Use pathlib in place of os.path

1aa13123 · Thomas Kluyver · 1505a482 · 1aa13123
Commit 1aa13123 authored 1 year ago by Thomas Kluyver
--- a/notebooks/LPD/LPDChar_Darks_NBC.ipynb
+++ b/notebooks/LPD/LPDChar_Darks_NBC.ipynb
@@ -69,11 +69,11 @@
   "source": [
    "import copy\n",
    "import multiprocessing\n",
-    "import os\n",
    "import warnings\n",
    "from datetime import datetime\n",
    "from functools import partial\n",
    "from logging import warning\n",
+    "from pathlib import Path\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
@@ -268,7 +268,7 @@
    "\n",
    "inp = []\n",
    "for gg, run_num in enumerate([run_high, run_med, run_low]):\n",
-    "    run_path = os.path.join(in_folder, f\"r{run_num:04d}\")\n",
+    "    run_path = Path(in_folder, f\"r{run_num:04d}\")\n",
    "    for i in modules:\n",
    "        inp.append((run_path, i, gg))\n",
    "\n",
@@ -404,14 +404,14 @@
    "            }\n",
    "        else:\n",
    "            timestamp = mdata.calibration_constant_version.begin_at.isoformat()\n",
-    "            filepath = os.path.join(\n",
+    "            filepath = Path(\n",
    "                mdata.calibration_constant_version.hdf5path,\n",
    "                mdata.calibration_constant_version.filename\n",
    "            )\n",
    "            h5path = mdata.calibration_constant_version.h5path\n",
    "            old_mdata[qm][const] = {\n",
    "                \"timestamp\": timestamp,\n",
-    "                \"filepath\": filepath,\n",
+    "                \"filepath\": str(filepath),\n",
    "                \"h5path\": h5path\n",
    "            }\n",
    "\n",

 %% Cell type:markdown id: tags:
 # LPD Offset, Noise and Dead Pixels Characterization #
 Author: M. Karnevskiy, S. Hauf
 This notebook performs re-characterize of dark images to derive offset, noise and bad-pixel maps. All three types of constants are evaluated per-pixel and per-memory cell.
 The notebook will correctly handle veto settings, but note that if you veto cells you will not be able to use these offsets for runs with different veto settings - vetoed cells will have zero offset.
 The evaluated calibration constants are stored locally and injected in the calibration data base.
 %% Cell type:code id: tags:
 ``` python
 in_folder = "/gpfs/exfel/exp/FXE/202304/p003338/raw" # path to input data, required
 out_folder = "/gpfs/exfel/data/scratch/kluyvert/lpd-darks-p3338-r133-134-135/" # path to output to, required
 metadata_folder = ""  # Directory containing calibration_metadata.yml when run by xfel-calibrate
 modules = [-1] # list of modules to evaluate, RANGE ALLOWED
 run_high = 133 # run number in which high gain data was recorded, required
 run_med = 134 # run number in which medium gain data was recorded, required
 run_low = 135 # run number in which low gain data was recorded, required
 karabo_id = "FXE_DET_LPD1M-1" # karabo karabo_id
 karabo_da = ['-1']  # a list of data aggregators names, Default [-1] for selecting all data aggregators
 source_name = "{}/DET/{}CH0:xtdf"  # Source name for raw detector data - filled with karabo_id & module number
 use_dir_creation_date = True # use the creation date of the directory for database time derivation
 cal_db_interface = "tcp://max-exfl016:8015#8025" # the database interface to use
 cal_db_timeout = 300000 # timeout on caldb requests"
 local_output = True # output constants locally
 db_output = False # output constants to database
 capacitor_setting = 5 # capacitor_setting for which data was taken
 mem_cells = 512 # number of memory cells used
 bias_voltage = 250 # detector bias voltage
 thresholds_offset_sigma = 3. # bad pixel relative threshold in terms of n sigma offset
 thresholds_offset_hard = [400, 1500] # bad pixel hard threshold
 thresholds_noise_sigma = 7. # bad pixel relative threshold in terms of n sigma noise
 thresholds_noise_hard = [1, 35] # bad pixel hard threshold
 ntrains = 500  # maximum number of trains to use in each gain stage
 skip_first_ntrains = 10  # Number of first trains to skip
 min_trains = 370  # minimum number of trains needed for each gain stage
 # Parameters for plotting
 skip_plots = False  # exit after writing corrected files
 high_res_badpix_3d = False # plot bad-pixel summary in high resolution
 test_for_normality = False # permorm normality test
 inject_cell_order = False  # Include memory cell order as part of the detector condition
 operation_mode = ''  # Detector operation mode, optional
 ```
 %% Cell type:code id: tags:
 ``` python
 import copy
 import multiprocessing
-import os
 import warnings
 from datetime import datetime
 from functools import partial
 from logging import warning
+from pathlib import Path
 warnings.filterwarnings('ignore')
 import dateutil.parser
 import matplotlib
 import pasha as psh
 import scipy.stats
 from IPython.display import Latex, Markdown, display
 matplotlib.use("agg")
 import matplotlib.patches as patches
 import matplotlib.pyplot as plt
 %matplotlib inline
 import numpy as np
 import tabulate
 import yaml
 from iCalibrationDB import Conditions, Constants, Detectors, Versions
 from XFELDetAna.plotting.heatmap import heatmapPlot
 from XFELDetAna.plotting.simpleplot import simplePlot
 from extra_data import RunDirectory
 from cal_tools.enums import BadPixels
 from cal_tools.plotting import (
    create_constant_overview,
    plot_badpix_3d,
    show_overview,
    show_processed_modules,
 )
 from cal_tools.tools import (
    get_dir_creation_date,
    get_from_db,
    get_notebook_name,
    get_pdu_from_db,
    get_random_db_interface,
    get_report,
    map_gain_stages,
    module_index_to_qm,
    parse_runs,
    reorder_axes,
    run_prop_seq_from_path,
    save_const_to_h5,
    send_to_db,
 )
 ```
 %% Cell type:code id: tags:
 ``` python
 gains = np.arange(3)
 max_cells = mem_cells
 cells = np.arange(max_cells)
 gain_names = ['High', 'Medium', 'Low']
 if karabo_da[0] == '-1':
    if modules[0] == -1:
        modules = list(range(16))
    karabo_da = ['LPD{:02d}'.format(i) for i in modules]
 else:
    modules = [int(x[-2:]) for x in karabo_da]
 capacitor_setting_s = f'{capacitor_setting}pf'
 creation_time = None
 if use_dir_creation_date:
    creation_time = get_dir_creation_date(in_folder, run_high)
    print("Using {} as creation time".format(creation_time))
 run, prop, seq = run_prop_seq_from_path(in_folder)
 cal_db_interface = get_random_db_interface(cal_db_interface)
 display(Markdown('## Evaluated parameters'))
 print('CalDB Interface {}'.format(cal_db_interface))
 print("Proposal: {}".format(prop))
 print("Memory cells: {}/{}".format(mem_cells, max_cells))
 print("Runs: {}, {}, {}".format(run_high, run_med, run_low))
 print("Using DB: {}".format(db_output))
 print("Input: {}".format(in_folder))
 print("Output: {}".format(out_folder))
 print("Bias voltage: {}V".format(bias_voltage))
 print(f"Capacitor setting: {capacitor_setting_s}")
 ```
 %% Cell type:markdown id: tags:
 ## Data processing
 %% Cell type:code id: tags:
 ``` python
 parallel_num_procs = min(6, len(modules)*3)
 parallel_num_threads = multiprocessing.cpu_count() // parallel_num_procs
 # the actual characterization
 def characterize_module(run_path, channel, gg):
    run = RunDirectory(run_path)
    det_source = source_name.format(karabo_id, channel)
    data = run[det_source, 'image.data'].drop_empty_trains()
    data = data[skip_first_ntrains : skip_first_ntrains + ntrains]
    cell_ids = run[det_source, 'image.cellId'].drop_empty_trains()
    cell_ids = cell_ids[skip_first_ntrains : skip_first_ntrains + ntrains]
    if len(data.train_ids) < min_trains:
        raise Exception(f"Run {run_path} only contains {len(data.train_ids)} trains, but {min_trains} required")
    im = data.ndarray()
    if im.ndim > 3:
        im = im[:, 0]  # Drop extra dimension
    cellid = cell_ids.ndarray()
    cellid_pattern = cell_ids[0].ndarray()
    if cellid.ndim > 1:
        cellid = cellid[:, 0]
        cellid_pattern = cellid_pattern[:, 0]
    # Mask off gain bits, leaving only data
    im &= 0b0000111111111111
    im = im.astype(np.float32)
    im = reorder_axes(im,
        from_order=('frames', 'slow_scan', 'fast_scan'),
        to_order=('fast_scan', 'slow_scan', 'frames'),
    )
    context = psh.context.ThreadContext(num_workers=parallel_num_threads)
    offset = context.alloc(shape=(im.shape[0], im.shape[1], max_cells), dtype=np.float64)
    noise = context.alloc(like=offset)
    normal_test = context.alloc(like=offset)
    def process_cell(worker_id, array_index, cc):
        idx = cellid == cc
        im_slice = im[..., idx]
        if np.any(idx):
            offset[..., cc] = np.median(im_slice, axis=2)
            noise[..., cc] = np.std(im_slice, axis=2)
            if test_for_normality:
                _, normal_test[..., cc] = scipy.stats.normaltest(
                    im[:, :, idx], axis=2)
    context.map(process_cell, np.unique(cellid))
    # bad pixels
    bp = np.zeros(offset.shape, np.uint32)
    # offset related bad pixels
    offset_mn = np.nanmedian(offset, axis=(0, 1))
    offset_std = np.nanstd(offset, axis=(0, 1))
    bp[(offset < offset_mn-thresholds_offset_sigma*offset_std) |
       (offset > offset_mn+thresholds_offset_sigma*offset_std)] |= BadPixels.OFFSET_OUT_OF_THRESHOLD.value
    bp[(offset < thresholds_offset_hard[0]) | (
        offset > thresholds_offset_hard[1])] |= BadPixels.OFFSET_OUT_OF_THRESHOLD.value
    bp[~np.isfinite(offset)] |= BadPixels.OFFSET_NOISE_EVAL_ERROR.value
    # noise related bad pixels
    noise_mn = np.nanmedian(noise, axis=(0, 1))
    noise_std = np.nanstd(noise, axis=(0, 1))
    bp[(noise < noise_mn-thresholds_noise_sigma*noise_std) |
       (noise > noise_mn+thresholds_noise_sigma*noise_std)] |= BadPixels.NOISE_OUT_OF_THRESHOLD.value
    bp[(noise < thresholds_noise_hard[0]) | (
        noise > thresholds_noise_hard[1])] |= BadPixels.NOISE_OUT_OF_THRESHOLD.value
    bp[~np.isfinite(noise)] |= BadPixels.OFFSET_NOISE_EVAL_ERROR.value
    idx = (cellid == cellid[0])
    return offset, noise, channel, gg, bp, im[12, 12, idx], normal_test, cellid_pattern
 ```
 %% Cell type:code id: tags:
 ``` python
 offset_g = {}
 noise_g = {}
 badpix_g = {}
 data_g = {}
 ntest_g = {}
 # Should be the same cell order for all modules & all gain stages
 cellid_patterns_g = {}
 inp = []
 for gg, run_num in enumerate([run_high, run_med, run_low]):
-    run_path = os.path.join(in_folder, f"r{run_num:04d}")
+    run_path = Path(in_folder, f"r{run_num:04d}")
    for i in modules:
        inp.append((run_path, i, gg))
 with multiprocessing.Pool(processes=parallel_num_procs) as pool:
    results = pool.starmap(characterize_module, inp)
 for ir, r in enumerate(results):
    offset, noise, i, gg, bp, data, normal, cellid_pattern = r
    if data is None:
        warning(f"No data for module {i} of gain {gg}")
        skip_plots = True
        continue
    qm = module_index_to_qm(i)
    if qm not in offset_g:
        offset_g[qm] = np.zeros(offset.shape[:3] + (3,))
        print("Constant shape:", offset_g[qm].shape)
        noise_g[qm] = np.zeros_like(offset_g[qm])
        badpix_g[qm] = np.zeros_like(offset_g[qm], dtype=np.uint32)
        data_g[qm] = np.full((ntrains, 3), np.nan)
        ntest_g[qm] = np.zeros_like(offset_g[qm])
        cellid_patterns_g[qm] = cellid_pattern
    else:
        if not np.array_equal(cellid_pattern, cellid_patterns_g[qm]):
            raise ValueError("Inconsistent cell ID pattern between gain stages")
    offset_g[qm][..., gg] = offset
    noise_g[qm][..., gg] = noise
    badpix_g[qm][..., gg] = bp
    data_g[qm][:data.shape[0], gg] = data
    ntest_g[qm][..., gg] = normal
    hn, cn = np.histogram(data, bins=20)
    print(f"{gain_names[gg]} gain, Module: {qm}. "
          f"Number of processed trains per cell: {data.shape[0]}.")
 ```
 %% Cell type:code id: tags:
 ``` python
 if skip_plots:
    import sys
    print('Skipping plots')
    sys.exit(0)
 ```
 %% Cell type:code id: tags:
 ``` python
 # Read report path and create file location tuple to add with the injection
 proposal = list(filter(None, in_folder.strip('/').split('/')))[-2]
 file_loc = 'proposal:{} runs:{} {} {}'.format(proposal, run_low, run_med, run_high)
 report = get_report(metadata_folder)
 ```
 %% Cell type:code id: tags:
 ``` python
 # TODO: add db_module when received from myMDC
 # Create the modules dict of karabo_das and PDUs
 qm_dict = {}
 for i, k_da in zip(modules, karabo_da):
    qm = module_index_to_qm(i)
    qm_dict[qm] = {"karabo_da": k_da,
                   "db_module": ""}
 ```
 %% Cell type:code id: tags:
 ``` python
 # Retrieve existing constants for comparison
 clist = ["Offset", "Noise", "BadPixelsDark"]
 dinstance = "LPD1M1"
 detinst = getattr(Detectors, dinstance)
 print('Retrieve pre-existing constants for comparison.')
 old_const = {}
 old_mdata = {}
 for qm in offset_g.keys():
    old_const[qm] = {}
    old_mdata[qm] = {}
    qm_db = qm_dict[qm]
    karabo_da = qm_db["karabo_da"]
    cellid_pattern = cellid_patterns_g[qm]
    if inject_cell_order:
        mem_cell_order = ",".join([str(c) for c in cellid_pattern]) + ","
    else:
        mem_cell_order = None
    condition = Conditions.Dark.LPD(memory_cells=max_cells,
                                    bias_voltage=bias_voltage,
                                    capacitor=capacitor_setting_s,
                                    memory_cell_order=mem_cell_order,
                                   )
    for const in clist:
        constant = getattr(Constants.LPD, const)()
        if not qm_db["db_module"]:
            # This should be used in case of running notebook
            # by a different method other than myMDC which already
            # sends CalCat info.
            qm_db["db_module"] = get_pdu_from_db(karabo_id, [karabo_da], constant,
                                                 condition, cal_db_interface,
                                                 snapshot_at=creation_time)[0]
        data, mdata = get_from_db(karabo_id, karabo_da,
                                  constant,
                                  condition, None,
                                  cal_db_interface,
                                  creation_time=creation_time,
                                  verbosity=2, timeout=cal_db_timeout)
        old_const[qm][const] = data
        if mdata is None or data is None:
            old_mdata[qm][const] = {
                "timestamp": "Not found",
                "filepath": None,
                "h5path": None
            }
        else:
            timestamp = mdata.calibration_constant_version.begin_at.isoformat()
-            filepath = os.path.join(
+            filepath = Path(
                mdata.calibration_constant_version.hdf5path,
                mdata.calibration_constant_version.filename
            )
            h5path = mdata.calibration_constant_version.h5path
            old_mdata[qm][const] = {
                "timestamp": timestamp,
-                "filepath": filepath,
+                "filepath": str(filepath),
                "h5path": h5path
            }
    with open(f"{out_folder}/module_metadata_{qm}.yml","w") as fd:
        yaml.safe_dump(
            {
                "module": qm,
                "pdu": qm_db["db_module"],
                "old-constants": old_mdata[qm]
            }, fd)
 ```
 %% Cell type:code id: tags:
 ``` python
 res = {}
 for i in modules:
    qm = module_index_to_qm(i)
    res[qm] = {'Offset': offset_g[qm],
               'Noise': noise_g[qm],
               'BadPixelsDark': badpix_g[qm]
              }
 ```
 %% Cell type:code id: tags:
 ``` python
 # Save constants in the calibration DB
 md = None
 for qm in res:
    karabo_da = qm_dict[qm]["karabo_da"]
    db_module = qm_dict[qm]["db_module"]
    cellid_pattern = cellid_patterns_g[qm]
    if inject_cell_order:
        mem_cell_order = ",".join([str(c) for c in cellid_pattern]) + ","
    else:
        mem_cell_order = None
    # Do not store empty constants
    # In case of 0 trains data_g is initiated with nans and never refilled.
    if np.count_nonzero(~np.isnan(data_g[qm]))==0:
        print(f"Constant ({qm}) would be empty, skipping saving")
        continue
    for const in res[qm]:
        dconst = getattr(Constants.LPD, const)()
        dconst.data = res[qm][const]
        # set the operating condition
        condition = Conditions.Dark.LPD(memory_cells=max_cells,
                                    bias_voltage=bias_voltage,
                                    capacitor=capacitor_setting_s,
                                    memory_cell_order=mem_cell_order,
                                   )
        if db_output:
            md = send_to_db(db_module, karabo_id, dconst, condition,
                            file_loc, report_path=report,
                            cal_db_interface=cal_db_interface,
                            creation_time=creation_time,
                            timeout=cal_db_timeout)
        if local_output:
            md = save_const_to_h5(db_module, karabo_id, dconst, condition,
                                  dconst.data, file_loc, report, creation_time, out_folder)
            print(f"Calibration constant {const} is stored locally.\n")
    print("Constants parameter conditions are:\n")
    print(f"• memory_cells: {max_cells}\n"
          f"• bias_voltage: {bias_voltage}\n"
          f"• capacitor: {capacitor_setting_s}\n"
          f"• memory cell order: {mem_cell_order}\n"
          f"• creation_time: {md.calibration_constant_version.begin_at if md is not None else creation_time}\n")
 ```
 %% Cell type:code id: tags:
 ``` python
 show_processed_modules(
    dinstance=dinstance,
    constants=None,
    mnames=[module_index_to_qm(i) for i in modules],
    mode="position"
 )
 ```
 %% Cell type:markdown id: tags:
 ## Raw pedestal distribution ##
 Distribution of a pedestal (ADUs) over trains for the pixel (12,12), memory cell 12. A median of the distribution is shown in yellow. A standard deviation is shown in red. The green line shows average over all pixels for a given memory cell and gain stage.
 %% Cell type:code id: tags:
 ``` python
 fig, grid = plt.subplots(3, 1, sharex="col", sharey="row", figsize=(10, 7))
 fig.subplots_adjust(wspace=0, hspace=0)
 for i in modules:
    qm = module_index_to_qm(i)
    if np.count_nonzero(~np.isnan(data_g[qm])) == 0:
        break
    for gain in range(3):
        data = data_g[qm][:, gain]
        offset = np.nanmedian(data)
        noise = np.nanstd(data)
        xrange = [np.nanmin(data_g[qm]), np.nanmax(data_g[qm])]
        if xrange[1] == xrange[0]:
            xrange = [0, xrange[0]+xrange[0]//2]
            nbins = data_g[qm].shape[0]
        else:
            nbins = int(xrange[1] - xrange[0])
        hn, cn = np.histogram(data, bins=nbins, range=xrange)
        grid[gain].hist(data, range=xrange, bins=nbins)
        grid[gain].plot([offset-noise, offset-noise], [0, np.nanmax(hn)],
                        linewidth=1.5, color='red',
                        label='1 $\sigma$ deviation')
        grid[gain].plot([offset+noise, offset+noise],
                        [0, np.nanmax(hn)], linewidth=1.5, color='red')
        grid[gain].plot([offset, offset], [0, 0],
                        linewidth=1.5, color='y', label='median')
        grid[gain].plot([np.nanmedian(offset_g[qm][:, :, 12, gain]),
                         np.nanmedian(offset_g[qm][:, :, 12, gain])],
                        [0, np.nanmax(hn)], linewidth=1.5, color='green',
                        label='average over pixels')
        grid[gain].set_xlim(xrange)
        grid[gain].set_ylim(0, np.nanmax(hn)*1.1)
        grid[gain].set_xlabel("Offset value [ADU]")
        grid[gain].set_ylabel("# of occurance")
        if gain == 0:
            leg = grid[gain].legend(
                loc='upper center', ncol=3,
                bbox_to_anchor=(0.1, 0.25, 0.7, 1.0))
        grid[gain].text(820, np.nanmax(hn)*0.4,
                        "{} gain".format(gain_names[gain]), fontsize=20)
        a = plt.axes([.125, .1, 0.775, .8], frame_on=False)
        a.patch.set_alpha(0.05)
        a.set_xlim(xrange)
        plt.plot([offset, offset], [0, 1], linewidth=1.5, color='y')
        plt.xticks([])
        plt.yticks([])
    ypos = 0.9
    x1pos = (np.nanmedian(data_g[qm][:, 0]) +
             np.nanmedian(data_g[qm][:, 2]))/2.
    x2pos = (np.nanmedian(data_g[qm][:, 2]) +
             np.nanmedian(data_g[qm][:, 1]))/2.-10
    plt.annotate("", xy=(np.nanmedian(data_g[qm][:, 0]), ypos), xycoords='data',
                 xytext=(np.nanmedian(data_g[qm][:, 2]), ypos), textcoords='data',
                 arrowprops=dict(arrowstyle="<->", connectionstyle="arc3"))
    plt.annotate('{}'.format(np.nanmedian(data_g[qm][:, 0])-np.nanmedian(data_g[qm][:, 2])),
                 xy=(x1pos, ypos), xycoords='data', xytext=(5, 5), textcoords='offset points')
    plt.annotate("", xy=(np.nanmedian(data_g[qm][:, 2]), ypos), xycoords='data',
                 xytext=(np.nanmedian(data_g[qm][:, 1]), ypos), textcoords='data',
                 arrowprops=dict(arrowstyle="<->", connectionstyle="arc3"))
    plt.annotate('{}'.format(np.nanmedian(data_g[qm][:, 2])-np.nanmedian(data_g[qm][:, 1])),
                 xy=(x2pos, ypos), xycoords='data', xytext=(5, 5), textcoords='offset points')
 plt.show()
 ```
 %% Cell type:markdown id: tags:
 ## Normality test ##
 Distributions of raw pedestal values have been tested if they are normally distributed. A normality test have been performed for each pixel and each memory cell. Plots below show histogram of p-Values and a 2D distribution for the  memory cell 12.
 %% Cell type:code id: tags:
 ``` python
 # Loop over modules, constants
 if not test_for_normality:
    print('Normality test was not requested. Flag `test_for_normality` False')
 else:
    for i in modules:
        qm = module_index_to_qm(i)
        data = np.copy(ntest_g[qm][:,:,:,:])
        data[badpix_g[qm][:,:,:,:]>0] = 1.01
        hn,cn = np.histogram(data[:,:,:,0], bins=100)
        d = [{'x': np.arange(100)*0.01+0.01,
              'y': np.histogram(data[:,:,:,0], bins=100)[0],
              'drawstyle': 'steps-pre',
              'label' : 'High gain',
              },
             {'x': np.arange(100)*0.01+0.01,
              'y': np.histogram(data[:,:,:,1], bins=100)[0],
              'drawstyle': 'steps-pre',
              'label' : 'Medium gain',
              },
             {'x': np.arange(100)*0.01+0.01,
              'y': np.histogram(data[:,:,:,2], bins=100)[0],
              'drawstyle': 'steps-pre',
              'label' : 'Low gain',
              },
            ]
        fig = plt.figure(figsize=(15,15), tight_layout={'pad': 0.5, 'w_pad': 0.3})
        for gain in range(3):
            ax = fig.add_subplot(221+gain)
            heatmapPlot(data[:,:,12,gain], add_panels=False, cmap='viridis', figsize=(10,10),
                y_label='Rows', x_label='Columns',
                lut_label='p-Value',
                use_axis=ax,
                title='p-Value for cell 12, {} gain'.format(gain_names[gain]) )
        ax = fig.add_subplot(224)
        _ = simplePlot(d, #aspect=1.6,
                              x_label = "p-Value".format(gain),
                              y_label="# of occurance",
                              use_axis=ax,
                               y_log=False, legend='outside-top-ncol3-frame', legend_pad=0.05, legend_size='5%')
        ax.ticklabel_format(style='sci', axis='y', scilimits=(4,6))
 ```
 %% Cell type:raw id: tags:
 .. raw:: latex
    \newpage
 %% Cell type:markdown id: tags:
 ## Single-Cell Overviews ##
 Single cell overviews allow to identify potential effects on all memory cells, e.g. on a sensor level. Additionally, they should serve as a first sanity check on expected behaviour, e.g. if structuring on the ASIC level is visible in the offsets, but otherwise no immediate artifacts are visible.
 %% Cell type:code id: tags:
 ``` python
 cell = 12
 for gain in range(3):
    display(
        Markdown('### Cell-12 overview - {} gain'.format(gain_names[gain])))
    fig = plt.figure(figsize=(18, 22) , tight_layout={'pad': 0.1, 'w_pad': 0.1})
    for qm in res:
        for iconst, const in enumerate(['Offset', 'Noise', 'BadPixelsDark']):
            ax = fig.add_subplot(321+iconst)
            data = res[qm][const][:, :, 12, gain]
            vmax = 1.5 * np.nanmedian(res[qm][const][:, :, 12, gain])
            title = const
            label = '{} value [ADU]'.format(const)
            title = '{} value'.format(const)
            if const == 'BadPixelsDark':
                vmax = 4
                bpix_code = data.astype(np.float32)
                bpix_code[bpix_code == 0] = np.nan
                title = 'Bad pixel code'
                label = title
                cb_labels = ['1 {}'.format(BadPixels.NOISE_OUT_OF_THRESHOLD.name),
                             '2 {}'.format(BadPixels.OFFSET_NOISE_EVAL_ERROR.name),
                             '3 {}'.format(BadPixels.OFFSET_OUT_OF_THRESHOLD.name),
                             '4 {}'.format('MIXED')]
                heatmapPlot(bpix_code, add_panels=False, cmap='viridis',
                            y_label='Rows', x_label='Columns',
                            lut_label='', vmax=vmax,
                            use_axis=ax, cb_ticklabels=cb_labels, cb_ticks = np.arange(4)+1,
                            title='{}'.format(title))
                del bpix_code
            else:
                heatmapPlot(data, add_panels=False, cmap='viridis',
                            y_label='Rows', x_label='Columns',
                            lut_label=label, vmax=vmax,
                            use_axis=ax,
                            title='{}'.format(title))
    for qm in res:
        for iconst, const in enumerate(['Offset', 'Noise']):
            data = res[qm][const]
            dataBP = np.copy(data)
            dataBP[res[qm]['BadPixelsDark'] > 0] = -1
            x_ranges = [[0, 1500], [0, 40]]
            hn, cn = np.histogram(
                data[:, :, :, gain], bins=100, range=x_ranges[iconst])
            hnBP, cnBP = np.histogram(dataBP[:, :, :, gain], bins=cn)
            d = [{'x': cn[:-1],
                  'y': hn,
                  'drawstyle': 'steps-pre',
                  'label': 'All data',
                  },
                 {'x': cnBP[:-1],
                  'y': hnBP,
                  'drawstyle': 'steps-pre',
                  'label': 'Bad pixels masked',
                  },
                 ]
            ax = fig.add_subplot(325+iconst)
            _ = simplePlot(d, figsize=(5, 7), aspect=1,
                                x_label="{} value [ADU]".format(const),
                                y_label="# of occurance",
                                title='', legend_pad=0.1, legend_size='10%',
                                use_axis=ax,
                                y_log=True, legend='outside-top-2col-frame')
    plt.show()
 ```
 %% Cell type:raw id: tags:
 .. raw:: latex
    \newpage
 %% Cell type:markdown id: tags:
 %% Cell type:code id: tags:
 ``` python
 cols = {BadPixels.NOISE_OUT_OF_THRESHOLD.value: (BadPixels.NOISE_OUT_OF_THRESHOLD.name, '#FF000080'),
        BadPixels.OFFSET_NOISE_EVAL_ERROR.value: (BadPixels.OFFSET_NOISE_EVAL_ERROR.name, '#0000FF80'),
        BadPixels.OFFSET_OUT_OF_THRESHOLD.value: (BadPixels.OFFSET_OUT_OF_THRESHOLD.name, '#00FF0080'),
        BadPixels.OFFSET_OUT_OF_THRESHOLD.value | BadPixels.NOISE_OUT_OF_THRESHOLD.value: ('MIXED', '#DD00DD80')}
 if high_res_badpix_3d:
    display(Markdown("""
    ## Global Bad Pixel Behaviour ##
    The following plots shows the results of a bad pixel evaluation for all evaluated memory cells.
    Cells are stacked in the Z-dimension, while pixels values in x/y are re-binned with a factor of 2.
    This excludes single bad pixels present only in disconnected pixels.
    Hence, any bad pixels spanning at least 4 pixels in the x/y-plane, or across at least two memory cells are indicated.
    Colors encode the bad pixel type, or mixed type.
        """))
    # Switch rebin to 1 for full resolution and
    # no interpolation for badpixel values.
    rebin = 2
    for gain in range(3):
        display(Markdown('### Bad pixel behaviour - {} gain ###'.format(gain_names[gain])))
        for mod, data in badpix_g.items():
            plot_badpix_3d(data[...,gain], cols, title='', rebin_fac=rebin)
            ax = plt.gca()
            leg = ax.get_legend()
            leg.set(alpha=0.5)
        plt.show()
 ```
 %% Cell type:raw id: tags:
 .. raw:: latex
    \newpage
 %% Cell type:markdown id: tags:
 ## Summary across tiles ##
 Plots give an overview of calibration constants averaged across tiles. A bad pixel mask is applied. Constants are compared with pre-existing constants retrieved from the calibration database. Differences $\Delta$ between the old and new constants is shown.
