Lots of bug-fixes, to adjust for newer version of packages

Author: thomas-haslwanter

ISP/Code_Quantlets/04_DataDisplay/gettingStarted/ISP_gettingStarted.py

@@ -1,102 +1,102 @@
-'''Short demonstration of Python for scientific data analysis
-
-This script covers the following points:
-* Plotting a sine wave
-* Generating a column matrix of data
-* Writing data to a text-file, and reading data from a text-file
-* Waiting for a button-press to continue the program exectution
-    (Note: this does NOT work in ipython, if you run it with inline figures!)
-* Using a dictionary, which is similar to MATLAB structures
-* Extracting data which fulfill a certain condition
-* Calculating the best-fit-line to noisy data
-* Formatting text-output
-* Waiting for a keyboard-press
-* Calculating confidence intervals for line-fits
-* Saving figures
-
-For such a short program, the definition of a "main" function, and calling
-it by default when the module is imported by the main program, is a bit
-superfluous. But it shows good Python coding style.
-'''
-
-# Copyright(c) 2017, Thomas Haslwanter. All rights reserved, under the CC BY-SA 4.0 International License
-
-# In contrast to MATLAB, you explicitly have to load the modules that you need.
-import numpy as np
-import matplotlib.pyplot as plt
-
-def main():
-    '''Define the main function. '''
-    
-    # Create a sine-wave
-    t = np.arange(0,10,0.1)
-    x = np.sin(t)
-
-    # Save the data in a text-file, in column form
-    # The formatting is a bit clumsy: data are by default row variables; so to
-    # get a matrix, you stack the two rows above each other, and then transpose
-    # the matrix
-    outFile = 'test.txt'
-    np.savetxt(outFile, np.vstack([t,x]).T)
-
-    # Read the data into a different variable
-    inData = np.loadtxt(outFile)
-    t2 = inData[:,0] # Note that Python starts at "0"!
-    x2 = inData[:,1]
-
-    # Plot the data, and wait for the user to click
-    plt.plot(t2,x2)
-    plt.title('Hit any key to continue')
-    plt.waitforbuttonpress()
-
-    # Generate a noisy line
-    t = np.arange(-100,100)
-    # use a Python "dictionary" for named variables
-    par = {'offset':100, 'slope':0.5, 'noiseAmp':4}
-    x = par['offset'] + par['slope']*t + par['noiseAmp']*np.random.randn(len(t))
-
-    # Select "late" values, i.e. with t>10
-    xHigh = x[t>10]
-    tHigh = t[t>10]
-
-    # Plot the "late" data
-    plt.close()
-    plt.plot(tHigh, xHigh)
-
-    # Determine the best-fit line
-    # To do so, you have to generate a matrix with "time" in the first
-    # column, and a column of "1" in the second column:
-    xMat = np.vstack((tHigh, np.ones_like(tHigh))).T
-    slope, intercept = np.linalg.lstsq(xMat, xHigh)[0]
-
-    # Show and plot the fit, and save it to a PNG-file with a medium resolution.
-    # The "modern" way of Python-formatting is used
-    plt.plot(tHigh, intercept + slope*tHigh, 'r')
-    plt.title('Hit any key to continue')
-    plt.savefig('linefit.png', dpi=200)
-    plt.waitforbuttonpress()
-    plt.close()
-    print(('Fit line: intercept = {0:5.3f}, and slope = {1:5.3f}'.format(intercept, slope)))
-
-    # If you want to know confidence intervals, best switch to "pandas"
-    # Note that this is an advanced topic, and requires new data structures
-    # such ad "DataFrames" and "ordinary-least-squares" or "ols-models".
-    
-    import pandas as pd
-    import statsmodels.formula.api as smf
-    
-    # Put the data into a pandas DataFrame
-    myDict = {'x':tHigh, 'y':xHigh}
-    df = pd.DataFrame(myDict)
-    
-    # Fit the model: here the "formula"-syntax commonly used in statistics is employed
-    # 'y~x' means "y is a linear function of x, taking a possible offset into consideration"
-    results = smf.ols('y~x', data=df).fit()
-    
-    # Print the results
-    print(results.summary())
-    #raw_input('These are the summary results from Pandas - Hit any key to continue')
