def temporal_and_area_info_metric(finalMCCList):
'''
Purpose:: To provide information regarding the temporal properties of the MCCs found
Inputs:: finalMCCList: a list of dictionaries representing a list of nodes representing a MCC
Returns:: allMCCtimes: a list of dictionaries {mccTimes, starttime, endtime, duration, area} representing the MCC
temporal details for each MCC in the period considered
Assumptions::
the final time hour --> the event lasted throughout that hr, therefore +1 to endtime
'''
# TODO: in real data edit this to use datetime
mccTimes = []
allMCCtimes = []
mcsArea = []
if finalMCCList:
for eachMCC in finalMCCList:
# Get the info from the node
for eachNode in eachMCC:
mccTimes.append(
mccSearch.this_dict(eachNode)['cloudElementTime'])
mcsArea.append(
mccSearch.this_dict(eachNode)['cloudElementArea'])
# Sort and remove duplicates
mccTimes = list(set(mccTimes))
mccTimes.sort()
tdelta = mccTimes[1] - mccTimes[0]
starttime = mccTimes[0]
endtime = mccTimes[-1]
duration = (endtime - starttime) + tdelta
print 'starttime ', starttime, 'endtime ', endtime, 'tdelta ', tdelta, 'duration ', duration, 'MCSAreas ', mcsArea
allMCCtimes.append({
'MCCtimes': mccTimes,
'starttime': starttime,
'endtime': endtime,
'duration': duration,
'MCSArea': mcsArea
})
mccTimes = []
mcsArea = []
else:
allMCCtimes = []
tdelta = 0
return allMCCtimes, tdelta
def precip_max_min(finalMCCList):
'''
TODO: this doesnt work the np.min/max function seems to be not working with the nonzero option
..possibly a problem upstream with cloudElementLatLonTRMM
Purpose:: Precipitation maximum and min rates associated with each cloud elements in MCS
Inputs:: finalMCCList: a list of dictionaries representing a list of nodes representing a MCC
Returns:: mcsPrecip: a list representing the max and min rate for each cloud elements identified
'''
maxCEprecip = 0.0
minCEprecip = 0.0
mcsPrecip = []
allmcsPrecip = []
if finalMCCList:
if type(finalMCCList[0]) is str: # len(finalMCCList) == 1:
for node in finalMCCList:
eachNode = mccSearch.this_dict(node)
#cloudElementTRMM = eachNode['cloudElementLatLonTRMM']
maxCEprecip = np.max(
eachNode['cloudElementLatLonTRMM'][np.nonzero(
eachNode['cloudElementLatLonTRMM'])])
minCEprecip = np.min(
eachNode['cloudElementLatLonTRMM'][np.nonzero(
eachNode['cloudElementLatLonTRMM'])])
mcsPrecip.append(
(eachNode['uniqueID'], minCEprecip, maxCEprecip))
else:
for eachMCC in finalMCCList:
# Get the info from the node
for node in eachMCC:
eachNode = mccSearch.this_dict(node)
# Find min and max precip
maxCEprecip = np.max(
eachNode['cloudElementLatLonTRMM'][np.nonzero(
eachNode['cloudElementLatLonTRMM'])])
minCEprecip = np.min(
eachNode['cloudElementLatLonTRMM'][np.nonzero(
eachNode['cloudElementLatLonTRMM'])])
mcsPrecip.append(
(eachNode['uniqueID'], minCEprecip, maxCEprecip))
allmcsPrecip.append(mcsPrecip)
mcsPrecip = []
return mcsPrecip
def average_feature_size(finalMCCList):
'''
Purpose:: To determine the average MCC size for the period
Inputs:: finalMCCList: a list of list of strings representing a list of list of nodes representing a MCC
Returns::a floating-point representing the average area of a MCC in the period
Assumptions::
'''
thisMCC = 0.0
thisMCCAvg = 0.0
# For each node in the list, get the are information from the dictionary
# in the graph and calculate the area
for eachPath in finalMCCList:
for eachNode in eachPath:
thisMCC += mccSearch.this_dict(eachNode)['cloudElementArea']
thisMCCAvg += (thisMCC / len(eachPath))
thisMCC = 0.0
# Calculate final average
return thisMCCAvg / (len(finalMCCList))
def average_feature_size(finalMCCList):
'''
Purpose:: To determine the average MCC size for the period
Inputs:: finalMCCList: a list of list of strings representing a list of list of nodes representing a MCC
Returns::a floating-point representing the average area of a MCC in the period
Assumptions::
'''
thisMCC = 0.0
thisMCCAvg = 0.0
# For each node in the list, get the are information from the dictionary
# in the graph and calculate the area
for eachPath in finalMCCList:
for eachNode in eachPath:
thisMCC += mccSearch.this_dict(eachNode)['cloudElementArea']
thisMCCAvg += (thisMCC/len(eachPath))
thisMCC = 0.0
# Calculate final average
return thisMCCAvg/(len(finalMCCList))
def temporal_and_area_info_metric(finalMCCList):
'''
Purpose:: To provide information regarding the temporal properties of the MCCs found
Inputs:: finalMCCList: a list of dictionaries representing a list of nodes representing a MCC
Returns:: allMCCtimes: a list of dictionaries {mccTimes, starttime, endtime, duration, area} representing the MCC
temporal details for each MCC in the period considered
Assumptions::
the final time hour --> the event lasted throughout that hr, therefore +1 to endtime
'''
# TODO: in real data edit this to use datetime
mccTimes = []
allMCCtimes = []
mcsArea = []
if finalMCCList:
for eachMCC in finalMCCList:
# Get the info from the node
for eachNode in eachMCC:
mccTimes.append(mccSearch.this_dict(eachNode)['cloudElementTime'])
mcsArea.append(mccSearch.this_dict(eachNode)['cloudElementArea'])
# Sort and remove duplicates
mccTimes = list(set(mccTimes))
mccTimes.sort()
tdelta = mccTimes[1] - mccTimes[0]
starttime = mccTimes[0]
endtime = mccTimes[-1]
duration = (endtime - starttime) + tdelta
print 'starttime ', starttime, 'endtime ', endtime, 'tdelta ', tdelta, 'duration ', duration, 'MCSAreas ', mcsArea
allMCCtimes.append({'MCCtimes': mccTimes, 'starttime': starttime, 'endtime': endtime,
'duration': duration, 'MCSArea': mcsArea})
mccTimes = []
mcsArea = []
else:
allMCCtimes = []
tdelta = 0
return allMCCtimes, tdelta
def precip_totals(finalMCCList):
'''
Purpose:: Precipitation totals associated with a cloud element
Inputs:: finalMCCList: a list of dictionaries representing a list of nodes per a MCC
Returns:: precipTotal: a floating-point number representing the total amount of precipitation associated
with the feature
'''
precipTotal = 0.0
cloudElementPrecip = 0.0
mcsPrecip = []
allmcsPrecip = []
count = 0
if finalMCCList:
# Print 'len finalMCCList is: ', len(finalMCCList)
for eachMCC in finalMCCList:
# Get the info from the node
for node in eachMCC:
eachNode = mccSearch.this_dict(node)
count += 1
if count == 1:
prevHr = int(
str(eachNode['cloudElementTime']).replace(' ',
'')[-8:-6])
currHr = int(
str(eachNode['cloudElementTime']).replace(' ', '')[-8:-6])
if prevHr == currHr:
cloudElementPrecip += eachNode['cloudElementPrecipTotal']
else:
mcsPrecip.append((prevHr, cloudElementPrecip))
cloudElementPrecip = eachNode['cloudElementPrecipTotal']
# Last value in for loop
if count == len(eachMCC):
mcsPrecip.append((currHr, cloudElementPrecip))
precipTotal += eachNode['cloudElementPrecipTotal']
prevHr = currHr
mcsPrecip.append(('0', precipTotal))
allmcsPrecip.append(mcsPrecip)
precipTotal = 0.0
cloudElementPrecip = 0.0
mcsPrecip = []
count = 0
print 'allmcsPrecip ', allmcsPrecip
return allmcsPrecip
def precip_totals(finalMCCList):
'''
Purpose:: Precipitation totals associated with a cloud element
Inputs:: finalMCCList: a list of dictionaries representing a list of nodes per a MCC
Returns:: precipTotal: a floating-point number representing the total amount of precipitation associated
with the feature
'''
precipTotal = 0.0
cloudElementPrecip = 0.0
mcsPrecip = []
allmcsPrecip = []
count = 0
if finalMCCList:
# Print 'len finalMCCList is: ', len(finalMCCList)
for eachMCC in finalMCCList:
# Get the info from the node
for node in eachMCC:
eachNode = mccSearch.this_dict(node)
count += 1
if count == 1:
prevHr = int(str(eachNode['cloudElementTime']).replace(' ', '')[-8:-6])
currHr = int(str(eachNode['cloudElementTime']).replace(' ', '')[-8:-6])
if prevHr == currHr:
cloudElementPrecip += eachNode['cloudElementPrecipTotal']
else:
mcsPrecip.append((prevHr, cloudElementPrecip))
cloudElementPrecip = eachNode['cloudElementPrecipTotal']
# Last value in for loop
if count == len(eachMCC):
mcsPrecip.append((currHr, cloudElementPrecip))
precipTotal += eachNode['cloudElementPrecipTotal']
prevHr = currHr
mcsPrecip.append(('0', precipTotal))
allmcsPrecip.append(mcsPrecip)
precipTotal = 0.0
cloudElementPrecip = 0.0
mcsPrecip = []
count = 0
print 'allmcsPrecip ', allmcsPrecip
return allmcsPrecip
def precip_max_min(finalMCCList):
'''
TODO: this doesnt work the np.min/max function seems to be not working with the nonzero option
..possibly a problem upstream with cloudElementLatLonTRMM
Purpose:: Precipitation maximum and min rates associated with each cloud elements in MCS
Inputs:: finalMCCList: a list of dictionaries representing a list of nodes representing a MCC
Returns:: mcsPrecip: a list representing the max and min rate for each cloud elements identified
'''
maxCEprecip = 0.0
minCEprecip = 0.0
mcsPrecip = []
allmcsPrecip = []
if finalMCCList:
if type(finalMCCList[0]) is str: # len(finalMCCList) == 1:
for node in finalMCCList:
eachNode = mccSearch.this_dict(node)
#cloudElementTRMM = eachNode['cloudElementLatLonTRMM']
maxCEprecip = np.max(eachNode['cloudElementLatLonTRMM'][np.nonzero(eachNode['cloudElementLatLonTRMM'])])
minCEprecip = np.min(eachNode['cloudElementLatLonTRMM'][np.nonzero(eachNode['cloudElementLatLonTRMM'])])
mcsPrecip.append((eachNode['uniqueID'], minCEprecip, maxCEprecip))
else:
for eachMCC in finalMCCList:
# Get the info from the node
for node in eachMCC:
eachNode = mccSearch.this_dict(node)
# Find min and max precip
maxCEprecip = np.max(eachNode['cloudElementLatLonTRMM'][np.nonzero(eachNode['cloudElementLatLonTRMM'])])
minCEprecip = np.min(eachNode['cloudElementLatLonTRMM'][np.nonzero(eachNode['cloudElementLatLonTRMM'])])
mcsPrecip.append((eachNode['uniqueID'], minCEprecip, maxCEprecip))
allmcsPrecip.append(mcsPrecip)
mcsPrecip = []
return mcsPrecip
def find_cloud_element_speed(node, mcsList, theList):
'''
Purpose:: To determine the speed of the cloud elements uses vector displacement deltaLat/deltaLon (y/x)
Inputs:: node: a string representing the cloud element
mcsList: a list of strings representing the feature
Returns:: cloudElementSpeed: a floating-point number representing the speed of the cloud element
'''
deltaLon = 0.0
deltaLat = 0.0
cloudElementSpeed = []
theSpeed = 0.0
nodeLatLon = mccSearch.this_dict(node)['cloudElementCenter']
for aNode in theList:
if aNode in mcsList:
# If aNode is part of the mcsList then determine distance
aNodeLatLon = mccSearch.this_dict(aNode)['cloudElementCenter']
# Calculate CE speed
# Checking the lats
# nodeLatLon[0] += 90.0
# aNodeLatLon[0] += 90.0
# deltaLat = (nodeLatLon[0] - aNodeLatLon[0])
deltaLat = (
(mccSearch.this_dict(node)['cloudElementCenter'][0] + 90.0) -
(mccSearch.this_dict(aNode)['cloudElementCenter'][0] + 90.0))
# nodeLatLon[1] += 360.0
# aNodeLatLon[1] += 360.0
# deltaLon = (nodeLatLon[1] - aNodeLatLon[1])
deltaLon = (
(mccSearch.this_dict(node)['cloudElementCenter'][1] + 360.0) -
(mccSearch.this_dict(aNode)['cloudElementCenter'][1] + 360.0))
# Fail safe for movement only in one dir
if deltaLat == 0.0:
deltaLat = 1.0
if deltaLon == 0.0:
deltaLon = 1.0
try:
theSpeed = abs((((deltaLat / deltaLon) * LAT_DISTANCE * 1000) /
(TRES * 3600))) # Convert to s --> m/s
except:
theSpeed = 0.0
cloudElementSpeed.append(theSpeed)
if not cloudElementSpeed:
return 0.0
else:
return min(cloudElementSpeed)
def find_cloud_element_speed(node, mcsList, theList):
'''
Purpose:: To determine the speed of the cloud elements uses vector displacement deltaLat/deltaLon (y/x)
Inputs:: node: a string representing the cloud element
mcsList: a list of strings representing the feature
Returns:: cloudElementSpeed: a floating-point number representing the speed of the cloud element
'''
deltaLon = 0.0
deltaLat = 0.0
cloudElementSpeed = []
theSpeed = 0.0
nodeLatLon = mccSearch.this_dict(node)['cloudElementCenter']
for aNode in theList:
if aNode in mcsList:
# If aNode is part of the mcsList then determine distance
aNodeLatLon = mccSearch.this_dict(aNode)['cloudElementCenter']
# Calculate CE speed
# Checking the lats
# nodeLatLon[0] += 90.0
# aNodeLatLon[0] += 90.0
# deltaLat = (nodeLatLon[0] - aNodeLatLon[0])
deltaLat = ((mccSearch.this_dict(node)['cloudElementCenter'][0] + 90.0) -
(mccSearch.this_dict(aNode)['cloudElementCenter'][0]+90.0))
# nodeLatLon[1] += 360.0
# aNodeLatLon[1] += 360.0
# deltaLon = (nodeLatLon[1] - aNodeLatLon[1])
deltaLon = ((mccSearch.this_dict(node)['cloudElementCenter'][1]+360.0) -
(mccSearch.this_dict(aNode)['cloudElementCenter'][1]+360.0))
# Fail safe for movement only in one dir
if deltaLat == 0.0:
deltaLat = 1.0
if deltaLon == 0.0:
deltaLon = 1.0
try:
theSpeed = abs((((deltaLat/deltaLon)*LAT_DISTANCE*1000)/(TRES*3600))) # Convert to s --> m/s
except:
theSpeed = 0.0
cloudElementSpeed.append(theSpeed)
if not cloudElementSpeed:
return 0.0
else:
return min(cloudElementSpeed)
def display_size(finalMCCList, MAIN_DIRECTORY):
'''
Purpose:: To create a figure showing the area verse time for each MCS
Inputs:: finalMCCList: a list of list of strings representing the list of nodes representing a MCC
MAIN_DIRECTORY: a string representing the path to the main directory where the data generated is saved
Returns:: None
Generates:: A plot with the area contribution of each node in a feature in the list
'''
timeList = []
count = 1
imgFilename = ''
minArea = 10000.0
maxArea = 0.0
eachNode = {}
# For each node in the list, get the area information from the dictionary
# in the graph and calculate the area
if finalMCCList:
for eachMCC in finalMCCList:
# Get the info from the node
for node in eachMCC:
eachNode = mccSearch.this_dict(node)
timeList.append(eachNode['cloudElementTime'])
if eachNode['cloudElementArea'] < minArea:
minArea = eachNode['cloudElementArea']
if eachNode['cloudElementArea'] > maxArea:
maxArea = eachNode['cloudElementArea']
# Sort and remove duplicates
timeList = list(set(timeList))
timeList.sort()
tdelta = timeList[1] - timeList[0]
starttime = timeList[0] - tdelta
endtime = timeList[-1] + tdelta
timeList.insert(0, starttime)
timeList.append(endtime)
# Plot info
plt.close('all')
title = 'Area distribution of the MCC over somewhere'
fig = plt.figure(facecolor='white', figsize=(18, 10))
fig, ax = plt.subplots(1, facecolor='white', figsize=(10, 10))
# The data
for node in eachMCC:
eachNode = mccSearch.this_dict(node)
if eachNode['cloudElementArea'] < 80000:
ax.plot(eachNode['cloudElementTime'],
eachNode['cloudElementArea'],
'bo',
markersize=10)
elif eachNode['cloudElementArea'] >= 80000.00 and eachNode[
'cloudElementArea'] < 160000.00:
ax.plot(eachNode['cloudElementTime'],
eachNode['cloudElementArea'],
'yo',
markersize=20)
else:
ax.plot(eachNode['cloudElementTime'],
eachNode['cloudElementArea'],
'ro',
markersize=30)
# Axes and labels
maxArea += 1000.00
ax.set_xlim(starttime, endtime)
ax.set_ylim(minArea, maxArea)
ax.set_ylabel('Area in km^2', fontsize=12)
ax.set_title(title)
ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d%H:%M:%S')
fig.autofmt_xdate()
plt.subplots_adjust(bottom=0.2)
imgFilename = MAIN_DIRECTORY + '/images/' + str(count) + 'MCS.gif'
plt.savefig(imgFilename,
facecolor=fig.get_facecolor(),
transparent=True)
# If time in not already in the time list, append it
timeList = []
count += 1
return
def display_size(finalMCCList, MAIN_DIRECTORY):
'''
Purpose:: To create a figure showing the area verse time for each MCS
Inputs:: finalMCCList: a list of list of strings representing the list of nodes representing a MCC
MAIN_DIRECTORY: a string representing the path to the main directory where the data generated is saved
Returns:: None
Generates:: A plot with the area contribution of each node in a feature in the list
'''
timeList = []
count = 1
imgFilename = ''
minArea = 10000.0
maxArea = 0.0
eachNode = {}
# For each node in the list, get the area information from the dictionary
# in the graph and calculate the area
if finalMCCList:
for eachMCC in finalMCCList:
# Get the info from the node
for node in eachMCC:
eachNode = mccSearch.this_dict(node)
timeList.append(eachNode['cloudElementTime'])
if eachNode['cloudElementArea'] < minArea:
minArea = eachNode['cloudElementArea']
if eachNode['cloudElementArea'] > maxArea:
maxArea = eachNode['cloudElementArea']
# Sort and remove duplicates
timeList = list(set(timeList))
timeList.sort()
tdelta = timeList[1] - timeList[0]
