def overlayPoesBnd( baseMapObj, axisHandle, startTime, coords = 'geo', hemi = 1, equBnd = True, polBnd = False ) :
"""This function reads POES TED data with in +/- 45min of the given time, fits the auroral oval boundaries and overlays them on a map object. The poleward boundary is not accurate all the times due to lesser number of satellite passes identifying it.
**Args**:
* **baseMapObj** (`datetime <http://tinyurl.com/bl352yx>`_ or None): the map object you want data to be overlayed on.
* **axisHandle** (`datetime <http://tinyurl.com/bl352yx>`_ or None): the Axis Handle used.
* **startTime** (`datetime <http://tinyurl.com/bl352yx>`_ or None): the starttime you want data for. If endTime is not given overlays data from satellites with in +/- 45 min of the startTime
* [**coords**] (list or None): Coordinates of the map object on which you want data to be overlayed on. Default 'geo'
* [**hemi**] (list or None): Hemisphere of the map object on which you want data to be overlayed on. Value is 1 for northern hemisphere and -1 for the southern hemisphere.Default 1
* [**equBnd**] (list or None): If this is True the equatorward auroral oval boundary fit from the TED data is overlayed on the map object. Default True
* [**polBnd**] (list or None): If this is True the poleward auroral oval boundary fit from the TED data is overlayed on the map object. Default False
**Returns**:
POES TED data is overlayed on the map object. If no data is found, None is returned.
**Example**:
::
import datetime as dt
poesList = gme.sat.overlayPoesTed(MapObj, sTime=dt.datetime(2011,3,4,4))
written by Bharat Kunduri, 20130216
"""
import utils
import matplotlib as mp
import datetime
import numpy
import matplotlib.pyplot as plt
import gme.sat.poes as Poes
import math
import matplotlib.cm as cm
from scipy import optimize
import models
#check all the inputs for validity
assert(isinstance(startTime,datetime.datetime)), \
'error, sTime must be a datetime object'
# Check the hemisphere and get the appropriate folat
folat = [ 45. * hemi, 90. * hemi ]
# Get the time range we choose +/- 45 minutes....
pltTimeInterval = numpy.array( datetime.timedelta( minutes = 45 ) )
timeRange = numpy.array( [ startTime - pltTimeInterval, startTime + pltTimeInterval ] )
satNum = [ 15, 16, 17, 18, 19 ]
# We set the TED cut-off value to -0.75,
# From observed cases this appeared to do well...
# though fails sometimes especially during geomagnetically quiet times...
# However this is version 1.0 and there always is room for improvement
equBndCutoffVal = -0.75
# If any particular satellite number is not chosen by user loop through all the available one's
satNum = numpy.array( satNum ) # I like numpy arrays better that's why I'm converting the satNum list to a numpy array
latPoesAll = [[] for j in range(len(satNum))]
lonPoesAll = [[] for j in range(len(satNum))]
tedPoesAll = [[] for j in range(len(satNum))]
timePoesAll = [[] for j in range(len(satNum))]
lenDataAll = [[] for j in range(len(satNum))]
for sN in range( len(satNum) ) :
currPoesList = Poes.readPoes( timeRange[0], eTime = timeRange[1], satnum = int(satNum[sN]), folat = folat )
# Check if the data is loaded...
if currPoesList == None :
print 'No data found'
continue
# Loop through the list and store the data into arrays
lenDataAll.append( len( currPoesList ) )
for l in range( lenDataAll[-1] ) :
# Store our data in arrays if the TED data value is > than the cutoff value
try:
x = math.log10(currPoesList[l].ted)
except:
continue
if x > equBndCutoffVal:
if coords == 'mag' or coords == 'mlt':
lat,lon,_ = models.aacgm.aacgmConv(currPoesList[l].folat,currPoesList[l].folon, 0., currPoesList[l].time.year, 0)
latPoesAll[sN].append(lat)
if coords == 'mag':
lonPoesAll[sN].append(lon)
else:
lonPoesAll[sN].append(models.aacgm.mltFromEpoch(utils.timeUtils.datetimeToEpoch(currPoesList[l].time),lon)*360./24.)
else:
latPoesAll[sN].append(currPoesList[l].folat)
lonPoesAll[sN].append(currPoesList[l].folon)
# latPoesAll[sN].append( currPoesList[l].folat )
# lonPoesAll[sN].append( currPoesList[l].folon )
tedPoesAll[sN].append( math.log10(currPoesList[l].ted) )
timePoesAll[sN].append( currPoesList[l].time )
latPoesAll = numpy.array( latPoesAll )
lonPoesAll = numpy.array( lonPoesAll )
tedPoesAll = numpy.array( tedPoesAll )
timePoesAll = numpy.array( timePoesAll )
lenDataAll = numpy.array( lenDataAll )
# Now to identify the boundaries...
# Also need to check if the boundary is equatorward or poleward..
# When satellite is moving from high-lat to low-lat decrease in flux would mean equatorward boundary
# When satellite is moving from low-lat to high-lat increase in flux would mean equatorward boundary
# that is what we are trying to check here
eqBndLats = []
eqBndLons = []
poBndLats = []
poBndLons = []
for n1 in range( len(satNum) ) :
currSatLats = latPoesAll[n1]
currSatLons = lonPoesAll[n1]
currSatTeds = tedPoesAll[n1]
testLatArrLtoh = []
testLonArrLtoh = []
testLatArrHtol = []
testLonArrHtol = []
testLatArrLtohP = []
testLonArrLtohP = []
testLatArrHtolP = []
testLonArrHtolP = []
for n2 in range( len(currSatLats)-1 ) :
#Check if the satellite is moving form low-lat to high-lat or otherwise
if ( math.fabs( currSatLats[n2] ) < math.fabs( currSatLats[n2+1] ) ) :
if ( currSatTeds[n2] < currSatTeds[n2+1] ) :
testLatArrLtoh.append( currSatLats[n2] )
testLonArrLtoh.append( currSatLons[n2] )
if ( currSatTeds[n2] > currSatTeds[n2+1] ) :
testLatArrLtohP.append( currSatLats[n2] )
testLonArrLtohP.append( currSatLons[n2] )
if ( math.fabs( currSatLats[n2] ) > math.fabs( currSatLats[n2+1] ) ) :
if ( currSatTeds[n2] > currSatTeds[n2+1] ) :
testLatArrHtol.append( currSatLats[n2] )
testLonArrHtol.append( currSatLons[n2] )
if ( currSatTeds[n2] < currSatTeds[n2+1] ) :
testLatArrHtolP.append( currSatLats[n2] )
testLonArrHtolP.append( currSatLons[n2] )
# I do this to find the index of the min lat...
if ( testLatArrLtoh != [] ) :
testLatArrLtoh = numpy.array( testLatArrLtoh )
testLonArrLtoh = numpy.array( testLonArrLtoh )
VarEqLat1 = testLatArrLtoh[ numpy.where( testLatArrLtoh == min(testLatArrLtoh) ) ]
VarEqLon1 = testLonArrLtoh[ numpy.where( testLatArrLtoh == min(testLatArrLtoh) ) ]
eqBndLats.append( VarEqLat1[0] )
eqBndLons.append( VarEqLon1[0] )
if ( testLatArrHtol != [] ) :
testLatArrHtol = numpy.array( testLatArrHtol )
testLonArrHtol = numpy.array( testLonArrHtol )
VarEqLat2 = testLatArrHtol[ numpy.where( testLatArrHtol == min(testLatArrHtol) ) ]
VarEqLon2 = testLonArrHtol[ numpy.where( testLatArrHtol == min(testLatArrHtol) ) ]
eqBndLats.append( VarEqLat2[0] )
eqBndLons.append( VarEqLon2[0] )
if ( testLatArrLtohP != [] ) :
testLatArrLtohP = numpy.array( testLatArrLtohP )
testLonArrLtohP = numpy.array( testLonArrLtohP )
VarEqLatP1 = testLatArrLtohP[ numpy.where( testLatArrLtohP == min(testLatArrLtohP) ) ]
VarEqLonP1 = testLonArrLtohP[ numpy.where( testLatArrLtohP == min(testLatArrLtohP) ) ]
if VarEqLatP1[0] > 64. :
poBndLats.append( VarEqLatP1[0] )
poBndLons.append( VarEqLonP1[0] )
if ( testLatArrHtolP != [] ) :
testLatArrHtolP = numpy.array( testLatArrHtolP )
testLonArrHtolP = numpy.array( testLonArrHtolP )
VarEqLatP2 = testLatArrHtolP[ numpy.where( testLatArrHtolP == min(testLatArrHtolP) ) ]
VarEqLonP2 = testLonArrHtolP[ numpy.where( testLatArrHtolP == min(testLatArrHtolP) ) ]
if VarEqLatP2[0] > 64 :
poBndLats.append( VarEqLatP2[0] )
poBndLons.append( VarEqLonP2[0] )
eqBndLats = numpy.array( eqBndLats )
eqBndLons = numpy.array( eqBndLons )
poBndLats = numpy.array( poBndLats )
poBndLons = numpy.array( poBndLons )
#get the axis Handle used
ax = axisHandle
# Now we do the fitting part...
fitfunc = lambda p, x: p[0] + p[1]*numpy.cos(2*math.pi*(x/360.)+p[2]) # Target function
errfunc = lambda p, x, y: fitfunc(p, x) - y # Distance to the target function
# Initial guess for the parameters
# Equatorward boundary
p0Equ = [ 1., 1., 1.]
p1Equ, successEqu = optimize.leastsq(errfunc, p0Equ[:], args=(eqBndLons, eqBndLats))
if polBnd == True :
p0Pol = [ 1., 1., 1.]
p1Pol, successPol = optimize.leastsq(errfunc, p0Pol[:], args=(poBndLons, poBndLats))
allPlotLons = numpy.linspace(0., 360., 25.)
allPlotLons[-1] = 0.
eqPlotLats = []
if polBnd == True :
poPlotLats = []
for xx in allPlotLons :
if equBnd == True :
eqPlotLats.append( p1Equ[0] + p1Equ[1]*numpy.cos(2*math.pi*(xx/360.)+p1Equ[2] ) )
if polBnd == True :
poPlotLats.append( p1Pol[0] + p1Pol[1]*numpy.cos(2*math.pi*(xx/360.)+p1Pol[2] ) )
xEqu, yEqu = baseMapObj(allPlotLons, eqPlotLats)
bpltpoes = baseMapObj.plot( xEqu,yEqu, zorder = 7., color = 'b' )
if polBnd == True :
xPol, yPol = baseMapObj(allPlotLons, poPlotLats)
bpltpoes = baseMapObj.plot( xPol,yPol, zorder = 7., color = 'r' )
def overlayPoesBnd( baseMapObj, axisHandle, startTime, coords = 'geo', hemi = 1, equBnd = True, polBnd = False ) :
"""This function reads POES TED data with in +/- 45min of the given time, fits the auroral oval boundaries and overlays them on a map object. The poleward boundary is not accurate all the times due to lesser number of satellite passes identifying it.
**Args**:
* **baseMapObj** (`datetime <http://tinyurl.com/bl352yx>`_ or None): the map object you want data to be overlayed on.
* **axisHandle** (`datetime <http://tinyurl.com/bl352yx>`_ or None): the Axis Handle used.
* **startTime** (`datetime <http://tinyurl.com/bl352yx>`_ or None): the starttime you want data for. If endTime is not given overlays data from satellites with in +/- 45 min of the startTime
* [**coords**] (list or None): Coordinates of the map object on which you want data to be overlayed on. Default 'geo'
* [**hemi**] (list or None): Hemisphere of the map object on which you want data to be overlayed on. Value is 1 for northern hemisphere and -1 for the southern hemisphere.Default 1
* [**equBnd**] (list or None): If this is True the equatorward auroral oval boundary fit from the TED data is overlayed on the map object. Default True
* [**polBnd**] (list or None): If this is True the poleward auroral oval boundary fit from the TED data is overlayed on the map object. Default False
**Returns**:
POES TED data is overlayed on the map object. If no data is found, None is returned.
**Example**:
::
import datetime as dt
poesList = gme.sat.overlayPoesTed(MapObj, sTime=dt.datetime(2011,3,4,4))
written by Bharat Kunduri, 20130216
"""
import utils
import matplotlib as mp
import datetime
import numpy
import matplotlib.pyplot as plt
import gme.sat.poes as Poes
import math
import matplotlib.cm as cm
from scipy import optimize
import models
#check all the inputs for validity
assert(isinstance(startTime,datetime.datetime)), \
'error, sTime must be a datetime object'
# Check the hemisphere and get the appropriate folat
folat = [ 45. * hemi, 90. * hemi ]
# Get the time range we choose +/- 45 minutes....
pltTimeInterval = numpy.array( datetime.timedelta( minutes = 45 ) )
timeRange = numpy.array( [ startTime - pltTimeInterval, startTime + pltTimeInterval ] )
satNum = [ 15, 16, 17, 18, 19 ]
# We set the TED cut-off value to -0.75,
# From observed cases this appeared to do well...
# though fails sometimes especially during geomagnetically quiet times...
# However this is version 1.0 and there always is room for improvement
equBndCutoffVal = -0.75
# If any particular satellite number is not chosen by user loop through all the available one's
satNum = numpy.array( satNum ) # I like numpy arrays better that's why I'm converting the satNum list to a numpy array
latPoesAll = [[] for j in range(len(satNum))]
lonPoesAll = [[] for j in range(len(satNum))]
tedPoesAll = [[] for j in range(len(satNum))]
timePoesAll = [[] for j in range(len(satNum))]
lenDataAll = [[] for j in range(len(satNum))]
for sN in range( len(satNum) ) :
currPoesList = Poes.readPoes( timeRange[0], eTime = timeRange[1], satnum = int(satNum[sN]), folat = folat )
# Check if the data is loaded...
if currPoesList == None :
print 'No data found'
continue
# Loop through the list and store the data into arrays
lenDataAll.append( len( currPoesList ) )
for l in range( lenDataAll[-1] ) :
# Store our data in arrays if the TED data value is > than the cutoff value
try:
x = math.log10(currPoesList[l].ted)
except:
continue
if x > equBndCutoffVal:
if coords == 'mag' or coords == 'mlt':
lat,lon,_ = models.aacgm.aacgmConv(currPoesList[l].folat,currPoesList[l].folon, 0., currPoesList[l].time.year, 0)
latPoesAll[sN].append(lat)
if coords == 'mag':
lonPoesAll[sN].append(lon)
else:
lonPoesAll[sN].append(models.aacgm.mltFromEpoch(utils.timeUtils.datetimeToEpoch(currPoesList[l].time),lon)*360./24.)
else:
latPoesAll[sN].append(currPoesList[l].folat)
lonPoesAll[sN].append(currPoesList[l].folon)
# latPoesAll[sN].append( currPoesList[l].folat )
# lonPoesAll[sN].append( currPoesList[l].folon )
tedPoesAll[sN].append( math.log10(currPoesList[l].ted) )
timePoesAll[sN].append( currPoesList[l].time )
latPoesAll = numpy.array( latPoesAll )
lonPoesAll = numpy.array( lonPoesAll )
tedPoesAll = numpy.array( tedPoesAll )
timePoesAll = numpy.array( timePoesAll )
lenDataAll = numpy.array( lenDataAll )
# Now to identify the boundaries...
# Also need to check if the boundary is equatorward or poleward..
# When satellite is moving from high-lat to low-lat decrease in flux would mean equatorward boundary
# When satellite is moving from low-lat to high-lat increase in flux would mean equatorward boundary
# that is what we are trying to check here
eqBndLats = []
eqBndLons = []
poBndLats = []
poBndLons = []
for n1 in range( len(satNum) ) :
currSatLats = latPoesAll[n1]
currSatLons = lonPoesAll[n1]
currSatTeds = tedPoesAll[n1]
testLatArrLtoh = []
testLonArrLtoh = []
testLatArrHtol = []
testLonArrHtol = []
testLatArrLtohP = []
testLonArrLtohP = []
testLatArrHtolP = []
testLonArrHtolP = []
for n2 in range( len(currSatLats)-1 ) :
#Check if the satellite is moving form low-lat to high-lat or otherwise
if ( math.fabs( currSatLats[n2] ) < math.fabs( currSatLats[n2+1] ) ) :
if ( currSatTeds[n2] < currSatTeds[n2+1] ) :
testLatArrLtoh.append( currSatLats[n2] )
testLonArrLtoh.append( currSatLons[n2] )
if ( currSatTeds[n2] > currSatTeds[n2+1] ) :
testLatArrLtohP.append( currSatLats[n2] )
testLonArrLtohP.append( currSatLons[n2] )
if ( math.fabs( currSatLats[n2] ) > math.fabs( currSatLats[n2+1] ) ) :
if ( currSatTeds[n2] > currSatTeds[n2+1] ) :
testLatArrHtol.append( currSatLats[n2] )
testLonArrHtol.append( currSatLons[n2] )
if ( currSatTeds[n2] < currSatTeds[n2+1] ) :
testLatArrHtolP.append( currSatLats[n2] )
testLonArrHtolP.append( currSatLons[n2] )
# I do this to find the index of the min lat...
if ( testLatArrLtoh != [] ) :
testLatArrLtoh = numpy.array( testLatArrLtoh )
testLonArrLtoh = numpy.array( testLonArrLtoh )
VarEqLat1 = testLatArrLtoh[ numpy.where( testLatArrLtoh == min(testLatArrLtoh) ) ]
VarEqLon1 = testLonArrLtoh[ numpy.where( testLatArrLtoh == min(testLatArrLtoh) ) ]
eqBndLats.append( VarEqLat1[0] )
eqBndLons.append( VarEqLon1[0] )
if ( testLatArrHtol != [] ) :
testLatArrHtol = numpy.array( testLatArrHtol )
testLonArrHtol = numpy.array( testLonArrHtol )
VarEqLat2 = testLatArrHtol[ numpy.where( testLatArrHtol == min(testLatArrHtol) ) ]
VarEqLon2 = testLonArrHtol[ numpy.where( testLatArrHtol == min(testLatArrHtol) ) ]
eqBndLats.append( VarEqLat2[0] )
eqBndLons.append( VarEqLon2[0] )
if ( testLatArrLtohP != [] ) :
testLatArrLtohP = numpy.array( testLatArrLtohP )
testLonArrLtohP = numpy.array( testLonArrLtohP )
VarEqLatP1 = testLatArrLtohP[ numpy.where( testLatArrLtohP == min(testLatArrLtohP) ) ]
VarEqLonP1 = testLonArrLtohP[ numpy.where( testLatArrLtohP == min(testLatArrLtohP) ) ]
if VarEqLatP1[0] > 64. :
poBndLats.append( VarEqLatP1[0] )
poBndLons.append( VarEqLonP1[0] )
if ( testLatArrHtolP != [] ) :
testLatArrHtolP = numpy.array( testLatArrHtolP )
testLonArrHtolP = numpy.array( testLonArrHtolP )
VarEqLatP2 = testLatArrHtolP[ numpy.where( testLatArrHtolP == min(testLatArrHtolP) ) ]
VarEqLonP2 = testLonArrHtolP[ numpy.where( testLatArrHtolP == min(testLatArrHtolP) ) ]
if VarEqLatP2[0] > 64 :
poBndLats.append( VarEqLatP2[0] )
poBndLons.append( VarEqLonP2[0] )
eqBndLats = numpy.array( eqBndLats )
eqBndLons = numpy.array( eqBndLons )
poBndLats = numpy.array( poBndLats )
poBndLons = numpy.array( poBndLons )
#get the axis Handle used
ax = axisHandle
# Now we do the fitting part...
fitfunc = lambda p, x: p[0] + p[1]*numpy.cos(2*math.pi*(x/360.)+p[2]) # Target function
errfunc = lambda p, x, y: fitfunc(p, x) - y # Distance to the target function
# Initial guess for the parameters
# Equatorward boundary
p0Equ = [ 1., 1., 1.]
p1Equ, successEqu = optimize.leastsq(errfunc, p0Equ[:], args=(eqBndLons, eqBndLats))
if polBnd == True :
p0Pol = [ 1., 1., 1.]
p1Pol, successPol = optimize.leastsq(errfunc, p0Pol[:], args=(poBndLons, poBndLats))
allPlotLons = numpy.linspace(0., 360., 25.)
allPlotLons[-1] = 0.
eqPlotLats = []
if polBnd == True :
poPlotLats = []
for xx in allPlotLons :
if equBnd == True :
eqPlotLats.append( p1Equ[0] + p1Equ[1]*numpy.cos(2*math.pi*(xx/360.)+p1Equ[2] ) )
if polBnd == True :
poPlotLats.append( p1Pol[0] + p1Pol[1]*numpy.cos(2*math.pi*(xx/360.)+p1Pol[2] ) )
xEqu, yEqu = baseMapObj(allPlotLons, eqPlotLats)
bpltpoes = baseMapObj.plot( xEqu,yEqu, zorder = 7., color = 'b' )
if polBnd == True :
xPol, yPol = baseMapObj(allPlotLons, poPlotLats)
bpltpoes = baseMapObj.plot( xPol,yPol, zorder = 7., color = 'r' )
def overlayPoesTed( baseMapObj, axisHandle, startTime, endTime = None, coords = 'geo', \
hemi = 1, folat = [45., 90.], satNum = None, param='ted', scMin=-3.,scMax=0.5) :
"""This function overlays POES TED data onto a map object.
**Args**:
* **baseMapObj** (`datetime <http://tinyurl.com/bl352yx>`_ or None): the map object you want data to be overlayed on.
* **axisHandle** (`datetime <http://tinyurl.com/bl352yx>`_ or None): the Axis Handle used.
* **startTime** (`datetime <http://tinyurl.com/bl352yx>`_ or None): the starttime you want data for. If endTime is not given overlays data from satellites with in +/- 45 min of the startTime
* [**endTime**] (`datetime <http://tinyurl.com/bl352yx>`_ or None): the latest time you want data for. if this is None, data from satellites with in +/- 45 min of the startTime is overlayed. default = None
* [**satnum**] (int): the satellite you want data for. eg 17 for noaa17. if this is None, data for all satellites will be returned. default = None
* [**coords**] (str): Coordinates of the map object on which you want data to be overlayed on, 'geo', 'mag', 'mlt'. Default 'geo'
* [**hemi**] (list or None): Hemisphere of the map object on which you want data to be overlayed on. Value is 1 for northern hemisphere and -1 for the southern hemisphere.Default 1
[**folat**] (list or None): if this is not None, it must be a 2-element list of numbers, [a,b]. In this case, only data with latitude values in the range [a,b] will be returned. default = None
* [**param**] (str): the name of the poes parameter to be plotted. default='ted'
**Returns**:
POES TED data is overlayed on the map object. If no data is found, None is returned.
**Example**:
::
import datetime as dt
poesList = gme.sat.overlayPoesTed(MapObj, sTime=dt.datetime(2011,3,4,4))
written by Bharat Kunduri, 20130216
"""
import utils
import matplotlib as mp
import datetime
import numpy
import matplotlib.pyplot as plt
import gme.sat.poes as Poes
import math
import models
import matplotlib.cm as cm
from scipy import optimize
#check all the inputs for validity
assert(isinstance(startTime,datetime.datetime)), \
'error, sTime must be a datetime object'
assert(endTime == None or isinstance(endTime,datetime.datetime)), \
'error, eTime must be either None or a datetime object'
var = locals()
assert(var['satNum'] == None or (isinstance(var['satNum'],list) )), \
'error, satNum must None or a list of satellite (integer) numbers'
if satNum != None :
assert( len(satNum) <= 5 ), \
'error, there are only 5 POES satellites in operation (atleast when I wrote this code)'
assert(var['folat'] == None or (isinstance(var['folat'],list) and \
isinstance(var['folat'][0],(int,float)) and isinstance(var['folat'][1],(int,float)))), \
'error, folat must None or a list of 2 numbers'
# Check the hemisphere and get the appropriate folat
folat = [ math.fabs( folat[0] ) * hemi, math.fabs( folat[1] ) * hemi ]
# Check if the endTime is given in which case the user wants a specific time interval to search for
# If not we'll give him the best available passes for the selected start time...
if ( endTime != None ) :
timeRange = numpy.array( [ startTime, endTime ] )
else :
timeRange = None
pltTimeInterval = numpy.array( datetime.timedelta( minutes = 45 ) )
# check if the timeRange is set... if not set the timeRange to +/- pltTimeInterval of the startTime
if timeRange == None:
timeRange = numpy.array( [ startTime - pltTimeInterval, startTime + pltTimeInterval ] )
# SatNums - currently operational POES satellites are 15, 16, 17, 18, 19
if satNum == None:
satNum = [None]
# If any particular satellite number is not chosen by user loop through all the available one's
satNum = numpy.array( satNum ) # I like numpy arrays better that's why I'm converting the satNum list to a numpy array
latPoesAll = [[] for j in range(len(satNum))]
lonPoesAll = [[] for j in range(len(satNum))]
tedPoesAll = [[] for j in range(len(satNum))]
timePoesAll = [[] for j in range(len(satNum))]
lenDataAll = [[] for j in range(len(satNum))]
goodFlg=False
for sN in range(len(satNum)) :
if(satNum[sN] != None):
currPoesList = Poes.readPoes(timeRange[0], eTime = timeRange[1], satnum = int(satNum[sN]), folat = folat)
else:
currPoesList = Poes.readPoes(timeRange[0], eTime = timeRange[1], satnum = satNum[sN], folat = folat)
# Check if the data is loaded...
if currPoesList == None :
print 'No data found'
continue
#return None
else:
goodFlg=True
# Loop through the list and store the data into arrays
lenDataAll.append(len(currPoesList))
for l in currPoesList :
# Store our data in arrays
try:
tedPoesAll[sN].append(math.log10(getattr(l,param)))
if coords == 'mag' or coords == 'mlt':
lat,lon,_ = models.aacgm.aacgmConv(l.folat,l.folon, 0., l.time.year, 0)
latPoesAll[sN].append(lat)
if coords == 'mag':
lonPoesAll[sN].append(lon)
else:
lonPoesAll[sN].append(models.aacgm.mltFromEpoch(utils.timeUtils.datetimeToEpoch(l.time),lon)*360./24.)
else:
latPoesAll[sN].append(l.folat)
lonPoesAll[sN].append(l.folon)
timePoesAll[sN].append(l.time)
except Exception,e:
print e
print 'could not get parameter for time',l.time
def overlayPoesTed( baseMapObj, axisHandle, startTime, endTime = None, coords = 'geo', \
hemi = 1, folat = [45., 90.], satNum = None, param='ted', scMin=-3.,scMax=0.5) :
"""This function overlays POES TED data onto a map object.
**Args**:
* **baseMapObj** (`datetime <http://tinyurl.com/bl352yx>`_ or None): the map object you want data to be overlayed on.
* **axisHandle** (`datetime <http://tinyurl.com/bl352yx>`_ or None): the Axis Handle used.
* **startTime** (`datetime <http://tinyurl.com/bl352yx>`_ or None): the starttime you want data for. If endTime is not given overlays data from satellites with in +/- 45 min of the startTime
* [**endTime**] (`datetime <http://tinyurl.com/bl352yx>`_ or None): the latest time you want data for. if this is None, data from satellites with in +/- 45 min of the startTime is overlayed. default = None
* [**satnum**] (int): the satellite you want data for. eg 17 for noaa17. if this is None, data for all satellites will be returned. default = None
* [**coords**] (str): Coordinates of the map object on which you want data to be overlayed on, 'geo', 'mag', 'mlt'. Default 'geo'
* [**hemi**] (list or None): Hemisphere of the map object on which you want data to be overlayed on. Value is 1 for northern hemisphere and -1 for the southern hemisphere.Default 1
[**folat**] (list or None): if this is not None, it must be a 2-element list of numbers, [a,b]. In this case, only data with latitude values in the range [a,b] will be returned. default = None
* [**param**] (str): the name of the poes parameter to be plotted. default='ted'
**Returns**:
POES TED data is overlayed on the map object. If no data is found, None is returned.
**Example**:
::
import datetime as dt
poesList = gme.sat.overlayPoesTed(MapObj, sTime=dt.datetime(2011,3,4,4))
written by Bharat Kunduri, 20130216
"""
import utils
import matplotlib as mp
import datetime
import numpy
import matplotlib.pyplot as plt
import gme.sat.poes as Poes
import math
import models
import matplotlib.cm as cm
from scipy import optimize
#check all the inputs for validity
assert(isinstance(startTime,datetime.datetime)), \
'error, sTime must be a datetime object'
assert(endTime == None or isinstance(endTime,datetime.datetime)), \
'error, eTime must be either None or a datetime object'
var = locals()
assert(var['satNum'] == None or (isinstance(var['satNum'],list) )), \
'error, satNum must None or a list of satellite (integer) numbers'
if satNum != None :
assert( len(satNum) <= 5 ), \
'error, there are only 5 POES satellites in operation (atleast when I wrote this code)'
assert(var['folat'] == None or (isinstance(var['folat'],list) and \
isinstance(var['folat'][0],(int,float)) and isinstance(var['folat'][1],(int,float)))), \
'error, folat must None or a list of 2 numbers'
# Check the hemisphere and get the appropriate folat
folat = [ math.fabs( folat[0] ) * hemi, math.fabs( folat[1] ) * hemi ]
# Check if the endTime is given in which case the user wants a specific time interval to search for
# If not we'll give him the best available passes for the selected start time...
if ( endTime != None ) :
timeRange = numpy.array( [ startTime, endTime ] )
else :
timeRange = None
pltTimeInterval = numpy.array( datetime.timedelta( minutes = 45 ) )
# check if the timeRange is set... if not set the timeRange to +/- pltTimeInterval of the startTime
if timeRange == None:
timeRange = numpy.array( [ startTime - pltTimeInterval, startTime + pltTimeInterval ] )
# SatNums - currently operational POES satellites are 15, 16, 17, 18, 19
if satNum == None:
satNum = [None]
# If any particular satellite number is not chosen by user loop through all the available one's
satNum = numpy.array( satNum ) # I like numpy arrays better that's why I'm converting the satNum list to a numpy array
latPoesAll = [[] for j in range(len(satNum))]
lonPoesAll = [[] for j in range(len(satNum))]
tedPoesAll = [[] for j in range(len(satNum))]
timePoesAll = [[] for j in range(len(satNum))]
lenDataAll = [[] for j in range(len(satNum))]
goodFlg=False
for sN in range(len(satNum)) :
if(satNum[sN] != None):
currPoesList = Poes.readPoes(timeRange[0], eTime = timeRange[1], satnum = int(satNum[sN]), folat = folat)
else:
currPoesList = Poes.readPoes(timeRange[0], eTime = timeRange[1], satnum = satNum[sN], folat = folat)
# Check if the data is loaded...
if currPoesList == None :
print 'No data found'
continue
#return None
else:
goodFlg=True
# Loop through the list and store the data into arrays
lenDataAll.append(len(currPoesList))
for l in currPoesList :
# Store our data in arrays
try:
tedPoesAll[sN].append(math.log10(getattr(l,param)))
if coords == 'mag' or coords == 'mlt':
lat,lon,_ = models.aacgm.aacgmConv(l.folat,l.folon, 0., l.time.year, 0)
latPoesAll[sN].append(lat)
if coords == 'mag':
lonPoesAll[sN].append(lon)
else:
lonPoesAll[sN].append(models.aacgm.mltFromEpoch(utils.timeUtils.datetimeToEpoch(l.time),lon)*360./24.)
else:
latPoesAll[sN].append(l.folat)
lonPoesAll[sN].append(l.folon)
timePoesAll[sN].append(l.time)
except Exception,e:
print e
print 'could not get parameter for time',l.time
