change binder example location

.binder/environment.yml

@@ -3,23 +3,23 @@ channels:
   - conda-forge
 dependencies:
   - python=3.10
-  - numpy >= 1.24.3
-  - xarray >= 0.17.0,<=2023.12.0
-  - cartopy >= 0.20
-  - matplotlib
-  - pandas
-  - intel-fortran-rt
-  - scipy  >=1.8.0
-  - statsmodels
-  - dask
   - pip:
-    - easyclimate
-    - geocat.viz <= 2023.10.0
+    - numpy >= 1.24.3
+    - xarray >= 0.17.0,<=2023.12.0
+    - geocat.viz
+    - cartopy >= 0.20
     - xeofs >= 2.2.2
+    - matplotlib
+    - pandas
     - fast-barnes-py
     - oceans
+    - intel-fortran-rt
+    - scipy >=1.8.0
+    - statsmodels
     - geocat-comp
     - pyspharm-syl
+    - windspharm-syl
+    - dask
     - pymannkendall
     - flox
     - xarray-datatree
@@ -29,3 +29,4 @@ dependencies:
     - tqdm
     - zarr
     - metpy
+    - easyclimate

docs/clean.sh

@@ -0,0 +1,3 @@
+rm -r ".\source\auto_gallery_output\*"
+rm ".\source\api_index\generated\*"
+rm ".\source\sg_execution_times.rst"

docs/copy_ipynb2example.ps1

@@ -0,0 +1,2 @@
+Remove-Item .\example\*.ipynb -Recurse
+Copy-Item .\source\auto_gallery_output\*.ipynb -Destination .\example -Recurse

docs/copy_ipynb2example.sh

@@ -0,0 +1,2 @@
+rm -r ./example/*.ipynb
+cp -r ./source/auto_gallery_output/*.ipynb ./example

