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1 | import numpy | |
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2 | import sys | |
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3 | import zmq | |
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4 | import time | |
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5 | import h5py | |
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6 | import os | |
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7 | ||
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8 | path="/home/alex/Downloads/pedestal" | |
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9 | ext=".hdf5" | |
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10 | ||
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11 | port ="5556" | |
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12 | if len(sys.argv)>1: | |
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13 | port = sys.argv[1] | |
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14 | int(port) | |
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15 | ||
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16 | if len(sys.argv)>2: | |
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17 | port1 = sys.argv[2] | |
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18 | int(port1) | |
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19 | ||
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20 | #Socket to talk to server | |
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21 | context = zmq.Context() | |
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22 | socket = context.socket(zmq.SUB) | |
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23 | ||
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24 | print("Collecting updates from weather server...") | |
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25 | socket.connect("tcp://localhost:%s"%port) | |
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26 | ||
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27 | if len(sys.argv)>2: | |
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28 | socket.connect("tcp://localhost:%s"%port1) | |
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29 | ||
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30 | #Subscribe to zipcode, default is NYC,10001 | |
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31 | topicfilter = "10001" | |
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32 | socket.setsockopt_string(zmq.SUBSCRIBE,topicfilter) | |
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33 | #Process 5 updates | |
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34 | total_value=0 | |
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35 | count= -1 | |
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36 | azi= [] | |
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37 | elev=[] | |
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38 | time0=[] | |
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39 | #for update_nbr in range(250): | |
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40 | while(True): | |
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41 | string= socket.recv() | |
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42 | topic,ang_elev,ang_elev_dec,ang_azi,ang_azi_dec,seconds,seconds_dec= string.split() | |
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43 | ang_azi =float(ang_azi)+1e-3*float(ang_azi_dec) | |
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44 | ang_elev =float(ang_elev)+1e-3*float(ang_elev_dec) | |
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45 | seconds =float(seconds) +1e-6*float(seconds_dec) | |
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46 | azi.append(ang_azi) | |
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47 | elev.append(ang_elev) | |
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48 | time0.append(seconds) | |
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49 | count +=1 | |
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50 | if count == 100: | |
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51 | timetuple=time.localtime() | |
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52 | epoc = time.mktime(timetuple) | |
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53 | #print(epoc) | |
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54 | fullpath = path + ("/" if path[-1]!="/" else "") | |
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55 | ||
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56 | if not os.path.exists(fullpath): | |
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57 | os.mkdir(fullpath) | |
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58 | ||
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59 | azi_array = numpy.array(azi) | |
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60 | elev_array = numpy.array(elev) | |
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61 | time0_array= numpy.array(time0) | |
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62 | pedestal_array=numpy.array([azi,elev,time0]) | |
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63 | count=0 | |
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64 | azi= [] | |
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65 | elev=[] | |
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66 | time0=[] | |
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67 | #print(pedestal_array[0]) | |
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68 | #print(pedestal_array[1]) | |
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69 | ||
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70 | meta='PE' | |
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71 | filex="%s%4.4d%3.3d%10.4d%s"%(meta,timetuple.tm_year,timetuple.tm_yday,epoc,ext) | |
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72 | filename = os.path.join(fullpath,filex) | |
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73 | fp = h5py.File(filename,'w') | |
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74 | #print("Escribiendo HDF5...",epoc) | |
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75 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· DataΒ·....Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
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76 | grp = fp.create_group("Data") | |
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77 | dset = grp.create_dataset("azimuth" , data=pedestal_array[0]) | |
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78 | dset = grp.create_dataset("elevacion", data=pedestal_array[1]) | |
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79 | dset = grp.create_dataset("utc" , data=pedestal_array[2]) | |
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80 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· MetadataΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
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81 | grp = fp.create_group("Metadata") | |
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82 | dset = grp.create_dataset("utctimeInit", data=pedestal_array[2][0]) | |
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83 | timeInterval = pedestal_array[2][1]-pedestal_array[2][0] | |
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84 | dset = grp.create_dataset("timeInterval", data=timeInterval) | |
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85 | fp.close() | |
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86 | ||
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87 | #print ("Average messagedata value for topic '%s' was %dF" % ( topicfilter,total_value / update_nbr)) | |
| @@ -0,0 +1,48 | |||
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1 | ########################################################################### | |
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2 | ############################### SERVIDOR################################### | |
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3 | ######################### SIMULADOR DE PEDESTAL############################ | |
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4 | ########################################################################### | |
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5 | import time | |
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6 | import math | |
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7 | import numpy | |
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8 | import struct | |
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9 | from time import sleep | |
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10 | import zmq | |
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11 | import pickle | |
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12 | port="5556" | |
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13 | context = zmq.Context() | |
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14 | socket = context.socket(zmq.PUB) | |
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15 | socket.bind("tcp://*:%s"%port) | |
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16 | ###### PARAMETROS DE ENTRADA################################ | |
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17 | print("PEDESTAL RESOLUCION 0.01") | |
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18 | print("MAXIMA VELOCIDAD DEL PEDESTAL") | |
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19 | ang_elev = 4.12 | |
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20 | ang_azi = 30 | |
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21 | velocidad= input ("Ingresa velocidad:") | |
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22 | velocidad= float(velocidad) | |
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23 | print (velocidad) | |
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24 | ############################################################ | |
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25 | sleep(3) | |
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26 | print("Start program") | |
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27 | t1 = time.time() | |
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28 | count=0 | |
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29 | while(True): | |
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30 | tmp_vuelta = int(360/velocidad) | |
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31 | t1=t1+tmp_vuelta*count | |
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32 | count= count+1 | |
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33 | muestras_seg = 100 | |
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34 | t2 = time.time() | |
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35 | for i in range(tmp_vuelta): | |
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36 | for j in range(muestras_seg): | |
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37 | tmp_variable = (i+j/100.0) | |
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38 | ang_azi = (tmp_variable)*float(velocidad) | |
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39 | seconds = t1+ tmp_variable | |
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40 | topic=10001 | |
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41 | print ("AzimΒ°: ","%.4f"%ang_azi,"Time:" ,"%.5f"%seconds) | |
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42 | seconds_dec=(seconds-int(seconds))*1e6 | |
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43 | ang_azi_dec= (ang_azi-int(ang_azi))*1e3 | |
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44 | ang_elev_dec=(ang_elev-int(ang_elev))*1e3 | |
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45 | sleep(0.0088) | |
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46 | socket.send_string("%d %d %d %d %d %d %d"%(topic,ang_elev,ang_elev_dec,ang_azi,ang_azi_dec,seconds,seconds_dec)) | |
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47 | t3 = time.time() | |
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48 | print ("Total time for 1 vuelta in Seconds",t3-t2) | |
| @@ -0,0 +1,82 | |||
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1 | import os, sys | |
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2 | import datetime | |
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3 | import time | |
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4 | from schainpy.controller import Project | |
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5 | ||
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6 | desc = "USRP_test" | |
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7 | filename = "USRP_processing.xml" | |
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8 | controllerObj = Project() | |
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9 | controllerObj.setup(id = '191', name='Test_USRP', description=desc) | |
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10 | ||
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11 | ############## USED TO PLOT IQ VOLTAGE, POWER AND SPECTRA ############# | |
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12 | ######PATH DE LECTURA, ESCRITURA, GRAFICOS Y ENVIO WEB################# | |
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13 | path = '/home/alex/Downloads/test_rawdata' | |
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14 | figpath = '/home/alex/Downloads/hdf5_test' | |
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15 | ######################## UNIDAD DE LECTURA############################# | |
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16 | ''' | |
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17 | readUnitConfObj = controllerObj.addReadUnit(datatype='VoltageReader', | |
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18 | path=path, | |
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19 | startDate="2020/01/01", #"2020/01/01",#today, | |
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20 | endDate= "2020/12/01", #"2020/12/30",#today, | |
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21 | startTime='00:00:00', | |
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22 | endTime='23:59:59', | |
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23 | delay=0, | |
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24 | #set=0, | |
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25 | online=0, | |
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26 | walk=1) | |
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27 | ||
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28 | ''' | |
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29 | readUnitConfObj = controllerObj.addReadUnit(datatype='SimulatorReader', | |
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30 | frequency=9.345e9, | |
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31 | FixRCP_IPP= 60, | |
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32 | Tau_0 = 30, | |
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33 | AcqH0_0=0, | |
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34 | samples=330, | |
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35 | AcqDH_0=0.15, | |
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36 | FixRCP_TXA=0.15, | |
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37 | FixRCP_TXB=0.15, | |
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38 | Fdoppler=600.0, | |
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39 | Hdoppler=36, | |
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40 | Adoppler=300,#300 | |
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41 | delay=0, | |
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42 | online=0, | |
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43 | walk=0, | |
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44 | profilesPerBlock=625, | |
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45 | dataBlocksPerFile=100) | |
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46 | #nTotalReadFiles=2) | |
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47 | ||
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48 | ||
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49 | #opObj11 = readUnitConfObj.addOperation(name='printInfo') | |
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50 | ||
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51 | procUnitConfObjA = controllerObj.addProcUnit(datatype='VoltageProc', inputId=readUnitConfObj.getId()) | |
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52 | ||
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53 | procUnitConfObjB = controllerObj.addProcUnit(datatype='SpectraProc', inputId=procUnitConfObjA.getId()) | |
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54 | procUnitConfObjB.addParameter(name='nFFTPoints', value=625, format='int') | |
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55 | procUnitConfObjB.addParameter(name='nProfiles', value=625, format='int') | |
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56 | ||
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57 | opObj11 = procUnitConfObjB.addOperation(name='removeDC') | |
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58 | opObj11.addParameter(name='mode', value=2) | |
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59 | #opObj11 = procUnitConfObjB.addOperation(name='SpectraPlot') | |
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60 | #opObj11 = procUnitConfObjB.addOperation(name='PowerProfilePlot') | |
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61 | ||
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62 | procUnitConfObjC= controllerObj.addProcUnit(datatype='ParametersProc',inputId=procUnitConfObjB.getId()) | |
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63 | procUnitConfObjC.addOperation(name='SpectralMoments') | |
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64 | #opObj11 = procUnitConfObjC.addOperation(name='PowerPlot') | |
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65 | ||
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66 | ''' | |
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67 | opObj11 = procUnitConfObjC.addOperation(name='SpectralMomentsPlot') | |
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68 | #opObj11.addParameter(name='xmin', value=14) | |
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69 | #opObj11.addParameter(name='xmax', value=15) | |
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70 | #opObj11.addParameter(name='save', value=figpath) | |
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71 | opObj11.addParameter(name='showprofile', value=1) | |
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72 | #opObj11.addParameter(name='save_period', value=10) | |
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73 | ''' | |
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74 | ||
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75 | opObj10 = procUnitConfObjC.addOperation(name='ParameterWriter') | |
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76 | opObj10.addParameter(name='path',value=figpath) | |
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77 | #opObj10.addParameter(name='mode',value=0) | |
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78 | opObj10.addParameter(name='blocksPerFile',value='100',format='int') | |
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79 | opObj10.addParameter(name='metadataList',value='utctimeInit,timeInterval',format='list') | |
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80 | opObj10.addParameter(name='dataList',value='data_POW,data_DOP,data_WIDTH,data_SNR')#,format='list' | |
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81 | ||
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82 | controllerObj.start() | |
| @@ -0,0 +1,162 | |||
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1 | import os,numpy,h5py | |
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2 | from shutil import copyfile | |
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3 | ||
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4 | def isNumber(str): | |
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5 | try: | |
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6 | float(str) | |
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7 | return True | |
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8 | except: | |
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9 | return False | |
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10 | ||
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11 | def getfirstFilefromPath(path,meta,ext): | |
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12 | validFilelist = [] | |
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13 | fileList = os.listdir(path) | |
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14 | if len(fileList)<1: | |
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15 | return None | |
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16 | # meta 1234 567 8-18 BCDE | |
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17 | # H,D,PE YYYY DDD EPOC .ext | |
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18 | ||
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19 | for thisFile in fileList: | |
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20 | if meta =="PE": | |
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21 | try: | |
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22 | number= int(thisFile[len(meta)+7:len(meta)+17]) | |
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23 | except: | |
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24 | print("There is a file or folder with different format") | |
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25 | if meta == "D": | |
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26 | try: | |
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27 | number= int(thisFile[8:11]) | |
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28 | except: | |
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29 | print("There is a file or folder with different format") | |
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30 | ||
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31 | if not isNumber(str=number): | |
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32 | continue | |
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33 | if (os.path.splitext(thisFile)[-1].lower() != ext.lower()): | |
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34 | continue | |
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35 | validFilelist.sort() | |
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36 | validFilelist.append(thisFile) | |
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37 | if len(validFilelist)>0: | |
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38 | validFilelist = sorted(validFilelist,key=str.lower) | |
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39 | return validFilelist | |
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40 | return None | |
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41 | ||
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42 | def gettimeutcfromDirFilename(path,file): | |
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43 | dir_file= path+"/"+file | |
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44 | fp = h5py.File(dir_file,'r') | |
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45 | epoc = fp['Metadata'].get('utctimeInit')[()] | |
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46 | fp.close() | |
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47 | return epoc | |
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48 | ||
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49 | def getDatavaluefromDirFilename(path,file,value): | |
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50 | dir_file= path+"/"+file | |
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51 | fp = h5py.File(dir_file,'r') | |
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52 | array = fp['Data'].get(value)[()] | |
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53 | fp.close() | |
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54 | return array | |
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55 | ||
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56 | ||
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57 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Velocidad de PedestalΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
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58 | w = input ("Ingresa velocidad de Pedestal: ") | |
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59 | w = 4 | |
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60 | w = float(w) | |
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61 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Resolucion minimo en gradosΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
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62 | alfa = input ("Ingresa resolucion minima en grados: ") | |
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63 | alfa = 1 | |
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64 | alfa = float(alfa) | |
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65 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· IPP del Experimento Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
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66 | IPP = input ("Ingresa el IPP del experimento: ") | |
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67 | IPP = 0.0004 | |
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68 | IPP = float(IPP) | |
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69 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· MODE Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
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70 | mode = input ("Ingresa el MODO del experimento T or F: ") | |
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71 | mode = "T" | |
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72 | mode = str(mode) | |
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73 | ||
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74 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Tiempo en generar la resolucion minΒ·Β·Β· | |
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75 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· MCU Β·Β· var_ang = w * (var_tiempo)Β·Β·Β· | |
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76 | var_tiempo = alfa/w | |
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77 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Tiempo Equivalente en perfilesΒ·Β·Β·Β·Β·Β·Β·Β· | |
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78 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· var_tiempo = IPP * ( num_perfiles )Β· | |
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79 | num_perfiles = int(var_tiempo/IPP) | |
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80 | ||
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81 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·DATA PEDESTALΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
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82 | dir_pedestal = "/home/alex/Downloads/pedestal" | |
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83 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· DATA ADQΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
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84 | if mode=="T": | |
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85 | dir_adq = "/home/alex/Downloads/hdf5_testPP/d2020194" # Time domain | |
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86 | else: | |
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87 | dir_adq = "/home/alex/Downloads/hdf5_test/d2020194" # Frequency domain | |
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88 | ||
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89 | print( "Velocidad angular :", w) | |
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90 | print( "Resolucion minima en grados :", alfa) | |
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91 | print( "Numero de perfiles equivalente:", num_perfiles) | |
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92 | print( "Mode :", mode) | |
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93 | ||
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94 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· First FileΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
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95 | list_pedestal = getfirstFilefromPath(path=dir_pedestal,meta="PE",ext=".hdf5") | |
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96 | list_adq = getfirstFilefromPath(path=dir_adq ,meta="D",ext=".hdf5") | |
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97 | ||
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98 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· utc time Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
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99 | utc_pedestal= gettimeutcfromDirFilename(path=dir_pedestal,file=list_pedestal[0]) | |
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100 | utc_adq = gettimeutcfromDirFilename(path=dir_adq ,file=list_adq[0]) | |
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101 | ||
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102 | print("utc_pedestal :",utc_pedestal) | |
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103 | print("utc_adq :",utc_adq) | |
|
|
104 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Relacion: utc_adq (+/-) var_tiempo*nro_file= utc_pedestal | |
|
|
105 | time_Interval_p = 0.01 | |
|
|
106 | n_perfiles_p = 100 | |
|
|
107 | if utc_adq>utc_pedestal: | |
|
|
108 | nro_file = int((int(utc_adq) - int(utc_pedestal))/(time_Interval_p*n_perfiles_p)) | |
|
|
109 | ff_pedestal = list_pedestal[nro_file] | |
|
|
110 | utc_pedestal = gettimeutcfromDirFilename(path=dir_pedestal,file=ff_pedestal) | |
|
|
111 | nro_key_p = int((utc_adq-utc_pedestal)/time_Interval_p) | |
|
|
112 | if utc_adq >utc_pedestal: | |
|
|
113 | ff_pedestal = ff_pedestal | |
|
|
114 | else: | |
|
|
115 | nro_file = nro_file-1 | |
|
|
116 | ff_pedestal = list_pedestal[nro_file] | |
|
|
117 | angulo = getDatavaluefromDirFilename(path=dir_pedestal,file=ff_pedestal,value="azimuth") | |
|
|
118 | nro_key_p = int((utc_adq-utc_pedestal)/time_Interval_p) | |
|
|
119 | print("nro_file :",nro_file) | |
|
|
120 | print("name_file :",ff_pedestal) | |
|
|
121 | print("utc_pedestal_file :",utc_pedestal) | |
|
|
122 | print("nro_key_p :",nro_key_p) | |
|
|
123 | print("utc_pedestal_init :",utc_pedestal+nro_key_p*time_Interval_p) | |
|
|
124 | print("angulo_array :",angulo[nro_key_p]) | |
|
|
125 | #4+25+25+25+21 | |
|
|
126 | #while True: | |
|
|
127 | list_pedestal = getfirstFilefromPath(path=dir_pedestal,meta="PE",ext=".hdf5") | |
|
|
128 | list_adq = getfirstFilefromPath(path=dir_adq ,meta="D",ext=".hdf5") | |
|
|
129 | ||
|
|
130 | nro_file = nro_file #10 | |
|
|
131 | nro_key_perfil = nro_key_p | |
|
|
132 | blocksPerFile = 100 | |
|
|
133 | wr_path = "/home/alex/Downloads/hdf5_wr/" | |
|
|
134 | # Lectura de archivos de adquisicion para adicion de azimuth | |
|
|
135 | for thisFile in range(len(list_adq)): | |
|
|
136 | print("thisFileAdq",thisFile) | |
|
|
137 | angulo_adq = numpy.zeros(blocksPerFile) | |
|
|
138 | tmp = 0 | |
|
|
139 | for j in range(blocksPerFile): | |
|
|
140 | iterador = nro_key_perfil + 25*(j-tmp) | |
|
|
141 | if iterador < n_perfiles_p: | |
|
|
142 | nro_file = nro_file | |
|
|
143 | else: | |
|
|
144 | nro_file = nro_file+1 | |
|
|
145 | tmp = j | |
|
|
146 | iterador = nro_key_perfil | |
|
|
147 | ff_pedestal = list_pedestal[nro_file] | |
|
|
148 | angulo = getDatavaluefromDirFilename(path=dir_pedestal,file=ff_pedestal,value="azimuth") | |
|
|
149 | angulo_adq[j]= angulo[iterador] | |
|
|
150 | copyfile(dir_adq+"/"+list_adq[thisFile],wr_path+list_adq[thisFile]) | |
|
|
151 | fp = h5py.File(wr_path+list_adq[thisFile],'a') | |
|
|
152 | grp = fp.create_group("Pedestal") | |
|
|
153 | dset = grp.create_dataset("azimuth" , data=angulo_adq) | |
|
|
154 | fp.close() | |
|
|
155 | print("Angulo",angulo_adq) | |
|
|
156 | print("Angulo",len(angulo_adq)) | |
|
|
157 | nro_key_perfil=iterador + 25 | |
|
|
158 | if nro_key_perfil< n_perfiles_p: | |
|
|
159 | nro_file = nro_file | |
|
|
160 | else: | |
|
|
161 | nro_file = nro_file+1 | |
|
|
162 | nro_key_perfil= nro_key_p | |
| @@ -1,1397 +1,1403 | |||
|
|
1 | 1 | ''' |
|
|
2 | 2 | |
|
|
3 | 3 | $Author: murco $ |
|
|
4 | 4 | $Id: JROData.py 173 2012-11-20 15:06:21Z murco $ |
|
|
5 | 5 | ''' |
|
|
6 | 6 | |
|
|
7 | 7 | import copy |
|
|
8 | 8 | import numpy |
|
|
9 | 9 | import datetime |
|
|
10 | 10 | import json |
|
|
11 | 11 | |
|
|
12 | 12 | import schainpy.admin |
|
|
13 | 13 | from schainpy.utils import log |
|
|
14 | 14 | from .jroheaderIO import SystemHeader, RadarControllerHeader |
|
|
15 | 15 | from schainpy.model.data import _noise |
|
|
16 | 16 | |
|
|
17 | 17 | |
|
|
18 | 18 | def getNumpyDtype(dataTypeCode): |
|
|
19 | 19 | |
|
|
20 | 20 | if dataTypeCode == 0: |
|
|
21 | 21 | numpyDtype = numpy.dtype([('real', '<i1'), ('imag', '<i1')]) |
|
|
22 | 22 | elif dataTypeCode == 1: |
|
|
23 | 23 | numpyDtype = numpy.dtype([('real', '<i2'), ('imag', '<i2')]) |
|
|
24 | 24 | elif dataTypeCode == 2: |
|
|
25 | 25 | numpyDtype = numpy.dtype([('real', '<i4'), ('imag', '<i4')]) |
|
|
26 | 26 | elif dataTypeCode == 3: |
|
|
27 | 27 | numpyDtype = numpy.dtype([('real', '<i8'), ('imag', '<i8')]) |
|
|
28 | 28 | elif dataTypeCode == 4: |
|
|
29 | 29 | numpyDtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')]) |
|
|
30 | 30 | elif dataTypeCode == 5: |
|
|
31 | 31 | numpyDtype = numpy.dtype([('real', '<f8'), ('imag', '<f8')]) |
|
|
32 | 32 | else: |
|
|
33 | 33 | raise ValueError('dataTypeCode was not defined') |
|
|
34 | 34 | |
|
|
35 | 35 | return numpyDtype |
|
|
36 | 36 | |
|
|
37 | 37 | |
|
|
38 | 38 | def getDataTypeCode(numpyDtype): |
|
|
39 | 39 | |
|
|
40 | 40 | if numpyDtype == numpy.dtype([('real', '<i1'), ('imag', '<i1')]): |
|
|
41 | 41 | datatype = 0 |
|
|
42 | 42 | elif numpyDtype == numpy.dtype([('real', '<i2'), ('imag', '<i2')]): |
|
|
43 | 43 | datatype = 1 |
|
|
44 | 44 | elif numpyDtype == numpy.dtype([('real', '<i4'), ('imag', '<i4')]): |
|
|
45 | 45 | datatype = 2 |
|
|
46 | 46 | elif numpyDtype == numpy.dtype([('real', '<i8'), ('imag', '<i8')]): |
|
|
47 | 47 | datatype = 3 |
|
|
48 | 48 | elif numpyDtype == numpy.dtype([('real', '<f4'), ('imag', '<f4')]): |
|
|
49 | 49 | datatype = 4 |
|
|
50 | 50 | elif numpyDtype == numpy.dtype([('real', '<f8'), ('imag', '<f8')]): |
|
|
51 | 51 | datatype = 5 |
|
|
52 | 52 | else: |
|
|
53 | 53 | datatype = None |
|
|
54 | 54 | |
|
|
55 | 55 | return datatype |
|
|
56 | 56 | |
|
|
57 | 57 | |
|
|
58 | 58 | def hildebrand_sekhon(data, navg): |
|
|
59 | 59 | """ |
|
|
60 | 60 | This method is for the objective determination of the noise level in Doppler spectra. This |
|
|
61 | 61 | implementation technique is based on the fact that the standard deviation of the spectral |
|
|
62 | 62 | densities is equal to the mean spectral density for white Gaussian noise |
|
|
63 | 63 | |
|
|
64 | 64 | Inputs: |
|
|
65 | 65 | Data : heights |
|
|
66 | 66 | navg : numbers of averages |
|
|
67 | 67 | |
|
|
68 | 68 | Return: |
|
|
69 | 69 | mean : noise's level |
|
|
70 | 70 | """ |
|
|
71 | 71 | |
|
|
72 | 72 | sortdata = numpy.sort(data, axis=None) |
|
|
73 | 73 | ''' |
|
|
74 | 74 | lenOfData = len(sortdata) |
|
|
75 | 75 | nums_min = lenOfData*0.2 |
|
|
76 | 76 | |
|
|
77 | 77 | if nums_min <= 5: |
|
|
78 | 78 | |
|
|
79 | 79 | nums_min = 5 |
|
|
80 | 80 | |
|
|
81 | 81 | sump = 0. |
|
|
82 | 82 | sumq = 0. |
|
|
83 | 83 | |
|
|
84 | 84 | j = 0 |
|
|
85 | 85 | cont = 1 |
|
|
86 | 86 | |
|
|
87 | 87 | while((cont == 1)and(j < lenOfData)): |
|
|
88 | 88 | |
|
|
89 | 89 | sump += sortdata[j] |
|
|
90 | 90 | sumq += sortdata[j]**2 |
|
|
91 | 91 | |
|
|
92 | 92 | if j > nums_min: |
|
|
93 | 93 | rtest = float(j)/(j-1) + 1.0/navg |
|
|
94 | 94 | if ((sumq*j) > (rtest*sump**2)): |
|
|
95 | 95 | j = j - 1 |
|
|
96 | 96 | sump = sump - sortdata[j] |
|
|
97 | 97 | sumq = sumq - sortdata[j]**2 |
|
|
98 | 98 | cont = 0 |
|
|
99 | 99 | |
|
|
100 | 100 | j += 1 |
|
|
101 | 101 | |
|
|
102 | 102 | lnoise = sump / j |
|
|
103 | 103 | ''' |
|
|
104 | 104 | return _noise.hildebrand_sekhon(sortdata, navg) |
|
|
105 | 105 | |
|
|
106 | 106 | |
|
|
107 | 107 | class Beam: |
|
|
108 | 108 | |
|
|
109 | 109 | def __init__(self): |
|
|
110 | 110 | self.codeList = [] |
|
|
111 | 111 | self.azimuthList = [] |
|
|
112 | 112 | self.zenithList = [] |
|
|
113 | 113 | |
|
|
114 | 114 | |
|
|
115 | 115 | class GenericData(object): |
|
|
116 | 116 | |
|
|
117 | 117 | flagNoData = True |
|
|
118 | 118 | |
|
|
119 | 119 | def copy(self, inputObj=None): |
|
|
120 | 120 | |
|
|
121 | 121 | if inputObj == None: |
|
|
122 | 122 | return copy.deepcopy(self) |
|
|
123 | 123 | |
|
|
124 | 124 | for key in list(inputObj.__dict__.keys()): |
|
|
125 | 125 | |
|
|
126 | 126 | attribute = inputObj.__dict__[key] |
|
|
127 | 127 | |
|
|
128 | 128 | # If this attribute is a tuple or list |
|
|
129 | 129 | if type(inputObj.__dict__[key]) in (tuple, list): |
|
|
130 | 130 | self.__dict__[key] = attribute[:] |
|
|
131 | 131 | continue |
|
|
132 | 132 | |
|
|
133 | 133 | # If this attribute is another object or instance |
|
|
134 | 134 | if hasattr(attribute, '__dict__'): |
|
|
135 | 135 | self.__dict__[key] = attribute.copy() |
|
|
136 | 136 | continue |
|
|
137 | 137 | |
|
|
138 | 138 | self.__dict__[key] = inputObj.__dict__[key] |
|
|
139 | 139 | |
|
|
140 | 140 | def deepcopy(self): |
|
|
141 | 141 | |
|
|
142 | 142 | return copy.deepcopy(self) |
|
|
143 | 143 | |
|
|
144 | 144 | def isEmpty(self): |
|
|
145 | 145 | |
|
|
146 | 146 | return self.flagNoData |
|
|
147 | 147 | |
|
|
148 | 148 | def isReady(self): |
|
|
149 | 149 | |
|
|
150 | 150 | return not self.flagNoData |
|
|
151 | 151 | |
|
|
152 | 152 | |
|
|
153 | 153 | class JROData(GenericData): |
|
|
154 | 154 | |
|
|
155 | 155 | # m_BasicHeader = BasicHeader() |
|
|
156 | 156 | # m_ProcessingHeader = ProcessingHeader() |
|
|
157 | 157 | |
|
|
158 | 158 | systemHeaderObj = SystemHeader() |
|
|
159 | 159 | radarControllerHeaderObj = RadarControllerHeader() |
|
|
160 | 160 | # data = None |
|
|
161 | 161 | type = None |
|
|
162 | 162 | datatype = None # dtype but in string |
|
|
163 | 163 | # dtype = None |
|
|
164 | 164 | # nChannels = None |
|
|
165 | 165 | # nHeights = None |
|
|
166 | 166 | nProfiles = None |
|
|
167 | 167 | heightList = None |
|
|
168 | 168 | channelList = None |
|
|
169 | 169 | flagDiscontinuousBlock = False |
|
|
170 | 170 | useLocalTime = False |
|
|
171 | 171 | utctime = None |
|
|
172 | 172 | timeZone = None |
|
|
173 | 173 | dstFlag = None |
|
|
174 | 174 | errorCount = None |
|
|
175 | 175 | blocksize = None |
|
|
176 | 176 | # nCode = None |
|
|
177 | 177 | # nBaud = None |
|
|
178 | 178 | # code = None |
|
|
179 | 179 | flagDecodeData = False # asumo q la data no esta decodificada |
|
|
180 | 180 | flagDeflipData = False # asumo q la data no esta sin flip |
|
|
181 | 181 | flagShiftFFT = False |
|
|
182 | 182 | # ippSeconds = None |
|
|
183 | 183 | # timeInterval = None |
|
|
184 | 184 | nCohInt = None |
|
|
185 | 185 | # noise = None |
|
|
186 | 186 | windowOfFilter = 1 |
|
|
187 | 187 | # Speed of ligth |
|
|
188 | 188 | C = 3e8 |
|
|
189 | 189 | frequency = 49.92e6 |
|
|
190 | 190 | realtime = False |
|
|
191 | 191 | beacon_heiIndexList = None |
|
|
192 | 192 | last_block = None |
|
|
193 | 193 | blocknow = None |
|
|
194 | 194 | azimuth = None |
|
|
195 | 195 | zenith = None |
|
|
196 | 196 | beam = Beam() |
|
|
197 | 197 | profileIndex = None |
|
|
198 | 198 | error = None |
|
|
199 | 199 | data = None |
|
|
200 | 200 | nmodes = None |
|
|
201 | 201 | |
|
|
202 | 202 | def __str__(self): |
|
|
203 | 203 | |
|
|
204 | 204 | return '{} - {}'.format(self.type, self.getDatatime()) |
|
|
205 | 205 | |
|
|
206 | 206 | def getNoise(self): |
|
|
207 | 207 | |
|
|
208 | 208 | raise NotImplementedError |
|
|
209 | 209 | |
|
|
210 | 210 | def getNChannels(self): |
|
|
211 | 211 | |
|
|
212 | 212 | return len(self.channelList) |
|
|
213 | 213 | |
|
|
214 | 214 | def getChannelIndexList(self): |
|
|
215 | 215 | |
|
|
216 | 216 | return list(range(self.nChannels)) |
|
|
217 | 217 | |
|
|
218 | 218 | def getNHeights(self): |
|
|
219 | 219 | |
|
|
220 | 220 | return len(self.heightList) |
|
|
221 | 221 | |
|
|
222 | 222 | def getHeiRange(self, extrapoints=0): |
|
|
223 | 223 | |
|
|
224 | 224 | heis = self.heightList |
|
|
225 | 225 | # deltah = self.heightList[1] - self.heightList[0] |
|
|
226 | 226 | # |
|
|
227 | 227 | # heis.append(self.heightList[-1]) |
|
|
228 | 228 | |
|
|
229 | 229 | return heis |
|
|
230 | 230 | |
|
|
231 | 231 | def getDeltaH(self): |
|
|
232 | 232 | |
|
|
233 | 233 | delta = self.heightList[1] - self.heightList[0] |
|
|
234 | 234 | |
|
|
235 | 235 | return delta |
|
|
236 | 236 | |
|
|
237 | 237 | def getltctime(self): |
|
|
238 | 238 | |
|
|
239 | 239 | if self.useLocalTime: |
|
|
240 | 240 | return self.utctime - self.timeZone * 60 |
|
|
241 | 241 | |
|
|
242 | 242 | return self.utctime |
|
|
243 | 243 | |
|
|
244 | 244 | def getDatatime(self): |
|
|
245 | 245 | |
|
|
246 | 246 | datatimeValue = datetime.datetime.utcfromtimestamp(self.ltctime) |
|
|
247 | 247 | return datatimeValue |
|
|
248 | 248 | |
|
|
249 | 249 | def getTimeRange(self): |
|
|
250 | 250 | |
|
|
251 | 251 | datatime = [] |
|
|
252 | 252 | |
|
|
253 | 253 | datatime.append(self.ltctime) |
|
|
254 | 254 | datatime.append(self.ltctime + self.timeInterval + 1) |
|
|
255 | 255 | |
|
|
256 | 256 | datatime = numpy.array(datatime) |
|
|
257 | 257 | |
|
|
258 | 258 | return datatime |
|
|
259 | 259 | |
|
|
260 | 260 | def getFmaxTimeResponse(self): |
|
|
261 | 261 | |
|
|
262 | 262 | period = (10**-6) * self.getDeltaH() / (0.15) |
|
|
263 | 263 | |
|
|
264 | 264 | PRF = 1. / (period * self.nCohInt) |
|
|
265 | 265 | |
|
|
266 | 266 | fmax = PRF |
|
|
267 | 267 | |
|
|
268 | 268 | return fmax |
|
|
269 | 269 | |
|
|
270 | 270 | def getFmax(self): |
|
|
271 | 271 | PRF = 1. / (self.ippSeconds * self.nCohInt) |
|
|
272 | 272 | |
|
|
273 | 273 | fmax = PRF |
|
|
274 | 274 | return fmax |
|
|
275 | 275 | |
|
|
276 | 276 | def getVmax(self): |
|
|
277 | 277 | |
|
|
278 | 278 | _lambda = self.C / self.frequency |
|
|
279 | 279 | |
|
|
280 | 280 | vmax = self.getFmax() * _lambda / 2 |
|
|
281 | 281 | |
|
|
282 | 282 | return vmax |
|
|
283 | 283 | |
|
|
284 | 284 | def get_ippSeconds(self): |
|
|
285 | 285 | ''' |
|
|
286 | 286 | ''' |
|
|
287 | 287 | return self.radarControllerHeaderObj.ippSeconds |
|
|
288 | 288 | |
|
|
289 | 289 | def set_ippSeconds(self, ippSeconds): |
|
|
290 | 290 | ''' |
|
|
291 | 291 | ''' |
|
|
292 | 292 | |
|
|
293 | 293 | self.radarControllerHeaderObj.ippSeconds = ippSeconds |
|
|
294 | 294 | |
|
|
295 | 295 | return |
|
|
296 | 296 | |
|
|
297 | 297 | def get_dtype(self): |
|
|
298 | 298 | ''' |
|
|
299 | 299 | ''' |
|
|
300 | 300 | return getNumpyDtype(self.datatype) |
|
|
301 | 301 | |
|
|
302 | 302 | def set_dtype(self, numpyDtype): |
|
|
303 | 303 | ''' |
|
|
304 | 304 | ''' |
|
|
305 | 305 | |
|
|
306 | 306 | self.datatype = getDataTypeCode(numpyDtype) |
|
|
307 | 307 | |
|
|
308 | 308 | def get_code(self): |
|
|
309 | 309 | ''' |
|
|
310 | 310 | ''' |
|
|
311 | 311 | return self.radarControllerHeaderObj.code |
|
|
312 | 312 | |
|
|
313 | 313 | def set_code(self, code): |
|
|
314 | 314 | ''' |
|
|
315 | 315 | ''' |
|
|
316 | 316 | self.radarControllerHeaderObj.code = code |
|
|
317 | 317 | |
|
|
318 | 318 | return |
|
|
319 | 319 | |
|
|
320 | 320 | def get_ncode(self): |
|
|
321 | 321 | ''' |
|
|
322 | 322 | ''' |
|
|
323 | 323 | return self.radarControllerHeaderObj.nCode |
|
|
324 | 324 | |
|
|
325 | 325 | def set_ncode(self, nCode): |
|
|
326 | 326 | ''' |
|
|
327 | 327 | ''' |
|
|
328 | 328 | self.radarControllerHeaderObj.nCode = nCode |
|
|
329 | 329 | |
|
|
330 | 330 | return |
|
|
331 | 331 | |
|
|
332 | 332 | def get_nbaud(self): |
|
|
333 | 333 | ''' |
|
|
334 | 334 | ''' |
|
|
335 | 335 | return self.radarControllerHeaderObj.nBaud |
|
|
336 | 336 | |
|
|
337 | 337 | def set_nbaud(self, nBaud): |
|
|
338 | 338 | ''' |
|
|
339 | 339 | ''' |
|
|
340 | 340 | self.radarControllerHeaderObj.nBaud = nBaud |
|
|
341 | 341 | |
|
|
342 | 342 | return |
|
|
343 | 343 | |
|
|
344 | 344 | nChannels = property(getNChannels, "I'm the 'nChannel' property.") |
|
|
345 | 345 | channelIndexList = property( |
|
|
346 | 346 | getChannelIndexList, "I'm the 'channelIndexList' property.") |
|
|
347 | 347 | nHeights = property(getNHeights, "I'm the 'nHeights' property.") |
|
|
348 | 348 | #noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
349 | 349 | datatime = property(getDatatime, "I'm the 'datatime' property") |
|
|
350 | 350 | ltctime = property(getltctime, "I'm the 'ltctime' property") |
|
|
351 | 351 | ippSeconds = property(get_ippSeconds, set_ippSeconds) |
|
|
352 | 352 | dtype = property(get_dtype, set_dtype) |
|
|
353 | 353 | # timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
354 | 354 | code = property(get_code, set_code) |
|
|
355 | 355 | nCode = property(get_ncode, set_ncode) |
|
|
356 | 356 | nBaud = property(get_nbaud, set_nbaud) |
|
|
357 | 357 | |
|
|
358 | 358 | |
|
|
359 | 359 | class Voltage(JROData): |
|
|
360 | 360 | |
|
|
361 | 361 | # data es un numpy array de 2 dmensiones (canales, alturas) |
|
|
362 | 362 | data = None |
|
|
363 |
data |
|
|
|
364 |
data |
|
|
|
365 |
data |
|
|
|
363 | dataPP_POW = None | |
|
|
364 | dataPP_DOP = None | |
|
|
365 | dataPP_WIDTH = None | |
|
|
366 | dataPP_SNR = None | |
|
|
367 | ||
|
|
366 | 368 | def __init__(self): |
|
|
367 | 369 | ''' |
|
|
368 | 370 | Constructor |
|
|
369 | 371 | ''' |
|
|
370 | 372 | |
|
|
371 | 373 | self.useLocalTime = True |
|
|
372 | 374 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
373 | 375 | self.systemHeaderObj = SystemHeader() |
|
|
374 | 376 | self.type = "Voltage" |
|
|
375 | 377 | self.data = None |
|
|
376 | 378 | # self.dtype = None |
|
|
377 | 379 | # self.nChannels = 0 |
|
|
378 | 380 | # self.nHeights = 0 |
|
|
379 | 381 | self.nProfiles = None |
|
|
380 | 382 | self.heightList = None |
|
|
381 | 383 | self.channelList = None |
|
|
382 | 384 | # self.channelIndexList = None |
|
|
383 | 385 | self.flagNoData = True |
|
|
384 | 386 | self.flagDiscontinuousBlock = False |
|
|
385 | 387 | self.utctime = None |
|
|
386 | 388 | self.timeZone = None |
|
|
387 | 389 | self.dstFlag = None |
|
|
388 | 390 | self.errorCount = None |
|
|
389 | 391 | self.nCohInt = None |
|
|
390 | 392 | self.blocksize = None |
|
|
391 | 393 | self.flagCohInt = False |
|
|
392 | 394 | self.flagDecodeData = False # asumo q la data no esta decodificada |
|
|
393 | 395 | self.flagDeflipData = False # asumo q la data no esta sin flip |
|
|
394 | 396 | self.flagShiftFFT = False |
|
|
395 | 397 | self.flagDataAsBlock = False # Asumo que la data es leida perfil a perfil |
|
|
396 | 398 | self.profileIndex = 0 |
|
|
397 | 399 | |
|
|
398 | 400 | def getNoisebyHildebrand(self, channel=None): |
|
|
399 | 401 | """ |
|
|
400 | 402 | Determino el nivel de ruido usando el metodo Hildebrand-Sekhon |
|
|
401 | 403 | |
|
|
402 | 404 | Return: |
|
|
403 | 405 | noiselevel |
|
|
404 | 406 | """ |
|
|
405 | 407 | |
|
|
406 | 408 | if channel != None: |
|
|
407 | 409 | data = self.data[channel] |
|
|
408 | 410 | nChannels = 1 |
|
|
409 | 411 | else: |
|
|
410 | 412 | data = self.data |
|
|
411 | 413 | nChannels = self.nChannels |
|
|
412 | 414 | |
|
|
413 | 415 | noise = numpy.zeros(nChannels) |
|
|
414 | 416 | power = data * numpy.conjugate(data) |
|
|
415 | 417 | |
|
|
416 | 418 | for thisChannel in range(nChannels): |
|
|
417 | 419 | if nChannels == 1: |
|
|
418 | 420 | daux = power[:].real |
|
|
419 | 421 | else: |
|
|
420 | 422 | daux = power[thisChannel, :].real |
|
|
421 | 423 | noise[thisChannel] = hildebrand_sekhon(daux, self.nCohInt) |
|
|
422 | 424 | |
|
|
423 | 425 | return noise |
|
|
424 | 426 | |
|
|
425 | 427 | def getNoise(self, type=1, channel=None): |
|
|
426 | 428 | |
|
|
427 | 429 | if type == 1: |
|
|
428 | 430 | noise = self.getNoisebyHildebrand(channel) |
|
|
429 | 431 | |
|
|
430 | 432 | return noise |
|
|
431 | 433 | |
|
|
432 | 434 | def getPower(self, channel=None): |
|
|
433 | 435 | |
|
|
434 | 436 | if channel != None: |
|
|
435 | 437 | data = self.data[channel] |
|
|
436 | 438 | else: |
|
|
437 | 439 | data = self.data |
|
|
438 | 440 | |
|
|
439 | 441 | power = data * numpy.conjugate(data) |
|
|
440 | 442 | powerdB = 10 * numpy.log10(power.real) |
|
|
441 | 443 | powerdB = numpy.squeeze(powerdB) |
|
|
442 | 444 | |
|
|
443 | 445 | return powerdB |
|
|
444 | 446 | |
|
|
445 | 447 | def getTimeInterval(self): |
|
|
446 | 448 | |
|
|
447 | 449 | timeInterval = self.ippSeconds * self.nCohInt |
|
|
448 | 450 | |
|
|
449 | 451 | return timeInterval |
|
|
450 | 452 | |
|
|
451 | 453 | noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
452 | 454 | timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
453 | 455 | |
|
|
454 | 456 | |
|
|
455 | 457 | class Spectra(JROData): |
|
|
456 | 458 | |
|
|
457 | 459 | # data spc es un numpy array de 2 dmensiones (canales, perfiles, alturas) |
|
|
458 | 460 | data_spc = None |
|
|
459 | 461 | # data cspc es un numpy array de 2 dmensiones (canales, pares, alturas) |
|
|
460 | 462 | data_cspc = None |
|
|
461 | 463 | # data dc es un numpy array de 2 dmensiones (canales, alturas) |
|
|
462 | 464 | data_dc = None |
|
|
463 | 465 | # data power |
|
|
464 | 466 | data_pwr = None |
|
|
465 | 467 | nFFTPoints = None |
|
|
466 | 468 | # nPairs = None |
|
|
467 | 469 | pairsList = None |
|
|
468 | 470 | nIncohInt = None |
|
|
469 | 471 | wavelength = None # Necesario para cacular el rango de velocidad desde la frecuencia |
|
|
470 | 472 | nCohInt = None # se requiere para determinar el valor de timeInterval |
|
|
471 | 473 | ippFactor = None |
|
|
472 | 474 | profileIndex = 0 |
|
|
473 | 475 | plotting = "spectra" |
|
|
474 | 476 | |
|
|
475 | 477 | def __init__(self): |
|
|
476 | 478 | ''' |
