statistics.py 11.9 KB
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# Copyright 2018 Markus Scheidgen
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an"AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
A command that generates various statistics.
"""

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from matplotlib import scale as mscale
from matplotlib import transforms as mtransforms
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
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import click
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import json
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from .client import client
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def codes(client, minimum=1, **kwargs):
    data = client.repo.search(per_page=1, **kwargs).response().result
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    x_values = sorted([
        code for code, values in data.quantities['code_name'].items()
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        if code != 'not processed' and values['code_runs'] >= minimum], key=lambda x: x.lower())
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    return data.quantities, x_values, 'code_name', 'code'


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def dates(client, minimum=1, **kwargs):
    data = client.repo.search(per_page=1, date_histogram=True, **kwargs).response().result
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    x_values = list([
        x for x in data.quantities['date_histogram'].keys()])

    return data.quantities, x_values, 'date_histogram', 'month'

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def error_fig(client):
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    _, labels, _, _ = codes(client)
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    def code_values(metric='code_runs', **kwargs):
        result = client.repo.search(
            per_page=1,
            owner='admin',
            metrics=[] if metric == 'code_runs' else metric,
            **kwargs).response().result

        return {
            code: values[metric]
            for code, values in result.quantities['code_name'].items()
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            if code != 'not processed' and (not labels or code in labels) > 0}
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    # get the data
    all_entries = code_values()
    parser_failure_label = 'parser failure'
    error_types = [
        {'name': parser_failure_label, 'search': dict(system='not processed')},
        {'name': 'failed system classification', 'search': dict(system='unavailable')},
        {'name': 'no basis set available', 'search': dict(basis_set='unavailable')},
        {'name': 'no XC functional available', 'search': dict(xc_functional='unavailable')}
    ]
    errors = {
        error_type['name']: {
            code: failures
            for code, failures in code_values(**error_type['search']).items()}
        for error_type in error_types}
    errors_rates = {
        error_type['name']: {
            code: 0 if all_entries[code] == 0 else failures / all_entries[code]
            for code, failures in code_values(**error_type['search']).items()}
        for error_type in error_types}

    fig, axs = plt.subplots(figsize=(15, 12), dpi=72, nrows=2)

    def draw_error_chart(errors, ax, colors, entries=None, mul=1, scale=0.5):
        n_bars = len(errors) - 1
        leg_colors = list(colors)

        x = np.arange(len(labels))  # the label locations
        width = 0.7 / n_bars  # the width of the bars
        plt.sca(ax)
        plt.xticks(rotation=90)

        if entries is not None:
            ax.bar(x, [entries[code] for code in labels], width * n_bars, label='all entries', color=colors.pop(0))

        i = -1
        not_processed = [errors[parser_failure_label][code] * mul for code in labels]
        ax.bar(x, not_processed, width * n_bars, label=parser_failure_label, color=colors.pop(0))
        for key, values in errors.items():
            if key != parser_failure_label:
                ax.bar(x + i * width, [values[code] * mul for code in labels], width, label=key, bottom=not_processed, color=colors.pop(0))
                i += 1

        # Add some text for labels, title and custom x-axis tick labels, etc.
        ax.set_yscale('power', exponent=scale)
        ax.set_xticks(x)
        ax.set_xticklabels(labels)
        ax.legend()
        leg = ax.get_legend()
        for i in range(0, len(leg_colors)):
            leg.legendHandles[i].set_color(leg_colors[i])

        fig.tight_layout()

    ax = axs[0]
    ax.set_title('Absolute number of entries with parser errors or missing repository metadata compared to all entries per code')
    ax.set_ylabel('number of entries', )
    colors = ['grey', 'red', 'yellow', 'orange', 'brown']
    draw_error_chart(errors, ax, entries=all_entries, mul=1, scale=0.25, colors=colors)
    ax.yaxis.set_major_formatter(ticker.StrMethodFormatter('{x:,.0f}'))

    ax = axs[1]
    ax.set_title('Relative rates of entries with parser errors or missing repository metadata per code')
    ax.set_ylabel('rate in %', )
    colors = ['red', 'yellow', 'orange', 'brown']
    draw_error_chart(errors_rates, ax, mul=100, colors=colors)

    plt.show()

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    return fig, plt

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class Metric:
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    def __init__(self, metric, label=None, power=None, multiplier=1, format=None, cumulate=False):
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        if label is None:
            label = metric
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        self.metric = metric
        self.agg = None
        self.label = label
        self.multiplier = multiplier
        self.power = power
        self.format = format
        self.cumulate = cumulate

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    def draw_axis(self, axis, data, x_values, x_positions, width, color, only=False):
        label_color = 'black' if only else color
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        value_map = {
            x: values[self.metric]
            for x, values in data[self.agg].items()
            if x in x_values}

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        if self.power is not None:
            axis.set_yscale('power', exponent=self.power)
        else:
            axis.set_yscale('log')
        axis.set_ylabel(self.label, color=label_color)
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        if self.format is not None:
            axis.yaxis.set_major_formatter(ticker.StrMethodFormatter(self.format))

        y_values = [value_map[x] * self.multiplier for x in x_values]
        if self.cumulate:
            y_values = np.array(y_values).cumsum()
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        axis.bar(x_positions, y_values, width, label=self.label, color=color, align='edge')
        axis.tick_params(axis='y', labelcolor=label_color)

        for x, v in zip(x_positions, y_values):
            axis.text(x + .1, v, ' {:,}'.format(int(v)), color=color, fontweight='bold', rotation=90)
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        # TODO remove
        if color.endswith('red'):
            import matplotlib.lines as mlines
            line = mlines.Line2D([min(x_positions), max(x_positions)], [80, 80], color=color)
            axis.add_line(line)
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def bar_plot(client, retrieve, metric1, metric2=None, title=None, **kwargs):
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    metrics = [] if metric1.metric == 'code_runs' else [metric1.metric]
    if metric2 is not None:
        metrics += [] if metric2.metric == 'code_runs' else [metric2.metric]

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    data, x_values, agg, agg_label = retrieve(client, metrics=metrics, **kwargs)
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    metric1.agg = agg
    if metric2 is not None:
        metric2.agg = agg
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    fig, ax1 = plt.subplots(figsize=(8, 6), dpi=72)
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    x = np.arange(len(x_values))
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    width = 0.8 / 2
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    if metric2 is None:
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        width = 0.8
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    plt.sca(ax1)
    plt.xticks(rotation=90)
    ax1.set_xticks(x)
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    ax1.set_xticklabels([value if value != 'Quantum Espresso' else 'Q. Espresso' for value in x_values])
    ax1.margins(x=0.01)
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    if title is None:
        title = 'Number of %s' % metric1.label
        if metric2 is not None:
            title += ' and %s' % metric2.label
        title += ' per %s' % agg_label
        ax1.set_title(title)
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    metric1.draw_axis(ax1, data, x_values, x - (width / 2), width, 'tab:blue', only=metric2 is None)
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    if metric2:
        ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis
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        metric2.draw_axis(ax2, data, x_values, x + width / 2, width, 'tab:red')
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    fig.tight_layout()
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    return fig, plt
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@client.command(help='Generate various matplotlib charts')
@click.option('--errors', is_flag=True, help='Two charts with relative and absolute parser/normalizer errors per code.')
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@click.option('--x-axis', type=str, help='Aggregation used for x-axis, values are "code" and "time".')
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@click.option('--y-axis', multiple=True, type=str, help='Metrics used for y-axis, values are "entries", "energies", "calculations", "users".')
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@click.option('--cumulate', is_flag=True, help='Cumulate over x-axis.')
@click.option('--title', type=str, help='Override chart title with given value.')
@click.option('--total', is_flag=True, help='Provide total sums of key metrics.')
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@click.option('--save', type=str, help='Save to given file instead of showing the plot.')
@click.option('--power', type=float, help='User power scale instead of log with the given inverse power.')
@click.option('--open-access', is_flag=True, help='Only consider Open-Access data.')
@click.option('--minimum', type=int, default=1, help='Only consider codes with at least the given ammount of entries.')
def statistics(errors, title, x_axis, y_axis, cumulate, total, save, power, open_access, minimum):
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    from .client import create_client
    client = create_client()

    class PowerScale(mscale.ScaleBase):
        name = 'power'

        def __init__(self, axis, exponent, **kwargs):
            mscale.ScaleBase.__init__(self, axis, **kwargs)
            self.exponent = exponent

        def set_default_locators_and_formatters(self, axis):
            axis.set_major_locator(ticker.AutoLocator())
            axis.set_major_formatter(ticker.ScalarFormatter())
            axis.set_minor_locator(ticker.NullLocator())
            axis.set_minor_formatter(ticker.NullFormatter())

        def limit_range_for_scale(self, vmin, vmax, minpos):
            return max(0., vmin), vmax

        class Transform(mtransforms.Transform):
            input_dims = 1
            output_dims = 1
            is_separable = True

            def __init__(self, exponent):
                super().__init__()
                self.exponent = exponent

            def transform_non_affine(self, a):
                return np.array(a)**self.exponent

            def inverted(self):
                return PowerScale.Transform(1 / self.exponent)

        def get_transform(self):
            return self.Transform(self.exponent)

    mscale.register_scale(PowerScale)

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    kwargs = {}
    if cumulate:
        kwargs.update(
            power=1,
            multiplier=1e-6,
            format='{x:,.1f}M')
    elif power is not None:
        kwargs.update(
            power=1 / power,
            multiplier=1e-6,
            format='{x:,.1f}M')

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    metrics = {
        'entries': Metric(
            'code_runs',
            label='entries (code runs)',
            cumulate=cumulate,
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            **kwargs),
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        'users': Metric(
            'users',
            cumulate=cumulate,
            label='users that provided data'),
        'energies': Metric(
            'total_energies',
            label='total energy calculations',
            cumulate=cumulate,
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            **kwargs),
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        'calculations': Metric(
            'calculations',
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            label='calculations (e.g. total energy)',
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            cumulate=cumulate,
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            **kwargs)
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    }

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    if errors:
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        fig, plt = error_fig(client)

    owner = 'all' if open_access else 'admin'
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    if x_axis is not None:
        assert 1 <= len(y_axis) <= 2, 'Need 1 or 2 y axis'

        if x_axis == 'code':
            x_axis = codes
        elif x_axis == 'time':
            x_axis = dates
        else:
            assert False, 'x axis can only be "code" or "time"'

        y_axis = [metrics[y] for y in y_axis]

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        fig, plt = bar_plot(client, x_axis, *y_axis, title=title, owner=owner, minimum=minimum, code_name="VASP")

    if errors or x_axis is not None:
        if save is not None:
            fig.savefig(save, bbox_inches='tight')
        else:
            plt.show()
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    if total:
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        data = client.repo.search(per_page=1, owner=owner, metrics=['total_energies', 'calculations', 'users', 'datasets']).response().result
        print(json.dumps(data.quantities['total'], indent=4))