avaframe/out3Plot/outQuickPlot.py
"""
Functions to plot 2D avalanche simulation results as well as comparison plots
between two datasets of identical shape.
"""
import matplotlib.pyplot as plt
import numpy as np
import numpy.ma as ma
import pathlib
import logging
from cmcrameri import cm as cmapCrameri
import matplotlib as mpl
# Local imports
import avaframe.in2Trans.ascUtils as IOf
import avaframe.in3Utils.geoTrans as geoTrans
import avaframe.out3Plot.plotUtils as pU
from avaframe.in3Utils import fileHandlerUtils as fU
from avaframe.out3Plot import statsPlots as sPlot
import avaframe.out3Plot.plotUtils as pU
# create local logger
# change log level in calling module to DEBUG to see log messages
log = logging.getLogger(__name__)
def generatePlot(dataDict, avaName, outDir, cfg, plotDict, crossProfile=True):
""" Create comparison plots of two ascii datasets
This function creates two plots, one plot with four panels with, first dataset, second dataset,
the absolute difference of the two datasets and the absolute difference capped to a smaller range
of differences (ppr: +- 100kPa, pft: +-1m, pfv:+- 10ms-1).
The difference plots also include an insert showing the histogram and the cumulative density function
of the differences. The second plot shows a cross- and along profile cut of the two datasets.
The folder and simulation name of the datasets has to be passed to the function.
The origin of the plots is set to 0,0 referring to the lower left center coordinate of the datasets.
Parameters
----------
dataDict : dict
dictionary with info on both datasets to be plotted:
name1: str
string with the name of the first data set
name2: str
string with the name of the second data set
data1: 2D numpy arrays
raster of the first data sets
data2: 2D numpy arrays
raster of the first second sets
cellSize: float
cell size
suffix: str
optional information about the data type compared ('ppr', 'pft', 'pfv', 'P', 'FV', 'FT', 'Vx'...)
avaName : str
name of avalanche
outDir : pathlib path
path to dictionary where plots shall be saved to
cfg : configParser
cfg['FLAGS'].getboolean('showPlot')
plotDict : dict
dictionary with information about plots, for example release area
Returns
-------
plotDict : dict
updated plot dictionary with info about e.g. min, mean, max of difference between datasets
"""
# Extract info for plotting
data1 = dataDict['data1']
data2 = dataDict['data2']
name1 = dataDict['name1']
name2 = dataDict['name2']
cellSize = dataDict['cellSize']
if 'suffix' in dataDict:
simName = dataDict['simName'] + '_' + dataDict['suffix']
unit = pU.cfgPlotUtils['unit' + dataDict['suffix']]
cmapType = pU.colorMaps[dataDict['suffix']]
else:
simName = 'compare'
unit = ''
cmapType = pU.cmapNN
# Set dimensions of plots
ny = data2.shape[0]
nx = data2.shape[1]
Ly = ny*cellSize
Lx = nx*cellSize
# Location of Profiles
ny_loc = int(nx * 0.5)
nx_loc = int(ny * 0.5)
# Difference between datasets
dataDiff = data1 - data2
dataDiff = np.where((data1 == 0) & (data2 == 0), np.nan, dataDiff)
dataExtend = np.where((data1 == 0) | (data2 == 0), 0, 1)
diffMax = np.nanmax(dataDiff)
diffMin = np.nanmin(dataDiff)
