View dbt_flow_simple_orders_example_postgres.sql
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{{ | |
config( | |
materialized='incremental' | |
) | |
}} | |
with orders as ( | |
select * from {{ ref('stg__orders') }} | |
), |
View cipherize.py
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""" | |
Encrypts or Decrypts messages. For encryption: | |
python cipherize.py --message=test --action=encrypt | |
or | |
python3 cipherize.py --filepath /path/to/file --action encrypt | |
Decryption: |
View top_unit_test_example.py
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@pytest.mark.django_db | |
def test_data_2(): | |
""" | |
Force overbooking of ships. | |
""" | |
name = 'test2' | |
f = Files.objects.create(name=name) | |
f.save() | |
demand_data = [ |
View top_final_solution_cvxpy.py
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def optimize(data_models, train_limit, ob_cost_factor=2, container_cost_factor=10): | |
results = {} | |
containers = data_models['containers'] | |
shippers = data_models['shippers'] | |
orig_ports = data_models['orig_port'] | |
transp = data_models['transp'] | |
transp_train_map = data_models['transp_train_map'] | |
shipper_trade_spaces = data_models['shipper_trade_spaces'] | |
overbooking_spaces = shipper_trade_spaces.matrix | |
dest_shipper_map = data_models['dest_shipper_map'] |
View top_complete_example_cvxpy.py
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import numpy as np | |
import cvxpy as cx | |
# Begin with variables | |
x_shippers = cx.Variable((4, 2), boolean=True, name='shippers') | |
x_transports = cx.Variable((4, 2), boolean=True, name='transports') | |
x_docks = cx.Variable((4, 2), boolean=True, name='docks') | |
# 'y' represents variables that are built by introducing an AND conjunction between | |
# 2 other variables. |
View top_medium_example_cvxpy.py
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import numpy as np | |
import cvxpy as cx | |
# Begin with the variables that describe the problem | |
x_shippers = cx.Variable((4, 2), boolean=True, name='shippers') | |
x_transports = cx.Variable((4, 2), boolean=True, name='transports') | |
# Data that helps implement the model | |
containers = np.array([[200, 0], [300, 1], [400, 0], [500, 1]]) |
View top_simple_example_cvxpy.py
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import numpy as np | |
import cvxpy as cx | |
# First the data that sets the problem | |
container_costs = np.array([200, 300, 400, 500]) | |
shippers_costs = np.array([100, 130]) | |
shippers_spaces = np.array([2, 1]) | |
# Main variable to be optimized | |
x_shippers = cx.Variable((4, 2), boolean=True, name='shippers') |
View top_shippers.csv
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Trade | Shipper | 1 | 2 | 3 | 4 | ... | 52 | |
---|---|---|---|---|---|---|---|---|
North Europe | Shipper 0 | 7 | 7 | 6 | 5 | ... | 7 | |
North America | Shipper 1 | 8 | 8 | 8 | 9 | ... | 6 | |
Far East | Shipper 2 | 6 | 6 | 7 | 8 | ... | 9 |
View top_freight.csv
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Factory | Trade | Destine Port | Shipper | Modal | Origin Port | Transport | Road Freight | Shipper Freight | |
---|---|---|---|---|---|---|---|---|---|
factory 0 | Far East | Hong Kong | Shipper 0 | Road | Santos | Third Party | 6000 | 8000 | |
factory 1 | Mediterranean | Barcelona | Shipper 1 | Train | Santos | Private | 5000 | 9000 | |
factory 2 | North America | Seattle | Shipper 3 | Road | Santos | Third Party | 9000 | 5000 |
View top_containers_input_data.csv
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Container ID | Factory | Destination | Worth | Shipper | Transportation | Origin | |
---|---|---|---|---|---|---|---|
0 | Factory 0 | Hong Kong | 1000 | ? | ? | ? | |
1 | Factory 1 | Barcelona 1 | 1200 | ? | ? | ? | |
2 | Factory 0 | Seattle 2 | 1300 | ? | ? | ? |
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