README.md

@@ -105,20 +105,6 @@ options:
        * `'spot_x/spot_y/spot_intensity': 3*list[float]` - Provides coordinates and intensity of the identified peaks within the frame.
        * `'is_hit_frame': True/False` - Marks whether a frame qualifies as a hit based on the number of identified peaks exceeding the defined threshold.
 
-   * **White field correction Algorithm**
-
-     Does the IN PLACE white field correction of the image
-
-     Input parameters:
-       * `'do_whitefield_correction': 1/0` - Specifies whether to do in-place white field correction.
-       * `'wf_data_file': str` - Path to the hdf5 file with corrected white field image.
-       * `'wf_dataset': str` [Optional] - Name of the dataset containing white field image in the hdf5 file, default is `"data/data"`.
-       * `'wf_method': 'div'|'sub'` - Method of white field correction - either division or subtraction is supported.
-
-     Algorithm Output:
-       * `'white_field_correction_applied': 1/0` - Indicates whether the algorithm ran successfully.
-       * Image is changed **in-place**.
-
    * **streakfinder Algorithm**
 
      This algorithm is using [streak-finder package](https://github.com/simply-nicky/streak_finder) - a connection-based streak finding algorithm for convergent beam diffraction patterns.
@@ -234,18 +220,6 @@ options:
 
        Use the `'apply_additional_mask': 0/1`  - Input flag to enable this functionality.
 
-   * **Additional Mask from file**
-
-       Alternative to previous additional masking, mask data is read from specified file. NumPy and HDF5 formats are supported.
-
-     Input parameters:
-       * `'apply_additional_mask_from_file': 1/0` - Input flag to enable this functionality.
-       * `'mask_file': str` - Path to the hdf5 file with mask data.
-       * `'mask_ds': str` [Optional] - Name of the dataset containing mask in the hdf5 file, default is `"data/data"`.
-
-     Algorithm Output:
-        * `'mask_from_file_applied': 1/0` - Indicates whether the algorithm ran successfully.
-
    * **Filter based on pulse picker information**
 
        If the event propagation capability is accessible for the detector and the pulse picker information is correctly configured for propagation, the filtration based on pulse picker information becomes feasible by using the

dap/algos/__init__.py

@@ -1,6 +1,5 @@
 
 from .addmask import calc_apply_additional_mask
-from .addmaskfile import calc_apply_additional_mask_from_file
 from .aggregation import calc_apply_aggregation
 from .jfdata import JFData
 from .mask import calc_mask_pixels
@@ -9,7 +8,6 @@ from .radprof import calc_radial_integration
 from .roi import calc_roi
 from .spiana import calc_spi_analysis
 from .streakfind import calc_streakfinder_analysis
-from .whitefield_correction import calc_apply_whitefield_correction
 from .thresh import calc_apply_threshold
 
 

dap/algos/addmaskfile.py

@@ -1,35 +0,0 @@
-import h5py
-import numpy as np
-
-
-def calc_apply_additional_mask_from_file(results, pixel_mask_pf):
-    apply_additional_mask = results.get("apply_additional_mask_from_file", False)
-    if not apply_additional_mask:
-        return
-    results["mask_from_file_applied"] = 0
-    mask_file = results.get("mask_file", None)
-    if not mask_file:
-        return
-    mask_dataset = results.get("mask_ds", "data/data")
-
-    # Support for hdf5 and npy
-    if mask_file.endswith(".npy"):
-        try:
-            mask = np.asarray(np.load(mask_file), dtype=bool)
-        except Exception as error:
-            print(f"Error loading mask data from NumPy file {mask_file}:\n{error}")
-            return
-    else:
-        try:
-            with h5py.File(mask_file, "r") as mask_file:
-                mask = np.asarray(mask_file[mask_dataset], dtype=bool)
-        except Exception as error:
-            print(f"Error loading mask from hdf5 file {mask_file}:\n{error}")
-            return
-
-    try:
-        np.multiply(pixel_mask_pf, mask, out=pixel_mask_pf)
-    except Exception as error:
-        print(f"Error applying additional mask from file {mask_file}:\n{error}")
-    else:
-        results["mask_from_file_applied"] = 1

dap/algos/jfdata.py

@@ -3,7 +3,6 @@ import numpy as np
 import jungfrau_utils as ju
 
 from .addmask import calc_apply_additional_mask
-from .addmaskfile import calc_apply_additional_mask_from_file
 
 
 class JFData:
@@ -59,7 +58,6 @@ class JFData:
 
         pixel_mask_pf = np.ascontiguousarray(pixel_mask_corrected)
         calc_apply_additional_mask(results, pixel_mask_pf) # changes pixel_mask_pf in place
-        calc_apply_additional_mask_from_file(results, pixel_mask_pf)  # changes pixel_mask_pf in place
 
         self.id_pixel_mask_corrected = new_id_pixel_mask_corrected
         self.pixel_mask_pf = pixel_mask_pf

dap/algos/streakfind.py

@@ -100,14 +100,11 @@ def _calc_streakfinder_analysis(results, cryst_data: CrystData):
         "sf_npts",
         "sf_xtol",
         "sf_nfa",
-
         "sf_num_threads",
-        # "beam_center_x",
-        # "beam_center_y"
     ]
 
     if not all([param in results.keys() for param in params_required]):
-        print(f"ERROR: Not enough parameters for streak finder analysis. Skipping\n"
+        print(f"ERROR: Not enough parameters for streak finder analysis. Skipping.\n"
               f"{params_required=}")
         return
 

dap/algos/whitefield_correction.py

@@ -1,74 +0,0 @@
-import numpy as np
-import h5py
-
-
-def _div(image, whitefield):
-    np.divide(
-        image,
-        whitefield,
-        out=image,
-        where=whitefield != 0
-    )
-
-
-def _sub(image, whitefield):
-    np.subtract(
-        image,
-        whitefield,
-        out=image,
-    )
-
-
-WF_METHODS = {
-    "div": _div,
-    "sub": _sub
-}
-
-
-def calc_apply_whitefield_correction(results, data):
-    """
-    In-place white field correction of the detector data
-    """
-    do_whitefield_correction = results.get("do_whitefield_correction", False)
-    if not do_whitefield_correction:
-        return
-
-    results["white_field_correction_applied"] = 0
-    params_required = [
-        "wf_data_file",
-        "wf_method",
-    ]
-
-    if not all([param in results.keys() for param in params_required]):
-        print(f"ERROR: Not enough parameters for whitefield correction. Skipping\n"
-              f"{params_required=}")
-        return
-
-    wf_data_file = results["wf_data_file"]
-    wf_method = results["wf_method"]
-
-    if wf_method not in WF_METHODS.keys():
-        print(f"ERROR: Unknown whitefield correction method {wf_method}. Skipping\n"
-              f"{params_required=}")
-        return
-
-    wf_dataset = results.get("wf_dataset", "data/data")
-    # TODO: cache white field data, only reload if file changed
-    #  maybe store checksum in results as "_checksum"
-    try:
-        with h5py.File(wf_data_file, "r") as wfile:
-            whitefield_image = np.asarray(wfile[wf_dataset])
-    except Exception as error:
-        print(f"ERROR: Can't read whitefield from file {wf_data_file}. Skipping\n"
-              f"{error=}")
-        return
-
-    try:
-        WF_METHODS[wf_method](data, whitefield_image)
-    except Exception as error:
-        print(f"ERROR: White field correction failed.\n"
-              f"{error=}")
-    else:
-        results["white_field_correction_applied"] = 1
-
-    return whitefield_image

dap/worker.py

@@ -2,8 +2,9 @@ import argparse
 
 import numpy as np
 
-from algos import (calc_apply_aggregation, calc_apply_threshold, calc_mask_pixels, calc_peakfinder_analysis, calc_radial_integration, calc_roi, calc_spi_analysis,
-                   calc_apply_whitefield_correction, calc_streakfinder_analysis, JFData)
+from algos import (calc_apply_aggregation, calc_apply_threshold, calc_mask_pixels, calc_peakfinder_analysis,
+                   calc_radial_integration, calc_roi, calc_spi_analysis,
+                   calc_streakfinder_analysis, JFData)
 from utils import Aggregator, BufferedJSON, randskip, read_bit
 from zmqsocks import ZMQSockets
 
@@ -117,11 +118,9 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
 
 # ???
 
-        # White-field correction and streak finder processing for convergent-beam diffraction
-        print(f"Applying whitefield correction")
-        calc_apply_whitefield_correction(results, image)  # changes image in place
-        print(f"Searching streaks")
-        image = calc_streakfinder_analysis(results, image, pixel_mask_pf)  # changes image in place is do_snr=True
+        # Streak finder processing for convergent-beam diffraction experiments
+        # changes image and mask in place if do_snr=True in parameters file
+        image = calc_streakfinder_analysis(results, image, pixel_mask_pf)
         print(f"Done\n{results=}")
 
         image, aggregation_is_ready = calc_apply_aggregation(results, image, pixel_mask_pf, aggregator)