class iterable_loader(DataStreamProcessor):
def __init__(self, iterable, name=None):
super(iterable_loader, self).__init__()
self.iterable = iterable
self.name = name
def handle_iterable(self):
mode = None
for x in self.iterable:
if mode is None:
assert isinstance(x, (dict, list))
mode = dict if isinstance(x, dict) else list
assert isinstance(x, mode)
if mode == dict:
yield x
else:
yield dict(zip(('col{}'.format(i) for i in range(len(x))), x))
def process_datapackage(self, dp: Package):
name = self.name
if name is None:
name = 'res_{}'.format(len(dp.resources) + 1)
self.res = Resource(dict(name=name, path='{}.csv'.format(name)),
storage=iterable_storage(self.handle_iterable()))
self.res.infer()
dp.descriptor.setdefault('resources', []).append(self.res.descriptor)
return dp
def process_resources(self, resources):
yield from super(iterable_loader, self).process_resources(resources)
yield self.res.iter(keyed=True)
def save_as_data_packages(row):
""" save dataset from data.json as data package
We will use this files as a queue to process later """
# TODO check if ckanext-datapackager is useful for import
# or export resources:
# https://github.com/frictionlessdata/ckanext-datapackager
package = Package()
# TODO check this, I'm learning datapackages.
resource = Resource({'data': row})
resource.infer() # adds "name": "inline"
if not resource.valid:
raise Exception('Invalid resource')
encoded_identifier = encode_identifier(identifier=row['identifier'])
# resource_path = os.path.join(path, f'{prefix}_{encoded_identifier}.json')
# resource.save(resource_path)
package.add_resource(descriptor=resource.descriptor)
folder = config.get_data_packages_folder_path()
filename = f'data-json-{encoded_identifier}.json'
package_path = os.path.join(folder, filename)
# no not rewrite if exists
if not os.path.isfile(package_path):
package.save(target=package_path)
def get_resource(self, idx=None, path=None, name=None, source_only=True):
if idx is None:
if path:
all_paths = [
r.descriptor.get("path") for r in self.datapackage.resources
]
if path in all_paths:
idx = all_paths.index(path)
else:
logger.error(f"path = {path} is not in resources.")
elif name:
all_names = [
r.descriptor.get("name") for r in self.datapackage.resources
]
if name in all_names:
idx = all_names.index(name)
else:
logger.error(f"name = {name} is not in resources.")
else:
raise Exception(
f"Please specify at least one of the keywords: idx, path, name."
)
if self.is_local:
logger.debug(
f"Using local dataset for {self.id}, sync it if you need the updated version."
)
r = self.datapackage.resources[idx]
logger.debug(f"using base_path: {str(self.base_path)}")
logger.debug(f"using descriptor: {r.descriptor}")
resource = Resource(r.descriptor, base_path=str(self.base_path))
logger.debug(f"base_path of r_1: {resource._Resource__base_path}")
elif (not self.is_local) and (self.source == "git"):
logger.debug(f"Using remote data")
self.remote_path = f"{self.metadata_uri[:-16]}"
r = self.datapackage.resources[idx]
resource = Resource(
{
**(r.descriptor),
**{"path": self.remote_path + r.descriptor.get("path", "")},
}
)
elif (not self.is_local) and (self.source == "s3"):
logger.debug(f"Using remote data")
logger.debug(
f"Direct resource from S3 is not supported yet. "
f"Please sync the dataset to local using the command line first.\n"
f"TODO: Sync S3 to local after confirmation from here."
)
resource = self.datapackage.resources[idx]
else:
logger.error("Resource is not supported. Currently supporting S3 and git.")
