./out/ set as default in fig. scripts in ./python/

python/compute_scaling_experiments.py

@@ -24,9 +24,10 @@
 else:
     simulation = 'lichtman_chii2_different_seed'
 
+base_path = os.path.join(os.getcwd(), 'out/')
+
 if simulation == 'lichtman_chii2_different_seed':
     from compute_scaling_experiments_helpers import state_scaling_lichtman_chii2_different_seed as state
-    base_path = '/p/scratch/cjinb33/jinb3330/huvi_fraction_EI_stable_localEtoI1_lichtman_chiI2/'
     save_data_dir = 'simulated_data/scaling_experiment_lichtman_chii2_different_seed'
     interesting_paths = [
             '07338a82a7f3331ed0309d0aefe2512b/',
@@ -52,7 +53,6 @@
     ana_path = 'd8057e7a966caf3eefdc9d234a0785d3/'
 elif simulation == 'lower_etoi':
     from compute_scaling_experiments_helpers import state_lower_e_to_i as state
-    base_path = '/p/scratch/cjinb33/jinb3330/huvi_fraction_EI_lower_e_to_i/'
     save_data_dir = 'simulated_data/scaling_experiment_lower_e_to_i'
     interesting_paths = [
             '12742e24273ad73c879fc396b575e2e9/',
@@ -72,7 +72,6 @@
     ana_path = '099ed2eb40bf4285921a916d32bf5343/'
 elif simulation == 'lichtman_chii2_smaller_g':
     from compute_scaling_experiments_helpers import state_scaling_lichtman_chii2_smaller_g as state
-    base_path = '/p/scratch/cjinb33/shimoura1/huvi_fraction_EI_stable_localEtoI1_lichtman_chiI2_smaller_g/'
     save_data_dir = 'simulated_data/scaling_experiment_lichtman_chii2_smaller_g'
     interesting_paths = [
             '08e8618517e6e057db688d6257680da6/',
@@ -85,7 +84,6 @@
     ana_path = 'd8057e7a966caf3eefdc9d234a0785d3/'
 elif simulation == 'lichtman_chii2_different_time_consts':
     from compute_scaling_experiments_helpers import state_scaling_lichtman_chii2_different_time_consts as state
-    base_path = '/p/scratch/cjinb33/shimoura1/huvi_fraction_EI_stable_localEtoI1_lichtman_chiI2_different_synaptic_time_constants/'
     save_data_dir = 'simulated_data/scaling_experiment_lichtman_chii2_different_time_consts'
     interesting_paths = [
             '013292ec8fa6c9230d1c9518b71d19ad/',
@@ -103,7 +101,6 @@
     ana_path = 'd8057e7a966caf3eefdc9d234a0785d3/'
 elif simulation == 'lichtman_chii2_distributed_params':
     from compute_scaling_experiments_helpers import state_scaling_lichtman_chii2_distributed_params as state
-    base_path = '/p/scratch/cjinb33/shimoura1/huvi_fraction_EI_stable_localEtoI1_lichtman_chiI2_distributed_parameters/'
     save_data_dir = 'simulated_data/scaling_experiment_lichtman_chii2_distributed_params'
     interesting_paths = [
             '2c60ac72127fa0089cf8c65a9f4aaf8e/',
@@ -142,7 +139,7 @@
 # ==============================================================================
 # ==============================================================================
 
-filename = '/p/project/cjinb33/jinb3330/gitordner/huvi/data/rutishauser/spikes/'
+filename = base_path + 'data/rutishauser/spikes/'
 data = load_data(filename)
 print(f'loaded {len(data)} neurons')
 all_ids = range(len(data))

python/compute_scaling_experiments_helpers.py

@@ -363,7 +363,7 @@ def LVr(sts, t_ref, t_start=None, t_stop=None, LvR_min_spikes=10):
     return 0.
 
 def calculateFuncionalConnectivityCorrelations(ana_path, tmin=2000, tmax=8000,
-        base_path='/p/project/cjinb33/shimoura1/huvi-master',
+        base_path=os.path.join(os.getcwd(), 'out'),
         exclude_diagonal=True, use_corrcoeff=True):
     """
     Calculates the correlation between experimental and simulated functional

python/figure_appendix_rasterplot.py

@@ -1,6 +1,7 @@
 import random
 import numpy as np
 import pandas as pd
+import os
 from os.path import join as p_join
 
 import matplotlib.pyplot as plt
@@ -23,7 +24,7 @@
 random.seed(1234)
 