|
|
477 | 479 | Constructor |
|
|
478 | 480 | ''' |
|
|
479 | 481 | |
|
|
480 | 482 | self.useLocalTime = True |
|
|
481 | 483 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
482 | 484 | self.systemHeaderObj = SystemHeader() |
|
|
483 | 485 | self.type = "Spectra" |
|
|
484 | 486 | # self.data = None |
|
|
485 | 487 | # self.dtype = None |
|
|
486 | 488 | # self.nChannels = 0 |
|
|
487 | 489 | # self.nHeights = 0 |
|
|
488 | 490 | self.nProfiles = None |
|
|
489 | 491 | self.heightList = None |
|
|
490 | 492 | self.channelList = None |
|
|
491 | 493 | # self.channelIndexList = None |
|
|
492 | 494 | self.pairsList = None |
|
|
493 | 495 | self.flagNoData = True |
|
|
494 | 496 | self.flagDiscontinuousBlock = False |
|
|
495 | 497 | self.utctime = None |
|
|
496 | 498 | self.nCohInt = None |
|
|
497 | 499 | self.nIncohInt = None |
|
|
498 | 500 | self.blocksize = None |
|
|
499 | 501 | self.nFFTPoints = None |
|
|
500 | 502 | self.wavelength = None |
|
|
501 | 503 | self.flagDecodeData = False # asumo q la data no esta decodificada |
|
|
502 | 504 | self.flagDeflipData = False # asumo q la data no esta sin flip |
|
|
503 | 505 | self.flagShiftFFT = False |
|
|
504 | 506 | self.ippFactor = 1 |
|
|
505 | 507 | #self.noise = None |
|
|
506 | 508 | self.beacon_heiIndexList = [] |
|
|
507 | 509 | self.noise_estimation = None |
|
|
508 | 510 | |
|
|
509 | 511 | def getNoisebyHildebrand(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None): |
|
|
510 | 512 | """ |
|
|
511 | 513 | Determino el nivel de ruido usando el metodo Hildebrand-Sekhon |
|
|
512 | 514 | |
|
|
513 | 515 | Return: |
|
|
514 | 516 | noiselevel |
|
|
515 | 517 | """ |
|
|
516 | 518 | |
|
|
517 | 519 | noise = numpy.zeros(self.nChannels) |
|
|
518 | 520 | |
|
|
519 | 521 | for channel in range(self.nChannels): |
|
|
520 | 522 | daux = self.data_spc[channel, |
|
|
521 | 523 | xmin_index:xmax_index, ymin_index:ymax_index] |
|
|
522 | 524 | noise[channel] = hildebrand_sekhon(daux, self.nIncohInt) |
|
|
523 | 525 | |
|
|
524 | 526 | return noise |
|
|
525 | 527 | |
|
|
526 | 528 | def getNoise(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None): |
|
|
527 | 529 | |
|
|
528 | 530 | if self.noise_estimation is not None: |
|
|
529 | 531 | # this was estimated by getNoise Operation defined in jroproc_spectra.py |
|
|
530 | 532 | return self.noise_estimation |
|
|
531 | 533 | else: |
|
|
532 | 534 | noise = self.getNoisebyHildebrand( |
|
|
533 | 535 | xmin_index, xmax_index, ymin_index, ymax_index) |
|
|
534 | 536 | return noise |
|
|
535 | 537 | |
|
|
536 | 538 | def getFreqRangeTimeResponse(self, extrapoints=0): |
|
|
537 | 539 | |
|
|
538 | 540 | deltafreq = self.getFmaxTimeResponse() / (self.nFFTPoints * self.ippFactor) |
|
|
539 | 541 | freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) - self.nFFTPoints / 2.) - deltafreq / 2 |
|
|
540 | 542 | |
|
|
541 | 543 | return freqrange |
|
|
542 | 544 | |
|
|
543 | 545 | def getAcfRange(self, extrapoints=0): |
|
|
544 | 546 | |
|
|
545 | 547 | deltafreq = 10. / (self.getFmax() / (self.nFFTPoints * self.ippFactor)) |
|
|
546 | 548 | freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2 |
|
|
547 | 549 | |
|
|
548 | 550 | return freqrange |
|
|
549 | 551 | |
|
|
550 | 552 | def getFreqRange(self, extrapoints=0): |
|
|
551 | 553 | |
|
|
552 | 554 | deltafreq = self.getFmax() / (self.nFFTPoints * self.ippFactor) |
|
|
553 | 555 | freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2 |
|
|
554 | 556 | |
|
|
555 | 557 | return freqrange |
|
|
556 | 558 | |
|
|
557 | 559 | def getVelRange(self, extrapoints=0): |
|
|
558 | 560 | |
|
|
559 | 561 | deltav = self.getVmax() / (self.nFFTPoints * self.ippFactor) |
|
|
560 | 562 | velrange = deltav * (numpy.arange(self.nFFTPoints + extrapoints) - self.nFFTPoints / 2.) |
|
|
561 | 563 | |
|
|
562 | 564 | if self.nmodes: |
|
|
563 | 565 | return velrange/self.nmodes |
|
|
564 | 566 | else: |
|
|
565 | 567 | return velrange |
|
|
566 | 568 | |
|
|
567 | 569 | def getNPairs(self): |
|
|
568 | 570 | |
|
|
569 | 571 | return len(self.pairsList) |
|
|
570 | 572 | |
|
|
571 | 573 | def getPairsIndexList(self): |
|
|
572 | 574 | |
|
|
573 | 575 | return list(range(self.nPairs)) |
|
|
574 | 576 | |
|
|
575 | 577 | def getNormFactor(self): |
|
|
576 | 578 | |
|
|
577 | 579 | pwcode = 1 |
|
|
578 | 580 | |
|
|
579 | 581 | if self.flagDecodeData: |
|
|
580 | 582 | pwcode = numpy.sum(self.code[0]**2) |
|
|
581 | 583 | #normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter |
|
|
582 | 584 | normFactor = self.nProfiles * self.nIncohInt * self.nCohInt * pwcode * self.windowOfFilter |
|
|
583 | 585 | |
|
|
584 | 586 | return normFactor |
|
|
585 | 587 | |
|
|
586 | 588 | def getFlagCspc(self): |
|
|
587 | 589 | |
|
|
588 | 590 | if self.data_cspc is None: |
|
|
589 | 591 | return True |
|
|
590 | 592 | |
|
|
591 | 593 | return False |
|
|
592 | 594 | |
|
|
593 | 595 | def getFlagDc(self): |
|
|
594 | 596 | |
|
|
595 | 597 | if self.data_dc is None: |
|
|
596 | 598 | return True |
|
|
597 | 599 | |
|
|
598 | 600 | return False |
|
|
599 | 601 | |
|
|
600 | 602 | def getTimeInterval(self): |
|
|
601 | 603 | |
|
|
602 | 604 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt * self.nProfiles * self.ippFactor |
|
|
603 | 605 | if self.nmodes: |
|
|
604 | 606 | return self.nmodes*timeInterval |
|
|
605 | 607 | else: |
|
|
606 | 608 | return timeInterval |
|
|
607 | 609 | |
|
|
608 | 610 | def getPower(self): |
|
|
609 | 611 | |
|
|
610 | 612 | factor = self.normFactor |
|
|
611 | 613 | z = self.data_spc / factor |
|
|
612 | 614 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
613 | 615 | avg = numpy.average(z, axis=1) |
|
|
614 | 616 | |
|
|
615 | 617 | return 10 * numpy.log10(avg) |
|
|
616 | 618 | |
|
|
617 | 619 | def getCoherence(self, pairsList=None, phase=False): |
|
|
618 | 620 | |
|
|
619 | 621 | z = [] |
|
|
620 | 622 | if pairsList is None: |
|
|
621 | 623 | pairsIndexList = self.pairsIndexList |
|
|
622 | 624 | else: |
|
|
623 | 625 | pairsIndexList = [] |
|
|
624 | 626 | for pair in pairsList: |
|
|
625 | 627 | if pair not in self.pairsList: |
|
|
626 | 628 | raise ValueError("Pair %s is not in dataOut.pairsList" % ( |
|
|
627 | 629 | pair)) |
|
|
628 | 630 | pairsIndexList.append(self.pairsList.index(pair)) |
|
|
629 | 631 | for i in range(len(pairsIndexList)): |
|
|
630 | 632 | pair = self.pairsList[pairsIndexList[i]] |
|
|
631 | 633 | ccf = numpy.average(self.data_cspc[pairsIndexList[i], :, :], axis=0) |
|
|
632 | 634 | powa = numpy.average(self.data_spc[pair[0], :, :], axis=0) |
|
|
633 | 635 | powb = numpy.average(self.data_spc[pair[1], :, :], axis=0) |
|
|
634 | 636 | avgcoherenceComplex = ccf / numpy.sqrt(powa * powb) |
|
|
635 | 637 | if phase: |
|
|
636 | 638 | data = numpy.arctan2(avgcoherenceComplex.imag, |
|
|
637 | 639 | avgcoherenceComplex.real) * 180 / numpy.pi |
|
|
638 | 640 | else: |
|
|
639 | 641 | data = numpy.abs(avgcoherenceComplex) |
|
|
640 | 642 | |
|
|
641 | 643 | z.append(data) |
|
|
642 | 644 | |
|
|
643 | 645 | return numpy.array(z) |
|
|
644 | 646 | |
|
|
645 | 647 | def setValue(self, value): |
|
|
646 | 648 | |
|
|
647 | 649 | print("This property should not be initialized") |
|
|
648 | 650 | |
|
|
649 | 651 | return |
|
|
650 | 652 | |
|
|
651 | 653 | nPairs = property(getNPairs, setValue, "I'm the 'nPairs' property.") |
|
|
652 | 654 | pairsIndexList = property( |
|
|
653 | 655 | getPairsIndexList, setValue, "I'm the 'pairsIndexList' property.") |
|
|
654 | 656 | normFactor = property(getNormFactor, setValue, |
|
|
655 | 657 | "I'm the 'getNormFactor' property.") |
|
|
656 | 658 | flag_cspc = property(getFlagCspc, setValue) |
|
|
657 | 659 | flag_dc = property(getFlagDc, setValue) |
|
|
658 | 660 | noise = property(getNoise, setValue, "I'm the 'nHeights' property.") |
|
|
659 | 661 | timeInterval = property(getTimeInterval, setValue, |
|
|
660 | 662 | "I'm the 'timeInterval' property") |
|
|
661 | 663 | |
|
|
662 | 664 | |
|
|
663 | 665 | class SpectraHeis(Spectra): |
|
|
664 | 666 | |
|
|
665 | 667 | data_spc = None |
|
|
666 | 668 | data_cspc = None |
|
|
667 | 669 | data_dc = None |
|
|
668 | 670 | nFFTPoints = None |
|
|
669 | 671 | # nPairs = None |
|
|
670 | 672 | pairsList = None |
|
|
671 | 673 | nCohInt = None |
|
|
672 | 674 | nIncohInt = None |
|
|
673 | 675 | |
|
|
674 | 676 | def __init__(self): |
|
|
675 | 677 | |
|
|
676 | 678 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
677 | 679 | |
|
|
678 | 680 | self.systemHeaderObj = SystemHeader() |
|
|
679 | 681 | |
|
|
680 | 682 | self.type = "SpectraHeis" |
|
|
681 | 683 | |
|
|
682 | 684 | # self.dtype = None |
|
|
683 | 685 | |
|
|
684 | 686 | # self.nChannels = 0 |
|
|
685 | 687 | |
|
|
686 | 688 | # self.nHeights = 0 |
|
|
687 | 689 | |
|
|
688 | 690 | self.nProfiles = None |
|
|
689 | 691 | |
|
|
690 | 692 | self.heightList = None |
|
|
691 | 693 | |
|
|
692 | 694 | self.channelList = None |
|
|
693 | 695 | |
|
|
694 | 696 | # self.channelIndexList = None |
|
|
695 | 697 | |
|
|
696 | 698 | self.flagNoData = True |
|
|
697 | 699 | |
|
|
698 | 700 | self.flagDiscontinuousBlock = False |
|
|
699 | 701 | |
|
|
700 | 702 | # self.nPairs = 0 |
|
|
701 | 703 | |
|
|
702 | 704 | self.utctime = None |
|
|
703 | 705 | |
|
|
704 | 706 | self.blocksize = None |
|
|
705 | 707 | |
|
|
706 | 708 | self.profileIndex = 0 |
|
|
707 | 709 | |
|
|
708 | 710 | self.nCohInt = 1 |
|
|
709 | 711 | |
|
|
710 | 712 | self.nIncohInt = 1 |
|
|
711 | 713 | |
|
|
712 | 714 | def getNormFactor(self): |
|
|
713 | 715 | pwcode = 1 |
|
|
714 | 716 | if self.flagDecodeData: |
|
|
715 | 717 | pwcode = numpy.sum(self.code[0]**2) |
|
|
716 | 718 | |
|
|
717 | 719 | normFactor = self.nIncohInt * self.nCohInt * pwcode |
|
|
718 | 720 | |
|
|
719 | 721 | return normFactor |
|
|
720 | 722 | |
|
|
721 | 723 | def getTimeInterval(self): |
|
|
722 | 724 | |
|
|
723 | 725 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt |
|
|
724 | 726 | |
|
|
725 | 727 | return timeInterval |
|
|
726 | 728 | |
|
|
727 | 729 | normFactor = property(getNormFactor, "I'm the 'getNormFactor' property.") |
|
|
728 | 730 | timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
729 | 731 | |
|
|
730 | 732 | |
|
|
731 | 733 | class Fits(JROData): |
|
|
732 | 734 | |
|
|
733 | 735 | heightList = None |
|
|
734 | 736 | channelList = None |
|
|
735 | 737 | flagNoData = True |
|
|
736 | 738 | flagDiscontinuousBlock = False |
|
|
737 | 739 | useLocalTime = False |
|
|
738 | 740 | utctime = None |
|
|
739 | 741 | timeZone = None |
|
|
740 | 742 | # ippSeconds = None |
|
|
741 | 743 | # timeInterval = None |
|
|
742 | 744 | nCohInt = None |
|
|
743 | 745 | nIncohInt = None |
|
|
744 | 746 | noise = None |
|
|
745 | 747 | windowOfFilter = 1 |
|
|
746 | 748 | # Speed of ligth |
|
|
747 | 749 | C = 3e8 |
|
|
748 | 750 | frequency = 49.92e6 |
|
|
749 | 751 | realtime = False |
|
|
750 | 752 | |
|
|
751 | 753 | def __init__(self): |
|
|
752 | 754 | |
|
|
753 | 755 | self.type = "Fits" |
|
|
754 | 756 | |
|
|
755 | 757 | self.nProfiles = None |
|
|
756 | 758 | |
|
|
757 | 759 | self.heightList = None |
|
|
758 | 760 | |
|
|
759 | 761 | self.channelList = None |
|
|
760 | 762 | |
|
|
761 | 763 | # self.channelIndexList = None |
|
|
762 | 764 | |
|
|
763 | 765 | self.flagNoData = True |
|
|
764 | 766 | |
|
|
765 | 767 | self.utctime = None |
|
|
766 | 768 | |
|
|
767 | 769 | self.nCohInt = 1 |
|
|
768 | 770 | |
|
|
769 | 771 | self.nIncohInt = 1 |
|
|
770 | 772 | |
|
|
771 | 773 | self.useLocalTime = True |
|
|
772 | 774 | |
|
|
773 | 775 | self.profileIndex = 0 |
|
|
774 | 776 | |
|
|
775 | 777 | # self.utctime = None |
|
|
776 | 778 | # self.timeZone = None |
|
|
777 | 779 | # self.ltctime = None |
|
|
778 | 780 | # self.timeInterval = None |
|
|
779 | 781 | # self.header = None |
|
|
780 | 782 | # self.data_header = None |
|
|
781 | 783 | # self.data = None |
|
|
782 | 784 | # self.datatime = None |
|
|
783 | 785 | # self.flagNoData = False |
|
|
784 | 786 | # self.expName = '' |
|
|
785 | 787 | # self.nChannels = None |
|
|
786 | 788 | # self.nSamples = None |
|
|
787 | 789 | # self.dataBlocksPerFile = None |
|
|
788 | 790 | # self.comments = '' |
|
|
789 | 791 | # |
|
|
790 | 792 | |
|
|
791 | 793 | def getltctime(self): |
|
|
792 | 794 | |
|
|
793 | 795 | if self.useLocalTime: |
|
|
794 | 796 | return self.utctime - self.timeZone * 60 |
|
|
795 | 797 | |
|
|
796 | 798 | return self.utctime |
|
|
797 | 799 | |
|
|
798 | 800 | def getDatatime(self): |
|
|
799 | 801 | |
|
|
800 | 802 | datatime = datetime.datetime.utcfromtimestamp(self.ltctime) |
|
|
801 | 803 | return datatime |
|
|
802 | 804 | |
|
|
803 | 805 | def getTimeRange(self): |
|
|
804 | 806 | |
|
|
805 | 807 | datatime = [] |
|
|
806 | 808 | |
|
|
807 | 809 | datatime.append(self.ltctime) |
|
|
808 | 810 | datatime.append(self.ltctime + self.timeInterval) |
|
|
809 | 811 | |
|
|
810 | 812 | datatime = numpy.array(datatime) |
|
|
811 | 813 | |
|
|
812 | 814 | return datatime |
|
|
813 | 815 | |
|
|
814 | 816 | def getHeiRange(self): |
|
|
815 | 817 | |
|
|
816 | 818 | heis = self.heightList |
|
|
817 | 819 | |
|
|
818 | 820 | return heis |
|
|
819 | 821 | |
|
|
820 | 822 | def getNHeights(self): |
|
|
821 | 823 | |
|
|
822 | 824 | return len(self.heightList) |
|
|
823 | 825 | |
|
|
824 | 826 | def getNChannels(self): |
|
|
825 | 827 | |
|
|
826 | 828 | return len(self.channelList) |
|
|
827 | 829 | |
|
|
828 | 830 | def getChannelIndexList(self): |
|
|
829 | 831 | |
|
|
830 | 832 | return list(range(self.nChannels)) |
|
|
831 | 833 | |
|
|
832 | 834 | def getNoise(self, type=1): |
|
|
833 | 835 | |
|
|
834 | 836 | #noise = numpy.zeros(self.nChannels) |
|
|
835 | 837 | |
|
|
836 | 838 | if type == 1: |
|
|
837 | 839 | noise = self.getNoisebyHildebrand() |
|
|
838 | 840 | |
|
|
839 | 841 | if type == 2: |
|
|
840 | 842 | noise = self.getNoisebySort() |
|
|
841 | 843 | |
|
|
842 | 844 | if type == 3: |
|
|
843 | 845 | noise = self.getNoisebyWindow() |
|
|
844 | 846 | |
|
|
845 | 847 | return noise |
|
|
846 | 848 | |
|
|
847 | 849 | def getTimeInterval(self): |
|
|
848 | 850 | |
|
|
849 | 851 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt |
|
|
850 | 852 | |
|
|
851 | 853 | return timeInterval |
|
|
852 | 854 | |
|
|
853 | 855 | def get_ippSeconds(self): |
|
|
854 | 856 | ''' |
|
|
855 | 857 | ''' |
|
|
856 | 858 | return self.ipp_sec |
|
|
857 | 859 | |
|
|
858 | 860 | |
|
|
859 | 861 | datatime = property(getDatatime, "I'm the 'datatime' property") |
|
|
860 | 862 | nHeights = property(getNHeights, "I'm the 'nHeights' property.") |
|
|
861 | 863 | nChannels = property(getNChannels, "I'm the 'nChannel' property.") |
|
|
862 | 864 | channelIndexList = property( |
|
|
863 | 865 | getChannelIndexList, "I'm the 'channelIndexList' property.") |
|
|
864 | 866 | noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
865 | 867 | |
|
|
866 | 868 | ltctime = property(getltctime, "I'm the 'ltctime' property") |
|
|
867 | 869 | timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
868 | 870 | ippSeconds = property(get_ippSeconds, '') |
|
|
869 | 871 | |
|
|
870 | 872 | class Correlation(JROData): |
|
|
871 | 873 | |
|
|
872 | 874 | noise = None |
|
|
873 | 875 | SNR = None |
|
|
874 | 876 | #-------------------------------------------------- |
|
|
875 | 877 | mode = None |
|
|
876 | 878 | split = False |
|
|
877 | 879 | data_cf = None |
|
|
878 | 880 | lags = None |
|
|
879 | 881 | lagRange = None |
|
|
880 | 882 | pairsList = None |
|
|
881 | 883 | normFactor = None |
|
|
882 | 884 | #-------------------------------------------------- |
|
|
883 | 885 | # calculateVelocity = None |
|
|
884 | 886 | nLags = None |
|
|
885 | 887 | nPairs = None |
|
|
886 | 888 | nAvg = None |
|
|
887 | 889 | |
|
|
888 | 890 | def __init__(self): |
|
|
889 | 891 | ''' |
|
|
890 | 892 | Constructor |
|
|
891 | 893 | ''' |
|
|
892 | 894 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
893 | 895 | |
|
|
894 | 896 | self.systemHeaderObj = SystemHeader() |
|
|
895 | 897 | |
|
|
896 | 898 | self.type = "Correlation" |
|
|
897 | 899 | |
|
|
898 | 900 | self.data = None |
|
|
899 | 901 | |
|
|
900 | 902 | self.dtype = None |
|
|
901 | 903 | |
|
|
902 | 904 | self.nProfiles = None |
|
|
903 | 905 | |
|
|
904 | 906 | self.heightList = None |
|
|
905 | 907 | |
|
|
906 | 908 | self.channelList = None |
|
|
907 | 909 | |
|
|
908 | 910 | self.flagNoData = True |
|
|
909 | 911 | |
|
|
910 | 912 | self.flagDiscontinuousBlock = False |
|
|
911 | 913 | |
|
|
912 | 914 | self.utctime = None |
|
|
913 | 915 | |
|
|
914 | 916 | self.timeZone = None |
|
|
915 | 917 | |
|
|
916 | 918 | self.dstFlag = None |
|
|
917 | 919 | |
|
|
918 | 920 | self.errorCount = None |
|
|
919 | 921 | |
|
|
920 | 922 | self.blocksize = None |
|
|
921 | 923 | |
|
|
922 | 924 | self.flagDecodeData = False # asumo q la data no esta decodificada |
|
|
923 | 925 | |
|
|
924 | 926 | self.flagDeflipData = False # asumo q la data no esta sin flip |
|
|
925 | 927 | |
|
|
926 | 928 | self.pairsList = None |
|
|
927 | 929 | |
|
|
928 | 930 | self.nPoints = None |
|
|
929 | 931 | |
|
|
930 | 932 | def getPairsList(self): |
|
|
931 | 933 | |
|
|
932 | 934 | return self.pairsList |
|
|
933 | 935 | |
|
|
934 | 936 | def getNoise(self, mode=2): |
|
|
935 | 937 | |
|
|
936 | 938 | indR = numpy.where(self.lagR == 0)[0][0] |
|
|
937 | 939 | indT = numpy.where(self.lagT == 0)[0][0] |
|
|
938 | 940 | |
|
|
939 | 941 | jspectra0 = self.data_corr[:, :, indR, :] |
|
|
940 | 942 | jspectra = copy.copy(jspectra0) |
|
|
941 | 943 | |
|
|
942 | 944 | num_chan = jspectra.shape[0] |
|
|
943 | 945 | num_hei = jspectra.shape[2] |
|
|
944 | 946 | |
|
|
945 | 947 | freq_dc = jspectra.shape[1] / 2 |
|
|
946 | 948 | ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc |
|
|
947 | 949 | |
|
|
948 | 950 | if ind_vel[0] < 0: |
|
|
949 | 951 | ind_vel[list(range(0, 1))] = ind_vel[list( |
|
|
950 | 952 | range(0, 1))] + self.num_prof |
|
|
951 | 953 | |
|
|
952 | 954 | if mode == 1: |
|
|
953 | 955 | jspectra[:, freq_dc, :] = ( |
|
|
954 | 956 | jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2 # CORRECCION |
|
|
955 | 957 | |
|
|
956 | 958 | if mode == 2: |
|
|
957 | 959 | |
|
|
958 | 960 | vel = numpy.array([-2, -1, 1, 2]) |
|
|
959 | 961 | xx = numpy.zeros([4, 4]) |
|
|
960 | 962 | |
|
|
961 | 963 | for fil in range(4): |
|
|
962 | 964 | xx[fil, :] = vel[fil]**numpy.asarray(list(range(4))) |
|
|
963 | 965 | |
|
|
964 | 966 | xx_inv = numpy.linalg.inv(xx) |
|
|
965 | 967 | xx_aux = xx_inv[0, :] |
|
|
966 | 968 | |
|
|
967 | 969 | for ich in range(num_chan): |
|
|
968 | 970 | yy = jspectra[ich, ind_vel, :] |
|
|
969 | 971 | jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy) |
|
|
970 | 972 | |
|
|
971 | 973 | junkid = jspectra[ich, freq_dc, :] <= 0 |
|
|
972 | 974 | cjunkid = sum(junkid) |
|
|
973 | 975 | |
|
|
974 | 976 | if cjunkid.any(): |
|
|
975 | 977 | jspectra[ich, freq_dc, junkid.nonzero()] = ( |
|
|
976 | 978 | jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2 |
|
|
977 | 979 | |
|
|
978 | 980 | noise = jspectra0[:, freq_dc, :] - jspectra[:, freq_dc, :] |
|
|
979 | 981 | |
|
|
980 | 982 | return noise |
|
|
981 | 983 | |
|
|
982 | 984 | def getTimeInterval(self): |
|
|
983 | 985 | |
|
|
984 | 986 | timeInterval = self.ippSeconds * self.nCohInt * self.nProfiles |
|
|
985 | 987 | |
|
|
986 | 988 | return timeInterval |
|
|
987 | 989 | |
|
|
988 | 990 | def splitFunctions(self): |
|
|
989 | 991 | |
|
|
990 | 992 | pairsList = self.pairsList |
|
|
991 | 993 | ccf_pairs = [] |
|
|
992 | 994 | acf_pairs = [] |
|
|
993 | 995 | ccf_ind = [] |
|
|
994 | 996 | acf_ind = [] |
|
|
995 | 997 | for l in range(len(pairsList)): |
|
|
996 | 998 | chan0 = pairsList[l][0] |
|
|
997 | 999 | chan1 = pairsList[l][1] |
|
|
998 | 1000 | |
|
|
999 | 1001 | # Obteniendo pares de Autocorrelacion |
|
|
1000 | 1002 | if chan0 == chan1: |
|
|
1001 | 1003 | acf_pairs.append(chan0) |
|
|
1002 | 1004 | acf_ind.append(l) |
|
|
1003 | 1005 | else: |
|
|
1004 | 1006 | ccf_pairs.append(pairsList[l]) |
|
|
1005 | 1007 | ccf_ind.append(l) |
|
|
1006 | 1008 | |
|
|
1007 | 1009 | data_acf = self.data_cf[acf_ind] |
|
|
1008 | 1010 | data_ccf = self.data_cf[ccf_ind] |
|
|
1009 | 1011 | |
|
|
1010 | 1012 | return acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf |
|
|
1011 | 1013 | |
|
|
1012 | 1014 | def getNormFactor(self): |
|
|
1013 | 1015 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.splitFunctions() |
|
|
1014 | 1016 | acf_pairs = numpy.array(acf_pairs) |
|
|
1015 | 1017 | normFactor = numpy.zeros((self.nPairs, self.nHeights)) |
|
|
1016 | 1018 | |
|
|
1017 | 1019 | for p in range(self.nPairs): |
|
|
1018 | 1020 | pair = self.pairsList[p] |
|
|
1019 | 1021 | |
|
|
1020 | 1022 | ch0 = pair[0] |
|
|
1021 | 1023 | ch1 = pair[1] |
|
|
1022 | 1024 | |
|
|
1023 | 1025 | ch0_max = numpy.max(data_acf[acf_pairs == ch0, :, :], axis=1) |
|
|
1024 | 1026 | ch1_max = numpy.max(data_acf[acf_pairs == ch1, :, :], axis=1) |
|
|
1025 | 1027 | normFactor[p, :] = numpy.sqrt(ch0_max * ch1_max) |
|
|
1026 | 1028 | |
|
|
1027 | 1029 | return normFactor |
|
|
1028 | 1030 | |
|
|
1029 | 1031 | timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
1030 | 1032 | normFactor = property(getNormFactor, "I'm the 'normFactor property'") |
|
|
1031 | 1033 | |
|
|
1032 | 1034 | |
|
|
1033 | 1035 | class Parameters(Spectra): |
|
|
1034 | 1036 | |
|
|
1035 | 1037 | experimentInfo = None # Information about the experiment |
|
|
1036 | 1038 | # Information from previous data |
|
|
1037 | 1039 | inputUnit = None # Type of data to be processed |
|
|
1038 | 1040 | operation = None # Type of operation to parametrize |
|
|
1039 | 1041 | # normFactor = None #Normalization Factor |
|
|
1040 | 1042 | groupList = None # List of Pairs, Groups, etc |
|
|
1041 | 1043 | # Parameters |
|
|
1042 | 1044 | data_param = None # Parameters obtained |
|
|
1043 | 1045 | data_pre = None # Data Pre Parametrization |
|
|
1044 | 1046 | data_SNR = None # Signal to Noise Ratio |
|
|
1045 | 1047 | # heightRange = None #Heights |
|
|
1046 | 1048 | abscissaList = None # Abscissa, can be velocities, lags or time |
|
|
1047 | 1049 | # noise = None #Noise Potency |
|
|
1048 | 1050 | utctimeInit = None # Initial UTC time |
|
|
1049 | 1051 | paramInterval = None # Time interval to calculate Parameters in seconds |
|
|
1050 | 1052 | useLocalTime = True |
|
|
1051 | 1053 | # Fitting |
|
|
1052 | 1054 | data_error = None # Error of the estimation |
|
|
1053 | 1055 | constants = None |
|
|
1054 | 1056 | library = None |
|
|
1055 | 1057 | # Output signal |
|
|
1056 | 1058 | outputInterval = None # Time interval to calculate output signal in seconds |
|
|
1057 | 1059 | data_output = None # Out signal |
|
|
1058 | 1060 | nAvg = None |
|
|
1059 | 1061 | noise_estimation = None |
|
|
1060 | 1062 | GauSPC = None # Fit gaussian SPC |
|
|
1061 | 1063 | |
|
|
1062 | 1064 | def __init__(self): |
|
|
1063 | 1065 | ''' |
|
|
1064 | 1066 | Constructor |
|
|
1065 | 1067 | ''' |
|
|
1066 | 1068 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
1067 | 1069 | |
|
|
1068 | 1070 | self.systemHeaderObj = SystemHeader() |
|
|
1069 | 1071 | |
|
|
1070 | 1072 | self.type = "Parameters" |
|
|
1071 | 1073 | |
|
|
1072 | 1074 | def getTimeRange1(self, interval): |
|
|
1073 | 1075 | |
|
|
1074 | 1076 | datatime = [] |
|
|
1075 | 1077 | |
|
|
1076 | 1078 | if self.useLocalTime: |
|
|
1077 | 1079 | time1 = self.utctimeInit - self.timeZone * 60 |
|
|
1078 | 1080 | else: |
|
|
1079 | 1081 | time1 = self.utctimeInit |
|
|
1080 | 1082 | |
|
|
1081 | 1083 | datatime.append(time1) |
|
|
1082 | 1084 | datatime.append(time1 + interval) |
|
|
1083 | 1085 | datatime = numpy.array(datatime) |
|
|
1084 | 1086 | |
|
|
1085 | 1087 | return datatime |
|
|
1086 | 1088 | |
|
|
1087 | 1089 | def getTimeInterval(self): |
|
|
1088 | 1090 | |
|
|
1089 | 1091 | if hasattr(self, 'timeInterval1'): |
|
|
1090 | 1092 | return self.timeInterval1 |
|
|
1091 | 1093 | else: |
|
|
1092 | 1094 | return self.paramInterval |
|
|
1093 | 1095 | |
|
|
1094 | 1096 | def setValue(self, value): |
|
|
1095 | 1097 | |
|
|
1096 | 1098 | print("This property should not be initialized") |
|
|
1097 | 1099 | |
|
|
1098 | 1100 | return |
|
|
1099 | 1101 | |
|
|
1100 | 1102 | def getNoise(self): |
|
|
1101 | 1103 | |
|
|
1102 | 1104 | return self.spc_noise |
|
|
1103 | 1105 | |
|
|
1104 | 1106 | timeInterval = property(getTimeInterval) |
|
|
1105 | 1107 | noise = property(getNoise, setValue, "I'm the 'Noise' property.") |
|
|
1106 | 1108 | |
|
|
1107 | 1109 | |
|
|
1108 | 1110 | class PlotterData(object): |
|
|
1109 | 1111 | ''' |
|
|
1110 | 1112 | Object to hold data to be plotted |
|
|
1111 | 1113 | ''' |
|
|
1112 | 1114 | |
|
|
1113 | 1115 | MAXNUMX = 200 |
|
|
1114 | 1116 | MAXNUMY = 200 |
|
|
1115 | 1117 | |
|
|
1116 | 1118 | def __init__(self, code, throttle_value, exp_code, buffering=True, snr=False): |
|
|
1117 | 1119 | |
|
|
1118 | 1120 | self.key = code |
|
|
1119 | 1121 | self.throttle = throttle_value |
|
|
1120 | 1122 | self.exp_code = exp_code |
|
|
1121 | 1123 | self.buffering = buffering |
|
|
1122 | 1124 | self.ready = False |
|
|
1123 | 1125 | self.flagNoData = False |
|
|
1124 | 1126 | self.localtime = False |
|
|
1125 | 1127 | self.data = {} |
|
|
1126 | 1128 | self.meta = {} |
|
|
1127 | 1129 | self.__heights = [] |
|
|
1128 | 1130 | |
|
|
1129 | 1131 | if 'snr' in code: |
|
|
1130 | 1132 | self.plottypes = ['snr'] |
|
|
1131 | 1133 | elif code == 'spc': |
|
|
1132 | 1134 | self.plottypes = ['spc', 'noise', 'rti'] |
|
|
1133 | 1135 | elif code == 'cspc': |
|
|
1134 | 1136 | self.plottypes = ['cspc', 'spc', 'noise', 'rti'] |
|
|
1135 | 1137 | elif code == 'rti': |
|
|
1136 | 1138 | self.plottypes = ['noise', 'rti'] |
|
|
1137 | 1139 | else: |
|
|
1138 | 1140 | self.plottypes = [code] |
|
|
1139 | 1141 | |
|
|
1140 | 1142 | if 'snr' not in self.plottypes and snr: |
|
|
1141 | 1143 | self.plottypes.append('snr') |
|
|
1142 | 1144 | |
|
|
1143 | 1145 | for plot in self.plottypes: |
|
|
1144 | 1146 | self.data[plot] = {} |
|
|
1145 | 1147 | |
|
|
1146 | 1148 | |
|
|
1147 | 1149 | def __str__(self): |
|
|
1148 | 1150 | dum = ['{}{}'.format(key, self.shape(key)) for key in self.data] |
|
|
1149 | 1151 | return 'Data[{}][{}]'.format(';'.join(dum), len(self.times)) |
|
|
1150 | 1152 | |
|
|
1151 | 1153 | def __len__(self): |
|
|
1152 | 1154 | return len(self.data[self.key]) |
|
|
1153 | 1155 | |
|
|
1154 | 1156 | def __getitem__(self, key): |
|
|
1155 | 1157 | |
|
|
1156 | 1158 | if key not in self.data: |
|
|
1157 | 1159 | raise KeyError(log.error('Missing key: {}'.format(key))) |
|
|
1158 | 1160 | if 'spc' in key or not self.buffering: |
|
|
1159 | 1161 | ret = self.data[key][self.tm] |
|
|
1160 | 1162 | elif 'scope' in key: |
|
|
1161 | 1163 | ret = numpy.array(self.data[key][float(self.tm)]) |
|
|
1162 | 1164 | else: |
|
|
1163 | 1165 | ret = numpy.array([self.data[key][x] for x in self.times]) |
|
|
1164 | 1166 | if ret.ndim > 1: |
|
|
1165 | 1167 | ret = numpy.swapaxes(ret, 0, 1) |
|
|
1166 | 1168 | return ret |
|
|
1167 | 1169 | |
|
|
1168 | 1170 | def __contains__(self, key): |
|
|
1169 | 1171 | return key in self.data |
|
|
1170 | 1172 | |
|
|
1171 | 1173 | def setup(self): |
|
|
1172 | 1174 | ''' |
|
|
1173 | 1175 | Configure object |
|
|
1174 | 1176 | ''' |
|
|
1175 | 1177 | self.type = '' |
|
|
1176 | 1178 | self.ready = False |
|
|
1177 | 1179 | del self.data |
|
|
1178 | 1180 | self.data = {} |
|
|
1179 | 1181 | self.__heights = [] |
|
|
1180 | 1182 | self.__all_heights = set() |
|
|
1181 | 1183 | for plot in self.plottypes: |
|
|
1182 | 1184 | if 'snr' in plot: |
|
|
1183 | 1185 | plot = 'snr' |
|
|
1184 | 1186 | elif 'spc_moments' == plot: |
|
|
1185 | 1187 | plot = 'moments' |
|
|
1186 | 1188 | self.data[plot] = {} |
|
|
1187 | 1189 | |
|
|
1188 | 1190 | if 'spc' in self.data or 'rti' in self.data or 'cspc' in self.data or 'moments' in self.data: |
|
|
1189 | 1191 | self.data['noise'] = {} |
|
|
1190 | 1192 | self.data['rti'] = {} |
|
|
1191 | 1193 | if 'noise' not in self.plottypes: |
|
|
1192 | 1194 | self.plottypes.append('noise') |
|
|
1193 | 1195 | if 'rti' not in self.plottypes: |
|
|
1194 | 1196 | self.plottypes.append('rti') |
|
|
1195 | 1197 | |
|
|
1196 | 1198 | def shape(self, key): |
|
|
1197 | 1199 | ''' |
|
|
1198 | 1200 | Get the shape of the one-element data for the given key |
|
|
1199 | 1201 | ''' |
|
|
1200 | 1202 | |
|
|
1201 | 1203 | if len(self.data[key]): |
|
|
1202 | 1204 | if 'spc' in key or not self.buffering: |
|
|
1203 | 1205 | return self.data[key].shape |
|
|
1204 | 1206 | return self.data[key][self.times[0]].shape |
|
|
1205 | 1207 | return (0,) |
|
|
1206 | 1208 | |
|
|
1207 | 1209 | def update(self, dataOut, tm): |
|
|
1208 | 1210 | ''' |
|
|
1209 | 1211 | Update data object with new dataOut |
|
|
1210 | 1212 | ''' |
|
|
1211 | ||
|
|
1213 | ||
|
|
1212 | 1214 | self.profileIndex = dataOut.profileIndex |
|
|
1213 | 1215 | self.tm = tm |
|
|
1214 | 1216 | self.type = dataOut.type |
|
|
1215 | 1217 | self.parameters = getattr(dataOut, 'parameters', []) |
|
|
1216 | 1218 | |
|
|
1217 | 1219 | if hasattr(dataOut, 'meta'): |
|
|
1218 | 1220 | self.meta.update(dataOut.meta) |
|
|
1219 | 1221 | |
|
|
1220 | 1222 | if hasattr(dataOut, 'pairsList'): |
|
|
1221 | 1223 | self.pairs = dataOut.pairsList |
|
|
1222 | 1224 | |
|
|
1223 | 1225 | self.interval = dataOut.getTimeInterval() |
|
|
1224 | 1226 | self.localtime = dataOut.useLocalTime |
|
|
1225 | 1227 | if True in ['spc' in ptype for ptype in self.plottypes]: |
|
|
1226 | 1228 | self.xrange = (dataOut.getFreqRange(1)/1000., |
|
|
1227 | 1229 | dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
|
|
1228 | 1230 | self.__heights.append(dataOut.heightList) |
|
|
1229 | 1231 | self.__all_heights.update(dataOut.heightList) |
|
|
1230 | 1232 | |
|
|
1231 | 1233 | for plot in self.plottypes: |
|
|
1232 | 1234 | if plot in ('spc', 'spc_moments', 'spc_cut'): |
|
|
1233 | 1235 | z = dataOut.data_spc/dataOut.normFactor |
|
|
1234 | 1236 | buffer = 10*numpy.log10(z) |
|
|
1235 | 1237 | if plot == 'cspc': |
|
|
1236 | 1238 | buffer = (dataOut.data_spc, dataOut.data_cspc) |
|
|
1237 | 1239 | if plot == 'noise': |
|
|
1238 | 1240 | buffer = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) |
|
|
1239 | 1241 | if plot in ('rti', 'spcprofile'): |
|
|
1240 | 1242 | buffer = dataOut.getPower() |
|
|
1241 | 1243 | if plot == 'snr_db': |
|
|
1242 | 1244 | buffer = dataOut.data_SNR |
|
|
1243 | 1245 | if plot == 'snr': |
|
|
1244 | 1246 | buffer = 10*numpy.log10(dataOut.data_SNR) |
|
|
1245 | 1247 | if plot == 'dop': |
|
|
1246 | 1248 | buffer = dataOut.data_DOP |
|
|
1247 | 1249 | if plot == 'pow': |
|
|
1248 | 1250 | buffer = 10*numpy.log10(dataOut.data_POW) |
|
|
1249 | 1251 | if plot == 'width': |
|
|
1250 | 1252 | buffer = dataOut.data_WIDTH |
|
|
1251 | 1253 | if plot == 'coh': |
|
|
1252 | 1254 | buffer = dataOut.getCoherence() |
|
|
1253 | 1255 | if plot == 'phase': |
|
|
1254 | 1256 | buffer = dataOut.getCoherence(phase=True) |
|
|
1255 | 1257 | if plot == 'output': |
|
|
1256 | 1258 | buffer = dataOut.data_output |
|
|
1257 | 1259 | if plot == 'param': |
|
|
1258 | 1260 | buffer = dataOut.data_param |
|
|
1259 | 1261 | if plot == 'scope': |
|
|
1260 | 1262 | buffer = dataOut.data |
|
|
1261 | 1263 | self.flagDataAsBlock = dataOut.flagDataAsBlock |
|
|
1262 | 1264 | self.nProfiles = dataOut.nProfiles |
|
|
1263 | 1265 | if plot == 'pp_power': |
|
|
1264 |
buffer = dataOut.data |
|
|
|
1266 | buffer = dataOut.dataPP_POWER | |
|
|
1267 | self.flagDataAsBlock = dataOut.flagDataAsBlock | |
|
|
1268 | self.nProfiles = dataOut.nProfiles | |
|
|
1269 | if plot == 'pp_signal': | |
|
|
1270 | buffer = dataOut.dataPP_POW | |
|
|
1265 | 1271 | self.flagDataAsBlock = dataOut.flagDataAsBlock |
|
|
1266 | 1272 | self.nProfiles = dataOut.nProfiles |
|
|
1267 | 1273 | if plot == 'pp_velocity': |
|
|
1268 |
buffer = dataOut.data |
|
|
|
1274 | buffer = dataOut.dataPP_DOP | |
|
|
1269 | 1275 | self.flagDataAsBlock = dataOut.flagDataAsBlock |
|
|
1270 | 1276 | self.nProfiles = dataOut.nProfiles |
|
|
1271 | 1277 | if plot == 'pp_specwidth': |
|
|
1272 |
buffer = dataOut.data |
|
|
|
1278 | buffer = dataOut.dataPP_WIDTH | |
|
|
1273 | 1279 | self.flagDataAsBlock = dataOut.flagDataAsBlock |
|
|
1274 | 1280 | self.nProfiles = dataOut.nProfiles |
|
|
1275 | 1281 | |
|
|
1276 | 1282 | if plot == 'spc': |
|
|
1277 | 1283 | self.data['spc'][tm] = buffer |
|
|
1278 | 1284 | elif plot == 'cspc': |
|
|
1279 | 1285 | self.data['cspc'][tm] = buffer |
|
|
1280 | 1286 | elif plot == 'spc_moments': |
|
|
1281 | 1287 | self.data['spc'][tm] = buffer |
|
|
1282 | 1288 | self.data['moments'][tm] = dataOut.moments |
|
|
1283 | 1289 | else: |
|
|
1284 | 1290 | if self.buffering: |
|
|
1285 | 1291 | self.data[plot][tm] = buffer |
|
|
1286 | 1292 | else: |
|
|
1287 | 1293 | self.data[plot][tm] = buffer |
|
|
1288 | 1294 | |
|
|
1289 | 1295 | if dataOut.channelList is None: |
|
|
1290 | 1296 | self.channels = range(buffer.shape[0]) |
|
|
1291 | 1297 | else: |
|
|
1292 | 1298 | self.channels = dataOut.channelList |
|
|
1293 | 1299 | |
|
|
1294 | 1300 | if buffer is None: |
|
|
1295 | 1301 | self.flagNoData = True |
|
|
1296 | 1302 | raise schainpy.admin.SchainWarning('Attribute data_{} is empty'.format(self.key)) |
|
|
1297 | 1303 | |
|
|
1298 | 1304 | def normalize_heights(self): |
|
|
1299 | 1305 | ''' |
|
|
1300 | 1306 | Ensure same-dimension of the data for different heighList |
|
|
1301 | 1307 | ''' |
|
|
1302 | 1308 | |
|
|
1303 | 1309 | H = numpy.array(list(self.__all_heights)) |
|
|
1304 | 1310 | H.sort() |
|
|
1305 | 1311 | for key in self.data: |
|
|
1306 | 1312 | shape = self.shape(key)[:-1] + H.shape |
|
|
1307 | 1313 | for tm, obj in list(self.data[key].items()): |
|
|
1308 | 1314 | h = self.__heights[self.times.tolist().index(tm)] |
|
|
1309 | 1315 | if H.size == h.size: |
|
|
1310 | 1316 | continue |
|
|
1311 | 1317 | index = numpy.where(numpy.in1d(H, h))[0] |
|
|
1312 | 1318 | dummy = numpy.zeros(shape) + numpy.nan |
|
|
1313 | 1319 | if len(shape) == 2: |
|
|
1314 | 1320 | dummy[:, index] = obj |
|
|
1315 | 1321 | else: |
|
|
1316 | 1322 | dummy[index] = obj |
|
|
1317 | 1323 | self.data[key][tm] = dummy |
|
|
1318 | 1324 | |
|
|
1319 | 1325 | self.__heights = [H for tm in self.times] |
|
|
1320 | 1326 | |
|
|
1321 | 1327 | def jsonify(self, tm, plot_name, plot_type, decimate=False): |
|
|
1322 | 1328 | ''' |
|
|
1323 | 1329 | Convert data to json |
|
|
1324 | 1330 | ''' |
|
|
1325 | 1331 | |
|
|
1326 | 1332 | dy = int(self.heights.size/self.MAXNUMY) + 1 |
|
|
1327 | 1333 | if self.key in ('spc', 'cspc'): |
|
|
1328 | 1334 | dx = int(self.data[self.key][tm].shape[1]/self.MAXNUMX) + 1 |
|
|
1329 | 1335 | data = self.roundFloats( |
|
|
1330 | 1336 | self.data[self.key][tm][::, ::dx, ::dy].tolist()) |
|
|
1331 | 1337 | else: |
|
|
1332 | 1338 | if self.key is 'noise': |
|
|
1333 | 1339 | data = [[x] for x in self.roundFloats(self.data[self.key][tm].tolist())] |
|
|
1334 | 1340 | else: |
|
|
1335 | 1341 | data = self.roundFloats(self.data[self.key][tm][::, ::dy].tolist()) |
|
|
1336 | 1342 | |
|
|
1337 | 1343 | meta = {} |
|
|
1338 | 1344 | ret = { |
|
|
1339 | 1345 | 'plot': plot_name, |
|
|
1340 | 1346 | 'code': self.exp_code, |
|
|
1341 | 1347 | 'time': float(tm), |
|
|
1342 | 1348 | 'data': data, |
|
|
1343 | 1349 | } |
|
|
1344 | 1350 | meta['type'] = plot_type |
|
|
1345 | 1351 | meta['interval'] = float(self.interval) |
|
|
1346 | 1352 | meta['localtime'] = self.localtime |
|
|
1347 | 1353 | meta['yrange'] = self.roundFloats(self.heights[::dy].tolist()) |
|
|
1348 | 1354 | if 'spc' in self.data or 'cspc' in self.data: |
|
|
1349 | 1355 | meta['xrange'] = self.roundFloats(self.xrange[2][::dx].tolist()) |
|
|
1350 | 1356 | else: |
|
|
1351 | 1357 | meta['xrange'] = [] |
|
|
1352 | 1358 | |
|
|
1353 | 1359 | meta.update(self.meta) |
|
|
1354 | 1360 | ret['metadata'] = meta |
|
|
1355 | 1361 | return json.dumps(ret) |
|
|
1356 | 1362 | |
|
|
1357 | 1363 | @property |
|
|
1358 | 1364 | def times(self): |
|
|
1359 | 1365 | ''' |
|
|
1360 | 1366 | Return the list of times of the current data |
|
|
1361 | 1367 | ''' |
|
|
1362 | 1368 | |
|
|
1363 | 1369 | ret = numpy.array([*self.data[self.key]]) |
|
|
1364 | 1370 | if self: |
|
|
1365 | 1371 | ret.sort() |
|
|
1366 | 1372 | return ret |
|
|
1367 | 1373 | |
|
|
1368 | 1374 | @property |
|
|
1369 | 1375 | def min_time(self): |
|
|
1370 | 1376 | ''' |
|
|
1371 | 1377 | Return the minimun time value |
|
|
1372 | 1378 | ''' |
|
|
1373 | 1379 | |
|
|
1374 | 1380 | return self.times[0] |
|
|
1375 | 1381 | |
|
|
1376 | 1382 | @property |
|
|
1377 | 1383 | def max_time(self): |
|
|
1378 | 1384 | ''' |
|
|
1379 | 1385 | Return the maximun time value |
|
|
1380 | 1386 | ''' |
|
|
1381 | 1387 | |
|
|
1382 | 1388 | return self.times[-1] |
|
|
1383 | 1389 | |
|
|
1384 | 1390 | @property |
|
|
1385 | 1391 | def heights(self): |
|
|
1386 | 1392 | ''' |
|
|
1387 | 1393 | Return the list of heights of the current data |
|
|
1388 | 1394 | ''' |
|
|
1389 | 1395 | |
|
|
1390 | 1396 | return numpy.array(self.__heights[-1]) |
|
|
1391 | 1397 | |
|
|
1392 | 1398 | @staticmethod |
|
|
1393 | 1399 | def roundFloats(obj): |
|
|
1394 | 1400 | if isinstance(obj, list): |
|
|
1395 | 1401 | return list(map(PlotterData.roundFloats, obj)) |
|
|
1396 | 1402 | elif isinstance(obj, float): |
|
|
1397 | 1403 | return round(obj, 2) |
| @@ -1,276 +1,302 | |||
|
|
1 | 1 | ''' |
|
|
2 | 2 | Created on Jul 9, 2014 |
|
|
3 | 3 | |
|
|
4 | 4 | @author: roj-idl71 |
|
|
5 | 5 | ''' |
|
|
6 | 6 | import os |
|
|
7 | 7 | import datetime |
|
|
8 | 8 | import numpy |
|
|
9 | 9 | |
|
|
10 | 10 | from schainpy.model.graphics.jroplot_base import Plot, plt |
|
|
11 | 11 | |
|
|
12 | 12 | |
|
|
13 | 13 | class ScopePlot(Plot): |
|
|
14 | 14 | |
|
|
15 | 15 | ''' |
|
|
16 | 16 | Plot for Scope |
|
|
17 | 17 | ''' |
|
|
18 | 18 | |
|
|
19 | 19 | CODE = 'scope' |
|
|
20 | 20 | plot_name = 'Scope' |
|
|
21 | 21 | plot_type = 'scatter' |
|
|
22 | 22 | |
|
|
23 | 23 | def setup(self): |
|
|
24 | 24 | |
|
|
25 | 25 | self.xaxis = 'Range (Km)' |
|
|
26 | 26 | self.ncols = 1 |
|
|
27 | 27 | self.nrows = 1 |
|
|
28 | 28 | self.nplots = 1 |
|
|
29 | 29 | self.ylabel = 'Intensity [dB]' |
|
|
30 | 30 | self.titles = ['Scope'] |
|
|
31 | 31 | self.colorbar = False |
|
|
32 | 32 | self.width = 6 |
|
|
33 | 33 | self.height = 4 |
|
|
34 | 34 | |
|
|
35 | 35 | def plot_iq(self, x, y, channelIndexList, thisDatetime, wintitle): |
|
|
36 | 36 | |
|
|
37 | 37 | yreal = y[channelIndexList,:].real |
|
|
38 | 38 | yimag = y[channelIndexList,:].imag |
|
|
39 | 39 | title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
40 | 40 | self.xlabel = "Range (Km)" |
|
|
41 | 41 | self.ylabel = "Intensity - IQ" |
|
|
42 | 42 | |
|
|
43 | 43 | self.y = yreal |
|
|
44 | 44 | self.x = x |
|
|
45 | 45 | self.xmin = min(x) |
|
|
46 | 46 | self.xmax = max(x) |
|
|
47 | 47 | |
|
|
48 | 48 | |
|
|
49 | 49 | self.titles[0] = title |
|
|
50 | 50 | |
|
|
51 | 51 | for i,ax in enumerate(self.axes): |
|
|
52 | 52 | title = "Channel %d" %(i) |
|
|
53 | 53 | if ax.firsttime: |
|
|
54 | 54 | ax.plt_r = ax.plot(x, yreal[i,:], color='b')[0] |
|
|
55 | 55 | ax.plt_i = ax.plot(x, yimag[i,:], color='r')[0] |
|
|
56 | 56 | else: |
|
|
57 | 57 | ax.plt_r.set_data(x, yreal[i,:]) |
|
|
58 | 58 | ax.plt_i.set_data(x, yimag[i,:]) |
|
|
59 | 59 | |
|
|
60 | 60 | def plot_power(self, x, y, channelIndexList, thisDatetime, wintitle): |
|
|
61 | 61 | y = y[channelIndexList,:] * numpy.conjugate(y[channelIndexList,:]) |
|
|
62 | 62 | yreal = y.real |
|
|
63 | 63 | yreal = 10*numpy.log10(yreal) |
|
|
64 | 64 | self.y = yreal |
|
|
65 | 65 | title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
66 | 66 | self.xlabel = "Range (Km)" |
|
|
67 | 67 | self.ylabel = "Intensity" |
|
|
68 | 68 | self.xmin = min(x) |
|
|
69 | 69 | self.xmax = max(x) |
|
|
70 | 70 | |
|
|
71 | 71 | |
|
|
72 | 72 | self.titles[0] = title |
|
|
73 | 73 | |
|
|
74 | 74 | for i,ax in enumerate(self.axes): |
|
|
75 | 75 | title = "Channel %d" %(i) |
|
|
76 | 76 | |
|
|
77 | 77 | ychannel = yreal[i,:] |
|
|
78 | 78 | |
|
|
79 | 79 | if ax.firsttime: |
|
|
80 | 80 | ax.plt_r = ax.plot(x, ychannel)[0] |
|
|
81 | 81 | else: |
|
|
82 | 82 | #pass |
|
|
83 | 83 | ax.plt_r.set_data(x, ychannel) |
|
|
84 | 84 | |
|
|
85 | 85 | def plot_weatherpower(self, x, y, channelIndexList, thisDatetime, wintitle): |
|
|
86 | 86 | |
|
|
87 | 87 | |
|
|
88 | 88 | y = y[channelIndexList,:] |
|
|
89 | 89 | yreal = y.real |
|
|
90 | 90 | yreal = 10*numpy.log10(yreal) |
|
|
91 | 91 | self.y = yreal |
|
|
92 | 92 | title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
93 | 93 | self.xlabel = "Range (Km)" |
|
|
94 | 94 | self.ylabel = "Intensity" |
|
|
95 | 95 | self.xmin = min(x) |
|
|
96 | 96 | self.xmax = max(x) |
|
|
97 | 97 | |
|
|
98 | 98 | self.titles[0] =title |
|
|
99 | 99 | for i,ax in enumerate(self.axes): |
|
|
100 | 100 | title = "Channel %d" %(i) |
|
|
101 | 101 | |
|
|
102 | 102 | ychannel = yreal[i,:] |
|
|
103 | 103 | |
|
|
104 | 104 | if ax.firsttime: |
|
|
105 | 105 | ax.plt_r = ax.plot(x, ychannel)[0] |
|
|
106 | 106 | else: |
|
|
107 | 107 | #pass |
|
|
108 | 108 | ax.plt_r.set_data(x, ychannel) |
|
|
109 | 109 | |
|
|
110 | 110 | def plot_weathervelocity(self, x, y, channelIndexList, thisDatetime, wintitle): |
|
|
111 | 111 | |
|
|
112 | 112 | x = x[channelIndexList,:] |
|
|
113 | 113 | yreal = y |
|
|
114 | 114 | self.y = yreal |
|
|
115 | 115 | title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
116 | 116 | self.xlabel = "Velocity (m/s)" |
|
|
117 | 117 | self.ylabel = "Range (Km)" |
|
|
118 | 118 | self.xmin = numpy.min(x) |
|
|
119 | 119 | self.xmax = numpy.max(x) |
|
|
120 | 120 | self.titles[0] =title |
|
|
121 | 121 | for i,ax in enumerate(self.axes): |
|
|
122 | 122 | title = "Channel %d" %(i) |
|
|
123 | 123 | xchannel = x[i,:] |
|
|
124 | 124 | if ax.firsttime: |
|
|
125 | 125 | ax.plt_r = ax.plot(xchannel, yreal)[0] |
|
|
126 | 126 | else: |
|
|
127 | 127 | #pass |
|
|
128 | 128 | ax.plt_r.set_data(xchannel, yreal) |
|
|
129 | 129 | |
|
|
130 | 130 | def plot_weatherspecwidth(self, x, y, channelIndexList, thisDatetime, wintitle): |
|
|
131 | 131 | |
|
|
132 | 132 | x = x[channelIndexList,:] |
|
|
133 | 133 | yreal = y |
|
|
134 | 134 | self.y = yreal |
|
|
135 | 135 | title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
136 | 136 | self.xlabel = "width " |
|
|
137 | 137 | self.ylabel = "Range (Km)" |
|
|
138 | 138 | self.xmin = numpy.min(x) |
|
|
139 | 139 | self.xmax = numpy.max(x) |
|
|
140 | 140 | self.titles[0] =title |
|
|
141 | 141 | for i,ax in enumerate(self.axes): |
|
|
142 | 142 | title = "Channel %d" %(i) |
|
|
143 | 143 | xchannel = x[i,:] |
|
|
144 | 144 | if ax.firsttime: |
|
|
145 | 145 | ax.plt_r = ax.plot(xchannel, yreal)[0] |
|
|
146 | 146 | else: |
|
|
147 | 147 | #pass |
|
|
148 | 148 | ax.plt_r.set_data(xchannel, yreal) |
|
|
149 | 149 | |
|
|
150 | 150 | def plot(self): |
|
|
151 | 151 | if self.channels: |
|
|
152 | 152 | channels = self.channels |
|
|
153 | 153 | else: |
|
|
154 | 154 | channels = self.data.channels |
|
|
155 | 155 | |
|
|
156 | 156 | thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]) |
|
|
157 | 157 | if self.CODE == "pp_power": |
|
|
158 | 158 | scope = self.data['pp_power'] |
|
|
159 | elif self.CODE == "pp_signal": | |
|
|
160 | scope = self.data["pp_signal"] | |
|
|
159 | 161 | elif self.CODE == "pp_velocity": |
|
|
160 | 162 | scope = self.data["pp_velocity"] |
|
|
161 | 163 | elif self.CODE == "pp_specwidth": |
|
|
162 | 164 | scope = self.data["pp_specwidth"] |
|
|
163 | 165 | else: |
|
|
164 | 166 | scope =self.data["scope"] |
|
|
165 | 167 | |
|
|
166 | 168 | if self.data.flagDataAsBlock: |
|
|
167 | 169 | |
|
|
168 | 170 | for i in range(self.data.nProfiles): |
|
|
169 | 171 | |
|
|
170 | 172 | wintitle1 = " [Profile = %d] " %i |
|
|
171 | 173 | if self.CODE =="scope": |
|
|
172 | 174 | if self.type == "power": |
|
|
173 | 175 | self.plot_power(self.data.heights, |
|
|
174 | 176 | scope[:,i,:], |
|
|
175 | 177 | channels, |
|
|
176 | 178 | thisDatetime, |
|
|
177 | 179 | wintitle1 |
|
|
178 | 180 | ) |
|
|
179 | 181 | |
|
|
180 | 182 | if self.type == "iq": |
|
|
181 | 183 | self.plot_iq(self.data.heights, |
|
|
182 | 184 | scope[:,i,:], |
|
|
183 | 185 | channels, |
|
|
184 | 186 | thisDatetime, |
|
|
185 | 187 | wintitle1 |
|
|
186 | 188 | ) |
|
|
187 | 189 | if self.CODE=="pp_power": |
|
|
188 | 190 | self.plot_weatherpower(self.data.heights, |
|
|
189 | 191 | scope[:,i,:], |
|
|
190 | 192 | channels, |
|
|
191 | 193 | thisDatetime, |
|
|
192 | 194 | wintitle |
|
|
193 | 195 | ) |
|
|
196 | if self.CODE=="pp_signal": | |
|
|
197 | self.plot_weatherpower(self.data.heights, | |
|
|
198 | scope[:,i,:], | |
|
|
199 | channels, | |
|
|
200 | thisDatetime, | |
|
|
201 | wintitle | |
|
|
202 | ) | |
|
|
194 | 203 | if self.CODE=="pp_velocity": |
|
|
195 | 204 | self.plot_weathervelocity(scope[:,i,:], |
|
|
196 | 205 | self.data.heights, |
|
|
197 | 206 | channels, |
|
|
198 | 207 | thisDatetime, |
|
|
199 | 208 | wintitle |
|
|
200 | 209 | ) |
|
|
201 | 210 | if self.CODE=="pp_spcwidth": |
|
|
202 | 211 | self.plot_weatherspecwidth(scope[:,i,:], |
|
|
203 | 212 | self.data.heights, |
|
|
204 | 213 | channels, |
|
|
205 | 214 | thisDatetime, |
|
|
206 | 215 | wintitle |
|
|
207 | 216 | ) |
|
|
208 | 217 | else: |
|
|
209 | 218 | wintitle = " [Profile = %d] " %self.data.profileIndex |
|
|
210 | 219 | if self.CODE== "scope": |
|
|
211 | 220 | if self.type == "power": |
|
|
212 | 221 | self.plot_power(self.data.heights, |
|
|
213 | 222 | scope, |
|
|
214 | 223 | channels, |
|
|
215 | 224 | thisDatetime, |
|
|
216 | 225 | wintitle |
|
|
217 | 226 | ) |
|
|
218 | 227 | |
|
|
219 | 228 | if self.type == "iq": |
|
|
220 | 229 | self.plot_iq(self.data.heights, |
|
|
221 | 230 | scope, |
|
|
222 | 231 | channels, |
|
|
223 | 232 | thisDatetime, |
|
|
224 | 233 | wintitle |
|
|
225 | 234 | ) |
|
|
226 | 235 | if self.CODE=="pp_power": |
|
|
227 | 236 | self.plot_weatherpower(self.data.heights, |
|
|
228 | 237 | scope, |
|
|
229 | 238 | channels, |
|
|
230 | 239 | thisDatetime, |
|
|
231 | 240 | wintitle |
|
|
232 | 241 | ) |
|
|
242 | if self.CODE=="pp_signal": | |
|
|
243 | self.plot_weatherpower(self.data.heights, | |
|
|
244 | scope, | |
|
|
245 | channels, | |
|
|
246 | thisDatetime, | |
|
|
247 | wintitle | |
|
|
248 | ) | |
|
|
233 | 249 | if self.CODE=="pp_velocity": |
|
|
234 | 250 | self.plot_weathervelocity(scope, |
|
|
235 | 251 | self.data.heights, |
|
|
236 | 252 | channels, |
|
|
237 | 253 | thisDatetime, |
|
|
238 | 254 | wintitle |
|
|
239 | 255 | ) |
|
|
240 | 256 | if self.CODE=="pp_specwidth": |
|
|
241 | 257 | self.plot_weatherspecwidth(scope, |
|
|
242 | 258 | self.data.heights, |
|
|
243 | 259 | channels, |
|
|
244 | 260 | thisDatetime, |
|
|
245 | 261 | wintitle |
|
|
246 | 262 | ) |
|
|
247 | 263 | |
|
|
248 | 264 | |
|
|
249 | 265 | |
|
|
250 | 266 | class PulsepairPowerPlot(ScopePlot): |
|
|
251 | 267 | ''' |
|
|
252 | Plot for | |
|
|
268 | Plot for P= S+N | |
|
|
253 | 269 | ''' |
|
|
254 | 270 | |
|
|
255 | 271 | CODE = 'pp_power' |
|
|
256 | 272 | plot_name = 'PulsepairPower' |
|
|
257 | 273 | plot_type = 'scatter' |
|
|
258 | 274 | buffering = False |
|
|
259 | 275 | |
|
|
260 | 276 | class PulsepairVelocityPlot(ScopePlot): |
|
|
261 | 277 | ''' |
|
|
262 | Plot for | |
|
|
278 | Plot for VELOCITY | |
|
|
263 | 279 | ''' |
|
|
264 | 280 | CODE = 'pp_velocity' |
|
|
265 | 281 | plot_name = 'PulsepairVelocity' |
|
|
266 | 282 | plot_type = 'scatter' |
|
|
267 | 283 | buffering = False |
|
|
268 | 284 | |
|
|
269 | 285 | class PulsepairSpecwidthPlot(ScopePlot): |
|
|
270 | 286 | ''' |
|
|
271 | Plot for | |
|
|
287 | Plot for WIDTH | |
|
|
272 | 288 | ''' |
|
|
273 | 289 | CODE = 'pp_specwidth' |
|
|
274 | 290 | plot_name = 'PulsepairSpecwidth' |
|
|
275 | 291 | plot_type = 'scatter' |
|
|
276 | 292 | buffering = False |
|
|
293 | ||
|
|
294 | class PulsepairSignalPlot(ScopePlot): | |
|
|
295 | ''' | |
|
|
296 | Plot for S | |
|
|
297 | ''' | |
|
|
298 | ||
|
|
299 | CODE = 'pp_signal' | |
|
|
300 | plot_name = 'PulsepairSignal' | |
|
|
301 | plot_type = 'scatter' | |
|
|
302 | buffering = False | |
| @@ -1,512 +1,519 | |||
|
|
1 | 1 | import numpy,math,random,time |
|
|
2 | 2 | #---------------1 Heredamos JRODatareader |
|
|
3 | 3 | from schainpy.model.io.jroIO_base import * |
|
|
4 | 4 | #---------------2 Heredamos las propiedades de ProcessingUnit |
|
|
5 | 5 | from schainpy.model.proc.jroproc_base import ProcessingUnit,Operation,MPDecorator |
|
|
6 | 6 | #---------------3 Importaremos las clases BascicHeader, SystemHeader, RadarControlHeader, ProcessingHeader |
|
|
7 | 7 | from schainpy.model.data.jroheaderIO import PROCFLAG, BasicHeader,SystemHeader,RadarControllerHeader, ProcessingHeader |
|
|
8 | 8 | #---------------4 Importaremos el objeto Voltge |
|
|
9 | 9 | from schainpy.model.data.jrodata import Voltage |
|
|
10 | 10 | |
|
|
11 | 11 | class SimulatorReader(JRODataReader, ProcessingUnit): |
|
|
12 | 12 | incIntFactor = 1 |
|
|
13 | 13 | nFFTPoints = 0 |
|
|
14 | 14 | FixPP_IncInt = 1 |
|
|
15 | 15 | FixRCP_IPP = 1000 |
|
|
16 | 16 | FixPP_CohInt = 1 |
|
|
17 | 17 | Tau_0 = 250 |
|
|
18 | 18 | AcqH0_0 = 70 |
|
|
19 | 19 | H0 = AcqH0_0 |
|
|
20 | 20 | AcqDH_0 = 1.25 |
|
|
21 | 21 | DH0 = AcqDH_0 |
|
|
22 | 22 | Bauds = 32 |
|
|
23 | 23 | BaudWidth = None |
|
|
24 | 24 | FixRCP_TXA = 40 |
|
|
25 | 25 | FixRCP_TXB = 70 |
|
|
26 | 26 | fAngle = 2.0*math.pi*(1/16) |
|
|
27 | 27 | DC_level = 500 |
|
|
28 | 28 | stdev = 8 |
|
|
29 | 29 | Num_Codes = 2 |
|
|
30 | 30 | #code0 = numpy.array([1,1,1,0,1,1,0,1,1,1,1,0,0,0,1,0,1,1,1,0,1,1,0,1,0,0,0,1,1,1,0,1]) |
|
|
31 | 31 | #code1 = numpy.array([1,1,1,0,1,1,0,1,1,1,1,0,0,0,1,0,0,0,0,1,0,0,1,0,1,1,1,0,0,0,1,0]) |
|
|
32 | 32 | #Dyn_snCode = numpy.array([Num_Codes,Bauds]) |
|
|
33 | 33 | Dyn_snCode = None |
|
|
34 | 34 | Samples = 200 |
|
|
35 | 35 | channels = 2 |
|
|
36 | 36 | pulses = None |
|
|
37 | 37 | Reference = None |
|
|
38 | 38 | pulse_size = None |
|
|
39 | 39 | prof_gen = None |
|
|
40 | 40 | Fdoppler = 100 |
|
|
41 | 41 | Hdoppler = 36 |
|
|
42 | 42 | Adoppler = 300 |
|
|
43 | 43 | frequency = 9345 |
|
|
44 | 44 | nTotalReadFiles = 1000 |
|
|
45 | 45 | |
|
|
46 | 46 | def __init__(self): |
|
|
47 | 47 | """ |
|
|
48 | 48 | Inicializador de la clases SimulatorReader para |
|
|
49 | 49 | generar datos de voltage simulados. |
|
|
50 | 50 | Input: |
|
|
51 | 51 | dataOut: Objeto de la clase Voltage. |
|
|
52 | 52 | Este Objeto sera utilizado apra almacenar |
|
|
53 | 53 | un perfil de datos cada vez qe se haga |
|
|
54 | 54 | un requerimiento (getData) |
|
|
55 | 55 | """ |
|
|
56 | 56 | ProcessingUnit.__init__(self) |
|
|
57 | 57 | print(" [ START ] init - Metodo Simulator Reader") |
|
|
58 | 58 | |
|
|
59 | 59 | self.isConfig = False |
|
|
60 | 60 | self.basicHeaderObj = BasicHeader(LOCALTIME) |
|
|
61 | 61 | self.systemHeaderObj = SystemHeader() |
|
|
62 | 62 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
63 | 63 | self.processingHeaderObj = ProcessingHeader() |
|
|
64 | 64 | self.profileIndex = 2**32-1 |
|
|
65 | 65 | self.dataOut = Voltage() |
|
|
66 | 66 | #code0 = numpy.array([1,1,1,0,1,1,0,1,1,1,1,0,0,0,1,0,1,1,1,0,1,1,0,1,0,0,0,1,1,1,0,1]) |
|
|
67 | 67 | code0 = numpy.array([1,1,1,-1,1,1,-1,1,1,1,1,-1,-1,-1,1,-1,1,1,1,-1,1,1,-1,1,-1,-1,-1,1,1,1,-1,1]) |
|
|
68 | 68 | #code1 = numpy.array([1,1,1,0,1,1,0,1,1,1,1,0,0,0,1,0,0,0,0,1,0,0,1,0,1,1,1,0,0,0,1,0]) |
|
|
69 | 69 | code1 = numpy.array([1,1,1,-1,1,1,-1,1,1,1,1,-1,-1,-1,1,-1,-1,-1,-1,1,-1,-1,1,-1,1,1,1,-1,-1,-1,1,-1]) |
|
|
70 | 70 | #self.Dyn_snCode = numpy.array([code0,code1]) |
|
|
71 | 71 | self.Dyn_snCode = None |
|
|
72 | 72 | |
|
|
73 | 73 | def set_kwargs(self, **kwargs): |
|
|
74 | 74 | for key, value in kwargs.items(): |
|
|
75 | 75 | setattr(self, key, value) |
|
|
76 | 76 | |
|
|
77 | 77 | def __hasNotDataInBuffer(self): |
|
|
78 | 78 | |
|
|
79 | 79 | if self.profileIndex >= self.processingHeaderObj.profilesPerBlock* self.nTxs: |
|
|
80 | 80 | if self.nReadBlocks>0: |
|
|
81 | 81 | tmp = self.dataOut.utctime |
|
|
82 | 82 | tmp_utc = int(self.dataOut.utctime) |
|
|
83 | 83 | tmp_milisecond = int((tmp-tmp_utc)*1000) |
|
|
84 | 84 | self.basicHeaderObj.utc = tmp_utc |
|
|
85 | 85 | self.basicHeaderObj.miliSecond= tmp_milisecond |
|
|
86 | 86 | return 1 |
|
|
87 | 87 | return 0 |
|
|
88 | 88 | |
|
|
89 | 89 | def setNextFile(self): |
|
|
90 | 90 | """Set the next file to be readed open it and parse de file header""" |
|
|
91 | 91 | |
|
|
92 | 92 | if (self.nReadBlocks >= self.processingHeaderObj.dataBlocksPerFile): |
|
|
93 | 93 | self.nReadFiles=self.nReadFiles+1 |
|
|
94 | 94 | if self.nReadFiles > self.nTotalReadFiles: |
|
|
95 | 95 | self.flagNoMoreFiles=1 |
|
|
96 | 96 | raise schainpy.admin.SchainWarning('No more files to read') |
|
|
97 | 97 | |
|
|
98 | 98 | print('------------------- [Opening file] ------------------------------',self.nReadFiles) |
|
|
99 | 99 | self.nReadBlocks = 0 |
|
|
100 | 100 | #if self.nReadBlocks==0: |
|
|
101 | 101 | # self.readFirstHeader() |
|
|
102 | 102 | |
|
|
103 | 103 | def __setNewBlock(self): |
|
|
104 | 104 | self.setNextFile() |
|
|
105 | 105 | if self.flagIsNewFile: |
|
|
106 | 106 | return 1 |
|
|
107 | 107 | |
|
|
108 | 108 | def readNextBlock(self): |
|
|
109 | 109 | while True: |
|
|
110 | 110 | self.__setNewBlock() |
|
|
111 | 111 | if not(self.readBlock()): |
|
|
112 | 112 | return 0 |
|
|
113 | 113 | self.getBasicHeader() |
|
|
114 | 114 | break |
|
|
115 | 115 | if self.verbose: |
|
|
116 | 116 | print("[Reading] Block No. %d/%d -> %s" %(self.nReadBlocks, |
|
|
117 | 117 | self.processingHeaderObj.dataBlocksPerFile, |
|
|
118 | 118 | self.dataOut.datatime.ctime()) ) |
|
|
119 | 119 | return 1 |
|
|
120 | 120 | |
|
|
121 | 121 | def getFirstHeader(self): |
|
|
122 | 122 | self.getBasicHeader() |
|
|
123 | 123 | self.dataOut.processingHeaderObj = self.processingHeaderObj.copy() |
|
|
124 | 124 | self.dataOut.systemHeaderObj = self.systemHeaderObj.copy() |
|
|
125 | 125 | self.dataOut.radarControllerHeaderObj = self.radarControllerHeaderObj.copy() |
|
|
126 | 126 | self.dataOut.dtype = self.dtype |
|
|
127 | 127 | |
|
|
128 | 128 | self.dataOut.nProfiles = self.processingHeaderObj.profilesPerBlock |
|
|
129 | 129 | self.dataOut.heightList = numpy.arange(self.processingHeaderObj.nHeights) * self.processingHeaderObj.deltaHeight + self.processingHeaderObj.firstHeight |
|
|
130 | 130 | self.dataOut.channelList = list(range(self.systemHeaderObj.nChannels)) |
|
|
131 | 131 | self.dataOut.nCohInt = self.processingHeaderObj.nCohInt |
|
|
132 | 132 | # asumo q la data no esta decodificada |
|
|
133 | 133 | self.dataOut.flagDecodeData = self.processingHeaderObj.flag_decode |
|
|
134 | 134 | # asumo q la data no esta sin flip |
|
|
135 | 135 | self.dataOut.flagDeflipData = self.processingHeaderObj.flag_deflip |
|
|
136 | 136 | self.dataOut.flagShiftFFT = self.processingHeaderObj.shif_fft |
|
|
137 | 137 | self.dataOut.frequency = self.frequency |
|
|
138 | 138 | |
|
|
139 | 139 | def getBasicHeader(self): |
|
|
140 | 140 | self.dataOut.utctime = self.basicHeaderObj.utc + self.basicHeaderObj.miliSecond / \ |
|
|
141 | 141 | 1000. + self.profileIndex * self.radarControllerHeaderObj.ippSeconds |
|
|
142 | 142 | |
|
|
143 | 143 | self.dataOut.flagDiscontinuousBlock = self.flagDiscontinuousBlock |
|
|
144 | 144 | self.dataOut.timeZone = self.basicHeaderObj.timeZone |
|
|
145 | 145 | self.dataOut.dstFlag = self.basicHeaderObj.dstFlag |
|
|
146 | 146 | self.dataOut.errorCount = self.basicHeaderObj.errorCount |
|
|
147 | 147 | self.dataOut.useLocalTime = self.basicHeaderObj.useLocalTime |
|
|
148 | 148 | self.dataOut.ippSeconds = self.radarControllerHeaderObj.ippSeconds / self.nTxs |
|
|
149 | 149 | |
|
|
150 | 150 | def readFirstHeader(self): |
|
|
151 | 151 | |
|
|
152 | 152 | datatype = int(numpy.log2((self.processingHeaderObj.processFlags & |
|
|
153 | 153 | PROCFLAG.DATATYPE_MASK)) - numpy.log2(PROCFLAG.DATATYPE_CHAR)) |
|
|
154 | 154 | if datatype == 0: |
|
|
155 | 155 | datatype_str = numpy.dtype([('real', '<i1'), ('imag', '<i1')]) |
|
|
156 | 156 | elif datatype == 1: |
|
|
157 | 157 | datatype_str = numpy.dtype([('real', '<i2'), ('imag', '<i2')]) |
|
|
158 | 158 | elif datatype == 2: |
|
|
159 | 159 | datatype_str = numpy.dtype([('real', '<i4'), ('imag', '<i4')]) |
|
|
160 | 160 | elif datatype == 3: |
|
|
161 | 161 | datatype_str = numpy.dtype([('real', '<i8'), ('imag', '<i8')]) |
|
|
162 | 162 | elif datatype == 4: |
|
|
163 | 163 | datatype_str = numpy.dtype([('real', '<f4'), ('imag', '<f4')]) |
|
|
164 | 164 | elif datatype == 5: |
|
|
165 | 165 | datatype_str = numpy.dtype([('real', '<f8'), ('imag', '<f8')]) |
|
|
166 | 166 | else: |
|
|
167 | 167 | raise ValueError('Data type was not defined') |
|
|
168 | 168 | |
|
|
169 | 169 | self.dtype = datatype_str |
|
|
170 | 170 | |
|
|
171 | 171 | |
|
|
172 | 172 | def set_RCH(self, expType=2, nTx=1,ipp=None, txA=0, txB=0, |
|
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173 | 173 | nWindows=None, nHeights=None, firstHeight=None, deltaHeight=None, |
|
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174 | 174 | numTaus=0, line6Function=0, line5Function=0, fClock=None, |
|
|
175 | 175 | prePulseBefore=0, prePulseAfter=0, |
|
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176 | 176 | codeType=0, nCode=0, nBaud=0, code=None, |
|
|
177 | 177 | flip1=0, flip2=0,Taus=0): |
|
|
178 | 178 | self.radarControllerHeaderObj.expType = expType |
|
|
179 | 179 | self.radarControllerHeaderObj.nTx = nTx |
|
|
180 | 180 | self.radarControllerHeaderObj.ipp = float(ipp) |
|
|
181 | 181 | self.radarControllerHeaderObj.txA = float(txA) |
|
|
182 | 182 | self.radarControllerHeaderObj.txB = float(txB) |
|
|
183 | 183 | self.radarControllerHeaderObj.rangeIpp = b'A\n'#ipp |
|
|
184 | 184 | self.radarControllerHeaderObj.rangeTxA = b'' |
|
|
185 | 185 | self.radarControllerHeaderObj.rangeTxB = b'' |
|
|
186 | 186 | |
|
|
187 | 187 | self.radarControllerHeaderObj.nHeights = int(nHeights) |
|
|
188 | 188 | self.radarControllerHeaderObj.firstHeight = numpy.array([firstHeight]) |
|
|
189 | 189 | self.radarControllerHeaderObj.deltaHeight = numpy.array([deltaHeight]) |
|
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190 | 190 | self.radarControllerHeaderObj.samplesWin = numpy.array([nHeights]) |
|
|
191 | 191 | |
|
|
192 | 192 | |
|
|
193 | 193 | self.radarControllerHeaderObj.nWindows = nWindows |
|
|
194 | 194 | self.radarControllerHeaderObj.numTaus = numTaus |
|
|
195 | 195 | self.radarControllerHeaderObj.codeType = codeType |
|
|
196 | 196 | self.radarControllerHeaderObj.line6Function = line6Function |
|
|
197 | 197 | self.radarControllerHeaderObj.line5Function = line5Function |
|
|
198 | 198 | #self.radarControllerHeaderObj.fClock = fClock |
|
|
199 | 199 | self.radarControllerHeaderObj.prePulseBefore= prePulseBefore |
|
|
200 | 200 | self.radarControllerHeaderObj.prePulseAfter = prePulseAfter |
|
|
201 | 201 | |
|
|
202 | 202 | self.radarControllerHeaderObj.flip1 = flip1 |
|
|
203 | 203 | self.radarControllerHeaderObj.flip2 = flip2 |
|
|
204 | 204 | |
|
|
205 | 205 | self.radarControllerHeaderObj.code_size = 0 |
|
|
206 | 206 | if self.radarControllerHeaderObj.codeType != 0: |
|
|
207 | 207 | self.radarControllerHeaderObj.nCode = nCode |
|
|
208 | 208 | self.radarControllerHeaderObj.nBaud = nBaud |
|
|
209 | 209 | self.radarControllerHeaderObj.code = code |
|
|
210 | 210 | self.radarControllerHeaderObj.code_size = int(numpy.ceil(nBaud / 32.)) * nCode * 4 |
|
|
211 | 211 | |
|
|
212 | 212 | if fClock is None and deltaHeight is not None: |
|
|
213 | 213 | self.fClock = 0.15 / (deltaHeight * 1e-6) |
|
|
214 | 214 | self.radarControllerHeaderObj.fClock = self.fClock |
|
|
215 | 215 | if numTaus==0: |
|
|
216 | 216 | self.radarControllerHeaderObj.Taus = numpy.array(0,'<f4') |
|
|
217 | 217 | else: |
|
|
218 | 218 | self.radarControllerHeaderObj.Taus = numpy.array(Taus,'<f4') |
|
|
219 | 219 | |
|
|
220 | 220 | def set_PH(self, dtype=0, blockSize=0, profilesPerBlock=0, |
|
|
221 | 221 | dataBlocksPerFile=0, nWindows=0, processFlags=0, nCohInt=0, |
|
|
222 | 222 | nIncohInt=0, totalSpectra=0, nHeights=0, firstHeight=0, |
|
|
223 | 223 | deltaHeight=0, samplesWin=0, spectraComb=0, nCode=0, |
|
|
224 | 224 | code=0, nBaud=None, shif_fft=False, flag_dc=False, |
|
|
225 | 225 | flag_cspc=False, flag_decode=False, flag_deflip=False): |
|
|
226 | 226 | |
|
|
227 | 227 | self.processingHeaderObj.dtype = dtype |
|
|
228 | 228 | self.processingHeaderObj.profilesPerBlock = profilesPerBlock |
|
|
229 | 229 | self.processingHeaderObj.dataBlocksPerFile = dataBlocksPerFile |
|
|
230 | 230 | self.processingHeaderObj.nWindows = nWindows |
|
|
231 | 231 | self.processingHeaderObj.processFlags = processFlags |
|
|
232 | 232 | self.processingHeaderObj.nCohInt = nCohInt |
|
|
233 | 233 | self.processingHeaderObj.nIncohInt = nIncohInt |
|
|
234 | 234 | self.processingHeaderObj.totalSpectra = totalSpectra |
|
|
235 | 235 | |
|
|
236 | 236 | self.processingHeaderObj.nHeights = int(nHeights) |
|
|
237 | 237 | self.processingHeaderObj.firstHeight = firstHeight#numpy.array([firstHeight])#firstHeight |
|
|
238 | 238 | self.processingHeaderObj.deltaHeight = deltaHeight#numpy.array([deltaHeight])#deltaHeight |
|
|
239 | 239 | self.processingHeaderObj.samplesWin = nHeights#numpy.array([nHeights])#nHeights |
|
|
240 | 240 | |
|
|
241 | 241 | def set_BH(self, utc = 0, miliSecond = 0, timeZone = 0): |
|
|
242 | 242 | self.basicHeaderObj.utc = utc |
|
|
243 | 243 | self.basicHeaderObj.miliSecond = miliSecond |
|
|
244 | 244 | self.basicHeaderObj.timeZone = timeZone |
|
|
245 | 245 | |
|
|
246 | 246 | def set_SH(self, nSamples=0, nProfiles=0, nChannels=0, adcResolution=14, pciDioBusWidth=32): |
|
|
247 | 247 | #self.systemHeaderObj.size = size |
|
|
248 | 248 | self.systemHeaderObj.nSamples = nSamples |
|
|
249 | 249 | self.systemHeaderObj.nProfiles = nProfiles |
|
|
250 | 250 | self.systemHeaderObj.nChannels = nChannels |
|
|
251 | 251 | self.systemHeaderObj.adcResolution = adcResolution |
|
|
252 | 252 | self.systemHeaderObj.pciDioBusWidth = pciDioBusWidth |
|
|
253 | 253 | |
|
|
254 | 254 | def init_acquisition(self): |
|
|
255 | 255 | |
|
|
256 | 256 | if self.nFFTPoints != 0: |
|
|
257 | 257 | self.incIntFactor = m_nProfilesperBlock/self.nFFTPoints |
|
|
258 | 258 | if (self.FixPP_IncInt > self.incIntFactor): |
|
|
259 | 259 | self.incIntFactor = self.FixPP_IncInt/ self.incIntFactor |
|
|
260 | 260 | elif(self.FixPP_IncInt< self.incIntFactor): |
|
|
261 | 261 | print("False alert...") |
|
|
262 | 262 | |
|
|
263 | 263 | ProfilesperBlock = self.processingHeaderObj.profilesPerBlock |
|
|
264 | 264 | |
|
|
265 | 265 | self.timeperblock =int(((self.FixRCP_IPP |
|
|
266 | 266 | *ProfilesperBlock |
|
|
267 | 267 | *self.FixPP_CohInt |
|
|
268 | 268 | *self.incIntFactor) |
|
|
269 | 269 | /150.0) |
|
|
270 | 270 | *0.9 |
|
|
271 | 271 | +0.5) |
|
|
272 | 272 | # para cada canal |
|
|
273 | 273 | self.profiles = ProfilesperBlock*self.FixPP_CohInt |
|
|
274 | 274 | self.profiles = ProfilesperBlock |
|
|
275 | 275 | self.Reference = int((self.Tau_0-self.AcqH0_0)/(self.AcqDH_0)+0.5) |
|
|
276 | 276 | self.BaudWidth = int((self.FixRCP_TXA/self.AcqDH_0)/self.Bauds + 0.5 ) |
|
|
277 | 277 | |
|
|
278 | 278 | if (self.BaudWidth==0): |
|
|
279 | 279 | self.BaudWidth=1 |
|
|
280 | 280 | |
|
|
281 | 281 | def init_pulse(self,Num_Codes=Num_Codes,Bauds=Bauds,BaudWidth=BaudWidth,Dyn_snCode=Dyn_snCode): |
|
|
282 | 282 | |
|
|
283 | 283 | Num_Codes = Num_Codes |
|
|
284 | 284 | Bauds = Bauds |
|
|
285 | 285 | BaudWidth = BaudWidth |
|
|
286 | 286 | Dyn_snCode = Dyn_snCode |
|
|
287 | 287 | |
|
|
288 | 288 | if Dyn_snCode: |
|
|
289 | 289 | print("EXISTE") |
|
|
290 | 290 | else: |
|
|
291 | 291 | print("No existe") |
|
|
292 | 292 | |
|
|
293 | 293 | if Dyn_snCode: # if Bauds: |
|
|
294 | 294 | pulses = list(range(0,Num_Codes)) |
|
|
295 | 295 | num_codes = Num_Codes |
|
|
296 | 296 | for i in range(num_codes): |
|
|
297 | 297 | pulse_size = Bauds*BaudWidth |
|
|
298 | 298 | pulses[i] = numpy.zeros(pulse_size) |
|
|
299 | 299 | for j in range(Bauds): |
|
|
300 | 300 | for k in range(BaudWidth): |
|
|
301 | 301 | pulses[i][j*BaudWidth+k] = int(Dyn_snCode[i][j]*600) |
|
|
302 | 302 | else: |
|
|
303 | 303 | print("sin code") |
|
|
304 | 304 | pulses = list(range(1)) |
|
|
305 | 305 | if self.AcqDH_0>0.149: |
|
|
306 | 306 | pulse_size = int(self.FixRCP_TXB/0.15+0.5) |
|
|
307 | 307 | else: |
|
|
308 | 308 | pulse_size = int((self.FixRCP_TXB/self.AcqDH_0)+0.5) #0.0375 |
|
|
309 | 309 | pulses[0] = numpy.ones(pulse_size) |
|
|
310 | 310 | pulses = 600*pulses[0] |
|
|
311 | 311 | |
|
|
312 | 312 | return pulses,pulse_size |
|
|
313 | 313 | |
|
|
314 | 314 | def jro_GenerateBlockOfData(self,Samples=Samples,DC_level= DC_level,stdev=stdev, |
|
|
315 | 315 | Reference= Reference,pulses= pulses, |
|
|
316 | 316 | Num_Codes= Num_Codes,pulse_size=pulse_size, |
|
|
317 | 317 | prof_gen= prof_gen,H0 = H0,DH0=DH0, |
|
|
318 | 318 | Adoppler=Adoppler,Fdoppler= Fdoppler,Hdoppler=Hdoppler): |
|
|
319 | 319 | Samples = Samples |
|
|
320 | 320 | DC_level = DC_level |
|
|
321 | 321 | stdev = stdev |
|
|
322 | 322 | m_nR = Reference |
|
|
323 | 323 | pulses = pulses |
|
|
324 | 324 | num_codes = Num_Codes |
|
|
325 | 325 | ps = pulse_size |
|
|
326 | 326 | prof_gen = prof_gen |
|
|
327 | 327 | channels = self.channels |
|
|
328 | 328 | H0 = H0 |
|
|
329 | 329 | DH0 = DH0 |
|
|
330 | 330 | ippSec = self.radarControllerHeaderObj.ippSeconds |
|
|
331 | 331 | Fdoppler = self.Fdoppler |
|
|
332 | 332 | Hdoppler = self.Hdoppler |
|
|
333 | 333 | Adoppler = self.Adoppler |
|
|
334 | 334 | |
|
|
335 | 335 | self.datablock = numpy.zeros([channels,prof_gen,Samples],dtype= numpy.complex64) |
|
|
336 | 336 | for i in range(channels): |
|
|
337 | 337 | for k in range(prof_gen): |
|
|
338 | 338 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·NOISEΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· |
|
|
339 | 339 | Noise_r = numpy.random.normal(DC_level,stdev,Samples) |
|
|
340 | 340 | Noise_i = numpy.random.normal(DC_level,stdev,Samples) |
|
|
341 | 341 | Noise = numpy.zeros(Samples,dtype=complex) |
|
|
342 | 342 | Noise.real = Noise_r |
|
|
343 | 343 | Noise.imag = Noise_i |
|
|
344 | 344 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·PULSOSΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· |
|
|
345 | 345 | Pulso = numpy.zeros(pulse_size,dtype=complex) |
|
|
346 | 346 | Pulso.real = pulses[k%num_codes] |
|
|
347 | 347 | Pulso.imag = pulses[k%num_codes] |
|
|
348 | 348 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· PULSES+NOISEΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β· |
|
|
349 | 349 | InBuffer = numpy.zeros(Samples,dtype=complex) |
|
|
350 | 350 | InBuffer[m_nR:m_nR+ps] = Pulso |
|
|
351 | 351 | InBuffer = InBuffer+Noise |
|
|
352 | 352 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· ANGLE Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· |
|
|
353 | 353 | InBuffer.real[m_nR:m_nR+ps] = InBuffer.real[m_nR:m_nR+ps]*(math.cos( self.fAngle)*5) |
|
|
354 | 354 | InBuffer.imag[m_nR:m_nR+ps] = InBuffer.imag[m_nR:m_nR+ps]*(math.sin( self.fAngle)*5) |
|
|
355 | 355 | InBuffer=InBuffer |
|
|
356 | 356 | self.datablock[i][k]= InBuffer |
|
|
357 | 357 | |
|
|
358 | 358 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·DOPPLER SIGNAL............................................... |
|
|
359 | 359 | time_vec = numpy.linspace(0,(prof_gen-1)*ippSec,int(prof_gen))+self.nReadBlocks*ippSec*prof_gen+(self.nReadFiles-1)*ippSec*prof_gen |
|
|
360 | 360 | fd = Fdoppler #+(600.0/120)*self.nReadBlocks |
|
|
361 | 361 | d_signal = Adoppler*numpy.array(numpy.exp(1.0j*2.0*math.pi*fd*time_vec),dtype=numpy.complex64) |
|
|
362 | 362 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·SeΓ±al con ancho espectralΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· |
|
|
363 | #specw_sig = numpy.linspace(-149,150,300) | |
|
|
364 | #w = 8 | |
|
|
365 | #A = 20 | |
|
|
366 | #specw_sig = specw_sig/w | |
|
|
367 | #specw_sig = numpy.sinc(specw_sig) | |
|
|
368 | #specw_sig = A*numpy.array(specw_sig,dtype=numpy.complex64) | |
|
|
363 | if prof_gen%2==0: | |
|
|
364 | min = int(prof_gen/2.0-1.0) | |
|
|
365 | max = int(prof_gen/2.0) | |
|
|
366 | else: | |
|
|
367 | min = int(prof_gen/2.0) | |
|
|
368 | max = int(prof_gen/2.0) | |
|
|
369 | specw_sig = numpy.linspace(-min,max,prof_gen) | |
|
|
370 | w = 4 | |
|
|
371 | A = 20 | |
|
|
372 | specw_sig = specw_sig/w | |
|
|
373 | specw_sig = numpy.sinc(specw_sig) | |
|
|
374 | specw_sig = A*numpy.array(specw_sig,dtype=numpy.complex64) | |
|
|
369 | 375 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· DATABLOCK + DOPPLERΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· |
|
|
370 | 376 | HD=int(Hdoppler/self.AcqDH_0) |
|
|
371 | 377 | for i in range(12): |
|
|
372 | 378 | self.datablock[0,:,HD+i]=self.datablock[0,:,HD+i]+ d_signal# RESULT |
|
|
373 | 379 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· DATABLOCK + DOPPLER*Sinc(x)Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· |
|
|
374 |
|
|
|
|
375 |
|
|
|
|
376 |
|
|
|
|
377 |
|
|
|
|
380 | HD=int(Hdoppler/self.AcqDH_0) | |
|
|
381 | HD=int(HD/2) | |
|
|
382 | for i in range(12): | |
|
|
383 | self.datablock[0,:,HD+i]=self.datablock[0,:,HD+i]+ specw_sig*d_signal# RESULT | |
|
|
378 | 384 | |
|
|
379 | 385 | def readBlock(self): |
|
|
380 | 386 | |
|
|
381 | 387 | self.jro_GenerateBlockOfData(Samples= self.samples,DC_level=self.DC_level, |
|
|
382 | 388 | stdev=self.stdev,Reference= self.Reference, |
|
|
383 | 389 | pulses = self.pulses,Num_Codes=self.Num_Codes, |
|
|
384 | 390 | pulse_size=self.pulse_size,prof_gen=self.profiles, |
|
|
385 | 391 | H0=self.H0,DH0=self.DH0) |
|
|
386 | 392 | |
|
|
387 | 393 | self.profileIndex = 0 |
|
|
388 | 394 | self.flagIsNewFile = 0 |
|
|
389 | 395 | self.flagIsNewBlock = 1 |
|
|
390 | 396 | self.nTotalBlocks += 1 |
|
|
391 | 397 | self.nReadBlocks += 1 |
|
|
392 | 398 | |
|
|
393 | 399 | return 1 |
|
|
394 | 400 | |
|
|
395 | 401 | |
|
|
396 | 402 | def getData(self): |
|
|
397 | 403 | if self.flagNoMoreFiles: |
|
|
398 | 404 | self.dataOut.flagNodata = True |
|
|
399 | 405 | return 0 |
|
|
400 | 406 | self.flagDiscontinuousBlock = 0 |
|
|
401 | 407 | self.flagIsNewBlock = 0 |
|
|
402 | 408 | if self.__hasNotDataInBuffer(): # aqui es verdad |
|
|
403 | 409 | if not(self.readNextBlock()): # return 1 y por eso el if not salta a getBasic Header |
|
|
404 | 410 | return 0 |
|
|
405 | 411 | self.getFirstHeader() # atributo |
|
|
406 | 412 | |
|
|
407 | 413 | if not self.getByBlock: |
|
|
408 | 414 | self.dataOut.flagDataAsBlock = False |
|
|
409 | 415 | self.dataOut.data = self.datablock[:, self.profileIndex, :] |
|
|
410 | 416 | self.dataOut.profileIndex = self.profileIndex |
|
|
411 | 417 | self.profileIndex += 1 |
|
|
412 | 418 | else: |
|
|
413 | 419 | pass |
|
|
414 | 420 | self.dataOut.flagNoData = False |
|
|
415 | 421 | self.getBasicHeader() |
|
|
416 | 422 | self.dataOut.realtime = self.online |
|
|
417 | 423 | return self.dataOut.data |
|
|
418 | 424 | |
|
|
419 | 425 | |
|
|
420 | 426 | def setup(self,frequency=49.92e6,incIntFactor= 1, nFFTPoints = 0, FixPP_IncInt=1,FixRCP_IPP=1000, |
|
|
421 | 427 | FixPP_CohInt= 1,Tau_0= 250,AcqH0_0 = 70 ,AcqDH_0=1.25, Bauds= 32, |
|
|
422 | 428 | FixRCP_TXA = 40, FixRCP_TXB = 50, fAngle = 2.0*math.pi*(1/16),DC_level= 50, |
|
|
423 | 429 | stdev= 8,Num_Codes = 1 , Dyn_snCode = None, samples=200, |
|
|
424 |
channels=2,Fdoppler=20,Hdoppler=36,Adoppler=500, |
|
|
|
430 | channels=2,Fdoppler=20,Hdoppler=36,Adoppler=500, | |
|
|
431 | profilesPerBlock=300,dataBlocksPerFile=120,nTotalReadFiles=10000, | |
|
|
425 | 432 | **kwargs): |
|
|
426 | 433 | |
|
|
427 | 434 | self.set_kwargs(**kwargs) |
|
|
428 | 435 | self.nReadBlocks = 0 |
|
|
429 | 436 | self.nReadFiles = 1 |
|
|
430 | 437 | print('------------------- [Opening file: ] ------------------------------',self.nReadFiles) |
|
|
431 | 438 | |
|
|
432 | 439 | tmp = time.time() |
|
|
433 | 440 | tmp_utc = int(tmp) |
|
|
434 | 441 | tmp_milisecond = int((tmp-tmp_utc)*1000) |
|
|
435 | 442 | print(" SETUP -basicHeaderObj.utc",datetime.datetime.utcfromtimestamp(tmp)) |
|
|
436 | 443 | if Dyn_snCode is None: |
|
|
437 | 444 | Num_Codes=1 |
|
|
438 | 445 | Bauds =1 |
|
|
439 | 446 | |
|
|
440 | 447 | |
|
|
441 | 448 | |
|
|
442 | 449 | self.set_BH(utc= tmp_utc,miliSecond= tmp_milisecond,timeZone=300 ) |
|
|
443 | 450 | self.set_RCH( expType=0, nTx=150,ipp=FixRCP_IPP, txA=FixRCP_TXA, txB= FixRCP_TXB, |
|
|
444 | 451 | nWindows=1 , nHeights=samples, firstHeight=AcqH0_0, deltaHeight=AcqDH_0, |
|
|
445 | 452 | numTaus=1, line6Function=0, line5Function=0, fClock=None, |
|
|
446 | 453 | prePulseBefore=0, prePulseAfter=0, |
|
|
447 | 454 | codeType=0, nCode=Num_Codes, nBaud=32, code=Dyn_snCode, |
|
|
448 | 455 | flip1=0, flip2=0,Taus=Tau_0) |
|
|
449 | 456 | |
|
|
450 |
self.set_PH(dtype=0, blockSize=0, profilesPerBlock= |
|
|
|
451 |
dataBlocksPerFile= |
|
|
|
457 | self.set_PH(dtype=0, blockSize=0, profilesPerBlock=profilesPerBlock, | |
|
|
458 | dataBlocksPerFile=dataBlocksPerFile, nWindows=1, processFlags=numpy.array([1024]), nCohInt=1, | |
|
|
452 | 459 | nIncohInt=1, totalSpectra=0, nHeights=samples, firstHeight=AcqH0_0, |
|
|
453 | 460 | deltaHeight=AcqDH_0, samplesWin=samples, spectraComb=0, nCode=0, |
|
|
454 | 461 | code=0, nBaud=None, shif_fft=False, flag_dc=False, |
|
|
455 | 462 | flag_cspc=False, flag_decode=False, flag_deflip=False) |
|
|
456 | 463 | |
|
|
457 |
self.set_SH(nSamples=samples, nProfiles= |
|
|
|
464 | self.set_SH(nSamples=samples, nProfiles=profilesPerBlock, nChannels=channels) | |
|
|
458 | 465 | |
|
|
459 | 466 | self.readFirstHeader() |
|
|
460 | 467 | |
|
|
461 | 468 | self.frequency = frequency |
|
|
462 | 469 | self.incIntFactor = incIntFactor |
|
|
463 | 470 | self.nFFTPoints = nFFTPoints |
|
|
464 | 471 | self.FixPP_IncInt = FixPP_IncInt |
|
|
465 | 472 | self.FixRCP_IPP = FixRCP_IPP |
|
|
466 | 473 | self.FixPP_CohInt = FixPP_CohInt |
|
|
467 | 474 | self.Tau_0 = Tau_0 |
|
|
468 | 475 | self.AcqH0_0 = AcqH0_0 |
|
|
469 | 476 | self.H0 = AcqH0_0 |
|
|
470 | 477 | self.AcqDH_0 = AcqDH_0 |
|
|
471 | 478 | self.DH0 = AcqDH_0 |
|
|
472 | 479 | self.Bauds = Bauds |
|
|
473 | 480 | self.FixRCP_TXA = FixRCP_TXA |
|
|
474 | 481 | self.FixRCP_TXB = FixRCP_TXB |
|
|
475 | 482 | self.fAngle = fAngle |
|
|
476 | 483 | self.DC_level = DC_level |
|
|
477 | 484 | self.stdev = stdev |
|
|
478 | 485 | self.Num_Codes = Num_Codes |
|
|
479 | 486 | self.Dyn_snCode = Dyn_snCode |
|
|
480 | 487 | self.samples = samples |
|
|
481 | 488 | self.channels = channels |
|
|
482 | 489 | self.profiles = None |
|
|
483 | 490 | self.m_nReference = None |
|
|
484 | 491 | self.Baudwidth = None |
|
|
485 | 492 | self.Fdoppler = Fdoppler |
|
|
486 | 493 | self.Hdoppler = Hdoppler |
|
|
487 | 494 | self.Adoppler = Adoppler |
|
|
488 | 495 | self.nTotalReadFiles = int(nTotalReadFiles) |
|
|
489 | 496 | |
|
|
490 | 497 | print("IPP ", self.FixRCP_IPP) |
|
|
491 | 498 | print("Tau_0 ",self.Tau_0) |
|
|
492 | 499 | print("AcqH0_0",self.AcqH0_0) |
|
|
493 | 500 | print("samples,window ",self.samples) |
|
|
494 | 501 | print("AcqDH_0",AcqDH_0) |
|
|
495 | 502 | print("FixRCP_TXA",self.FixRCP_TXA) |
|
|
496 | 503 | print("FixRCP_TXB",self.FixRCP_TXB) |
|
|
497 | 504 | print("Dyn_snCode",Dyn_snCode) |
|
|
498 | 505 | print("Fdoppler", Fdoppler) |
|
|
499 | 506 | print("Hdoppler",Hdoppler) |
|
|
500 | 507 | print("Vdopplermax",Fdoppler*(3.0e8/self.frequency)/2.0) |
|
|
501 | 508 | print("nTotalReadFiles", nTotalReadFiles) |
|
|
502 | 509 | |
|
|
503 | 510 | self.init_acquisition() |
|
|
504 | 511 | self.pulses,self.pulse_size=self.init_pulse(Num_Codes=self.Num_Codes,Bauds=self.Bauds,BaudWidth=self.BaudWidth,Dyn_snCode=Dyn_snCode) |
|
|
505 | 512 | print(" [ END ] - SETUP metodo") |
|
|
506 | 513 | return |
|
|
507 | 514 | |
|
|
508 | 515 | def run(self,**kwargs): # metodo propio |
|
|
509 | 516 | if not(self.isConfig): |
|
|
510 | 517 | self.setup(**kwargs) |
|
|
511 | 518 | self.isConfig = True |
|
|
512 | 519 | self.getData() |
| This diff has been collapsed as it changes many lines, (2141 lines changed) Show them Hide them | |||
| @@ -1,3987 +1,4000 | |||
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1 | 1 |
import numpy |
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2 | 2 |
import math |
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|
3 | 3 |
from scipy import optimize, interpolate, signal, stats, ndimage |
|
|
4 | 4 |
import scipy |
|
|
5 | 5 |
import re |
|
|
6 | 6 |
import datetime |
|
|
7 | 7 |
import copy |
|
|
8 | 8 |
import sys |
|
|
9 | 9 |
import importlib |
|
|
10 | 10 |
import itertools |
|
|
11 |
from multiprocessing import Pool, TimeoutError |
|
|
|
11 | from multiprocessing import Pool, TimeoutError | |
|
|
12 | 12 |
from multiprocessing.pool import ThreadPool |
|
|
13 | 13 |
import time |
|
|
14 | 14 | |
|
|
15 | 15 |
from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters |
|
|
16 |
from .jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
|
|
16 | from .jroproc_base import ProcessingUnit, Operation, MPDecorator | |
|
|
17 | 17 |
from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon |
|
|
18 | 18 |
from scipy import asarray as ar,exp |
|
|
19 | 19 |
from scipy.optimize import curve_fit |
|
|
20 | 20 |
from schainpy.utils import log |
|
|
21 | 21 |
import warnings |
|
|
22 | 22 |
from numpy import NaN |
|
|
23 | 23 |
from scipy.optimize.optimize import OptimizeWarning |
|
|
24 | 24 |
warnings.filterwarnings('ignore') |
|
|
25 | 25 | |
|
|
26 | 26 |
import matplotlib.pyplot as plt |
|
|
27 | 27 | |
|
|
28 | 28 |
SPEED_OF_LIGHT = 299792458 |
|
|
29 | 29 | |
|
|
30 | 30 | |
|
|
31 | 31 |
'''solving pickling issue''' |
|
|
32 | 32 | |
|
|
33 | 33 |
def _pickle_method(method): |
|
|
34 | 34 |
func_name = method.__func__.__name__ |
|
|
35 | 35 |
obj = method.__self__ |
|
|
36 | 36 |
cls = method.__self__.__class__ |
|
|
37 | 37 |
return _unpickle_method, (func_name, obj, cls) |
|
|
38 | 38 | |
|
|
39 | 39 |
def _unpickle_method(func_name, obj, cls): |
|
|
40 | 40 |
for cls in cls.mro(): |
|
|
41 | 41 |
try: |
|
|
42 | 42 |
func = cls.__dict__[func_name] |
|
|
43 | 43 |
except KeyError: |
|
|
44 | 44 |
pass |
|
|
45 | 45 |
else: |
|
|
46 | 46 |
break |
|
|
47 | 47 |
return func.__get__(obj, cls) |
|
|
48 | 48 | |
|
|
49 | 49 | |
|
|
50 | 50 |
class ParametersProc(ProcessingUnit): |
|
|
51 | ||
|
|
51 | ||
|
|
52 | 52 |
METHODS = {} |
|
|
53 | 53 |
nSeconds = None |
|
|
54 | 54 | |
|
|
55 | 55 |
def __init__(self): |
|
|
56 | 56 |
ProcessingUnit.__init__(self) |
|
|
57 | ||
|
|
57 | ||
|
|
58 | 58 |
# self.objectDict = {} |
|
|
59 | 59 |
self.buffer = None |
|
|
60 | 60 |
self.firstdatatime = None |
|
|
61 | 61 |
self.profIndex = 0 |
|
|
62 | 62 |
self.dataOut = Parameters() |
|
|
63 | 63 |
self.setupReq = False #Agregar a todas las unidades de proc |
|
|
64 | 64 | |
|
|
65 | 65 |
def __updateObjFromInput(self): |
|
|
66 | ||
|
|
66 | ||
|
|
67 | 67 |
self.dataOut.inputUnit = self.dataIn.type |
|
|
68 | ||
|
|
68 | ||
|
|
69 | 69 |
self.dataOut.timeZone = self.dataIn.timeZone |
|
|
70 | 70 |
self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
71 | 71 |
self.dataOut.errorCount = self.dataIn.errorCount |
|
|
72 | 72 |
self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
73 | ||
|
|
73 | ||
|
|
74 | 74 |
self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
75 | 75 |
self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
|
76 | 76 |
self.dataOut.channelList = self.dataIn.channelList |
|
|
77 | 77 |
self.dataOut.heightList = self.dataIn.heightList |
|
|
78 | 78 |
self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
|
79 | 79 |
# self.dataOut.nHeights = self.dataIn.nHeights |
|
|
80 | 80 |
# self.dataOut.nChannels = self.dataIn.nChannels |
|
|
81 | 81 |
self.dataOut.nBaud = self.dataIn.nBaud |
|
|
82 | 82 |
self.dataOut.nCode = self.dataIn.nCode |
|
|
83 | 83 |
self.dataOut.code = self.dataIn.code |
|
|
84 | 84 |
# self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
85 | 85 |
self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
|
86 | 86 |
# self.dataOut.utctime = self.firstdatatime |
|
|
87 | 87 |
self.dataOut.utctime = self.dataIn.utctime |
|
|
88 | 88 |
self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
|
|
89 | 89 |
self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
|
|
90 | 90 |
self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
91 | 91 |
# self.dataOut.nIncohInt = 1 |
|
|
92 | 92 |
self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
93 | 93 |
# self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
|
94 | 94 |
self.dataOut.timeInterval1 = self.dataIn.timeInterval |
|
|
95 |
self.dataOut.heightList = self.dataIn.getHeiRange() |
|
|
|
95 | self.dataOut.heightList = self.dataIn.getHeiRange() | |
|
|
96 | 96 |
self.dataOut.frequency = self.dataIn.frequency |
|
|
97 | 97 |
# self.dataOut.noise = self.dataIn.noise |
|
|
98 | ||
|
|
98 | ||
|
|
99 | 99 |
def run(self): |
|
|
100 | 100 | |
|
|
101 | 101 | |
|
|
102 | 102 | |
|
|
103 | 103 |
#---------------------- Voltage Data --------------------------- |
|
|
104 | ||
|
|
104 | ||
|
|
105 | 105 |
if self.dataIn.type == "Voltage": |
|
|
106 | 106 | |
|
|
107 | 107 |
self.__updateObjFromInput() |
|
|
108 | 108 |
self.dataOut.data_pre = self.dataIn.data.copy() |
|
|
109 | 109 |
self.dataOut.flagNoData = False |
|
|
110 | 110 |
self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
111 |
self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds |
|
|
|
111 | self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds | |
|
|
112 | if hasattr(self.dataIn, 'dataPP_POW'): | |
|
|
113 | self.dataOut.dataPP_POW = self.dataIn.dataPP_POW | |
|
|
114 | ||
|
|
115 | if hasattr(self.dataIn, 'dataPP_POWER'): | |
|
|
116 | self.dataOut.dataPP_POWER = self.dataIn.dataPP_POWER | |
|
|
117 | ||
|
|
118 | if hasattr(self.dataIn, 'dataPP_DOP'): | |
|
|
119 | self.dataOut.dataPP_DOP = self.dataIn.dataPP_DOP | |
|
|
120 | ||
|
|
121 | if hasattr(self.dataIn, 'dataPP_SNR'): | |
|
|
122 | self.dataOut.dataPP_SNR = self.dataIn.dataPP_SNR | |
|
|
123 | ||
|
|
124 | if hasattr(self.dataIn, 'dataPP_WIDTH'): | |
|
|
125 | self.dataOut.dataPP_WIDTH = self.dataIn.dataPP_WIDTH | |
|
|
112 | 126 |
return |
|
|
113 | ||
|
|
127 | ||
|
|
114 | 128 |
#---------------------- Spectra Data --------------------------- |
|
|
115 | ||
|
|
129 | ||
|
|
116 | 130 |
if self.dataIn.type == "Spectra": |
|
|
117 | 131 | |
|
|
118 | 132 |
self.dataOut.data_pre = (self.dataIn.data_spc, self.dataIn.data_cspc) |
|
|
119 | 133 |
self.dataOut.data_spc = self.dataIn.data_spc |
|
|
120 | 134 |
self.dataOut.data_cspc = self.dataIn.data_cspc |
|
|
121 | 135 |
self.dataOut.nProfiles = self.dataIn.nProfiles |
|
|
122 | 136 |
self.dataOut.nIncohInt = self.dataIn.nIncohInt |
|
|
123 | 137 |
self.dataOut.nFFTPoints = self.dataIn.nFFTPoints |
|
|
124 | 138 |
self.dataOut.ippFactor = self.dataIn.ippFactor |
|
|
125 | 139 |
self.dataOut.abscissaList = self.dataIn.getVelRange(1) |
|
|
126 | 140 |
self.dataOut.spc_noise = self.dataIn.getNoise() |
|
|
127 | 141 |
self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1)) |
|
|
128 | 142 |
# self.dataOut.normFactor = self.dataIn.normFactor |
|
|
129 |
self.dataOut.pairsList = self.dataIn.pairsList |
|
|
|
143 | self.dataOut.pairsList = self.dataIn.pairsList | |
|
|
130 | 144 |
self.dataOut.groupList = self.dataIn.pairsList |
|
|
131 |
self.dataOut.flagNoData = False |
|
|
|
132 | ||
|
|
145 | self.dataOut.flagNoData = False | |
|
|
146 | ||
|
|
133 | 147 |
if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels |
|
|
134 | 148 |
self.dataOut.ChanDist = self.dataIn.ChanDist |
|
|
135 |
else: self.dataOut.ChanDist = None |
|
|
|
136 | ||
|
|
149 | else: self.dataOut.ChanDist = None | |
|
|
150 | ||
|
|
137 | 151 |
#if hasattr(self.dataIn, 'VelRange'): #Velocities range |
|
|
138 | 152 |
# self.dataOut.VelRange = self.dataIn.VelRange |
|
|
139 | 153 |
#else: self.dataOut.VelRange = None |
|
|
140 | ||
|
|
154 | ||
|
|
141 | 155 |
if hasattr(self.dataIn, 'RadarConst'): #Radar Constant |
|
|
142 | 156 |
self.dataOut.RadarConst = self.dataIn.RadarConst |
|
|
143 | ||
|
|
157 | ||
|
|
144 | 158 |
if hasattr(self.dataIn, 'NPW'): #NPW |
|
|
145 | 159 |
self.dataOut.NPW = self.dataIn.NPW |
|
|
146 | ||
|
|
160 | ||
|
|
147 | 161 |
if hasattr(self.dataIn, 'COFA'): #COFA |
|
|
148 | 162 |
self.dataOut.COFA = self.dataIn.COFA |
|
|
149 | ||
|
|
150 | ||
|
|
151 | ||
|
|
163 | ||
|
|
164 | ||
|
|
165 | ||
|
|
152 | 166 |
#---------------------- Correlation Data --------------------------- |
|
|
153 | ||
|
|
167 | ||
|
|
154 | 168 |
if self.dataIn.type == "Correlation": |
|
|
155 | 169 |
acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions() |
|
|
156 | ||
|
|
170 | ||
|
|
157 | 171 |
self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:]) |
|
|
158 | 172 |
self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:]) |
|
|
159 | 173 |
self.dataOut.groupList = (acf_pairs, ccf_pairs) |
|
|
160 | ||
|
|
174 | ||
|
|
161 | 175 |
self.dataOut.abscissaList = self.dataIn.lagRange |
|
|
162 | 176 |
self.dataOut.noise = self.dataIn.noise |
|
|
163 | 177 |
self.dataOut.data_SNR = self.dataIn.SNR |
|
|
164 | 178 |
self.dataOut.flagNoData = False |
|
|
165 | 179 |
self.dataOut.nAvg = self.dataIn.nAvg |
|
|
166 | ||
|
|
180 | ||
|
|
167 | 181 |
#---------------------- Parameters Data --------------------------- |
|
|
168 | ||
|
|
182 | ||
|
|
169 | 183 |
if self.dataIn.type == "Parameters": |
|
|
170 | 184 |
self.dataOut.copy(self.dataIn) |
|
|
171 | 185 |
self.dataOut.flagNoData = False |
|
|
172 | ||
|
|
186 | ||
|
|
173 | 187 |
return True |
|
|
174 | ||
|
|
188 | ||
|
|
175 | 189 |
self.__updateObjFromInput() |
|
|
176 | 190 |
self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
177 | 191 |
self.dataOut.paramInterval = self.dataIn.timeInterval |
|
|
178 | ||
|
|
192 | ||
|
|
179 | 193 |
return |
|
|
180 | 194 | |
|
|
181 | 195 | |
|
|
182 | 196 |
def target(tups): |
|
|
183 | ||
|
|
197 | ||
|
|
184 | 198 |
obj, args = tups |
|
|
185 | ||
|
|
199 | ||
|
|
186 | 200 |
return obj.FitGau(args) |
|
|
187 | ||
|
|
188 | ||
|
|
201 | ||
|
|
202 | ||
|
|
189 | 203 |
class SpectralFilters(Operation): |
|
|
190 | ||
|
|
204 | ||
|
|
191 | 205 |
'''This class allows the Rainfall / Wind Selection for CLAIRE RADAR |
|
|
192 | ||
|
|
206 | ||
|
|
193 | 207 |
LimitR : It is the limit in m/s of Rainfall |
|
|
194 | 208 |
LimitW : It is the limit in m/s for Winds |
|
|
195 | ||
|
|
209 | ||
|
|
196 | 210 |
Input: |
|
|
197 | ||
|
|
211 | ||
|
|
198 | 212 |
self.dataOut.data_pre : SPC and CSPC |
|
|
199 | 213 |
self.dataOut.spc_range : To select wind and rainfall velocities |
|
|
200 | ||
|
|
214 | ||
|
|
201 | 215 |
Affected: |
|
|
202 | ||
|
|
216 | ||
|
|
203 | 217 |
self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind |
|
|
204 |
self.dataOut.spcparam_range : Used in SpcParamPlot |
|
|
|
218 | self.dataOut.spcparam_range : Used in SpcParamPlot | |
|
|
205 | 219 |
self.dataOut.SPCparam : Used in PrecipitationProc |
|
|
206 | ||
|
|
207 | ||
|
|
220 | ||
|
|
221 | ||
|
|
208 | 222 |
''' |
|
|
209 | ||
|
|
223 | ||
|
|
210 | 224 |
def __init__(self): |
|
|
211 | 225 |
Operation.__init__(self) |
|
|
212 | 226 |
self.i=0 |
|
|
213 | ||
|
|
214 |
def run(self, dataOut, PositiveLimit=1.5, NegativeLimit=2.5): |
|
|
|
215 | ||
|
|
216 | ||
|
|
217 |
#Limite de vientos |
|
|
|
227 | ||
|
|
228 | def run(self, dataOut, PositiveLimit=1.5, NegativeLimit=2.5): | |
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|
229 | ||
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|
230 | ||
|
|
231 | #Limite de vientos | |
|
|
218 | 232 |
LimitR = PositiveLimit |
|
|
219 | 233 |
LimitN = NegativeLimit |
|
|
220 | ||
|
|
234 | ||
|
|
221 | 235 |
self.spc = dataOut.data_pre[0].copy() |
|
|
222 | 236 |
self.cspc = dataOut.data_pre[1].copy() |
|
|
223 | ||
|
|
237 | ||
|
|
224 | 238 |
self.Num_Hei = self.spc.shape[2] |
|
|
225 | 239 |
self.Num_Bin = self.spc.shape[1] |
|
|
226 | 240 |
self.Num_Chn = self.spc.shape[0] |
|
|
227 | ||
|
|
241 | ||
|
|
228 | 242 |
VelRange = dataOut.spc_range[2] |
|
|
229 | 243 |
TimeRange = dataOut.spc_range[1] |
|
|
230 | 244 |
FrecRange = dataOut.spc_range[0] |
|
|
231 | ||
|
|
245 | ||
|
|
232 | 246 |
Vmax= 2*numpy.max(dataOut.spc_range[2]) |
|
|
233 | 247 |
Tmax= 2*numpy.max(dataOut.spc_range[1]) |
|
|
234 | 248 |
Fmax= 2*numpy.max(dataOut.spc_range[0]) |
|
|
235 | ||
|
|
249 | ||
|
|
236 | 250 |
Breaker1R=VelRange[numpy.abs(VelRange-(-LimitN)).argmin()] |
|
|
237 | 251 |
Breaker1R=numpy.where(VelRange == Breaker1R) |
|
|
238 | ||
|
|
239 |
Delta = self.Num_Bin/2 - Breaker1R[0] |
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|
240 | ||
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|
241 | ||
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|
252 | ||
|
|
253 | Delta = self.Num_Bin/2 - Breaker1R[0] | |
|
|
254 | ||
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|
255 | ||
|
|
242 | 256 |
'''Reacomodando SPCrange''' |
|
|
243 | 257 | |
|
|
244 | 258 |
VelRange=numpy.roll(VelRange,-(int(self.Num_Bin/2)) ,axis=0) |
|
|
245 | ||
|
|
259 | ||
|
|
246 | 260 |
VelRange[-(int(self.Num_Bin/2)):]+= Vmax |
|
|
247 | ||
|
|
261 | ||
|
|
248 | 262 |
FrecRange=numpy.roll(FrecRange,-(int(self.Num_Bin/2)),axis=0) |
|
|
249 | ||
|
|
263 | ||
|
|
250 | 264 |
FrecRange[-(int(self.Num_Bin/2)):]+= Fmax |
|
|
251 | ||
|
|
265 | ||
|
|
252 | 266 |
TimeRange=numpy.roll(TimeRange,-(int(self.Num_Bin/2)),axis=0) |
|
|
253 | ||
|
|
267 | ||
|
|
254 | 268 |
TimeRange[-(int(self.Num_Bin/2)):]+= Tmax |
|
|
255 | ||
|
|
269 | ||
|
|
256 | 270 |
''' ------------------ ''' |
|
|
257 | ||
|
|
271 | ||
|
|
258 | 272 |
Breaker2R=VelRange[numpy.abs(VelRange-(LimitR)).argmin()] |
|
|
259 | 273 |
Breaker2R=numpy.where(VelRange == Breaker2R) |
|
|
260 | ||
|
|
261 | ||
|
|
274 | ||
|
|
275 | ||
|
|
262 | 276 |
SPCroll = numpy.roll(self.spc,-(int(self.Num_Bin/2)) ,axis=1) |
|
|
263 | ||
|
|
277 | ||
|
|
264 | 278 |
SPCcut = SPCroll.copy() |
|
|
265 | 279 |
for i in range(self.Num_Chn): |
|
|
266 | ||
|
|
280 | ||
|
|
267 | 281 |
SPCcut[i,0:int(Breaker2R[0]),:] = dataOut.noise[i] |
|
|
268 | 282 |
SPCcut[i,-int(Delta):,:] = dataOut.noise[i] |
|
|
269 | ||
|
|
283 | ||
|
|
270 | 284 |
SPCcut[i]=SPCcut[i]- dataOut.noise[i] |
|
|
271 | 285 |
SPCcut[ numpy.where( SPCcut<0 ) ] = 1e-20 |
|
|
272 | ||
|
|
286 | ||
|
|
273 | 287 |
SPCroll[i]=SPCroll[i]-dataOut.noise[i] |
|
|
274 | 288 |
SPCroll[ numpy.where( SPCroll<0 ) ] = 1e-20 |
|
|
275 | ||
|
|
289 | ||
|
|
276 | 290 |
SPC_ch1 = SPCroll |
|
|
277 | ||
|
|
291 | ||
|
|
278 | 292 |
SPC_ch2 = SPCcut |
|
|
279 | ||
|
|
293 | ||
|
|
280 | 294 |
SPCparam = (SPC_ch1, SPC_ch2, self.spc) |
|
|
281 |
dataOut.SPCparam = numpy.asarray(SPCparam) |
|
|
|
282 | ||
|
|
283 | ||
|
|
295 | dataOut.SPCparam = numpy.asarray(SPCparam) | |
|
|
296 | ||
|
|
297 | ||
|
|
284 | 298 |
dataOut.spcparam_range=numpy.zeros([self.Num_Chn,self.Num_Bin+1]) |
|
|
285 | ||
|
|
299 | ||
|
|
286 | 300 |
dataOut.spcparam_range[2]=VelRange |
|
|
287 | 301 |
dataOut.spcparam_range[1]=TimeRange |
|
|
288 | 302 |
dataOut.spcparam_range[0]=FrecRange |
|
|
289 | 303 |
return dataOut |
|
|
290 | ||
|
|
304 | ||
|
|
291 | 305 |
class GaussianFit(Operation): |
|
|
292 | ||
|
|
306 | ||
|
|
293 | 307 |
''' |
|
|
294 |
Function that fit of one and two generalized gaussians (gg) based |
|
|
|
295 |
on the PSD shape across an "power band" identified from a cumsum of |
|
|
|
308 | Function that fit of one and two generalized gaussians (gg) based | |
|
|
309 | on the PSD shape across an "power band" identified from a cumsum of | |
|
|
296 | 310 |
the measured spectrum - noise. |
|
|
297 | ||
|
|
311 | ||
|
|
298 | 312 |
Input: |
|
|
299 | 313 |
self.dataOut.data_pre : SelfSpectra |
|
|
300 | ||
|
|
314 | ||
|
|
301 | 315 |
Output: |
|
|
302 | 316 |
self.dataOut.SPCparam : SPC_ch1, SPC_ch2 |
|
|
303 | ||
|
|
317 | ||
|
|
304 | 318 |
''' |
|
|
305 | 319 |
def __init__(self): |
|
|
306 | 320 |
Operation.__init__(self) |
|
|
307 | 321 |
self.i=0 |
|
|
308 | ||
|
|
309 | ||
|
|
322 | ||
|
|
323 | ||
|
|
310 | 324 |
def run(self, dataOut, num_intg=7, pnoise=1., SNRlimit=-9): #num_intg: Incoherent integrations, pnoise: Noise, vel_arr: range of velocities, similar to the ftt points |
|
|
311 | 325 |
"""This routine will find a couple of generalized Gaussians to a power spectrum |
|
|
312 | 326 |
input: spc |
|
|
313 | 327 |
output: |
|
|
314 | 328 |
Amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1,noise |
|
|
315 | 329 |
""" |
|
|
316 | ||
|
|
330 | ||
|
|
317 | 331 |
self.spc = dataOut.data_pre[0].copy() |
|
|
318 | 332 |
self.Num_Hei = self.spc.shape[2] |
|
|
319 | 333 |
self.Num_Bin = self.spc.shape[1] |
|
|
320 | 334 |
self.Num_Chn = self.spc.shape[0] |
|
|
321 | 335 |
Vrange = dataOut.abscissaList |
|
|
322 | ||
|
|
336 | ||
|
|
323 | 337 |
GauSPC = numpy.empty([self.Num_Chn,self.Num_Bin,self.Num_Hei]) |
|
|
324 | 338 |
SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei]) |
|
|
325 | 339 |
SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei]) |
|
|
326 | 340 |
SPC_ch1[:] = numpy.NaN |
|
|
327 | 341 |
SPC_ch2[:] = numpy.NaN |
|
|
328 | 342 | |
|
|
329 | ||
|
|
343 | ||
|
|
330 | 344 |
start_time = time.time() |
|
|
331 | ||
|
|
345 | ||
|
|
332 | 346 |
noise_ = dataOut.spc_noise[0].copy() |
|
|
333 | ||
|
|
334 | ||
|
|
335 |
pool = Pool(processes=self.Num_Chn) |
|
|
|
347 | ||
|
|
348 | ||
|
|
349 | pool = Pool(processes=self.Num_Chn) | |
|
|
336 | 350 |
args = [(Vrange, Ch, pnoise, noise_, num_intg, SNRlimit) for Ch in range(self.Num_Chn)] |
|
|
337 |
objs = [self for __ in range(self.Num_Chn)] |
|
|
|
338 |
attrs = list(zip(objs, args)) |
|
|
|
351 | objs = [self for __ in range(self.Num_Chn)] | |
|
|
352 | attrs = list(zip(objs, args)) | |
|
|
339 | 353 |
gauSPC = pool.map(target, attrs) |
|
|
340 | 354 |
dataOut.SPCparam = numpy.asarray(SPCparam) |
|
|
341 | ||
|
|
355 | ||
|
|
342 | 356 |
''' Parameters: |
|
|
343 | 357 |
1. Amplitude |
|
|
344 | 358 |
2. Shift |
|
|
345 | 359 |
3. Width |
|
|
346 | 360 |
4. Power |
|
|
347 | 361 |
''' |
|
|
348 | ||
|
|
362 | ||
|
|
349 | 363 |
def FitGau(self, X): |
|
|
350 | ||
|
|
364 | ||
|
|
351 | 365 |
Vrange, ch, pnoise, noise_, num_intg, SNRlimit = X |
|
|
352 | ||
|
|
366 | ||
|
|
353 | 367 |
SPCparam = [] |
|
|
354 | 368 |
SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei]) |
|
|
355 | 369 |
SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei]) |
|
|
356 | 370 |
SPC_ch1[:] = 0#numpy.NaN |
|
|
357 | 371 |
SPC_ch2[:] = 0#numpy.NaN |
|
|
358 | ||
|
|
359 | ||
|
|
360 | ||
|
|
372 | ||
|
|
373 | ||
|
|
374 | ||
|
|
361 | 375 |
for ht in range(self.Num_Hei): |
|
|
362 | ||
|
|
363 | ||
|
|
376 | ||
|
|
377 | ||
|
|
364 | 378 |
spc = numpy.asarray(self.spc)[ch,:,ht] |
|
|
365 | ||
|
|
379 | ||
|
|
366 | 380 |
############################################# |
|
|
367 | 381 |
# normalizing spc and noise |
|
|
368 | 382 |
# This part differs from gg1 |
|
|
369 | 383 |
spc_norm_max = max(spc) |
|
|
370 | 384 |
#spc = spc / spc_norm_max |
|
|
371 | 385 |
pnoise = pnoise #/ spc_norm_max |
|
|
372 | 386 |
############################################# |
|
|
373 | ||
|
|
387 | ||
|
|
374 | 388 |
fatspectra=1.0 |
|
|
375 | ||
|
|
389 | ||
|
|
376 | 390 |
wnoise = noise_ #/ spc_norm_max |
|
|
377 | 391 |
#wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used |
|
|
378 |
#if wnoise>1.1*pnoise: # to be tested later |
|
|
|
392 | #if wnoise>1.1*pnoise: # to be tested later | |
|
|
379 | 393 |
# wnoise=pnoise |
|
|
380 |
noisebl=wnoise*0.9; |
|
|
|
394 | noisebl=wnoise*0.9; | |
|
|
381 | 395 |
noisebh=wnoise*1.1 |
|
|
382 | 396 |
spc=spc-wnoise |
|
|
383 | ||
|
|
397 | ||
|
|
384 | 398 |
minx=numpy.argmin(spc) |
|
|
385 |
#spcs=spc.copy() |
|
|
|
399 | #spcs=spc.copy() | |
|
|
386 | 400 |
spcs=numpy.roll(spc,-minx) |
|
|
387 | 401 |
cum=numpy.cumsum(spcs) |
|
|
388 | 402 |
tot_noise=wnoise * self.Num_Bin #64; |
|
|
389 | ||
|
|
403 | ||
|
|
390 | 404 |
snr = sum(spcs)/tot_noise |
|
|
391 | 405 |
snrdB=10.*numpy.log10(snr) |
|
|
392 | ||
|
|
406 | ||
|
|
393 | 407 |
if snrdB < SNRlimit : |
|
|
394 | 408 |
snr = numpy.NaN |
|
|
395 | 409 |
SPC_ch1[:,ht] = 0#numpy.NaN |
|
|
396 | 410 |
SPC_ch1[:,ht] = 0#numpy.NaN |
|
|
397 | 411 |
SPCparam = (SPC_ch1,SPC_ch2) |
|
|
398 | 412 |
continue |
|
|
399 | ||
|
|
400 | ||
|
|
413 | ||
|
|
414 | ||
|
|
401 | 415 |
#if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4: |
|
|
402 | 416 |
# return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None |
|
|
403 | ||
|
|
404 |
cummax=max(cum); |
|
|
|
417 | ||
|
|
418 | cummax=max(cum); | |
|
|
405 | 419 |
epsi=0.08*fatspectra # cumsum to narrow down the energy region |
|
|
406 |
cumlo=cummax*epsi; |
|
|
|
420 | cumlo=cummax*epsi; | |
|
|
407 | 421 |
cumhi=cummax*(1-epsi) |
|
|
408 | 422 |
powerindex=numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0]) |
|
|
409 | ||
|
|
410 | ||
|
|
423 | ||
|
|
424 | ||
|
|
411 | 425 |
if len(powerindex) < 1:# case for powerindex 0 |
|
|
412 | 426 |
continue |
|
|
413 | 427 |
powerlo=powerindex[0] |
|
|
414 | 428 |
powerhi=powerindex[-1] |
|
|
415 | 429 |
powerwidth=powerhi-powerlo |
|
|
416 | ||
|
|
430 | ||
|
|
417 | 431 |
firstpeak=powerlo+powerwidth/10.# first gaussian energy location |
|
|
418 | 432 |
secondpeak=powerhi-powerwidth/10.#second gaussian energy location |
|
|
419 | 433 |
midpeak=(firstpeak+secondpeak)/2. |
|
|
420 | 434 |
firstamp=spcs[int(firstpeak)] |
|
|
421 | 435 |
secondamp=spcs[int(secondpeak)] |
|
|
422 | 436 |
midamp=spcs[int(midpeak)] |
|
|
423 | ||
|
|
437 | ||
|
|
424 | 438 |
x=numpy.arange( self.Num_Bin ) |
|
|
425 | 439 |
y_data=spc+wnoise |
|
|
426 | ||
|
|
440 | ||
|
|
427 | 441 |
''' single Gaussian ''' |
|
|
428 | 442 |
shift0=numpy.mod(midpeak+minx, self.Num_Bin ) |
|
|
429 | 443 |
width0=powerwidth/4.#Initialization entire power of spectrum divided by 4 |
|
|
430 | 444 |
power0=2. |
|
|
431 | 445 |
amplitude0=midamp |
|
|
432 | 446 |
state0=[shift0,width0,amplitude0,power0,wnoise] |
|
|
433 | 447 |
bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
434 | 448 |
lsq1=fmin_l_bfgs_b(self.misfit1,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True) |
|
|
435 | ||
|
|
436 | chiSq1=lsq1[1]; | |
|
|
437 | 449 | |
|
|
438 |
|
|
|
|
450 | chiSq1=lsq1[1]; | |
|
|
451 | ||
|
|
452 | ||
|
|
439 | 453 |
if fatspectra<1.0 and powerwidth<4: |
|
|
440 | 454 |
choice=0 |
|
|
441 | 455 |
Amplitude0=lsq1[0][2] |
|
|
442 | 456 |
shift0=lsq1[0][0] |
|
|
443 | 457 |
width0=lsq1[0][1] |
|
|
444 | 458 |
p0=lsq1[0][3] |
|
|
445 | 459 |
Amplitude1=0. |
|
|
446 | 460 |
shift1=0. |
|
|
447 | 461 |
width1=0. |
|
|
448 | 462 |
p1=0. |
|
|
449 | 463 |
noise=lsq1[0][4] |
|
|
450 | 464 |
#return (numpy.array([shift0,width0,Amplitude0,p0]), |
|
|
451 | 465 |
# numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice) |
|
|
452 | ||
|
|
466 | ||
|
|
453 | 467 |
''' two gaussians ''' |
|
|
454 | 468 |
#shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64) |
|
|
455 |
shift0=numpy.mod(firstpeak+minx, self.Num_Bin ); |
|
|
|
469 | shift0=numpy.mod(firstpeak+minx, self.Num_Bin ); | |
|
|
456 | 470 |
shift1=numpy.mod(secondpeak+minx, self.Num_Bin ) |
|
|
457 |
width0=powerwidth/6.; |
|
|
|
471 | width0=powerwidth/6.; | |
|
|
458 | 472 |
width1=width0 |
|
|
459 |
power0=2.; |
|
|
|
473 | power0=2.; | |
|
|
460 | 474 |
power1=power0 |
|
|
461 |
amplitude0=firstamp; |
|
|
|
475 | amplitude0=firstamp; | |
|
|
462 | 476 |
amplitude1=secondamp |
|
|
463 | 477 |
state0=[shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise] |
|
|
464 | 478 |
#bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
465 | 479 |
bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
466 | 480 |
#bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(0.1,0.5)) |
|
|
467 | ||
|
|
481 | ||
|
|
468 | 482 |
lsq2 = fmin_l_bfgs_b( self.misfit2 , state0 , args=(y_data,x,num_intg) , bounds=bnds , approx_grad=True ) |
|
|
469 | ||
|
|
470 | ||
|
|
471 |
chiSq2=lsq2[1]; |
|
|
|
472 | ||
|
|
473 | ||
|
|
474 | ||
|
|
483 | ||
|
|
484 | ||
|
|
485 | chiSq2=lsq2[1]; | |
|
|
486 | ||
|
|
487 | ||
|
|
488 | ||
|
|
475 | 489 |
oneG=(chiSq1<5 and chiSq1/chiSq2<2.0) and (abs(lsq2[0][0]-lsq2[0][4])<(lsq2[0][1]+lsq2[0][5])/3. or abs(lsq2[0][0]-lsq2[0][4])<10) |
|
|
476 | ||
|
|
490 | ||
|
|
477 | 491 |
if snrdB>-12: # when SNR is strong pick the peak with least shift (LOS velocity) error |
|
|
478 | 492 |
if oneG: |
|
|
479 | 493 |
choice=0 |
|
|
480 | 494 |
else: |
|
|
481 | 495 |
w1=lsq2[0][1]; w2=lsq2[0][5] |
|
|
482 | 496 |
a1=lsq2[0][2]; a2=lsq2[0][6] |
|
|
483 | 497 |
p1=lsq2[0][3]; p2=lsq2[0][7] |
|
|
484 |
s1=(2**(1+1./p1))*scipy.special.gamma(1./p1)/p1; |
|
|
|
498 | s1=(2**(1+1./p1))*scipy.special.gamma(1./p1)/p1; | |
|
|
485 | 499 |
s2=(2**(1+1./p2))*scipy.special.gamma(1./p2)/p2; |
|
|
486 | 500 |
gp1=a1*w1*s1; gp2=a2*w2*s2 # power content of each ggaussian with proper p scaling |
|
|
487 | ||
|
|
501 | ||
|
|
488 | 502 |
if gp1>gp2: |
|
|
489 | 503 |
if a1>0.7*a2: |
|
|
490 | 504 |
choice=1 |
|
|
491 | 505 |
else: |
|
|
492 | 506 |
choice=2 |
|
|
493 | 507 |
elif gp2>gp1: |
|
|
494 | 508 |
if a2>0.7*a1: |
|
|
495 | 509 |
choice=2 |
|
|
496 | 510 |
else: |
|
|
497 | 511 |
choice=1 |
|
|
498 | 512 |
else: |
|
|
499 | 513 |
choice=numpy.argmax([a1,a2])+1 |
|
|
500 | 514 |
#else: |
|
|
501 | 515 |
#choice=argmin([std2a,std2b])+1 |
|
|
502 | ||
|
|
516 | ||
|
|
503 | 517 |
else: # with low SNR go to the most energetic peak |
|
|
504 | 518 |
choice=numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]]) |
|
|
505 | ||
|
|
506 | ||
|
|
507 |
shift0=lsq2[0][0]; |
|
|
|
519 | ||
|
|
520 | ||
|
|
521 | shift0=lsq2[0][0]; | |
|
|
508 | 522 |
vel0=Vrange[0] + shift0*(Vrange[1]-Vrange[0]) |
|
|
509 |
shift1=lsq2[0][4]; |
|
|
|
523 | shift1=lsq2[0][4]; | |
|
|
510 | 524 |
vel1=Vrange[0] + shift1*(Vrange[1]-Vrange[0]) |
|
|
511 | ||
|
|
525 | ||
|
|
512 | 526 |
max_vel = 1.0 |
|
|
513 | ||
|
|
527 | ||
|
|
514 | 528 |
#first peak will be 0, second peak will be 1 |
|
|
515 | 529 |
if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range |
|
|
516 | 530 |
shift0=lsq2[0][0] |
|
|
517 | 531 |
width0=lsq2[0][1] |
|
|
518 | 532 |
Amplitude0=lsq2[0][2] |
|
|
519 | 533 |
p0=lsq2[0][3] |
|
|
520 | ||
|
|
534 | ||
|
|
521 | 535 |
shift1=lsq2[0][4] |
|
|
522 | 536 |
width1=lsq2[0][5] |
|
|
523 | 537 |
Amplitude1=lsq2[0][6] |
|
|
524 | 538 |
p1=lsq2[0][7] |
|
|
525 |
noise=lsq2[0][8] |
|
|
|
539 | noise=lsq2[0][8] | |
|
|
526 | 540 |
else: |
|
|
527 | 541 |
shift1=lsq2[0][0] |
|
|
528 | 542 |
width1=lsq2[0][1] |
|
|
529 | 543 |
Amplitude1=lsq2[0][2] |
|
|
530 | 544 |
p1=lsq2[0][3] |
|
|
531 | ||
|
|
545 | ||
|
|
532 | 546 |
shift0=lsq2[0][4] |
|
|
533 | 547 |
width0=lsq2[0][5] |
|
|
534 | 548 |
Amplitude0=lsq2[0][6] |
|
|
535 |
p0=lsq2[0][7] |
|
|
|
536 |
noise=lsq2[0][8] |
|
|
|
537 | ||
|
|
549 | p0=lsq2[0][7] | |
|
|
550 | noise=lsq2[0][8] | |
|
|
551 | ||
|
|
538 | 552 |
if Amplitude0<0.05: # in case the peak is noise |
|
|
539 |
shift0,width0,Amplitude0,p0 = [0,0,0,0]#4*[numpy.NaN] |
|
|
|
553 | shift0,width0,Amplitude0,p0 = [0,0,0,0]#4*[numpy.NaN] | |
|
|
540 | 554 |
if Amplitude1<0.05: |
|
|
541 |
shift1,width1,Amplitude1,p1 = [0,0,0,0]#4*[numpy.NaN] |
|
|
|
542 | ||
|
|
543 | ||
|
|
555 | shift1,width1,Amplitude1,p1 = [0,0,0,0]#4*[numpy.NaN] | |
|
|
556 | ||
|
|
557 | ||
|
|
544 | 558 |
SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0 |
|
|
545 | 559 |
SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1))/width1)**p1 |
|
|
546 | 560 |
SPCparam = (SPC_ch1,SPC_ch2) |
|
|
547 | ||
|
|
548 | ||
|
|
561 | ||
|
|
562 | ||
|
|
549 | 563 |
return GauSPC |
|
|
550 | ||
|
|
564 | ||
|
|
551 | 565 |
def y_model1(self,x,state): |
|
|
552 | 566 |
shift0,width0,amplitude0,power0,noise=state |
|
|
553 | 567 |
model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) |
|
|
554 | ||
|
|
568 | ||
|
|
555 | 569 |
model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0) |
|
|
556 | ||
|
|
570 | ||
|
|
557 | 571 |
model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0) |
|
|
558 | 572 |
return model0+model0u+model0d+noise |
|
|
559 | ||
|
|
560 |
def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist |
|
|
|
573 | ||
|
|
574 | def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist | |
|
|
561 | 575 |
shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,noise=state |
|
|
562 | 576 |
model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) |
|
|
563 | ||
|
|
577 | ||
|
|
564 | 578 |
model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0) |
|
|
565 | ||
|
|
579 | ||
|
|
566 | 580 |
model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0) |
|
|
567 | 581 |
model1=amplitude1*numpy.exp(-0.5*abs((x-shift1)/width1)**power1) |
|
|
568 | ||
|
|
582 | ||
|
|
569 | 583 |
model1u=amplitude1*numpy.exp(-0.5*abs((x-shift1- self.Num_Bin )/width1)**power1) |
|
|
570 | ||
|
|
584 | ||
|
|
571 | 585 |
model1d=amplitude1*numpy.exp(-0.5*abs((x-shift1+ self.Num_Bin )/width1)**power1) |
|
|
572 | 586 |
return model0+model0u+model0d+model1+model1u+model1d+noise |
|
|
573 | ||
|
|
574 |
def misfit1(self,state,y_data,x,num_intg): # This function compares how close real data is with the model data, the close it is, the better it is. |
|
|
|
587 | ||
|
|
588 | def misfit1(self,state,y_data,x,num_intg): # This function compares how close real data is with the model data, the close it is, the better it is. | |
|
|
575 | 589 | |
|
|
576 | 590 |
return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented |
|
|
577 | ||
|
|
591 | ||
|
|
578 | 592 |
def misfit2(self,state,y_data,x,num_intg): |
|
|
579 | 593 |
return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.) |
|
|
580 | ||
|
|
581 | ||
|
|
594 | ||
|
|
595 | ||
|
|
582 | 596 | |
|
|
583 | 597 |
class PrecipitationProc(Operation): |
|
|
584 | ||
|
|
598 | ||
|
|
585 | 599 |
''' |
|
|
586 | 600 |
Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R) |
|
|
587 | ||
|
|
588 |
Input: |
|
|
|
601 | ||
|
|
602 | Input: | |
|
|
589 | 603 |
self.dataOut.data_pre : SelfSpectra |
|
|
590 | ||
|
|
591 |
Output: |
|
|
|
592 | ||
|
|
593 |
self.dataOut.data_output : Reflectivity factor, rainfall Rate |
|
|
|
594 | ||
|
|
595 | ||
|
|
596 |
Parameters affected: |
|
|
|
604 | ||
|
|
605 | Output: | |
|
|
606 | ||
|
|
607 | self.dataOut.data_output : Reflectivity factor, rainfall Rate | |
|
|
608 | ||
|
|
609 | ||
|
|
610 | Parameters affected: | |
|
|
597 | 611 |
''' |
|
|
598 | ||
|
|
612 | ||
|
|
599 | 613 |
def __init__(self): |
|
|
600 | 614 |
Operation.__init__(self) |
|
|
601 | 615 |
self.i=0 |
|
|
602 | ||
|
|
603 | ||
|
|
616 | ||
|
|
617 | ||
|
|
604 | 618 |
def gaus(self,xSamples,Amp,Mu,Sigma): |
|
|
605 | 619 |
return ( Amp / ((2*numpy.pi)**0.5 * Sigma) ) * numpy.exp( -( xSamples - Mu )**2 / ( 2 * (Sigma**2) )) |
|
|
606 | ||
|
|
607 | ||
|
|
608 | ||
|
|
620 | ||
|
|
621 | ||
|
|
622 | ||
|
|
609 | 623 |
def Moments(self, ySamples, xSamples): |
|
|
610 | 624 |
Pot = numpy.nansum( ySamples ) # Potencia, momento 0 |
|
|
611 | 625 |
yNorm = ySamples / Pot |
|
|
612 | ||
|
|
626 | ||
|
|
613 | 627 |
Vr = numpy.nansum( yNorm * xSamples ) # Velocidad radial, mu, corrimiento doppler, primer momento |
|
|
614 |
Sigma2 = abs(numpy.nansum( yNorm * ( xSamples - Vr )**2 )) # Segundo Momento |
|
|
|
628 | Sigma2 = abs(numpy.nansum( yNorm * ( xSamples - Vr )**2 )) # Segundo Momento | |
|
|
615 | 629 |
Desv = Sigma2**0.5 # Desv. Estandar, Ancho espectral |
|
|
616 | ||
|
|
617 |
return numpy.array([Pot, Vr, Desv]) |
|
|
|
618 | ||
|
|
619 |
def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118, |
|
|
|
630 | ||
|
|
631 | return numpy.array([Pot, Vr, Desv]) | |
|
|
632 | ||
|
|
633 | def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118, | |
|
|
620 | 634 |
tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km = 0.93, Altitude=3350): |
|
|
621 | ||
|
|
622 | ||
|
|
635 | ||
|
|
636 | ||
|
|
623 | 637 |
Velrange = dataOut.spcparam_range[2] |
|
|
624 | 638 |
FrecRange = dataOut.spcparam_range[0] |
|
|
625 | ||
|
|
639 | ||
|
|
626 | 640 |
dV= Velrange[1]-Velrange[0] |
|
|
627 | 641 |
dF= FrecRange[1]-FrecRange[0] |
|
|
628 | ||
|
|
642 | ||
|
|
629 | 643 |
if radar == "MIRA35C" : |
|
|
630 | ||
|
|
644 | ||
|
|
631 | 645 |
self.spc = dataOut.data_pre[0].copy() |
|
|
632 | 646 |
self.Num_Hei = self.spc.shape[2] |
|
|
633 | 647 |
self.Num_Bin = self.spc.shape[1] |
|
|
634 | 648 |
self.Num_Chn = self.spc.shape[0] |
|
|
635 | 649 |
Ze = self.dBZeMODE2(dataOut) |
|
|
636 | ||
|
|
650 | ||
|
|
637 | 651 |
else: |
|
|
638 | ||
|
|
652 | ||
|
|
639 | 653 |
self.spc = dataOut.SPCparam[1].copy() #dataOut.data_pre[0].copy() # |
|
|
640 | ||
|
|
654 | ||
|
|
641 | 655 |
"""NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX""" |
|
|
642 | ||
|
|
643 |
self.spc[:,:,0:7]= numpy.NaN |
|
|
|
644 | ||
|
|
656 | ||
|
|
657 | self.spc[:,:,0:7]= numpy.NaN | |
|
|
658 | ||
|
|
645 | 659 |
"""##########################################""" |
|
|
646 | ||
|
|
660 | ||
|
|
647 | 661 |
self.Num_Hei = self.spc.shape[2] |
|
|
648 | 662 |
self.Num_Bin = self.spc.shape[1] |
|
|
649 | 663 |
self.Num_Chn = self.spc.shape[0] |
|
|
650 | ||
|
|
664 | ||
|
|
651 | 665 |
''' Se obtiene la constante del RADAR ''' |
|
|
652 | ||
|
|
666 | ||
|
|
653 | 667 |
self.Pt = Pt |
|
|
654 | 668 |
self.Gt = Gt |
|
|
655 | 669 |
self.Gr = Gr |
|
|
656 | 670 |
self.Lambda = Lambda |
|
|
657 | 671 |
self.aL = aL |
|
|
658 | 672 |
self.tauW = tauW |
|
|
659 | 673 |
self.ThetaT = ThetaT |
|
|
660 | 674 |
self.ThetaR = ThetaR |
|
|
661 | ||
|
|
675 | ||
|
|
662 | 676 |
Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
|
663 | 677 |
Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR) |
|
|
664 | 678 |
RadarConstant = 10e-26 * Numerator / Denominator # |
|
|
665 | ||
|
|
679 | ||
|
|
666 | 680 |
''' ============================= ''' |
|
|
667 | ||
|
|
668 |
self.spc[0] = (self.spc[0]-dataOut.noise[0]) |
|
|
|
669 |
self.spc[1] = (self.spc[1]-dataOut.noise[1]) |
|
|
|
670 |
self.spc[2] = (self.spc[2]-dataOut.noise[2]) |
|
|
|
671 | ||
|
|
681 | ||
|
|
682 | self.spc[0] = (self.spc[0]-dataOut.noise[0]) | |
|
|
683 | self.spc[1] = (self.spc[1]-dataOut.noise[1]) | |
|
|
684 | self.spc[2] = (self.spc[2]-dataOut.noise[2]) | |
|
|
685 | ||
|
|
672 | 686 |
self.spc[ numpy.where(self.spc < 0)] = 0 |
|
|
673 | ||
|
|
674 |
SPCmean = (numpy.mean(self.spc,0) - numpy.mean(dataOut.noise)) |
|
|
|
687 | ||
|
|
688 | SPCmean = (numpy.mean(self.spc,0) - numpy.mean(dataOut.noise)) | |
|
|
675 | 689 |
SPCmean[ numpy.where(SPCmean < 0)] = 0 |
|
|
676 | ||
|
|
690 | ||
|
|
677 | 691 |
ETAn = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
678 | 692 |
ETAv = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
679 | 693 |
ETAd = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
680 | ||
|
|
694 | ||
|
|
681 | 695 |
Pr = SPCmean[:,:] |
|
|
682 | ||
|
|
696 | ||
|
|
683 | 697 |
VelMeteoro = numpy.mean(SPCmean,axis=0) |
|
|
684 | ||
|
|
698 | ||
|
|
685 | 699 |
D_range = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
686 | 700 |
SIGMA = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
687 | 701 |
N_dist = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
688 | 702 |
V_mean = numpy.zeros(self.Num_Hei) |
|
|
689 | 703 |
del_V = numpy.zeros(self.Num_Hei) |
|
|
690 | 704 |
Z = numpy.zeros(self.Num_Hei) |
|
|
691 | 705 |
Ze = numpy.zeros(self.Num_Hei) |
|
|
692 | 706 |
RR = numpy.zeros(self.Num_Hei) |
|
|
693 | ||
|
|
707 | ||
|
|
694 | 708 |
Range = dataOut.heightList*1000. |
|
|
695 | ||
|
|
709 | ||
|
|
696 | 710 |
for R in range(self.Num_Hei): |
|
|
697 | ||
|
|
711 | ||
|
|
698 | 712 |
h = Range[R] + Altitude #Range from ground to radar pulse altitude |
|
|
699 | 713 |
del_V[R] = 1 + 3.68 * 10**-5 * h + 1.71 * 10**-9 * h**2 #Density change correction for velocity |
|
|
700 | ||
|
|
714 | ||
|
|
701 | 715 |
D_range[:,R] = numpy.log( (9.65 - (Velrange[0:self.Num_Bin] / del_V[R])) / 10.3 ) / -0.6 #Diameter range [m]x10**-3 |
|
|
702 | ||
|
|
716 | ||
|
|
703 | 717 |
'''NOTA: ETA(n) dn = ETA(f) df |
|
|
704 | ||
|
|
718 | ||
|
|
705 | 719 |
dn = 1 Diferencial de muestreo |
|
|
706 | 720 |
df = ETA(n) / ETA(f) |
|
|
707 | ||
|
|
721 | ||
|
|
708 | 722 |
''' |
|
|
709 | ||
|
|
723 | ||
|
|
710 | 724 |
ETAn[:,R] = RadarConstant * Pr[:,R] * (Range[R] )**2 #Reflectivity (ETA) |
|
|
711 | ||
|
|
725 | ||
|
|
712 | 726 |
ETAv[:,R]=ETAn[:,R]/dV |
|
|
713 | ||
|
|
727 | ||
|
|
714 | 728 |
ETAd[:,R]=ETAv[:,R]*6.18*exp(-0.6*D_range[:,R]) |
|
|
715 | ||
|
|
729 | ||
|
|
716 | 730 |
SIGMA[:,R] = Km * (D_range[:,R] * 1e-3 )**6 * numpy.pi**5 / Lambda**4 #Equivalent Section of drops (sigma) |
|
|
717 | ||
|
|
718 |
N_dist[:,R] = ETAn[:,R] / SIGMA[:,R] |
|
|
|
719 | ||
|
|
731 | ||
|
|
732 | N_dist[:,R] = ETAn[:,R] / SIGMA[:,R] | |
|
|
733 | ||
|
|
720 | 734 |
DMoments = self.Moments(Pr[:,R], Velrange[0:self.Num_Bin]) |
|
|
721 | ||
|
|
735 | ||
|
|
722 | 736 |
try: |
|
|
723 | 737 |
popt01,pcov = curve_fit(self.gaus, Velrange[0:self.Num_Bin] , Pr[:,R] , p0=DMoments) |
|
|
724 |
except: |
|
|
|
738 | except: | |
|
|
725 | 739 |
popt01=numpy.zeros(3) |
|
|
726 | 740 |
popt01[1]= DMoments[1] |
|
|
727 | ||
|
|
741 | ||
|
|
728 | 742 |
if popt01[1]<0 or popt01[1]>20: |
|
|
729 | 743 |
popt01[1]=numpy.NaN |
|
|
730 | ||
|
|
731 | ||
|
|
744 | ||
|
|
745 | ||
|
|
732 | 746 |
V_mean[R]=popt01[1] |
|
|
733 | ||
|
|
747 | ||
|
|
734 | 748 |
Z[R] = numpy.nansum( N_dist[:,R] * (D_range[:,R])**6 )#*10**-18 |
|
|
735 | ||
|
|
749 | ||
|
|
736 | 750 |
RR[R] = 0.0006*numpy.pi * numpy.nansum( D_range[:,R]**3 * N_dist[:,R] * Velrange[0:self.Num_Bin] ) #Rainfall rate |
|
|
737 | ||
|
|
751 | ||
|
|
738 | 752 |
Ze[R] = (numpy.nansum( ETAn[:,R]) * Lambda**4) / ( 10**-18*numpy.pi**5 * Km) |
|
|
739 | ||
|
|
740 | ||
|
|
741 | ||
|
|
753 | ||
|
|
754 | ||
|
|
755 | ||
|
|
742 | 756 |
RR2 = (Z/200)**(1/1.6) |
|
|
743 | 757 |
dBRR = 10*numpy.log10(RR) |
|
|
744 | 758 |
dBRR2 = 10*numpy.log10(RR2) |
|
|
745 | ||
|
|
759 | ||
|
|
746 | 760 |
dBZe = 10*numpy.log10(Ze) |
|
|
747 | 761 |
dBZ = 10*numpy.log10(Z) |
|
|
748 | ||
|
|
762 | ||
|
|
749 | 763 |
dataOut.data_output = RR[8] |
|
|
750 | 764 |
dataOut.data_param = numpy.ones([3,self.Num_Hei]) |
|
|
751 | 765 |
dataOut.channelList = [0,1,2] |
|
|
752 | ||
|
|
766 | ||
|
|
753 | 767 |
dataOut.data_param[0]=dBZ |
|
|
754 | 768 |
dataOut.data_param[1]=V_mean |
|
|
755 | 769 |
dataOut.data_param[2]=RR |
|
|
756 | 770 | |
|
|
757 | 771 |
return dataOut |
|
|
758 | ||
|
|
772 | ||
|
|
759 | 773 |
def dBZeMODE2(self, dataOut): # Processing for MIRA35C |
|
|
760 | ||
|
|
774 | ||
|
|
761 | 775 |
NPW = dataOut.NPW |
|
|
762 | 776 |
COFA = dataOut.COFA |
|
|
763 | ||
|
|
777 | ||
|
|
764 | 778 |
SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]]) |
|
|
765 | 779 |
RadarConst = dataOut.RadarConst |
|
|
766 | 780 |
#frequency = 34.85*10**9 |
|
|
767 | ||
|
|
781 | ||
|
|
768 | 782 |
ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei])) |
|
|
769 | 783 |
data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN |
|
|
770 | ||
|
|
784 | ||
|
|
771 | 785 |
ETA = numpy.sum(SNR,1) |
|
|
772 | ||
|
|
786 | ||
|
|
773 | 787 |
ETA = numpy.where(ETA is not 0. , ETA, numpy.NaN) |
|
|
774 | ||
|
|
788 | ||
|
|
775 | 789 |
Ze = numpy.ones([self.Num_Chn, self.Num_Hei] ) |
|
|
776 | ||
|
|
790 | ||
|
|
777 | 791 |
for r in range(self.Num_Hei): |
|
|
778 | ||
|
|
792 | ||
|
|
779 | 793 |
Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2) |
|
|
780 | 794 |
#Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2) |
|
|
781 | ||
|
|
795 | ||
|
|
782 | 796 |
return Ze |
|
|
783 | ||
|
|
797 | ||
|
|
784 | 798 |
# def GetRadarConstant(self): |
|
|
785 | # | |
|
|
786 |
# """ |
|
|
|
799 | # | |
|
|
800 | # """ | |
|
|
787 | 801 |
# Constants: |
|
|
788 | # | |
|
|
802 | # | |
|
|
789 | 803 |
# Pt: Transmission Power dB 5kW 5000 |
|
|
790 | 804 |
# Gt: Transmission Gain dB 24.7 dB 295.1209 |
|
|
791 | 805 |
# Gr: Reception Gain dB 18.5 dB 70.7945 |
|
|
792 | 806 |
# Lambda: Wavelenght m 0.6741 m 0.6741 |
|
|
793 | 807 |
# aL: Attenuation loses dB 4dB 2.5118 |
|
|
794 | 808 |
# tauW: Width of transmission pulse s 4us 4e-6 |
|
|
795 | 809 |
# ThetaT: Transmission antenna bean angle rad 0.1656317 rad 0.1656317 |
|
|
796 | 810 |
# ThetaR: Reception antenna beam angle rad 0.36774087 rad 0.36774087 |
|
|
797 | # | |
|
|
811 | # | |
|
|
798 | 812 |
# """ |
|
|
799 | # | |
|
|
813 | # | |
|
|
800 | 814 |
# Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
|
801 | 815 |
# Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR) |
|
|
802 | 816 |
# RadarConstant = Numerator / Denominator |
|
|
803 | # | |
|
|
817 | # | |
|
|
804 | 818 |
# return RadarConstant |
|
|
805 | ||
|
|
806 | ||
|
|
807 | ||
|
|
808 |
class FullSpectralAnalysis(Operation): |
|
|
|
809 | ||
|
|
819 | ||
|
|
820 | ||
|
|
821 | ||
|
|
822 | class FullSpectralAnalysis(Operation): | |
|
|
823 | ||
|
|
810 | 824 |
""" |
|
|
811 | 825 |
Function that implements Full Spectral Analysis technique. |
|
|
812 | ||
|
|
813 |
Input: |
|
|
|
826 | ||
|
|
827 | Input: | |
|
|
814 | 828 |
self.dataOut.data_pre : SelfSpectra and CrossSpectra data |
|
|
815 | 829 |
self.dataOut.groupList : Pairlist of channels |
|
|
816 | 830 |
self.dataOut.ChanDist : Physical distance between receivers |
|
|
817 | ||
|
|
818 | ||
|
|
819 |
Output: |
|
|
|
820 | ||
|
|
821 |
self.dataOut.data_output : Zonal wind, Meridional wind and Vertical wind |
|
|
|
822 | ||
|
|
823 | ||
|
|
831 | ||
|
|
832 | ||
|
|
833 | Output: | |
|
|
834 | ||
|
|
835 | self.dataOut.data_output : Zonal wind, Meridional wind and Vertical wind | |
|
|
836 | ||
|
|
837 | ||
|
|
824 | 838 |
Parameters affected: Winds, height range, SNR |
|
|
825 | ||
|
|
839 | ||
|
|
826 | 840 |
""" |
|
|
827 | 841 |
def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRlimit=7, minheight=None, maxheight=None): |
|
|
828 | ||
|
|
829 |
self.indice=int(numpy.random.rand()*1000) |
|
|
|
830 | ||
|
|
842 | ||
|
|
843 | self.indice=int(numpy.random.rand()*1000) | |
|
|
844 | ||
|
|
831 | 845 |
spc = dataOut.data_pre[0].copy() |
|
|
832 | 846 |
cspc = dataOut.data_pre[1] |
|
|
833 | ||
|
|
847 | ||
|
|
834 | 848 |
"""Erick: NOTE THE RANGE OF THE PULSE TX MUST BE REMOVED""" |
|
|
835 | 849 | |
|
|
836 | 850 |
SNRspc = spc.copy() |
|
|
837 | 851 |
SNRspc[:,:,0:7]= numpy.NaN |
|
|
838 | ||
|
|
852 | ||
|
|
839 | 853 |
"""##########################################""" |
|
|
840 | ||
|
|
841 | ||
|
|
854 | ||
|
|
855 | ||
|
|
842 | 856 |
nChannel = spc.shape[0] |
|
|
843 | 857 |
nProfiles = spc.shape[1] |
|
|
844 | 858 |
nHeights = spc.shape[2] |
|
|
845 | ||
|
|
859 | ||
|
|
846 | 860 |
# first_height = 0.75 #km (ref: data header 20170822) |
|
|
847 | 861 |
# resolution_height = 0.075 #km |
|
|
848 | 862 |
''' |
|
|
849 | 863 |
finding height range. check this when radar parameters are changed! |
|
|
850 | 864 |
''' |
|
|
851 | 865 |
if maxheight is not None: |
|
|
852 | 866 |
# range_max = math.ceil((maxheight - first_height) / resolution_height) # theoretical |
|
|
853 | 867 |
range_max = math.ceil(13.26 * maxheight - 3) # empirical, works better |
|
|
854 | 868 |
else: |
|
|
855 | 869 |
range_max = nHeights |
|
|
856 | 870 |
if minheight is not None: |
|
|
857 | 871 |
# range_min = int((minheight - first_height) / resolution_height) # theoretical |
|
|
858 | 872 |
range_min = int(13.26 * minheight - 5) # empirical, works better |
|
|
859 | 873 |
if range_min < 0: |
|
|
860 | 874 |
range_min = 0 |
|
|
861 | 875 |
else: |
|
|
862 | 876 |
range_min = 0 |
|
|
863 | 877 | |
|
|
864 | 878 |
pairsList = dataOut.groupList |
|
|
865 | 879 |
if dataOut.ChanDist is not None : |
|
|
866 | 880 |
ChanDist = dataOut.ChanDist |
|
|
867 | 881 |
else: |
|
|
868 | 882 |
ChanDist = numpy.array([[Xi01, Eta01],[Xi02,Eta02],[Xi12,Eta12]]) |
|
|
869 | ||
|
|
883 | ||
|
|
870 | 884 |
FrecRange = dataOut.spc_range[0] |
|
|
871 | ||
|
|
885 | ||
|
|
872 | 886 |
data_SNR=numpy.zeros([nProfiles]) |
|
|
873 | 887 |
noise = dataOut.noise |
|
|
874 | ||
|
|
888 | ||
|
|
875 | 889 |
dataOut.data_SNR = (numpy.mean(SNRspc,axis=1)- noise[0]) / noise[0] |
|
|
876 | ||
|
|
890 | ||
|
|
877 | 891 |
dataOut.data_SNR[numpy.where( dataOut.data_SNR <0 )] = 1e-20 |
|
|
878 | ||
|
|
879 | ||
|
|
892 | ||
|
|
893 | ||
|
|
880 | 894 |
data_output=numpy.ones([spc.shape[0],spc.shape[2]])*numpy.NaN |
|
|
881 | ||
|
|
895 | ||
|
|
882 | 896 |
velocityX=[] |
|
|
883 | 897 |
velocityY=[] |
|
|
884 |
velocityV=[] |
|
|
|
885 | ||
|
|
898 | velocityV=[] | |
|
|
899 | ||
|
|
886 | 900 |
dbSNR = 10*numpy.log10(dataOut.data_SNR) |
|
|
887 | 901 |
dbSNR = numpy.average(dbSNR,0) |
|
|
888 | ||
|
|
902 | ||
|
|
889 | 903 |
'''***********************************************WIND ESTIMATION**************************************''' |
|
|
890 | ||
|
|
904 | ||
|
|
891 | 905 |
for Height in range(nHeights): |
|
|
892 | ||
|
|
893 |
if Height >= range_min and Height < range_max: |
|
|
|
894 |
# error_code unused, yet maybe useful for future analysis. |
|
|
|
906 | ||
|
|
907 | if Height >= range_min and Height < range_max: | |
|
|
908 | # error_code unused, yet maybe useful for future analysis. | |
|
|
895 | 909 |
[Vzon,Vmer,Vver, error_code] = self.WindEstimation(spc[:,:,Height], cspc[:,:,Height], pairsList, ChanDist, Height, noise, dataOut.spc_range, dbSNR[Height], SNRlimit) |
|
|
896 | 910 |
else: |
|
|
897 | 911 |
Vzon,Vmer,Vver = 0., 0., numpy.NaN |
|
|
898 | ||
|
|
899 | ||
|
|
912 | ||
|
|
913 | ||
|
|
900 | 914 |
if abs(Vzon) < 100. and abs(Vzon) > 0. and abs(Vmer) < 100. and abs(Vmer) > 0.: |
|
|
901 | 915 |
velocityX=numpy.append(velocityX, Vzon) |
|
|
902 | 916 |
velocityY=numpy.append(velocityY, -Vmer) |
|
|
903 | 917 | |
|
|
904 | 918 |
else: |
|
|
905 | 919 |
velocityX=numpy.append(velocityX, numpy.NaN) |
|
|
906 | 920 |
velocityY=numpy.append(velocityY, numpy.NaN) |
|
|
907 | ||
|
|
921 | ||
|
|
908 | 922 |
if dbSNR[Height] > SNRlimit: |
|
|
909 | 923 |
velocityV=numpy.append(velocityV, -Vver) # reason for this minus sign -> convention? (taken from Ericks version) |
|
|
910 | 924 |
else: |
|
|
911 | 925 |
velocityV=numpy.append(velocityV, numpy.NaN) |
|
|
912 | ||
|
|
913 | ||
|
|
926 | ||
|
|
927 | ||
|
|
914 | 928 |
'''Change the numpy.array (velocityX) sign when trying to process BLTR data (Erick)''' |
|
|
915 |
data_output[0] = numpy.array(velocityX) |
|
|
|
916 |
data_output[1] = numpy.array(velocityY) |
|
|
|
929 | data_output[0] = numpy.array(velocityX) | |
|
|
930 | data_output[1] = numpy.array(velocityY) | |
|
|
917 | 931 |
data_output[2] = velocityV |
|
|
918 | ||
|
|
919 | ||
|
|
932 | ||
|
|
933 | ||
|
|
920 | 934 |
dataOut.data_output = data_output |
|
|
921 | ||
|
|
935 | ||
|
|
922 | 936 |
return dataOut |
|
|
923 | ||
|
|
937 | ||
|
|
924 | 938 | |
|
|
925 | 939 |
def moving_average(self,x, N=2): |
|
|
926 | 940 |
""" convolution for smoothenig data. note that last N-1 values are convolution with zeroes """ |
|
|
927 | 941 |
return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):] |
|
|
928 | ||
|
|
942 | ||
|
|
929 | 943 |
def gaus(self,xSamples,Amp,Mu,Sigma): |
|
|
930 | 944 |
return ( Amp / ((2*numpy.pi)**0.5 * Sigma) ) * numpy.exp( -( xSamples - Mu )**2 / ( 2 * (Sigma**2) )) |
|
|
931 | ||
|
|
945 | ||
|
|
932 | 946 |
def Moments(self, ySamples, xSamples): |
|
|
933 |
'''*** |
|
|
|
947 | '''*** | |
|
|
934 | 948 |
Variables corresponding to moments of distribution. |
|
|
935 | 949 |
Also used as initial coefficients for curve_fit. |
|
|
936 | 950 |
Vr was corrected. Only a velocity when x is velocity, of course. |
|
|
937 | 951 |
***''' |
|
|
938 | 952 |
Pot = numpy.nansum( ySamples ) # Potencia, momento 0 |
|
|
939 | 953 |
yNorm = ySamples / Pot |
|
|
940 | 954 |
x_range = (numpy.max(xSamples)-numpy.min(xSamples)) |
|
|
941 | 955 |
Vr = numpy.nansum( yNorm * xSamples )*x_range/len(xSamples) # Velocidad radial, mu, corrimiento doppler, primer momento |
|
|
942 |
Sigma2 = abs(numpy.nansum( yNorm * ( xSamples - Vr )**2 )) # Segundo Momento |
|
|
|
956 | Sigma2 = abs(numpy.nansum( yNorm * ( xSamples - Vr )**2 )) # Segundo Momento | |
|
|
943 | 957 |
Desv = Sigma2**0.5 # Desv. Estandar, Ancho espectral |
|
|
944 | ||
|
|
958 | ||
|
|
945 | 959 |
return numpy.array([Pot, Vr, Desv]) |
|
|
946 | 960 | |
|
|
947 | 961 |
def StopWindEstimation(self, error_code): |
|
|
948 | 962 |
''' |
|
|
949 | 963 |
the wind calculation and returns zeros |
|
|
950 | 964 |
''' |
|
|
951 | 965 |
Vzon = 0 |
|
|
952 | 966 |
Vmer = 0 |
|
|
953 | 967 |
Vver = numpy.nan |
|
|
954 | 968 |
return Vzon, Vmer, Vver, error_code |
|
|
955 | 969 | |
|
|
956 | 970 |
def AntiAliasing(self, interval, maxstep): |
|
|
957 |
""" |
|
|
|
971 | """ | |
|
|
958 | 972 |
function to prevent errors from aliased values when computing phaseslope |
|
|
959 | 973 |
""" |
|
|
960 | 974 |
antialiased = numpy.zeros(len(interval))*0.0 |
|
|
961 | 975 |
copyinterval = interval.copy() |
|
|
962 | 976 | |
|
|
963 | 977 |
antialiased[0] = copyinterval[0] |
|
|
964 | 978 | |
|
|
965 | 979 |
for i in range(1,len(antialiased)): |
|
|
966 | 980 | |
|
|
967 |
step = interval[i] - interval[i-1] |
|
|
|
968 | ||
|
|
981 | step = interval[i] - interval[i-1] | |
|
|
982 | ||
|
|
969 | 983 |
if step > maxstep: |
|
|
970 | 984 |
copyinterval -= 2*numpy.pi |
|
|
971 | 985 |
antialiased[i] = copyinterval[i] |
|
|
972 | 986 | |
|
|
973 | 987 |
elif step < maxstep*(-1): |
|
|
974 | 988 |
copyinterval += 2*numpy.pi |
|
|
975 | 989 |
antialiased[i] = copyinterval[i] |
|
|
976 | ||
|
|
990 | ||
|
|
977 | 991 |
else: |
|
|
978 | 992 |
antialiased[i] = copyinterval[i].copy() |
|
|
979 | 993 | |
|
|
980 | 994 |
return antialiased |
|
|
981 | 995 | |
|
|
982 | 996 |
def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit): |
|
|
983 | 997 |
""" |
|
|
984 | 998 |
Function that Calculates Zonal, Meridional and Vertical wind velocities. |
|
|
985 | 999 |
Initial Version by E. Bocanegra updated by J. Zibell until Nov. 2019. |
|
|
986 | 1000 | |
|
|
987 | 1001 |
Input: |
|
|
988 | 1002 |
spc, cspc : self spectra and cross spectra data. In Briggs notation something like S_i*(S_i)_conj, (S_j)_conj respectively. |
|
|
989 | 1003 |
pairsList : Pairlist of channels |
|
|
990 | 1004 |
ChanDist : array of xi_ij and eta_ij |
|
|
991 | 1005 |
Height : height at which data is processed |
|
|
992 | 1006 |
noise : noise in [channels] format for specific height |
|
|
993 | 1007 |
Abbsisarange : range of the frequencies or velocities |
|
|
994 | 1008 |
dbSNR, SNRlimit : signal to noise ratio in db, lower limit |
|
|
995 | 1009 | |
|
|
996 | 1010 |
Output: |
|
|
997 | 1011 |
Vzon, Vmer, Vver : wind velocities |
|
|
998 | 1012 |
error_code : int that states where code is terminated |
|
|
999 | 1013 | |
|
|
1000 | 1014 |
0 : no error detected |
|
|
1001 | 1015 |
1 : Gaussian of mean spc exceeds widthlimit |
|
|
1002 | 1016 |
2 : no Gaussian of mean spc found |
|
|
1003 | 1017 |
3 : SNR to low or velocity to high -> prec. e.g. |
|
|
1004 | 1018 |
4 : at least one Gaussian of cspc exceeds widthlimit |
|
|
1005 | 1019 |
5 : zero out of three cspc Gaussian fits converged |
|
|
1006 |
6 : phase slope fit could not be found |
|
|
|
1020 | 6 : phase slope fit could not be found | |
|
|
1007 | 1021 |
7 : arrays used to fit phase have different length |
|
|
1008 | 1022 |
8 : frequency range is either too short (len <= 5) or very long (> 30% of cspc) |
|
|
1009 | 1023 | |
|
|
1010 | 1024 |
""" |
|
|
1011 | 1025 | |
|
|
1012 | 1026 |
error_code = 0 |
|
|
1013 | ||
|
|
1027 | ||
|
|
1014 | 1028 | |
|
|
1015 | 1029 |
SPC_Samples = numpy.ones([spc.shape[0],spc.shape[1]]) # for normalized spc values for one height |
|
|
1016 | 1030 |
phase = numpy.ones([spc.shape[0],spc.shape[1]]) # phase between channels |
|
|
1017 | 1031 |
CSPC_Samples = numpy.ones([spc.shape[0],spc.shape[1]],dtype=numpy.complex_) # for normalized cspc values |
|
|
1018 | 1032 |
PhaseSlope = numpy.zeros(spc.shape[0]) # slope of the phases, channelwise |
|
|
1019 | 1033 |
PhaseInter = numpy.ones(spc.shape[0]) # intercept to the slope of the phases, channelwise |
|
|
1020 |
xFrec = AbbsisaRange[0][0:spc.shape[1]] # frequency range |
|
|
|
1034 | xFrec = AbbsisaRange[0][0:spc.shape[1]] # frequency range | |
|
|
1021 | 1035 |
xVel = AbbsisaRange[2][0:spc.shape[1]] # velocity range |
|
|
1022 | 1036 |
SPCav = numpy.average(spc, axis=0)-numpy.average(noise) # spc[0]-noise[0] |
|
|
1023 | ||
|
|
1037 | ||
|
|
1024 | 1038 |
SPCmoments_vel = self.Moments(SPCav, xVel ) # SPCmoments_vel[1] corresponds to vertical velocity and is used to determine if signal corresponds to wind (if .. <3) |
|
|
1025 | 1039 |
CSPCmoments = [] |
|
|
1026 | ||
|
|
1040 | ||
|
|
1027 | 1041 | |
|
|
1028 | 1042 |
'''Getting Eij and Nij''' |
|
|
1029 | 1043 | |
|
|
1030 | 1044 |
Xi01, Xi02, Xi12 = ChanDist[:,0] |
|
|
1031 | 1045 |
Eta01, Eta02, Eta12 = ChanDist[:,1] |
|
|
1032 | 1046 | |
|
|
1033 | 1047 |
# update nov 19 |
|
|
1034 | 1048 |
widthlimit = 7 # maximum width in Hz of the gaussian, empirically determined. Anything above 10 is unrealistic, often values between 1 and 5 correspond to proper fits. |
|
|
1035 | 1049 | |
|
|
1036 | 1050 |
'''************************* SPC is normalized ********************************''' |
|
|
1037 | 1051 | |
|
|
1038 | 1052 |
spc_norm = spc.copy() # need copy() because untouched spc is needed for normalization of cspc below |
|
|
1039 | 1053 |
spc_norm = numpy.where(numpy.isfinite(spc_norm), spc_norm, numpy.NAN) |
|
|
1040 | 1054 | |
|
|
1041 |
for i in range(spc.shape[0]): |
|
|
|
1042 | ||
|
|
1055 | for i in range(spc.shape[0]): | |
|
|
1056 | ||
|
|
1043 | 1057 |
spc_sub = spc_norm[i,:] - noise[i] # spc not smoothed here or in previous version. |
|
|
1044 | 1058 | |
|
|
1045 | 1059 |
Factor_Norm = 2*numpy.max(xFrec) / numpy.count_nonzero(~numpy.isnan(spc_sub)) # usually = Freq range / nfft |
|
|
1046 |
normalized_spc = spc_sub / (numpy.nansum(numpy.abs(spc_sub)) * Factor_Norm) |
|
|
|
1047 | ||
|
|
1060 | normalized_spc = spc_sub / (numpy.nansum(numpy.abs(spc_sub)) * Factor_Norm) | |
|
|
1061 | ||
|
|
1048 | 1062 |
xSamples = xFrec # the frequency range is taken |
|
|
1049 | 1063 |
SPC_Samples[i] = normalized_spc # Normalized SPC values are taken |
|
|
1050 | 1064 | |
|
|
1051 | 1065 |
'''********************** FITTING MEAN SPC GAUSSIAN **********************''' |
|
|
1052 | 1066 | |
|
|
1053 | 1067 |
""" the gaussian of the mean: first subtract noise, then normalize. this is legal because |
|
|
1054 | 1068 |
you only fit the curve and don't need the absolute value of height for calculation, |
|
|
1055 | 1069 |
only for estimation of width. for normalization of cross spectra, you need initial, |
|
|
1056 | 1070 |
unnormalized self-spectra With noise. |
|
|
1057 | 1071 | |
|
|
1058 |
Technically, you don't even need to normalize the self-spectra, as you only need the |
|
|
|
1072 | Technically, you don't even need to normalize the self-spectra, as you only need the | |
|
|
1059 | 1073 |
width of the peak. However, it was left this way. Note that the normalization has a flaw: |
|
|
1060 | 1074 |
due to subtraction of the noise, some values are below zero. Raw "spc" values should be |
|
|
1061 | 1075 |
>= 0, as it is the modulus squared of the signals (complex * it's conjugate) |
|
|
1062 | 1076 |
""" |
|
|
1063 | 1077 | |
|
|
1064 |
SPCMean = numpy.average(SPC_Samples, axis=0) |
|
|
|
1065 | ||
|
|
1078 | SPCMean = numpy.average(SPC_Samples, axis=0) | |
|
|
1079 | ||
|
|
1066 | 1080 |
popt = [1e-10,0,1e-10] |
|
|
1067 | 1081 |
SPCMoments = self.Moments(SPCMean, xSamples) |
|
|
1068 | 1082 | |
|
|
1069 |
if dbSNR > SNRlimit and numpy.abs(SPCmoments_vel[1]) < 3: |
|
|
|
1083 | if dbSNR > SNRlimit and numpy.abs(SPCmoments_vel[1]) < 3: | |
|
|
1070 | 1084 |
try: |
|
|
1071 | 1085 |
popt,pcov = curve_fit(self.gaus,xSamples,SPCMean,p0=SPCMoments)#, bounds=(-numpy.inf, [numpy.inf, numpy.inf, 10])). Setting bounds does not make the code faster but only keeps the fit from finding the minimum. |
|
|
1072 | 1086 |
if popt[2] > widthlimit: # CONDITION |
|
|
1073 | 1087 |
return self.StopWindEstimation(error_code = 1) |
|
|
1074 | 1088 | |
|
|
1075 | 1089 |
FitGauss = self.gaus(xSamples,*popt) |
|
|
1076 | ||
|
|
1090 | ||
|
|
1077 | 1091 |
except :#RuntimeError: |
|
|
1078 | 1092 |
return self.StopWindEstimation(error_code = 2) |
|
|
1079 | 1093 | |
|
|
1080 | 1094 |
else: |
|
|
1081 | 1095 |
return self.StopWindEstimation(error_code = 3) |
|
|
1082 | ||
|
|
1096 | ||
|
|
1083 | 1097 | |
|
|
1084 | 1098 | |
|
|
1085 | 1099 |
'''***************************** CSPC Normalization ************************* |
|
|
1086 | 1100 |
new section: |
|
|
1087 | 1101 |
The Spc spectra are used to normalize the crossspectra. Peaks from precipitation |
|
|
1088 |
influence the norm which is not desired. First, a range is identified where the |
|
|
|
1089 |
wind peak is estimated -> sum_wind is sum of those frequencies. Next, the area |
|
|
|
1090 |
around it gets cut off and values replaced by mean determined by the boundary |
|
|
|
1102 | influence the norm which is not desired. First, a range is identified where the | |
|
|
1103 | wind peak is estimated -> sum_wind is sum of those frequencies. Next, the area | |
|
|
1104 | around it gets cut off and values replaced by mean determined by the boundary | |
|
|
1091 | 1105 |
data -> sum_noise (spc is not normalized here, thats why the noise is important) |
|
|
1092 | 1106 | |
|
|
1093 | 1107 |
The sums are then added and multiplied by range/datapoints, because you need |
|
|
1094 | 1108 |
an integral and not a sum for normalization. |
|
|
1095 | ||
|
|
1096 |
A norm is found according to Briggs 92. |
|
|
|
1109 | ||
|
|
1110 | A norm is found according to Briggs 92. | |
|
|
1097 | 1111 |
''' |
|
|
1098 | 1112 | |
|
|
1099 | 1113 |
radarWavelength = 0.6741 # meters |
|
|
1100 |
count_limit_freq = numpy.abs(popt[1]) + widthlimit # Hz, m/s can be also used if velocity is desired abscissa. |
|
|
|
1114 | count_limit_freq = numpy.abs(popt[1]) + widthlimit # Hz, m/s can be also used if velocity is desired abscissa. | |
|
|
1101 | 1115 |
# count_limit_freq = numpy.max(xFrec) |
|
|
1102 | 1116 | |
|
|
1103 | 1117 |
channel_integrals = numpy.zeros(3) |
|
|
1104 | 1118 | |
|
|
1105 | 1119 |
for i in range(spc.shape[0]): |
|
|
1106 | 1120 |
''' |
|
|
1107 | 1121 |
find the point in array corresponding to count_limit frequency. |
|
|
1108 | 1122 |
sum over all frequencies in the range around zero Hz @ math.ceil(N_freq/2) |
|
|
1109 | 1123 |
''' |
|
|
1110 | 1124 |
N_freq = numpy.count_nonzero(~numpy.isnan(spc[i,:])) |
|
|
1111 |
count_limit_int = int(math.ceil( count_limit_freq / numpy.max(xFrec) * (N_freq / 2) )) # gives integer point |
|
|
|
1125 | count_limit_int = int(math.ceil( count_limit_freq / numpy.max(xFrec) * (N_freq / 2) )) # gives integer point | |
|
|
1112 | 1126 |
sum_wind = numpy.nansum( spc[i, (math.ceil(N_freq/2) - count_limit_int) : (math.ceil(N_freq / 2) + count_limit_int)] ) #N_freq/2 is where frequency (velocity) is zero, i.e. middle of spectrum. |
|
|
1113 | 1127 |
sum_noise = (numpy.mean(spc[i, :4]) + numpy.mean(spc[i, -6:-2]))/2.0 * (N_freq - 2*count_limit_int) |
|
|
1114 | 1128 |
channel_integrals[i] = (sum_noise + sum_wind) * (2*numpy.max(xFrec) / N_freq) |
|
|
1115 | ||
|
|
1129 | ||
|
|
1116 | 1130 | |
|
|
1117 | 1131 |
cross_integrals_peak = numpy.zeros(3) |
|
|
1118 | 1132 |
# cross_integrals_totalrange = numpy.zeros(3) |
|
|
1119 | 1133 | |
|
|
1120 | 1134 |
for i in range(spc.shape[0]): |
|
|
1121 | 1135 | |
|
|
1122 | 1136 |
cspc_norm = cspc[i,:].copy() # cspc not smoothed here or in previous version |
|
|
1123 | 1137 | |
|
|
1124 | 1138 |
chan_index0 = pairsList[i][0] |
|
|
1125 | 1139 |
chan_index1 = pairsList[i][1] |
|
|
1126 | 1140 | |
|
|
1127 | 1141 |
cross_integrals_peak[i] = channel_integrals[chan_index0]*channel_integrals[chan_index1] |
|
|
1128 |
normalized_cspc = cspc_norm / numpy.sqrt(cross_integrals_peak[i]) |
|
|
|
1142 | normalized_cspc = cspc_norm / numpy.sqrt(cross_integrals_peak[i]) | |
|
|
1129 | 1143 |
CSPC_Samples[i] = normalized_cspc |
|
|
1130 | 1144 | |
|
|
1131 | 1145 |
''' Finding cross integrals without subtracting any peaks:''' |
|
|
1132 | 1146 |
# FactorNorm0 = 2*numpy.max(xFrec) / numpy.count_nonzero(~numpy.isnan(spc[chan_index0,:])) |
|
|
1133 | 1147 |
# FactorNorm1 = 2*numpy.max(xFrec) / numpy.count_nonzero(~numpy.isnan(spc[chan_index1,:])) |
|
|
1134 |
# cross_integrals_totalrange[i] = (numpy.nansum(spc[chan_index0,:])) * FactorNorm0 * (numpy.nansum(spc[chan_index1,:])) * FactorNorm1 |
|
|
|
1135 |
# normalized_cspc = cspc_norm / numpy.sqrt(cross_integrals_totalrange[i]) |
|
|
|
1148 | # cross_integrals_totalrange[i] = (numpy.nansum(spc[chan_index0,:])) * FactorNorm0 * (numpy.nansum(spc[chan_index1,:])) * FactorNorm1 | |
|
|
1149 | # normalized_cspc = cspc_norm / numpy.sqrt(cross_integrals_totalrange[i]) | |
|
|
1136 | 1150 |
# CSPC_Samples[i] = normalized_cspc |
|
|
1137 | ||
|
|
1138 | ||
|
|
1151 | ||
|
|
1152 | ||
|
|
1139 | 1153 |
phase[i] = numpy.arctan2(CSPC_Samples[i].imag, CSPC_Samples[i].real) |
|
|
1140 | 1154 | |
|
|
1141 | 1155 | |
|
|
1142 | 1156 |
CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPC_Samples[0]), xSamples), |
|
|
1143 | 1157 |
self.Moments(numpy.abs(CSPC_Samples[1]), xSamples), |
|
|
1144 | 1158 |
self.Moments(numpy.abs(CSPC_Samples[2]), xSamples)]) |
|
|
1145 | ||
|
|
1159 | ||
|
|
1146 | 1160 | |
|
|
1147 | 1161 |
'''***Sorting out NaN entries***''' |
|
|
1148 | 1162 |
CSPCMask01 = numpy.abs(CSPC_Samples[0]) |
|
|
1149 | 1163 |
CSPCMask02 = numpy.abs(CSPC_Samples[1]) |
|
|
1150 | 1164 |
CSPCMask12 = numpy.abs(CSPC_Samples[2]) |
|
|
1151 | ||
|
|
1165 | ||
|
|
1152 | 1166 |
mask01 = ~numpy.isnan(CSPCMask01) |
|
|
1153 | 1167 |
mask02 = ~numpy.isnan(CSPCMask02) |
|
|
1154 | 1168 |
mask12 = ~numpy.isnan(CSPCMask12) |
|
|
1155 | ||
|
|
1169 | ||
|
|
1156 | 1170 |
CSPCMask01 = CSPCMask01[mask01] |
|
|
1157 | 1171 |
CSPCMask02 = CSPCMask02[mask02] |
|
|
1158 | 1172 |
CSPCMask12 = CSPCMask12[mask12] |
|
|
1159 | 1173 | |
|
|
1160 | ||
|
|
1161 |
popt01, popt02, popt12 = [1e-10,1e-10,1e-10], [1e-10,1e-10,1e-10] ,[1e-10,1e-10,1e-10] |
|
|
|
1174 | ||
|
|
1175 | popt01, popt02, popt12 = [1e-10,1e-10,1e-10], [1e-10,1e-10,1e-10] ,[1e-10,1e-10,1e-10] | |
|
|
1162 | 1176 |
FitGauss01, FitGauss02, FitGauss12 = numpy.empty(len(xSamples))*0, numpy.empty(len(xSamples))*0, numpy.empty(len(xSamples))*0 |
|
|
1163 | ||
|
|
1177 | ||
|
|
1164 | 1178 |
'''*******************************FIT GAUSS CSPC************************************''' |
|
|
1165 | 1179 | |
|
|
1166 |
try: |
|
|
|
1180 | try: | |
|
|
1167 | 1181 |
popt01,pcov = curve_fit(self.gaus,xSamples[mask01],numpy.abs(CSPCMask01),p0=CSPCmoments[0]) |
|
|
1168 | 1182 |
if popt01[2] > widthlimit: # CONDITION |
|
|
1169 | 1183 |
return self.StopWindEstimation(error_code = 4) |
|
|
1170 | 1184 | |
|
|
1171 | 1185 |
popt02,pcov = curve_fit(self.gaus,xSamples[mask02],numpy.abs(CSPCMask02),p0=CSPCmoments[1]) |
|
|
1172 | 1186 |
if popt02[2] > widthlimit: # CONDITION |
|
|
1173 | 1187 |
return self.StopWindEstimation(error_code = 4) |
|
|
1174 | 1188 | |
|
|
1175 | 1189 |
popt12,pcov = curve_fit(self.gaus,xSamples[mask12],numpy.abs(CSPCMask12),p0=CSPCmoments[2]) |
|
|
1176 | 1190 |
if popt12[2] > widthlimit: # CONDITION |
|
|
1177 | 1191 |
return self.StopWindEstimation(error_code = 4) |
|
|
1178 | 1192 | |
|
|
1179 | 1193 |
FitGauss01 = self.gaus(xSamples, *popt01) |
|
|
1180 | 1194 |
FitGauss02 = self.gaus(xSamples, *popt02) |
|
|
1181 | 1195 |
FitGauss12 = self.gaus(xSamples, *popt12) |
|
|
1182 | 1196 | |
|
|
1183 | 1197 |
except: |
|
|
1184 | 1198 |
return self.StopWindEstimation(error_code = 5) |
|
|
1185 | 1199 | |
|
|
1186 | 1200 | |
|
|
1187 | 1201 |
'''************* Getting Fij ***************''' |
|
|
1188 | 1202 | |
|
|
1189 | ||
|
|
1190 |
#Punto en Eje X de la Gaussiana donde se encuentra el centro -- x-axis point of the gaussian where the center is located |
|
|
|
1191 |
# -> PointGauCenter |
|
|
|
1192 |
GaussCenter = popt[1] |
|
|
|
1203 | ||
|
|
1204 | #Punto en Eje X de la Gaussiana donde se encuentra el centro -- x-axis point of the gaussian where the center is located | |
|
|
1205 | # -> PointGauCenter | |
|
|
1206 | GaussCenter = popt[1] | |
|
|
1193 | 1207 |
ClosestCenter = xSamples[numpy.abs(xSamples-GaussCenter).argmin()] |
|
|
1194 | 1208 |
PointGauCenter = numpy.where(xSamples==ClosestCenter)[0][0] |
|
|
1195 | ||
|
|
1209 | ||
|
|
1196 | 1210 |
#Punto e^-1 hubicado en la Gaussiana -- point where e^-1 is located in the gaussian |
|
|
1197 | 1211 |
PeMinus1 = numpy.max(FitGauss) * numpy.exp(-1) |
|
|
1198 | 1212 |
FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # El punto mas cercano a "Peminus1" dentro de "FitGauss" |
|
|
1199 | 1213 |
PointFij = numpy.where(FitGauss==FijClosest)[0][0] |
|
|
1200 | 1214 | |
|
|
1201 | 1215 |
Fij = numpy.abs(xSamples[PointFij] - xSamples[PointGauCenter]) |
|
|
1202 | ||
|
|
1216 | ||
|
|
1203 | 1217 |
'''********** Taking frequency ranges from mean SPCs **********''' |
|
|
1204 | ||
|
|
1218 | ||
|
|
1205 | 1219 |
#GaussCenter = popt[1] #Primer momento 01 |
|
|
1206 | 1220 |
GauWidth = popt[2] * 3/2 #Ancho de banda de Gau01 -- Bandwidth of Gau01 TODO why *3/2? |
|
|
1207 | 1221 |
Range = numpy.empty(2) |
|
|
1208 | 1222 |
Range[0] = GaussCenter - GauWidth |
|
|
1209 |
Range[1] = GaussCenter + GauWidth |
|
|
|
1223 | Range[1] = GaussCenter + GauWidth | |
|
|
1210 | 1224 |
#Punto en Eje X de la Gaussiana donde se encuentra ancho de banda (min:max) -- Point in x-axis where the bandwidth is located (min:max) |
|
|
1211 | 1225 |
ClosRangeMin = xSamples[numpy.abs(xSamples-Range[0]).argmin()] |
|
|
1212 | 1226 |
ClosRangeMax = xSamples[numpy.abs(xSamples-Range[1]).argmin()] |
|
|
1213 | ||
|
|
1227 | ||
|
|
1214 | 1228 |
PointRangeMin = numpy.where(xSamples==ClosRangeMin)[0][0] |
|
|
1215 | 1229 |
PointRangeMax = numpy.where(xSamples==ClosRangeMax)[0][0] |
|
|
1216 | ||
|
|
1230 | ||
|
|
1217 | 1231 |
Range = numpy.array([ PointRangeMin, PointRangeMax ]) |
|
|
1218 | ||
|
|
1232 | ||
|
|
1219 | 1233 |
FrecRange = xFrec[ Range[0] : Range[1] ] |
|
|
1220 | 1234 | |
|
|
1221 | ||
|
|
1222 |
'''************************** Getting Phase Slope ***************************''' |
|
|
|
1223 | ||
|
|
1224 |
for i in range(1,3): # Changed to only compute two |
|
|
|
1225 | ||
|
|
1235 | ||
|
|
1236 | '''************************** Getting Phase Slope ***************************''' | |
|
|
1237 | ||
|
|
1238 | for i in range(1,3): # Changed to only compute two | |
|
|
1239 | ||
|
|
1226 | 1240 |
if len(FrecRange) > 5 and len(FrecRange) < spc.shape[1] * 0.3: |
|
|
1227 | 1241 |
# PhaseRange=self.moving_average(phase[i,Range[0]:Range[1]],N=1) #used before to smooth phase with N=3 |
|
|
1228 | 1242 |
PhaseRange = phase[i,Range[0]:Range[1]].copy() |
|
|
1229 | ||
|
|
1243 | ||
|
|
1230 | 1244 |
mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange) |
|
|
1231 | ||
|
|
1245 | ||
|
|
1232 | 1246 | |
|
|
1233 | 1247 |
if len(FrecRange) == len(PhaseRange): |
|
|
1234 | ||
|
|
1235 |
try: |
|
|
|
1248 | ||
|
|
1249 | try: | |
|
|
1236 | 1250 |
slope, intercept, _, _, _ = stats.linregress(FrecRange[mask], self.AntiAliasing(PhaseRange[mask], 4.5)) |
|
|
1237 | 1251 |
PhaseSlope[i] = slope |
|
|
1238 | 1252 |
PhaseInter[i] = intercept |
|
|
1239 | 1253 | |
|
|
1240 | 1254 |
except: |
|
|
1241 | 1255 |
return self.StopWindEstimation(error_code = 6) |
|
|
1242 | 1256 | |
|
|
1243 | 1257 |
else: |
|
|
1244 | 1258 |
return self.StopWindEstimation(error_code = 7) |
|
|
1245 | ||
|
|
1259 | ||
|
|
1246 | 1260 |
else: |
|
|
1247 | 1261 |
return self.StopWindEstimation(error_code = 8) |
|
|
1248 | ||
|
|
1249 | ||
|
|
1250 | ||
|
|
1262 | ||
|
|
1263 | ||
|
|
1264 | ||
|
|
1251 | 1265 |
'''*** Constants A-H correspond to the convention as in Briggs and Vincent 1992 ***''' |
|
|
1252 | 1266 | |
|
|
1253 | 1267 |
'''Getting constant C''' |
|
|
1254 | 1268 |
cC=(Fij*numpy.pi)**2 |
|
|
1255 | ||
|
|
1269 | ||
|
|
1256 | 1270 |
'''****** Getting constants F and G ******''' |
|
|
1257 | 1271 |
MijEijNij = numpy.array([[Xi02,Eta02], [Xi12,Eta12]]) |
|
|
1258 | 1272 |
MijResult0 = (-PhaseSlope[1] * cC) / (2*numpy.pi) |
|
|
1259 |
MijResult1 = (-PhaseSlope[2] * cC) / (2*numpy.pi) |
|
|
|
1273 | MijResult1 = (-PhaseSlope[2] * cC) / (2*numpy.pi) | |
|
|
1260 | 1274 |
MijResults = numpy.array([MijResult0,MijResult1]) |
|
|
1261 |
(cF,cG) = numpy.linalg.solve(MijEijNij, MijResults) |
|
|
|
1262 | ||
|
|
1275 | (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults) | |
|
|
1276 | ||
|
|
1263 | 1277 |
'''****** Getting constants A, B and H ******''' |
|
|
1264 |
W01 = numpy.nanmax( FitGauss01 ) |
|
|
|
1265 |
W02 = numpy.nanmax( FitGauss02 ) |
|
|
|
1266 |
W12 = numpy.nanmax( FitGauss12 ) |
|
|
|
1267 | ||
|
|
1278 | W01 = numpy.nanmax( FitGauss01 ) | |
|
|
1279 | W02 = numpy.nanmax( FitGauss02 ) | |
|
|
1280 | W12 = numpy.nanmax( FitGauss12 ) | |
|
|
1281 | ||
|
|
1268 | 1282 |
WijResult0 = ((cF * Xi01 + cG * Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi / cC)) |
|
|
1269 | 1283 |
WijResult1 = ((cF * Xi02 + cG * Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi / cC)) |
|
|
1270 | 1284 |
WijResult2 = ((cF * Xi12 + cG * Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi / cC)) |
|
|
1271 | ||
|
|
1285 | ||
|
|
1272 | 1286 |
WijResults = numpy.array([WijResult0, WijResult1, WijResult2]) |
|
|
1273 | ||
|
|
1274 |
WijEijNij = numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ]) |
|
|
|
1287 | ||
|
|
1288 | WijEijNij = numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ]) | |
|
|
1275 | 1289 |
(cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults) |
|
|
1276 | ||
|
|
1290 | ||
|
|
1277 | 1291 |
VxVy = numpy.array([[cA,cH],[cH,cB]]) |
|
|
1278 | 1292 |
VxVyResults = numpy.array([-cF,-cG]) |
|
|
1279 | 1293 |
(Vx,Vy) = numpy.linalg.solve(VxVy, VxVyResults) |
|
|
1280 | ||
|
|
1294 | ||
|
|
1281 | 1295 |
Vzon = Vy |
|
|
1282 | 1296 |
Vmer = Vx |
|
|
1283 | ||
|
|
1297 | ||
|
|
1284 | 1298 |
# Vmag=numpy.sqrt(Vzon**2+Vmer**2) # unused |
|
|
1285 | 1299 |
# Vang=numpy.arctan2(Vmer,Vzon) # unused |
|
|
1286 | 1300 | |
|
|
1287 | ||
|
|
1301 | ||
|
|
1288 | 1302 |
''' using frequency as abscissa. Due to three channels, the offzenith angle is zero |
|
|
1289 | 1303 |
and Vrad equal to Vver. formula taken from Briggs 92, figure 4. |
|
|
1290 | 1304 |
''' |
|
|
1291 | 1305 |
if numpy.abs( popt[1] ) < 3.5 and len(FrecRange) > 4: |
|
|
1292 | 1306 |
Vver = 0.5 * radarWavelength * popt[1] * 100 # *100 to get cm (/s) |
|
|
1293 | 1307 |
else: |
|
|
1294 | 1308 |
Vver = numpy.NaN |
|
|
1295 | 1309 | |
|
|
1296 | 1310 |
error_code = 0 |
|
|
1297 | 1311 | |
|
|
1298 |
return Vzon, Vmer, Vver, error_code |
|
|
|
1312 | return Vzon, Vmer, Vver, error_code | |
|
|
1299 | 1313 | |
|
|
1300 | 1314 | |
|
|
1301 | 1315 |
class SpectralMoments(Operation): |
|
|
1302 | ||
|
|
1316 | ||
|
|
1303 | 1317 |
''' |
|
|
1304 | 1318 |
Function SpectralMoments() |
|
|
1305 | ||
|
|
1319 | ||
|
|
1306 | 1320 |
Calculates moments (power, mean, standard deviation) and SNR of the signal |
|
|
1307 | ||
|
|
1321 | ||
|
|
1308 | 1322 |
Type of dataIn: Spectra |
|
|
1309 | ||
|
|
1323 | ||
|
|
1310 | 1324 |
Configuration Parameters: |
|
|
1311 | ||
|
|
1325 | ||
|
|
1312 | 1326 |
dirCosx : Cosine director in X axis |
|
|
1313 | 1327 |
dirCosy : Cosine director in Y axis |
|
|
1314 | ||
|
|
1328 | ||
|
|
1315 | 1329 |
elevation : |
|
|
1316 | 1330 |
azimuth : |
|
|
1317 | ||
|
|
1331 | ||
|
|
1318 | 1332 |
Input: |
|
|
1319 |
channelList : simple channel list to select e.g. [2,3,7] |
|
|
|
1333 | channelList : simple channel list to select e.g. [2,3,7] | |
|
|
1320 | 1334 |
self.dataOut.data_pre : Spectral data |
|
|
1321 | 1335 |
self.dataOut.abscissaList : List of frequencies |
|
|
1322 | 1336 |
self.dataOut.noise : Noise level per channel |
|
|
1323 | ||
|
|
1337 | ||
|
|
1324 | 1338 |
Affected: |
|
|
1325 | 1339 |
self.dataOut.moments : Parameters per channel |
|
|
1326 | 1340 |
self.dataOut.data_SNR : SNR per channel |
|
|
1327 | ||
|
|
1341 | ||
|
|
1328 | 1342 |
''' |
|
|
1329 | ||
|
|
1343 | ||
|
|
1330 | 1344 |
def run(self, dataOut): |
|
|
1331 | ||
|
|
1345 | ||
|
|
1332 | 1346 |
data = dataOut.data_pre[0] |
|
|
1333 | 1347 |
absc = dataOut.abscissaList[:-1] |
|
|
1334 | 1348 |
noise = dataOut.noise |
|
|
1335 | 1349 |
nChannel = data.shape[0] |
|
|
1336 | 1350 |
data_param = numpy.zeros((nChannel, 4, data.shape[2])) |
|
|
1337 | 1351 | |
|
|
1338 | 1352 |
for ind in range(nChannel): |
|
|
1339 | 1353 |
data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind] ) |
|
|
1340 | ||
|
|
1354 | ||
|
|
1341 | 1355 |
dataOut.moments = data_param[:,1:,:] |
|
|
1342 | 1356 |
dataOut.data_SNR = data_param[:,0] |
|
|
1343 | 1357 |
dataOut.data_POW = data_param[:,1] |
|
|
1344 | 1358 |
dataOut.data_DOP = data_param[:,2] |
|
|
1345 | 1359 |
dataOut.data_WIDTH = data_param[:,3] |
|
|
1346 | 1360 | |
|
|
1347 | 1361 |
return dataOut |
|
|
1348 | ||
|
|
1349 |
def __calculateMoments(self, oldspec, oldfreq, n0, |
|
|
|
1362 | ||
|
|
1363 | def __calculateMoments(self, oldspec, oldfreq, n0, | |
|
|
1350 | 1364 |
nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None): |
|
|
1351 | ||
|
|
1365 | ||
|
|
1352 | 1366 |
if (nicoh is None): nicoh = 1 |
|
|
1353 |
if (graph is None): graph = 0 |
|
|
|
1367 | if (graph is None): graph = 0 | |
|
|
1354 | 1368 |
if (smooth is None): smooth = 0 |
|
|
1355 | 1369 |
elif (self.smooth < 3): smooth = 0 |
|
|
1356 | 1370 | |
|
|
1357 | 1371 |
if (type1 is None): type1 = 0 |
|
|
1358 | 1372 |
if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1 |
|
|
1359 | 1373 |
if (snrth is None): snrth = -3 |
|
|
1360 | 1374 |
if (dc is None): dc = 0 |
|
|
1361 | 1375 |
if (aliasing is None): aliasing = 0 |
|
|
1362 | 1376 |
if (oldfd is None): oldfd = 0 |
|
|
1363 | 1377 |
if (wwauto is None): wwauto = 0 |
|
|
1364 | ||
|
|
1378 | ||
|
|
1365 | 1379 |
if (n0 < 1.e-20): n0 = 1.e-20 |
|
|
1366 | ||
|
|
1380 | ||
|
|
1367 | 1381 |
freq = oldfreq |
|
|
1368 | 1382 |
vec_power = numpy.zeros(oldspec.shape[1]) |
|
|
1369 | 1383 |
vec_fd = numpy.zeros(oldspec.shape[1]) |
|
|
1370 | 1384 |
vec_w = numpy.zeros(oldspec.shape[1]) |
|
|
1371 | 1385 |
vec_snr = numpy.zeros(oldspec.shape[1]) |
|
|
1372 | ||
|
|
1386 | ||
|
|
1373 | 1387 |
# oldspec = numpy.ma.masked_invalid(oldspec) |
|
|
1374 | ||
|
|
1388 | ||
|
|
1375 | 1389 |
for ind in range(oldspec.shape[1]): |
|
|
1376 | ||
|
|
1390 | ||
|
|
1377 | 1391 |
spec = oldspec[:,ind] |
|
|
1378 | 1392 |
aux = spec*fwindow |
|
|
1379 | 1393 |
max_spec = aux.max() |
|
|
1380 | 1394 |
m = aux.tolist().index(max_spec) |
|
|
1381 | ||
|
|
1382 |
#Smooth |
|
|
|
1395 | ||
|
|
1396 | #Smooth | |
|
|
1383 | 1397 |
if (smooth == 0): |
|
|
1384 | 1398 |
spec2 = spec |
|
|
1385 | 1399 |
else: |
|
|
1386 | 1400 |
spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
|
|
1387 | ||
|
|
1401 | ||
|
|
1388 | 1402 |
# Calculo de Momentos |
|
|
1389 | 1403 |
bb = spec2[numpy.arange(m,spec2.size)] |
|
|
1390 | 1404 |
bb = (bb<n0).nonzero() |
|
|
1391 | 1405 |
bb = bb[0] |
|
|
1392 | ||
|
|
1406 | ||
|
|
1393 | 1407 |
ss = spec2[numpy.arange(0,m + 1)] |
|
|
1394 | 1408 |
ss = (ss<n0).nonzero() |
|
|
1395 | 1409 |
ss = ss[0] |
|
|
1396 | ||
|
|
1410 | ||
|
|
1397 | 1411 |
if (bb.size == 0): |
|
|
1398 | 1412 |
bb0 = spec.size - 1 - m |
|
|
1399 |
else: |
|
|
|
1413 | else: | |
|
|
1400 | 1414 |
bb0 = bb[0] - 1 |
|
|
1401 | 1415 |
if (bb0 < 0): |
|
|
1402 | 1416 |
bb0 = 0 |
|
|
1403 | ||
|
|
1417 | ||
|
|
1404 | 1418 |
if (ss.size == 0): |
|
|
1405 | 1419 |
ss1 = 1 |
|
|
1406 | 1420 |
else: |
|
|
1407 | 1421 |
ss1 = max(ss) + 1 |
|
|
1408 | ||
|
|
1422 | ||
|
|
1409 | 1423 |
if (ss1 > m): |
|
|
1410 | 1424 |
ss1 = m |
|
|
1411 | ||
|
|
1412 |
valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1 |
|
|
|
1413 | ||
|
|
1425 | ||
|
|
1426 | valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1 | |
|
|
1427 | ||
|
|
1414 | 1428 |
power = ((spec2[valid] - n0) * fwindow[valid]).sum() |
|
|
1415 | 1429 |
fd = ((spec2[valid]- n0)*freq[valid] * fwindow[valid]).sum() / power |
|
|
1416 | 1430 |
w = numpy.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum() / power) |
|
|
1417 | 1431 |
snr = (spec2.mean()-n0)/n0 |
|
|
1418 |
if (snr < 1.e-20) : |
|
|
|
1432 | if (snr < 1.e-20) : | |
|
|
1419 | 1433 |
snr = 1.e-20 |
|
|
1420 | ||
|
|
1434 | ||
|
|
1421 | 1435 |
vec_power[ind] = power |
|
|
1422 | 1436 |
vec_fd[ind] = fd |
|
|
1423 | 1437 |
vec_w[ind] = w |
|
|
1424 | 1438 |
vec_snr[ind] = snr |
|
|
1425 | 1439 | |
|
|
1426 | 1440 |
return numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
|
|
1427 | ||
|
|
1441 | ||
|
|
1428 | 1442 |
#------------------ Get SA Parameters -------------------------- |
|
|
1429 | ||
|
|
1443 | ||
|
|
1430 | 1444 |
def GetSAParameters(self): |
|
|
1431 | 1445 |
#SA en frecuencia |
|
|
1432 | 1446 |
pairslist = self.dataOut.groupList |
|
|
1433 | 1447 |
num_pairs = len(pairslist) |
|
|
1434 | ||
|
|
1448 | ||
|
|
1435 | 1449 |
vel = self.dataOut.abscissaList |
|
|
1436 | 1450 |
spectra = self.dataOut.data_pre |
|
|
1437 | 1451 |
cspectra = self.dataIn.data_cspc |
|
|
1438 |
delta_v = vel[1] - vel[0] |
|
|
|
1439 | ||
|
|
1452 | delta_v = vel[1] - vel[0] | |
|
|
1453 | ||
|
|
1440 | 1454 |
#Calculating the power spectrum |
|
|
1441 | 1455 |
spc_pow = numpy.sum(spectra, 3)*delta_v |
|
|
1442 | 1456 |
#Normalizing Spectra |
|
|
1443 | 1457 |
norm_spectra = spectra/spc_pow |
|
|
1444 | 1458 |
#Calculating the norm_spectra at peak |
|
|
1445 |
max_spectra = numpy.max(norm_spectra, 3) |
|
|
|
1446 | ||
|
|
1459 | max_spectra = numpy.max(norm_spectra, 3) | |
|
|
1460 | ||
|
|
1447 | 1461 |
#Normalizing Cross Spectra |
|
|
1448 | 1462 |
norm_cspectra = numpy.zeros(cspectra.shape) |
|
|
1449 | ||
|
|
1463 | ||
|
|
1450 | 1464 |
for i in range(num_chan): |
|
|
1451 | 1465 |
norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) |
|
|
1452 | ||
|
|
1466 | ||
|
|
1453 | 1467 |
max_cspectra = numpy.max(norm_cspectra,2) |
|
|
1454 | 1468 |
max_cspectra_index = numpy.argmax(norm_cspectra, 2) |
|
|
1455 | ||
|
|
1469 | ||
|
|
1456 | 1470 |
for i in range(num_pairs): |
|
|
1457 | 1471 |
cspc_par[i,:,:] = __calculateMoments(norm_cspectra) |
|
|
1458 | 1472 |
#------------------- Get Lags ---------------------------------- |
|
|
1459 | ||
|
|
1473 | ||
|
|
1460 | 1474 |
class SALags(Operation): |
|
|
1461 | 1475 |
''' |
|
|
1462 | 1476 |
Function GetMoments() |
|
|
1463 | 1477 | |
|
|
1464 | 1478 |
Input: |
|
|
1465 | 1479 |
self.dataOut.data_pre |
|
|
1466 | 1480 |
self.dataOut.abscissaList |
|
|
1467 | 1481 |
self.dataOut.noise |
|
|
1468 | 1482 |
self.dataOut.normFactor |
|
|
1469 | 1483 |
self.dataOut.data_SNR |
|
|
1470 | 1484 |
self.dataOut.groupList |
|
|
1471 | 1485 |
self.dataOut.nChannels |
|
|
1472 | ||
|
|
1486 | ||
|
|
1473 | 1487 |
Affected: |
|
|
1474 | 1488 |
self.dataOut.data_param |
|
|
1475 | ||
|
|
1489 | ||
|
|
1476 | 1490 |
''' |
|
|
1477 |
def run(self, dataOut): |
|
|
|
1491 | def run(self, dataOut): | |
|
|
1478 | 1492 |
data_acf = dataOut.data_pre[0] |
|
|
1479 | 1493 |
data_ccf = dataOut.data_pre[1] |
|
|
1480 | 1494 |
normFactor_acf = dataOut.normFactor[0] |
|
|
1481 | 1495 |
normFactor_ccf = dataOut.normFactor[1] |
|
|
1482 | 1496 |
pairs_acf = dataOut.groupList[0] |
|
|
1483 | 1497 |
pairs_ccf = dataOut.groupList[1] |
|
|
1484 | ||
|
|
1498 | ||
|
|
1485 | 1499 |
nHeights = dataOut.nHeights |
|
|
1486 | 1500 |
absc = dataOut.abscissaList |
|
|
1487 | 1501 |
noise = dataOut.noise |
|
|
1488 | 1502 |
SNR = dataOut.data_SNR |
|
|
1489 | 1503 |
nChannels = dataOut.nChannels |
|
|
1490 | 1504 |
# pairsList = dataOut.groupList |
|
|
1491 | 1505 |
# pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
|
|
1492 | 1506 | |
|
|
1493 | 1507 |
for l in range(len(pairs_acf)): |
|
|
1494 | 1508 |
data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:] |
|
|
1495 | ||
|
|
1509 | ||
|
|
1496 | 1510 |
for l in range(len(pairs_ccf)): |
|
|
1497 | 1511 |
data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:] |
|
|
1498 | ||
|
|
1512 | ||
|
|
1499 | 1513 |
dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights)) |
|
|
1500 | 1514 |
dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc) |
|
|
1501 | 1515 |
dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc) |
|
|
1502 | 1516 |
return |
|
|
1503 | ||
|
|
1517 | ||
|
|
1504 | 1518 |
# def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
1505 | # | |
|
|
1519 | # | |
|
|
1506 | 1520 |
# pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
1507 | # | |
|
|
1508 |
# for l in range(len(pairsList)): |
|
|
|
1521 | # | |
|
|
1522 | # for l in range(len(pairsList)): | |
|
|
1509 | 1523 |
# firstChannel = pairsList[l][0] |
|
|
1510 | 1524 |
# secondChannel = pairsList[l][1] |
|
|
1511 | # | |
|
|
1512 |
# #Obteniendo pares de Autocorrelacion |
|
|
|
1525 | # | |
|
|
1526 | # #Obteniendo pares de Autocorrelacion | |
|
|
1513 | 1527 |
# if firstChannel == secondChannel: |
|
|
1514 | 1528 |
# pairsAutoCorr[firstChannel] = int(l) |
|
|
1515 | # | |
|
|
1529 | # | |
|
|
1516 | 1530 |
# pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
1517 | # | |
|
|
1531 | # | |
|
|
1518 | 1532 |
# pairsCrossCorr = range(len(pairsList)) |
|
|
1519 | 1533 |
# pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
1520 | # | |
|
|
1534 | # | |
|
|
1521 | 1535 |
# return pairsAutoCorr, pairsCrossCorr |
|
|
1522 | ||
|
|
1536 | ||
|
|
1523 | 1537 |
def __calculateTaus(self, data_acf, data_ccf, lagRange): |
|
|
1524 | ||
|
|
1538 | ||
|
|
1525 | 1539 |
lag0 = data_acf.shape[1]/2 |
|
|
1526 | 1540 |
#Funcion de Autocorrelacion |
|
|
1527 | 1541 |
mean_acf = stats.nanmean(data_acf, axis = 0) |
|
|
1528 | ||
|
|
1542 | ||
|
|
1529 | 1543 |
#Obtencion Indice de TauCross |
|
|
1530 | 1544 |
ind_ccf = data_ccf.argmax(axis = 1) |
|
|
1531 | 1545 |
#Obtencion Indice de TauAuto |
|
|
1532 | 1546 |
ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int') |
|
|
1533 | 1547 |
ccf_lag0 = data_ccf[:,lag0,:] |
|
|
1534 | ||
|
|
1548 | ||
|
|
1535 | 1549 |
for i in range(ccf_lag0.shape[0]): |
|
|
1536 | 1550 |
ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0) |
|
|
1537 | ||
|
|
1551 | ||
|
|
1538 | 1552 |
#Obtencion de TauCross y TauAuto |
|
|
1539 | 1553 |
tau_ccf = lagRange[ind_ccf] |
|
|
1540 | 1554 |
tau_acf = lagRange[ind_acf] |
|
|
1541 | ||
|
|
1555 | ||
|
|
1542 | 1556 |
Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0]) |
|
|
1543 | ||
|
|
1557 | ||
|
|
1544 | 1558 |
tau_ccf[Nan1,Nan2] = numpy.nan |
|
|
1545 | 1559 |
tau_acf[Nan1,Nan2] = numpy.nan |
|
|
1546 | 1560 |
tau = numpy.vstack((tau_ccf,tau_acf)) |
|
|
1547 | ||
|
|
1561 | ||
|
|
1548 | 1562 |
return tau |
|
|
1549 | ||
|
|
1563 | ||
|
|
1550 | 1564 |
def __calculateLag1Phase(self, data, lagTRange): |
|
|
1551 | 1565 |
data1 = stats.nanmean(data, axis = 0) |
|
|
1552 | 1566 |
lag1 = numpy.where(lagTRange == 0)[0][0] + 1 |
|
|
1553 | 1567 | |
|
|
1554 | 1568 |
phase = numpy.angle(data1[lag1,:]) |
|
|
1555 | ||
|
|
1569 | ||
|
|
1556 | 1570 |
return phase |
|
|
1557 | ||
|
|
1571 | ||
|
|
1558 | 1572 |
class SpectralFitting(Operation): |
|
|
1559 | 1573 |
''' |
|
|
1560 | 1574 |
Function GetMoments() |
|
|
1561 | ||
|
|
1575 | ||
|
|
1562 | 1576 |
Input: |
|
|
1563 | 1577 |
Output: |
|
|
1564 | 1578 |
Variables modified: |
|
|
1565 | 1579 |
''' |
|
|
1566 | ||
|
|
1567 |
def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None): |
|
|
|
1568 | ||
|
|
1569 | ||
|
|
1580 | ||
|
|
1581 | def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None): | |
|
|
1582 | ||
|
|
1583 | ||
|
|
1570 | 1584 |
if path != None: |
|
|
1571 | 1585 |
sys.path.append(path) |
|
|
1572 | 1586 |
self.dataOut.library = importlib.import_module(file) |
|
|
1573 | ||
|
|
1587 | ||
|
|
1574 | 1588 |
#To be inserted as a parameter |
|
|
1575 | 1589 |
groupArray = numpy.array(groupList) |
|
|
1576 |
# groupArray = numpy.array([[0,1],[2,3]]) |
|
|
|
1590 | # groupArray = numpy.array([[0,1],[2,3]]) | |
|
|
1577 | 1591 |
self.dataOut.groupList = groupArray |
|
|
1578 | ||
|
|
1592 | ||
|
|
1579 | 1593 |
nGroups = groupArray.shape[0] |
|
|
1580 | 1594 |
nChannels = self.dataIn.nChannels |
|
|
1581 | 1595 |
nHeights=self.dataIn.heightList.size |
|
|
1582 | ||
|
|
1596 | ||
|
|
1583 | 1597 |
#Parameters Array |
|
|
1584 | 1598 |
self.dataOut.data_param = None |
|
|
1585 | ||
|
|
1599 | ||
|
|
1586 | 1600 |
#Set constants |
|
|
1587 | 1601 |
constants = self.dataOut.library.setConstants(self.dataIn) |
|
|
1588 | 1602 |
self.dataOut.constants = constants |
|
|
1589 | 1603 |
M = self.dataIn.normFactor |
|
|
1590 | 1604 |
N = self.dataIn.nFFTPoints |
|
|
1591 | 1605 |
ippSeconds = self.dataIn.ippSeconds |
|
|
1592 | 1606 |
K = self.dataIn.nIncohInt |
|
|
1593 | 1607 |
pairsArray = numpy.array(self.dataIn.pairsList) |
|
|
1594 | ||
|
|
1608 | ||
|
|
1595 | 1609 |
#List of possible combinations |
|
|
1596 | 1610 |
listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) |
|
|
1597 | 1611 |
indCross = numpy.zeros(len(list(listComb)), dtype = 'int') |
|
|
1598 | ||
|
|
1612 | ||
|
|
1599 | 1613 |
if getSNR: |
|
|
1600 | 1614 |
listChannels = groupArray.reshape((groupArray.size)) |
|
|
1601 | 1615 |
listChannels.sort() |
|
|
1602 | 1616 |
noise = self.dataIn.getNoise() |
|
|
1603 | 1617 |
self.dataOut.data_SNR = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels]) |
|
|
1604 | ||
|
|
1605 |
for i in range(nGroups): |
|
|
|
1618 | ||
|
|
1619 | for i in range(nGroups): | |
|
|
1606 | 1620 |
coord = groupArray[i,:] |
|
|
1607 | ||
|
|
1621 | ||
|
|
1608 | 1622 |
#Input data array |
|
|
1609 | 1623 |
data = self.dataIn.data_spc[coord,:,:]/(M*N) |
|
|
1610 | 1624 |
data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) |
|
|
1611 | ||
|
|
1625 | ||
|
|
1612 | 1626 |
#Cross Spectra data array for Covariance Matrixes |
|
|
1613 | 1627 |
ind = 0 |
|
|
1614 | 1628 |
for pairs in listComb: |
|
|
1615 | 1629 |
pairsSel = numpy.array([coord[x],coord[y]]) |
|
|
1616 | 1630 |
indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) |
|
|
1617 | 1631 |
ind += 1 |
|
|
1618 | 1632 |
dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N) |
|
|
1619 | 1633 |
dataCross = dataCross**2/K |
|
|
1620 | ||
|
|
1634 | ||
|
|
1621 | 1635 |
for h in range(nHeights): |
|
|
1622 | ||
|
|
1636 | ||
|
|
1623 | 1637 |
#Input |
|
|
1624 | 1638 |
d = data[:,h] |
|
|
1625 | 1639 | |
|
|
1626 | 1640 |
#Covariance Matrix |
|
|
1627 | 1641 |
D = numpy.diag(d**2/K) |
|
|
1628 | 1642 |
ind = 0 |
|
|
1629 | 1643 |
for pairs in listComb: |
|
|
1630 | 1644 |
#Coordinates in Covariance Matrix |
|
|
1631 |
x = pairs[0] |
|
|
|
1645 | x = pairs[0] | |
|
|
1632 | 1646 |
y = pairs[1] |
|
|
1633 | 1647 |
#Channel Index |
|
|
1634 | 1648 |
S12 = dataCross[ind,:,h] |
|
|
1635 | 1649 |
D12 = numpy.diag(S12) |
|
|
1636 | 1650 |
#Completing Covariance Matrix with Cross Spectras |
|
|
1637 | 1651 |
D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 |
|
|
1638 | 1652 |
D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 |
|
|
1639 | 1653 |
ind += 1 |
|
|
1640 | 1654 |
Dinv=numpy.linalg.inv(D) |
|
|
1641 | 1655 |
L=numpy.linalg.cholesky(Dinv) |
|
|
1642 | 1656 |
LT=L.T |
|
|
1643 | 1657 | |
|
|
1644 | 1658 |
dp = numpy.dot(LT,d) |
|
|
1645 | ||
|
|
1659 | ||
|
|
1646 | 1660 |
#Initial values |
|
|
1647 | 1661 |
data_spc = self.dataIn.data_spc[coord,:,h] |
|
|
1648 | ||
|
|
1662 | ||
|
|
1649 | 1663 |
if (h>0)and(error1[3]<5): |
|
|
1650 | 1664 |
p0 = self.dataOut.data_param[i,:,h-1] |
|
|
1651 | 1665 |
else: |
|
|
1652 | 1666 |
p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i)) |
|
|
1653 | ||
|
|
1667 | ||
|
|
1654 | 1668 |
try: |
|
|
1655 | 1669 |
#Least Squares |
|
|
1656 | 1670 |
minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) |
|
|
1657 | 1671 |
# minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) |
|
|
1658 | 1672 |
#Chi square error |
|
|
1659 | 1673 |
error0 = numpy.sum(infodict['fvec']**2)/(2*N) |
|
|
1660 | 1674 |
#Error with Jacobian |
|
|
1661 | 1675 |
error1 = self.dataOut.library.errorFunction(minp,constants,LT) |
|
|
1662 | 1676 |
except: |
|
|
1663 | 1677 |
minp = p0*numpy.nan |
|
|
1664 | 1678 |
error0 = numpy.nan |
|
|
1665 | 1679 |
error1 = p0*numpy.nan |
|
|
1666 | ||
|
|
1680 | ||
|
|
1667 | 1681 |
#Save |
|
|
1668 | 1682 |
if self.dataOut.data_param is None: |
|
|
1669 | 1683 |
self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan |
|
|
1670 | 1684 |
self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan |
|
|
1671 | ||
|
|
1685 | ||
|
|
1672 | 1686 |
self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) |
|
|
1673 | 1687 |
self.dataOut.data_param[i,:,h] = minp |
|
|
1674 | 1688 |
return |
|
|
1675 | ||
|
|
1689 | ||
|
|
1676 | 1690 |
def __residFunction(self, p, dp, LT, constants): |
|
|
1677 | 1691 | |
|
|
1678 | 1692 |
fm = self.dataOut.library.modelFunction(p, constants) |
|
|
1679 | 1693 |
fmp=numpy.dot(LT,fm) |
|
|
1680 | ||
|
|
1694 | ||
|
|
1681 | 1695 |
return dp-fmp |
|
|
1682 | 1696 | |
|
|
1683 | 1697 |
def __getSNR(self, z, noise): |
|
|
1684 | ||
|
|
1698 | ||
|
|
1685 | 1699 |
avg = numpy.average(z, axis=1) |
|
|
1686 | 1700 |
SNR = (avg.T-noise)/noise |
|
|
1687 | 1701 |
SNR = SNR.T |
|
|
1688 | 1702 |
return SNR |
|
|
1689 | ||
|
|
1703 | ||
|
|
1690 | 1704 |
def __chisq(p,chindex,hindex): |
|
|
1691 | 1705 |
#similar to Resid but calculates CHI**2 |
|
|
1692 | 1706 |
[LT,d,fm]=setupLTdfm(p,chindex,hindex) |
|
|
1693 | 1707 |
dp=numpy.dot(LT,d) |
|
|
1694 | 1708 |
fmp=numpy.dot(LT,fm) |
|
|
1695 | 1709 |
chisq=numpy.dot((dp-fmp).T,(dp-fmp)) |
|
|
1696 | 1710 |
return chisq |
|
|
1697 | ||
|
|
1711 | ||
|
|
1698 | 1712 |
class WindProfiler(Operation): |
|
|
1699 | ||
|
|
1713 | ||
|
|
1700 | 1714 |
__isConfig = False |
|
|
1701 | ||
|
|
1715 | ||
|
|
1702 | 1716 |
__initime = None |
|
|
1703 | 1717 |
__lastdatatime = None |
|
|
1704 | 1718 |
__integrationtime = None |
|
|
1705 | ||
|
|
1719 | ||
|
|
1706 | 1720 |
__buffer = None |
|
|
1707 | ||
|
|
1721 | ||
|
|
1708 | 1722 |
__dataReady = False |
|
|
1709 | ||
|
|
1723 | ||
|
|
1710 | 1724 |
__firstdata = None |
|
|
1711 | ||
|
|
1725 | ||
|
|
1712 | 1726 |
n = None |
|
|
1713 | ||
|
|
1714 |
def __init__(self): |
|
|
|
1727 | ||
|
|
1728 | def __init__(self): | |
|
|
1715 | 1729 |
Operation.__init__(self) |
|
|
1716 | ||
|
|
1730 | ||
|
|
1717 | 1731 |
def __calculateCosDir(self, elev, azim): |
|
|
1718 | 1732 |
zen = (90 - elev)*numpy.pi/180 |
|
|
1719 | 1733 |
azim = azim*numpy.pi/180 |
|
|
1720 |
cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) |
|
|
|
1734 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) | |
|
|
1721 | 1735 |
cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) |
|
|
1722 | ||
|
|
1736 | ||
|
|
1723 | 1737 |
signX = numpy.sign(numpy.cos(azim)) |
|
|
1724 | 1738 |
signY = numpy.sign(numpy.sin(azim)) |
|
|
1725 | ||
|
|
1739 | ||
|
|
1726 | 1740 |
cosDirX = numpy.copysign(cosDirX, signX) |
|
|
1727 | 1741 |
cosDirY = numpy.copysign(cosDirY, signY) |
|
|
1728 | 1742 |
return cosDirX, cosDirY |
|
|
1729 | ||
|
|
1743 | ||
|
|
1730 | 1744 |
def __calculateAngles(self, theta_x, theta_y, azimuth): |
|
|
1731 | ||
|
|
1745 | ||
|
|
1732 | 1746 |
dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) |
|
|
1733 | 1747 |
zenith_arr = numpy.arccos(dir_cosw) |
|
|
1734 | 1748 |
azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 |
|
|
1735 | ||
|
|
1749 | ||
|
|
1736 | 1750 |
dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1737 | 1751 |
dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1738 | ||
|
|
1752 | ||
|
|
1739 | 1753 |
return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw |
|
|
1740 | 1754 | |
|
|
1741 | 1755 |
def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): |
|
|
1742 | ||
|
|
1743 | # | |
|
|
1756 | ||
|
|
1757 | # | |
|
|
1744 | 1758 |
if horOnly: |
|
|
1745 | 1759 |
A = numpy.c_[dir_cosu,dir_cosv] |
|
|
1746 | 1760 |
else: |
|
|
1747 | 1761 |
A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] |
|
|
1748 | 1762 |
A = numpy.asmatrix(A) |
|
|
1749 | 1763 |
A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() |
|
|
1750 | 1764 | |
|
|
1751 | 1765 |
return A1 |
|
|
1752 | 1766 | |
|
|
1753 | 1767 |
def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
1754 | 1768 |
listPhi = phi.tolist() |
|
|
1755 | 1769 |
maxid = listPhi.index(max(listPhi)) |
|
|
1756 | 1770 |
minid = listPhi.index(min(listPhi)) |
|
|
1757 | ||
|
|
1758 |
rango = list(range(len(phi))) |
|
|
|
1771 | ||
|
|
1772 | rango = list(range(len(phi))) | |
|
|
1759 | 1773 |
# rango = numpy.delete(rango,maxid) |
|
|
1760 | ||
|
|
1774 | ||
|
|
1761 | 1775 |
heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
1762 | 1776 |
heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
1763 | 1777 |
indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
1764 | 1778 |
heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
1765 | ||
|
|
1779 | ||
|
|
1766 | 1780 |
velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1767 | 1781 |
SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1768 | ||
|
|
1782 | ||
|
|
1769 | 1783 |
for i in rango: |
|
|
1770 | 1784 |
x = heiRang*math.cos(phi[i]) |
|
|
1771 | 1785 |
y1 = velRadial[i,:] |
|
|
1772 | 1786 |
f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
1773 | ||
|
|
1787 | ||
|
|
1774 | 1788 |
x1 = heiRang1 |
|
|
1775 | 1789 |
y11 = f1(x1) |
|
|
1776 | ||
|
|
1790 | ||
|
|
1777 | 1791 |
y2 = SNR[i,:] |
|
|
1778 | 1792 |
f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
1779 | 1793 |
y21 = f2(x1) |
|
|
1780 | ||
|
|
1794 | ||
|
|
1781 | 1795 |
velRadial1[i,:] = y11 |
|
|
1782 | 1796 |
SNR1[i,:] = y21 |
|
|
1783 | ||
|
|
1797 | ||
|
|
1784 | 1798 |
return heiRang1, velRadial1, SNR1 |
|
|
1785 | 1799 | |
|
|
1786 | 1800 |
def __calculateVelUVW(self, A, velRadial): |
|
|
1787 | ||
|
|
1801 | ||
|
|
1788 | 1802 |
#Operacion Matricial |
|
|
1789 | 1803 |
# velUVW = numpy.zeros((velRadial.shape[1],3)) |
|
|
1790 | 1804 |
# for ind in range(velRadial.shape[1]): |
|
|
1791 | 1805 |
# velUVW[ind,:] = numpy.dot(A,velRadial[:,ind]) |
|
|
1792 | 1806 |
# velUVW = velUVW.transpose() |
|
|
1793 | 1807 |
velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) |
|
|
1794 | 1808 |
velUVW[:,:] = numpy.dot(A,velRadial) |
|
|
1795 | ||
|
|
1796 | ||
|
|
1809 | ||
|
|
1810 | ||
|
|
1797 | 1811 |
return velUVW |
|
|
1798 | ||
|
|
1812 | ||
|
|
1799 | 1813 |
# def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0): |
|
|
1800 | ||
|
|
1814 | ||
|
|
1801 | 1815 |
def techniqueDBS(self, kwargs): |
|
|
1802 | 1816 |
""" |
|
|
1803 | 1817 |
Function that implements Doppler Beam Swinging (DBS) technique. |
|
|
1804 | ||
|
|
1818 | ||
|
|
1805 | 1819 |
Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1806 | 1820 |
Direction correction (if necessary), Ranges and SNR |
|
|
1807 | ||
|
|
1821 | ||
|
|
1808 | 1822 |
Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1809 | ||
|
|
1823 | ||
|
|
1810 | 1824 |
Parameters affected: Winds, height range, SNR |
|
|
1811 | 1825 |
""" |
|
|
1812 | 1826 |
velRadial0 = kwargs['velRadial'] |
|
|
1813 | 1827 |
heiRang = kwargs['heightList'] |
|
|
1814 | 1828 |
SNR0 = kwargs['SNR'] |
|
|
1815 | ||
|
|
1829 | ||
|
|
1816 | 1830 |
if 'dirCosx' in kwargs and 'dirCosy' in kwargs: |
|
|
1817 | 1831 |
theta_x = numpy.array(kwargs['dirCosx']) |
|
|
1818 | 1832 |
theta_y = numpy.array(kwargs['dirCosy']) |
|
|
1819 | 1833 |
else: |
|
|
1820 | 1834 |
elev = numpy.array(kwargs['elevation']) |
|
|
1821 | 1835 |
azim = numpy.array(kwargs['azimuth']) |
|
|
1822 | 1836 |
theta_x, theta_y = self.__calculateCosDir(elev, azim) |
|
|
1823 |
azimuth = kwargs['correctAzimuth'] |
|
|
|
1837 | azimuth = kwargs['correctAzimuth'] | |
|
|
1824 | 1838 |
if 'horizontalOnly' in kwargs: |
|
|
1825 | 1839 |
horizontalOnly = kwargs['horizontalOnly'] |
|
|
1826 | 1840 |
else: horizontalOnly = False |
|
|
1827 | 1841 |
if 'correctFactor' in kwargs: |
|
|
1828 | 1842 |
correctFactor = kwargs['correctFactor'] |
|
|
1829 | 1843 |
else: correctFactor = 1 |
|
|
1830 | 1844 |
if 'channelList' in kwargs: |
|
|
1831 | 1845 |
channelList = kwargs['channelList'] |
|
|
1832 | 1846 |
if len(channelList) == 2: |
|
|
1833 | 1847 |
horizontalOnly = True |
|
|
1834 | 1848 |
arrayChannel = numpy.array(channelList) |
|
|
1835 | 1849 |
param = param[arrayChannel,:,:] |
|
|
1836 | 1850 |
theta_x = theta_x[arrayChannel] |
|
|
1837 | 1851 |
theta_y = theta_y[arrayChannel] |
|
|
1838 | ||
|
|
1839 |
azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
|
|
1840 |
heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) |
|
|
|
1852 | ||
|
|
1853 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) | |
|
|
1854 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) | |
|
|
1841 | 1855 |
A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) |
|
|
1842 | ||
|
|
1856 | ||
|
|
1843 | 1857 |
#Calculo de Componentes de la velocidad con DBS |
|
|
1844 | 1858 |
winds = self.__calculateVelUVW(A,velRadial1) |
|
|
1845 | ||
|
|
1859 | ||
|
|
1846 | 1860 |
return winds, heiRang1, SNR1 |
|
|
1847 | ||
|
|
1861 | ||
|
|
1848 | 1862 |
def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None): |
|
|
1849 | ||
|
|
1863 | ||
|
|
1850 | 1864 |
nPairs = len(pairs_ccf) |
|
|
1851 | 1865 |
posx = numpy.asarray(posx) |
|
|
1852 | 1866 |
posy = numpy.asarray(posy) |
|
|
1853 | ||
|
|
1867 | ||
|
|
1854 | 1868 |
#Rotacion Inversa para alinear con el azimuth |
|
|
1855 | 1869 |
if azimuth!= None: |
|
|
1856 | 1870 |
azimuth = azimuth*math.pi/180 |
|
|
1857 | 1871 |
posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) |
|
|
1858 | 1872 |
posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) |
|
|
1859 | 1873 |
else: |
|
|
1860 | 1874 |
posx1 = posx |
|
|
1861 | 1875 |
posy1 = posy |
|
|
1862 | ||
|
|
1876 | ||
|
|
1863 | 1877 |
#Calculo de Distancias |
|
|
1864 | 1878 |
distx = numpy.zeros(nPairs) |
|
|
1865 | 1879 |
disty = numpy.zeros(nPairs) |
|
|
1866 | 1880 |
dist = numpy.zeros(nPairs) |
|
|
1867 | 1881 |
ang = numpy.zeros(nPairs) |
|
|
1868 | ||
|
|
1882 | ||
|
|
1869 | 1883 |
for i in range(nPairs): |
|
|
1870 | 1884 |
distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] |
|
|
1871 |
disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] |
|
|
|
1885 | disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] | |
|
|
1872 | 1886 |
dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) |
|
|
1873 | 1887 |
ang[i] = numpy.arctan2(disty[i],distx[i]) |
|
|
1874 | ||
|
|
1888 | ||
|
|
1875 | 1889 |
return distx, disty, dist, ang |
|
|
1876 |
#Calculo de Matrices |
|
|
|
1890 | #Calculo de Matrices | |
|
|
1877 | 1891 |
# nPairs = len(pairs) |
|
|
1878 | 1892 |
# ang1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1879 | 1893 |
# dist1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1880 | # | |
|
|
1894 | # | |
|
|
1881 | 1895 |
# for j in range(nPairs): |
|
|
1882 | 1896 |
# dist1[j,0,0] = dist[pairs[j][0]] |
|
|
1883 | 1897 |
# dist1[j,1,0] = dist[pairs[j][1]] |
|
|
1884 | 1898 |
# ang1[j,0,0] = ang[pairs[j][0]] |
|
|
1885 | 1899 |
# ang1[j,1,0] = ang[pairs[j][1]] |
|
|
1886 | # | |
|
|
1900 | # | |
|
|
1887 | 1901 |
# return distx,disty, dist1,ang1 |
|
|
1888 | 1902 | |
|
|
1889 | ||
|
|
1903 | ||
|
|
1890 | 1904 |
def __calculateVelVer(self, phase, lagTRange, _lambda): |
|
|
1891 | 1905 | |
|
|
1892 | 1906 |
Ts = lagTRange[1] - lagTRange[0] |
|
|
1893 | 1907 |
velW = -_lambda*phase/(4*math.pi*Ts) |
|
|
1894 | ||
|
|
1908 | ||
|
|
1895 | 1909 |
return velW |
|
|
1896 | ||
|
|
1910 | ||
|
|
1897 | 1911 |
def __calculateVelHorDir(self, dist, tau1, tau2, ang): |
|
|
1898 | 1912 |
nPairs = tau1.shape[0] |
|
|
1899 | 1913 |
nHeights = tau1.shape[1] |
|
|
1900 |
vel = numpy.zeros((nPairs,3,nHeights)) |
|
|
|
1914 | vel = numpy.zeros((nPairs,3,nHeights)) | |
|
|
1901 | 1915 |
dist1 = numpy.reshape(dist, (dist.size,1)) |
|
|
1902 | ||
|
|
1916 | ||
|
|
1903 | 1917 |
angCos = numpy.cos(ang) |
|
|
1904 | 1918 |
angSin = numpy.sin(ang) |
|
|
1905 | ||
|
|
1906 |
vel0 = dist1*tau1/(2*tau2**2) |
|
|
|
1919 | ||
|
|
1920 | vel0 = dist1*tau1/(2*tau2**2) | |
|
|
1907 | 1921 |
vel[:,0,:] = (vel0*angCos).sum(axis = 1) |
|
|
1908 | 1922 |
vel[:,1,:] = (vel0*angSin).sum(axis = 1) |
|
|
1909 | ||
|
|
1923 | ||
|
|
1910 | 1924 |
ind = numpy.where(numpy.isinf(vel)) |
|
|
1911 | 1925 |
vel[ind] = numpy.nan |
|
|
1912 | ||
|
|
1926 | ||
|
|
1913 | 1927 |
return vel |
|
|
1914 | ||
|
|
1928 | ||
|
|
1915 | 1929 |
# def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
1916 | # | |
|
|
1930 | # | |
|
|
1917 | 1931 |
# pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
1918 | # | |
|
|
1919 |
# for l in range(len(pairsList)): |
|
|
|
1932 | # | |
|
|
1933 | # for l in range(len(pairsList)): | |
|
|
1920 | 1934 |
# firstChannel = pairsList[l][0] |
|
|
1921 | 1935 |
# secondChannel = pairsList[l][1] |
|
|
1922 | # | |
|
|
1923 |
# #Obteniendo pares de Autocorrelacion |
|
|
|
1936 | # | |
|
|
1937 | # #Obteniendo pares de Autocorrelacion | |
|
|
1924 | 1938 |
# if firstChannel == secondChannel: |
|
|
1925 | 1939 |
# pairsAutoCorr[firstChannel] = int(l) |
|
|
1926 | # | |
|
|
1940 | # | |
|
|
1927 | 1941 |
# pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
1928 | # | |
|
|
1942 | # | |
|
|
1929 | 1943 |
# pairsCrossCorr = range(len(pairsList)) |
|
|
1930 | 1944 |
# pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
1931 | # | |
|
|
1945 | # | |
|
|
1932 | 1946 |
# return pairsAutoCorr, pairsCrossCorr |
|
|
1933 | ||
|
|
1947 | ||
|
|
1934 | 1948 |
# def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor): |
|
|
1935 | 1949 |
def techniqueSA(self, kwargs): |
|
|
1936 | ||
|
|
1937 |
""" |
|
|
|
1950 | ||
|
|
1951 | """ | |
|
|
1938 | 1952 |
Function that implements Spaced Antenna (SA) technique. |
|
|
1939 | ||
|
|
1953 | ||
|
|
1940 | 1954 |
Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1941 | 1955 |
Direction correction (if necessary), Ranges and SNR |
|
|
1942 | ||
|
|
1956 | ||
|
|
1943 | 1957 |
Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1944 | ||
|
|
1958 | ||
|
|
1945 | 1959 |
Parameters affected: Winds |
|
|
1946 | 1960 |
""" |
|
|
1947 | 1961 |
position_x = kwargs['positionX'] |
|
|
1948 | 1962 |
position_y = kwargs['positionY'] |
|
|
1949 | 1963 |
azimuth = kwargs['azimuth'] |
|
|
1950 | ||
|
|
1964 | ||
|
|
1951 | 1965 |
if 'correctFactor' in kwargs: |
|
|
1952 | 1966 |
correctFactor = kwargs['correctFactor'] |
|
|
1953 | 1967 |
else: |
|
|
1954 | 1968 |
correctFactor = 1 |
|
|
1955 | ||
|
|
1969 | ||
|
|
1956 | 1970 |
groupList = kwargs['groupList'] |
|
|
1957 | 1971 |
pairs_ccf = groupList[1] |
|
|
1958 | 1972 |
tau = kwargs['tau'] |
|
|
1959 | 1973 |
_lambda = kwargs['_lambda'] |
|
|
1960 | ||
|
|
1974 | ||
|
|
1961 | 1975 |
#Cross Correlation pairs obtained |
|
|
1962 | 1976 |
# pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels) |
|
|
1963 | 1977 |
# pairsArray = numpy.array(pairsList)[pairsCrossCorr] |
|
|
1964 | 1978 |
# pairsSelArray = numpy.array(pairsSelected) |
|
|
1965 | 1979 |
# pairs = [] |
|
|
1966 | # | |
|
|
1980 | # | |
|
|
1967 | 1981 |
# #Wind estimation pairs obtained |
|
|
1968 | 1982 |
# for i in range(pairsSelArray.shape[0]/2): |
|
|
1969 | 1983 |
# ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0] |
|
|
1970 | 1984 |
# ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0] |
|
|
1971 | 1985 |
# pairs.append((ind1,ind2)) |
|
|
1972 | ||
|
|
1986 | ||
|
|
1973 | 1987 |
indtau = tau.shape[0]/2 |
|
|
1974 | 1988 |
tau1 = tau[:indtau,:] |
|
|
1975 | 1989 |
tau2 = tau[indtau:-1,:] |
|
|
1976 | 1990 |
# tau1 = tau1[pairs,:] |
|
|
1977 | 1991 |
# tau2 = tau2[pairs,:] |
|
|
1978 | 1992 |
phase1 = tau[-1,:] |
|
|
1979 | ||
|
|
1993 | ||
|
|
1980 | 1994 |
#--------------------------------------------------------------------- |
|
|
1981 |
#Metodo Directo |
|
|
|
1995 | #Metodo Directo | |
|
|
1982 | 1996 |
distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth) |
|
|
1983 | 1997 |
winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) |
|
|
1984 | 1998 |
winds = stats.nanmean(winds, axis=0) |
|
|
1985 | 1999 |
#--------------------------------------------------------------------- |
|
|
1986 | 2000 |
#Metodo General |
|
|
1987 | 2001 |
# distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth) |
|
|
1988 | 2002 |
# #Calculo Coeficientes de Funcion de Correlacion |
|
|
1989 | 2003 |
# F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n) |
|
|
1990 | 2004 |
# #Calculo de Velocidades |
|
|
1991 | 2005 |
# winds = self.calculateVelUV(F,G,A,B,H) |
|
|
1992 | 2006 | |
|
|
1993 | 2007 |
#--------------------------------------------------------------------- |
|
|
1994 | 2008 |
winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) |
|
|
1995 | 2009 |
winds = correctFactor*winds |
|
|
1996 | 2010 |
return winds |
|
|
1997 | ||
|
|
2011 | ||
|
|
1998 | 2012 |
def __checkTime(self, currentTime, paramInterval, outputInterval): |
|
|
1999 | ||
|
|
2013 | ||
|
|
2000 | 2014 |
dataTime = currentTime + paramInterval |
|
|
2001 | 2015 |
deltaTime = dataTime - self.__initime |
|
|
2002 | ||
|
|
2016 | ||
|
|
2003 | 2017 |
if deltaTime >= outputInterval or deltaTime < 0: |
|
|
2004 | 2018 |
self.__dataReady = True |
|
|
2005 |
return |
|
|
|
2006 | ||
|
|
2019 | return | |
|
|
2020 | ||
|
|
2007 | 2021 |
def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): |
|
|
2008 | 2022 |
''' |
|
|
2009 | 2023 |
Function that implements winds estimation technique with detected meteors. |
|
|
2010 | ||
|
|
2024 | ||
|
|
2011 | 2025 |
Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
|
2012 | ||
|
|
2026 | ||
|
|
2013 | 2027 |
Output: Winds estimation (Zonal and Meridional) |
|
|
2014 | ||
|
|
2028 | ||
|
|
2015 | 2029 |
Parameters affected: Winds |
|
|
2016 |
''' |
|
|
|
2030 | ''' | |
|
|
2017 | 2031 |
#Settings |
|
|
2018 | 2032 |
nInt = (heightMax - heightMin)/2 |
|
|
2019 | 2033 |
nInt = int(nInt) |
|
|
2020 |
winds = numpy.zeros((2,nInt))*numpy.nan |
|
|
|
2021 | ||
|
|
2034 | winds = numpy.zeros((2,nInt))*numpy.nan | |
|
|
2035 | ||
|
|
2022 | 2036 |
#Filter errors |
|
|
2023 | 2037 |
error = numpy.where(arrayMeteor[:,-1] == 0)[0] |
|
|
2024 | 2038 |
finalMeteor = arrayMeteor[error,:] |
|
|
2025 | ||
|
|
2039 | ||
|
|
2026 | 2040 |
#Meteor Histogram |
|
|
2027 | 2041 |
finalHeights = finalMeteor[:,2] |
|
|
2028 | 2042 |
hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) |
|
|
2029 | 2043 |
nMeteorsPerI = hist[0] |
|
|
2030 | 2044 |
heightPerI = hist[1] |
|
|
2031 | ||
|
|
2045 | ||
|
|
2032 | 2046 |
#Sort of meteors |
|
|
2033 | 2047 |
indSort = finalHeights.argsort() |
|
|
2034 | 2048 |
finalMeteor2 = finalMeteor[indSort,:] |
|
|
2035 | ||
|
|
2049 | ||
|
|
2036 | 2050 |
# Calculating winds |
|
|
2037 | 2051 |
ind1 = 0 |
|
|
2038 |
ind2 = 0 |
|
|
|
2039 | ||
|
|
2052 | ind2 = 0 | |
|
|
2053 | ||
|
|
2040 | 2054 |
for i in range(nInt): |
|
|
2041 | 2055 |
nMet = nMeteorsPerI[i] |
|
|
2042 | 2056 |
ind1 = ind2 |
|
|
2043 | 2057 |
ind2 = ind1 + nMet |
|
|
2044 | ||
|
|
2058 | ||
|
|
2045 | 2059 |
meteorAux = finalMeteor2[ind1:ind2,:] |
|
|
2046 | ||
|
|
2060 | ||
|
|
2047 | 2061 |
if meteorAux.shape[0] >= meteorThresh: |
|
|
2048 | 2062 |
vel = meteorAux[:, 6] |
|
|
2049 | 2063 |
zen = meteorAux[:, 4]*numpy.pi/180 |
|
|
2050 | 2064 |
azim = meteorAux[:, 3]*numpy.pi/180 |
|
|
2051 | ||
|
|
2065 | ||
|
|
2052 | 2066 |
n = numpy.cos(zen) |
|
|
2053 | 2067 |
# m = (1 - n**2)/(1 - numpy.tan(azim)**2) |
|
|
2054 | 2068 |
# l = m*numpy.tan(azim) |
|
|
2055 | 2069 |
l = numpy.sin(zen)*numpy.sin(azim) |
|
|
2056 | 2070 |
m = numpy.sin(zen)*numpy.cos(azim) |
|
|
2057 | ||
|
|
2071 | ||
|
|
2058 | 2072 |
A = numpy.vstack((l, m)).transpose() |
|
|
2059 | 2073 |
A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) |
|
|
2060 | 2074 |
windsAux = numpy.dot(A1, vel) |
|
|
2061 | ||
|
|
2075 | ||
|
|
2062 | 2076 |
winds[0,i] = windsAux[0] |
|
|
2063 | 2077 |
winds[1,i] = windsAux[1] |
|
|
2064 | ||
|
|
2078 | ||
|
|
2065 | 2079 |
return winds, heightPerI[:-1] |
|
|
2066 | ||
|
|
2080 | ||
|
|
2067 | 2081 |
def techniqueNSM_SA(self, **kwargs): |
|
|
2068 | 2082 |
metArray = kwargs['metArray'] |
|
|
2069 | 2083 |
heightList = kwargs['heightList'] |
|
|
2070 | 2084 |
timeList = kwargs['timeList'] |
|
|
2071 | ||
|
|
2085 | ||
|
|
2072 | 2086 |
rx_location = kwargs['rx_location'] |
|
|
2073 | 2087 |
groupList = kwargs['groupList'] |
|
|
2074 | 2088 |
azimuth = kwargs['azimuth'] |
|
|
2075 | 2089 |
dfactor = kwargs['dfactor'] |
|
|
2076 | 2090 |
k = kwargs['k'] |
|
|
2077 | ||
|
|
2091 | ||
|
|
2078 | 2092 |
azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) |
|
|
2079 | 2093 |
d = dist*dfactor |
|
|
2080 | 2094 |
#Phase calculation |
|
|
2081 | 2095 |
metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) |
|
|
2082 | ||
|
|
2096 | ||
|
|
2083 | 2097 |
metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities |
|
|
2084 | ||
|
|
2098 | ||
|
|
2085 | 2099 |
velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
|
2086 | 2100 |
azimuth1 = azimuth1*numpy.pi/180 |
|
|
2087 | ||
|
|
2101 | ||
|
|
2088 | 2102 |
for i in range(heightList.size): |
|
|
2089 | 2103 |
h = heightList[i] |
|
|
2090 | 2104 |
indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] |
|
|
2091 | 2105 |
metHeight = metArray1[indH,:] |
|
|
2092 | 2106 |
if metHeight.shape[0] >= 2: |
|
|
2093 | 2107 |
velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities |
|
|
2094 | 2108 |
iazim = metHeight[:,1].astype(int) |
|
|
2095 | 2109 |
azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths |
|
|
2096 | 2110 |
A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) |
|
|
2097 | 2111 |
A = numpy.asmatrix(A) |
|
|
2098 | 2112 |
A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() |
|
|
2099 | 2113 |
velHor = numpy.dot(A1,velAux) |
|
|
2100 | ||
|
|
2114 | ||
|
|
2101 | 2115 |
velEst[i,:] = numpy.squeeze(velHor) |
|
|
2102 | 2116 |
return velEst |
|
|
2103 | ||
|
|
2117 | ||
|
|
2104 | 2118 |
def __getPhaseSlope(self, metArray, heightList, timeList): |
|
|
2105 | 2119 |
meteorList = [] |
|
|
2106 | 2120 |
#utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 |
|
|
2107 | 2121 |
#Putting back together the meteor matrix |
|
|
2108 | 2122 |
utctime = metArray[:,0] |
|
|
2109 | 2123 |
uniqueTime = numpy.unique(utctime) |
|
|
2110 | ||
|
|
2124 | ||
|
|
2111 | 2125 |
phaseDerThresh = 0.5 |
|
|
2112 | 2126 |
ippSeconds = timeList[1] - timeList[0] |
|
|
2113 | 2127 |
sec = numpy.where(timeList>1)[0][0] |
|
|
2114 | 2128 |
nPairs = metArray.shape[1] - 6 |
|
|
2115 | 2129 |
nHeights = len(heightList) |
|
|
2116 | ||
|
|
2130 | ||
|
|
2117 | 2131 |
for t in uniqueTime: |
|
|
2118 | 2132 |
metArray1 = metArray[utctime==t,:] |
|
|
2119 | 2133 |
# phaseDerThresh = numpy.pi/4 #reducir Phase thresh |
|
|
2120 | 2134 |
tmet = metArray1[:,1].astype(int) |
|
|
2121 | 2135 |
hmet = metArray1[:,2].astype(int) |
|
|
2122 | ||
|
|
2136 | ||
|
|
2123 | 2137 |
metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) |
|
|
2124 | 2138 |
metPhase[:,:] = numpy.nan |
|
|
2125 | 2139 |
metPhase[:,hmet,tmet] = metArray1[:,6:].T |
|
|
2126 | ||
|
|
2140 | ||
|
|
2127 | 2141 |
#Delete short trails |
|
|
2128 | 2142 |
metBool = ~numpy.isnan(metPhase[0,:,:]) |
|
|
2129 | 2143 |
heightVect = numpy.sum(metBool, axis = 1) |
|
|
2130 | 2144 |
metBool[heightVect<sec,:] = False |
|
|
2131 | 2145 |
metPhase[:,heightVect<sec,:] = numpy.nan |
|
|
2132 | ||
|
|
2146 | ||
|
|
2133 | 2147 |
#Derivative |
|
|
2134 | 2148 |
metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) |
|
|
2135 | 2149 |
phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) |
|
|
2136 | 2150 |
metPhase[phDerAux] = numpy.nan |
|
|
2137 | ||
|
|
2151 | ||
|
|
2138 | 2152 |
#--------------------------METEOR DETECTION ----------------------------------------- |
|
|
2139 | 2153 |
indMet = numpy.where(numpy.any(metBool,axis=1))[0] |
|
|
2140 | ||
|
|
2154 | ||
|
|
2141 | 2155 |
for p in numpy.arange(nPairs): |
|
|
2142 | 2156 |
phase = metPhase[p,:,:] |
|
|
2143 | 2157 |
phDer = metDer[p,:,:] |
|
|
2144 | ||
|
|
2158 | ||
|
|
2145 | 2159 |
for h in indMet: |
|
|
2146 | 2160 |
height = heightList[h] |
|
|
2147 | 2161 |
phase1 = phase[h,:] #82 |
|
|
2148 | 2162 |
phDer1 = phDer[h,:] |
|
|
2149 | ||
|
|
2163 | ||
|
|
2150 | 2164 |
phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap |
|
|
2151 | ||
|
|
2165 | ||
|
|
2152 | 2166 |
indValid = numpy.where(~numpy.isnan(phase1))[0] |
|
|
2153 | 2167 |
initMet = indValid[0] |
|
|
2154 | 2168 |
endMet = 0 |
|
|
2155 | ||
|
|
2169 | ||
|
|
2156 | 2170 |
for i in range(len(indValid)-1): |
|
|
2157 | ||
|
|
2171 | ||
|
|
2158 | 2172 |
#Time difference |
|
|
2159 | 2173 |
inow = indValid[i] |
|
|
2160 | 2174 |
inext = indValid[i+1] |
|
|
2161 | 2175 |
idiff = inext - inow |
|
|
2162 | 2176 |
#Phase difference |
|
|
2163 |
phDiff = numpy.abs(phase1[inext] - phase1[inow]) |
|
|
|
2164 | ||
|
|
2177 | phDiff = numpy.abs(phase1[inext] - phase1[inow]) | |
|
|
2178 | ||
|
|
2165 | 2179 |
if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor |
|
|
2166 | 2180 |
sizeTrail = inow - initMet + 1 |
|
|
2167 | 2181 |
if sizeTrail>3*sec: #Too short meteors |
|
|
2168 | 2182 |
x = numpy.arange(initMet,inow+1)*ippSeconds |
|
|
2169 | 2183 |
y = phase1[initMet:inow+1] |
|
|
2170 | 2184 |
ynnan = ~numpy.isnan(y) |
|
|
2171 | 2185 |
x = x[ynnan] |
|
|
2172 | 2186 |
y = y[ynnan] |
|
|
2173 | 2187 |
slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) |
|
|
2174 | 2188 |
ylin = x*slope + intercept |
|
|
2175 | 2189 |
rsq = r_value**2 |
|
|
2176 | 2190 |
if rsq > 0.5: |
|
|
2177 | 2191 |
vel = slope#*height*1000/(k*d) |
|
|
2178 | 2192 |
estAux = numpy.array([utctime,p,height, vel, rsq]) |
|
|
2179 | 2193 |
meteorList.append(estAux) |
|
|
2180 |
initMet = inext |
|
|
|
2194 | initMet = inext | |
|
|
2181 | 2195 |
metArray2 = numpy.array(meteorList) |
|
|
2182 | ||
|
|
2196 | ||
|
|
2183 | 2197 |
return metArray2 |
|
|
2184 | ||
|
|
2198 | ||
|
|
2185 | 2199 |
def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): |
|
|
2186 | ||
|
|
2200 | ||
|
|
2187 | 2201 |
azimuth1 = numpy.zeros(len(pairslist)) |
|
|
2188 | 2202 |
dist = numpy.zeros(len(pairslist)) |
|
|
2189 | ||
|
|
2203 | ||
|
|
2190 | 2204 |
for i in range(len(rx_location)): |
|
|
2191 | 2205 |
ch0 = pairslist[i][0] |
|
|
2192 | 2206 |
ch1 = pairslist[i][1] |
|
|
2193 | ||
|
|
2207 | ||
|
|
2194 | 2208 |
diffX = rx_location[ch0][0] - rx_location[ch1][0] |
|
|
2195 | 2209 |
diffY = rx_location[ch0][1] - rx_location[ch1][1] |
|
|
2196 | 2210 |
azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi |
|
|
2197 | 2211 |
dist[i] = numpy.sqrt(diffX**2 + diffY**2) |
|
|
2198 | ||
|
|
2212 | ||
|
|
2199 | 2213 |
azimuth1 -= azimuth0 |
|
|
2200 | 2214 |
return azimuth1, dist |
|
|
2201 | ||
|
|
2215 | ||
|
|
2202 | 2216 |
def techniqueNSM_DBS(self, **kwargs): |
|
|
2203 | 2217 |
metArray = kwargs['metArray'] |
|
|
2204 | 2218 |
heightList = kwargs['heightList'] |
|
|
2205 |
timeList = kwargs['timeList'] |
|
|
|
2219 | timeList = kwargs['timeList'] | |
|
|
2206 | 2220 |
azimuth = kwargs['azimuth'] |
|
|
2207 | 2221 |
theta_x = numpy.array(kwargs['theta_x']) |
|
|
2208 | 2222 |
theta_y = numpy.array(kwargs['theta_y']) |
|
|
2209 | ||
|
|
2223 | ||
|
|
2210 | 2224 |
utctime = metArray[:,0] |
|
|
2211 | 2225 |
cmet = metArray[:,1].astype(int) |
|
|
2212 | 2226 |
hmet = metArray[:,3].astype(int) |
|
|
2213 | 2227 |
SNRmet = metArray[:,4] |
|
|
2214 | 2228 |
vmet = metArray[:,5] |
|
|
2215 | 2229 |
spcmet = metArray[:,6] |
|
|
2216 | ||
|
|
2230 | ||
|
|
2217 | 2231 |
nChan = numpy.max(cmet) + 1 |
|
|
2218 | 2232 |
nHeights = len(heightList) |
|
|
2219 | 2233 | |
|
|
2220 | 2234 |
azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
|
2221 | 2235 |
hmet = heightList[hmet] |
|
|
2222 | 2236 |
h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights |
|
|
2223 | 2237 | |
|
|
2224 | 2238 |
velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
|
2225 | 2239 | |
|
|
2226 | 2240 |
for i in range(nHeights - 1): |
|
|
2227 | 2241 |
hmin = heightList[i] |
|
|
2228 | 2242 |
hmax = heightList[i + 1] |
|
|
2229 | 2243 | |
|
|
2230 | 2244 |
thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10) |
|
|
2231 | 2245 |
indthisH = numpy.where(thisH) |
|
|
2232 | ||
|
|
2246 | ||
|
|
2233 | 2247 |
if numpy.size(indthisH) > 3: |
|
|
2234 | ||
|
|
2248 | ||
|
|
2235 | 2249 |
vel_aux = vmet[thisH] |
|
|
2236 | 2250 |
chan_aux = cmet[thisH] |
|
|
2237 | 2251 |
cosu_aux = dir_cosu[chan_aux] |
|
|
2238 | 2252 |
cosv_aux = dir_cosv[chan_aux] |
|
|
2239 | 2253 |
cosw_aux = dir_cosw[chan_aux] |
|
|
2240 | ||
|
|
2241 |
nch = numpy.size(numpy.unique(chan_aux)) |
|
|
|
2254 | ||
|
|
2255 | nch = numpy.size(numpy.unique(chan_aux)) | |
|
|
2242 | 2256 |
if nch > 1: |
|
|
2243 | 2257 |
A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True) |
|
|
2244 | 2258 |
velEst[i,:] = numpy.dot(A,vel_aux) |
|
|
2245 | ||
|
|
2259 | ||
|
|
2246 | 2260 |
return velEst |
|
|
2247 | 2261 | |
|
|
2248 | 2262 |
def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs): |
|
|
2249 | 2263 | |
|
|
2250 | 2264 |
param = dataOut.data_param |
|
|
2251 | 2265 |
if dataOut.abscissaList != None: |
|
|
2252 | 2266 |
absc = dataOut.abscissaList[:-1] |
|
|
2253 | 2267 |
# noise = dataOut.noise |
|
|
2254 | 2268 |
heightList = dataOut.heightList |
|
|
2255 | 2269 |
SNR = dataOut.data_SNR |
|
|
2256 | ||
|
|
2270 | ||
|
|
2257 | 2271 |
if technique == 'DBS': |
|
|
2258 | ||
|
|
2259 |
kwargs['velRadial'] = param[:,1,:] #Radial velocity |
|
|
|
2272 | ||
|
|
2273 | kwargs['velRadial'] = param[:,1,:] #Radial velocity | |
|
|
2260 | 2274 |
kwargs['heightList'] = heightList |
|
|
2261 | 2275 |
kwargs['SNR'] = SNR |
|
|
2262 | ||
|
|
2276 | ||
|
|
2263 | 2277 |
dataOut.data_output, dataOut.heightList, dataOut.data_SNR = self.techniqueDBS(kwargs) #DBS Function |
|
|
2264 | 2278 |
dataOut.utctimeInit = dataOut.utctime |
|
|
2265 | 2279 |
dataOut.outputInterval = dataOut.paramInterval |
|
|
2266 | ||
|
|
2280 | ||
|
|
2267 | 2281 |
elif technique == 'SA': |
|
|
2268 | ||
|
|
2282 | ||
|
|
2269 | 2283 |
#Parameters |
|
|
2270 | 2284 |
# position_x = kwargs['positionX'] |
|
|
2271 | 2285 |
# position_y = kwargs['positionY'] |
|
|
2272 | 2286 |
# azimuth = kwargs['azimuth'] |
|
|
2273 | # | |
|
|
2287 | # | |
|
|
2274 | 2288 |
# if kwargs.has_key('crosspairsList'): |
|
|
2275 | 2289 |
# pairs = kwargs['crosspairsList'] |
|
|
2276 | 2290 |
# else: |
|
|
2277 |
# pairs = None |
|
|
|
2278 | # | |
|
|
2291 | # pairs = None | |
|
|
2292 | # | |
|
|
2279 | 2293 |
# if kwargs.has_key('correctFactor'): |
|
|
2280 | 2294 |
# correctFactor = kwargs['correctFactor'] |
|
|
2281 | 2295 |
# else: |
|
|
2282 | 2296 |
# correctFactor = 1 |
|
|
2283 | ||
|
|
2297 | ||
|
|
2284 | 2298 |
# tau = dataOut.data_param |
|
|
2285 | 2299 |
# _lambda = dataOut.C/dataOut.frequency |
|
|
2286 | 2300 |
# pairsList = dataOut.groupList |
|
|
2287 | 2301 |
# nChannels = dataOut.nChannels |
|
|
2288 | ||
|
|
2302 | ||
|
|
2289 | 2303 |
kwargs['groupList'] = dataOut.groupList |
|
|
2290 | 2304 |
kwargs['tau'] = dataOut.data_param |
|
|
2291 | 2305 |
kwargs['_lambda'] = dataOut.C/dataOut.frequency |
|
|
2292 | 2306 |
# dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) |
|
|
2293 | 2307 |
dataOut.data_output = self.techniqueSA(kwargs) |
|
|
2294 | 2308 |
dataOut.utctimeInit = dataOut.utctime |
|
|
2295 | 2309 |
dataOut.outputInterval = dataOut.timeInterval |
|
|
2296 | ||
|
|
2297 |
elif technique == 'Meteors': |
|
|
|
2310 | ||
|
|
2311 | elif technique == 'Meteors': | |
|
|
2298 | 2312 |
dataOut.flagNoData = True |
|
|
2299 | 2313 |
self.__dataReady = False |
|
|
2300 | ||
|
|
2314 | ||
|
|
2301 | 2315 |
if 'nHours' in kwargs: |
|
|
2302 | 2316 |
nHours = kwargs['nHours'] |
|
|
2303 |
else: |
|
|
|
2317 | else: | |
|
|
2304 | 2318 |
nHours = 1 |
|
|
2305 | ||
|
|
2319 | ||
|
|
2306 | 2320 |
if 'meteorsPerBin' in kwargs: |
|
|
2307 | 2321 |
meteorThresh = kwargs['meteorsPerBin'] |
|
|
2308 | 2322 |
else: |
|
|
2309 | 2323 |
meteorThresh = 6 |
|
|
2310 | ||
|
|
2324 | ||
|
|
2311 | 2325 |
if 'hmin' in kwargs: |
|
|
2312 | 2326 |
hmin = kwargs['hmin'] |
|
|
2313 | 2327 |
else: hmin = 70 |
|
|
2314 | 2328 |
if 'hmax' in kwargs: |
|
|
2315 | 2329 |
hmax = kwargs['hmax'] |
|
|
2316 | 2330 |
else: hmax = 110 |
|
|
2317 | ||
|
|
2331 | ||
|
|
2318 | 2332 |
dataOut.outputInterval = nHours*3600 |
|
|
2319 | ||
|
|
2333 | ||
|
|
2320 | 2334 |
if self.__isConfig == False: |
|
|
2321 | 2335 |
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
2322 | 2336 |
#Get Initial LTC time |
|
|
2323 | 2337 |
self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
2324 | 2338 |
self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
2325 | 2339 | |
|
|
2326 | 2340 |
self.__isConfig = True |
|
|
2327 | ||
|
|
2341 | ||
|
|
2328 | 2342 |
if self.__buffer is None: |
|
|
2329 | 2343 |
self.__buffer = dataOut.data_param |
|
|
2330 | 2344 |
self.__firstdata = copy.copy(dataOut) |
|
|
2331 | 2345 | |
|
|
2332 | 2346 |
else: |
|
|
2333 | 2347 |
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
2334 | ||
|
|
2348 | ||
|
|
2335 | 2349 |
self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
2336 | ||
|
|
2350 | ||
|
|
2337 | 2351 |
if self.__dataReady: |
|
|
2338 | 2352 |
dataOut.utctimeInit = self.__initime |
|
|
2339 | ||
|
|
2353 | ||
|
|
2340 | 2354 |
self.__initime += dataOut.outputInterval #to erase time offset |
|
|
2341 | ||
|
|
2355 | ||
|
|
2342 | 2356 |
dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) |
|
|
2343 | 2357 |
dataOut.flagNoData = False |
|
|
2344 | 2358 |
self.__buffer = None |
|
|
2345 | ||
|
|
2359 | ||
|
|
2346 | 2360 |
elif technique == 'Meteors1': |
|
|
2347 | 2361 |
dataOut.flagNoData = True |
|
|
2348 | 2362 |
self.__dataReady = False |
|
|
2349 | ||
|
|
2363 | ||
|
|
2350 | 2364 |
if 'nMins' in kwargs: |
|
|
2351 | 2365 |
nMins = kwargs['nMins'] |
|
|
2352 | 2366 |
else: nMins = 20 |
|
|
2353 | 2367 |
if 'rx_location' in kwargs: |
|
|
2354 | 2368 |
rx_location = kwargs['rx_location'] |
|
|
2355 | 2369 |
else: rx_location = [(0,1),(1,1),(1,0)] |
|
|
2356 | 2370 |
if 'azimuth' in kwargs: |
|
|
2357 | 2371 |
azimuth = kwargs['azimuth'] |
|
|
2358 | 2372 |
else: azimuth = 51.06 |
|
|
2359 | 2373 |
if 'dfactor' in kwargs: |
|
|
2360 | 2374 |
dfactor = kwargs['dfactor'] |
|
|
2361 | 2375 |
if 'mode' in kwargs: |
|
|
2362 | 2376 |
mode = kwargs['mode'] |
|
|
2363 | 2377 |
if 'theta_x' in kwargs: |
|
|
2364 |
theta_x = kwargs['theta_x'] |
|
|
|
2378 | theta_x = kwargs['theta_x'] | |
|
|
2365 | 2379 |
if 'theta_y' in kwargs: |
|
|
2366 | 2380 |
theta_y = kwargs['theta_y'] |
|
|
2367 | 2381 |
else: mode = 'SA' |
|
|
2368 | 2382 | |
|
|
2369 | 2383 |
#Borrar luego esto |
|
|
2370 | 2384 |
if dataOut.groupList is None: |
|
|
2371 | 2385 |
dataOut.groupList = [(0,1),(0,2),(1,2)] |
|
|
2372 | 2386 |
groupList = dataOut.groupList |
|
|
2373 | 2387 |
C = 3e8 |
|
|
2374 | 2388 |
freq = 50e6 |
|
|
2375 | 2389 |
lamb = C/freq |
|
|
2376 | 2390 |
k = 2*numpy.pi/lamb |
|
|
2377 | ||
|
|
2391 | ||
|
|
2378 | 2392 |
timeList = dataOut.abscissaList |
|
|
2379 | 2393 |
heightList = dataOut.heightList |
|
|
2380 | ||
|
|
2394 | ||
|
|
2381 | 2395 |
if self.__isConfig == False: |
|
|
2382 | 2396 |
dataOut.outputInterval = nMins*60 |
|
|
2383 | 2397 |
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
2384 | 2398 |
#Get Initial LTC time |
|
|
2385 | 2399 |
initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
2386 | 2400 |
minuteAux = initime.minute |
|
|
2387 | 2401 |
minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) |
|
|
2388 | 2402 |
self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
2389 | 2403 | |
|
|
2390 | 2404 |
self.__isConfig = True |
|
|
2391 | ||
|
|
2405 | ||
|
|
2392 | 2406 |
if self.__buffer is None: |
|
|
2393 | 2407 |
self.__buffer = dataOut.data_param |
|
|
2394 | 2408 |
self.__firstdata = copy.copy(dataOut) |
|
|
2395 | 2409 | |
|
|
2396 | 2410 |
else: |
|
|
2397 | 2411 |
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
2398 | ||
|
|
2412 | ||
|
|
2399 | 2413 |
self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
2400 | ||
|
|
2414 | ||
|
|
2401 | 2415 |
if self.__dataReady: |
|
|
2402 | 2416 |
dataOut.utctimeInit = self.__initime |
|
|
2403 | 2417 |
self.__initime += dataOut.outputInterval #to erase time offset |
|
|
2404 | ||
|
|
2418 | ||
|
|
2405 | 2419 |
metArray = self.__buffer |
|
|
2406 | 2420 |
if mode == 'SA': |
|
|
2407 | 2421 |
dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) |
|
|
2408 | 2422 |
elif mode == 'DBS': |
|
|
2409 | 2423 |
dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y) |
|
|
2410 | 2424 |
dataOut.data_output = dataOut.data_output.T |
|
|
2411 | 2425 |
dataOut.flagNoData = False |
|
|
2412 | 2426 |
self.__buffer = None |
|
|
2413 | 2427 | |
|
|
2414 | 2428 |
return |
|
|
2415 | ||
|
|
2429 | ||
|
|
2416 | 2430 |
class EWDriftsEstimation(Operation): |
|
|
2417 | ||
|
|
2418 |
def __init__(self): |
|
|
|
2419 |
Operation.__init__(self) |
|
|
|
2420 | ||
|
|
2431 | ||
|
|
2432 | def __init__(self): | |
|
|
2433 | Operation.__init__(self) | |
|
|
2434 | ||
|
|
2421 | 2435 |
def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
2422 | 2436 |
listPhi = phi.tolist() |
|
|
2423 | 2437 |
maxid = listPhi.index(max(listPhi)) |
|
|
2424 | 2438 |
minid = listPhi.index(min(listPhi)) |
|
|
2425 | ||
|
|
2426 |
rango = list(range(len(phi))) |
|
|
|
2439 | ||
|
|
2440 | rango = list(range(len(phi))) | |
|
|
2427 | 2441 |
# rango = numpy.delete(rango,maxid) |
|
|
2428 | ||
|
|
2442 | ||
|
|
2429 | 2443 |
heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
2430 | 2444 |
heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
2431 | 2445 |
indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
2432 | 2446 |
heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
2433 | ||
|
|
2447 | ||
|
|
2434 | 2448 |
velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
2435 | 2449 |
SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
2436 | ||
|
|
2450 | ||
|
|
2437 | 2451 |
for i in rango: |
|
|
2438 | 2452 |
x = heiRang*math.cos(phi[i]) |
|
|
2439 | 2453 |
y1 = velRadial[i,:] |
|
|
2440 | 2454 |
f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
2441 | ||
|
|
2455 | ||
|
|
2442 | 2456 |
x1 = heiRang1 |
|
|
2443 | 2457 |
y11 = f1(x1) |
|
|
2444 | ||
|
|
2458 | ||
|
|
2445 | 2459 |
y2 = SNR[i,:] |
|
|
2446 | 2460 |
f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
2447 | 2461 |
y21 = f2(x1) |
|
|
2448 | ||
|
|
2462 | ||
|
|
2449 | 2463 |
velRadial1[i,:] = y11 |
|
|
2450 | 2464 |
SNR1[i,:] = y21 |
|
|
2451 | ||
|
|
2465 | ||
|
|
2452 | 2466 |
return heiRang1, velRadial1, SNR1 |
|
|
2453 | 2467 | |
|
|
2454 | 2468 |
def run(self, dataOut, zenith, zenithCorrection): |
|
|
2455 | 2469 |
heiRang = dataOut.heightList |
|
|
2456 | 2470 |
velRadial = dataOut.data_param[:,3,:] |
|
|
2457 | 2471 |
SNR = dataOut.data_SNR |
|
|
2458 | ||
|
|
2472 | ||
|
|
2459 | 2473 |
zenith = numpy.array(zenith) |
|
|
2460 |
zenith -= zenithCorrection |
|
|
|
2474 | zenith -= zenithCorrection | |
|
|
2461 | 2475 |
zenith *= numpy.pi/180 |
|
|
2462 | ||
|
|
2476 | ||
|
|
2463 | 2477 |
heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) |
|
|
2464 | ||
|
|
2478 | ||
|
|
2465 | 2479 |
alp = zenith[0] |
|
|
2466 | 2480 |
bet = zenith[1] |
|
|
2467 | ||
|
|
2481 | ||
|
|
2468 | 2482 |
w_w = velRadial1[0,:] |
|
|
2469 | 2483 |
w_e = velRadial1[1,:] |
|
|
2470 | ||
|
|
2471 |
w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) |
|
|
|
2472 |
u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) |
|
|
|
2473 | ||
|
|
2484 | ||
|
|
2485 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) | |
|
|
2486 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) | |
|
|
2487 | ||
|
|
2474 | 2488 |
winds = numpy.vstack((u,w)) |
|
|
2475 | ||
|
|
2489 | ||
|
|
2476 | 2490 |
dataOut.heightList = heiRang1 |
|
|
2477 | 2491 |
dataOut.data_output = winds |
|
|
2478 | 2492 |
dataOut.data_SNR = SNR1 |
|
|
2479 | ||
|
|
2493 | ||
|
|
2480 | 2494 |
dataOut.utctimeInit = dataOut.utctime |
|
|
2481 | 2495 |
dataOut.outputInterval = dataOut.timeInterval |
|
|
2482 | 2496 |
return |
|
|
2483 | 2497 | |
|
|
2484 | 2498 |
#--------------- Non Specular Meteor ---------------- |
|
|
2485 | 2499 | |
|
|
2486 | 2500 |
class NonSpecularMeteorDetection(Operation): |
|
|
2487 | 2501 | |
|
|
2488 | 2502 |
def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False): |
|
|
2489 | 2503 |
data_acf = dataOut.data_pre[0] |
|
|
2490 | 2504 |
data_ccf = dataOut.data_pre[1] |
|
|
2491 | 2505 |
pairsList = dataOut.groupList[1] |
|
|
2492 | ||
|
|
2506 | ||
|
|
2493 | 2507 |
lamb = dataOut.C/dataOut.frequency |
|
|
2494 | 2508 |
tSamp = dataOut.ippSeconds*dataOut.nCohInt |
|
|
2495 | 2509 |
paramInterval = dataOut.paramInterval |
|
|
2496 | ||
|
|
2510 | ||
|
|
2497 | 2511 |
nChannels = data_acf.shape[0] |
|
|
2498 | 2512 |
nLags = data_acf.shape[1] |
|
|
2499 | 2513 |
nProfiles = data_acf.shape[2] |
|
|
2500 | 2514 |
nHeights = dataOut.nHeights |
|
|
2501 | 2515 |
nCohInt = dataOut.nCohInt |
|
|
2502 | 2516 |
sec = numpy.round(nProfiles/dataOut.paramInterval) |
|
|
2503 | 2517 |
heightList = dataOut.heightList |
|
|
2504 | 2518 |
ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg |
|
|
2505 | 2519 |
utctime = dataOut.utctime |
|
|
2506 | ||
|
|
2520 | ||
|
|
2507 | 2521 |
dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds) |
|
|
2508 | 2522 | |
|
|
2509 | 2523 |
#------------------------ SNR -------------------------------------- |
|
|
2510 | 2524 |
power = data_acf[:,0,:,:].real |
|
|
2511 | 2525 |
noise = numpy.zeros(nChannels) |
|
|
2512 | 2526 |
SNR = numpy.zeros(power.shape) |
|
|
2513 | 2527 |
for i in range(nChannels): |
|
|
2514 | 2528 |
noise[i] = hildebrand_sekhon(power[i,:], nCohInt) |
|
|
2515 | 2529 |
SNR[i] = (power[i]-noise[i])/noise[i] |
|
|
2516 | 2530 |
SNRm = numpy.nanmean(SNR, axis = 0) |
|
|
2517 | 2531 |
SNRdB = 10*numpy.log10(SNR) |
|
|
2518 | ||
|
|
2532 | ||
|
|
2519 | 2533 |
if mode == 'SA': |
|
|
2520 | 2534 |
dataOut.groupList = dataOut.groupList[1] |
|
|
2521 | 2535 |
nPairs = data_ccf.shape[0] |
|
|
2522 | 2536 |
#---------------------- Coherence and Phase -------------------------- |
|
|
2523 | 2537 |
phase = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
|
2524 | 2538 |
# phase1 = numpy.copy(phase) |
|
|
2525 | 2539 |
coh1 = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
|
2526 | ||
|
|
2540 | ||
|
|
2527 | 2541 |
for p in range(nPairs): |
|
|
2528 | 2542 |
ch0 = pairsList[p][0] |
|
|
2529 | 2543 |
ch1 = pairsList[p][1] |
|
|
2530 | 2544 |
ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:]) |
|
|
2531 |
phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter |
|
|
|
2532 |
# phase1[p,:,:] = numpy.angle(ccf) #median filter |
|
|
|
2533 |
coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter |
|
|
|
2534 |
# coh1[p,:,:] = numpy.abs(ccf) #median filter |
|
|
|
2545 | phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter | |
|
|
2546 | # phase1[p,:,:] = numpy.angle(ccf) #median filter | |
|
|
2547 | coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter | |
|
|
2548 | # coh1[p,:,:] = numpy.abs(ccf) #median filter | |
|
|
2535 | 2549 |
coh = numpy.nanmax(coh1, axis = 0) |
|
|
2536 | 2550 |
# struc = numpy.ones((5,1)) |
|
|
2537 | 2551 |
# coh = ndimage.morphology.grey_dilation(coh, size=(10,1)) |
|
|
2538 | 2552 |
#---------------------- Radial Velocity ---------------------------- |
|
|
2539 | 2553 |
phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0) |
|
|
2540 | 2554 |
velRad = phaseAux*lamb/(4*numpy.pi*tSamp) |
|
|
2541 | ||
|
|
2555 | ||
|
|
2542 | 2556 |
if allData: |
|
|
2543 | 2557 |
boolMetFin = ~numpy.isnan(SNRm) |
|
|
2544 | 2558 |
# coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
|
2545 | 2559 |
else: |
|
|
2546 | 2560 |
#------------------------ Meteor mask --------------------------------- |
|
|
2547 | 2561 |
# #SNR mask |
|
|
2548 | 2562 |
# boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB)) |
|
|
2549 | # | |
|
|
2563 | # | |
|
|
2550 | 2564 |
# #Erase small objects |
|
|
2551 |
# boolMet1 = self.__erase_small(boolMet, 2*sec, 5) |
|
|
|
2552 | # | |
|
|
2565 | # boolMet1 = self.__erase_small(boolMet, 2*sec, 5) | |
|
|
2566 | # | |
|
|
2553 | 2567 |
# auxEEJ = numpy.sum(boolMet1,axis=0) |
|
|
2554 | 2568 |
# indOver = auxEEJ>nProfiles*0.8 #Use this later |
|
|
2555 | 2569 |
# indEEJ = numpy.where(indOver)[0] |
|
|
2556 | 2570 |
# indNEEJ = numpy.where(~indOver)[0] |
|
|
2557 | # | |
|
|
2571 | # | |
|
|
2558 | 2572 |
# boolMetFin = boolMet1 |
|
|
2559 | # | |
|
|
2573 | # | |
|
|
2560 | 2574 |
# if indEEJ.size > 0: |
|
|
2561 |
# boolMet1[:,indEEJ] = False #Erase heights with EEJ |
|
|
|
2562 | # | |
|
|
2575 | # boolMet1[:,indEEJ] = False #Erase heights with EEJ | |
|
|
2576 | # | |
|
|
2563 | 2577 |
# boolMet2 = coh > cohThresh |
|
|
2564 | 2578 |
# boolMet2 = self.__erase_small(boolMet2, 2*sec,5) |
|
|
2565 | # | |
|
|
2579 | # | |
|
|
2566 | 2580 |
# #Final Meteor mask |
|
|
2567 | 2581 |
# boolMetFin = boolMet1|boolMet2 |
|
|
2568 | ||
|
|
2582 | ||
|
|
2569 | 2583 |
#Coherence mask |
|
|
2570 | 2584 |
boolMet1 = coh > 0.75 |
|
|
2571 | 2585 |
struc = numpy.ones((30,1)) |
|
|
2572 | 2586 |
boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc) |
|
|
2573 | ||
|
|
2587 | ||
|
|
2574 | 2588 |
#Derivative mask |
|
|
2575 | 2589 |
derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
|
2576 | 2590 |
boolMet2 = derPhase < 0.2 |
|
|
2577 | 2591 |
# boolMet2 = ndimage.morphology.binary_opening(boolMet2) |
|
|
2578 | 2592 |
# boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1))) |
|
|
2579 | 2593 |
boolMet2 = ndimage.median_filter(boolMet2,size=5) |
|
|
2580 | 2594 |
boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool))) |
|
|
2581 | 2595 |
# #Final mask |
|
|
2582 | 2596 |
# boolMetFin = boolMet2 |
|
|
2583 | 2597 |
boolMetFin = boolMet1&boolMet2 |
|
|
2584 | 2598 |
# boolMetFin = ndimage.morphology.binary_dilation(boolMetFin) |
|
|
2585 | 2599 |
#Creating data_param |
|
|
2586 | 2600 |
coordMet = numpy.where(boolMetFin) |
|
|
2587 | 2601 | |
|
|
2588 | 2602 |
tmet = coordMet[0] |
|
|
2589 | 2603 |
hmet = coordMet[1] |
|
|
2590 | ||
|
|
2604 | ||
|
|
2591 | 2605 |
data_param = numpy.zeros((tmet.size, 6 + nPairs)) |
|
|
2592 | 2606 |
data_param[:,0] = utctime |
|
|
2593 | 2607 |
data_param[:,1] = tmet |
|
|
2594 | 2608 |
data_param[:,2] = hmet |
|
|
2595 | 2609 |
data_param[:,3] = SNRm[tmet,hmet] |
|
|
2596 | 2610 |
data_param[:,4] = velRad[tmet,hmet] |
|
|
2597 | 2611 |
data_param[:,5] = coh[tmet,hmet] |
|
|
2598 | 2612 |
data_param[:,6:] = phase[:,tmet,hmet].T |
|
|
2599 | ||
|
|
2613 | ||
|
|
2600 | 2614 |
elif mode == 'DBS': |
|
|
2601 | 2615 |
dataOut.groupList = numpy.arange(nChannels) |
|
|
2602 | 2616 | |
|
|
2603 | 2617 |
#Radial Velocities |
|
|
2604 | 2618 |
phase = numpy.angle(data_acf[:,1,:,:]) |
|
|
2605 | 2619 |
# phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1)) |
|
|
2606 | 2620 |
velRad = phase*lamb/(4*numpy.pi*tSamp) |
|
|
2607 | ||
|
|
2621 | ||
|
|
2608 | 2622 |
#Spectral width |
|
|
2609 | 2623 |
# acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1)) |
|
|
2610 | 2624 |
# acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1)) |
|
|
2611 | 2625 |
acf1 = data_acf[:,1,:,:] |
|
|
2612 | 2626 |
acf2 = data_acf[:,2,:,:] |
|
|
2613 | 2627 | |
|
|
2614 | 2628 |
spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2)) |
|
|
2615 | 2629 |
# velRad = ndimage.median_filter(velRad, size = (1,5,1)) |
|
|
2616 | 2630 |
if allData: |
|
|
2617 | 2631 |
boolMetFin = ~numpy.isnan(SNRdB) |
|
|
2618 | 2632 |
else: |
|
|
2619 | 2633 |
#SNR |
|
|
2620 | 2634 |
boolMet1 = (SNRdB>SNRthresh) #SNR mask |
|
|
2621 | 2635 |
boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5)) |
|
|
2622 | ||
|
|
2636 | ||
|
|
2623 | 2637 |
#Radial velocity |
|
|
2624 | 2638 |
boolMet2 = numpy.abs(velRad) < 20 |
|
|
2625 | 2639 |
boolMet2 = ndimage.median_filter(boolMet2, (1,5,5)) |
|
|
2626 | ||
|
|
2640 | ||
|
|
2627 | 2641 |
#Spectral Width |
|
|
2628 | 2642 |
boolMet3 = spcWidth < 30 |
|
|
2629 | 2643 |
boolMet3 = ndimage.median_filter(boolMet3, (1,5,5)) |
|
|
2630 | 2644 |
# boolMetFin = self.__erase_small(boolMet1, 10,5) |
|
|
2631 | 2645 |
boolMetFin = boolMet1&boolMet2&boolMet3 |
|
|
2632 | ||
|
|
2646 | ||
|
|
2633 | 2647 |
#Creating data_param |
|
|
2634 | 2648 |
coordMet = numpy.where(boolMetFin) |
|
|
2635 | 2649 | |
|
|
2636 | 2650 |
cmet = coordMet[0] |
|
|
2637 | 2651 |
tmet = coordMet[1] |
|
|
2638 | 2652 |
hmet = coordMet[2] |
|
|
2639 | ||
|
|
2653 | ||
|
|
2640 | 2654 |
data_param = numpy.zeros((tmet.size, 7)) |
|
|
2641 | 2655 |
data_param[:,0] = utctime |
|
|
2642 | 2656 |
data_param[:,1] = cmet |
|
|
2643 | 2657 |
data_param[:,2] = tmet |
|
|
2644 | 2658 |
data_param[:,3] = hmet |
|
|
2645 | 2659 |
data_param[:,4] = SNR[cmet,tmet,hmet].T |
|
|
2646 | 2660 |
data_param[:,5] = velRad[cmet,tmet,hmet].T |
|
|
2647 | 2661 |
data_param[:,6] = spcWidth[cmet,tmet,hmet].T |
|
|
2648 | ||
|
|
2662 | ||
|
|
2649 | 2663 |
# self.dataOut.data_param = data_int |
|
|
2650 | 2664 |
if len(data_param) == 0: |
|
|
2651 | 2665 |
dataOut.flagNoData = True |
|
|
2652 | 2666 |
else: |
|
|
2653 | 2667 |
dataOut.data_param = data_param |
|
|
2654 | 2668 | |
|
|
2655 | 2669 |
def __erase_small(self, binArray, threshX, threshY): |
|
|
2656 | 2670 |
labarray, numfeat = ndimage.measurements.label(binArray) |
|
|
2657 | 2671 |
binArray1 = numpy.copy(binArray) |
|
|
2658 | ||
|
|
2672 | ||
|
|
2659 | 2673 |
for i in range(1,numfeat + 1): |
|
|
2660 | 2674 |
auxBin = (labarray==i) |
|
|
2661 | 2675 |
auxSize = auxBin.sum() |
|
|
2662 | ||
|
|
2676 | ||
|
|
2663 | 2677 |
x,y = numpy.where(auxBin) |
|
|
2664 | 2678 |
widthX = x.max() - x.min() |
|
|
2665 | 2679 |
widthY = y.max() - y.min() |
|
|
2666 | ||
|
|
2680 | ||
|
|
2667 | 2681 |
#width X: 3 seg -> 12.5*3 |
|
|
2668 |
#width Y: |
|
|
|
2669 | ||
|
|
2682 | #width Y: | |
|
|
2683 | ||
|
|
2670 | 2684 |
if (auxSize < 50) or (widthX < threshX) or (widthY < threshY): |
|
|
2671 | 2685 |
binArray1[auxBin] = False |
|
|
2672 | ||
|
|
2686 | ||
|
|
2673 | 2687 |
return binArray1 |
|
|
2674 | 2688 | |
|
|
2675 | 2689 |
#--------------- Specular Meteor ---------------- |
|
|
2676 | 2690 | |
|
|
2677 | 2691 |
class SMDetection(Operation): |
|
|
2678 | 2692 |
''' |
|
|
2679 | 2693 |
Function DetectMeteors() |
|
|
2680 | 2694 |
Project developed with paper: |
|
|
2681 | 2695 |
HOLDSWORTH ET AL. 2004 |
|
|
2682 | ||
|
|
2696 | ||
|
|
2683 | 2697 |
Input: |
|
|
2684 | 2698 |
self.dataOut.data_pre |
|
|
2685 | ||
|
|
2699 | ||
|
|
2686 | 2700 |
centerReceiverIndex: From the channels, which is the center receiver |
|
|
2687 | ||
|
|
2701 | ||
|
|
2688 | 2702 |
hei_ref: Height reference for the Beacon signal extraction |
|
|
2689 | 2703 |
tauindex: |
|
|
2690 | 2704 |
predefinedPhaseShifts: Predefined phase offset for the voltge signals |
|
|
2691 | ||
|
|
2705 | ||
|
|
2692 | 2706 |
cohDetection: Whether to user Coherent detection or not |
|
|
2693 | 2707 |
cohDet_timeStep: Coherent Detection calculation time step |
|
|
2694 | 2708 |
cohDet_thresh: Coherent Detection phase threshold to correct phases |
|
|
2695 | ||
|
|
2709 | ||
|
|
2696 | 2710 |
noise_timeStep: Noise calculation time step |
|
|
2697 | 2711 |
noise_multiple: Noise multiple to define signal threshold |
|
|
2698 | ||
|
|
2712 | ||
|
|
2699 | 2713 |
multDet_timeLimit: Multiple Detection Removal time limit in seconds |
|
|
2700 | 2714 |
multDet_rangeLimit: Multiple Detection Removal range limit in km |
|
|
2701 | ||
|
|
2715 | ||
|
|
2702 | 2716 |
phaseThresh: Maximum phase difference between receiver to be consider a meteor |
|
|
2703 |
SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor |
|
|
|
2704 | ||
|
|
2717 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor | |
|
|
2718 | ||
|
|
2705 | 2719 |
hmin: Minimum Height of the meteor to use it in the further wind estimations |
|
|
2706 | 2720 |
hmax: Maximum Height of the meteor to use it in the further wind estimations |
|
|
2707 | 2721 |
azimuth: Azimuth angle correction |
|
|
2708 | ||
|
|
2722 | ||
|
|
2709 | 2723 |
Affected: |
|
|
2710 | 2724 |
self.dataOut.data_param |
|
|
2711 | ||
|
|
2725 | ||
|
|
2712 | 2726 |
Rejection Criteria (Errors): |
|
|
2713 | 2727 |
0: No error; analysis OK |
|
|
2714 | 2728 |
1: SNR < SNR threshold |
|
|
2715 | 2729 |
2: angle of arrival (AOA) ambiguously determined |
|
|
2716 | 2730 |
3: AOA estimate not feasible |
|
|
2717 | 2731 |
4: Large difference in AOAs obtained from different antenna baselines |
|
|
2718 | 2732 |
5: echo at start or end of time series |
|
|
2719 | 2733 |
6: echo less than 5 examples long; too short for analysis |
|
|
2720 | 2734 |
7: echo rise exceeds 0.3s |
|
|
2721 | 2735 |
8: echo decay time less than twice rise time |
|
|
2722 | 2736 |
9: large power level before echo |
|
|
2723 | 2737 |
10: large power level after echo |
|
|
2724 | 2738 |
11: poor fit to amplitude for estimation of decay time |
|
|
2725 | 2739 |
12: poor fit to CCF phase variation for estimation of radial drift velocity |
|
|
2726 | 2740 |
13: height unresolvable echo: not valid height within 70 to 110 km |
|
|
2727 | 2741 |
14: height ambiguous echo: more then one possible height within 70 to 110 km |
|
|
2728 | 2742 |
15: radial drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
2729 | 2743 |
16: oscilatory echo, indicating event most likely not an underdense echo |
|
|
2730 | ||
|
|
2744 | ||
|
|
2731 | 2745 |
17: phase difference in meteor Reestimation |
|
|
2732 | ||
|
|
2746 | ||
|
|
2733 | 2747 |
Data Storage: |
|
|
2734 | 2748 |
Meteors for Wind Estimation (8): |
|
|
2735 | 2749 |
Utc Time | Range Height |
|
|
2736 | 2750 |
Azimuth Zenith errorCosDir |
|
|
2737 | 2751 |
VelRad errorVelRad |
|
|
2738 | 2752 |
Phase0 Phase1 Phase2 Phase3 |
|
|
2739 | 2753 |
TypeError |
|
|
2740 | ||
|
|
2741 |
''' |
|
|
|
2742 | ||
|
|
2754 | ||
|
|
2755 | ''' | |
|
|
2756 | ||
|
|
2743 | 2757 |
def run(self, dataOut, hei_ref = None, tauindex = 0, |
|
|
2744 | 2758 |
phaseOffsets = None, |
|
|
2745 |
cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, |
|
|
|
2759 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, | |
|
|
2746 | 2760 |
noise_timeStep = 4, noise_multiple = 4, |
|
|
2747 | 2761 |
multDet_timeLimit = 1, multDet_rangeLimit = 3, |
|
|
2748 | 2762 |
phaseThresh = 20, SNRThresh = 5, |
|
|
2749 | 2763 |
hmin = 50, hmax=150, azimuth = 0, |
|
|
2750 | 2764 |
channelPositions = None) : |
|
|
2751 | ||
|
|
2752 | ||
|
|
2765 | ||
|
|
2766 | ||
|
|
2753 | 2767 |
#Getting Pairslist |
|
|
2754 | 2768 |
if channelPositions is None: |
|
|
2755 | 2769 |
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
2756 | 2770 |
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
2757 | 2771 |
meteorOps = SMOperations() |
|
|
2758 | 2772 |
pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
2759 | 2773 |
heiRang = dataOut.getHeiRange() |
|
|
2760 | 2774 |
#Get Beacon signal - No Beacon signal anymore |
|
|
2761 | 2775 |
# newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
|
2762 | # | |
|
|
2776 | # | |
|
|
2763 | 2777 |
# if hei_ref != None: |
|
|
2764 | 2778 |
# newheis = numpy.where(self.dataOut.heightList>hei_ref) |
|
|
2765 | # | |
|
|
2766 | ||
|
|
2767 | ||
|
|
2779 | # | |
|
|
2780 | ||
|
|
2781 | ||
|
|
2768 | 2782 |
#****************REMOVING HARDWARE PHASE DIFFERENCES*************** |
|
|
2769 | 2783 |
# see if the user put in pre defined phase shifts |
|
|
2770 | 2784 |
voltsPShift = dataOut.data_pre.copy() |
|
|
2771 | ||
|
|
2785 | ||
|
|
2772 | 2786 |
# if predefinedPhaseShifts != None: |
|
|
2773 | 2787 |
# hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 |
|
|
2774 | # | |
|
|
2788 | # | |
|
|
2775 | 2789 |
# # elif beaconPhaseShifts: |
|
|
2776 | 2790 |
# # #get hardware phase shifts using beacon signal |
|
|
2777 | 2791 |
# # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) |
|
|
2778 | 2792 |
# # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) |
|
|
2779 | # | |
|
|
2793 | # | |
|
|
2780 | 2794 |
# else: |
|
|
2781 |
# hardwarePhaseShifts = numpy.zeros(5) |
|
|
|
2782 | # | |
|
|
2795 | # hardwarePhaseShifts = numpy.zeros(5) | |
|
|
2796 | # | |
|
|
2783 | 2797 |
# voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex') |
|
|
2784 | 2798 |
# for i in range(self.dataOut.data_pre.shape[0]): |
|
|
2785 | 2799 |
# voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i]) |
|
|
2786 | 2800 | |
|
|
2787 | 2801 |
#******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* |
|
|
2788 | ||
|
|
2802 | ||
|
|
2789 | 2803 |
#Remove DC |
|
|
2790 | 2804 |
voltsDC = numpy.mean(voltsPShift,1) |
|
|
2791 | 2805 |
voltsDC = numpy.mean(voltsDC,1) |
|
|
2792 | 2806 |
for i in range(voltsDC.shape[0]): |
|
|
2793 | 2807 |
voltsPShift[i] = voltsPShift[i] - voltsDC[i] |
|
|
2794 | ||
|
|
2795 |
#Don't considerate last heights, theyre used to calculate Hardware Phase Shift |
|
|
|
2808 | ||
|
|
2809 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift | |
|
|
2796 | 2810 |
# voltsPShift = voltsPShift[:,:,:newheis[0][0]] |
|
|
2797 | ||
|
|
2811 | ||
|
|
2798 | 2812 |
#************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** |
|
|
2799 | 2813 |
#Coherent Detection |
|
|
2800 | 2814 |
if cohDetection: |
|
|
2801 | 2815 |
#use coherent detection to get the net power |
|
|
2802 | 2816 |
cohDet_thresh = cohDet_thresh*numpy.pi/180 |
|
|
2803 | 2817 |
voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh) |
|
|
2804 | ||
|
|
2818 | ||
|
|
2805 | 2819 |
#Non-coherent detection! |
|
|
2806 | 2820 |
powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) |
|
|
2807 | 2821 |
#********** END OF COH/NON-COH POWER CALCULATION********************** |
|
|
2808 | ||
|
|
2822 | ||
|
|
2809 | 2823 |
#********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** |
|
|
2810 | 2824 |
#Get noise |
|
|
2811 | 2825 |
noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval) |
|
|
2812 | 2826 |
# noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval) |
|
|
2813 | 2827 |
#Get signal threshold |
|
|
2814 | 2828 |
signalThresh = noise_multiple*noise |
|
|
2815 | 2829 |
#Meteor echoes detection |
|
|
2816 | 2830 |
listMeteors = self.__findMeteors(powerNet, signalThresh) |
|
|
2817 | 2831 |
#******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** |
|
|
2818 | ||
|
|
2832 | ||
|
|
2819 | 2833 |
#************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** |
|
|
2820 | 2834 |
#Parameters |
|
|
2821 | 2835 |
heiRange = dataOut.getHeiRange() |
|
|
2822 | 2836 |
rangeInterval = heiRange[1] - heiRange[0] |
|
|
2823 | 2837 |
rangeLimit = multDet_rangeLimit/rangeInterval |
|
|
2824 | 2838 |
timeLimit = multDet_timeLimit/dataOut.timeInterval |
|
|
2825 | 2839 |
#Multiple detection removals |
|
|
2826 | 2840 |
listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) |
|
|
2827 | 2841 |
#************ END OF REMOVE MULTIPLE DETECTIONS ********************** |
|
|
2828 | ||
|
|
2842 | ||
|
|
2829 | 2843 |
#********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** |
|
|
2830 | 2844 |
#Parameters |
|
|
2831 | 2845 |
phaseThresh = phaseThresh*numpy.pi/180 |
|
|
2832 | 2846 |
thresh = [phaseThresh, noise_multiple, SNRThresh] |
|
|
2833 | 2847 |
#Meteor reestimation (Errors N 1, 6, 12, 17) |
|
|
2834 | 2848 |
listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency) |
|
|
2835 | 2849 |
# listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise) |
|
|
2836 | 2850 |
#Estimation of decay times (Errors N 7, 8, 11) |
|
|
2837 | 2851 |
listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency) |
|
|
2838 | 2852 |
#******************* END OF METEOR REESTIMATION ******************* |
|
|
2839 | ||
|
|
2853 | ||
|
|
2840 | 2854 |
#********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** |
|
|
2841 | 2855 |
#Calculating Radial Velocity (Error N 15) |
|
|
2842 | 2856 |
radialStdThresh = 10 |
|
|
2843 |
listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval) |
|
|
|
2857 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval) | |
|
|
2844 | 2858 | |
|
|
2845 | 2859 |
if len(listMeteors4) > 0: |
|
|
2846 | 2860 |
#Setting New Array |
|
|
2847 | 2861 |
date = dataOut.utctime |
|
|
2848 | 2862 |
arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) |
|
|
2849 | ||
|
|
2863 | ||
|
|
2850 | 2864 |
#Correcting phase offset |
|
|
2851 | 2865 |
if phaseOffsets != None: |
|
|
2852 | 2866 |
phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
|
2853 | 2867 |
arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
|
2854 | ||
|
|
2868 | ||
|
|
2855 | 2869 |
#Second Pairslist |
|
|
2856 | 2870 |
pairsList = [] |
|
|
2857 | 2871 |
pairx = (0,1) |
|
|
2858 | 2872 |
pairy = (2,3) |
|
|
2859 | 2873 |
pairsList.append(pairx) |
|
|
2860 | 2874 |
pairsList.append(pairy) |
|
|
2861 | ||
|
|
2875 | ||
|
|
2862 | 2876 |
jph = numpy.array([0,0,0,0]) |
|
|
2863 | 2877 |
h = (hmin,hmax) |
|
|
2864 | 2878 |
arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
|
2865 | ||
|
|
2879 | ||
|
|
2866 | 2880 |
# #Calculate AOA (Error N 3, 4) |
|
|
2867 | 2881 |
# #JONES ET AL. 1998 |
|
|
2868 | 2882 |
# error = arrayParameters[:,-1] |
|
|
2869 | 2883 |
# AOAthresh = numpy.pi/8 |
|
|
2870 | 2884 |
# phases = -arrayParameters[:,9:13] |
|
|
2871 | 2885 |
# arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth) |
|
|
2872 | # | |
|
|
2886 | # | |
|
|
2873 | 2887 |
# #Calculate Heights (Error N 13 and 14) |
|
|
2874 | 2888 |
# error = arrayParameters[:,-1] |
|
|
2875 | 2889 |
# Ranges = arrayParameters[:,2] |
|
|
2876 | 2890 |
# zenith = arrayParameters[:,5] |
|
|
2877 | 2891 |
# arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
2878 | 2892 |
# error = arrayParameters[:,-1] |
|
|
2879 | 2893 |
#********************* END OF PARAMETERS CALCULATION ************************** |
|
|
2880 | ||
|
|
2881 |
#***************************+ PASS DATA TO NEXT STEP ********************** |
|
|
|
2894 | ||
|
|
2895 | #***************************+ PASS DATA TO NEXT STEP ********************** | |
|
|
2882 | 2896 |
# arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1])) |
|
|
2883 | 2897 |
dataOut.data_param = arrayParameters |
|
|
2884 | ||
|
|
2898 | ||
|
|
2885 | 2899 |
if arrayParameters is None: |
|
|
2886 | 2900 |
dataOut.flagNoData = True |
|
|
2887 | 2901 |
else: |
|
|
2888 | 2902 |
dataOut.flagNoData = True |
|
|
2889 | ||
|
|
2903 | ||
|
|
2890 | 2904 |
return |
|
|
2891 | ||
|
|
2905 | ||
|
|
2892 | 2906 |
def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): |
|
|
2893 | ||
|
|
2907 | ||
|
|
2894 | 2908 |
minIndex = min(newheis[0]) |
|
|
2895 | 2909 |
maxIndex = max(newheis[0]) |
|
|
2896 | ||
|
|
2910 | ||
|
|
2897 | 2911 |
voltage = voltage0[:,:,minIndex:maxIndex+1] |
|
|
2898 | 2912 |
nLength = voltage.shape[1]/n |
|
|
2899 | 2913 |
nMin = 0 |
|
|
2900 | 2914 |
nMax = 0 |
|
|
2901 | 2915 |
phaseOffset = numpy.zeros((len(pairslist),n)) |
|
|
2902 | ||
|
|
2916 | ||
|
|
2903 | 2917 |
for i in range(n): |
|
|
2904 | 2918 |
nMax += nLength |
|
|
2905 | 2919 |
phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) |
|
|
2906 | 2920 |
phaseCCF = numpy.mean(phaseCCF, axis = 2) |
|
|
2907 |
phaseOffset[:,i] = phaseCCF.transpose() |
|
|
|
2921 | phaseOffset[:,i] = phaseCCF.transpose() | |
|
|
2908 | 2922 |
nMin = nMax |
|
|
2909 | 2923 |
# phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist) |
|
|
2910 | ||
|
|
2924 | ||
|
|
2911 | 2925 |
#Remove Outliers |
|
|
2912 | 2926 |
factor = 2 |
|
|
2913 | 2927 |
wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) |
|
|
2914 | 2928 |
dw = numpy.std(wt,axis = 1) |
|
|
2915 | 2929 |
dw = dw.reshape((dw.size,1)) |
|
|
2916 |
ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) |
|
|
|
2930 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) | |
|
|
2917 | 2931 |
phaseOffset[ind] = numpy.nan |
|
|
2918 |
phaseOffset = stats.nanmean(phaseOffset, axis=1) |
|
|
|
2919 | ||
|
|
2932 | phaseOffset = stats.nanmean(phaseOffset, axis=1) | |
|
|
2933 | ||
|
|
2920 | 2934 |
return phaseOffset |
|
|
2921 | ||
|
|
2935 | ||
|
|
2922 | 2936 |
def __shiftPhase(self, data, phaseShift): |
|
|
2923 | 2937 |
#this will shift the phase of a complex number |
|
|
2924 |
dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) |
|
|
|
2938 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) | |
|
|
2925 | 2939 |
return dataShifted |
|
|
2926 | ||
|
|
2940 | ||
|
|
2927 | 2941 |
def __estimatePhaseDifference(self, array, pairslist): |
|
|
2928 | 2942 |
nChannel = array.shape[0] |
|
|
2929 | 2943 |
nHeights = array.shape[2] |
|
|
2930 | 2944 |
numPairs = len(pairslist) |
|
|
2931 | 2945 |
# phaseCCF = numpy.zeros((nChannel, 5, nHeights)) |
|
|
2932 | 2946 |
phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) |
|
|
2933 | ||
|
|
2947 | ||
|
|
2934 | 2948 |
#Correct phases |
|
|
2935 | 2949 |
derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] |
|
|
2936 | 2950 |
indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
2937 | ||
|
|
2938 |
if indDer[0].shape[0] > 0: |
|
|
|
2951 | ||
|
|
2952 | if indDer[0].shape[0] > 0: | |
|
|
2939 | 2953 |
for i in range(indDer[0].shape[0]): |
|
|
2940 | 2954 |
signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) |
|
|
2941 | 2955 |
phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi |
|
|
2942 | ||
|
|
2956 | ||
|
|
2943 | 2957 |
# for j in range(numSides): |
|
|
2944 | 2958 |
# phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2]) |
|
|
2945 | 2959 |
# phaseCCF[j,:,:] = numpy.angle(phaseCCFAux) |
|
|
2946 | # | |
|
|
2960 | # | |
|
|
2947 | 2961 |
#Linear |
|
|
2948 | 2962 |
phaseInt = numpy.zeros((numPairs,1)) |
|
|
2949 | 2963 |
angAllCCF = phaseCCF[:,[0,1,3,4],0] |
|
|
2950 | 2964 |
for j in range(numPairs): |
|
|
2951 | 2965 |
fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) |
|
|
2952 | 2966 |
phaseInt[j] = fit[1] |
|
|
2953 | 2967 |
#Phase Differences |
|
|
2954 | 2968 |
phaseDiff = phaseInt - phaseCCF[:,2,:] |
|
|
2955 | 2969 |
phaseArrival = phaseInt.reshape(phaseInt.size) |
|
|
2956 | ||
|
|
2970 | ||
|
|
2957 | 2971 |
#Dealias |
|
|
2958 | 2972 |
phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival)) |
|
|
2959 | 2973 |
# indAlias = numpy.where(phaseArrival > numpy.pi) |
|
|
2960 | 2974 |
# phaseArrival[indAlias] -= 2*numpy.pi |
|
|
2961 | 2975 |
# indAlias = numpy.where(phaseArrival < -numpy.pi) |
|
|
2962 | 2976 |
# phaseArrival[indAlias] += 2*numpy.pi |
|
|
2963 | ||
|
|
2977 | ||
|
|
2964 | 2978 |
return phaseDiff, phaseArrival |
|
|
2965 | ||
|
|
2979 | ||
|
|
2966 | 2980 |
def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): |
|
|
2967 | 2981 |
#this function will run the coherent detection used in Holdworth et al. 2004 and return the net power |
|
|
2968 | 2982 |
#find the phase shifts of each channel over 1 second intervals |
|
|
2969 | 2983 |
#only look at ranges below the beacon signal |
|
|
2970 | 2984 |
numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
2971 | 2985 |
numBlocks = int(volts.shape[1]/numProfPerBlock) |
|
|
2972 | 2986 |
numHeights = volts.shape[2] |
|
|
2973 | 2987 |
nChannel = volts.shape[0] |
|
|
2974 | 2988 |
voltsCohDet = volts.copy() |
|
|
2975 | ||
|
|
2989 | ||
|
|
2976 | 2990 |
pairsarray = numpy.array(pairslist) |
|
|
2977 | 2991 |
indSides = pairsarray[:,1] |
|
|
2978 | 2992 |
# indSides = numpy.array(range(nChannel)) |
|
|
2979 | 2993 |
# indSides = numpy.delete(indSides, indCenter) |
|
|
2980 | # | |
|
|
2994 | # | |
|
|
2981 | 2995 |
# listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0) |
|
|
2982 | 2996 |
listBlocks = numpy.array_split(volts, numBlocks, 1) |
|
|
2983 | ||
|
|
2997 | ||
|
|
2984 | 2998 |
startInd = 0 |
|
|
2985 | 2999 |
endInd = 0 |
|
|
2986 | ||
|
|
3000 | ||
|
|
2987 | 3001 |
for i in range(numBlocks): |
|
|
2988 | 3002 |
startInd = endInd |
|
|
2989 |
endInd = endInd + listBlocks[i].shape[1] |
|
|
|
2990 | ||
|
|
3003 | endInd = endInd + listBlocks[i].shape[1] | |
|
|
3004 | ||
|
|
2991 | 3005 |
arrayBlock = listBlocks[i] |
|
|
2992 | 3006 |
# arrayBlockCenter = listCenter[i] |
|
|
2993 | ||
|
|
3007 | ||
|
|
2994 | 3008 |
#Estimate the Phase Difference |
|
|
2995 | 3009 |
phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) |
|
|
2996 | 3010 |
#Phase Difference RMS |
|
|
2997 | 3011 |
arrayPhaseRMS = numpy.abs(phaseDiff) |
|
|
2998 | 3012 |
phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) |
|
|
2999 | 3013 |
indPhase = numpy.where(phaseRMSaux==4) |
|
|
3000 | 3014 |
#Shifting |
|
|
3001 | 3015 |
if indPhase[0].shape[0] > 0: |
|
|
3002 | 3016 |
for j in range(indSides.size): |
|
|
3003 | 3017 |
arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) |
|
|
3004 | 3018 |
voltsCohDet[:,startInd:endInd,:] = arrayBlock |
|
|
3005 | ||
|
|
3019 | ||
|
|
3006 | 3020 |
return voltsCohDet |
|
|
3007 | ||
|
|
3021 | ||
|
|
3008 | 3022 |
def __calculateCCF(self, volts, pairslist ,laglist): |
|
|
3009 | ||
|
|
3023 | ||
|
|
3010 | 3024 |
nHeights = volts.shape[2] |
|
|
3011 |
nPoints = volts.shape[1] |
|
|
|
3025 | nPoints = volts.shape[1] | |
|
|
3012 | 3026 |
voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') |
|
|
3013 | ||
|
|
3027 | ||
|
|
3014 | 3028 |
for i in range(len(pairslist)): |
|
|
3015 | 3029 |
volts1 = volts[pairslist[i][0]] |
|
|
3016 |
volts2 = volts[pairslist[i][1]] |
|
|
|
3017 | ||
|
|
3030 | volts2 = volts[pairslist[i][1]] | |
|
|
3031 | ||
|
|
3018 | 3032 |
for t in range(len(laglist)): |
|
|
3019 |
idxT = laglist[t] |
|
|
|
3033 | idxT = laglist[t] | |
|
|
3020 | 3034 |
if idxT >= 0: |
|
|
3021 | 3035 |
vStacked = numpy.vstack((volts2[idxT:,:], |
|
|
3022 | 3036 |
numpy.zeros((idxT, nHeights),dtype='complex'))) |
|
|
3023 | 3037 |
else: |
|
|
3024 | 3038 |
vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), |
|
|
3025 | 3039 |
volts2[:(nPoints + idxT),:])) |
|
|
3026 | 3040 |
voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) |
|
|
3027 | ||
|
|
3041 | ||
|
|
3028 | 3042 |
vStacked = None |
|
|
3029 | 3043 |
return voltsCCF |
|
|
3030 | ||
|
|
3044 | ||
|
|
3031 | 3045 |
def __getNoise(self, power, timeSegment, timeInterval): |
|
|
3032 | 3046 |
numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
3033 | 3047 |
numBlocks = int(power.shape[0]/numProfPerBlock) |
|
|
3034 | 3048 |
numHeights = power.shape[1] |
|
|
3035 | 3049 | |
|
|
3036 | 3050 |
listPower = numpy.array_split(power, numBlocks, 0) |
|
|
3037 | 3051 |
noise = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
3038 | 3052 |
noise1 = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
3039 | ||
|
|
3053 | ||
|
|
3040 | 3054 |
startInd = 0 |
|
|
3041 | 3055 |
endInd = 0 |
|
|
3042 | ||
|
|
3056 | ||
|
|
3043 | 3057 |
for i in range(numBlocks): #split por canal |
|
|
3044 | 3058 |
startInd = endInd |
|
|
3045 |
endInd = endInd + listPower[i].shape[0] |
|
|
|
3046 | ||
|
|
3059 | endInd = endInd + listPower[i].shape[0] | |
|
|
3060 | ||
|
|
3047 | 3061 |
arrayBlock = listPower[i] |
|
|
3048 | 3062 |
noiseAux = numpy.mean(arrayBlock, 0) |
|
|
3049 | 3063 |
# noiseAux = numpy.median(noiseAux) |
|
|
3050 | 3064 |
# noiseAux = numpy.mean(arrayBlock) |
|
|
3051 |
noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux |
|
|
|
3052 | ||
|
|
3065 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux | |
|
|
3066 | ||
|
|
3053 | 3067 |
noiseAux1 = numpy.mean(arrayBlock) |
|
|
3054 |
noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 |
|
|
|
3055 | ||
|
|
3068 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 | |
|
|
3069 | ||
|
|
3056 | 3070 |
return noise, noise1 |
|
|
3057 | ||
|
|
3071 | ||
|
|
3058 | 3072 |
def __findMeteors(self, power, thresh): |
|
|
3059 | 3073 |
nProf = power.shape[0] |
|
|
3060 | 3074 |
nHeights = power.shape[1] |
|
|
3061 | 3075 |
listMeteors = [] |
|
|
3062 | ||
|
|
3076 | ||
|
|
3063 | 3077 |
for i in range(nHeights): |
|
|
3064 | 3078 |
powerAux = power[:,i] |
|
|
3065 | 3079 |
threshAux = thresh[:,i] |
|
|
3066 | ||
|
|
3080 | ||
|
|
3067 | 3081 |
indUPthresh = numpy.where(powerAux > threshAux)[0] |
|
|
3068 | 3082 |
indDNthresh = numpy.where(powerAux <= threshAux)[0] |
|
|
3069 | ||
|
|
3083 | ||
|
|
3070 | 3084 |
j = 0 |
|
|
3071 | ||
|
|
3085 | ||
|
|
3072 | 3086 |
while (j < indUPthresh.size - 2): |
|
|
3073 | 3087 |
if (indUPthresh[j + 2] == indUPthresh[j] + 2): |
|
|
3074 | 3088 |
indDNAux = numpy.where(indDNthresh > indUPthresh[j]) |
|
|
3075 | 3089 |
indDNthresh = indDNthresh[indDNAux] |
|
|
3076 | ||
|
|
3090 | ||
|
|
3077 | 3091 |
if (indDNthresh.size > 0): |
|
|
3078 | 3092 |
indEnd = indDNthresh[0] - 1 |
|
|
3079 | 3093 |
indInit = indUPthresh[j] |
|
|
3080 | ||
|
|
3094 | ||
|
|
3081 | 3095 |
meteor = powerAux[indInit:indEnd + 1] |
|
|
3082 | 3096 |
indPeak = meteor.argmax() + indInit |
|
|
3083 | 3097 |
FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) |
|
|
3084 | ||
|
|
3098 | ||
|
|
3085 | 3099 |
listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! |
|
|
3086 | 3100 |
j = numpy.where(indUPthresh == indEnd)[0] + 1 |
|
|
3087 | 3101 |
else: j+=1 |
|
|
3088 | 3102 |
else: j+=1 |
|
|
3089 | ||
|
|
3103 | ||
|
|
3090 | 3104 |
return listMeteors |
|
|
3091 | ||
|
|
3105 | ||
|
|
3092 | 3106 |
def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): |
|
|
3093 | ||
|
|
3094 |
arrayMeteors = numpy.asarray(listMeteors) |
|
|
|
3107 | ||
|
|
3108 | arrayMeteors = numpy.asarray(listMeteors) | |
|
|
3095 | 3109 |
listMeteors1 = [] |
|
|
3096 | ||
|
|
3110 | ||
|
|
3097 | 3111 |
while arrayMeteors.shape[0] > 0: |
|
|
3098 | 3112 |
FLAs = arrayMeteors[:,4] |
|
|
3099 | 3113 |
maxFLA = FLAs.argmax() |
|
|
3100 | 3114 |
listMeteors1.append(arrayMeteors[maxFLA,:]) |
|
|
3101 | ||
|
|
3115 | ||
|
|
3102 | 3116 |
MeteorInitTime = arrayMeteors[maxFLA,1] |
|
|
3103 | 3117 |
MeteorEndTime = arrayMeteors[maxFLA,3] |
|
|
3104 | 3118 |
MeteorHeight = arrayMeteors[maxFLA,0] |
|
|
3105 | ||
|
|
3119 | ||
|
|
3106 | 3120 |
#Check neighborhood |
|
|
3107 | 3121 |
maxHeightIndex = MeteorHeight + rangeLimit |
|
|
3108 | 3122 |
minHeightIndex = MeteorHeight - rangeLimit |
|
|
3109 | 3123 |
minTimeIndex = MeteorInitTime - timeLimit |
|
|
3110 | 3124 |
maxTimeIndex = MeteorEndTime + timeLimit |
|
|
3111 | ||
|
|
3125 | ||
|
|
3112 | 3126 |
#Check Heights |
|
|
3113 | 3127 |
indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) |
|
|
3114 | 3128 |
indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) |
|
|
3115 | 3129 |
indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) |
|
|
3116 | ||
|
|
3130 | ||
|
|
3117 | 3131 |
arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) |
|
|
3118 | ||
|
|
3132 | ||
|
|
3119 | 3133 |
return listMeteors1 |
|
|
3120 | ||
|
|
3134 | ||
|
|
3121 | 3135 |
def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): |
|
|
3122 | 3136 |
numHeights = volts.shape[2] |
|
|
3123 | 3137 |
nChannel = volts.shape[0] |
|
|
3124 | ||
|
|
3138 | ||
|
|
3125 | 3139 |
thresholdPhase = thresh[0] |
|
|
3126 | 3140 |
thresholdNoise = thresh[1] |
|
|
3127 | 3141 |
thresholdDB = float(thresh[2]) |
|
|
3128 | ||
|
|
3142 | ||
|
|
3129 | 3143 |
thresholdDB1 = 10**(thresholdDB/10) |
|
|
3130 | 3144 |
pairsarray = numpy.array(pairslist) |
|
|
3131 | 3145 |
indSides = pairsarray[:,1] |
|
|
3132 | ||
|
|
3146 | ||
|
|
3133 | 3147 |
pairslist1 = list(pairslist) |
|
|
3134 | 3148 |
pairslist1.append((0,1)) |
|
|
3135 | 3149 |
pairslist1.append((3,4)) |
|
|
3136 | 3150 | |
|
|
3137 | 3151 |
listMeteors1 = [] |
|
|
3138 | 3152 |
listPowerSeries = [] |
|
|
3139 | 3153 |
listVoltageSeries = [] |
|
|
3140 | 3154 |
#volts has the war data |
|
|
3141 | ||
|
|
3155 | ||
|
|
3142 | 3156 |
if frequency == 30e6: |
|
|
3143 | 3157 |
timeLag = 45*10**-3 |
|
|
3144 | 3158 |
else: |
|
|
3145 | 3159 |
timeLag = 15*10**-3 |
|
|
3146 | 3160 |
lag = numpy.ceil(timeLag/timeInterval) |
|
|
3147 | ||
|
|
3161 | ||
|
|
3148 | 3162 |
for i in range(len(listMeteors)): |
|
|
3149 | ||
|
|
3163 | ||
|
|
3150 | 3164 |
###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### |
|
|
3151 | 3165 |
meteorAux = numpy.zeros(16) |
|
|
3152 | ||
|
|
3166 | ||
|
|
3153 | 3167 |
#Loading meteor Data (mHeight, mStart, mPeak, mEnd) |
|
|
3154 | 3168 |
mHeight = listMeteors[i][0] |
|
|
3155 | 3169 |
mStart = listMeteors[i][1] |
|
|
3156 | 3170 |
mPeak = listMeteors[i][2] |
|
|
3157 | 3171 |
mEnd = listMeteors[i][3] |
|
|
3158 | ||
|
|
3172 | ||
|
|
3159 | 3173 |
#get the volt data between the start and end times of the meteor |
|
|
3160 | 3174 |
meteorVolts = volts[:,mStart:mEnd+1,mHeight] |
|
|
3161 | 3175 |
meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
3162 | ||
|
|
3176 | ||
|
|
3163 | 3177 |
#3.6. Phase Difference estimation |
|
|
3164 | 3178 |
phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) |
|
|
3165 | ||
|
|
3179 | ||
|
|
3166 | 3180 |
#3.7. Phase difference removal & meteor start, peak and end times reestimated |
|
|
3167 | 3181 |
#meteorVolts0.- all Channels, all Profiles |
|
|
3168 | 3182 |
meteorVolts0 = volts[:,:,mHeight] |
|
|
3169 | 3183 |
meteorThresh = noise[:,mHeight]*thresholdNoise |
|
|
3170 | 3184 |
meteorNoise = noise[:,mHeight] |
|
|
3171 | 3185 |
meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting |
|
|
3172 | 3186 |
powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power |
|
|
3173 | ||
|
|
3187 | ||
|
|
3174 | 3188 |
#Times reestimation |
|
|
3175 | 3189 |
mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] |
|
|
3176 | 3190 |
if mStart1.size > 0: |
|
|
3177 | 3191 |
mStart1 = mStart1[-1] + 1 |
|
|
3178 | ||
|
|
3179 |
else: |
|
|
|
3192 | ||
|
|
3193 | else: | |
|
|
3180 | 3194 |
mStart1 = mPeak |
|
|
3181 | ||
|
|
3195 | ||
|
|
3182 | 3196 |
mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 |
|
|
3183 | 3197 |
mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] |
|
|
3184 | 3198 |
if mEndDecayTime1.size == 0: |
|
|
3185 | 3199 |
mEndDecayTime1 = powerNet0.size |
|
|
3186 | 3200 |
else: |
|
|
3187 | 3201 |
mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 |
|
|
3188 | 3202 |
# mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax() |
|
|
3189 | ||
|
|
3203 | ||
|
|
3190 | 3204 |
#meteorVolts1.- all Channels, from start to end |
|
|
3191 | 3205 |
meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] |
|
|
3192 | 3206 |
meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] |
|
|
3193 | 3207 |
if meteorVolts2.shape[1] == 0: |
|
|
3194 | 3208 |
meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] |
|
|
3195 | 3209 |
meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) |
|
|
3196 | 3210 |
meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) |
|
|
3197 | 3211 |
##################### END PARAMETERS REESTIMATION ######################### |
|
|
3198 | ||
|
|
3212 | ||
|
|
3199 | 3213 |
##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## |
|
|
3200 | 3214 |
# if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
3201 |
if meteorVolts2.shape[1] > 0: |
|
|
|
3215 | if meteorVolts2.shape[1] > 0: | |
|
|
3202 | 3216 |
#Phase Difference re-estimation |
|
|
3203 | 3217 |
phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation |
|
|
3204 | 3218 |
# phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist) |
|
|
3205 | 3219 |
meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) |
|
|
3206 | 3220 |
phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) |
|
|
3207 | 3221 |
meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting |
|
|
3208 | ||
|
|
3222 | ||
|
|
3209 | 3223 |
#Phase Difference RMS |
|
|
3210 | 3224 |
phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) |
|
|
3211 | 3225 |
powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) |
|
|
3212 | 3226 |
#Data from Meteor |
|
|
3213 | 3227 |
mPeak1 = powerNet1.argmax() + mStart1 |
|
|
3214 | 3228 |
mPeakPower1 = powerNet1.max() |
|
|
3215 | 3229 |
noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) |
|
|
3216 | 3230 |
mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux |
|
|
3217 | 3231 |
Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) |
|
|
3218 | 3232 |
Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) |
|
|
3219 | 3233 |
PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] |
|
|
3220 | 3234 |
#Vectorize |
|
|
3221 | 3235 |
meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] |
|
|
3222 | 3236 |
meteorAux[7:11] = phaseDiffint[0:4] |
|
|
3223 | ||
|
|
3237 | ||
|
|
3224 | 3238 |
#Rejection Criterions |
|
|
3225 | 3239 |
if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation |
|
|
3226 | 3240 |
meteorAux[-1] = 17 |
|
|
3227 | 3241 |
elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB |
|
|
3228 | 3242 |
meteorAux[-1] = 1 |
|
|
3229 | ||
|
|
3230 | ||
|
|
3231 |
else: |
|
|
|
3243 | ||
|
|
3244 | ||
|
|
3245 | else: | |
|
|
3232 | 3246 |
meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] |
|
|
3233 | 3247 |
meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
3234 | 3248 |
PowerSeries = 0 |
|
|
3235 | ||
|
|
3249 | ||
|
|
3236 | 3250 |
listMeteors1.append(meteorAux) |
|
|
3237 | 3251 |
listPowerSeries.append(PowerSeries) |
|
|
3238 | 3252 |
listVoltageSeries.append(meteorVolts1) |
|
|
3239 | ||
|
|
3240 |
return listMeteors1, listPowerSeries, listVoltageSeries |
|
|
|
3241 | ||
|
|
3253 | ||
|
|
3254 | return listMeteors1, listPowerSeries, listVoltageSeries | |
|
|
3255 | ||
|
|
3242 | 3256 |
def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): |
|
|
3243 | ||
|
|
3257 | ||
|
|
3244 | 3258 |
threshError = 10 |
|
|
3245 | 3259 |
#Depending if it is 30 or 50 MHz |
|
|
3246 | 3260 |
if frequency == 30e6: |
|
|
3247 | 3261 |
timeLag = 45*10**-3 |
|
|
3248 | 3262 |
else: |
|
|
3249 | 3263 |
timeLag = 15*10**-3 |
|
|
3250 | 3264 |
lag = numpy.ceil(timeLag/timeInterval) |
|
|
3251 | ||
|
|
3265 | ||
|
|
3252 | 3266 |
listMeteors1 = [] |
|
|
3253 | ||
|
|
3267 | ||
|
|
3254 | 3268 |
for i in range(len(listMeteors)): |
|
|
3255 | 3269 |
meteorPower = listPower[i] |
|
|
3256 | 3270 |
meteorAux = listMeteors[i] |
|
|
3257 | ||
|
|
3271 | ||
|
|
3258 | 3272 |
if meteorAux[-1] == 0: |
|
|
3259 | 3273 | |
|
|
3260 |
try: |
|
|
|
3274 | try: | |
|
|
3261 | 3275 |
indmax = meteorPower.argmax() |
|
|
3262 | 3276 |
indlag = indmax + lag |
|
|
3263 | ||
|
|
3277 | ||
|
|
3264 | 3278 |
y = meteorPower[indlag:] |
|
|
3265 | 3279 |
x = numpy.arange(0, y.size)*timeLag |
|
|
3266 | ||
|
|
3280 | ||
|
|
3267 | 3281 |
#first guess |
|
|
3268 | 3282 |
a = y[0] |
|
|
3269 | 3283 |
tau = timeLag |
|
|
3270 | 3284 |
#exponential fit |
|
|
3271 | 3285 |
popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) |
|
|
3272 | 3286 |
y1 = self.__exponential_function(x, *popt) |
|
|
3273 | 3287 |
#error estimation |
|
|
3274 | 3288 |
error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) |
|
|
3275 | ||
|
|
3289 | ||
|
|
3276 | 3290 |
decayTime = popt[1] |
|
|
3277 | 3291 |
riseTime = indmax*timeInterval |
|
|
3278 | 3292 |
meteorAux[11:13] = [decayTime, error] |
|
|
3279 | ||
|
|
3293 | ||
|
|
3280 | 3294 |
#Table items 7, 8 and 11 |
|
|
3281 | 3295 |
if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s |
|
|
3282 |
meteorAux[-1] = 7 |
|
|
|
3296 | meteorAux[-1] = 7 | |
|
|
3283 | 3297 |
elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time |
|
|
3284 | 3298 |
meteorAux[-1] = 8 |
|
|
3285 | 3299 |
if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time |
|
|
3286 |
meteorAux[-1] = 11 |
|
|
|
3287 | ||
|
|
3288 | ||
|
|
3300 | meteorAux[-1] = 11 | |
|
|
3301 | ||
|
|
3302 | ||
|
|
3289 | 3303 |
except: |
|
|
3290 |
meteorAux[-1] = 11 |
|
|
|
3291 | ||
|
|
3292 | ||
|
|
3304 | meteorAux[-1] = 11 | |
|
|
3305 | ||
|
|
3306 | ||
|
|
3293 | 3307 |
listMeteors1.append(meteorAux) |
|
|
3294 | ||
|
|
3308 | ||
|
|
3295 | 3309 |
return listMeteors1 |
|
|
3296 | 3310 | |
|
|
3297 | 3311 |
#Exponential Function |
|
|
3298 | 3312 | |
|
|
3299 | 3313 |
def __exponential_function(self, x, a, tau): |
|
|
3300 | 3314 |
y = a*numpy.exp(-x/tau) |
|
|
3301 | 3315 |
return y |
|
|
3302 | ||
|
|
3316 | ||
|
|
3303 | 3317 |
def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): |
|
|
3304 | ||
|
|
3318 | ||
|
|
3305 | 3319 |
pairslist1 = list(pairslist) |
|
|
3306 | 3320 |
pairslist1.append((0,1)) |
|
|
3307 | 3321 |
pairslist1.append((3,4)) |
|
|
3308 | 3322 |
numPairs = len(pairslist1) |
|
|
3309 | 3323 |
#Time Lag |
|
|
3310 | 3324 |
timeLag = 45*10**-3 |
|
|
3311 | 3325 |
c = 3e8 |
|
|
3312 | 3326 |
lag = numpy.ceil(timeLag/timeInterval) |
|
|
3313 | 3327 |
freq = 30e6 |
|
|
3314 | ||
|
|
3328 | ||
|
|
3315 | 3329 |
listMeteors1 = [] |
|
|
3316 | ||
|
|
3330 | ||
|
|
3317 | 3331 |
for i in range(len(listMeteors)): |
|
|
3318 | 3332 |
meteorAux = listMeteors[i] |
|
|
3319 | 3333 |
if meteorAux[-1] == 0: |
|
|
3320 | 3334 |
mStart = listMeteors[i][1] |
|
|
3321 |
mPeak = listMeteors[i][2] |
|
|
|
3335 | mPeak = listMeteors[i][2] | |
|
|
3322 | 3336 |
mLag = mPeak - mStart + lag |
|
|
3323 | ||
|
|
3337 | ||
|
|
3324 | 3338 |
#get the volt data between the start and end times of the meteor |
|
|
3325 | 3339 |
meteorVolts = listVolts[i] |
|
|
3326 | 3340 |
meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
3327 | 3341 | |
|
|
3328 | 3342 |
#Get CCF |
|
|
3329 | 3343 |
allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) |
|
|
3330 | ||
|
|
3344 | ||
|
|
3331 | 3345 |
#Method 2 |
|
|
3332 | 3346 |
slopes = numpy.zeros(numPairs) |
|
|
3333 | 3347 |
time = numpy.array([-2,-1,1,2])*timeInterval |
|
|
3334 | 3348 |
angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) |
|
|
3335 | ||
|
|
3349 | ||
|
|
3336 | 3350 |
#Correct phases |
|
|
3337 | 3351 |
derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] |
|
|
3338 | 3352 |
indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
3339 | ||
|
|
3340 |
if indDer[0].shape[0] > 0: |
|
|
|
3353 | ||
|
|
3354 | if indDer[0].shape[0] > 0: | |
|
|
3341 | 3355 |
for i in range(indDer[0].shape[0]): |
|
|
3342 | 3356 |
signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) |
|
|
3343 | 3357 |
angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi |
|
|
3344 | 3358 | |
|
|
3345 | 3359 |
# fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]])) |
|
|
3346 | 3360 |
for j in range(numPairs): |
|
|
3347 | 3361 |
fit = stats.linregress(time, angAllCCF[j,:]) |
|
|
3348 | 3362 |
slopes[j] = fit[0] |
|
|
3349 | ||
|
|
3363 | ||
|
|
3350 | 3364 |
#Remove Outlier |
|
|
3351 | 3365 |
# indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
3352 | 3366 |
# slopes = numpy.delete(slopes,indOut) |
|
|
3353 | 3367 |
# indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
3354 | 3368 |
# slopes = numpy.delete(slopes,indOut) |
|
|
3355 | ||
|
|
3369 | ||
|
|
3356 | 3370 |
radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
3357 | 3371 |
radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
3358 | 3372 |
meteorAux[-2] = radialError |
|
|
3359 | 3373 |
meteorAux[-3] = radialVelocity |
|
|
3360 | ||
|
|
3374 | ||
|
|
3361 | 3375 |
#Setting Error |
|
|
3362 | 3376 |
#Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
3363 |
if numpy.abs(radialVelocity) > 200: |
|
|
|
3377 | if numpy.abs(radialVelocity) > 200: | |
|
|
3364 | 3378 |
meteorAux[-1] = 15 |
|
|
3365 | 3379 |
#Number 12: Poor fit to CCF variation for estimation of radial drift velocity |
|
|
3366 | 3380 |
elif radialError > radialStdThresh: |
|
|
3367 | 3381 |
meteorAux[-1] = 12 |
|
|
3368 | ||
|
|
3382 | ||
|
|
3369 | 3383 |
listMeteors1.append(meteorAux) |
|
|
3370 | 3384 |
return listMeteors1 |
|
|
3371 | ||
|
|
3385 | ||
|
|
3372 | 3386 |
def __setNewArrays(self, listMeteors, date, heiRang): |
|
|
3373 | ||
|
|
3387 | ||
|
|
3374 | 3388 |
#New arrays |
|
|
3375 | 3389 |
arrayMeteors = numpy.array(listMeteors) |
|
|
3376 | 3390 |
arrayParameters = numpy.zeros((len(listMeteors), 13)) |
|
|
3377 | ||
|
|
3391 | ||
|
|
3378 | 3392 |
#Date inclusion |
|
|
3379 | 3393 |
# date = re.findall(r'\((.*?)\)', date) |
|
|
3380 | 3394 |
# date = date[0].split(',') |
|
|
3381 | 3395 |
# date = map(int, date) |
|
|
3382 | # | |
|
|
3396 | # | |
|
|
3383 | 3397 |
# if len(date)<6: |
|
|
3384 | 3398 |
# date.append(0) |
|
|
3385 | # | |
|
|
3399 | # | |
|
|
3386 | 3400 |
# date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]] |
|
|
3387 | 3401 |
# arrayDate = numpy.tile(date, (len(listMeteors), 1)) |
|
|
3388 | 3402 |
arrayDate = numpy.tile(date, (len(listMeteors))) |
|
|
3389 | ||
|
|
3403 | ||
|
|
3390 | 3404 |
#Meteor array |
|
|
3391 | 3405 |
# arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] |
|
|
3392 | 3406 |
# arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) |
|
|
3393 | ||
|
|
3407 | ||
|
|
3394 | 3408 |
#Parameters Array |
|
|
3395 | 3409 |
arrayParameters[:,0] = arrayDate #Date |
|
|
3396 | 3410 |
arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range |
|
|
3397 | 3411 |
arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error |
|
|
3398 | 3412 |
arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases |
|
|
3399 | 3413 |
arrayParameters[:,-1] = arrayMeteors[:,-1] #Error |
|
|
3400 | 3414 | |
|
|
3401 | ||
|
|
3415 | ||
|
|
3402 | 3416 |
return arrayParameters |
|
|
3403 | ||
|
|
3417 | ||
|
|
3404 | 3418 |
class CorrectSMPhases(Operation): |
|
|
3405 | ||
|
|
3419 | ||
|
|
3406 | 3420 |
def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None): |
|
|
3407 | ||
|
|
3421 | ||
|
|
3408 | 3422 |
arrayParameters = dataOut.data_param |
|
|
3409 | 3423 |
pairsList = [] |
|
|
3410 | 3424 |
pairx = (0,1) |
|
|
3411 | 3425 |
pairy = (2,3) |
|
|
3412 | 3426 |
pairsList.append(pairx) |
|
|
3413 | 3427 |
pairsList.append(pairy) |
|
|
3414 | 3428 |
jph = numpy.zeros(4) |
|
|
3415 | ||
|
|
3429 | ||
|
|
3416 | 3430 |
phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
|
3417 | 3431 |
# arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
|
3418 | 3432 |
arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets))) |
|
|
3419 | ||
|
|
3433 | ||
|
|
3420 | 3434 |
meteorOps = SMOperations() |
|
|
3421 | 3435 |
if channelPositions is None: |
|
|
3422 | 3436 |
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
3423 | 3437 |
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
3424 | ||
|
|
3438 | ||
|
|
3425 | 3439 |
pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
3426 | 3440 |
h = (hmin,hmax) |
|
|
3427 | ||
|
|
3441 | ||
|
|
3428 | 3442 |
arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
|
3429 | ||
|
|
3443 | ||
|
|
3430 | 3444 |
dataOut.data_param = arrayParameters |
|
|
3431 | 3445 |
return |
|
|
3432 | 3446 | |
|
|
3433 | 3447 |
class SMPhaseCalibration(Operation): |
|
|
3434 | ||
|
|
3448 | ||
|
|
3435 | 3449 |
__buffer = None |
|
|
3436 | 3450 | |
|
|
3437 | 3451 |
__initime = None |
|
|
3438 | 3452 | |
|
|
3439 | 3453 |
__dataReady = False |
|
|
3440 | ||
|
|
3454 | ||
|
|
3441 | 3455 |
__isConfig = False |
|
|
3442 | ||
|
|
3456 | ||
|
|
3443 | 3457 |
def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): |
|
|
3444 | ||
|
|
3458 | ||
|
|
3445 | 3459 |
dataTime = currentTime + paramInterval |
|
|
3446 | 3460 |
deltaTime = dataTime - initTime |
|
|
3447 | ||
|
|
3461 | ||
|
|
3448 | 3462 |
if deltaTime >= outputInterval or deltaTime < 0: |
|
|
3449 | 3463 |
return True |
|
|
3450 | ||
|
|
3464 | ||
|
|
3451 | 3465 |
return False |
|
|
3452 | ||
|
|
3466 | ||
|
|
3453 | 3467 |
def __getGammas(self, pairs, d, phases): |
|
|
3454 | 3468 |
gammas = numpy.zeros(2) |
|
|
3455 | ||
|
|
3469 | ||
|
|
3456 | 3470 |
for i in range(len(pairs)): |
|
|
3457 | ||
|
|
3471 | ||
|
|
3458 | 3472 |
pairi = pairs[i] |
|
|
3459 | 3473 | |
|
|
3460 | 3474 |
phip3 = phases[:,pairi[0]] |
|
|
3461 | 3475 |
d3 = d[pairi[0]] |
|
|
3462 | 3476 |
phip2 = phases[:,pairi[1]] |
|
|
3463 | 3477 |
d2 = d[pairi[1]] |
|
|
3464 | 3478 |
#Calculating gamma |
|
|
3465 | 3479 |
# jdcos = alp1/(k*d1) |
|
|
3466 | 3480 |
# jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0))) |
|
|
3467 | 3481 |
jgamma = -phip2*d3/d2 - phip3 |
|
|
3468 | 3482 |
jgamma = numpy.angle(numpy.exp(1j*jgamma)) |
|
|
3469 | 3483 |
# jgamma[jgamma>numpy.pi] -= 2*numpy.pi |
|
|
3470 | 3484 |
# jgamma[jgamma<-numpy.pi] += 2*numpy.pi |
|
|
3471 | ||
|
|
3485 | ||
|
|
3472 | 3486 |
#Revised distribution |
|
|
3473 | 3487 |
jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) |
|
|
3474 | 3488 | |
|
|
3475 | 3489 |
#Histogram |
|
|
3476 | 3490 |
nBins = 64 |
|
|
3477 | 3491 |
rmin = -0.5*numpy.pi |
|
|
3478 | 3492 |
rmax = 0.5*numpy.pi |
|
|
3479 | 3493 |
phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) |
|
|
3480 | ||
|
|
3494 | ||
|
|
3481 | 3495 |
meteorsY = phaseHisto[0] |
|
|
3482 | 3496 |
phasesX = phaseHisto[1][:-1] |
|
|
3483 | 3497 |
width = phasesX[1] - phasesX[0] |
|
|
3484 | 3498 |
phasesX += width/2 |
|
|
3485 | ||
|
|
3499 | ||
|
|
3486 | 3500 |
#Gaussian aproximation |
|
|
3487 | 3501 |
bpeak = meteorsY.argmax() |
|
|
3488 | 3502 |
peak = meteorsY.max() |
|
|
3489 | 3503 |
jmin = bpeak - 5 |
|
|
3490 | 3504 |
jmax = bpeak + 5 + 1 |
|
|
3491 | ||
|
|
3505 | ||
|
|
3492 | 3506 |
if jmin<0: |
|
|
3493 | 3507 |
jmin = 0 |
|
|
3494 | 3508 |
jmax = 6 |
|
|
3495 | 3509 |
elif jmax > meteorsY.size: |
|
|
3496 | 3510 |
jmin = meteorsY.size - 6 |
|
|
3497 | 3511 |
jmax = meteorsY.size |
|
|
3498 | ||
|
|
3512 | ||
|
|
3499 | 3513 |
x0 = numpy.array([peak,bpeak,50]) |
|
|
3500 | 3514 |
coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) |
|
|
3501 | ||
|
|
3515 | ||
|
|
3502 | 3516 |
#Gammas |
|
|
3503 | 3517 |
gammas[i] = coeff[0][1] |
|
|
3504 | ||
|
|
3518 | ||
|
|
3505 | 3519 |
return gammas |
|
|
3506 | ||
|
|
3520 | ||
|
|
3507 | 3521 |
def __residualFunction(self, coeffs, y, t): |
|
|
3508 | ||
|
|
3522 | ||
|
|
3509 | 3523 |
return y - self.__gauss_function(t, coeffs) |
|
|
3510 | 3524 | |
|
|
3511 | 3525 |
def __gauss_function(self, t, coeffs): |
|
|
3512 | ||
|
|
3526 | ||
|
|
3513 | 3527 |
return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) |
|
|
3514 | 3528 | |
|
|
3515 | 3529 |
def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): |
|
|
3516 | 3530 |
meteorOps = SMOperations() |
|
|
3517 | 3531 |
nchan = 4 |
|
|
3518 | 3532 |
pairx = pairsList[0] #x es 0 |
|
|
3519 | 3533 |
pairy = pairsList[1] #y es 1 |
|
|
3520 | 3534 |
center_xangle = 0 |
|
|
3521 | 3535 |
center_yangle = 0 |
|
|
3522 | 3536 |
range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) |
|
|
3523 | 3537 |
ntimes = len(range_angle) |
|
|
3524 | 3538 | |
|
|
3525 | 3539 |
nstepsx = 20 |
|
|
3526 | 3540 |
nstepsy = 20 |
|
|
3527 | 3541 | |
|
|
3528 | 3542 |
for iz in range(ntimes): |
|
|
3529 | 3543 |
min_xangle = -range_angle[iz]/2 + center_xangle |
|
|
3530 | 3544 |
max_xangle = range_angle[iz]/2 + center_xangle |
|
|
3531 | 3545 |
min_yangle = -range_angle[iz]/2 + center_yangle |
|
|
3532 | 3546 |
max_yangle = range_angle[iz]/2 + center_yangle |
|
|
3533 | ||
|
|
3547 | ||
|
|
3534 | 3548 |
inc_x = (max_xangle-min_xangle)/nstepsx |
|
|
3535 | 3549 |
inc_y = (max_yangle-min_yangle)/nstepsy |
|
|
3536 | ||
|
|
3550 | ||
|
|
3537 | 3551 |
alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle |
|
|
3538 | 3552 |
alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle |
|
|
3539 | 3553 |
penalty = numpy.zeros((nstepsx,nstepsy)) |
|
|
3540 | 3554 |
jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) |
|
|
3541 | 3555 |
jph = numpy.zeros(nchan) |
|
|
3542 | ||
|
|
3556 | ||
|
|
3543 | 3557 |
# Iterations looking for the offset |
|
|
3544 | 3558 |
for iy in range(int(nstepsy)): |
|
|
3545 | 3559 |
for ix in range(int(nstepsx)): |
|
|
3546 | 3560 |
d3 = d[pairsList[1][0]] |
|
|
3547 | 3561 |
d2 = d[pairsList[1][1]] |
|
|
3548 | 3562 |
d5 = d[pairsList[0][0]] |
|
|
3549 | 3563 |
d4 = d[pairsList[0][1]] |
|
|
3550 | ||
|
|
3564 | ||
|
|
3551 | 3565 |
alp2 = alpha_y[iy] #gamma 1 |
|
|
3552 |
alp4 = alpha_x[ix] #gamma 0 |
|
|
|
3553 | ||
|
|
3566 | alp4 = alpha_x[ix] #gamma 0 | |
|
|
3567 | ||
|
|
3554 | 3568 |
alp3 = -alp2*d3/d2 - gammas[1] |
|
|
3555 | 3569 |
alp5 = -alp4*d5/d4 - gammas[0] |
|
|
3556 | 3570 |
# jph[pairy[1]] = alpha_y[iy] |
|
|
3557 |
# jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]] |
|
|
|
3558 | ||
|
|
3571 | # jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]] | |
|
|
3572 | ||
|
|
3559 | 3573 |
# jph[pairx[1]] = alpha_x[ix] |
|
|
3560 | 3574 |
# jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]] |
|
|
3561 | 3575 |
jph[pairsList[0][1]] = alp4 |
|
|
3562 | 3576 |
jph[pairsList[0][0]] = alp5 |
|
|
3563 | 3577 |
jph[pairsList[1][0]] = alp3 |
|
|
3564 |
jph[pairsList[1][1]] = alp2 |
|
|
|
3578 | jph[pairsList[1][1]] = alp2 | |
|
|
3565 | 3579 |
jph_array[:,ix,iy] = jph |
|
|
3566 | 3580 |
# d = [2.0,2.5,2.5,2.0] |
|
|
3567 |
#falta chequear si va a leer bien los meteoros |
|
|
|
3581 | #falta chequear si va a leer bien los meteoros | |
|
|
3568 | 3582 |
meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph) |
|
|
3569 | 3583 |
error = meteorsArray1[:,-1] |
|
|
3570 | 3584 |
ind1 = numpy.where(error==0)[0] |
|
|
3571 | 3585 |
penalty[ix,iy] = ind1.size |
|
|
3572 | ||
|
|
3586 | ||
|
|
3573 | 3587 |
i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) |
|
|
3574 | 3588 |
phOffset = jph_array[:,i,j] |
|
|
3575 | ||
|
|
3589 | ||
|
|
3576 | 3590 |
center_xangle = phOffset[pairx[1]] |
|
|
3577 | 3591 |
center_yangle = phOffset[pairy[1]] |
|
|
3578 | ||
|
|
3592 | ||
|
|
3579 | 3593 |
phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) |
|
|
3580 |
phOffset = phOffset*180/numpy.pi |
|
|
|
3594 | phOffset = phOffset*180/numpy.pi | |
|
|
3581 | 3595 |
return phOffset |
|
|
3582 | ||
|
|
3583 | ||
|
|
3596 | ||
|
|
3597 | ||
|
|
3584 | 3598 |
def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1): |
|
|
3585 | ||
|
|
3599 | ||
|
|
3586 | 3600 |
dataOut.flagNoData = True |
|
|
3587 |
self.__dataReady = False |
|
|
|
3601 | self.__dataReady = False | |
|
|
3588 | 3602 |
dataOut.outputInterval = nHours*3600 |
|
|
3589 | ||
|
|
3603 | ||
|
|
3590 | 3604 |
if self.__isConfig == False: |
|
|
3591 | 3605 |
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
3592 | 3606 |
#Get Initial LTC time |
|
|
3593 | 3607 |
self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
3594 | 3608 |
self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
3595 | 3609 | |
|
|
3596 | 3610 |
self.__isConfig = True |
|
|
3597 | ||
|
|
3611 | ||
|
|
3598 | 3612 |
if self.__buffer is None: |
|
|
3599 | 3613 |
self.__buffer = dataOut.data_param.copy() |
|
|
3600 | 3614 | |
|
|
3601 | 3615 |
else: |
|
|
3602 | 3616 |
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
3603 | ||
|
|
3617 | ||
|
|
3604 | 3618 |
self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
3605 | ||
|
|
3619 | ||
|
|
3606 | 3620 |
if self.__dataReady: |
|
|
3607 | 3621 |
dataOut.utctimeInit = self.__initime |
|
|
3608 | 3622 |
self.__initime += dataOut.outputInterval #to erase time offset |
|
|
3609 | ||
|
|
3623 | ||
|
|
3610 | 3624 |
freq = dataOut.frequency |
|
|
3611 | 3625 |
c = dataOut.C #m/s |
|
|
3612 | 3626 |
lamb = c/freq |
|
|
3613 | 3627 |
k = 2*numpy.pi/lamb |
|
|
3614 | 3628 |
azimuth = 0 |
|
|
3615 | 3629 |
h = (hmin, hmax) |
|
|
3616 | 3630 |
# pairs = ((0,1),(2,3)) #Estrella |
|
|
3617 | 3631 |
# pairs = ((1,0),(2,3)) #T |
|
|
3618 | 3632 | |
|
|
3619 | 3633 |
if channelPositions is None: |
|
|
3620 | 3634 |
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
3621 | 3635 |
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
3622 | 3636 |
meteorOps = SMOperations() |
|
|
3623 | 3637 |
pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
3624 | 3638 | |
|
|
3625 | 3639 |
#Checking correct order of pairs |
|
|
3626 | 3640 |
pairs = [] |
|
|
3627 | 3641 |
if distances[1] > distances[0]: |
|
|
3628 | 3642 |
pairs.append((1,0)) |
|
|
3629 | 3643 |
else: |
|
|
3630 | 3644 |
pairs.append((0,1)) |
|
|
3631 | ||
|
|
3645 | ||
|
|
3632 | 3646 |
if distances[3] > distances[2]: |
|
|
3633 | 3647 |
pairs.append((3,2)) |
|
|
3634 | 3648 |
else: |
|
|
3635 | 3649 |
pairs.append((2,3)) |
|
|
3636 | 3650 |
# distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb] |
|
|
3637 | ||
|
|
3651 | ||
|
|
3638 | 3652 |
meteorsArray = self.__buffer |
|
|
3639 | 3653 |
error = meteorsArray[:,-1] |
|
|
3640 | 3654 |
boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) |
|
|
3641 | 3655 |
ind1 = numpy.where(boolError)[0] |
|
|
3642 | 3656 |
meteorsArray = meteorsArray[ind1,:] |
|
|
3643 | 3657 |
meteorsArray[:,-1] = 0 |
|
|
3644 | 3658 |
phases = meteorsArray[:,8:12] |
|
|
3645 | ||
|
|
3659 | ||
|
|
3646 | 3660 |
#Calculate Gammas |
|
|
3647 | 3661 |
gammas = self.__getGammas(pairs, distances, phases) |
|
|
3648 | 3662 |
# gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180 |
|
|
3649 | 3663 |
#Calculate Phases |
|
|
3650 | 3664 |
phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray) |
|
|
3651 | 3665 |
phasesOff = phasesOff.reshape((1,phasesOff.size)) |
|
|
3652 | 3666 |
dataOut.data_output = -phasesOff |
|
|
3653 | 3667 |
dataOut.flagNoData = False |
|
|
3654 | 3668 |
self.__buffer = None |
|
|
3655 | ||
|
|
3656 | ||
|
|
3669 | ||
|
|
3670 | ||
|
|
3657 | 3671 |
return |
|
|
3658 | ||
|
|
3672 | ||
|
|
3659 | 3673 |
class SMOperations(): |
|
|
3660 | ||
|
|
3674 | ||
|
|
3661 | 3675 |
def __init__(self): |
|
|
3662 | ||
|
|
3676 | ||
|
|
3663 | 3677 |
return |
|
|
3664 | ||
|
|
3678 | ||
|
|
3665 | 3679 |
def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph): |
|
|
3666 | ||
|
|
3680 | ||
|
|
3667 | 3681 |
arrayParameters = arrayParameters0.copy() |
|
|
3668 | 3682 |
hmin = h[0] |
|
|
3669 | 3683 |
hmax = h[1] |
|
|
3670 | ||
|
|
3684 | ||
|
|
3671 | 3685 |
#Calculate AOA (Error N 3, 4) |
|
|
3672 | 3686 |
#JONES ET AL. 1998 |
|
|
3673 | 3687 |
AOAthresh = numpy.pi/8 |
|
|
3674 | 3688 |
error = arrayParameters[:,-1] |
|
|
3675 | 3689 |
phases = -arrayParameters[:,8:12] + jph |
|
|
3676 | 3690 |
# phases = numpy.unwrap(phases) |
|
|
3677 | 3691 |
arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth) |
|
|
3678 | ||
|
|
3692 | ||
|
|
3679 | 3693 |
#Calculate Heights (Error N 13 and 14) |
|
|
3680 | 3694 |
error = arrayParameters[:,-1] |
|
|
3681 | 3695 |
Ranges = arrayParameters[:,1] |
|
|
3682 | 3696 |
zenith = arrayParameters[:,4] |
|
|
3683 | 3697 |
arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
3684 | ||
|
|
3698 | ||
|
|
3685 | 3699 |
#----------------------- Get Final data ------------------------------------ |
|
|
3686 | 3700 |
# error = arrayParameters[:,-1] |
|
|
3687 | 3701 |
# ind1 = numpy.where(error==0)[0] |
|
|
3688 | 3702 |
# arrayParameters = arrayParameters[ind1,:] |
|
|
3689 | ||
|
|
3703 | ||
|
|
3690 | 3704 |
return arrayParameters |
|
|
3691 | ||
|
|
3705 | ||
|
|
3692 | 3706 |
def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth): |
|
|
3693 | ||
|
|
3707 | ||
|
|
3694 | 3708 |
arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
3695 | 3709 |
cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions) |
|
|
3696 | ||
|
|
3710 | ||
|
|
3697 | 3711 |
arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
3698 | 3712 |
cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
3699 | 3713 |
arrayAOA[:,2] = cosDirError |
|
|
3700 | ||
|
|
3714 | ||
|
|
3701 | 3715 |
azimuthAngle = arrayAOA[:,0] |
|
|
3702 | 3716 |
zenithAngle = arrayAOA[:,1] |
|
|
3703 | ||
|
|
3717 | ||
|
|
3704 | 3718 |
#Setting Error |
|
|
3705 | 3719 |
indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0] |
|
|
3706 | 3720 |
error[indError] = 0 |
|
|
3707 | 3721 |
#Number 3: AOA not fesible |
|
|
3708 | 3722 |
indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
3709 |
error[indInvalid] = 3 |
|
|
|
3723 | error[indInvalid] = 3 | |
|
|
3710 | 3724 |
#Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
3711 | 3725 |
indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
3712 |
error[indInvalid] = 4 |
|
|
|
3726 | error[indInvalid] = 4 | |
|
|
3713 | 3727 |
return arrayAOA, error |
|
|
3714 | ||
|
|
3728 | ||
|
|
3715 | 3729 |
def __getDirectionCosines(self, arrayPhase, pairsList, distances): |
|
|
3716 | ||
|
|
3730 | ||
|
|
3717 | 3731 |
#Initializing some variables |
|
|
3718 | 3732 |
ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
3719 | 3733 |
ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
3720 | ||
|
|
3734 | ||
|
|
3721 | 3735 |
cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3722 | 3736 |
cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3723 | ||
|
|
3724 | ||
|
|
3737 | ||
|
|
3738 | ||
|
|
3725 | 3739 |
for i in range(2): |
|
|
3726 | 3740 |
ph0 = arrayPhase[:,pairsList[i][0]] |
|
|
3727 | 3741 |
ph1 = arrayPhase[:,pairsList[i][1]] |
|
|
3728 | 3742 |
d0 = distances[pairsList[i][0]] |
|
|
3729 | 3743 |
d1 = distances[pairsList[i][1]] |
|
|
3730 | ||
|
|
3731 |
ph0_aux = ph0 + ph1 |
|
|
|
3744 | ||
|
|
3745 | ph0_aux = ph0 + ph1 | |
|
|
3732 | 3746 |
ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux)) |
|
|
3733 | 3747 |
# ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi |
|
|
3734 |
# ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi |
|
|
|
3748 | # ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi | |
|
|
3735 | 3749 |
#First Estimation |
|
|
3736 | 3750 |
cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1)) |
|
|
3737 | ||
|
|
3751 | ||
|
|
3738 | 3752 |
#Most-Accurate Second Estimation |
|
|
3739 | 3753 |
phi1_aux = ph0 - ph1 |
|
|
3740 | 3754 |
phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
3741 | 3755 |
#Direction Cosine 1 |
|
|
3742 | 3756 |
cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1)) |
|
|
3743 | ||
|
|
3757 | ||
|
|
3744 | 3758 |
#Searching the correct Direction Cosine |
|
|
3745 | 3759 |
cosdir0_aux = cosdir0[:,i] |
|
|
3746 | 3760 |
cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
3747 | 3761 |
#Minimum Distance |
|
|
3748 | 3762 |
cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
3749 | 3763 |
indcos = cosDiff.argmin(axis = 1) |
|
|
3750 | 3764 |
#Saving Value obtained |
|
|
3751 | 3765 |
cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
3752 | ||
|
|
3766 | ||
|
|
3753 | 3767 |
return cosdir0, cosdir |
|
|
3754 | ||
|
|
3768 | ||
|
|
3755 | 3769 |
def __calculateAOA(self, cosdir, azimuth): |
|
|
3756 | 3770 |
cosdirX = cosdir[:,0] |
|
|
3757 | 3771 |
cosdirY = cosdir[:,1] |
|
|
3758 | ||
|
|
3772 | ||
|
|
3759 | 3773 |
zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
3760 | 3774 |
azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east |
|
|
3761 | 3775 |
angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
3762 | ||
|
|
3776 | ||
|
|
3763 | 3777 |
return angles |
|
|
3764 | ||
|
|
3778 | ||
|
|
3765 | 3779 |
def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
3766 | ||
|
|
3780 | ||
|
|
3767 | 3781 |
Ramb = 375 #Ramb = c/(2*PRF) |
|
|
3768 | 3782 |
Re = 6371 #Earth Radius |
|
|
3769 | 3783 |
heights = numpy.zeros(Ranges.shape) |
|
|
3770 | ||
|
|
3784 | ||
|
|
3771 | 3785 |
R_aux = numpy.array([0,1,2])*Ramb |
|
|
3772 | 3786 |
R_aux = R_aux.reshape(1,R_aux.size) |
|
|
3773 | 3787 | |
|
|
3774 | 3788 |
Ranges = Ranges.reshape(Ranges.size,1) |
|
|
3775 | ||
|
|
3789 | ||
|
|
3776 | 3790 |
Ri = Ranges + R_aux |
|
|
3777 | 3791 |
hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
3778 | ||
|
|
3792 | ||
|
|
3779 | 3793 |
#Check if there is a height between 70 and 110 km |
|
|
3780 | 3794 |
h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
3781 | 3795 |
ind_h = numpy.where(h_bool == 1)[0] |
|
|
3782 | ||
|
|
3796 | ||
|
|
3783 | 3797 |
hCorr = hi[ind_h, :] |
|
|
3784 | 3798 |
ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
3785 | ||
|
|
3799 | ||
|
|
3786 | 3800 |
hCorr = hi[ind_hCorr][:len(ind_h)] |
|
|
3787 | 3801 |
heights[ind_h] = hCorr |
|
|
3788 | ||
|
|
3802 | ||
|
|
3789 | 3803 |
#Setting Error |
|
|
3790 | 3804 |
#Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
3791 |
#Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
|
3805 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km | |
|
|
3792 | 3806 |
indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0] |
|
|
3793 | 3807 |
error[indError] = 0 |
|
|
3794 |
indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
|
3808 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] | |
|
|
3795 | 3809 |
error[indInvalid2] = 14 |
|
|
3796 | 3810 |
indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
3797 |
error[indInvalid1] = 13 |
|
|
|
3798 | ||
|
|
3811 | error[indInvalid1] = 13 | |
|
|
3812 | ||
|
|
3799 | 3813 |
return heights, error |
|
|
3800 | ||
|
|
3814 | ||
|
|
3801 | 3815 |
def getPhasePairs(self, channelPositions): |
|
|
3802 | 3816 |
chanPos = numpy.array(channelPositions) |
|
|
3803 | 3817 |
listOper = list(itertools.combinations(list(range(5)),2)) |
|
|
3804 | ||
|
|
3818 | ||
|
|
3805 | 3819 |
distances = numpy.zeros(4) |
|
|
3806 | 3820 |
axisX = [] |
|
|
3807 | 3821 |
axisY = [] |
|
|
3808 | 3822 |
distX = numpy.zeros(3) |
|
|
3809 | 3823 |
distY = numpy.zeros(3) |
|
|
3810 | 3824 |
ix = 0 |
|
|
3811 | 3825 |
iy = 0 |
|
|
3812 | ||
|
|
3826 | ||
|
|
3813 | 3827 |
pairX = numpy.zeros((2,2)) |
|
|
3814 | 3828 |
pairY = numpy.zeros((2,2)) |
|
|
3815 | ||
|
|
3829 | ||
|
|
3816 | 3830 |
for i in range(len(listOper)): |
|
|
3817 | 3831 |
pairi = listOper[i] |
|
|
3818 | ||
|
|
3832 | ||
|
|
3819 | 3833 |
posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:]) |
|
|
3820 | ||
|
|
3834 | ||
|
|
3821 | 3835 |
if posDif[0] == 0: |
|
|
3822 | 3836 |
axisY.append(pairi) |
|
|
3823 | 3837 |
distY[iy] = posDif[1] |
|
|
3824 | 3838 |
iy += 1 |
|
|
3825 | 3839 |
elif posDif[1] == 0: |
|
|
3826 | 3840 |
axisX.append(pairi) |
|
|
3827 | 3841 |
distX[ix] = posDif[0] |
|
|
3828 | 3842 |
ix += 1 |
|
|
3829 | ||
|
|
3843 | ||
|
|
3830 | 3844 |
for i in range(2): |
|
|
3831 | 3845 |
if i==0: |
|
|
3832 | 3846 |
dist0 = distX |
|
|
3833 | 3847 |
axis0 = axisX |
|
|
3834 | 3848 |
else: |
|
|
3835 | 3849 |
dist0 = distY |
|
|
3836 | 3850 |
axis0 = axisY |
|
|
3837 | ||
|
|
3851 | ||
|
|
3838 | 3852 |
side = numpy.argsort(dist0)[:-1] |
|
|
3839 | 3853 |
axis0 = numpy.array(axis0)[side,:] |
|
|
3840 | 3854 |
chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0]) |
|
|
3841 | 3855 |
axis1 = numpy.unique(numpy.reshape(axis0,4)) |
|
|
3842 | 3856 |
side = axis1[axis1 != chanC] |
|
|
3843 | 3857 |
diff1 = chanPos[chanC,i] - chanPos[side[0],i] |
|
|
3844 | 3858 |
diff2 = chanPos[chanC,i] - chanPos[side[1],i] |
|
|
3845 |
if diff1<0: |
|
|
|
3859 | if diff1<0: | |
|
|
3846 | 3860 |
chan2 = side[0] |
|
|
3847 | 3861 |
d2 = numpy.abs(diff1) |
|
|
3848 | 3862 |
chan1 = side[1] |
|
|
3849 | 3863 |
d1 = numpy.abs(diff2) |
|
|
3850 | 3864 |
else: |
|
|
3851 | 3865 |
chan2 = side[1] |
|
|
3852 | 3866 |
d2 = numpy.abs(diff2) |
|
|
3853 | 3867 |
chan1 = side[0] |
|
|
3854 | 3868 |
d1 = numpy.abs(diff1) |
|
|
3855 | ||
|
|
3869 | ||
|
|
3856 | 3870 |
if i==0: |
|
|
3857 | 3871 |
chanCX = chanC |
|
|
3858 | 3872 |
chan1X = chan1 |
|
|
3859 | 3873 |
chan2X = chan2 |
|
|
3860 | 3874 |
distances[0:2] = numpy.array([d1,d2]) |
|
|
3861 | 3875 |
else: |
|
|
3862 | 3876 |
chanCY = chanC |
|
|
3863 | 3877 |
chan1Y = chan1 |
|
|
3864 | 3878 |
chan2Y = chan2 |
|
|
3865 | 3879 |
distances[2:4] = numpy.array([d1,d2]) |
|
|
3866 | 3880 |
# axisXsides = numpy.reshape(axisX[ix,:],4) |
|
|
3867 | # | |
|
|
3881 | # | |
|
|
3868 | 3882 |
# channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0]) |
|
|
3869 | 3883 |
# channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0]) |
|
|
3870 | # | |
|
|
3884 | # | |
|
|
3871 | 3885 |
# ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0] |
|
|
3872 | 3886 |
# ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0] |
|
|
3873 | 3887 |
# channel25X = int(pairX[0,ind25X]) |
|
|
3874 | 3888 |
# channel20X = int(pairX[1,ind20X]) |
|
|
3875 | 3889 |
# ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0] |
|
|
3876 | 3890 |
# ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0] |
|
|
3877 | 3891 |
# channel25Y = int(pairY[0,ind25Y]) |
|
|
3878 | 3892 |
# channel20Y = int(pairY[1,ind20Y]) |
|
|
3879 | ||
|
|
3893 | ||
|
|
3880 | 3894 |
# pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)] |
|
|
3881 |
pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)] |
|
|
|
3882 | ||
|
|
3895 | pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)] | |
|
|
3896 | ||
|
|
3883 | 3897 |
return pairslist, distances |
|
|
3884 | 3898 |
# def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): |
|
|
3885 | # | |
|
|
3899 | # | |
|
|
3886 | 3900 |
# arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
3887 | 3901 |
# cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) |
|
|
3888 | # | |
|
|
3902 | # | |
|
|
3889 | 3903 |
# arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
3890 | 3904 |
# cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
3891 | 3905 |
# arrayAOA[:,2] = cosDirError |
|
|
3892 | # | |
|
|
3906 | # | |
|
|
3893 | 3907 |
# azimuthAngle = arrayAOA[:,0] |
|
|
3894 | 3908 |
# zenithAngle = arrayAOA[:,1] |
|
|
3895 | # | |
|
|
3909 | # | |
|
|
3896 | 3910 |
# #Setting Error |
|
|
3897 | 3911 |
# #Number 3: AOA not fesible |
|
|
3898 | 3912 |
# indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
3899 |
# error[indInvalid] = 3 |
|
|
|
3913 | # error[indInvalid] = 3 | |
|
|
3900 | 3914 |
# #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
3901 | 3915 |
# indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
3902 |
# error[indInvalid] = 4 |
|
|
|
3916 | # error[indInvalid] = 4 | |
|
|
3903 | 3917 |
# return arrayAOA, error |
|
|
3904 | # | |
|
|
3918 | # | |
|
|
3905 | 3919 |
# def __getDirectionCosines(self, arrayPhase, pairsList): |
|
|
3906 | # | |
|
|
3920 | # | |
|
|
3907 | 3921 |
# #Initializing some variables |
|
|
3908 | 3922 |
# ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
3909 | 3923 |
# ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
3910 | # | |
|
|
3924 | # | |
|
|
3911 | 3925 |
# cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3912 | 3926 |
# cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3913 | # | |
|
|
3914 | # | |
|
|
3927 | # | |
|
|
3928 | # | |
|
|
3915 | 3929 |
# for i in range(2): |
|
|
3916 | 3930 |
# #First Estimation |
|
|
3917 | 3931 |
# phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] |
|
|
3918 | 3932 |
# #Dealias |
|
|
3919 | 3933 |
# indcsi = numpy.where(phi0_aux > numpy.pi) |
|
|
3920 |
# phi0_aux[indcsi] -= 2*numpy.pi |
|
|
|
3934 | # phi0_aux[indcsi] -= 2*numpy.pi | |
|
|
3921 | 3935 |
# indcsi = numpy.where(phi0_aux < -numpy.pi) |
|
|
3922 |
# phi0_aux[indcsi] += 2*numpy.pi |
|
|
|
3936 | # phi0_aux[indcsi] += 2*numpy.pi | |
|
|
3923 | 3937 |
# #Direction Cosine 0 |
|
|
3924 | 3938 |
# cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) |
|
|
3925 | # | |
|
|
3939 | # | |
|
|
3926 | 3940 |
# #Most-Accurate Second Estimation |
|
|
3927 | 3941 |
# phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] |
|
|
3928 | 3942 |
# phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
3929 | 3943 |
# #Direction Cosine 1 |
|
|
3930 | 3944 |
# cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) |
|
|
3931 | # | |
|
|
3945 | # | |
|
|
3932 | 3946 |
# #Searching the correct Direction Cosine |
|
|
3933 | 3947 |
# cosdir0_aux = cosdir0[:,i] |
|
|
3934 | 3948 |
# cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
3935 | 3949 |
# #Minimum Distance |
|
|
3936 | 3950 |
# cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
3937 | 3951 |
# indcos = cosDiff.argmin(axis = 1) |
|
|
3938 | 3952 |
# #Saving Value obtained |
|
|
3939 | 3953 |
# cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
3940 | # | |
|
|
3954 | # | |
|
|
3941 | 3955 |
# return cosdir0, cosdir |
|
|
3942 | # | |
|
|
3956 | # | |
|
|
3943 | 3957 |
# def __calculateAOA(self, cosdir, azimuth): |
|
|
3944 | 3958 |
# cosdirX = cosdir[:,0] |
|
|
3945 | 3959 |
# cosdirY = cosdir[:,1] |
|
|
3946 | # | |
|
|
3960 | # | |
|
|
3947 | 3961 |
# zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
3948 | 3962 |
# azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east |
|
|
3949 | 3963 |
# angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
3950 | # | |
|
|
3964 | # | |
|
|
3951 | 3965 |
# return angles |
|
|
3952 | # | |
|
|
3966 | # | |
|
|
3953 | 3967 |
# def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
3954 | # | |
|
|
3968 | # | |
|
|
3955 | 3969 |
# Ramb = 375 #Ramb = c/(2*PRF) |
|
|
3956 | 3970 |
# Re = 6371 #Earth Radius |
|
|
3957 | 3971 |
# heights = numpy.zeros(Ranges.shape) |
|
|
3958 | # | |
|
|
3972 | # | |
|
|
3959 | 3973 |
# R_aux = numpy.array([0,1,2])*Ramb |
|
|
3960 | 3974 |
# R_aux = R_aux.reshape(1,R_aux.size) |
|
|
3961 | # | |
|
|
3975 | # | |
|
|
3962 | 3976 |
# Ranges = Ranges.reshape(Ranges.size,1) |
|
|
3963 | # | |
|
|
3977 | # | |
|
|
3964 | 3978 |
# Ri = Ranges + R_aux |
|
|
3965 | 3979 |
# hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
3966 | # | |
|
|
3980 | # | |
|
|
3967 | 3981 |
# #Check if there is a height between 70 and 110 km |
|
|
3968 | 3982 |
# h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
3969 | 3983 |
# ind_h = numpy.where(h_bool == 1)[0] |
|
|
3970 | # | |
|
|
3984 | # | |
|
|
3971 | 3985 |
# hCorr = hi[ind_h, :] |
|
|
3972 | 3986 |
# ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
3973 | # | |
|
|
3974 |
# hCorr = hi[ind_hCorr] |
|
|
|
3987 | # | |
|
|
3988 | # hCorr = hi[ind_hCorr] | |
|
|
3975 | 3989 |
# heights[ind_h] = hCorr |
|
|
3976 | # | |
|
|
3990 | # | |
|
|
3977 | 3991 |
# #Setting Error |
|
|
3978 | 3992 |
# #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
3979 |
# #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
|
3980 | # | |
|
|
3981 |
# indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
|
3993 | # #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km | |
|
|
3994 | # | |
|
|
3995 | # indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] | |
|
|
3982 | 3996 |
# error[indInvalid2] = 14 |
|
|
3983 | 3997 |
# indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
3984 |
# error[indInvalid1] = 13 |
|
|
|
3985 | # | |
|
|
3986 |
# return heights, error |
|
|
|
3987 | No newline at end of file | |
|
|
3998 | # error[indInvalid1] = 13 | |
|
|
3999 | # | |
|
|
4000 | # return heights, error | |
| @@ -1,1604 +1,1627 | |||
|
|
1 | 1 | import sys |
|
|
2 | 2 | import numpy,math |
|
|
3 | 3 | from scipy import interpolate |
|
|
4 | 4 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
|
5 | from schainpy.model.data.jrodata import Voltage | |
|
|
5 | from schainpy.model.data.jrodata import Voltage,hildebrand_sekhon | |
|
|
6 | 6 | from schainpy.utils import log |
|
|
7 | 7 | from time import time |
|
|
8 | 8 | |
|
|
9 | 9 | |
|
|
10 | 10 | |
|
|
11 | 11 | class VoltageProc(ProcessingUnit): |
|
|
12 | 12 | |
|
|
13 | 13 | def __init__(self): |
|
|
14 | 14 | |
|
|
15 | 15 | ProcessingUnit.__init__(self) |
|
|
16 | 16 | |
|
|
17 | 17 | self.dataOut = Voltage() |
|
|
18 | 18 | self.flip = 1 |
|
|
19 | 19 | self.setupReq = False |
|
|
20 | 20 | |
|
|
21 | 21 | def run(self): |
|
|
22 | 22 | |
|
|
23 | 23 | if self.dataIn.type == 'AMISR': |
|
|
24 | 24 | self.__updateObjFromAmisrInput() |
|
|
25 | 25 | |
|
|
26 | 26 | if self.dataIn.type == 'Voltage': |
|
|
27 | 27 | self.dataOut.copy(self.dataIn) |
|
|
28 | 28 | |
|
|
29 | 29 | def __updateObjFromAmisrInput(self): |
|
|
30 | 30 | |
|
|
31 | 31 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
32 | 32 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
33 | 33 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
34 | 34 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
35 | 35 | |
|
|
36 | 36 | self.dataOut.flagNoData = self.dataIn.flagNoData |
|
|
37 | 37 | self.dataOut.data = self.dataIn.data |
|
|
38 | 38 | self.dataOut.utctime = self.dataIn.utctime |
|
|
39 | 39 | self.dataOut.channelList = self.dataIn.channelList |
|
|
40 | 40 | #self.dataOut.timeInterval = self.dataIn.timeInterval |
|
|
41 | 41 | self.dataOut.heightList = self.dataIn.heightList |
|
|
42 | 42 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
|
43 | 43 | |
|
|
44 | 44 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
45 | 45 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
46 | 46 | self.dataOut.frequency = self.dataIn.frequency |
|
|
47 | 47 | |
|
|
48 | 48 | self.dataOut.azimuth = self.dataIn.azimuth |
|
|
49 | 49 | self.dataOut.zenith = self.dataIn.zenith |
|
|
50 | 50 | |
|
|
51 | 51 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
|
|
52 | 52 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
|
|
53 | 53 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
|
|
54 | 54 | |
|
|
55 | 55 | |
|
|
56 | 56 | class selectChannels(Operation): |
|
|
57 | 57 | |
|
|
58 | 58 | def run(self, dataOut, channelList): |
|
|
59 | 59 | |
|
|
60 | 60 | channelIndexList = [] |
|
|
61 | 61 | self.dataOut = dataOut |
|
|
62 | 62 | for channel in channelList: |
|
|
63 | 63 | if channel not in self.dataOut.channelList: |
|
|
64 | 64 | raise ValueError("Channel %d is not in %s" %(channel, str(self.dataOut.channelList))) |
|
|
65 | 65 | |
|
|
66 | 66 | index = self.dataOut.channelList.index(channel) |
|
|
67 | 67 | channelIndexList.append(index) |
|
|
68 | 68 | self.selectChannelsByIndex(channelIndexList) |
|
|
69 | 69 | return self.dataOut |
|
|
70 | 70 | |
|
|
71 | 71 | def selectChannelsByIndex(self, channelIndexList): |
|
|
72 | 72 | """ |
|
|
73 | 73 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
|
|
74 | 74 | |
|
|
75 | 75 | Input: |
|
|
76 | 76 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
|
|
77 | 77 | |
|
|
78 | 78 | Affected: |
|
|
79 | 79 | self.dataOut.data |
|
|
80 | 80 | self.dataOut.channelIndexList |
|
|
81 | 81 | self.dataOut.nChannels |
|
|
82 | 82 | self.dataOut.m_ProcessingHeader.totalSpectra |
|
|
83 | 83 | self.dataOut.systemHeaderObj.numChannels |
|
|
84 | 84 | self.dataOut.m_ProcessingHeader.blockSize |
|
|
85 | 85 | |
|
|
86 | 86 | Return: |
|
|
87 | 87 | None |
|
|
88 | 88 | """ |
|
|
89 | 89 | |
|
|
90 | 90 | for channelIndex in channelIndexList: |
|
|
91 | 91 | if channelIndex not in self.dataOut.channelIndexList: |
|
|
92 | 92 | raise ValueError("The value %d in channelIndexList is not valid" %channelIndex) |
|
|
93 | 93 | |
|
|
94 | 94 | if self.dataOut.type == 'Voltage': |
|
|
95 | 95 | if self.dataOut.flagDataAsBlock: |
|
|
96 | 96 | """ |
|
|
97 | 97 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
98 | 98 | """ |
|
|
99 | 99 | data = self.dataOut.data[channelIndexList,:,:] |
|
|
100 | 100 | else: |
|
|
101 | 101 | data = self.dataOut.data[channelIndexList,:] |
|
|
102 | 102 | |
|
|
103 | 103 | self.dataOut.data = data |
|
|
104 | 104 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
|
105 | 105 | self.dataOut.channelList = range(len(channelIndexList)) |
|
|
106 | 106 | |
|
|
107 | 107 | elif self.dataOut.type == 'Spectra': |
|
|
108 | 108 | data_spc = self.dataOut.data_spc[channelIndexList, :] |
|
|
109 | 109 | data_dc = self.dataOut.data_dc[channelIndexList, :] |
|
|
110 | 110 | |
|
|
111 | 111 | self.dataOut.data_spc = data_spc |
|
|
112 | 112 | self.dataOut.data_dc = data_dc |
|
|
113 | 113 | |
|
|
114 | 114 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
|
115 | 115 | self.dataOut.channelList = range(len(channelIndexList)) |
|
|
116 | 116 | self.__selectPairsByChannel(channelIndexList) |
|
|
117 | 117 | |
|
|
118 | 118 | return 1 |
|
|
119 | 119 | |
|
|
120 | 120 | def __selectPairsByChannel(self, channelList=None): |
|
|
121 | 121 | |
|
|
122 | 122 | if channelList == None: |
|
|
123 | 123 | return |
|
|
124 | 124 | |
|
|
125 | 125 | pairsIndexListSelected = [] |
|
|
126 | 126 | for pairIndex in self.dataOut.pairsIndexList: |
|
|
127 | 127 | # First pair |
|
|
128 | 128 | if self.dataOut.pairsList[pairIndex][0] not in channelList: |
|
|
129 | 129 | continue |
|
|
130 | 130 | # Second pair |
|
|
131 | 131 | if self.dataOut.pairsList[pairIndex][1] not in channelList: |
|
|
132 | 132 | continue |
|
|
133 | 133 | |
|
|
134 | 134 | pairsIndexListSelected.append(pairIndex) |
|
|
135 | 135 | |
|
|
136 | 136 | if not pairsIndexListSelected: |
|
|
137 | 137 | self.dataOut.data_cspc = None |
|
|
138 | 138 | self.dataOut.pairsList = [] |
|
|
139 | 139 | return |
|
|
140 | 140 | |
|
|
141 | 141 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] |
|
|
142 | 142 | self.dataOut.pairsList = [self.dataOut.pairsList[i] |
|
|
143 | 143 | for i in pairsIndexListSelected] |
|
|
144 | 144 | |
|
|
145 | 145 | return |
|
|
146 | 146 | |
|
|
147 | 147 | class selectHeights(Operation): |
|
|
148 | 148 | |
|
|
149 | 149 | def run(self, dataOut, minHei=None, maxHei=None): |
|
|
150 | 150 | """ |
|
|
151 | 151 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango |
|
|
152 | 152 | minHei <= height <= maxHei |
|
|
153 | 153 | |
|
|
154 | 154 | Input: |
|
|
155 | 155 | minHei : valor minimo de altura a considerar |
|
|
156 | 156 | maxHei : valor maximo de altura a considerar |
|
|
157 | 157 | |
|
|
158 | 158 | Affected: |
|
|
159 | 159 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex |
|
|
160 | 160 | |
|
|
161 | 161 | Return: |
|
|
162 | 162 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
163 | 163 | """ |
|
|
164 | 164 | |
|
|
165 | 165 | self.dataOut = dataOut |
|
|
166 | 166 | |
|
|
167 | 167 | if minHei == None: |
|
|
168 | 168 | minHei = self.dataOut.heightList[0] |
|
|
169 | 169 | |
|
|
170 | 170 | if maxHei == None: |
|
|
171 | 171 | maxHei = self.dataOut.heightList[-1] |
|
|
172 | 172 | |
|
|
173 | 173 | if (minHei < self.dataOut.heightList[0]): |
|
|
174 | 174 | minHei = self.dataOut.heightList[0] |
|
|
175 | 175 | |
|
|
176 | 176 | if (maxHei > self.dataOut.heightList[-1]): |
|
|
177 | 177 | maxHei = self.dataOut.heightList[-1] |
|
|
178 | 178 | |
|
|
179 | 179 | minIndex = 0 |
|
|
180 | 180 | maxIndex = 0 |
|
|
181 | 181 | heights = self.dataOut.heightList |
|
|
182 | 182 | |
|
|
183 | 183 | inda = numpy.where(heights >= minHei) |
|
|
184 | 184 | indb = numpy.where(heights <= maxHei) |
|
|
185 | 185 | |
|
|
186 | 186 | try: |
|
|
187 | 187 | minIndex = inda[0][0] |
|
|
188 | 188 | except: |
|
|
189 | 189 | minIndex = 0 |
|
|
190 | 190 | |
|
|
191 | 191 | try: |
|
|
192 | 192 | maxIndex = indb[0][-1] |
|
|
193 | 193 | except: |
|
|
194 | 194 | maxIndex = len(heights) |
|
|
195 | 195 | |
|
|
196 | 196 | self.selectHeightsByIndex(minIndex, maxIndex) |
|
|
197 | 197 | |
|
|
198 | 198 | return self.dataOut |
|
|
199 | 199 | |
|
|
200 | 200 | def selectHeightsByIndex(self, minIndex, maxIndex): |
|
|
201 | 201 | """ |
|
|
202 | 202 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango |
|
|
203 | 203 | minIndex <= index <= maxIndex |
|
|
204 | 204 | |
|
|
205 | 205 | Input: |
|
|
206 | 206 | minIndex : valor de indice minimo de altura a considerar |
|
|
207 | 207 | maxIndex : valor de indice maximo de altura a considerar |
|
|
208 | 208 | |
|
|
209 | 209 | Affected: |
|
|
210 | 210 | self.dataOut.data |
|
|
211 | 211 | self.dataOut.heightList |
|
|
212 | 212 | |
|
|
213 | 213 | Return: |
|
|
214 | 214 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
215 | 215 | """ |
|
|
216 | 216 | |
|
|
217 | 217 | if self.dataOut.type == 'Voltage': |
|
|
218 | 218 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
219 | 219 | raise ValueError("Height index range (%d,%d) is not valid" % (minIndex, maxIndex)) |
|
|
220 | 220 | |
|
|
221 | 221 | if (maxIndex >= self.dataOut.nHeights): |
|
|
222 | 222 | maxIndex = self.dataOut.nHeights |
|
|
223 | 223 | |
|
|
224 | 224 | #voltage |
|
|
225 | 225 | if self.dataOut.flagDataAsBlock: |
|
|
226 | 226 | """ |
|
|
227 | 227 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
228 | 228 | """ |
|
|
229 | 229 | data = self.dataOut.data[:,:, minIndex:maxIndex] |
|
|
230 | 230 | else: |
|
|
231 | 231 | data = self.dataOut.data[:, minIndex:maxIndex] |
|
|
232 | 232 | |
|
|
233 | 233 | # firstHeight = self.dataOut.heightList[minIndex] |
|
|
234 | 234 | |
|
|
235 | 235 | self.dataOut.data = data |
|
|
236 | 236 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex] |
|
|
237 | 237 | |
|
|
238 | 238 | if self.dataOut.nHeights <= 1: |
|
|
239 | 239 | raise ValueError("selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights)) |
|
|
240 | 240 | elif self.dataOut.type == 'Spectra': |
|
|
241 | 241 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
242 | 242 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % ( |
|
|
243 | 243 | minIndex, maxIndex)) |
|
|
244 | 244 | |
|
|
245 | 245 | if (maxIndex >= self.dataOut.nHeights): |
|
|
246 | 246 | maxIndex = self.dataOut.nHeights - 1 |
|
|
247 | 247 | |
|
|
248 | 248 | # Spectra |
|
|
249 | 249 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] |
|
|
250 | 250 | |
|
|
251 | 251 | data_cspc = None |
|
|
252 | 252 | if self.dataOut.data_cspc is not None: |
|
|
253 | 253 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] |
|
|
254 | 254 | |
|
|
255 | 255 | data_dc = None |
|
|
256 | 256 | if self.dataOut.data_dc is not None: |
|
|
257 | 257 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] |
|
|
258 | 258 | |
|
|
259 | 259 | self.dataOut.data_spc = data_spc |
|
|
260 | 260 | self.dataOut.data_cspc = data_cspc |
|
|
261 | 261 | self.dataOut.data_dc = data_dc |
|
|
262 | 262 | |
|
|
263 | 263 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex + 1] |
|
|
264 | 264 | |
|
|
265 | 265 | return 1 |
|
|
266 | 266 | |
|
|
267 | 267 | |
|
|
268 | 268 | class filterByHeights(Operation): |
|
|
269 | 269 | |
|
|
270 | 270 | def run(self, dataOut, window): |
|
|
271 | 271 | |
|
|
272 | 272 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
273 | 273 | |
|
|
274 | 274 | if window == None: |
|
|
275 | 275 | window = (dataOut.radarControllerHeaderObj.txA/dataOut.radarControllerHeaderObj.nBaud) / deltaHeight |
|
|
276 | 276 | |
|
|
277 | 277 | newdelta = deltaHeight * window |
|
|
278 | 278 | r = dataOut.nHeights % window |
|
|
279 | 279 | newheights = (dataOut.nHeights-r)/window |
|
|
280 | 280 | |
|
|
281 | 281 | if newheights <= 1: |
|
|
282 | 282 | raise ValueError("filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(dataOut.nHeights, window)) |
|
|
283 | 283 | |
|
|
284 | 284 | if dataOut.flagDataAsBlock: |
|
|
285 | 285 | """ |
|
|
286 | 286 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
287 | 287 | """ |
|
|
288 | 288 | buffer = dataOut.data[:, :, 0:int(dataOut.nHeights-r)] |
|
|
289 | 289 | buffer = buffer.reshape(dataOut.nChannels, dataOut.nProfiles, int(dataOut.nHeights/window), window) |
|
|
290 | 290 | buffer = numpy.sum(buffer,3) |
|
|
291 | 291 | |
|
|
292 | 292 | else: |
|
|
293 | 293 | buffer = dataOut.data[:,0:int(dataOut.nHeights-r)] |
|
|
294 | 294 | buffer = buffer.reshape(dataOut.nChannels,int(dataOut.nHeights/window),int(window)) |
|
|
295 | 295 | buffer = numpy.sum(buffer,2) |
|
|
296 | 296 | |
|
|
297 | 297 | dataOut.data = buffer |
|
|
298 | 298 | dataOut.heightList = dataOut.heightList[0] + numpy.arange( newheights )*newdelta |
|
|
299 | 299 | dataOut.windowOfFilter = window |
|
|
300 | 300 | |
|
|
301 | 301 | return dataOut |
|
|
302 | 302 | |
|
|
303 | 303 | |
|
|
304 | 304 | class setH0(Operation): |
|
|
305 | 305 | |
|
|
306 | 306 | def run(self, dataOut, h0, deltaHeight = None): |
|
|
307 | 307 | |
|
|
308 | 308 | if not deltaHeight: |
|
|
309 | 309 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
310 | 310 | |
|
|
311 | 311 | nHeights = dataOut.nHeights |
|
|
312 | 312 | |
|
|
313 | 313 | newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight |
|
|
314 | 314 | |
|
|
315 | 315 | dataOut.heightList = newHeiRange |
|
|
316 | 316 | |
|
|
317 | 317 | return dataOut |
|
|
318 | 318 | |
|
|
319 | 319 | |
|
|
320 | 320 | class deFlip(Operation): |
|
|
321 | 321 | |
|
|
322 | 322 | def run(self, dataOut, channelList = []): |
|
|
323 | 323 | |
|
|
324 | 324 | data = dataOut.data.copy() |
|
|
325 | 325 | |
|
|
326 | 326 | if dataOut.flagDataAsBlock: |
|
|
327 | 327 | flip = self.flip |
|
|
328 | 328 | profileList = list(range(dataOut.nProfiles)) |
|
|
329 | 329 | |
|
|
330 | 330 | if not channelList: |
|
|
331 | 331 | for thisProfile in profileList: |
|
|
332 | 332 | data[:,thisProfile,:] = data[:,thisProfile,:]*flip |
|
|
333 | 333 | flip *= -1.0 |
|
|
334 | 334 | else: |
|
|
335 | 335 | for thisChannel in channelList: |
|
|
336 | 336 | if thisChannel not in dataOut.channelList: |
|
|
337 | 337 | continue |
|
|
338 | 338 | |
|
|
339 | 339 | for thisProfile in profileList: |
|
|
340 | 340 | data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip |
|
|
341 | 341 | flip *= -1.0 |
|
|
342 | 342 | |
|
|
343 | 343 | self.flip = flip |
|
|
344 | 344 | |
|
|
345 | 345 | else: |
|
|
346 | 346 | if not channelList: |
|
|
347 | 347 | data[:,:] = data[:,:]*self.flip |
|
|
348 | 348 | else: |
|
|
349 | 349 | for thisChannel in channelList: |
|
|
350 | 350 | if thisChannel not in dataOut.channelList: |
|
|
351 | 351 | continue |
|
|
352 | 352 | |
|
|
353 | 353 | data[thisChannel,:] = data[thisChannel,:]*self.flip |
|
|
354 | 354 | |
|
|
355 | 355 | self.flip *= -1. |
|
|
356 | 356 | |
|
|
357 | 357 | dataOut.data = data |
|
|
358 | 358 | |
|
|
359 | 359 | return dataOut |
|
|
360 | 360 | |
|
|
361 | 361 | |
|
|
362 | 362 | class setAttribute(Operation): |
|
|
363 | 363 | ''' |
|
|
364 | 364 | Set an arbitrary attribute(s) to dataOut |
|
|
365 | 365 | ''' |
|
|
366 | 366 | |
|
|
367 | 367 | def __init__(self): |
|
|
368 | 368 | |
|
|
369 | 369 | Operation.__init__(self) |
|
|
370 | 370 | self._ready = False |
|
|
371 | 371 | |
|
|
372 | 372 | def run(self, dataOut, **kwargs): |
|
|
373 | 373 | |
|
|
374 | 374 | for key, value in kwargs.items(): |
|
|
375 | 375 | setattr(dataOut, key, value) |
|
|
376 | 376 | |
|
|
377 | 377 | return dataOut |
|
|
378 | 378 | |
|
|
379 | 379 | |
|
|
380 | 380 | @MPDecorator |
|
|
381 | 381 | class printAttribute(Operation): |
|
|
382 | 382 | ''' |
|
|
383 | 383 | Print an arbitrary attribute of dataOut |
|
|
384 | 384 | ''' |
|
|
385 | 385 | |
|
|
386 | 386 | def __init__(self): |
|
|
387 | 387 | |
|
|
388 | 388 | Operation.__init__(self) |
|
|
389 | 389 | |
|
|
390 | 390 | def run(self, dataOut, attributes): |
|
|
391 | 391 | |
|
|
392 | 392 | for attr in attributes: |
|
|
393 | 393 | if hasattr(dataOut, attr): |
|
|
394 | 394 | log.log(getattr(dataOut, attr), attr) |
|
|
395 | 395 | |
|
|
396 | 396 | |
|
|
397 | 397 | class interpolateHeights(Operation): |
|
|
398 | 398 | |
|
|
399 | 399 | def run(self, dataOut, topLim, botLim): |
|
|
400 | 400 | #69 al 72 para julia |
|
|
401 | 401 | #82-84 para meteoros |
|
|
402 | 402 | if len(numpy.shape(dataOut.data))==2: |
|
|
403 | 403 | sampInterp = (dataOut.data[:,botLim-1] + dataOut.data[:,topLim+1])/2 |
|
|
404 | 404 | sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1))) |
|
|
405 | 405 | #dataOut.data[:,botLim:limSup+1] = sampInterp |
|
|
406 | 406 | dataOut.data[:,botLim:topLim+1] = sampInterp |
|
|
407 | 407 | else: |
|
|
408 | 408 | nHeights = dataOut.data.shape[2] |
|
|
409 | 409 | x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights))) |
|
|
410 | 410 | y = dataOut.data[:,:,list(range(botLim))+list(range(topLim+1,nHeights))] |
|
|
411 | 411 | f = interpolate.interp1d(x, y, axis = 2) |
|
|
412 | 412 | xnew = numpy.arange(botLim,topLim+1) |
|
|
413 | 413 | ynew = f(xnew) |
|
|
414 | 414 | dataOut.data[:,:,botLim:topLim+1] = ynew |
|
|
415 | 415 | |
|
|
416 | 416 | return dataOut |
|
|
417 | 417 | |
|
|
418 | 418 | |
|
|
419 | 419 | class CohInt(Operation): |
|
|
420 | 420 | |
|
|
421 | 421 | isConfig = False |
|
|
422 | 422 | __profIndex = 0 |
|
|
423 | 423 | __byTime = False |
|
|
424 | 424 | __initime = None |
|
|
425 | 425 | __lastdatatime = None |
|
|
426 | 426 | __integrationtime = None |
|
|
427 | 427 | __buffer = None |
|
|
428 | 428 | __bufferStride = [] |
|
|
429 | 429 | __dataReady = False |
|
|
430 | 430 | __profIndexStride = 0 |
|
|
431 | 431 | __dataToPutStride = False |
|
|
432 | 432 | n = None |
|
|
433 | 433 | |
|
|
434 | 434 | def __init__(self, **kwargs): |
|
|
435 | 435 | |
|
|
436 | 436 | Operation.__init__(self, **kwargs) |
|
|
437 | 437 | |
|
|
438 | 438 | def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False): |
|
|
439 | 439 | """ |
|
|
440 | 440 | Set the parameters of the integration class. |
|
|
441 | 441 | |
|
|
442 | 442 | Inputs: |
|
|
443 | 443 | |
|
|
444 | 444 | n : Number of coherent integrations |
|
|
445 | 445 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
446 | 446 | overlapping : |
|
|
447 | 447 | """ |
|
|
448 | 448 | |
|
|
449 | 449 | self.__initime = None |
|
|
450 | 450 | self.__lastdatatime = 0 |
|
|
451 | 451 | self.__buffer = None |
|
|
452 | 452 | self.__dataReady = False |
|
|
453 | 453 | self.byblock = byblock |
|
|
454 | 454 | self.stride = stride |
|
|
455 | 455 | |
|
|
456 | 456 | if n == None and timeInterval == None: |
|
|
457 | 457 | raise ValueError("n or timeInterval should be specified ...") |
|
|
458 | 458 | |
|
|
459 | 459 | if n != None: |
|
|
460 | 460 | self.n = n |
|
|
461 | 461 | self.__byTime = False |
|
|
462 | 462 | else: |
|
|
463 | 463 | self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line |
|
|
464 | 464 | self.n = 9999 |
|
|
465 | 465 | self.__byTime = True |
|
|
466 | 466 | |
|
|
467 | 467 | if overlapping: |
|
|
468 | 468 | self.__withOverlapping = True |
|
|
469 | 469 | self.__buffer = None |
|
|
470 | 470 | else: |
|
|
471 | 471 | self.__withOverlapping = False |
|
|
472 | 472 | self.__buffer = 0 |
|
|
473 | 473 | |
|
|
474 | 474 | self.__profIndex = 0 |
|
|
475 | 475 | |
|
|
476 | 476 | def putData(self, data): |
|
|
477 | 477 | |
|
|
478 | 478 | """ |
|
|
479 | 479 | Add a profile to the __buffer and increase in one the __profileIndex |
|
|
480 | 480 | |
|
|
481 | 481 | """ |
|
|
482 | 482 | |
|
|
483 | 483 | if not self.__withOverlapping: |
|
|
484 | 484 | self.__buffer += data.copy() |
|
|
485 | 485 | self.__profIndex += 1 |
|
|
486 | 486 | return |
|
|
487 | 487 | |
|
|
488 | 488 | #Overlapping data |
|
|
489 | 489 | nChannels, nHeis = data.shape |
|
|
490 | 490 | data = numpy.reshape(data, (1, nChannels, nHeis)) |
|
|
491 | 491 | |
|
|
492 | 492 | #If the buffer is empty then it takes the data value |
|
|
493 | 493 | if self.__buffer is None: |
|
|
494 | 494 | self.__buffer = data |
|
|
495 | 495 | self.__profIndex += 1 |
|
|
496 | 496 | return |
|
|
497 | 497 | |
|
|
498 | 498 | #If the buffer length is lower than n then stakcing the data value |
|
|
499 | 499 | if self.__profIndex < self.n: |
|
|
500 | 500 | self.__buffer = numpy.vstack((self.__buffer, data)) |
|
|
501 | 501 | self.__profIndex += 1 |
|
|
502 | 502 | return |
|
|
503 | 503 | |
|
|
504 | 504 | #If the buffer length is equal to n then replacing the last buffer value with the data value |
|
|
505 | 505 | self.__buffer = numpy.roll(self.__buffer, -1, axis=0) |
|
|
506 | 506 | self.__buffer[self.n-1] = data |
|
|
507 | 507 | self.__profIndex = self.n |
|
|
508 | 508 | return |
|
|
509 | 509 | |
|
|
510 | 510 | |
|
|
511 | 511 | def pushData(self): |
|
|
512 | 512 | """ |
|
|
513 | 513 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
514 | 514 | |
|
|
515 | 515 | Affected: |
|
|
516 | 516 | |
|
|
517 | 517 | self.__profileIndex |
|
|
518 | 518 | |
|
|
519 | 519 | """ |
|
|
520 | 520 | |
|
|
521 | 521 | if not self.__withOverlapping: |
|
|
522 | 522 | data = self.__buffer |
|
|
523 | 523 | n = self.__profIndex |
|
|
524 | 524 | |
|
|
525 | 525 | self.__buffer = 0 |
|
|
526 | 526 | self.__profIndex = 0 |
|
|
527 | 527 | |
|
|
528 | 528 | return data, n |
|
|
529 | 529 | |
|
|
530 | 530 | #Integration with Overlapping |
|
|
531 | 531 | data = numpy.sum(self.__buffer, axis=0) |
|
|
532 | 532 | # print data |
|
|
533 | 533 | # raise |
|
|
534 | 534 | n = self.__profIndex |
|
|
535 | 535 | |
|
|
536 | 536 | return data, n |
|
|
537 | 537 | |
|
|
538 | 538 | def byProfiles(self, data): |
|
|
539 | 539 | |
|
|
540 | 540 | self.__dataReady = False |
|
|
541 | 541 | avgdata = None |
|
|
542 | 542 | # n = None |
|
|
543 | 543 | # print data |
|
|
544 | 544 | # raise |
|
|
545 | 545 | self.putData(data) |
|
|
546 | 546 | |
|
|
547 | 547 | if self.__profIndex == self.n: |
|
|
548 | 548 | avgdata, n = self.pushData() |
|
|
549 | 549 | self.__dataReady = True |
|
|
550 | 550 | |
|
|
551 | 551 | return avgdata |
|
|
552 | 552 | |
|
|
553 | 553 | def byTime(self, data, datatime): |
|
|
554 | 554 | |
|
|
555 | 555 | self.__dataReady = False |
|
|
556 | 556 | avgdata = None |
|
|
557 | 557 | n = None |
|
|
558 | 558 | |
|
|
559 | 559 | self.putData(data) |
|
|
560 | 560 | |
|
|
561 | 561 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
562 | 562 | avgdata, n = self.pushData() |
|
|
563 | 563 | self.n = n |
|
|
564 | 564 | self.__dataReady = True |
|
|
565 | 565 | |
|
|
566 | 566 | return avgdata |
|
|
567 | 567 | |
|
|
568 | 568 | def integrateByStride(self, data, datatime): |
|
|
569 | 569 | # print data |
|
|