diffMean = np.nanmean(dataDiff)
minVal = min(np.nanmin(data1), np.nanmin(data2))
maxVal = max(np.nanmax(data1), np.nanmax(data2))
# constrain data to where there is data
rowsMinPlot, rowsMaxPlot, colsMinPlot, colsMaxPlot = pU.constrainPlotsToData(dataExtend, cellSize,
extentOption=True)
# fetch extent of cellcenters or corners for imshow plot of raster data
extentCellCenters, extentCellCorners = pU.createExtentMinMax(dataExtend, {'cellsize':cellSize, 'nrows': ny, 'ncols': nx}, originLLCenter=False)
# Location of box
nybox = 0.05
nxbox = 0.05
# Plot data
# Figure 1 shows the result parameter data
fig = plt.figure(figsize=(pU.figW*3, pU.figH*2))
suptitle = fig.suptitle(avaName, fontsize=14, color='0.5')
ax1 = fig.add_subplot(221)
cmap, _, ticks, norm = pU.makeColorMap(cmapType, minVal, maxVal, continuous=pU.contCmap)
cmap.set_bad('w')
data1P = ma.masked_where(data1 == 0.0, data1)
im1 = plt.imshow(data1P, cmap=cmap, extent=extentCellCorners, origin='lower',
aspect=nx/ny, norm=norm)
ax1.set_xlim([colsMinPlot, colsMaxPlot])
ax1.set_ylim([rowsMinPlot, rowsMaxPlot])
pU.addColorBar(im1, ax1, ticks, unit)
ax1.set_aspect('auto')
title = str('%s - simulation' % name1)
ax1.set_title(title)
ax1.set_xlabel('x [m]')
ax1.set_ylabel('y [m]')
ax2 = fig.add_subplot(222)
cmap, _, ticks, norm = pU.makeColorMap(cmapType, minVal, maxVal, continuous=pU.contCmap)
cmap.set_bad('w')
data2P = ma.masked_where(data2 == 0.0, data2)
im2 = plt.imshow(data2P, cmap=cmap, extent=extentCellCorners, origin='lower',
aspect=nx/ny, norm=norm)
pU.addColorBar(im2, ax2, ticks, unit)
ax2.set_xlim([colsMinPlot, colsMaxPlot])
ax2.set_ylim([rowsMinPlot, rowsMaxPlot])
ax2.set_aspect('auto')
ax2.set_xlabel('x [m]')
title = str('%s - reference' % name2)
ax2.set_title(title)
ax3 = fig.add_subplot(223)
cmap = pU.cmapdiv
elevMax = np.nanmax(np.abs(dataDiff))
im3 = plt.imshow(dataDiff, cmap=cmap, clim=(-elevMax, elevMax),
extent=extentCellCorners,
origin='lower', aspect=nx/ny)
ax3.set_xlim([colsMinPlot, colsMaxPlot])
ax3.set_ylim([rowsMinPlot, rowsMaxPlot])
pU.addColorBar(im3, ax3, None, unit)
ax3.text(nybox, nxbox, 'Mean: %.2e %s\n Max: %.2e %s\n Min: %.2e %s' %
(diffMean, unit, diffMax, unit, diffMin, unit),
horizontalalignment='left', verticalalignment='bottom', transform=ax3.transAxes)
ax3.set_aspect('auto')
ax3.set_xlabel('x [m]')
ax3.set_title('Difference simulation-reference')
# for difference histogramm - remove dataDiff == 0 values from array
dataDiffPlot = dataDiff[np.isnan(dataDiff) == False]
ax4 = fig.add_subplot(224)
cmap = pU.cmapdiv
if 'suffix' in dataDict:
elevMax = pU.cfgPlotUtils.getfloat('elevMax' + dataDict['suffix'])
ax4.set_title('Difference capped at max difference in %s: +-%.2g %s' % (dataDict['suffix'], elevMax, unit))
else:
cutVal = 0.5
elevMax = cutVal * elevMax
ax4.set_title('Difference capped at %.1f times max difference: +-%.2f' % (cutVal, elevMax))
# for difference histogramm - remove dataDiff == 0 values from array
dataDiffZoom = np.where((dataDiffPlot < -elevMax) | (dataDiffPlot > elevMax), np.nan, dataDiffPlot)
diffMaxZoom = np.nanmax(dataDiffZoom)
diffMinZoom = np.nanmin(dataDiffZoom)
