def main_loop():
# requested query
Service = request.query.Service
#Converting the Service value to String
Service = str(Service)
csv = petl.fromcsv(file)
response.headers['Content-type'] = 'application/json'
response.headers['Access-Control-Allow-Origin'] = '*'
for i in csv:
if Service == i[1]:
# select the data according to the given requested query
dataSelect = petl.select(csv, "{Service} == '" + Service + "'")
# cutting out the required column names
jsonData = petl.cut(dataSelect, 'ClinicID', 'Suburb', 'Lat', 'Lon')
# convert the dictionary data into json data
jsonData = json.JSONEncoder().encode(list(petl.dicts(jsonData)))
# return the json data
return jsonData
# this is requested link of getting all the distinct list of clinics offering any service.
if Service == "0":
anyServices = petl.unique(csv, key='Name')
jsonData = petl.cut(anyServices, 'ClinicID', 'Suburb', 'Lat',
'Lon')
jsonData = json.JSONEncoder().encode(list(petl.dicts(jsonData)))
return jsonData
else:
jsonData = json.JSONEncoder().encode('Please Enter a Service.')
return jsonData
def main_loop():
# requested query
inputServiceID = request.query.serviceid
csv = pt.fromcsv('clinicservicelocations.csv')
response.headers['Content-type'] = 'application/json'
response.headers['Access-Control-Allow-Origin'] = '*'
for i in csv:
if inputServiceID == i[0]:
# select the data according to the given requested query
dataSelect = pt.select(
csv, "{ServiceID} == '" + str(inputServiceID) + "'")
# cutting out the required column names
jsonData = pt.cut(dataSelect, 'Name', 'Service', 'Suburb', 'State',
'Email', 'Lat', 'Lon')
# convert the dictionary data into json data
jsonData = json.JSONEncoder().encode(list(pt.dicts(jsonData)))
# return the json data
return jsonData
# this is requested link of getting all the distinct list of
# clinics offering any service.
if inputServiceID == "0":
anyServices = pt.unique(csv, key='Name')
jsonData = pt.cut(anyServices, 'Name', 'Service', 'Suburb',
'State', 'Email', 'Lat', 'Lon')
jsonData = json.JSONEncoder().encode(list(pt.dicts(jsonData)))
return jsonData
else:
jsonData = json.JSONEncoder().encode('Unable to find this id.')
return jsonData
def analyze(season_bins, game_bins, season_param, game_param, position):
# extract bucketed season / game performance
gbs = game_buckets(game_param, game_bins, position)
sbs = season_buckets(season_param, season_bins, position)
# create probability distribution tables for seasons / games
s_bucket_probs = bucket_dist(sbs)
sg_bucket_probs = season_game_bucket_combo_probs(sbs, gbs)
# generate likelihoods and prior
s_bucket_dicts = list(petl.dicts(s_bucket_probs))
sg_bucket_dicts = list(petl.dicts(sg_bucket_probs))
s_buckets = sorted(set(s['bucket'] for s in s_bucket_dicts))
likelihoods = {
sb: bayes.Pdf({
gb['g_bucket']: gb['prob']
for gb in sg_bucket_dicts
if gb['s_bucket'] == sb
})
for sb in s_buckets
}
prior = {s['bucket']: s['prob'] for s in s_bucket_dicts}
# get appropriately-formed data
data = data_dicts(sbs, gbs, game_param)
# run analysis on data
runs = conduct_runs(data, likelihoods, prior)
# conduct rmse analysis overall and by bucket
rmse_results = dict(
overall=rmse_analysis(runs),
by_bucket=dict(),
)
for sb in range(season_bins):
tmp_runs = [r for r in runs if r['data']['season']['bucket'] == sb]
rmse_results['by_bucket'][sb] = rmse_analysis(tmp_runs)
# return all data!
return dict(
runs=runs,
likelihoods={k:v.probs for k,v in likelihoods.items()},
prior=prior,
rmse_analysis=rmse_results,
)
def dicts(self, sql):
try:
if isinstance(sql, str):
sql = execute(sql, self)
return etl.dicts(sql)
except SQLError as e:
print(e)
def drained_entries(ctx: typer.Context, issues, entries, project):
config = ctx.meta['config']
empty_entries, unset_entries = petl.biselect(
entries, lambda row: row['issue_id'] is None)
drain_issues = list(
petl.dicts(
transform.select_drain_issues(
issues,
assignee_id=ctx.meta['rdm_user']['id'],
drain_cf_id=get_proj_attr(config, project,
'rdm_drain_cf_id'))))
if not len(drain_issues):
log.error('No drain issues found')
return petl.head(unset_entries, 0), entries
if len(drain_issues) > 1:
log.warning(
f'Found {len(drain_issues)} drain issues. Will use only first one')
drain_issue = drain_issues[0]
drained = petl.addfield(petl.cutout(empty_entries, 'issue_id'), 'issue_id',
drain_issue['id'])
return drained, unset_entries
def anyServices():
# requested query
Postcode = request.query.Postcode
#Converting the Service value to String
Postcode = str(Postcode)
# reading the csv file
csv = petl.fromcsv(file)
# json content type declaration
response.headers['Content-type'] = 'application/json'
response.headers['Access-Control-Allow-Origin'] = '*'
for i in csv:
if Postcode == i[4]:
# select the data according to the given requested query
dataSelect = petl.select(csv, "{Postcode} == '" + Postcode + "'")
# cutting out the required column names
jsonData = petl.cut(dataSelect, 'Service', 'Suburb')
# convert the dictionary data into json data
jsonData = json.JSONEncoder().encode(list(petl.dicts(jsonData)))
# return the json data
return jsonData
else:
jsonData = json.JSONEncoder().encode('Unable to find this Service.')
