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axotrace_example_log_hist_random_data.py
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axotrace_example_log_hist_random_data.py
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#import pyhht as HHT
import matplotlib as MP
import matplotlib.pyplot as MPP
import pylab as PL
import re as RE
import pandas as PD
import numpy as NP
import scipy.signal as SIG
#import scipy.ndimage as NDI
from lmfit import minimize as FIT, Parameters as FIT_Params
# import openpyxl as XL
import axographio as AG
import os as OS
### Coverage:
"""
- dictionaries
- lambda
- Objects
- axograph io
- data frames
- plotting & LaTeX
- fiting
"""
### possible ToDos:
"""
- fit double gaussian
- empirical mode decomposition
- plot smoothed curve (NDI.gaussian_filter1d)
"""
###########################
### Global functions ###
### and definitions ###
###########################
def ParamsToDict(params):
params_dict = {}
for key in params.keys():
params_dict[key] = params[key].value
return params_dict
rmse = lambda params, fcn, x, data: \
NP.abs(data - fcn(ParamsToDict(params), x))
units = { '1': 1 \
, 'ms': 1e3 \
, 'pA': 1e12 \
}
the_font = {'family':'sans-serif'
,'sans-serif':['Liberation Sans'] # Helvetica # Liberation Sans
,'weight' : 'normal' \
,'size' : 12 \
}
###########################
### Trace Object ###
###########################
class AxoTrace(object):
# CONSTRUCTOR:
def __init__(self, file_path, trace_nr = 0):
# load trace from axograph file
self.file = file_path
self.date = RE.search(r'([0-9]{6})', self.file ).group(0)
self.filenum = int(RE.search(r' ([0-9]{3}) ', self.file ).group(0))
self.episode = RE.sub('ep', 'episode ', RE.search(r'ep([0-9]{2})', self.file ).group(0) )
axodata = AG.read(self.file)
# extract time
times = [not(elmt is None) for elmt in [RE.search(r'(?i)(Time)', nstr) for nstr in axodata.names]]
trace_t = (NP.array([axodata.data[idx2][:] for idx2 in [idx for idx in range(len(times)) if times[idx]] ] , dtype = float)[0]) * units['ms']
# extract current
restr = r'(?i)(Ipatch)' # current
da = [not(elmt is None) for elmt in [RE.search(restr, nstr) \
for nstr in axodata.names]]
trace_i = [NP.array(axodata.data[idx2]) \
for idx2 in [idx for idx in range(len(da)) if da[idx]] ]
pd_dict = { \
"time": trace_t \
, "current": NP.array(trace_i[trace_nr]) * units['pA'] \
}
self.raw = PD.DataFrame.from_dict(pd_dict)
# print self.raw[self.raw.time <= 1].current
self.sampling_rate = (len(self.raw.current)-1) / ((NP.max(self.raw.time)-NP.min(self.raw.time))/units['ms'])
print self.sampling_rate
#+trace_t[:-1]/2
pd_dict = { \
"time": trace_t[1:] \
, "current": NP.diff(self.raw.current) \
}
self.diff = PD.DataFrame.from_dict(pd_dict)
def PlotTrace(self, ax):
ax.plot(self.raw.time, self.raw.current, 'k-')
ax.set_xlabel(r'time $\left( ms\right)$')
ax.set_ylabel(r'current $\left( pA\right)$')
def PlotDiffTrace(self, ax):
ax.plot(self.diff.time, self.diff.current, 'k-')
ax.set_title(r'gradient $\nabla \mathrm{I}$')
ax.set_xlabel(r'time $\left( ms\right)$')
ax.set_ylabel(r'$\Delta$I $\left( pA\right)$')
def HistoDiff(self, ax):
data = NP.random.normal(size=10000)
PL.hist(data, bins=NP.logspace(0.1, 1.0, 50))
PL.gca().set_xscale("log")
PL.show()
#hist, edges = NP.histogram(a = (self.raw.current), bins = 50, density = True)
#width = 0.9 * (edges[1] - edges[0])
#bin_centers = (edges[1:]+edges[:-1])/2
#ax.bar(bin_centers, hist, align='center'\
# , width=width, edgecolor = (0,0,0), facecolor = (1,1,1)\
# , label = r'binned data'\
# )
# fit with single gauss
params = FIT_Params()
params.add('a1', value=0.8, min = 0, max = 1) # , min = 0
params.add('mu1', value=0, min = -5000, max = 5000) # , min = 0
params.add('sigma1', value=1, min = 1e-6, max = 5000) # , min = 0
params.add('a2', value=0.2, min = 0, max = 1) # , min = 0
params.add('mu2', value=-5, min = -5000, max = 5000) # , min = 0
params.add('sigma2', value=1, min = 1e-6, max = 5000) # , min = 0
# print ParamsToDict(params)
gauss = lambda p, x: \
p['a1']*NP.exp(-((x-p['mu1'])**2)/(2*(p['sigma1']**2)))/(p['sigma1']*NP.sqrt(2*NP.pi)) \
+ p['a2']*NP.exp(-((x-p['mu2'])**2)/(2*(p['sigma2']**2)))/(p['sigma2']*NP.sqrt(2*NP.pi))
# TODO: fit with double gaussian!
fit_fcn = gauss
out = FIT(rmse, params, args=( fit_fcn \
, bin_centers \
, hist \
) \
, method = 'leastsq' \
#, maxfev= 10000 # optional for "leastsq"
#, tol = 1e-12 # optional for "nelder"
)
print "fit results: %s" % out.values
ax.plot(bin_centers, gauss(out.values, bin_centers), 'r:', label = r'mono-gaussian fit')
# polish plot
ax.set_title(r'histogram of $\nabla \mathrm{I}$')
ax.set_xlabel(r'current step')
ax.set_ylabel(r'probability')
ax.legend(loc=1)
def EMD(self, cycles = 3):
pass
# TODO: implement this
###########################
### Main process ###
###########################
def main():
### Load data ###
trace = AxoTrace( \
"140905 001 Average-ep61-2kHz-filt.axgx" \
# "/Users/Jelena/files/140905 001 Average-ep61-not-filt.axgx" \
)
# print trace.sampling_rate
### HHT ###
# TODO: decompose to IMF
### figure ###
figwidth = 16/2.54*1.25
figheight = 16/2.54*1.25
# define figure
fig = MPP.figure(figsize=(figwidth, figheight), facecolor = None, dpi = 150)
fig.hold(True) #(if multiple figures)
fig.subplots_adjust(top = 0.95, right=0.95, bottom = 0.1, left = 0.06, wspace=0.2, hspace=0.2)
print ( trace.filenum )
fig.suptitle( "%s, file %04.0f, %s" % ( trace.date, trace.filenum, trace.episode ) )
# define subplots
gs = MP.gridspec.GridSpec(2, 2) # height_ratios=[2, 3]
axarr = []
axarr.append(fig.add_subplot(gs[0, :]))
axarr.append(fig.add_subplot(gs[1, 0], sharex=axarr[0]))
axarr.append(fig.add_subplot(gs[1, 1]))
for ax in axarr:
ax.get_xaxis().set_tick_params(which='both', direction='out')
ax.get_yaxis().set_tick_params(which='both', direction='out')
ax.tick_params(top="off")
ax.tick_params(right="off")
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(True)
ax.spines['left'].set_visible(True)
ax.spines['right'].set_visible(False)
# # log scaled axes:
#ax.set_xscale('log')
#ax.xaxis.set_major_formatter(MP.ticker.ScalarFormatter())
# plot trace etc.
trace.PlotTrace(axarr[0])
trace.PlotDiffTrace(axarr[1])
trace.HistoDiff(axarr[2])
#LaTeX in python plots:
MP.rc( 'font', **the_font)
MPP.rcParams.update(**{'text.usetex': True})
MP.rcParams['text.latex.preamble'] = [\
r'\usepackage{upgreek}'
, r'\usepackage{cmbright}'
, r'\usepackage{sansmath}'
, r'\sansmath'
]
# save as svg (or pdf)
fig.savefig( RE.sub('.axgx', '.svg', "%s/%s" % ( OS.getcwd(), trace.file ) )\
, transparent=False, dpi=150, format = 'svg')
# don't forget to show!
MPP.show()
if __name__ == "__main__":
# this is here because all the function definitions should happen before processing the main algorithm.
main()