diffMeanZoom = np.nanmean(dataDiffZoom)
im4 = plt.imshow(dataDiff, cmap=cmap, clim=(-elevMax, elevMax),
extent=extentCellCorners,
origin='lower', aspect=nx/ny)
ax4.set_xlim([colsMinPlot, colsMaxPlot])
ax4.set_ylim([rowsMinPlot, rowsMaxPlot])
pU.addColorBar(im4, ax4, None, unit, extend='both')
ax4.set_aspect('auto')
ax4.set_xlabel('x [m]')
# if difference is zero dont insert CDF plots
indDiff = dataDiffPlot > 0
if indDiff.any():
axin3 = ax3.inset_axes([0.6, 0.1, 0.4, 0.25])
axin3.patch.set_alpha(0.0)
axin4 = ax4.inset_axes([0.6, 0.1, 0.4, 0.25])
axin4.patch.set_alpha(0.0)
centiles = sPlot.plotHistCDFDiff(dataDiffPlot, axin4, axin3)
ax4.text(nybox, nxbox, '95%% centile: %.2e %s\n 99%% centile: %.2e %s' %
(centiles[0], unit, centiles[1], unit),
horizontalalignment='left', verticalalignment='bottom', transform=ax4.transAxes)
saveNameDiff = outDir / ('Diff_%s_%s.%s' % (avaName, simName, pU.outputFormat))
# save and or show figure
plotPath = pU.saveAndOrPlot({'pathResult': outDir}, saveNameDiff.stem, fig)
if crossProfile:
# Figure 2 cross and longprofile
fig1, ax = plt.subplots(ncols=2, nrows=2, figsize=(pU.figW*2, pU.figH*2))
suptitle = fig1.suptitle(avaName, fontsize=14, color='0.5')
ax[0, 0].plot(data1[:, ny_loc], 'k', label='Simulation')
ax[0, 0].plot(data2[:, ny_loc], 'b--', label='Reference')
ax[0, 0].set_xlabel('Location across track [nrows]')
ax[0, 0].set_ylabel('Result parameter')
ax[0, 0].set_title('Cross profile at x = %d' % ny_loc)
ax[0, 1].plot(data1[nx_loc, :], 'k', label='Simulation')
ax[0, 1].plot(data2[nx_loc, :], 'b--', label='Reference')
ax[0, 1].set_xlabel('Location along track [ncols]')
ax[0, 1].set_ylabel('Result parameter')
ax[0, 1].set_title('Long profile at y = %d' % nx_loc)
ax[0, 0].legend()
ax[0, 1].legend()
ax[1, 0].imshow(data1P, cmap=cmap, extent=[0, nx, 0, ny], origin='lower', aspect=nx/ny)
ax[1, 1].imshow(data2P, cmap=cmap, extent=[0, nx, 0, ny], origin='lower', aspect=nx/ny)
ax[1, 1].axhline(ny - nx_loc, label='profile y=%d' % nx_loc)
ax[1, 0].axvline(ny_loc, label='profile x=%d' % ny_loc)
ax[1, 0].legend()
ax[1, 1].legend()
saveNameProfile = outDir / ('Profiles_%s_%s.%s' % (avaName, simName, pU.outputFormat))
# save and or show figure
plotPath = pU.saveAndOrPlot({'pathResult': outDir}, saveNameProfile.stem, fig1)
log.info('Figures saved to: %s' % outDir)
if cfg['FLAGS'].getboolean('showPlot'):
plt.show()
plotDict['plots'].append(saveNameDiff)
plotDict['difference'].append(diffMax)
plotDict['difference'].append(diffMean)
plotDict['difference'].append(diffMin)
# stats is the max and min value of the reference
plotDict['stats'].append(np.amax(data2))
plotDict['stats'].append(np.amin(data2))
if 'differenceZoom' in plotDict:
plotDict['differenceZoom'].append(diffMaxZoom)
plotDict['differenceZoom'].append(diffMeanZoom)
plotDict['differenceZoom'].append(diffMinZoom)
return plotDict
def quickPlotBench(avaDir, simNameRef, simNameComp, refDir, compDir, cfg, suffix):
""" Plot simulation result and compare to reference solution
(two raster datasets of identical dimension) and save to
Outputs/out3Plot within avalanche directoy.
