def ResetErosionRaster():
DataDirectory = "T://analysis_for_papers//Manny_Idaho//Revised//nested//"
#DataDirectory = "C://basin_data//Manny_Idaho//nested//"
ConstFname = "ConstEros.bil"
NewErateName = "HarringCreek_ERKnown.bil"
ThisFile = DataDirectory + ConstFname
NewFilename = DataDirectory + NewErateName
LSDP.CheckNoData(ThisFile)
# now print the constant value file
constant_value = 0.0092
LSDP.SetToConstantValue(ThisFile, NewFilename, constant_value)
LSDP.CheckNoData(NewFilename)
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()
