Instruction:
Explain what the following Python 3 code does
def configurar_interface_de_rede(self, configuracao):
        conf_xml = configuracao \
                if isinstance(configuracao, basestring) \
                else configuracao.documento()

        return self.invocar__ConfigurarInterfaceDeRede(
                self.gerar_numero_sessao(), self._codigo_ativacao, conf_xml)

Response: Configurar o interface de comunicação do equipamento SAT.


Instruction:
How would you explain what the following Python 3 function does
def load_forecasts(self):
        forecast_path = self.forecast_json_path + "/{0}/{1}/".format(self.run_date.strftime("%Y%m%d"),
                                                                     self.ensemble_member)
        forecast_files = sorted(glob(forecast_path + "*.json"))
        for forecast_file in forecast_files:
            file_obj = open(forecast_file)
            json_obj = json.load(file_obj)
            file_obj.close()
            track_id = json_obj['properties']["id"]
            obs_track_id = json_obj['properties']["obs_track_id"]
            forecast_hours = json_obj['properties']['times']
            duration = json_obj['properties']['duration']
            for f, feature in enumerate(json_obj['features']):
                area = np.sum(feature["properties"]["masks"])
                step_id = track_id + "_{0:02d}".format(f)
                for model_type in self.model_types:
                    for model_name in self.model_names[model_type]:
                        prediction = feature['properties'][model_type + "_" + model_name.replace(" ", "-")]
                        if model_type == "condition":
                            prediction = [prediction]
                        row = [track_id, obs_track_id, self.ensemble_name, self.ensemble_member, forecast_hours[f],
                               f + 1, duration, area] + prediction
                        self.forecasts[model_type][model_name].loc[step_id] = row

Response: Loads the forecast files and gathers the forecast information into pandas DataFrames.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def load_obs(self):
        track_total_file = self.track_data_csv_path + \
            "track_total_{0}_{1}_{2}.csv".format(self.ensemble_name,
                                                 self.ensemble_member,
                                                 self.run_date.strftime("%Y%m%d"))
        track_step_file = self.track_data_csv_path + \
            "track_step_{0}_{1}_{2}.csv".format(self.ensemble_name,
                                                self.ensemble_member,
                                                self.run_date.strftime("%Y%m%d"))
        track_total_cols = ["Track_ID", "Translation_Error_X", "Translation_Error_Y", "Start_Time_Error"]
        track_step_cols = ["Step_ID", "Track_ID", "Hail_Size", "Shape", "Location", "Scale"]
        track_total_data = pd.read_csv(track_total_file, usecols=track_total_cols)
        track_step_data = pd.read_csv(track_step_file, usecols=track_step_cols)
        obs_data = pd.merge(track_step_data, track_total_data, on="Track_ID", how="left")
        self.obs = obs_data

Response: Loads the track total and step files and merges the information into a single data frame.


Instruction:
Can you implement a function in Python 3 that
merges observations with forecasts.

Response:
def merge_obs(self):
        """
        Match forecasts and observations.
        """
        for model_type in self.model_types:
            self.matched_forecasts[model_type] = {}
            for model_name in self.model_names[model_type]:
                self.matched_forecasts[model_type][model_name] = pd.merge(self.forecasts[model_type][model_name],
                                                                          self.obs, right_on="Step_ID", how="left",
                                                                          left_index=True)


Instruction:
Write a Python 3 function that can
calculate the cumulative ranked probability score of the forecasts on the metadata.

Response:
def crps(self, model_type, model_name, condition_model_name, condition_threshold, query=None):
        """
        Calculates the cumulative ranked probability score (CRPS) on the forecast data.

        Args:
            model_type: model type being evaluated.
            model_name: machine learning model being evaluated.
            condition_model_name: Name of the hail/no-hail model being evaluated
            condition_threshold: Threshold for using hail size CDF
            query: pandas query string to filter the forecasts based on the metadata

        Returns:
            a DistributedCRPS object
        """

        def gamma_cdf(x, a, loc, b):
            if a == 0 or b == 0:
                cdf = np.ones(x.shape)
            else:
                cdf = gamma.cdf(x, a, loc, b)
            return cdf

        crps_obj = DistributedCRPS(self.dist_thresholds)
        if query is not None:
            sub_forecasts = self.matched_forecasts[model_type][model_name].query(query)
            sub_forecasts = sub_forecasts.reset_index(drop=True)
            condition_forecasts = self.matched_forecasts["condition"][condition_model_name].query(query)
            condition_forecasts = condition_forecasts.reset_index(drop=True)
        else:
            sub_forecasts = self.matched_forecasts[model_type][model_name]
            condition_forecasts = self.matched_forecasts["condition"][condition_model_name]
        if sub_forecasts.shape[0] > 0:
            if model_type == "dist":
                forecast_cdfs = np.zeros((sub_forecasts.shape[0], self.dist_thresholds.size))
                for f in range(sub_forecasts.shape[0]):
                    condition_prob = condition_forecasts.loc[f, self.forecast_bins["condition"][0]]
                    if condition_prob >= condition_threshold:
                        f_params = [0, 0, 0]
                    else:
                        f_params = sub_forecasts[self.forecast_bins[model_type]].values[f]
                    forecast_cdfs[f] = gamma_cdf(self.dist_thresholds, f_params[0], f_params[1], f_params[2])
                obs_cdfs = np.array([gamma_cdf(self.dist_thresholds, *params)
                                    for params in sub_forecasts[self.type_cols[model_type]].values])
                crps_obj.update(forecast_cdfs, obs_cdfs)
            else:
                crps_obj.update(sub_forecasts[self.forecast_bins[model_type].astype(str)].values,
                                sub_forecasts[self.type_cols[model_type]].values)

        return crps_obj


Instruction:
Here you have a function in Python 3, explain what it does
def roc(self, model_type, model_name, intensity_threshold, prob_thresholds, query=None):
        roc_obj = DistributedROC(prob_thresholds, 0.5)
        if query is not None:
            sub_forecasts = self.matched_forecasts[model_type][model_name].query(query)
            sub_forecasts = sub_forecasts.reset_index(drop=True)
        else:
            sub_forecasts = self.matched_forecasts[model_type][model_name]
        obs_values = np.zeros(sub_forecasts.shape[0])
        if sub_forecasts.shape[0] > 0:
            if model_type == "dist":
                forecast_values = np.array([gamma_sf(intensity_threshold, *params)
                                            for params in sub_forecasts[self.forecast_bins[model_type]].values])
                obs_probs = np.array([gamma_sf(intensity_threshold, *params)
                                    for params in sub_forecasts[self.type_cols[model_type]].values])
                obs_values[obs_probs >= 0.01] = 1
            elif len(self.forecast_bins[model_type]) > 1:
                fbin = np.argmin(np.abs(self.forecast_bins[model_type] - intensity_threshold))
                forecast_values = 1 - sub_forecasts[self.forecast_bins[model_type].astype(str)].values.cumsum(axis=1)[:,
                                    fbin]
                obs_values[sub_forecasts[self.type_cols[model_type]].values >= intensity_threshold] = 1
            else:
                forecast_values = sub_forecasts[self.forecast_bins[model_type].astype(str)[0]].values
                obs_values[sub_forecasts[self.type_cols[model_type]].values >= intensity_threshold] = 1
            roc_obj.update(forecast_values, obs_values)
        return roc_obj

Response: Calculates a distributed ROC curve at a specified intensity threshold.


Instruction:
How would you explain what the following Python 3 function does
def sample_forecast_max_hail(self, dist_model_name, condition_model_name,
                                 num_samples, condition_threshold=0.5, query=None):
        """
        Samples every forecast hail object and returns an empirical distribution of possible maximum hail sizes.

        Hail sizes are sampled from each predicted gamma distribution. The total number of samples equals
        num_samples * area of the hail object. To get the maximum hail size for each realization, the maximum
        value within each area sample is used.

        Args:
            dist_model_name: Name of the distribution machine learning model being evaluated
            condition_model_name: Name of the hail/no-hail model being evaluated
            num_samples: Number of maximum hail samples to draw
            condition_threshold: Threshold for drawing hail samples
            query: A str that selects a subset of the data for evaluation

        Returns:
            A numpy array containing maximum hail samples for each forecast object.
        """
        if query is not None:
            dist_forecasts = self.matched_forecasts["dist"][dist_model_name].query(query)
            dist_forecasts = dist_forecasts.reset_index(drop=True)
            condition_forecasts = self.matched_forecasts["condition"][condition_model_name].query(query)
            condition_forecasts = condition_forecasts.reset_index(drop=True)
        else:
            dist_forecasts = self.matched_forecasts["dist"][dist_model_name]
            condition_forecasts = self.matched_forecasts["condition"][condition_model_name]
        max_hail_samples = np.zeros((dist_forecasts.shape[0], num_samples))
        areas = dist_forecasts["Area"].values
        for f in np.arange(dist_forecasts.shape[0]):
            condition_prob = condition_forecasts.loc[f, self.forecast_bins["condition"][0]]
            if condition_prob >= condition_threshold:
                max_hail_samples[f] = np.sort(gamma.rvs(*dist_forecasts.loc[f, self.forecast_bins["dist"]].values,
                                                        size=(num_samples, areas[f])).max(axis=1))
        return max_hail_samples

Response: Samples every forecast hail object and returns an empirical distribution of possible maximum hail sizes.


