""" functions to plot range-time diagrams """
import numpy as np
from matplotlib import pyplot as plt
import logging
import pathlib
from cmcrameri import cm
from matplotlib.colors import LightSource
import pandas as pd
# Local imports
import avaframe.out3Plot.plotUtils as pU
import avaframe.ana5Utils.distanceTimeAnalysis as dtAna
import avaframe.in3Utils.geoTrans as gT
# from avaframe.Tools import NodeTools
log = logging.getLogger(__name__)
[docs]def plotRangeTime(mtiInfo, cfgRangeTime):
"""plot range-time diagram with avalanche front and colorcoded average values of result parameter
Parameters
-----------
mtiInfo: dict
dictionary with average values for range to reference point (mti), timeStep list,
list with distance to reference point of avalanche front (rangeList)
cfgRangeTime: configparser object
configuration settings for range time diagram - here used avalancheDir, rangeTimeResType,
simHash, and from plots width, height, lw, ..
"""
# create plot
fig = plt.figure(figsize=(pU.figW, pU.figH))
ax = fig.add_subplot(1, 1, 1)
plt.title(mtiInfo["plotTitle"])
# add plot to axes
ax, rangeTimeResType = addRangeTimePlotToAxes(mtiInfo, cfgRangeTime, ax)
# set path for saving figure
outDir = pathlib.Path(cfgRangeTime["GENERAL"]["avalancheDir"], "Outputs", "ana5Utils")
outFileName = mtiInfo["type"] + "_" + rangeTimeResType + "_" + cfgRangeTime["GENERAL"]["simHash"]
plotPath = pU.saveAndOrPlot({"pathResult": outDir}, outFileName, fig)
[docs]def addRangeTimePlotToAxes(mtiInfo, cfgRangeTime, ax):
"""add plot range-time diagram with avalanche front and colorcoded average values of result parameter
Parameters
-----------
mtiInfo: dict
dictionary with average values for range to reference point (mti), timeStep list,
list with distance to reference point of avalanche front (rangeList)
cfgRangeTime: configparser object
configuration settings for range time diagram - here used avalancheDir, rangeTimeResType,
simHash, and from plots width, height, lw, ..
ax: matplotlib axes object
axes where plot shall be added to
"""
# fetch required input info
mti = mtiInfo["mti"]
rangeGates = mtiInfo["rangeGates"]
timeList = mtiInfo["timeList"]
rangeList = mtiInfo["rangeList"]
rangeTimeResType = cfgRangeTime["GENERAL"]["rangeTimeResType"]
maxVel, rangeVel, timeVel = dtAna.approachVelocity(mtiInfo)
# in case time steps are not ordered - the colormesh x and y need to be ordered
timeIndex = np.argsort(np.array(timeList))
timeListNew = np.array(timeList)[timeIndex]
mti = mti[:, timeIndex]
# fetch velocity legend style info
width = cfgRangeTime["PLOTS"].getfloat("width")
height = cfgRangeTime["PLOTS"].getfloat("height")
lw = cfgRangeTime["PLOTS"].getfloat("lw")
textsize = cfgRangeTime["PLOTS"].getfloat("textsize")
pc = plt.pcolormesh(timeListNew, rangeGates, mti, cmap=pU.cmapRangeTime)
ax.plot(timeList, rangeList, ".", color="black", markersize=4, label="avalanche front")
ax.set_xlabel("Time [s]")
# add y label axis
if mtiInfo["type"] == "thalwegTime":
sTypeCapital = mtiInfo["sType"][0].upper() + mtiInfo["sType"][1:]
if cfgRangeTime['GENERAL'].getboolean('originStart'):
ax.set_ylabel("$S_{XY}$ [m]")
else:
labelY = r'$\beta_{%.1f°}$' % mtiInfo['betaPointAngle']
ax.set_ylabel("%s distance to %s [m]" % (sTypeCapital, labelY))
else:
ax.set_ylabel("Distance to %s [m]" % mtiInfo["referencePointName"])
# add colorbar and infobox
unit = pU.cfgPlotUtils["unit" + rangeTimeResType]
if mtiInfo["type"] == "thalwegTime" and cfgRangeTime["GENERAL"]["maxOrMean"].lower() == "max":
avgType = "max"
else:
avgType = "avg."
cName = "%s " % avgType + pU.cfgPlotUtils["name" + rangeTimeResType]
pU.addColorBar(pc, ax, None, unit, title=cName)
pU.putAvaNameOnPlot(ax, cfgRangeTime["GENERAL"]["avalancheDir"])
# add range time velocity legend
rangeTimeVelocityLegend(ax, maxVel, width, height, lw, textsize)
# add max velocity location
ax.plot(timeVel, rangeVel, "r*", label="max velocity location")
# FSO: is this needed?
# cbar.ax.axhline(y=maxVel, color='r', lw=1, label='max velocity')
# add info on avalanche front in legend
ax.legend(facecolor="grey", framealpha=0.2, loc="lower right", fontsize=8)
# if tt-diagram add beta point info
if mtiInfo["type"] == "thalwegTime":
# invert y axis as ava flow starts from minus distance to beta point
ax.invert_yaxis()
ax.axhline(
y=0.0,
color="gray",
linestyle="--",
linewidth=1,
alpha=0.5,
label="beta point: %.1f°" % mtiInfo["betaPointAngle"],
)
return ax, rangeTimeResType
[docs]def radarFieldOfViewPlot(radarFov, radarRange, cfgRangeTime, rangeGates, dem):
"""Create radar field of view plot
Parameters
-----------
radarFov: numpy array
list with radar location and end point of field of view, x and y coors
radarRange: masked array
masked array of DEM with radar field of view - showing distance to radar
cfgRangeTime: configparser object
configuration settings section - here used avalancheDir, simHash,
aperture angle [degree]
rangeGates: numpy array
range gates of radar field of view
dem: dict
dictionary with dem header and data
"""
# get input parameters
aperture = cfgRangeTime["GENERAL"].getfloat("aperture")
# fetch header info - required for creating coordinate grid
xllc = dem["header"]["xllcenter"]
yllc = dem["header"]["yllcenter"]
ncols = dem["header"]["ncols"]
nrows = dem["header"]["nrows"]
cellSize = dem["header"]["cellsize"]
# Set coordinate grid with given origin
X, Y = gT.makeCoordinateGrid(xllc, yllc, cellSize, ncols, nrows)
# load required input parameters for contour plot
gateContours = cfgRangeTime["PLOTS"].getint("gateContours")
# get field of view with radar location and aperture angle
xR = gT.rotate(radarFov, aperture)
xL = gT.rotate(radarFov, -aperture)
radarFovArrow = np.diff(radarFov) * 0.2
# create plot of radar range values and field of view
fig = plt.figure(figsize=(pU.figW, pU.figH))
ax1 = plt.subplot(111)
cmapRadar = pU.cmapRadarFOV
cmapRadar.set_bad("w", alpha=0.0)
pc = plt.pcolormesh(X, Y, radarRange, cmap=cmapRadar)
ax1.plot(radarFov[0], radarFov[1], marker="o", color="k", zorder=1)
ax1.contour(X, Y, radarRange, rangeGates[::gateContours], colors=["brown"], alpha=0.5, linewidths=0.5)
ax1.plot(xR[0], xR[1], marker="o", color="k")
ax1.plot(xL[0], xL[1], marker="o", color="k")
ax1.arrow(
x=radarFov[0][0],
y=radarFov[1][0],
dx=radarFovArrow[0][0],
dy=radarFovArrow[1][0],
fc="r",
ec="r",
width=10,
zorder=2,
)
ax1.set_title("Radar range")
ax1.set_xlabel("x [m]")
ax1.set_ylabel("y [m]")
plt.xticks(rotation=45)
# add colorbar and infoboxes
unit = "m"
cName = "range"
pU.addColorBar(pc, ax1, None, unit, title=cName)
pU.putAvaNameOnPlot(ax1, cfgRangeTime["GENERAL"]["avalancheDir"])
pU.putInfoBox(ax1, "- range gates [%d]" % gateContours, location="lowerRight", color="brown")
# create plot of DEM and radar field of view
fig2 = plt.figure(figsize=(pU.figW, pU.figH))
# add second panel with DEM and radar location
ax2 = plt.subplot(111)
# pc0 = plt.pcolormesh(X, Y, dem['rasterData'], cmap=cm.grayC)
ls = LightSource(azdeg=315, altdeg=45)
ax2.imshow(
ls.hillshade(
dem["rasterData"], vert_exag=10, dx=dem["rasterData"].shape[1], dy=dem["rasterData"].shape[0]
),
cmap="gray",
extent=[np.amin(X), np.amax(X), np.amin(Y), np.amax(Y)],
origin="lower",
)
CS = ax2.contour(X, Y, dem["rasterData"], colors=["brown"], alpha=0.75, linewidths=0.5)
# add radar field of view
ax2.plot(radarFov[0], radarFov[1], marker="o", color="k", zorder=1)
ax2.plot(xR[0], xR[1], marker="o", color="k")
ax2.plot(xL[0], xL[1], marker="o", color="k")
ax2.set_title("DEM with radar location")
ax2.set_xlabel("x [m]")
ax2.set_ylabel("y [m]")
plt.xticks(rotation=45)
# add infoboxes
pU.putAvaNameOnPlot(ax2, cfgRangeTime["GENERAL"]["avalancheDir"])
pU.putInfoBox(ax2, "- elevation [m]", location="lowerRight", color="brown")
ax2.clabel(CS, CS.levels, inline=True, fontsize=10)
# set path for saving figure
outDir = pathlib.Path(cfgRangeTime["GENERAL"]["avalancheDir"], "Outputs", "ana5Utils")
outFileName = "radarFieldOfViewPlot_%s" % cfgRangeTime["GENERAL"]["simHash"]
plotPath = pU.saveAndOrPlot({"pathResult": outDir}, outFileName, fig)
outFileName = "radarFieldOfViewPlotDEM_%s" % cfgRangeTime["GENERAL"]["simHash"]
plotPath = pU.saveAndOrPlot({"pathResult": outDir}, outFileName, fig2)
[docs]def plotRangeRaster(slRaster, rasterTransfo, cfgRangeTime, rangeRaster, cLower):
"""create plot of distance from start of runout area point extracted avalanche front and result field
Parameters
-----------
slRaster: numpy array
transformed result field
rasterTransfo: dict
info on coordinate transformation
cfgRangeTime: configparser object
configuration settings of range time diagram
rangeRaster: np array
distance to runout area point in s coordinate
cLower: int
index of avalanche front
"""
# fetch unit for result parameter
resType = cfgRangeTime["rangeTimeResType"]
unitRes = pU.cfgPlotUtils["unit" + resType]
# fetch s. l coordinates
l = rasterTransfo["l"]
s = rasterTransfo["s"]
indStartOfRunout = rasterTransfo["indStartOfRunout"]
# create figure
fig = plt.figure(figsize=(pU.figW * 1.5, pU.figH))
ax1 = plt.subplot(111)
plt.title("Distance to reference point and %s field" % pU.cfgPlotUtils["name" + resType])
# create colormap for range field
cmapRange, _, ticksRange, normRange = pU.makeColorMap(
cm.grayC.reversed(), np.nanmin(rangeRaster), np.nanmax(rangeRaster), continuous=pU.contCmap
)
# add imshow plot of range
ref0, im1 = pU.NonUnifIm(
ax1, l, s, rangeRaster, "l [m]", "s [m]", extent=[l.min(), l.max(), s.min(), s.max()], cmap=cmapRange
)
# add horizontal line indicating location of start of runout area and avalanche front
ax1.axhline(
y=s[indStartOfRunout],
color="w",
linestyle="--",
label="start of run-out area point : %.1f °" % rasterTransfo["startOfRunoutAreaAngle"],
)
if np.isnan(cLower):
log.debug("No avalanche front found for this time step")
else:
ax1.axhline(
y=s[cLower],
color="y",
linestyle="--",
label="avalanche front based on %s limit %s" % (resType, cfgRangeTime["thresholdResult"]),
)
# add result field masked with threshold
bmaskRes = slRaster < cfgRangeTime.getfloat("thresholdResult")
maskResType = np.ma.array(slRaster, mask=bmaskRes)
# create colormap
cmapRes, _, ticksRes, normRes = pU.makeColorMap(
pU.colorMaps[resType], np.nanmin(maskResType), np.nanmax(maskResType), continuous=True
)
cmapRes.set_bad(alpha=0.0)
ref2, im2 = pU.NonUnifIm(
ax1, l, s, maskResType, "l [m]", "s [m]", extent=[l.min(), l.max(), s.min(), s.max()], cmap=cmapRes
)
# add legend and colorbar
ax1.legend(loc="center", facecolor="black", framealpha=0.2)
pU.addColorBar(im1, ax1, ticksRange, "m", pad=0.005)
pU.addColorBar(im2, ax1, ticksRes, unitRes)
# create plot name and location for saving and save/plot
outDir = pathlib.Path(cfgRangeTime["avalancheDir"], "Outputs", "ana5Utils")
pathDict = {"pathResult": outDir}
outFileName = "distanceToStartOfRunoutArea_%s_%s" % (resType, cfgRangeTime["simHash"])
pU.saveAndOrPlot(pathDict, outFileName, fig)
[docs]def getMaxVelocityPoint(width, height, maxVel, diagVel):
"""get point of max approach velocity in axes coordinates
Parameters
-----------
width: float
fractional percentage of legend width
height: float
fractional percentage of legend height
maxVel: float
maximum approach velocity value
diagVel: float
x/y ratio of axes
Returns
--------
point: tuple
x, y axes coordinates of maximum velocity
"""
# along bottom line
if maxVel > diagVel:
point = [(1 - width) + width / maxVel * diagVel, 1 - height]
else:
# along re vert line
point = [1, 1 - height * maxVel / diagVel]
return point
[docs]def rangeTimeVelocityLegend(ax, maxVel, width, height, lw, textsize):
"""set legend in range time diagram for velocity in terms of steepness of front position
connects to figure callback to react to zoom/pan events
Parameters
----------
ax: matplotlib axes object
figure axes object
maxVel: float
maximum approach velocity value
width : float , *0.25
fractional percentage of legend width
height: float
fractional percentage of legend height
lw: float
linewidth for legend line
textsize: float
textsize for legend entries
"""
# keep track of which text elements belong to this legend thing...
inititalNrOfTextElements = len(ax.texts)
# get legend points in axes coordinates
point = getVelocityPoints(1, 1, width, height, 1, 1)
# add title and boundary box and diagonal lines using point
lVmax = addTitleBox(ax, width, height, lw, point, textsize, maxVel=True)
# ---- internal functions ----
def zoomLvlChange(eventAx, first=False):
"""
Function to annotate the diagonal lines with velocity values.
Flag first decides if the text elements are created (must be called once)
or if just the text is changed (figure callback)
"""
# get current axes limits
xdMax = max(eventAx.get_xlim()) # timesteps[-1]
ydMax = max(eventAx.get_ylim()) # max(rangegates)
xdIntervall = max(eventAx.get_xlim()) - min(eventAx.get_xlim())
ydIntervall = max(eventAx.get_ylim()) - min(
eventAx.get_ylim()
) # y_max-rangegates[0] # from min to max
vDiag = ydIntervall / xdIntervall
# calculate current velocities of diagonal lines
dataPoint = getVelocityPoints(xdMax, ydMax, width, height, xdIntervall, ydIntervall)
textElementNr = addVelocityValues(
ax, dataPoint, point, first=first, inititalNrOfTextElements=inititalNrOfTextElements
)
# make or update max velocity line and text
maxVelPoint = getMaxVelocityPoint(width, height, maxVel, vDiag)
if maxVel < vDiag:
xLoc = maxVelPoint[0] - 0.04
yLoc = maxVelPoint[1] - 0.004
else:
xLoc = maxVelPoint[0]
yLoc = maxVelPoint[1] + 0.004
if first:
eventAx.text(
xLoc,
yLoc,
"max %.1f" % maxVel,
size=5,
fontweight="bold",
color="r",
transform=ax.transAxes,
gid=len(point) - 2,
va="center",
ha="left",
bbox=dict(facecolor="white", alpha=0.5),
)
else:
ax.texts[textElementNr + 1].set_position((xLoc, yLoc))
lVmax.set_data([point[0][0], maxVelPoint[0]], [point[0][1], maxVelPoint[1]])
# ---- internal functions end ----
# make the text, e.g. the mandatory first=True call
zoomLvlChange(ax, first=True)
# connect callback
ax.callbacks.connect("xlim_changed", zoomLvlChange)
ax.callbacks.connect("ylim_changed", zoomLvlChange)
[docs]def addTitleBox(ax, width, height, lw, point, textsize, maxVel=True):
"""add velocity legend title and boundary box and diagonal lines
if maxVel=True add max velocity line in red
Parameters
-----------
ax: matplotlib axes object
axes object for legend
width: float
fractional percentage of legend width
height: float
fractional percentage of legend height
lw: float
linewidth
point: list
list of points coordinates for velocity legend
textsize: float
size of legend entries
maxVel: bool
if True add max velocity line in red
"""
# add legend title
ax.text(
point[1][0],
point[1][1],
"Approach \n velocity [m/s]",
size=textsize,
transform=ax.transAxes,
va="top",
ha="right",
fontweight="bold",
bbox=dict(facecolor="white", alpha=0.5),
)
# plot boundary of velocity box
ax.plot(
[point[0][0], point[1][0]],
[point[0][1], point[1][1]],
color="k",
linewidth=lw,
transform=ax.transAxes,
) # top hor
ax.plot(
[point[1][0], point[5][0]],
[point[1][1], point[5][1]],
color="k",
linewidth=lw,
transform=ax.transAxes,
) # re vert
ax.plot(
[point[5][0], point[9][0]],
[point[5][1], point[9][1]],
color="k",
linewidth=lw,
transform=ax.transAxes,
) # bottom hor
ax.plot(
[point[9][0], point[0][0]],
[point[9][1], point[0][1]],
color="k",
linewidth=lw,
transform=ax.transAxes,
) # le vert
# these are the diagonal lines referring to velocity
for j in range(2, len(point) - 1):
x1 = [point[0][0], point[j][0]]
y1 = [point[0][1], point[j][1]]
ax.plot(x1, y1, color="k", linewidth=lw, transform=ax.transAxes)
if maxVel:
# draw red line for max velocity
xdIntervall = max(ax.get_xlim()) - min(ax.get_xlim())
ydIntervall = max(ax.get_ylim()) - min(ax.get_ylim())
vDiag = ydIntervall / xdIntervall
pVmax = getMaxVelocityPoint(width, height, maxVel, vDiag)
lVmax = ax.plot(
[point[0][0], pVmax[0]],
[point[0][1], pVmax[1]],
color="r",
linewidth=lw * 1.5,
transform=ax.transAxes,
)[
0
] # return handle not list
return lVmax
[docs]def addVelocityValues(ax, dataPoint, point, first=True, inititalNrOfTextElements=np.nan):
"""add velocity values as labels
Parameters
-----------
ax: matplotlib axis object
dataPoint: list
list of points coordinates for velocity label locations
point: list
list of points coordinates for velocity lines
first: bool
if True text elements are created - initially required, if False then only changed
inititalNrOfTextElements:
"""
# compute velocity values
velocity = []
for i in range(2, len(dataPoint) - 1):
deltaS = dataPoint[0][1] - dataPoint[i][1]
deltaT = dataPoint[i][0] - dataPoint[0][0]
velocity.append(deltaS / deltaT)
textElementNr = inititalNrOfTextElements
# add velocity labels
for j in range(2, len(point) - 1):
x1 = [point[0][0], point[j][0]]
y1 = [point[0][1], point[j][1]]
textElementNr = textElementNr + 1
if first:
if j < 5: # text along vertical axes
ax.text(
x1[1] + 0.01,
y1[1] + 0.002,
"%.1f" % velocity[j - 2],
size=5,
fontweight="bold",
color="k",
transform=ax.transAxes,
gid=j,
va="center",
ha="left",
)
else: # text along horizontal axes
ax.text(
x1[1],
y1[1] - 0.01,
"%.1f" % velocity[j - 2],
size=5,
fontweight="bold",
color="k",
transform=ax.transAxes,
gid=j,
va="top",
ha="center",
)
else:
ax.texts[textElementNr].set_text("%.1f" % velocity[j - 2])
return textElementNr
[docs]def getVelocityPoints(xMax, yMax, width, height, xinterval, yinterval):
"""get points for legend creation (box and sloping lines)
if xMax, yMax, xinterval, yinterval are all 1 -> points refer to
axes coordinates, that range from 0 to 1
if given true values, e.g. in seconds and meters, points are in
data coordinates and can be used for velocity calculus etc.
Note: used to be an internal function to rangeTimeVelocityLegend,
so use with care.
Parameters
-----------
xMax: float
max x extent of data (timeSteps)
yMax: float
max y extent of data (rangeGates)
width: float
fractional percentage of legend width
height: float
fractional percentage of legend height
xinterval: float
interval on x
yinterval: float
interval on y
Returns
--------
points: list
list of point coordinates
"""
points = []
points.append([xMax - width * xinterval, yMax]) # p0 left, top
points.append([xMax, yMax]) # p1 right, top
points.append([xMax, yMax - height / 4.0 * yinterval]) # p2 right, 0.75*top
points.append([xMax, yMax - height / 2.0 * yinterval]) # p3 right, 0.5*top
points.append([xMax, yMax - height / 4.0 * 3.0 * yinterval]) # p4 right, 0.25*top
points.append([xMax, yMax - height * yinterval]) # p5 right, bottom
points.append([xMax - width / 4 * xinterval, yMax - height * yinterval]) # p6 0.75*right, bottom
points.append([xMax - width / 2 * xinterval, yMax - height * yinterval]) # p7 0.5*right, bottom
points.append([xMax - width / 4 * 3 * xinterval, yMax - height * yinterval]) # p8 0.25*right, bottom
points.append([xMax - width * xinterval, yMax - height * yinterval]) # p9 left, bottom
return points
[docs]def animationPlot(demData, data, cellSize, resType, cfgRangeTime, mtiInfo, timeStep):
"""3 panel plot: result in x,y, result in s, l, tt diagram
Parameters
-----------
demData: dict
info on dem with header and rasterData
data: numpy array
result type data
cellSize: float
cell size of result type and dem
resType: str
name of result type, e.g. FV, FT
cfgRangeTime: configparser object
configuration of range time diagram settings
mtiInfo: dict
info dictionary for color plot of tt diagram, also info on domain transformation into s,l
timeStep: float
actual time step of sim result
"""
# read required configuration for final plots, e.g. result min and max for colorbar range
resMin = cfgRangeTime["ANIMATE"].getfloat("resMin")
resMax = cfgRangeTime["ANIMATE"].getfloat("resMax")
# fetch unit of result type
unit = pU.cfgPlotUtils["unit%s" % resType]
# choose colormap with min max result type range
cmapRes, col, ticksRes, normRes = pU.makeColorMap(
pU.colorMaps[resType], resMin, resMax, continuous=pU.contCmap
)
# set up 3 panel plot
fig = plt.figure(figsize=(pU.figW * 3, pU.figH))
# +++++++++++++++PANEL 1++++++++++++++++++
# result field in x,y with s,l domain on top
ax1 = fig.add_subplot(131)
# check if nodata_value is found and if replace with nans for plotting
demField = np.where(
demData["rasterData"] == demData["header"]["nodata_value"], np.nan, demData["rasterData"]
)
# initialize x, y vectors of result field domain
xllc = demData["header"]["xllcenter"]
yllc = demData["header"]["yllcenter"]
x = np.arange(demData["header"]["ncols"]) * cellSize + xllc
y = np.arange(demData["header"]["nrows"]) * cellSize + yllc
# mask data for plot where it is zero
data = np.ma.masked_where(data == 0.0, data)
# set alpha to zero
cmapRes.set_bad(alpha=0)
# uncomment this to set the under value for discrete cmap transparent
# cmap.set_under(alpha=0)
# add DEM hillshade with contour lines
ls, CS = pU.addHillShadeContours(
ax1, demField, cellSize, [x.min(), x.max(), y.min(), y.max()], colors=["white"]
)
# add peak field data
im1 = ax1.imshow(
data,
cmap=cmapRes,
norm=normRes,
extent=[x.min(), x.max(), y.min(), y.max()],
origin="lower",
aspect="equal",
zorder=4,
)
cName1 = "%s [%s]" % (pU.cfgPlotUtils["name" + resType], pU.cfgPlotUtils["unit" + resType])
pU.addColorBar(im1, ax1, ticksRes, None, title=cName1)
# add domain transformation info
# read avaPath with scale
rasterTransfo = mtiInfo["rasterTransfo"]
xPath = rasterTransfo["x"]
yPath = rasterTransfo["y"]
# read domain boundaries with scale
cellSizeSL = rasterTransfo["cellSizeSL"]
DBXl = rasterTransfo["DBXl"] * cellSizeSL
DBXr = rasterTransfo["DBXr"] * cellSizeSL
DBYl = rasterTransfo["DBYl"] * cellSizeSL
DBYr = rasterTransfo["DBYr"] * cellSizeSL
# add indication for run out area based on beta point but fetch for x, y coordinates
indStartOfRunout = rasterTransfo["indStartOfRunout"]
betaPointX = rasterTransfo["gridx"][indStartOfRunout]
betaPointY = rasterTransfo["gridy"][indStartOfRunout]
# add indication for avalanche front based on s,l but fetch for x, y coordinates
cLower = mtiInfo["cLower"]
if np.isnan(cLower):
log.debug("No avalanche front found for this time step")
else:
frontLineX = rasterTransfo["gridx"][cLower]
frontLineY = rasterTransfo["gridy"][cLower]
ax1.plot(frontLineX, frontLineY, "k:", linewidth=2, zorder=5, label="avalanche front")
# add lines to plot about path following domain
ax1.plot(xPath, yPath, "k--", zorder=5, label="thalweg")
ax1.plot(DBXl, DBYl, "k-", label="s,l domain")
ax1.plot(DBXr, DBYr, "k-")
ax1.plot([DBXl, DBXr], [DBYl, DBYr], "k-")
ax1.plot(betaPointX, betaPointY, "b--", zorder=5, label="beta Point")
ax1.legend(loc="upper right")
# label x, y axes
if cfgRangeTime["ANIMATE"].getboolean("xyEastNorth"):
ax1.set_xlabel("East x [m]")
ax1.set_ylabel("North y [m]")
else:
ax1.set_xlabel("x [m]")
ax1.set_ylabel("y [m]")
# optional add title to panel
if cfgRangeTime["ANIMATE"].getboolean("panelTitles"):
ax1.set_title("simulation x,y extent at t=%.2fs" % timeStep)
# add avaName
pU.putAvaNameOnPlot(ax1, cfgRangeTime["GENERAL"]["avalancheDir"], date=False)
# +++++++++++++++PANEL 2#############
# result field bigger than threshold in s,l with ava front and runout area start
ax2 = fig.add_subplot(132)
# fetch avalanche front info
cLower = mtiInfo["cLower"]
slRaster = mtiInfo["slRaster"]
rasterTransfo = mtiInfo["rasterTransfo"]
rangeRaster = mtiInfo["rangeRaster"]
sType = mtiInfo["sType"]
# fetch s. l coordinates
l = rasterTransfo["l"]
indStartOfRunout = rasterTransfo["indStartOfRunout"]
# determine if sParallel or sProjected has been used
if sType.lower() == "parallel":
s = rasterTransfo["sParallel"] - rasterTransfo["sParallel"][indStartOfRunout]
sLabel = "Parallel distance to beta point [m]"
elif sType.lower() == "projected":
s = rasterTransfo["s"] - rasterTransfo["s"][indStartOfRunout]
sLabel = "Projected distance to beta point [m]"
# create figure
# add line indicating location of start of runout area and avalanche front
ax2.axhline(
y=s[indStartOfRunout],
color="b",
linestyle="--",
label="beta point (%.1f°)" % mtiInfo["betaPointAngle"],
)
# only plot front if a front found - e.g. if FV is zero in first time step no front found
if np.isnan(cLower):
log.debug("No avalanche front found for this time step")
else:
ax2.axhline(
y=s[cLower],
color="k",
linewidth=2.5,
linestyle=(0, (0.1, 2)),
dash_capstyle="round",
label="avalanche front",
)
# add result field masked with threshold
bmaskRes = slRaster < cfgRangeTime["GENERAL"].getfloat("thresholdResult")
maskResType = np.ma.array(slRaster, mask=bmaskRes)
# plot masked result type, add norm res to scale to colorbar
cmapRes.set_bad(alpha=0.0)
ref2, im3 = pU.NonUnifIm(
ax2,
l,
s,
maskResType,
