micantis 0.1.13

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()

Specifications Table

Download Specifications List

Retrieve specifications with their associated user properties.

# Get all specifications with their user properties
specs_df = api.get_specifications_table()
specs_df.head()

Test Management

Download Test Requests List

Retrieve test request data with flexible date filtering.

Optional parameters

• since: Date string in various formats (defaults to January 1, 2020 if not provided)
  • Full month names: "May 1, 2025", "January 15, 2024"
  • ISO format: "2025-05-01" or "25-05-01"

# Get all test requests (defaults to since 2020-01-01)
test_requests = api.get_test_request_list()

# Get test requests since a specific date using month name
test_requests = api.get_test_request_list(since="May 1, 2024")

# Get test requests using ISO format
test_requests = api.get_test_request_list(since="2024-05-01")

Download Failed Test Requests

Retrieve only failed test requests with the same date filtering options.

# Get failed test requests since a specific date
failed_requests = api.get_failed_test_requests(since="January 1, 2024")
failed_requests.head()

Get Individual Test Request Details

Retrieve full details for a specific test request by ID.

New Feature: Multiple output format options for better data analysis!

Format Options

• return_format='dict': Raw dictionary (default, backwards compatible)
• return_format='dataframes': Returns 3 DataFrames - summary, tests, and status_log ⭐ Recommended
• return_format='flat': Single-row DataFrame with basic info

# Option 1: Dictionary format (default, backwards compatible)
request_id = "your-test-request-guid"
test_details = api.get_test_request(request_id)

# Option 2: DataFrames format (recommended for analysis) ⭐
test_details = api.get_test_request(request_id, return_format='dataframes')
print(test_details['summary'])      # Basic request information
print(test_details['tests'])        # All requested tests
print(test_details['status_log'])   # Status change history

# Option 3: Flat DataFrame (best for combining multiple requests)
test_details = api.get_test_request(request_id, return_format='flat')

Batch Processing Multiple Requests

# Get summaries for multiple test requests
request_ids = test_requests['id'].head(10).to_list()

all_summaries = []
for req_id in request_ids:
    summary = api.get_test_request(req_id, return_format='flat')
    all_summaries.append(summary)

# Combine into single DataFrame
combined_df = pd.concat(all_summaries, ignore_index=True)
print(f"Retrieved {len(combined_df)} test requests")
combined_df.head()

Write Cell Metadata

Micantis lets you programmatically assign or update metadata for each cell using either:

• the human-readable field name (e.g., "Technician", "Weight (g)")
• or the internal propertyDefinitionId (UUID)

📘 Examples

# Example 1: Update the technician field for a cell
changes = [
    {
        "id": "your-cell-test-guid-here",  # cell test GUID
        "field": "Technician",
        "value": "Mykela"
    },
    {
        "id": "your-cell-test-guid-here",
        "field": "Weight (g)",
        "value": 98.7
    }
]

api.write_cell_metadata(changes=changes)

# Verify the changes
api.get_cell_metadata(cell_ids=["your-cell-test-guid-here"], metadata=['Weight (g)', 'Technician'])

# Example 2: Update using propertyDefinitionId (advanced)
changes = [
    {
        "id": "your-cell-test-guid-here",
        "propertyDefinitionId": "your-property-definition-guid",
        "value": 98.7
    }
]

api.write_cell_metadata(changes=changes)

# Verify the changes
api.get_cell_metadata(cell_ids=["your-cell-test-guid-here"], metadata=['Weight (g)', 'Technician'])