Docs/add code block copy button (#2918)

* add copy button to code blocks

* improve code copying

* update code examples

* update changelog

Author: dennisbader

CHANGELOG.md

@@ -25,6 +25,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 - It is now possible to control the fit and predict verbosity in `ForecastingModel.historical_forecasts()` by passing `verbose` in parameters `fit_kwargs` and `predict_kwargs`. [#2805](https://github.com/unit8co/darts/pull/2805) by [Timon Erhart](https://github.com/turbotimon) and [Dennis Bader](https://github.com/dennisbader).
 - Added support for applying historical forecast, backtest and residuals globally on all series with parameter `apply_globally: bool`. If `True`, computes the output only the time intersection of all series. Additionally, with `retrain=True`, activates global model- and data transformer fitting (for global forecasting models). If `False` (default), computes the output on the entire extent of each individual series and performs local fitting. [#2916](https://github.com/unit8co/darts/pull/2916) by [Dennis Bader](https://github.com/dennisbader).
 - `TimeSeries.drop_before()` and `drop_after()` now support keeping the split point in the returned series by passing parameter `keep=True`. [#2916](https://github.com/unit8co/darts/pull/2916) by [Dennis Bader](https://github.com/dennisbader).
+- Added support for copying all example code blocks from the documentation to the clipboard. [#2918](https://github.com/unit8co/darts/pull/2918) by [Dennis Bader](https://github.com/dennisbader).
 
 **Fixed**
 

darts/dataprocessing/pipeline.py

@@ -52,24 +52,19 @@ def __init__(
         --------
         >>> import numpy as np
         >>> from darts import TimeSeries
-        >>> from darts.datasets import AirPassengersDataset
         >>> from darts.dataprocessing.transformers import Scaler, MissingValuesFiller
         >>> from darts.dataprocessing.pipeline import Pipeline
-        >>> values = np.arange(start=0, stop=12.5, step=2.5)
+        >>> values = np.arange(start=0, stop=10, step=2.)
         >>> values[1:3] = np.nan
-        >>> series = series.from_values(values)
+        >>> series = TimeSeries.from_values(values)
         >>> pipeline = Pipeline([MissingValuesFiller(), Scaler()])
         >>> series_transformed = pipeline.fit_transform(series)
-        <TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>
-        array([[[0.  ]],
-            [[0.25]],
-            [[0.5 ]],
-            [[0.75]],
-            [[1.  ]]])
-        Coordinates:
-        * time       (time) int64 0 1 2 3 4
-        * component  (component) object '0'
-        Dimensions without coordinates: sample
+        >>> print(series_transformed.values())
+        [[0.  ]
+         [0.25]
+         [0.5 ]
+         [0.75]
+         [1.  ]]
         """
 
         raise_if_not(

darts/dataprocessing/transformers/base_data_transformer.py

@@ -131,55 +131,30 @@ def __init__(
         >>> from darts.utils.timeseries_generation import linear_timeseries
         >>>
         >>> class SimpleTransform(BaseDataTransformer):
+        >>>     def __init__(self, a):
+        >>>         self._a = a
+        >>>         super().__init__()
         >>>
-        >>>         def __init__(self, a):
-        >>>             self._a = a
-        >>>             super().__init__()
-        >>>
-        >>>         @staticmethod
-        >>>         def ts_transform(series, params, **kwargs):
-        >>>             a = params['fixed']['_a']
-        >>>             b = kwargs.pop('b')
-        >>>             return a*series + b
+        >>>     @staticmethod
+        >>>     def ts_transform(series, params, **kwargs):
+        >>>         a = params['fixed']['_a']
+        >>>         b = kwargs.pop('b')
+        >>>         return a*series + b
         >>>
         >>> series = linear_timeseries(length=5)
-        >>> print(series)
-        <TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>
-        array([[[0.  ]],
-
-            [[0.25]],
-
-            [[0.5 ]],
-
-            [[0.75]],
-
-            [[1.  ]]])
-        Coordinates:
-        * time       (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-05
-        * component  (component) object 'linear'
-        Dimensions without coordinates: sample
-        Attributes:
-            static_covariates:  None
-            hierarchy:          None
+        >>> print(series.values())
+        [[0.  ]
+         [0.25]
+         [0.5 ]
+         [0.75]
+         [1.  ]]
         >>> series = SimpleTransform(a=2).transform(series, b=3)
-        >>> print(series)
-        <TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>
-        array([[[3. ]],
-
-            [[3.5]],
-
-            [[4. ]],
-
-            [[4.5]],
-
-            [[5. ]]])
-        Coordinates:
-        * time       (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-05
-        * component  (component) object 'linear'
-        Dimensions without coordinates: sample
-        Attributes:
-            static_covariates:  None
-            hierarchy:          None
+        >>> print(series.values())
+        [[3. ]
+         [3.5]
+         [4. ]
+         [4.5]
+         [5. ]]
         """
         # Assume `super().__init__` called at *very end* of
         # child-most class's `__init__`, so `vars(self)` contains

