from __future__ import print_function
from builtins import str
from random import random
import argparse
import functools
import getpass
import itertools
import os
import sys
import time
import json
# Python3 support
try:
# Python3
from urllib.parse import urlencode
except ImportError:
# Python2
from urllib import urlencode
from api.client import cfg, lib
from api.client.constants import DATA_SERIES_UNIQUE_TYPES_ID, ENTITY_KEY_TO_TYPE
from api.client.utils import intersect, zip_selections, dict_unnest
from api.client.lib import APIError
import pandas
import unicodecsv
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
API_HOST = "api.gro-intelligence.com"
OUTPUT_FILENAME = "gro_client_output.csv"
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
)
try:
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(
self.url,
self.retry_count,
"retry" if self.retry_count == 1 else "retries",
response,
)
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()
try:
self._async_http_client = AsyncHTTPClient()
self._ioloop = IOLoop()
except Exception as e:
self._logger.warn(
"Unable to initialize an event loop. Async methods disabled."
)
self._async_http_client = None
self._ioloop = None
def get_logger(self):
return self._logger
@gen.coroutine
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)
else:
self._logger.warning(msg, extra=log_record)
"""General 'make api request' function.
Assigns headers and builds in retries and logging.
"""
self._logger.debug(url)
# append version info
headers.update(lib.get_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)),
method="GET",
headers=headers,
request_timeout=cfg.TIMEOUT,
connect_timeout=cfg.TIMEOUT,
)
try:
try:
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"}[
status_code
]
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"))[
"data"
][0]
new_params = lib.redirect(params, redirected_ids)
log_request(
start_time,
retry_count,
"Redirecting {} to {}".format(params, new_params),
status_code,
)
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 = (
e.response.error
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, 503, 504]:
# First retry is immediate.
# After that, exponential backoff before retrying.
if retry_count > 0:
time.sleep(2 ** retry_count)
continue
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.
Parameters
----------
func : function
The function to be batched. Typically a Client method.
batched_args : list of dicts
Inputs
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)
else:
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()
@gen.coroutine
def consumer():
"""Execute func on all items in queue asynchronously."""
while q.qsize():
try:
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)
else:
result = yield func(item)
output_data["result"] = map_result(
idx, item, result, output_data["result"]
)
self._logger.debug("Done with {}".format(idx))
q.task_done()
except Exception:
# Cease processing
# IOLoop raises "Operation timed out after None seconds"
self._ioloop.stop()
self._ioloop.close()
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
self._logger.info("Queued {} requests in {}".format(q.qsize(), elapsed))
@gen.coroutine
def main():
# Start consumer without waiting (since it never finishes).
for i in range(cfg.MAX_QUERIES_PER_SECOND):
self._ioloop.spawn_callback(consumer)
producer() # Wait for producer to put all tasks.
yield q.join() # Wait for consumer to finish all tasks.
self._ioloop.run_sync(main)
return output_data["result"]
# TODO: deprecate the following two methods, standardize on one
# approach with get_data_points and get_df
@gen.coroutine
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)
try:
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.
Parameters
----------
batched_args : list of dicts
Each dict should be a `selections` object like would be passed to
:meth:`~get_data_points`.
Example::
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
Example::
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,
output_list=output_list,
map_result=map_response)
Returns
-------
any
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
)
@gen.coroutine
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.
Parameters
----------
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.
Parameters
----------
entity_type : {'metrics', 'items', 'regions'}
Returns
-------
data : list of dicts
Example::
[ { '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.
Parameters
----------
selected_entities : dict
Example::
{ '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
Returns
-------
list of dicts
Example::
[ { '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.
https://developers.gro-intelligence.com/gro-ontology.html
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.
https://developers.gro-intelligence.com/data-series-definition.html
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 lib.search(self.access_token, 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:`~.search` 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:`~.search`, 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)
[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 https://developers.gro-intelligence.com/data-point-definition.html
If index_by_series is set, the dataframe is indexed by series.
See https://developers.gro-intelligence.com/data-series-definition.html
"""
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.
https://developers.gro-intelligence.com/data-point-definition.html
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:`quick_start.py` 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.warn("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 list(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 https://developers.gro-intelligence.com/data-series-definition.html
Returns
-------
None
"""
series_hash = frozenset(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))
self._logger.info("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 https://developers.gro-intelligence.com/data-series-definition.html
: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:`~.search`, :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:
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 self.search(
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 data_series
[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 = self.search(entity_type, 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 https://github.com/gro-intelligence/api-client/wiki/Data-Series-Definition
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()
]
###
# Convenience methods that automatically fill in partial selections with random entities
###
def pick_random_entities(self): # pragma: no cover
"""Pick a random item that has some data associated with it, and a random metric and region
pair for that item with data available.
