def ExampleOne_PartOne_SimpleHillshade(DataDirectory,Base_file):
"""
This function makes a shaded relief plot of the DEM with the basins coloured
by the basin ID.
Args:
DataDirectory (str): the data directory with the m/n csv files
Base_file (str): The prefix for the m/n csv files
Returns:
Shaded relief plot with the basins coloured by basin ID
Author: FJC
"""
# specify the figure size and format
fig_size_inches = 12
ax_style = "Normal"
# Get the filenames you want
BackgroundRasterName = Base_file+"_hs.bil"
DrapeRasterName = Base_file+".bil"
# clear the plot
plt.clf()
# this is where we want the colourbar
cbar_loc = "right"
# set up the base image and the map
MF = MapFigure(BackgroundRasterName, DataDirectory,coord_type="UTM_km",colourbar_location = cbar_loc)
MF.add_drape_image(DrapeRasterName,DataDirectory,colourmap = "jet", alpha = 0.6, colorbarlabel = "Elevation (m)")
# Save the image
ImageName = DataDirectory+"Xian_example1_hillshade.png"
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, Fig_dpi = 250)
def PrintChannels(DataDirectory,fname_prefix, add_basin_labels = True, cmap = "jet", cbar_loc = "right", size_format = "ESURF", fig_format = "png", dpi = 250, out_fname_prefix = ""):
"""
This function prints a channel map over a hillshade.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
cbar_lox (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
Shaded relief plot with the basins coloured by basin ID. Uses a colourbar to show each basin
Author: SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# Get the filenames you want
BackgroundRasterName = fname_prefix+"_hs.bil"
DrapeRasterName = fname_prefix+".bil"
ChannelFileName = fname_prefix+"_chi_data_map.csv"
chi_csv_fname = DataDirectory+ChannelFileName
thisPointData = LSDMap_PD.LSDMap_PointData(chi_csv_fname)
# clear the plot
plt.clf()
# set up the base image and the map
MF = MapFigure(BackgroundRasterName, DataDirectory,coord_type="UTM_km",colourbar_location = "None")
MF.add_drape_image(DrapeRasterName,DataDirectory,colourmap = cmap, alpha = 0.6)
MF.add_point_data(thisPointData,column_for_plotting = "basin_key",
scale_points = True,column_for_scaling = "drainage_area",
scaled_data_in_log = True,
max_point_size = 5, min_point_size = 1)
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory+fname_prefix+"_channels_coloured_by_basin."+fig_format
else:
ImageName = DataDirectory+out_fname_prefix+"_channels_coloured_by_basin."+fig_format
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, FigFormat=fig_format, Fig_dpi = dpi)
def SimpleHillshade(DataDirectory,Base_file, cmap = "jet", cbar_loc = "right", size_format = "ESURF", fig_format = "png", dpi = 250, out_fname_prefix = ""):
"""
This function makes a shaded relief plot of the DEM.
Args:
DataDirectory (str): the data directory with the rasters
Base_file (str): The prefix for the rasters
cmap (str or colourmap): The colourmap to use for the plot
cbar_loc (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
Shaded relief plot. The elevation is also included in the plot.
Author: FJC, SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# Get the filenames you want
BackgroundRasterName = Base_file+"_hs.bil"
DrapeRasterName = Base_file+".bil"
# clear the plot
plt.clf()
# set up the base image and the map
MF = MapFigure(BackgroundRasterName, DataDirectory,coord_type="UTM_km",colourbar_location = cbar_loc)
MF.add_drape_image(DrapeRasterName,DataDirectory,colourmap = cmap, alpha = 0.6, colorbarlabel = "Elevation (m)")
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory+Base_file+"_hillshade."+fig_format
else:
ImageName = DataDirectory+out_fname_prefix+"_hillshade."+fig_format
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, FigFormat=fig_format, Fig_dpi = dpi)
def map_custom():
"""
Testing function to plot custom maps before creating real function for mapping routines
Args:
Yes.
returns:
No.
Author:
BG
"""
###### Parameters ######
Directory = "/home/s1675537/PhD/DataStoreBoris/GIS/Data/Carpathian/knickpoint/" # reading directory
wDirectory = Directory # writing directory
Base_file = "Buzau" # It will be the cabkground raster. Each other raster you want to drap on it will be cropped to its extents including nodata
csv_file = Directory + "test_sign_m_chi.csv" # Name of your point file, add a similar line with different name if you have more than one point file
DrapeRasterName = "Buzau_hs.bil" # if you want to drap a raster on your background one. Just add a similar line in case you want another raster to drap and so on
wname = "sign_test" # name of your output file
dpi = 500 # Quality of your output image, don't exceed 900
fig_size_inches = 7 # Figure size in Inches
##### Now we can load and plot the data
BackgroundRasterName = Base_file + ".bil" # Ignore this line
thisPointData = LSDP.LSDMap_PointData(csv_file, PANDEX = True) # Load the point file #1, add a similar line with different name if you have more than one point file.
plt.clf() # Ignore this line
MF = MapFigure(BackgroundRasterName, Directory,coord_type="UTM_km") # load the background raster
MF.add_drape_image(DrapeRasterName,Directory, # Calling the function will add a drapped raster on the top of the background one
colourmap = "gray", # colormap used for this raster, see http://matplotlib.org/users/colormaps.html for examples, put _r at the end of a colormap to get the reversed version
alpha=0.5, # transparency of this specific layer, 0 for fully transparent (why not) and 1 for fully opaque
show_colourbar = False, # Well, this one is explicit I think
colorbarlabel = "Colourbar") # Name of your Colourbar, it might bug though
MF.add_point_data( thisPointData, # this function plot the requested point file using the lat/long column in the csv file
column_for_plotting = "m_chi_sign", # Column used to color the data
this_colourmap = "cubehelix", # Colormap used, see http://matplotlib.org/users/colormaps.html for examples, put _r at the end of a colormap to get the reversed version
colorbarlabel = "Colourbar", # Label
scale_points = False, # All the point will have the same size if False
column_for_scaling = "None", # If scale point True, you can scale the size of your points using one of the columns
scaled_data_in_log = False, # If scale point True, you can log the scaling
max_point_size = 5, # max size if scale point True again
min_point_size = 0.5, # You should be able to guess that one now
coulor_log = False, # do you want a log scale for your colorbar ?