darts/dataprocessing/transformers/boxcox.py

@@ -89,17 +89,12 @@ def __init__(
         >>> series = AirPassengersDataset().load()
         >>> transformer = BoxCox(lmbda=0.2)
         >>> series_transformed = transformer.fit_transform(series)
-        >>> print(series_transformed.head())
-        <TimeSeries (DataArray) (Month: 5, component: 1, sample: 1)>
-        array([[[7.84735157]],
-            [[7.98214351]],
-            [[8.2765364 ]],
-            [[8.21563229]],
-            [[8.04749318]]])
-        Coordinates:
-        * Month      (Month) datetime64[ns] 1949-01-01 1949-02-01 ... 1949-05-01
-        * component  (component) object '#Passengers'
-        Dimensions without coordinates: sample
+        >>> print(series_transformed.values()[:5])
+        [[7.84735157]
+         [7.98214351]
+         [8.2765364 ]
+         [8.21563229]
+         [8.04749318]]
 
         References
         ----------

darts/dataprocessing/transformers/diff.py

@@ -81,27 +81,20 @@ def __init__(
         >>> from darts.dataprocessing.transformers import Diff
         >>> series = AirPassengersDataset().load()
         >>> first_order_diff = Diff(lags=1, dropna=True).fit_transform(series)
-        >>> print(first_order_diff.head())
-        <TimeSeries (DataArray) (Month: 5, component: 1, sample: 1)>
-        array([[[ 6.]],
-            [[14.]],
-            [[-3.]],
-            [[-8.]],
-            [[14.]]])
-        Coordinates:
-        * Month      (Month) datetime64[ns] 1949-02-01 1949-03-01 ... 1949-06-01
-        * component  (component) object '#Passengers'
+        >>> print(first_order_diff.values()[:5])
+        [[ 6.]
+         [14.]
+         [-3.]
+         [-8.]
+         [14.]]
         >>> second_order_diff = Diff(lags=[1, 2], dropna=False).fit_transform(series)
-        >>> print(second_order_diff.head())
-        <TimeSeries (DataArray) (Month: 5, component: 1, sample: 1)>
-        array([[[ nan]],
-            [[ nan]],
-            [[ nan]],
-            [[ -9.]],
-            [[-22.]]])
-        Coordinates:
-        * Month      (Month) datetime64[ns] 1949-01-01 1949-02-01 ... 1949-05-01
-        * component  (component) object '#Passengers'
+        >>> print(second_order_diff.values()[:5])
+        [[ nan]
+         [ nan]
+         [ nan]
+         [ -9.]
+         [-22.]]
+
         References
         ----------
         .. [1] https://otexts.com/fpp2/stationarity.html

darts/dataprocessing/transformers/fittable_data_transformer.py

@@ -126,43 +126,19 @@ class should first inherit from `FittableDataTransformer` and then from `Inverti
         >>>         return {'scale': scale, 'position': position}
         >>>
         >>> series = linear_timeseries(length=5, start_value=1, end_value=5)
-        >>> print(series)
-        <TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>
-        array([[[1.]],
-
-            [[2.]],
-
-            [[3.]],
-
-            [[4.]],
-
-            [[5.]]])
-        Coordinates:
-        * time       (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-05
-        * component  (component) object 'linear'
-        Dimensions without coordinates: sample
-        Attributes:
-            static_covariates:  None
-            hierarchy:          None
+        >>> print(series.values())
+        [[1.]
+         [2.]
+         [3.]
+         [4.]
+         [5.]]
         >>> series = SimpleRangeScaler(scale=2, position=-1).fit_transform(series)
-        >>> print(series)
-        <TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>
-        array([[[-1. ]],
-
-            [[-0.5]],
-
-            [[ 0. ]],
-
-            [[ 0.5]],
-
-            [[ 1. ]]])
-        Coordinates:
-        * time       (time) int64 0 1 2 3 4
-        * component  (component) <U1 '0'
-        Dimensions without coordinates: sample
-        Attributes:
-            static_covariates:  None
-            hierarchy:          None
+        >>> print(series.values())
+        [[-1. ]
+         [-0.5]
+         [ 0. ]
+         [ 0.5]
+         [ 1. ]]
         """
         super().__init__(
             name=name,