"""
item_list = list(self.get_available("items").values())
num = 0
while not num:
item = item_list[int(len(item_list) * random())]
selected_entities = {"itemId": item["id"]}
entity_list = self.list_available(selected_entities)
num = len(entity_list)
entities = entity_list[int(num * random())]
self._logger.info("Using randomly selected entities: {}".format(str(entities)))
selected_entities.update(entities)
return selected_entities
def pick_random_data_series(self, selected_entities): # pragma: no cover
"""Given a selection of tentities, pick a random available data series the given selection
of entities.
"""
data_series_list = self.get_data_series(**selected_entities)
if not data_series_list:
raise Exception("No data series available for {}".format(selected_entities))
selected_data_series = data_series_list[int(len(data_series_list) * random())]
return selected_data_series
# TODO: rename function to "write_..." rather than "print_..."
def print_one_data_series(self, data_series, filename): # pragma: no cover
"""Output a data series to a CSV file."""
self._logger.warning("Using data series: {}".format(str(data_series)))
self._logger.warning("Outputing to file: {}".format(filename))
writer = unicodecsv.writer(open(filename, "wb"))
for point in self.get_data_points(**data_series):
writer.writerow(
[
point["start_date"],
point["end_date"],
point["value"],
self.lookup_unit_abbreviation(point["unit_id"]),
]
)
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:
value_in_base_unit = (
point["value"] * from_convert_factor.get("factor")
) + from_convert_factor.get("offset", 0)
point["value"] = float(
value_in_base_unit - to_convert_factor.get("offset", 0)
) / to_convert_factor.get("factor")
point["unit_id"] = target_unit_id
return point
def main(): # pragma: no cover
"""Basic Gro API command line interface.
Note that results are chosen randomly from matching selections, and so results are not
deterministic. This tool is useful for simple queries, but anything more complex should be done
using the provided Python packages.
Usage examples:
gro_client --item=soybeans --region=brazil --partner_region china --metric export
gro_client --item=sesame --region=ethiopia
gro_client --user_email=john.doe@example.com --print_token
For more information use --help
"""
parser = argparse.ArgumentParser(description="Gro API command line interface")
parser.add_argument("--user_email")
parser.add_argument("--user_password")
parser.add_argument("--item")
parser.add_argument("--metric")
parser.add_argument("--region")
parser.add_argument("--partner_region")
parser.add_argument(
"--print_token",
action="store_true",
help="Ouput API access token for the given user email and password. "
"Save it in GROAPI_TOKEN environment variable.",
)
parser.add_argument(
"--token",
default=os.environ.get("GROAPI_TOKEN"),
help="Defaults to GROAPI_TOKEN environment variable.",
)
args = parser.parse_args()
assert (
args.user_email or args.token
), "Need --token, or --user_email, or $GROAPI_TOKEN"
access_token = None
if args.token:
access_token = args.token
else:
if not args.user_password:
args.user_password = getpass.getpass()
access_token = lib.get_access_token(
API_HOST, args.user_email, args.user_password
)
if args.print_token:
print(access_token)
sys.exit(0)
client = GroClient(API_HOST, access_token)
if (
not args.metric
and not args.item
and not args.region
and not args.partner_region
):
ds = client.pick_random_data_series(client.pick_random_entities())
else:
ds = next(
client.find_data_series(
item=args.item,
metric=args.metric,
region=args.region,
partner_region=args.partner_region,
)
)
client.print_one_data_series(ds, OUTPUT_FILENAME)
def get_df(client, **selected_entities): # pragma: no cover
"""Deprecated: use the corresponding method in GroClient instead."""
return pandas.DataFrame(client.get_data_points(**selected_entities))
def search_for_entity(client, entity_type, keywords): # pragma: no cover
"""Deprecated: use the corresponding method in GroClient instead."""
return client.search_for_entity(entity_type, keywords)
def pick_random_entities(client): # pragma: no cover
"""Deprecated: use the corresponding method in GroClient instead."""
return client.pick_random_entities()
def print_random_data_series(client, selected_entities): # pragma: no cover
"""Example which prints out a CSV of a random data series that
satisfies the (optional) given selection.
"""
return client.print_one_data_series(
client.pick_random_data_series(selected_entities), OUTPUT_FILENAME
)
if __name__ == "__main__": # pragma: no cover
main()