def plot_data(plot_variable):
MPV = False
# These variables are stored per plate, so we have 2 or 4 values
# for one event instead of just 1.
if plot_variable[0][0] in ('pulseheights', 'integrals',
'baseline', 'std_dev', "n_peaks"):
if plot_variable[0][0] in ('pulseheights', 'integrals'):
MPV = query_yes_no('Do you want to PLOT the MPV (Most probable value) of the %s?' % plot_variable[0][0])
if MPV == True:
print ''
interval = question.digit("Select the time interval (in seconds) over which the MPV values must be calculated ( e.g. for a day enter '86400' ): ")
seconds = int(interval)
variable_parts, time, number_of_plates = split_data_file_in_parts(plot_variable, seconds)
#e.g. variable_parts = [[p1, p2...pn], [p1, p2...pn], ....]
# time [t1, t2...tn] times are timestamps in the middle of every time interval
if plot_variable[0][0] == 'pulseheights':
MPV_list, number_of_plates, times = find_MPV_pulseheights(variable_parts, plot_variable, time, number_of_plates)
#times = array(time) necessary?
times_dates = [datetime.fromtimestamp(x) for x in times]
plot_variable1 = [('pulseheights', plot_variable[0][1], plot_variable[0][2], 'events')]
plot_variable2 = [('time', plot_variable[0][1], plot_variable[0][2], 'events')]
values1 = MPV_list
values2 = time
filename = create_correlation_table(plot_variable1, plot_variable2, values1, values2, seconds)
elif plot_variable[0][0] == 'integrals':
MPV_list, number_of_plates = find_MPV_integrals(variable_parts, plot_variable)
values = array(MPV_list)
for i in range(number_of_plates):
y = values[:, i]
plt.plot(times_dates, y)
plt.xlabel('time')
plt.ylabel('%s (%s)' % (plot_variable[0][0], units[plot_variable[0][0]]))
# create filename for correlation table from data filenames
intermediate1 = plot_variable[0][1].replace('data_s%s_' % str(plot_variable[0][2]), '')
intermediate2 = intermediate1.partition('_')
start_date = intermediate2[0]
intermediate1b = plot_variable[-1][1].replace('data_s%s_' % str(plot_variable[-1][2]), '')
intermediate2b = intermediate1b.partition('_')
intermediate3b = intermediate2b[2][1:]
end_date = intermediate3b.replace('.h5', '')
fname = ('MPV_%s_s%s_%s-%s_timeinterval_%d_seconds.png' %
(plot_variable[0][0], plot_variable[0][2], start_date, end_date, seconds))
plt.savefig(fname)
plt.show()
returntype = 'MPV'
else:
time_list = []
plate1 = []
plate2 = []
plate3 = []
plate4 = []
number_of_plates = 0
for i in range(len(plot_variable)):
with tables.openFile(plot_variable[i][1], 'r') as data:
tree_time = "data.root.s%s.%s.col('timestamp')" % (plot_variable[i][2], plot_variable[i][3])
time = eval(tree_time)
tree_variable = "data.root.s%s.%s.col('%s')" % (plot_variable[i][2], plot_variable[i][3], plot_variable[i][0])
variable = eval(tree_variable)
time_list.extend(time)
number_of_plates = get_number_of_plates(variable[0])
plate1.extend(list(variable[:, 0]))
plate2.extend(list(variable[:, 1]))
if number_of_plates == 4:
plate3.extend(list(variable[:, 2]))
plate4.extend(list(variable[:, 3]))
dat_sorted1 = array(sorted(zip(time_list, plate1)))
dat_sorted2 = array(sorted(zip(time_list, plate2)))
if number_of_plates == 4:
dat_sorted3 = array(sorted(zip(time_list, plate3)))
dat_sorted4 = array(sorted(zip(time_list, plate4)))
print ''
print 'Your file(s) contain(s) data from ' + str(datetime.fromtimestamp(dat_sorted1[0][0])) + ' until ' + str(datetime.fromtimestamp(dat_sorted1[-1][0]))
print ''
whole = query_yes_no('Do you want to PLOT this whole time interval')
if whole == True:
times = dat_sorted1[:, 0]
x = [datetime.fromtimestamp(i) for i in times]
y = dat_sorted1[:, 1]
plt.plot(x, y)
y = dat_sorted2[:, 1]
plt.plot(x, y)
if number_of_plates == 4:
y = dat_sorted3[:, 1]
plt.plot(x, y)
y = dat_sorted4[:, 1]
plt.plot(x, y)
values = array(zip(dat_sorted1[:, 1], dat_sorted2[:, 1],
dat_sorted3[:, 1], dat_sorted4[:, 1]))
else:
values = array(zip(dat_sorted1[:, 1], dat_sorted2[:, 1]))
plt.ylabel('%s (%s)' % (plot_variable[0][0], units[plot_variable[0][0]]))
plt.grid(True)
plt.show()
returntype = 'whole'
elif whole == False:
x_lim_low = 2
x_lim_up = 2
while True:
start = datetime.fromtimestamp(dat_sorted1[0][0])
print ''
print 'Start time: ' + str(start)
print 'Seconds in interval: ' + str(dat_sorted1[-1][0] - dat_sorted1[0][0])
print ''
print 'First you are going to enter the LOWER time limit.'
while True:
x_lim_low = question.digit('Enter the number of seconds after the start time shown above ( e.g. input "3600" means x_begin = timestamp + 3600 s ): ')
x_lim_low = int(x_lim_low)
if dat_sorted1[0][0] + x_lim_low <= dat_sorted1[-1][0]:
break
else:
print "Oops! Your lower time limit lies beyond your data set. Try again."
print ''
print 'Now you are going to enter the UPPER time limit.'
while True:
x_lim_up = question.digit('Enter the number of seconds after the start time shown above ( e.g. input "86400" means x_end = timestamp + 86400 s ): ')
x_lim_up = int(x_lim_up)
if x_lim_up > x_lim_low and dat_sorted1[0][0] + x_lim_up <= dat_sorted1[-1][0]:
break
elif x_lim_up <= x_lim_low:
print "Oops! The upper time limit less than or equal to the lower time limit. Try again."
elif dat_sorted1[0][0] + x_lim_up > dat_sorted1[-1][0]:
print "Oops! Your upper time limit lies beyond your data set. Try again."
plot_list1 = array([[t, v] for t, v in dat_sorted1 if dat_sorted1[0][0] + x_lim_low < t < dat_sorted1[0][0] + x_lim_up])
plot_list2 = array([[t, v] for t, v in dat_sorted2 if dat_sorted2[0][0] + x_lim_low < t < dat_sorted2[0][0] + x_lim_up])
if number_of_plates == 4:
plot_list3 = array([[t, v] for t, v in dat_sorted3 if dat_sorted3[0][0] + x_lim_low < t < dat_sorted3[0][0] + x_lim_up])
plot_list4 = array([[t, v] for t, v in dat_sorted4 if dat_sorted4[0][0] + x_lim_low < t < dat_sorted4[0][0] + x_lim_up])
values = array(zip(plot_list1[:, 1], plot_list2[:, 1],
plot_list3[:, 1], plot_list4[:, 1]))
else:
values = array(zip(plot_list1[:, 1], plot_list2[:, 1]))
times = plot_list1[:, 0]
x = [datetime.fromtimestamp(i) for i in times]
plt.plot(x, plot_list1[:, 1])
plt.plot(x, plot_list2[:, 1])
if number_of_plates == 4:
plt.plot(x, plot_list3[:, 1])
plt.plot(x, plot_list4[:, 1])
plt.ylabel('%s (%s)' % (plot_variable[0][0], units[plot_variable[0][0]]))
returntype = 'part'
plt.show()
again = query_yes_no('Do you want to plot again with different values for the UPPER and LOWER time?')
if again != True:
break
else:
variable_list = []
time_list = []
for i in range(len(plot_variable)):
with tables.openFile(plot_variable[i][1], 'r') as data:
tree_time = "data.root.s%s.%s.col('timestamp')" % (plot_variable[i][2], plot_variable[i][3])
time = eval(tree_time)
tree_variable = "data.root.s%s.%s.col('%s')" % (plot_variable[i][2], plot_variable[i][3], plot_variable[i][0])
variable = eval(tree_variable)
time_list.extend(time)
variable_list.extend(variable)
dat_sorted = array(sorted(zip(time_list, variable_list)))
print ''
print 'Your file(s) contain(s) data from %s until %s' % (str(datetime.fromtimestamp(dat_sorted[0][0])), str(datetime.fromtimestamp(dat_sorted[-1][0])))
print ''
whole = query_yes_no('Do you want to PLOT this whole time interval?')
