We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Feedstock failing due to pandas datetime
### Description
CI is failing due to datetime units not being set for csv reader

### What I Did

See the PR here that was failing
https://github.com/conda-forge/act-atmos-feedstock/pull/63


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `act/io/csvfiles.py`
Content:
```
1 """
2 This module contains I/O operations for loading csv files.
3 
4 """
5 
6 import pathlib
7 
8 import pandas as pd
9 
10 from .armfiles import check_arm_standards
11 
12 
13 def read_csv(filename, sep=',', engine='python', column_names=None, skipfooter=0, ignore_index=True, **kwargs):
14 
15     """
16     Returns an `xarray.Dataset` with stored data and metadata from user-defined
17     query of CSV files.
18 
19     Parameters
20     ----------
21     filenames : str or list
22         Name of file(s) to read.
23     sep : str
24         The separator between columns in the csv file.
25     column_names : list or None
26         The list of column names in the csv file.
27     verbose : bool
28         If true, will print if a file is not found.
29     ignore_index : bool
30          Keyword for pandas concat function.  If True, do not use the index
31          values along the concatenation axis. The resulting axis will be labeled
32          0, …, n - 1. This is useful if you are concatenating datasets where the
33          concatenation axis does not have meaningful indexing information. Note
34          the index values on the other axes are still respected in the join.
35 
36     Additional keyword arguments will be passed into pandas.read_csv.
37 
38     Returns
39     -------
40     ds : xarray.Dataset
41         ACT Xarray dataset. Will be None if the file is not found.
42 
43     Examples
44     --------
45     This example will load the example sounding data used for unit testing:
46 
47     .. code-block:: python
48 
49         import act
50 
51         ds = act.io.csvfiles.read(act.tests.sample_files.EXAMPLE_CSV_WILDCARD)
52 
53     """
54 
55     # Convert to string if filename is a pathlib or not a list
56     if isinstance(filename, (pathlib.PurePath, str)):
57         filename = [str(filename)]
58 
59     if isinstance(filename, list) and isinstance(filename[0], pathlib.PurePath):
60         filename = [str(ii) for ii in filename]
61 
62     # Read data using pandas read_csv one file at a time and append to
63     # list. Then concatinate the list into one pandas dataframe.
64     li = []
65     for fl in filename:
66         df = pd.read_csv(
67             fl, sep=sep, names=column_names, skipfooter=skipfooter, engine=engine, **kwargs
68         )
69         li.append(df)
70 
71     if len(li) == 1:
72         df = li[0]
73     else:
74         df = pd.concat(li, axis=0, ignore_index=ignore_index)
75 
76     # Set Coordinates if there's a variable date_time
77     if 'date_time' in df:
78         df.date_time = df.date_time.astype('datetime64')
79         df.time = df.date_time
80         df = df.set_index('time')
81 
82     # Convert to xarray DataSet
83     ds = df.to_xarray()
84 
85     # Set additional variables
86     # Since we cannot assume a standard naming convention setting
87     # file_date and file_time to the first time in the file
88     x_coord = ds.coords.to_index().values[0]
89     if isinstance(x_coord, str):
90         x_coord_dt = pd.to_datetime(x_coord)
91         ds.attrs['_file_dates'] = x_coord_dt.strftime('%Y%m%d')
92         ds.attrs['_file_times'] = x_coord_dt.strftime('%H%M%S')
93 
94     # Check for standard ARM datastream name, if none, assume the file is ARM
95     # standard format.
96     is_arm_file_flag = check_arm_standards(ds)
97     if is_arm_file_flag == 0:
98 
99         ds.attrs['_datastream'] = '.'.join(filename[0].split('/')[-1].split('.')[0:2])
100 
101     # Add additional attributes, site, standards flag, etc...
102     ds.attrs['_site'] = str(ds.attrs['_datastream'])[0:3]
103     ds.attrs['_arm_standards_flag'] = is_arm_file_flag
104 
105     return ds
106
```
--- 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/act/io/csvfiles.py b/act/io/csvfiles.py
--- a/act/io/csvfiles.py
+++ b/act/io/csvfiles.py
@@ -75,7 +75,7 @@
 
