Perturbed indicators

.github/workflows/main.yml

@@ -71,11 +71,6 @@ jobs:
           create-args: >-
             mamba
             python=${{ matrix.python-version }}
-      - name: Downgrade intake-esm (for xscen) and xclim
-        if: matrix.python-version == '3.9'
-        run: |
-          micromamba install -y intake-esm=2023.11.10
-          micromamba install -y xclim=0.47.0
       - name: Conda and Mamba versions
         run: |
           mamba --version

CHANGES.rst

@@ -4,7 +4,7 @@ Changelog
 
 v0.4.0 (unreleased)
 -------------------
-Contributors to this version: Trevor James Smith (:user:`Zeitsperre`), Thomas-Charles Fortier Filion (:user:`TC-FF`).
+Contributors to this version: Trevor James Smith (:user:`Zeitsperre`), Thomas-Charles Fortier Filion (:user:`TC-FF`), Gabriel Rondeau-Genesse (:user:`RondeauG`).
 
 New features and enhancements
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -15,10 +15,13 @@ New features and enhancements
 * Added new functions to `xhydro.frequency_analysis.local` to calculate plotting positions and to prepare plots. (:pull:`87`).
 * `xscen` now supports Python3.12. (:pull:`99`).
 * `xscen` now supports `pandas` >= 2.2.0, `xarray` >= 2023.11.0, and `xclim` >= 0.47.0. (:pull:`99`).
+* Added `xh.cc.sampled_indicators` to compute future indicators using a perturbation approach and random sampling. (:pull:`54`).
 
 Breaking changes
 ^^^^^^^^^^^^^^^^
 * Added `pooch` as an installation dependency. (:pull:`62`).
+* `xhydro` now requires `xarray`>=2023.11.0, `xclim`>=0.48.2, `xscen`>=0.8.3, and, indirectly, `pandas`>=2.2.0. The main breaking change is in how yearly frequencies are called ('YS-' instead of 'AS-'). (:pull:`54`).
+* Functions that output a dict with keys as xrfreq (namely, ``xh.indicators.compute_indicators``) will now return the new nomenclature (e.g. "YS-JAN" instead of "AS-JAN"). (:pull:`54`).
 
 Internal changes
 ^^^^^^^^^^^^^^^^

docs/index.rst

@@ -10,6 +10,7 @@ Welcome to xHydro's documentation!
    installation
    usage
    notebooks/local_frequency_analysis
+   notebooks/climate_change
    planification
    apidoc/modules
    contributing

docs/notebooks/local_frequency_analysis.ipynb

@@ -69,7 +69,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ds.streamflow.dropna('time', 'all').hvplot(x='time', grid=True, widget_location='bottom', groupby='id')"
+        "ds.streamflow.dropna('time', how='all').hvplot(x='time', grid=True, widget_location='bottom', groupby='id')"
       ]
     },
     {
@@ -100,7 +100,7 @@
         "    \"spring\": {\"date_bounds\": [\"02-11\", \"06-19\"]},\n",
         "    \"summer\": {\"doy_bounds\": [152, 243]},\n",
         "    \"fall\": {\"month\": [9, 10, 11]},\n",
-        "    \"winter\": {\"season\": ['DJF'], \"freq\": \"AS-DEC\"},  # To correctly wrap around the year, we need to specify the resampling frequency.\n",
+        "    \"winter\": {\"season\": ['DJF'], \"freq\": \"YS-DEC\"},  # To correctly wrap around the year, we need to specify the resampling frequency.\n",
         "    \"august\": {\"month\": [8]},\n",
         "    \"annual\": {}\n",
         "    }"
@@ -140,13 +140,6 @@
         "ds_4fa"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -155,7 +148,7 @@
       },
       "outputs": [],
       "source": [
-        "ds_4fa.streamflow_max_summer.dropna('time', 'all').hvplot(x='time', grid=True, widget_location='bottom', groupby='id')"
+        "ds_4fa.streamflow_max_summer.dropna('time', how='all').hvplot(x='time', grid=True, widget_location='bottom', groupby='id')"
       ]
     },
     {
@@ -219,7 +212,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ds_4fa.streamflow_max_custom.dropna('time', 'all').hvplot(x='time', grid=True, widget_location='bottom', groupby='id')"
+        "ds_4fa.streamflow_max_custom.dropna('time', how='all').hvplot(x='time', grid=True, widget_location='bottom', groupby='id')"
       ]
     },
     {
@@ -256,7 +249,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ds_4fa.volume_sum_spring.dropna('time', 'all').hvplot(x='time', grid=True, widget_location='bottom', groupby='id')"
+        "ds_4fa.volume_sum_spring.dropna('time', how='all').hvplot(x='time', grid=True, widget_location='bottom', groupby='id')"
       ]
     },
     {
@@ -329,7 +322,7 @@
       "source": [
         "In a future release, plotting will be handled by a proper function. For now, we'll show an example in this Notebook using preliminary utilities.\n",
         "\n",
-        "`xhfa.local._prepare_plots` generates datapoints required to plot the results of the frequency analysis. If `log=True`,will it return log-spaced x values between `xmin` and `xmax`.\n"
+        "`xhfa.local._prepare_plots` generates datapoints required to plot the results of the frequency analysis. If `log=True`, it will return log-spaced x values between `xmin` and `xmax`.\n"
       ]
     },
     {
@@ -390,20 +383,6 @@
         "#And now combining the plots\n",
         "p1 * p2"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
     }
   ],
   "metadata": {
@@ -422,7 +401,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.11.7"
+      "version": "3.12.2"
     },
     "vscode": {
       "interpreter": {

environment-dev.yml

@@ -10,12 +10,12 @@ dependencies:
   - pydantic >=2.0,<2.5.3  # FIXME: Remove pin once our dependencies (xclim, xscen) support pydantic 2.5.3
   - spotpy
   - statsmodels
-  - xarray
-  - xclim >=0.47.0
-  - xscen >=0.7.1
+  - xarray >=2023.11.0
+  - xclim >=0.48.2
+  - xscen >=0.8.3
   - pip
   - pip:
-    - xdatasets
+    - xdatasets >=0.3.1
   # Dev
   - black ==24.1.1
   - blackdoc ==0.3.9

environment.yml

@@ -10,9 +10,9 @@ dependencies:
   - pydantic >=2.0,<2.5.3  # FIXME: Remove pin once our dependencies (xclim, xscen) support pydantic 2.5.3
   - spotpy
   - statsmodels
-  - xarray
-  - xclim >=0.47.0
-  - xscen >=0.7.1
+  - xarray >=2023.11.0
+  - xclim >=0.48.2
+  - xscen >=0.8.3
   - pip
   - pip:
-    - xdatasets
+    - xdatasets >=0.3.1

pyproject.toml

@@ -42,10 +42,10 @@ dependencies = [
   "pydantic>=2.0,<2.5.3",
   "spotpy",
   "statsmodels",
-  "xarray",
-  "xclim>=0.47.0",
+  "xarray>=2023.11.0",
+  "xclim>=0.48.2",
   "xdatasets>=0.3.1",
-  "xscen>=0.7.1"
+  "xscen>=0.8.3"
 ]
 
 [project.optional-dependencies]