restructured modules

ideas.md

@@ -0,0 +1,5 @@
+Furhter Ideas:
+
+- color the roi
+- create a movie
+- automatic use of cached values

mtor/__init__.py

@@ -1,21 +1,23 @@
 import warnings
 
-from .prescan import prescan_workflow
-from .imageproc import image_workflow
-from .dataproc import data_workflow, cached_workflow
-from .filesort import filesort_workflow
+from .workflows import (
+    prescan_workflow,
+    image_workflow,
+    data_workflow,
+    cached_data_workflow,
+    postprocessing_workflow,
+)
 
 warnings.filterwarnings("ignore")
 
 
 def run():
-    #settings = prescan_workflow("mtor-bilder", 50, 910, 300, 660)
-    settings = prescan_workflow("mtor-bilder", 50, 725+150, 300, 725)
-    stats_results = image_workflow(settings)
-    selected_files = data_workflow(stats_results, settings)
-    filesort_workflow(selected_files, settings)
+    # settings = prescan_workflow("mtor-bilder", 50, 910, 300, 660)
+    pw = prescan_workflow("mtor-bilder", 50, 725 + 150, 300, 725)
+    iw = image_workflow(pw.parameters)
+    dw = data_workflow(iw.data, iw.parameters)
+    fw = postprocessing_workflow(dw.data, dw.parameters)  # noqa: F841
 
 
 def run_data():
-    selected_files, settings = cached_workflow("mtor-bilder")
-    filesort_workflow(selected_files, settings)
+    cw = cached_data_workflow("mtor-bilder")

mtor/commons.py

@@ -0,0 +1,38 @@
+import functools
+import numpy
+import re
+
+from collections import namedtuple
+
+from .luts import FIRE_LUT  # noqa: F401
+
+
+IMG_MAX_INTENSITY = 65535  # 2^16 -  1
+
+LABEL_SELECTED = "selected"
+LABEL_DISCARDED = "discarded"
+
+OUTPUT_FOLDER_NAMES = (
+    "colored",
+    "cuts",
+    f"cuts_{LABEL_DISCARDED}",
+    f"cuts_{LABEL_SELECTED}",
+    "data",
+)
+
+RE_DIGITS = re.compile(r"(\d+)")
+
+ROI_STATISTIC_FUNCTIONS = {
+    "min": numpy.amin,
+    "max": numpy.amax,
+    "1quantile": functools.partial(numpy.percentile, q=25),
+    "2quantile": functools.partial(numpy.percentile, q=50),
+    "3quantile": functools.partial(numpy.percentile, q=75),
+    "average": numpy.average,
+    "median": numpy.median,
+    "std": numpy.std,
+}
+
+TifImage = namedtuple("TifImage", ["path", "frame"])
+TifArray = namedtuple("TifArray", ["path", "frame", "data"])
+WorkflowResult = namedtuple("WorkflowResult", ["data", "parameters"])

mtor/dataproc.py

@@ -6,13 +6,12 @@ import pickle
 import seaborn
 import matplotlib.pyplot as pyplot
 
-from peakutils.plot import plot as peakplot
 from scipy.signal import savgol_filter
 
-from .imageproc import STATS_FUNCS, LEFT_GUARD, ROI_NAME, RIGHT_GUARD
+from .commons import ROI_STATISTIC_FUNCTIONS
 
 
-def peakplot_iloc(x, y, ind):
+def peakplot_iloc(x, y, ix1, ix1_label="peaks", ix2=None, ix2_label="peaks"):
     """
     Plots the original data with the peaks that were identified
 
@@ -29,15 +28,26 @@ def peakplot_iloc(x, y, ind):
     ind : array-like
         Indexes of the identified peaks
     """
-    pyplot.plot(x, y, "--")
+    pyplot.plot(x, y)
+    num_items = len(ix1)
     pyplot.plot(
-        x.iloc[ind],
-        y.iloc[ind],
+        x.iloc[ix1],
+        y.iloc[ix1],
         "r+",
         ms=5,
         mew=2,
-        label="{} peaks".format(len(ind)),
+        label=f"{num_items} {ix1_label}",
     )
+    if ix2 is not None:
+        num_items = len(ix2)
+        pyplot.plot(
+            x.iloc[ix2],
+            y.iloc[ix2],
+            "g1",
+            ms=5,
+            mew=2,
+            label=f"{num_items} {ix2_label}",
+        )
     pyplot.legend()
 
 
@@ -56,26 +66,31 @@ def set_plotting_styles():
     seaborn.set_context("talk")
 
 
-def init_pandas_data_frame():
+def init_pandas_data_frame(parameters):
     columns = ["file", "frame"]
-    for prefix in (LEFT_GUARD, ROI_NAME, RIGHT_GUARD):
-        for key in STATS_FUNCS:
-            columns.append(f"{prefix}.{key}")
+    groups = (
+        parameters.roi_name,
+        parameters.left_guard_name,
+        parameters.right_guard_name,
+    )
+    for group in groups:
+        for func_name in ROI_STATISTIC_FUNCTIONS:
+            columns.append(f"{group}.{func_name}")
     return pandas.DataFrame(columns=columns)
 
 
-def construct_data_frame(stats_results):
-    data_frame = init_pandas_data_frame()
+def construct_data_frame(stats_results, parameters):
+    data_frame = init_pandas_data_frame(parameters)
     data_frame = data_frame.append(stats_results, ignore_index=True)
     return data_frame.sort_values(by=["frame"]).reset_index(drop=True)
 
 
-def find_guard_value(data_frame, settings):
-    left_avg = data_frame["left.average"]
-    right_avg = data_frame["right.average"]
-    guards_avg = left_avg.append(right_avg, ignore_index=True)
+def find_guard_threshold(data_frame, parameters):
+    left_values = data_frame[parameters.left_guard_column]
+    right_values = data_frame[parameters.right_guard_column]
+    guard_values = left_values.append(right_values, ignore_index=True)
     guard_data = numpy.histogram(
-        guards_avg, bins=settings.guard_histogram_bins
+        guard_values, bins=parameters.guard_histogram_bins
     )
     guard_counts = guard_data[0].astype(numpy.float16)
     guard_edges = guard_data[1][1:]  # edges enclose the counts
@@ -85,13 +100,15 @@ def find_guard_value(data_frame, settings):
     pyplot.title("Histogram of Guard Avarages (not filtered)")
     pyplot.xlabel("Average Intensity [au]")
     pyplot.ylabel("Number of Observations [1]")
-    path = settings.data_dir / "1-histogram-of-guard-avarages-not-filtered.png"
+    path = (
+        parameters.data_dir / "1-histogram-of-guard-avarages-not-filtered.png"
+    )
     pyplot.savefig(str(path))
 
     guard_counts_filtered = savgol_filter(
         guard_counts,
-        settings.guard_filter_window,
-        settings.guard_filter_polynom,
+        parameters.guard_filter_window,
+        parameters.guard_filter_polynom,
     )
 
     pyplot.clf()
@@ -99,7 +116,7 @@ def find_guard_value(data_frame, settings):
     pyplot.title("Histogram of Guard Avarages (Savitzky-Golay filter)")
     pyplot.xlabel("Average Intensity [au]")
     pyplot.ylabel("Number of Observations [1]")
-    path = settings.data_dir / "2-histogram-of-guard-avarages-filtered.png"
+    path = parameters.data_dir / "2-histogram-of-guard-avarages-filtered.png"
     pyplot.savefig(str(path))
 
     # Finding the first minima after the first peak
@@ -114,47 +131,53 @@ def find_guard_value(data_frame, settings):
     inverted_series = max(guard_counts_filtered) - guard_counts_filtered
     indexes = peakutils.indexes(
         inverted_series[first_peak_position:],
-        min_dist=settings.guards_minima_min_dist,
+        min_dist=parameters.guards_minima_min_dist,
     )
 
     # since we shortened the data, we need to add the first peak position
     first_minima_position = indexes[0] + first_peak_position
-    settings.guard_max_value = guard_edges[first_minima_position]
+    parameters.guard_max_value = guard_edges[first_minima_position]
 
     pyplot.clf()
-    peakplot(guard_edges, guard_counts_filtered, [first_minima_position])
+    peakplot_iloc(
+        pandas.Series(guard_edges), pandas.Series(guard_counts_filtered), [first_minima_position], "minima"
+    )
     pyplot.title(
         (
             f"Histogram of Guard Avarages (Savitzky-Golay filter),"
-            f" first minima at {int(settings.guard_max_value)} au"
+            f" first minima at {int(parameters.guard_max_value)} au"
         )
     )
     pyplot.xlabel("Average Intensity [au]")
     pyplot.ylabel("Number of Observations [1]")
     path = (
-        settings.data_dir
+        parameters.data_dir
         / "3-histogram-of-guard-avarages-filtered-with-first-minima.png"
     )
     pyplot.savefig(str(path))
 
 
-def check_guards(data_frame, settings):
-    for what in (LEFT_GUARD, RIGHT_GUARD):
+def check_guards(data_frame, parameters):
+    for what in (parameters.left_guard_name, parameters.right_guard_name):
         ok_col = f"{what}.ok"
         data_frame[ok_col] = False
-        mask = data_frame[f"{what}.average"] < settings.guard_max_value
+        mask = (
+            data_frame[f"{what}.{parameters.guard_stats}"]
+            < parameters.guard_max_value
+        )
         data_frame.loc[mask, ok_col] = True
     data_frame["guards.ok"] = (
-        data_frame[f"{LEFT_GUARD}.ok"] & data_frame[f"{RIGHT_GUARD}.ok"]
+        data_frame[f"{parameters.left_guard_name}.ok"]
+        & data_frame[f"{parameters.right_guard_name}.ok"]
     )
 
-    mask = data_frame["guards.ok"] == True
+    mask = data_frame["guards.ok"] == True  # noqa: E712
     guarded_df = data_frame[mask].copy()
 
     pyplot.clf()
     ax = seaborn.scatterplot(
         x="frame",
-        y=f"{ROI_NAME}.average",
+        y=parameters.roi_column,
         data=data_frame,
         hue="guards.ok",
         hue_order=[True, False],
@@ -163,13 +186,13 @@ def check_guards(data_frame, settings):
     pyplot.title("Selection based on guard values")
     pyplot.ylabel("Average Intensity [au]")
     pyplot.ylabel("Frame Number [1]")
-    path = settings.data_dir / "4-image-selection-based-on-guard-values.png"
+    path = parameters.data_dir / "4-image-selection-based-on-guard-values.png"
     pyplot.savefig(str(path))
-    settings.charts_y_limit = ax.get_ylim()
+    parameters.charts_y_limit = ax.get_ylim()
 
     pyplot.clf()
     ax = seaborn.scatterplot(
-        x="frame", y=f"{ROI_NAME}.average", data=guarded_df
+        x="frame", y=parameters.roi_column, data=guarded_df
     )
     count_all_images = len(data_frame)
     count_guarded_images = len(guarded_df)
@@ -181,81 +204,80 @@ def check_guards(data_frame, settings):
     )
     pyplot.xlabel("Frame Number [1]")
     pyplot.ylabel("Average Intensity [au]")
-    ax.set_ylim(settings.charts_y_limit)
-    path = settings.data_dir / "5-selected-values-based-on-guard-values.png"
+    ax.set_ylim(parameters.charts_y_limit)
+    path = parameters.data_dir / "5-selected-values-based-on-guard-values.png"
     pyplot.savefig(str(path))
 
-
     return data_frame
 
 
-def find_outliers(data_frame, settings):
+def find_outliers(data_frame, parameters):
 
-    mask = data_frame["guards.ok"] == True
+    mask = data_frame["guards.ok"] == True  # noqa: E712
     guarded_df = data_frame[mask].copy()
 
     pyplot.clf()
-    seaborn.boxplot(data=guarded_df, x=f"{ROI_NAME}.average")
+    seaborn.boxplot(data=guarded_df, x=parameters.roi_column)
     pyplot.title(f"Boxblot of guarded values")
     pyplot.xlabel("Average Intensity [au]")
-    path = settings.data_dir / "6-boxplot-of-guarded-values.png"
+    path = parameters.data_dir / "6-boxplot-of-guarded-values.png"
     pyplot.savefig(str(path))
 
