fix: rtd github download

docs/example/plot_formatting_coordinates.ipynb

@@ -251,7 +251,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.14"
+      "version": "3.10.15"
     }
   },
   "nbformat": 4,

docs/example/plot_interp.ipynb

@@ -143,6 +143,114 @@
       "source": [
         "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n\nu_data.sel(level=500).isel(time=0).plot(ax=ax[0])\nax[0].set_title(\"Before\", size=20)\n\nregriding_data.sel(level=500).isel(time=0).plot(ax=ax[1])\nax[1].set_title(\"After\", size=20)"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Interpolation from model layers to altitude layers\nSuppose the following data are available\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "uwnd_data = xr.DataArray(\n  np.array([-15.080393 , -10.749071 ,  -4.7920494,  -2.3813725,  -1.4152431,\n        -0.6465206,  -7.8181705, -14.718096 , -14.65539  , -14.948015 ,\n       -13.705519 , -11.443476 ,  -8.865583 ,  -7.9528713,  -8.329103 ,\n        -7.2445316,  -6.7150173,  -5.5189686,  -4.139448 ,  -3.2731838,\n        -2.2931194,  -1.0041752,  -1.8983078,  -2.3674374,  -2.8203106,\n        -3.2940865,  -3.526329 ,  -3.8654022,  -4.164995 ,  -4.2834396,\n        -4.2950516,  -4.3438225,  -4.3716908,  -4.7688255,  -4.6155453,\n        -4.5528393,  -4.4831676,  -4.385626 ,  -4.2950516,  -4.0953217]),\n    dims = ('model_level',),\n    coords = {'model_level': np.array([ 36,  44,  51,  56,  60,  63,  67,  70,  73,  75,  78,  81,  83,  85,\n                                        88,  90,  92,  94,  96,  98, 100, 102, 104, 105, 107, 109, 111, 113,\n                                        115, 117, 119, 122, 124, 126, 129, 131, 133, 135, 136, 137])\n    }\n)\n\np_data = xr.DataArray(\n  np.array([  2081.4756,   3917.6995,   6162.6455,   8171.3506,  10112.652 ,\n        11811.783 ,  14447.391 ,  16734.607 ,  19317.787 ,  21218.21  ,\n        24357.875 ,  27871.277 ,  30436.492 ,  33191.027 ,  37698.96  ,\n        40969.438 ,  44463.73  ,  48191.92  ,  52151.29  ,  56291.098 ,\n        60525.63  ,  64770.367 ,  68943.69  ,  70979.66  ,  74908.17  ,\n        78599.67  ,  82012.95  ,  85122.69  ,  87918.29  ,  90401.77  ,\n        92584.94  ,  95338.72  ,  96862.08  ,  98165.305 ,  99763.38  ,\n       100626.21  , 101352.69  , 101962.28  , 102228.875 , 102483.055 ]),\n    dims = ('model_level',),\n    coords = {'model_level': np.array([ 36,  44,  51,  56,  60,  63,  67,  70,  73,  75,  78,  81,  83,  85,\n                                        88,  90,  92,  94,  96,  98, 100, 102, 104, 105, 107, 109, 111, 113,\n                                        115, 117, 119, 122, 124, 126, 129, 131, 133, 135, 136, 137])\n    }\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we interpolate the data located on the mode plane to the isobaric plane. \nNote that the units of the given isobaric surface are consistent with `pressure_data`.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "result = ecl.interp.interp1d_vertical_model2pressure(\n    pressure_data = p_data,\n    variable_data = uwnd_data,\n    vertical_input_dim = 'model_level',\n    vertical_output_dim = 'plev',\n    vertical_output_level = np.array([100000, 92500, 85000, 70000, 60000, 50000, 40000, 30000, 20000, 10000])\n)\nresult"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Simply calibrate the interpolation effect.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots()\nax.plot(p_data, uwnd_data, label = 'Original')\nax.plot(result.plev, result.data, 'o', label = 'Interpolated')\nax.invert_xaxis()\nax.set_xlabel('Pressure [Pa]')\nax.set_ylabel('Zonal Wind [m/s]')\nplt.legend()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Interpolation from pressure layers to altitude layers\nSuppose the following data are available\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "z_data = xr.DataArray(\n    np.array([  214.19354,   841.6774 ,  1516.871  ,  3055.7097 ,  4260.5806 ,\n        5651.4194 ,  7288.032  ,  9288.193  , 10501.097  , 11946.71   ,\n       13762.322  , 16233.451  , 18370.902  , 20415.227  , 23619.033  ,\n       26214.322  , 30731.807  ]),\n    dims=('level'),\n    coords={'level': np.array([1000.,  925.,  850.,  700.,  600.,  500.,  400.,  300.,  250.,  200.,\n        150.,  100.,   70.,   50.,   30.,   20.,   10.])}\n)\nuwnd_data = xr.DataArray(\n    np.array([-2.3200073, -3.5700073, -2.5800018,  8.080002 , 14.059998 ,\n       22.119995 , 33.819992 , 49.339996 , 57.86     , 64.009995 ,\n       62.940002 , 49.809998 , 31.160004 , 16.59999  , 10.300003 ,\n       10.459991 ,  9.880005 ]),\n    dims=('level'), \n    coords={'level': np.array([1000.,  925.,  850.,  700.,  600.,  500.,  400.,  300.,  250.,  200.,\n        150.,  100.,   70.,   50.,   30.,   20.,   10.])}    \n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Then we need to interpolate the zonal wind data (located on the isobaric surface) to the altitude layers.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "target_heights = np.linspace(0, 10000, 101)\n\nresult = ecl.interp.interp1d_vertical_pressure2altitude(\n    z_data = z_data,\n    variable_data = uwnd_data,\n    target_heights = target_heights,\n    vertical_input_dim = 'level',\n    vertical_output_dim = 'altitude',\n)\nresult"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we can check the interpolation results.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "plt.plot(z_data[:9], uwnd_data[:9], 'o', label = 'Original')\nplt.plot(result.altitude, result.data, label = 'Interpolated')\nplt.xlabel('Altitude [m]')\nplt.ylabel('Zonal Wind [m/s]')\nplt.legend()"
+      ]
     }
   ],
   "metadata": {
@@ -161,7 +269,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.14"
+      "version": "3.10.15"
     }
   },
   "nbformat": 4,

docs/example/plot_taylor_diagram.ipynb

@@ -197,7 +197,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.14"
+      "version": "3.10.15"
     }
   },
   "nbformat": 4,

docs/example/plot_time_scale_average.ipynb

@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.14"
+      "version": "3.10.15"
     }
   },
   "nbformat": 4,

docs/requirements.txt

@@ -26,6 +26,7 @@ metpy
 tcpypi
 netCDF4
 h5netcdf
+seaborn
 
 sphinx == 8.0.2
 recommonmark == 0.7.1