fixed dspeed version

.github/workflows/distribution.yml

@@ -38,6 +38,6 @@ jobs:
         name: artifact
         path: dist
 
-    - uses: pypa/[email protected].7
+    - uses: pypa/[email protected].10
       with:
         password: ${{ secrets.pypi_password }}

.github/workflows/main.yml

@@ -4,10 +4,8 @@ on:
   push:
     branches:
       - main
-      - refactor
       - 'releases/**'
   pull_request:
-  merge_group:
   release:
 
 concurrency:
@@ -22,7 +20,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ['3.9', '3.10']
+        python-version: ['3.9', '3.10', '3.11']
         os: [ubuntu-latest, macOS-latest]
 
     steps:
@@ -54,7 +52,7 @@ jobs:
         run: |
           python -m pip install --upgrade pip wheel setuptools
           python -m pip install --upgrade .[test]
-          pytest --cov=pygama --cov-report=xml
+          python -m pytest --cov=pygama --cov-report=xml
       - name: Upload Coverage to codecov.io
         uses: codecov/codecov-action@v3
         with:

.pre-commit-config.yaml

@@ -26,7 +26,7 @@ repos:
   - id: trailing-whitespace
 
 - repo: https://github.com/asottile/setup-cfg-fmt
-  rev: "v2.2.0"
+  rev: "v2.4.0"
   hooks:
   - id: setup-cfg-fmt
 
@@ -36,7 +36,7 @@ repos:
   - id: isort
 
 - repo: https://github.com/asottile/pyupgrade
-  rev: "v3.3.1"
+  rev: "v3.8.0"
   hooks:
   - id: pyupgrade
     args: ["--py38-plus"]
@@ -47,14 +47,14 @@ repos:
   - id: black-jupyter
 
 - repo: https://github.com/pre-commit/mirrors-mypy
-  rev: "v1.1.1"
+  rev: "v1.4.1"
   hooks:
   - id: mypy
     files: src
     stages: [manual]
 
 - repo: https://github.com/hadialqattan/pycln
-  rev: "v2.1.3"
+  rev: "v2.1.5"
   hooks:
   - id: pycln
     exclude: ^src/pygama/pargen
@@ -85,12 +85,12 @@ repos:
     stages: [manual]
 
 - repo: https://github.com/codespell-project/codespell
-  rev: "v2.2.4"
+  rev: "v2.2.5"
   hooks:
   - id: codespell
 
 - repo: https://github.com/shellcheck-py/shellcheck-py
-  rev: "v0.9.0.2"
+  rev: "v0.9.0.5"
   hooks:
   - id: shellcheck
 
@@ -103,7 +103,7 @@ repos:
   - id: rst-inline-touching-normal
 
 - repo: https://github.com/pre-commit/mirrors-prettier
-  rev: "v3.0.0-alpha.6"
+  rev: "v3.0.0-alpha.9-for-vscode"
   hooks:
     - id: prettier
       types_or: [json]

README.md

@@ -13,9 +13,15 @@
 
 *pygama* is a Python package for:
 
-* converting physics data acquisition system output to [LH5-format](https://github.com/legend-exp/legend-data-format-specs) HDF5 files
-* performing bulk digital signal processing (DSP) on time-series data
-* optimizing DSP routines and tuning associated analysis parameters
-* generating and selecting high-level event data for further analysis
+- converting physics data acquisition system output to
+  [LH5-format](https://legend-exp.github.io/legend-data-format-specs) HDF5
+  files (functionality provided by the
+  [legend-pydataobj](https://legend-pydataobj.readthedocs.io) and
+  [legend-daq2lh5](https://legend-daq2lh5.readthedocs.io) packages)
+- performing bulk digital signal processing (DSP) on time-series data
+  (functionality provided by the [dspeed](https://dspeed.readthedocs.io)
+  package)
+- optimizing DSP routines and tuning associated analysis parameters
+- generating and selecting high-level event data for further analysis
 
 Check out the [online documentation](https://pygama.readthedocs.io).

codecov.yml

@@ -5,7 +5,7 @@ coverage:
   status:
     project:
       default:
-        target: 20%
+        informational: true
     patch: false
 
 github_checks:

setup.cfg

@@ -7,7 +7,7 @@ url = https://github.com/legend-exp/pygama
 author = The LEGEND collaboration
 maintainer = The LEGEND collaboration
 license = GPL-3.0
-license_file = LICENSE
+license_files = LICENSE
 classifiers =
     Development Status :: 4 - Beta
     Intended Audience :: Developers
@@ -32,11 +32,11 @@ project_urls =
 packages = find:
 install_requires =
     colorlog
-    dspeed==1.2.*
+    dspeed>=1.2
     h5py>=3.2
     iminuit
-    legend-daq2lh5==1.0.*
-    legend-pydataobj==1.1.*
+    legend-daq2lh5>=1.0
+    legend-pydataobj>=1.1
     matplotlib
     numba!=0.53.*,!=0.54.*,!=0.57
     numpy>=1.21

src/pygama/evt/tcm.py

@@ -20,11 +20,22 @@ def generate_tcm_cols(
     coincidence data (e.g. hit times from different channels). Returns 3
     :class:`numpy.ndarray`\ s representing a vector-of-vector-like structure:
     two flattened arrays ``array_id`` (e.g. channel number) and  ``array_idx``
-    (e.g. hit ID) that specify the location in the input ``coin_data`` of each
-    datum belonging to a coincidence event, and a ``cumulative_length`` array
-    that specifies which rows of the other two output arrays correspond to
-    which coincidence event. These can be used to retrieve other data at the
-    same tier as the input data into coincidence structures.
+    (e.g. hit index) that specify the location in the input ``coin_data`` of
+    each datum belonging to a coincidence event, and a ``cumulative_length``
+    array that specifies which rows of the other two output arrays correspond
+    to which coincidence event. These can be used to retrieve other data at
+    the same tier as the input data into coincidence structures.
+
+    The 0'th entry of ``cumulative_length`` contains the number of hits in the
+    zeroth coincidence event, and the i'th entry is set to
+    ``cumulative_length[i-1]`` plus the number of hits in the i'th event.
+    Thus, the hits of the i'th event can be found in rows
+    ``cumulative_length[i-1]`` to ``cumulative_length[i] - 1`` of ``array_id``
+    and ``array_idx``.
+
+    An example: ``cumulative_length = [4, 7, ...]``.  Then rows 0 to 3 in
+    `array_id` and `array_idx` correspond to the hits in event 0, rows 4 to 6
+    correspond to event 1, and so on.
 
     Makes use of :func:`pandas.concat`, :meth:`pandas.DataFrame.sort_values`,
     and :meth:`pandas.DataFrame.diff` functions:

src/pygama/flow/data_loader.py

@@ -873,8 +873,13 @@ def build_hit_entries(
                     tb_df[f"{low_level}_idx"] = tb_df.index
 
                 # final DataFrame
-                f_entries = pd.concat((f_entries, tb_df), ignore_index=True)[entry_cols]
-                # end tb loop
+                if f_entries.empty:
+                    f_entries = tb_df
+                else:
+                    f_entries = pd.concat((f_entries, tb_df), ignore_index=True)[
+                        entry_cols
+                    ]
+
             if self.merge_files:
                 f_entries["file"] = file
             if in_memory: