We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Recover trim, a method of channel


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `WrightTools/data/_channel.py`
Content:
```
1 """Channel class and associated."""
2 
3 
4 # --- import --------------------------------------------------------------------------------------
5 
6 
7 import numpy as np
8 
9 import h5py
10 
11 from .. import kit as wt_kit
12 from .._dataset import Dataset
13 
14 __all__ = ["Channel"]
15 
16 # --- class ---------------------------------------------------------------------------------------
17 
18 
19 class Channel(Dataset):
20     """Channel."""
21 
22     class_name = "Channel"
23 
24     def __init__(
25         self,
26         parent,
27         id,
28         *,
29         units=None,
30         null=None,
31         signed=None,
32         label=None,
33         label_seed=None,
34         **kwargs
35     ):
36         """Construct a channel object.
37 
38         Parameters
39         ----------
40         values : array-like
41             Values.
42         name : string
43             Channel name.
44         units : string (optional)
45             Channel units. Default is None.
46         null : number (optional)
47             Channel null. Default is None (0).
48         signed : booelan (optional)
49             Channel signed flag. Default is None (guess).
50         label : string.
51             Label. Default is None.
52         label_seed : list of strings
53             Label seed. Default is None.
54         **kwargs
55             Additional keyword arguments are added to the attrs dictionary
56             and to the natural namespace of the object (if possible).
57         """
58         self._parent = parent
59         super().__init__(id)
60         self.label = label
61         self.label_seed = label_seed
62         self.units = units
63         self.dimensionality = len(self.shape)
64         # attrs
65         self.attrs.update(kwargs)
66         self.attrs["name"] = h5py.h5i.get_name(self.id).decode().split("/")[-1]
67         self.attrs["class"] = "Channel"
68         if signed is not None:
69             self.attrs["signed"] = signed
70         if null is not None:
71             self.attrs["null"] = null
72         for key, value in self.attrs.items():
73             identifier = wt_kit.string2identifier(key)
74             if not hasattr(self, identifier):
75                 setattr(self, identifier, value)
76 
77     @property
78     def major_extent(self) -> complex:
79         """Maximum deviation from null."""
80         return max((self.max() - self.null, self.null - self.min()))
81 
82     @property
83     def minor_extent(self) -> complex:
84         """Minimum deviation from null."""
85         return min((self.max() - self.null, self.null - self.min()))
86 
87     @property
88     def null(self) -> complex:
89         if "null" not in self.attrs.keys():
90             self.attrs["null"] = 0
91         return self.attrs["null"]
92 
93     @null.setter
94     def null(self, value):
95         self.attrs["null"] = value
96 
97     @property
98     def signed(self) -> bool:
99         if "signed" not in self.attrs.keys():
100             self.attrs["signed"] = False
101         return self.attrs["signed"]
102 
103     @signed.setter
104     def signed(self, value):
105         self.attrs["signed"] = value
106 
107     def mag(self) -> complex:
108         """Channel magnitude (maximum deviation from null)."""
109         return self.major_extent
110 
111     def normalize(self, mag=1.):
112         """Normalize a Channel, set `null` to 0 and the mag to given value.
113 
114         Parameters
115         ----------
116         mag : float (optional)
117             New value of mag. Default is 1.
118         """
119 
120         def f(dataset, s, null, mag):
121             dataset[s] -= null
122             dataset[s] /= mag
123 
124         if self.signed:
125             mag = self.mag() / mag
126         else:
127             mag = self.max() / mag
128         self.chunkwise(f, null=self.null, mag=mag)
129         self._null = 0
130 
131     def trim(self, neighborhood, method="ztest", factor=3, replace="nan", verbose=True):
132         """Remove outliers from the dataset.
133 
134         Identifies outliers by comparing each point to its
135         neighbors using a statistical test.
136 
137         Parameters
138         ----------
139         neighborhood : list of integers
140             Size of the neighborhood in each dimension. Length of the list must
141             be equal to the dimensionality of the channel.
142         method : {'ztest'} (optional)
143             Statistical test used to detect outliers. Default is ztest.
144 
145             ztest
146                 Compare point deviation from neighborhood mean to neighborhood
147                 standard deviation.
148 
149         factor : number (optional)
150             Tolerance factor.  Default is 3.
151         replace : {'nan', 'mean', 'mask', number} (optional)
152             Behavior of outlier replacement. Default is nan.
153 
154             nan
155                 Outliers are replaced by numpy nans.
156 
157             mean
158                 Outliers are replaced by the mean of its neighborhood.
159 
160             mask
161                 Array is masked at outliers.
162 
163             number
164                 Array becomes given number.
165 
166         Returns
167         -------
168         list of tuples
169             Indicies of trimmed outliers.
170 
171         See Also
172         --------
173         clip
174             Remove pixels outside of a certain range.
175         """
176         raise NotImplementedError
177         outliers = []
178         means = []
179         # find outliers
180         for idx in np.ndindex(self.shape):
181             slices = []
182             for i, di, size in zip(idx, neighborhood, self.shape):
183                 start = max(0, i - di)
184                 stop = min(size, i + di + 1)
185                 slices.append(slice(start, stop, 1))
186             neighbors = self[slices]
187             mean = np.nanmean(neighbors)
188             limit = np.nanstd(neighbors) * factor
189             if np.abs(self[idx] - mean) > limit:
190                 outliers.append(idx)
191                 means.append(mean)
192         # replace outliers
193         i = tuple(zip(*outliers))
194         if replace == "nan":
195             self[i] = np.nan
196         elif replace == "mean":
197             self[i] = means
198         elif replace == "mask":
199             self[:] = np.ma.array(self[:])
200             self[i] = np.ma.masked
201         elif type(replace) in [int, float]:
202             self[i] = replace
203         else:
204             raise KeyError("replace must be one of {nan, mean, mask} or some number")
205         # finish
206         if verbose:
207             print("%i outliers removed" % len(outliers))
208         return outliers
209
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/WrightTools/data/_channel.py b/WrightTools/data/_channel.py
--- a/WrightTools/data/_channel.py
+++ b/WrightTools/data/_channel.py
@@ -8,8 +8,12 @@
 
 import h5py
 
+import warnings
+import numbers
+
 from .. import kit as wt_kit
 from .._dataset import Dataset
+from .. import exceptions as wt_exceptions
 
 __all__ = ["Channel"]
 
@@ -148,7 +152,7 @@
 
         factor : number (optional)
             Tolerance factor.  Default is 3.
-        replace : {'nan', 'mean', 'mask', number} (optional)
+        replace : {'nan', 'mean', number} (optional)
             Behavior of outlier replacement. Default is nan.
 
             nan
@@ -157,9 +161,6 @@
             mean
                 Outliers are replaced by the mean of its neighborhood.
 
-            mask
-                Array is masked at outliers.
-
             number
                 Array becomes given number.
 
@@ -173,7 +174,7 @@
         clip
             Remove pixels outside of a certain range.
         """
-        raise NotImplementedError
+        warnings.warn("trim", category=wt_exceptions.EntireDatasetInMemoryWarning)
         outliers = []
         means = []
         # find outliers
@@ -192,16 +193,19 @@
         # replace outliers
         i = tuple(zip(*outliers))
         if replace == "nan":
-            self[i] = np.nan
+            arr = self[:]
+            arr[i] = np.nan
+            self[:] = arr
         elif replace == "mean":
-            self[i] = means
-        elif replace == "mask":
-            self[:] = np.ma.array(self[:])
-            self[i] = np.ma.masked
-        elif type(replace) in [int, float]:
-            self[i] = replace
+            arr = self[:]
+            arr[i] = means
+            self[:] = arr
+        elif isinstance(replace, numbers.Number):
+            arr = self[:]
+            arr[i] = replace
+            self[:] = arr
         else:
-            raise KeyError("replace must be one of {nan, mean, mask} or some number")
+            raise KeyError("replace must be one of {nan, mean} or some number")
         # finish
         if verbose:
             print("%i outliers removed" % len(outliers))

