Updating for version 0.7.11

This is an outdated way of pyzebra deployment

.gitea/workflows/deploy.yaml

@@ -0,0 +1,53 @@
+name: pyzebra CI/CD pipeline
+
+on:
+  push:
+    branches:
+      - main
+    tags:
+      - '*'
+
+env:
+  CONDA: /opt/miniforge3
+
+jobs:
+  prepare:
+    runs-on: pyzebra
+    steps:
+      - run: $CONDA/bin/conda config --add channels conda-forge
+      - run: $CONDA/bin/conda config --set solver libmamba
+
+  test-env:
+    runs-on: pyzebra
+    needs: prepare
+    if: github.ref == 'refs/heads/main'
+    env:
+      BUILD_DIR: ${{ runner.temp }}/conda_build
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      - run: $CONDA/bin/conda build --no-anaconda-upload --output-folder $BUILD_DIR ./conda-recipe
+      - run: $CONDA/bin/conda remove --name test --all --keep-env -y
+      - run: $CONDA/bin/conda install --name test --channel $BUILD_DIR python=3.8 pyzebra -y
+      - run: sudo systemctl restart pyzebra-test.service
+
+  prod-env:
+    runs-on: pyzebra
+    needs: prepare
+    if: startsWith(github.ref, 'refs/tags/')
+    env:
+      BUILD_DIR: ${{ runner.temp }}/conda_build
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      - run: $CONDA/bin/conda build --token ${{ secrets.ANACONDA_TOKEN }} --output-folder $BUILD_DIR ./conda-recipe
+      - run: $CONDA/bin/conda remove --name prod --all --keep-env -y
+      - run: $CONDA/bin/conda install --name prod --channel $BUILD_DIR python=3.8 pyzebra -y
+      - run: sudo systemctl restart pyzebra-prod.service
+
+  cleanup:
+    runs-on: pyzebra
+    needs: [test-env, prod-env]
+    if: always()
+    steps:
+      - run: $CONDA/bin/conda build purge-all

.github/workflows/deployment.yaml

@@ -1,26 +0,0 @@
-name: Deployment
-
-on:
-  push:
-    tags:
-      - '*'
-
-jobs:
-  publish-conda-package:
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v2
-
-    - name: Prepare
-      run: |
-        $CONDA/bin/conda install --quiet --yes conda-build anaconda-client conda-libmamba-solver
-        $CONDA/bin/conda config --append channels conda-forge
-        $CONDA/bin/conda config --set solver libmamba
-        $CONDA/bin/conda config --set anaconda_upload yes
-
-    - name: Build and upload
-      env:
-        ANACONDA_TOKEN: ${{ secrets.ANACONDA_TOKEN }}
-      run: |
-        $CONDA/bin/conda build --token $ANACONDA_TOKEN conda-recipe

conda-recipe/meta.yaml

@@ -28,7 +28,7 @@ requirements:
 
 
 about:
-  home: https://github.com/paulscherrerinstitute/pyzebra
+  home: https://gitlab.psi.ch/zebra/pyzebra
   summary: {{ data['description'] }}
   license: GNU GPLv3
   license_file: LICENSE

make_release.py

@@ -7,18 +7,19 @@ import subprocess
 
 
 def main():
+    default_branch = "main"
     branch = subprocess.check_output("git rev-parse --abbrev-ref HEAD", shell=True).decode().strip()
-    if branch != "master":
-        print("Aborting, not on 'master' branch.")
+    if branch != default_branch:
+        print(f"Aborting, not on '{default_branch}' branch.")
         return
 
-    filepath = "pyzebra/__init__.py"
+    version_filepath = os.path.join(os.path.basename(os.path.dirname(__file__)), "__init__.py")
 
     parser = argparse.ArgumentParser()
     parser.add_argument("level", type=str, choices=["patch", "minor", "major"])
     args = parser.parse_args()
 
-    with open(filepath) as f:
+    with open(version_filepath) as f:
         file_content = f.read()
 
     version = re.search(r'__version__ = "(.*?)"', file_content).group(1)
@@ -36,11 +37,12 @@ def main():
 
     new_version = f"{major}.{minor}.{patch}"
 
