exclude notebooks from format

Author: MridulS

packages/essimaging/docs/odin/odin-data-reduction.ipynb

@@ -201,14 +201,8 @@
    "outputs": [],
    "source": [
     "wf[MaskingRules] = {\n",
-    "    'x_pixel_offset': lambda x: (\n",
-    "        (x < sc.scalar(-5.8e-3, unit='m').to(unit=x.unit))\n",
-    "        | (x > sc.scalar(5.8e-3, unit='m').to(unit=x.unit))\n",
-    "    ),\n",
-    "    'y_pixel_offset': lambda y: (\n",
-    "        (y < sc.scalar(-5.8e-3, unit='m').to(unit=y.unit))\n",
-    "        | (y > sc.scalar(5.8e-3, unit='m').to(unit=y.unit))\n",
-    "    ),\n",
+    "    'x_pixel_offset': lambda x: (x < sc.scalar(-5.8e-3, unit='m').to(unit=x.unit)) | (x > sc.scalar(5.8e-3, unit='m').to(unit=x.unit)),\n",
+    "    'y_pixel_offset': lambda y: (y < sc.scalar(-5.8e-3, unit='m').to(unit=y.unit)) | (y > sc.scalar(5.8e-3, unit='m').to(unit=y.unit))\n",
     "}"
    ]
   },
@@ -244,9 +238,10 @@
    "source": [
     "wbins = sc.linspace('wavelength', 1.1, 9.4, 301, unit='angstrom')\n",
     "\n",
-    "normalized = wf.compute(CorrectedDetector[SampleRun]).bins.concat().hist(\n",
-    "    wavelength=wbins\n",
-    ") / wf.compute(CorrectedDetector[OpenBeamRun]).bins.concat().hist(wavelength=wbins)\n",
+    "normalized = (\n",
+    "        wf.compute(CorrectedDetector[SampleRun]).bins.concat().hist(wavelength=wbins) /\n",
+    "        wf.compute(CorrectedDetector[OpenBeamRun]).bins.concat().hist(wavelength=wbins)\n",
+    ")\n",
     "\n",
     "normalized.plot()"
    ]

packages/essimaging/docs/odin/odin-make-tof-lookup-table.ipynb

@@ -42,9 +42,7 @@
     "wf = time_of_flight.TofLookupTableWorkflow()\n",
     "wf[time_of_flight.DiskChoppers[AnyRun]] = disk_choppers\n",
     "wf[time_of_flight.SourcePosition] = source_position\n",
-    "wf[time_of_flight.NumberOfSimulatedNeutrons] = (\n",
-    "    200_000  # Increase this number for more reliable results\n",
-    ")\n",
+    "wf[time_of_flight.NumberOfSimulatedNeutrons] = 200_000  # Increase this number for more reliable results\n",
     "wf[time_of_flight.SimulationSeed] = 1234\n",
     "wf[time_of_flight.PulseStride] = 2\n",
     "wf[time_of_flight.LtotalRange] = sc.scalar(5.0, unit=\"m\"), sc.scalar(65.0, unit=\"m\")\n",

packages/essimaging/docs/tbl/orca-image-normalization.ipynb

@@ -133,20 +133,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "results = wf.compute(\n",
-    "    [\n",
-    "        RawDetector[SampleRun],\n",
-    "        CorrectedDetector[SampleRun],\n",
-    "        BackgroundSubtractedDetector[SampleRun],\n",
-    "    ]\n",
-    ")\n",
+    "results = wf.compute([\n",
+    "    RawDetector[SampleRun],\n",
+    "    CorrectedDetector[SampleRun],\n",
+    "    BackgroundSubtractedDetector[SampleRun]\n",
+    "])\n",
     "\n",
     "fig = pp.tiled(2, 2, hspace=0.3, wspace=0.3)\n",
     "fig[0, 0] = results[RawDetector[SampleRun]]['time', 0].plot(title='Raw data')\n",
     "fig[0, 1] = results[CorrectedDetector[SampleRun]]['time', 0].plot(title='Masks applied')\n",
-    "fig[1, 0] = results[BackgroundSubtractedDetector[SampleRun]]['time', 0].plot(\n",
-    "    title='Background subtracted'\n",
-    ")\n",
+    "fig[1, 0] = results[BackgroundSubtractedDetector[SampleRun]]['time', 0].plot(title='Background subtracted')\n",
     "fig[1, 1] = image['time', 0].plot(title='Final image')\n",
     "fig"
    ]

