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Commit 1cd1d896 authored by Abhijeet Krishna's avatar Abhijeet Krishna
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Visualize pathlengths

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Experiments/notebooks/visualize_lambdas.ipynb
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%% Cell type:markdown id: tags:
# Visualize lambdas
In this notebook we will visualize lambdas with normal 2D plots and circular plots
To run this notebook you must have the following files:
- "../analyzed_output/stages_comparison_df_ecadGFPnbG4.csv"
%% Cell type:code id: tags:
``` python
%load_ext autoreload
%autoreload 2
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os
codeRepo = "/Users/krishna/PhD/WDeversion_theory/"
dataout = codeRepo + "/Experiments/analyzed_output/plots/"
os.makedirs(dataout, exist_ok=True)
plt.style.use(codeRepo + 'src/WDeversion_theory_methods/seaborn-custom-whitegrid.mplstyle')
```
%% Cell type:code id: tags:
``` python
genotype = "ecadGFPnbG4"
#genotype = 'ecadGFPnbG4myoVI'
df_comparison = pd.read_csv(codeRepo + 'Experiments/analyzed_output/stages_comparison_df_' + genotype + '.csv')
df_comparison['roi'] = df_comparison['region']
```
%% Cell type:code id: tags:
``` python
C96hAEL = '#F9F06C'
C120hAEL = '#EBA434'
Cupcrawling = '#414042'
CwhitePupa = '#003399'
C2hAPF = '#0080FF'
C4hAPF = '#33FFFF'
C6hAPF = '#71C382'
CecadGFPnbG4 = '#404040'
CecadGFPnbG4myoVI = '#0099CC'
colors = {'ecadGFPnbG4': CecadGFPnbG4,
'ecadGFPnbG4myoVI': CecadGFPnbG4myoVI,
'96hAEL': C96hAEL,
'120hAEL':C120hAEL,
'wL3': Cupcrawling,
'0hAPF': CwhitePupa,
'2hAPF': C2hAPF,
'4hAPF': C4hAPF,
'6hAPF': C6hAPF}
devstage_combinations = pd.DataFrame({'devstage_init':['wL3','wL3','wL3',],
'devstage_final':['0hAPF','2hAPF','4hAPF',],}
)
rois = ["DV", "outDV"]
regions = rois
```
%% Cell type:code id: tags:
``` python
x_col = 'ref_pathlength_scaled'
props = ['lambda_isotropic', 'lambda_Q', 'lambda_rearrangement', 'lambda_anisotropic']
ylabels = [r'$\lambda^*$', r'$\tilde{\lambda}^*_{Q}$', r'$\tilde{\lambda}^*_{R}$', r'$\tilde{\lambda}^*$']
prop = props[0]
ylabel = ylabels[0]
for prop,ylabel in zip(props,ylabels):
for i in range(len(rois)):
fig,ax = plt.subplots(figsize = (2.5, 2.5)
)
roi = rois[i]
if roi == 'DV':
#style = '--'
xlabel = r'$l$'
title = 'DVB'
else:
#style = '-'
xlabel = r'$r$'
title = 'outDVB'
style = '-'
ax.axhline(1, linestyle = '-', color ='grey')
for j in range(len(devstage_combinations)):
devstage_init = devstage_combinations.loc[j,'devstage_init']
devstage_final = devstage_combinations.loc[j,'devstage_final']
ax.set_yticks(np.arange(0.7,2, 0.3))
ax.set_xticks([0.0,0.5,1.0])
query_str = '(devstage_init == "' + devstage_init + '") & (devstage_final == "' + devstage_final + '") & (roi == "' + roi + '")'
data = df_comparison.query(query_str)
ax.fill_between(x = data[x_col], y1 = data[prop +'_mean'] - data[prop +'_std'], y2 = data[prop +'_mean'] + data[prop +'_std'],color = colors[devstage_final], alpha = 0.2, linewidth = 0)
ax.plot(data[x_col], data['fit_' + prop +'_mean'], color = colors[devstage_final], linestyle = style, linewidth = 2)
ax.tick_params(axis='both', which='major', labelsize=15)
ax.set_xlabel(xlabel, rotation = 'horizontal')
ax.set_ylabel(ylabel, rotation = 'horizontal')
ax.set_title(title, loc ='center')
ax.set_ylim(0.7,1.6)
ax.set_xlim(0,1.05)
plt.savefig(dataout + genotype + prop + roi + '_' + '_std_lamdavsP_cumulative_wFit_eversion.pdf',dpi = 600,bbox_inches = 'tight')
```
%% Output
%% Cell type:markdown id: tags:
# Plot pathlengths
%% Cell type:code id: tags:
``` python
nbcols = 1
nbrows = 1
devstages = ['0hAPF', '2hAPF', '4hAPF']
fig,ax = plt.subplots(nbrows, nbcols,
figsize = (3, 3)
)
for i in [0,1]:
roi = rois[i]
if roi == 'DV':
linestyle = '--'
elif roi == 'outDV':
linestyle = '-'
for j in range(len(devstages)):
devstage = devstages[j]
query_str = '(devstage_final == "' + devstage + '") & (region == "' + roi + '")'
ax.set_ylim(0,900)
# ax.set_xlim(0,220)
ax.set_yticks(np.arange(0,910,250))
data = df_comparison.query(query_str)
x_col_mean = 'pathlength_scaled'
y_col_mean = 'cumcount_mean'
#ax.fill_between(stat_df[y_col_mean], stat_df[x_col_mean] - yerr, stat_df[x_col_mean] + yerr, color = colors[devstage], alpha = 0.2)
ax.plot(data[x_col_mean], data[y_col_mean], color = colors[devstage], lw = 2, label = devstage, linestyle = linestyle)
query_str = '(devstage_init == "' + 'wL3' + '") & (region == "' + roi + '")'
data = df_comparison.query(query_str)
#this will give us three sets of wL3 data, one for each devstage_final, hence we choose the first one
query_str = 'devstage_final == "0hAPF"'
data = data.query(query_str)
ax.plot(data['ref_pathlength_scaled'], data['cumcount_mean'], linestyle = linestyle, color = colors["wL3"], linewidth = 2, label = 'wL3')
ax.set_xlabel(r'$\rho$')
ax.set_ylabel(r'$N$')
plt.savefig(dataout +'/' + genotype + '_rho_vs_N-noIncreaselimits.pdf',dpi = 600,bbox_inches='tight')
```
%% Output
......
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