resource = self.datapackage.resources[idx]
if source_only:
return resource.source
else:
return resource
def process_datapackage(self, dp: Package):
name = self.name
if name is None:
name = 'res_{}'.format(len(dp.resources) + 1)
self.res = Resource(dict(name=name, path='{}.csv'.format(name)),
storage=iterable_storage(self.handle_iterable()))
self.res.infer()
dp.descriptor.setdefault('resources', []).append(self.res.descriptor)
return dp
def process_datapackage(self, dp: Package):
if isinstance(self.load_source, tuple):
datapackage_descriptor, _ = self.load_source
dp.descriptor.setdefault('resources', [])
self.resource_matcher = ResourceMatcher(self.resources,
datapackage_descriptor)
for resource_descriptor in datapackage_descriptor['resources']:
if self.resource_matcher.match(resource_descriptor['name']):
dp.add_resource(resource_descriptor)
else: # load_source is string:
if self.load_source.startswith('env://'):
env_var = self.load_source[6:]
self.load_source = os.environ.get(env_var)
if self.load_source is None:
raise ValueError(
f"Couldn't find value for env var '{env_var}'")
if os.path.basename(self.load_source) == 'datapackage.json':
self.load_dp = Package(self.load_source)
self.resource_matcher = ResourceMatcher(
self.resources, self.load_dp)
dp.descriptor.setdefault('resources', [])
for resource in self.load_dp.resources:
if self.resource_matcher.match(resource.name):
dp.add_resource(resource.descriptor)
else:
if os.path.exists(self.load_source):
base_path = os.path.dirname(self.load_source) or '.'
self.load_source = os.path.basename(self.load_source)
else:
base_path = None
descriptor = dict(path=self.load_source,
profile='tabular-data-resource')
descriptor['format'] = self.options.get('format')
if 'encoding' in self.options:
descriptor['encoding'] = self.options['encoding']
if descriptor['format'] == 'xml' or self.load_source.endswith(
'.xml'):
self.options.setdefault('custom_parsers',
{})['xml'] = XMLParser
self.options.setdefault('ignore_blank_headers', True)
self.options.setdefault('headers', 1)
self.res = Resource(descriptor,
base_path=base_path,
**self.options)
self.res.infer(confidence=1, limit=1000)
if self.name is not None:
self.res.descriptor['name'] = self.name
if self.force_strings:
for f in self.res.descriptor['schema']['fields']:
f['type'] = 'string'
self.res.commit()
self.res.descriptor['path'] = '{name}.{format}'.format(
**self.res.descriptor)
dp.add_resource(self.res.descriptor)
return dp
def test_raw_data(self, simpsons_dataset, simpsons_datapackage,
simpsons_descriptor_path):
for r in simpsons_datapackage.resources:
resource = Resource(
r.descriptor, base_path=path.dirname(simpsons_descriptor_path))
once = simpsons_dataset.raw_data[r.descriptor['name']]
twice = simpsons_dataset.raw_data[r.descriptor['name']]
assert_that(once, equal_to(resource.raw_read()))
# Not a generator
for _ in once:
pass # Consume iterable
assert_that(once, equal_to(twice))
def package_from_resources(resource_path, output_path, clean=True):
""" Collects resource descriptors and merges them in a datapackage.json
Parameters
----------
resource_path: string
Path to directory with resources (in .json format)
output_path: string
Root path of datapackage where the newly created datapckage.json is
stored
clean: boolean
If true, resources will be deleted
"""
p = Package()
p.descriptor["profile"] = "tabular-data-package"
p.commit()
for f in os.listdir(resource_path):
path = os.path.join(resource_path, f)
r = Resource(path)
p.add_resource(r.descriptor)
p.commit()
os.remove(path)
if clean:
os.rmdir(resource_path)
p.save(os.path.join(output_path, "datapackage.json"))
def _make_package(source, publisher, config):
os.chdir(source)
files = [f for f in os.listdir('data') if f.endswith('.csv')]
package = Package({'publisher': publisher})
for f in files:
path = f"data/{f}"
name = f.replace('.csv', '')
schema = f"https://raw.githubusercontent.com/digital-land/alpha-data/master/schema/{name}-schema.json"
resource = Resource({'path': path, 'schema': schema})
package.add_resource(resource.descriptor)
package.commit()
package.infer()
errors = False
for r in package.resources:
try:
r.read(keyed=True)
r.check_relations()
except (CastError, RelationError) as e:
print('Error in', os.path.join(source, r.descriptor['path']))
print(e, e.errors)
errors = True
if not errors:
package.save('datapackage.zip')
print('saved datapackage.json to', source)
s3 = boto3.client(
's3',
aws_access_key_id=config['AWS_ACCESS_KEY_ID'],
aws_secret_access_key=config['AWS_SECRET_ACCESS_KEY'])
bucket = 'developer-contributions-datapackages'
key = f'{publisher}/{uuid.uuid4()}/datapackage.zip'
s3.upload_file(f'{source}/datapackage.zip',
bucket,
key,
ExtraArgs={'ACL': 'public-read'})
config = s3._client_config
config.signature_version = botocore.UNSIGNED
datapackage_url = boto3.resource(
's3',
config=config).meta.client.generate_presigned_url('get_object',
ExpiresIn=0,
Params={
'Bucket':
bucket,
'Key': key
})
return datapackage_url
def save_datasets_as_data_packages(self, folder_path):
""" save each dataset from a data.json source as _datapackage_ """
for dataset in self.package_list:
package = Package()
#TODO check this, I'm learning datapackages
resource = Resource({'data': dataset})
resource.infer() #adds "name": "inline"
identifier = dataset['id']
bytes_identifier = identifier.encode('utf-8')
encoded = base64.b64encode(bytes_identifier)
encoded_identifier = str(encoded, "utf-8")
resource_path = os.path.join(
folder_path, f'resource_ckan_api_{encoded_identifier}.json')
if not resource.valid:
raise Exception('Invalid resource')
resource.save(resource_path)
package.add_resource(descriptor=resource.descriptor)
package_path = os.path.join(
folder_path, f'pkg_ckan_api_{encoded_identifier}.zip')
package.save(target=package_path)
def save_datasets_as_data_packages(self, folder_path):
""" save each dataset from a data.json source as _datapackage_ """
for dataset in self.datasets:
package = Package()
#TODO check this, I'm learning datapackages
resource = Resource({'data': dataset})
resource.infer() #adds "name": "inline"
#FIXME identifier uses incompables characthers as paths (e.g. /).
# could exist duplicates paths from different resources
# use BASE64 or hashes
idf = slugify(dataset['identifier'])
resource_path = os.path.join(folder_path,
f'resource_data_json_{idf}.json')
if not resource.valid:
raise Exception('Invalid resource')
resource.save(resource_path)
package.add_resource(descriptor=resource.descriptor)
package_path = os.path.join(folder_path,
f'pkg_data_json_{idf}.zip')
package.save(target=package_path)
def update_package_descriptor():
"""
"""
p = Package("datapackage.json")
for f in os.listdir("resources"):
path = os.path.join("resources", f)
r = Resource(path)
p.add_resource(r.descriptor)
p.commit()
os.remove(path)
os.rmdir("resources")
p.save("datapackage.json")
def infer_resources(directory="data/elements"):
""" Method looks at all files in `directory` and creates
datapackage.Resource object that will be stored
Parameters
----------
directory: string
Path to directory from where resources are inferred
"""
if not os.path.exists("resources"):
os.makedirs("resources")
# create meta data resources
for f in os.listdir(directory):
r = Resource({"path": os.path.join(directory, f)})
r.infer()
r.save(os.path.join("resources", f.replace(".csv", ".json")))
def save_datasets_as_data_packages(self, folder_path, identifier_field):
""" save each dataset from a data.json source as _datapackage_ """
for dataset in self.datasets:
package = Package()
#TODO check this, I'm learning datapackages
resource = Resource({'data': dataset})
resource.infer() #adds "name": "inline"
idf = slugify(dataset[identifier_field])
resource_path = os.path.join(folder_path, f'resource_data_json_{idf}.json')
if not resource.valid:
raise Exception('Invalid resource')
resource.save(resource_path)
package.add_resource(descriptor=resource.descriptor)
package_path = os.path.join(folder_path, f'pkg_data_json_{idf}.zip')
package.save(target=package_path)
def infer_metadata(
package_name="default-name",
keep_resources=False,
foreign_keys={
"bus": [
"volatile",
"dispatchable",
"storage",
"load",
"reservoir",
"shortage",
"excess",
],
"profile": ["load", "volatile", "ror"],
"from_to_bus": ["connection", "line", "conversion"],
"chp": ["backpressure", "extraction", "chp"],
},
path=None,
):
""" Add basic meta data for a datapackage
Parameters
----------
package_name: string
Name of the data package
keep_resource: boolean
Flag indicating of the resources meta data json-files should be kept
after main datapackage.json is created. The reource meta data will
be stored in the `resources` directory.
foreign_keys: dict
Dictionary with foreign key specification. Keys for dictionary are:
'bus', 'profile', 'from_to_bus'. Values are list with
strings with the name of the resources
path: string
Absoltue path to root-folder of the datapackage
"""
current_path = os.getcwd()
if path:
print("Setting current work directory to {}".format(path))
os.chdir(path)
p = Package()
p.descriptor["name"] = package_name
p.descriptor["profile"] = "tabular-data-package"
p.commit()
if not os.path.exists("resources"):
os.makedirs("resources")
# create meta data resources elements
if not os.path.exists("data/elements"):
print("No data path found in directory {}. Skipping...".format(
os.getcwd()))
else:
for f in os.listdir("data/elements"):
r = Resource({"path": os.path.join("data/elements", f)})
r.infer()
r.descriptor["schema"]["primaryKey"] = "name"
if r.name in foreign_keys.get("bus", []):
r.descriptor["schema"]["foreignKeys"] = [{
"fields": "bus",
"reference": {
"resource": "bus",
"fields": "name"
},
}]
if r.name in foreign_keys.get("profile", []):
r.descriptor["schema"]["foreignKeys"].append({
"fields": "profile",
"reference": {
"resource": r.name + "_profile"
},
})
elif r.name in foreign_keys.get("from_to_bus", []):
r.descriptor["schema"]["foreignKeys"] = [
{
"fields": "from_bus",
"reference": {
"resource": "bus",
"fields": "name"
},
},
{
"fields": "to_bus",
"reference": {
"resource": "bus",
"fields": "name"
},
},
]
elif r.name in foreign_keys.get("chp", []):
r.descriptor["schema"]["foreignKeys"] = [
{
"fields": "fuel_bus",
"reference": {
"resource": "bus",
"fields": "name"
},
},
{
"fields": "electricity_bus",
"reference": {
"resource": "bus",
"fields": "name"
},
},
{
"fields": "heat_bus",
"reference": {
"resource": "bus",
"fields": "name"
},
},
]
r.commit()
r.save(os.path.join("resources", f.replace(".csv", ".json")))
p.add_resource(r.descriptor)
# create meta data resources elements
if not os.path.exists("data/sequences"):
print("No data path found in directory {}. Skipping...".format(
os.getcwd()))
else:
for f in os.listdir("data/sequences"):
r = Resource({"path": os.path.join("data/sequences", f)})
r.infer()
r.commit()
r.save(os.path.join("resources", f.replace(".csv", ".json")))
p.add_resource(r.descriptor)
p.commit()
p.save("datapackage.json")
if not keep_resources:
shutil.rmtree("resources")
os.chdir(current_path)
def create_resource(path):
from datapackage import Resource
resource = Resource({'path': path})
resource.infer()
resource.descriptor['schema']['primaryKey'] = 'timeindex'
resource.descriptor['description'] = (
'Profiles for Run of River (ROR) components. The profile is assumed' +
' to be constant during the year.')
resource.descriptor['title'] = 'ROR profiles'
resource.descriptor['sources'] = [{'title': 'Assumption'}]
resource.commit()
if resource.valid:
resource.save('resources/' + resource.name + '.json')
def create_resource(path):
"""
"""
mapper = {}
from datapackage import Resource
resource = Resource({'path': path})
resource.infer()
resource.descriptor['schema']['primaryKey'] = 'name'
resource.descriptor[
'description'] = 'Installed capacities, costs and technical parameters for components'
resource.descriptor['title'] = '{} components'.format(
resource.name.title())
resource.descriptor['sources'] = [{
'title':
'E-Highway 2050 installed capacities',
'path':
'http://www.e-highway2050.eu/fileadmin/documents/Results/e-Highway2050_2050_Country_and_cluster_installed_capacities_31-03-2015.xlsx'
}]
resource.descriptor['schema']['foreignKeys'] = [{
"fields": "bus",
"reference": {
"resource": "bus",
"fields": "name"
}
}]
if 'demand' in resource.name:
resource.descriptor['schema']['foreignKeys'].append({
"fields": "profile",
"reference": {
"resource": "demand-profiles"
}
})
elif 'volatile-generator' in resource.name:
resource.descriptor['schema']['foreignKeys'].append({
"fields": "profile",
"reference": {
"resource": "generator-profiles"
}
})
resource.commit()
if resource.valid:
resource.save('resources/' + resource.name + '.json')
else:
print('Resource is not valid, writing resource anyway...')
