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GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
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The licenses for most software and other practical works are designed
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Nothing in this License shall be construed as excluding or limiting
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12. No Surrender of Others' Freedom.
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not convey it at all. For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
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14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU General Public License from time to time. Such new versions will
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If the Program specifies that a proxy can decide which future
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THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
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HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
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IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
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17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
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Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.
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# Acknowledgements
The development of this package benefitted from the support of ERA-Net Smart Energy Systems and Innovation Fond Denmark through the TOP-UP project (project references 91176 and 9045-00017A, respectively).
# License
This package is released under version 3 of the GNU General Public License.
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[build-system]
requires = [
"setuptools>=42",
"wheel"
]
build-backend = "setuptools.build_meta"
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[metadata]
name = topupopt
version = 0.0.1
author = Pedro L. Magalhães
author_email = pmlpm@posteo.de
description = A package for energy system optimisation.
long_description = file: README.md
long_description_content_type = text/markdown
url = https://lab.compute.dtu.dk/pmag/topupopt
project_urls =
Bug Tracker = https://lab.compute.dtu.dk/pmag/topupopt/issues
classifiers =
Programming Language :: Python :: 3
License :: OSI Approved :: GPLv3
Operating System :: OS Independent
[options]
package_dir =
= src
packages = find:
install_requires =
pyomo
numpy
scipy
networkx
shapely
pandas
geopandas
topupheat
matplotlib
contextily
scikit-learn
python_requires = >=3.8
[options.packages.find]
where = src
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# -*- coding: utf-8 -*-
# from . import mvesipp
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# -*- coding: utf-8 -*-
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# -*- coding: utf-8 -*-
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# -*- coding: utf-8 -*-
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# *****************************************************************************
# *****************************************************************************
import numpy as np
from geopandas import GeoDataFrame
from ...misc.utils import discrete_sinusoid_matching_integral
from ...misc.utils import create_profile_using_time_weighted_state
from ...misc.utils import generate_manual_state_correlated_profile
from ...misc.utils import generate_state_correlated_profile, generate_profile
from .bbr import label_bbr_entrance_id, label_bbr_housing_area
# *****************************************************************************
# *****************************************************************************
# labels
selected_bbr_adgang_labels = ["Opgang_id", "AdgAdr_id", "Bygning_id"]
selected_bbr_building_point_labels = [
"KoorOest",
"KoorNord",
"KoorSystem",
# "koordinater" # corresponds to a list, which cannot be written to a file
]
selected_bbr_building_labels = [
"BYG_ANVEND_KODE",
"OPFOERELSE_AAR", # new
"OMBYG_AAR", # new
"BYG_ARL_SAML",
"BYG_BOLIG_ARL_SAML",
"ERHV_ARL_SAML",
"BYG_BEBYG_ARL",
"GARAGE_INDB_ARL",
"CARPORT_INDB_ARL",
"UDHUS_INDB_ARL",
"UDESTUE_ARL",
"LukOverdaekAreal",
"AFFALDSRUM_ARL",
"ANDET_ARL",
"OverdaekAreal",
"AabenOverdaekAreal",
"AdgangsAreal",
"ETAGER_ANT",
"ETAGER_AFVIG_KODE",
"VARMEINSTAL_KODE",
"OPVARMNING_KODE",
"VARME_SUPPL_KODE",
"BygPkt_id",
]
# label under which building entrance ids can be found in OSM
label_osm_entrance_id = "osak:identifier"
# *****************************************************************************
# *****************************************************************************
def heat_demand_dict_by_building_entrance(
gdf_osm: GeoDataFrame,
gdf_buildings: GeoDataFrame,
number_intervals: int,
time_interval_durations: list,
bdg_specific_demand: dict,
bdg_min_max_ratio: dict,
bdg_demand_phase_shift: dict = None,
key_osm_entr_id: str = label_osm_entrance_id,
key_bbr_entr_id: str = label_bbr_entrance_id,
avg_state: list = None,
state_correlates_with_output: bool = False,
deviation_gain: float = 1.0,
solver: str = 'glpk',
**kwargs
) -> dict:
# initialise dict for each building entrance
demand_dict = {}
# for each building entrance
for osm_index in gdf_osm.index:
# initialise dict for each building consumption point
heat_demand_profiles = []
# find the indexes for each building leading to the curr. cons. point
building_indexes = gdf_buildings[
gdf_buildings[key_bbr_entr_id] == gdf_osm.loc[osm_index][key_osm_entr_id]
].index
# for each building
for building_index in building_indexes:
# get relevant data
area = gdf_buildings.loc[building_index][label_bbr_housing_area]
# generate the profile
if type(avg_state) == type(None):
# ignore states
heat_demand_profiles.append(
np.array(
discrete_sinusoid_matching_integral(
bdg_specific_demand[building_index] * area,
time_interval_durations=time_interval_durations,
min_max_ratio=bdg_min_max_ratio[building_index],
phase_shift_radians=(
bdg_demand_phase_shift[building_index]
),
)
)
)
elif type(deviation_gain) == type(None):
# states matter but the gain must be determined
# heat_demand_profiles.append(
# np.array(
# create_profile_using_time_weighted_state(
# integration_result=(
# bdg_specific_demand[building_index] * area
# ),
# avg_state=avg_state,
# time_interval_durations=time_interval_durations,
# min_max_ratio=bdg_min_max_ratio[building_index],
# state_correlates_with_output=state_correlates_with_output,
# )
# )
# )
heat_demand_profiles.append(
np.array(
generate_state_correlated_profile(
integration_result=(
bdg_specific_demand[building_index] * area
),
states=avg_state,
time_interval_durations=time_interval_durations,
states_correlate_profile=state_correlates_with_output,
min_max_ratio=bdg_min_max_ratio[building_index],
solver=solver
)
)
)
else:
# states matter and the gain is predefined
heat_demand_profiles.append(
np.array(
generate_manual_state_correlated_profile(
integration_result=(
bdg_specific_demand[building_index] * area
),
states=avg_state,
time_interval_durations=time_interval_durations,
deviation_gain=deviation_gain
)
)
)
# add the profiles, time step by time step
if len(heat_demand_profiles) == 0:
final_profile = []
else:
final_profile = sum(profile for profile in heat_demand_profiles)
# store the demand profile
demand_dict[osm_index] = final_profile
# return
return demand_dict
# *****************************************************************************
# *****************************************************************************
# TODO: allow reusing the gain
def heat_demand_profiles(
gdf_osm: GeoDataFrame,
gdf_buildings: GeoDataFrame,
time_interval_durations: list,
assessments: list,
annual_heat_demand: dict,
air_temperature: dict = None,
reuse_deviation_gain: bool = True,
**kwargs
) -> dict:
# calculate the total area
total_area = total_heating_area(gdf_osm, gdf_buildings)
# initialise data dict
heat_demand_dict = {}
# for each building entrance
for osm_index in gdf_osm.index:
# initialise dict for each building entrance
bdg_entr_dict = {}
# find the indexes for each building leading to the curr. cons. point
building_indexes = gdf_buildings[
gdf_buildings[label_bbr_entrance_id] == gdf_osm.loc[osm_index][label_osm_entrance_id]
].index
for q in assessments:
# define the specific heat demand
specific_demand = annual_heat_demand[q]/total_area
#
# initialise dict for each building consumption point
bdg_profile_list = []
# for each building
for building_index in building_indexes:
# get relevant data
area = gdf_buildings.loc[building_index][label_bbr_housing_area]
# handle states
if type(air_temperature) != type(None):
kwargs['states'] = air_temperature[q]
# append the profile for each building to the list
_profile = generate_profile(
integration_result=specific_demand*area,
time_interval_durations=time_interval_durations,
**kwargs
# min_max_ratio,
# states,
# states_correlate_profile,
# solver,
# deviation_gain
)
bdg_profile_list.append(np.array(_profile))
# aggregate profiles for the same building entrance
bdg_entr_profile = (
[]
if len(bdg_profile_list) == 0 else
sum(profile for profile in bdg_profile_list)
)
# store the demand profile
bdg_entr_dict[q] = bdg_entr_profile
# add to the main dict
heat_demand_dict[osm_index] = bdg_entr_dict
return heat_demand_dict, total_area
# *****************************************************************************
# *****************************************************************************
def total_heating_area(
gdf_osm: GeoDataFrame,
gdf_buildings: GeoDataFrame,
key_osm_entr_id: str = label_osm_entrance_id,
key_bbr_entr_id: str = label_bbr_entrance_id,
) -> float:
area = 0
for osm_index in gdf_osm.index:
# find the indexes for each building leading to the curr. cons. point
building_indexes = gdf_buildings[
gdf_buildings[label_bbr_entrance_id]
== gdf_osm.loc[osm_index][label_osm_entrance_id]
].index
# for each building
for building_index in building_indexes:
# get relevant data
area += gdf_buildings.loc[building_index][label_bbr_housing_area]
return area
# *****************************************************************************
# *****************************************************************************
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# -*- coding: utf-8 -*-
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# imports
# standard imports
from numbers import Real
# local, external imports
# from topupheat.pipes.trenches import SupplyReturnPipeTrenchWithIdenticalPipes
from topupheat.pipes.trenches import SupplyReturnPipeTrench
# local, internal imports
from ...problems.esipp.network import ArcsWithoutProportionalLosses
# TODO: add way to define polarity of cash flows
from ...data.finance.utils import ArcInvestments
# *****************************************************************************
# *****************************************************************************
# scenarios: different datasets/trench objects
# options: fully specified trenches, including prices
# constants
KEY_DHT_OPTIONS_OBJ = "trench"
KEY_DHT_LENGTH = "length"
KEY_DHT_UCF = "capacity_unit_conversion_factor"
KEY_HHT_DHT_PIPES = "pipes"
KEY_HHT_STD_PIPES = "pipe_tuple"
# *****************************************************************************
# *****************************************************************************
class PipeTrenchOptions(ArcsWithoutProportionalLosses):
"A class for defining investments in district heating trenches."
def __init__(
self,
trench: SupplyReturnPipeTrench,
name: str,
length: float,
specific_capacity_cost: float or list = None,
minimum_cost: list or tuple = None, # default: pipes
capacity_is_instantaneous: bool = False,
unit_conversion_factor: float = 1.0,
):
# store the unit conversion
self.unit_conversion_factor = unit_conversion_factor
# keep the trench object
self.trench = trench
# keep the trench length
self.length = (
[length for i in range(trench.number_options())]
if trench.vector_mode
else length
)
# determine the rated heat capacity
rhc = trench.rated_heat_capacity(unit_conversion_factor=unit_conversion_factor)
# initialise the object using the mother class
ArcsWithoutProportionalLosses.__init__(
self,
name=name,
static_loss=None,
capacity=[rhc] if isinstance(rhc, Real) else rhc,
minimum_cost=minimum_cost,
specific_capacity_cost=(
0
if type(specific_capacity_cost) == type(None)
else specific_capacity_cost
),
capacity_is_instantaneous=False,
)
# initialise the minimum cost
if type(minimum_cost) == type(None):
self.set_minimum_cost()
# *************************************************************************
# *************************************************************************
def set_minimum_cost(self, minimum_cost=None):
# minimum arc cost
# if no external minimum cost list was provided, calculate it
if type(minimum_cost) == type(None):
# use the specific pipe cost that features in the database
if self.trench.vector_mode:
# multiple options
self.minimum_cost = tuple(
(
pipe.sp * length # twin pipes: one twin pipe
if self.trench.twin_pipes
else pipe.sp * length * 2
) # single pipes: two single pipes
for pipe, length in zip(self.trench.supply_pipe, self.length)
)
else: # only one option
self.minimum_cost = (self.trench.supply_pipe.sp * self.length,)
else: # use an external minimum cost
self.minimum_cost = tuple(minimum_cost)
# *************************************************************************
# *************************************************************************
def set_capacity(self, **kwargs):
# retrieve the rated heat capacity
rhc = self.trench.rated_heat_capacity(**kwargs)
if self.trench.vector_mode:
# multiple options, rhc is already a list of values
self.capacity = rhc
else:
# one option, rhc is one value
self.capacity = (rhc,)
# *************************************************************************
# *************************************************************************
def set_static_losses(
self,
scenario_key,
ground_thermal_conductivity: float or list,
ground_air_heat_transfer_coefficient: float or list,
time_interval_duration: float or list,
temperature_surroundings: float or list,
length: float or list = None,
unit_conversion_factor: float = None,
**kwargs
):
hts = self.trench.heat_transfer_surroundings(
ground_thermal_conductivity=ground_thermal_conductivity,
ground_air_heat_transfer_coefficient=(ground_air_heat_transfer_coefficient),
time_interval_duration=time_interval_duration,
temperature_surroundings=temperature_surroundings,
length=(self.length if type(length) == type(None) else length),
unit_conversion_factor=(
self.unit_conversion_factor
if type(unit_conversion_factor) == type(None)
else unit_conversion_factor
),
**kwargs
)
if self.trench.vector_mode:
# multiple options: hts is a vector
if hasattr(self, "static_loss") and type(self.static_loss) != type(None):
# update the static loss dictionary
if type(hts[0]) == list:
# multiple time intervals
self.static_loss.update(
{
(h, scenario_key, k): hts[h][k]
for h, hts_h in enumerate(hts)
for k, hts_hk in enumerate(hts_h)
}
)
else: # not a list: one time interval
self.static_loss.update(
{(h, scenario_key, 0): hts[h] for h, hts_h in enumerate(hts)}
)
else:
# no static loss dictionary, create it
if type(hts[0]) == list:
# multiple time intervals
self.static_loss = {
(h, scenario_key, k): hts[h][k]
for h, hts_h in enumerate(hts)
for k, hts_hk in enumerate(hts_h)
}
else: # not a list: one time interval
self.static_loss = {
(h, scenario_key, 0): hts[h] for h, hts_h in enumerate(hts)
}
else:
# one option: hts might be a number
if hasattr(self, "static_loss") and type(self.static_loss) != type(None):
# update the static loss dictionary
if not isinstance(hts, Real):
# multiple time intervals
self.static_loss.update(
{(0, scenario_key, k): hts[k] for k, hts_k in enumerate(hts)}
)
else: # not a list: one time interval
self.static_loss.update({(0, scenario_key, 0): hts})
else:
# no static loss dictionary, create it
if not isinstance(hts, Real):
# multiple time intervals
self.static_loss = {
(0, scenario_key, k): hts_k for k, hts_k in enumerate(hts)
}
else: # not a list: one time interval
self.static_loss = {(0, scenario_key, 0): hts}
# *****************************************************************************
# *****************************************************************************
class PipeTrenchInvestments(ArcInvestments, PipeTrenchOptions):
"A class for defining investments in district heating trenches."
def __init__(
self,
trench: SupplyReturnPipeTrench,
name: str,
length: float,
investments: tuple,
static_loss: dict = None,
specific_capacity_cost: float or list = None,
capacity_is_instantaneous: bool = False,
unit_conversion_factor: float = 1.0,
**kwargs
):
# store the unit conversion
self.unit_conversion_factor = unit_conversion_factor
# keep the trench object
self.trench = trench
# keep the trench length
self.length = (
[length for i in range(trench.number_options())]
if trench.vector_mode
else length
)
# determine the rated heat capacity
rhc = trench.rated_heat_capacity(unit_conversion_factor=unit_conversion_factor)
# initialise the object using the mother class
ArcInvestments.__init__(
self,
investments=investments,
name=name,
efficiency=None,
efficiency_reverse=None,
static_loss=static_loss,
capacity=[rhc] if isinstance(rhc, Real) else rhc,
specific_capacity_cost=(
0
if type(specific_capacity_cost) == type(None)
else specific_capacity_cost
),
capacity_is_instantaneous=False,
validate=False,
)
# # *************************************************************************
# # *************************************************************************
# def set_minimum_cost(self, minimum_cost = None):
# # minimum arc cost
# # if no external minimum cost list was provided, calculate it
# if type(minimum_cost) == type(None):
# # use the specific pipe cost that features in the database
# if self.trench.vector_mode:
# # multiple options
# self.minimum_cost = tuple(
# (pipe.sp*length # twin pipes: one twin pipe
# if self.trench.twin_pipes else
# pipe.sp*length*2) # single pipes: two single pipes
# for pipe, length in zip(
# self.trench.supply_pipe,
# self.length
# )
# )
# else: # only one option
# self.minimum_cost = (self.trench.supply_pipe.sp*self.length,)
# else: # use an external minimum cost
# self.minimum_cost = tuple(minimum_cost)
# # *************************************************************************
# # *************************************************************************
# def set_capacity(self, **kwargs):
# # retrieve the rated heat capacity
# rhc = self.trench.rated_heat_capacity(**kwargs)
# if self.trench.vector_mode:
# # multiple options, rhc is already a list of values
# self.capacity = rhc
# else:
# # one option, rhc is one value
# self.capacity = (rhc,)
# # *************************************************************************
# # *************************************************************************
# def set_static_losses(
# self,
# scenario_key,
# ground_thermal_conductivity: float or list,
# ground_air_heat_transfer_coefficient: float or list,
# time_interval_duration: float or list,
# temperature_surroundings: float or list,
# length: float or list = None,
# unit_conversion_factor: float = None,
# **kwargs):
# hts = self.trench.heat_transfer_surroundings(
# ground_thermal_conductivity=ground_thermal_conductivity,
# ground_air_heat_transfer_coefficient=(
# ground_air_heat_transfer_coefficient),
# time_interval_duration=time_interval_duration,
# temperature_surroundings=temperature_surroundings,
# length=(
# self.length
# if type(length) == type(None) else
# length
# ),
# unit_conversion_factor=(
# self.unit_conversion_factor
# if type(unit_conversion_factor) == type(None) else
# unit_conversion_factor
# ),
# **kwargs)
# if self.trench.vector_mode:
# # multiple options: hts is a vector
# if (hasattr(self, "static_loss") and
# type(self.static_loss) != type(None)):
# # update the static loss dictionary
# if type(hts[0]) == list:
# # multiple time intervals
# self.static_loss.update({
# (h, scenario_key, k): hts[h][k]
# for h, hts_h in enumerate(hts)
# for k, hts_hk in enumerate(hts_h)
# })
# else: # not a list: one time interval
# self.static_loss.update({
# (h, scenario_key, 0): hts[h]
# for h, hts_h in enumerate(hts)
# })
# else:
# # no static loss dictionary, create it
# if type(hts[0]) == list:
# # multiple time intervals
# self.static_loss = {
# (h, scenario_key, k): hts[h][k]
# for h, hts_h in enumerate(hts)
# for k, hts_hk in enumerate(hts_h)
# }
# else: # not a list: one time interval
# self.static_loss = {
# (h, scenario_key, 0): hts[h]
# for h, hts_h in enumerate(hts)
# }
# else:
# # one option: hts might be a number
# if (hasattr(self, "static_loss") and
# type(self.static_loss) != type(None)):
# # update the static loss dictionary
# if not isinstance(hts, Real):
# # multiple time intervals
# self.static_loss.update({
# (0, scenario_key, k): hts[k]
# for k, hts_k in enumerate(hts)
# })
# else: # not a list: one time interval
# self.static_loss.update({
# (0, scenario_key, 0): hts
# })
# else:
# # no static loss dictionary, create it
# if not isinstance(hts, Real):
# # multiple time intervals
# self.static_loss = {
# (0, scenario_key, k): hts_k
# for k, hts_k in enumerate(hts)
# }
# else: # not a list: one time interval
# self.static_loss = {
# (0, scenario_key, 0): hts
# }
# *****************************************************************************
# *****************************************************************************
class ExistingPipeTrench(PipeTrenchOptions):
"A class for existing pipe trenches."
