def test_parameters_pd(self):
schema = test_schema + "_parameters_pd"
pdf = PanDatFactory(parameters=[["Key"], ["Value"]])
pdf.add_parameter("Something", 100)
pdf.add_parameter("Different",
'boo',
strings_allowed='*',
number_allowed=False)
dat = TicDatFactory(**pdf.schema()).TicDat(
parameters=[["Something", float("inf")], ["Different", "inf"]])
dat = TicDatFactory(**pdf.schema()).copy_to_pandas(
dat, drop_pk_columns=False)
pdf.pgsql.write_schema(self.engine, schema)
pdf.pgsql.write_data(dat, self.engine, schema)
dat_ = pdf.pgsql.create_pan_dat(self.engine, schema)
self.assertTrue(pdf._same_data(dat, dat_))
input_schema.set_data_type("iris",
fld,
inclusive_min=False,
inclusive_max=False,
min=0,
max=float("inf"))
input_schema.set_data_type("iris",
'Species',
number_allowed=False,
strings_allowed='*')
# the number of clusters is our only parameter, but using a parameters table makes it easy to add more as needed
input_schema.add_parameter("Number of Clusters",
default_value=4,
inclusive_min=False,
inclusive_max=False,
min=0,
max=float("inf"),
must_be_int=True)
# ---------------------------------------------------------------------------------
# ------------------------ define the output schema -------------------------------
solution_schema = PanDatFactory(
iris=[[],
[
'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width',
'Species', 'Cluster ID'
]])
# ---------------------------------------------------------------------------------
plants=[["Name"], []],
warehouses=[["Name"], ["Max Assignment Capacity", "Fixed Cost"]],
customers=[["Name"], []],
products=[["Name"], ["Warehouse Volume"]],
demand=[["Customer", "Product"], ["Demand"]],
supply=[["Plant", "Product"], ["Supply"]],
plant_to_warehouse_costs=[["Plant", "Warehouse", "Product"], ["Cost"]],
warehouse_to_customer_costs=[["Warehouse", "Customer", "Product"],
["Cost"]],
warehouse_to_customer_distances=[["Warehouse", "Customer"], ["Distance"]],
parameters=[["Parameter"], ["Value"]])
input_schema.add_parameter("Number of Warehouses",
default_value=4,
inclusive_min=False,
inclusive_max=False,
min=0,
max=float("inf"),
must_be_int=True)
input_schema.add_parameter("High Service Distance",
default_value=0,
inclusive_min=True,
inclusive_max=True,
min=0,
max=float("inf"),
must_be_int=False)
input_schema.add_parameter("Maximum Average Service Distance",
default_value=float("inf"),
inclusive_min=True,
inclusive_max=True,
min=0,
min=0,
max=float("inf"),
inclusive_min=False,
inclusive_max=False,
must_be_int=True)
input_schema.add_data_row_predicate(
"roster_requirements",
predicate=lambda row: row["Max Num Starters"] >= row["Min Num Starters"])
input_schema.add_data_row_predicate(
"roster_requirements",
predicate=lambda row: row["Max Num Reserve"] >= row["Min Num Reserve"])
input_schema.add_parameter("Starter Weight",
default_value=1.2,
min=0,
max=float("inf"),
inclusive_min=False,
inclusive_max=False)
input_schema.add_parameter("Reserve Weight",
default_value=0.9,
min=0,
max=float("inf"),
inclusive_min=False,
inclusive_max=False)
input_schema.add_parameter("Maximum Number of Flex Starters",
default_value=float("inf"),
min=0,
max=float("inf"),
inclusive_min=True,
inclusive_max=True)
# ---------------------------------------------------------------------------------
# to and from .csv files.
#
# Command line interface works like this
# python simple_datetime_solver.py -i sample_data -o solution_directory
#
# This is a very simple app that demos datetime functionality that might be useful for a routing application.
# A parameter defines the start of the model, and each order has a "Deliver By" time requirement. The solution
# (which is just diagnostic information) is the time elapsed (in days) between the start time of the model and the
# "Delvery By" time for each order
from ticdat import PanDatFactory, standard_main
# ------------------------ define the input schema --------------------------------
input_schema = PanDatFactory(parameters=[["Name"], ["Value"]],
orders=[["Name"], ["Deliver By"]])
input_schema.set_data_type("orders", "Deliver By", datetime=True)
input_schema.add_parameter("Start Of Model", "Jan 1 2019 8 AM", datetime=True)
# ---------------------------------------------------------------------------------
# ------------------------ define the output schema -------------------------------
solution_schema = PanDatFactory(
time_to_deliver=[["Name"], ["Maximum Time To Deliver"]])
# ---------------------------------------------------------------------------------
# ------------------------ create a solve function --------------------------------
def solve(dat):
assert input_schema.good_pan_dat_object(dat)
assert not input_schema.find_duplicates(dat)
assert not input_schema.find_data_type_failures(
dat) # "Delvery By" will be a datetime.datetime
input_schema = PanDatFactory (
parameters=[["Parameter"], ["Value"]],
load_amounts=[["Amount"],[]],
number_of_one_way_trips=[["Number"],[]],
amount_leftover=[["Amount"], []])
input_schema.set_data_type("load_amounts", "Amount", min=0, max=float("inf"),
inclusive_min=False, inclusive_max=False)
input_schema.set_data_type("number_of_one_way_trips", "Number", min=0, max=float("inf"),
inclusive_min=False, inclusive_max=False, must_be_int=True)
input_schema.set_data_type("amount_leftover", "Amount", min=0, max=float("inf"),
inclusive_min=True, inclusive_max=False)
input_schema.add_parameter("One Way Price", default_value=2.25, min=0, max=float("inf"), inclusive_min=True,
inclusive_max=False)
input_schema.add_parameter("Amount Leftover Constraint", default_value="Upper Bound", number_allowed=False,
strings_allowed=["Equality", "Upper Bound", "Upper Bound With Leftover Multiple Rule"])
# ---------------------------------------------------------------------------------
# ------------------------ define the output schema -------------------------------
solution_schema = PanDatFactory(
load_amount_details=[["Number One Way Trips", "Amount Leftover", "Load Amount"],
["Number Of Visits"]],
load_amount_summary=[["Number One Way Trips", "Amount Leftover"],["Number Of Visits"]])
# ---------------------------------------------------------------------------------
# ------------------------ create a solve function --------------------------------
def solve(dat):