diff --git a/config.json b/config.json
new file mode 100644
index 0000000..5063425
--- /dev/null
+++ b/config.json
@@ -0,0 +1,4 @@
+{
+    "datasource": "/tmp/data",
+    "results": "/tmp/Graded_Remapping"
+}
diff --git a/iso_dist_hist.py b/iso_dist_hist.py
index 4f1a1af..f53a75f 100644
--- a/iso_dist_hist.py
+++ b/iso_dist_hist.py
@@ -11,6 +11,7 @@
 """
 import os
 import glob
+import json
 import numpy as np
 import matplotlib.pyplot as pl
 from scipy.io import loadmat
@@ -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)
 
@@ -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
 
@@ -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()
diff --git a/maximum_likelihood_estimation.py b/maximum_likelihood_estimation.py
index abc6adf..3c6057c 100644
--- a/maximum_likelihood_estimation.py
+++ b/maximum_likelihood_estimation.py
@@ -12,6 +12,7 @@
 
 import os
 import glob
+import json
 import numpy as np
 import matplotlib.pyplot as pl
 import pandas as pd
@@ -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)
 
@@ -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],
@@ -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')
@@ -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)
@@ -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)]
@@ -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
@@ -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)
diff --git a/plot_place_cells.py b/plot_place_cells.py
index 9713b34..fe2cf90 100644
--- a/plot_place_cells.py
+++ b/plot_place_cells.py
@@ -10,6 +10,7 @@
 """
 import os
 import glob
+import json
 import numpy as np
 import matplotlib.pyplot as pl
 import pandas as pd
@@ -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)
 
@@ -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],
@@ -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:
@@ -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
@@ -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()
diff --git a/pop_vec_analysis.py b/pop_vec_analysis.py
index 5ba62f2..bf1f259 100644
--- a/pop_vec_analysis.py
+++ b/pop_vec_analysis.py
@@ -13,6 +13,7 @@
 """
 import os
 import glob
+import json
 import itertools as it
 import numpy as np
 import matplotlib.pyplot as pl
@@ -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)
 
@@ -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],
@@ -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
@@ -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)
 
@@ -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
@@ -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)
@@ -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
@@ -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
@@ -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
@@ -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()
diff --git a/rate_remapping.py b/rate_remapping.py
index 820fa9b..b3d1ab1 100644
--- a/rate_remapping.py
+++ b/rate_remapping.py
@@ -12,6 +12,7 @@
 
 import os
 import glob
+import json
 import itertools as it
 import numpy as np
 import matplotlib.pyplot as pl
@@ -59,10 +60,12 @@
 itmp.append(Nbins-1)
 iparts = [i+1 for i in itmp]
 
-# Load data from this folder
-hdf5Dir = '/home/fetterhoff/Graded_Remapping/'
+with open("config.json") as f:
+    config = json.load(f)
 
-combinedResultDir = hdf5Dir+'rate_remapping/' # Save in subdirectory
+# Load data from this folder
+hdf5Dir = config['datasource']
+combinedResultDir = os.path.join(config['results'], 'rate_remapping') # Save in subdirectory
 if not os.path.exists(combinedResultDir):
     os.makedirs(combinedResultDir)
 
@@ -78,11 +81,11 @@
     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],
@@ -93,13 +96,13 @@
         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
@@ -197,7 +200,7 @@
 #%% Save to csv files
 if plotSummary:
 
-    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'))
 
     #%% Peak comparison data to assess rate remapping
     df_ci_rateRemapping = pd.DataFrame([])
@@ -274,7 +277,7 @@ def r_confidence_interval(r_, n_, alpha=0.05):
     ax[15].set_xlabel('L*', labelpad=0)
 
     df_ci_rateRemapping.to_csv(combinedResultDir+'table_S2_rate_remapping_single_peaks_CI.csv')
-    pl.savefig(combinedResultDir+'fig_2A_rate_remapping_single_peaks.pdf', format='pdf', bbox_inches='tight', pad_inches=0.05)
+    pl.savefig(os.path.join(combinedResultDir, 'fig_2A_rate_remapping_single_peaks.pdf'), format='pdf', bbox_inches='tight', pad_inches=0.05)
     pl.close()
 
     #%% Overlap summary figure
@@ -437,6 +440,6 @@ def r_confidence_interval(r_, n_, alpha=0.05):
         ax[i].spines['bottom'].set_visible(False)
         ax[i].tick_params(axis=u'both', which=u'both', length=0)
 
-    df_overlap_stats.to_csv(combinedResultDir+'table_S3_overlap_KW_MWU_stats.csv')
-    pl.savefig(combinedResultDir+'fig_2BCD_rate_remap_overlap_summary_.pdf', format='pdf', bbox_inches='tight', pad_inches=0.05)
+    df_overlap_stats.to_csv(os.path.join(combinedResultDir, 'table_S3_overlap_KW_MWU_stats.csv'))
+    pl.savefig(os.path.join(combinedResultDir, 'fig_2BCD_rate_remap_overlap_summary_.pdf'), format='pdf', bbox_inches='tight', pad_inches=0.05)
     pl.close()
diff --git a/trajectory_plots.py b/trajectory_plots.py
index c83e7a2..d054eea 100644
--- a/trajectory_plots.py
+++ b/trajectory_plots.py
@@ -11,6 +11,7 @@
 """
 
 import os
+import json
 import numpy as np
 import matplotlib.pyplot as pl
 import pandas as pd
@@ -38,10 +39,12 @@
 
 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+'trajectory_plots/' # Save in subdirectory
+# Load data from this folder
+hdf5Dir = config['datasource']
+combinedResultDir = os.path.join(config['results'], 'trajectory_plots') # Save in subdirectory
 if not os.path.exists(combinedResultDir):
     os.makedirs(combinedResultDir)
 
@@ -66,9 +69,9 @@
     session = s[0]
     print(session) # current session
 
-    sd = hdf5Dir+session+'/' # session directory
+    sd = os.path.join(hdf5Dir, session) # session directory
 
-    f2 = sd+session+'_laps_traj.h5'
+    f2 = os.path.join(sd, session+'_laps_traj.h5')
     lapsDF = pd.read_hdf(f2, 'lapsDF')
     trajDF = pd.read_hdf(f2, 'trj')
     lapsDB = np.array(lapsDF)
@@ -156,7 +159,7 @@
 
     ax[3,1].legend(['First Hall','First Corner','Middle Hall','Last Corner','Last Hall'])
 
-    pl.savefig(combinedResultDir+'fig_S2A_lap_trajectories_{}.pdf'.format(session),format='pdf', dpi=300, bbox_inches = 'tight', pad_inches = 0.01)
+    pl.savefig(os.path.join(combinedResultDir, 'fig_S2A_lap_trajectories_{}.pdf'.format(session)),format='pdf', dpi=300, bbox_inches = 'tight', pad_inches = 0.01)
     pl.close()
 
     all_vr_y = np.append(all_vr_y, places_xy[1,:] * -1)
@@ -206,7 +209,7 @@
 ax[0].set_ylabel('PDF')
 pl.setp(ax, xticks=[-0.5,0.5], xticklabels=['Left','Right'])
 
-pl.savefig(combinedResultDir+'fig_S2C_y_position_hist.pdf',format='pdf', dpi=300, bbox_inches = 'tight', pad_inches = 0.01)
+pl.savefig(os.path.join(combinedResultDir, 'fig_S2C_y_position_hist.pdf'), format='pdf', dpi=300, bbox_inches = 'tight', pad_inches = 0.01)
 pl.close()
 
 #%% average speed by maze segment
@@ -230,5 +233,5 @@
 pl.xticks(np.arange(5), ('First Hall', 'First Corner', 'Middle Hall', 'Last Corner', 'Last Hall'),rotation=45)
 pl.ylabel('Average Speed (cm/s)')
 
-pl.savefig(combinedResultDir+'fig_S2B_avg_speed_by_maze_segment.pdf',format='pdf', dpi=300, bbox_inches = 'tight', pad_inches = 0.05)
+pl.savefig(os.path.join(combinedResultDir, 'fig_S2B_avg_speed_by_maze_segment.pdf'),format='pdf', dpi=300, bbox_inches = 'tight', pad_inches = 0.05)
 pl.close()
diff --git a/waveform_stats.py b/waveform_stats.py
index 63ef86c..b4de248 100644
--- a/waveform_stats.py
+++ b/waveform_stats.py
@@ -11,19 +11,22 @@
 """
 
 import os
+import json
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as pl
 import seaborn as sns
 
-# 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)
 
-wavedf = pd.read_csv(hdf5Dir+'allwavecombined_final.csv')
+wavedf = pd.read_csv(os.path.join(hdf5Dir, 'allwavecombined_final.csv'))
 wavedf['neuron_type'] = 0
 fil=(wavedf.spike_ratio < 1.5)
 wavedf.loc[fil, 'neuron_type'] = 'INT'
@@ -44,7 +47,7 @@
 ajp.ax_joint.set_ylabel('Log of Mean Firing Rate (Hz)')
 ajp.ax_marg_x.axvline(1.5,color='C1',alpha=0.5)
 ajp.ax_joint.set_ylim(yl1, yl2)
-ajp.savefig(combinedResultDir+'fig_S1C_mfr_spike_ratio.pdf',format='pdf', dpi=300, bbox_inches = 'tight', pad_inches = 0.05)
+ajp.savefig(os.path.join(combinedResultDir, 'fig_S1C_mfr_spike_ratio.pdf'),format='pdf', dpi=300, bbox_inches = 'tight', pad_inches = 0.05)
 pl.close()
 perc_lost = (wavedf.spike_ratio > 10).sum() / float(len(wavedf)) *100
 print('{}% of neurons not shown (SR > 10)'.format(perc_lost))
@@ -54,5 +57,5 @@
 ax = sns.catplot(x='neuron_type', y='spike_width_ms', kind='boxen', data=wavedf, height=2.2)
 ax.set_ylabels('Spike Width (ms)')
 ax.set_xlabels('Neuron Type')
-ax.savefig(combinedResultDir+'fig_S1D_spike_width.pdf',format='pdf', dpi=300, bbox_inches = 'tight', pad_inches = 0.05)
+ax.savefig(os.path.join(combinedResultDir, 'fig_S1D_spike_width.pdf'),format='pdf', dpi=300, bbox_inches = 'tight', pad_inches = 0.05)
 pl.close()