resource.save('resources/' + resource.name + '.json')
herb = herb.get("herb")
return herb
else:
logger.error(f"Could not find herb {id}")
return
if __name__ == "__main__":
from datapackage import Resource
fl = Flora(flora="/Users/leima/dataherb/flora/flora.json")
hb = fl.herb("git-data-science-job")
print(f"herb base_path: {hb.base_path}")
rs = hb.resources[0]
rs_1 = Resource(rs.descriptor, base_path=str(hb.base_path))
print(f"{rs.tabular}")
# rs_2.read()
rs.read()
print(hb.get_resource(path="dataset/stackoverflow_job_listing.csv"))
logger.debug("End of Game")
'Pinst': 'installed capacity in MW',
'efactor': 'energy that can be gained from the water kwh/m3',
'head': 'difference in altitude in m',
'total_flow': 'inflow to the power plant in mio m3',
'flo_river_ror': 'next downstream res_nr',
'status': 'operational status of the plant',
'company': None,
'turbtype': 'optional: turbine type',
'geodbid': 'specified id for geo referencing',
'river': 'river in which the plant is located',
'river_km': 'km from stream source',
'level_meter': 'assigned level meter for flow curve'
}
# create resource
r = Resource({'path': 'data/runofriver.csv'})
# get basic metadata from data
r.infer()
# add description for fields based on mapper
for i in range(len(r.descriptor['schema']['fields'])):
r.descriptor['schema']['fields'][i]['description'] = \
description_mapper[r.descriptor['schema']['fields'][i]['name']]
# commit (apply) changes to resource
r.commit()
# save the resource
r.save('dataresource.json')
def create_resource(path, title):
from datapackage import Resource
resource = Resource({'path': path})
resource.infer()
resource.descriptor['schema']['primaryKey'] = 'name'
resource.descriptor[
'description'] = 'Installed capacities, costs and technical parameters for components'
resource.descriptor['title'] = title
resource.descriptor['sources'] = [{
'title':
'Restore 2050 hydro inflow timeseries',
'path':
'https://zenodo.org/record/804244/files/Hydro_Inflow.zip'
}, {
'title':
'E-Highway 2050 installed capacities',
'path':
'http://www.e-highway2050.eu/fileadmin/documents/Results/e-Highway2050_2050_Country_and_cluster_installed_capacities_31-03-2015.xlsx'
}, {
'title':
'DIW Berlin - Current and Prospective Costs of Electricity Generation until 2050',
'path':
'https://www.diw.de/documents/publikationen/73/diw_01.c.424566.de/diw_datadoc_2013-068.pdf'
}]
resource.descriptor['schema']['foreignKeys'] = [{
"fields": "bus",
"reference": {
"resource": "bus",
"fields": "name"
}
}]
resource.commit()
if resource.valid:
resource.save('resources/' + resource.name + '.json')
def create_resource(path):
from datapackage import Resource
resource = Resource({'path': path})
resource.infer()
resource.descriptor['schema']['primaryKey'] = 'timeindex'
resource.descriptor['description'] = 'Demand profiles per country'
resource.descriptor['title'] = 'Demand profiles'
resource.descriptor['sources'] = [{
'title':
'OPSD timeseries',
'path':
'https://data.open-power-system-data.org/time_series/2017-07-09/' +
'time_series_60min_singleindex.csv'
}]
resource.commit()
if resource.valid:
resource.save('resources/' + resource.name + '.json')
def temporal_clustering(datapackage, n, path="/tmp", how="daily"):
""" Creates a new datapackage by aggregating sequences inside the
`sequence` folder of the specified datapackage by clustering `n` timesteps
Parameters
----------
datapackage: string
String of meta data file datapackage.json
n: integer
Number of clusters
path: string
Path to directory where the aggregated datapackage is stored
how: string
How to cluster 'daily' or 'hourly'
"""
if how == "weekly":
raise NotImplementedError("Weekly clustering is not implemented!")