figure 1, plot raster data for dataset1, dataset2 and their difference, their difference limited to
specified range, including a histogram and the cumulative density function of the differences
figure 2, plot cross and longprofiles for both datasets (ny_loc and nx_loc define location of profiles)
plots are saved to Outputs/out3Plot
Parameters
----------
avaDir : str or pathlib path
path to avalanche directory
simNameRef: str
name of reference simulation
simNameComp: str
name of comparison simulation
refDir: str or pathlib path
path to reference file
compDir: str or pathlib path
path to comparison file
cfg : dict
global configuration settings
suffix: str
result type
Returns
-------
plotList : dict
plot dictionaries (path to plots, min, mean and max difference
between plotted datasets, max and mean value of reference dataset )
"""
# Create required directories
avaDir = fU.checkPathlib(avaDir)
outDir = avaDir / 'Outputs' / 'out3Plot'
fU.makeADir(outDir)
# Initialise plotDictList
plotList = []
# Initialise plotList
plotDict = {'plots': [], 'difference': [], 'stats': []}
refDir = fU.checkPathlib(refDir)
compDir = fU.checkPathlib(compDir)
simRefFile = refDir / (simNameRef + '_' + suffix + '.asc')
simCompFile = compDir / (simNameComp + '_' + suffix + '.asc')
if not simRefFile.is_file() or not simCompFile.is_file():
log.error('File for result type: %s not found' % suffix)
# Load data
raster = IOf.readRaster(simCompFile, noDataToNan=True)
rasterRef = IOf.readRaster(simRefFile, noDataToNan=True)
dataComp, dataRef = geoTrans.resizeData(raster, rasterRef)
log.debug('dataset1: %s' % simCompFile)
log.debug('dataset2: %s' % simRefFile)
cellSize = rasterRef['header']['cellsize']
unit = pU.cfgPlotUtils['unit%s' % suffix]
# Get name of Avalanche
avaName = avaDir.stem
# Create dataDict to be passed to generatePlot
dataDict = {'data1': dataComp, 'data2': dataRef, 'name1': simNameComp + '_' + suffix,
'name2': simNameRef + '_' + suffix, 'compareType': 'compToRef',
'simName': simNameComp, 'suffix': suffix, 'cellSize': cellSize, 'unit': unit}
# Create Plots
plotDictNew = generatePlot(dataDict, avaName, outDir, cfg, plotDict)
return plotDictNew
def quickPlotSimple(avaDir, inputDir, cfg):
""" Plot two raster datasets of identical dimension and difference between two datasets
figure 1: plot raster data for dataset1, dataset2 and their difference
figure 2: plot cross and longprofiles for both datasets (ny_loc and nx_loc define location of profiles)
-plots are saved to Outputs/out3Plot
Be aware: files are being sorted after getting them from the directory!
(Important for the differences)
Parameters
----------
avaDir : str or pathlib path
path to avalanche directory
inputDir : str or pathlib path
path to directory of input data (only 2 raster files allowed)
cfg: configParser object
global configuration settings
"""
avaDir = fU.checkPathlib(avaDir)
outDir = avaDir / 'Outputs' / 'out3Plot'
fU.makeADir(outDir)
# Get name of Avalanche
avaName = avaDir.stem
# Load input datasets from input directory
inputDir = fU.checkPathlib(inputDir)
datafiles = sorted(list(inputDir.glob('*.asc')))
datafiles.extend(list(inputDir.glob('*.txt')))
name1 = datafiles[0].name
name2 = datafiles[1].name
log.info('input dataset #1 is %s' % name1)
log.info('input dataset #2 is %s' % name2)
# Load data
raster = IOf.readRaster(datafiles[0], noDataToNan=True)
rasterRef = IOf.readRaster(datafiles[1], noDataToNan=True)
data1, data2 = geoTrans.resizeData(raster, rasterRef)
header = IOf.readASCheader(datafiles[0])
cellSize = header['cellsize']
# Create dataDict to be passed to generatePlot
dataDict = {'data1': data1, 'data2': data2, 'name1': name1,
'name2': name2, 'compareType': '', 'cellSize': cellSize}
# Initialise plotList
plotDict = {'plots': [], 'difference': [], 'stats': []}
# Create Plots
plotDictNew = generatePlot(dataDict, avaName, outDir, cfg, plotDict)
return plotDictNew
def quickPlotOne(avaDir, datafile, cfg, locVal, axis, resType=''):
""" Plots one raster dataset and a cross profile
figure 1: plot raster data for dataset and profile
