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performance_plots.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 25 11:01:49 2021
@author: saad
"""
import h5py
import numpy as np
import os
import re
from model import utils_si
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator
import socket
hostname=socket.gethostname()
if hostname=='sandwolf':
base = '/home/saad/data_hdd/'
elif hostname=='sandhound':
base = '/home/saad/postdoc_db/'
from scipy.stats import wilcoxon
import gc
import csv
from collections import namedtuple
Exptdata = namedtuple('Exptdata', ['X', 'y'])
from model.load_savedModel import load
from model.data_handler import load_data, load_h5Dataset, prepare_data_cnn2d, prepare_data_cnn3d, prepare_data_convLSTM, prepare_data_pr_cnn2d
from model.performance import getModelParams, model_evaluate,model_evaluate_new,paramsToName, get_weightsDict, get_weightsOfLayer
from model import metrics
from model import featureMaps
from model.models import modelFileName
# from pyret.filtertools import sta, decompose
# import seaborn
import tensorflow as tf
gpu_devices = tf.config.experimental.list_physical_devices("GPU")
for device in gpu_devices:
tf.config.experimental.set_memory_growth(device, True)
from tensorflow.keras import Model
from tensorflow.keras.layers import BatchNormalization, Input, Reshape
data_pers = 'mike'
expDates = ('20230725C',)
subFold = '' #'PR_BP' #'8ms_clark' #'8ms_trainablePR' # test_coneParams
mdl_subFold = ''
# lightLevel_1 = 'CB_photopic-Rstar'
lightLevel_1 = ('CB_photopic-Rstar','NATSTIM_photopic-Rstar')
models_all = ('CNN_2D_NORM3',) # (PR_CNN2D, 'CNN_2D','CNN_3D','CNN_3D_LSTM','convLSTM','BP_CNN2D_PRFRTRAINABLEGAMMA','BP_CNN2D_MULTIBP_PRFRTRAINABLEGAMMA')
writeToCSV = False
path_save_performance = '/home/saad/postdoc_db/projects/RetinaPredictors/performance_mike'
models_list = []
param_list_keys = ['U', 'T','C1_n','C1_s','C1_3d','C2_n','C2_s','C2_3d','C3_n','C3_s','C3_3d','BN','MP','TR','P','LR','TRSAMPS'] # model parameters to group by
csv_header = ['mdl_name','expDate','temp_window','chan1_n','filt1_size','filt1_3rdDim','chan2_n','filt2_size','filt2_3rdDim','chan3_n','filt3_size','filt3_3rdDim',
'FEV_median','FracExVar','corr_median','rr_corr_median']
cols_lightLevels = {
'photopic': 'r',
'scotopic': 'b',
'scot-0.3':'b',
'scot-0.3-R':'b',
'scot-3': 'r',
'scot-3-R': 'r',
'scot-30':'g',
'scot-30-':'g'
}
# Dataset info
dataset_nameForPaths = ''
for i in range(len(lightLevel_1)):
dataset_nameForPaths = dataset_nameForPaths+lightLevel_1[i]+'+'
dataset_nameForPaths = dataset_nameForPaths[:-1]
path_dataset_base = os.path.join('/home/saad/postdoc_db/analyses/data_'+data_pers+'/',expDates[0],subFold)
name_datasetFile = expDates[0]+'_dataset_train_val_test_'+lightLevel_1[0]+'.h5'
fname_data_train_val_test = os.path.join(path_dataset_base,'datasets',name_datasetFile) + '+'
perf_allExps = {} # Performance dict for all experiments, models, conditions
params_allExps = {} # Dict containing parameter values for all experiments, models, conditions
paramNames_allExps = {} #Dict containing the folder name of the model, which is basically concatenating the params together
# %%
idx_exp = 0
mdl_select = models_all[0]
for idx_exp in range(len(expDates)):
path_mdl_base = os.path.join(base,'analyses/data_'+data_pers+'/',expDates[idx_exp],'models',subFold)
path_dataset_base = os.path.join('/home/saad/postdoc_db/analyses/data_'+data_pers+'/',expDates[idx_exp],subFold)
perf_allModels = {}
params_allModels = {}
paramNames_allModels = {}
exp_select = expDates[idx_exp]
path_mdl_drive = os.path.join(path_mdl_base,dataset_nameForPaths,mdl_subFold)
## -- this is temporary here for extracting median response of units
# path_dataset = os.path.join(path_dataset_base,'datasets')
# fname_data_train_val_test = os.path.join(path_dataset,(exp_select+'_dataset_train_val_test_'+lightLevel_1+'.h5'))
# _,_,_,data_quality,_,_,_ = load_h5Dataset(fname_data_train_val_test,LOAD_TR=False,LOAD_VAL=False)
# resp_median_allUnits = data_quality['resp_median_allUnits']
# -- this is temporary here for extracting median response of units
# mdl_select = models_all[0]
for mdl_select in models_all:
if writeToCSV==True:
fname_csv_file = 'performance_'+exp_select+'_'+lightLevel_1+'_avgAcrossTrials_'+mdl_select+'.csv'
fname_csv_file = os.path.join(path_save_performance,fname_csv_file)
with open(fname_csv_file,'a',encoding='utf-8') as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerow(csv_header)
# Get the folder names (paramNames) for the different runs of mdl_select
paramNames_temp = os.listdir(os.path.join(path_mdl_drive,mdl_select))
paramNames = ([])
for p in paramNames_temp:
try: # some model runs are incomplete so don't consider them. They will not contain the performance file
if os.listdir(os.path.join(path_mdl_drive,mdl_select,p,'performance')):
paramNames.append(p)
except:
pass
