emobpy package

emobpy.availability module

This module contains the Availability class that creates the grid availability time series. The class requires to be provided with the name of the driving consumption profile on which will build up the new time series. It will also require the charging station power rating and charging station probability distribution based on the location. The location can also be associated with the trip purpose or destination. The time series indicate the location as a state.

The Availability class reads the consumption profile. Every row contains a location, arriving time, duration, distance and energy consumption from battery for driving. Following the availability probability distribution, the tool looks at every row’s location (state) and sample a charging station. After the charging stations have been allocated to every row, the tool tests if the allocation complies with the energy requirements. The state of charge (SOC) is determined assuming an immediate charging strategy. This strategy consists of charging at the maximum power rate whenever the current SOC is between 0 - 1. If the resulting SOC never goes below zero. Then the allocation is considered correct, and the charging availability time series is done; otherwise, a new allocation occurs.

The allocation of charging stations is carried out several times until a successful allocation is reached or the maximum number of attempts is attained. If the latter, the file name will contain FAIL word.

For more details see the article and cite:

@article{Gaete-Morales_2021,
author={Gaete-Morales, Carlos and Kramer, Hendrik and Schill, Wolf-Peter and Zerrahn, Alexander},
title={An open tool for creating battery-electric vehicle time series from empirical data, emobpy},
journal={Scientific Data}, year={2021}, month={Jun}, day={11}, volume={8}, number={1}, pages={152},
issn={2052-4463}, doi={10.1038/s41597-021-00932-9}, url={https://doi.org/10.1038/s41597-021-00932-9}}

See also the examples in the documentation https://diw-evu.gitlab.io/emobpy/emobpy

class emobpy.availability.Availability(inpt, db)[source]

Bases: object

Instance that represents a grid availability time series. It requires the driving consumption profile name (inpt) on which will build up the new time series and the database instace (db) where the consumption profiles are hosted.

Parameters

inpt (str) – driving consumption profile name
db (DataBase()) – class instance that contains the profiles

Example

GA = Availability('ev1_abc_tesla3_def', DB)
GA.set_scenario(charging_data)
GA.run()
GA.save_profile('path to folder')

run()[source]

No input required. Once it finishes the following attributes can be called.

Attributes:

kind
input
chargingdata
battery_capacity
charging_eff
discharging_eff
soc_init
soc_min
storage_altern
profile
timeseries
success
name
proportion_ts_modified

save_profile(folder, description=' ')[source]

Saves object profile as a pickle file.

Parameters

folder (str) – Where the files will be stored. Folder is created in case it does not exist.
description (str, optional) – Description which can be saved in object attribute. Defaults to ” “.

set_scenario(charging_data)[source]

Sets given charging_data to object.

Parameters

charging_data (dict) – E.g.

{
    'prob_charging_point' :
        {'errands':  {'public':0.3,'none':0.7},
        'escort':   {'public':0.3,'none':0.7},
        'leisure':  {'public':0.3,'none':0.7},
        'shopping': {'public':0.3,'none':0.7},
        'home':     {'public':0.3,'none':0.7},
        'workplace':{'public':0.0,'workplace':0.3,'none':0.7},
        'driving':  {'none':1.0}
        },
    'capacity_charging_point' :
        {'public':11,'home':1.8,'workplace':5.5,'none':0}
}

emobpy.availability.add_column_datetime(df, totalrows, reference_date, t)[source]

Useful to convert the time series from hours index to datetime index.

Parameters

df (pd.DataFrame) – Table on which datetime column should be added.
totalrows (int) – Number of rows on which datetime column should be added.
reference_date (str) – Starting date for adding. E.g. ‘01/01/2020’.
t (float) – Float frequency, will be changed to string.

Returns

Table with added datetime column.

Return type

pd.DataFrame

emobpy.charging module

While a grid availability time series contains at each interval information of the charging stations available, such as the maximum power rating allocated to them, a grid electricity demand time series is the one that indicates the actual consumption of electricity from the grid to charge the battery of a vehicle according to its driving needs and grid availability. There are different options available to create a grid electricity demand time series. For example, “Immediate-Full capacity” is an option that informs the energy drawn from the grid at a maximum power rating of a respective charging station until the battery is fully charged or “Immediate-Balanced” option that creates a time series taking into account the duration of a vehicle is connected to a charging station and the energy required to get the battery fully charged, allowing to charge the battery at a lower capacity than the maximum capacity available.

For more details see the article and cite:

@article{Gaete-Morales_2021,
author={Gaete-Morales, Carlos and Kramer, Hendrik and Schill, Wolf-Peter and Zerrahn, Alexander},
title={An open tool for creating battery-electric vehicle time series from empirical data, emobpy},
journal={Scientific Data}, year={2021}, month={Jun}, day={11}, volume={8}, number={1}, pages={152},
issn={2052-4463}, doi={10.1038/s41597-021-00932-9}, url={https://doi.org/10.1038/s41597-021-00932-9}}

See also the examples in the documentation https://diw-evu.gitlab.io/emobpy/emobpy

class emobpy.charging.Charging(inpt)[source]

Bases: object

Parameters

self.__init__ (input) – input: string. File name of the input profile (not the path). The input should be in this case a grid availability profile name.

Methods in the following order:

self.loadScenario(DataBase)
self.setSubScenario(option)
self.run()
self.save_profile(folder, description=’ ‘)

load_scenario(database)[source]

Loads scenario data from given database into object.

Parameters: database (DataBase()) – E.g. manager = DataBase(). “manager” is a class instance that contains the profiles.
Raises: ValueError – Raised if charging profile can not be found in the database.

run()[source]

No input required. Once it finishes the following attributes can be called.

Attributes:

kind
input
change_battery_cap
pointmissing
success
option
profile
timeseries
name

save_profile(folder, description=' ')[source]

Saves object profile as a pickle file.

Parameters

folder (str) – Where the files will be stored. Folder is created in case it does not exist.
description (str, optional) – Description which can be saved in object attribute. Defaults to ” “.

set_sub_scenario(option)[source]

Sets sub scenario in self.option.

Parameters: option (str) – ‘immediate’, ‘balanced’ or ‘from_22_to_6_at_home’.

emobpy.charging.add_column_datetime(df, totalrows, reference_date, t)[source]

Useful to convert the time series from hours index to datetime index.

Parameters

df (pd.DataFrame) – Table on which datetime column should be added.
totalrows (int) – Number of rows on which datetime column should be added.
reference_date (str) – Starting date for adding. E.g. ‘01/01/2020’.
t (float) – Float frequency, will be changed to string.

Returns

Table with added datetime column.

Return type

pd.DataFrame

emobpy.charging.is_between(t, time_range)[source]

Checks if given value is between given time_range.

