def plot_sum_for_each_animal(axsum,mn_by_animal,summary_data_location):
kcol=ml.colors.colorConverter.to_rgba('k', alpha=.5)
rcol=ml.colors.colorConverter.to_rgba('r', alpha=.5)
axsum.scatter(np.zeros(len(mn_by_animal['on'])),mn_by_animal['on'],s=15,facecolor='none',edgecolor=kcol)
axsum.scatter(np.ones(len(mn_by_animal['off']))-.5,mn_by_animal['off'],s=15,facecolor='none',edgecolor=rcol)
for crind, cr_on in enumerate(mn_by_animal['on']):
cr_off=mn_by_animal['off'][crind]
axsum.plot([0,0.5],[cr_on,cr_off],color='k',linewidth=0.2)
mn_on=np.mean(mn_by_animal['on'])
mn_off=np.mean(mn_by_animal['off'])
rel_stats=scipy.stats.ttest_rel(mn_by_animal['on'],mn_by_animal['off'])
off_target_rel_to_zero_stats=scipy.stats.ttest_1samp(mn_by_animal['off'],0)
plt.plot(1.1,mn_on,'<',Markersize=3,MarkerEdgeColor=None,color='k')
plt.plot(1.1,mn_off,'<',Markersize=3,MarkerEdgeColor=None,color='r')
axsum.set_ylim(-.25,1.25)
axsum.set_aspect(4)
axsum.set_yticks([-.25,0,.25,.5,.75])
axsum.set_xlim(-.2,1.2)
fpl.adjust_spines(axsum,['left'])
axsum.set_ylim(-.25,1.25)
axsum.set_aspect(4)
axsum.set_yticks([-.25,0,.25,.5,.75])
axsum.set_xlim(-.2,1.2)
sumdt={}
sumdt['mn_by_animal']=mn_by_animal
sumdt['rel_stats']=rel_stats
sumdt['off_targ_stats']=off_target_rel_to_zero_stats
fh.save_to_pickle(summary_data_location+'summary_datwt.pck',sumdt)
#march26_animal_9_meta_dt
from py_utilities import tw_filehandling as fh
meta_dt = {}
meta_dt['data_path'] = '/Volumes/LaCie/2pdata/march25/animal4/'
meta_dt['meta_file_name'] = 'anim4_plotdata.pck'
#file_names=['---Streaming Phasor Capture - 2 - 2_XY0_Z0_T0000_C0.tif']
meta_dt['file_names'] = [
'---Streaming Phasor Capture - 2 - 2_XY0_Z0_T0000_C0.tif',
'---Streaming Phasor Capture - 2 - 1_XY0_Z0_T0000_C0.tif',
'---Streaming Phasor Capture - 2_XY0_Z0_T0000_C0.tif'
]
meta_dt['roi_to_plot'] = 0
meta_dt['on_target_depth'] = 231
meta_dt['off_target'] = 251
meta_dt['on_depths'] = 'odd'
meta_dt['off_depths'] = 'even'
fh.save_to_pickle(meta_dt['data_path'] + meta_dt['meta_file_name'], meta_dt)
sumdt['roi'] = my_roi
sumdt['zoom_vls'] = {}
sumdt['zoom_vls']['xvls'] = xvls
sumdt['zoom_vls']['yvls'] = yvls
(stim_tms, stim_frame_nums) = util.convert_stim_times(
stim['stim_frame_nums'],
frame_flag=True,
time_between_frames=TIME_BETWEEN_FRAMES)
frame_tms = [float(i) for i in im['time_stamps']]
deltaf_vls = util.get_delta_f(mn_roi,
stim_frame_nums,
preframes,
postframes,
pre_frame_buffer=2)
sumdt['im_mean'] = im_zoom_mean
sumdt['stim_mask'] = stim_mask
sumdt['mn_roi'] = mn_roi
sumdt['roi_masks'] = masks
sumdt['frame_nums'] = [int(i) for i in im['frame_nums']]
sumdt['frame_tms'] = frame_tms
sumdt['stim_tms'] = stim_tms
sumdt['deltaf_vls'] = deltaf_vls
sumdt['stim_depths'] = [float(i) for i in stim_depths]
sumdt['stim_region'] = stim_region
sumdt['stim_edges'] = stim_edges
sumdt['stim_frame_nums'] = stim_frame_nums
fh.save_to_pickle(data_path + file_name + '.pck', sumdt)
