Initial commit of autogain detector plot scheme

src/toffy/normalize.py

@@ -1,7 +1,11 @@
 # adapted from https://machinelearningmastery.com/curve-fitting-with-python/
 import copy
+import glob
+import json
 import os
+import pathlib
 import warnings
+from typing import Literal, Union
 
 import matplotlib.pyplot as plt
 import natsort as ns
@@ -892,3 +896,82 @@ def check_detector_voltage(run_dir):
  # non-empty list of changes will raise an error
  if changes_in_voltage:
  raise ValueError("Changes in detector voltage were found during the run:\n" + err_str)
+
+
+def plot_detector_voltage(
+ base_dir: Union[pathlib.Path, str],
+ mph_run_dir: Union[pathlib.Path, str],
+ detector_volt_type: Literal["hvAdc", "hvDac"] = "hvAdc",
+) -> None:
+ """Extracts saved detector voltage values during fov acquisition and creates a txt file and png plot with
+ detector voltage over fov progression.
+
+ Args:
+ base_dir (Union[pathlib.Path, str]):
+ Path to where the run data (and json files) are stored.
+ mph_run_dir (Union[pathlib.Path, str]):
+ Path to where the run metrics are stored.
+ detector_volt_type (Union["hvAdc", "hvDac"]):
+ Either "hvAdc" (default) or "hvDac" voltage metric to plot.
+ """
+ # Get all the JSON run files
+ json_files = sorted(glob.glob(os.path.join(base_dir, "fov-*-scan-?.json")))
+
+ # Map each FOV to their observed detector voltage
+ detector_voltage_data = {"FOVs": [], "Detector Voltage": []}
+
+ # Iterate over the list of files
+ for json_file in json_files:
+ # Open the JSON file and load the data
+ with open(json_file, "r") as f:
+ json_data = json.load(f)
+
+ # Extract the FOV from the filename
+ fov = os.path.basename(json_file).split("-")[1]
+
+ # Extract the Detector voltage
+ # Assuming the "hvAdc" and/or "hvDac" field is always present and has the "Detector" entry
+ if detector_volt_type == "hvAdc":
+ detector_voltage = [
+ item["value"] for item in json_data["hvAdc"] if item["name"] == "Detector"
+ ][0]
+ else:
+ detector_voltage = [
+ item["currentSetPoint"] for item in json_data["hvDac"] if item["name"] == "Detector"
+ ][0]
+
+ # Add the data to our dictionary
+ detector_voltage_data["FOVs"].append(int(fov))
+ detector_voltage_data["Detector Voltage"].append(detector_voltage)
+
+ # Save data in a text log
+ detector_gain_file = os.path.join(mph_run_dir, "detector_gain_values.csv")
+ detector_gain_df = pd.DataFrame(detector_voltage_data)
+ detector_gain_df.to_csv(detector_gain_file, index=False)
+
+ # Create a new figure and axes
+ fig, ax = plt.subplots()
+
+ # Plot the data with color coding and without a line connecting the points
+ for (
+ fov,
+ voltage,
+ ) in zip(detector_voltage_data["FOVs"], detector_voltage_data["Detector Voltage"]):
+ ax.plot(fov, voltage, marker="o", color="blue")
+
+ # Set the labels for the axes
+ ax.set_xlabel("FOVs")
+ ax.set_ylabel("Detector Voltage (v)")
+
+ # Show the plot
+ plt.savefig(
+ fname=os.path.join(mph_run_dir, "detector_gain_plot.png"),
+ dpi="figure",
+ format="png",
+ metadata=None,
+ bbox_inches=None,
+ pad_inches=0.1,
+ facecolor="auto",
+ edgecolor="auto",
+ backend=None,
+ )

templates/4b_normalize_image_data.ipynb

@@ -41,6 +41,7 @@
  " - `run_name` should contain the exact name of the MIBI run to extract from\n",
  " - `panel_path` should point to a panel csv specifying the targets on your panel (see [panel format](https://github.com/angelolab/toffy#panel-format) for more information)\n",
  " - `tuning_curve_file` should point to a tuning curve contained in `toffy/tuning_curves` (`avg_norm_func_450.json`, `avg_norm_func_1300.json`, or `avg_norm_func_2600.json`).\n",
+ " - `autogain`: if the Ionpath autogain setting was used for this run\n",
  "\n",
  "`avg_norm_func_2600.json` is the curve that should be used when running the MIBI with Ionpath's default settings. `avg_norm_func_450.json` and `avg_norm_func_1300.json` are curves generated for Angelo Lab-specific parameters. Use the power supply settings to determine which curve is most applicable."
  ]
@@ -59,7 +60,10 @@
  "panel_path = 'C:\\\\Users\\\\Customer.ION\\\\Documents\\\\panel_files\\\\my_cool_panel.csv'\n",
  "\n",
  "# Name of tuning curve file\n",
- "tuning_curve_file = 'avg_norm_func_2600.json'"
+ "tuning_curve_file = 'avg_norm_func_2600.json'\n",
+ "\n",
+ "# Autogain setting of run\n",
+ "autogain = True"
  ]
  },
  {
@@ -85,8 +89,15 @@
  "normalized_base_dir = 'D:\\\\Normalized_Images'\n",
  "mph_run_dir = os.path.join('C:\\\\Users\\\\Customer.ION\\\\Documents\\\\run_metrics', run_name, 'fov_data')\n",
  "\n",
- "# check the run for changes in detector voltage\n",
- "normalize.check_detector_voltage(os.path.join(bin_base_dir, run_name))"
+ "# plot the voltage change across FOVs if autogain set\n",
+ "# otherwise, verify the voltage across all FOVs is constant\n",
+ "if autogain:\n",
+ " normalize.plot_detector_voltage(\n",
+ " base_dir=os.path.join(bin_base_dir, run_name),\n",
+ " mph_run_dir=os.path.dirname(mph_run_dir, run_name)\n",
+ " )\n",
+ "else:\n",
+ " normalize.check_detector_voltage(os.path.join(bin_base_dir, run_name))"
  ]
  },
  {