packages/essimaging/docs/tbl/tbl-data-reduction.ipynb

@@ -81,9 +81,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ngem['dim_0', 0].hist().plot(aspect='equal', title='First detection') + ngem[\n",
-    "    'dim_0', 1\n",
-    "].hist().plot(aspect='equal', title='Last detection')"
+    "ngem['dim_0', 0].hist().plot(aspect='equal', title='First detection') + ngem['dim_0', 1].hist().plot(aspect='equal', title='Last detection')"
    ]
   },
   {
@@ -141,10 +139,7 @@
     "ngem_tofs = wf.compute(TofDetector[SampleRun])\n",
     "ngem_wavs = wf.compute(WavelengthDetector[SampleRun])\n",
     "\n",
-    "(\n",
-    "    ngem_tofs.bins.concat().hist(tof=100).plot()\n",
-    "    + ngem_wavs.bins.concat().hist(wavelength=100).plot()\n",
-    ")"
+    "ngem_tofs.bins.concat().hist(tof=100).plot() + ngem_wavs.bins.concat().hist(wavelength=100).plot()"
    ]
   },
   {
@@ -179,9 +174,7 @@
    "source": [
     "lims = {'vmin': 0, 'vmax': 4500}\n",
     "\n",
-    "he3['he3_detector_bank0'].hist().plot(title='he3_bank0', **lims) + he3[\n",
-    "    'he3_detector_bank1'\n",
-    "].hist().plot(title='he3_bank1', **lims)"
+    "he3['he3_detector_bank0'].hist().plot(title='he3_bank0', **lims) + he3['he3_detector_bank1'].hist().plot(title='he3_bank1', **lims)"
    ]
   },
   {
@@ -205,9 +198,7 @@
     "for bank in ('he3_detector_bank0', 'he3_detector_bank1'):\n",
     "    he3_wf[NeXusDetectorName] = bank\n",
     "    he3_tofs[bank] = he3_wf.compute(TofDetector[SampleRun]).bins.concat().hist(tof=100)\n",
-    "    he3_wavs[bank] = (\n",
-    "        he3_wf.compute(WavelengthDetector[SampleRun]).bins.concat().hist(wavelength=100)\n",
-    "    )\n",
+    "    he3_wavs[bank] = he3_wf.compute(WavelengthDetector[SampleRun]).bins.concat().hist(wavelength=100)\n",
     "\n",
     "pp.plot(he3_tofs) + pp.plot(he3_wavs)"
    ]

packages/essimaging/docs/tbl/tbl-make-tof-lookup-table.ipynb

@@ -40,9 +40,7 @@
     "wf = time_of_flight.TofLookupTableWorkflow()\n",
     "wf[time_of_flight.DiskChoppers[AnyRun]] = {}\n",
     "wf[time_of_flight.SourcePosition] = source_position\n",
-    "wf[time_of_flight.NumberOfSimulatedNeutrons] = (\n",
-    "    200_000  # Increase this number for more reliable results\n",
-    ")\n",
+    "wf[time_of_flight.NumberOfSimulatedNeutrons] = 200_000  # Increase this number for more reliable results\n",
     "wf[time_of_flight.SimulationSeed] = 1234\n",
     "wf[time_of_flight.PulseStride] = 1\n",
     "wf[time_of_flight.LtotalRange] = sc.scalar(5.0, unit=\"m\"), sc.scalar(35.0, unit=\"m\")\n",
@@ -76,9 +74,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "table.plot(title=\"Predicted time-of-flight\") + sc.stddevs(table.array).plot(\n",
-    "    title=\"Standard deviation\"\n",
-    ")"
+    "table.plot(title=\"Predicted time-of-flight\") + sc.stddevs(table.array).plot(title=\"Standard deviation\")"
    ]
   },
   {