-    lower_quartil = guarded_df[f"{ROI_NAME}.average"].quantile(0.25)
-    upper_quartil = guarded_df[f"{ROI_NAME}.average"].quantile(0.75)
+    lower_quartil = guarded_df[parameters.roi_column].quantile(0.25)
+    upper_quartil = guarded_df[parameters.roi_column].quantile(0.75)
     inter_quartil_range = upper_quartil - lower_quartil
-    settings.outlier_upper_limit = upper_quartil + 1.5 * inter_quartil_range
+    parameters.outlier_upper_limit = upper_quartil + 1.5 * inter_quartil_range
 
     data_frame["outlier.ok"] = (
-        data_frame[f"{ROI_NAME}.average"] < settings.outlier_upper_limit
+        data_frame[parameters.roi_column] < parameters.outlier_upper_limit
     )
     return data_frame
 
 
-def select_value_on_guards_and_outliers(data_frame, settings):
+def select_on_guards_and_outliers(data_frame, parameters):
     data_frame["outlier_guards.ok"] = (
         data_frame["guards.ok"] & data_frame["outlier.ok"]
     )
-    mask = data_frame["outlier_guards.ok"] == True
+    mask = data_frame["outlier_guards.ok"] == True  # noqa: E712
     selected_df = data_frame[mask].copy()
 
     pyplot.clf()
     ax = seaborn.scatterplot(
-        x="frame", y=f"{ROI_NAME}.average", data=selected_df
+        x="frame", y=parameters.roi_column, data=selected_df
     )
     pyplot.title(f"Selected Images, outliers removed")
     pyplot.xlabel("Frame Number [1]")
     pyplot.ylabel("Average Intensity [au]")
-    ax.set_ylim(settings.charts_y_limit)
-    path = settings.data_dir / "7-selected-images-outliers-removed.png"
+    ax.set_ylim(parameters.charts_y_limit)
+    path = parameters.data_dir / "7-selected-images-outliers-removed.png"
     pyplot.savefig(str(path))
 
     return selected_df
 
 
-def smooth_rolling_min(selected_df, settings):
+def smooth_rolling_min(selected_df, parameters):
     rm = (
-        selected_df[f"{ROI_NAME}.average"]
-        .rolling(settings.rolling_min_window)
+        selected_df[parameters.roi_column]
+        .rolling(parameters.rolling_min_window)
         .min()
     )
 
     # after a rolling window calculation, the first values will be NaN,
     # we need to fill them
-    selected_df[f"{ROI_NAME}.average.rolling.min"] = rm.fillna(
+    selected_df[f"{parameters.roi_name}.rolling.min"] = rm.fillna(
         method="backfill"
     )
 
     pyplot.clf()
     ax = seaborn.scatterplot(
-        x="frame", y=f"{ROI_NAME}.average.rolling.min", data=selected_df
+        x="frame", y=f"{parameters.roi_name}.rolling.min", data=selected_df
     )
     pyplot.title(f"Selected Images, outliers removed, rolling min applied")
     pyplot.xlabel("Frame Number [1]")
     pyplot.ylabel("Average Intensity [au]")
-    ax.set_ylim(settings.charts_y_limit)
+    ax.set_ylim(parameters.charts_y_limit)
     path = (
-        settings.data_dir
+        parameters.data_dir
         / "8-selected-images-outliers-removed-rolling-min-applied.png"
     )
     pyplot.savefig(str(path))
@@ -263,17 +285,17 @@ def smooth_rolling_min(selected_df, settings):
     return selected_df
 
 
-def smooth_savgol_filter(selected_df, settings):
+def smooth_savgol_filter(selected_df, parameters):
     filtered = savgol_filter(
-        selected_df[f"{ROI_NAME}.average.rolling.min"],
-        settings.savgol_filter_window,
-        settings.savgol_filter_polynom,
+        selected_df[f"{parameters.roi_name}.rolling.min"],
+        parameters.savgol_filter_window,
+        parameters.savgol_filter_polynom,
     )
-    selected_df[f"{ROI_NAME}.average.savgol"] = filtered
+    selected_df[f"{parameters.roi_name}.savgol"] = filtered
 
     pyplot.clf()
     seaborn.lineplot(
-        x="frame", y=f"{ROI_NAME}.average.savgol", data=selected_df
+        x="frame", y=f"{parameters.roi_name}.savgol", data=selected_df
     )
     pyplot.title(
         (
@@ -284,7 +306,7 @@ def smooth_savgol_filter(selected_df, settings):
     pyplot.xlabel("Frame Number [1]")
     pyplot.ylabel("Average Intensity [au]")
     path = (
-        settings.data_dir
+        parameters.data_dir
         / "9-selected-images-outliers-removed-rolling-min-savgol-filtered.png"
     )
     pyplot.savefig(str(path))
@@ -292,46 +314,50 @@ def smooth_savgol_filter(selected_df, settings):
     return selected_df
 
 
-def find_extremas(selected_df, settings):
+def find_extremas(selected_df, parameters):
 
-    indexes = peakutils.indexes(
-        selected_df[f"{ROI_NAME}.average.savgol"],
-        thres=settings.peak_threshold,
-        min_dist=settings.peak_min_distance,
+    max_indexes = peakutils.indexes(
+        selected_df[f"{parameters.roi_name}.savgol"],
+        thres=parameters.peak_threshold,
+        min_dist=parameters.peak_min_distance,
     )
-    maximas = selected_df.iloc[indexes].copy()
+    maximas = selected_df.iloc[max_indexes].copy()
 
     pyplot.clf()
     peakplot_iloc(
         selected_df["frame"],
-        selected_df[f"{ROI_NAME}.average.savgol"],
-        indexes,
+        selected_df[f"{parameters.roi_name}.savgol"],
+        max_indexes,
+        "maxima",
     )
     pyplot.title(f"Finding Maximas")
     pyplot.xlabel("Frame Number [1]")
     pyplot.ylabel("Average Intensity [au]")
-    path = settings.data_dir / "10-finding-maximas.png"
+    path = parameters.data_dir / "10-finding-maximas.png"
     pyplot.savefig(str(path))
 
     inverted_series = (
-        max(selected_df[f"{ROI_NAME}.average.savgol"])
-        - selected_df[f"{ROI_NAME}.average.savgol"]
+        max(selected_df[f"{parameters.roi_name}.savgol"])
+        - selected_df[f"{parameters.roi_name}.savgol"]
     )
-    indexes = peakutils.indexes(
-        inverted_series, min_dist=settings.peak_min_distance
+    min_indexes = peakutils.indexes(
+        inverted_series, min_dist=parameters.peak_min_distance
     )
-    minimas = selected_df.iloc[indexes].copy()
+    minimas = selected_df.iloc[min_indexes].copy()
 
     pyplot.clf()
     peakplot_iloc(
         selected_df["frame"],
-        selected_df[f"{ROI_NAME}.average.savgol"],
-        indexes,
+        selected_df[f"{parameters.roi_name}.savgol"],
+        max_indexes,
+        "maxima",
+        min_indexes,
+        "minima",
     )
     pyplot.title(f"Finding Minimas")
     pyplot.xlabel("Frame Number [1]")
     pyplot.ylabel("Average Intensity [au]")
-    path = settings.data_dir / "11-finding-minimas.png"
+    path = parameters.data_dir / "11-finding-minimas.png"
     pyplot.savefig(str(path))
 
     maximas["is_maxima"] = True
@@ -340,91 +366,38 @@ def find_extremas(selected_df, settings):
     return extremas_df
 
 
-def save_data(data_frame, selected_df, extremas_df, settings):
-    path = settings.data_dir / "numeric-data.xlsx"
+def save_data(data_frame, selected_df, extremas_df, parameters):
+    path = parameters.data_dir / "numeric-data.xlsx"
     writer = pandas.ExcelWriter(path, engine="xlsxwriter")
 
     extremas_df.to_excel(writer, sheet_name="extremas")
     selected_df.to_excel(writer, sheet_name="selected data")
     data_frame.to_excel(writer, sheet_name="raw data")
 
-    ignore_settings = {"tif_list", "cuts_dir"}
-    tmp_settings = {
-        k: [v] for k, v in settings.items() if k not in ignore_settings
+    ignore_parameters = {"tif_list", "cuts_dir"}
+    tmp_parameters = {
+        k: [v] for k, v in parameters.items() if k not in ignore_parameters
     }
-    tmp_setings_df = pandas.DataFrame(tmp_settings).T
+    tmp_setings_df = pandas.DataFrame(tmp_parameters).T
     tmp_setings_df.to_excel(writer, sheet_name="parameters")
 
     writer.save()
 
 
-def data_workflow(stats_results, settings):
-    print("3/4: Data analysis")
-    set_plotting_styles()
-    data_frame = construct_data_frame(stats_results)
-    save_temp(data_frame, settings)
-    find_guard_value(data_frame, settings)
-    data_frame = check_guards(data_frame, settings)
-    data_frame = find_outliers(data_frame, settings)
-
-    selected_df = select_value_on_guards_and_outliers(data_frame, settings)
-    selected_df = smooth_rolling_min(selected_df, settings)
-    selected_df = smooth_savgol_filter(selected_df, settings)
-
-    extremas_df = find_extremas(selected_df, settings)
-
-    save_data(data_frame, selected_df, extremas_df, settings)
-
-    return list(selected_df["file"])
-
-
-def save_temp(data_frame, settings):
-    csv_path = settings.tif_dir / "_data.csv"
+def save_temp(data_frame, parameters):
+    csv_path = parameters.tif_dir / "_data.csv"
     data_frame.to_csv(csv_path, sep="\t")
-    xls_path = settings.tif_dir / "_data.xlsx"
-    data_frame.to_excel(xls_path)
-    settings_path = settings.tif_dir / "_settings.pickle"
-    with open(settings_path, "wb") as fh:
-        pickle.dump(settings, fh)
+    parameters_path = parameters.tif_dir / "_parameters.pickle"
+    with open(parameters_path, "wb") as fh:
+        pickle.dump(parameters, fh)
 
 
 def load_temp(folder):
     dir_path = pathlib.Path(folder)
     csv_path = dir_path / "_data.csv"
     data_frame = pandas.read_csv(csv_path, sep="\t", index_col=0)
-    settings_path = dir_path / "_settings.pickle"
-    with open(settings_path, "rb") as fh:
-        settings = pickle.load(fh)
-
-    settings.cut_pad_x = 50
-    settings.cut_pad_y = 25
-    settings.guard_histogram_bins = 100
-    settings.guard_filter_window = 7
-    settings.guard_filter_polynom = 3
-    settings.guards_minima_min_dist = 10
-    settings.rolling_min_window = 5
-    settings.savgol_filter_window = 51
-    settings.savgol_filter_polynom = 1
-    settings.peak_min_distance = 5
-    settings.peak_threshold = 0.3
-
-    return data_frame, settings
-
-
-def cached_workflow(folder):
-    print("3/4: Data analysis")
-    set_plotting_styles()
-    data_frame, settings = load_temp(folder)
-    find_guard_value(data_frame, settings)
-    data_frame = check_guards(data_frame, settings)
-    data_frame = find_outliers(data_frame, settings)
-
-    selected_df = select_value_on_guards_and_outliers(data_frame, settings)
-    selected_df = smooth_rolling_min(selected_df, settings)
-    selected_df = smooth_savgol_filter(selected_df, settings)
-
-    extremas_df = find_extremas(selected_df, settings)
-
-    save_data(data_frame, selected_df, extremas_df, settings)
-
-    return list(selected_df["file"]), settings
+    parameters_path = dir_path / "_parameters.pickle"
+    with open(parameters_path, "rb") as fh:
+        parameters = pickle.load(fh)
+
+    return data_frame, parameters

