def main():
recipe_name = input('Recipe Name: ')
recipe = Recipe(name=recipe_name)
print('Ingredients:')
name = input('Name: ')
quantity = input('Quantity (eg 200): ')
measurement = input('Measurement (eg grams): ')
ingredient = recipe.build_ingredient(name, quantity, measurement)
recipe.add_ingredient_to_recipe_ingredients(ingredient)
print('Instructions:')
instruction = input('Instruction: ')
recipe.add_instruction_to_recipe_instructions(instruction)
print('Recipe: ', recipe.build_recipe())
recipe_book = RecipeBook()
recipe_book.add_recipe_to_recipe_book(recipe.build_recipe())
print('Recipe Book: ', recipe_book.view_recipes())
subject_type_label='trafficCounter')
attr = AttributeMatcher(provider='uk.gov.dft', label='CountPedalCycles')
sub_field_latest = LatestValueField(attribute_matcher=attr)
field_1 = GeographicAggregationField(label='BicycleCount',
subject=a_sub,
function='sum',
field=sub_field_latest)
attr_2 = AttributeMatcher(provider='uk.gov.dft', label='CountCarsTaxis')
sub_field_latest_2 = LatestValueField(attribute_matcher=attr_2)
field_2 = GeographicAggregationField(label='CarCount',
subject=a_sub,
function='sum',
field=sub_field_latest_2)
arithmetic_field = ArithmeticField(label='BicycleFraction',
operation='div',
operation_on_field_1=field_1,
operation_on_field_2=field_2)
# Passing everything to a Dataset Object as a list. Building and running the recipe by
# passing as arguments location of DigitalConnector and recipe output location.
# The latter needs to be relative to user's home directory.
dataset = Dataset(subjects=[main_subject],
datasources=[m_datasource_2, m_datasource_3, m_datasource_4],
fields=[parent_field, arithmetic_field])
recipe = Recipe(dataset=dataset)
recipe.build_recipe(
output_location='Desktop/london-cycle-traffic-air-quality.json')
recipe.run_recipe(
tombolo_path='Desktop/TomboloDigitalConnector',
output_path='Desktop/london-cycle-traffic-air-quality.geojson')
from os import path, pardir import sys sys.path.append(path.join(path.dirname(path.realpath(__file__)), pardir)) tombolo_path = 'Desktop/TomboloDigitalConnector' recipe_output_location = 'Desktop/london-no2.json' model_output = 'Desktop/london-no2.geojson' from recipe import Recipe, Field, Datasource, AttributeMatcher, Subject, Match_Rule, LatestValueField, Dataset subjects = Subject(subject_type_label='airQualityControl', provider_label='erg.kcl.ac.uk') datasources = Datasource(importer_class='uk.org.tombolo.importer.lac.LAQNImporter', datasource_id='airQualityControl') attribute_matcher = AttributeMatcher(provider='erg.kcl.ac.uk', label='NO2 40 ug/m3 as an annual me') lvf = LatestValueField(attribute_matcher=attribute_matcher, label='Anual NO2') dataset = Dataset(subjects=[subjects], datasources=[datasources], fields=[lvf]) recipe = Recipe(dataset=dataset) recipe.build_recipe(output_location=recipe_output_location) recipe.run_recipe(tombolo_path=tombolo_path, output_path=model_output)
sys.path.append(path.join(path.dirname(path.realpath(__file__)), pardir))
# Importing all the relevant objects which are necessary
from recipe import Dataset, Subject, AttributeMatcher, GeographicAggregationField, LatestValueField, Match_Rule, Datasource, Recipe
# Creating match rule for London
match_rule = Match_Rule(attribute_to_match_on='label', pattern='E090%')
subject = Subject(subject_type_label='localAuthority', provider_label='uk.gov.ons', match_rule=match_rule)
# Creating datasource to tell DC which importers to call in order to download dataset
datasource_1 = Datasource(importer_class='uk.org.tombolo.importer.ons.OaImporter', datasource_id='localAuthority')
datasource_2 = Datasource(importer_class='uk.org.tombolo.importer.dft.TrafficCountImporter',
datasource_id= 'trafficCounts', geography_scope=["London"])
# Creating Attribute matcher, which means getting only those values from database where
# attribute name is CountPedalCycles
attribute_matcher = AttributeMatcher(provider='uk.gov.dft', label='CountPedalCycles')
field = LatestValueField(attribute_matcher=attribute_matcher, label='CountPedalCycles')
# Creating Subject for Geographic aggregation field
subject_2 = Subject(subject_type_label='trafficCounter', provider_label='uk.gov.dft')
geo_agg_field = GeographicAggregationField(subject=subject_2, field=field, function='sum', label='SumCountPedalCycles')
# Creating the dataset and calling DC to run the recipe
dataset = Dataset(subjects=[subject], datasources=[datasource_1, datasource_2], fields=[geo_agg_field])
_recipe = Recipe(dataset=dataset)
_recipe.build_recipe(output_location='Desktop/aggregate-traffic-count-data-within-localauthorities.json')
_recipe.run_recipe(tombolo_path='Desktop/UptodateProject/TomboloDigitalConnector', output_path='Desktop/aggregate-traffic-count-data-within-localauthorities.geojson')
from os import path, pardir
import sys
sys.path.append(path.join(path.dirname(path.realpath(__file__)), pardir))
from recipe import Recipe, Field, Datasource, AttributeMatcher, Subject, Match_Rule, LatestValueField, Dataset
from Importers import importer_london_air_quality
subjects = Subject(subject_type_label='airQualityControl',
provider_label='erg.kcl.ac.uk')
datasources = Datasource(importer_class='', datasource_id='')
attribute_matcher = AttributeMatcher(provider='erg.kcl.ac.uk',
label='NO2 40 ug/m3 as an annual mean')
lvf = LatestValueField(attribute_matcher=attribute_matcher, label='NO2 Value')
dataset = Dataset(subjects=[subjects], datasources=[datasources], fields=[lvf])
recipe = Recipe(dataset=dataset)
recipe.build_recipe(output_location='Desktop/london-no2-python-importer.json')
recipe.run_recipe(
tombolo_path='Desktop/UptodateProject/TomboloDigitalConnector',
output_path='Desktop/london-no2-python-importer.geojson')
