"""
Functions to plot 2D simulation results: plot of all peak files at once
"""
import logging
import numpy as np
import matplotlib
matplotlib.use("agg")
from matplotlib import pyplot as plt
import pathlib
import avaframe.out3Plot.plotUtils as pU
import avaframe.in1Data.getInput as gI
from avaframe.in3Utils import fileHandlerUtils as fU
import avaframe.in2Trans.ascUtils as IOf
# create local logger
log = logging.getLogger(__name__)
[docs]def plotAllPeakFields(avaDir, cfgFLAGS, modName, demData=""):
"""Plot all peak fields and return dictionary with paths to plots
with DEM in background
Parameters
----------
avaDir : str
path to avalanche directoy
cfgFLAGS : str
general configuration, required to define if plots saved to reports directoy
modName : str
name of module that has been used to produce data to be plotted
demData: dictionary
optional - if not the dem in the avaDir/Inputs folder has been used but a different one
Returns
-------
plotDict : dict
dictionary with info on plots, like path to plot
"""
# Load all infos on simulations
avaDir = pathlib.Path(avaDir)
inputDir = avaDir / "Outputs" / modName / "peakFiles"
peakFilesDF = fU.makeSimDF(inputDir, avaDir=avaDir)
if demData == "":
demFile = gI.getDEMPath(avaDir)
demData = IOf.readRaster(demFile, noDataToNan=True)
demDataField = demData["rasterData"]
else:
# check if nodata_value is found and if replace with nans for plotting
demDataField = np.where(
demData["rasterData"] == demData["header"]["nodata_value"], np.nan, demData["rasterData"]
)
demField = demDataField
# Output directory
if cfgFLAGS.getboolean("ReportDir"):
outDir = avaDir / "Outputs" / modName / "reports"
fU.makeADir(outDir)
else:
outDir = avaDir / "Outputs" / "out1Peak"
fU.makeADir(outDir)
# Initialise plot dictionary with simulation names
plotDict = {}
for sName in peakFilesDF["simName"]:
plotDict[sName] = {}
# Loop through peakFiles and generate plot
for m in range(len(peakFilesDF["names"])):
# Load names and paths of peakFiles
name = peakFilesDF["names"][m]
fileName = peakFilesDF["files"][m]
avaName = peakFilesDF["avaName"][m]
resType = peakFilesDF["resType"][m]
log.debug("now plot %s:" % (fileName))
plotName = outDir / ("%s.%s" % (name, pU.outputFormat))
# only produce a plot if it does not already exists
# make sure to remove the Outputs folder if you want to regenerate the plot
# this enables to append simulations to an already existing output without regenerating all plots
if not plotName.is_file():
# Figure shows the result parameter data
fig, ax = plt.subplots(figsize=(pU.figW, pU.figH))
# fetch cellSize of peak field
cellSize = peakFilesDF["cellSize"][m]
# add peak field data now
ax, rowsMinPlot, colsMinPlot = addConstrainedDataField(fileName, resType, demField, ax, cellSize)
# add title, labels and ava Info
title = str("%s" % name)
ax.set_title(title)
ax.set_xlabel("x [m]")
ax.set_ylabel("y [m]")
pU.putAvaNameOnPlot(ax, avaDir)
# save and or show figure
plotPath = pU.saveAndOrPlot({"pathResult": outDir}, plotName.stem, fig)
plotDict[peakFilesDF["simName"][m]].update({peakFilesDF["resType"][m]: plotPath})
return plotDict
[docs]def addConstrainedDataField(fileName, resType, demField, ax, cellSize, alpha=1.0, setLimits=False, oneColor=''):
""" find fileName data, constrain data and demField to where there is data,
create colormap, define extent, add hillshade contours, add to axes
and add colorbar
Parameters
-----------
fileName: pathlib path
path to data
resType: str
name of result variable type
demField: numpy ndarray
array of dem data
ax: matplotlib axes object
axes where to add data plot to
cellSize: float
cellSize of data
alpha: float
from 0 transparent to 1 opaque for plot of constrained data
setLimits: bool
if True set limits of constrained data to plot
oneColor: str
optional to add a color for a single color for field
Return
--------
ax: matplotlib axes object
axes updated
"""
# Load data
raster = IOf.readRaster(fileName, noDataToNan=True)
