift_scale_depth(self, shift, scale, framenumber, IM_SZ, show_flag=False):
"""
Wudi added this method to extract segmented depth frame,
by a shift and scale
"""
depth_original = self.getDepth(framenumber)
mask = numpy.mean(self.getUser(framenumber), axis=2) > 150
resize_final_out = numpy.zeros((IM_SZ,IM_SZ))
if mask.sum() < 1000: # Kinect detect nothing
print "skip "+ str(framenumber)
flag = False
else:
flag = True
depth_user = depth_original * mask
depth_user_normalized = depth_user * 1.0 / float(self.data['maxDepth'])
depth_user_normalized = depth_user_normalized *255 /depth_user_normalized.max()
rows,cols = depth_user_normalized.shape
M = numpy.float32([[1,0, shift[1]],[0,1, shift[0]]])
affine_image = cv2.warpAffine(depth_user_normalized, M,(cols, rows))
resize_image = imresize(affine_image, scale)
resize_image_median = cv2.medianBlur(resize_image,5)
rows, cols = resize_image_median.shape
image_crop = resize_image_median[rows/2-160:rows/2+160, cols/2-160:cols/2+160]
resize_final_out = imresize(image_crop, (IM_SZ,IM_SZ))
if show_flag: # show the segmented images here
cv2.imshow('image',image_crop)
cv2.waitKey(10)
return [resize_final_out, flag]
#¶¯×÷Êý¾ÝÀà
class ActionSample(object):
""" Class that allows to access all the information for a certain action database sample """
#define class to access actions data samples
def __init__ (self,fileName):
""" Constructor. Read the sample file and unzip it if it is necessary. All the data is loaded.
sample=ActionSample('Sec01.zip')
"""
# Check the given file
if not os.path.exists(fileName) and not os.path.isfile(fileName):
raise Exception("Sample path does not exist: " + fileName)
# Prepare sample information
self.fullFile = fileName
self.dataPath = os.path.split(fileName)[0]
self.file=os.path.split(fileName)[1]
self.seqID=os.path.splitext(self.file)[0]
self.samplePath=self.dataPath + os.path.sep + self.seqID;
# Unzip sample if it is necessary
if os.path.isdir(self.samplePath) :
self.unzip = False
else:
self.unzip = True
zipFile=zipfile.ZipFile(self.fullFile,"r")
zipFile.extractall(self.samplePath)
# Open video access for RGB information
rgbVideoPath=self.samplePath + os.path.sep + self.seqID + '_color.mp4'
if not os.path.exists(rgbVideoPath):
raise Exception("Invalid sample file. RGB data is not available")
self.rgb = cv2.VideoCapture(rgbVideoPath)
while not self.rgb.isOpened():
self.rgb = cv2.VideoCapture(rgbVideoPath)
cv2.waitKey(500)
# Read sample data
sampleDataPath=self.samplePath + os.path.sep + self.seqID + '_data.csv'
if not os.path.exists(sampleDataPath):
raise Exception("Invalid sample file. Sample data is not available")
self.data=dict()
with open(sampleDataPath, 'rb') as csvfile:
filereader = csv.reader(csvfile, delimiter=',')
for row in filereader:
self.data['numFrames']=int(row[0])
del filereader
# Read labels data
labelsPath=self.samplePath + os.path.sep + self.seqID + '_labels.csv'
self.labels=[]
if not os.path.exists(labelsPath):
warnings.warn("Labels are not available", Warning)
else:
with open(labelsPath, 'rb') as csvfile:
filereader = csv.reader(csvfile, delimiter=',')
for row in filereader:
self.labels.append(map(int,row))
del filereader
def __del__(self):
""" Destructor. If the object unziped the sample, it remove the temporal data """
if self.unzip:
self.clean()
def clean(self):
""" Clean temporal unziped data """
del self.rgb;
shutil.rmtree(self.samplePath)
def getFrame(self,video, frameNum):
""" Get a single frame from given video object """
# Check frame number
# Get total number of frames
numFrames = video.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT)
# Check the given file
if frameNum<1 or frameNum>numFrames:
raise Exception("Invalid frame number <" + str(frameNum) + ">. Valid frames are values between 1 and " + str(int(numFrames)))
# Set the frame index
video.set(cv2.cv.CV_CAP_PROP_POS_FRAMES,frameNum-1)
ret,frame=video.read()
if ret==False:
raise Exception("Cannot read the frame")
return frame
def getNumFrames(self):
""" Get the number of frames for this sample """
return self.data['numFrames']
def getRGB(self, frameNum):
""" Get the RGB color image for the given frame """
#get RGB frame
return self.getFrame(self.rgb,frameNum)
def getActions(self):
""" Get the list of gesture for this sample. Each row is an action, with the format (actionID,startFrame,endFrame) """
return self.labels
