diff --git a/cal_tools/cal_tools/ana_tools.py b/cal_tools/cal_tools/ana_tools.py
index 689c946814c010052f115765926e3b20989be8d1..2015576b9b6391fc30c092a3ecf8c820ba9dbc9a 100644
--- a/cal_tools/cal_tools/ana_tools.py
+++ b/cal_tools/cal_tools/ana_tools.py
@@ -2,6 +2,7 @@ import h5py
import numpy as np
import matplotlib.pyplot as plt
import datetime
+import dateutil.parser
import glob
@@ -43,16 +44,16 @@ def load_data_from_hdf5(filelist):
if mKey not in data[cKey]:
data[cKey][mKey] = {}
# Loop over module data
- for tKey in f.get(cKey + '/' + mKey):
+ for tKey in f.get('{}/{}'.format(cKey, mKey)):
if tKey == 'ctime':
- if "ctime" not in data[cKey][mKey]:
- data[cKey][mKey]["ctime"] = []
+ ctime = data[cKey][mKey].get("ctime", [])
for timeKey in f.get("/".join((cKey, mKey, tKey))):
path = "/".join((cKey, mKey, tKey, timeKey))
value = f.get(path).value
- value = datetime.datetime.fromtimestamp(value)
- data[cKey][mKey]["ctime"].append(value)
+ value = dateutil.parser.parse(value)
+ ctime.append(value)
+ data[cKey][mKey]["ctime"] = ctime
continue
# Load ndarray if they are there
@@ -63,7 +64,7 @@ def load_data_from_hdf5(filelist):
continue
# Loop over stored data
- for dKay in f.get( "/".join((cKey, mKey, tKey))):
+ for dKay in f.get("/".join((cKey, mKey, tKey))):
# Loop over metadata
if dKay == "mdata":
@@ -74,19 +75,19 @@ def load_data_from_hdf5(filelist):
"/".join(
(cKey, mKey, tKey, 'mdata',
mdKey))).value
- if "mdata" not in data[cKey][mKey]:
- data[cKey][mKey]["mdata"] = []
- data[cKey][mKey]["mdata"].append(mdata)
+ mdata_l = data[cKey][mKey].get("mdata", [])
+ mdata_l.append(mdata)
+ data[cKey][mKey]["mdata"] = mdata_l
continue
if dKay not in data[cKey][mKey]:
data[cKey][mKey][dKay] = []
value = f.get(
- "/".join((cKey, mKey, tKey, dKay))).value
+ "/".join((cKey, mKey, tKey, dKay))).value
if dKay == "ctime":
- value = datetime.datetime.fromtimestamp(value)
+ value = dateutil.parser.parse(value)
data[cKey][mKey][dKay].append(value)
return data
@@ -106,7 +107,7 @@ def recursively_save_dict_contents_to_group(h5file, path, dic):
# Prepare datatime to save
if isinstance(item, datetime.datetime):
- item = item.timestamp()
+ item = item.isoformat()
# Iterate over dictionary
if isinstance(item, dict):
@@ -119,7 +120,7 @@ def recursively_save_dict_contents_to_group(h5file, path, dic):
if isinstance(x, dict):
recursively_save_dict_contents_to_group(h5file, newpath, x)
elif isinstance(x, datetime.datetime):
- h5file[newpath] = x.timestamp()
+ h5file[newpath] = x.isoformat()
else:
raise ValueError('Cannot save a list of %s ' % type(item))
@@ -302,11 +303,11 @@ def multi_intersect(a, b):
return from_multiset(aa & bb)
-def HMCombine(data, fname=None, type=1, **kwargs):
+def hm_combine(data, fname=None, type=1, **kwargs):
"""
Plot heatmap for calibration report
- :param data: data to plot
+ :param data: 2D numpy.array of data points to plot
