| @@ -1,126 +1,126 | |||
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1 | 1 | #include <Python.h> |
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2 | 2 | #include <numpy/arrayobject.h> |
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3 | 3 | #include <math.h> |
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4 | 4 | |
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5 | 5 | |
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6 | 6 | static PyObject *hildebrand_sekhon(PyObject *self, PyObject *args) { |
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7 | 7 | double navg; |
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8 | 8 | PyObject *data_obj, *data_array; |
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9 | 9 | |
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10 | 10 | if (!PyArg_ParseTuple(args, "Od", &data_obj, &navg)) { |
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11 | 11 | return NULL; |
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12 | 12 | } |
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13 | 13 | |
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14 | 14 | data_array = PyArray_FROM_OTF(data_obj, NPY_FLOAT64, NPY_IN_ARRAY); |
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15 | 15 | |
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16 | 16 | if (data_array == NULL) { |
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17 | 17 | Py_XDECREF(data_array); |
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18 | 18 | Py_XDECREF(data_obj); |
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19 | 19 | return NULL; |
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20 | 20 | } |
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21 | 21 | double *sortdata = (double*)PyArray_DATA(data_array); |
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22 | 22 | int lenOfData = (int)PyArray_SIZE(data_array) ; |
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23 | 23 | double nums_min = lenOfData*0.2; |
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24 | 24 | if (nums_min <= 5) nums_min = 5; |
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25 | 25 | double sump = 0; |
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26 | 26 | double sumq = 0; |
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27 | 27 | long j = 0; |
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28 | 28 | int cont = 1; |
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29 | 29 | double rtest = 0; |
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30 | 30 | while ((cont == 1) && (j < lenOfData)) { |
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31 | 31 | sump = sump + sortdata[j]; |
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32 | 32 | sumq = sumq + pow(sortdata[j], 2); |
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33 | 33 | if (j > nums_min) { |
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34 | 34 | rtest = (double)j/(j-1) + 1/navg; |
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35 | 35 | if ((sumq*j) > (rtest*pow(sump, 2))) { |
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36 | 36 | j = j - 1; |
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37 | 37 | sump = sump - sortdata[j]; |
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38 | 38 | sumq = sumq - pow(sortdata[j],2); |
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39 | 39 | cont = 0; |
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40 | 40 | } |
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41 | 41 | } |
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42 | 42 | j = j + 1; |
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43 | 43 | } |
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44 | 44 | |
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45 | 45 | double lnoise = sump / j; |
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46 | 46 | |
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47 | 47 | Py_DECREF(data_array); |
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48 | 48 | |
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49 | 49 | // return PyLong_FromLong(lnoise); |
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50 | 50 | return PyFloat_FromDouble(lnoise); |
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51 | 51 | } |
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52 | 52 | |
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53 | 53 | static PyObject *hildebrand_sekhon2(PyObject *self, PyObject *args) { |
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54 | 54 | double navg; |
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55 | 55 | PyObject *data_obj, *data_array; |
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56 | 56 | |
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57 | 57 | if (!PyArg_ParseTuple(args, "Od", &data_obj, &navg)) { |
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58 | 58 | return NULL; |
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59 | 59 | } |
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60 | 60 | data_array = PyArray_FROM_OTF(data_obj, NPY_FLOAT64, NPY_IN_ARRAY); |
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61 | 61 | |
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62 | 62 | if (data_array == NULL) { |
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63 | 63 | Py_XDECREF(data_array); |
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64 | 64 | Py_XDECREF(data_obj); |
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65 | 65 | return NULL; |
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66 | 66 | } |
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67 | 67 | double *sortdata = (double*)PyArray_DATA(data_array); |
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68 | 68 | int lenOfData = (int)PyArray_SIZE(data_array) ; |
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69 |
double nums_min = lenOfData*0. |
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69 | double nums_min = lenOfData*0.85; | |
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70 | 70 | if (nums_min <= 5) nums_min = 5; |
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71 | 71 | double sump = 0; |
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72 | 72 | double sumq = 0; |
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73 | 73 | long j = 0; |
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74 | 74 | int cont = 1; |
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75 | 75 | double rtest = 0; |
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76 | 76 | while ((cont == 1) && (j < lenOfData)) { |
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77 | 77 | sump = sump + sortdata[j]; |
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78 | 78 | sumq = sumq + pow(sortdata[j], 2); |
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79 | 79 | if (j > nums_min) { |
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80 | 80 | rtest = (double)j/(j-1) + 1/navg; |
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81 | 81 | if ((sumq*j) > (rtest*pow(sump, 2))) { |
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82 | 82 | j = j - 1; |
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83 | 83 | sump = sump - sortdata[j]; |
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84 | 84 | sumq = sumq - pow(sortdata[j],2); |
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85 | 85 | cont = 0; |
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86 | 86 | } |
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87 | 87 | } |
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88 | 88 | j = j + 1; |
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89 | 89 | } |
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90 | 90 | |
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91 | 91 | Py_DECREF(data_array); |
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92 | 92 | |
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93 | 93 | return PyLong_FromLong(j); |
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94 | 94 | } |
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95 | 95 | |
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96 | 96 | |
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97 | 97 | static PyMethodDef noiseMethods[] = { |
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98 | 98 | { "hildebrand_sekhon", hildebrand_sekhon, METH_VARARGS, "Get noise with hildebrand_sekhon algorithm" }, |
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99 | 99 | { "hildebrand_sekhon2", hildebrand_sekhon2, METH_VARARGS, "Variation for satellite cleaning" }, |
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100 | 100 | { NULL, NULL, 0, NULL } |
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101 | 101 | }; |
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102 | 102 | |
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103 | 103 | #if PY_MAJOR_VERSION >= 3 |
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104 | 104 | |
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105 | 105 | static struct PyModuleDef noisemodule = { |
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106 | 106 | PyModuleDef_HEAD_INIT, |
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107 | 107 | "_noise", |
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108 | 108 | "Get noise with hildebrand_sekhon algorithm", |
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109 | 109 | -1, |
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110 | 110 | noiseMethods |
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111 | 111 | }; |
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112 | 112 | |
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113 | 113 | #endif |
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114 | 114 | |
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115 | 115 | #if PY_MAJOR_VERSION >= 3 |
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116 | 116 | PyMODINIT_FUNC PyInit__noise(void) { |
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117 | 117 | Py_Initialize(); |
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118 | 118 | import_array(); |
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119 | 119 | return PyModule_Create(&noisemodule); |
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120 | 120 | } |
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121 | 121 | #else |
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122 | 122 | PyMODINIT_FUNC init_noise() { |
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123 | 123 | Py_InitModule("_noise", noiseMethods); |
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124 | 124 | import_array(); |
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125 | 125 | } |
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126 | 126 | #endif |
| @@ -1,687 +1,685 | |||
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1 | 1 | # Copyright (c) 2012-2020 Jicamarca Radio Observatory |
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2 | 2 | # All rights reserved. |
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3 | 3 | # |
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4 | 4 | # Distributed under the terms of the BSD 3-clause license. |
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5 | 5 | """API to create signal chain projects |
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6 | 6 | |
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7 | 7 | The API is provide through class: Project |
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8 | 8 | """ |
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9 | 9 | |
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10 | 10 | import re |
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11 | 11 | import sys |
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12 | 12 | import ast |
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13 | 13 | import datetime |
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14 | 14 | import traceback |
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15 | 15 | import time |
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16 | 16 | import multiprocessing |
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17 | 17 | from multiprocessing import Process, Queue |
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18 | 18 | from threading import Thread |
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19 | 19 | from xml.etree.ElementTree import ElementTree, Element, SubElement |
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20 | 20 | |
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21 | 21 | from schainpy.admin import Alarm, SchainWarning |
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22 | 22 | from schainpy.model import * |
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23 | 23 | from schainpy.utils import log |
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24 | 24 | |
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25 | 25 | if 'darwin' in sys.platform and sys.version_info[0] == 3 and sys.version_info[1] > 7: |
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26 | 26 | multiprocessing.set_start_method('fork') |
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27 | 27 | |
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28 | 28 | class ConfBase(): |
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29 | 29 | |
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30 | 30 | def __init__(self): |
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31 | 31 | |
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32 | 32 | self.id = '0' |
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33 | 33 | self.name = None |
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34 | 34 | self.priority = None |
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35 | 35 | self.parameters = {} |
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36 | 36 | self.object = None |
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37 | 37 | self.operations = [] |
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38 | 38 | |
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39 | 39 | def getId(self): |
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40 | 40 | |
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41 | 41 | return self.id |
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42 | 42 | |
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43 | 43 | def getNewId(self): |
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44 | 44 | |
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45 | 45 | return int(self.id) * 10 + len(self.operations) + 1 |
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46 | 46 | |
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47 | 47 | def updateId(self, new_id): |
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48 | 48 | |
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49 | 49 | self.id = str(new_id) |
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50 | 50 | |
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51 | 51 | n = 1 |
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52 | 52 | for conf in self.operations: |
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53 | 53 | conf_id = str(int(new_id) * 10 + n) |
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54 | 54 | conf.updateId(conf_id) |
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55 | 55 | n += 1 |
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56 | 56 | |
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57 | 57 | def getKwargs(self): |
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58 | 58 | |
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59 | 59 | params = {} |
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60 | 60 | |
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61 | 61 | for key, value in self.parameters.items(): |
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62 | 62 | if value not in (None, '', ' '): |
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63 | 63 | params[key] = value |
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64 | 64 | |
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65 | 65 | return params |
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66 | 66 | |
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67 | 67 | def update(self, **kwargs): |
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68 | 68 | |
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69 | 69 | for key, value in kwargs.items(): |
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70 | 70 | self.addParameter(name=key, value=value) |
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71 | 71 | |
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72 | 72 | def addParameter(self, name, value, format=None): |
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73 | 73 | ''' |
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74 | 74 | ''' |
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75 | 75 | |
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76 | 76 | if isinstance(value, str) and re.search(r'(\d+/\d+/\d+)', value): |
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77 | 77 | self.parameters[name] = datetime.date(*[int(x) for x in value.split('/')]) |
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78 | 78 | elif isinstance(value, str) and re.search(r'(\d+:\d+:\d+)', value): |
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79 | 79 | self.parameters[name] = datetime.time(*[int(x) for x in value.split(':')]) |
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80 | 80 | else: |
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81 | 81 | try: |
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82 | 82 | self.parameters[name] = ast.literal_eval(value) |
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83 | 83 | except: |
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84 | 84 | if isinstance(value, str) and ',' in value: |
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85 | 85 | self.parameters[name] = value.split(',') |
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86 | 86 | else: |
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87 | 87 | self.parameters[name] = value |
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88 | 88 | |
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89 | 89 | def getParameters(self): |
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90 | 90 | |
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91 | 91 | params = {} |
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92 | 92 | for key, value in self.parameters.items(): |
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93 | 93 | s = type(value).__name__ |
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94 | 94 | if s == 'date': |
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95 | 95 | params[key] = value.strftime('%Y/%m/%d') |
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96 | 96 | elif s == 'time': |
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97 | 97 | params[key] = value.strftime('%H:%M:%S') |
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98 | 98 | else: |
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99 | 99 | params[key] = str(value) |
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100 | 100 | |
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101 | 101 | return params |
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102 | 102 | |
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103 | 103 | def makeXml(self, element): |
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104 | 104 | |
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105 | 105 | xml = SubElement(element, self.ELEMENTNAME) |
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106 | 106 | for label in self.xml_labels: |
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107 | 107 | xml.set(label, str(getattr(self, label))) |
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108 | 108 | |
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109 | 109 | for key, value in self.getParameters().items(): |
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110 | 110 | xml_param = SubElement(xml, 'Parameter') |
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111 | 111 | xml_param.set('name', key) |
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112 | 112 | xml_param.set('value', value) |
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113 | 113 | |
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114 | 114 | for conf in self.operations: |
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115 | 115 | conf.makeXml(xml) |
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116 | 116 | |
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117 | 117 | def __str__(self): |
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118 | 118 | |
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119 | 119 | if self.ELEMENTNAME == 'Operation': |
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120 | 120 | s = ' {}[id={}]\n'.format(self.name, self.id) |
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121 | 121 | else: |
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122 | 122 | s = '{}[id={}, inputId={}]\n'.format(self.name, self.id, self.inputId) |
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123 | 123 | |
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124 | 124 | for key, value in self.parameters.items(): |
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125 | 125 | if self.ELEMENTNAME == 'Operation': |
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126 | 126 | s += ' {}: {}\n'.format(key, value) |
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127 | 127 | else: |
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128 | 128 | s += ' {}: {}\n'.format(key, value) |
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129 | 129 | |
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130 | 130 | for conf in self.operations: |
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131 | 131 | s += str(conf) |
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132 | 132 | |
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133 | 133 | return s |
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134 | 134 | |
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135 | 135 | class OperationConf(ConfBase): |
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136 | 136 | |
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137 | 137 | ELEMENTNAME = 'Operation' |
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138 | 138 | xml_labels = ['id', 'name'] |
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139 | 139 | |
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140 | 140 | def setup(self, id, name, priority, project_id, err_queue): |
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141 | 141 | |
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142 | 142 | self.id = str(id) |
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143 | 143 | self.project_id = project_id |
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144 | 144 | self.name = name |
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145 | 145 | self.type = 'other' |
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146 | 146 | self.err_queue = err_queue |
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147 | 147 | |
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148 | 148 | def readXml(self, element, project_id, err_queue): |
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149 | 149 | |
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150 | 150 | self.id = element.get('id') |
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151 | 151 | self.name = element.get('name') |
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152 | 152 | self.type = 'other' |
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153 | 153 | self.project_id = str(project_id) |
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154 | 154 | self.err_queue = err_queue |
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155 | 155 | |
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156 | 156 | for elm in element.iter('Parameter'): |
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157 | 157 | self.addParameter(elm.get('name'), elm.get('value')) |
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158 | 158 | |
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159 | 159 | def createObject(self): |
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160 | 160 | |
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161 | 161 | className = eval(self.name) |
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162 | 162 | |
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163 | 163 | if 'Plot' in self.name or 'Writer' in self.name or 'Send' in self.name or 'print' in self.name: |
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164 | 164 | kwargs = self.getKwargs() |
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165 | 165 | opObj = className(self.name, self.id, self.project_id, self.err_queue, **kwargs) |
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166 | 166 | opObj.start() |
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167 | 167 | self.type = 'external' |
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168 | 168 | else: |
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169 | 169 | opObj = className() |
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170 | 170 | |
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171 | 171 | self.object = opObj |
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172 | 172 | return opObj |
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173 | 173 | |
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174 | 174 | class ProcUnitConf(ConfBase): |
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175 | 175 | |
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176 | 176 | ELEMENTNAME = 'ProcUnit' |
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177 | 177 | xml_labels = ['id', 'inputId', 'name'] |
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178 | 178 | |
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179 | 179 | def setup(self, project_id, id, name, datatype, inputId, err_queue): |
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180 | 180 | ''' |
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181 | 181 | ''' |
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182 | 182 | |
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183 | 183 | if datatype == None and name == None: |
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184 | 184 | raise ValueError('datatype or name should be defined') |
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185 | 185 | |
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186 | 186 | if name == None: |
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187 | 187 | if 'Proc' in datatype: |
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188 | 188 | name = datatype |
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189 | 189 | else: |
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190 | 190 | name = '%sProc' % (datatype) |
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191 | 191 | |
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192 | 192 | if datatype == None: |
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193 | 193 | datatype = name.replace('Proc', '') |
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194 | 194 | |
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195 | 195 | self.id = str(id) |
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196 | 196 | self.project_id = project_id |
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197 | 197 | self.name = name |
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198 | 198 | self.datatype = datatype |
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199 | 199 | self.inputId = inputId |
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200 | 200 | self.err_queue = err_queue |
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201 | 201 | self.operations = [] |
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202 | 202 | self.parameters = {} |
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203 | 203 | |
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204 | 204 | def removeOperation(self, id): |
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205 | 205 | |
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206 | 206 | i = [1 if x.id==id else 0 for x in self.operations] |
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207 | 207 | self.operations.pop(i.index(1)) |
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208 | 208 | |
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209 | 209 | def getOperation(self, id): |
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210 | 210 | |
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211 | 211 | for conf in self.operations: |
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212 | 212 | if conf.id == id: |
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213 | 213 | return conf |
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214 | 214 | |
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215 | 215 | def addOperation(self, name, optype='self'): |
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216 | 216 | ''' |
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217 | 217 | ''' |
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218 | 218 | |
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219 | 219 | id = self.getNewId() |
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220 | 220 | conf = OperationConf() |
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221 | 221 | conf.setup(id, name=name, priority='0', project_id=self.project_id, err_queue=self.err_queue) |
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222 | 222 | self.operations.append(conf) |
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223 | 223 | |
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224 | 224 | return conf |
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225 | 225 | |
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226 | 226 | def readXml(self, element, project_id, err_queue): |
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227 | 227 | |
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228 | 228 | self.id = element.get('id') |
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229 | 229 | self.name = element.get('name') |
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230 | 230 | self.inputId = None if element.get('inputId') == 'None' else element.get('inputId') |
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231 | 231 | self.datatype = element.get('datatype', self.name.replace(self.ELEMENTNAME.replace('Unit', ''), '')) |
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232 | 232 | self.project_id = str(project_id) |
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233 | 233 | self.err_queue = err_queue |
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234 | 234 | self.operations = [] |
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235 | 235 | self.parameters = {} |
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236 | 236 | |
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237 | 237 | for elm in element: |
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238 | 238 | if elm.tag == 'Parameter': |
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239 | 239 | self.addParameter(elm.get('name'), elm.get('value')) |
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240 | 240 | elif elm.tag == 'Operation': |
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241 | 241 | conf = OperationConf() |
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242 | 242 | conf.readXml(elm, project_id, err_queue) |
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243 | 243 | self.operations.append(conf) |
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244 | 244 | |
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245 | 245 | def createObjects(self): |
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246 | 246 | ''' |
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247 | 247 | Instancia de unidades de procesamiento. |
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248 | 248 | ''' |
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249 | 249 | |
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250 | 250 | className = eval(self.name) |
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251 | 251 | kwargs = self.getKwargs() |
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252 | 252 | procUnitObj = className() |
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253 | 253 | procUnitObj.name = self.name |
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254 | 254 | log.success('creating process... id: {}, inputId: {}'.format(self.id,self.inputId), self.name) |
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255 | 255 | |
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256 | 256 | for conf in self.operations: |
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257 | 257 | |
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258 | 258 | opObj = conf.createObject() |
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259 | 259 | |
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260 | 260 | log.success('adding operation: {}, type:{}'.format(conf.name,conf.type), self.name) |
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261 | 261 | |
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262 | 262 | procUnitObj.addOperation(conf, opObj) |
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263 | 263 | |
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264 | 264 | self.object = procUnitObj |
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265 | 265 | |
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266 | 266 | def run(self): |
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267 | 267 | ''' |
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268 | 268 | ''' |
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269 | 269 | return self.object.call(**self.getKwargs()) |
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270 | 270 | |
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271 | 271 | class ReadUnitConf(ProcUnitConf): |
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272 | 272 | |
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273 | 273 | ELEMENTNAME = 'ReadUnit' |
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274 | 274 | |
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275 | 275 | def __init__(self): |
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276 | 276 | |
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277 | 277 | self.id = None |
|
|
278 | 278 | self.datatype = None |
|
|
279 | 279 | self.name = None |
|
|
280 | 280 | self.inputId = None |
|
|
281 | 281 | self.operations = [] |
|
|
282 | 282 | self.parameters = {} |
|
|
283 | 283 | |
|
|
284 | 284 | def setup(self, project_id, id, name, datatype, err_queue, path='', startDate='', endDate='', |
|
|
285 | 285 | startTime='', endTime='', server=None, **kwargs): |
|
|
286 | 286 | |
|
|
287 | 287 | if datatype == None and name == None: |
|
|
288 | 288 | raise ValueError('datatype or name should be defined') |
|
|
289 | 289 | if name == None: |
|
|
290 | 290 | if 'Reader' in datatype: |
|
|
291 | 291 | name = datatype |
|
|
292 | 292 | datatype = name.replace('Reader','') |
|
|
293 | 293 | else: |
|
|
294 | 294 | name = '{}Reader'.format(datatype) |
|
|
295 | 295 | if datatype == None: |
|
|
296 | 296 | if 'Reader' in name: |
|
|
297 | 297 | datatype = name.replace('Reader','') |
|
|
298 | 298 | else: |
|
|
299 | 299 | datatype = name |
|
|
300 | 300 | name = '{}Reader'.format(name) |
|
|
301 | 301 | |
|
|
302 | 302 | self.id = id |
|
|
303 | 303 | self.project_id = project_id |
|
|
304 | 304 | self.name = name |
|
|
305 | 305 | self.datatype = datatype |
|
|
306 | 306 | self.err_queue = err_queue |
|
|
307 | 307 | |
|
|
308 | 308 | self.addParameter(name='path', value=path) |
|
|
309 | 309 | self.addParameter(name='startDate', value=startDate) |
|
|
310 | 310 | self.addParameter(name='endDate', value=endDate) |
|
|
311 | 311 | self.addParameter(name='startTime', value=startTime) |
|
|
312 | 312 | self.addParameter(name='endTime', value=endTime) |
|
|
313 | 313 | |
|
|
314 | 314 | for key, value in kwargs.items(): |
|
|
315 | 315 | self.addParameter(name=key, value=value) |
|
|
316 | 316 | |
|
|
317 | 317 | |
|
|
318 | 318 | class Project(Process): |
|
|
319 | 319 | """API to create signal chain projects""" |
|
|
320 | 320 | |
|
|
321 | 321 | ELEMENTNAME = 'Project' |
|
|
322 | 322 | |
|
|
323 | 323 | def __init__(self, name=''): |
|
|
324 | 324 | |
|
|
325 | 325 | Process.__init__(self) |
|
|
326 | 326 | self.id = '1' |
|
|
327 | 327 | if name: |
|
|
328 | 328 | self.name = '{} ({})'.format(Process.__name__, name) |
|
|
329 | 329 | self.filename = None |
|
|
330 | 330 | self.description = None |
|
|
331 | 331 | self.email = None |
|
|
332 | 332 | self.alarm = [] |
|
|
333 | 333 | self.configurations = {} |
|
|
334 | 334 | # self.err_queue = Queue() |
|
|
335 | 335 | self.err_queue = None |
|
|
336 | 336 | self.started = False |
|
|
337 | 337 | |
|
|
338 | 338 | def getNewId(self): |
|
|
339 | 339 | |
|
|
340 | 340 | idList = list(self.configurations.keys()) |
|
|
341 | 341 | id = int(self.id) * 10 |
|
|
342 | 342 | |
|
|
343 | 343 | while True: |
|
|
344 | 344 | id += 1 |
|
|
345 | 345 | |
|
|
346 | 346 | if str(id) in idList: |
|
|
347 | 347 | continue |
|
|
348 | 348 | |
|
|
349 | 349 | break |
|
|
350 | 350 | |
|
|
351 | 351 | return str(id) |
|
|
352 | 352 | |
|
|
353 | 353 | def updateId(self, new_id): |
|
|
354 | 354 | |
|
|
355 | 355 | self.id = str(new_id) |
|
|
356 | 356 | |
|
|
357 | 357 | keyList = list(self.configurations.keys()) |
|
|
358 | 358 | keyList.sort() |
|
|
359 | 359 | |
|
|
360 | 360 | n = 1 |
|
|
361 | 361 | new_confs = {} |
|
|
362 | 362 | |
|
|
363 | 363 | for procKey in keyList: |
|
|
364 | 364 | |
|
|
365 | 365 | conf = self.configurations[procKey] |
|
|
366 | 366 | idProcUnit = str(int(self.id) * 10 + n) |
|
|
367 | 367 | conf.updateId(idProcUnit) |
|
|
368 | 368 | new_confs[idProcUnit] = conf |
|
|
369 | 369 | n += 1 |
|
|
370 | 370 | |
|
|
371 | 371 | self.configurations = new_confs |
|
|
372 | 372 | |
|
|
373 | 373 | def setup(self, id=1, name='', description='', email=None, alarm=[]): |
|
|
374 | 374 | |
|
|
375 | 375 | self.id = str(id) |
|
|
376 | 376 | self.description = description |
|
|
377 | 377 | self.email = email |
|
|
378 | 378 | self.alarm = alarm |
|
|
379 | 379 | if name: |
|
|
380 | 380 | self.name = '{} ({})'.format(Process.__name__, name) |
|
|
381 | 381 | |
|
|
382 | 382 | def update(self, **kwargs): |
|
|
383 | 383 | |
|
|
384 | 384 | for key, value in kwargs.items(): |
|
|
385 | 385 | setattr(self, key, value) |
|
|
386 | 386 | |
|
|
387 | 387 | def clone(self): |
|
|
388 | 388 | |
|
|
389 | 389 | p = Project() |
|
|
390 | 390 | p.id = self.id |
|
|
391 | 391 | p.name = self.name |
|
|
392 | 392 | p.description = self.description |
|
|
393 | 393 | p.configurations = self.configurations.copy() |
|
|
394 | 394 | |
|
|
395 | 395 | return p |
|
|
396 | 396 | |
|
|
397 | 397 | def addReadUnit(self, id=None, datatype=None, name=None, **kwargs): |
|
|
398 | 398 | |
|
|
399 | 399 | ''' |
|
|
400 | 400 | ''' |
|
|
401 | 401 | |
|
|
402 | 402 | if id is None: |
|
|
403 | 403 | idReadUnit = self.getNewId() |
|
|
404 | 404 | else: |
|
|
405 | 405 | idReadUnit = str(id) |
|
|
406 | 406 | |
|
|
407 | 407 | conf = ReadUnitConf() |
|
|
408 | 408 | conf.setup(self.id, idReadUnit, name, datatype, self.err_queue, **kwargs) |
|
|
409 | 409 | self.configurations[conf.id] = conf |
|
|
410 | 410 | |
|
|
411 | 411 | return conf |
|
|
412 | 412 | |
|
|
413 | 413 | def addProcUnit(self, id=None, inputId='0', datatype=None, name=None): |
|
|
414 | 414 | |
|
|
415 | 415 | ''' |
|
|
416 | 416 | ''' |
|
|
417 | 417 | |
|
|
418 | 418 | if id is None: |
|
|
419 | 419 | idProcUnit = self.getNewId() |
|
|
420 | 420 | else: |
|
|
421 | 421 | idProcUnit = id |
|
|
422 | 422 | |
|
|
423 | 423 | conf = ProcUnitConf() |
|
|
424 | 424 | conf.setup(self.id, idProcUnit, name, datatype, inputId, self.err_queue) |
|
|
425 | 425 | self.configurations[conf.id] = conf |
|
|
426 | 426 | |
|
|
427 | 427 | return conf |
|
|
428 | 428 | |
|
|
429 | 429 | def removeProcUnit(self, id): |
|
|
430 | 430 | |
|
|
431 | 431 | if id in self.configurations: |
|
|
432 | 432 | self.configurations.pop(id) |
|
|
433 | 433 | |
|
|
434 | 434 | def getReadUnit(self): |
|
|
435 | 435 | |
|
|
436 | 436 | for obj in list(self.configurations.values()): |
|
|
437 | 437 | if obj.ELEMENTNAME == 'ReadUnit': |
|
|
438 | 438 | return obj |
|
|
439 | 439 | |
|
|
440 | 440 | return None |
|
|
441 | 441 | |
|
|
442 | 442 | def getProcUnit(self, id): |
|
|
443 | 443 | |
|
|
444 | 444 | return self.configurations[id] |
|
|
445 | 445 | |
|
|
446 | 446 | def getUnits(self): |
|
|
447 | 447 | |
|
|
448 | 448 | keys = list(self.configurations) |
|
|
449 | 449 | keys.sort() |
|
|
450 | 450 | |
|
|
451 | 451 | for key in keys: |
|
|
452 | 452 | yield self.configurations[key] |
|
|
453 | 453 | |
|
|
454 | 454 | def updateUnit(self, id, **kwargs): |
|
|
455 | 455 | |
|
|
456 | 456 | conf = self.configurations[id].update(**kwargs) |
|
|
457 | 457 | |
|
|
458 | 458 | def makeXml(self): |
|
|
459 | 459 | |
|
|
460 | 460 | xml = Element('Project') |
|
|
461 | 461 | xml.set('id', str(self.id)) |
|
|
462 | 462 | xml.set('name', self.name) |
|
|
463 | 463 | xml.set('description', self.description) |
|
|
464 | 464 | |
|
|
465 | 465 | for conf in self.configurations.values(): |
|
|
466 | 466 | conf.makeXml(xml) |
|
|
467 | 467 | |
|
|
468 | 468 | self.xml = xml |
|
|
469 | 469 | |
|
|
470 | 470 | def writeXml(self, filename=None): |
|
|
471 | 471 | |
|
|
472 | 472 | if filename == None: |
|
|
473 | 473 | if self.filename: |
|
|
474 | 474 | filename = self.filename |
|
|
475 | 475 | else: |
|
|
476 | 476 | filename = 'schain.xml' |
|
|
477 | 477 | |
|
|
478 | 478 | if not filename: |
|
|
479 | 479 | print('filename has not been defined. Use setFilename(filename) for do it.') |
|
|
480 | 480 | return 0 |
|
|
481 | 481 | |
|
|
482 | 482 | abs_file = os.path.abspath(filename) |
|
|
483 | 483 | |
|
|
484 | 484 | if not os.access(os.path.dirname(abs_file), os.W_OK): |
|
|
485 | 485 | print('No write permission on %s' % os.path.dirname(abs_file)) |
|
|
486 | 486 | return 0 |
|
|
487 | 487 | |
|
|
488 | 488 | if os.path.isfile(abs_file) and not(os.access(abs_file, os.W_OK)): |
|
|
489 | 489 | print('File %s already exists and it could not be overwriten' % abs_file) |
|
|
490 | 490 | return 0 |
|
|
491 | 491 | |
|
|
492 | 492 | self.makeXml() |
|
|
493 | 493 | |
|
|
494 | 494 | ElementTree(self.xml).write(abs_file, method='xml') |
|
|
495 | 495 | |
|
|
496 | 496 | self.filename = abs_file |
|
|
497 | 497 | |
|
|
498 | 498 | return 1 |
|
|
499 | 499 | |
|
|
500 | 500 | def readXml(self, filename): |
|
|
501 | 501 | |
|
|
502 | 502 | abs_file = os.path.abspath(filename) |
|
|
503 | 503 | |
|
|
504 | 504 | self.configurations = {} |
|
|
505 | 505 | |
|
|
506 | 506 | try: |
|
|
507 | 507 | self.xml = ElementTree().parse(abs_file) |
|
|
508 | 508 | except: |
|
|
509 | 509 | log.error('Error reading %s, verify file format' % filename) |
|
|
510 | 510 | return 0 |
|
|
511 | 511 | |
|
|
512 | 512 | self.id = self.xml.get('id') |
|
|
513 | 513 | self.name = self.xml.get('name') |
|
|
514 | 514 | self.description = self.xml.get('description') |
|
|
515 | 515 | |
|
|
516 | 516 | for element in self.xml: |
|
|
517 | 517 | if element.tag == 'ReadUnit': |
|
|
518 | 518 | conf = ReadUnitConf() |
|
|
519 | 519 | conf.readXml(element, self.id, self.err_queue) |
|
|
520 | 520 | self.configurations[conf.id] = conf |
|
|
521 | 521 | elif element.tag == 'ProcUnit': |
|
|
522 | 522 | conf = ProcUnitConf() |
|
|
523 | 523 | input_proc = self.configurations[element.get('inputId')] |
|
|
524 | 524 | conf.readXml(element, self.id, self.err_queue) |
|
|
525 | 525 | self.configurations[conf.id] = conf |
|
|
526 | 526 | |
|
|
527 | 527 | self.filename = abs_file |
|
|
528 | 528 | |
|
|
529 | 529 | return 1 |
|
|
530 | 530 | |
|
|
531 | 531 | def __str__(self): |
|
|
532 | 532 | |
|
|
533 | 533 | text = '\nProject[id=%s, name=%s, description=%s]\n\n' % ( |
|
|
534 | 534 | self.id, |
|
|
535 | 535 | self.name, |
|
|
536 | 536 | self.description, |
|
|
537 | 537 | ) |
|
|
538 | 538 | |
|
|
539 | 539 | for conf in self.configurations.values(): |
|
|
540 | 540 | text += '{}'.format(conf) |
|
|
541 | 541 | |
|
|
542 | 542 | return text |
|
|
543 | 543 | |
|
|
544 | 544 | def createObjects(self): |
|
|
545 | 545 | |
|
|
546 | 546 | keys = list(self.configurations.keys()) |
|
|
547 | 547 | keys.sort() |
|
|
548 | 548 | for key in keys: |
|
|
549 | 549 | conf = self.configurations[key] |
|
|
550 | 550 | conf.createObjects() |
|
|
551 | 551 | if conf.inputId is not None: |
|
|
552 | 552 | conf.object.setInput(self.configurations[conf.inputId].object) |
|
|
553 | 553 | |
|
|
554 | 554 | def monitor(self): |
|
|
555 | 555 | |
|
|
556 | 556 | t = Thread(target=self._monitor, args=(self.err_queue, self.ctx)) |
|
|
557 | 557 | t.start() |
|
|
558 | 558 | |
|
|
559 | 559 | def _monitor(self, queue, ctx): |
|
|
560 | 560 | |
|
|
561 | 561 | import socket |
|
|
562 | 562 | |
|
|
563 | 563 | procs = 0 |
|
|
564 | 564 | err_msg = '' |
|
|
565 | 565 | |
|
|
566 | 566 | while True: |
|
|
567 | 567 | msg = queue.get() |
|
|
568 | 568 | if '#_start_#' in msg: |
|
|
569 | 569 | procs += 1 |
|
|
570 | 570 | elif '#_end_#' in msg: |
|
|
571 | 571 | procs -=1 |
|
|
572 | 572 | else: |
|
|
573 | 573 | err_msg = msg |
|
|
574 | 574 | |
|
|
575 | 575 | if procs == 0 or 'Traceback' in err_msg: |
|
|
576 | 576 | break |
|
|
577 | 577 | time.sleep(0.1) |
|
|
578 | 578 | |
|
|
579 | 579 | if '|' in err_msg: |
|
|
580 | 580 | name, err = err_msg.split('|') |
|
|
581 | 581 | if 'SchainWarning' in err: |
|
|
582 | 582 | log.warning(err.split('SchainWarning:')[-1].split('\n')[0].strip(), name) |
|
|
583 | 583 | elif 'SchainError' in err: |
|
|
584 | 584 | log.error(err.split('SchainError:')[-1].split('\n')[0].strip(), name) |
|
|
585 | 585 | else: |
|
|
586 | 586 | log.error(err, name) |
|
|
587 | 587 | else: |
|
|
588 | 588 | name, err = self.name, err_msg |
|
|
589 | 589 | |
|
|
590 | 590 | time.sleep(1) |
|
|
591 | 591 | |
|
|
592 | 592 | ctx.term() |
|
|
593 | 593 | |
|
|
594 | 594 | message = ''.join(err) |
|
|
595 | 595 | |
|
|
596 | 596 | if err_msg: |
|
|
597 | 597 | subject = 'SChain v%s: Error running %s\n' % ( |
|
|
598 | 598 | schainpy.__version__, self.name) |
|
|
599 | 599 | |
|
|
600 | 600 | subtitle = 'Hostname: %s\n' % socket.gethostbyname( |
|
|
601 | 601 | socket.gethostname()) |
|
|
602 | 602 | subtitle += 'Working directory: %s\n' % os.path.abspath('./') |
|
|
603 | 603 | subtitle += 'Configuration file: %s\n' % self.filename |
|
|
604 | 604 | subtitle += 'Time: %s\n' % str(datetime.datetime.now()) |
|
|
605 | 605 | |
|
|
606 | 606 | readUnitConfObj = self.getReadUnit() |
|
|
607 | 607 | if readUnitConfObj: |
|
|
608 | 608 | subtitle += '\nInput parameters:\n' |
|
|
609 | 609 | subtitle += '[Data path = %s]\n' % readUnitConfObj.parameters['path'] |
|
|
610 | 610 | subtitle += '[Start date = %s]\n' % readUnitConfObj.parameters['startDate'] |
|
|
611 | 611 | subtitle += '[End date = %s]\n' % readUnitConfObj.parameters['endDate'] |
|
|
612 | 612 | subtitle += '[Start time = %s]\n' % readUnitConfObj.parameters['startTime'] |
|
|
613 | 613 | subtitle += '[End time = %s]\n' % readUnitConfObj.parameters['endTime'] |
|
|
614 | 614 | |
|
|
615 | 615 | a = Alarm( |
|
|
616 | 616 | modes=self.alarm, |
|
|
617 | 617 | email=self.email, |
|
|
618 | 618 | message=message, |
|
|
619 | 619 | subject=subject, |
|
|
620 | 620 | subtitle=subtitle, |
|
|
621 | 621 | filename=self.filename |
|
|
622 | 622 | ) |
|
|
623 | 623 | |
|
|
624 | 624 | a.start() |
|
|
625 | 625 | |
|
|
626 | 626 | def setFilename(self, filename): |
|
|
627 | 627 | |
|
|
628 | 628 | self.filename = filename |
|
|
629 | 629 | |
|
|
630 | 630 | def runProcs(self): |
|
|
631 | 631 | |
|
|
632 | 632 | err = False |
|
|
633 | 633 | n = len(self.configurations) |
|
|
634 | 634 | flag_no_read = False |
|
|
635 | 635 | nProc_noRead = 0 |
|
|
636 | 636 | while not err: |
|
|
637 | 637 | #print("STAR") |
|
|
638 | 638 | n_proc = 0 |
|
|
639 | 639 | for conf in self.getUnits(): |
|
|
640 | 640 | #print("CONF: ",conf) |
|
|
641 | 641 | #print( n_proc, nProc_noRead,flag_no_read) |
|
|
642 | 642 | if flag_no_read: |
|
|
643 | 643 | if n_proc >= nProc_noRead: |
|
|
644 | 644 | ok = conf.run() |
|
|
645 | 645 | else: |
|
|
646 | 646 | #print("break") |
|
|
647 | 647 | #print(ok, n_proc, nProc_noRead, n, flag_no_read) |
|
|
648 | 648 | n_proc += 1 |
|
|
649 | 649 | continue |
|
|
650 | 650 | else: |
|
|
651 | 651 | ok = conf.run() |
|
|
652 | 652 | |
|
|
653 | 653 | n_proc += 1 |
|
|
654 | 654 | #print(ok, n_proc, nProc_noRead, n, flag_no_read) |
|
|
655 | 655 | |
|
|
656 | 656 | if ok == 'Error': |
|
|
657 | 657 | #self.removeProcUnit(conf.id) #remove proc Unit |
|
|
658 | 658 | n -= 1 |
|
|
659 | 659 | continue |
|
|
660 | 660 | |
|
|
661 | 661 | elif ok == 'no_Read' and (not flag_no_read): |
|
|
662 | 662 | nProc_noRead = n_proc - 1 |
|
|
663 | 663 | flag_no_read = True |
|
|
664 | print("No read") | |
|
|
665 | 664 | continue |
|
|
666 | 665 | elif ok == 'new_Read': |
|
|
667 | 666 | nProc_noRead = 0 |
|
|
668 | 667 | flag_no_read = False |
|
|
669 | print("read again") | |
|
|
670 | 668 | continue |
|
|
671 | 669 | elif not ok: |
|
|
672 | 670 | #print("not ok",ok) |
|
|
673 | 671 | break |
|
|
674 | 672 | |
|
|
675 | 673 | if n == 0: |
|
|
676 | 674 | err = True |
|
|
677 | 675 | |
|
|
678 | 676 | def run(self): |
|
|
679 | 677 | |
|
|
680 | 678 | log.success('\nStarting Project {} [id={}]'.format(self.name, self.id), tag='') |
|
|
681 | 679 | self.started = True |
|
|
682 | 680 | self.start_time = time.time() |
|
|
683 | 681 | self.createObjects() |
|
|
684 | 682 | self.runProcs() |
|
|
685 | 683 | log.success('{} Done (Time: {:4.2f}s)'.format( |
|
|
686 | 684 | self.name, |
|
|
687 | 685 | time.time()-self.start_time), '') |
| @@ -1,1079 +1,1102 | |||
|
|
1 | 1 | # Copyright (c) 2012-2020 Jicamarca Radio Observatory |
|
|
2 | 2 | # All rights reserved. |
|
|
3 | 3 | # |
|
|
4 | 4 | # Distributed under the terms of the BSD 3-clause license. |
|
|
5 | 5 | """Definition of diferent Data objects for different types of data |
|
|
6 | 6 | |
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7 | 7 | Here you will find the diferent data objects for the different types |
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8 | 8 | of data, this data objects must be used as dataIn or dataOut objects in |
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9 | 9 | processing units and operations. Currently the supported data objects are: |
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10 | 10 | Voltage, Spectra, SpectraHeis, Fits, Correlation and Parameters |
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11 | 11 | """ |
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12 | 12 | |
|
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13 | 13 | import copy |
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14 | 14 | import numpy |
|
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15 | 15 | import datetime |
|
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16 | 16 | import json |
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17 | 17 | |
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18 | 18 | import schainpy.admin |
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19 | 19 | from schainpy.utils import log |
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20 | 20 | from .jroheaderIO import SystemHeader, RadarControllerHeader |
|
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21 | 21 | from schainpy.model.data import _noise |
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22 | 22 | |
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23 | 23 | |
|
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24 | 24 | def getNumpyDtype(dataTypeCode): |
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25 | 25 | |
|
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26 | 26 | if dataTypeCode == 0: |
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27 | 27 | numpyDtype = numpy.dtype([('real', '<i1'), ('imag', '<i1')]) |
|
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28 | 28 | elif dataTypeCode == 1: |
|
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29 | 29 | numpyDtype = numpy.dtype([('real', '<i2'), ('imag', '<i2')]) |
|
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30 | 30 | elif dataTypeCode == 2: |
|
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31 | 31 | numpyDtype = numpy.dtype([('real', '<i4'), ('imag', '<i4')]) |
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32 | 32 | elif dataTypeCode == 3: |
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33 | 33 | numpyDtype = numpy.dtype([('real', '<i8'), ('imag', '<i8')]) |
|
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34 | 34 | elif dataTypeCode == 4: |
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35 | 35 | numpyDtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')]) |
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36 | 36 | elif dataTypeCode == 5: |
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37 | 37 | numpyDtype = numpy.dtype([('real', '<f8'), ('imag', '<f8')]) |
|
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38 | 38 | else: |
|
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39 | 39 | raise ValueError('dataTypeCode was not defined') |
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40 | 40 | |
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41 | 41 | return numpyDtype |
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42 | 42 | |
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43 | 43 | |
|
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44 | 44 | def getDataTypeCode(numpyDtype): |
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45 | 45 | |
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46 | 46 | if numpyDtype == numpy.dtype([('real', '<i1'), ('imag', '<i1')]): |
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47 | 47 | datatype = 0 |
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48 | 48 | elif numpyDtype == numpy.dtype([('real', '<i2'), ('imag', '<i2')]): |
|
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49 | 49 | datatype = 1 |
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50 | 50 | elif numpyDtype == numpy.dtype([('real', '<i4'), ('imag', '<i4')]): |
|
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51 | 51 | datatype = 2 |
|
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52 | 52 | elif numpyDtype == numpy.dtype([('real', '<i8'), ('imag', '<i8')]): |
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53 | 53 | datatype = 3 |
|
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54 | 54 | elif numpyDtype == numpy.dtype([('real', '<f4'), ('imag', '<f4')]): |
|
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55 | 55 | datatype = 4 |
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56 | 56 | elif numpyDtype == numpy.dtype([('real', '<f8'), ('imag', '<f8')]): |
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57 | 57 | datatype = 5 |
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58 | 58 | else: |
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59 | 59 | datatype = None |
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60 | 60 | |
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61 | 61 | return datatype |
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62 | 62 | |
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63 | 63 | |
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64 | 64 | def hildebrand_sekhon(data, navg): |
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65 | 65 | """ |
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66 | 66 | This method is for the objective determination of the noise level in Doppler spectra. This |
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67 | 67 | implementation technique is based on the fact that the standard deviation of the spectral |
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68 | 68 | densities is equal to the mean spectral density for white Gaussian noise |
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69 | 69 | |
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70 | 70 | Inputs: |
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71 | 71 | Data : heights |
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72 | 72 | navg : numbers of averages |
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73 | 73 | |
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74 | 74 | Return: |
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75 | 75 | mean : noise's level |
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76 | 76 | """ |
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77 | 77 | |
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78 | 78 | sortdata = numpy.sort(data, axis=None) |
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79 | 79 | ''' |
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80 | 80 | lenOfData = len(sortdata) |
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81 |
nums_min = lenOfData*0. |
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|
|
81 | nums_min = lenOfData*0.5 | |
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82 | 82 | |
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83 | 83 | if nums_min <= 5: |
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84 | 84 | |
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85 | 85 | nums_min = 5 |
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86 | 86 | |
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87 | 87 | sump = 0. |
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88 | 88 | sumq = 0. |
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89 | 89 | |
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90 | 90 | j = 0 |
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91 | 91 | cont = 1 |
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92 | 92 | |
|
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93 | 93 | while((cont == 1)and(j < lenOfData)): |
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94 | 94 | |
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95 | 95 | sump += sortdata[j] |
|
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96 | 96 | sumq += sortdata[j]**2 |
|
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97 | 97 | |
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98 | 98 | if j > nums_min: |
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99 | 99 | rtest = float(j)/(j-1) + 1.0/navg |
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100 | 100 | if ((sumq*j) > (rtest*sump**2)): |
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101 | 101 | j = j - 1 |
|
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102 | 102 | sump = sump - sortdata[j] |
|
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103 | 103 | sumq = sumq - sortdata[j]**2 |
|
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104 | 104 | cont = 0 |
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105 | 105 | |
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106 | 106 | j += 1 |
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107 | 107 | |
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108 | 108 | lnoise = sump / j |
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109 | return lnoise | |
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109 | 110 | ''' |
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110 | 111 | return _noise.hildebrand_sekhon(sortdata, navg) |
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111 | 112 | |
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112 | 113 | |
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113 | 114 | class Beam: |
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114 | 115 | |
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115 | 116 | def __init__(self): |
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116 | 117 | self.codeList = [] |
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117 | 118 | self.azimuthList = [] |
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118 | 119 | self.zenithList = [] |
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119 | 120 | |
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120 | 121 | |
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121 | 122 | |
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122 | 123 | class GenericData(object): |
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123 | 124 | |
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124 | 125 | flagNoData = True |
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125 | 126 | |
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126 | 127 | def copy(self, inputObj=None): |
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127 | 128 | |
|
|
128 | 129 | if inputObj == None: |
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129 | 130 | return copy.deepcopy(self) |
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130 | 131 | |
|
|
131 | 132 | for key in list(inputObj.__dict__.keys()): |
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132 | 133 | |
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133 | 134 | attribute = inputObj.__dict__[key] |
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134 | 135 | |
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135 | 136 | # If this attribute is a tuple or list |
|
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136 | 137 | if type(inputObj.__dict__[key]) in (tuple, list): |
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137 | 138 | self.__dict__[key] = attribute[:] |
|
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138 | 139 | continue |
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139 | 140 | |
|
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140 | 141 | # If this attribute is another object or instance |
|
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141 | 142 | if hasattr(attribute, '__dict__'): |
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142 | 143 | self.__dict__[key] = attribute.copy() |
|
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143 | 144 | continue |
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144 | 145 | |
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145 | 146 | self.__dict__[key] = inputObj.__dict__[key] |
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146 | 147 | |
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147 | 148 | def deepcopy(self): |
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148 | 149 | |
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|
149 | 150 | return copy.deepcopy(self) |
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150 | 151 | |
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|
151 | 152 | def isEmpty(self): |
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152 | 153 | |
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|
153 | 154 | return self.flagNoData |
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154 | 155 | |
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155 | 156 | def isReady(self): |
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156 | 157 | |
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157 | 158 | return not self.flagNoData |
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158 | 159 | |
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159 | 160 | |
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160 | 161 | class JROData(GenericData): |
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161 | 162 | |
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162 | 163 | useInputBuffer = False |
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163 | 164 | buffer_empty = True |
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164 | 165 | |
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165 | 166 | systemHeaderObj = SystemHeader() |
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166 | 167 | radarControllerHeaderObj = RadarControllerHeader() |
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167 | 168 | type = None |
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168 | 169 | datatype = None # dtype but in string |
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169 | 170 | nProfiles = None |
|
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170 | 171 | heightList = None |
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171 | 172 | channelList = None |
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172 | 173 | flagDiscontinuousBlock = False |
|
|
173 | 174 | useLocalTime = False |
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174 | 175 | utctime = None |
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175 | 176 | timeZone = None |
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176 | 177 | dstFlag = None |
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177 | 178 | errorCount = None |
|
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178 | 179 | blocksize = None |
|
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179 | 180 | flagDecodeData = False # asumo q la data no esta decodificada |
|
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180 | 181 | flagDeflipData = False # asumo q la data no esta sin flip |
|
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181 | 182 | flagShiftFFT = False |
|
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182 | 183 | nCohInt = None |
|
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183 | 184 | windowOfFilter = 1 |
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184 | 185 | C = 3e8 |
|
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185 | 186 | frequency = 49.92e6 |
|
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186 | 187 | realtime = False |
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187 | 188 | beacon_heiIndexList = None |
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188 | 189 | last_block = None |
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189 | 190 | blocknow = None |
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190 | 191 | azimuth = None |
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191 | 192 | zenith = None |
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192 | 193 | beam = Beam() |
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193 | 194 | profileIndex = None |
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194 | 195 | error = None |
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195 | 196 | data = None |
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196 | 197 | nmodes = None |
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197 | 198 | metadata_list = ['heightList', 'timeZone', 'type'] |
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198 | 199 | codeList = [] |
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199 | 200 | azimuthList = [] |
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200 | 201 | elevationList = [] |
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201 | 202 | |
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202 | 203 | def __str__(self): |
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203 | 204 | |
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204 | 205 | return '{} - {}'.format(self.type, self.datatime()) |
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205 | 206 | |
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206 | 207 | def getNoise(self): |
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207 | 208 | |
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208 | 209 | raise NotImplementedError |
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209 | 210 | |
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210 | 211 | @property |
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211 | 212 | def nChannels(self): |
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212 | 213 | |
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213 | 214 | return len(self.channelList) |
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214 | 215 | |
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215 | 216 | @property |
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216 | 217 | def channelIndexList(self): |
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217 | 218 | |
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218 | 219 | return list(range(self.nChannels)) |
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219 | 220 | |
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220 | 221 | @property |
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221 | 222 | def nHeights(self): |
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222 | 223 | |
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223 | 224 | return len(self.heightList) |
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224 | 225 | |
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225 | 226 | def getDeltaH(self): |
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226 | 227 | |
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227 | 228 | return self.heightList[1] - self.heightList[0] |
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228 | 229 | |
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229 | 230 | @property |
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230 | 231 | def ltctime(self): |
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231 | 232 | |
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232 | 233 | if self.useLocalTime: |
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233 | 234 | return self.utctime - self.timeZone * 60 |
|
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234 | 235 | |
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235 | 236 | return self.utctime |
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236 | 237 | |
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237 | 238 | @property |
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238 | 239 | def datatime(self): |
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239 | 240 | |
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240 | 241 | datatimeValue = datetime.datetime.utcfromtimestamp(self.ltctime) |
|
|
241 | 242 | return datatimeValue |
|
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242 | 243 | |
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243 | 244 | def getTimeRange(self): |
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244 | 245 | |
|
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245 | 246 | datatime = [] |
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246 | 247 | |
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247 | 248 | datatime.append(self.ltctime) |
|
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248 | 249 | datatime.append(self.ltctime + self.timeInterval + 1) |
|
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249 | 250 | |
|
|
250 | 251 | datatime = numpy.array(datatime) |
|
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251 | 252 | |
|
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252 | 253 | return datatime |
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253 | 254 | |
|
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254 | 255 | def getFmaxTimeResponse(self): |
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255 | 256 | |
|
|
256 | 257 | period = (10**-6) * self.getDeltaH() / (0.15) |
|
|
257 | 258 | |
|
|
258 | 259 | PRF = 1. / (period * self.nCohInt) |
|
|
259 | 260 | |
|
|
260 | 261 | fmax = PRF |
|
|
261 | 262 | |
|
|
262 | 263 | return fmax |
|
|
263 | 264 | |
|
|
264 | 265 | def getFmax(self): |
|
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265 | 266 | PRF = 1. / (self.ippSeconds * self.nCohInt) |
|
|
266 | 267 | |
|
|
267 | 268 | fmax = PRF |
|
|
268 | 269 | return fmax |
|
|
269 | 270 | |
|
|
270 | 271 | def getVmax(self): |
|
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271 | 272 | |
|
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272 | 273 | _lambda = self.C / self.frequency |
|
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273 | 274 | |
|
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274 | 275 | vmax = self.getFmax() * _lambda / 2 |
|
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275 | 276 | |
|
|
276 | 277 | return vmax |
|
|
277 | 278 | |
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278 | 279 | @property |
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279 | 280 | def ippSeconds(self): |
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280 | 281 | ''' |
|
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281 | 282 | ''' |
|
|
282 | 283 | return self.radarControllerHeaderObj.ippSeconds |
|
|
283 | 284 | |
|
|
284 | 285 | @ippSeconds.setter |
|
|
285 | 286 | def ippSeconds(self, ippSeconds): |
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286 | 287 | ''' |
|
|
287 | 288 | ''' |
|
|
288 | 289 | self.radarControllerHeaderObj.ippSeconds = ippSeconds |
|
|
289 | 290 | |
|
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290 | 291 | @property |
|
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291 | 292 | def code(self): |
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292 | 293 | ''' |
|
|
293 | 294 | ''' |
|
|
294 | 295 | return self.radarControllerHeaderObj.code |
|
|
295 | 296 | |
|
|
296 | 297 | @code.setter |
|
|
297 | 298 | def code(self, code): |
|
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298 | 299 | ''' |
|
|
299 | 300 | ''' |
|
|
300 | 301 | self.radarControllerHeaderObj.code = code |
|
|
301 | 302 | |
|
|
302 | 303 | @property |
|
|
303 | 304 | def nCode(self): |
|
|
304 | 305 | ''' |
|
|
305 | 306 | ''' |
|
|
306 | 307 | return self.radarControllerHeaderObj.nCode |
|
|
307 | 308 | |
|
|
308 | 309 | @nCode.setter |
|
|
309 | 310 | def nCode(self, ncode): |
|
|
310 | 311 | ''' |
|
|
311 | 312 | ''' |
|
|
312 | 313 | self.radarControllerHeaderObj.nCode = ncode |
|
|
313 | 314 | |
|
|
314 | 315 | @property |
|
|
315 | 316 | def nBaud(self): |
|
|
316 | 317 | ''' |
|
|
317 | 318 | ''' |
|
|
318 | 319 | return self.radarControllerHeaderObj.nBaud |
|
|
319 | 320 | |
|
|
320 | 321 | @nBaud.setter |
|
|
321 | 322 | def nBaud(self, nbaud): |
|
|
322 | 323 | ''' |
|
|
323 | 324 | ''' |
|
|
324 | 325 | self.radarControllerHeaderObj.nBaud = nbaud |
|
|
325 | 326 | |
|
|
326 | 327 | @property |
|
|
327 | 328 | def ipp(self): |
|
|
328 | 329 | ''' |
|
|
329 | 330 | ''' |
|
|
330 | 331 | return self.radarControllerHeaderObj.ipp |
|
|
331 | 332 | |
|
|
332 | 333 | @ipp.setter |
|
|
333 | 334 | def ipp(self, ipp): |
|
|
334 | 335 | ''' |
|
|
335 | 336 | ''' |
|
|
336 | 337 | self.radarControllerHeaderObj.ipp = ipp |
|
|
337 | 338 | |
|
|
338 | 339 | @property |
|
|
339 | 340 | def metadata(self): |
|
|
340 | 341 | ''' |
|
|
341 | 342 | ''' |
|
|
342 | 343 | |
|
|
343 | 344 | return {attr: getattr(self, attr) for attr in self.metadata_list} |
|
|
344 | 345 | |
|
|
345 | 346 | |
|
|
346 | 347 | class Voltage(JROData): |
|
|
347 | 348 | |
|
|
348 | 349 | dataPP_POW = None |
|
|
349 | 350 | dataPP_DOP = None |
|
|
350 | 351 | dataPP_WIDTH = None |
|
|
351 | 352 | dataPP_SNR = None |
|
|
352 | 353 | |
|
|
353 | 354 | def __init__(self): |
|
|
354 | 355 | ''' |
|
|
355 | 356 | Constructor |
|
|
356 | 357 | ''' |
|
|
357 | 358 | |
|
|
358 | 359 | self.useLocalTime = True |
|
|
359 | 360 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
360 | 361 | self.systemHeaderObj = SystemHeader() |
|
|
361 | 362 | self.type = "Voltage" |
|
|
362 | 363 | self.data = None |
|
|
363 | 364 | self.nProfiles = None |
|
|
364 | 365 | self.heightList = None |
|
|
365 | 366 | self.channelList = None |
|
|
366 | 367 | self.flagNoData = True |
|
|
367 | 368 | self.flagDiscontinuousBlock = False |
|
|
368 | 369 | self.utctime = None |
|
|
369 | 370 | self.timeZone = 0 |
|
|
370 | 371 | self.dstFlag = None |
|
|
371 | 372 | self.errorCount = None |
|
|
372 | 373 | self.nCohInt = None |
|
|
373 | 374 | self.blocksize = None |
|
|
374 | 375 | self.flagCohInt = False |
|
|
375 | 376 | self.flagDecodeData = False # asumo q la data no esta decodificada |
|
|
376 | 377 | self.flagDeflipData = False # asumo q la data no esta sin flip |
|
|
377 | 378 | self.flagShiftFFT = False |
|
|
378 | 379 | self.flagDataAsBlock = False # Asumo que la data es leida perfil a perfil |
|
|
379 | 380 | self.profileIndex = 0 |
|
|
380 | 381 | self.metadata_list = ['type', 'heightList', 'timeZone', 'nProfiles', 'channelList', 'nCohInt', |
|
|
381 | 382 | 'code', 'nCode', 'nBaud', 'ippSeconds', 'ipp'] |
|
|
382 | 383 | |
|
|
383 | def getNoisebyHildebrand(self, channel=None): | |
|
|
384 | def getNoisebyHildebrand(self, channel=None, ymin_index=None, ymax_index=None): | |
|
|
384 | 385 | """ |
|
|
385 | 386 | Determino el nivel de ruido usando el metodo Hildebrand-Sekhon |
|
|
386 | 387 | |
|
|
387 | 388 | Return: |
|
|
388 | 389 | noiselevel |
|
|
389 | 390 | """ |
|
|
390 | 391 | |
|
|
391 | 392 | if channel != None: |
|
|
392 | data = self.data[channel] | |
|
|
393 | data = self.data[channel,ymin_index:ymax_index] | |
|
|
393 | 394 | nChannels = 1 |
|
|
394 | 395 | else: |
|
|
395 | data = self.data | |
|
|
396 | data = self.data[:,ymin_index:ymax_index] | |
|
|
396 | 397 | nChannels = self.nChannels |
|
|
397 | 398 | |
|
|
398 | 399 | noise = numpy.zeros(nChannels) |
|
|
399 | 400 | power = data * numpy.conjugate(data) |
|
|
400 | 401 | |
|
|
401 | 402 | for thisChannel in range(nChannels): |
|
|
402 | 403 | if nChannels == 1: |
|
|
403 | 404 | daux = power[:].real |
|
|
404 | 405 | else: |
|
|
405 | 406 | daux = power[thisChannel, :].real |
|
|
406 | 407 | noise[thisChannel] = hildebrand_sekhon(daux, self.nCohInt) |
|
|
407 | 408 | |
|
|
408 | 409 | return noise |
|
|
409 | 410 | |
|
|
410 | def getNoise(self, type=1, channel=None): | |
|
|
411 | def getNoise(self, type=1, channel=None,ymin_index=None, ymax_index=None): | |
|
|
411 | 412 | |
|
|
412 | 413 | if type == 1: |
|
|
413 | noise = self.getNoisebyHildebrand(channel) | |
|
|
414 | noise = self.getNoisebyHildebrand(channel,ymin_index, ymax_index) | |
|
|
414 | 415 | |
|
|
415 | 416 | return noise |
|
|
416 | 417 | |
|
|
417 | 418 | def getPower(self, channel=None): |
|
|
418 | 419 | |
|
|
419 | 420 | if channel != None: |
|
|
420 | 421 | data = self.data[channel] |
|
|
421 | 422 | else: |
|
|
422 | 423 | data = self.data |
|
|
423 | 424 | |
|
|
424 | 425 | power = data * numpy.conjugate(data) |
|
|
425 | 426 | powerdB = 10 * numpy.log10(power.real) |
|
|
426 | 427 | powerdB = numpy.squeeze(powerdB) |
|
|
427 | 428 | |
|
|
428 | 429 | return powerdB |
|
|
429 | 430 | |
|
|
430 | 431 | @property |
|
|
431 | 432 | def timeInterval(self): |
|
|
432 | 433 | |
|
|
433 | 434 | return self.ippSeconds * self.nCohInt |
|
|
434 | 435 | |
|
|
435 | 436 | noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
436 | 437 | |
|
|
437 | 438 | |
|
|
438 | 439 | class Spectra(JROData): |
|
|
439 | 440 | |
|
|
441 | data_outlier = 0 | |
|
|
442 | ||
|
|
440 | 443 | def __init__(self): |
|
|
441 | 444 | ''' |
|
|
442 | 445 | Constructor |
|
|
443 | 446 | ''' |
|
|
444 | 447 | |
|
|
445 | 448 | self.data_dc = None |
|
|
446 | 449 | self.data_spc = None |
|
|
447 | 450 | self.data_cspc = None |
|
|
448 | 451 | self.useLocalTime = True |
|
|
449 | 452 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
450 | 453 | self.systemHeaderObj = SystemHeader() |
|
|
451 | 454 | self.type = "Spectra" |
|
|
452 | 455 | self.timeZone = 0 |
|
|
453 | 456 | self.nProfiles = None |
|
|
454 | 457 | self.heightList = None |
|
|
455 | 458 | self.channelList = None |
|
|
456 | 459 | self.pairsList = None |
|
|
457 | 460 | self.flagNoData = True |
|
|
458 | 461 | self.flagDiscontinuousBlock = False |
|
|
459 | 462 | self.utctime = None |
|
|
460 | 463 | self.nCohInt = None |
|
|
461 | 464 | self.nIncohInt = None |
|
|
462 | 465 | self.blocksize = None |
|
|
463 | 466 | self.nFFTPoints = None |
|
|
464 | 467 | self.wavelength = None |
|
|
465 | 468 | self.flagDecodeData = False # asumo q la data no esta decodificada |
|
|
466 | 469 | self.flagDeflipData = False # asumo q la data no esta sin flip |
|
|
467 | 470 | self.flagShiftFFT = False |
|
|
468 | 471 | self.ippFactor = 1 |
|
|
469 | 472 | self.beacon_heiIndexList = [] |
|
|
470 | 473 | self.noise_estimation = None |
|
|
471 | 474 | self.codeList = [] |
|
|
472 | 475 | self.azimuthList = [] |
|
|
473 | 476 | self.elevationList = [] |
|
|
474 | 477 | self.metadata_list = ['type', 'heightList', 'timeZone', 'pairsList', 'channelList', 'nCohInt', |
|
|
475 | 478 | 'code', 'nCode', 'nBaud', 'ippSeconds', 'ipp','nIncohInt', 'nFFTPoints', 'nProfiles'] |
|
|
476 | 479 | |
|
|
477 | 480 | |
|
|
481 | self.max_nIncohInt = 1 | |
|
|
482 | ||
|
|
483 | ||
|
|
478 | 484 | def getNoisebyHildebrand(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None): |
|
|
479 | 485 | """ |
|
|
480 | 486 | Determino el nivel de ruido usando el metodo Hildebrand-Sekhon |
|
|
481 | 487 | |
|
|
482 | 488 | Return: |
|
|
483 | 489 | noiselevel |
|
|
484 | 490 | """ |
|
|
485 | ||
|
|
491 | # if hasattr(self.nIncohInt, "__len__"): #nIncohInt is a matrix | |
|
|
492 | # | |
|
|
493 | # heis = self.data_spc.shape[2] | |
|
|
494 | # | |
|
|
495 | # noise = numpy.zeros((self.nChannels, heis)) | |
|
|
496 | # for hei in range(heis): | |
|
|
497 | # for channel in range(self.nChannels): | |
|
|
498 | # daux = self.data_spc[channel, xmin_index:xmax_index, hei] | |
|
|
499 | # | |
|
|
500 | # noise[channel,hei] = hildebrand_sekhon(daux, self.nIncohInt[channel,hei]) | |
|
|
501 | # | |
|
|
502 | # else: | |
|
|
503 | # noise = numpy.zeros(self.nChannels) | |
|
|
504 | # for channel in range(self.nChannels): | |
|
|
505 | # daux = self.data_spc[channel,xmin_index:xmax_index, ymin_index:ymax_index] | |
|
|
506 | # | |
|
|
507 | # noise[channel] = hildebrand_sekhon(daux, self.nIncohInt) | |
|
|
486 | 508 | noise = numpy.zeros(self.nChannels) |
|
|
487 | 509 | for channel in range(self.nChannels): |
|
|
488 | daux = self.data_spc[channel, | |
|
|
489 | xmin_index:xmax_index, ymin_index:ymax_index] | |
|
|
490 | noise[channel] = hildebrand_sekhon(daux, self.nIncohInt) | |
|
|
510 | daux = self.data_spc[channel,xmin_index:xmax_index, ymin_index:ymax_index] | |
|
|
511 | ||
|
|
512 | noise[channel] = hildebrand_sekhon(daux, self.max_nIncohInt) | |
|
|
491 | 513 | |
|
|
492 | 514 | return noise |
|
|
493 | 515 | |
|
|
494 | 516 | def getNoise(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None): |
|
|
495 | 517 | |
|
|
496 | 518 | if self.noise_estimation is not None: |
|
|
497 | 519 | # this was estimated by getNoise Operation defined in jroproc_spectra.py |
|
|
498 | 520 | return self.noise_estimation |
|
|
499 | 521 | else: |
|
|
500 | 522 | noise = self.getNoisebyHildebrand( |
|
|
501 | 523 | xmin_index, xmax_index, ymin_index, ymax_index) |
|
|
502 | 524 | return noise |
|
|
503 | 525 | |
|
|
504 | 526 | def getFreqRangeTimeResponse(self, extrapoints=0): |
|
|
505 | 527 | |
|
|
506 | 528 | deltafreq = self.getFmaxTimeResponse() / (self.nFFTPoints * self.ippFactor) |
|
|
507 | 529 | freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) - self.nFFTPoints / 2.) - deltafreq / 2 |
|
|
508 | 530 | |
|
|
509 | 531 | return freqrange |
|
|
510 | 532 | |
|
|
511 | 533 | def getAcfRange(self, extrapoints=0): |
|
|
512 | 534 | |
|
|
513 | 535 | deltafreq = 10. / (self.getFmax() / (self.nFFTPoints * self.ippFactor)) |
|
|
514 | 536 | freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2 |
|
|
515 | 537 | |
|
|
516 | 538 | return freqrange |
|
|
517 | 539 | |
|
|
518 | 540 | def getFreqRange(self, extrapoints=0): |
|
|
519 | 541 | |
|
|
520 | 542 | deltafreq = self.getFmax() / (self.nFFTPoints * self.ippFactor) |
|
|
521 | 543 | freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2 |
|
|
522 | 544 | |
|
|
523 | 545 | return freqrange |
|
|
524 | 546 | |
|
|
525 | 547 | def getVelRange(self, extrapoints=0): |
|
|
526 | 548 | |
|
|
527 | 549 | deltav = self.getVmax() / (self.nFFTPoints * self.ippFactor) |
|
|
528 | 550 | velrange = deltav * (numpy.arange(self.nFFTPoints + extrapoints) - self.nFFTPoints / 2.) |
|
|
529 | 551 | |
|
|
530 | 552 | if self.nmodes: |
|
|
531 | 553 | return velrange/self.nmodes |
|
|
532 | 554 | else: |
|
|
533 | 555 | return velrange |
|
|
534 | 556 | |
|
|
535 | 557 | @property |
|
|
536 | 558 | def nPairs(self): |
|
|
537 | 559 | |
|
|
538 | 560 | return len(self.pairsList) |
|
|
539 | 561 | |
|
|
540 | 562 | @property |
|
|
541 | 563 | def pairsIndexList(self): |
|
|
542 | 564 | |
|
|
543 | 565 | return list(range(self.nPairs)) |
|
|
544 | 566 | |
|
|
545 | 567 | @property |
|
|
546 | 568 | def normFactor(self): |
|
|
547 | 569 | |
|
|
548 | 570 | pwcode = 1 |
|
|
549 | 571 | |
|
|
550 | 572 | if self.flagDecodeData: |
|
|
551 | 573 | pwcode = numpy.sum(self.code[0]**2) |
|
|
552 | 574 | #normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter |
|
|
553 | 575 | normFactor = self.nProfiles * self.nIncohInt * self.nCohInt * pwcode * self.windowOfFilter |
|
|
554 | 576 | |
|
|
577 | ||
|
|
555 | 578 | return normFactor |
|
|
556 | 579 | |
|
|
557 | 580 | @property |
|
|
558 | 581 | def flag_cspc(self): |
|
|
559 | 582 | |
|
|
560 | 583 | if self.data_cspc is None: |
|
|
561 | 584 | return True |
|
|
562 | 585 | |
|
|
563 | 586 | return False |
|
|
564 | 587 | |
|
|
565 | 588 | @property |
|
|
566 | 589 | def flag_dc(self): |
|
|
567 | 590 | |
|
|
568 | 591 | if self.data_dc is None: |
|
|
569 | 592 | return True |
|
|
570 | 593 | |
|
|
571 | 594 | return False |
|
|
572 | 595 | |
|
|
573 | 596 | @property |
|
|
574 | 597 | def timeInterval(self): |
|
|
575 | 598 | |
|
|
576 | 599 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt * self.nProfiles * self.ippFactor |
|
|
577 | 600 | if self.nmodes: |
|
|
578 | 601 | return self.nmodes*timeInterval |
|
|
579 | 602 | else: |
|
|
580 | 603 | return timeInterval |
|
|
581 | 604 | |
|
|
582 | 605 | def getPower(self): |
|
|
583 | 606 | |
|
|
584 | 607 | factor = self.normFactor |
|
|
585 |
z = self.data_spc |
|
|
|
608 | z = numpy.divide(self.data_spc,factor) | |
|
|
586 | 609 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
587 | 610 | avg = numpy.average(z, axis=1) |
|
|
588 | 611 | |
|
|
589 | 612 | return 10 * numpy.log10(avg) |
|
|
590 | 613 | |
|
|
591 | 614 | def getCoherence(self, pairsList=None, phase=False): |
|
|
592 | 615 | |
|
|
593 | 616 | z = [] |
|
|
594 | 617 | if pairsList is None: |
|
|
595 | 618 | pairsIndexList = self.pairsIndexList |
|
|
596 | 619 | else: |
|
|
597 | 620 | pairsIndexList = [] |
|
|
598 | 621 | for pair in pairsList: |
|
|
599 | 622 | if pair not in self.pairsList: |
|
|
600 | 623 | raise ValueError("Pair %s is not in dataOut.pairsList" % ( |
|
|
601 | 624 | pair)) |
|
|
602 | 625 | pairsIndexList.append(self.pairsList.index(pair)) |
|
|
603 | 626 | for i in range(len(pairsIndexList)): |
|
|
604 | 627 | pair = self.pairsList[pairsIndexList[i]] |
|
|
605 | 628 | ccf = numpy.average(self.data_cspc[pairsIndexList[i], :, :], axis=0) |
|
|
606 | 629 | powa = numpy.average(self.data_spc[pair[0], :, :], axis=0) |
|
|
607 | 630 | powb = numpy.average(self.data_spc[pair[1], :, :], axis=0) |
|
|
608 | 631 | avgcoherenceComplex = ccf / numpy.sqrt(powa * powb) |
|
|
609 | 632 | if phase: |
|
|
610 | 633 | data = numpy.arctan2(avgcoherenceComplex.imag, |
|
|
611 | 634 | avgcoherenceComplex.real) * 180 / numpy.pi |
|
|
612 | 635 | else: |
|
|
613 | 636 | data = numpy.abs(avgcoherenceComplex) |
|
|
614 | 637 | |
|
|
615 | 638 | z.append(data) |
|
|
616 | 639 | |
|
|
617 | 640 | return numpy.array(z) |
|
|
618 | 641 | |
|
|
619 | 642 | def setValue(self, value): |
|
|
620 | 643 | |
|
|
621 | 644 | print("This property should not be initialized") |
|
|
622 | 645 | |
|
|
623 | 646 | return |
|
|
624 | 647 | |
|
|
625 | 648 | noise = property(getNoise, setValue, "I'm the 'nHeights' property.") |
|
|
626 | 649 | |
|
|
627 | 650 | |
|
|
628 | 651 | class SpectraHeis(Spectra): |
|
|
629 | 652 | |
|
|
630 | 653 | def __init__(self): |
|
|
631 | 654 | |
|
|
632 | 655 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
633 | 656 | self.systemHeaderObj = SystemHeader() |
|
|
634 | 657 | self.type = "SpectraHeis" |
|
|
635 | 658 | self.nProfiles = None |
|
|
636 | 659 | self.heightList = None |
|
|
637 | 660 | self.channelList = None |
|
|
638 | 661 | self.flagNoData = True |
|
|
639 | 662 | self.flagDiscontinuousBlock = False |
|
|
640 | 663 | self.utctime = None |
|
|
641 | 664 | self.blocksize = None |
|
|
642 | 665 | self.profileIndex = 0 |
|
|
643 | 666 | self.nCohInt = 1 |
|
|
644 | 667 | self.nIncohInt = 1 |
|
|
645 | 668 | |
|
|
646 | 669 | @property |
|
|
647 | 670 | def normFactor(self): |
|
|
648 | 671 | pwcode = 1 |
|
|
649 | 672 | if self.flagDecodeData: |
|
|
650 | 673 | pwcode = numpy.sum(self.code[0]**2) |
|
|
651 | 674 | |
|
|
652 | 675 | normFactor = self.nIncohInt * self.nCohInt * pwcode |
|
|
653 | 676 | |
|
|
654 | 677 | return normFactor |
|
|
655 | 678 | |
|
|
656 | 679 | @property |
|
|
657 | 680 | def timeInterval(self): |
|
|
658 | 681 | |
|
|
659 | 682 | return self.ippSeconds * self.nCohInt * self.nIncohInt |
|
|
660 | 683 | |
|
|
661 | 684 | |
|
|
662 | 685 | class Fits(JROData): |
|
|
663 | 686 | |
|
|
664 | 687 | def __init__(self): |
|
|
665 | 688 | |
|
|
666 | 689 | self.type = "Fits" |
|
|
667 | 690 | self.nProfiles = None |
|
|
668 | 691 | self.heightList = None |
|
|
669 | 692 | self.channelList = None |
|
|
670 | 693 | self.flagNoData = True |
|
|
671 | 694 | self.utctime = None |
|
|
672 | 695 | self.nCohInt = 1 |
|
|
673 | 696 | self.nIncohInt = 1 |
|
|
674 | 697 | self.useLocalTime = True |
|
|
675 | 698 | self.profileIndex = 0 |
|
|
676 | 699 | self.timeZone = 0 |
|
|
677 | 700 | |
|
|
678 | 701 | def getTimeRange(self): |
|
|
679 | 702 | |
|
|
680 | 703 | datatime = [] |
|
|
681 | 704 | |
|
|
682 | 705 | datatime.append(self.ltctime) |
|
|
683 | 706 | datatime.append(self.ltctime + self.timeInterval) |
|
|
684 | 707 | |
|
|
685 | 708 | datatime = numpy.array(datatime) |
|
|
686 | 709 | |
|
|
687 | 710 | return datatime |
|
|
688 | 711 | |
|
|
689 | 712 | def getChannelIndexList(self): |
|
|
690 | 713 | |
|
|
691 | 714 | return list(range(self.nChannels)) |
|
|
692 | 715 | |
|
|
693 | 716 | def getNoise(self, type=1): |
|
|
694 | 717 | |
|
|
695 | 718 | |
|
|
696 | 719 | if type == 1: |
|
|
697 | 720 | noise = self.getNoisebyHildebrand() |
|
|
698 | 721 | |
|
|
699 | 722 | if type == 2: |
|
|
700 | 723 | noise = self.getNoisebySort() |
|
|
701 | 724 | |
|
|
702 | 725 | if type == 3: |
|
|
703 | 726 | noise = self.getNoisebyWindow() |
|
|
704 | 727 | |
|
|
705 | 728 | return noise |
|
|
706 | 729 | |
|
|
707 | 730 | @property |
|
|
708 | 731 | def timeInterval(self): |
|
|
709 | 732 | |
|
|
710 | 733 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt |
|
|
711 | 734 | |
|
|
712 | 735 | return timeInterval |
|
|
713 | 736 | |
|
|
714 | 737 | @property |
|
|
715 | 738 | def ippSeconds(self): |
|
|
716 | 739 | ''' |
|
|
717 | 740 | ''' |
|
|
718 | 741 | return self.ipp_sec |
|
|
719 | 742 | |
|
|
720 | 743 | noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
721 | 744 | |
|
|
722 | 745 | |
|
|
723 | 746 | class Correlation(JROData): |
|
|
724 | 747 | |
|
|
725 | 748 | def __init__(self): |
|
|
726 | 749 | ''' |
|
|
727 | 750 | Constructor |
|
|
728 | 751 | ''' |
|
|
729 | 752 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
730 | 753 | self.systemHeaderObj = SystemHeader() |
|
|
731 | 754 | self.type = "Correlation" |
|
|
732 | 755 | self.data = None |
|
|
733 | 756 | self.dtype = None |
|
|
734 | 757 | self.nProfiles = None |
|
|
735 | 758 | self.heightList = None |
|
|
736 | 759 | self.channelList = None |
|
|
737 | 760 | self.flagNoData = True |
|
|
738 | 761 | self.flagDiscontinuousBlock = False |
|
|
739 | 762 | self.utctime = None |
|
|
740 | 763 | self.timeZone = 0 |
|
|
741 | 764 | self.dstFlag = None |
|
|
742 | 765 | self.errorCount = None |
|
|
743 | 766 | self.blocksize = None |
|
|
744 | 767 | self.flagDecodeData = False # asumo q la data no esta decodificada |
|
|
745 | 768 | self.flagDeflipData = False # asumo q la data no esta sin flip |
|
|
746 | 769 | self.pairsList = None |
|
|
747 | 770 | self.nPoints = None |
|
|
748 | 771 | |
|
|
749 | 772 | def getPairsList(self): |
|
|
750 | 773 | |
|
|
751 | 774 | return self.pairsList |
|
|
752 | 775 | |
|
|
753 | 776 | def getNoise(self, mode=2): |
|
|
754 | 777 | |
|
|
755 | 778 | indR = numpy.where(self.lagR == 0)[0][0] |
|
|
756 | 779 | indT = numpy.where(self.lagT == 0)[0][0] |
|
|
757 | 780 | |
|
|
758 | 781 | jspectra0 = self.data_corr[:, :, indR, :] |
|
|
759 | 782 | jspectra = copy.copy(jspectra0) |
|
|
760 | 783 | |
|
|
761 | 784 | num_chan = jspectra.shape[0] |
|
|
762 | 785 | num_hei = jspectra.shape[2] |
|
|
763 | 786 | |
|
|
764 | 787 | freq_dc = jspectra.shape[1] / 2 |
|
|
765 | 788 | ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc |
|
|
766 | 789 | |
|
|
767 | 790 | if ind_vel[0] < 0: |
|
|
768 | 791 | ind_vel[list(range(0, 1))] = ind_vel[list( |
|
|
769 | 792 | range(0, 1))] + self.num_prof |
|
|
770 | 793 | |
|
|
771 | 794 | if mode == 1: |
|
|
772 | 795 | jspectra[:, freq_dc, :] = ( |
|
|
773 | 796 | jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2 # CORRECCION |
|
|
774 | 797 | |
|
|
775 | 798 | if mode == 2: |
|
|
776 | 799 | |
|
|
777 | 800 | vel = numpy.array([-2, -1, 1, 2]) |
|
|
778 | 801 | xx = numpy.zeros([4, 4]) |
|
|
779 | 802 | |
|
|
780 | 803 | for fil in range(4): |
|
|
781 | 804 | xx[fil, :] = vel[fil]**numpy.asarray(list(range(4))) |
|
|
782 | 805 | |
|
|
783 | 806 | xx_inv = numpy.linalg.inv(xx) |
|
|
784 | 807 | xx_aux = xx_inv[0, :] |
|
|
785 | 808 | |
|
|
786 | 809 | for ich in range(num_chan): |
|
|
787 | 810 | yy = jspectra[ich, ind_vel, :] |
|
|
788 | 811 | jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy) |
|
|
789 | 812 | |
|
|
790 | 813 | junkid = jspectra[ich, freq_dc, :] <= 0 |
|
|
791 | 814 | cjunkid = sum(junkid) |
|
|
792 | 815 | |
|
|
793 | 816 | if cjunkid.any(): |
|
|
794 | 817 | jspectra[ich, freq_dc, junkid.nonzero()] = ( |
|
|
795 | 818 | jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2 |
|
|
796 | 819 | |
|
|
797 | 820 | noise = jspectra0[:, freq_dc, :] - jspectra[:, freq_dc, :] |
|
|
798 | 821 | |
|
|
799 | 822 | return noise |
|
|
800 | 823 | |
|
|
801 | 824 | @property |
|
|
802 | 825 | def timeInterval(self): |
|
|
803 | 826 | |
|
|
804 | 827 | return self.ippSeconds * self.nCohInt * self.nProfiles |
|
|
805 | 828 | |
|
|
806 | 829 | def splitFunctions(self): |
|
|
807 | 830 | |
|
|
808 | 831 | pairsList = self.pairsList |
|
|
809 | 832 | ccf_pairs = [] |
|
|
810 | 833 | acf_pairs = [] |
|
|
811 | 834 | ccf_ind = [] |
|
|
812 | 835 | acf_ind = [] |
|
|
813 | 836 | for l in range(len(pairsList)): |
|
|
814 | 837 | chan0 = pairsList[l][0] |
|
|
815 | 838 | chan1 = pairsList[l][1] |
|
|
816 | 839 | |
|
|
817 | 840 | # Obteniendo pares de Autocorrelacion |
|
|
818 | 841 | if chan0 == chan1: |
|
|
819 | 842 | acf_pairs.append(chan0) |
|
|
820 | 843 | acf_ind.append(l) |
|
|
821 | 844 | else: |
|
|
822 | 845 | ccf_pairs.append(pairsList[l]) |
|
|
823 | 846 | ccf_ind.append(l) |
|
|
824 | 847 | |
|
|
825 | 848 | data_acf = self.data_cf[acf_ind] |
|
|
826 | 849 | data_ccf = self.data_cf[ccf_ind] |
|
|
827 | 850 | |
|
|
828 | 851 | return acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf |
|
|
829 | 852 | |
|
|
830 | 853 | @property |
|
|
831 | 854 | def normFactor(self): |
|
|
832 | 855 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.splitFunctions() |
|
|
833 | 856 | acf_pairs = numpy.array(acf_pairs) |
|
|
834 | 857 | normFactor = numpy.zeros((self.nPairs, self.nHeights)) |
|
|
835 | 858 | |
|
|
836 | 859 | for p in range(self.nPairs): |
|
|
837 | 860 | pair = self.pairsList[p] |
|
|
838 | 861 | |
|
|
839 | 862 | ch0 = pair[0] |
|
|
840 | 863 | ch1 = pair[1] |
|
|
841 | 864 | |
|
|
842 | 865 | ch0_max = numpy.max(data_acf[acf_pairs == ch0, :, :], axis=1) |
|
|
843 | 866 | ch1_max = numpy.max(data_acf[acf_pairs == ch1, :, :], axis=1) |
|
|
844 | 867 | normFactor[p, :] = numpy.sqrt(ch0_max * ch1_max) |
|
|
845 | 868 | |
|
|
846 | 869 | return normFactor |
|
|
847 | 870 | |
|
|
848 | 871 | |
|
|
849 | 872 | class Parameters(Spectra): |
|
|
850 | 873 | |
|
|
851 | 874 | groupList = None # List of Pairs, Groups, etc |
|
|
852 | 875 | data_param = None # Parameters obtained |
|
|
853 | 876 | data_pre = None # Data Pre Parametrization |
|
|
854 | 877 | data_SNR = None # Signal to Noise Ratio |
|
|
855 | 878 | abscissaList = None # Abscissa, can be velocities, lags or time |
|
|
856 | 879 | utctimeInit = None # Initial UTC time |
|
|
857 | 880 | paramInterval = None # Time interval to calculate Parameters in seconds |
|
|
858 | 881 | useLocalTime = True |
|
|
859 | 882 | # Fitting |
|
|
860 | 883 | data_error = None # Error of the estimation |
|
|
861 | 884 | constants = None |
|
|
862 | 885 | library = None |
|
|
863 | 886 | # Output signal |
|
|
864 | 887 | outputInterval = None # Time interval to calculate output signal in seconds |
|
|
865 | 888 | data_output = None # Out signal |
|
|
866 | 889 | nAvg = None |
|
|
867 | 890 | noise_estimation = None |
|
|
868 | 891 | GauSPC = None # Fit gaussian SPC |
|
|
869 | 892 | |
|
|
870 | 893 | def __init__(self): |
|
|
871 | 894 | ''' |
|
|
872 | 895 | Constructor |
|
|
873 | 896 | ''' |
|
|
874 | 897 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
875 | 898 | self.systemHeaderObj = SystemHeader() |
|
|
876 | 899 | self.type = "Parameters" |
|
|
877 | 900 | self.timeZone = 0 |
|
|
878 | 901 | |
|
|
879 | 902 | def getTimeRange1(self, interval): |
|
|
880 | 903 | |
|
|
881 | 904 | datatime = [] |
|
|
882 | 905 | |
|
|
883 | 906 | if self.useLocalTime: |
|
|
884 | 907 | time1 = self.utctimeInit - self.timeZone * 60 |
|
|
885 | 908 | else: |
|
|
886 | 909 | time1 = self.utctimeInit |
|
|
887 | 910 | |
|
|
888 | 911 | datatime.append(time1) |
|
|
889 | 912 | datatime.append(time1 + interval) |
|
|
890 | 913 | datatime = numpy.array(datatime) |
|
|
891 | 914 | |
|
|
892 | 915 | return datatime |
|
|
893 | 916 | |
|
|
894 | 917 | @property |
|
|
895 | 918 | def timeInterval(self): |
|
|
896 | 919 | |
|
|
897 | 920 | if hasattr(self, 'timeInterval1'): |
|
|
898 | 921 | return self.timeInterval1 |
|
|
899 | 922 | else: |
|
|
900 | 923 | return self.paramInterval |
|
|
901 | 924 | |
|
|
902 | 925 | def setValue(self, value): |
|
|
903 | 926 | |
|
|
904 | 927 | print("This property should not be initialized") |
|
|
905 | 928 | |
|
|
906 | 929 | return |
|
|
907 | 930 | |
|
|
908 | 931 | def getNoise(self): |
|
|
909 | 932 | |
|
|
910 | 933 | return self.spc_noise |
|
|
911 | 934 | |
|
|
912 | 935 | noise = property(getNoise, setValue, "I'm the 'Noise' property.") |
|
|
913 | 936 | |
|
|
914 | 937 | |
|
|
915 | 938 | class PlotterData(object): |
|
|
916 | 939 | ''' |
|
|
917 | 940 | Object to hold data to be plotted |
|
|
918 | 941 | ''' |
|
|
919 | 942 | |
|
|
920 | 943 | MAXNUMX = 200 |
|
|
921 | 944 | MAXNUMY = 200 |
|
|
922 | 945 | |
|
|
923 | 946 | def __init__(self, code, exp_code, localtime=True): |
|
|
924 | 947 | |
|
|
925 | 948 | self.key = code |
|
|
926 | 949 | self.exp_code = exp_code |
|
|
927 | 950 | self.ready = False |
|
|
928 | 951 | self.flagNoData = False |
|
|
929 | 952 | self.localtime = localtime |
|
|
930 | 953 | self.data = {} |
|
|
931 | 954 | self.meta = {} |
|
|
932 | 955 | self.__heights = [] |
|
|
933 | 956 | |
|
|
934 | 957 | def __str__(self): |
|
|
935 | 958 | dum = ['{}{}'.format(key, self.shape(key)) for key in self.data] |
|
|
936 | 959 | return 'Data[{}][{}]'.format(';'.join(dum), len(self.times)) |
|
|
937 | 960 | |
|
|
938 | 961 | def __len__(self): |
|
|
939 | 962 | return len(self.data) |
|
|
940 | 963 | |
|
|
941 | 964 | def __getitem__(self, key): |
|
|
942 | 965 | if isinstance(key, int): |
|
|
943 | 966 | return self.data[self.times[key]] |
|
|
944 | 967 | elif isinstance(key, str): |
|
|
945 | 968 | ret = numpy.array([self.data[x][key] for x in self.times]) |
|
|
946 | 969 | if ret.ndim > 1: |
|
|
947 | 970 | ret = numpy.swapaxes(ret, 0, 1) |
|
|
948 | 971 | return ret |
|
|
949 | 972 | |
|
|
950 | 973 | def __contains__(self, key): |
|
|
951 | 974 | return key in self.data[self.min_time] |
|
|
952 | 975 | |
|
|
953 | 976 | def setup(self): |
|
|
954 | 977 | ''' |
|
|
955 | 978 | Configure object |
|
|
956 | 979 | ''' |
|
|
957 | 980 | self.type = '' |
|
|
958 | 981 | self.ready = False |
|
|
959 | 982 | del self.data |
|
|
960 | 983 | self.data = {} |
|
|
961 | 984 | self.__heights = [] |
|
|
962 | 985 | self.__all_heights = set() |
|
|
963 | 986 | |
|
|
964 | 987 | def shape(self, key): |
|
|
965 | 988 | ''' |
|
|
966 | 989 | Get the shape of the one-element data for the given key |
|
|
967 | 990 | ''' |
|
|
968 | 991 | |
|
|
969 | 992 | if len(self.data[self.min_time][key]): |
|
|
970 | 993 | return self.data[self.min_time][key].shape |
|
|
971 | 994 | return (0,) |
|
|
972 | 995 | |
|
|
973 | 996 | def update(self, data, tm, meta={}): |
|
|
974 | 997 | ''' |
|
|
975 | 998 | Update data object with new dataOut |
|
|
976 | 999 | ''' |
|
|
977 | 1000 | |
|
|
978 | 1001 | self.data[tm] = data |
|
|
979 | 1002 | |
|
|
980 | 1003 | for key, value in meta.items(): |
|
|
981 | 1004 | setattr(self, key, value) |
|
|
982 | 1005 | |
|
|
983 | 1006 | def normalize_heights(self): |
|
|
984 | 1007 | ''' |
|
|
985 | 1008 | Ensure same-dimension of the data for different heighList |
|
|
986 | 1009 | ''' |
|
|
987 | 1010 | |
|
|
988 | 1011 | H = numpy.array(list(self.__all_heights)) |
|
|
989 | 1012 | H.sort() |
|
|
990 | 1013 | for key in self.data: |
|
|
991 | 1014 | shape = self.shape(key)[:-1] + H.shape |
|
|
992 | 1015 | for tm, obj in list(self.data[key].items()): |
|
|
993 | 1016 | h = self.__heights[self.times.tolist().index(tm)] |
|
|
994 | 1017 | if H.size == h.size: |
|
|
995 | 1018 | continue |
|
|
996 | 1019 | index = numpy.where(numpy.in1d(H, h))[0] |
|
|
997 | 1020 | dummy = numpy.zeros(shape) + numpy.nan |
|
|
998 | 1021 | if len(shape) == 2: |
|
|
999 | 1022 | dummy[:, index] = obj |
|
|
1000 | 1023 | else: |
|
|
1001 | 1024 | dummy[index] = obj |
|
|
1002 | 1025 | self.data[key][tm] = dummy |
|
|
1003 | 1026 | |
|
|
1004 | 1027 | self.__heights = [H for tm in self.times] |
|
|
1005 | 1028 | |
|
|
1006 | 1029 | def jsonify(self, tm, plot_name, plot_type, decimate=False): |
|
|
1007 | 1030 | ''' |
|
|
1008 | 1031 | Convert data to json |
|
|
1009 | 1032 | ''' |
|
|
1010 | 1033 | |
|
|
1011 | 1034 | meta = {} |
|
|
1012 | 1035 | meta['xrange'] = [] |
|
|
1013 | 1036 | dy = int(len(self.yrange)/self.MAXNUMY) + 1 |
|
|
1014 | 1037 | tmp = self.data[tm][self.key] |
|
|
1015 | 1038 | shape = tmp.shape |
|
|
1016 | 1039 | if len(shape) == 2: |
|
|
1017 | 1040 | data = self.roundFloats(self.data[tm][self.key][::, ::dy].tolist()) |
|
|
1018 | 1041 | elif len(shape) == 3: |
|
|
1019 | 1042 | dx = int(self.data[tm][self.key].shape[1]/self.MAXNUMX) + 1 |
|
|
1020 | 1043 | data = self.roundFloats( |
|
|
1021 | 1044 | self.data[tm][self.key][::, ::dx, ::dy].tolist()) |
|
|
1022 | 1045 | meta['xrange'] = self.roundFloats(self.xrange[2][::dx].tolist()) |
|
|
1023 | 1046 | else: |
|
|
1024 | 1047 | data = self.roundFloats(self.data[tm][self.key].tolist()) |
|
|
1025 | 1048 | |
|
|
1026 | 1049 | ret = { |
|
|
1027 | 1050 | 'plot': plot_name, |
|
|
1028 | 1051 | 'code': self.exp_code, |
|
|
1029 | 1052 | 'time': float(tm), |
|
|
1030 | 1053 | 'data': data, |
|
|
1031 | 1054 | } |
|
|
1032 | 1055 | meta['type'] = plot_type |
|
|
1033 | 1056 | meta['interval'] = float(self.interval) |
|
|
1034 | 1057 | meta['localtime'] = self.localtime |
|
|
1035 | 1058 | meta['yrange'] = self.roundFloats(self.yrange[::dy].tolist()) |
|
|
1036 | 1059 | meta.update(self.meta) |
|
|
1037 | 1060 | ret['metadata'] = meta |
|
|
1038 | 1061 | return json.dumps(ret) |
|
|
1039 | 1062 | |
|
|
1040 | 1063 | @property |
|
|
1041 | 1064 | def times(self): |
|
|
1042 | 1065 | ''' |
|
|
1043 | 1066 | Return the list of times of the current data |
|
|
1044 | 1067 | ''' |
|
|
1045 | 1068 | |
|
|
1046 | 1069 | ret = [t for t in self.data] |
|
|
1047 | 1070 | ret.sort() |
|
|
1048 | 1071 | return numpy.array(ret) |
|
|
1049 | 1072 | |
|
|
1050 | 1073 | @property |
|
|
1051 | 1074 | def min_time(self): |
|
|
1052 | 1075 | ''' |
|
|
1053 | 1076 | Return the minimun time value |
|
|
1054 | 1077 | ''' |
|
|
1055 | 1078 | |
|
|
1056 | 1079 | return self.times[0] |
|
|
1057 | 1080 | |
|
|
1058 | 1081 | @property |
|
|
1059 | 1082 | def max_time(self): |
|
|
1060 | 1083 | ''' |
|
|
1061 | 1084 | Return the maximun time value |
|
|
1062 | 1085 | ''' |
|
|
1063 | 1086 | |
|
|
1064 | 1087 | return self.times[-1] |
|
|
1065 | 1088 | |
|
|
1066 | 1089 | # @property |
|
|
1067 | 1090 | # def heights(self): |
|
|
1068 | 1091 | # ''' |
|
|
1069 | 1092 | # Return the list of heights of the current data |
|
|
1070 | 1093 | # ''' |
|
|
1071 | 1094 | |
|
|
1072 | 1095 | # return numpy.array(self.__heights[-1]) |
|
|
1073 | 1096 | |
|
|
1074 | 1097 | @staticmethod |
|
|
1075 | 1098 | def roundFloats(obj): |
|
|
1076 | 1099 | if isinstance(obj, list): |
|
|
1077 | 1100 | return list(map(PlotterData.roundFloats, obj)) |
|
|
1078 | 1101 | elif isinstance(obj, float): |
|
|
1079 | 1102 | return round(obj, 2) |
| @@ -1,1016 +1,1180 | |||
|
|
1 | 1 | # Copyright (c) 2012-2020 Jicamarca Radio Observatory |
|
|
2 | 2 | # All rights reserved. |
|
|
3 | 3 | # |
|
|
4 | 4 | # Distributed under the terms of the BSD 3-clause license. |
|
|
5 | 5 | """Classes to plot Spectra data |
|
|
6 | 6 | |
|
|
7 | 7 | """ |
|
|
8 | 8 | |
|
|
9 | 9 | import os |
|
|
10 | 10 | import numpy |
|
|
11 | 11 | |
|
|
12 | 12 | from schainpy.model.graphics.jroplot_base import Plot, plt, log |
|
|
13 | 13 | from itertools import combinations |
|
|
14 | 14 | from matplotlib.ticker import LinearLocator |
|
|
15 | 15 | |
|
|
16 | 16 | class SpectraPlot(Plot): |
|
|
17 | 17 | ''' |
|
|
18 | 18 | Plot for Spectra data |
|
|
19 | 19 | ''' |
|
|
20 | 20 | |
|
|
21 | 21 | CODE = 'spc' |
|
|
22 | 22 | colormap = 'jet' |
|
|
23 | 23 | plot_type = 'pcolor' |
|
|
24 | 24 | buffering = False |
|
|
25 | 25 | channelList = [] |
|
|
26 | 26 | elevationList = [] |
|
|
27 | 27 | azimuthList = [] |
|
|
28 | 28 | |
|
|
29 | 29 | def setup(self): |
|
|
30 | 30 | |
|
|
31 | 31 | self.nplots = len(self.data.channels) |
|
|
32 | 32 | self.ncols = int(numpy.sqrt(self.nplots) + 0.9) |
|
|
33 | 33 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
|
|
34 | 34 | self.height = 3.4 * self.nrows |
|
|
35 | 35 | |
|
|
36 | 36 | self.cb_label = 'dB' |
|
|
37 | 37 | if self.showprofile: |
|
|
38 | 38 | self.width = 5.2 * self.ncols |
|
|
39 | 39 | else: |
|
|
40 | 40 | self.width = 4.2* self.ncols |
|
|
41 | 41 | self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.12}) |
|
|
42 | 42 | self.ylabel = 'Range [km]' |
|
|
43 | 43 | |
|
|
44 | 44 | |
|
|
45 | 45 | def update_list(self,dataOut): |
|
|
46 | 46 | if len(self.channelList) == 0: |
|
|
47 | 47 | self.channelList = dataOut.channelList |
|
|
48 | 48 | if len(self.elevationList) == 0: |
|
|
49 | 49 | self.elevationList = dataOut.elevationList |
|
|
50 | 50 | if len(self.azimuthList) == 0: |
|
|
51 | 51 | self.azimuthList = dataOut.azimuthList |
|
|
52 | 52 | |
|
|
53 | 53 | def update(self, dataOut): |
|
|
54 | 54 | |
|
|
55 | 55 | self.update_list(dataOut) |
|
|
56 | 56 | data = {} |
|
|
57 | 57 | meta = {} |
|
|
58 | 58 | spc = 10*numpy.log10(dataOut.data_spc/dataOut.normFactor) |
|
|
59 | 59 | data['spc'] = spc |
|
|
60 | 60 | data['rti'] = dataOut.getPower() |
|
|
61 | data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) | |
|
|
61 | #data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) | |
|
|
62 | 62 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
|
|
63 | 63 | if self.CODE == 'spc_moments': |
|
|
64 | 64 | data['moments'] = dataOut.moments |
|
|
65 | 65 | |
|
|
66 | 66 | return data, meta |
|
|
67 | 67 | |
|
|
68 | 68 | def plot(self): |
|
|
69 | 69 | if self.xaxis == "frequency": |
|
|
70 | 70 | x = self.data.xrange[0] |
|
|
71 | 71 | self.xlabel = "Frequency (kHz)" |
|
|
72 | 72 | elif self.xaxis == "time": |
|
|
73 | 73 | x = self.data.xrange[1] |
|
|
74 | 74 | self.xlabel = "Time (ms)" |
|
|
75 | 75 | else: |
|
|
76 | 76 | x = self.data.xrange[2] |
|
|
77 | 77 | self.xlabel = "Velocity (m/s)" |
|
|
78 | 78 | |
|
|
79 | 79 | if self.CODE == 'spc_moments': |
|
|
80 | 80 | x = self.data.xrange[2] |
|
|
81 | 81 | self.xlabel = "Velocity (m/s)" |
|
|
82 | 82 | |
|
|
83 | 83 | self.titles = [] |
|
|
84 | 84 | y = self.data.yrange |
|
|
85 | 85 | self.y = y |
|
|
86 | 86 | |
|
|
87 | 87 | data = self.data[-1] |
|
|
88 | 88 | z = data['spc'] |
|
|
89 | print(z.shape, x.shape, y.shape) | |
|
|
89 | #print(z.shape, x.shape, y.shape) | |
|
|
90 | 90 | for n, ax in enumerate(self.axes): |
|
|
91 | noise = data['noise'][n] | |
|
|
91 | #noise = data['noise'][n] | |
|
|
92 | noise=62 | |
|
|
92 | 93 | if self.CODE == 'spc_moments': |
|
|
93 | 94 | mean = data['moments'][n, 1] |
|
|
94 | 95 | if ax.firsttime: |
|
|
95 | 96 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
96 | 97 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
97 | 98 | self.zmin = self.zmin if self.zmin else numpy.nanmin(z) |
|
|
98 | 99 | self.zmax = self.zmax if self.zmax else numpy.nanmax(z) |
|
|
99 | 100 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
100 | 101 | vmin=self.zmin, |
|
|
101 | 102 | vmax=self.zmax, |
|
|
102 | 103 | cmap=plt.get_cmap(self.colormap) |
|
|
103 | 104 | ) |
|
|
104 | 105 | |
|
|
105 | 106 | if self.showprofile: |
|
|
106 | 107 | ax.plt_profile = self.pf_axes[n].plot( |
|
|
107 | 108 | data['rti'][n], y)[0] |
|
|
108 | ax.plt_noise = self.pf_axes[n].plot(numpy.repeat(noise, len(y)), y, | |
|
|
109 | color="k", linestyle="dashed", lw=1)[0] | |
|
|
109 | # ax.plt_noise = self.pf_axes[n].plot(numpy.repeat(noise, len(y)), y, | |
|
|
110 | # color="k", linestyle="dashed", lw=1)[0] | |
|
|
110 | 111 | if self.CODE == 'spc_moments': |
|
|
111 | 112 | ax.plt_mean = ax.plot(mean, y, color='k')[0] |
|
|
112 | 113 | else: |
|
|
113 | 114 | ax.plt.set_array(z[n].T.ravel()) |
|
|
114 | 115 | if self.showprofile: |
|
|
115 | 116 | ax.plt_profile.set_data(data['rti'][n], y) |
|
|
116 | ax.plt_noise.set_data(numpy.repeat(noise, len(y)), y) | |
|
|
117 | #ax.plt_noise.set_data(numpy.repeat(noise, len(y)), y) | |
|
|
117 | 118 | if self.CODE == 'spc_moments': |
|
|
118 | 119 | ax.plt_mean.set_data(mean, y) |
|
|
119 | 120 | if len(self.azimuthList) > 0 and len(self.elevationList) > 0: |
|
|
120 | 121 | self.titles.append('CH {}: {:2.1f}elv {:2.1f}az {:3.2f}dB'.format(self.channelList[n], noise, self.elevationList[n], self.azimuthList[n])) |
|
|
121 | 122 | else: |
|
|
122 | 123 | self.titles.append('CH {}: {:3.2f}dB'.format(self.channelList[n], noise)) |
|
|
123 | 124 | |
|
|
124 | 125 | |
|
|
125 | 126 | class CrossSpectraPlot(Plot): |
|
|
126 | 127 | |
|
|
127 | 128 | CODE = 'cspc' |
|
|
128 | 129 | colormap = 'jet' |
|
|
129 | 130 | plot_type = 'pcolor' |
|
|
130 | 131 | zmin_coh = None |
|
|
131 | 132 | zmax_coh = None |
|
|
132 | 133 | zmin_phase = None |
|
|
133 | 134 | zmax_phase = None |
|
|
134 | 135 | realChannels = None |
|
|
135 | 136 | crossPairs = None |
|
|
136 | 137 | |
|
|
137 | 138 | def setup(self): |
|
|
138 | 139 | |
|
|
139 | 140 | self.ncols = 4 |
|
|
140 | 141 | self.nplots = len(self.data.pairs) * 2 |
|
|
141 | 142 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
|
|
142 | 143 | self.width = 3.1 * self.ncols |
|
|
143 | 144 | self.height = 2.6 * self.nrows |
|
|
144 | 145 | self.ylabel = 'Range [km]' |
|
|
145 | 146 | self.showprofile = False |
|
|
146 | 147 | self.plots_adjust.update({'left': 0.08, 'right': 0.92, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08}) |
|
|
147 | 148 | |
|
|
148 | 149 | def update(self, dataOut): |
|
|
149 | 150 | |
|
|
150 | 151 | data = {} |
|
|
151 | 152 | meta = {} |
|
|
152 | 153 | |
|
|
153 | 154 | spc = dataOut.data_spc |
|
|
154 | 155 | cspc = dataOut.data_cspc |
|
|
155 | 156 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
|
|
156 | 157 | rawPairs = list(combinations(list(range(dataOut.nChannels)), 2)) |
|
|
157 | 158 | meta['pairs'] = rawPairs |
|
|
158 | 159 | |
|
|
159 | 160 | if self.crossPairs == None: |
|
|
160 | 161 | self.crossPairs = dataOut.pairsList |
|
|
161 | 162 | |
|
|
162 | 163 | tmp = [] |
|
|
163 | 164 | |
|
|
164 | 165 | for n, pair in enumerate(meta['pairs']): |
|
|
165 | 166 | |
|
|
166 | 167 | out = cspc[n] / numpy.sqrt(spc[pair[0]] * spc[pair[1]]) |
|
|
167 | 168 | coh = numpy.abs(out) |
|
|
168 | 169 | phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi |
|
|
169 | 170 | tmp.append(coh) |
|
|
170 | 171 | tmp.append(phase) |
|
|
171 | 172 | |
|
|
172 | 173 | data['cspc'] = numpy.array(tmp) |
|
|
173 | 174 | |
|
|
174 | 175 | return data, meta |
|
|
175 | 176 | |
|
|
176 | 177 | def plot(self): |
|
|
177 | 178 | |
|
|
178 | 179 | if self.xaxis == "frequency": |
|
|
179 | 180 | x = self.data.xrange[0] |
|
|
180 | 181 | self.xlabel = "Frequency (kHz)" |
|
|
181 | 182 | elif self.xaxis == "time": |
|
|
182 | 183 | x = self.data.xrange[1] |
|
|
183 | 184 | self.xlabel = "Time (ms)" |
|
|
184 | 185 | else: |
|
|
185 | 186 | x = self.data.xrange[2] |
|
|
186 | 187 | self.xlabel = "Velocity (m/s)" |
|
|
187 | 188 | |
|
|
188 | 189 | self.titles = [] |
|
|
189 | 190 | |
|
|
190 | 191 | y = self.data.yrange |
|
|
191 | 192 | self.y = y |
|
|
192 | 193 | |
|
|
193 | 194 | data = self.data[-1] |
|
|
194 | 195 | cspc = data['cspc'] |
|
|
195 | 196 | |
|
|
196 | 197 | for n in range(len(self.data.pairs)): |
|
|
197 | 198 | |
|
|
198 | 199 | pair = self.crossPairs[n] |
|
|
199 | 200 | |
|
|
200 | 201 | coh = cspc[n*2] |
|
|
201 | 202 | phase = cspc[n*2+1] |
|
|
202 | 203 | ax = self.axes[2 * n] |
|
|
203 | 204 | |
|
|
204 | 205 | if ax.firsttime: |
|
|
205 | 206 | ax.plt = ax.pcolormesh(x, y, coh.T, |
|
|
206 | 207 | vmin=self.zmin_coh, |
|
|
207 | 208 | vmax=self.zmax_coh, |
|
|
208 | 209 | cmap=plt.get_cmap(self.colormap_coh) |
|
|
209 | 210 | ) |
|
|
210 | 211 | else: |
|
|
211 | 212 | ax.plt.set_array(coh.T.ravel()) |
|
|
212 | 213 | self.titles.append( |
|
|
213 | 214 | 'Coherence Ch{} * Ch{}'.format(pair[0], pair[1])) |
|
|
214 | 215 | |
|
|
215 | 216 | ax = self.axes[2 * n + 1] |
|
|
216 | 217 | if ax.firsttime: |
|
|
217 | 218 | ax.plt = ax.pcolormesh(x, y, phase.T, |
|
|
218 | 219 | vmin=-180, |
|
|
219 | 220 | vmax=180, |
|
|
220 | 221 | cmap=plt.get_cmap(self.colormap_phase) |
|
|
221 | 222 | ) |
|
|
222 | 223 | else: |
|
|
223 | 224 | ax.plt.set_array(phase.T.ravel()) |
|
|
224 | 225 | |
|
|
225 | 226 | self.titles.append('Phase CH{} * CH{}'.format(pair[0], pair[1])) |
|
|
226 | 227 | |
|
|
227 | 228 | |
|
|
228 | 229 | class RTIPlot(Plot): |
|
|
229 | 230 | ''' |
|
|
230 | 231 | Plot for RTI data |
|
|
231 | 232 | ''' |
|
|
232 | 233 | |
|
|
233 | 234 | CODE = 'rti' |
|
|
234 | 235 | colormap = 'jet' |
|
|
235 | 236 | plot_type = 'pcolorbuffer' |
|
|
236 | 237 | titles = None |
|
|
237 | 238 | channelList = [] |
|
|
238 | 239 | elevationList = [] |
|
|
239 | 240 | azimuthList = [] |
|
|
240 | 241 | |
|
|
241 | 242 | def setup(self): |
|
|
242 | 243 | self.xaxis = 'time' |
|
|
243 | 244 | self.ncols = 1 |
|
|
244 | 245 | #print("dataChannels ",self.data.channels) |
|
|
245 | 246 | self.nrows = len(self.data.channels) |
|
|
246 | 247 | self.nplots = len(self.data.channels) |
|
|
247 | 248 | self.ylabel = 'Range [km]' |
|
|
248 | 249 | self.xlabel = 'Time' |
|
|
249 | 250 | self.cb_label = 'dB' |
|
|
250 | 251 | self.plots_adjust.update({'hspace':0.8, 'left': 0.08, 'bottom': 0.2, 'right':0.94}) |
|
|
251 | 252 | self.titles = ['{} Channel {}'.format( |
|
|
252 | 253 | self.CODE.upper(), x) for x in range(self.nplots)] |
|
|
253 | 254 | |
|
|
254 | 255 | def update_list(self,dataOut): |
|
|
255 | 256 | |
|
|
256 | 257 | if len(self.channelList) == 0: |
|
|
257 | 258 | self.channelList = dataOut.channelList |
|
|
258 | 259 | if len(self.elevationList) == 0: |
|
|
259 | 260 | self.elevationList = dataOut.elevationList |
|
|
260 | 261 | if len(self.azimuthList) == 0: |
|
|
261 | 262 | self.azimuthList = dataOut.azimuthList |
|
|
262 | 263 | |
|
|
263 | 264 | |
|
|
264 | 265 | def update(self, dataOut): |
|
|
265 | 266 | if len(self.channelList) == 0: |
|
|
266 | 267 | self.update_list(dataOut) |
|
|
267 | 268 | data = {} |
|
|
268 | 269 | meta = {} |
|
|
269 | 270 | data['rti'] = dataOut.getPower() |
|
|
270 | 271 | data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) |
|
|
271 | 272 | return data, meta |
|
|
272 | 273 | |
|
|
273 | 274 | def plot(self): |
|
|
274 | 275 | |
|
|
275 | 276 | self.x = self.data.times |
|
|
276 | 277 | self.y = self.data.yrange |
|
|
277 | ||
|
|
278 | #print(" x, y: ",self.x, self.y) | |
|
|
278 | 279 | self.z = self.data[self.CODE] |
|
|
279 | 280 | self.z = numpy.array(self.z, dtype=float) |
|
|
280 | 281 | self.z = numpy.ma.masked_invalid(self.z) |
|
|
281 | 282 | |
|
|
282 | 283 | try: |
|
|
283 | 284 | if self.channelList != None: |
|
|
284 | 285 | if len(self.elevationList) > 0 and len(self.azimuthList) > 0: |
|
|
285 | 286 | self.titles = ['{} Channel {} ({:2.1f} Elev, {:2.1f} Azth)'.format( |
|
|
286 | 287 | self.CODE.upper(), x, self.elevationList[x], self.azimuthList[x]) for x in self.channelList] |
|
|
287 | 288 | else: |
|
|
288 | 289 | self.titles = ['{} Channel {}'.format( |
|
|
289 | 290 | self.CODE.upper(), x) for x in self.channelList] |
|
|
290 | 291 | except: |
|
|
291 | 292 | if self.channelList.any() != None: |
|
|
292 | 293 | |
|
|
293 | 294 | self.titles = ['{} Channel {}'.format( |
|
|
294 | 295 | self.CODE.upper(), x) for x in self.channelList] |
|
|
295 | 296 | |
|
|
296 | 297 | if self.decimation is None: |
|
|
297 | 298 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
|
|
298 | 299 | else: |
|
|
299 | 300 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
300 | 301 | |
|
|
301 | 302 | #dummy_var = self.axes #Extrañamente esto actualiza el valor axes |
|
|
302 | 303 | for n, ax in enumerate(self.axes): |
|
|
303 | 304 | self.zmin = self.zmin if self.zmin else numpy.min(self.z) |
|
|
304 | 305 | self.zmax = self.zmax if self.zmax else numpy.max(self.z) |
|
|
305 | 306 | data = self.data[-1] |
|
|
306 | 307 | |
|
|
307 | 308 | if ax.firsttime: |
|
|
308 | 309 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
309 | 310 | vmin=self.zmin, |
|
|
310 | 311 | vmax=self.zmax, |
|
|
311 | 312 | cmap=plt.get_cmap(self.colormap) |
|
|
312 | 313 | ) |
|
|
313 | 314 | if self.showprofile: |
|
|
314 | 315 | ax.plot_profile = self.pf_axes[n].plot(data[self.CODE][n], self.y)[0] |
|
|
315 | 316 | if "noise" in self.data: |
|
|
316 | 317 | |
|
|
317 | 318 | ax.plot_noise = self.pf_axes[n].plot(numpy.repeat(data['noise'][n], len(self.y)), self.y, |
|
|
318 | 319 | color="k", linestyle="dashed", lw=1)[0] |
|
|
319 | 320 | else: |
|
|
320 | 321 | ax.collections.remove(ax.collections[0]) |
|
|
321 | 322 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
322 | 323 | vmin=self.zmin, |
|
|
323 | 324 | vmax=self.zmax, |
|
|
324 | 325 | cmap=plt.get_cmap(self.colormap) |
|
|
325 | 326 | ) |
|
|
326 | 327 | if self.showprofile: |
|
|
327 | 328 | ax.plot_profile.set_data(data[self.CODE][n], self.y) |
|
|
328 | 329 | if "noise" in self.data: |
|
|
329 | 330 | |
|
|
330 | 331 | ax.plot_noise.set_data(numpy.repeat( |
|
|
331 | 332 | data['noise'][n], len(self.y)), self.y) |
|
|
332 | 333 | |
|
|
333 | 334 | |
|
|
334 | 335 | class CoherencePlot(RTIPlot): |
|
|
335 | 336 | ''' |
|
|
336 | 337 | Plot for Coherence data |
|
|
337 | 338 | ''' |
|
|
338 | 339 | |
|
|
339 | 340 | CODE = 'coh' |
|
|
340 | 341 | |
|
|
341 | 342 | def setup(self): |
|
|
342 | 343 | self.xaxis = 'time' |
|
|
343 | 344 | self.ncols = 1 |
|
|
344 | 345 | self.nrows = len(self.data.pairs) |
|
|
345 | 346 | self.nplots = len(self.data.pairs) |
|
|
346 | 347 | self.ylabel = 'Range [km]' |
|
|
347 | 348 | self.xlabel = 'Time' |
|
|
348 | 349 | self.plots_adjust.update({'hspace':0.6, 'left': 0.1, 'bottom': 0.1,'right':0.95}) |
|
|
349 | 350 | if self.CODE == 'coh': |
|
|
350 | 351 | self.cb_label = '' |
|
|
351 | 352 | self.titles = [ |
|
|
352 | 353 | 'Coherence Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs] |
|
|
353 | 354 | else: |
|
|
354 | 355 | self.cb_label = 'Degrees' |
|
|
355 | 356 | self.titles = [ |
|
|
356 | 357 | 'Phase Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs] |
|
|
357 | 358 | |
|
|
358 | 359 | def update(self, dataOut): |
|
|
359 | 360 | self.update_list(dataOut) |
|
|
360 | 361 | data = {} |
|
|
361 | 362 | meta = {} |
|
|
362 | 363 | data['coh'] = dataOut.getCoherence() |
|
|
363 | 364 | meta['pairs'] = dataOut.pairsList |
|
|
364 | 365 | |
|
|
365 | 366 | |
|
|
366 | 367 | return data, meta |
|
|
367 | 368 | |
|
|
368 | 369 | class PhasePlot(CoherencePlot): |
|
|
369 | 370 | ''' |
|
|
370 | 371 | Plot for Phase map data |
|
|
371 | 372 | ''' |
|
|
372 | 373 | |
|
|
373 | 374 | CODE = 'phase' |
|
|
374 | 375 | colormap = 'seismic' |
|
|
375 | 376 | |
|
|
376 | 377 | def update(self, dataOut): |
|
|
377 | 378 | |
|
|
378 | 379 | data = {} |
|
|
379 | 380 | meta = {} |
|
|
380 | 381 | data['phase'] = dataOut.getCoherence(phase=True) |
|
|
381 | 382 | meta['pairs'] = dataOut.pairsList |
|
|
382 | 383 | |
|
|
383 | 384 | return data, meta |
|
|
384 | 385 | |
|
|
385 | 386 | class NoisePlot(Plot): |
|
|
386 | 387 | ''' |
|
|
387 | 388 | Plot for noise |
|
|
388 | 389 | ''' |
|
|
389 | 390 | |
|
|
390 | 391 | CODE = 'noise' |
|
|
391 | 392 | plot_type = 'scatterbuffer' |
|
|
392 | 393 | |
|
|
393 | 394 | def setup(self): |
|
|
394 | 395 | self.xaxis = 'time' |
|
|
395 | 396 | self.ncols = 1 |
|
|
396 | 397 | self.nrows = 1 |
|
|
397 | 398 | self.nplots = 1 |
|
|
398 | 399 | self.ylabel = 'Intensity [dB]' |
|
|
399 | 400 | self.xlabel = 'Time' |
|
|
400 | 401 | self.titles = ['Noise'] |
|
|
401 | 402 | self.colorbar = False |
|
|
402 | 403 | self.plots_adjust.update({'right': 0.85 }) |
|
|
403 | 404 | |
|
|
404 | 405 | def update(self, dataOut): |
|
|
405 | 406 | |
|
|
406 | 407 | data = {} |
|
|
407 | 408 | meta = {} |
|
|
408 | 409 | noise = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor).reshape(dataOut.nChannels, 1) |
|
|
409 | 410 | data['noise'] = noise |
|
|
410 | 411 | meta['yrange'] = numpy.array([]) |
|
|
411 | 412 | |
|
|
412 | 413 | return data, meta |
|
|
413 | 414 | |
|
|
414 | 415 | def plot(self): |
|
|
415 | 416 | |
|
|
416 | 417 | x = self.data.times |
|
|
417 | 418 | xmin = self.data.min_time |
|
|
418 | 419 | xmax = xmin + self.xrange * 60 * 60 |
|
|
419 | 420 | Y = self.data['noise'] |
|
|
420 | 421 | |
|
|
421 | 422 | if self.axes[0].firsttime: |
|
|
422 | 423 | if self.ymin is None: self.ymin = numpy.nanmin(Y) - 5 |
|
|
423 | 424 | if self.ymax is None: self.ymax = numpy.nanmax(Y) + 5 |
|
|
424 | 425 | for ch in self.data.channels: |
|
|
425 | 426 | y = Y[ch] |
|
|
426 | 427 | self.axes[0].plot(x, y, lw=1, label='Ch{}'.format(ch)) |
|
|
427 | 428 | plt.legend(bbox_to_anchor=(1.18, 1.0)) |
|
|
428 | 429 | else: |
|
|
429 | 430 | for ch in self.data.channels: |
|
|
430 | 431 | y = Y[ch] |
|
|
431 | 432 | self.axes[0].lines[ch].set_data(x, y) |
|
|
432 | 433 | |
|
|
433 | 434 | |
|
|
434 | 435 | class PowerProfilePlot(Plot): |
|
|
435 | 436 | |
|
|
436 | 437 | CODE = 'pow_profile' |
|
|
437 | 438 | plot_type = 'scatter' |
|
|
438 | 439 | |
|
|
439 | 440 | def setup(self): |
|
|
440 | 441 | |
|
|
441 | 442 | self.ncols = 1 |
|
|
442 | 443 | self.nrows = 1 |
|
|
443 | 444 | self.nplots = 1 |
|
|
444 | 445 | self.height = 4 |
|
|
445 | 446 | self.width = 3 |
|
|
446 | 447 | self.ylabel = 'Range [km]' |
|
|
447 | 448 | self.xlabel = 'Intensity [dB]' |
|
|
448 | 449 | self.titles = ['Power Profile'] |
|
|
449 | 450 | self.colorbar = False |
|
|
450 | 451 | |
|
|
451 | 452 | def update(self, dataOut): |
|
|
452 | 453 | |
|
|
453 | 454 | data = {} |
|
|
454 | 455 | meta = {} |
|
|
455 | 456 | data[self.CODE] = dataOut.getPower() |
|
|
456 | 457 | |
|
|
457 | 458 | return data, meta |
|
|
458 | 459 | |
|
|
459 | 460 | def plot(self): |
|
|
460 | 461 | |
|
|
461 | 462 | y = self.data.yrange |
|
|
462 | 463 | self.y = y |
|
|
463 | 464 | |
|
|
464 | 465 | x = self.data[-1][self.CODE] |
|
|
465 | 466 | |
|
|
466 | 467 | if self.xmin is None: self.xmin = numpy.nanmin(x)*0.9 |
|
|
467 | 468 | if self.xmax is None: self.xmax = numpy.nanmax(x)*1.1 |
|
|
468 | 469 | |
|
|
469 | 470 | if self.axes[0].firsttime: |
|
|
470 | 471 | for ch in self.data.channels: |
|
|
471 | 472 | self.axes[0].plot(x[ch], y, lw=1, label='Ch{}'.format(ch)) |
|
|
472 | 473 | plt.legend() |
|
|
473 | 474 | else: |
|
|
474 | 475 | for ch in self.data.channels: |
|
|
475 | 476 | self.axes[0].lines[ch].set_data(x[ch], y) |
|
|
476 | 477 | |
|
|
477 | 478 | |
|
|
478 | 479 | class SpectraCutPlot(Plot): |
|
|
479 | 480 | |
|
|
480 | 481 | CODE = 'spc_cut' |
|
|
481 | 482 | plot_type = 'scatter' |
|
|
482 | 483 | buffering = False |
|
|
483 | 484 | heights = [] |
|
|
484 | 485 | channelList = [] |
|
|
485 | 486 | maintitle = "Spectra Cuts" |
|
|
486 | 487 | flag_setIndex = False |
|
|
487 | 488 | |
|
|
488 | 489 | def setup(self): |
|
|
489 | 490 | |
|
|
490 | 491 | self.nplots = len(self.data.channels) |
|
|
491 | 492 | self.ncols = int(numpy.sqrt(self.nplots) + 0.9) |
|
|
492 | 493 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
|
|
493 | 494 | self.width = 4.2 * self.ncols + 2.5 |
|
|
494 | 495 | self.height = 4.8 * self.nrows |
|
|
495 | 496 | self.ylabel = 'Power [dB]' |
|
|
496 | 497 | self.colorbar = False |
|
|
497 | 498 | self.plots_adjust.update({'left':0.05, 'hspace':0.3, 'right': 0.9, 'bottom':0.08}) |
|
|
498 | 499 | |
|
|
499 | 500 | if len(self.selectedHeightsList) > 0: |
|
|
500 | 501 | self.maintitle = "Spectra Cut"# for %d km " %(int(self.selectedHeight)) |
|
|
501 | 502 | |
|
|
502 | 503 | |
|
|
503 | 504 | |
|
|
504 | 505 | def update(self, dataOut): |
|
|
505 | 506 | if len(self.channelList) == 0: |
|
|
506 | 507 | self.channelList = dataOut.channelList |
|
|
507 | 508 | |
|
|
508 | 509 | self.heights = dataOut.heightList |
|
|
509 | 510 | #print("sels: ",self.selectedHeightsList) |
|
|
510 | 511 | if len(self.selectedHeightsList)>0 and not self.flag_setIndex: |
|
|
511 | 512 | |
|
|
512 | 513 | for sel_height in self.selectedHeightsList: |
|
|
513 | 514 | index_list = numpy.where(self.heights >= sel_height) |
|
|
514 | 515 | index_list = index_list[0] |
|
|
515 | 516 | self.height_index.append(index_list[0]) |
|
|
516 | 517 | #print("sels i:"", self.height_index) |
|
|
517 | 518 | self.flag_setIndex = True |
|
|
518 | 519 | #print(self.height_index) |
|
|
519 | 520 | data = {} |
|
|
520 | 521 | meta = {} |
|
|
521 | 522 | spc = 10*numpy.log10(dataOut.data_spc/dataOut.normFactor) |
|
|
522 | 523 | |
|
|
523 | 524 | data['spc'] = spc |
|
|
524 | 525 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
|
|
525 | 526 | |
|
|
526 | 527 | return data, meta |
|
|
527 | 528 | |
|
|
528 | 529 | def plot(self): |
|
|
529 | 530 | if self.xaxis == "frequency": |
|
|
530 | 531 | x = self.data.xrange[0][1:] |
|
|
531 | 532 | self.xlabel = "Frequency (kHz)" |
|
|
532 | 533 | elif self.xaxis == "time": |
|
|
533 | 534 | x = self.data.xrange[1] |
|
|
534 | 535 | self.xlabel = "Time (ms)" |
|
|
535 | 536 | else: |
|
|
536 | 537 | x = self.data.xrange[2] |
|
|
537 | 538 | self.xlabel = "Velocity (m/s)" |
|
|
538 | 539 | |
|
|
539 | 540 | self.titles = [] |
|
|
540 | 541 | |
|
|
541 | 542 | y = self.data.yrange |
|
|
542 | 543 | z = self.data[-1]['spc'] |
|
|
543 | 544 | #print(z.shape) |
|
|
544 | 545 | if len(self.height_index) > 0: |
|
|
545 | 546 | index = self.height_index |
|
|
546 | 547 | else: |
|
|
547 | 548 | index = numpy.arange(0, len(y), int((len(y))/9)) |
|
|
548 | 549 | #print("inde x ", index, self.axes) |
|
|
549 | 550 | |
|
|
550 | 551 | for n, ax in enumerate(self.axes): |
|
|
551 | 552 | |
|
|
552 | 553 | if ax.firsttime: |
|
|
553 | 554 | |
|
|
554 | 555 | |
|
|
555 | 556 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
556 | 557 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
557 | 558 | self.ymin = self.ymin if self.ymin else numpy.nanmin(z) |
|
|
558 | 559 | self.ymax = self.ymax if self.ymax else numpy.nanmax(z) |
|
|
559 | 560 | |
|
|
560 | 561 | |
|
|
561 | 562 | ax.plt = ax.plot(x, z[n, :, index].T) |
|
|
562 | 563 | labels = ['Range = {:2.1f}km'.format(y[i]) for i in index] |
|
|
563 | 564 | self.figures[0].legend(ax.plt, labels, loc='center right', prop={'size': 8}) |
|
|
564 | 565 | ax.minorticks_on() |
|
|
565 | 566 | ax.grid(which='major', axis='both') |
|
|
566 | 567 | ax.grid(which='minor', axis='x') |
|
|
567 | 568 | else: |
|
|
568 | 569 | for i, line in enumerate(ax.plt): |
|
|
569 | 570 | line.set_data(x, z[n, :, index[i]]) |
|
|
570 | 571 | |
|
|
571 | 572 | |
|
|
572 | 573 | self.titles.append('CH {}'.format(self.channelList[n])) |
|
|
573 | 574 | plt.suptitle(self.maintitle, fontsize=10) |
|
|
574 | 575 | |
|
|
575 | 576 | |
|
|
576 | 577 | class BeaconPhase(Plot): |
|
|
577 | 578 | |
|
|
578 | 579 | __isConfig = None |
|
|
579 | 580 | __nsubplots = None |
|
|
580 | 581 | |
|
|
581 | 582 | PREFIX = 'beacon_phase' |
|
|
582 | 583 | |
|
|
583 | 584 | def __init__(self): |
|
|
584 | 585 | Plot.__init__(self) |
|
|
585 | 586 | self.timerange = 24*60*60 |
|
|
586 | 587 | self.isConfig = False |
|
|
587 | 588 | self.__nsubplots = 1 |
|
|
588 | 589 | self.counter_imagwr = 0 |
|
|
589 | 590 | self.WIDTH = 800 |
|
|
590 | 591 | self.HEIGHT = 400 |
|
|
591 | 592 | self.WIDTHPROF = 120 |
|
|
592 | 593 | self.HEIGHTPROF = 0 |
|
|
593 | 594 | self.xdata = None |
|
|
594 | 595 | self.ydata = None |
|
|
595 | 596 | |
|
|
596 | 597 | self.PLOT_CODE = BEACON_CODE |
|
|
597 | 598 | |
|
|
598 | 599 | self.FTP_WEI = None |
|
|
599 | 600 | self.EXP_CODE = None |
|
|
600 | 601 | self.SUB_EXP_CODE = None |
|
|
601 | 602 | self.PLOT_POS = None |
|
|
602 | 603 | |
|
|
603 | 604 | self.filename_phase = None |
|
|
604 | 605 | |
|
|
605 | 606 | self.figfile = None |
|
|
606 | 607 | |
|
|
607 | 608 | self.xmin = None |
|
|
608 | 609 | self.xmax = None |
|
|
609 | 610 | |
|
|
610 | 611 | def getSubplots(self): |
|
|
611 | 612 | |
|
|
612 | 613 | ncol = 1 |
|
|
613 | 614 | nrow = 1 |
|
|
614 | 615 | |
|
|
615 | 616 | return nrow, ncol |
|
|
616 | 617 | |
|
|
617 | 618 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
618 | 619 | |
|
|
619 | 620 | self.__showprofile = showprofile |
|
|
620 | 621 | self.nplots = nplots |
|
|
621 | 622 | |
|
|
622 | 623 | ncolspan = 7 |
|
|
623 | 624 | colspan = 6 |
|
|
624 | 625 | self.__nsubplots = 2 |
|
|
625 | 626 | |
|
|
626 | 627 | self.createFigure(id = id, |
|
|
627 | 628 | wintitle = wintitle, |
|
|
628 | 629 | widthplot = self.WIDTH+self.WIDTHPROF, |
|
|
629 | 630 | heightplot = self.HEIGHT+self.HEIGHTPROF, |
|
|
630 | 631 | show=show) |
|
|
631 | 632 | |
|
|
632 | 633 | nrow, ncol = self.getSubplots() |
|
|
633 | 634 | |
|
|
634 | 635 | self.addAxes(nrow, ncol*ncolspan, 0, 0, colspan, 1) |
|
|
635 | 636 | |
|
|
636 | 637 | def save_phase(self, filename_phase): |
|
|
637 | 638 | f = open(filename_phase,'w+') |
|
|
638 | 639 | f.write('\n\n') |
|
|
639 | 640 | f.write('JICAMARCA RADIO OBSERVATORY - Beacon Phase \n') |
|
|
640 | 641 | f.write('DD MM YYYY HH MM SS pair(2,0) pair(2,1) pair(2,3) pair(2,4)\n\n' ) |
|
|
641 | 642 | f.close() |
|
|
642 | 643 | |
|
|
643 | 644 | def save_data(self, filename_phase, data, data_datetime): |
|
|
644 | 645 | f=open(filename_phase,'a') |
|
|
645 | 646 | timetuple_data = data_datetime.timetuple() |
|
|
646 | 647 | day = str(timetuple_data.tm_mday) |
|
|
647 | 648 | month = str(timetuple_data.tm_mon) |
|
|
648 | 649 | year = str(timetuple_data.tm_year) |
|
|
649 | 650 | hour = str(timetuple_data.tm_hour) |
|
|
650 | 651 | minute = str(timetuple_data.tm_min) |
|
|
651 | 652 | second = str(timetuple_data.tm_sec) |
|
|
652 | 653 | f.write(day+' '+month+' '+year+' '+hour+' '+minute+' '+second+' '+str(data[0])+' '+str(data[1])+' '+str(data[2])+' '+str(data[3])+'\n') |
|
|
653 | 654 | f.close() |
|
|
654 | 655 | |
|
|
655 | 656 | def plot(self): |
|
|
656 | 657 | log.warning('TODO: Not yet implemented...') |
|
|
657 | 658 | |
|
|
658 | 659 | def run(self, dataOut, id, wintitle="", pairsList=None, showprofile='True', |
|
|
659 | 660 | xmin=None, xmax=None, ymin=None, ymax=None, hmin=None, hmax=None, |
|
|
660 | 661 | timerange=None, |
|
|
661 | 662 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
662 | 663 | server=None, folder=None, username=None, password=None, |
|
|
663 | 664 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
664 | 665 | |
|
|
665 | 666 | if dataOut.flagNoData: |
|
|
666 | 667 | return dataOut |
|
|
667 | 668 | |
|
|
668 | 669 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
669 | 670 | return |
|
|
670 | 671 | |
|
|
671 | 672 | if pairsList == None: |
|
|
672 | 673 | pairsIndexList = dataOut.pairsIndexList[:10] |
|
|
673 | 674 | else: |
|
|
674 | 675 | pairsIndexList = [] |
|
|
675 | 676 | for pair in pairsList: |
|
|
676 | 677 | if pair not in dataOut.pairsList: |
|
|
677 | 678 | raise ValueError("Pair %s is not in dataOut.pairsList" %(pair)) |
|
|
678 | 679 | pairsIndexList.append(dataOut.pairsList.index(pair)) |
|
|
679 | 680 | |
|
|
680 | 681 | if pairsIndexList == []: |
|
|
681 | 682 | return |
|
|
682 | 683 | |
|
|
683 | 684 | # if len(pairsIndexList) > 4: |
|
|
684 | 685 | # pairsIndexList = pairsIndexList[0:4] |
|
|
685 | 686 | |
|
|
686 | 687 | hmin_index = None |
|
|
687 | 688 | hmax_index = None |
|
|
688 | 689 | |
|
|
689 | 690 | if hmin != None and hmax != None: |
|
|
690 | 691 | indexes = numpy.arange(dataOut.nHeights) |
|
|
691 | 692 | hmin_list = indexes[dataOut.heightList >= hmin] |
|
|
692 | 693 | hmax_list = indexes[dataOut.heightList <= hmax] |
|
|
693 | 694 | |
|
|
694 | 695 | if hmin_list.any(): |
|
|
695 | 696 | hmin_index = hmin_list[0] |
|
|
696 | 697 | |
|
|
697 | 698 | if hmax_list.any(): |
|
|
698 | 699 | hmax_index = hmax_list[-1]+1 |
|
|
699 | 700 | |
|
|
700 | 701 | x = dataOut.getTimeRange() |
|
|
701 | 702 | |
|
|
702 | 703 | thisDatetime = dataOut.datatime |
|
|
703 | 704 | |
|
|
704 | 705 | title = wintitle + " Signal Phase" # : %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
705 | 706 | xlabel = "Local Time" |
|
|
706 | 707 | ylabel = "Phase (degrees)" |
|
|
707 | 708 | |
|
|
708 | 709 | update_figfile = False |
|
|
709 | 710 | |
|
|
710 | 711 | nplots = len(pairsIndexList) |
|
|
711 | 712 | #phase = numpy.zeros((len(pairsIndexList),len(dataOut.beacon_heiIndexList))) |
|
|
712 | 713 | phase_beacon = numpy.zeros(len(pairsIndexList)) |
|
|
713 | 714 | for i in range(nplots): |
|
|
714 | 715 | pair = dataOut.pairsList[pairsIndexList[i]] |
|
|
715 | 716 | ccf = numpy.average(dataOut.data_cspc[pairsIndexList[i], :, hmin_index:hmax_index], axis=0) |
|
|
716 | 717 | powa = numpy.average(dataOut.data_spc[pair[0], :, hmin_index:hmax_index], axis=0) |
|
|
717 | 718 | powb = numpy.average(dataOut.data_spc[pair[1], :, hmin_index:hmax_index], axis=0) |
|
|
718 | 719 | avgcoherenceComplex = ccf/numpy.sqrt(powa*powb) |
|
|
719 | 720 | phase = numpy.arctan2(avgcoherenceComplex.imag, avgcoherenceComplex.real)*180/numpy.pi |
|
|
720 | 721 | |
|
|
721 | 722 | if dataOut.beacon_heiIndexList: |
|
|
722 | 723 | phase_beacon[i] = numpy.average(phase[dataOut.beacon_heiIndexList]) |
|
|
723 | 724 | else: |
|
|
724 | 725 | phase_beacon[i] = numpy.average(phase) |
|
|
725 | 726 | |
|
|
726 | 727 | if not self.isConfig: |
|
|
727 | 728 | |
|
|
728 | 729 | nplots = len(pairsIndexList) |
|
|
729 | 730 | |
|
|
730 | 731 | self.setup(id=id, |
|
|
731 | 732 | nplots=nplots, |
|
|
732 | 733 | wintitle=wintitle, |
|
|
733 | 734 | showprofile=showprofile, |
|
|
734 | 735 | show=show) |
|
|
735 | 736 | |
|
|
736 | 737 | if timerange != None: |
|
|
737 | 738 | self.timerange = timerange |
|
|
738 | 739 | |
|
|
739 | 740 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
740 | 741 | |
|
|
741 | 742 | if ymin == None: ymin = 0 |
|
|
742 | 743 | if ymax == None: ymax = 360 |
|
|
743 | 744 | |
|
|
744 | 745 | self.FTP_WEI = ftp_wei |
|
|
745 | 746 | self.EXP_CODE = exp_code |
|
|
746 | 747 | self.SUB_EXP_CODE = sub_exp_code |
|
|
747 | 748 | self.PLOT_POS = plot_pos |
|
|
748 | 749 | |
|
|
749 | 750 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
750 | 751 | self.isConfig = True |
|
|
751 | 752 | self.figfile = figfile |
|
|
752 | 753 | self.xdata = numpy.array([]) |
|
|
753 | 754 | self.ydata = numpy.array([]) |
|
|
754 | 755 | |
|
|
755 | 756 | update_figfile = True |
|
|
756 | 757 | |
|
|
757 | 758 | #open file beacon phase |
|
|
758 | 759 | path = '%s%03d' %(self.PREFIX, self.id) |
|
|
759 | 760 | beacon_file = os.path.join(path,'%s.txt'%self.name) |
|
|
760 | 761 | self.filename_phase = os.path.join(figpath,beacon_file) |
|
|
761 | 762 | #self.save_phase(self.filename_phase) |
|
|
762 | 763 | |
|
|
763 | 764 | |
|
|
764 | 765 | #store data beacon phase |
|
|
765 | 766 | #self.save_data(self.filename_phase, phase_beacon, thisDatetime) |
|
|
766 | 767 | |
|
|
767 | 768 | self.setWinTitle(title) |
|
|
768 | 769 | |
|
|
769 | 770 | |
|
|
770 | 771 | title = "Phase Plot %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
771 | 772 | |
|
|
772 | 773 | legendlabels = ["Pair (%d,%d)"%(pair[0], pair[1]) for pair in dataOut.pairsList] |
|
|
773 | 774 | |
|
|
774 | 775 | axes = self.axesList[0] |
|
|
775 | 776 | |
|
|
776 | 777 | self.xdata = numpy.hstack((self.xdata, x[0:1])) |
|
|
777 | 778 | |
|
|
778 | 779 | if len(self.ydata)==0: |
|
|
779 | 780 | self.ydata = phase_beacon.reshape(-1,1) |
|
|
780 | 781 | else: |
|
|
781 | 782 | self.ydata = numpy.hstack((self.ydata, phase_beacon.reshape(-1,1))) |
|
|
782 | 783 | |
|
|
783 | 784 | |
|
|
784 | 785 | axes.pmultilineyaxis(x=self.xdata, y=self.ydata, |
|
|
785 | 786 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, |
|
|
786 | 787 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid", |
|
|
787 | 788 | XAxisAsTime=True, grid='both' |
|
|
788 | 789 | ) |
|
|
789 | 790 | |
|
|
790 | 791 | self.draw() |
|
|
791 | 792 | |
|
|
792 | 793 | if dataOut.ltctime >= self.xmax: |
|
|
793 | 794 | self.counter_imagwr = wr_period |
|
|
794 | 795 | self.isConfig = False |
|
|
795 | 796 | update_figfile = True |
|
|
796 | 797 | |
|
|
797 | 798 | self.save(figpath=figpath, |
|
|
798 | 799 | figfile=figfile, |
|
|
799 | 800 | save=save, |
|
|
800 | 801 | ftp=ftp, |
|
|
801 | 802 | wr_period=wr_period, |
|
|
802 | 803 | thisDatetime=thisDatetime, |
|
|
803 | 804 | update_figfile=update_figfile) |
|
|
804 | 805 | |
|
|
805 | 806 | return dataOut |
|
|
806 | 807 | |
|
|
807 | 808 | class NoiselessSpectraPlot(Plot): |
|
|
808 | 809 | ''' |
|
|
809 | 810 | Plot for Spectra data, subtracting |
|
|
810 | 811 | the noise in all channels, using for |
|
|
811 | 812 | amisr-14 data |
|
|
812 | 813 | ''' |
|
|
813 | 814 | |
|
|
814 | 815 | CODE = 'noiseless_spc' |
|
|
815 | 816 | colormap = 'nipy_spectral' |
|
|
816 | 817 | plot_type = 'pcolor' |
|
|
817 | 818 | buffering = False |
|
|
818 | 819 | channelList = [] |
|
|
820 | last_noise = None | |
|
|
819 | 821 | |
|
|
820 | 822 | def setup(self): |
|
|
821 | 823 | |
|
|
822 | 824 | self.nplots = len(self.data.channels) |
|
|
823 | 825 | self.ncols = int(numpy.sqrt(self.nplots) + 0.9) |
|
|
824 | 826 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
|
|
825 | 827 | self.height = 2.6 * self.nrows |
|
|
826 | 828 | |
|
|
827 | 829 | self.cb_label = 'dB' |
|
|
828 | 830 | if self.showprofile: |
|
|
829 | 831 | self.width = 4 * self.ncols |
|
|
830 | 832 | else: |
|
|
831 | 833 | self.width = 3.5 * self.ncols |
|
|
832 | 834 | self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08}) |
|
|
833 | 835 | self.ylabel = 'Range [km]' |
|
|
834 | 836 | |
|
|
835 | 837 | |
|
|
836 | 838 | def update_list(self,dataOut): |
|
|
837 | 839 | if len(self.channelList) == 0: |
|
|
838 | 840 | self.channelList = dataOut.channelList |
|
|
839 | 841 | |
|
|
840 | 842 | def update(self, dataOut): |
|
|
841 | 843 | |
|
|
842 | 844 | self.update_list(dataOut) |
|
|
843 | 845 | data = {} |
|
|
844 | 846 | meta = {} |
|
|
845 | n0 = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) | |
|
|
846 | (nch, nff, nh) = dataOut.data_spc.shape | |
|
|
847 | n1 = numpy.repeat(n0,nh, axis=0).reshape((nch,nh)) | |
|
|
848 | noise = numpy.repeat(n1,nff, axis=1).reshape((nch,nff,nh)) | |
|
|
849 | #print(noise.shape, "noise", noise) | |
|
|
850 | 847 | |
|
|
851 | spc = 10*numpy.log10(dataOut.data_spc/dataOut.normFactor) - noise | |
|
|
848 | norm = dataOut.nProfiles * dataOut.max_nIncohInt * dataOut.nCohInt * dataOut.windowOfFilter | |
|
|
849 | n0 = 10*numpy.log10(dataOut.getNoise()/float(norm)) | |
|
|
852 | 850 | |
|
|
853 | data['spc'] = spc | |
|
|
854 | data['rti'] = dataOut.getPower() - n1 | |
|
|
851 | if self.last_noise == None: | |
|
|
852 | self.last_noise = n0 | |
|
|
853 | else: | |
|
|
854 | n0 = (n0*0.2 + self.last_noise*0.8) | |
|
|
855 | self.last_noise = n0 | |
|
|
856 | ||
|
|
857 | spc = 10*numpy.log10(dataOut.data_spc/dataOut.normFactor) | |
|
|
855 | 858 | |
|
|
856 |
data[' |
|
|
|
859 | data['spc'] = spc - n0 | |
|
|
860 | data['rti'] = dataOut.getPower() - n0 | |
|
|
861 | ||
|
|
862 | #data['noise'] = noise | |
|
|
857 | 863 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
|
|
858 | 864 | |
|
|
859 | 865 | return data, meta |
|
|
860 | 866 | |
|
|
861 | 867 | def plot(self): |
|
|
862 | 868 | if self.xaxis == "frequency": |
|
|
863 | 869 | x = self.data.xrange[0] |
|
|
864 | 870 | self.xlabel = "Frequency (kHz)" |
|
|
865 | 871 | elif self.xaxis == "time": |
|
|
866 | 872 | x = self.data.xrange[1] |
|
|
867 | 873 | self.xlabel = "Time (ms)" |
|
|
868 | 874 | else: |
|
|
869 | 875 | x = self.data.xrange[2] |
|
|
870 | 876 | self.xlabel = "Velocity (m/s)" |
|
|
871 | 877 | |
|
|
872 | 878 | self.titles = [] |
|
|
873 | 879 | y = self.data.yrange |
|
|
874 | 880 | self.y = y |
|
|
875 | 881 | |
|
|
876 | 882 | data = self.data[-1] |
|
|
877 | 883 | z = data['spc'] |
|
|
878 | 884 | |
|
|
879 | 885 | for n, ax in enumerate(self.axes): |
|
|
880 | noise = data['noise'][n] | |
|
|
886 | #noise = data['noise'][n] | |
|
|
881 | 887 | |
|
|
882 | 888 | if ax.firsttime: |
|
|
883 | 889 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
884 | 890 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
885 | 891 | self.zmin = self.zmin if self.zmin else numpy.nanmin(z) |
|
|
886 | 892 | self.zmax = self.zmax if self.zmax else numpy.nanmax(z) |
|
|
887 | 893 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
888 | 894 | vmin=self.zmin, |
|
|
889 | 895 | vmax=self.zmax, |
|
|
890 | 896 | cmap=plt.get_cmap(self.colormap) |
|
|
891 | 897 | ) |
|
|
892 | 898 | |
|
|
893 | 899 | if self.showprofile: |
|
|
894 | 900 | ax.plt_profile = self.pf_axes[n].plot( |
|
|
895 | 901 | data['rti'][n], y)[0] |
|
|
896 | ax.plt_noise = self.pf_axes[n].plot(numpy.repeat(noise, len(y)), y, | |
|
|
897 | color="k", linestyle="dashed", lw=1)[0] | |
|
|
902 | ||
|
|
898 | 903 | |
|
|
899 | 904 | else: |
|
|
900 | 905 | ax.plt.set_array(z[n].T.ravel()) |
|
|
901 | 906 | if self.showprofile: |
|
|
902 | 907 | ax.plt_profile.set_data(data['rti'][n], y) |
|
|
903 | ax.plt_noise.set_data(numpy.repeat(noise, len(y)), y) | |
|
|
904 | 908 | |
|
|
905 | self.titles.append('CH {}: {:3.2f}dB'.format(self.channelList[n], noise)) | |
|
|
909 | ||
|
|
910 | self.titles.append('CH {}'.format(self.channelList[n])) | |
|
|
906 | 911 | |
|
|
907 | 912 | |
|
|
908 | 913 | class NoiselessRTIPlot(Plot): |
|
|
909 | 914 | ''' |
|
|
910 | 915 | Plot for RTI data |
|
|
911 | 916 | ''' |
|
|
912 | 917 | |
|
|
913 | 918 | CODE = 'noiseless_rti' |
|
|
914 | 919 | colormap = 'jet' |
|
|
915 | 920 | plot_type = 'pcolorbuffer' |
|
|
916 | 921 | titles = None |
|
|
917 | 922 | channelList = [] |
|
|
918 | 923 | elevationList = [] |
|
|
919 | 924 | azimuthList = [] |
|
|
925 | last_noise = None | |
|
|
920 | 926 | |
|
|
921 | 927 | def setup(self): |
|
|
922 | 928 | self.xaxis = 'time' |
|
|
923 | 929 | self.ncols = 1 |
|
|
924 | 930 | #print("dataChannels ",self.data.channels) |
|
|
925 | 931 | self.nrows = len(self.data.channels) |
|
|
926 | 932 | self.nplots = len(self.data.channels) |
|
|
927 | 933 | self.ylabel = 'Range [km]' |
|
|
928 | 934 | self.xlabel = 'Time' |
|
|
929 | 935 | self.cb_label = 'dB' |
|
|
930 | 936 | self.plots_adjust.update({'hspace':0.8, 'left': 0.08, 'bottom': 0.2, 'right':0.94}) |
|
|
931 | 937 | self.titles = ['{} Channel {}'.format( |
|
|
932 | 938 | self.CODE.upper(), x) for x in range(self.nplots)] |
|
|
933 | 939 | |
|
|
934 | 940 | def update_list(self,dataOut): |
|
|
935 | 941 | if len(self.channelList) == 0: |
|
|
936 | 942 | self.channelList = dataOut.channelList |
|
|
937 | 943 | if len(self.elevationList) == 0: |
|
|
938 | 944 | self.elevationList = dataOut.elevationList |
|
|
939 | 945 | if len(self.azimuthList) == 0: |
|
|
940 | 946 | self.azimuthList = dataOut.azimuthList |
|
|
941 | 947 | |
|
|
942 | 948 | def update(self, dataOut): |
|
|
943 | 949 | if len(self.channelList) == 0: |
|
|
944 | 950 | self.update_list(dataOut) |
|
|
945 | 951 | data = {} |
|
|
946 | 952 | meta = {} |
|
|
947 | 953 | |
|
|
948 | n0 = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) | |
|
|
949 | (nch, nff, nh) = dataOut.data_spc.shape | |
|
|
950 | #print(nch, nff, nh) | |
|
|
951 | if nch != 1: | |
|
|
952 | aux = [] | |
|
|
953 |
|
|
|
|
954 | aux.append(n0[c]) | |
|
|
955 | n0 = numpy.asarray(aux) | |
|
|
956 | noise = numpy.repeat(n0,nh, axis=0).reshape((nch,nh)) | |
|
|
957 | #print(dataOut.elevationList, dataOut.azimuthList) | |
|
|
958 | #print(dataOut.channelList) | |
|
|
959 |
data['noiseless_rti'] = dataOut.getPower() - n |
|
|
|
960 | data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) | |
|
|
954 | ||
|
|
955 | norm = dataOut.nProfiles * dataOut.max_nIncohInt * dataOut.nCohInt * dataOut.windowOfFilter | |
|
|
956 | n0 = 10*numpy.log10(dataOut.getNoise()/float(norm)) | |
|
|
957 | #print("noise: ",n0, dataOut.normFactor, norm, dataOut.nIncohInt, dataOut.max_nIncohInt) | |
|
|
958 | if self.last_noise == None: | |
|
|
959 | self.last_noise = n0 | |
|
|
960 | else: | |
|
|
961 | n0 = (n0*0.2 + self.last_noise*0.8) | |
|
|
962 | self.last_noise = n0 | |
|
|
963 | ||
|
|
964 | ||
|
|
965 | data['noiseless_rti'] = dataOut.getPower() - n0 | |
|
|
966 | ||
|
|
967 | #data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) | |
|
|
968 | #print(noise) | |
|
|
961 | 969 | return data, meta |
|
|
962 | 970 | |
|
|
963 | 971 | def plot(self): |
|
|
964 | 972 | |
|
|
965 | 973 | self.x = self.data.times |
|
|
966 | 974 | self.y = self.data.yrange |
|
|
967 | 975 | self.z = self.data['noiseless_rti'] |
|
|
968 | 976 | self.z = numpy.array(self.z, dtype=float) |
|
|
969 | 977 | self.z = numpy.ma.masked_invalid(self.z) |
|
|
970 | 978 | |
|
|
971 | 979 | try: |
|
|
972 | 980 | if self.channelList != None: |
|
|
973 | 981 | if len(self.elevationList) > 0 and len(self.azimuthList) > 0: |
|
|
974 | 982 | self.titles = ['{} Channel {} ({:2.1f} Elev, {:2.1f} Azth)'.format( |
|
|
975 | 983 | self.CODE.upper(), x, self.elevationList[x], self.azimuthList[x]) for x in self.channelList] |
|
|
976 | 984 | else: |
|
|
977 | 985 | self.titles = ['{} Channel {}'.format( |
|
|
978 | 986 | self.CODE.upper(), x) for x in self.channelList] |
|
|
979 | 987 | except: |
|
|
980 | 988 | if self.channelList.any() != None: |
|
|
981 | 989 | |
|
|
982 | 990 | self.titles = ['{} Channel {}'.format( |
|
|
983 | 991 | self.CODE.upper(), x) for x in self.channelList] |
|
|
984 | 992 | if self.decimation is None: |
|
|
985 | 993 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
|
|
986 | 994 | else: |
|
|
987 | 995 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
988 | 996 | dummy_var = self.axes #Extrañamente esto actualiza el valor axes |
|
|
997 | #print("plot shapes ", z.shape, x.shape, y.shape) | |
|
|
989 | 998 | for n, ax in enumerate(self.axes): |
|
|
990 | 999 | self.zmin = self.zmin if self.zmin else numpy.min(self.z) |
|
|
991 | 1000 | self.zmax = self.zmax if self.zmax else numpy.max(self.z) |
|
|
992 | 1001 | data = self.data[-1] |
|
|
993 | 1002 | if ax.firsttime: |
|
|
994 | 1003 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
995 | 1004 | vmin=self.zmin, |
|
|
996 | 1005 | vmax=self.zmax, |
|
|
997 | 1006 | cmap=plt.get_cmap(self.colormap) |
|
|
998 | 1007 | ) |
|
|
999 | 1008 | if self.showprofile: |
|
|
1000 | 1009 | ax.plot_profile = self.pf_axes[n].plot(data['noiseless_rti'][n], self.y)[0] |
|
|
1001 | 1010 | |
|
|
1002 | if "noise" in self.data: | |
|
|
1003 | ax.plot_noise = self.pf_axes[n].plot(numpy.repeat(data['noise'][n], len(self.y)), self.y, | |
|
|
1004 | color="k", linestyle="dashed", lw=1)[0] | |
|
|
1005 | 1011 | else: |
|
|
1006 | 1012 | ax.collections.remove(ax.collections[0]) |
|
|
1007 | 1013 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
1008 | 1014 | vmin=self.zmin, |
|
|
1009 | 1015 | vmax=self.zmax, |
|
|
1010 | 1016 | cmap=plt.get_cmap(self.colormap) |
|
|
1011 | 1017 | ) |
|
|
1012 | 1018 | if self.showprofile: |
|
|
1013 | 1019 | ax.plot_profile.set_data(data['noiseless_rti'][n], self.y) |
|
|
1014 | if "noise" in self.data: | |
|
|
1015 | ax.plot_noise.set_data(numpy.repeat( | |
|
|
1016 |
data['noise'][n] |
|
|
|
1020 | # if "noise" in self.data: | |
|
|
1021 | # #ax.plot_noise.set_data(numpy.repeat(data['noise'][n], len(self.y)), self.y) | |
|
|
1022 | # ax.plot_noise.set_data(data['noise'][n], self.y) | |
|
|
1023 | ||
|
|
1024 | ||
|
|
1025 | class OutliersRTIPlot(Plot): | |
|
|
1026 | ''' | |
|
|
1027 | Plot for data_xxxx object | |
|
|
1028 | ''' | |
|
|
1029 | ||
|
|
1030 | CODE = 'outlier' | |
|
|
1031 | colormap = 'cool' | |
|
|
1032 | plot_type = 'pcolorbuffer' | |
|
|
1033 | ||
|
|
1034 | def setup(self): | |
|
|
1035 | self.xaxis = 'time' | |
|
|
1036 | self.ncols = 1 | |
|
|
1037 | self.nrows = self.data.shape('outlier')[0] | |
|
|
1038 | self.nplots = self.nrows | |
|
|
1039 | self.plots_adjust.update({'hspace':0.8, 'left': 0.08, 'bottom': 0.2, 'right':0.94}) | |
|
|
1040 | ||
|
|
1041 | ||
|
|
1042 | if not self.xlabel: | |
|
|
1043 | self.xlabel = 'Time' | |
|
|
1044 | ||
|
|
1045 | self.ylabel = 'Height [km]' | |
|
|
1046 | if not self.titles: | |
|
|
1047 | self.titles = ['Outliers Ch:{}'.format(x) for x in range(self.nrows)] | |
|
|
1048 | ||
|
|
1049 | def update(self, dataOut): | |
|
|
1050 | ||
|
|
1051 | data = {} | |
|
|
1052 | data['outlier'] = dataOut.data_outlier | |
|
|
1053 | ||
|
|
1054 | meta = {} | |
|
|
1055 | ||
|
|
1056 | return data, meta | |
|
|
1057 | ||
|
|
1058 | def plot(self): | |
|
|
1059 | # self.data.normalize_heights() | |
|
|
1060 | self.x = self.data.times | |
|
|
1061 | self.y = self.data.yrange | |
|
|
1062 | self.z = self.data['outlier'] | |
|
|
1063 | ||
|
|
1064 | #self.z = numpy.ma.masked_invalid(self.z) | |
|
|
1065 | ||
|
|
1066 | if self.decimation is None: | |
|
|
1067 | x, y, z = self.fill_gaps(self.x, self.y, self.z) | |
|
|
1068 | else: | |
|
|
1069 | x, y, z = self.fill_gaps(*self.decimate()) | |
|
|
1070 | ||
|
|
1071 | for n, ax in enumerate(self.axes): | |
|
|
1072 | ||
|
|
1073 | self.zmax = self.zmax if self.zmax is not None else numpy.max( | |
|
|
1074 | self.z[n]) | |
|
|
1075 | self.zmin = self.zmin if self.zmin is not None else numpy.min( | |
|
|
1076 | self.z[n]) | |
|
|
1077 | data = self.data[-1] | |
|
|
1078 | if ax.firsttime: | |
|
|
1079 | if self.zlimits is not None: | |
|
|
1080 | self.zmin, self.zmax = self.zlimits[n] | |
|
|
1081 | ||
|
|
1082 | ax.plt = ax.pcolormesh(x, y, z[n].T, | |
|
|
1083 | vmin=self.zmin, | |
|
|
1084 | vmax=self.zmax, | |
|
|
1085 | cmap=self.cmaps[n] | |
|
|
1086 | ) | |
|
|
1087 | if self.showprofile: | |
|
|
1088 | ax.plot_profile = self.pf_axes[n].plot(data['outlier'][n], self.y)[0] | |
|
|
1089 | self.pf_axes[n].set_xlabel('') | |
|
|
1090 | else: | |
|
|
1091 | if self.zlimits is not None: | |
|
|
1092 | self.zmin, self.zmax = self.zlimits[n] | |
|
|
1093 | ax.collections.remove(ax.collections[0]) | |
|
|
1094 | ax.plt = ax.pcolormesh(x, y, z[n].T , | |
|
|
1095 | vmin=self.zmin, | |
|
|
1096 | vmax=self.zmax, | |
|
|
1097 | cmap=self.cmaps[n] | |
|
|
1098 | ) | |
|
|
1099 | if self.showprofile: | |
|
|
1100 | ax.plot_profile.set_data(data['outlier'][n], self.y) | |
|
|
1101 | self.pf_axes[n].set_xlabel('') | |
|
|
1102 | ||
|
|
1103 | # class NoiseRTIPlot(Plot): | |
|
|
1104 | # ''' | |
|
|
1105 | # Plot for data_xxxx object | |
|
|
1106 | # ''' | |
|
|
1107 | # | |
|
|
1108 | # CODE = 'noise' | |
|
|
1109 | # colormap = 'jet' | |
|
|
1110 | # plot_type = 'pcolorbuffer' | |
|
|
1111 | # | |
|
|
1112 | # def setup(self): | |
|
|
1113 | # self.xaxis = 'time' | |
|
|
1114 | # self.ncols = 1 | |
|
|
1115 | # self.nrows = self.data.shape('noise')[0] | |
|
|
1116 | # self.nplots = self.nrows | |
|
|
1117 | # self.plots_adjust.update({'hspace':0.8, 'left': 0.08, 'bottom': 0.2, 'right':0.94}) | |
|
|
1118 | # | |
|
|
1119 | # | |
|
|
1120 | # if not self.xlabel: | |
|
|
1121 | # self.xlabel = 'Time' | |
|
|
1122 | # | |
|
|
1123 | # self.ylabel = 'Height [km]' | |
|
|
1124 | # if not self.titles: | |
|
|
1125 | # self.titles = ['noise Ch:{}'.format(x) for x in range(self.nrows)] | |
|
|
1126 | # | |
|
|
1127 | # def update(self, dataOut): | |
|
|
1128 | # | |
|
|
1129 | # data = {} | |
|
|
1130 | # norm = dataOut.max_nIncohInt*dataOut.nProfiles* dataOut.nCohInt*dataOut.windowOfFilter | |
|
|
1131 | # print("max incoh: ",dataOut.max_nIncohInt ) | |
|
|
1132 | # n0 = 10*numpy.log10(dataOut.getNoise()/norm) | |
|
|
1133 | # data['noise'] = n0 | |
|
|
1134 | # | |
|
|
1135 | # meta = {} | |
|
|
1136 | # | |
|
|
1137 | # return data, meta | |
|
|
1138 | # | |
|
|
1139 | # def plot(self): | |
|
|
1140 | # # self.data.normalize_heights() | |
|
|
1141 | # self.x = self.data.times | |
|
|
1142 | # self.y = self.data.yrange | |
|
|
1143 | # self.z = self.data['noise'] | |
|
|
1144 | # | |
|
|
1145 | # #self.z = numpy.ma.masked_invalid(self.z) | |
|
|
1146 | # | |
|
|
1147 | # if self.decimation is None: | |
|
|
1148 | # x, y, z = self.fill_gaps(self.x, self.y, self.z) | |
|
|
1149 | # else: | |
|
|
1150 | # x, y, z = self.fill_gaps(*self.decimate()) | |
|
|
1151 | # | |
|
|
1152 | # for n, ax in enumerate(self.axes): | |
|
|
1153 | # | |
|
|
1154 | # self.zmax = self.zmax if self.zmax is not None else numpy.max( | |
|
|
1155 | # self.z[n]) | |
|
|
1156 | # self.zmin = self.zmin if self.zmin is not None else numpy.min( | |
|
|
1157 | # self.z[n]) | |
|
|
1158 | # data = self.data[-1] | |
|
|
1159 | # if ax.firsttime: | |
|
|
1160 | # if self.zlimits is not None: | |
|
|
1161 | # self.zmin, self.zmax = self.zlimits[n] | |
|
|
1162 | # | |
|
|
1163 | # ax.plt = ax.pcolormesh(x, y, z[n].T, | |
|
|
1164 | # vmin=self.zmin, | |
|
|
1165 | # vmax=self.zmax, | |
|
|
1166 | # cmap=self.cmaps[n] | |
|
|
1167 | # ) | |
|
|
1168 | # if self.showprofile: | |
|
|
1169 | # ax.plot_profile = self.pf_axes[n].plot(data['noise'][n], self.y)[0] | |
|
|
1170 | # else: | |
|
|
1171 | # if self.zlimits is not None: | |
|
|
1172 | # self.zmin, self.zmax = self.zlimits[n] | |
|
|
1173 | # ax.collections.remove(ax.collections[0]) | |
|
|
1174 | # ax.plt = ax.pcolormesh(x, y, z[n].T , | |
|
|
1175 | # vmin=self.zmin, | |
|
|
1176 | # vmax=self.zmax, | |
|
|
1177 | # cmap=self.cmaps[n] | |
|
|
1178 | # ) | |
|
|
1179 | # if self.showprofile: | |
|
|
1180 | # ax.plot_profile.set_data(data['noise'][n], self.y) | |
| @@ -1,665 +1,666 | |||
|
|
1 | 1 | '''' |
|
|
2 | 2 | Created on Set 9, 2015 |
|
|
3 | 3 | |
|
|
4 | 4 | @author: roj-idl71 Karim Kuyeng |
|
|
5 | 5 | |
|
|
6 | 6 | @update: 2021, Joab Apaza |
|
|
7 | 7 | ''' |
|
|
8 | 8 | |
|
|
9 | 9 | import os |
|
|
10 | 10 | import sys |
|
|
11 | 11 | import glob |
|
|
12 | 12 | import fnmatch |
|
|
13 | 13 | import datetime |
|
|
14 | 14 | import time |
|
|
15 | 15 | import re |
|
|
16 | 16 | import h5py |
|
|
17 | 17 | import numpy |
|
|
18 | 18 | |
|
|
19 | 19 | try: |
|
|
20 | 20 | from gevent import sleep |
|
|
21 | 21 | except: |
|
|
22 | 22 | from time import sleep |
|
|
23 | 23 | |
|
|
24 | 24 | from schainpy.model.data.jroheaderIO import RadarControllerHeader, SystemHeader |
|
|
25 | 25 | from schainpy.model.data.jrodata import Voltage |
|
|
26 | 26 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
|
27 | 27 | from numpy import imag |
|
|
28 | 28 | from schainpy.utils import log |
|
|
29 | 29 | |
|
|
30 | 30 | |
|
|
31 | 31 | class AMISRReader(ProcessingUnit): |
|
|
32 | 32 | ''' |
|
|
33 | 33 | classdocs |
|
|
34 | 34 | ''' |
|
|
35 | 35 | |
|
|
36 | 36 | def __init__(self): |
|
|
37 | 37 | ''' |
|
|
38 | 38 | Constructor |
|
|
39 | 39 | ''' |
|
|
40 | 40 | |
|
|
41 | 41 | ProcessingUnit.__init__(self) |
|
|
42 | 42 | |
|
|
43 | 43 | self.set = None |
|
|
44 | 44 | self.subset = None |
|
|
45 | 45 | self.extension_file = '.h5' |
|
|
46 | 46 | self.dtc_str = 'dtc' |
|
|
47 | 47 | self.dtc_id = 0 |
|
|
48 | 48 | self.status = True |
|
|
49 | 49 | self.isConfig = False |
|
|
50 | 50 | self.dirnameList = [] |
|
|
51 | 51 | self.filenameList = [] |
|
|
52 | 52 | self.fileIndex = None |
|
|
53 | 53 | self.flagNoMoreFiles = False |
|
|
54 | 54 | self.flagIsNewFile = 0 |
|
|
55 | 55 | self.filename = '' |
|
|
56 | 56 | self.amisrFilePointer = None |
|
|
57 | 57 | self.realBeamCode = [] |
|
|
58 | 58 | self.beamCodeMap = None |
|
|
59 | 59 | self.azimuthList = [] |
|
|
60 | 60 | self.elevationList = [] |
|
|
61 | 61 | self.dataShape = None |
|
|
62 | 62 | self.flag_old_beams = False |
|
|
63 | 63 | |
|
|
64 | 64 | |
|
|
65 | 65 | self.profileIndex = 0 |
|
|
66 | 66 | |
|
|
67 | 67 | |
|
|
68 | 68 | self.beamCodeByFrame = None |
|
|
69 | 69 | self.radacTimeByFrame = None |
|
|
70 | 70 | |
|
|
71 | 71 | self.dataset = None |
|
|
72 | 72 | |
|
|
73 | 73 | self.__firstFile = True |
|
|
74 | 74 | |
|
|
75 | 75 | self.buffer = None |
|
|
76 | 76 | |
|
|
77 | 77 | self.timezone = 'ut' |
|
|
78 | 78 | |
|
|
79 | 79 | self.__waitForNewFile = 20 |
|
|
80 | 80 | self.__filename_online = None |
|
|
81 | 81 | #Is really necessary create the output object in the initializer |
|
|
82 | 82 | self.dataOut = Voltage() |
|
|
83 | 83 | self.dataOut.error=False |
|
|
84 | 84 | self.margin_days = 1 |
|
|
85 | 85 | |
|
|
86 | 86 | def setup(self,path=None, |
|
|
87 | 87 | startDate=None, |
|
|
88 | 88 | endDate=None, |
|
|
89 | 89 | startTime=None, |
|
|
90 | 90 | endTime=None, |
|
|
91 | 91 | walk=True, |
|
|
92 | 92 | timezone='ut', |
|
|
93 | 93 | all=0, |
|
|
94 | 94 | code = None, |
|
|
95 | 95 | nCode = 0, |
|
|
96 | 96 | nBaud = 0, |
|
|
97 | 97 | online=False, |
|
|
98 | 98 | old_beams=False, |
|
|
99 | 99 | margin_days=1): |
|
|
100 | 100 | |
|
|
101 | 101 | |
|
|
102 | 102 | |
|
|
103 | 103 | self.timezone = timezone |
|
|
104 | 104 | self.all = all |
|
|
105 | 105 | self.online = online |
|
|
106 | 106 | self.flag_old_beams = old_beams |
|
|
107 | 107 | self.code = code |
|
|
108 | 108 | self.nCode = int(nCode) |
|
|
109 | 109 | self.nBaud = int(nBaud) |
|
|
110 | 110 | self.margin_days = margin_days |
|
|
111 | 111 | |
|
|
112 | 112 | |
|
|
113 | 113 | #self.findFiles() |
|
|
114 | 114 | if not(online): |
|
|
115 | 115 | #Busqueda de archivos offline |
|
|
116 | 116 | self.searchFilesOffLine(path, startDate, endDate, startTime, endTime, walk) |
|
|
117 | 117 | else: |
|
|
118 | 118 | self.searchFilesOnLine(path, startDate, endDate, startTime,endTime,walk) |
|
|
119 | 119 | |
|
|
120 | 120 | if not(self.filenameList): |
|
|
121 | 121 | raise schainpy.admin.SchainWarning("There is no files into the folder: %s"%(path)) |
|
|
122 | 122 | sys.exit() |
|
|
123 | 123 | |
|
|
124 | 124 | self.fileIndex = 0 |
|
|
125 | 125 | |
|
|
126 | 126 | self.readNextFile(online) |
|
|
127 | 127 | |
|
|
128 | 128 | ''' |
|
|
129 | 129 | Add code |
|
|
130 | 130 | ''' |
|
|
131 | 131 | self.isConfig = True |
|
|
132 | 132 | # print("Setup Done") |
|
|
133 | 133 | pass |
|
|
134 | 134 | |
|
|
135 | 135 | |
|
|
136 | 136 | def readAMISRHeader(self,fp): |
|
|
137 | 137 | |
|
|
138 | 138 | if self.isConfig and (not self.flagNoMoreFiles): |
|
|
139 | 139 | newShape = fp.get('Raw11/Data/Samples/Data').shape[1:] |
|
|
140 | 140 | if self.dataShape != newShape and newShape != None: |
|
|
141 | 141 | raise schainpy.admin.SchainError("NEW FILE HAS A DIFFERENT SHAPE: ") |
|
|
142 | 142 | print(self.dataShape,newShape,"\n") |
|
|
143 | 143 | return 0 |
|
|
144 | 144 | else: |
|
|
145 | 145 | self.dataShape = fp.get('Raw11/Data/Samples/Data').shape[1:] |
|
|
146 | 146 | |
|
|
147 | 147 | |
|
|
148 | 148 | header = 'Raw11/Data/RadacHeader' |
|
|
149 | 149 | self.beamCodeByPulse = fp.get(header+'/BeamCode') # LIST OF BEAMS PER PROFILE, TO BE USED ON REARRANGE |
|
|
150 | 150 | if (self.startDate> datetime.date(2021, 7, 15)) or self.flag_old_beams: #Se cambió la forma de extracción de Apuntes el 17 o forzar con flag de reorganización |
|
|
151 | 151 | self.beamcodeFile = fp['Setup/Beamcodefile'][()].decode() |
|
|
152 | 152 | self.trueBeams = self.beamcodeFile.split("\n") |
|
|
153 | 153 | self.trueBeams.pop()#remove last |
|
|
154 | 154 | [self.realBeamCode.append(x) for x in self.trueBeams if x not in self.realBeamCode] |
|
|
155 | 155 | self.beamCode = [int(x, 16) for x in self.realBeamCode] |
|
|
156 | 156 | else: |
|
|
157 | 157 | _beamCode= fp.get('Raw11/Data/Beamcodes') #se usa la manera previa al cambio de apuntes |
|
|
158 | 158 | self.beamCode = _beamCode[0,:] |
|
|
159 | 159 | |
|
|
160 | 160 | if self.beamCodeMap == None: |
|
|
161 | 161 | self.beamCodeMap = fp['Setup/BeamcodeMap'] |
|
|
162 | 162 | for beam in self.beamCode: |
|
|
163 | 163 | beamAziElev = numpy.where(self.beamCodeMap[:,0]==beam) |
|
|
164 | 164 | beamAziElev = beamAziElev[0].squeeze() |
|
|
165 | 165 | self.azimuthList.append(self.beamCodeMap[beamAziElev,1]) |
|
|
166 | 166 | self.elevationList.append(self.beamCodeMap[beamAziElev,2]) |
|
|
167 | 167 | #print("Beamssss: ",self.beamCodeMap[beamAziElev,1],self.beamCodeMap[beamAziElev,2]) |
|
|
168 | 168 | #print(self.beamCode) |
|
|
169 | 169 | #self.code = fp.get(header+'/Code') # NOT USE FOR THIS |
|
|
170 | 170 | self.frameCount = fp.get(header+'/FrameCount')# NOT USE FOR THIS |
|
|
171 | 171 | self.modeGroup = fp.get(header+'/ModeGroup')# NOT USE FOR THIS |
|
|
172 | 172 | self.nsamplesPulse = fp.get(header+'/NSamplesPulse')# TO GET NSA OR USING DATA FOR THAT |
|
|
173 | 173 | self.pulseCount = fp.get(header+'/PulseCount')# NOT USE FOR THIS |
|
|
174 | 174 | self.radacTime = fp.get(header+'/RadacTime')# 1st TIME ON FILE ANDE CALCULATE THE REST WITH IPP*nindexprofile |
|
|
175 | 175 | self.timeCount = fp.get(header+'/TimeCount')# NOT USE FOR THIS |
|
|
176 | 176 | self.timeStatus = fp.get(header+'/TimeStatus')# NOT USE FOR THIS |
|
|
177 | 177 | self.rangeFromFile = fp.get('Raw11/Data/Samples/Range') |
|
|
178 | 178 | self.frequency = fp.get('Rx/Frequency') |
|
|
179 | 179 | txAus = fp.get('Raw11/Data/Pulsewidth') |
|
|
180 | 180 | |
|
|
181 | 181 | |
|
|
182 | 182 | self.nblocks = self.pulseCount.shape[0] #nblocks |
|
|
183 | 183 | |
|
|
184 | 184 | self.nprofiles = self.pulseCount.shape[1] #nprofile |
|
|
185 | 185 | self.nsa = self.nsamplesPulse[0,0] #ngates |
|
|
186 | 186 | self.nchannels = len(self.beamCode) |
|
|
187 | 187 | self.ippSeconds = (self.radacTime[0][1] -self.radacTime[0][0]) #Ipp in seconds |
|
|
188 | #print("IPPS secs: ",self.ippSeconds) | |
|
|
188 | 189 | #self.__waitForNewFile = self.nblocks # wait depending on the number of blocks since each block is 1 sec |
|
|
189 | 190 | self.__waitForNewFile = self.nblocks * self.nprofiles * self.ippSeconds # wait until new file is created |
|
|
190 | 191 | |
|
|
191 | 192 | #filling radar controller header parameters |
|
|
192 | 193 | self.__ippKm = self.ippSeconds *.15*1e6 # in km |
|
|
193 | 194 | self.__txA = (txAus[()])*.15 #(ipp[us]*.15km/1us) in km |
|
|
194 | 195 | self.__txB = 0 |
|
|
195 | 196 | nWindows=1 |
|
|
196 | 197 | self.__nSamples = self.nsa |
|
|
197 | 198 | self.__firstHeight = self.rangeFromFile[0][0]/1000 #in km |
|
|
198 | 199 | self.__deltaHeight = (self.rangeFromFile[0][1] - self.rangeFromFile[0][0])/1000 |
|
|
199 | 200 | #print("amisr-ipp:",self.ippSeconds, self.__ippKm) |
|
|
200 | 201 | #for now until understand why the code saved is different (code included even though code not in tuf file) |
|
|
201 | 202 | #self.__codeType = 0 |
|
|
202 | 203 | # self.__nCode = None |
|
|
203 | 204 | # self.__nBaud = None |
|
|
204 | 205 | self.__code = self.code |
|
|
205 | 206 | self.__codeType = 0 |
|
|
206 | 207 | if self.code != None: |
|
|
207 | 208 | self.__codeType = 1 |
|
|
208 | 209 | self.__nCode = self.nCode |
|
|
209 | 210 | self.__nBaud = self.nBaud |
|
|
210 | 211 | #self.__code = 0 |
|
|
211 | 212 | |
|
|
212 | 213 | #filling system header parameters |
|
|
213 | 214 | self.__nSamples = self.nsa |
|
|
214 | 215 | self.newProfiles = self.nprofiles/self.nchannels |
|
|
215 | 216 | self.__channelList = list(range(self.nchannels)) |
|
|
216 | 217 | |
|
|
217 | 218 | self.__frequency = self.frequency[0][0] |
|
|
218 | 219 | |
|
|
219 | 220 | |
|
|
220 | 221 | return 1 |
|
|
221 | 222 | |
|
|
222 | 223 | |
|
|
223 | 224 | def createBuffers(self): |
|
|
224 | 225 | |
|
|
225 | 226 | pass |
|
|
226 | 227 | |
|
|
227 | 228 | def __setParameters(self,path='', startDate='',endDate='',startTime='', endTime='', walk=''): |
|
|
228 | 229 | self.path = path |
|
|
229 | 230 | self.startDate = startDate |
|
|
230 | 231 | self.endDate = endDate |
|
|
231 | 232 | self.startTime = startTime |
|
|
232 | 233 | self.endTime = endTime |
|
|
233 | 234 | self.walk = walk |
|
|
234 | 235 | |
|
|
235 | 236 | def __checkPath(self): |
|
|
236 | 237 | if os.path.exists(self.path): |
|
|
237 | 238 | self.status = 1 |
|
|
238 | 239 | else: |
|
|
239 | 240 | self.status = 0 |
|
|
240 | 241 | print('Path:%s does not exists'%self.path) |
|
|
241 | 242 | |
|
|
242 | 243 | return |
|
|
243 | 244 | |
|
|
244 | 245 | |
|
|
245 | 246 | def __selDates(self, amisr_dirname_format): |
|
|
246 | 247 | try: |
|
|
247 | 248 | year = int(amisr_dirname_format[0:4]) |
|
|
248 | 249 | month = int(amisr_dirname_format[4:6]) |
|
|
249 | 250 | dom = int(amisr_dirname_format[6:8]) |
|
|
250 | 251 | thisDate = datetime.date(year,month,dom) |
|
|
251 | 252 | #margen de un día extra, igual luego se filtra for fecha y hora |
|
|
252 | 253 | if (thisDate>=(self.startDate - datetime.timedelta(days=self.margin_days)) and thisDate <= (self.endDate)+ datetime.timedelta(days=1)): |
|
|
253 | 254 | return amisr_dirname_format |
|
|
254 | 255 | except: |
|
|
255 | 256 | return None |
|
|
256 | 257 | |
|
|
257 | 258 | |
|
|
258 | 259 | def __findDataForDates(self,online=False): |
|
|
259 | 260 | |
|
|
260 | 261 | if not(self.status): |
|
|
261 | 262 | return None |
|
|
262 | 263 | |
|
|
263 | 264 | pat = '\d+.\d+' |
|
|
264 | 265 | dirnameList = [re.search(pat,x) for x in os.listdir(self.path)] |
|
|
265 | 266 | dirnameList = [x for x in dirnameList if x!=None] |
|
|
266 | 267 | dirnameList = [x.string for x in dirnameList] |
|
|
267 | 268 | if not(online): |
|
|
268 | 269 | dirnameList = [self.__selDates(x) for x in dirnameList] |
|
|
269 | 270 | dirnameList = [x for x in dirnameList if x!=None] |
|
|
270 | 271 | if len(dirnameList)>0: |
|
|
271 | 272 | self.status = 1 |
|
|
272 | 273 | self.dirnameList = dirnameList |
|
|
273 | 274 | self.dirnameList.sort() |
|
|
274 | 275 | else: |
|
|
275 | 276 | self.status = 0 |
|
|
276 | 277 | return None |
|
|
277 | 278 | |
|
|
278 | 279 | def __getTimeFromData(self): |
|
|
279 | 280 | startDateTime_Reader = datetime.datetime.combine(self.startDate,self.startTime) |
|
|
280 | 281 | endDateTime_Reader = datetime.datetime.combine(self.endDate,self.endTime) |
|
|
281 | 282 | |
|
|
282 | 283 | print('Filtering Files from %s to %s'%(startDateTime_Reader, endDateTime_Reader)) |
|
|
283 | 284 | print('........................................') |
|
|
284 | 285 | filter_filenameList = [] |
|
|
285 | 286 | self.filenameList.sort() |
|
|
286 | 287 | total_files = len(self.filenameList) |
|
|
287 | 288 | #for i in range(len(self.filenameList)-1): |
|
|
288 | 289 | for i in range(total_files): |
|
|
289 | 290 | filename = self.filenameList[i] |
|
|
290 | 291 | #print("file-> ",filename) |
|
|
291 | 292 | try: |
|
|
292 | 293 | fp = h5py.File(filename,'r') |
|
|
293 | 294 | time_str = fp.get('Time/RadacTimeString') |
|
|
294 | 295 | |
|
|
295 | 296 | startDateTimeStr_File = time_str[0][0].decode('UTF-8').split('.')[0] |
|
|
296 | 297 | #startDateTimeStr_File = "2019-12-16 09:21:11" |
|
|
297 | 298 | junk = time.strptime(startDateTimeStr_File, '%Y-%m-%d %H:%M:%S') |
|
|
298 | 299 | startDateTime_File = datetime.datetime(junk.tm_year,junk.tm_mon,junk.tm_mday,junk.tm_hour, junk.tm_min, junk.tm_sec) |
|
|
299 | 300 | |
|
|
300 | 301 | #endDateTimeStr_File = "2019-12-16 11:10:11" |
|
|
301 | 302 | endDateTimeStr_File = time_str[-1][-1].decode('UTF-8').split('.')[0] |
|
|
302 | 303 | junk = time.strptime(endDateTimeStr_File, '%Y-%m-%d %H:%M:%S') |
|
|
303 | 304 | endDateTime_File = datetime.datetime(junk.tm_year,junk.tm_mon,junk.tm_mday,junk.tm_hour, junk.tm_min, junk.tm_sec) |
|
|
304 | 305 | |
|
|
305 | 306 | fp.close() |
|
|
306 | 307 | |
|
|
307 | 308 | #print("check time", startDateTime_File) |
|
|
308 | 309 | if self.timezone == 'lt': |
|
|
309 | 310 | startDateTime_File = startDateTime_File - datetime.timedelta(minutes = 300) |
|
|
310 | 311 | endDateTime_File = endDateTime_File - datetime.timedelta(minutes = 300) |
|
|
311 | 312 | if (startDateTime_File >=startDateTime_Reader and endDateTime_File<=endDateTime_Reader): |
|
|
312 | 313 | filter_filenameList.append(filename) |
|
|
313 | 314 | |
|
|
314 | 315 | if (startDateTime_File>endDateTime_Reader): |
|
|
315 | 316 | break |
|
|
316 | 317 | except Exception as e: |
|
|
317 | 318 | log.warning("Error opening file {} -> {}".format(os.path.split(filename)[1],e)) |
|
|
318 | 319 | |
|
|
319 | 320 | filter_filenameList.sort() |
|
|
320 | 321 | self.filenameList = filter_filenameList |
|
|
321 | 322 | |
|
|
322 | 323 | return 1 |
|
|
323 | 324 | |
|
|
324 | 325 | def __filterByGlob1(self, dirName): |
|
|
325 | 326 | filter_files = glob.glob1(dirName, '*.*%s'%self.extension_file) |
|
|
326 | 327 | filter_files.sort() |
|
|
327 | 328 | filterDict = {} |
|
|
328 | 329 | filterDict.setdefault(dirName) |
|
|
329 | 330 | filterDict[dirName] = filter_files |
|
|
330 | 331 | return filterDict |
|
|
331 | 332 | |
|
|
332 | 333 | def __getFilenameList(self, fileListInKeys, dirList): |
|
|
333 | 334 | for value in fileListInKeys: |
|
|
334 | 335 | dirName = list(value.keys())[0] |
|
|
335 | 336 | for file in value[dirName]: |
|
|
336 | 337 | filename = os.path.join(dirName, file) |
|
|
337 | 338 | self.filenameList.append(filename) |
|
|
338 | 339 | |
|
|
339 | 340 | |
|
|
340 | 341 | def __selectDataForTimes(self, online=False): |
|
|
341 | 342 | #aun no esta implementado el filtro for tiempo |
|
|
342 | 343 | if not(self.status): |
|
|
343 | 344 | return None |
|
|
344 | 345 | |
|
|
345 | 346 | dirList = [os.path.join(self.path,x) for x in self.dirnameList] |
|
|
346 | 347 | fileListInKeys = [self.__filterByGlob1(x) for x in dirList] |
|
|
347 | 348 | self.__getFilenameList(fileListInKeys, dirList) |
|
|
348 | 349 | if not(online): |
|
|
349 | 350 | #filtro por tiempo |
|
|
350 | 351 | if not(self.all): |
|
|
351 | 352 | self.__getTimeFromData() |
|
|
352 | 353 | |
|
|
353 | 354 | if len(self.filenameList)>0: |
|
|
354 | 355 | self.status = 1 |
|
|
355 | 356 | self.filenameList.sort() |
|
|
356 | 357 | else: |
|
|
357 | 358 | self.status = 0 |
|
|
358 | 359 | return None |
|
|
359 | 360 | |
|
|
360 | 361 | else: |
|
|
361 | 362 | #get the last file - 1 |
|
|
362 | 363 | self.filenameList = [self.filenameList[-2]] |
|
|
363 | 364 | new_dirnameList = [] |
|
|
364 | 365 | for dirname in self.dirnameList: |
|
|
365 | 366 | junk = numpy.array([dirname in x for x in self.filenameList]) |
|
|
366 | 367 | junk_sum = junk.sum() |
|
|
367 | 368 | if junk_sum > 0: |
|
|
368 | 369 | new_dirnameList.append(dirname) |
|
|
369 | 370 | self.dirnameList = new_dirnameList |
|
|
370 | 371 | return 1 |
|
|
371 | 372 | |
|
|
372 | 373 | def searchFilesOnLine(self, path, startDate, endDate, startTime=datetime.time(0,0,0), |
|
|
373 | 374 | endTime=datetime.time(23,59,59),walk=True): |
|
|
374 | 375 | |
|
|
375 | 376 | if endDate ==None: |
|
|
376 | 377 | startDate = datetime.datetime.utcnow().date() |
|
|
377 | 378 | endDate = datetime.datetime.utcnow().date() |
|
|
378 | 379 | |
|
|
379 | 380 | self.__setParameters(path=path, startDate=startDate, endDate=endDate,startTime = startTime,endTime=endTime, walk=walk) |
|
|
380 | 381 | |
|
|
381 | 382 | self.__checkPath() |
|
|
382 | 383 | |
|
|
383 | 384 | self.__findDataForDates(online=True) |
|
|
384 | 385 | |
|
|
385 | 386 | self.dirnameList = [self.dirnameList[-1]] |
|
|
386 | 387 | |
|
|
387 | 388 | self.__selectDataForTimes(online=True) |
|
|
388 | 389 | |
|
|
389 | 390 | return |
|
|
390 | 391 | |
|
|
391 | 392 | |
|
|
392 | 393 | def searchFilesOffLine(self, |
|
|
393 | 394 | path, |
|
|
394 | 395 | startDate, |
|
|
395 | 396 | endDate, |
|
|
396 | 397 | startTime=datetime.time(0,0,0), |
|
|
397 | 398 | endTime=datetime.time(23,59,59), |
|
|
398 | 399 | walk=True): |
|
|
399 | 400 | |
|
|
400 | 401 | self.__setParameters(path, startDate, endDate, startTime, endTime, walk) |
|
|
401 | 402 | |
|
|
402 | 403 | self.__checkPath() |
|
|
403 | 404 | |
|
|
404 | 405 | self.__findDataForDates() |
|
|
405 | 406 | |
|
|
406 | 407 | self.__selectDataForTimes() |
|
|
407 | 408 | |
|
|
408 | 409 | for i in range(len(self.filenameList)): |
|
|
409 | 410 | print("%s" %(self.filenameList[i])) |
|
|
410 | 411 | |
|
|
411 | 412 | return |
|
|
412 | 413 | |
|
|
413 | 414 | def __setNextFileOffline(self): |
|
|
414 | 415 | |
|
|
415 | 416 | try: |
|
|
416 | 417 | self.filename = self.filenameList[self.fileIndex] |
|
|
417 | 418 | self.amisrFilePointer = h5py.File(self.filename,'r') |
|
|
418 | 419 | self.fileIndex += 1 |
|
|
419 | 420 | except: |
|
|
420 | 421 | self.flagNoMoreFiles = 1 |
|
|
421 | 422 | raise schainpy.admin.SchainError('No more files to read') |
|
|
422 | 423 | return 0 |
|
|
423 | 424 | |
|
|
424 | 425 | self.flagIsNewFile = 1 |
|
|
425 | 426 | print("Setting the file: %s"%self.filename) |
|
|
426 | 427 | |
|
|
427 | 428 | return 1 |
|
|
428 | 429 | |
|
|
429 | 430 | |
|
|
430 | 431 | def __setNextFileOnline(self): |
|
|
431 | 432 | filename = self.filenameList[0] |
|
|
432 | 433 | if self.__filename_online != None: |
|
|
433 | 434 | self.__selectDataForTimes(online=True) |
|
|
434 | 435 | filename = self.filenameList[0] |
|
|
435 | 436 | wait = 0 |
|
|
436 | 437 | self.__waitForNewFile=300 ## DEBUG: |
|
|
437 | 438 | while self.__filename_online == filename: |
|
|
438 | 439 | print('waiting %d seconds to get a new file...'%(self.__waitForNewFile)) |
|
|
439 | 440 | if wait == 5: |
|
|
440 | 441 | self.flagNoMoreFiles = 1 |
|
|
441 | 442 | return 0 |
|
|
442 | 443 | sleep(self.__waitForNewFile) |
|
|
443 | 444 | self.__selectDataForTimes(online=True) |
|
|
444 | 445 | filename = self.filenameList[0] |
|
|
445 | 446 | wait += 1 |
|
|
446 | 447 | |
|
|
447 | 448 | self.__filename_online = filename |
|
|
448 | 449 | |
|
|
449 | 450 | self.amisrFilePointer = h5py.File(filename,'r') |
|
|
450 | 451 | self.flagIsNewFile = 1 |
|
|
451 | 452 | self.filename = filename |
|
|
452 | 453 | print("Setting the file: %s"%self.filename) |
|
|
453 | 454 | return 1 |
|
|
454 | 455 | |
|
|
455 | 456 | |
|
|
456 | 457 | def readData(self): |
|
|
457 | 458 | buffer = self.amisrFilePointer.get('Raw11/Data/Samples/Data') |
|
|
458 | 459 | re = buffer[:,:,:,0] |
|
|
459 | 460 | im = buffer[:,:,:,1] |
|
|
460 | 461 | dataset = re + im*1j |
|
|
461 | 462 | |
|
|
462 | 463 | self.radacTime = self.amisrFilePointer.get('Raw11/Data/RadacHeader/RadacTime') |
|
|
463 | 464 | timeset = self.radacTime[:,0] |
|
|
464 | 465 | |
|
|
465 | 466 | return dataset,timeset |
|
|
466 | 467 | |
|
|
467 | 468 | def reshapeData(self): |
|
|
468 | 469 | #self.beamCodeByPulse, self.beamCode, self.nblocks, self.nprofiles, self.nsa, |
|
|
469 | 470 | channels = self.beamCodeByPulse[0,:] |
|
|
470 | 471 | nchan = self.nchannels |
|
|
471 | 472 | #self.newProfiles = self.nprofiles/nchan #must be defined on filljroheader |
|
|
472 | 473 | nblocks = self.nblocks |
|
|
473 | 474 | nsamples = self.nsa |
|
|
474 | 475 | |
|
|
475 | 476 | #Dimensions : nChannels, nProfiles, nSamples |
|
|
476 | 477 | new_block = numpy.empty((nblocks, nchan, numpy.int_(self.newProfiles), nsamples), dtype="complex64") |
|
|
477 | 478 | ############################################ |
|
|
478 | 479 | |
|
|
479 | 480 | for thisChannel in range(nchan): |
|
|
480 | 481 | new_block[:,thisChannel,:,:] = self.dataset[:,numpy.where(channels==self.beamCode[thisChannel])[0],:] |
|
|
481 | 482 | |
|
|
482 | 483 | |
|
|
483 | 484 | new_block = numpy.transpose(new_block, (1,0,2,3)) |
|
|
484 | 485 | new_block = numpy.reshape(new_block, (nchan,-1, nsamples)) |
|
|
485 | 486 | |
|
|
486 | 487 | return new_block |
|
|
487 | 488 | |
|
|
488 | 489 | def updateIndexes(self): |
|
|
489 | 490 | |
|
|
490 | 491 | pass |
|
|
491 | 492 | |
|
|
492 | 493 | def fillJROHeader(self): |
|
|
493 | 494 | |
|
|
494 | 495 | #fill radar controller header |
|
|
495 | 496 | self.dataOut.radarControllerHeaderObj = RadarControllerHeader(ipp=self.__ippKm, |
|
|
496 | 497 | txA=self.__txA, |
|
|
497 | 498 | txB=0, |
|
|
498 | 499 | nWindows=1, |
|
|
499 | 500 | nHeights=self.__nSamples, |
|
|
500 | 501 | firstHeight=self.__firstHeight, |
|
|
501 | 502 | deltaHeight=self.__deltaHeight, |
|
|
502 | 503 | codeType=self.__codeType, |
|
|
503 | 504 | nCode=self.__nCode, nBaud=self.__nBaud, |
|
|
504 | 505 | code = self.__code, |
|
|
505 | 506 | fClock=1) |
|
|
506 | 507 | #fill system header |
|
|
507 | 508 | self.dataOut.systemHeaderObj = SystemHeader(nSamples=self.__nSamples, |
|
|
508 | 509 | nProfiles=self.newProfiles, |
|
|
509 | 510 | nChannels=len(self.__channelList), |
|
|
510 | 511 | adcResolution=14, |
|
|
511 | 512 | pciDioBusWidth=32) |
|
|
512 | 513 | |
|
|
513 | 514 | self.dataOut.type = "Voltage" |
|
|
514 | 515 | self.dataOut.data = None |
|
|
515 | 516 | self.dataOut.dtype = numpy.dtype([('real','<i8'),('imag','<i8')]) |
|
|
516 | 517 | # self.dataOut.nChannels = 0 |
|
|
517 | 518 | |
|
|
518 | 519 | # self.dataOut.nHeights = 0 |
|
|
519 | 520 | |
|
|
520 | 521 | self.dataOut.nProfiles = self.newProfiles*self.nblocks |
|
|
521 | 522 | #self.dataOut.heightList = self.__firstHeigth + numpy.arange(self.__nSamples, dtype = numpy.float)*self.__deltaHeigth |
|
|
522 | 523 | ranges = numpy.reshape(self.rangeFromFile[()],(-1)) |
|
|
523 | 524 | self.dataOut.heightList = ranges/1000.0 #km |
|
|
524 | 525 | self.dataOut.channelList = self.__channelList |
|
|
525 | 526 | self.dataOut.blocksize = self.dataOut.nChannels * self.dataOut.nHeights |
|
|
526 | 527 | |
|
|
527 | 528 | # self.dataOut.channelIndexList = None |
|
|
528 | 529 | |
|
|
529 | 530 | |
|
|
530 | 531 | self.dataOut.azimuthList = numpy.array(self.azimuthList) |
|
|
531 | 532 | self.dataOut.elevationList = numpy.array(self.elevationList) |
|
|
532 | 533 | self.dataOut.codeList = numpy.array(self.beamCode) |
|
|
533 | 534 | #print(self.dataOut.elevationList) |
|
|
534 | 535 | self.dataOut.flagNoData = True |
|
|
535 | 536 | |
|
|
536 | 537 | #Set to TRUE if the data is discontinuous |
|
|
537 | 538 | self.dataOut.flagDiscontinuousBlock = False |
|
|
538 | 539 | |
|
|
539 | 540 | self.dataOut.utctime = None |
|
|
540 | 541 | |
|
|
541 | 542 | #self.dataOut.timeZone = -5 #self.__timezone/60 #timezone like jroheader, difference in minutes between UTC and localtime |
|
|
542 | 543 | if self.timezone == 'lt': |
|
|
543 | 544 | self.dataOut.timeZone = time.timezone / 60. #get the timezone in minutes |
|
|
544 | 545 | else: |
|
|
545 | 546 | self.dataOut.timeZone = 0 #by default time is UTC |
|
|
546 | 547 | |
|
|
547 | 548 | self.dataOut.dstFlag = 0 |
|
|
548 | 549 | self.dataOut.errorCount = 0 |
|
|
549 | 550 | self.dataOut.nCohInt = 1 |
|
|
550 | 551 | self.dataOut.flagDecodeData = False #asumo que la data esta decodificada |
|
|
551 | 552 | self.dataOut.flagDeflipData = False #asumo que la data esta sin flip |
|
|
552 | 553 | self.dataOut.flagShiftFFT = False |
|
|
553 | 554 | self.dataOut.ippSeconds = self.ippSeconds |
|
|
554 | 555 | |
|
|
555 | 556 | #Time interval between profiles |
|
|
556 | 557 | #self.dataOut.timeInterval = self.dataOut.ippSeconds * self.dataOut.nCohInt |
|
|
557 | 558 | |
|
|
558 | 559 | self.dataOut.frequency = self.__frequency |
|
|
559 | 560 | self.dataOut.realtime = self.online |
|
|
560 | 561 | pass |
|
|
561 | 562 | |
|
|
562 | 563 | def readNextFile(self,online=False): |
|
|
563 | 564 | |
|
|
564 | 565 | if not(online): |
|
|
565 | 566 | newFile = self.__setNextFileOffline() |
|
|
566 | 567 | else: |
|
|
567 | 568 | newFile = self.__setNextFileOnline() |
|
|
568 | 569 | |
|
|
569 | 570 | if not(newFile): |
|
|
570 | 571 | self.dataOut.error = True |
|
|
571 | 572 | return 0 |
|
|
572 | 573 | |
|
|
573 | 574 | if not self.readAMISRHeader(self.amisrFilePointer): |
|
|
574 | 575 | self.dataOut.error = True |
|
|
575 | 576 | return 0 |
|
|
576 | 577 | |
|
|
577 | 578 | self.createBuffers() |
|
|
578 | 579 | self.fillJROHeader() |
|
|
579 | 580 | |
|
|
580 | 581 | #self.__firstFile = False |
|
|
581 | 582 | |
|
|
582 | 583 | |
|
|
583 | 584 | |
|
|
584 | 585 | self.dataset,self.timeset = self.readData() |
|
|
585 | 586 | |
|
|
586 | 587 | if self.endDate!=None: |
|
|
587 | 588 | endDateTime_Reader = datetime.datetime.combine(self.endDate,self.endTime) |
|
|
588 | 589 | time_str = self.amisrFilePointer.get('Time/RadacTimeString') |
|
|
589 | 590 | startDateTimeStr_File = time_str[0][0].decode('UTF-8').split('.')[0] |
|
|
590 | 591 | junk = time.strptime(startDateTimeStr_File, '%Y-%m-%d %H:%M:%S') |
|
|
591 | 592 | startDateTime_File = datetime.datetime(junk.tm_year,junk.tm_mon,junk.tm_mday,junk.tm_hour, junk.tm_min, junk.tm_sec) |
|
|
592 | 593 | if self.timezone == 'lt': |
|
|
593 | 594 | startDateTime_File = startDateTime_File - datetime.timedelta(minutes = 300) |
|
|
594 | 595 | if (startDateTime_File>endDateTime_Reader): |
|
|
595 | 596 | return 0 |
|
|
596 | 597 | |
|
|
597 | 598 | self.jrodataset = self.reshapeData() |
|
|
598 | 599 | #----self.updateIndexes() |
|
|
599 | 600 | self.profileIndex = 0 |
|
|
600 | 601 | |
|
|
601 | 602 | return 1 |
|
|
602 | 603 | |
|
|
603 | 604 | |
|
|
604 | 605 | def __hasNotDataInBuffer(self): |
|
|
605 | 606 | if self.profileIndex >= (self.newProfiles*self.nblocks): |
|
|
606 | 607 | return 1 |
|
|
607 | 608 | return 0 |
|
|
608 | 609 | |
|
|
609 | 610 | |
|
|
610 | 611 | def getData(self): |
|
|
611 | 612 | |
|
|
612 | 613 | if self.flagNoMoreFiles: |
|
|
613 | 614 | self.dataOut.flagNoData = True |
|
|
614 | 615 | return 0 |
|
|
615 | 616 | |
|
|
616 | 617 | if self.__hasNotDataInBuffer(): |
|
|
617 | 618 | if not (self.readNextFile(self.online)): |
|
|
618 | 619 | return 0 |
|
|
619 | 620 | |
|
|
620 | 621 | |
|
|
621 | 622 | if self.dataset is None: # setear esta condicion cuando no hayan datos por leer |
|
|
622 | 623 | self.dataOut.flagNoData = True |
|
|
623 | 624 | return 0 |
|
|
624 | 625 | |
|
|
625 | 626 | #self.dataOut.data = numpy.reshape(self.jrodataset[self.profileIndex,:],(1,-1)) |
|
|
626 | 627 | |
|
|
627 | 628 | self.dataOut.data = self.jrodataset[:,self.profileIndex,:] |
|
|
628 | 629 | |
|
|
629 | 630 | #print("R_t",self.timeset) |
|
|
630 | 631 | |
|
|
631 | 632 | #self.dataOut.utctime = self.jrotimeset[self.profileIndex] |
|
|
632 | 633 | #verificar basic header de jro data y ver si es compatible con este valor |
|
|
633 | 634 | #self.dataOut.utctime = self.timeset + (self.profileIndex * self.ippSeconds * self.nchannels) |
|
|
634 | 635 | indexprof = numpy.mod(self.profileIndex, self.newProfiles) |
|
|
635 | 636 | indexblock = self.profileIndex/self.newProfiles |
|
|
636 | 637 | #print (indexblock, indexprof) |
|
|
637 | 638 | diffUTC = 0 |
|
|
638 | 639 | t_comp = (indexprof * self.ippSeconds * self.nchannels) + diffUTC # |
|
|
639 | 640 | |
|
|
640 | 641 | #print("utc :",indexblock," __ ",t_comp) |
|
|
641 | 642 | #print(numpy.shape(self.timeset)) |
|
|
642 | 643 | self.dataOut.utctime = self.timeset[numpy.int_(indexblock)] + t_comp |
|
|
643 | 644 | #self.dataOut.utctime = self.timeset[self.profileIndex] + t_comp |
|
|
644 | 645 | |
|
|
645 | 646 | self.dataOut.profileIndex = self.profileIndex |
|
|
646 | 647 | #print("N profile:",self.profileIndex,self.newProfiles,self.nblocks,self.dataOut.utctime) |
|
|
647 | 648 | self.dataOut.flagNoData = False |
|
|
648 | 649 | # if indexprof == 0: |
|
|
649 | 650 | # print("kamisr: ",self.dataOut.utctime) |
|
|
650 | 651 | |
|
|
651 | 652 | self.profileIndex += 1 |
|
|
652 | 653 | |
|
|
653 | 654 | return self.dataOut.data #retorno necesario?? |
|
|
654 | 655 | |
|
|
655 | 656 | |
|
|
656 | 657 | def run(self, **kwargs): |
|
|
657 | 658 | ''' |
|
|
658 | 659 | This method will be called many times so here you should put all your code |
|
|
659 | 660 | ''' |
|
|
660 | 661 | #print("running kamisr") |
|
|
661 | 662 | if not self.isConfig: |
|
|
662 | 663 | self.setup(**kwargs) |
|
|
663 | 664 | self.isConfig = True |
|
|
664 | 665 | |
|
|
665 | 666 | self.getData() |
| @@ -1,1890 +1,1948 | |||
|
|
1 | 1 | # Copyright (c) 2012-2020 Jicamarca Radio Observatory |
|
|
2 | 2 | # All rights reserved. |
|
|
3 | 3 | # |
|
|
4 | 4 | # Distributed under the terms of the BSD 3-clause license. |
|
|
5 | 5 | """Spectra processing Unit and operations |
|
|
6 | 6 | |
|
|
7 | 7 | Here you will find the processing unit `SpectraProc` and several operations |
|
|
8 | 8 | to work with Spectra data type |
|
|
9 | 9 | """ |
|
|
10 | 10 | |
|
|
11 | 11 | import time |
|
|
12 | 12 | import itertools |
|
|
13 | 13 | |
|
|
14 | 14 | import numpy |
|
|
15 | 15 | import math |
|
|
16 | 16 | |
|
|
17 | 17 | from schainpy.model.proc.jroproc_base import ProcessingUnit, MPDecorator, Operation |
|
|
18 | 18 | from schainpy.model.data.jrodata import Spectra |
|
|
19 | 19 | from schainpy.model.data.jrodata import hildebrand_sekhon |
|
|
20 | 20 | from schainpy.model.data import _noise |
|
|
21 | 21 | |
|
|
22 | 22 | from schainpy.utils import log |
|
|
23 | ||
|
|
23 | import matplotlib.pyplot as plt | |
|
|
24 | 24 | from scipy.optimize import curve_fit |
|
|
25 | 25 | |
|
|
26 | 26 | class SpectraProc(ProcessingUnit): |
|
|
27 | 27 | |
|
|
28 | 28 | def __init__(self): |
|
|
29 | 29 | |
|
|
30 | 30 | ProcessingUnit.__init__(self) |
|
|
31 | 31 | |
|
|
32 | 32 | self.buffer = None |
|
|
33 | 33 | self.firstdatatime = None |
|
|
34 | 34 | self.profIndex = 0 |
|
|
35 | 35 | self.dataOut = Spectra() |
|
|
36 | 36 | self.id_min = None |
|
|
37 | 37 | self.id_max = None |
|
|
38 | 38 | self.setupReq = False #Agregar a todas las unidades de proc |
|
|
39 | 39 | |
|
|
40 | 40 | def __updateSpecFromVoltage(self): |
|
|
41 | 41 | |
|
|
42 | 42 | |
|
|
43 | 43 | |
|
|
44 | 44 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
45 | 45 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
46 | 46 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
47 | 47 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
48 | 48 | try: |
|
|
49 | 49 | self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy() |
|
|
50 | 50 | except: |
|
|
51 | 51 | pass |
|
|
52 | 52 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
53 | 53 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
|
54 | 54 | self.dataOut.channelList = self.dataIn.channelList |
|
|
55 | 55 | self.dataOut.heightList = self.dataIn.heightList |
|
|
56 | 56 | self.dataOut.dtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')]) |
|
|
57 | 57 | self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
58 | 58 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
|
59 | 59 | self.dataOut.utctime = self.firstdatatime |
|
|
60 | 60 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData |
|
|
61 | 61 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData |
|
|
62 | 62 | self.dataOut.flagShiftFFT = False |
|
|
63 | 63 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
64 | 64 | self.dataOut.nIncohInt = 1 |
|
|
65 | 65 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
|
66 | 66 | self.dataOut.frequency = self.dataIn.frequency |
|
|
67 | 67 | self.dataOut.realtime = self.dataIn.realtime |
|
|
68 | 68 | self.dataOut.azimuth = self.dataIn.azimuth |
|
|
69 | 69 | self.dataOut.zenith = self.dataIn.zenith |
|
|
70 | 70 | self.dataOut.codeList = self.dataIn.codeList |
|
|
71 | 71 | self.dataOut.azimuthList = self.dataIn.azimuthList |
|
|
72 | 72 | self.dataOut.elevationList = self.dataIn.elevationList |
|
|
73 | 73 | |
|
|
74 | 74 | |
|
|
75 | 75 | def __getFft(self): |
|
|
76 | 76 | """ |
|
|
77 | 77 | Convierte valores de Voltaje a Spectra |
|
|
78 | 78 | |
|
|
79 | 79 | Affected: |
|
|
80 | 80 | self.dataOut.data_spc |
|
|
81 | 81 | self.dataOut.data_cspc |
|
|
82 | 82 | self.dataOut.data_dc |
|
|
83 | 83 | self.dataOut.heightList |
|
|
84 | 84 | self.profIndex |
|
|
85 | 85 | self.buffer |
|
|
86 | 86 | self.dataOut.flagNoData |
|
|
87 | 87 | """ |
|
|
88 | 88 | fft_volt = numpy.fft.fft( |
|
|
89 | 89 | self.buffer, n=self.dataOut.nFFTPoints, axis=1) |
|
|
90 | 90 | fft_volt = fft_volt.astype(numpy.dtype('complex')) |
|
|
91 | 91 | dc = fft_volt[:, 0, :] |
|
|
92 | 92 | |
|
|
93 | 93 | # calculo de self-spectra |
|
|
94 | 94 | fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,)) |
|
|
95 | 95 | spc = fft_volt * numpy.conjugate(fft_volt) |
|
|
96 | 96 | spc = spc.real |
|
|
97 | 97 | |
|
|
98 | 98 | blocksize = 0 |
|
|
99 | 99 | blocksize += dc.size |
|
|
100 | 100 | blocksize += spc.size |
|
|
101 | 101 | |
|
|
102 | 102 | cspc = None |
|
|
103 | 103 | pairIndex = 0 |
|
|
104 | 104 | if self.dataOut.pairsList != None: |
|
|
105 | 105 | # calculo de cross-spectra |
|
|
106 | 106 | cspc = numpy.zeros( |
|
|
107 | 107 | (self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex') |
|
|
108 | 108 | for pair in self.dataOut.pairsList: |
|
|
109 | 109 | if pair[0] not in self.dataOut.channelList: |
|
|
110 | 110 | raise ValueError("Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" % ( |
|
|
111 | 111 | str(pair), str(self.dataOut.channelList))) |
|
|
112 | 112 | if pair[1] not in self.dataOut.channelList: |
|
|
113 | 113 | raise ValueError("Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" % ( |
|
|
114 | 114 | str(pair), str(self.dataOut.channelList))) |
|
|
115 | 115 | |
|
|
116 | 116 | cspc[pairIndex, :, :] = fft_volt[pair[0], :, :] * \ |
|
|
117 | 117 | numpy.conjugate(fft_volt[pair[1], :, :]) |
|
|
118 | 118 | pairIndex += 1 |
|
|
119 | 119 | blocksize += cspc.size |
|
|
120 | 120 | |
|
|
121 | 121 | self.dataOut.data_spc = spc |
|
|
122 | 122 | self.dataOut.data_cspc = cspc |
|
|
123 | 123 | self.dataOut.data_dc = dc |
|
|
124 | 124 | self.dataOut.blockSize = blocksize |
|
|
125 | 125 | self.dataOut.flagShiftFFT = False |
|
|
126 | 126 | |
|
|
127 | 127 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=None, ippFactor=None, shift_fft=False): |
|
|
128 | ||
|
|
128 | #print("run spc proc") | |
|
|
129 | 129 | if self.dataIn.type == "Spectra": |
|
|
130 | 130 | |
|
|
131 | 131 | try: |
|
|
132 | 132 | self.dataOut.copy(self.dataIn) |
|
|
133 | 133 | |
|
|
134 | 134 | except Exception as e: |
|
|
135 | 135 | print("Error dataIn ",e) |
|
|
136 | 136 | |
|
|
137 | 137 | if shift_fft: |
|
|
138 | 138 | #desplaza a la derecha en el eje 2 determinadas posiciones |
|
|
139 | 139 | shift = int(self.dataOut.nFFTPoints/2) |
|
|
140 | 140 | self.dataOut.data_spc = numpy.roll(self.dataOut.data_spc, shift , axis=1) |
|
|
141 | 141 | |
|
|
142 | 142 | if self.dataOut.data_cspc is not None: |
|
|
143 | 143 | #desplaza a la derecha en el eje 2 determinadas posiciones |
|
|
144 | 144 | self.dataOut.data_cspc = numpy.roll(self.dataOut.data_cspc, shift, axis=1) |
|
|
145 | 145 | if pairsList: |
|
|
146 | 146 | self.__selectPairs(pairsList) |
|
|
147 | 147 | |
|
|
148 | 148 | |
|
|
149 | 149 | elif self.dataIn.type == "Voltage": |
|
|
150 | 150 | |
|
|
151 | 151 | self.dataOut.flagNoData = True |
|
|
152 | 152 | |
|
|
153 | 153 | if nFFTPoints == None: |
|
|
154 | 154 | raise ValueError("This SpectraProc.run() need nFFTPoints input variable") |
|
|
155 | 155 | |
|
|
156 | 156 | if nProfiles == None: |
|
|
157 | 157 | nProfiles = nFFTPoints |
|
|
158 | 158 | |
|
|
159 | 159 | if ippFactor == None: |
|
|
160 | 160 | self.dataOut.ippFactor = 1 |
|
|
161 | 161 | |
|
|
162 | 162 | self.dataOut.nFFTPoints = nFFTPoints |
|
|
163 | 163 | #print(" volts ch,prof, h: ", self.dataIn.data.shape) |
|
|
164 | 164 | if self.buffer is None: |
|
|
165 | 165 | self.buffer = numpy.zeros((self.dataIn.nChannels, |
|
|
166 | 166 | nProfiles, |
|
|
167 | 167 | self.dataIn.nHeights), |
|
|
168 | 168 | dtype='complex') |
|
|
169 | 169 | |
|
|
170 | 170 | if self.dataIn.flagDataAsBlock: |
|
|
171 | 171 | nVoltProfiles = self.dataIn.data.shape[1] |
|
|
172 | 172 | |
|
|
173 | 173 | if nVoltProfiles == nProfiles: |
|
|
174 | 174 | self.buffer = self.dataIn.data.copy() |
|
|
175 | 175 | self.profIndex = nVoltProfiles |
|
|
176 | 176 | |
|
|
177 | 177 | elif nVoltProfiles < nProfiles: |
|
|
178 | 178 | |
|
|
179 | 179 | if self.profIndex == 0: |
|
|
180 | 180 | self.id_min = 0 |
|
|
181 | 181 | self.id_max = nVoltProfiles |
|
|
182 | 182 | |
|
|
183 | 183 | self.buffer[:, self.id_min:self.id_max, |
|
|
184 | 184 | :] = self.dataIn.data |
|
|
185 | 185 | self.profIndex += nVoltProfiles |
|
|
186 | 186 | self.id_min += nVoltProfiles |
|
|
187 | 187 | self.id_max += nVoltProfiles |
|
|
188 | 188 | else: |
|
|
189 | 189 | raise ValueError("The type object %s has %d profiles, it should just has %d profiles" % ( |
|
|
190 | 190 | self.dataIn.type, self.dataIn.data.shape[1], nProfiles)) |
|
|
191 | 191 | self.dataOut.flagNoData = True |
|
|
192 | 192 | else: |
|
|
193 | 193 | self.buffer[:, self.profIndex, :] = self.dataIn.data.copy() |
|
|
194 | 194 | self.profIndex += 1 |
|
|
195 | 195 | |
|
|
196 | 196 | if self.firstdatatime == None: |
|
|
197 | 197 | self.firstdatatime = self.dataIn.utctime |
|
|
198 | 198 | |
|
|
199 | 199 | if self.profIndex == nProfiles: |
|
|
200 | 200 | |
|
|
201 | 201 | self.__updateSpecFromVoltage() |
|
|
202 | ||
|
|
202 | 203 | if pairsList == None: |
|
|
203 | 204 | self.dataOut.pairsList = [pair for pair in itertools.combinations(self.dataOut.channelList, 2)] |
|
|
204 | 205 | else: |
|
|
205 | 206 | self.dataOut.pairsList = pairsList |
|
|
206 | 207 | self.__getFft() |
|
|
207 | 208 | self.dataOut.flagNoData = False |
|
|
208 | 209 | self.firstdatatime = None |
|
|
209 | 210 | self.profIndex = 0 |
|
|
210 | 211 | |
|
|
212 | ||
|
|
211 | 213 | else: |
|
|
212 | 214 | raise ValueError("The type of input object '%s' is not valid".format( |
|
|
213 | 215 | self.dataIn.type)) |
|
|
214 | 216 | |
|
|
215 | 217 | |
|
|
216 | 218 | def __selectPairs(self, pairsList): |
|
|
217 | 219 | |
|
|
218 | 220 | if not pairsList: |
|
|
219 | 221 | return |
|
|
220 | 222 | |
|
|
221 | 223 | pairs = [] |
|
|
222 | 224 | pairsIndex = [] |
|
|
223 | 225 | |
|
|
224 | 226 | for pair in pairsList: |
|
|
225 | 227 | if pair[0] not in self.dataOut.channelList or pair[1] not in self.dataOut.channelList: |
|
|
226 | 228 | continue |
|
|
227 | 229 | pairs.append(pair) |
|
|
228 | 230 | pairsIndex.append(pairs.index(pair)) |
|
|
229 | 231 | |
|
|
230 | 232 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndex] |
|
|
231 | 233 | self.dataOut.pairsList = pairs |
|
|
232 | 234 | |
|
|
233 | 235 | return |
|
|
234 | 236 | |
|
|
235 | 237 | def selectFFTs(self, minFFT, maxFFT ): |
|
|
236 | 238 | """ |
|
|
237 | 239 | Selecciona un bloque de datos en base a un grupo de valores de puntos FFTs segun el rango |
|
|
238 | 240 | minFFT<= FFT <= maxFFT |
|
|
239 | 241 | """ |
|
|
240 | 242 | |
|
|
241 | 243 | if (minFFT > maxFFT): |
|
|
242 | 244 | raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minFFT, maxFFT)) |
|
|
243 | 245 | |
|
|
244 | 246 | if (minFFT < self.dataOut.getFreqRange()[0]): |
|
|
245 | 247 | minFFT = self.dataOut.getFreqRange()[0] |
|
|
246 | 248 | |
|
|
247 | 249 | if (maxFFT > self.dataOut.getFreqRange()[-1]): |
|
|
248 | 250 | maxFFT = self.dataOut.getFreqRange()[-1] |
|
|
249 | 251 | |
|
|
250 | 252 | minIndex = 0 |
|
|
251 | 253 | maxIndex = 0 |
|
|
252 | 254 | FFTs = self.dataOut.getFreqRange() |
|
|
253 | 255 | |
|
|
254 | 256 | inda = numpy.where(FFTs >= minFFT) |
|
|
255 | 257 | indb = numpy.where(FFTs <= maxFFT) |
|
|
256 | 258 | |
|
|
257 | 259 | try: |
|
|
258 | 260 | minIndex = inda[0][0] |
|
|
259 | 261 | except: |
|
|
260 | 262 | minIndex = 0 |
|
|
261 | 263 | |
|
|
262 | 264 | try: |
|
|
263 | 265 | maxIndex = indb[0][-1] |
|
|
264 | 266 | except: |
|
|
265 | 267 | maxIndex = len(FFTs) |
|
|
266 | 268 | |
|
|
267 | 269 | self.selectFFTsByIndex(minIndex, maxIndex) |
|
|
268 | 270 | |
|
|
269 | 271 | return 1 |
|
|
270 | 272 | |
|
|
271 | 273 | def getBeaconSignal(self, tauindex=0, channelindex=0, hei_ref=None): |
|
|
272 | 274 | newheis = numpy.where( |
|
|
273 | 275 | self.dataOut.heightList > self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
|
274 | 276 | |
|
|
275 | 277 | if hei_ref != None: |
|
|
276 | 278 | newheis = numpy.where(self.dataOut.heightList > hei_ref) |
|
|
277 | 279 | |
|
|
278 | 280 | minIndex = min(newheis[0]) |
|
|
279 | 281 | maxIndex = max(newheis[0]) |
|
|
280 | 282 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] |
|
|
281 | 283 | heightList = self.dataOut.heightList[minIndex:maxIndex + 1] |
|
|
282 | 284 | |
|
|
283 | 285 | # determina indices |
|
|
284 | 286 | nheis = int(self.dataOut.radarControllerHeaderObj.txB / |
|
|
285 | 287 | (self.dataOut.heightList[1] - self.dataOut.heightList[0])) |
|
|
286 | 288 | avg_dB = 10 * \ |
|
|
287 | 289 | numpy.log10(numpy.sum(data_spc[channelindex, :, :], axis=0)) |
|
|
288 | 290 | beacon_dB = numpy.sort(avg_dB)[-nheis:] |
|
|
289 | 291 | beacon_heiIndexList = [] |
|
|
290 | 292 | for val in avg_dB.tolist(): |
|
|
291 | 293 | if val >= beacon_dB[0]: |
|
|
292 | 294 | beacon_heiIndexList.append(avg_dB.tolist().index(val)) |
|
|
293 | 295 | |
|
|
294 | 296 | #data_spc = data_spc[:,:,beacon_heiIndexList] |
|
|
295 | 297 | data_cspc = None |
|
|
296 | 298 | if self.dataOut.data_cspc is not None: |
|
|
297 | 299 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] |
|
|
298 | 300 | #data_cspc = data_cspc[:,:,beacon_heiIndexList] |
|
|
299 | 301 | |
|
|
300 | 302 | data_dc = None |
|
|
301 | 303 | if self.dataOut.data_dc is not None: |
|
|
302 | 304 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] |
|
|
303 | 305 | #data_dc = data_dc[:,beacon_heiIndexList] |
|
|
304 | 306 | |
|
|
305 | 307 | self.dataOut.data_spc = data_spc |
|
|
306 | 308 | self.dataOut.data_cspc = data_cspc |
|
|
307 | 309 | self.dataOut.data_dc = data_dc |
|
|
308 | 310 | self.dataOut.heightList = heightList |
|
|
309 | 311 | self.dataOut.beacon_heiIndexList = beacon_heiIndexList |
|
|
310 | 312 | |
|
|
311 | 313 | return 1 |
|
|
312 | 314 | |
|
|
313 | 315 | def selectFFTsByIndex(self, minIndex, maxIndex): |
|
|
314 | 316 | """ |
|
|
315 | 317 | |
|
|
316 | 318 | """ |
|
|
317 | 319 | |
|
|
318 | 320 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
319 | 321 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex)) |
|
|
320 | 322 | |
|
|
321 | 323 | if (maxIndex >= self.dataOut.nProfiles): |
|
|
322 | 324 | maxIndex = self.dataOut.nProfiles-1 |
|
|
323 | 325 | |
|
|
324 | 326 | #Spectra |
|
|
325 | 327 | data_spc = self.dataOut.data_spc[:,minIndex:maxIndex+1,:] |
|
|
326 | 328 | |
|
|
327 | 329 | data_cspc = None |
|
|
328 | 330 | if self.dataOut.data_cspc is not None: |
|
|
329 | 331 | data_cspc = self.dataOut.data_cspc[:,minIndex:maxIndex+1,:] |
|
|
330 | 332 | |
|
|
331 | 333 | data_dc = None |
|
|
332 | 334 | if self.dataOut.data_dc is not None: |
|
|
333 | 335 | data_dc = self.dataOut.data_dc[minIndex:maxIndex+1,:] |
|
|
334 | 336 | |
|
|
335 | 337 | self.dataOut.data_spc = data_spc |
|
|
336 | 338 | self.dataOut.data_cspc = data_cspc |
|
|
337 | 339 | self.dataOut.data_dc = data_dc |
|
|
338 | 340 | |
|
|
339 | 341 | self.dataOut.ippSeconds = self.dataOut.ippSeconds*(self.dataOut.nFFTPoints / numpy.shape(data_cspc)[1]) |
|
|
340 | 342 | self.dataOut.nFFTPoints = numpy.shape(data_cspc)[1] |
|
|
341 | 343 | self.dataOut.profilesPerBlock = numpy.shape(data_cspc)[1] |
|
|
342 | 344 | |
|
|
343 | 345 | return 1 |
|
|
344 | 346 | |
|
|
345 | 347 | def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None): |
|
|
346 | 348 | # validacion de rango |
|
|
347 | 349 | if minHei == None: |
|
|
348 | 350 | minHei = self.dataOut.heightList[0] |
|
|
349 | 351 | |
|
|
350 | 352 | if maxHei == None: |
|
|
351 | 353 | maxHei = self.dataOut.heightList[-1] |
|
|
352 | 354 | |
|
|
353 | 355 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): |
|
|
354 | 356 | print('minHei: %.2f is out of the heights range' % (minHei)) |
|
|
355 | 357 | print('minHei is setting to %.2f' % (self.dataOut.heightList[0])) |
|
|
356 | 358 | minHei = self.dataOut.heightList[0] |
|
|
357 | 359 | |
|
|
358 | 360 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): |
|
|
359 | 361 | print('maxHei: %.2f is out of the heights range' % (maxHei)) |
|
|
360 | 362 | print('maxHei is setting to %.2f' % (self.dataOut.heightList[-1])) |
|
|
361 | 363 | maxHei = self.dataOut.heightList[-1] |
|
|
362 | 364 | |
|
|
363 | 365 | # validacion de velocidades |
|
|
364 | 366 | velrange = self.dataOut.getVelRange(1) |
|
|
365 | 367 | |
|
|
366 | 368 | if minVel == None: |
|
|
367 | 369 | minVel = velrange[0] |
|
|
368 | 370 | |
|
|
369 | 371 | if maxVel == None: |
|
|
370 | 372 | maxVel = velrange[-1] |
|
|
371 | 373 | |
|
|
372 | 374 | if (minVel < velrange[0]) or (minVel > maxVel): |
|
|
373 | 375 | print('minVel: %.2f is out of the velocity range' % (minVel)) |
|
|
374 | 376 | print('minVel is setting to %.2f' % (velrange[0])) |
|
|
375 | 377 | minVel = velrange[0] |
|
|
376 | 378 | |
|
|
377 | 379 | if (maxVel > velrange[-1]) or (maxVel < minVel): |
|
|
378 | 380 | print('maxVel: %.2f is out of the velocity range' % (maxVel)) |
|
|
379 | 381 | print('maxVel is setting to %.2f' % (velrange[-1])) |
|
|
380 | 382 | maxVel = velrange[-1] |
|
|
381 | 383 | |
|
|
382 | 384 | # seleccion de indices para rango |
|
|
383 | 385 | minIndex = 0 |
|
|
384 | 386 | maxIndex = 0 |
|
|
385 | 387 | heights = self.dataOut.heightList |
|
|
386 | 388 | |
|
|
387 | 389 | inda = numpy.where(heights >= minHei) |
|
|
388 | 390 | indb = numpy.where(heights <= maxHei) |
|
|
389 | 391 | |
|
|
390 | 392 | try: |
|
|
391 | 393 | minIndex = inda[0][0] |
|
|
392 | 394 | except: |
|
|
393 | 395 | minIndex = 0 |
|
|
394 | 396 | |
|
|
395 | 397 | try: |
|
|
396 | 398 | maxIndex = indb[0][-1] |
|
|
397 | 399 | except: |
|
|
398 | 400 | maxIndex = len(heights) |
|
|
399 | 401 | |
|
|
400 | 402 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
401 | 403 | raise ValueError("some value in (%d,%d) is not valid" % ( |
|
|
402 | 404 | minIndex, maxIndex)) |
|
|
403 | 405 | |
|
|
404 | 406 | if (maxIndex >= self.dataOut.nHeights): |
|
|
405 | 407 | maxIndex = self.dataOut.nHeights - 1 |
|
|
406 | 408 | |
|
|
407 | 409 | # seleccion de indices para velocidades |
|
|
408 | 410 | indminvel = numpy.where(velrange >= minVel) |
|
|
409 | 411 | indmaxvel = numpy.where(velrange <= maxVel) |
|
|
410 | 412 | try: |
|
|
411 | 413 | minIndexVel = indminvel[0][0] |
|
|
412 | 414 | except: |
|
|
413 | 415 | minIndexVel = 0 |
|
|
414 | 416 | |
|
|
415 | 417 | try: |
|
|
416 | 418 | maxIndexVel = indmaxvel[0][-1] |
|
|
417 | 419 | except: |
|
|
418 | 420 | maxIndexVel = len(velrange) |
|
|
419 | 421 | |
|
|
420 | 422 | # seleccion del espectro |
|
|
421 | 423 | data_spc = self.dataOut.data_spc[:, |
|
|
422 | 424 | minIndexVel:maxIndexVel + 1, minIndex:maxIndex + 1] |
|
|
423 | 425 | # estimacion de ruido |
|
|
424 | 426 | noise = numpy.zeros(self.dataOut.nChannels) |
|
|
425 | 427 | |
|
|
426 | 428 | for channel in range(self.dataOut.nChannels): |
|
|
427 | 429 | daux = data_spc[channel, :, :] |
|
|
428 | 430 | sortdata = numpy.sort(daux, axis=None) |
|
|
429 | 431 | noise[channel] = hildebrand_sekhon(sortdata, self.dataOut.nIncohInt) |
|
|
430 | 432 | |
|
|
431 | 433 | self.dataOut.noise_estimation = noise.copy() |
|
|
432 | 434 | |
|
|
433 | 435 | return 1 |
|
|
434 | 436 | |
|
|
435 | 437 | class removeDC(Operation): |
|
|
436 | 438 | |
|
|
437 | 439 | def run(self, dataOut, mode=2): |
|
|
438 | 440 | self.dataOut = dataOut |
|
|
439 | 441 | jspectra = self.dataOut.data_spc |
|
|
440 | 442 | jcspectra = self.dataOut.data_cspc |
|
|
441 | 443 | |
|
|
442 | 444 | num_chan = jspectra.shape[0] |
|
|
443 | 445 | num_hei = jspectra.shape[2] |
|
|
444 | 446 | |
|
|
445 | 447 | if jcspectra is not None: |
|
|
446 | 448 | jcspectraExist = True |
|
|
447 | 449 | num_pairs = jcspectra.shape[0] |
|
|
448 | 450 | else: |
|
|
449 | 451 | jcspectraExist = False |
|
|
450 | 452 | |
|
|
451 | 453 | freq_dc = int(jspectra.shape[1] / 2) |
|
|
452 | 454 | ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc |
|
|
453 | 455 | ind_vel = ind_vel.astype(int) |
|
|
454 | 456 | |
|
|
455 | 457 | if ind_vel[0] < 0: |
|
|
456 | 458 | ind_vel[list(range(0, 1))] = ind_vel[list(range(0, 1))] + self.num_prof |
|
|
457 | 459 | |
|
|
458 | 460 | if mode == 1: |
|
|
459 | 461 | jspectra[:, freq_dc, :] = ( |
|
|
460 | 462 | jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2 # CORRECCION |
|
|
461 | 463 | |
|
|
462 | 464 | if jcspectraExist: |
|
|
463 | 465 | jcspectra[:, freq_dc, :] = ( |
|
|
464 | 466 | jcspectra[:, ind_vel[1], :] + jcspectra[:, ind_vel[2], :]) / 2 |
|
|
465 | 467 | |
|
|
466 | 468 | if mode == 2: |
|
|
467 | 469 | |
|
|
468 | 470 | vel = numpy.array([-2, -1, 1, 2]) |
|
|
469 | 471 | xx = numpy.zeros([4, 4]) |
|
|
470 | 472 | |
|
|
471 | 473 | for fil in range(4): |
|
|
472 | 474 | xx[fil, :] = vel[fil]**numpy.asarray(list(range(4))) |
|
|
473 | 475 | |
|
|
474 | 476 | xx_inv = numpy.linalg.inv(xx) |
|
|
475 | 477 | xx_aux = xx_inv[0, :] |
|
|
476 | 478 | |
|
|
477 | 479 | for ich in range(num_chan): |
|
|
478 | 480 | yy = jspectra[ich, ind_vel, :] |
|
|
479 | 481 | jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy) |
|
|
480 | 482 | |
|
|
481 | 483 | junkid = jspectra[ich, freq_dc, :] <= 0 |
|
|
482 | 484 | cjunkid = sum(junkid) |
|
|
483 | 485 | |
|
|
484 | 486 | if cjunkid.any(): |
|
|
485 | 487 | jspectra[ich, freq_dc, junkid.nonzero()] = ( |
|
|
486 | 488 | jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2 |
|
|
487 | 489 | |
|
|
488 | 490 | if jcspectraExist: |
|
|
489 | 491 | for ip in range(num_pairs): |
|
|
490 | 492 | yy = jcspectra[ip, ind_vel, :] |
|
|
491 | 493 | jcspectra[ip, freq_dc, :] = numpy.dot(xx_aux, yy) |
|
|
492 | 494 | |
|
|
493 | 495 | self.dataOut.data_spc = jspectra |
|
|
494 | 496 | self.dataOut.data_cspc = jcspectra |
|
|
495 | 497 | |
|
|
496 | 498 | return self.dataOut |
|
|
497 | 499 | |
|
|
498 | 500 | class getNoise(Operation): |
|
|
499 | 501 | warnings = False |
|
|
500 | 502 | def __init__(self): |
|
|
501 | 503 | |
|
|
502 | 504 | Operation.__init__(self) |
|
|
503 | 505 | |
|
|
504 | 506 | def run(self, dataOut, minHei=None, maxHei=None,minVel=None, maxVel=None, minFreq= None, maxFreq=None, warnings=False): |
|
|
505 | 507 | self.dataOut = dataOut |
|
|
506 | 508 | self.warnings = warnings |
|
|
507 | 509 | if minHei == None: |
|
|
508 | 510 | minHei = self.dataOut.heightList[0] |
|
|
509 | 511 | |
|
|
510 | 512 | if maxHei == None: |
|
|
511 | 513 | maxHei = self.dataOut.heightList[-1] |
|
|
512 | 514 | |
|
|
513 | 515 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): |
|
|
514 | 516 | if self.warnings: |
|
|
515 | 517 | print('minHei: %.2f is out of the heights range' % (minHei)) |
|
|
516 | 518 | print('minHei is setting to %.2f' % (self.dataOut.heightList[0])) |
|
|
517 | 519 | minHei = self.dataOut.heightList[0] |
|
|
518 | 520 | |
|
|
519 | 521 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): |
|
|
520 | 522 | if self.warnings: |
|
|
521 | 523 | print('maxHei: %.2f is out of the heights range' % (maxHei)) |
|
|
522 | 524 | print('maxHei is setting to %.2f' % (self.dataOut.heightList[-1])) |
|
|
523 | 525 | maxHei = self.dataOut.heightList[-1] |
|
|
524 | 526 | |
|
|
525 | 527 | |
|
|
526 | 528 | #indices relativos a los puntos de fft, puede ser de acuerdo a velocidad o frecuencia |
|
|
527 | 529 | minIndexFFT = 0 |
|
|
528 | 530 | maxIndexFFT = 0 |
|
|
529 | 531 | # validacion de velocidades |
|
|
530 | 532 | indminPoint = None |
|
|
531 | 533 | indmaxPoint = None |
|
|
534 | if self.dataOut.type == 'Spectra': | |
|
|
535 | if minVel == None and maxVel == None : | |
|
|
532 | 536 | |
|
|
533 | if minVel == None and maxVel == None: | |
|
|
537 | freqrange = self.dataOut.getFreqRange(1) | |
|
|
534 | 538 | |
|
|
535 | freqrange = self.dataOut.getFreqRange(1) | |
|
|
539 | if minFreq == None: | |
|
|
540 | minFreq = freqrange[0] | |
|
|
536 | 541 | |
|
|
537 |
if m |
|
|
|
538 |
|
|
|
|
542 | if maxFreq == None: | |
|
|
543 | maxFreq = freqrange[-1] | |
|
|
539 | 544 | |
|
|
540 | if maxFreq == None: | |
|
|
541 | maxFreq = freqrange[-1] | |
|
|
545 | if (minFreq < freqrange[0]) or (minFreq > maxFreq): | |
|
|
546 | if self.warnings: | |
|
|
547 | print('minFreq: %.2f is out of the frequency range' % (minFreq)) | |
|
|
548 | print('minFreq is setting to %.2f' % (freqrange[0])) | |
|
|
549 | minFreq = freqrange[0] | |
|
|
542 | 550 | |
|
|
543 |
if (m |
|
|
|
544 | if self.warnings: | |
|
|
545 |
print('m |
|
|
|
546 |
print('m |
|
|
|
547 |
|
|
|
|
551 | if (maxFreq > freqrange[-1]) or (maxFreq < minFreq): | |
|
|
552 | if self.warnings: | |
|
|
553 | print('maxFreq: %.2f is out of the frequency range' % (maxFreq)) | |
|
|
554 | print('maxFreq is setting to %.2f' % (freqrange[-1])) | |
|
|
555 | maxFreq = freqrange[-1] | |
|
|
548 | 556 | |
|
|
549 | if (maxFreq > freqrange[-1]) or (maxFreq < minFreq): | |
|
|
550 | if self.warnings: | |
|
|
551 | print('maxFreq: %.2f is out of the frequency range' % (maxFreq)) | |
|
|
552 | print('maxFreq is setting to %.2f' % (freqrange[-1])) | |
|
|
553 | maxFreq = freqrange[-1] | |
|
|
557 | indminPoint = numpy.where(freqrange >= minFreq) | |
|
|
558 | indmaxPoint = numpy.where(freqrange <= maxFreq) | |
|
|
554 | 559 | |
|
|
555 | indminPoint = numpy.where(freqrange >= minFreq) | |
|
|
556 | indmaxPoint = numpy.where(freqrange <= maxFreq) | |
|
|
560 | else: | |
|
|
557 | 561 | |
|
|
558 | else: | |
|
|
559 | velrange = self.dataOut.getVelRange(1) | |
|
|
562 | velrange = self.dataOut.getVelRange(1) | |
|
|
560 | 563 | |
|
|
561 | if minVel == None: | |
|
|
562 | minVel = velrange[0] | |
|
|
564 | if minVel == None: | |
|
|
565 | minVel = velrange[0] | |
|
|
563 | 566 | |
|
|
564 | if maxVel == None: | |
|
|
565 | maxVel = velrange[-1] | |
|
|
567 | if maxVel == None: | |
|
|
568 | maxVel = velrange[-1] | |
|
|
566 | 569 | |
|
|
567 | if (minVel < velrange[0]) or (minVel > maxVel): | |
|
|
568 | if self.warnings: | |
|
|
569 | print('minVel: %.2f is out of the velocity range' % (minVel)) | |
|
|
570 | print('minVel is setting to %.2f' % (velrange[0])) | |
|
|
571 | minVel = velrange[0] | |
|
|
570 | if (minVel < velrange[0]) or (minVel > maxVel): | |
|
|
571 | if self.warnings: | |
|
|
572 | print('minVel: %.2f is out of the velocity range' % (minVel)) | |
|
|
573 | print('minVel is setting to %.2f' % (velrange[0])) | |
|
|
574 | minVel = velrange[0] | |
|
|
572 | 575 | |
|
|
573 | if (maxVel > velrange[-1]) or (maxVel < minVel): | |
|
|
574 | if self.warnings: | |
|
|
575 | print('maxVel: %.2f is out of the velocity range' % (maxVel)) | |
|
|
576 | print('maxVel is setting to %.2f' % (velrange[-1])) | |
|
|
577 | maxVel = velrange[-1] | |
|
|
576 | if (maxVel > velrange[-1]) or (maxVel < minVel): | |
|
|
577 | if self.warnings: | |
|
|
578 | print('maxVel: %.2f is out of the velocity range' % (maxVel)) | |
|
|
579 | print('maxVel is setting to %.2f' % (velrange[-1])) | |
|
|
580 | maxVel = velrange[-1] | |
|
|
578 | 581 | |
|
|
579 | indminPoint = numpy.where(velrange >= minVel) | |
|
|
580 | indmaxPoint = numpy.where(velrange <= maxVel) | |
|
|
582 | indminPoint = numpy.where(velrange >= minVel) | |
|
|
583 | indmaxPoint = numpy.where(velrange <= maxVel) | |
|
|
581 | 584 | |
|
|
582 | 585 | |
|
|
583 | 586 | # seleccion de indices para rango |
|
|
584 | 587 | minIndex = 0 |
|
|
585 | 588 | maxIndex = 0 |
|
|
586 | 589 | heights = self.dataOut.heightList |
|
|
587 | 590 | |
|
|
588 | 591 | inda = numpy.where(heights >= minHei) |
|
|
589 | 592 | indb = numpy.where(heights <= maxHei) |
|
|
590 | 593 | |
|
|
591 | 594 | try: |
|
|
592 | 595 | minIndex = inda[0][0] |
|
|
593 | 596 | except: |
|
|
594 | 597 | minIndex = 0 |
|
|
595 | 598 | |
|
|
596 | 599 | try: |
|
|
597 | 600 | maxIndex = indb[0][-1] |
|
|
598 | 601 | except: |
|
|
599 | 602 | maxIndex = len(heights) |
|
|
600 | 603 | |
|
|
601 | 604 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
602 | 605 | raise ValueError("some value in (%d,%d) is not valid" % ( |
|
|
603 | 606 | minIndex, maxIndex)) |
|
|
604 | 607 | |
|
|
605 | 608 | if (maxIndex >= self.dataOut.nHeights): |
|
|
606 | 609 | maxIndex = self.dataOut.nHeights - 1 |
|
|
607 | 610 | #############################################################3 |
|
|
608 | 611 | # seleccion de indices para velocidades |
|
|
612 | if self.dataOut.type == 'Spectra': | |
|
|
613 | try: | |
|
|
614 | minIndexFFT = indminPoint[0][0] | |
|
|
615 | except: | |
|
|
616 | minIndexFFT = 0 | |
|
|
609 | 617 | |
|
|
610 | try: | |
|
|
611 |
|
|
|
|
612 | except: | |
|
|
613 | minIndexFFT = 0 | |
|
|
618 | try: | |
|
|
619 | maxIndexFFT = indmaxPoint[0][-1] | |
|
|
620 | except: | |
|
|
621 | maxIndexFFT = len( self.dataOut.getFreqRange(1)) | |
|
|
614 | 622 | |
|
|
615 | try: | |
|
|
616 | maxIndexFFT = indmaxPoint[0][-1] | |
|
|
617 | except: | |
|
|
618 | maxIndexFFT = len( self.dataOut.getFreqRange(1)) | |
|
|
619 | 623 | |
|
|
620 | #print(minIndex, maxIndex,minIndexVel, maxIndexVel) | |
|
|
621 | 624 | self.dataOut.noise_estimation = None |
|
|
622 | noise = self.dataOut.getNoise(xmin_index=minIndexFFT, xmax_index=maxIndexFFT, ymin_index=minIndex, ymax_index=maxIndex) | |
|
|
623 | ||
|
|
625 | noise = None | |
|
|
626 | if self.dataOut.type == 'Voltage': | |
|
|
627 | noise = self.dataOut.getNoise(ymin_index=minIndex, ymax_index=maxIndex) | |
|
|
628 | #print(minIndex, maxIndex,minIndexVel, maxIndexVel) | |
|
|
629 | elif self.dataOut.type == 'Spectra': | |
|
|
630 | noise = self.dataOut.getNoise(xmin_index=minIndexFFT, xmax_index=maxIndexFFT, ymin_index=minIndex, ymax_index=maxIndex) | |
|
|
631 | else: | |
|
|
632 | noise = self.dataOut.getNoise(xmin_index=minIndexFFT, xmax_index=maxIndexFFT, ymin_index=minIndex, ymax_index=maxIndex) | |
|
|
624 | 633 | self.dataOut.noise_estimation = noise.copy() # dataOut.noise |
|
|
625 | 634 | #print("2: ",10*numpy.log10(self.dataOut.noise_estimation/64)) |
|
|
635 | ||
|
|
626 | 636 | return self.dataOut |
|
|
627 | 637 | |
|
|
628 | 638 | |
|
|
629 | 639 | |
|
|
630 | 640 | # import matplotlib.pyplot as plt |
|
|
631 | 641 | |
|
|
632 | 642 | def fit_func( x, a0, a1, a2): #, a3, a4, a5): |
|
|
633 | 643 | z = (x - a1) / a2 |
|
|
634 | 644 | y = a0 * numpy.exp(-z**2 / a2) #+ a3 + a4 * x + a5 * x**2 |
|
|
635 | 645 | return y |
|
|
636 | 646 | |
|
|
637 | 647 | |
|
|
638 | 648 | class CleanRayleigh(Operation): |
|
|
639 | 649 | |
|
|
640 | 650 | def __init__(self): |
|
|
641 | 651 | |
|
|
642 | 652 | Operation.__init__(self) |
|
|
643 | 653 | self.i=0 |
|
|
644 | 654 | self.isConfig = False |
|
|
645 | 655 | self.__dataReady = False |
|
|
646 | 656 | self.__profIndex = 0 |
|
|
647 | 657 | self.byTime = False |
|
|
648 | 658 | self.byProfiles = False |
|
|
649 | 659 | |
|
|
650 | 660 | self.bloques = None |
|
|
651 | 661 | self.bloque0 = None |
|
|
652 | 662 | |
|
|
653 | 663 | self.index = 0 |
|
|
654 | 664 | |
|
|
655 | 665 | self.buffer = 0 |
|
|
656 | 666 | self.buffer2 = 0 |
|
|
657 | 667 | self.buffer3 = 0 |
|
|
658 | 668 | |
|
|
659 | 669 | |
|
|
660 | 670 | def setup(self,dataOut,min_hei,max_hei,n, timeInterval,factor_stdv): |
|
|
661 | 671 | |
|
|
662 | 672 | self.nChannels = dataOut.nChannels |
|
|
663 | 673 | self.nProf = dataOut.nProfiles |
|
|
664 | 674 | self.nPairs = dataOut.data_cspc.shape[0] |
|
|
665 | 675 | self.pairsArray = numpy.array(dataOut.pairsList) |
|
|
666 | 676 | self.spectra = dataOut.data_spc |
|
|
667 | 677 | self.cspectra = dataOut.data_cspc |
|
|
668 | 678 | self.heights = dataOut.heightList #alturas totales |
|
|
669 | 679 | self.nHeights = len(self.heights) |
|
|
670 | 680 | self.min_hei = min_hei |
|
|
671 | 681 | self.max_hei = max_hei |
|
|
672 | 682 | if (self.min_hei == None): |
|
|
673 | 683 | self.min_hei = 0 |
|
|
674 | 684 | if (self.max_hei == None): |
|
|
675 | 685 | self.max_hei = dataOut.heightList[-1] |
|
|
676 | 686 | self.hval = ((self.max_hei>=self.heights) & (self.heights >= self.min_hei)).nonzero() |
|
|
677 | 687 | self.heightsClean = self.heights[self.hval] #alturas filtradas |
|
|
678 | 688 | self.hval = self.hval[0] # forma (N,), an solo N elementos -> Indices de alturas |
|
|
679 | 689 | self.nHeightsClean = len(self.heightsClean) |
|
|
680 | 690 | self.channels = dataOut.channelList |
|
|
681 | 691 | self.nChan = len(self.channels) |
|
|
682 | 692 | self.nIncohInt = dataOut.nIncohInt |
|
|
683 | 693 | self.__initime = dataOut.utctime |
|
|
684 | 694 | self.maxAltInd = self.hval[-1]+1 |
|
|
685 | 695 | self.minAltInd = self.hval[0] |
|
|
686 | 696 | |
|
|
687 | 697 | self.crosspairs = dataOut.pairsList |
|
|
688 | 698 | self.nPairs = len(self.crosspairs) |
|
|
689 | 699 | self.normFactor = dataOut.normFactor |
|
|
690 | 700 | self.nFFTPoints = dataOut.nFFTPoints |
|
|
691 | 701 | self.ippSeconds = dataOut.ippSeconds |
|
|
692 | 702 | self.currentTime = self.__initime |
|
|
693 | 703 | self.pairsArray = numpy.array(dataOut.pairsList) |
|
|
694 | 704 | self.factor_stdv = factor_stdv |
|
|
695 | 705 | |
|
|
696 | 706 | if n != None : |
|
|
697 | 707 | self.byProfiles = True |
|
|
698 | 708 | self.nIntProfiles = n |
|
|
699 | 709 | else: |
|
|
700 | 710 | self.__integrationtime = timeInterval |
|
|
701 | 711 | |
|
|
702 | 712 | self.__dataReady = False |
|
|
703 | 713 | self.isConfig = True |
|
|
704 | 714 | |
|
|
705 | 715 | |
|
|
706 | 716 | |
|
|
707 | 717 | def run(self, dataOut,min_hei=None,max_hei=None, n=None, timeInterval=10,factor_stdv=2.5): |
|
|
708 | ||
|
|
718 | #print("runing cleanRayleigh") | |
|
|
709 | 719 | if not self.isConfig : |
|
|
710 | 720 | |
|
|
711 | 721 | self.setup(dataOut, min_hei,max_hei,n,timeInterval,factor_stdv) |
|
|
712 | 722 | |
|
|
713 | 723 | tini=dataOut.utctime |
|
|
714 | 724 | |
|
|
715 | 725 | if self.byProfiles: |
|
|
716 | 726 | if self.__profIndex == self.nIntProfiles: |
|
|
717 | 727 | self.__dataReady = True |
|
|
718 | 728 | else: |
|
|
719 | 729 | if (tini - self.__initime) >= self.__integrationtime: |
|
|
720 | 730 | |
|
|
721 | 731 | self.__dataReady = True |
|
|
722 | 732 | self.__initime = tini |
|
|
723 | 733 | |
|
|
724 | 734 | #if (tini.tm_min % 2) == 0 and (tini.tm_sec < 5 and self.fint==0): |
|
|
725 | 735 | |
|
|
726 | 736 | if self.__dataReady: |
|
|
727 | 737 | |
|
|
728 | 738 | self.__profIndex = 0 |
|
|
729 | 739 | jspc = self.buffer |
|
|
730 | 740 | jcspc = self.buffer2 |
|
|
731 | 741 | #jnoise = self.buffer3 |
|
|
732 | 742 | self.buffer = dataOut.data_spc |
|
|
733 | 743 | self.buffer2 = dataOut.data_cspc |
|
|
734 | 744 | #self.buffer3 = dataOut.noise |
|
|
735 | 745 | self.currentTime = dataOut.utctime |
|
|
736 | 746 | if numpy.any(jspc) : |
|
|
737 | 747 | #print( jspc.shape, jcspc.shape) |
|
|
738 | 748 | jspc = numpy.reshape(jspc,(int(len(jspc)/self.nChannels),self.nChannels,self.nFFTPoints,self.nHeights)) |
|
|
739 | jcspc= numpy.reshape(jcspc,(int(len(jcspc)/self.nPairs),self.nPairs,self.nFFTPoints,self.nHeights)) | |
|
|
749 | try: | |
|
|
750 | jcspc= numpy.reshape(jcspc,(int(len(jcspc)/self.nPairs),self.nPairs,self.nFFTPoints,self.nHeights)) | |
|
|
751 | except: | |
|
|
752 | print("no cspc") | |
|
|
740 | 753 | self.__dataReady = False |
|
|
741 | 754 | #print( jspc.shape, jcspc.shape) |
|
|
742 | 755 | dataOut.flagNoData = False |
|
|
743 | 756 | else: |
|
|
744 | 757 | dataOut.flagNoData = True |
|
|
745 | 758 | self.__dataReady = False |
|
|
746 | 759 | return dataOut |
|
|
747 | 760 | else: |
|
|
748 | 761 | #print( len(self.buffer)) |
|
|
749 | 762 | if numpy.any(self.buffer): |
|
|
750 | 763 | self.buffer = numpy.concatenate((self.buffer,dataOut.data_spc), axis=0) |
|
|
751 | self.buffer2 = numpy.concatenate((self.buffer2,dataOut.data_cspc), axis=0) | |
|
|
752 |
self.buffer |
|
|
|
764 | try: | |
|
|
765 | self.buffer2 = numpy.concatenate((self.buffer2,dataOut.data_cspc), axis=0) | |
|
|
766 | self.buffer3 += dataOut.data_dc | |
|
|
767 | except: | |
|
|
768 | pass | |
|
|
753 | 769 | else: |
|
|
754 | 770 | self.buffer = dataOut.data_spc |
|
|
755 | 771 | self.buffer2 = dataOut.data_cspc |
|
|
756 | 772 | self.buffer3 = dataOut.data_dc |
|
|
757 | 773 | #print self.index, self.fint |
|
|
758 | 774 | #print self.buffer2.shape |
|
|
759 | 775 | dataOut.flagNoData = True ## NOTE: ?? revisar LUEGO |
|
|
760 | 776 | self.__profIndex += 1 |
|
|
761 | 777 | return dataOut ## NOTE: REV |
|
|
762 | 778 | |
|
|
763 | 779 | |
|
|
764 | 780 | #index = tini.tm_hour*12+tini.tm_min/5 |
|
|
765 |
''' |
|
|
|
781 | ''' | |
|
|
782 | REVISAR | |
|
|
783 | ''' | |
|
|
766 | 784 | # jspc = jspc/self.nFFTPoints/self.normFactor |
|
|
767 | 785 | # jcspc = jcspc/self.nFFTPoints/self.normFactor |
|
|
768 | 786 | |
|
|
769 | 787 | |
|
|
770 | 788 | |
|
|
771 | 789 | tmp_spectra,tmp_cspectra = self.cleanRayleigh(dataOut,jspc,jcspc,self.factor_stdv) |
|
|
772 | 790 | dataOut.data_spc = tmp_spectra |
|
|
773 | 791 | dataOut.data_cspc = tmp_cspectra |
|
|
774 | 792 | |
|
|
775 | 793 | #dataOut.data_spc,dataOut.data_cspc = self.cleanRayleigh(dataOut,jspc,jcspc,self.factor_stdv) |
|
|
776 | 794 | |
|
|
777 | 795 | dataOut.data_dc = self.buffer3 |
|
|
778 | 796 | dataOut.nIncohInt *= self.nIntProfiles |
|
|
797 | dataOut.max_nIncohInt = self.nIntProfiles | |
|
|
779 | 798 | dataOut.utctime = self.currentTime #tiempo promediado |
|
|
780 | 799 | #print("Time: ",time.localtime(dataOut.utctime)) |
|
|
781 | 800 | # dataOut.data_spc = sat_spectra |
|
|
782 | 801 | # dataOut.data_cspc = sat_cspectra |
|
|
783 | 802 | self.buffer = 0 |
|
|
784 | 803 | self.buffer2 = 0 |
|
|
785 | 804 | self.buffer3 = 0 |
|
|
786 | 805 | |
|
|
787 | 806 | return dataOut |
|
|
788 | 807 | |
|
|
789 | 808 | def cleanRayleigh(self,dataOut,spectra,cspectra,factor_stdv): |
|
|
790 |
|
|
|
|
809 | print("OP cleanRayleigh") | |
|
|
791 | 810 | #import matplotlib.pyplot as plt |
|
|
792 | 811 | #for k in range(149): |
|
|
793 | 812 | #channelsProcssd = [] |
|
|
794 | 813 | #channelA_ok = False |
|
|
795 | 814 | #rfunc = cspectra.copy() #self.bloques |
|
|
796 | 815 | rfunc = spectra.copy() |
|
|
797 | 816 | #rfunc = cspectra |
|
|
798 | 817 | #val_spc = spectra*0.0 #self.bloque0*0.0 |
|
|
799 | 818 | #val_cspc = cspectra*0.0 #self.bloques*0.0 |
|
|
800 | 819 | #in_sat_spectra = spectra.copy() #self.bloque0 |
|
|
801 | 820 | #in_sat_cspectra = cspectra.copy() #self.bloques |
|
|
802 | 821 | |
|
|
803 | 822 | |
|
|
804 | 823 | ###ONLY FOR TEST: |
|
|
805 | 824 | raxs = math.ceil(math.sqrt(self.nPairs)) |
|
|
825 | if raxs == 0: | |
|
|
826 | raxs = 1 | |
|
|
806 | 827 | caxs = math.ceil(self.nPairs/raxs) |
|
|
807 | 828 | if self.nPairs <4: |
|
|
808 | 829 | raxs = 2 |
|
|
809 | 830 | caxs = 2 |
|
|
810 | 831 | #print(raxs, caxs) |
|
|
811 | 832 | fft_rev = 14 #nFFT to plot |
|
|
812 | 833 | hei_rev = ((self.heights >= 550) & (self.heights <= 551)).nonzero() #hei to plot |
|
|
813 | 834 | hei_rev = hei_rev[0] |
|
|
814 | 835 | #print(hei_rev) |
|
|
815 | 836 | |
|
|
816 | 837 | #print numpy.absolute(rfunc[:,0,0,14]) |
|
|
817 | 838 | |
|
|
818 | 839 | gauss_fit, covariance = None, None |
|
|
819 | 840 | for ih in range(self.minAltInd,self.maxAltInd): |
|
|
820 | 841 | for ifreq in range(self.nFFTPoints): |
|
|
821 | 842 | ''' |
|
|
822 | 843 | ###ONLY FOR TEST: |
|
|
823 | 844 | if ifreq ==fft_rev and ih==hei_rev: #TO VIEW A SIGNLE FREQUENCY |
|
|
824 | 845 | fig, axs = plt.subplots(raxs, caxs) |
|
|
825 | 846 | fig2, axs2 = plt.subplots(raxs, caxs) |
|
|
826 | 847 | col_ax = 0 |
|
|
827 | 848 | row_ax = 0 |
|
|
828 | 849 | ''' |
|
|
829 | 850 | #print(self.nPairs) |
|
|
830 | 851 | for ii in range(self.nChan): #PARES DE CANALES SELF y CROSS |
|
|
831 | 852 | # if self.crosspairs[ii][1]-self.crosspairs[ii][0] > 1: # APLICAR SOLO EN PARES CONTIGUOS |
|
|
832 | 853 | # continue |
|
|
833 | 854 | # if not self.crosspairs[ii][0] in channelsProcssd: |
|
|
834 | 855 | # channelA_ok = True |
|
|
835 | 856 | #print("pair: ",self.crosspairs[ii]) |
|
|
836 | 857 | ''' |
|
|
837 | 858 | ###ONLY FOR TEST: |
|
|
838 | 859 | if (col_ax%caxs==0 and col_ax!=0 and self.nPairs !=1): |
|
|
839 | 860 | col_ax = 0 |
|
|
840 | 861 | row_ax += 1 |
|
|
841 | 862 | ''' |
|
|
842 | 863 | func2clean = 10*numpy.log10(numpy.absolute(rfunc[:,ii,ifreq,ih])) #Potencia? |
|
|
843 | 864 | #print(func2clean.shape) |
|
|
844 | 865 | val = (numpy.isfinite(func2clean)==True).nonzero() |
|
|
845 | 866 | |
|
|
846 | 867 | if len(val)>0: #limitador |
|
|
847 | 868 | min_val = numpy.around(numpy.amin(func2clean)-2) #> (-40) |
|
|
848 | 869 | if min_val <= -40 : |
|
|
849 | 870 | min_val = -40 |
|
|
850 | 871 | max_val = numpy.around(numpy.amax(func2clean)+2) #< 200 |
|
|
851 | 872 | if max_val >= 200 : |
|
|
852 | 873 | max_val = 200 |
|
|
853 | 874 | #print min_val, max_val |
|
|
854 | 875 | step = 1 |
|
|
855 | 876 | #print("Getting bins and the histogram") |
|
|
856 | 877 | x_dist = min_val + numpy.arange(1 + ((max_val-(min_val))/step))*step |
|
|
857 | 878 | y_dist,binstep = numpy.histogram(func2clean,bins=range(int(min_val),int(max_val+2),step)) |
|
|
858 | 879 | #print(len(y_dist),len(binstep[:-1])) |
|
|
859 | 880 | #print(row_ax,col_ax, " ..") |
|
|
860 | 881 | #print(self.pairsArray[ii][0],self.pairsArray[ii][1]) |
|
|
861 | 882 | mean = numpy.sum(x_dist * y_dist) / numpy.sum(y_dist) |
|
|
862 | 883 | sigma = numpy.sqrt(numpy.sum(y_dist * (x_dist - mean)**2) / numpy.sum(y_dist)) |
|
|
863 | 884 | parg = [numpy.amax(y_dist),mean,sigma] |
|
|
864 | 885 | |
|
|
865 | 886 | newY = None |
|
|
866 | 887 | |
|
|
867 | 888 | try : |
|
|
868 | 889 | gauss_fit, covariance = curve_fit(fit_func, x_dist, y_dist,p0=parg) |
|
|
869 | 890 | mode = gauss_fit[1] |
|
|
870 | 891 | stdv = gauss_fit[2] |
|
|
871 | 892 | #print(" FIT OK",gauss_fit) |
|
|
872 | 893 | ''' |
|
|
873 | 894 | ###ONLY FOR TEST: |
|
|
874 | 895 | if ifreq ==fft_rev and ih==hei_rev: #TO VIEW A SIGNLE FREQUENCY |
|
|
875 | 896 | newY = fit_func(x_dist,gauss_fit[0],gauss_fit[1],gauss_fit[2]) |
|
|
876 | 897 | axs[row_ax,col_ax].plot(binstep[:-1],y_dist,color='green') |
|
|
877 | 898 | axs[row_ax,col_ax].plot(binstep[:-1],newY,color='red') |
|
|
878 | 899 | axs[row_ax,col_ax].set_title("CH "+str(self.channels[ii])) |
|
|
879 | 900 | ''' |
|
|
880 | 901 | except: |
|
|
881 | 902 | mode = mean |
|
|
882 | 903 | stdv = sigma |
|
|
883 | 904 | #print("FIT FAIL") |
|
|
884 | 905 | #continue |
|
|
885 | 906 | |
|
|
886 | 907 | |
|
|
887 | 908 | #print(mode,stdv) |
|
|
888 | 909 | #Removing echoes greater than mode + std_factor*stdv |
|
|
889 | 910 | noval = (abs(func2clean - mode)>=(factor_stdv*stdv)).nonzero() |
|
|
890 | 911 | #noval tiene los indices que se van a remover |
|
|
891 | 912 | #print("Chan ",ii," novals: ",len(noval[0])) |
|
|
892 | 913 | if len(noval[0]) > 0: #forma de array (N,) es igual a longitud (N) |
|
|
893 | 914 | novall = ((func2clean - mode) >= (factor_stdv*stdv)).nonzero() |
|
|
894 | 915 | #print(novall) |
|
|
895 | 916 | #print(" ",self.pairsArray[ii]) |
|
|
896 | 917 | #cross_pairs = self.pairsArray[ii] |
|
|
897 | 918 | #Getting coherent echoes which are removed. |
|
|
898 | 919 | # if len(novall[0]) > 0: |
|
|
899 | 920 | # |
|
|
900 | 921 | # val_spc[novall[0],cross_pairs[0],ifreq,ih] = 1 |
|
|
901 | 922 | # val_spc[novall[0],cross_pairs[1],ifreq,ih] = 1 |
|
|
902 | 923 | # val_cspc[novall[0],ii,ifreq,ih] = 1 |
|
|
903 | 924 | #print("OUT NOVALL 1") |
|
|
904 | 925 | try: |
|
|
905 | 926 | pair = (self.channels[ii],self.channels[ii + 1]) |
|
|
906 | 927 | except: |
|
|
907 | 928 | pair = (99,99) |
|
|
908 | 929 | #print("par ", pair) |
|
|
909 | 930 | if ( pair in self.crosspairs): |
|
|
910 | 931 | q = self.crosspairs.index(pair) |
|
|
911 | 932 | #print("está aqui: ", q, (ii,ii + 1)) |
|
|
912 | 933 | new_a = numpy.delete(cspectra[:,q,ifreq,ih], noval[0]) |
|
|
913 | 934 | cspectra[noval,q,ifreq,ih] = numpy.mean(new_a) #mean CrossSpectra |
|
|
914 | 935 | |
|
|
915 | 936 | #if channelA_ok: |
|
|
916 | 937 | #chA = self.channels.index(cross_pairs[0]) |
|
|
917 | 938 | new_b = numpy.delete(spectra[:,ii,ifreq,ih], noval[0]) |
|
|
918 | 939 | spectra[noval,ii,ifreq,ih] = numpy.mean(new_b) #mean Spectra Pair A |
|
|
919 | 940 | #channelA_ok = False |
|
|
920 | 941 | |
|
|
921 | 942 | # chB = self.channels.index(cross_pairs[1]) |
|
|
922 | 943 | # new_c = numpy.delete(spectra[:,chB,ifreq,ih], noval[0]) |
|
|
923 | 944 | # spectra[noval,chB,ifreq,ih] = numpy.mean(new_c) #mean Spectra Pair B |
|
|
924 | 945 | # |
|
|
925 | 946 | # channelsProcssd.append(self.crosspairs[ii][0]) # save channel A |
|
|
926 | 947 | # channelsProcssd.append(self.crosspairs[ii][1]) # save channel B |
|
|
927 | 948 | ''' |
|
|
928 | 949 | ###ONLY FOR TEST: |
|
|
929 | 950 | if ifreq ==fft_rev and ih==hei_rev: #TO VIEW A SIGNLE FREQUENCY |
|
|
930 | 951 | func2clean = 10*numpy.log10(numpy.absolute(spectra[:,ii,ifreq,ih])) |
|
|
931 | 952 | y_dist,binstep = numpy.histogram(func2clean,bins=range(int(min_val),int(max_val+2),step)) |
|
|
932 | 953 | axs2[row_ax,col_ax].plot(binstep[:-1],newY,color='red') |
|
|
933 | 954 | axs2[row_ax,col_ax].plot(binstep[:-1],y_dist,color='green') |
|
|
934 | 955 | axs2[row_ax,col_ax].set_title("CH "+str(self.channels[ii])) |
|
|
935 | 956 | ''' |
|
|
936 | 957 | ''' |
|
|
937 | 958 | ###ONLY FOR TEST: |
|
|
938 | 959 | col_ax += 1 #contador de ploteo columnas |
|
|
939 | 960 | ##print(col_ax) |
|
|
940 | 961 | ###ONLY FOR TEST: |
|
|
941 | 962 | if ifreq ==fft_rev and ih==hei_rev: #TO VIEW A SIGNLE FREQUENCY |
|
|
942 | 963 | title = str(dataOut.datatime)+" nFFT: "+str(ifreq)+" Alt: "+str(self.heights[ih])+ " km" |
|
|
943 | 964 | title2 = str(dataOut.datatime)+" nFFT: "+str(ifreq)+" Alt: "+str(self.heights[ih])+ " km CLEANED" |
|
|
944 | 965 | fig.suptitle(title) |
|
|
945 | 966 | fig2.suptitle(title2) |
|
|
946 | 967 | plt.show() |
|
|
947 | 968 | ''' |
|
|
948 | 969 | ################################################################################################## |
|
|
949 | 970 | |
|
|
950 | 971 | #print("Getting average of the spectra and cross-spectra from incoherent echoes.") |
|
|
951 | 972 | out_spectra = numpy.zeros([self.nChan,self.nFFTPoints,self.nHeights], dtype=float) #+numpy.nan |
|
|
952 | 973 | out_cspectra = numpy.zeros([self.nPairs,self.nFFTPoints,self.nHeights], dtype=complex) #+numpy.nan |
|
|
953 | 974 | for ih in range(self.nHeights): |
|
|
954 | 975 | for ifreq in range(self.nFFTPoints): |
|
|
955 | 976 | for ich in range(self.nChan): |
|
|
956 | 977 | tmp = spectra[:,ich,ifreq,ih] |
|
|
957 | 978 | valid = (numpy.isfinite(tmp[:])==True).nonzero() |
|
|
958 | 979 | |
|
|
959 | 980 | if len(valid[0]) >0 : |
|
|
960 | 981 | out_spectra[ich,ifreq,ih] = numpy.nansum(tmp)#/len(valid[0]) |
|
|
961 | 982 | |
|
|
962 | 983 | for icr in range(self.nPairs): |
|
|
963 | 984 | tmp = numpy.squeeze(cspectra[:,icr,ifreq,ih]) |
|
|
964 | 985 | valid = (numpy.isfinite(tmp)==True).nonzero() |
|
|
965 | 986 | if len(valid[0]) > 0: |
|
|
966 | 987 | out_cspectra[icr,ifreq,ih] = numpy.nansum(tmp)#/len(valid[0]) |
|
|
967 | 988 | |
|
|
968 | 989 | return out_spectra, out_cspectra |
|
|
969 | 990 | |
|
|
970 | 991 | def REM_ISOLATED_POINTS(self,array,rth): |
|
|
971 | 992 | # import matplotlib.pyplot as plt |
|
|
972 | 993 | if rth == None : |
|
|
973 | 994 | rth = 4 |
|
|
974 | 995 | #print("REM ISO") |
|
|
975 | 996 | num_prof = len(array[0,:,0]) |
|
|
976 | 997 | num_hei = len(array[0,0,:]) |
|
|
977 | 998 | n2d = len(array[:,0,0]) |
|
|
978 | 999 | |
|
|
979 | 1000 | for ii in range(n2d) : |
|
|
980 | 1001 | #print ii,n2d |
|
|
981 | 1002 | tmp = array[ii,:,:] |
|
|
982 | 1003 | #print tmp.shape, array[ii,101,:],array[ii,102,:] |
|
|
983 | 1004 | |
|
|
984 | 1005 | # fig = plt.figure(figsize=(6,5)) |
|
|
985 | 1006 | # left, bottom, width, height = 0.1, 0.1, 0.8, 0.8 |
|
|
986 | 1007 | # ax = fig.add_axes([left, bottom, width, height]) |
|
|
987 | 1008 | # x = range(num_prof) |
|
|
988 | 1009 | # y = range(num_hei) |
|
|
989 | 1010 | # cp = ax.contour(y,x,tmp) |
|
|
990 | 1011 | # ax.clabel(cp, inline=True,fontsize=10) |
|
|
991 | 1012 | # plt.show() |
|
|
992 | 1013 | |
|
|
993 | 1014 | #indxs = WHERE(FINITE(tmp) AND tmp GT 0,cindxs) |
|
|
994 | 1015 | tmp = numpy.reshape(tmp,num_prof*num_hei) |
|
|
995 | 1016 | indxs1 = (numpy.isfinite(tmp)==True).nonzero() |
|
|
996 | 1017 | indxs2 = (tmp > 0).nonzero() |
|
|
997 | 1018 | |
|
|
998 | 1019 | indxs1 = (indxs1[0]) |
|
|
999 | 1020 | indxs2 = indxs2[0] |
|
|
1000 | 1021 | #indxs1 = numpy.array(indxs1[0]) |
|
|
1001 | 1022 | #indxs2 = numpy.array(indxs2[0]) |
|
|
1002 | 1023 | indxs = None |
|
|
1003 | 1024 | #print indxs1 , indxs2 |
|
|
1004 | 1025 | for iv in range(len(indxs2)): |
|
|
1005 | 1026 | indv = numpy.array((indxs1 == indxs2[iv]).nonzero()) |
|
|
1006 | 1027 | #print len(indxs2), indv |
|
|
1007 | 1028 | if len(indv[0]) > 0 : |
|
|
1008 | 1029 | indxs = numpy.concatenate((indxs,indxs2[iv]), axis=None) |
|
|
1009 | 1030 | # print indxs |
|
|
1010 | 1031 | indxs = indxs[1:] |
|
|
1011 | 1032 | #print(indxs, len(indxs)) |
|
|
1012 | 1033 | if len(indxs) < 4 : |
|
|
1013 | 1034 | array[ii,:,:] = 0. |
|
|
1014 | 1035 | return |
|
|
1015 | 1036 | |
|
|
1016 | 1037 | xpos = numpy.mod(indxs ,num_hei) |
|
|
1017 | 1038 | ypos = (indxs / num_hei) |
|
|
1018 | 1039 | sx = numpy.argsort(xpos) # Ordering respect to "x" (time) |
|
|
1019 | 1040 | #print sx |
|
|
1020 | 1041 | xpos = xpos[sx] |
|
|
1021 | 1042 | ypos = ypos[sx] |
|
|
1022 | 1043 | |
|
|
1023 | 1044 | # *********************************** Cleaning isolated points ********************************** |
|
|
1024 | 1045 | ic = 0 |
|
|
1025 | 1046 | while True : |
|
|
1026 | 1047 | r = numpy.sqrt(list(numpy.power((xpos[ic]-xpos),2)+ numpy.power((ypos[ic]-ypos),2))) |
|
|
1027 | 1048 | #no_coh = WHERE(FINITE(r) AND (r LE rth),cno_coh) |
|
|
1028 | 1049 | #plt.plot(r) |
|
|
1029 | 1050 | #plt.show() |
|
|
1030 | 1051 | no_coh1 = (numpy.isfinite(r)==True).nonzero() |
|
|
1031 | 1052 | no_coh2 = (r <= rth).nonzero() |
|
|
1032 | 1053 | #print r, no_coh1, no_coh2 |
|
|
1033 | 1054 | no_coh1 = numpy.array(no_coh1[0]) |
|
|
1034 | 1055 | no_coh2 = numpy.array(no_coh2[0]) |
|
|
1035 | 1056 | no_coh = None |
|
|
1036 | 1057 | #print valid1 , valid2 |
|
|
1037 | 1058 | for iv in range(len(no_coh2)): |
|
|
1038 | 1059 | indv = numpy.array((no_coh1 == no_coh2[iv]).nonzero()) |
|
|
1039 | 1060 | if len(indv[0]) > 0 : |
|
|
1040 | 1061 | no_coh = numpy.concatenate((no_coh,no_coh2[iv]), axis=None) |
|
|
1041 | 1062 | no_coh = no_coh[1:] |
|
|
1042 | 1063 | #print len(no_coh), no_coh |
|
|
1043 | 1064 | if len(no_coh) < 4 : |
|
|
1044 | 1065 | #print xpos[ic], ypos[ic], ic |
|
|
1045 | 1066 | # plt.plot(r) |
|
|
1046 | 1067 | # plt.show() |
|
|
1047 | 1068 | xpos[ic] = numpy.nan |
|
|
1048 | 1069 | ypos[ic] = numpy.nan |
|
|
1049 | 1070 | |
|
|
1050 | 1071 | ic = ic + 1 |
|
|
1051 | 1072 | if (ic == len(indxs)) : |
|
|
1052 | 1073 | break |
|
|
1053 | 1074 | #print( xpos, ypos) |
|
|
1054 | 1075 | |
|
|
1055 | 1076 | indxs = (numpy.isfinite(list(xpos))==True).nonzero() |
|
|
1056 | 1077 | #print indxs[0] |
|
|
1057 | 1078 | if len(indxs[0]) < 4 : |
|
|
1058 | 1079 | array[ii,:,:] = 0. |
|
|
1059 | 1080 | return |
|
|
1060 | 1081 | |
|
|
1061 | 1082 | xpos = xpos[indxs[0]] |
|
|
1062 | 1083 | ypos = ypos[indxs[0]] |
|
|
1063 | 1084 | for i in range(0,len(ypos)): |
|
|
1064 | 1085 | ypos[i]=int(ypos[i]) |
|
|
1065 | 1086 | junk = tmp |
|
|
1066 | 1087 | tmp = junk*0.0 |
|
|
1067 | 1088 | |
|
|
1068 | 1089 | tmp[list(xpos + (ypos*num_hei))] = junk[list(xpos + (ypos*num_hei))] |
|
|
1069 | 1090 | array[ii,:,:] = numpy.reshape(tmp,(num_prof,num_hei)) |
|
|
1070 | 1091 | |
|
|
1071 | 1092 | #print array.shape |
|
|
1072 | 1093 | #tmp = numpy.reshape(tmp,(num_prof,num_hei)) |
|
|
1073 | 1094 | #print tmp.shape |
|
|
1074 | 1095 | |
|
|
1075 | 1096 | # fig = plt.figure(figsize=(6,5)) |
|
|
1076 | 1097 | # left, bottom, width, height = 0.1, 0.1, 0.8, 0.8 |
|
|
1077 | 1098 | # ax = fig.add_axes([left, bottom, width, height]) |
|
|
1078 | 1099 | # x = range(num_prof) |
|
|
1079 | 1100 | # y = range(num_hei) |
|
|
1080 | 1101 | # cp = ax.contour(y,x,array[ii,:,:]) |
|
|
1081 | 1102 | # ax.clabel(cp, inline=True,fontsize=10) |
|
|
1082 | 1103 | # plt.show() |
|
|
1083 | 1104 | return array |
|
|
1084 | 1105 | |
|
|
1085 | 1106 | |
|
|
1086 | 1107 | class IntegrationFaradaySpectra(Operation): |
|
|
1087 | 1108 | |
|
|
1088 | 1109 | __profIndex = 0 |
|
|
1089 | 1110 | __withOverapping = False |
|
|
1090 | 1111 | |
|
|
1091 | 1112 | __byTime = False |
|
|
1092 | 1113 | __initime = None |
|
|
1093 | 1114 | __lastdatatime = None |
|
|
1094 | 1115 | __integrationtime = None |
|
|
1095 | 1116 | |
|
|
1096 | 1117 | __buffer_spc = None |
|
|
1097 | 1118 | __buffer_cspc = None |
|
|
1098 | 1119 | __buffer_dc = None |
|
|
1099 | 1120 | |
|
|
1100 | 1121 | __dataReady = False |
|
|
1101 | 1122 | |
|
|
1102 | 1123 | __timeInterval = None |
|
|
1103 | ||
|
|
1124 | n_ints = None #matriz de numero de integracions (CH,HEI) | |
|
|
1104 | 1125 | n = None |
|
|
1105 | 1126 | minHei_ind = None |
|
|
1106 | 1127 | maxHei_ind = None |
|
|
1107 | 1128 | navg = 1.0 |
|
|
1108 | 1129 | factor = 0.0 |
|
|
1130 | dataoutliers = None # (CHANNELS, HEIGHTS) | |
|
|
1109 | 1131 | |
|
|
1110 | 1132 | def __init__(self): |
|
|
1111 | 1133 | |
|
|
1112 | 1134 | Operation.__init__(self) |
|
|
1113 | 1135 | |
|
|
1114 | 1136 | def setup(self, dataOut,n=None, timeInterval=None, overlapping=False, DPL=None, minHei=None, maxHei=None, avg=1,factor=0.75): |
|
|
1115 | 1137 | """ |
|
|
1116 | 1138 | Set the parameters of the integration class. |
|
|
1117 | 1139 | |
|
|
1118 | 1140 | Inputs: |
|
|
1119 | 1141 | |
|
|
1120 | 1142 | n : Number of coherent integrations |
|
|
1121 | 1143 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
1122 | 1144 | overlapping : |
|
|
1123 | 1145 | |
|
|
1124 | 1146 | """ |
|
|
1125 | 1147 | |
|
|
1126 | 1148 | self.__initime = None |
|
|
1127 | 1149 | self.__lastdatatime = 0 |
|
|
1128 | 1150 | |
|
|
1129 | 1151 | self.__buffer_spc = [] |
|
|
1130 | 1152 | self.__buffer_cspc = [] |
|
|
1131 | 1153 | self.__buffer_dc = 0 |
|
|
1132 | 1154 | |
|
|
1133 | 1155 | self.__profIndex = 0 |
|
|
1134 | 1156 | self.__dataReady = False |
|
|
1135 | 1157 | self.__byTime = False |
|
|
1136 | 1158 | |
|
|
1137 | 1159 | self.factor = factor |
|
|
1138 | 1160 | self.navg = avg |
|
|
1139 | 1161 | #self.ByLags = dataOut.ByLags ###REDEFINIR |
|
|
1140 | 1162 | self.ByLags = False |
|
|
1163 | self.maxProfilesInt = 1 | |
|
|
1141 | 1164 | |
|
|
1142 | 1165 | if DPL != None: |
|
|
1143 | 1166 | self.DPL=DPL |
|
|
1144 | 1167 | else: |
|
|
1145 | 1168 | #self.DPL=dataOut.DPL ###REDEFINIR |
|
|
1146 | 1169 | self.DPL=0 |
|
|
1147 | 1170 | |
|
|
1148 | 1171 | if n is None and timeInterval is None: |
|
|
1149 | 1172 | raise ValueError("n or timeInterval should be specified ...") |
|
|
1150 | 1173 | |
|
|
1151 | 1174 | if n is not None: |
|
|
1152 | 1175 | self.n = int(n) |
|
|
1153 | 1176 | else: |
|
|
1154 | 1177 | self.__integrationtime = int(timeInterval) |
|
|
1155 | 1178 | self.n = None |
|
|
1156 | 1179 | self.__byTime = True |
|
|
1157 | 1180 | |
|
|
1158 | 1181 | if minHei == None: |
|
|
1159 | 1182 | minHei = self.dataOut.heightList[0] |
|
|
1160 | 1183 | |
|
|
1161 | 1184 | if maxHei == None: |
|
|
1162 | 1185 | maxHei = self.dataOut.heightList[-1] |
|
|
1163 | 1186 | |
|
|
1164 | 1187 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): |
|
|
1165 | 1188 | print('minHei: %.2f is out of the heights range' % (minHei)) |
|
|
1166 | 1189 | print('minHei is setting to %.2f' % (self.dataOut.heightList[0])) |
|
|
1167 | 1190 | minHei = self.dataOut.heightList[0] |
|
|
1168 | 1191 | |
|
|
1169 | 1192 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): |
|
|
1170 | 1193 | print('maxHei: %.2f is out of the heights range' % (maxHei)) |
|
|
1171 | 1194 | print('maxHei is setting to %.2f' % (self.dataOut.heightList[-1])) |
|
|
1172 | 1195 | maxHei = self.dataOut.heightList[-1] |
|
|
1173 | 1196 | |
|
|
1174 | 1197 | ind_list1 = numpy.where(self.dataOut.heightList >= minHei) |
|
|
1175 | 1198 | ind_list2 = numpy.where(self.dataOut.heightList <= maxHei) |
|
|
1176 | 1199 | self.minHei_ind = ind_list1[0][0] |
|
|
1177 | 1200 | self.maxHei_ind = ind_list2[0][-1] |
|
|
1178 | 1201 | |
|
|
1179 | 1202 | def putData(self, data_spc, data_cspc, data_dc): |
|
|
1180 | 1203 | """ |
|
|
1181 | 1204 | Add a profile to the __buffer_spc and increase in one the __profileIndex |
|
|
1182 | 1205 | |
|
|
1183 | 1206 | """ |
|
|
1184 | 1207 | |
|
|
1185 | 1208 | self.__buffer_spc.append(data_spc) |
|
|
1186 | 1209 | |
|
|
1187 | 1210 | if data_cspc is None: |
|
|
1188 | 1211 | self.__buffer_cspc = None |
|
|
1189 | 1212 | else: |
|
|
1190 | 1213 | self.__buffer_cspc.append(data_cspc) |
|
|
1191 | 1214 | |
|
|
1192 | 1215 | if data_dc is None: |
|
|
1193 | 1216 | self.__buffer_dc = None |
|
|
1194 | 1217 | else: |
|
|
1195 | 1218 | self.__buffer_dc += data_dc |
|
|
1196 | 1219 | |
|
|
1197 | 1220 | self.__profIndex += 1 |
|
|
1198 | 1221 | |
|
|
1199 | 1222 | return |
|
|
1200 | 1223 | |
|
|
1201 | 1224 | def hildebrand_sekhon_Integration(self,sortdata,navg, factor): |
|
|
1202 | 1225 | #data debe estar ordenado |
|
|
1203 | 1226 | #sortdata = numpy.sort(data, axis=None) |
|
|
1204 | 1227 | #sortID=data.argsort() |
|
|
1205 | 1228 | lenOfData = len(sortdata) |
|
|
1206 | 1229 | nums_min = lenOfData*factor |
|
|
1207 | 1230 | if nums_min <= 5: |
|
|
1208 | 1231 | nums_min = 5 |
|
|
1209 | 1232 | sump = 0. |
|
|
1210 | 1233 | sumq = 0. |
|
|
1211 | 1234 | j = 0 |
|
|
1212 | 1235 | cont = 1 |
|
|
1213 | 1236 | while((cont == 1)and(j < lenOfData)): |
|
|
1214 | 1237 | sump += sortdata[j] |
|
|
1215 | 1238 | sumq += sortdata[j]**2 |
|
|
1216 | 1239 | if j > nums_min: |
|
|
1217 | 1240 | rtest = float(j)/(j-1) + 1.0/navg |
|
|
1218 | 1241 | if ((sumq*j) > (rtest*sump**2)): |
|
|
1219 | 1242 | j = j - 1 |
|
|
1220 | 1243 | sump = sump - sortdata[j] |
|
|
1221 | 1244 | sumq = sumq - sortdata[j]**2 |
|
|
1222 | 1245 | cont = 0 |
|
|
1223 | 1246 | j += 1 |
|
|
1224 | 1247 | #lnoise = sump / j |
|
|
1225 | 1248 | #print("H S done") |
|
|
1226 | 1249 | #return j,sortID |
|
|
1227 | 1250 | return j |
|
|
1228 | 1251 | |
|
|
1229 | 1252 | |
|
|
1230 | 1253 | def pushData(self): |
|
|
1231 | 1254 | """ |
|
|
1232 | 1255 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
1233 | 1256 | |
|
|
1234 | 1257 | Affected: |
|
|
1235 | 1258 | |
|
|
1236 | 1259 | self.__profileIndex |
|
|
1237 | 1260 | |
|
|
1238 | 1261 | """ |
|
|
1239 | 1262 | bufferH=None |
|
|
1240 | 1263 | buffer=None |
|
|
1241 | 1264 | buffer1=None |
|
|
1242 | 1265 | buffer_cspc=None |
|
|
1243 | 1266 | self.__buffer_spc=numpy.array(self.__buffer_spc) |
|
|
1244 | 1267 | try: |
|
|
1245 | 1268 | self.__buffer_cspc=numpy.array(self.__buffer_cspc) |
|
|
1246 | 1269 | except : |
|
|
1247 | 1270 | #print("No cpsc",e) |
|
|
1248 | 1271 | pass |
|
|
1249 | 1272 | #print("FREQ_DC",self.__buffer_spc.shape,self.__buffer_cspc.shape) |
|
|
1250 | 1273 | |
|
|
1251 | 1274 | freq_dc = int(self.__buffer_spc.shape[2] / 2) |
|
|
1252 | 1275 | #print("FREQ_DC",freq_dc,self.__buffer_spc.shape,self.nHeights) |
|
|
1253 | 1276 | |
|
|
1277 | self.dataOutliers = numpy.zeros((self.nChannels,self.nHeights)) # --> almacen de outliers | |
|
|
1278 | ||
|
|
1254 | 1279 | for k in range(self.minHei_ind,self.maxHei_ind): |
|
|
1255 | 1280 | try: |
|
|
1256 | 1281 | buffer_cspc=numpy.copy(self.__buffer_cspc[:,:,:,k]) |
|
|
1257 | 1282 | except: |
|
|
1258 | 1283 | #print("No cpsc",e) |
|
|
1259 | 1284 | pass |
|
|
1260 | 1285 | outliers_IDs_cspc=[] |
|
|
1261 | 1286 | cspc_outliers_exist=False |
|
|
1262 | 1287 | for i in range(self.nChannels):#dataOut.nChannels): |
|
|
1263 | 1288 | |
|
|
1264 | 1289 | buffer1=numpy.copy(self.__buffer_spc[:,i,:,k]) |
|
|
1265 | 1290 | indexes=[] |
|
|
1266 | 1291 | #sortIDs=[] |
|
|
1267 | 1292 | outliers_IDs=[] |
|
|
1268 | 1293 | |
|
|
1269 | for j in range(self.nProfiles): | |
|
|
1294 | for j in range(self.nProfiles): #frecuencias en el tiempo | |
|
|
1270 | 1295 | # if i==0 and j==freq_dc: #NOT CONSIDERING DC PROFILE AT CHANNEL 0 |
|
|
1271 | 1296 | # continue |
|
|
1272 | 1297 | # if i==1 and j==0: #NOT CONSIDERING DC PROFILE AT CHANNEL 1 |
|
|
1273 | 1298 | # continue |
|
|
1274 | 1299 | buffer=buffer1[:,j] |
|
|
1275 | 1300 | sortdata = numpy.sort(buffer, axis=None) |
|
|
1301 | ||
|
|
1276 | 1302 | sortID=buffer.argsort() |
|
|
1277 | 1303 | index = _noise.hildebrand_sekhon2(sortdata,self.navg) |
|
|
1278 | 1304 | |
|
|
1279 | 1305 | #index,sortID=self.hildebrand_sekhon_Integration(buffer,1,self.factor) |
|
|
1280 | 1306 | |
|
|
1307 | # fig,ax = plt.subplots() | |
|
|
1308 | # ax.set_title(str(k)+" "+str(j)) | |
|
|
1309 | # x=range(len(sortdata)) | |
|
|
1310 | # ax.scatter(x,sortdata) | |
|
|
1311 | # ax.axvline(index) | |
|
|
1312 | # plt.show() | |
|
|
1313 | ||
|
|
1281 | 1314 | indexes.append(index) |
|
|
1282 | 1315 | #sortIDs.append(sortID) |
|
|
1283 | 1316 | outliers_IDs=numpy.append(outliers_IDs,sortID[index:]) |
|
|
1284 | 1317 | |
|
|
1318 | #print("Outliers: ",outliers_IDs) | |
|
|
1285 | 1319 | outliers_IDs=numpy.array(outliers_IDs) |
|
|
1286 | 1320 | outliers_IDs=outliers_IDs.ravel() |
|
|
1287 | 1321 | outliers_IDs=numpy.unique(outliers_IDs) |
|
|
1288 | 1322 | outliers_IDs=outliers_IDs.astype(numpy.dtype('int64')) |
|
|
1289 | 1323 | indexes=numpy.array(indexes) |
|
|
1290 | 1324 | indexmin=numpy.min(indexes) |
|
|
1291 | 1325 | |
|
|
1326 | ||
|
|
1327 | #print(indexmin,buffer1.shape[0], k) | |
|
|
1328 | ||
|
|
1329 | # fig,ax = plt.subplots() | |
|
|
1330 | # ax.plot(sortdata) | |
|
|
1331 | # ax2 = ax.twinx() | |
|
|
1332 | # x=range(len(indexes)) | |
|
|
1333 | # #plt.scatter(x,indexes) | |
|
|
1334 | # ax2.scatter(x,indexes) | |
|
|
1335 | # plt.show() | |
|
|
1336 | ||
|
|
1292 | 1337 | if indexmin != buffer1.shape[0]: |
|
|
1293 | 1338 | if self.nChannels > 1: |
|
|
1294 | 1339 | cspc_outliers_exist= True |
|
|
1295 | 1340 | |
|
|
1296 | 1341 | lt=outliers_IDs |
|
|
1297 | avg=numpy.mean(buffer1[[t for t in range(buffer1.shape[0]) if t not in lt],:],axis=0) | |
|
|
1342 | #avg=numpy.mean(buffer1[[t for t in range(buffer1.shape[0]) if t not in lt],:],axis=0) | |
|
|
1298 | 1343 | |
|
|
1299 | 1344 | for p in list(outliers_IDs): |
|
|
1300 | buffer1[p,:]=avg | |
|
|
1345 | #buffer1[p,:]=avg | |
|
|
1346 | buffer1[p,:] = numpy.NaN | |
|
|
1347 | ||
|
|
1348 | self.dataOutliers[i,k] = len(outliers_IDs) | |
|
|
1301 | 1349 | |
|
|
1302 | 1350 | self.__buffer_spc[:,i,:,k]=numpy.copy(buffer1) |
|
|
1303 | ###cspc IDs | |
|
|
1304 | #indexmin_cspc+=indexmin_cspc | |
|
|
1351 | ||
|
|
1305 | 1352 | outliers_IDs_cspc=numpy.append(outliers_IDs_cspc,outliers_IDs) |
|
|
1306 | 1353 | |
|
|
1307 | #if not breakFlag: | |
|
|
1354 | ||
|
|
1355 | ||
|
|
1308 | 1356 | outliers_IDs_cspc=outliers_IDs_cspc.astype(numpy.dtype('int64')) |
|
|
1309 | 1357 | if cspc_outliers_exist : |
|
|
1310 | #sortdata=numpy.sort(buffer_cspc,axis=0) | |
|
|
1311 | #avg=numpy.mean(sortdata[:indexmin_cpsc,:],axis=0) | |
|
|
1358 | ||
|
|
1312 | 1359 | lt=outliers_IDs_cspc |
|
|
1313 | 1360 | |
|
|
1314 | avg=numpy.mean(buffer_cspc[[t for t in range(buffer_cspc.shape[0]) if t not in lt],:],axis=0) | |
|
|
1361 | #avg=numpy.mean(buffer_cspc[[t for t in range(buffer_cspc.shape[0]) if t not in lt],:],axis=0) | |
|
|
1315 | 1362 | for p in list(outliers_IDs_cspc): |
|
|
1316 | buffer_cspc[p,:]=avg | |
|
|
1363 | #buffer_cspc[p,:]=avg | |
|
|
1364 | buffer_cspc[p,:] = numpy.NaN | |
|
|
1317 | 1365 | |
|
|
1318 | 1366 | try: |
|
|
1319 | 1367 | self.__buffer_cspc[:,:,:,k]=numpy.copy(buffer_cspc) |
|
|
1320 | 1368 | except: |
|
|
1321 | 1369 | #print("No cpsc",e) |
|
|
1322 | 1370 | pass |
|
|
1323 | 1371 | #else: |
|
|
1324 | 1372 | #break |
|
|
1325 | 1373 | |
|
|
1326 | 1374 | |
|
|
1327 | 1375 | |
|
|
1328 | ||
|
|
1376 | nOutliers = len(outliers_IDs) | |
|
|
1377 | #print("Outliers n: ",self.dataOutliers,nOutliers) | |
|
|
1329 | 1378 | buffer=None |
|
|
1330 | 1379 | bufferH=None |
|
|
1331 | 1380 | buffer1=None |
|
|
1332 | 1381 | buffer_cspc=None |
|
|
1333 | 1382 | |
|
|
1334 | #print("cpsc",self.__buffer_cspc[:,0,0,0,0]) | |
|
|
1335 | #exit() | |
|
|
1336 | 1383 | |
|
|
1337 | 1384 | buffer=None |
|
|
1338 | #print(self.__buffer_spc[:,1,3,20,0]) | |
|
|
1339 |
# |
|
|
|
1340 | data_spc = numpy.sum(self.__buffer_spc,axis=0) | |
|
|
1385 | ||
|
|
1386 | #data_spc = numpy.sum(self.__buffer_spc,axis=0) | |
|
|
1387 | data_spc = numpy.nansum(self.__buffer_spc,axis=0) | |
|
|
1341 | 1388 | try: |
|
|
1342 | data_cspc = numpy.sum(self.__buffer_cspc,axis=0) | |
|
|
1389 | #data_cspc = numpy.sum(self.__buffer_cspc,axis=0) | |
|
|
1390 | data_cspc = numpy.nansum(self.__buffer_cspc,axis=0) | |
|
|
1343 | 1391 | except: |
|
|
1344 | 1392 | #print("No cpsc",e) |
|
|
1345 | 1393 | pass |
|
|
1346 | 1394 | |
|
|
1347 | 1395 | |
|
|
1348 | #print(numpy.shape(data_spc)) | |
|
|
1349 | #data_spc[1,4,20,0]=numpy.nan | |
|
|
1350 | ||
|
|
1351 | #data_cspc = self.__buffer_cspc | |
|
|
1352 | #print("pushData pre Done") | |
|
|
1353 | 1396 | data_dc = self.__buffer_dc |
|
|
1354 | n = self.__profIndex | |
|
|
1397 | #(CH, HEIGH) | |
|
|
1398 | self.maxProfilesInt = self.__profIndex | |
|
|
1399 | n = self.__profIndex - self.dataOutliers | |
|
|
1355 | 1400 | |
|
|
1356 | 1401 | self.__buffer_spc = [] |
|
|
1357 | 1402 | self.__buffer_cspc = [] |
|
|
1358 | 1403 | self.__buffer_dc = 0 |
|
|
1359 | 1404 | self.__profIndex = 0 |
|
|
1360 | #print("pushData Done") | |
|
|
1405 | ||
|
|
1361 | 1406 | return data_spc, data_cspc, data_dc, n |
|
|
1362 | 1407 | |
|
|
1363 | 1408 | def byProfiles(self, *args): |
|
|
1364 | 1409 | |
|
|
1365 | 1410 | self.__dataReady = False |
|
|
1366 | 1411 | avgdata_spc = None |
|
|
1367 | 1412 | avgdata_cspc = None |
|
|
1368 | 1413 | avgdata_dc = None |
|
|
1369 | 1414 | |
|
|
1370 | 1415 | self.putData(*args) |
|
|
1371 | 1416 | |
|
|
1372 | 1417 | if self.__profIndex == self.n: |
|
|
1373 | 1418 | |
|
|
1374 | 1419 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
1375 | self.n = n | |
|
|
1420 | self.n_ints = n | |
|
|
1376 | 1421 | self.__dataReady = True |
|
|
1377 | 1422 | |
|
|
1378 | 1423 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1379 | 1424 | |
|
|
1380 | 1425 | def byTime(self, datatime, *args): |
|
|
1381 | 1426 | |
|
|
1382 | 1427 | self.__dataReady = False |
|
|
1383 | 1428 | avgdata_spc = None |
|
|
1384 | 1429 | avgdata_cspc = None |
|
|
1385 | 1430 | avgdata_dc = None |
|
|
1386 | 1431 | |
|
|
1387 | 1432 | self.putData(*args) |
|
|
1388 | 1433 | |
|
|
1389 | 1434 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
1390 | 1435 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
1391 | self.n = n | |
|
|
1436 | self.n_ints = n | |
|
|
1392 | 1437 | self.__dataReady = True |
|
|
1393 | 1438 | |
|
|
1394 | 1439 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1395 | 1440 | |
|
|
1396 | 1441 | def integrate(self, datatime, *args): |
|
|
1397 | 1442 | |
|
|
1398 | 1443 | if self.__profIndex == 0: |
|
|
1399 | 1444 | self.__initime = datatime |
|
|
1400 | 1445 | |
|
|
1401 | 1446 | if self.__byTime: |
|
|
1402 | 1447 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime( |
|
|
1403 | 1448 | datatime, *args) |
|
|
1404 | 1449 | else: |
|
|
1405 | 1450 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args) |
|
|
1406 | 1451 | |
|
|
1407 | 1452 | if not self.__dataReady: |
|
|
1408 | 1453 | return None, None, None, None |
|
|
1409 | 1454 | |
|
|
1410 | 1455 | return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1411 | 1456 | |
|
|
1412 | 1457 | def run(self, dataOut, n=None, DPL = None,timeInterval=None, overlapping=False, minHei=None, maxHei=None, avg=1, factor=0.75): |
|
|
1413 | 1458 | self.dataOut = dataOut.copy() |
|
|
1414 | 1459 | if n == 1: |
|
|
1415 | 1460 | return self.dataOut |
|
|
1416 | 1461 | |
|
|
1417 | 1462 | self.dataOut.flagNoData = True |
|
|
1418 | 1463 | if self.dataOut.nChannels == 1: |
|
|
1419 | 1464 | self.dataOut.data_cspc = None #si es un solo canal no vale la pena acumular DATOS |
|
|
1420 | 1465 | #print(self.dataOut.data_spc.shape, self.dataOut.data_cspc) |
|
|
1421 | 1466 | if not self.isConfig: |
|
|
1422 | 1467 | self.setup(self.dataOut, n, timeInterval, overlapping,DPL ,minHei, maxHei, avg, factor) |
|
|
1423 | 1468 | self.isConfig = True |
|
|
1424 | 1469 | |
|
|
1425 | 1470 | if not self.ByLags: |
|
|
1426 | 1471 | self.nProfiles=self.dataOut.nProfiles |
|
|
1427 | 1472 | self.nChannels=self.dataOut.nChannels |
|
|
1428 | 1473 | self.nHeights=self.dataOut.nHeights |
|
|
1429 | 1474 | avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(self.dataOut.utctime, |
|
|
1430 | 1475 | self.dataOut.data_spc, |
|
|
1431 | 1476 | self.dataOut.data_cspc, |
|
|
1432 | 1477 | self.dataOut.data_dc) |
|
|
1433 | 1478 | else: |
|
|
1434 | 1479 | self.nProfiles=self.dataOut.nProfiles |
|
|
1435 | 1480 | self.nChannels=self.dataOut.nChannels |
|
|
1436 | 1481 | self.nHeights=self.dataOut.nHeights |
|
|
1437 | 1482 | avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(self.dataOut.utctime, |
|
|
1438 | 1483 | self.dataOut.dataLag_spc, |
|
|
1439 | 1484 | self.dataOut.dataLag_cspc, |
|
|
1440 | 1485 | self.dataOut.dataLag_dc) |
|
|
1441 | 1486 | |
|
|
1442 | 1487 | if self.__dataReady: |
|
|
1443 | 1488 | |
|
|
1444 | 1489 | if not self.ByLags: |
|
|
1445 | 1490 | if self.nChannels == 1: |
|
|
1446 | 1491 | #print("f int", avgdata_spc.shape) |
|
|
1447 | 1492 | self.dataOut.data_spc = avgdata_spc |
|
|
1448 | 1493 | self.dataOut.data_cspc = avgdata_spc |
|
|
1449 | 1494 | else: |
|
|
1450 | 1495 | self.dataOut.data_spc = numpy.squeeze(avgdata_spc) |
|
|
1451 | 1496 | self.dataOut.data_cspc = numpy.squeeze(avgdata_cspc) |
|
|
1452 | 1497 | self.dataOut.data_dc = avgdata_dc |
|
|
1453 | ||
|
|
1498 | self.dataOut.data_outlier = self.dataOutliers | |
|
|
1454 | 1499 | |
|
|
1455 | 1500 | else: |
|
|
1456 | 1501 | self.dataOut.dataLag_spc = avgdata_spc |
|
|
1457 | 1502 | self.dataOut.dataLag_cspc = avgdata_cspc |
|
|
1458 | 1503 | self.dataOut.dataLag_dc = avgdata_dc |
|
|
1459 | 1504 | |
|
|
1460 | 1505 | self.dataOut.data_spc=self.dataOut.dataLag_spc[:,:,:,self.dataOut.LagPlot] |
|
|
1461 | 1506 | self.dataOut.data_cspc=self.dataOut.dataLag_cspc[:,:,:,self.dataOut.LagPlot] |
|
|
1462 | 1507 | self.dataOut.data_dc=self.dataOut.dataLag_dc[:,:,self.dataOut.LagPlot] |
|
|
1463 | 1508 | |
|
|
1464 | 1509 | |
|
|
1465 | self.dataOut.nIncohInt *= self.n | |
|
|
1510 | self.dataOut.nIncohInt *= self.n_ints | |
|
|
1511 | self.dataOut.max_nIncohInt = self.maxProfilesInt | |
|
|
1512 | #print(self.dataOut.max_nIncohInt) | |
|
|
1466 | 1513 | self.dataOut.utctime = avgdatatime |
|
|
1467 | 1514 | self.dataOut.flagNoData = False |
|
|
1468 | ||
|
|
1515 | #print("Faraday Integration DONE...") | |
|
|
1469 | 1516 | return self.dataOut |
|
|
1470 | 1517 | |
|
|
1471 | 1518 | class removeInterference(Operation): |
|
|
1472 | 1519 | |
|
|
1473 | 1520 | def removeInterference2(self): |
|
|
1474 | 1521 | |
|
|
1475 | 1522 | cspc = self.dataOut.data_cspc |
|
|
1476 | 1523 | spc = self.dataOut.data_spc |
|
|
1477 | 1524 | Heights = numpy.arange(cspc.shape[2]) |
|
|
1478 | 1525 | realCspc = numpy.abs(cspc) |
|
|
1479 | 1526 | |
|
|
1480 | 1527 | for i in range(cspc.shape[0]): |
|
|
1481 | 1528 | LinePower= numpy.sum(realCspc[i], axis=0) |
|
|
1482 | 1529 | Threshold = numpy.amax(LinePower)-numpy.sort(LinePower)[len(Heights)-int(len(Heights)*0.1)] |
|
|
1483 | 1530 | SelectedHeights = Heights[ numpy.where( LinePower < Threshold ) ] |
|
|
1484 | 1531 | InterferenceSum = numpy.sum( realCspc[i,:,SelectedHeights], axis=0 ) |
|
|
1485 | 1532 | InterferenceThresholdMin = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.98)] |
|
|
1486 | 1533 | InterferenceThresholdMax = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.99)] |
|
|
1487 | 1534 | |
|
|
1488 | 1535 | |
|
|
1489 | 1536 | InterferenceRange = numpy.where( ([InterferenceSum > InterferenceThresholdMin]))# , InterferenceSum < InterferenceThresholdMax]) ) |
|
|
1490 | 1537 | #InterferenceRange = numpy.where( ([InterferenceRange < InterferenceThresholdMax])) |
|
|
1491 | 1538 | if len(InterferenceRange)<int(cspc.shape[1]*0.3): |
|
|
1492 | 1539 | cspc[i,InterferenceRange,:] = numpy.NaN |
|
|
1493 | 1540 | |
|
|
1494 | 1541 | self.dataOut.data_cspc = cspc |
|
|
1495 | 1542 | |
|
|
1496 | 1543 | def removeInterference(self, interf = 2, hei_interf = None, nhei_interf = None, offhei_interf = None): |
|
|
1497 | 1544 | |
|
|
1498 | 1545 | jspectra = self.dataOut.data_spc |
|
|
1499 | 1546 | jcspectra = self.dataOut.data_cspc |
|
|
1500 | 1547 | jnoise = self.dataOut.getNoise() |
|
|
1501 | 1548 | num_incoh = self.dataOut.nIncohInt |
|
|
1502 | 1549 | |
|
|
1503 | 1550 | num_channel = jspectra.shape[0] |
|
|
1504 | 1551 | num_prof = jspectra.shape[1] |
|
|
1505 | 1552 | num_hei = jspectra.shape[2] |
|
|
1506 | 1553 | |
|
|
1507 | 1554 | # hei_interf |
|
|
1508 | 1555 | if hei_interf is None: |
|
|
1509 | 1556 | count_hei = int(num_hei / 2) |
|
|
1510 | 1557 | hei_interf = numpy.asmatrix(list(range(count_hei))) + num_hei - count_hei |
|
|
1511 | 1558 | hei_interf = numpy.asarray(hei_interf)[0] |
|
|
1512 | 1559 | # nhei_interf |
|
|
1513 | 1560 | if (nhei_interf == None): |
|
|
1514 | 1561 | nhei_interf = 5 |
|
|
1515 | 1562 | if (nhei_interf < 1): |
|
|
1516 | 1563 | nhei_interf = 1 |
|
|
1517 | 1564 | if (nhei_interf > count_hei): |
|
|
1518 | 1565 | nhei_interf = count_hei |
|
|
1519 | 1566 | if (offhei_interf == None): |
|
|
1520 | 1567 | offhei_interf = 0 |
|
|
1521 | 1568 | |
|
|
1522 | 1569 | ind_hei = list(range(num_hei)) |
|
|
1523 | 1570 | # mask_prof = numpy.asarray(range(num_prof - 2)) + 1 |
|
|
1524 | 1571 | # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1 |
|
|
1525 | 1572 | mask_prof = numpy.asarray(list(range(num_prof))) |
|
|
1526 | 1573 | num_mask_prof = mask_prof.size |
|
|
1527 | 1574 | comp_mask_prof = [0, num_prof / 2] |
|
|
1528 | 1575 | |
|
|
1529 | 1576 | # noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal |
|
|
1530 | 1577 | if (jnoise.size < num_channel or numpy.isnan(jnoise).any()): |
|
|
1531 | 1578 | jnoise = numpy.nan |
|
|
1532 | 1579 | noise_exist = jnoise[0] < numpy.Inf |
|
|
1533 | 1580 | |
|
|
1534 | 1581 | # Subrutina de Remocion de la Interferencia |
|
|
1535 | 1582 | for ich in range(num_channel): |
|
|
1536 | 1583 | # Se ordena los espectros segun su potencia (menor a mayor) |
|
|
1537 | 1584 | power = jspectra[ich, mask_prof, :] |
|
|
1538 | 1585 | power = power[:, hei_interf] |
|
|
1539 | 1586 | power = power.sum(axis=0) |
|
|
1540 | 1587 | psort = power.ravel().argsort() |
|
|
1541 | 1588 | |
|
|
1542 | 1589 | # Se estima la interferencia promedio en los Espectros de Potencia empleando |
|
|
1543 | 1590 | junkspc_interf = jspectra[ich, :, hei_interf[psort[list(range( |
|
|
1544 | 1591 | offhei_interf, nhei_interf + offhei_interf))]]] |
|
|
1545 | 1592 | |
|
|
1546 | 1593 | if noise_exist: |
|
|
1547 | 1594 | # tmp_noise = jnoise[ich] / num_prof |
|
|
1548 | 1595 | tmp_noise = jnoise[ich] |
|
|
1549 | 1596 | junkspc_interf = junkspc_interf - tmp_noise |
|
|
1550 | 1597 | #junkspc_interf[:,comp_mask_prof] = 0 |
|
|
1551 | 1598 | |
|
|
1552 | 1599 | jspc_interf = junkspc_interf.sum(axis=0) / nhei_interf |
|
|
1553 | 1600 | jspc_interf = jspc_interf.transpose() |
|
|
1554 | 1601 | # Calculando el espectro de interferencia promedio |
|
|
1555 | 1602 | noiseid = numpy.where( |
|
|
1556 | 1603 | jspc_interf <= tmp_noise / numpy.sqrt(num_incoh)) |
|
|
1557 | 1604 | noiseid = noiseid[0] |
|
|
1558 | 1605 | cnoiseid = noiseid.size |
|
|
1559 | 1606 | interfid = numpy.where( |
|
|
1560 | 1607 | jspc_interf > tmp_noise / numpy.sqrt(num_incoh)) |
|
|
1561 | 1608 | interfid = interfid[0] |
|
|
1562 | 1609 | cinterfid = interfid.size |
|
|
1563 | 1610 | |
|
|
1564 | 1611 | if (cnoiseid > 0): |
|
|
1565 | 1612 | jspc_interf[noiseid] = 0 |
|
|
1566 | 1613 | |
|
|
1567 | 1614 | # Expandiendo los perfiles a limpiar |
|
|
1568 | 1615 | if (cinterfid > 0): |
|
|
1569 | 1616 | new_interfid = ( |
|
|
1570 | 1617 | numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof) % num_prof |
|
|
1571 | 1618 | new_interfid = numpy.asarray(new_interfid) |
|
|
1572 | 1619 | new_interfid = {x for x in new_interfid} |
|
|
1573 | 1620 | new_interfid = numpy.array(list(new_interfid)) |
|
|
1574 | 1621 | new_cinterfid = new_interfid.size |
|
|
1575 | 1622 | else: |
|
|
1576 | 1623 | new_cinterfid = 0 |
|
|
1577 | 1624 | |
|
|
1578 | 1625 | for ip in range(new_cinterfid): |
|
|
1579 | 1626 | ind = junkspc_interf[:, new_interfid[ip]].ravel().argsort() |
|
|
1580 | 1627 | jspc_interf[new_interfid[ip] |
|
|
1581 | 1628 | ] = junkspc_interf[ind[nhei_interf // 2], new_interfid[ip]] |
|
|
1582 | 1629 | |
|
|
1583 | 1630 | jspectra[ich, :, ind_hei] = jspectra[ich, :, |
|
|
1584 | 1631 | ind_hei] - jspc_interf # Corregir indices |
|
|
1585 | 1632 | |
|
|
1586 | 1633 | # Removiendo la interferencia del punto de mayor interferencia |
|
|
1587 | 1634 | ListAux = jspc_interf[mask_prof].tolist() |
|
|
1588 | 1635 | maxid = ListAux.index(max(ListAux)) |
|
|
1589 | 1636 | |
|
|
1590 | 1637 | if cinterfid > 0: |
|
|
1591 | 1638 | for ip in range(cinterfid * (interf == 2) - 1): |
|
|
1592 | 1639 | ind = (jspectra[ich, interfid[ip], :] < tmp_noise * |
|
|
1593 | 1640 | (1 + 1 / numpy.sqrt(num_incoh))).nonzero() |
|
|
1594 | 1641 | cind = len(ind) |
|
|
1595 | 1642 | |
|
|
1596 | 1643 | if (cind > 0): |
|
|
1597 | 1644 | jspectra[ich, interfid[ip], ind] = tmp_noise * \ |
|
|
1598 | 1645 | (1 + (numpy.random.uniform(cind) - 0.5) / |
|
|
1599 | 1646 | numpy.sqrt(num_incoh)) |
|
|
1600 | 1647 | |
|
|
1601 | 1648 | ind = numpy.array([-2, -1, 1, 2]) |
|
|
1602 | 1649 | xx = numpy.zeros([4, 4]) |
|
|
1603 | 1650 | |
|
|
1604 | 1651 | for id1 in range(4): |
|
|
1605 | 1652 | xx[:, id1] = ind[id1]**numpy.asarray(list(range(4))) |
|
|
1606 | 1653 | |
|
|
1607 | 1654 | xx_inv = numpy.linalg.inv(xx) |
|
|
1608 | 1655 | xx = xx_inv[:, 0] |
|
|
1609 | 1656 | ind = (ind + maxid + num_mask_prof) % num_mask_prof |
|
|
1610 | 1657 | yy = jspectra[ich, mask_prof[ind], :] |
|
|
1611 | 1658 | jspectra[ich, mask_prof[maxid], :] = numpy.dot( |
|
|
1612 | 1659 | yy.transpose(), xx) |
|
|
1613 | 1660 | |
|
|
1614 | 1661 | indAux = (jspectra[ich, :, :] < tmp_noise * |
|
|
1615 | 1662 | (1 - 1 / numpy.sqrt(num_incoh))).nonzero() |
|
|
1616 | 1663 | jspectra[ich, indAux[0], indAux[1]] = tmp_noise * \ |
|
|
1617 | 1664 | (1 - 1 / numpy.sqrt(num_incoh)) |
|
|
1618 | 1665 | |
|
|
1619 | 1666 | # Remocion de Interferencia en el Cross Spectra |
|
|
1620 | 1667 | if jcspectra is None: |
|
|
1621 | 1668 | return jspectra, jcspectra |
|
|
1622 | 1669 | num_pairs = int(jcspectra.size / (num_prof * num_hei)) |
|
|
1623 | 1670 | jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei) |
|
|
1624 | 1671 | |
|
|
1625 | 1672 | for ip in range(num_pairs): |
|
|
1626 | 1673 | |
|
|
1627 | 1674 | #------------------------------------------- |
|
|
1628 | 1675 | |
|
|
1629 | 1676 | cspower = numpy.abs(jcspectra[ip, mask_prof, :]) |
|
|
1630 | 1677 | cspower = cspower[:, hei_interf] |
|
|
1631 | 1678 | cspower = cspower.sum(axis=0) |
|
|
1632 | 1679 | |
|
|
1633 | 1680 | cspsort = cspower.ravel().argsort() |
|
|
1634 | 1681 | junkcspc_interf = jcspectra[ip, :, hei_interf[cspsort[list(range( |
|
|
1635 | 1682 | offhei_interf, nhei_interf + offhei_interf))]]] |
|
|
1636 | 1683 | junkcspc_interf = junkcspc_interf.transpose() |
|
|
1637 | 1684 | jcspc_interf = junkcspc_interf.sum(axis=1) / nhei_interf |
|
|
1638 | 1685 | |
|
|
1639 | 1686 | ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort() |
|
|
1640 | 1687 | |
|
|
1641 | 1688 | median_real = int(numpy.median(numpy.real( |
|
|
1642 | 1689 | junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :]))) |
|
|
1643 | 1690 | median_imag = int(numpy.median(numpy.imag( |
|
|
1644 | 1691 | junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :]))) |
|
|
1645 | 1692 | comp_mask_prof = [int(e) for e in comp_mask_prof] |
|
|
1646 | 1693 | junkcspc_interf[comp_mask_prof, :] = numpy.complex( |
|
|
1647 | 1694 | median_real, median_imag) |
|
|
1648 | 1695 | |
|
|
1649 | 1696 | for iprof in range(num_prof): |
|
|
1650 | 1697 | ind = numpy.abs(junkcspc_interf[iprof, :]).ravel().argsort() |
|
|
1651 | 1698 | jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf // 2]] |
|
|
1652 | 1699 | |
|
|
1653 | 1700 | # Removiendo la Interferencia |
|
|
1654 | 1701 | jcspectra[ip, :, ind_hei] = jcspectra[ip, |
|
|
1655 | 1702 | :, ind_hei] - jcspc_interf |
|
|
1656 | 1703 | |
|
|
1657 | 1704 | ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist() |
|
|
1658 | 1705 | maxid = ListAux.index(max(ListAux)) |
|
|
1659 | 1706 | |
|
|
1660 | 1707 | ind = numpy.array([-2, -1, 1, 2]) |
|
|
1661 | 1708 | xx = numpy.zeros([4, 4]) |
|
|
1662 | 1709 | |
|
|
1663 | 1710 | for id1 in range(4): |
|
|
1664 | 1711 | xx[:, id1] = ind[id1]**numpy.asarray(list(range(4))) |
|
|
1665 | 1712 | |
|
|
1666 | 1713 | xx_inv = numpy.linalg.inv(xx) |
|
|
1667 | 1714 | xx = xx_inv[:, 0] |
|
|
1668 | 1715 | |
|
|
1669 | 1716 | ind = (ind + maxid + num_mask_prof) % num_mask_prof |
|
|
1670 | 1717 | yy = jcspectra[ip, mask_prof[ind], :] |
|
|
1671 | 1718 | jcspectra[ip, mask_prof[maxid], :] = numpy.dot(yy.transpose(), xx) |
|
|
1672 | 1719 | |
|
|
1673 | 1720 | # Guardar Resultados |
|
|
1674 | 1721 | self.dataOut.data_spc = jspectra |
|
|
1675 | 1722 | self.dataOut.data_cspc = jcspectra |
|
|
1676 | 1723 | |
|
|
1677 | 1724 | return 1 |
|
|
1678 | 1725 | |
|
|
1679 | 1726 | def run(self, dataOut, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None, mode=1): |
|
|
1680 | 1727 | |
|
|
1681 | 1728 | self.dataOut = dataOut |
|
|
1682 | 1729 | |
|
|
1683 | 1730 | if mode == 1: |
|
|
1684 | 1731 | self.removeInterference(interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None) |
|
|
1685 | 1732 | elif mode == 2: |
|
|
1686 | 1733 | self.removeInterference2() |
|
|
1687 | 1734 | |
|
|
1688 | 1735 | return self.dataOut |
|
|
1689 | 1736 | |
|
|
1690 | 1737 | |
|
|
1691 | 1738 | class IncohInt(Operation): |
|
|
1692 | 1739 | |
|
|
1693 | 1740 | __profIndex = 0 |
|
|
1694 | 1741 | __withOverapping = False |
|
|
1695 | 1742 | |
|
|
1696 | 1743 | __byTime = False |
|
|
1697 | 1744 | __initime = None |
|
|
1698 | 1745 | __lastdatatime = None |
|
|
1699 | 1746 | __integrationtime = None |
|
|
1700 | 1747 | |
|
|
1701 | 1748 | __buffer_spc = None |
|
|
1702 | 1749 | __buffer_cspc = None |
|
|
1703 | 1750 | __buffer_dc = None |
|
|
1704 | 1751 | |
|
|
1705 | 1752 | __dataReady = False |
|
|
1706 | 1753 | |
|
|
1707 | 1754 | __timeInterval = None |
|
|
1708 | ||
|
|
1755 | incohInt = 0 | |
|
|
1756 | nOutliers = 0 | |
|
|
1709 | 1757 | n = None |
|
|
1710 | 1758 | |
|
|
1711 | 1759 | def __init__(self): |
|
|
1712 | 1760 | |
|
|
1713 | 1761 | Operation.__init__(self) |
|
|
1714 | 1762 | |
|
|
1715 | 1763 | def setup(self, n=None, timeInterval=None, overlapping=False): |
|
|
1716 | 1764 | """ |
|
|
1717 | 1765 | Set the parameters of the integration class. |
|
|
1718 | 1766 | |
|
|
1719 | 1767 | Inputs: |
|
|
1720 | 1768 | |
|
|
1721 | 1769 | n : Number of coherent integrations |
|
|
1722 | 1770 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
1723 | 1771 | overlapping : |
|
|
1724 | 1772 | |
|
|
1725 | 1773 | """ |
|
|
1726 | 1774 | |
|
|
1727 | 1775 | self.__initime = None |
|
|
1728 | 1776 | self.__lastdatatime = 0 |
|
|
1729 | 1777 | |
|
|
1730 | 1778 | self.__buffer_spc = 0 |
|
|
1731 | 1779 | self.__buffer_cspc = 0 |
|
|
1732 | 1780 | self.__buffer_dc = 0 |
|
|
1733 | 1781 | |
|
|
1734 | 1782 | self.__profIndex = 0 |
|
|
1735 | 1783 | self.__dataReady = False |
|
|
1736 | 1784 | self.__byTime = False |
|
|
1737 | ||
|
|
1785 | self.incohInt = 0 | |
|
|
1786 | self.nOutliers = 0 | |
|
|
1738 | 1787 | if n is None and timeInterval is None: |
|
|
1739 | 1788 | raise ValueError("n or timeInterval should be specified ...") |
|
|
1740 | 1789 | |
|
|
1741 | 1790 | if n is not None: |
|
|
1742 | 1791 | self.n = int(n) |
|
|
1743 | 1792 | else: |
|
|
1744 | 1793 | |
|
|
1745 | 1794 | self.__integrationtime = int(timeInterval) |
|
|
1746 | 1795 | self.n = None |
|
|
1747 | 1796 | self.__byTime = True |
|
|
1748 | 1797 | |
|
|
1749 | 1798 | def putData(self, data_spc, data_cspc, data_dc): |
|
|
1750 | 1799 | """ |
|
|
1751 | 1800 | Add a profile to the __buffer_spc and increase in one the __profileIndex |
|
|
1752 | 1801 | |
|
|
1753 | 1802 | """ |
|
|
1754 | 1803 | if data_spc.all() == numpy.nan : |
|
|
1755 | 1804 | print("nan ") |
|
|
1756 | 1805 | return |
|
|
1757 | 1806 | self.__buffer_spc += data_spc |
|
|
1758 | 1807 | |
|
|
1759 | 1808 | if data_cspc is None: |
|
|
1760 | 1809 | self.__buffer_cspc = None |
|
|
1761 | 1810 | else: |
|
|
1762 | 1811 | self.__buffer_cspc += data_cspc |
|
|
1763 | 1812 | |
|
|
1764 | 1813 | if data_dc is None: |
|
|
1765 | 1814 | self.__buffer_dc = None |
|
|
1766 | 1815 | else: |
|
|
1767 | 1816 | self.__buffer_dc += data_dc |
|
|
1768 | 1817 | |
|
|
1769 | 1818 | self.__profIndex += 1 |
|
|
1770 | 1819 | |
|
|
1771 | 1820 | return |
|
|
1772 | 1821 | |
|
|
1773 | 1822 | def pushData(self): |
|
|
1774 | 1823 | """ |
|
|
1775 | 1824 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
1776 | 1825 | |
|
|
1777 | 1826 | Affected: |
|
|
1778 | 1827 | |
|
|
1779 | 1828 | self.__profileIndex |
|
|
1780 | 1829 | |
|
|
1781 | 1830 | """ |
|
|
1782 | 1831 | |
|
|
1783 | 1832 | data_spc = self.__buffer_spc |
|
|
1784 | 1833 | data_cspc = self.__buffer_cspc |
|
|
1785 | 1834 | data_dc = self.__buffer_dc |
|
|
1786 | 1835 | n = self.__profIndex |
|
|
1787 | 1836 | |
|
|
1788 | 1837 | self.__buffer_spc = 0 |
|
|
1789 | 1838 | self.__buffer_cspc = 0 |
|
|
1790 | 1839 | self.__buffer_dc = 0 |
|
|
1791 | self.__profIndex = 0 | |
|
|
1840 | ||
|
|
1792 | 1841 | |
|
|
1793 | 1842 | return data_spc, data_cspc, data_dc, n |
|
|
1794 | 1843 | |
|
|
1795 | 1844 | def byProfiles(self, *args): |
|
|
1796 | 1845 | |
|
|
1797 | 1846 | self.__dataReady = False |
|
|
1798 | 1847 | avgdata_spc = None |
|
|
1799 | 1848 | avgdata_cspc = None |
|
|
1800 | 1849 | avgdata_dc = None |
|
|
1801 | 1850 | |
|
|
1802 | 1851 | self.putData(*args) |
|
|
1803 | 1852 | |
|
|
1804 | 1853 | if self.__profIndex == self.n: |
|
|
1805 | 1854 | |
|
|
1806 | 1855 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
1807 | 1856 | self.n = n |
|
|
1808 | 1857 | self.__dataReady = True |
|
|
1809 | 1858 | |
|
|
1810 | 1859 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1811 | 1860 | |
|
|
1812 | 1861 | def byTime(self, datatime, *args): |
|
|
1813 | 1862 | |
|
|
1814 | 1863 | self.__dataReady = False |
|
|
1815 | 1864 | avgdata_spc = None |
|
|
1816 | 1865 | avgdata_cspc = None |
|
|
1817 | 1866 | avgdata_dc = None |
|
|
1818 | 1867 | |
|
|
1819 | 1868 | self.putData(*args) |
|
|
1820 | 1869 | |
|
|
1821 | 1870 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
1822 | 1871 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
1823 | 1872 | self.n = n |
|
|
1824 | 1873 | self.__dataReady = True |
|
|
1825 | 1874 | |
|
|
1826 | 1875 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1827 | 1876 | |
|
|
1828 | 1877 | def integrate(self, datatime, *args): |
|
|
1829 | 1878 | |
|
|
1830 | 1879 | if self.__profIndex == 0: |
|
|
1831 | 1880 | self.__initime = datatime |
|
|
1832 | 1881 | |
|
|
1833 | 1882 | if self.__byTime: |
|
|
1834 | 1883 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime( |
|
|
1835 | 1884 | datatime, *args) |
|
|
1836 | 1885 | else: |
|
|
1837 | 1886 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args) |
|
|
1838 | 1887 | |
|
|
1839 | 1888 | if not self.__dataReady: |
|
|
1840 | 1889 | return None, None, None, None |
|
|
1841 | 1890 | |
|
|
1842 | 1891 | return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1843 | 1892 | |
|
|
1844 | 1893 | def run(self, dataOut, n=None, timeInterval=None, overlapping=False): |
|
|
1845 | 1894 | if n == 1: |
|
|
1846 | 1895 | return dataOut |
|
|
1847 | 1896 | |
|
|
1897 | if dataOut.flagNoData == True: | |
|
|
1898 | return dataOut | |
|
|
1899 | ||
|
|
1848 | 1900 | dataOut.flagNoData = True |
|
|
1849 | 1901 | |
|
|
1850 | 1902 | if not self.isConfig: |
|
|
1851 | 1903 | self.setup(n, timeInterval, overlapping) |
|
|
1852 | 1904 | self.isConfig = True |
|
|
1853 | 1905 | |
|
|
1854 | 1906 | avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime, |
|
|
1855 | 1907 | dataOut.data_spc, |
|
|
1856 | 1908 | dataOut.data_cspc, |
|
|
1857 | 1909 | dataOut.data_dc) |
|
|
1858 | ||
|
|
1910 | self.incohInt += dataOut.nIncohInt | |
|
|
1911 | self.nOutliers += dataOut.data_outlier | |
|
|
1859 | 1912 | if self.__dataReady: |
|
|
1860 | 1913 | |
|
|
1861 | 1914 | dataOut.data_spc = avgdata_spc |
|
|
1862 | 1915 | dataOut.data_cspc = avgdata_cspc |
|
|
1863 | 1916 | dataOut.data_dc = avgdata_dc |
|
|
1864 |
dataOut.nIncohInt |
|
|
|
1917 | dataOut.nIncohInt = self.incohInt | |
|
|
1918 | dataOut.data_outlier = self.nOutliers | |
|
|
1865 | 1919 | dataOut.utctime = avgdatatime |
|
|
1866 | 1920 | dataOut.flagNoData = False |
|
|
1921 | dataOut.max_nIncohInt = self.__profIndex | |
|
|
1922 | self.incohInt = 0 | |
|
|
1923 | self.nOutliers = 0 | |
|
|
1924 | self.__profIndex = 0 | |
|
|
1867 | 1925 | |
|
|
1868 | 1926 | return dataOut |
|
|
1869 | 1927 | |
|
|
1870 | 1928 | class dopplerFlip(Operation): |
|
|
1871 | 1929 | |
|
|
1872 | 1930 | def run(self, dataOut): |
|
|
1873 | 1931 | # arreglo 1: (num_chan, num_profiles, num_heights) |
|
|
1874 | 1932 | self.dataOut = dataOut |
|
|
1875 | 1933 | # JULIA-oblicua, indice 2 |
|
|
1876 | 1934 | # arreglo 2: (num_profiles, num_heights) |
|
|
1877 | 1935 | jspectra = self.dataOut.data_spc[2] |
|
|
1878 | 1936 | jspectra_tmp = numpy.zeros(jspectra.shape) |
|
|
1879 | 1937 | num_profiles = jspectra.shape[0] |
|
|
1880 | 1938 | freq_dc = int(num_profiles / 2) |
|
|
1881 | 1939 | # Flip con for |
|
|
1882 | 1940 | for j in range(num_profiles): |
|
|
1883 | 1941 | jspectra_tmp[num_profiles-j-1]= jspectra[j] |
|
|
1884 | 1942 | # Intercambio perfil de DC con perfil inmediato anterior |
|
|
1885 | 1943 | jspectra_tmp[freq_dc-1]= jspectra[freq_dc-1] |
|
|
1886 | 1944 | jspectra_tmp[freq_dc]= jspectra[freq_dc] |
|
|
1887 | 1945 | # canal modificado es re-escrito en el arreglo de canales |
|
|
1888 | 1946 | self.dataOut.data_spc[2] = jspectra_tmp |
|
|
1889 | 1947 | |
|
|
1890 | 1948 | return self.dataOut |
| @@ -1,2234 +1,2361 | |||
|
|
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 | 5 | from schainpy.model.data.jrodata import Voltage,hildebrand_sekhon |
|
|
6 | 6 | from schainpy.utils import log |
|
|
7 | 7 | from schainpy.model.io.utils import getHei_index |
|
|
8 | 8 | from time import time |
|
|
9 | 9 | import datetime |
|
|
10 | 10 | import numpy |
|
|
11 | import copy | |
|
|
11 | #import copy | |
|
|
12 | from schainpy.model.data import _noise | |
|
|
12 | 13 | |
|
|
13 | 14 | class VoltageProc(ProcessingUnit): |
|
|
14 | 15 | |
|
|
15 | 16 | def __init__(self): |
|
|
16 | 17 | |
|
|
17 | 18 | ProcessingUnit.__init__(self) |
|
|
18 | 19 | |
|
|
19 | 20 | self.dataOut = Voltage() |
|
|
20 | 21 | self.flip = 1 |
|
|
21 | 22 | self.setupReq = False |
|
|
22 | 23 | |
|
|
23 | 24 | def run(self): |
|
|
24 | 25 | #print("running volt proc") |
|
|
25 | 26 | if self.dataIn.type == 'AMISR': |
|
|
26 | 27 | self.__updateObjFromAmisrInput() |
|
|
27 | 28 | |
|
|
28 | 29 | if self.dataOut.buffer_empty: |
|
|
29 | 30 | if self.dataIn.type == 'Voltage': |
|
|
30 | 31 | self.dataOut.copy(self.dataIn) |
|
|
31 | 32 | #print("new volts reading") |
|
|
32 | 33 | |
|
|
33 | 34 | |
|
|
34 | 35 | def __updateObjFromAmisrInput(self): |
|
|
35 | 36 | |
|
|
36 | 37 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
37 | 38 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
38 | 39 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
39 | 40 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
40 | 41 | |
|
|
41 | 42 | self.dataOut.flagNoData = self.dataIn.flagNoData |
|
|
42 | 43 | self.dataOut.data = self.dataIn.data |
|
|
43 | 44 | self.dataOut.utctime = self.dataIn.utctime |
|
|
44 | 45 | self.dataOut.channelList = self.dataIn.channelList |
|
|
45 | 46 | #self.dataOut.timeInterval = self.dataIn.timeInterval |
|
|
46 | 47 | self.dataOut.heightList = self.dataIn.heightList |
|
|
47 | 48 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
|
48 | 49 | |
|
|
49 | 50 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
50 | 51 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
51 | 52 | self.dataOut.frequency = self.dataIn.frequency |
|
|
52 | 53 | |
|
|
53 | 54 | self.dataOut.azimuth = self.dataIn.azimuth |
|
|
54 | 55 | self.dataOut.zenith = self.dataIn.zenith |
|
|
55 | 56 | |
|
|
56 | 57 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
|
|
57 | 58 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
|
|
58 | 59 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
|
|
59 | 60 | |
|
|
60 | 61 | |
|
|
61 | 62 | class selectChannels(Operation): |
|
|
62 | 63 | |
|
|
63 | 64 | def run(self, dataOut, channelList=None): |
|
|
64 | 65 | self.channelList = channelList |
|
|
65 | 66 | if self.channelList == None: |
|
|
66 | 67 | print("Missing channelList") |
|
|
67 | 68 | return dataOut |
|
|
68 | 69 | channelIndexList = [] |
|
|
69 | 70 | |
|
|
70 | 71 | if type(dataOut.channelList) is not list: #leer array desde HDF5 |
|
|
71 | 72 | try: |
|
|
72 | 73 | dataOut.channelList = dataOut.channelList.tolist() |
|
|
73 | 74 | except Exception as e: |
|
|
74 | 75 | print("Select Channels: ",e) |
|
|
75 | 76 | for channel in self.channelList: |
|
|
76 | 77 | if channel not in dataOut.channelList: |
|
|
77 | 78 | raise ValueError("Channel %d is not in %s" %(channel, str(dataOut.channelList))) |
|
|
78 | 79 | |
|
|
79 | 80 | index = dataOut.channelList.index(channel) |
|
|
80 | 81 | channelIndexList.append(index) |
|
|
81 | 82 | dataOut = self.selectChannelsByIndex(dataOut,channelIndexList) |
|
|
82 | 83 | return dataOut |
|
|
83 | 84 | |
|
|
84 | 85 | def selectChannelsByIndex(self, dataOut, channelIndexList): |
|
|
85 | 86 | """ |
|
|
86 | 87 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
|
|
87 | 88 | |
|
|
88 | 89 | Input: |
|
|
89 | 90 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
|
|
90 | 91 | |
|
|
91 | 92 | Affected: |
|
|
92 | 93 | dataOut.data |
|
|
93 | 94 | dataOut.channelIndexList |
|
|
94 | 95 | dataOut.nChannels |
|
|
95 | 96 | dataOut.m_ProcessingHeader.totalSpectra |
|
|
96 | 97 | dataOut.systemHeaderObj.numChannels |
|
|
97 | 98 | dataOut.m_ProcessingHeader.blockSize |
|
|
98 | 99 | |
|
|
99 | 100 | Return: |
|
|
100 | 101 | None |
|
|
101 | 102 | """ |
|
|
102 | 103 | #print("selectChannelsByIndex") |
|
|
103 | 104 | # for channelIndex in channelIndexList: |
|
|
104 | 105 | # if channelIndex not in dataOut.channelIndexList: |
|
|
105 | 106 | # raise ValueError("The value %d in channelIndexList is not valid" %channelIndex) |
|
|
106 | 107 | |
|
|
107 | 108 | if dataOut.type == 'Voltage': |
|
|
108 | 109 | if dataOut.flagDataAsBlock: |
|
|
109 | 110 | """ |
|
|
110 | 111 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
111 | 112 | """ |
|
|
112 | 113 | data = dataOut.data[channelIndexList,:,:] |
|
|
113 | 114 | else: |
|
|
114 | 115 | data = dataOut.data[channelIndexList,:] |
|
|
115 | 116 | |
|
|
116 | 117 | dataOut.data = data |
|
|
117 | 118 | # dataOut.channelList = [dataOut.channelList[i] for i in channelIndexList] |
|
|
118 | 119 | dataOut.channelList = range(len(channelIndexList)) |
|
|
119 | 120 | |
|
|
120 | 121 | elif dataOut.type == 'Spectra': |
|
|
121 | 122 | if hasattr(dataOut, 'data_spc'): |
|
|
122 | 123 | if dataOut.data_spc is None: |
|
|
123 | 124 | raise ValueError("data_spc is None") |
|
|
124 | 125 | return dataOut |
|
|
125 | 126 | else: |
|
|
126 | 127 | data_spc = dataOut.data_spc[channelIndexList, :] |
|
|
127 | 128 | dataOut.data_spc = data_spc |
|
|
128 | 129 | |
|
|
129 | 130 | # if hasattr(dataOut, 'data_dc') :# and |
|
|
130 | 131 | # if dataOut.data_dc is None: |
|
|
131 | 132 | # raise ValueError("data_dc is None") |
|
|
132 | 133 | # return dataOut |
|
|
133 | 134 | # else: |
|
|
134 | 135 | # data_dc = dataOut.data_dc[channelIndexList, :] |
|
|
135 | 136 | # dataOut.data_dc = data_dc |
|
|
136 | 137 | # dataOut.channelList = [dataOut.channelList[i] for i in channelIndexList] |
|
|
137 | 138 | dataOut.channelList = channelIndexList |
|
|
138 | 139 | dataOut = self.__selectPairsByChannel(dataOut,channelIndexList) |
|
|
139 | 140 | |
|
|
140 | 141 | return dataOut |
|
|
141 | 142 | |
|
|
142 | 143 | def __selectPairsByChannel(self, dataOut, channelList=None): |
|
|
143 | 144 | #print("__selectPairsByChannel") |
|
|
144 | 145 | if channelList == None: |
|
|
145 | 146 | return |
|
|
146 | 147 | |
|
|
147 | 148 | pairsIndexListSelected = [] |
|
|
148 | 149 | for pairIndex in dataOut.pairsIndexList: |
|
|
149 | 150 | # First pair |
|
|
150 | 151 | if dataOut.pairsList[pairIndex][0] not in channelList: |
|
|
151 | 152 | continue |
|
|
152 | 153 | # Second pair |
|
|
153 | 154 | if dataOut.pairsList[pairIndex][1] not in channelList: |
|
|
154 | 155 | continue |
|
|
155 | 156 | |
|
|
156 | 157 | pairsIndexListSelected.append(pairIndex) |
|
|
157 | 158 | if not pairsIndexListSelected: |
|
|
158 | 159 | dataOut.data_cspc = None |
|
|
159 | 160 | dataOut.pairsList = [] |
|
|
160 | 161 | return |
|
|
161 | 162 | |
|
|
162 | 163 | dataOut.data_cspc = dataOut.data_cspc[pairsIndexListSelected] |
|
|
163 | 164 | dataOut.pairsList = [dataOut.pairsList[i] |
|
|
164 | 165 | for i in pairsIndexListSelected] |
|
|
165 | 166 | |
|
|
166 | 167 | return dataOut |
|
|
167 | 168 | |
|
|
168 | 169 | class selectHeights(Operation): |
|
|
169 | 170 | |
|
|
170 | 171 | def run(self, dataOut, minHei=None, maxHei=None, minIndex=None, maxIndex=None): |
|
|
171 | 172 | """ |
|
|
172 | 173 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango |
|
|
173 | 174 | minHei <= height <= maxHei |
|
|
174 | 175 | |
|
|
175 | 176 | Input: |
|
|
176 | 177 | minHei : valor minimo de altura a considerar |
|
|
177 | 178 | maxHei : valor maximo de altura a considerar |
|
|
178 | 179 | |
|
|
179 | 180 | Affected: |
|
|
180 | 181 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex |
|
|
181 | 182 | |
|
|
182 | 183 | Return: |
|
|
183 | 184 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
184 | 185 | """ |
|
|
185 | 186 | |
|
|
186 | 187 | self.dataOut = dataOut |
|
|
187 | 188 | |
|
|
188 | 189 | if minHei and maxHei: |
|
|
189 | 190 | |
|
|
190 | 191 | if (minHei < dataOut.heightList[0]): |
|
|
191 | 192 | minHei = dataOut.heightList[0] |
|
|
192 | 193 | |
|
|
193 | 194 | if (maxHei > dataOut.heightList[-1]): |
|
|
194 | 195 | maxHei = dataOut.heightList[-1] |
|
|
195 | 196 | |
|
|
196 | 197 | minIndex = 0 |
|
|
197 | 198 | maxIndex = 0 |
|
|
198 | 199 | heights = dataOut.heightList |
|
|
199 | 200 | |
|
|
200 | 201 | inda = numpy.where(heights >= minHei) |
|
|
201 | 202 | indb = numpy.where(heights <= maxHei) |
|
|
202 | 203 | |
|
|
203 | 204 | try: |
|
|
204 | 205 | minIndex = inda[0][0] |
|
|
205 | 206 | except: |
|
|
206 | 207 | minIndex = 0 |
|
|
207 | 208 | |
|
|
208 | 209 | try: |
|
|
209 | 210 | maxIndex = indb[0][-1] |
|
|
210 | 211 | except: |
|
|
211 | 212 | maxIndex = len(heights) |
|
|
212 | 213 | |
|
|
213 | 214 | self.selectHeightsByIndex(minIndex, maxIndex) |
|
|
214 | 215 | |
|
|
215 | 216 | return dataOut |
|
|
216 | 217 | |
|
|
217 | 218 | def selectHeightsByIndex(self, minIndex, maxIndex): |
|
|
218 | 219 | """ |
|
|
219 | 220 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango |
|
|
220 | 221 | minIndex <= index <= maxIndex |
|
|
221 | 222 | |
|
|
222 | 223 | Input: |
|
|
223 | 224 | minIndex : valor de indice minimo de altura a considerar |
|
|
224 | 225 | maxIndex : valor de indice maximo de altura a considerar |
|
|
225 | 226 | |
|
|
226 | 227 | Affected: |
|
|
227 | 228 | self.dataOut.data |
|
|
228 | 229 | self.dataOut.heightList |
|
|
229 | 230 | |
|
|
230 | 231 | Return: |
|
|
231 | 232 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
232 | 233 | """ |
|
|
233 | 234 | |
|
|
234 | 235 | if self.dataOut.type == 'Voltage': |
|
|
235 | 236 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
236 | 237 | raise ValueError("Height index range (%d,%d) is not valid" % (minIndex, maxIndex)) |
|
|
237 | 238 | |
|
|
238 | 239 | if (maxIndex >= self.dataOut.nHeights): |
|
|
239 | 240 | maxIndex = self.dataOut.nHeights |
|
|
240 | 241 | |
|
|
241 | 242 | #voltage |
|
|
242 | 243 | if self.dataOut.flagDataAsBlock: |
|
|
243 | 244 | """ |
|
|
244 | 245 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
245 | 246 | """ |
|
|
246 | 247 | data = self.dataOut.data[:,:, minIndex:maxIndex] |
|
|
247 | 248 | else: |
|
|
248 | 249 | data = self.dataOut.data[:, minIndex:maxIndex] |
|
|
249 | 250 | |
|
|
250 | 251 | # firstHeight = self.dataOut.heightList[minIndex] |
|
|
251 | 252 | |
|
|
252 | 253 | self.dataOut.data = data |
|
|
253 | 254 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex] |
|
|
254 | 255 | |
|
|
255 | 256 | if self.dataOut.nHeights <= 1: |
|
|
256 | 257 | raise ValueError("selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights)) |
|
|
257 | 258 | elif self.dataOut.type == 'Spectra': |
|
|
258 | 259 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
259 | 260 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % ( |
|
|
260 | 261 | minIndex, maxIndex)) |
|
|
261 | 262 | |
|
|
262 | 263 | if (maxIndex >= self.dataOut.nHeights): |
|
|
263 | 264 | maxIndex = self.dataOut.nHeights - 1 |
|
|
264 | 265 | |
|
|
265 | 266 | # Spectra |
|
|
266 | 267 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] |
|
|
267 | 268 | |
|
|
268 | 269 | data_cspc = None |
|
|
269 | 270 | if self.dataOut.data_cspc is not None: |
|
|
270 | 271 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] |
|
|
271 | 272 | |
|
|
272 | 273 | data_dc = None |
|
|
273 | 274 | if self.dataOut.data_dc is not None: |
|
|
274 | 275 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] |
|
|
275 | 276 | |
|
|
276 | 277 | self.dataOut.data_spc = data_spc |
|
|
277 | 278 | self.dataOut.data_cspc = data_cspc |
|
|
278 | 279 | self.dataOut.data_dc = data_dc |
|
|
279 | 280 | |
|
|
280 | 281 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex + 1] |
|
|
281 | 282 | |
|
|
282 | 283 | return 1 |
|
|
283 | 284 | |
|
|
284 | 285 | |
|
|
285 | 286 | class filterByHeights(Operation): |
|
|
286 | 287 | |
|
|
287 | 288 | def run(self, dataOut, window): |
|
|
288 | 289 | |
|
|
289 | 290 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
290 | 291 | |
|
|
291 | 292 | if window == None: |
|
|
292 | 293 | window = (dataOut.radarControllerHeaderObj.txA/dataOut.radarControllerHeaderObj.nBaud) / deltaHeight |
|
|
293 | 294 | |
|
|
294 | 295 | newdelta = deltaHeight * window |
|
|
295 | 296 | r = dataOut.nHeights % window |
|
|
296 | 297 | newheights = (dataOut.nHeights-r)/window |
|
|
297 | 298 | |
|
|
298 | 299 | if newheights <= 1: |
|
|
299 | 300 | raise ValueError("filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(dataOut.nHeights, window)) |
|
|
300 | 301 | |
|
|
301 | 302 | if dataOut.flagDataAsBlock: |
|
|
302 | 303 | """ |
|
|
303 | 304 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
304 | 305 | """ |
|
|
305 | 306 | buffer = dataOut.data[:, :, 0:int(dataOut.nHeights-r)] |
|
|
306 | 307 | buffer = buffer.reshape(dataOut.nChannels, dataOut.nProfiles, int(dataOut.nHeights/window), window) |
|
|
307 | 308 | buffer = numpy.sum(buffer,3) |
|
|
308 | 309 | |
|
|
309 | 310 | else: |
|
|
310 | 311 | buffer = dataOut.data[:,0:int(dataOut.nHeights-r)] |
|
|
311 | 312 | buffer = buffer.reshape(dataOut.nChannels,int(dataOut.nHeights/window),int(window)) |
|
|
312 | 313 | buffer = numpy.sum(buffer,2) |
|
|
313 | 314 | |
|
|
314 | 315 | dataOut.data = buffer |
|
|
315 | 316 | dataOut.heightList = dataOut.heightList[0] + numpy.arange( newheights )*newdelta |
|
|
316 | 317 | dataOut.windowOfFilter = window |
|
|
317 | 318 | |
|
|
318 | 319 | return dataOut |
|
|
319 | 320 | |
|
|
320 | 321 | |
|
|
321 | 322 | class setH0(Operation): |
|
|
322 | 323 | |
|
|
323 | 324 | def run(self, dataOut, h0, deltaHeight = None): |
|
|
324 | 325 | |
|
|
325 | 326 | if not deltaHeight: |
|
|
326 | 327 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
327 | 328 | |
|
|
328 | 329 | nHeights = dataOut.nHeights |
|
|
329 | 330 | |
|
|
330 | 331 | newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight |
|
|
331 | 332 | |
|
|
332 | 333 | dataOut.heightList = newHeiRange |
|
|
333 | 334 | |
|
|
334 | 335 | return dataOut |
|
|
335 | 336 | |
|
|
336 | 337 | |
|
|
337 | 338 | class deFlip(Operation): |
|
|
338 | 339 | |
|
|
339 | 340 | def run(self, dataOut, channelList = []): |
|
|
340 | 341 | |
|
|
341 | 342 | data = dataOut.data.copy() |
|
|
342 | 343 | |
|
|
343 | 344 | if dataOut.flagDataAsBlock: |
|
|
344 | 345 | flip = self.flip |
|
|
345 | 346 | profileList = list(range(dataOut.nProfiles)) |
|
|
346 | 347 | |
|
|
347 | 348 | if not channelList: |
|
|
348 | 349 | for thisProfile in profileList: |
|
|
349 | 350 | data[:,thisProfile,:] = data[:,thisProfile,:]*flip |
|
|
350 | 351 | flip *= -1.0 |
|
|
351 | 352 | else: |
|
|
352 | 353 | for thisChannel in channelList: |
|
|
353 | 354 | if thisChannel not in dataOut.channelList: |
|
|
354 | 355 | continue |
|
|
355 | 356 | |
|
|
356 | 357 | for thisProfile in profileList: |
|
|
357 | 358 | data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip |
|
|
358 | 359 | flip *= -1.0 |
|
|
359 | 360 | |
|
|
360 | 361 | self.flip = flip |
|
|
361 | 362 | |
|
|
362 | 363 | else: |
|
|
363 | 364 | if not channelList: |
|
|
364 | 365 | data[:,:] = data[:,:]*self.flip |
|
|
365 | 366 | else: |
|
|
366 | 367 | for thisChannel in channelList: |
|
|
367 | 368 | if thisChannel not in dataOut.channelList: |
|
|
368 | 369 | continue |
|
|
369 | 370 | |
|
|
370 | 371 | data[thisChannel,:] = data[thisChannel,:]*self.flip |
|
|
371 | 372 | |
|
|
372 | 373 | self.flip *= -1. |
|
|
373 | 374 | |
|
|
374 | 375 | dataOut.data = data |
|
|
375 | 376 | |
|
|
376 | 377 | return dataOut |
|
|
377 | 378 | |
|
|
378 | 379 | |
|
|
379 | 380 | class setAttribute(Operation): |
|
|
380 | 381 | ''' |
|
|
381 | 382 | Set an arbitrary attribute(s) to dataOut |
|
|
382 | 383 | ''' |
|
|
383 | 384 | |
|
|
384 | 385 | def __init__(self): |
|
|
385 | 386 | |
|
|
386 | 387 | Operation.__init__(self) |
|
|
387 | 388 | self._ready = False |
|
|
388 | 389 | |
|
|
389 | 390 | def run(self, dataOut, **kwargs): |
|
|
390 | 391 | |
|
|
391 | 392 | for key, value in kwargs.items(): |
|
|
392 | 393 | setattr(dataOut, key, value) |
|
|
393 | 394 | |
|
|
394 | 395 | return dataOut |
|
|
395 | 396 | |
|
|
396 | 397 | |
|
|
397 | 398 | @MPDecorator |
|
|
398 | 399 | class printAttribute(Operation): |
|
|
399 | 400 | ''' |
|
|
400 | 401 | Print an arbitrary attribute of dataOut |
|
|
401 | 402 | ''' |
|
|
402 | 403 | |
|
|
403 | 404 | def __init__(self): |
|
|
404 | 405 | |
|
|
405 | 406 | Operation.__init__(self) |
|
|
406 | 407 | |
|
|
407 | 408 | def run(self, dataOut, attributes): |
|
|
408 | 409 | |
|
|
409 | 410 | if isinstance(attributes, str): |
|
|
410 | 411 | attributes = [attributes] |
|
|
411 | 412 | for attr in attributes: |
|
|
412 | 413 | if hasattr(dataOut, attr): |
|
|
413 | 414 | log.log(getattr(dataOut, attr), attr) |
|
|
414 | 415 | |
|
|
415 | 416 | |
|
|
416 | 417 | class interpolateHeights(Operation): |
|
|
417 | 418 | |
|
|
418 | 419 | def run(self, dataOut, topLim, botLim): |
|
|
419 | 420 | #69 al 72 para julia |
|
|
420 | 421 | #82-84 para meteoros |
|
|
421 | 422 | if len(numpy.shape(dataOut.data))==2: |
|
|
422 | 423 | sampInterp = (dataOut.data[:,botLim-1] + dataOut.data[:,topLim+1])/2 |
|
|
423 | 424 | sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1))) |
|
|
424 | 425 | #dataOut.data[:,botLim:limSup+1] = sampInterp |
|
|
425 | 426 | dataOut.data[:,botLim:topLim+1] = sampInterp |
|
|
426 | 427 | else: |
|
|
427 | 428 | nHeights = dataOut.data.shape[2] |
|
|
428 | 429 | x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights))) |
|
|
429 | 430 | y = dataOut.data[:,:,list(range(botLim))+list(range(topLim+1,nHeights))] |
|
|
430 | 431 | f = interpolate.interp1d(x, y, axis = 2) |
|
|
431 | 432 | xnew = numpy.arange(botLim,topLim+1) |
|
|
432 | 433 | ynew = f(xnew) |
|
|
433 | 434 | dataOut.data[:,:,botLim:topLim+1] = ynew |
|
|
434 | 435 | |
|
|
435 | 436 | return dataOut |
|
|
436 | 437 | |
|
|
437 | 438 | |
|
|
438 | 439 | class CohInt(Operation): |
|
|
439 | 440 | |
|
|
440 | 441 | isConfig = False |
|
|
441 | 442 | __profIndex = 0 |
|
|
442 | 443 | __byTime = False |
|
|
443 | 444 | __initime = None |
|
|
444 | 445 | __lastdatatime = None |
|
|
445 | 446 | __integrationtime = None |
|
|
446 | 447 | __buffer = None |
|
|
447 | 448 | __bufferStride = [] |
|
|
448 | 449 | __dataReady = False |
|
|
449 | 450 | __profIndexStride = 0 |
|
|
450 | 451 | __dataToPutStride = False |
|
|
451 | 452 | n = None |
|
|
452 | 453 | |
|
|
453 | 454 | def __init__(self, **kwargs): |
|
|
454 | 455 | |
|
|
455 | 456 | Operation.__init__(self, **kwargs) |
|
|
456 | 457 | |
|
|
457 | 458 | def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False): |
|
|
458 | 459 | """ |
|
|
459 | 460 | Set the parameters of the integration class. |
|
|
460 | 461 | |
|
|
461 | 462 | Inputs: |
|
|
462 | 463 | |
|
|
463 | 464 | n : Number of coherent integrations |
|
|
464 | 465 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
465 | 466 | overlapping : |
|
|
466 | 467 | """ |
|
|
467 | 468 | |
|
|
468 | 469 | self.__initime = None |
|
|
469 | 470 | self.__lastdatatime = 0 |
|
|
470 | 471 | self.__buffer = None |
|
|
471 | 472 | self.__dataReady = False |
|
|
472 | 473 | self.byblock = byblock |
|
|
473 | 474 | self.stride = stride |
|
|
474 | 475 | |
|
|
475 | 476 | if n == None and timeInterval == None: |
|
|
476 | 477 | raise ValueError("n or timeInterval should be specified ...") |
|
|
477 | 478 | |
|
|
478 | 479 | if n != None: |
|
|
479 | 480 | self.n = n |
|
|
480 | 481 | self.__byTime = False |
|
|
481 | 482 | else: |
|
|
482 | 483 | self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line |
|
|
483 | 484 | self.n = 9999 |
|
|
484 | 485 | self.__byTime = True |
|
|
485 | 486 | |
|
|
486 | 487 | if overlapping: |
|
|
487 | 488 | self.__withOverlapping = True |
|
|
488 | 489 | self.__buffer = None |
|
|
489 | 490 | else: |
|
|
490 | 491 | self.__withOverlapping = False |
|
|
491 | 492 | self.__buffer = 0 |
|
|
492 | 493 | |
|
|
493 | 494 | self.__profIndex = 0 |
|
|
494 | 495 | |
|
|
495 | 496 | def putData(self, data): |
|
|
496 | 497 | |
|
|
497 | 498 | """ |
|
|
498 | 499 | Add a profile to the __buffer and increase in one the __profileIndex |
|
|
499 | 500 | |
|
|
500 | 501 | """ |
|
|
501 | 502 | |
|
|
502 | 503 | if not self.__withOverlapping: |
|
|
503 | 504 | self.__buffer += data.copy() |
|
|
504 | 505 | self.__profIndex += 1 |
|
|
505 | 506 | return |
|
|
506 | 507 | |
|
|
507 | 508 | #Overlapping data |
|
|
508 | 509 | nChannels, nHeis = data.shape |
|
|
509 | 510 | data = numpy.reshape(data, (1, nChannels, nHeis)) |
|
|
510 | 511 | |
|
|
511 | 512 | #If the buffer is empty then it takes the data value |
|
|
512 | 513 | if self.__buffer is None: |
|
|
513 | 514 | self.__buffer = data |
|
|
514 | 515 | self.__profIndex += 1 |
|
|
515 | 516 | return |
|
|
516 | 517 | |
|
|
517 | 518 | #If the buffer length is lower than n then stakcing the data value |
|
|
518 | 519 | if self.__profIndex < self.n: |
|
|
519 | 520 | self.__buffer = numpy.vstack((self.__buffer, data)) |
|
|
520 | 521 | self.__profIndex += 1 |
|
|
521 | 522 | return |
|
|
522 | 523 | |
|
|
523 | 524 | #If the buffer length is equal to n then replacing the last buffer value with the data value |
|
|
524 | 525 | self.__buffer = numpy.roll(self.__buffer, -1, axis=0) |
|
|
525 | 526 | self.__buffer[self.n-1] = data |
|
|
526 | 527 | self.__profIndex = self.n |
|
|
527 | 528 | return |
|
|
528 | 529 | |
|
|
529 | 530 | |
|
|
530 | 531 | def pushData(self): |
|
|
531 | 532 | """ |
|
|
532 | 533 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
533 | 534 | |
|
|
534 | 535 | Affected: |
|
|
535 | 536 | |
|
|
536 | 537 | self.__profileIndex |
|
|
537 | 538 | |
|
|
538 | 539 | """ |
|
|
539 | 540 | |
|
|
540 | 541 | if not self.__withOverlapping: |
|
|
541 | 542 | data = self.__buffer |
|
|
542 | 543 | n = self.__profIndex |
|
|
543 | 544 | |
|
|
544 | 545 | self.__buffer = 0 |
|
|
545 | 546 | self.__profIndex = 0 |
|
|
546 | 547 | |
|
|
547 | 548 | return data, n |
|
|
548 | 549 | |
|
|
549 | 550 | #Integration with Overlapping |
|
|
550 | 551 | data = numpy.sum(self.__buffer, axis=0) |
|
|
551 | 552 | # print data |
|
|
552 | 553 | # raise |
|
|
553 | 554 | n = self.__profIndex |
|
|
554 | 555 | |
|
|
555 | 556 | return data, n |
|
|
556 | 557 | |
|
|
557 | 558 | def byProfiles(self, data): |
|
|
558 | 559 | |
|
|
559 | 560 | self.__dataReady = False |
|
|
560 | 561 | avgdata = None |
|
|
561 | 562 | # n = None |
|
|
562 | 563 | # print data |
|
|
563 | 564 | # raise |
|
|
564 | 565 | self.putData(data) |
|
|
565 | 566 | |
|
|
566 | 567 | if self.__profIndex == self.n: |
|
|
567 | 568 | avgdata, n = self.pushData() |
|
|
568 | 569 | self.__dataReady = True |
|
|
569 | 570 | |
|
|
570 | 571 | return avgdata |
|
|
571 | 572 | |
|
|
572 | 573 | def byTime(self, data, datatime): |
|
|
573 | 574 | |
|
|
574 | 575 | self.__dataReady = False |
|
|
575 | 576 | avgdata = None |
|
|
576 | 577 | n = None |
|
|
577 | 578 | |
|
|
578 | 579 | self.putData(data) |
|
|
579 | 580 | |
|
|
580 | 581 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
581 | 582 | avgdata, n = self.pushData() |
|
|
582 | 583 | self.n = n |
|
|
583 | 584 | self.__dataReady = True |
|
|
584 | 585 | |
|
|
585 | 586 | return avgdata |
|
|
586 | 587 | |
|
|
587 | 588 | def integrateByStride(self, data, datatime): |
|
|
588 | 589 | # print data |
|
|
589 | 590 | if self.__profIndex == 0: |
|
|
590 | 591 | self.__buffer = [[data.copy(), datatime]] |
|
|
591 | 592 | else: |
|
|
592 | 593 | self.__buffer.append([data.copy(),datatime]) |
|
|
593 | 594 | self.__profIndex += 1 |
|
|
594 | 595 | self.__dataReady = False |
|
|
595 | 596 | |
|
|
596 | 597 | if self.__profIndex == self.n * self.stride : |
|
|
597 | 598 | self.__dataToPutStride = True |
|
|
598 | 599 | self.__profIndexStride = 0 |
|
|
599 | 600 | self.__profIndex = 0 |
|
|
600 | 601 | self.__bufferStride = [] |
|
|
601 | 602 | for i in range(self.stride): |
|
|
602 | 603 | current = self.__buffer[i::self.stride] |
|
|
603 | 604 | data = numpy.sum([t[0] for t in current], axis=0) |
|
|
604 | 605 | avgdatatime = numpy.average([t[1] for t in current]) |
|
|
605 | 606 | # print data |
|
|
606 | 607 | self.__bufferStride.append((data, avgdatatime)) |
|
|
607 | 608 | |
|
|
608 | 609 | if self.__dataToPutStride: |
|
|
609 | 610 | self.__dataReady = True |
|
|
610 | 611 | self.__profIndexStride += 1 |
|
|
611 | 612 | if self.__profIndexStride == self.stride: |
|
|
612 | 613 | self.__dataToPutStride = False |
|
|
613 | 614 | # print self.__bufferStride[self.__profIndexStride - 1] |
|
|
614 | 615 | # raise |
|
|
615 | 616 | return self.__bufferStride[self.__profIndexStride - 1] |
|
|
616 | 617 | |
|
|
617 | 618 | |
|
|
618 | 619 | return None, None |
|
|
619 | 620 | |
|
|
620 | 621 | def integrate(self, data, datatime=None): |
|
|
621 | 622 | |
|
|
622 | 623 | if self.__initime == None: |
|
|
623 | 624 | self.__initime = datatime |
|
|
624 | 625 | |
|
|
625 | 626 | if self.__byTime: |
|
|
626 | 627 | avgdata = self.byTime(data, datatime) |
|
|
627 | 628 | else: |
|
|
628 | 629 | avgdata = self.byProfiles(data) |
|
|
629 | 630 | |
|
|
630 | 631 | |
|
|
631 | 632 | self.__lastdatatime = datatime |
|
|
632 | 633 | |
|
|
633 | 634 | if avgdata is None: |
|
|
634 | 635 | return None, None |
|
|
635 | 636 | |
|
|
636 | 637 | avgdatatime = self.__initime |
|
|
637 | 638 | |
|
|
638 | 639 | deltatime = datatime - self.__lastdatatime |
|
|
639 | 640 | |
|
|
640 | 641 | if not self.__withOverlapping: |
|
|
641 | 642 | self.__initime = datatime |
|
|
642 | 643 | else: |
|
|
643 | 644 | self.__initime += deltatime |
|
|
644 | 645 | |
|
|
645 | 646 | return avgdata, avgdatatime |
|
|
646 | 647 | |
|
|
647 | 648 | def integrateByBlock(self, dataOut): |
|
|
648 | 649 | |
|
|
649 | 650 | times = int(dataOut.data.shape[1]/self.n) |
|
|
650 | 651 | avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex) |
|
|
651 | 652 | |
|
|
652 | 653 | id_min = 0 |
|
|
653 | 654 | id_max = self.n |
|
|
654 | 655 | |
|
|
655 | 656 | for i in range(times): |
|
|
656 | 657 | junk = dataOut.data[:,id_min:id_max,:] |
|
|
657 | 658 | avgdata[:,i,:] = junk.sum(axis=1) |
|
|
658 | 659 | id_min += self.n |
|
|
659 | 660 | id_max += self.n |
|
|
660 | 661 | |
|
|
661 | 662 | timeInterval = dataOut.ippSeconds*self.n |
|
|
662 | 663 | avgdatatime = (times - 1) * timeInterval + dataOut.utctime |
|
|
663 | 664 | self.__dataReady = True |
|
|
664 | 665 | return avgdata, avgdatatime |
|
|
665 | 666 | |
|
|
666 | 667 | def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs): |
|
|
667 | 668 | |
|
|
668 | 669 | if not self.isConfig: |
|
|
669 | 670 | self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs) |
|
|
670 | 671 | self.isConfig = True |
|
|
671 | 672 | |
|
|
672 | 673 | if dataOut.flagDataAsBlock: |
|
|
673 | 674 | """ |
|
|
674 | 675 | Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
675 | 676 | """ |
|
|
676 | 677 | avgdata, avgdatatime = self.integrateByBlock(dataOut) |
|
|
677 | 678 | dataOut.nProfiles /= self.n |
|
|
678 | 679 | else: |
|
|
679 | 680 | if stride is None: |
|
|
680 | 681 | avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime) |
|
|
681 | 682 | else: |
|
|
682 | 683 | avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime) |
|
|
683 | 684 | |
|
|
684 | 685 | |
|
|
685 | 686 | # dataOut.timeInterval *= n |
|
|
686 | 687 | dataOut.flagNoData = True |
|
|
687 | 688 | |
|
|
688 | 689 | if self.__dataReady: |
|
|
689 | 690 | dataOut.data = avgdata |
|
|
690 | 691 | if not dataOut.flagCohInt: |
|
|
691 | 692 | dataOut.nCohInt *= self.n |
|
|
692 | 693 | dataOut.flagCohInt = True |
|
|
693 | 694 | dataOut.utctime = avgdatatime |
|
|
694 | 695 | # print avgdata, avgdatatime |
|
|
695 | 696 | # raise |
|
|
696 | 697 | # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt |
|
|
697 | 698 | dataOut.flagNoData = False |
|
|
698 | 699 | return dataOut |
|
|
699 | 700 | |
|
|
700 | 701 | class Decoder(Operation): |
|
|
701 | 702 | |
|
|
702 | 703 | isConfig = False |
|
|
703 | 704 | __profIndex = 0 |
|
|
704 | 705 | |
|
|
705 | 706 | code = None |
|
|
706 | 707 | |
|
|
707 | 708 | nCode = None |
|
|
708 | 709 | nBaud = None |
|
|
709 | 710 | |
|
|
710 | 711 | def __init__(self, **kwargs): |
|
|
711 | 712 | |
|
|
712 | 713 | Operation.__init__(self, **kwargs) |
|
|
713 | 714 | |
|
|
714 | 715 | self.times = None |
|
|
715 | 716 | self.osamp = None |
|
|
716 | 717 | # self.__setValues = False |
|
|
717 | 718 | self.isConfig = False |
|
|
718 | 719 | self.setupReq = False |
|
|
719 | 720 | def setup(self, code, osamp, dataOut): |
|
|
720 | 721 | |
|
|
721 | 722 | self.__profIndex = 0 |
|
|
722 | 723 | |
|
|
723 | 724 | self.code = code |
|
|
724 | 725 | |
|
|
725 | 726 | self.nCode = len(code) |
|
|
726 | 727 | self.nBaud = len(code[0]) |
|
|
727 | 728 | if (osamp != None) and (osamp >1): |
|
|
728 | 729 | self.osamp = osamp |
|
|
729 | 730 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) |
|
|
730 | 731 | self.nBaud = self.nBaud*self.osamp |
|
|
731 | 732 | |
|
|
732 | 733 | self.__nChannels = dataOut.nChannels |
|
|
733 | 734 | self.__nProfiles = dataOut.nProfiles |
|
|
734 | 735 | self.__nHeis = dataOut.nHeights |
|
|
735 | 736 | |
|
|
736 | 737 | if self.__nHeis < self.nBaud: |
|
|
737 | 738 | raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)) |
|
|
738 | 739 | |
|
|
739 | 740 | #Frequency |
|
|
740 | 741 | __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex) |
|
|
741 | 742 | |
|
|
742 | 743 | __codeBuffer[:,0:self.nBaud] = self.code |
|
|
743 | 744 | |
|
|
744 | 745 | self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) |
|
|
745 | 746 | |
|
|
746 | 747 | if dataOut.flagDataAsBlock: |
|
|
747 | 748 | |
|
|
748 | 749 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
749 | 750 | |
|
|
750 | 751 | self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex) |
|
|
751 | 752 | |
|
|
752 | 753 | else: |
|
|
753 | 754 | |
|
|
754 | 755 | #Time |
|
|
755 | 756 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
756 | 757 | |
|
|
757 | 758 | self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex) |
|
|
758 | 759 | |
|
|
759 | 760 | def __convolutionInFreq(self, data): |
|
|
760 | 761 | |
|
|
761 | 762 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
762 | 763 | |
|
|
763 | 764 | fft_data = numpy.fft.fft(data, axis=1) |
|
|
764 | 765 | |
|
|
765 | 766 | conv = fft_data*fft_code |
|
|
766 | 767 | |
|
|
767 | 768 | data = numpy.fft.ifft(conv,axis=1) |
|
|
768 | 769 | |
|
|
769 | 770 | return data |
|
|
770 | 771 | |
|
|
771 | 772 | def __convolutionInFreqOpt(self, data): |
|
|
772 | 773 | |
|
|
773 | 774 | raise NotImplementedError |
|
|
774 | 775 | |
|
|
775 | 776 | def __convolutionInTime(self, data): |
|
|
776 | 777 | |
|
|
777 | 778 | code = self.code[self.__profIndex] |
|
|
778 | 779 | for i in range(self.__nChannels): |
|
|
779 | 780 | self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:] |
|
|
780 | 781 | |
|
|
781 | 782 | return self.datadecTime |
|
|
782 | 783 | |
|
|
783 | 784 | def __convolutionByBlockInTime(self, data): |
|
|
784 | 785 | |
|
|
785 | 786 | repetitions = int(self.__nProfiles / self.nCode) |
|
|
786 | 787 | junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) |
|
|
787 | 788 | junk = junk.flatten() |
|
|
788 | 789 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) |
|
|
789 | 790 | profilesList = range(self.__nProfiles) |
|
|
790 | 791 | |
|
|
791 | 792 | for i in range(self.__nChannels): |
|
|
792 | 793 | for j in profilesList: |
|
|
793 | 794 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] |
|
|
794 | 795 | return self.datadecTime |
|
|
795 | 796 | |
|
|
796 | 797 | def __convolutionByBlockInFreq(self, data): |
|
|
797 | 798 | |
|
|
798 | 799 | raise NotImplementedError("Decoder by frequency fro Blocks not implemented") |
|
|
799 | 800 | |
|
|
800 | 801 | |
|
|
801 | 802 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
802 | 803 | |
|
|
803 | 804 | fft_data = numpy.fft.fft(data, axis=2) |
|
|
804 | 805 | |
|
|
805 | 806 | conv = fft_data*fft_code |
|
|
806 | 807 | |
|
|
807 | 808 | data = numpy.fft.ifft(conv,axis=2) |
|
|
808 | 809 | |
|
|
809 | 810 | return data |
|
|
810 | 811 | |
|
|
811 | 812 | |
|
|
812 | 813 | def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None): |
|
|
813 | 814 | |
|
|
814 | 815 | if dataOut.flagDecodeData: |
|
|
815 | 816 | print("This data is already decoded, recoding again ...") |
|
|
816 | 817 | |
|
|
817 | 818 | if not self.isConfig: |
|
|
818 | 819 | |
|
|
819 | 820 | if code is None: |
|
|
820 | 821 | if dataOut.code is None: |
|
|
821 | 822 | raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type) |
|
|
822 | 823 | |
|
|
823 | 824 | code = dataOut.code |
|
|
824 | 825 | else: |
|
|
825 | 826 | code = numpy.array(code).reshape(nCode,nBaud) |
|
|
826 | 827 | self.setup(code, osamp, dataOut) |
|
|
827 | 828 | |
|
|
828 | 829 | self.isConfig = True |
|
|
829 | 830 | |
|
|
830 | 831 | if mode == 3: |
|
|
831 | 832 | sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode) |
|
|
832 | 833 | |
|
|
833 | 834 | if times != None: |
|
|
834 | 835 | sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n") |
|
|
835 | 836 | |
|
|
836 | 837 | if self.code is None: |
|
|
837 | 838 | print("Fail decoding: Code is not defined.") |
|
|
838 | 839 | return |
|
|
839 | 840 | |
|
|
840 | 841 | self.__nProfiles = dataOut.nProfiles |
|
|
841 | 842 | datadec = None |
|
|
842 | 843 | |
|
|
843 | 844 | if mode == 3: |
|
|
844 | 845 | mode = 0 |
|
|
845 | 846 | |
|
|
846 | 847 | if dataOut.flagDataAsBlock: |
|
|
847 | 848 | """ |
|
|
848 | 849 | Decoding when data have been read as block, |
|
|
849 | 850 | """ |
|
|
850 | 851 | |
|
|
851 | 852 | if mode == 0: |
|
|
852 | 853 | datadec = self.__convolutionByBlockInTime(dataOut.data) |
|
|
853 | 854 | if mode == 1: |
|
|
854 | 855 | datadec = self.__convolutionByBlockInFreq(dataOut.data) |
|
|
855 | 856 | else: |
|
|
856 | 857 | """ |
|
|
857 | 858 | Decoding when data have been read profile by profile |
|
|
858 | 859 | """ |
|
|
859 | 860 | if mode == 0: |
|
|
860 | 861 | datadec = self.__convolutionInTime(dataOut.data) |
|
|
861 | 862 | |
|
|
862 | 863 | if mode == 1: |
|
|
863 | 864 | datadec = self.__convolutionInFreq(dataOut.data) |
|
|
864 | 865 | |
|
|
865 | 866 | if mode == 2: |
|
|
866 | 867 | datadec = self.__convolutionInFreqOpt(dataOut.data) |
|
|
867 | 868 | |
|
|
868 | 869 | if datadec is None: |
|
|
869 | 870 | raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode) |
|
|
870 | 871 | |
|
|
871 | 872 | dataOut.code = self.code |
|
|
872 | 873 | dataOut.nCode = self.nCode |
|
|
873 | 874 | dataOut.nBaud = self.nBaud |
|
|
874 | 875 | |
|
|
875 | 876 | dataOut.data = datadec |
|
|
876 | 877 | |
|
|
877 | 878 | dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]] |
|
|
878 | 879 | |
|
|
879 | 880 | dataOut.flagDecodeData = True #asumo q la data esta decodificada |
|
|
880 | 881 | |
|
|
881 | 882 | if self.__profIndex == self.nCode-1: |
|
|
882 | 883 | self.__profIndex = 0 |
|
|
883 | 884 | return dataOut |
|
|
884 | 885 | |
|
|
885 | 886 | self.__profIndex += 1 |
|
|
886 | 887 | |
|
|
887 | 888 | return dataOut |
|
|
888 | 889 | # dataOut.flagDeflipData = True #asumo q la data no esta sin flip |
|
|
889 | 890 | |
|
|
890 | 891 | |
|
|
891 | 892 | class ProfileConcat(Operation): |
|
|
892 | 893 | |
|
|
893 | 894 | isConfig = False |
|
|
894 | 895 | buffer = None |
|
|
895 | 896 | |
|
|
896 | 897 | def __init__(self, **kwargs): |
|
|
897 | 898 | |
|
|
898 | 899 | Operation.__init__(self, **kwargs) |
|
|
899 | 900 | self.profileIndex = 0 |
|
|
900 | 901 | |
|
|
901 | 902 | def reset(self): |
|
|
902 | 903 | self.buffer = numpy.zeros_like(self.buffer) |
|
|
903 | 904 | self.start_index = 0 |
|
|
904 | 905 | self.times = 1 |
|
|
905 | 906 | |
|
|
906 | 907 | def setup(self, data, m, n=1): |
|
|
907 | 908 | self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0])) |
|
|
908 | 909 | self.nHeights = data.shape[1]#.nHeights |
|
|
909 | 910 | self.start_index = 0 |
|
|
910 | 911 | self.times = 1 |
|
|
911 | 912 | |
|
|
912 | 913 | def concat(self, data): |
|
|
913 | 914 | |
|
|
914 | 915 | self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy() |
|
|
915 | 916 | self.start_index = self.start_index + self.nHeights |
|
|
916 | 917 | |
|
|
917 | 918 | def run(self, dataOut, m): |
|
|
918 | 919 | dataOut.flagNoData = True |
|
|
919 | 920 | |
|
|
920 | 921 | if not self.isConfig: |
|
|
921 | 922 | self.setup(dataOut.data, m, 1) |
|
|
922 | 923 | self.isConfig = True |
|
|
923 | 924 | |
|
|
924 | 925 | if dataOut.flagDataAsBlock: |
|
|
925 | 926 | raise ValueError("ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False") |
|
|
926 | 927 | |
|
|
927 | 928 | else: |
|
|
928 | 929 | self.concat(dataOut.data) |
|
|
929 | 930 | self.times += 1 |
|
|
930 | 931 | if self.times > m: |
|
|
931 | 932 | dataOut.data = self.buffer |
|
|
932 | 933 | self.reset() |
|
|
933 | 934 | dataOut.flagNoData = False |
|
|
934 | 935 | # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas |
|
|
935 | 936 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
936 | 937 | xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m |
|
|
937 | 938 | dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight) |
|
|
938 | 939 | dataOut.ippSeconds *= m |
|
|
939 | 940 | return dataOut |
|
|
940 | 941 | |
|
|
941 | 942 | class ProfileSelector(Operation): |
|
|
942 | 943 | |
|
|
943 | 944 | profileIndex = None |
|
|
944 | 945 | # Tamanho total de los perfiles |
|
|
945 | 946 | nProfiles = None |
|
|
946 | 947 | |
|
|
947 | 948 | def __init__(self, **kwargs): |
|
|
948 | 949 | |
|
|
949 | 950 | Operation.__init__(self, **kwargs) |
|
|
950 | 951 | self.profileIndex = 0 |
|
|
951 | 952 | |
|
|
952 | 953 | def incProfileIndex(self): |
|
|
953 | 954 | |
|
|
954 | 955 | self.profileIndex += 1 |
|
|
955 | 956 | |
|
|
956 | 957 | if self.profileIndex >= self.nProfiles: |
|
|
957 | 958 | self.profileIndex = 0 |
|
|
958 | 959 | |
|
|
959 | 960 | def isThisProfileInRange(self, profileIndex, minIndex, maxIndex): |
|
|
960 | 961 | |
|
|
961 | 962 | if profileIndex < minIndex: |
|
|
962 | 963 | return False |
|
|
963 | 964 | |
|
|
964 | 965 | if profileIndex > maxIndex: |
|
|
965 | 966 | return False |
|
|
966 | 967 | |
|
|
967 | 968 | return True |
|
|
968 | 969 | |
|
|
969 | 970 | def isThisProfileInList(self, profileIndex, profileList): |
|
|
970 | 971 | |
|
|
971 | 972 | if profileIndex not in profileList: |
|
|
972 | 973 | return False |
|
|
973 | 974 | |
|
|
974 | 975 | return True |
|
|
975 | 976 | |
|
|
976 | 977 | def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None): |
|
|
977 | 978 | |
|
|
978 | 979 | """ |
|
|
979 | 980 | ProfileSelector: |
|
|
980 | 981 | |
|
|
981 | 982 | Inputs: |
|
|
982 | 983 | profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8) |
|
|
983 | 984 | |
|
|
984 | 985 | profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30) |
|
|
985 | 986 | |
|
|
986 | 987 | rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256)) |
|
|
987 | 988 | |
|
|
988 | 989 | """ |
|
|
989 | 990 | |
|
|
990 | 991 | if rangeList is not None: |
|
|
991 | 992 | if type(rangeList[0]) not in (tuple, list): |
|
|
992 | 993 | rangeList = [rangeList] |
|
|
993 | 994 | |
|
|
994 | 995 | dataOut.flagNoData = True |
|
|
995 | 996 | |
|
|
996 | 997 | if dataOut.flagDataAsBlock: |
|
|
997 | 998 | """ |
|
|
998 | 999 | data dimension = [nChannels, nProfiles, nHeis] |
|
|
999 | 1000 | """ |
|
|
1000 | 1001 | if profileList != None: |
|
|
1001 | 1002 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
1002 | 1003 | |
|
|
1003 | 1004 | if profileRangeList != None: |
|
|
1004 | 1005 | minIndex = profileRangeList[0] |
|
|
1005 | 1006 | maxIndex = profileRangeList[1] |
|
|
1006 | 1007 | profileList = list(range(minIndex, maxIndex+1)) |
|
|
1007 | 1008 | |
|
|
1008 | 1009 | dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:] |
|
|
1009 | 1010 | |
|
|
1010 | 1011 | if rangeList != None: |
|
|
1011 | 1012 | |
|
|
1012 | 1013 | profileList = [] |
|
|
1013 | 1014 | |
|
|
1014 | 1015 | for thisRange in rangeList: |
|
|
1015 | 1016 | minIndex = thisRange[0] |
|
|
1016 | 1017 | maxIndex = thisRange[1] |
|
|
1017 | 1018 | |
|
|
1018 | 1019 | profileList.extend(list(range(minIndex, maxIndex+1))) |
|
|
1019 | 1020 | |
|
|
1020 | 1021 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
1021 | 1022 | |
|
|
1022 | 1023 | dataOut.nProfiles = len(profileList) |
|
|
1023 | 1024 | dataOut.profileIndex = dataOut.nProfiles - 1 |
|
|
1024 | 1025 | dataOut.flagNoData = False |
|
|
1025 | 1026 | |
|
|
1026 | 1027 | return dataOut |
|
|
1027 | 1028 | |
|
|
1028 | 1029 | """ |
|
|
1029 | 1030 | data dimension = [nChannels, nHeis] |
|
|
1030 | 1031 | """ |
|
|
1031 | 1032 | |
|
|
1032 | 1033 | if profileList != None: |
|
|
1033 | 1034 | |
|
|
1034 | 1035 | if self.isThisProfileInList(dataOut.profileIndex, profileList): |
|
|
1035 | 1036 | |
|
|
1036 | 1037 | self.nProfiles = len(profileList) |
|
|
1037 | 1038 | dataOut.nProfiles = self.nProfiles |
|
|
1038 | 1039 | dataOut.profileIndex = self.profileIndex |
|
|
1039 | 1040 | dataOut.flagNoData = False |
|
|
1040 | 1041 | |
|
|
1041 | 1042 | self.incProfileIndex() |
|
|
1042 | 1043 | return dataOut |
|
|
1043 | 1044 | |
|
|
1044 | 1045 | if profileRangeList != None: |
|
|
1045 | 1046 | |
|
|
1046 | 1047 | minIndex = profileRangeList[0] |
|
|
1047 | 1048 | maxIndex = profileRangeList[1] |
|
|
1048 | 1049 | |
|
|
1049 | 1050 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
1050 | 1051 | |
|
|
1051 | 1052 | self.nProfiles = maxIndex - minIndex + 1 |
|
|
1052 | 1053 | dataOut.nProfiles = self.nProfiles |
|
|
1053 | 1054 | dataOut.profileIndex = self.profileIndex |
|
|
1054 | 1055 | dataOut.flagNoData = False |
|
|
1055 | 1056 | |
|
|
1056 | 1057 | self.incProfileIndex() |
|
|
1057 | 1058 | return dataOut |
|
|
1058 | 1059 | |
|
|
1059 | 1060 | if rangeList != None: |
|
|
1060 | 1061 | |
|
|
1061 | 1062 | nProfiles = 0 |
|
|
1062 | 1063 | |
|
|
1063 | 1064 | for thisRange in rangeList: |
|
|
1064 | 1065 | minIndex = thisRange[0] |
|
|
1065 | 1066 | maxIndex = thisRange[1] |
|
|
1066 | 1067 | |
|
|
1067 | 1068 | nProfiles += maxIndex - minIndex + 1 |
|
|
1068 | 1069 | |
|
|
1069 | 1070 | for thisRange in rangeList: |
|
|
1070 | 1071 | |
|
|
1071 | 1072 | minIndex = thisRange[0] |
|
|
1072 | 1073 | maxIndex = thisRange[1] |
|
|
1073 | 1074 | |
|
|
1074 | 1075 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
1075 | 1076 | |
|
|
1076 | 1077 | self.nProfiles = nProfiles |
|
|
1077 | 1078 | dataOut.nProfiles = self.nProfiles |
|
|
1078 | 1079 | dataOut.profileIndex = self.profileIndex |
|
|
1079 | 1080 | dataOut.flagNoData = False |
|
|
1080 | 1081 | |
|
|
1081 | 1082 | self.incProfileIndex() |
|
|
1082 | 1083 | |
|
|
1083 | 1084 | break |
|
|
1084 | 1085 | |
|
|
1085 | 1086 | return dataOut |
|
|
1086 | 1087 | |
|
|
1087 | 1088 | |
|
|
1088 | 1089 | if beam != None: #beam is only for AMISR data |
|
|
1089 | 1090 | if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]): |
|
|
1090 | 1091 | dataOut.flagNoData = False |
|
|
1091 | 1092 | dataOut.profileIndex = self.profileIndex |
|
|
1092 | 1093 | |
|
|
1093 | 1094 | self.incProfileIndex() |
|
|
1094 | 1095 | |
|
|
1095 | 1096 | return dataOut |
|
|
1096 | 1097 | |
|
|
1097 | 1098 | raise ValueError("ProfileSelector needs profileList, profileRangeList or rangeList parameter") |
|
|
1098 | 1099 | |
|
|
1099 | 1100 | |
|
|
1100 | 1101 | class Reshaper(Operation): |
|
|
1101 | 1102 | |
|
|
1102 | 1103 | def __init__(self, **kwargs): |
|
|
1103 | 1104 | |
|
|
1104 | 1105 | Operation.__init__(self, **kwargs) |
|
|
1105 | 1106 | |
|
|
1106 | 1107 | self.__buffer = None |
|
|
1107 | 1108 | self.__nitems = 0 |
|
|
1108 | 1109 | |
|
|
1109 | 1110 | def __appendProfile(self, dataOut, nTxs): |
|
|
1110 | 1111 | |
|
|
1111 | 1112 | if self.__buffer is None: |
|
|
1112 | 1113 | shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) ) |
|
|
1113 | 1114 | self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype) |
|
|
1114 | 1115 | |
|
|
1115 | 1116 | ini = dataOut.nHeights * self.__nitems |
|
|
1116 | 1117 | end = ini + dataOut.nHeights |
|
|
1117 | 1118 | |
|
|
1118 | 1119 | self.__buffer[:, ini:end] = dataOut.data |
|
|
1119 | 1120 | |
|
|
1120 | 1121 | self.__nitems += 1 |
|
|
1121 | 1122 | |
|
|
1122 | 1123 | return int(self.__nitems*nTxs) |
|
|
1123 | 1124 | |
|
|
1124 | 1125 | def __getBuffer(self): |
|
|
1125 | 1126 | |
|
|
1126 | 1127 | if self.__nitems == int(1./self.__nTxs): |
|
|
1127 | 1128 | |
|
|
1128 | 1129 | self.__nitems = 0 |
|
|
1129 | 1130 | |
|
|
1130 | 1131 | return self.__buffer.copy() |
|
|
1131 | 1132 | |
|
|
1132 | 1133 | return None |
|
|
1133 | 1134 | |
|
|
1134 | 1135 | def __checkInputs(self, dataOut, shape, nTxs): |
|
|
1135 | 1136 | |
|
|
1136 | 1137 | if shape is None and nTxs is None: |
|
|
1137 | 1138 | raise ValueError("Reshaper: shape of factor should be defined") |
|
|
1138 | 1139 | |
|
|
1139 | 1140 | if nTxs: |
|
|
1140 | 1141 | if nTxs < 0: |
|
|
1141 | 1142 | raise ValueError("nTxs should be greater than 0") |
|
|
1142 | 1143 | |
|
|
1143 | 1144 | if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0: |
|
|
1144 | 1145 | raise ValueError("nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))) |
|
|
1145 | 1146 | |
|
|
1146 | 1147 | shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs] |
|
|
1147 | 1148 | |
|
|
1148 | 1149 | return shape, nTxs |
|
|
1149 | 1150 | |
|
|
1150 | 1151 | if len(shape) != 2 and len(shape) != 3: |
|
|
1151 | 1152 | 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)) |
|
|
1152 | 1153 | |
|
|
1153 | 1154 | if len(shape) == 2: |
|
|
1154 | 1155 | shape_tuple = [dataOut.nChannels] |
|
|
1155 | 1156 | shape_tuple.extend(shape) |
|
|
1156 | 1157 | else: |
|
|
1157 | 1158 | shape_tuple = list(shape) |
|
|
1158 | 1159 | |
|
|
1159 | 1160 | nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles |
|
|
1160 | 1161 | |
|
|
1161 | 1162 | return shape_tuple, nTxs |
|
|
1162 | 1163 | |
|
|
1163 | 1164 | def run(self, dataOut, shape=None, nTxs=None): |
|
|
1164 | 1165 | |
|
|
1165 | 1166 | shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs) |
|
|
1166 | 1167 | |
|
|
1167 | 1168 | dataOut.flagNoData = True |
|
|
1168 | 1169 | profileIndex = None |
|
|
1169 | 1170 | |
|
|
1170 | 1171 | if dataOut.flagDataAsBlock: |
|
|
1171 | 1172 | |
|
|
1172 | 1173 | dataOut.data = numpy.reshape(dataOut.data, shape_tuple) |
|
|
1173 | 1174 | dataOut.flagNoData = False |
|
|
1174 | 1175 | |
|
|
1175 | 1176 | profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1 |
|
|
1176 | 1177 | |
|
|
1177 | 1178 | else: |
|
|
1178 | 1179 | |
|
|
1179 | 1180 | if self.__nTxs < 1: |
|
|
1180 | 1181 | |
|
|
1181 | 1182 | self.__appendProfile(dataOut, self.__nTxs) |
|
|
1182 | 1183 | new_data = self.__getBuffer() |
|
|
1183 | 1184 | |
|
|
1184 | 1185 | if new_data is not None: |
|
|
1185 | 1186 | dataOut.data = new_data |
|
|
1186 | 1187 | dataOut.flagNoData = False |
|
|
1187 | 1188 | |
|
|
1188 | 1189 | profileIndex = dataOut.profileIndex*nTxs |
|
|
1189 | 1190 | |
|
|
1190 | 1191 | else: |
|
|
1191 | 1192 | raise ValueError("nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)") |
|
|
1192 | 1193 | |
|
|
1193 | 1194 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1194 | 1195 | |
|
|
1195 | 1196 | dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0] |
|
|
1196 | 1197 | |
|
|
1197 | 1198 | dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs) |
|
|
1198 | 1199 | |
|
|
1199 | 1200 | dataOut.profileIndex = profileIndex |
|
|
1200 | 1201 | |
|
|
1201 | 1202 | dataOut.ippSeconds /= self.__nTxs |
|
|
1202 | 1203 | |
|
|
1203 | 1204 | return dataOut |
|
|
1204 | 1205 | |
|
|
1205 | 1206 | class SplitProfiles(Operation): |
|
|
1206 | 1207 | |
|
|
1207 | 1208 | def __init__(self, **kwargs): |
|
|
1208 | 1209 | |
|
|
1209 | 1210 | Operation.__init__(self, **kwargs) |
|
|
1210 | 1211 | |
|
|
1211 | 1212 | def run(self, dataOut, n): |
|
|
1212 | 1213 | |
|
|
1213 | 1214 | dataOut.flagNoData = True |
|
|
1214 | 1215 | profileIndex = None |
|
|
1215 | 1216 | |
|
|
1216 | 1217 | if dataOut.flagDataAsBlock: |
|
|
1217 | 1218 | |
|
|
1218 | 1219 | #nchannels, nprofiles, nsamples |
|
|
1219 | 1220 | shape = dataOut.data.shape |
|
|
1220 | 1221 | |
|
|
1221 | 1222 | if shape[2] % n != 0: |
|
|
1222 | 1223 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[2])) |
|
|
1223 | 1224 | |
|
|
1224 | 1225 | new_shape = shape[0], shape[1]*n, int(shape[2]/n) |
|
|
1225 | 1226 | |
|
|
1226 | 1227 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
1227 | 1228 | dataOut.flagNoData = False |
|
|
1228 | 1229 | |
|
|
1229 | 1230 | profileIndex = int(dataOut.nProfiles/n) - 1 |
|
|
1230 | 1231 | |
|
|
1231 | 1232 | else: |
|
|
1232 | 1233 | |
|
|
1233 | 1234 | raise ValueError("Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)") |
|
|
1234 | 1235 | |
|
|
1235 | 1236 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1236 | 1237 | |
|
|
1237 | 1238 | dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0] |
|
|
1238 | 1239 | |
|
|
1239 | 1240 | dataOut.nProfiles = int(dataOut.nProfiles*n) |
|
|
1240 | 1241 | |
|
|
1241 | 1242 | dataOut.profileIndex = profileIndex |
|
|
1242 | 1243 | |
|
|
1243 | 1244 | dataOut.ippSeconds /= n |
|
|
1244 | 1245 | |
|
|
1245 | 1246 | return dataOut |
|
|
1246 | 1247 | |
|
|
1247 | 1248 | class CombineProfiles(Operation): |
|
|
1248 | 1249 | def __init__(self, **kwargs): |
|
|
1249 | 1250 | |
|
|
1250 | 1251 | Operation.__init__(self, **kwargs) |
|
|
1251 | 1252 | |
|
|
1252 | 1253 | self.__remData = None |
|
|
1253 | 1254 | self.__profileIndex = 0 |
|
|
1254 | 1255 | |
|
|
1255 | 1256 | def run(self, dataOut, n): |
|
|
1256 | 1257 | |
|
|
1257 | 1258 | dataOut.flagNoData = True |
|
|
1258 | 1259 | profileIndex = None |
|
|
1259 | 1260 | |
|
|
1260 | 1261 | if dataOut.flagDataAsBlock: |
|
|
1261 | 1262 | |
|
|
1262 | 1263 | #nchannels, nprofiles, nsamples |
|
|
1263 | 1264 | shape = dataOut.data.shape |
|
|
1264 | 1265 | new_shape = shape[0], shape[1]/n, shape[2]*n |
|
|
1265 | 1266 | |
|
|
1266 | 1267 | if shape[1] % n != 0: |
|
|
1267 | 1268 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[1])) |
|
|
1268 | 1269 | |
|
|
1269 | 1270 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
1270 | 1271 | dataOut.flagNoData = False |
|
|
1271 | 1272 | |
|
|
1272 | 1273 | profileIndex = int(dataOut.nProfiles*n) - 1 |
|
|
1273 | 1274 | |
|
|
1274 | 1275 | else: |
|
|
1275 | 1276 | |
|
|
1276 | 1277 | #nchannels, nsamples |
|
|
1277 | 1278 | if self.__remData is None: |
|
|
1278 | 1279 | newData = dataOut.data |
|
|
1279 | 1280 | else: |
|
|
1280 | 1281 | newData = numpy.concatenate((self.__remData, dataOut.data), axis=1) |
|
|
1281 | 1282 | |
|
|
1282 | 1283 | self.__profileIndex += 1 |
|
|
1283 | 1284 | |
|
|
1284 | 1285 | if self.__profileIndex < n: |
|
|
1285 | 1286 | self.__remData = newData |
|
|
1286 | 1287 | #continue |
|
|
1287 | 1288 | return |
|
|
1288 | 1289 | |
|
|
1289 | 1290 | self.__profileIndex = 0 |
|
|
1290 | 1291 | self.__remData = None |
|
|
1291 | 1292 | |
|
|
1292 | 1293 | dataOut.data = newData |
|
|
1293 | 1294 | dataOut.flagNoData = False |
|
|
1294 | 1295 | |
|
|
1295 | 1296 | profileIndex = dataOut.profileIndex/n |
|
|
1296 | 1297 | |
|
|
1297 | 1298 | |
|
|
1298 | 1299 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1299 | 1300 | |
|
|
1300 | 1301 | dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0] |
|
|
1301 | 1302 | |
|
|
1302 | 1303 | dataOut.nProfiles = int(dataOut.nProfiles/n) |
|
|
1303 | 1304 | |
|
|
1304 | 1305 | dataOut.profileIndex = profileIndex |
|
|
1305 | 1306 | |
|
|
1306 | 1307 | dataOut.ippSeconds *= n |
|
|
1307 | 1308 | |
|
|
1308 | 1309 | return dataOut |
|
|
1309 | 1310 | |
|
|
1310 | 1311 | class PulsePairVoltage(Operation): |
|
|
1311 | 1312 | ''' |
|
|
1312 | 1313 | Function PulsePair(Signal Power, Velocity) |
|
|
1313 | 1314 | The real component of Lag[0] provides Intensity Information |
|
|
1314 | 1315 | The imag component of Lag[1] Phase provides Velocity Information |
|
|
1315 | 1316 | |
|
|
1316 | 1317 | Configuration Parameters: |
|
|
1317 | 1318 | nPRF = Number of Several PRF |
|
|
1318 | 1319 | theta = Degree Azimuth angel Boundaries |
|
|
1319 | 1320 | |
|
|
1320 | 1321 | Input: |
|
|
1321 | 1322 | self.dataOut |
|
|
1322 | 1323 | lag[N] |
|
|
1323 | 1324 | Affected: |
|
|
1324 | 1325 | self.dataOut.spc |
|
|
1325 | 1326 | ''' |
|
|
1326 | 1327 | isConfig = False |
|
|
1327 | 1328 | __profIndex = 0 |
|
|
1328 | 1329 | __initime = None |
|
|
1329 | 1330 | __lastdatatime = None |
|
|
1330 | 1331 | __buffer = None |
|
|
1331 | 1332 | noise = None |
|
|
1332 | 1333 | __dataReady = False |
|
|
1333 | 1334 | n = None |
|
|
1334 | 1335 | __nch = 0 |
|
|
1335 | 1336 | __nHeis = 0 |
|
|
1336 | 1337 | removeDC = False |
|
|
1337 | 1338 | ipp = None |
|
|
1338 | 1339 | lambda_ = 0 |
|
|
1339 | 1340 | |
|
|
1340 | 1341 | def __init__(self,**kwargs): |
|
|
1341 | 1342 | Operation.__init__(self,**kwargs) |
|
|
1342 | 1343 | |
|
|
1343 | 1344 | def setup(self, dataOut, n = None, removeDC=False): |
|
|
1344 | 1345 | ''' |
|
|
1345 | 1346 | n= Numero de PRF's de entrada |
|
|
1346 | 1347 | ''' |
|
|
1347 | 1348 | self.__initime = None |
|
|
1348 | 1349 | self.__lastdatatime = 0 |
|
|
1349 | 1350 | self.__dataReady = False |
|
|
1350 | 1351 | self.__buffer = 0 |
|
|
1351 | 1352 | self.__profIndex = 0 |
|
|
1352 | 1353 | self.noise = None |
|
|
1353 | 1354 | self.__nch = dataOut.nChannels |
|
|
1354 | 1355 | self.__nHeis = dataOut.nHeights |
|
|
1355 | 1356 | self.removeDC = removeDC |
|
|
1356 | 1357 | self.lambda_ = 3.0e8/(9345.0e6) |
|
|
1357 | 1358 | self.ippSec = dataOut.ippSeconds |
|
|
1358 | 1359 | self.nCohInt = dataOut.nCohInt |
|
|
1359 | 1360 | |
|
|
1360 | 1361 | if n == None: |
|
|
1361 | 1362 | raise ValueError("n should be specified.") |
|
|
1362 | 1363 | |
|
|
1363 | 1364 | if n != None: |
|
|
1364 | 1365 | if n<2: |
|
|
1365 | 1366 | raise ValueError("n should be greater than 2") |
|
|
1366 | 1367 | |
|
|
1367 | 1368 | self.n = n |
|
|
1368 | 1369 | self.__nProf = n |
|
|
1369 | 1370 | |
|
|
1370 | 1371 | self.__buffer = numpy.zeros((dataOut.nChannels, |
|
|
1371 | 1372 | n, |
|
|
1372 | 1373 | dataOut.nHeights), |
|
|
1373 | 1374 | dtype='complex') |
|
|
1374 | 1375 | |
|
|
1375 | 1376 | def putData(self,data): |
|
|
1376 | 1377 | ''' |
|
|
1377 | 1378 | Add a profile to he __buffer and increase in one the __profiel Index |
|
|
1378 | 1379 | ''' |
|
|
1379 | 1380 | self.__buffer[:,self.__profIndex,:]= data |
|
|
1380 | 1381 | self.__profIndex += 1 |
|
|
1381 | 1382 | return |
|
|
1382 | 1383 | |
|
|
1383 | 1384 | def pushData(self,dataOut): |
|
|
1384 | 1385 | ''' |
|
|
1385 | 1386 | Return the PULSEPAIR and the profiles used in the operation |
|
|
1386 | 1387 | Affected : self.__profileIndex |
|
|
1387 | 1388 | ''' |
|
|
1388 | 1389 | #----------------- Remove DC----------------------------------- |
|
|
1389 | 1390 | if self.removeDC==True: |
|
|
1390 | 1391 | mean = numpy.mean(self.__buffer,1) |
|
|
1391 | 1392 | tmp = mean.reshape(self.__nch,1,self.__nHeis) |
|
|
1392 | 1393 | dc= numpy.tile(tmp,[1,self.__nProf,1]) |
|
|
1393 | 1394 | self.__buffer = self.__buffer - dc |
|
|
1394 | 1395 | #------------------Calculo de Potencia ------------------------ |
|
|
1395 | 1396 | pair0 = self.__buffer*numpy.conj(self.__buffer) |
|
|
1396 | 1397 | pair0 = pair0.real |
|
|
1397 | 1398 | lag_0 = numpy.sum(pair0,1) |
|
|
1398 | 1399 | #------------------Calculo de Ruido x canal-------------------- |
|
|
1399 | 1400 | self.noise = numpy.zeros(self.__nch) |
|
|
1400 | 1401 | for i in range(self.__nch): |
|
|
1401 | 1402 | daux = numpy.sort(pair0[i,:,:],axis= None) |
|
|
1402 | 1403 | self.noise[i]=hildebrand_sekhon( daux ,self.nCohInt) |
|
|
1403 | 1404 | |
|
|
1404 | 1405 | self.noise = self.noise.reshape(self.__nch,1) |
|
|
1405 | 1406 | self.noise = numpy.tile(self.noise,[1,self.__nHeis]) |
|
|
1406 | 1407 | noise_buffer = self.noise.reshape(self.__nch,1,self.__nHeis) |
|
|
1407 | 1408 | noise_buffer = numpy.tile(noise_buffer,[1,self.__nProf,1]) |
|
|
1408 | 1409 | #------------------ Potencia recibida= P , Potencia senal = S , Ruido= N-- |
|
|
1409 | 1410 | #------------------ P= S+N ,P=lag_0/N --------------------------------- |
|
|
1410 | 1411 | #-------------------- Power -------------------------------------------------- |
|
|
1411 | 1412 | data_power = lag_0/(self.n*self.nCohInt) |
|
|
1412 | 1413 | #------------------ Senal --------------------------------------------------- |
|
|
1413 | 1414 | data_intensity = pair0 - noise_buffer |
|
|
1414 | 1415 | data_intensity = numpy.sum(data_intensity,axis=1)*(self.n*self.nCohInt)#*self.nCohInt) |
|
|
1415 | 1416 | #data_intensity = (lag_0-self.noise*self.n)*(self.n*self.nCohInt) |
|
|
1416 | 1417 | for i in range(self.__nch): |
|
|
1417 | 1418 | for j in range(self.__nHeis): |
|
|
1418 | 1419 | if data_intensity[i][j] < 0: |
|
|
1419 | 1420 | data_intensity[i][j] = numpy.min(numpy.absolute(data_intensity[i][j])) |
|
|
1420 | 1421 | |
|
|
1421 | 1422 | #----------------- Calculo de Frecuencia y Velocidad doppler-------- |
|
|
1422 | 1423 | pair1 = self.__buffer[:,:-1,:]*numpy.conjugate(self.__buffer[:,1:,:]) |
|
|
1423 | 1424 | lag_1 = numpy.sum(pair1,1) |
|
|
1424 | 1425 | data_freq = (-1/(2.0*math.pi*self.ippSec*self.nCohInt))*numpy.angle(lag_1) |
|
|
1425 | 1426 | data_velocity = (self.lambda_/2.0)*data_freq |
|
|
1426 | 1427 | |
|
|
1427 | 1428 | #---------------- Potencia promedio estimada de la Senal----------- |
|
|
1428 | 1429 | lag_0 = lag_0/self.n |
|
|
1429 | 1430 | S = lag_0-self.noise |
|
|
1430 | 1431 | |
|
|
1431 | 1432 | #---------------- Frecuencia Doppler promedio --------------------- |
|
|
1432 | 1433 | lag_1 = lag_1/(self.n-1) |
|
|
1433 | 1434 | R1 = numpy.abs(lag_1) |
|
|
1434 | 1435 | |
|
|
1435 | 1436 | #---------------- Calculo del SNR---------------------------------- |
|
|
1436 | 1437 | data_snrPP = S/self.noise |
|
|
1437 | 1438 | for i in range(self.__nch): |
|
|
1438 | 1439 | for j in range(self.__nHeis): |
|
|
1439 | 1440 | if data_snrPP[i][j] < 1.e-20: |
|
|
1440 | 1441 | data_snrPP[i][j] = 1.e-20 |
|
|
1441 | 1442 | |
|
|
1442 | 1443 | #----------------- Calculo del ancho espectral ---------------------- |
|
|
1443 | 1444 | L = S/R1 |
|
|
1444 | 1445 | L = numpy.where(L<0,1,L) |
|
|
1445 | 1446 | L = numpy.log(L) |
|
|
1446 | 1447 | tmp = numpy.sqrt(numpy.absolute(L)) |
|
|
1447 | 1448 | data_specwidth = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec*self.nCohInt))*tmp*numpy.sign(L) |
|
|
1448 | 1449 | n = self.__profIndex |
|
|
1449 | 1450 | |
|
|
1450 | 1451 | self.__buffer = numpy.zeros((self.__nch, self.__nProf,self.__nHeis), dtype='complex') |
|
|
1451 | 1452 | self.__profIndex = 0 |
|
|
1452 | 1453 | return data_power,data_intensity,data_velocity,data_snrPP,data_specwidth,n |
|
|
1453 | 1454 | |
|
|
1454 | 1455 | |
|
|
1455 | 1456 | def pulsePairbyProfiles(self,dataOut): |
|
|
1456 | 1457 | |
|
|
1457 | 1458 | self.__dataReady = False |
|
|
1458 | 1459 | data_power = None |
|
|
1459 | 1460 | data_intensity = None |
|
|
1460 | 1461 | data_velocity = None |
|
|
1461 | 1462 | data_specwidth = None |
|
|
1462 | 1463 | data_snrPP = None |
|
|
1463 | 1464 | self.putData(data=dataOut.data) |
|
|
1464 | 1465 | if self.__profIndex == self.n: |
|
|
1465 | 1466 | data_power,data_intensity, data_velocity,data_snrPP,data_specwidth, n = self.pushData(dataOut=dataOut) |
|
|
1466 | 1467 | self.__dataReady = True |
|
|
1467 | 1468 | |
|
|
1468 | 1469 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth |
|
|
1469 | 1470 | |
|
|
1470 | 1471 | |
|
|
1471 | 1472 | def pulsePairOp(self, dataOut, datatime= None): |
|
|
1472 | 1473 | |
|
|
1473 | 1474 | if self.__initime == None: |
|
|
1474 | 1475 | self.__initime = datatime |
|
|
1475 | 1476 | data_power, data_intensity, data_velocity, data_snrPP, data_specwidth = self.pulsePairbyProfiles(dataOut) |
|
|
1476 | 1477 | self.__lastdatatime = datatime |
|
|
1477 | 1478 | |
|
|
1478 | 1479 | if data_power is None: |
|
|
1479 | 1480 | return None, None, None,None,None,None |
|
|
1480 | 1481 | |
|
|
1481 | 1482 | avgdatatime = self.__initime |
|
|
1482 | 1483 | deltatime = datatime - self.__lastdatatime |
|
|
1483 | 1484 | self.__initime = datatime |
|
|
1484 | 1485 | |
|
|
1485 | 1486 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, avgdatatime |
|
|
1486 | 1487 | |
|
|
1487 | 1488 | def run(self, dataOut,n = None,removeDC= False, overlapping= False,**kwargs): |
|
|
1488 | 1489 | |
|
|
1489 | 1490 | if not self.isConfig: |
|
|
1490 | 1491 | self.setup(dataOut = dataOut, n = n , removeDC=removeDC , **kwargs) |
|
|
1491 | 1492 | self.isConfig = True |
|
|
1492 | 1493 | data_power, data_intensity, data_velocity,data_snrPP,data_specwidth, avgdatatime = self.pulsePairOp(dataOut, dataOut.utctime) |
|
|
1493 | 1494 | dataOut.flagNoData = True |
|
|
1494 | 1495 | |
|
|
1495 | 1496 | if self.__dataReady: |
|
|
1496 | 1497 | dataOut.nCohInt *= self.n |
|
|
1497 | 1498 | dataOut.dataPP_POW = data_intensity # S |
|
|
1498 | 1499 | dataOut.dataPP_POWER = data_power # P |
|
|
1499 | 1500 | dataOut.dataPP_DOP = data_velocity |
|
|
1500 | 1501 | dataOut.dataPP_SNR = data_snrPP |
|
|
1501 | 1502 | dataOut.dataPP_WIDTH = data_specwidth |
|
|
1502 | 1503 | dataOut.PRFbyAngle = self.n #numero de PRF*cada angulo rotado que equivale a un tiempo. |
|
|
1503 | 1504 | dataOut.utctime = avgdatatime |
|
|
1504 | 1505 | dataOut.flagNoData = False |
|
|
1505 | 1506 | return dataOut |
|
|
1506 | 1507 | |
|
|
1507 | 1508 | |
|
|
1508 | 1509 | |
|
|
1509 | 1510 | # import collections |
|
|
1510 | 1511 | # from scipy.stats import mode |
|
|
1511 | 1512 | # |
|
|
1512 | 1513 | # class Synchronize(Operation): |
|
|
1513 | 1514 | # |
|
|
1514 | 1515 | # isConfig = False |
|
|
1515 | 1516 | # __profIndex = 0 |
|
|
1516 | 1517 | # |
|
|
1517 | 1518 | # def __init__(self, **kwargs): |
|
|
1518 | 1519 | # |
|
|
1519 | 1520 | # Operation.__init__(self, **kwargs) |
|
|
1520 | 1521 | # # self.isConfig = False |
|
|
1521 | 1522 | # self.__powBuffer = None |
|
|
1522 | 1523 | # self.__startIndex = 0 |
|
|
1523 | 1524 | # self.__pulseFound = False |
|
|
1524 | 1525 | # |
|
|
1525 | 1526 | # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): |
|
|
1526 | 1527 | # |
|
|
1527 | 1528 | # #Read data |
|
|
1528 | 1529 | # |
|
|
1529 | 1530 | # powerdB = dataOut.getPower(channel = channel) |
|
|
1530 | 1531 | # noisedB = dataOut.getNoise(channel = channel)[0] |
|
|
1531 | 1532 | # |
|
|
1532 | 1533 | # self.__powBuffer.extend(powerdB.flatten()) |
|
|
1533 | 1534 | # |
|
|
1534 | 1535 | # dataArray = numpy.array(self.__powBuffer) |
|
|
1535 | 1536 | # |
|
|
1536 | 1537 | # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") |
|
|
1537 | 1538 | # |
|
|
1538 | 1539 | # maxValue = numpy.nanmax(filteredPower) |
|
|
1539 | 1540 | # |
|
|
1540 | 1541 | # if maxValue < noisedB + 10: |
|
|
1541 | 1542 | # #No se encuentra ningun pulso de transmision |
|
|
1542 | 1543 | # return None |
|
|
1543 | 1544 | # |
|
|
1544 | 1545 | # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] |
|
|
1545 | 1546 | # |
|
|
1546 | 1547 | # if len(maxValuesIndex) < 2: |
|
|
1547 | 1548 | # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX |
|
|
1548 | 1549 | # return None |
|
|
1549 | 1550 | # |
|
|
1550 | 1551 | # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples |
|
|
1551 | 1552 | # |
|
|
1552 | 1553 | # #Seleccionar solo valores con un espaciamiento de nSamples |
|
|
1553 | 1554 | # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) |
|
|
1554 | 1555 | # |
|
|
1555 | 1556 | # if len(pulseIndex) < 2: |
|
|
1556 | 1557 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
1557 | 1558 | # return None |
|
|
1558 | 1559 | # |
|
|
1559 | 1560 | # spacing = pulseIndex[1:] - pulseIndex[:-1] |
|
|
1560 | 1561 | # |
|
|
1561 | 1562 | # #remover senales que se distancien menos de 10 unidades o muestras |
|
|
1562 | 1563 | # #(No deberian existir IPP menor a 10 unidades) |
|
|
1563 | 1564 | # |
|
|
1564 | 1565 | # realIndex = numpy.where(spacing > 10 )[0] |
|
|
1565 | 1566 | # |
|
|
1566 | 1567 | # if len(realIndex) < 2: |
|
|
1567 | 1568 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
1568 | 1569 | # return None |
|
|
1569 | 1570 | # |
|
|
1570 | 1571 | # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) |
|
|
1571 | 1572 | # realPulseIndex = pulseIndex[realIndex] |
|
|
1572 | 1573 | # |
|
|
1573 | 1574 | # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] |
|
|
1574 | 1575 | # |
|
|
1575 | 1576 | # print "IPP = %d samples" %period |
|
|
1576 | 1577 | # |
|
|
1577 | 1578 | # self.__newNSamples = dataOut.nHeights #int(period) |
|
|
1578 | 1579 | # self.__startIndex = int(realPulseIndex[0]) |
|
|
1579 | 1580 | # |
|
|
1580 | 1581 | # return 1 |
|
|
1581 | 1582 | # |
|
|
1582 | 1583 | # |
|
|
1583 | 1584 | # def setup(self, nSamples, nChannels, buffer_size = 4): |
|
|
1584 | 1585 | # |
|
|
1585 | 1586 | # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), |
|
|
1586 | 1587 | # maxlen = buffer_size*nSamples) |
|
|
1587 | 1588 | # |
|
|
1588 | 1589 | # bufferList = [] |
|
|
1589 | 1590 | # |
|
|
1590 | 1591 | # for i in range(nChannels): |
|
|
1591 | 1592 | # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN, |
|
|
1592 | 1593 | # maxlen = buffer_size*nSamples) |
|
|
1593 | 1594 | # |
|
|
1594 | 1595 | # bufferList.append(bufferByChannel) |
|
|
1595 | 1596 | # |
|
|
1596 | 1597 | # self.__nSamples = nSamples |
|
|
1597 | 1598 | # self.__nChannels = nChannels |
|
|
1598 | 1599 | # self.__bufferList = bufferList |
|
|
1599 | 1600 | # |
|
|
1600 | 1601 | # def run(self, dataOut, channel = 0): |
|
|
1601 | 1602 | # |
|
|
1602 | 1603 | # if not self.isConfig: |
|
|
1603 | 1604 | # nSamples = dataOut.nHeights |
|
|
1604 | 1605 | # nChannels = dataOut.nChannels |
|
|
1605 | 1606 | # self.setup(nSamples, nChannels) |
|
|
1606 | 1607 | # self.isConfig = True |
|
|
1607 | 1608 | # |
|
|
1608 | 1609 | # #Append new data to internal buffer |
|
|
1609 | 1610 | # for thisChannel in range(self.__nChannels): |
|
|
1610 | 1611 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
1611 | 1612 | # bufferByChannel.extend(dataOut.data[thisChannel]) |
|
|
1612 | 1613 | # |
|
|
1613 | 1614 | # if self.__pulseFound: |
|
|
1614 | 1615 | # self.__startIndex -= self.__nSamples |
|
|
1615 | 1616 | # |
|
|
1616 | 1617 | # #Finding Tx Pulse |
|
|
1617 | 1618 | # if not self.__pulseFound: |
|
|
1618 | 1619 | # indexFound = self.__findTxPulse(dataOut, channel) |
|
|
1619 | 1620 | # |
|
|
1620 | 1621 | # if indexFound == None: |
|
|
1621 | 1622 | # dataOut.flagNoData = True |
|
|
1622 | 1623 | # return |
|
|
1623 | 1624 | # |
|
|
1624 | 1625 | # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex) |
|
|
1625 | 1626 | # self.__pulseFound = True |
|
|
1626 | 1627 | # self.__startIndex = indexFound |
|
|
1627 | 1628 | # |
|
|
1628 | 1629 | # #If pulse was found ... |
|
|
1629 | 1630 | # for thisChannel in range(self.__nChannels): |
|
|
1630 | 1631 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
1631 | 1632 | # #print self.__startIndex |
|
|
1632 | 1633 | # x = numpy.array(bufferByChannel) |
|
|
1633 | 1634 | # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] |
|
|
1634 | 1635 | # |
|
|
1635 | 1636 | # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1636 | 1637 | # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight |
|
|
1637 | 1638 | # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 |
|
|
1638 | 1639 | # |
|
|
1639 | 1640 | # dataOut.data = self.__arrayBuffer |
|
|
1640 | 1641 | # |
|
|
1641 | 1642 | # self.__startIndex += self.__newNSamples |
|
|
1642 | 1643 | # |
|
|
1643 | 1644 | # return |
|
|
1644 | 1645 | class SSheightProfiles(Operation): |
|
|
1645 | 1646 | |
|
|
1646 | 1647 | step = None |
|
|
1647 | 1648 | nsamples = None |
|
|
1648 | 1649 | bufferShape = None |
|
|
1649 | 1650 | profileShape = None |
|
|
1650 | 1651 | sshProfiles = None |
|
|
1651 | 1652 | profileIndex = None |
|
|
1652 | 1653 | |
|
|
1653 | 1654 | def __init__(self, **kwargs): |
|
|
1654 | 1655 | |
|
|
1655 | 1656 | Operation.__init__(self, **kwargs) |
|
|
1656 | 1657 | self.isConfig = False |
|
|
1657 | 1658 | |
|
|
1658 | 1659 | def setup(self,dataOut ,step = None , nsamples = None): |
|
|
1659 | 1660 | |
|
|
1660 | 1661 | if step == None and nsamples == None: |
|
|
1661 | 1662 | raise ValueError("step or nheights should be specified ...") |
|
|
1662 | 1663 | |
|
|
1663 | 1664 | self.step = step |
|
|
1664 | 1665 | self.nsamples = nsamples |
|
|
1665 | 1666 | self.__nChannels = dataOut.nChannels |
|
|
1666 | 1667 | self.__nProfiles = dataOut.nProfiles |
|
|
1667 | 1668 | self.__nHeis = dataOut.nHeights |
|
|
1668 | 1669 | shape = dataOut.data.shape #nchannels, nprofiles, nsamples |
|
|
1669 | 1670 | |
|
|
1670 | 1671 | residue = (shape[1] - self.nsamples) % self.step |
|
|
1671 | 1672 | if residue != 0: |
|
|
1672 | 1673 | print("The residue is %d, step=%d should be multiple of %d to avoid loss of %d samples"%(residue,step,shape[1] - self.nsamples,residue)) |
|
|
1673 | 1674 | |
|
|
1674 | 1675 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1675 | 1676 | numberProfile = self.nsamples |
|
|
1676 | 1677 | numberSamples = (shape[1] - self.nsamples)/self.step |
|
|
1677 | 1678 | |
|
|
1678 | 1679 | self.bufferShape = int(shape[0]), int(numberSamples), int(numberProfile) # nchannels, nsamples , nprofiles |
|
|
1679 | 1680 | self.profileShape = int(shape[0]), int(numberProfile), int(numberSamples) # nchannels, nprofiles, nsamples |
|
|
1680 | 1681 | |
|
|
1681 | 1682 | self.buffer = numpy.zeros(self.bufferShape , dtype=numpy.complex) |
|
|
1682 | 1683 | self.sshProfiles = numpy.zeros(self.profileShape, dtype=numpy.complex) |
|
|
1683 | 1684 | |
|
|
1684 | 1685 | def run(self, dataOut, step, nsamples, code = None, repeat = None): |
|
|
1685 | 1686 | dataOut.flagNoData = True |
|
|
1686 | 1687 | |
|
|
1687 | 1688 | profileIndex = None |
|
|
1688 | 1689 | #print("nProfiles, nHeights ",dataOut.nProfiles, dataOut.nHeights) |
|
|
1689 | 1690 | #print(dataOut.getFreqRange(1)/1000.) |
|
|
1690 | 1691 | #exit(1) |
|
|
1691 | 1692 | if dataOut.flagDataAsBlock: |
|
|
1692 | 1693 | dataOut.data = numpy.average(dataOut.data,axis=1) |
|
|
1693 | 1694 | #print("jee") |
|
|
1694 | 1695 | dataOut.flagDataAsBlock = False |
|
|
1695 | 1696 | if not self.isConfig: |
|
|
1696 | 1697 | self.setup(dataOut, step=step , nsamples=nsamples) |
|
|
1697 | 1698 | #print("Setup done") |
|
|
1698 | 1699 | self.isConfig = True |
|
|
1699 | 1700 | |
|
|
1700 | 1701 | |
|
|
1701 | 1702 | if code is not None: |
|
|
1702 | 1703 | code = numpy.array(code) |
|
|
1703 | 1704 | code_block = code |
|
|
1704 | 1705 | |
|
|
1705 | 1706 | if repeat is not None: |
|
|
1706 | 1707 | code_block = numpy.repeat(code_block, repeats=repeat, axis=1) |
|
|
1707 | 1708 | #print(code_block.shape) |
|
|
1708 | 1709 | for i in range(self.buffer.shape[1]): |
|
|
1709 | 1710 | |
|
|
1710 | 1711 | if code is not None: |
|
|
1711 | 1712 | self.buffer[:,i] = dataOut.data[:,i*self.step:i*self.step + self.nsamples]*code_block |
|
|
1712 | 1713 | |
|
|
1713 | 1714 | else: |
|
|
1714 | 1715 | |
|
|
1715 | 1716 | self.buffer[:,i] = dataOut.data[:,i*self.step:i*self.step + self.nsamples]#*code[dataOut.profileIndex,:] |
|
|
1716 | 1717 | |
|
|
1717 | 1718 | #self.buffer[:,j,self.__nHeis-j*self.step - self.nheights:self.__nHeis-j*self.step] = numpy.flip(dataOut.data[:,j*self.step:j*self.step + self.nheights]) |
|
|
1718 | 1719 | |
|
|
1719 | 1720 | for j in range(self.buffer.shape[0]): |
|
|
1720 | 1721 | self.sshProfiles[j] = numpy.transpose(self.buffer[j]) |
|
|
1721 | 1722 | |
|
|
1722 | 1723 | profileIndex = self.nsamples |
|
|
1723 | 1724 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1724 | 1725 | ippSeconds = (deltaHeight*1.0e-6)/(0.15) |
|
|
1725 | 1726 | #print("ippSeconds, dH: ",ippSeconds,deltaHeight) |
|
|
1726 | 1727 | try: |
|
|
1727 | 1728 | if dataOut.concat_m is not None: |
|
|
1728 | 1729 | ippSeconds= ippSeconds/float(dataOut.concat_m) |
|
|
1729 | 1730 | #print "Profile concat %d"%dataOut.concat_m |
|
|
1730 | 1731 | except: |
|
|
1731 | 1732 | pass |
|
|
1732 | 1733 | |
|
|
1733 | 1734 | dataOut.data = self.sshProfiles |
|
|
1734 | 1735 | dataOut.flagNoData = False |
|
|
1735 | 1736 | dataOut.heightList = numpy.arange(self.buffer.shape[1]) *self.step*deltaHeight + dataOut.heightList[0] |
|
|
1736 | 1737 | dataOut.nProfiles = int(dataOut.nProfiles*self.nsamples) |
|
|
1737 | 1738 | |
|
|
1738 | 1739 | dataOut.profileIndex = profileIndex |
|
|
1739 | 1740 | dataOut.flagDataAsBlock = True |
|
|
1740 | 1741 | dataOut.ippSeconds = ippSeconds |
|
|
1741 | 1742 | dataOut.step = self.step |
|
|
1742 | 1743 | #print(numpy.shape(dataOut.data)) |
|
|
1743 | 1744 | #exit(1) |
|
|
1744 | 1745 | |
|
|
1745 | 1746 | return dataOut |
|
|
1746 | 1747 | ################################################################################3############################3 |
|
|
1747 | 1748 | ################################################################################3############################3 |
|
|
1748 | 1749 | ################################################################################3############################3 |
|
|
1749 | 1750 | ################################################################################3############################3 |
|
|
1750 | 1751 | |
|
|
1751 | 1752 | class SSheightProfiles2(Operation): |
|
|
1752 | 1753 | ''' |
|
|
1753 | 1754 | Procesa por perfiles y por bloques |
|
|
1754 | 1755 | ''' |
|
|
1755 | 1756 | |
|
|
1756 | 1757 | step = None |
|
|
1757 | 1758 | nsamples = None |
|
|
1758 | 1759 | bufferShape = None |
|
|
1759 | 1760 | profileShape = None |
|
|
1760 | 1761 | sshProfiles = None |
|
|
1761 | 1762 | profileIndex = None |
|
|
1763 | deltaHeight = None | |
|
|
1764 | init_range = None | |
|
|
1762 | 1765 | |
|
|
1763 | 1766 | def __init__(self, **kwargs): |
|
|
1764 | 1767 | |
|
|
1765 | 1768 | Operation.__init__(self, **kwargs) |
|
|
1766 | 1769 | self.isConfig = False |
|
|
1767 | 1770 | |
|
|
1768 | 1771 | def setup(self,dataOut ,step = None , nsamples = None): |
|
|
1769 | 1772 | |
|
|
1770 | 1773 | if step == None and nsamples == None: |
|
|
1771 | 1774 | raise ValueError("step or nheights should be specified ...") |
|
|
1772 | 1775 | |
|
|
1773 | 1776 | self.step = step |
|
|
1774 | 1777 | self.nsamples = nsamples |
|
|
1775 | 1778 | self.__nChannels = int(dataOut.nChannels) |
|
|
1776 | 1779 | self.__nProfiles = int(dataOut.nProfiles) |
|
|
1777 | 1780 | self.__nHeis = int(dataOut.nHeights) |
|
|
1778 | 1781 | |
|
|
1779 | 1782 | residue = (self.__nHeis - self.nsamples) % self.step |
|
|
1780 | 1783 | if residue != 0: |
|
|
1781 | 1784 | print("The residue is %d, step=%d should be multiple of %d to avoid loss of %d samples"%(residue,step,shape[1] - self.nsamples,residue)) |
|
|
1782 | 1785 | |
|
|
1783 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
|
|
1786 | self.deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
|
|
1787 | self.init_range = dataOut.heightList[0] | |
|
|
1784 | 1788 | #numberProfile = self.nsamples |
|
|
1785 | 1789 | self.numberSamples = (self.__nHeis - self.nsamples)/self.step |
|
|
1786 | 1790 | |
|
|
1787 | 1791 | self.new_nHeights = self.numberSamples |
|
|
1788 | 1792 | |
|
|
1789 | 1793 | self.bufferShape = int(self.__nChannels), int(self.numberSamples), int(self.nsamples) # nchannels, nsamples , nprofiles |
|
|
1790 | 1794 | self.profileShape = int(self.__nChannels), int(self.nsamples), int(self.numberSamples) # nchannels, nprofiles, nsamples |
|
|
1791 | 1795 | |
|
|
1792 | 1796 | self.buffer = numpy.zeros(self.bufferShape , dtype=numpy.complex) |
|
|
1793 | 1797 | self.sshProfiles = numpy.zeros(self.profileShape, dtype=numpy.complex) |
|
|
1794 | 1798 | |
|
|
1795 | 1799 | def getNewProfiles(self, data, code=None, repeat=None): |
|
|
1796 | 1800 | |
|
|
1797 | 1801 | if code is not None: |
|
|
1798 | 1802 | code = numpy.array(code) |
|
|
1799 | 1803 | code_block = code |
|
|
1800 | 1804 | |
|
|
1801 | 1805 | if repeat is not None: |
|
|
1802 | 1806 | code_block = numpy.repeat(code_block, repeats=repeat, axis=1) |
|
|
1803 | #print("buff, data, :",self.buffer.shape, data.shape) | |
|
|
1807 | if data.ndim == 2: | |
|
|
1808 | data = data.reshape(1,1,self.__nHeis ) | |
|
|
1809 | #print("buff, data, :",self.buffer.shape, data.shape,self.sshProfiles.shape) | |
|
|
1804 | 1810 | for i in range(int(self.new_nHeights)): #nuevas alturas |
|
|
1805 | 1811 | if code is not None: |
|
|
1806 | self.buffer[:,i] = data[:,i*self.step:i*self.step + self.nsamples]*code_block | |
|
|
1812 | self.buffer[:,i,:] = data[:,:,i*self.step:i*self.step + self.nsamples]*code_block | |
|
|
1807 | 1813 | else: |
|
|
1808 |
self.buffer[:,i] |
|
|
|
1814 | self.buffer[:,i,:] = data[:,:,i*self.step:i*self.step + self.nsamples]#*code[dataOut.profileIndex,:] | |
|
|
1809 | 1815 | |
|
|
1810 | 1816 | for j in range(self.__nChannels): #en los cananles |
|
|
1811 | self.sshProfiles[j] = numpy.transpose(self.buffer[j]) | |
|
|
1812 | ||
|
|
1817 | self.sshProfiles[j,:,:] = numpy.transpose(self.buffer[j,:,:]) | |
|
|
1818 | #print("new profs Done") | |
|
|
1813 | 1819 | |
|
|
1814 | 1820 | |
|
|
1815 | 1821 | |
|
|
1816 | 1822 | def run(self, dataOut, step, nsamples, code = None, repeat = None): |
|
|
1817 | 1823 | |
|
|
1824 | if dataOut.flagNoData == True: | |
|
|
1825 | return dataOut | |
|
|
1818 | 1826 | dataOut.flagNoData = True |
|
|
1819 | 1827 | #print("init data shape:", dataOut.data.shape) |
|
|
1820 | 1828 | #print("ch: {} prof: {} hs: {}".format(int(dataOut.nChannels), |
|
|
1821 | 1829 | # int(dataOut.nProfiles),int(dataOut.nHeights))) |
|
|
1822 | 1830 | |
|
|
1823 | 1831 | profileIndex = None |
|
|
1824 | 1832 | # if not dataOut.flagDataAsBlock: |
|
|
1825 | 1833 | # dataOut.nProfiles = 1 |
|
|
1826 | 1834 | |
|
|
1827 | 1835 | if not self.isConfig: |
|
|
1828 | 1836 | self.setup(dataOut, step=step , nsamples=nsamples) |
|
|
1829 | 1837 | #print("Setup done") |
|
|
1830 | 1838 | self.isConfig = True |
|
|
1831 | 1839 | |
|
|
1832 | 1840 | dataBlock = None |
|
|
1833 | 1841 | |
|
|
1834 | 1842 | nprof = 1 |
|
|
1835 | 1843 | if dataOut.flagDataAsBlock: |
|
|
1836 | 1844 | nprof = int(dataOut.nProfiles) |
|
|
1837 | 1845 | |
|
|
1838 | 1846 | #print("dataOut nProfiles:", dataOut.nProfiles) |
|
|
1839 | 1847 | for profile in range(nprof): |
|
|
1840 | 1848 | if dataOut.flagDataAsBlock: |
|
|
1849 | #print("read blocks") | |
|
|
1841 | 1850 | self.getNewProfiles(dataOut.data[:,profile,:], code=code, repeat=repeat) |
|
|
1842 | 1851 | else: |
|
|
1843 | self.getNewProfiles(dataOut.data, code=code, repeat=repeat) | |
|
|
1852 | #print("read profiles") | |
|
|
1853 | self.getNewProfiles(dataOut.data, code=code, repeat=repeat) #only one channe | |
|
|
1844 | 1854 | if profile == 0: |
|
|
1845 | 1855 | dataBlock = self.sshProfiles.copy() |
|
|
1846 | 1856 | else: #by blocks |
|
|
1847 | 1857 | dataBlock = numpy.concatenate((dataBlock,self.sshProfiles), axis=1) #profile axis |
|
|
1848 | print("by blocks: ",dataBlock.shape, self.sshProfiles.shape) | |
|
|
1858 | #print("by blocks: ",dataBlock.shape, self.sshProfiles.shape) | |
|
|
1849 | 1859 | |
|
|
1850 | 1860 | profileIndex = self.nsamples |
|
|
1851 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
|
|
1852 | ippSeconds = (deltaHeight*1.0e-6)/(0.15) | |
|
|
1861 | #deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
|
|
1862 | ippSeconds = (self.deltaHeight*1.0e-6)/(0.15) | |
|
|
1853 | 1863 | |
|
|
1854 | 1864 | |
|
|
1855 | 1865 | dataOut.data = dataBlock |
|
|
1856 | dataOut.heightList = numpy.arange(self.new_nHeights) *self.step*deltaHeight + dataOut.heightList[0] | |
|
|
1857 | #print("show me: ",dataOut.nHeights, self.new_nHeights) | |
|
|
1858 | #dataOut.nHeights = int(self.new_nHeights) | |
|
|
1866 | #print("show me: ",self.step,self.deltaHeight, dataOut.heightList, self.new_nHeights) | |
|
|
1867 | dataOut.heightList = numpy.arange(int(self.new_nHeights)) *self.step*self.deltaHeight + self.init_range | |
|
|
1868 | ||
|
|
1859 | 1869 | dataOut.ippSeconds = ippSeconds |
|
|
1860 | 1870 | dataOut.step = self.step |
|
|
1861 | 1871 | dataOut.flagNoData = False |
|
|
1862 | 1872 | if dataOut.flagDataAsBlock: |
|
|
1863 | 1873 | dataOut.nProfiles = int(dataOut.nProfiles*self.nsamples) |
|
|
1864 | 1874 | |
|
|
1865 | 1875 | else: |
|
|
1866 | 1876 | dataOut.nProfiles = int(self.nsamples) |
|
|
1867 | 1877 | dataOut.profileIndex = dataOut.nProfiles |
|
|
1868 | 1878 | dataOut.flagDataAsBlock = True |
|
|
1869 | 1879 | |
|
|
1870 | 1880 | dataBlock = None |
|
|
1871 | 1881 | #print("new data shape:", dataOut.data.shape) |
|
|
1872 | 1882 | |
|
|
1873 | 1883 | return dataOut |
|
|
1874 | 1884 | |
|
|
1875 | 1885 | |
|
|
1876 | 1886 | |
|
|
1877 | 1887 | |
|
|
1878 | 1888 | #import skimage.color |
|
|
1879 | 1889 | #import skimage.io |
|
|
1880 | import matplotlib.pyplot as plt | |
|
|
1890 | #import matplotlib.pyplot as plt | |
|
|
1881 | 1891 | |
|
|
1882 |
class |
|
|
|
1892 | class removeProfileByFaradayHS(Operation): | |
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1883 | 1893 | ''' |
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1884 | 1894 | |
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1885 | 1895 | ''' |
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|
1886 | 1896 | isConfig = False |
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|
1887 | 1897 | n = None |
|
|
1888 | __timeInterval = None | |
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|
1898 | ||
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1889 | 1899 | __profIndex = 0 |
|
|
1890 | __byTime = False | |
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|
1900 | ||
|
|
1891 | 1901 | __dataReady = False |
|
|
1892 | 1902 | __buffer_data = [] |
|
|
1893 | 1903 | __buffer_times = [] |
|
|
1894 | 1904 | __initime = None |
|
|
1895 | 1905 | __count_exec = 0 |
|
|
1896 | 1906 | __profIndex = 0 |
|
|
1897 | 1907 | buffer = None |
|
|
1898 | 1908 | lenProfileOut = 1 |
|
|
1899 | 1909 | |
|
|
1900 | 1910 | init_prof = 0 |
|
|
1901 | 1911 | end_prof = 0 |
|
|
1902 | 1912 | n_profiles = 0 |
|
|
1903 | 1913 | first_utcBlock = None |
|
|
1914 | outliers_IDs_list = [] | |
|
|
1904 | 1915 | __dh = 0 |
|
|
1905 | 1916 | |
|
|
1906 | 1917 | def __init__(self, **kwargs): |
|
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1907 | 1918 | |
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|
1908 | 1919 | Operation.__init__(self, **kwargs) |
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1909 | 1920 | self.isConfig = False |
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1910 | 1921 | |
|
|
1911 |
def setup(self,dataOut, n=None , |
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|
|
1922 | def setup(self,dataOut, n=None , navg=0.8, profileMargin=50,thHistOutlier=3, minHei=None, maxHei=None): | |
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1912 | 1923 | |
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|
1913 | 1924 | if n == None and timeInterval == None: |
|
|
1914 | 1925 | raise ValueError("nprofiles or timeInterval should be specified ...") |
|
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1915 | 1926 | |
|
|
1916 | 1927 | if n != None: |
|
|
1917 | 1928 | self.n = n |
|
|
1918 | self.__byTime = False | |
|
|
1919 | else: | |
|
|
1920 | self.__timeInterval = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line | |
|
|
1921 | self.n = 9999 | |
|
|
1922 | self.__byTime = True | |
|
|
1923 | 1929 | |
|
|
1930 | self.navg = navg | |
|
|
1931 | self.profileMargin = profileMargin | |
|
|
1932 | self.thHistOutlier = thHistOutlier | |
|
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1924 | 1933 | self.__profIndex = 0 |
|
|
1925 | 1934 | self.buffer = None |
|
|
1926 | 1935 | self.lenProfileOut = 1 |
|
|
1927 | ||
|
|
1936 | self.n_prof_released = 0 | |
|
|
1937 | self.heightList = dataOut.heightList | |
|
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1928 | 1938 | self.init_prof = 0 |
|
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1929 | 1939 | self.end_prof = 0 |
|
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1930 | 1940 | self.n_profiles = 0 |
|
|
1931 | 1941 | self.first_utcBlock = None |
|
|
1932 | 1942 | self.__dh = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1943 | minHei = minHei | |
|
|
1944 | maxHei = maxHei | |
|
|
1945 | if minHei==None : | |
|
|
1946 | minHei = dataOut.heightList[0] | |
|
|
1947 | if maxHei==None : | |
|
|
1948 | maxHei = dataOut.heightList[-1] | |
|
|
1949 | self.minHei_idx,self.maxHei_idx = getHei_index(minHei, maxHei, dataOut.heightList) | |
|
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1950 | ||
|
|
1951 | ||
|
|
1952 | def filterSatsProfiles(self): | |
|
|
1953 | data = self.__buffer_data | |
|
|
1954 | #print(data.shape) | |
|
|
1955 | nChannels, profiles, heights = data.shape | |
|
|
1956 | indexes=[] | |
|
|
1957 | outliers_IDs=[] | |
|
|
1958 | for c in range(nChannels): | |
|
|
1959 | for h in range(self.minHei_idx, self.maxHei_idx): | |
|
|
1960 | power = data[c,:,h] * numpy.conjugate(data[c,:,h]) | |
|
|
1961 | power = power.real | |
|
|
1962 | #power = (numpy.abs(data[c,:,h].real)) | |
|
|
1963 | sortdata = numpy.sort(power, axis=None) | |
|
|
1964 | sortID=power.argsort() | |
|
|
1965 | index = _noise.hildebrand_sekhon2(sortdata,self.navg) #0.75-> buen valor | |
|
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1966 | ||
|
|
1967 | indexes.append(index) | |
|
|
1968 | outliers_IDs=numpy.append(outliers_IDs,sortID[index:]) | |
|
|
1969 | # print(outliers_IDs) | |
|
|
1970 | # fig,ax = plt.subplots() | |
|
|
1971 | # #ax.set_title(str(k)+" "+str(j)) | |
|
|
1972 | # x=range(len(sortdata)) | |
|
|
1973 | # ax.scatter(x,sortdata) | |
|
|
1974 | # ax.axvline(index) | |
|
|
1975 | # plt.grid() | |
|
|
1976 | # plt.show() | |
|
|
1977 | ||
|
|
1978 | outliers_IDs = outliers_IDs.astype(numpy.dtype('int64')) | |
|
|
1979 | outliers_IDs = numpy.unique(outliers_IDs) | |
|
|
1980 | outs_lines = numpy.sort(outliers_IDs) | |
|
|
1981 | # #print("outliers Ids: ", outs_lines, outs_lines.shape) | |
|
|
1982 | #hist, bin_edges = numpy.histogram(outs_lines, bins=10, density=True) | |
|
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1983 | ||
|
|
1984 | ||
|
|
1985 | #Agrupando el histograma de outliers, | |
|
|
1986 | my_bins = numpy.linspace(0,9600, 96, endpoint=False) | |
|
|
1987 | ||
|
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1988 | ||
|
|
1989 | hist, bins = numpy.histogram(outs_lines,bins=my_bins) | |
|
|
1990 | hist_outliers_indexes = numpy.where(hist > self.thHistOutlier) #es outlier | |
|
|
1991 | #print(hist_outliers_indexes[0]) | |
|
|
1992 | bins_outliers_indexes = [int(i) for i in bins[hist_outliers_indexes]] # | |
|
|
1993 | #print(bins_outliers_indexes) | |
|
|
1994 | outlier_loc_index = [] | |
|
|
1995 | ||
|
|
1996 | #outlier_loc_index = [k for k in range(bins_outliers_indexes[n]-50,bins_outliers_indexes[n+1]+50) for n in range(len(bins_outliers_indexes)-1) ] | |
|
|
1997 | for n in range(len(bins_outliers_indexes)-1): | |
|
|
1998 | #outlier_loc_index = [k for k in range(bins_outliers_indexes[n]-50,bins_outliers_indexes[n+1]+50)] | |
|
|
1999 | for k in range(bins_outliers_indexes[n]-self.profileMargin,bins_outliers_indexes[n+1]+self.profileMargin): | |
|
|
2000 | outlier_loc_index.append(k) | |
|
|
2001 | ||
|
|
2002 | outlier_loc_index = numpy.asarray(outlier_loc_index) | |
|
|
2003 | #print(numpy.unique(outlier_loc_index)) | |
|
|
2004 | ||
|
|
2005 | ||
|
|
2006 | ||
|
|
2007 | # x, y = numpy.meshgrid(numpy.arange(profiles), self.heightList) | |
|
|
2008 | # fig, ax = plt.subplots(1,2,figsize=(8, 6)) | |
|
|
2009 | # | |
|
|
2010 | # dat = data[0,:,:].real | |
|
|
2011 | # m = numpy.nanmean(dat) | |
|
|
2012 | # o = numpy.nanstd(dat) | |
|
|
2013 | # #print(m, o, x.shape, y.shape) | |
|
|
2014 | # c = ax[0].pcolormesh(x, y, dat.T, cmap ='YlGnBu', vmin = (m-2*o), vmax = (m+2*o)) | |
|
|
2015 | # ax[0].vlines(outs_lines,200,600, linestyles='dashed', label = 'outs', color='w') | |
|
|
2016 | # fig.colorbar(c) | |
|
|
2017 | # ax[0].vlines(outlier_loc_index,650,750, linestyles='dashed', label = 'outs', color='r') | |
|
|
2018 | # ax[1].hist(outs_lines,bins=my_bins) | |
|
|
2019 | # plt.show() | |
|
|
2020 | ||
|
|
2021 | ||
|
|
2022 | self.outliers_IDs_list = numpy.unique(outlier_loc_index) | |
|
|
2023 | return data | |
|
|
1933 | 2024 | |
|
|
1934 | 2025 | def cleanOutliersByBlock(self): |
|
|
1935 | 2026 | #print(self.__buffer_data[0].shape) |
|
|
1936 | 2027 | data = self.__buffer_data#.copy() |
|
|
1937 | 2028 | #print("cleaning shape inpt: ",data.shape) |
|
|
1938 | ||
|
|
2029 | ''' | |
|
|
1939 | 2030 | self.__buffer_data = [] |
|
|
1940 | 2031 |
|
|
|
1941 | 2032 | spectrum = numpy.fft.fft2(data, axes=(0,2)) |
|
|
1942 | 2033 | #print("spc : ",spectrum.shape) |
|
|
1943 | 2034 | (nch,nsamples, nh) = spectrum.shape |
|
|
1944 | ||
|
|
1945 | #nch = | |
|
|
1946 | ||
|
|
2035 | data2 = None | |
|
|
2036 | #print(data.shape) | |
|
|
2037 | spectrum2 = spectrum.copy() | |
|
|
1947 | 2038 | for ch in range(nch): |
|
|
1948 | 2039 | dh = self.__dh |
|
|
1949 | 2040 | dt1 = (dh*1.0e-6)/(0.15) |
|
|
1950 | 2041 | dt2 = self.__buffer_times[1]-self.__buffer_times[0] |
|
|
1951 | 2042 | freqv = numpy.fft.fftfreq(nh, d=dt1) |
|
|
1952 | 2043 | freqh = numpy.fft.fftfreq(self.n, d=dt2) |
|
|
1953 | 2044 | #print("spc loop: ") |
|
|
1954 | x, y = numpy.meshgrid(numpy.sort(freqh),numpy.sort(freqv)) | |
|
|
1955 | z = numpy.abs(spectrum[ch,:,:]) | |
|
|
1956 | ||
|
|
1957 | ||
|
|
1958 | dat = numpy.log10(z.T) | |
|
|
1959 | m = numpy.mean(dat) | |
|
|
1960 | o = numpy.std(dat) | |
|
|
1961 | fig, ax = plt.subplots(figsize=(8, 6)) | |
|
|
1962 | 2045 |
|
|
|
1963 | c = ax.pcolormesh(x, y, dat, cmap ='YlGnBu', vmin = (m-2*o), vmax = (m+2*o)) | |
|
|
1964 | #c = ax.pcolor( z.T , cmap ='gray', vmin = (m-2*o), vmax = (m+2*o)) | |
|
|
1965 | date_time = datetime.datetime.fromtimestamp(self.__buffer_times[0]).strftime('%Y-%m-%d %H:%M:%S.%f') | |
|
|
1966 | #strftime('%Y-%m-%d %H:%M:%S') | |
|
|
1967 | ax.set_title('Spectrum magnitude '+date_time) | |
|
|
1968 | fig.canvas.set_window_title('Spectrum magnitude {} '.format(self.n)+date_time) | |
|
|
1969 | fig.colorbar(c) | |
|
|
1970 | 2046 |
|
|
|
1971 | plt.show() | |
|
|
1972 | 2047 |
|
|
|
2048 | x, y = numpy.meshgrid(numpy.sort(freqh),numpy.sort(freqv)) | |
|
|
2049 | z = numpy.abs(spectrum[ch,:,:]) | |
|
|
2050 | # Find all peaks higher than the 98th percentile | |
|
|
2051 | peaks = z < numpy.percentile(z, 98) | |
|
|
2052 | #print(peaks) | |
|
|
2053 | # Set those peak coefficients to zero | |
|
|
2054 | spectrum2 = spectrum2 * peaks.astype(int) | |
|
|
2055 | data2 = numpy.fft.ifft2(spectrum2) | |
|
|
1973 | 2056 |
|
|
|
2057 | dat = numpy.log10(z.T) | |
|
|
2058 | dat2 = numpy.log10(spectrum2.T) | |
|
|
2059 | ||
|
|
2060 | # m = numpy.mean(dat) | |
|
|
2061 | # o = numpy.std(dat) | |
|
|
2062 | # fig, ax = plt.subplots(2,1,figsize=(8, 6)) | |
|
|
2063 | # | |
|
|
2064 | # c = ax[0].pcolormesh(x, y, dat, cmap ='YlGnBu', vmin = (m-2*o), vmax = (m+2*o)) | |
|
|
2065 | # #c = ax.pcolor( z.T , cmap ='gray', vmin = (m-2*o), vmax = (m+2*o)) | |
|
|
2066 | # date_time = datetime.datetime.fromtimestamp(self.__buffer_times[0]).strftime('%Y-%m-%d %H:%M:%S.%f') | |
|
|
2067 | # #strftime('%Y-%m-%d %H:%M:%S') | |
|
|
2068 | # ax[0].set_title('Spectrum magnitude '+date_time) | |
|
|
2069 | # fig.canvas.set_window_title('Spectrum magnitude {} '.format(self.n)+date_time) | |
|
|
2070 | # | |
|
|
2071 | # | |
|
|
2072 | # c = ax[1].pcolormesh(x, y, dat, cmap ='YlGnBu', vmin = (m-2*o), vmax = (m+2*o)) | |
|
|
2073 | # fig.colorbar(c) | |
|
|
2074 | # plt.show() | |
|
|
2075 | ||
|
|
2076 | #print(data2.shape) | |
|
|
2077 | ||
|
|
2078 | data = data2 | |
|
|
1974 | 2079 |
|
|
|
1975 | 2080 | #cleanBlock = numpy.fft.ifft2(spectrum, axes=(0,2)).reshape() |
|
|
2081 | ''' | |
|
|
2082 | #print("cleanOutliersByBlock Done") | |
|
|
1976 | 2083 | |
|
|
1977 | print("cleanOutliersByBlock Done") | |
|
|
1978 | return data | |
|
|
1979 | ||
|
|
1980 | def byTime(self, data, datatime): | |
|
|
1981 | ||
|
|
1982 | self.__dataReady = False | |
|
|
1983 | ||
|
|
1984 | self.fillBuffer(data, datatime) | |
|
|
1985 | dataBlock = None | |
|
|
1986 | ||
|
|
1987 | if (datatime - self.__initime) >= self.__timeInterval: | |
|
|
1988 | dataBlock = self.cleanOutliersByBlock() | |
|
|
1989 | self.__dataReady = True | |
|
|
1990 | self.n = self.__profIndex | |
|
|
1991 | return dataBlock | |
|
|
1992 | ||
|
|
1993 | def byProfiles(self, data, datatime): | |
|
|
1994 | self.__dataReady = False | |
|
|
1995 | ||
|
|
1996 | self.fillBuffer(data, datatime) | |
|
|
1997 | dataBlock = None | |
|
|
2084 | return self.filterSatsProfiles() | |
|
|
1998 | 2085 | |
|
|
1999 | if self.__profIndex == self.n: | |
|
|
2000 | #print("apnd : ",data) | |
|
|
2001 | dataBlock = self.cleanOutliersByBlock() | |
|
|
2002 | self.__dataReady = True | |
|
|
2003 | 2086 | |
|
|
2004 | return dataBlock | |
|
|
2005 | 2087 | |
|
|
2006 | 2088 | def fillBuffer(self, data, datatime): |
|
|
2007 | 2089 | |
|
|
2008 | 2090 | if self.__profIndex == 0: |
|
|
2009 | 2091 | self.__buffer_data = data.copy() |
|
|
2010 | 2092 | |
|
|
2011 | 2093 | else: |
|
|
2012 | 2094 | self.__buffer_data = numpy.concatenate((self.__buffer_data,data), axis=1)#en perfiles |
|
|
2013 | 2095 | self.__profIndex += 1 |
|
|
2014 | self.__buffer_times.append(datatime) | |
|
|
2096 | #self.__buffer_times.append(datatime) | |
|
|
2015 | 2097 | |
|
|
2016 | 2098 | def getData(self, data, datatime=None): |
|
|
2017 | 2099 | |
|
|
2018 | 2100 | if self.__profIndex == 0: |
|
|
2019 | 2101 | self.__initime = datatime |
|
|
2020 | 2102 | |
|
|
2021 | if self.__byTime: | |
|
|
2022 | dataBlock = self.byTime(data, datatime) | |
|
|
2023 | else: | |
|
|
2024 | dataBlock = self.byProfiles(data, datatime) | |
|
|
2025 | 2103 | |
|
|
2104 | self.__dataReady = False | |
|
|
2026 | 2105 | |
|
|
2106 | self.fillBuffer(data, datatime) | |
|
|
2107 | dataBlock = None | |
|
|
2108 | ||
|
|
2109 | if self.__profIndex == self.n: | |
|
|
2110 | #print("apnd : ",data) | |
|
|
2111 | #dataBlock = self.cleanOutliersByBlock() | |
|
|
2112 | dataBlock = self.filterSatsProfiles() | |
|
|
2113 | self.__dataReady = True | |
|
|
2114 | ||
|
|
2115 | return dataBlock | |
|
|
2027 | 2116 | |
|
|
2028 | 2117 | if dataBlock is None: |
|
|
2029 | return None | |
|
|
2118 | return None, None | |
|
|
2030 | 2119 | |
|
|
2031 | 2120 | |
|
|
2032 | 2121 | |
|
|
2033 | 2122 | return dataBlock |
|
|
2034 | 2123 | |
|
|
2035 |
def releaseBlock(self |
|
|
|
2124 | def releaseBlock(self): | |
|
|
2036 | 2125 | |
|
|
2037 | 2126 | if self.n % self.lenProfileOut != 0: |
|
|
2038 | 2127 | raise ValueError("lenProfileOut %d must be submultiple of nProfiles %d" %(self.lenProfileOut, self.n_profiles)) |
|
|
2039 | 2128 | return None |
|
|
2040 | 2129 | |
|
|
2041 |
|
|
|
|
2042 | dataOut.profileIndex = self.lenProfileOut | |
|
|
2043 | dataOut.utctime = self.first_utcBlock + self.init_prof*dataOut.ippSeconds | |
|
|
2130 | data = self.buffer[:,self.init_prof:self.end_prof:,:] #ch, prof, alt | |
|
|
2131 | ||
|
|
2044 | 2132 | self.init_prof = self.end_prof |
|
|
2045 | 2133 | self.end_prof += self.lenProfileOut |
|
|
2046 | print("data release shape: ",dataOut.data.shape, self.end_prof) | |
|
|
2134 | #print("data release shape: ",dataOut.data.shape, self.end_prof) | |
|
|
2135 | self.n_prof_released += 1 | |
|
|
2047 | 2136 | |
|
|
2048 | if self.end_prof >= (self.n +self.lenProfileOut): | |
|
|
2049 | self.init_prof = 0 | |
|
|
2050 | self.__profIndex = 0 | |
|
|
2051 | self.buffer = None | |
|
|
2052 | dataOut.buffer_empty = True | |
|
|
2053 | return dataOut | |
|
|
2054 | 2137 | |
|
|
2055 | def run(self, dataOut, n=None, timeInterval=None, nProfilesOut=1): | |
|
|
2138 | #print("f_no_data ", dataOut.flagNoData) | |
|
|
2139 | return data | |
|
|
2140 | ||
|
|
2141 | def run(self, dataOut, n=None, navg=0.8, nProfilesOut=1, profile_margin=50,th_hist_outlier=3,minHei=None, maxHei=None): | |
|
|
2056 | 2142 | #print("run op buffer 2D") |
|
|
2057 | 2143 | self.nChannels = dataOut.nChannels |
|
|
2058 | 2144 | self.nHeights = dataOut.nHeights |
|
|
2059 | 2145 | |
|
|
2060 | 2146 | if not self.isConfig: |
|
|
2061 | self.setup(dataOut,n=n, timeInterval=timeInterval) | |
|
|
2147 | #print("init p idx: ", dataOut.profileIndex ) | |
|
|
2148 | self.setup(dataOut,n=n, navg=navg,profileMargin=profile_margin, | |
|
|
2149 | thHistOutlier=th_hist_outlier,minHei=minHei, maxHei=maxHei) | |
|
|
2062 | 2150 | self.isConfig = True |
|
|
2063 | 2151 | |
|
|
2064 | 2152 | dataBlock = None |
|
|
2065 | 2153 | |
|
|
2066 | 2154 | if not dataOut.buffer_empty: #hay datos acumulados |
|
|
2067 | 2155 | |
|
|
2068 | 2156 | if self.init_prof == 0: |
|
|
2157 | self.n_prof_released = 0 | |
|
|
2069 | 2158 | self.lenProfileOut = nProfilesOut |
|
|
2070 | 2159 | dataOut.flagNoData = False |
|
|
2071 | 2160 | #print("tp 2 ",dataOut.data.shape) |
|
|
2072 | 2161 | #(ch, self.n_profiles, nh) = self.buffer.shape |
|
|
2073 | 2162 | #print("tp 3 ",self.dataOut.data.shape) |
|
|
2074 | 2163 | #print("rel: ",self.buffer[:,-1,:]) |
|
|
2075 | 2164 | self.init_prof = 0 |
|
|
2076 | 2165 | self.end_prof = self.lenProfileOut |
|
|
2077 | self.first_utcBlock = dataOut.utctime | |
|
|
2166 | ||
|
|
2078 | 2167 | dataOut.nProfiles = self.lenProfileOut |
|
|
2079 | dataOut.error = False | |
|
|
2168 | if nProfilesOut == 1: | |
|
|
2169 | dataOut.flagDataAsBlock = False | |
|
|
2170 | else: | |
|
|
2080 | 2171 | dataOut.flagDataAsBlock = True |
|
|
2081 | 2172 | #print("prof: ",self.init_prof) |
|
|
2082 | 2173 | dataOut.flagNoData = False |
|
|
2083 | return self.releaseBlock(dataOut) | |
|
|
2174 | if numpy.isin(self.n_prof_released, self.outliers_IDs_list): | |
|
|
2175 | #print("omitting: ", self.n_prof_released) | |
|
|
2176 | dataOut.flagNoData = True | |
|
|
2177 | ||
|
|
2178 | dataOut.utctime = self.first_utcBlock + self.init_prof*dataOut.ippSeconds | |
|
|
2179 | #dataOut.data = self.releaseBlock() | |
|
|
2180 | #########################################################3 | |
|
|
2181 | if self.n % self.lenProfileOut != 0: | |
|
|
2182 | raise ValueError("lenProfileOut %d must be submultiple of nProfiles %d" %(self.lenProfileOut, self.n_profiles)) | |
|
|
2183 | return None | |
|
|
2184 | ||
|
|
2185 | dataOut.data = self.buffer[:,self.init_prof:self.end_prof:,:] #ch, prof, alt | |
|
|
2186 | ||
|
|
2187 | self.init_prof = self.end_prof | |
|
|
2188 | self.end_prof += self.lenProfileOut | |
|
|
2189 | #print("data release shape: ",dataOut.data.shape, self.end_prof) | |
|
|
2190 | self.n_prof_released += 1 | |
|
|
2191 | ||
|
|
2192 | if self.end_prof >= (self.n +self.lenProfileOut): | |
|
|
2193 | ||
|
|
2194 | self.init_prof = 0 | |
|
|
2195 | self.__profIndex = 0 | |
|
|
2196 | self.buffer = None | |
|
|
2197 | dataOut.buffer_empty = True | |
|
|
2198 | self.outliers_IDs_list = [] | |
|
|
2199 | self.n_prof_released = 0 | |
|
|
2200 | dataOut.flagNoData = False #enviar ultimo aunque sea outlier :( | |
|
|
2201 | #print("cleaning...", dataOut.buffer_empty) | |
|
|
2202 | dataOut.profileIndex = 0 #self.lenProfileOut | |
|
|
2203 | #################################################################### | |
|
|
2204 | return dataOut | |
|
|
2084 | 2205 | |
|
|
2085 | 2206 | |
|
|
2086 | 2207 | #print("tp 223 ",dataOut.data.shape) |
|
|
2087 | 2208 | dataOut.flagNoData = True |
|
|
2088 | 2209 | |
|
|
2089 | 2210 | |
|
|
2090 | 2211 | |
|
|
2091 | 2212 | try: |
|
|
2092 | 2213 | #dataBlock = self.getData(dataOut.data.reshape(self.nChannels,1,self.nHeights), dataOut.utctime) |
|
|
2093 | 2214 | dataBlock = self.getData(numpy.reshape(dataOut.data,(self.nChannels,1,self.nHeights)), dataOut.utctime) |
|
|
2094 | 2215 | self.__count_exec +=1 |
|
|
2095 | 2216 | except Exception as e: |
|
|
2096 | 2217 | print("Error getting profiles data",self.__count_exec ) |
|
|
2097 | 2218 | print(e) |
|
|
2098 | 2219 | sys.exit() |
|
|
2099 | 2220 | |
|
|
2100 | 2221 | if self.__dataReady: |
|
|
2222 | #print("omitting: ", len(self.outliers_IDs_list)) | |
|
|
2101 | 2223 | self.__count_exec = 0 |
|
|
2102 | 2224 | #dataOut.data = |
|
|
2103 | self.buffer = numpy.flip(dataBlock, axis=1) | |
|
|
2104 | ||
|
|
2225 | #self.buffer = numpy.flip(dataBlock, axis=1) | |
|
|
2226 | self.buffer = dataBlock | |
|
|
2227 | self.first_utcBlock = self.__initime | |
|
|
2105 | 2228 | dataOut.utctime = self.__initime |
|
|
2106 | 2229 | dataOut.nProfiles = self.__profIndex |
|
|
2107 | 2230 | #dataOut.flagNoData = False |
|
|
2231 | self.init_prof = 0 | |
|
|
2108 | 2232 | self.__profIndex = 0 |
|
|
2109 | 2233 | self.__initime = None |
|
|
2110 | 2234 | dataBlock = None |
|
|
2111 | 2235 | self.__buffer_times = [] |
|
|
2112 | 2236 | dataOut.error = False |
|
|
2113 | 2237 | dataOut.useInputBuffer = True |
|
|
2114 | 2238 | dataOut.buffer_empty = False |
|
|
2115 | print("1 ch: {} prof: {} hs: {}".format(int(dataOut.nChannels), | |
|
|
2116 | int(dataOut.nProfiles),int(dataOut.nHeights))) | |
|
|
2117 | #return None | |
|
|
2239 | #print("1 ch: {} prof: {} hs: {}".format(int(dataOut.nChannels),int(dataOut.nProfiles),int(dataOut.nHeights))) | |
|
|
2240 | ||
|
|
2118 | 2241 | |
|
|
2119 | 2242 | |
|
|
2120 | 2243 | #print(self.__count_exec) |
|
|
2121 | 2244 | |
|
|
2122 | 2245 | return dataOut |
|
|
2123 | 2246 | |
|
|
2124 |
class |
|
|
|
2247 | class RemoveProfileSats(Operation): | |
|
|
2125 | 2248 | ''' |
|
|
2126 | 2249 | Omite los perfiles contaminados con señal de satelites, |
|
|
2127 | 2250 | In: minHei = min_sat_range |
|
|
2128 | 2251 | max_sat_range |
|
|
2129 | 2252 | min_hei_ref |
|
|
2130 | 2253 | max_hei_ref |
|
|
2131 | 2254 | th = diference between profiles mean, ref and sats |
|
|
2132 | 2255 | Out: |
|
|
2133 | 2256 | profile clean |
|
|
2134 | 2257 | ''' |
|
|
2135 | 2258 | |
|
|
2136 | 2259 | isConfig = False |
|
|
2137 | 2260 | min_sats = 0 |
|
|
2138 | 2261 | max_sats = 999999999 |
|
|
2139 | 2262 | min_ref= 0 |
|
|
2140 | 2263 | max_ref= 9999999999 |
|
|
2141 | 2264 | needReshape = False |
|
|
2142 | 2265 | count = 0 |
|
|
2143 | 2266 | thdB = 0 |
|
|
2144 | 2267 | byRanges = False |
|
|
2145 | 2268 | min_sats = None |
|
|
2146 | 2269 | max_sats = None |
|
|
2270 | noise = 0 | |
|
|
2147 | 2271 | |
|
|
2148 | 2272 | def __init__(self, **kwargs): |
|
|
2149 | 2273 | |
|
|
2150 | 2274 | Operation.__init__(self, **kwargs) |
|
|
2151 | 2275 | self.isConfig = False |
|
|
2152 | 2276 | |
|
|
2153 | 2277 | |
|
|
2154 | 2278 | def setup(self, dataOut, minHei, maxHei, minRef, maxRef, th, thdB, rangeHeiList): |
|
|
2155 | 2279 | |
|
|
2156 | 2280 | if rangeHeiList!=None: |
|
|
2157 | 2281 | self.byRanges = True |
|
|
2158 | 2282 | else: |
|
|
2159 | 2283 | if minHei==None or maxHei==None : |
|
|
2160 | 2284 | raise ValueError("Parameters heights are required") |
|
|
2161 | 2285 | if minRef==None or maxRef==None: |
|
|
2162 | 2286 | raise ValueError("Parameters heights are required") |
|
|
2163 | 2287 | |
|
|
2164 | 2288 | if self.byRanges: |
|
|
2165 | 2289 | self.min_sats = [] |
|
|
2166 | 2290 | self.max_sats = [] |
|
|
2167 | 2291 | for min,max in rangeHeiList: |
|
|
2168 | 2292 | a,b = getHei_index(min, max, dataOut.heightList) |
|
|
2169 | 2293 | self.min_sats.append(a) |
|
|
2170 | 2294 | self.max_sats.append(b) |
|
|
2171 | 2295 | else: |
|
|
2172 | 2296 | self.min_sats, self.max_sats = getHei_index(minHei, maxHei, dataOut.heightList) |
|
|
2173 | 2297 | self.min_ref, self.max_ref = getHei_index(minRef, maxRef, dataOut.heightList) |
|
|
2174 | 2298 | self.th = th |
|
|
2175 | 2299 | self.thdB = thdB |
|
|
2176 | 2300 | self.isConfig = True |
|
|
2177 | 2301 | |
|
|
2178 | 2302 | |
|
|
2179 | 2303 | def compareRanges(self,data, minHei,maxHei): |
|
|
2180 | ref = data[0,self.min_ref:self.max_ref] * numpy.conjugate(data[0,self.min_ref:self.max_ref]) | |
|
|
2181 | p_ref = 10*numpy.log10(ref.real) | |
|
|
2182 |
|
|
|
|
2304 | ||
|
|
2305 | # ref = data[0,self.min_ref:self.max_ref] * numpy.conjugate(data[0,self.min_ref:self.max_ref]) | |
|
|
2306 | # p_ref = 10*numpy.log10(ref.real) | |
|
|
2307 | # m_ref = numpy.mean(p_ref) | |
|
|
2308 | ||
|
|
2309 | m_ref = self.noise | |
|
|
2183 | 2310 | |
|
|
2184 | 2311 | sats = data[0,minHei:maxHei] * numpy.conjugate(data[0,minHei:maxHei]) |
|
|
2185 | 2312 | p_sats = 10*numpy.log10(sats.real) |
|
|
2186 | 2313 | m_sats = numpy.mean(p_sats) |
|
|
2187 | 2314 | |
|
|
2188 | if m_sats > (m_ref + self.th) and (m_sats > self.thdB): | |
|
|
2189 | #print("msats: ",m_sats," mRef: ", m_ref, (m_sats - m_ref)) | |
|
|
2315 | if m_sats > (m_ref + self.th): #and (m_sats > self.thdB): | |
|
|
2316 | #print("msats: ",m_sats," \tmRef: ", m_ref, "\t",(m_sats - m_ref)) | |
|
|
2190 | 2317 | #print("Removing profiles...") |
|
|
2191 | 2318 | return False |
|
|
2192 | 2319 | |
|
|
2193 | 2320 | return True |
|
|
2194 | 2321 | |
|
|
2195 | 2322 | def isProfileClean(self, data): |
|
|
2196 | 2323 | ''' |
|
|
2197 | 2324 | Analiza solo 1 canal, y descarta todos... |
|
|
2198 | 2325 | ''' |
|
|
2199 | 2326 | |
|
|
2200 | 2327 | clean = True |
|
|
2201 | 2328 | |
|
|
2202 | 2329 | if self.byRanges: |
|
|
2203 | 2330 | |
|
|
2204 | 2331 | for n in range(len(self.min_sats)): |
|
|
2205 | 2332 | c = self.compareRanges(data,self.min_sats[n],self.max_sats[n]) |
|
|
2206 | 2333 | clean = clean and c |
|
|
2207 | 2334 | else: |
|
|
2208 | 2335 | |
|
|
2209 | 2336 | clean = (self.compareRanges(data, self.min_sats,self.max_sats)) |
|
|
2210 | 2337 | |
|
|
2211 | 2338 | return clean |
|
|
2212 | 2339 | |
|
|
2213 | 2340 | |
|
|
2214 | 2341 | |
|
|
2215 | 2342 | def run(self, dataOut, minHei=None, maxHei=None, minRef=None, maxRef=None, th=5, thdB=65, rangeHeiList=None): |
|
|
2216 | 2343 | dataOut.flagNoData = True |
|
|
2217 | 2344 | |
|
|
2218 | 2345 | if not self.isConfig: |
|
|
2219 | 2346 | self.setup(dataOut, minHei, maxHei, minRef, maxRef, th, thdB, rangeHeiList) |
|
|
2220 | 2347 | self.isConfig = True |
|
|
2221 | ||
|
|
2348 | #print(self.min_sats,self.max_sats) | |
|
|
2222 | 2349 | if dataOut.flagDataAsBlock: |
|
|
2223 | 2350 | raise ValueError("ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False") |
|
|
2224 | 2351 | |
|
|
2225 | 2352 | else: |
|
|
2226 | ||
|
|
2353 | self.noise =10*numpy.log10(dataOut.getNoisebyHildebrand(ymin_index=self.min_ref, ymax_index=self.max_ref)) | |
|
|
2227 | 2354 | if not self.isProfileClean(dataOut.data): |
|
|
2228 | 2355 | return dataOut |
|
|
2229 | 2356 | #dataOut.data = numpy.full((dataOut.nChannels,dataOut.nHeights),numpy.NAN) |
|
|
2230 | 2357 | #self.count += 1 |
|
|
2231 | 2358 | |
|
|
2232 | 2359 | dataOut.flagNoData = False |
|
|
2233 | 2360 | |
|
|
2234 | 2361 | return dataOut |
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