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Use of delays instead of input queue to keep dataouts and avoid loose of them
Use of delays instead of input queue to keep dataouts and avoid loose of them

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jroplot_correlation.py
186 lines | 5.9 KiB | text/x-python | PythonLexer
import os
import datetime
import numpy
import copy
from schainpy.model import *
from .figure import Figure, isRealtime
class CorrelationPlot_(Figure):
isConfig = None
__nsubplots = None
WIDTHPROF = None
HEIGHTPROF = None
PREFIX = 'corr'
def __init__(self, **kwargs):
Figure.__init__(self, **kwargs)
self.isConfig = False
self.__nsubplots = 1
self.WIDTH = 280
self.HEIGHT = 250
self.WIDTHPROF = 120
self.HEIGHTPROF = 0
self.counter_imagwr = 0
self.PLOT_CODE = 1
self.FTP_WEI = None
self.EXP_CODE = None
self.SUB_EXP_CODE = None
self.PLOT_POS = None
def getSubplots(self):
ncol = int(numpy.sqrt(self.nplots)+0.9)
nrow = int(self.nplots*1./ncol + 0.9)
return nrow, ncol
def setup(self, id, nplots, wintitle, showprofile=False, show=True):
showprofile = False
self.__showprofile = showprofile
self.nplots = nplots
ncolspan = 1
colspan = 1
if showprofile:
ncolspan = 3
colspan = 2
self.__nsubplots = 2
self.createFigure(id = id,
wintitle = wintitle,
widthplot = self.WIDTH + self.WIDTHPROF,
heightplot = self.HEIGHT + self.HEIGHTPROF,
show=show)
nrow, ncol = self.getSubplots()
counter = 0
for y in range(nrow):
for x in range(ncol):
if counter >= self.nplots:
break
self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1)
if showprofile:
self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1)
counter += 1
def run(self, dataOut, id, wintitle="", channelList=None, showprofile=False,
xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None,
save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1,
server=None, folder=None, username=None, password=None,
ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False):
"""
Input:
dataOut :
id :
wintitle :
channelList :
showProfile :
xmin : None,
xmax : None,
ymin : None,
ymax : None,
zmin : None,
zmax : None
"""
if dataOut.flagNoData:
return None
if realtime:
if not(isRealtime(utcdatatime = dataOut.utctime)):
print('Skipping this plot function')
return
if channelList == None:
channelIndexList = dataOut.channelIndexList
else:
channelIndexList = []
for channel in channelList:
if channel not in dataOut.channelList:
raise ValueError("Channel %d is not in dataOut.channelList")
channelIndexList.append(dataOut.channelList.index(channel))
factor = dataOut.normFactor
lenfactor = factor.shape[1]
x = dataOut.getLagTRange(1)
y = dataOut.getHeiRange()
z = copy.copy(dataOut.data_corr[:,:,0,:])
for i in range(dataOut.data_corr.shape[0]):
z[i,:,:] = z[i,:,:]/factor[i,:]
zdB = numpy.abs(z)
avg = numpy.average(z, axis=1)
# avg = numpy.nanmean(z, axis=1)
# noise = dataOut.noise/factor
#thisDatetime = dataOut.datatime
thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0])
title = wintitle + " Correlation"
xlabel = "Lag T (s)"
ylabel = "Range (Km)"
if not self.isConfig:
nplots = dataOut.data_corr.shape[0]
self.setup(id=id,
nplots=nplots,
wintitle=wintitle,
showprofile=showprofile,
show=show)
if xmin == None: xmin = numpy.nanmin(x)
if xmax == None: xmax = numpy.nanmax(x)
if ymin == None: ymin = numpy.nanmin(y)
if ymax == None: ymax = numpy.nanmax(y)
if zmin == None: zmin = 0
if zmax == None: zmax = 1
self.FTP_WEI = ftp_wei
self.EXP_CODE = exp_code
self.SUB_EXP_CODE = sub_exp_code
self.PLOT_POS = plot_pos
self.isConfig = True
self.setWinTitle(title)
for i in range(self.nplots):
str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S"))
title = "Channel %d and %d: : %s" %(dataOut.pairsList[i][0],dataOut.pairsList[i][1] , str_datetime)
axes = self.axesList[i*self.__nsubplots]
axes.pcolor(x, y, zdB[i,:,:],
xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax,
xlabel=xlabel, ylabel=ylabel, title=title,
ticksize=9, cblabel='')
# if self.__showprofile:
# axes = self.axesList[i*self.__nsubplots +1]
# axes.pline(avgdB[i], y,
# xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax,
# xlabel='dB', ylabel='', title='',
# ytick_visible=False,
# grid='x')
#
# noiseline = numpy.repeat(noisedB[i], len(y))
# axes.addpline(noiseline, y, idline=1, color="black", linestyle="dashed", lw=2)
self.draw()
self.save(figpath=figpath,
figfile=figfile,
save=save,
ftp=ftp,
wr_period=wr_period,
thisDatetime=thisDatetime)