Source code for jwql.utils.plotting

#! /usr/bin/env python

This module is a collection of plotting functions that may be used
across the ``jwql`` application.


    - Joe Filippazzo


    This module can be use as follows:


        from jwql.utils import plotting
        from pandas import DataFrame
        data = DataFrame({'meow': {'foo': 12, 'bar': 23, 'baz': 2},
                          'mix': {'foo': 45, 'bar': 31, 'baz': 23},
                          'deliver': {'foo': 62, 'bar': 20, 'baz': 9}})
        data = data.reset_index()
        plt = plotting.bar_chart(data, 'index')

from bokeh.models import ColumnDataSource, FactorRange, HoverTool
from bokeh.palettes import Category20c
from bokeh.plotting import figure
from bokeh.transform import factor_cmap

[docs]def bar_chart(dataframe, groupcol, datacols=None, **kwargs): """Create a pie chart from a Pandas DataFrame Parameters ---------- dataframe : pandas.DataFrame A dataframe of values groupcol : str The name of the column with the group labels datacol : str, sequence (optional) The name or list of names of the column containing the data. In None, uses all columns except **groupcol** Returns ------- plt : obj The generated bokeh.figure object """ # Get the groups groups = list(dataframe[groupcol]) # Get the datacols if datacols is None: datacols = [col for col in list(dataframe.columns) if col != groupcol] # Make a dictionary of the groups and data data = {'groups': groups} for col in datacols: data.update({col: list(dataframe[col])}) # hstack it x = [(group, datacol) for group in groups for datacol in datacols] counts = sum(zip(*[data[col] for col in datacols]), ()) colors = max(3, len(datacols)) source = ColumnDataSource(data=dict(x=x, counts=counts)) # Make the figure hover = HoverTool(tooltips=[('count', '@counts')]) plt = figure(x_range=FactorRange(*x), plot_height=250, tools=[hover], **kwargs) plt.vbar(x='x', top='counts', width=0.9, source=source, line_color="white", fill_color=factor_cmap('x', palette=Category20c[colors], factors=datacols, start=1, end=2)) # Formatting plt.y_range.start = 0 plt.x_range.range_padding = 0.1 plt.xaxis.major_label_orientation = 1 plt.xgrid.grid_line_color = None return plt