Welcome to mvm_smart_meter’s documentation!

mvm_smart_meter package

Submodules

mvm_smart_meter.smart_meter module

exception GuidNotFoundException(message='Guid not found in URL', url=None)[source]

Bases: Exception

Exception raised when the GUID is not found in a URL.

Parameters:

  • message (str) – Custom error message describing the exception. Defaults to “Guid not found in URL”.

  • url (str) – URL where the GUID was not found. Defaults to None.

Variables:

  • message – Custom error message describing the exception.

  • url – URL where the GUID was not found.

class Smart_meter(username: str, password: str)[source]

Bases: object

All the main function to gather date from the smart metering site is gathered here

download_ldc_data() → object[source]

This function downloads the data inbetwen the previusly set dates.

Returns:

returns text object

Return type:

object

get_base_cookies()[source]

Gets cookies from the main site to be used for later

get_cookies_smart_meter_site()[source]

Get cookies from the main site.Need to find a workaround as requestium uses old version of selenium.

get_custumer_data()[source]

Gets custumer data as it’s needs to be provided in the url’s later on.

get_ldc()[source]

Loads the load curve site, as data is stored in html.

get_login_cookies()[source]

Log’s into the main site, gets the cookies and the AuthCode for grabbing the token later on.

get_smart_meter_data()[source]

The smart metering site is on a external site, this function gets thoose links into a list.

get_token()[source]

Gets Oauth 2.0 token to be used on the main site for authorization

set_date_for_ldc(date_from: str, date_to: str)[source]

Function for posting dates, as load curve data is stored in the html.

Parameters:

  • date_from (str) – date where to start

  • date_to (str) – date where to end

smart_site_accept_cookes()[source]

Accapts the cookies on the smart metering site.

clean_data(df: DataFrame) → DataFrame[source]

This function can clean up text response

Parameters:

df (pandas.DataFrame) – Dataframe to be cleaned

Returns:

Return a cleaned up dataframe

Return type:

pandas.DataFrame

data_to_dataframe(data: object) → DataFrame[source]

Converts html/text request to dataframe

Parameters:

data (object) – Text from request

Returns:

Returns a dataframe

Return type:

pandas.DataFrame

date_list(date_from: str, date_to: str) → list[source]

Makes a list with date’s between date_from and date_to

Parameters:

  • date_from (str) – date_from in string

  • date_to (str) – date_to in string

Returns:

Returns a list with all the dates

Return type:

list

get_all_load_curve(username: str, password: str, date_from=None, date_to=None, raw_data: bool = False) → DataFrame[source]

It’s similar to get_load_curve(), but this function was intented to get all the available data from the smart matering site day by day

Parameters:

  • username (str) – Username for smart metering site

  • password (str) – Password for smart metering site

  • date_from (_type_, optional) – Start to data where to start from, defaults to None

  • date_to (_type_, optional) – End date where to stop, defaults to None

  • raw_data (bool, optional) – Flag for cleaning up response txt, defaults to False

Returns:

Return all valid daily load curve in Dataframe

Return type:

pandas.DataFrame

get_load_curve(username: str, password: str, date_from=None, date_to=None, raw_data: bool = False) → DataFrame[source]

Get’s load curve data from smart metering site into a Dataframe

Parameters:

  • username (str) – Username for smart metering site

  • password (str) – Password for smart metering site

  • date_from (_type_, optional) – Usualy date_from and date_to is the same date., defaults to None

  • date_to (_type_, optional) – Usualy date_from and date_to is the same date., defaults to None

  • raw_data (bool, optional) – Flag for cleaning up response txt, defaults to False

Returns:

Returns load curve data between set dates into a Dataframe

Return type:

pandas.DataFrame

main()[source]

Main funtion intented for debuging

sum_load_curve(load_curve_df: DataFrame) → DataFrame[source]

Summation function for load curve data set

Parameters:

load_curve_df (pandas.DataFrame) – Dataframe which summoned

Returns:

Returns summed dataset

Return type:

pandas.DataFrame

validate_df(df_to_validate: DataFrame) → bool[source]

Usualy smart metering site stores daily data for 3 months, after that all the daily data is deleted.Helper function for get_all_load_curve() to validate the date is valid on that date.

Parameters:

df_to_validate (pandas.DataFrame) – Dataframe which validation is performed

Returns:

Returns True when the Dataframe is valid

Return type:

bool

Module contents

Indices and tables