570 | 570 | if self.__profIndex == 0: |
|
|
571 | 571 | self.__buffer = [[data.copy(), datatime]] |
|
|
572 | 572 | else: |
|
|
573 | 573 | self.__buffer.append([data.copy(),datatime]) |
|
|
574 | 574 | self.__profIndex += 1 |
|
|
575 | 575 | self.__dataReady = False |
|
|
576 | 576 | |
|
|
577 | 577 | if self.__profIndex == self.n * self.stride : |
|
|
578 | 578 | self.__dataToPutStride = True |
|
|
579 | 579 | self.__profIndexStride = 0 |
|
|
580 | 580 | self.__profIndex = 0 |
|
|
581 | 581 | self.__bufferStride = [] |
|
|
582 | 582 | for i in range(self.stride): |
|
|
583 | 583 | current = self.__buffer[i::self.stride] |
|
|
584 | 584 | data = numpy.sum([t[0] for t in current], axis=0) |
|
|
585 | 585 | avgdatatime = numpy.average([t[1] for t in current]) |
|
|
586 | 586 | # print data |
|
|
587 | 587 | self.__bufferStride.append((data, avgdatatime)) |
|
|
588 | 588 | |
|
|
589 | 589 | if self.__dataToPutStride: |
|
|
590 | 590 | self.__dataReady = True |
|
|
591 | 591 | self.__profIndexStride += 1 |
|
|
592 | 592 | if self.__profIndexStride == self.stride: |
|
|
593 | 593 | self.__dataToPutStride = False |
|
|
594 | 594 | # print self.__bufferStride[self.__profIndexStride - 1] |
|
|
595 | 595 | # raise |
|
|
596 | 596 | return self.__bufferStride[self.__profIndexStride - 1] |
|
|
597 | 597 | |
|
|
598 | 598 | |
|
|
599 | 599 | return None, None |
|
|
600 | 600 | |
|
|
601 | 601 | def integrate(self, data, datatime=None): |
|
|
602 | 602 | |
|
|
603 | 603 | if self.__initime == None: |
|
|
604 | 604 | self.__initime = datatime |
|
|
605 | 605 | |
|
|
606 | 606 | if self.__byTime: |
|
|
607 | 607 | avgdata = self.byTime(data, datatime) |
|
|
608 | 608 | else: |
|
|
609 | 609 | avgdata = self.byProfiles(data) |
|
|
610 | 610 | |
|
|
611 | 611 | |
|
|
612 | 612 | self.__lastdatatime = datatime |
|
|
613 | 613 | |
|
|
614 | 614 | if avgdata is None: |
|
|
615 | 615 | return None, None |
|
|
616 | 616 | |
|
|
617 | 617 | avgdatatime = self.__initime |
|
|
618 | 618 | |
|
|
619 | 619 | deltatime = datatime - self.__lastdatatime |
|
|
620 | 620 | |
|
|
621 | 621 | if not self.__withOverlapping: |
|
|
622 | 622 | self.__initime = datatime |
|
|
623 | 623 | else: |
|
|
624 | 624 | self.__initime += deltatime |
|
|
625 | 625 | |
|
|
626 | 626 | return avgdata, avgdatatime |
|
|
627 | 627 | |
|
|
628 | 628 | def integrateByBlock(self, dataOut): |
|
|
629 | 629 | |
|
|
630 | 630 | times = int(dataOut.data.shape[1]/self.n) |
|
|
631 | 631 | avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex) |
|
|
632 | 632 | |
|
|
633 | 633 | id_min = 0 |
|
|
634 | 634 | id_max = self.n |
|
|
635 | 635 | |
|
|
636 | 636 | for i in range(times): |
|
|
637 | 637 | junk = dataOut.data[:,id_min:id_max,:] |
|
|
638 | 638 | avgdata[:,i,:] = junk.sum(axis=1) |
|
|
639 | 639 | id_min += self.n |
|
|
640 | 640 | id_max += self.n |
|
|
641 | 641 | |
|
|
642 | 642 | timeInterval = dataOut.ippSeconds*self.n |
|
|
643 | 643 | avgdatatime = (times - 1) * timeInterval + dataOut.utctime |
|
|
644 | 644 | self.__dataReady = True |
|
|
645 | 645 | return avgdata, avgdatatime |
|
|
646 | 646 | |
|
|
647 | 647 | def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs): |
|
|
648 | 648 | |
|
|
649 | 649 | if not self.isConfig: |
|
|
650 | 650 | self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs) |
|
|
651 | 651 | self.isConfig = True |
|
|
652 | 652 | |
|
|
653 | 653 | if dataOut.flagDataAsBlock: |
|
|
654 | 654 | """ |
|
|
655 | 655 | Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
656 | 656 | """ |
|
|
657 | 657 | avgdata, avgdatatime = self.integrateByBlock(dataOut) |
|
|
658 | 658 | dataOut.nProfiles /= self.n |
|
|
659 | 659 | else: |
|
|
660 | 660 | if stride is None: |
|
|
661 | 661 | avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime) |
|
|
662 | 662 | else: |
|
|
663 | 663 | avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime) |
|
|
664 | 664 | |
|
|
665 | 665 | |
|
|
666 | 666 | # dataOut.timeInterval *= n |
|
|
667 | 667 | dataOut.flagNoData = True |
|
|
668 | 668 | |
|
|
669 | 669 | if self.__dataReady: |
|
|
670 | 670 | dataOut.data = avgdata |
|
|
671 | 671 | if not dataOut.flagCohInt: |
|
|
672 | 672 | dataOut.nCohInt *= self.n |
|
|
673 | 673 | dataOut.flagCohInt = True |
|
|
674 | 674 | dataOut.utctime = avgdatatime |
|
|
675 | 675 | # print avgdata, avgdatatime |
|
|
676 | 676 | # raise |
|
|
677 | 677 | # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt |
|
|
678 | 678 | dataOut.flagNoData = False |
|
|
679 | 679 | return dataOut |
|
|
680 | 680 | |
|
|
681 | 681 | class Decoder(Operation): |
|
|
682 | 682 | |
|
|
683 | 683 | isConfig = False |
|
|
684 | 684 | __profIndex = 0 |
|
|
685 | 685 | |
|
|
686 | 686 | code = None |
|
|
687 | 687 | |
|
|
688 | 688 | nCode = None |
|
|
689 | 689 | nBaud = None |
|
|
690 | 690 | |
|
|
691 | 691 | def __init__(self, **kwargs): |
|
|
692 | 692 | |
|
|
693 | 693 | Operation.__init__(self, **kwargs) |
|
|
694 | 694 | |
|
|
695 | 695 | self.times = None |
|
|
696 | 696 | self.osamp = None |
|
|
697 | 697 | # self.__setValues = False |
|
|
698 | 698 | self.isConfig = False |
|
|
699 | 699 | self.setupReq = False |
|
|
700 | 700 | def setup(self, code, osamp, dataOut): |
|
|
701 | 701 | |
|
|
702 | 702 | self.__profIndex = 0 |
|
|
703 | 703 | |
|
|
704 | 704 | self.code = code |
|
|
705 | 705 | |
|
|
706 | 706 | self.nCode = len(code) |
|
|
707 | 707 | self.nBaud = len(code[0]) |
|
|
708 | 708 | |
|
|
709 | 709 | if (osamp != None) and (osamp >1): |
|
|
710 | 710 | self.osamp = osamp |
|
|
711 | 711 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) |
|
|
712 | 712 | self.nBaud = self.nBaud*self.osamp |
|
|
713 | 713 | |
|
|
714 | 714 | self.__nChannels = dataOut.nChannels |
|
|
715 | 715 | self.__nProfiles = dataOut.nProfiles |
|
|
716 | 716 | self.__nHeis = dataOut.nHeights |
|
|
717 | 717 | |
|
|
718 | 718 | if self.__nHeis < self.nBaud: |
|
|
719 | 719 | raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)) |
|
|
720 | 720 | |
|
|
721 | 721 | #Frequency |
|
|
722 | 722 | __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex) |
|
|
723 | 723 | |
|
|
724 | 724 | __codeBuffer[:,0:self.nBaud] = self.code |
|
|
725 | 725 | |
|
|
726 | 726 | self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) |
|
|
727 | 727 | |
|
|
728 | 728 | if dataOut.flagDataAsBlock: |
|
|
729 | 729 | |
|
|
730 | 730 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
731 | 731 | |
|
|
732 | 732 | self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex) |
|
|
733 | 733 | |
|
|
734 | 734 | else: |
|
|
735 | 735 | |
|
|
736 | 736 | #Time |
|
|
737 | 737 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
738 | 738 | |
|
|
739 | 739 | self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex) |
|
|
740 | 740 | |
|
|
741 | 741 | def __convolutionInFreq(self, data): |
|
|
742 | 742 | |
|
|
743 | 743 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
744 | 744 | |
|
|
745 | 745 | fft_data = numpy.fft.fft(data, axis=1) |
|
|
746 | 746 | |
|
|
747 | 747 | conv = fft_data*fft_code |
|
|
748 | 748 | |
|
|
749 | 749 | data = numpy.fft.ifft(conv,axis=1) |
|
|
750 | 750 | |
|
|
751 | 751 | return data |
|
|
752 | 752 | |
|
|
753 | 753 | def __convolutionInFreqOpt(self, data): |
|
|
754 | 754 | |
|
|
755 | 755 | raise NotImplementedError |
|
|
756 | 756 | |
|
|
757 | 757 | def __convolutionInTime(self, data): |
|
|
758 | 758 | |
|
|
759 | 759 | code = self.code[self.__profIndex] |
|
|
760 | 760 | for i in range(self.__nChannels): |
|
|
761 | 761 | self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:] |
|
|
762 | 762 | |
|
|
763 | 763 | return self.datadecTime |
|
|
764 | 764 | |
|
|
765 | 765 | def __convolutionByBlockInTime(self, data): |
|
|
766 | 766 | |
|
|
767 | 767 | repetitions = int(self.__nProfiles / self.nCode) |
|
|
768 | 768 | junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) |
|
|
769 | 769 | junk = junk.flatten() |
|
|
770 | 770 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) |
|
|
771 | 771 | profilesList = range(self.__nProfiles) |
|
|
772 | 772 | |
|
|
773 | 773 | for i in range(self.__nChannels): |
|
|
774 | 774 | for j in profilesList: |
|
|
775 | 775 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] |
|
|
776 | 776 | return self.datadecTime |
|
|
777 | 777 | |
|
|
778 | 778 | def __convolutionByBlockInFreq(self, data): |
|
|
779 | 779 | |
|
|
780 | 780 | raise NotImplementedError("Decoder by frequency fro Blocks not implemented") |
|
|
781 | 781 | |
|
|
782 | 782 | |
|
|
783 | 783 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
784 | 784 | |
|
|
785 | 785 | fft_data = numpy.fft.fft(data, axis=2) |
|
|
786 | 786 | |
|
|
787 | 787 | conv = fft_data*fft_code |
|
|
788 | 788 | |
|
|
789 | 789 | data = numpy.fft.ifft(conv,axis=2) |
|
|
790 | 790 | |
|
|
791 | 791 | return data |
|
|
792 | 792 | |
|
|
793 | 793 | |
|
|
794 | 794 | def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None): |
|
|
795 | 795 | |
|
|
796 | 796 | if dataOut.flagDecodeData: |
|
|
797 | 797 | print("This data is already decoded, recoding again ...") |
|
|
798 | 798 | |
|
|
799 | 799 | if not self.isConfig: |
|
|
800 | 800 | |
|
|
801 | 801 | if code is None: |
|
|
802 | 802 | if dataOut.code is None: |
|
|
803 | 803 | raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type) |
|
|
804 | 804 | |
|
|
805 | 805 | code = dataOut.code |
|
|
806 | 806 | else: |
|
|
807 | 807 | code = numpy.array(code).reshape(nCode,nBaud) |
|
|
808 | 808 | self.setup(code, osamp, dataOut) |
|
|
809 | 809 | |
|
|
810 | 810 | self.isConfig = True |
|
|
811 | 811 | |
|
|
812 | 812 | if mode == 3: |
|
|
813 | 813 | sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode) |
|
|
814 | 814 | |
|
|
815 | 815 | if times != None: |
|
|
816 | 816 | sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n") |
|
|
817 | 817 | |
|
|
818 | 818 | if self.code is None: |
|
|
819 | 819 | print("Fail decoding: Code is not defined.") |
|
|
820 | 820 | return |
|
|
821 | 821 | |
|
|
822 | 822 | self.__nProfiles = dataOut.nProfiles |
|
|
823 | 823 | datadec = None |
|
|
824 | 824 | |
|
|
825 | 825 | if mode == 3: |
|
|
826 | 826 | mode = 0 |
|
|
827 | 827 | |
|
|
828 | 828 | if dataOut.flagDataAsBlock: |
|
|
829 | 829 | """ |
|
|
830 | 830 | Decoding when data have been read as block, |
|
|
831 | 831 | """ |
|
|
832 | 832 | |
|
|
833 | 833 | if mode == 0: |
|
|
834 | 834 | datadec = self.__convolutionByBlockInTime(dataOut.data) |
|
|
835 | 835 | if mode == 1: |
|
|
836 | 836 | datadec = self.__convolutionByBlockInFreq(dataOut.data) |
|
|
837 | 837 | else: |
|
|
838 | 838 | """ |
|
|
839 | 839 | Decoding when data have been read profile by profile |
|
|
840 | 840 | """ |
|
|
841 | 841 | if mode == 0: |
|
|
842 | 842 | datadec = self.__convolutionInTime(dataOut.data) |
|
|
843 | 843 | |
|
|
844 | 844 | if mode == 1: |
|
|
845 | 845 | datadec = self.__convolutionInFreq(dataOut.data) |
|
|
846 | 846 | |
|
|
847 | 847 | if mode == 2: |
|
|
848 | 848 | datadec = self.__convolutionInFreqOpt(dataOut.data) |
|
|
849 | 849 | |
|
|
850 | 850 | if datadec is None: |
|
|
851 | 851 | raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode) |
|
|
852 | 852 | |
|
|
853 | 853 | dataOut.code = self.code |
|
|
854 | 854 | dataOut.nCode = self.nCode |
|
|
855 | 855 | dataOut.nBaud = self.nBaud |
|
|
856 | 856 | |
|
|
857 | 857 | dataOut.data = datadec |
|
|
858 | 858 | |
|
|
859 | 859 | dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]] |
|
|
860 | 860 | |
|
|
861 | 861 | dataOut.flagDecodeData = True #asumo q la data esta decodificada |
|
|
862 | 862 | |
|
|
863 | 863 | if self.__profIndex == self.nCode-1: |
|
|
864 | 864 | self.__profIndex = 0 |
|
|
865 | 865 | return dataOut |
|
|
866 | 866 | |
|
|
867 | 867 | self.__profIndex += 1 |
|
|
868 | 868 | |
|
|
869 | 869 | return dataOut |
|
|
870 | 870 | # dataOut.flagDeflipData = True #asumo q la data no esta sin flip |
|
|
871 | 871 | |
|
|
872 | 872 | |
|
|
873 | 873 | class ProfileConcat(Operation): |
|
|
874 | 874 | |
|
|
875 | 875 | isConfig = False |
|
|
876 | 876 | buffer = None |
|
|
877 | 877 | |
|
|
878 | 878 | def __init__(self, **kwargs): |
|
|
879 | 879 | |
|
|
880 | 880 | Operation.__init__(self, **kwargs) |
|
|
881 | 881 | self.profileIndex = 0 |
|
|
882 | 882 | |
|
|
883 | 883 | def reset(self): |
|
|
884 | 884 | self.buffer = numpy.zeros_like(self.buffer) |
|
|
885 | 885 | self.start_index = 0 |
|
|
886 | 886 | self.times = 1 |
|
|
887 | 887 | |
|
|
888 | 888 | def setup(self, data, m, n=1): |
|
|
889 | 889 | self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0])) |
|
|
890 | 890 | self.nHeights = data.shape[1]#.nHeights |
|
|
891 | 891 | self.start_index = 0 |
|
|
892 | 892 | self.times = 1 |
|
|
893 | 893 | |
|
|
894 | 894 | def concat(self, data): |
|
|
895 | 895 | |
|
|
896 | 896 | self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy() |
|
|
897 | 897 | self.start_index = self.start_index + self.nHeights |
|
|
898 | 898 | |
|
|
899 | 899 | def run(self, dataOut, m): |
|
|
900 | 900 | dataOut.flagNoData = True |
|
|
901 | 901 | |
|
|
902 | 902 | if not self.isConfig: |
|
|
903 | 903 | self.setup(dataOut.data, m, 1) |
|
|
904 | 904 | self.isConfig = True |
|
|
905 | 905 | |
|
|
906 | 906 | if dataOut.flagDataAsBlock: |
|
|
907 | 907 | raise ValueError("ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False") |
|
|
908 | 908 | |
|
|
909 | 909 | else: |
|
|
910 | 910 | self.concat(dataOut.data) |
|
|
911 | 911 | self.times += 1 |
|
|
912 | 912 | if self.times > m: |
|
|
913 | 913 | dataOut.data = self.buffer |
|
|
914 | 914 | self.reset() |
|
|
915 | 915 | dataOut.flagNoData = False |
|
|
916 | 916 | # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas |
|
|
917 | 917 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
918 | 918 | xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m |
|
|
919 | 919 | dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight) |
|
|
920 | 920 | dataOut.ippSeconds *= m |
|
|
921 | 921 | return dataOut |
|
|
922 | 922 | |
|
|
923 | 923 | class ProfileSelector(Operation): |
|
|
924 | 924 | |
|
|
925 | 925 | profileIndex = None |
|
|
926 | 926 | # Tamanho total de los perfiles |
|
|
927 | 927 | nProfiles = None |
|
|
928 | 928 | |
|
|
929 | 929 | def __init__(self, **kwargs): |
|
|
930 | 930 | |
|
|
931 | 931 | Operation.__init__(self, **kwargs) |
|
|
932 | 932 | self.profileIndex = 0 |
|
|
933 | 933 | |
|
|
934 | 934 | def incProfileIndex(self): |
|
|
935 | 935 | |
|
|
936 | 936 | self.profileIndex += 1 |
|
|
937 | 937 | |
|
|
938 | 938 | if self.profileIndex >= self.nProfiles: |
|
|
939 | 939 | self.profileIndex = 0 |
|
|
940 | 940 | |
|
|
941 | 941 | def isThisProfileInRange(self, profileIndex, minIndex, maxIndex): |
|
|
942 | 942 | |
|
|
943 | 943 | if profileIndex < minIndex: |
|
|
944 | 944 | return False |
|
|
945 | 945 | |
|
|
946 | 946 | if profileIndex > maxIndex: |
|
|
947 | 947 | return False |
|
|
948 | 948 | |
|
|
949 | 949 | return True |
|
|
950 | 950 | |
|
|
951 | 951 | def isThisProfileInList(self, profileIndex, profileList): |
|
|
952 | 952 | |
|
|
953 | 953 | if profileIndex not in profileList: |
|
|
954 | 954 | return False |
|
|
955 | 955 | |
|
|
956 | 956 | return True |
|
|
957 | 957 | |
|
|
958 | 958 | def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None): |
|
|
959 | 959 | |
|
|
960 | 960 | """ |
|
|
961 | 961 | ProfileSelector: |
|
|
962 | 962 | |
|
|
963 | 963 | Inputs: |
|
|
964 | 964 | profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8) |
|
|
965 | 965 | |
|
|
966 | 966 | profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30) |
|
|
967 | 967 | |
|
|
968 | 968 | rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256)) |
|
|
969 | 969 | |
|
|
970 | 970 | """ |
|
|
971 | 971 | |
|
|
972 | 972 | if rangeList is not None: |
|
|
973 | 973 | if type(rangeList[0]) not in (tuple, list): |
|
|
974 | 974 | rangeList = [rangeList] |
|
|
975 | 975 | |
|
|
976 | 976 | dataOut.flagNoData = True |
|
|
977 | 977 | |
|
|
978 | 978 | if dataOut.flagDataAsBlock: |
|
|
979 | 979 | """ |
|
|
980 | 980 | data dimension = [nChannels, nProfiles, nHeis] |
|
|
981 | 981 | """ |
|
|
982 | 982 | if profileList != None: |
|
|
983 | 983 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
984 | 984 | |
|
|
985 | 985 | if profileRangeList != None: |
|
|
986 | 986 | minIndex = profileRangeList[0] |
|
|
987 | 987 | maxIndex = profileRangeList[1] |
|
|
988 | 988 | profileList = list(range(minIndex, maxIndex+1)) |
|
|
989 | 989 | |
|
|
990 | 990 | dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:] |
|
|
991 | 991 | |
|
|
992 | 992 | if rangeList != None: |
|
|
993 | 993 | |
|
|
994 | 994 | profileList = [] |
|
|
995 | 995 | |
|
|
996 | 996 | for thisRange in rangeList: |
|
|
997 | 997 | minIndex = thisRange[0] |
|
|
998 | 998 | maxIndex = thisRange[1] |
|
|
999 | 999 | |
|
|
1000 | 1000 | profileList.extend(list(range(minIndex, maxIndex+1))) |
|
|
1001 | 1001 | |
|
|
1002 | 1002 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
1003 | 1003 | |
|
|
1004 | 1004 | dataOut.nProfiles = len(profileList) |
|
|
1005 | 1005 | dataOut.profileIndex = dataOut.nProfiles - 1 |
|
|
1006 | 1006 | dataOut.flagNoData = False |
|
|
1007 | 1007 | |
|
|
1008 | 1008 | return dataOut |
|
|
1009 | 1009 | |
|
|
1010 | 1010 | """ |
|
|
1011 | 1011 | data dimension = [nChannels, nHeis] |
|
|
1012 | 1012 | """ |
|
|
1013 | 1013 | |
|
|
1014 | 1014 | if profileList != None: |
|
|
1015 | 1015 | |
|
|
1016 | 1016 | if self.isThisProfileInList(dataOut.profileIndex, profileList): |
|
|
1017 | 1017 | |
|
|
1018 | 1018 | self.nProfiles = len(profileList) |
|
|
1019 | 1019 | dataOut.nProfiles = self.nProfiles |
|
|
1020 | 1020 | dataOut.profileIndex = self.profileIndex |
|
|
1021 | 1021 | dataOut.flagNoData = False |
|
|
1022 | 1022 | |
|
|
1023 | 1023 | self.incProfileIndex() |
|
|
1024 | 1024 | return dataOut |
|
|
1025 | 1025 | |
|
|
1026 | 1026 | if profileRangeList != None: |
|
|
1027 | 1027 | |
|
|
1028 | 1028 | minIndex = profileRangeList[0] |
|
|
1029 | 1029 | maxIndex = profileRangeList[1] |
|
|
1030 | 1030 | |
|
|
1031 | 1031 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
1032 | 1032 | |
|
|
1033 | 1033 | self.nProfiles = maxIndex - minIndex + 1 |
|
|
1034 | 1034 | dataOut.nProfiles = self.nProfiles |
|
|
1035 | 1035 | dataOut.profileIndex = self.profileIndex |
|
|
1036 | 1036 | dataOut.flagNoData = False |
|
|
1037 | 1037 | |
|
|
1038 | 1038 | self.incProfileIndex() |
|
|
1039 | 1039 | return dataOut |
|
|
1040 | 1040 | |
|
|
1041 | 1041 | if rangeList != None: |
|
|
1042 | 1042 | |
|
|
1043 | 1043 | nProfiles = 0 |
|
|
1044 | 1044 | |
|
|
1045 | 1045 | for thisRange in rangeList: |
|
|
1046 | 1046 | minIndex = thisRange[0] |
|
|
1047 | 1047 | maxIndex = thisRange[1] |
|
|
1048 | 1048 | |
|
|
1049 | 1049 | nProfiles += maxIndex - minIndex + 1 |
|
|
1050 | 1050 | |
|
|
1051 | 1051 | for thisRange in rangeList: |
|
|
1052 | 1052 | |
|
|
1053 | 1053 | minIndex = thisRange[0] |
|
|
1054 | 1054 | maxIndex = thisRange[1] |
|
|
1055 | 1055 | |
|
|
1056 | 1056 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
1057 | 1057 | |
|
|
1058 | 1058 | self.nProfiles = nProfiles |
|
|
1059 | 1059 | dataOut.nProfiles = self.nProfiles |
|
|
1060 | 1060 | dataOut.profileIndex = self.profileIndex |
|
|
1061 | 1061 | dataOut.flagNoData = False |
|
|
1062 | 1062 | |
|
|
1063 | 1063 | self.incProfileIndex() |
|
|
1064 | 1064 | |
|
|
1065 | 1065 | break |
|
|
1066 | 1066 | |
|
|
1067 | 1067 | return dataOut |
|
|
1068 | 1068 | |
|
|
1069 | 1069 | |
|
|
1070 | 1070 | if beam != None: #beam is only for AMISR data |
|
|
1071 | 1071 | if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]): |
|
|
1072 | 1072 | dataOut.flagNoData = False |
|
|
1073 | 1073 | dataOut.profileIndex = self.profileIndex |
|
|
1074 | 1074 | |
|
|
1075 | 1075 | self.incProfileIndex() |
|
|
1076 | 1076 | |
|
|
1077 | 1077 | return dataOut |
|
|
1078 | 1078 | |
|
|
1079 | 1079 | raise ValueError("ProfileSelector needs profileList, profileRangeList or rangeList parameter") |
|
|
1080 | 1080 | |
|
|
1081 | 1081 | |
|
|
1082 | 1082 | class Reshaper(Operation): |
|
|
1083 | 1083 | |
|
|
1084 | 1084 | def __init__(self, **kwargs): |
|
|
1085 | 1085 | |
|
|
1086 | 1086 | Operation.__init__(self, **kwargs) |
|
|
1087 | 1087 | |
|
|
1088 | 1088 | self.__buffer = None |
|
|
1089 | 1089 | self.__nitems = 0 |
|
|
1090 | 1090 | |
|
|
1091 | 1091 | def __appendProfile(self, dataOut, nTxs): |
|
|
1092 | 1092 | |
|
|
1093 | 1093 | if self.__buffer is None: |
|
|
1094 | 1094 | shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) ) |
|
|
1095 | 1095 | self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype) |
|
|
1096 | 1096 | |
|
|
1097 | 1097 | ini = dataOut.nHeights * self.__nitems |
|
|
1098 | 1098 | end = ini + dataOut.nHeights |
|
|
1099 | 1099 | |
|
|
1100 | 1100 | self.__buffer[:, ini:end] = dataOut.data |
|
|
1101 | 1101 | |
|
|
1102 | 1102 | self.__nitems += 1 |
|
|
1103 | 1103 | |
|
|
1104 | 1104 | return int(self.__nitems*nTxs) |
|
|
1105 | 1105 | |
|
|
1106 | 1106 | def __getBuffer(self): |
|
|
1107 | 1107 | |
|
|
1108 | 1108 | if self.__nitems == int(1./self.__nTxs): |
|
|
1109 | 1109 | |
|
|
1110 | 1110 | self.__nitems = 0 |
|
|
1111 | 1111 | |
|
|
1112 | 1112 | return self.__buffer.copy() |
|
|
1113 | 1113 | |
|
|
1114 | 1114 | return None |
|
|
1115 | 1115 | |
|
|
1116 | 1116 | def __checkInputs(self, dataOut, shape, nTxs): |
|
|
1117 | 1117 | |
|
|
1118 | 1118 | if shape is None and nTxs is None: |
|
|
1119 | 1119 | raise ValueError("Reshaper: shape of factor should be defined") |
|
|
1120 | 1120 | |
|
|
1121 | 1121 | if nTxs: |
|
|
1122 | 1122 | if nTxs < 0: |
|
|
1123 | 1123 | raise ValueError("nTxs should be greater than 0") |
|
|
1124 | 1124 | |
|
|
1125 | 1125 | if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0: |
|
|
1126 | 1126 | raise ValueError("nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))) |
|
|
1127 | 1127 | |
|
|
1128 | 1128 | shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs] |
|
|
1129 | 1129 | |
|
|
1130 | 1130 | return shape, nTxs |
|
|
1131 | 1131 | |
|
|
1132 | 1132 | if len(shape) != 2 and len(shape) != 3: |
|
|
1133 | 1133 | raise ValueError("shape dimension should be equal to 2 or 3. shape = (nProfiles, nHeis) or (nChannels, nProfiles, nHeis). Actually shape = (%d, %d, %d)" %(dataOut.nChannels, dataOut.nProfiles, dataOut.nHeights)) |
|
|
1134 | 1134 | |
|
|
1135 | 1135 | if len(shape) == 2: |
|
|
1136 | 1136 | shape_tuple = [dataOut.nChannels] |
|
|
1137 | 1137 | shape_tuple.extend(shape) |
|
|
1138 | 1138 | else: |
|
|
1139 | 1139 | shape_tuple = list(shape) |
|
|
1140 | 1140 | |
|
|
1141 | 1141 | nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles |
|
|
1142 | 1142 | |
|
|
1143 | 1143 | return shape_tuple, nTxs |
|
|
1144 | 1144 | |
|
|
1145 | 1145 | def run(self, dataOut, shape=None, nTxs=None): |
|
|
1146 | 1146 | |
|
|
1147 | 1147 | shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs) |
|
|
1148 | 1148 | |
|
|
1149 | 1149 | dataOut.flagNoData = True |
|
|
1150 | 1150 | profileIndex = None |
|
|
1151 | 1151 | |
|
|
1152 | 1152 | if dataOut.flagDataAsBlock: |
|
|
1153 | 1153 | |
|
|
1154 | 1154 | dataOut.data = numpy.reshape(dataOut.data, shape_tuple) |
|
|
1155 | 1155 | dataOut.flagNoData = False |
|
|
1156 | 1156 | |
|
|
1157 | 1157 | profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1 |
|
|
1158 | 1158 | |
|
|
1159 | 1159 | else: |
|
|
1160 | 1160 | |
|
|
1161 | 1161 | if self.__nTxs < 1: |
|
|
1162 | 1162 | |
|
|
1163 | 1163 | self.__appendProfile(dataOut, self.__nTxs) |
|
|
1164 | 1164 | new_data = self.__getBuffer() |
|
|
1165 | 1165 | |
|
|
1166 | 1166 | if new_data is not None: |
|
|
1167 | 1167 | dataOut.data = new_data |
|
|
1168 | 1168 | dataOut.flagNoData = False |
|
|
1169 | 1169 | |
|
|
1170 | 1170 | profileIndex = dataOut.profileIndex*nTxs |
|
|
1171 | 1171 | |
|
|
1172 | 1172 | else: |
|
|
1173 | 1173 | raise ValueError("nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)") |
|
|
1174 | 1174 | |
|
|
1175 | 1175 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1176 | 1176 | |
|
|
1177 | 1177 | dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0] |
|
|
1178 | 1178 | |
|
|
1179 | 1179 | dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs) |
|
|
1180 | 1180 | |
|
|
1181 | 1181 | dataOut.profileIndex = profileIndex |
|
|
1182 | 1182 | |
|
|
1183 | 1183 | dataOut.ippSeconds /= self.__nTxs |
|
|
1184 | 1184 | |
|
|
1185 | 1185 | return dataOut |
|
|
1186 | 1186 | |
|
|
1187 | 1187 | class SplitProfiles(Operation): |
|
|
1188 | 1188 | |
|
|
1189 | 1189 | def __init__(self, **kwargs): |
|
|
1190 | 1190 | |
|
|
1191 | 1191 | Operation.__init__(self, **kwargs) |
|
|
1192 | 1192 | |
|
|
1193 | 1193 | def run(self, dataOut, n): |
|
|
1194 | 1194 | |
|
|
1195 | 1195 | dataOut.flagNoData = True |
|
|
1196 | 1196 | profileIndex = None |
|
|
1197 | 1197 | |
|
|
1198 | 1198 | if dataOut.flagDataAsBlock: |
|
|
1199 | 1199 | |
|
|
1200 | 1200 | #nchannels, nprofiles, nsamples |
|
|
1201 | 1201 | shape = dataOut.data.shape |
|
|
1202 | 1202 | |
|
|
1203 | 1203 | if shape[2] % n != 0: |
|
|
1204 | 1204 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[2])) |
|
|
1205 | 1205 | |
|
|
1206 | 1206 | new_shape = shape[0], shape[1]*n, int(shape[2]/n) |
|
|
1207 | 1207 | |
|
|
1208 | 1208 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
1209 | 1209 | dataOut.flagNoData = False |
|
|
1210 | 1210 | |
|
|
1211 | 1211 | profileIndex = int(dataOut.nProfiles/n) - 1 |
|
|
1212 | 1212 | |
|
|
1213 | 1213 | else: |
|
|
1214 | 1214 | |
|
|
1215 | 1215 | raise ValueError("Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)") |
|
|
1216 | 1216 | |
|
|
1217 | 1217 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1218 | 1218 | |
|
|
1219 | 1219 | dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0] |
|
|
1220 | 1220 | |
|
|
1221 | 1221 | dataOut.nProfiles = int(dataOut.nProfiles*n) |
|
|
1222 | 1222 | |
|
|
1223 | 1223 | dataOut.profileIndex = profileIndex |
|
|
1224 | 1224 | |
|
|
1225 | 1225 | dataOut.ippSeconds /= n |
|
|
1226 | 1226 | |
|
|
1227 | 1227 | return dataOut |
|
|
1228 | 1228 | |
|
|
1229 | 1229 | class CombineProfiles(Operation): |
|
|
1230 | 1230 | def __init__(self, **kwargs): |
|
|
1231 | 1231 | |
|
|
1232 | 1232 | Operation.__init__(self, **kwargs) |
|
|
1233 | 1233 | |
|
|
1234 | 1234 | self.__remData = None |
|
|
1235 | 1235 | self.__profileIndex = 0 |
|
|
1236 | 1236 | |
|
|
1237 | 1237 | def run(self, dataOut, n): |
|
|
1238 | 1238 | |
|
|
1239 | 1239 | dataOut.flagNoData = True |
|
|
1240 | 1240 | profileIndex = None |
|
|
1241 | 1241 | |
|
|
1242 | 1242 | if dataOut.flagDataAsBlock: |
|
|
1243 | 1243 | |
|
|
1244 | 1244 | #nchannels, nprofiles, nsamples |
|
|
1245 | 1245 | shape = dataOut.data.shape |
|
|
1246 | 1246 | new_shape = shape[0], shape[1]/n, shape[2]*n |
|
|
1247 | 1247 | |
|
|
1248 | 1248 | if shape[1] % n != 0: |
|
|
1249 | 1249 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[1])) |
|
|
1250 | 1250 | |
|
|
1251 | 1251 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
1252 | 1252 | dataOut.flagNoData = False |
|
|
1253 | 1253 | |
|
|
1254 | 1254 | profileIndex = int(dataOut.nProfiles*n) - 1 |
|
|
1255 | 1255 | |
|
|
1256 | 1256 | else: |
|
|
1257 | 1257 | |
|
|
1258 | 1258 | #nchannels, nsamples |
|
|
1259 | 1259 | if self.__remData is None: |
|
|
1260 | 1260 | newData = dataOut.data |
|
|
1261 | 1261 | else: |
|
|
1262 | 1262 | newData = numpy.concatenate((self.__remData, dataOut.data), axis=1) |
|
|
1263 | 1263 | |
|
|
1264 | 1264 | self.__profileIndex += 1 |
|
|
1265 | 1265 | |
|
|
1266 | 1266 | if self.__profileIndex < n: |
|
|
1267 | 1267 | self.__remData = newData |
|
|
1268 | 1268 | #continue |
|
|
1269 | 1269 | return |
|
|
1270 | 1270 | |
|
|
1271 | 1271 | self.__profileIndex = 0 |
|
|
1272 | 1272 | self.__remData = None |
|
|
1273 | 1273 | |
|
|
1274 | 1274 | dataOut.data = newData |
|
|
1275 | 1275 | dataOut.flagNoData = False |
|
|
1276 | 1276 | |
|
|
1277 | 1277 | profileIndex = dataOut.profileIndex/n |
|
|
1278 | 1278 | |
|
|
1279 | 1279 | |
|
|
1280 | 1280 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1281 | 1281 | |
|
|
1282 | 1282 | dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0] |
|
|
1283 | 1283 | |
|
|
1284 | 1284 | dataOut.nProfiles = int(dataOut.nProfiles/n) |
|
|
1285 | 1285 | |
|
|
1286 | 1286 | dataOut.profileIndex = profileIndex |
|
|
1287 | 1287 | |
|
|
1288 | 1288 | dataOut.ippSeconds *= n |
|
|
1289 | 1289 | |
|
|
1290 | 1290 | return dataOut |
|
|
1291 | 1291 | |
|
|
1292 | 1292 | class PulsePairVoltage(Operation): |
|
|
1293 | 1293 | ''' |
|
|
1294 | 1294 | Function PulsePair(Signal Power, Velocity) |
|
|
1295 | 1295 | The real component of Lag[0] provides Intensity Information |
|
|
1296 | 1296 | The imag component of Lag[1] Phase provides Velocity Information |
|
|
1297 | 1297 | |
|
|
1298 | 1298 | Configuration Parameters: |
|
|
1299 | 1299 | nPRF = Number of Several PRF |
|
|
1300 | 1300 | theta = Degree Azimuth angel Boundaries |
|
|
1301 | 1301 | |
|
|
1302 | 1302 | Input: |
|
|
1303 | 1303 | self.dataOut |
|
|
1304 | 1304 | lag[N] |
|
|
1305 | 1305 | Affected: |
|
|
1306 | 1306 | self.dataOut.spc |
|
|
1307 | 1307 | ''' |
|
|
1308 | 1308 | isConfig = False |
|
|
1309 | 1309 | __profIndex = 0 |
|
|
1310 | 1310 | __initime = None |
|
|
1311 | 1311 | __lastdatatime = None |
|
|
1312 | 1312 | __buffer = None |
|
|
1313 | 1313 | noise = None |
|
|
1314 | 1314 | __dataReady = False |
|
|
1315 | 1315 | n = None |
|
|
1316 | 1316 | __nch = 0 |
|
|
1317 | 1317 | __nHeis = 0 |
|
|
1318 | 1318 | removeDC = False |
|
|
1319 | 1319 | ipp = None |
|
|
1320 | 1320 | lambda_ = 0 |
|
|
1321 | 1321 | |
|
|
1322 | 1322 | def __init__(self,**kwargs): |
|
|
1323 | 1323 | Operation.__init__(self,**kwargs) |
|
|
1324 | 1324 | |
|
|
1325 | 1325 | def setup(self, dataOut, n = None, removeDC=False): |
|
|
1326 | 1326 | ''' |
|
|
1327 | 1327 | n= Numero de PRF's de entrada |
|
|
1328 | 1328 | ''' |
|
|
1329 | 1329 | self.__initime = None |
|
|
1330 | 1330 | self.__lastdatatime = 0 |
|
|
1331 | 1331 | self.__dataReady = False |
|
|
1332 | 1332 | self.__buffer = 0 |
|
|
1333 | 1333 | self.__profIndex = 0 |
|
|
1334 | 1334 | self.noise = None |
|
|
1335 | 1335 | self.__nch = dataOut.nChannels |
|
|
1336 | 1336 | self.__nHeis = dataOut.nHeights |
|
|
1337 | 1337 | self.removeDC = removeDC |
|
|
1338 | 1338 | self.lambda_ = 3.0e8/(9345.0e6) |
|
|
1339 | 1339 | self.ippSec = dataOut.ippSeconds |
|
|
1340 | 1340 | self.nCohInt = dataOut.nCohInt |
|
|
1341 | 1341 | print("IPPseconds",dataOut.ippSeconds) |
|
|
1342 | 1342 | |
|
|
1343 | 1343 | print("ELVALOR DE n es:", n) |
|
|
1344 | 1344 | if n == None: |
|
|
1345 | 1345 | raise ValueError("n should be specified.") |
|
|
1346 | 1346 | |
|
|
1347 | 1347 | if n != None: |
|
|
1348 | 1348 | if n<2: |
|
|
1349 | 1349 | raise ValueError("n should be greater than 2") |
|
|
1350 | 1350 | |
|
|
1351 | 1351 | self.n = n |
|
|
1352 | 1352 | self.__nProf = n |
|
|
1353 | 1353 | |
|
|
1354 | 1354 | self.__buffer = numpy.zeros((dataOut.nChannels, |
|
|
1355 | 1355 | n, |
|
|
1356 | 1356 | dataOut.nHeights), |
|
|
1357 | 1357 | dtype='complex') |
|
|
1358 | #self.noise = numpy.zeros([self.__nch,self.__nHeis]) | |
|
|
1359 | #for i in range(self.__nch): | |
|
|
1360 | # self.noise[i]=dataOut.getNoise(channel=i) | |
|
|
1361 | 1358 | |
|
|
1362 | 1359 | def putData(self,data): |
|
|
1363 | 1360 | ''' |
|
|
1364 | 1361 | Add a profile to he __buffer and increase in one the __profiel Index |
|
|
1365 | 1362 | ''' |
|
|
1366 | 1363 | self.__buffer[:,self.__profIndex,:]= data |
|
|
1367 | 1364 | self.__profIndex += 1 |
|
|
1368 | 1365 | return |
|
|
1369 | 1366 | |
|
|
1370 | 1367 | def pushData(self,dataOut): |
|
|
1371 | 1368 | ''' |
|
|
1372 | 1369 | Return the PULSEPAIR and the profiles used in the operation |
|
|
1373 | 1370 | Affected : self.__profileIndex |
|
|
1374 | 1371 | ''' |
|
|
1372 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Remove DCΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
|
|
1375 | 1373 | if self.removeDC==True: |
|
|
1376 | 1374 | mean = numpy.mean(self.__buffer,1) |
|
|
1377 | 1375 | tmp = mean.reshape(self.__nch,1,self.__nHeis) |
|
|
1378 | 1376 | dc= numpy.tile(tmp,[1,self.__nProf,1]) |
|
|
1379 | 1377 | self.__buffer = self.__buffer - dc |
|
|
1378 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Calculo de Potencia Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
|
|
1379 | pair0 = self.__buffer*numpy.conj(self.__buffer) | |
|
|
1380 | pair0 = pair0.real | |
|
|
1381 | lag_0 = numpy.sum(pair0,1) | |
|
|
1382 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Calculo de Ruido x canalΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
|
|
1383 | self.noise = numpy.zeros(self.__nch) | |
|
|
1384 | for i in range(self.__nch): | |
|
|
1385 | daux = numpy.sort(pair0[i,:,:],axis= None) | |
|
|
1386 | self.noise[i]=hildebrand_sekhon( daux ,self.nCohInt) | |
|
|
1387 | ||
|
|
1388 | self.noise = self.noise.reshape(self.__nch,1) | |
|
|
1389 | self.noise = numpy.tile(self.noise,[1,self.__nHeis]) | |
|
|
1390 | noise_buffer = self.noise.reshape(self.__nch,1,self.__nHeis) | |
|
|
1391 | noise_buffer = numpy.tile(noise_buffer,[1,self.__nProf,1]) | |
|
|
1392 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Potencia recibida= P , Potencia senal = S , Ruido= NΒ·Β· | |
|
|
1393 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· P= S+N ,P=lag_0/N Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
|
|
1394 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Power Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
|
|
1395 | data_power = lag_0/(self.n*self.nCohInt) | |
|
|
1396 | #------------------ Senal Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
|
|
1397 | data_intensity = pair0 - noise_buffer | |
|
|
1398 | data_intensity = numpy.sum(data_intensity,axis=1)*(self.n*self.nCohInt)#*self.nCohInt) | |
|
|
1399 | #data_intensity = (lag_0-self.noise*self.n)*(self.n*self.nCohInt) | |
|
|
1400 | for i in range(self.__nch): | |
|
|
1401 | for j in range(self.__nHeis): | |
|
|
1402 | if data_intensity[i][j] < 0: | |
|
|
1403 | data_intensity[i][j] = numpy.min(numpy.absolute(data_intensity[i][j])) | |
|
|
1380 | 1404 | |
|
|
1381 | lag_0 = numpy.sum(self.__buffer*numpy.conj(self.__buffer),1) | |
|
|
1382 | data_intensity = lag_0/(self.n*self.nCohInt)#*self.nCohInt) | |
|
|
1383 | ||
|
|
1405 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Calculo de Frecuencia y Velocidad dopplerΒ·Β·Β·Β·Β·Β·Β·Β· | |
|
|
1384 | 1406 | pair1 = self.__buffer[:,:-1,:]*numpy.conjugate(self.__buffer[:,1:,:]) |
|
|
1385 | 1407 | lag_1 = numpy.sum(pair1,1) |
|
|
1386 | #angle = numpy.angle(numpy.sum(pair1,1))*180/(math.pi) | |
|
|
1387 | data_velocity = (-1.0*self.lambda_/(4*math.pi*self.ippSec))*numpy.angle(lag_1)#self.ippSec*self.nCohInt | |
|
|
1408 | data_freq = (-1/(2.0*math.pi*self.ippSec*self.nCohInt))*numpy.angle(lag_1) | |
|
|
1409 | data_velocity = (self.lambda_/2.0)*data_freq | |
|
|
1388 | 1410 | |
|
|
1389 | self.noise = numpy.zeros([self.__nch,self.__nHeis]) | |
|
|
1390 | for i in range(self.__nch): | |
|
|
1391 | self.noise[i]=dataOut.getNoise(channel=i) | |
|
|
1411 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Potencia promedio estimada de la SenalΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
|
|
1412 | lag_0 = lag_0/self.n | |
|
|
1413 | S = lag_0-self.noise | |
|
|
1392 | 1414 | |
|
|
1393 | lag_0 = lag_0.real/(self.n) | |
|
|
1415 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Frecuencia Doppler promedio Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
|
|
1394 | 1416 | lag_1 = lag_1/(self.n-1) |
|
|
1395 | 1417 | R1 = numpy.abs(lag_1) |
|
|
1396 | S = (lag_0-self.noise) | |
|
|
1397 | 1418 | |
|
|
1419 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Calculo del SNRΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
|
|
1398 | 1420 | data_snrPP = S/self.noise |
|
|
1399 | data_snrPP = numpy.where(data_snrPP<0,1,data_snrPP) | |
|
|
1421 | for i in range(self.__nch): | |
|
|
1422 | for j in range(self.__nHeis): | |
|
|
1423 | if data_snrPP[i][j] < 1.e-20: | |
|
|
1424 | data_snrPP[i][j] = 1.e-20 | |
|
|
1400 | 1425 | |
|
|
1426 | #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Calculo del ancho espectral Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· | |
|
|
1401 | 1427 | L = S/R1 |
|
|
1402 | 1428 | L = numpy.where(L<0,1,L) |
|
|
1403 | 1429 | L = numpy.log(L) |
|
|
1404 | ||
|
|
1405 | 1430 | tmp = numpy.sqrt(numpy.absolute(L)) |
|
|
1406 | ||
|
|
1407 | data_specwidth = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec))*tmp*numpy.sign(L) | |
|
|
1408 | #data_specwidth = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec))*k | |
|
|
1431 | data_specwidth = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec*self.nCohInt))*tmp*numpy.sign(L) | |
|
|
1409 | 1432 | n = self.__profIndex |
|
|
1410 | 1433 | |
|
|
1411 | 1434 | self.__buffer = numpy.zeros((self.__nch, self.__nProf,self.__nHeis), dtype='complex') |
|
|
1412 | 1435 | self.__profIndex = 0 |
|
|
1413 | return data_intensity,data_velocity,data_snrPP,data_specwidth,n | |
|
|
1436 | return data_power,data_intensity,data_velocity,data_snrPP,data_specwidth,n | |
|
|
1437 | ||
|
|
1414 | 1438 | |
|
|
1415 | 1439 | def pulsePairbyProfiles(self,dataOut): |
|
|
1416 | 1440 | |
|
|
1417 | 1441 | self.__dataReady = False |
|
|
1442 | data_power = None | |
|
|
1418 | 1443 | data_intensity = None |
|
|
1419 | 1444 | data_velocity = None |
|
|
1420 | 1445 | data_specwidth = None |
|
|
1421 | 1446 | data_snrPP = None |
|
|
1422 | 1447 | self.putData(data=dataOut.data) |
|
|
1423 | 1448 | if self.__profIndex == self.n: |
|
|
1424 | #self.noise = numpy.zeros([self.__nch,self.__nHeis]) | |
|
|
1425 | #for i in range(self.__nch): | |
|
|
1426 | # self.noise[i]=data.getNoise(channel=i) | |
|
|
1427 | #print(self.noise.shape) | |
|
|
1428 | data_intensity, data_velocity,data_snrPP,data_specwidth, n = self.pushData(dataOut=dataOut) | |
|
|
1449 | data_power,data_intensity, data_velocity,data_snrPP,data_specwidth, n = self.pushData(dataOut=dataOut) | |
|
|
1429 | 1450 | self.__dataReady = True |
|
|
1430 | 1451 | |
|
|
1431 | return data_intensity, data_velocity,data_snrPP,data_specwidth | |
|
|
1452 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth | |
|
|
1453 | ||
|
|
1432 | 1454 | |
|
|
1433 | 1455 | def pulsePairOp(self, dataOut, datatime= None): |
|
|
1434 | 1456 | |
|
|
1435 | 1457 | if self.__initime == None: |
|
|
1436 | 1458 | self.__initime = datatime |
|
|
1437 | #print("hola") | |
|
|
1438 | data_intensity, data_velocity,data_snrPP,data_specwidth = self.pulsePairbyProfiles(dataOut) | |
|
|
1459 | data_power, data_intensity, data_velocity, data_snrPP, data_specwidth = self.pulsePairbyProfiles(dataOut) | |
|
|
1439 | 1460 | self.__lastdatatime = datatime |
|
|
1440 | 1461 | |
|
|
1441 |
if data_ |
|
|
|
1442 | return None, None,None,None,None | |
|
|
1462 | if data_power is None: | |
|
|
1463 | return None, None, None,None,None,None | |
|
|
1443 | 1464 | |
|
|
1444 | 1465 | avgdatatime = self.__initime |
|
|
1445 | 1466 | deltatime = datatime - self.__lastdatatime |
|
|
1446 | 1467 | self.__initime = datatime |
|
|
1447 | 1468 | |
|
|
1448 | return data_intensity, data_velocity,data_snrPP,data_specwidth,avgdatatime | |
|
|
1469 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, avgdatatime | |
|
|
1449 | 1470 | |
|
|
1450 | 1471 | def run(self, dataOut,n = None,removeDC= False, overlapping= False,**kwargs): |
|
|
1451 | 1472 | |
|
|
1452 | 1473 | if not self.isConfig: |
|
|
1453 | 1474 | self.setup(dataOut = dataOut, n = n , removeDC=removeDC , **kwargs) |
|
|
1454 | 1475 | self.isConfig = True |
|
|
1455 | data_intensity, data_velocity,data_snrPP,data_specwidth, avgdatatime = self.pulsePairOp(dataOut, dataOut.utctime) | |
|
|
1476 | data_power, data_intensity, data_velocity,data_snrPP,data_specwidth, avgdatatime = self.pulsePairOp(dataOut, dataOut.utctime) | |
|
|
1456 | 1477 | dataOut.flagNoData = True |
|
|
1457 | 1478 | |
|
|
1458 | 1479 | if self.__dataReady: |
|
|
1459 | 1480 | dataOut.nCohInt *= self.n |
|
|
1460 |
dataOut.data |
|
|
|
1461 |
dataOut.data |
|
|
|
1462 |
dataOut.data |
|
|
|
1463 |
dataOut.data |
|
|
|
1481 | dataOut.dataPP_POW = data_intensity # S | |
|
|
1482 | dataOut.dataPP_POWER = data_power # P | |
|
|
1483 | dataOut.dataPP_DOP = data_velocity | |
|
|
1484 | dataOut.dataPP_SNR = data_snrPP | |
|
|
1485 | dataOut.dataPP_WIDTH = data_specwidth | |
|
|
1464 | 1486 | dataOut.PRFbyAngle = self.n #numero de PRF*cada angulo rotado que equivale a un tiempo. |
|
|
1465 | 1487 | dataOut.utctime = avgdatatime |
|
|
1466 | 1488 | dataOut.flagNoData = False |
|
|
1467 | 1489 | return dataOut |
|
|
1468 | 1490 | |
|
|
1469 | 1491 | |
|
|
1492 | ||
|
|
1470 | 1493 | # import collections |
|
|
1471 | 1494 | # from scipy.stats import mode |
|
|
1472 | 1495 | # |
|
|
1473 | 1496 | # class Synchronize(Operation): |
|
|
1474 | 1497 | # |
|
|
1475 | 1498 | # isConfig = False |
|
|
1476 | 1499 | # __profIndex = 0 |
|
|
1477 | 1500 | # |
|
|
1478 | 1501 | # def __init__(self, **kwargs): |
|
|
1479 | 1502 | # |
|
|
1480 | 1503 | # Operation.__init__(self, **kwargs) |
|
|
1481 | 1504 | # # self.isConfig = False |
|
|
1482 | 1505 | # self.__powBuffer = None |
|
|
1483 | 1506 | # self.__startIndex = 0 |
|
|
1484 | 1507 | # self.__pulseFound = False |
|
|
1485 | 1508 | # |
|
|
1486 | 1509 | # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): |
|
|
1487 | 1510 | # |
|
|
1488 | 1511 | # #Read data |
|
|
1489 | 1512 | # |
|
|
1490 | 1513 | # powerdB = dataOut.getPower(channel = channel) |
|
|
1491 | 1514 | # noisedB = dataOut.getNoise(channel = channel)[0] |
|
|
1492 | 1515 | # |
|
|
1493 | 1516 | # self.__powBuffer.extend(powerdB.flatten()) |
|
|
1494 | 1517 | # |
|
|
1495 | 1518 | # dataArray = numpy.array(self.__powBuffer) |
|
|
1496 | 1519 | # |
|
|
1497 | 1520 | # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") |
|
|
1498 | 1521 | # |
|
|
1499 | 1522 | # maxValue = numpy.nanmax(filteredPower) |
|
|
1500 | 1523 | # |
|
|
1501 | 1524 | # if maxValue < noisedB + 10: |
|
|
1502 | 1525 | # #No se encuentra ningun pulso de transmision |
|
|
1503 | 1526 | # return None |
|
|
1504 | 1527 | # |
|
|
1505 | 1528 | # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] |
|
|
1506 | 1529 | # |
|
|
1507 | 1530 | # if len(maxValuesIndex) < 2: |
|
|
1508 | 1531 | # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX |
|
|
1509 | 1532 | # return None |
|
|
1510 | 1533 | # |
|
|
1511 | 1534 | # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples |
|
|
1512 | 1535 | # |
|
|
1513 | 1536 | # #Seleccionar solo valores con un espaciamiento de nSamples |
|
|
1514 | 1537 | # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) |
|
|
1515 | 1538 | # |
|
|
1516 | 1539 | # if len(pulseIndex) < 2: |
|
|
1517 | 1540 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
1518 | 1541 | # return None |
|
|
1519 | 1542 | # |
|
|
1520 | 1543 | # spacing = pulseIndex[1:] - pulseIndex[:-1] |
|
|
1521 | 1544 | # |
|
|
1522 | 1545 | # #remover senales que se distancien menos de 10 unidades o muestras |
|
|
1523 | 1546 | # #(No deberian existir IPP menor a 10 unidades) |
|
|
1524 | 1547 | # |
|
|
1525 | 1548 | # realIndex = numpy.where(spacing > 10 )[0] |
|
|
1526 | 1549 | # |
|
|
1527 | 1550 | # if len(realIndex) < 2: |
|
|
1528 | 1551 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
1529 | 1552 | # return None |
|
|
1530 | 1553 | # |
|
|
1531 | 1554 | # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) |
|
|
1532 | 1555 | # realPulseIndex = pulseIndex[realIndex] |
|
|
1533 | 1556 | # |
|
|
1534 | 1557 | # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] |
|
|
1535 | 1558 | # |
|
|
1536 | 1559 | # print "IPP = %d samples" %period |
|
|
1537 | 1560 | # |
|
|
1538 | 1561 | # self.__newNSamples = dataOut.nHeights #int(period) |
|
|
1539 | 1562 | # self.__startIndex = int(realPulseIndex[0]) |
|
|
1540 | 1563 | # |
|
|
1541 | 1564 | # return 1 |
|
|
1542 | 1565 | # |
|
|
1543 | 1566 | # |
|
|
1544 | 1567 | # def setup(self, nSamples, nChannels, buffer_size = 4): |
|
|
1545 | 1568 | # |
|
|
1546 | 1569 | # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), |
|
|
1547 | 1570 | # maxlen = buffer_size*nSamples) |
|
|
1548 | 1571 | # |
|
|
1549 | 1572 | # bufferList = [] |
|
|
1550 | 1573 | # |
|
|
1551 | 1574 | # for i in range(nChannels): |
|
|
1552 | 1575 | # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN, |
|
|
1553 | 1576 | # maxlen = buffer_size*nSamples) |
|
|
1554 | 1577 | # |
|
|
1555 | 1578 | # bufferList.append(bufferByChannel) |
|
|
1556 | 1579 | # |
|
|
1557 | 1580 | # self.__nSamples = nSamples |
|
|
1558 | 1581 | # self.__nChannels = nChannels |
|
|
1559 | 1582 | # self.__bufferList = bufferList |
|
|
1560 | 1583 | # |
|
|
1561 | 1584 | # def run(self, dataOut, channel = 0): |
|
|
1562 | 1585 | # |
|
|
1563 | 1586 | # if not self.isConfig: |
|
|
1564 | 1587 | # nSamples = dataOut.nHeights |
|
|
1565 | 1588 | # nChannels = dataOut.nChannels |
|
|
1566 | 1589 | # self.setup(nSamples, nChannels) |
|
|
1567 | 1590 | # self.isConfig = True |
|
|
1568 | 1591 | # |
|
|
1569 | 1592 | # #Append new data to internal buffer |
|
|
1570 | 1593 | # for thisChannel in range(self.__nChannels): |
|
|
1571 | 1594 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
1572 | 1595 | # bufferByChannel.extend(dataOut.data[thisChannel]) |
|
|
1573 | 1596 | # |
|
|
1574 | 1597 | # if self.__pulseFound: |
|
|
1575 | 1598 | # self.__startIndex -= self.__nSamples |
|
|
1576 | 1599 | # |
|
|
1577 | 1600 | # #Finding Tx Pulse |
|
|
1578 | 1601 | # if not self.__pulseFound: |
|
|
1579 | 1602 | # indexFound = self.__findTxPulse(dataOut, channel) |
|
|
1580 | 1603 | # |
|
|
1581 | 1604 | # if indexFound == None: |
|
|
1582 | 1605 | # dataOut.flagNoData = True |
|
|
1583 | 1606 | # return |
|
|
1584 | 1607 | # |
|
|
1585 | 1608 | # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex) |
|
|
1586 | 1609 | # self.__pulseFound = True |
|
|
1587 | 1610 | # self.__startIndex = indexFound |
|
|
1588 | 1611 | # |
|
|
1589 | 1612 | # #If pulse was found ... |
|
|
1590 | 1613 | # for thisChannel in range(self.__nChannels): |
|
|
1591 | 1614 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
1592 | 1615 | # #print self.__startIndex |
|
|
1593 | 1616 | # x = numpy.array(bufferByChannel) |
|
|
1594 | 1617 | # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] |
|
|
1595 | 1618 | # |
|
|
1596 | 1619 | # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1597 | 1620 | # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight |
|
|
1598 | 1621 | # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 |
|
|
1599 | 1622 | # |
|
|
1600 | 1623 | # dataOut.data = self.__arrayBuffer |
|
|
1601 | 1624 | # |
|
|
1602 | 1625 | # self.__startIndex += self.__newNSamples |
|
|
1603 | 1626 | # |
|
|
1604 | 1627 | # return |
| @@ -1,49 +1,73 | |||
|
|
1 | 1 | import os,sys |
|
|
2 | 2 | import datetime |
|
|
3 | 3 | import time |
|
|
4 | 4 | from schainpy.controller import Project |
|
|
5 | 5 | path = '/home/alex/Downloads/NEW_WR2/spc16removeDC' |
|
|
6 | 6 | figpath = path |
|
|
7 | 7 | desc = "Simulator Test" |
|
|
8 | 8 | |
|
|
9 | 9 | controllerObj = Project() |
|
|
10 | 10 | |
|
|
11 | 11 | controllerObj.setup(id='10',name='Test Simulator',description=desc) |
|
|
12 | 12 | |
|
|
13 | 13 | readUnitConfObj = controllerObj.addReadUnit(datatype='SimulatorReader', |
|
|
14 | 14 | frequency=9.345e9, |
|
|
15 | 15 | FixRCP_IPP= 60, |
|
|
16 | 16 | Tau_0 = 30, |
|
|
17 | 17 | AcqH0_0=0, |
|
|
18 | 18 | samples=330, |
|
|
19 | 19 | AcqDH_0=0.15, |
|
|
20 | 20 | FixRCP_TXA=0.15, |
|
|
21 | 21 | FixRCP_TXB=0.15, |
|
|
22 | 22 | Fdoppler=600.0, |
|
|
23 | 23 | Hdoppler=36, |
|
|
24 | 24 | Adoppler=300,#300 |
|
|
25 | 25 | delay=0, |
|
|
26 | 26 | online=0, |
|
|
27 | 27 | walk=0, |
|
|
28 |
|
|
|
|
29 | ||
|
|
28 | profilesPerBlock=625, | |
|
|
29 | dataBlocksPerFile=100)#,#nTotalReadFiles=2) | |
|
|
30 | ''' | |
|
|
31 | readUnitConfObj = controllerObj.addReadUnit(datatype='VoltageReader', | |
|
|
32 | path=path, | |
|
|
33 | startDate="2020/01/01", #"2020/01/01",#today, | |
|
|
34 | endDate= "2020/12/01", #"2020/12/30",#today, | |
|
|
35 | startTime='00:00:00', | |
|
|
36 | endTime='23:59:59', | |
|
|
37 | delay=0, | |
|
|
38 | #set=0, | |
|
|
39 | online=0, | |
|
|
40 | walk=1) | |
|
|
41 | ''' | |
|
|
30 | 42 | opObj11 = readUnitConfObj.addOperation(name='printInfo') |
|
|
31 | 43 | |
|
|
32 | 44 | procUnitConfObjA = controllerObj.addProcUnit(datatype='VoltageProc', inputId=readUnitConfObj.getId()) |
|
|
33 | 45 | #opObj11 = procUnitConfObjA.addOperation(name='CohInt', optype='other') |
|
|
34 | 46 | #opObj11.addParameter(name='n', value='10', format='int') |
|
|
35 | 47 | |
|
|
36 | 48 | #opObj10 = procUnitConfObjA.addOperation(name='selectChannels') |
|
|
37 | 49 | #opObj10.addParameter(name='channelList', value=[0]) |
|
|
38 | 50 | opObj11 = procUnitConfObjA.addOperation(name='PulsePairVoltage', optype='other') |
|
|
39 |
opObj11.addParameter(name='n', value=' |
|
|
|
51 | opObj11.addParameter(name='n', value='625', format='int')#10 | |
|
|
40 | 52 | opObj11.addParameter(name='removeDC', value=1, format='int') |
|
|
41 | 53 | |
|
|
42 | 54 | #opObj11 = procUnitConfObjA.addOperation(name='PulsepairPowerPlot', optype='other') |
|
|
55 | #opObj11 = procUnitConfObjA.addOperation(name='PulsepairSignalPlot', optype='other') | |
|
|
56 | ||
|
|
43 | 57 | |
|
|
44 | opObj11 = procUnitConfObjA.addOperation(name='PulsepairVelocityPlot', optype='other') | |
|
|
58 | #opObj11 = procUnitConfObjA.addOperation(name='PulsepairVelocityPlot', optype='other') | |
|
|
45 | 59 | #opObj11.addParameter(name='xmax', value=8) |
|
|
46 | 60 | |
|
|
47 | opObj11 = procUnitConfObjA.addOperation(name='PulsepairSpecwidthPlot', optype='other') | |
|
|
61 | #opObj11 = procUnitConfObjA.addOperation(name='PulsepairSpecwidthPlot', optype='other') | |
|
|
62 | ||
|
|
63 | procUnitConfObjB= controllerObj.addProcUnit(datatype='ParametersProc',inputId=procUnitConfObjA.getId()) | |
|
|
64 | ||
|
|
65 | ||
|
|
66 | opObj10 = procUnitConfObjB.addOperation(name='ParameterWriter') | |
|
|
67 | opObj10.addParameter(name='path',value=figpath) | |
|
|
68 | #opObj10.addParameter(name='mode',value=0) | |
|
|
69 | opObj10.addParameter(name='blocksPerFile',value='100',format='int') | |
|
|
70 | opObj10.addParameter(name='metadataList',value='utctimeInit,timeInterval',format='list') | |
|
|
71 | opObj10.addParameter(name='dataList',value='dataPP_POW,dataPP_DOP,dataPP_SNR,dataPP_WIDTH')#,format='list' | |
|
|
48 | 72 | |
|
|
49 | 73 | controllerObj.start() |
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