Table Boxplot

Data:
       org   abx    sari        sart     r2      f_prob       jb      jb_prob    ptm    ptn     dw surveillance
0     ABAU  AAMI  0.3906 -5.0000e-03  0.181  2.9400e-05    4.648   9.7900e-02  0.000  0.000  1.177            M
1     ABAU  AAMO  0.9603 -1.2000e-03  0.053  1.9400e-02  631.965  5.9000e-138  0.019  0.000  1.848            M
2     ABAU  AAUG  0.7578 -8.0000e-04 -0.004  4.2400e-01   13.083   1.4400e-03  0.424  0.000  1.640            M
3     ABAU  AAZT  0.9706 -9.0000e-04  0.057  1.5600e-02  129.263   8.5300e-29  0.016  0.000  1.604            M
4     ABAU  ACAZ  0.9417 -7.0000e-04  0.015  1.3200e-01  103.616   3.1600e-23  0.132  0.000  1.776            M
...    ...   ...     ...         ...    ...         ...      ...          ...    ...    ...    ...          ...
1726  SSAP  AGEN  0.0026 -6.3420e-05  0.027  7.2100e-02   40.688   1.4600e-09  0.072  0.004  0.180           SW
1727  SSAP  AMET  0.2897  3.8000e-03  0.539  5.1000e-16    2.913   2.3300e-01  0.000  0.000  0.115           SW
1728  SSAP  ANIT  0.0118 -2.0000e-04  0.418  1.0700e-11    3.205   2.0100e-01  0.000  0.000  0.443           SW
1729  SSAP  APEN  0.8494 -5.0000e-04  0.042  3.2400e-02    5.323   6.9800e-02  0.032  0.000  0.308           SW
1730  SSAP  ATRI  0.0608 -1.6810e-05 -0.011  8.0200e-01    0.007   9.9700e-01  0.802  0.000  0.316           SW

[1731 rows x 12 columns]

# Libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

from matplotlib import colors

from pyamr.utils.plot import hlinebgplot

import pyamr
pyamr.utils.plot.scalar_colormap([1,2,3,4], 'coolwarm', vmin=0, vmax=10)

# -------------------
# PLOTTING SETTINGS
# -------------------
# Configuration for each columns
info_sari = {
  'cmap':'Reds',
  'title':'SARI',
  'xlim':[-0.1, 1.1],
  'xticks':[0, 1],
}

info_sart = {
  'cmap':'RdBu_r',
  'title':'SART',
  'xlim':[-1.2, 1.2],
  'xticks':[-1, 1],
}

info_r2 = {
  'cmap':'YlGn',
  'title':'R2',
  'ylim': [-0.2, 1.2],
  'yticks': [.0, .2, .4, .6, .8 ]
}

info_fprob = {
  'name':'f_prob',
  'title':'Prob(F)',
  'ylim': [-0.2, 1.2],
  'yticks': [.0, .2, .4, .6, .8 ]
}

info_jb_prob = {
  'cmap':'YlGn',
  'title':'Prob(JB)',
  'ylim': [-0.2, 1.2],
  'hline': [{'yv':0.05, 'bg':0.0}],
  'yticks': [.0, .2, .4, .6, .8 ]
}

info_dw = {
  'cmap':'YlGn_r',
  'title':'DW',
  'hline': [{'yv':0.8, 'bg':4.0}],
  'ylim': [-0, 4.2],
  'yticks': [1, 2, 3]
}

info_slope = {
  'name':'x1_tprob',
  'cmap':'YlGn_r',
  'title':'P>|t| m',
  'hline': [{'yv':0.05, 'bg':1.0}],
  'ylim': [-0.2, 1.2],
  'yticks': [.0, .2, .4, .6, .8 ]
}

info_coefficient = {
  'name':'c_tprob',
  'cmap':'YlGn_r',
  'title':'P>|t| n',
  'hline': [{'yv':0.05, 'bg':1.0}],
  'ylim': [-0.2, 1.2],
  'yticks': [.0, .2, .4, .6, .8 ]
}


# Now we combine all of them together. Note
# that the key value corresponds to the name
# of the column it should be applied to.
info = {
    'sari': info_sari,
    'sart': info_sart,
    'dw': info_dw,
    'r2': info_r2,
    'jb_prob': info_jb_prob,
    'ptm': info_slope,
    'ptn': info_coefficient,
    'f_prob': info_fprob
}

# -------------------------------------------
# Main
# -------------------------------------------
# Data path.
path = '../../pyamr/fixtures/fixture_surveillance.csv'

# Rename
rename = {
    'organismCode': 'org',
    'antibioticCode': 'abx',
    'sari': 'sari',
    'x1_coef': 'sart',
    'adj_rsquared': 'r2',
    'f_prob': 'f_prob',
    'jarque-bera': 'jb',
    'jarque_bera_prob': 'jb_prob',
    'x1_tprob': 'ptm',
    'c_tprob': 'ptn',
    'durbin-watson': 'dw',
    'surveillance': 'surveillance'
}

# Create data
data = pd.read_csv(path)
data = data[rename.keys()]
data = data.rename(columns=rename)

# Show
print("\nData:")
print(data)


# -------------------------------------------
# Pair Grid
# -------------------------------------------
# Features
statistics = ['r2', 'f_prob', 'jb_prob', 'ptm', 'ptn', 'dw']

# Create figure
f, axes = plt.subplots(1, len(statistics), figsize=(12, 3))
axes = axes.flatten()

# Plot strips and format axes
for ax, c in zip(axes, statistics):

    # Get information
    d = info[c] if c in info else {}

    # Plot distributions
    sns.boxplot(x='surveillance', y=data[c],
                ax=ax, data=data, whis=1e30,
                palette="Set3", linewidth=0.75)

    # Plot horizontal lines
    for e in d.get('hline', []):
        hlinebgplot(ax, right=3, **e)

    # Configure axes
    ax.set(title=d.get('title', c),
           xlabel='', ylabel='',
           ylim=d.get('ylim', None),
           yticks=d.get('yticks', ax.get_yticks()))
    #ax.tick_params(axis='y', which='both', length=0)
    ax.xaxis.grid(False)
    ax.yaxis.grid(False)

# Despine
sns.despine(bottom=True, left=True)

# Change last ticks to right
axes[-1].yaxis.tick_right()

# Adjust space (overwritten by tight)
plt.subplots_adjust(wspace=0.75)

# Configure layout
#plt.tight_layout()

# Show
plt.show()