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@dsoto
Created April 9, 2013 12:44
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{
"metadata": {
"name": "2013-04-09-rural-energy-PCA"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2013-04-09\n",
"# PCA on load data from mali shared solar\n",
"# Principal Component Analysis on timeseries data\n",
"\n",
"This script finds an aggregated time series for each circuit in the data set and then projects them onto a subspace using PCA.\n",
"\n",
"_Note: This analysis was done months ago, but I'm including it now in my lab notebook._"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# read in csv to data frame\n",
"%pylab inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"sd_watts = pd.read_csv('/Users/dsoto/repos/processed_ss_data/sd_card/sd_watts_5min.csv', \n",
" index_col='date', \n",
" parse_dates=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
"For more information, type 'help(pylab)'.\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assemble mean or median timeseries in a dataframe\n",
"\n",
"This outputs length 288 (5 minute data over one day) of load data for each customer in the database that is not a mains in Mali. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#col = 'ml05_11'\n",
"ds = '2012-01-01'\n",
"de = '2012-06-01'\n",
"\n",
"# algorithm to average timeseries\n",
"\n",
"data_list = []\n",
"labels = []\n",
"\n",
"mean_power = {}\n",
"median_power = {}\n",
"for col in sd_watts.columns:\n",
"#for col in ['ml05_11', 'ml05_12']:\n",
" if '_00' in col:\n",
" continue\n",
" # skip uganda meters\n",
" if 'ug' in col:\n",
" continue\n",
" # loop over days in range\n",
" # subselect one circuit of data over time frame and replace missing with zeros\n",
" data = sd_watts[col][ds:de].fillna(0)\n",
" # 288 samples per day for 5 minute data\n",
" stride = 288\n",
" # accumulator \n",
" a = np.zeros(stride)\n",
" for i in range(0, 8640, stride):\n",
" a = np.vstack((a, data.ix[i:i + stride]))\n",
" # could use median instead of mean here\n",
" median_power[col] = np.median(a, 0)\n",
" mean_power[col] = np.mean(a, 0)\n",
" \n",
"mean_power = pd.DataFrame(mean_power)\n",
"median_power = pd.DataFrame(median_power)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate the covariance matrix and SVD\n",
"\n",
"We calculate the covariance of the matrix of daily timeseries in order to find the vectors of highest variance."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"cov = np.cov(median_power)\n",
"#cov = np.cov(mean_power)\n",
"(U, S, V) = np.linalg.svd(cov)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot the N-th eigenvector\n",
"\n",
"These should represent the vectors of highest variance in the data set. Except for the minus sign, it seems reasonable for a village with lighting predominantly in the evening."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"N = 0\n",
"plt.plot(U[:, N])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 4,
"text": [
"[<matplotlib.lines.Line2D at 0x114e5da10>]"
]
},
{
"output_type": "display_data",
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wGIiISIXBQEREKgwGDdntdq1L8Cl/bp8/tw1g+wY7t4OhsbERNpsNcXFxSE9P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\nDEREpMJgICIilf8H6wxeusBXI7sAAAAASUVORK5CYII=\n"
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"My interpretation of this vector is that it represents a shift of the overall pattern from earlier to later in the evening."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"N = 1\n",
"plt.plot(U[:, N])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 5,
"text": [
"[<matplotlib.lines.Line2D at 0x102748250>]"
]
},
{
"output_type": "display_data",
"png": 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h+f2mKylSVq40cxU6mPMbNxQMIpLQItli2LkTHnsM3noLBg2KzGe6QcEgIgmt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\nRK5CYII=\n"
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"N = 2\n",
"plt.plot(U[:, N])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 6,
"text": [
"[<matplotlib.lines.Line2D at 0x10271ad50>]"
]
},
{
"output_type": "display_data",
"png": 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p3ZzEYCAignFqDADQty8wciQQGytrEFo3J/l7UxI7n4nIJ1ypMfhLMABAcrK8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\n"
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"N = 3\n",
"plt.plot(U[:, N])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 7,
"text": [
"[<matplotlib.lines.Line2D at 0x1068bd650>]"
]
},
{
"output_type": "display_data",
"png": 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v3z63y+/du9ffj9YlXhKD9IzBoC4Wq0awKYn0LCpKNB31xGAIHharRvB0VdIz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\n/wcWf6MUH64NLgAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Project onto first two eigenvectors and plot\n",
"\n",
"This was meant to try to discriminate between customers. Conjecturing from 'eigen-consumptions' maybe one axis is overall consumption and one is time of evening weighting? "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import ex7\n",
"projected = ex7.project_data(median_power.T.values, U, 4)\n",
"scatter(projected[:,0], projected[:,1])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 0.99999507]\n",
" [ 0.99999507]]\n",
"[[ 0.99999013 0.00314157]\n",
" [ 0.99999013 0.00314157]]\n"
]
},
{
"output_type": "pyout",
"prompt_number": 8,
"text": [
"<matplotlib.collections.PathCollection at 0x106dad990>"
]
},
{
"output_type": "display_data",
"png": 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K8fHHn9K+fQ969hzI3r17S9/77rsv6N+/EtWrdyU4eDKrVy+hadOmN23r7Nmz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\n"
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# most variation is in first vectors\n",
"\n",
"This plots the magnitude of the covariance for first 20 vectors in the decomposition. Far and away the most variation is in the first. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(S[:20], 'ko')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 9,
"text": [
"[<matplotlib.lines.Line2D at 0x106dd9850>]"
]
},
{
"output_type": "display_data",
"png": 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}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# find extrema in projected values and corresponding time series\n",
"\n",
"The following few plots were a space where I was looking at the time series of the projected time series and trying to see if the households with high or low values in the second eigenvector had shifts in the time of day of consumption."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(projected[:, 1])\n",
"colind_max = projected[:, 1].argmax()\n",
"colind_min = projected[:, 1].argmin()\n",
"print colind_min\n",
"print colind_max"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"106\n",
"16\n"
]
},
{
"output_type": "display_data",
"png": 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U/SQJBq2tteFl5syRUc0f/hB1+oB0+7/+tRQq5cRVvHPuXHRVUa3oGwkdED/i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\nkSNHxpx+JsfU29srysrKxl2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}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"col = median_power.columns[16]\n",
"print col \n",
"median_power[col].plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"ml01_05\n"
]
},
{
"output_type": "pyout",
"prompt_number": 11,
"text": [
"<matplotlib.axes.AxesSubplot at 0x106d77910>"
]
},
{
"output_type": "display_data",
"png": 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}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"col = median_power.columns[106]\n",
"print col \n",
"median_power[col].plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"ml05_15\n"
]
},
{
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<matplotlib.axes.AxesSubplot at 0x106d08210>"
]
},
{
"output_type": "display_data",
"png": 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}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
}
],
"metadata": {}
}
]
}
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