packages/essimaging/tools/make-tbl-images-from-ymir.ipynb

@@ -53,8 +53,8 @@
     "path = 'entry/instrument/histogram_mode_detectors/orca/data/'\n",
     "\n",
     "with h5.File(ymir_lego_images_path()) as f:\n",
-    "    lego_data = f[path + 'value'][()]\n",
-    "    lego_time = f[path + 'time'][()]"
+    "    lego_data = f[path+'value'][()]\n",
+    "    lego_time = f[path+'time'][()]"
    ]
   },
   {
@@ -93,29 +93,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "exposure_time = int(5e8)  # 0.5s (made up)\n",
-    "period = 1e9 / 14\n",
+    "exposure_time = int(5e8) # 0.5s (made up)\n",
+    "period = 1e9/14\n",
     "\n",
-    "dark_charge_time = np.arange(\n",
-    "    float(dark_time[0]), float(dark_time[-1] + exposure_time), period\n",
-    ").astype('uint64')\n",
-    "dark_charge_value = np.random.uniform(0.99, 1.01, size=len(dark_charge_time)).astype(\n",
-    "    'float64'\n",
-    ")\n",
+    "dark_charge_time = np.arange(float(dark_time[0]), float(dark_time[-1] + exposure_time), period).astype('uint64')\n",
+    "dark_charge_value = np.random.uniform(0.99, 1.01, size=len(dark_charge_time)).astype('float64')\n",
     "\n",
-    "ob_charge_time = np.arange(\n",
-    "    float(ob_time[0]), float(ob_time[-1] + exposure_time), period\n",
-    ").astype('uint64')\n",
-    "ob_charge_value = np.random.uniform(0.99, 1.01, size=len(ob_charge_time)).astype(\n",
-    "    'float64'\n",
-    ")\n",
+    "ob_charge_time = np.arange(float(ob_time[0]), float(ob_time[-1] + exposure_time), period).astype('uint64')\n",
+    "ob_charge_value = np.random.uniform(0.99, 1.01, size=len(ob_charge_time)).astype('float64')\n",
     "\n",
-    "sample_charge_time = np.arange(\n",
-    "    float(sample_time[0]), float(sample_time[-1] + exposure_time), period\n",
-    ").astype('uint64')\n",
-    "sample_charge_value = np.random.uniform(\n",
-    "    0.99, 1.01, size=len(sample_charge_time)\n",
-    ").astype('float64')"
+    "sample_charge_time = np.arange(float(sample_time[0]), float(sample_time[-1] + exposure_time), period).astype('uint64')\n",
+    "sample_charge_value = np.random.uniform(0.99, 1.01, size=len(sample_charge_time)).astype('float64')"
    ]
   },
   {
@@ -157,32 +145,31 @@
     "    dset = entry.create_dataset(name, data=values)\n",
     "    dset.attrs.update(attrs)\n",
     "\n",
-    "\n",
     "def make_new_file(template_nexus_file, outfile, data, proton_charge):\n",
     "    import shutil\n",
-    "\n",
     "    shutil.copyfile(template_nexus_file, outfile)\n",
     "\n",
     "    # detector_path = 'entry/instrument/histogram_mode_detectors/orca'  # ODIN\n",
     "    detector_path = 'entry/instrument/orca_detector/'  # TBL\n",
     "    proton_charge_path = 'entry/neutron_prod_info/pulse_charge'\n",
     "\n",
     "    with h5.File(outfile, \"r+\") as f:\n",
-    "        detector_data = f[detector_path + \"data\"]\n",
+    "\n",
+    "        detector_data = f[detector_path+\"data\"]\n",
     "        replace_dataset(detector_data, name=\"value\", values=data[\"value\"])\n",
     "        replace_dataset(detector_data, name=\"time\", values=data[\"time\"])\n",
     "\n",
     "        detector = f[detector_path]\n",
     "        detector.copy('intensifier', detector, 'camera_exposure')\n",
-    "        exp = f[detector_path + \"camera_exposure\"]\n",
+    "        exp = f[detector_path+\"camera_exposure\"]\n",
     "        replace_dataset(exp, name=\"value\", values=np.array([exposure_time]))\n",
     "        replace_dataset(exp, name=\"average_value\", values=exposure_time)\n",
     "        replace_dataset(exp, name=\"minimum_value\", values=exposure_time)\n",
     "        replace_dataset(exp, name=\"maximum_value\", values=exposure_time)\n",
-    "        f[detector_path + \"camera_exposure/value\"].attrs['units'] = 'ns'\n",
-    "        f[detector_path + \"camera_exposure/average_value\"].attrs['units'] = 'ns'\n",
-    "        f[detector_path + \"camera_exposure/minimum_value\"].attrs['units'] = 'ns'\n",
-    "        f[detector_path + \"camera_exposure/maximum_value\"].attrs['units'] = 'ns'\n",
+    "        f[detector_path+\"camera_exposure/value\"].attrs['units'] = 'ns'\n",
+    "        f[detector_path+\"camera_exposure/average_value\"].attrs['units'] = 'ns'\n",
+    "        f[detector_path+\"camera_exposure/minimum_value\"].attrs['units'] = 'ns'\n",
+    "        f[detector_path+\"camera_exposure/maximum_value\"].attrs['units'] = 'ns'\n",
     "\n",
     "        xoff = detector.create_dataset('x_pixel_offset', data=x_pixel_offset.values)\n",
     "        xoff.attrs['units'] = str(x_pixel_offset.unit)\n",
@@ -195,15 +182,9 @@
     "        pcharge = f[proton_charge_path]\n",
     "        replace_dataset(pcharge, name=\"value\", values=proton_charge[\"value\"])\n",
     "        replace_dataset(pcharge, name=\"time\", values=proton_charge[\"time\"])\n",
-    "        replace_dataset(\n",
-    "            pcharge, name=\"average_value\", values=proton_charge[\"value\"].mean()\n",
-    "        )\n",
-    "        replace_dataset(\n",
-    "            pcharge, name=\"minimum_value\", values=proton_charge[\"value\"].min()\n",
-    "        )\n",
-    "        replace_dataset(\n",
-    "            pcharge, name=\"maximum_value\", values=proton_charge[\"value\"].max()\n",
-    "        )"
+    "        replace_dataset(pcharge, name=\"average_value\", values=proton_charge[\"value\"].mean())\n",
+    "        replace_dataset(pcharge, name=\"minimum_value\", values=proton_charge[\"value\"].min())\n",
+    "        replace_dataset(pcharge, name=\"maximum_value\", values=proton_charge[\"value\"].max())"
    ]
   },
   {
@@ -219,7 +200,7 @@
     "    template_nexus_file=template_nexus_file,\n",
     "    outfile=\"ymir_lego_dark_run.hdf\",\n",
     "    data={\"value\": dark_data, \"time\": dark_time},\n",
-    "    proton_charge={\"value\": dark_charge_value, \"time\": dark_charge_time},\n",
+    "    proton_charge={\"value\": dark_charge_value, \"time\": dark_charge_time}\n",
     ")"
    ]
   },
@@ -234,7 +215,7 @@
     "    template_nexus_file=template_nexus_file,\n",
     "    outfile=\"ymir_lego_openbeam_run.hdf\",\n",
     "    data={\"value\": ob_data, \"time\": ob_time},\n",
-    "    proton_charge={\"value\": ob_charge_value, \"time\": ob_charge_time},\n",
+    "    proton_charge={\"value\": ob_charge_value, \"time\": ob_charge_time}\n",
     ")"
    ]
   },
@@ -249,7 +230,7 @@
     "    template_nexus_file=template_nexus_file,\n",
     "    outfile=\"ymir_lego_sample_run.hdf\",\n",
     "    data={\"value\": sample_data, \"time\": sample_time},\n",
-    "    proton_charge={\"value\": sample_charge_value, \"time\": sample_charge_time},\n",
+    "    proton_charge={\"value\": sample_charge_value, \"time\": sample_charge_time}\n",
     ")"
    ]
   }

