From cffb3fcf688bcbdf68d0655e6210de83fad00e5f Mon Sep 17 00:00:00 2001 From: Mathieu Doucet Date: Mon, 2 Sep 2024 12:31:47 -0400 Subject: [PATCH] update IS code --- launcher/apps/reduction.py | 58 ++- launcher/launcher.py | 8 +- miniDAS/db_collector.py | 15 +- miniDAS/instrument.py | 3 +- reduction/notebooks/workflow-fixed-tthd.ipynb | 356 ++++++++++++++---- scripts/autoreduce/reduce_REF_L.py | 15 +- .../livereduce/reduce_REF_L_live_post_proc.py | 47 ++- scripts/shared/batch_reduce.py | 27 +- 8 files changed, 431 insertions(+), 98 deletions(-) diff --git a/launcher/apps/reduction.py b/launcher/apps/reduction.py index 0fb8239..02372a0 100644 --- a/launcher/apps/reduction.py +++ b/launcher/apps/reduction.py @@ -23,7 +23,7 @@ def __init__(self): layout.addWidget(self.choose_template, 1, 1) self.template_path = QLabel(self) - layout.addWidget(self.template_path, 1, 2) + layout.addWidget(self.template_path, 1, 3) # First run to process self.first_run_number_ledit = QtWidgets.QLineEdit() @@ -31,7 +31,7 @@ def __init__(self): layout.addWidget(self.first_run_number_ledit, 3, 1) self.first_run_number_label = QLabel(self) self.first_run_number_label.setText("First run to process") - layout.addWidget(self.first_run_number_label, 3, 2) + layout.addWidget(self.first_run_number_label, 3, 3) # Last run to process self.last_run_number_ledit = QtWidgets.QLineEdit() @@ -39,7 +39,7 @@ def __init__(self): layout.addWidget(self.last_run_number_ledit, 4, 1) self.last_run_number_label = QLabel(self) self.last_run_number_label.setText("Last run to process") - layout.addWidget(self.last_run_number_label, 4, 2) + layout.addWidget(self.last_run_number_label, 4, 3) # Select version self.select_version_label = QLabel(self) @@ -48,7 +48,7 @@ def __init__(self): layout.addWidget(self.select_version_label, 5, 1) self.select_version_check = QtWidgets.QCheckBox() self.select_version_check.setChecked(False) - layout.addWidget(self.select_version_check, 5, 2) + layout.addWidget(self.select_version_check, 5, 3) # Select const-q binning self.const_q_label = QLabel(self) @@ -57,7 +57,7 @@ def __init__(self): layout.addWidget(self.const_q_label, 6, 1) self.const_q_check = QtWidgets.QCheckBox() self.const_q_check.setChecked(False) - layout.addWidget(self.const_q_check, 6, 2) + layout.addWidget(self.const_q_check, 6, 3) # Select how to treat overlap self.average_overlapp_label = QLabel(self) @@ -66,11 +66,33 @@ def __init__(self): layout.addWidget(self.average_overlapp_label, 7, 1) self.average_overlapp_check = QtWidgets.QCheckBox() self.average_overlapp_check.setChecked(True) - layout.addWidget(self.average_overlapp_check, 7, 2) + layout.addWidget(self.average_overlapp_check, 7, 3) + + # Fit first peak to compute offset + self.first_peak_label = QLabel(self) + self.first_peak_label.setText("Offset from first peak") + self.first_peak_label.setAlignment(QtCore.Qt.AlignRight) + layout.addWidget(self.first_peak_label, 8, 1) + self.first_peak_check = QtWidgets.QCheckBox() + self.first_peak_check.setChecked(True) + layout.addWidget(self.first_peak_check, 8, 3) + + # Theta offset + self.fix_offset_label = QLabel(self) + self.fix_offset_label.setText("Use fixed theta offset") + self.fix_offset_label.setAlignment(QtCore.Qt.AlignRight) + layout.addWidget(self.fix_offset_label, 9, 1) + self.fix_offset_check = QtWidgets.QCheckBox() + self.fix_offset_check.setChecked(False) + layout.addWidget(self.fix_offset_check, 9, 2) + + self.fix_offset_ledit = QtWidgets.QLineEdit() + self.fix_offset_ledit.setValidator(QtGui.QDoubleValidator()) + layout.addWidget(self.fix_offset_ledit, 9, 3) # Process button self.perform_reduction = QPushButton('Reduce') - layout.addWidget(self.perform_reduction, 8, 1) + layout.addWidget(self.perform_reduction, 10, 1) # connections self.choose_template.clicked.connect(self.template_selection) @@ -87,7 +109,12 @@ def select_version(self): self.average_overlapp_label.setEnabled(not use_old) self.const_q_check.setEnabled(not use_old) self.const_q_label.setEnabled(not use_old) - + self.fix_offset_check.setEnabled(not use_old) + self.fix_offset_label.setEnabled(not use_old) + self.fix_offset_ledit.setEnabled(not use_old) + self.first_peak_label.setEnabled(not