Files for working with reduced data from 2020-12 experiments at XPD

README.md

Collection of notebooks an files for playing with gpcam data

1. explore_pgcam_data.ipynb -> demo of how to pulll reduced data out of the msgpack databroker
2. extract.py -> code run on xpd against raw data broker to sort out what raw runs we are interested in
3. reprocess.py -> code run to re-process the raw data and produce the reduced outputs

explore_gpcam_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "terminal-flight",
   "metadata": {},
   "outputs": [],
   "source": [
    "# switch back to this to get interactive plots\n",
    "#%matplotlib widget\n",
    "# but inline saves images in the output!\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "champion-bikini",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lifted from https://github.com/NSLS-II-XPD/ae-gpcam/blob/e0196e1cfa1dfdd611782cfb52dc78b4ab0a9f49/ae_gpcam/bluesky_config/scripts/roi_reduction_consumer.py#L256-L294\n",
    "import numpy as np\n",
    "\n",
    "peak_location = (2.925, 2.974)\n",
    "\n",
    "def compute_peak_area(Q, I, q_start, q_stop):\n",
    "    \"\"\"\n",
    "    Integrated area under a peak with estimated background removed.\n",
    "    Estimates the background by averaging the 3 values on either side\n",
    "    of the peak and subtracting that as a constant from I before\n",
    "    integrating.\n",
    "    Parameters\n",
    "    ----------\n",
    "    Q, I : array\n",
    "        The q-values and binned intensity.  Assumed to be same length.\n",
    "    q_start, q_stop : float\n",
    "        The region of q to integrate.  Must be in same units as the Q.\n",
    "    Returns\n",
    "    -------\n",
    "    peak_area : float\n",
    "    \"\"\"\n",
    "\n",
    "    # figure out the index of the start and stop of the q\n",
    "    # region of interest\n",
    "    start, stop = np.searchsorted(Q, (q_start, q_stop))\n",
    "    # add one to stop because we want the index after the end\n",
    "    # value not the one before\n",
    "    stop += 1\n",
    "    # pull out the region of interest from I.\n",
    "    data_section = I[start:stop]\n",
    "    # pull out one more q value than I because we want the bin widths.\n",
    "    q_section = Q[start : stop + 1]\n",
    "    # compute width of each of the Q bins.\n",
    "    dQ = np.diff(q_section)\n",
    "    # estimate the background level by averaging the 3 and and 3 I(q) outside of\n",
    "    # our ROI in either direction.\n",
    "    background = (np.mean(I[start - 3 : start]) + np.mean(I[stop : stop + 3])) / 2\n",
    "    # do the integration!\n",
    "    return np.sum((data_section - background) * dQ)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "fallen-australian",
   "metadata": {},
   "outputs": [],
   "source": [
    "# adjust these path to point to where you have unpacked the tarball\n",
    "\n",
    "from databroker._drivers.msgpack import BlueskyMsgpackCatalog\n",
    "xca_db = BlueskyMsgpackCatalog(['/mnt/data/bnl/2020-12_ae/adaptive_reduced/xca/*msgpack'])\n",
    "gpcam_db = BlueskyMsgpackCatalog(['/mnt/data/bnl/2020-12_ae/adaptive_reduced/gpcam/*msgpack'])\n",
    "grid_db = BlueskyMsgpackCatalog(['/mnt/data/bnl/2020-12_ae/adaptive_reduced/grid/*msgpack'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "least-vacation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "218"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at how runs we have\n",
    "len(gpcam_db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "boring-advertiser",
   "metadata": {},
   "outputs": [],
   "source": [
    "# you can throw mongo queries at the db object to filter down\n",
    "thick_measurements = list(gpcam_db.search({'adaptive_step.snapped.ctrl_thickness': 1}))\n",
    "thin_measurements = list(gpcam_db.search({'adaptive_step.snapped.ctrl_thickness': 0}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "compatible-password",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(thin_measurements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "fifteen-divide",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pull out a measurment\n",
    "h = gpcam_db[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "retained-colorado",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Start({'adaptive_step': {'requested': {'ctrl_Ti': 71.91510525551067,\n",
       "                                 'ctrl_annealing_time': 8.153374338566547,\n",
       "                                 'ctrl_temp': 370.35262012869214,\n",
       "                                 'ctrl_thickness': 0.3174752095944964},\n",
       "                   'snapped': {'ctrl_Ti': 68.0,\n",
       "                               'ctrl_annealing_time': 450,\n",
       "                               'ctrl_temp': 400,\n",
       "                               'ctrl_thickness': 0}},\n",
       " 'analysis_stage': 'integration',\n",
       " 'batch_count': 27,\n",
       " 'batch_id': '6ae65817-064e-46d3-9d0a-b2c3027a3878',\n",
       " 'beamline_config': {'Filters': ['1Cu', '2Cu', '3Cu', '4Cu'],\n",
       "                     'Verification time': '2020-12-10 21:27:42',\n",
       "                     'Verified by': 'SG',\n",
       "                     'beamstop': 'W'},\n",
       " 'beamline_id': '28-id-2',\n",
       " 'bkgd_geometry': 'A common sample name',\n",
       " 'bt_experimenters': ['Ghose',\n",
       "                      'Zhang',\n",
       "                      'Fukuto',\n",
       "                      'Caswell',\n",
       "                      'Lynch',\n",
       "                      'Yager',\n",
       "                      'Noak'],\n",
       " 'bt_piLast': 'Chen-Waigart',\n",
       " 'bt_safN': '306612',\n",
       " 'bt_uid': '98812e29',\n",
       " 'bt_wavelength': 0.1874,\n",
       " 'calibration_md': {'calibrant_name': 'undefined',\n",
       "                    'centerX': 1909.2229638919803,\n",
       "                    'centerY': 981.1330069606032,\n",
       "                    'dSpacing': [2.03458234862,\n",
       "                                 1.761935,\n",
       "                                 1.24592214845,\n",
       "                                 1.06252597829,\n",
       "                                 1.01729117431,\n",
       "                                 0.881,\n",
       "                                 0.80846104616,\n",
       "                                 0.787990355271,\n",
       "                                 0.719333487797,\n",
       "                                 0.678194116208,\n",
       "                                 0.622961074225,\n",
       "                                 0.595664718733,\n",
       "                                 0.587333333333,\n",
       "                                 0.557193323722,\n",
       "                                 0.537404961852,\n",
       "                                 0.531262989146,\n",
       "                                 0.508645587156,\n",
       "                                 0.493458701611,\n",
       "                                 0.488690872874,\n",
       "                                 0.47091430825,\n",
       "                                 0.458785722296,\n",
       "                                 0.4405,\n",
       "                                 0.430525121912,\n",
       "                                 0.427347771314],\n",
       "                    'detector': 'Perkin detector',\n",
       "                    'directDist': 1356.0373163457173,\n",
       "                    'dist': 1.3554105722159373,\n",
       "                    'max_shape': [4096, 4096],\n",
       "                    'pixel1': 0.0002,\n",
       "                    'pixel2': 0.0002,\n",
       "                    'pixelX': 200.0,\n",
       "                    'pixelY': 200.0,\n",
       "                    'poni1': 0.23691649230757097,\n",
       "                    'poni2': 0.37523272279712333,\n",
       "                    'poni_file_name': '/tmp/tmpy6aweuxx/from_calib_func.poni',\n",
       "                    'rot1': -0.004878092049634572,\n",
       "                    'rot2': -0.030010973161420894,\n",
       "                    'rot3': 3.961908527821151e-06,\n",
       "                    'splineFile': None,\n",
       "                    'tilt': 1.742062274104331,\n",
       "                    'tiltPlanRotation': -80.77043929391112,\n",
       "                    'time': '20201210-100647',\n",
       "                    'wavelength': 1.874e-11},\n",
       " 'composition_string': 'Cu0.33Mg0.33Ti0.33',\n",
       " 'detector_calibration_client_uid': '614f9df8-7c1f-4c73-a547-1b44e74fafc5',\n",
       " 'detectors': ['pe2', 'sample_x', 'ss_stg2_y', 'ctrl'],\n",
       " 'facility': 'NSLS-II',\n",
       " 'filter_positions': {'flt1': 'Out',\n",
       "                      'flt2': 'Out',\n",
       "                      'flt3': 'Out',\n",
       "                      'flt4': 'Out'},\n",
       " 'group': 'XPD',\n",
       " 'hints': {'dimensions': [[['ss_stg2_y'], 'primary'],\n",
       "                          [['q'], 'primary'],\n",
       "                          [['tth'], 'primary']]},\n",
       " 'motors': ['ss_stg2_y'],\n",
       " 'num_intervals': 2,\n",
       " 'num_points': 3,\n",
       " 'original_start_time': 1607663444.9758844,\n",
       " 'original_start_uid': 'b86d2d24-b5e0-4de5-b913-b36587cb97cf',\n",
       " 'outbound_node': '7044feed-e959-4396-a724-1907d6d16b23',\n",
       " 'parent_node_map': {'01f42312-e9e9-42af-9547-5277ed96c5d8': '288f1afd-64d6-4716-bdea-d451e8a9f1cb',\n",
       "                     '07664283-2ada-47ee-8cc1-71237e70845f': '288f1afd-64d6-4716-bdea-d451e8a9f1cb',\n",
       "                     '07858321-4e53-4baf-9458-15ec5093eee5': '288f1afd-64d6-4716-bdea-d451e8a9f1cb',\n",
       "                     '1c699cb4-93e1-4bc3-a0e2-30b0d267d39e': '67492233-94b0-4279-be8d-bdcc06c86f5e',\n",
       "                     '268a8e9e-c011-46a1-be74-48e50c05de3e': '288f1afd-64d6-4716-bdea-d451e8a9f1cb',\n",
       "                     '45539362-8bdb-4b52-af7f-98e0b775991f': 'b86d2d24-b5e0-4de5-b913-b36587cb97cf',\n",
       "                     '7b21784f-b9f0-485d-ab30-81bef367dbaa': 'b86d2d24-b5e0-4de5-b913-b36587cb97cf',\n",
       "                     '838d7800-d004-4a85-8ac3-11fab4c7ee38': '7c866dbd-dc7a-4cac-bea9-41522633334c',\n",
       "                     'af2f9f50-f3b7-49f9-9604-b7722f0e49b7': '288f1afd-64d6-4716-bdea-d451e8a9f1cb',\n",
       "                     'e0993a3f-47e5-47cc-8750-d9e14af069c7': 'bdb347c1-9999-4870-9a20-452a23cd908a',\n",
       "                     'f858bdef-4520-499e-b533-751718c68617': '22e26791-8318-4d79-8dc3-7233e6561c03'},\n",
       " 'parent_uids': ['b86d2d24-b5e0-4de5-b913-b36587cb97cf',\n",
       "                 '288f1afd-64d6-4716-bdea-d451e8a9f1cb',\n",
       "                 '67492233-94b0-4279-be8d-bdcc06c86f5e',\n",
       "                 '22e26791-8318-4d79-8dc3-7233e6561c03',\n",
       "                 'bdb347c1-9999-4870-9a20-452a23cd908a',\n",
       "                 '7c866dbd-dc7a-4cac-bea9-41522633334c'],\n",
       " 'plan_args': {'args': [\"EpicsMotor(prefix='XF:28IDC-ES:1{Stg:Smpl2-Ax:Y}Mtr', \"\n",
       "                        \"name='ss_stg2_y', settle_time=0.0, timeout=None, \"\n",
       "                        \"read_attrs=['user_readback', 'user_setpoint'], \"\n",
       "                        \"configuration_attrs=['user_offset', \"\n",
       "                        \"'user_offset_dir', 'velocity', 'acceleration', \"\n",
       "                        \"'motor_egu'])\",\n",
       "                        17.086850000000002,\n",
       "                        18.086850000000002],\n",
       "               'detectors': [\"PerkinElmerContinuous(prefix='XF:28IDC-ES:1{Det:PE2}', \"\n",
       "                             \"name='pe2', read_attrs=['tiff', 'stats1', \"\n",
       "                             \"'stats1.total'], configuration_attrs=['cam', \"\n",
       "                             \"'cam.acquire_period', 'cam.acquire_time', \"\n",
       "                             \"'cam.bin_x', 'cam.bin_y', 'cam.image_mode', \"\n",
       "                             \"'cam.manufacturer', 'cam.model', \"\n",
       "                             \"'cam.num_exposures', 'cam.trigger_mode', 'tiff', \"\n",
       "                             \"'tiff.configuration_names', 'tiff.port_name', \"\n",
       "                             \"'tiff.asyn_pipeline_config', \"\n",
       "                             \"'tiff.blocking_callbacks', 'tiff.enable', \"\n",
       "                             \"'tiff.nd_array_port', 'tiff.plugin_type', \"\n",
       "                             \"'tiff.auto_increment', 'tiff.auto_save', \"\n",
       "                             \"'tiff.file_format', 'tiff.file_name', \"\n",
       "                             \"'tiff.file_path', 'tiff.file_path_exists', \"\n",
       "                             \"'tiff.file_template', 'tiff.file_write_mode', \"\n",
       "                             \"'tiff.full_file_name', 'tiff.num_capture', \"\n",
       "                             \"'images_per_set', 'number_of_sets', \"\n",
       "                             \"'pixel_size', 'detector_type', 'stats1', \"\n",
       "                             \"'stats1.configuration_names', \"\n",
       "                             \"'stats1.port_name', \"\n",
       "                             \"'stats1.asyn_pipeline_config', \"\n",
       "                             \"'stats1.blocking_callbacks', 'stats1.enable', \"\n",
       "                             \"'stats1.nd_array_port', 'stats1.plugin_type', \"\n",
       "                             \"'stats1.bgd_width', 'stats1.centroid_threshold', \"\n",
       "                             \"'stats1.compute_centroid', \"\n",
       "                             \"'stats1.compute_histogram', \"\n",
       "                             \"'stats1.compute_profiles', \"\n",
       "                             \"'stats1.compute_statistics', 'stats1.hist_max', \"\n",
       "                             \"'stats1.hist_min', 'stats1.hist_size', \"\n",
       "                             \"'stats1.profile_cursor', 'stats1.profile_size', \"\n",
       "                             \"'stats1.ts_num_points'])\",\n",
       "                             \"EpicsMotor(prefix='XF:28IDC-ES:1{SampArray-Ax:X}Mtr', \"\n",
       "                             \"name='sample_x', settle_time=0.0, timeout=None, \"\n",
       "                             \"read_attrs=['user_readback', 'user_setpoint'], \"\n",
       "                             \"configuration_attrs=['user_offset', \"\n",
       "                             \"'user_offset_dir', 'velocity', 'acceleration', \"\n",
       "                             \"'motor_egu'])\",\n",
       "                             \"EpicsMotor(prefix='XF:28IDC-ES:1{Stg:Smpl2-Ax:Y}Mtr', \"\n",
       "                             \"name='ss_stg2_y', settle_time=0.0, timeout=None, \"\n",
       "                             \"read_attrs=['user_readback', 'user_setpoint'], \"\n",
       "                             \"configuration_attrs=['user_offset', \"\n",
       "                             \"'user_offset_dir', 'velocity', 'acceleration', \"\n",
       "                             \"'motor_egu'])\",\n",
       "                             \"Control(prefix='', name='ctrl', \"\n",
       "                             \"read_attrs=['Ti', 'temp', 'annealing_time', \"\n",
       "                             \"'thickness'], configuration_attrs=[])\"],\n",
       "               'num': 3,\n",
       "               'per_step': 'None'},\n",
       " 'plan_name': 'scan',\n",
       " 'plan_pattern': 'inner_product',\n",
       " 'plan_pattern_args': {'args': [\"EpicsMotor(prefix='XF:28IDC-ES:1{Stg:Smpl2-Ax:Y}Mtr', \"\n",
       "                                \"name='ss_stg2_y', settle_time=0.0, \"\n",
       "                                \"timeout=None, read_attrs=['user_readback', \"\n",
       "                                \"'user_setpoint'], \"\n",
       "                                \"configuration_attrs=['user_offset', \"\n",
       "                                \"'user_offset_dir', 'velocity', \"\n",
       "                                \"'acceleration', 'motor_egu'])\",\n",
       "                                17.086850000000002,\n",
       "                                18.086850000000002],\n",
       "                       'num': 3},\n",
       " 'plan_pattern_module': 'bluesky.plan_patterns',\n",
       " 'plan_type': 'generator',\n",
       " 'sa_uid': '44c91564',\n",
       " 'sample_composition': {'Cu': 0.33, 'Mg': 0.33, 'Ti': 0.33},\n",
       " 'sample_name': 'TiCuMg_alloy_auto_1',\n",
       " 'sample_phase': {'Cu': 0.33, 'Mg': 0.33, 'Ti': 0.33},\n",
       " 'sc_dk_field_uid': '0f62e611-fdb3-4c4a-ab56-88ac37716639',\n",
       " 'scan_id': 3768,\n",
       " 'sp': {'computed_exposure': 20,\n",
       "        'num_frames': 0,\n",
       "        'plan_name': 'ct',\n",
       "        'requested_exposure': 20,\n",
       "        'time_per_frame': 0,\n",
       "        'type': 'ct',\n",
       "        'uid': '56a0b8be-57cf-4afd-ad60-384861b561f0'},\n",
       " 'sp_computed_exposure': 20,\n",
       " 'sp_num_frames': 0,\n",
       " 'sp_plan_name': 'ct',\n",
       " 'sp_requested_exposure': 20,\n",
       " 'sp_time_per_frame': 0,\n",
       " 'sp_type': 'ct',\n",
       " 'sp_uid': '56a0b8be-57cf-4afd-ad60-384861b561f0',\n",
       " 'ticu_adaptive': {'num': 3,\n",
       "                   'rocking_range': 0.5,\n",
       "                   'snap_tolerance': {},\n",
       "                   'snapped': True,\n",
       "                   'take_data': 'stepping_ct'},\n",
       " 'time': 1613578302.285961,\n",
       " 'uid': 'e291b3e7-e7f7-41f5-9189-f524afadbe1b',\n",
       " 'xpdacq_md_version': 0.1})"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inpsect the start metadata \n",
    "h.metadata['start']\n",
    "# h.metadata['start']['original_start_uid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "behind-style",
   "metadata": {},
   "outputs": [
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       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (dim_0: 3091, dim_1: 3091, dim_2: 3091, time: 3)\n",
       "Coordinates:\n",
       "  * time       (time) float64 1.614e+09 1.614e+09 1.614e+09\n",
       "Dimensions without coordinates: dim_0, dim_1, dim_2\n",
       "Data variables:\n",
       "    mean       (time, dim_0) float64 16.0 10.75 18.38 18.08 ... 0.0 0.0 0.0 0.0\n",
       "    q          (time, dim_1) float64 0.002883 0.005239 0.008734 ... 10.43 10.45\n",
       "    tth        (time, dim_2) float64 0.004927 0.008952 0.01493 ... 17.9 17.94\n",
       "    ss_stg2_y  (time) float64 17.09 17.59 18.09</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-47830b59-1589-4c9e-a301-e96b50477b15' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-47830b59-1589-4c9e-a301-e96b50477b15' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>dim_0</span>: 3091</li><li><span>dim_1</span>: 3091</li><li><span>dim_2</span>: 3091</li><li><span class='xr-has-index'>time</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e9ecd482-32af-4cfc-a4f0-c31c70928676' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e9ecd482-32af-4cfc-a4f0-c31c70928676' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.614e+09 1.614e+09 1.614e+09</div><input id='attrs-068d1be5-05d8-436b-af89-a0e3e5069a21' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-068d1be5-05d8-436b-af89-a0e3e5069a21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c305cba0-fa3f-4923-8e64-f9890e46067b' class='xr-var-data-in' type='checkbox'><label for='data-c305cba0-fa3f-4923-8e64-f9890e46067b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.613578e+09, 1.613578e+09, 1.613578e+09])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-136391f2-2c2d-470b-bb62-94c2fed14682' class='xr-section-summary-in' type='checkbox'  checked><label for='section-136391f2-2c2d-470b-bb62-94c2fed14682' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mean</span></div><div class='xr-var-dims'>(time, dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.0 10.75 18.38 ... 0.0 0.0 0.0</div><input id='attrs-39862841-dfc4-4606-84aa-3d51dfa659f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39862841-dfc4-4606-84aa-3d51dfa659f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de0ac623-af2e-406c-81dc-4a6d5988e8c3' class='xr-var-data-in' type='checkbox'><label for='data-de0ac623-af2e-406c-81dc-4a6d5988e8c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>mean</dd></dl></div><div class='xr-var-data'><pre>array([[16.        , 10.75000024, 18.37500097, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [20.        , 13.50000048, 18.75000095, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [17.        , 10.75000024, 20.25000119, ...,  0.        ,\n",
       "         0.        ,  0.        ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time, dim_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.002883 0.005239 ... 10.43 10.45</div><input id='attrs-60f6467e-9a98-4eaf-933a-679fc1b04e3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60f6467e-9a98-4eaf-933a-679fc1b04e3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb2f2593-5fa6-4f13-99c3-322ba1d41f39' class='xr-var-data-in' type='checkbox'><label for='data-eb2f2593-5fa6-4f13-99c3-322ba1d41f39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>q</dd></dl></div><div class='xr-var-data'><pre>array([[2.88310908e-03, 5.23871965e-03, 8.73385053e-03, ...,\n",
       "        1.04298716e+01, 1.04329219e+01, 1.04542286e+01],\n",
       "       [2.88310908e-03, 5.23871965e-03, 8.73385053e-03, ...,\n",
       "        1.04298716e+01, 1.04329219e+01, 1.04542286e+01],\n",
       "       [2.88310908e-03, 5.23871965e-03, 8.73385053e-03, ...,\n",
       "        1.04298716e+01, 1.04329219e+01, 1.04542286e+01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tth</span></div><div class='xr-var-dims'>(time, dim_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.004927 0.008952 ... 17.9 17.94</div><input id='attrs-72b51107-8374-49b9-9abb-33b352013a80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72b51107-8374-49b9-9abb-33b352013a80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b14d4dc2-e117-4f52-ad80-5a0ce43cb215' class='xr-var-data-in' type='checkbox'><label for='data-b14d4dc2-e117-4f52-ad80-5a0ce43cb215' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>tth</dd></dl></div><div class='xr-var-data'><pre>array([[4.92689634e-03, 8.95235940e-03, 1.49251295e-02, ...,\n",
       "        1.78960899e+01, 1.79013668e+01, 1.79382273e+01],\n",
       "       [4.92689634e-03, 8.95235940e-03, 1.49251295e-02, ...,\n",
       "        1.78960899e+01, 1.79013668e+01, 1.79382273e+01],\n",
       "       [4.92689634e-03, 8.95235940e-03, 1.49251295e-02, ...,\n",
       "        1.78960899e+01, 1.79013668e+01, 1.79382273e+01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ss_stg2_y</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>17.09 17.59 18.09</div><input id='attrs-c4e33384-0f7a-4c65-85cc-20c2aa47ac72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4e33384-0f7a-4c65-85cc-20c2aa47ac72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0ba3db8-32da-4270-8d24-a4430b2d972e' class='xr-var-data-in' type='checkbox'><label for='data-f0ba3db8-32da-4270-8d24-a4430b2d972e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>ss_stg2_y</dd></dl></div><div class='xr-var-data'><pre>array([17.08685, 17.58685, 18.08685])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3df3c698-be61-4e2e-a72e-3ffa64268ef1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3df3c698-be61-4e2e-a72e-3ffa64268ef1' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (dim_0: 3091, dim_1: 3091, dim_2: 3091, time: 3)\n",
       "Coordinates:\n",
       "  * time       (time) float64 1.614e+09 1.614e+09 1.614e+09\n",
       "Dimensions without coordinates: dim_0, dim_1, dim_2\n",
       "Data variables:\n",
       "    mean       (time, dim_0) float64 16.0 10.75 18.38 18.08 ... 0.0 0.0 0.0 0.0\n",
       "    q          (time, dim_1) float64 0.002883 0.005239 0.008734 ... 10.43 10.45\n",
       "    tth        (time, dim_2) float64 0.004927 0.008952 0.01493 ... 17.9 17.94\n",
       "    ss_stg2_y  (time) float64 17.09 17.59 18.09"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read out the data payload.  These only have a primary stream\n",
    "d = h.primary.read()\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "different-remedy",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a helper function to pull out a sub-set of the data in the run\n",
    "def extract_data(h):\n",
    "    \n",
    "    d = h.primary.read()   # this is an xarray \n",
    "    step = h.metadata['start']['adaptive_step'] \n",
    "    # average the Q, I(Q) in time (we have 3 measurements at different ys), the snapped ctrl and requested ctrl\n",
    "    return d['q'].mean('time'), d['mean'].mean('time'), step['snapped'], step['requested']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "enabling-structure",
   "metadata": {},
   "outputs": [],
   "source": [
    "q, I, snapped, requested = extract_data(gpcam_db[-50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "billion-robin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ctrl_Ti': 43.20103197890273,\n",
       " 'ctrl_annealing_time': 7.850903355659998,\n",
       " 'ctrl_temp': 363.06637444462586,\n",
       " 'ctrl_thickness': 0.4169027366367482}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "requested"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "sustainable-kernel",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in add axes internal key=((GridSpec(1, 1)[0:1, 0:1],), (('sharex', None), ('sharey', None)))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'I')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a plot of a single measurement \n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(q, I, label=str(snapped.values()))\n",
    "ax.legend()\n",
    "ax.set_xlabel('q')\n",
    "ax.set_xlim(*peak_location)\n",
    "ax.set_ylabel('I')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "independent-definition",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in add axes internal key=((GridSpec(1, 1)[0:1, 0:1],), (('sharex', None), ('sharey', None)))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 391
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a plot of a handful of curves\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "\n",
    "def plot_bunch_of_IofQ(ax, h_list):\n",
    "    line_style = {0: \"-\", 1: \"--\"}\n",
    "\n",
    "    for h in h_list:\n",
    "        q, I, snapped, requested = extract_data(h)\n",
    "        ti, at, temp, thick = [\n",
    "            snapped[k]\n",
    "            for k in [\"ctrl_Ti\", \"ctrl_annealing_time\", \"ctrl_temp\", \"ctrl_thickness\"]\n",
    "        ]\n",
    "        ax.plot(\n",
    "            q,\n",
    "            I,\n",
    "            linestyle=line_style[thick],\n",
    "            label=f\"Ti: {ti:.2f} time: {at}s temp: {temp}C\",\n",
    "        )\n",
    "    ax.legend()\n",
    "    ax.set_xlabel(\"Q\")\n",
    "    ax.set_xlim(peak_location[0] - 0.05, peak_location[1] + 0.05)\n",
    "    ax.set_ylim(50, 150)\n",
    "    # label the ROI range\n",
    "    ax.axvspan(*peak_location, alpha=0.5, zorder=-5, color=\"r\")\n",
    "    ax.set_ylabel(\"I(Q)\")\n",
    "\n",
    "\n",
    "plot_bunch_of_IofQ(ax, [gpcam_db[indx] for indx in [-175, -15, -30, -50, -100, -150]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "corresponding-fantasy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in add axes internal key=((GridSpec(2, 1)[0:1, 0:1],), (('sharex', None), ('sharey', None)))\n",
      "in add axes internal key=((GridSpec(2, 1)[1:2, 0:1],), (('sharex', <AxesSubplot:>), ('sharey', <AxesSubplot:>)))\n",
      "in add axes internal key=((Bbox([[0.78375, 0.5368181818181819], [0.9, 0.88]]),), (('label', ('<', 'c', 'o', 'l', 'o', 'r', 'b', 'a', 'r', '>')),))\n",
      "in add axes internal key=((Bbox([[0.78375, 0.1250000000000001], [0.9, 0.46818181818181825]]),), (('label', ('<', 'c', 'o', 'l', 'o', 'r', 'b', 'a', 'r', '>')),))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'thin annealing_time 3600')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 296,
       "width": 440
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a plot with a whole lot of curves\n",
    "\n",
    "from matplotlib.colors import Normalize\n",
    "from matplotlib.cm import get_cmap, ScalarMappable\n",
    "\n",
    "\n",
    "\n",
    "def plot_bunch_of_IofQ_colored(\n",
    "    ax,\n",
    "    h_list,\n",
    "    color_by=\"ctrl_Ti\",\n",
    "    *,\n",
    "    cmap=\"viridis\",\n",
    "    norm=None,\n",
    "    peak_location=peak_location,\n",
    "):\n",
    "    line_style = {0: \"-\", 1: \"--\"}\n",
    "    scale_values = []\n",
    "    lines = []\n",
    "    for h in h_list:\n",
    "        q, I, snapped, requested = extract_data(h)\n",
    "        ti, at, temp, thick = [\n",
    "            snapped[k]\n",
    "            for k in [\"ctrl_Ti\", \"ctrl_annealing_time\", \"ctrl_temp\", \"ctrl_thickness\"]\n",
    "        ]\n",
    "        (ln,) = ax.plot(\n",
    "            q,\n",
    "            I,\n",
    "            linestyle=line_style[thick],\n",
    "            label=f\"Ti: {ti:.2f} time: {at}s temp: {temp}C\",\n",
    "            alpha=0.5,\n",
    "        )\n",
    "        lines.append(ln)\n",
    "        scale_values.append(snapped[color_by])\n",
    "    cmap = get_cmap(cmap)\n",
    "    if norm is None:\n",
    "        norm = Normalize()\n",
    "    colors = cmap(norm(scale_values))\n",
    "    for c, ln in zip(colors, lines):\n",
    "        ln.set_color(c)\n",
    "    sm = ScalarMappable(cmap=cmap, norm=norm)\n",
    "    cbar = ax.figure.colorbar(sm, ax=ax)\n",
    "    cbar.set_label(color_by)\n",
    "    \n",
    "    ax.set_xlabel(\"Q\")\n",
    "    ax.set_ylabel(\"I(Q)\")\n",
    "    \n",
    "    ax.set_xlim(peak_location[0] - 0.05, peak_location[1] + 0.05)\n",
    "    ax.set_ylim(50, 150)\n",
    "    # label the ROI range\n",
    "    ax.axvspan(*peak_location, alpha=0.5, zorder=-5, color=\"r\")\n",
    "\n",
    "\n",
    "\n",
    "annealing_time = 3600\n",
    "\n",
    "# make a plot\n",
    "fig, (ax1, ax2) = plt.subplots(2, constrained_layout=True, sharex=True, sharey=True)\n",
    "plot_bunch_of_IofQ_colored(\n",
    "    ax1,\n",
    "    [\n",
    "        gpcam_db[indx]\n",
    "        for indx in gpcam_db.search(\n",
    "            {\n",
    "                \"adaptive_step.snapped.ctrl_thickness\": 1,\n",
    "                \"adaptive_step.snapped.ctrl_annealing_time\": annealing_time,\n",
    "            }\n",
    "        )\n",
    "    ],\n",
    ")\n",
    "ax1.set_title(f\"thick annealing_time {annealing_time}\")\n",
    "plot_bunch_of_IofQ_colored(\n",
    "    ax2,\n",
    "    [\n",
    "        gpcam_db[indx]\n",
    "        for indx in gpcam_db.search(\n",
    "            {\n",
    "                \"adaptive_step.snapped.ctrl_thickness\": 0,\n",
    "                \"adaptive_step.snapped.ctrl_annealing_time\": annealing_time,\n",
    "            }\n",
    "        )\n",
    "    ],\n",
    ")\n",
    "ax2.set_title(f\"thin annealing_time {annealing_time}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "material-integer",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "continued-tractor",
   "metadata": {},
   "outputs": [],
   "source": [
    "# how to go from the original_start_uid back to raw data (but you need the raw databroker!)\n",
    "# orig_uid = xca_db[-1].metadata['start']['original_start_uid']\n",
    "# raw_data = real_xpd_raw_db[orig_uid].primary.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "established-messenger",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;mean&#x27; ()&gt;\n",
       "array(0.07114276)</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'mean'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3e43f904-1fe1-461a-8439-3be6c54a598b' class='xr-array-in' type='checkbox' checked><label for='section-3e43f904-1fe1-461a-8439-3be6c54a598b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.07114</span></div><div class='xr-array-data'><pre>array(0.07114276)</pre></div></div></li><li class='xr-section-item'><input id='section-2a3a8cb8-379b-4b93-aaad-058ca621f2ac' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2a3a8cb8-379b-4b93-aaad-058ca621f2ac' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-ef3b72ba-96ba-4482-b68c-fe4cab94f0dd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ef3b72ba-96ba-4482-b68c-fe4cab94f0dd' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "q, I, snapped, requested = extract_data(gpcam_db[-150])\n",
    "compute_peak_area(q, I, *peak_location)\n"
   ]
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extract.py

from databroker import Broker
from databroker.queries import TimeRange
from databroker._drivers.msgpack import BlueskyMsgpackCatalog
import datetime
import itertools
from collections import defaultdict
import json

def summerize(by_sample):
    tmp = []
    for k, v in by_sample.items():
        times = [h.metadata["start"]["time"] for h in v]
        start_time = min(times)
        stop_time = max(times)
        tmp.append(
            (
                start_time,
                f"{k!r:<25}:{len(v):<4} "
                + f"{datetime.datetime.fromtimestamp(start_time)} - "
                + f"{datetime.datetime.fromtimestamp(stop_time)}",
            )
        )
    for _, pp in sorted(tmp):
        print(pp)


def groupby_sample(hdrs):
    ret = defaultdict(list)
    for h in hdrs:
        ret[h.metadata["start"].get("sample_name", None)].append(h)

    return ret


def by_run_phase(by_sample, groups):
    out = {}
    for k, v in groups.items():
        out[k] = list(itertools.chain(*(by_sample[s] for s in v)))

    return out


def by_reduced_status(by_phase, has_reduction):
    out = {}
    for k, v in by_phase.items():
        wd = out[k] = {"needs": [], "has": []}
        for h in v:
            if h.metadata["start"]["uid"] in has_reduction:
                wd["has"].append(h)
            else:
                wd["needs"].append(h)
    return out


db = Broker.named("xpd")
cat = db.v2
tr = TimeRange(since="2020-12-07 19:00", until="2020-12-12 12:00")
hdrs = list(cat[d] for d in cat.search(tr).search({"bt_uid": "98812e29"}))

by_sample = groupby_sample(hdrs)
print({k: len(v) for k, v in by_sample.items()})

ticumg_data = {k: v for k, v in by_sample.items() if k is not None and "TiCuMg" in k}

by_phase = by_run_phase(
    ticumg_data,
    {
        "grid": ["TiCuMg_alloy", "TiCuMg_alloy_2", "TiCuMg_alloy_3"],
        "gpcam": ["TiCuMg_alloy_auto_1"],
        "xca": ["TiCuMg_alloy_adapt"],
    },
)

print({k: len(v) for k, v in by_phase.items()})

local_cat = BlueskyMsgpackCatalog(["reduced/*.msgpack"])

have_reduced = set(
    local_cat[d].metadata["start"]["original_start_uid"] for d in local_cat.search({})
)


work_plan = by_reduced_status(by_phase, have_reduced)
print({k: {_k: len(_v) for _k, _v in v.items()} for k, v in work_plan.items()})

just_uids = {k: {_k: [_h.metadata['start']['uid'] for _h in _v] for _k, _v in v.items()} for k, v in work_plan.items()}

with open(Path('~/work_plan.json').expanduser(), 'w') as fout:
    json.dump(just_uids, fout)

# len(have_reduced)
# have_reduced - data_uids
# len(have_reduced - data_uids)
# len(data_uids - have_reduced)
# to_export = list(data_uids - have_reduced)
# to_export
# len(data_uids | have_reduced)
# len(data_uids & have_reduced)
# len(to_export)
# just_copy = data_uids - have_reduced
# import itertools

# list(itertools.chain(*(local_cat.search({"original_start_uid": d}) for d in just_copy)))
# just_copy
# local_cat.search({"oiginal_start_uid": "05006b0d-e55b-4e9c-b359-e59aee82bad0"})
# list(local_cat.search({"oiginal_start_uid": "05006b0d-e55b-4e9c-b359-e59aee82bad0"}))
# have_reduced
# list(local_cat.search({"oiginal_start_uid": "ffe934b2-46ec-4607-993c-dbe509447c1a"}))
# list(local_cat.search(oiginal_start_uid="ffe934b2-46ec-4607-993c-dbe509447c1a"))
# reduced_headers = [local_cat[d] for d in local_cat.search({})]
# just_export_headers = [
#     h for h in reduced_headers if h["original_start_uid"] in just_export
# ]
# just_export_headers = [
#     h
#     for h in reduced_headers
#     if h.metadata["start"]["original_start_uid"] in just_export
# ]
# just_export_headers = [
#     h for h in reduced_headers if h.metadata["start"]["original_start_uid"] in just_copy
# ]
# len(just_export_headers)
# just_copy = data_uids & have_reduced
# list(itertools.chain(*(local_cat.search({"original_start_uid": d}) for d in just_copy)))
# just_copy_reduced_uids = list(
#     itertools.chain(*(local_cat.search({"original_start_uid": d}) for d in just_copy))
# )

# len(local_cat)
# len(res)
# res = list(cat.search(dict(bt_safN="306612")))
# len(res)

reprocess.py

"""Run data reduction in-line
"""
import copy
from pathlib import Path
import json
from functools import partial
import itertools

from event_model import RunRouter as RealRunRouter
from event_model import unpack_datum_page, unpack_event_page
from rapidz import Stream, move_to_first
from rapidz.link import link
from shed import SimpleToEventStream
from suitcase.msgpack import Serializer

from xpdan.pipelines.main import (
    image_process,
    calibration,
    clear_geo_gen,
    scattering_correction,
    gen_mask,
    integration,
    clear_comp,
    start_gen,
    pdf_gen,
)

from xpdan.pipelines.to_event_model import pipeline_order as tem_pipeline_order
from xpdan.pipelines.to_event_model import to_event_stream_with_ind

from xpdan.vend.callbacks.core import RunRouter, StripDepVar

from databroker.v0 import Broker


db = Broker.named("xpd")

pipeline_order = [
    partial(start_gen, db=db, image_names=["pe2_image", "pe1_image"]),
    image_process,
    calibration,
    clear_geo_gen,
    scattering_correction,
    gen_mask,
    integration,
    pdf_gen,
    clear_comp,
]

order = pipeline_order + tem_pipeline_order


def create_analysis_pipeline(
    *, order, publisher, stage_blacklist=(), **kwargs,
):
    """Create the analysis pipeline from an list of chunks and pipeline kwargs

    Parameters
    ----------
    order : list of functions
        The list of pipeline chunk functions
    kwargs : Any
        The kwargs to pass to the pipeline creation

    Returns
    -------
    namespace : dict
        The namespace of the pipeline

    """
    namespace = link(*order, raw_source=Stream(stream_name="raw source"), **kwargs)
    source = namespace["source"]

    # do inspection of pipeline for ToEventModel nodes, maybe?
    # for analyzed data with independent data (vis and save)
    # strip the dependant vars form the raw data
    raw_stripped = move_to_first(source.starmap(StripDepVar()))
    namespace.update(
        to_event_stream_with_ind(
            raw_stripped,
            *[
                node
                for node in namespace.values()
                if isinstance(node, SimpleToEventStream)
                and node.md.get("analysis_stage", None) not in stage_blacklist
            ],
            publisher=publisher,
        )
    )

    return namespace


def diffraction_router(start, diffraction_dets, xrd_namespace):
    # This does not support concurrent radiograms and diffractograms
    # If there are diffraction detectors in the list, this is diffraction
    if any(d in diffraction_dets for d in start["detectors"]):
        print("analyzing as diffraction")
        return lambda *x: xrd_namespace["raw_source"].emit(x)


def create_analysis_pipeline(
    order, stage_blacklist=(), *, publisher, **kwargs,
):
    """Create the analysis pipeline from an list of chunks and pipeline kwargs

    Parameters
    ----------
    order : list of functions
        The list of pipeline chunk functions
    kwargs : Any
        The kwargs to pass to the pipeline creation

    Returns
    -------
    namespace : dict
        The namespace of the pipeline

    """
    namespace = link(*order, raw_source=Stream(stream_name="raw source"), **kwargs)
    source = namespace["source"]

    # do inspection of pipeline for ToEventModel nodes, maybe?
    # for analyzed data with independent data (vis and save)
    # strip the dependant vars form the raw data
    raw_stripped = move_to_first(source.starmap(StripDepVar()))
    namespace.update(
        to_event_stream_with_ind(
            raw_stripped,
            *[
                node
                for node in namespace.values()
                if isinstance(node, SimpleToEventStream)
                and node.md.get("analysis_stage", None) not in stage_blacklist
            ],
            publisher=publisher,
        )
    )

    return namespace


def run_processor(
    order, *, db, diffraction_dets, stage_blacklist=(), publisher=None, **kwargs,
):

    print(kwargs)
    db.prepare_hook = lambda x, y: copy.deepcopy(y)

    rr = RunRouter(
        [diffraction_router],
        xrd_namespace=create_analysis_pipeline(
            order=order,
            stage_blacklist=stage_blacklist,
            publisher=publisher,
            **kwargs,
            db=db,
        ),
        diffraction_dets=diffraction_dets,
    )

    return rr


def filter_factory(base_name):
    def filter(name, doc):
        if doc["analysis_stage"] == "integration":
            print("got one!")
            return (
                [Serializer(Path("~").expanduser() / "adaptive_reduced" / base_name)],
                [],
            )
        return [], []

    return filter


with open(Path("~/work_plan.json").expanduser(), "r") as fin:
    just_uids = json.load(fin)

for k, v in just_uids.items():
    filter = filter_factory(k)
    outputter = RealRunRouter([filter])
    rr = run_processor(
        order=order, db=db, diffraction_dets=["pe1", "pe2"], publisher=outputter
    )
    for uid in itertools.chain(*v.values()):
        h = db[uid]
        for name, doc in h.documents():
            doc = dict(doc)
            if name == "datum_page":
                for d in unpack_datum_page(doc):
                    rr("datum", d)
            elif name == "event_page":
                for d in unpack_event_page(doc):
                    rr("event", d)
            else:
                rr(name, doc)