-    with open(filepath, "w") as f:
+    with open(version_filepath, "w") as f:
         f.write(re.sub(r'__version__ = "(.*?)"', f'__version__ = "{new_version}"', file_content))
 
-    os.system(f"git commit {filepath} -m 'Updating for version {new_version}'")
+    os.system(f"git commit {version_filepath} -m 'Updating for version {new_version}'")
     os.system(f"git tag -a {new_version} -m 'Release {new_version}'")
+    os.system("git push --follow-tags")
 
 
 if __name__ == "__main__":

pyzebra/__init__.py

@@ -6,4 +6,4 @@ from pyzebra.sxtal_refgen import *
 from pyzebra.utils import *
 from pyzebra.xtal import *
 
-__version__ = "0.7.5"
+__version__ = "0.7.11"

pyzebra/anatric.py

@@ -1,6 +1,9 @@
+import logging
 import subprocess
 import xml.etree.ElementTree as ET
 
+logger = logging.getLogger(__name__)
+
 DATA_FACTORY_IMPLEMENTATION = ["trics", "morph", "d10"]
 
 REFLECTION_PRINTER_FORMATS = [
@@ -16,11 +19,11 @@ REFLECTION_PRINTER_FORMATS = [
     "oksana",
 ]
 
-ANATRIC_PATH = "/afs/psi.ch/project/sinq/rhel7/bin/anatric"
+ANATRIC_PATH = "/afs/psi.ch/project/sinq/rhel8/bin/anatric"
 ALGORITHMS = ["adaptivemaxcog", "adaptivedynamic"]
 
 
-def anatric(config_file, anatric_path=ANATRIC_PATH, cwd=None):
+def anatric(config_file, anatric_path=ANATRIC_PATH, cwd=None, log=logger):
     comp_proc = subprocess.run(
         [anatric_path, config_file],
         stdout=subprocess.PIPE,
@@ -29,8 +32,8 @@ def anatric(config_file, anatric_path=ANATRIC_PATH, cwd=None):
         check=True,
         text=True,
     )
-    print(" ".join(comp_proc.args))
-    print(comp_proc.stdout)
+    log.info(" ".join(comp_proc.args))
+    log.info(comp_proc.stdout)
 
 
 class AnatricConfig:

pyzebra/app/app_hooks.py

@@ -1,17 +0,0 @@
-import logging
-import sys
-from io import StringIO
-
-
-def on_server_loaded(_server_context):
-    formatter = logging.Formatter(
-        fmt="%(asctime)s %(levelname)s: %(message)s", datefmt="%Y-%m-%d %H:%M:%S"
-    )
-
-    sys.stdout = StringIO()
-
-    bokeh_handler = logging.StreamHandler(StringIO())
-    bokeh_handler.setFormatter(formatter)
-    bokeh_logger = logging.getLogger("bokeh")
-    bokeh_logger.setLevel(logging.WARNING)
-    bokeh_logger.addHandler(bokeh_handler)

pyzebra/app/cli.py

@@ -4,7 +4,7 @@ import sys
 
 
 def main():
-    app_path = os.path.join(os.path.dirname(os.path.abspath(__file__)))
+    app_path = os.path.dirname(os.path.abspath(__file__))
     subprocess.run(["bokeh", "serve", app_path, *sys.argv[1:]], check=True)
 
 

pyzebra/app/fit_controls.py

@@ -1,5 +1,6 @@
 import types
 
+from bokeh.io import curdoc
 from bokeh.models import (
     Button,
     CellEditor,
@@ -51,6 +52,7 @@ def _params_factory(function):
 
 class FitControls:
     def __init__(self):
+        self.log = curdoc().logger
         self.params = {}
 
         def add_function_button_callback(click):
@@ -145,7 +147,11 @@ class FitControls:
 
     def _process_scan(self, scan):
         pyzebra.fit_scan(
-            scan, self.params, fit_from=self.from_spinner.value, fit_to=self.to_spinner.value
+            scan,
+            self.params,
+            fit_from=self.from_spinner.value,
+            fit_to=self.to_spinner.value,
+            log=self.log,
         )
         pyzebra.get_area(
             scan,

pyzebra/app/input_controls.py

@@ -11,6 +11,7 @@ import pyzebra
 class InputControls:
     def __init__(self, dataset, dlfiles, on_file_open=lambda: None, on_monitor_change=lambda: None):
         doc = curdoc()
+        log = doc.logger
 
         def filelist_select_update_for_proposal():
             proposal_path = proposal_textinput.name
@@ -45,19 +46,19 @@ class InputControls:
                     f_name = os.path.basename(f_path)
                     base, ext = os.path.splitext(f_name)
                     try:
-                        file_data = pyzebra.parse_1D(file, ext)
-                    except:
-                        print(f"Error loading {f_name}")
+                        file_data = pyzebra.parse_1D(file, ext, log=log)
+                    except Exception as e:
+                        log.exception(e)
                         continue
 
                 pyzebra.normalize_dataset(file_data, monitor_spinner.value)
 
                 if not new_data:  # first file
                     new_data = file_data
-                    pyzebra.merge_duplicates(new_data)
+                    pyzebra.merge_duplicates(new_data, log=log)
                     dlfiles.set_names([base] * dlfiles.n_files)
                 else:
-                    pyzebra.merge_datasets(new_data, file_data)
+                    pyzebra.merge_datasets(new_data, file_data, log=log)
 
             if new_data:
                 dataset.clear()
@@ -76,13 +77,13 @@ class InputControls:
                     f_name = os.path.basename(f_path)
                     _, ext = os.path.splitext(f_name)
                     try:
-                        file_data = pyzebra.parse_1D(file, ext)
-                    except:
-                        print(f"Error loading {f_name}")
+                        file_data = pyzebra.parse_1D(file, ext, log=log)
+                    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:
                 on_file_open()
@@ -97,19 +98,19 @@ class InputControls:
                 with io.StringIO(base64.b64decode(f_str).decode()) as file:
                     base, ext = os.path.splitext(f_name)
                     try:
-                        file_data = pyzebra.parse_1D(file, ext)
-                    except:
-                        print(f"Error loading {f_name}")
+                        file_data = pyzebra.parse_1D(file, ext, log=log)
+                    except Exception as e:
+                        log.exception(e)
                         continue
 
                 pyzebra.normalize_dataset(file_data, monitor_spinner.value)
 
                 if not new_data:  # first file
                     new_data = file_data
-                    pyzebra.merge_duplicates(new_data)
+                    pyzebra.merge_duplicates(new_data, log=log)
                     dlfiles.set_names([base] * dlfiles.n_files)
                 else:
-                    pyzebra.merge_datasets(new_data, file_data)
+                    pyzebra.merge_datasets(new_data, file_data, log=log)
 
             if new_data:
                 dataset.clear()
@@ -129,13 +130,13 @@ class InputControls:
                 with io.StringIO(base64.b64decode(f_str).decode()) as file:
                     _, ext = os.path.splitext(f_name)
                     try:
-                        file_data = pyzebra.parse_1D(file, ext)
-                    except:
-                        print(f"Error loading {f_name}")
+                        file_data = pyzebra.parse_1D(file, ext, log=log)
+                    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:
                 on_file_open()

pyzebra/app/main.py

@@ -1,6 +1,6 @@
 import argparse
 import logging
-import sys
+from io import StringIO
 
 from bokeh.io import curdoc
 from bokeh.layouts import column, row
@@ -43,11 +43,17 @@ doc.anatric_path = args.anatric_path
 doc.spind_path = args.spind_path
 doc.sxtal_refgen_path = args.sxtal_refgen_path
 
-# In app_hooks.py a StreamHandler was added to "bokeh" logger
-bokeh_stream = logging.getLogger("bokeh").handlers[0].stream
+stream = StringIO()
+handler = logging.StreamHandler(stream)
+handler.setFormatter(
+    logging.Formatter(fmt="%(asctime)s %(levelname)s: %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
+)
+logger = logging.getLogger(str(id(doc)))
+logger.setLevel(logging.INFO)
+logger.addHandler(handler)
+doc.logger = logger
 