Parameters

t (float) – Value to check.
time_range (list) – Time range list. E.g. [1,100].

Returns

Value if t is between time_range.

Return type

bool

emobpy.charging.represents_int(s)[source]

Check if argument is an int value.

Parameters: s (any type) – Value to check.
Returns: True if the argument is an int value.
Return type: bool

emobpy.constants module

List of some constants used for the calculations.

For more details see the article and cite:

@article{Gaete-Morales_2021,
author={Gaete-Morales, Carlos and Kramer, Hendrik and Schill, Wolf-Peter and Zerrahn, Alexander},
title={An open tool for creating battery-electric vehicle time series from empirical data, emobpy},
journal={Scientific Data}, year={2021}, month={Jun}, day={11}, volume={8}, number={1}, pages={152},
issn={2052-4463}, doi={10.1038/s41597-021-00932-9}, url={https://doi.org/10.1038/s41597-021-00932-9}}

emobpy.consumption module

Based on the vehicle mobility time series, the driving electricity consumption (ii) time series is derived. This requires further input data, such as information on nominal motor power, curb weight, drag coefficient, and dimensions, which the tool includes for several current BEV models. Ambient temperature is also a significant parameter that affects the consumption of BEV. For that reason, emobpy is endowed with a database of hourly temperature for European countries with a registry of the last 17 years. Additionally, the vehicle cabin insulation characteristics are required; this data is not widely available and thus assumed independently of the BEV models database. Driving cycles are also important input parameters that are used to simulate every individual trip. The model includes two driving cycles, Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and Environmental Protection Agency (EPA). This input data is already provided within the tool, and the user can select a particular BEV model, country weather, and driving cycle. Alternatively, emobpy also allows providing user-defined custom data.

For more details see the article and cite:

@article{Gaete-Morales_2021,
author={Gaete-Morales, Carlos and Kramer, Hendrik and Schill, Wolf-Peter and Zerrahn, Alexander},
title={An open tool for creating battery-electric vehicle time series from empirical data, emobpy},
journal={Scientific Data}, year={2021}, month={Jun}, day={11}, volume={8}, number={1}, pages={152},
issn={2052-4463}, doi={10.1038/s41597-021-00932-9}, url={https://doi.org/10.1038/s41597-021-00932-9}}

See also the examples in the documentation https://diw-evu.gitlab.io/emobpy/emobpy

class emobpy.consumption.BEVspecs(filename=None)[source]

Bases: object

average(parameter)[source]

Returns average of a given parameter from self.data.

Parameters: parameter (str) – Parameter of which the average is required.
Returns: Average of the parameter.
Return type: float

dropna_model(parameter)[source]

Delete all na in self.data for given parameter.

Parameters: parameter (str) – Parameter from which to delete.

get(brand, model, year, parameter)[source]

Search for specific information in the vehicle database and returns it.

Parameters

brand (str) – E.g ‘Volkswagen’
model (str) – E.g. ‘ID.3’
year (int) – E.g. 2020
parameter (str) – E.g. ‘acc_0_100_kmh’

Returns

Requested value. None if nothing was found.

Return type

[float]

get_fallback_parameter(parameter)[source]

Get data for a given parameter if it is missing.

Parameters: parameter (str) – Parameter to get data value for.
Returns: Fallback data value for given parameter.
Return type: float

maximum(parameter)[source]

Returns maximum of specific parameter for the object.

Parameters: parameter (str) – Parameter of which the maximum is required.
Returns: Maximum of the parameter.
Return type: float

model(model, use_fallback=True, msg=True)[source]

Initializes ModelSpecs object, adds parameters and checks them.

Parameters

model (tuple) – Data of model. E.g (‘Volkswagen’, ‘ID.3’, 2020)
use_fallback
msg (bool, optional) – Flag, whether to inform about missing parameters. Defaults to True.

Returns

Initialized object.

Return type

ModelSpecs object

replacena_model(parameter, default)[source]

Replace all na in self.data for given parameter with default value.

Parameters

parameter (str) – Parameter from which to delete.
default (float) – Value to be used instead.

save()[source]: Save self.data into .yml file.

search_by_parameter(parameter='power', first_x=10, brand_filter=[], model_filter=[], year_filter=[])[source]

Searching for vehicles sorted in descending order of given parameter. It returns a Pandas DataFrame.

Parameters

parameter (str) – Vehicle parameter to compare. Defaults to ‘power’
first_x (int) – Number of vehicles to show. Defaults to 10.
brand_filter (int) – Filter for brands. Defaults to [].
model_filter (int) – Filter for models. Defaults to [].
year_filter (int) – Filter for years. Defaults to [].

Returns

data (pd.DataFrame)

show_models(brand='', model='', year='')[source]

Shows a list of all cars from the database. Can be filtered by brand, model and year.

Parameters

brand (str, optional) – Show only cars that match the brand. Defaults to ‘’.
model (str, optional) – Show only cars that match the model. Defaults to ‘’.
year (str, optional) – Show only cars that match the year. Defaults to ‘’.

class emobpy.consumption.Consumption(inpt, ev_model)[source]

Bases: object

load_setting_mobility(DataBase)[source]

Load certain attributes of the object with data from the transferred database.

Attributes:

self.df
self.t
self.totalrows
self.hours
self.freq
self.refdate
self.energy_consumption
self.states

Parameters: DataBase (DataBase()) – Database from which the data is to be loaded.
Raises: ValueError – A driving profile can not be found in the database.

run(heat_insulation, weather_country='DE', weather_year=2016, passenger_mass=75, passenger_sensible_heat=70, passenger_nr=1.5, air_cabin_heat_transfer_coef=10, air_flow=0.01, driving_cycle_type='WLTC', road_type=0, wind_speed=0, road_slope=0)[source]

#TODO Docstring

Parameters

heat_insulation (object) – [description]
weather_country (str, optional – [description]. Defaults to “DE”.
weather_year (int, optional) – [description]. Defaults to 2016.
passenger_mass (int, optional) – Passenger mass in kg. Defaults to 75.
passenger_sensible_heat (int, optional) – Passenger sensible heat in W. Defaults to 70.
air_cabin_heat_transfer_coef (int, optional) – Coefficient in W/(m2K). Defaults to 10.
air_flow (float, optional) – Ranges from 0.02 (high ventilation) to 0.001 (minimum ventilation) in me/s.
Defaults to 0.01.
driving_cycle_type (str, optional) – [desc]. Defaults to “WLTC”.
road_type (int, optional) – See function rolling_resistance_coef(method=’M1’) if an integer then all trips
have the same value, if list must fit the length of the profile. Defaults to 0.
wind_speed (int, optional) – m/s if an integer then all trips have the same value, if list must fit the
length of the profile. Defaults to 0.
road_slope (int, optional) – Radians if an integer then all trips have the same value, if list must fit the
length of the profile. Defaults to 0.

Raises

Exception – [description]

save_profile(folder, description=' ')[source]

Saves object profile as a pickle file.

Parameters

folder (str) – Where the files will be stored. Folder is created in case it does not exist.
description (str, optional) – Description which can be saved in object attribute. Defaults to ” “.

class emobpy.consumption.DrivingCycle[source]

Bases: object

create_data()[source]: Create self.data from self.dc_df.

driving_cycle(trip, model, full_driving_cycle=False)[source]

Calculates driving cycle from Trip and ModelSpecs object.

Parameters

trip (Trip) – Trip for the driving cycle.
model (ModelSpecs) – Vehicle Model for the driving cycle.
full_driving_cycle (bool, optional) – [description]. Defaults to False.

get_csv(csv_path='/home/docs/checkouts/readthedocs.org/user_builds/emobpy/envs/latest/lib/python3.8/site-packages/emobpy/data/driving_cycles.csv')[source]

Load csv as dataframe into self.dc_df

Parameters: csv_path (str, optional) – Path of file. Defaults to os.path.join(MODULE_DATA_PATH, “driving_cycles.csv”).

load_data()[source]: Load data from self.datafile to self.data.

save_data()[source]: Save self.tmpdata to file.

class emobpy.consumption.HeatInsulation(default=False)[source]

Bases: object

compile()[source]: Set all zones from self.zone_names to self.zone_layers.

show()[source]: Prints Heat Insulation attributes.

class emobpy.consumption.MGefficiency(filename=None)[source]

Bases: object

get_efficiency(load_fraction, g_m_code)[source]

g_m_code: 1 -> motor, -1 -> generator #TODO DOCSTRING :Parameters: * load_fraction ([type])

g_m_code (-1, 1) – 1 for motor, -1 for generator

Raises: Exception – Raised if g_m_code is not 1 or -1.
Returns: [description]
Return type: type

class emobpy.consumption.ModelSpecs(model, BEVspecs_instance)[source]

Bases: object

add(parameters_dict, msg=True)[source]

Adds parameter and associated value to the object.

Parameters

parameters_dict (dict) – Contains the name of the parameters and the corresponding value.
msg (bool, optional) – Flag, whether to inform about added parameters. Defaults to True.

add_calculated_param()[source]: Calculate all parameters that can be calculated from existing information.

add_fallback_data()[source]

add_parameters()[source]: Adds a value from the database to all parameters in self.db.parameters.

addtodb()[source]

Adds parameters, which are not None, to database.

Returns: Returns list of None parameters, which can not be added to database.
Return type: None, List of None parameters

class emobpy.consumption.Trip(trips)[source]

Bases: object

add_distance_duration(distance, duration)[source]

Sets loaded distance and duration to self.distance and self.duration and saves mean_speed.

Parameters

distance (dict) – Dictionary containing value and unit. E.g. {‘value’: 10.0, ‘unit’: ‘km’}
duration (dict) – Dictionary containing value and unit. E.g. {‘value’: 15.0, ‘unit’: ‘min’}

get_mean_speed()[source]: Calculates mean_speed ans saves it in object attribute.

class emobpy.consumption.Trips[source]

Bases: object

add_trip(trip)[source]

Adds single trip to trips object.[summary]

Parameters: trip (Trip) – Object to be added to the trips collection.

get_code()[source]

Returns trip code based on quantity of trip plus 1.

Returns: Trip code.
Return type: int

get_trip(code)[source]

Returns specific trip based on code.

Parameters: code (int) – Code of the requested trip.
Returns: Requested trip object.
Return type: Trip

class emobpy.consumption.Weather[source]

Bases: object

static air_density_from_ideal_gas_law(t, p)[source]

Calculate Air density from ideal gas law.

Parameters

t (float) – Temperature in degree Celsius.
p (float) – Pressure in mega bar.

Returns

Air density

Return type

array

static calc_dew_point(t, h)[source]

Calculate dew point. :Parameters: * t (float) – air temperature in degree Celsius.

h (float) – Relative humidity in percent.

Return type: array

static calc_dry_air_partial_pressure(P, pv)[source]

Calculate dry air partial pressure.

Parameters

P (float) – [description]
pv (float) – [description]

Returns

Dry air partial pressure.

Return type

array

calc_rel_humidity(Dp, T)[source]

Calculate humidity.

Parameters

Dp (array) – Dew point temperature in degree Celsius.
T (array) – Temperature in degree Celsius.

Returns

Relative humidity in percent.

Return type

array

static calc_vapor_pressure(t)[source]

Calculate vapor pressure.

Parameters: t (array) – Dew point or air temperature in degree Celsius.
Returns: Vapor pressure array
Return type: array

dewpoint(country_code, year)[source]

Loads selected dew point data in Kelvin into object.

Parameters

country_code (str) – E.g. ‘DE’.
year (int) – E.g. 2016.

Returns

Dew point data.

Return type

list

static download_weather_data(location=None)[source]

Download weather data from zenodo.

Parameters: location (str, optional) – Path to user path. Defaults to None.
Returns: Weather data.
Return type: list

humidair_density(t, p, dp=None, h=None)[source]

Calculate humid air density.

Parameters

t (array) – Temperature in degree Celsius.
p (array) – Pressure in mbar.
dp (array, optional) – Dew point temperature in degree Celsius. Defaults to None.
h (array, optional) – Humidity in percent. Defaults to None.

Raises

Exception – Dp or h is missing.

Returns

Humid air density.

Return type

array

pressure(country_code, year)[source]

Loads selected pressure data in Pascal into object.

Parameters

country_code (str) – E.g. ‘DE’.
year (int) – E.g. 2016.

Returns

Pressure data.

Return type

list

temp(country_code, year)[source]

Loads selected temperature data in Kelvin into object.

Parameters

country_code (str) – E.g. ‘DE’.
year (int) – E.g. 2016.

Returns

Temperature data.

Return type

list

emobpy.consumption.acceleration(V0, V2)[source]

Calculate and returns acceleration.

Parameters

V0 (float) – Old speed.
V2 (float) – New speed.

Returns

Acceleration.

Return type

float

emobpy.consumption.acceleration_array(speed_array)[source]

Calculates and returns acceleration array from speed_array. The acceleration of the adjoining values is calculated.

Parameters: speed_array (ndarray) – Array with speed values.
Returns: Array with acceleration values.
Return type: ndarray

emobpy.consumption.bar_progress(*args)[source]

Prints actual progress in format: “Downloading: 80% [8 / 10] kilobyte” :Parameters: * current (int) – Current download.

total (int) – Total number of downloads.

emobpy.consumption.consumption_progress(current, total)[source]

Prints message about consumption progress.

Parameters

current (int) – Current index.
total (int) – Total number of loops.
width (int, optional) – Not used. Defaults to 80.

emobpy.consumption.include_weather(pf, refdate, temp_arr, pres_arr, dp_arr, H, r_ha)[source]

Adds weather data to given DataFrame.

Parameters

pf (pd.DataFrame) – DataFrame where weather data should be added.
refdate (str) – E.g. ‘01/01/2020’.
temp_arr (ndarray) – Temperature in degree Celsius.
pres_arr (ndarray) – Pressure in mbar.
dp_arr (ndarray) – Dewpoint data in degree Celsius.
H (ndarray) – Humidity data.
r_ha (ndarray) – Air density in kg/m3.

Returns

[description]

Return type

pd.DataFrame

emobpy.consumption.inertial_mass(curb_weight, gear_ratio)[source]

Calculates and returns inertial mass.

Parameters

curb_weight (float) – Curb weight of the car.
gear_ratio (float) – Gear ratio of the car.

Returns

Inertial mass of the car.

Return type

float

emobpy.database module

This module contains data organisation classes to read, load and edit the resulting time series. See also the examples in the documentation https://diw-evu.gitlab.io/emobpy/emobpy

For more details see the article and cite:

@article{Gaete-Morales_2021,
author={Gaete-Morales, Carlos and Kramer, Hendrik and Schill, Wolf-Peter and Zerrahn, Alexander},
title={An open tool for creating battery-electric vehicle time series from empirical data, emobpy},
journal={Scientific Data}, year={2021}, month={Jun}, day={11}, volume={8}, number={1}, pages={152},
issn={2052-4463}, doi={10.1038/s41597-021-00932-9}, url={https://doi.org/10.1038/s41597-021-00932-9}}

class emobpy.database.DataBase(folder)[source]

Bases: object

DataBase object useful to manage many.

important attribute: self.db : It is a dictionary that contains all profiles. The dictionary keys are the name of the profile

Every profile in this dict has nested dictionary. The keys depend on the type of profile. Common keys:

self.db[“name of the profile”][“kind”] that can be [“driving”, “availability”, “charging”] self.db[“name of the profile”][“input”] that is a string only for [“availability”, “charging”] profiles

self.__init__(folder): folder: path as string of folder where profiles are hosted.

getdb()[source]

Run self.loadfiles() and return imported database.

Returns: Loaded database object.
Return type: DataBase

loadfiles(loaddir='')[source]

Load profiles and host in a directory other than the “folder”. So that directory must be indicated (loaddir). In this way profiles from many directories can be loaded.

Parameters: loaddir (str, optional) – Directory to load from. Defaults to ‘’.

loadfiles_batch(loaddir='', batch=10, nr_workers=4, kind='', add_variables=[])[source]

Load datafiles into DataBase object for further usage.

Parameters

loaddir (str, optional) – Directory to load from. Defaults to ‘’.
batch (int, optional) – Number of batches to load. Defaults to 10.
nr_workers (int, optional) – Number of workers to load. Defaults to 4.
kind (str, optional) – Data kind to load. E.g ‘consumption’. Defaults to ‘’.
add_variables (list, optional) – New variables to load. Defaults to [].

static loadpkl(f, variables, kind)[source]

Load from pickle file.

Parameters

f (str) – Path to pickle file.
variables (str) – Variables which should be loaded.
kind ([type]) – Data kind to load.

Returns

Loaded object.

Return type

DataBase

remove(name)[source]

Remove part of database.

Parameters: name (str) – Key which is to be deleted.

update()[source]: Run self.laodfiles() to load files from database “folder”.

class emobpy.database.DataManager[source]

Bases: object

Data Manager to load and save files.

loaddb(dbfilepath, profilesdir)[source]

Load database from pickle file.

Parameters

dbfilepath (str) – Path to pickle file.
profilesdir (str) – Path to profiles directory.

Returns

Loaded database from pickle file.

Return type

object

savedb(obj, dbdir='db_files')[source]

Save database to pickle file.

Parameters

obj (object) – Database to be saved.
dbdir (str, optional) – Path to database directory. Defaults to ‘db_files’.

emobpy.export module

This module contains a class that can be used to export results to CSV file in a format that is useful for modelling BEV in the power system model DIETER and DIETERpy https://diw-evu.gitlab.io/dieter_public/dieterpy.

The documentation contains examples of Export class https://diw-evu.gitlab.io/emobpy/emobpy

class emobpy.export.Export(groupbyhr=True, rows=None, kwto='MW')[source]

Bases: object

exp = Export() exp.loaddata(db), where db is an instance of DataBase Class. exp.to_csv() exp.save_files(‘csv’)

loaddata(db)[source]

Load data from database into self.data.

Parameters: db () – Database to be loaded.

save_files(repository='')[source]

Saves object as an csv.

Parameters: repository (str, optional) – Path to repository. Defaults to “”.

to_csv()[source]: Change object data to csv structure.

emobpy.functions module

Fuctions used for Consumption class

emobpy.functions.EFFICIENCYregenerative_braking(acceleration)[source]

#TODO DOCSTRING

Parameters: acceleration ([type]) – [description]
Returns: [description]
Return type: [type]

emobpy.functions.balance(db, tscode, include=None)[source]

#TODO DOCSTRING

Parameters

db ([type]) – [description]
tscode ([type]) – [description]
include ([type], optional) – [description]. Defaults to None.

Raises

Exception – [description]

Returns

[description]

Return type

[type]

emobpy.functions.cp(T)[source]

#TODO DOCSTRING

Parameters: T ([type]) – [description]
Returns: [description]
Return type: [type]

emobpy.functions.htc_air_out(vehicle_speed, limit=5)[source]

#TODO DOCSTRING

Parameters

vehicle_speed ([type]) – [description]
limit (int, optional) – [description]. Defaults to 5.

Returns

[description]

Return type

[type]

emobpy.functions.p_generatorin(p_wheel, transmission_eff, regenerative_braking_eff)[source]

#TODO DOCSTRING

Parameters

p_wheel ([type]) – [description]
transmission_eff ([type]) – [description]
regenerative_braking_eff ([type]) – [description]

Returns

[description]

Return type

[type]

emobpy.functions.p_generatorout(p_generator_in, generator_eff)[source]

#TODO DOCSTRING

Parameters

p_generator_in ([type]) – [description]
generator_eff ([type]) – [description]

Returns

[description]

Return type

[type]

emobpy.functions.p_gravity(vehicle_mass, g, v, slop_angle=0)[source]

#TODO DOCSTRING

Parameters

vehicle_mass ([type]) – [description]
g ([type]) – [description]
v ([type]) – [description]
slop_angle (int, optional) – [description]. Defaults to 0.

Returns

[description]

Return type

[type]

emobpy.functions.p_motorin(p_motor_out, motor_eff)[source]

#TODO DOCSTRING

Parameters

p_motor_out ([type]) – [description]
motor_eff ([type]) – [description]

Returns

[description]

Return type

[type]

emobpy.functions.p_motorout(p_wheel, transmission_eff)[source]

#TODO DOCSTRING

Parameters

p_wheel ([type]) – [description]
transmission_eff ([type]) – [description]

Returns

[description]

Return type

[type]

emobpy.functions.p_wheel(p_rollingresistance, p_airdrag, p_gravity, p_inertia)[source]

#TODO DOCSTRING

Parameters

p_rollingresistance ([type]) – [description]
p_airdrag ([type]) – [description]
p_gravity ([type]) – [description]
p_inertia ([type]) – [description]

Returns

[description]

Return type

[type]

emobpy.functions.pairdrag(air_density, frontal_area, drag_coeff, v, wind_speed=0)[source]

#TODO DOCSTRING Reference: Wang, J.; Besselink, I.; Nijmeijer, H. Electric Vehicle Energy Consumption Modelling and Prediction Based on Road Information. World Electr. Veh. J. 2015, 7, 447-458. https://doi.org/10.3390/wevj7030447

Parameters

air_density ([type]) – [description]
frontal_area ([type]) – [description]
drag_coeff ([type]) – [description]
v ([type]) – [description]
wind_speed (int, optional) – Wind speed in direction of the vehicle.. Defaults to 0.

Returns

[description]

Return type

float

emobpy.functions.pinertia(inertial_mass, vehicle_mass, acceleration, v)[source]

#TODO DOCSTRING

Parameters

inertial_mass ([type]) – [description]
vehicle_mass ([type]) – [description]
acceleration ([type]) – [description]
v ([type]) – [description]

Returns

[description]

Return type

[type]

emobpy.functions.plot_multi(data, cols=None, spacing=0.1, **kwargs)[source]

#TODO DOCSTRING

Parameters

data ([type]) – [description]
cols ([type], optional) – [description]. Defaults to None.
spacing (float, optional) – [description]. Defaults to .1.

Returns

[description]

Return type

[type]

emobpy.functions.prollingresistance(rolling_resistance_coeff, vehicle_mass, g, v, slop_angle=0)[source]

Calculates and returns polling resistance.

#TODO DOCSTRING :Parameters: * rolling_resistance_coeff ([type]) – [description]

vehicle_mass ([type]) – [description]

g ([type]) – [description]

v ([type]) – [description]

slop_angle (int, optional) – [description]. Defaults to 0.

Returns: Polling resistance.
Return type: float

emobpy.functions.q_person(q_sensible, persons=1)[source]

#TODO DOCSTRING

Parameters

q_sensible ([type]) – [description]
persons (int, optional) – [description]. Defaults to 1.

Returns

[description]

Return type

[type]

emobpy.functions.q_transfer(zone_layer, zone_area, layer_conductivity, layer_thickness, t_air_cabin, t_air_out, vehicle_speed, air_cabin_heat_transfer_coef=10)[source]

#TODO DOCSTRING

Parameters

zone_layer ([type]) – [description]
zone_area ([type]) – [description]
layer_conductivity ([type]) – [description]
layer_thickness ([type]) – [description]
t_air_cabin ([type]) – [description]
t_air_out ([type]) – [description]
vehicle_speed ([type]) – [description]
air_cabin_heat_transfer_coef (int, optional) – [description]. Defaults to 10.

Returns

[description]

Return type

[type]

emobpy.functions.q_ventilation(density_air, flow_air, Cp_air, temp_air)[source]

#TODO DOCSTRING

Density_air: kg/m3, Flow_air: m3/s, Cp_air: J/(kg*K), Temp_air: degC :Parameters: * density_air ([type]) – [description]

flow_air ([type]) – [description]

Cp_air ([type]) – [description]

temp_air ([type]) – [description]

Returns: [description]
Return type: [type]

emobpy.functions.qhvac(D, T_out, T_targ, cabin_volume, flow_air, zone_layer, zone_area, layer_conductivity, layer_thickness, vehicle_speed, Q_sensible=70, persons=1, P_out=1013.25, h_out=60, air_cabin_heat_transfer_coef=10)[source]

#TODO DOCUMENTATION Q indexes 0: Qtotal, 1: Q_in_per, 2: Q_in_vent, 3: Q_out_vent, 4: Q_tr

Parameters

D (method) – [description]
T_out (float) – [description]
T_targ (int) – [description]
cabin_volume (float) – [description]
flow_air (float) – [description]
zone_layer (ndarray) – [description]
zone_area (ndarray) – [description]
layer_conductivity (ndarray) – [description]
layer_thickness (ndarray) – [description]
vehicle_speed (ndarray) – [description]
Q_sensible (int, optional) – [description]. Defaults to 70.
persons (int, optional) – [description]. Defaults to 1.
P_out (float, optional) – [description]. Defaults to 1013.25.
h_out (int, optional) – [description]. Defaults to 60.
air_cabin_heat_transfer_coef (int, optional) – [description]. Defaults to 10.

Returns

[description]

Return type

[type]

emobpy.functions.resistances(zone_layer, zone_area, layer_conductivity, layer_thickness, vehicle_speed, air_cabin_heat_transfer_coef)[source]

[summary] #TODO DOCSTRING

Parameters

zone_layer ([type]) – [description]
zone_area ([type]) – [description]
layer_conductivity ([type]) – [description]
layer_thickness ([type]) – [description]
vehicle_speed ([type]) – [description]
air_cabin_heat_transfer_coef ([type]) – [description]

Returns

[description]

Return type

[type]

emobpy.functions.rolling_resistance_coeff(method='M1', **kwargs)[source]

Returns calculated rolling resistance coeff depending on method.

M1 depends on v:velocity (km/h), temp: degC and road_type: int -> Wang et al. M2 depends on v:velocity (km/h), tire_type: int, road_type: int -> Rahka et al.

M1 options:

v: km/h temp: degC road_type

0: ordinary car tires on concrete, new asphalt, cobbles small new, coeff: 0.01 - 0.015 1: car tires on gravel - rolled new, on tar or asphalt, coeff: 0.02 2: car tires on cobbles - large worn, coeff: 0.03 3: car tire on solid sand, gravel loose worn, soil medium hard, coeff: 0.04 - 0.08 4: car tire on loose sand, coeff: 0.2 - 0.4

M2 options:

v: km/h tire_type

0: Radial, c2:0.0328, c3: 4.575 1: Bias ply, c2:0.0438, c3: 6.100

road_type: Concrete: excellent 0: 1.00, good 1: 1.50, poor 2: 2.00 Asphalt: good 3: 1.25, fair 4: 1.75, poor 5: 2.25 Macadam: good 6: 1.50, fair 7: 2.25, poor 8: 3.75 Cobbles: ordinary 9: 5.50, poor 10: 8.50 Snow: 2 inch 11: 2.50, 4 inch 12: 3.75 Dirt: Smooth 13: 2.50, sandy 14: 3.75 Sand not implemented range 6.00 - 30.00

Parameters: method (str, optional) – [description]. Defaults to ‘M1’.
Raises: Exception – Raised if Method is not M1 or M2.
Returns: [description]
Return type: [type]

emobpy.functions.rolling_resistance_coeff_M1(temp, v, road_type=0)[source]

Returns calculated rolling resistance coeff for M1.

