new release 0.6.1

.github/workflows/ci-on-pullreq.yaml

@@ -5,12 +5,17 @@ on: [pull_request]
 jobs:
   test:
     runs-on: ${{ matrix.os }}
+    env:
+      MPLBACKEND: Agg
     strategy:
       fail-fast: true
       matrix:
         os: ["ubuntu-latest", "macos-latest", "windows-latest"]
-        python-version: ["3.9", "3.10", "3.11", "3.12"]
-
+        python-version: ["3.10", "3.11", "3.12"]
+        exclude:
+          # Exclude Python 3.10 on macOS latest
+          - os: "macos-latest"
+            python-version: "3.10"
     steps:
       - name: Checkout source
         uses: actions/checkout@v2
@@ -20,8 +25,9 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           architecture: x64
+
 
-      - name: Install
+      - name: Install poetry
         run: |
           pip install poetry
           poetry install --with dev,test

.gitignore

@@ -49,6 +49,7 @@ coverage.xml
 *.py,cover
 .hypothesis/
 .pytest_cache/
+test-output.xml
 cover/
 
 # Translations
@@ -160,3 +161,4 @@ cython_debug/
 .issues/
 .DS_Store
 test.xlsx
+

.readthedocs.yml

@@ -5,16 +5,16 @@ build:
   tools:
     python: "3.10"
   jobs:
-    post_create_environment:
-      # Install poetry
-      # https://python-poetry.org/docs/#installing-manually
+
+    post_install:
       - pip install poetry
       # Tell poetry to not use a virtual environment
-      - poetry config virtualenvs.create false
-    post_install:
       # Install dependencies with 'docs' dependency group
       # https://python-poetry.org/docs/managing-dependencies/#dependency-groups
-      - poetry install --with docs
+      - poetry export -f requirements.txt --without-hashes --without-urls --with docs -o requirements.txt
+      - pip install -r requirements.txt
+      #- pip list
+
 
 sphinx:
   configuration: docs/conf.py

README.rst

@@ -1,3 +1,4 @@
+=========
 portfolyo
 =========
 
@@ -28,6 +29,8 @@ portfolyo
 Portfolyo is a Python package to analyse and manipulate timeseries related to power 
 and gas offtake portfolios.
 
+
+------------
 Installation
 ------------
 
@@ -45,13 +48,16 @@ NB: this package is under active development and the API will change without pri
    portfolyo = "x.y.z"
 
 
+-------------
 Documentation
 -------------
 
 Documentation is hosted on readthedocs:
 
 https://portfolyo.readthedocs.io/
 
+
+----------
 Repository
 ----------
 
@@ -60,6 +66,7 @@ The git repository is hosted on github:
 http://www.github.com/rwijtvliet/portfolyo
 
 
+----------
 Developing
 ----------
 
@@ -74,6 +81,16 @@ the commit hooks.
 
 Feature branches are merged into the ``develop`` branch via pull request.
 
+
+Internal dependencies
+---------------------
+
+Inside the package, modules depend on each other in the following chain. A module may depend on another module if it is further to the left:
+
+tools >> pfline >> pfstate >> tools2
+
+
+----------
 Publishing
 ----------
 

benchmark/benchmark.py

@@ -65,7 +65,7 @@ def sizeof(o):
 
 def benchmark_pfline(df):
     for le, ru in tqdm.tqdm(zip(LENGTHS, RUNS)):
-        i = pd.date_range("2020", periods=le, freq="15T")
+        i = pd.date_range("2020", periods=le, freq="15min")
         for cols in ["q", "p", "qr", "wp"]:
             print(f"{le=} {cols=}")
             data = pd.DataFrame({c: np.linspace(1, 1000, le) for c in cols}, i)
@@ -99,7 +99,7 @@ def benchmark_pfline(df):
 def benchmark_pfstate(df):
     for le, ru in tqdm.tqdm(zip(LENGTHS, RUNS)):
         print(f"{le=}")
-        i = pd.date_range("2020", periods=le, freq="15T")
+        i = pd.date_range("2020", periods=le, freq="15min")
         w_offtake = pf.dev.w_offtake(i)
         offtake = pf.PfLine(w_offtake)
         unsourced = pf.PfLine(pf.dev.p_marketprices(i))

docs/core/interoperability.rst

@@ -19,7 +19,7 @@ In the code examples below, the following imports are assumed and variables are
 
    import portfolyo as pf
    import pandas as pd
-   idx = pd.date_range("2023", freq="AS", periods=2)
+   idx = pd.date_range("2023", freq="YS", periods=2)
 
 ---------
 One value
@@ -82,7 +82,7 @@ For timeseries, ``pandas.Series`` are used. These can be "unit-agnostic" (i.e.,
    # --- hide: start ---
    import portfolyo as pf 
    import pandas as pd
-   idx = pd.date_range("2023", freq="AS", periods=2)
+   idx = pd.date_range("2023", freq="YS", periods=2)
    # --- hide: stop ---
    pd.Series([50, 56.0], idx, dtype="pint[Eur/MWh]")  # unit-aware
    # --- hide: start ---
@@ -103,7 +103,7 @@ To pass several timeseries, we can use:
      # --- hide: start ---
      import portfolyo as pf 
      import pandas as pd
-     idx = pd.date_range("2023", freq="AS", periods=2)
+     idx = pd.date_range("2023", freq="YS", periods=2)
      # --- hide: stop ---
      {"p": pd.Series([50, 56], idx), "w": pd.Series([120, 125], idx, dtype="pint[MW]")}
      # --- hide: start ---
@@ -118,7 +118,7 @@ To pass several timeseries, we can use:
      # --- hide: start ---
      import portfolyo as pf 
      import pandas as pd
-     idx = pd.date_range("2023", freq="AS", periods=2)
+     idx = pd.date_range("2023", freq="YS", periods=2)
      # --- hide: stop ---
      pd.DataFrame({"p": [50, 56], "w": [120, 125]}, idx)
      # --- hide: start ---
@@ -139,7 +139,7 @@ Dictionaries are the most versatily of these objects. They can be used to pass a
    # --- hide: start ---
    import portfolyo as pf 
    import pandas as pd
-   idx = pd.date_range("2023", freq="AS", periods=2)
+   idx = pd.date_range("2023", freq="YS", periods=2)
    # --- hide: stop ---
    d1 = {"p": 50}
    d2 = {"p": 50, "w": 120}
@@ -194,7 +194,7 @@ Footnotes
    .. code-block:: python 
        :emphasize-lines: 3,4
 
-       >>> idx = pandas.date_range("2023", freq="AS", periods=2)
+       >>> idx = pandas.date_range("2023", freq="YS", periods=2)
        >>> s_agn = pandas.Series([50, 56], idx)  # unit-agnostic
        >>> s1 = s_agn.astype("pint[Eur/MWh]")  # unit-aware
 
@@ -212,7 +212,7 @@ Footnotes
    .. code-block:: python
       :emphasize-lines: 4, 7
 
-      >>> idx = pandas.date_range("2023", freq="AS", periods=2)
+      >>> idx = pandas.date_range("2023", freq="YS", periods=2)
       >>> s_price = pandas.Series([50, 56], idx, dtype="pint[Eur/MWh]")
       >>> s_volume = pandas.Series([120, 125], idx, dtype="pint[MW]")
       >>> df1 = pandas.DataFrame({"p": s_price, "w": s_volume})