{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"Patrick BROCKMANN - LSCE (Climate and Environment Sciences Laboratory)
\n",
"![](\"http://www.lsce.ipsl.fr/Css/img/banniere_LSCE_75.png\")
\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Updated: 2019/11/13"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load the ferret extension"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext ferretmagic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Put data from python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### First example: 1D array"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['name', 'data'])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"b = {}\n",
"b['name']='myvar1' \n",
"x=np.linspace(-np.pi*4, np.pi*4, 500)\n",
"b['data']=np.sin(x)/x\n",
"b.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A dataset must have been opened before putting data to ferret to get list of variables latter.\n",
"\n",
"https://github.com/NOAA-PMEL/PyFerret/issues/64"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%%ferret\n",
"use levitus_climatology "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Message: b is now available in ferret as myvar1
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%ferret_putdata --axis_pos (0,1,2,3,4,5) b"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" currently SET data sets:\n",
" 1> /opt/ferret_dsets/data/levitus_climatology.cdf (default)\n",
" name title I J K L\n",
" TEMP TEMPERATURE 1:360 1:180 1:20 ...\n",
" SALT SALINITY 1:360 1:180 1:20 ...\n",
" ------ Python Variables ------\n",
" MYVAR1 myvar1 1:500 ... ... ... ... ...\n",
" \n",
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%ferret\n",
"set text/font=arial\n",
"\n",
"show data\n",
"ppl color 2, 0, 50, 100, 75\n",
"ppl color 3, 100, 50, 0, 75\n",
"plot/thick=3/color myvar1, myvar1[x=@shf:50]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### Second example: 3D array (XYZ)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a dummy 3D array (XY and a Z axis)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(145, 73, 10)\n"
]
}
],
"source": [
"nlons, nlats, dim3 = (145, 73, 10)\n",
"\n",
"lats = np.linspace(-np.pi / 2, np.pi / 2, nlats)\n",
"lons = np.linspace(0, 2 * np.pi, nlons)\n",
"lons, lats = np.meshgrid(lons, lats, indexing='ij')\n",
"\n",
"wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons)\n",
"mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2)\n",
"\n",
"lats = np.rad2deg(lats)\n",
"lons = np.rad2deg(lons)\n",
"data2D = wave + mean \n",
"\n",
"myaxis = np.linspace(1, 1000, dim3)\n",
"dataXYZ = np.repeat(np.expand_dims(data2D,axis=-1), dim3, axis=2)\n",
"\n",
"print(dataXYZ.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Please refer to http://ferret.pmel.noaa.gov/Ferret/documentation/pyferret/data-dictionaries/"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['name', 'axis_names', 'axis_units', 'axis_types', 'axis_coords', 'data'])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pyferret\n",
"data2ferret = {}\n",
"data2ferret['name']='myvar2' \n",
"data2ferret['axis_names']=('lons', 'lats', 'depth')\n",
"data2ferret['axis_units']=('degrees_east', 'degrees_north', 'meters')\n",
"data2ferret['axis_types']=(\n",
" pyferret.AXISTYPE_LONGITUDE,\n",
" pyferret.AXISTYPE_LATITUDE,\n",
" pyferret.AXISTYPE_LEVEL\n",
" )\n",
"data2ferret['axis_coords']=(lons[:,0], lats[0,:], myaxis[:])\n",
"data2ferret['data']=dataXYZ\n",
"data2ferret.keys()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Message: data2ferret is now available in ferret as myvar2
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%ferret_putdata data2ferret"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" currently SET data sets:\n",
" 1> /opt/ferret_dsets/data/levitus_climatology.cdf (default)\n",
" name title I J K L\n",
" TEMP TEMPERATURE 1:360 1:180 1:20 ...\n",
" SALT SALINITY 1:360 1:180 1:20 ...\n",
" ------ Python Variables ------\n",
" MYVAR1 myvar1 1:500 ... ... ... ... ...\n",
" MYVAR2 myvar2 1:145 1:73 1:10 ... ... ...\n",
" \n",
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%ferret\n",
"show data\n",
"shade myvar2[k=1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### Third example: 3D array (XYT)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a dummy 3D array (XY and a T axis)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(145, 73, 1, 10)\n"
]
}
],
"source": [
"dataXYT = np.reshape(dataXYZ, (nlons, nlats, 1, dim3))\n",
"print(dataXYT.shape)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['name', 'axis_names', 'axis_units', 'axis_types', 'axis_coords', 'data'])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pyferret\n",
"data2ferret = {}\n",
"data2ferret['name']='myvar3' \n",
"data2ferret['axis_names']=('lons', 'lats', '', 'time')\n",
"data2ferret['axis_units']=('degrees_east', 'degrees_north', '', '')\n",
"data2ferret['axis_types']=(\n",
" pyferret.AXISTYPE_LONGITUDE,\n",
" pyferret.AXISTYPE_LATITUDE,\n",
" pyferret.AXISTYPE_NORMAL,\n",
" pyferret.AXISTYPE_ABSTRACT\n",
" )\n",
"data2ferret['axis_coords']=(lons[:,0], lats[0,:], None, None)\n",
"data2ferret['data']=dataXYT\n",
"data2ferret.keys()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Message: data2ferret is now available in ferret as myvar3
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%ferret_putdata data2ferret"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" currently SET data sets:\n",
" 1> /opt/ferret_dsets/data/levitus_climatology.cdf (default)\n",
" name title I J K L\n",
" TEMP TEMPERATURE 1:360 1:180 1:20 ...\n",
" SALT SALINITY 1:360 1:180 1:20 ...\n",
" ------ Python Variables ------\n",
" MYVAR1 myvar1 1:500 ... ... ... ... ...\n",
" MYVAR2 myvar2 1:145 1:73 1:10 ... ... ...\n",
" MYVAR3 myvar3 1:145 1:73 ... 1:10 ... ...\n",
" \n",
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%ferret\n",
"show data\n",
"shade myvar3[l=1]"
]
}
],
"metadata": {
"anaconda-cloud": {},
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}