x = [str(i) for i in x]

y = [str(i) for i in y]

combo = zip(x, y)

list = []

for i in range(len(combo)):

if combo[i][0] != 'nan' and combo[i][1] != 'nan':

list.append(combo[i])

x, y = zip(*list)

x = [float(i) for i in x]

x = array(x)

y = [float(i) for i in y]

y = array(y)

first_file = var1[0][1]

station_ID = var1[0][2]

inter_string1a = first_file.partition(station_ID + '_')

inter_string1b = inter_string1a[2].partition('_')

start_date_interval = inter_string1b[0]

second_file = var1[-1][1]

inter_string2a = second_file.partition(station_ID + '_')

inter_string2b = inter_string2a[2].partition('_')

stop_date_interval = inter_string2b[2].replace('.h5', '')

with tables.openFile(filename, 'r') as data:

# fetch values variable 1 and 2

variable_1 = data.root.correlation.table.col('variable1')

variable_2 = data.root.correlation.table.col('variable2')

y_axis = query_yes_no("Do you want to plot %s on the y-axis?" % variable1[0][0])

if len(variable_1.shape) != 1:

print 'There are %d plates with an individual %s value.' % (variable_1.shape[1], variable1[0][0])

plate_number1 = int(question.digit_plate("Enter the plate number that you want to you use in your correlation analysis ( e.g. '1' ): ", variable_1.shape[1]))

variable_1 = variable_1[:, plate_number1 - 1]

if len(variable_2.shape) != 1:

print 'There are %d plates with an individual %s value.' % (variable_2.shape[1], variable2[0][0])

plate_number2 = int(question.digit_plate("Enter the plate number that you want to you use in your correlation analysis ( e.g. '1' ): ", variable_2.shape[1]))

variable_2 = variable_2[:, plate_number2 - 1]

if y_axis == True:

y = variable_1 # e.g. 'event_rates'

x = variable_2 # e.g. 'barometric pressure'

x, y = lose_nans(x, y)

elif y_axis == False:

x = variable_1 # e.g. 'event_rates'

y = variable_2 # e.g. 'barometric pressure'

else:

print 'weird'

del variable_1, variable_2

# Apply a linear least square fit:

# a line, ``y = mx + c``, through the data-points:

# We can rewrite the line equation as ``y = Ap``, where ``A = [[x 1]]``

# and ``p = [[m], [c]]``. Now use `lstsq` to solve for `p`:

A = np.vstack([x, np.ones(len(x))]).T

a, b = np.linalg.lstsq(A, y)[0]

del A

if y_axis == True:

print ''

print "The equation for the linear fit line is: ( y = a * x + b ) y = " + str(a) + " * x + " + str(b)

print ''

print "or '" + variable1[0][0] + "' = " + str(a) + " * '" + variable2[0][0] + "' + " + str(b)

elif y_axis == False:

print ''

print "The equation for the linear fit line is: ( y = a * x + b ) y = " + str(a) + " * x + " + str(b)

print ''

print "or '" + variable2[0][0] + "' = " + str(a) + " * '" + variable1[0][0] + "' + " + str(b)

# Calculate sample pearson correlation coefficient

cor_coef = np.corrcoef([x, y])[0, 1]

absolute_cor_coef = abs(cor_coef)

print ''

pearson_text = "The Pearson correlation coefficient between '%s' and '%s' is: %s" % (variable1[0][0], variable2[0][0], str(cor_coef))

print pearson_text

print ''

if absolute_cor_coef < 0.1:

correlation = 'NO'

elif 0.1 <= absolute_cor_coef <= 0.3:

correlation = 'a SMALL'

elif 0.3 <= absolute_cor_coef <= 0.5:

correlation = 'a MEDIUM'

elif 0.5 <= absolute_cor_coef <= 1:

correlation = 'a STRONG'

else:

correlation = ''

if cor_coef >= 0.1:

pos_neg = ' POSITIVE'

elif cor_coef <= -0.1:

pos_neg = ' NEGATIVE'

else:

pos_neg = ''

conclusion = "For this sample you have found %s%s correlation between '%s' and '%s'." % (correlation, pos_neg, variable1[0][0], variable2[0][0])

print conclusion

"""

# calculate chi squared

list_exp = array([a*i + b for i in x])

begin3 = datetime.now()

chi2, p = chisquare(y,list_exp)

end3 = datetime.now()

print end3 - begin3

combo = zip(y,list_exp)

begin = datetime.now()

ch2 = 0

for i in combo:

ch2 = ch2 + (i[0]-i[1]-0.5)**2/i[1]

print 'chi squared is ', ch2

end = datetime.now()

print end - begin

print ''

print 'chi squared:', chi2

print 'associated p-value: ', p

print ''

degrees_of_freedom = (len(x) - 1)

print 'chi squared divided by the number of measurements: ', chi2/degrees_of_freedom

chi2_prob = chisqprob(chi2,degrees_of_freedom) # probability value associated with the provided chi-square value and degrees of freedom

print 'probability value associated with the provided chi-square value and degrees of freedom:', chi2_prob

"""

# Plot the data along with the fitted line:

if(len(x) > 500000):

x, y = downsample(x, y)

plt.plot(x, y, 'o', label='Original data', markersize=1)

plt.plot(x, a * x + b, 'r', label='Fitted line')

if y_axis == True:

plt.ylabel(variable1[0][0] + ' (' + units[variable1[0][0]] + ')')

plt.xlabel(variable2[0][0] + ' (' + units[variable2[0][0]] + ')')

elif y_axis == False:

plt.ylabel(variable2[0][0] + ' (' + units[variable2[0][0]] + ')')

plt.xlabel(variable1[0][0] + ' (' + units[variable1[0][0]] + ')')

tit = "Fit line: ( y = ax + b ) y = " + str(a) + " * x + " + str(b)

plt.legend()

plt.title(tit)

start_date_interval, stop_date_interval = get_date_interval_from_file_names(variable1, variable2)

inter_filename = filename.replace('.h5', '')

fname = inter_filename + ' ' + start_date_interval + '_' + stop_date_interval

plt.savefig(fname + ".png")

plt.show()

fit_info = open(fname + '.txt', 'w')

fit_info.write(tit)

fit_info.write("%s\n" % (''))

fit_info.write(str(pearson_text))

fit_info.write("%s\n" % (''))

fit_info.write(str(conclusion))

fit_info.close

"""

# calculate mean y value

mean_y = sum(y) / len(y)

print 'mean_y = ', mean_y

relative_deviation_from_mean_y_list = []

#relative deviation of the cosmic ray intensity (deltaI/I) from the mean intensity.

for i in range(len(y)):

deviation_of_mean_y = y[i] - mean_y

relative_deviation_from_mean_y = deviation_of_mean_y/mean_y

relative_deviation_from_mean_y_list.append(relative_deviation_from_mean_y)

plt.plot(x,relative_deviation_from_mean_y_list,'o',markersize=1)

plt.ylabel('deltaMPV_p/<MPV_p>')

plt.xlabel('Outside temperature (degrees Celsius)')

tit = "Correlation between the Relative deviation of the MPV of the pulseheight (3h intervals) from the mean MPV value with the outside temperature."

plt.title(tit)

fname = 'Correlation between relative deviation of the MPV of the pulseheights (3h intervals) from the mean MPV value with T_out'

plt.savefig(fname +".png")

plt.show()

"""

"""

# histogram

ys = sorted(y)

bins = int(ys[-1] - ys[0])

histo = plt.hist(y,bins)

print histo[0]

print histo[1]

plt.show()