MarcelPower/github-jupyter-text-code-pairs

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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	82792e254dde479300d9c8bcafa265cd
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	9914ba8608c30b8693c848e8bacb2b2a
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	0536d8f5d269c29373831969c9175259
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	b61c5097f3f28757719bd85ee1f47d25
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	e04b3278b10630d46348a4aa725df15a
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	3ba1cad072899434499c7c7afcec077b
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	e1690be08af655803c07cb9faf972c45
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	d7927394c14e450da6d900cbeeb69a3d
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	ddf13ecdda2528f433aa976fd5810da6
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	9f84a367c6d06841b3331c228a512fc1
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	7c7e2b22efef956d795d2dbea3969704
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	7b2d9d34a84fcd3f7e660a11f9c29ad2
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	dddcf043c2709adda6b2b07b635f07d9
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	9ffd9c86ab22b478d3c0c37368a685b4
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	9b0393b2af5c01b8de80072cc822c0fe
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	6b70d183434775000de3edff905eb059
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	34fadb76f25251b81874aadf5c68d958
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	0db12b08fa3291dc086e4fc874bce527
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	bbc957f4e200f1d10ba145e6bfdfd118
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	4e12f001bedb26703e369d5fafc244a7
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	ccb656d1ebf55c26f0f1bc9ae133f83f
<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	c3e3bf83b36bcea205de6145392e15d1
<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	9bdca26abc87060ac4a2979af8a3f1f0
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	2860b58a930f5617357ee7bee5b3a6a1
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	c753ea6a92b67a103af46609c4bf5d65
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	b2581165e90555636634678748a139dd
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	226ef3680c942cd0dfb6046c103fcf78
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	4171860ee6cd4de51e30e8666ee9c027
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	371ab38f95b9f438862b33ef29096d3c
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	2b793f479ff6f66708b9d15b0bf09af0
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	0f7fee47897dd2dc807b5a1309fae285
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 - x2) (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 - x2) (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	95243834d0525d35771430d79eec1f76
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	1003f4d3d39a9f97cb86e34c0cda370d
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	17bff2b26ad56c0e87cf99d76f5d0522
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	1a51370490409576988377d27d6ec77f
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	6e4137f6ab84f22e09b2aa76a646f58d
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	227a36362e4b1f4cece532df7c50330d
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	19d7b1e7e6ba077bcc6f4acd4bcc66a1
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	fb93f09f278128ff90ee7ebe9d3f2095
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	76d976e80f9c2f576a07ea0e30cdeebb
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	2d6d35f8a12670eafd41cbaa2aa3672e
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	38902e195de63ae1c46bdb55110e89e6
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	09c41e4517ea24f1da7a5fd94ac2408d
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	83cece3a5c0442440ea78e99492ea1d5
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	7aa2f8d9af58c6b1962ee3d9d27ac1c5
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	c9ea88cd9c169a0bc133b074047a718b
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	e5887bc4ebef4238c1533b599d966119
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	871758e8194fbf58e0cc4de9f991aa07
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	664c4ed27605d2951cb8e30446fcc39c
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	bd061f655c69438eba1f0a4309db6dc3
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	463d0463ae15902cd20172df6e4206c3
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	e8178bff99c801bb7fd9d706226b6bae
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	16cd5997ea72b596fe8abd83d98a3c45
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	5c0c5c18bb1ad51f67e6d14644337a63
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	d7177af1fab7a53e2b6d8abcfe954bed
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	0821411b8d801b4ed4d9c2912bb02b89
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	ce7b44349d43e92ae8918cd8d63edc1f
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	d7a980f5e97d813e8b85e11f2f3a6c22
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	81783b4ab755df339c838450ae0b9fac
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	0d07eac006049dc89b43e5e7ed2d701b
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	45d99bd8d7c37f986444904555c70e8d
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	b2123b426b7fb22fcde4d9df9e06e0a2
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	e3a2657c8fdb7a768a466698046db6ba
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	c18c22bf829d4229e80abbc3dd1e2cc8
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	416c7e85955151b9f6963699e7435fe2
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	b09fbf49faa26c353d6cedc7eefac683
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	77b7e63e370e9d9b9496f0d8a1544f52
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	74dd648f90bfe974dbd7497f2ad80c57
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	dd893d8590fa246e84162354787cabbf
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	c9ff22f8688d21ca375d513ffadaec83
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	4eb0b3bd3448bc48b86c6bb49f6a82af
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	ef9fce0f2f6a2ccec016d69683e139bf
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	b533a872aa0c463994d15ec1840e2494
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	0fea1ed118e4ab6b6aff393a98620039
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	96593537a21dfac2ed80f2f762658f7c
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	e6d1da7bc59e5e6d15ef29552a9623c7
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	256de5dbc2d1ecd65a1692478fdab8a5
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	7378006ec7bae37d7138b2c20a915a8f
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	83eadb7cb32875569e7bdfaaac8fc84b
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	d1e73bd60177bcd6a9ec71513ab1b4de
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	ee745f8cb67c157871e6724aa36a993d
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	4e7578ccf72826a208dda46cdefcbf4c
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	ae63ff3ebeb00c7165f1b8b14fe823f1
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	b24df9ef86830bcd78c201747b4b579c
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	7757ce2cb342d29c08086c0bc3b1b621
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	47b73679937c1cfecd13d959979b4cbe
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	338ef669860d3c9f8123fac5afd062b2
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	54ade35a46119f3cb03c33deba2ee036
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	cb383618369609a6ecab413c4a2e018e
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	a0ee0c49c10ce2a04c1cf087539b45cf
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	8c9c1312eb10231d42f48969c1c2120a
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	daa78dd15f4045e64708abbfb718c2f3
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	5d26e549274543ef807ad8a39d7eb701
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	9ca4a41caf0cd23ac241edfefc748075
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	0a39f432c9174c3b744b794d4113b7e9
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	1a14163049e50f10a3fd8d07deb7a6f1
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	4f8c2d41bf83f48f491a1b3065448a52
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	a323bf7c1daff63549ab34f6d71fdc80
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	84cb71c6e68af4ea3cac0b3e565e8fea
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	07848d5fdee52f0c9363d132051f34a8

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

82792e254dde479300d9c8bcafa265cd

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

9914ba8608c30b8693c848e8bacb2b2a

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

0536d8f5d269c29373831969c9175259

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

b61c5097f3f28757719bd85ee1f47d25

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

e04b3278b10630d46348a4aa725df15a

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

3ba1cad072899434499c7c7afcec077b

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

e1690be08af655803c07cb9faf972c45

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

d7927394c14e450da6d900cbeeb69a3d

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

ddf13ecdda2528f433aa976fd5810da6

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

9f84a367c6d06841b3331c228a512fc1

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

7c7e2b22efef956d795d2dbea3969704

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

7b2d9d34a84fcd3f7e660a11f9c29ad2

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

dddcf043c2709adda6b2b07b635f07d9

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

9ffd9c86ab22b478d3c0c37368a685b4

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

9b0393b2af5c01b8de80072cc822c0fe

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

6b70d183434775000de3edff905eb059

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

34fadb76f25251b81874aadf5c68d958

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

0db12b08fa3291dc086e4fc874bce527

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

bbc957f4e200f1d10ba145e6bfdfd118

The $z = x$ % $y$ operation means that the remainder of $x$ when divided with $y$ will be assigned to $z$. The $if/else$ 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 if/elif/else condition The $if/elif/else$ 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

4e12f001bedb26703e369d5fafc244a7

In the example above, the first condition always use the $if$ condition expression (i.e. $int1$ > $int2$). Only if this is not fulfilled will the second condition be evaluated i.e. the $elif$ condition expression. If this condition is also not fulfilled, then the $else$ 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. Example 3.1: 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

ccb656d1ebf55c26f0f1bc9ae133f83f

Example 3.2: 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

c3e3bf83b36bcea205de6145392e15d1

Exercise 3.1: 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

9bdca26abc87060ac4a2979af8a3f1f0

3.3 The for and while conditions The $for$ and $while$ 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

2860b58a930f5617357ee7bee5b3a6a1

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

c753ea6a92b67a103af46609c4bf5d65

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

b2581165e90555636634678748a139dd

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

226ef3680c942cd0dfb6046c103fcf78

The $while$ function works similarly like $for$ but initialization of the variable is performed before the $while$ 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

4171860ee6cd4de51e30e8666ee9c027

3.3 The $enumerate$( ) function The $enumerate$( ) function will make the $for$ 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

371ab38f95b9f438862b33ef29096d3c

The $enumerate$( ) 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 $enumerate$( ) function.

for item in enumerate('Numpy',5): print item

Tutorial 3 - Conditional Expression.ipynb

megatharun/basic-python-for-researcher

artistic-2.0

2b793f479ff6f66708b9d15b0bf09af0

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

0f7fee47897dd2dc807b5a1309fae285

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

95243834d0525d35771430d79eec1f76

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

1003f4d3d39a9f97cb86e34c0cda370d

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

17bff2b26ad56c0e87cf99d76f5d0522

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

1a51370490409576988377d27d6ec77f

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

6e4137f6ab84f22e09b2aa76a646f58d

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

227a36362e4b1f4cece532df7c50330d

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

19d7b1e7e6ba077bcc6f4acd4bcc66a1

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

fb93f09f278128ff90ee7ebe9d3f2095

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

76d976e80f9c2f576a07ea0e30cdeebb

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

2d6d35f8a12670eafd41cbaa2aa3672e

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

38902e195de63ae1c46bdb55110e89e6

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

09c41e4517ea24f1da7a5fd94ac2408d

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

83cece3a5c0442440ea78e99492ea1d5

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

7aa2f8d9af58c6b1962ee3d9d27ac1c5

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

c9ea88cd9c169a0bc133b074047a718b

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

e5887bc4ebef4238c1533b599d966119

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

871758e8194fbf58e0cc4de9f991aa07

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

664c4ed27605d2951cb8e30446fcc39c

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

bd061f655c69438eba1f0a4309db6dc3

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

463d0463ae15902cd20172df6e4206c3

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)