xmin = min([x[0] for x in xlims])
xmax = max([x[1] for x in xlims])
for i,ax in enumerate(plt.gcf().axes):
ax.set_xlim((xmin, xmax))
ax.set_ylim(ylim)
ax.legend()
# add a scale bar
if i==0:
add_scalebar(ax, bbox_transform = ax.transAxes, **sb_kwds)
else:
# hide axis and spines
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
for loc, spine in ax.spines.iteritems():
spine.set_color('none')
return plt.gcf()
if __name__=='__main__':
# make list of dicts like so:
files_dict = [{'abf':abf_reader('./2012_08_24_0000.abf'),
'color':'black',
'label':'control'},
{'abf':abf_reader('./2012_08_24_0004.abf'),
'color':'red',
'label':'frf'}]
fig = main(files_dict)
fig.set_size_inches(10,8)
fig.savefig('default_fig_name.png', dpi = 300)
self.MsecLeadIn = MsecLeadIn
super(waterfall_int_current, self).__init__(*args, **kwds)
def gen_iter(self):
for i, swp_idx in enumerate(self._swps):
xs = self.xs + self.offset*i
# have to get the lead in here
lead_dp = int((self.MsecLeadIn / 1000.) * self.sr)
Vm = self.abf_epoch._data(swp_idx,leadin_dp=lead_dp)[:,self.channo] + self.yoffset*i
tarr = np.r_[0:len(Vm)/self.sr:self.xstep]
current = self.CurrentSpecifier(Vm, tarr)
ys = current[lead_dp:] + self.yoffset*i
lvl = self.abf_epoch._lvl(swp_idx)
yield xs, ys, lvl
self.xlim = (0, max(xs))
if __name__=='__main__':
''' debug script '''
import os
labdir = os.environ.get("LABDIR")
abfpath = os.path.join(labdir,
'B44_B48_experiments',
'b48_fI_summary',
'frf', '2012_08_24_0004.abf')
abf = abf_reader(abfpath)
epch = epoch(abf, 1, 1)
epch.set_pading(left = 1000, right = 1000)
for xs,ys,lvl in waterfall(epch, selectd = np.r_[0:18:2]):
plt.plot(xs,ys, '-k', linewidth = 0.5)
plt.show()
import os
from abf_reader import *
import matplotlib.pyplot as plt
from matt_axes_cust import xscale, yscale, clean_axes, no_spines
import pdb
########## load fixture data ##########
data_path = os.path.join(os.environ.get('LABDIR'),
'B44_B48_experiments','b48_fI_summary','control')
fI = abf_reader(os.path.join(data_path, '2012_09_08_0009.abf'))
########## end load fixture data ##########
#################### time steps and DACs ####################
DAC_num = 1
ADC_num = 0
xstep = 1/sample_rate(fI.header)
sr = sample_rate(fI.header)
#################### end time steps and DACs ################
#################### PLOT fI ####################
for swp_num, swp in enumerate(make_swp_indxs(fI.header)):
swp_len = swp[1]-swp[0]
swp_x_offset = 1.4 * swp_num
swp_y_offset = 0 * swp_num
xs = np.r_[0:swp_len/sr:xstep] + swp_x_offset
ys = fI.read_data()[slice(swp[0],swp[1]),0] + swp_y_offset
# make sure xs arnt too long, with float fups
xs = xs[0:len(ys)]
plt.plot(xs,ys, linewidth = 0.3, color = 'black')
xmax = np.max(xs)
ax = plt.gca()
