madrigal.ui.madrigalPlot module
madrigalPlot is the module that produces plots of Madrigal data.
Presently based on madplotlib: http://matplotlib.sourceforge.net/
$Id: madrigalPlot.py 7043 2019-10-07 19:10:59Z brideout $
"""madrigalPlot is the module that produces plots of Madrigal data.
Presently based on madplotlib: http://matplotlib.sourceforge.net/
$Id: madrigalPlot.py 7043 2019-10-07 19:10:59Z brideout $
"""
import sys,os
import os.path
import traceback
import types
import datetime
import bisect
import math
import colorsys
import subprocess
import numpy
import madrigal.metadata
import madrigal._derive
def defineHomeEnvVariable():
"""defineHomeEnvVariable makes sure HOME env variable is defined, as required by matplotlib.
If not defined, sets HOME to MADROOT/metadata/userdata
"""
try:
os.environ['HOME']
return
except KeyError:
import madrigal.metadata
madDB = madrigal.metadata.MadrigalDB()
os.environ['HOME'] = os.path.join(madDB.getMadroot(), 'metadata/userdata')
defineHomeEnvVariable()
import matplotlib
# set rendering
matplotlib.use('Agg')
import matplotlib.pylab
import matplotlib.colors
import matplotlib.cm
import numpy.ma
import numpy
def convertToAbsoluteTimeStr(xticks, noTime=False, noDate=False, timezone=0):
"""convertToAbsoluteTimeStr converts a list of strings containing seconds since 1/1/1950 to datetime string.
Input: xticks - a list of strings containing seconds since 1/1/1950
Returns: a list of strings formated as YYYY-MM-DD HH-MM-SS. If noTime, format as YYYY-MM-DD
"""
datetime1950 = datetime.datetime(1950,1,1,0,0,0)
newList = []
seconds = int(xticks[0]) - timezone
iniDoy = datetime1950 + datetime.timedelta(0, seconds)
iniDoy = int(iniDoy.strftime('%j'))
for item in xticks:
seconds = int(item) - timezone
newDatetime = datetime1950 + datetime.timedelta(0, seconds)
if noTime:
newList.append(newDatetime.strftime('%Y-%m-%d'))
elif noDate:
curDoy = int(newDatetime.strftime('%j'))
currHour = newDatetime.hour
currMin = newDatetime.minute
currHour += (curDoy - iniDoy)*24
newList.append('%02d:%02d' %(currHour,currMin))
else:
newList.append(newDatetime.strftime('%Y-%m-%d %H:%M:%S'))
return newList
def get_vo_cmap():
"""get_vo_cmap is a function to return a colormap optimized to show sign changes in the middle of the range.
"""
LUTSIZE = matplotlib.rcParams['image.lut']
_vo_cm_data = {'red': ((0, 0.75, 0.75), (0.4, 0.0, 0.0), (0.5, 0.0, 0.0), (0.6, 1.0, 1.0), (1.0, 1.0, 1.0)),
'green': ((0, 1.0, 1.0), (0.4, 0.5, 0.5), (0.5, 0.25, 0.25), (0.6, 0.25, 0.25), (1.0, 1.0, 1.0)),
'blue': ((0, 1.0, 1.0), (0.4, 1.0, 1.0), (0.5, 0.5, 0.5), (0.6, 0.0, 0.0), (1.0, 0.5, 0.5))}
vo_cm = matplotlib.colors.LinearSegmentedColormap('vo_cm',_vo_cm_data, LUTSIZE)
return vo_cm
def getIsprintString(filename, parms, filterStr):
"""getIsprintString returns the output of isprint given inputs
Inputs:
filename - Madrigal filename
parms - comma-delimited parameters
filterStr - arguments to pass to isprint cmd
"""
madDB = madrigal.metadata.MadrigalDB()
cmd = '%s/bin/isprint file=%s ' % (madDB.getMadroot(), filename)
for parm in parms.split(','):
cmd += '%s ' % (parm)
cmd += filterStr
cmd += ' header=f summary=f '
pipe = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE).stdout
result = pipe.read()
delimiter = '\n'
if type(result) in (bytes, numpy.bytes_):
result = result.decode('utf-8')
lines = result.split(delimiter)
return(delimiter.join(lines[1:]).strip())
class madScatterPlot:
"""madScatterPlot is the class the produces two dimensional scatter plots of x versus y.
"""
def __init__(self, isprintText,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
useAbsoluteTime = False,
startTime = None,
endTime = None,
maxNumPoints = None,
noTime = False,
noDate = False,
yMinimum = None,
yMaximum = None,
timezone = 0):
"""__init__ writes a madScatter plot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The second must be the parameter to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
maxNumPoints - maximum number of points to plot. If not None, truncate isprintText if
needed to have at most maxNumPoints lines.
noTime - if True and useAbsoluteTime True, then dates without times printed on x axis.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
yMinimum - set y minimum. If default=None, use data minimum
yMaximum - set y maximum. If default=None, use data maximum
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
Returns: void
Affects: None
"""
self.__missing = 1.0E30 # special value, since numpy doesn't handle NaN
self.__parameter_count = 2
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
if maxNumPoints != None:
isprintText = self.__truncateIsprint(isprintText, maxNumPoints)
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
# find min, max of x (time)
if startTime == None:
xMin = array_data[0,0]
else:
xMin = startTime
if endTime == None:
xMax = array_data[-1,0]
else:
xMax = endTime
# find min, max of y, not including missing
yMin = None
yMax = None
for y in array_data[:,1]:
if y == self.__missing:
continue
if yMin == None:
yMin = y
elif yMin > y:
yMin = y
if yMax == None:
yMax = y
elif yMax < y:
yMax = y
if yMin == None:
raise ValueError('No valid y data found')
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(9,3), facecolor = 'w')
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(10,6), facecolor = 'w')
if useAbsoluteTime:
# leave room for rotated datetime string
if size == 'large':
matplotlib.pylab.axes([0.1, 0.2, 0.8, 0.7])
elif size == 'wide':
matplotlib.pylab.axes([0.07, 0.2, 0.66, 0.7])
elif size == 'small':
matplotlib.pylab.axes([0.14, 0.18, 0.83, 0.7])
matplotlib.pylab.scatter(array_data[:,0],array_data[:,1])
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.xlim(xMin, xMax)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
matplotlib.pylab.grid(True)
if useAbsoluteTime:
# if plot is less than 24 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
xmin = xmin / (60*60)
xmax = xmax / (60*60)
if noDate == True:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
step = int((xmax-xmin-1)/6)+1
xRange = list(range(0,int(newXmax-xmin),step))
for i in xRange:
tmp = (xmin + i)*60*60
newXLocs.append(tmp)
if (xmax - xmin) < 1: newXLocs.append(xmax*60*60)
newXLabels = convertToAbsoluteTimeStr(newXLocs, noDate=noDate, timezone=timezone)
matplotlib.pylab.xticks(newXLocs, newXLabels)
else:
locs, labels = matplotlib.pylab.xticks()
if len(locs) > 5:
# truncate by len(locs) / 5
scale = 1 + int(len(locs) / 5)
new_locs = []
for i in range(len(locs)):
if i % scale == 0:
new_locs.append(locs[i])
locs = new_locs
newXTicks = convertToAbsoluteTimeStr(locs)
matplotlib.pylab.xticks(locs, newXTicks, rotation=15)
matplotlib.pylab.xticks(fontsize=fontSize)
if yMinimum != None and yMaximum != None:
matplotlib.pylab.ylim(yMinimum, yMaximum)
elif yMinimum != None and yMaximum == None:
matplotlib.pylab.ylim(yMin=yMinimum)
elif yMinimum == None and yMaximum != None:
matplotlib.pylab.ylim(yMax=yMaximum)
# get the handle to the figure now that it has been created so we can manipulate on subsequent calls.
#self.__figure = matplotlib.pylab.gcf()
matplotlib.pylab.savefig(fullFilename)
#matplotlib.pylab.show()
matplotlib.pylab.clf()
def __truncateIsprint(self, isprintText, maxLines):
"""__truncateIsprint truncates isprintText to have maxLines at most.
"""
isprintList = isprintText.split('\n')
if len(isprintList) < maxLines:
return isprintText
else:
dropNumber = int(1 + len(isprintList)/maxLines)
newline = '\n'
newIsprintText = newline.join(isprintList[::dropNumber])
return newIsprintText
def __filter_missing(self,x):
try:
return float(x)
except:
return self.__missing
class madLineTimePlot:
"""madLineTimePlot is the class the produces line plots of one or more parameters versus time.
"""
def __init__(self, isprintText,
yParmList,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
useAbsoluteTime = False,
startTime = None,
endTime = None,
maxNumPoints = None,
noTime = False,
yMinimum = None,
yMaximum = None,
noDate = False,
timezone = 0):
"""__init__ writes a madLineTimePlot plot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The the following must be the parameters to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
yParmList - a list of y parameters (strings). Length must == num columns in isprintText - 1
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
maxNumPoints - maximum number of points to plot. If not None, truncate isprintText if
needed to have at most maxNumPoints lines.
noTime - if True and useAbsoluteTime True, then dates without times printed on x axis.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
yMinimum - set y minimum. If default=None, use data minimum
yMaximum - set y maximum. If default=None, use data maximum
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
Returns: void
Affects: None
"""
self.__missing = 1.0E30 # special value, since numpy doesn't handle NaN
self.__parameter_count = 1 + len(yParmList)
self.__colorList = 'gbrcmykw' # a list of colors for the lines
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
if maxNumPoints != None:
isprintText = self.__truncateIsprint(isprintText, maxNumPoints)
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
threshold = self.__missing*0.9999
array_data = numpy.ma.masked_where(array_data > threshold, array_data)
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
# find min, max of x (time)
if startTime == None:
xMin = array_data[0,0]
else:
xMin = startTime
if endTime == None:
xMax = array_data[-1,0]
else:
xMax = endTime
# find yMin just to ensure there is data
yMin = None
for column in range(len(yParmList)):
for y in array_data[:,1+column]:
if y == self.__missing:
continue
if yMin == None:
yMin = y
elif yMin > y:
yMin = y
if yMin == None:
raise ValueError('No valid y data found')
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(8,3), facecolor = 'w')
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(12,6), facecolor = 'w')
if useAbsoluteTime:
# leave room for rotated datetime string
if size == 'large':
matplotlib.pylab.axes([0.1, 0.2, 0.8, 0.7])
elif size == 'wide':
matplotlib.pylab.axes([0.07, 0.2, 0.9, 0.7])
elif size == 'small':
matplotlib.pylab.axes([0.1, 0.18, 0.87, 0.7])
else:
if size == 'large':
matplotlib.pylab.axes([0.1, 0.15, 0.71, 0.73])
for column in range(len(yParmList)):
color = self.__colorList[column % len(self.__colorList)]
matplotlib.pylab.plot(array_data[:,0],array_data[:,1 + column], '%so-' % (color), ms=5, mew=0.5)
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.xlim(xMin, xMax)
matplotlib.pylab.grid(True)
if useAbsoluteTime:
# if plot is less than 24 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
xmin = xmin / (60*60)
xmax = xmax / (60*60)
if noDate == True:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
step = int((xmax-xmin-1)/6)+1
xRange = list(range(0,int(newXmax-xmin),step))
for i in xRange:
tmp = (xmin + i)*60*60
newXLocs.append(tmp)
if (xmax - xmin) < 1: newXLocs.append(xmax*60*60)
newXLabels = convertToAbsoluteTimeStr(newXLocs, noDate=noDate, timezone=timezone)
matplotlib.pylab.xticks(newXLocs, newXLabels)
else:
locs, labels = matplotlib.pylab.xticks()
if len(locs) > 5:
# truncate by len(locs) / 5
scale = 1 + int(len(locs) / 5)
new_locs = []
for i in range(len(locs)):
if i % scale == 0:
new_locs.append(locs[i])
locs = new_locs
newXTicks = convertToAbsoluteTimeStr(locs, noTime)
matplotlib.pylab.xticks(locs, newXTicks, rotation=15)
else:
# if plot is less than 48 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
if xmax < 49:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
for i in range(0,int(4+newXmax),4):
newXLocs.append(i)
newXLabels.append(str(i))
matplotlib.pylab.xticks(newXLocs, newXLabels)
matplotlib.pylab.xticks(fontsize=fontSize)
if yMinimum != None and yMaximum != None:
matplotlib.pylab.ylim(yMinimum, yMaximum)
elif yMinimum != None and yMaximum == None:
matplotlib.pylab.ylim(yMin=yMinimum)
elif yMinimum == None and yMaximum != None:
matplotlib.pylab.ylim(yMax=yMaximum)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
matplotlib.pylab.legend(yParmList, numpoints=1)
matplotlib.pylab.savefig(fullFilename)
matplotlib.pylab.clf()
def __truncateIsprint(self, isprintText, maxLines):
"""__truncateIsprint truncates isprintText to have maxLines at most.
"""
isprintList = isprintText.split('\n')
if len(isprintList) < maxLines:
return isprintText
else:
dropNumber = int(1 + len(isprintList)/maxLines)
newline = '\n'
newIsprintText = newline.join(isprintList[::dropNumber])
return newIsprintText
def __filter_missing(self,x):
try:
return float(x)
except:
return self.__missing
def writeToFile(self, fullFilename):
pass
def displayToScreen(self):
pass
def getFigureHandle(self):
pass
class mad2YTimePlot:
"""mad2YTimePlot is the class the produces line plots of two parameters with different Y axes versus time.
"""
def __init__(self, isprintText,
yParmList,
titleStr,
xLabelStr,
fullFilename,
size = 'small',
useAbsoluteTime = False,
startTime = None,
endTime = None,
maxNumPoints = None,
noTime = False,
yMinimum = None,
yMaximum = None,
noDate = False,
timezone = 0):
"""__init__ writes a madLineTimePlot plot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The following must be two parameters to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
yParmList - a list of two y parameters (strings)
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
maxNumPoints - maximum number of points to plot. If not None, truncate isprintText if
needed to have at most maxNumPoints lines.
noTime - if True and useAbsoluteTime True, then dates without times printed on x axis.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
yMinimum - set y minimum of first parm. If default=None, use data minimum
yMaximum - set y maximum of first. If default=None, use data maximum
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
Returns: void
Affects: None
"""
self.__missing = 1.0E30 # special value, since numpy doesn't handle NaN
self.__parameter_count = 1 + len(yParmList)
self.__colorList = 'gbrcmykw' # a list of colors for the lines
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
if maxNumPoints != None:
isprintText = self.__truncateIsprint(isprintText, maxNumPoints)
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
threshold = self.__missing*0.9999
array_data = numpy.ma.masked_where(array_data > threshold, array_data)
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
# find min, max of x (time)
if startTime == None:
xMin = array_data[0,0]
else:
xMin = startTime
if endTime == None:
xMax = array_data[-1,0]
else:
xMax = endTime
# find yMin just to ensure there is data
yMin = None
for column in range(len(yParmList)):
for y in array_data[:,1+column]:
if y == self.__missing:
continue
if yMin == None:
yMin = y
elif yMin > y:
yMin = y
if yMin == None:
raise ValueError('No valid y data found')
fig, ax1 = matplotlib.pylab.subplots()
# select the plot size
if size == 'small':
fig.set_size_inches(8, 3)
elif size == 'wide':
fig.set_size_inches(10,4)
elif size == 'large':
fig.set_size_inches(12,6)
if useAbsoluteTime:
# leave room for rotated datetime string
if size == 'large':
matplotlib.pylab.axes([0.1, 0.2, 0.8, 0.7])
elif size == 'wide':
matplotlib.pylab.axes([0.07, 0.2, 0.9, 0.7])
elif size == 'small':
matplotlib.pylab.axes([0.1, 0.18, 0.87, 0.7])
else:
if size == 'large':
matplotlib.pylab.axes([0.1, 0.15, 0.71, 0.73])
# plot first
color = self.__colorList[0]
ax1.plot(array_data[:,0],array_data[:,1], '%so-' % (color))
ax1.set_ylabel(yParmList[0], color=color)
ax2 = ax1.twinx()
color = self.__colorList[1]
ax2.plot(array_data[:,0],array_data[:,2], '%so-' % (color))
ax2.set_ylabel(yParmList[1], color=color)
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.xlim(xMin, xMax)
matplotlib.pylab.grid(True)
if useAbsoluteTime:
# if plot is less than 24 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
xmin = xmin / (60*60)
xmax = xmax / (60*60)
if noDate == True:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
step = int((xmax-xmin-1)/6)+1
xRange = list(range(0,int(newXmax-xmin),step))
for i in xRange:
tmp = (xmin + i)*60*60
newXLocs.append(tmp)
if (xmax - xmin) < 1: newXLocs.append(xmax*60*60)
newXLabels = convertToAbsoluteTimeStr(newXLocs, noDate=noDate, timezone=timezone)
matplotlib.pylab.xticks(newXLocs, newXLabels)
else:
locs, labels = matplotlib.pylab.xticks()
if len(locs) > 5:
# truncate by len(locs) / 5
scale = 1 + int(len(locs) / 5)
new_locs = []
for i in range(len(locs)):
if i % scale == 0:
new_locs.append(locs[i])
locs = new_locs
newXTicks = convertToAbsoluteTimeStr(locs, noTime)
matplotlib.pylab.xticks(locs, newXTicks, rotation=15)
else:
# if plot is less than 48 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
if xmax < 49:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
for i in range(0,int(4+newXmax),4):
newXLocs.append(i)
ax1.set_xticks(newXLocs)
ax2.set_xticks(newXLocs)
matplotlib.pylab.xticks(fontsize=fontSize)
if yMinimum != None or yMaximum != None:
ax1.set_ylim(yMinimum, yMaximum)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
matplotlib.pylab.savefig(fullFilename)
matplotlib.pylab.clf()
def __truncateIsprint(self, isprintText, maxLines):
"""__truncateIsprint truncates isprintText to have maxLines at most.
"""
isprintList = isprintText.split('\n')
if len(isprintList) < maxLines:
return isprintText
else:
dropNumber = int(1 + len(isprintList)/maxLines)
newline = '\n'
newIsprintText = newline.join(isprintList[::dropNumber])
return newIsprintText
def __filter_missing(self,x):
try:
return float(x)
except:
return self.__missing
class madXYScatterPlot:
"""madXYScatterPlot is the class the produces XY scatter plots.
Change History:
Written by "Brandon Scott Fines":mailto:bfines@haystack.mit.edu Aug 2, 2006
Written by "Bill Rideout":mailto:brideout@haystack.mit.edu Aug 2, 2006
"""
def __init__(self,inputText,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size='small',
lowBound=None,
highBound=None,
maxNumPoints=None,
yLegend=None):
"""
Inputs:
inputText - string of values to be plotted. The formatting is as
follows:
xval yval
xval yval
xval yval
titleStr - title of the Plot
xLabelStr - label for the x axis
yLabelStr - label for the y axis
fullFilename - full file path to save the resulting picture to
size - size of plot to be saved. Must be 'small','wide', or 'large'.
defaults to 'small'.
lowBound - lower bound on the x-axis value. If no bound is specified,
the lowest value found in inputText will be used.
highBound - upper bound on the x-axis value. If no bound is specified,
the highest value found in inputText will be used.
maxNumPoints - maximum number of points to be plotted.
Outputs: None
Affects: Creates a scatter plot using matplotlib and writes that to the
file designated by the variable 'fullFilename'
Exceptions: ValueError if lowBound or highBound cannot be converted to
a float value
Non-standard python modules used:
matplotlib
"""
#verify input
if size not in ('small','wide','large'):
raise ValueError('size must be "small","wide", or "large", not %s'%(str(size)))
if size in ('small','wide'):
fontSize = 12
elif size in 'large':
fontSize = 18
if maxNumPoints !=None:
inputText = self.__truncateInput(inputText, maxNumPoints)
if lowBound !=None:
#check that it is a number
try:
lowBound = float(lowBound)
except ValueError:
raise ValueError('lowBound not a number')
try:
highBound = float(highBound)
except:
raise ValueError('lowBound not a number')
#convert the input data into numeric arrays
x=[]
y=[]
split_data = inputText.split()
#send x-values to x, y-values to y
i=0
while i xhigh:
xhigh = i
xlow = lowBound
elif lowBound==None and highBound !=None:
#find the lower bound in x
for i in x:
if xlow==None:
xlow = i
if ixhigh:
xhigh = i
#find upper and lower bounds in y
ylow = None
yhigh = None
for i in y:
if ylow==None:
ylow = i
elif iyhigh:
yhigh = i
#check for good numbers
if ylow == None:
raise ValueError('no valid y data found')
#select plot sizes
if size=='small':
matplotlib.pylab.figure(1,figsize=(6,4),facecolor='w')
elif size=='wide':
matplotlib.pylab.figure(1,figsize=(10,4),facecolor='w')
elif size=='large':
matplotlib.pylab.figure(1,figsize=(12,6),facecolor='w')
#draw scatter plot
#matplotlib.pylab.scatter(x,y)
matplotlib.pylab.plot(x,y, 'gd',ms=5,mew=0.5)
thisFont = {'size' : fontSize}
matplotlib.pylab.rc('font', **thisFont) #pass in font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr,fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr,fontsize=fontSize)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.xticks(fontsize=fontSize)
matplotlib.pylab.xlim(xlow,xhigh)
matplotlib.pylab.title(titleStr,fontsize=fontSize)
matplotlib.pylab.grid(True)
if yLegend != None: matplotlib.pylab.legend(yLegend,numpoints=1)
#save the figure
matplotlib.pylab.savefig(fullFilename)
matplotlib.pylab.clf()
class madXYwithErrorPlot:
"""madXYwithErrorPlot is the class the produces two dimensional scatter plots of x versus y more its error.
Written by "Miguel Urco":mailto:murco@jro.igp.gob.pe Jul 2, 2009
"""
def __init__(self, isprintText,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
useAbsoluteTime = False,
startTime = None,
endTime = None,
maxNumPoints = None,
noTime = False,
noDate = False,
yMinimum = None,
yMaximum = None,
timezone = 0):
"""__init__ writes a madScatter plot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First of three parameters
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The second must be the parameter to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - y label string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
maxNumPoints - maximum number of points to plot. If not None, truncate isprintText if
needed to have at most maxNumPoints lines.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
yMinimum - set y minimum. If default=None, use data minimum
yMaximum - set y maximum. If default=None, use data maximum
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
Returns: void
Affects: None
Now removes all lines with missing
"""
self.__missing = 1.0E30 # special value, since numpy doesn't handle NaN
self.__parameter_count = 3
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
# filter out missing lines
newIsprint = ''
lines = isprintText.split('\n')
for line in lines:
try:
items = line.split()
float(items[0])
float(items[1])
float(items[2])
newIsprint += line + ' '
except:
continue
isprintText = newIsprint
if maxNumPoints != None:
isprintText = self.__truncateIsprint(isprintText, maxNumPoints)
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
# find min, max of x (time)
if startTime == None:
xMin = array_data[0,0]
else:
xMin = startTime
if endTime == None:
xMax = array_data[-1,0]
else:
xMax = endTime
# find min, max of y, not including missing
yMin = None
yMax = None
for y in array_data[:,1]:
if y == self.__missing:
continue
if yMin == None:
yMin = y
elif yMin > y:
yMin = y
if yMax == None:
yMax = y
elif yMax < y:
yMax = y
if yMin == None:
raise ValueError('No valid y data found')
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(8,3), facecolor = 'w')
matplotlib.pylab.axes([0.11, 0.18, 0.78, 0.7])
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(10,6), facecolor = 'w')
if useAbsoluteTime:
# leave room for rotated datetime string
if size == 'large':
matplotlib.pylab.axes([0.1, 0.2, 0.8, 0.7])
elif size == 'wide':
matplotlib.pylab.axes([0.07, 0.2, 0.66, 0.7])
elif size == 'small':
matplotlib.pylab.axes([0.1, 0.18, 0.87, 0.7])
matplotlib.pylab.errorbar(array_data[:,0],array_data[:,2],array_data[:,1],ecolor='red',fmt='s',mfc='red',mec='green',ms=1,mew=1)
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.xlim(xMin, xMax)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
matplotlib.pylab.grid(True)
if yMinimum != None and yMaximum != None:
matplotlib.pylab.ylim(yMinimum, yMaximum)
elif yMinimum != None and yMaximum == None:
matplotlib.pylab.ylim(yMin=yMinimum)
elif yMinimum == None and yMaximum != None:
matplotlib.pylab.ylim(yMax=yMaximum)
if useAbsoluteTime:
# if plot is less than 24 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
xmin = xmin / (60*60)
xmax = xmax / (60*60)
if noDate == True:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0)+1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
step = int((xmax-xmin-1)/6)+1
xRange = list(range(0,int(newXmax-xmin),step))
for i in xRange:
tmp = (xmin + i)*60*60
newXLocs.append(tmp)
if (xmax - xmin) < 1: newXLocs.append(xmax*60*60)
newXLabels = convertToAbsoluteTimeStr(newXLocs, noDate=noDate, timezone=timezone)
matplotlib.pylab.xticks(newXLocs, newXLabels)
else:
locs, labels = matplotlib.pylab.xticks()
if len(locs) > 5:
# truncate by len(locs) / 5
scale = 1 + int(len(locs) / 5)
new_locs = []
for i in range(len(locs)):
if i % scale == 0:
new_locs.append(locs[i])
locs = new_locs
newXTicks = convertToAbsoluteTimeStr(locs)
matplotlib.pylab.xticks(locs, newXTicks, rotation=15)
matplotlib.pylab.xticks(fontsize=fontSize)
# get the handle to the figure now that it has been created so we can manipulate on subsequent calls.
#self.__figure = matplotlib.pylab.gcf()
matplotlib.pylab.savefig(fullFilename)
#matplotlib.pylab.show()
matplotlib.pylab.clf()
def __truncateInput(self,inputStr,maxLines):
"""__truncateInput truncates inputStr to have maxLines at most
"""
inputList = inputStr.split('\n')
if len(inputList) zMax and z != self.__missing:
zMax = z
elif z != self.__missing:
zMax = z
except:
continue
if zMin == None:
raise ValueError('No valid z data found')
# if both minColormap and maxColormap == None, use autoscaling
if minColormap == None and maxColormap == None:
zList.sort()
d10 = zList[int(len(zList)*0.10)]
d90 = zList[int(len(zList)*0.90)]
zMin = d10 - (d90-d10) * 0.75
zMax = d90 + (d90-d10) * 0.75
# now sort the X and Y axis lists and pull their length
xList.sort()
if startTime == None:
xMin = xList[0]
else:
xMin = startTime
if endTime == None:
xMax = xList[-1]
else:
xMax = endTime
yList.sort()
max_x_dimension = len(xList)
max_y_dimension = len(yList)
if yMinimum == None:
yMinimum = yList[0]
if yMaximum == None:
yMaximum = yList[-1]
self.truncateAlt = False
if maxNumAlt != None:
if max_y_dimension > maxNumAlt:
self.truncateAlt = True
# build dictonary of indexes into xList
self.xListDict = {}
for i in range(len(xList)):
self.xListDict[xList[i]] = i
# if self.truncateAlt == False, build dictonary of indexes into yList,
# else truncate y values by builing a list of maxNumAlt ranges
if self.truncateAlt == False:
self.yListDict = {}
for i in range(len(yList)):
self.yListDict[yList[i]] = i
else:
self.yListRanges = []
for i in range(maxNumAlt):
self.yListRanges.append(yList[int(i*(len(yList)/maxNumAlt))])
max_y_dimension = maxNumAlt
# now build arrays to handle the X axis label, Y axis label, and the Z data
X = numpy.zeros((max_x_dimension, max_y_dimension), numpy.float32)
Y = numpy.zeros((max_x_dimension, max_y_dimension), numpy.float32)
Z = numpy.ones((max_x_dimension, max_y_dimension), numpy.float32)
# all parameter values default to missing
Z = Z * self.__missing
# fill the X and Y arrays
for i in range(max_x_dimension):
for j in range(max_y_dimension):
X[i][j] = float(xList[i])
if self.truncateAlt:
Y[i][j] = float(yList[int(j*(len(yList)/maxNumAlt))])
else:
Y[i][j] = float(yList[j])
# Now load up the data array Z with the array_data measurements as a function of x and y
previousIndex = None
previousValue = None
presentTime = None
newTimeFound = True
for k in range(len(array_data)):
try:
xdata = array_data[k][0]
ydata = array_data[k][1]
zdata = array_data[k][2]
if zdata == self.__missing:
continue
if xdata != presentTime:
newTimeFound = True
else:
newTimeFound = False
presentTime = xdata
# now find the right place in the array for this data point
i = self.xListDict[xdata]
j = self.__getYIndex(ydata)
Z[i][j] = zdata
# now see if we need to fill in any gaps
if (not newTimeFound) and smoothAltitude:
if previousIndex < j - 1:
# fill in all missing points
for l in range(previousIndex + 1, j):
# simply average between the points based on index
thisValue = previousValue + (zdata - previousValue)*(float(l-previousIndex)/float(j-previousIndex))
Z[i][l] = thisValue
previousIndex = j
previousValue = zdata
except:
continue
# insert missing data to represent gaps if needed
if insertDataGap != None:
# first find the time interval greater than 90% of others
timeIntervalList = []
for i in range(len(xList) - 1):
timeIntervalList.append(xList[i+1] - xList[i])
timeIntervalList.sort()
index = int(len(timeIntervalList)*0.9)
maxInterval = timeIntervalList[index]
for i in range(len(xList) - 1):
if xList[i+1] - xList[i] > maxInterval * insertDataGap:
Z[i,:] = self.__missing
# insert missing data to represent gaps if needed in Altitude
if insertDataGap != None and not smoothAltitude:
# first find the time interval greater than 90% of others
altitudeIntervalList = []
for i in range(len(yList) - 1):
altitudeIntervalList.append(yList[i+1] - yList[i])
altitudeIntervalList.sort()
index = int(len(altitudeIntervalList)*0.9)
maxInterval = altitudeIntervalList[index]
for j in range(len(yList) - 1):
if yList[j+1] - yList[j] > maxInterval * insertDataGap and j < max_y_dimension:
Z[:,j] = self.__missing
# make Z a masked array
Znew = numpy.ma.masked_inside(Z, 0.99*self.__missing, 1.01*self.__missing)
# set up plotting parameters
if minColormap == None:
minColormap = zMin
if maxColormap == None:
maxColormap = zMax
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(8,3), facecolor = 'w')
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(14,5), facecolor = 'w')
if useAbsoluteTime:
# leave room for rotated datetime string
if size == 'large':
matplotlib.pylab.axes([0.1, 0.1, 0.85, 0.7])
elif size == 'wide':
matplotlib.pylab.axes([0.07, 0.2, 0.97, 0.7])
elif size == 'small':
matplotlib.pylab.axes([0.1, 0.18, 0.97, 0.7])
else:
if size == 'small':
matplotlib.pylab.axes([0.1, 0.18, 0.97, 0.7])
elif size == 'large':
matplotlib.pylab.axes([0.1, 0.2, 0.9, 0.73])
matplotlib.pylab.pcolor(X,Y,Znew, edgecolors='none', vmin=minColormap, vmax=maxColormap, cmap = colorMap,
norm = matplotlib.pylab.Normalize(), edgecolor='face')
matplotlib.pylab.colorbar()
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.xlim(xMin, xMax)
matplotlib.pylab.ylim(yMinimum, yMaximum)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
if useAbsoluteTime:
# if plot is less than 49 hours
xmin, xmax = matplotlib.pylab.xlim()
xmin = numpy.floor(xmin / (60*60))
xmax = numpy.ceil(xmax / (60*60))
if noDate == True:
newXLabels = []
if (xmax - xmin) <= 1:
newXLocs = []
xRange = numpy.arange(0,1,0.25)
for i in xRange:
tmp=(xmin+i)*60*60
newXLocs.append(tmp)
elif (xmax - xmin) <= 2:
newXLocs = []
xRange = numpy.arange(0,2,0.5)
for i in xRange:
tmp=(xmin+i)*60*60
newXLocs.append(tmp)
else:
newXLocs = []
step = int((xmax-xmin-1)/6)+1
xRange = list(range(0,int(xmax-xmin),step))
for i in xRange:
tmp = (xmin + i)*60*60
newXLocs.append(tmp)
#newXLocs.append(xmax*60*60)
newXLabels = convertToAbsoluteTimeStr(newXLocs, noDate=noDate, timezone=timezone)
matplotlib.pylab.xticks(newXLocs, newXLabels)
else:
locs, labels = matplotlib.pylab.xticks()
if len(locs) > 5:
# truncate by len(locs) / 5
scale = 1 + int(len(locs) / 5)
new_locs = []
for i in range(len(locs)):
if i % scale == 0:
new_locs.append(locs[i])
locs = new_locs
newXTicks = convertToAbsoluteTimeStr(locs)
matplotlib.pylab.xticks(locs, newXTicks, rotation=15)
else:
# if plot is less than 48 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
if xmax < 49:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
for i in range(0,int(4+newXmax),4):
newXLocs.append(i)
newXLabels.append(str(i))
matplotlib.pylab.xticks(newXLocs, newXLabels)
matplotlib.pylab.xticks(fontsize=fontSize)
# add second y-axis if desired
if altYTitle != None and altYLabels != None:
ax2 = matplotlib.pylab.twinx()
matplotlib.pylab.ylabel(altYTitle)
ax2.yaxis.tick_right()
matplotlib.pylab.yticks(list(range(len(altYLabels))), altYLabels)
matplotlib.pylab.yticks(fontsize=fontSize)
# get the handle to the figure now that it has been created so we can manipulate on subsequent calls.
#self.__figure = matplotlib.pylab.gcf()
if peakAltStr:
# plot peak altitudes
data = peakAltStr.split()
xdata = [float(item) for i, item in enumerate(data) if i % 2 == 0]
ydata = [float(item) for i, item in enumerate(data) if i % 2 == 1]
matplotlib.pylab.plot(xdata, ydata, 'b+')
matplotlib.pylab.savefig(fullFilename)
#matplotlib.pylab.show()
matplotlib.pylab.clf()
def __filter_missing(self,x):
try:
return float(x)
except:
return self.__missing
def __getYIndex(self, yvalue):
"""__getYIndex returns the correct index into the y dimension for a given y value.
Input: yvalue - value of y parameter
Returns: the correct index into the y dimension
Algorithm: if self.truncateAlt == False, simple use the dictionary self.yListDict. Else
loop through self.yListRanges and return the first greater than the requested value
"""
if self.truncateAlt == False:
return self.yListDict[yvalue]
else:
i = bisect.bisect_left(self.yListRanges, yvalue)
if i >= len(self.yListRanges):
i = len(self.yListRanges) - 1
return i
def __truncateIsprint(self, isprintText, maxLines):
"""__truncateIsprint truncates isprintText to have maxLines at most.
"""
isprintList = isprintText.split('\n')
if len(isprintList) < maxLines:
return isprintText
else:
dropNumber = int(1 + len(isprintList)/maxLines)
newIsprintText = ''
for i in range(0,len(isprintList),dropNumber):
newIsprintText += isprintList[i] + '\n'
return newIsprintText
def displayToScreen(self):
" to implement this takes a reworking away from pylab to use the underlying matplotlib code "
pass
def getFigureHandle(self):
return self.__figure
def getAverage(self, X):
"""returns the average of items in a float array. Does not including missing data.
If all data missing, returns self.__missing
"""
count = 0
total = 0.0
for i in range(X.shape[0]):
if X[i] != self.__missing:
count += 1
total += X[i]
if count == 0:
return self.__missing
else:
return total / float(count)
def sortArrayInTime(self, array_data):
"""sortArrayInTime sorts a two-dimensional array so that the first element in each row (time) is in ascending order.
Input: array_data - two-dimensional array to be sorted by rearranging rows so
that the first element in each row (time) is in ascending order
Returns: new_array
"""
sortIndex = numpy.argsort(array_data[:,0])[0]
# if already sorted, just return original array
if sortIndex == numpy.sort(sortIndex):
return array_data
new_array = numpy.zeros(array_data.shape, array_data.dtype)
for i in range(len(sortIndex)):
new_array[sortIndex[i],:] = array_data[i,:]
return new_array
def decimateTimes(self, array_data, maxNumTimes, insertDataGap):
"""decimateTimes decimates array_data to have at most maxNumTimes times.
Input: array_data - two-dimensional array to be decimated by deleting times and missing data.
maxNumTimes: int representing the maximum number of times to keep in array_data
insertDataGap - this parameter sets the threshold for inserting a data gap. The time intervals
being plotted are ordered, and the time gap larger than 90% of the rest is determined.
Note that this parameter is used here to stop the truncation of isprint lines that
will eventually be considered edge lines.
Returns: new array built from decimated array_data
"""
# get the number of times in array_data, and make a list of all unique times
numTimes = 0
uniqueTimes = []
time_array = array_data[:, 0]
for i in range(len(time_array)):
if i == 0:
numTimes = 1
uniqueTimes.append(time_array[i])
elif time_array[i-1] != time_array[i]:
uniqueTimes.append(time_array[i])
numTimes += 1
if numTimes <= maxNumTimes:
return array_data
# insert missing data to represent gaps if needed
gapTimes = []
if insertDataGap != None:
# first find the time interval greater than 90% of others
timeIntervalList = []
for i in range(len(time_array) - 1):
if time_array[i+1] == time_array[i]:
continue
timeIntervalList.append(time_array[i+1] - time_array[i])
timeIntervalList.sort()
index = int(len(timeIntervalList)*0.9)
maxInterval = timeIntervalList[index]
for i in range(len(time_array) - 1):
if time_array[i+1] - time_array[i] > maxInterval * insertDataGap:
gapTimes.append(time_array[i+1])
gapTimes.append(time_array[i])
# get the number of times to skip each time
numSkip = numTimes/maxNumTimes
# get the number of rows in the new_array
numRows = 0
numTimes = 0
useThisTime = False
for i in range(len(time_array)):
if i == 0:
numTimes = 1
elif time_array[i-1] != time_array[i]:
numTimes += 1
if numTimes % (numSkip + 1) == 0 or time_array[i] in gapTimes:
useThisTime = True
if array_data[i, -1] != self.__missing:
numRows += 1
else:
useThisTime = False
else:
if useThisTime:
if array_data[i, -1] != self.__missing:
numRows += 1
# create new_array
new_array = numpy.zeros((numRows, array_data.shape[1]), array_data.dtype)
# copy selected rows to new_array
numRows = 0
numTimes = 0
useThisTime = False
for i in range(len(time_array)):
if i == 0:
numTimes = 1
elif time_array[i-1] != time_array[i]:
numTimes += 1
if numTimes % (numSkip + 1) == 0 or time_array[i] in gapTimes:
useThisTime = True
if array_data[i, -1] != self.__missing:
new_array[numRows,:] = array_data[i,:]
numRows += 1
else:
useThisTime = False
else:
if useThisTime:
if array_data[i, -1] != self.__missing:
new_array[numRows,:] = array_data[i,:]
numRows += 1
return new_array
class madPcolorScan:
"""madPcolorScan is the class that produces pcolor scans.
Usage example::
obj = madPcolorScan(isprintText,
'Nel (log(m^-3)) - 26 June 2006 13:49:43-14:07:36',
'Longitude',
'Latitude',
'./isprint.png',
size = 'large',
minColormap = 9,
maxColormap = 12)
Non-standard Python modules used:
matplotlib
Change history:
Written by "Bill Rideout":mailto:wrideout@haystack.mit.edu Jul. 20, 2006
"""
def __init__(self, isprintText,
xGridSize,
yGridSize,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
xMinimum = None,
xMaximum = None,
yMinimum = None,
yMaximum = None,
minColormap = None,
maxColormap = None,
colorMap = matplotlib.cm.jet,
maxNumLines = None):
"""__init__ writes a madPcolorScan to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be the X axis value, and the second must be the Y axis value.
The third column is the value (intensity). Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
xGridSize - grid size for x data (for example 0.1 for 0.1 degree longitude grid)
yGridSize - grid size for x data (for example 0.1 for 0.1 degree latitude grid)
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
xMinimum = minumum x value. If None (default), uses lowest x value found.
xMaximum = maximum x value. If None (default), uses highest x value found.
yMinimum = minumum y value. If None (default), uses lowest y value found.
yMaximum = maximum y value. If None (default), uses highest y value found.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
maxNumLine - max number of lines in isprintText before truncating. If None, no truncation
Returns: void
Affects: None
"""
self.__missing = 1.0E30 # special value, used to create a masked array
self.__parameter_count = 3
self.__xGridSize = int(xGridSize)
self.__yGridSize = int(yGridSize)
self.__parseCount = 0 # used to tell which column self.__filter_input is working on
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
if maxNumLines != None:
isprintText = self.__truncateIsprint(isprintText, maxNumLines)
# since matplotlib pcolor wants a regular grid, we create a grid with all altitudes
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_input, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
array_data = self.sortArrayInX(array_data)
# The first pass through is to obtain the number and range of the x and y variables
xList = []
yList = []
zList = []
zMin = None
zMax = None
# loop over all the lines of data in the array
for j in range(len(array_data)):
try:
x = array_data[j][0]
y = array_data[j][1]
z = array_data[j][2]
if x not in xList:
xList.append(x)
if y not in yList:
yList.append(y)
if z != self.__missing:
zList.append(z)
if zMin != None:
if z < zMin and z != self.__missing:
zMin = z
elif z != self.__missing:
zMin = z
if zMax != None:
if z > zMax and z != self.__missing:
zMax = z
elif z != self.__missing:
zMax = z
except:
continue
if zMin == None:
raise ValueError('No valid z data found')
# if both minColormap and maxColormap == None, use autoscaling
if minColormap == None and maxColormap == None:
zList.sort()
d10 = zList[int(len(zList)*0.10)]
d90 = zList[int(len(zList)*0.90)]
zMin = d10 - (d90-d10) * 0.75
zMax = d90 + (d90-d10) * 0.75
# now sort the X and Y axis lists and pull their length
xList.sort()
xMin = xList[0]
xMax = xList[-1]
yList.sort()
yMin = yList[0]
yMax = yList[-1]
max_x_dimension = len(xList)
max_y_dimension = len(yList)
# build dictonary of indexes into xList
self.xListDict = {}
for i in range(len(xList)):
self.xListDict[xList[i]] = i
# build dictonary of indexes into yList,
self.yListDict = {}
for i in range(len(yList)):
self.yListDict[yList[i]] = i
# now build arrays to handle the X axis label, Y axis label, and the Z data
X = numpy.zeros((max_x_dimension, max_y_dimension), numpy.float32)
Y = numpy.zeros((max_x_dimension, max_y_dimension), numpy.float32)
Z = numpy.ones((max_x_dimension, max_y_dimension), numpy.float32)
# all parameter values default to missing
Z = Z * self.__missing
# fill the X and Y arrays
for i in range(max_x_dimension):
for j in range(max_y_dimension):
X[i][j] = float(xList[i])
Y[i][j] = float(yList[j])
# Now load up the data array Z with the array_data measurements as a function of x and y
previousIndex = None
previousValue = None
presentTime = None
newTimeFound = True
for k in range(len(array_data)):
try:
xdata = self.__round(array_data[k][0], self.__xGridSize)
ydata = self.__round(array_data[k][1], self.__yGridSize)
zdata = array_data[k][2]
if zdata == self.__missing:
continue
if xdata != presentTime:
newTimeFound = True
else:
newTimeFound = False
presentTime = xdata
# now find the right place in the array for this data point
i = self.xListDict[xdata]
j = self.yListDict[ydata]
Z[i][j] = zdata
previousIndex = j
previousValue = zdata
except:
continue
# make Z a masked array
Znew = numpy.ma.masked_inside(Z, 0.99*self.__missing, 1.01*self.__missing)
# set up plotting parameters
if xMinimum == None:
xMinimum = xMin
if xMaximum == None:
xMaximum = xMax
if yMinimum == None:
yMinimum = yMin
if yMaximum == None:
yMaximum = yMax
if minColormap == None:
minColormap = zMin
if maxColormap == None:
maxColormap = zMax
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(6,4), facecolor = 'w')
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(12,6), facecolor = 'w')
matplotlib.pylab.pcolor(X,Y,Znew, edgecolors='none', vmin=minColormap, vmax=maxColormap, cmap = colorMap, norm = matplotlib.pylab.Normalize())
matplotlib.pylab.colorbar()
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.xlim(xMinimum, xMaximum)
matplotlib.pylab.ylim(yMinimum, yMaximum)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
# get the handle to the figure now that it has been created so we can manipulate on subsequent calls.
#self.__figure = matplotlib.pylab.gcf()
try:
matplotlib.pylab.savefig(fullFilename)
except:
matplotlib.pylab.clf()
raise
#matplotlib.pylab.show()
matplotlib.pylab.clf()
def __round(self, value, increment):
"""__round returns a value to the nearest increment
"""
return value - math.fmod(value, increment)
def __filter_input(self,x):
"""__filter_input is called in map to convert missing strings to self.__missing, and to
round x and y vales to the nearest xGridSize or yGridSize
"""
try:
value = float(x)
if self.__parseCount % 3 == 0:
# x value
value = self.__round(value, self.__xGridSize)
elif self.__parseCount % 3 == 1:
# y value
value = self.__round(value, self.__yGridSize)
except:
value = self.__missing
self.__parseCount += 1
return value
def __truncateIsprint(self, isprintText, maxLines):
"""__truncateIsprint truncates isprintText to have maxLines at most.
"""
isprintList = isprintText.split('\n')
if len(isprintList) < maxLines:
return isprintText
else:
dropNumber = int(1 + len(isprintList)/maxLines)
newIsprintText = ''
for i in range(0,len(isprintList),dropNumber):
newIsprintText += isprintList[i] + '\n'
return newIsprintText
def displayToScreen(self):
" to implement this takes a reworking away from pylab to use the underlying matplotlib code "
pass
def getFigureHandle(self):
return self.__figure
def getAverage(self, X):
"""returns the average of items in a float array. Does not including missing data.
If all data missing, returns self.__missing
"""
count = 0
total = 0.0
for i in range(X.shape[0]):
if X[i] != self.__missing:
count += 1
total += X[i]
if count == 0:
return self.__missing
else:
return total / float(count)
def sortArrayInX(self, array_data):
"""sortArrayInX sorts a two-dimensional array so that the first element in each row (x) is in ascending order.
Input: array_data - two-dimensional array to be sorted by rearranging rows so
that the first element in each row (x) is in ascending order
Returns: new_array
"""
sortIndex = numpy.argsort(array_data[:,0])[0]
# if already sorted, just return original array
if sortIndex == numpy.sort(sortIndex):
return array_data
new_array = numpy.zeros(array_data.shape, array_data.dtype)
for i in range(len(sortIndex)):
new_array[sortIndex[i],:] = array_data[i,:]
return new_array
class madPcolorWedgeScan:
"""madPcolorWedgeScan is the class that produces pcolor scans where data is drawn as wedge shapes.
Usage example::
obj = madPcolorScan(isprintText,
'Nel (log(m^-3)) - 26 June 2006 13:49:43-14:07:36',
'Longitude',
'Latitude',
'./isprint.png',
size = 'large',
minColormap = 9,
maxColormap = 12)
Non-standard Python modules used:
matplotlib
Change history:
Written by "Bill Rideout":mailto:wrideout@haystack.mit.edu Jul. 20, 2006
"""
def __init__(self, isprintText,
scanType,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
xMinimum = None,
xMaximum = None,
yMinimum = None,
yMaximum = None,
minColormap = None,
maxColormap = None,
colorMap = matplotlib.cm.jet):
"""__init__ writes a madPcolorWedgeScan to a file.
Inputs:
isprintText - a string giving isprint output without headers. The 9 parameters
are (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2, ). Any
missing data should be written as "missing" or other string that
cannot be converted to a float. gdlatr, gdlonr, galtr are the station location
scanType - must be 'az' or 'el_lat' (for north or south el scans), 'el_lon'
(for east or west el scans) or 'el_gcdist' (for all other el scans.
For az - x is long, y is lat
for el_* y is always alt, and x is either lat, lon, or great circle distance
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
xMinimum = minumum x value. If None (default), uses lowest value found.
xMaximum = maximum x value. If None (default), uses highest value found.
yMinimum = minumum y value. If None (default), uses lowest value found.
yMaximum = maximum y value. If None (default), uses highest value found.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
Returns: void
Affects: Writes fullFilename to disk
"""
# verify input
if scanType not in ('az', 'el_lat', 'el_lon', 'el_gcdist'):
raise ValueError('scanType must be "az", "el_lat", "el_lon", "el_gcdist", not %s' % (str(scanType)))
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
isprintLines = isprintText.split('\n')
if xMaximum == None or xMinimum == None or yMaximum == None or yMinimum or maxColormap == None or minColormap == None:
newLonMax, newLonMin, newLatMax, newLatMin, newAltMax, newGreatCDist, newParmMax, newParmMin = self._getLimits(isprintLines)
# set values
xMaximum, xMinimum, yMaximum, yMinimum = self._setLimits(scanType, xMaximum, xMinimum, yMaximum, yMinimum,
newLonMax, newLonMin, newLatMax, newLatMin, newAltMax,
newGreatCDist, newParmMax, newParmMin)
if maxColormap == None:
maxColormap = newParmMax
if minColormap == None:
minColormap = newParmMin
# set up wedge generator
dataGen = self._generateWedges(isprintLines, scanType)
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(6,5), facecolor = 'w')
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(12,6), facecolor = 'w')
try:
while True:
data = next(dataGen)
x = [data[1][0], data[2][0], data[3][0], data[4][0]]
y = [data[1][1], data[2][1], data[3][1], data[4][1]]
dataRatio = (data[0] - minColormap) / (maxColormap - minColormap)
colorStr = matplotlib.colors.rgb2hex(colorMap(dataRatio)[:3])
thisPlot = matplotlib.pyplot.fill(x, y, colorStr, edgecolor='none')
except StopIteration:
pass
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
scalableMappable = matplotlib.cm.ScalarMappable(cmap=colorMap)
scalableMappable.set_array(numpy.array([minColormap, maxColormap]))
scalableMappable.set_clim(minColormap, maxColormap)
def get_alpha(*args, **kw):
return(1.0)
scalableMappable.get_alpha = get_alpha
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.xlim(xMinimum, xMaximum)
matplotlib.pylab.ylim(yMinimum, yMaximum)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
matplotlib.pylab.colorbar(mappable=scalableMappable)
matplotlib.pylab.gcf().subplots_adjust(bottom=0.15)
try:
matplotlib.pylab.savefig(fullFilename)
except:
matplotlib.pylab.clf()
raise
matplotlib.pylab.clf()
def getGreatCircleDist(self, gdlatr, glonr, gdlat, glon):
"""getGreatCircleDist return the great circle distance in. Taken from madDeriveMethods c code.
Inputs: gdlatr, glonr: reference point on earth's surface
gdlat, glon: second point on earth's surface
"""
dlon = glonr/57.2958 - glon/57.2958
dlat = gdlatr/57.2958 - gdlat/57.2958;
a = math.sin(dlat/2.0)*math.sin(dlat/2.0) + math.cos(gdlatr/57.2958)*math.cos(gdlat/57.2958) * math.sin(dlon/2.0)*math.sin(dlon/2.0)
if math.sqrt(a) < 1.0:
c = 2.0 * math.asin(math.sqrt(a))
else:
c = 2.0 * math.asin(1.0)
return(c * 6371.2)
def _getLimits(self, isprintLines):
"""returns (newLonMax, newLonMin, newLatMax, newLatMin, newAltMax, newGreatCDist, newParmMax, newParmMin)
given isprint lines with
parameters (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2, )
We only look at the end points of ranges and the station location
"""
# set initial values to station location
items = isprintLines[1].split()
slat = float(items[0])
slon = float(items[1])
if slon > 180.0:
slon -= 360.0
salt = float(items[2])
maxLon = slon
minLon = slon
maxLat = slat
minLat = slat
maxAlt = salt
maxGreatCDist = 0.0
maxParm = -1.0e-30
minParm = 1.0e30
lastAz = None
lastEl = None
lastRange = None
for i, isprintLine in enumerate(isprintLines):
items = isprintLine.split()
if len(items) < 9:
continue
range = float(items[3])
az1 = float(items[4])
az2 = float(items[5])
thisAz = (az1 + az2)/2
el1 = float(items[6])
el2 = float(items[7])
thisEl = (el1 + el2)/2
try:
parm = float(items[8])
if parm > maxParm:
maxParm = parm
if parm < minParm:
minParm = parm
except ValueError:
continue
# see if we're at an end point
if lastAz != None:
if abs(lastAz - thisAz) > 0.001 or abs(lastEl - thisEl) > 0.001:
# the last point was an end point
gdlat,glon,gdalt = madrigal._derive.radarToGeodetic(slat,slon,salt,lastAz,lastEl,lastRange)
if gdlat > maxLat:
maxLat = gdlat
if gdlat < minLat:
minLat = gdlat
if glon > maxLon:
maxLon = glon
if glon < minLon:
minLon = glon
if gdalt > maxAlt:
maxAlt = gdalt
greatCDist = self.getGreatCircleDist(slat, slon, gdlat, glon)
if greatCDist > maxGreatCDist:
maxGreatCDist = greatCDist
lastAz = thisAz
lastEl = thisEl
lastRange = range
# check last point
gdlat,glon,gdalt = madrigal._derive.radarToGeodetic(slat,slon,slat,lastAz,lastEl,lastRange)
if gdlat > maxLat:
maxLat = gdlat
if gdlat < minLat:
minLat = gdlat
if glon > maxLon:
maxLon = glon
if glon < minLon:
minLon = glon
if gdalt > maxAlt:
maxAlt = gdalt
greatCDist = self.getGreatCircleDist(slat, slon, gdlat, glon)
if greatCDist > maxGreatCDist:
maxGreatCDist = greatCDist
return((maxLon, minLon, maxLat, minLat, maxAlt, maxGreatCDist, maxParm, minParm))
def _setLimits(self, scanType, xMaximum, xMinimum, yMaximum, yMinimum,
newLonMax, newLonMin, newLatMax, newLatMin, newAltMax,
newGreatCDist, newParmMax, newParmMin):
""" _setLimits sets xMaximum, xMinimum, yMaximum, yMinimum based on values
found in self._getLimits and scanType. If any value in (xMaximum, xMinimum, yMaximum, yMinimum)
is not None, it will not be changed. """
newXMax = xMaximum
newXMin = xMinimum
newYMax = yMaximum
newYMin = yMinimum
if scanType == 'az':
if xMaximum == None:
newXMax = newLonMax
if xMinimum == None:
newXMin = newLonMin
if yMaximum == None:
newYMax = newLatMax
if yMinimum == None:
newYMin = newLatMin
elif scanType == 'el_lat':
if xMaximum == None:
newXMax = newLatMax
if xMinimum == None:
newXMin = newLatMin
if yMaximum == None:
newYMax = newAltMax
if yMinimum == None:
newYMin = 0.0
elif scanType == 'el_lon':
if xMaximum == None:
newXMax = newLonMax
if xMinimum == None:
newXMin = newLonMin
if yMaximum == None:
newYMax = newAltMax
if yMinimum == None:
newYMin = 0.0
elif scanType == 'el_gcdist':
if xMaximum == None:
newXMax = newGreatCDist
if xMinimum == None:
newXMin = 0.0
if yMaximum == None:
newYMax = newAltMax
if yMinimum == None:
newYMin = 0.0
return((newXMax, newXMin, newYMax, newYMin))
def _generateWedges(self, isprintLines, scanType):
"""_generateWedges is a python generator that return a data wedge in the form
(dataValue, (point1_x, point1_y), (point2_x, point2_y),
(point3_x, point3_y), (point4_x, point4_y))
Inputs:
isprintLines - a list of line with 9 parameters that
are (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2, ). Any
missing data should be written as "missing" or other string that
cannot be converted to a float. gdlatr, gdlonr, galtr are the station location.
No wedge is returned for a missing data line
scanType - must be 'az' or 'el_lat' (for north or south el scans), 'el_lon'
(for east or west el scans) or 'el_gcdist' (for all other el scans.
For az - x is long, y is lat
for el_* y is always alt, and x is either lat, lon, or great circle distance
"""
# state variables
lastAz1 = None
lastAz2 = None
lastEl1 = None
lastEl2 = None
lastRange = None
lastDiffRangeLow = None
lastValue = None
for isprintLine in isprintLines:
items = isprintLine.split()
if len(items) < 9:
continue
gdlatr = float(items[0])
gdlonr = float(items[1])
galtr = float(items[2])
range = float(items[3])
az1 = float(items[4])
az2 = float(items[5])
el1 = float(items[6])
el2 = float(items[7])
valueStr = items[8]
if lastAz1 != None and ( abs(lastAz1-az1) > 0.001 or abs(lastEl1-el1) > 0.001 ):
# new radar line found, send last point if needed
if lastValue != None:
dataWedge = self._getWedge(gdlatr, gdlonr, galtr, lastRange,
lastDiffRangeLow, lastDiffRangeLow, lastAz1, lastAz2,
lastEl1, lastEl2, lastValue, scanType)
yield(dataWedge)
# this is the first point in the line, so no lastDiffRangeLow
lastDiffRangeLow = None
elif lastAz1 != None and ( abs(lastAz1-az1) < 0.001 and abs(lastEl1-el1) < 0.001 ):
# continuing radar line
if lastValue != None:
lastDiffRangeLow = (range - lastRange)/2.0
dataWedge = self._getWedge(gdlatr, gdlonr, galtr, lastRange,
lastDiffRangeLow, (range-lastRange)/2.0, lastAz1, lastAz2,
lastEl1, lastEl2, lastValue, scanType)
yield(dataWedge)
else:
lastDiffRangeLow = (range - lastRange)/2.0
# reset state variables
lastAz1 = az1
lastAz2 = az2
lastEl1 = el1
lastEl2 = el2
lastRange = range
try:
lastValue = float(valueStr)
except:
lastValue = None
# finally, send last wedge if needed
if lastValue != None:
dataWedge = self._getWedge(gdlatr, gdlonr, galtr, lastRange,
lastDiffRangeLow, lastDiffRangeLow, lastAz1, lastAz2,
lastEl1, lastEl2, lastValue, scanType)
yield(dataWedge)
def _getWedge(self, gdlatr, gdlonr, galtr, range,
diffRange1, diffRange2, az1, az2,
el1, el2, value, scanType):
"""_getWedge returns return a data wedge in the form
(dataValue, (point1_x, point1_y), (point2_x, point2_y),
(point3_x, point3_y), (point4_x, point4_y)). The x and y dimensions depend on
scanType:
az: x: lon, y: lat
el_lat: x: lat, y: alt
el_lon: x: lon, y: alt
el_gcdist: x: great circle distance, y: alt
Inputs:
gdlatr, gdlonr, galtr - location of radar
range, diffRange1, diffRange2 - range of center of wedge, and distance in and out from there
az1, az2, el1, el2 - start and end aximuth and elevation. Which is used depends on scanType
value - float data value
scanType - az, el_lat, el_lon, el_gcdist
"""
if scanType == 'az':
point1_y,point1_x,gdalt = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range-diffRange1)
point2_y,point2_x,gdalt = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az2,el1,range-diffRange1)
point3_y,point3_x,gdalt = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az2,el1,range+diffRange2)
point4_y,point4_x,gdalt = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range+diffRange2)
elif scanType == 'el_lat':
point1_x,glon,point1_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range-diffRange1)
point2_x,glon,point2_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el2,range-diffRange1)
point3_x,glon,point3_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el2,range+diffRange2)
point4_x,glon,point4_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range+diffRange2)
elif scanType == 'el_lon':
gdlat,point1_x,point1_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range-diffRange1)
gdlat,point2_x,point2_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el2,range-diffRange1)
gdlat,point3_x,point3_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el2,range+diffRange2)
gdlat,point4_x,point4_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range+diffRange2)
elif scanType == 'el_gcdist':
gdlat,glon,point1_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range-diffRange1)
point1_x = self.getGreatCircleDist(gdlatr, gdlonr, gdlat, glon)
gdlat,glon,point2_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el2,range-diffRange1)
point2_x = self.getGreatCircleDist(gdlatr, gdlonr, gdlat, glon)
gdlat,glon,point3_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el2,range+diffRange2)
point3_x = self.getGreatCircleDist(gdlatr, gdlonr, gdlat, glon)
gdlat,glon,point4_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range+diffRange2)
point4_x = self.getGreatCircleDist(gdlatr, gdlonr, gdlat, glon)
return((value, (point1_x, point1_y), (point2_x, point2_y), (point3_x, point3_y), (point4_x, point4_y)))
class madPcolorWedgeKmlScan:
"""madPcolorWedgeKmlScan is the class that produces pcolor scan kml files where data is drawn as wedge shapes.
Usage example::
obj = madPcolorWedgeKmlScan(isprintText,
'az',
'./isprint.kml',
minColormap = 9,
maxColormap = 12)
Non-standard Python modules used:
matplotlib
Change history:
Written by "Bill Rideout":mailto:wrideout@haystack.mit.edu Dec 15, 2010
"""
def __init__(self, isprintText,
scanType,
fullFilename,
minColormap = None,
maxColormap = None,
instName = None,
instDesc = None,
instLat = None,
instLon = None,
instAlt = None,
colorMap = None):
"""__init__ writes a madPcolorWedgeKmlScan to a file.
Inputs:
isprintText - a string giving isprint output without headers. The 9 parameters
are (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2, ). Any
missing data should be written as "missing" or other string that
cannot be converted to a float. gdlatr, gdlonr, galtr are the station location
scanType - must be 'az' or 'el_lat' (for north or south el scans), 'el_lon'
(for east or west el scans) or 'el_gcdist' (for all other el scans.
For az - x is long, y is lat
for el_* y is always alt, and x is either lat, lon, or great circle distance
fullFilename - full path of file containing kml file to be saved. Extension must
be kml, or exception thrown.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
instName, instDesc, instLat, instLon, instAlt - used to create a placemark for the radar. If
instLat or instLon missing, no placemark added.
colorMap - A matplotlib colormap to use to create colors. If None, then blue minimum and red maximum
Returns: void
Affects: Writes fullFilename to disk
"""
# verify input
if scanType not in ('az', 'el_lat', 'el_lon', 'el_gcdist'):
raise ValueError('scanType must be "az", "el_lat", "el_lon", "el_gcdist", not %s' % (str(scanType)))
isprintLines = isprintText.split('\n')
newLonMax, newLonMin, newLatMax, newLatMin, newAltMax, newGreatCDist, newParmMax, newParmMin = self._getLimits(isprintLines)
# get look point
lookLat = (newLatMax + newLatMin)/2.0
lookLon = (newLonMax + newLonMin)/2.0
lookAlt = newAltMax * 10.0
if maxColormap == None:
maxColormap = newParmMax
if minColormap == None:
minColormap = newParmMin
# set up wedge generator
dataGen = self._generateWedges(isprintLines, scanType)
text = self._createKmlText(dataGen, maxColormap, minColormap, lookLat, lookLon, lookAlt, scanType,
instName, instDesc, instLat, instLon, instAlt, colorMap)
f = open(fullFilename, 'w')
f.write(text)
f.close()
def getGreatCircleDist(self, gdlatr, glonr, gdlat, glon):
"""getGreatCircleDist return the great circle distance in. Taken from madDeriveMethods c code.
Inputs: gdlatr, glonr: reference point on earth's surface
gdlat, glon: second point on earth's surface
"""
dlon = glonr/57.2958 - glon/57.2958
dlat = gdlatr/57.2958 - gdlat/57.2958;
a = math.sin(dlat/2.0)*math.sin(dlat/2.0) + math.cos(gdlatr/57.2958)*math.cos(gdlat/57.2958) * math.sin(dlon/2.0)*math.sin(dlon/2.0)
if math.sqrt(a) < 1.0:
c = 2.0 * math.asin(math.sqrt(a))
else:
c = 2.0 * math.asin(1.0)
return(c * 6371.2)
def _getLimits(self, isprintLines):
"""returns (newLonMax, newLonMin, newLatMax, newLatMin, newAltMax, newGreatCDist, newParmMax, newParmMin)
given isprint lines with
parameters (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2, )
We only look at the end points of ranges and the station location
"""
# set initial values to station location
items = isprintLines[1].split()
slat = float(items[0])
slon = float(items[1])
if slon > 180.0:
slon -= 360.0
salt = float(items[2])
maxLon = slon
minLon = slon
maxLat = slat
minLat = slat
maxAlt = salt
maxGreatCDist = 0.0
maxParm = -1.0e-30
minParm = 1.0e30
lastAz = None
lastEl = None
lastRange = None
for i, isprintLine in enumerate(isprintLines):
items = isprintLine.split()
if len(items) < 9:
continue
range = float(items[3])
az1 = float(items[4])
az2 = float(items[5])
thisAz = (az1 + az2)/2
el1 = float(items[6])
el2 = float(items[7])
thisEl = (el1 + el2)/2
try:
parm = float(items[8])
if parm > maxParm:
maxParm = parm
if parm < minParm:
minParm = parm
except ValueError:
continue
# see if we're at an end point
if lastAz != None:
if abs(lastAz - thisAz) > 0.001 or abs(lastEl - thisEl) > 0.001:
# the last point was an end point
gdlat,glon,gdalt = madrigal._derive.radarToGeodetic(slat,slon,salt,lastAz,lastEl,lastRange)
if gdlat > maxLat:
maxLat = gdlat
if gdlat < minLat:
minLat = gdlat
if glon > maxLon:
maxLon = glon
if glon < minLon:
minLon = glon
if gdalt > maxAlt:
maxAlt = gdalt
greatCDist = self.getGreatCircleDist(slat, slon, gdlat, glon)
if greatCDist > maxGreatCDist:
maxGreatCDist = greatCDist
lastAz = thisAz
lastEl = thisEl
lastRange = range
# check last point
gdlat,glon,gdalt = madrigal._derive.radarToGeodetic(slat,slon,slat,lastAz,lastEl,lastRange)
if gdlat > maxLat:
maxLat = gdlat
if gdlat < minLat:
minLat = gdlat
if glon > maxLon:
maxLon = glon
if glon < minLon:
minLon = glon
if gdalt > maxAlt:
maxAlt = gdalt
greatCDist = self.getGreatCircleDist(slat, slon, gdlat, glon)
if greatCDist > maxGreatCDist:
maxGreatCDist = greatCDist
return((maxLon, minLon, maxLat, minLat, maxAlt, maxGreatCDist, maxParm, minParm))
def _generateWedges(self, isprintLines, scanType):
"""_generateWedges is a python generator that return a data wedge in the form
(dataValue, (point1_lat, point1_lon, point1_alt), (point2_lat, point2_lon, point2_alt),
(point3_lat, point3_lon, point3_alt), (point4_lat, point4_lon, point4_alt))
Inputs:
isprintLines - a list of line with 9 parameters that
are (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2, ). Any
missing data should be written as "missing" or other string that
cannot be converted to a float. gdlatr, gdlonr, galtr are the station location.
No wedge is returned for a missing data line
scanType - must be 'az' or 'el_lat' (for north or south el scans), 'el_lon'
(for east or west el scans) or 'el_gcdist' (for all other el scans.
For az - x is long, y is lat
for el_* y is always alt, and x is either lat, lon, or great circle distance
"""
# state variables
lastAz1 = None
lastAz2 = None
lastEl1 = None
lastEl2 = None
lastRange = None
lastDiffRangeLow = None
lastValue = None
for isprintLine in isprintLines:
items = isprintLine.split()
if len(items) < 9:
continue
gdlatr = float(items[0])
gdlonr = float(items[1])
galtr = float(items[2])
range = float(items[3])
az1 = float(items[4])
az2 = float(items[5])
el1 = float(items[6])
el2 = float(items[7])
valueStr = items[8]
if lastAz1 != None and ( abs(lastAz1-az1) > 0.001 or abs(lastEl1-el1) > 0.001 ):
# new radar line found, send last point if needed
if lastValue != None:
dataWedge = self._getWedge(gdlatr, gdlonr, galtr, lastRange,
lastDiffRangeLow, lastDiffRangeLow, lastAz1, lastAz2,
lastEl1, lastEl2, lastValue)
yield(dataWedge)
# this is the first point in the line, so no lastDiffRangeLow
lastDiffRangeLow = None
elif lastAz1 != None and ( abs(lastAz1-az1) < 0.001 and abs(lastEl1-el1) < 0.001 ):
# continuing radar line
if lastValue != None:
lastDiffRangeLow = (range - lastRange)/2.0
dataWedge = self._getWedge(gdlatr, gdlonr, galtr, lastRange,
lastDiffRangeLow, (range-lastRange)/2.0, lastAz1, lastAz2,
lastEl1, lastEl2, lastValue)
yield(dataWedge)
else:
lastDiffRangeLow = (range - lastRange)/2.0
# reset state variables
lastAz1 = az1
lastAz2 = az2
lastEl1 = el1
lastEl2 = el2
lastRange = range
try:
lastValue = float(valueStr)
except:
lastValue = None
# finally, send last wedge if needed
if lastValue != None:
dataWedge = self._getWedge(gdlatr, gdlonr, galtr, lastRange,
lastDiffRangeLow, lastDiffRangeLow, lastAz1, lastAz2,
lastEl1, lastEl2, lastValue)
yield(dataWedge)
def _getWedge(self, gdlatr, gdlonr, galtr, range,
diffRange1, diffRange2, az1, az2,
el1, el2, value):
"""_getWedge returns return a data wedge in the form
(dataValue, (point1_lat, point1_lon, point1_alt), (point2_lat, point2_lon, point2_alt),
(point3_lat, point3_lon, point3_alt), (point4_lat, point4_lon, point4_alt)).
Inputs:
gdlatr, gdlonr, galtr - location of radar
range, diffRange1, diffRange2 - range of center of wedge, and distance in and out from there
az1, az2, el1, el2 - start and end aximuth and elevation. Which is used depends on scanType
value - float data value
"""
point1_lat,point1_lon,point1_alt = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range-diffRange1)
point2_lat,point2_lon,point2_alt = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az2,el2,range-diffRange1)
point3_lat,point3_lon,point3_alt = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az2,el2,range+diffRange2)
point4_lat,point4_lon,point4_alt = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range+diffRange2)
return((value, (point1_lat,point1_lon,point1_alt), (point2_lat,point2_lon,point2_alt),
(point3_lat,point3_lon,point3_alt), (point4_lat,point4_lon,point4_alt)))
def _createKmlText(self, dataGen, maxColormap, minColormap, lookLat, lookLon, lookAlt, scanType,
instName, instDesc, instLat, instLon, instAlt, colorMap):
"""_createKmlText creates kml text
Inputs:
dataGen - list in form (dataValue, (point1_lat, point1_lon, point1_alt), (point2_lat, point2_lon, point2_alt),
(point3_lat, point3_lon, point3_alt), (point4_lat, point4_lon, point4_alt))
maxColormap
minColormap
lookLat
lookLon
lookAlt
scanType
instName, instDesc, instLat, instLon, instAlt - - used to create a placemark for the radar. If
instLat or instLon missing, no placemark added.
colorMap - A matplotlib colormap to use to create colors. If None, then blue minimum and red maximum
"""
# need to fill in id and color
styleTemplate = """
"""
# need to fill in id and coordinates
placemarkTemplate = """
#%s
absolute
%s
"""
instTemplate = """
%s
%s
%f,%f,%f
"""
# need to fill in styles and placemarks
documentTemplate = """
ScanKmlFile
%f
%f
%f
%f
50
%f
relativeToGround
%s
%s
%s
"""
retStr = ''
styleStr = ''
# create 100 colors
for i in range(100):
if not colorMap:
colorStr = self._getKmlRGB(i/100.0)
else:
matLabColorStr = matplotlib.colors.rgb2hex(colorMap(i/100.0)[:3])
red = matLabColorStr[1:3]
green = matLabColorStr[3:5]
blue = matLabColorStr[5:7]
colorStr = 'ff' + blue + green + red # this is the google earth standard for opaque color
styleStr += styleTemplate % (i, colorStr)
placemarkStr = ''
# loop through data adding strings to placemark string
while(True):
try:
p = next(dataGen)
except StopIteration:
break
data = p[0]
if data > maxColormap:
styleIndex = 99
elif data < minColormap:
styleIndex = 0
else:
styleIndex = int(99.0 * ((data-minColormap)/(maxColormap-minColormap)))
styleId = 'p%i' % (styleIndex)
coordStr = ('%f,%f,%f %f,%f,%f %f,%f,%f %f,%f,%f %f,%f,%f' ) % \
(p[1][1], p[1][0], p[1][2]*1000.0,
p[2][1], p[2][0], p[2][2]*1000.0,
p[3][1], p[3][0], p[3][2]*1000.0,
p[4][1], p[4][0], p[4][2]*1000.0,
p[1][1], p[1][0], p[1][2]*1000.0)
placemarkStr += placemarkTemplate % (styleId, coordStr)
if scanType in ('el_lat'):
heading = 90.0
distance = 200.0
elif scanType in ('el_lon', 'el_gcdist'):
heading = 0.0
distance = 200.0
else:
heading = 0.0
distance = 1000.0
# create instStr
instStr = ''
if instLat != None and instLon != None:
if instAlt == None:
instAlt = 0.0
if instName == None:
instName = 'Radar'
if instDesc == None:
instDesc = 'This is the location of the radar that produced this scan.'
instStr += instTemplate % (instName, instDesc, instLon, instLat, instAlt)
retStr += documentTemplate % (lookLon, lookLat, lookAlt*100.0, heading, lookAlt*distance, styleStr, instStr, placemarkStr)
return(retStr)
def _getKmlRGB(self, value, transparent=False, wrap=False):
"""_getKmlRGB returns a Kml color string given a value between 0 qnd 1.
If transparent == False, solid color. Otherwise transpanency set to 50%
If wrap, colors at zero and 1 wrap. Otherwise, extremes red and blue
If invert, use value = 1 - value
"""
if value < 0.0 or value > 1.0:
raise ValueError('value must be between 0 and 1, not %f' % (value))
if wrap:
r,g,b = colorsys.hsv_to_rgb(value, 0.9, 0.9)
else:
r,g,b = colorsys.hsv_to_rgb(value*0.67, 0.9, 0.9)
if not transparent:
transStr = 'ff'
else:
transStr = '80'
retStr = '%s%s%s%s' % (transStr,
hex(int(r*255))[-2:],
hex(int(g*255))[-2:],
hex(int(b*255))[-2:])
return(retStr)
class scanPlotter:
"""scanPlotter is the class that produces a series of scan plots for a single Madrigal file
Non-standard Python modules used:
matplotlib
Change history:
Written by "Bill Rideout":mailto:wrideout@haystack.mit.edu Jul. 26, 2006
"""
def __init__(self, madFile,
madDBObj=None):
"""__init__ initializes a scanPlotter object.
Inputs:
madFile - the Madrigal file to be analyzed. Must contain SCNTYP and CYCN parameters.
madDBObj - a madrigal.metadata.MadrigalDB object. If None (default),
one created
Returns: void
Affects: sets self.madFile, self.madDBObj
"""
if os.access(madFile, os.R_OK):
self.madFile = madFile
else:
raise IOError('unable to read %s' % (str(madFile)))
if madDBObj == None:
self.madDBObj = madrigal.metadata.MadrigalDB()
else:
self.madDBObj = madDBObj
def plotAllScans(self,
scanType,
fullFilenameTemplate,
plotParm,
size = 'small',
xMinimum = None,
xMaximum = None,
yMinimum = None,
yMaximum = None,
xGridSize = None,
yGridSize = None,
minColormap = None,
maxColormap = None,
colorMap = matplotlib.cm.jet,
maxNumLines = None,
filterStr = ''):
"""plotAllScans creates a series of az or el scans.
Inputs:
scanType - must be 'az' or 'el'
fullFilename - full path of file containing pcolor plot to be saved. Each image
created will have _#.png appended, with # starting at 0
plotParm - mnemonic of parameter to be plotted.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
xMinimum = minumum x value. If None (default), uses lowest x value found for each scan.
xMaximum = maximum x value. If None (default), uses highest x value found for each scan.
yMinimum = minumum y value. If None (default), uses lowest y value found for each scan.
yMaximum = maximum y value. If None (default), uses highest y value found for each scan.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
maxNumLine - max number of lines in isprintText before truncating. If None, no truncation
filterStr - a filter string to pass to isprint. Defaults to no filtering
Returns - a list of tuples, where each tuple has two items: 1. time string,
2. metadata string in form for scanTab.txt. List length = number
of plots created
"""
if scanType not in ('az', 'el'):
raise ValueError('scantype must be az or el, not %s' % (str(scanType)))
self.scanType = scanType
retList = []
# handle ion velocity
if plotParm.lower() == 'vo':
cmap = madrigal.ui.madrigalPlot.get_vo_cmap()
else:
cmap = matplotlib.cm.jet
# create isprint string
parms = 'ut1,scntyp,cycn,%s,gdalt,gdlat,glon,azm,elm,gcdist' % (plotParm.strip())
isprintStr = getIsprintString(self.madFile, parms, filterStr)
try:
scanList = self.createScanInfoList(isprintStr)
except:
traceback.print_exc()
return (retList)
# loop through each scan
scanFailureCount = 0 # keep track of how many scans fail
for i in range(len(scanList)):
scanCount = i - scanFailureCount
scanInfo = scanList[i]
try:
startDateStr = self.getDateStrFromUT(scanInfo[1])
except:
# no scans may have been found
continue
endDateStr = self.getTimeStrFromUT(scanInfo[4])
# create title
if scanType == 'el':
if scanInfo[3] > scanInfo[6]:
ind = 'down'
direction = 1
else:
ind = 'up'
direction = 0
titleStr = 'El %s scan (%s) %s-%s, az=%i' % (plotParm,
ind,
startDateStr,
endDateStr,
int(scanInfo[2]))
timeStr = '%s-%s' % (startDateStr, endDateStr)
metaStr = 'El scan: startTime %i el %f az %f stopTime %i el %f az %f dir %i' % (scanInfo[1],
scanInfo[3],
scanInfo[2],
scanInfo[4],
scanInfo[6],
scanInfo[5],
direction)
retList.append((timeStr,metaStr))
if scanInfo[7] == 'Gdlat':
xLabelStr = 'Geodetic latitude'
xGridSize = 1.0
elif scanInfo[7] == 'Glon':
xLabelStr = 'Longitude'
xGridSize = 1.0
elif scanInfo[7] == 'Gcdist':
xLabelStr = 'Ground distance in km'
xGridSize = 100.0
xLabelStr = ''
yLabelStr = 'Altitude (km)'
if xGridSize == None:
xGridSize = 1.0
if yGridSize == None:
yGridSize = 20.0
else:
if scanInfo[2] > scanInfo[5]:
ind = 'ccw'
direction = 1
else:
ind = 'cw'
direction = 0
titleStr = 'Az %s scan (%s) %s-%s, el=%i' % (plotParm,
ind,
startDateStr,
endDateStr,
int(scanInfo[3]))
timeStr = '%s-%s' % (startDateStr, endDateStr)
metaStr = 'Az scan: startTime %i az %f el %f stopTime %i az %f el %f dir %i' % (scanInfo[1],
scanInfo[2],
scanInfo[3],
scanInfo[4],
scanInfo[5],
scanInfo[6],
direction)
retList.append((timeStr,metaStr))
xLabelStr = 'Longitude'
yLabelStr = 'Geodetic latitude'
xGridSize = 1.0
yGridSize = 1.0
name = '%s_%06i.png' % (fullFilenameTemplate, scanCount)
try:
madPcolorScan(scanInfo[0],
xGridSize,
yGridSize,
titleStr,
xLabelStr,
yLabelStr,
name,
minColormap = minColormap,
maxColormap = maxColormap,
colorMap = cmap)
except:
print('Problem creating scan %s' % (str(retList[-1])))
traceback.print_exc()
scanFailureCount += 1
retList.remove(retList[-1])
return (retList)
def plotAllWedgeScans(self,
scanType,
fullFilenameTemplate,
plotParm,
size = 'small',
xMinimum = None,
xMaximum = None,
yMinimum = None,
yMaximum = None,
minColormap = None,
maxColormap = None,
colorMap = matplotlib.cm.jet,
maxNumLines = None,
filterStr = '',
addTitle = '',
includeKml = False,
radarName = None,
radarDesc = None):
"""plotAllWedgeScans creates a series of az or el scans. Similar to plotAllScans, except produces
fancier wedge plots with uniform scalling.
Inputs:
scanType - must be 'az' or 'el'
fullFilename - full path of file containing pcolor plot to be saved. Each image
created will have _#.png appended, with # starting at 0
plotParm - mnemonic of parameter to be plotted.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
xMinimum = minumum x value. If None (default), uses lowest x value found for each scan.
xMaximum = maximum x value. If None (default), uses highest x value found for each scan.
yMinimum = minumum y value. If None (default), uses lowest y value found for each scan.
yMaximum = maximum y value. If None (default), uses highest y value found for each scan.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
maxNumLine - max number of lines in isprintText before truncating. If None, no truncation
filterStr - a filter string to pass to isprint. Defaults to no filtering
addTitle - a string with additional title. If empty string (the default), no additional title string.
includeKml - if True, create a corresponding kml file for every png file. Same names
as png files, except extension = .kml. If False, do not.
radarName - name to label radar placemark if kml generated.
radarDesc - description text for radar placemark if kml generated
Returns - a list of tuples, where each tuple has two items: 1. time string,
2. metadata string in form for scanTab.txt. List length = number
of plots created
"""
if scanType not in ('az', 'el'):
raise ValueError('scantype must be az or el, not %s' % (str(scanType)))
self.scanType = scanType
if scanType == 'az':
wedgeScanType = 'az'
retList = []
# handle ion velocity
if plotParm.lower() == 'vo':
cmap = madrigal.ui.madrigalPlot.get_vo_cmap()
else:
cmap = matplotlib.cm.jet
# create isprint string
parms = 'ut1,scntyp,cycn,%s,gdalt,gdlat,glon,azm,elm,gcdist,gdlatr,gdlonr,galtr,range,az1,az2,el1,el2' % (plotParm.strip())
filterStr += ' filter=%s,, ' % (plotParm.strip())
isprintStr = getIsprintString(self.madFile, parms, filterStr)
try:
scanList = self.createWedgeScanInfoList(isprintStr)
except:
traceback.print_exc()
return (retList)
gdlatr, gdlonr, galtr = self._getInstLocation(isprintStr)
# create limit dictionary used to keep all scans of the same type with the same limits
limitDict = self._getLimits(scanList, xMinimum, xMaximum, yMinimum, yMaximum)
# loop through each scan
scanFailureCount = 0 # keep track of how many scans fail
for i in range(len(scanList)):
scanCount = i - scanFailureCount
scanInfo = scanList[i]
try:
startDateStr = self.getDateStrFromUT(scanInfo[1])
except:
# no scans may have been found
continue
endDateStr = self.getTimeStrFromUT(scanInfo[4])
# create title
if scanType == 'el':
if scanInfo[3] > scanInfo[6]:
ind = 'down'
direction = 1
else:
ind = 'up'
direction = 0
if int(scanInfo[2]) == int(scanInfo[5]):
# no change in az
titleStr = 'El %s scan (%s) %s-%s, az=%i %s' % (plotParm,
ind,
startDateStr,
endDateStr,
int(scanInfo[2]),
addTitle)
else:
# az changed
titleStr = 'El (%i-%i) %s scan %s-%s, az=%i-%i %s' % (int(scanInfo[3]),
int(scanInfo[6]),
plotParm,
startDateStr,
endDateStr,
int(scanInfo[2]),
int(scanInfo[5]),
addTitle)
timeStr = '%s-%s' % (startDateStr, endDateStr)
metaStr = 'El scan: startTime %i el %f az %f stopTime %i el %f az %f dir %i' % (scanInfo[1],
scanInfo[3],
scanInfo[2],
scanInfo[4],
scanInfo[6],
scanInfo[5],
direction)
retList.append((timeStr,metaStr))
if scanInfo[7] == 'Gdlat':
xLabelStr = 'Geodetic latitude'
wedgeScanType = 'el_lat'
elif scanInfo[7] == 'Glon':
xLabelStr = 'Longitude'
wedgeScanType = 'el_lon'
elif scanInfo[7] == 'Gcdist':
xLabelStr = 'Ground distance in km'
wedgeScanType = 'el_gcdist'
xLabelStr = ''
yLabelStr = 'Altitude (km)'
else:
if scanInfo[2] > scanInfo[5]:
ind = 'ccw'
direction = 1
else:
ind = 'cw'
direction = 0
titleStr = 'Az %s scan (%s) %s-%s, el=%i %s' % (plotParm,
ind,
startDateStr,
endDateStr,
int(scanInfo[3]),
addTitle)
timeStr = '%s-%s' % (startDateStr, endDateStr)
metaStr = 'Az scan: startTime %i az %f el %f stopTime %i az %f el %f dir %i' % (scanInfo[1],
scanInfo[2],
scanInfo[3],
scanInfo[4],
scanInfo[5],
scanInfo[6],
direction)
retList.append((timeStr,metaStr))
xLabelStr = 'Longitude'
yLabelStr = 'Geodetic latitude'
name = '%s_%06i.png' % (fullFilenameTemplate, scanCount)
try:
madPcolorWedgeScan(scanInfo[0],
wedgeScanType,
titleStr,
xLabelStr,
yLabelStr,
name,
xMinimum = limitDict[wedgeScanType][0],
xMaximum = limitDict[wedgeScanType][1],
yMinimum = limitDict[wedgeScanType][2],
yMaximum = limitDict[wedgeScanType][3],
minColormap = minColormap,
maxColormap = maxColormap,
colorMap = cmap)
success = True
except:
print('Problem creating scan %s' % (str(retList[-1])))
traceback.print_exc()
scanFailureCount += 1
retList.remove(retList[-1])
success = False
if includeKml and success:
kmlName = name[:-3] + 'kml'
try:
madPcolorWedgeKmlScan(scanInfo[0],
wedgeScanType,
kmlName,
minColormap = minColormap,
maxColormap = maxColormap,
instName = radarName,
instDesc = radarDesc,
instLat = gdlatr,
instLon = gdlonr,
instAlt = galtr,
colorMap = cmap)
except:
print('Problem creating kml scan')
traceback.print_exc()
return (retList)
def getDateStrFromUT(self, ut):
"""getDateStrFromUT returns a date string formated as YYYY-MM-DD HH:MM:SS from a ut time
(seconds since 1/1/1950)
"""
timeList = madrigal._derive.getDateFromUt(float(ut))
return '%04i-%02i-%02i %02i:%02i:%02i' % (timeList[0],
timeList[1],
timeList[2],
timeList[3],
timeList[4],
timeList[5])
def getTimeStrFromUT(self, ut):
"""getTimeStrFromUT returns a time string formated as HH:MM:SS from a ut time
(seconds since 1/1/1950)
"""
timeList = madrigal._derive.getDateFromUt(float(ut))
return '%02i:%02i:%02i' % (timeList[3],
timeList[4],
timeList[5])
def createScanInfoList(self, isprintStr):
"""createScanInfoList creates a list of tuples, which each tuple representing a single scan, and
values of:
(isprintString, startUT, startAz, startEl, endUT, endAz, endEl, type,
minGdlat, maxGdlat,minGlon, maxGdlon,minGdalt, maxGdalt, minGcdist, maxGcdist). Isprint string
has values (x,y,plotParm), where for az scan x=lon, y=lat, and for el scan y=alt, x=lat if starting
az within 15 degrees of north or south, x=lon if starting az within 15 degrees of east or west,
of x=gcdist if other az. Type is Gdlat or Glon or Gcdist for el scans, None for az scans.
Input isprint string has parameters ut1,scntyp,cycn,,gdalt,gdlat,glon,azm,elm,gcdist
Azimuth scans are split whenever direction or elevation changes. For elevation scans, there will be zero or one
north-south scans, zero or one east-west scans, and zero or more off azimuth scans. An off azimuth
scan is not within 15 degrees of north, south, east or west. Off azimuth scans are not combined with
scans 180 degrees in the other direction, because the x axis is ground distance, which does not reverse
sign.
"""
isprintItems = isprintStr.split()
retList = []
# loop variables
thisIsprintStr = []
cycNum = None
startUT = []
startAz = []
startEl = []
endUT = []
endAz = []
endEl = []
elScan = []
minGdlat = []
maxGdlat = []
minGlon = []
maxGlon = []
minGdalt = []
maxGdalt = []
minGcdist = []
maxGcdist = []
thisAzDirection = None # used to detect new azimuth scans
lastAzDirection = None # used to detect new azimuth scans
lastEl = None # used to detect new azimuth scans
thisAz = None
lastUt = None # keep track of new ut
isNewUt = None # true only when ut changes
inAzScan = False
for i in range(len(isprintItems)):
item = isprintItems[i]
mod = i % 10
if mod == 0:
thisUt = float(item)
if lastUt == None:
lastUt = thisUt
elif mod == 1:
thisScntyp = int(item)
elif mod == 2:
thisCycn = int(float(item))
if cycNum == None:
cycNum = thisCycn
elif mod == 3:
thisParm = item
elif mod == 4:
thisGdalt = float(item)
elif mod == 5:
thisGdlat = float(item)
elif mod == 6:
thisGlon = float(item)
elif mod == 7:
if thisAz != None and lastUt < thisUt and inAzScan:
if thisAz > float(item):
thisAzDirection = 'cw'
elif thisAz < float(item):
thisAzDirection = 'ccw'
thisAz = float(item)
elif mod == 8:
thisEl = float(item)
elif mod == 9:
thisGcdist = float(item)
# check whether this is a new time
if lastUt < thisUt:
isNewUt = True
lastUt = thisUt
else:
isNewUt = False
# if new cycle, close out value if data exists
if thisCycn != cycNum:
for i in range(len(thisIsprintStr)):
if len(thisIsprintStr[i]) == 0:
continue
retList.append((thisIsprintStr[i],
startUT[i], startAz[i], startEl[i], endUT[i], endAz[i], endEl[i], elScan[i],
minGdlat[i], maxGdlat[i], minGlon[i], maxGlon[i], minGdalt[i], maxGdalt[i],
minGcdist[i], maxGcdist[i]))
thisIsprintStr = []
cycNum = None
startUT = []
startAz = []
startEl = []
endUT = []
endAz = []
endEl = []
elScan = []
minGdlat = []
maxGdlat = []
minGlon = []
maxGlon = []
minGdalt = []
maxGdalt = []
minGcdist = []
maxGcdist = []
thisAzDirection = None
lastAzDirection = None
lastEl = None
inAzScan = False
# end of line - process it
if self.scanType == 'el' and thisScntyp == 3:
# add this elevation scan point
# check type of el scan
inAzScan = False
if -15.0 <= thisAz <= 15.0 or -165.0 >= thisAz or thisAz >= 165.0:
thisElScan = 'Gdlat'
elif 75.0 <= thisAz <= 105.0 or -105.0 <= thisAz <= -75.0:
thisElScan = 'Glon'
else:
thisElScan = 'Gcdist'
# get index of this scan, None if a new scan
index = None
for i, item in enumerate(elScan):
if item == 'Gdlat' and thisElScan == 'Gdlat':
index = i
break
elif item == 'Glon' and thisElScan == 'Glon':
index = i
break
elif item == 'Gcdist' and thisElScan == 'Gcdist' and int(thisAz) == int(startAz[i]):
index = i
break
if index == None:
# a new scan has been found
thisIsprintStr.append('')
startUT.append(thisUt)
startAz.append(thisAz)
startEl.append(thisEl)
endUT.append(None)
endAz.append(None)
endEl.append(None)
elScan.append(thisElScan)
minGdlat.append(None)
maxGdlat.append(None)
minGlon.append(None)
maxGlon.append(None)
minGdalt.append(None)
maxGdalt.append(None)
minGcdist.append(None)
maxGcdist.append(None)
index = len(thisIsprintStr) - 1
endUT[index] = thisUt
endAz[index] = thisAz
endEl[index] = thisEl
if thisElScan == 'Gdlat':
thisIsprintStr[index] += '%f %f %s\n' % (thisGdlat, thisGdalt, thisParm)
elif thisElScan == 'Glon':
thisIsprintStr[index] += '%f %f %s\n' % (thisGlon, thisGdalt, thisParm)
else:
thisIsprintStr[index] += '%f %f %s\n' % (thisGcdist, thisGdalt, thisParm)
elif self.scanType == 'az' and thisScntyp == 2:
# az scan
inAzScan = True
# add this azimuth scan point
# check if this is a new scan
newAzScan = False
if lastEl == None:
newAzScan = True
# check for elevation change
elif abs(lastEl - int(thisEl)) > 3:
newAzScan = True
# check for direction change
if lastAzDirection != None:
if lastAzDirection != thisAzDirection:
newAzScan = True
thisAzDirection = None
if newAzScan:
# a new azimuth scan has been found
thisIsprintStr.append('')
startUT.append(thisUt)
startAz.append(thisAz)
startEl.append(thisEl)
endUT.append(None)
endAz.append(None)
endEl.append(None)
elScan.append(None)
minGdlat.append(None)
maxGdlat.append(None)
minGlon.append(None)
maxGlon.append(None)
minGdalt.append(None)
maxGdalt.append(None)
minGcdist.append(None)
maxGcdist.append(None)
index = len(thisIsprintStr) - 1
lastEl = int(thisEl)
lastAzDirection = None
else:
# this is at least the second line of the scan
if not thisAzDirection is None:
lastAzDirection = thisAzDirection
endUT[-1] = thisUt
endAz[-1] = thisAz
endEl[-1] = thisEl
thisIsprintStr[-1] += '%f %f %s\n' % (thisGlon, thisGdlat, thisParm)
else:
# not a scan line
inAzScan = False
continue
if minGdlat[index] == None:
minGdlat[index] = thisGdlat
maxGdlat[index] = thisGdlat
minGlon[index] = thisGlon
maxGlon[index] = thisGlon
minGdalt[index] = thisGdalt
maxGdalt[index] = thisGdalt
minGcdist[index] = thisGcdist
maxGcdist[index] = thisGcdist
else:
if minGdlat[index] > thisGdlat:
minGdlat[index] = thisGdlat
if maxGdlat[index] < thisGdlat:
maxGdlat[index] = thisGdlat
if minGlon[index] > thisGlon:
minGlon[index] = thisGlon
if maxGlon[index] < thisGlon:
maxGlon[index] = thisGlon
if minGdalt[index] > thisGdalt:
minGdalt[index] = thisGdalt
if maxGdalt[index] < thisGdalt:
maxGdalt[index] = thisGdalt
if minGcdist[index] > thisGcdist:
minGcdist[index] = thisGcdist
if maxGcdist[index] < thisGcdist:
maxGcdist[index] = thisGcdist
# end, close out last cycle
for i in range(len(thisIsprintStr)):
if len(thisIsprintStr[i]) == 0:
continue
retList.append((thisIsprintStr[i],
startUT[i], startAz[i], startEl[i], endUT[i], endAz[i], endEl[i], elScan[i],
minGdlat[i], maxGdlat[i], minGlon[i], maxGlon[i], minGdalt[i], maxGdalt[i],
minGcdist[i], maxGcdist[i]))
return(retList)
def createWedgeScanInfoList(self, isprintStr):
"""createWedgeScanInfoList creates a list of tuples, which each tuple representing a single scan, and
values of:
(isprintString, startUT, startAz, startEl, endUT, endAz, endEl, type,
minGdlat, maxGdlat,minGlon, maxGdlon,minGdalt, maxGdalt, minGcdist, maxGcdist).
Input isprint string has parameters ut1,scntyp,cycn,,gdalt,gdlat,glon,azm,elm,gcdist,
gdlatr,gdlonr,galtr,range,az1,az2,el1,el2
Azimuth scans are split whenever direction or elevation changes. For elevation scans, there will be zero or more
north-south scans, zero or more east-west scans, and zero or more off azimuth scans. A south to north
elevation scan or a west to east elevation scan is called clockwise, and a switch from clockwise
to counterclockwise will create a new elevation scan. An off azimuth
scan is not within 15 degrees of north, south, east or west. Off azimuth scans are not combined with
scans 180 degrees in the other direction, because the x axis is ground distance, which does not reverse
sign.
"""
isprintItems = isprintStr.split()
retList = []
# loop variables
thisIsprintStr = []
cycNum = None
startUT = []
startAz = []
startEl = []
endUT = []
endAz = []
endEl = []
elScan = []
minGdlat = []
maxGdlat = []
minGlon = []
maxGlon = []
minGdalt = []
maxGdalt = []
minGcdist = []
maxGcdist = []
thisAzDirection = None # used to detect new azimuth scans
lastAzDirection = None # used to detect new azimuth scans
thisElDirection = None # used to detect new elevation scans (cw or ccw)
lastElDirection = None # used to detect new elevation scans (cw or ccw)
lastEl = None # used to detect new azimuth scans
thisAz = None
thisEl = None
lastUt = None # keep track of new ut
isNewUt = None # true only when ut changes
inAzScan = False
inElScan = False
for i in range(len(isprintItems)):
item = isprintItems[i]
mod = i % 18
if mod == 0:
thisUt = float(item)
if lastUt == None:
lastUt = thisUt
elif mod == 1:
thisScntyp = int(item)
elif mod == 2:
thisCycn = int(float(item))
if cycNum == None:
cycNum = thisCycn
elif mod == 3:
thisParm = item
elif mod == 4:
thisGdalt = float(item)
elif mod == 5:
thisGdlat = float(item)
elif mod == 6:
thisGlon = float(item)
elif mod == 7:
if thisAz != None and lastUt < thisUt and inAzScan:
if thisAz > float(item):
thisAzDirection = 'cw'
elif thisAz < float(item):
thisAzDirection = 'ccw'
thisAz = float(item)
elif mod == 8:
if thisEl != None and inElScan:
if abs(float(item) - thisEl) > 1.0:
if thisElScan == 'Gdlat':
if -165.0 >= thisAz or thisAz >= 165.0:
# southern scan
if float(item) > thisEl:
thisElDirection = 'cw'
else:
thisElDirection = 'ccw'
else:
# northern scan
if float(item) < thisEl:
thisElDirection = 'cw'
else:
thisElDirection = 'ccw'
elif thisElScan == 'Glon':
if -105.0 <= thisAz <= -75.0:
# western scan
if float(item) > thisEl:
thisElDirection = 'cw'
else:
thisElDirection = 'ccw'
else:
# eastern scan
if float(item) < thisEl:
thisElDirection = 'cw'
else:
thisElDirection = 'ccw'
elif thisElScan == 'Gcdist':
if float(item) > thisEl:
thisElDirection = 'up'
else:
thisElDirection = 'down'
thisEl = float(item)
elif mod == 9:
thisGcdist = float(item)
elif mod == 10:
thisGdlatr = float(item)
elif mod == 11:
thisGdlonr = float(item)
elif mod == 12:
thisGaltr = float(item)
elif mod == 13:
thisRange = float(item)
elif mod == 14:
thisAz1 = float(item)
elif mod == 15:
thisAz2 = float(item)
elif mod == 16:
thisEl1 = float(item)
elif mod == 17:
thisEl2 = float(item)
# check whether this is a new time
if lastUt < thisUt:
isNewUt = True
lastUt = thisUt
else:
isNewUt = False
# if new cycle or elevation issue, close out value if data exists
if (thisCycn != cycNum) or \
(thisElDirection == 'cw' and lastElDirection == 'ccw') or \
(thisElDirection == 'ccw' and lastElDirection == 'cw') or \
(thisElDirection == 'down' and lastElDirection == 'up') or \
(thisElDirection == 'up' and lastElDirection == 'down') or \
(thisElDirection != None and not inElScan) or \
(thisAzDirection != None and not inAzScan):
for i in range(len(thisIsprintStr)):
if len(thisIsprintStr[i]) == 0:
continue
retList.append((thisIsprintStr[i],
startUT[i], startAz[i], startEl[i], endUT[i], endAz[i], endEl[i], elScan[i],
minGdlat[i], maxGdlat[i], minGlon[i], maxGlon[i], minGdalt[i], maxGdalt[i],
minGcdist[i], maxGcdist[i]))
thisIsprintStr = []
cycNum = None
startUT = []
startAz = []
startEl = []
endUT = []
endAz = []
endEl = []
elScan = []
minGdlat = []
maxGdlat = []
minGlon = []
maxGlon = []
minGdalt = []
maxGdalt = []
minGcdist = []
maxGcdist = []
thisAzDirection = None
lastAzDirection = None
thisElDirection = None # used to detect new elevation scans (cw or ccw)
lastElDirection = None
lastEl = None
inAzScan = False
inElScan = False
# end of line - process it
if self.scanType == 'el' and thisScntyp == 3:
# add this elevation scan point
# check type of el scan
inAzScan = False
inElScan = True
if (-15.0 <= thisAz <= 15.0 or -165.0 >= thisAz or thisAz >= 165.0) and \
abs(thisAz1 - thisAz2) < 0.5:
thisElScan = 'Gdlat'
elif (75.0 <= thisAz <= 105.0 or -105.0 <= thisAz <= -75.0) and \
abs(thisAz1 - thisAz2) < 0.5:
thisElScan = 'Glon'
else:
thisElScan = 'Gcdist'
# get index of this scan, None if a new scan
index = None
for i, item in enumerate(elScan):
if item == 'Gdlat' and thisElScan == 'Gdlat':
index = i
break
elif item == 'Glon' and thisElScan == 'Glon':
index = i
break
elif item == 'Gcdist' and thisElScan == 'Gcdist' and int(thisAz) == int(startAz[i]):
index = i
break
elif item == 'Gcdist' and thisElScan == 'Gcdist' and i == len(elScan)-1:
index = i
break
if index == None:
# a new scan has been found
thisIsprintStr.append('')
startUT.append(thisUt)
startAz.append(thisAz)
startEl.append(thisEl)
endUT.append(None)
endAz.append(None)
endEl.append(None)
elScan.append(thisElScan)
minGdlat.append(None)
maxGdlat.append(None)
minGlon.append(None)
maxGlon.append(None)
minGdalt.append(None)
maxGdalt.append(None)
minGcdist.append(None)
maxGcdist.append(None)
index = len(thisIsprintStr) - 1
lastElDirection = thisElDirection
endUT[index] = thisUt
endAz[index] = thisAz
endEl[index] = thisEl
# gdlatr, gdlonr, galtr, range, az1, az2, el1, el2,plotParm
thisIsprintStr[index] += '%f %f %f %f %f %f %f %f %s\n' % (thisGdlatr, thisGdlonr, thisGaltr,
thisRange, thisAz1, thisAz2,
thisEl1, thisEl2, thisParm)
elif self.scanType == 'az' and thisScntyp == 2:
# az scan
inAzScan = True
inElScan = False
# add this azimuth scan point
# check if this is a new scan
newAzScan = False
if lastEl == None:
newAzScan = True
# check for elevation change
elif abs(lastEl - int(thisEl)) > 3:
newAzScan = True
# check for direction change
if lastAzDirection != None:
if lastAzDirection != thisAzDirection:
newAzScan = True
thisAzDirection = None
if newAzScan:
# a new azimuth scan has been found
thisIsprintStr.append('')
startUT.append(thisUt)
startAz.append(thisAz)
startEl.append(thisEl)
endUT.append(None)
endAz.append(None)
endEl.append(None)
elScan.append(None)
minGdlat.append(None)
maxGdlat.append(None)
minGlon.append(None)
maxGlon.append(None)
minGdalt.append(None)
maxGdalt.append(None)
minGcdist.append(None)
maxGcdist.append(None)
index = len(thisIsprintStr) - 1
lastEl = int(thisEl)
lastAzDirection = None
else:
# this is at least the second line of the scan
if not thisAzDirection is None:
lastAzDirection = thisAzDirection
endUT[-1] = thisUt
endAz[-1] = thisAz
endEl[-1] = thisEl
thisIsprintStr[-1] += '%f %f %f %f %f %f %f %f %s\n' % (thisGdlatr, thisGdlonr, thisGaltr,
thisRange, thisAz1, thisAz2,
thisEl1, thisEl2, thisParm)
else:
# not a scan line
inAzScan = False
inElScan = False
continue
if minGdlat[index] == None:
minGdlat[index] = thisGdlat
maxGdlat[index] = thisGdlat
minGlon[index] = thisGlon
maxGlon[index] = thisGlon
minGdalt[index] = thisGdalt
maxGdalt[index] = thisGdalt
minGcdist[index] = thisGcdist
maxGcdist[index] = thisGcdist
else:
if minGdlat[index] > thisGdlat:
minGdlat[index] = thisGdlat
if maxGdlat[index] < thisGdlat:
maxGdlat[index] = thisGdlat
if minGlon[index] > thisGlon:
minGlon[index] = thisGlon
if maxGlon[index] < thisGlon:
maxGlon[index] = thisGlon
if minGdalt[index] > thisGdalt:
minGdalt[index] = thisGdalt
if maxGdalt[index] < thisGdalt:
maxGdalt[index] = thisGdalt
if minGcdist[index] > thisGcdist:
minGcdist[index] = thisGcdist
if maxGcdist[index] < thisGcdist:
maxGcdist[index] = thisGcdist
# end, close out last cycle
for i in range(len(thisIsprintStr)):
if len(thisIsprintStr[i]) == 0:
continue
retList.append((thisIsprintStr[i],
startUT[i], startAz[i], startEl[i], endUT[i], endAz[i], endEl[i], elScan[i],
minGdlat[i], maxGdlat[i], minGlon[i], maxGlon[i], minGdalt[i], maxGdalt[i],
minGcdist[i], maxGcdist[i]))
return(retList)
def _getLimits(self, scanList, xMinimum, xMaximum, yMinimum, yMaximum):
"""_getLimits returns a dictionary with keys that may include ('az', 'el_lat', 'el_lon', 'el_gcdist'),
though not all will neccessarily be there. Values are overall tuple of
(xMinimum, xMaximum, yMinimum, yMaximum). These values are set by the input
arguments unless it is None, in which case the scanList limits set the values.
Inputs:
scanList: a list of tuples, which each tuple representing a single scan, and
has values of: (isprintString, startUT, startAz, startEl, endUT, endAz, endEl, type,
minGdlat, maxGdlat,minGlon, maxGdlon,minGdalt, maxGdalt, minGcdist, maxGcdist). Isprint string
has values (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2,plotParm),. Type is Gdlat or Glon or
Gcdist for el scans, None for az scans.
xMinimum, xMaximum, yMinimum, yMaximum - as passed into scanPlotter
"""
retDict = {}
xMinAz = None
xMaxAz = None
yMinAz = None
yMaxAz = None
xMinElLat = None
xMaxElLat = None
yMinElLat = None
yMaxElLat = None
xMinElLon = None
xMaxElLon = None
yMinElLon = None
yMaxElLon = None
xMinElGcdist = None
xMaxElGcdist = None
yMinElGcdist = None
yMaxElGcdist = None
minValue = None
maxValue = None
for scanItem in scanList:
if scanItem[7] == None: # az scan
if xMinAz == None:
xMinAz = scanItem[10]
elif xMinAz > scanItem[10]:
xMinAz = scanItem[10]
if xMaxAz == None:
xMaxAz = scanItem[11]
elif xMaxAz < scanItem[11]:
xMaxAz = scanItem[11]
if yMinAz == None:
yMinAz = scanItem[8]
elif yMinAz > scanItem[8]:
yMinAz = scanItem[8]
if yMaxAz == None:
yMaxAz = scanItem[9]
elif yMaxAz < scanItem[9]:
yMaxAz = scanItem[9]
elif scanItem[7] == 'Gdlat': # el_lat scan
if xMinElLat == None:
xMinElLat = scanItem[8]
elif xMinElLat > scanItem[8]:
xMinElLat = scanItem[8]
if xMaxElLat == None:
xMaxElLat = scanItem[9]
elif xMaxElLat < scanItem[9]:
xMaxElLat = scanItem[9]
if yMinElLat == None:
yMinElLat = scanItem[12]
elif yMinElLat > scanItem[12]:
yMinElLat = scanItem[12]
if yMaxElLat == None:
yMaxElLat = scanItem[13]
elif yMaxElLat < scanItem[13]:
yMaxElLat = scanItem[13]
elif scanItem[7] == 'Glon': # el_lon scan
if xMinElLon == None:
xMinElLon = scanItem[10]
elif xMinElLon > scanItem[10]:
xMinElLon = scanItem[10]
if xMaxElLon == None:
xMaxElLon = scanItem[11]
elif xMaxElLon < scanItem[11]:
xMaxElLon = scanItem[11]
if yMinElLon == None:
yMinElLon = scanItem[12]
elif yMinElLon > scanItem[12]:
yMinElLon = scanItem[12]
if yMaxElLon == None:
yMaxElLon = scanItem[13]
elif yMaxElLon < scanItem[13]:
yMaxElLon = scanItem[13]
elif scanItem[7] == 'Gcdist': # el_gcdist scan
if xMinElGcdist == None:
xMinElGcdist = scanItem[14]
elif xMinElGcdist > scanItem[14]:
xMinElGcdist = scanItem[14]
if xMaxElGcdist == None:
xMaxElGcdist = scanItem[15]
elif xMaxElGcdist < scanItem[15]:
xMaxElGcdist = scanItem[15]
if yMinElGcdist == None:
yMinElGcdist = scanItem[12]
elif yMinElGcdist > scanItem[12]:
yMinElGcdist = scanItem[12]
if yMaxElGcdist == None:
yMaxElGcdist = scanItem[13]
elif yMaxElGcdist < scanItem[13]:
yMaxElGcdist = scanItem[13]
# fill out the dictionary
if xMinAz != None:
if xMinimum == None:
xMin = xMinAz
else:
xMin = xMinimum
if xMaximum == None:
xMax = xMaxAz
else:
xMax = xMaximum
if yMinimum == None:
yMin = yMinAz
else:
yMin = yMinimum
if yMaximum == None:
yMax = yMaxAz
else:
yMax = yMaximum
retDict['az'] = (xMin, xMax, yMin, yMax)
if xMinElLat != None:
if xMinimum == None:
xMin = xMinElLat
else:
xMin = xMinimum
if xMaximum == None:
xMax = xMaxElLat
else:
xMax = xMaximum
if yMinimum == None:
yMin = yMinElLat
else:
yMin = yMinimum
if yMaximum == None:
yMax = yMaxElLat
else:
yMax = yMaximum
retDict['el_lat'] = (xMin, xMax, yMin, yMax)
if xMinElLon != None:
if xMinimum == None:
xMin = xMinElLon
else:
xMin = xMinimum
if xMaximum == None:
xMax = xMaxElLon
else:
xMax = xMaximum
if yMinimum == None:
yMin = yMinElLon
else:
yMin = yMinimum
if yMaximum == None:
yMax = yMaxElLon
else:
yMax = yMaximum
retDict['el_lon'] = (xMin, xMax, yMin, yMax)
if xMinElGcdist != None:
if xMinimum == None:
xMin = xMinElGcdist
else:
xMin = xMinimum
if xMaximum == None:
xMax = xMaxElGcdist
else:
xMax = xMaximum
if yMinimum == None:
yMin = yMinElGcdist
else:
yMin = yMinimum
if yMaximum == None:
yMax = yMaxElGcdist
else:
yMax = yMaximum
retDict['el_gcdist'] = (xMin, xMax, yMin, yMax)
return(retDict)
def _getInstLocation(self, isprintStr):
"""_getInstLocation returns a tuple of instrument location (gdlatr, gdlonr, galtr)
Input isprint string has parameters ut1,scntyp,cycn,,gdalt,gdlat,glon,azm,elm,gcdist,
gdlatr,gdlonr,galtr,range,az1,az2,el1,el2
"""
isprintItems = isprintStr.split()
gdlatr = float(isprintItems[10])
gdlonr = float(isprintItems[11])
if gdlonr > 180.0:
gdlonr -= 360.0
galtr = float(isprintItems[12])
return((gdlatr, gdlonr, galtr))
class madHistogram:
"""madHistogram is the class that produces Histogram plots of Madrigal
Data.
Usage example::
obj = madHistogram(isprintText,
'Nel (log(m^-3)) - Millstone Hill - Oct. 30, 2005 - Alt. code',
'Hours since midnight UT Oct. 30,2003'
'Altitude',
'./isprint.png'
size = 'large')
Inputs:
isprintText - a string giving isprint output without headers. Any missing data should
be written as 'missing' or other string that cannot be converted into
a float. Only one column.
TitleStr - plot title(string) - should describe the plot being made
xLabelStr - Label for x axis
fullFilename - full path of file containing histogram plot to be saved. Extension must be .jpeg
or .png, or exception thrown
size - size of plot to be saved. Must be 'small','wide', or 'large'. Defaults to small
maxNumPoints - maximum number of points to be considered. Defaults to None, so all points in string shown
numBins - number of bins to be used to store the data. More bins means closer resolution in Histogram.
Default is 30
orientation - whether histogram shows horizontal or vertical. Must be written as 'horizontal' or
'vertical', or exception thrown--defaults to 'vertical'
isNorm - whether histogram should be normalized or not. Default is 0, or False
isBottom - If true, sets the lowest passed value as zero
sdevs - number of standard deviations to take into account. When 0 is passed to it, this option is ignored and all data
is included in the distribution; otherwise, the range of values accepted is sdevs*(standard deviation of sample)
Outputs:
A .png file is written to the fullFilename path given, resulting in a Histogram drawn by matplotlib
Change History:
Written by "Brandon Scott Fines":mailto:bfines@haystack.mit.edu Aug. 1,2006
Written by "Bill Rideout":mailto:brideout@haystack.mit.edu Aug. 1,2006
"""
def __init__(self,isprintText,
titleStr,
xLabelStr,
fullFilename,
size = 'small',
maxNumPoints = None,
numBins = 30,
Orientation='vertical',
isNorm =0,
isBottom=0,
sdevs=0):
"""__init__ writes a madHistogram string to file
"""
self.__missing = 1.0E30 #special value, since numpy doesn't Handle NaN
self.__parameter_count = 2
#verify input
if not size in ('small','wide','large'):
raise ValueError('size must be "small","wide" or "large", not %s'%(str(size)))
if size in ('small','wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
# remove all non-float values
items = isprintText.split()
keptItems = []
for item in items:
try:
float(item)
keptItems.append(item)
except:
continue
delimiter = ' '
isprintText = delimiter.join(keptItems)
if maxNumPoints !=None:
isprintText = self._truncateIsprint(isprintText, maxNumPoints)
#convert the input data into numeric arrays of floats assuming no headers and filter the missing values
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing,split_data))
array_data = numpy.asarray(float_data)
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
#if needed, throw away outliers
if sdevs !=0:
array_data = self.removeOutliers(sdevs,array_data)
#select the plot size
if size=='small':
matplotlib.pylab.figure(1,figsize=(6,4), facecolor = 'w')
elif size=='wide':
matplotlib.pylab.figure(1,figsize=(10,4),facecolor='w')
elif size=='large':
matplotlib.pylab.figure(1,figsize=(12,6),facecolor='w')
matplotlib.pylab.hist(array_data,bins = numBins,normed=isNorm,bottom=isBottom,orientation = Orientation)
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) #pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.title(titleStr,fontsize=fontSize)
#self.__figure = matplotlib.pylab.gcf()
matplotlib.pylab.savefig(fullFilename)
matplotlib.pylab.clf()
def __truncateIsprintStr(self,isprintText, maxLines):
"""__truncateIsprintStr truncates isprintText to have maxLines at most.
"""
isprintList = isprintText.split('\n')
if len(isprintList)=ndist) and (i<=pdist):
retArray.append(i)
return retArray
#end eliminateOutliers()
class madIsrRecordSummary:
"""madIsrRecordSummary is the class that produces a summary plot of a single ISR record.
"""
def __init__(self, TiData,
TiError,
TeData,
TeError,
VoData,
VoError,
NelData,
NelError,
isPopl,
description,
fullFilename,
useRange = False,
altResl = None):
"""__init__ writes a madIsrRecordSummary to a file.
Inputs:
TiData - an Nx2 numeric array of Ti (col 0) and altitude in km (col 1)
TiError - an N length array of error in Ti
TeData - an Nx2 numeric array of Te (col 0) and altitude in km (col 1)
TeError - an N length array of error in Te
VoData - an Nx2 numeric array of Vo (col 0) and altitude in km (col 1)
VoError - an N length array of error in Vo
NelData - an Nx2 numeric array of (Popl or Nel in m^-3) (col 0) and altitude in km (col 1)
NelError - an 2xN length array of lower, upper error in Popl or Nel
isPopl - true if Nel contains Popl data, false if Nel data
description - text to put in fourth panel of plot - use carriage returns to separate lines
fullFilename - full filename to save output png file.
useRange - if False (the default), y axis = Altitude. If True, y axis = Range.
altResl - altitude resolution in km. If not None, show altitude resolution on each graph.
Returns: void
Affects: None
"""
if useRange:
yAxisStr = 'Range (km)'
else:
yAxisStr = 'Altitude (km)'
# upper left - Ti and Te
fig = matplotlib.pylab.figure(figsize=(10,10))
axis = matplotlib.pylab.subplot(2,2,1)
if TiData.shape[0] > 0 and TeData.shape[0] > 0:
axis.errorbar(TiData[:,0], TiData[:,1], xerr=TiError, fmt='bo-')
axis.errorbar(TeData[:,0], TeData[:,1], xerr=TeError, fmt='rD-')
matplotlib.pylab.text(0.65, 0.1, 'Ti: blue circle\nTe: red diamond',
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=10)
matplotlib.pylab.text(0.5,0.5, 'Preliminary data\n\n\nfrom the\n\n\nMadrigal database',
horizontalalignment='center',
verticalalignment='center',
fontsize=18,
transform = axis.transAxes, alpha=0.2)
matplotlib.pylab.xlabel('Temperature (K)')
matplotlib.pylab.ylabel(yAxisStr)
matplotlib.pylab.grid(True)
# set limits based on data only
tiMax = numpy.nanmax(TiData[:,0])
tiMin = numpy.nanmin(TiData[:,0])
teMax = numpy.nanmax(TeData[:,0])
teMin = numpy.nanmin(TeData[:,0])
lowerLimit = (int(min(tiMin, teMin)) / 1000) * 1000
upperLimit = (1+int(max(tiMax, teMax)) / 1000) * 1000
# alt resolution
if altResl != None:
altLabel = 'Alt res: \n%i km' % (int(altResl))
y_min, y_max = matplotlib.pylab.ylim()
startX = lowerLimit + 0.8*(upperLimit - lowerLimit)
startY = 0.15
reslPercentage = altResl/(y_max-y_min)
matplotlib.pylab.axvline(x=startX,
ymin=startY,
ymax=reslPercentage + startY,
linewidth=2, color='b')
matplotlib.pylab.text(0.83, 0.2, altLabel,
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=10)
matplotlib.pylab.xlim(lowerLimit, upperLimit)
# upper right - Vo and 10*Vo
axis = matplotlib.pylab.subplot(2,2,2)
if VoData.shape[0] > 0:
axis.errorbar(VoData[:,0], VoData[:,1], xerr=VoError, fmt='bo-')
axis.errorbar(10.0*VoData[:,0], VoData[:,1], xerr=10.0*VoError, fmt='rD-')
# set x limits to be even around 0
voMax = numpy.nanmax(10.0*VoData[:,0])
voMin = numpy.nanmin(10.0*VoData[:,0])
limit = max(abs(voMax), abs(voMin))
matplotlib.pylab.xlim(-1.0*limit, limit)
matplotlib.pylab.text(0.58, 0.1, 'Vo: blue circle\n10*Vo: red diamond',
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=10)
matplotlib.pylab.text(0.5,0.5, 'Preliminary data\n\n\nfrom the\n\n\nMadrigal database',
horizontalalignment='center',
verticalalignment='center',
fontsize=18,
transform = axis.transAxes, alpha=0.2)
matplotlib.pylab.xlabel('Line of sight drift (m/s)')
matplotlib.pylab.ylabel(yAxisStr)
matplotlib.pylab.grid(True)
# alt resolution
if altResl != None:
altLabel = 'Alt res: \n%i km' % (int(altResl))
y_min, y_max = matplotlib.pylab.ylim()
startX = limit*0.6
startY = 0.15
reslPercentage = altResl/(y_max-y_min)
matplotlib.pylab.axvline(x=startX,
ymin=startY,
ymax=reslPercentage + startY,
linewidth=2, color='b')
matplotlib.pylab.text(0.83, 0.2, altLabel,
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=10)
matplotlib.pylab.xlim(-1.0*limit, limit)
# lower left - Nel or Popl
axis = matplotlib.pylab.subplot(2,2,3)
if isPopl:
labelStr = 'Log Power (m^-3)'
else:
labelStr = 'Log Electron density (m^-3)'
if NelData.shape[0] > 0:
# check that not every point equal
if numpy.nanmax(NelData[:,0]) == numpy.nanmin(NelData[:,0]):
pass
else:
axis.errorbar(NelData[:,0], NelData[:,1], xerr=NelError, fmt='bo-')
matplotlib.pylab.xlabel(labelStr)
matplotlib.pylab.ylabel(yAxisStr)
matplotlib.pylab.grid(True)
nelMax = numpy.nanmax(NelData[:,0])
nelMin = numpy.nanmin(NelData[:,0])
matplotlib.pylab.xlim(math.floor(nelMin), math.ceil(nelMax))
matplotlib.pylab.text(0.5,0.5, 'Preliminary data\n\n\nfrom the\n\n\nMadrigal database',
horizontalalignment='center',
verticalalignment='center',
fontsize=18,
transform = axis.transAxes, alpha=0.2)
# alt resolution
if altResl != None:
altLabel = 'Alt res: \n%i km' % (int(altResl))
y_min, y_max = matplotlib.pylab.ylim()
x_min, x_max = matplotlib.pylab.xlim()
startX = x_min + 0.8*(x_max-x_min)
startY = 0.15
reslPercentage = altResl/(y_max-y_min)
matplotlib.pylab.axvline(x=startX,
ymin=startY,
ymax=reslPercentage + startY,
linewidth=2, color='b')
matplotlib.pylab.text(0.83, 0.2, altLabel,
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=10)
matplotlib.pylab.xlim(math.floor(nelMin), math.ceil(nelMax))
# lower right - text
axis = matplotlib.pylab.subplot(2,2,4)
matplotlib.pylab.text(0.05, 0.9, description,
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=9)
matplotlib.pylab.xticks([])
matplotlib.pylab.yticks([])
matplotlib.pylab.subplots_adjust(wspace = 0.25)
matplotlib.pylab.savefig(fullFilename)
#matplotlib.pylab.show()
matplotlib.pylab.clf()
matplotlib.pylab.close()
if __name__ == '__main__':
import time
# test madPcolorScan
# az scan
f = open('testFiles/isprint_az_scan.txt')
isprintText = f.read()
f.close()
print('creating az scan at /tmp/isprint_az_scan.png')
obj = madPcolorScan(isprintText,
1.0, 1.0,
'Az scan - NEL 2006-06-26 13:49:43-14:07:36',
'Longitude',
'Latitude',
'/tmp/isprint_az_scan.png',
'small',
-100,-50,40,60,
9,12)
# el scan
f = open('testFiles/isprint_el_scan.txt')
isprintText = f.read()
f.close()
print('creating el scan at /tmp/isprint_el_scan.png')
obj = madPcolorScan(isprintText,
1.0, 40.0,
'El scan - NEL 2006-06-26 13:49:43-14:07:36',
'Latitude',
'Altitude (km)',
'/tmp/isprint_el_scan.png',
'small')
# test scanPlotter
print('creating az scan series using mlh060622a.000')
scanObj = scanPlotter('/home/grail/brideout/madroot/experiments/2006/mlh/22jun06/mlh060622a.000')
scanObj.plotAllScans('az','/tmp/junkAz', 'Nel', filterStr='filter=nel,,11.0')
t = time.time()
# test madIsrRecordSummary
TiData = numpy.array([[800, 150],[900, 200],[910, 300], [945, 500], [980, 600]])
TiError = numpy.array([50,100,50,100,50])
TeData = numpy.array([[850, 150],[1000, 200],[1800, 300], [2000, 500], [2000, 600]])
TeError = numpy.array([50,100,50,100,50])
VoData = numpy.array([[-30, 150],[0, 200],[10, 300], [35, 500], [10, 600]])
VoError = numpy.array([5,10,5,10,5])
NelData = numpy.array([[11, 150],[11.5, 200],[11.3, 300], [11, 500], [10.5, 600]])
NelError = numpy.array(((2.0,0.5),(2.0,1.0), (2.0,0.5), (2.0,1.0), (2.0,0.5)))
isPopl = True
text = 'Kinst = Millstone Zenith Antenna\nMore stuff here\nand more\nand more'
fullFilename = '/tmp/junk.png'
madIsrRecordSummary(TiData,TiError,TeData,TeError,VoData,VoError,NelData,numpy.transpose(NelError),isPopl,text,fullFilename)
f = open('testFiles/isprint_uth_fof2.txt')
isprintText = f.read()
f.close()
print('creating scatterplot at /tmp/isprint_fof2.png')
obj = madScatterPlot(isprintText,
'Fof2 - Millstone Hill - Oct. 30, 2003',
'Hours since midnight UT Oct. 30, 2003',
'Fof2',
'/tmp/isprint_fof2.png',
size = 'large',
useAbsoluteTime = False)
f = open('testFiles/isprint_nel.txt')
isprintText = f.read()
f.close()
print('creating pcolor plot at /tmp/isprint_nel.png')
obj = madPcolorPlot(isprintText,
'Nel (log(m^-3)) - Millstone Hill - Oct. 30, 2003 - Alt code',
'Hours since midnight UT Oct. 30, 2003',
'Altitude (km)',
'/tmp/isprint_nel.png',
size = 'large',
minColormap = 9,
maxColormap = 12,
smoothAltitude = True,
insertDataGap = 5,
useAbsoluteTime = False,
startTime = None,
endTime = None,
sortTimeFlag = False,
maxNumTimes = None,
maxNumAlt = 50,
truncateIsprint = True,
colorMap = matplotlib.cm.spring,
altYTitle = 'Latitude',
altYLabels = ('40', '41', '42', '43', '44', '45'))
print('took %i secs' % (time.time() - t))
Functions
def convertToAbsoluteTimeStr(
xticks, noTime=False, noDate=False, timezone=0)
convertToAbsoluteTimeStr converts a list of strings containing seconds since 1/1/1950 to datetime string.
Input: xticks - a list of strings containing seconds since 1/1/1950
Returns: a list of strings formated as YYYY-MM-DD HH-MM-SS. If noTime, format as YYYY-MM-DD
def convertToAbsoluteTimeStr(xticks, noTime=False, noDate=False, timezone=0):
"""convertToAbsoluteTimeStr converts a list of strings containing seconds since 1/1/1950 to datetime string.
Input: xticks - a list of strings containing seconds since 1/1/1950
Returns: a list of strings formated as YYYY-MM-DD HH-MM-SS. If noTime, format as YYYY-MM-DD
"""
datetime1950 = datetime.datetime(1950,1,1,0,0,0)
newList = []
seconds = int(xticks[0]) - timezone
iniDoy = datetime1950 + datetime.timedelta(0, seconds)
iniDoy = int(iniDoy.strftime('%j'))
for item in xticks:
seconds = int(item) - timezone
newDatetime = datetime1950 + datetime.timedelta(0, seconds)
if noTime:
newList.append(newDatetime.strftime('%Y-%m-%d'))
elif noDate:
curDoy = int(newDatetime.strftime('%j'))
currHour = newDatetime.hour
currMin = newDatetime.minute
currHour += (curDoy - iniDoy)*24
newList.append('%02d:%02d' %(currHour,currMin))
else:
newList.append(newDatetime.strftime('%Y-%m-%d %H:%M:%S'))
return newList
def defineHomeEnvVariable(
)
defineHomeEnvVariable makes sure HOME env variable is defined, as required by matplotlib.
If not defined, sets HOME to MADROOT/metadata/userdata
def defineHomeEnvVariable():
"""defineHomeEnvVariable makes sure HOME env variable is defined, as required by matplotlib.
If not defined, sets HOME to MADROOT/metadata/userdata
"""
try:
os.environ['HOME']
return
except KeyError:
import madrigal.metadata
madDB = madrigal.metadata.MadrigalDB()
os.environ['HOME'] = os.path.join(madDB.getMadroot(), 'metadata/userdata')
def getIsprintString(
filename, parms, filterStr)
getIsprintString returns the output of isprint given inputs
Inputs: filename - Madrigal filename parms - comma-delimited parameters filterStr - arguments to pass to isprint cmd
def getIsprintString(filename, parms, filterStr):
"""getIsprintString returns the output of isprint given inputs
Inputs:
filename - Madrigal filename
parms - comma-delimited parameters
filterStr - arguments to pass to isprint cmd
"""
madDB = madrigal.metadata.MadrigalDB()
cmd = '%s/bin/isprint file=%s ' % (madDB.getMadroot(), filename)
for parm in parms.split(','):
cmd += '%s ' % (parm)
cmd += filterStr
cmd += ' header=f summary=f '
pipe = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE).stdout
result = pipe.read()
delimiter = '\n'
if type(result) in (bytes, numpy.bytes_):
result = result.decode('utf-8')
lines = result.split(delimiter)
return(delimiter.join(lines[1:]).strip())
def get_vo_cmap(
)
get_vo_cmap is a function to return a colormap optimized to show sign changes in the middle of the range.
def get_vo_cmap():
"""get_vo_cmap is a function to return a colormap optimized to show sign changes in the middle of the range.
"""
LUTSIZE = matplotlib.rcParams['image.lut']
_vo_cm_data = {'red': ((0, 0.75, 0.75), (0.4, 0.0, 0.0), (0.5, 0.0, 0.0), (0.6, 1.0, 1.0), (1.0, 1.0, 1.0)),
'green': ((0, 1.0, 1.0), (0.4, 0.5, 0.5), (0.5, 0.25, 0.25), (0.6, 0.25, 0.25), (1.0, 1.0, 1.0)),
'blue': ((0, 1.0, 1.0), (0.4, 1.0, 1.0), (0.5, 0.5, 0.5), (0.6, 0.0, 0.0), (1.0, 0.5, 0.5))}
vo_cm = matplotlib.colors.LinearSegmentedColormap('vo_cm',_vo_cm_data, LUTSIZE)
return vo_cm
Classes
class mad2YTimePlot
mad2YTimePlot is the class the produces line plots of two parameters with different Y axes versus time.
class mad2YTimePlot:
"""mad2YTimePlot is the class the produces line plots of two parameters with different Y axes versus time.
"""
def __init__(self, isprintText,
yParmList,
titleStr,
xLabelStr,
fullFilename,
size = 'small',
useAbsoluteTime = False,
startTime = None,
endTime = None,
maxNumPoints = None,
noTime = False,
yMinimum = None,
yMaximum = None,
noDate = False,
timezone = 0):
"""__init__ writes a madLineTimePlot plot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The following must be two parameters to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
yParmList - a list of two y parameters (strings)
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
maxNumPoints - maximum number of points to plot. If not None, truncate isprintText if
needed to have at most maxNumPoints lines.
noTime - if True and useAbsoluteTime True, then dates without times printed on x axis.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
yMinimum - set y minimum of first parm. If default=None, use data minimum
yMaximum - set y maximum of first. If default=None, use data maximum
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
Returns: void
Affects: None
"""
self.__missing = 1.0E30 # special value, since numpy doesn't handle NaN
self.__parameter_count = 1 + len(yParmList)
self.__colorList = 'gbrcmykw' # a list of colors for the lines
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
if maxNumPoints != None:
isprintText = self.__truncateIsprint(isprintText, maxNumPoints)
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
threshold = self.__missing*0.9999
array_data = numpy.ma.masked_where(array_data > threshold, array_data)
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
# find min, max of x (time)
if startTime == None:
xMin = array_data[0,0]
else:
xMin = startTime
if endTime == None:
xMax = array_data[-1,0]
else:
xMax = endTime
# find yMin just to ensure there is data
yMin = None
for column in range(len(yParmList)):
for y in array_data[:,1+column]:
if y == self.__missing:
continue
if yMin == None:
yMin = y
elif yMin > y:
yMin = y
if yMin == None:
raise ValueError('No valid y data found')
fig, ax1 = matplotlib.pylab.subplots()
# select the plot size
if size == 'small':
fig.set_size_inches(8, 3)
elif size == 'wide':
fig.set_size_inches(10,4)
elif size == 'large':
fig.set_size_inches(12,6)
if useAbsoluteTime:
# leave room for rotated datetime string
if size == 'large':
matplotlib.pylab.axes([0.1, 0.2, 0.8, 0.7])
elif size == 'wide':
matplotlib.pylab.axes([0.07, 0.2, 0.9, 0.7])
elif size == 'small':
matplotlib.pylab.axes([0.1, 0.18, 0.87, 0.7])
else:
if size == 'large':
matplotlib.pylab.axes([0.1, 0.15, 0.71, 0.73])
# plot first
color = self.__colorList[0]
ax1.plot(array_data[:,0],array_data[:,1], '%so-' % (color))
ax1.set_ylabel(yParmList[0], color=color)
ax2 = ax1.twinx()
color = self.__colorList[1]
ax2.plot(array_data[:,0],array_data[:,2], '%so-' % (color))
ax2.set_ylabel(yParmList[1], color=color)
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.xlim(xMin, xMax)
matplotlib.pylab.grid(True)
if useAbsoluteTime:
# if plot is less than 24 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
xmin = xmin / (60*60)
xmax = xmax / (60*60)
if noDate == True:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
step = int((xmax-xmin-1)/6)+1
xRange = list(range(0,int(newXmax-xmin),step))
for i in xRange:
tmp = (xmin + i)*60*60
newXLocs.append(tmp)
if (xmax - xmin) < 1: newXLocs.append(xmax*60*60)
newXLabels = convertToAbsoluteTimeStr(newXLocs, noDate=noDate, timezone=timezone)
matplotlib.pylab.xticks(newXLocs, newXLabels)
else:
locs, labels = matplotlib.pylab.xticks()
if len(locs) > 5:
# truncate by len(locs) / 5
scale = 1 + int(len(locs) / 5)
new_locs = []
for i in range(len(locs)):
if i % scale == 0:
new_locs.append(locs[i])
locs = new_locs
newXTicks = convertToAbsoluteTimeStr(locs, noTime)
matplotlib.pylab.xticks(locs, newXTicks, rotation=15)
else:
# if plot is less than 48 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
if xmax < 49:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
for i in range(0,int(4+newXmax),4):
newXLocs.append(i)
ax1.set_xticks(newXLocs)
ax2.set_xticks(newXLocs)
matplotlib.pylab.xticks(fontsize=fontSize)
if yMinimum != None or yMaximum != None:
ax1.set_ylim(yMinimum, yMaximum)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
matplotlib.pylab.savefig(fullFilename)
matplotlib.pylab.clf()
def __truncateIsprint(self, isprintText, maxLines):
"""__truncateIsprint truncates isprintText to have maxLines at most.
"""
isprintList = isprintText.split('\n')
if len(isprintList) < maxLines:
return isprintText
else:
dropNumber = int(1 + len(isprintList)/maxLines)
newline = '\n'
newIsprintText = newline.join(isprintList[::dropNumber])
return newIsprintText
def __filter_missing(self,x):
try:
return float(x)
except:
return self.__missing
Ancestors (in MRO)
- mad2YTimePlot
- builtins.object
Static methods
def __init__(
self, isprintText, yParmList, titleStr, xLabelStr, fullFilename, size='small', useAbsoluteTime=False, startTime=None, endTime=None, maxNumPoints=None, noTime=False, yMinimum=None, yMaximum=None, noDate=False, timezone=0)
init writes a madLineTimePlot plot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The following must be two parameters to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
yParmList - a list of two y parameters (strings)
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
maxNumPoints - maximum number of points to plot. If not None, truncate isprintText if
needed to have at most maxNumPoints lines.
noTime - if True and useAbsoluteTime True, then dates without times printed on x axis.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
yMinimum - set y minimum of first parm. If default=None, use data minimum
yMaximum - set y maximum of first. If default=None, use data maximum
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
Returns: void
Affects: None
def __init__(self, isprintText,
yParmList,
titleStr,
xLabelStr,
fullFilename,
size = 'small',
useAbsoluteTime = False,
startTime = None,
endTime = None,
maxNumPoints = None,
noTime = False,
yMinimum = None,
yMaximum = None,
noDate = False,
timezone = 0):
"""__init__ writes a madLineTimePlot plot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The following must be two parameters to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
yParmList - a list of two y parameters (strings)
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
maxNumPoints - maximum number of points to plot. If not None, truncate isprintText if
needed to have at most maxNumPoints lines.
noTime - if True and useAbsoluteTime True, then dates without times printed on x axis.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
yMinimum - set y minimum of first parm. If default=None, use data minimum
yMaximum - set y maximum of first. If default=None, use data maximum
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
Returns: void
Affects: None
"""
self.__missing = 1.0E30 # special value, since numpy doesn't handle NaN
self.__parameter_count = 1 + len(yParmList)
self.__colorList = 'gbrcmykw' # a list of colors for the lines
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
if maxNumPoints != None:
isprintText = self.__truncateIsprint(isprintText, maxNumPoints)
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
threshold = self.__missing*0.9999
array_data = numpy.ma.masked_where(array_data > threshold, array_data)
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
# find min, max of x (time)
if startTime == None:
xMin = array_data[0,0]
else:
xMin = startTime
if endTime == None:
xMax = array_data[-1,0]
else:
xMax = endTime
# find yMin just to ensure there is data
yMin = None
for column in range(len(yParmList)):
for y in array_data[:,1+column]:
if y == self.__missing:
continue
if yMin == None:
yMin = y
elif yMin > y:
yMin = y
if yMin == None:
raise ValueError('No valid y data found')
fig, ax1 = matplotlib.pylab.subplots()
# select the plot size
if size == 'small':
fig.set_size_inches(8, 3)
elif size == 'wide':
fig.set_size_inches(10,4)
elif size == 'large':
fig.set_size_inches(12,6)
if useAbsoluteTime:
# leave room for rotated datetime string
if size == 'large':
matplotlib.pylab.axes([0.1, 0.2, 0.8, 0.7])
elif size == 'wide':
matplotlib.pylab.axes([0.07, 0.2, 0.9, 0.7])
elif size == 'small':
matplotlib.pylab.axes([0.1, 0.18, 0.87, 0.7])
else:
if size == 'large':
matplotlib.pylab.axes([0.1, 0.15, 0.71, 0.73])
# plot first
color = self.__colorList[0]
ax1.plot(array_data[:,0],array_data[:,1], '%so-' % (color))
ax1.set_ylabel(yParmList[0], color=color)
ax2 = ax1.twinx()
color = self.__colorList[1]
ax2.plot(array_data[:,0],array_data[:,2], '%so-' % (color))
ax2.set_ylabel(yParmList[1], color=color)
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.xlim(xMin, xMax)
matplotlib.pylab.grid(True)
if useAbsoluteTime:
# if plot is less than 24 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
xmin = xmin / (60*60)
xmax = xmax / (60*60)
if noDate == True:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
step = int((xmax-xmin-1)/6)+1
xRange = list(range(0,int(newXmax-xmin),step))
for i in xRange:
tmp = (xmin + i)*60*60
newXLocs.append(tmp)
if (xmax - xmin) < 1: newXLocs.append(xmax*60*60)
newXLabels = convertToAbsoluteTimeStr(newXLocs, noDate=noDate, timezone=timezone)
matplotlib.pylab.xticks(newXLocs, newXLabels)
else:
locs, labels = matplotlib.pylab.xticks()
if len(locs) > 5:
# truncate by len(locs) / 5
scale = 1 + int(len(locs) / 5)
new_locs = []
for i in range(len(locs)):
if i % scale == 0:
new_locs.append(locs[i])
locs = new_locs
newXTicks = convertToAbsoluteTimeStr(locs, noTime)
matplotlib.pylab.xticks(locs, newXTicks, rotation=15)
else:
# if plot is less than 48 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
if xmax < 49:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
for i in range(0,int(4+newXmax),4):
newXLocs.append(i)
ax1.set_xticks(newXLocs)
ax2.set_xticks(newXLocs)
matplotlib.pylab.xticks(fontsize=fontSize)
if yMinimum != None or yMaximum != None:
ax1.set_ylim(yMinimum, yMaximum)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
matplotlib.pylab.savefig(fullFilename)
matplotlib.pylab.clf()
class madHistogram
madHistogram is the class that produces Histogram plots of Madrigal Data.
