def PlotBasinsWithHillshade(DataDirectory, OutDirectory, fname_prefix, stream_order=1):
"""
Read in the basins and plot them over a hillshade coloured by their cluster ID
"""
df = pd.read_csv(OutDirectory+fname_prefix+'_profiles_clustered_SO{}.csv'.format(stream_order))
clusters = df.cluster_id.unique()
# make a figure
fig = plt.figure(1, facecolor='white')
gs = plt.GridSpec(100,100,bottom=0.1,left=0.1,right=0.9,top=0.9)
ax = fig.add_subplot(gs[5:100,5:100])
# plot the raster
hs_raster = IO.ReadRasterArrayBlocks(DataDirectory+fname_prefix+'_hs.bil')
extent = LSDP.GetRasterExtent(DataDirectory+fname_prefix+'_hs.bil')
plt.imshow(hs_raster, cmap=cm.gray, extent=extent)
means = {}
for i,cl in enumerate(clusters):
this_df = df[df.cluster_id == cl]
# get the polygons
polygons = ReadBasinPolygons(DataDirectory, OutDirectory, fname_prefix+'_basins_SO{}_CL{}'.format(stream_order,int(cl)))
for p in polygons:
#print(list(p.exterior.coords))
patch = PolygonPatch(p, facecolor=this_df.iloc[0]['colour'], alpha=1.0, zorder=2, lw=0.2)
ax.add_patch(patch)
#print(polygons)
# for each
plt.savefig(OutDirectory+'polygons.png', FigFormat='png', dpi=500)
def TestNewMappingTools_asc():
DataDirectory = "T:\\analysis_for_papers\\Beaches\\"
#Filename1 = "BedThickness_050.asc"
#Filename2 = "BedThickness_100.asc"
Filename1 = "20m_bl.asc"
ThisFile = DataDirectory + Filename1
yo = LSDP.GetRasterExtent(ThisFile)
print "raster extent is: "
print yo
#MB = LSDP.BasicMassBalance(DataDirectory, Filename1, Filename2)
#print "Mass balance between these two time steps is: " + str(MB) + " cubic metres"
#Mean1 = LSDP.RasterMeanValue(DataDirectory, Filename1)
#Mean2 = LSDP.RasterMeanValue(DataDirectory, Filename2)
#print "The mean values of the two rasters are: " + str(Mean1) +" and "+ str(Mean2)
# now try the swath plotting
#axis = 0
#LSDP.SwathPlot(DataDirectory, Filename1, axis)
axis = 1
LSDP.SwathPlot(DataDirectory, Filename1, axis)
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 __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()
def TestAxisLocation():
tick_label_size = 10
text_size = 12
# Set up fonts for plots
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = text_size
rcParams['xtick.labelsize'] = tick_label_size
rcParams['ytick.labelsize'] = tick_label_size
DataDirectory = "C:\\Vagrantboxes\\LSDTopoTools\\Topographic_projects\\Meghalaya\\Divides\\"
#DataDirectory = "T:\\analysis_for_papers\\Meghalaya\\divide_migration\\"
Base_file = "Mega_divide"
bil = ".bil"
#Filename = "Mega_clip.bil"
#HSFilename = "Mega_clip_hs.bil"
#BasinFilename = "Mega_clip_AllBasins.bil"
DEMname = DataDirectory + Base_file + bil
FigFileName = DataDirectory + Base_file + "Picture.png"
FigFormat = "png"
# get the data
#raster = LSDP.ReadRasterArrayBlocks(DEMname)
# now get the extent
extent_raster = LSDP.GetRasterExtent(DEMname)
x_min = extent_raster[0]
x_max = extent_raster[1]
y_min = extent_raster[2]
y_max = extent_raster[3]
print(extent_raster)
fig = plt.figure(1, facecolor='white', figsize=(10, 10))
# Add an axis. This will be used to check how high the text is.
ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.7], zorder=-1)
# turn off the ticks
ax2.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelbottom='off') # labels along the bottom edge are off
ax2.tick_params(
axis='y', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
left='off', # ticks along the bottom edge are off
right='off', # ticks along the top edge are off
labelleft='off') # labels along the bottom edge are off
ax1 = fig.add_axes([0.0, 0.09, 0.85, 0.85])
#ax1.set(alpha=0.25)
ax1.text(
0.1,
0.1,
"x: Tick 10pt, Font 12pt need 0.9 inches.\n",
transform=ax1.transAxes,
)
#im = ax1.imshow(raster[::-1], "jet", extent = extent_raster, interpolation="nearest")
n_target_tics = 5
xlocs, ylocs, new_x_labels, new_y_labels = LSDP.GetTicksForUTM(
DEMname, x_max, x_min, y_max, y_min, n_target_tics)
ax1.set_xticks(xlocs)
ax1.set_yticks(ylocs)
ax1.set_xticklabels(new_x_labels, rotation=60)
ax1.set_yticklabels(new_y_labels)
# This gets all the ticks, and pads them away from the axis so that the corners don't overlap
ax1.tick_params(axis='both', width=1, pad=2)
for tick in ax1.xaxis.get_major_ticks():
tick.set_pad(2)
# This affects all axes because we set share_all = True.
ax1.set_xlim(x_min, x_max)
ax1.set_ylim(y_max, y_min)
ax1.set_xlabel("YumYumDonuts")
ax1.set_ylabel("Bigmama")
#ax3 = fig.add_axes([0.0,0.5,0.5,0.5])
#ax1.text(0,0,"Hello",fontsize = 94)
plt.savefig(FigFileName, format=FigFormat, dpi=100)
yo = 284 / 3
print(yo)
# So in every inch there are 94 points of text
# The bottom
plt.show()
