lime-internal-getter 0.3.2

data handling library for Lime.ai

lime_internal_getter- A Data Handling Library for Lime.ai Documentation

Overview

This document provides a description of the functions defined in the lime_internal_getter package. The functions facilitate various operations including data fetching, IoT dashboard integration, data filtering, and process execution.

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Functions

1. get_imei

Description: Retrieves the IMEI of a battery pack from the Lime.ai dashboard.

Parameters:

• IMEI (str): The IMEI string of the device.

Returns:

• IMEI (str): Retrieved IMEI of the battery pack.

Example Usage:

imei = get_imei("MD0AIOALAA00638")

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2. get_pimdata

Description: Retrieves and processes IoT dashboard data for Local PIM testing.(Only for developers of PIM)

Parameters:

• IMEI (str): IMEI of the device.
• start_time (str): Start time in YYYY-MM-DD HH:MM format.
• end_time (str): End time in YYYY-MM-DD HH:MM format.
• filter_data (bool): Whether to filter data. Default is False.
• serial_no (bool): Whether the input is a serial number. Default is False.
• interpolation (bool): Whether to interpolate data. Default is True.
• period (float): Interpolation period in seconds. Default is 0.1.
• nas (bool): Use NAS storage if True. Default is True.

Returns:

• DataFrame: A pandas DataFrame containing processed data.

Example Usage:

pim_data = get_pimdata("MD0AIOALAA00638", "2024-10-25 14:30", "2024-10-26 02:17")

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3. get_fwdata

Description: Fetches a list of packs with specific firmware versions.

Parameters:

• fWVersion (str): Firmware version to filter.
• battery_prefix (str): Battery prefix for filtering.

Returns:

• DataFrame: A pandas DataFrame containing the filtered data.

Example Usage:

fw_data = get_fwdata(fWVersion="8183D", battery_prefix="MH")

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4. get_data

Description: Fetches battery data from NAS storage or IoT dashboard.

Parameters:

• imei (str): IMEI or serial number.
• start_time (str): Start time in YYYY-MM-DD HH:MM format.
• end_time (str): End time in YYYY-MM-DD HH:MM format.
• filter_data (bool): Whether to filter data. Default is False.
• skip (bool): Skip missing files if True. Default is False.
• nas (bool): Use NAS storage if True. Default is True.

Returns:

• DataFrame: A pandas DataFrame containing fetched data.

Example Usage:

battery_data = get_data("MD0AIOALAA00638", "2024-10-25 14:30", "2024-10-26 02:17")

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5. pim_make

Description: Executes the PIM model after setting configurations in C code.(Only for developers of PIM)

Parameters:

• directory_path (str): Path to the directory containing PIM configurations.
• model (int): Model type. Default is 4.
• filename (str): File extension for the output. Default is "_iot_data.csv".

Example Usage:

pim_make("/path/to/directory", model=4, filename="_iot_data.csv")

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6. PIMProcessor (Class)

Description: Class for processing PIM models and generating reports. (Not required for non-developers of PIM Model) results avaiable through some list of data through batch process as list

Methods:

• __init__(directory_path, model,step): Initializes the processor.
• fetch_and_process_data(...): Processes data for a list of serial numbers.
• generate_final_table(save_csv): Generates the final data table.
• plot(): Plots SOH comparison for packs.

Example Usage:

processor = PIMProcessor("/path/to/directory", model=4)
processor.fetch_and_process_data([...], "2024-06-27", "2024-06-28")
processor.correction_monitor()
print(processor.bms_errors)

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7. KalmanFilter (Class)

Description: Class for running PIM models implemented locally through python (Not required for non-developers of PIM Model but can be used) soc and soh available through internal variable parameters

Methods:

• __init__(model) : Initialises model parameters
• process_filter(data): Performs Kalman filter for the given data
  • data a dataframe variable which has the structure: [Time in s,Current(A), Voltage 1 , Voltage2 ....]

Example Usage:

import lime_internal_getter as ig
kf= ig.KalmanFilter(model=9)
kf.process_filter(ig.get_pimdata("863674072212077",'2025-01-26')) #Put actual parameters here
print(kf.soc[0])# print soc for cell 1 corresponding to voltage 1 
print(kf.soh[0])# print soh for cell 1 corresponding to voltage 1

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8. Reports (Class)

Description: Class for generating reports and corrections scripts for different firmware versions

Methods:

• __init__(model): Generates a variable which has functionality of docx. Use df_todocx to update this and save using document.save(filpath+filename.docx)
• df_to_docx(data) : Use this generate a table in the document for any dataframe
• correction_script(fWlist,model,start_date) : Use for running correction monitor with different correction scripts in PIMProcessor class for big data in batch mode for processing entire firm like for max errors, errors in soc_ranges etc. . Example Usage:

import pandas as pd
import lime_internal_getter as ig
ar=ig.Reports()
ar.df_to_docx(pd.DataFrame([1,2,3],[1.1,2.5,4.2]),save=True)
fWlist=["51828"]
ar.correction_script(fWlist,model=3,start_date="2025-01-21")
#### Process for atleast 5 Min
print(pd.read_csv('51828.csv'))

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