Merge branch 'develop'

Author: AlessioZanga

.travis.yml

@@ -8,6 +8,8 @@ jobs:
       os: linux
       dist: xenial
       python: 3.6
+      before_install:
+        - python3.6 -m pip install dataclasses
     - name: "Python 3.7.0 on Bionic Linux"
       dist: bionic
       python: 3.7

CHANGELOG.md

@@ -12,6 +12,25 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Removed
 ### Fixed
 
+## [0.9.2] - 2020-06-04
+### Added
+* DOI reference link using Zenodo
+* @dataclass decorator to datadabase tables ORM
+* Install instruction for Python 3.6 dataclasses
+
+### Changed
+* Renamed JoinedPreprocessor to ForkedPreprocessor
+* Renamed JoinDataFrames to ToMergedDataframes
+* Renamed SinglePickleCache to PickleCache
+
+### Removed
+* Python 3.6 full support due to @dataclass
+    (workaround: python3.6 -m pip install dataclasses)
+* Conda environment.yml configuration file
+* ChunksPickleCache cache manager
+* VerticalPipeline pipeline executor
+* TextMiner textual analysis
+
 ## [0.9.1] - 2020-06-03
 ### Added
 * Add min_value and max_value for each file Metadata

README.md

@@ -1,6 +1,6 @@
 # PyEEGLab
 
-[![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip) [![Build Status](https://travis-ci.org/AlessioZanga/PyEEGLab.svg?branch=master)](https://travis-ci.org/AlessioZanga/PyEEGLab) [![Documentation Status](https://readthedocs.org/projects/pyeeglab/badge/?version=latest)](https://pyeeglab.readthedocs.io/en/latest/?badge=latest) [![Gitpod Ready-to-Code](https://img.shields.io/badge/Gitpod-Ready--to--Code-blue?logo=gitpod)](https://gitpod.io/#https://github.com/AlessioZanga/PyEEGLab) [![codecov](https://codecov.io/gh/AlessioZanga/PyEEGLab/branch/master/graph/badge.svg)](https://codecov.io/gh/AlessioZanga/PyEEGLab) [![Maintainability](https://api.codeclimate.com/v1/badges/c55f67ee28e9e8bd8038/maintainability)](https://codeclimate.com/github/AlessioZanga/PyEEGLab/maintainability)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3874461.svg)](https://doi.org/10.5281/zenodo.3874461) [![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip) [![Build Status](https://travis-ci.org/AlessioZanga/PyEEGLab.svg?branch=master)](https://travis-ci.org/AlessioZanga/PyEEGLab) [![Documentation Status](https://readthedocs.org/projects/pyeeglab/badge/?version=latest)](https://pyeeglab.readthedocs.io/en/latest/?badge=latest) [![Gitpod Ready-to-Code](https://img.shields.io/badge/Gitpod-Ready--to--Code-blue?logo=gitpod)](https://gitpod.io/#https://github.com/AlessioZanga/PyEEGLab) [![codecov](https://codecov.io/gh/AlessioZanga/PyEEGLab/branch/master/graph/badge.svg)](https://codecov.io/gh/AlessioZanga/PyEEGLab) [![Maintainability](https://api.codeclimate.com/v1/badges/c55f67ee28e9e8bd8038/maintainability)](https://codeclimate.com/github/AlessioZanga/PyEEGLab/maintainability)
 
 Analyze and manipulate EEG data using PyEEGLab.
 
@@ -18,6 +18,10 @@ PyEEGLab is distributed using the pip repository:
 
     pip install PyEEGLab
 
+If you use Python 3.6, the dataclasses package must be installed as backport of Python 3.7 dataclasses:
+
+    pip install dataclasses
+
 If you need a bleeding edge version, you can install it directly from GitHub:
 
     pip install git+https://github.com/AlessioZanga/PyEEGLab@develop
@@ -48,6 +52,8 @@ If you use this code in your project use the citation below:
         title={PyEEGLab: A simple tool for EEG manipulation},
         author={Alessio Zanga},
         year={2019},
+        doi={10.5281/zenodo.3874461},
+        url={https://dx.doi.org/10.5281/zenodo.3874461},
         howpublished={\url{https://github.com/AlessioZanga/PyEEGLab}},
     }
 

environment.yml

@@ -21,12 +21,12 @@
 import sys
 
 sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
-from pyeeglab import    TUHEEGAbnormalDataset, SinglePickleCache, Pipeline, CommonChannelSet, \
+from pyeeglab import    TUHEEGAbnormalDataset, PickleCache, Pipeline, CommonChannelSet, \
                         LowestFrequency, ToDataframe, DynamicWindow, BinarizedSpearmanCorrelation, \
                         ToNumpy
 
 dataset = TUHEEGAbnormalDataset('../../data/tuh_eeg_abnormal/v2.0.0/edf')
-dataset.set_cache_manager(SinglePickleCache('../../export'))
+dataset.set_cache_manager(PickleCache('../../export'))
 
