Make code work with updated dependencies

.github/workflows/build_doc.yml

@@ -16,7 +16,7 @@ jobs:
       - name: Install Python
         uses: actions/setup-python@v2
         with:
-          python-version: 3.9
+          python-version: 3.12
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip

.github/workflows/deploy_pypi.yml

@@ -17,7 +17,7 @@ jobs:
       - name: Install Python
         uses: actions/setup-python@v2
         with:
-          python-version: 3.8
+          python-version: 3.12
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip

.github/workflows/tests.yml

@@ -6,7 +6,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python: ['3.8', '3.9', '3.10']
+        python: ['3.11', '3.12', '3.13', '3.14']
     runs-on: ${{ matrix.os }}
     steps:
       - uses: actions/checkout@v2
@@ -20,7 +20,7 @@ jobs:
       - name: run tests
         run: python3 -m pytest --cov=tlviz
       - name: upload coverage to Codecov
-        if: ${{matrix.os == 'ubuntu-latest' && matrix.python == '3.10'}}
+        if: ${{matrix.os == 'ubuntu-latest' && matrix.python == '3.13'}}
         uses: codecov/codecov-action@v2
         with:
           verbose: true

tlviz/_xarray_wrapper.py

@@ -56,13 +56,13 @@ def add_factor_metadata(cp_tensor, dataset):
     >>> bikes = load_oslo_city_bike()
     >>> bikes.coords
     Coordinates:
-      * End station name  (End station name) object '7 Juni Plassen' ... 'Økernve...
-        lat               (End station name) float64 59.92 59.93 ... 59.93 59.92
-        lon               (End station name) float64 10.73 10.75 ... 10.8 10.78
-      * Hour              (Hour) int32 0 1 2 3 4 5 6 7 8 ... 16 17 18 19 20 21 22 23
-      * Month             (Month) int32 1 2 3 4 5 6 7 8 9 10 11 12
-      * Day of week       (Day of week) int32 0 1 2 3 4 5 6
-      * Year              (Year) int32 2020 2021
+      * End station name  (End station name) object 2kB '7 Juni Plassen' ... 'Øke...
+      * Year              (Year) int32 8B 2020 2021
+      * Month             (Month) int32 48B 1 2 3 4 5 6 7 8 9 10 11 12
+      * Day of week       (Day of week) int32 28B 0 1 2 3 4 5 6
+      * Hour              (Hour) int32 96B 0 1 2 3 4 5 6 7 ... 17 18 19 20 21 22 23
+        lat               (End station name) float64 2kB 59.92 59.93 ... 59.93 59.92
+        lon               (End station name) float64 2kB 10.73 10.75 ... 10.8 10.78
 
     We see that the ``End station name`` dimension has two additional columns: ``lat`` and ``lon``.
     These contain metadata about the end station coordinates, and it can be useful to have these

tlviz/model_evaluation.py

@@ -194,7 +194,7 @@ def sse(cp_tensor, dataset):
     >>> cp = random_cp((4, 5, 6), 3, random_state=rng)
     >>> X = rng.random_sample((4, 5, 6))
     >>> sse(cp, X)
-    18.948918157419186
+    np.float64(18.948918157419186)
     """
     X_hat = cp_to_tensor(cp_tensor)
     return np.sum((dataset - X_hat) ** 2)
@@ -236,7 +236,7 @@ def relative_sse(cp_tensor, dataset, sum_squared_dataset=None):
     >>> cp = random_cp((4, 5, 6), 3, random_state=rng)
     >>> X = rng.random_sample((4, 5, 6))
     >>> relative_sse(cp, X)
-    0.4817407254961442
+    np.float64(0.4817407254961442)
     """
     if sum_squared_dataset is None:
         sum_squared_x = np.sum(dataset**2)
@@ -279,13 +279,13 @@ def fit(cp_tensor, dataset, sum_squared_dataset=None):
     >>> cp = random_cp((4, 5, 6), 3, random_state=rng)
     >>> X = rng.random_sample((4, 5, 6))
     >>> fit(cp, X)
-    0.5182592745038558
+    np.float64(0.5182592745038558)
 
     We can see that it is equal to 1 - relative SSE
 
     >>> from tlviz.model_evaluation import relative_sse
     >>> 1 - relative_sse(cp, X)
-    0.5182592745038558
+    np.float64(0.5182592745038558)
     """
     return 1 - relative_sse(cp_tensor, dataset, sum_squared_dataset=sum_squared_dataset)
 

tlviz/multimodel_evaluation.py

@@ -149,12 +149,12 @@ def get_model_with_lowest_error(cp_tensors, dataset, error_function=None, return
     And that it is the model that has the lowest error
 
     >>> errors[index] == min(errors)
-    True
+    np.True_
 
     And finally that this error is equal to the relative SSE
 
     >>> errors[index] == relative_sse(model, dataset)
-    True
+    np.True_
     """
     if error_function is None:
         error_function = model_evaluation.relative_sse

tlviz/visualisation.py

@@ -39,6 +39,21 @@
 ]
 
 
+def _get_next_style(ax):
+    """Get the next style of the matplotlib axes property cycler and increment its position.
+
+    Before Matplotlib v3.8, we directly accessed the prop_cycler of ax._get_lines. However this attribute
+    was deleted to make pickling work correctly for Matplotlib axes, so now we need this workaround.
+    """
+    lines = ax._get_lines
+    if hasattr(lines, "prop_cycler"):
+        return next(lines.prop_cycler)
+    else:
+        out = lines._cycler_items[lines._idx]
+        lines._idx = (lines._idx + 1) % len(lines._cycler_items)
+        return out
+
+
 def scree_plot(cp_tensors, dataset, errors=None, metric="Fit", ax=None):
     """Create scree plot for the given cp tensors.
 
@@ -177,7 +192,7 @@ def histogram_of_residuals(cp_tensor, dataset, ax=None, standardised=True, **kwa
         >>> true_cp, X = simulated_random_cp_tensor((10, 20, 30), 3, seed=0)
         >>> est_cp = parafac(X, 3)
         >>> histogram_of_residuals(est_cp, X)
-        <AxesSubplot: title={'center': 'Histogram of residuals'}, xlabel='Standardised residuals', ylabel='Frequency'>
+        <Axes: title={'center': 'Histogram of residuals'}, xlabel='Standardised residuals', ylabel='Frequency'>
         >>> plt.show()
     """
     estimated_dataset = cp_to_tensor(cp_tensor)
@@ -244,7 +259,7 @@ def residual_qq(cp_tensor, dataset, ax=None, use_pingouin=False, **kwargs):
         >>> true_cp, X = simulated_random_cp_tensor((10, 20, 30), 3, seed=0)
         >>> est_cp = parafac(X, 3)
         >>> residual_qq(est_cp, X)
-        <AxesSubplot: title={'center': 'QQ-plot of residuals'}, xlabel='Theoretical Quantiles', ylabel='Sample Quantiles'>
+        <Axes: title={'center': 'QQ-plot of residuals'}, xlabel='Theoretical Quantiles', ylabel='Sample Quantiles'>
         >>> plt.show()
     """
     estimated_dataset = cp_to_tensor(cp_tensor)
@@ -337,7 +352,7 @@ def outlier_plot(
         >>> outlier_plot(
         ...     cp, data, leverage_rules_of_thumb='p-value', residual_rules_of_thumb='p-value', p_value=[0.05, 0.01]
         ... )
-        <AxesSubplot: title={'center': 'Outlier plot for End station name'}, xlabel='Leverage score', ylabel='Slabwise SSE'>
+        <Axes: title={'center': 'Outlier plot for End station name'}, xlabel='Leverage score', ylabel='Slabwise SSE'>
         >>> plt.show()
 
     We can also provide multiple types of rules of thumb
@@ -360,7 +375,7 @@ def outlier_plot(
         >>> outlier_plot(
         ...     cp, data, leverage_rules_of_thumb=['huber lower', 'hw higher'], residual_rules_of_thumb='two sigma'
         ... )
-        <AxesSubplot: title={'center': 'Outlier plot for End station name'}, xlabel='Leverage score', ylabel='Slabwise SSE'>
+        <Axes: title={'center': 'Outlier plot for End station name'}, xlabel='Leverage score', ylabel='Slabwise SSE'>
         >>> plt.show()
 