{"golden_diff": "diff --git a/WrightTools/data/_channel.py b/WrightTools/data/_channel.py\n--- a/WrightTools/data/_channel.py\n+++ b/WrightTools/data/_channel.py\n@@ -8,8 +8,12 @@\n \n import h5py\n \n+import warnings\n+import numbers\n+\n from .. import kit as wt_kit\n from .._dataset import Dataset\n+from .. import exceptions as wt_exceptions\n \n __all__ = [\"Channel\"]\n \n@@ -148,7 +152,7 @@\n \n         factor : number (optional)\n             Tolerance factor.  Default is 3.\n-        replace : {'nan', 'mean', 'mask', number} (optional)\n+        replace : {'nan', 'mean', number} (optional)\n             Behavior of outlier replacement. Default is nan.\n \n             nan\n@@ -157,9 +161,6 @@\n             mean\n                 Outliers are replaced by the mean of its neighborhood.\n \n-            mask\n-                Array is masked at outliers.\n-\n             number\n                 Array becomes given number.\n \n@@ -173,7 +174,7 @@\n         clip\n             Remove pixels outside of a certain range.\n         \"\"\"\n-        raise NotImplementedError\n+        warnings.warn(\"trim\", category=wt_exceptions.EntireDatasetInMemoryWarning)\n         outliers = []\n         means = []\n         # find outliers\n@@ -192,16 +193,19 @@\n         # replace outliers\n         i = tuple(zip(*outliers))\n         if replace == \"nan\":\n-            self[i] = np.nan\n+            arr = self[:]\n+            arr[i] = np.nan\n+            self[:] = arr\n         elif replace == \"mean\":\n-            self[i] = means\n-        elif replace == \"mask\":\n-            self[:] = np.ma.array(self[:])\n-            self[i] = np.ma.masked\n-        elif type(replace) in [int, float]:\n-            self[i] = replace\n+            arr = self[:]\n+            arr[i] = means\n+            self[:] = arr\n+        elif isinstance(replace, numbers.Number):\n+            arr = self[:]\n+            arr[i] = replace\n+            self[:] = arr\n         else:\n-            raise KeyError(\"replace must be one of {nan, mean, mask} or some number\")\n+            raise KeyError(\"replace must be one of {nan, mean} or some number\")\n         # finish\n         if verbose:\n             print(\"%i outliers removed\" % len(outliers))\n", "issue": "Recover trim, a method of channel\n\n", "before_files": [{"content": "\"\"\"Channel class and associated.\"\"\"\n\n\n# --- import --------------------------------------------------------------------------------------\n\n\nimport numpy as np\n\nimport h5py\n\nfrom .. import kit as wt_kit\nfrom .._dataset import Dataset\n\n__all__ = [\"Channel\"]\n\n# --- class ---------------------------------------------------------------------------------------\n\n\nclass Channel(Dataset):\n    \"\"\"Channel.\"\"\"\n\n    class_name = \"Channel\"\n\n    def __init__(\n        self,\n        parent,\n        id,\n        *,\n        units=None,\n        null=None,\n        signed=None,\n        label=None,\n        label_seed=None,\n        **kwargs\n    ):\n        \"\"\"Construct a channel object.\n\n        Parameters\n        ----------\n        values : array-like\n            Values.\n        name : string\n            Channel name.\n        units : string (optional)\n            Channel units. Default is None.\n        null : number (optional)\n            Channel null. Default is None (0).\n        signed : booelan (optional)\n            Channel signed flag. Default is None (guess).\n        label : string.\n            Label. Default is None.\n        label_seed : list of strings\n            Label seed. Default is None.\n        **kwargs\n            Additional keyword arguments are added to the attrs dictionary\n            and to the natural namespace of the object (if possible).\n        \"\"\"\n        self._parent = parent\n        super().__init__(id)\n        self.label = label\n        self.label_seed = label_seed\n        self.units = units\n        self.dimensionality = len(self.shape)\n        # attrs\n        self.attrs.update(kwargs)\n        self.attrs[\"name\"] = h5py.h5i.get_name(self.id).decode().split(\"/\")[-1]\n        self.attrs[\"class\"] = \"Channel\"\n        if signed is not None:\n            self.attrs[\"signed\"] = signed\n        if null is not None:\n            self.attrs[\"null\"] = null\n        for key, value in self.attrs.items():\n            identifier = wt_kit.string2identifier(key)\n            if not hasattr(self, identifier):\n                setattr(self, identifier, value)\n\n    @property\n    def major_extent(self) -> complex:\n        \"\"\"Maximum deviation from null.\"\"\"\n        return max((self.max() - self.null, self.null - self.min()))\n\n    @property\n    def minor_extent(self) -> complex:\n        \"\"\"Minimum deviation from null.\"\"\"\n        return min((self.max() - self.null, self.null - self.min()))\n\n    @property\n    def null(self) -> complex:\n        if \"null\" not in self.attrs.keys():\n            self.attrs[\"null\"] = 0\n        return self.attrs[\"null\"]\n\n    @null.setter\n    def null(self, value):\n        self.attrs[\"null\"] = value\n\n    @property\n    def signed(self) -> bool:\n        if \"signed\" not in self.attrs.keys():\n            self.attrs[\"signed\"] = False\n        return self.attrs[\"signed\"]\n\n    @signed.setter\n    def signed(self, value):\n        self.attrs[\"signed\"] = value\n\n    def mag(self) -> complex:\n        \"\"\"Channel magnitude (maximum deviation from null).\"\"\"\n        return self.major_extent\n\n    def normalize(self, mag=1.):\n        \"\"\"Normalize a Channel, set `null` to 0 and the mag to given value.\n\n        Parameters\n        ----------\n        mag : float (optional)\n            New value of mag. Default is 1.\n        \"\"\"\n\n        def f(dataset, s, null, mag):\n            dataset[s] -= null\n            dataset[s] /= mag\n\n        if self.signed:\n            mag = self.mag() / mag\n        else:\n            mag = self.max() / mag\n        self.chunkwise(f, null=self.null, mag=mag)\n        self._null = 0\n\n    def trim(self, neighborhood, method=\"ztest\", factor=3, replace=\"nan\", verbose=True):\n        \"\"\"Remove outliers from the dataset.\n\n        Identifies outliers by comparing each point to its\n        neighbors using a statistical test.\n\n        Parameters\n        ----------\n        neighborhood : list of integers\n            Size of the neighborhood in each dimension. Length of the list must\n            be equal to the dimensionality of the channel.\n        method : {'ztest'} (optional)\n            Statistical test used to detect outliers. Default is ztest.\n\n            ztest\n                Compare point deviation from neighborhood mean to neighborhood\n                standard deviation.\n\n        factor : number (optional)\n            Tolerance factor.  Default is 3.\n        replace : {'nan', 'mean', 'mask', number} (optional)\n            Behavior of outlier replacement. Default is nan.\n\n            nan\n                Outliers are replaced by numpy nans.\n\n            mean\n                Outliers are replaced by the mean of its neighborhood.\n\n            mask\n                Array is masked at outliers.\n\n            number\n                Array becomes given number.\n\n        Returns\n        -------\n        list of tuples\n            Indicies of trimmed outliers.\n\n        See Also\n        --------\n        clip\n            Remove pixels outside of a certain range.\n        \"\"\"\n        raise NotImplementedError\n        outliers = []\n        means = []\n        # find outliers\n        for idx in np.ndindex(self.shape):\n            slices = []\n            for i, di, size in zip(idx, neighborhood, self.shape):\n                start = max(0, i - di)\n                stop = min(size, i + di + 1)\n                slices.append(slice(start, stop, 1))\n            neighbors = self[slices]\n            mean = np.nanmean(neighbors)\n            limit = np.nanstd(neighbors) * factor\n            if np.abs(self[idx] - mean) > limit:\n                outliers.append(idx)\n                means.append(mean)\n        # replace outliers\n        i = tuple(zip(*outliers))\n        if replace == \"nan\":\n            self[i] = np.nan\n        elif replace == \"mean\":\n            self[i] = means\n        elif replace == \"mask\":\n            self[:] = np.ma.array(self[:])\n            self[i] = np.ma.masked\n        elif type(replace) in [int, float]:\n            self[i] = replace\n        else:\n            raise KeyError(\"replace must be one of {nan, mean, mask} or some number\")\n        # finish\n        if verbose:\n            print(\"%i outliers removed\" % len(outliers))\n        return outliers\n", "path": "WrightTools/data/_channel.py"}], "after_files": [{"content": "\"\"\"Channel class and associated.\"\"\"\n\n\n# --- import --------------------------------------------------------------------------------------\n\n\nimport numpy as np\n\nimport h5py\n\nimport warnings\nimport numbers\n\nfrom .. import kit as wt_kit\nfrom .._dataset import Dataset\nfrom .. import exceptions as wt_exceptions\n\n__all__ = [\"Channel\"]\n\n# --- class ---------------------------------------------------------------------------------------\n\n\nclass Channel(Dataset):\n    \"\"\"Channel.\"\"\"\n\n    class_name = \"Channel\"\n\n    def __init__(\n        self,\n        parent,\n        id,\n        *,\n        units=None,\n        null=None,\n        signed=None,\n        label=None,\n        label_seed=None,\n        **kwargs\n    ):\n        \"\"\"Construct a channel object.\n\n        Parameters\n        ----------\n        values : array-like\n            Values.\n        name : string\n            Channel name.\n        units : string (optional)\n            Channel units. Default is None.\n        null : number (optional)\n            Channel null. Default is None (0).\n        signed : booelan (optional)\n            Channel signed flag. Default is None (guess).\n        label : string.\n            Label. Default is None.\n        label_seed : list of strings\n            Label seed. Default is None.\n        **kwargs\n            Additional keyword arguments are added to the attrs dictionary\n            and to the natural namespace of the object (if possible).\n        \"\"\"\n        self._parent = parent\n        super().