Isolate loggers per document
This commit is contained in:
parent
14d122b947
commit
9b48fb7a24
@ -1,6 +1,9 @@
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import logging
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import subprocess
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import xml.etree.ElementTree as ET
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logger = logging.getLogger(__name__)
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DATA_FACTORY_IMPLEMENTATION = ["trics", "morph", "d10"]
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REFLECTION_PRINTER_FORMATS = [
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@ -20,7 +23,7 @@ ANATRIC_PATH = "/afs/psi.ch/project/sinq/rhel7/bin/anatric"
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ALGORITHMS = ["adaptivemaxcog", "adaptivedynamic"]
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def anatric(config_file, anatric_path=ANATRIC_PATH, cwd=None):
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def anatric(config_file, anatric_path=ANATRIC_PATH, cwd=None, log=logger):
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comp_proc = subprocess.run(
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[anatric_path, config_file],
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stdout=subprocess.PIPE,
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@ -29,8 +32,8 @@ def anatric(config_file, anatric_path=ANATRIC_PATH, cwd=None):
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check=True,
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text=True,
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)
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print(" ".join(comp_proc.args))
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print(comp_proc.stdout)
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log.info(" ".join(comp_proc.args))
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log.info(comp_proc.stdout)
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class AnatricConfig:
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@ -1,6 +0,0 @@
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import sys
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from io import StringIO
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def on_server_loaded(_server_context):
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sys.stdout = StringIO()
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@ -1,5 +1,6 @@
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import types
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from bokeh.io import curdoc
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from bokeh.models import (
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Button,
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CellEditor,
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@ -51,6 +52,7 @@ def _params_factory(function):
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class FitControls:
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def __init__(self):
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self.log = curdoc().logger
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self.params = {}
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def add_function_button_callback(click):
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@ -145,7 +147,11 @@ class FitControls:
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def _process_scan(self, scan):
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pyzebra.fit_scan(
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scan, self.params, fit_from=self.from_spinner.value, fit_to=self.to_spinner.value
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scan,
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self.params,
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fit_from=self.from_spinner.value,
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fit_to=self.to_spinner.value,
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log=self.log,
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)
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pyzebra.get_area(
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scan,
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@ -11,6 +11,7 @@ import pyzebra
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class InputControls:
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def __init__(self, dataset, dlfiles, on_file_open=lambda: None, on_monitor_change=lambda: None):
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doc = curdoc()
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log = doc.logger
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def filelist_select_update_for_proposal():
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proposal_path = proposal_textinput.name
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@ -45,19 +46,19 @@ class InputControls:
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f_name = os.path.basename(f_path)
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base, ext = os.path.splitext(f_name)
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try:
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file_data = pyzebra.parse_1D(file, ext)
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except:
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print(f"Error loading {f_name}")
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file_data = pyzebra.parse_1D(file, ext, log=log)
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except Exception as e:
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log.exception(e)
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continue
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pyzebra.normalize_dataset(file_data, monitor_spinner.value)
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if not new_data: # first file
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new_data = file_data
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pyzebra.merge_duplicates(new_data)
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pyzebra.merge_duplicates(new_data, log=log)
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dlfiles.set_names([base] * dlfiles.n_files)
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else:
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pyzebra.merge_datasets(new_data, file_data)
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pyzebra.merge_datasets(new_data, file_data, log=log)
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if new_data:
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dataset.clear()
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@ -76,13 +77,13 @@ class InputControls:
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f_name = os.path.basename(f_path)
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_, ext = os.path.splitext(f_name)
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try:
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file_data = pyzebra.parse_1D(file, ext)
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except:
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print(f"Error loading {f_name}")
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file_data = pyzebra.parse_1D(file, ext, log=log)
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except Exception as e:
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log.exception(e)
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continue
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pyzebra.normalize_dataset(file_data, monitor_spinner.value)
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pyzebra.merge_datasets(dataset, file_data)
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pyzebra.merge_datasets(dataset, file_data, log=log)
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if file_data:
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on_file_open()
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@ -97,19 +98,19 @@ class InputControls:
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with io.StringIO(base64.b64decode(f_str).decode()) as file:
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base, ext = os.path.splitext(f_name)
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try:
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file_data = pyzebra.parse_1D(file, ext)