     See Also
@@ -438,7 +453,7 @@ def outlier_plot(
 
     # Draw the lines
     for key, value in leverage_thresholds.items():
-        ax.axvline(value, label=key, **next(ax._get_lines.prop_cycler))
+        ax.axvline(value, label=key, **_get_next_style(ax))
 
     residual_thresholds = {}
     if residual_rules_of_thumb is not None:
@@ -465,7 +480,7 @@ def outlier_plot(
                     name = residual_rule_of_thumb
                 residual_thresholds[name] = threshold
     for key, value in residual_thresholds.items():
-        ax.axhline(value, label=key, **next(ax._get_lines.prop_cycler))
+        ax.axhline(value, label=key, **_get_next_style(ax))
 
     if len(leverage_thresholds) > 0 or len(residual_thresholds) > 0:
         ax.legend()
@@ -521,7 +536,7 @@ def component_scatterplot(cp_tensor, mode, x_component=0, y_component=1, ax=None
         >>> import matplotlib.pyplot as plt
         >>> cp_tensor = random_cp(shape=(5,10,15), rank=2)
         >>> component_scatterplot(cp_tensor, mode=0)
-        <AxesSubplot: title={'center': 'Component plot'}, xlabel='Component 0', ylabel='Component 1'>
+        <Axes: title={'center': 'Component plot'}, xlabel='Component 0', ylabel='Component 1'>
         >>> plt.show()
 
     Eexample with PCA of a real stock dataset
@@ -553,7 +568,7 @@ def component_scatterplot(cp_tensor, mode, x_component=0, y_component=1, ax=None
         >>>
         >>> # Visualise the components with components_plot
         >>> component_scatterplot(cp_tensor, mode=1)
-        <AxesSubplot: title={'center': 'Component plot'}, xlabel='Component 0', ylabel='Component 1'>
+        <Axes: title={'center': 'Component plot'}, xlabel='Component 0', ylabel='Component 1'>
         >>> plt.show()
     """
     if ax is None:
@@ -624,7 +639,7 @@ def core_element_plot(cp_tensor, dataset, normalised=False, ax=None):
         >>> true_cp, X = simulated_random_cp_tensor((10, 20, 30), 3, seed=42)
         >>> est_cp = parafac(X, 3)
         >>> core_element_plot(est_cp, X)
-        <AxesSubplot: title={'center': 'Core consistency: 99.8'}, xlabel='Core element', ylabel='Value'>
+        <Axes: title={'center': 'Core consistency: 99.8'}, xlabel='Core element', ylabel='Value'>
         >>> plt.show()
     """
     weights, factors = cp_tensor
@@ -1300,7 +1315,7 @@ def percentage_variation_plot(
         >>> import matplotlib.pyplot as plt
         >>> cp_tensor, dataset = simulated_random_cp_tensor(shape=(5,10,15), rank=3, noise_level=0.5, seed=0)
         >>> percentage_variation_plot(cp_tensor)
-        <AxesSubplot: xlabel='Component number', ylabel='Percentage variation explained [%]'>
+        <Axes: xlabel='Component number', ylabel='Percentage variation explained [%]'>
         >>> plt.show()
 
     We can also get the percentage of variation in the data that each component explains
@@ -1314,7 +1329,7 @@ def percentage_variation_plot(
         >>> import matplotlib.pyplot as plt
         >>> cp_tensor, dataset = simulated_random_cp_tensor(shape=(5,10,15), rank=3, noise_level=0.5, seed=0)
         >>> percentage_variation_plot(cp_tensor, dataset, method="data")
-        <AxesSubplot: xlabel='Component number', ylabel='Percentage variation explained [%]'>
+        <Axes: xlabel='Component number', ylabel='Percentage variation explained [%]'>
         >>> plt.show()
 
     Or both the variation in the data and in the model
@@ -1328,7 +1343,7 @@ def percentage_variation_plot(
         >>> import matplotlib.pyplot as plt
         >>> cp_tensor, dataset = simulated_random_cp_tensor(shape=(5,10,15), rank=3, noise_level=0.5, seed=0)
         >>> percentage_variation_plot(cp_tensor, dataset, method="both")
-        <AxesSubplot: xlabel='Component number', ylabel='Percentage variation explained [%]'>
+        <Axes: xlabel='Component number', ylabel='Percentage variation explained [%]'>
         >>> plt.show()
     """
     if ax is None: