def simulation_inundation_timeseries(glob_wildcard,
floodplain_mask,
stream_mask,
threshold=0,
savefilename="inundation_metrics.txt"):
"""Creates a timeseries of a given inundation metric.
Options should be:
Inundation Area (Entire catchment)
Mean Water Depth (Entire catchment)
Mean Water Depth (Floodplain only)
Mean Water Depth (Channel)
"""
# Create an empty array with the correct number of columns
data_array = _np.empty((0, 5), dtype=_np.float32)
print("Data array shape: ", data_array.shape)
for water_raster_file in sorted(glob.glob(glob_wildcard), key=natural_key):
print(water_raster_file)
water_raster = lsdgdal.ReadRasterArrayBlocks(water_raster_file)
timestep_row = [] # Empty list to store elements of the row
cur_timestep = timestep_string_from_filename(
water_raster_file
) # get the current timestep by parsing the filename
# Inundation area
this_inundation_area = calculate_waterinundation_area(
water_raster, DX, 0.02)
this_mean_catchment_waterdepth = calculate_mean_waterdepth(
water_raster)
this_mean_floodplain_waterdepth = floodplain_mean_depth(
water_raster, floodplain_mask)
this_mean_mainchannel_waterdepth = main_channel_mean_depth(
water_raster, floodplain_mask, stream_mask)
# Append each value to the current row list object
timestep_row.append([
cur_timestep, this_inundation_area, this_mean_catchment_waterdepth,
this_mean_floodplain_waterdepth, this_mean_mainchannel_waterdepth
])
# Convert the list into a numpy array
timestep_row = _np.asarray(timestep_row, dtype=_np.float32)
# Now append (stack) that row onto the bottom of the array (axis=0)
data_array = _np.append(data_array, timestep_row, axis=0)
print(data_array)
print(data_array.shape)
with open(savefilename, 'wb') as f:
_np.savetxt(f, data_array, fmt='%i %f %f %f %f')
def BasicMassBalance(path, file1, file2):
# make sure names are in correct format
NewPath = LSDOst.AppendSepToDirectoryPath(path)
raster_file1 = NewPath + file1
raster_file2 = NewPath + file2
PixelArea = LSDMap_IO.GetPixelArea(raster_file1)
print "PixelArea is: " + str(PixelArea)
print "The formatted path is: " + NewPath
Raster1 = LSDMap_IO.ReadRasterArrayBlocks(raster_file1, raster_band=1)
Raster2 = LSDMap_IO.ReadRasterArrayBlocks(raster_file2, raster_band=1)
NewRaster = np.subtract(Raster2, Raster1)
mass_balance = np.sum(NewRaster) * PixelArea
print "linear dif " + str(np.sum(NewRaster))
return mass_balance
def RasterMeanValue(path, file1):
# make sure names are in correct format
NewPath = LSDOst.AppendSepToDirectoryPath(path)
raster_file1 = NewPath + file1
NPixels = LSDMap_IO.GetNPixelsInRaster(raster_file1)
Raster1 = LSDMap_IO.ReadRasterArrayBlocks(raster_file1, raster_band=1)
mean_value = np.sum(Raster1) / float(NPixels)
return mean_value
def Hillshade(raster_file, azimuth=315, angle_altitude=45):
array = LSDMap_IO.ReadRasterArrayBlocks(raster_file, raster_band=1)
x, y = np.gradient(array)
slope = np.pi / 2. - np.arctan(np.sqrt(x * x + y * y))
aspect = np.arctan2(-x, y)
azimuthrad = azimuth * np.pi / 180.
altituderad = angle_altitude * np.pi / 180.
shaded = np.sin(altituderad) * np.sin(slope)\
+ np.cos(altituderad) * np.cos(slope)\
* np.cos(azimuthrad - aspect)
#this_array = 255*(shaded + 1)/2
return 255 * (shaded + 1) / 2
def SimpleSwath(path, file1, axis):
# make sure names are in correct format
NewPath = LSDOst.AppendSepToDirectoryPath(path)
raster_file1 = NewPath + file1
# get some information about the raster
NDV, xsize, ysize, GeoT, Projection, DataType = LSDMap_IO.GetGeoInfo(
raster_file1)
print "NDV is: "
print NDV
if NDV == None:
NDV = -9999
print "No NDV defined"
Raster1 = LSDMap_IO.ReadRasterArrayBlocks(raster_file1, raster_band=1)
#nan_raster = Raster1[Raster1==NDV]=np.nan
#print nan_raster
#now mask the nodata
masked_Raster1 = np.ma.masked_values(Raster1, NDV)
means = np.mean(masked_Raster1, axis)
medians = np.median(masked_Raster1, axis)
std_deviations = np.std(masked_Raster1, axis)
twentyfifth_percentile = np.percentile(masked_Raster1, 25, axis)
seventyfifth_percentile = np.percentile(masked_Raster1, 75, axis)
# This stuff only works with numpy 1.8 or later, wich we don't have
#means = np.nanmean(nan_raster, axis)
#medians = np.nanmedian(nan_raster, axis)
#std_deviations = np.nanstd(nan_raster, axis)
#twentyfifth_percentile = np.nanpercentile(nan_raster, 25, axis)
#seventyfifth_percentile = np.nanpercentile(nan_raster, 75, axis)
#print means
#print medians
#print std_deviations
#print twentyfifth_percentile
#print seventyfifth_percentile
return means, medians, std_deviations, twentyfifth_percentile, seventyfifth_percentile
def SetNoDataBelowThreshold(raster_filename,
new_raster_filename,
threshold=0,
driver_name="ENVI",
NoDataValue=-9999):
# read the data
rasterArray = LSDMap_IO.ReadRasterArrayBlocks(raster_filename)
print "Read the data"
# set any point on the raster below the threshold as nodata
rasterArray[rasterArray <= threshold] = NoDataValue
print "Reset raster values"
# write the data to a new file
LSDMap_IO.array2raster(raster_filename, new_raster_filename, rasterArray,
driver_name, NoDataValue)
print "Wrote raster"
def SetToConstantValue(raster_filename,
new_raster_filename,
constant_value,
driver_name="ENVI"):
# get the nodata value
NoDataValue = LSDMap_IO.getNoDataValue(raster_filename)
# read the data
rasterArray = LSDMap_IO.ReadRasterArrayBlocks(raster_filename)
print "Read the data"
# set any nodata to a constant value
rasterArray[rasterArray != NoDataValue] = constant_value
print "Changed to a constant value"
# write the data to a new file
LSDMap_IO.array2raster(raster_filename, new_raster_filename, rasterArray,
driver_name, NoDataValue)
print "Wrote raster"
def Hillshade(raster_file, azimuth=315, angle_altitude=45, NoDataVal=-9999.0):
array = LSDMap_IO.ReadRasterArrayBlocks(raster_file, raster_band=1)
# Addition DAV to cope with rasters that are not rectangles (e.g. clipped basins)
nodata = NoDataVal
array[array == nodata] = np.nan #Set nodatas to NaN
x, y = np.gradient(array)
slope = np.pi / 2. - np.arctan(np.sqrt(x * x + y * y))
aspect = np.arctan2(-x, y)
azimuthrad = azimuth * np.pi / 180.
altituderad = angle_altitude * np.pi / 180.
shaded = np.sin(altituderad) * np.sin(slope)\
+ np.cos(altituderad) * np.cos(slope)\
* np.cos(azimuthrad - aspect)
#this_array = 255*(shaded + 1)/2
return 255 * (shaded + 1) / 2
print(data_array)
print(data_array.shape)
with open(savefilename, 'wb') as f:
_np.savetxt(f, data_array, fmt='%i %f %f %f %f')
"""Get your rasters into arrays"""
water_raster_wildcard = "/run/media/dav/SHETLAND/ModelRuns/Ryedale_storms/Gridded/Hydro/WaterDepths*.asc"
water_raster_file = "/mnt/SCRATCH/Analyses/HydrogeomorphPaper/peak_flood_maps/ryedale/WaterDepths2880_GRID_TLIM.asc"
#raster_file = "/run/media/dav/SHETLAND/Analyses/HydrogeomorphPaper/peak_flood_maps/boscastle/peak_flood/WaterDepths2400_GRID_HYDRO.asc"
floodplain_file = "/mnt/SCRATCH/Analyses/ChannelMaskAnalysis/floodplain_ryedale/RyedaleElevations_FP.bil"
stream_raster_file = "/mnt/SCRATCH/Analyses/ChannelMaskAnalysis/floodplain_ryedale/RyedaleElevations_SO.bil"
water_raster = lsdgdal.ReadRasterArrayBlocks(water_raster_file)
floodplain_mask = lsdgdal.ReadRasterArrayBlocks(floodplain_file)
stream_mask = lsdgdal.ReadRasterArrayBlocks(stream_raster_file)
#print(stream_mask)
DX = lsdgdal.GetUTMMaxMin(water_raster_file)[
0] # I never realised you could do this!
print(DX)
"""Calculate the depths and areas"""
#calculate_mean_waterdepth(water_raster)
#calcualte_max_waterdepth(water_raster)
#calculate_waterinundation_area(water_raster, DX, 0.02)
#floodplain_mean_depth(water_raster, floodplain_mask)
#main_channel_mean_depth(water_raster, floodplain_mask, stream_mask)
"""Make the timeseries file"""
def DrapedOverHillshade(FileName,
DrapeName,
thiscmap='gray',
drape_cmap='gray',
colorbarlabel='Elevation in meters',
clim_val=(0, 0),
drape_alpha=0.6,
ShowColorbar=False):
import matplotlib.pyplot as plt
import matplotlib.lines as mpllines
from mpl_toolkits.axes_grid1 import AxesGrid
label_size = 20
#title_size = 30
axis_size = 28
# Set up fonts for plots
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
hillshade = Hillshade(FileName)
#hillshade = LSDMap_IO.ReadRasterArrayBlocks(DrapeName)
raster_drape = LSDMap_IO.ReadRasterArrayBlocks(DrapeName)
# now get the extent
extent_raster = LSDMap_IO.GetRasterExtent(FileName)
x_min = extent_raster[0]
x_max = extent_raster[1]
y_min = extent_raster[2]
y_max = extent_raster[3]
# make a figure, sized for a ppt slide
fig = plt.figure(1, facecolor='white', figsize=(10, 7.5))
if ShowColorbar:
grid = AxesGrid(
fig,
111,
nrows_ncols=(1, 1),
axes_pad=(0.45, 0.15),
label_mode="1",
share_all=True,
cbar_location="right",
cbar_mode="each",
cbar_size="7%",
cbar_pad="2%",
)
else:
grid = AxesGrid(
fig,
111,
nrows_ncols=(1, 1),
axes_pad=(0.45, 0.15),
label_mode="1",
share_all=True,
)
# now get the tick marks
n_target_tics = 5
xlocs, ylocs, new_x_labels, new_y_labels = GetTicksForUTM(
FileName, 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)
im = grid[0].imshow(hillshade[::-1],
thiscmap,
extent=extent_raster,
interpolation="nearest")
#im = grid[0].imshow(raster, thiscmap, interpolation="nearest")
if ShowColorbar:
cbar = grid.cbar_axes[0].colorbar(im)
cbar.set_label_text(colorbarlabel)
# set the colour limits
print "Setting colour limits to " + str(clim_val[0]) + " and " + str(
clim_val[1])
if (clim_val == (0, 0)):
print "I don't think I should be here"
im.set_clim(0, np.max(hillshade))
else:
print "Now setting colour limits to " + str(
clim_val[0]) + " and " + str(clim_val[1])
im.set_clim(clim_val[0], clim_val[1])
# Now for the drape: it is in grayscape
im = grid[0].imshow(raster_drape[::-1],
drape_cmap,
extent=extent_raster,
alpha=drape_alpha,
interpolation="nearest")
# This affects all axes because we set share_all = True.
grid.axes_llc.set_xlim(x_min, x_max)
grid.axes_llc.set_ylim(y_max, y_min)
grid.axes_llc.set_xticks(xlocs)
grid.axes_llc.set_yticks(ylocs)
grid.axes_llc.set_xticklabels(new_x_labels, rotation=60)
grid.axes_llc.set_yticklabels(new_y_labels)
grid.axes_llc.set_xlabel("Easting (m)")
grid.axes_llc.set_ylabel("Northing (m)")
plt.show()
def BasicDrapedPlotGridPlot(FileName,
DrapeName,
thiscmap='gray',
drape_cmap='gray',
colorbarlabel='Elevation in meters',
clim_val=(0, 0),
drape_alpha=0.6,
FigFileName='Image.pdf',
FigFormat='show'):
print "======================================"
print "Yo, I'm doing a draped plot"
print FigFileName
print FigFormat
print "======================================"
import matplotlib.pyplot as plt
import matplotlib.lines as mpllines
from mpl_toolkits.axes_grid1 import AxesGrid
label_size = 20
#title_size = 30
axis_size = 28
# Set up fonts for plots
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
# get the data
raster = LSDMap_IO.ReadRasterArrayBlocks(FileName)
raster_drape = LSDMap_IO.ReadRasterArrayBlocks(DrapeName)
# now get the extent
extent_raster = LSDMap_IO.GetRasterExtent(FileName)
x_min = extent_raster[0]
x_max = extent_raster[1]
y_min = extent_raster[2]
y_max = extent_raster[3]
# make a figure, sized for a ppt slide
fig = plt.figure(1, facecolor='white', figsize=(10, 7.5))
gs = plt.GridSpec(100, 75, bottom=0.1, left=0.1, right=0.9, top=1.0)
ax = fig.add_subplot(gs[10:100, 10:75])
#grid = AxesGrid(fig, 111,
# nrows_ncols=(1, 1),
# axes_pad=(0.45, 0.15),
# label_mode="1",
# share_all=True,
# cbar_location="right",
# cbar_mode="each",
# cbar_size="7%",
# cbar_pad="2%",
# )
# now get the tick marks
n_target_tics = 5
xlocs, ylocs, new_x_labels, new_y_labels = GetTicksForUTM(
FileName, 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)
im = ax.imshow(raster[::-1],
thiscmap,
extent=extent_raster,
interpolation="nearest")
#im = grid[0].imshow(raster, thiscmap, interpolation="nearest")
cbar = plt.colorbar(im)
cbar.set_label(colorbarlabel)
#cbar.set_height(1)
#cbar = fig.cbar_axes[0].colorbar(im)
#cbar.set_label_text(colorbarlabel)
# set the colour limits
print "Setting colour limits to " + str(clim_val[0]) + " and " + str(
clim_val[1])
if (clim_val == (0, 0)):
print "I don't think I should be here"
im.set_clim(0, np.max(raster))
else:
print "Now setting colour limits to " + str(
clim_val[0]) + " and " + str(clim_val[1])
im.set_clim(clim_val[0], clim_val[1])
# Now for the drape: it is in grayscape
im = ax.imshow(raster_drape[::-1],
drape_cmap,
extent=extent_raster,
alpha=drape_alpha,
interpolation="nearest")
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)
# This affects all axes because we set share_all = True.
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_max, y_min)
ax.set_xticks(xlocs)
ax.set_yticks(ylocs)
ax.set_xticklabels(new_x_labels, rotation=60)
ax.set_yticklabels(new_y_labels)
ax.set_xlabel("Easting (m)")
ax.set_ylabel("Northing (m)")
# This gets all the ticks, and pads them away from the axis so that the corners don't overlap
ax.tick_params(axis='both', width=2.5, pad=10)
for tick in ax.xaxis.get_major_ticks():
tick.set_pad(10)
print "The figure format is: " + FigFormat
if FigFormat == 'show':
plt.show()
else:
plt.savefig(FigFileName, format=FigFormat)
fig.clf()
def BasicDensityPlot(FileName,
thiscmap='gray',
colorbarlabel='Elevation in meters',
clim_val=(0, 0)):
import matplotlib.pyplot as plt
import matplotlib.lines as mpllines
label_size = 20
#title_size = 30
axis_size = 28
# Set up fonts for plots
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
# get the data
raster = LSDMap_IO.ReadRasterArrayBlocks(FileName)
# now get the extent
extent_raster = LSDMap_IO.GetRasterExtent(FileName)
x_min = extent_raster[0]
x_max = extent_raster[1]
y_min = extent_raster[2]
y_max = extent_raster[3]
# make a figure, sized for a ppt slide
fig = plt.figure(1, facecolor='white', figsize=(10, 7.5))
# make room for the colorbar
#fig.subplots_adjust(bottom=0.1)
#fig.subplots_adjust(top=0.9)
#fig.subplots_adjust(left=0.2)
#fig.subplots_adjust(right=0.8)
ax1 = fig.add_subplot(1, 1, 1)
im = ax1.imshow(raster[::-1], thiscmap, extent=extent_raster)
print "The is the extent raster data element"
print extent_raster
print "now I am in the mapping routine"
print "x_min: " + str(x_min)
print "x_max: " + str(x_max)
print "y_min: " + str(y_min)
print "y_max: " + str(y_max)
# now get the tick marks
n_target_tics = 5
xlocs, ylocs, new_x_labels, new_y_labels = GetTicksForUTM(
FileName, x_max, x_min, y_max, y_min, n_target_tics)
plt.xticks(xlocs, new_x_labels,
rotation=60) #[1:-1] skips ticks where we have no data
plt.yticks(ylocs, new_y_labels)
print "The x locs are: "
print xlocs
print "The x labels are: "
print new_x_labels
# some formatting to make some of the ticks point outward
for line in ax1.get_xticklines():
line.set_marker(mpllines.TICKDOWN)
#line.set_markeredgewidth(3)
for line in ax1.get_yticklines():
line.set_marker(mpllines.TICKLEFT)
#line.set_markeredgewidth(3)
plt.xlim(x_min, x_max)
plt.ylim(y_max, y_min)
plt.xlabel('Easting (m)', fontsize=axis_size)
plt.ylabel('Northing (m)', fontsize=axis_size)
ax1.set_xlabel("Easting (m)")
ax1.set_ylabel("Northing (m)")
# set the colour limits
print "Setting colour limits to " + str(clim_val[0]) + " and " + str(
clim_val[1])
if (clim_val == (0, 0)):
print "I don't think I should be here"
im.set_clim(0, np.max(raster))
else:
print "Now setting colour limits to " + str(
clim_val[0]) + " and " + str(clim_val[1])
im.set_clim(clim_val[0], clim_val[1])
cbar = fig.colorbar(im, orientation='vertical')
cbar.set_label(colorbarlabel)
#plt.tight_layout()
plt.show()
def BasicChiPlotGridPlot(FileName,
DrapeName,
chi_csv_fname,
thiscmap='gray',
drape_cmap='gray',
colorbarlabel='Elevation in meters',
clim_val=(0, 0),
drape_alpha=0.6,
FigFileName='Image.pdf',
FigFormat='show',
elevation_threshold=0):
import matplotlib.pyplot as plt
import matplotlib.lines as mpllines
from mpl_toolkits.axes_grid1 import AxesGrid
from matplotlib import colors
label_size = 10
#title_size = 30
axis_size = 12
# Set up fonts for plots
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
#plt.rc('text', usetex=True)
# get the data
raster = LSDMap_IO.ReadRasterArrayBlocks(FileName)
raster_drape = LSDMap_IO.ReadRasterArrayBlocks(DrapeName)
# now get the extent
extent_raster = LSDMap_IO.GetRasterExtent(FileName)
x_min = extent_raster[0]
x_max = extent_raster[1]
y_min = extent_raster[2]
y_max = extent_raster[3]
# make a figure, sized for a ppt slide
fig = plt.figure(1, facecolor='white', figsize=(4.92126, 3.5))
gs = plt.GridSpec(100, 100, bottom=0.25, left=0.1, right=1.0, top=1.0)
ax = fig.add_subplot(gs[25:100, 10:95])
# This is the axis for the colorbar
ax2 = fig.add_subplot(gs[10:15, 15:70])
#grid = AxesGrid(fig, 111,
# nrows_ncols=(1, 1),
# axes_pad=(0.45, 0.15),
# label_mode="1",
# share_all=True,
# cbar_location="right",
# cbar_mode="each",
# cbar_size="7%",
# cbar_pad="2%",
# )
# now get the tick marks
n_target_tics = 5
xlocs, ylocs, new_x_labels, new_y_labels = LSDMap_BP.GetTicksForUTM(
FileName, 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)
#Z1 = np.array(([0, 1]*4 + [1, 0]*4)*4)
#Z1.shape = (8, 8) # chessboard
#im2 = ax.imshow(Z1, cmap=plt.cm.gray, interpolation='nearest',
# extent=extent_raster)
#plt.hold(True)
im1 = ax.imshow(raster[::-1],
thiscmap,
extent=extent_raster,
interpolation="nearest")
# set the colour limits
print "Setting colour limits to " + str(clim_val[0]) + " and " + str(
clim_val[1])
if (clim_val == (0, 0)):
print "Im setting colour limits based on minimum and maximum values"
im1.set_clim(0, np.max(raster))
else:
print "Now setting colour limits to " + str(
clim_val[0]) + " and " + str(clim_val[1])
im1.set_clim(clim_val[0], clim_val[1])
plt.hold(True)
# Now for the drape: it is in grayscale
#print "drape_cmap is: "+drape_cmap
im3 = ax.imshow(raster_drape[::-1],
drape_cmap,
extent=extent_raster,
alpha=drape_alpha,
interpolation="nearest")
# Set the colour limits of the drape
im3.set_clim(0, np.max(raster_drape))
ax.spines['top'].set_linewidth(1)
ax.spines['left'].set_linewidth(1)
ax.spines['right'].set_linewidth(1)
ax.spines['bottom'].set_linewidth(1)
#ax.spines['bottom'].set_capstyle('projecting')
#for spine in ax.spines.values():
# spine.set_capstyle('projecting')
ax.set_xticklabels(new_x_labels, rotation=60)
ax.set_yticklabels(new_y_labels)
ax.set_xlabel("Easting (m)")
ax.set_ylabel("Northing (m)")
# This gets all the ticks, and pads them away from the axis so that the corners don't overlap
ax.tick_params(axis='both', width=1, pad=2)
for tick in ax.xaxis.get_major_ticks():
tick.set_pad(2)
# Now we get the chi points
EPSG_string = LSDMap_IO.GetUTMEPSG(FileName)
print "EPSG string is: " + EPSG_string
thisPointData = LSDMap_PD.LSDMap_PointData(chi_csv_fname)
thisPointData.ThinData('elevation', elevation_threshold)
# convert to easting and northing
[easting, northing] = thisPointData.GetUTMEastingNorthing(EPSG_string)
# The image is inverted so we have to invert the northing coordinate
Ncoord = np.asarray(northing)
Ncoord = np.subtract(extent_raster[3], Ncoord)
Ncoord = np.add(Ncoord, extent_raster[2])
M_chi = thisPointData.QueryData('m_chi')
#print M_chi
M_chi = [float(x) for x in M_chi]
# make a color map of fixed colors
this_cmap = colors.ListedColormap(
['#2c7bb6', '#abd9e9', '#ffffbf', '#fdae61', '#d7191c'])
bounds = [0, 50, 100, 175, 250, 1205]
norm = colors.BoundaryNorm(bounds, this_cmap.N)
sc = ax.scatter(easting,
Ncoord,
s=0.5,
c=M_chi,
cmap=this_cmap,
norm=norm,
edgecolors='none')
# This affects all axes because we set share_all = True.
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_max, y_min)
ax.set_xticks(xlocs)
ax.set_yticks(ylocs)
cbar = plt.colorbar(sc,
cmap=this_cmap,
norm=norm,
spacing='uniform',
ticks=bounds,
boundaries=bounds,
orientation='horizontal',
cax=ax2)
cbar.set_label(colorbarlabel, fontsize=10)
ax2.set_xlabel(colorbarlabel, fontname='Arial', labelpad=-35)
print "The figure format is: " + FigFormat
if FigFormat == 'show':
plt.show()
elif FigFormat == 'return':
return fig
else:
plt.savefig(FigFileName, format=FigFormat, dpi=500)
fig.clf()
def DrapedOverHillshade(FileName,
DrapeName,
thiscmap='gray',
drape_cmap='gray',
colorbarlabel='Elevation in meters',
clim_val=(0, 0),
drape_alpha=0.6,
ShowColorbar=False,
ShowDrapeColorbar=False,
drape_cbarlabel=None):
import matplotlib.pyplot as plt
import matplotlib.lines as mpllines
from mpl_toolkits.axes_grid1 import AxesGrid, make_axes_locatable
label_size = 20
#title_size = 30
axis_size = 28
# Set up fonts for plots
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
hillshade = Hillshade(FileName)
#hillshade = LSDMap_IO.ReadRasterArrayBlocks(DrapeName)
# DAV - option to supply array directly (after masking for example, rather
# than reading directly from a file. Should not break anyone's code)
# (You can't overload functions in Python...)
if isinstance(DrapeName, str):
raster_drape = LSDMap_IO.ReadRasterArrayBlocks(DrapeName)
elif isinstance(DrapeName, np.ndarray):
raster_drape = DrapeName
else:
print "DrapeName supplied is of type: ", type(DrapeName)
raise ValueError('DrapeName must either be a string to a filename, \
or a numpy ndarray type. Please try again.')
# now get the extent
extent_raster = LSDMap_IO.GetRasterExtent(FileName)
x_min = extent_raster[0]
x_max = extent_raster[1]
y_min = extent_raster[2]
y_max = extent_raster[3]
# make a figure, sized for a ppt slide
fig = plt.figure(1, facecolor='white', figsize=(10, 7.5))
if ShowColorbar:
grid = AxesGrid(
fig,
111,
nrows_ncols=(1, 1),
axes_pad=(0.45, 0.15),
label_mode="1",
share_all=True,
cbar_location="right",
cbar_mode="each",
cbar_size="7%",
cbar_pad="2%",
)
#ShowDrapeColorbar = True
#if ShowDrapeColorbar and ShowColorbar:
#divider = make_axes_locatable(fig)
#cax = divider.append_axes("right", size = "5%", pad=0.05)
else:
grid = AxesGrid(
fig,
111,
nrows_ncols=(1, 1),
axes_pad=(0.45, 0.15),
label_mode="1",
share_all=True,
)
# now get the tick marks
n_target_tics = 5
xlocs, ylocs, new_x_labels, new_y_labels = GetTicksForUTM(
FileName, 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)
im = grid[0].imshow(hillshade[::-1],
thiscmap,
extent=extent_raster,
interpolation="nearest")
#im = grid[0].imshow(raster, thiscmap, interpolation="nearest")
if ShowColorbar:
cbar = grid.cbar_axes[0].colorbar(im)
cbar.set_label_text(colorbarlabel)
# set the colour limits
print "Setting colour limits to " + str(clim_val[0]) + " and " + str(
clim_val[1])
if (clim_val == (0, 0)):
print "I don't think I should be here"
im.set_clim(0, np.max(hillshade))
else:
print "Now setting colour limits to " + str(
clim_val[0]) + " and " + str(clim_val[1])
im.set_clim(clim_val[0], clim_val[1])
# Now for the drape: it is in grayscape
im2 = grid[0].imshow(raster_drape[::-1],
drape_cmap,
extent=extent_raster,
alpha=drape_alpha,
interpolation="nearest")
if ShowDrapeColorbar:
cbar2 = grid.cbar_axes[0].colorbar(im2)
cbar2.set_label_text(drape_cbarlabel)
#plt.colorbar(im)
# This affects all axes because we set share_all = True.
grid.axes_llc.set_xlim(x_min, x_max)
grid.axes_llc.set_ylim(y_max, y_min)
grid.axes_llc.set_xticks(xlocs)
grid.axes_llc.set_yticks(ylocs)
grid.axes_llc.set_xticklabels(new_x_labels, rotation=60)
grid.axes_llc.set_yticklabels(new_y_labels)
grid.axes_llc.set_xlabel("Easting (m)")
grid.axes_llc.set_ylabel("Northing (m)")
plt.show()