def calc()

in analysis/webservice/algorithms/CorrelationMap.py [0:0]
52 lines of code
22 McCabe index (conditional complexity)

    def calc(self, computeOptions, **args):
        minLat = computeOptions.get_min_lat()
        maxLat = computeOptions.get_max_lat()
        minLon = computeOptions.get_min_lon()
        maxLon = computeOptions.get_max_lon()
        ds = computeOptions.get_dataset()
        startTime = computeOptions.get_start_time()
        endTime = computeOptions.get_end_time()
        resolution = computeOptions.get_decimal_arg("res", default=1.0)

        if not len(ds) == 2:
            raise Exception("Requires two datasets for comparison. Specify request parameter ds=Dataset_1,Dataset_2")

        ds1tiles = self._get_tile_service().find_tiles_in_polygon(box(minLon, minLat, maxLon, maxLat), ds[0], startTime,
                                                            endTime)
        ds2tiles = self._get_tile_service().find_tiles_in_polygon(box(minLon, minLat, maxLon, maxLat), ds[1], startTime,
                                                            endTime)

        matches = self._match_tiles(ds1tiles, ds2tiles)

        if len(matches) == 0:
            raise NexusProcessingException(reason="Could not find any data temporally co-located")

        results = [[{
            'cnt': 0,
            'slope': 0,
            'intercept': 0,
            'r': 0,
            'p': 0,
            'stderr': 0,
            'lat': float(lat),
            'lon': float(lon)
        } for lon in np.arange(minLon, maxLon, resolution)] for lat in
            np.arange(minLat, maxLat, resolution)]

        for stats in results:
            for stat in stats:
                values_x = []
                values_y = []
                for tile_matches in matches:

                    tile_1_list = tile_matches[0]
                    value_1 = get_approximate_value_for_lat_lon(tile_1_list, stat["lat"], stat["lon"])

                    tile_2_list = tile_matches[1]
                    value_2 = get_approximate_value_for_lat_lon(tile_2_list, stat["lat"], stat["lon"])

                    if not (math.isnan(value_1) or math.isnan(value_2)):
                        values_x.append(value_1)
                        values_y.append(value_2)

                if len(values_x) > 2 and len(values_y) > 2:
                    stats = linregress(values_x, values_y)

                    stat["slope"] = stats[0] if not math.isnan(stats[0]) and not math.isinf(stats[0]) else str(stats[0])
                    stat["intercept"] = stats[1] if not math.isnan(stats[1]) and not math.isinf(stats[1]) else str(
                        stats[1])
                    stat["r"] = stats[2] if not math.isnan(stats[2]) and not math.isinf(stats[2]) else str(stats[2])
                    stat["p"] = stats[3] if not math.isnan(stats[3]) and not math.isinf(stats[3]) else str(stats[3])
                    stat["stderr"] = stats[4] if not math.isnan(stats[4]) and not math.isinf(stats[4]) else str(
                        stats[4])
                    stat["cnt"] = len(values_x)

        return CorrelationResults(results)