deploy: ae875c0

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 49604998998f56e35a68417e02711300
+tags: d77d1c0d9ca2f4c8421862c7c5a0d620

_downloads/397ee019b6b388442dffa61ae3629d85/lint_example.py

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+import numpy
+import matplotlib.pyplot as plt
+
+x = np.linspace(0, np.pi, 100))
+y = np.sin(x)
+
+plt.plot(x, y)
+
+plt.show()

_downloads/747df499c1e18239511ea006e0433951/exercise1.py

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+"""
+pylint exercise 1
+"""
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn import linear_model
+
+
+def f(x):
+    """
+    Example function:
+
+    f(x) = x/2 + 2
+    """"
+    return 0.5*x + 2
+
+
+# Create example data
+x_data = np.linspace(0, 10, 100)
+err = 2 * np.random.random(x_data.shape[0])
+y_data = f(x_data) + err
+
+# Put data into dataframe
+df = pd.DataFrame({'x': x_data, 'y': y_data})
+
+# Create linear model and fit data
+reg = linear_model.LinearRegression(fit_intercept=True)
+
+reg.fit(df[['x'], df[['y']])
+
+slope = reg.coef_[0][0]
+intercept = reg.intercept_[0]
+
+df['pred'] = reg.predict(df[['x']])
+
+fig, ax = plt.subplots()
+
+ax.scater(df[['x']], df[['y']], alpha=0.5)
+ax.plot(df[['x']], df[['pred']]
+        color='black', linestyle='--',
+        label=f'Prediction with slope {slope:.2f} and intercept {intercept:.2f}')
+ax.set_ylabel('y')
+ax.set_xlabel('x')
+ax.legend()
+
+plt.show()

_downloads/75c4ab69c0f59fbb1589b03be360a485/optionsparser.py

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+import yaml
+
+def get_parameters(config_file, required, defaults):
+    '''
+    Parameters:
+    Optionfile:  FileName of the yaml file containing the options
+    required:    Dict of required argument names and their object types.
+    defaults:    Dict of default parameters mapping to their default values
+    
+    Returns:     An object with fields named according to required and optional values.
+    '''
+    f = open(config_file)
+    options = yaml.safe_load(f)
+    # create a parameters object that allows setting attributes.
+    parameters = type('Options', (), {})()
+    # check required arguments
+    for arg in required:
+        if not arg in options:
+            raise Exception("Could not find required Argument " + arg + " aborting...")
+        else:
+            if not isinstance(options[arg],required[arg]):
+                raise Exception("Expected input of type " + str(required[arg]) + " but got " + str(type(options[arg])))                
+        print("Setting " + arg + " to " + str(options[arg]))
+        setattr(parameters,arg,options[arg])
+    # check the default values.          
+    for arg in defaults:
+        if arg in options:
+            if not isinstance(options[arg],type(defaults[arg])):
+                #Wrong type for the parameter
+                raise Exception("Expected input of type " + str(type(defaults[arg])) + " but got " + str(type(options[arg])))
+            print("Setting " + arg + " to " + str(options[arg]))
+            setattr(parameters,arg,options[arg])
+        else:
+            print( arg + " not found in option file. Using default: " +str(defaults[arg]))
+            setattr(parameters,arg,defaults[arg])
+    return parameters
+
+

_downloads/a99f82e01864794e5780d2697d273d9e/code_style_example.py

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+import  numpy  as np
+
+def  PI_estimate(n):
+    """This function calculates an estimate of pi with dart thrower algorithm.
+    """
+
+    pi_Numbers =  np.random.random(size = 2*n)
+    x = pi_Numbers[ :n ]
+    y = pi_Numbers[ n: ]
+
+    return 4*np.sum((x * x + y*y ) < 1)/n
+
+
+for number  in range(1,8):
+
+    n = 10** number
+
+    print(f'Estimate for PI with {n:8d} dart throws: {PI_estimate( n )}')

_downloads/b1df8a26f353860c500cc194df1641aa/exercise2_solution.py

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+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def dice_toss(n, m):
+    """Throw n dice m times and the total value together."""
+    dice_rolls = np.random.randint(1, 6, size=(m, n))
+
+    roll_averages = np.sum(dice_rolls, axis=-1)
+
+    return roll_averages
+
+
+fig, ax = plt.subplots()
+
+n = int(input("Number of dices to toss:\n"))
+
+bins = np.arange(1, 6 * n + 1)
+
+m = 1000
+
+ax.hist(dice_toss(n, m), bins=bins)
+
+ax.set_title(f"Histogram of {n} dice tosses")
+
+ax.set_xlabel("Total value")
+
+ax.set_ylabel("Number of instances")
+
+plt.show()

_downloads/bd9ea3f34382e553b2a8efaca3708746/exercise1_solution.py

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+"""
+pylint exercise 1
+"""
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn import linear_model
+
+
+def f(x):
+    """
+    Example function:
+
+    f(x) = x/2 + 2
+    """
+    return 0.5*x + 2
+
+
+# Create example data
+x_data = np.linspace(0, 10, 100)
+err = 2 * np.random.random(x_data.shape[0])
+y_data = f(x_data) + err
+
+# Put data into dataframe
+df = pd.DataFrame({'x': x_data, 'y': y_data})
+
+# Create linear model and fit data
+reg = linear_model.LinearRegression(fit_intercept=True)
+
+reg.fit(df[['x']], df[['y']])
+
+slope = reg.coef_[0][0]
+intercept = reg.intercept_[0]
+
+df['pred'] = reg.predict(df[['x']])
+
+fig, ax = plt.subplots()
+
+ax.scatter(df[['x']], df[['y']], alpha=0.5)
+ax.plot(df[['x']], df[['pred']],
+        color='black', linestyle='--',
+        label=f'Prediction with slope {slope:.2f} and intercept {intercept:.2f}')
+ax.set_ylabel('y')
+ax.set_xlabel('x')
+ax.legend()
+
+plt.show()

_downloads/c0ff880c08336404fab105236689d632/exercise2.py

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+import numpy as np
+import matplotlib.pyplot  as plt
+
+def dice_toss(n,m):
+
+    """Throw n dice m times and the total value together."""
+    dice_rolls    = np.random.randint(1,6,size=(m, n))
+
+    roll_averages = np.sum(dice_rolls,axis = -1)
+
+    return roll_averages
+fig,ax = plt.subplots( )
+
+n = int( input('Number of dices to toss:\n'))
+
+bins = np.arange(1, 6 * n+1)
+
+m = 1000
+
+ax.hist(dice_toss(n,m), bins = bins)
+
+ax.set_title(f'Histogram of {n} dice tosses')
+
+ax.set_xlabel('Total value' )
+
+ax.set_ylabel('Number of instances')
+
+plt.show()