#
###########################################################################
from matplotlib.ticker import FuncFormatter, MaxNLocator
import numpy as np
import pickle; import numpy as np; import matplotlib.pyplot as plt;
import os
import sys
import scipy.stats as stats
import mol_utils
codepath = os.getcwd()+'/'
sys.path.append(codepath)
DRM_words = mol_utils.pickleload(codepath+'DRM_words.dump')['DRM_words']
labels = [DRM_words[i] for i in np.array([10, 16, 11, 4, 12, 17, 8, 14, 19 , 21, 7, 13, 5, 20, 3, 9, 15, 18, 6])-3]
def format_fn(tick_val, tick_pos):
if int(tick_val)<len(labels):
return labels[int(tick_val)]
else:
return ''
###########################################################################
#
# Z-score version of figures
#
###########################################################################
mediansZscores = mol_utils.pickleload(codepath+'factor_analysis_results_overlap_plus_mean_FT0.dump')['mediansPvals'];
keys1 = sorted(mediansZscores['Semantics2'].keys())
import gensim import re import copy import sklearn.cross_decomposition as cross from sklearn import linear_model as lm from sklearn import model_selection as mod_sel import matplotlib.pyplot as plt import scipy.stats as stats import os import pickle import mol_utils basepath = os.getcwd() + '/' sys.path.append(basepath) v = mol_utils.pickleload(basepath + 'aux/horizontal_barchart_data.dump') corr = v['corr'] corr2 = v['corr2'] DRV_words2 = v['DRV_words2'] mols = v['mols'] chems = v['chems'] corr2a = v['corr2a'] sorted_correls_DRV = v['sorted_correls_DRV'] sorted_correls_DRV_mols = [x for (y, x) in sorted(zip(corr2, mols))] sorted_correls_DRV_mols = [x for (y, x) in sorted(zip(corr2, mols))] # Create the bars # The parameters are:
import os
import matplotlib.pyplot as plt
import scipy.stats as stats
import pickle
import numpy as np
import sys
import mol_utils as mu
basepath = os.getcwd() + '/'
sys.path.append(basepath)
codepath = basepath
try:
mediansPvals = mu.pickleload(
codepath +
'moleculeAnalysis_results_overlap_plus_mean_2.0_FT0_expandSet.dump'
)['mediansZscores']
except:
mediansPvals = mu.pickleload(
codepath +
'moleculeAnalysis_results_overlap_plus_mean_2.0_FT0_expandSet.dump'
)['mediansPvals']
markers = ['s', '<', 'o']
#markerfacecolors = ['white','black','lightgrey']
linestyles = ['solid', 'solid', 'solid'] #'dashed','-.']
fig, ax = plt.subplots()
for style, key, marker in zip(linestyles,
['Semantics2', 'Perceptual', 'Half2'], markers):
keys0 = [k for k in sorted(mediansPvals[key].keys()) if not k == 0]
if key == 'Perceptual':
key2 = 'DirRat'