packages/essimaging/tools/mcstas_to_nexus.ipynb

@@ -176,6 +176,7 @@
     "    shutil.copyfile(template_nexus_file, outfile)\n",
     "\n",
     "    with h5.File(outfile, \"r+\") as f:\n",
+    "\n",
     "        # Detector data\n",
     "        if detector_entry_path is not None:\n",
     "            # remove translation offset\n",

packages/essnmx/docs/developer/test-dataset.ipynb

@@ -52,7 +52,6 @@
     "    zip(*[global_rng.integers(*HKL_RANGE, size=100) for _ in range(3)], strict=False)\n",
     ")\n",
     "\n",
-    "\n",
     "def create_mtz_data_frame(random_seed: int) -> pd.DataFrame:\n",
     "    rng = np.random.default_rng(random_seed)\n",
     "    intensities = np.sort(rng.normal(50, 20, size=10_000))[::-1] + (\n",

packages/essnmx/docs/user-guide/mcstas_workflow.ipynb

@@ -103,9 +103,7 @@
     "from cyclebane.graph import NodeName, IndexValues\n",
     "\n",
     "# Data from all selected detectors binned by panel, pixel and timeslice\n",
-    "targets = [\n",
-    "    NodeName(NMXReducedDataGroup, IndexValues((\"panel\",), (i,))) for i in range(3)\n",
-    "]\n",
+    "targets = [NodeName(NMXReducedDataGroup, IndexValues((\"panel\",), (i,))) for i in range(3)]\n",
     "dg = merge_panels(*wf.compute(targets).values())\n",
     "dg"
    ]

packages/essnmx/docs/user-guide/scaling_workflow.ipynb

@@ -73,15 +73,9 @@
     "import sciline as sl\n",
     "import scipp as sc\n",
     "\n",
-    "from ess.nmx.mtz_io import (\n",
-    "    providers as mtz_io_providers,\n",
-    "    default_parameters as mtz_io_params,\n",
-    ")\n",
+    "from ess.nmx.mtz_io import providers as mtz_io_providers, default_parameters as mtz_io_params\n",
     "from ess.nmx.mtz_io import SpaceGroupDesc\n",
-    "from ess.nmx.scaling import (\n",
-    "    providers as scaling_providers,\n",
-    "    default_parameters as scaling_params,\n",
-    ")\n",
+    "from ess.nmx.scaling import providers as scaling_providers, default_parameters as scaling_params\n",
     "from ess.nmx.scaling import (\n",
     "    WavelengthBins,\n",
     "    FilteredEstimatedScaledIntensities,\n",