Merge pull request #70 from SpikeInterface/prepare_release

Prepare release

.travis.yml

@@ -5,6 +5,7 @@ os:
 python:
 - '3.6'
 install:
+- pip install matplotlib==3.2.2
 - pip install -r requirements.txt
 - pip install -r requirements_rtd.txt
 - pip install sphinx pillow sphinx-gallery

README.md

@@ -36,16 +36,14 @@ python setup.py develop
 **Important**: installing with `python setup.py develop` DOES NOT install the latest version of the different modules.
 In order to get the latest updates, clone the above-mentioned repositories and install them from source.
 
-## Examples
-
-For using SpikeInterface, please checkout these [examples](https://github.com/SpikeInterface/spikeinterface/tree/master/examples).
-
-Also, you can checkout this [tutorial](https://github.com/SpikeInterface/spiketutorials/tree/master/Spike_sorting_workshop_2019) for getting started with SpikeInterface.
-
 ## Documentation
 
 All documentation for SpikeInterface can be found here: https://spikeinterface.readthedocs.io/en/latest/.
 
+You can also check out this 1-hour video tutorial for the NWB User Days 2019:
+https://www.youtube.com/watch?v=nWJGwFB7oII
+
+
 ## Citation
 
 If you find SpikeInterface useful in your research, please cite:

doc/index.rst

@@ -33,9 +33,13 @@ With SpikeInterface, users can:
    whatisnew
    authors
 
+For more informatio, please have a look at:
+
+- The bioRxiv `preprint <https://www.biorxiv.org/content/10.1101/796599v1>`_
+
+- 1-hour `video tutorial <https://www.youtube.com/watch?v=nWJGwFB7oII>`_, recorded for the NWB User Days (May 2020)
 
-Please have a look at the `preprint paper <https://www.biorxiv.org/content/10.1101/796599v1>`_  for more info.
-
+- A collection of analysis notebook `spikeinterface report <https://spikeinterface.github.io/>`_
 
 .. Indices and tables
 .. ==================

doc/sorting_extractor.rst

@@ -26,7 +26,7 @@ The contributed extractors are in the **spikeextractors/extractors** folder. You
             SortingExtractor.__init__(self)
 
             ## All file specific initialization code can go here.
-            # If your format stores the sampling frequency, you can overweite the self._sampling_frequency. This way,
+            # If your format stores the sampling frequency, you can overwrite the self._sampling_frequency. This way,
             # the base method self.get_sampling_frequency() will return the correct sampling frequency
 
             self._sampling_frequency = my_sampling_frequency

doc/spike_sorter.rst

@@ -49,17 +49,10 @@ Then, you can start creating a new class:
         'param2': 2,
         }
 
-    # (optional) rewrite the params as a list for the Spikely GUI to use
-    _extra_gui_params = [
-        {'name': 'param1', 'type': 'str', 'value': None, 'default': None,
-         'title': "My first param"},
-        {'name': 'param2', 'type': 'int', 'value': 2, 'default': 2,
-         'title': "My second param"},
-    ]
-
-    sorter_gui_params = copy.deepcopy(BaseSorter.sorter_gui_params)
-    for param in _extra_gui_params:
-        sorter_gui_params.append(param)
+    _params_description = {
+        'param1': 'Description for param1',
+        'param1': 'Description for param1',
+    }
 
     installation_mesg = """
         >>> pip install myspikesorter
@@ -79,12 +72,18 @@ Now you can start filling out the required methods:
     def get_sorter_version():
         return myspikesorter.__version__
 
+    @classmethod
+    def is_installed(cls):
+
+        # Fill code to check sorter installation. It returns a boolean
+        return HAVE_MSS
+
     def _setup_recording(self, recording, output_folder):
 
         # Fill code to set up the recording: convert to required file, parse config files, etc.
         # The files should be placed in the 'output_folder'
 
-    def _run(self,  recording, output_folder):
+    def _run(self, recording, output_folder):
 
         # Fill code to run your spike sorter based on the files created in the _setup_recording()
         # You can run CLI commands (e.g. klusta, spykingcircus, tridescous), pure Python code (e.g. Mountainsort4,

doc/supported.rst

@@ -170,22 +170,22 @@ First, import the spikesorters package,
 
 .. code:: python
 
-  import spikesorters as sorters
+  import spikesorters as ss
 
 Then you can check the installed Sorter list,
 
 .. code:: python
 
-  sorters.installed_sorter_list
+  ss.installed_sorters()
   
 which outputs,
 
 .. parsed-literal::
-  [spikesorters.klusta.klusta.KlustaSorter,
-   spikesorters.tridesclous.tridesclous.TridesclousSorter,
-   spikesorters.mountainsort4.mountainsort4.Mountainsort4Sorter,
-   spikesorters.spyking_circus.spyking_circus.SpykingcircusSorter,
-   spikesorters.herdingspikes.herdingspikes.HerdingspikesSorter]
+  ['herdingspikes',
+   'klusta',
+   'mountainsort4',
+   'spykingcircus',
+   'tridesclous']
 
 
 When trying to use an sorter that has not been installed in your environment, an installation message will appear indicating how to install the given sorter,

examples/getting_started/plot_getting_started.py

@@ -118,11 +118,11 @@
 # Let's first check which sorters are implemented and which are installed
 
 print('Available sorters', ss.available_sorters())
-print('Installed sorters', ss.installed_sorter_list)
+print('Installed sorters', ss.installed_sorters())
 