:param fname: file name of output file
:param type: type of figure
:param kwargs: other keywords supported by pyDetLib heatmapPlot
@@ -334,7 +335,7 @@ def HMCombine(data, fname=None, type=1, **kwargs):
vmin = kwargs.get('vmin', None)
vmax = kwargs.get('vmax', None)
- h_frame = (1 - pad_b - pad_t)
+ h_frame = 1 - pad_b - pad_t
w_frame = 1 - pad_l - pad_r
if type == 2:
@@ -347,7 +348,8 @@ def HMCombine(data, fname=None, type=1, **kwargs):
mean = np.nanmean(data[y])
if vmin and vmax:
mean = np.nanmean(
- data[y, np.where((data[y] > vmin) & (data[y] < vmax))])
+ data[y, np.where((data[y] > vmin) & (data[y] < vmax))]
+ )
ax = plt.axes(
[pad_l, pad_b + h_frame * y + mar, w_frame, h_frame - 2 * mar],
@@ -356,17 +358,16 @@ def HMCombine(data, fname=None, type=1, **kwargs):
yticklabels=['{:4.2f}'.format(mean)],
xticks=[],
xticklabels=[]
- )
+ )
ax.tick_params(axis='y', which='major', pad=25)
if vmin and vmax:
- ax.set_ylim([vmin, vmax])
+ ax.set_ylim(vmin, vmax)
d = [{'x': np.arange(data.shape[1]),
'y': data[y],
'drawstyle': 'steps-mid',
'linestyle': 'dotted',
- # 'marker': '^',
'linewidth': 2,
'color': 'white',
@@ -381,15 +382,16 @@ def HMCombine(data, fname=None, type=1, **kwargs):
]
y_lab = ''
- if y == data.shape[0] / 2:
+ # Locate label in a middle of the axis
+ if y == int(data.shape[0] / 2):
y_lab = 'Average over time'
- _ = xana.simplePlot(d, # aspect=1.6,
+ _ = xana.simplePlot(d,
x_label="",
y_label=y_lab,
use_axis=ax,
y_log=False)
- elif type == 1:
+ if type == 1:
ax2 = plt.axes([pad_l, pad_b, w_frame, h_frame / 16.], frame_on=False,
yticks=range(3), yticklabels=[32, 16, 0],
@@ -399,8 +401,6 @@ def HMCombine(data, fname=None, type=1, **kwargs):
ax2.set_ylabel('Memory cell ID', color='r')
ax2.tick_params('y', colors='r', right=True, left=False,
labelright=True, labelleft=False, )
- else:
- pass
if fname:
fig.savefig(fname, bbox_inches='tight')
diff --git a/notebooks/AGIPD/PlotFromCalDB_AGIPD_NBC.ipynb b/notebooks/AGIPD/PlotFromCalDB_AGIPD_NBC.ipynb
index ebd3e6629a64f2085bc766c597efce9d70cf54a4..6d74ac1ef738b01bdddc25762f9edf9d336aef28 100644
--- a/notebooks/AGIPD/PlotFromCalDB_AGIPD_NBC.ipynb
+++ b/notebooks/AGIPD/PlotFromCalDB_AGIPD_NBC.ipynb
@@ -35,8 +35,8 @@
"bias_voltages = [300, 500] # Bias voltage\n",
"mem_cells = [128, 176] # Number of used memory cells. Typically: 4,32,64,128,176.\n",
"photon_energy = 9.2 # Photon energy of the beam\n",
- "out_folder = \"/gpfs/exfel/data/scratch/karnem/testAGIPD16_14/\" # Output folder, required\n",
- "use_existing = \"/gpfs/exfel/data/scratch/karnem/testAGIPD16_9/\" # Input folder\n",
+ "out_folder = \"/gpfs/exfel/data/scratch/karnem/testAGIPD16_10/\" # Output folder, required\n",
+ "use_existing = \"\" # Input folder\n",
"cal_db_timeout = 1200000 # timeout on caldb requests\",\n",
"detectorDB = \"AGIPD1M1\" # detector entry in the DB to investigate\n",
"dclass = \"AGIPD\" # Detector class\n",
@@ -52,24 +52,20 @@
},
"outputs": [],
"source": [
- "import os\n",
- "import numpy as np\n",
- "import matplotlib\n",
- "\n",
- "##matplotlib.use(\"agg\")\n",
- "import matplotlib.pyplot as plt\n",
- "% matplotlib inline\n",
- "\n",
- "import sys\n",
"import datetime\n",
"import dateutil.parser\n",
+ "import numpy as np\n",
+ "import os\n",
+ "import sys\n",
"import warnings\n",
- "\n",
"warnings.filterwarnings('ignore')\n",
"\n",
+ "import matplotlib\n",
+ "% matplotlib inline\n",
+ "\n",
"from iCalibrationDB import Constants, Conditions, Detectors\n",
"from cal_tools.tools import get_from_db\n",
- "from cal_tools.ana_tools import *\n"
+ "from cal_tools.ana_tools import *"
]
},
{
@@ -83,8 +79,10 @@
"# Prepare variables\n",
"interval = 1 # interval for evaluation in days\n",
"\n",
- "if modules[0] == -1:\n",
- " modules = list(range(16))\n",
+ "nMem = 176 # Number of mem Cells to store\n",
+ "spShape = (64,64) # Shape of superpixel\n",
+ "\n",
+ "adu_to_photon = 8000 / 3.6 / 67.# ADU to photon conversion factor\n",
"\n",
"in_mod_names = []\n",
"for mod in modules:\n",
@@ -120,15 +118,6 @@
"print('CalDB Interface {}'.format(cal_db_interface))"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- },
{
"cell_type": "code",
"execution_count": null,
@@ -140,7 +129,7 @@
"import copy\n",
"\n",
"def prepare_to_store(a, nMem):\n",
- " shape = list(a.shape[:2])+[nMem,2]\n",
+ " shape = list(a.shape[:2])+[nMem, 2]\n",
" b = np.full(shape, np.nan)\n",
" b[:, :, :a.shape[2]] = a[:, :, :, :2]\n",
" return b\n",
@@ -154,9 +143,32 @@
" a.shape[2],\n",
" a.shape[3])\n",
" \n",
+ "def modify_const(const, data, isBP = False):\n",
+ " if const in ['SlopesFF']:\n",
+ " if (len(data.shape) == 4):\n",
+ " data = data[:, :, :, 0][..., None]\n",
+ " else:\n",
+ " data = data[..., None]\n",
"\n",
- "nMem = 176 # Number of mem Cells to store\n",
- "spShape = (64,64) # Shape of superpixel\n",
+ " if not isBP:\n",
+ " if data.shape[0] != 128:\n",
+ " data = data.swapaxes(0, 2).swapaxes(1, 3).swapaxes(2, 3)\n",
+ "\n",
+ " # Copy slope medium to be saved later\n",
+ " if const in ['SlopesPC']:\n",
+ " data[:, :, :, 1] = data[:, :, :, 3]\n",
+ " else:\n",
+ " if const in ['SlopesPC']:\n",
+ " if len(data.shape) == 3:\n",
+ " data = data[:, :, :, None].repeat(10, axis=3)\n",
+ "\n",
+ " if data.shape[0] != 128:\n",
+ " data = data.swapaxes(0, 1).swapaxes(1, 2)\n",
+ " \n",
+ " if len(data.shape) < 4:\n",
+ " print(data.shape, \"Unexpected shape !!!!!!!!!\")\n",
+ "\n",
+ " return data\n",
"\n",
"ret_constants = {}\n",
"\n",
@@ -223,29 +235,11 @@
" cdata is not None),\n",
" ctime)\n",
" print(cdata.shape)\n",
- "\n",
- " if const in ['SlopesFF']:\n",
- " if (len(cdata.shape) == 4):\n",
- " cdata = cdata[:, :, :, 0][..., None]\n",
- " else:\n",
- " cdata = cdata[..., None]\n",
- "\n",