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except:
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print(f"Error loading {f_name}")
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file_data = pyzebra.parse_1D(file, ext, log=log)
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except Exception as e:
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log.exception(e)
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continue
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pyzebra.normalize_dataset(file_data, monitor_spinner.value)
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if not new_data: # first file
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new_data = file_data
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pyzebra.merge_duplicates(new_data)
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pyzebra.merge_duplicates(new_data, log=log)
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dlfiles.set_names([base] * dlfiles.n_files)
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else:
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pyzebra.merge_datasets(new_data, file_data)
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pyzebra.merge_datasets(new_data, file_data, log=log)
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if new_data:
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dataset.clear()
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@ -129,13 +130,13 @@ class InputControls:
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with io.StringIO(base64.b64decode(f_str).decode()) as file:
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_, ext = os.path.splitext(f_name)
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try:
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file_data = pyzebra.parse_1D(file, ext)
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except:
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print(f"Error loading {f_name}")
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file_data = pyzebra.parse_1D(file, ext, log=log)
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except Exception as e:
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log.exception(e)
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continue
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pyzebra.normalize_dataset(file_data, monitor_spinner.value)
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pyzebra.merge_datasets(dataset, file_data)
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pyzebra.merge_datasets(dataset, file_data, log=log)
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if file_data:
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on_file_open()
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@ -1,5 +1,6 @@
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import argparse
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import sys
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import logging
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from io import StringIO
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from bokeh.io import curdoc
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from bokeh.layouts import column, row
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@ -42,6 +43,16 @@ doc.anatric_path = args.anatric_path
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doc.spind_path = args.spind_path
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doc.sxtal_refgen_path = args.sxtal_refgen_path
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stream = StringIO()
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handler = logging.StreamHandler(stream)
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handler.setFormatter(
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logging.Formatter(fmt="%(asctime)s %(levelname)s: %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
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)
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logger = logging.getLogger(str(id(doc)))
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logger.setLevel(logging.INFO)
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logger.addHandler(handler)
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doc.logger = logger
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log_textareainput = TextAreaInput(title="Logging output:")
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@ -60,7 +71,7 @@ def apply_button_callback():
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try:
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proposal_path = pyzebra.find_proposal_path(proposal)
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except ValueError as e:
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print(e)
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logger.exception(e)
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return
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apply_button.disabled = True
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else:
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@ -95,7 +106,7 @@ doc.add_root(
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def update_stdout():
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log_textareainput.value = sys.stdout.getvalue()
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log_textareainput.value = stream.getvalue()
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doc.add_periodic_callback(update_stdout, 1000)
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@ -33,6 +33,7 @@ from pyzebra import EXPORT_TARGETS, app
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def create():
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doc = curdoc()
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log = doc.logger
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dataset1 = []
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dataset2 = []
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app_dlfiles = app.DownloadFiles(n_files=2)
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@ -94,7 +95,7 @@ def create():
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def file_open_button_callback():
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if len(file_select.value) != 2:
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print("WARNING: Select exactly 2 .ccl files.")
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log.warning("Select exactly 2 .ccl files.")
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return
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new_data1 = []
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@ -104,13 +105,13 @@ def create():
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f_name = os.path.basename(f_path)
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base, ext = os.path.splitext(f_name)
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try:
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file_data = pyzebra.parse_1D(file, ext)
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except:
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print(f"Error loading {f_name}")
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file_data = pyzebra.parse_1D(file, ext, log=log)
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except Exception as e:
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log.exception(e)
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return
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pyzebra.normalize_dataset(file_data, monitor_spinner.value)
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pyzebra.merge_duplicates(file_data)
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pyzebra.merge_duplicates(file_data, log=log)
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if ind == 0:
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app_dlfiles.set_names([base, base])
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@ -133,7 +134,7 @@ def create():
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def upload_button_callback(_attr, _old, _new):
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if len(upload_button.filename) != 2:
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print("WARNING: Upload exactly 2 .ccl files.")
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log.warning("Upload exactly 2 .ccl files.")
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return
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new_data1 = []
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@ -142,13 +143,13 @@ def create():
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with io.StringIO(base64.b64decode(f_str).decode()) as file:
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base, ext = os.path.splitext(f_name)
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try:
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file_data = pyzebra.parse_1D(file, ext)
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except:
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print(f"Error loading {f_name}")
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file_data = pyzebra.parse_1D(file, ext, log=log)
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except Exception as e:
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log.exception(e)
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return
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pyzebra.normalize_dataset(file_data, monitor_spinner.value)
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pyzebra.merge_duplicates(file_data)
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pyzebra.merge_duplicates(file_data, log=log)
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if ind == 0:
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app_dlfiles.set_names([base, base])
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@ -377,11 +378,11 @@ def create():
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scan_from2 = dataset2[int(merge_from_select.value)]
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if scan_into1 is scan_from1:
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print("WARNING: Selected scans for merging are identical")
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log.warning("Selected scans for merging are identical")
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return
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pyzebra.merge_scans(scan_into1, scan_from1)
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pyzebra.merge_scans(scan_into2, scan_from2)
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pyzebra.merge_scans(scan_into1, scan_from1, log=log)
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pyzebra.merge_scans(scan_into2, scan_from2, log=log)
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_update_table()
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_update_plot()
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@ -2,6 +2,7 @@ import os
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import tempfile
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import numpy as np
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from bokeh.io import curdoc
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from bokeh.layouts import column, row
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from bokeh.models import (
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Button,
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@ -25,6 +26,8 @@ from pyzebra import EXPORT_TARGETS, app
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def create():
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doc = curdoc()
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log = doc.logger
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dataset = []
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app_dlfiles = app.DownloadFiles(n_files=2)
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@ -214,10 +217,10 @@ def create():
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scan_from = dataset[int(merge_from_select.value)]
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if scan_into is scan_from:
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print("WARNING: Selected scans for merging are identical")
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log.warning("Selected scans for merging are identical")
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return
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pyzebra.merge_scans(scan_into, scan_from)
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pyzebra.merge_scans(scan_into, scan_from, log=log)
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_update_table()
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_update_plot()
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@ -5,6 +5,7 @@ import subprocess
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import tempfile