p = Package(datapackage)
cwd = os.getcwd()
copied_package_name = (p.descriptor["name"] + "__temporal_cluster__" +
how + "_" + str(n))
copy_path = os.path.join(path, p.descriptor["name"], copied_package_name)
copied_root = copy_datapackage(datapackage,
os.path.abspath(copy_path),
subset="data")
sequence_resources = [
r for r in p.resources
if re.match(r"^data/sequences/.*$", r.descriptor["path"])
]
dfs = {
r.name:
pd.DataFrame(r.read(keyed="True")).set_index("timeindex").astype(float)
for r in sequence_resources
}
sequences = pd.concat(dfs.values(), axis=1)
if how == "daily":
hoursPerPeriod = 24
elif how == "hourly":
hoursPerPeriod = 1
elif how == "weekly":
hoursPerPeriod = 24 * 7
aggregation = tsam.TimeSeriesAggregation(
sequences,
noTypicalPeriods=n,
rescaleClusterPeriods=False,
hoursPerPeriod=hoursPerPeriod,
clusterMethod="hierarchical",
)
cluster_weights = {
aggregation.clusterCenterIndices[n]: w
for n, w in aggregation.clusterPeriodNoOccur.items()
}
if how == "daily":
temporal = pd.Series(
{
d: cluster_weights[d.dayofyear]
for d in sequences.index
if d.dayofyear in aggregation.clusterCenterIndices
},
name="weighting",
)
temporal.index.name = "timeindex"
elif how == "hourly":
temporal = pd.Series(
{
h: cluster_weights[sequences.index.get_loc(h)]
for h in sequences.index if sequences.index.get_loc(h) in
aggregation.clusterCenterIndices
},
name="weighting",
)
temporal.index.name = "timeindex"
# write resources to copied package (should not interfer with meta data)
# as columns are not removed and sorted when written.
os.chdir(copied_root)
for r in sequence_resources:
write_sequences(r.name + ".csv",
dfs[r.name].loc[temporal.index],
replace=True)
# write temporal information from clustering
temporal.to_csv(
"data/temporal.csv",
header=True,
sep=";",
date_format="%Y-%m-%dT%H:%M:%SZ",
)
# add meta data for new temporal information
r = Resource({"path": "data/temporal.csv"})
r.infer()
# TODO: Add meta-data description
r.descriptor[
"description"] = "Temporal selection based on hierachical clustering..."
# Update meta-data of copied package
cp = Package("datapackage.json")
cp.descriptor["name"] = copied_package_name
cp.descriptor["resources"].append(r.descriptor)
cp.commit()
cp.save("datapackage.json")
# set back to 'old' workdirectory
os.chdir(cwd)
return copied_root
# -*- coding: utf-8 -*-
"""
"""
import os
from datapackage import Package, Resource
p = Package('datapackage.json')
p.descriptor['profile'] = 'tabular-data-package'
for f in os.listdir('resources'):
path = os.path.join('resources', f)
r = Resource(path)
p.add_resource(r.descriptor)
p.commit()
os.remove(path)
os.rmdir('resources')
p.save('datapackage.json')
from datapackage import Resource
# Create
resource = Resource({'path': 'data/data.csv'})
resource.tabular # true
resource.headers # ['city', 'location']
print(resource.read(keyed=True))
# [
# {city: 'london', location: '51.50,-0.11'},
# {city: 'paris', location: '48.85,2.30'},
# {city: 'rome', location: 'N/A'},
# ]
# Infer
resource.infer()
print(resource.descriptor)
#{ path: 'data.csv',
# profile: 'tabular-data-resource',
# encoding: 'utf-8',
# name: 'data',
# format: 'csv',
# mediatype: 'text/csv',
# schema: { fields: [ [Object], [Object] ], missingValues: [ '' ] } }
# resource.read(keyed=True)
# Fails with a data validation error
# Tweak
resource.descriptor['schema']['missingValues'] = 'N/A'
resource.commit()
resource.valid # False
print(resource.errors)
def create_resource(path):
"""
"""
from datapackage import Resource
resource = Resource({'path': path})
resource.infer()
resource.descriptor['schema']['primaryKey'] = 'name'
resource.descriptor[
'description'] = 'Contains the hubs (nodes) for the energy system representation'
resource.descriptor[
'title'] = 'Energy system hubs for DE and its electrical neighbours'
