statistics.py 7.8 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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from .client import client
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def codes(client):
    result = client.repo.search(per_page=1, owner='admin').response().result
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    return sorted([
        code for code, values in result.quantities['code_name'].items()
        if code != 'not processed' and values['code_runs'] > 0], key=lambda x: x.lower())
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def error_fig(client):
    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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def codes_fig(client):
    labels = codes(client)

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    # get the data
    result = client.repo.search(
        per_page=1, owner='admin', metrics=['total_energies']).response().result
    all_entries = {
        code: values['code_runs']
        for code, values in result.quantities['code_name'].items()
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        if code != 'not processed' and code in labels}
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    total_energies = {
        code: values['total_energies']
        for code, values in result.quantities['code_name'].items()
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        if code != 'not processed' and code in labels}
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    fig, ax1 = plt.subplots(figsize=(15, 6), dpi=72)
    x = np.arange(len(labels))  # the label locations
    width = 0.7 / 2  # the width of the bars
    plt.sca(ax1)
    plt.xticks(rotation=90)
    ax1.set_xticks(x)
    ax1.set_xticklabels(labels)
    ax1.set_title('Number of entries (code runs, sets of input/output files) and total energy calculations per code')

    color = 'tab:red'
    ax1.set_yscale('power', exponent=0.25)
    ax1.set_ylabel('number of entries', color=color)
    ax1.yaxis.set_major_formatter(ticker.StrMethodFormatter('{x:,.1f}M'))
    ax1.bar(x - width / 2, [all_entries[code] / 1e6 for code in labels], width, label='entries', color=color)
    ax1.tick_params(axis='y', labelcolor=color)

    ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis

    color = 'tab:blue'
    ax2.set_yscale('power', exponent=0.25)
    ax2.set_ylabel('number of total energy calculations', color=color)
    ax2.yaxis.set_major_formatter(ticker.StrMethodFormatter('{x:,.1f}M'))
    ax2.bar(x + width / 2, [total_energies[code] / 1e6 for code in labels], width, label='total energy calculations', color=color)
    ax2.tick_params(axis='y', labelcolor=color)

    fig.tight_layout()
    plt.show()


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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.')
@click.option('--per-code', is_flag=True, help='Entries and total energy calculations per code.')
def statistics(errors, per_code):
    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)

    if errors:
        error_fig(client)
    if per_code:
        codes_fig(client)