use_old) + self.first_peak_check.setEnabled(not use_old) + def template_selection(self): _template_file, _ = QFileDialog.getOpenFileName(self, 'Open file', self.template_path.text(), @@ -114,6 +141,14 @@ def read_settings(self): _const_q = self.settings.value("reduction_const_q", "false") self.const_q_check.setChecked(_const_q=='true') + _first_peak = self.settings.value("fit_first_peak", "false") + self.first_peak_check.setChecked(_first_peak=='true') + _fix_offset = self.settings.value("reduction_fix_use_offset", "false") + self.fix_offset_check.setChecked(_fix_offset=='true') + _fix_offset = self.settings.value("reduction_fix_offset", "0") + self.fix_offset_ledit.setText(_fix_offset) + + def save_settings(self): self.settings.setValue('reduction_template', self.template_path.text()) self.settings.setValue('reduction_first_run_number', self.first_run_number_ledit.text()) @@ -122,6 +157,10 @@ def save_settings(self): self.settings.setValue('reduction_avg_overlap', self.average_overlapp_check.isChecked()) self.settings.setValue('reduction_const_q', self.const_q_check.isChecked()) + self.settings.setValue('fit_first_peak', self.first_peak_check.isChecked()) + self.settings.setValue('reduction_fix_use_offset', self.fix_offset_check.isChecked()) + self.settings.setValue('reduction_fix_offset', self.fix_offset_ledit.text()) + def check_inputs(self): error = None # Check files and output directory @@ -167,6 +206,9 @@ def reduce(self): options.append(self.template_path.text()) options.append(str(self.average_overlapp_check.isChecked())) options.append(str(self.const_q_check.isChecked())) + options.append(str(self.first_peak_check.isChecked())) + if self.fix_offset_check.isChecked(): + options.append(str(self.fix_offset_ledit.text())) else: options.append('old') options.append(self.template_path.text()) diff --git a/launcher/launcher.py b/launcher/launcher.py index 6708800..317a769 100755 --- a/launcher/launcher.py +++ b/launcher/launcher.py @@ -35,14 +35,14 @@ def __init__(self): # 60Hz time-resolved tab_id += 1 self.time_60Hz_tab = Dynamic60Hz() - self.addTab(self.time_60Hz_tab, "Time-resolved 60Hz") - self.setTabText(tab_id, "Time-resolved 60Hz") + self.addTab(self.time_60Hz_tab, "Time-resolved") + self.setTabText(tab_id, "Time-resolved") # 30 Hz time-resolved tab_id += 1 self.time_30Hz_tab = Dynamic30Hz() - self.addTab(self.time_30Hz_tab, "Time-resolved 30Hz") - self.setTabText(tab_id, "Time-resolved 30Hz") + self.addTab(self.time_30Hz_tab, "Time-resolved (D2O ref)") + self.setTabText(tab_id, "Time-resolved (D2O ref)") # Off-specular data tab_id += 1 diff --git a/miniDAS/db_collector.py b/miniDAS/db_collector.py index 1715a70..6377792 100644 --- a/miniDAS/db_collector.py +++ b/miniDAS/db_collector.py @@ -97,6 +97,10 @@ def scan_centers(self, charge: float = None): s1_width = self.positions[1]['s1:X:Gap'] self.lr.move({'si:X:Gap': si_width/self.grid_size[0], 's1:X:Gap': s1_width/self.grid_size[1]}) + # Set scale multiplier + multiplier = self.grid_size[0] * self.grid_size[1] + instrument.ScaleMultiplier.put(multiplier) + # The starting center should half a step from the left-most position si_start = si_width * (-1 + 1 / self.grid_size[0]) / 2 s1_start = s1_width * (-1 + 1 / self.grid_size[1]) / 2 @@ -110,10 +114,13 @@ def scan_centers(self, charge: float = None): print("Charge to acquire per configuration:", charge_to_acquire_per_point) # Iterate over Si + counter = 0 for si in si_positions: # Iterate over S1 for s1 in s1_positions: - print(f"Si X center: {si}\tS1 X center: {s1}") + counter += 1 + t0 = time.time() + print(f"{counter} -> Si X center: {si}\tS1 X center: {s1}") # Move motors to the specified positions self.lr.move({'si:X:Center': si, 's1:X:Center': s1}) time.sleep(1.) @@ -124,13 +131,15 @@ def scan_centers(self, charge: float = None): self.lr.pause() rate = self.lr.get_rate() - print(f" Rate: {rate}") + elapsed = time.time() - t0 + print(f" Rate: {rate} Elapsed: {elapsed} sec\n\n") + time.sleep(1.) self.lr.stop() # Move centers back to zero self.lr.move({'si:X:Center': 0, 's1:X:Center': 0}) - + instrument.ScaleMultiplier.put(multiplier) time.sleep(2) # Example usage diff --git a/miniDAS/instrument.py b/miniDAS/instrument.py index ec1c065..84be142 100644 --- a/miniDAS/instrument.py +++ b/miniDAS/instrument.py @@ -49,6 +49,7 @@ def get(self): Lcen = PV('BL4B:Det:TH:BL:Lambda') Lset = PV('BL4B:Chop:Gbl:WavelengthReq') ChopStat = PV('BL4B:Chop:Gbl:Busy:Stat') +ScaleMultiplier = PV('BL4B:CS:Autoreduce:ScaleMultiplier') BL4B_MOT_PREFIX = 'BL4B:Mot:' @@ -155,7 +156,7 @@ def start_or_resume(self, counts: int = 0, seconds: int = 0, charge: float = 200 - time.sleep(1) + time.sleep(2) # Wait for the neutron count to reach the desired value if counts > 0: while neutrons.get() < counts: diff --git a/reduction/notebooks/workflow-fixed-tthd.ipynb b/reduction/notebooks/workflow-fixed-tthd.ipynb index f50042f..fc05687 100644 --- a/reduction/notebooks/workflow-fixed-tthd.ipynb +++ b/reduction/notebooks/workflow-fixed-tthd.ipynb @@ -9,14 +9,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 78, "metadata": { "execution": { - "iopub.execute_input": "2024-08-29T20:09:54.900738Z", - "iopub.status.busy": "2024-08-29T20:09:54.900423Z", - "iopub.status.idle": "2024-08-29T20:09:55.489466Z", - "shell.execute_reply": "2024-08-29T20:09:55.489044Z", - "shell.execute_reply.started": "2024-08-29T20:09:54.900716Z" + "iopub.execute_input": "2024-08-31T14:23:09.716471Z", + "iopub.status.busy": "2024-08-31T14:23:09.716170Z", + "iopub.status.idle": "2024-08-31T14:23:09.720906Z", + "shell.execute_reply": "2024-08-31T14:23:09.720520Z", + "shell.execute_reply.started": "2024-08-31T14:23:09.716453Z" }, "tags": [] }, @@ -41,14 +41,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 79, "metadata": { "execution": { - "iopub.execute_input": "2024-08-29T20:09:55.490475Z", - "iopub.status.busy": "2024-08-29T20:09:55.490230Z", - "iopub.status.idle": "2024-08-29T20:09:56.659367Z", - "shell.execute_reply": "2024-08-29T20:09:56.658917Z", - "shell.execute_reply.started": "2024-08-29T20:09:55.490458Z" + "iopub.execute_input": "2024-08-31T14:23:10.269155Z", + "iopub.status.busy": "2024-08-31T14:23:10.268693Z", + "iopub.status.idle": "2024-08-31T14:23:10.272225Z", + "shell.execute_reply": "2024-08-31T14:23:10.271852Z", + "shell.execute_reply.started": "2024-08-31T14:23:10.269129Z" }, "tags": [] }, @@ -69,14 +69,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 80, "metadata": { "execution": { - "iopub.execute_input": "2024-08-29T20:09:56.660456Z", - "iopub.status.busy": "2024-08-29T20:09:56.660142Z", - "iopub.status.idle": "2024-08-29T20:09:56.662899Z", - "shell.execute_reply": "2024-08-29T20:09:56.662496Z", - "shell.execute_reply.started": "2024-08-29T20:09:56.660438Z" + "iopub.execute_input": "2024-08-31T14:23:10.779163Z", + "iopub.status.busy": "2024-08-31T14:23:10.778933Z", + "iopub.status.idle": "2024-08-31T14:23:10.781684Z", + "shell.execute_reply": "2024-08-31T14:23:10.781324Z", + "shell.execute_reply.started": "2024-08-31T14:23:10.779146Z" }, "tags": [] }, @@ -97,14 +97,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 81, "metadata": { "execution": { - "iopub.execute_input": "2024-08-29T20:09:56.931547Z", - "iopub.status.busy": "2024-08-29T20:09:56.931310Z", - "iopub.status.idle": "2024-08-29T20:09:57.109343Z", - "shell.execute_reply": "2024-08-29T20:09:57.108902Z", - "shell.execute_reply.started": "2024-08-29T20:09:56.931531Z" + "iopub.execute_input": "2024-08-31T14:23:13.334429Z", + "iopub.status.busy": "2024-08-31T14:23:13.334117Z", + "iopub.status.idle": "2024-08-31T14:23:13.337197Z", + "shell.execute_reply": "2024-08-31T14:23:13.336802Z", + "shell.execute_reply.started": "2024-08-31T14:23:13.334410Z" }, "tags": [] }, @@ -171,16 +171,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 157, "metadata": { "execution": { - "iopub.execute_input": "2023-11-06T17:20:31.849380Z", - "iopub.status.busy": "2023-11-06T17:20:31.849042Z", - "iopub.status.idle": "2023-11-06T17:20:34.380862Z", - "shell.execute_reply": "2023-11-06T17:20:34.379829Z", - "shell.execute_reply.started": "2023-11-06T17:20:31.849358Z" - }, - "tags": [] + "iopub.execute_input": "2024-09-01T23:31:38.133109Z", + "iopub.status.busy": "2024-09-01T23:31:38.132775Z", + "iopub.status.idle": "2024-09-01T23:31:48.948929Z", + "shell.execute_reply": "2024-09-01T23:31:48.948485Z", + "shell.execute_reply.started": "2024-09-01T23:31:38.133087Z" + } }, "outputs": [ { @@ -188,47 +187,142 @@ "output_type": "stream", "text": [ "\n", - "Processing: 198409\n", - " DB center: 141.619\t Width: 1.70416 from [137 147]\n", - " SC center: 141.47\t Width: 1.63214\n", - " Two-theta = -1.19853\n", - " Template peak: [136 147]\n", - " New peak: [136 147]\n", - " New bck: [133 150]\n", - "wl=15; ths=-0.600382; thi=-0.00812677; No offset\n", - "Background on both sides: [133 135] [148 150]\n" + "Processing: 211906\n", + " DB center: 260\t Width: 4.91008 from [250 275]\n", + " SC center: 230.975\t Width: 3\n", + " Theta = -0.42953\n", + " Template peak: [218 240]\n", + "wl=6.2; ths=-0.449066; thi=-0.000140031; No offset\n", + "Left side background: [100, 120]\n", + "Left side background: [100, 120]\n", + "Normalization options: True True\n", + "\n", + "Processing: 211911\n", + " DB center: 260\t Width: 4.91008 from [250 275]\n", + " SC center: 231\t Width: 3\n", + " Theta = -0.429158\n", + " Template peak: [218 240]\n", + "wl=6.2; ths=-0.45012; thi=-0.000140031; No offset\n", + "Left side background: [100, 120]\n", + "Left side background: [100, 120]\n", + "Normalization options: True True\n" ] }, { - "ename": "ValueError", - "evalue": "operands could not be broadcast together with shapes (12,34) (3,34) (12,34) ", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[6], line 15\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m198409\u001b[39m, \u001b[38;5;241m198417\u001b[39m):\n\u001b[1;32m 14\u001b[0m ws \u001b[38;5;241m=\u001b[39m api\u001b[38;5;241m.\u001b[39mLoad(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mREF_L_\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m i)\n\u001b[0;32m---> 15\u001b[0m \u001b[43mworkflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce_fixed_two_theta\u001b[49m\u001b[43m(\u001b[49m\u001b[43mws\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemplate_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maverage_overlap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m#workflow.reduce(ws, template_path, output_dir=data_dir, pre_cut=1, post_cut=1, average_overlap=False)\u001b[39;00m\n\u001b[1;32m 18\u001b[0m reduced_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(data_dir, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mREFL_198409_\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m_partial.txt\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (seq, i))\n", - "File \u001b[0;32m~/git/LiquidsReflectometer/reduction/lr_reduction/workflow.py:195\u001b[0m, in \u001b[0;36mreduce_fixed_two_theta\u001b[0;34m(ws, template_file, output_dir, average_overlap, q_summing, bck_in_q, peak_width, offset_from_first)\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m New bck: [\u001b[39m\u001b[38;5;132;01m%g\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%g\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (template_data\u001b[38;5;241m.\u001b[39mbackground_roi[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 192\u001b[0m template_data\u001b[38;5;241m.\u001b[39mbackground_roi[\u001b[38;5;241m1\u001b[39m]))\n\u001b[1;32m 194\u001b[0m \u001b[38;5;66;03m# Call the reduction using the template\u001b[39;00m\n\u001b[0;32m--> 195\u001b[0m qz_mid, refl, d_refl, meta_data \u001b[38;5;241m=\u001b[39m \u001b[43mtemplate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess_from_template_ws\u001b[49m\u001b[43m(\u001b[49m\u001b[43mws\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemplate_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 196\u001b[0m \u001b[43m \u001b[49m\u001b[43mq_summing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq_summing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 197\u001b[0m \u001b[43m \u001b[49m\u001b[43mtof_weighted\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq_summing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 198\u001b[0m \u001b[43m \u001b[49m\u001b[43mbck_in_q\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbck_in_q\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minfo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 199\u001b[0m \u001b[43m \u001b[49m\u001b[43mtheta_value\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtwotheta\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m2.0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 