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37.5. Wave Effects
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe if/how wave effects are modelled at ocean surface. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.ocean.boundary_forcing.wave_effects')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/cccr-iitm/cmip6/models/sandbox-1/ocean.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
37.6. River Runoff Budget
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe how river runoff from land surface is routed to ocean and any global adjustment done. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.ocean.boundary_forcing.river_runoff_budget')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/cccr-iitm/cmip6/models/sandbox-1/ocean.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
37.7. Geothermal Heating
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe if/how geothermal heating is present at ocean bottom. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.ocean.boundary_forcing.geothermal_heating')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/cccr-iitm/cmip6/models/sandbox-1/ocean.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
38. Boundary Forcing --> Momentum --> Bottom Friction
Properties of momentum bottom friction in ocean
38.1. Type
Is Required: TRUE Type: ENUM Cardinality: 1.1
Type of momentum bottom friction in ocean | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.ocean.boundary_forcing.momentum.bottom_friction.type')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Linear"
# "Non-linear"
# "Non-linear (drag function of speed of tides)"
# "Constant drag coefficient"
# "None"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/cccr-iitm/cmip6/models/sandbox-1/ocean.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
39. Boundary Forcing --> Momentum --> Lateral Friction
Properties of momentum lateral friction in ocean
39.1. Type
Is Required: TRUE Type: ENUM Cardinality: 1.1
Type of momentum lateral friction in ocean | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.ocean.boundary_forcing.momentum.lateral_friction.type')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "None"
# "Free-slip"
# "No-slip"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/cccr-iitm/cmip6/models/sandbox-1/ocean.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
40. Boundary Forcing --> Tracers --> Sunlight Penetration
Properties of sunlight penetration scheme in ocean
40.1. Scheme
Is Required: TRUE Type: ENUM Cardinality: 1.1
Type of sunlight penetration scheme in ocean | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.scheme')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "1 extinction depth"
# "2 extinction depth"
# "3 extinction depth"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/cccr-iitm/cmip6/models/sandbox-1/ocean.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
40.2. Ocean Colour
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Is the ocean sunlight penetration scheme ocean colour dependent ? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.ocean_colour')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/cccr-iitm/cmip6/models/sandbox-1/ocean.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
40.3. Extinction Depth
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe and list extinctions depths for sunlight penetration scheme (if applicable). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.extinction_depth')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/cccr-iitm/cmip6/models/sandbox-1/ocean.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
41. Boundary Forcing --> Tracers --> Fresh Water Forcing
Properties of surface fresh water forcing in ocean
41.1. From Atmopshere
Is Required: TRUE Type: ENUM Cardinality: 1.1
Type of surface fresh water forcing from atmos in ocean | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.from_atmopshere')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Freshwater flux"
# "Virtual salt flux"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/cccr-iitm/cmip6/models/sandbox-1/ocean.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
41.2. From Sea Ice
Is Required: TRUE Type: ENUM Cardinality: 1.1
Type of surface fresh water forcing from sea-ice in ocean | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.from_sea_ice')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Freshwater flux"
# "Virtual salt flux"
# "Real salt flux"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/cccr-iitm/cmip6/models/sandbox-1/ocean.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
41.3. Forced Mode Restoring
Is Required: TRUE Type: STRING Cardinality: 1.1
Type of surface salinity restoring in forced mode (OMIP) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.forced_mode_restoring')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/cccr-iitm/cmip6/models/sandbox-1/ocean.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