-plot is saved to Outputs/out3Plot
Parameters
----------
avaDir : str or pathlib Path
path to avalanche directory
datafile : str or pathlib path
path to data file
cfg : dict
configuration including flags for plotting
locVal : float
location of cross profile
resType : str
result parameter type e.g. 'pft' - optional
"""
avaDir = fU.checkPathlib(avaDir)
outDir = avaDir / 'Outputs' / 'out3Plot'
fU.makeADir(outDir)
datafile = fU.checkPathlib(datafile)
name1 = datafile.stem
log.info('input dataset #1 is %s' % name1)
# Load data
raster = IOf.readRaster(datafile, noDataToNan=True)
data1 = raster['rasterData']
header = IOf.readASCheader(datafile)
cellSize = header['cellsize']
# Create dataDict to be passed to generatePlot
dataDict = {'data1': data1, 'name1': name1,
'cellSize': cellSize}
# Initialise plotList
plotDict = {'plots': [], 'location': locVal, 'resType': resType, 'axis': axis}
# Create Plots
plotList = generateOnePlot(dataDict, outDir, cfg, plotDict)
return plotDict
def generateOnePlot(dataDict, outDir, cfg, plotDict):
""" Create plot of ascii dataset
Parameters
----------
dataDict : dict
dictionary with info of the dataset to be plotted
outDir : pathlib path
path to dictionary where plots shall be saved to
cfg : dict
main configuration settings
plotDict : dict
dictionary with information about plots, for example release area...
Returns
-------
plotDict : dict
updated plot dictionary with path to plot
"""
# Extract info for plotting
data1 = dataDict['data1']
name1 = dataDict['name1']
cellSize = dataDict['cellSize']
simName = 'Analyse'
if plotDict['resType'] != '':
unit = pU.cfgPlotUtils['unit%s' % plotDict['resType']]
nameRes = pU.cfgPlotUtils['name%s' % plotDict['resType']]
cmapType = pU.colorMaps[plotDict['resType']]
else:
unit = ''
nameRes = 'Result parameter'
cmapType = pU.cmapNN
# Set dimensions of plots
ny = data1.shape[0]
nx = data1.shape[1]
Ly = ny*cellSize
Lx = nx*cellSize
axis = plotDict['axis']
# fetch extent of cellcenters or corners for imshow plot of raster data
extentCellCenters, extentCellCorners = pU.createExtentMinMax(data1, {'cellsize':cellSize, 'nrows': ny, 'ncols': nx}, originLLCenter=False)
# Location of Profiles
location = float(plotDict['location'])
if axis == 'x':
nx_loc = int(location / cellSize)
elif axis == 'y':
ny_loc = int(location / cellSize)
else:
log.error('Not an axis, please provide axis of profile')
# Plot data
# Figure 1 shows the result parameter data
fig = plt.figure(figsize=(pU.figW*2, pU.figH))
suptitle = fig.suptitle(name1, fontsize=14, color='0.5')
ax1 = fig.add_subplot(121)
cmap, _, ticks, norm = pU.makeColorMap(cmapType, np.nanmin(data1), np.nanmax(data1), continuous=pU.contCmap)
cmap.set_bad('w')
data1P = ma.masked_where(data1 == 0.0, data1)
im1 = plt.imshow(data1P, cmap=cmap, extent=extentCellCorners, origin='lower', aspect=nx/ny, norm=norm)
pU.addColorBar(im1, ax1, ticks, unit)
ax1.set_aspect('auto')
title = str('%s' % name1)
ax1.set_title(title)
ax1.set_xlabel('x [m]')
ax1.set_ylabel('y [m]')
ax3 = fig.add_subplot(122)
if axis == 'x':
ax3.plot(data1[:, nx_loc], 'k', label='Reference')
else:
ax3.plot(data1[ny_loc, :], 'k', label='Reference')
ax3.set_xlabel('Location across track [nrows]')
ax3.set_ylabel('%s [%s]' % (nameRes, unit))
if axis == 'x':
ax3.set_title('Profile at x ~ %d [%s] (%d)' % (location, unit, nx_loc))
else:
ax3.set_title('Profile at y ~ %d [%s] (%d)' % (location, unit, ny_loc))
saveNameProfile = outDir / ('Profiles_%s.%s' % (name1, pU.outputFormat))
# save and or show figure
plotPath = pU.saveAndOrPlot({'pathResult': outDir}, saveNameProfile.stem, fig)
log.info('Figures saved to: %s' % outDir)
plotDict['plots'].append(saveNameProfile)
return plotDict
def plotContours(contourDict, resType, thresholdValue, pathDict, addLegend=True):
""" plot contour lines of all transformed fields
Parameters
-----------
contourDict: dict
dictionary with contourline coordinates
resType: str
result type
thresholdValue: float
value for contour level
pathDict: dict
dictionary with info on project name, ...
addLegend: bool
if True add legend to plot
"""
unit = pU.cfgPlotUtils['unit' + resType]
fig = plt.figure(figsize=(pU.figW*2, pU.figH))
# show flow path
ax1 = fig.add_subplot(121)
ax1.set_title('%s %s %s contour lines' % (resType, thresholdValue, unit))
ax1.set_ylabel('x [m]')
ax1.set_xlabel('y [m]')
pU.putAvaNameOnPlot(ax1, pathDict['avaDir'])
# create color map
nSims = len(contourDict)
norm = mpl.colors.Normalize(vmin=0, vmax=nSims)
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=cmapCrameri.hawaii)
cmap.set_array([])
# loop over all sims
for ind, simName in enumerate(contourDict):
keyLabel = list(contourDict[simName])[0]
for key in contourDict[simName]:
if key == keyLabel and 'line' in key:
ax1.plot(contourDict[simName][key]['x'], contourDict[simName][key]['y'],
c=cmap.to_rgba(ind), label=simName)
elif 'line' in key:
ax1.plot(contourDict[simName][key]['x'], contourDict[simName][key]['y'],
c=cmap.to_rgba(ind))
if addLegend:
ax1.legend()
ax1.axis('equal')
# save and or plot
outFileName = pathDict['plotScenario'] + '_ContourLines'
pU.saveAndOrPlot(pathDict, outFileName, fig)
def plotAllContours(avaDir, modName, resType, level, specDir=''):
""" Create a plot of resType level contour lines for all resType files found in
avaDir/Outputs/modName/peakFiles or specDir optional
file format needs to be of type _resType.asc
Parameters
------------
avaDir: str or pathlib path
path to avalanche directory
modName: str
name of computational module
resType: str
result variable type
level: float
contour line level
specDir: str or pathlib path
path to peak files folder - optional
"""
# directory with peak files
if specDir == '':
inDir = pathlib.Path(avaDir, 'Outputs', modName, 'peakFiles')
elif pathlib.Path(specDir).is_dir():
inDir = pathlib.Path(specDir)
else:
message = 'specdir: %s is not a directory' % str(specDir)
log.error(message)
raise AssertionError(message)
# fetch all files for resType (file format needs to be of type _resType.asc)
pFiles = list(inDir.glob('*_%s.asc' % resType))
# loop over all pFiles and create contourLines dictionary
contourDict = {}
for pf in pFiles:
# load data
rasterF = IOf.readRaster(pf)
rasterData = rasterF["rasterData"]
# load info on extent to create coordinate mesh
x = rasterF['header']['xllcenter']
y = rasterF['header']['yllcenter']
nrows = rasterF['header']['nrows']
ncols = rasterF['header']['ncols']
cellSize = rasterF['header']['cellsize']
x1 = np.linspace(x, x + (ncols-1) * cellSize, ncols)
y1 = np.linspace(y, y + (nrows-1) * cellSize, nrows)
xm, ym = np.meshgrid(x1, y1)
# fetch contour line coords for data and level
contourDictXY = pU.fetchContourCoords(xm, ym, rasterData, level)
contourDict[pf.stem.split('_')[1]] = contourDictXY
# save and or plot figure
outDir = pathlib.Path(avaDir, 'Outputs', 'out3Plot')
pathDict = {'pathResult': outDir, 'plotScenario': pathlib.Path(avaDir).stem,
'avaDir': avaDir}
plotContours(contourDict, resType, level, pathDict, addLegend=True)