-
-
-if __name__=='__main__':
-    main()    # Execute the main function
+'''Short demonstration of Python for scientific data analysis
+
+This script covers the following points:
+* Plotting a sine wave
+* Generating a column matrix of data
+* Writing data to a text-file, and reading data from a text-file
+* Waiting for a button-press to continue the program exectution
+    (Note: this does NOT work in ipython, if you run it with inline figures!)
+* Using a dictionary, which is similar to MATLAB structures
+* Extracting data which fulfill a certain condition
+* Calculating the best-fit-line to noisy data
+* Formatting text-output
+* Waiting for a keyboard-press
+* Calculating confidence intervals for line-fits
+* Saving figures
+
+For such a short program, the definition of a "main" function, and calling
+it by default when the module is imported by the main program, is a bit
+superfluous. But it shows good Python coding style.
+'''
+
+# Copyright(c) 2017, Thomas Haslwanter. All rights reserved, under the CC BY-SA 4.0 International License
+
+# In contrast to MATLAB, you explicitly have to load the modules that you need.
+import numpy as np
+import matplotlib.pyplot as plt
+
+def main():
+    '''Define the main function. '''
+    
+    # Create a sine-wave
+    t = np.arange(0,10,0.1)
+    x = np.sin(t)
+
+    # Save the data in a text-file, in column form
+    # The formatting is a bit clumsy: data are by default row variables; so to
+    # get a matrix, you stack the two rows above each other, and then transpose
+    # the matrix
+    outFile = 'test.txt'
+    np.savetxt(outFile, np.vstack([t,x]).T)
+
+    # Read the data into a different variable
+    inData = np.loadtxt(outFile)
+    t2 = inData[:,0] # Note that Python starts at "0"!
+    x2 = inData[:,1]
+
+    # Plot the data, and wait for the user to click
+    plt.plot(t2,x2)
+    plt.title('Hit any key to continue')
+    plt.waitforbuttonpress()
+
+    # Generate a noisy line
+    t = np.arange(-100,100)
+    # use a Python "dictionary" for named variables
+    par = {'offset':100, 'slope':0.5, 'noiseAmp':4}
+    x = par['offset'] + par['slope']*t + par['noiseAmp']*np.random.randn(len(t))
+
+    # Select "late" values, i.e. with t>10
+    xHigh = x[t>10]
+    tHigh = t[t>10]
+
+    # Plot the "late" data
+    plt.close()
+    plt.plot(tHigh, xHigh)
+
+    # Determine the best-fit line
+    # To do so, you have to generate a matrix with "time" in the first
+    # column, and a column of "1" in the second column:
+    xMat = np.vstack((tHigh, np.ones_like(tHigh))).T
+    slope, intercept = np.linalg.lstsq(xMat, xHigh, rcond=None)[0]
+
+    # Show and plot the fit, and save it to a PNG-file with a medium resolution.
+    # The "modern" way of Python-formatting is used
+    plt.plot(tHigh, intercept + slope*tHigh, 'r')
+    plt.title('Hit any key to continue')
+    plt.savefig('linefit.png', dpi=200)
+    plt.waitforbuttonpress()
+    plt.close()
+    print(('Fit line: intercept = {0:5.3f}, and slope = {1:5.3f}'.format(intercept, slope)))
+
+    # If you want to know confidence intervals, best switch to "pandas"
+    # Note that this is an advanced topic, and requires new data structures
+    # such ad "DataFrames" and "ordinary-least-squares" or "ols-models".
+    
+    import pandas as pd
+    import statsmodels.formula.api as smf
+    
+    # Put the data into a pandas DataFrame
+    myDict = {'x':tHigh, 'y':xHigh}
+    df = pd.DataFrame(myDict)
+    
+    # Fit the model: here the "formula"-syntax commonly used in statistics is employed
+    # 'y~x' means "y is a linear function of x, taking a possible offset into consideration"
+    results = smf.ols('y~x', data=df).fit()
+    
+    # Print the results
+    print(results.summary())
+    #raw_input('These are the summary results from Pandas - Hit any key to continue')
+
+
+if __name__=='__main__':
+    main()    # Execute the main function