 %% Cell type:code id: tags:
 ``` python
 time_summary = []
 time_summary.append(f"The following pre-existing constants are used for comparison:")
 for qm, qm_data in old_mdata.items():
    time_summary.append(f"- Module {qm}")
    for const, const_data in qm_data.items():
        time_summary.append(f"    - {const} created at {const_data['timestamp']}")
 display(Markdown("\n".join(time_summary)))
 ```
 %% Cell type:code id: tags:
 ``` python
 # Loop over modules, constants
 for qm in res:
    for gain in range(3):
        display(Markdown('### Summary across tiles - {} gain'.format(gain_names[gain])))
        for const in res[qm]:
            data = np.copy(res[qm][const][:, :, :, gain])
            label = 'Fraction of bad pixels'
            if const != 'BadPixelsDark':
                data[badpix_g[qm][:, :, :, gain] > 0] = np.nan
                label = '{} value [ADU]'.format(const)
            else:
                data[data>0] = 1.0
            data = data.reshape(
                int(data.shape[0] / 32),
                32,
                int(data.shape[1] / 128),
                128,
                data.shape[2])
            data = np.nanmean(data, axis=(1, 3)).swapaxes(
                0, 2).reshape(512, 16)
            fig = plt.figure(figsize=(15, 6))
            ax = fig.add_subplot(121)
            _ = heatmapPlot(data[:510, :], add_panels=True,
                            y_label='Momery Cell ID', x_label='Tile ID',
                            lut_label=label, use_axis=ax,
                            panel_y_label=label, panel_x_label=label,
                            cmap='viridis',  # cb_loc='right',cb_aspect=15,
                            x_ticklabels=np.arange(16)+1,
                            x_ticks=np.arange(16)+0.5)
            if old_const[qm][const] is not None:
                ax = fig.add_subplot(122)
                dataold = np.copy(old_const[qm][const][:, :, :, gain])
                label = '$\Delta$ {}'.format(label)
                if const != 'BadPixelsDark':
                    if old_const[qm]['BadPixelsDark'] is not None:
                        dataold[old_const[qm]['BadPixelsDark'][:, :, :, gain] > 0] = np.nan
                    else:
                        dataold[:] = np.nan
                else:
                    dataold[dataold>0]=1.0
                dataold = dataold.reshape(
                    int(dataold.shape[0] / 32),
                    32,
                    int(dataold.shape[1] / 128),
                    128,
                    dataold.shape[2])
                dataold = np.nanmean(dataold, axis=(
                    1, 3)).swapaxes(0, 2).reshape(512, 16)
                dataold = dataold - data
                _ = heatmapPlot(dataold[:510, :], add_panels=True,
                                y_label='Momery Cell ID', x_label='Tile ID',
                                lut_label=label, use_axis=ax,
                                panel_y_label=label, panel_x_label=label,
                                cmap='viridis',  # cb_loc='right',cb_aspect=15,
                                x_ticklabels=np.arange(16)+1,
                                x_ticks=np.arange(16)+0.5)
        plt.show()
 ```
 %% Cell type:raw id: tags:
 .. raw:: latex
    \newpage
 %% Cell type:markdown id: tags:
 ## Variation of offset and noise across Tiles and ASICs ##
 The following plots show a standard deviation $\sigma$ of the calibration constant. The plot of standard deviations across tiles show pixels of one tile ($128 \times 32$). Value for each pixel shows a standard deviation across 16 tiles. The standard deviation across ASICs are shown overall tiles. The plot shows pixels of one ASIC ($16 \times 32$), where the value shows a standard deviation across all ACIS of the module.
 %% Cell type:code id: tags:
 ``` python
 # Loop over modules, constants
 for qm in res:
    for gain in range(3):
        display(Markdown('### Variation of offset and noise across ASICs - {} gain'.format(gain_names[gain])))
        fig = plt.figure(figsize=(15, 6))
        for iconst, const in enumerate(['Offset', 'Noise']):
            data = np.copy(res[qm][const][:, :, :, gain])
            data[badpix_g[qm][:, :, :, gain] > 0] = np.nan
            label = '$\sigma$ {} [ADU]'.format(const)
            dataA = np.nanmean(data, axis=2)  # average over cells
            dataA = dataA.reshape(8, 32, 16, 16)
            dataA = np.nanstd(dataA, axis=(0, 2))  # average across ASICs
            ax = fig.add_subplot(121+iconst)
            _ = heatmapPlot(dataA, add_panels=True,
                            y_label='rows', x_label='columns',
                            lut_label=label, use_axis=ax,
                            panel_y_label=label, panel_x_label=label,
                            cmap='viridis'
                            )
        plt.show()
 ```
 %% Cell type:code id: tags:
 ``` python
 # Loop over modules, constants
 for qm in res:
    for gain in range(3):
        display(Markdown('### Variation of offset and noise across tiles - {} gain'.format(gain_names[gain])))
        fig = plt.figure(figsize=(15, 6))
        for iconst, const in enumerate(['Offset', 'Noise']):
            data = np.copy(res[qm][const][:, :, :, gain])
            data[badpix_g[qm][:, :, :, gain] > 0] = np.nan
            label = '$\sigma$ {} [ADU]'.format(const)
            dataT = data.reshape(
                int(data.shape[0] / 32),
                32,
                int(data.shape[1] / 128),
                128,
                data.shape[2])
            dataT = np.nanstd(dataT, axis=(0, 2))
            dataT = np.nanmean(dataT, axis=2)
            ax = fig.add_subplot(121+iconst)
            _ = heatmapPlot(dataT, add_panels=True,
                            y_label='rows', x_label='columns',
                            lut_label=label, use_axis=ax,
                            panel_y_label=label, panel_x_label=label,
                            cmap='viridis')
        plt.show()
 ```
 %% Cell type:raw id: tags:
 .. raw:: latex
    \newpage
 %% Cell type:markdown id: tags:
 ## Aggregate values and per cell behaviour ##
 The following tables and plots give an overview of statistical aggregates for each constant, as well as per-cell behavior, averaged across pixels.