# Cut out the trial number to group different trials of same models/params together
paramNames_cut = []
for i in range(0,len(paramNames)):
rgb = re.compile(r'_TR-(\d+)')
idx_paramMention = rgb.search(paramNames[i]).span()
paramNames_cut_rgb = paramNames[i][:idx_paramMention[0]] + paramNames[i][idx_paramMention[1]:]
paramNames_cut.append(paramNames_cut_rgb)
paramNames_unique = list(set(paramNames_cut)) # This contains the unique runs
# Locate where the unique names lie in the non-unique array. So this will also tell how many trials for each model/param
idx_paramName = ([])
for i in range(len(paramNames_unique)):
name = paramNames_unique[i]
rgb = [j for j,k in enumerate(paramNames_cut) if name==k]
idx_paramName.append(rgb)
perf_paramNames = {}
param_list = dict([(key, []) for key in param_list_keys])
# param_unique_idx = 0
for param_unique_idx in range(len(paramNames_unique)):
# initialize variables where the last dimension size is number of trials for that model/params combo
num_trials = len(idx_paramName[param_unique_idx])
fev_allUnits_allEpochs_allTr = np.zeros((1000,1000,num_trials))
fev_allUnits_bestEpoch_allTr = np.zeros((1000,num_trials))
fev_medianUnits_allEpochs_allTr = np.zeros((1000,num_trials))
fev_medianUnits_bestEpoch_allTr = np.zeros((num_trials))
predCorr_allUnits_allEpochs_allTr = np.zeros((1000,1000,num_trials))
predCorr_allUnits_bestEpoch_allTr = np.zeros((1000,num_trials))
predCorr_medianUnits_allEpochs_allTr = np.zeros((1000,num_trials))
predCorr_medianUnits_bestEpoch_allTr = np.zeros((num_trials))
trial_id = np.zeros((num_trials))
for c_tr in range(num_trials):
paramFileName = paramNames[idx_paramName[param_unique_idx][c_tr]]
paramName_path = os.path.join(path_mdl_drive,mdl_select,paramFileName)
fname_performanceFile = os.path.join(paramName_path,'performance',exp_select+'_'+paramFileName+'.h5')
f = h5py.File(fname_performanceFile,'r')
rgb = np.atleast_1d(f['model_performance']['fev_allUnits_allEpochs'])
num_units = rgb.shape[1]
num_epochs = rgb.shape[0]
fev_allUnits_allEpochs_allTr[:num_epochs,:num_units,c_tr] = rgb
rgb = np.array(f['model_performance']['fev_allUnits_bestEpoch'])
fev_allUnits_bestEpoch_allTr[:rgb.shape[0],c_tr] = rgb
try:
rgb = np.array(f['model_performance']['fev_median_allEpochs'])
except:
rgb = np.array(f['model_performance']['fev_medianUnits_allEpochs'])
rgb = np.array(f['model_performance']['fev_medianUnits_allEpochs'])
fev_medianUnits_allEpochs_allTr[:rgb.shape[0],c_tr] = rgb
rgb = np.atleast_1d(f['model_performance']['fev_medianUnits_bestEpoch'])
fev_medianUnits_bestEpoch_allTr[c_tr] = rgb
rgb = np.array(f['model_performance']['predCorr_allUnits_bestEpoch'])
predCorr_allUnits_bestEpoch_allTr[:rgb.shape[0],c_tr] = rgb
rgb = np.array(f['model_performance']['predCorr_medianUnits_allEpochs'])
predCorr_medianUnits_allEpochs_allTr[:rgb.shape[0],c_tr] = rgb
rgb = np.atleast_1d(f['model_performance']['predCorr_medianUnits_bestEpoch'])
predCorr_medianUnits_bestEpoch_allTr[c_tr] = rgb
rgb = getModelParams(os.path.split(fname_performanceFile)[-1])
trial_id[c_tr] = np.atleast_1d(rgb['TR'])
fev_allUnits_allEpochs_allTr = fev_allUnits_allEpochs_allTr[:num_epochs,:num_units,:]
fev_allUnits_bestEpoch_allTr = fev_allUnits_bestEpoch_allTr[:num_units,:]
fev_medianUnits_allEpochs_allTr = fev_medianUnits_allEpochs_allTr[:num_epochs,:]
fev_medianUnits_bestEpoch_allTr = fev_medianUnits_bestEpoch_allTr
fracExVar_allUnits = np.array(f['model_performance']['fracExVar_allUnits'])
fracExVar_medianUnits = np.array(f['model_performance']['fracExVar_medianUnits'])
predCorr_allUnits_allEpochs_allTr = fev_allUnits_allEpochs_allTr[:num_epochs,:num_units,:]
predCorr_allUnits_bestEpoch_allTr = predCorr_allUnits_bestEpoch_allTr[:num_units,:]
predCorr_medianUnits_allEpochs_allTr = fev_medianUnits_allEpochs_allTr[:num_epochs,:]
predCorr_medianUnits_bestEpoch_allTr = predCorr_medianUnits_bestEpoch_allTr
rrCorr_allUnits = np.array(f['model_performance']['rrCorr_allUnits'])
rrCorr_medianUnits = np.array(f['model_performance']['rrCorr_medianUnits'])
perf = {
'fev_allUnits_allEpochs_allTr': fev_allUnits_allEpochs_allTr,
'fev_allUnits_bestEpoch_allTr': fev_allUnits_bestEpoch_allTr,
'fev_medianUnits_allEpochs_allTr': fev_medianUnits_allEpochs_allTr,
'fev_medianUnits_bestEpoch_allTr': fev_medianUnits_bestEpoch_allTr,
'fracExVar_allUnits': fracExVar_allUnits,
'fracExVar_medianUnits': fracExVar_medianUnits,
'idx_bestEpoch': np.array(f['model_performance']['idx_bestEpoch']),
'predCorr_allUnits_allEpochs_allTr': predCorr_allUnits_allEpochs_allTr,
'predCorr_allUnits_bestEpoch_allTr': predCorr_allUnits_bestEpoch_allTr,
'predCorr_medianUnits_allEpochs_allTr': predCorr_medianUnits_allEpochs_allTr,
'predCorr_medianUnits_bestEpoch_allTr': predCorr_medianUnits_bestEpoch_allTr,
'rrCorr_allUnits': rrCorr_allUnits,
'rrCorr_medianUnits': rrCorr_medianUnits,
'num_trials': num_trials,
# 'val_dataset_name': utils_si.h5_tostring(np.array(f['model_performance']['val_dataset_name'])),
'trial_id': trial_id,
}
performance = {}
performance['model_performance'] = perf
select_groups = ('model_params','data_quality')
for j in select_groups:
perf_group = {}
keys = list(f[j].keys())
for i in keys:
rgb = np.array(f[j][i])
rgb_type = rgb.dtype.name
if 'bytes' in rgb_type:
perf_group[i] = utils_si.h5_tostring(rgb)
else:
perf_group[i] = rgb
performance[j] = perf_group
filt_temporal_width = np.array(f['stim_info']['temporal_width'])
performance['model_params']['thresh_rr'] = np.array(f['thresh_rr'])
performance['model_params']['idx_unitsToTake'] = np.array(f['idx_unitsToTake'])
performance['model_params']['temporal_width'] = filt_temporal_width
pr_param_list = ('pr_alpha','pr_beta','pr_gamma','pr_nY','pr_nZ','pr_tauY','pr_tauZ')
perf_keys = list(f['model_performance'].keys())
if 'pr_alpha' in perf_keys:
for j in pr_param_list:
performance['model_params'][j] = np.array(f['model_performance'][j])
rgb = getModelParams(paramFileName)
for i in list(rgb.keys()):
param_list[i].append(rgb[i])
dataset_pred = {
'obs_rate': np.array(f['dataset_pred']['obs_rate']),
'pred_rate': np.array(f['dataset_pred']['pred_rate']),
}
performance['dataset_pred'] = dataset_pred
# performance['resp_median_trainingData_allUnits'] = resp_median_allUnits
perf_paramNames[paramNames_unique[param_unique_idx]] = performance
if writeToCSV == True:
csv_data = [mdl_select,exp_select,filt_temporal_width,rgb['C1_n'], rgb['C1_s'], rgb['C1_3d'], rgb['C2_n'], rgb['C2_s'], rgb['C2_3d'], rgb['C3_n'], rgb['C3_s'], rgb['C3_3d'],np.nanmean(fev_medianUnits_bestEpoch_allTr),fracExVar_medianUnits,np.mean(predCorr_medianUnits_bestEpoch_allTr),rrCorr_medianUnits]
with open(fname_csv_file,'a',encoding='utf-8') as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerow(csv_data)
f.close()
# p_U
perf_allModels[mdl_select] = perf_paramNames
params_allModels[mdl_select] = param_list
paramNames_allModels[mdl_select] = paramNames_unique
perf_allExps[exp_select] = perf_allModels
params_allExps[exp_select] = params_allModels
paramNames_allExps[exp_select] = paramNames_allModels
# del perf_allModels, perf_paramNames, performance, perf_group, param_list, params_allModels, paramNames_allModels, paramNames
# %% D1: Test model
select_exp = expDates[0]
select_mdl = models_all[0] #'CNN_2D' #'CNN_2D_chansVary'#'CNN_2D_filtsVary'
# params_mdl = params_allExps[select_exp][select_mdl]
val_dataset_1 = lightLevel_1 #'scot-3-Rstar_mdl-rieke_s-7.07_p-7.07_e-2.53_k-0.01_h-3_b-25_hc-4_gd-15.5_g-50.0_preproc-rods_norm-0_tb-8_Euler_RF-2' #lightLevel_1 #lightLevel_1 # # ['scotopic','photopic']
correctMedian = False
select_LR = '0.001'
select_U = 57#31#0.15
select_P = 0#180
select_T = 120#120
select_BN = 1
select_MP = 2
# select_TR = 1
select_CB = 0
select_C1_n = 8#1#2#13 #13 #20
select_C1_s = 9
select_C1_3d = 0#50#25
select_C2_n = 16#40#26 #26 #24
select_C2_s = 7#2#2
select_C2_3d = 0#10#5
select_C3_n = 20 #36 #24 #22
select_C3_s = 7#1#1
select_C3_3d = 0#62#32
select_TRSAMPS = 40
# paramFileName = paramsToName(select_mdl,U=select_U,P=select_P,T=select_T,BN=select_BN,MP=select_MP,LR=select_LR,
# C1_n=select_C1_n,C1_s=select_C1_s,C1_3d=select_C1_3d,
# C2_n=select_C2_n,C2_s=select_C2_s,C2_3d=select_C2_3d,
# C3_n=select_C3_n,C3_s=select_C3_s,C3_3d=select_C3_3d)
paramFileName,_ = modelFileName(U=select_U,P=select_P,T=select_T,BN=select_BN,MP=select_MP,LR=select_LR,CB_n=select_CB,
C1_n=select_C1_n,C1_s=select_C1_s,C1_3d=select_C1_3d,
C2_n=select_C2_n,C2_s=select_C2_s,C2_3d=select_C2_3d,
C3_n=select_C3_n,C3_s=select_C3_s,C3_3d=select_C3_3d,with_TR=False,
TRSAMPS=select_TRSAMPS,with_TRSAMPS=True)
# val_dataset_1 = perf_allExps[select_exp][select_mdl][paramFileName]['model_performance']['val_dataset_name'][0]
# assert val_dataset_1 != val_dataset_1, 'same datasets selected'
idx_bestTrial = np.nanargmax(perf_allExps[select_exp][select_mdl][paramFileName]['model_performance']['fev_medianUnits_bestEpoch_allTr'])
idx_bestEpoch = np.nanargmax(perf_allExps[select_exp][select_mdl][paramFileName]['model_performance']['fev_medianUnits_allEpochs_allTr'][:,idx_bestTrial])
trial_num = perf_allExps[select_exp][select_mdl][paramFileName]['model_performance']['trial_id'][idx_bestTrial]
plt.plot(perf_allExps[select_exp][select_mdl][paramFileName]['model_performance']['fev_medianUnits_allEpochs_allTr'][:,idx_bestTrial])
select_TR = int(trial_num)
mdlFolder = paramFileName+'_TR-%02d' % select_TR
path_model = os.path.join(path_mdl_drive,select_mdl,mdlFolder)
mdl = load(os.path.join(path_model,mdlFolder))
fname_bestWeight = 'weights_'+mdlFolder+'_epoch-%03d' % (idx_bestEpoch+1)
try:
mdl.load_weights(os.path.join(path_model,fname_bestWeight))
except:
mdl.load_weights(os.path.join(path_model,fname_bestWeight+'.h5'))
weights_dict = get_weightsDict(mdl)
fname_data_train_val_test = os.path.join(path_dataset,(exp_select+'_dataset_train_val_test_'+val_dataset_1+'.h5'))
data_train,data_val,_,_,dataset_rr,parameters,resp_orig = load_h5Dataset(fname_data_train_val_test,LOAD_TR=False)
samps_shift = int(np.array(parameters['samps_shift']))
# samps_shift = 0
resp_orig = resp_orig['train']
filt_temporal_width = select_T
obs_rate_allStimTrials_d1 = dataset_rr['stim_0']['val'][:,filt_temporal_width:,:]
if select_mdl[:6]=='CNN_2D' or select_mdl == 'replaceDense_2D':
data_val = prepare_data_cnn2d(data_val,select_T,np.arange(data_val.y.shape[1]))
# data_test = prepare_data_cnn2d(data_test,select_T,np.arange(data_test.y.shape[1]))
elif select_mdl[:6]=='CNN_3D':
data_val = prepare_data_cnn3d(data_val,select_T,np.arange(data_val.y.shape[1]))
# data_test = prepare_data_cnn3d(data_test,select_T,np.arange(data_test.y.shape[1]))
elif select_mdl[:8]=='PR_CNN2D' or select_mdl[:10]=='PRFR_CNN2D' or select_mdl[:8]=='BP_CNN2D' or select_mdl=='BPFELIX_CNN2D':
pr_temporal_width = perf_allExps[select_exp][select_mdl][paramFileName]['model_params']['pr_temporal_width']
data_val = prepare_data_pr_cnn2d(data_val,pr_temporal_width,np.arange(data_val.y.shape[1]))
obs_rate_allStimTrials_d1 = dataset_rr['stim_0']['val'][:,pr_temporal_width:,:]
elif select_mdl[:8]=='PR_CNN3D':
pr_temporal_width = perf_allExps[select_exp][select_mdl][paramFileName]['model_params']['pr_temporal_width']
data_val = prepare_data_cnn3d(data_val,pr_temporal_width,np.arange(data_val.y.shape[1]))
obs_rate_allStimTrials_d1 = dattaset_rr['stim_0']['val'][:,pr_temporal_width:,:]
idx_unitsToTake = perf_allExps[select_exp][select_mdl][paramFileName]['model_params']['idx_unitsToTake']
obs_rate_allStimTrials_d1 = obs_rate_allStimTrials_d1[:,:,idx_unitsToTake]
# plt.plot(x[:,-1,0])
# plt.plot(x_pred[:,-1,0])
# plt.show()
obs_rate = data_val.y
obs_rate = obs_rate[:,idx_unitsToTake]
if correctMedian==True:
fname_data_train_val_test_d1 = os.path.join(path_dataset,(exp_select+'_dataset_train_val_test_'+val_dataset_1+'.h5'))
_,_,_,_,_,_,resp_med_d1 = load_h5Dataset(fname_data_train_val_test_d1)
resp_med_d1 = np.nanmedian(resp_med_d1['train'],axis=0)
resp_med_d2 = np.nanmedian(resp_orig,axis=0)
resp_mulFac = resp_med_d2/resp_med_d1;
pred_rate = mdl.predict(data_val.X)
pred_rate = pred_rate * resp_mulFac[None,:]
else:
pred_rate = mdl.predict(data_val.X,batch_size = 100)
# obs_rate = data_test.y
# pred_rate = mdl.predict(data_test.X)
num_iters = 500
fev_d1_allUnits = np.empty((pred_rate.shape[1],num_iters))
fracExplainableVar = np.empty((pred_rate.shape[1],num_iters))
predCorr_d1_allUnits = np.empty((pred_rate.shape[1],num_iters))
rrCorr_d1_allUnits = np.empty((pred_rate.shape[1],num_iters))
for i in range(num_iters):
fev_d1_allUnits[:,i], fracExplainableVar[:,i], predCorr_d1_allUnits[:,i], rrCorr_d1_allUnits[:,i] = model_evaluate_new(obs_rate_allStimTrials_d1,pred_rate,0,RR_ONLY=False,lag = samps_shift)
fev_d1_allUnits = np.mean(fev_d1_allUnits,axis=1)
fracExplainableVar = np.mean(fracExplainableVar,axis=1)
predCorr_d1_allUnits = np.mean(predCorr_d1_allUnits,axis=1)
rrCorr_d1_allUnits = np.mean(rrCorr_d1_allUnits,axis=1)
idx_allUnits = np.arange(fev_d1_allUnits.shape[0])
idx_d1_valid = idx_allUnits
# idx_d1_valid = np.logical_and(fev_d1_allUnits>-1,fev_d1_allUnits<1.1)
# idx_d1_valid = idx_allUnits[idx_d1_valid]
# fev_d1_validUnits = fev_d1_allUnits[idx_d1_valid]
# predCorr_d1_validUnits = predCorr_d1_allUnits[idx_d1_valid]
# rrCorr_d1_validUnits = rrCorr_d1_allUnits[idx_d1_valid]
# # fev_d1_allUnits = fev_d1_allUnits[idx_valid]
fev_d1_medianUnits = np.median(fev_d1_allUnits[idx_d1_valid])
print('FEV = %0.2f' %(fev_d1_medianUnits*100))
fev_d1_stdUnits = np.std(fev_d1_allUnits[idx_d1_valid])
fev_d1_ci = 1.96*(fev_d1_stdUnits/len(idx_d1_valid)**.5)
predCorr_d1_medianUnits = np.median(predCorr_d1_allUnits[idx_d1_valid])
print('R = %0.2f' %predCorr_d1_medianUnits)
predCorr_d1_stdUnits = np.std(predCorr_d1_allUnits[idx_d1_valid])
predCorr_d1_ci = 1.96*(predCorr_d1_stdUnits/len(idx_d1_valid)**.5)
rrCorr_d1_medianUnits = np.median(rrCorr_d1_allUnits[idx_d1_valid])
rrCorr_d1_stdUnits = np.std(rrCorr_d1_allUnits[idx_d1_valid])
rrCorr_d1_ci = 1.96*(rrCorr_d1_stdUnits/len(idx_d1_valid)**.5)
idx_units_sorted = np.argsort(fev_d1_allUnits[idx_d1_valid])
idx_units_sorted = idx_d1_valid[idx_units_sorted]
idx_unitsToPred = [idx_units_sorted[-1],idx_units_sorted[-2],idx_units_sorted[1],idx_units_sorted[0]]
# idx_unitsToPred = [26,3,16,32]
t_start = 10
t_dur = obs_rate.shape[0]
t_end = t_dur-20
win_display = (t_start,t_start+t_dur)
font_size_ticks = 14
t_frame = 8
t_axis = np.arange(0,obs_rate.shape[0]*t_frame,t_frame)
col_mdl = ('r')
lineWidth_mdl = [2]
lim_y = (0,6)
fig,axs = plt.subplots(2,2,figsize=(25,10))
axs = np.ravel(axs)
fig.suptitle('Training: '+lightLevel_1+' | Prediction: '+ val_dataset_1,fontsize=16)
for i in range(len(idx_unitsToPred)):
l_base, = axs[i].plot(t_axis[t_start+samps_shift:t_end],obs_rate[t_start:t_end-samps_shift,idx_unitsToPred[i]],linewidth=4,color='darkgrey')
l_base.set_label('Actual')
l, = axs[i].plot(t_axis[t_start+samps_shift:t_end],pred_rate[t_start+samps_shift:t_end,idx_unitsToPred[i]],cols_lightLevels[val_dataset_1[:8]],linewidth=lineWidth_mdl[0])
l.set_label('Predicted: FEV = %.02f, Corr = %0.2f' %(fev_d1_allUnits[idx_unitsToPred[i]],predCorr_d1_allUnits[idx_unitsToPred[i]]))
# axs[i].set_ylim(lim_y)
axs[i].set_xlabel('Time (ms)',fontsize=font_size_ticks)
axs[i].set_ylabel('Normalized spike rate (spikes/second)',fontsize=font_size_ticks)
axs[i].set_title('unit_id: %d' %idx_unitsToPred[i])
axs[i].legend()
plt.setp(axs[i].get_xticklabels(), fontsize=font_size_ticks)
fevs = [fev_d1_medianUnits,0]
cis = [fev_d1_ci,0]
fig,axs = plt.subplots(2,1,figsize=(5,10))
fig.suptitle('Training: '+lightLevel_1,fontsize=16)
font_size_ticks = 20
col_scheme = ('darkgrey',cols_lightLevels[val_dataset_1[:8]])
# ax.yaxis.grid(True)
xlabel = [val_dataset_1,'']
axs[0].bar(xlabel,fevs,yerr=cis,align='center',capsize=6,alpha=.7,color=[col_scheme[1]],width=0.4)
axs[0].set_ylabel('FEV',fontsize=font_size_ticks)
axs[0].set_title('',fontsize=font_size_ticks)
axs[0].set_ylim((0,1.1))
axs[0].set_yticks(np.arange(0,1.1,.2))
axs[0].text(-0.2,1,'FEV = %0.2f' %fevs[0],fontsize=font_size_ticks)
axs[0].tick_params(axis='both',labelsize=font_size_ticks)
fevs = [predCorr_d1_medianUnits,0]
cis = [predCorr_d1_ci,0]
xlabel = [val_dataset_1,'']
axs[1].bar(xlabel,fevs,yerr=cis,align='center',capsize=6,alpha=.7,color=[col_scheme[1]],width=0.4)
axs[1].set_ylabel('Correlation Coeff',fontsize=font_size_ticks)
axs[1].set_title('',fontsize=font_size_ticks)
axs[1].set_ylim((0,1.1))
axs[1].set_yticks(np.arange(0,1.1,.2))
# axs[0].text(.75,.45,'N = %d RGCs' %fev_allUnits.shape[1],fontsize=font_size_ticks)
axs[1].tick_params(axis='both',labelsize=font_size_ticks)
# plt.hist(b,np.arange(-1,1.1,.1))
# plt.hist(a,np.arange(-1,1.1,.1))
# plt.ylim((0,6))
# plt.ylabel('num of cells')
# plt.xlabel('FEV')
# plt.show()
_ = gc.collect()
# %% D2: Test model
transferModel = False
if transferModel == True:
mdl_select_d2 = 'PRFR_CNN2D'
subFold_d2 = subFold
idx_mdl_start = 1
idx_mdl_end = 4
else:
mdl_select_d2 = mdl_select
subFold_d2 = subFold
idx_currMdl_start = 0#4
val_dataset_2_all = ('scot-0.3','scot-3','scot-30')
suffix = '-Rstar' #'-Rstar_mdl-rieke_s-7.07_p-7.07_e-2.53_k-0.01_h-3_b-25_hc-4_gd-15.5_g-50.0_preproc-rods_norm-0_tb-8_Euler_RF-2'
med_fevs = 0
idx_cells_valid = np.arange(fev_d1_allUnits.shape[0])
fev_stack = fev_d1_allUnits[idx_cells_valid]
v_idx = 0
for v_idx in range(len(val_dataset_2_all)):
val_dataset_2 = val_dataset_2_all[v_idx]+suffix #'scot-30-Rstar'#-Rstar_mdl-rieke_s-25.8898_p-21.1589_e-76.97_k-0.01_h-3_b-47.38_hc-4_gd-28_g-1048.0_preproc-rods_norm-0_tb-8_Euler_RF-2'
correctMedian = True
samps_shift_2 = 0#samps_shift
path_dataset_d2 = os.path.join('/home/saad/data/analyses/data_kiersten/',expDates[idx_exp],subFold_d2,'datasets')
fname_data_train_val_test = os.path.join(path_dataset_d2,(exp_select+'_dataset_train_val_test_'+val_dataset_2+'.h5'))
_,data_val,_,_,dataset_rr,params_d2,resp_orig = load_h5Dataset(fname_data_train_val_test)
resp_orig = resp_orig['train']
obs_rate_allStimTrials_d2 = dataset_rr['stim_0']['val'][:,filt_temporal_width:,:]
filt_width = filt_temporal_width
if mdl_select_d2[:6]=='CNN_2D' or mdl_select_d2 == 'replaceDense_2D':
data_val = prepare_data_cnn2d(data_val,select_T,np.arange(data_val.y.shape[1]))
# data_test = prepare_data_cnn2d(data_test,select_T,np.arange(data_test.y.shape[1]))
elif mdl_select_d2[:6]=='CNN_3D':
data_val = prepare_data_cnn3d(data_val,select_T,np.arange(data_val.y.shape[1]))
# data_test = prepare_data_cnn3d(data_test,select_T,np.arange(data_test.y.shape[1]))
elif mdl_select_d2[:8]=='PR_CNN2D' or mdl_select_d2[:10]=='PRFR_CNN2D' or mdl_select_d2[:8]=='BP_CNN2D' or select_mdl=='BPFELIX_CNN2D':
pr_temporal_width = perf_allExps[select_exp][select_mdl][paramFileName]['model_params']['pr_temporal_width']
data_val = prepare_data_pr_cnn2d(data_val,pr_temporal_width,np.arange(data_val.y.shape[1]))
obs_rate_allStimTrials_d2 = dataset_rr['stim_0']['val'][:,pr_temporal_width:,:]
elif mdl_select_d2[:8]=='PR_CNN3D':
pr_temporal_width = perf_allExps[select_exp][select_mdl][paramFileName]['model_params']['pr_temporal_width']
data_val = prepare_data_cnn3d(data_val,pr_temporal_width,np.arange(data_val.y.shape[1]))
if transferModel == True:
if idx_mdl_end == -1:
idx_mdl_end = len(mdl.layers)
x = Input(shape=data_val.X.shape[1:])
n_cells = data_val.y.shape[1]
# y = Reshape((x.shape[1],x.shape[-2]*x.shape[-1]),name='start')(x)
y = x
for layer in mdl.layers[idx_mdl_start:idx_mdl_end]:
y = layer(y)
y = y[:,x.shape[1]-filt_temporal_width:,:,:]
# y = model.models.Normalize_PRDA_GF(units=1)(y)
for layer in mdl.layers[5:6]:
y = layer(y)
mdl_d2 = Model(x, y, name='CNN_2D')
elif idx_currMdl_start > 0: # If we want to remove the beginning layers from the current model
x = Input(shape=data_val.X.shape[1:])
n_cells = data_val.y.shape[1]
y = x
for layer in mdl.layers[idx_currMdl_start:2]:
if layer.name=='tf.__operators__.getitem':
y = y[:,x.shape[1]-filt_temporal_width:,:,:]
# elif layer.name=='tf.__operators__.getitem_1':
# y = y[:,x.shape[1]-filt_temporal_width:,:,:,:]
else:
y = layer(y)
mdl_d2 = Model(x, y, name=mdl_select_d2)
else:
mdl_d2 = mdl
pred_rate = mdl_d2.predict(data_val.X,batch_size=64)
obs_rate = data_val.y
if correctMedian==True:
fname_data_train_val_test_d1 = os.path.join(path_dataset,(exp_select+'_dataset_train_val_test_'+val_dataset_1+'.h5'))
_,_,_,data_quality,_,_,resp_med_d1 = load_h5Dataset(fname_data_train_val_test_d1)
resp_med_d1 = np.nanmedian(resp_med_d1['train'],axis=0)
resp_med_d2 = np.nanmedian(resp_orig,axis=0)
resp_mulFac = resp_med_d2/resp_med_d1;
obs_rate_allStimTrials_d2 = obs_rate_allStimTrials_d2/resp_mulFac
obs_rate = obs_rate/resp_mulFac
# pred_rate = mdl_d2.predict(data_val.X)
# pred_rate = pred_rate * resp_mulFac[None,:]
# else:
# pred_rate = mdl_d2.predict(data_val.X) # /1.4 for 0.3Rstar
num_iters = 50
fev_d2_allUnits = np.empty((pred_rate.shape[1],num_iters))
fracExplainableVar = np.empty((pred_rate.shape[1],num_iters))
predCorr_d2_allUnits = np.empty((pred_rate.shape[1],num_iters))
rrCorr_d2_allUnits = np.empty((pred_rate.shape[1],num_iters))
for i in range(num_iters):
fev_d2_allUnits[:,i], fracExplainableVar[:,i], predCorr_d2_allUnits[:,i], rrCorr_d2_allUnits[:,i] = model_evaluate_new(obs_rate_allStimTrials_d2,pred_rate,0,RR_ONLY=False,lag = samps_shift_2)
fev_d2_allUnits = np.mean(fev_d2_allUnits,axis=1)
# fev_d2_allUnits[fev_d2_allUnits<0] = 0
fracExplainableVar = np.mean(fracExplainableVar,axis=1)
predCorr_d2_allUnits = np.mean(predCorr_d2_allUnits,axis=1)
rrCorr_d2_allUnits = np.mean(rrCorr_d2_allUnits,axis=1)
idx_allUnits = np.arange(fev_d2_allUnits.shape[0])
idx_d2_valid = idx_allUnits
idx_d2_valid = np.logical_and(fev_d2_allUnits>-1,fev_d2_allUnits<1.1)
idx_d2_valid = idx_allUnits[idx_d2_valid]
# idx_d2_valid = idx_d1_valid
# fev_d2_validUnits = fev_d2_allUnits[idx_d2_valid]
# predCorr_d2_validUnits = predCorr_d2_allUnits[idx_d2_valid]
# rrCorr_d2_validUnits = rrCorr_d2_allUnits[idx_d2_valid]
# # fev_d2_allUnits = fev_d2_allUnits[idx_valid]
fev_d2_medianUnits = np.median(fev_d2_allUnits[idx_d2_valid])
print('FEV = %0.2f' %(fev_d2_medianUnits*100))
fev_d2_stdUnits = np.std(fev_d2_allUnits[idx_d2_valid])
fev_d2_ci = 1.96*(fev_d2_stdUnits/len(idx_d2_valid)**.5)
predCorr_d2_medianUnits = np.median(predCorr_d2_allUnits[idx_d2_valid])
print('R = %0.2f' %(predCorr_d2_medianUnits*100))
predCorr_d2_stdUnits = np.std(predCorr_d2_allUnits[idx_d2_valid])
predCorr_d2_ci = 1.96*(predCorr_d2_stdUnits/len(idx_d2_valid)**.5)
rrCorr_d2_medianUnits = np.median(rrCorr_d2_allUnits[idx_d2_valid])
rrCorr_d2_stdUnits = np.std(rrCorr_d2_allUnits[idx_d2_valid])
rrCorr_d2_ci = 1.96*(rrCorr_d2_stdUnits/len(idx_d2_valid)**.5)
# idx_units_sorted = np.argsort(predCorr_d2_allUnits[idx_d2_valid])
idx_units_sorted = np.argsort(fev_d2_allUnits[idx_d2_valid])
idx_units_sorted = idx_d2_valid[idx_units_sorted]
# idx_unitsToPred = [idx_units_sorted[-1],idx_units_sorted[-2],idx_units_sorted[1],idx_units_sorted[0]]
# idx_unitsToPred = [26,3,16,32]
# %
t_start = 10 + 45
t_dur = obs_rate.shape[0]
t_end = t_dur-20
win_display = (t_start,t_start+t_dur)
font_size_ticks = 14
t_frame = 8
t_axis = np.arange(0,obs_rate.shape[0]*t_frame,t_frame)
col_mdl = ('r')
lineWidth_mdl = [2]
lim_y = (0,6)
fig,axs = plt.subplots(2,2,figsize=(25,10))
axs = np.ravel(axs)
fig.suptitle('Training: '+lightLevel_1+' | Prediction: '+ val_dataset_2,fontsize=16)
for i in range(len(idx_unitsToPred)):
l_base, = axs[i].plot(t_axis[t_start+samps_shift_2:t_end],obs_rate[t_start:t_end-samps_shift_2,idx_unitsToPred[i]],linewidth=4,color='darkgrey')
l_base.set_label('Actual')
l, = axs[i].plot(t_axis[t_start+samps_shift_2:t_end],pred_rate[t_start+samps_shift_2:t_end,idx_unitsToPred[i]],cols_lightLevels[val_dataset_2[:8]],linewidth=lineWidth_mdl[0])
l.set_label('Predicted: FEV = %.02f, Corr = %0.2f' %(fev_d2_allUnits[idx_unitsToPred[i]],predCorr_d2_allUnits[idx_unitsToPred[i]]))
# axs[i].set_ylim(lim_y)
axs[i].set_xlabel('Time (ms)',fontsize=font_size_ticks)
axs[i].set_ylabel('Normalized spike rate (spikes/second)',fontsize=font_size_ticks)
axs[i].set_title('unit_id: %d' %idx_unitsToPred[i])
axs[i].legend()
plt.setp(axs[i].get_xticklabels(), fontsize=font_size_ticks)
# idx_cells_notvalid = np.union1d(np.setdiff1d(idx_allUnits,idx_d1_valid),np.setdiff1d(idx_allUnits,idx_d2_valid))
# idx_cells_valid = np.setdiff1d(idx_allUnits,idx_cells_notvalid)
fev_stack = np.vstack((fev_stack,fev_d2_allUnits[idx_cells_valid]))
med_fevs = np.vstack((med_fevs,fev_d2_medianUnits))
# % Violin plots
# med_fevs = med_fevs[1:]*100
fev_stack_plot = fev_stack[1:]*100
idx_cells_valid = np.logical_and(fev_stack_plot>-110,fev_stack_plot<110)
fev_stack_plot[~idx_cells_valid] = np.nan
fev_stack_plot = fev_stack_plot.T
med_fevs = np.nanmedian(fev_stack_plot,axis=0)
# %Bar plots of photopic and scotopic light levels
font_size_ticks = 50
font_size_labels = 50
mdls_order = val_dataset_2_all #[val_dataset_1[:8],val_dataset_2[:8]]
# fev_stack = np.vstack((fev_d1_allUnits,fev_d2_allUnits))*100
figure,axs = plt.subplots(1,1,figsize=(20,20))
axs = np.ravel(axs)
figure.suptitle('')
# col_scheme = ('blue','red')
col_scheme = ('b','r','g')
# ax.yaxis.grid(True)
seaborn.violinplot(data=fev_stack_plot,ax=axs[0],palette=col_scheme)
axs[0].set_ylabel('FEV (%)',fontsize=font_size_ticks)
axs[0].set_title('Trained at '+val_dataset_1,fontsize=font_size_labels)
axs[0].set_yticks(np.arange(0,150,10))
axs[0].set_ylim((0,150))
axs[0].set_xticklabels(mdls_order)
for i in range(len(mdls_order)):
axs[0].text(i+.2,120,'med\n%0.2f%%' %med_fevs[i],fontsize=font_size_labels,color=col_scheme[i])
axs[0].text(-0.4,100,'N = %d RGCs' %fev_stack_plot.shape[0],fontsize=font_size_ticks)
axs[0].tick_params(axis='both',labelsize=font_size_labels)
# %% Sustained, Transient split
unames_valid = perf_allExps[select_exp][select_mdl][paramFileName]['data_quality']['uname_selectedUnits'][idx_d2_valid]
idx_sust = np.arange(0,29)
idx_trans = np.arange(29,64)
idx_sust_valid = np.intersect1d(idx_d2_valid,idx_sust)
idx_trans_valid = np.intersect1d(idx_d2_valid,idx_trans)
fev_sust_allUnits = fev_d2_allUnits[idx_sust_valid]
fev_sust_medianUnits = np.median(fev_sust_allUnits)
fev_sust_stdUnits = np.std(fev_sust_allUnits)
fev_sust_ci = 1.96*(fev_sust_stdUnits/idx_sust_valid.shape[0]**.5)
fev_trans_allUnits = fev_d2_allUnits[idx_trans_valid]
fev_trans_medianUnits = np.median(fev_trans_allUnits)
fev_trans_stdUnits = np.std(fev_trans_allUnits)
fev_trans_ci = 1.96*(fev_trans_stdUnits/idx_trans_valid.shape[0]**.5)
fevs_d1 = [fev_d1_medianUnits,0]
fevs_d2 = [fev_sust_medianUnits,0]
fevs_d3 = [fev_trans_medianUnits,0]
cis_d1 = [fev_d1_ci,0]
cis_d2 = [fev_sust_ci,0]
cis_d3 = [fev_trans_ci,0]
fig,axs = plt.subplots(2,1,figsize=(5,10))
fig.suptitle(select_exp+'\nTraining: '+lightLevel_1,fontsize=16)
font_size_ticks = 16
font_size_labels = 16
col_scheme = (cols_lightLevels[val_dataset_1[:8]],cols_lightLevels[val_dataset_2[:8]])
# ax.yaxis.grid(True)
xpoints = np.atleast_1d((0,1))
xlabel_fev = [select_mdl,'']
axs[0].bar(xpoints-.2,fevs_d1,yerr=cis_d1,align='center',capsize=6,alpha=.7,color=col_scheme[0],width=0.4,label=val_dataset_1[:8])
axs[0].bar(xpoints+.2,fevs_d2,yerr=cis_d2,align='center',capsize=6,alpha=.7,color=col_scheme[1],width=0.4,label=val_dataset_2[:8]+'_sust')
axs[0].bar(xpoints+.6,fevs_d2,yerr=cis_d3,align='center',capsize=6,alpha=.7,color='m',width=0.4,label=val_dataset_2[:8]+'_trans')
axs[0].plot((-0.5,len(xlabel_fev)-0.5),(np.nanmax(fevs_d1),np.nanmax(fevs_d1)),color='green')
axs[0].set_xticks(xpoints)#(2*np.arange(0,fev_d1_medianUnits_allMdls.shape[0]))
axs[0].set_xticklabels(xlabel_fev)
axs[0].set_ylabel('FEV',fontsize=font_size_ticks)
axs[0].set_title('',fontsize=font_size_ticks)
axs[0].set_yticks(np.arange(-1,1.1,.1))
axs[0].set_ylim((0,1.1))
# axs[0].text(.75,.45,'N = %d RGCs' %fev_allUnits.shape[1],fontsize=font_size_ticks)
axs[0].tick_params(axis='both',labelsize=16)
axs[0].legend(loc='best',fontsize=font_size_labels)
# %% D1: Heat maps
RECALC_PERF = 0
select_exp = 'retina1'
select_mdl = models_all[0]
select_param_x = 'C2_n'
select_param_y = 'C3_n'
select_param_z = 'C1_n'
thresh_fev = 0.0
params_mdl = params_allExps[select_exp][select_mdl]
select_U = 0
select_T = 120
select_BN = 1
select_MP = 0
# select_TR = 1
select_C1_n = np.unique(params_mdl['C1_n'])#13
select_C1_s = np.unique(params_mdl['C1_s'])
select_C1_3d = np.unique(params_mdl['C1_3d'])
select_C2_n = np.unique(params_mdl['C2_n'])#13
select_C2_s = np.unique(params_mdl['C2_s'])
select_C2_3d = np.unique(params_mdl['C2_3d'])
select_C3_n = np.unique(params_mdl['C3_n'])#25
select_C3_s = np.unique(params_mdl['C3_s'])
select_C3_3d = np.unique(params_mdl['C3_3d'])
idx_interest = np.in1d(params_mdl['U'],select_U)
idx_interest = idx_interest & np.in1d(params_mdl['T'],select_T)
idx_interest = idx_interest & np.in1d(params_mdl['BN'],select_BN)
idx_interest = idx_interest & np.in1d(params_mdl['MP'],select_MP)
# idx_interest = idx_interest & np.in1d(params_mdl['TR'],select_TR)
idx_interest = idx_interest & np.in1d(params_mdl['C1_n'],select_C1_n)
idx_interest = idx_interest & np.in1d(params_mdl['C1_s'],select_C1_s)
idx_interest = idx_interest & np.in1d(params_mdl['C1_3d'],select_C1_3d)
idx_interest = idx_interest & np.in1d(params_mdl['C2_n'],select_C2_n)
idx_interest = idx_interest & np.in1d(params_mdl['C2_s'],select_C2_s)
idx_interest = idx_interest & np.in1d(params_mdl['C2_3d'],select_C2_3d)
idx_interest = idx_interest & np.in1d(params_mdl['C3_n'],select_C3_n)
