diff --git a/docs/examples/tutorial_pynapple_numpy.py b/docs/examples/tutorial_pynapple_numpy.py
index 73d0b899..40c13da8 100644
--- a/docs/examples/tutorial_pynapple_numpy.py
+++ b/docs/examples/tutorial_pynapple_numpy.py
@@ -144,7 +144,7 @@
tsdframe = nap.TsdFrame(t=np.arange(5), d=np.random.randn(5, 3))
-print(np.concatenate(tsdframe, tsdframe), 1)
+print(np.concatenate((tsdframe, tsdframe), 1))
# %%
# Spliting
diff --git a/pynapple/core/interval_set.py b/pynapple/core/interval_set.py
index 652a4a65..ca1046aa 100644
--- a/pynapple/core/interval_set.py
+++ b/pynapple/core/interval_set.py
@@ -562,7 +562,7 @@ def save(self, filename):
objects. For example, you determined some epochs for one session that you want to save
to avoid recomputing them.
- You can load the object with numpy.load. Keys are 'start', 'end' and 'type'.
+ You can load the object with `nap.load_file`. Keys are 'start', 'end' and 'type'.
See the example below.
Parameters
@@ -577,16 +577,10 @@ def save(self, filename):
>>> ep = nap.IntervalSet(start=[0, 10, 20], end=[5, 12, 33])
>>> ep.save("my_ep.npz")
- Here I can retrieve my data with numpy directly:
+ To load you file, you can use the `nap.load_file` function :
- >>> file = np.load("my_ep.npz")
- >>> print(list(file.keys()))
- ['start', 'end', 'type']
- >>> print(file['start'])
- [0. 10. 20.]
-
- It is then easy to recreate the IntervalSet object.
- >>> nap.IntervalSet(file['start'], file['end'])
+ >>> ep = nap.load_file("my_path/my_ep.npz")
+ >>> ep
start end
0 0.0 5.0
1 10.0 12.0
diff --git a/pynapple/core/time_index.py b/pynapple/core/time_index.py
index c1c1333d..d5eb64a1 100644
--- a/pynapple/core/time_index.py
+++ b/pynapple/core/time_index.py
@@ -2,7 +2,8 @@
Similar to pandas.Index, `TsIndex` holds the timestamps associated with the data of a time series.
This class deals with conversion between different time units for all pynapple objects as well
- as making sure that timestamps are property sorted before initializing any objects.
+ as making sure that timestamps are property sorted before initializing any objects.
+
- `us`: microseconds
- `ms`: milliseconds
- `s`: seconds (overall default)
diff --git a/pynapple/core/time_series.py b/pynapple/core/time_series.py
index bb1f5b8f..5b4c24c4 100644
--- a/pynapple/core/time_series.py
+++ b/pynapple/core/time_series.py
@@ -658,7 +658,7 @@ def save(self, filename):
filtered them. You can save the filtered channels as a npz to avoid
reprocessing it.
- You can load the object with numpy.load. Keys are 't', 'd', 'start', 'end', 'type'
+ You can load the object with `nap.load_file`. Keys are 't', 'd', 'start', 'end', 'type'
and 'columns' for columns names.
Parameters
@@ -673,21 +673,9 @@ def save(self, filename):
>>> tsdtensor = nap.TsdTensor(t=np.array([0., 1.]), d = np.zeros((2,3,4)))
>>> tsdtensor.save("my_path/my_tsdtensor.npz")
- Here I can retrieve my data with numpy directly:
-
- >>> file = np.load("my_path/my_tsdtensor.npz")
- >>> print(list(file.keys()))
- ['t', 'd', 'start', 'end', ''type']
- >>> print(file['t'])
- [0. 1.]
-
- It is then easy to recreate the TsdTensor object.
- >>> time_support = nap.IntervalSet(file['start'], file['end'])
- >>> nap.TsdTensor(t=file['t'], d=file['d'], time_support=time_support)
- Time (s)
- 0.0 [[[0.0 ...]]]
- 1.0 [[[0.0 ...]]]
+ To load you file, you can use the `nap.load_file` function :
+ >>> tsdtensor = nap.load_file("my_path/my_tsdtensor.npz")
Raises
------
@@ -914,7 +902,7 @@ def save(self, filename):
filtered them. You can save the filtered channels as a npz to avoid
reprocessing it.
- You can load the object with numpy.load. Keys are 't', 'd', 'start', 'end', 'type'
+ You can load the object with `nap.load_file`. Keys are 't', 'd', 'start', 'end', 'type'
and 'columns' for columns names.
Parameters
@@ -929,17 +917,10 @@ def save(self, filename):
>>> tsdframe = nap.TsdFrame(t=np.array([0., 1.]), d = np.array([[2, 3],[4,5]]), columns=['a', 'b'])
>>> tsdframe.save("my_path/my_tsdframe.npz")
- Here I can retrieve my data with numpy directly:
-
- >>> file = np.load("my_path/my_tsdframe.npz")
- >>> print(list(file.keys()))
- ['t', 'd', 'start', 'end', 'columns', 'type']
- >>> print(file['t'])
- [0. 1.]
+ To load you file, you can use the `nap.load_file` function :
- It is then easy to recreate the Tsd object.
- >>> time_support = nap.IntervalSet(file['start'], file['end'])
- >>> nap.TsdFrame(t=file['t'], d=file['d'], time_support=time_support, columns=file['columns'])
+ >>> tsdframe = nap.load_file("my_path/my_tsdframe.npz")
+ >>> tsdframe
a b
Time (s)
0.0 2 3
@@ -1236,7 +1217,7 @@ def save(self, filename):
filtered it. You can save the filtered channel as a npz to avoid
reprocessing it.
- You can load the object with numpy.load. Keys are 't', 'd', 'start', 'end' and 'type'.
+ You can load the object with `nap.load_file`. Keys are 't', 'd', 'start', 'end' and 'type'.
See the example below.
Parameters
@@ -1251,17 +1232,10 @@ def save(self, filename):
>>> tsd = nap.Tsd(t=np.array([0., 1.]), d = np.array([2, 3]))
>>> tsd.save("my_path/my_tsd.npz")
- Here I can retrieve my data with numpy directly:
+ To load you file, you can use the `nap.load_file` function :
- >>> file = np.load("my_path/my_tsd.npz")
- >>> print(list(file.keys()))
- ['t', 'd', 'start', 'end', 'type']
- >>> print(file['t'])
- [0. 1.]
-
- It is then easy to recreate the Tsd object.
- >>> time_support = nap.IntervalSet(file['start'], file['end'])
- >>> nap.Tsd(t=file['t'], d=file['d'], time_support=time_support)
+ >>> tsd = nap.load_file("my_path/my_tsd.npz")
+ >>> tsd
Time (s)
0.0 2
1.0 3
@@ -1530,7 +1504,7 @@ def save(self, filename):
The main purpose of this function is to save small/medium sized timestamps
object.
- You can load the object with numpy.load. Keys are 't', 'start' and 'end' and 'type'.
+ You can load the object with `nap.load_file`. Keys are 't', 'start' and 'end' and 'type'.
See the example below.
Parameters
@@ -1545,23 +1519,15 @@ def save(self, filename):
>>> ts = nap.Ts(t=np.array([0., 1., 1.5]))
>>> ts.save("my_path/my_ts.npz")
- Here I can retrieve my data with numpy directly:
-
- >>> file = np.load("my_path/my_ts.npz")
- >>> print(list(file.keys()))
- ['t', 'start', 'end', 'type']
- >>> print(file['t'])
- [0. 1. 1.5]
+ To load you file, you can use the `nap.load_file` function :
- It is then easy to recreate the Tsd object.
- >>> time_support = nap.IntervalSet(file['start'], file['end'])
- >>> nap.Ts(t=file['t'], time_support=time_support)
+ >>> ts = nap.load_file("my_path/my_ts.npz")
+ >>> ts
Time (s)
0.0
1.0
1.5
-
Raises
------
RuntimeError
diff --git a/pynapple/core/ts_group.py b/pynapple/core/ts_group.py
index 0e545e29..325c9e2e 100644
--- a/pynapple/core/ts_group.py
+++ b/pynapple/core/ts_group.py
@@ -888,28 +888,33 @@ def save(self, filename):
and assigning to each the corresponding index. Typically, a TsGroup like
this :
- TsGroup({
- 0 : Tsd(t=[0, 2, 4], d=[1, 2, 3])
- 1 : Tsd(t=[1, 5], d=[5, 6])
- })
+ ``` py
+ TsGroup({
+ 0 : Tsd(t=[0, 2, 4], d=[1, 2, 3])
+ 1 : Tsd(t=[1, 5], d=[5, 6])
+ })
+ ```
will be saved as npz with the following keys:
- {
- 't' : [0, 1, 2, 4, 5],
- 'd' : [1, 5, 2, 3, 5],
- 'index' : [0, 1, 0, 0, 1],
- 'start' : [0],
- 'end' : [5],
- 'type' : 'TsGroup'
- }
+ ``` py
+ {
+ 't' : [0, 1, 2, 4, 5],
+ 'd' : [1, 5, 2, 3, 5],
+ 'index' : [0, 1, 0, 0, 1],
+ 'start' : [0],
+ 'end' : [5],
+ 'keys' : [0, 1],
+ 'type' : 'TsGroup'
+ }
+ ```
Metadata are saved by columns with the column name as the npz key. To avoid
potential conflicts, make sure the columns name of the metadata are different
- from ['t', 'd', 'start', 'end', 'index']
+ from ['t', 'd', 'start', 'end', 'index', 'keys']
- You can load the object with numpy.load. Default keys are 't', 'd'(optional),
- 'start', 'end', 'index' and 'type'.
