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jgoad/Zeeman.ipynb

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Zeeman.ipynb
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"input": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import scipy.optimize as optimization\n",
"import os\n",
"from scipy.signal import argrelextrema\n",
"from IPython.html.widgets import interact\n",
"from IPython.html import widgets\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'svg' "
],
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"input": [
"def smooth(x,window_len=11,window='hanning'):\n",
" \"\"\"smooth the data using a window with requested size.\n",
" \n",
" This method is based on the convolution of a scaled window with the signal.\n",
" The signal is prepared by introducing reflected copies of the signal \n",
" (with the window size) in both ends so that transient parts are minimized\n",
" in the begining and end part of the output signal.\n",
" \n",
" input:\n",
" x: the input signal \n",
" window_len: the dimension of the smoothing window; should be an odd integer\n",
" window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\n",
" flat window will produce a moving average smoothing.\n",
"\n",
" output:\n",
" the smoothed signal\n",
" \n",
" example:\n",
"\n",
" t=linspace(-2,2,0.1)\n",
" x=sin(t)+randn(len(t))*0.1\n",
" y=smooth(x)\n",
" \n",
" see also: \n",
" \n",
" numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve\n",
" scipy.signal.lfilter\n",
" \n",
" TODO: the window parameter could be the window itself if an array instead of a string\n",
" NOTE: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y.\n",
" \"\"\"\n",
"\n",
" if x.ndim != 1:\n",
" raise ValueError, \"smooth only accepts 1 dimension arrays.\"\n",
"\n",
" if x.size < window_len:\n",
" raise ValueError, \"Input vector needs to be bigger than window size.\"\n",
"\n",
"\n",
" if window_len<3:\n",
" return x\n",
"\n",
"\n",
" if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n",
" raise ValueError, \"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\"\n",
"\n",
"\n",
" s=np.r_[x[window_len-1:0:-1],x,x[-1:-window_len:-1]]\n",
" #print(len(s))\n",
" if window == 'flat': #moving average\n",
" w=np.ones(window_len,'d')\n",
" else:\n",
" w=eval('np.'+window+'(window_len)')\n",
"\n",
" y=np.convolve(w/w.sum(),s,mode='valid')\n",
" return y"
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"input": [
"folder_path = '/Users/jgoad-admin/Desktop/ZeemanData/'\n",
"I = np.array([0,2.12,2.63,3.12,3.61,4.10,4.60,5.10,5.60,6.20,6.80,7.30])\n",
"os.listdir(folder_path)[1:]"
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"text": [
"['0.xlsx',\n",
" '212.xlsx',\n",
" '263.xlsx',\n",
" '312.xlsx',\n",
" '361.xlsx',\n",
" '410.xlsx',\n",
" '460.xlsx',\n",
" '510.xlsx',\n",
" '560.xlsx',\n",
" '620.xlsx',\n",
" '680.xlsx',\n",
" '730.xlsx',\n",
" 'magnet_calibration.xlsx']"
]
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"input": [
"calib = pd.read_excel(folder_path+'magnet_calibration.xlsx', index_col=False)\n",
"data = []\n",
"files = []\n",
"for file_name in os.listdir(folder_path)[1:-1]:\n",
" print folder_path+file_name\n",
" run = pd.read_excel(folder_path+file_name, index_col=False)\n",
" run.columns = ['alpha','intensity']\n",
" data.append(run)\n",
" files.append(file_name)"
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"language": "python",
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{
"output_type": "stream",
"stream": "stdout",
"text": [
"/Users/jgoad-admin/Desktop/ZeemanData/0.xlsx\n",
"/Users/jgoad-admin/Desktop/ZeemanData/212.xlsx\n",
"/Users/jgoad-admin/Desktop/ZeemanData/263.xlsx\n",
"/Users/jgoad-admin/Desktop/ZeemanData/312.xlsx\n",
"/Users/jgoad-admin/Desktop/ZeemanData/361.xlsx\n",
"/Users/jgoad-admin/Desktop/ZeemanData/410.xlsx\n",
"/Users/jgoad-admin/Desktop/ZeemanData/460.xlsx\n",
"/Users/jgoad-admin/Desktop/ZeemanData/510.xlsx\n",
"/Users/jgoad-admin/Desktop/ZeemanData/560.xlsx\n",
"/Users/jgoad-admin/Desktop/ZeemanData/620.xlsx\n",
"/Users/jgoad-admin/Desktop/ZeemanData/680.xlsx\n",
"/Users/jgoad-admin/Desktop/ZeemanData/730.xlsx\n"
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"input": [
"def interfunc(thresh,run,smoothing,smoothing_type,window_size): \n",
" \n",
" #run = 11\n",
" smoothing_process = ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']\n",
" #'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\n",
" run_data = data[run]#[data[run].alpha >= -2.2]\n",
" run_data = run_data#[run_data.alpha <= 2.2]\n",
" maxima_loc = argrelextrema(smooth(run_data.intensity*-1, smoothing, window =smoothing_process[smoothing_type]), \n",
" np.less,order = window_size)[0]\n",
"\n",
" #picks out maxima datapoints\n",
" maxima = run_data.loc[maxima_loc]\n",
"\n",
" #resets the index\n",
" #maxima.index = np.arange(0,len(maxima))\n",
"\n",
" splitting1 = maxima.shift(1) - maxima\n",
" splitting2 = maxima.shift(-1) - maxima\n",
" \n",
"\n",
" mu = (splitting1.dropna().mean().alpha + splitting1.dropna().mean().alpha)/2.0\n",
" sig = (splitting1.dropna().std().alpha + splitting1.dropna().std().alpha)/2.0\n",
" \n",
" df1 = maxima[splitting1.alpha >= (mu - thresh*sig)]\n",
" df2 = maxima[splitting2.alpha >= (mu - thresh*sig)]\n",
" df3 = df1[splitting1.alpha <= (mu + thresh*sig)]\n",
" df4 = df2[splitting2.alpha <= (mu + thresh*sig)]\n",
"\n",
" df7 = pd.concat([df4,df3], axis = 0).drop_duplicates()\n",
" \n",
" x_axis = (-2.5,2.5)\n",
"\n",
" plt.subplot(2, 1, 1)\n",
" plt.xlim(x_axis[0],x_axis[1])\n",
" plt.plot(run_data.alpha,run_data.intensity,label = 'Data')\n",
" plt.scatter(df7.alpha,df7.intensity)\n",
" #for point in df7.values:\n",
" # plt.annotate('(%(x)2.2f, %(y)2.2f)' % {\"x\": float(point[0]), \"y\": float(point[1])}, xy=point, textcoords='data')\n",
" \n",
" plt.subplot(2, 1, 2)\n",
" plt.xlim(x_axis[0],x_axis[1])\n",
" plt.plot(run_data.alpha,run_data.intensity,label = 'Data')\n",
" \n",
"\n",
"interact(interfunc, thresh=widgets.FloatSlider(min=0.01, max=10, value=3.51),run = widgets.IntSlider(min=0, max=11, value=0),\n",
" smoothing = widgets.IntSlider(min=3, max=50, value=4),smoothing_type = widgets.IntSlider(min=0, max=4, value=2),\n",
" window_size = widgets.IntSlider(min=1, max=50, value=1))"
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"maxima_by_I = []\n",
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" \n",
" thresh = 3.51\n",
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