resource.descriptor['sources'] = [{
'title':
'NUTS Shapefiles',
'path':
'http://ec.europa.eu/eurostat/cache/GISCO/geodatafiles/NUTS_2013_10M_SH.zip',
'files': [
'NUTS_2013_10M_SH/data/NUTS_RG_10M_2013.shp',
'NUTS_2013_10M_SH/data/NUTS_RG_10M_2013.dbf'
]
}]
resource.commit()
resource.descriptor
if resource.valid:
resource.save('resources/' + resource.name + '.json')
def create_resource(path):
from datapackage import Resource
resource = Resource({'path': path})
resource.infer()
resource.descriptor['schema']['primaryKey'] = 'name'
resource.descriptor[
'description'] = 'Installed transmission capacities from the e-highway 2050 scenario'
resource.descriptor['title'] = 'Installed transmission capacities'
resource.descriptor['sources'] = [{
'title':
'E-Highway 2050 transmission capacities',
'path':
'http://www.e-highway2050.eu/fileadmin/documents/' +
'Results/e-Highway_database_per_country-08022016.xlsx'
}]
resource.descriptor['schema']['foreignKeys'] = [{
"fields": "from_bus",
"reference": {
"resource": "bus",
"fields": "name"
}
}, {
"fields": "to_bus",
"reference": {
"resource": "bus",
"fields": "name"
}
}]
resource.commit()
if resource.valid:
resource.save('resources/' + resource.name + '.json')
def create_resource(path):
from datapackage import Resource
resource = Resource({'path': path})
resource.infer()
resource.descriptor['schema']['primaryKey'] = 'timeindex'
resource.descriptor[
'description'] = 'PV profiles (capacity factors) from renewables ninja for each country'
resource.descriptor['title'] = 'PV profiles'
resource.descriptor['sources'] = [{
'title':
'Renewables Ninja PV Capacity Factors',
'path':
'https://www.renewables.ninja/static/downloads/ninja_europe_pv_v1.1.zip'
}]
resource.commit()
if resource.valid:
resource.save('resources/' + resource.name + '.json')
def temporal_skip(datapackage, n, path="/tmp", name=None, *args):
""" Creates a new datapackage by aggregating sequences inside the
`sequence` folder of the specified datapackage by skipping `n` timesteps
Parameters
----------
datapackage: string
String of meta data file datapackage.json
n: integer
Number of timesteps to skip
path: string
Path to directory where the aggregated datapackage is stored
name: string
Name of the new, aggregated datapackage. If not specified a name will
be given
"""
p = Package(datapackage)
cwd = os.getcwd()
if name is None:
copied_package_name = (p.descriptor["name"] + "__temporal_skip__" +
str(n))
else:
copied_package_name = name
copy_path = os.path.join(path, copied_package_name)
copied_root = copy_datapackage(datapackage,
os.path.abspath(copy_path),
subset="data")
sequence_resources = [
r for r in p.resources
if re.match(r"^data/sequences/.*$", r.descriptor["path"])
]
dfs = {
r.name:
pd.DataFrame(r.read(keyed="True")).set_index("timeindex").astype(float)
for r in sequence_resources
}
sequences = pd.concat(dfs.values(), axis=1)
skip_sequences = sequences.loc[::n]
temporal = pd.Series(data=n, index=skip_sequences.index, name="weighting")
temporal.index.name = "timeindex"
os.chdir(copied_root)
for r in sequence_resources:
write_sequences(r.name + ".csv",
dfs[r.name].loc[temporal.index],
replace=True)
# write temporal information from clustering
temporal.to_csv(
"data/temporal.csv",
header=True,
sep=";",
date_format="%Y-%m-%dT%H:%M:%SZ",
)
# add meta data for new temporal information
r = Resource({"path": "data/temporal.csv"})
r.infer()
r.descriptor[
"description"] = "Temporal selection based on skipped timesteps. Skipped n={}".format(
n)
# Update meta-data of copied package
cp = Package("datapackage.json")
cp.descriptor["name"] = copied_package_name
cp.descriptor["resources"].append(r.descriptor)
cp.commit()
cp.save("datapackage.json")
# set back to 'old' workdirectory
os.chdir(cwd)
return copied_root