-log_textareainput = TextAreaInput(title="logging output:")
-bokeh_log_textareainput = TextAreaInput(title="server output:")
+log_textareainput = TextAreaInput(title="Logging output:")
 
 
 def proposal_textinput_callback(_attr, _old, _new):
@@ -65,7 +71,7 @@ def apply_button_callback():
         try:
             proposal_path = pyzebra.find_proposal_path(proposal)
         except ValueError as e:
-            print(e)
+            logger.exception(e)
             return
         apply_button.disabled = True
     else:
@@ -94,14 +100,13 @@ doc.add_root(
                 panel_spind.create(),
             ]
         ),
-        row(log_textareainput, bokeh_log_textareainput, sizing_mode="scale_both"),
+        row(log_textareainput, sizing_mode="scale_both"),
     )
 )
 
 
 def update_stdout():
-    log_textareainput.value = sys.stdout.getvalue()
-    bokeh_log_textareainput.value = bokeh_stream.getvalue()
+    log_textareainput.value = stream.getvalue()
 
 
 doc.add_periodic_callback(update_stdout, 1000)

pyzebra/app/panel_ccl_compare.py

@@ -33,6 +33,7 @@ from pyzebra import EXPORT_TARGETS, app
 
 def create():
     doc = curdoc()
+    log = doc.logger
     dataset1 = []
     dataset2 = []
     app_dlfiles = app.DownloadFiles(n_files=2)
@@ -94,7 +95,7 @@ def create():
 
     def file_open_button_callback():
         if len(file_select.value) != 2:
-            print("WARNING: Select exactly 2 .ccl files.")
+            log.warning("Select exactly 2 .ccl files.")
             return
 
         new_data1 = []
@@ -104,13 +105,13 @@ def create():
                 f_name = os.path.basename(f_path)
                 base, ext = os.path.splitext(f_name)
                 try:
-                    file_data = pyzebra.parse_1D(file, ext)
-                except:
-                    print(f"Error loading {f_name}")
+                    file_data = pyzebra.parse_1D(file, ext, log=log)
+                except Exception as e:
+                    log.exception(e)
                     return
 
             pyzebra.normalize_dataset(file_data, monitor_spinner.value)
-            pyzebra.merge_duplicates(file_data)
+            pyzebra.merge_duplicates(file_data, log=log)
 
             if ind == 0:
                 app_dlfiles.set_names([base, base])
@@ -133,7 +134,7 @@ def create():
 
     def upload_button_callback(_attr, _old, _new):
         if len(upload_button.filename) != 2:
-            print("WARNING: Upload exactly 2 .ccl files.")
+            log.warning("Upload exactly 2 .ccl files.")
             return
 
         new_data1 = []
@@ -142,13 +143,13 @@ def create():
             with io.StringIO(base64.b64decode(f_str).decode()) as file:
                 base, ext = os.path.splitext(f_name)
                 try:
-                    file_data = pyzebra.parse_1D(file, ext)
-                except:
-                    print(f"Error loading {f_name}")
+                    file_data = pyzebra.parse_1D(file, ext, log=log)
+                except Exception as e:
+                    log.exception(e)
                     return
 
             pyzebra.normalize_dataset(file_data, monitor_spinner.value)
-            pyzebra.merge_duplicates(file_data)
+            pyzebra.merge_duplicates(file_data, log=log)
 
             if ind == 0:
                 app_dlfiles.set_names([base, base])
@@ -377,11 +378,11 @@ def create():
         scan_from2 = dataset2[int(merge_from_select.value)]
 
         if scan_into1 is scan_from1:
-            print("WARNING: Selected scans for merging are identical")
+            log.warning("Selected scans for merging are identical")
             return
 
-        pyzebra.merge_scans(scan_into1, scan_from1)
-        pyzebra.merge_scans(scan_into2, scan_from2)
+        pyzebra.merge_scans(scan_into1, scan_from1, log=log)
+        pyzebra.merge_scans(scan_into2, scan_from2, log=log)
         _update_table()
         _update_plot()
 

pyzebra/app/panel_ccl_integrate.py

@@ -2,6 +2,7 @@ import os
 import tempfile
 
 import numpy as np
+from bokeh.io import curdoc
 from bokeh.layouts import column, row
 from bokeh.models import (
     Button,
@@ -25,6 +26,8 @@ from pyzebra import EXPORT_TARGETS, app
 
 
 def create():
+    doc = curdoc()
+    log = doc.logger
     dataset = []
     app_dlfiles = app.DownloadFiles(n_files=2)
 
@@ -214,10 +217,10 @@ 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_plot()
 

pyzebra/app/panel_ccl_prepare.py

@@ -5,6 +5,7 @@ import subprocess
 import tempfile
 
 import numpy as np
+from bokeh.io import curdoc
 from bokeh.layouts import column, row
 from bokeh.models import (
     Arrow,
@@ -39,6 +40,8 @@ SORT_OPT_NB = ["gamma", "nu", "omega"]
 
 
 def create():
+    doc = curdoc()
+    log = doc.logger
     ang_lims = {}
     cif_data = {}
     params = {}
@@ -132,7 +135,11 @@ def create():
         params = dict()
         params["SPGR"] = cryst_space_group.value
         params["CELL"] = cryst_cell.value
-        ub = pyzebra.calc_ub_matrix(params)
+        try:
+            ub = pyzebra.calc_ub_matrix(params, log=log)
+        except Exception as e:
+            log.exception(e)
+            return
         ub_matrix.value = " ".join(ub)
 
     ub_matrix_calc = Button(label="UB matrix:", button_type="primary", width=100)
@@ -221,9 +228,9 @@ def create():
                 geom_template = None
             pyzebra.export_geom_file(geom_path, ang_lims, geom_template)
 
-            print(f"Content of {geom_path}:")
+            log.info(f"Content of {geom_path}:")
             with open(geom_path) as f:
-                print(f.read())
+                log.info(f.read())
 
             priority = [sorting_0.value, sorting_1.value, sorting_2.value]
             chunks = [sorting_0_dt.value, sorting_1_dt.value, sorting_2_dt.value]
@@ -248,9 +255,9 @@ def create():
                     cfl_template = None
                 pyzebra.export_cfl_file(cfl_path, params, cfl_template)
 
-                print(f"Content of {cfl_path}:")
+                log.info(f"Content of {cfl_path}:")
                 with open(cfl_path) as f:
-                    print(f.read())
+                    log.info(f.read())
 
                 comp_proc = subprocess.run(
                     [pyzebra.SXTAL_REFGEN_PATH, cfl_path],
@@ -260,8 +267,8 @@ 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)
 
                 if i == 1:  # all hkl files are identical, so keep only one
                     hkl_fname = base_fname + ".hkl"
@@ -591,8 +598,8 @@ def create():
                 _, ext = os.path.splitext(fname)
                 try:
                     file_data = pyzebra.parse_hkl(file, ext)
-                except:
-                    print(f"Error loading {fname}")
+                except Exception as e:
+                    log.exception(e)
                     return
 
             fnames.append(fname)

pyzebra/app/panel_hdf_anatric.py

@@ -24,6 +24,7 @@ from pyzebra import DATA_FACTORY_IMPLEMENTATION, REFLECTION_PRINTER_FORMATS
 
 def create():
     doc = curdoc()
+    log = doc.logger
     config = pyzebra.AnatricConfig()
 
     def _load_config_file(file):
@@ -347,7 +348,11 @@ def create():
         with tempfile.TemporaryDirectory() as temp_dir:
             temp_file = temp_dir + "/config.xml"
             config.save_as(temp_file)
-            pyzebra.anatric(temp_file, anatric_path=doc.anatric_path, cwd=temp_dir)
+            try:
+                pyzebra.anatric(temp_file, anatric_path=doc.anatric_path, cwd=temp_dir, log=log)
+            except Exception as e:
+                log.exception(e)
+                return
 
             with open(os.path.join(temp_dir, config.logfile)) as f_log:
                 output_log.value = f_log.read()

pyzebra/app/panel_hdf_param_study.py

@@ -36,6 +36,7 @@ IMAGE_PLOT_H = int(IMAGE_H * 2.4) + 27
 
 def create():
     doc = curdoc()
+    log = doc.logger
     dataset = []
     cami_meta = {}
 
@@ -133,8 +134,8 @@ def create():
         for f_name in file_select.value:
             try:
                 new_data.append(pyzebra.read_detector_data(f_name))
-            except KeyError:
-                print("Could not read data from the file.")
+            except KeyError as e:
+                log.exception(e)
                 return
 
         dataset.extend(new_data)
@@ -275,7 +276,7 @@ def create():
         frame_range.bounds = (0, n_im)
 
         scan_motor = scan["scan_motor"]
-        proj_y_plot.axis[1].axis_label = f"Scanning motor, {scan_motor}"
+        proj_y_plot.yaxis.axis_label = f"Scanning motor, {scan_motor}"
 
         var = scan[scan_motor]
         var_start = var[0]

pyzebra/app/panel_hdf_viewer.py

@@ -43,6 +43,7 @@ IMAGE_PLOT_H = int(IMAGE_H * 2.4) + 27
 
 def create():
     doc = curdoc()
+    log = doc.logger
     dataset = []
     cami_meta = {}
 
@@ -102,8 +103,8 @@ def create():
         nonlocal dataset
         try:
             scan = pyzebra.read_detector_data(io.BytesIO(base64.b64decode(new)), None)
-        except KeyError:
-            print("Could not read data from the file.")
+        except Exception as e:
+            log.exception(e)
             return
 
         dataset = [scan]
@@ -137,8 +138,8 @@ def create():
             f_name = os.path.basename(f_path)
             try:
                 file_data = [pyzebra.read_detector_data(f_path, cm)]
-            except:
-                print(f"Error loading {f_name}")
+            except Exception as e:
+                log.exception(e)
                 continue
 
             pyzebra.normalize_dataset(file_data, monitor_spinner.value)
@@ -146,7 +147,7 @@ def create():
             if not new_data:  # first file
                 new_data = file_data
             else:
-                pyzebra.merge_datasets(new_data, file_data)
+                pyzebra.merge_datasets(new_data, file_data, log=log)
 
         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()
@@ -406,7 +407,7 @@ def create():
         frame_range.bounds = (0, n_im)
 
         scan_motor = scan["scan_motor"]
-        proj_y_plot.axis[1].axis_label = f"Scanning motor, {scan_motor}"
+        proj_y_plot.yaxis.axis_label = f"Scanning motor, {scan_motor}"
 
         var = scan[scan_motor]
         var_start = var[0]

pyzebra/app/panel_param_study.py

@@ -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()

pyzebra/app/panel_plot_data.py

@@ -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"])):

pyzebra/app/panel_spind.py

@@ -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)
 

pyzebra/app/plot_hkl.py

@@ -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"])):

pyzebra/ccl_io.py

@@ -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",
@@ -14,6 +17,7 @@ META_VARS_STR = (
     "original_filename",
     "date",
     "zebra_mode",
+    "zebramode",
     "sample_name",
 )
 
@@ -110,7 +114,7 @@ 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
@@ -133,6 +137,8 @@ def parse_1D(fileobj, data_type):
 
         try:
             if var_name in META_VARS_STR:
+                if var_name == "zebramode":
+                    var_name = "zebra_mode"
                 metadata[var_name] = value
 
             elif var_name in META_VARS_FLOAT:
@@ -156,7 +162,7 @@ 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
@@ -232,7 +238,7 @@ def parse_1D(fileobj, data_type):
         scan["export"] = True
 
         match = re.search("Scanning Variables: (.*), Steps: (.*)", next(fileobj))
-        motors = [motor.lower() for motor in match.group(1).split(", ")]
+        motors = [motor.strip().lower() for motor in match.group(1).split(",")]
         # Steps can be separated by " " or ", "
         steps = [float(step.strip(",")) for step in match.group(2).split()]
 
@@ -294,7 +300,7 @@ def parse_1D(fileobj, data_type):
         dataset.append({**metadata, **scan})
 
     else:
-        print("Unknown file extention")
+        log.error("Unknown file extention")
 
     return dataset
 

pyzebra/ccl_process.py

@@ -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)
@@ -87,16 +92,16 @@ def merge_datasets(dataset_into, dataset_from):
         for ind, scan_from in enumerate(dataset_from):
             if _parameters_match(scan_into, scan_from) and not merged[ind]:
                 if scan_into["counts"].ndim == 3:
-                    merge_h5_scans(scan_into, scan_from)
+                    merge_h5_scans(scan_into, scan_from, log=log)
                 else:  # scan_into["counts"].ndim == 1
-                    merge_scans(scan_into, scan_from)
+                    merge_scans(scan_into, scan_from, log=log)
                 merged[ind] = True
 
     for scan_from in 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]

pyzebra/h5.py

@@ -148,11 +148,21 @@ def read_detector_data(filepath, cami_meta=None):
         if "/entry1/sample/magnetic_field" in h5f:
             scan["mf"] = h5f["/entry1/sample/magnetic_field"][:]
 
+        if "mf" in scan:
+            # TODO: NaNs are not JSON compliant, so replace them with None
+            # this is not a great solution, but makes it safe to use the array in bokeh
+            scan["mf"] = np.where(np.isnan(scan["mf"]), None, scan["mf"])
+
         if "/entry1/sample/temperature" in h5f:
             scan["temp"] = h5f["/entry1/sample/temperature"][:]
         elif "/entry1/sample/Ts/value" in h5f:
             scan["temp"] = h5f["/entry1/sample/Ts/value"][:]
 
+        if "temp" in scan:
+            # TODO: NaNs are not JSON compliant, so replace them with None
+            # this is not a great solution, but makes it safe to use the array in bokeh
+            scan["temp"] = np.where(np.isnan(scan["temp"]), None, scan["temp"])
+
         # overwrite metadata from .cami
         if cami_meta is not None:
             if "crystal" in cami_meta:

pyzebra/sxtal_refgen.py

@@ -1,4 +1,5 @@
 import io
+import logging
 import os
 import subprocess
 import tempfile
@@ -6,7 +7,9 @@ from math import ceil, floor
 
 import numpy as np
 
-SXTAL_REFGEN_PATH = "/afs/psi.ch/project/sinq/rhel7/bin/Sxtal_Refgen"
+logger = logging.getLogger(__name__)
+
+SXTAL_REFGEN_PATH = "/afs/psi.ch/project/sinq/rhel8/bin/Sxtal_Refgen"
 
 _zebraBI_default_geom = """GEOM          2 Bissecting - HiCHI
 BLFR          z-up
@@ -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 = []

scripts/pyzebra-test.service

@@ -1,11 +0,0 @@
-[Unit]
-Description=pyzebra-test web server
-
-[Service]
-Type=simple
-User=pyzebra
-ExecStart=/bin/bash /usr/local/sbin/pyzebra-test.sh
-Restart=always
-
-[Install]
-WantedBy=multi-user.target

scripts/pyzebra-test.sh

@@ -1,4 +0,0 @@
-source /opt/miniconda3/etc/profile.d/conda.sh
-
-conda activate test
-python /opt/pyzebra/pyzebra/app/cli.py --port=5010 --allow-websocket-origin=pyzebra.psi.ch:5010 --args --spind-path=/opt/spind

scripts/pyzebra.service

@@ -1,10 +0,0 @@
-[Unit]
-Description=pyzebra web server
-
-[Service]
-Type=simple
-ExecStart=/bin/bash /usr/local/sbin/pyzebra.sh
-Restart=always
-
-[Install]
-WantedBy=multi-user.target

scripts/pyzebra.sh

@@ -1,4 +0,0 @@
-source /opt/miniconda3/etc/profile.d/conda.sh
-
-conda activate prod
-pyzebra --port=80 --allow-websocket-origin=pyzebra.psi.ch:80 --args --spind-path=/opt/spind