return jsonData
def _get_record_identifiers(self, cfg):
p = cfg.get('record_identifiers')
if not (p and os.path.isfile(p)):
p = os.path.join(cfg['root'], 'record_ids.json')
table = petl.fromjson(p)
return petl.dicts(table)
def sustain_summary(self):
success, result = self.clip_and_dissolve_esri_feature_layer(
feature_layer_query_url=RAINWAYS_RESOURCES['sustain'],
feature_layer_fields=['GI_Type'],
clipping_mask_gdf=self.aoi_gdf,
clipping_mask_bbox=self.aoi_bbox,
in_epsg_code=self.aoi_gdf_epsg)
if success:
# post-process the dataframe; add additional field
t = etl\
.fromdataframe(result)\
.cutout('geometry')\
.addfield('area_acres', lambda r: r['area'] * 0.00002295682)
# convert to list of dictionaries
results = list(etl.dicts(t))
self.results.sustain.extend(results)
return results
else:
self.status = 'failed'
messages = result['error']['details'] + result['message']
self.messages.extend(messages)
return None
def upsert_many(table, rows, keys, ensure=None, types=None):
if isinstance(rows, Table):
rows = etl.dicts(rows)
elif isinstance(rows, DataFrame):
rows = rows.to_dict(orient='records')
for row in rows:
table.upsert(row, keys, ensure, types)
def update_user_logins(data, url, api_key, source, account_id_field,
login_id_field, unique_id_field, password_field,
sis_user_id_field, integration_id_field, results_table):
if not url.startswith('http') or not url.startswith('https'):
url = 'https://' + url
client = LoginsAPI(url, api_key)
source = data.get(source)
debug = data.config.debug
results = []
for row in petl.dicts(source):
account_id = row[account_id_field]
login_id = row[login_id_field]
kwargs = {}
if unique_id_field is not None and row[unique_id_field] is not None:
kwargs['login_unique_id'] = row[unique_id_field]
if password_field is not None and row[password_field] is not None:
kwargs['login_password'] = row[password_field]
if sis_user_id_field is not None and row[sis_user_id_field] is not None:
kwargs['login_sis_user_id'] = row[sis_user_id_field]
if integration_id_field is not None and row[
integration_id_field] is not None:
kwargs['login_integration_id'] = row[integration_id_field]
try:
r = client.edit_user_login(login_id, account_id, **kwargs)
unsync.secho('Successfully updated login: {} with data: {}'.format(
login_id, str(kwargs)),
fg='green')
if results_table:
row['_data'] = str(kwargs)
row['_response_status'] = r
row['_response_content'] = r
results.append(row)
if debug:
unsync.secho(str(r), fg='yellow')
except (CanvasAPIError) as e:
unsync.secho('Failed updating login: {} with data: {}'.format(
login_id, str(kwargs)),
fg='red')
unsync.secho('Response Status: {} Response Reason: {}'.format(
e.response.status_code, e.response.content),
fg='red')
if results_table:
row['_data'] = str(kwargs)
row['_response_status'] = e.response.status_code
row['_response_content'] = e.response.content
results.append(row)
results = petl.fromdicts(results)
data.cat(results_table, results)
def precip_table_etl_noaa(
precip_table,
rainfall_adjustment=1,
frequency_min=1,
frequency_max=1000,
conversion_factor=2.54,
desc_field="by duration for ARI (years):",
duration_val="24-hr:"
):
"""
Extract, Transform, and Load data from a NOAA PRECIPITATION FREQUENCY
ESTIMATES matrix (in a csv) into an array used by the runoff calculator.
Required Inputs:
- precip_table: NOAA PRECIPITATION FREQUENCY ESTIMATES csv, in inches.
Optional Inputs:
- rainfall_adjustment: multipler to adjust for future rainfall
conditions. defaults to 1.
- frequency_min: the min. annual frequency to be returned. Default: 1
- frequency_max: the max. annual frequency to be returned. Default: 1000
- conversion_factor: apply to rainfall values. Default: 2.54
(convert inches to centimeters).
- desc_field: exact field name from NOAA table in first column.
Defaults to "by duration for ARI (years):". Used for selecting
data.
- duration_val: exact row value in the desc_field from NOAA table that
contains the duration of interest. Defaults to "24-hr:". Used for
selecting data.
Outputs:
- precip_array: 1D array containing 24-hour duration estimate for
frequencies 1,2,5,10,25,50,100,200,500,and 1000 year storm events
"""
# load the csv table, skip the file header information, extract rows we need
t1 = etl\
.fromcsv(precip_table)\
.skip(13)\
.rowslice(0,19)
# grab raw data from the row containing the x-hour duration event info
t2 = etl\
.select(t1, desc_field, lambda v: v == duration_val)\
.cutout(desc_field)
# generate a new header with only columns within frequency min/max
h = tuple(
i
for i in list(etl.header(t2))
if (int(i) >= frequency_min and int(i) <= frequency_max)
)
# for events within freq range, convert to cm, adjust for future rainfall
t3 = etl\
.cut(t2, h)\
.convertall(lambda v: round(float(v) * conversion_factor * rainfall_adjustment, 2))
# convert to a 1D array (values cast to floats)
precips = list(etl.data(t3)[0])
# also convert to a dictionary, for lookup by event
precips_lookup = list(etl.dicts(t3))[0]
# return 1D array and dictionary
return precips, precips_lookup
def to_dicts(self):
"""
Output table as a list of dicts.
`Returns:`
list
"""
return list(petl.dicts(self.table))
def _medical_limits(id, source_db):
"""
get the member limits
"""
sql = ("SELECT dispensary_id, daily_purchase_limit, visit_purchase_limit, "
"daily_visit_limit, two_week_purchase_limit "
"FROM red_flags "
"WHERE dispensary_id={0}").format(id)
data = etl.fromdb(source_db, sql)
limits = etl.select(data, lambda rec: rec.dispensary_id == id)
return etl.dicts(limits)
def anyServices():
# reading the csv file
csv = pt.fromcsv('services.csv')
# json content type declaration
response.headers['Content-type'] = 'application/json'
response.headers['Access-Control-Allow-Origin'] = '*'
# cutting out the required column names
jsonData = pt.cut(csv, 'ServiceID', 'Service')
# convert the dictionary data into json data
jsonData = json.JSONEncoder().encode(list(pt.dicts(jsonData)))
# returning the json data
return jsonData
def __getitem__(self, index):
self._index_count += 1
if self._index_count >= DIRECT_INDEX_WARNING_COUNT:
logger.warning("""
You have indexed directly into this Table multiple times. This can be inefficient,
as data transformations you've made will be computed _each time_ you index into the
Table. If you are accessing many rows of data, consider switching to this style of
iteration, which is much more efficient:
`for row in table:`
""")
return petl.dicts(self.table)[index]
def _get_taxes(id, source_db):
"""
get the dispensary taxes settings for each dispensary_id
"""
sql = ("SELECT DISTINCT dispensary_id, amount, name "
"FROM taxes "
"WHERE dispensary_id={0}").format(id)
data = etl.fromdb(source_db, sql)
try:
lookup_taxes = etl.select(data, lambda rec: rec.dispensary_id == id)
return etl.dicts(lookup_taxes)
except KeyError:
return 0
def data_dicts(sbs, gbs, game_param):
season_dicts = petl.dicts(sbs)
game_dicts = petl.dicts(gbs)
data = []
for s in season_dicts:
gd = [
{
k:v for k,v in x.items()
if k in [game_param, 'week', 'bucket']
}
for x in game_dicts
if x['year'] == s['year']
and x['name'] == s['name']
]
data.append(dict(
season=s,
games=sorted(gd, key=lambda r: r['week']),
))
return data
def etl(self, record_id):
for model in self.__models__:
location_table = self.extract(model, record_id)
nrows = petl.nrows(location_table)
if nrows == 1:
record = petl.dicts(location_table)[0]
if self._has_observations(record):
self._added = True
location_id = self._post_location(record, model)
thing_id = self._post_thing(record, model, location_id)
self.add_package(record)
self.observation.etl(tids=self._make_tids(thing_id, record),
models=(model,))
else:
print(f'multipe records found for given record_id. Skipping {record_id}')
def dictFromTable():
insights = {}
#number of applications filed
insights['Building Permits Filed'] = 0
#number of commercial in the works
insights['Commercial Project Count'] = 0
#number of residential in the works
insights['Residential Project Count'] = 0
listings = []
# print "in here "
years = ['2016']
quarters = ['Q1', 'Q2', 'Q3', 'Q4']
tableList = openFileTable(years, quarters)
for table in tableList:
#transforms into dictionary
d = etl.dicts(table)
# print etl.look(table)
#list of dictionaries
b = list(d)
for i in range(0, len(b)):
#print b[i]
if 'BP FILED' == b[i]['BESTSTAT']:
insights['Building Permits Filed'] = insights[
'Building Permits Filed'] + 1
if 'Resident' == b[i]['PROJECT_TYPE']:
insights['Residential Project Count'] = insights[
'Residential Project Count'] + 1
if 'Mixed' == b[i]['PROJECT_TYPE']:
insights['Commercial Project Count'] = insights[
'Commercial Project Count'] + 1
listings.append(b[i])
listings.insert(0, insights)
return listings
def process_animal_extended(shelter_id, session, input_directory):
table = petl.fromxls(os.path.join(input_directory,
'AnimalIntakeExtended.xls'),
sheet='AnimalIntakeExtended')
## Because an animal can appear in the intake report more than once,
## we must sort the table in order to upsert the latest value
table_sorted = petl.sort(table, key='Intake Date/Time')
for row in petl.dicts(table_sorted):
id = row['Animal ID']
set_values = {
'arn': normalize_string(row['ARN']),
'name': normalize_string(row['Animal Name']),
'species': normalize_string(row['Species']),
'primary_breed': normalize_string(row['Primary Breed']),
'secondary_bred': normalize_string(row['Secondary Breed']),
'gender': normalize_string(row['Gender']),
'pre_altered': to_bool(row['Pre Altered']),
'altered': to_bool(row['Altered']),
'primary_color': normalize_string(row['Primary Colour']),
'secondary_color': normalize_string(row['Secondary Colour']),
'third_color': normalize_string(row['Third Colour']),
'color_pattern': normalize_string(row['Colour Pattern']),
'second_color_pattern':
normalize_string(row['Second Colour Pattern']),
'size': normalize_string(row['Size'])
}
insert_stmt = insert(Animal)\
.values(
id=id,
shelter_id=shelter_id, ## TODO: add to index for constraint? make composite pk?
**set_values)\
.on_conflict_do_update(
constraint='animals_pkey',
set_={
'shelter_id': shelter_id,
**set_values,
'updated_at': func.now()
})
session.execute(insert_stmt)
session.commit()
def _add_observations(self, datastream_id, records, model):
if not isinstance(records, list):
records = petl.dicts(records)
for wti in tqdm(records):
t = self._timestamp_extract(wti[model.timestamp_column])
t = MT_TIMEZONE.localize(t)
v = wti[model.mapped_column]
if v is not None:
payload = {
'phenomenonTime': t.isoformat(timespec='milliseconds'),
'resultTime': t.isoformat(timespec='milliseconds'),
'result': v,
'Datastream': make_id(datastream_id)
}
self._post_item(f'Observations', payload)
def row_data(self, row_index):
"""
Returns a row in table
`Args:`
row_index: int
`Returns:`
dict
A dictionary of the row with the column as the key and the cell
as the value.
"""
self._index_count += 1
if self._index_count >= DIRECT_INDEX_WARNING_COUNT:
logger.warning("""
You have indexed directly into this Table multiple times. This can be inefficient,
as data transformations you've made will be computed _each time_ you index into the
Table. If you are accessing many rows of data, consider switching to this style of
iteration, which is much more efficient:
`for row in table:`
""")
return petl.dicts(self.table)[row_index]
def test_dicts_shortrows():
table = (('foo', 'bar'), ('a', 1), ('b', ))
actual = dicts(table)
expect = ({'foo': 'a', 'bar': 1}, {'foo': 'b', 'bar': None})
ieq(expect, actual)
def test_dicts():
table = (("foo", "bar"), ("a", 1), ("b", 2))
actual = dicts(table)
expect = ({"foo": "a", "bar": 1}, {"foo": "b", "bar": 2})
ieq(expect, actual)
def test_dicts():
table = (('foo', 'bar'), ('a', 1), ('b', 2))
actual = dicts(table)
expect = ({'foo': 'a', 'bar': 1}, {'foo': 'b', 'bar': 2})
ieq(expect, actual)
def collect_import_objects(self):
"""
Query and put together all QA objects which will be imported.
"""
def get_tables(mappings):
"""
Recursively collect all WOCAT tables of the mappings.
Args:
mappings: list.
Returns:
list. A list of tables.
"""
tables = []
for mapping in mappings:
table = mapping.get('wocat_table')
if table:
tables.append(table)
tables.extend(get_tables(mapping.get('mapping', [])))
tables.extend(get_tables(mapping.get('conditions', [])))
return tables
self.output('Fetching data from WOCAT QA database.', v=1)
# Extend the default tables by adding the ones from the mapping.
tables = self.default_tables
for qg_properties in self.mapping.values():
questions = qg_properties.get('questions', {})
for q_properties in questions.values():
tables.extend(get_tables(q_properties.get('mapping', [])))
# Remove duplicates
tables = list(set(tables))
# Try to query the lookup table and collect its values.
try:
lookup_query = """
SELECT *
FROM {schema}.{table_name};
""".format(schema=self.schema,
table_name=self.lookup_table_name)
lookup_table = {}
for row in petl.dicts(petl.fromdb(self.connection, lookup_query)):
lookup_table[row.get('id')] = row
except AttributeError:
lookup_table = {}
# So far, lookup_text is never used. Therefore it can be left empty.
lookup_table_text = {}
# Try to query file infos
try:
lookup_query_files = """
SELECT *
FROM {schema}.{table_name};
""".format(schema=self.schema,
table_name=self.file_info_table)
file_infos = {}
for row in petl.dicts(
petl.fromdb(self.connection, lookup_query_files)):
file_infos[row.get('blob_id')] = row
except AttributeError:
file_infos = {}
for table_name in tables:
query = 'SELECT {columns} FROM {schema}.{table_name};'.format(
columns='*', schema=self.schema, table_name=table_name)
queried_table = petl.fromdb(self.connection, query)
row_errors = False
for row in petl.dicts(queried_table):
if row_errors is True:
continue
# Inconsistent naming throughout the tables
questionnaire_identifier = self.questionnaire_identifier
if table_name == 'approach':
questionnaire_identifier = 'id'
elif table_name == 'qa_quality_review':
questionnaire_identifier = 'qa_id'
identifier = row.get(questionnaire_identifier)
if identifier is None:
self.output('No identifier found for table "{}".'.format(
table_name), v=1, l='error')
row_errors = True
if identifier in self.import_objects_exclude:
continue
import_object = self.get_import_object(identifier)
if import_object is None:
import_object = QAImportObject(
identifier, self.command_options, lookup_table,
lookup_table_text, file_infos, self.image_url)
import_object.add_custom_mapping_messages(
self.custom_mapping_messages)
self.import_objects.append(import_object)
# Set the code if it is available in the current table
code = row.get(self.questionnaire_code)
if code:
import_object.set_code(code)
# The main contributor is the compiler
compiler_id = row.get(self.questionnaire_owner)
if compiler_id:
# If the main contributer is "Not registered" (ID 661), use
# the default compiler
if compiler_id == 661:
compiler_id = self.default_compiler_id
import_object.add_mapping_message(
'Using "Unknown User" as compiler in QCAT as main '
'contributor in QA was "Not registered"')
# The following QAs have a main contributor which is not
# available through the API call. Set the default user and
# add a mapping message.
elif identifier in [131, 128, 89, 47, 106, 82, 195, 212,
76, 107, 84, 139, 130, 276, 72, 147,
138, 43, 44, 46, 49, 50, 52, 57, 173,
171, 170, 166, 125, 78, 102, 45, 197,
48]:
compiler_id = self.default_compiler_id
import_object.add_mapping_message(
'The compiler needs to be set manually. Use the '
'main contributor of QA.')
import_object.set_owner(compiler_id)
# Use the creation date available on the approach table
created = row.get('date')
if created and table_name == 'approach':
creation_time = datetime.strptime(
created, WOCAT_DATE_FORMAT)
import_object.created = timezone.make_aware(
creation_time, timezone.get_current_timezone())
import_object.add_wocat_data(table_name, row)
etl.header(table) # data() ######## import petl as etl table = [['foo', 'bar'], ['a', 1], ['b', 2]] d = etl.data(table) list(d) # dicts() ######### import petl as etl table = [['foo', 'bar'], ['a', 1], ['b', 2]] d = etl.dicts(table) d list(d) # namedtuples() ############### import petl as etl table = [['foo', 'bar'], ['a', 1], ['b', 2]] d = etl.namedtuples(table) d list(d) # records() ###############
def test_dicts_shortrows():
table = (("foo", "bar"), ("a", 1), ("b",))
actual = dicts(table)
expect = ({"foo": "a", "bar": 1}, {"foo": "b", "bar": None})
ieq(expect, actual)
def test_dicts():
table = (('foo', 'bar'), ('a', 1), ('b', 2))
actual = dicts(table)
expect = ({'foo': 'a', 'bar': 1}, {'foo': 'b', 'bar': 2})
ieq(expect, actual)
def __iter__(self):
return iter(petl.dicts(self.table))
def test_dicts_shortrows():
table = (('foo', 'bar'), ('a', 1), ('b',))
actual = dicts(table)
expect = ({'foo': 'a', 'bar': 1}, {'foo': 'b', 'bar': None})
ieq(expect, actual)
######## import petl as etl table = [["foo", "bar"], ["a", 1], ["b", 2]] d = etl.data(table) list(d) # dicts() ######### import petl as etl table = [["foo", "bar"], ["a", 1], ["b", 2]] d = etl.dicts(table) d list(d) # namedtuples() ############### import petl as etl table = [["foo", "bar"], ["a", 1], ["b", 2]] d = etl.namedtuples(table) d list(d)
def transform_and_aggregate_datetimes(query_results, rollup):
"""transform datetime to the correct TZ; aggregate the values in the query results
based on the datetime rollup args. Aggregation is performed for:
* hourly or daily time intervals
* total
NOTE: in order to handle potential No-Data values in the DB during aggregation, we
convert them to 0. The `src` field then indicates if any values in the rollup were N/D.
Then, if the value field in the aggregated row still shows 0 after summation, *and*
the src field shows N/D, we turn that zero into None. If there was a partial reading
(e.g., the sensor has values for the first half hour but N/D for the second, and we are
doing an hourly rollup), then the values will stay there, but the source field will indicate
both N/D and whatever the source was for the workable sensor values.
TODO: move this work over to the database query
"""
t1 = etl\
.fromdicts(query_results)\
.convert('xts', lambda v: v.astimezone(TZ).isoformat(), failonerror=True)
#.rename('xts', 'ts')
# print("t1")
# print(t1)
# print("rollup", rollup)
if rollup in [INTERVAL_DAILY, INTERVAL_HOURLY]:
petl_aggs = OrderedDict(
val=('val', _sumround), # sum the rainfall vales
src=('src', _listset) # create a list of all rainfall sources included in the rollup
)
t2 = etl\
.convert(
t1,
'xts',
lambda v: _rollup_date(v, rollup), # convert datetimes to their rolled-up value in iso-format
failonerror=True
)\
.convert(
'val',
lambda v: 0 if v is None else v, # convert rainfall values to 0 if no-data
failonerror=True
)\
.aggregate(
('xts', 'sid'),
petl_aggs # aggregate rainfall values (sum) and sources (list) for each timestamp+ID combo,
)\
.convert(
'val',
lambda v, r: None if ('N/D' in r.src and v == 0) else v, # replace 0 values with no data if aggregated source says its N/D
pass_row=True,
failonerror=True
)\
.sort('sid')
# .convert(
# 'xts',
# lambda v: TZ.localize(parse(v)).isoformat(), # convert that datetime to iso format w/ timezone
# failonerror=True
# )
# print("t2 time rollup")
elif rollup in [INTERVAL_SUM]:
petl_aggs = OrderedDict(
val=('val', _sumround), # sum the rainfall vales
src=('src', _listset), # create a list of all rainfall sources included in the rollup
xts=('xts', _minmax) # create a iso datetime range string from the min and max datetimes found
)
t2 = etl\
.aggregate(
t1,
'sid',
petl_aggs # aggregate rainfall values (sum) and sources (list), and datetimes (str) for each ID,
)\
.convert(
'val',
lambda v, r: None if ('N/D' in r.src and v == 0) else v, # replace 0 values with no data if aggregated source says its N/D
pass_row=True
)\
.sort('sid')\
# print("t2 sum")
else:
t2 = t1
# print("t2 = t1")
# print(t2)
# h = etl.header(t2)
# rename the timestamp and sensor id fields,
# print("t2 header:", list(etl.header(t2)))
# rename_kw = {}
# for h1, h0 in [('xts', 'ts'), ('sid', 'id')]:
# if h1 in h:
# rename_kw[h1] = h0
# if len(rename_kw.items()) > 0:
# {'xts':'ts', 'sid':'id'}
t3 = etl.rename(t2, {'xts':'ts', 'sid':'id'}, strict=False)
# else:
# t3 = t2
# print("t3")
# print(t3)
# convert to list of dicts and return
return list(etl.dicts(t3))
def transform(mmj_menu_items, mmj_categories, prices, organization_id,
source_db, debug):
"""
Transform data
"""
# source data table
source_dt = utils.view_to_list(mmj_menu_items)
cut_menu_data = [
'id', 'vendor_id', 'menu_id', 'dispensary_id', 'strain_id',
'created_at', 'updated_at', 'category_id', 'name', 'sativa', 'indica',
'on_hold', 'product_type', 'image_file_name', 'medicine_amount',
'product_type'
]
cut_prices = [
'menu_item_id', 'dispensary_id', 'price_half_gram', 'price_gram',
'price_two_gram', 'price_eigth', 'price_quarter', 'price_half',
'price_ounce'
]
# Cut out all the fields we don't need to load
menu_items = etl.cut(source_dt, cut_menu_data)
prices_data = etl.cut(prices, cut_prices)
menu_items = (etl.addfield(
menu_items, 'createdAtEpoch').addfield('unitOfMeasure').addfield(
'locationProductDetails').addfield('keys').addfield('restockLevel')
)
# Two-step transform and cut. First we need to cut the name
# and id from the source data to map to.
cut_source_cats = etl.cut(mmj_categories, 'name', 'id', 'measurement')
source_values = etl.values(cut_source_cats, 'name', 'id')
# Then we nede a dict of categories to compare against.
# id is stored to match against when transforming and mapping categories
mmj_categories = dict([(value, id) for (value, id) in source_values])
mappings = OrderedDict()
mappings['id'] = 'id'
mappings['createdAt'] = 'created_at'
mappings['updatedAt'] = 'updated_at'
mappings['createdAtEpoch'] = lambda x: utils.create_epoch(x.created_at)
mappings['name'] = 'name'
mappings['shareOnWM'] = lambda x: _wm_integration(x.id, source_db)
"""
1 = Units
2 = Grams (weight)
"""
mappings['unitOfMeasure'] = \
lambda x: _map_uom(x.category_id, source_db)
fields = etl.fieldmap(menu_items, mappings)
data = etl.merge(menu_items, fields, key='id')
items = []
for item in etl.dicts(data):
breakpoint_pricing = (etl.select(
prices_data,
lambda x: x.dispensary_id == item['dispensary_id']).rename({
'price_eigth':
'price_eighth'
}).cutout('menu_item_id'))
# Set image url for load to download
url = None
if debug and item['image_file_name'] is not None:
url = ("https://wm-mmjmenu-images-development.s3."
"amazonaws.com/menu_items/images/{0}/large/"
"{1}").format(item['id'], item['image_file_name'])
elif item['image_file_name'] is not None:
url = ("https://wm-mmjmenu-images-production.s3."
