Note: This document is for an older version of the Gro API Client. Please see the latest documentation.

Source code for groclient.client

from __future__ import print_function
import functools
import itertools
import time
import json

# Python3 support
    # Python3
    from urllib.parse import urlencode
except ImportError:
    # Python2
    from urllib import urlencode

from groclient import cfg, lib
from groclient.constants import DATA_SERIES_UNIQUE_TYPES_ID, ENTITY_KEY_TO_TYPE
from groclient.utils import intersect, zip_selections, dict_unnest
from groclient.lib import APIError

import pandas
from tornado import gen
from tornado.escape import json_decode
from tornado.httpclient import AsyncHTTPClient, HTTPRequest, HTTPError
from tornado.ioloop import IOLoop
from tornado.queues import Queue

class BatchError(APIError):
    """Replicate the APIError interface given a Tornado HTTPError."""

    def __init__(self, response, retry_count, url, params):
        self.response = response
        self.retry_count = retry_count
        self.url = url
        self.params = params
        self.status_code = (
            self.response.code if hasattr(self.response, "code") else None
            json_content = json_decode(self.response.body)
            # 'error' should be something like 'Not Found' or 'Bad Request'
            self.message = json_content.get("error", "")
            # Some error responses give additional info.
            # For example, a 400 Bad Request might say "metricId is required"
            if "message" in json_content:
                self.message += ": {}".format(json_content["message"])
        except Exception:
            # If the error message can't be parsed, fall back to a generic "giving up" message.
            self.message = "Giving up on {} after {} {}: {}".format(
                "retry" if self.retry_count == 1 else "retries",

class GroClient(object):
    """API client with stateful authentication for lib functions and extra convenience methods."""

    def __init__(self, api_host, access_token):
        self.api_host = api_host
        self.access_token = access_token
        self._logger = lib.get_default_logger()
        self._data_series_list = set()  # all that have been added
        self._data_series_queue = []  # added but not loaded in data frame
        self._data_frame = pandas.DataFrame()
            # Each GroClient has its own IOLoop and AsyncHTTPClient.
            self._ioloop = IOLoop()
            # Note: force_instance is needed to disable Tornado's
            # pseudo-singleton AsyncHTTPClient caching behavior.
            self._async_http_client = AsyncHTTPClient(force_instance=True)
        except Exception as e:
                "Unable to initialize event loop, async methods disabled: {}".format(e)
            self._async_http_client = None
            self._ioloop = None

    def __del__(self):

    def get_logger(self):
        return self._logger

    def async_get_data(self, url, headers, params=None):
        base_log_record = dict(route=url, params=params)

        def log_request(start_time, retry_count, msg, status_code):
            elapsed_time = time.time() - start_time
            log_record = dict(base_log_record)
            log_record["elapsed_time_in_ms"] = 1000 * elapsed_time
            log_record["retry_count"] = retry_count
            log_record["status_code"] = status_code
            if status_code == 200:
                self._logger.debug(msg, extra=log_record)
                self._logger.warning(msg, extra=log_record)

        """General 'make api request' function.

        Assigns headers and builds in retries and logging.

        # append version info

        # Initialize to -1 so first attempt will be retry 0
        retry_count = -1
        while retry_count <= cfg.MAX_RETRIES:
            retry_count += 1
            start_time = time.time()
            http_request = HTTPRequest(
                "{url}?{params}".format(url=url, params=urlencode(params)),
                    response = yield self._async_http_client.fetch(http_request)
                    status_code = response.code
                except HTTPError as e:
                    # Catch non-200 codes that aren't errors
                    status_code = e.code if hasattr(e, "code") else None
                    if status_code in [204, 206]:
                        log_msg = {204: "No Content", 206: "Partial Content"}[
                        response = e.response
                        log_request(start_time, retry_count, log_msg, status_code)
                        # Do not retry.
                    elif status_code == 301:
                        redirected_ids = json.loads(e.response.body.decode("utf-8"))[
                        new_params = lib.redirect(params, redirected_ids)
                            "Redirecting {} to {}".format(params, new_params),
                        params = new_params
                        continue  # retry
                    else:  # Otherwise, propagate to error handling
                        raise e
            except Exception as e:
                # HTTPError raised when there's a non-200 status code
                # socket.gaio error raised when there's a connection error
                response = e.response if hasattr(e, "response") else e
                status_code = e.code if hasattr(e, "code") else None
                error_msg = (
                    if (hasattr(e, "response") and hasattr(e.response, "error"))
                    else e
                log_request(start_time, retry_count, error_msg, status_code)
                if status_code in [429, 500, 502, 503, 504]:
                    # First retry is immediate.
                    # After that, exponential backoff before retrying.
                    if retry_count > 0:
                        time.sleep(2 ** retry_count)
                elif status_code in [400, 401, 402, 404]:
                    break  # Do not retry. Go right to raising an Exception.

            # Request was successful
            log_request(start_time, retry_count, "OK", status_code)
            raise gen.Return(
                json_decode(response.body) if hasattr(response, "body") else None

        # Retries failed. Raise exception
        raise BatchError(response, retry_count, url, params)

    def batch_async_queue(self, func, batched_args, output_list, map_result):
        """Asynchronously call func.

        func : function
            The function to be batched. Typically a Client method.
        batched_args : list of dicts
        output_list : any, optional
            A custom accumulator to use in map_result. For example: may pass in a non-empty list
            to append results to it, or may pass in a pandas dataframe, etc. By default, is a list
            of n 0s, where n is the length of batched_args.
        map_result : function, optional
            Function to apply changes to individual requests' responses before returning. Must
            return an accumulator, like a map() function.
            Takes 4 params:
            1. the index in batched_args
            2. the element from batched_args
            3. the result from that input
            4. `output_list`. The accumulator of all results

        assert (
            type(batched_args) is list
        ), "Only argument to a batch async decorated function should be a \
            list of a list of the individual non-keyword arguments being \
            passed to the original function."

        # Wrap output_list in an object so it can be modified within inner functions' scope
        # In Python 3, can accomplish the same thing with `nonlocal` keyword.
        output_data = {}
        if output_list is None:
            output_data["result"] = [0] * len(batched_args)
            output_data["result"] = output_list

        if not map_result:
            # Default map_result function separates output by index of the query. For example:
            # batched_args: [exports of corn, exports of soybeans]
            # accumulator: [[corn datapoint, corn datapoint],
            #               [soybean data point, soybean data point]]
            def map_result(idx, query, response, accumulator):
                accumulator[idx] = response
                return accumulator

        q = Queue()

        def consumer():
            """Execute func on all items in queue asynchronously."""
            while q.qsize():
                    idx, item = q.get().result()
                    self._logger.debug("Doing work on {}".format(idx))
                    if type(item) is dict:
                        # Assume that dict types should be unpacked as kwargs
                        result = yield func(**item)
                    elif type(item) is list:
                        # Assume that list types should be unpacked as positional args
                        result = yield func(*item)
                        result = yield func(item)
                    output_data["result"] = map_result(
                        idx, item, result, output_data["result"]
                    self._logger.debug("Done with {}".format(idx))
                except Exception:
                    # Cease processing
                    # IOLoop raises "Operation timed out after None seconds"

        def producer():
            """Immediately enqueue the whole batch of requests."""
            lasttime = time.time()
            for idx, item in enumerate(batched_args):
                q.put((idx, item))
            elapsed = time.time() - lasttime
  "Queued {} requests in {}".format(q.qsize(), elapsed))

        def main():
            # Start consumer without waiting (since it never finishes).
            for i in range(cfg.MAX_QUERIES_PER_SECOND):
            producer()  # Wait for producer to put all tasks.
            yield q.join()  # Wait for consumer to finish all tasks.

        return output_data["result"]

    # TODO: deprecate  the following  two methods, standardize  on one
    # approach with get_data_points and get_df
    def get_data_points_generator(self, **selection):
        headers = {"authorization": "Bearer " + self.access_token}
        url = "/".join(["https:", "", self.api_host, "v2/data"])
        params = lib.get_data_call_params(**selection)
            list_of_series_points = yield self.async_get_data(url, headers, params)
            include_historical = selection.get("include_historical", True)
            points = lib.list_of_series_to_single_series(
                list_of_series_points, False, include_historical
            raise gen.Return(points)
        except BatchError as b:
            raise gen.Return(b)

    def batch_async_get_data_points(
        self, batched_args, output_list=None, map_result=None
        """Make many :meth:`~get_data_points` requests asynchronously.

        batched_args : list of dicts
            Each dict should be a `selections` object like would be passed to


                input_list = [
                    {'metric_id': 860032, 'item_id': 274, 'region_id': 1215, 'frequency_id': 9, 'source_id': 2},
                    {'metric_id': 860032, 'item_id': 270, 'region_id': 1215, 'frequency_id': 9, 'source_id': 2}

        output_list : any, optional
            A custom accumulator to use in map_result. For example: may pass in a non-empty list
            to append results to it, or may pass in a pandas dataframe, etc. By default, is a list
            of n 0s, where n is the length of batched_args.
        map_result : function, optional
            Function to apply changes to individual requests' responses before returning.
            Takes 4 params:
            1. the index in batched_args
            2. the element from batched_args
            3. the result from that input
            4. `output_list`. The accumulator of all results


                output_list = []

                # Merge all responses into a single list
                def map_response(inputIndex, inputObject, response, output_list):
                    output_list += response
                    return output_list

                batch_output = client.batch_async_get_data_points(input_list,

            By default, returns a list of lists of data points. Likely either objects or lists of
            dictionaries. If using a custom map_result function, can return any type.

            Example of the default output format::

                        {'metric_id': 1, 'item_id': 2, 'start_date': 2000-01-01, 'value': 41, ...},
                        {'metric_id': 1, 'item_id': 2, 'start_date': 2001-01-01, 'value': 39, ...},
                        {'metric_id': 1, 'item_id': 2, 'start_date': 2002-01-01, 'value': 50, ...},
                        {'metric_id': 1, 'item_id': 6, 'start_date': 2000-01-01, 'value': 12, ...},
                        {'metric_id': 1, 'item_id': 6, 'start_date': 2001-01-01, 'value': 13, ...},
                        {'metric_id': 1, 'item_id': 6, 'start_date': 2002-01-01, 'value': 4, ...},

        return self.batch_async_queue(
            self.get_data_points_generator, batched_args, output_list, map_result

    def async_rank_series_by_source(self, *selections_list):
        """Get all sources, in ranked order, for a given selection."""
        response = self.rank_series_by_source(selections_list)
        raise gen.Return(list(response))

    def batch_async_rank_series_by_source(
        self, batched_args, output_list=None, map_result=None
        """Perform multiple rank_series_by_source requests asynchronously.

        batched_args : list of lists of dicts
            See :meth:`~.rank_series_by_source` `selections_list`. A list of those lists.

        return self.batch_async_queue(
            self.async_rank_series_by_source, batched_args, output_list, map_result

    def get_available(self, entity_type):
        """List the first 5000 available entities of the given type.

        entity_type : {'metrics', 'items', 'regions'}

        data : list of dicts


                [ { 'id': 0, 'contains': [1, 2, 3], 'name': 'World', 'level': 1},
                  { 'id': 1, 'contains': [4, 5, 6], 'name': 'Asia', 'level': 2},
                ... ]

        return lib.get_available(self.access_token, self.api_host, entity_type)

    def list_available(self, selected_entities):
        """List available entities given some selected entities.

        Given one or more selections, return entities combinations that have
        data for the given selections.

        selected_entities : dict


                { 'metric_id': 123, 'item_id': 456, 'source_id': 7 }

            Keys may include: metric_id, item_id, region_id, partner_region_id,
            source_id, frequency_id

        list of dicts


                [ { 'metric_id': 11078, 'metric_name': 'Export Value (currency)',
                    'item_id': 274, 'item_name': 'Corn',
                    'region_id': 1215, 'region_name': 'United States',
                    'source_id': 15, 'source_name': 'USDA GATS' },
                  { ... },
                ... ]

        return lib.list_available(self.access_token, self.api_host, selected_entities)

[docs] def lookup(self, entity_type, entity_ids): """Retrieve details about a given id or list of ids of type entity_type. Parameters ---------- entity_type : { 'metrics', 'items', 'regions', 'frequencies', 'sources', 'units' } entity_ids : int or list of ints Returns ------- dict or dict of dicts A dict with entity details is returned if an integer is given for entity_ids. A dict of dicts with entity details, keyed by id, is returned if a list of integers is given for entity_ids. Example:: { 'id': 274, 'contains': [779, 780, ...] 'name': 'Corn', 'definition': 'The seeds of the widely cultivated corn plant <i>Zea mays</i>,' ' which is one of the world\'s most popular grains.' } Example:: { '274': { 'id': 274, 'contains': [779, 780, ...], 'belongsTo': [4138, 8830, ...], 'name': 'Corn', 'definition': 'The seeds of the widely cultivated corn plant' ' <i>Zea mays</i>, which is one of the world\'s most popular' ' grains.' }, '270': { 'id': 270, 'contains': [1737, 7401, ...], 'belongsTo': [8830, 9053, ...], 'name': 'Soybeans', 'definition': 'The seeds and harvested crops of plants belonging to the' ' species <i>Glycine max</i> that are used in the production' ' of oil and both human and livestock consumption.' } } """ return lib.lookup(self.access_token, self.api_host, entity_type, entity_ids)
def lookup_unit_abbreviation(self, unit_id): return self.lookup("units", unit_id)["abbreviation"] def get_allowed_units(self, metric_id, item_id=None): """Get a list of unit that can be used with the given metric (and optionally, item). Parameters ---------- metric_id: int item_id: int, optional. Returns ------- list of unit ids """ return lib.get_allowed_units( self.access_token, self.api_host, metric_id, item_id )
[docs] def get_data_series(self, **selection): """Get available data series for the given selections. Parameters ---------- metric_id : integer, optional item_id : integer, optional region_id : integer, optional partner_region_id : integer, optional source_id : integer, optional frequency_id : integer, optional Returns ------- list of dicts Example:: [{ 'metric_id': 2020032, 'metric_name': 'Seed Use', 'item_id': 274, 'item_name': 'Corn', 'region_id': 1215, 'region_name': 'United States', 'source_id': 24, 'source_name': 'USDA FEEDGRAINS', 'frequency_id': 7, 'start_date': '1975-03-01T00:00:00.000Z', 'end_date': '2018-05-31T00:00:00.000Z' }, { ... }, ... ] """ return lib.get_data_series(self.access_token, self.api_host, **selection)
[docs] def search(self, entity_type, search_terms): """Search for the given search term. Better matches appear first. Parameters ---------- entity_type : { 'metrics', 'items', 'regions', 'sources' } search_terms : string Returns ------- list of dicts Example:: [{'id': 5604}, {'id': 10204}, {'id': 10210}, ....] """ return, self.api_host, entity_type, search_terms)
[docs] def search_and_lookup(self, entity_type, search_terms, num_results=10): """Search for the given search terms and look up their details. For each result, yield a dict of the entity and it's properties. Parameters ---------- entity_type : { 'metrics', 'items', 'regions', 'sources' } search_terms : string num_results: int Maximum number of results to return. Defaults to 10. Yields ------ dict Result from :meth:`` passed to :meth:`~.lookup` to get additional details. Example:: { 'id': 274, 'contains': [779, 780, ...], 'name': 'Corn', 'definition': 'The seeds of the widely cultivated...' } See output of :meth:`~.lookup`. Note that as with :meth:``, the first result is the best match for the given search term(s). """ return lib.search_and_lookup( self.access_token, self.api_host, entity_type, search_terms, num_results )
[docs] def lookup_belongs(self, entity_type, entity_id): """Look up details of entities containing the given entity. Parameters ---------- entity_type : { 'metrics', 'items', 'regions' } entity_id : int Yields ------ dict Result of :meth:`~.lookup` on each entity the given entity belongs to. For example: For the region 'United States', one yielded result will be for 'North America.' The format of which matches the output of :meth:`~.lookup`:: { 'id': 15, 'contains': [ 1008, 1009, 1012, 1215, ... ], 'name': 'North America', 'level': 2 } """ return lib.lookup_belongs( self.access_token, self.api_host, entity_type, entity_id )
[docs] def rank_series_by_source(self, selections_list): """Given a list of series selections, for each unique combination excluding source, expand to all available sources and return them in ranked order. The order corresponds to how well that source covers the selection (metrics, items, regions, and time range and frequency). Parameters ---------- selections_list : list of dicts See the output of :meth:`~.get_data_series`. Yields ------ dict The input selections_list, expanded out to each possible source, ordered by coverage. """ return lib.rank_series_by_source( self.access_token, self.api_host, selections_list )