darts/dataprocessing/transformers/invertible_data_transformer.py

@@ -108,63 +108,27 @@ def __init__(
         >>>             return (series - b) / a
         >>>
         >>> series = linear_timeseries(length=5)
-        >>> print(series)
-        <TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>
-        array([[[0.  ]],
-
-            [[0.25]],
-
-            [[0.5 ]],
-
-            [[0.75]],
-
-            [[1.  ]]])
-        Coordinates:
-        * time       (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-05
-        * component  (component) object 'linear'
-        Dimensions without coordinates: sample
-        Attributes:
-            static_covariates:  None
-            hierarchy:          None
+        >>> print(series.values())
+        [[0.  ]
+         [0.25]
+         [0.5 ]
+         [0.75]
+         [1.  ]]
         >>> transform = SimpleTransform(a=2)
         >>> series = transform.transform(series, b=3)
-        >>> print(series)
-        <TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>
-        array([[[3. ]],
-
-            [[3.5]],
-
-            [[4. ]],
-
-            [[4.5]],
-
-            [[5. ]]])
-        Coordinates:
-        * time       (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-05
-        * component  (component) object 'linear'
-        Dimensions without coordinates: sample
-        Attributes:
-            static_covariates:  None
-            hierarchy:          None
+        >>> print(series.values())
+        [[3. ]
+         [3.5]
+         [4. ]
+         [4.5]
+         [5. ]]
         >>> series = transform.inverse_transform(series, b=3)
-        >>> print(series)
-        <TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>
-        array([[[0.  ]],
-
-            [[0.25]],
-
-            [[0.5 ]],
-
-            [[0.75]],
-
-            [[1.  ]]])
-        Coordinates:
-        * time       (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-05
-        * component  (component) object 'linear'
-        Dimensions without coordinates: sample
-        Attributes:
-            static_covariates:  None
-            hierarchy:          None
+        >>> print(series.values())
+        [[0.  ]
+         [0.25]
+         [0.5 ]
+         [0.75]
+         [1.  ]]
         """
         super().__init__(
             name=name,

darts/dataprocessing/transformers/mappers.py

@@ -61,20 +61,14 @@ def __init__(
         --------
         >>> import numpy as np
         >>> from darts import TimeSeries
-        >>> from darts.dataprocessing.transformers import InvertibleMapper
-        >>> series = TimeSeries.from_values(np.array([1e0, 1e1, 1e2, 1e3]))
-        >>> transformer = InvertibleMapper(np.log10, lambda x: 10**x)
+        >>> from darts.dataprocessing.transformers import Mapper
+        >>> series = TimeSeries.from_values(np.array([1, 10, 100]))
+        >>> transformer = Mapper(np.log10)
         >>> series_transformed = transformer.transform(series)
-        >>> print(series_transformed)
-        <TimeSeries (DataArray) (time: 4, component: 1, sample: 1)>
-        array([[[0.]],
-            [[1.]],
-            [[2.]],
-            [[3.]]])
-        Coordinates:
-        * time       (time) int64 0 1 2 3
-        * component  (component) <U1 '0'
-        Dimensions without coordinates: sample
+        >>> print(series_transformed.values())
+        [[0.]
+         [1.]
+         [2.]]
         """
         # Define fixed params (i.e. attributes defined before calling `super().__init__`):
         self._fn = fn
@@ -128,31 +122,20 @@ def __init__(
         --------
         >>> import numpy as np
         >>> from darts import TimeSeries
-        >>> from darts.dataprocessing.transformers import Mapper
-        >>> series = TimeSeries.from_values(np.array([1e0, 1e1, 1e2, 1e3]))
-        >>> transformer = Mapper(np.log10)
+        >>> from darts.dataprocessing.transformers import InvertibleMapper
+        >>> series = TimeSeries.from_values(np.array([1, 10, 100]))
+        >>> transformer = InvertibleMapper(np.log10, lambda x: 10 ** x)
         >>> series_transformed = transformer.transform(series)
-        >>> print(series_transformed)
-        <TimeSeries (DataArray) (time: 4, component: 1, sample: 1)>
-        array([[[0.]],
-            [[1.]],
-            [[2.]],
-            [[3.]]])
-        Coordinates:
-        * time       (time) int64 0 1 2 3
-        * component  (component) <U1 '0'
-        Dimensions without coordinates: sample
-        >>> series_restaured = transformer.inverse_transform(series_transformed)
-        >>> print(series_restaured)
-        <TimeSeries (DataArray) (time: 4, component: 1, sample: 1)>
-        array([[[   1.]],
-            [[  10.]],
-            [[ 100.]],
-            [[1000.]]])
-        Coordinates:
-        * time       (time) int64 0 1 2 3
-        * component  (component) <U1 '0'
-        Dimensions without coordinates: sample
+        >>> print(series_transformed.values())
+        [[0.]
+         [1.]
+         [2.]]
+         [3.]]
+        >>> series_restored = transformer.inverse_transform(series_transformed)
+        >>> print(series_restored.values())
+        [[  1.]
+         [ 10.]
+         [100.]]
         """
 
         self._fn = fn