Usage example::
obj = madHistogram(isprintText,
'Nel (log(m^-3)) - Millstone Hill - Oct. 30, 2005 - Alt. code',
'Hours since midnight UT Oct. 30,2003'
'Altitude',
'./isprint.png'
size = 'large')
Inputs:
isprintText - a string giving isprint output without headers. Any missing data should
be written as 'missing' or other string that cannot be converted into
a float. Only one column.
TitleStr - plot title(string) - should describe the plot being made
xLabelStr - Label for x axis
fullFilename - full path of file containing histogram plot to be saved. Extension must be .jpeg
or .png, or exception thrown
size - size of plot to be saved. Must be 'small','wide', or 'large'. Defaults to small
maxNumPoints - maximum number of points to be considered. Defaults to None, so all points in string shown
numBins - number of bins to be used to store the data. More bins means closer resolution in Histogram.
Default is 30
orientation - whether histogram shows horizontal or vertical. Must be written as 'horizontal' or
'vertical', or exception thrown--defaults to 'vertical'
isNorm - whether histogram should be normalized or not. Default is 0, or False
isBottom - If true, sets the lowest passed value as zero
sdevs - number of standard deviations to take into account. When 0 is passed to it, this option is ignored and all data
is included in the distribution; otherwise, the range of values accepted is sdevs*(standard deviation of sample)
Outputs:
A .png file is written to the fullFilename path given, resulting in a Histogram drawn by matplotlib
Change History:
Written by "Brandon Scott Fines":mailto:bfines@haystack.mit.edu Aug. 1,2006
Written by "Bill Rideout":mailto:brideout@haystack.mit.edu Aug. 1,2006
class madHistogram:
"""madHistogram is the class that produces Histogram plots of Madrigal
Data.
Usage example::
obj = madHistogram(isprintText,
'Nel (log(m^-3)) - Millstone Hill - Oct. 30, 2005 - Alt. code',
'Hours since midnight UT Oct. 30,2003'
'Altitude',
'./isprint.png'
size = 'large')
Inputs:
isprintText - a string giving isprint output without headers. Any missing data should
be written as 'missing' or other string that cannot be converted into
a float. Only one column.
TitleStr - plot title(string) - should describe the plot being made
xLabelStr - Label for x axis
fullFilename - full path of file containing histogram plot to be saved. Extension must be .jpeg
or .png, or exception thrown
size - size of plot to be saved. Must be 'small','wide', or 'large'. Defaults to small
maxNumPoints - maximum number of points to be considered. Defaults to None, so all points in string shown
numBins - number of bins to be used to store the data. More bins means closer resolution in Histogram.
Default is 30
orientation - whether histogram shows horizontal or vertical. Must be written as 'horizontal' or
'vertical', or exception thrown--defaults to 'vertical'
isNorm - whether histogram should be normalized or not. Default is 0, or False
isBottom - If true, sets the lowest passed value as zero
sdevs - number of standard deviations to take into account. When 0 is passed to it, this option is ignored and all data
is included in the distribution; otherwise, the range of values accepted is sdevs*(standard deviation of sample)
Outputs:
A .png file is written to the fullFilename path given, resulting in a Histogram drawn by matplotlib
Change History:
Written by "Brandon Scott Fines":mailto:bfines@haystack.mit.edu Aug. 1,2006
Written by "Bill Rideout":mailto:brideout@haystack.mit.edu Aug. 1,2006
"""
def __init__(self,isprintText,
titleStr,
xLabelStr,
fullFilename,
size = 'small',
maxNumPoints = None,
numBins = 30,
Orientation='vertical',
isNorm =0,
isBottom=0,
sdevs=0):
"""__init__ writes a madHistogram string to file
"""
self.__missing = 1.0E30 #special value, since numpy doesn't Handle NaN
self.__parameter_count = 2
#verify input
if not size in ('small','wide','large'):
raise ValueError('size must be "small","wide" or "large", not %s'%(str(size)))
if size in ('small','wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
# remove all non-float values
items = isprintText.split()
keptItems = []
for item in items:
try:
float(item)
keptItems.append(item)
except:
continue
delimiter = ' '
isprintText = delimiter.join(keptItems)
if maxNumPoints !=None:
isprintText = self._truncateIsprint(isprintText, maxNumPoints)
#convert the input data into numeric arrays of floats assuming no headers and filter the missing values
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing,split_data))
array_data = numpy.asarray(float_data)
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
#if needed, throw away outliers
if sdevs !=0:
array_data = self.removeOutliers(sdevs,array_data)
#select the plot size
if size=='small':
matplotlib.pylab.figure(1,figsize=(6,4), facecolor = 'w')
elif size=='wide':
matplotlib.pylab.figure(1,figsize=(10,4),facecolor='w')
elif size=='large':
matplotlib.pylab.figure(1,figsize=(12,6),facecolor='w')
matplotlib.pylab.hist(array_data,bins = numBins,normed=isNorm,bottom=isBottom,orientation = Orientation)
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) #pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.title(titleStr,fontsize=fontSize)
#self.__figure = matplotlib.pylab.gcf()
matplotlib.pylab.savefig(fullFilename)
matplotlib.pylab.clf()
def __truncateIsprintStr(self,isprintText, maxLines):
"""__truncateIsprintStr truncates isprintText to have maxLines at most.
"""
isprintList = isprintText.split('\n')
if len(isprintList)=ndist) and (i<=pdist):
retArray.append(i)
return retArray
Ancestors (in MRO)
- madHistogram
- builtins.object
Static methods
def __init__(
self, isprintText, titleStr, xLabelStr, fullFilename, size='small', maxNumPoints=None, numBins=30, Orientation='vertical', isNorm=0, isBottom=0, sdevs=0)
init writes a madHistogram string to file
def __init__(self,isprintText,
titleStr,
xLabelStr,
fullFilename,
size = 'small',
maxNumPoints = None,
numBins = 30,
Orientation='vertical',
isNorm =0,
isBottom=0,
sdevs=0):
"""__init__ writes a madHistogram string to file
"""
self.__missing = 1.0E30 #special value, since numpy doesn't Handle NaN
self.__parameter_count = 2
#verify input
if not size in ('small','wide','large'):
raise ValueError('size must be "small","wide" or "large", not %s'%(str(size)))
if size in ('small','wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
# remove all non-float values
items = isprintText.split()
keptItems = []
for item in items:
try:
float(item)
keptItems.append(item)
except:
continue
delimiter = ' '
isprintText = delimiter.join(keptItems)
if maxNumPoints !=None:
isprintText = self._truncateIsprint(isprintText, maxNumPoints)
#convert the input data into numeric arrays of floats assuming no headers and filter the missing values
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing,split_data))
array_data = numpy.asarray(float_data)
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
#if needed, throw away outliers
if sdevs !=0:
array_data = self.removeOutliers(sdevs,array_data)
#select the plot size
if size=='small':
matplotlib.pylab.figure(1,figsize=(6,4), facecolor = 'w')
elif size=='wide':
matplotlib.pylab.figure(1,figsize=(10,4),facecolor='w')
elif size=='large':
matplotlib.pylab.figure(1,figsize=(12,6),facecolor='w')
matplotlib.pylab.hist(array_data,bins = numBins,normed=isNorm,bottom=isBottom,orientation = Orientation)
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) #pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.title(titleStr,fontsize=fontSize)
#self.__figure = matplotlib.pylab.gcf()
matplotlib.pylab.savefig(fullFilename)
matplotlib.pylab.clf()
def removeOutliers(
self, ndevs, values=[])
def removeOutliers(self,ndevs,values=[]):
#find the mean value in values
sums = 0.0
count = 0
for i in values:
sums = sums+i
count = count+1
mean = sums/count
#calculate a standard deviation
innersum = 0
for i in values:
innersum = innersum + math.pow((i-mean),2)
sdev = math.sqrt((1.0/count)*innersum)
#throw away all data points falling outside three standard deviations of the mean, then return the resulting list
retArray = []
ndist = mean-ndevs*sdev
pdist = mean+ndevs*sdev
for i in values:
if (i>=ndist) and (i<=pdist):
retArray.append(i)
return retArray
class madIsrRecordSummary
madIsrRecordSummary is the class that produces a summary plot of a single ISR record.
class madIsrRecordSummary:
"""madIsrRecordSummary is the class that produces a summary plot of a single ISR record.
"""
def __init__(self, TiData,
TiError,
TeData,
TeError,
VoData,
VoError,
NelData,
NelError,
isPopl,
description,
fullFilename,
useRange = False,
altResl = None):
"""__init__ writes a madIsrRecordSummary to a file.
Inputs:
TiData - an Nx2 numeric array of Ti (col 0) and altitude in km (col 1)
TiError - an N length array of error in Ti
TeData - an Nx2 numeric array of Te (col 0) and altitude in km (col 1)
TeError - an N length array of error in Te
VoData - an Nx2 numeric array of Vo (col 0) and altitude in km (col 1)
VoError - an N length array of error in Vo
NelData - an Nx2 numeric array of (Popl or Nel in m^-3) (col 0) and altitude in km (col 1)
NelError - an 2xN length array of lower, upper error in Popl or Nel
isPopl - true if Nel contains Popl data, false if Nel data
description - text to put in fourth panel of plot - use carriage returns to separate lines
fullFilename - full filename to save output png file.
useRange - if False (the default), y axis = Altitude. If True, y axis = Range.
altResl - altitude resolution in km. If not None, show altitude resolution on each graph.
Returns: void
Affects: None
"""
if useRange:
yAxisStr = 'Range (km)'
else:
yAxisStr = 'Altitude (km)'
# upper left - Ti and Te
fig = matplotlib.pylab.figure(figsize=(10,10))
axis = matplotlib.pylab.subplot(2,2,1)
if TiData.shape[0] > 0 and TeData.shape[0] > 0:
axis.errorbar(TiData[:,0], TiData[:,1], xerr=TiError, fmt='bo-')
axis.errorbar(TeData[:,0], TeData[:,1], xerr=TeError, fmt='rD-')
matplotlib.pylab.text(0.65, 0.1, 'Ti: blue circle\nTe: red diamond',
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=10)
matplotlib.pylab.text(0.5,0.5, 'Preliminary data\n\n\nfrom the\n\n\nMadrigal database',
horizontalalignment='center',
verticalalignment='center',
fontsize=18,
transform = axis.transAxes, alpha=0.2)
matplotlib.pylab.xlabel('Temperature (K)')
matplotlib.pylab.ylabel(yAxisStr)
matplotlib.pylab.grid(True)
# set limits based on data only
tiMax = numpy.nanmax(TiData[:,0])
tiMin = numpy.nanmin(TiData[:,0])
teMax = numpy.nanmax(TeData[:,0])
teMin = numpy.nanmin(TeData[:,0])
lowerLimit = (int(min(tiMin, teMin)) / 1000) * 1000
upperLimit = (1+int(max(tiMax, teMax)) / 1000) * 1000
# alt resolution
if altResl != None:
altLabel = 'Alt res: \n%i km' % (int(altResl))
y_min, y_max = matplotlib.pylab.ylim()
startX = lowerLimit + 0.8*(upperLimit - lowerLimit)
startY = 0.15
reslPercentage = altResl/(y_max-y_min)
matplotlib.pylab.axvline(x=startX,
ymin=startY,
ymax=reslPercentage + startY,
linewidth=2, color='b')
matplotlib.pylab.text(0.83, 0.2, altLabel,
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=10)
matplotlib.pylab.xlim(lowerLimit, upperLimit)
# upper right - Vo and 10*Vo
axis = matplotlib.pylab.subplot(2,2,2)
if VoData.shape[0] > 0:
axis.errorbar(VoData[:,0], VoData[:,1], xerr=VoError, fmt='bo-')
axis.errorbar(10.0*VoData[:,0], VoData[:,1], xerr=10.0*VoError, fmt='rD-')
# set x limits to be even around 0
voMax = numpy.nanmax(10.0*VoData[:,0])
voMin = numpy.nanmin(10.0*VoData[:,0])
limit = max(abs(voMax), abs(voMin))
matplotlib.pylab.xlim(-1.0*limit, limit)
matplotlib.pylab.text(0.58, 0.1, 'Vo: blue circle\n10*Vo: red diamond',
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=10)
matplotlib.pylab.text(0.5,0.5, 'Preliminary data\n\n\nfrom the\n\n\nMadrigal database',
horizontalalignment='center',
verticalalignment='center',
fontsize=18,
transform = axis.transAxes, alpha=0.2)
matplotlib.pylab.xlabel('Line of sight drift (m/s)')
matplotlib.pylab.ylabel(yAxisStr)
matplotlib.pylab.grid(True)
# alt resolution
if altResl != None:
altLabel = 'Alt res: \n%i km' % (int(altResl))
y_min, y_max = matplotlib.pylab.ylim()
startX = limit*0.6
startY = 0.15
reslPercentage = altResl/(y_max-y_min)
matplotlib.pylab.axvline(x=startX,
ymin=startY,
ymax=reslPercentage + startY,
linewidth=2, color='b')
matplotlib.pylab.text(0.83, 0.2, altLabel,
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=10)
matplotlib.pylab.xlim(-1.0*limit, limit)
# lower left - Nel or Popl
axis = matplotlib.pylab.subplot(2,2,3)
if isPopl:
labelStr = 'Log Power (m^-3)'
else:
labelStr = 'Log Electron density (m^-3)'
if NelData.shape[0] > 0:
# check that not every point equal
if numpy.nanmax(NelData[:,0]) == numpy.nanmin(NelData[:,0]):
pass
else:
axis.errorbar(NelData[:,0], NelData[:,1], xerr=NelError, fmt='bo-')
matplotlib.pylab.xlabel(labelStr)
matplotlib.pylab.ylabel(yAxisStr)
matplotlib.pylab.grid(True)
nelMax = numpy.nanmax(NelData[:,0])
nelMin = numpy.nanmin(NelData[:,0])
matplotlib.pylab.xlim(math.floor(nelMin), math.ceil(nelMax))
matplotlib.pylab.text(0.5,0.5, 'Preliminary data\n\n\nfrom the\n\n\nMadrigal database',
horizontalalignment='center',
verticalalignment='center',
fontsize=18,
transform = axis.transAxes, alpha=0.2)
# alt resolution
if altResl != None:
altLabel = 'Alt res: \n%i km' % (int(altResl))
y_min, y_max = matplotlib.pylab.ylim()
x_min, x_max = matplotlib.pylab.xlim()
startX = x_min + 0.8*(x_max-x_min)
startY = 0.15
reslPercentage = altResl/(y_max-y_min)
matplotlib.pylab.axvline(x=startX,
ymin=startY,
ymax=reslPercentage + startY,
linewidth=2, color='b')
matplotlib.pylab.text(0.83, 0.2, altLabel,
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=10)
matplotlib.pylab.xlim(math.floor(nelMin), math.ceil(nelMax))
# lower right - text
axis = matplotlib.pylab.subplot(2,2,4)
matplotlib.pylab.text(0.05, 0.9, description,
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=9)
matplotlib.pylab.xticks([])
matplotlib.pylab.yticks([])
matplotlib.pylab.subplots_adjust(wspace = 0.25)
matplotlib.pylab.savefig(fullFilename)
#matplotlib.pylab.show()
matplotlib.pylab.clf()
matplotlib.pylab.close()
Ancestors (in MRO)
- madIsrRecordSummary
- builtins.object
Static methods
def __init__(
self, TiData, TiError, TeData, TeError, VoData, VoError, NelData, NelError, isPopl, description, fullFilename, useRange=False, altResl=None)
init writes a madIsrRecordSummary to a file.
Inputs:
TiData - an Nx2 numeric array of Ti (col 0) and altitude in km (col 1)
TiError - an N length array of error in Ti
TeData - an Nx2 numeric array of Te (col 0) and altitude in km (col 1)
TeError - an N length array of error in Te
VoData - an Nx2 numeric array of Vo (col 0) and altitude in km (col 1)
VoError - an N length array of error in Vo
NelData - an Nx2 numeric array of (Popl or Nel in m^-3) (col 0) and altitude in km (col 1)
NelError - an 2xN length array of lower, upper error in Popl or Nel
isPopl - true if Nel contains Popl data, false if Nel data
description - text to put in fourth panel of plot - use carriage returns to separate lines
fullFilename - full filename to save output png file.
useRange - if False (the default), y axis = Altitude. If True, y axis = Range.
altResl - altitude resolution in km. If not None, show altitude resolution on each graph.
Returns: void
Affects: None
def __init__(self, TiData,
TiError,
TeData,
TeError,
VoData,
VoError,
NelData,
NelError,
sPopl,
description,
fullFilename,
useRange = False,
altResl = None):
"""__init__ writes a madIsrRecordSummary to a file.
Inputs:
TiData - an Nx2 numeric array of Ti (col 0) and altitude in km (col 1)
TiError - an N length array of error in Ti
TeData - an Nx2 numeric array of Te (col 0) and altitude in km (col 1)
TeError - an N length array of error in Te
VoData - an Nx2 numeric array of Vo (col 0) and altitude in km (col 1)
VoError - an N length array of error in Vo
NelData - an Nx2 numeric array of (Popl or Nel in m^-3) (col 0) and altitude in km (col 1)
NelError - an 2xN length array of lower, upper error in Popl or Nel
isPopl - true if Nel contains Popl data, false if Nel data
description - text to put in fourth panel of plot - use carriage returns to separate lines
fullFilename - full filename to save output png file.
useRange - if False (the default), y axis = Altitude. If True, y axis = Range.
altResl - altitude resolution in km. If not None, show altitude resolution on each graph.
Returns: void
Affects: None
"""
if useRange:
yAxisStr = 'Range (km)'
else:
yAxisStr = 'Altitude (km)'
# upper left - Ti and Te
fig = matplotlib.pylab.figure(figsize=(10,10))
axis = matplotlib.pylab.subplot(2,2,1)
if TiData.shape[0] > 0 and TeData.shape[0] > 0:
axis.errorbar(TiData[:,0], TiData[:,1], xerr=TiError, fmt='bo-')
axis.errorbar(TeData[:,0], TeData[:,1], xerr=TeError, fmt='rD-')
matplotlib.pylab.text(0.65, 0.1, 'Ti: blue circle\nTe: red diamond',
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=10)
matplotlib.pylab.text(0.5,0.5, 'Preliminary data\n\n\nfrom the\n\n\nMadrigal database',
horizontalalignment='center',
verticalalignment='center',
fontsize=18,
transform = axis.transAxes, alpha=0.2)
matplotlib.pylab.xlabel('Temperature (K)')
matplotlib.pylab.ylabel(yAxisStr)
matplotlib.pylab.grid(True)
# set limits based on data only
tiMax = numpy.nanmax(TiData[:,0])
tiMin = numpy.nanmin(TiData[:,0])
teMax = numpy.nanmax(TeData[:,0])
teMin = numpy.nanmin(TeData[:,0])
lowerLimit = (int(min(tiMin, teMin)) / 1000) * 1000
upperLimit = (1+int(max(tiMax, teMax)) / 1000) * 1000
# alt resolution
if altResl != None:
altLabel = 'Alt res: \n%i km' % (int(altResl))
y_min, y_max = matplotlib.pylab.ylim()
startX = lowerLimit + 0.8*(upperLimit - lowerLimit)
startY = 0.15
reslPercentage = altResl/(y_max-y_min)
matplotlib.pylab.axvline(x=startX,
ymin=startY,
ymax=reslPercentage + startY,
linewidth=2, color='b')
matplotlib.pylab.text(0.83, 0.2, altLabel,
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=10)
matplotlib.pylab.xlim(lowerLimit, upperLimit)
# upper right - Vo and 10*Vo
axis = matplotlib.pylab.subplot(2,2,2)
if VoData.shape[0] > 0:
axis.errorbar(VoData[:,0], VoData[:,1], xerr=VoError, fmt='bo-')
axis.errorbar(10.0*VoData[:,0], VoData[:,1], xerr=10.0*VoError, fmt='rD-')
# set x limits to be even around 0
voMax = numpy.nanmax(10.0*VoData[:,0])
voMin = numpy.nanmin(10.0*VoData[:,0])
limit = max(abs(voMax), abs(voMin))
matplotlib.pylab.xlim(-1.0*limit, limit)
matplotlib.pylab.text(0.58, 0.1, 'Vo: blue circle\n10*Vo: red diamond',
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=10)
matplotlib.pylab.text(0.5,0.5, 'Preliminary data\n\n\nfrom the\n\n\nMadrigal database',
horizontalalignment='center',
verticalalignment='center',
fontsize=18,
transform = axis.transAxes, alpha=0.2)
matplotlib.pylab.xlabel('Line of sight drift (m/s)')
matplotlib.pylab.ylabel(yAxisStr)
matplotlib.pylab.grid(True)
# alt resolution
if altResl != None:
altLabel = 'Alt res: \n%i km' % (int(altResl))
y_min, y_max = matplotlib.pylab.ylim()
startX = limit*0.6
startY = 0.15
reslPercentage = altResl/(y_max-y_min)
matplotlib.pylab.axvline(x=startX,
ymin=startY,
ymax=reslPercentage + startY,
linewidth=2, color='b')
matplotlib.pylab.text(0.83, 0.2, altLabel,
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=10)
matplotlib.pylab.xlim(-1.0*limit, limit)
# lower left - Nel or Popl
axis = matplotlib.pylab.subplot(2,2,3)
if isPopl:
labelStr = 'Log Power (m^-3)'
else:
labelStr = 'Log Electron density (m^-3)'
if NelData.shape[0] > 0:
# check that not every point equal
if numpy.nanmax(NelData[:,0]) == numpy.nanmin(NelData[:,0]):
pass
else:
axis.errorbar(NelData[:,0], NelData[:,1], xerr=NelError, fmt='bo-')
matplotlib.pylab.xlabel(labelStr)
matplotlib.pylab.ylabel(yAxisStr)
matplotlib.pylab.grid(True)
nelMax = numpy.nanmax(NelData[:,0])
nelMin = numpy.nanmin(NelData[:,0])
matplotlib.pylab.xlim(math.floor(nelMin), math.ceil(nelMax))
matplotlib.pylab.text(0.5,0.5, 'Preliminary data\n\n\nfrom the\n\n\nMadrigal database',
horizontalalignment='center',
verticalalignment='center',
fontsize=18,
transform = axis.transAxes, alpha=0.2)
# alt resolution
if altResl != None:
altLabel = 'Alt res: \n%i km' % (int(altResl))
y_min, y_max = matplotlib.pylab.ylim()
x_min, x_max = matplotlib.pylab.xlim()
startX = x_min + 0.8*(x_max-x_min)
startY = 0.15
reslPercentage = altResl/(y_max-y_min)
matplotlib.pylab.axvline(x=startX,
ymin=startY,
ymax=reslPercentage + startY,
linewidth=2, color='b')
matplotlib.pylab.text(0.83, 0.2, altLabel,
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=10)
matplotlib.pylab.xlim(math.floor(nelMin), math.ceil(nelMax))
# lower right - text
axis = matplotlib.pylab.subplot(2,2,4)
matplotlib.pylab.text(0.05, 0.9, description,
horizontalalignment='left',
verticalalignment='top',
transform = axis.transAxes,
fontsize=9)
matplotlib.pylab.xticks([])
matplotlib.pylab.yticks([])
matplotlib.pylab.subplots_adjust(wspace = 0.25)
matplotlib.pylab.savefig(fullFilename)
#matplotlib.pylab.show()
matplotlib.pylab.clf()
matplotlib.pylab.close()
class madLineTimePlot
madLineTimePlot is the class the produces line plots of one or more parameters versus time.
class madLineTimePlot:
"""madLineTimePlot is the class the produces line plots of one or more parameters versus time.
"""
def __init__(self, isprintText,
yParmList,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
useAbsoluteTime = False,
startTime = None,
endTime = None,
maxNumPoints = None,
noTime = False,
yMinimum = None,
yMaximum = None,
noDate = False,
timezone = 0):
"""__init__ writes a madLineTimePlot plot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The the following must be the parameters to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
yParmList - a list of y parameters (strings). Length must == num columns in isprintText - 1
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
maxNumPoints - maximum number of points to plot. If not None, truncate isprintText if
needed to have at most maxNumPoints lines.
noTime - if True and useAbsoluteTime True, then dates without times printed on x axis.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
yMinimum - set y minimum. If default=None, use data minimum
yMaximum - set y maximum. If default=None, use data maximum
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
Returns: void
Affects: None
"""
self.__missing = 1.0E30 # special value, since numpy doesn't handle NaN
self.__parameter_count = 1 + len(yParmList)
self.__colorList = 'gbrcmykw' # a list of colors for the lines
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
if maxNumPoints != None:
isprintText = self.__truncateIsprint(isprintText, maxNumPoints)
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
threshold = self.__missing*0.9999
array_data = numpy.ma.masked_where(array_data > threshold, array_data)
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
# find min, max of x (time)
if startTime == None:
xMin = array_data[0,0]
else:
xMin = startTime
if endTime == None:
xMax = array_data[-1,0]
else:
xMax = endTime
# find yMin just to ensure there is data
yMin = None
for column in range(len(yParmList)):
for y in array_data[:,1+column]:
if y == self.__missing:
continue
if yMin == None:
yMin = y
elif yMin > y:
yMin = y
if yMin == None:
raise ValueError('No valid y data found')
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(8,3), facecolor = 'w')
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(12,6), facecolor = 'w')
if useAbsoluteTime:
# leave room for rotated datetime string
if size == 'large':
matplotlib.pylab.axes([0.1, 0.2, 0.8, 0.7])
elif size == 'wide':
matplotlib.pylab.axes([0.07, 0.2, 0.9, 0.7])
elif size == 'small':
matplotlib.pylab.axes([0.1, 0.18, 0.87, 0.7])
else:
if size == 'large':
matplotlib.pylab.axes([0.1, 0.15, 0.71, 0.73])
for column in range(len(yParmList)):
color = self.__colorList[column % len(self.__colorList)]
matplotlib.pylab.plot(array_data[:,0],array_data[:,1 + column], '%so-' % (color), ms=5, mew=0.5)
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.xlim(xMin, xMax)
matplotlib.pylab.grid(True)
if useAbsoluteTime:
# if plot is less than 24 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
xmin = xmin / (60*60)
xmax = xmax / (60*60)
if noDate == True:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
step = int((xmax-xmin-1)/6)+1
xRange = list(range(0,int(newXmax-xmin),step))
for i in xRange:
tmp = (xmin + i)*60*60
newXLocs.append(tmp)
if (xmax - xmin) < 1: newXLocs.append(xmax*60*60)
newXLabels = convertToAbsoluteTimeStr(newXLocs, noDate=noDate, timezone=timezone)
matplotlib.pylab.xticks(newXLocs, newXLabels)
else:
locs, labels = matplotlib.pylab.xticks()
if len(locs) > 5:
# truncate by len(locs) / 5
scale = 1 + int(len(locs) / 5)
new_locs = []
for i in range(len(locs)):
if i % scale == 0:
new_locs.append(locs[i])
locs = new_locs
newXTicks = convertToAbsoluteTimeStr(locs, noTime)
matplotlib.pylab.xticks(locs, newXTicks, rotation=15)
else:
# if plot is less than 48 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
if xmax < 49:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
for i in range(0,int(4+newXmax),4):
newXLocs.append(i)
newXLabels.append(str(i))
matplotlib.pylab.xticks(newXLocs, newXLabels)
matplotlib.pylab.xticks(fontsize=fontSize)
if yMinimum != None and yMaximum != None:
matplotlib.pylab.ylim(yMinimum, yMaximum)
elif yMinimum != None and yMaximum == None:
matplotlib.pylab.ylim(yMin=yMinimum)
elif yMinimum == None and yMaximum != None:
matplotlib.pylab.ylim(yMax=yMaximum)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
matplotlib.pylab.legend(yParmList, numpoints=1)
matplotlib.pylab.savefig(fullFilename)
matplotlib.pylab.clf()
def __truncateIsprint(self, isprintText, maxLines):
"""__truncateIsprint truncates isprintText to have maxLines at most.
"""
isprintList = isprintText.split('\n')
if len(isprintList) < maxLines:
return isprintText
else:
dropNumber = int(1 + len(isprintList)/maxLines)
newline = '\n'
newIsprintText = newline.join(isprintList[::dropNumber])
return newIsprintText
def __filter_missing(self,x):
try:
return float(x)
except:
return self.__missing
def writeToFile(self, fullFilename):
pass
def displayToScreen(self):
pass
def getFigureHandle(self):
pass
Ancestors (in MRO)
- madLineTimePlot
- builtins.object
Static methods
def __init__(
self, isprintText, yParmList, titleStr, xLabelStr, yLabelStr, fullFilename, size='small', useAbsoluteTime=False, startTime=None, endTime=None, maxNumPoints=None, noTime=False, yMinimum=None, yMaximum=None, noDate=False, timezone=0)
init writes a madLineTimePlot plot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The the following must be the parameters to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
yParmList - a list of y parameters (strings). Length must == num columns in isprintText - 1
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
maxNumPoints - maximum number of points to plot. If not None, truncate isprintText if
needed to have at most maxNumPoints lines.
noTime - if True and useAbsoluteTime True, then dates without times printed on x axis.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
yMinimum - set y minimum. If default=None, use data minimum
yMaximum - set y maximum. If default=None, use data maximum
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
Returns: void
Affects: None
def __init__(self, isprintText,
yParmList,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
useAbsoluteTime = False,
startTime = None,
endTime = None,
maxNumPoints = None,
noTime = False,
yMinimum = None,
yMaximum = None,
noDate = False,
timezone = 0):
"""__init__ writes a madLineTimePlot plot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The the following must be the parameters to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
yParmList - a list of y parameters (strings). Length must == num columns in isprintText - 1
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
maxNumPoints - maximum number of points to plot. If not None, truncate isprintText if
needed to have at most maxNumPoints lines.
noTime - if True and useAbsoluteTime True, then dates without times printed on x axis.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
yMinimum - set y minimum. If default=None, use data minimum
yMaximum - set y maximum. If default=None, use data maximum
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
Returns: void
Affects: None
"""
self.__missing = 1.0E30 # special value, since numpy doesn't handle NaN
self.__parameter_count = 1 + len(yParmList)
self.__colorList = 'gbrcmykw' # a list of colors for the lines
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
if maxNumPoints != None:
isprintText = self.__truncateIsprint(isprintText, maxNumPoints)
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
threshold = self.__missing*0.9999
array_data = numpy.ma.masked_where(array_data > threshold, array_data)
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
# find min, max of x (time)
if startTime == None:
xMin = array_data[0,0]
else:
xMin = startTime
if endTime == None:
xMax = array_data[-1,0]
else:
xMax = endTime
# find yMin just to ensure there is data
yMin = None
for column in range(len(yParmList)):
for y in array_data[:,1+column]:
if y == self.__missing:
continue
if yMin == None:
yMin = y
elif yMin > y:
yMin = y
if yMin == None:
raise ValueError('No valid y data found')
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(8,3), facecolor = 'w')
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(12,6), facecolor = 'w')
if useAbsoluteTime:
# leave room for rotated datetime string
if size == 'large':
matplotlib.pylab.axes([0.1, 0.2, 0.8, 0.7])
elif size == 'wide':
matplotlib.pylab.axes([0.07, 0.2, 0.9, 0.7])
elif size == 'small':
matplotlib.pylab.axes([0.1, 0.18, 0.87, 0.7])
else:
if size == 'large':
matplotlib.pylab.axes([0.1, 0.15, 0.71, 0.73])
for column in range(len(yParmList)):
color = self.__colorList[column % len(self.__colorList)]
matplotlib.pylab.plot(array_data[:,0],array_data[:,1 + column], '%so-' % (color), ms=5, mew=0.5)
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.xlim(xMin, xMax)
matplotlib.pylab.grid(True)
if useAbsoluteTime:
# if plot is less than 24 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
xmin = xmin / (60*60)
xmax = xmax / (60*60)
if noDate == True:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
step = int((xmax-xmin-1)/6)+1
xRange = list(range(0,int(newXmax-xmin),step))
for i in xRange:
tmp = (xmin + i)*60*60
newXLocs.append(tmp)
if (xmax - xmin) < 1: newXLocs.append(xmax*60*60)
newXLabels = convertToAbsoluteTimeStr(newXLocs, noDate=noDate, timezone=timezone)
matplotlib.pylab.xticks(newXLocs, newXLabels)
else:
locs, labels = matplotlib.pylab.xticks()
if len(locs) > 5:
# truncate by len(locs) / 5
scale = 1 + int(len(locs) / 5)
new_locs = []
for i in range(len(locs)):
if i % scale == 0:
new_locs.append(locs[i])
locs = new_locs
newXTicks = convertToAbsoluteTimeStr(locs, noTime)
matplotlib.pylab.xticks(locs, newXTicks, rotation=15)
else:
# if plot is less than 48 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
if xmax < 49:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
for i in range(0,int(4+newXmax),4):
newXLocs.append(i)
newXLabels.append(str(i))
matplotlib.pylab.xticks(newXLocs, newXLabels)
matplotlib.pylab.xticks(fontsize=fontSize)
if yMinimum != None and yMaximum != None:
matplotlib.pylab.ylim(yMinimum, yMaximum)
elif yMinimum != None and yMaximum == None:
matplotlib.pylab.ylim(yMin=yMinimum)
elif yMinimum == None and yMaximum != None:
matplotlib.pylab.ylim(yMax=yMaximum)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
matplotlib.pylab.legend(yParmList, numpoints=1)
matplotlib.pylab.savefig(fullFilename)
matplotlib.pylab.clf()
def displayToScreen(
self)
def displayToScreen(self):
pass
def getFigureHandle(
self)
def getFigureHandle(self):
pass
def writeToFile(
self, fullFilename)
def writeToFile(self, fullFilename):
pass
class madPcolorPlot
madPcolorPlot is the class that produces pcolor plots of x versus y with z intensity.
Assumes the x axis is time.
Usage example::
obj = madPcolorPlot(isprintText,
'Nel (log(m^-3)) - Millstone Hill - Oct. 30, 2003 - Alt code',
'Hours since midnight UT Oct. 30, 2003',
'Altitude (km)',
'./isprint.png',
size = 'large',
minColormap = 9,
maxColormap = 12,
smoothAltitude = False)
Non-standard Python modules used: matplotlib
Change history:
Written by "Bill Rideout":mailto:wrideout@haystack.mit.edu Mar. 31, 2005
class madPcolorPlot:
"""madPcolorPlot is the class that produces pcolor plots of x versus y with z intensity.
Assumes the x axis is time.
Usage example::
obj = madPcolorPlot(isprintText,
'Nel (log(m^-3)) - Millstone Hill - Oct. 30, 2003 - Alt code',
'Hours since midnight UT Oct. 30, 2003',
'Altitude (km)',
'./isprint.png',
size = 'large',
minColormap = 9,
maxColormap = 12,
smoothAltitude = False)
Non-standard Python modules used:
matplotlib
Change history:
Written by "Bill Rideout":mailto:wrideout@haystack.mit.edu Mar. 31, 2005
"""
def __init__(self, isprintText,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
minColormap = None,
maxColormap = None,
smoothAltitude = True,
insertDataGap = 5,
useAbsoluteTime = False,
startTime = None,
endTime = None,
sortTimeFlag = False,
maxNumTimes = None,
maxNumAlt = None,
truncateIsprint = False,
colorMap = matplotlib.cm.jet,
yMinimum = None,
yMaximum = None,
altYTitle = None,
altYLabels = None,
noTime = False,
noDate = False,
timezone = 0,
background = 'w',
peakAltStr = None):
"""__init__ writes a madPColorPlot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The second must be gdalt, and third parameter to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
smoothAltitude - if True, extrapolate between existing data between altitudes to fill
in missing data; if False, leave missing
insertDataGap - this parameter sets the threshold for inserting a data gap. The time intervals
being plotted are ordered, and the time gap larger than 90% of the rest is determined.
Any time interval more than insertDataGap times bigger is then considered missing
data. Defaults to five. If None, no gaps are ever inserted. For data with close
to uniform time intervals, no gaps will be inserted.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
sortTimeFlag - if true, check that time is correctly sorted. If false (the default),
assume time already sorted
maxNumTimes - if not None, decimate the number of times in the isprint string to
maxNumTimes. If None (the default), plot all times.
maxNumAlt - if not None, reduce the number of altitudes to maxNumAlt. If None (the default),
plot all altitudes.
truncateIsprint - if True, and both maxNumTimes and maxNumAlt not = None, then truncate
the number of isprint lines to be maxNumTimes * maxNumAlt
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
yMinimum - minumum y value. If None (default), set by data y minimum.
yMaximum - maximum y value. If None (default), set by data y maximum.
altYTitle - title of right (second) y axis. If None (the default),
no Y axis on the right side.
altYLabels - a list of Y labels (strings) for the right axis. If None (the default),
no Y labels on the right axis.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
peakAltStr - if not None (the default), plot the peak altitude with x. Format is a
str with each line (time in same format as data, peak altitude)
Returns: void
Affects: None
"""
self.__missing = 1.0E30 # special value, used to create a masked array
self.__parameter_count = 3
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small'):
fontSize = 10
elif size in ('wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
if truncateIsprint and maxNumTimes != None and maxNumAlt != None:
isprintText = self.__truncateIsprint(isprintText, maxNumTimes * maxNumAlt)
# since matplotlib pcolor wants a regular grid, we create a grid with all altitudes
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
if sortTimeFlag:
array_data = self.sortArrayInTime(array_data)
if maxNumTimes != None and maxNumTimes != 0 and not truncateIsprint:
array_data = self.decimateTimes(array_data, int(maxNumTimes), insertDataGap)
# The first pass through is to obtain the number and range of the x and y variables
xList = []
yList = []
zList = []
zMin = None
zMax = None
# loop over all the lines of data in the array
for j in range(len(array_data)):
try:
x = array_data[j][0]
y = array_data[j][1]
z = array_data[j][2]
if x not in xList:
xList.append(x)
if y not in yList:
yList.append(y)
if z != self.__missing:
zList.append(z)
if zMin != None:
if z < zMin and z != self.__missing:
zMin = z
elif z != self.__missing:
zMin = z
if zMax != None:
if z > zMax and z != self.__missing:
zMax = z
elif z != self.__missing:
zMax = z
except:
continue
if zMin == None:
raise ValueError('No valid z data found')
# if both minColormap and maxColormap == None, use autoscaling
if minColormap == None and maxColormap == None:
zList.sort()
d10 = zList[int(len(zList)*0.10)]
d90 = zList[int(len(zList)*0.90)]
zMin = d10 - (d90-d10) * 0.75
zMax = d90 + (d90-d10) * 0.75
# now sort the X and Y axis lists and pull their length
xList.sort()
if startTime == None:
xMin = xList[0]
else:
xMin = startTime
if endTime == None:
xMax = xList[-1]
else:
xMax = endTime
yList.sort()
max_x_dimension = len(xList)
max_y_dimension = len(yList)
if yMinimum == None:
yMinimum = yList[0]
if yMaximum == None:
yMaximum = yList[-1]
self.truncateAlt = False
if maxNumAlt != None:
if max_y_dimension > maxNumAlt:
self.truncateAlt = True
# build dictonary of indexes into xList
self.xListDict = {}
for i in range(len(xList)):
self.xListDict[xList[i]] = i
# if self.truncateAlt == False, build dictonary of indexes into yList,
# else truncate y values by builing a list of maxNumAlt ranges
if self.truncateAlt == False:
self.yListDict = {}
for i in range(len(yList)):
self.yListDict[yList[i]] = i
else:
self.yListRanges = []
for i in range(maxNumAlt):
self.yListRanges.append(yList[int(i*(len(yList)/maxNumAlt))])
max_y_dimension = maxNumAlt
# now build arrays to handle the X axis label, Y axis label, and the Z data
X = numpy.zeros((max_x_dimension, max_y_dimension), numpy.float32)
Y = numpy.zeros((max_x_dimension, max_y_dimension), numpy.float32)
Z = numpy.ones((max_x_dimension, max_y_dimension), numpy.float32)
# all parameter values default to missing
Z = Z * self.__missing
# fill the X and Y arrays
for i in range(max_x_dimension):
for j in range(max_y_dimension):
X[i][j] = float(xList[i])
if self.truncateAlt:
Y[i][j] = float(yList[int(j*(len(yList)/maxNumAlt))])
else:
Y[i][j] = float(yList[j])
# Now load up the data array Z with the array_data measurements as a function of x and y
previousIndex = None
previousValue = None
presentTime = None
newTimeFound = True
for k in range(len(array_data)):
try:
xdata = array_data[k][0]
ydata = array_data[k][1]
zdata = array_data[k][2]
if zdata == self.__missing:
continue
if xdata != presentTime:
newTimeFound = True
else:
newTimeFound = False
presentTime = xdata
# now find the right place in the array for this data point
i = self.xListDict[xdata]
j = self.__getYIndex(ydata)
Z[i][j] = zdata
# now see if we need to fill in any gaps
if (not newTimeFound) and smoothAltitude:
if previousIndex < j - 1:
# fill in all missing points
for l in range(previousIndex + 1, j):
# simply average between the points based on index
thisValue = previousValue + (zdata - previousValue)*(float(l-previousIndex)/float(j-previousIndex))
Z[i][l] = thisValue
previousIndex = j
previousValue = zdata
except:
continue
# insert missing data to represent gaps if needed
if insertDataGap != None:
# first find the time interval greater than 90% of others
timeIntervalList = []
for i in range(len(xList) - 1):
timeIntervalList.append(xList[i+1] - xList[i])
timeIntervalList.sort()
index = int(len(timeIntervalList)*0.9)
maxInterval = timeIntervalList[index]
for i in range(len(xList) - 1):
if xList[i+1] - xList[i] > maxInterval * insertDataGap:
Z[i,:] = self.__missing
# insert missing data to represent gaps if needed in Altitude
if insertDataGap != None and not smoothAltitude:
# first find the time interval greater than 90% of others
altitudeIntervalList = []
for i in range(len(yList) - 1):
altitudeIntervalList.append(yList[i+1] - yList[i])
altitudeIntervalList.sort()
index = int(len(altitudeIntervalList)*0.9)
maxInterval = altitudeIntervalList[index]
for j in range(len(yList) - 1):
if yList[j+1] - yList[j] > maxInterval * insertDataGap and j < max_y_dimension:
Z[:,j] = self.__missing
# make Z a masked array
Znew = numpy.ma.masked_inside(Z, 0.99*self.__missing, 1.01*self.__missing)
# set up plotting parameters
if minColormap == None:
minColormap = zMin
if maxColormap == None:
maxColormap = zMax
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(8,3), facecolor = 'w')
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(14,5), facecolor = 'w')
if useAbsoluteTime:
# leave room for rotated datetime string
if size == 'large':
matplotlib.pylab.axes([0.1, 0.1, 0.85, 0.7])
elif size == 'wide':
matplotlib.pylab.axes([0.07, 0.2, 0.97, 0.7])
elif size == 'small':
matplotlib.pylab.axes([0.1, 0.18, 0.97, 0.7])
else:
if size == 'small':
matplotlib.pylab.axes([0.1, 0.18, 0.97, 0.7])
elif size == 'large':
matplotlib.pylab.axes([0.1, 0.2, 0.9, 0.73])
matplotlib.pylab.pcolor(X,Y,Znew, edgecolors='none', vmin=minColormap, vmax=maxColormap, cmap = colorMap,
norm = matplotlib.pylab.Normalize(), edgecolor='face')
matplotlib.pylab.colorbar()
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.xlim(xMin, xMax)
matplotlib.pylab.ylim(yMinimum, yMaximum)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
if useAbsoluteTime:
# if plot is less than 49 hours
xmin, xmax = matplotlib.pylab.xlim()
xmin = numpy.floor(xmin / (60*60))
xmax = numpy.ceil(xmax / (60*60))
if noDate == True:
newXLabels = []
if (xmax - xmin) <= 1:
newXLocs = []
xRange = numpy.arange(0,1,0.25)
for i in xRange:
tmp=(xmin+i)*60*60
newXLocs.append(tmp)
elif (xmax - xmin) <= 2:
newXLocs = []
xRange = numpy.arange(0,2,0.5)
for i in xRange:
tmp=(xmin+i)*60*60
newXLocs.append(tmp)
else:
newXLocs = []
step = int((xmax-xmin-1)/6)+1
xRange = list(range(0,int(xmax-xmin),step))
for i in xRange:
tmp = (xmin + i)*60*60
newXLocs.append(tmp)
#newXLocs.append(xmax*60*60)
newXLabels = convertToAbsoluteTimeStr(newXLocs, noDate=noDate, timezone=timezone)
matplotlib.pylab.xticks(newXLocs, newXLabels)
else:
locs, labels = matplotlib.pylab.xticks()
if len(locs) > 5:
# truncate by len(locs) / 5
scale = 1 + int(len(locs) / 5)
new_locs = []
for i in range(len(locs)):
if i % scale == 0:
new_locs.append(locs[i])
locs = new_locs
newXTicks = convertToAbsoluteTimeStr(locs)
matplotlib.pylab.xticks(locs, newXTicks, rotation=15)
else:
# if plot is less than 48 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
if xmax < 49:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
for i in range(0,int(4+newXmax),4):
newXLocs.append(i)
newXLabels.append(str(i))
matplotlib.pylab.xticks(newXLocs, newXLabels)
matplotlib.pylab.xticks(fontsize=fontSize)
# add second y-axis if desired
if altYTitle != None and altYLabels != None:
ax2 = matplotlib.pylab.twinx()
matplotlib.pylab.ylabel(altYTitle)
ax2.yaxis.tick_right()
matplotlib.pylab.yticks(list(range(len(altYLabels))), altYLabels)
matplotlib.pylab.yticks(fontsize=fontSize)
# get the handle to the figure now that it has been created so we can manipulate on subsequent calls.
#self.__figure = matplotlib.pylab.gcf()
if peakAltStr:
# plot peak altitudes
data = peakAltStr.split()
xdata = [float(item) for i, item in enumerate(data) if i % 2 == 0]
ydata = [float(item) for i, item in enumerate(data) if i % 2 == 1]
matplotlib.pylab.plot(xdata, ydata, 'b+')
matplotlib.pylab.savefig(fullFilename)
#matplotlib.pylab.show()
matplotlib.pylab.clf()
def __filter_missing(self,x):
try:
return float(x)
except:
return self.__missing
def __getYIndex(self, yvalue):
"""__getYIndex returns the correct index into the y dimension for a given y value.
Input: yvalue - value of y parameter
Returns: the correct index into the y dimension
Algorithm: if self.truncateAlt == False, simple use the dictionary self.yListDict. Else
loop through self.yListRanges and return the first greater than the requested value
"""
if self.truncateAlt == False:
return self.yListDict[yvalue]
else:
i = bisect.bisect_left(self.yListRanges, yvalue)
if i >= len(self.yListRanges):
i = len(self.yListRanges) - 1
return i
def __truncateIsprint(self, isprintText, maxLines):
"""__truncateIsprint truncates isprintText to have maxLines at most.
"""
isprintList = isprintText.split('\n')
if len(isprintList) < maxLines:
return isprintText
else:
dropNumber = int(1 + len(isprintList)/maxLines)
newIsprintText = ''
for i in range(0,len(isprintList),dropNumber):
newIsprintText += isprintList[i] + '\n'
return newIsprintText
def displayToScreen(self):
" to implement this takes a reworking away from pylab to use the underlying matplotlib code "
pass
def getFigureHandle(self):
return self.__figure
def getAverage(self, X):
"""returns the average of items in a float array. Does not including missing data.
If all data missing, returns self.__missing
"""
count = 0
total = 0.0
for i in range(X.shape[0]):
if X[i] != self.__missing:
count += 1
total += X[i]
if count == 0:
return self.__missing
else:
return total / float(count)
def sortArrayInTime(self, array_data):
"""sortArrayInTime sorts a two-dimensional array so that the first element in each row (time) is in ascending order.
Input: array_data - two-dimensional array to be sorted by rearranging rows so
that the first element in each row (time) is in ascending order
Returns: new_array
"""
sortIndex = numpy.argsort(array_data[:,0])[0]
# if already sorted, just return original array
if sortIndex == numpy.sort(sortIndex):
return array_data
new_array = numpy.zeros(array_data.shape, array_data.dtype)
for i in range(len(sortIndex)):
new_array[sortIndex[i],:] = array_data[i,:]
return new_array
def decimateTimes(self, array_data, maxNumTimes, insertDataGap):
"""decimateTimes decimates array_data to have at most maxNumTimes times.
Input: array_data - two-dimensional array to be decimated by deleting times and missing data.
maxNumTimes: int representing the maximum number of times to keep in array_data
insertDataGap - this parameter sets the threshold for inserting a data gap. The time intervals
being plotted are ordered, and the time gap larger than 90% of the rest is determined.
Note that this parameter is used here to stop the truncation of isprint lines that
will eventually be considered edge lines.
Returns: new array built from decimated array_data
"""
# get the number of times in array_data, and make a list of all unique times
numTimes = 0
uniqueTimes = []
time_array = array_data[:, 0]
for i in range(len(time_array)):
if i == 0:
numTimes = 1
uniqueTimes.append(time_array[i])
elif time_array[i-1] != time_array[i]:
uniqueTimes.append(time_array[i])
numTimes += 1
if numTimes <= maxNumTimes:
return array_data
# insert missing data to represent gaps if needed
gapTimes = []
if insertDataGap != None:
# first find the time interval greater than 90% of others
timeIntervalList = []
for i in range(len(time_array) - 1):
if time_array[i+1] == time_array[i]:
continue
timeIntervalList.append(time_array[i+1] - time_array[i])
timeIntervalList.sort()
index = int(len(timeIntervalList)*0.9)
maxInterval = timeIntervalList[index]
for i in range(len(time_array) - 1):
if time_array[i+1] - time_array[i] > maxInterval * insertDataGap:
gapTimes.append(time_array[i+1])
gapTimes.append(time_array[i])
# get the number of times to skip each time
numSkip = numTimes/maxNumTimes
# get the number of rows in the new_array
numRows = 0
numTimes = 0
useThisTime = False
for i in range(len(time_array)):
if i == 0:
numTimes = 1
elif time_array[i-1] != time_array[i]:
numTimes += 1
if numTimes % (numSkip + 1) == 0 or time_array[i] in gapTimes:
useThisTime = True
if array_data[i, -1] != self.__missing:
numRows += 1
else:
useThisTime = False
else:
if useThisTime:
if array_data[i, -1] != self.__missing:
numRows += 1
# create new_array
new_array = numpy.zeros((numRows, array_data.shape[1]), array_data.dtype)
# copy selected rows to new_array
numRows = 0
numTimes = 0
useThisTime = False
for i in range(len(time_array)):
if i == 0:
numTimes = 1
elif time_array[i-1] != time_array[i]:
numTimes += 1
if numTimes % (numSkip + 1) == 0 or time_array[i] in gapTimes:
useThisTime = True
if array_data[i, -1] != self.__missing:
new_array[numRows,:] = array_data[i,:]
numRows += 1
else:
useThisTime = False
else:
if useThisTime:
if array_data[i, -1] != self.__missing:
new_array[numRows,:] = array_data[i,:]
numRows += 1
return new_array
Ancestors (in MRO)
- madPcolorPlot
- builtins.object
Static methods
def __init__(
self, isprintText, titleStr, xLabelStr, yLabelStr, fullFilename, size='small', minColormap=None, maxColormap=None, smoothAltitude=True, insertDataGap=5, useAbsoluteTime=False, startTime=None, endTime=None, sortTimeFlag=False, maxNumTimes=None, maxNumAlt=None, truncateIsprint=False, colorMap=<matplotlib.colors.LinearSegmentedColormap object at 0x1169fb450>, yMinimum=None, yMaximum=None, altYTitle=None, altYLabels=None, noTime=False, noDate=False, timezone=0, background='w', peakAltStr=None)
init writes a madPColorPlot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The second must be gdalt, and third parameter to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
smoothAltitude - if True, extrapolate between existing data between altitudes to fill
in missing data; if False, leave missing
insertDataGap - this parameter sets the threshold for inserting a data gap. The time intervals
being plotted are ordered, and the time gap larger than 90% of the rest is determined.
Any time interval more than insertDataGap times bigger is then considered missing
data. Defaults to five. If None, no gaps are ever inserted. For data with close
to uniform time intervals, no gaps will be inserted.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
sortTimeFlag - if true, check that time is correctly sorted. If false (the default),
assume time already sorted
maxNumTimes - if not None, decimate the number of times in the isprint string to
maxNumTimes. If None (the default), plot all times.
maxNumAlt - if not None, reduce the number of altitudes to maxNumAlt. If None (the default),
plot all altitudes.
truncateIsprint - if True, and both maxNumTimes and maxNumAlt not = None, then truncate
the number of isprint lines to be maxNumTimes * maxNumAlt
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
yMinimum - minumum y value. If None (default), set by data y minimum.
yMaximum - maximum y value. If None (default), set by data y maximum.
altYTitle - title of right (second) y axis. If None (the default),
no Y axis on the right side.
altYLabels - a list of Y labels (strings) for the right axis. If None (the default),
no Y labels on the right axis.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
peakAltStr - if not None (the default), plot the peak altitude with x. Format is a
str with each line (time in same format as data, peak altitude)
Returns: void
Affects: None
def __init__(self, isprintText,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
minColormap = None,
maxColormap = None,
smoothAltitude = True,
insertDataGap = 5,
useAbsoluteTime = False,
startTime = None,
endTime = None,
sortTimeFlag = False,
maxNumTimes = None,
maxNumAlt = None,
truncateIsprint = False,
colorMap = matplotlib.cm.jet,
yMinimum = None,
yMaximum = None,
altYTitle = None,
altYLabels = None,
noTime = False,
noDate = False,
timezone = 0,
background = 'w',
peakAltStr = None):
"""__init__ writes a madPColorPlot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The second must be gdalt, and third parameter to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
smoothAltitude - if True, extrapolate between existing data between altitudes to fill
in missing data; if False, leave missing
insertDataGap - this parameter sets the threshold for inserting a data gap. The time intervals
being plotted are ordered, and the time gap larger than 90% of the rest is determined.
Any time interval more than insertDataGap times bigger is then considered missing
data. Defaults to five. If None, no gaps are ever inserted. For data with close
to uniform time intervals, no gaps will be inserted.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
sortTimeFlag - if true, check that time is correctly sorted. If false (the default),
assume time already sorted
maxNumTimes - if not None, decimate the number of times in the isprint string to
maxNumTimes. If None (the default), plot all times.
maxNumAlt - if not None, reduce the number of altitudes to maxNumAlt. If None (the default),
plot all altitudes.
truncateIsprint - if True, and both maxNumTimes and maxNumAlt not = None, then truncate
the number of isprint lines to be maxNumTimes * maxNumAlt
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
yMinimum - minumum y value. If None (default), set by data y minimum.
yMaximum - maximum y value. If None (default), set by data y maximum.
altYTitle - title of right (second) y axis. If None (the default),
no Y axis on the right side.
altYLabels - a list of Y labels (strings) for the right axis. If None (the default),
no Y labels on the right axis.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
peakAltStr - if not None (the default), plot the peak altitude with x. Format is a
str with each line (time in same format as data, peak altitude)
Returns: void
Affects: None
"""
self.__missing = 1.0E30 # special value, used to create a masked array
self.__parameter_count = 3
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small'):
fontSize = 10
elif size in ('wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
if truncateIsprint and maxNumTimes != None and maxNumAlt != None:
isprintText = self.__truncateIsprint(isprintText, maxNumTimes * maxNumAlt)
# since matplotlib pcolor wants a regular grid, we create a grid with all altitudes
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
if sortTimeFlag:
array_data = self.sortArrayInTime(array_data)
if maxNumTimes != None and maxNumTimes != 0 and not truncateIsprint:
array_data = self.decimateTimes(array_data, int(maxNumTimes), insertDataGap)
# The first pass through is to obtain the number and range of the x and y variables
xList = []
yList = []
zList = []
zMin = None
zMax = None
# loop over all the lines of data in the array
for j in range(len(array_data)):
try:
x = array_data[j][0]
y = array_data[j][1]
z = array_data[j][2]
if x not in xList:
xList.append(x)
if y not in yList:
yList.append(y)
if z != self.__missing:
zList.append(z)
if zMin != None:
if z < zMin and z != self.__missing:
zMin = z
elif z != self.__missing:
zMin = z
if zMax != None:
if z > zMax and z != self.__missing:
zMax = z
elif z != self.__missing:
zMax = z
except:
continue
if zMin == None:
raise ValueError('No valid z data found')
# if both minColormap and maxColormap == None, use autoscaling
if minColormap == None and maxColormap == None:
zList.sort()
d10 = zList[int(len(zList)*0.10)]
d90 = zList[int(len(zList)*0.90)]
zMin = d10 - (d90-d10) * 0.75
zMax = d90 + (d90-d10) * 0.75
# now sort the X and Y axis lists and pull their length
xList.sort()
if startTime == None:
xMin = xList[0]
else:
xMin = startTime
if endTime == None:
xMax = xList[-1]
else:
xMax = endTime
yList.sort()
max_x_dimension = len(xList)
max_y_dimension = len(yList)
if yMinimum == None:
yMinimum = yList[0]
if yMaximum == None:
yMaximum = yList[-1]
self.truncateAlt = False
if maxNumAlt != None:
if max_y_dimension > maxNumAlt:
self.truncateAlt = True
# build dictonary of indexes into xList
self.xListDict = {}
for i in range(len(xList)):
self.xListDict[xList[i]] = i
# if self.truncateAlt == False, build dictonary of indexes into yList,
# else truncate y values by builing a list of maxNumAlt ranges
if self.truncateAlt == False:
self.yListDict = {}
for i in range(len(yList)):
self.yListDict[yList[i]] = i
else:
self.yListRanges = []
for i in range(maxNumAlt):
self.yListRanges.append(yList[int(i*(len(yList)/maxNumAlt))])
max_y_dimension = maxNumAlt
# now build arrays to handle the X axis label, Y axis label, and the Z data
X = numpy.zeros((max_x_dimension, max_y_dimension), numpy.float32)
Y = numpy.zeros((max_x_dimension, max_y_dimension), numpy.float32)
Z = numpy.ones((max_x_dimension, max_y_dimension), numpy.float32)
# all parameter values default to missing
Z = Z * self.__missing
# fill the X and Y arrays
for i in range(max_x_dimension):
for j in range(max_y_dimension):
X[i][j] = float(xList[i])
if self.truncateAlt:
Y[i][j] = float(yList[int(j*(len(yList)/maxNumAlt))])
else:
Y[i][j] = float(yList[j])
# Now load up the data array Z with the array_data measurements as a function of x and y
previousIndex = None
previousValue = None
presentTime = None
newTimeFound = True
for k in range(len(array_data)):
try:
xdata = array_data[k][0]
ydata = array_data[k][1]
zdata = array_data[k][2]
if zdata == self.__missing:
continue
if xdata != presentTime:
newTimeFound = True
else:
newTimeFound = False
presentTime = xdata
# now find the right place in the array for this data point
i = self.xListDict[xdata]
j = self.__getYIndex(ydata)
Z[i][j] = zdata
# now see if we need to fill in any gaps
if (not newTimeFound) and smoothAltitude:
if previousIndex < j - 1:
# fill in all missing points
for l in range(previousIndex + 1, j):
# simply average between the points based on index
thisValue = previousValue + (zdata - previousValue)*(float(l-previousIndex)/float(j-previousIndex))
Z[i][l] = thisValue
previousIndex = j
previousValue = zdata
except:
continue
# insert missing data to represent gaps if needed
if insertDataGap != None:
# first find the time interval greater than 90% of others
timeIntervalList = []
for i in range(len(xList) - 1):
timeIntervalList.append(xList[i+1] - xList[i])
timeIntervalList.sort()
index = int(len(timeIntervalList)*0.9)
maxInterval = timeIntervalList[index]
for i in range(len(xList) - 1):
if xList[i+1] - xList[i] > maxInterval * insertDataGap:
Z[i,:] = self.__missing
# insert missing data to represent gaps if needed in Altitude
if insertDataGap != None and not smoothAltitude:
# first find the time interval greater than 90% of others
altitudeIntervalList = []
for i in range(len(yList) - 1):
altitudeIntervalList.append(yList[i+1] - yList[i])
altitudeIntervalList.sort()
index = int(len(altitudeIntervalList)*0.9)
maxInterval = altitudeIntervalList[index]
for j in range(len(yList) - 1):
if yList[j+1] - yList[j] > maxInterval * insertDataGap and j < max_y_dimension:
Z[:,j] = self.__missing
# make Z a masked array
Znew = numpy.ma.masked_inside(Z, 0.99*self.__missing, 1.01*self.__missing)
# set up plotting parameters
if minColormap == None:
minColormap = zMin
if maxColormap == None:
maxColormap = zMax
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(8,3), facecolor = 'w')
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(14,5), facecolor = 'w')
if useAbsoluteTime:
# leave room for rotated datetime string
if size == 'large':
matplotlib.pylab.axes([0.1, 0.1, 0.85, 0.7])
elif size == 'wide':
matplotlib.pylab.axes([0.07, 0.2, 0.97, 0.7])
elif size == 'small':
matplotlib.pylab.axes([0.1, 0.18, 0.97, 0.7])
else:
if size == 'small':
matplotlib.pylab.axes([0.1, 0.18, 0.97, 0.7])
elif size == 'large':
matplotlib.pylab.axes([0.1, 0.2, 0.9, 0.73])
matplotlib.pylab.pcolor(X,Y,Znew, edgecolors='none', vmin=minColormap, vmax=maxColormap, cmap = colorMap,
norm = matplotlib.pylab.Normalize(), edgecolor='face')
matplotlib.pylab.colorbar()
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.xlim(xMin, xMax)
matplotlib.pylab.ylim(yMinimum, yMaximum)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
if useAbsoluteTime:
# if plot is less than 49 hours
xmin, xmax = matplotlib.pylab.xlim()
xmin = numpy.floor(xmin / (60*60))
xmax = numpy.ceil(xmax / (60*60))
if noDate == True:
newXLabels = []
if (xmax - xmin) <= 1:
newXLocs = []
xRange = numpy.arange(0,1,0.25)
for i in xRange:
tmp=(xmin+i)*60*60
newXLocs.append(tmp)
elif (xmax - xmin) <= 2:
newXLocs = []
xRange = numpy.arange(0,2,0.5)
for i in xRange:
tmp=(xmin+i)*60*60
newXLocs.append(tmp)
else:
newXLocs = []
step = int((xmax-xmin-1)/6)+1
xRange = list(range(0,int(xmax-xmin),step))
for i in xRange:
tmp = (xmin + i)*60*60
newXLocs.append(tmp)
#newXLocs.append(xmax*60*60)
newXLabels = convertToAbsoluteTimeStr(newXLocs, noDate=noDate, timezone=timezone)
matplotlib.pylab.xticks(newXLocs, newXLabels)
else:
locs, labels = matplotlib.pylab.xticks()
if len(locs) > 5:
# truncate by len(locs) / 5
scale = 1 + int(len(locs) / 5)
new_locs = []
for i in range(len(locs)):
if i % scale == 0:
new_locs.append(locs[i])
locs = new_locs
newXTicks = convertToAbsoluteTimeStr(locs)
matplotlib.pylab.xticks(locs, newXTicks, rotation=15)
else:
# if plot is less than 48 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
if xmax < 49:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
for i in range(0,int(4+newXmax),4):
newXLocs.append(i)
newXLabels.append(str(i))
matplotlib.pylab.xticks(newXLocs, newXLabels)
matplotlib.pylab.xticks(fontsize=fontSize)
# add second y-axis if desired
if altYTitle != None and altYLabels != None:
ax2 = matplotlib.pylab.twinx()
matplotlib.pylab.ylabel(altYTitle)
ax2.yaxis.tick_right()
matplotlib.pylab.yticks(list(range(len(altYLabels))), altYLabels)
matplotlib.pylab.yticks(fontsize=fontSize)
# get the handle to the figure now that it has been created so we can manipulate on subsequent calls.
#self.__figure = matplotlib.pylab.gcf()
if peakAltStr:
# plot peak altitudes
data = peakAltStr.split()
xdata = [float(item) for i, item in enumerate(data) if i % 2 == 0]
ydata = [float(item) for i, item in enumerate(data) if i % 2 == 1]
matplotlib.pylab.plot(xdata, ydata, 'b+')
matplotlib.pylab.savefig(fullFilename)
#matplotlib.pylab.show()
matplotlib.pylab.clf()
def decimateTimes(
self, array_data, maxNumTimes, insertDataGap)
decimateTimes decimates array_data to have at most maxNumTimes times.
Input: array_data - two-dimensional array to be decimated by deleting times and missing data.
maxNumTimes: int representing the maximum number of times to keep in array_data
insertDataGap - this parameter sets the threshold for inserting a data gap. The time intervals
being plotted are ordered, and the time gap larger than 90% of the rest is determined.
Note that this parameter is used here to stop the truncation of isprint lines that
will eventually be considered edge lines.
Returns: new array built from decimated array_data
def decimateTimes(self, array_data, maxNumTimes, insertDataGap):
"""decimateTimes decimates array_data to have at most maxNumTimes times.
Input: array_data - two-dimensional array to be decimated by deleting times and missing data.
maxNumTimes: int representing the maximum number of times to keep in array_data
insertDataGap - this parameter sets the threshold for inserting a data gap. The time intervals
being plotted are ordered, and the time gap larger than 90% of the rest is determined.
Note that this parameter is used here to stop the truncation of isprint lines that
will eventually be considered edge lines.
Returns: new array built from decimated array_data
"""
# get the number of times in array_data, and make a list of all unique times
numTimes = 0
uniqueTimes = []
time_array = array_data[:, 0]
for i in range(len(time_array)):
if i == 0:
numTimes = 1
uniqueTimes.append(time_array[i])
elif time_array[i-1] != time_array[i]:
uniqueTimes.append(time_array[i])
numTimes += 1
if numTimes <= maxNumTimes:
return array_data
# insert missing data to represent gaps if needed
gapTimes = []
if insertDataGap != None:
# first find the time interval greater than 90% of others
timeIntervalList = []
for i in range(len(time_array) - 1):
if time_array[i+1] == time_array[i]:
continue
timeIntervalList.append(time_array[i+1] - time_array[i])
timeIntervalList.sort()
index = int(len(timeIntervalList)*0.9)
maxInterval = timeIntervalList[index]
for i in range(len(time_array) - 1):
if time_array[i+1] - time_array[i] > maxInterval * insertDataGap:
gapTimes.append(time_array[i+1])
gapTimes.append(time_array[i])
# get the number of times to skip each time
numSkip = numTimes/maxNumTimes
# get the number of rows in the new_array
numRows = 0
numTimes = 0
useThisTime = False
for i in range(len(time_array)):
if i == 0:
numTimes = 1
elif time_array[i-1] != time_array[i]:
numTimes += 1
if numTimes % (numSkip + 1) == 0 or time_array[i] in gapTimes:
useThisTime = True
if array_data[i, -1] != self.__missing:
numRows += 1
else:
useThisTime = False
else:
if useThisTime:
if array_data[i, -1] != self.__missing:
numRows += 1
# create new_array
new_array = numpy.zeros((numRows, array_data.shape[1]), array_data.dtype)
# copy selected rows to new_array
numRows = 0
numTimes = 0
useThisTime = False
for i in range(len(time_array)):
if i == 0:
numTimes = 1
elif time_array[i-1] != time_array[i]:
numTimes += 1
if numTimes % (numSkip + 1) == 0 or time_array[i] in gapTimes:
useThisTime = True
if array_data[i, -1] != self.__missing:
new_array[numRows,:] = array_data[i,:]
numRows += 1
else:
useThisTime = False
else:
if useThisTime:
if array_data[i, -1] != self.__missing:
new_array[numRows,:] = array_data[i,:]
numRows += 1
return new_array
def displayToScreen(
self)
to implement this takes a reworking away from pylab to use the underlying matplotlib code
def displayToScreen(self):
" to implement this takes a reworking away from pylab to use the underlying matplotlib code "
pass
def getAverage(
self, X)
returns the average of items in a float array. Does not including missing data. If all data missing, returns self.__missing
def getAverage(self, X):
"""returns the average of items in a float array. Does not including missing data.
If all data missing, returns self.__missing
"""
count = 0
total = 0.0
for i in range(X.shape[0]):
if X[i] != self.__missing:
count += 1
total += X[i]
if count == 0:
return self.__missing
else:
return total / float(count)
def getFigureHandle(
self)
def getFigureHandle(self):
return self.__figure
def sortArrayInTime(
self, array_data)
sortArrayInTime sorts a two-dimensional array so that the first element in each row (time) is in ascending order.
Input: array_data - two-dimensional array to be sorted by rearranging rows so that the first element in each row (time) is in ascending order
Returns: new_array
def sortArrayInTime(self, array_data):
"""sortArrayInTime sorts a two-dimensional array so that the first element in each row (time) is in ascending order.
Input: array_data - two-dimensional array to be sorted by rearranging rows so
that the first element in each row (time) is in ascending order
Returns: new_array
"""
sortIndex = numpy.argsort(array_data[:,0])[0]
# if already sorted, just return original array
if sortIndex == numpy.sort(sortIndex):
return array_data
new_array = numpy.zeros(array_data.shape, array_data.dtype)
for i in range(len(sortIndex)):
new_array[sortIndex[i],:] = array_data[i,:]
return new_array
Instance variables
var truncateAlt
var xListDict
class madPcolorScan
madPcolorScan is the class that produces pcolor scans.
Usage example::
obj = madPcolorScan(isprintText,
'Nel (log(m^-3)) - 26 June 2006 13:49:43-14:07:36',
'Longitude',
'Latitude',
'./isprint.png',
size = 'large',
minColormap = 9,
maxColormap = 12)
Non-standard Python modules used: matplotlib
Change history:
Written by "Bill Rideout":mailto:wrideout@haystack.mit.edu Jul. 20, 2006
class madPcolorScan:
"""madPcolorScan is the class that produces pcolor scans.
Usage example::
obj = madPcolorScan(isprintText,
'Nel (log(m^-3)) - 26 June 2006 13:49:43-14:07:36',
'Longitude',
'Latitude',
'./isprint.png',
size = 'large',
minColormap = 9,
maxColormap = 12)
Non-standard Python modules used:
matplotlib
Change history:
Written by "Bill Rideout":mailto:wrideout@haystack.mit.edu Jul. 20, 2006
"""
def __init__(self, isprintText,
xGridSize,
yGridSize,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
xMinimum = None,
xMaximum = None,
yMinimum = None,
yMaximum = None,
minColormap = None,
maxColormap = None,
colorMap = matplotlib.cm.jet,
maxNumLines = None):
"""__init__ writes a madPcolorScan to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be the X axis value, and the second must be the Y axis value.
The third column is the value (intensity). Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
xGridSize - grid size for x data (for example 0.1 for 0.1 degree longitude grid)
yGridSize - grid size for x data (for example 0.1 for 0.1 degree latitude grid)
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
xMinimum = minumum x value. If None (default), uses lowest x value found.
xMaximum = maximum x value. If None (default), uses highest x value found.
yMinimum = minumum y value. If None (default), uses lowest y value found.
yMaximum = maximum y value. If None (default), uses highest y value found.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
maxNumLine - max number of lines in isprintText before truncating. If None, no truncation
Returns: void
Affects: None
"""
self.__missing = 1.0E30 # special value, used to create a masked array
self.__parameter_count = 3
self.__xGridSize = int(xGridSize)
self.__yGridSize = int(yGridSize)
self.__parseCount = 0 # used to tell which column self.__filter_input is working on
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
if maxNumLines != None:
isprintText = self.__truncateIsprint(isprintText, maxNumLines)
# since matplotlib pcolor wants a regular grid, we create a grid with all altitudes
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_input, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
array_data = self.sortArrayInX(array_data)
# The first pass through is to obtain the number and range of the x and y variables
xList = []
yList = []
zList = []
zMin = None
zMax = None
# loop over all the lines of data in the array
for j in range(len(array_data)):
try:
x = array_data[j][0]
y = array_data[j][1]
z = array_data[j][2]
if x not in xList:
xList.append(x)
if y not in yList:
yList.append(y)
if z != self.__missing:
zList.append(z)
if zMin != None:
if z < zMin and z != self.__missing:
zMin = z
elif z != self.__missing:
zMin = z
if zMax != None:
if z > zMax and z != self.__missing:
zMax = z
elif z != self.__missing:
zMax = z
except:
continue
if zMin == None:
raise ValueError('No valid z data found')
# if both minColormap and maxColormap == None, use autoscaling
if minColormap == None and maxColormap == None:
zList.sort()
d10 = zList[int(len(zList)*0.10)]
d90 = zList[int(len(zList)*0.90)]
zMin = d10 - (d90-d10) * 0.75
zMax = d90 + (d90-d10) * 0.75
# now sort the X and Y axis lists and pull their length
xList.sort()
xMin = xList[0]
xMax = xList[-1]
yList.sort()
yMin = yList[0]
yMax = yList[-1]
max_x_dimension = len(xList)
max_y_dimension = len(yList)
# build dictonary of indexes into xList
self.xListDict = {}
for i in range(len(xList)):
self.xListDict[xList[i]] = i
# build dictonary of indexes into yList,
self.yListDict = {}
for i in range(len(yList)):
self.yListDict[yList[i]] = i
# now build arrays to handle the X axis label, Y axis label, and the Z data
X = numpy.zeros((max_x_dimension, max_y_dimension), numpy.float32)
Y = numpy.zeros((max_x_dimension, max_y_dimension), numpy.float32)
Z = numpy.ones((max_x_dimension, max_y_dimension), numpy.float32)
# all parameter values default to missing
Z = Z * self.__missing
# fill the X and Y arrays
for i in range(max_x_dimension):
for j in range(max_y_dimension):
X[i][j] = float(xList[i])
Y[i][j] = float(yList[j])
# Now load up the data array Z with the array_data measurements as a function of x and y
previousIndex = None
previousValue = None
presentTime = None
newTimeFound = True
for k in range(len(array_data)):
try:
xdata = self.__round(array_data[k][0], self.__xGridSize)
ydata = self.__round(array_data[k][1], self.__yGridSize)
zdata = array_data[k][2]
if zdata == self.__missing:
continue
if xdata != presentTime:
newTimeFound = True
else:
newTimeFound = False
presentTime = xdata
# now find the right place in the array for this data point
i = self.xListDict[xdata]
j = self.yListDict[ydata]
Z[i][j] = zdata
previousIndex = j
previousValue = zdata
except:
continue
# make Z a masked array
Znew = numpy.ma.masked_inside(Z, 0.99*self.__missing, 1.01*self.__missing)
# set up plotting parameters
if xMinimum == None:
xMinimum = xMin
if xMaximum == None:
xMaximum = xMax
if yMinimum == None:
yMinimum = yMin
if yMaximum == None:
yMaximum = yMax
if minColormap == None:
minColormap = zMin
if maxColormap == None:
maxColormap = zMax
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(6,4), facecolor = 'w')
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(12,6), facecolor = 'w')
matplotlib.pylab.pcolor(X,Y,Znew, edgecolors='none', vmin=minColormap, vmax=maxColormap, cmap = colorMap, norm = matplotlib.pylab.Normalize())
matplotlib.pylab.colorbar()
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.xlim(xMinimum, xMaximum)
matplotlib.pylab.ylim(yMinimum, yMaximum)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
# get the handle to the figure now that it has been created so we can manipulate on subsequent calls.
#self.__figure = matplotlib.pylab.gcf()
try:
matplotlib.pylab.savefig(fullFilename)
except:
matplotlib.pylab.clf()
raise
#matplotlib.pylab.show()
matplotlib.pylab.clf()
def __round(self, value, increment):
"""__round returns a value to the nearest increment
"""
return value - math.fmod(value, increment)
def __filter_input(self,x):
"""__filter_input is called in map to convert missing strings to self.__missing, and to
round x and y vales to the nearest xGridSize or yGridSize
"""
try:
value = float(x)
if self.__parseCount % 3 == 0:
# x value
value = self.__round(value, self.__xGridSize)
elif self.__parseCount % 3 == 1:
# y value
value = self.__round(value, self.__yGridSize)
except:
value = self.__missing
self.__parseCount += 1
return value
def __truncateIsprint(self, isprintText, maxLines):
"""__truncateIsprint truncates isprintText to have maxLines at most.
"""
isprintList = isprintText.split('\n')
if len(isprintList) < maxLines:
return isprintText
else:
dropNumber = int(1 + len(isprintList)/maxLines)
newIsprintText = ''
for i in range(0,len(isprintList),dropNumber):
newIsprintText += isprintList[i] + '\n'
return newIsprintText
def displayToScreen(self):
" to implement this takes a reworking away from pylab to use the underlying matplotlib code "
pass
def getFigureHandle(self):
return self.__figure
def getAverage(self, X):
"""returns the average of items in a float array. Does not including missing data.
If all data missing, returns self.__missing
"""
count = 0
total = 0.0
for i in range(X.shape[0]):
if X[i] != self.__missing:
count += 1
total += X[i]
if count == 0:
return self.__missing
else:
return total / float(count)
def sortArrayInX(self, array_data):
"""sortArrayInX sorts a two-dimensional array so that the first element in each row (x) is in ascending order.
Input: array_data - two-dimensional array to be sorted by rearranging rows so
that the first element in each row (x) is in ascending order
Returns: new_array
"""
sortIndex = numpy.argsort(array_data[:,0])[0]
# if already sorted, just return original array
if sortIndex == numpy.sort(sortIndex):
return array_data
new_array = numpy.zeros(array_data.shape, array_data.dtype)
for i in range(len(sortIndex)):
new_array[sortIndex[i],:] = array_data[i,:]
return new_array
Ancestors (in MRO)
- madPcolorScan
- builtins.object
Static methods
def __init__(
self, isprintText, xGridSize, yGridSize, titleStr, xLabelStr, yLabelStr, fullFilename, size='small', xMinimum=None, xMaximum=None, yMinimum=None, yMaximum=None, minColormap=None, maxColormap=None, colorMap=<matplotlib.colors.LinearSegmentedColormap object at 0x1169fb450>, maxNumLines=None)
init writes a madPcolorScan to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be the X axis value, and the second must be the Y axis value.
The third column is the value (intensity). Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
xGridSize - grid size for x data (for example 0.1 for 0.1 degree longitude grid)
yGridSize - grid size for x data (for example 0.1 for 0.1 degree latitude grid)
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
xMinimum = minumum x value. If None (default), uses lowest x value found.
xMaximum = maximum x value. If None (default), uses highest x value found.
yMinimum = minumum y value. If None (default), uses lowest y value found.
yMaximum = maximum y value. If None (default), uses highest y value found.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
maxNumLine - max number of lines in isprintText before truncating. If None, no truncation
Returns: void
Affects: None
def __init__(self, isprintText,
xGridSize,
yGridSize,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
xMinimum = None,
xMaximum = None,
yMinimum = None,
yMaximum = None,
minColormap = None,
maxColormap = None,
colorMap = matplotlib.cm.jet,
maxNumLines = None):
"""__init__ writes a madPcolorScan to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be the X axis value, and the second must be the Y axis value.
The third column is the value (intensity). Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
xGridSize - grid size for x data (for example 0.1 for 0.1 degree longitude grid)
yGridSize - grid size for x data (for example 0.1 for 0.1 degree latitude grid)
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
xMinimum = minumum x value. If None (default), uses lowest x value found.
xMaximum = maximum x value. If None (default), uses highest x value found.
yMinimum = minumum y value. If None (default), uses lowest y value found.
yMaximum = maximum y value. If None (default), uses highest y value found.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
maxNumLine - max number of lines in isprintText before truncating. If None, no truncation
Returns: void
Affects: None
"""
self.__missing = 1.0E30 # special value, used to create a masked array
self.__parameter_count = 3
self.__xGridSize = int(xGridSize)
self.__yGridSize = int(yGridSize)
self.__parseCount = 0 # used to tell which column self.__filter_input is working on
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
if maxNumLines != None:
isprintText = self.__truncateIsprint(isprintText, maxNumLines)
# since matplotlib pcolor wants a regular grid, we create a grid with all altitudes
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_input, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
array_data = self.sortArrayInX(array_data)
# The first pass through is to obtain the number and range of the x and y variables
xList = []
yList = []
zList = []
zMin = None
zMax = None
# loop over all the lines of data in the array
for j in range(len(array_data)):
try:
x = array_data[j][0]
y = array_data[j][1]
z = array_data[j][2]
if x not in xList:
xList.append(x)
if y not in yList:
yList.append(y)
if z != self.__missing:
zList.append(z)
if zMin != None:
if z < zMin and z != self.__missing:
zMin = z
elif z != self.__missing:
zMin = z
if zMax != None:
if z > zMax and z != self.__missing:
zMax = z
elif z != self.__missing:
zMax = z
except:
continue
if zMin == None:
raise ValueError('No valid z data found')
# if both minColormap and maxColormap == None, use autoscaling
if minColormap == None and maxColormap == None:
zList.sort()
d10 = zList[int(len(zList)*0.10)]
d90 = zList[int(len(zList)*0.90)]
zMin = d10 - (d90-d10) * 0.75
zMax = d90 + (d90-d10) * 0.75
# now sort the X and Y axis lists and pull their length
xList.sort()
xMin = xList[0]
xMax = xList[-1]
yList.sort()
yMin = yList[0]
yMax = yList[-1]
max_x_dimension = len(xList)
max_y_dimension = len(yList)
# build dictonary of indexes into xList
self.xListDict = {}
for i in range(len(xList)):
self.xListDict[xList[i]] = i
# build dictonary of indexes into yList,
self.yListDict = {}
for i in range(len(yList)):
self.yListDict[yList[i]] = i
# now build arrays to handle the X axis label, Y axis label, and the Z data
X = numpy.zeros((max_x_dimension, max_y_dimension), numpy.float32)
Y = numpy.zeros((max_x_dimension, max_y_dimension), numpy.float32)
Z = numpy.ones((max_x_dimension, max_y_dimension), numpy.float32)
# all parameter values default to missing
Z = Z * self.__missing
# fill the X and Y arrays
for i in range(max_x_dimension):
for j in range(max_y_dimension):
X[i][j] = float(xList[i])
Y[i][j] = float(yList[j])
# Now load up the data array Z with the array_data measurements as a function of x and y
previousIndex = None
previousValue = None
presentTime = None
newTimeFound = True
for k in range(len(array_data)):
try:
xdata = self.__round(array_data[k][0], self.__xGridSize)
ydata = self.__round(array_data[k][1], self.__yGridSize)
zdata = array_data[k][2]
if zdata == self.__missing:
continue
if xdata != presentTime:
newTimeFound = True
else:
newTimeFound = False
presentTime = xdata
# now find the right place in the array for this data point
i = self.xListDict[xdata]
j = self.yListDict[ydata]
Z[i][j] = zdata
previousIndex = j
previousValue = zdata
except:
continue
# make Z a masked array
Znew = numpy.ma.masked_inside(Z, 0.99*self.__missing, 1.01*self.__missing)
# set up plotting parameters
if xMinimum == None:
xMinimum = xMin
if xMaximum == None:
xMaximum = xMax
if yMinimum == None:
yMinimum = yMin
if yMaximum == None:
yMaximum = yMax
if minColormap == None:
minColormap = zMin
if maxColormap == None:
maxColormap = zMax
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(6,4), facecolor = 'w')
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(12,6), facecolor = 'w')
matplotlib.pylab.pcolor(X,Y,Znew, edgecolors='none', vmin=minColormap, vmax=maxColormap, cmap = colorMap, norm = matplotlib.pylab.Normalize())
matplotlib.pylab.colorbar()
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.xlim(xMinimum, xMaximum)
matplotlib.pylab.ylim(yMinimum, yMaximum)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
# get the handle to the figure now that it has been created so we can manipulate on subsequent calls.
#self.__figure = matplotlib.pylab.gcf()
try:
matplotlib.pylab.savefig(fullFilename)
except:
matplotlib.pylab.clf()
raise
#matplotlib.pylab.show()
matplotlib.pylab.clf()
def displayToScreen(
self)
to implement this takes a reworking away from pylab to use the underlying matplotlib code
def displayToScreen(self):
" to implement this takes a reworking away from pylab to use the underlying matplotlib code "
pass
def getAverage(
self, X)
returns the average of items in a float array. Does not including missing data. If all data missing, returns self.__missing
def getAverage(self, X):
"""returns the average of items in a float array. Does not including missing data.
If all data missing, returns self.__missing
"""
count = 0
total = 0.0
for i in range(X.shape[0]):
if X[i] != self.__missing:
count += 1
total += X[i]
if count == 0:
return self.__missing
else:
return total / float(count)
def getFigureHandle(
self)
def getFigureHandle(self):
return self.__figure
def sortArrayInX(
self, array_data)
sortArrayInX sorts a two-dimensional array so that the first element in each row (x) is in ascending order.
Input: array_data - two-dimensional array to be sorted by rearranging rows so that the first element in each row (x) is in ascending order
Returns: new_array
def sortArrayInX(self, array_data):
"""sortArrayInX sorts a two-dimensional array so that the first element in each row (x) is in ascending order.
Input: array_data - two-dimensional array to be sorted by rearranging rows so
that the first element in each row (x) is in ascending order
Returns: new_array
"""
sortIndex = numpy.argsort(array_data[:,0])[0]
# if already sorted, just return original array
if sortIndex == numpy.sort(sortIndex):
return array_data
new_array = numpy.zeros(array_data.shape, array_data.dtype)
for i in range(len(sortIndex)):
new_array[sortIndex[i],:] = array_data[i,:]
return new_array
Instance variables
var xListDict
var yListDict
class madPcolorWedgeKmlScan
madPcolorWedgeKmlScan is the class that produces pcolor scan kml files where data is drawn as wedge shapes.
Usage example::
obj = madPcolorWedgeKmlScan(isprintText,
'az',
'./isprint.kml',
minColormap = 9,
maxColormap = 12)
Non-standard Python modules used: matplotlib
Change history:
Written by "Bill Rideout":mailto:wrideout@haystack.mit.edu Dec 15, 2010
class madPcolorWedgeKmlScan:
"""madPcolorWedgeKmlScan is the class that produces pcolor scan kml files where data is drawn as wedge shapes.
Usage example::
obj = madPcolorWedgeKmlScan(isprintText,
'az',
'./isprint.kml',
minColormap = 9,
maxColormap = 12)
Non-standard Python modules used:
matplotlib
Change history:
Written by "Bill Rideout":mailto:wrideout@haystack.mit.edu Dec 15, 2010
"""
def __init__(self, isprintText,
scanType,
fullFilename,
minColormap = None,
maxColormap = None,
instName = None,
instDesc = None,
instLat = None,
instLon = None,
instAlt = None,
colorMap = None):
"""__init__ writes a madPcolorWedgeKmlScan to a file.
Inputs:
isprintText - a string giving isprint output without headers. The 9 parameters
are (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2, ). Any
missing data should be written as "missing" or other string that
cannot be converted to a float. gdlatr, gdlonr, galtr are the station location
scanType - must be 'az' or 'el_lat' (for north or south el scans), 'el_lon'
(for east or west el scans) or 'el_gcdist' (for all other el scans.
For az - x is long, y is lat
for el_* y is always alt, and x is either lat, lon, or great circle distance
fullFilename - full path of file containing kml file to be saved. Extension must
be kml, or exception thrown.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
instName, instDesc, instLat, instLon, instAlt - used to create a placemark for the radar. If
instLat or instLon missing, no placemark added.
colorMap - A matplotlib colormap to use to create colors. If None, then blue minimum and red maximum
Returns: void
Affects: Writes fullFilename to disk
"""
# verify input
if scanType not in ('az', 'el_lat', 'el_lon', 'el_gcdist'):
raise ValueError('scanType must be "az", "el_lat", "el_lon", "el_gcdist", not %s' % (str(scanType)))
isprintLines = isprintText.split('\n')
newLonMax, newLonMin, newLatMax, newLatMin, newAltMax, newGreatCDist, newParmMax, newParmMin = self._getLimits(isprintLines)
# get look point
lookLat = (newLatMax + newLatMin)/2.0
lookLon = (newLonMax + newLonMin)/2.0
lookAlt = newAltMax * 10.0
if maxColormap == None:
maxColormap = newParmMax
if minColormap == None:
minColormap = newParmMin
# set up wedge generator
dataGen = self._generateWedges(isprintLines, scanType)
text = self._createKmlText(dataGen, maxColormap, minColormap, lookLat, lookLon, lookAlt, scanType,
instName, instDesc, instLat, instLon, instAlt, colorMap)
f = open(fullFilename, 'w')
f.write(text)
f.close()
def getGreatCircleDist(self, gdlatr, glonr, gdlat, glon):
"""getGreatCircleDist return the great circle distance in. Taken from madDeriveMethods c code.
Inputs: gdlatr, glonr: reference point on earth's surface
gdlat, glon: second point on earth's surface
"""
dlon = glonr/57.2958 - glon/57.2958
dlat = gdlatr/57.2958 - gdlat/57.2958;
a = math.sin(dlat/2.0)*math.sin(dlat/2.0) + math.cos(gdlatr/57.2958)*math.cos(gdlat/57.2958) * math.sin(dlon/2.0)*math.sin(dlon/2.0)
if math.sqrt(a) < 1.0:
c = 2.0 * math.asin(math.sqrt(a))
else:
c = 2.0 * math.asin(1.0)
return(c * 6371.2)
def _getLimits(self, isprintLines):
"""returns (newLonMax, newLonMin, newLatMax, newLatMin, newAltMax, newGreatCDist, newParmMax, newParmMin)
given isprint lines with
parameters (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2, )
We only look at the end points of ranges and the station location
"""
# set initial values to station location
items = isprintLines[1].split()
slat = float(items[0])
slon = float(items[1])
if slon > 180.0:
slon -= 360.0
salt = float(items[2])
maxLon = slon
minLon = slon
maxLat = slat
minLat = slat
maxAlt = salt
maxGreatCDist = 0.0
maxParm = -1.0e-30
minParm = 1.0e30
lastAz = None
lastEl = None
lastRange = None
for i, isprintLine in enumerate(isprintLines):
items = isprintLine.split()
if len(items) < 9:
continue
range = float(items[3])
az1 = float(items[4])
az2 = float(items[5])
thisAz = (az1 + az2)/2
el1 = float(items[6])
el2 = float(items[7])
thisEl = (el1 + el2)/2
try:
parm = float(items[8])
if parm > maxParm:
maxParm = parm
if parm < minParm:
minParm = parm
except ValueError:
continue
# see if we're at an end point
if lastAz != None:
if abs(lastAz - thisAz) > 0.001 or abs(lastEl - thisEl) > 0.001:
# the last point was an end point
gdlat,glon,gdalt = madrigal._derive.radarToGeodetic(slat,slon,salt,lastAz,lastEl,lastRange)
if gdlat > maxLat:
maxLat = gdlat
if gdlat < minLat:
minLat = gdlat
if glon > maxLon:
maxLon = glon
if glon < minLon:
minLon = glon
if gdalt > maxAlt:
maxAlt = gdalt
greatCDist = self.getGreatCircleDist(slat, slon, gdlat, glon)
if greatCDist > maxGreatCDist:
maxGreatCDist = greatCDist
lastAz = thisAz
lastEl = thisEl
lastRange = range
# check last point
gdlat,glon,gdalt = madrigal._derive.radarToGeodetic(slat,slon,slat,lastAz,lastEl,lastRange)
if gdlat > maxLat:
maxLat = gdlat
if gdlat < minLat:
minLat = gdlat
if glon > maxLon:
maxLon = glon
if glon < minLon:
minLon = glon
if gdalt > maxAlt:
maxAlt = gdalt
greatCDist = self.getGreatCircleDist(slat, slon, gdlat, glon)
if greatCDist > maxGreatCDist:
maxGreatCDist = greatCDist
return((maxLon, minLon, maxLat, minLat, maxAlt, maxGreatCDist, maxParm, minParm))
def _generateWedges(self, isprintLines, scanType):
"""_generateWedges is a python generator that return a data wedge in the form
(dataValue, (point1_lat, point1_lon, point1_alt), (point2_lat, point2_lon, point2_alt),
(point3_lat, point3_lon, point3_alt), (point4_lat, point4_lon, point4_alt))
Inputs:
isprintLines - a list of line with 9 parameters that
are (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2, ). Any
missing data should be written as "missing" or other string that
cannot be converted to a float. gdlatr, gdlonr, galtr are the station location.
No wedge is returned for a missing data line
scanType - must be 'az' or 'el_lat' (for north or south el scans), 'el_lon'
(for east or west el scans) or 'el_gcdist' (for all other el scans.
For az - x is long, y is lat
for el_* y is always alt, and x is either lat, lon, or great circle distance
"""
# state variables
lastAz1 = None
lastAz2 = None
lastEl1 = None
lastEl2 = None
lastRange = None
lastDiffRangeLow = None
lastValue = None
for isprintLine in isprintLines:
items = isprintLine.split()
if len(items) < 9:
continue
gdlatr = float(items[0])
gdlonr = float(items[1])
galtr = float(items[2])
range = float(items[3])
az1 = float(items[4])
az2 = float(items[5])
el1 = float(items[6])
el2 = float(items[7])
valueStr = items[8]
if lastAz1 != None and ( abs(lastAz1-az1) > 0.001 or abs(lastEl1-el1) > 0.001 ):
# new radar line found, send last point if needed
if lastValue != None:
dataWedge = self._getWedge(gdlatr, gdlonr, galtr, lastRange,
lastDiffRangeLow, lastDiffRangeLow, lastAz1, lastAz2,
lastEl1, lastEl2, lastValue)
yield(dataWedge)
# this is the first point in the line, so no lastDiffRangeLow
lastDiffRangeLow = None
elif lastAz1 != None and ( abs(lastAz1-az1) < 0.001 and abs(lastEl1-el1) < 0.001 ):
# continuing radar line
if lastValue != None:
lastDiffRangeLow = (range - lastRange)/2.0
dataWedge = self._getWedge(gdlatr, gdlonr, galtr, lastRange,
lastDiffRangeLow, (range-lastRange)/2.0, lastAz1, lastAz2,
lastEl1, lastEl2, lastValue)
yield(dataWedge)
else:
lastDiffRangeLow = (range - lastRange)/2.0
# reset state variables
lastAz1 = az1
lastAz2 = az2
lastEl1 = el1
lastEl2 = el2
lastRange = range
try:
lastValue = float(valueStr)
except:
lastValue = None
# finally, send last wedge if needed
if lastValue != None:
dataWedge = self._getWedge(gdlatr, gdlonr, galtr, lastRange,
lastDiffRangeLow, lastDiffRangeLow, lastAz1, lastAz2,
lastEl1, lastEl2, lastValue)
yield(dataWedge)
def _getWedge(self, gdlatr, gdlonr, galtr, range,
diffRange1, diffRange2, az1, az2,
el1, el2, value):
"""_getWedge returns return a data wedge in the form
(dataValue, (point1_lat, point1_lon, point1_alt), (point2_lat, point2_lon, point2_alt),
(point3_lat, point3_lon, point3_alt), (point4_lat, point4_lon, point4_alt)).
Inputs:
gdlatr, gdlonr, galtr - location of radar
range, diffRange1, diffRange2 - range of center of wedge, and distance in and out from there
az1, az2, el1, el2 - start and end aximuth and elevation. Which is used depends on scanType
value - float data value
"""
point1_lat,point1_lon,point1_alt = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range-diffRange1)
point2_lat,point2_lon,point2_alt = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az2,el2,range-diffRange1)
point3_lat,point3_lon,point3_alt = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az2,el2,range+diffRange2)
point4_lat,point4_lon,point4_alt = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range+diffRange2)
return((value, (point1_lat,point1_lon,point1_alt), (point2_lat,point2_lon,point2_alt),
(point3_lat,point3_lon,point3_alt), (point4_lat,point4_lon,point4_alt)))
def _createKmlText(self, dataGen, maxColormap, minColormap, lookLat, lookLon, lookAlt, scanType,
instName, instDesc, instLat, instLon, instAlt, colorMap):
"""_createKmlText creates kml text
Inputs:
dataGen - list in form (dataValue, (point1_lat, point1_lon, point1_alt), (point2_lat, point2_lon, point2_alt),
(point3_lat, point3_lon, point3_alt), (point4_lat, point4_lon, point4_alt))
maxColormap
minColormap
lookLat
lookLon
lookAlt
scanType
instName, instDesc, instLat, instLon, instAlt - - used to create a placemark for the radar. If
instLat or instLon missing, no placemark added.
colorMap - A matplotlib colormap to use to create colors. If None, then blue minimum and red maximum
"""
# need to fill in id and color
styleTemplate = """
"""
# need to fill in id and coordinates
placemarkTemplate = """
#%s
absolute
%s
"""
instTemplate = """
%s
%s
%f,%f,%f
"""
# need to fill in styles and placemarks
documentTemplate = """
ScanKmlFile
%f
%f
%f
%f
50
%f
relativeToGround
%s
%s
%s
"""
retStr = ''
styleStr = ''
# create 100 colors
for i in range(100):
if not colorMap:
colorStr = self._getKmlRGB(i/100.0)
else:
matLabColorStr = matplotlib.colors.rgb2hex(colorMap(i/100.0)[:3])
red = matLabColorStr[1:3]
green = matLabColorStr[3:5]
blue = matLabColorStr[5:7]
colorStr = 'ff' + blue + green + red # this is the google earth standard for opaque color
styleStr += styleTemplate % (i, colorStr)
placemarkStr = ''
# loop through data adding strings to placemark string
while(True):
try:
p = next(dataGen)
except StopIteration:
break
data = p[0]
if data > maxColormap:
styleIndex = 99
elif data < minColormap:
styleIndex = 0
else:
styleIndex = int(99.0 * ((data-minColormap)/(maxColormap-minColormap)))
styleId = 'p%i' % (styleIndex)
coordStr = ('%f,%f,%f %f,%f,%f %f,%f,%f %f,%f,%f %f,%f,%f' ) % \
(p[1][1], p[1][0], p[1][2]*1000.0,
p[2][1], p[2][0], p[2][2]*1000.0,
p[3][1], p[3][0], p[3][2]*1000.0,
p[4][1], p[4][0], p[4][2]*1000.0,
p[1][1], p[1][0], p[1][2]*1000.0)
placemarkStr += placemarkTemplate % (styleId, coordStr)
if scanType in ('el_lat'):
heading = 90.0
distance = 200.0
elif scanType in ('el_lon', 'el_gcdist'):
heading = 0.0
distance = 200.0
else:
heading = 0.0
distance = 1000.0
# create instStr
instStr = ''
if instLat != None and instLon != None:
if instAlt == None:
instAlt = 0.0
if instName == None:
instName = 'Radar'
if instDesc == None:
instDesc = 'This is the location of the radar that produced this scan.'
instStr += instTemplate % (instName, instDesc, instLon, instLat, instAlt)
retStr += documentTemplate % (lookLon, lookLat, lookAlt*100.0, heading, lookAlt*distance, styleStr, instStr, placemarkStr)
return(retStr)
def _getKmlRGB(self, value, transparent=False, wrap=False):
"""_getKmlRGB returns a Kml color string given a value between 0 qnd 1.
If transparent == False, solid color. Otherwise transpanency set to 50%
If wrap, colors at zero and 1 wrap. Otherwise, extremes red and blue
If invert, use value = 1 - value
"""
if value < 0.0 or value > 1.0:
raise ValueError('value must be between 0 and 1, not %f' % (value))
if wrap:
r,g,b = colorsys.hsv_to_rgb(value, 0.9, 0.9)
else:
r,g,b = colorsys.hsv_to_rgb(value*0.67, 0.9, 0.9)
if not transparent:
transStr = 'ff'
else:
transStr = '80'
retStr = '%s%s%s%s' % (transStr,
hex(int(r*255))[-2:],
hex(int(g*255))[-2:],
hex(int(b*255))[-2:])
return(retStr)
Ancestors (in MRO)
- madPcolorWedgeKmlScan
- builtins.object
Static methods
def __init__(
self, isprintText, scanType, fullFilename, minColormap=None, maxColormap=None, instName=None, instDesc=None, instLat=None, instLon=None, instAlt=None, colorMap=None)
init writes a madPcolorWedgeKmlScan to a file.
Inputs:
isprintText - a string giving isprint output without headers. The 9 parameters
are (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2, <parameter value>). Any
missing data should be written as "missing" or other string that
cannot be converted to a float. gdlatr, gdlonr, galtr are the station location
scanType - must be 'az' or 'el_lat' (for north or south el scans), 'el_lon'
(for east or west el scans) or 'el_gcdist' (for all other el scans.
For az - x is long, y is lat
for el_* y is always alt, and x is either lat, lon, or great circle distance
fullFilename - full path of file containing kml file to be saved. Extension must
be kml, or exception thrown.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
instName, instDesc, instLat, instLon, instAlt - used to create a placemark for the radar. If
instLat or instLon missing, no placemark added.
colorMap - A matplotlib colormap to use to create colors. If None, then blue minimum and red maximum
Returns: void
Affects: Writes fullFilename to disk
def __init__(self, isprintText,
scanType,
fullFilename,
minColormap = None,
maxColormap = None,
instName = None,
instDesc = None,
instLat = None,
instLon = None,
instAlt = None,
colorMap = None):
"""__init__ writes a madPcolorWedgeKmlScan to a file.
Inputs:
isprintText - a string giving isprint output without headers. The 9 parameters
are (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2, ). Any
missing data should be written as "missing" or other string that
cannot be converted to a float. gdlatr, gdlonr, galtr are the station location
scanType - must be 'az' or 'el_lat' (for north or south el scans), 'el_lon'
(for east or west el scans) or 'el_gcdist' (for all other el scans.
For az - x is long, y is lat
for el_* y is always alt, and x is either lat, lon, or great circle distance
fullFilename - full path of file containing kml file to be saved. Extension must
be kml, or exception thrown.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
instName, instDesc, instLat, instLon, instAlt - used to create a placemark for the radar. If
instLat or instLon missing, no placemark added.
colorMap - A matplotlib colormap to use to create colors. If None, then blue minimum and red maximum
Returns: void
Affects: Writes fullFilename to disk
"""
# verify input
if scanType not in ('az', 'el_lat', 'el_lon', 'el_gcdist'):
raise ValueError('scanType must be "az", "el_lat", "el_lon", "el_gcdist", not %s' % (str(scanType)))
isprintLines = isprintText.split('\n')
newLonMax, newLonMin, newLatMax, newLatMin, newAltMax, newGreatCDist, newParmMax, newParmMin = self._getLimits(isprintLines)
# get look point
lookLat = (newLatMax + newLatMin)/2.0
lookLon = (newLonMax + newLonMin)/2.0
lookAlt = newAltMax * 10.0
if maxColormap == None:
maxColormap = newParmMax
if minColormap == None:
minColormap = newParmMin
# set up wedge generator
dataGen = self._generateWedges(isprintLines, scanType)
text = self._createKmlText(dataGen, maxColormap, minColormap, lookLat, lookLon, lookAlt, scanType,
instName, instDesc, instLat, instLon, instAlt, colorMap)
f = open(fullFilename, 'w')
f.write(text)
f.close()
def getGreatCircleDist(
self, gdlatr, glonr, gdlat, glon)
getGreatCircleDist return the great circle distance in. Taken from madDeriveMethods c code.
Inputs: gdlatr, glonr: reference point on earth's surface gdlat, glon: second point on earth's surface
def getGreatCircleDist(self, gdlatr, glonr, gdlat, glon):
"""getGreatCircleDist return the great circle distance in. Taken from madDeriveMethods c code.
Inputs: gdlatr, glonr: reference point on earth's surface
gdlat, glon: second point on earth's surface
"""
dlon = glonr/57.2958 - glon/57.2958
dlat = gdlatr/57.2958 - gdlat/57.2958;
a = math.sin(dlat/2.0)*math.sin(dlat/2.0) + math.cos(gdlatr/57.2958)*math.cos(gdlat/57.2958) * math.sin(dlon/2.0)*math.sin(dlon/2.0)
if math.sqrt(a) < 1.0:
c = 2.0 * math.asin(math.sqrt(a))
else:
c = 2.0 * math.asin(1.0)
return(c * 6371.2)
class madPcolorWedgeScan
madPcolorWedgeScan is the class that produces pcolor scans where data is drawn as wedge shapes.
Usage example::
obj = madPcolorScan(isprintText,
'Nel (log(m^-3)) - 26 June 2006 13:49:43-14:07:36',
'Longitude',
'Latitude',
'./isprint.png',
size = 'large',
minColormap = 9,
maxColormap = 12)
Non-standard Python modules used: matplotlib
Change history:
Written by "Bill Rideout":mailto:wrideout@haystack.mit.edu Jul. 20, 2006
class madPcolorWedgeScan:
"""madPcolorWedgeScan is the class that produces pcolor scans where data is drawn as wedge shapes.
Usage example::
obj = madPcolorScan(isprintText,
'Nel (log(m^-3)) - 26 June 2006 13:49:43-14:07:36',
'Longitude',
'Latitude',
'./isprint.png',
size = 'large',
minColormap = 9,
maxColormap = 12)
Non-standard Python modules used:
matplotlib
Change history:
Written by "Bill Rideout":mailto:wrideout@haystack.mit.edu Jul. 20, 2006
"""
def __init__(self, isprintText,
scanType,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
xMinimum = None,
xMaximum = None,
yMinimum = None,
yMaximum = None,
minColormap = None,
maxColormap = None,
colorMap = matplotlib.cm.jet):
"""__init__ writes a madPcolorWedgeScan to a file.
Inputs:
isprintText - a string giving isprint output without headers. The 9 parameters
are (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2, ). Any
missing data should be written as "missing" or other string that
cannot be converted to a float. gdlatr, gdlonr, galtr are the station location
scanType - must be 'az' or 'el_lat' (for north or south el scans), 'el_lon'
(for east or west el scans) or 'el_gcdist' (for all other el scans.
For az - x is long, y is lat
for el_* y is always alt, and x is either lat, lon, or great circle distance
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
xMinimum = minumum x value. If None (default), uses lowest value found.
xMaximum = maximum x value. If None (default), uses highest value found.
yMinimum = minumum y value. If None (default), uses lowest value found.
yMaximum = maximum y value. If None (default), uses highest value found.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
Returns: void
Affects: Writes fullFilename to disk
"""
# verify input
if scanType not in ('az', 'el_lat', 'el_lon', 'el_gcdist'):
raise ValueError('scanType must be "az", "el_lat", "el_lon", "el_gcdist", not %s' % (str(scanType)))
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
isprintLines = isprintText.split('\n')
if xMaximum == None or xMinimum == None or yMaximum == None or yMinimum or maxColormap == None or minColormap == None:
newLonMax, newLonMin, newLatMax, newLatMin, newAltMax, newGreatCDist, newParmMax, newParmMin = self._getLimits(isprintLines)
# set values
xMaximum, xMinimum, yMaximum, yMinimum = self._setLimits(scanType, xMaximum, xMinimum, yMaximum, yMinimum,
newLonMax, newLonMin, newLatMax, newLatMin, newAltMax,
newGreatCDist, newParmMax, newParmMin)
if maxColormap == None:
maxColormap = newParmMax
if minColormap == None:
minColormap = newParmMin
# set up wedge generator
dataGen = self._generateWedges(isprintLines, scanType)
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(6,5), facecolor = 'w')
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(12,6), facecolor = 'w')
try:
while True:
data = next(dataGen)
x = [data[1][0], data[2][0], data[3][0], data[4][0]]
y = [data[1][1], data[2][1], data[3][1], data[4][1]]
dataRatio = (data[0] - minColormap) / (maxColormap - minColormap)
colorStr = matplotlib.colors.rgb2hex(colorMap(dataRatio)[:3])
thisPlot = matplotlib.pyplot.fill(x, y, colorStr, edgecolor='none')
except StopIteration:
pass
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
scalableMappable = matplotlib.cm.ScalarMappable(cmap=colorMap)
scalableMappable.set_array(numpy.array([minColormap, maxColormap]))
scalableMappable.set_clim(minColormap, maxColormap)
def get_alpha(*args, **kw):
return(1.0)
scalableMappable.get_alpha = get_alpha
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.xlim(xMinimum, xMaximum)
matplotlib.pylab.ylim(yMinimum, yMaximum)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
matplotlib.pylab.colorbar(mappable=scalableMappable)
matplotlib.pylab.gcf().subplots_adjust(bottom=0.15)
try:
matplotlib.pylab.savefig(fullFilename)
except:
matplotlib.pylab.clf()
raise
matplotlib.pylab.clf()
def getGreatCircleDist(self, gdlatr, glonr, gdlat, glon):
"""getGreatCircleDist return the great circle distance in. Taken from madDeriveMethods c code.
Inputs: gdlatr, glonr: reference point on earth's surface
gdlat, glon: second point on earth's surface
"""
dlon = glonr/57.2958 - glon/57.2958
dlat = gdlatr/57.2958 - gdlat/57.2958;
a = math.sin(dlat/2.0)*math.sin(dlat/2.0) + math.cos(gdlatr/57.2958)*math.cos(gdlat/57.2958) * math.sin(dlon/2.0)*math.sin(dlon/2.0)
if math.sqrt(a) < 1.0:
c = 2.0 * math.asin(math.sqrt(a))
else:
c = 2.0 * math.asin(1.0)
return(c * 6371.2)
def _getLimits(self, isprintLines):
"""returns (newLonMax, newLonMin, newLatMax, newLatMin, newAltMax, newGreatCDist, newParmMax, newParmMin)
given isprint lines with
parameters (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2, )
We only look at the end points of ranges and the station location
"""
# set initial values to station location
items = isprintLines[1].split()
slat = float(items[0])
slon = float(items[1])
if slon > 180.0:
slon -= 360.0
salt = float(items[2])
maxLon = slon
minLon = slon
maxLat = slat
minLat = slat
maxAlt = salt
maxGreatCDist = 0.0
maxParm = -1.0e-30
minParm = 1.0e30
lastAz = None
lastEl = None
lastRange = None
for i, isprintLine in enumerate(isprintLines):
items = isprintLine.split()
if len(items) < 9:
continue
range = float(items[3])
az1 = float(items[4])
az2 = float(items[5])
thisAz = (az1 + az2)/2
el1 = float(items[6])
el2 = float(items[7])
thisEl = (el1 + el2)/2
try:
parm = float(items[8])
if parm > maxParm:
maxParm = parm
if parm < minParm:
minParm = parm
except ValueError:
continue
# see if we're at an end point
if lastAz != None:
if abs(lastAz - thisAz) > 0.001 or abs(lastEl - thisEl) > 0.001:
# the last point was an end point
gdlat,glon,gdalt = madrigal._derive.radarToGeodetic(slat,slon,salt,lastAz,lastEl,lastRange)
if gdlat > maxLat:
maxLat = gdlat
if gdlat < minLat:
minLat = gdlat
if glon > maxLon:
maxLon = glon
if glon < minLon:
minLon = glon
if gdalt > maxAlt:
maxAlt = gdalt
greatCDist = self.getGreatCircleDist(slat, slon, gdlat, glon)
if greatCDist > maxGreatCDist:
maxGreatCDist = greatCDist
lastAz = thisAz
lastEl = thisEl
lastRange = range
# check last point
gdlat,glon,gdalt = madrigal._derive.radarToGeodetic(slat,slon,slat,lastAz,lastEl,lastRange)
if gdlat > maxLat:
maxLat = gdlat
if gdlat < minLat:
minLat = gdlat
if glon > maxLon:
maxLon = glon
if glon < minLon:
minLon = glon
if gdalt > maxAlt:
maxAlt = gdalt
greatCDist = self.getGreatCircleDist(slat, slon, gdlat, glon)
if greatCDist > maxGreatCDist:
maxGreatCDist = greatCDist
return((maxLon, minLon, maxLat, minLat, maxAlt, maxGreatCDist, maxParm, minParm))
def _setLimits(self, scanType, xMaximum, xMinimum, yMaximum, yMinimum,
newLonMax, newLonMin, newLatMax, newLatMin, newAltMax,
newGreatCDist, newParmMax, newParmMin):
""" _setLimits sets xMaximum, xMinimum, yMaximum, yMinimum based on values
found in self._getLimits and scanType. If any value in (xMaximum, xMinimum, yMaximum, yMinimum)
is not None, it will not be changed. """
newXMax = xMaximum
newXMin = xMinimum
newYMax = yMaximum
newYMin = yMinimum
if scanType == 'az':
if xMaximum == None:
newXMax = newLonMax
if xMinimum == None:
newXMin = newLonMin
if yMaximum == None:
newYMax = newLatMax
if yMinimum == None:
newYMin = newLatMin
elif scanType == 'el_lat':
if xMaximum == None:
newXMax = newLatMax
if xMinimum == None:
newXMin = newLatMin
if yMaximum == None:
newYMax = newAltMax
if yMinimum == None:
newYMin = 0.0
elif scanType == 'el_lon':
if xMaximum == None:
newXMax = newLonMax
if xMinimum == None:
newXMin = newLonMin
if yMaximum == None:
newYMax = newAltMax
if yMinimum == None:
newYMin = 0.0
elif scanType == 'el_gcdist':
if xMaximum == None:
newXMax = newGreatCDist
if xMinimum == None:
newXMin = 0.0
if yMaximum == None:
newYMax = newAltMax
if yMinimum == None:
newYMin = 0.0
return((newXMax, newXMin, newYMax, newYMin))
def _generateWedges(self, isprintLines, scanType):
"""_generateWedges is a python generator that return a data wedge in the form
(dataValue, (point1_x, point1_y), (point2_x, point2_y),
(point3_x, point3_y), (point4_x, point4_y))
Inputs:
isprintLines - a list of line with 9 parameters that
are (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2, ). Any
missing data should be written as "missing" or other string that
cannot be converted to a float. gdlatr, gdlonr, galtr are the station location.
No wedge is returned for a missing data line
scanType - must be 'az' or 'el_lat' (for north or south el scans), 'el_lon'
(for east or west el scans) or 'el_gcdist' (for all other el scans.
For az - x is long, y is lat
for el_* y is always alt, and x is either lat, lon, or great circle distance
"""
# state variables
lastAz1 = None
lastAz2 = None
lastEl1 = None
lastEl2 = None
lastRange = None
lastDiffRangeLow = None
lastValue = None
for isprintLine in isprintLines:
items = isprintLine.split()
if len(items) < 9:
continue
gdlatr = float(items[0])
gdlonr = float(items[1])
galtr = float(items[2])
range = float(items[3])
az1 = float(items[4])
az2 = float(items[5])
el1 = float(items[6])
el2 = float(items[7])
valueStr = items[8]
if lastAz1 != None and ( abs(lastAz1-az1) > 0.001 or abs(lastEl1-el1) > 0.001 ):
# new radar line found, send last point if needed
if lastValue != None:
dataWedge = self._getWedge(gdlatr, gdlonr, galtr, lastRange,
lastDiffRangeLow, lastDiffRangeLow, lastAz1, lastAz2,
lastEl1, lastEl2, lastValue, scanType)
yield(dataWedge)
# this is the first point in the line, so no lastDiffRangeLow
lastDiffRangeLow = None
elif lastAz1 != None and ( abs(lastAz1-az1) < 0.001 and abs(lastEl1-el1) < 0.001 ):
# continuing radar line
if lastValue != None:
lastDiffRangeLow = (range - lastRange)/2.0
dataWedge = self._getWedge(gdlatr, gdlonr, galtr, lastRange,
lastDiffRangeLow, (range-lastRange)/2.0, lastAz1, lastAz2,
lastEl1, lastEl2, lastValue, scanType)
yield(dataWedge)
else:
lastDiffRangeLow = (range - lastRange)/2.0
# reset state variables
lastAz1 = az1
lastAz2 = az2
lastEl1 = el1
lastEl2 = el2
lastRange = range
try:
lastValue = float(valueStr)
except:
lastValue = None
# finally, send last wedge if needed
if lastValue != None:
dataWedge = self._getWedge(gdlatr, gdlonr, galtr, lastRange,
lastDiffRangeLow, lastDiffRangeLow, lastAz1, lastAz2,
lastEl1, lastEl2, lastValue, scanType)
yield(dataWedge)
def _getWedge(self, gdlatr, gdlonr, galtr, range,
diffRange1, diffRange2, az1, az2,
el1, el2, value, scanType):
"""_getWedge returns return a data wedge in the form
(dataValue, (point1_x, point1_y), (point2_x, point2_y),
(point3_x, point3_y), (point4_x, point4_y)). The x and y dimensions depend on
scanType:
az: x: lon, y: lat
el_lat: x: lat, y: alt
el_lon: x: lon, y: alt
el_gcdist: x: great circle distance, y: alt
Inputs:
gdlatr, gdlonr, galtr - location of radar
range, diffRange1, diffRange2 - range of center of wedge, and distance in and out from there
az1, az2, el1, el2 - start and end aximuth and elevation. Which is used depends on scanType
value - float data value
scanType - az, el_lat, el_lon, el_gcdist
"""
if scanType == 'az':
point1_y,point1_x,gdalt = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range-diffRange1)
point2_y,point2_x,gdalt = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az2,el1,range-diffRange1)
point3_y,point3_x,gdalt = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az2,el1,range+diffRange2)
point4_y,point4_x,gdalt = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range+diffRange2)
elif scanType == 'el_lat':
point1_x,glon,point1_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range-diffRange1)
point2_x,glon,point2_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el2,range-diffRange1)
point3_x,glon,point3_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el2,range+diffRange2)
point4_x,glon,point4_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range+diffRange2)
elif scanType == 'el_lon':
gdlat,point1_x,point1_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range-diffRange1)
gdlat,point2_x,point2_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el2,range-diffRange1)
gdlat,point3_x,point3_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el2,range+diffRange2)
gdlat,point4_x,point4_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range+diffRange2)
elif scanType == 'el_gcdist':
gdlat,glon,point1_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range-diffRange1)
point1_x = self.getGreatCircleDist(gdlatr, gdlonr, gdlat, glon)
gdlat,glon,point2_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el2,range-diffRange1)
point2_x = self.getGreatCircleDist(gdlatr, gdlonr, gdlat, glon)
gdlat,glon,point3_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el2,range+diffRange2)
point3_x = self.getGreatCircleDist(gdlatr, gdlonr, gdlat, glon)
gdlat,glon,point4_y = madrigal._derive.radarToGeodetic(gdlatr, gdlonr, galtr,az1,el1,range+diffRange2)
point4_x = self.getGreatCircleDist(gdlatr, gdlonr, gdlat, glon)
return((value, (point1_x, point1_y), (point2_x, point2_y), (point3_x, point3_y), (point4_x, point4_y)))
Ancestors (in MRO)
- madPcolorWedgeScan
- builtins.object
Static methods
def __init__(
self, isprintText, scanType, titleStr, xLabelStr, yLabelStr, fullFilename, size='small', xMinimum=None, xMaximum=None, yMinimum=None, yMaximum=None, minColormap=None, maxColormap=None, colorMap=<matplotlib.colors.LinearSegmentedColormap object at 0x1169fb450>)
init writes a madPcolorWedgeScan to a file.
Inputs:
isprintText - a string giving isprint output without headers. The 9 parameters
are (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2, <parameter value>). Any
missing data should be written as "missing" or other string that
cannot be converted to a float. gdlatr, gdlonr, galtr are the station location
scanType - must be 'az' or 'el_lat' (for north or south el scans), 'el_lon'
(for east or west el scans) or 'el_gcdist' (for all other el scans.
For az - x is long, y is lat
for el_* y is always alt, and x is either lat, lon, or great circle distance
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
xMinimum = minumum x value. If None (default), uses lowest value found.
xMaximum = maximum x value. If None (default), uses highest value found.
yMinimum = minumum y value. If None (default), uses lowest value found.
yMaximum = maximum y value. If None (default), uses highest value found.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
Returns: void
Affects: Writes fullFilename to disk
def __init__(self, isprintText,
scanType,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
xMinimum = None,
xMaximum = None,
yMinimum = None,
yMaximum = None,
minColormap = None,
maxColormap = None,
colorMap = matplotlib.cm.jet):
"""__init__ writes a madPcolorWedgeScan to a file.
Inputs:
isprintText - a string giving isprint output without headers. The 9 parameters
are (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2, ). Any
missing data should be written as "missing" or other string that
cannot be converted to a float. gdlatr, gdlonr, galtr are the station location
scanType - must be 'az' or 'el_lat' (for north or south el scans), 'el_lon'
(for east or west el scans) or 'el_gcdist' (for all other el scans.
For az - x is long, y is lat
for el_* y is always alt, and x is either lat, lon, or great circle distance
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
xMinimum = minumum x value. If None (default), uses lowest value found.
xMaximum = maximum x value. If None (default), uses highest value found.
yMinimum = minumum y value. If None (default), uses lowest value found.
yMaximum = maximum y value. If None (default), uses highest value found.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
Returns: void
Affects: Writes fullFilename to disk
"""
# verify input
if scanType not in ('az', 'el_lat', 'el_lon', 'el_gcdist'):
raise ValueError('scanType must be "az", "el_lat", "el_lon", "el_gcdist", not %s' % (str(scanType)))
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
isprintLines = isprintText.split('\n')
if xMaximum == None or xMinimum == None or yMaximum == None or yMinimum or maxColormap == None or minColormap == None:
newLonMax, newLonMin, newLatMax, newLatMin, newAltMax, newGreatCDist, newParmMax, newParmMin = self._getLimits(isprintLines)
# set values
xMaximum, xMinimum, yMaximum, yMinimum = self._setLimits(scanType, xMaximum, xMinimum, yMaximum, yMinimum,
newLonMax, newLonMin, newLatMax, newLatMin, newAltMax,
newGreatCDist, newParmMax, newParmMin)
if maxColormap == None:
maxColormap = newParmMax
if minColormap == None:
minColormap = newParmMin
# set up wedge generator
dataGen = self._generateWedges(isprintLines, scanType)
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(6,5), facecolor = 'w')
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(12,6), facecolor = 'w')
try:
while True:
data = next(dataGen)
x = [data[1][0], data[2][0], data[3][0], data[4][0]]
y = [data[1][1], data[2][1], data[3][1], data[4][1]]
dataRatio = (data[0] - minColormap) / (maxColormap - minColormap)
colorStr = matplotlib.colors.rgb2hex(colorMap(dataRatio)[:3])
thisPlot = matplotlib.pyplot.fill(x, y, colorStr, edgecolor='none')
except StopIteration:
pass
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
scalableMappable = matplotlib.cm.ScalarMappable(cmap=colorMap)
scalableMappable.set_array(numpy.array([minColormap, maxColormap]))
scalableMappable.set_clim(minColormap, maxColormap)
def get_alpha(*args, **kw):
return(1.0)
scalableMappable.get_alpha = get_alpha
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.xlim(xMinimum, xMaximum)
matplotlib.pylab.ylim(yMinimum, yMaximum)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
matplotlib.pylab.colorbar(mappable=scalableMappable)
matplotlib.pylab.gcf().subplots_adjust(bottom=0.15)
try:
matplotlib.pylab.savefig(fullFilename)
except:
matplotlib.pylab.clf()
raise
matplotlib.pylab.clf()
def getGreatCircleDist(
self, gdlatr, glonr, gdlat, glon)
getGreatCircleDist return the great circle distance in. Taken from madDeriveMethods c code.
Inputs: gdlatr, glonr: reference point on earth's surface gdlat, glon: second point on earth's surface
def getGreatCircleDist(self, gdlatr, glonr, gdlat, glon):
"""getGreatCircleDist return the great circle distance in. Taken from madDeriveMethods c code.
Inputs: gdlatr, glonr: reference point on earth's surface
gdlat, glon: second point on earth's surface
"""
dlon = glonr/57.2958 - glon/57.2958
dlat = gdlatr/57.2958 - gdlat/57.2958;
a = math.sin(dlat/2.0)*math.sin(dlat/2.0) + math.cos(gdlatr/57.2958)*math.cos(gdlat/57.2958) * math.sin(dlon/2.0)*math.sin(dlon/2.0)
if math.sqrt(a) < 1.0:
c = 2.0 * math.asin(math.sqrt(a))
else:
c = 2.0 * math.asin(1.0)
return(c * 6371.2)
class madScatterPlot
madScatterPlot is the class the produces two dimensional scatter plots of x versus y.
class madScatterPlot:
"""madScatterPlot is the class the produces two dimensional scatter plots of x versus y.
"""
def __init__(self, isprintText,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
useAbsoluteTime = False,
startTime = None,
endTime = None,
maxNumPoints = None,
noTime = False,
noDate = False,
yMinimum = None,
yMaximum = None,
timezone = 0):
"""__init__ writes a madScatter plot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The second must be the parameter to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
maxNumPoints - maximum number of points to plot. If not None, truncate isprintText if
needed to have at most maxNumPoints lines.
noTime - if True and useAbsoluteTime True, then dates without times printed on x axis.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
yMinimum - set y minimum. If default=None, use data minimum
yMaximum - set y maximum. If default=None, use data maximum
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
Returns: void
Affects: None
"""
self.__missing = 1.0E30 # special value, since numpy doesn't handle NaN
self.__parameter_count = 2
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
if maxNumPoints != None:
isprintText = self.__truncateIsprint(isprintText, maxNumPoints)
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
# find min, max of x (time)
if startTime == None:
xMin = array_data[0,0]
else:
xMin = startTime
if endTime == None:
xMax = array_data[-1,0]
else:
xMax = endTime
# find min, max of y, not including missing
yMin = None
yMax = None
for y in array_data[:,1]:
if y == self.__missing:
continue
if yMin == None:
yMin = y
elif yMin > y:
yMin = y
if yMax == None:
yMax = y
elif yMax < y:
yMax = y
if yMin == None:
raise ValueError('No valid y data found')
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(9,3), facecolor = 'w')
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(10,6), facecolor = 'w')
if useAbsoluteTime:
# leave room for rotated datetime string
if size == 'large':
matplotlib.pylab.axes([0.1, 0.2, 0.8, 0.7])
elif size == 'wide':
matplotlib.pylab.axes([0.07, 0.2, 0.66, 0.7])
elif size == 'small':
matplotlib.pylab.axes([0.14, 0.18, 0.83, 0.7])
matplotlib.pylab.scatter(array_data[:,0],array_data[:,1])
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.xlim(xMin, xMax)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
matplotlib.pylab.grid(True)
if useAbsoluteTime:
# if plot is less than 24 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
xmin = xmin / (60*60)
xmax = xmax / (60*60)
if noDate == True:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
step = int((xmax-xmin-1)/6)+1
xRange = list(range(0,int(newXmax-xmin),step))
for i in xRange:
tmp = (xmin + i)*60*60
newXLocs.append(tmp)
if (xmax - xmin) < 1: newXLocs.append(xmax*60*60)
newXLabels = convertToAbsoluteTimeStr(newXLocs, noDate=noDate, timezone=timezone)
matplotlib.pylab.xticks(newXLocs, newXLabels)
else:
locs, labels = matplotlib.pylab.xticks()
if len(locs) > 5:
# truncate by len(locs) / 5
scale = 1 + int(len(locs) / 5)
new_locs = []
for i in range(len(locs)):
if i % scale == 0:
new_locs.append(locs[i])
locs = new_locs
newXTicks = convertToAbsoluteTimeStr(locs)
matplotlib.pylab.xticks(locs, newXTicks, rotation=15)
matplotlib.pylab.xticks(fontsize=fontSize)
if yMinimum != None and yMaximum != None:
matplotlib.pylab.ylim(yMinimum, yMaximum)
elif yMinimum != None and yMaximum == None:
matplotlib.pylab.ylim(yMin=yMinimum)
elif yMinimum == None and yMaximum != None:
matplotlib.pylab.ylim(yMax=yMaximum)
# get the handle to the figure now that it has been created so we can manipulate on subsequent calls.
#self.__figure = matplotlib.pylab.gcf()
matplotlib.pylab.savefig(fullFilename)
#matplotlib.pylab.show()
matplotlib.pylab.clf()
def __truncateIsprint(self, isprintText, maxLines):
"""__truncateIsprint truncates isprintText to have maxLines at most.
"""
isprintList = isprintText.split('\n')
if len(isprintList) < maxLines:
return isprintText
else:
dropNumber = int(1 + len(isprintList)/maxLines)
newline = '\n'
newIsprintText = newline.join(isprintList[::dropNumber])
return newIsprintText
def __filter_missing(self,x):
try:
return float(x)
except:
return self.__missing
Ancestors (in MRO)
- madScatterPlot
- builtins.object
Static methods
def __init__(
self, isprintText, titleStr, xLabelStr, yLabelStr, fullFilename, size='small', useAbsoluteTime=False, startTime=None, endTime=None, maxNumPoints=None, noTime=False, noDate=False, yMinimum=None, yMaximum=None, timezone=0)
init writes a madScatter plot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The second must be the parameter to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
maxNumPoints - maximum number of points to plot. If not None, truncate isprintText if
needed to have at most maxNumPoints lines.
noTime - if True and useAbsoluteTime True, then dates without times printed on x axis.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
yMinimum - set y minimum. If default=None, use data minimum
yMaximum - set y maximum. If default=None, use data maximum
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
Returns: void
Affects: None
def __init__(self, isprintText,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
useAbsoluteTime = False,
startTime = None,
endTime = None,
maxNumPoints = None,
noTime = False,
noDate = False,
yMinimum = None,
yMaximum = None,
timezone = 0):
"""__init__ writes a madScatter plot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First parameter
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The second must be the parameter to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - ylabel string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
maxNumPoints - maximum number of points to plot. If not None, truncate isprintText if
needed to have at most maxNumPoints lines.
noTime - if True and useAbsoluteTime True, then dates without times printed on x axis.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
yMinimum - set y minimum. If default=None, use data minimum
yMaximum - set y maximum. If default=None, use data maximum
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
Returns: void
Affects: None
"""
self.__missing = 1.0E30 # special value, since numpy doesn't handle NaN
self.__parameter_count = 2
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
if maxNumPoints != None:
isprintText = self.__truncateIsprint(isprintText, maxNumPoints)
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
# find min, max of x (time)
if startTime == None:
xMin = array_data[0,0]
else:
xMin = startTime
if endTime == None:
xMax = array_data[-1,0]
else:
xMax = endTime
# find min, max of y, not including missing
yMin = None
yMax = None
for y in array_data[:,1]:
if y == self.__missing:
continue
if yMin == None:
yMin = y
elif yMin > y:
yMin = y
if yMax == None:
yMax = y
elif yMax < y:
yMax = y
if yMin == None:
raise ValueError('No valid y data found')
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(9,3), facecolor = 'w')
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(10,6), facecolor = 'w')
if useAbsoluteTime:
# leave room for rotated datetime string
if size == 'large':
matplotlib.pylab.axes([0.1, 0.2, 0.8, 0.7])
elif size == 'wide':
matplotlib.pylab.axes([0.07, 0.2, 0.66, 0.7])
elif size == 'small':
matplotlib.pylab.axes([0.14, 0.18, 0.83, 0.7])
matplotlib.pylab.scatter(array_data[:,0],array_data[:,1])
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.xlim(xMin, xMax)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
matplotlib.pylab.grid(True)
if useAbsoluteTime:
# if plot is less than 24 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
xmin = xmin / (60*60)
xmax = xmax / (60*60)
if noDate == True:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0) + 1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
step = int((xmax-xmin-1)/6)+1
xRange = list(range(0,int(newXmax-xmin),step))
for i in xRange:
tmp = (xmin + i)*60*60
newXLocs.append(tmp)
if (xmax - xmin) < 1: newXLocs.append(xmax*60*60)
newXLabels = convertToAbsoluteTimeStr(newXLocs, noDate=noDate, timezone=timezone)
matplotlib.pylab.xticks(newXLocs, newXLabels)
else:
locs, labels = matplotlib.pylab.xticks()
if len(locs) > 5:
# truncate by len(locs) / 5
scale = 1 + int(len(locs) / 5)
new_locs = []
for i in range(len(locs)):
if i % scale == 0:
new_locs.append(locs[i])
locs = new_locs
newXTicks = convertToAbsoluteTimeStr(locs)
matplotlib.pylab.xticks(locs, newXTicks, rotation=15)
matplotlib.pylab.xticks(fontsize=fontSize)
if yMinimum != None and yMaximum != None:
matplotlib.pylab.ylim(yMinimum, yMaximum)
elif yMinimum != None and yMaximum == None:
matplotlib.pylab.ylim(yMin=yMinimum)
elif yMinimum == None and yMaximum != None:
matplotlib.pylab.ylim(yMax=yMaximum)
# get the handle to the figure now that it has been created so we can manipulate on subsequent calls.
#self.__figure = matplotlib.pylab.gcf()
matplotlib.pylab.savefig(fullFilename)
#matplotlib.pylab.show()
matplotlib.pylab.clf()
class madXYScatterPlot
madXYScatterPlot is the class the produces XY scatter plots.
Change History:
Written by "Brandon Scott Fines":mailto:bfines@haystack.mit.edu Aug 2, 2006
Written by "Bill Rideout":mailto:brideout@haystack.mit.edu Aug 2, 2006
class madXYScatterPlot:
"""madXYScatterPlot is the class the produces XY scatter plots.
Change History:
Written by "Brandon Scott Fines":mailto:bfines@haystack.mit.edu Aug 2, 2006
Written by "Bill Rideout":mailto:brideout@haystack.mit.edu Aug 2, 2006
"""
def __init__(self,inputText,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size='small',
lowBound=None,
highBound=None,
maxNumPoints=None,
yLegend=None):
"""
Inputs:
inputText - string of values to be plotted. The formatting is as
follows:
xval yval
xval yval
xval yval
titleStr - title of the Plot
xLabelStr - label for the x axis
yLabelStr - label for the y axis
fullFilename - full file path to save the resulting picture to
size - size of plot to be saved. Must be 'small','wide', or 'large'.
defaults to 'small'.
lowBound - lower bound on the x-axis value. If no bound is specified,
the lowest value found in inputText will be used.
highBound - upper bound on the x-axis value. If no bound is specified,
the highest value found in inputText will be used.
maxNumPoints - maximum number of points to be plotted.
Outputs: None
Affects: Creates a scatter plot using matplotlib and writes that to the
file designated by the variable 'fullFilename'
Exceptions: ValueError if lowBound or highBound cannot be converted to
a float value
Non-standard python modules used:
matplotlib
"""
#verify input
if size not in ('small','wide','large'):
raise ValueError('size must be "small","wide", or "large", not %s'%(str(size)))
if size in ('small','wide'):
fontSize = 12
elif size in 'large':
fontSize = 18
if maxNumPoints !=None:
inputText = self.__truncateInput(inputText, maxNumPoints)
if lowBound !=None:
#check that it is a number
try:
lowBound = float(lowBound)
except ValueError:
raise ValueError('lowBound not a number')
try:
highBound = float(highBound)
except:
raise ValueError('lowBound not a number')
#convert the input data into numeric arrays
x=[]
y=[]
split_data = inputText.split()
#send x-values to x, y-values to y
i=0
while i xhigh:
xhigh = i
xlow = lowBound
elif lowBound==None and highBound !=None:
#find the lower bound in x
for i in x:
if xlow==None:
xlow = i
if ixhigh:
xhigh = i
#find upper and lower bounds in y
ylow = None
yhigh = None
for i in y:
if ylow==None:
ylow = i
elif iyhigh:
yhigh = i
#check for good numbers
if ylow == None:
raise ValueError('no valid y data found')
#select plot sizes
if size=='small':
matplotlib.pylab.figure(1,figsize=(6,4),facecolor='w')
elif size=='wide':
matplotlib.pylab.figure(1,figsize=(10,4),facecolor='w')
elif size=='large':
matplotlib.pylab.figure(1,figsize=(12,6),facecolor='w')
#draw scatter plot
#matplotlib.pylab.scatter(x,y)
matplotlib.pylab.plot(x,y, 'gd',ms=5,mew=0.5)
thisFont = {'size' : fontSize}
matplotlib.pylab.rc('font', **thisFont) #pass in font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr,fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr,fontsize=fontSize)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.xticks(fontsize=fontSize)
matplotlib.pylab.xlim(xlow,xhigh)
matplotlib.pylab.title(titleStr,fontsize=fontSize)
matplotlib.pylab.grid(True)
if yLegend != None: matplotlib.pylab.legend(yLegend,numpoints=1)
#save the figure
matplotlib.pylab.savefig(fullFilename)
matplotlib.pylab.clf()
Ancestors (in MRO)
- madXYScatterPlot
- builtins.object
Static methods
def __init__(
self, inputText, titleStr, xLabelStr, yLabelStr, fullFilename, size='small', lowBound=None, highBound=None, maxNumPoints=None, yLegend=None)
Inputs:
inputText - string of values to be plotted. The formatting is as
follows:
xval yval
xval yval
xval yval
titleStr - title of the Plot
xLabelStr - label for the x axis
yLabelStr - label for the y axis
fullFilename - full file path to save the resulting picture to
size - size of plot to be saved. Must be 'small','wide', or 'large'.
defaults to 'small'.
lowBound - lower bound on the x-axis value. If no bound is specified,
the lowest value found in inputText will be used.
highBound - upper bound on the x-axis value. If no bound is specified,
the highest value found in inputText will be used.
maxNumPoints - maximum number of points to be plotted.
Outputs: None
Affects: Creates a scatter plot using matplotlib and writes that to the file designated by the variable 'fullFilename'
Exceptions: ValueError if lowBound or highBound cannot be converted to a float value
Non-standard python modules used:
matplotlib
def __init__(self,inputText,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size='small',
lowBound=None,
highBound=None,
maxNumPoints=None,
yLegend=None):
"""
Inputs:
inputText - string of values to be plotted. The formatting is as
follows:
xval yval
xval yval
xval yval
titleStr - title of the Plot
xLabelStr - label for the x axis
yLabelStr - label for the y axis
fullFilename - full file path to save the resulting picture to
size - size of plot to be saved. Must be 'small','wide', or 'large'.
defaults to 'small'.
lowBound - lower bound on the x-axis value. If no bound is specified,
the lowest value found in inputText will be used.
highBound - upper bound on the x-axis value. If no bound is specified,
the highest value found in inputText will be used.
maxNumPoints - maximum number of points to be plotted.
Outputs: None
Affects: Creates a scatter plot using matplotlib and writes that to the
file designated by the variable 'fullFilename'
Exceptions: ValueError if lowBound or highBound cannot be converted to
a float value
Non-standard python modules used:
matplotlib
"""
#verify input
if size not in ('small','wide','large'):
raise ValueError('size must be "small","wide", or "large", not %s'%(str(size)))
if size in ('small','wide'):
fontSize = 12
elif size in 'large':
fontSize = 18
if maxNumPoints !=None:
inputText = self.__truncateInput(inputText, maxNumPoints)
if lowBound !=None:
#check that it is a number
try:
lowBound = float(lowBound)
except ValueError:
raise ValueError('lowBound not a number')
try:
highBound = float(highBound)
except:
raise ValueError('lowBound not a number')
#convert the input data into numeric arrays
x=[]
y=[]
split_data = inputText.split()
#send x-values to x, y-values to y
i=0
while i xhigh:
xhigh = i
xlow = lowBound
elif lowBound==None and highBound !=None:
#find the lower bound in x
for i in x:
if xlow==None:
xlow = i
if ixhigh:
xhigh = i
#find upper and lower bounds in y
ylow = None
yhigh = None
for i in y:
if ylow==None:
ylow = i
elif iyhigh:
yhigh = i
#check for good numbers
if ylow == None:
raise ValueError('no valid y data found')
#select plot sizes
if size=='small':
matplotlib.pylab.figure(1,figsize=(6,4),facecolor='w')
elif size=='wide':
matplotlib.pylab.figure(1,figsize=(10,4),facecolor='w')
elif size=='large':
matplotlib.pylab.figure(1,figsize=(12,6),facecolor='w')
#draw scatter plot
#matplotlib.pylab.scatter(x,y)
matplotlib.pylab.plot(x,y, 'gd',ms=5,mew=0.5)
thisFont = {'size' : fontSize}
matplotlib.pylab.rc('font', **thisFont) #pass in font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr,fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr,fontsize=fontSize)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.xticks(fontsize=fontSize)
matplotlib.pylab.xlim(xlow,xhigh)
matplotlib.pylab.title(titleStr,fontsize=fontSize)
matplotlib.pylab.grid(True)
if yLegend != None: matplotlib.pylab.legend(yLegend,numpoints=1)
#save the figure
matplotlib.pylab.savefig(fullFilename)
matplotlib.pylab.clf()
class madXYwithErrorPlot
madXYwithErrorPlot is the class the produces two dimensional scatter plots of x versus y more its error. Written by "Miguel Urco":mailto:murco@jro.igp.gob.pe Jul 2, 2009
class madXYwithErrorPlot:
"""madXYwithErrorPlot is the class the produces two dimensional scatter plots of x versus y more its error.
Written by "Miguel Urco":mailto:murco@jro.igp.gob.pe Jul 2, 2009
"""
def __init__(self, isprintText,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
useAbsoluteTime = False,
startTime = None,
endTime = None,
maxNumPoints = None,
noTime = False,
noDate = False,
yMinimum = None,
yMaximum = None,
timezone = 0):
"""__init__ writes a madScatter plot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First of three parameters
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The second must be the parameter to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - y label string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
maxNumPoints - maximum number of points to plot. If not None, truncate isprintText if
needed to have at most maxNumPoints lines.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
yMinimum - set y minimum. If default=None, use data minimum
yMaximum - set y maximum. If default=None, use data maximum
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
Returns: void
Affects: None
Now removes all lines with missing
"""
self.__missing = 1.0E30 # special value, since numpy doesn't handle NaN
self.__parameter_count = 3
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
# filter out missing lines
newIsprint = ''
lines = isprintText.split('\n')
for line in lines:
try:
items = line.split()
float(items[0])
float(items[1])
float(items[2])
newIsprint += line + ' '
except:
continue
isprintText = newIsprint
if maxNumPoints != None:
isprintText = self.__truncateIsprint(isprintText, maxNumPoints)
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
# find min, max of x (time)
if startTime == None:
xMin = array_data[0,0]
else:
xMin = startTime
if endTime == None:
xMax = array_data[-1,0]
else:
xMax = endTime
# find min, max of y, not including missing
yMin = None
yMax = None
for y in array_data[:,1]:
if y == self.__missing:
continue
if yMin == None:
yMin = y
elif yMin > y:
yMin = y
if yMax == None:
yMax = y
elif yMax < y:
yMax = y
if yMin == None:
raise ValueError('No valid y data found')
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(8,3), facecolor = 'w')
matplotlib.pylab.axes([0.11, 0.18, 0.78, 0.7])
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(10,6), facecolor = 'w')
if useAbsoluteTime:
# leave room for rotated datetime string
if size == 'large':
matplotlib.pylab.axes([0.1, 0.2, 0.8, 0.7])
elif size == 'wide':
matplotlib.pylab.axes([0.07, 0.2, 0.66, 0.7])
elif size == 'small':
matplotlib.pylab.axes([0.1, 0.18, 0.87, 0.7])
matplotlib.pylab.errorbar(array_data[:,0],array_data[:,2],array_data[:,1],ecolor='red',fmt='s',mfc='red',mec='green',ms=1,mew=1)
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.xlim(xMin, xMax)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
matplotlib.pylab.grid(True)
if yMinimum != None and yMaximum != None:
matplotlib.pylab.ylim(yMinimum, yMaximum)
elif yMinimum != None and yMaximum == None:
matplotlib.pylab.ylim(yMin=yMinimum)
elif yMinimum == None and yMaximum != None:
matplotlib.pylab.ylim(yMax=yMaximum)
if useAbsoluteTime:
# if plot is less than 24 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
xmin = xmin / (60*60)
xmax = xmax / (60*60)
if noDate == True:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0)+1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
step = int((xmax-xmin-1)/6)+1
xRange = list(range(0,int(newXmax-xmin),step))
for i in xRange:
tmp = (xmin + i)*60*60
newXLocs.append(tmp)
if (xmax - xmin) < 1: newXLocs.append(xmax*60*60)
newXLabels = convertToAbsoluteTimeStr(newXLocs, noDate=noDate, timezone=timezone)
matplotlib.pylab.xticks(newXLocs, newXLabels)
else:
locs, labels = matplotlib.pylab.xticks()
if len(locs) > 5:
# truncate by len(locs) / 5
scale = 1 + int(len(locs) / 5)
new_locs = []
for i in range(len(locs)):
if i % scale == 0:
new_locs.append(locs[i])
locs = new_locs
newXTicks = convertToAbsoluteTimeStr(locs)
matplotlib.pylab.xticks(locs, newXTicks, rotation=15)
matplotlib.pylab.xticks(fontsize=fontSize)
# get the handle to the figure now that it has been created so we can manipulate on subsequent calls.
#self.__figure = matplotlib.pylab.gcf()
matplotlib.pylab.savefig(fullFilename)
#matplotlib.pylab.show()
matplotlib.pylab.clf()
def __truncateInput(self,inputStr,maxLines):
"""__truncateInput truncates inputStr to have maxLines at most
"""
inputList = inputStr.split('\n')
if len(inputList)Ancestors (in MRO)
- madXYwithErrorPlot
- builtins.object
Static methods
def __init__(
self, isprintText, titleStr, xLabelStr, yLabelStr, fullFilename, size='small', useAbsoluteTime=False, startTime=None, endTime=None, maxNumPoints=None, noTime=False, noDate=False, yMinimum=None, yMaximum=None, timezone=0)
init writes a madScatter plot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First of three parameters
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The second must be the parameter to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - y label string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
maxNumPoints - maximum number of points to plot. If not None, truncate isprintText if
needed to have at most maxNumPoints lines.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
yMinimum - set y minimum. If default=None, use data minimum
yMaximum - set y maximum. If default=None, use data maximum
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
Returns: void
Affects: None
Now removes all lines with missing
def __init__(self, isprintText,
titleStr,
xLabelStr,
yLabelStr,
fullFilename,
size = 'small',
useAbsoluteTime = False,
startTime = None,
endTime = None,
maxNumPoints = None,
noTime = False,
noDate = False,
yMinimum = None,
yMaximum = None,
timezone = 0):
"""__init__ writes a madScatter plot to a file.
Inputs:
isprintText - a string giving isprint output without headers. First of three parameters
must be UTH or UT1, depending on whether time scale should be relative
to the experiment beginning (UTH) or absolute (UT1).
The second must be the parameter to be plotted. Any
missing data should be written as "missing" or other string that
cannot be converted to a float.
titleStr - plot title (string) - should describe parameter being plotted
xLabelStr - x label string
yLabelStr - y label string
fullFilename - full path of file containing pcolor plot to be saved. Extension must
be jpeg or png, or exception thrown.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
useAbsoluteTime - if true, print time as YYYY-MM-DD HH:MM:SS. If false (default), print time as hour
since beginning of experiment (UTH). If useAbsoluteTime is true, first
parameter in isprintText must be UT1, if false, it must be UTH.
startTime - start plot at given time. If useAbsoluteTime == True, then startTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
startTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means start at lowest time found.
endTime - end plot at given time. If useAbsoluteTime == True, then endTime must be
in units of seconds since 1/1/1950. If useAbsoluteTime == False, then
endTime must be in units of UTH (hours since midnight UT of first day of
experiment). Default is None, which means end at largest time found.
maxNumPoints - maximum number of points to plot. If not None, truncate isprintText if
needed to have at most maxNumPoints lines.
noDate - if True and useAbsoluteTime True, then times without dates printed on x axis.
yMinimum - set y minimum. If default=None, use data minimum
yMaximum - set y maximum. If default=None, use data maximum
timezone - The offset of the local (non-DST) timezone, in seconds west of UTC
(negative in most of Western Europe, positive in the US, zero in the UK).
Returns: void
Affects: None
Now removes all lines with missing
"""
self.__missing = 1.0E30 # special value, since numpy doesn't handle NaN
self.__parameter_count = 3
# verify input
if size not in ('small', 'wide', 'large'):
raise ValueError('size must be "small", "wide", or "large", not %s' % (str(size)))
if size in ('small', 'wide'):
fontSize = 12
elif size in ('large'):
fontSize = 18
# filter out missing lines
newIsprint = ''
lines = isprintText.split('\n')
for line in lines:
try:
items = line.split()
float(items[0])
float(items[1])
float(items[2])
newIsprint += line + ' '
except:
continue
isprintText = newIsprint
if maxNumPoints != None:
isprintText = self.__truncateIsprint(isprintText, maxNumPoints)
# convert the input data into numeric arrays of float assuming no headers and filter the missing values.
try:
split_data = isprintText.split()
float_data = list(map(self.__filter_missing, split_data))
array_data = numpy.asarray(float_data)
array_data = numpy.reshape(array_data,(-1,self.__parameter_count))
except:
traceback.print_exc()
raise ValueError('input text is not parseable')
# find min, max of x (time)
if startTime == None:
xMin = array_data[0,0]
else:
xMin = startTime
if endTime == None:
xMax = array_data[-1,0]
else:
xMax = endTime
# find min, max of y, not including missing
yMin = None
yMax = None
for y in array_data[:,1]:
if y == self.__missing:
continue
if yMin == None:
yMin = y
elif yMin > y:
yMin = y
if yMax == None:
yMax = y
elif yMax < y:
yMax = y
if yMin == None:
raise ValueError('No valid y data found')
# select the plot size
if size == 'small':
matplotlib.pylab.figure(1, figsize=(8,3), facecolor = 'w')
matplotlib.pylab.axes([0.11, 0.18, 0.78, 0.7])
elif size == 'wide':
matplotlib.pylab.figure(1, figsize=(10,4), facecolor = 'w')
elif size == 'large':
matplotlib.pylab.figure(1, figsize=(10,6), facecolor = 'w')
if useAbsoluteTime:
# leave room for rotated datetime string
if size == 'large':
matplotlib.pylab.axes([0.1, 0.2, 0.8, 0.7])
elif size == 'wide':
matplotlib.pylab.axes([0.07, 0.2, 0.66, 0.7])
elif size == 'small':
matplotlib.pylab.axes([0.1, 0.18, 0.87, 0.7])
matplotlib.pylab.errorbar(array_data[:,0],array_data[:,2],array_data[:,1],ecolor='red',fmt='s',mfc='red',mec='green',ms=1,mew=1)
thisFont = {'size': fontSize}
matplotlib.pylab.rc('font', **thisFont) # pass in the font dict as kwargs
matplotlib.pylab.xlabel(xLabelStr, fontsize=fontSize)
matplotlib.pylab.ylabel(yLabelStr, fontsize=fontSize)
matplotlib.pylab.yticks(fontsize=fontSize)
matplotlib.pylab.xlim(xMin, xMax)
matplotlib.pylab.title(titleStr, fontsize=fontSize)
matplotlib.pylab.grid(True)
if yMinimum != None and yMaximum != None:
matplotlib.pylab.ylim(yMinimum, yMaximum)
elif yMinimum != None and yMaximum == None:
matplotlib.pylab.ylim(yMin=yMinimum)
elif yMinimum == None and yMaximum != None:
matplotlib.pylab.ylim(yMax=yMaximum)
if useAbsoluteTime:
# if plot is less than 24 hours, force ticks to be every 4 hours
xmin, xmax = matplotlib.pylab.xlim()
xmin = xmin / (60*60)
xmax = xmax / (60*60)
if noDate == True:
if xmax % 4 != 0:
newXmax = 4 * (int(xmax/4.0)+1)
else:
newXmax = xmax
newXLocs = []
newXLabels = []
step = int((xmax-xmin-1)/6)+1
xRange = list(range(0,int(newXmax-xmin),step))
for i in xRange:
tmp = (xmin + i)*60*60
newXLocs.append(tmp)
if (xmax - xmin) < 1: newXLocs.append(xmax*60*60)
newXLabels = convertToAbsoluteTimeStr(newXLocs, noDate=noDate, timezone=timezone)
matplotlib.pylab.xticks(newXLocs, newXLabels)
else:
locs, labels = matplotlib.pylab.xticks()
if len(locs) > 5:
# truncate by len(locs) / 5
scale = 1 + int(len(locs) / 5)
new_locs = []
for i in range(len(locs)):
if i % scale == 0:
new_locs.append(locs[i])
locs = new_locs
newXTicks = convertToAbsoluteTimeStr(locs)
matplotlib.pylab.xticks(locs, newXTicks, rotation=15)
matplotlib.pylab.xticks(fontsize=fontSize)
# get the handle to the figure now that it has been created so we can manipulate on subsequent calls.
#self.__figure = matplotlib.pylab.gcf()
matplotlib.pylab.savefig(fullFilename)
#matplotlib.pylab.show()
matplotlib.pylab.clf()
class scanPlotter
scanPlotter is the class that produces a series of scan plots for a single Madrigal file
Non-standard Python modules used: matplotlib
Change history:
Written by "Bill Rideout":mailto:wrideout@haystack.mit.edu Jul. 26, 2006
class scanPlotter:
"""scanPlotter is the class that produces a series of scan plots for a single Madrigal file
Non-standard Python modules used:
matplotlib
Change history:
Written by "Bill Rideout":mailto:wrideout@haystack.mit.edu Jul. 26, 2006
"""
def __init__(self, madFile,
madDBObj=None):
"""__init__ initializes a scanPlotter object.
Inputs:
madFile - the Madrigal file to be analyzed. Must contain SCNTYP and CYCN parameters.
madDBObj - a madrigal.metadata.MadrigalDB object. If None (default),
one created
Returns: void
Affects: sets self.madFile, self.madDBObj
"""
if os.access(madFile, os.R_OK):
self.madFile = madFile
else:
raise IOError('unable to read %s' % (str(madFile)))
if madDBObj == None:
self.madDBObj = madrigal.metadata.MadrigalDB()
else:
self.madDBObj = madDBObj
def plotAllScans(self,
scanType,
fullFilenameTemplate,
plotParm,
size = 'small',
xMinimum = None,
xMaximum = None,
yMinimum = None,
yMaximum = None,
xGridSize = None,
yGridSize = None,
minColormap = None,
maxColormap = None,
colorMap = matplotlib.cm.jet,
maxNumLines = None,
filterStr = ''):
"""plotAllScans creates a series of az or el scans.
Inputs:
scanType - must be 'az' or 'el'
fullFilename - full path of file containing pcolor plot to be saved. Each image
created will have _#.png appended, with # starting at 0
plotParm - mnemonic of parameter to be plotted.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
xMinimum = minumum x value. If None (default), uses lowest x value found for each scan.
xMaximum = maximum x value. If None (default), uses highest x value found for each scan.
yMinimum = minumum y value. If None (default), uses lowest y value found for each scan.
yMaximum = maximum y value. If None (default), uses highest y value found for each scan.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
maxNumLine - max number of lines in isprintText before truncating. If None, no truncation
filterStr - a filter string to pass to isprint. Defaults to no filtering
Returns - a list of tuples, where each tuple has two items: 1. time string,
2. metadata string in form for scanTab.txt. List length = number
of plots created
"""
if scanType not in ('az', 'el'):
raise ValueError('scantype must be az or el, not %s' % (str(scanType)))
self.scanType = scanType
retList = []
# handle ion velocity
if plotParm.lower() == 'vo':
cmap = madrigal.ui.madrigalPlot.get_vo_cmap()
else:
cmap = matplotlib.cm.jet
# create isprint string
parms = 'ut1,scntyp,cycn,%s,gdalt,gdlat,glon,azm,elm,gcdist' % (plotParm.strip())
isprintStr = getIsprintString(self.madFile, parms, filterStr)
try:
scanList = self.createScanInfoList(isprintStr)
except:
traceback.print_exc()
return (retList)
# loop through each scan
scanFailureCount = 0 # keep track of how many scans fail
for i in range(len(scanList)):
scanCount = i - scanFailureCount
scanInfo = scanList[i]
try:
startDateStr = self.getDateStrFromUT(scanInfo[1])
except:
# no scans may have been found
continue
endDateStr = self.getTimeStrFromUT(scanInfo[4])
# create title
if scanType == 'el':
if scanInfo[3] > scanInfo[6]:
ind = 'down'
direction = 1
else:
ind = 'up'
direction = 0
titleStr = 'El %s scan (%s) %s-%s, az=%i' % (plotParm,
ind,
startDateStr,
endDateStr,
int(scanInfo[2]))
timeStr = '%s-%s' % (startDateStr, endDateStr)
metaStr = 'El scan: startTime %i el %f az %f stopTime %i el %f az %f dir %i' % (scanInfo[1],
scanInfo[3],
scanInfo[2],
scanInfo[4],
scanInfo[6],
scanInfo[5],
direction)
retList.append((timeStr,metaStr))
if scanInfo[7] == 'Gdlat':
xLabelStr = 'Geodetic latitude'
xGridSize = 1.0
elif scanInfo[7] == 'Glon':
xLabelStr = 'Longitude'
xGridSize = 1.0
elif scanInfo[7] == 'Gcdist':
xLabelStr = 'Ground distance in km'
xGridSize = 100.0
xLabelStr = ''
yLabelStr = 'Altitude (km)'
if xGridSize == None:
xGridSize = 1.0
if yGridSize == None:
yGridSize = 20.0
else:
if scanInfo[2] > scanInfo[5]:
ind = 'ccw'
direction = 1
else:
ind = 'cw'
direction = 0
titleStr = 'Az %s scan (%s) %s-%s, el=%i' % (plotParm,
ind,
startDateStr,
endDateStr,
int(scanInfo[3]))
timeStr = '%s-%s' % (startDateStr, endDateStr)
metaStr = 'Az scan: startTime %i az %f el %f stopTime %i az %f el %f dir %i' % (scanInfo[1],
scanInfo[2],
scanInfo[3],
scanInfo[4],
scanInfo[5],
scanInfo[6],
direction)
retList.append((timeStr,metaStr))
xLabelStr = 'Longitude'
yLabelStr = 'Geodetic latitude'
xGridSize = 1.0
yGridSize = 1.0
name = '%s_%06i.png' % (fullFilenameTemplate, scanCount)
try:
madPcolorScan(scanInfo[0],
xGridSize,
yGridSize,
titleStr,
xLabelStr,
yLabelStr,
name,
minColormap = minColormap,
maxColormap = maxColormap,
colorMap = cmap)
except:
print('Problem creating scan %s' % (str(retList[-1])))
traceback.print_exc()
scanFailureCount += 1
retList.remove(retList[-1])
return (retList)
def plotAllWedgeScans(self,
scanType,
fullFilenameTemplate,
plotParm,
size = 'small',
xMinimum = None,
xMaximum = None,
yMinimum = None,
yMaximum = None,
minColormap = None,
maxColormap = None,
colorMap = matplotlib.cm.jet,
maxNumLines = None,
filterStr = '',
addTitle = '',
includeKml = False,
radarName = None,
radarDesc = None):
"""plotAllWedgeScans creates a series of az or el scans. Similar to plotAllScans, except produces
fancier wedge plots with uniform scalling.
Inputs:
scanType - must be 'az' or 'el'
fullFilename - full path of file containing pcolor plot to be saved. Each image
created will have _#.png appended, with # starting at 0
plotParm - mnemonic of parameter to be plotted.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
xMinimum = minumum x value. If None (default), uses lowest x value found for each scan.
xMaximum = maximum x value. If None (default), uses highest x value found for each scan.
yMinimum = minumum y value. If None (default), uses lowest y value found for each scan.
yMaximum = maximum y value. If None (default), uses highest y value found for each scan.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
maxNumLine - max number of lines in isprintText before truncating. If None, no truncation
filterStr - a filter string to pass to isprint. Defaults to no filtering
addTitle - a string with additional title. If empty string (the default), no additional title string.
includeKml - if True, create a corresponding kml file for every png file. Same names
as png files, except extension = .kml. If False, do not.
radarName - name to label radar placemark if kml generated.
radarDesc - description text for radar placemark if kml generated
Returns - a list of tuples, where each tuple has two items: 1. time string,
2. metadata string in form for scanTab.txt. List length = number
of plots created
"""
if scanType not in ('az', 'el'):
raise ValueError('scantype must be az or el, not %s' % (str(scanType)))
self.scanType = scanType
if scanType == 'az':
wedgeScanType = 'az'
retList = []
# handle ion velocity
if plotParm.lower() == 'vo':
cmap = madrigal.ui.madrigalPlot.get_vo_cmap()
else:
cmap = matplotlib.cm.jet
# create isprint string
parms = 'ut1,scntyp,cycn,%s,gdalt,gdlat,glon,azm,elm,gcdist,gdlatr,gdlonr,galtr,range,az1,az2,el1,el2' % (plotParm.strip())
filterStr += ' filter=%s,, ' % (plotParm.strip())
isprintStr = getIsprintString(self.madFile, parms, filterStr)
try:
scanList = self.createWedgeScanInfoList(isprintStr)
except:
traceback.print_exc()
return (retList)
gdlatr, gdlonr, galtr = self._getInstLocation(isprintStr)
# create limit dictionary used to keep all scans of the same type with the same limits
limitDict = self._getLimits(scanList, xMinimum, xMaximum, yMinimum, yMaximum)
# loop through each scan
scanFailureCount = 0 # keep track of how many scans fail
for i in range(len(scanList)):
scanCount = i - scanFailureCount
scanInfo = scanList[i]
try:
startDateStr = self.getDateStrFromUT(scanInfo[1])
except:
# no scans may have been found
continue
endDateStr = self.getTimeStrFromUT(scanInfo[4])
# create title
if scanType == 'el':
if scanInfo[3] > scanInfo[6]:
ind = 'down'
direction = 1
else:
ind = 'up'
direction = 0
if int(scanInfo[2]) == int(scanInfo[5]):
# no change in az
titleStr = 'El %s scan (%s) %s-%s, az=%i %s' % (plotParm,
ind,
startDateStr,
endDateStr,
int(scanInfo[2]),
addTitle)
else:
# az changed
titleStr = 'El (%i-%i) %s scan %s-%s, az=%i-%i %s' % (int(scanInfo[3]),
int(scanInfo[6]),
plotParm,
startDateStr,
endDateStr,
int(scanInfo[2]),
int(scanInfo[5]),
addTitle)
timeStr = '%s-%s' % (startDateStr, endDateStr)
metaStr = 'El scan: startTime %i el %f az %f stopTime %i el %f az %f dir %i' % (scanInfo[1],
scanInfo[3],
scanInfo[2],
scanInfo[4],
scanInfo[6],
scanInfo[5],
direction)
retList.append((timeStr,metaStr))
if scanInfo[7] == 'Gdlat':
xLabelStr = 'Geodetic latitude'
wedgeScanType = 'el_lat'
elif scanInfo[7] == 'Glon':
xLabelStr = 'Longitude'
wedgeScanType = 'el_lon'
elif scanInfo[7] == 'Gcdist':
xLabelStr = 'Ground distance in km'
wedgeScanType = 'el_gcdist'
xLabelStr = ''
yLabelStr = 'Altitude (km)'
else:
if scanInfo[2] > scanInfo[5]:
ind = 'ccw'
direction = 1
else:
ind = 'cw'
direction = 0
titleStr = 'Az %s scan (%s) %s-%s, el=%i %s' % (plotParm,
ind,
startDateStr,
endDateStr,
int(scanInfo[3]),
addTitle)
timeStr = '%s-%s' % (startDateStr, endDateStr)
metaStr = 'Az scan: startTime %i az %f el %f stopTime %i az %f el %f dir %i' % (scanInfo[1],
scanInfo[2],
scanInfo[3],
scanInfo[4],
scanInfo[5],
scanInfo[6],
direction)
retList.append((timeStr,metaStr))
xLabelStr = 'Longitude'
yLabelStr = 'Geodetic latitude'
name = '%s_%06i.png' % (fullFilenameTemplate, scanCount)
try:
madPcolorWedgeScan(scanInfo[0],
wedgeScanType,
titleStr,
xLabelStr,
yLabelStr,
name,
xMinimum = limitDict[wedgeScanType][0],
xMaximum = limitDict[wedgeScanType][1],
yMinimum = limitDict[wedgeScanType][2],
yMaximum = limitDict[wedgeScanType][3],
minColormap = minColormap,
maxColormap = maxColormap,
colorMap = cmap)
success = True
except:
print('Problem creating scan %s' % (str(retList[-1])))
traceback.print_exc()
scanFailureCount += 1
retList.remove(retList[-1])
success = False
if includeKml and success:
kmlName = name[:-3] + 'kml'
try:
madPcolorWedgeKmlScan(scanInfo[0],
wedgeScanType,
kmlName,
minColormap = minColormap,
maxColormap = maxColormap,
instName = radarName,
instDesc = radarDesc,
instLat = gdlatr,
instLon = gdlonr,
instAlt = galtr,
colorMap = cmap)
except:
print('Problem creating kml scan')
traceback.print_exc()
return (retList)
def getDateStrFromUT(self, ut):
"""getDateStrFromUT returns a date string formated as YYYY-MM-DD HH:MM:SS from a ut time
(seconds since 1/1/1950)
"""
timeList = madrigal._derive.getDateFromUt(float(ut))
return '%04i-%02i-%02i %02i:%02i:%02i' % (timeList[0],
timeList[1],
timeList[2],
timeList[3],
timeList[4],
timeList[5])
def getTimeStrFromUT(self, ut):
"""getTimeStrFromUT returns a time string formated as HH:MM:SS from a ut time
(seconds since 1/1/1950)
"""
timeList = madrigal._derive.getDateFromUt(float(ut))
return '%02i:%02i:%02i' % (timeList[3],
timeList[4],
timeList[5])
def createScanInfoList(self, isprintStr):
"""createScanInfoList creates a list of tuples, which each tuple representing a single scan, and
values of:
(isprintString, startUT, startAz, startEl, endUT, endAz, endEl, type,
minGdlat, maxGdlat,minGlon, maxGdlon,minGdalt, maxGdalt, minGcdist, maxGcdist). Isprint string
has values (x,y,plotParm), where for az scan x=lon, y=lat, and for el scan y=alt, x=lat if starting
az within 15 degrees of north or south, x=lon if starting az within 15 degrees of east or west,
of x=gcdist if other az. Type is Gdlat or Glon or Gcdist for el scans, None for az scans.
Input isprint string has parameters ut1,scntyp,cycn,,gdalt,gdlat,glon,azm,elm,gcdist
Azimuth scans are split whenever direction or elevation changes. For elevation scans, there will be zero or one
north-south scans, zero or one east-west scans, and zero or more off azimuth scans. An off azimuth
scan is not within 15 degrees of north, south, east or west. Off azimuth scans are not combined with
scans 180 degrees in the other direction, because the x axis is ground distance, which does not reverse
sign.
"""
isprintItems = isprintStr.split()
retList = []
# loop variables
thisIsprintStr = []
cycNum = None
startUT = []
startAz = []
startEl = []
endUT = []
endAz = []
endEl = []
elScan = []
minGdlat = []
maxGdlat = []
minGlon = []
maxGlon = []
minGdalt = []
maxGdalt = []
minGcdist = []
maxGcdist = []
thisAzDirection = None # used to detect new azimuth scans
lastAzDirection = None # used to detect new azimuth scans
lastEl = None # used to detect new azimuth scans
thisAz = None
lastUt = None # keep track of new ut
isNewUt = None # true only when ut changes
inAzScan = False
for i in range(len(isprintItems)):
item = isprintItems[i]
mod = i % 10
if mod == 0:
thisUt = float(item)
if lastUt == None:
lastUt = thisUt
elif mod == 1:
thisScntyp = int(item)
elif mod == 2:
thisCycn = int(float(item))
if cycNum == None:
cycNum = thisCycn
elif mod == 3:
thisParm = item
elif mod == 4:
thisGdalt = float(item)
elif mod == 5:
thisGdlat = float(item)
elif mod == 6:
thisGlon = float(item)
elif mod == 7:
if thisAz != None and lastUt < thisUt and inAzScan:
if thisAz > float(item):
thisAzDirection = 'cw'
elif thisAz < float(item):
thisAzDirection = 'ccw'
thisAz = float(item)
elif mod == 8:
thisEl = float(item)
elif mod == 9:
thisGcdist = float(item)
# check whether this is a new time
if lastUt < thisUt:
isNewUt = True
lastUt = thisUt
else:
isNewUt = False
# if new cycle, close out value if data exists
if thisCycn != cycNum:
for i in range(len(thisIsprintStr)):
if len(thisIsprintStr[i]) == 0:
continue
retList.append((thisIsprintStr[i],
startUT[i], startAz[i], startEl[i], endUT[i], endAz[i], endEl[i], elScan[i],
minGdlat[i], maxGdlat[i], minGlon[i], maxGlon[i], minGdalt[i], maxGdalt[i],
minGcdist[i], maxGcdist[i]))
thisIsprintStr = []
cycNum = None
startUT = []
startAz = []
startEl = []
endUT = []
endAz = []
endEl = []
elScan = []
minGdlat = []
maxGdlat = []
minGlon = []
maxGlon = []
minGdalt = []
maxGdalt = []
minGcdist = []
maxGcdist = []
thisAzDirection = None
lastAzDirection = None
lastEl = None
inAzScan = False
# end of line - process it
if self.scanType == 'el' and thisScntyp == 3:
# add this elevation scan point
# check type of el scan
inAzScan = False
if -15.0 <= thisAz <= 15.0 or -165.0 >= thisAz or thisAz >= 165.0:
thisElScan = 'Gdlat'
elif 75.0 <= thisAz <= 105.0 or -105.0 <= thisAz <= -75.0:
thisElScan = 'Glon'
else:
thisElScan = 'Gcdist'
# get index of this scan, None if a new scan
index = None
for i, item in enumerate(elScan):
if item == 'Gdlat' and thisElScan == 'Gdlat':
index = i
break
elif item == 'Glon' and thisElScan == 'Glon':
index = i
break
elif item == 'Gcdist' and thisElScan == 'Gcdist' and int(thisAz) == int(startAz[i]):
index = i
break
if index == None:
# a new scan has been found
thisIsprintStr.append('')
startUT.append(thisUt)
startAz.append(thisAz)
startEl.append(thisEl)
endUT.append(None)
endAz.append(None)
endEl.append(None)
elScan.append(thisElScan)
minGdlat.append(None)
maxGdlat.append(None)
minGlon.append(None)
maxGlon.append(None)
minGdalt.append(None)
maxGdalt.append(None)
minGcdist.append(None)
maxGcdist.append(None)
index = len(thisIsprintStr) - 1
endUT[index] = thisUt
endAz[index] = thisAz
endEl[index] = thisEl
if thisElScan == 'Gdlat':
thisIsprintStr[index] += '%f %f %s\n' % (thisGdlat, thisGdalt, thisParm)
elif thisElScan == 'Glon':
thisIsprintStr[index] += '%f %f %s\n' % (thisGlon, thisGdalt, thisParm)
else:
thisIsprintStr[index] += '%f %f %s\n' % (thisGcdist, thisGdalt, thisParm)
elif self.scanType == 'az' and thisScntyp == 2:
# az scan
inAzScan = True
# add this azimuth scan point
# check if this is a new scan
newAzScan = False
if lastEl == None:
newAzScan = True
# check for elevation change
elif abs(lastEl - int(thisEl)) > 3:
newAzScan = True
# check for direction change
if lastAzDirection != None:
if lastAzDirection != thisAzDirection:
newAzScan = True
thisAzDirection = None
if newAzScan:
# a new azimuth scan has been found
thisIsprintStr.append('')
startUT.append(thisUt)
startAz.append(thisAz)
startEl.append(thisEl)
endUT.append(None)
endAz.append(None)
endEl.append(None)
elScan.append(None)
minGdlat.append(None)
maxGdlat.append(None)
minGlon.append(None)
maxGlon.append(None)
minGdalt.append(None)
maxGdalt.append(None)
minGcdist.append(None)
maxGcdist.append(None)
index = len(thisIsprintStr) - 1
lastEl = int(thisEl)
lastAzDirection = None
else:
# this is at least the second line of the scan
if not thisAzDirection is None:
lastAzDirection = thisAzDirection
endUT[-1] = thisUt
endAz[-1] = thisAz
endEl[-1] = thisEl
thisIsprintStr[-1] += '%f %f %s\n' % (thisGlon, thisGdlat, thisParm)
else:
# not a scan line
inAzScan = False
continue
if minGdlat[index] == None:
minGdlat[index] = thisGdlat
maxGdlat[index] = thisGdlat
minGlon[index] = thisGlon
maxGlon[index] = thisGlon
minGdalt[index] = thisGdalt
maxGdalt[index] = thisGdalt
minGcdist[index] = thisGcdist
maxGcdist[index] = thisGcdist
else:
if minGdlat[index] > thisGdlat:
minGdlat[index] = thisGdlat
if maxGdlat[index] < thisGdlat:
maxGdlat[index] = thisGdlat
if minGlon[index] > thisGlon:
minGlon[index] = thisGlon
if maxGlon[index] < thisGlon:
maxGlon[index] = thisGlon
if minGdalt[index] > thisGdalt:
minGdalt[index] = thisGdalt
if maxGdalt[index] < thisGdalt:
maxGdalt[index] = thisGdalt
if minGcdist[index] > thisGcdist:
minGcdist[index] = thisGcdist
if maxGcdist[index] < thisGcdist:
maxGcdist[index] = thisGcdist
# end, close out last cycle
for i in range(len(thisIsprintStr)):
if len(thisIsprintStr[i]) == 0:
continue
retList.append((thisIsprintStr[i],
startUT[i], startAz[i], startEl[i], endUT[i], endAz[i], endEl[i], elScan[i],
minGdlat[i], maxGdlat[i], minGlon[i], maxGlon[i], minGdalt[i], maxGdalt[i],
minGcdist[i], maxGcdist[i]))
return(retList)
def createWedgeScanInfoList(self, isprintStr):
"""createWedgeScanInfoList creates a list of tuples, which each tuple representing a single scan, and
values of:
(isprintString, startUT, startAz, startEl, endUT, endAz, endEl, type,
minGdlat, maxGdlat,minGlon, maxGdlon,minGdalt, maxGdalt, minGcdist, maxGcdist).
Input isprint string has parameters ut1,scntyp,cycn,,gdalt,gdlat,glon,azm,elm,gcdist,
gdlatr,gdlonr,galtr,range,az1,az2,el1,el2
Azimuth scans are split whenever direction or elevation changes. For elevation scans, there will be zero or more
north-south scans, zero or more east-west scans, and zero or more off azimuth scans. A south to north
elevation scan or a west to east elevation scan is called clockwise, and a switch from clockwise
to counterclockwise will create a new elevation scan. An off azimuth
scan is not within 15 degrees of north, south, east or west. Off azimuth scans are not combined with
scans 180 degrees in the other direction, because the x axis is ground distance, which does not reverse
sign.
"""
isprintItems = isprintStr.split()
retList = []
# loop variables
thisIsprintStr = []
cycNum = None
startUT = []
startAz = []
startEl = []
endUT = []
endAz = []
endEl = []
elScan = []
minGdlat = []
maxGdlat = []
minGlon = []
maxGlon = []
minGdalt = []
maxGdalt = []
minGcdist = []
maxGcdist = []
thisAzDirection = None # used to detect new azimuth scans
lastAzDirection = None # used to detect new azimuth scans
thisElDirection = None # used to detect new elevation scans (cw or ccw)
lastElDirection = None # used to detect new elevation scans (cw or ccw)
lastEl = None # used to detect new azimuth scans
thisAz = None
thisEl = None
lastUt = None # keep track of new ut
isNewUt = None # true only when ut changes
inAzScan = False
inElScan = False
for i in range(len(isprintItems)):
item = isprintItems[i]
mod = i % 18
if mod == 0:
thisUt = float(item)
if lastUt == None:
lastUt = thisUt
elif mod == 1:
thisScntyp = int(item)
elif mod == 2:
thisCycn = int(float(item))
if cycNum == None:
cycNum = thisCycn
elif mod == 3:
thisParm = item
elif mod == 4:
thisGdalt = float(item)
elif mod == 5:
thisGdlat = float(item)
elif mod == 6:
thisGlon = float(item)
elif mod == 7:
if thisAz != None and lastUt < thisUt and inAzScan:
if thisAz > float(item):
thisAzDirection = 'cw'
elif thisAz < float(item):
thisAzDirection = 'ccw'
thisAz = float(item)
elif mod == 8:
if thisEl != None and inElScan:
if abs(float(item) - thisEl) > 1.0:
if thisElScan == 'Gdlat':
if -165.0 >= thisAz or thisAz >= 165.0:
# southern scan
if float(item) > thisEl:
thisElDirection = 'cw'
else:
thisElDirection = 'ccw'
else:
# northern scan
if float(item) < thisEl:
thisElDirection = 'cw'
else:
thisElDirection = 'ccw'
elif thisElScan == 'Glon':
if -105.0 <= thisAz <= -75.0:
# western scan
if float(item) > thisEl:
thisElDirection = 'cw'
else:
thisElDirection = 'ccw'
else:
# eastern scan
if float(item) < thisEl:
thisElDirection = 'cw'
else:
thisElDirection = 'ccw'
elif thisElScan == 'Gcdist':
if float(item) > thisEl:
thisElDirection = 'up'
else:
thisElDirection = 'down'
thisEl = float(item)
elif mod == 9:
thisGcdist = float(item)
elif mod == 10:
thisGdlatr = float(item)
elif mod == 11:
thisGdlonr = float(item)
elif mod == 12:
thisGaltr = float(item)
elif mod == 13:
thisRange = float(item)
elif mod == 14:
thisAz1 = float(item)
elif mod == 15:
thisAz2 = float(item)
elif mod == 16:
thisEl1 = float(item)
elif mod == 17:
thisEl2 = float(item)
# check whether this is a new time
if lastUt < thisUt:
isNewUt = True
lastUt = thisUt
else:
isNewUt = False
# if new cycle or elevation issue, close out value if data exists
if (thisCycn != cycNum) or \
(thisElDirection == 'cw' and lastElDirection == 'ccw') or \
(thisElDirection == 'ccw' and lastElDirection == 'cw') or \
(thisElDirection == 'down' and lastElDirection == 'up') or \
(thisElDirection == 'up' and lastElDirection == 'down') or \
(thisElDirection != None and not inElScan) or \
(thisAzDirection != None and not inAzScan):
for i in range(len(thisIsprintStr)):
if len(thisIsprintStr[i]) == 0:
continue
retList.append((thisIsprintStr[i],
startUT[i], startAz[i], startEl[i], endUT[i], endAz[i], endEl[i], elScan[i],
minGdlat[i], maxGdlat[i], minGlon[i], maxGlon[i], minGdalt[i], maxGdalt[i],
minGcdist[i], maxGcdist[i]))
thisIsprintStr = []
cycNum = None
startUT = []
startAz = []
startEl = []
endUT = []
endAz = []
endEl = []
elScan = []
minGdlat = []
maxGdlat = []
minGlon = []
maxGlon = []
minGdalt = []
maxGdalt = []
minGcdist = []
maxGcdist = []
thisAzDirection = None
lastAzDirection = None
thisElDirection = None # used to detect new elevation scans (cw or ccw)
lastElDirection = None
lastEl = None
inAzScan = False
inElScan = False
# end of line - process it
if self.scanType == 'el' and thisScntyp == 3:
# add this elevation scan point
# check type of el scan
inAzScan = False
inElScan = True
if (-15.0 <= thisAz <= 15.0 or -165.0 >= thisAz or thisAz >= 165.0) and \
abs(thisAz1 - thisAz2) < 0.5:
thisElScan = 'Gdlat'
elif (75.0 <= thisAz <= 105.0 or -105.0 <= thisAz <= -75.0) and \
abs(thisAz1 - thisAz2) < 0.5:
thisElScan = 'Glon'
else:
thisElScan = 'Gcdist'
# get index of this scan, None if a new scan
index = None
for i, item in enumerate(elScan):
if item == 'Gdlat' and thisElScan == 'Gdlat':
index = i
break
elif item == 'Glon' and thisElScan == 'Glon':
index = i
break
elif item == 'Gcdist' and thisElScan == 'Gcdist' and int(thisAz) == int(startAz[i]):
index = i
break
elif item == 'Gcdist' and thisElScan == 'Gcdist' and i == len(elScan)-1:
index = i
break
if index == None:
# a new scan has been found
thisIsprintStr.append('')
startUT.append(thisUt)
startAz.append(thisAz)
startEl.append(thisEl)
endUT.append(None)
endAz.append(None)
endEl.append(None)
elScan.append(thisElScan)
minGdlat.append(None)
maxGdlat.append(None)
minGlon.append(None)
maxGlon.append(None)
minGdalt.append(None)
maxGdalt.append(None)
minGcdist.append(None)
maxGcdist.append(None)
index = len(thisIsprintStr) - 1
lastElDirection = thisElDirection
endUT[index] = thisUt
endAz[index] = thisAz
endEl[index] = thisEl
# gdlatr, gdlonr, galtr, range, az1, az2, el1, el2,plotParm
thisIsprintStr[index] += '%f %f %f %f %f %f %f %f %s\n' % (thisGdlatr, thisGdlonr, thisGaltr,
thisRange, thisAz1, thisAz2,
thisEl1, thisEl2, thisParm)
elif self.scanType == 'az' and thisScntyp == 2:
# az scan
inAzScan = True
inElScan = False
# add this azimuth scan point
# check if this is a new scan
newAzScan = False
if lastEl == None:
newAzScan = True
# check for elevation change
elif abs(lastEl - int(thisEl)) > 3:
newAzScan = True
# check for direction change
if lastAzDirection != None:
if lastAzDirection != thisAzDirection:
newAzScan = True
thisAzDirection = None
if newAzScan:
# a new azimuth scan has been found
thisIsprintStr.append('')
startUT.append(thisUt)
startAz.append(thisAz)
startEl.append(thisEl)
endUT.append(None)
endAz.append(None)
endEl.append(None)
elScan.append(None)
minGdlat.append(None)
maxGdlat.append(None)
minGlon.append(None)
maxGlon.append(None)
minGdalt.append(None)
maxGdalt.append(None)
minGcdist.append(None)
maxGcdist.append(None)
index = len(thisIsprintStr) - 1
lastEl = int(thisEl)
lastAzDirection = None
else:
# this is at least the second line of the scan
if not thisAzDirection is None:
lastAzDirection = thisAzDirection
endUT[-1] = thisUt
endAz[-1] = thisAz
endEl[-1] = thisEl
thisIsprintStr[-1] += '%f %f %f %f %f %f %f %f %s\n' % (thisGdlatr, thisGdlonr, thisGaltr,
thisRange, thisAz1, thisAz2,
thisEl1, thisEl2, thisParm)
else:
# not a scan line
inAzScan = False
inElScan = False
continue
if minGdlat[index] == None:
minGdlat[index] = thisGdlat
maxGdlat[index] = thisGdlat
minGlon[index] = thisGlon
maxGlon[index] = thisGlon
minGdalt[index] = thisGdalt
maxGdalt[index] = thisGdalt
minGcdist[index] = thisGcdist
maxGcdist[index] = thisGcdist
else:
if minGdlat[index] > thisGdlat:
minGdlat[index] = thisGdlat
if maxGdlat[index] < thisGdlat:
maxGdlat[index] = thisGdlat
if minGlon[index] > thisGlon:
minGlon[index] = thisGlon
if maxGlon[index] < thisGlon:
maxGlon[index] = thisGlon
if minGdalt[index] > thisGdalt:
minGdalt[index] = thisGdalt
if maxGdalt[index] < thisGdalt:
maxGdalt[index] = thisGdalt
if minGcdist[index] > thisGcdist:
minGcdist[index] = thisGcdist
if maxGcdist[index] < thisGcdist:
maxGcdist[index] = thisGcdist
# end, close out last cycle
for i in range(len(thisIsprintStr)):
if len(thisIsprintStr[i]) == 0:
continue
retList.append((thisIsprintStr[i],
startUT[i], startAz[i], startEl[i], endUT[i], endAz[i], endEl[i], elScan[i],
minGdlat[i], maxGdlat[i], minGlon[i], maxGlon[i], minGdalt[i], maxGdalt[i],
minGcdist[i], maxGcdist[i]))
return(retList)
def _getLimits(self, scanList, xMinimum, xMaximum, yMinimum, yMaximum):
"""_getLimits returns a dictionary with keys that may include ('az', 'el_lat', 'el_lon', 'el_gcdist'),
though not all will neccessarily be there. Values are overall tuple of
(xMinimum, xMaximum, yMinimum, yMaximum). These values are set by the input
arguments unless it is None, in which case the scanList limits set the values.
Inputs:
scanList: a list of tuples, which each tuple representing a single scan, and
has values of: (isprintString, startUT, startAz, startEl, endUT, endAz, endEl, type,
minGdlat, maxGdlat,minGlon, maxGdlon,minGdalt, maxGdalt, minGcdist, maxGcdist). Isprint string
has values (gdlatr, gdlonr, galtr, range, az1, az2, el1, el2,plotParm),. Type is Gdlat or Glon or
Gcdist for el scans, None for az scans.
xMinimum, xMaximum, yMinimum, yMaximum - as passed into scanPlotter
"""
retDict = {}
xMinAz = None
xMaxAz = None
yMinAz = None
yMaxAz = None
xMinElLat = None
xMaxElLat = None
yMinElLat = None
yMaxElLat = None
xMinElLon = None
xMaxElLon = None
yMinElLon = None
yMaxElLon = None
xMinElGcdist = None
xMaxElGcdist = None
yMinElGcdist = None
yMaxElGcdist = None
minValue = None
maxValue = None
for scanItem in scanList:
if scanItem[7] == None: # az scan
if xMinAz == None:
xMinAz = scanItem[10]
elif xMinAz > scanItem[10]:
xMinAz = scanItem[10]
if xMaxAz == None:
xMaxAz = scanItem[11]
elif xMaxAz < scanItem[11]:
xMaxAz = scanItem[11]
if yMinAz == None:
yMinAz = scanItem[8]
elif yMinAz > scanItem[8]:
yMinAz = scanItem[8]
if yMaxAz == None:
yMaxAz = scanItem[9]
elif yMaxAz < scanItem[9]:
yMaxAz = scanItem[9]
elif scanItem[7] == 'Gdlat': # el_lat scan
if xMinElLat == None:
xMinElLat = scanItem[8]
elif xMinElLat > scanItem[8]:
xMinElLat = scanItem[8]
if xMaxElLat == None:
xMaxElLat = scanItem[9]
elif xMaxElLat < scanItem[9]:
xMaxElLat = scanItem[9]
if yMinElLat == None:
yMinElLat = scanItem[12]
elif yMinElLat > scanItem[12]:
yMinElLat = scanItem[12]
if yMaxElLat == None:
yMaxElLat = scanItem[13]
elif yMaxElLat < scanItem[13]:
yMaxElLat = scanItem[13]
elif scanItem[7] == 'Glon': # el_lon scan
if xMinElLon == None:
xMinElLon = scanItem[10]
elif xMinElLon > scanItem[10]:
xMinElLon = scanItem[10]
if xMaxElLon == None:
xMaxElLon = scanItem[11]
elif xMaxElLon < scanItem[11]:
xMaxElLon = scanItem[11]
if yMinElLon == None:
yMinElLon = scanItem[12]
elif yMinElLon > scanItem[12]:
yMinElLon = scanItem[12]
if yMaxElLon == None:
yMaxElLon = scanItem[13]
elif yMaxElLon < scanItem[13]:
yMaxElLon = scanItem[13]
elif scanItem[7] == 'Gcdist': # el_gcdist scan
if xMinElGcdist == None:
xMinElGcdist = scanItem[14]
elif xMinElGcdist > scanItem[14]:
xMinElGcdist = scanItem[14]
if xMaxElGcdist == None:
xMaxElGcdist = scanItem[15]
elif xMaxElGcdist < scanItem[15]:
xMaxElGcdist = scanItem[15]
if yMinElGcdist == None:
yMinElGcdist = scanItem[12]
elif yMinElGcdist > scanItem[12]:
yMinElGcdist = scanItem[12]
if yMaxElGcdist == None:
yMaxElGcdist = scanItem[13]
elif yMaxElGcdist < scanItem[13]:
yMaxElGcdist = scanItem[13]
# fill out the dictionary
if xMinAz != None:
if xMinimum == None:
xMin = xMinAz
else:
xMin = xMinimum
if xMaximum == None:
xMax = xMaxAz
else:
xMax = xMaximum
if yMinimum == None:
yMin = yMinAz
else:
yMin = yMinimum
if yMaximum == None:
yMax = yMaxAz
else:
yMax = yMaximum
retDict['az'] = (xMin, xMax, yMin, yMax)
if xMinElLat != None:
if xMinimum == None:
xMin = xMinElLat
else:
xMin = xMinimum
if xMaximum == None:
xMax = xMaxElLat
else:
xMax = xMaximum
if yMinimum == None:
yMin = yMinElLat
else:
yMin = yMinimum
if yMaximum == None:
yMax = yMaxElLat
else:
yMax = yMaximum
retDict['el_lat'] = (xMin, xMax, yMin, yMax)
if xMinElLon != None:
if xMinimum == None:
xMin = xMinElLon
else:
xMin = xMinimum
if xMaximum == None:
xMax = xMaxElLon
else:
xMax = xMaximum
if yMinimum == None:
yMin = yMinElLon
else:
yMin = yMinimum
if yMaximum == None:
yMax = yMaxElLon
else:
yMax = yMaximum
retDict['el_lon'] = (xMin, xMax, yMin, yMax)
if xMinElGcdist != None:
if xMinimum == None:
xMin = xMinElGcdist
else:
xMin = xMinimum
if xMaximum == None:
xMax = xMaxElGcdist
else:
xMax = xMaximum
if yMinimum == None:
yMin = yMinElGcdist
else:
yMin = yMinimum
if yMaximum == None:
yMax = yMaxElGcdist
else:
yMax = yMaximum
retDict['el_gcdist'] = (xMin, xMax, yMin, yMax)
return(retDict)
def _getInstLocation(self, isprintStr):
"""_getInstLocation returns a tuple of instrument location (gdlatr, gdlonr, galtr)
Input isprint string has parameters ut1,scntyp,cycn,,gdalt,gdlat,glon,azm,elm,gcdist,
gdlatr,gdlonr,galtr,range,az1,az2,el1,el2
"""
isprintItems = isprintStr.split()
gdlatr = float(isprintItems[10])
gdlonr = float(isprintItems[11])
if gdlonr > 180.0:
gdlonr -= 360.0
galtr = float(isprintItems[12])
return((gdlatr, gdlonr, galtr))
Ancestors (in MRO)
- scanPlotter
- builtins.object
Static methods
def __init__(
self, madFile, madDBObj=None)
init initializes a scanPlotter object.
Inputs:
madFile - the Madrigal file to be analyzed. Must contain SCNTYP and CYCN parameters.
madDBObj - a madrigal.metadata.MadrigalDB object. If None (default),
one created
Returns: void
Affects: sets self.madFile, self.madDBObj
def __init__(self, madFile,
madDBObj=None):
"""__init__ initializes a scanPlotter object.
Inputs:
madFile - the Madrigal file to be analyzed. Must contain SCNTYP and CYCN parameters.
madDBObj - a madrigal.metadata.MadrigalDB object. If None (default),
one created
Returns: void
Affects: sets self.madFile, self.madDBObj
"""
if os.access(madFile, os.R_OK):
self.madFile = madFile
else:
raise IOError('unable to read %s' % (str(madFile)))
if madDBObj == None:
self.madDBObj = madrigal.metadata.MadrigalDB()
else:
self.madDBObj = madDBObj
def createScanInfoList(
self, isprintStr)
createScanInfoList creates a list of tuples, which each tuple representing a single scan, and values of:
(isprintString, startUT, startAz, startEl, endUT, endAz, endEl, type, minGdlat, maxGdlat,minGlon, maxGdlon,minGdalt, maxGdalt, minGcdist, maxGcdist). Isprint string has values (x,y,plotParm), where for az scan x=lon, y=lat, and for el scan y=alt, x=lat if starting az within 15 degrees of north or south, x=lon if starting az within 15 degrees of east or west, of x=gcdist if other az. Type is Gdlat or Glon or Gcdist for el scans, None for az scans.
Input isprint string has parameters ut1,scntyp,cycn,
Azimuth scans are split whenever direction or elevation changes. For elevation scans, there will be zero or one north-south scans, zero or one east-west scans, and zero or more off azimuth scans. An off azimuth scan is not within 15 degrees of north, south, east or west. Off azimuth scans are not combined with scans 180 degrees in the other direction, because the x axis is ground distance, which does not reverse sign.
def createScanInfoList(self, isprintStr):
"""createScanInfoList creates a list of tuples, which each tuple representing a single scan, and
values of:
(isprintString, startUT, startAz, startEl, endUT, endAz, endEl, type,
minGdlat, maxGdlat,minGlon, maxGdlon,minGdalt, maxGdalt, minGcdist, maxGcdist). Isprint string
has values (x,y,plotParm), where for az scan x=lon, y=lat, and for el scan y=alt, x=lat if starting
az within 15 degrees of north or south, x=lon if starting az within 15 degrees of east or west,
of x=gcdist if other az. Type is Gdlat or Glon or Gcdist for el scans, None for az scans.
Input isprint string has parameters ut1,scntyp,cycn,,gdalt,gdlat,glon,azm,elm,gcdist
Azimuth scans are split whenever direction or elevation changes. For elevation scans, there will be zero or one
north-south scans, zero or one east-west scans, and zero or more off azimuth scans. An off azimuth
scan is not within 15 degrees of north, south, east or west. Off azimuth scans are not combined with
scans 180 degrees in the other direction, because the x axis is ground distance, which does not reverse
sign.
"""
isprintItems = isprintStr.split()
retList = []
# loop variables
thisIsprintStr = []
cycNum = None
startUT = []
startAz = []
startEl = []
endUT = []
endAz = []
endEl = []
elScan = []
minGdlat = []
maxGdlat = []
minGlon = []
maxGlon = []
minGdalt = []
maxGdalt = []
minGcdist = []
maxGcdist = []
thisAzDirection = None # used to detect new azimuth scans
lastAzDirection = None # used to detect new azimuth scans
lastEl = None # used to detect new azimuth scans
thisAz = None
lastUt = None # keep track of new ut
isNewUt = None # true only when ut changes
inAzScan = False
for i in range(len(isprintItems)):
item = isprintItems[i]
mod = i % 10
if mod == 0:
thisUt = float(item)
if lastUt == None:
lastUt = thisUt
elif mod == 1:
thisScntyp = int(item)
elif mod == 2:
thisCycn = int(float(item))
if cycNum == None:
cycNum = thisCycn
elif mod == 3:
thisParm = item
elif mod == 4:
thisGdalt = float(item)
elif mod == 5:
thisGdlat = float(item)
elif mod == 6:
thisGlon = float(item)
elif mod == 7:
if thisAz != None and lastUt < thisUt and inAzScan:
if thisAz > float(item):
thisAzDirection = 'cw'
elif thisAz < float(item):
thisAzDirection = 'ccw'
thisAz = float(item)
elif mod == 8:
thisEl = float(item)
elif mod == 9:
thisGcdist = float(item)
# check whether this is a new time
if lastUt < thisUt:
isNewUt = True
lastUt = thisUt
else:
isNewUt = False
# if new cycle, close out value if data exists
if thisCycn != cycNum:
for i in range(len(thisIsprintStr)):
if len(thisIsprintStr[i]) == 0:
continue
retList.append((thisIsprintStr[i],
startUT[i], startAz[i], startEl[i], endUT[i], endAz[i], endEl[i], elScan[i],
minGdlat[i], maxGdlat[i], minGlon[i], maxGlon[i], minGdalt[i], maxGdalt[i],
minGcdist[i], maxGcdist[i]))
thisIsprintStr = []
cycNum = None
startUT = []
startAz = []
startEl = []
endUT = []
endAz = []
endEl = []
elScan = []
minGdlat = []
maxGdlat = []
minGlon = []
maxGlon = []
minGdalt = []
maxGdalt = []
minGcdist = []
maxGcdist = []
thisAzDirection = None
lastAzDirection = None
lastEl = None
inAzScan = False
# end of line - process it
if self.scanType == 'el' and thisScntyp == 3:
# add this elevation scan point
# check type of el scan
inAzScan = False
if -15.0 <= thisAz <= 15.0 or -165.0 >= thisAz or thisAz >= 165.0:
thisElScan = 'Gdlat'
elif 75.0 <= thisAz <= 105.0 or -105.0 <= thisAz <= -75.0:
thisElScan = 'Glon'
else:
thisElScan = 'Gcdist'
# get index of this scan, None if a new scan
index = None
for i, item in enumerate(elScan):
if item == 'Gdlat' and thisElScan == 'Gdlat':
index = i
break
elif item == 'Glon' and thisElScan == 'Glon':
index = i
break
elif item == 'Gcdist' and thisElScan == 'Gcdist' and int(thisAz) == int(startAz[i]):
index = i
break
if index == None:
# a new scan has been found
thisIsprintStr.append('')
startUT.append(thisUt)
startAz.append(thisAz)
startEl.append(thisEl)
endUT.append(None)
endAz.append(None)
endEl.append(None)
elScan.append(thisElScan)
minGdlat.append(None)
maxGdlat.append(None)
minGlon.append(None)
maxGlon.append(None)
minGdalt.append(None)
maxGdalt.append(None)
minGcdist.append(None)
maxGcdist.append(None)
index = len(thisIsprintStr) - 1
endUT[index] = thisUt
endAz[index] = thisAz
endEl[index] = thisEl
if thisElScan == 'Gdlat':
thisIsprintStr[index] += '%f %f %s\n' % (thisGdlat, thisGdalt, thisParm)
elif thisElScan == 'Glon':
thisIsprintStr[index] += '%f %f %s\n' % (thisGlon, thisGdalt, thisParm)
else:
thisIsprintStr[index] += '%f %f %s\n' % (thisGcdist, thisGdalt, thisParm)
elif self.scanType == 'az' and thisScntyp == 2:
# az scan
inAzScan = True
# add this azimuth scan point
# check if this is a new scan
newAzScan = False
if lastEl == None:
newAzScan = True
# check for elevation change
elif abs(lastEl - int(thisEl)) > 3:
newAzScan = True
# check for direction change
if lastAzDirection != None:
if lastAzDirection != thisAzDirection:
newAzScan = True
thisAzDirection = None
if newAzScan:
# a new azimuth scan has been found
thisIsprintStr.append('')
startUT.append(thisUt)
startAz.append(thisAz)
startEl.append(thisEl)
endUT.append(None)
endAz.append(None)
endEl.append(None)
elScan.append(None)
minGdlat.append(None)
maxGdlat.append(None)
minGlon.append(None)
maxGlon.append(None)
minGdalt.append(None)
maxGdalt.append(None)
minGcdist.append(None)
maxGcdist.append(None)
index = len(thisIsprintStr) - 1
lastEl = int(thisEl)
lastAzDirection = None
else:
# this is at least the second line of the scan
if not thisAzDirection is None:
lastAzDirection = thisAzDirection
endUT[-1] = thisUt
endAz[-1] = thisAz
endEl[-1] = thisEl
thisIsprintStr[-1] += '%f %f %s\n' % (thisGlon, thisGdlat, thisParm)
else:
# not a scan line
inAzScan = False
continue
if minGdlat[index] == None:
minGdlat[index] = thisGdlat
maxGdlat[index] = thisGdlat
minGlon[index] = thisGlon
maxGlon[index] = thisGlon
minGdalt[index] = thisGdalt
maxGdalt[index] = thisGdalt
minGcdist[index] = thisGcdist
maxGcdist[index] = thisGcdist
else:
if minGdlat[index] > thisGdlat:
minGdlat[index] = thisGdlat
if maxGdlat[index] < thisGdlat:
maxGdlat[index] = thisGdlat
if minGlon[index] > thisGlon:
minGlon[index] = thisGlon
if maxGlon[index] < thisGlon:
maxGlon[index] = thisGlon
if minGdalt[index] > thisGdalt:
minGdalt[index] = thisGdalt
if maxGdalt[index] < thisGdalt:
maxGdalt[index] = thisGdalt
if minGcdist[index] > thisGcdist:
minGcdist[index] = thisGcdist
if maxGcdist[index] < thisGcdist:
maxGcdist[index] = thisGcdist
# end, close out last cycle
for i in range(len(thisIsprintStr)):
if len(thisIsprintStr[i]) == 0:
continue
retList.append((thisIsprintStr[i],
startUT[i], startAz[i], startEl[i], endUT[i], endAz[i], endEl[i], elScan[i],
minGdlat[i], maxGdlat[i], minGlon[i], maxGlon[i], minGdalt[i], maxGdalt[i],
minGcdist[i], maxGcdist[i]))
return(retList)
def createWedgeScanInfoList(
self, isprintStr)
createWedgeScanInfoList creates a list of tuples, which each tuple representing a single scan, and values of:
(isprintString, startUT, startAz, startEl, endUT, endAz, endEl, type, minGdlat, maxGdlat,minGlon, maxGdlon,minGdalt, maxGdalt, minGcdist, maxGcdist).
Input isprint string has parameters ut1,scntyp,cycn,
Azimuth scans are split whenever direction or elevation changes. For elevation scans, there will be zero or more north-south scans, zero or more east-west scans, and zero or more off azimuth scans. A south to north elevation scan or a west to east elevation scan is called clockwise, and a switch from clockwise to counterclockwise will create a new elevation scan. An off azimuth scan is not within 15 degrees of north, south, east or west. Off azimuth scans are not combined with scans 180 degrees in the other direction, because the x axis is ground distance, which does not reverse sign.
def createWedgeScanInfoList(self, isprintStr):
"""createWedgeScanInfoList creates a list of tuples, which each tuple representing a single scan, and
values of:
(isprintString, startUT, startAz, startEl, endUT, endAz, endEl, type,
minGdlat, maxGdlat,minGlon, maxGdlon,minGdalt, maxGdalt, minGcdist, maxGcdist).
Input isprint string has parameters ut1,scntyp,cycn,,gdalt,gdlat,glon,azm,elm,gcdist,
gdlatr,gdlonr,galtr,range,az1,az2,el1,el2
Azimuth scans are split whenever direction or elevation changes. For elevation scans, there will be zero or more
north-south scans, zero or more east-west scans, and zero or more off azimuth scans. A south to north
elevation scan or a west to east elevation scan is called clockwise, and a switch from clockwise
to counterclockwise will create a new elevation scan. An off azimuth
scan is not within 15 degrees of north, south, east or west. Off azimuth scans are not combined with
scans 180 degrees in the other direction, because the x axis is ground distance, which does not reverse
sign.
"""
isprintItems = isprintStr.split()
retList = []
# loop variables
thisIsprintStr = []
cycNum = None
startUT = []
startAz = []
startEl = []
endUT = []
endAz = []
endEl = []
elScan = []
minGdlat = []
maxGdlat = []
minGlon = []
maxGlon = []
minGdalt = []
maxGdalt = []
minGcdist = []
maxGcdist = []
thisAzDirection = None # used to detect new azimuth scans
lastAzDirection = None # used to detect new azimuth scans
thisElDirection = None # used to detect new elevation scans (cw or ccw)
lastElDirection = None # used to detect new elevation scans (cw or ccw)
lastEl = None # used to detect new azimuth scans
thisAz = None
thisEl = None
lastUt = None # keep track of new ut
isNewUt = None # true only when ut changes
inAzScan = False
inElScan = False
for i in range(len(isprintItems)):
item = isprintItems[i]
mod = i % 18
if mod == 0:
thisUt = float(item)
if lastUt == None:
lastUt = thisUt
elif mod == 1:
thisScntyp = int(item)
elif mod == 2:
thisCycn = int(float(item))
if cycNum == None:
cycNum = thisCycn
elif mod == 3:
thisParm = item
elif mod == 4:
thisGdalt = float(item)
elif mod == 5:
thisGdlat = float(item)
elif mod == 6:
thisGlon = float(item)
elif mod == 7:
if thisAz != None and lastUt < thisUt and inAzScan:
if thisAz > float(item):
thisAzDirection = 'cw'
elif thisAz < float(item):
thisAzDirection = 'ccw'
thisAz = float(item)
elif mod == 8:
if thisEl != None and inElScan:
if abs(float(item) - thisEl) > 1.0:
if thisElScan == 'Gdlat':
if -165.0 >= thisAz or thisAz >= 165.0:
# southern scan
if float(item) > thisEl:
thisElDirection = 'cw'
else:
thisElDirection = 'ccw'
else:
# northern scan
if float(item) < thisEl:
thisElDirection = 'cw'
else:
thisElDirection = 'ccw'
elif thisElScan == 'Glon':
if -105.0 <= thisAz <= -75.0:
# western scan
if float(item) > thisEl:
thisElDirection = 'cw'
else:
thisElDirection = 'ccw'
else:
# eastern scan
if float(item) < thisEl:
thisElDirection = 'cw'
else:
thisElDirection = 'ccw'
elif thisElScan == 'Gcdist':
if float(item) > thisEl:
thisElDirection = 'up'
else:
thisElDirection = 'down'
thisEl = float(item)
elif mod == 9:
thisGcdist = float(item)
elif mod == 10:
thisGdlatr = float(item)
elif mod == 11:
thisGdlonr = float(item)
elif mod == 12:
thisGaltr = float(item)
elif mod == 13:
thisRange = float(item)
elif mod == 14:
thisAz1 = float(item)
elif mod == 15:
thisAz2 = float(item)
elif mod == 16:
thisEl1 = float(item)
elif mod == 17:
thisEl2 = float(item)
# check whether this is a new time
if lastUt < thisUt:
isNewUt = True
lastUt = thisUt
else:
isNewUt = False
# if new cycle or elevation issue, close out value if data exists
if (thisCycn != cycNum) or \
(thisElDirection == 'cw' and lastElDirection == 'ccw') or \
(thisElDirection == 'ccw' and lastElDirection == 'cw') or \
(thisElDirection == 'down' and lastElDirection == 'up') or \
(thisElDirection == 'up' and lastElDirection == 'down') or \
(thisElDirection != None and not inElScan) or \
(thisAzDirection != None and not inAzScan):
for i in range(len(thisIsprintStr)):
if len(thisIsprintStr[i]) == 0:
continue
retList.append((thisIsprintStr[i],
startUT[i], startAz[i], startEl[i], endUT[i], endAz[i], endEl[i], elScan[i],
minGdlat[i], maxGdlat[i], minGlon[i], maxGlon[i], minGdalt[i], maxGdalt[i],
minGcdist[i], maxGcdist[i]))
thisIsprintStr = []
cycNum = None
startUT = []
startAz = []
startEl = []
endUT = []
endAz = []
endEl = []
elScan = []
minGdlat = []
maxGdlat = []
minGlon = []
maxGlon = []
minGdalt = []
maxGdalt = []
minGcdist = []
maxGcdist = []
thisAzDirection = None
lastAzDirection = None
thisElDirection = None # used to detect new elevation scans (cw or ccw)
lastElDirection = None
lastEl = None
inAzScan = False
inElScan = False
# end of line - process it
if self.scanType == 'el' and thisScntyp == 3:
# add this elevation scan point
# check type of el scan
inAzScan = False
inElScan = True
if (-15.0 <= thisAz <= 15.0 or -165.0 >= thisAz or thisAz >= 165.0) and \
abs(thisAz1 - thisAz2) < 0.5:
thisElScan = 'Gdlat'
elif (75.0 <= thisAz <= 105.0 or -105.0 <= thisAz <= -75.0) and \
abs(thisAz1 - thisAz2) < 0.5:
thisElScan = 'Glon'
else:
thisElScan = 'Gcdist'
# get index of this scan, None if a new scan
index = None
for i, item in enumerate(elScan):
if item == 'Gdlat' and thisElScan == 'Gdlat':
index = i
break
elif item == 'Glon' and thisElScan == 'Glon':
index = i
break
elif item == 'Gcdist' and thisElScan == 'Gcdist' and int(thisAz) == int(startAz[i]):
index = i
break
elif item == 'Gcdist' and thisElScan == 'Gcdist' and i == len(elScan)-1:
index = i
break
if index == None:
# a new scan has been found
thisIsprintStr.append('')
startUT.append(thisUt)
startAz.append(thisAz)
startEl.append(thisEl)
endUT.append(None)
endAz.append(None)
endEl.append(None)
elScan.append(thisElScan)
minGdlat.append(None)
maxGdlat.append(None)
minGlon.append(None)
maxGlon.append(None)
minGdalt.append(None)
maxGdalt.append(None)
minGcdist.append(None)
maxGcdist.append(None)
index = len(thisIsprintStr) - 1
lastElDirection = thisElDirection
endUT[index] = thisUt
endAz[index] = thisAz
endEl[index] = thisEl
# gdlatr, gdlonr, galtr, range, az1, az2, el1, el2,plotParm
thisIsprintStr[index] += '%f %f %f %f %f %f %f %f %s\n' % (thisGdlatr, thisGdlonr, thisGaltr,
thisRange, thisAz1, thisAz2,
thisEl1, thisEl2, thisParm)
elif self.scanType == 'az' and thisScntyp == 2:
# az scan
inAzScan = True
inElScan = False
# add this azimuth scan point
# check if this is a new scan
newAzScan = False
if lastEl == None:
newAzScan = True
# check for elevation change
elif abs(lastEl - int(thisEl)) > 3:
newAzScan = True
# check for direction change
if lastAzDirection != None:
if lastAzDirection != thisAzDirection:
newAzScan = True
thisAzDirection = None
if newAzScan:
# a new azimuth scan has been found
thisIsprintStr.append('')
startUT.append(thisUt)
startAz.append(thisAz)
startEl.append(thisEl)
endUT.append(None)
endAz.append(None)
endEl.append(None)
elScan.append(None)
minGdlat.append(None)
maxGdlat.append(None)
minGlon.append(None)
maxGlon.append(None)
minGdalt.append(None)
maxGdalt.append(None)
minGcdist.append(None)
maxGcdist.append(None)
index = len(thisIsprintStr) - 1
lastEl = int(thisEl)
lastAzDirection = None
else:
# this is at least the second line of the scan
if not thisAzDirection is None:
lastAzDirection = thisAzDirection
endUT[-1] = thisUt
endAz[-1] = thisAz
endEl[-1] = thisEl
thisIsprintStr[-1] += '%f %f %f %f %f %f %f %f %s\n' % (thisGdlatr, thisGdlonr, thisGaltr,
thisRange, thisAz1, thisAz2,
thisEl1, thisEl2, thisParm)
else:
# not a scan line
inAzScan = False
inElScan = False
continue
if minGdlat[index] == None:
minGdlat[index] = thisGdlat
maxGdlat[index] = thisGdlat
minGlon[index] = thisGlon
maxGlon[index] = thisGlon
minGdalt[index] = thisGdalt
maxGdalt[index] = thisGdalt
minGcdist[index] = thisGcdist
maxGcdist[index] = thisGcdist
else:
if minGdlat[index] > thisGdlat:
minGdlat[index] = thisGdlat
if maxGdlat[index] < thisGdlat:
maxGdlat[index] = thisGdlat
if minGlon[index] > thisGlon:
minGlon[index] = thisGlon
if maxGlon[index] < thisGlon:
maxGlon[index] = thisGlon
if minGdalt[index] > thisGdalt:
minGdalt[index] = thisGdalt
if maxGdalt[index] < thisGdalt:
maxGdalt[index] = thisGdalt
if minGcdist[index] > thisGcdist:
minGcdist[index] = thisGcdist
if maxGcdist[index] < thisGcdist:
maxGcdist[index] = thisGcdist
# end, close out last cycle
for i in range(len(thisIsprintStr)):
if len(thisIsprintStr[i]) == 0:
continue
retList.append((thisIsprintStr[i],
startUT[i], startAz[i], startEl[i], endUT[i], endAz[i], endEl[i], elScan[i],
minGdlat[i], maxGdlat[i], minGlon[i], maxGlon[i], minGdalt[i], maxGdalt[i],
minGcdist[i], maxGcdist[i]))
return(retList)
def getDateStrFromUT(
self, ut)
getDateStrFromUT returns a date string formated as YYYY-MM-DD HH:MM:SS from a ut time (seconds since 1/1/1950)
def getDateStrFromUT(self, ut):
"""getDateStrFromUT returns a date string formated as YYYY-MM-DD HH:MM:SS from a ut time
(seconds since 1/1/1950)
"""
timeList = madrigal._derive.getDateFromUt(float(ut))
return '%04i-%02i-%02i %02i:%02i:%02i' % (timeList[0],
timeList[1],
timeList[2],
timeList[3],
timeList[4],
timeList[5])
def getTimeStrFromUT(
self, ut)
getTimeStrFromUT returns a time string formated as HH:MM:SS from a ut time (seconds since 1/1/1950)
def getTimeStrFromUT(self, ut):
"""getTimeStrFromUT returns a time string formated as HH:MM:SS from a ut time
(seconds since 1/1/1950)
"""
timeList = madrigal._derive.getDateFromUt(float(ut))
return '%02i:%02i:%02i' % (timeList[3],
timeList[4],
timeList[5])
def plotAllScans(
self, scanType, fullFilenameTemplate, plotParm, size='small', xMinimum=None, xMaximum=None, yMinimum=None, yMaximum=None, xGridSize=None, yGridSize=None, minColormap=None, maxColormap=None, colorMap=<matplotlib.colors.LinearSegmentedColormap object at 0x1169fb450>, maxNumLines=None, filterStr='')
plotAllScans creates a series of az or el scans.
Inputs:
scanType - must be 'az' or 'el'
fullFilename - full path of file containing pcolor plot to be saved. Each image
created will have _#.png appended, with # starting at 0
plotParm - mnemonic of parameter to be plotted.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
xMinimum = minumum x value. If None (default), uses lowest x value found for each scan.
xMaximum = maximum x value. If None (default), uses highest x value found for each scan.
yMinimum = minumum y value. If None (default), uses lowest y value found for each scan.
yMaximum = maximum y value. If None (default), uses highest y value found for each scan.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
maxNumLine - max number of lines in isprintText before truncating. If None, no truncation
filterStr - a filter string to pass to isprint. Defaults to no filtering
Returns - a list of tuples, where each tuple has two items: 1. time string, 2. metadata string in form for scanTab.txt. List length = number of plots created
def plotAllScans(self,
scanType,
fullFilenameTemplate,
plotParm,
size = 'small',
xMinimum = None,
xMaximum = None,
yMinimum = None,
yMaximum = None,
xGridSize = None,
yGridSize = None,
minColormap = None,
maxColormap = None,
colorMap = matplotlib.cm.jet,
maxNumLines = None,
filterStr = ''):
"""plotAllScans creates a series of az or el scans.
Inputs:
scanType - must be 'az' or 'el'
fullFilename - full path of file containing pcolor plot to be saved. Each image
created will have _#.png appended, with # starting at 0
plotParm - mnemonic of parameter to be plotted.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
xMinimum = minumum x value. If None (default), uses lowest x value found for each scan.
xMaximum = maximum x value. If None (default), uses highest x value found for each scan.
yMinimum = minumum y value. If None (default), uses lowest y value found for each scan.
yMaximum = maximum y value. If None (default), uses highest y value found for each scan.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
maxNumLine - max number of lines in isprintText before truncating. If None, no truncation
filterStr - a filter string to pass to isprint. Defaults to no filtering
Returns - a list of tuples, where each tuple has two items: 1. time string,
2. metadata string in form for scanTab.txt. List length = number
of plots created
if scanType not in ('az', 'el'):
raise ValueError('scantype must be az or el, not %s' % (str(scanType)))
self.scanType = scanType
retList = []
# handle ion velocity
if plotParm.lower() == 'vo':
cmap = madrigal.ui.madrigalPlot.get_vo_cmap()
else:
cmap = matplotlib.cm.jet
# create isprint string
parms = 'ut1,scntyp,cycn,%s,gdalt,gdlat,glon,azm,elm,gcdist' % (plotParm.strip())
isprintStr = getIsprintString(self.madFile, parms, filterStr)
try:
scanList = self.createScanInfoList(isprintStr)
except:
traceback.print_exc()
return (retList)
# loop through each scan
scanFailureCount = 0 # keep track of how many scans fail
for i in range(len(scanList)):
scanCount = i - scanFailureCount
scanInfo = scanList[i]
try:
startDateStr = self.getDateStrFromUT(scanInfo[1])
except:
# no scans may have been found
continue
endDateStr = self.getTimeStrFromUT(scanInfo[4])
# create title
if scanType == 'el':
if scanInfo[3] > scanInfo[6]:
ind = 'down'
direction = 1
else:
ind = 'up'
direction = 0
titleStr = 'El %s scan (%s) %s-%s, az=%i' % (plotParm,
ind,
startDateStr,
endDateStr,
int(scanInfo[2]))
timeStr = '%s-%s' % (startDateStr, endDateStr)
metaStr = 'El scan: startTime %i el %f az %f stopTime %i el %f az %f dir %i' % (scanInfo[1],
scanInfo[3],
scanInfo[2],
scanInfo[4],
scanInfo[6],
scanInfo[5],
direction)
retList.append((timeStr,metaStr))
if scanInfo[7] == 'Gdlat':
xLabelStr = 'Geodetic latitude'
xGridSize = 1.0
elif scanInfo[7] == 'Glon':
xLabelStr = 'Longitude'
xGridSize = 1.0
elif scanInfo[7] == 'Gcdist':
xLabelStr = 'Ground distance in km'
xGridSize = 100.0
xLabelStr = ''
yLabelStr = 'Altitude (km)'
if xGridSize == None:
xGridSize = 1.0
if yGridSize == None:
yGridSize = 20.0
else:
if scanInfo[2] > scanInfo[5]:
ind = 'ccw'
direction = 1
else:
ind = 'cw'
direction = 0
titleStr = 'Az %s scan (%s) %s-%s, el=%i' % (plotParm,
ind,
startDateStr,
endDateStr,
int(scanInfo[3]))
timeStr = '%s-%s' % (startDateStr, endDateStr)
metaStr = 'Az scan: startTime %i az %f el %f stopTime %i az %f el %f dir %i' % (scanInfo[1],
scanInfo[2],
scanInfo[3],
scanInfo[4],
scanInfo[5],
scanInfo[6],
direction)
retList.append((timeStr,metaStr))
xLabelStr = 'Longitude'
yLabelStr = 'Geodetic latitude'
xGridSize = 1.0
yGridSize = 1.0
name = '%s_%06i.png' % (fullFilenameTemplate, scanCount)
try:
madPcolorScan(scanInfo[0],
xGridSize,
yGridSize,
titleStr,
xLabelStr,
yLabelStr,
name,
minColormap = minColormap,
maxColormap = maxColormap,
colorMap = cmap)
except:
print('Problem creating scan %s' % (str(retList[-1])))
traceback.print_exc()
scanFailureCount += 1
retList.remove(retList[-1])
return (retList)
def plotAllWedgeScans(
self, scanType, fullFilenameTemplate, plotParm, size='small', xMinimum=None, xMaximum=None, yMinimum=None, yMaximum=None, minColormap=None, maxColormap=None, colorMap=<matplotlib.colors.LinearSegmentedColormap object at 0x1169fb450>, maxNumLines=None, filterStr='', addTitle='', includeKml=False, radarName=None, radarDesc=None)
plotAllWedgeScans creates a series of az or el scans. Similar to plotAllScans, except produces fancier wedge plots with uniform scalling.
Inputs:
scanType - must be 'az' or 'el'
fullFilename - full path of file containing pcolor plot to be saved. Each image
created will have _#.png appended, with # starting at 0
plotParm - mnemonic of parameter to be plotted.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
xMinimum = minumum x value. If None (default), uses lowest x value found for each scan.
xMaximum = maximum x value. If None (default), uses highest x value found for each scan.
yMinimum = minumum y value. If None (default), uses lowest y value found for each scan.
yMaximum = maximum y value. If None (default), uses highest y value found for each scan.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
maxNumLine - max number of lines in isprintText before truncating. If None, no truncation
filterStr - a filter string to pass to isprint. Defaults to no filtering
addTitle - a string with additional title. If empty string (the default), no additional title string.
includeKml - if True, create a corresponding kml file for every png file. Same names
as png files, except extension = .kml. If False, do not.
radarName - name to label radar placemark if kml generated.
radarDesc - description text for radar placemark if kml generated
Returns - a list of tuples, where each tuple has two items: 1. time string, 2. metadata string in form for scanTab.txt. List length = number of plots created
def plotAllWedgeScans(self,
scanType,
fullFilenameTemplate,
plotParm,
size = 'small',
xMinimum = None,
xMaximum = None,
yMinimum = None,
yMaximum = None,
minColormap = None,
maxColormap = None,
colorMap = matplotlib.cm.jet,
maxNumLines = None,
filterStr = '',
addTitle = '',
includeKml = False,
radarName = None,
radarDesc = None):
"""plotAllWedgeScans creates a series of az or el scans. Similar to plotAllScans, except produces
fancier wedge plots with uniform scalling.
Inputs:
scanType - must be 'az' or 'el'
fullFilename - full path of file containing pcolor plot to be saved. Each image
created will have _#.png appended, with # starting at 0
plotParm - mnemonic of parameter to be plotted.
size - size of plot to save. Must be "small", "wide", or "large". Defaults to small.
xMinimum = minumum x value. If None (default), uses lowest x value found for each scan.
xMaximum = maximum x value. If None (default), uses highest x value found for each scan.
yMinimum = minumum y value. If None (default), uses lowest y value found for each scan.
yMaximum = maximum y value. If None (default), uses highest y value found for each scan.
minColormap - minimum parameter value (defaults to lowest parameter value)
maxColormap - maximum parameter value (defaults to highest parameter value). However, if
both minColormap and maxColormap == None, autoscaling applied.
colorMap - sets colormap. It not given, defaults to matplotlib.cm.jet
maxNumLine - max number of lines in isprintText before truncating. If None, no truncation
filterStr - a filter string to pass to isprint. Defaults to no filtering
addTitle - a string with additional title. If empty string (the default), no additional title string.
includeKml - if True, create a corresponding kml file for every png file. Same names
as png files, except extension = .kml. If False, do not.
radarName - name to label radar placemark if kml generated.
radarDesc - description text for radar placemark if kml generated
Returns - a list of tuples, where each tuple has two items: 1. time string,
2. metadata string in form for scanTab.txt. List length = number
of plots created
"""
if scanType not in ('az', 'el'):
raise ValueError('scantype must be az or el, not %s' % (str(scanType)))
self.scanType = scanType
if scanType == 'az':
wedgeScanType = 'az'
retList = []
# handle ion velocity
if plotParm.lower() == 'vo':
cmap = madrigal.ui.madrigalPlot.get_vo_cmap()
else:
cmap = matplotlib.cm.jet
# create isprint string
parms = 'ut1,scntyp,cycn,%s,gdalt,gdlat,glon,azm,elm,gcdist,gdlatr,gdlonr,galtr,range,az1,az2,el1,el2' % (plotParm.strip())
filterStr += ' filter=%s,, ' % (plotParm.strip())
isprintStr = getIsprintString(self.madFile, parms, filterStr)
try:
scanList = self.createWedgeScanInfoList(isprintStr)
except:
traceback.print_exc()
return (retList)
gdlatr, gdlonr, galtr = self._getInstLocation(isprintStr)
# create limit dictionary used to keep all scans of the same type with the same limits
limitDict = self._getLimits(scanList, xMinimum, xMaximum, yMinimum, yMaximum)
# loop through each scan
scanFailureCount = 0 # keep track of how many scans fail
for i in range(len(scanList)):
scanCount = i - scanFailureCount
scanInfo = scanList[i]
try:
startDateStr = self.getDateStrFromUT(scanInfo[1])
except:
# no scans may have been found
continue
endDateStr = self.getTimeStrFromUT(scanInfo[4])
# create title
if scanType == 'el':
if scanInfo[3] > scanInfo[6]:
ind = 'down'
direction = 1
else:
ind = 'up'
direction = 0
if int(scanInfo[2]) == int(scanInfo[5]):
# no change in az
titleStr = 'El %s scan (%s) %s-%s, az=%i %s' % (plotParm,
ind,
startDateStr,
endDateStr,
int(scanInfo[2]),
addTitle)
else:
# az changed
titleStr = 'El (%i-%i) %s scan %s-%s, az=%i-%i %s' % (int(scanInfo[3]),
int(scanInfo[6]),
plotParm,
startDateStr,
endDateStr,
int(scanInfo[2]),
int(scanInfo[5]),
addTitle)
timeStr = '%s-%s' % (startDateStr, endDateStr)
metaStr = 'El scan: startTime %i el %f az %f stopTime %i el %f az %f dir %i' % (scanInfo[1],
scanInfo[3],
scanInfo[2],
scanInfo[4],
scanInfo[6],
scanInfo[5],
direction)
retList.append((timeStr,metaStr))
if scanInfo[7] == 'Gdlat':
xLabelStr = 'Geodetic latitude'
wedgeScanType = 'el_lat'
elif scanInfo[7] == 'Glon':
xLabelStr = 'Longitude'
wedgeScanType = 'el_lon'
elif scanInfo[7] == 'Gcdist':
xLabelStr = 'Ground distance in km'
wedgeScanType = 'el_gcdist'
xLabelStr = ''
yLabelStr = 'Altitude (km)'
else:
if scanInfo[2] > scanInfo[5]:
ind = 'ccw'
direction = 1
else:
ind = 'cw'
direction = 0
titleStr = 'Az %s scan (%s) %s-%s, el=%i %s' % (plotParm,
ind,
startDateStr,
endDateStr,
int(scanInfo[3]),
addTitle)
timeStr = '%s-%s' % (startDateStr, endDateStr)
metaStr = 'Az scan: startTime %i az %f el %f stopTime %i az %f el %f dir %i' % (scanInfo[1],
scanInfo[2],
scanInfo[3],
scanInfo[4],
scanInfo[5],
scanInfo[6],
direction)
retList.append((timeStr,metaStr))
xLabelStr = 'Longitude'
yLabelStr = 'Geodetic latitude'
name = '%s_%06i.png' % (fullFilenameTemplate, scanCount)
try:
madPcolorWedgeScan(scanInfo[0],
wedgeScanType,
titleStr,
xLabelStr,
yLabelStr,
name,
xMinimum = limitDict[wedgeScanType][0],
xMaximum = limitDict[wedgeScanType][1],
yMinimum = limitDict[wedgeScanType][2],
yMaximum = limitDict[wedgeScanType][3],
minColormap = minColormap,
maxColormap = maxColormap,
colorMap = cmap)
success = True
except:
print('Problem creating scan %s' % (str(retList[-1])))
traceback.print_exc()
scanFailureCount += 1
retList.remove(retList[-1])
success = False
if includeKml and success:
kmlName = name[:-3] + 'kml'
try:
madPcolorWedgeKmlScan(scanInfo[0],
wedgeScanType,
kmlName,
minColormap = minColormap,
maxColormap = maxColormap,
instName = radarName,
instDesc = radarDesc,
instLat = gdlatr,
instLon = gdlonr,
instAlt = galtr,
colorMap = cmap)
except:
print('Problem creating kml scan')
traceback.print_exc()
return (retList)