<gh_stars>0
# testErrorComp.py

# Import Packages
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from diststats import *

# Initialize some stuff
loc, scale = 0., 100.
samples = 60
bincount = 29



def setBins(bincount,lbound,ubound):
    # Define the bins we will use.
    return [r * ((ubound-lbound)/bincount) for r in range(0,bincount + 1)]


def plotComp(pdist,qdist,statstitle):
    # Settings for plot
    pos = list(range(len(pdist)))
    width = 0.25

    # Plotting the bars
    fig, ax = plt.subplots(figsize=(10,5))

    # Create plot for P
    plt.bar(
        pos,
        pdist,
        width,
        alpha=0.5,
        color='#EE3224',
        label="P"
        )

    # Create plot for Q
    plt.bar(
        [j + width for j in pos],
        qdist,
        width,
        alpha=0.5,
        color='#3539B8',
        label="Q"
        )

    # Set Title
    ax.set_title(statstitle)
    
    # Setting the x-axis and y-axis limits
    plt.xlim(min(pos)-width, max(pos)+width*4)
    plt.ylim([0, max(max(pdist),max(qdist))] )

    # Plot it
    plt.legend(['P','Q'])
    plt.grid()
    plt.show()




# Build out the dummy data structure
# These samples have their own parameters, including what kind of distributions.
# These parameters - to some extent - could be controlled by users.
psamp = pd.DataFrame(np.random.uniform(loc,scale,samples), columns = ['P'])
qsamp = pd.DataFrame(np.random.uniform(loc,scale,samples), columns = ['Q'])
#psamp = pd.DataFrame(np.random.normal(30,20,samples), columns = ['P'])
#qsamp = pd.DataFrame(np.random.normal(40,10,samples), columns = ['Q'])

# Get the bins. The number of bins is user settable,
# but the loc and scale are internal params that don't need to change.
bins = setBins(bincount,loc,scale)

# Create the histogram
pcount, pbins = np.histogram(psamp,bins)
qcount, qbins = np.histogram(qsamp,bins)

# Calculate the stats from the histograms.
# These can be displayed on the chart as shown below.
# Here, we also print to the terminal for convenience.
jsd1 = jsd(pcount,qcount)
print("jsd = %f" % jsd1)
print("rjsd = %f" % sqrt(jsd1))
rmse1 = rmse(pcount,qcount)
print("rmse = %f" % rmse1)
#print("normed rmse = %f" % (rmse1/np.sum(pcount)))

# Create the title and plot the histograms.
stats = "Jensen-Shannon Divergence (JSD) = %f\n Root Mean Squared Error = %f" % (jsd1,rmse1)
plotComp(pcount,qcount,stats)




# testErrorComp.py

# Import Packages
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from diststats import *

# Initialize some stuff
loc, scale = 0., 100.
samples = 60
bincount = 29



def setBins(bincount,lbound,ubound):
    # Define the bins we will use.
    return [r * ((ubound-lbound)/bincount) for r in range(0,bincount + 1)]


def plotComp(pdist,qdist,statstitle):
    # Settings for plot
    pos = list(range(len(pdist)))
    width = 0.25

    # Plotting the bars
    fig, ax = plt.subplots(figsize=(10,5))

    # Create plot for P
    plt.bar(
        pos,
        pdist,
        width,
        alpha=0.5,
        color='#EE3224',
        label="P"
        )

    # Create plot for Q
    plt.bar(
        [j + width for j in pos],
        qdist,
        width,
        alpha=0.5,
        color='#3539B8',
        label="Q"
        )

    # Set Title
    ax.set_title(statstitle)
    
    # Setting the x-axis and y-axis limits
    plt.xlim(min(pos)-width, max(pos)+width*4)
    plt.ylim([0, max(max(pdist),max(qdist))] )

    # Plot it
    plt.legend(['P','Q'])
    plt.grid()
    plt.show()




# Build out the dummy data structure
# These samples have their own parameters, including what kind of distributions.
# These parameters - to some extent - could be controlled by users.
psamp = pd.DataFrame(np.random.uniform(loc,scale,samples), columns = ['P'])
qsamp = pd.DataFrame(np.random.uniform(loc,scale,samples), columns = ['Q'])
#psamp = pd.DataFrame(np.random.normal(30,20,samples), columns = ['P'])
#qsamp = pd.DataFrame(np.random.normal(40,10,samples), columns = ['Q'])

# Get the bins. The number of bins is user settable,
# but the loc and scale are internal params that don't need to change.
bins = setBins(bincount,loc,scale)

# Create the histogram
pcount, pbins = np.histogram(psamp,bins)
qcount, qbins = np.histogram(qsamp,bins)

# Calculate the stats from the histograms.
# These can be displayed on the chart as shown below.
# Here, we also print to the terminal for convenience.
jsd1 = jsd(pcount,qcount)
print("jsd = %f" % jsd1)
print("rjsd = %f" % sqrt(jsd1))
rmse1 = rmse(pcount,qcount)
print("rmse = %f" % rmse1)
#print("normed rmse = %f" % (rmse1/np.sum(pcount)))

# Create the title and plot the histograms.
stats = "Jensen-Shannon Divergence (JSD) = %f\n Root Mean Squared Error = %f" % (jsd1,rmse1)
plotComp(pcount,qcount,stats)
# testErrorComp.py # Import Packages # Initialize some stuff # Define the bins we will use. # Settings for plot # Plotting the bars # Create plot for P # Create plot for Q # Set Title # Setting the x-axis and y-axis limits # Plot it # Build out the dummy data structure # These samples have their own parameters, including what kind of distributions. # These parameters - to some extent - could be controlled by users. #psamp = pd.DataFrame(np.random.normal(30,20,samples), columns = ['P']) #qsamp = pd.DataFrame(np.random.normal(40,10,samples), columns = ['Q']) # Get the bins. The number of bins is user settable, # but the loc and scale are internal params that don't need to change. # Create the histogram # Calculate the stats from the histograms. # These can be displayed on the chart as shown below. # Here, we also print to the terminal for convenience. #print("normed rmse = %f" % (rmse1/np.sum(pcount))) # Create the title and plot the histograms.