starttime = timeList[0] - tdelta
endtime = timeList[-1] + tdelta
timeList.insert(0, starttime)
timeList.append(endtime)
# Plot info
plt.close('all')
title = 'Area distribution of the MCC over somewhere'
fig = plt.figure(facecolor='white', figsize=(18,10))
fig, ax = plt.subplots(1, facecolor='white', figsize=(10,10))
# The data
for node in eachMCC:
eachNode = mccSearch.this_dict(node)
if eachNode['cloudElementArea'] < 80000:
ax.plot(eachNode['cloudElementTime'], eachNode['cloudElementArea'], 'bo', markersize=10)
elif eachNode['cloudElementArea'] >= 80000.00 and eachNode['cloudElementArea'] < 160000.00:
ax.plot(eachNode['cloudElementTime'], eachNode['cloudElementArea'], 'yo', markersize=20)
else:
ax.plot(eachNode['cloudElementTime'], eachNode['cloudElementArea'], 'ro', markersize=30)
# Axes and labels
maxArea += 1000.00
ax.set_xlim(starttime, endtime)
ax.set_ylim(minArea, maxArea)
ax.set_ylabel('Area in km^2', fontsize=12)
ax.set_title(title)
ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d%H:%M:%S')
fig.autofmt_xdate()
plt.subplots_adjust(bottom=0.2)
imgFilename = MAIN_DIRECTORY+'/images/' + str(count)+'MCS.gif'
plt.savefig(imgFilename, facecolor=fig.get_facecolor(), transparent=True)
# If time in not already in the time list, append it
timeList = []
count += 1
return
def create_text_file(finalMCCList, identifier, userVariables, graphVariables):
'''
Purpose::
Create a text file with information about the MCS
This function is expected to be especially of use regarding long term record checks
Inputs::
finalMCCList: a list of dictionaries representing a list of nodes representing a MCC
identifier: an integer representing the type of list that has been entered...this is for creating file purposes
1 - MCCList; 2- mcsList
Returns:: None
Generates::
mcsUserFile: a user readable text file with all information about each MCS
mcsSummaryFile: a user readable text file with the summary of the MCS
mcsPostFile: a user readable text file with each MCS identified
Assumptions::
TODOs: provide visualizations for the calculations used to create the textFile
'''
durations = 0.0
startTimes = []
endTimes = []
averagePropagationSpeed = 0.0
speedCounter = 0
maxArea = 0.0
amax = 0.0
avgMaxArea = []
maxAreaCounter = 0.0
maxAreaTime = ''
eccentricity = 0.0
firstTime = True
matureFlag = True
timeMCSMatures = ''
maxCEprecipRate = 0.0
minCEprecipRate = 0.0
averageArea = 0.0
averageAreaCounter = 0
durationOfMatureMCC = 0
avgMaxPrecipRate = 0.0
avgMaxPrecipRateCounter = 0
avgMinPrecipRate = 0.0
avgMinPrecipRateCounter = 0
cloudElementSpeed = 0.0
mcsSpeed = 0.0
mcsSpeedCounter = 0
mcsPrecipTotal = 0.0
avgmcsPrecipTotalCounter = 0
bigPtotal = 0.0
bigPtotalCounter = 0
allPropagationSpeeds = []
averageAreas = []
areaAvg = 0.0
avgPrecipTotal = 0.0
avgPrecipTotalCounter = 0
avgMaxmcsPrecipRate = 0.0
avgMaxmcsPrecipRateCounter = 0
avgMinmcsPrecipRate = 0.0
avgMinmcsPrecipRateCounter = 0
avgPrecipArea = []
location = []
avgPrecipAreaPercent = 0.0
precipArea = 0.0
precipAreaPercent = 0.0
precipPercent = []
precipCounter = 0
precipAreaAvg = 0.0
minSpeed = 0.0
maxSpeed = 0.0
if identifier == 1:
mcsUserFile = open((userVariables.DIRS['mainDirStr'] + '/textFiles/MCCsUserFile.txt'), 'wb')
mcsSummaryFile = open((userVariables.DIRS['mainDirStr'] + '/textFiles/MCCSummary.txt'), 'wb')
mcsPostFile = open((userVariables.DIRS['mainDirStr'] + '/textFiles/MCCPostPrecessing.txt'), 'wb')
if identifier == 2:
mcsUserFile = open((userVariables.DIRS['mainDirStr'] + '/textFiles/MCSsUserFile.txt'), 'wb')
mcsSummaryFile = open((userVariables.DIRS['mainDirStr'] + '/textFiles/MCSSummary.txt'), 'wb')
mcsPostFile = open((userVariables.DIRS['mainDirStr'] + '/textFiles/MCSPostPrecessing.txt'), 'wb')
for eachPath in finalMCCList:
eachPath.sort(key=lambda nodeID: (len(nodeID.split('C')[0]), nodeID.split('C')[0], nodeID.split('CE')[1]))
mcsPostFile.write('\n %s' % eachPath)
startTime = mccSearch.this_dict(eachPath[0], graphVariables)['cloudElementTime']
endTime = mccSearch.this_dict(eachPath[-1], graphVariables)['cloudElementTime']
duration = (endTime - startTime) + timedelta(hours=userVariables.TRES)
# Convert datatime duration to seconds and add to the total for the average duration of all MCS in finalMCCList
durations += (duration.total_seconds())
#durations += duration
startTimes.append(startTime)
endTimes.append(endTime)
# Get the precip info
for eachNode in eachPath:
thisNode = mccSearch.this_dict(eachNode, graphVariables)
# Set first time min 'fake' values
if firstTime is True:
minCEprecipRate = thisNode['CETRMMmin']
avgMinmcsPrecipRate += thisNode['CETRMMmin']
firstTime = False
# Calculate the speed
# if thisNode['cloudElementArea'] >= OUTER_CLOUD_SHIELD_AREA:
# averagePropagationSpeed += find_cloud_element_speed(eachNode, eachPath)
# speedCounter +=1
# Amax: find max area
if thisNode['cloudElementArea'] > maxArea:
maxArea = thisNode['cloudElementArea']
maxAreaTime = str(thisNode['cloudElementTime'])
eccentricity = thisNode['cloudElementEccentricity']
location = thisNode['cloudElementCenter']
# Determine the time the feature matures
if matureFlag is True:
timeMCSMatures = str(thisNode['cloudElementTime'])
matureFlag = False
# Find min and max precip rate
if thisNode['CETRMMmin'] < minCEprecipRate:
minCEprecipRate = thisNode['CETRMMmin']
if thisNode['CETRMMmax'] > maxCEprecipRate:
maxCEprecipRate = thisNode['CETRMMmax']
# If MCC, then calculate for only mature phase else, calculate for full MCS
if identifier == 1:
# Calculations for only the mature stage
try:
if thisNode['nodeMCSIdentifier'] == 'M':
# Calculate average area of the maturity feature only
averageArea += thisNode['cloudElementArea']
averageAreaCounter += 1
durationOfMatureMCC += 1
avgMaxPrecipRate += thisNode['CETRMMmax']
avgMaxPrecipRateCounter += 1
avgMinPrecipRate += thisNode['CETRMMmin']
avgMinPrecipRateCounter += 1
avgMaxmcsPrecipRate += thisNode['CETRMMmax']
avgMaxmcsPrecipRateCounter += 1
avgMinmcsPrecipRate += thisNode['CETRMMmin']
avgMinmcsPrecipRateCounter += 1
# The precip percentage (TRMM area/CE area)
if thisNode['cloudElementArea'] >= 0.0 and thisNode['TRMMArea'] >= 0.0:
precipArea += thisNode['TRMMArea']
avgPrecipArea.append(thisNode['TRMMArea'])
avgPrecipAreaPercent += (thisNode['TRMMArea']/thisNode['cloudElementArea'])
precipPercent.append((thisNode['TRMMArea']/thisNode['cloudElementArea']))
precipCounter += 1
# System speed for only mature stage
# cloudElementSpeed = find_cloud_element_speed(eachNode,eachPath)
# if cloudElementSpeed > 0.0 :
# mcsSpeed += cloudElementSpeed
# mcsSpeedCounter += 1
except:
print 'MCS node has no nodeMCSIdentifier ', thisNode['uniqueID']
avgMaxmcsPrecipRate += thisNode['CETRMMmax']
avgMaxmcsPrecipRateCounter += 1
avgMinmcsPrecipRate += thisNode['CETRMMmin']
avgMinmcsPrecipRateCounter += 1
else:
averageArea += thisNode['cloudElementArea']
averageAreaCounter += 1
durationOfMatureMCC += 1
avgMaxPrecipRate += thisNode['CETRMMmax']
avgMaxPrecipRateCounter += 1
avgMinPrecipRate += thisNode['CETRMMmin']
avgMinPrecipRateCounter += 1
avgMaxmcsPrecipRate += thisNode['CETRMMmax']
avgMaxmcsPrecipRateCounter += 1
avgMinmcsPrecipRate += thisNode['CETRMMmin']
avgMinmcsPrecipRateCounter += 1
# The precip percentage (TRMM area/CE area)
if thisNode['cloudElementArea'] >= 0.0 and thisNode['TRMMArea'] >= 0.0:
precipArea += thisNode['TRMMArea']
avgPrecipArea.append(thisNode['TRMMArea'])
avgPrecipAreaPercent += (thisNode['TRMMArea']/thisNode['cloudElementArea'])
precipPercent.append((thisNode['TRMMArea']/thisNode['cloudElementArea']))
precipCounter += 1
# System speed for only mature stage
# cloudElementSpeed = find_cloud_element_speed(eachNode,eachPath)
# if cloudElementSpeed > 0.0 :
# mcsSpeed += cloudElementSpeed
# mcsSpeedCounter += 1
# Find accumulated precip
if thisNode['cloudElementPrecipTotal'] > 0.0:
mcsPrecipTotal += thisNode['cloudElementPrecipTotal']
avgmcsPrecipTotalCounter += 1
# A: calculate the average Area of the (mature) MCS
if averageAreaCounter > 0: # and averageAreaCounter > 0:
averageArea /= averageAreaCounter
averageAreas.append(averageArea)
# V: MCS speed
if mcsSpeedCounter > 0: # and mcsSpeed > 0.0:
mcsSpeed /= mcsSpeedCounter
# smallP_max: calculate the average max precip rate (mm/h)
if avgMaxmcsPrecipRateCounter > 0: # and avgMaxPrecipRate > 0.0:
avgMaxmcsPrecipRate /= avgMaxmcsPrecipRateCounter
# smallP_min: calculate the average min precip rate (mm/h)
if avgMinmcsPrecipRateCounter > 0: # and avgMinPrecipRate > 0.0:
avgMinmcsPrecipRate /= avgMinmcsPrecipRateCounter
# smallP_avg: calculate the average precipitation (mm hr-1)
if mcsPrecipTotal > 0.0: # and avgmcsPrecipTotalCounter> 0:
avgmcsPrecipTotal = mcsPrecipTotal/avgmcsPrecipTotalCounter
avgPrecipTotal += avgmcsPrecipTotal
avgPrecipTotalCounter += 1
#smallP_total = mcsPrecipTotal
# Precip over the MCS lifetime prep for bigP_total
if mcsPrecipTotal > 0.0:
bigPtotal += mcsPrecipTotal
bigPtotalCounter += 1
if maxArea > 0.0:
avgMaxArea.append(maxArea)
maxAreaCounter += 1
# Average precipate area percentage (TRMM/CE area)
if precipCounter > 0:
avgPrecipAreaPercent /= precipCounter
precipArea /= precipCounter
# Write stuff to file
mcsUserFile.write('\n\n\nStarttime is: %s ' % (str(startTime)))
mcsUserFile.write('\nEndtime is: %s ' % (str(endTime)))
mcsUserFile.write('\nLife duration is %s hrs' % (str(duration)))
mcsUserFile.write('\nTime of maturity is %s ' % (timeMCSMatures))
mcsUserFile.write('\nDuration mature stage is: %s ' % durationOfMatureMCC*userVariables.TRES)
mcsUserFile.write('\nAverage area is: %.4f km^2 ' % (averageArea))
mcsUserFile.write('\nMax area is: %.4f km^2 ' % (maxArea))
mcsUserFile.write('\nMax area time is: %s ' % (maxAreaTime))
mcsUserFile.write('\nEccentricity at max area is: %.4f ' % (eccentricity))
mcsUserFile.write('\nCenter (lat,lon) at max area is: %.2f\t%.2f' % (location[0], location[1]))
mcsUserFile.write('\nPropagation speed is %.4f ' % (mcsSpeed))
mcsUserFile.write('\nMCS minimum precip rate is %.4f mmh^-1' % (minCEprecipRate))
mcsUserFile.write('\nMCS maximum precip rate is %.4f mmh^-1' % (maxCEprecipRate))
mcsUserFile.write('\nTotal precipitation during MCS is %.4f mm/lifetime' % (mcsPrecipTotal))
mcsUserFile.write('\nAverage MCS precipitation is %.4f mm' % (avgmcsPrecipTotal))
mcsUserFile.write('\nAverage MCS maximum precipitation is %.4f mmh^-1' % (avgMaxmcsPrecipRate))
mcsUserFile.write('\nAverage MCS minimum precipitation is %.4f mmh^-1' % (avgMinmcsPrecipRate))
mcsUserFile.write('\nAverage precipitation area is %.4f km^2 ' % (precipArea))
mcsUserFile.write('\nPrecipitation area percentage of mature system %.4f percent ' % (avgPrecipAreaPercent*100))
# Append stuff to lists for the summary file
if mcsSpeed > 0.0:
allPropagationSpeeds.append(mcsSpeed)
averagePropagationSpeed += mcsSpeed
speedCounter += 1
# Reset vars for next MCS in list
averageArea = 0.0
averageAreaCounter = 0
durationOfMatureMCC = 0
mcsSpeed = 0.0
mcsSpeedCounter = 0
mcsPrecipTotal = 0.0
avgMaxmcsPrecipRate = 0.0
avgMaxmcsPrecipRateCounter = 0
avgMinmcsPrecipRate = 0.0
avgMinmcsPrecipRateCounter = 0
firstTime = True
matureFlag = True
avgmcsPrecipTotalCounter = 0
avgPrecipAreaPercent = 0.0
precipArea = 0.0
precipCounter = 0
maxArea = 0.0
maxAreaTime = ''
eccentricity = 0.0
timeMCSMatures = ''
maxCEprecipRate = 0.0
minCEprecipRate = 0.0
location = []
# LD: average duration
if len(finalMCCList) > 1:
durations /= len(finalMCCList)
durations /= 3600.0 # Convert to hours
# A: average area
areaAvg = sum(averageAreas) / len(finalMCCList)
# Create histogram plot here
# if len(averageAreas) > 1:
# plotHistogram(averageAreas, 'Average Area [km^2]', 'Area [km^2]')
# Amax: average maximum area
if maxAreaCounter > 0.0: # and avgMaxArea > 0.0 :
amax = sum(avgMaxArea) / maxAreaCounter
# Create histogram plot here
#if len(avgMaxArea) > 1:
# plotHistogram(avgMaxArea, 'Maximum Area [km^2]', 'Area [km^2]')
# v_avg: calculate the average propagation speed
if speedCounter > 0: # and averagePropagationSpeed > 0.0
averagePropagationSpeed /= speedCounter
# bigP_min: calculate the min rate in mature system
if avgMinPrecipRate > 0.0: # and avgMinPrecipRateCounter > 0.0:
avgMinPrecipRate /= avgMinPrecipRateCounter
# bigP_max: calculate the max rate in mature system
if avgMinPrecipRateCounter > 0.0: # and avgMaxPrecipRate > 0.0:
avgMaxPrecipRate /= avgMaxPrecipRateCounter
# bigP_avg: average total preicip rate mm/hr
if avgPrecipTotalCounter > 0.0: # and avgPrecipTotal > 0.0:
avgPrecipTotal /= avgPrecipTotalCounter
# bigP_total: total precip rate mm/LD
if bigPtotalCounter > 0.0: # and bigPtotal > 0.0:
bigPtotal /= bigPtotalCounter
# Precipitation area percentage
if len(precipPercent) > 0:
precipAreaPercent = (sum(precipPercent)/len(precipPercent))*100.0
# average precipitation area
if len(avgPrecipArea) > 0:
precipAreaAvg = sum(avgPrecipArea)/len(avgPrecipArea)
#if len(avgPrecipArea) > 1:
# plotHistogram(avgPrecipArea, 'Average Rainfall Area [km^2]', 'Area [km^2]')
sTime = str(average_time(startTimes))
eTime = str(average_time(endTimes))
if len(allPropagationSpeeds) > 1:
maxSpeed = max(allPropagationSpeeds)
minSpeed = min(allPropagationSpeeds)
# Write stuff to the summary file
mcsSummaryFile.write('\nNumber of features is %d ' % (len(finalMCCList)))
mcsSummaryFile.write('\nAverage duration is %.4f hrs ' % (durations))
mcsSummaryFile.write('\nAverage startTime is %s ' % (sTime[-8:]))
mcsSummaryFile.write('\nAverage endTime is %s ' % (eTime[-8:]))
mcsSummaryFile.write('\nAverage size is %.4f km^2 ' % (areaAvg))
mcsSummaryFile.write('\nAverage precipitation area is %.4f km^2 ' % (precipAreaAvg))
mcsSummaryFile.write('\nAverage maximum size is %.4f km^2 ' % (amax))
mcsSummaryFile.write('\nAverage propagation speed is %.4f ms^-1' % (averagePropagationSpeed))
mcsSummaryFile.write('\nMaximum propagation speed is %.4f ms^-1 ' % (maxSpeed))
mcsSummaryFile.write('\nMinimum propagation speed is %.4f ms^-1 ' % (minSpeed))
mcsSummaryFile.write('\nAverage minimum precipitation rate is %.4f mmh^-1' % (avgMinPrecipRate))
mcsSummaryFile.write('\nAverage maximum precipitation rate is %.4f mm h^-1' % (avgMaxPrecipRate))
mcsSummaryFile.write('\nAverage precipitation is %.4f mm ' % (avgPrecipTotal))
mcsSummaryFile.write('\nAverage total precipitation during MCSs is %.4f mm/LD ' % (bigPtotal))
mcsSummaryFile.write('\nAverage precipitation area percentage is %.4f percent ' % (precipAreaPercent))
mcsUserFile.close()
mcsSummaryFile.close()
mcsPostFile.close()
return
def plot_precip_histograms(finalMCCList, MAIN_DIRECTORY):
'''
Purpose:: To create plots (histograms) of the each TRMMnetcdfCEs files
Input:: finalMCCList: a list of dictionaries representing a list of nodes representing a MCC
MAIN_DIRECTORY: a string representing the path to the main directory where the data generated is saved
Returns:: None
Generates:: Histogram plots for each feature in the list passed
'''
numBins = 5
precip = []
imgFilename = ' '
startTime = ' '
firstTime = True
if finalMCCList:
for eachMCC in finalMCCList:
firstTime = True
for node in eachMCC:
eachNode = mccSearch.this_dict(node)
thisTime = eachNode['cloudElementTime']
thisFileName = MAIN_DIRECTORY+'/TRMMnetcdfCEs/TRMM' + str(thisTime).replace(' ', '_') + \
eachNode['uniqueID'] + '.nc'
TRMMData = Dataset(thisFileName, 'r', format='NETCDF4')
precipRate = TRMMData.variables['precipitation_Accumulation'][:, :, :]
cloudElementPrecipRate = precipRate[0, :, :]
TRMMData.close()
if firstTime is True:
totalPrecip = np.zeros(cloudElementPrecipRate.shape)
startTime = str(thisTime) + eachNode['uniqueID']
firstTime = False
totalPrecip = np.add(totalPrecip, precipRate)
for _, value in np.ndenumerate(cloudElementPrecipRate):
if value != 0.0:
precip.append(value)
plt.close('all')
title = 'TRMM precipitation distribution for ' + str(thisTime)
imgFilename = MAIN_DIRECTORY+'/images/'+str(thisTime)+eachNode['uniqueID']+'TRMMMCS.gif'
xlabel = 'Precipitation [mm]'
ylabel = 'Area [km^2]'
numBins = 5
if all(value == 0 for value in precip):
print 'NO Precipitation at '+str(thisTime)+eachNode['uniqueID']
else:
plot_histogram(precip, title, imgFilename, xlabel, ylabel, numBins)
precip = []
# The image at the end of each MCS
title = 'TRMM precipitation distribution for '+startTime+' to '+str(thisTime)
imgFilename = MAIN_DIRECTORY+'/images/'+startTime+'_'+str(thisTime)+eachNode['uniqueID']+'TRMMMCS.gif'
xlabel = 'Precipitation [mm]'
ylabel = 'Area [km^2]'
numBins = 10
for _, value in np.ndenumerate(totalPrecip):
if value != 0.0:
precip.append(value)
if all(value == 0 for value in precip):
print 'No precipitation for MCS starting at '+startTime+' and ending at '+str(thisTime)+eachNode['uniqueID']
else:
plot_histogram(precip, title, imgFilename, xlabel, ylabel, numBins)
precip = []
return
def plot_accu_TRMM(finalMCCList, MAIN_DIRECTORY):
'''
Purpose:: (1) generate a file with the accumulated precipitation for the MCS (2) generate the appropriate image
Input:: finalMCCList: a list of dictionaries representing a list of nodes representing a MCC
MAIN_DIRECTORY: a string representing the path to the main directory where the data generated is saved
Returns:: a netcdf file containing the accumulated precip
Generates: A plot (generated in Grads) of the accumulated precipitation
'''
os.chdir((MAIN_DIRECTORY+'/TRMMnetcdfCEs'))
fname = ''
imgFilename = ''
firstPartName = ''
firstTime = True
replaceExpXDef = ''
thisNode = ''
subprocess.call('export DISPLAY=:0.0', shell=True)
# Generate the file name using MCCTimes
# if the file name exists, add it to the accTRMM file
for path in finalMCCList:
firstTime = True
for eachNode in path:
thisNode = mccSearch.this_dict(eachNode)
fname = 'TRMM' + str(thisNode['cloudElementTime']).replace(' ', '_') + thisNode['uniqueID'] + '.nc'
if os.path.isfile(fname):
# Open NetCDF file add info to the accu
cloudElementTRMMData = Dataset(fname, 'r', format='NETCDF4')
precipRate = cloudElementTRMMData.variables['precipitation_Accumulation'][:]
lats = cloudElementTRMMData.variables['latitude'][:]
lons = cloudElementTRMMData.variables['longitude'][:]
LONTRMM, LATTRMM = np.meshgrid(lons, lats)
nygrdTRMM = len(LATTRMM[:, 0])
nxgrdTRMM = len(LONTRMM[0, :])
precipRate = ma.masked_array(precipRate, mask=(precipRate < 0.0))
cloudElementTRMMData.close()
if firstTime is True:
firstPartName = str(thisNode['uniqueID'])+str(thisNode['cloudElementTime']).replace(' ', '_')+'-'
accuPrecipRate = ma.zeros(precipRate.shape)
firstTime = False
accuPrecipRate += precipRate
imgFilename = MAIN_DIRECTORY+'/images/MCS_'+firstPartName+str(thisNode['cloudElementTime']).replace(' ', '_')+'.gif'
# Create new netCDF file
accuTRMMFile = MAIN_DIRECTORY+'/TRMMnetcdfCEs/accu'+firstPartName+str(thisNode['cloudElementTime']).replace(' ', '_')+'.nc'
# Write the file
accuTRMMData = Dataset(accuTRMMFile, 'w', format='NETCDF4')
accuTRMMData.description = 'Accumulated precipitation data'
accuTRMMData.calendar = 'standard'
accuTRMMData.conventions = 'COARDS'
# Dimensions
accuTRMMData.createDimension('time', None)
accuTRMMData.createDimension('lat', nygrdTRMM)
accuTRMMData.createDimension('lon', nxgrdTRMM)
# Variables
TRMMprecip = ('time', 'lat', 'lon',)
times = accuTRMMData.createVariable('time', 'f8', ('time',))
times.units = 'hours since ' + str(thisNode['cloudElementTime']).replace(' ', '_')[:-6]
latitude = accuTRMMData.createVariable('latitude', 'f8', ('lat',))
longitude = accuTRMMData.createVariable('longitude', 'f8', ('lon',))
rainFallacc = accuTRMMData.createVariable('precipitation_Accumulation', 'f8', TRMMprecip)
rainFallacc.units = 'mm'
longitude[:] = LONTRMM[0, :]
longitude.units = 'degrees_east'
longitude.long_name = 'Longitude'
latitude[:] = LATTRMM[:, 0]
latitude.units = 'degrees_north'
latitude.long_name = 'Latitude'
rainFallacc[:] = accuPrecipRate[:]
accuTRMMData.close()
# Generate the image with GrADS
subprocess.call('rm acc.ctl', shell=True)
subprocess.call('touch acc.ctl', shell=True)
replaceExpDset = 'echo DSET ' + accuTRMMFile + ' >> acc.ctl'
subprocess.call(replaceExpDset, shell=True)
subprocess.call('echo "OPTIONS yrev little_endian template" >> acc.ctl', shell=True)
subprocess.call('echo "DTYPE netcdf" >> acc.ctl', shell=True)
subprocess.call('echo "UNDEF 0" >> acc.ctl', shell=True)
subprocess.call('echo "TITLE TRMM MCS accumulated precipitation" >> acc.ctl', shell=True)
replaceExpXDef = 'echo XDEF ' + str(nxgrdTRMM) + ' LINEAR ' + str(min(lons)) + ' ' + \
str((max(lons)-min(lons))/nxgrdTRMM) + ' >> acc.ctl'
subprocess.call(replaceExpXDef, shell=True)
replaceExpYDef = 'echo YDEF '+str(nygrdTRMM)+' LINEAR '+str(min(lats)) + ' ' + \
str((max(lats)-min(lats))/nygrdTRMM)+' >>acc.ctl'
subprocess.call(replaceExpYDef, shell=True)
subprocess.call('echo "ZDEF 01 LEVELS 1" >> acc.ctl', shell=True)
subprocess.call('echo "TDEF 99999 linear 31aug2009 1hr" >> acc.ctl', shell=True)
subprocess.call('echo "VARS 1" >> acc.ctl', shell=True)
subprocess.call('echo "precipitation_Accumulation=>precipAcc 1 t,y,x precipAccu" >> acc.ctl', shell=True)
subprocess.call('echo "ENDVARS" >> acc.ctl', shell=True)
# Generate GrADS script
subprocess.call('rm accuTRMM1.gs', shell=True)
subprocess.call('touch accuTRMM1.gs', shell=True)
subprocess.call('echo "''\'reinit''\'" >> accuTRMM1.gs', shell=True)
subprocess.call('echo "''\'open acc.ctl ''\'" >> accuTRMM1.gs', shell=True)
subprocess.call('echo "''\'set grads off''\'" >> accuTRMM1.gs', shell=True)
subprocess.call('echo "''\'set mpdset hires''\'" >> accuTRMM1.gs', shell=True)
subprocess.call('echo "''\'set gxout shaded''\'" >> accuTRMM1.gs', shell=True)
subprocess.call('echo "''\'set datawarn off''\'" >> accuTRMM1.gs', shell=True)
subprocess.call('echo "''\'d precipacc''\'" >> accuTRMM1.gs', shell=True)
subprocess.call('echo "''\'draw title TRMM Accumulated Precipitation [mm]''\'" >> accuTRMM1.gs', shell=True)
subprocess.call('echo "''\'run cbarn''\'" >> accuTRMM1.gs', shell=True)
subprocess.call('echo "''\'printim '+imgFilename + ' x1000 y800 white''\'" >> accuTRMM1.gs', shell=True)
subprocess.call('echo "''\'quit''\'" >> accuTRMM1.gs', shell=True)
gradscmd = 'grads -blc ' + '\'run accuTRMM1.gs''\''
subprocess.call(gradscmd, shell=True)
# Clean up
subprocess.call('rm accuTRMM1.gs', shell=True)
subprocess.call('rm acc.ctl', shell=True)
return
def display_precip(finalMCCList, MAIN_DIRECTORY):
'''
Purpose:: To create a figure showing the precip rate verse time for each MCS
Inputs:: finalMCCList: a list of dictionaries representing a list of nodes representing a MCC
MAIN_DIRECTORY: a string representing the path to the main directory where the data generated is saved
Returns:: None
Generates:: A plot with the precipitation distribution over each feature in the list
'''
timeList = []
oriTimeList = []
colorBarTime = []
count = 1
imgFilename = ''
percentagePrecipitating = [] #0.0
cloudElementArea = []
nodes = []
xy = []
x = []
y = []
precip = []
partialArea = []
totalSize = 0.0
firstTime = True
xStart = 0.0
yStart = 0.0
# For each node in the list, get the area information from the dictionary
# in the graph and calculate the area
if finalMCCList:
for eachMCC in finalMCCList:
# Get the info from the node
for node in eachMCC:
eachNode = mccSearch.this_dict(node)
if firstTime is True:
xStart = eachNode['cloudElementCenter'][1] # lon
yStart = eachNode['cloudElementCenter'][0] # lat
timeList.append(eachNode['cloudElementTime'])
percentagePrecipitating.append(
(eachNode['TRMMArea'] / eachNode['cloudElementArea']) *
100.0)
cloudElementArea.append(eachNode['cloudElementArea'])
nodes.append(eachNode['uniqueID'])
x.append(eachNode['cloudElementCenter'][1]) # -xStart)
y.append(eachNode['cloudElementCenter'][0]) # -yStart)
firstTime = False
# Convert the timeList[] to list of floats
for i in xrange(len(timeList)):
colorBarTime.append(time.mktime(timeList[i].timetuple()))
totalSize = sum(cloudElementArea)
partialArea = [(a / totalSize) * 30000 for a in cloudElementArea]
# Plot info
plt.close('all')
title = 'Precipitation distribution of the MCS '
fig, ax = plt.subplots(1, facecolor='white', figsize=(20, 7))
cmap = cm.jet
ax.scatter(x,
y,
s=partialArea,
c=colorBarTime,
edgecolors='none',
marker='o',
cmap=cmap)
colorBarTime = []
colorBarTime = list(set(timeList))
colorBarTime.sort()
cb = colorbar_index(ncolors=len(colorBarTime),
nlabels=colorBarTime,
cmap=cmap)
# Axes and labels
ax.set_xlabel('Degrees Longtude', fontsize=12)
ax.set_ylabel('Degrees Latitude', fontsize=12)
ax.set_title(title)
ax.grid(True)
plt.subplots_adjust(bottom=0.2)
for i, txt in enumerate(nodes):
if cloudElementArea[i] >= 2400.00:
ax.annotate('%d' % percentagePrecipitating[i] + '%',
(x[i], y[i]))
precip = []
imgFilename = MAIN_DIRECTORY + '/images/MCSprecip' + str(
count) + '.gif'
plt.savefig(imgFilename,
facecolor=fig.get_facecolor(),
transparent=True)
# Reset for next image
timeList = []
percentagePrecipitating = []
cloudElementArea = []
x = []
y = []
colorBarTime = []
nodes = []
precip = []
count += 1
firstTime = True
return
def plot_accu_TRMM(finalMCCList, MAIN_DIRECTORY):
'''
Purpose:: (1) generate a file with the accumulated precipitation for the MCS (2) generate the appropriate image
Input:: finalMCCList: a list of dictionaries representing a list of nodes representing a MCC
MAIN_DIRECTORY: a string representing the path to the main directory where the data generated is saved
Returns:: a netcdf file containing the accumulated precip
Generates: A plot (generated in Grads) of the accumulated precipitation
'''
os.chdir((MAIN_DIRECTORY + '/TRMMnetcdfCEs'))
fname = ''
imgFilename = ''
firstPartName = ''
firstTime = True
replaceExpXDef = ''
thisNode = ''
subprocess.call('export DISPLAY=:0.0', shell=True)
# Generate the file name using MCCTimes
# if the file name exists, add it to the accTRMM file
for path in finalMCCList:
firstTime = True
for eachNode in path:
thisNode = mccSearch.this_dict(eachNode)
fname = 'TRMM' + str(thisNode['cloudElementTime']).replace(
' ', '_') + thisNode['uniqueID'] + '.nc'
if os.path.isfile(fname):
# Open NetCDF file add info to the accu
cloudElementTRMMData = Dataset(fname, 'r', format='NETCDF4')
precipRate = cloudElementTRMMData.variables[
'precipitation_Accumulation'][:]
lats = cloudElementTRMMData.variables['latitude'][:]
lons = cloudElementTRMMData.variables['longitude'][:]
LONTRMM, LATTRMM = np.meshgrid(lons, lats)
nygrdTRMM = len(LATTRMM[:, 0])
nxgrdTRMM = len(LONTRMM[0, :])
precipRate = ma.masked_array(precipRate,
mask=(precipRate < 0.0))
cloudElementTRMMData.close()
if firstTime is True:
firstPartName = str(thisNode['uniqueID']) + str(
thisNode['cloudElementTime']).replace(' ', '_') + '-'
accuPrecipRate = ma.zeros(precipRate.shape)
firstTime = False
accuPrecipRate += precipRate
imgFilename = MAIN_DIRECTORY + '/images/MCS_' + firstPartName + str(
thisNode['cloudElementTime']).replace(' ', '_') + '.gif'
# Create new netCDF file
accuTRMMFile = MAIN_DIRECTORY + '/TRMMnetcdfCEs/accu' + firstPartName + str(
thisNode['cloudElementTime']).replace(' ', '_') + '.nc'
# Write the file
accuTRMMData = Dataset(accuTRMMFile, 'w', format='NETCDF4')
accuTRMMData.description = 'Accumulated precipitation data'
accuTRMMData.calendar = 'standard'
accuTRMMData.conventions = 'COARDS'
# Dimensions
accuTRMMData.createDimension('time', None)
accuTRMMData.createDimension('lat', nygrdTRMM)
accuTRMMData.createDimension('lon', nxgrdTRMM)
# Variables
TRMMprecip = (
'time',
'lat',
'lon',
)
times = accuTRMMData.createVariable('time', 'f8', ('time', ))
times.units = 'hours since ' + str(
thisNode['cloudElementTime']).replace(' ', '_')[:-6]
latitude = accuTRMMData.createVariable('latitude', 'f8', ('lat', ))
longitude = accuTRMMData.createVariable('longitude', 'f8', ('lon', ))
rainFallacc = accuTRMMData.createVariable('precipitation_Accumulation',
'f8', TRMMprecip)
rainFallacc.units = 'mm'
longitude[:] = LONTRMM[0, :]
longitude.units = 'degrees_east'
longitude.long_name = 'Longitude'
latitude[:] = LATTRMM[:, 0]
latitude.units = 'degrees_north'
latitude.long_name = 'Latitude'
rainFallacc[:] = accuPrecipRate[:]
accuTRMMData.close()
# Generate the image with GrADS
subprocess.call('rm acc.ctl', shell=True)
subprocess.call('touch acc.ctl', shell=True)
replaceExpDset = 'echo DSET ' + accuTRMMFile + ' >> acc.ctl'
subprocess.call(replaceExpDset, shell=True)
subprocess.call(
'echo "OPTIONS yrev little_endian template" >> acc.ctl',
shell=True)
subprocess.call('echo "DTYPE netcdf" >> acc.ctl', shell=True)
subprocess.call('echo "UNDEF 0" >> acc.ctl', shell=True)
subprocess.call(
'echo "TITLE TRMM MCS accumulated precipitation" >> acc.ctl',
shell=True)
replaceExpXDef = 'echo XDEF ' + str(nxgrdTRMM) + ' LINEAR ' + str(min(lons)) + ' ' + \
str((max(lons)-min(lons))/nxgrdTRMM) + ' >> acc.ctl'
subprocess.call(replaceExpXDef, shell=True)
replaceExpYDef = 'echo YDEF '+str(nygrdTRMM)+' LINEAR '+str(min(lats)) + ' ' + \
str((max(lats)-min(lats))/nygrdTRMM)+' >>acc.ctl'
subprocess.call(replaceExpYDef, shell=True)
subprocess.call('echo "ZDEF 01 LEVELS 1" >> acc.ctl', shell=True)
subprocess.call('echo "TDEF 99999 linear 31aug2009 1hr" >> acc.ctl',
shell=True)
subprocess.call('echo "VARS 1" >> acc.ctl', shell=True)
subprocess.call(
'echo "precipitation_Accumulation=>precipAcc 1 t,y,x precipAccu" >> acc.ctl',
shell=True)
subprocess.call('echo "ENDVARS" >> acc.ctl', shell=True)
# Generate GrADS script
subprocess.call('rm accuTRMM1.gs', shell=True)
subprocess.call('touch accuTRMM1.gs', shell=True)
subprocess.call('echo "' '\'reinit' '\'" >> accuTRMM1.gs', shell=True)
subprocess.call('echo "'
'\'open acc.ctl '
'\'" >> accuTRMM1.gs',
shell=True)
subprocess.call('echo "'
'\'set grads off'
'\'" >> accuTRMM1.gs',
shell=True)
subprocess.call('echo "'
'\'set mpdset hires'
'\'" >> accuTRMM1.gs',
shell=True)
subprocess.call('echo "'
'\'set gxout shaded'
'\'" >> accuTRMM1.gs',
shell=True)
subprocess.call('echo "'
'\'set datawarn off'
'\'" >> accuTRMM1.gs',
shell=True)
subprocess.call('echo "'
'\'d precipacc'
'\'" >> accuTRMM1.gs',
shell=True)
subprocess.call(
'echo "'
'\'draw title TRMM Accumulated Precipitation [mm]'
'\'" >> accuTRMM1.gs',
shell=True)
subprocess.call('echo "'
'\'run cbarn'
'\'" >> accuTRMM1.gs',
shell=True)
subprocess.call('echo "'
'\'printim ' + imgFilename + ' x1000 y800 white'
'\'" >> accuTRMM1.gs',
shell=True)
subprocess.call('echo "' '\'quit' '\'" >> accuTRMM1.gs', shell=True)
gradscmd = 'grads -blc ' + '\'run accuTRMM1.gs' '\''
subprocess.call(gradscmd, shell=True)
# Clean up
subprocess.call('rm accuTRMM1.gs', shell=True)
subprocess.call('rm acc.ctl', shell=True)
return
def plot_precip_histograms(finalMCCList, MAIN_DIRECTORY):
'''
Purpose:: To create plots (histograms) of the each TRMMnetcdfCEs files
Input:: finalMCCList: a list of dictionaries representing a list of nodes representing a MCC
MAIN_DIRECTORY: a string representing the path to the main directory where the data generated is saved
Returns:: None
Generates:: Histogram plots for each feature in the list passed
'''
numBins = 5
precip = []
imgFilename = ' '
startTime = ' '
firstTime = True
if finalMCCList:
for eachMCC in finalMCCList:
firstTime = True
for node in eachMCC:
eachNode = mccSearch.this_dict(node)
thisTime = eachNode['cloudElementTime']
thisFileName = MAIN_DIRECTORY+'/TRMMnetcdfCEs/TRMM' + str(thisTime).replace(' ', '_') + \
eachNode['uniqueID'] + '.nc'
TRMMData = Dataset(thisFileName, 'r', format='NETCDF4')
precipRate = TRMMData.variables[
'precipitation_Accumulation'][:, :, :]
cloudElementPrecipRate = precipRate[0, :, :]
TRMMData.close()
if firstTime is True:
totalPrecip = np.zeros(cloudElementPrecipRate.shape)
startTime = str(thisTime) + eachNode['uniqueID']
firstTime = False
totalPrecip = np.add(totalPrecip, precipRate)
for _, value in np.ndenumerate(cloudElementPrecipRate):
if value != 0.0:
precip.append(value)
plt.close('all')
title = 'TRMM precipitation distribution for ' + str(thisTime)
imgFilename = MAIN_DIRECTORY + '/images/' + str(
thisTime) + eachNode['uniqueID'] + 'TRMMMCS.gif'
xlabel = 'Precipitation [mm]'
ylabel = 'Area [km^2]'
numBins = 5
if all(value == 0 for value in precip):
print 'NO Precipitation at ' + str(
thisTime) + eachNode['uniqueID']
else:
plot_histogram(precip, title, imgFilename, xlabel, ylabel,
numBins)
precip = []
# The image at the end of each MCS
title = 'TRMM precipitation distribution for ' + startTime + ' to ' + str(
thisTime)
imgFilename = MAIN_DIRECTORY + '/images/' + startTime + '_' + str(
thisTime) + eachNode['uniqueID'] + 'TRMMMCS.gif'
xlabel = 'Precipitation [mm]'
ylabel = 'Area [km^2]'
numBins = 10
for _, value in np.ndenumerate(totalPrecip):
if value != 0.0:
precip.append(value)
if all(value == 0 for value in precip):
print 'No precipitation for MCS starting at ' + startTime + ' and ending at ' + str(
thisTime) + eachNode['uniqueID']
else:
plot_histogram(precip, title, imgFilename, xlabel, ylabel,
numBins)
precip = []
return
def display_precip(finalMCCList, MAIN_DIRECTORY):
'''
Purpose:: To create a figure showing the precip rate verse time for each MCS
Inputs:: finalMCCList: a list of dictionaries representing a list of nodes representing a MCC
MAIN_DIRECTORY: a string representing the path to the main directory where the data generated is saved
Returns:: None
Generates:: A plot with the precipitation distribution over each feature in the list
'''
timeList = []
oriTimeList = []
colorBarTime = []
count = 1
imgFilename = ''
percentagePrecipitating = [] #0.0
cloudElementArea = []
nodes = []
xy = []
x = []
y = []
precip = []
partialArea = []
totalSize = 0.0
firstTime = True
xStart = 0.0
yStart = 0.0
# For each node in the list, get the area information from the dictionary
# in the graph and calculate the area
if finalMCCList:
for eachMCC in finalMCCList:
# Get the info from the node
for node in eachMCC:
eachNode = mccSearch.this_dict(node)
if firstTime is True:
xStart = eachNode['cloudElementCenter'][1] # lon
yStart = eachNode['cloudElementCenter'][0] # lat
timeList.append(eachNode['cloudElementTime'])
percentagePrecipitating.append((eachNode['TRMMArea'] / eachNode['cloudElementArea']) * 100.0)
cloudElementArea.append(eachNode['cloudElementArea'])
nodes.append(eachNode['uniqueID'])
x.append(eachNode['cloudElementCenter'][1]) # -xStart)
y.append(eachNode['cloudElementCenter'][0]) # -yStart)
firstTime = False
# Convert the timeList[] to list of floats
for i in xrange(len(timeList)):
colorBarTime.append(time.mktime(timeList[i].timetuple()))
totalSize = sum(cloudElementArea)
partialArea = [(a/totalSize)*30000 for a in cloudElementArea]
# Plot info
plt.close('all')
title = 'Precipitation distribution of the MCS '
fig, ax = plt.subplots(1, facecolor='white', figsize=(20, 7))
cmap = cm.jet
ax.scatter(x, y, s=partialArea, c=colorBarTime, edgecolors='none', marker='o', cmap=cmap)
colorBarTime = []
colorBarTime = list(set(timeList))
colorBarTime.sort()
cb = colorbar_index(ncolors=len(colorBarTime), nlabels=colorBarTime, cmap=cmap)
# Axes and labels
ax.set_xlabel('Degrees Longtude', fontsize=12)
ax.set_ylabel('Degrees Latitude', fontsize=12)
ax.set_title(title)
ax.grid(True)
plt.subplots_adjust(bottom=0.2)
for i, txt in enumerate(nodes):
if cloudElementArea[i] >= 2400.00:
ax.annotate('%d' % percentagePrecipitating[i]+'%', (x[i], y[i]))
precip = []
imgFilename = MAIN_DIRECTORY+'/images/MCSprecip' + str(count)+'.gif'
plt.savefig(imgFilename, facecolor=fig.get_facecolor(), transparent=True)
# Reset for next image
timeList = []
percentagePrecipitating = []
cloudElementArea = []
x = []
y = []
colorBarTime = []
nodes = []
precip = []
count += 1
firstTime = True
return
def create_text_file(finalMCCList, identifier, userVariables, graphVariables):
'''
Purpose::
Create a text file with information about the MCS
This function is expected to be especially of use regarding long term record checks
Inputs::
finalMCCList: a list of dictionaries representing a list of nodes representing a MCC
identifier: an integer representing the type of list that has been entered...this is for creating file purposes
1 - MCCList; 2- mcsList
Returns:: None
Generates::
mcsUserFile: a user readable text file with all information about each MCS
mcsSummaryFile: a user readable text file with the summary of the MCS
mcsPostFile: a user readable text file with each MCS identified
Assumptions::
TODOs: provide visualizations for the calculations used to create the textFile
'''
durations = 0.0
startTimes = []
endTimes = []
averagePropagationSpeed = 0.0
speedCounter = 0
maxArea = 0.0
amax = 0.0
avgMaxArea = []
maxAreaCounter = 0.0
maxAreaTime = ''
eccentricity = 0.0
firstTime = True
matureFlag = True
timeMCSMatures = ''
maxCEprecipRate = 0.0
minCEprecipRate = 0.0
averageArea = 0.0
averageAreaCounter = 0
durationOfMatureMCC = 0
avgMaxPrecipRate = 0.0
avgMaxPrecipRateCounter = 0
avgMinPrecipRate = 0.0
avgMinPrecipRateCounter = 0
cloudElementSpeed = 0.0
mcsSpeed = 0.0
mcsSpeedCounter = 0
mcsPrecipTotal = 0.0
avgmcsPrecipTotalCounter = 0
bigPtotal = 0.0
bigPtotalCounter = 0
allPropagationSpeeds = []
averageAreas = []
areaAvg = 0.0
avgPrecipTotal = 0.0
avgPrecipTotalCounter = 0
avgMaxmcsPrecipRate = 0.0
avgMaxmcsPrecipRateCounter = 0
avgMinmcsPrecipRate = 0.0
avgMinmcsPrecipRateCounter = 0
avgPrecipArea = []
location = []
avgPrecipAreaPercent = 0.0
precipArea = 0.0
precipAreaPercent = 0.0
precipPercent = []
precipCounter = 0
precipAreaAvg = 0.0
minSpeed = 0.0
maxSpeed = 0.0
if identifier == 1:
mcsUserFile = open(
(userVariables.DIRS['mainDirStr'] + '/textFiles/MCCsUserFile.txt'),
'wb')
mcsSummaryFile = open(
(userVariables.DIRS['mainDirStr'] + '/textFiles/MCCSummary.txt'),
'wb')
mcsPostFile = open((userVariables.DIRS['mainDirStr'] +
'/textFiles/MCCPostPrecessing.txt'), 'wb')
if identifier == 2:
mcsUserFile = open(
(userVariables.DIRS['mainDirStr'] + '/textFiles/MCSsUserFile.txt'),
'wb')
mcsSummaryFile = open(
(userVariables.DIRS['mainDirStr'] + '/textFiles/MCSSummary.txt'),
'wb')
mcsPostFile = open((userVariables.DIRS['mainDirStr'] +
'/textFiles/MCSPostPrecessing.txt'), 'wb')
for eachPath in finalMCCList:
eachPath.sort(key=lambda nodeID: (len(nodeID.split('C')[
0]), nodeID.split('C')[0], nodeID.split('CE')[1]))
mcsPostFile.write('\n %s' % eachPath)
startTime = mccSearch.this_dict(eachPath[0],
graphVariables)['cloudElementTime']
endTime = mccSearch.this_dict(eachPath[-1],
graphVariables)['cloudElementTime']
duration = (endTime - startTime) + timedelta(hours=userVariables.TRES)
# Convert datatime duration to seconds and add to the total for the average duration of all MCS in finalMCCList
durations += (duration.total_seconds())
#durations += duration
startTimes.append(startTime)
endTimes.append(endTime)
# Get the precip info
for eachNode in eachPath:
thisNode = mccSearch.this_dict(eachNode, graphVariables)
# Set first time min 'fake' values
if firstTime is True:
minCEprecipRate = thisNode['CETRMMmin']
avgMinmcsPrecipRate += thisNode['CETRMMmin']
firstTime = False
# Calculate the speed
# if thisNode['cloudElementArea'] >= OUTER_CLOUD_SHIELD_AREA:
# averagePropagationSpeed += find_cloud_element_speed(eachNode, eachPath)
# speedCounter +=1
# Amax: find max area
if thisNode['cloudElementArea'] > maxArea:
maxArea = thisNode['cloudElementArea']
maxAreaTime = str(thisNode['cloudElementTime'])
eccentricity = thisNode['cloudElementEccentricity']
location = thisNode['cloudElementCenter']
# Determine the time the feature matures
if matureFlag is True:
timeMCSMatures = str(thisNode['cloudElementTime'])
matureFlag = False
# Find min and max precip rate
if thisNode['CETRMMmin'] < minCEprecipRate:
minCEprecipRate = thisNode['CETRMMmin']
if thisNode['CETRMMmax'] > maxCEprecipRate:
maxCEprecipRate = thisNode['CETRMMmax']
# If MCC, then calculate for only mature phase else, calculate for full MCS
if identifier == 1:
# Calculations for only the mature stage
try:
if thisNode['nodeMCSIdentifier'] == 'M':
# Calculate average area of the maturity feature only
averageArea += thisNode['cloudElementArea']
averageAreaCounter += 1
durationOfMatureMCC += 1
avgMaxPrecipRate += thisNode['CETRMMmax']
avgMaxPrecipRateCounter += 1
avgMinPrecipRate += thisNode['CETRMMmin']
avgMinPrecipRateCounter += 1
avgMaxmcsPrecipRate += thisNode['CETRMMmax']
avgMaxmcsPrecipRateCounter += 1
avgMinmcsPrecipRate += thisNode['CETRMMmin']
avgMinmcsPrecipRateCounter += 1
# The precip percentage (TRMM area/CE area)
if thisNode['cloudElementArea'] >= 0.0 and thisNode[
'TRMMArea'] >= 0.0:
precipArea += thisNode['TRMMArea']
avgPrecipArea.append(thisNode['TRMMArea'])
avgPrecipAreaPercent += (
thisNode['TRMMArea'] /
thisNode['cloudElementArea'])
precipPercent.append(
(thisNode['TRMMArea'] /
thisNode['cloudElementArea']))
precipCounter += 1
# System speed for only mature stage
# cloudElementSpeed = find_cloud_element_speed(eachNode,eachPath)
# if cloudElementSpeed > 0.0 :
# mcsSpeed += cloudElementSpeed
# mcsSpeedCounter += 1
except:
print 'MCS node has no nodeMCSIdentifier ', thisNode[
'uniqueID']
avgMaxmcsPrecipRate += thisNode['CETRMMmax']
avgMaxmcsPrecipRateCounter += 1
avgMinmcsPrecipRate += thisNode['CETRMMmin']
avgMinmcsPrecipRateCounter += 1
else:
averageArea += thisNode['cloudElementArea']
averageAreaCounter += 1
durationOfMatureMCC += 1
avgMaxPrecipRate += thisNode['CETRMMmax']
avgMaxPrecipRateCounter += 1
avgMinPrecipRate += thisNode['CETRMMmin']
avgMinPrecipRateCounter += 1
avgMaxmcsPrecipRate += thisNode['CETRMMmax']
avgMaxmcsPrecipRateCounter += 1
avgMinmcsPrecipRate += thisNode['CETRMMmin']
avgMinmcsPrecipRateCounter += 1
# The precip percentage (TRMM area/CE area)
if thisNode['cloudElementArea'] >= 0.0 and thisNode[
'TRMMArea'] >= 0.0:
precipArea += thisNode['TRMMArea']
avgPrecipArea.append(thisNode['TRMMArea'])
avgPrecipAreaPercent += (thisNode['TRMMArea'] /
thisNode['cloudElementArea'])
precipPercent.append(
(thisNode['TRMMArea'] / thisNode['cloudElementArea']))
precipCounter += 1
# System speed for only mature stage
# cloudElementSpeed = find_cloud_element_speed(eachNode,eachPath)
# if cloudElementSpeed > 0.0 :
# mcsSpeed += cloudElementSpeed
# mcsSpeedCounter += 1
# Find accumulated precip
if thisNode['cloudElementPrecipTotal'] > 0.0:
mcsPrecipTotal += thisNode['cloudElementPrecipTotal']
avgmcsPrecipTotalCounter += 1
# A: calculate the average Area of the (mature) MCS
if averageAreaCounter > 0: # and averageAreaCounter > 0:
averageArea /= averageAreaCounter
averageAreas.append(averageArea)
# V: MCS speed
if mcsSpeedCounter > 0: # and mcsSpeed > 0.0:
mcsSpeed /= mcsSpeedCounter
# smallP_max: calculate the average max precip rate (mm/h)
if avgMaxmcsPrecipRateCounter > 0: # and avgMaxPrecipRate > 0.0:
avgMaxmcsPrecipRate /= avgMaxmcsPrecipRateCounter
# smallP_min: calculate the average min precip rate (mm/h)
if avgMinmcsPrecipRateCounter > 0: # and avgMinPrecipRate > 0.0:
avgMinmcsPrecipRate /= avgMinmcsPrecipRateCounter
# smallP_avg: calculate the average precipitation (mm hr-1)
if mcsPrecipTotal > 0.0: # and avgmcsPrecipTotalCounter> 0:
avgmcsPrecipTotal = mcsPrecipTotal / avgmcsPrecipTotalCounter
avgPrecipTotal += avgmcsPrecipTotal
avgPrecipTotalCounter += 1
#smallP_total = mcsPrecipTotal
# Precip over the MCS lifetime prep for bigP_total
if mcsPrecipTotal > 0.0:
bigPtotal += mcsPrecipTotal
bigPtotalCounter += 1
if maxArea > 0.0:
avgMaxArea.append(maxArea)
maxAreaCounter += 1
# Average precipate area percentage (TRMM/CE area)
if precipCounter > 0:
avgPrecipAreaPercent /= precipCounter
precipArea /= precipCounter
# Write stuff to file
mcsUserFile.write('\n\n\nStarttime is: %s ' % (str(startTime)))
mcsUserFile.write('\nEndtime is: %s ' % (str(endTime)))
mcsUserFile.write('\nLife duration is %s hrs' % (str(duration)))
mcsUserFile.write('\nTime of maturity is %s ' % (timeMCSMatures))
mcsUserFile.write('\nDuration mature stage is: %s ' %
durationOfMatureMCC * userVariables.TRES)
mcsUserFile.write('\nAverage area is: %.4f km^2 ' % (averageArea))
mcsUserFile.write('\nMax area is: %.4f km^2 ' % (maxArea))
mcsUserFile.write('\nMax area time is: %s ' % (maxAreaTime))
mcsUserFile.write('\nEccentricity at max area is: %.4f ' %
(eccentricity))
mcsUserFile.write('\nCenter (lat,lon) at max area is: %.2f\t%.2f' %
(location[0], location[1]))
mcsUserFile.write('\nPropagation speed is %.4f ' % (mcsSpeed))
mcsUserFile.write('\nMCS minimum precip rate is %.4f mmh^-1' %
(minCEprecipRate))
mcsUserFile.write('\nMCS maximum precip rate is %.4f mmh^-1' %
(maxCEprecipRate))
mcsUserFile.write(
'\nTotal precipitation during MCS is %.4f mm/lifetime' %
(mcsPrecipTotal))
mcsUserFile.write('\nAverage MCS precipitation is %.4f mm' %
(avgmcsPrecipTotal))
mcsUserFile.write(
'\nAverage MCS maximum precipitation is %.4f mmh^-1' %
(avgMaxmcsPrecipRate))
mcsUserFile.write(
'\nAverage MCS minimum precipitation is %.4f mmh^-1' %
(avgMinmcsPrecipRate))
mcsUserFile.write('\nAverage precipitation area is %.4f km^2 ' %
(precipArea))
mcsUserFile.write(
'\nPrecipitation area percentage of mature system %.4f percent ' %
(avgPrecipAreaPercent * 100))
# Append stuff to lists for the summary file
if mcsSpeed > 0.0:
allPropagationSpeeds.append(mcsSpeed)
averagePropagationSpeed += mcsSpeed
speedCounter += 1
# Reset vars for next MCS in list
averageArea = 0.0
averageAreaCounter = 0
durationOfMatureMCC = 0
mcsSpeed = 0.0
mcsSpeedCounter = 0
mcsPrecipTotal = 0.0
avgMaxmcsPrecipRate = 0.0
avgMaxmcsPrecipRateCounter = 0
avgMinmcsPrecipRate = 0.0
avgMinmcsPrecipRateCounter = 0
firstTime = True
matureFlag = True
avgmcsPrecipTotalCounter = 0
avgPrecipAreaPercent = 0.0
precipArea = 0.0
precipCounter = 0
maxArea = 0.0
maxAreaTime = ''
eccentricity = 0.0
timeMCSMatures = ''
maxCEprecipRate = 0.0
minCEprecipRate = 0.0
location = []
# LD: average duration
if len(finalMCCList) > 1:
durations /= len(finalMCCList)
durations /= 3600.0 # Convert to hours
# A: average area
areaAvg = sum(averageAreas) / len(finalMCCList)
# Create histogram plot here
# if len(averageAreas) > 1:
# plotHistogram(averageAreas, 'Average Area [km^2]', 'Area [km^2]')
# Amax: average maximum area
if maxAreaCounter > 0.0: # and avgMaxArea > 0.0 :
amax = sum(avgMaxArea) / maxAreaCounter
# Create histogram plot here
#if len(avgMaxArea) > 1:
# plotHistogram(avgMaxArea, 'Maximum Area [km^2]', 'Area [km^2]')
# v_avg: calculate the average propagation speed
if speedCounter > 0: # and averagePropagationSpeed > 0.0
averagePropagationSpeed /= speedCounter
# bigP_min: calculate the min rate in mature system
if avgMinPrecipRate > 0.0: # and avgMinPrecipRateCounter > 0.0:
avgMinPrecipRate /= avgMinPrecipRateCounter
# bigP_max: calculate the max rate in mature system
if avgMinPrecipRateCounter > 0.0: # and avgMaxPrecipRate > 0.0:
avgMaxPrecipRate /= avgMaxPrecipRateCounter
# bigP_avg: average total preicip rate mm/hr
if avgPrecipTotalCounter > 0.0: # and avgPrecipTotal > 0.0:
avgPrecipTotal /= avgPrecipTotalCounter
# bigP_total: total precip rate mm/LD
if bigPtotalCounter > 0.0: # and bigPtotal > 0.0:
bigPtotal /= bigPtotalCounter
# Precipitation area percentage
if len(precipPercent) > 0:
precipAreaPercent = (sum(precipPercent) / len(precipPercent)) * 100.0
# average precipitation area
if len(avgPrecipArea) > 0:
precipAreaAvg = sum(avgPrecipArea) / len(avgPrecipArea)
#if len(avgPrecipArea) > 1:
# plotHistogram(avgPrecipArea, 'Average Rainfall Area [km^2]', 'Area [km^2]')
sTime = str(average_time(startTimes))
eTime = str(average_time(endTimes))
if len(allPropagationSpeeds) > 1:
maxSpeed = max(allPropagationSpeeds)
minSpeed = min(allPropagationSpeeds)
# Write stuff to the summary file
mcsSummaryFile.write('\nNumber of features is %d ' % (len(finalMCCList)))
mcsSummaryFile.write('\nAverage duration is %.4f hrs ' % (durations))
mcsSummaryFile.write('\nAverage startTime is %s ' % (sTime[-8:]))
mcsSummaryFile.write('\nAverage endTime is %s ' % (eTime[-8:]))
mcsSummaryFile.write('\nAverage size is %.4f km^2 ' % (areaAvg))
mcsSummaryFile.write('\nAverage precipitation area is %.4f km^2 ' %
(precipAreaAvg))
mcsSummaryFile.write('\nAverage maximum size is %.4f km^2 ' % (amax))
mcsSummaryFile.write('\nAverage propagation speed is %.4f ms^-1' %
(averagePropagationSpeed))
mcsSummaryFile.write('\nMaximum propagation speed is %.4f ms^-1 ' %
(maxSpeed))
mcsSummaryFile.write('\nMinimum propagation speed is %.4f ms^-1 ' %
(minSpeed))
mcsSummaryFile.write(
'\nAverage minimum precipitation rate is %.4f mmh^-1' %
(avgMinPrecipRate))
mcsSummaryFile.write(
'\nAverage maximum precipitation rate is %.4f mm h^-1' %
(avgMaxPrecipRate))
mcsSummaryFile.write('\nAverage precipitation is %.4f mm ' %
(avgPrecipTotal))
mcsSummaryFile.write(
'\nAverage total precipitation during MCSs is %.4f mm/LD ' %
(bigPtotal))
mcsSummaryFile.write(
'\nAverage precipitation area percentage is %.4f percent ' %
(precipAreaPercent))
mcsUserFile.close()
mcsSummaryFile.close()
mcsPostFile.close()
return