
REF1_HASH_DEFAULT = None
REF2_HASH_DEFAULT = 'upstream/master'


import os
import sys
import shutil
import tempfile
import subprocess
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from bokeh.plotting import figure, show, ColumnDataSource
from bokeh.layouts import gridplot
from bokeh.models.tools import HoverTool
from bokeh.io import output_notebook

output_notebook()


cd ..

/home/vsts/work/1/s/tardis-refdata


# data pickled with protocol 5 can't be opened with `python<3.8.3`, use the backport

if sys.version_info < (3, 8, 3):
    import pickle5

    sys.modules["pickle"] = pickle5


def highlight_missing(val):
    if val == True:
        return 'background-color: #BCF5A9'
    else:
        return 'background-color: #F5A9A9'
    
def highlight_relative_difference(val):
    ret = 'background-color: #BCF5A9'
    if val is None:
        ret = 'background-color: #BCF5A9'
    elif val > 1e-2:
        ret = 'background-color: #F2F5A9'
    elif val > 1e-1:
        ret = 'background-color: #F5D0A9'
    elif val > 1:
        ret = 'background-color: #F5A9A9'
    return ret


class ReferenceComparer(object):

    def __init__(self, ref1_hash=None, ref2_hash=None, compare_path='unit_test_data.h5'):
        assert not ((ref1_hash is None) and (ref2_hash is None)), "One hash can not be None"
        self.ref1_hash = ref1_hash
        self.ref2_hash = ref2_hash
        self.compare_path = compare_path
        self.tmp_dir = None
        self.setup()
    
    def setup(self):
        self.tmp_dir = tempfile.mkdtemp()
        print('Created temporary directory at {0}. Delete after use with .teardown'.format(self.tmp_dir))
        for ref_id, ref_hash in enumerate([self.ref1_hash, self.ref2_hash]):
            ref_id += 1
            if ref_hash is not None:
                self._copy_data_from_hash(ref_hash, 'ref{0}_'.format(ref_id))
            else:
                subprocess.Popen('cp {0} {1}'.format(self.compare_path, 
                                                     os.path.join(self.tmp_dir, 
                                                                  'ref{0}_{1}'.format(ref_id, self.compare_path))), 
                                                     shell=True)
            setattr(self, 'ref{0}_fname'.format(ref_id), 
                    os.path.join(self.tmp_dir, 'ref{0}_{1}'.format(ref_id, self.compare_path)))

    def teardown(self):
        shutil.rmtree(self.tmp_dir)

    def _copy_data_from_hash(self, ref_hash, prefix):
        git_cmd = ['git']
        git_cmd.append('--work-tree={0}'.format(self.tmp_dir))
        git_cmd += ['checkout', ref_hash, self.compare_path]
        p = subprocess.Popen(git_cmd)
        p.wait()
        shutil.move(os.path.join(self.tmp_dir, self.compare_path), 
                    os.path.join(self.tmp_dir, prefix + self.compare_path))

    def generate_test_table(self):
        rd1_hdfs = pd.HDFStore(self.ref1_fname, mode='r')
        rd2_hdfs = pd.HDFStore(self.ref2_fname, mode='r')
        rd1_keys = rd1_hdfs.keys()
        rd2_keys = rd2_hdfs.keys()
        rd1_hdfs.close()
        rd2_hdfs.close()
        rd1_df = pd.DataFrame(index=rd1_keys, columns=['exists'])
        rd2_df = pd.DataFrame(index=rd2_keys, columns=['exists'])
        rd1_df['exists'] = True
        rd2_df['exists'] = True
        joined_df = rd1_df.join(rd2_df, how='outer', lsuffix='_1', rsuffix='_2')
        joined_df = joined_df.fillna(False)
        return joined_df
    
    def compare_refdata(self, test_table):
        test_table['match'] = None
        test_table['abs_diff_mean'] = None
        test_table['abs_diff_max'] = None
        test_table['rel_diff_mean'] = None
        test_table['rel_diff_max'] = None
        for row_id, row in test_table.iterrows():
            
            if row[['exists_1', 'exists_2']].all():
                ref1_df = pd.read_hdf(self.ref1_fname, row_id)
                ref2_df = pd.read_hdf(self.ref2_fname, row_id)
                
                if isinstance(ref1_df, pd.Series):
                    try:
                        pd.testing.assert_series_equal(ref1_df, ref2_df)
                    except AssertionError:
                        test_table.loc[row_id, 'match'] = False
                        abs_diff = np.fabs(ref1_df - ref2_df)
                        rel_diff = (abs_diff / np.fabs(ref1_df))[ref1_df != 0]
                        test_table.loc[row_id, 'abs_diff_mean'] = abs_diff.mean()
                        test_table.loc[row_id, 'abs_diff_max'] = abs_diff.max()
                        test_table.loc[row_id, 'rel_diff_mean'] = rel_diff.mean()
                        test_table.loc[row_id, 'rel_diff_max'] = rel_diff.max()
                    else:
                        test_table.loc[row_id, 'match'] = True

                elif isinstance(ref1_df, pd.DataFrame):
                    try:
                        pd.testing.assert_frame_equal(ref1_df, ref2_df)
                    except AssertionError:
                        test_table.loc[row_id, 'match'] = False
                        abs_diff = np.fabs(ref1_df - ref2_df)
                        rel_diff = (abs_diff / np.fabs(ref1_df))[ref1_df != 0]
                        test_table.loc[row_id, 'abs_diff_mean'] = abs_diff.mean(skipna=True).mean()
                        test_table.loc[row_id, 'abs_diff_max'] = abs_diff.max(skipna=True).max()
                        test_table.loc[row_id, 'rel_diff_mean'] = rel_diff.mean(skipna=True).mean()
                        test_table.loc[row_id, 'rel_diff_max'] = rel_diff.max(skipna=True).max()
                    else:
                        test_table.loc[row_id, 'match'] = True

                else:
                    raise ValueError('Needs to be a Series or DataFrame but is' + str(type(ref1_df)))
        return test_table


try:
    REF1_HASH = os.environ['REF1_HASH']

    if not REF1_HASH:
        raise ValueError

except (KeyError, ValueError):
    REF1_HASH = REF1_HASH_DEFAULT

try:
    REF2_HASH = os.environ['REF2_HASH']

    if not REF2_HASH:
        raise ValueError

except (KeyError, ValueError):
    REF2_HASH = REF2_HASH_DEFAULT


REF1_HASH, REF2_HASH

(None, 'upstream/master')


comparer = ReferenceComparer(ref1_hash=REF1_HASH, ref2_hash=REF2_HASH)

Created temporary directory at /tmp/tmp3efw1gol. Delete after use with .teardown


tt = comparer.generate_test_table()


tt = comparer.compare_refdata(tt)


tt[["exists_1", "exists_2", 'rel_diff_mean', 'rel_diff_max', 'match']].style.applymap(
    highlight_missing, subset=['exists_1', 'exists_2', 'match']).applymap(
    highlight_relative_difference, subset=['rel_diff_mean', 'rel_diff_max'])


def compare_output_nu(df1, df2, mpl_backend=False):
    nu_min = np.min([df1.min(), df2.min()])
    nu_max = np.max([df1.max(), df2.max()])
    
    if mpl_backend:
        plt.figure(figsize=(14, 6))
        plt.subplot(121)
        plt.plot(df1, df2, ',')
        plt.xlabel("output_nu, ref 1")
        plt.ylabel("output_nu, ref 2")
        plt.subplot(122)
        plt.hist(df1, bins=np.linspace(nu_min, nu_max, 100), histtype="step", label="ref 1")
        plt.hist(df2, bins=np.linspace(nu_min, nu_max, 100), histtype="step", label="ref 2")
        plt.xlabel("output_nu")
        plt.legend(frameon=False)
        
        return

    
    TOOLTIPS = [("(x,y)", "(@x, @y)")]
    hover = HoverTool(tooltips=TOOLTIPS)
    
    p = figure()
    output_nu = ColumnDataSource(pd.DataFrame.from_records({'x': df1.values, 
                                                            'y': df2.values}))
    p.circle('x', 'y', size=1, source=output_nu)
    p.xaxis.axis_label = "output_nu, ref 1"
    p.yaxis.axis_label = "output_nu, ref 2"
    p.xaxis.formatter.precision = 1
    p.yaxis.formatter.precision = 1
    p.add_tools(hover)

    # Step lines are hacky way to make histograms with Bokeh
    arr_hist1, edges1 = np.histogram(df1.values, 
                                     bins = 100, 
                                     range = [nu_min, nu_max])
    arr_hist2, edges2 = np.histogram(df1.values, 
                                     bins = 100, 
                                     range = [nu_min, nu_max])
    
    hist1 = ColumnDataSource(pd.DataFrame.from_records({'x': np.linspace(nu_min, nu_max, 100),
                                                        'y': arr_hist1}))
    hist2 = ColumnDataSource(pd.DataFrame.from_records({'x': np.linspace(nu_min, nu_max, 100),
                                                        'y': arr_hist2}))
    q = figure()
    q.step('x', 'y', source=hist1, legend_label='ref 1')
    q.step('x', 'y', source=hist2, legend_label='ref 2', color='#ff7f0e')
    q.xaxis.axis_label = "output_nu"
    q.xaxis.formatter.precision = 1
    q.legend.click_policy="hide"
    
    # Currently HoverTool does not work for step line glyph. See: https://github.com/bokeh/bokeh/issues/7419
    q.add_tools(hover)
    
    plot = gridplot([p, q], ncols=2, plot_width=420, plot_height=360)
    
    show(plot)


def compare_spectrum(ref1_nu, ref1_L, ref2_nu, ref2_L, mpl_backend=False):
    
    if mpl_backend:
        plt.figure(figsize=(14, 6))
        plt.subplot(121)
        plt.plot(ref1_nu, ref1_L, label="ref 1")
        plt.plot(ref2_nu, ref2_L, label="ref 2")
        plt.xlabel("nu")
        plt.ylabel("L")
        plt.legend(frameon=False)
        plt.subplot(122)
        plt.plot(ref1_nu, ref1_L / ref2_L)
        plt.xlabel("nu")
        plt.ylabel("L ref 1 / L ref 2")
        
        return
    
    
    TOOLTIPS = [("(x,y)", "(@x, @y)")]
    hover = HoverTool(tooltips=TOOLTIPS)
    
    p = figure()
    spectrum1 = ColumnDataSource(pd.DataFrame.from_records({'x': ref1_nu.values, 
                                                            'y': ref1_L}))
    spectrum2 = ColumnDataSource(pd.DataFrame.from_records({'x': ref2_nu.values, 
                                                            'y': ref2_L}))
    p.line('x', 'y', source=spectrum1, legend_label='ref 1')
    p.line('x', 'y', source=spectrum2, legend_label='ref 2', color='#ff7f0e')
    p.xaxis.axis_label = "L"
    p.yaxis.axis_label = "nu"
    p.xaxis.formatter.precision = 1
    p.yaxis.formatter.precision = 1
    p.legend.click_policy="hide"
    p.add_tools(hover)
    
    q = figure()
    lum_ratio = ColumnDataSource(pd.DataFrame.from_records({'x': ref1_nu.values, 
                                                            'y': ref1_L.values/ref2_L.values}))
    q.circle('x', 'y', size=1, source=lum_ratio)
    q.xaxis.axis_label = "nu"
    q.yaxis.axis_label = "L ref 1 / L ref 2"
    q.xaxis.formatter.precision = 1
    q.yaxis.formatter.precision = 1
    q.add_tools(hover)
    
    
    plot = gridplot([p, q], ncols=2, plot_width=420, plot_height=360)
    
    show(plot)


tmp1 = pd.HDFStore(comparer.ref1_fname, "r")
tmp2 = pd.HDFStore(comparer.ref2_fname, "r")

diff_entries = tt.loc[(tt["match"] == False) & (tt["exists_1"] == True) & (tt["exists_2"] == True)].index


compare_output_nu(tmp1['/test_simulation/output_nu'], tmp2['/test_simulation/output_nu'])


compare_spectrum(tmp1['/test_runner_simple/spectrum/_frequency'][:-1], 
                 tmp1['/test_runner_simple/spectrum/luminosity'],
                 tmp2['/test_runner_simple/spectrum/_frequency'][:-1], 
                 tmp2['/test_runner_simple/spectrum/luminosity'])

/usr/share/miniconda/envs/tardis/lib/python3.7/site-packages/ipykernel_launcher.py:38: RuntimeWarning: divide by zero encountered in true_divide
/usr/share/miniconda/envs/tardis/lib/python3.7/site-packages/ipykernel_launcher.py:38: RuntimeWarning: invalid value encountered in true_divide


compare_spectrum(tmp1['/test_runner_simple_integral_macroatom_interp/spectrum/_frequency'][:-1], 
                 tmp1['/test_runner_simple_integral_macroatom_interp/spectrum_integrated/luminosity'],
                 tmp2['/test_runner_simple_integral_macroatom_interp/spectrum/_frequency'][:-1], 
                 tmp2['/test_runner_simple_integral_macroatom_interp/spectrum_integrated/luminosity'])


compare_spectrum(tmp1['/test_runner_simple_integral_macroatom/spectrum/_frequency'][:-1], 
                 tmp1['/test_runner_simple_integral_macroatom/spectrum_integrated/luminosity'],
                 tmp2['/test_runner_simple_integral_macroatom/spectrum/_frequency'][:-1], 
                 tmp2['/test_runner_simple_integral_macroatom/spectrum_integrated/luminosity'])


compare_spectrum(tmp1['/test_runner_simple_integral_downbranch/spectrum/_frequency'][:-1], 
                 tmp1['/test_runner_simple_integral_downbranch/spectrum_integrated/luminosity'],
                 tmp2['/test_runner_simple_integral_downbranch/spectrum/_frequency'][:-1], 
                 tmp2['/test_runner_simple_integral_downbranch/spectrum_integrated/luminosity'])


compare_spectrum(tmp1['/test_runner_simple/spectrum_virtual/_frequency'][:-1], 
                 tmp1['/test_runner_simple/spectrum_virtual/luminosity'],
                 tmp2['/test_runner_simple/spectrum_virtual/_frequency'][:-1], 
                 tmp2['/test_runner_simple/spectrum_virtual/luminosity'])

/usr/share/miniconda/envs/tardis/lib/python3.7/site-packages/ipykernel_launcher.py:38: RuntimeWarning: divide by zero encountered in true_divide
/usr/share/miniconda/envs/tardis/lib/python3.7/site-packages/ipykernel_launcher.py:38: RuntimeWarning: invalid value encountered in true_divide


comparer.teardown()

Compare reference data¶

Installation¶

Usage¶

Define classes and functions¶

Load data¶

Test table¶

Detailed inspection of the reference data¶

Results¶