 ##############################################################################
-# The :code:`ss.installed_sorter_list` will list the sorters installed in the machine. Each spike sorter
-# is implemented as a class. We can see we have Klusta and Mountainsort4 installed.
+# The :code:`ss.installed_sorters()` will list the sorters installed in the machine.
+# We can see we have Klusta and Mountainsort4 installed.
 # Spike sorters come with a set of parameters that users can change. The available parameters are dictionaries and
 # can be accessed with:
 

examples/modules/comparison/generate_erroneous_sorting.py

@@ -8,7 +8,6 @@
 import matplotlib.pyplot as plt
 
 import spikeinterface.extractors as se
-import spikeinterface.sorters as sorters
 import spikeinterface.comparison as sc
 import spikeinterface.widgets as sw
 

examples/modules/comparison/plot_1_compare_two_sorters.py

@@ -14,7 +14,7 @@
 import matplotlib.pyplot as plt
 
 import spikeinterface.extractors as se
-import spikeinterface.sorters as sorters
+import spikeinterface.sorters as ss
 import spikeinterface.comparison as sc
 import spikeinterface.widgets as sw
 
@@ -27,8 +27,8 @@
 #############################################################################
 # Then run two spike sorters and compare their ouput.
 
-sorting_KL = sorters.run_klusta(recording)
-sorting_MS4 = sorters.run_mountainsort4(recording)
+sorting_KL = ss.run_klusta(recording)
+sorting_MS4 = ss.run_mountainsort4(recording)
 
 
 #############################################################################

examples/modules/comparison/plot_2_compare_multiple_sorters.py

@@ -16,7 +16,7 @@
 import matplotlib.pyplot as plt
 
 import spikeinterface.extractors as se
-import spikeinterface.sorters as sorters
+import spikeinterface.sorters as ss
 import spikeinterface.comparison as sc
 import spikeinterface.widgets as sw
 
@@ -26,11 +26,11 @@
 recording, sorting = se.example_datasets.toy_example(num_channels=4, duration=20, seed=0)
 
 #############################################################################
-# Then run 3 spike sorters and compare their ouput.
+# Then run 3 spike sorters and compare their output.
 
-sorting_KL = sorters.run_klusta(recording)
-sorting_MS4 = sorters.run_mountainsort4(recording)
-sorting_TDC = sorters.run_tridesclous(recording)
+sorting_KL = ss.run_klusta(recording)
+sorting_MS4 = ss.run_mountainsort4(recording)
+sorting_TDC = ss.run_tridesclous(recording)
 
 #############################################################################
 # Compare multiple spike sorter outputs

examples/modules/comparison/plot_3_compare_sorter_with_ground_truth.py

@@ -22,15 +22,15 @@
 import seaborn as sns
 
 import spikeinterface.extractors as se
-import spikeinterface.sorters as sorters
+import spikeinterface.sorters as ss
 import spikeinterface.comparison as sc
 import spikeinterface.widgets as sw
 
 ##############################################################################
 
 recording, sorting_true = se.example_datasets.toy_example(num_channels=4, duration=10, seed=0)
 
-sorting_MS4 = sorters.run_mountainsort4(recording)
+sorting_MS4 = ss.run_mountainsort4(recording)
 
 ##############################################################################
 
@@ -108,7 +108,7 @@
 ##############################################################################
 # Lets do the same for klusta
 
-sorting_KL = sorters.run_klusta(recording)
+sorting_KL = ss.run_klusta(recording)
 cmp_gt_KL = sc.compare_sorter_to_ground_truth(sorting_true, sorting_KL, exhaustive_gt=True)
 
 ##############################################################################

examples/modules/comparison/plot_5_comparison_sorter_weaknesses.py

@@ -41,7 +41,7 @@
 import seaborn as sns
 
 import spikeinterface.extractors as se
-import spikeinterface.sorters as sorters
+import spikeinterface.sorters as ss
 import spikeinterface.comparison as sc
 import spikeinterface.widgets as sw
 

examples/modules/sorters/plot_1_sorters_example.py

@@ -21,10 +21,10 @@
 print(ss.available_sorters())
 
 ##############################################################################
-# This will list the sorters installed in the machine. Each spike sorter
-# is implemented in a class. To access the class names you can run:
+# This will list the sorters available through SpikeInterface. To see which sorters are installed on the machine
+# you can run:
 
-print(ss.installed_sorter_list)
+print(ss.installed_sorters())
 
 ##############################################################################
 # Change sorter parameters

examples/modules/sorters/plot_3_sorting_by_group.py

@@ -22,9 +22,10 @@
 # (using the :code:`set_channel_property` method), or by using a probe file. In this example we will create a 16 channel
 # recording and split it in four channel groups (tetrodes).
 #
-# Let's create a toy example with 16 channels:
+# Let's create a toy example with 16 channels (the :code:`dumpable=True` dumps the extractors to a file, which is
+# required for parallel sorting):
 
-recording_tetrodes, sorting_true = se.example_datasets.toy_example(duration=10, num_channels=16)
+recording_tetrodes, sorting_true = se.example_datasets.toy_example(duration=10, num_channels=16, dumpable=True)
 
 ##############################################################################
 # Initially there is no group information ('location' is loaded automatically when creating toy data):

requirements.txt

@@ -1,5 +1,5 @@
-spikeextractors>=0.8.4
-spiketoolkit>=0.6.3
-spikesorters>=0.3.2
-spikecomparison>=0.2.6
-spikewidgets>=0.4.3
+spikeextractors>=0.9.1
+spiketoolkit>=0.7.0
+spikesorters>=0.4.2
+spikecomparison>=0.3.0
+spikewidgets>=0.5.0

requirements_rtd.txt

@@ -18,4 +18,5 @@ klustakwik2==0.2.7
 
 # tridesclous
 pyqt5
+hdbscan
 tridesclous==1.6.0