diff --git a/pyglance/glance/imapp_plot.py b/pyglance/glance/imapp_plot.py
index 9cd6d194042adfc5370300e316334431be1fd247..7296b3cdcb8e4e9d049b77b5776bbae44b2ecde4 100644
--- a/pyglance/glance/imapp_plot.py
+++ b/pyglance/glance/imapp_plot.py
@@ -93,6 +93,23 @@ color_data = {
}
light_trajectory_pressure_color_map = matplotlib.colors.LinearSegmentedColormap('lightTrajPressCM', color_data, 256)
+lightningColorMapData = { 'red': ((0.0, 1.00, 1.00),
+ (0.25, 1.00, 1.00),
+ (0.5, 1.00, 1.00),
+ (0.75, 1.00, 0.00),
+ (1.00, 0.00, 0.00)),
+ 'green': ((0.0, 0.00, 0.00),
+ (0.25, 0.00, 0.00),
+ (0.5, 0.00, 1.00),
+ (0.75, 1.00, 0.55),
+ (1.00, 0.55, 0.55)),
+ 'blue': ((0.0, 1.00, 1.00),
+ (0.25, 1.00, 0.00),
+ (0.5, 0.00, 0.00),
+ (0.75, 0.00, 0.00),
+ (1.00, 0.00, 0.00)) }
+lightningColorMap = colors.LinearSegmentedColormap('lightningColorMap', lightningColorMapData, 256)
+
def _check_requested_projection (projection, doWarn=True, fileDescription="") :
"""
Check the requested projection, issuing a warning if it is not available and doWarn is true
@@ -475,14 +492,16 @@ def _create_imapp_figure (initAODdata, initLongitudeData, initLatitu
return figure
-def _create_thermal_couplets_figure(basemapObject, centersMask, longitudeData, latitudeData, colormap=cm.jet) :
+def _create_thermal_couplets_figure(basemapObject, centersMask, longitudeData, latitudeData, colormap=cm.jet,
+ plotWarningCircles=False, warningDistance=25.0, useDarkBackground=False, title="Overshooting Tops/Thermal Couplets",
+ datetime=None) :
"""
Plot the thermal couplet centers using a mask that identifies where they are
TODO, this is not finished at all
"""
# build the basic map plot plot
- axes, figure = _build_basic_figure_with_map (basemapObject, parallelWidth=5.0, meridianWidth=5.0, useDarkBackground=False,)
+ axes, figure = _build_basic_figure_with_map (basemapObject, parallelWidth=5.0, meridianWidth=5.0, useDarkBackground=useDarkBackground,)
# build extra info to go to the map plotting function
kwargs = { }
@@ -495,19 +514,75 @@ def _create_thermal_couplets_figure(basemapObject, centersMask, longitudeData, l
centersLon = longitudeData[centersMask]
centersLat = latitudeData[centersMask]
tempX, tempY = basemapObject(centersLon, centersLat)
- p = basemapObject.plot(tempX, tempY, 'bs')
- # if our colorbar has limits set those
- #if colorbarLimits is not None :
- # clim(vmin=colorbarLimits[0], vmax=colorbarLimits[-1])
- # make a color bar
- #cbar = colorbar(format='%.3g')
- # add the units to the colorbar
- #if str.lower(str(units)) != "none" :
- # cbar.set_label(units)
+ # if we're just plotting the positions of the centers, do that
+ if not plotWarningCircles :
+ p = basemapObject.plot(tempX, tempY, 'bo', markersize=3.0)
+ else : # otherwise we must be plotting circles to warn where something could happen!
+ # TODO, this is not really the way this should work in the long run
+ degreesToUse = (warningDistance / 111.0) / 2.0 # TODO this is a very rough approximation of 1 degree latitude = 111 km, if needed do more complex math
+ for centerNum in range(len(centersLat)) :
+ p = basemapObject.tissot(centersLon[centerNum], centersLat[centerNum], degreesToUse, 100, facecolor='yellow', alpha=0.5, lw=0)
# and some informational stuff
- axes.set_title("Overshooting Tops/Thermal Couplets")
+ tempTitle = title
+ if datetime is not None :
+ tempTitle = title + ": " + datetime.strftime("%Y-%m-%d at %H:%M UTC")
+ axes.set_title(tempTitle)
+
+ return figure
+
+def _create_lightning_risk_figure(basemapObject, riskAreasInfoList,
+ warningDistance=10.0, useDarkBackground=True, title="Lightning Risk",
+ colorbarLabel="Lightning Risk within 10 km of overshooting top (%)",
+ datetime=None) :
+ """
+ Plot areas of lightning risk colored based on the % chance of cloud-to-ground lightning
+
+ the riskAreasInfoList should be a list with entries in the form (lonArray, latArray, percentageChance, colorToPlotIn, alphaToPlotWith)
+
+ TODO, this is not finished at all
+ """
+
+ # build the basic map plot plot
+ axes, figure = _build_basic_figure_with_map (basemapObject, parallelWidth=5.0, meridianWidth=5.0, useDarkBackground=useDarkBackground,)
+
+ # build extra info to go to the map plotting function
+ kwargs = { }
+
+ # hang on to info about the percentages and their colors
+ riskPercentsColors = { }
+ # we're going to need to loop through all the sets to plot them
+ for (lonArray, latArray, percentageChance, colorToPlotIn, alphaToPlotWith) in riskAreasInfoList :
+ riskPercentsColors[percentageChance] = colorToPlotIn
+ # TODO, this is not really the way this should work in the long run
+ degreesToUse = (warningDistance / 111.0) # TODO this is a very rough approximation of 1 degree latitude = 111 km, if needed do more complex math
+ for centerNum in range(len(lonArray)) :
+ p = basemapObject.tissot(lonArray[centerNum], latArray[centerNum], degreesToUse, 100, facecolor=colorToPlotIn, alpha=alphaToPlotWith, lw=0)
+
+ # TODO, use riskPercentsColors to build a colorbar?
+
+ percentages = sorted(riskPercentsColors.keys())
+
+ tempMap = cm.ScalarMappable(cmap=lightningColorMap)
+ tempMap.set_clim(vmin=0.0, vmax=1.0)
+ tempMap.set_array([0.0, 1.0])
+ # this unfortunately leaves black tick marks on the bar itself, but it does let me separate it into four sections
+ colorBarToReturn = colorbar(tempMap, format='%.5g', orientation='horizontal', shrink=0.5, ticks=[(1./8.), (3./8.), (5./8.), (7./8.)])
+ colorBarToReturn.ax.set_xticklabels(["70", "65", "50", "35"])
+ # TODO, for some reason python doesn't think that colorbar has an update_ticks?
+ #colorBarToReturn.locator = matplotlib.ticker.FixedLocator([(1./8.), (3./8.), (5./8.), (7./8.)])
+ #colorBarToReturn.formatter = matplotlib.ticker.FixedFormatter(["70", "65", "50", "35"])
+ #colorBarToReturn.update_ticks()
+ #for tempText in colorBarToReturn.ax.get_xticklabels():
+ # tempText.set_fontsize(5)
+ colorBarToReturn.set_label(colorbarLabel, fontsize=8)
+
+ # and some informational stuff
+ tempTitle = title
+ if datetime is not None :
+ tempTitle = title + ": " + datetime.strftime("%Y-%m-%d at %H:%M UTC")
+ axes.set_title(tempTitle)
return figure
@@ -638,6 +713,7 @@ python -m glance.imapp_plot aodTraj traj.nc optionalGrid.nc
# create some masks to identify things about the data
centersMask = otQAFlag == 0
goodData = otPQIFlag == 1
+ goodCenters = centersMask & goodData
# get more display information TODO, this may not be used?
parallelWidth = options.parallelWidth
@@ -646,21 +722,44 @@ python -m glance.imapp_plot aodTraj traj.nc optionalGrid.nc
# get the longitude and latitude information, then use that to figure out the viewing window
longitudeData = otFileObject.file_object["pixel_longitude"]
latitudeData = otFileObject.file_object["pixel_latitude"]
- minLongitude = np.min(longitudeData)
- maxLongitude = np.max(longitudeData)
- minLatitude = np.min(latitudeData)
- maxLatitude = np.max(latitudeData)
+ missingLonVal = otFileObject.file_object.missing_value("pixel_longitude")
+ missingLatVal = otFileObject.file_object.missing_value("pixel_latitude")
+ tempMaskLon = (longitudeData != missingLonVal) & goodCenters # for now only look at good data
+ tempMaskLat = (latitudeData != missingLatVal) & goodCenters # for now only look at good data
+ lonTemp = longitudeData[tempMaskLon]
+ latTemp = latitudeData [tempMaskLat]
+ minLongitude = np.min(lonTemp)
+ maxLongitude = np.max(lonTemp)
+ minLatitude = np.min(latTemp)
+ maxLatitude = np.max(latTemp)
# do some more lon / lat calculations
latRange = maxLatitude - minLatitude
lonRange = maxLongitude - minLongitude
centerLat = minLatitude + (latRange / 2.0)
centerLon = minLongitude + (lonRange / 2.0)
-
+ # TEMP expand the range by a bit to alow space around our good data points
+ minLatitude = minLatitude - (latRange / 4.0)
+ maxLatitude = maxLatitude + (latRange / 4.0)
+ minLongitude = minLongitude - (lonRange / 4.0)
+ maxLongitude = maxLongitude + (lonRange / 4.0)
# make sure the view window longitudes are in ranges that matplotlib will accept
minLongitude, maxLongitude, shouldModifyNegativeLons = _modify_view_window_longitudes(minLongitude, maxLongitude)
+
# TODO, haven't hooked up the boolean to control modification of negative longitudes yet
+ # get the base time information and translate it into a date time object
+ dateString = otFileObject.file_object.get_global_attribute("Image_Date")
+ timeString = otFileObject.file_object.get_global_attribute("Image_Time")
+ dateTimeStringTemp = str(dateString) + "." + str(timeString)
+ timeReferenceObject = None
+ try :
+ timeReferenceObject = datetime.strptime(dateTimeStringTemp, "1%y%j.%H%M%S")
+ except ValueError :
+ LOG.warn ("Unable to parse datetime from file. No datetime information will be displayed on images.")
+ timeReferenceObject = None
+ LOG.debug("image time information: " + str(timeReferenceObject))
+
# build a basemap
LOG.info("Building basemap object.")
projectionName = 'lcc' # use the Lambert Conformal projection; TODO at some point this will need to be checked with a global attribute
@@ -672,7 +771,7 @@ python -m glance.imapp_plot aodTraj traj.nc optionalGrid.nc
# create a plot of the centers of the thermal couplets
LOG.info ("Creating plot of Overshooting Top center locations.")
- tempFigure = _create_thermal_couplets_figure(basemapObject, centersMask & goodData, longitudeData, latitudeData) # TODO, more will be needed here?
+ tempFigure = _create_thermal_couplets_figure(basemapObject, goodCenters, longitudeData, latitudeData, datetime=timeReferenceObject) # TODO, more will be needed here?
# save the plot and then get rid of the local copy
LOG.info("Saving plot to disk.")
@@ -680,6 +779,40 @@ python -m glance.imapp_plot aodTraj traj.nc optionalGrid.nc
tempFigure.savefig(figureNameAndPath, dpi=defaultValues['figureDPI'])
plt.close(tempFigure)
del(tempFigure)
+
+ # create a plot of the turbulence around the centers
+ LOG.info ("Creating plot of Turbulence Risk.")
+ tempFigure = _create_thermal_couplets_figure(basemapObject, goodCenters, longitudeData, latitudeData,
+ plotWarningCircles=True, useDarkBackground=True, title="Turbulence Risk", datetime=timeReferenceObject) # TODO, more will be needed here?
+
+ # save the plot and then get rid of the local copy
+ LOG.info("Saving plot to disk.")
+ figureNameAndPath = os.path.join(options.outpath, "turbulenceRisk" + defaultValues['figureName']) # TODO, need a constant other than 'figureName'
+ tempFigure.savefig(figureNameAndPath, dpi=defaultValues['figureDPI'])
+ plt.close(tempFigure)
+ del(tempFigure)
+
+ # create a plot of the lightning risks near the centers
+ LOG.info ("Creating plot of Lightning Risk.")
+ tempLon = longitudeData[goodCenters]
+ tempLat = latitudeData[goodCenters]
+ LOG.debug ("Number of OT centers: " + str(tempLat.size))
+ # the riskAreas should be a list with entries in the form (lonArray, latArray, percentageChance, colorToPlotIn, alphaToPlotWith)
+ # TODO this is temp test code to make dummy data and is very fragile
+ riskAreas = [ ]
+ if tempLat.size > 0 :
+ riskAreas = [(tempLon[ : 5], tempLat[ : 5], 65, 'red', 0.75),
+ (tempLon[ 5:10], tempLat[ 5:10], 50, 'yellow', 0.75),
+ (tempLon[10:20], tempLat[10:20], 70, 'magenta', 0.75),
+ (tempLon[20: ], tempLat[20: ], 35, 'green', 0.75)] # TODO, this is just some dummy data to test it
+ tempFigure = _create_lightning_risk_figure(basemapObject, riskAreas, datetime=timeReferenceObject)
+
+ # save the plot and then get rid of the local copy
+ LOG.info("Saving plot to disk.")
+ figureNameAndPath = os.path.join(options.outpath, "lightningRisk" + defaultValues['figureName']) # TODO, need a constant other than 'figureName'
+ tempFigure.savefig(figureNameAndPath, dpi=defaultValues['figureDPI'])
+ plt.close(tempFigure)
+ del(tempFigure)
def aodTraj(*args):
"""plot AOD trajectory frames