docs/example/plot_formatting_coordinates.ipynb

@@ -0,0 +1,259 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Formatting Coordinates\n\nBefore proceeding with all the steps, first import some necessary libraries and packages\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import easyclimate as ecl\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nimport cartopy.crs as ccrs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The data we use here is monthly data from Jan 2022 to Feb 2022 and contains 17 vertical levels.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "u_data = ecl.tutorial.open_tutorial_dataset(\"uwnd_202201_mon_mean\").sortby(\"lat\").uwnd\nu_data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Formatting of the Latitude and Lontitude Tickes\n`draw_data1` is extracted from time level 0 and 500hPa vertical level.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "draw_data1 = u_data.isel(time=0).sel(level=500)\ndraw_data1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we call :py:func:`xarray.plot.pcolormesh <xarray.plot.pcolormesh>` to plot the latitudinal wind field on the 500hPa isobaric surface.\nNoting that the x-axis and y-axis are not in standard geographic coordinate format, our next step is to format these coordinates.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(1, 1)\n\ndraw_data1.plot.pcolormesh(\n    ax=ax,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        ":py:func:`easyclimate.plot.set_lon_format_axis <easyclimate.plot.set_lon_format_axis>`,\n:py:func:`easyclimate.plot.set_lat_format_axis <easyclimate.plot.set_lat_format_axis>` can help us quickly\nformat the coordinates on the x-axis and y-axis, respectively, into a geographic coordinate format.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(1, 1)\n\ndraw_data1.plot.pcolormesh(\n    ax=ax,\n)\n\necl.plot.set_lon_format_axis(ax, axis=\"x\")\necl.plot.set_lat_format_axis(ax, axis=\"y\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "It is worth mentioning that the :py:func:`easyclimate.plot.set_lon_format_axis <easyclimate.plot.set_lon_format_axis>`,\n:py:func:`easyclimate.plot.set_lat_format_axis <easyclimate.plot.set_lat_format_axis>` methods contain a parameter `dmi`\nwhich helps us to convert DD (Decimal Degrees) format to DMS (Degrees Minutes Seconds) format.\n\nNow let's start by selecting a smaller area\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "draw_data1_1 = (\n    u_data.isel(time=0).sel(level=500).sel(lon=slice(100, 110), lat=slice(20, 23))\n)\ndraw_data1_1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Note the difference in geo-labeling on the x-axis.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(1, 2, figsize=(15, 5))\n\nfor axi in ax.flat:\n    draw_data1_1.plot(ax=axi, cmap=\"Reds\")\n    axi.xaxis.set_major_locator(ticker.LinearLocator(5))\n\necl.plot.set_lon_format_axis(ax[0], axis=\"x\")\necl.plot.set_lat_format_axis(ax[0], axis=\"y\")\nax[0].set_title(\"dms = False\")\n\necl.plot.set_lon_format_axis(ax[1], axis=\"x\", dms=True)\necl.plot.set_lat_format_axis(ax[1], axis=\"y\", dms=True)\nax[1].set_title(\"dms = True\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Barometric Profile Label Formatting\nHere we choose the longitudinally averaged latitudinal direction at time level 0 to plot the profile\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "draw_data2 = u_data.isel(time=0).mean(dim=\"lon\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Notice that the x-axis and y-axis labels are unformatted, so we'll take care of that next.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(1, 1)\n\ndraw_data2.plot.contourf(ax=ax, levels=21, yincrease=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        ":py:func:`easyclimate.plot.set_p_format_axis <easyclimate.plot.set_p_format_axis>` can help us format barometric vertical labels\nand similarly :py:func:`easyclimate.plot.set_lat_format_axis <easyclimate.plot.set_lat_format_axis>` can help us format latitude labels.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(1, 1)\n\ndraw_data2.plot.contourf(ax=ax, levels=21, yincrease=False)\n\necl.plot.set_lat_format_axis(ax, axis=\"x\")\necl.plot.set_p_format_axis(ax, axis=\"y\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Polar Stereo of the Circle Boundary\nFor the sake of illustration, we use here the sea ice concentration (SIC) data from the Barents-Kara Seas (30\u00b0\u221290\u00b0E, 65\u00b0\u221285\u00b0N).\nThe results of the data under the 10th time level are described below.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sic_data = ecl.tutorial.open_tutorial_dataset(\"mini_HadISST_ice\").sic.isel(time=10)\nsic_data.plot.contourf(cmap=\"Blues\", levels=11)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        ":py:func:`easyclimate.plot.draw_Circlemap_PolarStereo <easyclimate.plot.draw_Circlemap_PolarStereo>` helps us to easily\nestablish the boundary of the circle under the projection of the polar stereo.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(subplot_kw={\"projection\": ccrs.NorthPolarStereo()})\n\nax.coastlines(edgecolor=\"black\", linewidths=0.5)\nax.stock_img()\n\necl.plot.draw_Circlemap_PolarStereo(\n    ax=ax,\n    lon_step=30,\n    lat_step=10,\n    lat_range=[50, 90],\n    draw_labels=True,\n    gridlines_kwargs={\"color\": \"grey\", \"alpha\": 0.5, \"linestyle\": \"--\"},\n)\n\nsic_data.plot.contourf(cmap=\"Blues\", levels=11, transform=ccrs.PlateCarree())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Adjusting `north_pad` and `south_pad` appropriately can help us compensate for not completing the circle boundaries.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(subplot_kw={\"projection\": ccrs.NorthPolarStereo()})\n\nax.coastlines(edgecolor=\"black\", linewidths=0.5)\nax.stock_img()\n\necl.plot.draw_Circlemap_PolarStereo(\n    ax=ax,\n    lon_step=30,\n    lat_step=10,\n    lat_range=[50, 90],\n    draw_labels=True,\n    set_map_boundary_kwargs={\"north_pad\": 0.3, \"south_pad\": 0.4},\n    gridlines_kwargs={\"color\": \"grey\", \"alpha\": 0.5, \"linestyle\": \"--\"},\n)\n\nsic_data.plot(cmap=\"Blues\", levels=11, transform=ccrs.PlateCarree())"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.9"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}