def __init__(self, RasterName, Directory):
self._RasterFileName = RasterName
self._RasterDirectory = Directory
self._FullPathRaster = self._RasterDirectory + self._RasterFileName
# I think the BaseRaster should contain a numpy array of the Raster
self._RasterArray = LSDP.ReadRasterArrayBlocks(self._FullPathRaster)
# Get the extents as a list
self._RasterExtents = LSDP.GetRasterExtent(self._FullPathRaster)
def findmaxval_multirasters(FileList):
"""
Loops through a list or array of rasters (np arrays)
and finds the maximum single value in the set of arrays.
"""
overall_max_val = 0
for i in range (len(FileList)):
raster_as_array = LSDP.ReadRasterArrayBlocks(FileList[i])
this_max_val = np.max(raster_as_array)
if this_max_val > overall_max_val:
overall_max_val = this_max_val
print(overall_max_val)
return overall_max_val
def _render_background(self, fullpath_to_raster):
"""
Renders the background image that
will form the drape plot, e.g. a hillshade
"""
if self._backgroundtype == "Hillshade":
self.Hillshade = LSDP.Hillshade(self.fullpath_to_raster)
self.colourmap = "gray"
elif self._backgroundtype == "Terrain":
self.Hillshade = LSDP.ReadRasterArrayBlocks(
self.fullpath_to_raster)
self.colourmap = LSDP.colours.UsefulColourmaps.niceterrain
#self.colourmap = LSDP.colours.UsefulColourmaps.darkearth
#self.colourmap = "terrain"
else:
print ("That background style is not yet supported. Currently only " \
" 'Hillshade' and 'Terrain' are supported.")
def TestNewMappingTools2():
DataDirectory = "T://analysis_for_papers//Manny_idaho//"
Filename = "TestIdaho.bil"
NewFilename = "TestIdaho_after2.bil"
ThreshFname = "ThreshIdaho.bil"
ConstFname = "ConstEros.bil"
ThisFile = DataDirectory + Filename
NewFilename = DataDirectory + NewFilename
ThreshFname = DataDirectory + ThreshFname
ConstFname = DataDirectory + ConstFname
#FigFormat = 'svg'
#FigFileN= 'Sorbas_chi.svg'
#FigFileName= DataDirectory+FigFileN
#Plot the basin over the elevation
#tcmapcolorbarlabel = "Elevation (m)"
#tcmap = 'jet'
#tcmapcolorbarlabel='Chi'
#clim_val = (50,300)
#LSDP.BasicDensityPlotGridPlot(ThisFile,tcmap,tcmapcolorbarlabel,clim_val)
#get the nodata values
NData = LSDP.getNoDataValue(ThisFile)
print "NoData is: " + str(NData)
# get the data as an array
array = LSDP.ReadRasterArrayBlocks(ThisFile)
driver_name = "ENVI"
LSDP.array2raster(ThisFile, NewFilename, array, driver_name, -9999)
#get the nodata values
NData2 = LSDP.getNoDataValue(NewFilename)
#print "NoData 2 is: " + str(NData2)
LSDP.SetNoDataBelowThreshold(ThisFile, ThreshFname)
# now print the constant value file
constant_value = 0.001
LSDP.SetToConstantValue(ThreshFname, ConstFname, constant_value)
def __init__(self, RasterName, Directory):
self._RasterFileName = RasterName
self._RasterDirectory = Directory
self._FullPathRaster = self._RasterDirectory + self._RasterFileName
# I think the BaseRaster should contain a numpy array of the Raster
self._RasterArray = LSDP.ReadRasterArrayBlocks(self._FullPathRaster)
# Get the extents as a list
self._RasterExtents = LSDP.GetRasterExtent(self._FullPathRaster)
self._RasterAspectRatio = (
self._RasterExtents[1] - self._RasterExtents[0]) / (
self._RasterExtents[3] - self._RasterExtents[2])
# set the default colourmap
self._colourmap = "gray"
# get the EPSG string
self._EPSGString = LSDP.LSDMap_IO.GetUTMEPSG(self._FullPathRaster)
def MultiDrapeMaps(DataDir, ElevationRaster, DrapeRasterWild, cmap, drape_min_threshold=None, drape_max=None):
"""
Plots flood extents from water depth rasters
draped over the catchment elevation raster
in a series of subplots
Takes a wildcard for the drapes
Expexts a fixed elevation raster, but this could be
modified in future.
Thought: consider, if plotting multiple datasets, how you
are going to deal with min a max values in the colur range.
imshow will automatically set vmin and vmax and stretch the colour bar
over this - which can be visually misleading. Ideally, you
want to have the same colour map used for *all* subplots, and
this is not default behaviour.
"""
f, ax_arr = plt.subplots(2, 2, figsize=(10, 5), sharex=True, sharey=True)
ax_arr = ax_arr.ravel()
FPFiles = sorted(glob.glob(DataDirectory+DrapeRasterWild), key=str)
n_files = len(FPFiles)
print("Number of files = ", n_files)
elev_raster_file = DataDir + ElevationRaster
hillshade = LSDP.Hillshade(elev_raster_file)
#hillshade_array = LSDP.ReadRasterArrayBlocks(elev_raster_file)
# now get the extent
extent_raster = LSDP.GetRasterExtent(elev_raster_file)
x_min = extent_raster[0]
x_max = extent_raster[1]
y_min = extent_raster[2]
y_max = extent_raster[3]
# now get the tick marks
n_target_tics = 5
xlocs,ylocs,new_x_labels,new_y_labels = LSDP.GetTicksForUTM(elev_raster_file,x_max,x_min,y_max,y_min,n_target_tics)
print("xmax: " + str(x_max))
print("xmin: " + str(x_min))
print("ymax: " + str(y_max))
print("ymin: " + str(y_min))
"""
Find the maximum water depth in all rasters.
You need this to normalize the colourscale accross
all plots when teh imshow is done later.
"""
try:
print("Calculating max drape raster value by scanning rasters...")
max_water_depth = findmaxval_multirasters(FPFiles)
drape_max = max_water_depth
except:
print("Something went wrong trying to obtain the max value in \
your drape raster file list.")
finally:
print("The drape(s) max value is set to: ", drape_max)
for i in range(n_files):
print("The floodplain file name is: ", FPFiles[i])
FP_raster = LSDP.ReadRasterArrayBlocks(FPFiles[i])
#FP_raster = np.ma.masked_where(FP_raster <= 0, FP_raster)
filename = os.path.basename(FPFiles[i])
title = mplext.labels.make_line_label(filename)
print(title)
low_values_index = FP_raster < drape_min_threshold
FP_raster[low_values_index] = np.nan
im = ax_arr[i].imshow(hillshade, "gray", extent=extent_raster, interpolation="nearest")
"""
Now we can set vmax to be the maximum water depth we calcualted earlier, making our separate
subplots all have the same colourscale
"""
im = ax_arr[i].imshow(FP_raster, cmap, extent=extent_raster,
alpha=1.0, interpolation="none",
vmin=drape_min_threshold,
vmax=drape_max)
ax_arr[i].set_title(title)
f.subplots_adjust(right=0.8)
cax = f.add_axes([0.9, 0.1, 0.03, 0.8])
cbar = f.colorbar(im, cax=cax)
cbar.set_label("Water depth (m)")
#cbar.set_ticks(np.linspace(0, 8, 8))
#cbar = mplext.colours.colorbar_index(f, cax, 8, cmap,
# drape_min_threshold, drape_max)
cbar.set_label("Water depth (m)")
#tick_locator = ticker.MaxNLocator(nbins=8)
#cbar.locator = tick_locator
#cbar.update_ticks()
f.text(0.5, 0.04, 'Easting (m)', ha='center')
f.text(0.04, 0.5, 'Northing (m)', va='center', rotation='vertical')
def coloured_chans_like_graphs(hillshade_file, tree_file):
"""
Plots outlines of channels taken from the *.tree file over a hillshade
giving each channel a unique colour which corresponds to the colours used
in the Chi plotting routines in chi_visualisation.py.
"""
label_size = 20
#title_size = 30
axis_size = 28
import matplotlib.pyplot as pp
import numpy as np
import matplotlib.colors as colors
import matplotlib.cm as cmx
from matplotlib import rcParams
import matplotlib.lines as mpllines
# make sure the nodata is formatted
LSDP.CheckNoData(hillshade_file)
#get data
hillshade = LSDP.ReadRasterArrayBlocks(hillshade_file)
#ignore nodata values
hillshade = np.ma.masked_where(hillshade == -9999, hillshade)
# now get the extent
extent_raster = LSDP.GetRasterExtent(hillshade_file)
x_min = extent_raster[0]
x_max = extent_raster[1]
y_min = extent_raster[2]
y_max = extent_raster[3]
#fonts
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
#get coordinates of streams from tree file
channel_id = []
row = []
col = []
with open(tree_file, 'r') as f:
lines = f.readlines()
for q,line in enumerate(lines):
if q > 0: #skip first line
channel_id.append(int(line.split()[0]))
row.append(float(line.split()[4]))
col.append(float(line.split()[5]))
#get bounding box & pad to 10% of the dimension
x_max = max(col)
x_min = min(col)
y_max = max(row)
y_min = min(row)
pad_x = (x_max - x_min) * 0.1
pad_y = (x_max - y_min) * 0.1
if (pad_y > pad_x):
pad_x = pad_y
else:
pad_y = pad_x
x_max += pad_x
x_min -= pad_x
y_max += pad_y
y_min -= pad_y
fig = pp.figure(1, facecolor='white',figsize=(10,7.5))
ax = fig.add_subplot(1,1,1)
ax.imshow(hillshade, vmin=0, vmax=255, cmap=cmx.gray)
# now get the tick marks
n_target_tics = 5
xlocs,ylocs,new_x_labels,new_y_labels = LSDP.GetTicksForUTM(hillshade_file,x_max,x_min,y_max,y_min,n_target_tics)
pp.xticks(xlocs, new_x_labels, rotation=60) #[1:-1] skips ticks where we have no data
pp.yticks(ylocs, new_y_labels)
# some formatting to make some of the ticks point outward
for line in ax.get_xticklines():
line.set_marker(mpllines.TICKDOWN)
#line.set_markeredgewidth(3)
for line in ax.get_yticklines():
line.set_marker(mpllines.TICKLEFT)
#line.set_markeredgewidth(3)
pp.xlim(x_min,x_max)
pp.ylim(y_max,y_min)
pp.xlabel('Easting (m)',fontsize = axis_size)
pp.ylabel('Northing (m)', fontsize = axis_size)
# channel ID
Channel_ID_MIN = np.min(channel_id)
Channel_ID_MAX = np.max(channel_id)
cNorm_channel_ID = colors.Normalize(vmin=Channel_ID_MIN, vmax=Channel_ID_MAX) # the max number of channel segs is the 'top' colour
jet = pp.get_cmap('jet')
scalarMap_channel_ID = cmx.ScalarMappable(norm=cNorm_channel_ID, cmap=jet)
#for a,i in enumerate(reversed(channel_id)):
for a,i in enumerate(channel_id):
if i != 0:
# plot other stream segments
colorVal = scalarMap_channel_ID.to_rgba(i) # this gets the distinct colour for this segment
pp.scatter(col[a], row[a], 30,marker=".", color=colorVal,edgecolors='none')
for a,i in enumerate(channel_id):
if i == 0:
# plot trunk stream in black
pp.scatter(col[a], row[a], 40,marker=".", color='k',edgecolors='none')
ax.spines['top'].set_linewidth(2.5)
ax.spines['left'].set_linewidth(2.5)
ax.spines['right'].set_linewidth(2.5)
ax.spines['bottom'].set_linewidth(2.5)
ax.tick_params(axis='both', width=2.5)
pp.xlim(x_min,x_max)
pp.ylim(y_max,y_min)
pp.title("Channels colored by channel number",fontsize=label_size)
pp.tight_layout()
pp.show()
def m_values_over_hillshade(hillshade_file, tree_file):
"""
Plots m values of channels taken from the *.tree file over a hillshade
"""
label_size = 20
#title_size = 30
axis_size = 28
import matplotlib.pyplot as pp
import numpy as np
import matplotlib.colors as colors
import matplotlib.cm as cmx
from matplotlib import rcParams
import matplotlib.lines as mpllines
#get data
hillshade = LSDP.ReadRasterArrayBlocks(hillshade_file)
#ignore nodata values
hillshade = np.ma.masked_where(hillshade == -9999, hillshade)
# now get the extent
extent_raster = LSDP.GetRasterExtent(hillshade_file)
x_min = extent_raster[0]
x_max = extent_raster[1]
y_min = extent_raster[2]
y_max = extent_raster[3]
#ignore nodata values
hillshade = np.ma.masked_where(hillshade == -9999, hillshade)
#fonts
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
#get coordinates of streams from tree file
M_chi_value = []
channel_id = []
row = []
col = []
with open(tree_file, 'r') as f:
lines = f.readlines()
for q,line in enumerate(lines):
if q > 0: #skip first line
channel_id.append(float(line.split()[0]))
M_chi_value.append(float(line.split()[11]))
row.append(float(line.split()[4]))
col.append(float(line.split()[5]))
#get bounding box & pad to 10% of the dimension
x_max = max(col)
x_min = min(col)
y_max = max(row)
y_min = min(row)
pad_x = (x_max - x_min) * 0.1
pad_y = (x_max - y_min) * 0.1
if (pad_y > pad_x):
pad_x = pad_y
else:
pad_y = pad_x
x_max += pad_x
x_min -= pad_x
y_max += pad_y
y_min -= pad_y
fig = pp.figure(1, facecolor='white',figsize=(10,7.5))
ax = fig.add_subplot(1,1,1)
ax.imshow(hillshade, vmin=0, vmax=255, cmap=cmx.gray)
# now get the tick marks
n_target_tics = 5
xlocs,ylocs,new_x_labels,new_y_labels = LSDP.GetTicksForUTM(hillshade_file,x_max,x_min,y_max,y_min,n_target_tics)
pp.xticks(xlocs, new_x_labels, rotation=60) #[1:-1] skips ticks where we have no data
pp.yticks(ylocs, new_y_labels)
for line in ax.get_xticklines():
line.set_marker(mpllines.TICKDOWN)
#line.set_markeredgewidth(3)
for line in ax.get_yticklines():
line.set_marker(mpllines.TICKLEFT)
#line.set_markeredgewidth(3)
pp.xlim(x_min,x_max)
pp.ylim(y_max,y_min)
pp.xlabel('Easting (m)',fontsize = axis_size)
pp.ylabel('Northing (m)',fontsize = axis_size)
# channel ID
M_chi_value_MIN = np.min(M_chi_value)
M_chi_value_MAX = np.max(M_chi_value)
cNorm_M_chi_value = colors.Normalize(vmin=M_chi_value_MIN, vmax=M_chi_value_MAX) # the max number of channel segs is the 'top' colour
hot = pp.get_cmap('RdYlBu_r')
scalarMap_M_chi_value = cmx.ScalarMappable(norm=cNorm_M_chi_value, cmap=hot)
for a,i in enumerate(M_chi_value):
#print "a: " +str(a)+" i: " +str(i)
if channel_id[a] != 0:
# plot other stream segments
colorVal = scalarMap_M_chi_value.to_rgba(i) # this gets the distinct colour for this segment
pp.scatter(col[a], row[a], 30,marker=".", color=colorVal,edgecolors=colorVal)
for a,i in enumerate(M_chi_value):
if channel_id[a] == 0:
# plot trunk stream in black
colorVal = scalarMap_M_chi_value.to_rgba(i)
pp.scatter(col[a], row[a], 40,marker=".", color=colorVal,edgecolors=colorVal)
sm = pp.cm.ScalarMappable(cmap=hot, norm=pp.normalize(vmin=min(M_chi_value), vmax=max(M_chi_value)))
sm._A = []
ax.spines['top'].set_linewidth(2.5)
ax.spines['left'].set_linewidth(2.5)
ax.spines['right'].set_linewidth(2.5)
ax.spines['bottom'].set_linewidth(2.5)
ax.tick_params(axis='both', width=2.5)
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(pp.gca())
cax = divider.append_axes("right", "5%", pad="3%")
pp.colorbar(sm, cax=cax).set_label('$M_{\chi}$',fontsize=axis_size)
cax.tick_params(labelsize=label_size)
#pp.xlim(x_min,x_max)
#pp.ylim(y_max,y_min)
pp.tight_layout()
pp.show()
import numpy as np
import LSDPlottingTools as LSDP
import lsdmatplotlibextensions as mplext
#fit_weibull_from_file(sys.argv[1])
#TestNewMappingTools2()
#ResetErosionRaster()
#FloodThenHillshade()
#FixStupidNoData()
DataDirectory = "/run/media/dav/SHETLAND/Analyses/Ryedale_storms_simulation/Gridded/DetachLim/"
filename = DataDirectory + "Elevations0.asc"
drapename = DataDirectory + "WaterDepths2880.asc"
# Create the drape array from one of the Catchment model output rasters
drape_array = LSDP.ReadRasterArrayBlocks(drapename)
# Optional: A lot of the output rasters contain very small values for certain
# things like water depth or elevation difference, so you can mask this below:
low_values_index = drape_array < 0.005
drape_array[low_values_index] = np.nan
#cmap = plt.get_cmap("Blues")
trunc_cmap = mplext.colours.truncate_colormap("Blues", 0.4, 1.0)
LSDP.DrapedOverHillshade(filename,drape_array,clim_val=(0,400), \
drape_cmap=trunc_cmap, colorbarlabel='Elevation in meters',\
ShowColorbar=True, ShowDrapeColorbar=True,
drape_cbarlabel = "Water depth (m)",
drape_alpha=1.0)
