from analyze_by_deflection import *
import pandas as pd
import plotVG3D
import os
import seaborn as sns
# ============================ #
# edit here #
# ============================ #
p_save = os.path.join(os.environ['BOX_PATH'],
r'__VG3D/_deflection_trials/_NEO/results')
cell_list = ['201711B2', '201708D1']
figsize = plotVG3D.set_fig_style()[1]
sns.set_style('ticks')
# =================================
# Plot PCA variance explained
dat = pd.read_csv(os.path.join(p_save,
'PCA_decompositions_new_eigennames.csv'))
wd = figsize[0] / 3
ht = wd / .75
f = plt.figure(figsize=(wd, ht))
pvt = dat2.pivot_table(index=dat2.index,
columns='id',
values='ExplainedVarianceRatio')
exp_var = np.array([pvt[x].as_matrix() for x in pvt.columns]).T
plt.plot(exp_var, 'k', alpha=.15, linewidth=1)
plt.plot(np.mean(exp_var, axis=1),
'o-',
color=[182 / 255., 0, 0],
linewidth=2,
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib as mpl
from cycler import cycler
from plotVG3D import set_fig_style
figsize, ext = set_fig_style()[1:3]
cmap = mpl.cm.get_cmap('Greys')
import numpy as np
mpl.rcParams['axes.prop_cycle'] = cycler(
color=cmap(np.linspace(100, 205, 5).astype('int')))
## ================================ ##j
## ==== large NLIN comparisons ==== ##
## ================================ ##
fname_large = 'K_testing_large_nlin.csv'
fname_small = 'K_testing_small_nlin.csv'
sigma = 16
p_load = '__VG3D/_deflection_trials/_NEO/results'
p_load = os.path.join(os.environ['BOX_PATH'], p_load)
p_save = p_load
df1 = pd.read_csv(os.path.join(p_load, fname_large))
df2 = pd.read_csv(os.path.join(p_load, fname_small))
for cell in df1.id.unique():
sub_df1 = df1[df1.id == cell]
sub_df1.plot(
x='sigma',
linewidth=1.5,
import varTuning
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import plotVG3D
# ===================
dpi_res, figsize, ext = plotVG3D.set_fig_style()
p_save = os.path.join(os.environ['BOX_PATH'],
r'__VG3D\_deflection_trials\_NEO\results')
is_stim = pd.read_csv(os.path.join(p_save, r'cell_id_stim_responsive.csv'))
df = pd.read_csv(os.path.join(p_save, 'regularity_by_contact.csv'))
df = df.merge(is_stim, on='id')
df = df[df.stim_responsive]
cell_list = ['201708D1c0']
# =======================================
if len(cell_list) == 0:
cell_list = df.id.unique()
# Plot regularity by direction for a cell
wd = figsize[0] / 3
ht = wd
for cell in cell_list:
sub_df = df[df.id == cell]
df_dir = sub_df.groupby('dir_idx')
theta = df_dir.med_dir.mean()
# =========================
plt.figure(figsize=(wd, ht))
med_CV = df_dir.CV.quantile(0.5)
lower_CV = df_dir.CV.quantile(0.25)
upper_CV = df_dir.CV.quantile(0.75)