Parameters

temp (float) – Temperature ein degree celsius.
v (float) – Speed in km/h.
road_type (int, optional) – 0: ordinary car tires on concrete, new asphalt, cobbles small new, coeff: 0.01 - 0.015 (Default)
1: car tires on gravel - rolled new, on tar or asphalt, coeff: 0.02 2: car tires on cobbles - large worn, coeff: 0.03 3: car tire on solid sand, gravel loose worn, soil medium hard, coeff: 0.04 - 0.08 4: car tire on loose sand, coeff: 0.2 - 0.4

reference: Wang, J.; Besselink, I.; Nijmeijer, H. Electric Vehicle Energy Consumption Modelling and Prediction Based on Road Information. World Electr. Veh. J. 2015, 7, 447-458. https://doi.org/10.3390/wevj7030447

Returns

Rolling resistance coefficient

Return type

int

emobpy.functions.rolling_resistance_coeff_M2(v, tire_type=0, road_type=4)[source]

Returns calculated rolling resistance coeff for M2.

Parameters

v (float) – Speed in km/h.
tire_type (int, optional) – 0: Radial, c2:0.0328, c3: 4.575 1: Bias ply, c2:0.0438, c3: 6.100 (Default)
road_type (int, optional) – [description]. Defaults to 4. Concrete: excellent 0: 1.00, good 1: 1.50, poor 2: 2.00 Asphalt: good 3: 1.25, fair 4: 1.75, poor 5: 2.25 Macadam: good 6: 1.50, fair 7: 2.25, poor 8: 3.75 Cobbles: ordinary 9: 5.50, poor 10: 8.50 Snow: 2 inch 11: 2.50, 4 inch 12: 3.75 Dirt: Smooth 13: 2.50, sandy 14: 3.75 Sand not implemented range 6.00 - 30.00 reference: Rahka et al. 2001. Vehicle Dynamics Model for Predicting Maximum Truck Acceleration Levels. https://doi.org/10.1061/(ASCE)0733-947X(2001)127:5(418)

Returns

Rolling resistance coefficient

Return type

int

emobpy.functions.vehicle_mass(curb_weight, passengers_weight)[source]

Calculates and returns vehicle mass.

Parameters

curb_weight (float) – Curb weight of the vehicle.
passengers_weight (float) – Passengers weight.

Returns

Vehicle mass.

Return type

float

emobpy.init module

emobpy.init.copy_to_user_data_dir()[source]: This function combines the generation of user data directory and marges the user-defined data This usually runs when we create a project folder from command line. If we install emobpy and do not run the funtion create_project then it is likely that ‘emobpy user data folder’ has not been created and an error will ocour when using Consumption class. Check out the folder in # linux: ~/.local/share/emobpy # Windows: C:/Users/<USER>/AppData/Roaming/emobpy if embopy user data folder does not exist then run this function: from emobpy.init import copy_to_user_data_dir; copy_to_user_data_dir()

emobpy.init.create_project(project_name, template)[source]

Creates project based on selected template and copies these files.

Parameters

project_name (str) – Chosen project name.
template (str) – Chosen template.

Raises

Exception – Template arguments not valid.
Exception – Chosen folder does not exist.

emobpy.mobility module

Mobility class consists of creating individual driver time series that contains vehicle location and distance travelled at every time step. The vehicle’s locations are a group of common places obtained from mobility data.

Our approach consists of using three probability distributions from which to extract relevant features by samplings, such as the number of trips per day, the destination, departure time, trip distance and trip duration. Time steps and total time for every time series are parameters that have to be provided in advance.

To reach consistent and feasible mobility patterns, a set of rules can also be provided, for instance, establishing home as the destination of the last trip of the day.

Two different approaches have been developed—commuters and free-time drivers (non-commuters). Commuters are drivers that go during the weekdays to the workplace.

An instance of a mobility class contains a driving profile and a time series of one driver. The instance can be saved in a pickle file to access it later on and create other time series, such as consumption time series, grid availability, and eventually the grid demand.

For more details see the article and cite:

@article{
Gaete-Morales2021,
author={Gaete-Morales, Carlos and Kramer, Hendrik and Schill, Wolf-Peter and Zerrahn, Alexander},
title={An open tool for creating battery-electric vehicle time series from empirical data, emobpy},
journal={Scientific Data},
year={2021},
month={Jun},
day={11},
volume={8},
number={1},
pages={152},
abstract={There is substantial research interest in how future fleets of battery-electric vehicles will interact with the power sector. Various types of energy models are used for respective analyses. They depend on meaningful input parameters, in particular time series of vehicle mobility, driving electricity consumption, grid availability, or grid electricity demand. As the availability of such data is highly limited, we introduce the open-source tool emobpy. Based on mobility statistics, physical properties of battery-electric vehicles, and other customizable assumptions, it derives time series data that can readily be used in a wide range of model applications. For an illustration, we create and characterize 200 vehicle profiles for Germany. Depending on the hour of the day, a fleet of one million vehicles has a median grid availability between 5 and 7 gigawatts, as vehicles are parking most of the time. Four exemplary grid electricity demand time series illustrate the smoothing effect of balanced charging strategies.},
issn={2052-4463},
doi={10.1038/s41597-021-00932-9},
url={https://doi.org/10.1038/s41597-021-00932-9}
}

class emobpy.mobility.Mobility(config_folder='config_files')[source]

Bases: object

To create a mobility time series, after creating the Mobility class instance call the methods in the following order:

self.set_params(param)

self.set_stats(stat_ntrip, stat_dest, stat_km)

self.set_rules(rules_key, rules_file)

self.run()

self.save_profile(destination_folder)

Parameters: config_folder (string) – Folder name where the configuration files are hosted. Configuration files names are specified when setting stats and rules.

run()[source]

This function returns a driving profile. No input possible. Returns nothing. Once it finishes the following attributes can be called:

kind
name_prefix
refdate
hours
t
user_defined
df1
df2
df3
user_rules
states
numb_weeks
totalrows
name
profile
timeseries

save_profile(folder, description=' ')[source]

Saves a profile from the current object as a pickle file.

