Merge pull request #390 from pynapple-org/bump_0.8

Bump 0.8

README.md

@@ -1,10 +1,4 @@
-<!-- ![pic1](banner_logo.png) -->
-<p align="center">
-  <img width="60%" src="docs/images/banner_logo.png">
-</p>
-
 
-<!-- ========================== -->
 
 [![image](https://img.shields.io/pypi/v/pynapple.svg)](https://pypi.python.org/pypi/pynapple)
 [![pynapple CI](https://github.com/pynapple-org/pynapple/actions/workflows/main.yml/badge.svg)](https://github.com/pynapple-org/pynapple/actions/workflows/main.yml)
@@ -157,7 +151,7 @@ Shown below, the final figure from the example code displays the firing rate of
 
 <!-- ![pic1](readme_figure.png) -->
 <p align="center">
-  <img width="80%" src="docs/images/readme_figure.png">
+  <img width="80%" src="doc/_static/readme_figure.png">
 </p>
 
 

doc/releases.md

@@ -18,6 +18,11 @@ of the Flatiron institute.
 
 ## Releases
 
+### 0.8.0 (2025-01-15)
+
+- New private class: `_MetadataMixin` (core/metadata_class.py). Can be inherited by `IntervalSet`, `TsdFrame` and `TsGroup`.
+- `decode_1d` and `decode_2d` now accepts `TsdFrame` as input. 
+
 ### 0.7.1 (2024-09-24)
 
 - Fixing nan issue when computing 1d tuning curve (See issue #334).

doc/user_guide/11_wavelets.md

@@ -29,7 +29,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
 custom_params = {"axes.spines.right": False, "axes.spines.top": False}
-sns.set_theme(style="ticks", palette="colorblind", font_scale=1.5, rc=custom_params)
+sns.set_theme(style="ticks", palette="colorblind", font_scale=1.0, rc=custom_params)
 ```
 
 ***
@@ -121,6 +121,7 @@ underlying wavelets.
 window_lengths = [1.0, 3.0]
 gaussian_widths = [1.0, 3.0]
 colors = np.array([["r", "g"], ["b", "y"]])
+fig = plt.figure()
 fig, ax = plt.subplots(
     len(window_lengths) + 1,
     len(gaussian_widths) + 1,
@@ -153,6 +154,7 @@ for i in range(len(gaussian_widths)):
     ax[i, -1].set(
         xlim=(0, 2), yticks=[], ylabel="Frequency Response", xlabel="Frequency (Hz)"
     )
+
 ```
 
 Increasing `window_length` increases the number of wavelet cycles present in the oscillations (cycles), and

pynapple/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.7.1"
+__version__ = "0.8.0"
 from .core import (
     IntervalSet,
     Ts,

pynapple/core/time_series.py

@@ -647,6 +647,47 @@ class TsdTensor(_BaseTsd):
         Frequency of the time series (Hz) computed over the time support
     time_support : IntervalSet
         The time support of the time series
+
+    Examples
+    --------
+    Initialize a TsdTensor:
+
+    >>> import pynapple as nap
+    >>> import numpy as np
+    >>> t = np.arange(10)
+    >>> d = np.random.randn(10, 2, 3)
+    >>> tsdtensor = nap.TsdTensor(t=t, d=d)
+    >>> tsdtensor
+    Time (s)
+    ----------  -------------------------------
+    0           [[-1.493178 ... -1.281017] ...]
+    1           [[0.230829 ... 0.437679] ...]
+    2           [[-0.462031 ...  0.344506] ...]
+    3           [[0.497019 ... 0.469494] ...]
+    4           [[0.065921 ... 1.012917] ...]
+    5           [[0.158534 ... 1.455523] ...]
+    6           [[-2.567728 ...  0.61182 ] ...]
+    7           [[0.940799 ... 0.109203] ...]
+    8           [[2.340077 ... 0.21885 ] ...]
+    9           [[-0.306175 ... -0.447414] ...]
+    dtype: float64, shape: (10, 2, 3)
+
+    Initialize a TsdTensor with `time_support`:
+
+    >>> t = np.arange(10)
+    >>> d = np.random.randn(10, 2, 3)
+    >>> time_support = nap.IntervalSet(start=0, end=4)
+    >>> tsdtensor = nap.TsdTensor(t=t, d=d, time_support=time_support)
+    >>> tsdtensor
+    Time (s)
+    ----------  -------------------------------
+    0           [[-1.493178 ... -1.281017] ...]
+    1           [[0.230829 ... 0.437679] ...]
+    2           [[-0.462031 ...  0.344506] ...]
+    3           [[0.497019 ... 0.469494] ...]
+    4           [[0.065921 ... 1.012917] ...]
+    dtype: float64, shape: (5, 2, 3)
+
     """
 
     def __init__(
@@ -1519,6 +1560,55 @@ class Tsd(_BaseTsd):
         Frequency of the time series (Hz) computed over the time support
     time_support : IntervalSet
         The time support of the time series
+
+    Examples
+    --------
+    Initialize a Tsd:
+
+    >>> import pynapple as nap
+    >>> import numpy as np
+    >>> t = np.arange(100)
+    >>> d = np.ones(100)
+    >>> tsd = nap.Tsd(t=t, d=d)
+    >>> tsd
+    Time (s)
+    ----------  --
+    0.0          1
+    1.0          1
+    2.0          1
+    3.0          1
+    4.0          1
+    5.0          1
+    6.0          1
+    ...
+    93.0         1
+    94.0         1
+    95.0         1
+    96.0         1
+    97.0         1
+    98.0         1
+    99.0         1
+    dtype: float64, shape: (100,)
+
+    Initialize a Tsd with `time_support`:
+
+    >>> t = np.arange(100)
+    >>> d = np.ones(100)
+    >>> time_support = nap.IntervalSet(start=0.5, end=8)
+    >>> tsd = nap.Tsd(t=t, d=d, time_support=time_support)
+    >>> tsd
+    Time (s)
+    ----------  --
+    1            1
+    2            1
+    3            1
+    4            1
+    5            1
+    6            1
+    7            1
+    8            1
+    dtype: float64, shape: (8,)
+
     """
 
     def __init__(

pynapple/process/wavelets.py

@@ -106,7 +106,7 @@ def compute_wavelet_transform(
     if fs is None:
         fs = sig.rate
 
-    output_shape = (sig.shape[0], len(freqs), *sig.shape[1:])
+    output_shape = (sig.shape[0], *sig.shape[1:], len(freqs))
     sig = np.reshape(sig, (sig.shape[0], -1))
 
     filter_bank = generate_morlet_filterbank(
@@ -122,16 +122,17 @@ def compute_wavelet_transform(
         coef = convolved / (fs / np.sqrt(freqs))
     else:
         coef = convolved
+
     cwt = np.expand_dims(coef, -1) if len(coef.shape) == 2 else coef
 
     if len(output_shape) == 2:
         return nap.TsdFrame(
-            t=sig.index, d=cwt.reshape(output_shape), time_support=sig.time_support
+            t=sig.index, d=np.squeeze(cwt, axis=1), time_support=sig.time_support
+        )
+    else:
+        return nap.TsdTensor(
+            t=sig.index, d=np.reshape(cwt, output_shape), time_support=sig.time_support
         )
-
-    return nap.TsdTensor(
-        t=sig.index, d=cwt.reshape(output_shape), time_support=sig.time_support
-    )
 
 
 def generate_morlet_filterbank(

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pynapple"
-version = "0.7.1"
+version = "0.8.0"
 description = "PYthon Neural Analysis Package Pour Laboratoires d’Excellence"
 readme = "README.md"
 authors = [{ name = "Guillaume Viejo", email = "[email protected]" }]

setup.py

@@ -59,8 +59,8 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/pynapple-org/pynapple',
-    version='v0.7.1',
+    version='v0.8.0',
     zip_safe=False,
     long_description_content_type='text/markdown',
-    download_url='https://github.com/pynapple-org/pynapple/archive/refs/tags/v0.7.1.tar.gz'
+    download_url='https://github.com/pynapple-org/pynapple/archive/refs/tags/v0.8.0.tar.gz'
 )

tests/test_signal_processing.py

@@ -291,7 +291,17 @@ def get_1d_signal(fs=1000, fc=50):
 def get_2d_signal(fs=1000, fc=50):
     t = np.arange(0, 2, 1 / fs)
     d = np.sin(t * fc * np.pi * 2) * np.interp(t, [0, 1, 2], [0, 1, 0])
-    return nap.TsdFrame(t, d[:, np.newaxis], time_support=nap.IntervalSet(0, 2))
+    return nap.TsdFrame(
+        t, np.repeat(d[:, np.newaxis], 2, axis=1), time_support=nap.IntervalSet(0, 2)
+    )
+
+
+def get_3d_signal(fs=1000, fc=50):
+    t = np.arange(0, 2, 1 / fs)
+    d = np.sin(t * fc * np.pi * 2) * np.interp(t, [0, 1, 2], [0, 1, 0])
+    d = d[:, np.newaxis, np.newaxis]
+    d = np.repeat(np.repeat(d, 2, axis=1), 3, axis=2)
+    return nap.TsdTensor(t, d, time_support=nap.IntervalSet(0, 2))
 
 
 def get_output_1d(sig, wavelets):
@@ -308,15 +318,30 @@ def get_output_1d(sig, wavelets):
 def get_output_2d(sig, wavelets):
     T, K = sig.shape
     M, N = wavelets.shape
-    out = []
-    for k in range(K):
-        tmp = []
-        for n in range(N):
-            tmp.append(np.convolve(sig[:, k], wavelets[:, n], mode="full"))
-        out.append(np.array(tmp))
-    out = np.array(out).T
     cut = ((M - 1) // 2, T + M - 1 - ((M - 1) // 2) - (1 - M % 2))
-    return out[cut[0] : cut[1]]
+    out = np.zeros((T, K, N), dtype=np.complex128)
+    for n in range(N):  # wavelet
+        for k in range(K):
+            out[:, k, n] = np.convolve(sig[:, k], wavelets[:, n], mode="full")[
+                cut[0] : cut[1]
+            ]
+
+    return out
+
+
+def get_output_3d(sig, wavelets):
+    T, K, L = sig.shape
+    M, N = wavelets.shape
+    cut = ((M - 1) // 2, T + M - 1 - ((M - 1) // 2) - (1 - M % 2))
+    out = np.zeros((T, K, L, N), dtype=np.complex128)
+    for n in range(N):  # wavelet
+        for k in range(K):
+            for l in range(L):
+                out[:, k, l, n] = np.convolve(
+                    sig[:, k, l], wavelets[:, n], mode="full"
+                )[cut[0] : cut[1]]
+
+    return out
 
 
 @pytest.mark.parametrize(
@@ -331,6 +356,7 @@ def get_output_2d(sig, wavelets):
         (get_1d_signal, np.linspace(10, 100, 10), 1000, 1.5, 1.0, 16, "l2", 50, 1000),
         (get_1d_signal, np.linspace(10, 100, 10), 1000, 1.5, 1.0, 16, None, 20, 1000),
         (get_2d_signal, np.linspace(10, 100, 10), 1000, 1.5, 1.0, 16, None, 20, 1000),
+        (get_3d_signal, np.linspace(10, 100, 10), 1000, 1.5, 1.0, 16, None, 20, 1000),
     ],
 )
 def test_compute_wavelet_transform(
@@ -344,6 +370,8 @@ def test_compute_wavelet_transform(
         output = get_output_1d(sig.d, wavelets.values)
     if sig.ndim == 2:
         output = get_output_2d(sig.d, wavelets.values)
+    if sig.ndim == 3:
+        output = get_output_3d(sig.d, wavelets.values)
 
     if norm == "l1":
         output = output / (1000 / freqs)