benchling-examples-python migration

README.md

@@ -1,6 +1,6 @@
-# Benchling App Python Examples
+# Benchling Python Examples
 
-The `examples/` directory contains Benchling App samples written by Benchling.
+A collection of Python reference examples for the Benchling platform.
 
 ## chem-sync-local-flask
 
@@ -19,4 +19,23 @@ in a local development environment running [Flask](https://flask.palletsprojects
 * User Feedback via [App Status](https://docs.benchling.com/docs/introduction-to-app-status)
 * Data Mapping via [App Config](https://docs.benchling.com/docs/app-configuration)
 * Receiving and verifying [Webhooks](https://docs.benchling.com/docs/getting-started-with-webhooks)
-* Creating [molecule custom entities](https://benchling.com/api/reference#/Molecules/createMolecule)
+* Creating [molecule custom entities](https://benchling.com/api/reference#/Molecules/createMolecule)
+
+## Custom Code in Automation Designer
+This project contains example snippets of code that can be utilized in a Custom Code step of the Benchling Automation Designer with Benchling Analysis.
+
+![image info](./examples/custom-code-in-automation-designer/snippets/plot-chromatogram/docs/Example_Chromatogram_Plot.gif)
+
+## Overview
+These examples demonstrate how to extend Benchling's native capabilities using Python scripts within the Automation Designer context. They cover common use cases such as data visualization, file handling, and complex data transformations.
+
+## Key Capabilities
+The code examples included in this directory cover the following functionalities:
+
+- Visualizations: Create custom charts, graphs, and annotations (e.g., chromatograms).
+
+- File Parsing: Logic to read and parse various file formats.
+
+- File Creation: Generate new files, such as instruction lists for laboratory instruments.
+
+- Data Transformation: Apply transformations, merge/join datasets, and perform complex calculations on data.

examples/custom-code-in-automation-designer/README.md

@@ -0,0 +1,36 @@
+# Custom Code in Automation Designer
+This repository contains Python snippets compatible with the Custom Code feature in Automation Designer. This feature allows users to execute Python logic directly within Benchling Analysis.
+
+![image info](./snippets/plot-chromatogram/docs/Example_Chromatogram_Plot.gif)
+
+## Overview
+These examples demonstrate how to extend Benchling's native capabilities using Python scripts within the Automation Designer context. They cover common use cases such as data visualization, file handling, and complex data transformations.
+
+## Key Capabilities
+The code examples included in this directory cover the following functionalities:
+
+- Visualizations: Create custom charts, graphs, and annotations (e.g., chromatograms).
+
+- File Parsing: Logic to read and parse various file formats.
+
+- File Creation: Generate new files, such as instruction lists for laboratory instruments.
+
+- Data Transformation: Apply transformations, merge/join datasets, and perform complex calculations on data.
+
+## Dependencies
+See [requirements.txt](./requirements.txt) for the specific library versions used in these examples.
+
+## Constraints & Limitations
+When adapting these examples for your tenant, please note the current beta limitations:
+
+ - Runtime Limit: Execution is limited to 15 minutes per run.
+
+ - No Network Access: The environment does not support general network access.
+
+ - Fixed Packages: You cannot install custom libraries (e.g., via `pip`). You are limited to the pre-installed packages listed above
+
+ - No API Access: The Benchling SDK/API is not currently supported within the execution environment.
+
+ - Bring your own Container: BYOC is not supported; code runs in the standard Benchling runtime environment
+
+ - Lifecycle Management: The feature does not currently support native GitHub integration or versioning within the UI.

examples/custom-code-in-automation-designer/requirements.txt

@@ -0,0 +1,11 @@
+allotropy==0.1.111
+lmfit==1.2.0
+numpy==2.2.4
+openpyxl==3.1.5
+pandas==2.2.3
+plotly==5.22.0
+pyarrow==19.0.1
+pydantic==1.10.21
+scikit-learn==1.6.1
+scipy==1.15.2
+statsmodels==0.14.4

examples/custom-code-in-automation-designer/snippets/plot-chromatogram/HPLC_Chromatogram_Plot_(Absorbance, pH, Temperature).py

@@ -0,0 +1,101 @@
+
+"""
+Supported packages:
+
+allotropy
+benchling-sdk
+numpy
+openpyxl
+pandas
+plotly
+pyarrow
+pydantic
+scikit-learn
+scipy
+statsmodels
+"""
+from io import BytesIO
+import pandas as pd
+from typing import NamedTuple
+import plotly.graph_objects as go
+from scipy.signal import find_peaks
+
+class IOData(NamedTuple):
+    name: str
+    data: BytesIO | pd.DataFrame | go.Figure
+
+def custom_code(inputs: list[IOData], **kwargs) -> list[IOData]:
+    df = inputs[0].data
+    # Extract the relevant data series and convert them to floats
+    absorbance_data = df['absorbance (mAU)'].astype(float) 
+    retention_volume_data = df['retention volume (mL)'].astype(float)
+
+    # Find peaks in the absorbance data.
+    # We set a `height` threshold to ignore baseline noise. 
+    # A good starting point is 10% of the max absorbance.
+    min_height = absorbance_data.max() * 0.10
+    peak_indices, _ = find_peaks(absorbance_data, height=min_height)
+
+    # Set a default range in case no peaks are found
+    x_axis_range = None 
+
+    # Check if at least one peak was detected
+    if len(peak_indices) > 0:
+        # Get the retention volumes for the first and last detected peaks
+        first_peak_x = retention_volume_data.iloc[peak_indices[0]]
+        last_peak_x = retention_volume_data.iloc[peak_indices[-1]]
+
+        # Define the padding you want on each side
+        padding = 150 
+
+        # Calculate the new x-axis range
+        x_min = first_peak_x - padding
+        x_max = last_peak_x + padding
+
+        x_axis_range = [x_min, x_max]
+    fig = go.Figure()
+
+    # Add Absorbance Trace
+    fig.add_trace(go.Scatter(
+        x=df['retention volume (mL)'],
+        y=df['absorbance (mAU)'],
+        name="Absorbance (mAU)",
+        line=dict(color='royalblue'),
+        yaxis="y1"
+    ))
+
+    # Add other traces (pH, Conc. B)
+    fig.add_trace(go.Scatter(
+        x=df['retention volume (mL)'], y=df['pH (pH)'],
+        name="pH", line=dict(color='crimson', dash='dash'), yaxis="y2"
+    ))
+    fig.add_trace(go.Scatter(
+        x=df['retention volume (mL)'], y=df['temperature (degC)'],
+        name="Temperature (degC)", line=dict(color='green', dash='dot'), yaxis="y3"
+    ))
+
+    # --- Layout Definition ---
+    fig.update_layout(
+        title_text="Chromatogram with Abs, pH, Temperature Traces",
+        xaxis_title="Retention Volume (mL)",
+        plot_bgcolor='white',
+        legend_title="Traces",
+        xaxis=dict(domain=[0.1, 0.88]),
+        yaxis=dict(
+            title="<b>Absorbance (mAU)</b>",
+            tickfont=dict(color="royalblue"),
+            color="royalblue"
+        ),
+        yaxis2=dict(
+            title="<b>pH</b>",
+            tickfont=dict(color="crimson"), color="crimson",
+            anchor="x", overlaying="y", side="right"
+        ),
+        yaxis3=dict(
+            title="<b>Temperature (degC)</b>",
+            tickfont=dict(color="green"), color="green",
+            anchor="free", overlaying="y", side="right", position=0.92
+        ) 
+    )
+
+    return [IOData(name="Chromatogram_New", data=fig)]