data = raster["rasterData"]
# constrain data to where there is data
rowsMin, rowsMax, colsMin, colsMax = pU.constrainPlotsToData(data, cellSize)
dataConstrained = data[rowsMin:rowsMax + 1, colsMin:colsMax + 1]
demConstrained = demField[rowsMin:rowsMax + 1, colsMin:colsMax + 1]
data = np.ma.masked_where(dataConstrained == 0.0, dataConstrained)
unit = pU.cfgPlotUtils["unit%s" % resType]
# Set extent of peak file
ny = data.shape[0]
nx = data.shape[1]
Ly = ny * cellSize
Lx = nx * cellSize
# choose colormap
cmap, col, ticks, norm = pU.makeColorMap(
pU.colorMaps[resType], np.amin(data), np.amax(data), continuous=pU.contCmap)
cmap.set_bad(alpha=0)
# uncomment this to set the under value for discrete cmap transparent
# cmap.set_under(alpha=0)
# add origin of data to extent and cellssize to rows and columns
xllcenter = raster["header"]["xllcenter"]
yllcenter = raster["header"]["yllcenter"]
rowsMinPlot = rowsMin * cellSize + yllcenter
rowsMaxPlot = (rowsMax + 1) * cellSize + yllcenter
colsMinPlot = colsMin * cellSize + xllcenter
colsMaxPlot = (colsMax + 1) * cellSize + xllcenter
extent = [colsMinPlot, colsMaxPlot, rowsMinPlot, rowsMaxPlot]
# add DEM hillshade with contour lines
ls, CS = pU.addHillShadeContours(ax, demConstrained, cellSize, extent)
# add peak field data
if oneColor != '':
dataOneColor = np.where(data > 0.0, np.amax(data)*0.25, np.nan)
im1 = ax.imshow(dataOneColor, cmap=oneColor, norm=norm, extent=extent, origin="lower", aspect="equal", zorder=4,
alpha=alpha)
else:
im1 = ax.imshow(data, cmap=cmap, norm=norm, extent=extent, origin="lower", aspect="equal", zorder=4,
alpha=alpha)
if len(np.nonzero(data)[0]) > 0.:
pU.addColorBar(im1, ax, ticks, unit)
if setLimits:
ax.set_xlim([colsMinPlot, colsMaxPlot])
ax.set_ylim([rowsMinPlot, rowsMaxPlot])
return ax, rowsMinPlot, colsMinPlot
[docs]def plotAllFields(avaDir, inputDir, outDir, unit="", constrainData=True):
"""Plot all fields within given directory and save to outDir
Parameters
----------
avaDir : str
path to avalanche directoy
inputDir : str
path to input directoy
outDir : str
path to directoy where plots shall be saved to
unit: str
unit of result type
"""
# Load all infos on simulations
inputDir = pathlib.Path(inputDir)
outDir = pathlib.Path(outDir)
if outDir.is_dir() is False:
# create out dir if not already existing
outDir.mkdir()
peakFiles = list(inputDir.glob("*.asc"))
# Loop through peakFiles and generate plot
for filename in peakFiles:
# Load data
raster = IOf.readRaster(filename)
data = raster["rasterData"]
data = np.ma.masked_where(data == 0.0, data)
name = filename.stem
# get header info for file writing
header = raster["header"]
cellSize = header["cellsize"]
# Set extent of peak file
ny = data.shape[0]
nx = data.shape[1]
Ly = ny * cellSize
Lx = nx * cellSize
# Figure shows the result parameter data
fig = plt.figure(figsize=(pU.figW, pU.figH))
fig, ax = plt.subplots()
# choose colormap
cmap, _, ticks, norm = pU.makeColorMap(
pU.cmapNN, np.amin(data), np.amax(data), continuous=pU.contCmap
)
cmap.set_bad("w")
if constrainData:
rowsMin, rowsMax, colsMin, colsMax = pU.constrainPlotsToData(data, cellSize)
dataConstrained = data[rowsMin:rowsMax + 1, colsMin:colsMax + 1]
data = np.ma.masked_where(dataConstrained == 0.0, dataConstrained)
rowsMinPlot = rowsMin * cellSize
rowsMaxPlot = (rowsMax + 1) * cellSize
colsMinPlot = colsMin * cellSize
colsMaxPlot = (colsMax + 1) * cellSize
im1 = ax.imshow(
data,
cmap=cmap,
norm=norm,
extent=[colsMinPlot, colsMaxPlot, rowsMinPlot, rowsMaxPlot],
origin="lower",
aspect=nx / ny,
)
else:
im1 = ax.imshow(
data, cmap=cmap, norm=norm, extent=[0, Lx, 0, Ly], origin="lower", aspect=nx / ny
)
pU.addColorBar(im1, ax, ticks, unit)
title = str("%s" % name)
ax.set_title(title)
ax.set_xlabel("x [m]")
ax.set_ylabel("y [m]")
plotName = "%s" % (name)
pU.putAvaNameOnPlot(ax, avaDir)
# save and or show figure
plotPath = pU.saveAndOrPlot({"pathResult": outDir}, plotName, fig)