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gcamp_temperature.py
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gcamp_temperature.py
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import pandas as pd
import numpy as np
import argparse
from matplotlib import cm
import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms
import matplotlib.collections as collections
from matplotlib.collections import LineCollection
from matplotlib.colors import ListedColormap, BoundaryNorm
from scipy import stats as st
def twin_axis_plot(xvar, var1, var2, xlabel, label1, label2):
ax1 = plt.gca()# = plt.subplots()
ax1.plot(xvar, var1, 'b-')
ax1.set_xlabel(xlabel)
# Make the y-axis label and tick labels match the line color.
ax1.set_ylabel(label1, color='b')
for tl in ax1.get_yticklabels():
tl.set_color('b')
ax2 = ax1.twinx()
ax2.plot(xvar, var2, 'r.')
ax2.set_ylabel(label2, color='r')
for tick in ax1.xaxis.get_major_ticks():
tick.label.set_fontsize(10)
# specify integer or one of preset strings, e.g.
#tick.label.set_fontsize('x-small')
tick.label.set_rotation('vertical')
for tl in ax2.get_yticklabels():
tl.set_color('r')
return
def plot_data(means, sems, ns, measurement, ax):
means = means[means[measurement].notnull()]
#means = 1.0 - means
#ns = ns[ns[measurement].notnull()]
#sems = sems[sems[measurement].notnull()]
#fig = plt.figure()
group_number = 0
#ax = fig.add_subplot(1,1,1)
y_range = []
x_range = []
laser_x = []
for x in means.index.levels[0]:
max_n = ns.ix[x]['ExpID'].max()
x_values = []
y_values = []
psems = []
nsems = []
for w in means.ix[x].index:
laser_x.append((w-pd.to_datetime(0)).total_seconds())
if ns.ix[x]['ExpID'][w] > ((max_n)-3): #(max_n/3):
x_range.append((w-pd.to_datetime(0)).total_seconds())
#print ns.ix[x]['FlyID'][w]
x_values.append((w-pd.to_datetime(0)).total_seconds())
y_values.append(means.ix[x,w][measurement])
psems.append(sems.ix[x,w][measurement])
nsems.append(-1.0*sems.ix[x,w][measurement])
#x_values = list((means.ix[x].index - pd.to_datetime(0)).total_seconds())
#y_values = list(means.ix[x][measurement])
#psems = list(sems.ix[x][measurement])
#nsems = list(-1*(sems.ix[x][measurement]))
y_range.append(np.amin(y_values))
y_range.append(np.amax(y_values))
top_errbar = tuple(map(sum, zip(psems, y_values)))
bottom_errbar = tuple(map(sum, zip(nsems, y_values)))
p = plt.plot(x_values, y_values, linewidth=3, zorder=100,
linestyle = '-',
color=colourlist[group_number],
label=(str(x) + ', n= ' + str(max_n)))
q = plt.fill_between(x_values,
top_errbar,
bottom_errbar,
alpha=0.15,
linewidth=0,
zorder=90,
color=colourlist[group_number],
)
group_number += 1
if 'maxWingAngle' in measurement:
ax.set_ylabel('Mean maximum wing angle (rad)' + ' ' + u"\u00B1" + ' SEM', fontsize=16) # +/- sign is u"\u00B1"
elif 'dtarget' in measurement:
ax.set_ylabel('Mean min. distance to target (mm)' + ' ' + u"\u00B1" + ' SEM', fontsize=16) # +/- sign is u"\u00B1"
else:
ax.set_ylabel('Mean ' + measurement + ' ' + u"\u00B1" + ' SEM', fontsize=16)
ax.set_xlabel('Time (s)', fontsize=16)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
plt.legend()
return
def get_stats( _df):
ROI_START = _df[_df.Light > 0].index[-1]+1 #index of _df to start critical region
ROI_END = _df[_df.Temperature > 26.0].index[0] #index of _df to end critical region
BASELINE_END = _df[_df.Light > 0].index[0]
baseline = _df[0:BASELINE_END]
#roi = _df[ROI_START: ROI_END]
pvals = []
for x in _df[ROI_START:ROI_END].index:
pvals.append(st.kruskal(baseline['dff'], _df.loc[x:x+1, 'dff')[1])
return np.array(pvals).mean()
############################################multi-colour tools ########################################################################
def make_segments(x, y):
'''
Create list of line segments from x and y coordinates, in the correct format for LineCollection:
an array of the form numlines x (points per line) x 2 (x and y) array
'''
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
return segments
# Interface to LineCollection:
def colorline(x, y, z=None, cmap=plt.get_cmap('jet'), norm=plt.Normalize(0.0, 1.0), linewidth=3, alpha=1.0, zorder=100):
'''
Plot a colored line with coordinates x and y
Optionally specify colors in the array z
Optionally specify a colormap, a norm function and a line width
'''
# Default colors equally spaced on [0,1]:
if z is None:
z = np.linspace(0.0, 1.0, len(x))
# Special case if a single number:
if not hasattr(z, "__iter__"): # to check for numerical input -- this is a hack
z = np.array([z])
z = np.asarray(z)
segments = make_segments(x, y)
lc = LineCollection(segments, array=z, cmap=cmap, norm=norm, linewidth=linewidth, alpha=alpha, zorder=zorder)
ax = plt.gca()
ax.set_xlim(0.85*x.min(), 1.15*x.max())
ax.set_ylim(1.15*y.min(), 1.15*y.max())
ax.add_collection(lc)
return lc
def clear_frame(ax=None):
# Taken from a post by Tony S Yu
if ax is None:
ax = plt.gca()
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
for spine in ax.spines.itervalues():
spine.set_visible(False)
############################################################################################################
if __name__ == "__main__":
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--experiment', type=str, required=True,
help='path and filename of csv containing experiment info')
parser.add_argument('--savedir', type=str, required=False, default='undefined',
help='path to save directory, default is same directory as experiment file')
parser.add_argument('--tresholds', type=int, required=False, default='undefined',
help='List the upper and lower bounds of acceptable pvalues during the post-opto pre-thermo time period. eg: "0.05,0.0001"')
args = parser.parse_args()
if args.savedir == 'undefined':
SAVEDIR = args.experiment.rsplit('/',1)[0]
else:
SAVEDIR = args.SAVEDIR
if not SAVEDIR[-1] == '/':
SAVEDIR = SAVEDIR + '/'
if args.thresholds != 'undefined':
LOWER_BOUND, UPPER_BOUND = [float(x) for x in args.thresholds.split(',')]
else:
LOWER_BOUND, UPPER_BOUND = [0, 1]
read_column = 'Intensity Region 1'
background_column = 'Intensity Region 2'
BASELINE_DURATION = 10 #seconds
SAMPLING_RATE = '500ms'
jdict = pd.read_csv(args.experiment)
jdict = jdict[jdict['GENOTYPE'].notnull()]
jdict['calcfn'] = ['/groups/dickson/dicksonlab' + z.split('Z:')[1] for z in jdict['calcfn'].values]
jdict['tempfn'] = ['/groups/dickson/dicksonlab' + z.split('Z:')[1] for z in jdict['tempfn'].values]
#jdict = jdict[jdict['DATE'] >= 160919]
jdict['notes'] = jdict['notes'].fillna('')
#jdict = jdict[jdict['notes'].str.contains('Acetylcholine')]
jdict = jdict[jdict['include'] == 1].reset_index()
datadf = pd.DataFrame()
for expNum in range(len(jdict)):
try:
calcdata = pd.read_table(jdict['calcfn'][expNum])
print jdict['calcfn'][expNum]
#filename = '-'.join((jdict['calcfn'][expNum]).split(SAVEDIR.split('/')[-2])[1].split('/')).split('.txt')[0] + '.png'
filename = '-'.join((jdict['calcfn'][expNum]).split('/')[-3:]).rsplit('.',1)[0] + '_' + str(expNum) + '.png'
genotype = jdict['GENOTYPE'][expNum]
calccolumn = calcdata.columns[jdict['calcColNum'][expNum]-1]
bgcol = calcdata.columns[jdict['bgColNum'][expNum]-1]
tempdata = pd.read_table(jdict['tempfn'][expNum])
tempcolumn = jdict['tempColName'][expNum]
lightcolumn = jdict['lightColName'][expNum]
calcdata = calcdata[:-1]
calcdata.index = pd.to_datetime(calcdata['Time [s]'], unit='s')
calcdata['df'] = calcdata[calccolumn] - calcdata[bgcol]
baseline = calcdata[calcdata.index <= pd.to_datetime(BASELINE_DURATION*1E9)]['df']
calcdata['dff'] = (calcdata['df'] - baseline.mean()) / baseline.mean()
tempdata.index = pd.to_datetime(tempdata['Time'], unit='s')
#tempdata = tempdata[tempcolumn]
#tempdata.columns = ['Temperature']
foo = calcdata.merge(tempdata, how='outer', left_index=True, right_index=True).resample(SAMPLING_RATE)
tempdf = foo[['Time [s]','dff',tempcolumn,lightcolumn]]
tempdf.columns = ['Time','dff','Temperature','Light']
tempdf['ExpID'] = filename.split('.png')[0]
tempdf['Genotype'] = genotype
tempdf.to_csv(SAVEDIR +'-'.join((jdict['calcfn'][expNum]).split('/')[-3:]).rsplit('.',1)[0] + '_' + str(expNum) + '.csv', sep=',')
############# RESPONSE-DEPENDENT MERGE HERE
checkthis = get_stats(tempdf) ####
print checkthis
if checkthis < UPPER_BOUND:
datadf = pd.concat([datadf,tempdf], axis=0)
fig = plt.figure()
axA = fig.add_subplot(211)
twin_axis_plot(foo.index, foo['dff'], foo['28 Temp'], 'Time (hh:mm:ss)', 'dF/F0', 'Temperature (C)')
axB = fig.add_subplot(212)
colorline(foo['28 Temp'], foo['dff'])
axB.set_xlabel('Temperature (C)')
axB.set_ylabel('dF/F0')
plt.subplots_adjust(hspace=0.75)
plt.savefig(SAVEDIR+filename)
plt.close('all')
except:
print 'ERROR PROCESSING:', jdict['calcfn'][expNum]
continue
g = datadf.groupby(['Genotype',datadf.index])
g.mean().unstack(level=0)['dff'].plot()
plt.xlim(pd.to_datetime(0),pd.to_datetime(600*1e9))
g.mean().unstack(level=0)['Temperature'].plot()
plt.xlim(pd.to_datetime(0),pd.to_datetime(600*1e9))
plt.show()
means = g.mean()
sems = g.sem()
ns = g.count()
means.to_csv(SAVEDIR + 'means.csv', sep=',')
sems.to_csv(SAVEDIR + 'sems.csv', sep=',')
ns.to_csv(SAVEDIR + 'ns.csv', sep=',')
colourlist = ['#FF0000','#009020','#0000CC']
fig = plt.figure()
ax1 = fig.add_subplot(111)
plot_data(means,sems, ns, 'dff',ax1)
t = datadf.groupby([datadf.index]).mean().reset_index()
ax1.set_xlabel('Time')
# Make the y-axis label and tick labels match the line color.
ax1.set_ylabel('dF/F0', color='k')
ax1.set_yscale('symlog')
plt.axhline(y=0, linestyle='--', color='k')
for tl in ax1.get_yticklabels():
tl.set_color('b')
ax2 = ax1.twinx()
print t.columns
ax2.plot(t['Time'], t['Temperature'], 'k.')
ax2.set_ylabel('Temperature (C)', color='k')
#ax2.set_ylim(0,32)
plt.show()
fig = plt.figure()
ax1 = fig.add_subplot(111)
plot_data(means,sems, ns, 'Temperature',ax1)
t = datadf.groupby([datadf.index]).mean().reset_index()
ax1.set_xlabel('Time')
# Make the y-axis label and tick labels match the line color.
ax1.set_ylabel('Temperature', color='k')
for tl in ax1.get_yticklabels():
tl.set_color('b')
ax2 = ax1.twinx()
print t.columns
ax2.plot(t['Time'], t['Temperature'], 'k.')
ax2.set_ylabel('Temperature (C)', color='k')
#ax2.set_ylim(0,32)
plt.show()