"amazonaws.com/menu_items/images/{0}/large/"
"{1}").format(item['id'], item['image_file_name'])
item['image_file_name'] = url
item['categoryId'] = _map_categories(item['category_id'],
item['sativa'], item['indica'],
mmj_categories, menu_items)
item['keys'] = {
'dispensary_id': item['dispensary_id'],
'id': item['id'],
'menu_id': item['menu_id'],
'vendor_id': item['vendor_id'],
'strain_id': item['strain_id'],
'category_id': item['category_id']
}
# set a default netMJ value if the menu item is a unit product
if item['unitOfMeasure'] is 2:
item['netMarijuana'] = int(item['medicine_amount'])
for key in item['keys'].keys():
if not item['keys'][key]:
del item['keys'][key]
item['locationProductDetails'] = {
'id': item['id'],
'active': _active(item['on_hold'])
}
item['restockLevel'] = _restock_level(item['dispensary_id'],
item['product_type'], source_db)
if item['shareOnWM'] is None:
item['shareOnWM'] = False
for price in etl.dicts(breakpoint_pricing):
try:
price_two_gram = price['price_two_gram']
except KeyError:
price_two_gram = 0.0
item['locationProductDetails']['weightPricing'] = {
'price_half_gram':
utils.dollars_to_cents(price['price_half_gram']),
'price_two_gram': utils.dollars_to_cents(price_two_gram),
'price_gram': utils.dollars_to_cents(price['price_gram']),
'price_eighth': utils.dollars_to_cents(price['price_eighth']),
'price_quarter':
utils.dollars_to_cents(price['price_quarter']),
'price_half': utils.dollars_to_cents(price['price_half']),
'price_ounce': utils.dollars_to_cents(price['price_ounce'])
}
del item['vendor_id']
del item['indica']
del item['dispensary_id']
del item['id']
del item['strain_id']
del item['on_hold']
del item['menu_id']
del item['sativa']
del item['category_id']
del item['updated_at']
del item['created_at']
del item['product_type']
if item['image_file_name'] is None:
del item['image_file_name']
# set up final structure for API
items.append(item)
# Remove inactive items
for item in items:
if item['locationProductDetails']['active'] is False:
items.remove(item)
if debug:
result = json.dumps(items,
sort_keys=True,
indent=4,
default=utils.json_serial)
print(result)
return items
def transform(source_data, organization_id, debug):
"""
Load the transformed data into the destination(s)
"""
# source data table
source_dt = utils.view_to_list(source_data)
cut_data = [
'id', 'dispensary_id', 'picture_file_name', 'name', 'email',
'address', 'phone_number', 'dob', 'license_type', 'registry_no',
'membership_id', 'given_caregivership', 'tax_exempt',
'drivers_license_no', 'points', 'locked_visits',
'locked_visits_reason', 'caregiver_id', 'picture_file_name',
'card_expires_at', 'created_at', 'updated_at', 'physician_id',
'custom_membership_id', 'organization_membership_id', 'city',
'state', 'zip_code', 'address', 'organization_id'
]
member_data = etl.cut(source_dt, cut_data)
members = (
etl
.addfield(member_data, 'identificationType')
.addfield('createdAtEpoch')
)
member_mapping = OrderedDict()
member_mapping['id'] = 'id'
member_mapping['caregiver_id'] = 'caregiver_id'
member_mapping['dispensary_id'] = 'dispensary_id'
member_mapping['physician_id'] = 'physician_id'
member_mapping['custom_membership_id'] = 'custom_membership_id'
member_mapping['organization_membership_id'] = 'organization_membership_id'
member_mapping['picture_file_name'] = 'picture_file_name'
member_mapping['dateOfBirth'] = 'dob'
member_mapping['name'] = 'name'
member_mapping['phone_number'] = 'phone_number'
member_mapping['email'] = 'email'
member_mapping['organization_id'] = 'organization_id'
# MEDICAL 1, RECREATIONAL 2
member_mapping['memberType'] = \
lambda m: 'MEDICAL' if m.license_type == 1 else 'RECREATIONAL'
member_mapping['mmjCard'] = 'registry_no'
member_mapping['isCaregiver'] = \
lambda x: utils.true_or_false(x.given_caregivership)
member_mapping['identificationNumber'] = 'drivers_license_no'
member_mapping['points'] = 'points'
member_mapping['card_expires_at'] = 'card_expires_at'
member_mapping['taxExempt'] = lambda x: utils.true_or_false(x.tax_exempt)
member_mapping['locked_visits'] = 'locked_visits'
member_mapping['locked_visits_reason'] = 'accountStatusNotes'
member_mapping['address'] = 'address'
member_mapping['city'] = 'city'
member_mapping['zip_code'] = 'zip_code'
member_mapping['state'] = 'state'
member_mapping['createdAt'] = 'created_at'
member_mapping['updatedAt'] = 'updated_at'
member_mapping['accountStatus'] = \
lambda x: utils.account_status(x.locked_visits)
member_fields = etl.fieldmap(members, member_mapping)
members = []
for item in etl.dicts(member_fields):
item['keys'] = {
'id': item['id'],
'caregiver_id': item['caregiver_id'],
'dispensary_id': item['dispensary_id'],
'physician_id': item['physician_id'],
'custom_membership_id': item['custom_membership_id'],
'organization_membership_id': item['organization_membership_id'],
'picture_file_name': item['picture_file_name'],
'organization_id': item['organization_id'],
}
if item['card_expires_at'] is not None:
item['expiryDate'] = item['card_expires_at']
# remove any item['keys'] tuples with None values
for key in item['keys'].keys():
if not item['keys'][key]:
del item['keys'][key]
# set up final structure for API
item['identificationType'] = 'Drivers License'
# We may not need this in the data
item['address'] = [{
'line1': item['address'],
'city': item['city'],
'state': item['state'],
'zip': item['zip_code'],
}]
# replace None value dobs with the epoch beginning of time
if not item['dateOfBirth']:
dob = time.strftime('%Y-%m-%d %H:%M:%S.000Z', time.gmtime(0))
item['dateOfBirth'] = datetime.strptime(dob, '%Y-%m-%d %H:%M:%S.000Z')
#del item['address']
del item['city']
del item['zip_code']
del item['state']
del item['dispensary_id']
del item['id']
del item['physician_id']
del item['caregiver_id']
del item['custom_membership_id']
del item['organization_membership_id']
del item['organization_id']
del item['picture_file_name']
del item['locked_visits_reason']
del item['locked_visits']
del item['card_expires_at']
members.append(item)
if debug:
result = json.dumps(members, sort_keys=True,
indent=4, default=utils.json_serial)
print(result)
return members
"""Return a list of valid NUTS codes."""
with open(GEOCODES_FILE) as stream:
lines = csv.DictReader(stream)
geocodes = []
for i, line in enumerate(lines):
# The first line has an empty NUTS-code
if i > 0:
geocode = line['NUTS-Code']
geocodes.append(geocode)
logging.debug('Loaded %d NUTS geocodes', len(geocodes))
return tuple(geocodes)
GEOCODES = list(dicts(fromcsv(GEOCODES_FILE)))
def get_all_codelists():
"""Return all codelists as a dictionary of dictionaries."""
codelists = {}
for codelist_file in os.listdir(CODELISTS_DIR):
codelist_name, _ = os.path.splitext(codelist_file)
codelist = get_codelist(codelist_name)
codelists.update({codelist_name: codelist})
return codelists
def sales_summary(start_dt=None, end_dt=None):
"""tally up gross (sale over list) profits
TODO: tally up net profites (gross profit vs inventory purchase total)
TODO: Keyword Arguments:
start_dt {[type]} -- datetime for start of query (default: {None})
end_dt {[type]} -- datetime for start of query [description] (default: {None})
Returns:
[dict] -- various types of sales information, stored in a dictionary.
"""
# products = db.session.query(Product).all()
# sales = db.session.query(Sale).all()
# retrieve existing tables
products_records = etl.fromdb(db.engine, 'SELECT * FROM product')
sales_records = etl.fromdb(db.engine, 'SELECT * FROM sale')
# join product info to sales data
sales_data = etl.join(sales_records,
products_records,
lkey='product_id',
rkey='id')
# prep joined sales data for tabulation
sales_data = etl.convert(sales_data, 'date', lambda dt: format_date(dt))
sales_data = etl.sort(sales_data, 'date')
sales_data = etl.convert(sales_data, 'quantity',
lambda q: handle_none(q, replace_with=1))
sales_data = etl.addfield(sales_data, 'profit',
lambda rec: calculate_profit(rec))
sales_data = etl.addfield(sales_data, 'gross_sales',
lambda rec: calculate_gross_sales(rec))
# summarize data into charting-friendly data structures
chart_count = etl.fold(sales_data,
'date',
operator.add,
'quantity',
presorted=True)
chart_count = etl.rename(chart_count, {'key': 'x', 'value': 'y'})
chart_count, chart_count_missing_date = etl.biselect(
chart_count, lambda rec: rec.x is not None)
# print(chart_count)
# etl.lookall(chart_count)
chart_gross = etl.fold(sales_data,
'date',
operator.add,
'gross_sales',
presorted=True)
chart_gross = etl.rename(chart_gross, {'key': 'x', 'value': 'y'})
chart_gross, chart_gross_missing_date = etl.biselect(
chart_gross, lambda rec: rec.x is not None)
# print(chart_gross)
# etl.lookall(chart_gross)
chart_profit = etl.fold(sales_data,
'date',
operator.add,
'profit',
presorted=True)
chart_profit = etl.rename(chart_profit, {'key': 'x', 'value': 'y'})
chart_profit, chart_profit_missing_date = etl.biselect(
chart_profit, lambda rec: rec.x is not None)
# tabulate some figures
gross_sales = 0
profits = 0
for sale in etl.dicts(sales_data):
profits += calculate_profit(sale)
gross_sales += calculate_gross_sales(sale)
# for i in etl.dicts(chart_count):
# print(i)
# for i in etl.dicts(chart_gross):
# print(i)
return {
'gross_sales': gross_sales,
'profits': profits,
'chart_gross': list(etl.dicts(chart_gross)),
'chart_gross_missing_date': list(etl.dicts(chart_gross_missing_date)),
'chart_profit': list(etl.dicts(chart_profit)),
'chart_profit_missing_date':
list(etl.dicts(chart_profit_missing_date)),
'chart_count': list(etl.dicts(chart_count)),
'chart_count_missing_date': list(etl.dicts(chart_count_missing_date))
}
def __repr__(self):
return repr(petl.dicts(self.table))