 preprocessing = Pipeline([
     CommonChannelSet(),

examples/tuh_eeg_abnormal/example_cnn_dense_classification.py

@@ -21,12 +21,12 @@
 import sys
 
 sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
-from pyeeglab import    TUHEEGAbnormalDataset, SinglePickleCache, Pipeline, CommonChannelSet, \
+from pyeeglab import    TUHEEGAbnormalDataset, PickleCache, Pipeline, CommonChannelSet, \
                         LowestFrequency, BandPassFrequency, ToDataframe, DynamicWindow, \
                         BinarizedSpearmanCorrelation, ToNumpy
 
 dataset = TUHEEGAbnormalDataset('../../data/tuh_eeg_abnormal/v2.0.0/edf')
-dataset.set_cache_manager(SinglePickleCache('../../export'))
+dataset.set_cache_manager(PickleCache('../../export'))
 
 preprocessing = Pipeline([
     CommonChannelSet(),

examples/tuh_eeg_abnormal/example_cnn_dense_classification_with_filter.py

@@ -21,12 +21,12 @@
 import sys
 
 sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
-from pyeeglab import    TUHEEGAbnormalDataset, SinglePickleCache, Pipeline, CommonChannelSet, \
+from pyeeglab import    TUHEEGAbnormalDataset, PickleCache, Pipeline, CommonChannelSet, \
                         LowestFrequency, ToDataframe, DynamicWindow, BinarizedSpearmanCorrelation, \
                         ToNumpy
 
 dataset = TUHEEGAbnormalDataset('../../data/tuh_eeg_abnormal/v2.0.0/edf')
-dataset.set_cache_manager(SinglePickleCache('../../export'))
+dataset.set_cache_manager(PickleCache('../../export'))
 
 preprocessing = Pipeline([
     CommonChannelSet(),

examples/tuh_eeg_abnormal/example_cnn_lstm_classification.py

@@ -21,12 +21,12 @@
 import sys
 
 sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
-from pyeeglab import    TUHEEGAbnormalDataset, SinglePickleCache, Pipeline, CommonChannelSet, \
+from pyeeglab import    TUHEEGAbnormalDataset, PickleCache, Pipeline, CommonChannelSet, \
                         LowestFrequency, BandPassFrequency, ToDataframe, DynamicWindow, \
                         BinarizedSpearmanCorrelation, ToNumpy
 
 dataset = TUHEEGAbnormalDataset('../../data/tuh_eeg_abnormal/v2.0.0/edf')
-dataset.set_cache_manager(SinglePickleCache('../../export'))
+dataset.set_cache_manager(PickleCache('../../export'))
 
 preprocessing = Pipeline([
     CommonChannelSet(),

examples/tuh_eeg_abnormal/example_cnn_lstm_classification_with_filter.py

@@ -23,19 +23,19 @@
 import sys
 
 sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
-from pyeeglab import    TUHEEGAbnormalDataset, SinglePickleCache, Pipeline, CommonChannelSet, \
+from pyeeglab import    TUHEEGAbnormalDataset, PickleCache, Pipeline, CommonChannelSet, \
                         LowestFrequency, ToDataframe, DynamicWindow, BinarizedSpearmanCorrelation, \
-                        CorrelationToAdjacency, Bandpower, GraphWithFeatures, JoinedPreprocessor
+                        CorrelationToAdjacency, Bandpower, GraphWithFeatures, ForkedPreprocessor
 
 dataset = TUHEEGAbnormalDataset('../../data/tuh_eeg_abnormal/v2.0.0/edf')
-dataset.set_cache_manager(SinglePickleCache('../../export'))
+dataset.set_cache_manager(PickleCache('../../export'))
 
 preprocessing = Pipeline([
     CommonChannelSet(),
     LowestFrequency(),
     ToDataframe(),
     DynamicWindow(8),
-    JoinedPreprocessor(
+    ForkedPreprocessor(
         inputs=[
             [BinarizedSpearmanCorrelation(), CorrelationToAdjacency()],
             Bandpower()

examples/tuh_eeg_abnormal/example_gat_lstm_classification.py

@@ -23,21 +23,21 @@
 import sys
 
 sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
-from pyeeglab import    TUHEEGAbnormalDataset, SinglePickleCache, Pipeline, CommonChannelSet, \
+from pyeeglab import    TUHEEGAbnormalDataset, PickleCache, Pipeline, CommonChannelSet, \
                         LowestFrequency, BandPassFrequency, ToDataframe, DynamicWindow, \
                         BinarizedSpearmanCorrelation, CorrelationToAdjacency, Bandpower, \
-                        GraphWithFeatures, JoinedPreprocessor
+                        GraphWithFeatures, ForkedPreprocessor
 
 dataset = TUHEEGAbnormalDataset('../../data/tuh_eeg_abnormal/v2.0.0/edf')
-dataset.set_cache_manager(SinglePickleCache('../../export'))
+dataset.set_cache_manager(PickleCache('../../export'))
 
 preprocessing = Pipeline([
     CommonChannelSet(),
     LowestFrequency(),
     BandPassFrequency(0.1, 47),
     ToDataframe(),
     DynamicWindow(8),
-    JoinedPreprocessor(
+    ForkedPreprocessor(
         inputs=[
             [BinarizedSpearmanCorrelation(), CorrelationToAdjacency()],
             Bandpower()