Parameters

folder (str) – Where the files will be stored. Folder is created in case it does not exist.
description (str, optional) – Profile description. Defaults to ” “.

set_params(name_prefix='', total_hours=168, time_step_in_hrs=0.5, category='user_defined', reference_date='01/01/2020')[source]

Defines the attributes given to the class objects.

Parameters

name_prefix (str) – It is an identifier that takes part of the file name. The user can freely choose it, e.g. ‘worker’. Defaults to ‘’
total_hours (int) – Defaults 168 hrs (one week). Maximum value 8760.
time_step_in_hrs (float) – It is the time step in hours. Defaults to 0.5 . Options are 0.25, 0.5, 1. Recommennded values below 1.
category (str) – This is an identifier. A column in the time series will contain this string e.g. ‘worker’. Defauts to ‘user_defined’.
reference_date (str, optional) – With this date the time series will start. All consecutive time steps will follow this date, e.g. ‘01/01/2020’. Defaults to “01/01/2020”.

set_rules(rule_key=None, rules_path='rules.yml')[source]

A set of rules can be defined and provided in a YAML file. Several customized rules can be allocated in only one file. Hence, the rule_key points to the set of rules that wanted to be used in the current run. If no rule_key is provided, then the tool copy a rules dictionary with default values.

If you want to know all the possibles rules to create your own rules file afterwards, type .initial_conf() and then .rules, e.g. Mobility.rules

Parameters

rule_key (str, optional) – e.g. ‘user_defined’. Defaults to None.
rules_path (str, optional) – Path to rules file. Defaults to “rules.yml” in config_files folder.

Raises

Exception – Raised if .setStats(args) is not called before.

set_stats(stat_ntrip_path, stat_dest_path, stat_km_duration_path)[source]

Name of files that contain the required probabilities to generate a mobility time series. They are three: DepartureDestinationTrip, DistanceDurationTrip, and TripsPerDay.

The DepartureDestinationTrip file must contain a wide data format (https://en.wikipedia.org/wiki/Wide_and_narrow_data). The first two columns are days and departure time in hours. The following columns represent the expected destinations where ‘home’ must be one of the destinations. At least 2 destinations (columns) should be included. As emobpy takes into account weekend days as well, the column days must contain ‘weekdays’, ‘sunday’ and ‘saturday’. Suppose your data does not differentiate from these days. Repeat the probabilities in each of the three options.

The DistanceDurationTrip file must contain a wide data format. The first column is distance in kilometers. The following columns are the duration of the trip in minutes. The tool determines the distance of trips and the duration. Then calculates the average velocity. Make sure that all possible combinations in your joint probabilities lead to consistent average velocities.

The TripsPerDay file must contain a wide data format. The first column is ‘trip’ and represnts the number of trips per day. The column weekday and weekend contain the probability distribution. Bear in mind that zero trip per day represent not trips at all, two trips means a trip to some destination and then a retun trip. One trip is not possible, and considered a limitation of the tool, as the model needs to starts the following day from home again. Always set 1 trip with zero as probability value. Also the maximun number of trip per day is limited with the time steps selected. If the time step is one hour, then having a distribution with 8 trips per day could be challenging for the model, and it might hang off.

Parameters

stat_ntrip_path (str) – e.g. ‘DepartureDestinationTrip.csv’
stat_dest_path (str) – e.g. ‘DistanceDurationTrip.csv’
stat_km_duration_path (str) – ‘TripsPerDay.csv

Raises

Exception – Raised, if .set_params(args) is not called before.

emobpy.mobility.add_column_datetime(df, totalrows, reference_date, t)[source]

Useful to convert the time series from hours index to datetime index.

Parameters

df (pd.DataFrame) – Table on which datetime column should be added.
totalrows (int) – Number of rows on which datetime column should be added.
reference_date (str) – Starting date for adding. E.g. ‘01/01/2020’.
t (float) – Float frequency, will be changed to string.

Returns

Table with added datetime column.

Return type

pd.DataFrame

emobpy.mobility.bar_progress(current, total)[source]

Prints actual progress in format: “Progress: 80% [8 / 10] days”.

Parameters

current (int) – Current day.
total (int) – Total number of days.

emobpy.mobility.cmp(arg1, string_operator, arg2)[source]

Perform comparison operation according to operator module. This function is used on meet_all_conditions().

Parameters

arg1 (object) – First argument.
string_operator (‘<’, ‘<=’, ‘==’, ‘!=’, ‘>=’, ‘>’) – String operator which can be used.
arg2 (object) – Second Argument.

Returns

Result of comparison.

Return type

bool

emobpy.mobility.create_tour_nb(unsortedtrips, previous_dest, lasttriparrival, t)[source]

#TODO DOCSTRING Create tour tuple .

Parameters

unsortedtrips ([type]) – [description]
previous_dest ([type]) – [description]
lasttriparrival ([type]) – [description]
t ([type]) – [description]

Returns

[description]

Return type

[type]

emobpy.mobility.creation_unsorted_trips_nb(Rnd_week_trips, km_array, duration_array, km_dur_prob_array, km_sorted, duration_sorted, t, time_purpose_array, daycode)[source]

#TODO DOCSTRING It returns a unsorted time dataframe with trips. It includes number of trips, departure time, destination (purpose).

Parameters

Rnd_week_trips ([type]) – [description]
km_array ([type]) – [description]
duration_array ([type]) – [description]
km_dur_prob_array ([type]) – [description]
km_sorted ([type]) – [description]
duration_sorted ([type]) – [description]
t ([type]) – [description]
time_purpose_array ([type]) – [description]
daycode ([type]) – [description]

Returns

[description]

Return type

[type]

emobpy.mobility.days_week_sequence(reference_date, weeks)[source]

This determines the day of the week of the reference date and sets the day-sequence. It helps to differentiate the statistics from week working days to weekends.

Parameters

reference_date (str) – Reference date in form of ‘01/01/2020’
weeks (dict) – Contains tag, tag type and day code per day ID.

Returns

List of day IDs (Integers) indicating the sequence of the week.

Return type

list

emobpy.mobility.distance_duration_sample(km_array_, duration_array_, km_dur_prob_array, km_sorted_, duration_sorted)[source]

#TODO DOCSTRING Randomly choose a distance duration based on distance and probability distribution. :Parameters: * km_array_ ([type]) – [description]

duration_array_ ([type]) – [description]

km_dur_prob_array ([type]) – [description]

km_sorted_ ([type]) – [description]

duration_sorted ([type]) – [description]

Returns: [description]
Return type: [type]

emobpy.mobility.distances_collection(nr_trips, km_array, duration_array, km_dur_prob_array, km_sorted, duration_sorted, edge_dist=- 1, edge_dur=- 1, edge_nr=0)[source]

#TODO DOCSTRING Sample distances from nba - grams.

