pyloghub

Log-hub Python API library

Table of Contents

• Introduction
• Prerequisites
• Installation
  • Setting Up Python Environment
  • Installing pyloghub Package
• Configuration
• Usage
• Available Functionalities
• Contact Us
• License

Introduction

The pyloghub package provides convinient access to various Log-hub API services for Supply Chain Visualization, Network Design Optimization, and Transport Optimization as well as access to the Log-hub platform data.

Prerequisites

• Python 3.10 or later recommended
• Pip (Python package manager)
• Log-hub API key
• Supply Chain APPS PRO subscription

Installation

Setting Up Python Environment

Recommended Python Version

Python 3.10 or later is recommended for optimal performance and compatibility.

Optional: Setting Up a Virtual Environment

A virtual environment allows you to manage Python packages for different projects separately.

1. Create a Virtual Environment:

   • Windows:

     python -m venv loghub_env
     

   • macOS/Linux:

     python3 -m venv loghub_env
     

2. Activate the Virtual Environment:

   • Windows:

     .\loghub_env\Scripts\activate
     

   • macOS/Linux:

     source loghub_env/bin/activate
     

   Deactivate with deactivate when done.

Installing pyloghub Package

Within the environment, install the package using:

pip install pyloghub

Configuration

Obtaining an API Key

1. Sign up or log in at Log-hub Account Integration.
2. Obtain your API key.

Setting Up Your Environment

Securely store your API key for use in your Python scripts or as an environment variable.

Usage

Sample Code: Reverse Distance Calculation

This example demonstrates using the Reverse Distance Calculation feature:

1. Import Functions:

   from pyloghub.distance_calculation import reverse_distance_calculation, reverse_distance_calculation_sample_data
   

2. Load Sample Data:

   sample_data = reverse_distance_calculation_sample_data()
   geocode_data_df = sample_data['geocode_data']
   parameters = sample_data['parameters']
   

3. Perform Calculation:

   reverse_distance_result_df = reverse_distance_calculation(geocode_data_df, parameters, 'YOUR_API_KEY')
   

   Replace 'YOUR_API_KEY' with your actual Log-hub API key.

4. View Results:

   print(reverse_distance_result_df)
   

Available Functionalities

Overview

pyloghub offers a suite of functionalities to enhance your supply chain management processes. Below is a quick guide to the available features and sample usage for each.

Forward Geocoding

Convert addresses to geographic coordinates.

from pyloghub.geocoding import forward_geocoding, forward_geocoding_sample_data

sample_data = forward_geocoding_sample_data()
addresses_df = sample_data['addresses']
forward_geocoding_result_df = forward_geocoding(addresses_df, api_key)

Reverse Geocoding

Convert geographic coordinates to addresses.

from pyloghub.geocoding import reverse_geocoding, reverse_geocoding_sample_data

sample_data = reverse_geocoding_sample_data()
geocodes_df = sample_data['geocodes']
reverse_geocoding_result_df = reverse_geocoding(geocodes_df, api_key)

Forward Distance Calculation

Calculate distances based on address data.

from pyloghub.distance_calculation import forward_distance_calculation, forward_distance_calculation_sample_data

sample_data = forward_distance_calculation_sample_data()
address_data_df = sample_data['address_data']
parameters = sample_data['parameters']
forward_distance_calculation_result_df = forward_distance_calculation(address_data_df, parameters, api_key)

Reverse Distance Calculation

Calculate distances based on geocode data.

from pyloghub.distance_calculation import reverse_distance_calculation, reverse_distance_calculation_sample_data

sample_data = reverse_distance_calculation_sample_data()
geocode_data_df = sample_data['geocode_data']
parameters = sample_data['parameters']
reverse_center_of_gravity_result_df = reverse_distance_calculation(geocode_data_df, parameters, api_key)

Forward Center of Gravity

Determine optimal facility locations based on addresses.

from pyloghub.center_of_gravity import forward_center_of_gravity, forward_center_of_gravity_sample_data

sample_data = forward_center_of_gravity_sample_data()
addresses_df = sample_data['addresses']
parameters = sample_data['parameters']
assigned_addresses_df, centers_df = forward_center_of_gravity(addresses_df, parameters, api_key)

Reverse Center of Gravity

Determine optimal facility locations based on coordinates.

from pyloghub.center_of_gravity import reverse_center_of_gravity, reverse_center_of_gravity_sample_data

sample_data = reverse_center_of_gravity_sample_data()
coordinates_df = sample_data['coordinates']
parameters = sample_data['parameters']
assigned_geocodes_df, centers_df = reverse_center_of_gravity(coordinates_df, parameters, api_key)

Milkrun Optimization Plus

Optimize delivery routes with multiple stops.

from pyloghub.milkrun_optimization_plus import forward_milkrun_optimization_plus, forward_milkrun_optimization_plus_sample_data

sample_data = forward_milkrun_optimization_plus_sample_data()
depots_df = sample_data['depots']
vehicles_df = sample_data['vehicles']
jobs_df = sample_data['jobs']
timeWindowProfiles_df = sample_data['timeWindowProfiles']
breaks_df = sample_data['breaks']
parameters = sample_data['parameters']

route_overview_df, route_details_df, external_orders_df  = forward_milkrun_optimization_plus(depots_df, vehicles_df, jobs_df, timeWindowProfiles_df, breaks_df, parameters, api_key)

Transport Optimization Plus

Optimize transport routes for shipments.

from pyloghub.transport_optimization_plus import forward_transport_optimization_plus, forward_transport_optimization_plus_sample_data

sample_data = forward_transport_optimization_plus_sample_data()
vehicles_df = sample_data['vehicles']
shipments_df = sample_data['shipments']
timeWindowProfiles_df = sample_data['timeWindowProfiles']
breaks_df = sample_data['breaks']
parameters = sample_data['parameters']

route_overview_df, route_details_df, external_orders_df = forward_transport_optimization_plus(vehicles_df, shipments_df, timeWindowProfiles_df, breaks_df, parameters, api_key)

Shipment Analyzer

Analyze and optimize shipment costs and operations.

from pyloghub.shipment_analyzer import forward_shipment_analyzer, forward_shipment_analyzer_sample_data

sample_data = forward_shipment_analyzer_sample_data()
shipments_df = sample_data['shipments']
transport_costs_adjustments_df = sample_data['transportCostAdjustments']
consolidation_df = sample_data['consolidation']
surcharges_df = sample_data['surcharges']
parameters = sample_data['parameters']

shipments_analysis_df, transports_analysis_df = forward_shipment_analyzer(shipments_df, transport_costs_adjustments_df, consolidation_df, surcharges_df, parameters, api_key)

For the Milkrun Optimization, Transport Optimization as well as the Shipment Analyzer service there is also the reverse version available.

Freight Matrix

Evaluate shipments with costs based on your own freight cost matrices. The following matrix types are supported:

• Absolute weight distance matrix
• Relative weight distance matrix
• Absolute weight zone matrix
• Relative weight zone matrix
• Zone zone matrix
• Absolute weight zone distance matrix
• Relative weight zone distance matrix

from pyloghub.freight_matrix import forward_freight_matrix, forward_freight_matrix_sample_data

sample_data = forward_freight_matrix_sample_data()
shipments_df = sample_data['shipments']
matrix_id = "Your freight matrix id"

evaluated_shipments_df = forward_freight_matrix(shipments_df, matrix_id, api_key)
evaluated_shipments_df

You can create a freight matrix on the Log-hub Platform. Therefore, please create a workspace and click within the workspace on "Create Freight Matrix". There you can provide the matrix a name, select the matrix type and define all other parameters. To get the matrix id, please click on the "gear" icon. There you can copy & paste the matrix id that is needed in your API request.

Working with Log-hub Tables

To read or update a table, you need a table link from a table in the Log-hub platform. Therefore, please navigate in a workspace with an existing dataset and go to the table you would like to read or update. Click on the "three dots" and click on "Table Link". Then copy the corresponding table link. If no table exists create a dataset and a new table via the GUI.

Reading Data from a Table

The read_table function simplifies the process of fetching and formatting data from a specific table on the Log-hub platform into a pandas DataFrame. This function ensures that the data types in the DataFrame match those in the Log-hub table, leveraging metadata from the table for precise formatting.

from pyloghub.dataset import read_table
import pandas as pd

# Replace with actual table link, email, and API key
table_link = "https://production.supply-chain-apps.log-hub.com/api/v1/datasets/.../tables/.../rows"
email = "[e-mail]"
api_key = "your_api_key"

# Read data from table
dataframe = read_table(table_link, email, api_key)

# Check the DataFrame
if dataframe is not None:
    print(dataframe.head())
else:
    print("Failed to read data from the table.")

Updating Data in a Table

The update_table function is designed for uploading data from a local pandas DataFrame to a specific table on the Log-hub platform. It requires the table link, the DataFrame, metadata describing the table structure (optional). If no metadata are provided, the datatypes are automatically extracted from the pandas dataframe.

from pyloghub.dataset import update_table
import pandas as pd

# Replace with actual credentials and link
table_link = "https://production.supply-chain-apps.log-hub.com/api/v1/datasets/.../tables/.../rows"
email = "[e-mail]"
api_key = "your_api_key"

# Example DataFrame
dataframe = pd.DataFrame({
    'ColumnA': ['Value1', 'Value2'],
    'ColumnB': [123, 456]
})

# Metadata for the table
metadata = {
    'table_name': 'YourTableName',  # Optional, defaults to 'Table 01' if not provided
    'columns': [
        {
            'name': 'ColumnA',
            'propertyName': 'ColumnA',
            'dataType': 'string',
            'format': 'General'
        },
        {
            'name': 'ColumnB',
            'propertyName': 'ColumnB',
            'dataType': 'number',
            'format': 'General'
        }
        # More columns as needed
    ]
}

# Update table
response = update_table(table_link, dataframe, metadata, email, api_key)

if response is None:
    print("Table updated successfully.")
else:
    print("Failed to update the table.")

Contact Us

For any inquiries, assistance, or additional information, feel free to reach out to us at our office or via email.

Address:
Schwandweg 5,
8834 Schindellegi,
Switzerland

Email:
[e-mail]

Alternatively, for more information and resources, visit Log-hub API Documentation.

License

Distributed under the MIT License.