spelling and grammar fixes

docs/examples/tutorial_pynapple_core.py

@@ -146,7 +146,7 @@
 my_ts = {
     0: nap.Ts(
         t=np.sort(np.random.uniform(0, 100, 10)), time_units="s"
-    ),  # here a simple dictionnary
+    ),  # here a simple dictionary
     1: nap.Ts(t=np.sort(np.random.uniform(0, 100, 20)), time_units="s"),
     2: nap.Ts(t=np.sort(np.random.uniform(0, 100, 30)), time_units="s"),
 }

docs/examples/tutorial_pynapple_io.py

@@ -47,7 +47,7 @@
 # %%
 # Here it shows all the subjects (in this case only A2929), all the sessions and all of the derivatives folders. It shows as well all the NPZ files that contains a pynapple object and the NWB files.
 #
-# The object project behaves like a nested dictionnary. It is then easy to loop and navigate through a hierarchy of folders when doing analyses. In this case, we are gonna take only the session A2929-200711.
+# The object project behaves like a nested dictionary. It is then easy to loop and navigate through a hierarchy of folders when doing analyses. In this case, we are gonna take only the session A2929-200711.
 
 
 session = project["sub-A2929"]["ses-A2929-200711"]

docs/examples/tutorial_pynapple_quick_start.py

@@ -67,15 +67,15 @@
 print(spikes)
 
 # %%
-# In this case, the TsGroup holds 15 neurons and it is possible to access, similar to a dictionnary, the spike times of a single neuron:
+# In this case, the TsGroup holds 15 neurons and it is possible to access, similar to a dictionary, the spike times of a single neuron:
 neuron_0 = spikes[0]
 print(neuron_0)
 
 # %%
 # *neuron_0* is a [Ts](https://pynapple-org.github.io/pynapple/core.time_series/#pynapple.core.time_series.Ts) object containing the times of the spikes.
 
 # %%
-# The other information about the session is contained in `nwb["epochs"]`. In this case, the start and end of the sleep and wake epochs. If the NWB time intervals contains tags of the epochs, pynapple will try to group them together and return a dictionnary of IntervalSet instead of IntervalSet.
+# The other information about the session is contained in `nwb["epochs"]`. In this case, the start and end of the sleep and wake epochs. If the NWB time intervals contains tags of the epochs, pynapple will try to group them together and return a dictionary of IntervalSet instead of IntervalSet.
 epochs = nwb["epochs"]
 print(epochs)
 

docs/old_notebooks/pynapple-core-notebook.ipynb

@@ -484,7 +484,7 @@
    "source": [
     "time_support = nap.IntervalSet(start = 0, end = 200, time_units = 's')\n",
     "\n",
-    "my_ts = {0:nap.Ts(t = np.sort(np.random.uniform(0, 100, 10)), time_units = 's'), # here a simple dictionnary\n",
+    "my_ts = {0:nap.Ts(t = np.sort(np.random.uniform(0, 100, 10)), time_units = 's'), # here a simple dictionary\n",
     "         1:nap.Ts(t = np.sort(np.random.uniform(0, 100, 20)), time_units = 's'),\n",
     "         2:nap.Ts(t = np.sort(np.random.uniform(0, 100, 30)), time_units = 's')}\n",
     "\n",

docs/old_notebooks/pynapple-io-notebook.ipynb

@@ -134,7 +134,7 @@
    "source": [
     "Here it shows all the subjects (in this case only A2929), all the sessions and all of the derivatives folders. It shows as well all the NPZ files that contains a pynapple object and the NWB files.\n",
     "\n",
-    "The object project behaves like a nested dictionnary. It is then easy to loop and navigate through a hierarchy of folders when doing analyses. In this case, we are gonna take only the session A2929-200711."
+    "The object project behaves like a nested dictionary. It is then easy to loop and navigate through a hierarchy of folders when doing analyses. In this case, we are gonna take only the session A2929-200711."
    ]
   },
   {

docs/old_notebooks/pynapple-quick-start.ipynb

@@ -125,7 +125,7 @@
    "source": [
     "*spikes* is a [TsGroup](https://peyrachelab.github.io/pynapple/core.ts_group/) object. It allows to group together time series with different timestamps and couple metainformation to each neuron. In this case, the location of where the neuron was recorded has been added when loading the session for the first time.\n",
     "\n",
-    "In this case, the TsGroup holds 15 neurons and it is possible to access, similar to a dictionnary, the spike times of a single neuron: "
+    "In this case, the TsGroup holds 15 neurons and it is possible to access, similar to a dictionary, the spike times of a single neuron: "
    ]
   },
   {

pynapple/core/ts_group.py

@@ -58,7 +58,7 @@ def union_intervals(i_sets):
 
 class TsGroup(UserDict):
     """
-    The TsGroup is a dictionnary-like object to hold multiple [`Ts`][pynapple.core.time_series.Ts] or [`Tsd`][pynapple.core.time_series.Tsd] objects with different time index.
+    The TsGroup is a dictionary-like object to hold multiple [`Ts`][pynapple.core.time_series.Ts] or [`Tsd`][pynapple.core.time_series.Tsd] objects with different time index.
 
     Attributes
     ----------
@@ -77,7 +77,7 @@ def __init__(
         Parameters
         ----------
         data : dict
-            Dictionnary containing Ts/Tsd objects
+            Dictionary containing Ts/Tsd objects
         time_support : IntervalSet, optional
             The time support of the TsGroup. Ts/Tsd objects will be restricted to the time support if passed.
             If no time support is specified, TsGroup will merge time supports from all the Ts/Tsd objects in data.
@@ -88,7 +88,7 @@ def __init__(
             Useful to speed up initialization of TsGroup when Ts/Tsd objects have already been restricted beforehand
         **kwargs
             Meta-info about the Ts/Tsd objects. Can be either pandas.Series or numpy.ndarray.
-            Note that the index should match the index of the input dictionnary.
+            Note that the index should match the index of the input dictionary.
 
         Raises
         ------
@@ -812,7 +812,7 @@ def getby_category(self, key):
         Returns
         -------
         dict
-            A dictionnary of TsGroup
+            A dictionary of TsGroup
 
         Examples
         --------

pynapple/io/folder.py

@@ -86,7 +86,7 @@ class Folder(UserDict):
     Attributes
     ----------
     data : dict
-        Dictionnary holidng all the pynapple objects found in the folder.
+        Dictionary holding all the pynapple objects found in the folder.
     name : str
         Name of the folder
     npz_files : list
@@ -96,7 +96,7 @@ class Folder(UserDict):
     path : str
         Absolute path of the folder
     subfolds : dict
-        Dictionnary of all the subfolders
+        Dictionary of all the subfolders
 
     """
 
@@ -170,7 +170,7 @@ def __getitem__(self, key):
         Raises
         ------
         KeyError
-            If key is not in the dictionnary
+            If key is not in the dictionary
         """
         if key.__hash__:
             if self.__contains__(key):

pynapple/io/interface_npz.py

@@ -9,7 +9,7 @@
 """
 File classes help to validate and load pynapple objects or NWB files.
 Data are always lazy-loaded.
-Both classes behaves like dictionnary.
+Both classes behave like dictionary.
 """
 
 import os
@@ -49,7 +49,7 @@ def __init__(self, path):
         self.file = np.load(self.path, allow_pickle=True)
         self.type = ""
 
-        # First check if type is explicitely defined
+        # First check if type is explicitly defined
         possible = ["Ts", "Tsd", "TsdFrame", "TsGroup", "IntervalSet"]
         if "type" in self.file.keys():
             if len(self.file["type"]) == 1:

pynapple/io/interface_nwb.py

@@ -7,7 +7,7 @@
 """
 Pynapple class to interface with NWB files.
 Data are always lazy-loaded.
-Object behaves like dictionnary.
+Object behaves like dictionary.
 """
 
 import errno
@@ -35,7 +35,7 @@ def _extract_compatible_data_from_nwbfile(nwbfile):
     Returns
     -------
     dict
-        Dictionnary containing all the object found and their type in pynapple.
+        Dictionary containing all the object found and their type in pynapple.
     """
     data = {}
 
@@ -366,7 +366,7 @@ def __getitem__(self, key):
         Raises
         ------
         KeyError
-            If key is not in the dictionnary
+            If key is not in the dictionary
         """
         if key.__hash__:
             if self.__contains__(key):

pynapple/io/misc.py

@@ -58,7 +58,7 @@ def load_file(path):
 
 def load_folder(path):
     """Load folder containing files or other folder.
-    Pynapple will walk throught the subfolders to detect compatible npz files
+    Pynapple will walk through the subfolders to detect compatible npz files
     or nwb files.
 
     Parameters
@@ -69,7 +69,7 @@ def load_folder(path):
     Returns
     -------
     Folder
-        A dictionnary-like class containing all the sub-folders and compatible files (i.e. npz, nwb)
+        A dictionary-like class containing all the sub-folders and compatible files (i.e. npz, nwb)
 
     Raises
     ------
@@ -180,7 +180,7 @@ def load_eeg(
     Deleted Parameters
     ------------------
     extension : str, optional
-        The file extenstion (.eeg, .dat, .lfp). Make sure the frequency match
+        The file extension (.eeg, .dat, .lfp). Make sure the frequency match
 
     """
     # Need to check if a xml file exists

pynapple/io/neurosuite.py

@@ -155,7 +155,7 @@ def load_neurosuite_xml(self, path):
 
         Function reads :
         1. the number of channels
-        2. the sampling frequency of the dat file or the eeg file depending of what is present in the folder
+        2. the sampling frequency of the dat file or the eeg file depending on what is present in the folder
             eeg file first if both are present or both are absent
         3. the mappings shanks to channels as a dict
 

pynapple/io/phy.py

@@ -143,7 +143,7 @@ def load_phy_spikes(self, time_support=None):
         ----------
         path : Path object
             The path to the data
-        time_support : IntevalSet, optional
+        time_support : IntervalSet, optional
             The time support of the data
 
         Raises
@@ -342,9 +342,9 @@ def load_lfp(
         ----------
         filename : str, optional
             The filename of the lfp file.
-            It can be useful it multiple dat files are present in the data directory
+            It can be useful if multiple dat files are present in the data directory
         channel : int or list of int, optional
-            The channel(s) to load. If None return a memory map of the dat file to avoid memory error
+            The channel(s) to load. If None, return a memory map of the dat file to avoid memory error
         extension : str, optional
             The file extenstion (.eeg, .dat, .lfp). Make sure the frequency match
         frequency : float, optional

pynapple/io/suite2p.py

@@ -53,7 +53,7 @@ class Suite2P(BaseLoader):
     plane_info : pandas.DataFrame
         Contains plane identity of each cell
     stats : dict
-        dictionnay of statistics from stat.npy for each planes only for the neurons that were classified as cells
+        dictionary of statistics from stat.npy for each plane only for the neurons that were classified as cells
         (Can be smaller when loading from the NWB file)
     ops : dict
         Parameters from Suite2p. (Can be smaller when loading from the NWB file)

pynapple/process/decoding.py

@@ -128,7 +128,7 @@ def decode_2d(tuning_curves, group, ep, bin_size, xy, time_units="s", features=N
     tuning_curves : dict
         Dictionnay of 2d tuning curves (one for each neuron).
     group : TsGroup or dict of Ts/Tsd object.
-        A group of neurons with the same keys as tuning_curves dictionnary.
+        A group of neurons with the same keys as tuning_curves dictionary.
     ep : IntervalSet
         The epoch on which decoding is computed
     bin_size : float

pynapple/process/perievent.py

@@ -62,7 +62,7 @@ def compute_perievent(data, tref, minmax, time_unit="s"):
     data : Ts/Tsd/TsGroup
         The data to align to tref.
         If Ts/Tsd, returns a TsGroup.
-        If TsGroup, returns a dictionnary of TsGroup
+        If TsGroup, returns a dictionary of TsGroup
     tref : Ts/Tsd
         The timestamps of the event to align to
     minmax : tuple or int or float
@@ -74,7 +74,7 @@ def compute_perievent(data, tref, minmax, time_unit="s"):
     -------
     dict
         A TsGroup if data is a Ts/Tsd or
-        a dictionnary of TsGroup if data is a TsGroup.
+        a dictionary of TsGroup if data is a TsGroup.
 
     Raises
     ------

pynapple/process/randomize.py

@@ -295,7 +295,7 @@ def _resample_ts(ts):
 
 def _resample_tsgroup(tsgroup):
     """
-    Resamples the each timestamp series in the group, with uniform distribution and on the time
+    Resamples each timestamp series in the group, with uniform distribution and on the time
     support of the whole group.
 
     Parameters

pynapple/process/tuning_curves.py

@@ -17,12 +17,12 @@
 
 def compute_discrete_tuning_curves(group, dict_ep):
     """
-        Compute discrete tuning curves of a TsGroup using a dictionnary of epochs.
-    The function returns a pandas DataFrame with each row being a key of the dictionnary of epochs
+        Compute discrete tuning curves of a TsGroup using a dictionary of epochs.
+    The function returns a pandas DataFrame with each row being a key of the dictionary of epochs
     and each column being a neurons.
 
        This function can typically being used for a set of stimulus being presented for multiple epochs.
-    An example of the dictionnary is :
+    An example of the dictionary is :
 
         >>> dict_ep =  {
                 "stim0": nap.IntervalSet(start=0, end=1),
@@ -154,7 +154,7 @@ def compute_2d_tuning_curves(group, feature, nb_bins, ep=None, minmax=None):
     -------
     tuple
         A tuple containing: \n
-        tc (dict): Dictionnary of the tuning curves with dimensions (nb_bins, nb_bins).\n
+        tc (dict): Dictionary of the tuning curves with dimensions (nb_bins, nb_bins).\n
         xy (list): List of bins center in the two dimensions
 
     Raises
@@ -437,7 +437,7 @@ def compute_2d_tuning_curves_continuous(
     -------
     tuple
         A tuple containing: \n
-        tc (dict): Dictionnary of the tuning curves with dimensions (nb_bins, nb_bins).\n
+        tc (dict): Dictionary of the tuning curves with dimensions (nb_bins, nb_bins).\n
         xy (list): List of bins center in the two dimensions
 
     Raises