AMAROC-819 Add support for numpy >2.0

CHANGELOG

@@ -15,6 +15,7 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm
  - [regDaniel, 2024-04-09] Minor adaptions to better comply with PEP standards.
  - [fpavogt, 2024-03-26] Use "height" in "ft aal" throughout.
 ### Changed:
+ - [fpavogt, 2024-06-25] Add support for numpy >2.0
  - [fpavogt, 2024-04-28] Add a rogue point to the mock example.
  - [fpavogt, 2024-03-25] Set the fluffiness boost to 2 (fixes #123).
  - [fpavogt, 2024-03-23] Changed default mock dataset to be 900s long (instead of 2400s).

docs/source/index.rst

@@ -18,10 +18,19 @@ Welcome to the ampycloud documentation
 --------------------------------------
 
 * **What:** ampycloud refers to both a **Python package** and the **algorithm** at its core, designed
-  to characterize cloud layers (i.e. height and sky coverage fraction) using ceilometer measurements
-  (i.e. automatic cloud base *hits*), and derive a corresponding METAR-like message.
+  to characterize cloud layers (i.e. sky coverage fraction and base height) using ceilometer measurements
+  (i.e. cloud base *hits*), and derive a corresponding METAR-like message.
   A visual illustration of the algorithm is visible in :numref:`fig-demo`.
 
+  .. note::
+    At the moment, ampycloud **cannot** use backscatter profiles to derive cloud base hits independantly.
+
+  .. note::
+    ampycloud does not challenge the quality/nature of the cloud base hits that it is being provided.
+    It trusts them all fully and equally. The capacity of the algorithm to provide an accurate
+    assessment of cloud layers above an aerodrome is thus directly limited by the ability of
+    ceilometers to report clouds up to the aerodrome's Minimum Sector Altitude in the first place.
+
 * **Where:** ampycloud lives in `a dedicated repository <https://github.com/MeteoSwiss/ampycloud>`_
   under the `MeteoSwiss organization <https://github.com/MeteoSwiss>`_ on Github, where you can
   submit all your `questions <https://github.com/MeteoSwiss/ampycloud/discussions>`_ and

docs/source/running.rst

@@ -36,8 +36,8 @@ A no-words example for those that want to get started quickly
 The input data
 --------------
 
-The ampycloud algorithm is meant to process cloud base *hits* from ceilometer observations. A given
-set of hits to be processed by the ampycloud package must be stored inside a
+The ampycloud algorithm is meant to process cloud base *hits* derived from ceilometer observations.
+A given set of hits to be processed by the ampycloud package must be stored inside a
 :py:class:`pandas.DataFrame` with a specific set of characteristics outlined below. Users can use
 the following utility function to check whether a given :py:class:`pandas.DataFrame` meets all the
 requirements of ampycloud.

docs/source/scope.rst

@@ -3,11 +3,11 @@ which requires all its dependencies to be robust and stable.
 
 This has the following implications for ampycloud:
 
-    * The scope of ampycloud will remain limited to the **automatic processing of ceilometer
-      hits**.  In particular, ampycloud does not process Vertical Visibility (VV) measurements.
-      Depending on the ceilometer type, the user will need to decide how to treat VV hits *before*
-      passing them to ampycloud, e.g. by removing them or by converting them to cloud base
-      heights.
+    * The scope of ampycloud will remain limited to the **automatic processing of cloud base
+      hits derived using ceilometers**. Furthermore, ampycloud does not process
+      Vertical Visibility (VV) measurements. Depending on the ceilometer type, the user will need
+      to decide how to treat VV hits *before* passing them to ampycloud, e.g. by removing them or
+      by converting them to cloud base heights.
 
     * Note that regulation says that "if there are no clouds of operational significance
       and no restriction on vertical visibility and the abbreviation 'CAVOK' is not

src/ampycloud/data.py

@@ -1047,7 +1047,7 @@ def metar_msg(self, which: str = 'layers') -> str:
 
         # Deal with the MSA: set it to infinity if None was specified
         if self.msa is None:
-            msa_val = np.infty
+            msa_val = np.inf
         else:
             msa_val = self.msa
 

src/ampycloud/scaler.py

@@ -154,11 +154,11 @@ def step_scale(vals: np.ndarray,
     offsets = [0] + steps
 
     # Get the bin edges for the scale mode
-    edges_in = [-np.infty] + steps + [np.infty]
+    edges_in = [-np.inf] + steps + [np.inf]
     # Idem for the descale mode ... this is more complex because of the continuity requirement
     edges_out = [steps[0]/scales[0] + np.sum((np.diff(steps)/scales[1:-1])[:ind])
                  for ind in range(len(steps))]
-    edges_out = [-np.infty] + edges_out + [np.infty]
+    edges_out = [-np.inf] + edges_out + [np.inf]
 
     # Prepare the output
     out = np.full_like(vals, np.nan, dtype=float)

src/ampycloud/utils/utils.py

@@ -185,10 +185,10 @@ def check_data_consistency(pdf: pd.DataFrame,
         msgs += ['Some type=0 hits have non-NaNs height values ?!']
     if np.any(np.isnan(data.loc[data.type == 1, 'height'])):
         msgs += ['Some type=1 hits have NaNs height values ?!']
-    if not np.all(np.in1d(data.loc[data.type == 2, 'dt'].values,
+    if not np.all(np.isin(data.loc[data.type == 2, 'dt'].values,
                           data.loc[data.type == 1, 'dt'].values)):
         msgs += ['Some type=2 hits have no coincident type=1 hits ?!']
-    if not np.all(np.in1d(data.loc[data.type == 3, 'dt'].values,
+    if not np.all(np.isin(data.loc[data.type == 3, 'dt'].values,
                           data.loc[data.type == 2, 'dt'].values)):
         msgs += ['Some type=3 hits have no coincident type=2 hits ?!']