Define a year as a "Superman year" whose films feature more Superman characters than Batman. How many years in film history have been Superman years? | both = cast[(cast.character=='Superman') | (cast.character == 'Batman')].groupby(['year','character']).size().unstack().fillna(0)
diff = both.Superman - both.Batman
print("Superman: " + str(len(diff[diff>0]))) | Tutorial/Exercises-4.ipynb | RobbieNesmith/PandasTutorial | mit |
How many years have been "Batman years", with more Batman characters than Superman characters? | both = cast[(cast.character=='Superman') | (cast.character == 'Batman')].groupby(['year','character']).size().unstack().fillna(0)
diff = both.Batman - both.Superman
print("Batman: " + str(len(diff[diff>0]))) | Tutorial/Exercises-4.ipynb | RobbieNesmith/PandasTutorial | mit |
Plot the number of actor roles each year and the number of actress roles each year over the history of film. | cast.groupby(['year','type']).size().unstack().plot() | Tutorial/Exercises-4.ipynb | RobbieNesmith/PandasTutorial | mit |
Plot the number of actor roles each year and the number of actress roles each year, but this time as a kind='area' plot. | cast.groupby(['year','type']).size().unstack().plot(kind='area') | Tutorial/Exercises-4.ipynb | RobbieNesmith/PandasTutorial | mit |
Plot the difference between the number of actor roles each year and the number of actress roles each year over the history of film. | foo = cast.groupby(['year','type']).size().unstack().fillna(0)
foo['diff'] = foo['actor']-foo['actress']
foo['diff'].plot() | Tutorial/Exercises-4.ipynb | RobbieNesmith/PandasTutorial | mit |
Plot the fraction of roles that have been 'actor' roles each year in the hitsory of film. | foo['totalRoles'] = foo['actor']+foo['actress']
foo['manFrac'] = foo['actor']/foo['totalRoles']
foo['manFrac'].plot() | Tutorial/Exercises-4.ipynb | RobbieNesmith/PandasTutorial | mit |
Plot the fraction of supporting (n=2) roles that have been 'actor' roles each year in the history of film. | support = cast[cast.n==2]
bar = support.groupby(['year','type']).size().unstack().fillna(0)
bar['totalRoles'] = bar['actor']+bar['actress']
bar['manFrac'] = bar['actor']/bar['totalRoles']
bar['manFrac'].plot() | Tutorial/Exercises-4.ipynb | RobbieNesmith/PandasTutorial | mit |
Build a plot with a line for each rank n=1 through n=3, where the line shows what fraction of that rank's roles were 'actor' roles for each year in the history of film. | thirdWheel = cast[cast.n==3]
baz = thirdWheel.groupby(['year','type']).size().unstack().fillna(0)
baz['totalRoles'] = baz['actor']+baz['actress']
baz['manFrac'] = baz['actor']/baz['totalRoles']
foo['manFrac'].plot() + (bar['manFrac'].plot() + baz['manFrac'].plot()) | Tutorial/Exercises-4.ipynb | RobbieNesmith/PandasTutorial | mit |
The $z = x$ % $y$ operation means that the remainder of $x$ when divided with $y$ will be assigned to $z$. The <span style="color: #0000FF">$if/else$</span> conditional expression above resulted in the printing of the variable $z$ if its value is positive, i.e. if $x$ when divided with $y$ has a remainder. If this condition is not fulfilled, then the text "There's no remainder" will be printed.
Statements inside conditional expression must be indented with space (not tab). The indentation must be consistent throughout the condition.
3.2 The <span style="color: #0000FF">if/elif/else</span> condition
The <span style="color: #0000FF">$if/elif/else$</span> conditional expression allows multiple conditions to be applied. | # Compare the values of two integers
int1 = 45
int2 = 55
if int1 > int2:
print "%d is larger than %d" % (int1,int2)
elif int1 == int2:
print "%d is equal to %d" % (int1,int2)
else:
print "%d is less than %d" % (int1,int2) | Tutorial 3 - Conditional Expression.ipynb | megatharun/basic-python-for-researcher | artistic-2.0 |
In the example above, the first condition always use the <span style="color: #0000FF">$if$</span> condition expression (i.e. $int1$ > $int2$). Only if this is not fulfilled will the second condition be evaluated i.e. the <span style="color: #0000FF">$elif$</span> condition expression. If this condition is also not fulfilled, then the <span style="color: #0000FF">$else$</span> condition statement will be executed.
In multiple conditional expressions, all the conditions will be evaluated in sequence. When one of the condition is fulfilled, the sequential evaluation will stop and the statement for that conditions will be executed.
Some of the conditional operators that can be used in a conditional expression:
|Condition|Function|
|---|---|
|>|more than|
|<|less than|
|>=|equal or more than|
|<=|equal or less than|
|==|equal to|
|!=|not equal to|
|and|more than one conditional operations are true|
|or|either one conditional operations is true|
In general, the multiple conditional expressions format is:
if (condition/s 1):
statement 1.1
statement 1.2
......
elif (condition/s 2):
statement 2.1
......
elif (condition/s 3):
statement 3.1
......
......
......
......
else:
statement
......
The statement in each conditional expression can also be a conditional expression.
<span style="color: #F5DA81; background-color: #610B4B">Example 3.1</span>: Determine the maximum and minimum of three different integers: 34,12,67. | x = 34
y = 12
z = 67
if x > y:
if y > z:
print 'Maximum integer is %d' % x
print 'Minimum integer is %d' % z
elif z > x:
print 'Maximum integer is %d' % z
print 'Minimum integer is %d' % y
else:
print 'Maximum integer is %d' % x
print 'Minimum integer is %d' % y
else: # y > x
if x > z:
print 'Maximum integer is %d' % y
print 'Minimum integer is %d' % z
elif z > y:
print 'Maximum integer is %d' % z
print 'Minimum integer is %d' % x
else:
print 'Maximum integer is %d' % y
print 'Minimum integer is %d' % x
| Tutorial 3 - Conditional Expression.ipynb | megatharun/basic-python-for-researcher | artistic-2.0 |
<span style="color: #F5DA81; background-color: #610B4B">Example 3.2</span>: Use only one type of conditional operator for Exercise 3.1. | x = 34
y = 12
z = 67
if x > y > z:
print 'Maximum integer is %d' % x
print 'Minimum integer is %d' % z
elif x > z > y:
print 'Maximum integer is %d' % x
print 'Minimum integer is %d' % y
elif y > x > z:
print 'Maximum integer is %d' % y
print 'Minimum integer is %d' % z
elif y > z > x:
print 'Maximum integer is %d' % y
print 'Minimum integer is %d' % x
elif z > x > y:
print 'Maximum integer is %d' % z
print 'Minimum integer is %d' % y
else:
print 'Maximum integer is %d' % z
print 'Minimum integer is %d' % x | Tutorial 3 - Conditional Expression.ipynb | megatharun/basic-python-for-researcher | artistic-2.0 |
<span style="color: #F5DA81; background-color: #610B4B">Exercise 3.1</span>: What if two or all integers have the same value. Try this and run the codes that solve Examples 3.1 and 3.2.
Codes in Example 3.1 seems more robust but 3.2 can be made more robust adding '>=' instead of '>'. | x = 78
y = 78
z = 99
if x >= y >= z:
print 'Maximum integer is %d' % x
print 'Minimum integer is %d' % z
elif x >= z >= y:
print 'Maximum integer is %d' % x
print 'Minimum integer is %d' % y
elif y >= x >= z:
print 'Maximum integer is %d' % y
print 'Minimum integer is %d' % z
elif y >= z >= x:
print 'Maximum integer is %d' % y
print 'Minimum integer is %d' % x
elif z >= x >= y:
print 'Maximum integer is %d' % z
print 'Minimum integer is %d' % y
else:
print 'Maximum integer is %d' % z
print 'Minimum integer is %d' % x | Tutorial 3 - Conditional Expression.ipynb | megatharun/basic-python-for-researcher | artistic-2.0 |
3.3 The <span style="color: #0000FF">for</span> and <span style="color: #0000FF">while</span> conditions
The <span style="color: #0000FF">$for$</span> and <span style="color: #0000FF">$while$</span> functions can be used to do repetitive action. The indentation with space (not tab) for statements inside the loop is also applied and consistent throughout the condition. | for i in range(0,5,1):
print i | Tutorial 3 - Conditional Expression.ipynb | megatharun/basic-python-for-researcher | artistic-2.0 |
Here the variable $i$ will be assigned the value of $0$ and cyclically incremented $5$ times by adding the integer $1$ to it each time. Only integer values are accepted in the parenthesis of the range statement. The first integer is the intial value of the $i$ variable, the second integer indicates (not-inclusive) the limiting value of the $i$ variable and the third integer represent the integer added to the variable $i$ for each cycles. | for i in range(4,17,3):
print i*2 | Tutorial 3 - Conditional Expression.ipynb | megatharun/basic-python-for-researcher | artistic-2.0 |
The conditional looping can be nested as examplified below: | for i in range(1,6,1):
for j in range(6,11,1):
print '%d x %d = %d' % (i,j,i*j) | Tutorial 3 - Conditional Expression.ipynb | megatharun/basic-python-for-researcher | artistic-2.0 |
It is also possible to loop into the elements of a string (i.e. a $list$). | for name in 'Numpy':
print name | Tutorial 3 - Conditional Expression.ipynb | megatharun/basic-python-for-researcher | artistic-2.0 |
The <span style="color: #0000FF">$while$</span> function works similarly like <span style="color: #0000FF">$for$</span> but initialization of the variable is performed before the <span style="color: #0000FF">$while$</span> statement and incrementing process is carried out as part of the loop argument. | z = 0
while z < 27:
print z
z = z + 6 | Tutorial 3 - Conditional Expression.ipynb | megatharun/basic-python-for-researcher | artistic-2.0 |
3.3 The <span style="color: #0000FF">$enumerate$( )</span> function
The <span style="color: #0000FF">$enumerate$( )</span> function will make the <span style="color: #0000FF">$for$</span> looping condition looking more comprehensible. The argument for this function is a $list$. | for i,j in enumerate('Numpy'):
print i, '\t', j
for item in enumerate('Numpy'):
print item | Tutorial 3 - Conditional Expression.ipynb | megatharun/basic-python-for-researcher | artistic-2.0 |
The <span style="color: #0000FF">$enumerate$( )</span> function allows the extraction of both the default position and its element of a $list$. In the first example, the two variables $i$ and $j$ will be assigned the $list$ default positional number and its element, respectively. In the second example, the variable $item$ will be assigned a tuple that consists the pair of default positional number and its element of the $list$.
The default positional number can be initiated to a different number. This can be done by passing the initial number as another argument in the <span style="color: #0000FF">$enumerate$( )</span> function. | for item in enumerate('Numpy',5):
print item | Tutorial 3 - Conditional Expression.ipynb | megatharun/basic-python-for-researcher | artistic-2.0 |
Meshing and Volume Calculations | import numpy as np
import random
from scipy.spatial import ConvexHull
def compute_mesh(points):
hull = ConvexHull(points)
indices = hull.simplices
return indices, hull.vertices | 3d_meshing.ipynb | stitchfix/d3-jupyter-tutorial | mit |
Example: Randomly Sampled Points on a Cylinder | def cylinder_points_and_hull_given_sample_size(sample_size):
points = []
for i in range(sample_size/2):
x = random.uniform(-1,1)
z = random.uniform(0,1)
s = (-1.0, 1.0)[random.uniform(0,1) < 0.5]
y = s * (1 - x**2) ** (0.5)
points.append(np.array([x,y,z]))
for z in range(0,2):
for i in range(n/4):
x = random.uniform(-1,1)
s = (-1.0, 1.0)[random.uniform(0,1) < 0.5]
y = s * random.uniform(0,1) * (1 - x**2) ** (0.5)
points.append(np.array([x,y,z]))
points = np.array(points)
triangles_vertices, hull_points = compute_mesh(points)
return points, hull_points, triangles_vertices
random.seed(42)
n = 100
points, hull_vertices, triangles_vertices = cylinder_points_and_hull_given_sample_size(n)
points[:3]
triangles_vertices[:3]
graph_points_triangles([[points, triangles_vertices]]) | 3d_meshing.ipynb | stitchfix/d3-jupyter-tutorial | mit |
Import Python packages | from __future__ import print_function
import pandas as pd
import geopandas as gpd
import matplotlib as mpl
import matplotlib.pyplot as plt
from ipywidgets.widgets import interact, Text
from IPython.display import display
import numpy as np | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | mvdbosch/AtosCodexDemo | gpl-3.0 |
Set Jupyter Notebook graphical parameters | # use the notebook definition for interactive embedded graphics
# %matplotlib notebook
# use the inline definition for static embedded graphics
%matplotlib inline
rcParam = {
'figure.figsize': (12,6),
'font.weight': 'bold',
'axes.labelsize': 20.0,
'axes.titlesize': 20.0,
'axes.titleweight': 'bold',
'legend.fontsize': 14,
'xtick.labelsize': 14,
'ytick.labelsize': 14,
}
for key in rcParam:
mpl.rcParams[key] = rcParam[key] | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | mvdbosch/AtosCodexDemo | gpl-3.0 |
Read the combines CBS dataset
This is the file that we downladen & merged using Talend Open Studio for Big Data. (Note: please check the file path) | cbs_data = pd.read_csv('combined_data.csv',sep=',',na_values=['NA','.'],error_bad_lines=False); | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | mvdbosch/AtosCodexDemo | gpl-3.0 |
Let's inspect the contents of this file by looking at the first 5 rows.
As you can see, this file has a lot of columns. For a description of the fieldnames, please see the description file | cbs_data.head()
cbs_data_2015 = cbs_data.loc[cbs_data['YEAR'] == 2015];
#list(cbs_data_2015) | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | mvdbosch/AtosCodexDemo | gpl-3.0 |
We will subset the entire 2010-2015 into just the year 2015.
In the table below you will see summary statistics | cbs_data_2015.describe()
#cbs_data_2015.YEAR.describe()
cbs_data_2015 = cbs_data_2015.dropna();
cbs_data_2015.describe() | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | mvdbosch/AtosCodexDemo | gpl-3.0 |
Description of some of the demographic features of this dataset | cbs_data_2015.iloc[:,35:216].describe() | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | mvdbosch/AtosCodexDemo | gpl-3.0 |
We want to make a label and a set of features out of our data
Labelling: The relative amount of money and property crimes ( Vermogensmisdrijven_rel)
Features : All neighbourhood demographic columns in the dataset | labels = cbs_data_2015["Vermogensmisdrijven_rel"].values
columns = list(cbs_data_2015.iloc[:,37:215])
features = cbs_data_2015[list(columns)];
features = features.apply(lambda columns : pd.to_numeric(columns, errors='ignore')) | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | mvdbosch/AtosCodexDemo | gpl-3.0 |
Inspect our labels and features | print(labels[1:10])
features.head() | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | mvdbosch/AtosCodexDemo | gpl-3.0 |
Feature selection using Randomized Lasso
Import Randomized Lasso from the Python Scikit-learn package | from sklearn.linear_model import RandomizedLasso | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | mvdbosch/AtosCodexDemo | gpl-3.0 |
Run Randomized Lasso, with 3000 resampling and 100 iterations. | rlasso = RandomizedLasso(alpha='aic',verbose =True,normalize =True,n_resampling=3000,max_iter=100)
rlasso.fit(features, labels) | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | mvdbosch/AtosCodexDemo | gpl-3.0 |
Features sorted by their score
In the table below the top10 best features (i.e. columns) are shown with their score | dfResults = pd.DataFrame.from_dict(sorted(zip(map(lambda x: round(x, 4), rlasso.scores_), list(features)), reverse=True))
dfResults.columns = ['Score', 'FeatureName']
dfResults.head(10) | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | mvdbosch/AtosCodexDemo | gpl-3.0 |
Because in the beginning of the lasso results table, a lot of high-scoring features are present, we want
to check how the scores are devided across all features | dfResults.plot('FeatureName', 'Score', kind='bar', color='navy')
ax1 = plt.axes()
x_axis = ax1.axes.get_xaxis()
x_axis.set_visible(False)
plt.show() | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | mvdbosch/AtosCodexDemo | gpl-3.0 |
Scatterplot
Let's inspect one of the top variables and make a scatterplot for this one | plt.scatter(y=pd.to_numeric(cbs_data_2015['Vermogensmisdrijven_rel']),x=pd.to_numeric(cbs_data_2015['A_BED_GI']));
plt.ylabel('Vermogensmisdrijven_rel')
plt.xlabel('A_BED_GI ( Bedrijfsvestigingen: Handel en horeca )')
plt.show()
dfResults.tail(10) | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | mvdbosch/AtosCodexDemo | gpl-3.0 |
Let's also inspect one of the worst variables (Perc% of Low income households) and plot this one too | plt.scatter(y=pd.to_numeric(cbs_data_2015['Vermogensmisdrijven_rel']),x=pd.to_numeric(cbs_data_2015['P_LAAGINKH']));
plt.ylabel('Vermogensmisdrijven_rel')
plt.xlabel('Perc. Laaginkomen Huish.')
plt.show() | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | mvdbosch/AtosCodexDemo | gpl-3.0 |
Try-out another hypothese (e.g. Perc% of divorced vs. Rel% Domestic and Sexual violence crimes) | plt.scatter(y=pd.to_numeric(cbs_data_2015['Gewelds_en_seksuele_misdrijven_rel']),x=pd.to_numeric(cbs_data_2015['P_GESCHEID']));
plt.ylabel('Gewelds_en_seksuele_misdrijven_rel')
plt.xlabel('Perc_Gescheiden')
plt.show() | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | mvdbosch/AtosCodexDemo | gpl-3.0 |
Step 1: load in the SIF file (refer to Class 6 exercise) into a data frame sif_data, using the pandas.read_csv function, and name the columns species1, interaction_type, and species2. | sif_data = pandas.read_csv("shared/pathway_commons.sif",
sep="\t", names=["species1","interaction_type","species2"]) | class08_components_python3.ipynb | ramseylab/networkscompbio | apache-2.0 |
Step 2: restrict the interactions to protein-protein undirected ("in-complex-with", "interacts-with"), by using the isin function and then using [ to index rows into the data frame. Call the returned ata frame interac_ppi. | interaction_types_ppi = set(["interacts-with",
"in-complex-with"])
interac_ppi = sif_data[sif_data.interaction_type.isin(interaction_types_ppi)].copy() | class08_components_python3.ipynb | ramseylab/networkscompbio | apache-2.0 |
Step 3: restrict the data frame to only the unique interaction pairs of proteins (ignoring the interaction type), and call that data frame interac_ppi_unique. Make an igraph Graph object from interac_ppi_unique using Graph.TupleList, values, and tolist. Call summary on the Graph object. Refer to the notebooks for the in-class exercises in Class sessions 3 and 6. | boolean_vec = interac_ppi['species1'] > interac_ppi['species2']
interac_ppi.loc[boolean_vec, ['species1', 'species2']] = interac_ppi.loc[boolean_vec, ['species2', 'species1']].values
interac_ppi_unique = interac_ppi[["species1","species2"]].drop_duplicates()
ppi_igraph = Graph.TupleList(interac_ppi_unique.values.tolist(), directed=False)
summary(ppi_igraph) | class08_components_python3.ipynb | ramseylab/networkscompbio | apache-2.0 |
Step 4: Map the components of the network using the igraph.Graph.clusters method. That method returns a igraph.clustering.VertexClustering object. Call the sizes method on that VertexClustering object, to get a list of sizes of the components. What is the giant component size? | # call the `clusters` method on the `ppi_igraph` object, and assign the
# resulting `VertexClustering` object to have object name `ppi_components`
ppi_components = ppi_igraph.clusters()
# call the `sizes` method on the `ppi_components` object, and assign the
# resulting list object to have the name `ppi_component_sizes`.
ppi_component_sizes = ppi_components.sizes()
# make a `numpy.array` initialized by `ppi_component_sizes`, and find its
# maximum value using the `max` method on the `numpy.array` class
numpy.array(ppi_component_sizes).max() | class08_components_python3.ipynb | ramseylab/networkscompbio | apache-2.0 |
Document Table of Contents
1. Key Properties
2. Key Properties --> Conservation Properties
3. Key Properties --> Timestepping Framework
4. Key Properties --> Software Properties
5. Grid
6. Grid --> Horizontal
7. Grid --> Vertical
8. Soil
9. Soil --> Soil Map
10. Soil --> Snow Free Albedo
11. Soil --> Hydrology
12. Soil --> Hydrology --> Freezing
13. Soil --> Hydrology --> Drainage
14. Soil --> Heat Treatment
15. Snow
16. Snow --> Snow Albedo
17. Vegetation
18. Energy Balance
19. Carbon Cycle
20. Carbon Cycle --> Vegetation
21. Carbon Cycle --> Vegetation --> Photosynthesis
22. Carbon Cycle --> Vegetation --> Autotrophic Respiration
23. Carbon Cycle --> Vegetation --> Allocation
24. Carbon Cycle --> Vegetation --> Phenology
25. Carbon Cycle --> Vegetation --> Mortality
26. Carbon Cycle --> Litter
27. Carbon Cycle --> Soil
28. Carbon Cycle --> Permafrost Carbon
29. Nitrogen Cycle
30. River Routing
31. River Routing --> Oceanic Discharge
32. Lakes
33. Lakes --> Method
34. Lakes --> Wetlands
1. Key Properties
Land surface key properties
1.1. Model Overview
Is Required: TRUE Type: STRING Cardinality: 1.1
Overview of land surface model. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.model_overview')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
1.2. Model Name
Is Required: TRUE Type: STRING Cardinality: 1.1
Name of land surface model code (e.g. MOSES2.2) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.model_name')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
1.3. Description
Is Required: TRUE Type: STRING Cardinality: 1.1
General description of the processes modelled (e.g. dymanic vegation, prognostic albedo, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.description')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
1.4. Land Atmosphere Flux Exchanges
Is Required: FALSE Type: ENUM Cardinality: 0.N
Fluxes exchanged with the atmopshere. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.land_atmosphere_flux_exchanges')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "water"
# "energy"
# "carbon"
# "nitrogen"
# "phospherous"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
1.5. Atmospheric Coupling Treatment
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe the treatment of land surface coupling with the Atmosphere model component, which may be different for different quantities (e.g. dust: semi-implicit, water vapour: explicit) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.atmospheric_coupling_treatment')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
1.6. Land Cover
Is Required: TRUE Type: ENUM Cardinality: 1.N
Types of land cover defined in the land surface model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.land_cover')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "bare soil"
# "urban"
# "lake"
# "land ice"
# "lake ice"
# "vegetated"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
1.7. Land Cover Change
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe how land cover change is managed (e.g. the use of net or gross transitions) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.land_cover_change')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
1.8. Tiling
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe the general tiling procedure used in the land surface (if any). Include treatment of physiography, land/sea, (dynamic) vegetation coverage and orography/roughness | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.tiling')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
2. Key Properties --> Conservation Properties
TODO
2.1. Energy
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe if/how energy is conserved globally and to what level (e.g. within X [units]/year) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.conservation_properties.energy')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
2.2. Water
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe if/how water is conserved globally and to what level (e.g. within X [units]/year) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.conservation_properties.water')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
2.3. Carbon
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe if/how carbon is conserved globally and to what level (e.g. within X [units]/year) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.conservation_properties.carbon')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
3. Key Properties --> Timestepping Framework
TODO
3.1. Timestep Dependent On Atmosphere
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Is a time step dependent on the frequency of atmosphere coupling? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestep_dependent_on_atmosphere')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
3.2. Time Step
Is Required: TRUE Type: INTEGER Cardinality: 1.1
Overall timestep of land surface model (i.e. time between calls) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.timestepping_framework.time_step')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
3.3. Timestepping Method
Is Required: TRUE Type: STRING Cardinality: 1.1
General description of time stepping method and associated time step(s) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestepping_method')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
4. Key Properties --> Software Properties
Software properties of land surface code
4.1. Repository
Is Required: FALSE Type: STRING Cardinality: 0.1
Location of code for this component. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.software_properties.repository')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
4.2. Code Version
Is Required: FALSE Type: STRING Cardinality: 0.1
Code version identifier. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.software_properties.code_version')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
4.3. Code Languages
Is Required: FALSE Type: STRING Cardinality: 0.N
Code language(s). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.key_properties.software_properties.code_languages')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
5. Grid
Land surface grid
5.1. Overview
Is Required: TRUE Type: STRING Cardinality: 1.1
Overview of the grid in the land surface | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.grid.overview')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
6. Grid --> Horizontal
The horizontal grid in the land surface
6.1. Description
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe the general structure of the horizontal grid (not including any tiling) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.grid.horizontal.description')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
6.2. Matches Atmosphere Grid
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Does the horizontal grid match the atmosphere? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.grid.horizontal.matches_atmosphere_grid')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
7. Grid --> Vertical
The vertical grid in the soil
7.1. Description
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe the general structure of the vertical grid in the soil (not including any tiling) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.grid.vertical.description')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
7.2. Total Depth
Is Required: TRUE Type: INTEGER Cardinality: 1.1
The total depth of the soil (in metres) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.grid.vertical.total_depth')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8. Soil
Land surface soil
8.1. Overview
Is Required: TRUE Type: STRING Cardinality: 1.1
Overview of soil in the land surface | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.overview')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.2. Heat Water Coupling
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe the coupling between heat and water in the soil | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.heat_water_coupling')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.3. Number Of Soil layers
Is Required: TRUE Type: INTEGER Cardinality: 1.1
The number of soil layers | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.number_of_soil layers')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.4. Prognostic Variables
Is Required: TRUE Type: STRING Cardinality: 1.1
List the prognostic variables of the soil scheme | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.prognostic_variables')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9. Soil --> Soil Map
Key properties of the land surface soil map
9.1. Description
Is Required: TRUE Type: STRING Cardinality: 1.1
General description of soil map | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.soil_map.description')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9.2. Structure
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe the soil structure map | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.soil_map.structure')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9.3. Texture