def get_geo_centre(self, region_id): """Given a region ID, return the geographic centre in degrees lat/lon. Parameters ---------- region_id : integer Returns ------- list of dicts Example:: [{'centre': [ 39.8333, -98.5855 ], 'regionId': 1215, 'regionName': 'United States'}] """ return lib.get_geo_centre(self.access_token, self.api_host, region_id) def get_geojsons(self, region_id, descendant_level=None, zoom_level=7): """Given a region ID, return shape information in geojson, for the region and all its descendants at the given level (if specified). Parameters ---------- region_id : integer descendant_level : integer, admin region level (2, 3, 4 or 5) zoom_level : integer, optional(allow 1-8) Valid if include_geojson equals True. If zoom level is specified and it is less than 6, simplified shapefile will be returned. Otherwise, detailed shapefile will be used by default. Returns ------- list of dicts Example:: [{ 'centre': [ 39.8333, -98.5855 ], 'regionId': 1215, 'regionName': 'United States', u'geojson': u'{"type":"GeometryCollection","geometries":[{"type":"MultiPolygon","coordinates":[[[[-155.651382446,20.1647224430001], ...]]]}]}' }] """ return lib.get_geojsons( self.access_token, self.api_host, region_id, descendant_level, zoom_level)
[docs] def get_geojson(self, region_id, zoom_level=7): """Given a region ID, return shape information in geojson. Parameters ---------- region_id : integer zoom_level : integer, optional(allow 1-8) Valid if include_geojson equals True. If zoom level is specified and it is less than 6, simplified shapefile will be returned. Otherwise, detailed shapefile will be used by default. Returns ------- a geojson object or None Example:: { 'type': 'GeometryCollection', 'geometries': [{'type': 'MultiPolygon', 'coordinates': [[[[-38.394, -4.225], ...]]]}, ...]} """ return lib.get_geojson(self.access_token, self.api_host, region_id, zoom_level)
def get_descendant( self, entity_type, entity_id, distance=None, include_details=True, ): """Given an item, metric or region, returns all its descendants i.e. entities that are "contained" in the given entity Similar to :meth:~.get_descendant_regions, but also works on items and metrics. This method has a distance parameter (which returns all nested child entities) instead of a descendant_level parameter (which only returns child entities at a given depth/level). Parameters ---------- entity_type : { 'metrics', 'items', 'regions' } entity_id : integer distance: integer, optional Return all entity contained to entity_id at maximum distance. If not provided, get all descendants. include_details : boolean, optional True by default. Will perform a lookup() on each descendant to find name, definition, etc. If this option is set to False, only ids of descendant entities will be returned, which makes execution significantly faster. Returns ------- list of dicts Example:: [{ 'id': 134, 'name': 'Cattle hides, wet-salted', 'definition': 'Hides and skins of domesticated cattle-animals ...', } , { 'id': 382, 'name': 'Calf skins, wet-salted', 'definition': 'Wet-salted hides and skins of calves-animals of ...' }, ...] See output of :meth:`~.lookup` """ return lib.get_descendant( self.access_token, self.api_host, entity_type, entity_id, distance, include_details, )
[docs] def get_descendant_regions( self, region_id, descendant_level=None, include_historical=True, include_details=True, ): """Look up details of all regions of the given level contained by a region. Given any region by id, get all the descendant regions that are of the specified level. Parameters ---------- region_id : integer descendant_level : integer, optional The region level of interest. See REGION_LEVELS constant. If not provided, get all descendants. include_historical : boolean, optional True by default. If False is specified, regions that only exist in historical data (e.g. the Soviet Union) will be excluded. include_details : boolean, optional True by default. Will perform a lookup() on each descendant region to find name, latitude, longitude, etc. If this option is set to False, only ids of descendant regions will be returned, which makes execution significantly faster. Returns ------- list of dicts Example:: [{ 'id': 13100, 'contains': [139839, 139857, ...], 'name': 'Wisconsin', 'level': 4 } , { 'id': 13101, 'contains': [139891, 139890, ...], 'name': 'Wyoming', 'level': 4 }, ...] See output of :meth:`~.lookup` """ return lib.get_descendant_regions( self.access_token, self.api_host, region_id, descendant_level, include_historical, include_details, )
[docs] def get_available_timefrequency(self, **selection): """Given a selection, return a list of frequencies and time ranges. The results are ordered by coverage-optimized ranking. Parameters ---------- metric_id : integer, optional item_id : integer, optional region_id : integer, optional partner_region_id : integer, optional Returns ------- list of dicts Example:: [{ 'start_date': '2000-02-18T00:00:00.000Z', 'frequency_id': 3, 'end_date': '2020-03-12T00:00:00.000Z', 'name': '8-day' }, { 'start_date': '2019-09-02T00:00:00.000Z', 'frequency_id': 1, 'end_date': '2020-03-09T00:00:00.000Z', 'name': u'daily'}, ... ] """ return lib.get_available_timefrequency( self.access_token, self.api_host, **selection )
[docs] def get_top(self, entity_type, num_results=5, **selection): """Find the data series with the highest cumulative value for the given time range. Examples:: # To get FAO's top 5 corn-producing countries of all time: client.get_top('regions', metric_id=860032, item_id=274, frequency_id=9, source_id=2) # To get FAO's top 5 corn-producing countries of 2014: client.get_top('regions', metric_id=860032, item_id=274, frequency_id=9, source_id=2, start_date='2014-01-01', end_date='2014-12-31') # To get the United States' top 15 exports in the decade of 2010-2019: client.get_top('items', num_results=15, metric_id=20032, region_id=1215, frequency_id=9, source_id=2, start_date='2010-01-01', end_date='2019-12-31') Parameters ---------- entity_type : { 'items', 'regions' } The entity type to rank, all other selections being the same. Only items and regions are rankable at this time. num_results : integer, optional How many data series to rank. Top 5 by default. metric_id : integer item_id : integer Required if requesting top regions. Disallowed if requesting top items. region_id : integer Required if requesting top items. Disallowed if requesting top regions. partner_region_id : integer, optional frequency_id : integer source_id : integer start_date : string, optional If not provided, the cumulative value used for ranking will include data points as far back as the source provides. end_date : string, optional Returns ------- list of dicts Example:: [ {'metricId': 860032, 'itemId': 274, 'regionId': 1215, 'frequencyId': 9, 'sourceId': 2, 'value': 400, 'unitId': 14}, {'metricId': 860032, 'itemId': 274, 'regionId': 1215, 'frequencyId': 9, 'sourceId': 2, 'value': 395, 'unitId': 14}, {'metricId': 860032, 'itemId': 274, 'regionId': 1215, 'frequencyId': 9, 'sourceId': 2, 'value': 12, 'unitId': 14}, ] Along with the series attributes, value and unit are also given for the total cumulative value the series are ranked by. You may then use the results to call :meth:`~.get_data_points` to get the individual time series points. """ return lib.get_top( self.access_token, self.api_host, entity_type, num_results, **selection )
[docs] def get_df(self, show_revisions=False, index_by_series=False): """Call :meth:`~.get_data_points` for each saved data series and return as a combined dataframe. Note you must have first called either :meth:`~.add_data_series` or :meth:`~.add_single_data_series` to save data series into the GroClient's data_series_list. You can inspect the client's saved list using :meth:`~.get_data_series_list`. Returns ------- pandas.DataFrame The results to :meth:`~.get_data_points` for all the saved series, appended together into a single dataframe. See If index_by_series is set, the dataframe is indexed by series. See """ while self._data_series_queue: data_series = self._data_series_queue.pop() if show_revisions: data_series["show_revisions"] = True self.add_points_to_df( None, data_series, self.get_data_points(**data_series) ) if index_by_series and not self._data_frame.empty: columns = intersect(DATA_SERIES_UNIQUE_TYPES_ID, self._data_frame.columns) if len(columns) > 0: indexed_df = self._data_frame.set_index(columns) indexed_df.index.set_names(DATA_SERIES_UNIQUE_TYPES_ID, inplace=True) return indexed_df.sort_index() return self._data_frame
def async_get_df(self): self.batch_async_get_data_points( self._data_series_queue, output_list=self._data_frame, map_result=self.add_points_to_df, ) return self._data_frame def add_points_to_df(self, index, data_series, data_points, *args): """Add the given datapoints to a pandas dataframe. Parameters: ----------- index : unused data_series : dict data_points : list of dicts """ tmp = pandas.DataFrame(data=[dict_unnest(point) for point in data_points]) if tmp.empty: return # get_data_points response doesn't include the # source_id. We add it as a column, in case we have # several selections series which differ only by source id. tmp["source_id"] = data_series["source_id"] # tmp should always have end_date/start_date/reporting_date as columns if not empty tmp.end_date = pandas.to_datetime(tmp.end_date) tmp.start_date = pandas.to_datetime(tmp.start_date) tmp.reporting_date = pandas.to_datetime(tmp.reporting_date) if self._data_frame.empty: self._data_frame = tmp else: self._data_frame = pandas.concat([self._data_frame, tmp])
[docs] def get_data_points(self, **selections): """Get all the data points for a given selection. Example:: client.get_data_points(**{'metric_id': 860032, 'item_id': 274, 'region_id': 1215, 'frequency_id': 9, 'source_id': 2, 'start_date': '2017-01-01', 'end_date': '2017-12-31', 'unit_id': 15}) Returns:: [{ 'start_date': '2017-01-01T00:00:00.000Z', 'end_date': '2017-12-31T00:00:00.000Z', 'value': 408913833.8019222, 'unit_id': 15, 'reporting_date': None, 'metric_id': 860032, 'item_id': 274, 'region_id': 1215, 'partner_region_id': 0, 'frequency_id': 9, 'source_id': 2, 'belongs_to': { 'metric_id': 860032, 'item_id': 274, 'region_id': 1215, 'frequency_id': 9, 'source_id': 2 } }] Note: you can pass the output of :meth:`~.get_data_series` into :meth:`~.get_data_points` to check what series exist for some selections and then retrieve the data points for those series. See :sample:`` for an example of this. :meth:`~.get_data_points` also allows passing a list of ids for metric_id, item_id, and/or region_id to get multiple series in a single request. This can be faster if requesting many series. For example:: client.get_data_points(**{'metric_id': 860032, 'item_id': 274, 'region_id': [1215,1216], 'frequency_id': 9, 'source_id': 2, 'start_date': '2017-01-01', 'end_date': '2017-12-31', 'unit_id': 15}) Returns:: [{ 'start_date': '2017-01-01T00:00:00.000Z', 'end_date': '2017-12-31T00:00:00.000Z', 'value': 408913833.8019222, 'unit_id': 15, 'reporting_date': None, 'metric_id': 860032, 'item_id': 274, 'region_id': 1215, 'partner_region_id': 0, 'frequency_id': 9, 'source_id': 2, 'belongs_to': { 'metric_id': 860032, 'item_id': 274, 'region_id': 1215, 'frequency_id': 9, 'source_id': 2 } }, { 'start_date': '2017-01-01T00:00:00.000Z', 'end_date': '2017-12-31T00:00:00.000Z', 'value': 340614.19507563586, 'unit_id': 15, 'reporting_date': None, 'metric_id': 860032, 'item_id': 274, 'region_id': 1216, 'partner_region_id': 0, 'frequency_id': 9, 'source_id': 2, 'belongs_to': { 'metric_id': 860032, 'item_id': 274, 'region_id': 1216, 'frequency_id': 9, 'source_id': 2 } }] Parameters ---------- metric_id : integer or list of integers How something is measured. e.g. "Export Value" or "Area Harvested" item_id : integer or list of integers What is being measured. e.g. "Corn" or "Rainfall" region_id : integer or list of integers Where something is being measured e.g. "United States Corn Belt" or "China" partner_region_id : integer or list of integers, optional partner_region refers to an interaction between two regions, like trade or transportation. For example, for an Export metric, the "region" would be the exporter and the "partner_region" would be the importer. For most series, this can be excluded or set to 0 ("World") by default. source_id : integer frequency_id : integer unit_id : integer, optional start_date : string, optional All points with end dates equal to or after this date end_date : string, optional All points with start dates equal to or before this date show_revisions : boolean, optional False by default, meaning only the latest value for each period. If true, will return all values for a given period, differentiated by the `reporting_date` field. insert_null : boolean, optional False by default. If True, will include a data point with a None value for each period that does not have data. at_time : string, optional Estimate what data would have been available via Gro at a given time in the past. See :sample:`at-time-query-examples.ipynb` for more details. include_historical : boolean, optional True by default, will include historical regions that are part of your selections Returns ------- list of dicts """ data_points = lib.get_data_points( self.access_token, self.api_host, **selections ) # Apply unit conversion if a unit is specified if "unit_id" in selections: return list( map( functools.partial( self.convert_unit, target_unit_id=selections["unit_id"] ), data_points, ) ) # Return data points in input units if not unit is specified return data_points
def GDH(self, gdh_selection, **optional_selections): """Wrapper for :meth:`~.get_data_points`. with alternative input and output style. The data series selection to retrieve is encoded in a 'gdh_selection' string of the form <metric_id>-<item_id>-<region_id>-<partner_region_id>-<frequency_id>-<source_id> For example, client.GDH("860032-274-1231-0-9-14") will get the data points for Production of Corn in China from PS&D at an annual frequency, e.g. for csv_row in client.GDH("860032-274-1231-0-9-14"): print csv_row Parameters: ---------- gdh_selection: string optional_selections: dict, optional accepts optional params from :meth:`~.get_data_points`. Returns: ------ pandas.DataFrame the subset of the main DataFrame :meth:`~.get_df`. with the requested series. """ entity_ids = [int(x) for x in gdh_selection.split("-")] selection = zip_selections(entity_ids, **optional_selections) self.add_single_data_series(selection) try: return self.get_df(index_by_series=True).loc[[tuple(entity_ids)], :] except KeyError: self._logger.warning("GDH returned no data") return pandas.DataFrame()
[docs] def get_data_series_list(self): """Inspect the current list of saved data series contained in the GroClient. For use with :meth:`~.get_df`. Add new data series to the list using :meth:`~.add_data_series` and :meth:`~.add_single_data_series`. Returns ------- list of dicts A list of data_series objects, as returned by :meth:`~.get_data_series`. """ return [dict(data_series_hash) for data_series_hash in self._data_series_list]
[docs] def add_single_data_series(self, data_series): """Save a data series object to the GroClient's data_series_list. For use with :meth:`~.get_df`. Parameters ---------- data_series : dict A single data_series object, as returned by :meth:`~.get_data_series` or :meth:`~.find_data_series`. See Returns ------- None """ series_hash = frozenset(dict_unnest(data_series).items()) if series_hash not in self._data_series_list: self._data_series_list.add(series_hash) # Add a copy of the data series, in case the original is modified self._data_series_queue.append(dict(data_series))"Added {}".format(data_series)) else: self._logger.debug("Already added: {}".format(data_series)) return
[docs] def find_data_series(self, result_filter=None, **kwargs): """Find data series matching a combination of entities specified by name and yield them ranked by coverage. Example:: client.find_data_series(item="Corn", metric="Futures Open Interest", region="United States of America") will yield a sequence of dictionaries of the form:: { 'metric_id': 15610005, 'metric_name': 'Futures Open Interest', 'item_id': 274, 'item_name': 'Corn', 'region_id': 1215, 'region_name': 'United States', 'frequency_id': 15, 'source_id': 81, 'start_date': '1972-03-01T00:00:00.000Z', ...}, { ... }, ... See :code:`result_filter` can be used to filter entity searches. For example:: client.find_data_series(item="vegetation", metric="vegetation indices", region="Central", result_filter=lambda r: ('region_id' not in r or r['region_id'] == 10393)) will only consider that particular region, and not the many other regions with the same name. This method uses :meth:``, :meth:`~.get_data_series`, :meth:`~.get_available_timefrequency` and :meth:`~.rank_series_by_source`. Parameters ---------- metric : string, optional item : string, optional region : string, optional partner_region : string, optional start_date : string, optional YYYY-MM-DD end_date : string, optional YYYY-MM-DD result_filter: function, optional function taking data series selection dict returning boolean Yields ------ dict A sequence of data series matching the input selections See also -------- :meth:`~.get_data_series` """ results = [] # [[('item_id',1),('item_id',2),...],[('metric_id" 1),...],...] for kw in kwargs: if kwargs.get(kw) is None: continue id_key = "{}_id".format(kw) if id_key in ENTITY_KEY_TO_TYPE: type_results = [] # [('item_id',1),('item_id',2),...] for search_result in ENTITY_KEY_TO_TYPE[id_key], kwargs[kw] )[: cfg.MAX_RESULT_COMBINATION_DEPTH]: if result_filter is None or result_filter( {id_key: search_result["id"]} ): type_results.append((id_key, search_result["id"])) results.append(type_results) # Rank by frequency and source, while preserving search ranking in # permutations of search results. ranking_groups = set() for comb in itertools.product(*results): for data_series in self.get_data_series(**dict(comb))[ : cfg.MAX_SERIES_PER_COMB ]: self._logger.debug("Data series: {}".format(data_series)) # remove time and frequency to rank them data_series.pop("start_date", None) data_series.pop("end_date", None) data_series.pop("frequency_id", None) data_series.pop("frequency_name", None) # remove source to rank them data_series.pop("source_id", None) data_series.pop("source_name", None) # metadata is not hashable data_series.pop("metadata", None) series_hash = frozenset(data_series.items()) if series_hash not in ranking_groups: ranking_groups.add(series_hash) if kwargs.get("start_date"): data_series["start_date"] = kwargs["start_date"] if kwargs.get("end_date"): data_series["end_date"] = kwargs["end_date"] for tf in self.get_available_timefrequency(**data_series): ds = dict(data_series) ds["frequency_id"] = tf["frequency_id"] for data_series in self.rank_series_by_source([ds]): yield self.get_data_series(**data_series)[0]
[docs] def add_data_series(self, **kwargs): """Adds the top result of :meth:`~.find_data_series` to the saved data series list. For use with :meth:`~.get_df`. Parameters ---------- metric : string, optional item : string, optional region : string, optional partner_region : string, optional start_date : string, optional YYYY-MM-DD end_date : string, optional YYYY-MM-DD result_filter: function, optional function taking data series selection dict returning boolean Returns ------- data_series object, as returned by :meth:`~.get_data_series`. The data_series that was added or None if none were found. See also -------- :meth:`~.get_df` :meth:`~.add_single_data_series` :meth:`~.find_data_series` """ for the_data_series in self.find_data_series(**kwargs): self.add_single_data_series(the_data_series) return the_data_series return
### # Discovery shortcuts ###
[docs] def search_for_entity(self, entity_type, keywords): """Returns the first result of entity_type that matches the given keywords. Parameters ---------- entity_type : { 'metrics', 'items', 'regions', 'sources' } keywords : string Returns ---------- integer The id of the first search result """ results =, keywords) for result in results: self._logger.debug( "First result, out of {} {}: {}".format( len(results), entity_type, result["id"] ) ) return result["id"]
[docs] def get_provinces(self, country_name): """Given the name of a country, find its provinces. Parameters ---------- country_name : string Returns ---------- list of dicts Example:: [{ 'id': 13100, 'contains': [139839, 139857, ...], 'name': 'Wisconsin', 'level': 4 } , { 'id': 13101, 'contains': [139891, 139890, ...], 'name': 'Wyoming', 'level': 4 }, ...] See output of :meth:`~.lookup` See Also -------- :meth:`~.get_descendant_regions` """ for region in self.search_and_lookup("regions", country_name): if region["level"] == lib.REGION_LEVELS["country"]: provinces = self.get_descendant_regions( region["id"], lib.REGION_LEVELS["province"] ) self._logger.debug( "Provinces of {}: {}".format(country_name, provinces) ) return provinces return None
def get_names_for_selection(self, selection): """Convert a selection into entity names. Parameters: ----------- data_series : dict A single data_series object, as returned by get_data_series() or find_data_series(). See Returns: -------- list of pairs of strings [('item', 'Corn'), ('region', 'China') ...] """ return [ ( entity_key.split("_")[0], self.lookup(ENTITY_KEY_TO_TYPE[entity_key], entity_id)["name"], ) for entity_key, entity_id in selection.items() ] def convert_unit(self, point, target_unit_id): """Convert the data point from one unit to another unit. If original or target unit is non-convertible, throw an error. Parameters ---------- point : dict { value: float, unit_id: integer, ... } target_unit_id : integer Returns ------- dict Example :: { value: 14.2, unit_id: 4 } unit_id is changed to the target, and value is converted to use the new unit_id. Other properties are unchanged. """ if point.get("unit_id") is None or point.get("unit_id") == target_unit_id: return point from_convert_factor = self.lookup("units", point["unit_id"]).get( "baseConvFactor" ) if not from_convert_factor.get("factor"): raise Exception("unit_id {} is not convertible".format(point["unit_id"])) to_convert_factor = self.lookup("units", target_unit_id).get("baseConvFactor") if not to_convert_factor.get("factor"): raise Exception("unit_id {} is not convertible".format(target_unit_id)) if point.get("value") is not None: point["value"] = lib.convert_value(point["value"], from_convert_factor, to_convert_factor) if point.get("metadata") is not None and point["metadata"].get("conf_interval") is not None: point["metadata"]["conf_interval"] = lib.convert_value(point["metadata"]["conf_interval"], from_convert_factor, to_convert_factor) point["unit_id"] = target_unit_id return point