     # Set Coordinates if there's a variable date_time
     if 'date_time' in df:
-        df.date_time = df.date_time.astype('datetime64')
+        df.date_time = df.date_time.astype('datetime64[ns]')
         df.time = df.date_time
         df = df.set_index('time')

{"golden_diff": "diff --git a/act/io/csvfiles.py b/act/io/csvfiles.py\n--- a/act/io/csvfiles.py\n+++ b/act/io/csvfiles.py\n@@ -75,7 +75,7 @@\n \n     # Set Coordinates if there's a variable date_time\n     if 'date_time' in df:\n-        df.date_time = df.date_time.astype('datetime64')\n+        df.date_time = df.date_time.astype('datetime64[ns]')\n         df.time = df.date_time\n         df = df.set_index('time')\n", "issue": "Feedstock failing due to pandas datetime\n### Description\r\nCI is failing due to datetime units not being set for csv reader\r\n\r\n### What I Did\r\n\r\nSee the PR here that was failing\r\nhttps://github.com/conda-forge/act-atmos-feedstock/pull/63\r\n\n", "before_files": [{"content": "\"\"\"\nThis module contains I/O operations for loading csv files.\n\n\"\"\"\n\nimport pathlib\n\nimport pandas as pd\n\nfrom .armfiles import check_arm_standards\n\n\ndef read_csv(filename, sep=',', engine='python', column_names=None, skipfooter=0, ignore_index=True, **kwargs):\n\n    \"\"\"\n    Returns an `xarray.Dataset` with stored data and metadata from user-defined\n    query of CSV files.\n\n    Parameters\n    ----------\n    filenames : str or list\n        Name of file(s) to read.\n    sep : str\n        The separator between columns in the csv file.\n    column_names : list or None\n        The list of column names in the csv file.\n    verbose : bool\n        If true, will print if a file is not found.\n    ignore_index : bool\n         Keyword for pandas concat function.  If True, do not use the index\n         values along the concatenation axis. The resulting axis will be labeled\n         0, \u2026, n - 1. This is useful if you are concatenating datasets where the\n         concatenation axis does not have meaningful indexing information. Note\n         the index values on the other axes are still respected in the join.\n\n    Additional keyword arguments will be passed into pandas.read_csv.\n\n    Returns\n    -------\n    ds : xarray.Dataset\n        ACT Xarray dataset. Will be None if the file is not found.\n\n    Examples\n    --------\n    This example will load the example sounding data used for unit testing:\n\n    .. code-block:: python\n\n        import act\n\n        ds = act.io.csvfiles.read(act.tests.sample_files.EXAMPLE_CSV_WILDCARD)\n\n    \"\"\"\n\n    # Convert to string if filename is a pathlib or not a list\n    if isinstance(filename, (pathlib.PurePath, str)):\n        filename = [str(filename)]\n\n    if isinstance(filename, list) and isinstance(filename[0], pathlib.PurePath):\n        filename = [str(ii) for ii in filename]\n\n    # Read data using pandas read_csv one file at a time and append to\n    # list. Then concatinate the list into one pandas dataframe.\n    li = []\n    for fl in filename:\n        df = pd.read_csv(\n            fl, sep=sep, names=column_names, skipfooter=skipfooter, engine=engine, **kwargs\n        )\n        li.append(df)\n\n    if len(li) == 1:\n        df = li[0]\n    else:\n        df = pd.concat(li, axis=0, ignore_index=ignore_index)\n\n    # Set Coordinates if there's a variable date_time\n    if 'date_time' in df:\n        df.date_time = df.date_time.astype('datetime64')\n        df.time = df.date_time\n        df = df.set_index('time')\n\n    # Convert to xarray DataSet\n    ds = df.to_xarray()\n\n    # Set additional variables\n    # Since we cannot assume a standard naming convention setting\n    # file_date and file_time to the first time in the file\n    x_coord = ds.coords.to_index().values[0]\n    if isinstance(x_coord, str):\n        x_coord_dt = pd.to_datetime(x_coord)\n        ds.attrs['_file_dates'] = x_coord_dt.strftime('%Y%m%d')\n        ds.attrs['_file_times'] = x_coord_dt.strftime('%H%M%S')\n\n    # Check for standard ARM datastream name, if none, assume the file is ARM\n    # standard format.