coulor_manual_scale = [], #Do you want to manually limit the scale of your colorbar? if not let is false
manual_size = 0.5, # If none of above is choosen but you want to put another value than 0.5 to scale your point
alpha = 1, # transparency of this specific layer, 0 for fully transparent (why not) and 1 for fully opaque
minimum_log_scale_cut_off = -10) # you probably won't need this
ImageName = wDirectory+str(int(clock.time()))+wname+".png" # Ignore this
ax_style = "Normal" # Ignore this
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, Fig_dpi = dpi) # Save the figure
def PrintChannels(DataDirectory,fname_prefix, add_basin_labels = True, cmap = "jet", cbar_loc = "right", size_format = "ESURF", fig_format = "png", dpi = 250, out_fname_prefix = ""):
"""
This function prints a channel map over a hillshade.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
cbar_lox (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
Shaded relief plot with the basins coloured by basin ID. Uses a colourbar to show each basin
Author: SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# Get the filenames you want
BackgroundRasterName = fname_prefix+"_hs.bil"
DrapeRasterName = fname_prefix+".bil"
ChannelFileName = fname_prefix+"_chi_data_map.csv"
chi_csv_fname = DataDirectory+ChannelFileName
thisPointData = LSDMap_PD.LSDMap_PointData(chi_csv_fname)
# clear the plot
plt.clf()
# set up the base image and the map
MF = MapFigure(BackgroundRasterName, DataDirectory,coord_type="UTM_km",colourbar_location = "None")
MF.add_drape_image(DrapeRasterName,DataDirectory,colourmap = cmap, alpha = 0.6)
MF.add_point_data(thisPointData,column_for_plotting = "basin_key",
scale_points = True,column_for_scaling = "drainage_area",
scaled_data_in_log = True,
max_point_size = 5, min_point_size = 1)
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory+fname_prefix+"_channels_coloured_by_basin."+fig_format
else:
ImageName = DataDirectory+out_fname_prefix+"_channels_coloured_by_basin."+fig_format
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, FigFormat=fig_format, Fig_dpi = dpi)
def SimpleDrape(DataDirectory,Base_file, Drape_prefix, cmap = "jet", cbar_loc = "right", cbar_label = "drape colourbar", size_format = "ESURF", fig_format = "png", dpi = 250, out_fname_prefix = "", coord_type = "UTM_km", use_scalebar = False, drape_cnorm = "none", colour_min_max = []):
"""
This function makes a simple drape plot. You can choose the colourbar in this one. Similar to the PlotHillshade routine but a bit more flexible.
Args:
DataDirectory (str): the data directory with the rasters
Base_file (str): The prefix for the rasters
cmap (str or colourmap): The colourmap to use for the plot
cbar_loc (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
coord_type (str): this can be in UTM_km or UTM_m
use_scalebar (bool): If true inserts a scalebar in the image
drape_cnorm (str): Sets the normalisation of the colourbar.
colour_min_max (float list): Sets the minimum and maximum values of the colourbar
Returns:
Shaded relief plot. The elevation is also included in the plot.
Author: FJC, SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# Get the filenames you want
BackgroundRasterName = Base_file+"_hs.bil"
ElevationName = Base_file+".bil"
DrapeName = Drape_prefix+".bil"
# clear the plot
plt.clf()
# set up the base image and the map
MF = MapFigure(BackgroundRasterName, DataDirectory,coord_type=coord_type,colourbar_location = cbar_loc)
#MF.add_drape_image(ElevationName,DataDirectory,colourmap = "gray", alpha = 0.6, colorbarlabel = None)
MF.add_drape_image(DrapeName,DataDirectory,colourmap = cmap, alpha = 0.6, colorbarlabel = cbar_label, norm = drape_cnorm, colour_min_max = colour_min_max)
if(use_scalebar):
print("Let me add a scalebar")
MF.add_scalebar()
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory+Base_file+"_drape."+fig_format
else:
ImageName = DataDirectory+out_fname_prefix+"_drape."+fig_format
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, FigFormat=fig_format, Fig_dpi = dpi)
def ExampleOne_PartOne_SimpleHillshade(DataDirectory,Base_file):
"""
This function makes a shaded relief plot of the DEM with the basins coloured
by the basin ID.
Args:
DataDirectory (str): the data directory with the m/n csv files
Base_file (str): The prefix for the m/n csv files
Returns:
Shaded relief plot with the basins coloured by basin ID
Author: FJC
"""
# specify the figure size and format
fig_size_inches = 12
ax_style = "Normal"
# Get the filenames you want
BackgroundRasterName = Base_file+"_hs.bil"
DrapeRasterName = Base_file+".bil"
# clear the plot
plt.clf()
# this is where we want the colourbar
cbar_loc = "right"
# set up the base image and the map
MF = MapFigure(BackgroundRasterName, DataDirectory,coord_type="UTM_km",colourbar_location = cbar_loc)
MF.add_drape_image(DrapeRasterName,DataDirectory,colourmap = "jet", alpha = 0.6, colorbarlabel = "Elevation (m)")
# