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Config file with paths to source and destination folders + paths joining fixes for Windows #1

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Config file with paths to source and destination folders + paths joining fixes for Windows #1

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4 changes: 4 additions & 0 deletions config.json
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Original file line number Diff line number Diff line change
@@ -0,0 +1,4 @@
{
"datasource": "/tmp/data",
"results": "/tmp/Graded_Remapping"
}
15 changes: 9 additions & 6 deletions iso_dist_hist.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@
"""
import os
import glob
import json
import numpy as np
import matplotlib.pyplot as pl
from scipy.io import loadmat
Expand All @@ -36,10 +37,12 @@
['g2784_d3']
]

# Load data from this folder
hdf5Dir = '/home/fetterhoff/Graded_Remapping/'
with open("config.json") as f:
config = json.load(f)

combinedResultDir = hdf5Dir+'waveform_stats/' # Save in subdirectory
# Load data from this folder
hdf5Dir = config['datasource']
combinedResultDir = os.path.join(config['results'], 'waveform_stats') # Save in subdirectory
if not os.path.exists(combinedResultDir):
os.makedirs(combinedResultDir)

Expand All @@ -53,9 +56,9 @@
session = s[0]
print(session) # current session

sd = hdf5Dir+session+'/' # session directory
sd = os.path.join(hdf5Dir, session) # session directory

for mat_name in glob.glob(sd+'*TT*.mat'): # loop through all neuron files
for mat_name in glob.glob(os.path.join(sd, '*TT*.mat')): # loop through all neuron files
m = loadmat(mat_name)
iso_dist = np.append(iso_dist, m['isolation_distance'][0][0]) # save isolation distances

Expand All @@ -73,5 +76,5 @@
pl.ylabel('Cumulative Distribution')
pl.xlim([0,100])

pl.savefig(combinedResultDir+'Fig_S1B_IsolationDistance.pdf',format='pdf', dpi=300, bbox_inches = 'tight', pad_inches = 0.05)
pl.savefig(os.path.join(combinedResultDir, 'Fig_S1B_IsolationDistance.pdf'),format='pdf', dpi=300, bbox_inches = 'tight', pad_inches = 0.05)
pl.close()
24 changes: 13 additions & 11 deletions maximum_likelihood_estimation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@

import os
import glob
import json
import numpy as np
import matplotlib.pyplot as pl
import pandas as pd
Expand Down Expand Up @@ -84,11 +85,12 @@ def bin_spikes(spike_times, dT, wdw_start, wdw_end):
colors = ('r', 'b', 'm', 'c') # Colors for each maze-type
mazeSegList = ['Entire Maze', 'First Hallway', 'First Corner', 'Middle Hallway', 'Last Corner', 'Last Hallway']

# Load data from this folder
hdf5Dir = '/home/fetterhoff/Graded_Remapping/'
with open("config.json") as f:
config = json.load(f)

# Create a results subfolder inside the data folder
combinedResultDir = hdf5Dir+'mle_results_{}gamma/'.format(gamma) # Save in subdirectory
# Load data from this folder
hdf5Dir = config['datasource']
combinedResultDir = os.path.join(config['results'], 'mle_results_{}gamma/'.format(gamma)) # Save in subdirectory
if not os.path.exists(combinedResultDir):
os.makedirs(combinedResultDir)

Expand Down Expand Up @@ -119,11 +121,11 @@ def bin_spikes(spike_times, dT, wdw_start, wdw_end):
session = s[0]
print(session) # current session

sd = hdf5Dir+session+'/' # session directory
sd = os.path.join(hdf5Dir, session) # session directory

# Build a DataFrame using all tetrode (TT) files
spikeDF = pd.DataFrame()
for mat_name in glob.glob(sd+'*TT*.mat'):
for mat_name in glob.glob(os.path.join(sd, '*TT*.mat')): # loop through all neuron files
m = loadmat(mat_name)

frame = pd.DataFrame([[m['file'][0], m['times'][0], m['vr_x'][0], m['vr_y'][0], m['real_cm'][0], m['speed_cms'][0], m['lap_num'][0],
Expand All @@ -134,7 +136,7 @@ def bin_spikes(spike_times, dT, wdw_start, wdw_end):
spikeDF = spikeDF.append(frame)
spikeDF.sort_index(inplace=True)

f2 = sd+session+'_laps_traj.h5'
f2 = os.path.join(sd, session+'_laps_traj.h5')
trajDF = pd.read_hdf(f2, 'trj') # DataFrame of times/places/speed for each lap in VR
# LapsDF maze_type dictionary: {1:R, -1:L, 2: R*, -2: L*}
lapsDF = pd.read_hdf(f2, 'lapsDF')
Expand Down Expand Up @@ -363,7 +365,7 @@ def get_log_likelihood(z_source_dat, z_pattern_dat, Gamma):
ax[0].set_yticklabels(mazeTypeList)
ax[0].set_xticks([0, xlim0/20, xlim0/10])

fig.savefig(combinedResultDir+'Fig3A_mle_timesteps_{}.pdf'.format(session), format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
fig.savefig(os.path.join(combinedResultDir, 'Fig3A_mle_timesteps_{}.pdf'.format(session)), format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.close(fig)

#%% MLE while excuding the same maze as pattern (ex = excluding same maze-type)
Expand Down Expand Up @@ -427,7 +429,7 @@ def get_log_likelihood(z_source_dat, z_pattern_dat, Gamma):
#%% Plot MLE over all sessions
if toPlotAllSessions:

df_count.to_csv(combinedResultDir+'table_S1_place_cell_field_counts.csv')
df_count.to_csv(os.path.join(combinedResultDir, 'table_S1_place_cell_field_counts.csv'))

# plot as percentages
pctAll = [mle_sess.mean(axis=0), mle_sess_fh.mean(axis=0), mle_sess_fc.mean(axis=0), mle_sess_mh.mean(axis=0), mle_sess_lc.mean(axis=0), mle_sess_lh.mean(axis=0)]
Expand Down Expand Up @@ -457,7 +459,7 @@ def get_log_likelihood(z_source_dat, z_pattern_dat, Gamma):
pl.ylabel("Maximum Likelihood Estimate from Pattern Data", labelpad=1)
pl.xlabel("Maximum Likelihood Estimate from Source Data", labelpad=0)

fig.savefig(combinedResultDir+'Fig3B_allSessions_withReal_mle_gamma{}.pdf'.format(gamma), format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
fig.savefig(os.path.join(combinedResultDir,'Fig3B_allSessions_withReal_mle_gamma{}.pdf'.format(gamma)), format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.close(fig)

#%% Plot MLE excluding same maze-type
Expand Down Expand Up @@ -486,5 +488,5 @@ def get_log_likelihood(z_source_dat, z_pattern_dat, Gamma):
pl.ylabel("Maximum Likelihood Estimate from Pattern Data", labelpad=1)
pl.xlabel("Maximum Likelihood Estimate from Source Data", labelpad=0)

fig.savefig(combinedResultDir+'Fig3C_allSessions_noReal_mle_gamma{}.pdf'.format(gamma), format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
fig.savefig(os.path.join(combinedResultDir, 'Fig3C_allSessions_noReal_mle_gamma{}.pdf'.format(gamma)), format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.close(fig)
21 changes: 12 additions & 9 deletions plot_place_cells.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@
"""
import os
import glob
import json
import numpy as np
import matplotlib.pyplot as pl
import pandas as pd
Expand Down Expand Up @@ -40,10 +41,12 @@
fileList = fileList
speedThresh = 5 # cm/s, to discard spikes during stillness

# Load data from this folder
hdf5Dir = '/home/fetterhoff/Graded_Remapping/'
with open("config.json") as f:
config = json.load(f)

combinedResultDir = hdf5Dir+'place_cells/' # Save in subdirectory
# Load data from this folder
hdf5Dir = config['datasource']
combinedResultDir = os.path.join(config['results'], 'place_cells') # Save in subdirectory
if not os.path.exists(combinedResultDir):
os.makedirs(combinedResultDir)

Expand All @@ -63,11 +66,11 @@
session = s[0]
print(session) # current session

sd = hdf5Dir+session+'/' # session directory
sd = os.path.join(hdf5Dir, session) # session directory

# Build a DataFrame using all tetrode (TT) files
spikeDF = pd.DataFrame()
for mat_name in glob.glob(sd+'*TT*.mat'):
for mat_name in glob.glob(os.path.join(sd, '*TT*.mat')): # loop through all neuron files
m = loadmat(mat_name)

frame = pd.DataFrame([[m['file'][0], m['times'][0], m['vr_x'][0], m['vr_y'][0], m['real_cm'][0], m['speed_cms'][0], m['lap_num'][0],
Expand All @@ -78,13 +81,13 @@
spikeDF = spikeDF.append(frame)
spikeDF.sort_index(inplace=True)

f3 = sd+session+'_PCresultsDB.h5'
f3 = os.path.join(sd, session+'_PCresultsDB.h5')
cellResultsDB = pd.read_hdf(f3, 'cellResultsDB')

#%% Plot neurons as examples
for q, cell_id in enumerate(spikeDF.T):
sp = spikeDF.loc[cell_id]
title = combinedResultDir + session + '_' + sp.file[:-2]
title = os.path.join(combinedResultDir, session + '_' + sp.file[:-2])
session_list.append(session)
maze_seg_code.append(sp.segment_types)
if toPlotAllNeurons:
Expand Down Expand Up @@ -202,7 +205,7 @@
box.x0, box.y0, box.y1 = 0.7, 0.26, 0.5
ax[5].set_position(box)

pl.savefig(combinedResultDir+'fig_1HI_pieChart.pdf', format='pdf', bbox_inches='tight', pad_inches=0.01)
pl.savefig(os.path.join(combinedResultDir, 'fig_1HI_pieChart.pdf'), format='pdf', bbox_inches='tight', pad_inches=0.01)
pl.close()

#%% Pie for each gerbil
Expand Down Expand Up @@ -264,5 +267,5 @@
ax[5].legend(['RR*-dir', 'LL*-dir', 'RL*-im', 'LR*-im'], loc='upper right')
ax[1].legend(labels, loc='upper left', bbox_to_anchor=(0.3, 0.83))

pl.savefig(combinedResultDir+'fig_S3_pieChartByGerbil_{}.pdf'.format(gid), format='pdf', bbox_inches='tight', pad_inches=0.01)
pl.savefig(os.path.join(combinedResultDir, 'fig_S3_pieChartByGerbil_{}.pdf'.format(gid)), format='pdf', bbox_inches='tight', pad_inches=0.01)
pl.close()
43 changes: 23 additions & 20 deletions pop_vec_analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@
"""
import os
import glob
import json
import itertools as it
import numpy as np
import matplotlib.pyplot as pl
Expand Down Expand Up @@ -49,10 +50,12 @@
['g2784_ss1']
]

# Load data from this folder
hdf5Dir = '/home/fetterhoff/Graded_Remapping/'
with open("config.json") as f:
config = json.load(f)

combinedResultDir = hdf5Dir+'pop_vec_analysis/' # Save in subdirectory
# Load data from this folder
hdf5Dir = config['datasource']
combinedResultDir = os.path.join(config['results'], 'pop_vec_analysis') # Save in subdirectory
if not os.path.exists(combinedResultDir):
os.makedirs(combinedResultDir)

Expand Down Expand Up @@ -97,11 +100,11 @@
session = s[0]
print(session) # current session

sd = hdf5Dir+session+'/' # session directory
sd = os.path.join(hdf5Dir, session) # session directory

# Build a DataFrame using all tetrode (TT) files
spikeDF = pd.DataFrame()
for mat_name in glob.glob(sd+'*TT*.mat'):
for mat_name in glob.glob(os.path.join(sd, '*TT*.mat')): # loop through all neuron files
m = loadmat(mat_name)

frame = pd.DataFrame([[m['file'][0], m['times'][0], m['vr_x'][0], m['vr_y'][0], m['real_cm'][0], m['speed_cms'][0], m['lap_num'][0],
Expand All @@ -112,13 +115,13 @@
spikeDF = spikeDF.append(frame)
spikeDF.sort_index(inplace=True)

f2 = sd+session+'_laps_traj.h5'
f2 = os.path.join(sd, session+'_laps_traj.h5')
trajDF = pd.read_hdf(f2, 'trj') # DataFrame of times/places/speed for each lap in VR
# LapsDF maze_type dictionary: {1:R, -1:L, 2: R*, -2: L*}
lapsDF = pd.read_hdf(f2, 'lapsDF')
lapsDB = np.array(lapsDF) # Keep values as matrix

f3 = sd+session+'_PCresultsDB.h5'
f3 = os.path.join(sd, session+'_PCresultsDB.h5')
cellResultsDB = pd.read_hdf(f3, 'cellResultsDB')

nPlaceFields = 0 # Count the number of place fields
Expand Down Expand Up @@ -214,8 +217,8 @@
#%% Plot place field matrices sorted by maze R

if not (simSwap or best6 or toExcludeImageCells):
pop_dir = combinedResultDir + 'population_plots/'

pop_dir = os.path.join(combinedResultDir, 'population_plots')
if not os.path.exists(pop_dir):
os.makedirs(pop_dir)

