def PlotRasterLithology(DataDirectory, fname_prefix, geol_raster='geol'):
"""
Make a hillshade and rasterise a geology shapefile and drape on the top.
Uses the LSDPlottingTools libraries. https://github.com/LSDtopotools/LSDMappingTools
Args:
stream_order: the stream order of the profiles that you are analysing
shapefile_name: name of the lithology shapefile
geol_field: the field of the shapefile that has the lithology information
Author: FJC
"""
import LSDPlottingTools as LSDP
from LSDMapFigure.PlottingRaster import MapFigure
# set figure sizes based on format
fig_width_inches = 8
# some raster names
raster_ext = '.bil'
BackgroundRasterName = fname_prefix + raster_ext
HSName = fname_prefix + '_hs' + raster_ext
if not os.path.isfile(DataDirectory + HSName):
# make a hillshade
BM.GetHillshade(DataDirectory + BackgroundRasterName,
DataDirectory + HSName)
# create the map figure
MF = MapFigure(HSName, DataDirectory, coord_type="UTM")
#geology
LithName = geol_raster
print("The geology raster is" + LithName)
MF.add_drape_image(LithName,
DataDirectory,
colourmap=plt.cm.jet,
alpha=0.5,
show_colourbar=False,
discrete_cmap=True,
cbar_type=int,
mask_value=0)
MF.save_fig(fig_width_inches=fig_width_inches,
FigFileName=DataDirectory + fname_prefix + '_lith.png',
FigFormat='png',
Fig_dpi=300,
fixed_cbar_characters=6,
adjust_cbar_characters=False,
transparent=True) # Save the figure
def PlotKsnFromSlopeArea(DataDirectory,
fname_prefix,
theta=0.45,
cbar_loc='right',
custom_cbar_min_max=[]):
"""
Make a plot of the slope area data with a fixed concavity
"""
import numpy as np
print("Calculating ksn...")
# read the csv and get some info
df = pd.read_csv(DataDirectory + fname_prefix + "_slopes.csv")
# now force a fit of ks based on this concavity
area = df['drainage_area'].values
slope = df['slope'].values
ksn = slope / (area**(-theta))
df['ksn'] = ksn
print(np.max(ksn), np.min(ksn))
print(ksn)
df.to_csv(DataDirectory + fname_prefix + '_ksn.csv', index=False)
# set figure sizes based on format
fig_width_inches = 6
# some raster names
raster_ext = '.bil'
BackgroundRasterName = fname_prefix + raster_ext
HSName = fname_prefix + '_hs' + raster_ext
if not os.path.isfile(DataDirectory + HSName):
# make a hillshade
BM.GetHillshade(DataDirectory + BackgroundRasterName,
DataDirectory + HSName)
# create the map figure
MF = MapFigure(HSName,
DataDirectory,
coord_type="UTM",
cbar_loc='right',
font_size=16)
#MF.add_drape_image(BackgroundRasterName,DataDirectory,colourmap = 'gray', alpha=0.5)
# add the ksn data
ChannelPoints = LSDP.LSDMap_PointData(df, data_type="pandas", PANDEX=True)
MF.add_point_data(ChannelPoints,
this_colourmap='viridis',
column_for_plotting='ksn',
colour_log=True,
zorder=100,
show_colourbar=True,
colourbar_location='bottom',
font_size=24,
colorbarlabel="log$_{10}(k_{sn})",
manual_size=1.5,
colour_manual_scale=[0, 1.8])
#plt.show()
MF.save_fig(fig_width_inches=fig_width_inches,
FigFileName=DataDirectory + fname_prefix + '_ksn.png',
FigFormat='png',
Fig_dpi=300,
fixed_cbar_characters=6,
adjust_cbar_characters=False,
axis_style='Thin',
transparent=True) # Save the figure
plt.clf()
def PlotRasterLithologyWithClusters(DataDirectory,
OutDirectory,
fname_prefix,
stream_order=1,
geol_raster='geol'):
"""
Make a hillshade of the raster with the channels coloured by the cluster
value. Rasterise a geology shapefile and drape on the top.
Uses the LSDPlottingTools libraries. https://github.com/LSDtopotools/LSDMappingTools
Args:
stream_order: the stream order of the profiles that you are analysing
shapefile_name: name of the lithology shapefile
geol_field: the field of the shapefile that has the lithology information
Author: FJC
"""
import LSDPlottingTools as LSDP
from LSDMapFigure.PlottingRaster import MapFigure
df = pd.read_csv(DataDirectory + fname_prefix + '_all_tribs.csv')
cluster_df = pd.read_csv(
OutDirectory + fname_prefix +
'_profiles_clustered_SO{}.csv'.format(stream_order))
# set figure sizes based on format
fig_width_inches = 8
# some raster names
raster_ext = '.bil'
BackgroundRasterName = fname_prefix + raster_ext
HSName = fname_prefix + '_hs' + raster_ext
if not os.path.isfile(DataDirectory + HSName):
# make a hillshade
BM.GetHillshade(DataDirectory + BackgroundRasterName,
DataDirectory + HSName)
# create the map figure
MF = MapFigure(HSName, DataDirectory, coord_type="UTM")
#geology
LithName = geol_raster
print("The geology raster is" + LithName)
MF.add_drape_image(LithName,
DataDirectory,
colourmap=plt.cm.jet,
alpha=0.5,
show_colourbar=False,
discrete_cmap=True,
cbar_type=int,
mask_value=0)
clusters = cluster_df.cluster_id.unique()
for cl in clusters:
# plot the whole channel network in black
ChannelPoints = LSDP.LSDMap_PointData(df,
data_type="pandas",
PANDEX=True)
MF.add_point_data(ChannelPoints,
show_colourbar="False",
unicolor='white',
manual_size=1.5,
zorder=2,
alpha=0.5)
# plot the clustered profiles in the correct colour
this_df = cluster_df[cluster_df.cluster_id == cl]
this_colour = str(this_df.colour.unique()[0])
ClusteredPoints = LSDP.LSDMap_PointData(this_df,
data_type="pandas",
PANDEX=True)
MF.add_point_data(ClusteredPoints,
show_colourbar="False",
zorder=100,
unicolor=this_colour,
manual_size=2.5)
MF.save_fig(fig_width_inches=fig_width_inches,
FigFileName=OutDirectory + fname_prefix +
'_lith_clusters_SO{}.png'.format(stream_order),
FigFormat='png',
Fig_dpi=300,
fixed_cbar_characters=6,
adjust_cbar_characters=False,
transparent=True) # Save the figure
def PlotElevationWithClusters(DataDirectory,
OutDirectory,
fname_prefix,
stream_order=1,
cbar_loc='right',
custom_cbar_min_max=[]):
"""
Make a plot of the raster with the channels coloured by the cluster
value. Uses the LSDPlottingTools libraries. https://github.com/LSDtopotools/LSDMappingTools
Args:
stream_order: the stream order of the profiles that you are analysing
cbar_loc: location of the colourbar, can be right, top, bottom, left, or none.
custom_cbar_min_max: list of [min, max] to recast the raster to for display.
Author: FJC
"""
print("I'm plotting the elevation with channels coloured by cluster")
df = pd.read_csv(DataDirectory + fname_prefix + '_all_tribs.csv')
cluster_df = pd.read_csv(
OutDirectory + fname_prefix +
'_profiles_clustered_SO{}.csv'.format(stream_order))
# set figure sizes based on format
fig_width_inches = 4.92126
# some raster names
raster_ext = '.bil'
BackgroundRasterName = fname_prefix + raster_ext
HSName = fname_prefix + '_hs' + raster_ext
if not os.path.isfile(DataDirectory + HSName):
# make a hillshade
BM.GetHillshade(DataDirectory + BackgroundRasterName,
DataDirectory + HSName)
MF = MapFigure(HSName,
DataDirectory,
coord_type="UTM",
colourbar_location=cbar_loc)
MF.add_drape_image(BackgroundRasterName,
DataDirectory,
colourmap='gray',
alpha=0.5,
colorbarlabel="Elevation (m)",
colour_min_max=custom_cbar_min_max)
# # create the map figure
# MF = MapFigure(BackgroundRasterName, DataDirectory,coord_type="UTM",colour_min_max = custom_cbar_min_max)
clusters = cluster_df.cluster_id.unique()
for cl in clusters:
# plot the whole channel network in black
ChannelPoints = LSDP.LSDMap_PointData(df,
data_type="pandas",
PANDEX=True)
MF.add_point_data(ChannelPoints,
show_colourbar="False",
unicolor='w',
manual_size=1,
zorder=2,
alpha=1)
# plot the clustered profiles in the correct colour
this_df = cluster_df[cluster_df.cluster_id == cl]
this_colour = str(this_df.colour.unique()[0])
ClusteredPoints = LSDP.LSDMap_PointData(this_df,
data_type="pandas",
PANDEX=True)
MF.add_point_data(ClusteredPoints,
show_colourbar="False",
zorder=100,
unicolor=this_colour,
manual_size=2)
MF.save_fig(fig_width_inches=fig_width_inches,
FigFileName=OutDirectory + fname_prefix +
'_elev_clusters_SO{}.png'.format(stream_order),
FigFormat='png',
Fig_dpi=300,
fixed_cbar_characters=6,
adjust_cbar_characters=False,
axis_style='Thin',
transparent=True) # Save the figure
def PlotHillshadewithClusters(DataDirectory,
OutDirectory,
fname_prefix,
stream_order=1):
"""
Make a hillshade of the raster with the channels coloured by the cluster
value. Uses the LSDPlottingTools libraries. https://github.com/LSDtopotools/LSDMappingTools
Args:
stream_order: the stream order of the profiles that you are analysing
Author: FJC
"""
import LSDPlottingTools as LSDP
from LSDMapFigure.PlottingRaster import MapFigure
print(
"I'm plotting a shaded relief map with the channels coloured by cluster"
)
df = pd.read_csv(DataDirectory + fname_prefix + '_all_tribs.csv')
cluster_df = pd.read_csv(
OutDirectory + fname_prefix +
'_profiles_clustered_SO{}.csv'.format(stream_order))
# set figure sizes based on format
fig_width_inches = 8
# some raster names
raster_ext = '.bil'
BackgroundRasterName = fname_prefix + raster_ext
HSName = fname_prefix + '_hs' + raster_ext
if not os.path.isfile(DataDirectory + HSName):
# make a hillshade
BM.GetHillshade(DataDirectory + BackgroundRasterName,
DataDirectory + HSName)
# create the map figure
MF = MapFigure(HSName, DataDirectory, coord_type="UTM")
clusters = cluster_df.cluster_id.unique()
for cl in clusters:
# plot the whole channel network in black
ChannelPoints = LSDP.LSDMap_PointData(df,
data_type="pandas",
PANDEX=True)
MF.add_point_data(ChannelPoints,
show_colourbar="False",
unicolor='0.9',
manual_size=2,
zorder=1,
alpha=0.5)
# plot the clustered profiles in the correct colour
this_df = cluster_df[cluster_df.cluster_id == cl]
this_colour = str(this_df.colour.unique()[0])
ClusteredPoints = LSDP.LSDMap_PointData(this_df,
data_type="pandas",
PANDEX=True)
MF.add_point_data(ClusteredPoints,
show_colourbar="False",
zorder=100,
unicolor=this_colour,
manual_size=3)
MF.save_fig(fig_width_inches=fig_width_inches,
FigFileName=OutDirectory + fname_prefix +
'_hs_clusters_SO{}.png'.format(stream_order),
FigFormat='png',
Fig_dpi=300,
fixed_cbar_characters=6,
adjust_cbar_characters=False,
transparent=True) # Save the figure