{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import julia \n",
"import numpy as np\n",
"# as jl\n",
"# jl.install()\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# !pip install julia\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"julia.install()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from julia.api import Julia"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"jl = Julia(compiled_modules=False)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import julia"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"j = julia.Julia()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/amiraabdel-rahman/opt/anaconda3/lib/python3.7/site-packages/julia/core.py:691: FutureWarning: Accessing `Julia().<name>` to obtain Julia objects is deprecated. Use `from julia import Main; Main.<name>` or `jl = Julia(); jl.eval('<name>')`.\n",
" FutureWarning,\n"
]
}
],
"source": [
"fn = j.include('test.jl')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"x = np.array([[1,2,3], [4,5,6]], dtype=np.float64)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1., 2., 3.],\n",
" [4., 5., 6.]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 2., 4., 6.],\n",
" [ 8., 10., 12.]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fn(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Steps"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import julia\n",
"from julia.api import Julia\n",
"jl = Julia(compiled_modules=False)\n",
"from julia import Main"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<PyCall.jlwrap dataPlotTest>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Main.include(\"script.jl\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1 8 27]\n",
" [ 64 125 216]]\n"
]
}
],
"source": [
"x= np.array([[1,2,3],[4,5,6]])\n",
"print(Main.testFunc01(x, x,x))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"y= np.array([1,2,5,5,3,4,5,6,8,7,6,8])\n",
"Main.dataPlotTest(y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test frep julia"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"import math as Math\n",
"import matplotlib\n",
"matplotlib.use(\"Agg\")\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<PyCall.jlwrap lossFunction>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Main.include(\"frep_julia.jl\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"resolution=32\n",
"target=Main.circle(resolution, 1,False)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"matplotlib.image.imsave(\"./target_python.png\", target.reshape(resolution,resolution))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-1. , -0.87513007, -0.75858481, ..., -0.75858481,\n",
" -0.87513007, -1. ])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test rover julia"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import julia\n",
"from julia.api import Julia\n",
"jl = Julia(compiled_modules=False)\n",
"from julia import Main"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"Main.include(\"./initRover.jl\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"ename": "ImportError",
"evalue": "<PyCall.jlwrap (in a Julia function called from Python)\nJULIA: LoadError: SystemError: opening file \"../json/tutorial.json\": No such file or directory\nStacktrace:\n [1] systemerror(::String, ::Int32; extrainfo::Nothing) at .\\error.jl:168\n [2] #systemerror#48 at .\\error.jl:167 [inlined]\n [3] systemerror at .\\error.jl:167 [inlined]\n [4] open(::String; lock::Bool, read::Bool, write::Nothing, create::Nothing, truncate::Nothing, append::Nothing) at .\\iostream.jl:284\n [5] open(::String, ::String; lock::Bool) at .\\iostream.jl:346\n [6] open(::String, ::String) at .\\iostream.jl:346\n [7] open(::var\"#33#34\", ::String, ::Vararg{String,N} where N; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at .\\io.jl:323\n [8] open at .\\io.jl:323 [inlined]\n [9] getSetupFullPath(::String) at C:\\Users\\amira\\Dropbox (MIT)\\CBA\\MetaVoxels\\metavoxels-code\\julia\\include\\export.jl:177\n [10] top-level scope at C:\\Users\\amira\\Dropbox (MIT)\\CBA\\MetaVoxels\\metavoxels-code\\WANN\\julia\\runRover.jl:25\n [11] include(::String) at .\\client.jl:457\n [12] #invokelatest#1 at .\\essentials.jl:710 [inlined]\n [13] invokelatest(::Any, ::Any) at .\\essentials.jl:709\n [14] _pyjlwrap_call(::Function, ::Ptr{PyCall.PyObject_struct}, ::Ptr{PyCall.PyObject_struct}) at C:\\Users\\amira\\.julia\\packages\\PyCall\\BcTLp\\src\\callback.jl:28\n [15] pyjlwrap_call(::Ptr{PyCall.PyObject_struct}, ::Ptr{PyCall.PyObject_struct}, ::Ptr{PyCall.PyObject_struct}) at C:\\Users\\amira\\.julia\\packages\\PyCall\\BcTLp\\src\\callback.jl:49\nin expression starting at C:\\Users\\amira\\Dropbox (MIT)\\CBA\\MetaVoxels\\metavoxels-code\\WANN\\julia\\runRover.jl:25>",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-3-03f024c8456f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mMain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minclude\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"./runRover.jl\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mImportError\u001b[0m: <PyCall.jlwrap (in a Julia function called from Python)\nJULIA: LoadError: SystemError: opening file \"../json/tutorial.json\": No such file or directory\nStacktrace:\n [1] systemerror(::String, ::Int32; extrainfo::Nothing) at .\\error.jl:168\n [2] #systemerror#48 at .\\error.jl:167 [inlined]\n [3] systemerror at .\\error.jl:167 [inlined]\n [4] open(::String; lock::Bool, read::Bool, write::Nothing, create::Nothing, truncate::Nothing, append::Nothing) at .\\iostream.jl:284\n [5] open(::String, ::String; lock::Bool) at .\\iostream.jl:346\n [6] open(::String, ::String) at .\\iostream.jl:346\n [7] open(::var\"#33#34\", ::String, ::Vararg{String,N} where N; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at .\\io.jl:323\n [8] open at .\\io.jl:323 [inlined]\n [9] getSetupFullPath(::String) at C:\\Users\\amira\\Dropbox (MIT)\\CBA\\MetaVoxels\\metavoxels-code\\julia\\include\\export.jl:177\n [10] top-level scope at C:\\Users\\amira\\Dropbox (MIT)\\CBA\\MetaVoxels\\metavoxels-code\\WANN\\julia\\runRover.jl:25\n [11] include(::String) at .\\client.jl:457\n [12] #invokelatest#1 at .\\essentials.jl:710 [inlined]\n [13] invokelatest(::Any, ::Any) at .\\essentials.jl:709\n [14] _pyjlwrap_call(::Function, ::Ptr{PyCall.PyObject_struct}, ::Ptr{PyCall.PyObject_struct}) at C:\\Users\\amira\\.julia\\packages\\PyCall\\BcTLp\\src\\callback.jl:28\n [15] pyjlwrap_call(::Ptr{PyCall.PyObject_struct}, ::Ptr{PyCall.PyObject_struct}, ::Ptr{PyCall.PyObject_struct}) at C:\\Users\\amira\\.julia\\packages\\PyCall\\BcTLp\\src\\callback.jl:49\nin expression starting at C:\\Users\\amira\\Dropbox (MIT)\\CBA\\MetaVoxels\\metavoxels-code\\WANN\\julia\\runRover.jl:25>"
]
}
],
"source": [
"Main.include(\"./runRover.jl\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Main.target"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Main.habal(4)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<PyCall.jlwrap habal>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Main.eval(\"function habal(x)\\nreturn 4*x;\\nend\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Main.habal(4)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.49999753260092894"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Main.externalForce(1,Main.Vector3(0,0,0),Main.Vector3(0,0,1)).y"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"y=list(range(0, 10000))\n",
"z=list(range(0, 10000))\n",
"for currentTimeStep in range(0, 10000):\n",
" y[currentTimeStep]=Main.externalForce(currentTimeStep+1,Main.Vector3(0,0,0),Main.Vector3(0,0,1)).y\n",
" z[currentTimeStep]=Main.externalForce(currentTimeStep+1,Main.Vector3(0,0,0),Main.Vector3(0,0,1)).z\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max(y)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max(z)"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.000e+00],\n",
" [1.000e-04],\n",
" [2.000e-04],\n",
" ...,\n",
" [9.998e-01],\n",
" [9.999e-01],\n",
" [1.000e+00]])"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
" np.array([np.linspace(0,1.0, 10001)]).T\n"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.00000e+00],\n",
" [1.00001e-05],\n",
" [2.00002e-05],\n",
" ...,\n",
" [9.99980e-01],\n",
" [9.99990e-01],\n",
" [1.00000e+00]])"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array([np.linspace(0,1.0, 100000)]).T"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"strY+=str(M)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"strY=\"[\"\n",
"strZ=\"[\"\n",
"strY+=str(y[0])\n",
"strZ+=str(z[0])\n",
"for i in range(1, 10000):\n",
" strY+=\",\"\n",
" strZ+=\",\"\n",
" strY+=str(y[i])\n",
" strZ+=str(z[i])\n",
"strY+=\"]\"\n",
"strZ+=\"]\" "
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<PyCall.jlwrap externalForce>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Main.eval(\"function externalForce(currentTimeStep,N_position,N_force)\\n strY=\"+strY+\";\\n strZ=\"+strZ+\";\\n if (convert(Float64,N_force.z)>0.0) \\n z=strZ[currentTimeStep+1];\\n y=strY[currentTimeStep+1];\\n if (convert(Float64,N_force.z)>1.5) \\n z=-z;\\n y=-y;\\nend\\nreturn Vector3(0.0,y,z);\\n else\\n return Vector3(0.0,0.0,0.0);\\n end\\n end\")\n"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "runMetaVoxels",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-58-0a0161835a57>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mMain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetupSim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mMain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetSetupFullPath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m \u001b[1;31m#get simulation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mMain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrunMetaVoxels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetupSim\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msavedDataFolderPath\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"CPU\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\users\\amira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\julia\\core.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 174\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__getattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 175\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 176\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__try_getattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 177\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 178\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"_b\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\amira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\julia\\core.py\u001b[0m in \u001b[0;36m__try_getattr\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 197\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_julia\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjl_fullname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 198\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 199\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 200\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 201\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: runMetaVoxels"
]
}
],
"source": [
"simName=\"tutorial\"\n",
"savedDataFolderPath=\"../../json/\"+simName+\"/\" # make sure this folder exists, this is where the simulation result will be saved\n",
"path=\"../../json/\"+simName+\".json\"\n",
"\n",
"Main.setupSim=Main.getSetupFullPath(path); #get simulation \n",
"Main.runMetaVoxels(Main.setupSim,savedDataFolderPath,\"CPU\");"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'../../json/tutorial.json'"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
"m=np.array([np.linspace(0,10-1, 10)]).T"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"def pixelGrid(resolution=64):\n",
" x = np.linspace(-1,1, resolution)\n",
" X,Y = np.meshgrid(x,x)\n",
" return np.vstack([X.flatten(),Y.flatten()]).T"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
"P=pixelGrid(32).shape"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "tuple indices must be integers or slices, not tuple",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-84-68f53859d825>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mP\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m: tuple indices must be integers or slices, not tuple"
]
}
],
"source": [
"P[1:5,0]"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"x = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 5, 6],\n",
" [ 9, 10]])"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x[1:3, 0:2]\n"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10, 1)"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1.],\n",
" [2.],\n",
" [3.],\n",
" [4.]])"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m[1:5]"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "object too deep for desired array",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-94-e8f43e8d8fa1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minterp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\users\\amira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36minterp\u001b[1;34m(x, xp, fp, left, right, period)\u001b[0m\n\u001b[0;32m 1410\u001b[0m \u001b[0mfp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1411\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1412\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0minterp_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1413\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1414\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: object too deep for desired array"
]
}
],
"source": [
"m=interp(m,[min(m),max(m)],[-0.5,0.5])"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [],
"source": [
"from numpy import interp\n"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"def maprange(a, b, s):\n",
" (a1, a2), (b1, b2) = a, b\n",
" return b1 + ((s - a1) * (b2 - b1) / (a2 - a1))"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [],
"source": [
"m=maprange((min(m),max(m)),(-0.5,0.5), m)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.26237485, 0.95375265, 0.76825466, -0.12357312, -0.90178835,\n",
" -0.85090352, -0.01770193, 0.83177474, 0.91652155, 0.15862267,\n",
" -0.74511316, -0.96379539, -0.29636858, 0.64353813, 0.99177885,\n",
" 0.42818267, -0.52908269, -0.99991186, -0.55142668, 0.40403765,\n",
" 0.98803162, 0.66363388, -0.27090579, -0.95637593, -0.76255845,\n",
" 0.13235175, 0.90557836, 0.8462204 , 0.00885131, -0.83665564,\n",
" -0.91294525, -0.14987721, 0.75098725, 0.96139749, 0.28790332,\n",
" -0.65028784, -0.99060736, -0.42016704, 0.53657292, 0.99999021,\n",
" 0.54402111, -0.41211849, -0.98935825, -0.6569866 , 0.2794155 ,\n",
" 0.95892427, 0.7568025 , -0.14112001, -0.90929743, -0.84147098,\n",
" 0. , 0.84147098, 0.90929743, 0.14112001, -0.7568025 ,\n",
" -0.95892427, -0.2794155 , 0.6569866 , 0.98935825, 0.41211849,\n",
" -0.54402111, -0.99999021, -0.53657292, 0.42016704, 0.99060736,\n",
" 0.65028784, -0.28790332, -0.96139749, -0.75098725, 0.14987721,\n",
" 0.91294525, 0.83665564, -0.00885131, -0.8462204 , -0.90557836,\n",
" -0.13235175, 0.76255845, 0.95637593, 0.27090579, -0.66363388,\n",
" -0.98803162, -0.40403765, 0.55142668, 0.99991186, 0.52908269,\n",
" -0.42818267, -0.99177885, -0.64353813, 0.29636858, 0.96379539,\n",
" 0.74511316, -0.15862267, -0.91652155, -0.83177474, 0.01770193,\n",
" 0.85090352, 0.90178835, 0.12357312, -0.76825466, -0.95375265]])"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sin(np.array([np.linspace(0,100-1, 100)])-100/2.0)"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"x = y = z = np.arange(0.0,5.0,1.0)\n",
"# np.savetxt('test.out', x, delimiter=',',newline=',') # X is an array\n",
"np.savetxt('test.csv', (x,y,z)) # x,y,z equal sized 1D arrays\n",
"# np.savetxt('test.out', x, fmt='%1.4e') # use exponential notation"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"z = np.arange(0.0,10.0,1.0)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10,)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"z.shape"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"m=np.reshape(z,(5,2))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1., 3., 5., 7., 9.])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m[:,1]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"ename": "IndexError",
"evalue": "index 2 is out of bounds for axis 1 with size 2",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-17-cd00256ee3bc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mm\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mIndexError\u001b[0m: index 2 is out of bounds for axis 1 with size 2"
]
}
],
"source": [
"m[:,2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"@webio": {
"lastCommId": null,
"lastKernelId": null
},
"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",
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}
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"nbformat": 4,
"nbformat_minor": 2
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