"l [m]",
sLabel,
extent=[l.min(), l.max(), s.min(), s.max()],
cmap=cmapRes,
norm=normRes,
origin="lower",
)
# add elevation contours, first get z coordinates of s,l points
zPoints = {"x": rasterTransfo["gridx"], "y": rasterTransfo["gridy"]}
zPoints, _ = gT.projectOnRaster(demData, zPoints)
ls, CS = pU.addHillShadeContours(
ax2,
zPoints["z"],
rasterTransfo["cellSizeSL"],
[l.min(), l.max(), s.min(), s.max()],
colors=["gray"],
onlyContours=True,
)
# invert y axis
ax2.invert_yaxis()
# add legend and colorbar
ax2.legend(loc="lower right", facecolor="white", framealpha=0.2)
# optionally add title
if cfgRangeTime["ANIMATE"].getboolean("panelTitles"):
ax2.set_title("s,l domain extent")
# +++++++++++++++PANEL 3++++++++++++++++++
# tt-diagram of result field bigger than threshold
ax3 = fig.add_subplot(133)
# fetch required input info
mti = mtiInfo["mti"]
rangeGates = mtiInfo["rangeGates"]
timeList = mtiInfo["timeList"]
rangeList = mtiInfo["rangeList"]
# in case time steps are not ordered - the colormesh x and y need to be ordered
timeIndex = np.argsort(np.array(timeList))
timeListNew = np.array(timeList)[timeIndex]
mti = mti[:, timeIndex]
# create fig
pc = ax3.pcolormesh(timeListNew, rangeGates, mti, vmin=resMin, vmax=resMax, cmap=pU.cmapRangeTime)
ax3.plot(timeList, rangeList, ".", color="black", markersize=4, label="avalanche front")
ax3.set_xlabel("Time [s]")
sTypeCapital = sType[0].upper() + sType[1:]
ax3.set_ylabel("%s distance to %s [m]" % (sTypeCapital, mtiInfo["referencePointName"]))
# add colorbar and infobox
cName = "%s [%s]" % (
cfgRangeTime["GENERAL"]["maxOrMean"] + " " + pU.cfgPlotUtils["name" + resType],
pU.cfgPlotUtils["unit" + resType],
)
pU.addColorBar(pc, ax3, None, None, title=cName)
width = cfgRangeTime["PLOTS"].getfloat("width")
height = cfgRangeTime["PLOTS"].getfloat("height")
lw = cfgRangeTime["PLOTS"].getfloat("lw")
textsize = cfgRangeTime["PLOTS"].getfloat("textsize")
xMin = cfgRangeTime["ANIMATE"].getfloat("xMin")
xMax = cfgRangeTime["ANIMATE"].getfloat("xMax")
yMax = cfgRangeTime["ANIMATE"].getfloat("yMax")
yMin = cfgRangeTime["ANIMATE"].getfloat("yMin")
xinterval = xMax - xMin
yinterval = yMax - yMin
kPlot = yinterval / xinterval
# fetch points for drawing velocity legend
point = getVelocityPoints(1, 1, width, height, 1, 1)
# add title and boundary box and diagonal lines using point
lVmax = addTitleBox(ax3, width, height, lw, point, textsize, maxVel=False)
# compute velocity values
dataPoint = getVelocityPoints(xMax, yMax, width, height, xinterval, yinterval)
# add velocity legend labels
_ = addVelocityValues(ax3, dataPoint, point)
# set limits for plot (depends on final time step)
ax3.set_ylim([yMin, yMax])
ax3.set_xlim([xMin, xMax])
# add info on avalanche front in legend
plt.legend(facecolor="grey", framealpha=0.2, loc="lower right", fontsize=8)
# if tt-diagram add beta point info
# invert y axis as ava flow starts from minus distance to beta point
ax3.invert_yaxis()
ax3.axhline(y=0.0, color="b", linestyle="--", label="beta point: %.1f°" % mtiInfo["betaPointAngle"])
# optional - add title to panel
if cfgRangeTime["ANIMATE"].getboolean("panelTitles"):
ax3.set_title("tt-diagram")
# # set path for saving figure
outDir = pathlib.Path(cfgRangeTime["GENERAL"]["avalancheDir"], "Outputs", "ana5Utils")
outFileName = (
mtiInfo["type"] + "_" + resType + "_" + cfgRangeTime["GENERAL"]["simHash"] + "_%08.3f" % timeStep
)
plotPath = pU.saveAndOrPlot({"pathResult": outDir}, outFileName, fig)