diff --git a/schainpy/model/graphics/jroplot_parameters.py b/schainpy/model/graphics/jroplot_parameters.py index a089b81..06032b4 100644 --- a/schainpy/model/graphics/jroplot_parameters.py +++ b/schainpy/model/graphics/jroplot_parameters.py @@ -172,8 +172,7 @@ class GenericRTIPlot(Plot): self.ylabel = 'Height [km]' if not self.titles: - self.titles = self.data.parameters \ - if self.data.parameters else ['Param {}'.format(x) for x in range(self.nrows)] + self.titles = ['Param {}'.format(x) for x in range(self.nrows)] def update(self, dataOut): diff --git a/schainpy/model/io/bltrIO_spectra.py b/schainpy/model/io/bltrIO_spectra.py index 4045f34..f98874d 100644 --- a/schainpy/model/io/bltrIO_spectra.py +++ b/schainpy/model/io/bltrIO_spectra.py @@ -1,16 +1,8 @@ import os import sys import glob -import fnmatch -import datetime -import time -import re -import h5py import numpy -import pylab as plb -from scipy.optimize import curve_fit -from scipy import asarray as ar, exp SPEED_OF_LIGHT = 299792458 SPEED_OF_LIGHT = 3e8 @@ -19,9 +11,9 @@ from .utils import folder_in_range import schainpy.admin from schainpy.model.data.jrodata import Spectra -from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator +from schainpy.model.proc.jroproc_base import ProcessingUnit from schainpy.utils import log -from schainpy.model.io.jroIO_base import JRODataReader + def pol2cart(rho, phi): x = rho * numpy.cos(phi) @@ -423,7 +415,6 @@ class BLTRSpectraReader (ProcessingUnit): copy = self.data_block.copy() spc = copy * numpy.conjugate(copy) self.data_spc = numpy.absolute(spc) # valor absoluto o magnitud - self.dataOut.data_spc = self.data_spc cspc = self.data_block.copy() self.data_cspc = self.data_block.copy() diff --git a/schainpy/model/io/jroIO_param.py b/schainpy/model/io/jroIO_param.py index 4733785..9df02d5 100644 --- a/schainpy/model/io/jroIO_param.py +++ b/schainpy/model/io/jroIO_param.py @@ -196,11 +196,11 @@ class HDFReader(Reader, ProcessingUnit): if self.description: for key, value in self.description['Metadata'].items(): - meta[key] = self.fp[value].value + meta[key] = self.fp[value][()] else: grp = self.fp['Metadata'] for name in grp: - meta[name] = grp[name].value + meta[name] = grp[name][()] if self.extras: for key, value in self.extras.items(): @@ -217,26 +217,26 @@ class HDFReader(Reader, ProcessingUnit): for key, value in self.description['Data'].items(): if isinstance(value, str): if isinstance(self.fp[value], h5py.Dataset): - data[key] = self.fp[value].value + data[key] = self.fp[value][()] elif isinstance(self.fp[value], h5py.Group): array = [] for ch in self.fp[value]: - array.append(self.fp[value][ch].value) + array.append(self.fp[value][ch][()]) data[key] = numpy.array(array) elif isinstance(value, list): array = [] for ch in value: - array.append(self.fp[ch].value) + array.append(self.fp[ch][()]) data[key] = numpy.array(array) else: grp = self.fp['Data'] for name in grp: if isinstance(grp[name], h5py.Dataset): - array = grp[name].value + array = grp[name][()] elif isinstance(grp[name], h5py.Group): array = [] for ch in grp[name]: - array.append(grp[name][ch].value) + array.append(grp[name][ch][()]) array = numpy.array(array) else: log.warning('Unknown type: {}'.format(name)) diff --git a/schainpy/model/proc/bltrproc_parameters.py b/schainpy/model/proc/bltrproc_parameters.py index 3cc948c..8c109bf 100644 --- a/schainpy/model/proc/bltrproc_parameters.py +++ b/schainpy/model/proc/bltrproc_parameters.py @@ -5,14 +5,10 @@ Created on Oct 24, 2016 ''' import numpy -import copy import datetime import time -from time import gmtime -from numpy import transpose - -from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator +from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation from schainpy.model.data.jrodata import Parameters @@ -68,10 +64,11 @@ class BLTRParametersProc(ProcessingUnit): self.dataOut.data_param = self.dataOut.data[mode] self.dataOut.heightList = self.dataOut.height[0] self.dataOut.data_snr = self.dataOut.data_snr[mode] + SNRavg = numpy.average(self.dataOut.data_snr, axis=0) + SNRavgdB = 10*numpy.log10(SNRavg) + self.dataOut.data_snr_avg_db = SNRavgdB.reshape(1, *SNRavgdB.shape) if snr_threshold is not None: - SNRavg = numpy.average(self.dataOut.data_snr, axis=0) - SNRavgdB = 10*numpy.log10(SNRavg) for i in range(3): self.dataOut.data_param[i][SNRavgdB <= snr_threshold] = numpy.nan