idx_interest = idx_interest & np.in1d(params_mdl['C3_s'],select_C3_s)
idx_interest = idx_interest & np.in1d(params_mdl['C3_3d'],select_C3_3d)
# make array of all fevs for min max vals
fev_grand = ([])
for i in perf_allExps[select_exp][select_mdl]:
if perf_allExps[select_exp][select_mdl][i]['model_performance']['num_trials'] == 1:
rgb = perf_allExps[select_exp][select_mdl][i]['model_performance']['fev_medianUnits_bestEpoch_allTr']
else:
rgb = np.mean(perf_allExps[select_exp][select_mdl][i]['model_performance']['fev_medianUnits_bestEpoch_allTr'],axis=-1) # take average across trials
fev_grand.append(rgb)
fev_min, fev_max = np.round(np.nanmin(fev_grand),2), np.round(np.nanmax(fev_grand),2)
fev_min, fev_max = fev_min-.01, fev_max+.01
# get datasets if recalc_perf==1
if RECALC_PERF==1:
val_dataset_1 = lightLevel_1
paramName = paramNames_allExps[select_exp][select_mdl][0]
fname_data_train_val_test = os.path.join(path_dataset,(exp_select+'_dataset_train_val_test_'+val_dataset_1+'.h5'))
_,data_val,_,_,dataset_rr,parameters,resp_orig = load_h5Dataset(fname_data_train_val_test)
samps_shift = int(np.array(parameters['samps_shift']))
filt_temporal_width = select_T
obs_rate_allStimTrials_d1 = dataset_rr['stim_0']['val'][:,filt_temporal_width:,:]
if select_mdl[:6]=='CNN_2D' or select_mdl == 'replaceDense_2D':
data_val = prepare_data_cnn2d(data_val,select_T,np.arange(data_val.y.shape[1]))
elif select_mdl[:6]=='CNN_3D':
data_val = prepare_data_cnn3d(data_val,select_T,np.arange(data_val.y.shape[1]))
elif select_mdl[:8]=='PR_CNN2D' or select_mdl[:10]=='PRFR_CNN2D' or select_mdl[:8]=='BP_CNN2D' or select_mdl=='BPFELIX_CNN2D':
pr_temporal_width = perf_allExps[select_exp][select_mdl][paramName]['model_params']['pr_temporal_width']
data_val = prepare_data_pr_cnn2d(data_val,pr_temporal_width,np.arange(data_val.y.shape[1]))
obs_rate_allStimTrials_d1 = dataset_rr['stim_0']['val'][:,pr_temporal_width:,:]
else:
raise ValueError('model data handler not found')
obs_rate = data_val.y
# plot heatmap
fev_heatmap = np.zeros((len(eval('select_'+select_param_y)),len(eval('select_'+select_param_x)),len(eval('select_'+select_param_z))))
fev_heatmap[:] = np.nan
totalCombs = sum(idx_interest)
idx_interest = np.where(idx_interest)[0]
for i in idx_interest:
idx_x = np.where(params_mdl[select_param_x][i] == eval('select_'+select_param_x))[0][0]
idx_y = np.where(params_mdl[select_param_y][i] == eval('select_'+select_param_y))[0][0]
idx_z = np.where(params_mdl[select_param_z][i] == eval('select_'+select_param_z))[0][0]
paramName = paramNames_allExps[select_exp][select_mdl][i]
if RECALC_PERF == 0:
if perf_allExps[select_exp][select_mdl][paramName]['model_performance']['num_trials'] == 1:
rgb = perf_allExps[select_exp][select_mdl][paramName]['model_performance']['fev_allUnits_bestEpoch_allTr']
else:
rgb = np.mean(perf_allExps[select_exp][select_mdl][paramName]['model_performance']['fev_allUnits_bestEpoch_allTr'],axis=-1) # take average across trials
rgb = np.nanmedian(rgb,axis=0)
if rgb>thresh_fev:
fev_heatmap[idx_y,idx_x,idx_z] = rgb
else:
pred_rate = perf_allExps[select_exp][select_mdl][paramName]['dataset_pred']['pred_rate']
num_iters = 50
fev_d1_allUnits = np.empty((pred_rate.shape[1],num_iters))
fracExplainableVar = np.empty((pred_rate.shape[1],num_iters))
predCorr_d1_allUnits = np.empty((pred_rate.shape[1],num_iters))
rrCorr_d1_allUnits = np.empty((pred_rate.shape[1],num_iters))
for j in range(num_iters):
fev_d1_allUnits[:,j], fracExplainableVar[:,j], predCorr_d1_allUnits[:,j], rrCorr_d1_allUnits[:,j] = model_evaluate_new(obs_rate_allStimTrials_d1,pred_rate,0,RR_ONLY=False,lag = samps_shift)
fev_d1_allUnits = np.mean(fev_d1_allUnits,axis=1)
idx_allUnits = np.arange(fev_d1_allUnits.shape[0])
idx_d1_valid = idx_allUnits
idx_d1_valid = np.logical_and(fev_d1_allUnits>-1,fev_d1_allUnits<1.1)
idx_d1_valid = idx_allUnits[idx_d1_valid]
fev_d1_medianUnits = np.median(fev_d1_allUnits[idx_d1_valid])
if fev_d1_medianUnits>thresh_fev:
fev_heatmap[idx_y,idx_x,idx_z] = fev_d1_medianUnits
font_size_ticks = 10
fig_rows = 3
fig_cols = int(np.ceil(len(eval('select_'+select_param_z))/fig_rows))
fig,axs = plt.subplots(fig_rows,fig_cols,figsize=(20,15))
axs = np.ravel(axs)
axs = axs[:len(eval('select_'+select_param_z))]
color_map = plt.cm.get_cmap('autumn')
reversed_color_map = color_map.reversed()
for l in range(len(eval('select_'+select_param_z))):
param_z = eval('select_'+select_param_z)[l]
# im = axs[l].imshow(fev_heatmap_sorted[:,:,l],cmap=reversed_color_map,vmin = fev_min, vmax = fev_max)
im = axs[l].imshow(fev_heatmap[:,:,l],cmap=reversed_color_map,vmin = fev_min, vmax = fev_max)
axs[l].set_yticks(range(0,len(eval('select_'+select_param_y))))
axs[l].set_yticklabels(eval('select_'+select_param_y),fontsize=font_size_ticks)
axs[l].set_xticks(range(0,len(eval('select_'+select_param_x))))
axs[l].set_xticklabels(eval('select_'+select_param_x),fontsize=font_size_ticks)