+ You can load the object with `nap.load_file`. Default keys are 't', 'd'(optional),
+ 'start', 'end', 'index', 'keys' and 'type'.
See the example below.
Parameters
@@ -935,21 +940,9 @@ def save(self, filename):
6 0.4 1 left foot
>>> tsgroup.save("my_tsgroup.npz")
- Here I can retrieve my data with numpy directly:
-
- >>> file = np.load("my_tsgroup.npz")
- >>> print(list(file.keys()))
- ['rate', 'group', 'location', 't', 'index', 'start', 'end', 'type']
- >>> print(file['index'])
- [0 6 0 0 6]
+ To get back to pynapple, you can use the `nap.load_file` function :
- In the case where TsGroup is a set of Ts objects, it is very direct to
- recreate the TsGroup by using the function to_tsgroup :
-
- >>> time_support = nap.IntervalSet(file['start'], file['end'])
- >>> tsd = nap.Tsd(t=file['t'], d=file['index'], time_support = time_support)
- >>> tsgroup = tsd.to_tsgroup()
- >>> tsgroup.set_info(group = file['group'], location = file['location'])
+ >>> tsgroup = nap.load_file("my_tsgroup.npz")
>>> tsgroup
Index rate group location
------- ------ ------- ----------
@@ -981,7 +974,7 @@ def save(self, filename):
dicttosave = {"type": np.array(["TsGroup"], dtype=np.str_)}
for k in self._metadata.columns:
- if k not in ["t", "d", "start", "end", "index"]:
+ if k not in ["t", "d", "start", "end", "index", "keys"]:
tmp = self._metadata[k].values
if tmp.dtype == np.dtype("O"):
tmp = tmp.astype(np.str_)
@@ -1012,7 +1005,7 @@ def save(self, filename):
dicttosave["index"] = index
if not np.all(np.isnan(data)):
dicttosave["d"] = data[idx]
-
+ dicttosave["keys"] = np.array(self.keys())
dicttosave["start"] = self.time_support.start
dicttosave["end"] = self.time_support.end
diff --git a/pynapple/io/interface_npz.py b/pynapple/io/interface_npz.py
index efd738b8..968e7d6f 100644
--- a/pynapple/io/interface_npz.py
+++ b/pynapple/io/interface_npz.py
@@ -4,7 +4,7 @@
# @Author: Guillaume Viejo
# @Date: 2023-07-05 16:03:25
# @Last Modified by: Guillaume Viejo
-# @Last Modified time: 2023-09-26 18:00:54
+# @Last Modified time: 2024-04-02 14:32:25
"""
File classes help to validate and load pynapple objects or NWB files.
@@ -89,12 +89,35 @@ def load(self):
else:
time_support = nap.IntervalSet(self.file["start"], self.file["end"])
if self.type == "TsGroup":
- tsd = nap.Tsd(
- t=self.file["t"], d=self.file["index"], time_support=time_support
- )
- tsgroup = tsd.to_tsgroup()
+
+ times = self.file["t"]
+ index = self.file["index"]
+ has_data = False
if "d" in self.file.keys():
- print("TODO")
+ data = self.file["data"]
+ has_data = True
+
+ if "keys" in self.file.keys():
+ keys = self.file["keys"]
+ else:
+ keys = np.unique(index)
+
+ group = {}
+ for k in keys:
+ if has_data:
+ group[k] = nap.Tsd(
+ t=times[index == k],
+ d=data[index == k],
+ time_support=time_support,
+ )
+ else:
+ group[k] = nap.Ts(
+ t=times[index == k], time_support=time_support
+ )
+
+ tsgroup = nap.TsGroup(
+ group, time_support=time_support, bypass_check=True
+ )
metainfo = {}
for k in set(self.file.keys()) - {