def getActionsName(self,actionID):
""" Get the action label from a given action ID """
names=('wave','point','clap','crouch','jump','walk','run','shake hands', \
'hug','kiss','fight')
# Check the given file
if actionID<1 or actionID>11:
raise Exception("Invalid action ID <" + str(actionID) + ">. Valid IDs are values between 1 and 11")
return names[actionID-1]
def exportPredictions(self, prediction,predPath):
""" Export the given prediction to the correct file in the given predictions path """
if not os.path.exists(predPath):
os.makedirs(predPath)
output_filename = os.path.join(predPath, self.seqID + '_prediction.csv')
output_file = open(output_filename, 'wb')
for row in prediction:
output_file.write(repr(int(row[0])) + "," + repr(int(row[1])) + "," + repr(int(row[2])) + "\n")
output_file.close()
def evaluate(self,csvpathpred):
""" Evaluate this sample agains the ground truth file """
maxGestures=11
seqLength=self.getNumFrames()
# Get the list of gestures from the ground truth and frame activation
predGestures = []
binvec_pred = numpy.zeros((maxGestures, seqLength))
gtGestures = []
binvec_gt = numpy.zeros((maxGestures, seqLength))
with open(csvpathpred, 'rb') as csvfilegt:
csvgt = csv.reader(csvfilegt)
for row in csvgt:
binvec_pred[int(row[0])-1, int(row[1])-1:int(row[2])-1] = 1
predGestures.append(int(row[0]))
# Get the list of gestures from prediction and frame activation
for row in self.getActions():
binvec_gt[int(row[0])-1, int(row[1])-1:int(row[2])-1] = 1
gtGestures.append(int(row[0]))
# Get the list of gestures without repetitions for ground truth and predicton
gtGestures = numpy.unique(gtGestures)
predGestures = numpy.unique(predGestures)
# Find false positives
falsePos=numpy.setdiff1d(gtGestures, numpy.union1d(gtGestures,predGestures))
# Get overlaps for each gesture
overlaps = []
for idx in gtGestures:
intersec = sum(binvec_gt[idx-1] * binvec_pred[idx-1])
aux = binvec_gt[idx-1] + binvec_pred[idx-1]
union = sum(aux > 0)
overlaps.append(intersec/union)
# Use real gestures and false positive gestures to calculate the final score
return sum(overlaps)/(len(overlaps)+len(falsePos))
#×Ë̬Êý¾ÝÀà
class PoseSample(object):
""" Class that allows to access all the information for a certain pose database sample """
#define class to access gesture data samples
def __init__ (self,fileName):
""" Constructor. Read the sample file and unzip it if it is necessary. All the data is loaded.
sample=PoseSample('Seq01.zip')
"""
# Check the given file
if not os.path.exists(fileName) and not os.path.isfile(fileName):
raise Exception("Sequence path does not exist: " + fileName)
# Prepare sample information
self.fullFile = fileName
self.dataPath = os.path.split(fileName)[0]
self.file=os.path.split(fileName)[1]
self.seqID=os.path.splitext(self.file)[0]
self.samplePath=self.dataPath + os.path.sep + self.seqID;
# Unzip sample if it is necessary
if os.path.isdir(self.samplePath):
self.unzip = False
else:
self.unzip = True
zipFile=zipfile.ZipFile(self.fullFile,"r")
zipFile.extractall(self.samplePath)
# Set path for rgb images
rgbPath=self.samplePath + os.path.sep + 'imagesjpg'+ os.path.sep
if not os.path.exists(rgbPath):
raise Exception("Invalid sample file. RGB data is not available")
self.rgbpath = rgbPath
# Set path for gt images
gtPath=self.samplePath + os.path.sep + 'maskspng'+ os.path.sep
if not os.path.exists(gtPath):
self.gtpath= "empty"
else:
self.gtpath = gtPath
frames=os.listdir(self.rgbpath)
self.numberFrames=len(frames)
def __del__(self):
""" Destructor. If the object unziped the sample, it remove the temporal data """
if self.unzip:
self.clean()
def clean(self):
""" Clean temporal unziped data """
shutil.rmtree(self.samplePath)
def getRGB(self, frameNum):
""" Get the RGB color image for the given frame """
#get RGB frame
if frameNum>self.numberFrames:
raise Exception("Number of frame has to be less than: "+ self.numberFrames)
framepath=self.rgbpath+self.seqID[3:5]+'_'+ '%04d' %frameNum+'.jpg'
if not os.path.isfile(framepath):
raise Exception("RGB file does not exist: " + framepath)
return cv2.imread(framepath)
def getNumFrames(self):
return self.numberFrames
def getLimb(self, frameNum, actorID,limbID):
""" Get the BW limb image for a certain frame and a certain limbID """
if self.gtpath == "empty":
raise Exception("Limb labels are not available for this sequence. This sequence belong to the validation set.")
else:
limbpath=self.gtpath+self.seqID[3:5]+'_'+ '%04d' %frameNum+'_'+str(actorID)+'_'+str(limbID)+'.png'
if frameNum>self.numberFrames:
raise Exception("Number of frame has to be less than: "+ self.numberFrames)
if actorID<1 or actorID>2:
raise Exception("Invalid actor ID <" + str(actorID) + ">. Valid frames are values between 1 and 2 ")
if limbID<1 or limbID>14:
raise Exception("Invalid limb ID <" + str(limbID) + ">. Valid frames are values between 1 and 14")
return cv2.imread(limbpath,cv2.CV_LOAD_IMAGE_GRAYSCALE)
def getLimbsName(self,limbID):
""" Get the limb label from a given limb ID """
names=('head','torso','lhand','rhand','lforearm','rforearm','larm','rarm', \
'lfoot','rfoot','lleg','rleg','lthigh','rthigh')
# Check the given file
if limbID<1 or limbID>14:
raise Exception("Invalid limb ID <" + str(limbID) + ">. Valid IDs are values between 1 and 14")
return names[limbID-1]
def overlap_images(self, gtimage, predimage):
""" this function computes the hit measure of overlap between two binary images im1 and im2 """
[ret, im1] = cv2.threshold(gtimage, 127, 255, cv2.THRESH_BINARY)
[ret, im2] = cv2.threshold(predimage, 127, 255, cv2.THRESH_BINARY)
intersec = cv2.bitwise_and(im1, im2)
intersec_val = float(numpy.sum(intersec))
union = cv2.bitwise_or(im1, im2)
union_val = float(numpy.sum(union))
if union_val == 0:
return 0
else:
if float(intersec_val / union_val)>0.5:
return 1
else:
return 0
def exportPredictions(self, prediction,frame,actor,limb,predPath):
""" Export the given prediction to the correct file in the given predictions path """
if not os.path.exists(predPath):
os.makedirs(predPath)
prediction_filename = predPath+os.path.sep+ self.seqID[3:5] +'_'+ '%04d' %frame +'_'+str(actor)+'_'+str(limb)+'_prediction.png'
cv2.imwrite(prediction_filename,prediction)
def evaluate(self, predpath):
""" Evaluate this sample agains the ground truth file """
# Get the list of videos from ground truth
gt_list = os.listdir(self.gtpath)
# For each sample on the GT, search the given prediction
score = 0.0
nevals = 0
for gtlimbimage in gt_list:
# Avoid double check, use only labels file
if not gtlimbimage.lower().endswith(".png"):
continue
# Build paths for prediction and ground truth files
aux = gtlimbimage.split('.')
parts = aux[0].split('_')
seqID = parts[0]
gtlimbimagepath = os.path.join(self.gtpath,gtlimbimage)
predlimbimagepath= os.path.join(predpath) + os.path.sep + seqID+'_'+parts[1]+'_'+parts[2]+'_'+parts[3]+"_prediction.png"
#check predfile exists
if not os.path.exists(predlimbimagepath) or not os.path.isfile(predlimbimagepath):
raise Exception("Invalid video limb prediction file. Not all limb predictions are available")
#Load images
gtimage=cv2.imread(gtlimbimagepath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
predimage=cv2.imread(predlimbimagepath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
if cv2.cv.CountNonZero(cv2.cv.fromarray(gtimage)) >= 1:
score += self.overlap_images(gtimage, predimage)
nevals += 1
#release videos and return mean overlap
return score/nevals
| mit |
heli522/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 |
burjorjee/evolve-parities | evolveparities.py | 1 | 5098 | from contextlib import closing
from matplotlib.pyplot import plot, figure, hold, axis, ylabel, xlabel, savefig, title
from numpy import sort, logical_xor, transpose, logical_not
from numpy.numarray.functions import cumsum, zeros
from numpy.random import rand, shuffle
from numpy import mod, floor
import time
import cloud
from durus.file_storage import FileStorage
from durus.connection import Connection
def bitFreqVisualizer(effectiveAttrIndices, bitFreqs, gen):
f = figure(1)
n = len(bitFreqs)
hold(False)
plot(range(n), bitFreqs,'b.', markersize=10)
hold(True)
plot(effectiveAttrIndices, bitFreqs[effectiveAttrIndices],'r.', markersize=10)
axis([0, n-1, 0, 1])
title("Generation = %s" % (gen,))
ylabel('Frequency of the Bit 1')
xlabel('Locus')
f.canvas.draw()
f.show()
def showExperimentTimeStamps():
with closing(FileStorage("results.durus")) as durus:
conn = Connection(durus)
return conn.get_root().keys()
def neap_uga(m, n, gens, probMutation, effectiveAttrIndices, probMisclassification, bitFreqVisualizer=None):
""" neap = "noisy effective attribute parity"
"""
pop = rand(m,n)<0.5
bitFreqHist= zeros((n,gens+1))
for t in range(gens+1):
print "Generation %s" % t
bitFreqs = pop.astype('float').sum(axis=0)/m
bitFreqHist[:,t] = transpose(bitFreqs)
if bitFreqVisualizer:
bitFreqVisualizer(bitFreqs,t)
fitnessVals = mod(pop[:, effectiveAttrIndices].astype('byte').sum(axis=1) +
(rand(m) < probMisclassification).astype('byte'),2)
totalFitness = sum (fitnessVals)
cumNormFitnessVals = cumsum(fitnessVals).astype('float')/totalFitness
parentIndices = zeros(2*m, dtype='int16')
markers = sort(rand(2*m))
ctr = 0
for idx in xrange(2*m):
while markers[idx]>cumNormFitnessVals[ctr]:
ctr += 1
parentIndices[idx] = ctr
shuffle(parentIndices)
crossoverMasks = rand(m, n) < 0.5
newPop = zeros((m, n), dtype='bool')
newPop[crossoverMasks] = pop[parentIndices[:m], :][crossoverMasks]
newPop[logical_not(crossoverMasks)] = pop[parentIndices[m:], :][logical_not(crossoverMasks)]
mutationMasks = rand(m, n)<probMutation
pop = logical_xor(newPop,mutationMasks)
return bitFreqHist[0, :], bitFreqHist[-1, :]
def f(gens):
k = 7
n= k + 1
effectiveAttrIndices = range(k)
probMutation = 0.004
probMisclassification = 0.20
popSize = 1500
jid = cloud.call(neap_uga, **dict(m=popSize,
n=n,
gens=gens,
probMutation=probMutation,
effectiveAttrIndices=effectiveAttrIndices,
probMisclassification=probMisclassification))
print "Kicked off trial %s" % jid
return jid
def cloud_result(jid):
result = cloud.result(jid)
print "Retrieved results for trial %s" % jid
return result
def run_trials():
numTrials = 3000
gens = 1000
from multiprocessing.pool import ThreadPool as Pool
pool = Pool(50)
jids = pool.map(f,[gens]*numTrials)
print "Done spawning trials. Retrieving results..."
results = pool.map(cloud_result, jids)
firstLocusFreqsHists = zeros((numTrials,gens+1), dtype='float')
lastLocusFreqsHists = zeros((numTrials,gens+1), dtype='float')
print "Done retrieving results. Press Enter to serialize..."
raw_input()
for i, result in enumerate(results):
firstLocusFreqsHists[i, :], lastLocusFreqsHists[i, :] = result
with closing(FileStorage("results.durus")) as durus:
conn = Connection(durus)
conn.get_root()[str(int(floor(time.time())))] = (firstLocusFreqsHists, lastLocusFreqsHists)
conn.commit()
pool.close()
pool.join()
def render_results(timestamp=None):
with closing(FileStorage("results.durus")) as durus:
conn = Connection(durus)
db = conn.get_root()
if not timestamp:
timestamp = sorted(db.keys())[-1]
firstLocusFreqsHists, lastLocusFreqsHists = db[timestamp]
print "Done deserializing results. Plotting..."
x = [(2, 'First', firstLocusFreqsHists, "effective"),
(3, 'Last', lastLocusFreqsHists, "non-effective")]
for i, pos, freqsHists, filename in x :
freqsHists = freqsHists[:,:801]
f = figure(i)
hold(False)
plot(transpose(freqsHists), color='grey')
hold(True)
maxGens = freqsHists.shape[1]-1
plot([0, maxGens], [.05,.05], 'k--')
plot([0, maxGens], [.95,.95], 'k--')
axis([0, maxGens, 0, 1])
xlabel('Generation')
ylabel('1-Frequency of the '+pos+' Locus')
f.canvas.draw()
f.show()
savefig(filename+'.png', format='png', dpi=200)
if __name__ == "__main__":
cloud.start_simulator()
run_trials()
render_results()
print "Done plotting results. Press Enter to end..."
raw_input()
| gpl-3.0 |
matthiasrichter/AliceO2 | Analysis/Scripts/update_ccdb.py | 3 | 6042 | #!/usr/bin/env python3
# Copyright 2019-2020 CERN and copyright holders of ALICE O2.
# See https://alice-o2.web.cern.ch/copyright for details of the copyright holders.
# All rights not expressly granted are reserved.
#
# This software is distributed under the terms of the GNU General Public
# License v3 (GPL Version 3), copied verbatim in the file "COPYING".
#
# In applying this license CERN does not waive the privileges and immunities
# granted to it by virtue of its status as an Intergovernmental Organization
# or submit itself to any jurisdiction.
"""
Script to update the CCDB with timestamp non-overlapping objects.
If an object is found in the range specified, the object is split into two.