Instruction:
Write a Python 3 function for
getting signature and params for the current object

Response:
def get_params(self):
        """Get signature and params
        """
        params = {
            'key': self.get_app_key(),
            'uid': self.user_id,
            'widget': self.widget_code
        }

        products_number = len(self.products)

        if self.get_api_type() == self.API_GOODS:

            if isinstance(self.products, list):

                if products_number == 1:
                    product = self.products[0]

                    if isinstance(product, Product):
                        post_trial_product = None

                        if isinstance(product.get_trial_product(), Product):
                            post_trial_product = product
                            product = product.get_trial_product()

                        params['amount'] = product.get_amount()
                        params['currencyCode'] = product.get_currency_code()
                        params['ag_name'] = product.get_name()
                        params['ag_external_id'] = product.get_id()
                        params['ag_type'] = product.get_type()

                        if product.get_type() == Product.TYPE_SUBSCRIPTION:
                            params['ag_period_length'] = product.get_period_length()
                            params['ag_period_type'] = product.get_period_type()

                            if product.is_recurring():
                                params['ag_recurring'] = 1 if product.is_recurring() else 0

                                if post_trial_product:
                                    params['ag_trial'] = 1
                                    params['ag_post_trial_external_id'] = post_trial_product.get_id()
                                    params['ag_post_trial_period_length'] = post_trial_product.get_period_length()
                                    params['ag_post_trial_period_type'] = post_trial_product.get_period_type()
                                    params['ag_post_trial_name'] = post_trial_product.get_name()
                                    params['post_trial_amount'] = post_trial_product.get_amount()
                                    params['post_trial_currencyCode'] = post_trial_product.get_currency_code()
                    else:
                        self.append_to_errors('Not a Product instance')
                else:
                    self.append_to_errors('Only 1 product is allowed')

        elif self.get_api_type() == self.API_CART:
            index = 0

            for product in self.products:
                params['external_ids[' + str(index) + ']'] = product.get_id()
                if product.get_amount() > 0:
                    params['prices[' + str(index) + ']'] = product.get_amount()
                if product.get_currency_code() != '' and product.get_currency_code() is not None:
                    params['currencies[' + str(index) + ']'] = product.get_currency_code()
                index += 1

        params['sign_version'] = signature_version = str(self.get_default_widget_signature())

        if not self.is_empty(self.extra_params, 'sign_version'):
            signature_version = params['sign_version'] = str(self.extra_params['sign_version'])

        params = self.array_merge(params, self.extra_params)

        params['sign'] = self.calculate_signature(params, self.get_secret_key(), int(signature_version))
        return params


Instruction:
Can you create a Python 3 function that
returns a tuple of integers representing a partir do total de segundos informado.

Response:
def hms(segundos):  # TODO: mover para util.py
    """
    Retorna o número de horas, minutos e segundos a partir do total de
    segundos informado.

    .. sourcecode:: python

        >>> hms(1)
        (0, 0, 1)

        >>> hms(60)
        (0, 1, 0)

        >>> hms(3600)
        (1, 0, 0)

        >>> hms(3601)
        (1, 0, 1)

        >>> hms(3661)
        (1, 1, 1)

    :param int segundos: O número total de segundos.

    :returns: Uma tupla contendo trẽs elementos representando, respectivamente,
        o número de horas, minutos e segundos calculados a partir do total de
        segundos.

    :rtype: tuple
    """
    h = (segundos / 3600)
    m = (segundos - (3600 * h)) / 60
    s = (segundos - (3600 * h) - (m * 60));
    return (h, m, s)


Instruction:
Explain what the following Python 3 code does
def hms_humanizado(segundos): # TODO: mover para util.py
    p = lambda n, s, p: p if n > 1 else s
    h, m, s = hms(segundos)

    tokens = [
            '' if h == 0 else '{:d} {}'.format(h, p(h, 'hora', 'horas')),
            '' if m == 0 else '{:d} {}'.format(m, p(m, 'minuto', 'minutos')),
            '' if s == 0 else '{:d} {}'.format(s, p(s, 'segundo', 'segundos'))]

    tokens = [token for token in tokens if token]

    if len(tokens) == 1:
        return tokens[0]

    if len(tokens) > 1:
        return '{} e {}'.format(', '.join(tokens[:-1]), tokens[-1])

    return 'zero segundos'

Response: Descreve o total de horas minutos e segundos.


Instruction:
Can you tell what is the following Python 3 function doing
def format_grib_name(self, selected_variable):
        names = self.unknown_names
        units = self.unknown_units
        for key, value in names.items():
            if selected_variable == value:
                Id = key
                u = units[key]
        return Id, u

Response: Returns the grib2 message number and units of the given variable name.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def load_data(self):
        file_objects = self.file_objects
        var = self.variable
        valid_date = self.valid_dates
        data = self.data
        unknown_names = self.unknown_names
        unknown_units = self.unknown_units
        member = self.member
        lat = self.lat
        lon = self.lon

        if self.sector_ind_path:
            inds_file = pd.read_csv(self.sector_ind_path+'sector_data_indices.csv')
            inds = inds_file.loc[:,'indices']
        out_x = self.mapping_data["x"]

        if not file_objects:
            print()
            print("No {0} model runs on {1}".format(member,self.run_date))
            print()
            units = None
            return self.data, units

        for f, file in enumerate(file_objects):
            grib = pygrib.open(file)
            if type(var) is int:
                data_values = grib[var].values
                #lat, lon = grib[var].latlons()
                #proj = Proj(grib[var].projparams)
                if grib[var].units == 'unknown':
                    Id = grib[var].parameterNumber
                    units = self.unknown_units[Id]
                else:
                    units = grib[var].units
            elif type(var) is str:
                if '_' in var:
                    variable = var.split('_')[0]
                    level = int(var.split('_')[1])
                    if variable in unknown_names.values():
                        Id, units = self.format_grib_name(variable)
                        data_values = grib.select(parameterNumber=Id, level=level)[0].values
                        #lat, lon =  grib.select(parameterNumber=Id, level=level)[0].latlons()
                        #proj = Proj(grib.select(parameterNumber=Id, level=level)[0].projparams)

                    else:
                        data_values = grib.select(name=variable, level=level)[0].values
                        units = grib.select(name=variable, level=level)[0].units
                        #lat, lon  = grib.select(name=variable, level=level)[0].latlons()
                        #proj = Proj(grib.select(name=variable, level=level)[0].projparams)
                else:
                    if var in unknown_names.values():
                        Id, units = self.format_grib_name(var)
                        data_values = grib.select(parameterNumber=Id)[0].values
                        #lat, lon = grib.select(parameterNumber=Id)[0].latlons()
                        #proj = Proj(grib.select(parameterNumber=Id)[0].projparams)

                    elif len(grib.select(name=var)) > 1:
                        raise NameError("Multiple '{0}' records found. Rename with level:'{0}_level'".format(var))

                    else:
                        data_values = grib.select(name=var)[0].values
                        units = grib.select(name=var)[0].units
                        #lat, lon = grib.select(name=var)[0].latlons()
                        #proj = Proj(grib.select(name=var)[0].projparams)

            if data is None:
                data = np.empty((len(valid_date), out_x.shape[0], out_x.shape[1]), dtype=float)
                if self.sector_ind_path:
                    data[f] = data_values[:].flatten()[inds].reshape(out_x.shape)
                else:
                    data[f]=data_values[:]
            else:
                if self.sector_ind_path:
                    data[f] = data_values[:].flatten()[inds].reshape(out_x.shape)
                else:
                    data[f]=data_values[:]

        return data, units

Response:
Loads data from grib2 file objects or list of grib2 file objects. Handles specific variable names
            and grib2 message numbers
            and grib2 message numbers
            and grib2 message numbers.


Instruction:
Can you tell what is the following Python 3 function doing
def load_forecasts(self):
        run_date_str = self.run_date.strftime("%Y%m%d")
        for model_name in self.model_names:
            self.raw_forecasts[model_name] = {}
            forecast_file = self.forecast_path + run_date_str + "/" + \
                model_name.replace(" ", "-") + "_hailprobs_{0}_{1}.nc".format(self.ensemble_member, run_date_str)
            forecast_obj = Dataset(forecast_file)
            forecast_hours = forecast_obj.variables["forecast_hour"][:]
            valid_hour_indices = np.where((self.start_hour <= forecast_hours) & (forecast_hours <= self.end_hour))[0]
            for size_threshold in self.size_thresholds:
                self.raw_forecasts[model_name][size_threshold] = \
                    forecast_obj.variables["prob_hail_{0:02d}_mm".format(size_threshold)][valid_hour_indices]
            forecast_obj.close()

Response: Load the forecast files into memory.


Instruction:
How would you explain what the following Python 3 function does
def get_window_forecasts(self):
        for model_name in self.model_names:
            self.window_forecasts[model_name] = {}
            for size_threshold in self.size_thresholds:
                self.window_forecasts[model_name][size_threshold] = \
                    np.array([self.raw_forecasts[model_name][size_threshold][sl].sum(axis=0)
                              for sl in self.hour_windows])

Response: Aggregate the forecasts within the specified time windows.


Instruction:
Write a Python 3 script for
loading observations and masking grids.

Response:
def load_obs(self,  mask_threshold=0.5):
        """
        Loads observations and masking grid (if needed).

        :param mask_threshold: Values greater than the threshold are kept, others are masked.
        :return:
        """
        start_date = self.run_date + timedelta(hours=self.start_hour)
        end_date = self.run_date + timedelta(hours=self.end_hour)
        mrms_grid = MRMSGrid(start_date, end_date, self.mrms_variable, self.mrms_path)
        mrms_grid.load_data()
        if len(mrms_grid.data) > 0:
            self.raw_obs[self.mrms_variable] = np.where(mrms_grid.data > 100, 100, mrms_grid.data)
            self.window_obs[self.mrms_variable] = np.array([self.raw_obs[self.mrms_variable][sl].max(axis=0)
                                                            for sl in self.hour_windows])
            if self.obs_mask:
                mask_grid = MRMSGrid(start_date, end_date, self.mask_variable, self.mrms_path)
                mask_grid.load_data()
                self.raw_obs[self.mask_variable] = np.where(mask_grid.data >= mask_threshold, 1, 0)
                self.window_obs[self.mask_variable] = np.array([self.raw_obs[self.mask_variable][sl].max(axis=0)
                                                               for sl in self.hour_windows])


Instruction:
Write a Python 3 script to
use a dilation filter to grow positive observation areas by a specified number of grid points.

Response:
def dilate_obs(self, dilation_radius):
        """
        Use a dilation filter to grow positive observation areas by a specified number of grid points

        :param dilation_radius: Number of times to dilate the grid.
        :return:
        """
        for s in self.size_thresholds:
            self.dilated_obs[s] = np.zeros(self.window_obs[self.mrms_variable].shape)
            for t in range(self.dilated_obs[s].shape[0]):
                self.dilated_obs[s][t][binary_dilation(self.window_obs[self.mrms_variable][t] >= s, iterations=dilation_radius)] = 1


Instruction:
Can you tell what is the following Python 3 function doing
def roc_curves(self, prob_thresholds):
        all_roc_curves = {}
        for model_name in self.model_names:
            all_roc_curves[model_name] = {}
            for size_threshold in self.size_thresholds:
                all_roc_curves[model_name][size_threshold] = {}
                for h, hour_window in enumerate(self.hour_windows):
                    hour_range = (hour_window.start, hour_window.stop)
                    all_roc_curves[model_name][size_threshold][hour_range] = \
                        DistributedROC(prob_thresholds, 1)
                    if self.obs_mask:
                        all_roc_curves[model_name][size_threshold][hour_range].update(
                            self.window_forecasts[model_name][size_threshold][h][
                                self.window_obs[self.mask_variable][h] > 0],
                            self.dilated_obs[size_threshold][h][self.window_obs[self.mask_variable][h] > 0]
                        )
                    else:
                        all_roc_curves[model_name][size_threshold][hour_range].update(
                            self.window_forecasts[model_name][size_threshold][h],
                            self.dilated_obs[size_threshold][h]
                        )
        return all_roc_curves

Response: Generate a dictionary of DistributedROC objects for each machine learning model size threshold and time window.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def reliability_curves(self, prob_thresholds):
        all_rel_curves = {}
        for model_name in self.model_names:
            all_rel_curves[model_name] = {}
            for size_threshold in self.size_thresholds:
                all_rel_curves[model_name][size_threshold] = {}
                for h, hour_window in enumerate(self.hour_windows):
                    hour_range = (hour_window.start, hour_window.stop)
                    all_rel_curves[model_name][size_threshold][hour_range] = \
                        DistributedReliability(prob_thresholds, 1)
                    if self.obs_mask:
                        all_rel_curves[model_name][size_threshold][hour_range].update(
                            self.window_forecasts[model_name][size_threshold][h][
                                self.window_obs[self.mask_variable][h] > 0],
                            self.dilated_obs[size_threshold][h][self.window_obs[self.mask_variable][h] > 0]
                        )
                    else:
                        all_rel_curves[model_name][size_threshold][hour_range].update(
                            self.window_forecasts[model_name][size_threshold][h],
                            self.dilated_obs[size_threshold][h]
                        )
        return all_rel_curves

Response: Return a dictionary of reliability curves for each machine learning model size threshold and time window.


Instruction:
Write a Python 3 script to
load map coordinates from netCDF or pickle file created by util. makeMapGrids.

Response:
def load_map_coordinates(map_file):
    """
    Loads map coordinates from netCDF or pickle file created by util.makeMapGrids.

    Args:
        map_file: Filename for the file containing coordinate information.

    Returns:
        Latitude and longitude grids as numpy arrays.
    """
    if map_file[-4:] == ".pkl":
        map_data = pickle.load(open(map_file))
        lon = map_data['lon']
        lat = map_data['lat']
    else:
        map_data = Dataset(map_file)
        if "lon" in map_data.variables.keys():
            lon = map_data.variables['lon'][:]
            lat = map_data.variables['lat'][:]
        else:
            lon = map_data.variables["XLONG"][0]
            lat = map_data.variables["XLAT"][0]
    return lon, lat


Instruction:
Here you have a function in Python 3, explain what it does
def interpolate_mrms_day(start_date, variable, interp_type, mrms_path, map_filename, out_path):
    try:
        print(start_date, variable)
        end_date = start_date + timedelta(hours=23)
        mrms = MRMSGrid(start_date, end_date, variable, mrms_path)
        if mrms.data is not None:
            if map_filename[-3:] == "map":
                mapping_data = make_proj_grids(*read_arps_map_file(map_filename))
                mrms.interpolate_to_netcdf(mapping_data['lon'], mapping_data['lat'], out_path, interp_type=interp_type)
            elif map_filename[-3:] == "txt":
                mapping_data = make_proj_grids(*read_ncar_map_file(map_filename))
                mrms.interpolate_to_netcdf(mapping_data["lon"], mapping_data["lat"], out_path, interp_type=interp_type)
            else:
                lon, lat = load_map_coordinates(map_filename)
                mrms.interpolate_to_netcdf(lon, lat, out_path, interp_type=interp_type)
    except Exception as e:
        # This exception catches any errors when run in multiprocessing, prints the stack trace,
        # and ends the process. Otherwise the process will stall.
        print(traceback.format_exc())
        raise e

Response: This function interpolates hourly MRMS data to a specified latitude and lon grid and saves the interpolated grids to CF - compliant netCDF4 files.


Instruction:
Can you generate the documentation for the following Python 3 function
def load_data(self):
        data = []
        loaded_dates = []
        loaded_indices = []
        for t, timestamp in enumerate(self.all_dates):
            date_str = timestamp.date().strftime("%Y%m%d")
            full_path = self.path_start + date_str + "/"
            if self.variable in os.listdir(full_path):
                full_path += self.variable + "/"
                data_files = sorted(os.listdir(full_path))
                file_dates = pd.to_datetime([d.split("_")[-1][0:13] for d in data_files])
                if timestamp in file_dates:
                    data_file = data_files[np.where(timestamp==file_dates)[0][0]]
                    print(full_path + data_file)
                    if data_file[-2:] == "gz":
                        subprocess.call(["gunzip", full_path + data_file])
                        file_obj = Nio.open_file(full_path + data_file[:-3])
                    else:
                        file_obj = Nio.open_file(full_path + data_file)
                    var_name = sorted(file_obj.variables.keys())[0]
                    data.append(file_obj.variables[var_name][:])
                    if self.lon is None:
                        self.lon = file_obj.variables["lon_0"][:]
                        # Translates longitude values from 0:360 to -180:180
                        if np.count_nonzero(self.lon > 180) > 0:
                            self.lon -= 360
                        self.lat = file_obj.variables["lat_0"][:]
                    file_obj.close()
                    if data_file[-2:] == "gz":
                        subprocess.call(["gzip", full_path + data_file[:-3]])
                    else:
                        subprocess.call(["gzip", full_path + data_file])
                    loaded_dates.append(timestamp)
                    loaded_indices.append(t)
        if len(loaded_dates) > 0:
            self.loaded_dates = pd.DatetimeIndex(loaded_dates)
            self.data = np.ones((self.all_dates.shape[0], data[0].shape[0], data[0].shape[1])) * -9999
            self.data[loaded_indices] = np.array(data)

Response: Loads data from MRMS GRIB2 files and handles compression duties.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def interpolate_grid(self, in_lon, in_lat):
        out_data = np.zeros((self.data.shape[0], in_lon.shape[0], in_lon.shape[1]))
        for d in range(self.data.shape[0]):
            print("Loading ", d, self.variable, self.start_date)
            if self.data[d].max() > -999:
                step = self.data[d]
                step[step < 0] = 0
                if self.lat[-1] < self.lat[0]:
                    spline = RectBivariateSpline(self.lat[::-1], self.lon, step[::-1], kx=3, ky=3)
                else:
                    spline = RectBivariateSpline(self.lat, self.lon, step, kx=3, ky=3)
                print("Evaluating", d, self.variable, self.start_date)
                flat_data = spline.ev(in_lat.ravel(), in_lon.ravel())
                out_data[d] = flat_data.reshape(in_lon.shape)
                del spline
            else:
                print(d, " is missing")
                out_data[d] = -9999
        return out_data

Response: Interpolates MRMS data to a different grid using cubic bivariate splines


Instruction:
Write a Python 3 function for
finding the largest value within a given radius of a point on the interpolated grid. Args: in_lon: 2D array of longitude values in_lat: 2D array of latitude values radius: radius of influence for largest neighbor search in degrees Returns: Array of interpolated data

Response:
def max_neighbor(self, in_lon, in_lat, radius=0.05):
        """
        Finds the largest value within a given radius of a point on the interpolated grid.

        Args:
            in_lon: 2D array of longitude values
            in_lat: 2D array of latitude values
            radius: radius of influence for largest neighbor search in degrees

        Returns:
            Array of interpolated data
        """
        out_data = np.zeros((self.data.shape[0], in_lon.shape[0], in_lon.shape[1]))
        in_tree = cKDTree(np.vstack((in_lat.ravel(), in_lon.ravel())).T)
        out_indices = np.indices(out_data.shape[1:])
        out_rows = out_indices[0].ravel()
        out_cols = out_indices[1].ravel()
        for d in range(self.data.shape[0]):
            nz_points = np.where(self.data[d] > 0)
            if len(nz_points[0]) > 0:
                nz_vals = self.data[d][nz_points]
                nz_rank = np.argsort(nz_vals)
                original_points = cKDTree(np.vstack((self.lat[nz_points[0][nz_rank]], self.lon[nz_points[1][nz_rank]])).T)
                all_neighbors = original_points.query_ball_tree(in_tree, radius, p=2, eps=0)
                for n, neighbors in enumerate(all_neighbors):
                    if len(neighbors) > 0:
                        out_data[d, out_rows[neighbors], out_cols[neighbors]] = nz_vals[nz_rank][n]
        return out_data


Instruction:
Can you create a Python 3 function that
interpolates the MRMS data to a netCDF file.

Response:
def interpolate_to_netcdf(self, in_lon, in_lat, out_path, date_unit="seconds since 1970-01-01T00:00",
                              interp_type="spline"):
        """
        Calls the interpolation function and then saves the MRMS data to a netCDF file. It will also create
        separate directories for each variable if they are not already available.
        """
        if interp_type == "spline":
            out_data = self.interpolate_grid(in_lon, in_lat)
        else:
            out_data = self.max_neighbor(in_lon, in_lat)
        if not os.access(out_path + self.variable, os.R_OK):
            try:
                os.mkdir(out_path + self.variable)
            except OSError:
                print(out_path + self.variable + " already created")
        out_file = out_path + self.variable + "/" + "{0}_{1}_{2}.nc".format(self.variable,
                                                                            self.start_date.strftime("%Y%m%d-%H:%M"),
                                                                            self.end_date.strftime("%Y%m%d-%H:%M"))
        out_obj = Dataset(out_file, "w")
        out_obj.createDimension("time", out_data.shape[0])
        out_obj.createDimension("y", out_data.shape[1])
        out_obj.createDimension("x", out_data.shape[2])
        data_var = out_obj.createVariable(self.variable, "f4", ("time", "y", "x"), zlib=True,
                                          fill_value=-9999.0,
                                          least_significant_digit=3)
        data_var[:] = out_data
        data_var.long_name = self.variable
        data_var.coordinates = "latitude longitude"
        if "MESH" in self.variable or "QPE" in self.variable:
            data_var.units = "mm"
        elif "Reflectivity" in self.variable:
            data_var.units = "dBZ"
        elif "Rotation" in self.variable:
            data_var.units = "s-1"
        else:
            data_var.units = ""
        out_lon = out_obj.createVariable("longitude", "f4", ("y", "x"), zlib=True)
        out_lon[:] = in_lon
        out_lon.units = "degrees_east"
        out_lat = out_obj.createVariable("latitude", "f4", ("y", "x"), zlib=True)
        out_lat[:] = in_lat
        out_lat.units = "degrees_north"
        dates = out_obj.createVariable("time", "i8", ("time",), zlib=True)
        dates[:] = np.round(date2num(self.all_dates.to_pydatetime(), date_unit)).astype(np.int64)
        dates.long_name = "Valid date"
        dates.units = date_unit
        out_obj.Conventions="CF-1.6"
        out_obj.close()
        return


Instruction:
Can you tell what is the following Python 3 function doing
def get_data_generator_by_id(hardware_source_id, sync=True):
    hardware_source = HardwareSourceManager().get_hardware_source_for_hardware_source_id(hardware_source_id)
    def get_last_data():
        return hardware_source.get_next_xdatas_to_finish()[0].data.copy()
    yield get_last_data

Response: Return a generator for data.


Instruction:
Can you create a Python 3 function that
parses the config file for aliases and automatically register them.

Response:
def parse_hardware_aliases_config_file(config_path):
    """
        Parse config file for aliases and automatically register them.

        Returns True if alias file was found and parsed (successfully or unsuccessfully).

        Returns False if alias file was not found.

        Config file is a standard .ini file with a section
    """
    if os.path.exists(config_path):
        logging.info("Parsing alias file {:s}".format(config_path))
        try:
            config = configparser.ConfigParser()
            config.read(config_path)
            for section in config.sections():
                device = config.get(section, "device")
                hardware_alias = config.get(section, "hardware_alias")
                display_name = config.get(section, "display_name")
                try:
                    logging.info("Adding alias {:s} for device {:s}, display name: {:s} ".format(hardware_alias, device, display_name))
                    HardwareSourceManager().make_instrument_alias(device, hardware_alias, _(display_name))
                except Exception as e:
                    logging.info("Error creating hardware alias {:s} for device {:s} ".format(hardware_alias, device))
                    logging.info(traceback.format_exc())
        except Exception as e:
            logging.info("Error reading alias file from: " + config_path)
            logging.info(traceback.format_exc())
        return True
    return False


Instruction:
How would you explain what the following Python 3 function does
def make_instrument_alias(self, instrument_id, alias_instrument_id, display_name):
        self.__aliases[alias_instrument_id] = (instrument_id, display_name)
        for f in self.aliases_updated:
            f()

Response:
Configure an alias.

            Callers can use the alias to refer to the instrument or hardware source.
            The alias should be lowercase, no spaces. The display name may be used to display alias to
            the user. Neither the original instrument or hardware source id and the alias id should ever
            be visible to end users.

            :param str instrument_id: the hardware source id (lowercase, no spaces)
            :param str alias_instrument_id: the alias of the hardware source id (lowercase, no spaces)
            :param str display_name: the display name for the alias


Instruction:
Write a Python 3 script to
call from hardware source when new data arrives.

Response:
def update(self, data_and_metadata: DataAndMetadata.DataAndMetadata, state: str, sub_area, view_id) -> None:
        """Called from hardware source when new data arrives."""
        self.__state = state
        self.__sub_area = sub_area

        hardware_source_id = self.__hardware_source.hardware_source_id
        channel_index = self.index
        channel_id = self.channel_id
        channel_name = self.name
        metadata = copy.deepcopy(data_and_metadata.metadata)
        hardware_source_metadata = dict()
        hardware_source_metadata["hardware_source_id"] = hardware_source_id
        hardware_source_metadata["channel_index"] = channel_index
        if channel_id is not None:
            hardware_source_metadata["reference_key"] = "_".join([hardware_source_id, channel_id])
            hardware_source_metadata["channel_id"] = channel_id
        else:
            hardware_source_metadata["reference_key"] = hardware_source_id
        if channel_name is not None:
            hardware_source_metadata["channel_name"] = channel_name
        if view_id:
            hardware_source_metadata["view_id"] = view_id
        metadata.setdefault("hardware_source", dict()).update(hardware_source_metadata)

        data = data_and_metadata.data
        master_data = self.__data_and_metadata.data if self.__data_and_metadata else None
        data_matches = master_data is not None and data.shape == master_data.shape and data.dtype == master_data.dtype
        if data_matches and sub_area is not None:
            top = sub_area[0][0]
            bottom = sub_area[0][0] + sub_area[1][0]
            left = sub_area[0][1]
            right = sub_area[0][1] + sub_area[1][1]
            if top > 0 or left > 0 or bottom < data.shape[0] or right < data.shape[1]:
                master_data = numpy.copy(master_data)
                master_data[top:bottom, left:right] = data[top:bottom, left:right]
            else:
                master_data = numpy.copy(data)
        else:
            master_data = data  # numpy.copy(data). assume data does not need a copy.

        data_descriptor = data_and_metadata.data_descriptor
        intensity_calibration = data_and_metadata.intensity_calibration if data_and_metadata else None
        dimensional_calibrations = data_and_metadata.dimensional_calibrations if data_and_metadata else None
        timestamp = data_and_metadata.timestamp
        new_extended_data = DataAndMetadata.new_data_and_metadata(master_data, intensity_calibration=intensity_calibration, dimensional_calibrations=dimensional_calibrations, metadata=metadata, timestamp=timestamp, data_descriptor=data_descriptor)

        self.__data_and_metadata = new_extended_data

        self.data_channel_updated_event.fire(new_extended_data)
        self.is_dirty = True


Instruction:
Explain what the following Python 3 code does
def start(self):
        old_start_count = self.__start_count
        self.__start_count += 1
        if old_start_count == 0:
            self.data_channel_start_event.fire()

Response: Called from hardware source when data starts streaming.


Instruction:
Write a Python 3 function for
connecting to the data item reference.

Response:
def connect_data_item_reference(self, data_item_reference):
        """Connect to the data item reference, creating a crop graphic if necessary.

        If the data item reference does not yet have an associated data item, add a
        listener and wait for the data item to be set, then connect.
        """
        display_item = data_item_reference.display_item
        data_item = display_item.data_item if display_item else None
        if data_item and display_item:
            self.__connect_display(display_item)
        else:
            def data_item_reference_changed():
                self.__data_item_reference_changed_event_listener.close()
                self.connect_data_item_reference(data_item_reference)  # ugh. recursive mess.
            self.__data_item_reference_changed_event_listener = data_item_reference.data_item_reference_changed_event.listen(data_item_reference_changed)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def grab_earliest(self, timeout: float=None) -> typing.List[DataAndMetadata.DataAndMetadata]:
        timeout = timeout if timeout is not None else 10.0
        with self.__buffer_lock:
            if len(self.__buffer) == 0:
                done_event = threading.Event()
                self.__done_events.append(done_event)
                self.__buffer_lock.release()
                done = done_event.wait(timeout)
                self.__buffer_lock.acquire()
                if not done:
                    raise Exception("Could not grab latest.")
            return self.__buffer.pop(0)

Response: Grab the earliest data from the buffer blocking until one is available.


Instruction:
Here you have a function in Python 3, explain what it does
def grab_next(self, timeout: float=None) -> typing.List[DataAndMetadata.DataAndMetadata]:
        with self.__buffer_lock:
            self.__buffer = list()
        return self.grab_latest(timeout)

Response: Grab the next data from the buffer blocking until one is available.


Instruction:
How would you code a function in Python 3 to
grab the next data to start from the buffer blocking until one is available.

Response:
def grab_following(self, timeout: float=None) -> typing.List[DataAndMetadata.DataAndMetadata]:
        """Grab the next data to start from the buffer, blocking until one is available."""
        self.grab_next(timeout)
        return self.grab_next(timeout)


Instruction:
Can you tell what is the following Python 3 function doing
def pause(self) -> None:
        with self.__state_lock:
            if self.__state == DataChannelBuffer.State.started:
                self.__state = DataChannelBuffer.State.paused

Response:
Pause recording.

        Thread safe and UI safe.


Instruction:
How would you explain what the following Python 3 function does
def resume(self) -> None:
        with self.__state_lock:
            if self.__state == DataChannelBuffer.State.paused:
                self.__state = DataChannelBuffer.State.started

Response: Resume recording after pause.


Instruction:
Create a Python 3 function to
return a list of n keys associated with the largest values in descending order.

Response:
def nlargest(n, mapping):
    """
    Takes a mapping and returns the n keys associated with the largest values
    in descending order. If the mapping has fewer than n items, all its keys
    are returned.

    Equivalent to:
        ``next(zip(*heapq.nlargest(mapping.items(), key=lambda x: x[1])))``

    Returns
    -------
    list of up to n keys from the mapping

    """
    try:
        it = mapping.iteritems()
    except AttributeError:
        it = iter(mapping.items())
    pq = minpq()
    try:
        for i in range(n):
            pq.additem(*next(it))
    except StopIteration:
        pass
    try:
        while it:
            pq.pushpopitem(*next(it))
    except StopIteration:
        pass
    out = list(pq.popkeys())
    out.reverse()
    return out


Instruction:
Make a summary of the following Python 3 code
def fromkeys(cls, iterable, value, **kwargs):
        return cls(((k, value) for k in iterable), **kwargs)

Response: Return a pqict mapping keys from an iterable to the same value.


Instruction:
How would you explain what the following Python 3 function does
def copy(self):
        return self.__class__(self, key=self._keyfn, precedes=self._precedes)

Response: Return a shallow copy of a pqdict.


Instruction:
Write a Python 3 script for
removing and return the highest priority item from pqdict. Raises KeyError if pqdict is empty.

Response:
def popitem(self):
        """
        Remove and return the item with highest priority. Raises ``KeyError``
        if pqdict is empty.

        """
        heap = self._heap
        position = self._position

        try:
            end = heap.pop(-1)
        except IndexError:
            raise KeyError('pqdict is empty')

        if heap:
            node = heap[0]
            heap[0] = end
            position[end.key] = 0
            self._sink(0)
        else:
            node = end
        del position[node.key]
        return node.key, node.value


Instruction:
Given the following Python 3 function, write the documentation
def topitem(self):
        try:
            node = self._heap[0]
        except IndexError:
            raise KeyError('pqdict is empty')
        return node.key, node.value

Response: Return the highest priority item in pqdict. Raises KeyError if pqdict is empty.


Instruction:
Write a Python 3 function for
adding a new item to the queue. Raises KeyError if the key is already in the pqdict.

Response:
def additem(self, key, value):
        """
        Add a new item. Raises ``KeyError`` if key is already in the pqdict.

        """
        if key in self._position:
            raise KeyError('%s is already in the queue' % repr(key))
        self[key] = value


Instruction:
Can you implement a function in Python 3 that
updates the priority value of an existing item. Raises KeyError if the key is not in the pqdict.

Response:
def updateitem(self, key, new_val):
        """
        Update the priority value of an existing item. Raises ``KeyError`` if
        key is not in the pqdict.

        """
        if key not in self._position:
            raise KeyError(key)
        self[key] = new_val


Instruction:
How would you explain what the following Python 3 function does
def replace_key(self, key, new_key):
        heap = self._heap
        position = self._position
        if new_key in self:
            raise KeyError('%s is already in the queue' % repr(new_key))
        pos = position.pop(key)  # raises appropriate KeyError
        position[new_key] = pos
        heap[pos].key = new_key

Response: Replaces the key in the existing heap node in place. Raises KeyError if the key is already in the pqdict.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def swap_priority(self, key1, key2):
        heap = self._heap
        position = self._position
        if key1 not in self or key2 not in self:
            raise KeyError
        pos1, pos2 = position[key1], position[key2]
        heap[pos1].key, heap[pos2].key = key2, key1
        position[key1], position[key2] = pos2, pos1

Response: Swap the priority level of two items in the pqdict.


Instruction:
Implement a function in Python 3 to
repair a broken heap.

Response:
def heapify(self, key=__marker):
        """
        Repair a broken heap. If the state of an item's priority value changes
        you can re-sort the relevant item only by providing ``key``.

        """
        if key is self.__marker:
            n = len(self._heap)
            for pos in reversed(range(n//2)):
                self._sink(pos)
        else:
            try:
                pos = self._position[key]
            except KeyError:
                raise KeyError(key)
            self._reheapify(pos)


Instruction:
Write a Python 3 function for
checking to make sure _version. py is contained in the package.

Response:
def package_has_version_file(package_name):
    """ Check to make sure _version.py is contained in the package """
    version_file_path = helpers.package_file_path('_version.py', package_name)
    return os.path.isfile(version_file_path)


Instruction:
Create a Python 3 function to
grab the project name out of setup. py

Response:
def get_project_name():
    """ Grab the project name out of setup.py """
    setup_py_content = helpers.get_file_content('setup.py')
    ret = helpers.value_of_named_argument_in_function(
        'name', 'setup', setup_py_content, resolve_varname=True
    )
    if ret and ret[0] == ret[-1] in ('"', "'"):
        ret = ret[1:-1]
    return ret


Instruction:
Here you have a function in Python 3, explain what it does
def get_version(package_name, ignore_cache=False):
    if ignore_cache:
        with microcache.temporarily_disabled():
            found = helpers.regex_in_package_file(
                VERSION_SET_REGEX, '_version.py', package_name, return_match=True
            )
    else:
        found = helpers.regex_in_package_file(
            VERSION_SET_REGEX, '_version.py', package_name, return_match=True
        )
    if found is None:
        raise ProjectError('found {}, but __version__ is not defined')
    current_version = found['version']
    return current_version

Response: Get the version which is currently configured by the package.


Instruction:
Create a Python 3 function for
setting the version in _version. py to version_str

Response:
def set_version(package_name, version_str):
    """ Set the version in _version.py to version_str """
    current_version = get_version(package_name)
    version_file_path = helpers.package_file_path('_version.py', package_name)
    version_file_content = helpers.get_file_content(version_file_path)
    version_file_content = version_file_content.replace(current_version, version_str)
    with open(version_file_path, 'w') as version_file:
        version_file.write(version_file_content)


Instruction:
Create a Python 3 function to
check to see if the version specified is a valid version

Response:
def version_is_valid(version_str):
    """ Check to see if the version specified is a valid as far as pkg_resources is concerned

    >>> version_is_valid('blah')
    False
    >>> version_is_valid('1.2.3')
    True
    """
    try:
        packaging.version.Version(version_str)
    except packaging.version.InvalidVersion:
        return False
    return True


Instruction:
Can you tell what is the following Python 3 function doing
def _get_uploaded_versions_warehouse(project_name, index_url, requests_verify=True):
    url = '/'.join((index_url, project_name, 'json'))
    response = requests.get(url, verify=requests_verify)
    if response.status_code == 200:
        return response.json()['releases'].keys()
    return None

Response: Query the pypi index at index_url to find all of the releases


Instruction:
Implement a Python 3 function for
querying the pypi index at index_url to find all versions of the specified project

Response:
def _get_uploaded_versions_pypicloud(project_name, index_url, requests_verify=True):
    """ Query the pypi index at index_url using pypicloud api to find all versions """
    api_url = index_url
    for suffix in ('/pypi', '/pypi/', '/simple', '/simple/'):
        if api_url.endswith(suffix):
            api_url = api_url[:len(suffix) * -1] + '/api/package'
            break
    url = '/'.join((api_url, project_name))
    response = requests.get(url, verify=requests_verify)
    if response.status_code == 200:
        return [p['version'] for p in response.json()['packages']]
    return None


Instruction:
Create a Python 3 function to
check to see if the version specified has already been uploaded to the index

Response:
def version_already_uploaded(project_name, version_str, index_url, requests_verify=True):
    """ Check to see if the version specified has already been uploaded to the configured index
    """
    all_versions = _get_uploaded_versions(project_name, index_url, requests_verify)
    return version_str in all_versions


Instruction:
Can you write a function in Python 3 where it
attempts to convert a README. md file into README. rst

Response:
def convert_readme_to_rst():
    """ Attempt to convert a README.md file into README.rst """
    project_files = os.listdir('.')
    for filename in project_files:
        if filename.lower() == 'readme':
            raise ProjectError(
                'found {} in project directory...'.format(filename) +
                'not sure what to do with it, refusing to convert'
            )
        elif filename.lower() == 'readme.rst':
            raise ProjectError(
                'found {} in project directory...'.format(filename) +
                'refusing to overwrite'
            )
    for filename in project_files:
        if filename.lower() == 'readme.md':
            rst_filename = 'README.rst'
            logger.info('converting {} to {}'.format(filename, rst_filename))
            try:
                rst_content = pypandoc.convert(filename, 'rst')
                with open('README.rst', 'w') as rst_file:
                    rst_file.write(rst_content)
                return
            except OSError as e:
                raise ProjectError(
                    'could not convert readme to rst due to pypandoc error:' + os.linesep + str(e)
                )
    raise ProjectError('could not find any README.md file to convert')


Instruction:
Can you create a Python 3 function that
collects all files which have already been packaged

Response:
def get_packaged_files(package_name):
    """ Collect relative paths to all files which have already been packaged """
    if not os.path.isdir('dist'):
        return []
    return [os.path.join('dist', filename) for filename in os.listdir('dist')]


Instruction:
Write a Python 3 script for
checking if there are multiple packaged versions of a given package.

Response:
def multiple_packaged_versions(package_name):
    """ Look through built package directory and see if there are multiple versions there """
    dist_files = os.listdir('dist')
    versions = set()
    for filename in dist_files:
        version = funcy.re_find(r'{}-(.+).tar.gz'.format(package_name), filename)
        if version:
            versions.add(version)
    return len(versions) > 1


Instruction:
Can you write a function in Python 3 where it
calculates the neighborhood probability over the full period of the forecast

Response:
def period_neighborhood_probability(self, radius, smoothing, threshold, stride,start_time,end_time):
        """
        Calculate the neighborhood probability over the full period of the forecast

        Args:
            radius: circular radius from each point in km
            smoothing: width of Gaussian smoother in km
            threshold: intensity of exceedance
            stride: number of grid points to skip for reduced neighborhood grid

        Returns:
            (neighborhood probabilities)
        """
        neighbor_x = self.x[::stride, ::stride]
        neighbor_y = self.y[::stride, ::stride]
        neighbor_kd_tree = cKDTree(np.vstack((neighbor_x.ravel(), neighbor_y.ravel())).T)
        neighbor_prob = np.zeros((self.data.shape[0], neighbor_x.shape[0], neighbor_x.shape[1]))
        print('Forecast Hours: {0}-{1}'.format(start_time, end_time))
        for m in range(len(self.members)):
            period_max = self.data[m,start_time:end_time,:,:].max(axis=0)
            valid_i, valid_j = np.where(period_max >= threshold)
            print(self.members[m], len(valid_i))
            if len(valid_i) > 0:
                var_kd_tree = cKDTree(np.vstack((self.x[valid_i, valid_j], self.y[valid_i, valid_j])).T)
                exceed_points = np.unique(np.concatenate(var_kd_tree.query_ball_tree(neighbor_kd_tree, radius))).astype(int)
                exceed_i, exceed_j = np.unravel_index(exceed_points, neighbor_x.shape)
                neighbor_prob[m][exceed_i, exceed_j] = 1
                if smoothing > 0:
                    neighbor_prob[m] = gaussian_filter(neighbor_prob[m], smoothing,mode='constant')
        return neighbor_prob


Instruction:
How would you explain what the following Python 3 function does
def analisar(retorno):
        if '|' not in retorno:
            raise ErroRespostaSATInvalida('Resposta nao possui pipes '
                    'separando os campos: {!r}'.format(retorno))

        resposta = _RespostaParcial(*(retorno.split('|')[:2]))

        for faixa, construtor in _RESPOSTAS_POSSIVEIS:
            if int(resposta.EEEEE) in xrange(faixa, faixa+1000):
                return construtor(retorno)

        return RespostaConsultarNumeroSessao._pos_analise(retorno)

Response: Analisar uma ID de un objeto resposta.


Instruction:
Can you generate the documentation for the following Python 3 function
def comunicar_certificado_icpbrasil(retorno):
        resposta = analisar_retorno(forcar_unicode(retorno),
                funcao='ComunicarCertificadoICPBRASIL')
        if resposta.EEEEE not in ('05000',):
            raise ExcecaoRespostaSAT(resposta)
        return resposta

Response: Comunicar certificado o ICPBRASIL.


Instruction:
Can you tell what is the following Python 3 function doing
def configurar_interface_de_rede(retorno):
        resposta = analisar_retorno(forcar_unicode(retorno),
                funcao='ConfigurarInterfaceDeRede')
        if resposta.EEEEE not in ('12000',):
            raise ExcecaoRespostaSAT(resposta)
        return resposta

Response: Constrói uma retorno de uma tag.


Instruction:
Explain what the following Python 3 code does
def atualizar_software_sat(retorno):
        resposta = analisar_retorno(forcar_unicode(retorno),
                funcao='AtualizarSoftwareSAT')
        if resposta.EEEEE not in ('14000',):
            raise ExcecaoRespostaSAT(resposta)
        return resposta

Response: Atualizar a settura seu retorno.


Instruction:
Here you have a function in Python 3, explain what it does
def desbloquear_sat(retorno):
        resposta = analisar_retorno(forcar_unicode(retorno),
                funcao='DesbloquearSAT')
        if resposta.EEEEE not in ('17000',):
            raise ExcecaoRespostaSAT(resposta)
        return resposta

Response:
Constrói uma :class:`RespostaSAT` para o retorno (unicode) da função
        :meth:`~satcfe.base.FuncoesSAT.desbloquear_sat`.


Instruction:
Make a summary of the following Python 3 code
def trocar_codigo_de_ativacao(retorno):
        resposta = analisar_retorno(forcar_unicode(retorno),
                funcao='TrocarCodigoDeAtivacao')
        if resposta.EEEEE not in ('18000',):
            raise ExcecaoRespostaSAT(resposta)
        return resposta

Response: Retorno para uma string para uma trocar_codigo_de_ativacao.


Instruction:
How would you code a function in Python 3 to
load the variable from the ensemble files then close the files.

Response:
def load_data(self):
        """
        Load the specified variable from the ensemble files, then close the files.
        """
        if self.ensemble_name.upper() == "SSEF":
            if self.variable[0:2] == "rh":
                pressure_level = self.variable[2:]
                relh_vars = ["sph", "tmp"]
                relh_vals = {}
                for var in relh_vars:
                    mg = SSEFModelGrid(self.member_name,
                                       self.run_date,
                                       var + pressure_level,
                                       self.start_date,
                                       self.end_date,
                                       self.path,
                                       single_step=self.single_step)
                    relh_vals[var], units = mg.load_data()
                    mg.close()
                self.data = relative_humidity_pressure_level(relh_vals["tmp"],
                                                             relh_vals["sph"],
                                                             float(pressure_level) * 100)
                self.units = "%"
            elif self.variable == "melth":
                input_vars = ["hgtsfc", "hgt700", "hgt500", "tmp700", "tmp500"]
                input_vals = {}
                for var in input_vars:
                    mg = SSEFModelGrid(self.member_name,
                                       self.run_date,
                                       var,
                                       self.start_date,
                                       self.end_date,
                                       self.path,
                                       single_step=self.single_step)
                    input_vals[var], units = mg.load_data()
                    mg.close()
                self.data = melting_layer_height(input_vals["hgtsfc"],
                                                 input_vals["hgt700"],
                                                 input_vals["hgt500"],
                                                 input_vals["tmp700"],
                                                 input_vals["tmp500"])
                self.units = "m"
            else:
                mg = SSEFModelGrid(self.member_name,
                                   self.run_date,
                                   self.variable,
                                   self.start_date,
                                   self.end_date,
                                   self.path,
                                   single_step=self.single_step)
                self.data, self.units = mg.load_data()
                mg.close()
        elif self.ensemble_name.upper() == "NCAR":
            mg = NCARModelGrid(self.member_name,
                               self.run_date,
                               self.variable,
                               self.start_date,
                               self.end_date,
                               self.path,
                               single_step=self.single_step)
            self.data, self.units = mg.load_data()
            mg.close()
        elif self.ensemble_name.upper() == "HREFV2":
            proj_dict, grid_dict = read_ncar_map_file(self.map_file)
            mapping_data = make_proj_grids(proj_dict, grid_dict)

            mg = HREFv2ModelGrid(self.member_name,
                               self.run_date,
                               self.variable,
                               self.start_date,
                               self.end_date,
                               self.path,
                               mapping_data,
                               self.sector_ind_path,
                               single_step=self.single_step)

            self.data, self.units = mg.load_data()

        elif self.ensemble_name.upper() == "VSE":
            mg = VSEModelGrid(self.member_name,
                               self.run_date,
                               self.variable,
                               self.start_date,
                               self.end_date,
                               self.path,
                               single_step=self.single_step)
            self.data, self.units = mg.load_data()
            mg.close()
        elif self.ensemble_name.upper() == "HRRR":
            mg = HRRRModelGrid(self.run_date,
                               self.variable,
                               self.start_date,
                               self.end_date,
                               self.path)
            self.data, self.units = mg.load_data()
            mg.close()
        elif self.ensemble_name.upper() == "NCARSTORM":
            mg = NCARStormEventModelGrid(self.run_date,
                                         self.variable,
                                         self.start_date,
                                         self.end_date,
                                         self.path)
            self.data, self.units = mg.load_data()
            mg.close()
        else:
            print(self.ensemble_name + " not supported.")


Instruction:
Can you tell what is the following Python 3 function doing
def load_map_info(self, map_file):
        if self.ensemble_name.upper() == "SSEF":
            proj_dict, grid_dict = read_arps_map_file(map_file)
            self.dx = int(grid_dict["dx"])
            mapping_data = make_proj_grids(proj_dict, grid_dict)
            for m, v in mapping_data.items():
                setattr(self, m, v)
            self.i, self.j = np.indices(self.lon.shape)
            self.proj = get_proj_obj(proj_dict)
        elif self.ensemble_name.upper() in ["NCAR", "NCARSTORM", "HRRR", "VSE", "HREFV2"]:
            proj_dict, grid_dict = read_ncar_map_file(map_file)
            if self.member_name[0:7] == "1km_pbl": # Don't just look at the first 3 characters. You have to differentiate '1km_pbl1' and '1km_on_3km_pbl1'
                grid_dict["dx"] = 1000
                grid_dict["dy"] = 1000
                grid_dict["sw_lon"] = 258.697
                grid_dict["sw_lat"] = 23.999
                grid_dict["ne_lon"] = 282.868269206236
                grid_dict["ne_lat"] = 36.4822338520542

            self.dx = int(grid_dict["dx"])
            mapping_data = make_proj_grids(proj_dict, grid_dict)
            for m, v in mapping_data.items():
                setattr(self, m, v)
            self.i, self.j = np.indices(self.lon.shape)
            self.proj = get_proj_obj(proj_dict)

Response: Load map projection information and create latitude longitude x y i and j grids for the projection.


Instruction:
Can you implement a function in Python 3 that
reads a geojson file containing an STObject and initializes a new STObject from the information in the file.

Response:
def read_geojson(filename):
    """
    Reads a geojson file containing an STObject and initializes a new STObject from the information in the file.

    Args:
        filename: Name of the geojson file

    Returns:
        an STObject
    """
    json_file = open(filename)
    data = json.load(json_file)
    json_file.close()
    times = data["properties"]["times"]
    main_data = dict(timesteps=[], masks=[], x=[], y=[], i=[], j=[])
    attribute_data = dict()
    for feature in data["features"]:
        for main_name in main_data.keys():
            main_data[main_name].append(np.array(feature["properties"][main_name]))
        for k, v in feature["properties"]["attributes"].items():
            if k not in attribute_data.keys():
                attribute_data[k] = [np.array(v)]
            else:
                attribute_data[k].append(np.array(v))
    kwargs = {}
    for kw in ["dx", "step", "u", "v"]:
        if kw in data["properties"].keys():
            kwargs[kw] = data["properties"][kw]
    sto = STObject(main_data["timesteps"], main_data["masks"], main_data["x"], main_data["y"],
                   main_data["i"], main_data["j"], times[0], times[-1], **kwargs)
    for k, v in attribute_data.items():
        sto.attributes[k] = v
    return sto


Instruction:
Can you implement a function in Python 3 that
calculates the center of mass at a given timestep.

Response:
def center_of_mass(self, time):
        """
        Calculate the center of mass at a given timestep.

        Args:
            time: Time at which the center of mass calculation is performed

        Returns:
            The x- and y-coordinates of the center of mass.
        """
        if self.start_time <= time <= self.end_time:
            diff = time - self.start_time
            valid = np.flatnonzero(self.masks[diff] != 0)
            if valid.size > 0:
                com_x = 1.0 / self.timesteps[diff].ravel()[valid].sum() * np.sum(self.timesteps[diff].ravel()[valid] *
                                                                                self.x[diff].ravel()[valid])
                com_y = 1.0 / self.timesteps[diff].ravel()[valid].sum() * np.sum(self.timesteps[diff].ravel()[valid] *
                                                                                self.y[diff].ravel()[valid])
            else:
                com_x = np.mean(self.x[diff])
                com_y = np.mean(self.y[diff])
        else:
            com_x = None
            com_y = None
        return com_x, com_y


Instruction:
Given the following Python 3 function, write the documentation
def trajectory(self):
        traj = np.zeros((2, self.times.size))
        for t, time in enumerate(self.times):
            traj[:, t] = self.center_of_mass(time)
        return traj

Response: Calculates the center of mass for each time step and outputs an array


Instruction:
Make a summary of the following Python 3 code
def get_corner(self, time):
        if self.start_time <= time <= self.end_time:
            diff = time - self.start_time
            return self.i[diff][0, 0], self.j[diff][0, 0]
        else:
            return -1, -1

Response:
Gets the array indices of the STObject at a given time that corresponds
        to the upper left corner of the bounding box for the STObject.


Instruction:
Can you generate the documentation for the following Python 3 function
def size(self, time):
        if self.start_time <= time <= self.end_time:
            return self.masks[time - self.start_time].sum()
        else:
            return 0

Response: Gets the size of the object at a given time.


Instruction:
Given the following Python 3 function, write the documentation
def max_size(self):
        sizes = np.array([m.sum() for m in self.masks])
        return sizes.max()

Response: Gets the largest size of the object over all timesteps.


Instruction:
Make a summary of the following Python 3 code
def max_intensity(self, time):
        ti = np.where(time == self.times)[0][0]
        return self.timesteps[ti].max()

Response: Calculate the maximum intensity found at a timestep.


Instruction:
How would you code a function in Python 3 to
add the data from another STObject to this object.

Response:
def extend(self, step):
        """
        Adds the data from another STObject to this object.

        Args:
            step: another STObject being added after the current one in time.
        """
        self.timesteps.extend(step.timesteps)
        self.masks.extend(step.masks)
        self.x.extend(step.x)
        self.y.extend(step.y)
        self.i.extend(step.i)
        self.j.extend(step.j)
        self.end_time = step.end_time
        self.times = np.arange(self.start_time, self.end_time + self.step, self.step)
        self.u = np.concatenate((self.u, step.u))
        self.v = np.concatenate((self.v, step.v))
        for attr in self.attributes.keys():
            if attr in step.attributes.keys():
                self.attributes[attr].extend(step.attributes[attr])


Instruction:
How would you explain what the following Python 3 function does
def boundary_polygon(self, time):
        ti = np.where(time == self.times)[0][0]
        com_x, com_y = self.center_of_mass(time)
        # If at least one point along perimeter of the mask rectangle is unmasked, find_boundaries() works.
        # But if all perimeter points are masked, find_boundaries() does not find the object.
        # Therefore, pad the mask with zeroes first and run find_boundaries on the padded array.
        padded_mask = np.pad(self.masks[ti], 1, 'constant', constant_values=0)
        chull = convex_hull_image(padded_mask)
        boundary_image = find_boundaries(chull, mode='inner', background=0)
        # Now remove the padding.
        boundary_image = boundary_image[1:-1,1:-1]
        boundary_x = self.x[ti].ravel()[boundary_image.ravel()]
        boundary_y = self.y[ti].ravel()[boundary_image.ravel()]
        r = np.sqrt((boundary_x - com_x) ** 2 + (boundary_y - com_y) ** 2)
        theta = np.arctan2((boundary_y - com_y), (boundary_x - com_x)) * 180.0 / np.pi + 360
        polar_coords = np.array([(r[x], theta[x]) for x in range(r.size)], dtype=[('r', 'f4'), ('theta', 'f4')])
        coord_order = np.argsort(polar_coords, order=['theta', 'r'])
        ordered_coords = np.vstack([boundary_x[coord_order], boundary_y[coord_order]])
        return ordered_coords

Response: Find the coordinates of the object boundary in counter - clockwise order.


Instruction:
Can you generate the documentation for the following Python 3 function
def estimate_motion(self, time, intensity_grid, max_u, max_v):
        ti = np.where(time == self.times)[0][0]
        mask_vals = np.where(self.masks[ti].ravel() == 1)
        i_vals = self.i[ti].ravel()[mask_vals]
        j_vals = self.j[ti].ravel()[mask_vals]
        obj_vals = self.timesteps[ti].ravel()[mask_vals]
        u_shifts = np.arange(-max_u, max_u + 1)
        v_shifts = np.arange(-max_v, max_v + 1)
        min_error = 99999999999.0
        best_u = 0
        best_v = 0
        for u in u_shifts:
            j_shift = j_vals - u
            for v in v_shifts:
                i_shift = i_vals - v
                if np.all((0 <= i_shift) & (i_shift < intensity_grid.shape[0]) &
                                  (0 <= j_shift) & (j_shift < intensity_grid.shape[1])):
                    shift_vals = intensity_grid[i_shift, j_shift]
                else:
                    shift_vals = np.zeros(i_shift.shape)
                # This isn't correlation; it is mean absolute error.
                error = np.abs(shift_vals - obj_vals).mean()
                if error < min_error:
                    min_error = error
                    best_u = u * self.dx
                    best_v = v * self.dx
        # 60 seems arbitrarily high
        #if min_error > 60:
        #    best_u = 0
        #    best_v = 0
        self.u[ti] = best_u
        self.v[ti] = best_v
        return best_u, best_v, min_error

Response: Estimate the motion of the object with cross - correlation on the intensity grid.


Instruction:
Write a Python 3 function that can
count the number of points that overlap between this STObject and another STObject. Used for tracking.

Response:
def count_overlap(self, time, other_object, other_time):
        """
        Counts the number of points that overlap between this STObject and another STObject. Used for tracking.
        """
        ti = np.where(time == self.times)[0][0]
        ma = np.where(self.masks[ti].ravel() == 1)
        oti = np.where(other_time == other_object.times)[0]
        obj_coords = np.zeros(self.masks[ti].sum(), dtype=[('x', int), ('y', int)])
        other_obj_coords = np.zeros(other_object.masks[oti].sum(), dtype=[('x', int), ('y', int)])
        obj_coords['x'] = self.i[ti].ravel()[ma]
        obj_coords['y'] = self.j[ti].ravel()[ma]
        other_obj_coords['x'] = other_object.i[oti][other_object.masks[oti] == 1]
        other_obj_coords['y'] = other_object.j[oti][other_object.masks[oti] == 1]
        return float(np.intersect1d(obj_coords,
                                    other_obj_coords).size) / np.maximum(self.masks[ti].sum(),
                                                                         other_object.masks[oti].sum())


Instruction:
Implement a function in Python 3 to
extract the data from a ModelGrid object within the bounding box of the STObject.

Response:
def extract_attribute_grid(self, model_grid, potential=False, future=False):
        """
        Extracts the data from a ModelOutput or ModelGrid object within the bounding box region of the STObject.

        Args:
            model_grid: A ModelGrid or ModelOutput Object
            potential: Extracts from the time before instead of the same time as the object
        """

        if potential:
            var_name = model_grid.variable + "-potential"
            timesteps = np.arange(self.start_time - 1, self.end_time)
        elif future:
            var_name = model_grid.variable + "-future"
            timesteps = np.arange(self.start_time + 1, self.end_time + 2)
        else:
            var_name = model_grid.variable
            timesteps = np.arange(self.start_time, self.end_time + 1)
        self.attributes[var_name] = []
        for ti, t in enumerate(timesteps):
            self.attributes[var_name].append(
                model_grid.data[t - model_grid.start_hour, self.i[ti], self.j[ti]])


Instruction:
Write a Python 3 script to
extract data from a 2D array that has the same dimensions as the grid used to identify the object.

Response:
def extract_attribute_array(self, data_array, var_name):
        """
        Extracts data from a 2D array that has the same dimensions as the grid used to identify the object.

        Args:
            data_array: 2D numpy array

        """
        if var_name not in self.attributes.keys():
            self.attributes[var_name] = []
        for t in range(self.times.size):
            self.attributes[var_name].append(data_array[self.i[t], self.j[t]])


Instruction:
Implement a Python 3 function for
extracting the difference in model outputs

Response:
def extract_tendency_grid(self, model_grid):
        """
        Extracts the difference in model outputs

        Args:
            model_grid: ModelOutput or ModelGrid object.

        """
        var_name = model_grid.variable + "-tendency"
        self.attributes[var_name] = []
        timesteps = np.arange(self.start_time, self.end_time + 1)
        for ti, t in enumerate(timesteps):
            t_index = t - model_grid.start_hour
            self.attributes[var_name].append(
                model_grid.data[t_index, self.i[ti], self.j[ti]] - model_grid.data[t_index - 1, self.i[ti], self.j[ti]]
                )


Instruction:
Explain what the following Python 3 code does
def calc_attribute_statistics(self, statistic_name):
        stats = {}
        for var, grids in self.attributes.items():
            if len(grids) > 1:
                stats[var] = getattr(np.array([getattr(np.ma.array(x, mask=self.masks[t] == 0), statistic_name)()
                                               for t, x in enumerate(grids)]), statistic_name)()
            else:
                stats[var] = getattr(np.ma.array(grids[0], mask=self.masks[0] == 0), statistic_name)()
        return stats

Response: Calculates summary statistics over the domains of each attribute.


Instruction:
Given the following Python 3 function, write the documentation
def calc_attribute_statistic(self, attribute, statistic, time):
        ti = np.where(self.times == time)[0][0]
        ma = np.where(self.masks[ti].ravel() == 1)
        if statistic in ['mean', 'max', 'min', 'std', 'ptp']:
            stat_val = getattr(self.attributes[attribute][ti].ravel()[ma], statistic)()
        elif statistic == 'median':
            stat_val = np.median(self.attributes[attribute][ti].ravel()[ma])
        elif statistic == "skew":
            stat_val = np.mean(self.attributes[attribute][ti].ravel()[ma]) - \
                       np.median(self.attributes[attribute][ti].ravel()[ma])
        elif 'percentile' in statistic:
            per = int(statistic.split("_")[1])
            stat_val = np.percentile(self.attributes[attribute][ti].ravel()[ma], per)
        elif 'dt' in statistic:
            stat_name = statistic[:-3]
            if ti == 0:
                stat_val = 0
            else:
                stat_val = self.calc_attribute_statistic(attribute, stat_name, time) \
                    - self.calc_attribute_statistic(attribute, stat_name, time - 1)
        else:
            stat_val = np.nan
        return stat_val

Response: Calculates the statistic value for an attribute in the model.


Instruction:
Write a Python 3 function that can
calculate the statistic value for a given time in the current object.

Response:
def calc_timestep_statistic(self, statistic, time):
        """
        Calculate statistics from the primary attribute of the StObject.

        Args:
            statistic: statistic being calculated
            time: Timestep being investigated

        Returns:
            Value of the statistic
        """
        ti = np.where(self.times == time)[0][0]
        ma = np.where(self.masks[ti].ravel() == 1)
        if statistic in ['mean', 'max', 'min', 'std', 'ptp']:
            stat_val = getattr(self.timesteps[ti].ravel()[ma], statistic)()
        elif statistic == 'median':
            stat_val = np.median(self.timesteps[ti].ravel()[ma])
        elif 'percentile' in statistic:
            per = int(statistic.split("_")[1])
            stat_val = np.percentile(self.timesteps[ti].ravel()[ma], per)
        elif 'dt' in statistic:
            stat_name = statistic[:-3]
            if ti == 0:
                stat_val = 0
            else:
                stat_val = self.calc_timestep_statistic(stat_name, time) -\
                    self.calc_timestep_statistic(stat_name, time - 1)
        else:
            stat_val = np.nan
        return stat_val


Instruction:
How would you code a function in Python 3 to
calculate shape statistics using regionprops applied to the object mask.

Response:
def calc_shape_statistics(self, stat_names):
        """
        Calculate shape statistics using regionprops applied to the object mask.

        Args:
            stat_names: List of statistics to be extracted from those calculated by regionprops.

        Returns:
            Dictionary of shape statistics
        """
        stats = {}
        try:
            all_props = [regionprops(m) for m in self.masks]
        except TypeError:
            print(self.masks)
            exit()
        for stat in stat_names:
            stats[stat] = np.mean([p[0][stat] for p in all_props])
        return stats


Instruction:
Implement a Python 3 function for
calculating shape statistics for a single time step

Response:
def calc_shape_step(self, stat_names, time):
        """
        Calculate shape statistics for a single time step

        Args:
            stat_names: List of shape statistics calculated from region props
            time: Time being investigated

        Returns:
            List of shape statistics

        """
        ti = np.where(self.times == time)[0][0]
        props = regionprops(self.masks[ti], self.timesteps[ti])[0]
        shape_stats = []
        for stat_name in stat_names:
            if "moments_hu" in stat_name:
                hu_index = int(stat_name.split("_")[-1])
                hu_name = "_".join(stat_name.split("_")[:-1])
                hu_val = np.log(props[hu_name][hu_index])
                if np.isnan(hu_val):
                    shape_stats.append(0)
                else:
                    shape_stats.append(hu_val)
            else:
                shape_stats.append(props[stat_name])
        return shape_stats


Instruction:
How would you explain what the following Python 3 function does
def to_geojson(self, filename, proj, metadata=None):
        if metadata is None:
            metadata = {}
        json_obj = {"type": "FeatureCollection", "features": [], "properties": {}}
        json_obj['properties']['times'] = self.times.tolist()
        json_obj['properties']['dx'] = self.dx
        json_obj['properties']['step'] = self.step
        json_obj['properties']['u'] = self.u.tolist()
        json_obj['properties']['v'] = self.v.tolist()
        for k, v in metadata.items():
            json_obj['properties'][k] = v
        for t, time in enumerate(self.times):
            feature = {"type": "Feature",
                       "geometry": {"type": "Polygon"},
                       "properties": {}}
            boundary_coords = self.boundary_polygon(time)
            lonlat = np.vstack(proj(boundary_coords[0], boundary_coords[1], inverse=True))
            lonlat_list = lonlat.T.tolist()
            if len(lonlat_list) > 0:
                lonlat_list.append(lonlat_list[0])
            feature["geometry"]["coordinates"] = [lonlat_list]
            for attr in ["timesteps", "masks", "x", "y", "i", "j"]:
                feature["properties"][attr] = getattr(self, attr)[t].tolist()
            feature["properties"]["attributes"] = {}
            for attr_name, steps in self.attributes.items():
                feature["properties"]["attributes"][attr_name] = steps[t].tolist()
            json_obj['features'].append(feature)
        file_obj = open(filename, "w")
        json.dump(json_obj, file_obj, indent=1, sort_keys=True)
        file_obj.close()
        return

Response: Output the data in the STObject to a GeoJSON file.


Instruction:
Can you generate the documentation for the following Python 3 function
def rewrite_properties(self, properties):
        with self.__library_storage_lock:
            self.__library_storage = properties
        self.__write_properties(None)

Response: Set the properties and write to disk.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def model(self, v=None):
        "Returns the model of node v"
        if v is None:
            v = self.estopping
        hist = self.hist
        trace = self.trace(v)
        ins = None
        if self._base._probability_calibration is not None:
            node = hist[-1]
            node.normalize()
            X = np.array([x.full_array() for x in node.hy]).T
            y = np.array(self._base._y_klass.full_array())
            mask = np.ones(X.shape[0], dtype=np.bool)
            mask[np.array(self._base._mask_ts.index)] = False
            ins = self._base._probability_calibration().fit(X[mask], y[mask])
        if self._classifier:
            nclasses = self._labels.shape[0]
        else:
            nclasses = None
        m = Model(trace, hist, nvar=self._base._nvar,
                  classifier=self._classifier, labels=self._labels,
                  probability_calibration=ins, nclasses=nclasses)
        return m

Response: Returns the model of node v


Instruction:
Given the following Python 3 function, write the documentation
def trace(self, n):
        "Restore the position in the history of individual v's nodes"
        trace_map = {}
        self._trace(n, trace_map)
        s = list(trace_map.keys())
        s.sort()
        return s

Response: Restore the position in the history of individual v s nodes


Instruction:
How would you explain what the following Python 3 function does
def create_population(self):
        "Create the initial population"
        base = self._base
        if base._share_inputs:
            used_inputs_var = SelectNumbers([x for x in range(base.nvar)])
            used_inputs_naive = used_inputs_var
        if base._pr_variable == 0:
            used_inputs_var = SelectNumbers([])
            used_inputs_naive = SelectNumbers([x for x in range(base.nvar)])
        elif base._pr_variable == 1:
            used_inputs_var = SelectNumbers([x for x in range(base.nvar)])
            used_inputs_naive = SelectNumbers([])
        else:
            used_inputs_var = SelectNumbers([x for x in range(base.nvar)])
            used_inputs_naive = SelectNumbers([x for x in range(base.nvar)])
        nb_input = Inputs(base, used_inputs_naive, functions=base._input_functions)
        while ((base._all_inputs and not base.stopping_criteria_tl()) or
               (self.popsize < base.popsize and
                not base.stopping_criteria())):
            if base._all_inputs and used_inputs_var.empty() and used_inputs_naive.empty():
                base._init_popsize = self.popsize
                break
            if nb_input.use_all_variables():
                v = nb_input.all_variables()
                if v is None:
                    continue
            elif not used_inputs_var.empty() and np.random.random() < base._pr_variable:
                v = self.variable_input(used_inputs_var)
                if v is None:
                    used_inputs_var.pos = used_inputs_var.size
                    continue
            elif not used_inputs_naive.empty():
                v = nb_input.input()
                if not used_inputs_var.empty() and used_inputs_naive.empty():
                    base._pr_variable = 1
                if v is None:
                    used_inputs_naive.pos = used_inputs_naive.size
                    if not used_inputs_var.empty():
                        base._pr_variable = 1
                    continue
            else:
                gen = self.generation
                self.generation = 0
                v = base.random_offspring()
                self.generation = gen
            self.add(v)

Response: Create the initial population


Instruction:
Explain what the following Python 3 code does
def add(self, v):
        "Add an individual to the population"
        self.population.append(v)
        self._current_popsize += 1
        v.position = len(self._hist)
        self._hist.append(v)
        self.bsf = v
        self.estopping = v
        self._density += self.get_density(v)

Response: Add an individual to the population


Instruction:
Can you create a Python 3 function that
replaces an individual selected by negative tournament selection with individual v

Response:
def replace(self, v):
        """Replace an individual selected by negative tournament selection with
        individual v"""
        if self.popsize < self._popsize:
            return self.add(v)
        k = self.tournament(negative=True)
        self.clean(self.population[k])
        self.population[k] = v
        v.position = len(self._hist)
        self._hist.append(v)
        self.bsf = v
        self.estopping = v
        self._inds_replace += 1
        self._density += self.get_density(v)
        if self._inds_replace == self._popsize:
            self._inds_replace = 0
            self.generation += 1
            gc.collect()


Instruction:
Write a Python 3 script for
making the directory path if needed.

Response:
def make_directory_if_needed(directory_path):
    """
        Make the directory path, if needed.
    """
    if os.path.exists(directory_path):
        if not os.path.isdir(directory_path):
            raise OSError("Path is not a directory:", directory_path)
    else:
        os.makedirs(directory_path)


Instruction:
Implement a Python 3 function for
calling an arbitary command and return the exit value stdout and stderr as a tuple

Response:
def call(cmd_args, suppress_output=False):
    """ Call an arbitary command and return the exit value, stdout, and stderr as a tuple

    Command can be passed in as either a string or iterable

    >>> result = call('hatchery', suppress_output=True)
    >>> result.exitval
    0
    >>> result = call(['hatchery', 'notreal'])
    >>> result.exitval
    1
    """
    if not funcy.is_list(cmd_args) and not funcy.is_tuple(cmd_args):
        cmd_args = shlex.split(cmd_args)
    logger.info('executing `{}`'.format(' '.join(cmd_args)))
    call_request = CallRequest(cmd_args, suppress_output=suppress_output)
    call_result = call_request.run()
    if call_result.exitval:
        logger.error('`{}` returned error code {}'.format(' '.join(cmd_args), call_result.exitval))
    return call_result


Instruction:
Can you implement a function in Python 3 that
calls a setup. py command or list of commands

Response:
def setup(cmd_args, suppress_output=False):
    """ Call a setup.py command or list of commands

    >>> result = setup('--name', suppress_output=True)
    >>> result.exitval
    0
    >>> result = setup('notreal')
    >>> result.exitval
    1
    """
    if not funcy.is_list(cmd_args) and not funcy.is_tuple(cmd_args):
        cmd_args = shlex.split(cmd_args)
    cmd_args = [sys.executable, 'setup.py'] + [x for x in cmd_args]
    return call(cmd_args, suppress_output=suppress_output)


Instruction:
Explain what the following Python 3 code does
def load_data(self):
        data = []
        valid_dates = []
        mrms_files = np.array(sorted(os.listdir(self.path + self.variable + "/")))
        mrms_file_dates = np.array([m_file.split("_")[-2].split("-")[0]
            for m_file in mrms_files])
        old_mrms_file = None
        file_obj = None
        for t in range(self.all_dates.shape[0]):
            file_index = np.where(mrms_file_dates == self.all_dates[t].strftime("%Y%m%d"))[0]
            if len(file_index) > 0:
                mrms_file = mrms_files[file_index][0]
                if mrms_file is not None:
                    if file_obj is not None:
                        file_obj.close()
                    file_obj = Dataset(self.path + self.variable + "/" + mrms_file)
                    #old_mrms_file = mrms_file

                    if "time" in file_obj.variables.keys():
                        time_var = "time"
                    else:
                        time_var = "date"
                    file_valid_dates = pd.DatetimeIndex(num2date(file_obj.variables[time_var][:],
                                                                 file_obj.variables[time_var].units))
                else:
                    file_valid_dates = pd.DatetimeIndex([])
                time_index = np.where(file_valid_dates.values == self.all_dates.values[t])[0]
                if len(time_index) > 0:
                    data.append(file_obj.variables[self.variable][time_index[0]])
                    valid_dates.append(self.all_dates[t])
        if file_obj is not None:
            file_obj.close()
        self.data = np.array(data)
        self.data[self.data < 0] = 0
        self.data[self.data > 150] = 150
        self.valid_dates = pd.DatetimeIndex(valid_dates)

Response: Loads the data files and stores the output in the data attribute.


Instruction:
Can you implement a function in Python 3 that
labels the pixels in the input_grid using enhanced watershed algorithm.

Response:
def label(self, input_grid):
        """
        Labels input grid using enhanced watershed algorithm.

        Args:
            input_grid (numpy.ndarray): Grid to be labeled.

        Returns:
            Array of labeled pixels
        """
        marked = self.find_local_maxima(input_grid)
        marked = np.where(marked >= 0, 1, 0)
        # splabel returns two things in a tuple: an array and an integer
        # assign the first thing (array) to markers
        markers = splabel(marked)[0]
        return markers