increased sleep time for calibration routine

Signed-off-by: elistavitski <[email protected]>
NSLS-II-ISS · Oct 22, 2024 · 8304f4c · 8304f4c
1 parent 0dab9e3
commit 8304f4c
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Showing 4 changed files with 156 additions and 7 deletions.
diff --git a/xas/energy_calibration.py b/xas/energy_calibration.py
@@ -175,7 +175,7 @@ def get_energy_offset_old(uid, db, db_proc, dE=25, plot_fun=None, attempts=5, sl
         else:
             return e0, e_cor
 
-def get_energy_offset(uid, db, db_proc, dE=25, plot_fun=None, attempts=5, sleep_time=1, full_return=False):
+def get_energy_offset(uid, db, db_proc, dE=25, plot_fun=None, attempts=20, sleep_time=2, full_return=False):
     print('running get_energy_offset')
     start = db[uid].start
     fname_raw = start['interp_filename']
@@ -190,7 +190,7 @@ def get_energy_offset(uid, db, db_proc, dE=25, plot_fun=None, attempts=5, sleep_
                 # print('bla')
                 break
             except:
-                print(f'[Energy Calibration] Attempt to read data {i + 1}')
+                print(f'[Energy Calibration] Attempt to read data {i + 1} {ttime.ctime()}')
                 ttime.sleep(sleep_time)
                 df = None
 
@@ -204,7 +204,16 @@ def get_energy_offset(uid, db, db_proc, dE=25, plot_fun=None, attempts=5, sleep_
             edge = start['edge']
             e0 = float(start['e0'])
             # energy_ref, mu_ref = get_foil_spectrum(element, edge, db_proc)
-            energy_ref, mu_ref = db_proc.foil_spectrum(element, edge)
+            energy_ref, mu_ref = db_proc.foil_spectrum(element, edge) ## unsorted array of energy and mu
+
+            _foil_spectrum_tuple = db_proc.foil_spectrum(element, edge)
+            _dataframe_foil = pd.DataFrame(np.column_stack(_foil_spectrum_tuple))
+            _dataframe_foil = _dataframe_foil.sort_values(0)
+
+            energy_ref = np.array(_dataframe_foil[0])
+            mu_ref = np.array(_dataframe_foil[1])
+
+
             mask = (energy_ref >= (e0 - dE)) & (energy_ref <= (e0 + dE))
 
             energy_shift_coarse = energy_ref[np.argmin(np.abs(mu_ref - 0.5))] - energy[np.argmin(np.abs(mu - 0.5))]

diff --git a/xas/process.py b/xas/process.py
@@ -325,9 +325,10 @@ def get_processed_df_from_uid_for_epics_fly_scan(db, uid, save_interpolated_file
         raise e
     if 'spectrometer' in hdr.start:
         if 'roi_polygon' in hdr.start['detectors']['Pilatus 100k New']['config']:
-            johann_image_kwargs = filter_johann_image_kwargs(processing_kwargs)
-            if (hdr.start['spectrometer'] == 'johann') and (load_images):
-                interpolated_df = reduce_johann_images(interpolated_df, hdr, **johann_image_kwargs)
+        # if 'roi_polygon' in hdr.start['detectors']['Pilatus 100k']['config']:
+            # johann_image_kwargs = filter_johann_image_kwargs(processing_kwargs)
+            # if (hdr.start['spectrometer'] == 'johann') and (load_images):
+            #     interpolated_df = reduce_johann_images(interpolated_df, hdr, **johann_image_kwargs)
 
             johann_calibration_kwargs = filter_johann_calibration_kwargs(processing_kwargs)
             if (hdr.start['spectrometer'] == 'johann') and (load_images):

diff --git a/xas/scratch.py b/xas/scratch.py
@@ -1888,4 +1888,142 @@ def get_timestamps_for_pbs(uid, db):
 
 plt.figure(2, clear=True)
 plt.plot(img_closeset_arr, img_closeset_arr / img_mask_arr, 'k.')
-# plt.axis('square')
+# plt.axis('square')
+
+
+
+hhm_y_precise = {}
+hhm_y_precise['uid'] = []
+hhm_y_precise['value'] = []
+def find_optimal_hhm_Y_values():
+    energy_tab = [ 4900,  5100,  5500,  6000,  7000,  8000,  9000, 10000, 11000, 12000, 13000, 15000, 17500, 20000]
+    # energy_tab = [9000]
+    for energy in energy_tab:
+        yield from bps.mv(hhm.energy, energy)
+        yield from prepare_beamline_plan(energy, move_cm_mirror = True, move_hhm_y = True)
+        yield from quick_pitch_optimization()
+        yield from bp.relative_scan([bpm_fm], hhm.y_precise, -0.5, 0.5, 21)
+
+
+        hdr = db[-1]
+        hhm_y_precise['uid'].append(hdr.start['uid'])
+        t = hdr.table()
+        index = np.argmax(t['bpm_fm_stats1_total'])
+        hhm_y_precise['value'].append(t['hhm_y_precise'][index+1])
+
+
+
+
+
+#2024-09-19
+
+[9.937850000000001,
+ 9.57315,
+ 9.467450000000001,
+ 9.3162,
+ 9.177100000000001,
+ 9.08755,
+ 9.025,
+ 8.978050000000001,
+ 8.9411,
+ 8.9609,
+ 8.88475,
+ 8.84365,
+ 8.85205,
+ 8.816450000000001]
+
+
+uids =['d0166c48-ebcf-4647-820f-f10f53e768f8',
+ '93b12235-e1ba-4ef4-93e7-7ea558450f0d',
+ '3e49c021-69de-4011-937f-0637a56ddf40',
+ 'ebfafa6f-5ef5-4820-a77f-8f0b9436959e',
+ '9f8e8399-a0af-4099-ae57-b89f4e9687cf',
+ '021337a6-693c-4a0f-a529-5246e574f941',
+ '8a21ec71-c4ae-4a95-94ee-6b8e6e5b24c4',
+ '6ec12c6d-492e-44d1-93c9-9876a1b53f96',
+ '922fa456-e258-43c6-b6ba-b498f6f61bf9',
+ '7794afc8-4dd0-4591-98ff-c576df92790e',
+ 'c7354f86-c247-4ad4-858f-2d0e7043f326',
+ '496d2087-10dd-4ff1-83f1-7ab7410d8bd2',
+ '1baf04d6-a8e3-4a6b-882d-e31ca3a5b75a',
+ 'db7bb3d2-866d-47d4-a1fd-463d94c46416']
+
+with open('inclinometer_data.json') as jd:
+    d = json.load(jd)
+
+d = {'motor_det_th1': {'0': -27,
+  '1': -24,
+  '2': -21,
+  '3': -18,
+  '4': -15,
+  '5': -12,
+  '6': -9,
+  '7': -6,
+  '8': -3,
+  '9': 0,
+  '10': 3,
+  '11': 6,
+  '12': 9,
+  '13': 12,
+  '14': 15,
+  '15': 18,
+  '16': 21,
+  '17': 24,
+  '18': 27,
+  '19': 30,
+  '20': 33,
+  '21': 36,
+  '22': 39,
+  '23': 42,
+  '24': 45,
+  '25': 48,
+  '26': 51,
+  '27': 54,
+  '28': 57,
+  '29': 60,
+  '30': 63,
+  '31': 66,
+  '32': 69},
+ 'motor_det_inc1': {'0': 14653,
+  '1': 14388,
+  '2': 14119,
+  '3': 13855,
+  '4': 13575,
+  '5': 13316,
+  '6': 13047,
+  '7': 12782,
+  '8': 12514,
+  '9': 12244,
+  '10': 11980,
+  '11': 11705,
+  '12': 11436,
+  '13': 11172,
+  '14': 10903,
+  '15': 10639,
+  '16': 10370,
+  '17': 10106,
+  '18': 9836,
+  '19': 9567,
+  '20': 9303,
+  '21': 9039,
+  '22': 8774,
+  '23': 8505,
+  '24': 8241,
+  '25': 7972,
+  '26': 7707,
+  '27': 7438,
+  '28': 7164,
+  '29': 6900,
+  '30': 6631,
+  '31': 6366,
+  '32': 6097}}
+
+
+plt.figure();
+plt.plot(d['motor_det_th1'].keys(), d['motor_det_th1'].values())
+
+
+
+
+
+
diff --git a/xas/vonhamos.py b/xas/vonhamos.py
@@ -14,6 +14,7 @@
 from sklearn.covariance import MinCovDet
 
 PILATUS_KEY = 'Pilatus 100k New'
+# PILATUS_KEY = 'Pilatus 100k'
 
 DET2KEY = {'Pilatus 100k' : 'pil100k',
            'Pilatus 100k New': 'pil100k2'}