If the requested range was overlapping three objects are uploaded on CCDB:
1) latest object with requested timestamp validity
2) old object with validity [old_lower_validity-requested_lower_bound]
3) old object with validity [requested_upper_bound, old_upper_validity]
Author: Nicolo' Jacazio on 2020-06-22
TODO add support for 3 files update
"""
import subprocess
from datetime import datetime
import matplotlib.pyplot as plt
import argparse
def convert_timestamp(ts):
"""
Converts the timestamp in milliseconds in human readable format
"""
return datetime.utcfromtimestamp(ts/1000).strftime('%Y-%m-%d %H:%M:%S')
def get_ccdb_obj(path, timestamp, dest="/tmp/", verbose=0):
"""
Gets the ccdb object from 'path' and 'timestamp' and downloads it into 'dest'
"""
if verbose:
print("Getting obj", path, "with timestamp",
timestamp, convert_timestamp(timestamp))
cmd = f"o2-ccdb-downloadccdbfile --path {path} --dest {dest} --timestamp {timestamp}"
subprocess.run(cmd.split())
def get_ccdb_obj_validity(path, dest="/tmp/", verbose=0):
"""
Gets the timestamp validity for an object downloaded from CCDB.
Returns a list with the initial and end timestamps.
"""
cmd = f"o2-ccdb-inspectccdbfile {dest}{path}/snapshot.root"
process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)
output, error = process.communicate()
output = output.decode("utf-8").split("\n")
error = error.decode("utf-8").split("\n") if error is not None else error
if verbose:
print("out:")
print(*output, "\n")
print("err:")
print(error)
result = list(filter(lambda x: x.startswith('Valid-'), output))
ValidFrom = result[0].split()
ValidUntil = result[1].split()
return [int(ValidFrom[-1]), int(ValidUntil[-1])]
def upload_ccdb_obj(path, timestamp_from, timestamp_until, dest="/tmp/", meta=""):
"""
Uploads a new object to CCDB in the 'path' using the validity timestamp specified
"""
print("Uploading obj", path, "with timestamp", [timestamp_from, timestamp_until],
convert_timestamp(timestamp_from), convert_timestamp(timestamp_until))
key = path.split("/")[-1]
cmd = f"o2-ccdb-upload -f {dest}{path}/snapshot.root "
cmd += f"--key {key} --path {path} "
cmd += f"--starttimestamp {timestamp_from} --endtimestamp {timestamp_until} --meta \"{meta}\""
subprocess.run(cmd.split())
def main(path, timestamp_from, timestamp_until, verbose=0, show=False):
"""
Used to upload a new object to CCDB in 'path' valid from 'timestamp_from' to 'timestamp_until'
Gets the object from CCDB specified in 'path' and for 'timestamp_from-1'
Gets the object from CCDB specified in 'path' and for 'timestamp_until+1'
If required plots the situation before and after the update
"""
get_ccdb_obj(path, timestamp_from-1)
val_before = get_ccdb_obj_validity(path, verbose=verbose)
get_ccdb_obj(path, timestamp_until+1)
val_after = get_ccdb_obj_validity(path, verbose=verbose)
overlap_before = val_before[1] > timestamp_from
overlap_after = val_after[0] < timestamp_until
if verbose:
if overlap_before:
print("Previous objects overalps")
if overlap_after:
print("Next objects overalps")
trimmed_before = val_before if not overlap_before else [
val_before[0], timestamp_from - 1]
trimmed_after = val_after if not overlap_after else [
timestamp_until+1, val_after[1]]
if show:
fig, ax = plt.subplots()
fig
def bef_af(v, y):
return [v[0] - 1] + v + [v[1] + 1], [0, y, y, 0]
if True:
ax.plot(*bef_af(val_before, 0.95), label='before')
ax.plot(*bef_af(val_after, 1.05), label='after')
if False:
ax.plot(*bef_af(trimmed_before, 0.9), label='trimmed before')
ax.plot(*bef_af(trimmed_after, 1.1), label='trimmed after')
ax.plot(*bef_af([timestamp_from, timestamp_until], 1), label='object')
xlim = 10000000
plt.xlim([timestamp_from-xlim, timestamp_until+xlim])
plt.ylim(0, 2)
plt.xlabel('Timestamp')
plt.ylabel('Validity')
plt.legend()
plt.show()
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Uploads timestamp non overlapping objects to CCDB."
"Basic example: `./update_ccdb.py qc/TOF/TOFTaskCompressed/hDiagnostic 1588956517161 1588986517161 --show --verbose`")
parser.add_argument('path', metavar='path_to_object', type=str,
help='Path of the object in the CCDB repository')
parser.add_argument('timestamp_from', metavar='from_timestamp', type=int,
help='Timestamp of start for the new object to use')
parser.add_argument('timestamp_until', metavar='until_timestamp', type=int,
help='Timestamp of stop for the new object to use')
parser.add_argument('--verbose', '-v', action='count', default=0)
parser.add_argument('--show', '-s', action='count', default=0)
args = parser.parse_args()
main(path=args.path,
timestamp_from=args.timestamp_from,
timestamp_until=args.timestamp_until,
verbose=args.verbose,
show=args.show)
| gpl-3.0 |
annahs/atmos_research | WHI_long_term_2min_data_to_db.py | 1 | 8596 | import sys
import os
import numpy as np
from pprint import pprint
from datetime import datetime
from datetime import timedelta
import mysql.connector
import math
import calendar
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib import dates
start = datetime(2009,7,15,4) #2009 - 20090628 2010 - 20100610 2012 - 20100405
end = datetime(2009,8,17) #2009 - 20090816 2010 - 20100726 2012 - 20100601
timestep = 6.#1./30 #hours
sample_min = 117 #117 for all 2009-2012
sample_max = 123 #123 for all 2009-2012
yag_min = 3.8 #3.8 for all 2009-2012
yag_max = 6 #6 for all 2009-2012
BC_VED_min = 70
BC_VED_max = 220
min_scat_pkht = 20
mass_min = ((BC_VED_min/(10.**7))**3)*(math.pi/6.)*1.8*(10.**15)
mass_max = ((BC_VED_max/(10.**7))**3)*(math.pi/6.)*1.8*(10.**15)
lag_threshold_2009 = 0.1
lag_threshold_2010 = 0.25
lag_threshold_2012 = 1.5
print 'mass limits', mass_min, mass_max
cnx = mysql.connector.connect(user='root', password='Suresh15', host='localhost', database='black_carbon')
cursor = cnx.cursor()
def check_spike_times(particle_start_time,particle_end_time):
cursor.execute('''SELECT count(*)
FROM whi_spike_times_2009to2012
WHERE (spike_start_UTC <= %s AND spike_end_UTC > %s)
OR (spike_start_UTC <= %s AND spike_end_UTC > %s)
''',
(particle_start_time,particle_start_time,particle_end_time,particle_end_time))
spike_count = cursor.fetchall()[0][0]
return spike_count
def get_hysplit_id(particle_start_time):
cursor.execute('''SELECT id
FROM whi_hysplit_hourly_data
WHERE (UNIX_UTC_start_time <= %s AND UNIX_UTC_end_time > %s)
''',
(particle_start_time,particle_start_time))
hy_id_list = cursor.fetchall()
if hy_id_list == []:
hy_id = None
else:
hy_id = hy_id_list[0][0]
return hy_id
def get_met_info(particle_start_time):
cursor.execute('''SELECT id,pressure_Pa,room_temp_C
FROM whi_sampling_conditions
WHERE (UNIX_UTC_start_time <= %s AND UNIX_UTC_end_time > %s)
''',
(particle_start_time,particle_start_time))
met_list = cursor.fetchall()
if met_list == []:
met_list = [[np.nan,np.nan,np.nan]]
return met_list[0]
def get_gc_id(particle_start_time):
cursor.execute('''SELECT id
FROM whi_gc_hourly_bc_data
WHERE (UNIX_UTC_start_time <= %s AND UNIX_UTC_end_time > %s)
''',
(particle_start_time,particle_start_time))
gc_id_list = cursor.fetchall()
if gc_id_list == []:
gc_id = None
else:
gc_id = gc_id_list[0][0]
return gc_id
def get_sample_factor(UNIX_start):
date_time = datetime.utcfromtimestamp(UNIX_start)
sample_factors_2012 = [
[datetime(2012,4,4,19,43,4), datetime(2012,4,5,13,47,9), 3.0],
[datetime(2012,4,5,13,47,9), datetime(2012,4,10,3,3,25), 1.0],
[datetime(2012,4,10,3,3,25), datetime(2012,5,16,6,9,13), 3.0],
[datetime(2012,5,16,6,9,13), datetime(2012,6,7,18,14,39), 10.0],
]
if date_time.year in [2009,2010]:
sample_factor = 1.0
if date_time.year == 2012:
for date_range in sample_factors_2012:
start_date = date_range[0]
end_date = date_range[1]
range_sample_factor = date_range[2]
if start_date<= date_time < end_date:
sample_factor = range_sample_factor
return sample_factor
def lag_time_calc(BB_incand_pk_pos,BB_scat_pk_pos):
long_lags = 0
short_lags = 0
lag_time = np.nan
if (-10 < lag_time < 10):
lag_time = (BB_incand_pk_pos-BB_scat_pk_pos)*0.2 #us
if start_dt.year == 2009 and lag_time > lag_threshold_2009:
long_lags = 1
elif start_dt.year == 2010 and lag_time > lag_threshold_2010:
long_lags = 1
elif start_dt.year == 2012 and lag_time > lag_threshold_2012:
long_lags = 1
else:
short_lags = 1
return [lag_time,long_lags,short_lags]
#query to add 1h mass conc data
add_data = ('''INSERT INTO whi_sp2_2min_data
(UNIX_UTC_start_time,UNIX_UTC_end_time,number_particles,rBC_mass_conc,rBC_mass_conc_err,volume_air_sampled,sampling_duration,mean_lag_time,sample_factor,hysplit_hourly_id,whi_sampling_cond_id,gc_hourly_id)
VALUES (%(UNIX_UTC_start_time)s,%(UNIX_UTC_end_time)s,%(number_particles)s,%(rBC_mass_conc)s,%(rBC_mass_conc_err)s,%(volume_air_sampled)s,%(sampling_duration)s,%(mean_lag_time)s,%(sample_factor)s,%(hysplit_hourly_id)s,%(whi_sampling_cond_id)s,%(gc_hourly_id)s)'''
)
#
multiple_records = []
i=1
while start <= end:
long_lags = 0
short_lags = 0
if (4 <= start.hour < 16):
UNIX_start = calendar.timegm(start.utctimetuple())
UNIX_end = UNIX_start + timestep*3600.0
print start, UNIX_start+60
print datetime.utcfromtimestamp(UNIX_end)
#filter on hk data here
cursor.execute('''(SELECT
mn.UNIX_UTC_ts_int_start,
mn.UNIX_UTC_ts_int_end,
mn.rBC_mass_fg_BBHG,
mn.rBC_mass_fg_BBHG_err,
mn.BB_incand_pk_pos,
mn.BB_scat_pk_pos,
mn.BB_scat_pkht,
hk.sample_flow,
mn.BB_incand_HG
FROM whi_sp2_particle_data mn
FORCE INDEX (hourly_binning)
JOIN whi_hk_data hk on mn.HK_id = hk.id
WHERE
mn.UNIX_UTC_ts_int_start >= %s
AND mn.UNIX_UTC_ts_int_end < %s
AND hk.sample_flow >= %s
AND hk.sample_flow < %s
AND hk.yag_power >= %s
AND hk.yag_power < %s)''',
(UNIX_start,UNIX_end,sample_min,sample_max,yag_min,yag_max))
ind_data = cursor.fetchall()
data={
'rBC_mass_fg':[],
'rBC_mass_fg_err':[],
'lag_time':[]
}
total_sample_vol = 0
for row in ind_data:
ind_start_time = float(row[0])
ind_end_time = float(row[1])
bbhg_mass_corr11 = float(row[2])
bbhg_mass_corr_err = float(row[3])
BB_incand_pk_pos = float(row[4])
BB_scat_pk_pos = float(row[5])
BB_scat_pk_ht = float(row[6])
sample_flow = float(row[7]) #in vccm
incand_pkht = float(row[8])
#filter spike times here
if check_spike_times(ind_start_time,ind_end_time):
print 'spike'
continue
#skip the long interval
if (ind_end_time - ind_start_time) > 540:
print 'long interval'
continue
#skip if no sample flow
if sample_flow == None:
print 'no flow'
continue
#get sampling conditions id and met conditions
met_data = get_met_info(UNIX_start)
met_id = met_data[0]
pressure = met_data[1]
temperature = met_data[2]+273.15
correction_factor_for_STP = (273*pressure)/(101325*temperature)
sample_vol = (sample_flow*(ind_end_time-ind_start_time)/60)*correction_factor_for_STP #/60 b/c sccm and time in secs
total_sample_vol = total_sample_vol + sample_vol
bbhg_mass_corr = 0.01244+0.0172*incand_pkht
if (mass_min <= bbhg_mass_corr < mass_max):
#get sample factor
sample_factor = get_sample_factor(UNIX_start)
data['rBC_mass_fg'].append(bbhg_mass_corr*sample_factor)
data['rBC_mass_fg_err'].append(bbhg_mass_corr_err)
#only calc lag time if there is a scattering signal
if BB_scat_pk_ht > min_scat_pkht:
lags = lag_time_calc(BB_incand_pk_pos,BB_scat_pk_pos)
data['lag_time'].append(lags[0])
long_lags += lags[1]
short_lags += lags[2]
tot_rBC_mass_fg = sum(data['rBC_mass_fg'])
tot_rBC_mass_uncer = sum(data['rBC_mass_fg_err'])
rBC_number = len(data['rBC_mass_fg'])
mean_lag = float(np.mean(data['lag_time']))
if np.isnan(mean_lag):
mean_lag = None
#get hysplit_id
hysplit_id = None #get_hysplit_id(UNIX_start)
#get GC id
gc_id = None #get_gc_id(UNIX_start)
if total_sample_vol != 0:
mass_conc = (tot_rBC_mass_fg/total_sample_vol)
mass_conc_uncer = (tot_rBC_mass_uncer/total_sample_vol)
#add to db
single_record = {
'UNIX_UTC_start_time' :UNIX_start,
'UNIX_UTC_end_time' :UNIX_end,
'number_particles' :rBC_number,
'rBC_mass_conc' :mass_conc,
'rBC_mass_conc_err' :mass_conc_uncer,
'volume_air_sampled' :total_sample_vol,
'sampling_duration' :(total_sample_vol/2),
'mean_lag_time' :mean_lag,
'number_long_lag' :long_lags,
'number_short_lag' :short_lags,
'sample_factor' :sample_factor,
'hysplit_hourly_id' :hysplit_id,
'whi_sampling_cond_id' :met_id,
'gc_hourly_id' :gc_id,
}
multiple_records.append((single_record))
#bulk insert to db table
if i%1 == 0:
cursor.executemany(add_data, multiple_records)
cnx.commit()
multiple_records = []
#increment count
i+= 1
start += timedelta(hours = timestep)
#bulk insert of remaining records to db
if multiple_records != []:
cursor.executemany(add_data, multiple_records)
cnx.commit()
multiple_records = []
cnx.close()
| mit |
inviwo/inviwo | data/scripts/matplotlib_create_transferfunction.py | 2 | 1270 | # Inviwo Python script
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import inviwopy
from inviwopy.glm import vec2,vec3,vec4
#http://matplotlib.org/examples/color/colormaps_reference.html
#Perceptually Uniform Sequential : #['viridis', 'inferno', 'plasma', 'magma']
#Sequential : #['Blues', 'BuGn', 'BuPu','GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu','Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']
#Diverging : #['afmhot', 'autumn', 'bone', 'cool','copper', 'gist_heat', 'gray', 'hot','pink', 'spring', 'summer', 'winter']
#Qualitative : #['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr', 'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral', 'seismic']
#Miscellaneous : #['Accent', 'Dark2', 'Paired', 'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3']
#Sequential : #['gist_earth', 'terrain', 'ocean', 'gist_stern','brg', 'CMRmap', 'cubehelix','gnuplot', 'gnuplot2', 'gist_ncar', 'nipy_spectral', 'jet', 'rainbow', 'gist_rainbow', 'hsv', 'flag', 'prism']
tf = inviwopy.app.network.VolumeRaycaster.transferFunction
tf.clear()
cmapName = "viridis"
cmap=plt.get_cmap(cmapName)
N = 128
for i in range(0,N,1):
x = i / (N-1)
a = 1.0
color = cmap(x)
tf.add(x, vec4(color[0],color[1],color[2], a))
| bsd-2-clause |
xyguo/scikit-learn | examples/svm/plot_svm_nonlinear.py | 268 | 1091 | """
==============
Non-linear SVM
==============
Perform binary classification using non-linear SVC
with RBF kernel. The target to predict is a XOR of the
inputs.
The color map illustrates the decision function learned by the SVC.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
xx, yy = np.meshgrid(np.linspace(-3, 3, 500),
np.linspace(-3, 3, 500))
np.random.seed(0)
X = np.random.randn(300, 2)
Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)
# fit the model
clf = svm.NuSVC()
clf.fit(X, Y)
# plot the decision function for each datapoint on the grid
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.imshow(Z, interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()), aspect='auto',
origin='lower', cmap=plt.cm.PuOr_r)
contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2,
linetypes='--')
plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired)
plt.xticks(())
plt.yticks(())
plt.axis([-3, 3, -3, 3])
plt.show()
| bsd-3-clause |
acimmarusti/isl_exercises | chap3/chap3ex8.py | 1 | 1315 | from __future__ import print_function, division
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from pandas.tools.plotting import scatter_matrix
import statsmodels.formula.api as smf
#from sklearn.linear_model import LinearRegression
#import scipy, scipy.stats
#from statsmodels.sandbox.regression.predstd import wls_prediction_std
from statsmodels.stats.outliers_influence import variance_inflation_factor, summary_table
filename = '../Auto.csv'
data = pd.read_csv(filename, na_values='?').dropna()
#Quantitative and qualitative predictors#
print(data.dtypes)
#Simple linear regression#
slinreg = smf.ols('mpg ~ horsepower', data=data).fit()
print(slinreg.summary())
st, fitdat, ss2 = summary_table(slinreg, alpha=0.05)
fittedvalues = fitdat[:,2]
predict_mean_se = fitdat[:,3]
predict_mean_ci_low, predict_mean_ci_upp = fitdat[:,4:6].T
predict_ci_low, predict_ci_upp = fitdat[:,6:8].T
x = data['horsepower']
y = data['mpg']
#Residuals#
resd1 = y - fittedvalues
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot(x, y, 'o')
ax1.plot(x, fittedvalues, 'g-')
ax1.plot(x, predict_ci_low, 'r--')
ax1.plot(x, predict_ci_upp, 'r--')
ax1.plot(x, predict_mean_ci_low, 'b--')
ax1.plot(x, predict_mean_ci_upp, 'b--')
ax2.plot(resd1, fittedvalues, 'o')
plt.show()
| gpl-3.0 |