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe the soil texture map | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.soil_map.texture')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9.4. Organic Matter
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe the soil organic matter map | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.soil_map.organic_matter')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9.5. Albedo
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe the soil albedo map | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.soil_map.albedo')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9.6. Water Table
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe the soil water table map, if any | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.soil_map.water_table')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9.7. Continuously Varying Soil Depth
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Does the soil properties vary continuously with depth? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.soil_map.continuously_varying_soil_depth')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9.8. Soil Depth
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe the soil depth map | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.soil_map.soil_depth')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
10. Soil --> Snow Free Albedo
TODO
10.1. Prognostic
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Is snow free albedo prognostic? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.snow_free_albedo.prognostic')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
10.2. Functions
Is Required: FALSE Type: ENUM Cardinality: 0.N
If prognostic, describe the dependancies on snow free albedo calculations | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.snow_free_albedo.functions')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "vegetation type"
# "soil humidity"
# "vegetation state"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
10.3. Direct Diffuse
Is Required: FALSE Type: ENUM Cardinality: 0.1
If prognostic, describe the distinction between direct and diffuse albedo | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.snow_free_albedo.direct_diffuse')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "distinction between direct and diffuse albedo"
# "no distinction between direct and diffuse albedo"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
10.4. Number Of Wavelength Bands
Is Required: FALSE Type: INTEGER Cardinality: 0.1
If prognostic, enter the number of wavelength bands used | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.snow_free_albedo.number_of_wavelength_bands')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11. Soil --> Hydrology
Key properties of the land surface soil hydrology
11.1. Description
Is Required: TRUE Type: STRING Cardinality: 1.1
General description of the soil hydrological model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.hydrology.description')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11.2. Time Step
Is Required: TRUE Type: INTEGER Cardinality: 1.1
Time step of river soil hydrology in seconds | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.hydrology.time_step')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11.3. Tiling
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe the soil hydrology tiling, if any. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.hydrology.tiling')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11.4. Vertical Discretisation
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe the typical vertical discretisation | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.hydrology.vertical_discretisation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11.5. Number Of Ground Water Layers
Is Required: TRUE Type: INTEGER Cardinality: 1.1
The number of soil layers that may contain water | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.hydrology.number_of_ground_water_layers')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11.6. Lateral Connectivity
Is Required: TRUE Type: ENUM Cardinality: 1.N
Describe the lateral connectivity between tiles | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.hydrology.lateral_connectivity')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "perfect connectivity"
# "Darcian flow"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11.7. Method
Is Required: TRUE Type: ENUM Cardinality: 1.1
The hydrological dynamics scheme in the land surface model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.hydrology.method')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Bucket"
# "Force-restore"
# "Choisnel"
# "Explicit diffusion"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
12. Soil --> Hydrology --> Freezing
TODO
12.1. Number Of Ground Ice Layers
Is Required: TRUE Type: INTEGER Cardinality: 1.1
How many soil layers may contain ground ice | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.hydrology.freezing.number_of_ground_ice_layers')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
12.2. Ice Storage Method
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe the method of ice storage | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.hydrology.freezing.ice_storage_method')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
12.3. Permafrost
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe the treatment of permafrost, if any, within the land surface scheme | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.hydrology.freezing.permafrost')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13. Soil --> Hydrology --> Drainage
TODO
13.1. Description
Is Required: TRUE Type: STRING Cardinality: 1.1
General describe how drainage is included in the land surface scheme | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.land.soil.hydrology.drainage.description')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |