Variations in alpha over denticle number

Goal: graphing variations in calculated alpha with denticle number in embryos and larvae



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from IPython.core.display import HTML
HTML("<style>.container { width:90% !important; }</style>")



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from sys import argv
import glob
import os
import math

import numpy as np
import pandas as pd
import scipy.stats as sps
import statsmodels as sm

import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline



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sns.set_context('notebook')
sns.set_style('darkgrid')
# sns.set_style('white')
# sns.set_style('ticks')



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numbermap = {'five':5, 'six':6, 'seven':7, 'eight':8, 'nine':9}
modelmap = {'halfD':.5, 'twothirdsD':.666, 'sixtenthsD':.6, 'sevententhsD':.7, 
            'threequartersD':.75, 'eighttenthsD':.8, 'ninetenthsD':.9, 'oneD':1}



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basepath = os.getcwd()



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iterations = os.getcwd().split('_')[-2][0:-10]



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names = []
for fname in glob.glob('*ks_fractions.csv'):
# for fname in glob.glob('*ks_fractions*.csv'):
    names.append(fname)


fract_data = pd.DataFrame()


for name in names: 
    genotype = name.split('_')[0]
    stdev = numbermap[name.split('_')[3]]

    temp = pd.read_csv(name, header=0, names=['model','less','greater'])
    temp['stage'] = genotype
    temp['stdev'] = stdev
    temp['dentnumber'] = int(name.split('_')[1][0:2])

    fract_data = pd.concat([fract_data, temp], ignore_index=True)

os.chdir(basepath)

fract_data['modelnumber'] = fract_data['model'].map(modelmap)
fract_data.head()



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### the real graphs



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frac = 99

passes = fract_data[fract_data.greater >= frac]

with sns.axes_style('darkgrid'):
    fig, ax = plt.subplots(figsize=(15,5))
    ax.scatter(passes[passes.stage=='Larvae'].dentnumber, passes[passes.stage=='Larvae'].modelnumber, 
                color='r',marker='o')
    ax.scatter(passes[passes.stage=='yw'].dentnumber, passes[passes.stage=='yw'].modelnumber, 
                color='k',marker='o')
    
    ax.set_xticks(np.arange(0,18))
    ax.set_yticks([0.6, 0.667, 0.7, 0.75, 0.8, 0.9, 1.0, 1.1])
    
    ax.set_xlim(1.5,12.5)
    ax.set_ylim(0.59,1.1)


    ax.set_title('alpha values per denticle number, where at least %i of simulations pass (%s iterations)' % (frac, iterations))
    ax.set_ylabel('model (alpha value)')
    ax.set_xlabel('denticle number')
    
    sns.despine()

fig.savefig(basepath + '/alpha values per denticle number, where at least %i of simulations pass (%s iterations)_grid.svg' % (frac, iterations))



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frac = 99
passes = fract_data[fract_data.greater >= frac]

with sns.axes_style('ticks'):
    fig, ax = plt.subplots(figsize=(15,5))

    ax.scatter(passes[passes.stage=='Larvae'].dentnumber, passes[passes.stage=='Larvae'].modelnumber, 
                color='r',marker='o')
    ax.scatter(passes[passes.stage=='yw'].dentnumber, passes[passes.stage=='yw'].modelnumber, 
                color='k',marker='o')

    ax.set_xticks(np.arange(0,18))
    ax.set_yticks([0.6, 0.667, 0.7, 0.75, 0.8, 0.9, 1.0, 1.1])
    
    ax.set_xlim(1.5,12.5)
    ax.set_ylim(0.59,1.1)
    
    ax.set_title('alpha values per denticle number, where at least %i of simulations pass (%s iterations)' % (frac, iterations))
    ax.set_ylabel('model (alpha value)')
    ax.set_xlabel('denticle number')
    sns.despine()

fig.savefig(basepath +'/alpha values per denticle number, where at least %i of simulations pass (%s iterations).svg' % (frac, iterations))

some checks and validation things



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fract_data.describe()

pd.set_option('display.max_rows',100) pt = passes.set_index(['stage','model']) pt.sort_values(by=['dentnumber','modelnumber'])



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frac = 95
passes = fract_data[fract_data.greater >= frac]

fig, ax = plt.subplots(figsize=(15,5))
ax.set_xticks(np.arange(0,15))

ax.scatter(passes[passes.stage=='Larvae'].dentnumber, passes[passes.stage=='Larvae'].modelnumber, 
            color='k',marker='o')
ax.scatter(passes[passes.stage=='yw'].dentnumber, passes[passes.stage=='yw'].modelnumber, 
            color='r',marker='o')

ax.set_ylim(0,100)
sns.despine()

fig.savefig('alpha values per denticle number, where at least %i of simulations pass (1000 iterations).png' % frac)

ax = passes[passes.stage=='Larvae'].plot('dentnumber', 'modelnumber', kind='scatter') ax.set_ylim(0,1.1) sns.despine() ax.set_title('Larvae')



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with sns.axes_style('ticks'):
    frac = 95
    passes = fract_data[fract_data.greater >= frac]

    fig, ax = plt.subplots(figsize=(15,5))
    ax.set_xticks(np.arange(0,15))

    ax.scatter(passes[passes.stage=='Larvae'].dentnumber, passes[passes.stage=='Larvae'].modelnumber, 
                color='k',marker='o')
    ax.scatter(passes[passes.stage=='yw'].dentnumber, passes[passes.stage=='yw'].modelnumber, 
                color='r',marker='o')

    ax.set_title('alpha values per denticle number, where at least %i of simulations pass (1000 iterations)' % frac)
    ax.set_ylabel('model (alpha value)')
    ax.set_xlabel('denticle number')
    sns.despine()

    fig.savefig('alpha values per denticle number, where at least %i of simulations pass (1000 iterations).png' % frac)

pd.set_option('display.max_rows',100) pt = passes.set_index(['stage','model']) # pt.sort_values(by=['dentnumber','modelnumber'])