def __init__(self, option_selected: int, **kwargs):
# initialise
PipeTrenchOptions.__init__(
self,
minimum_cost=[0 for i in range(kwargs["trench"].number_options())],
**kwargs
)
# define the option that already exists
self.options_selected[option_selected] = True
# *****************************************************************************
# *****************************************************************************
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# imports
# standard
# local, external
import osmnx as ox
import contextily as cx
import matplotlib.pyplot as plt
import numpy as np
# local, internal
from ...problems.esipp.network import Network
from .network import PipeTrenchOptions
from topupheat.pipes.trenches import SupplyReturnPipeTrench
from numbers import Real
# *****************************************************************************
# *****************************************************************************
def cost_pipes(trench: SupplyReturnPipeTrench, length: float or tuple) -> tuple:
"""
Returns the costs of each trench option for a given trench length.
Parameters
----------
trench : SupplyReturnPipeTrench
The object describing the trench options.
length : float or tuple
The trench length in meters.
Raises
------
ValueError
This error is raised if unrecognised inputs are inserted.
Returns
-------
tuple
A tuple of the pipe costs for each option.
"""
# use the specific pipe cost that features in the database
if trench.vector_mode:
# multiple options
if type(length) == tuple and len(length) == trench.number_options():
# multiple trench lengths
return tuple(
(
pipe.sp * length # twin pipes: one twin pipe
if trench.twin_pipes
else pipe.sp * length * 2
) # single pipes: two single pipes
for pipe, length in zip(trench.supply_pipe, length)
)
elif isinstance(length, Real):
# one trench length
return tuple(
(
pipe.sp * length # twin pipes: one twin pipe
if trench.twin_pipes
else pipe.sp * length * 2
) # single pipes: two single pipes
for pipe in trench.supply_pipe
)
else:
raise ValueError("Unrecognised input combination.")
elif not trench.vector_mode and isinstance(length, Real):
# only one option
return (trench.supply_pipe.sp * length,)
else: # only one option
raise ValueError("Unrecognised input combination.")
# # keep the trench length
# self.length = (
# [length for i in range(trench.number_options())]
# if trench.vector_mode else
# length
# )
# *****************************************************************************
# *****************************************************************************
def summarise_network_by_pipe_technology(
network: Network, print_output: bool = False
) -> dict:
"A method to summarise a network by pipe technology."
# *************************************************************************
# *************************************************************************
# create a dictionary that compiles the lengths of each arc technology
length_dict = {}
# *************************************************************************
# *************************************************************************
# for each arc
for arc_key in network.edges(keys=True):
# check if it is a PipeTrench object
if not isinstance(
network.edges[arc_key][Network.KEY_ARC_TECH], PipeTrenchOptions
):
# if not, skip arc
continue
# for each arc technology option
for h, tech_option in enumerate(
network.edges[arc_key][Network.KEY_ARC_TECH].options_selected
):
# check if the tech option was selected
if tech_option:
# technology option was selected
# get the length of the arc
arc_length = (
network.edges[arc_key][Network.KEY_ARC_TECH].length[h]
if type(network.edges[arc_key][Network.KEY_ARC_TECH].length) == list
else network.edges[arc_key][Network.KEY_ARC_TECH].length
)
# identify the option
tech_option_label = network.edges[arc_key][
Network.KEY_ARC_TECH
].trench.printable_description(h)
# if the arc technology has been previously selected...
if tech_option_label in length_dict:
# ...increment the total length
length_dict[tech_option_label] += arc_length
else:
# if not, add a new arc technology to the dictionary
length_dict[tech_option_label] = arc_length
# *************************************************************************
# *************************************************************************
if print_output:
print("printing the arc technologies selected by pipe size...")
for key, value in sorted(
(tech, length) for tech, length in length_dict.items()
):
print(str(key) + ": " + str(value))
print("total: " + str(sum(length_dict.values())))
return length_dict
# *************************************************************************
# *************************************************************************
# *****************************************************************************
# *****************************************************************************
def plot_network_layout(
network: Network,
include_basemap: bool = False,
figure_size: tuple = (25, 25),
min_linewidth: float = 1.0,
max_linewidth: float = 3.0,
legend_fontsize: float = 20.0,
basemap_zoom_level: float = 15,
legend_location: str = "lower left",
legend_with_brand_model: bool = False,
legend_transparency: float = None,
):
# convert graph object to GDF
_, my_gdf_arcs = ox.graph_to_gdfs(network)
# convert to final plot CRS
my_gdf = my_gdf_arcs.to_crs(epsg=3857)
# dict: keys are the pipe tuples and the values are lists of edge keys
arc_tech_summary_dict = {}
# for each edge
for arc_key in my_gdf.index:
# check if it is a PipeTrenchOptions object
if not isinstance(
network.edges[arc_key][Network.KEY_ARC_TECH], PipeTrenchOptions
):
# if not, skip arc
continue
# find the trench's description, if it was selected
try:
selected_option = (
my_gdf[Network.KEY_ARC_TECH]
.loc[arc_key]
.trench.printable_description(
my_gdf[Network.KEY_ARC_TECH]
.loc[arc_key]
.options_selected.index(True)
)
)
except ValueError:
continue
# if the pipe tuple already exists as a key in the dict
if selected_option in arc_tech_summary_dict:
# append the edge_key to the list obtained via that pipe tuple key
arc_tech_summary_dict[selected_option].append(arc_key)
else: # if not
# add a new dict entry whose key is the pipe tuple and create a list
arc_tech_summary_dict[selected_option] = [arc_key]
list_sorted = sorted(
(int(printable_description[2:]), printable_description)
for printable_description in arc_tech_summary_dict.keys()
)
(list_sorted_dn, list_sorted_descriptions) = list(map(list, zip(*list_sorted)))
list_arc_widths = (
[
min_linewidth
+ (max_linewidth - min_linewidth) * iteration / (len(list_sorted_dn) - 1)
for iteration, _ in enumerate(list_sorted_dn)
]
if len(list_sorted_dn) != 1
else [(max_linewidth + min_linewidth) / 2]
)
# *************************************************************************
# *************************************************************************
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(*figure_size)
for description, arc_width in zip(list_sorted_descriptions, list_arc_widths):
# prepare plot
my_gdf.loc[arc_tech_summary_dict[description]].plot(
edgecolor="k", legend=True, linewidth=arc_width, ax=ax
)
# adjust legend labels
ax.legend(
list_sorted_descriptions,
fontsize=legend_fontsize,
loc=legend_location,
framealpha=(
legend_transparency if type(legend_transparency) != type(None) else None
),
)
# add base map
if include_basemap:
cx.add_basemap(
ax,
zoom=basemap_zoom_level,
source=cx.providers.OpenStreetMap.Mapnik,
# crs=gdf_map.crs,
)
# *************************************************************************
# *************************************************************************
# *****************************************************************************
# *****************************************************************************
def plot_heating_demand(
losses: list,
end_use_demand: list,
labels: list,
ylabel: str = "Heating demand [MWh]",
title: str = "Heat demand by month",
):
energy_totals = {
"Losses (optimised)": np.array(losses),
"End use (estimated)": np.array(end_use_demand),
}
colors = {
"Losses (optimised)": "tab:orange",
"End use (estimated)": "tab:blue",
}
# width = 0.8 # the width of the bars: can also be len(x) sequence
# make sure the grid lines are behind the bars
zorder_bars = 3
zorder_grid = 0
fig, ax = plt.subplots()
bottom = np.zeros(len(labels))
figure_size = (8, 4)
fig.set_size_inches(figure_size[0], figure_size[1])
for energy_category, energy_total in energy_totals.items():
p = ax.bar(
labels,
energy_total,
label=energy_category,
bottom=bottom,
color=colors[energy_category],
zorder=zorder_bars,
)
bottom += energy_total
ax.bar_label(p, fmt="{:,.0f}", label_type="center")
# ax.bar_label(p, fmt='{:,.0f}')
ax.grid(zorder=zorder_grid) # zorder=0 to make the grid
ax.set(ylabel=ylabel, title=title)
ax.legend()
plt.show()
# *****************************************************************************
# *****************************************************************************
src/topupopt/data/finance/__init__.py 0 → 100644
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# -*- coding: utf-8 -*-
src/topupopt/data/finance/invest.py 0 → 100644
+ 483
− 0
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# *****************************************************************************
# *****************************************************************************
from math import prod
from statistics import mean
# *****************************************************************************
# *****************************************************************************
# TODO: enable swapping the polarity
class Investment:
"""This class is meant to enable analysis of specific investments."""
# self.discount_rates: N samples
# self.net_cash_flows: N+1 samples
# TODO: consider using dicts to make things more intuitive, time-wise
def __init__(
self,
discount_rates: list,
net_cash_flows: list = None,
discount_rate: float = None,
analysis_period_span: int = None,
):
"""
Create an object for investment analysis using typical information.
Parameters
----------
discount_rates : list
A list specifying the discount rates to use in the calculation.
net_cash_flows : list
A list specifying the net cash flows to use in the calculation.
Returns
-------
None.
"""
# validate the inputs
if type(discount_rates) != type(None):
# discount_rates is not None:
if type(discount_rates) != tuple:
raise TypeError("The discount rates must be provided as a tuple.")
self.discount_rates = tuple(discount_rates)
self.analysis_period_span = len(self.discount_rates)
if self.analysis_period_span <= 0:
raise ValueError(
"The duration of the period under analysis must be " + "positive."
)
else:
# discount_rates is None:
# discount rate must be positive real under 1
# analysis_period_span must be an int
if type(discount_rate) != float:
raise TypeError("The discount rate must be provided as a float.")
if discount_rate <= 0 or discount_rate >= 1:
raise ValueError(
"The discount rate must be in the open interval between 0"
+ " and 1."
)
if type(analysis_period_span) != int:
raise TypeError(
"The duration of the period under consideration must be "
+ "provided as an integer."
)
if analysis_period_span <= 0:
raise ValueError(
"The duration of the period under analysis must be " + "positive."
)
self.analysis_period_span = analysis_period_span
self.discount_rates = tuple(
discount_rate for i in range(self.analysis_period_span)
)
# check the net cash flows
if type(net_cash_flows) != type(None):
if type(net_cash_flows) != list:
raise TypeError("The net cash flows must be provided as a list.")
if len(net_cash_flows) != self.analysis_period_span + 1:
raise ValueError("The inputs are consistent in terms of length.")
self.net_cash_flows = list(net_cash_flows)
else:
# net_cash_flows is None: initialise it as a list of zeros
self.net_cash_flows = list(0 for i in range(self.analysis_period_span + 1))
# discount factors
self.discount_factors = tuple(
discount_factor(self.discount_rates[:i])
for i in range(self.analysis_period_span + 1)
)
# *************************************************************************
# *************************************************************************
def add_investment(
self,
investment: float,
investment_period: int,
investment_longevity: int,
commissioning_delay_after_investment: int = 0,
salvage_value_method: str = "annuity",
):
if salvage_value_method == "annuity":
mean_discount_rate = mean(self.discount_rates)
residual_value = salvage_value_annuity(
investment=investment,
investment_longevity=investment_longevity,
investment_period=investment_period,
discount_rate=mean_discount_rate,
analysis_period_span=self.analysis_period_span,
)
self.net_cash_flows[investment_period] += investment
self.net_cash_flows[self.analysis_period_span] += -residual_value
else:
residual_value = salvage_value_linear_depreciation(
investment=investment,
investment_period=investment_period,
investment_longevity=investment_longevity,
analysis_period_span=self.analysis_period_span,
commissioning_delay_after_investment=(
commissioning_delay_after_investment
),
)
self.net_cash_flows[investment_period] += investment
self.net_cash_flows[self.analysis_period_span] += -residual_value
# *************************************************************************
# *************************************************************************
def add_operational_cash_flows(
self, cash_flow: float or int, start_period: int, longevity: int = None
):
"""Adds a sequence of cash flows to the analysis."""
if type(longevity) == type(None):
# until the planning horizon
for i in range(self.analysis_period_span - start_period + 1):
# add operational cash flows
self.net_cash_flows[i + start_period] += cash_flow
else:
# limited longevity
for i in range(longevity):
if i + start_period >= self.analysis_period_span + 1:
break
# add operational cash flows
self.net_cash_flows[i + start_period] += cash_flow
# *************************************************************************
# *************************************************************************
def net_present_value(self):
"""Returns the net present value for the investment under analysis."""
return npv(self.discount_rates, self.net_cash_flows)
# *************************************************************************
# *************************************************************************
# *****************************************************************************
# *****************************************************************************
def npv(
discount_rates: list, net_cash_flows: list, return_discount_factors: bool = False
) -> float or tuple:
"""
Calculates the net present value using the information provided.
Parameters
----------
discount_rates : list
A list with the discount rates for each period after the present.
net_cash_flows : list
A list with the net cash flows for each period including the present.
return_discount_factors : bool, optional
If True, return the discount factors too. The default is False.
Raises
------
ValueError
This error is raised if the lists for the discount rates and the
net cash flows do not have the same size.
Returns
-------
float or tuple
If return_discount_factors is False, returns the net present value.
If True, returns the net present value in addition to a list with
the discount factors used in the calculation.
"""
# check sizes
if len(discount_rates) != len(net_cash_flows) - 1:
# the inputs do not match, return None
raise ValueError("The inputs are inconsistent.")
discount_factors = [
discount_factor(discount_rates[:t]) for t in range(len(discount_rates) + 1)
]
if return_discount_factors:
return (
sum(
ncf_t * df_t for (ncf_t, df_t) in zip(net_cash_flows, discount_factors)
),
discount_factors,
)
else:
return sum(
ncf_t * df_t for (ncf_t, df_t) in zip(net_cash_flows, discount_factors)
)
# *****************************************************************************
# *****************************************************************************
def discount_factor(discount_rates: list) -> float:
"""
Return the discount factor consistent with the discount rates provided.
To calculate the net present value, we need to quantify the effect time
has on the net cash flows. This amounts to a factor, which depends on
the discount rates between the time of the cash flow and the present.
Parameters
----------
discount_rates : list
A list with the discount rates for each time interval between a
given time interval and the present. The order is irrelevant.
Returns
-------
float
The discount factor consistent with the discount rates provided. It
uses all discount rates in the list.
"""
return prod([1 / (1 + i) for i in discount_rates])
# *****************************************************************************
# *****************************************************************************
def salvage_value_linear_depreciation(
investment: int or float,
investment_period: int,
investment_longevity: int,
analysis_period_span: int,
commissioning_delay_after_investment: int = 1,
) -> float:
"""
Determine an asset\'s salvage value by the end of an analysis period.
The depreciation is assumed to be linear: the asset is initially rated at
100% of the investment made and then 0% by the time it is no longer usable.
The salvage value is the asset\'s value after the analysis period.
Parameters
----------
investment : int or float
The sum initially invested in the asset.
investment_period : int
The period during which the investment is made or reported. It has to
be between zero (present) and analysis_period_span+1 (planning horizon).
investment_longevity : int
The investment\'s expected longevity expressed as a number of periods.
commissioning_delay_after_investment : int, optional
The delay between the investment and the period within which the asset
is used, specified in reporting periods. The default is one (1).
Raises
------
ValueError
This error is raised if the investment takes place outside the planning
period under analysis.
Returns
-------
float
The salvage value.
"""
if investment_period >= analysis_period_span + 1:
raise ValueError(
"The investment has to be made within the period being analysed."
)
# calculate the salvage value
return (
(
investment_longevity
+ investment_period
+ commissioning_delay_after_investment
- 1
- analysis_period_span
)
* investment
/ investment_longevity
)
# *****************************************************************************
# *****************************************************************************
def salvage_value_annuity(
investment: int or float,
discount_rate: float,
investment_longevity: int,
investment_period: int,
analysis_period_span: int,
) -> float:
npv_salvage = present_salvage_value_annuity(
investment=investment,
investment_longevity=investment_longevity,
investment_period=investment_period,
discount_rate=discount_rate,
analysis_period_span=analysis_period_span,
return_annuity=False,
)
return npv_salvage / discount_factor(
tuple(discount_rate for i in range(analysis_period_span))
)
# *****************************************************************************
# *****************************************************************************
def annuity(
investment: int or float, investment_longevity: int, discount_rate: float
) -> float:
"Returns the annuity value for a given investment sum and longevity."
return (
investment
* discount_rate
/ (1 - (1 + discount_rate) ** (-investment_longevity))
)
# *****************************************************************************
# *****************************************************************************
def present_salvage_value_annuity(
investment: int or float,
investment_longevity: int,
investment_period: int,
discount_rate: float,
analysis_period_span: int,
return_annuity: bool = False,
) -> float:
"""
Calculates the present value of an asset after a given analysis period.
The calculation is based on the annuity method, which assumes that the
investment could produce a steady revenue stream for a given period after
the investment is made -- the investment longevity.
This method assumes that the investment precedes the annuity payments.
Parameters
----------
investment : int or float
The sum initially invested.
investment_longevity : int
The investment\'s expected longevity expressed as a number of periods.
investment_period : int
The period during which the investment is made or reported. It has to
be between 0 (present) and analysis_period_span+1 (planning horizon).
discount_rate : float
The discount rate for the periods under considerations.
analysis_period_span : int
The span of time from the present until the planning horizon, expressed
as a number of periods.
return_annuity : bool, optional
If True, returns the annuity as well. The default is False.
Raises
------
ValueError
This error is raised if the inputs are inconsistent.
Returns
-------
float
The present salvage value.
float
The annuity.
"""
if investment_period >= analysis_period_span + 1:
raise ValueError(
"The investment has to be made within the period being analysed."
)
# the present salvage value requires the lifetime to extend beyond the hor.
if analysis_period_span >= investment_longevity + investment_period:
if return_annuity:
return 0, annuity(
investment=investment,
investment_longevity=investment_longevity,
discount_rate=discount_rate,
)
else:
return 0
# the annuity has to consider the asset longevity and the commission. delay
value_annuity = annuity(
investment=investment,
investment_longevity=investment_longevity,
discount_rate=discount_rate,
)
discount_rates = tuple(
discount_rate for i in range(investment_longevity + investment_period)
)
net_cash_flows = list(
value_annuity for i in range(investment_longevity + investment_period + 1)
)
for year_index in range(analysis_period_span + 1):
net_cash_flows[year_index] = 0
if return_annuity:
return (
npv(discount_rates=discount_rates, net_cash_flows=net_cash_flows),
value_annuity,
)
else:
return npv(discount_rates=discount_rates, net_cash_flows=net_cash_flows)
# *****************************************************************************
# *****************************************************************************
src/topupopt/data/finance/utils.py 0 → 100644
+ 38
− 0
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# *****************************************************************************
# *****************************************************************************
from ...problems.esipp.network import Arcs
# *****************************************************************************
# *****************************************************************************
class ArcInvestments(Arcs):
"""A class for defining arcs linked to investments."""
# *************************************************************************
# *************************************************************************
def __init__(self, investments: tuple, **kwargs):
# keep investment data
self.investments = investments
# initialise object
Arcs.__init__(
self,
minimum_cost=tuple([inv.net_present_value() for inv in self.investments]),
# validate=False,
**kwargs
)
# *************************************************************************
# *************************************************************************
def update_minimum_cost(self):
"Updates the minimum costs using the Investment objects."
self.minimum_cost = tuple([inv.net_present_value() for inv in self.investments])
# *************************************************************************
# *************************************************************************
# *****************************************************************************
# *****************************************************************************
\ No newline at end of file
src/topupopt/data/gis/__init__.py 0 → 100644
+ 2
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# -*- coding: utf-8 -*-
# import osm
src/topupopt/data/gis/calculate.py 0 → 100644
+ 352
− 0
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# imports
from math import inf
# local, external
from networkx import MultiDiGraph, is_path
from geopandas import GeoDataFrame
from pandas import isna
from shapely.geometry import LineString, Point
from shapely import length
from osmnx.distance import great_circle
from osmnx.stats import count_streets_per_node
from osmnx.projection import is_projected
# local, internal
from ..gis import osm as osm
from ..gis import identify as ident
# *****************************************************************************
# *****************************************************************************
def edge_lengths(network: MultiDiGraph, edge_keys: tuple = None) -> dict:
"""
Calculate edge lengths in a OSMnx-formatted MultiDiGraph network object.
The calculation method changes depending on whether the coordinates are
projected and depending on whether the edges are simplified.
Parameters
----------
network : MultiDiGraph
The object describing the network.
edge_keys : tuple, optional
The keys for the edges under consideration. The default is None, which
means all edges in the network will be considered.
Returns
-------
dict
A dictionary with the lengths for each edge.
"""
# determine if the graph is projected or not
graph_is_projected = is_projected(network.graph["crs"])
# check if edge keys were specified
if type(edge_keys) == type(None):
# no particular edge keys were provided: consider all edges (default)
edge_keys = network.edges(keys=True) # tuple(network.edges(keys=True))
# initialise length dict
length_dict = {}
# for each edge on the graph
for edge_key in edge_keys:
# calculate it using the library
if graph_is_projected:
# calculate it using projected coordinates
if osm.KEY_OSMNX_GEOMETRY in network.edges[edge_key]:
# use geometry
length_dict[edge_key] = length(
network.edges[edge_key][osm.KEY_OSMNX_GEOMETRY]
)
else:
# use (projected) coordinates
start_point = Point(
(
network.nodes[edge_key[0]][osm.KEY_OSMNX_X],
network.nodes[edge_key[0]][osm.KEY_OSMNX_Y],
)
)
end_point = Point(
(
network.nodes[edge_key[1]][osm.KEY_OSMNX_X],
network.nodes[edge_key[1]][osm.KEY_OSMNX_Y],
)
)
length_dict[edge_key] = start_point.distance(end_point)
else:
# calculate it using unprojected coordinates (lat/long)
if osm.KEY_OSMNX_GEOMETRY in network.edges[edge_key]:
# use geometry
length_dict[edge_key] = great_circle_distance_along_path(
network.edges[edge_key][osm.KEY_OSMNX_GEOMETRY]
)
else:
# use (unprojected) coordinates
length_dict[edge_key] = great_circle(
lat1=network.nodes[edge_key[0]][osm.KEY_OSMNX_Y],
lon1=network.nodes[edge_key[0]][osm.KEY_OSMNX_X],
lat2=network.nodes[edge_key[1]][osm.KEY_OSMNX_Y],
lon2=network.nodes[edge_key[1]][osm.KEY_OSMNX_X],
)
# return the dict with lengths of each edge
return length_dict
# *****************************************************************************
# *****************************************************************************
def great_circle_distance_along_path(path: LineString) -> float:
"""
Computes the great circle distance along a given path.
The distance is to be calculated using a shapely LineString object made of
(longitude, latitude) coordinate tuples. The calculation is vectorised.
Parameters
----------
path : LineString
An object describing the path whose distance is to be calculated.
Returns
-------
float
The sum of the individual great circle distances along the path.
"""
# get coordinates
lon = tuple(path.coords.xy[0])
lat = tuple(path.coords.xy[1])
# sum individual distances and return
return sum(
great_circle(
lat[:-1], # latitudes of starting points
lon[:-1], # longitudes of starting points
lat[1:], # latitudes of ending points
lon[1:], # longitudes of ending points
)
)
# *****************************************************************************
# *****************************************************************************
def update_street_count(network: MultiDiGraph):
"""
Updates the street count attributes of nodes in a MultiDiGraph object.
Parameters
----------
network : MultiDiGraph
The object describing the network.
Returns
-------
None.
"""
# update street count
street_count_dict = count_streets_per_node(network)
network.add_nodes_from(
(
(key, {osm.KEY_OSMNX_STREET_COUNT: value})
for key, value in street_count_dict.items()
)
)
# *****************************************************************************
# *****************************************************************************
def node_path_length(
network: MultiDiGraph, path: list, return_minimum_length_only: bool = True
) -> list or float:
"""
Returns the length or lengths of a path defined using nodes.
If more than one edge connects adjacent nodes along the path, a length value
will be returned for each possible path combination.
Parameters
----------
network : MultiDiGraph
The object describing the network.
path : list
A list of node keys or edge keys describing the path.
return_minimum_length_only : bool, optional
If True, returns the minimum path length only. The default is True.
Returns
-------
list or float
The path\'s length or all lengths consistent with the path provided.
"""
# direction matters
path_length = len(path)
if path_length == 0:
return inf
# if the path is given as a list of node keys, then it is subjective
# i.e., it may refer to many paths, namely if parallel edges exist
# check if the path object qualifies as such
if not is_path(network, path):
# it does not, exit
if return_minimum_length_only:
return inf
else:
return [inf]
# prepare a list with all possible paths given as lists of edge keys
list_of_edge_key_paths = [[]] # a list of edge key lists
# for each pair of nodes in the path
for node_pair in range(path_length - 1):
# get the edges between these two nodes
edge_keys = ident.get_edges_from_a_to_b(
network, path[node_pair], path[node_pair + 1]
)
number_edge_keys = len(edge_keys)
if number_edge_keys == 1:
# only one edge exists: append its key to all existing lists/paths
for edge_key_path in list_of_edge_key_paths:
edge_key_path.append(edge_keys[0])
else: # multiple edges exist: each path identified so far has to be
# replicated a total of number_edge_keys times and then updated
number_paths = len(list_of_edge_key_paths)
# for each parallel edge
for edge_key_index in range(number_edge_keys - 1):
# replicate all paths
for path_index in range(number_paths):
list_of_edge_key_paths.append(
list(list_of_edge_key_paths[path_index])
)
# paths have been replicated, now add the edges
for edge_key_index in range(number_edge_keys):
for path_index in range(number_paths):
# add the new edge
list_of_edge_key_paths[
path_index + edge_key_index * number_paths
].append(edge_keys[edge_key_index])
# *************************************************************************
path_lenths = [
sum(network.edges[edge_key][osm.KEY_OSMNX_LENGTH] for edge_key in edge_key_path)
for edge_key_path in list_of_edge_key_paths
]
if return_minimum_length_only:
return min(path_lenths)
else:
return path_lenths
# *************************************************************************
# *****************************************************************************
# *****************************************************************************
def edge_path_length(network: MultiDiGraph, path: list, **kwargs) -> float:
"""
Returns the total length of a path defined using edges.
If the path does not exist, or if no path is provided, the result will be
infinity (math.inf).
Parameters
----------
network : MultiDiGraph
The object describing the network.
path : list
A list of edge keys describing the path.
Returns
-------
list or float
The path\'s length or all lengths consistent with the path provided.
"""
# check the number of
path_length = len(path)
if path_length == 0:
return inf
if ident.is_edge_path(network, path, **kwargs):
return sum(network.edges[edge_key][osm.KEY_OSMNX_LENGTH] for edge_key in path)
else:
# no path provided
return inf
# *****************************************************************************
# *****************************************************************************
def count_ocurrences(
gdf: GeoDataFrame, column: str, column_entries: list = None
) -> dict:
"""
Counts the number of occurrences per entry in a DataFrame object's column.
If a list is provided, only the entries that match those in the list are
counted. If no list is provided, all unique entries are counted.
Parameters
----------
gdf : GeoDataFrame
The object holding the data.
column : str
A string with the name of the column.
column_entries : list, optional
A list with the entries that are to be counted. The default is None, in
which case all the unique entries will be counted.
Returns
-------
dict
A dictionary with the counts whose keys are the values counted.
"""
if type(column_entries) == list:
# find entries also present in the dict
# initialise dict
count_dict = {}
# for each key in the dict
for key in column_entries:
# # store the number of rows
# count_dict[key] = gdf[gdf[column]==key].shape[0]
# count the number of rows with this key
if isna(key):
count_dict[key] = gdf[gdf[column].isnull()].shape[0]
else:
count_dict[key] = gdf[gdf[column] == key].shape[0]
else:
# find all unique entries
# initialise dict
count_dict = {}
for entry in gdf[column]:
# check if it is already in the dict
if entry in count_dict:
# it is, skip
continue
# it is not, count and store the number of rows with said entry
if isna(entry): # type(entry) == type(None):
count_dict[entry] = gdf[gdf[column].isnull()].shape[0]
else:
count_dict[entry] = gdf[gdf[column] == entry].shape[0]
# return statement
return count_dict
# *****************************************************************************
# *****************************************************************************
src/topupopt/data/gis/identify.py 0 → 100644
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src/topupopt/data/gis/modify.py 0 → 100644
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