Exemple #1
0
def get_stat_set_within_place():
    """Gets the statistical variable values for child places of a certain place
    type contained in a parent place at a given date. If no date given, will
    return values for most recent date.

    Returns:
        Dict keyed by statvar DCIDs with dicts as values. See `SourceSeries` in
        https://github.com/datacommonsorg/mixer/blob/master/proto/mixer.proto
        for the definition of the inner dicts. In particular, the values for "val"
        are dicts keyed by child place DCIDs with the statvar values as values.
    """
    parent_place = request.args.get("parent_place")
    if not parent_place:
        return Response(json.dumps("error: must provide a parent_place field"),
                        400,
                        mimetype='application/json')
    child_type = request.args.get("child_type")
    if not child_type:
        return Response(json.dumps("error: must provide a child_type field"),
                        400,
                        mimetype='application/json')
    stat_vars = request.args.getlist("stat_vars")
    if not stat_vars:
        return Response(json.dumps("error: must provide a stat_vars field"),
                        400,
                        mimetype='application/json')
    date = request.args.get("date")
    return Response(json.dumps(
        dc.get_stat_set_within_place(parent_place, child_type, stat_vars,
                                     date)),
                    200,
                    mimetype='application/json')
Exemple #2
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def choropleth_data(dcid):
    """
    Get stats var data needed for choropleth charts for a given place

    API Returns:
        {
            [stat var]: {
                date: string,
                data: {
                    [dcid]: number,
                    ...
                },
                numDataPoints: number,
                exploreUrl: string,
                sources: [],
            },
            ...
        }
    """
    configs = get_choropleth_configs()
    stat_vars, denoms = shared_api.get_stat_vars(configs)
    display_dcid, display_level = get_choropleth_display_level(dcid)
    geos = []
    if display_dcid and display_level:
        geos = dc_service.get_places_in([display_dcid],
                                        display_level).get(display_dcid, [])
    if not stat_vars or not geos:
        return Response(json.dumps({}), 200, mimetype='application/json')
    # Get data for all the stat vars for every place we will need and process the data
    sv_data = dc_service.get_stat_set_within_place(display_dcid, display_level,
                                                   list(stat_vars), "")
    sv_data_values = sv_data.get('data', {})
    sv_metadata = sv_data.get('metadata', {})
    denoms_data = get_denoms_data(geos, denoms)

    result = {}
    # Process the data for each config
    for cc in configs:
        # we should only be making choropleths for configs with a single stat var
        sv = cc['statsVars'][0]
        cc_sv_data_values = sv_data_values.get(sv, {}).get('stat', {})
        denom = landing_page_api.get_denom(cc, True)
        cc_denom_data = denoms_data.get(denom, {})
        scaling = cc.get('scaling', 1)
        if 'relatedChart' in cc:
            scaling = cc['relatedChart'].get('scaling', scaling)
        sources = set()
        dates = set()
        data_dict = dict()
        # Process the data for each place we have stat var data for
        for place_dcid in cc_sv_data_values:
            dcid_sv_data = cc_sv_data_values.get(place_dcid)
            # process and then update data_dict with the value for this
            # place_dcid
            val = get_value(place_dcid, dcid_sv_data, denom, cc_denom_data,
                            scaling)
            if not val:
                continue
            data_dict[place_dcid] = val
            # add the date of the stat var value for this place_dcid to the set
            # of dates
            dates.add(dcid_sv_data.get("date", ""))
            # add stat var source and denom source (if there is a denom) to the
            # set of sources
            metadata_hash = dcid_sv_data.get('metaHash', "")
            source = sv_metadata.get(str(metadata_hash),
                                     {}).get('provenanceUrl', "")
            sources.add(source)
            if denom:
                sources.add(
                    cc_denom_data.get(place_dcid, {}).get('provenanceUrl', ""))
        # build the exploreUrl
        # TODO: webdriver test to test that the right choropleth loads
        is_scaled = (('relatedChart' in cc
                      and cc['relatedChart'].get('scale', False))
                     or ('denominator' in cc))
        url_anchor = '&pd={}&ept={}&sv={}'.format(dcid, display_level, sv)
        if is_scaled:
            url_anchor += "&pc=1"
        explore_url = urllib.parse.unquote(
            url_for('tools.map', _anchor=url_anchor))
        # process the set of sources and set of dates collected for this chart
        # config
        sources = filter(lambda x: x != "", sources)
        date_range = shared_api.get_date_range(dates)
        # build the result for this chart config and add it to the result
        cc_result = {
            'date': date_range,
            'data': data_dict,
            'numDataPoints': len(data_dict.values()),
            # TODO (chejennifer): exploreUrl should link to choropleth tool once the tool is ready
            'exploreUrl': explore_url,
            'sources': sorted(list(sources))
        }
        result[sv] = cc_result
    return Response(json.dumps(result), 200, mimetype='application/json')