1 | import matplotlib.pyplot as plt
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2 | import numpy as np
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3 | import numpy.ma as ma
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4 |
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5 | class camplotter( object ):
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6 | def __init__( self ):
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7 | chid, y,x,xe,ye,yh,xh,softid,hardid = np.loadtxt("map_dn.txt",unpack=True)
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8 | self.fig = plt.figure()
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9 | self.xe = xe
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10 | self.ye = ye
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11 | self.H = (6,0,30./180.*3.1415926)
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12 |
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13 | def plotcam():
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14 | data = np.random.rand(1440)
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15 | chid, y,x,xe,ye,yh,xh,softid,hardid = np.loadtxt("map_dn.txt",unpack=True)
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16 | fig = plt.figure()
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17 | ax = fig.add_subplot(121, aspect='equal')
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18 | bx = fig.add_subplot(122, aspect='equal')
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19 |
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20 | H = (6,0,30./180.*3.1415926)
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21 |
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22 | ax.scatter( xe, ye , s=40 , alpha=0.75 , marker=H , linewidths=0.)
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23 | bx.scatter( xe, ye , s=40 , alpha=0.75 , marker=H , linewidths=0., c=data)
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24 | plt.show()
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25 |
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26 | def plotincam( data ):
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27 | ax = fig.add_subplot(aspect='equal')
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28 | ax.scatter(xe,ye,s=40,alpha=0.75,marker=H, c=data)
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29 | plt.show()
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30 |
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31 | def plotmaskedcam( thr):
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32 | chid, y,x,xe,ye,yh,xh,softid,hardid = np.loadtxt("map_dn.txt",unpack=True)
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33 | data = np.random.rand(1440)
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34 | mdata = ma.masked_greater(data, thr)
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35 | fig = plt.figure()
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36 | ax = fig.add_subplot(111, aspect='equal')
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37 | H = (6,0,30./180.*3.1415926)
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38 |
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39 | a = ma.masked_where(mdata.mask == True, xe)
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40 | b = ma.masked_where(mdata.mask == True, ye)
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41 |
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42 | xm = a.compressed()
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43 | ym = b.compressed()
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44 | dm = mdata.compressed()
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45 |
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46 | print xm
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47 | print ym
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48 | print dm
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49 | ax.scatter(xe,ye,s=80,alpha=0.75,marker=H, c=data, edgecolors='none')
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50 | ax.scatter( xm, ym ,s=80,alpha=1,marker=H,facecolors='none',linewidths=3)
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51 | plt.show()
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52 |
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53 |
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54 | def randomcam():
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55 | data = np.random.rand(1440)
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56 | plotincam( data )
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57 |
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58 |
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59 |
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60 |
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61 |
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62 | if __name__ == '__main__':
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63 | """
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64 | create an instance
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65 | """
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66 | cplt = camplotter()
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67 | # np.savez ( filename, amplitude=maxAmp, time=maxPos, integral=integ)
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68 | npz = np.load('test.npz')
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69 | print npz.files
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70 |
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71 | data = npz['integral']
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72 | print 'data.shape', data.shape()
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73 |
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74 | for event in data:
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75 | print 'event.shape', event.shape()
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76 |
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