__init__(id)\n        self.label = label\n        self.label_seed = label_seed\n        self.units = units\n        self.dimensionality = len(self.shape)\n        # attrs\n        self.attrs.update(kwargs)\n        self.attrs[\"name\"] = h5py.h5i.get_name(self.id).decode().split(\"/\")[-1]\n        self.attrs[\"class\"] = \"Channel\"\n        if signed is not None:\n            self.attrs[\"signed\"] = signed\n        if null is not None:\n            self.attrs[\"null\"] = null\n        for key, value in self.attrs.items():\n            identifier = wt_kit.string2identifier(key)\n            if not hasattr(self, identifier):\n                setattr(self, identifier, value)\n\n    @property\n    def major_extent(self) -> complex:\n        \"\"\"Maximum deviation from null.\"\"\"\n        return max((self.max() - self.null, self.null - self.min()))\n\n    @property\n    def minor_extent(self) -> complex:\n        \"\"\"Minimum deviation from null.\"\"\"\n        return min((self.max() - self.null, self.null - self.min()))\n\n    @property\n    def null(self) -> complex:\n        if \"null\" not in self.attrs.keys():\n            self.attrs[\"null\"] = 0\n        return self.attrs[\"null\"]\n\n    @null.setter\n    def null(self, value):\n        self.attrs[\"null\"] = value\n\n    @property\n    def signed(self) -> bool:\n        if \"signed\" not in self.attrs.keys():\n            self.attrs[\"signed\"] = False\n        return self.attrs[\"signed\"]\n\n    @signed.setter\n    def signed(self, value):\n        self.attrs[\"signed\"] = value\n\n    def mag(self) -> complex:\n        \"\"\"Channel magnitude (maximum deviation from null).\"\"\"\n        return self.major_extent\n\n    def normalize(self, mag=1.):\n        \"\"\"Normalize a Channel, set `null` to 0 and the mag to given value.\n\n        Parameters\n        ----------\n        mag : float (optional)\n            New value of mag. Default is 1.\n        \"\"\"\n\n        def f(dataset, s, null, mag):\n            dataset[s] -= null\n            dataset[s] /= mag\n\n        if self.signed:\n            mag = self.mag() / mag\n        else:\n            mag = self.max() / mag\n        self.chunkwise(f, null=self.null, mag=mag)\n        self._null = 0\n\n    def trim(self, neighborhood, method=\"ztest\", factor=3, replace=\"nan\", verbose=True):\n        \"\"\"Remove outliers from the dataset.\n\n        Identifies outliers by comparing each point to its\n        neighbors using a statistical test.\n\n        Parameters\n        ----------\n        neighborhood : list of integers\n            Size of the neighborhood in each dimension. Length of the list must\n            be equal to the dimensionality of the channel.\n        method : {'ztest'} (optional)\n            Statistical test used to detect outliers. Default is ztest.\n\n            ztest\n                Compare point deviation from neighborhood mean to neighborhood\n                standard deviation.\n\n        factor : number (optional)\n            Tolerance factor.  Default is 3.\n        replace : {'nan', 'mean', number} (optional)\n            Behavior of outlier replacement. Default is nan.\n\n            nan\n                Outliers are replaced by numpy nans.\n\n            mean\n                Outliers are replaced by the mean of its neighborhood.\n\n            number\n                Array becomes given number.\n\n        Returns\n        -------\n        list of tuples\n            Indicies of trimmed outliers.\n\n        See Also\n        --------\n        clip\n            Remove pixels outside of a certain range.\n        \"\"\"\n        warnings.warn(\"trim\", category=wt_exceptions.EntireDatasetInMemoryWarning)\n        outliers = []\n        means = []\n        # find outliers\n        for idx in np.ndindex(self.shape):\n            slices = []\n            for i, di, size in zip(idx, neighborhood, self.shape):\n                start = max(0, i - di)\n                stop = min(size, i + di + 1)\n                slices.append(slice(start, stop, 1))\n            neighbors = self[slices]\n            mean = np.nanmean(neighbors)\n            limit = np.nanstd(neighbors) * factor\n            if np.abs(self[idx] - mean) > limit:\n                outliers.append(idx)\n                means.append(mean)\n        # replace outliers\n        i = tuple(zip(*outliers))\n        if replace == \"nan\":\n            arr = self[:]\n            arr[i] = np.nan\n            self[:] = arr\n        elif replace == \"mean\":\n            arr = self[:]\n            arr[i] = means\n            self[:] = arr\n        elif isinstance(replace, numbers.Number):\n            arr = self[:]\n            arr[i] = replace\n            self[:] = arr\n        else:\n            raise KeyError(\"replace must be one of {nan, mean} or some number\")\n        # finish\n        if verbose:\n            print(\"%i outliers removed\" % len(outliers))\n        return outliers\n", "path": "WrightTools/data/_channel.py"}]}