\n    is_arm_file_flag = check_arm_standards(ds)\n    if is_arm_file_flag == 0:\n\n        ds.attrs['_datastream'] = '.'.join(filename[0].split('/')[-1].split('.')[0:2])\n\n    # Add additional attributes, site, standards flag, etc...\n    ds.attrs['_site'] = str(ds.attrs['_datastream'])[0:3]\n    ds.attrs['_arm_standards_flag'] = is_arm_file_flag\n\n    return ds\n", "path": "act/io/csvfiles.py"}], "after_files": [{"content": "\"\"\"\nThis module contains I/O operations for loading csv files.\n\n\"\"\"\n\nimport pathlib\n\nimport pandas as pd\n\nfrom .armfiles import check_arm_standards\n\n\ndef read_csv(filename, sep=',', engine='python', column_names=None, skipfooter=0, ignore_index=True, **kwargs):\n\n    \"\"\"\n    Returns an `xarray.Dataset` with stored data and metadata from user-defined\n    query of CSV files.\n\n    Parameters\n    ----------\n    filenames : str or list\n        Name of file(s) to read.\n    sep : str\n        The separator between columns in the csv file.\n    column_names : list or None\n        The list of column names in the csv file.\n    verbose : bool\n        If true, will print if a file is not found.\n    ignore_index : bool\n         Keyword for pandas concat function.  If True, do not use the index\n         values along the concatenation axis. The resulting axis will be labeled\n         0, \u2026, n - 1. This is useful if you are concatenating datasets where the\n         concatenation axis does not have meaningful indexing information. Note\n         the index values on the other axes are still respected in the join.\n\n    Additional keyword arguments will be passed into pandas.read_csv.\n\n    Returns\n    -------\n    ds : xarray.Dataset\n        ACT Xarray dataset. Will be None if the file is not found.\n\n    Examples\n    --------\n    This example will load the example sounding data used for unit testing:\n\n    .. code-block:: python\n\n        import act\n\n        ds = act.io.csvfiles.read(act.tests.sample_files.EXAMPLE_CSV_WILDCARD)\n\n    \"\"\"\n\n    # Convert to string if filename is a pathlib or not a list\n    if isinstance(filename, (pathlib.PurePath, str)):\n        filename = [str(filename)]\n\n    if isinstance(filename, list) and isinstance(filename[0], pathlib.PurePath):\n        filename = [str(ii) for ii in filename]\n\n    # Read data using pandas read_csv one file at a time and append to\n    # list. Then concatinate the list into one pandas dataframe.\n    li = []\n    for fl in filename:\n        df = pd.read_csv(\n            fl, sep=sep, names=column_names, skipfooter=skipfooter, engine=engine, **kwargs\n        )\n        li.append(df)\n\n    if len(li) == 1:\n        df = li[0]\n    else:\n        df = pd.concat(li, axis=0, ignore_index=ignore_index)\n\n    # Set Coordinates if there's a variable date_time\n    if 'date_time' in df:\n        df.date_time = df.date_time.astype('datetime64[ns]')\n        df.time = df.date_time\n        df = df.set_index('time')\n\n    # Convert to xarray DataSet\n    ds = df.to_xarray()\n\n    # Set additional variables\n    # Since we cannot assume a standard naming convention setting\n    # file_date and file_time to the first time in the file\n    x_coord = ds.coords.to_index().values[0]\n    if isinstance(x_coord, str):\n        x_coord_dt = pd.to_datetime(x_coord)\n        ds.attrs['_file_dates'] = x_coord_dt.strftime('%Y%m%d')\n        ds.attrs['_file_times'] = x_coord_dt.strftime('%H%M%S')\n\n    # Check for standard ARM datastream name, if none, assume the file is ARM\n    # standard format.\n    is_arm_file_flag = check_arm_standards(ds)\n    if is_arm_file_flag == 0:\n\n        ds.attrs['_datastream'] = '.'.join(filename[0].split('/')[-1].split('.')[0:2])\n\n    # Add additional attributes, site, standards flag, etc...\n    ds.attrs['_site'] = str(ds.attrs['_datastream'])[0:3]\n    ds.attrs['_arm_standards_flag'] = is_arm_file_flag\n\n    return ds\n", "path": "act/io/csvfiles.py"}]}