Bump to v1.0.0

Someone on the internet was making fun of VerZero and I took that personally. Enough is enough!

.github/workflows/python-publish.yml

@@ -0,0 +1,36 @@
+# This workflow will upload a Python Package using Twine when a release is created
+# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
+
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+name: Upload Python Package
+
+on:
+  release:
+    types: [published]
+
+jobs:
+  deploy:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.x'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install build
+    - name: Build package
+      run: python -m build
+    - name: Publish package
+      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
+      with:
+        user: __token__
+        password: ${{ secrets.EMD_PYPI_API_TOKEN }}

.gitignore

@@ -1,9 +1,17 @@
-build
-dist
-*.swp
-venv
-.mypy_cache
-.vscode
+venv/
+.venv/
+build/
+dist/
 *.egg-info
+
+.vscode/
+.idea/
+
+.mypy_cache
+__pycache__
+.pytest_cache/
+.ipynb_checkpoints/
+
+*.swp
 *.pyc
-*ipynb
+*.ipynb

PyEMD/CEEMDAN.py

@@ -39,6 +39,18 @@ class CEEMDAN:
     values. Two are `range_thr` and `total_power_thr` which relate to
     the value range (max - min) and check for total power below, respectively.
 
+    Configuration can be passed through keyword parameters.
+    For example, updating threshold would be through:
+
+    Example 1:
+
+    >>> config = {"range_thr": 0.001, "total_power_thr": 0.01}
+    >>> emd = EMD(**config)
+
+    Example 2:
+
+    >>> emd = EMD(range_thr=0.001, total_power_thr=0.01)
+
     Parameters
     ----------
 
@@ -62,6 +74,17 @@ class CEEMDAN:
     processes : int or None (optional)
         Number of processes harness when executing in parallel mode.
         The value should be between 1 and max that depends on your hardware.
+    noise_scale : float (default: 1)
+        Scale (amplitude) of the added noise.
+    noise_kind : str (default: "normal")
+        What type of noise to add. Allowed are "normal" (default) and "uniform".
+    range_thr : float (default: 0.01)
+        Range threshold used as an IMF check. The value is in percentage compared
+        to initial signal's amplitude. If absolute amplitude (max - min) is below
+        the `range_thr` then the decomposition is finished.
+    total_power_thr : float (default: 0.05)
+        Signal's power threshold. Finishes decomposition if sum(abs(r)) < thr.
+
 
     References
     ----------
@@ -80,16 +103,6 @@ class CEEMDAN:
     noise_kinds_all = ["normal", "uniform"]
 
     def __init__(self, trials: int=100, epsilon: float = 0.005, ext_EMD = None, parallel: bool = False, **kwargs):
-        """
-        Configuration can be passed through keyword parameters.
-        For example, updating threshold would be through:
-
-        Example 1:
-        >>> config = {"range_thr": 0.001, "total_power_thr": 0.01}
-        >>> emd = EMD(**config)
-        Example 2:
-        >>> emd = EMD(range_thr=0.001, total_power_thr=0.01)
-        """
 
         # Ensemble constants
         self.trials = trials

PyEMD/EMD.py

@@ -887,7 +887,14 @@ def emd(self, S: np.ndarray, T: Optional[np.ndarray] = None, max_imf: int = -1)
     def get_imfs_and_residue(self) -> Tuple[np.ndarray, np.ndarray]:
         """
         Provides access to separated imfs and residue from recently analysed signal.
-        :return: (imfs, residue)
+
+        Returns
+        -------
+        imfs : np.ndarray
+            Obtained IMFs
+        residue : np.ndarray
+            Residue.
+
         """
         if self.imfs is None or self.residue is None:
             raise ValueError('No IMF found. Please, run EMD method or its variant first.')
@@ -900,7 +907,14 @@ def get_imfs_and_trend(self) -> Tuple[np.ndarray, np.ndarray]:
         Note that this may differ from the `get_imfs_and_residue` as the trend isn't
         necessarily the residue. Residue is a point-wise difference between input signal
         and all obtained components, whereas trend is the slowest component (can be zero).
-        :return: (imfs, trend)
+
+        Returns
+        -------
+        imfs : np.ndarray
+            Obtained IMFs
+        trend : np.ndarray
+            The main trend.
+
         """
         if self.imfs is None or self.residue is None:
             raise ValueError('No IMF found. Please, run EMD method or its variant first.')

PyEMD/__init__.py

@@ -1,6 +1,6 @@
 import logging
 
-__version__ = "0.2.15"
+__version__ = "1.0.0"
 logger = logging.getLogger('pyemd')
 
 from PyEMD.EMD import EMD

doc/ceemdan.rst

@@ -7,9 +7,15 @@ Info
 Complete ensemble EMD with adaptive noise (CEEMDAN) performs an EEMD with
 the difference that the information about the noise is shared among all workers.
 
+
+.. note::
+    Given the nature of CEEMDAN, each time you decompose a signal you will obtain a different set of components.
+    That's the expected consequence of adding noise which is going to be random and different.
+    To make the decomposition reproducible, one needs to set a seed for the random number generator used in CEEMDAN.
+    This is done using :func:`PyEMD.CEEMDAN.noise_seed` method on the instance.
+
 Class
 -----
 
 .. autoclass:: PyEMD.CEEMDAN
     :members:
-    :special-members:

doc/eemd.rst

@@ -3,6 +3,7 @@ EEMD
 
 Info
 ----
+
 Ensemble empirical mode decomposition (EEMD) creates an ensemble of worker each
 of which performs an :doc:`EMD </emd>` on a copy of the input signal with added noise.
 When all workers finish their work a mean over all workers is considered as

doc/examples.rst

@@ -1,5 +1,5 @@
-Example
-*******
+Examples
+********
 
 Some examples can be found in PyEMD/example directory.
 

doc/intro.rst

@@ -28,7 +28,9 @@ The easiest way is to either add `EMD-signal`_ to your `requirements.txt` file,
 
     $ pip install EMD-signal
 
-And, yes, the package is updated every time there is a new algorithm or other cools features added.
+Once the package is installed it should be accessible in your Python as `PyEMD`, e.g. ::
+
+    >>> from PyEMD import EMD
 
 Research (github)
 `````````````````

setup.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 
-VERSION="0.2.15"
+VERSION="1.0.0"
 
 DESCRIPTION = "Implementation of the Empirical Mode Decomposition (EMD) and its variations"