 %% Cell type:code id: tags:
 ``` python
 # Loop over modules, constants
 for qm in res:
    for gain in range(3):
        display(Markdown('### Mean over pixels - {} gain'.format(gain_names[gain])))
        fig = plt.figure(figsize=(9,11))
        for iconst, const in enumerate(res[qm]):
            ax = fig.add_subplot(311+iconst)
            data = res[qm][const][:,:,:510,gain]
            if const == 'BadPixelsDark':
                data[data>0] = 1.0
            dataBP = np.copy(data)
            dataBP[badpix_g[qm][:,:,:510,gain]>0] = -10
            data = np.nanmean(data, axis=(0,1))
            dataBP = np.nanmean(dataBP, axis=(0,1))
            d = [{'y': data,
                  'x': np.arange(data.shape[0]),
                  'drawstyle': 'steps-mid',
                  'label' : 'All data'
                 }
                ]
            if const != 'BadPixelsDark':
                d.append({'y': dataBP,
                  'x': np.arange(data.shape[0]),
                  'drawstyle': 'steps-mid',
                  'label' : 'good pixels only'
                 })
                y_title = "{} value [ADU]".format(const)
                title = "{} value, {} gain".format(const, gain_names[gain])
            else:
                y_title = "Fraction of Bad Pixels"
                title = "Fraction of Bad Pixels, {} gain".format(gain_names[gain])
            data_min = np.min([data, dataBP])if const != 'BadPixelsDark' else np.min([data])
            data_max = np.max([data[20:], dataBP[20:]])
            data_dif = data_max - data_min
            local_max = np.max([data[200:300], dataBP[200:300]])
            frac = 0.35
            new_max = (local_max - data_min*(1-frac))/frac
            new_max = np.max([data_max, new_max])
            _ = simplePlot(d, figsize=(10,10), aspect=2, xrange=(-12, 510),
                              x_label = 'Memory Cell ID',
                              y_label=y_title, use_axis=ax,
                              title=title,
                              title_position=[0.5, 1.15],
                              inset='xy-coord-right', inset_x_range=(0,20), inset_indicated=True,
                              inset_labeled=True, inset_coord=[0.2,0.5,0.6,0.95],
                                inset_lw = 1.0, y_range = [data_min-data_dif*0.05, new_max+data_dif*0.05],
                              y_log=False, legend='outside-top-ncol2-frame', legend_size='18%',
                                 legend_pad=0.00)
            plt.tight_layout(pad=1.08, h_pad=0.35)
        plt.show()
 ```
 %% Cell type:raw id: tags:
 .. raw:: latex
    \newpage
 %% Cell type:markdown id: tags:
 ## Summary tables ##
 The following tables show summary information for the evaluated module. Values for currently evaluated constants are compared with values for pre-existing constants retrieved from the calibration database.
 %% Cell type:code id: tags:
 ``` python
 table = []
 bits = [
    BadPixels.NOISE_OUT_OF_THRESHOLD,
    BadPixels.OFFSET_OUT_OF_THRESHOLD,
    BadPixels.OFFSET_NOISE_EVAL_ERROR
 ]
 for qm in res:
    for gain in range(3):
        l_data = []
        l_data_old = []
        data = np.copy(res[qm]['BadPixelsDark'][:,:,:,gain])
        l_data.append(len(data[data>0].flatten()))
        for bit in bits:
            l_data.append(np.count_nonzero(badpix_g[qm][:,:,:,gain] & bit.value))
        if old_const[qm]['BadPixelsDark'] is not None:
            old_const[qm]['BadPixelsDark'] = old_const[qm]['BadPixelsDark'].astype(np.uint32)
            dataold = np.copy(old_const[qm]['BadPixelsDark'][:, :, :, gain])
            l_data_old.append(len(dataold[dataold>0].flatten()))
            for bit in bits:
                l_data_old.append(np.count_nonzero(old_const[qm]['BadPixelsDark'][:, :, :, gain] & bit.value))
        l_data_name = ['All bad pixels', 'NOISE_OUT_OF_THRESHOLD',
                       'OFFSET_OUT_OF_THRESHOLD', 'OFFSET_NOISE_EVAL_ERROR']
        l_threshold = ['', f'{thresholds_noise_sigma}', f'{thresholds_offset_sigma}',
                       f'{thresholds_offset_hard}/{thresholds_noise_hard}']
        for i in range(len(l_data)):
            line = [f'{l_data_name[i]}, gain {gain_names[gain]}', l_threshold[i], l_data[i]]
            if old_const[qm]['BadPixelsDark'] is not None:
                line += [l_data_old[i]]
            else:
                line += ['-']
            table.append(line)
        table.append(['', '', '', ''])
 display(Markdown('''
 ### Number of bad pixels ###
 One pixel can be bad for different reasons, therefore, the sum of all types of bad pixels can be more than the number of all bad pixels.
 '''))
 if len(table)>0:
    md = display(Latex(tabulate.tabulate(table, tablefmt='latex',
                                     headers=["Pixel type", "Threshold",
                                              "New constant", "Old constant"])))
 ```
 %% Cell type:code id: tags:
 ``` python
 header = ['Parameter',
          "New constant", "Old constant ",
          "New constant", "Old constant ",
          "New constant", "Old constant "]
 for const in ['Offset', 'Noise']:
    table = [['','High gain', 'High gain', 'Medium gain', 'Medium gain', 'Low gain', 'Low gain']]
    for qm in res:
        data = np.copy(res[qm][const])
        data[res[qm]['BadPixelsDark']>0] = np.nan
        if old_const[qm][const] is not None and old_const[qm]['BadPixelsDark'] is not None :
            dataold = np.copy(old_const[qm][const])
            dataold[old_const[qm]['BadPixelsDark']>0] = np.nan
        f_list = [np.nanmedian, np.nanmean, np.nanstd, np.nanmin, np.nanmax]
        n_list = ['Median', 'Mean', 'Std', 'Min', 'Max']
        for i, f in enumerate(f_list):
            line = [n_list[i]]
            for gain in range(3):
                line.append('{:6.1f}'.format(f(data[...,gain])))
                if old_const[qm][const] is not None and old_const[qm]['BadPixelsDark'] is not None:
                    line.append('{:6.1f}'.format(f(dataold[...,gain])))
                else:
                    line.append('-')
            table.append(line)
    display(Markdown('### {} [ADU], good pixels only ###'.format(const)))
    md = display(Latex(tabulate.tabulate(table, tablefmt='latex', headers=header)))
 ```