release info for v1.1.0

CITATION.cff

@@ -1,4 +1,4 @@
-cff-version: 1.0.0
+cff-version: 1.1.0
 message: "If you use this software, please cite it as below."
 authors:
   - family-names: "Khider"
@@ -28,7 +28,7 @@ authors:
     orcid: "https://orcid.org/0000-0003-1696-1407"
 
 title: "Pyleoclim: A Python package for the analysis and visualization of paleoclimate data"
-version: v1.0.0
+version: v1.1.0
 doi: 10.5281/zenodo.1205661
-date-released: 2024-06-04
+date-released: 2024-07-23
 url: "https://github.com/LinkedEarth/Pyleoclim_util"

README.md

@@ -17,13 +17,11 @@
 [Paleoclimate](https://www.ncdc.noaa.gov/news/what-paleoclimatology) data, whether from observations or model simulations, offer unique challenges to the analyst, as they usually come in the form of timeseries with missing values and age uncertainties, which trip up off-the-shelf methods.
 Pyleoclim is a Python package primarily geared towards the analysis and visualization of such timeseries. The package includes several low-level methods to deal with these issues under the hood, leaving paleoscientists to interact with intuitive, high-level analysis and plotting methods that support publication-quality scientific workflows.
 
-There are many entry points to Pyleoclim, thanks to its underlying [data structures](https://pyleoclim-util.readthedocs.io/en/latest/core/api.html). The package leverages the Linked Paleo Data ([LiPD](http://www.clim-past.net/12/1093/2016/)) standard container and its associated [utilities](https://pylipd.readthedocs.io/en/latest/). The package is aware of age ensembles stored via LiPD and uses them for time-uncertain analyses, very much like its R sidekick, [GeoChronR](https://doi.org/10.5194/gchron-2020-25).
+There are many entry points to Pyleoclim, thanks to its underlying [data structures](https://pyleoclim-util.readthedocs.io/en/latest/core/api.html). Low-level modules work on [NumPy](http://www.numpy.org) arrays or [Pandas](https://pandas.pydata.org) dataframes.
 
-LiPD is not an obligatory entry point to Pyleoclim. Low-level modules work on [NumPy](http://www.numpy.org) arrays or [Pandas](https://pandas.pydata.org) dataframes, so most Pyleoclim timeseries analysis functionalities can apply to these more common types as well, including those generated by numerical models (via [xarray](http://xarray.pydata.org)). This makes the package suitable for rigorous and efficient model-data comparisons, like [this one](https://www.pnas.org/content/116/18/8728.short).
+We've worked hard to make Pyleoclim accessible to a wide variety of users, from establisher researchers to high-school students, and from seasoned Pythonistas to first-time programmers. A progressive introduction to the package is available at [PyleoTutorials](http://linked.earth/PyleoTutorials/). Examples of scientific use are given [this paper](https://doi.org/10.1029/2022PA004509).  A growing collection of research-grade workflows using Pyleoclim and the LinkedEarth research ecosystem are available as Jupyter notebooks on [paleoBooks](http://linked.earth/PaleoBooks/index.html), with video tutorials on the LinkedEarth [YouTube channel](https://www.youtube.com/watch?v=LJaQBFMK2-Q&list=PL93NbaRnKAuF4WpIQf-4y_U4lo-GqcrcW). Python novices are encouraged to follow these [self-paced tutorials](http://linked.earth/LeapFROGS) before trying Pyleoclim.
 
-We've worked hard to make Pyleoclim accessible to a wide variety of users, from establisher researchers to high-school students, and from seasoned Pythonistas to first-time programmers. A progressive introduction to the package is available at [PyleoTutorials](http://linked.earth/PyleoTutorials/). Examples of scientific use are given [this paper](https://doi.org/10.1029/2022PA004509).  A growing collection of research-grade workflows using Pyleoclim and the LinkedEarth research ecosystem are available as Jupyter notebooks on [paleoBooks](https://github.com/LinkedEarth/PaleoBooks/tree/master/notebooks), with video tutorials on the LinkedEarth [YouTube channel](https://www.youtube.com/watch?v=LJaQBFMK2-Q&list=PL93NbaRnKAuF4WpIQf-4y_U4lo-GqcrcW). Python novices are encouraged to follow these [self-paced tutorials](http://linked.earth/ec_workshops_py/) before trying Pyleoclim.
-
-Science-based training materials are also available from the [paleoHackathon repository](https://github.com/LinkedEarth/paleoHackathon). You can run these training notebooks at any time on our [research hub](http://linked.earth/research_hub.html). We also run live training workshops every so often. Follow us on [Twitter](https://twitter.com/Linked_Earth), or join our [Discourse Forum](https://discourse.linked.earth) for more information.
+Science-based training materials are also available from the [paleoHackathon repository](https://github.com/LinkedEarth/paleoHackathon). We also run live training workshops every so often. Follow us on [Twitter](https://twitter.com/Linked_Earth), or join our [Discourse Forum](https://discourse.linked.earth) for more information.
 
 ### Versions
 
@@ -35,7 +33,7 @@ Online documentation is available through [readthedocs](https://pyleoclim-util.r
 
 ### Dependencies
 
-pyleoclim **only** supports Python 3.9, 3.10
+pyleoclim **only** supports Python 3.11
 
 ### Installation
 

setup.py

@@ -2,7 +2,7 @@
 
 from setuptools import setup, find_packages
 
-version = '1.0.0b0'
+version = '1.1.0'
 
 # Read the readme file contents into variable
 def read(fname):