def create_resource(path):
"""
"""
from datapackage import Resource
resource = Resource({'path': path})
resource.infer()
resource.descriptor['schema']['primaryKey'] = 'name'
resource.descriptor[
'description'] = 'Excess slacks for each electricity hub in the energy system representation'
resource.descriptor[
'title'] = 'Excess slacks for DE and its electrical neighbours'
resource.descriptor['schema']['foreignKeys'] = [{
"fields": "bus",
"reference": {
"resource": "bus",
"fields": "name"
}
}]
resource.commit()
resource.descriptor
if resource.valid:
resource.save('resources/' + resource.name + '.json')
class load(DataStreamProcessor):
def __init__(self,
load_source,
name=None,
resources=None,
validate=False,
strip=True,
**options):
super(load, self).__init__()
self.load_source = load_source
self.options = options
self.name = name
self.resources = resources
self.load_dp = None
self.validate = validate
self.strip = strip
self.force_strings = options.get('force_strings') is True
def process_datapackage(self, dp: Package):
if isinstance(self.load_source, tuple):
datapackage_descriptor, _ = self.load_source
dp.descriptor.setdefault('resources', [])
self.resource_matcher = ResourceMatcher(self.resources,
datapackage_descriptor)
for resource_descriptor in datapackage_descriptor['resources']:
if self.resource_matcher.match(resource_descriptor['name']):
dp.add_resource(resource_descriptor)
else: # load_source is string:
if self.load_source.startswith('env://'):
env_var = self.load_source[6:]
self.load_source = os.environ.get(env_var)
if self.load_source is None:
raise ValueError(
f"Couldn't find value for env var '{env_var}'")
if os.path.basename(self.load_source) == 'datapackage.json':
self.load_dp = Package(self.load_source)
self.resource_matcher = ResourceMatcher(
self.resources, self.load_dp)
dp.descriptor.setdefault('resources', [])
for resource in self.load_dp.resources:
if self.resource_matcher.match(resource.name):
dp.add_resource(resource.descriptor)
else:
if os.path.exists(self.load_source):
base_path = os.path.dirname(self.load_source) or '.'
self.load_source = os.path.basename(self.load_source)
else:
base_path = None
descriptor = dict(path=self.load_source,
profile='tabular-data-resource')
descriptor['format'] = self.options.get('format')
if 'encoding' in self.options:
descriptor['encoding'] = self.options['encoding']
if descriptor['format'] == 'xml' or self.load_source.endswith(
'.xml'):
self.options.setdefault('custom_parsers',
{})['xml'] = XMLParser
self.options.setdefault('ignore_blank_headers', True)
self.options.setdefault('headers', 1)
self.res = Resource(descriptor,
base_path=base_path,
**self.options)
self.res.infer(confidence=1, limit=1000)
if self.name is not None:
self.res.descriptor['name'] = self.name
if self.force_strings:
for f in self.res.descriptor['schema']['fields']:
f['type'] = 'string'
self.res.commit()
self.res.descriptor['path'] = '{name}.{format}'.format(
**self.res.descriptor)
dp.add_resource(self.res.descriptor)
return dp
def stripper(self, iterator):
for r in iterator:
yield dict((k, v.strip()) if isinstance(v, str) else (k, v)
for k, v in r.items())
def process_resources(self, resources):
yield from super(load, self).process_resources(resources)
if isinstance(self.load_source, tuple):
datapackage_descriptor, resources = self.load_source
yield from (resource for resource, descriptor in zip(
resources, datapackage_descriptor['resources'])
if self.resource_matcher.match(descriptor['name']))
elif self.load_dp is not None:
yield from (resource.iter(keyed=True)
for resource in self.load_dp.resources
if self.resource_matcher.match(resource.name))
else:
it = self.res.iter(keyed=True, cast=False)
if self.validate:
it = schema_validator(self.res, it)
if self.strip:
it = self.stripper(it)
yield it
