DataModel.py

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from io import BytesIO
from flask import Flask, send_file
import base64


# dit zijn de waardes uit de file
CATEGORY_NAMES = {0: 'onbekend',
                  1: 'helemaal oneens',
                  2: 'oneens',
                  3: 'neutraal',
                  4: 'eens',
                  5: 'helemaal mee eens',
                  6: '?'}

# dit zijn de categorie namen voor het plaatje
CATEGORY_NEW = {0: '% onbekend',
                1: '% helemaal oneens',
                2: '% oneens',
                3: '% neutraal',
                4: '% ?',
                5: '% eens',
                6: '% helemaal mee eens'}

QUESTIONMARK = 3  # drie omdat de index met 0 begint, dus eigenlijk categorie 4


def fill_array(df, colno):
    """
    Function to fill an array y with the numbers of occurrence per category
    it uses parameters:
    --------
    df: dataframe from file
    colno: collumno of the dataframe (usally a survey question)
    y: an array with all the counts per outcome in percentage
    """
    # get all the values from a column
    x = np.array(np.array(df.iloc[:, [colno]].dropna().astype(int)))
    # get unique answers and amount per unique answers
    unique_elements, counts_elements = np.unique(x, return_counts=True)
    # compose array with amount per answer
    y = np.array([0, 0, 0, 0, 0, 0, 0])
    for idx, i in enumerate(unique_elements):
        y[i] = counts_elements[idx]
    y_perc = np.array([i / y.sum() * 100 for i in y])

    return y_perc


def survey(results, category_names):
    """
    This funtion creates the graph. It uses the following parameters:
    ----------
    results : dict
        A mapping from question labels to a list of answers per category.
        It is assumed all lists contain the same number of entries and that
        it matches the length of *category_names*.
    category_names : list of str
        The category labels.
    """

    labels = list(results.keys())
    data = np.array(list(results.values()))
    data_cum = data.cumsum(axis=1)
    category_colors = plt.get_cmap('RdYlGn')(np.linspace(0.0, 0.95, data.shape[1]))

    fig, ax = plt.subplots(figsize=(10, 1))
    # ax.invert_yaxis()
    ax.xaxis.set_visible(False)
    ax.yaxis.set_visible(False)
    ax.set_xlim(0, np.sum(data, axis=1).max())
    # remove all the ticks (both axes), and tick labels on the Y axis
    ax.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='on')

    # remove the frame of the chart
    for spine in plt.gca().spines.values():
        spine.set_visible(False)

    for i, (colname, color) in enumerate(zip(category_names, category_colors)):
        widths = data[:, i]
        starts = data_cum[:, i] - widths

        if i == 2:  # 'neutraal'
            ax.barh(labels, widths, left=starts, height=0.5,
                    label=colname, color='lightblue')
            xcenters = starts + widths / 2
        elif i == 3:  # display bars with 'weet niet'
            ax.barh(labels, widths, left=starts, height=0.5,
                    label=colname, color='lightgrey')
            xcenters = starts + widths / 2
        else:
            ax.barh(labels, widths, left=starts, height=0.5,
                    label=colname, color=color)  # color is red to green
            xcenters = starts + widths / 2

        # tekst color
        r, g, b, _ = color
        text_color = 'white' if r * g * b < 0.5 else 'darkgrey'
        for y, (x, c) in enumerate(zip(xcenters, widths)):
            if c != 0:
                ax.text(x, y, str(int(round(c))), ha='center', va='center', color=text_color)

    ax.legend(ncol=len(category_names), bbox_to_anchor=(-0.006, 1), loc='lower left', fontsize='small')
    return fig, ax


class DataModel:
    def bar_plot(self, filename):
        df = pd.read_csv(filename, delimiter=';')

        """ functie om de weet niet in het midden te krijgen"""
        # neem de eerste kolom uit de file en map de inhoud
        df.iloc[:, 0] = df.iloc[:, 0].map({0: 0,
                                               1: 1,
                                               2: 2,
                                               3: 3,
                                               4: 5,
                                               5: 6,
                                               6: 4})

        results = {i: fill_array(df, i)[1:] for i in range(0, 1)}
        category_names = [CATEGORY_NEW[i] for i in CATEGORY_NEW][1:]
        fig, ax = survey(results, category_names)
        fig.tight_layout()
        
 
        figfile = BytesIO()
        fig.savefig(figfile, format='png')
        figfile.seek(0)  # rewind to beginning of file
        return base64.b64encode(figfile.getvalue()).decode('ascii')