 
-outpath = '/p/scratch/cjinb33/jinb3330/huvi_fraction_EI_stable_localEtoI1_lichtman_chiI2/'
+outpath = os.path.join(os.getcwd(), 'out/')
 net_folder = p_join(outpath, '10acdb2ae9247b49d4eeea4d78f308b7')  # metastable
 sim_folder = p_join(net_folder, 'de4934b8c7777751f7c516e2ad35f50a')
 ana_folder = p_join(sim_folder, 'd8057e7a966caf3eefdc9d234a0785d3')

python/figure_connectivity_construction.py

@@ -1,3 +1,4 @@
+import os
 import numpy as np
 import pandas as pd
 from scipy.special import ndtr
@@ -15,7 +16,7 @@
 except FileNotFoundError:
     print('Please run data/macaque/preprocessing.py first')
     exit(1)
-net_dict = networkDictFromDump('/p/scratch/cjinb33/jinb3330/huvi_fraction_EI_stable_localEtoI1_lichtman_chiI2/517f98422516bbe6cb324c5436e7f66f/')
+net_dict = networkDictFromDump(os.path.join(os.getcwd(), 'out/517f98422516bbe6cb324c5436e7f66f/'))
 
 roman_to_arabic_numerals = {
         'I': '1',

python/figure_connectivity_validation.py

@@ -17,7 +17,7 @@
 target_min_outdeg = 100
 work_dir = "figures/tmp"
 figname = "figure_connectivity_validation"
-net_dict = networkDictFromDump('/p/scratch/cjinb33/jinb3330/huvi_fraction_EI_stable_localEtoI1_lichtman_chiI2/517f98422516bbe6cb324c5436e7f66f/')
+net_dict = networkDictFromDump(os.path.join(os.getcwd(), 'out/517f98422516bbe6cb324c5436e7f66f/'))
 
 # ========== Preprocess ==========
 area_list = net_dict['area_list']

python/figure_scaling_experiments.py

@@ -475,7 +475,7 @@
 # Plot experimental fMRI
 # ==============================================================================
 
-data_dir = '/p/project/cjinb33/jinb3330/gitordner/huvi/data/senden/rsData_7T_DKparcel/'
+data_dir = os.path.join(os.getcwd(), 'data/senden/rsData_7T_DKparcel/')
 roi = pd.read_csv(os.path.join(data_dir, 'ROIs.txt'), header=None, names=['roi'], dtype=str, squeeze=True)
 roi = roi.apply(lambda x: x.split('-')[-1])
 roi = roi.drop(range(0, 14)).drop(range(82, 85))

python/figure_spike_journey.py

@@ -1,5 +1,6 @@
 from os.path import join as path_join
 from itertools import product, combinations
+import os
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -25,8 +26,7 @@
 ana_hash = '9869eeeab580621db9572c8aa8c5f789'
 
 # outpath = 'out'
-outpath = '/p/scratch/cjinb33/jinb3330/huvi_fraction_EI_stable_localEtoI1_lichtman_chiI2_perturbation'
-
+outpath = os.path.join(os.getcwd(), 'out')
 
 def load_diffs(net_hash_orig, net_hash_pert):
     """Calculate rate differences for the two hashes"""

python/figure_spike_statistics.py

@@ -1,3 +1,4 @@
+import os
 import random
 import numpy as np
 import pandas as pd
@@ -27,7 +28,7 @@
 
 for state in ['groundstate', 'metastable']:
     name_extension = '_lichtman_chiI2_different_seed'
-    outpath = '/p/scratch/cjinb33/jinb3330/huvi_fraction_EI_stable_localEtoI1_lichtman_chiI2/'
+    outpath = os.path.join(os.getcwd(), 'out/')
     if state == 'groundstate':
         net_folder = p_join(outpath, '517f98422516bbe6cb324c5436e7f66f')  # groundstate
     elif state == 'metastable':