200\u001b[0m \u001b[43m \u001b[49m\u001b[43mws_db\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mws_db\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 202\u001b[0m \u001b[38;5;66;03m# Save partial results\u001b[39;00m\n\u001b[1;32m 203\u001b[0m coll \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mRunCollection()\n", - "File \u001b[0;32m~/git/LiquidsReflectometer/reduction/lr_reduction/template.py:224\u001b[0m, in \u001b[0;36mprocess_from_template_ws\u001b[0;34m(ws_sc, template_data, q_summing, tof_weighted, bck_in_q, clean, info, normalize, theta_value, ws_db)\u001b[0m\n\u001b[1;32m 213\u001b[0m event_refl \u001b[38;5;241m=\u001b[39m event_reduction\u001b[38;5;241m.\u001b[39mEventReflectivity(ws_sc, ws_db,\n\u001b[1;32m 214\u001b[0m signal_peak\u001b[38;5;241m=\u001b[39mpeak, signal_bck\u001b[38;5;241m=\u001b[39mpeak_bck,\n\u001b[1;32m 215\u001b[0m norm_peak\u001b[38;5;241m=\u001b[39mnorm_peak, norm_bck\u001b[38;5;241m=\u001b[39mnorm_bck,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 220\u001b[0m theta\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mabs(theta),\n\u001b[1;32m 221\u001b[0m instrument\u001b[38;5;241m=\u001b[39mevent_reduction\u001b[38;5;241m.\u001b[39mEventReflectivity\u001b[38;5;241m.\u001b[39mINSTRUMENT_4B)\n\u001b[1;32m 223\u001b[0m \u001b[38;5;66;03m# R(Q)\u001b[39;00m\n\u001b[0;32m--> 224\u001b[0m qz, refl, d_refl \u001b[38;5;241m=\u001b[39m \u001b[43mevent_refl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mspecular\u001b[49m\u001b[43m(\u001b[49m\u001b[43mq_summing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq_summing\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtof_weighted\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtof_weighted\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 225\u001b[0m \u001b[43m \u001b[49m\u001b[43mbck_in_q\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbck_in_q\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclean\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnormalize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 226\u001b[0m qz_mid \u001b[38;5;241m=\u001b[39m (qz[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m qz[\u001b[38;5;241m1\u001b[39m:])\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2.0\u001b[39m\n\u001b[1;32m 228\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNormalization options: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (normalize, template_data\u001b[38;5;241m.\u001b[39mscaling_factor_flag))\n", - "File \u001b[0;32m~/git/LiquidsReflectometer/reduction/lr_reduction/event_reduction.py:240\u001b[0m, in \u001b[0;36mEventReflectivity.specular\u001b[0;34m(self, q_summing, tof_weighted, bck_in_q, clean, normalize)\u001b[0m\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspecular_weighted(q_summing\u001b[38;5;241m=\u001b[39mq_summing, bck_in_q\u001b[38;5;241m=\u001b[39mbck_in_q)\n\u001b[1;32m 239\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 240\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mspecular_unweighted\u001b[49m\u001b[43m(\u001b[49m\u001b[43mq_summing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq_summing\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnormalize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 242\u001b[0m \u001b[38;5;66;03m# Remove leading zeros\u001b[39;00m\n\u001b[1;32m 243\u001b[0m r \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mtrim_zeros(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrefl, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/git/LiquidsReflectometer/reduction/lr_reduction/event_reduction.py:281\u001b[0m, in \u001b[0;36mEventReflectivity.specular_unweighted\u001b[0;34m(self, q_summing, normalize)\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignal_bck \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 280\u001b[0m refl_bck, d_refl_bck \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbck_subtraction()\n\u001b[0;32m--> 281\u001b[0m refl \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m refl_bck\n\u001b[1;32m 282\u001b[0m d_refl \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msqrt(d_refl\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m \u001b[38;5;241m+\u001b[39m d_refl_bck\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 283\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSIG shape:\u001b[39m\u001b[38;5;124m\"\u001b[39m, refl\u001b[38;5;241m.\u001b[39mshape)\n", - "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (12,34) (3,34) (12,34) " + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "90c6f63daff9413b89d1f65265acfdd0", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "importlib.reload(workflow)\n", + "importlib.reload(output)\n", + "importlib.reload(event_reduction)\n", + "importlib.reload(peak_finding)\n", + "importlib.reload(template)\n", + "\n", + "data_dir = os.path.expanduser('~/git/LiquidsReflectometer/reduction/data')\n", + "template_path = os.path.join(data_dir, 'template.xml')\n", + "template_path = '/SNS/REF_L/IPTS-33612/shared/autoreduce/template_down.xml'\n", + "\n", + "fig, ax = plt.subplots(figsize=(10,5))\n", + "seq = 1\n", + "\n", + "run_number = 211906\n", + "\n", + "for i in [211906, 211911]: #, 211916, 211925]:\n", + " ws = api.Load(\"REF_L_%s\" % i)\n", + " workflow.reduce_fixed_two_theta(ws, template_path, output_dir=data_dir, average_overlap=False,\n", + " offset_from_first=False, fixed_offset=None)\n", + "\n", + " reduced_path = os.path.join(data_dir, 'REFL_%s_%s_%s_partial.txt' % (i, seq, i))\n", + " if os.path.isfile(reduced_path):\n", + " _refl = np.loadtxt(reduced_path).T\n", + " plt.errorbar(_refl[0], _refl[1]*_refl[0]**4, yerr=_refl[2]*_refl[0]**4, markersize=4, marker='.', linestyle='', label=str(i))\n", + "\n", + "\n", + "\n", + "plt.legend()\n", + "plt.xlabel('q [$1/\\AA$]')\n", + "plt.ylabel('R(q)')\n", + "ax.set_yscale('log')\n", + "ax.set_xscale('log')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": { + "execution": { + "iopub.execute_input": "2024-09-02T14:59:41.577504Z", + "iopub.status.busy": "2024-09-02T14:59:41.577188Z", + "iopub.status.idle": "2024-09-02T14:59:52.000371Z", + "shell.execute_reply": "2024-09-02T14:59:51.999941Z", + "shell.execute_reply.started": "2024-09-02T14:59:41.577481Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Processing: 211986\n", + " DB center: 261.404\t Width: 1.5691 from [250 275]\n", + " SC center: 229.014\t Width: 3\n", + " Theta = -0.479318\n", + " Template peak: [218 240]\n", + "wl=6.2; ths=-0.450408; thi=-0.000140031; No offset\n", + "Left side background: [100, 120]\n", + "Left side background: [100, 120]\n", + "Normalization options: True True\n", + "\n", + "Processing: 211987\n", + " Theta = -1.22894\n", + " Template peak: [172 188]\n", + "wl=6.2; ths=-1.20003; thi=-0.000140031; No offset\n", + "Background on both sides: [159 171] [189 193]\n", + "Left side background: [100, 120]\n", + "Normalization options: True True\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3e08b06f486c4999b9bede4e78b7cfdc", + "model_id": "2d704f1d7bd54317aa55afbd51a109b9", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", - " \n", + " \n", "
\n", " " ], @@ -247,24 +341,38 @@ "importlib.reload(peak_finding)\n", "importlib.reload(template)\n", "\n", + "ipts = 33612\n", + "ipts = 32814\n", + "\n", "data_dir = os.path.expanduser('~/git/LiquidsReflectometer/reduction/data')\n", "template_path = os.path.join(data_dir, 'template.xml')\n", + "template_path = '/SNS/REF_L/IPTS-%s/shared/autoreduce/template_down.xml' % ipts\n", "\n", "fig, ax = plt.subplots(figsize=(10,5))\n", "seq = 1\n", "\n", - "for i in range(198409, 198417):\n", + "run_number = 211906\n", + "run_number = 211986\n", + "\n", + "for i in range(run_number, run_number+2):\n", " ws = api.Load(\"REF_L_%s\" % i)\n", - " workflow.reduce_fixed_two_theta(ws, template_path, output_dir=data_dir, average_overlap=False)\n", + " workflow.reduce_fixed_two_theta(ws, template_path, output_dir=data_dir, average_overlap=False,\n", + " offset_from_first=True, fixed_offset=None)\n", " #workflow.reduce(ws, template_path, output_dir=data_dir, pre_cut=1, post_cut=1, average_overlap=False)\n", "\n", - " reduced_path = os.path.join(data_dir, 'REFL_198409_%s_%s_partial.txt' % (seq, i))\n", + " reduced_path = os.path.join(data_dir, 'REFL_%s_%s_%s_partial.txt' % (run_number, seq, i))\n", " if os.path.isfile(reduced_path):\n", " _refl = np.loadtxt(reduced_path).T\n", " plt.errorbar(_refl[0], _refl[1]*_refl[0]**4, yerr=_refl[2]*_refl[0]**4, markersize=4, marker='.', linestyle='', label='new reduction')\n", "\n", " seq += 1\n", "\n", + "if False:\n", + " reduced_path = '/SNS/REF_L/IPTS-%s/shared/autoreduce/REFL_%s_combined_data_auto.txt' % (ipts, run_number)\n", + " _refl = np.loadtxt(reduced_path).T\n", + " plt.errorbar(_refl[0], _refl[1]*_refl[0]**4, yerr=_refl[2]*_refl[0]**4, markersize=4, marker='.', linestyle='', label='auto-reduction')\n", + "\n", + "\n", "plt.xlabel('q [$1/\\AA$]')\n", "plt.ylabel('R(q)')\n", "ax.set_yscale('log')\n", @@ -274,14 +382,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 86, "metadata": { "execution": { - "iopub.execute_input": "2023-02-14T21:19:17.943373Z", - "iopub.status.busy": "2023-02-14T21:19:17.943120Z", - "iopub.status.idle": "2023-02-14T21:19:18.200765Z", - "shell.execute_reply": "2023-02-14T21:19:18.200137Z", - "shell.execute_reply.started": "2023-02-14T21:19:17.943355Z" + "iopub.execute_input": "2024-08-31T14:33:33.025587Z", + "iopub.status.busy": "2024-08-31T14:33:33.025058Z", + "iopub.status.idle": "2024-08-31T14:33:33.252165Z", + "shell.execute_reply": "2024-08-31T14:33:33.251729Z", + "shell.execute_reply.started": "2024-08-31T14:33:33.025559Z" }, "tags": [] }, @@ -289,18 +397,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7eaba68110544554a4540758a4e1fccd", + "model_id": "34b43470ace74e3ca66dbee36c2cc825", "version_major": 2, "version_minor": 0 }, - "image/png": 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", + "image/png": 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", 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\n", " " ], @@ -330,16 +438,116 @@ "plt.ylabel('R(q)')\n", "ax.set_yscale('log')\n", "ax.set_xscale('log')\n", - "plt.show()\n", - "\n" + "plt.show()\n" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "execution_count": 143, + "metadata": { + "execution": { + "iopub.execute_input": "2024-09-01T22:56:07.736455Z", + "iopub.status.busy": "2024-09-01T22:56:07.736119Z", + "iopub.status.idle": "2024-09-01T22:56:07.740781Z", + "shell.execute_reply": "2024-09-01T22:56:07.740384Z", + "shell.execute_reply.started": "2024-09-01T22:56:07.736432Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-1.9873584584723842\n" + ] + } + ], + "source": [ + "_pixel_width = 0.0007\n", + "det_distance = 1.355\n", + "dirpix = 261\n", + "peak_position = 85.5\n", + "#peak_position = 210\n", + "peak_position = 126.5\n", + "\n", + "\n", + "\n", + "\n", + "x0 = _pixel_width * (peak_position - dirpix)\n", + "theta = np.arctan(x0 / det_distance) / 2.0 * 180 / np.pi\n", + "print(theta)\n", + "\n", + "\n", + " \n", + "if False:\n", + " for i in range(304):\n", + " x0 = _pixel_width * (i - dirpix)\n", + " theta = np.arctan(x0 / det_distance) / 2.0 * 180 / np.pi\n", + " print(\"Pixel:%g Theta = %g\" % (i, theta))" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "execution": { + "iopub.execute_input": "2024-08-30T01:39:22.045519Z", + "iopub.status.busy": "2024-08-30T01:39:22.045153Z", + "iopub.status.idle": "2024-08-30T01:39:22.126224Z", + "shell.execute_reply": "2024-08-30T01:39:22.125855Z", + "shell.execute_reply.started": "2024-08-30T01:39:22.045498Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2ed42c932ddc43469f7dcd3f88396f39", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ths = np.asarray([-0.48, -0.75, -1.3, -1.87, -3.14])\n", + "pix = np.asarray([227, 210, 173, 85, 23])\n", + "all_pix = np.arange(300)\n", + "\n", + "x0 = _pixel_width * (pix - dirpix)\n", + "calc = np.arctan(x0 / det_distance) / 2.0 * 180 / np.pi\n", + "\n", + "x0 = _pixel_width * (all_pix - dirpix)\n", + "all_calc = np.arctan(x0 / det_distance) / 2.0 * 180 / np.pi\n", + "\n", + "fig, ax = plt.subplots(figsize=(10,5))\n", + "plt.plot(pix, ths, 'o', label='ths')\n", + "plt.plot(pix, calc, label='calculated from pixel')\n", + "plt.plot(all_pix, all_calc, color='gray')\n", + "\n", + "plt.legend()\n", + "plt.xlabel('pixel')\n", + "plt.ylabel('theta')\n", + "\n", + "\n", + "plt.show()\n" + ] }, { "cell_type": "code", diff --git a/scripts/autoreduce/reduce_REF_L.py b/scripts/autoreduce/reduce_REF_L.py index 75f30f9..b9a6b3e 100644 --- a/scripts/autoreduce/reduce_REF_L.py +++ b/scripts/autoreduce/reduce_REF_L.py @@ -40,6 +40,14 @@ if len(sys.argv) > 6: const_q = sys.argv[6].lower() == 'true' +fit_first_peak = True +if len(sys.argv) > 7: + fit_first_peak = sys.argv[7].lower() == 'true' + +theta_offset = None +if len(sys.argv) > 8: + theta_offset = float(sys.argv[8]) + event_file = os.path.split(event_file_path)[-1] # The legacy format is REF_L_xyz_event.nxs # The new format is REF_L_xyz.nxs.h5 @@ -112,7 +120,12 @@ print("Average overlap: %s" % avg_overlap) print("Constant-Q binning: %s" % const_q) from lr_reduction import workflow - first_run_of_set = workflow.reduce(ws, template_file, output_dir, + + #first_run_of_set = workflow.reduce(ws, template_file, output_dir, + first_run_of_set = workflow.reduce_fixed_two_theta(ws, template_file, output_dir, + offset_from_first=fit_first_peak, + fixed_offset=theta_offset, + peak_width=0, average_overlap=avg_overlap, q_summing=const_q, bck_in_q=False) else: diff --git a/scripts/livereduce/reduce_REF_L_live_post_proc.py b/scripts/livereduce/reduce_REF_L_live_post_proc.py index 3cecd74..8fa1708 100644 --- a/scripts/livereduce/reduce_REF_L_live_post_proc.py +++ b/scripts/livereduce/reduce_REF_L_live_post_proc.py @@ -8,7 +8,7 @@ import time sys.path.append("/SNS/REF_L/shared/reduction") -from lr_reduction import workflow +from lr_reduction import workflow, peak_finding DEBUG = True @@ -89,6 +89,47 @@ def reduction(): return '' +def find_peaks(): + ws = mtd_api.mtd[LIVE_DATA_WS] + tof, _x, _y = peak_finding.process_data(ws, summed=True, tof_step=200) + + + ths_value = ws.getRun()['ths'].value[0] + blocked = int(np.fabs(ths_value) * 50) + x_max = 261-blocked + + peak_center = np.argmax(_y[:x_max]) + + _center, _width, _ = peak_finding.fit_signal_flat_bck(_x, _y, + x_min=5, x_max=x_max, + center=peak_center, + sigma=5) + + ths_value = ws.getRun()['ths'].value[0] + + _pixel_width = 0.0007 + det_distance = 1.355 + dirpix = 261 + + x0 = _pixel_width * (_center - dirpix) + theta = np.arctan(x0 / det_distance) / 2.0 * 180 / np.pi + + fig, ax = plt.subplots(dpi=150, figsize=(5, 4.1)) + plt.subplots_adjust(left=0.15, right=.95, top=0.85, bottom=0.15) + + plt.plot(_x, _y) + + title = "ths=%g pixel=%g theta=%g" % (ths_value, _center, theta) + logthis(title+'\n') + logthis("xmax: %g\n" % x_max) + plt.title(title) + plt.legend(frameon=False) + plt.xlabel('Pixel', fontsize=15) + plt.ylabel('Counts', fontsize=15) + + plt.savefig('/SNS/REF_L/shared/peaks-live-data.png') + + def time_resolved(): logthis("\nStarting time-resolved processing\n") ws = mtd_api.mtd[LIVE_DATA_WS] @@ -214,9 +255,13 @@ def save_live_data(run_number, time_data): #except: # logthis(sys.exc_info) + # Find peaks + find_peaks() + # Time-resolved plot time_resolved() + except: logthis("failure: %s" % sys.exc_info()[1]) diff --git a/scripts/shared/batch_reduce.py b/scripts/shared/batch_reduce.py index 1ac43bc..35fe748 100644 --- a/scripts/shared/batch_reduce.py +++ b/scripts/shared/batch_reduce.py @@ -39,10 +39,20 @@ if len(sys.argv) > 7: const_q = sys.argv[7] +fit_first_peak = False +if len(sys.argv) > 8: + fit_first_peak = sys.argv[8] + +theta_offset = None +if len(sys.argv) > 9: + theta_offset = sys.argv[9] + print("Using new version: %s" % new_version) print("Using template: %s" % template_file) print(" Average overlap: %s" % avg_overlap) print(" Constant-Q binning: %s" % const_q) +print(" Fit first peak: %s" % fit_first_peak) +print(" Const theta offset: %s" % theta_offset) t_0 = time.time() for r in range(first_run, last_run+1): @@ -53,12 +63,17 @@ else: print("Processing %s" % _data_file_path) if new_version: - cmd = "%s /SNS/REF_L/shared/autoreduce/reduce_REF_L.py %s %s new %s %s %s" % (PYTHON_CMD, - _data_file_path, - _output_dir, - template_file, - avg_overlap, - const_q) + cmd = "%s /SNS/REF_L/shared/autoreduce/reduce_REF_L.py %s %s new %s %s %s %s" % ( + PYTHON_CMD, + _data_file_path, + _output_dir, + template_file, + avg_overlap, + const_q, + fit_first_peak + ) + if theta_offset is not None: + cmd += " %s" % theta_offset else: if template_file is not None: cmd = "%s /SNS/REF_L/shared/autoreduce/reduce_REF_L.py %s %s old %s" % (PYTHON_CMD,