Expand All @@ -241,7 +244,7 @@
axm3.set_xlabel('Track Position (cm)'); axm4.set_xlabel('Track Position (cm)')
axm1.set_ylabel('Neuron #'); axm3.set_ylabel('Neuron #')

pl.savefig(pop_dir+'all_neural_pop_viridis_Rsort_{}.png'.format(session), format='png', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.savefig(os.path.join(pop_dir, 'all_neural_pop_viridis_Rsort_{}.png'.format(session)), format='png', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.close()

# Plot place field matrices sorted by maze L
Expand All @@ -268,13 +271,13 @@
axm3.set_xlabel('Track Position (cm)'); axm4.set_xlabel('Track Position (cm)')
axm1.set_ylabel('Neuron #'); axm3.set_ylabel('Neuron #')

pl.savefig(pop_dir+'all_neural_pop_viridis_Lsort_{}.png'.format(session), format='png', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.savefig(os.path.join(pop_dir, 'all_neural_pop_viridis_Lsort_{}.png'.format(session)), format='png', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.close()

#%% pop_vec correlation - all vs shuffled

if not (simSwap or best6 or toExcludeImageCells):
df_count.to_csv(combinedResultDir+'table_S1_place_cell_counts_pop_vec.csv')
df_count.to_csv(os.path.join(combinedResultDir, 'table_S1_place_cell_counts_pop_vec.csv'))

if (best6 or simSwap):
fig, axx = pl.subplots(1, 2, figsize=(3.4, 1.4), sharey=True)
Expand Down Expand Up @@ -402,13 +405,13 @@
axx[1].legend()

if toExcludeImageCells:
pl.savefig(combinedResultDir+'fig_4B_pop_vec_correlation_noImageCells.pdf', format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.savefig(os.path.join(combinedResultDir, 'fig_4B_pop_vec_correlation_noImageCells.pdf'), format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
elif simSwap:
pl.savefig(combinedResultDir+'fig_S5A_pop_vec_correlation.pdf', format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.savefig(os.path.join(combinedResultDir, 'fig_S5A_pop_vec_correlation.pdf'), format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
elif best6:
pl.savefig(combinedResultDir+'fig_S5C_pop_vec_correlation.pdf', format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.savefig(os.path.join(combinedResultDir, 'fig_S5C_pop_vec_correlation.pdf'), format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
else:
pl.savefig(combinedResultDir+'fig_4A_pop_vec_correlation.pdf', format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.savefig(os.path.join(combinedResultDir, 'fig_4A_pop_vec_correlation.pdf'), format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.close(fig)

#%% pop_vec correlation - all vs shuffled for each gerbil
Expand Down Expand Up @@ -472,7 +475,7 @@
aq[ai].fill_between([bd[0], bd[1]], -.2, 1, facecolor='k', alpha=0.2)
aq[ai].fill_between([bd[2], bd[3]], -.2, 1, facecolor='k', alpha=0.2)

pl.savefig(combinedResultDir+'fig_S5E_pop_vec_correlation_g{}.pdf'.format(gid), format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.savefig(os.path.join(combinedResultDir, 'fig_S5E_pop_vec_correlation_g{}.pdf'.format(gid)), format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.close(fig)

#%% Plot the matrix for the poulation vector correlation divided by maze segment
Expand Down Expand Up @@ -527,7 +530,7 @@
cbar_ax.tick_params(labelsize=6)
cbar_ax.set_ylabel("r", rotation=0, labelpad=-13)

pl.savefig(combinedResultDir+'fig_4C_population_vector_correlation_bySegment_v.pdf', format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.savefig(os.path.join(combinedResultDir, 'fig_4C_population_vector_correlation_bySegment_v.pdf'), format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.close()

#%% Plot the matrix for the poulation vector correlation divided by maze segment
Expand Down Expand Up @@ -582,8 +585,8 @@
axw[i].grid(False)

if simSwap:
pl.savefig(combinedResultDir+'fig_S5B_population_vector_correlation_bySegment_simSwap.pdf', format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.savefig(os.path.join(combinedResultDir, 'fig_S5B_population_vector_correlation_bySegment_simSwap.pdf'), format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.close()
if best6:
pl.savefig(combinedResultDir+'fig_S5D_population_vector_correlation_bySegment_best6.pdf', format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.savefig(os.path.join(combinedResultDir, 'fig_S5D_population_vector_correlation_bySegment_best6.pdf'), format='pdf', dpi=300, bbox_inches='tight', pad_inches=0.05)
pl.close()
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