mtor/imageproc.py

@@ -1,32 +1,13 @@
-from collections import namedtuple
-from functools import partial
-from multiprocessing import Pool
 from PIL import Image
-from tqdm import tqdm
 
 import numpy
 
-from .luts import FIRE_LUT
-
-
-STATS_FUNCS = {
-    "min": numpy.amin,
-    "max": numpy.amax,
-    "1quantile": partial(numpy.percentile, q=25),
-    "2quantile": partial(numpy.percentile, q=50),
-    "3quantile": partial(numpy.percentile, q=75),
-    "average": numpy.average,
-    "median": numpy.median,
-    "std": numpy.std,
-}
-
-LEFT_GUARD = "left"
-ROI_NAME = "roi"
-RIGHT_GUARD = "right"
-
-ROI_BORDER_INTENSITY =  2**15
-
-TifArray = namedtuple("TifArray", ["path", "frame", "data"])
+from .commons import (
+    TifArray,
+    IMG_MAX_INTENSITY,
+    FIRE_LUT,
+    ROI_STATISTIC_FUNCTIONS,
+)
 
 
 def open_as_array(tif_image):
@@ -38,19 +19,19 @@ def open_as_array(tif_image):
     )
 
 
-def scale_and_colorize(tif_array, settings):
-    adjusted_array = (tif_array.data - settings.offset) // settings.scale
+def scale_and_colorize(tif_array, parameters):
+    adjusted_array = (tif_array.data - parameters.offset) // parameters.scale
 
     # paint roi
 
-    top = settings.roi_top
-    bottom = settings.roi_bottom
-    right = settings.roi_right
-    left = settings.roi_left
-    adjusted_array[top, left:right] = ROI_BORDER_INTENSITY
-    adjusted_array[bottom, left:right] = ROI_BORDER_INTENSITY
-    adjusted_array[top:bottom, left] = ROI_BORDER_INTENSITY
-    adjusted_array[top:bottom, right] = ROI_BORDER_INTENSITY
+    top = parameters.roi_top
+    bottom = parameters.roi_bottom
+    right = parameters.roi_right
+    left = parameters.roi_left
+    adjusted_array[top, left:right] = IMG_MAX_INTENSITY
+    adjusted_array[bottom, left:right] = IMG_MAX_INTENSITY
+    adjusted_array[top:bottom, left] = IMG_MAX_INTENSITY
+    adjusted_array[top:bottom, right] = IMG_MAX_INTENSITY
 
     adjusted = Image.fromarray(adjusted_array)
     converted = adjusted.convert(mode="L").convert(mode="P")
@@ -58,21 +39,21 @@ def scale_and_colorize(tif_array, settings):
     return converted.convert("RGB")
 
 
-def safe_color_coded(tif_array, settings):
-    png_image = scale_and_colorize(tif_array, settings)
-    png_path = settings.colored_dir / (tif_array.path.stem + ".jpg")
+def safe_color_coded(tif_array, parameters):
+    png_image = scale_and_colorize(tif_array, parameters)
+    png_path = parameters.colored_dir / (tif_array.path.stem + ".jpg")
     with png_path.open("wb") as outhandle:
         png_image.save(outhandle)
     del png_image
 
 
-def safe_cut(tif_array, settings):
-    cut_path = settings.cuts_dir / (tif_array.path.stem + "_cut.tif")
+def safe_cut(tif_array, parameters):
+    cut_path = parameters.cuts_dir / tif_array.path.name
 
-    top = settings.cut_top
-    bottom = settings.cut_bottom
-    right = settings.cut_right
-    left = settings.cut_left
+    top = parameters.cut_top
+    bottom = parameters.cut_bottom
+    right = parameters.cut_right
+    left = parameters.cut_left
 
     with cut_path.open("wb") as outhandle:
         cut_array = tif_array.data[top:bottom, left:right]
@@ -81,42 +62,35 @@ def safe_cut(tif_array, settings):
         del cut_img
 
 
-def get_roi(tif_array, settings):
-    top = settings.roi_top
-    bottom = settings.roi_bottom
-    right = settings.roi_right
-    left = settings.roi_left
+def get_roi(tif_array, parameters):
+    top = parameters.roi_top
+    bottom = parameters.roi_bottom
+    right = parameters.roi_right
+    left = parameters.roi_left
     return tif_array.data[top:bottom, left:right]
 
 
-def analyse_roi(tif_array, settings):
-    data = get_roi(tif_array, settings)
+def analyse_roi(tif_array, parameters):
+    data = get_roi(tif_array, parameters)
     results = {}
-    results.update(get_stats(data[:, 1], LEFT_GUARD))
-    results.update(get_stats(data[:, 1:-1], ROI_NAME))
-    results.update(get_stats(data[:, -1], RIGHT_GUARD))
+    results.update(get_stats(data[:, 1], parameters.left_guard_name))
+    results.update(get_stats(data[:, 1:-1], parameters.roi_name))
+    results.update(get_stats(data[:, -1], parameters.right_guard_name))
     return results
 
 
 def get_stats(array, prefix):
-    return {f"{prefix}.{k}": f(array) for k, f in STATS_FUNCS.items()}
+    return {
+        f"{prefix}.{name}": func(array)
+        for name, func in ROI_STATISTIC_FUNCTIONS.items()
+    }
 
 
-def process_one_tif(tif_image, settings):
+def process_one_tif(tif_image, parameters):
     tif_array = open_as_array(tif_image)
-    safe_color_coded(tif_array, settings)
-    safe_cut(tif_array, settings)
-    stats_result = analyse_roi(tif_array, settings)
+    safe_color_coded(tif_array, parameters)
+    safe_cut(tif_array, parameters)
+    stats_result = analyse_roi(tif_array, parameters)
     stats_result["file"] = tif_image.path.name
     stats_result["frame"] = tif_image.frame
     return stats_result
-
-
-def image_workflow(settings):
-    print("2/4: Image analysis and conversion")
-    func = partial(process_one_tif, settings=settings)
-    tif_list = settings.tif_list
-    num_items = len(tif_list)
-    with Pool(4) as p:
-        stat_results = list(tqdm(p.imap(func, tif_list), total=num_items))
-    return stat_results

mtor/parameters.py

@@ -0,0 +1,179 @@
+import pathlib
+
+from PIL import Image
+
+from .commons import RE_DIGITS, OUTPUT_FOLDER_NAMES, TifImage
+
+
+class Parameters(dict):
+    """ namespacing the parameters for a analysis with nice access
+
+    the following parameters are used:
+
+    boost:                  linear factor for brightness on color coded images
+    charts_y_limit:         common scale for the y axis in graphs
+    colored_dir:            path to the directory to hold color coded images
+    cut_bottom:             bottom edge for croping an image
+    cut_left:               left edge for croping an image
+    cut_pad_x:              pixels added to left or right edge to crop an image
+    cut_pad_y:              pixels added to top or bottom edge to crop an image
+    cut_right:              right edge for croping an image
+    cut_top:                left edge for croping an image
+    cuts_discarded_dir:     path to the directory of discarded croped images
+    cuts_selected_dir:      path to the directory of selected croped images
+    data_dir:               path to the directory for graphs and numeric data
+    folder:                 name of the directory that holds the tif images
+    guard_filter_polynom:   polynomal scalar for smoothing the guards histogram
+    guard_filter_window:    window scalar for smoothing the guards histogram
+    guard_histogram_bins:   number of bins when constructing guards histogram
+    guard_max_value:        calculated threshold for the gurards
+    guard_stats:            which values to use for caclulation of guards
+    guards_minima_min_dist: min distance between minimas in guards histogram
+    image_height:           height of an image
+    image_width:            width of an image
+    intensity_max:          highest intensity found in all images
+    intensity_min:          lowest intensity found in all images
+    left_guard_column:      name of the most used left guard value column
+    left_guard_name:        left guard value group name
+    offset:                 calculated the lowest intensity to scale by
+    outlier_upper_limit:    calculated threshold value for outliers
+    peak_min_distance:      min distance for peaks in the final results
+    peak_threshold:         threshold peak height in the final results
+    right_guard_column:     name of the most used left guard value column
+    right_guard_name:       right guard value group name
+    roi_bottom:             bottom edge of the ROI
+    roi_column:
+    roi_left:               bottom left of the ROI
+    roi_name:               roi value group name
+    roi_right:              bottom right of the ROI
+    roi_stats:              which values to use for caclulation of roi
+    roi_top:                top edge of the ROI
+    rolling_min_window:     size of the window for the rolling min calculation
+    savgol_filter_polynom:  polynomal scalar for smoothing the final results
+    savgol_filter_window:   window scalar for smoothing the final results
+    scale:                  calculated factor to boost image intensities
+    tif_dir:                path to the directory with the original tif images
+
+    """
+
+    def __init__(self, folder, top, right, bottom, left, **kargs):
+        super().__init__(self)
+
+        self.folder = folder
+
+        # defaults
+        self.boost = 10
+        self.cut_pad_x = 50
+        self.cut_pad_y = 25
+        self.guard_histogram_bins = 100
+        self.guard_filter_window = 7
+        self.guard_filter_polynom = 3
+        self.guards_minima_min_dist = 10
+        self.rolling_min_window = 5
+        self.savgol_filter_window = 51
+        self.savgol_filter_polynom = 1
+        self.peak_min_distance = 5
+        self.peak_threshold = 0.3
+
+        # labels for data items
+        self.roi_name = "roi"
+        self.roi_stats = "average"
+        self.roi_column = f"{self.roi_name}.{self.roi_stats}"
+        self.left_guard_name = "left"
+        self.right_guard_name = "right"
+        self.guard_stats = "average"
+        self.left_guard_column = f"{self.left_guard_name}.{self.guard_stats}"
+        self.right_guard_column = f"{self.right_guard_name}.{self.guard_stats}"
+
+        # arbitrary keyword arguments, updating defaults if necessary
+        self.update(kargs)
+
+        # create list of images
+        self.tif_dir = pathlib.Path(folder)
+        if not self.tif_dir.is_dir():
+            raise IOError(f"Not a directory: {self.folder}")
+        self.tif_list = self.scan_image_dir()
+
+        # set the names of output directories
+        for name in OUTPUT_FOLDER_NAMES:
+            sub_path = self.tif_dir / name
+            self[f"{name}_dir"] = sub_path
+
+        # get image dimensions
+        width, height = self.get_size_of_one_image()
+        self.image_width = width
+        self.image_height = height
+
+        self.set_roi_and_cut_size(top, right, bottom, left)
+
+    def __getattr__(self, key):
+        """ return a parameters value as instance property """
+        return self[key]
+
+    def __setattr__(self, key, value):
+        """ set a parameters value via instance property """
+        self[key] = value
+
+    def __getstate__(self):
+        # Copy the object's state from self.__dict__ which contains
+        # all our instance attributes. Always use the dict.copy()
+        # method to avoid modifying the original state.
+        return self.__dict__.copy()
+
+    def __setstate__(self, state):
+        # Restore instance attributes (i.e., filename and lineno).
+        self.__dict__.update(state)
+
+    def scan_image_dir(self):
+        """ scan a folder for tif images """
+        visible = (
+            item
+            for item in self.tif_dir.iterdir()
+            if not item.stem.startswith(".")
+        )
+        tifs = (item for item in visible if item.suffix == ".tif")
+        results = []
+        for path in tifs:
+            try:
+                match = RE_DIGITS.search(path.name).group()
+            except AttributeError as ex:
+                raise ValueError(
+                    f"no sequence number found in {path.name}"
+                ) from ex
+            results.append(TifImage(path=path, frame=int(match)))
+        return results
+
+    def get_size_of_one_image(self):
+        """ get the dimension of one Image
+
+        the dimensions should be the same on all images. this is ensured in
+        an other workflow.
+        """
+        image = self.tif_list[0]
+        with open(image.path, "rb") as filehandle:
+            image = Image.open(filehandle)
+            size = image.size
+        return size
+
+    def set_roi_and_cut_size(self, top, right, bottom, left):
+        if right < left:
+            left, right = right, left
+        if top > bottom:
+            top, bottom = bottom, top
+        if (
+            top < 0
+            or left < 0
+            or bottom > self.image_height
+            or right > self.image_width
+        ):
+            raise ValueError("ROI out of bounce")
+
+        self.roi_top = top
+        self.roi_bottom = bottom
+        self.roi_right = right
+        self.roi_left = left
+
+        self.cut_top = min(self.image_height, top - self.cut_pad_y)
+        self.cut_bottom = max(0, bottom + self.cut_pad_y)
+        self.cut_right = min(self.image_width, right + self.cut_pad_x)
+        self.cut_left = max(0, left - self.cut_pad_x)