if whole == True:
times = dat_sorted[:, 0]
x = [datetime.fromtimestamp(i) for i in times]
dat_sorted = array(dat_sorted)
values = dat_sorted[:, 1]
plt.plot(x, values)
plt.ylabel('%s (%s)' % (plot_variable[0][0], units[plot_variable[0][0]]))
plt.grid(True)
plt.show()
returntype = 'whole'
elif whole == False:
x_lim_low = 2
x_lim_up = 2
plot_list = []
while True:
start = datetime.fromtimestamp(dat_sorted[0][0])
print ''
print 'Start time = ' + str(start)
print 'Seconds in interval: ' + str(dat_sorted[-1][0] - dat_sorted[0][0])
print ''
print 'First you are going to enter the LOWER time limit.'
while True:
x_lim_low = question.digit('Enter the number of seconds after the start time shown above ( e.g. input "3600" means x_begin = timestamp + 3600 s ): ')
x_lim_low = int(x_lim_low)
if dat_sorted[0][0] + x_lim_low <= dat_sorted[-1][0]:
break
else:
print "Oops! Your lower time limit lies beyond your data set. Try again."
print ''
print 'Now you are going to enter the UPPER time limit.'
while True:
x_lim_up = question.digit('Enter the number of seconds after the start time shown above ( e.g. input "86400" means x_end = timestamp + 86400 s ): ')
x_lim_up = int(x_lim_up)
if x_lim_up > x_lim_low and dat_sorted[0][0] + x_lim_up <= dat_sorted[-1][0]:
break
elif x_lim_up <= x_lim_low:
print "Oops! The upper time limit less than or equal to the lower time limit. Try again."
elif dat_sorted[0][0] + x_lim_up > dat_sorted[-1][0]:
print "Oops! Your upper time limit lies beyond your data set. Try again."
plot_list = []
# e.g. dat_sorted = (timestamp, variable)
for t, v in dat_sorted:
if t > dat_sorted[0][0] + x_lim_low and t < dat_sorted[0][0] + x_lim_up:
plot_list.append([t, v])
plot_list = array(plot_list)
times = plot_list[:, 0]
values = plot_list[:, 1]
x = [datetime.fromtimestamp(i) for i in times]
plt.plot(x, values)
plt.ylabel('%s (%s)' % (plot_variable[0][0], units[plot_variable[0][0]]))
plt.grid(True)
plt.show()
returntype = 'part'
again = query_yes_no('Do you want to plot again with a different time limits?')
if again != True:
break
return values, times, returntype
ID = get_station_ID_from_filename(filename)
print stations
if ID in stations:
list_files.append(filename)
break
else:
print "Oops! The filename you entered does not match the station ID you entered earlier. Try again..."
else:
print "Oops! The filename you entered does not exist. Try again..."
kind_of_data_in_table = kind_of_data(list_files) # e.g. [('data_s501_2011,6,30_2011,6,30.h5', True, True), ('data_s502_2011,6,30_2011,6,30.h5', True, False)]
plot_variable2 = choose_one_variable(kind_of_data_in_table, stations) #e.g. plot_variable = [('event_rate','data_s501_2011,12,7_2011,12,8.h5','501','events')]
if returntype == 'MPV':
if len(times) > 1:
mean_variable_list = get_corresponding_values_MPV(plot_variable2, times)
filen = create_correlation_table(plot_variable1, plot_variable2, values1, mean_variable_list, int(times[1] - times[0]))
least_squares_fit(filen, plot_variable1, plot_variable2)
else:
print 'Correlation is not possible with one data point.'
elif returntype == 'whole':
print 'b'
filen = interpolate(plot_variable1, plot_variable2) # e.g. cor_data_barometer_501_event_rate_502.h5
least_squares_fit(filen, plot_variable1, plot_variable2)
elif returntype == 'part':
complementary_variable2_list = get_corresponding_values_selection(plot_variable2, times)
filen = interpolate_selection(array(zip(times, values1)), complementary_variable2_list)
least_squares_fit(filen, plot_variable1, plot_variable2)
else:
