Prepare 1.9.1 release

.github/workflows/python-package.yml

@@ -13,7 +13,7 @@ jobs:
       fail-fast: false
       matrix:
         os: ["ubuntu-latest"]
-        python-version: ["3.9"]
+        python-version: ["3.10"]
     steps:
       - uses: actions/checkout@v2
       - uses: s-weigand/setup-conda@v1

docs/generate_templates.rst

@@ -14,7 +14,7 @@ A set of 13 cell models from layer 5 is included in the basic installation and c
 the `download <https://bbp.epfl.ch/nmc-portal/downloads>`_ page, move them to the cell model folder (which can be
 retrieved with the :code:`mearec default-config` command or with the Python code:
 :code:`mr.get_default_cell_models_folder()`), and unzip them.
-Note also custom models cane be used. In `this notebook <https://github.com/alejoe91/MEArec/blob/master/notebooks/generate_recordings_with_allen_models.ipynb>`_ we show how to use models from the
+Note also custom models cane be used. In `this notebook <https://github.com/SpikeInterface/MEArec/blob/main/notebooks/generate_recordings_with_allen_models.ipynb>`_ we show how to use models from the
 `Allen database <https://celltypes.brain-map.org/>`_ to build templates (and recordings).
 
 Templates are generated in two steps:

docs/usage.rst

@@ -36,7 +36,7 @@ The :code:`recgen` is a :code:`RecordingGenerator` objects and contains the foll
 * spike_traces: (n_spiketrains, n_samples) clean spike trace for each spike train
 * info: dictionary with parameters used
 
-There are several notebooks available `here <https://github.com/SpikeInterface/MEArec/tree/master/notebooks>`_
+There are several notebooks available `here <https://github.com/SpikeInterface/MEArec/tree/main/notebooks>`_
 that show MEArec applications.
 
 Plotting

docs/whatisnew.rst

@@ -5,11 +5,35 @@ Release notes
 =============
 
 
+Version 1.9.1
+=============
+
+6th April 2024
+
+Bug fixes
+---------
+
+- Fix n_burst_spikes param (when missing from default dict) (#156)
+- Fix jitter generation in drifting (#153)
+
+Improvements
+------------
+
+- Load templates as memmap rather than in memory (#167)
+- Little clean of padding templates (#165)
+- Do not remove resampled templates if not resampled  enhancement (#163)
+- Fix numpy deprecations (#162)
+- Improve documentation (#159)
+
+
 Version 1.9.0
 =============
 
+23rd May 2023
+
 
-### Bug fixes
+Bug fixes
+---------
 
 - Propagate filter order and add filter mode (#145)
 - Fix resampling timing (#141)
@@ -18,33 +42,41 @@ Version 1.9.0
 - Fix passing template_ids (#120)
 
 
-### New features
+New features
+------------
+
 
 - Add option to control the drift linear gradient (#129)
 - Define external drifts (#128)
 - Add check_eap_shape parameter for template-generation (#126)
 - Add extract_units_drift_vector (#122)
 
 
-### Improvements
+Improvements
+------------
 
 - Convolution computed in float for more precision (#118)
 
-### Packaging
+Packaging
+---------
 
 - Packaging: move to pyproject.toml, src structure, and black (#146)
 
 
 Version 1.8.0
 =============
 
-### New features
+18th July 2022
+
+New features
+------------
 
 - Refactored drift generation (#97)
 - Add LSB, ADC_BIT_DEPTH, and GAIN concepts (#104, #105)
 - Add smoothing step to templates to make sure they sart and end at 0 (#110)
 
-### Improvements
+Improvements
+------------
 
 - Pre-generate random positions at template generation to improve speed (#95)
 - Improve random seed management (#94)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "MEArec"
-version = "1.9.0"
+version = "1.9.1"
 authors = [
   { name="Alessio Buccino", email="[email protected]" },
 ]

src/MEArec/generators/recgensteps.py

@@ -312,7 +312,7 @@ def chunk_uncorrelated_noise_(
 
     if noise_color:
         # iir peak filter
-        b_iir, a_iir = scipy.signal.iirpeak(color_peak, Q=color_q, fs=fs)
+        b_iir, a_iir = scipy.signal.iirpeak(color_peak, Q=color_q, fs=float(fs))
         additive_noise = scipy.signal.filtfilt(b_iir, a_iir, additive_noise, axis=0, padlen=1000)
         additive_noise += (
             color_noise_floor
@@ -354,7 +354,7 @@ def chunk_distance_correlated_noise_(
     additive_noise = noise_level * np.random.multivariate_normal(np.zeros(n_elec), cov_dist, size=length)
     if noise_color:
         # iir peak filter
-        b_iir, a_iir = scipy.signal.iirpeak(color_peak, Q=color_q, fs=fs)
+        b_iir, a_iir = scipy.signal.iirpeak(color_peak, Q=color_q, fs=float(fs))
         additive_noise = scipy.signal.filtfilt(b_iir, a_iir, additive_noise, axis=0, padlen=1000)
         additive_noise = additive_noise + color_noise_floor * np.std(additive_noise) * np.random.multivariate_normal(
             np.zeros(n_elec), cov_dist, size=length