In [1]:

    

import pandas as pd
import math
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from empcolors import get_empo_cat_color
%matplotlib inline


    

In [2]:

    

path_map = '../../data/mapping-files/emp_qiime_mapping_subset_2k.tsv' # already has 90bp alpha-div data
version = '2k'


    

In [3]:

    

path_map = '../../data/mapping-files/emp_qiime_mapping_qc_filtered.tsv' # already has 90bp alpha-div data
version = 'qc'


    

In [4]:

    

path_adiv100 = '../../data/alpha-div/emp.100.min25.deblur.withtax.onlytree_5000.txt'
path_adiv150 = '../../data/alpha-div/emp.150.min25.deblur.withtax.onlytree_5000.txt'


    

In [5]:

    

df_map = pd.read_csv(path_map, sep='\t', index_col=0)
df_adiv100 = pd.read_csv(path_adiv100, sep='\t', index_col=0)
df_adiv150 = pd.read_csv(path_adiv150, sep='\t', index_col=0)

df_adiv100.columns = ['adiv_chao1_100bp', 'adiv_observed_otus_100bp', 'adiv_faith_pd_100bp', 'adiv_shannon_100bp']
df_adiv150.columns = ['adiv_chao1_150bp', 'adiv_observed_otus_150bp', 'adiv_faith_pd_150bp', 'adiv_shannon_150bp']


    

In [6]:

    

df_merged = pd.concat([df_adiv100, df_adiv150, df_map], axis=1, join='outer')


    

In [7]:

    

df1 = df_merged[['empo_3', 'adiv_observed_otus', 'adiv_observed_otus_100bp', 'adiv_observed_otus_150bp']]
df1.columns = ['empo_3', 'observed_tag_sequences_90bp', 'observed_tag_sequences_100bp', 'observed_tag_sequences_150bp']
df1.dropna(axis=0, inplace=True)


    

    




/Users/luke.thompson/.local/lib/python3.5/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  app.launch_new_instance()

In [8]:

    

g = sns.PairGrid(df1, hue='empo_3', palette=get_empo_cat_color(returndict=True))
g = g.map(plt.scatter, alpha=0.5)
for i in [0, 1, 2]:
    for j in [0, 1, 2]:
        g.axes[i][j].set_xscale('log')
        g.axes[i][j].set_yscale('log')
        g.axes[i][j].set_xlim([1e0, 1e4])
        g.axes[i][j].set_ylim([1e0, 1e4])
        
g.savefig('adiv_%s_scatter.pdf' % version)


    

In [9]:

    

sns.lmplot(x='observed_tag_sequences_90bp', y='observed_tag_sequences_150bp', col='empo_3', hue="empo_3", data=df1,
           col_wrap=4, palette=get_empo_cat_color(returndict=True), size=3, markers='o',
           scatter_kws={"s": 20, "alpha": 1}, fit_reg=True)
plt.xlim([0, 3000])
plt.ylim([0, 3000])
plt.savefig('adiv_%s_lmplot.pdf' % version)


    

    
Out[9]:





(0, 3000)






    

In [10]:

    

df1melt = pd.melt(df1, id_vars='empo_3')
empo_list = list(set(df1melt.empo_3))
empo_list = [x for x in empo_list if type(x) is str]
empo_list.sort()
empo_colors = [get_empo_cat_color(returndict=True)[x] for x in empo_list]

for var in ['observed_tag_sequences_90bp', 'observed_tag_sequences_100bp', 'observed_tag_sequences_150bp']:
    list_of = [0] * len(empo_list)
    df1melt2 = df1melt[df1melt['variable'] == var].drop('variable', axis=1)
    for empo in np.arange(len(empo_list)):
        list_of[empo] = list(df1melt2.pivot(columns='empo_3')['value'][empo_list[empo]].dropna())

    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(2.5,2.5))    
    plt.hist(list_of, color=empo_colors, 
            bins=np.logspace(np.log10(1e0),np.log10(1e4), 20),
            stacked=True)
    plt.xscale('log')

    fig.savefig('adiv_%s_hist_%s.pdf' % (version, var))


    

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