Merge branch 'main' into auto_detector

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
@@ -919,3 +923,86 @@ 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(
+ run_folder: 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:
+ run_folder (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.
+ """
+ # Verify that detector_volt_type is valid
+ misc_utils.verify_in_list(
+ specified_volt_type=detector_volt_type, valid_volt_types=["hvAdc", "hvDac"]
+ )
+
+ # Get all the JSON run files
+ json_files = sorted(glob.glob(os.path.join(run_folder, "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:
+ # Load the JSON file
+ run_json_data = read_json_file(json_file, encoding="utf-8")
+
+ # Extract the FOV from the filename
+ fov = os.path.basename(json_file).split("-")[1]
+
+ # For hvAdc and hvDac, the key associated with the voltage is different
+ voltage_key = "value" if detector_volt_type == "hvAdc" else "currentSetPoint"
+
+ # Extract the Detector voltage
+ # Assuming the "hvAdc" and/or "hvDac" field is always present and has the "Detector" entry
+ detector_voltage = [
+ item[voltage_key]
+ for item in run_json_data[detector_volt_type]
+ 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

@@ -87,8 +87,15 @@
  "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))"
  ]
  },
  {

tests/normalize_test.py

@@ -1,6 +1,9 @@
+import json
 import os
+import pathlib
 import shutil
 import tempfile
+from typing import List
 from unittest.mock import patch
 
 import natsort
@@ -10,6 +13,7 @@
 import pytest
 import xarray as xr
 from alpineer import io_utils, load_utils, test_utils
+from pytest import TempPathFactory
 from pytest_cases import parametrize_with_cases
 
 from toffy import normalize
@@ -767,3 +771,37 @@ def test_check_detector_voltage():
  ),
  ):
  normalize.check_detector_voltage(tmp_dir)
+
+
+@parametrize("detector_volt_type", ["hvAdc", "hvDac"])
+def test_plot_detector_voltage(tmp_path_factory: TempPathFactory, detector_volt_type: str):
+ run_folder: pathlib.Path = tmp_path_factory.mktemp("run_folder")
+ mph_run_dir: pathlib.Path = tmp_path_factory.mktemp("mph_run_dir")
+ json_files: List[str] = [f"fov-{i}-scan-1.json" for i in np.arange(1, 4)]
+
+ for i, file in enumerate(json_files):
+ sample_data = {
+ "hvAdc": [{"name": "Detector", "value": 1 * (i + 1)}, {"name": "Not Detector"}],
+ "hvDac": [
+ {"name": "Detector", "currentSetPoint": 10 * (i + 1)},
+ {"name": "Not Detector"},
+ ],
+ }
+
+ write_json_file(run_folder / file, sample_data, encoding="utf-8")
+
+ normalize.plot_detector_voltage(
+ run_folder=run_folder, mph_run_dir=mph_run_dir, detector_volt_type=detector_volt_type
+ )
+
+ detector_gain_values_path: pathlib.Path = mph_run_dir / "detector_gain_values.csv"
+ assert os.path.exists(detector_gain_values_path)
+
+ detector_gain_values: pd.DataFrame = pd.read_csv(detector_gain_values_path)
+ expected_voltage_vals: np.ndarray = (
+ np.array([1, 2, 3]) if detector_volt_type == "hvAdc" else np.array([10, 20, 30])
+ )
+ assert np.all(detector_gain_values["FOVs"] == np.arange(1, 4))
+ assert np.all(detector_gain_values["Detector Voltage"] == expected_voltage_vals)
+
+ assert os.path.exists(mph_run_dir / "detector_gain_plot.png")