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import numpy as np
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from bokeh.io import curdoc
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from bokeh.layouts import column, row
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from bokeh.models import (
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Arrow,
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@ -39,6 +40,8 @@ SORT_OPT_NB = ["gamma", "nu", "omega"]
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def create():
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doc = curdoc()
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log = doc.logger
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ang_lims = {}
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cif_data = {}
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params = {}
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@ -132,7 +135,11 @@ def create():
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params = dict()
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params["SPGR"] = cryst_space_group.value
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params["CELL"] = cryst_cell.value
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ub = pyzebra.calc_ub_matrix(params)
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try:
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ub = pyzebra.calc_ub_matrix(params, log=log)
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except Exception as e:
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log.exception(e)
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return
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ub_matrix.value = " ".join(ub)
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ub_matrix_calc = Button(label="UB matrix:", button_type="primary", width=100)
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@ -221,9 +228,9 @@ def create():
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geom_template = None
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pyzebra.export_geom_file(geom_path, ang_lims, geom_template)
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print(f"Content of {geom_path}:")
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log.info(f"Content of {geom_path}:")
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with open(geom_path) as f:
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print(f.read())
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log.info(f.read())
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priority = [sorting_0.value, sorting_1.value, sorting_2.value]
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chunks = [sorting_0_dt.value, sorting_1_dt.value, sorting_2_dt.value]
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@ -248,9 +255,9 @@ def create():
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cfl_template = None
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pyzebra.export_cfl_file(cfl_path, params, cfl_template)
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print(f"Content of {cfl_path}:")
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log.info(f"Content of {cfl_path}:")
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with open(cfl_path) as f:
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print(f.read())
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log.info(f.read())
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comp_proc = subprocess.run(
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[pyzebra.SXTAL_REFGEN_PATH, cfl_path],
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@ -260,8 +267,8 @@ def create():
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stderr=subprocess.STDOUT,
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text=True,
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)
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print(" ".join(comp_proc.args))
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print(comp_proc.stdout)
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log.info(" ".join(comp_proc.args))
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log.info(comp_proc.stdout)
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if i == 1: # all hkl files are identical, so keep only one
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hkl_fname = base_fname + ".hkl"
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@ -591,8 +598,8 @@ def create():
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_, ext = os.path.splitext(fname)
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try:
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file_data = pyzebra.parse_hkl(file, ext)
