import glob

import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline

def analyze_stats(filename):
    data = np.loadtxt(fname=filename, delimiter=',')
    return data.mean(0), data.max(0), data.min(0)

datalen = 40
filenames = glob.glob('inflammation*.csv')
nofiles = len(filenames)
datamin = np.empty((nofiles, datalen))
datamean = np.empty_like(datamin)
datamax = np.empty_like(datamin)

count = 0
for f in filenames:
    datamean[count], datamax[count], datamin[count] = analyze_stats(f)
    count += 1

for count, f in enumerate(filenames):
    datamean[count], datamax[count], datamin[count] = analyze_stats(f)

plt.imshow(datamax)
plt.xlabel('Time')
plt.ylabel('Dataset')
plt.title('Max Values')
plt.show()

plt.imshow(datamean)
plt.xlabel('Time')
plt.ylabel('Dataset')
plt.title('Mean Values')
plt.show()

overallmax = datamax.max(0)
plt.plot(overallmax)

for count in range(nofiles):
    for time in range(datalen):
        if datamax[count,time] - overallmax[time] == -1:
            plt.plot(time, datamax[count, time], 's')
        elif datamax[count,time] < overallmax[time]:
            plt.plot(time, datamax[count, time], 'x')
        else:
            plt.plot(time, datamax[count, time], '.')

plt.xlabel('Time')
plt.ylabel('Max Value')
plt.show()

if (1 > 0) and (-1 > 0):
    print 'both parts are true'
else:
    print 'one part is not true'

if (1 < 0) or ('left' < 'right'):
    print 'at least one test is true'

def sherlock(filenames, datalen=40):
    datamax = np.empty((len(filenames), datalen))
    for count, f in enumerate(filenames):
        datamax[count] = analyze_stats(f)[1]
    plot_clues(datamax)

    
def plot_clues(datamax):
    overallmax = datamax.max(0)
    plt.plot(overallmax)
    size = datamax.shape

    for count in range(size[0]):
        for time in range(size[1]):
            if datamax[count, time] - overallmax[time] == -1:
                plt.plot(time, datamax[count, time], 's')
            elif datamax[count, time] < overallmax[time]:
                plt.plot(time, datamax[count, time], 'x')
            else:
                plt.plot(time, datamax[count, time], '.')
    plt.title(
        "Overall Maximum and Deviations Away from It\n"
        "dots = same as overall mean\n"
        "squares = exactly 1 unit less")
    plt.xlabel("Time (days)")
    plt.ylabel("Inflammation (units)")
    plt.show()

sherlock(glob.glob('inflammation*.csv'))