plots.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 11 13:45:38 2019

Look into validation results

Need to type:
    
    %matplotlib auto

if ipython and want to get the interactive plots!


@author: iregon
"""
import mdf_reader
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np


#------------------------------------------------------------------------------
def plot_numeric_validation(data,mask,element,valid_max,valid_min, units):
    bbox_props = dict(boxstyle="round", fc="w", ec="0.5", alpha=0.9)
    plt.figure()
    ax = data.plot(label = 'data')
    # Carefull, something may not be working here in the no data tag!!!
    no_data = True if len(data.loc[data.notna()]) == 0 else False
    data.where(~mask).plot(marker = 'o',color = 'r',ax=ax,label='not valid')
    true_value = data.where(mask).median()
    if not true_value or np.isnan(true_value):
        if valid_max != None and valid_min != None:
            true_value = valid_min + (valid_max - valid_min)/2.
        else:
            true_value = 1
    falses = pd.Series(index = data.index, data = true_value)
    falses.where(~mask & data.isna()).plot(marker = 'o',color = 'r',ax=ax,label='_nolegend_')
    trues = pd.Series(index = data.index,data = true_value )
    trues.where(mask).plot(color = 'YellowGreen',ax=ax,label='valid')
    if valid_max != None and valid_min != None:
        ax.fill_between(data.index, valid_min,valid_max,
                            facecolor='DarkSlateGrey', alpha=0.25, interpolate=False, label='valid range',zorder=5)
    ax.grid(linestyle=':',which='major',color='grey')
    if units:
        ax.set_ylabel(units, fontsize=10)
    ax.set_xlabel('idx')
    if no_data:
        ax.text((ax.get_xlim()[1] - ax.get_xlim()[0])/2,ax.get_ylim()[0] + (ax.get_ylim()[1] - ax.get_ylim()[0])/2, "no data", ha="center", va="center", size=20,bbox=bbox_props)
    plt.legend()
    plt.title(element + ' validation')
    plt.show()
    
def plot_categorical_validation(data,mask,element,codetable):
    merged = pd.concat([data,mask],axis = 1)
    merged.columns = ['data','mask']
    counts = pd.DataFrame(index =merged['data'].value_counts(dropna = False).sort_index().index )
    counts['Data'] = merged['data'].value_counts(dropna = False).sort_index()
    # Watch here, need to convert to str so that NaNs are not removed from the grouping!
    # Could be dangerous if we are not just counting, but then, we would not need the NaNs...
    counts.index = counts.index.fillna(str(np.nan))
    counts['Not valid'] = merged.astype(str).query('mask == "False"').groupby('data').count()
    counts['Valid'] = merged.astype(str).query('mask == "True"').groupby('data').count()
    fig = plt.figure() # Create matplotlib figure
    ax = fig.add_subplot(111) # Create matplotlib axes
    ax2 = ax.twinx() # Create another axes that shares the same x-axis as ax.
    width = 0.4
    counts['Data'].plot(kind='bar', ax=ax, width=width, position=1, color = 'DarkCyan', label = 'data counts')    
    counts[['Not valid','Valid']].plot(kind='bar', stacked=True, ax=ax2, width=width, position=0,legend = False, color = ['DarkRed','YellowGreen'])
    ax.set_yscale("log")
    ax.set_ylabel('data counts', fontsize=10)
    ax2.set_yscale("log")
    ax2.set_ylabel('validation counts', fontsize=10)
    ax.legend(loc = 2)
    ax2.legend()
    ax.grid(linestyle=':',which='major',color='grey')
    plt.title(element + " validation \n Codetable: " + codetable)
    ax.set_xlim(-1,len(counts))
    ax.set_xlabel('code')
    ax2.set_xlim(-1,len(counts))
    ax.set_ylim(0.5,ax.get_ylim()[1])
    ax2.set_ylim(0.5,ax2.get_ylim()[1])
    plt.show()
#------------------------------------------------------------------------------

def plot_model_validation(imodel):
    for element in imodel['atts'].keys():
        title_element = element if not isinstance(element,tuple) else element[1] + " (" + element[0] + ")"
        dtype = imodel['atts'].get(element).get('column_type')
        if dtype in mdf_reader.properties.numeric_types: 
            valid_max = imodel['atts'].get(element).get('valid_max')
            valid_min = imodel['atts'].get(element).get('valid_min')
            units = imodel['atts'].get(element).get('units')
            plot_numeric_validation(imodel['data'][element],imodel['valid_mask'][element],title_element, valid_max, valid_min, units)  
        elif dtype == 'key':
            # ...mmm should account for multi-keyed combinations
            codetable = imodel['atts'].get(element).get('codetable')
            if not codetable:
                codetable = 'undefined'
            plot_categorical_validation(imodel['data'][element],imodel['valid_mask'][element],title_element,codetable)