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except:
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print(f"Error loading {fname}")
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except Exception as e:
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log.exception(e)
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return
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fnames.append(fname)
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@ -24,6 +24,7 @@ from pyzebra import DATA_FACTORY_IMPLEMENTATION, REFLECTION_PRINTER_FORMATS
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def create():
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doc = curdoc()
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log = doc.logger
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config = pyzebra.AnatricConfig()
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def _load_config_file(file):
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@ -347,7 +348,11 @@ def create():
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with tempfile.TemporaryDirectory() as temp_dir:
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temp_file = temp_dir + "/config.xml"
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config.save_as(temp_file)
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pyzebra.anatric(temp_file, anatric_path=doc.anatric_path, cwd=temp_dir)
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try:
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pyzebra.anatric(temp_file, anatric_path=doc.anatric_path, cwd=temp_dir, log=log)
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except Exception as e:
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log.exception(e)
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return
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with open(os.path.join(temp_dir, config.logfile)) as f_log:
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output_log.value = f_log.read()
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@ -36,6 +36,7 @@ IMAGE_PLOT_H = int(IMAGE_H * 2.4) + 27
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def create():
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doc = curdoc()
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log = doc.logger
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dataset = []
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cami_meta = {}
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@ -133,8 +134,8 @@ def create():
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for f_name in file_select.value:
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try:
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new_data.append(pyzebra.read_detector_data(f_name))
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except KeyError:
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print("Could not read data from the file.")
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except KeyError as e:
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log.exception(e)
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return
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dataset.extend(new_data)
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@ -43,6 +43,7 @@ IMAGE_PLOT_H = int(IMAGE_H * 2.4) + 27
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def create():
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doc = curdoc()
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log = doc.logger
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dataset = []
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cami_meta = {}
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@ -102,8 +103,8 @@ def create():
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nonlocal dataset
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try:
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scan = pyzebra.read_detector_data(io.BytesIO(base64.b64decode(new)), None)
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except KeyError:
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print("Could not read data from the file.")
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except Exception as e:
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log.exception(e)
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return
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dataset = [scan]
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@ -137,8 +138,8 @@ def create():
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f_name = os.path.basename(f_path)