mtor/postproc.py

@@ -0,0 +1,40 @@
+import pathlib
+
+from .commons import LABEL_SELECTED, LABEL_DISCARDED
+
+
+def stem_file_list(selected_files):
+    return {pathlib.Path(filename).stem for filename in selected_files}
+
+
+def rename_color_coded_images(file_stems, parameters):
+    rename_pairs = []
+    for path in parameters.colored_dir.iterdir():
+        if not path.stem.startswith("."):
+            label = (
+                LABEL_SELECTED if path.stem in file_stems else LABEL_DISCARDED
+            )
+            new_name = path.with_name(f"{path.stem}_{label}{path.suffix}")
+            rename_pairs.append((path, new_name))
+    return rename_pairs
+
+
+def sort_cut_images(file_stems, parameters):
+    sort_pairs = []
+    for path in parameters.cuts_dir.iterdir():
+        if not path.stem.startswith("."):
+            label = (
+                LABEL_SELECTED if path.stem in file_stems else LABEL_DISCARDED
+            )
+            new_path = (
+                parameters[f"cuts_{label}_dir"]
+                / f"{path.stem}_cut_{label}{path.suffix}"
+            )
+            sort_pairs.append((path, new_path))
+    return sort_pairs
+
+
+def remove_cuts_dir(parameters):
+    for item in parameters["cuts_dir"].iterdir():
+        item.unlink()
+    parameters["cuts_dir"].rmdir()

mtor/prescan.py

@@ -1,163 +1,36 @@
-import re
-
-from collections import namedtuple
-from pathlib import Path
 from PIL import Image
 from tqdm import tqdm
 
+from .commons import OUTPUT_FOLDER_NAMES, IMG_MAX_INTENSITY
 
-RE_DIGITS = re.compile(r"(\d+)")
-
-LABEL_SELECTED = "selected"
-LABEL_DISCARDED = "discarded"
-
-
-TifImage = namedtuple("TifImage", ["path", "frame"])
-
-
-class Settings(dict):
-    """ namespacing the constant values for a analysis with nice access """
-
-    def __getattr__(self, key):
-        """ return a settings value as instance property """
-        return self[key]
-
-    def __setattr__(self, key, value):
-        """ set a settings value via instance property """
-        self[key] = value
-
-    def __getstate__(self):
-        # Copy the object's state from self.__dict__ which contains
-        # all our instance attributes. Always use the dict.copy()
-        # method to avoid modifying the original state.
-        return self.__dict__.copy()
-
-    def __setstate__(self, state):
-        # Restore instance attributes (i.e., filename and lineno).
-        self.__dict__.update(state)
-
-
-def scan_image_dir(settings):
-    """ scan a folder for tif images """
-    tif_dir = Path(settings.folder)
-    if not tif_dir.is_dir():
-        raise IOError(f"Not a directory: {settings.folder}")
-    visible = (i for i in tif_dir.iterdir() if not i.stem.startswith("."))
-    tifs = (item for item in visible if item.suffix == ".tif")
-    results = []
-    for path in tifs:
-        try:
-            match = RE_DIGITS.search(path.name).group()
-        except AttributeError as ex:
-            raise ValueError(
-                f"no sequence number found in {path.name}"
-            ) from ex
-        results.append(TifImage(path=path, frame=int(match)))
-    settings.tif_dir = tif_dir
-    settings.tif_list = results
-    return settings
-
-
-def prepare_output_dirs(settings):
-    dir_names = (
-        "colored",
-        "cuts",
-        f"cuts_{LABEL_DISCARDED}",
-        f"cuts_{LABEL_SELECTED}",
-        "data",
-    )
-    for subdir in dir_names:
-        sub_path = settings.tif_dir / subdir
-        sub_path.mkdir(exist_ok=True)
-        for item in sub_path.iterdir():
-            item.unlink()
-        settings[f"{subdir}_dir"] = sub_path
-    return settings
 
-
-def pre_scan_tifs(settings):
+def scan_tifs(parameters):
     """ scan a sequence of tif images for common values """
-    lowest = 2 ** 16
+    lowest = IMG_MAX_INTENSITY
     highest = 0
-    width = None
-    height = None
-
-    print("1/4: scanning tifs for common autocontrast values")
 
-    for tif in tqdm(settings.tif_list):
+    for tif in tqdm(parameters.tif_list):
         with tif.path.open("rb") as filehandle:
             image = Image.open(filehandle)
             mi, ma = image.getextrema()
             lowest = min(lowest, mi)
             highest = max(highest, ma)
-            if width is None:
-                width, height = image.size
-            elif width != image.width or height != image.height:
+            if (
+                image.width != parameters.image_width
+                or image.height != parameters.image_height
+            ):
                 raise ValueError("Images have different sizes")
 
-    print(f"intensity range: {lowest} to {highest}")
-    print(f"image size: {width} x {height} pixels")
-
-    settings.intensity_min = lowest
-    settings.intensity_max = highest
-    settings.image_width = width
-    settings.image_height = height
-
-    return settings
-
-
-def check_roi_size(settings, top, right, bottom, left):
-    if (
-        top > settings.image_height
-        or bottom > settings.image_height
-        or right > settings.image_width
-        or left > settings.image_width
-    ):
-        raise ValueError("ROI out of bounce")
-    if right < left:
-        left, right = right, left
-    if bottom < top:
-        top, bottom = bottom, top
-
-    settings.roi_top = top
-    settings.roi_bottom = bottom
-    settings.roi_right = right
-    settings.roi_left = left
-
-    settings.cut_top = min(settings.image_height, top - settings.cut_pad_y)
-    settings.cut_bottom = max(0, bottom + settings.cut_pad_y)
-    settings.cut_right = min(settings.image_width, right + settings.cut_pad_x)
-    settings.cut_left = max(0, left - settings.cut_pad_x)
+    parameters.intensity_min = lowest
+    parameters.intensity_max = highest
 
-    return settings
+    return parameters
 
 
-def prescan_workflow(folder, top, right, bottom, left, boost=10, **kargs):
-    settings = Settings()
-    settings.folder = folder
-    settings.boost = boost
-
-    settings.cut_pad_x = 50
-    settings.cut_pad_y = 25
-    settings.guard_histogram_bins = 100
-    settings.guard_filter_window = 7
-    settings.guard_filter_polynom = 3
-    settings.guards_minima_min_dist = 10
-    settings.rolling_min_window = 5
-    settings.savgol_filter_window = 51
-    settings.savgol_filter_polynom = 1
-    settings.peak_min_distance = 5
-    settings.peak_threshold = 0.3
-
-    settings.update(kargs)
-
-    settings = scan_image_dir(settings)
-    settings = prepare_output_dirs(settings)
-    settings = pre_scan_tifs(settings)
-    settings = check_roi_size(settings, top, right, bottom, left)
-
-    settings.offset = settings.intensity_min
-    max_offset = settings.intensity_max - settings.intensity_min
-    settings.scale = max_offset / 255 / boost
-
-    return settings
+def prepare_output_dirs(parameters):
+    for name in OUTPUT_FOLDER_NAMES:
+        sub_path = parameters[f"{name}_dir"]
+        sub_path.mkdir(exist_ok=True)
+        for item in sub_path.iterdir():
+            item.unlink()
+    return parameters

mtor/workflows.py

@@ -0,0 +1,108 @@
+import multiprocessing
+import functools
+
+from tqdm import tqdm
+
+from .commons import WorkflowResult
+from .parameters import Parameters
+from .prescan import scan_tifs, prepare_output_dirs
+from .imageproc import process_one_tif
+from .dataproc import (
+    set_plotting_styles,
+    construct_data_frame,
+    find_guard_threshold,
+    check_guards,
+    find_outliers,
+    select_on_guards_and_outliers,
+    smooth_rolling_min,
+    smooth_savgol_filter,
+    find_extremas,
+    save_data,
+)
+from .postproc import (
+    stem_file_list,
+    rename_color_coded_images,
+    sort_cut_images,
+    remove_cuts_dir,
+)
+
+
+def prescan_workflow(folder, top, right, bottom, left, **kargs):
+    print("1/4: scanning tifs for common autocontrast values")
+
+    parameters = Parameters(folder, top, right, bottom, left)
+    parameters = scan_tifs(parameters)
+
+    parameters.offset = parameters.intensity_min
+    max_offset = parameters.intensity_max - parameters.intensity_min
+    parameters.scale = max_offset / 255 / parameters.boost
+
+    prepare_output_dirs(parameters)
+
+    return WorkflowResult(None, parameters)
+
+
+def image_workflow(parameters):
+    print("2/4: Image analysis and conversion")
+    func = functools.partial(process_one_tif, parameters=parameters)
+    tif_list = parameters.tif_list
+    num_items = len(tif_list)
+    cpu_count = multiprocessing.cpu_count()
+    with multiprocessing.Pool(cpu_count) as mpp:
+        stat_results = list(tqdm(mpp.imap(func, tif_list), total=num_items))
+    return WorkflowResult(stat_results, parameters)
+
+
+def data_workflow(stats_results, parameters):
+    from .dataproc import save_temp
+
+    print("3/4: Data analysis")
+    set_plotting_styles()
+    data_frame = construct_data_frame(stats_results, parameters)
+    save_temp(data_frame, parameters)
+
+    find_guard_threshold(data_frame, parameters)
+    data_frame = check_guards(data_frame, parameters)
+    data_frame = find_outliers(data_frame, parameters)
+
+    selected_df = select_on_guards_and_outliers(data_frame, parameters)
+    selected_df = smooth_rolling_min(selected_df, parameters)
+    selected_df = smooth_savgol_filter(selected_df, parameters)
+
+    extremas_df = find_extremas(selected_df, parameters)
+
+    save_data(data_frame, selected_df, extremas_df, parameters)
+
+    return WorkflowResult(list(selected_df["file"]), parameters)
+
+
+def postprocessing_workflow(selected_files, parameters):
+    print("4/4: Post processing")
+    file_stems = stem_file_list(selected_files)
+    cc_rename_pairs = rename_color_coded_images(file_stems, parameters)
+    cut_sort_pairs = sort_cut_images(file_stems, parameters)
+    file_pairs = cc_rename_pairs + cut_sort_pairs
+    for old_path, new_path in tqdm(file_pairs):
+        old_path.rename(new_path)
+    remove_cuts_dir(parameters)
+
+
+def cached_data_workflow(folder):
+    from .dataproc import load_temp
+
+    print("3/4: Data analysis")
+    set_plotting_styles()
+    data_frame, parameters = load_temp(folder)
+    find_guard_threshold(data_frame, parameters)
+    data_frame = check_guards(data_frame, parameters)
+    data_frame = find_outliers(data_frame, parameters)
+
+    selected_df = select_on_guards_and_outliers(data_frame, parameters)
+    selected_df = smooth_rolling_min(selected_df, parameters)
+    selected_df = smooth_savgol_filter(selected_df, parameters)
+
+    extremas_df = find_extremas(selected_df, parameters)
+
+    save_data(data_frame, selected_df, extremas_df, parameters)
+
+    return WorkflowResult(list(selected_df["file"]), parameters)