Merge pull request #62 from leosaffin/merge-master

Merge master

README.md

@@ -3,13 +3,20 @@
 
 A python package for working with various forms of feature tracking data
 
+## Version 1 coming soon! Subscribe for notification.
+We will release version 1 soon (by the end of November 2024). We advice you wait for this version to install the package. To stay updated about it, there are two options:
+1. Most specific: Subscribe to [this discussion](https://github.com/Huracan-project/huracanpy/discussions/57), that we will use to communicate with our community of users.
+2. Less specific: "Watch" the repo by clicking the button on the top-right of this page. Select "custom" then tick "discussions". You can always go back if there turns out to be too much emails. 
+(We wish there was a better way for you to subscribe to announcements. If you agree with us, please up [this issue](https://github.com/orgs/community/discussions/3951).)
+
 ## Installation
 To install the package, you can use `pip`: `pip install huracanpy`.
 
 This can fail with older python versions due to issues with installing cartopy through
 pip. If this happens, use conda to install cartopy first
 (e.g. `conda install -c conda-forge cartopy`), then install huracanpy as normal
 
+
 ## Usage
 The idea of this package is to be a standard way for working with cyclone track data. We
 were all working on track data, but in slightly different ways which makes sharing code

huracanpy/info/_time.py

@@ -58,9 +58,15 @@ def season(track_id, lat, time, convention="short"):
     # Derive values
     hemi = hemisphere(lat)
 
-    time = pd.to_datetime(time)
-    year = time.year
-    month = time.month
+    try:
+        time = pd.to_datetime(time)
+        year = time.year
+        month = time.month
+    except TypeError:
+        # Fix for cftime
+        year = np.array([t.year for t in time])
+        month = np.array([t.month for t in time])
+
     # Store in a dataframe
     df = pd.DataFrame(
         {"hemi": hemi, "year": year, "month": month, "track_id": track_id}

tests/conftest.py

@@ -3,6 +3,7 @@
 import datetime
 from collections import namedtuple
 
+import cftime
 import numpy as np
 import xarray as xr
 
@@ -32,6 +33,21 @@ def tracks_with_extra_coord(tracks_csv):
     )
 
 
+@pytest.fixture()
+def tracks_year():
+    return huracanpy.load(huracanpy.example_year_file)
+
+
+@pytest.fixture()
+def tracks_year_cftime():
+    tracks = huracanpy.load(huracanpy.example_year_file)
+    time = [
+        cftime.datetime(t.dt.year, t.dt.month, t.dt.day, t.dt.hour) for t in tracks.time
+    ]
+    tracks["time"] = ("record", time)
+    return tracks
+
+
 @pytest.fixture()
 def tracks_minus180_plus180():
     return xr.Dataset(

tests/test_info/test_time.py

@@ -1,3 +1,4 @@
+import pytest
 import numpy as np
 
 import huracanpy
@@ -42,9 +43,11 @@ def test_time_components(tracks_csv):
     np.testing.assert_equal(hour, hours)
 
 
-def test_seasons():
-    data = huracanpy.load(huracanpy.example_year_file)
-    season = huracanpy.info.season(data.track_id, data.lat, data.time)
+@pytest.mark.parametrize(("tracks",), [("tracks_year",), ("tracks_year_cftime",)])
+def test_seasons(tracks, request):
+    tracks = request.getfixturevalue(tracks)
+
+    season = huracanpy.info.season(tracks.track_id, tracks.lat, tracks.time)
     assert season.astype(int).min() == 1996
     assert season.astype(int).max() == 1997
     np.testing.assert_approx_equal(season.astype(int).mean(), 1996.09894459, 1e-6)