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try:
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file_data = [pyzebra.read_detector_data(f_path, cm)]
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except:
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print(f"Error loading {f_name}")
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except Exception as e:
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log.exception(e)
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continue
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pyzebra.normalize_dataset(file_data, monitor_spinner.value)
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@ -146,7 +147,7 @@ def create():
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if not new_data: # first file
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new_data = file_data
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else:
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pyzebra.merge_datasets(new_data, file_data)
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pyzebra.merge_datasets(new_data, file_data, log=log)
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if new_data:
|
||||
dataset = new_data
|
||||
@ -161,12 +162,12 @@ def create():
|
||||
f_name = os.path.basename(f_path)
|
||||
try:
|
||||
file_data = [pyzebra.read_detector_data(f_path, None)]
|
||||
except:
|
||||
print(f"Error loading {f_name}")
|
||||
except Exception as e:
|
||||
log.exception(e)
|
||||
continue
|
||||
|
||||
pyzebra.normalize_dataset(file_data, monitor_spinner.value)
|
||||
pyzebra.merge_datasets(dataset, file_data)
|
||||
pyzebra.merge_datasets(dataset, file_data, log=log)
|
||||
|
||||
if file_data:
|
||||
_init_datatable()
|
||||
@ -292,10 +293,10 @@ def create():
|
||||
scan_from = dataset[int(merge_from_select.value)]
|
||||
|
||||
if scan_into is scan_from:
|
||||
print("WARNING: Selected scans for merging are identical")
|
||||
log.warning("Selected scans for merging are identical")
|
||||
return
|
||||
|
||||
pyzebra.merge_h5_scans(scan_into, scan_from)
|
||||
pyzebra.merge_h5_scans(scan_into, scan_from, log=log)
|
||||
_update_table()
|
||||
_update_image()
|
||||
_update_proj_plots()
|
||||
|
@ -3,6 +3,7 @@ import os
|
||||
import tempfile
|
||||
|
||||
import numpy as np
|
||||
from bokeh.io import curdoc
|
||||
from bokeh.layouts import column, row
|
||||
from bokeh.models import (
|
||||
Button,
|
||||
@ -38,6 +39,8 @@ def color_palette(n_colors):
|
||||
|
||||
|
||||
def create():
|
||||
doc = curdoc()
|
||||
log = doc.logger
|
||||
dataset = []
|
||||
app_dlfiles = app.DownloadFiles(n_files=1)
|
||||
|
||||
@ -361,10 +364,10 @@ def create():
|
||||
scan_from = dataset[int(merge_from_select.value)]
|
||||
|
||||
if scan_into is scan_from:
|
||||
print("WARNING: Selected scans for merging are identical")
|
||||
log.warning("Selected scans for merging are identical")
|
||||
return
|
||||
|
||||
pyzebra.merge_scans(scan_into, scan_from)
|
||||
pyzebra.merge_scans(scan_into, scan_from, log=log)
|
||||
_update_table()
|
||||
_update_single_scan_plot()
|
||||
_update_overview()
|
||||
|
@ -3,6 +3,7 @@ import io
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
from bokeh.io import curdoc
|
||||
from bokeh.layouts import column, row
|
||||
from bokeh.models import (
|
||||
Button,
|
||||
@ -31,6 +32,8 @@ from pyzebra.app.panel_hdf_viewer import calculate_hkl
|
||||
|
||||
|
||||
def create():
|
||||
doc = curdoc()
|
||||
log = doc.logger
|
||||
_update_slice = None
|
||||
measured_data_div = Div(text="Measured <b>HDF</b> data:")
|
||||
measured_data = FileInput(accept=".hdf", multiple=True, width=200)
|
||||
@ -59,8 +62,8 @@ def create():
|
||||
# Read data
|
||||
try:
|
||||
det_data = pyzebra.read_detector_data(io.BytesIO(base64.b64decode(fdata)))
|
||||
except:
|
||||
print(f"Error loading {fname}")
|
||||
except Exception as e:
|
||||
log.exception(e)
|
||||
return None
|
||||
|
||||
if ind == 0:
|
||||
@ -179,8 +182,8 @@ def create():
|
||||
_, ext = os.path.splitext(fname)
|
||||
try:
|
||||
fdata = pyzebra.parse_hkl(file, ext)
|
||||
except:
|
||||
print(f"Error loading {fname}")
|
||||
except Exception as e:
|
||||
log.exception(e)
|
||||
return
|
||||
|
||||
for ind in range(len(fdata["counts"])):
|
||||
|
@ -21,6 +21,7 @@ import pyzebra
|
||||
|
||||
def create():
|
||||
doc = curdoc()
|
||||
log = doc.logger
|
||||
events_data = doc.events_data
|
||||
|
||||
npeaks_spinner = Spinner(title="Number of peaks from hdf_view panel:", disabled=True)
|
||||
@ -63,8 +64,8 @@ def create():
|
||||
stderr=subprocess.STDOUT,
|
||||
text=True,
|
||||
)
|
||||
print(" ".join(comp_proc.args))
|
||||
print(comp_proc.stdout)
|
||||
log.info(" ".join(comp_proc.args))
|
||||
log.info(comp_proc.stdout)
|
||||
|
||||
# prepare an event file
|
||||
diff_vec = []
|
||||
@ -94,9 +95,9 @@ def create():
|
||||
f"{x_pos} {y_pos} {intensity} {snr_cnts} {dv1} {dv2} {dv3} {d_spacing}\n"
|
||||
)
|
||||
|
||||
print(f"Content of {temp_event_file}:")
|
||||
log.info(f"Content of {temp_event_file}:")
|
||||
with open(temp_event_file) as f:
|
||||
print(f.read())
|
||||
log.info(f.read())
|
||||
|
||||
comp_proc = subprocess.run(
|
||||
[
|
||||
@ -123,8 +124,8 @@ def create():
|
||||
stderr=subprocess.STDOUT,
|
||||
text=True,
|
||||
)
|
||||
print(" ".join(comp_proc.args))
|
||||
print(comp_proc.stdout)
|
||||
log.info(" ".join(comp_proc.args))
|
||||
log.info(comp_proc.stdout)
|
||||
|
||||
spind_out_file = os.path.join(temp_dir, "spind.txt")
|
||||
spind_res = dict(
|
||||
@ -146,12 +147,12 @@ def create():
|
||||
ub_matrices.append(ub_matrix_spind)
|
||||
spind_res["ub_matrix"].append(str(ub_matrix_spind * 1e-10))
|
||||
|
||||
print(f"Content of {spind_out_file}:")
|
||||
log.info(f"Content of {spind_out_file}:")
|
||||
with open(spind_out_file) as f:
|
||||
print(f.read())
|
||||
log.info(f.read())
|
||||
|
||||
except FileNotFoundError:
|
||||
print("No results from spind")
|
||||
log.warning("No results from spind")
|
||||
|
||||
results_table_source.data.update(spind_res)
|
||||
|
||||
|
@ -3,6 +3,7 @@ import io
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
from bokeh.io import curdoc
|
||||
from bokeh.layouts import column, row
|
||||
from bokeh.models import (
|
||||
Arrow,
|
||||
@ -30,6 +31,9 @@ import pyzebra
|
||||
|
||||
class PlotHKL:
|
||||
def __init__(self):
|
||||
doc = curdoc()
|
||||
log = doc.logger
|
||||
|
||||
_update_slice = None
|
||||
measured_data_div = Div(text="Measured <b>CCL</b> data:")
|
||||
measured_data = FileInput(accept=".ccl", multiple=True, width=200)
|
||||
@ -62,9 +66,9 @@ class PlotHKL:
|
||||
with io.StringIO(base64.b64decode(md_fdata[0]).decode()) as file:
|
||||
_, ext = os.path.splitext(md_fnames[0])
|
||||
try:
|
||||
file_data = pyzebra.parse_1D(file, ext)
|
||||
except:
|
||||
print(f"Error loading {md_fnames[0]}")
|
||||
file_data = pyzebra.parse_1D(file, ext, log=log)
|
||||
except Exception as e:
|
||||
log.exception(e)
|
||||
return None
|
||||
|
||||
alpha = file_data[0]["alpha_cell"] * np.pi / 180.0
|
||||
@ -144,9 +148,9 @@ class PlotHKL:
|
||||
with io.StringIO(base64.b64decode(md_fdata[j]).decode()) as file:
|
||||
_, ext = os.path.splitext(md_fname)
|
||||
try:
|
||||
file_data = pyzebra.parse_1D(file, ext)
|
||||
except:
|
||||
print(f"Error loading {md_fname}")
|
||||
file_data = pyzebra.parse_1D(file, ext, log=log)
|
||||
except Exception as e:
|
||||
log.exception(e)
|
||||
return None
|
||||
|
||||
pyzebra.normalize_dataset(file_data)
|
||||
@ -291,8 +295,8 @@ class PlotHKL:
|
||||
_, ext = os.path.splitext(fname)
|
||||
try:
|
||||
fdata = pyzebra.parse_hkl(file, ext)
|
||||
except:
|
||||
print(f"Error loading {fname}")
|
||||
except Exception as e:
|
||||
log.exception(e)
|
||||
return
|
||||
|
||||
for ind in range(len(fdata["counts"])):
|
||||
|
@ -1,3 +1,4 @@
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
from ast import literal_eval
|
||||
@ -5,6 +6,8 @@ from collections import defaultdict
|
||||
|
||||
import numpy as np
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
META_VARS_STR = (
|
||||
"instrument",
|
||||
"title",
|
||||
@ -110,7 +113,7 @@ def load_1D(filepath):
|
||||
return dataset
|
||||
|
||||
|
||||
def parse_1D(fileobj, data_type):
|
||||
def parse_1D(fileobj, data_type, log=logger):
|
||||
metadata = {"data_type": data_type}
|
||||
|
||||
# read metadata
|
||||
@ -156,7 +159,7 @@ def parse_1D(fileobj, data_type):
|
||||
metadata["ub"][row, :] = list(map(float, value.split()))
|
||||
|
||||
except Exception:
|
||||
print(f"Error reading {var_name} with value '{value}'")
|
||||
log.error(f"Error reading {var_name} with value '{value}'")
|
||||
metadata[var_name] = 0
|
||||
|
||||
# handle older files that don't contain "zebra_mode" metadata
|
||||
@ -294,7 +297,7 @@ def parse_1D(fileobj, data_type):
|
||||
dataset.append({**metadata, **scan})
|
||||
|
||||
else:
|
||||
print("Unknown file extention")
|
||||
log.error("Unknown file extention")
|
||||
|
||||
return dataset
|
||||
|
||||
|
@ -1,3 +1,4 @@
|
||||
import logging
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
@ -6,6 +7,8 @@ from scipy.integrate import simpson, trapezoid
|
||||
|
||||
from pyzebra import CCL_ANGLES
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
PARAM_PRECISIONS = {
|
||||
"twotheta": 0.1,
|
||||
"chi": 0.1,
|
||||
@ -33,12 +36,12 @@ def normalize_dataset(dataset, monitor=100_000):
|
||||
scan["monitor"] = monitor
|
||||
|
||||
|
