Merge remote-tracking branch 'origin/master' into elitherl/workflows

.github/workflows/docs.yml

@@ -29,33 +29,38 @@ jobs:
       with:
         python-version: 3.8 # should match value in .readthedocs.yml
 
+    - name: Install Poetry
+      uses: abatilo/[email protected]
+      with:
+        poetry-version: 1.4.2
+
+    - name: Configure poetry
+      shell: bash
+      run: python -m poetry config virtualenvs.in-project true
+
     - name: Set up cache
       uses: actions/cache@v2
       id: cache
       with:
         path: .venv
-        key: venv-pip-${{ runner.os }}-${{ hashFiles('**/poetry.lock', '.github/workflows/docs.yml')}}
+        key: venv-${{ runner.os }}-${{ steps.full-python-version.outputs.version }}-${{ hashFiles('**/poetry.lock') }}
 
     - name: Ensure cache is healthy
       if: steps.cache.outputs.cache-hit == 'true'
       shell: bash
-      run: source .venv/bin/activate &&
-           (timeout 10s pip --version || (rm -rf .venv && echo "::set-output name=cache-bad::true"))
-      id: cache-checkup
+      run: timeout 10s python -m poetry run pip --version || rm -rf .venv
+
+    - name: Install dependencies
+      shell: bash
+      run: |
+        python -m poetry install
 
     - name: Install plantuml
       shell: bash
       run: |
         sudo apt-get update
         sudo apt-get install -y plantuml
 
-    - name: Install dependencies
-      shell: bash
-      run: python -m venv .venv &&
-           source .venv/bin/activate &&
-           pip install .[docs]
-      if: ${{ steps.cache-venv.ouputs.cache-hit != 'true' || steps.cache-checkup.outputs.cache-bad == 'true'}}
-
     - name: Test docs build successfully
       run: source .venv/bin/activate &&
            make -C doc html SPHINXOPTS="-W --keep-going"

.github/workflows/quality.yml

@@ -46,7 +46,7 @@ jobs:
     - name: Install Poetry
       uses: abatilo/[email protected]
       with:
-        poetry-version: 1.1.15
+        poetry-version: 1.4.2
 
     - name: Configure poetry
       shell: bash

.github/workflows/tests.yml

@@ -37,7 +37,7 @@ jobs:
     - name: Install Poetry
       uses: abatilo/[email protected]
       with:
-        poetry-version: 1.1.15
+        poetry-version: 1.4.2
 
     - name: Configure poetry
       shell: bash
@@ -65,6 +65,9 @@ jobs:
 
     - name: "Upload coverage to Codecov"
       if: ${{ always() }}
-      uses: codecov/codecov-action@v1
+      uses: codecov/codecov-action@v3
       with:
+        token: ${{ secrets.CODECOV_TOKEN }}
+        files: ./coverage.xml
         fail_ci_if_error: true
+        verbose: true

.pre-commit-config.yaml

@@ -27,6 +27,6 @@ repos:
       - flake8-import-order
       name: "Check Code Style Using flake8"
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v0.942  # Use the sha / tag you want to point at
+    rev: v1.1.1  # Use the sha / tag you want to point at
     hooks:
     -   id: mypy

.readthedocs.yml

@@ -1,16 +1,24 @@
 # .readthedocs.yml
-
 version: 2
 
+build:
+  os: "ubuntu-22.04"
+  apt_packages:
+    - plantuml
+  tools:
+    python: "3.8"
+  jobs:
+    post_create_environment:
+      # Install poetry
+      # https://python-poetry.org/docs/#installing-manually
+      - 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 --without dev
+
 sphinx:
   configuration: doc/conf.py
   fail_on_warning: true
-
-python:
-  version: 3.8
-  install:
-    - path: .
-      method: pip
-      extra_requirements:
-        - docs
-  system_packages: true

indica/converters/abstractconverter.py

@@ -9,7 +9,6 @@
 from typing import Optional
 from typing import Tuple
 
-import numpy as np
 from xarray import DataArray
 from xarray import zeros_like
 
@@ -295,7 +294,7 @@ def distance(
         R, z = cast(Tuple[DataArray, DataArray], self.convert_to_Rz(x1, x2, t))
         if isinstance(R, (int, float)) or isinstance(z, (int, float)):
             raise ValueError("Arguments x1 and x2 must be xarray DataArray objects.")
-        spacings = np.sqrt(R.diff(direction) ** 2 + z.diff(direction) ** 2)
+        spacings = (R.diff(direction) ** 2 + z.diff(direction) ** 2) ** 0.5
         result = zeros_like(R.broadcast_like(z))
         result[{direction: slice(1, None)}] = spacings.cumsum(direction)
         return result
@@ -304,9 +303,9 @@ def encode(self) -> str:
         """Returns a JSON representation of this object. Should be sufficient
         to recreate it identically from scratch (except for the
         equilibrium)."""
-        return ""
+        raise NotImplementedError
 