- " if len(cdata.shape) < 4:\n",
- " print(cdata.shape, \"Unexpected shape !!!!!!!!!\")\n",
- "\n",
- " if cdata.shape[0] != 128:\n",
- " cdata = cdata.swapaxes(0, 2).swapaxes(1, 3).swapaxes(2, 3)\n",
- "\n",
- " # Copy slope medium to be saved later\n",
- " if const in ['SlopesPC']:\n",
- " cdata[:, :, :, 1] = cdata[:, :, :, 3]\n",
- "\n",
+ " cdata = modify_const(const, cdata)\n",
" print(cdata.shape)\n",
"\n",
" # Request bad pixel mask\n",
" if const in constantsDark:\n",
- " #for par in mcond.parameters:\n",
- " # print (par.name, par.value)\n",
" print('RequestBP ',\n",
" (creation_time + step + step + step + step))\n",
" cdataBP, ctimeBP = get_from_db(getattr(det, qm),\n",
@@ -269,26 +263,11 @@
"\n",
" print(\"Found BP {}\".format(ctimeBP))\n",
" print(cdataBP.shape)\n",
- "\n",
- " if const in ['SlopesFF']:\n",
- " if len(cdataBP.shape) == 4:\n",
- " cdataBP = cdataBP[:, :, :, 0][..., None]\n",
- " else:\n",
- " cdataBP = cdataBP[..., None]\n",
- "\n",
- " if const in ['SlopesPC']:\n",
- " if len(cdataBP.shape) == 3:\n",
- " cdataBP = cdataBP[:, :, :, None].repeat(10,\n",
- " axis=3)\n",
- "\n",
- " if cdataBP.shape[0] != 128:\n",
- " cdataBP = cdataBP.swapaxes(0, 1).swapaxes(1, 2)\n",
- "\n",
- " if (len(cdataBP.shape) < 4):\n",
- " print(cdataBP.shape, \"Unexpected shape !!!!!!!!!\")\n",
- "\n",
+ " cdataBP = modify_const(const, cdataBP, True)\n",
+ " print(cdataBP.shape)\n",
+ " \n",
" if cdataBP.shape != cdata.shape:\n",
- " print(cdataBP.shape, 'Wrong BP shape!!!')\n",
+ " print('Wrong BP shape!!!')\n",
" ctime = ctime.replace(tzinfo=None)\n",
" dt = ctime - step\n",
" continue\n",
@@ -329,7 +308,7 @@
" 'mdata': dpar})\n",
" \n",
" ctime = ctime.replace(tzinfo=None)\n",
- " dt = ctime - step\n"
+ " dt = ctime - step"
]
},
{
@@ -373,10 +352,12 @@
},
"outputs": [],
"source": [
- "print ('Combine Gain FF and PC and Noise in [e-]')\n",
+ "# Combine FF and PC data to calculate Gain\n",
+ "# Estimate Noise in units of electrons\n",
+ "print ('Calculate Gain and Noise in electron units')\n",
+ "\n",
"ret_constants[\"Gain\"] = {}\n",
"ret_constants[\"Noise-e\"] = {}\n",
- "\n",
"for module in list(range(16)):\n",
" if (\"SlopesFF\" not in ret_constants or\n",
" \"SlopesPC\" not in ret_constants):\n",
@@ -422,6 +403,7 @@
" \n",
" ret_constants[\"Gain\"][qm][\"ctime\"] = ctime\n",
" ret_constants[\"Gain\"][qm][\"data\"] = np.array(gain_vs_time)\n",
+ " # Fill missing data for compatibility with plotting code\n",
" ret_constants[\"Gain\"][qm][\"dataBP\"] = np.array(gain_vs_time)\n",
" ret_constants[\"Gain\"][qm][\"nBP\"] = np.array(gain_vs_time)\n",
"\n",
@@ -448,12 +430,13 @@
" data_vs_time = []\n",
" for iG, iN in icomb:\n",
" data_vs_time.append(\n",
- " cdataN_vs_time[iN] * (8000 / 3.6 / 67.) / cdataG_vs_time[iG])\n",
+ " cdataN_vs_time[iN] * adu_to_photon / cdataG_vs_time[iG])\n",
"\n",
" print(np.array(gain_vs_time).shape)\n",
" ctime_ts = [t.timestamp() for t in ctime]\n",
" ret_constants[\"Noise-e\"][qm][\"ctime\"] = ctime\n",
" ret_constants[\"Noise-e\"][qm][\"data\"] = np.array(data_vs_time)\n",
+ " # Fill missing data for compatibility with plotting code\n",
" ret_constants[\"Noise-e\"][qm][\"dataBP\"] = np.array(data_vs_time)\n",
" ret_constants[\"Noise-e\"][qm][\"nBP\"] = np.array(data_vs_time)\n",
" \n",
@@ -473,12 +456,12 @@
"\n",
"# Define range for plotting\n",
"rangevals = {\n",
- " \"Offset\": [[800., 1500], [600, 900]],\n",
- " \"Noise\": [[2.0, 16], [1.0, 7.0]],\n",
- " \"Gain\": [[20, 30], [20, 30]],\n",
- " \"Noise-e\": [[100., 600.], [100., 600.]],\n",
- " \"SlopesCI\": [[0.95, 1.05], [0.0, 0.5]],\n",
- " \"SlopesFF\": [[0.8, 1.2], [0.8, 1.2]]\n",
+ " \"Offset\": [[4000., 5500], [6500, 8500]],\n",
+ " \"Noise\": [[2.5, 15], [7.5, 17.0]],\n",
+ " \"Gain\": [[0.8, 1.2], [0.8, 1.2]],\n",
+ " \"Noise-e\": [[85., 500.], [85., 500.]],\n",
+ " \"SlopesPC\": [[22.0, 27.0], [-0.5, 1.5]],\n",
+ " \"SlopesFF\": [[0.8, 1.2], [0.6, 1.2]]\n",
"}\n",
"\n",
"nMemToShow = 32\n",
@@ -553,7 +536,6 @@
" if item[0] in data:\n",
" rdata[key] = np.array(data[item[0]])[sort_ind]\n",
"\n",
- " # print(rdata['Mean'].shape)\n",
" nTimes = rdata['Mean'].shape[0]\n",
" nPixels = rdata['Mean'].shape[1] * rdata['Mean'].shape[2]\n",
" nBins = nMemToShow * nPixels\n",
@@ -636,11 +618,7 @@
" title=title, cb_label=cb_label,\n",
" fname='{}/{}_{}_g{}_MEM_{}.png'.format(\n",
" out_folder, const, mod, gain, key),\n",
- " vmin=vmin, vmax=vmax)\n",
- "\n",
- " # break\n",
- " #break\n",
- " #break"
+ " vmin=vmin, vmax=vmax)"
]
}
],
diff --git a/notebooks/AGIPD/PlotFromCalDB_Summary_AGIPD_NBC.ipynb b/notebooks/AGIPD/PlotFromCalDB_Summary_AGIPD_NBC.ipynb
index 016a8ef0d9cefb06e346b1ebe10b238e1ae51718..23db813cc49ef042bb56f1975e278aed642cc1c3 100644
--- a/notebooks/AGIPD/PlotFromCalDB_Summary_AGIPD_NBC.ipynb
+++ b/notebooks/AGIPD/PlotFromCalDB_Summary_AGIPD_NBC.ipynb
@@ -34,15 +34,14 @@
},
"outputs": [],
"source": [
- "from cal_tools.ana_tools import *\n",
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')\n",
+ "\n",
"import numpy as np\n",
"import matplotlib\n",
- "matplotlib.use(\"agg\")\n",
"% matplotlib inline\n",
- "import matplotlib.pyplot as plt\n",
"\n",
- "import warnings\n",
- "warnings.filterwarnings('ignore')"
+ "from cal_tools.ana_tools import *"
]
},
{
@@ -243,7 +242,7 @@
" const, gain_name[gain], keys[key][1])\n",
" cb_label = '{}, {} {}'.format(const, keys[key][2], unit)\n",
"\n",
- " HMCombine(np.array(mod_data[key])[mod_idx][::-1],\n",
+ " hm_combine(np.array(mod_data[key])[mod_idx][::-1],\n",
" y_ticks=np.arange(nModules)[::-1]+0.8,\n",
" y_ticklabels=np.array(mod_names)[mod_idx],\n",
" x_label='Creation Time', y_label='Module ID',\n",
@@ -253,12 +252,7 @@
" fname='{}/{}_all_g{}_{}.png'.format(\n",
" out_folder, const, gain, key),\n",
" vmin=vmin, vmax=vmax,\n",
- " pad=[0.125, 0.151, 0.12, 0.17], type=htype)\n",
- "\n",
- " #plt.show()\n",
- " #break\n",
- " #break\n",
- " #break"
+ " pad=[0.125, 0.151, 0.12, 0.17], type=htype)"
]
}
],
diff --git a/notebooks/LPD/PlotFromCalDB_LPD_NBC.ipynb b/notebooks/LPD/PlotFromCalDB_LPD_NBC.ipynb
index abdbaf26b586131938a238c8b246be3577b863b2..63fb41601cc3cff3d178bb378b3f370dedfa46ef 100644
--- a/notebooks/LPD/PlotFromCalDB_LPD_NBC.ipynb
+++ b/notebooks/LPD/PlotFromCalDB_LPD_NBC.ipynb
@@ -31,12 +31,12 @@
"start_date = \"2018-01-30\" # Date to start investigation interval from\n",
"end_date = \"2018-12-12\" # Date to end investigation interval at, can be \"now\"\n",
"constants = [\"Offset\", \"Noise\", \"SlopesFF\", \"SlopesCI\"] # constants to plot\n",
- "modules = [1] # Modules, range allowed\n",
+ "modules = [2] # Modules, range allowed\n",
"bias_voltages = [250, 500] # Bias voltage\n",
"mem_cells = [1, 128, 256, 512] # Number of used memory cells. Typically: 4,32,64,128,176.\n",
"photon_energy = 9.2 # Photon energy of the beam\n",
- "out_folder = \"/gpfs/exfel/data/scratch/karnem/testLPD_16/\" # Output folder, required\n",
- "use_existing = \"\" # Input folder\n",
+ "out_folder = \"/gpfs/exfel/data/scratch/karnem/testLPD_15/\" # Output folder, required\n",
+ "use_existing = \"/gpfs/exfel/data/scratch/karnem/testLPD_16/\" # Input folder\n",
"cal_db_timeout = 180000 # timeout on caldb requests\",\n",
"detectorDB = \"LPD1M1\" # detector entry in the DB to investigate\n",
"dclass = \"LPD\" # Detector class\n",
@@ -52,24 +52,20 @@
},
"outputs": [],
"source": [
- "import os\n",
- "import numpy as np\n",
- "import matplotlib\n",
- "\n",
- "##matplotlib.use(\"agg\")\n",
- "import matplotlib.pyplot as plt\n",
- "% matplotlib inline\n",
- "\n",
- "import sys\n",
"import datetime\n",
"import dateutil.parser\n",
+ "import numpy as np\n",
+ "import os\n",
+ "import sys\n",
"import warnings\n",
- "\n",
"warnings.filterwarnings('ignore')\n",
"\n",
+ "import matplotlib\n",
+ "% matplotlib inline\n",
+ "\n",
"from iCalibrationDB import Constants, Conditions, Detectors\n",
"from cal_tools.tools import get_from_db\n",
- "from cal_tools.ana_tools import *\n"
+ "from cal_tools.ana_tools import *"
]
},
{
@@ -83,8 +79,10 @@
"# Prepare variables\n",
"interval = 1 # interval for evaluation in days\n",
"\n",
- "if modules[0] == -1:\n",
- " modules = list(range(16))\n",
+ "nMem = 512 # Number of mem Cells to store\n",
+ "spShape = (64,64) # Shape of superpixel\n",
+ "\n",
+ "adu_to_photon = 8000 / 3.6 / 67.# ADU to photon conversion factor\n",
"\n",
"in_mod_names = []\n",
"for mod in modules:\n",
@@ -130,12 +128,14 @@
"source": [
"import copy\n",
"\n",
+ "\n",
"def prepare_to_store(a, nMem):\n",
- " shape = list(a.shape[:2])+[nMem,2]\n",
+ " shape = list(a.shape[:2])+[nMem, 2]\n",
" b = np.full(shape, np.nan)\n",
" b[:, :, :a.shape[2]] = a[:, :, :, :2]\n",
" return b\n",
"\n",
+ "\n",
"def get_rebined(a, rebin):\n",
" return a.reshape(\n",
" int(a.shape[0] / rebin[0]),\n",
@@ -144,10 +144,24 @@
" rebin[1],\n",
" a.shape[2],\n",
" a.shape[3])\n",
- " \n",
"\n",
- "nMem = 512 # Number of mem Cells to store\n",
- "spShape = (64,64) # Shape of superpixel\n",
+ "\n",
+ "def modify_const(const, data):\n",
+ "\n",
+ " if const in ['SlopesFF']:\n",
+ " data = data[..., None, None]\n",
+ "\n",
+ " if(len(data.shape)==5):\n",
+ " data = data[:,:,:,:,0]\n",
+ "\n",
+ " if len(data.shape) < 4:\n",
+ " print(data.shape, \"Unexpected shape !!!!!!!!!\")\n",
+ "\n",
+ " if data.shape[0] != 256:\n",
+ " data = data.swapaxes(0, 2).swapaxes(1,3).swapaxes(2,3) \n",
+ " \n",
+ " return data\n",
+ "\n",
"\n",
"ret_constants = {}\n",
"\n",
@@ -214,25 +228,11 @@
" cdata is not None),\n",
" ctime)\n",
" print(cdata.shape)\n",
- "\n",
- " if const in ['SlopesFF']:\n",
- " cdata = cdata[...,None,None]\n",
- " \n",
- " if(len(cdata.shape)==5):\n",
- " cdata = cdata[:,:,:,:,0]\n",
- "\n",
- " if len(cdata.shape) < 4:\n",
- " print(cdata.shape, \"Unexpected shape !!!!!!!!!\")\n",
- "\n",
- " if cdata.shape[0] != 256:\n",
- " cdata = cdata.swapaxes(0, 2).swapaxes(1,3).swapaxes(2,3) \n",
- "\n",
+ " cdata = modify_const(const, cdata)\n",
" print(cdata.shape)\n",
"\n",
" # Request bad pixel mask\n",
" if const in constantsDark:\n",
- " #for par in mcond.parameters:\n",
- " # print (par.name, par.value)\n",
" print('RequestBP ',\n",
" (creation_time + step + step + step + step))\n",
" cdataBP, ctimeBP = get_from_db(getattr(det, qm),\n",
@@ -256,21 +256,11 @@
"\n",
" print(\"Found BP {}\".format(ctimeBP))\n",
" print(cdataBP.shape)\n",
- "\n",
- " if const in ['SlopesFF']:\n",
- " cdataBP = cdataBP[...,None,None]\n",
- " \n",
- " if(len(cdataBP.shape)==5):\n",
- " cdataBP = cdataBP[:,:,:,:,0]\n",
- "\n",
- " if cdataBP.shape[0] != 256:\n",
- " cdataBP = cdataBP.swapaxes(0, 2).swapaxes(1,3).swapaxes(2,3) \n",
- "\n",
- " if (len(cdataBP.shape) < 4):\n",
- " print(cdataBP.shape, \"Unexpected shape !!!!!!!!!\")\n",
+ " cdataBP = modify_const(const, cdataBP)\n",
+ " print(cdataBP.shape)\n",
"\n",
" if cdataBP.shape != cdata.shape:\n",
- " print(cdataBP.shape, 'Wrong BP shape!!!')\n",
+ " print('Wrong BP shape!!!')\n",
" ctime = ctime.replace(tzinfo=None)\n",
" dt = ctime - step\n",
" continue\n",
@@ -311,7 +301,7 @@
" 'mdata': dpar})\n",
" \n",
" ctime = ctime.replace(tzinfo=None)\n",
- " dt = ctime - step\n"
+ " dt = ctime - step"
]
},
{
@@ -355,13 +345,15 @@
},
"outputs": [],
"source": [
- "print ('Combine Gain FF and CI and Noise in [e-]')\n",
+ "# Combine FF and PC data to calculate Gain\n",
+ "# Estimate Noise in units of electrons\n",
+ "print ('Calculate Gain and Noise in electron units')\n",
+ "\n",
"ret_constants[\"Gain\"] = {}\n",
"ret_constants[\"Noise-e\"] = {}\n",
- "\n",
"for module in list(range(16)):\n",
" if (\"SlopesFF\" not in ret_constants or\n",
- " \"SlopesPC\" not in ret_constants):\n",
+ " \"SlopesCI\" not in ret_constants):\n",
" break\n",
"\n",
" qm = \"Q{}M{}\".format(module // 4 + 1, module % 4 + 1)\n",
@@ -404,6 +396,7 @@
" \n",
" ret_constants[\"Gain\"][qm][\"ctime\"] = ctime\n",
" ret_constants[\"Gain\"][qm][\"data\"] = np.array(gain_vs_time)\n",
+ " # Fill missing data for compatibility with plotting code\n",
" ret_constants[\"Gain\"][qm][\"dataBP\"] = np.array(gain_vs_time)\n",
" ret_constants[\"Gain\"][qm][\"nBP\"] = np.array(gain_vs_time)\n",
"\n",
@@ -430,12 +423,13 @@
" data_vs_time = []\n",
" for iG, iN in icomb:\n",
" data_vs_time.append(\n",
- " cdataN_vs_time[iN] * (8000 / 3.6 / 67.) / cdataG_vs_time[iG])\n",
+ " cdataN_vs_time[iN] * adu_to_photon / cdataG_vs_time[iG])\n",
"\n",
" print(np.array(gain_vs_time).shape)\n",
" ctime_ts = [t.timestamp() for t in ctime]\n",
" ret_constants[\"Noise-e\"][qm][\"ctime\"] = ctime\n",
" ret_constants[\"Noise-e\"][qm][\"data\"] = np.array(data_vs_time)\n",
+ " # Fill missing data for compatibility with plotting code\n",
" ret_constants[\"Noise-e\"][qm][\"dataBP\"] = np.array(data_vs_time)\n",
" ret_constants[\"Noise-e\"][qm][\"nBP\"] = np.array(data_vs_time)\n",
" \n",
@@ -536,7 +530,6 @@
" if item[0] in data:\n",
" rdata[key] = np.array(data[item[0]])[sort_ind]\n",
"\n",
- " # print(rdata['Mean'].shape)\n",
" nTimes = rdata['Mean'].shape[0]\n",
" nPixels = rdata['Mean'].shape[1] * rdata['Mean'].shape[2]\n",
" nBins = nMemToShow * nPixels\n",
@@ -619,12 +612,7 @@
" title=title, cb_label=cb_label,\n",
" fname='{}/{}_{}_g{}_MEM_{}.png'.format(\n",
" out_folder, const, mod, gain, key),\n",
- " vmin=vmin, vmax=vmax)\n",
- "\n",
- " #break\n",
- " #break\n",
- " #break\n",
- " #break"
+ " vmin=vmin, vmax=vmax)"
]
}
],
diff --git a/xfel_calibrate/notebooks.py b/xfel_calibrate/notebooks.py
index 3dcfef45e2f320a61d16c259a734e183f509e446..fa8da6fe36eed80986f70d8da740f4b2a062d786 100644
--- a/xfel_calibrate/notebooks.py
+++ b/xfel_calibrate/notebooks.py
@@ -33,6 +33,13 @@ notebooks = {
"default concurrency": None,
"cluster cores": 8},
},
+ "STATS_FROM_DB": {
+ "notebook": "notebooks/AGIPD/PlotFromCalDB_AGIPD_NBC.ipynb",
+ "dep_notebooks": ["notebooks/AGIPD/PlotFromCalDB_Summary_AGIPD_NBC.ipynb"],
+ "concurrency": {"parameter": "modules",
+ "default concurrency": None,
+ "cluster cores": 1},
+ },
},
"LPD": {
"DARK": {
@@ -68,8 +75,9 @@ notebooks = {
"cluster cores": 2},
},
"STATS_FROM_DB": {
- "notebook": "notebooks/LPD/PlotFromCalDB.ipynb",
- "concurrency": {"parameter": None,
+ "notebook": "notebooks/LPD/PlotFromCalDB_LPD_NBC.ipynb",
+ "dep_notebooks": ["notebooks/AGIPD/PlotFromCalDB_Summary_AGIPD_NBC.ipynb"],
+ "concurrency": {"parameter": "modules",
"default concurrency": None,
"cluster cores": 1},
},