||||
def merge_duplicates(dataset):
|
||||
def merge_duplicates(dataset, log=logger):
|
||||
merged = np.zeros(len(dataset), dtype=bool)
|
||||
for ind_into, scan_into in enumerate(dataset):
|
||||
for ind_from, scan_from in enumerate(dataset[ind_into + 1 :], start=ind_into + 1):
|
||||
if _parameters_match(scan_into, scan_from) and not merged[ind_from]:
|
||||
merge_scans(scan_into, scan_from)
|
||||
merge_scans(scan_into, scan_from, log=log)
|
||||
merged[ind_from] = True
|
||||
|
||||
|
||||
@ -75,11 +78,13 @@ def _parameters_match(scan1, scan2):
|
||||
return True
|
||||
|
||||
|
||||
def merge_datasets(dataset_into, dataset_from):
|
||||
def merge_datasets(dataset_into, dataset_from, log=logger):
|
||||
scan_motors_into = dataset_into[0]["scan_motors"]
|
||||
scan_motors_from = dataset_from[0]["scan_motors"]
|
||||
if scan_motors_into != scan_motors_from:
|
||||
print(f"Scan motors mismatch between datasets: {scan_motors_into} vs {scan_motors_from}")
|
||||
log.warning(
|
||||
f"Scan motors mismatch between datasets: {scan_motors_into} vs {scan_motors_from}"
|
||||
)
|
||||
return
|
||||
|
||||
merged = np.zeros(len(dataset_from), dtype=bool)
|
||||
@ -96,7 +101,7 @@ def merge_datasets(dataset_into, dataset_from):
|
||||
dataset_into.append(scan_from)
|
||||
|
||||
|
||||
def merge_scans(scan_into, scan_from):
|
||||
def merge_scans(scan_into, scan_from, log=logger):
|
||||
if "init_scan" not in scan_into:
|
||||
scan_into["init_scan"] = scan_into.copy()
|
||||
|
||||
@ -148,10 +153,10 @@ def merge_scans(scan_into, scan_from):
|
||||
|
||||
fname1 = os.path.basename(scan_into["original_filename"])
|
||||
fname2 = os.path.basename(scan_from["original_filename"])
|
||||
print(f'Merging scans: {scan_into["idx"]} ({fname1}) <-- {scan_from["idx"]} ({fname2})')
|
||||
log.info(f'Merging scans: {scan_into["idx"]} ({fname1}) <-- {scan_from["idx"]} ({fname2})')
|
||||
|
||||
|
||||
def merge_h5_scans(scan_into, scan_from):
|
||||
def merge_h5_scans(scan_into, scan_from, log=logger):
|
||||
if "init_scan" not in scan_into:
|
||||
scan_into["init_scan"] = scan_into.copy()
|
||||
|
||||
@ -160,7 +165,7 @@ def merge_h5_scans(scan_into, scan_from):
|
||||
|
||||
for scan in scan_into["merged_scans"]:
|
||||
if scan_from is scan:
|
||||
print("Already merged scan")
|
||||
log.warning("Already merged scan")
|
||||
return
|
||||
|
||||
scan_into["merged_scans"].append(scan_from)
|
||||
@ -212,7 +217,7 @@ def merge_h5_scans(scan_into, scan_from):
|
||||
|
||||
fname1 = os.path.basename(scan_into["original_filename"])
|
||||
fname2 = os.path.basename(scan_from["original_filename"])
|
||||
print(f'Merging scans: {scan_into["idx"]} ({fname1}) <-- {scan_from["idx"]} ({fname2})')
|
||||
log.info(f'Merging scans: {scan_into["idx"]} ({fname1}) <-- {scan_from["idx"]} ({fname2})')
|
||||
|
||||
|
||||
def restore_scan(scan):
|
||||
@ -230,7 +235,7 @@ def restore_scan(scan):
|
||||
scan["export"] = True
|
||||
|
||||
|
||||
def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
|
||||
def fit_scan(scan, model_dict, fit_from=None, fit_to=None, log=logger):
|
||||
if fit_from is None:
|
||||
fit_from = -np.inf
|
||||
if fit_to is None:
|
||||
@ -243,7 +248,7 @@ def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
|
||||
# apply fitting range
|
||||
fit_ind = (fit_from <= x_fit) & (x_fit <= fit_to)
|
||||
if not np.any(fit_ind):
|
||||
print(f"No data in fit range for scan {scan['idx']}")
|
||||
log.warning(f"No data in fit range for scan {scan['idx']}")
|
||||
return
|
||||
|
||||
y_fit = y_fit[fit_ind]
|
||||
|
@ -1,4 +1,5 @@
|
||||
import io
|
||||
import logging
|
||||
import os
|
||||
import subprocess
|
||||
import tempfile
|
||||
@ -6,6 +7,8 @@ from math import ceil, floor
|
||||
|
||||
import numpy as np
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
SXTAL_REFGEN_PATH = "/afs/psi.ch/project/sinq/rhel7/bin/Sxtal_Refgen"
|
||||
|
||||
_zebraBI_default_geom = """GEOM 2 Bissecting - HiCHI
|
||||
@ -144,7 +147,7 @@ def export_geom_file(path, ang_lims, template=None):
|
||||
out_file.write(f"{'':<8}{ang:<10}{vals[0]:<10}{vals[1]:<10}{vals[2]:<10}\n")
|
||||
|
||||
|
||||
def calc_ub_matrix(params):
|
||||
def calc_ub_matrix(params, log=logger):
|
||||
with tempfile.TemporaryDirectory() as temp_dir:
|
||||
cfl_file = os.path.join(temp_dir, "ub_matrix.cfl")
|
||||
|
||||
@ -160,8 +163,8 @@ def calc_ub_matrix(params):
|
||||
stderr=subprocess.STDOUT,
|
||||
text=True,
|
||||
)
|
||||
print(" ".join(comp_proc.args))
|
||||
print(comp_proc.stdout)
|
||||
log.info(" ".join(comp_proc.args))
|
||||
log.info(comp_proc.stdout)
|
||||
|
||||
sfa_file = os.path.join(temp_dir, "ub_matrix.sfa")
|
||||
ub_matrix = []
|
||||
|
Loading…
x
Reference in New Issue
Block a user