micantis 0.1.2

Package to simplify Micantis API usage

Micantis API Wrapper

A lightweight Python wrapper for interacting with the Micantis API plus some helpful utilities.
Built for ease of use, fast prototyping, and clean integration into data workflows.

---

🚀 Features

• Authenticate and connect to the Micantis API service
• Download and parse csvs and binary data into pandas DataFrames
• Filter, search, and retrieve metadata
• Utility functions to simplify common API tasks

---

⚠️ Important

This package is designed for authenticated Micantis customers only.
If you are not a Micantis customer, the API wrapper and utilities in this package will not work for you.

For more information on accessing the Micantis API, please contact us at [e-mail].

---

📦 Installation

pip install micantis

---

💻 Examples

Import functions

import pandas as pd
from micantis import MicantisAPI

Initialize API

# Option 1 - login with username and password
service_url = 'your service url'
username = 'your username'
password = 'your password'

api = MicantisAPI(service_url=service_url, username=username, password=password)

# Option 2 - login in with Microsoft Entra ID
SERVICE = 'your service url'
CLIENT_ID = 'your client id'
AUTHORITY = 'https://login.microsoftonline.com/organizations'
SCOPES = ['your scopes']

api = MicantisAPI(service_url=SERVICE, client_id=CLIENT_ID, authority=AUTHORITY, scopes=SCOPES)

Authenticate API

api.authenticate()

Download Data Table Summary

Optional parameters

• search: Search string (same syntax as the Micantis WebApp)
• barcode: Search for a specific barcode
• limit: Number of results to return (default: 500)
• min_date: Only return results after this date
• max_date: Only return results before this date
• show_ignored: Include soft-deleted files (default: True)

table = api.get_data_table(search=search, barcode=barcode, min_date=min_date, max_date=max_date, limit = 10, show_ignored=show_ignored)
table

Download Binary Files

# Download single file

file_id = 'File ID obtained from data table, id column'
df = api.download_binary_file(id)

# Download many files using list of files from the table

file_id_list = table['id'].to_list()
data = []

for id in file_id_list:
    df = api.download_csv_file(id)
    data.append(df)

all_data = pd.concat(data)

Download CSV Files

# Download single file

file_id = 'File ID obtained from data table, id column'
df = api.download_csv_file(id)

# Download multiple files

id_list = table['id'].to_list()
data = []

for id in id_list:
    df = api.download_csv_file(id)
    data.append(df)

all_data = pd.concat(data)

Cells Table

Download Cell ID Information

Retrieve a list of cell names and GUIDs from the Micantis database with flexible filtering options.

Optional parameters

• search: Search string (same syntax as the Micantis WebApp)
• barcode: Search for a specific barcode
• limit: Number of results to return (default: 500)
• min_date: Only return results after this date
• max_date: Only return results before this date
• show_ignored: Include soft-deleted files (default: True)

search = "*NPD*"
cells_df = api.get_cells_list(search=search)
cells_df.head()

Download Cell Metadata

Fetch per-cell metadata and return a clean, wide-format DataFrame.

Parameters:

• cell_ids: List[str]
  List of cell test GUIDs (required)

• metadata: List[str] (optional)
  List of metadata names (e.g., "OCV (V)") or IDs.
  If omitted, all non-image metadata will be returned by default.

• return_images: bool (optional)
  If True, includes image metadata fields. Default is False.

---

📘 Examples

# Example 1: Get all non-image metadata for a list of cells
cell_ids = cells_df["id"].to_list()
cell_metadata_df = api.get_cell_metadata(cell_ids=cell_ids)

# Example 2: Get specific metadata fields by name
cell_metadata_df = api.get_cell_metadata(
    cell_ids=cell_ids,
    metadata=["Cell width", "Cell height"],
    return_images=False
)

# Merge cell metadata table with cell names to get clean dataframe
# Merge id with Cell Name (as last column)
id_to_name = dict(zip(cells_df['id'], cells_df['name']))
cells_metadata_df['cell_name'] = cells_metadata_df['id'].map(id_to_name)
cells_metadata_df.head()