Parameters

nr_trips ([type]) – [description]
km_array ([type]) – [description]
duration_array ([type]) – [description]
km_dur_prob_array ([type]) – [description]
km_sorted ([type]) – [description]
duration_sorted ([type]) – [description]
edge_dist (int, optional) – [description]. Defaults to -1.
edge_dur (int, optional) – [description]. Defaults to -1.
edge_nr (int, optional) – [description]. Defaults to 0.

Returns

[description]

Return type

[type]

emobpy.mobility.expand_table(df, factor, time_step)[source]

Expands a table (DataFrame) by a factor. The table can then be more granular. :Parameters: * df ([pd.DataFrame]) – Table that is to be extended.

factor (float) – By how many times the table should be extended.

time_step (float) – The new time_step that should apply.

Returns: Table in an expanded format.
Return type: [pd.DataFrame]

emobpy.mobility.make_interval(x, factor, time_step)[source]

Make a new interval for the given factor.

Parameters

x (float) – Start value.
factor (int) – Number of repetition.
time_step (float) – Time step for each repetition.

Returns

[description]

Return type

[type]

emobpy.mobility.make_list(x, factor)[source]

Make a list of integers representing a number x .

Parameters

x (float) – Number which is repeated in the list.
factor (int) – Len of the produced list.

Returns

List which contains value x for factor times.

Return type

list

emobpy.mobility.nb_isin(A, C)[source]

#TODO DOCSTRING Return nb in - place of nb in c .

Parameters

A ([type]) – [description]
C ([type]) – [description]

Returns

[description]

Return type

[type]

emobpy.mobility.rand_choice_nb(arr, prob)[source]

Choose random sample from numpy array with a explicit probability.

Parameters

arr (numpy.array) – A 1D numpy array of values to sample from.
prob (numpy.array) – A 1D numpy array of probabilities for the given samples.

Returns

A random sample from the given array with a given probability.

Return type

int or float

emobpy.mobility.time_purpose_iter(array_tp, daycode, deltime)[source]

#TODO DOCSTRING Return a random value for time purpose

Parameters

array_tp ([type]) – [description]
daycode ([type]) – [description]
deltime ([type]) – [description]

Returns

[description]

Return type

[type]

emobpy.plot module

This module contains a class that can be used to visualise the data. There are different visualisation functions.

class emobpy.plot.NBplot(db)[source]

Bases: object

Work in Jupyter notebooks only. Set of plots for a single time series and groups. Three kind of plots:

self.sgplot_dp(tscode) for driving profiles
self.sgplot_ga(tscode) for grid availability profiles
self.sgplot_ged(tscode) for grid electricity demand profiles
tscode: time series code (string of profile name)

self.__init__(db): db is an instance of a DataBase class that contains the time series.

overview(tscode, date_range=None, to_html=False, path=None)[source]

Still in Development.

Do not use!

date_range=[‘01/01/2020 00:00:00’,’01/06/2020 23:00:00’]

sankey(tscode, include=None, to_html=False, path=None)[source]

Plot of sankey diagram which shows the energy consumption flows.

Parameters

tscode (str) – Time series code. Eg. ‘ev_user_W3_85e59_avai_65t2p’
include (int) – Index which part to include. Defaults to None.
to_html (bool) – Save as a html file. Defaults to False.
path (str) – Path if plot should be saved to file. Defaults to None.

Returns

Plot object.

Return type

plotly.plot

sgplot_dp(tscode, rng=None, to_html=False, path=None)[source]

Plot of a single driving profile.

Parameters

tscode (str) – Time series code. Eg. ‘ev_user_W3_85e59_avai_65t2p’
rng (tuple) – (a,b) index if only part of timeseries should be copied. Defaults to None.
to_html (bool) – Save as a html file. Defaults to False.
path (str) – Path if plot should be saved to file. Defaults to None.

Returns

Plot object.

Return type

plotly.plot

sgplot_ga(tscode, rng=None, to_html=False, path=None)[source]

Plot of a single grid availability profile.

Parameters

tscode (str) – Time series code. Eg. ‘ev_user_W3_85e59_avai_65t2p’
rng (tuple) – (a,b) index if only part of timeseries should be copied. Defaults to None.
to_html (bool) – Save as a html file. Defaults to False.
path (str) – Path if plot should be saved to file. Defaults to None.

Returns

Plot object.

Return type

plotly.plot

sgplot_ged(tscode, rng=None, to_html=False, path=None)[source]

Plot of grid electricity demand profiles associated with the same grid availability profile.

Parameters

tscode (str) – Time series code. Eg. ‘ev_user_W3_85e59_avai_65t2p’
rng (tuple) – (a,b) index if only part of timeseries should be copied. Defaults to None.
to_html (bool) – Save as a html file. Defaults to False.
path (str) – Path if plot should be saved to file. Defaults to None.

Returns

Plot object.

Return type

plotly.plot

emobpy.tools module

This module contains a tool functions used to create a new project.

class emobpy.tools.Unit(val, unit, description='No description given.')[source]

Bases: object

This class represents a unit. In this way a value and a unit can be saved and with class methods the unit can easily be changed. The default value used by the programme is always the 1 value in unit_scale.

convert_to(new_unit, msg=False)[source]

convert_to_default_value()[source]

info()[source]

emobpy.tools.check_for_new_function_name(attribute_error_name)[source]: In an earlier update function names have been changed from camelCase to snake_case. To prevent users confusing this function raises a specific AttributeError of the user trys to access to old function name, which does not exist anymore.

emobpy.tools.parallel_func(dc, queue=None, queue_lock=None, function=None, kargs={})[source]

#TODO DOCSTRING

Parameters

dc ([type]) – [description]
queue ([type], optional) – [description]. Defaults to None.
queue_lock ([type], optional) – [description]. Defaults to None.
function ([type], optional) – [description]. Defaults to None.
kargs (dict, optional) – [description]. Defaults to {}.

Returns

[description]

Return type

[type]

emobpy.tools.parallelize(function=None, inputdict: Optional[dict] = None, nr_workers=1, **kargs)[source]

Parallelize function to run program faster. The queue contains tuples of keys and objects, the function must be consistent when getting data from queue.

Parameters

function (function, optional) – Function that is to be parallelized. Defaults to None.
inputdict (dict, optional) – Contains numbered keys and as value any object. Defaults to None.
nr_workers (int, optional) – Number of workers, so their tasks can run parallel. Defaults to 1.

Returns

Dictionary the given functions creates.

Return type

dict

emobpy.tools.set_seed()[source]: Sets seed at the beginning of any python script or jupyter notebook. That allows to repeat the same calculations with exactly same results, without any random noise.