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Merge pull request #86 from shenyulu/dev
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fix: rtd github download
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@shenyulu
shenyulu authored Dec 3, 2024
2 parents 7d081ed + 6cc13cd commit 2faf56d
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568 changes: 61 additions & 507 deletions docs/example/plot_basic_statistical_analysis.ipynb
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2 changes: 1 addition & 1 deletion docs/example/plot_formatting_coordinates.ipynb
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Expand Up @@ -251,7 +251,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
"version": "3.10.15"
}
},
"nbformat": 4,
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439 changes: 58 additions & 381 deletions docs/example/plot_geographic_finite_difference.ipynb
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110 changes: 109 additions & 1 deletion docs/example/plot_interp.ipynb
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Expand Up @@ -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": {
Expand All @@ -161,7 +269,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
"version": "3.10.15"
}
},
"nbformat": 4,
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2 changes: 1 addition & 1 deletion docs/example/plot_taylor_diagram.ipynb
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Expand Up @@ -197,7 +197,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
"version": "3.10.15"
}
},
"nbformat": 4,
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2 changes: 1 addition & 1 deletion docs/example/plot_time_scale_average.ipynb
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Expand Up @@ -143,7 +143,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
"version": "3.10.15"
}
},
"nbformat": 4,
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1 change: 1 addition & 0 deletions docs/requirements.txt
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Expand Up @@ -26,6 +26,7 @@ metpy
tcpypi
netCDF4
h5netcdf
seaborn

sphinx == 8.0.2
recommonmark == 0.7.1
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