Merge pull request #336 from pynapple-org/dev

Dev

docs/HISTORY.md

@@ -14,6 +14,12 @@ and [Edoardo Balzani](https://www.simonsfoundation.org/people/edoardo-balzani/)
 of the Flatiron institute.
 
 
+0.7.1 (2024-09-24)
+------------------
+
+- Fixing nan issue when computing 1d tuning curve (See issue #334).
+- Refactor tuning curves and correlogram tests.
+- Adding validators decorators for tuning curves and correlogram modules.
 
 0.7.0 (2024-09-16)
 ------------------

docs/index.md

@@ -12,9 +12,7 @@ PYthon Neural Analysis Package.
 pynapple is a light-weight python library for neurophysiological data analysis. The goal is to offer a versatile set of tools to study typical data in the field, i.e. time series (spike times, behavioral events, etc.) and time intervals (trials, brain states, etc.). It also provides users with generic functions for neuroscience such as tuning curves and cross-correlograms.
 
 -   Free software: MIT License
--   __Documentation__: <https://pynapple-org.github.io/pynapple>
--   __Notebooks and tutorials__ : <https://pynapple-org.github.io/pynapple/generated/gallery/>
-<!-- -   __Collaborative repository__: <https://github.com/PeyracheLab/pynacollada> -->
+-   __Documentation__: <https://pynapple.org>
 
 
 > **Note**

pynapple/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.7.0"
+__version__ = "0.7.1"
 from .core import (
     IntervalSet,
     Ts,

pynapple/core/time_series.py

@@ -139,7 +139,7 @@ def size(self):
         return self.values.size
 
     def __array__(self, dtype=None):
-        return self.values.astype(dtype)
+        return np.asarray(self.values, dtype=dtype)
 
     def __array_ufunc__(self, ufunc, method, *args, **kwargs):
         # print("In __array_ufunc__")

pynapple/process/correlograms.py

@@ -1,6 +1,19 @@
-"""Cross-correlograms """
+"""
+This module holds the functions to compute discrete cross-correlogram
+for timestamps data (i.e. spike times).
 
+| Function | Description |
+|------|------|
+| `nap.compute_autocorrelogram` | Autocorrelograms from a TsGroup object |
+| `nap.compute_crosscorrelogram` | Crosscorrelogram from a TsGroup object |
+| `nap.compute_eventcorrelogram` | Crosscorrelogram between a TsGroup object and a Ts object |
+
+"""
+
+import inspect
+from functools import wraps
 from itertools import combinations, product
+from numbers import Number
 
 import numpy as np
 import pandas as pd
@@ -9,9 +22,53 @@
 from .. import core as nap
 
 
-#########################################################
-# CORRELATION
-#########################################################
+def _validate_correlograms_inputs(func):
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        # Validate each positional argument
+        sig = inspect.signature(func)
+        kwargs = sig.bind_partial(*args, **kwargs).arguments
+
+        # Only TypeError here
+        if getattr(func, "__name__") == "compute_crosscorrelogram" and isinstance(
+            kwargs["group"], (tuple, list)
+        ):
+            if (
+                not all([isinstance(g, nap.TsGroup) for g in kwargs["group"]])
+                or len(kwargs["group"]) != 2
+            ):
+                raise TypeError(
+                    "Invalid type. Parameter group must be of type TsGroup or a tuple/list of (TsGroup, TsGroup)."
+                )
+        else:
+            if not isinstance(kwargs["group"], nap.TsGroup):
+                msg = "Invalid type. Parameter group must be of type TsGroup"
+                if getattr(func, "__name__") == "compute_crosscorrelogram":
+                    msg = msg + " or a tuple/list of (TsGroup, TsGroup)."
+                raise TypeError(msg)
+
+        parameters_type = {
+            "binsize": Number,
+            "windowsize": Number,
+            "ep": nap.IntervalSet,
+            "norm": bool,
+            "time_units": str,
+            "reverse": bool,
+            "event": (nap.Ts, nap.Tsd),
+        }
+        for param, param_type in parameters_type.items():
+            if param in kwargs:
+                if not isinstance(kwargs[param], param_type):
+                    raise TypeError(
+                        f"Invalid type. Parameter {param} must be of type {param_type}."
+                    )
+
+        # Call the original function with validated inputs
+        return func(**kwargs)
+
+    return wrapper
+
+
 @jit(nopython=True)
 def _cross_correlogram(t1, t2, binsize, windowsize):
     """
@@ -81,6 +138,7 @@ def _cross_correlogram(t1, t2, binsize, windowsize):
     return C, B
 
 
+@_validate_correlograms_inputs
 def compute_autocorrelogram(
     group, binsize, windowsize, ep=None, norm=True, time_units="s"
 ):
@@ -118,13 +176,10 @@ def compute_autocorrelogram(
     RuntimeError
         group must be TsGroup
     """
-    if type(group) is nap.TsGroup:
-        if isinstance(ep, nap.IntervalSet):
-            newgroup = group.restrict(ep)
-        else:
-            newgroup = group
+    if isinstance(ep, nap.IntervalSet):
+        newgroup = group.restrict(ep)
     else:
-        raise RuntimeError("Unknown format for group")
+        newgroup = group
 
     autocorrs = {}
 
@@ -152,6 +207,7 @@ def compute_autocorrelogram(
     return autocorrs.astype("float")
 
 
+@_validate_correlograms_inputs
 def compute_crosscorrelogram(
     group, binsize, windowsize, ep=None, norm=True, time_units="s", reverse=False
 ):
@@ -207,7 +263,24 @@ def compute_crosscorrelogram(
         np.array([windowsize], dtype=np.float64), time_units
     )[0]
 
-    if isinstance(group, nap.TsGroup):
+    if isinstance(group, tuple):
+        if isinstance(ep, nap.IntervalSet):
+            newgroup = [group[i].restrict(ep) for i in range(2)]
+        else:
+            newgroup = group
+
+        pairs = product(list(newgroup[0].keys()), list(newgroup[1].keys()))
+
+        for i, j in pairs:
+            spk1 = newgroup[0][i].index
+            spk2 = newgroup[1][j].index
+            auc, times = _cross_correlogram(spk1, spk2, binsize, windowsize)
+            if norm:
+                auc /= newgroup[1][j].rate
+            crosscorrs[(i, j)] = pd.Series(index=times, data=auc, dtype="float")
+
+        crosscorrs = pd.DataFrame.from_dict(crosscorrs)
+    else:
         if isinstance(ep, nap.IntervalSet):
             newgroup = group.restrict(ep)
         else:
@@ -232,34 +305,10 @@ def compute_crosscorrelogram(
             )
             crosscorrs = crosscorrs / freq2
 
-    elif (
-        isinstance(group, (tuple, list))
-        and len(group) == 2
-        and all(map(lambda g: isinstance(g, nap.TsGroup), group))
-    ):
-        if isinstance(ep, nap.IntervalSet):
-            newgroup = [group[i].restrict(ep) for i in range(2)]
-        else:
-            newgroup = group
-
-        pairs = product(list(newgroup[0].keys()), list(newgroup[1].keys()))
-
-        for i, j in pairs:
-            spk1 = newgroup[0][i].index
-            spk2 = newgroup[1][j].index
-            auc, times = _cross_correlogram(spk1, spk2, binsize, windowsize)
-            if norm:
-                auc /= newgroup[1][j].rate
-            crosscorrs[(i, j)] = pd.Series(index=times, data=auc, dtype="float")
-
-        crosscorrs = pd.DataFrame.from_dict(crosscorrs)
-
-    else:
-        raise RuntimeError("Unknown format for group")
-
     return crosscorrs.astype("float")
 
 
+@_validate_correlograms_inputs
 def compute_eventcorrelogram(
     group, event, binsize, windowsize, ep=None, norm=True, time_units="s"
 ):
@@ -306,10 +355,7 @@ def compute_eventcorrelogram(
     else:
         tsd1 = event.restrict(ep).index
 
-    if type(group) is nap.TsGroup:
-        newgroup = group.restrict(ep)
-    else:
-        raise RuntimeError("Unknown format for group")
+    newgroup = group.restrict(ep)
 
     crosscorrs = {}