Merge pull request #297 from ecmwf/feature/irregular_grid_w_new_develop

Feature/irregular grid w new develop

performance/fdb_slice_many_numbers_timeseries.py

@@ -3,14 +3,12 @@
 import pandas as pd
 import pygribjump as gj
 
-from polytope_feature.datacube.backends.fdb import FDBDatacube
 from polytope_feature.polytope import Polytope, Request
 from polytope_feature.shapes import All, Point, Select
 
 time1 = time.time()
 # Create a dataarray with 3 labelled axes using different index types
 
-# config = {"class": "od", "expver": "0001", "levtype": "sfc", "type": "pf"}
 options = {
     "axis_config": [
         {"axis_name": "step", "transformations": [{"name": "type_change", "type": "int"}]},
@@ -63,7 +61,6 @@
     Select("class", ["od"]),
     Select("stream", ["enfo"]),
     Select("type", ["pf"]),
-    # Select("latitude", [0.035149384216], method="surrounding"),
     Point(["latitude", "longitude"], [[0.04, 0]], method="surrounding"),
     All("number"),
 )

polytope_feature/version.py

@@ -1 +1 @@
-__version__ = "1.0.20"
+__version__ = "1.0.21"

readme.md

@@ -180,6 +180,10 @@ If this software is useful in your work, please consider citing our [paper](http
 
 > Leuridan, M., Hawkes, J., Smart, S., Danovaro, E., and Quintino, T., “Polytope: An Algorithm for Efficient Feature Extraction on Hypercubes”, <i>arXiv e-prints</i>, 2023. doi:10.48550/arXiv.2306.11553.
 
+Other papers in preparation include: 
+
+> Leuridan, M., Bradley, C., Hawkes, J., Quintino, T., and Schultz, M., "Performance Analysis of an Efficient Algorithm for Feature Extraction from Large Scale Meteorological Data Stores".
+
 ## Acknowledgements
 
 Past and current funding and support for **Polytope** is listed in the adjoining [Acknowledgements](./ACKNOWLEDGEMENTS.rst).