     @staticmethod
     def decode(json: str) -> "CoordinateTransform":
         """Takes some JSON and decodes it into a CoordinateTransform object."""
-        pass
+        raise NotImplementedError

indica/converters/line_of_sight.py

@@ -2,6 +2,7 @@
 """
 
 from typing import cast
+from typing import Optional
 from typing import Tuple
 
 import numpy as np
@@ -205,9 +206,9 @@ def distance(
         x = self.x_start + (self.x_end - self.x_start) * x2
         y = self.y_start + (self.y_end - self.y_start) * x2
         z = self.z_start + (self.z_end - self.z_start) * x2
-        spacings = np.sqrt(
+        spacings = (
             x.diff(direction) ** 2 + z.diff(direction) ** 2 + y.diff(direction) ** 2
-        )
+        ) ** 0.5
         result = zeros_like(x)
         result[{direction: slice(1, None)}] = spacings.cumsum(direction)
         return result.values
@@ -225,7 +226,7 @@ def set_dl(self, dl: float):
         )
 
         # Find the number of points
-        npts = np.ceil(los_length.data / dl).astype(int)
+        npts = np.ceil(los_length.data / dl).astype(int)  # type: ignore
 
         # Set dl, calculate dl
         ind = np.linspace(0, 1, npts, dtype=float)
@@ -237,7 +238,7 @@ def set_dl(self, dl: float):
     def assign_flux_transform(self, flux_transform: FluxSurfaceCoordinates):
         self.flux_transform = flux_transform
 
-    def convert_to_rho(self, t: float = None):
+    def convert_to_rho(self, t: Optional[float] = None):
         self.rho = self.flux_transform.convert_from_Rz(self.R, self.z, t=t)
 
 

indica/converters/lines_of_sight.py

@@ -225,9 +225,9 @@ def distance(
         x = x_s + (x_e - x_s) * x2
         y = y_s + (y_e - y_s) * x2
         z = z_s + (z_e - z_s) * x2
-        spacings = np.sqrt(
+        spacings = (
             x.diff(direction) ** 2 + z.diff(direction) ** 2 + y.diff(direction) ** 2
-        )
+        ) ** 0.5
         result = zeros_like(x)
         result[{direction: slice(1, None)}] = spacings.cumsum(direction)
         return result

indica/converters/magnetic.py

@@ -94,7 +94,9 @@ def func(R: float) -> float:
                 f"scipy.optimize.root_scalar failed to converge with flag {result.flag}"
             )
 
-        return apply_ufunc(find_root, x1, x2, t, vectorize=True), x2 + self.z_los
+        # apply_ufunc vectorize=True does not seem to be working
+        vfunc = np.vectorize(find_root)
+        return apply_ufunc(vfunc, x1, x2, t, vectorize=False), x2 + self.z_los
 
     def convert_from_Rz(
         self, R: LabeledArray, z: LabeledArray, t: LabeledArray

indica/converters/time.py

@@ -3,6 +3,7 @@
 
 import numpy as np
 from xarray import DataArray
+from xarray.core.types import InterpOptions
 
 
 def strip_provenance(arr: DataArray):
@@ -19,7 +20,7 @@ def convert_in_time(
     tend: float,
     frequency: float,
     data: DataArray,
-    method: str = "linear",
+    method: InterpOptions = "linear",
 ) -> DataArray:
     """Interpolate or bin (as appropriate) the given data along the time
     axis, discarding data before or after the limits.
@@ -57,7 +58,7 @@ def convert_in_time_dt(
     tend: float,
     dt: float,
     data: DataArray,
-    method: str = "linear",
+    method: InterpOptions = "linear",
 ) -> DataArray:
     """
     Interpolate or bin given data along the time axis, discarding data before
@@ -90,7 +91,7 @@ def convert_in_time_dt(
 
 
 def interpolate_to_time_labels(
-    tlabels: np.ndarray, data: DataArray, method: str = "linear"
+    tlabels: np.ndarray, data: DataArray, method: InterpOptions = "linear"
 ) -> DataArray:
     """
     Interpolate data to sit on the specified time labels.
@@ -125,6 +126,9 @@ def interpolate_to_time_labels(
             )
         interpolated.attrs["dropped"] = dropped
 
+    if "transform" in data.attrs:
+        interpolated.attrs["transform"] = data.attrs["transform"]
+
     strip_provenance(interpolated)
 
     return interpolated
@@ -163,17 +167,14 @@ def bin_to_time_labels(tlabels: np.ndarray, data: DataArray) -> DataArray:
 
     if "error" in data.attrs:
         grouped = (
-            data.attrs["error"]
+            (data.attrs["error"] ** 2)
             .sel(t=slice(tbins[0], tbins[-1]))
             .groupby_bins("t", tbins, labels=tlabels)
         )
-        uncertainty = np.sqrt(
-            grouped.reduce(
-                lambda x, axis: np.sum(x**2, axis) / np.size(x, axis) ** 2, "t"
-            )
-        )
-        error = np.sqrt(uncertainty**2 + stdev**2)
+        uncertainty_square = grouped.sum("t") / grouped.count("t")
+        error = (uncertainty_square + stdev**2) ** 0.5
         averaged.attrs["error"] = error.rename(t_bins="t")
+
     if "dropped" in data.attrs:
         grouped = (
             data.attrs["dropped"]
@@ -190,14 +191,13 @@ def bin_to_time_labels(tlabels: np.ndarray, data: DataArray) -> DataArray:
                 .sel(t=slice(tbins[0], tbins[-1]))
                 .groupby_bins("t", tbins, labels=tlabels)
             )
-            uncertainty = np.sqrt(
-                grouped.reduce(
-                    lambda x, axis: np.sum(x**2, axis) / np.size(x, axis) ** 2, "t"
-                )
-            )
-            error = np.sqrt(uncertainty**2 + stdev**2)
+            uncertainty_square = grouped.sum("t") / grouped.count("t")
+            error = (uncertainty_square + stdev**2) ** 0.5
             averaged.attrs["dropped"].attrs["error"] = error.rename(t_bins="t")
 
+    if "transform" in data.attrs:
+        averaged.attrs["transform"] = data.attrs["transform"]
+
     strip_provenance(averaged)
 
     return averaged.rename(t_bins="t")
@@ -208,7 +208,7 @@ def interpolate_in_time(
     tend: float,
     frequency: float,
     data: DataArray,
-    method: str = "linear",
+    method: InterpOptions = "linear",
 ) -> DataArray:
     """Interpolate the given data along the time axis, discarding data
     before or after the limits.
@@ -245,7 +245,7 @@ def interpolate_in_time_dt(
     tend: float,
     dt: float,
     data: DataArray,
-    method: str = "linear",
+    method: InterpOptions = "linear",
 ) -> DataArray:
     """Interpolate the given data along the time axis, discarding data
     before or after the limits.

indica/data.py

@@ -34,6 +34,7 @@
 from scipy.interpolate import InterpolatedUnivariateSpline
 from scipy.interpolate import RectBivariateSpline
 import xarray as xr
+from xarray.core.types import InterpOptions
 from xarray.core.utils import either_dict_or_kwargs
 
 from . import session
@@ -159,7 +160,7 @@ def invert_interp(
         target: str,
         new_dims: Optional[Union[Tuple[str, ...], str]] = None,
         coords: Optional[Mapping[Hashable, ArrayLike]] = None,
-        method: str = "linear",
+        method: InterpOptions = "linear",
         assume_sorted: bool = False,
         kwargs: Optional[Mapping[str, Any]] = None,
         **coords_kwargs: ArrayLike,
@@ -279,7 +280,7 @@ def invert_root(
         guess: Optional[ArrayLike] = None,
         new_dims: Optional[Union[Tuple[str, ...], str]] = None,
         coords: Optional[Mapping[Hashable, ArrayLike]] = None,
-        method: str = "linear",
+        method: InterpOptions = "linear",
         assume_sorted: bool = False,
         kwargs: Optional[Mapping[str, Any]] = None,
         **coords_kwargs: ArrayLike,
@@ -787,10 +788,10 @@ def check_datatype(self, data_type: ArrayType) -> Tuple[bool, Optional[str]]:
         status
             Whether the datatype of this array matches the argument.
         message
-            If ``status == False``, an explaination of why.
+            If ``status == False``, an explanation of why.
 
         """
-        pass
+        return NotImplementedError  # type: ignore
 
     def _update_prov_for_equilibrium(
         self,
@@ -1043,7 +1044,7 @@ def attach(
         (unless the value is the same). This behaviour can be
         overridden with the `overwrite` argument.
 
-        This function will fail if the specific datatyp for ``array``
+        This function will fail if the specific datatype for ``array``
         differs from that for this Dataset. It will also fail if the
         dimensions of ``array`` differ from those of the Dataset.
 
@@ -1137,7 +1138,7 @@ def check_datatype(self, datatype: DatasetType) -> Tuple[bool, Optional[str]]:
             If ``status == False``, an explanation of why.
 
         """
-        pass
+        raise NotImplementedError
 
     def inclusive_timeslice(self, t_start: float, t_end: float) -> xr.Dataset:
         """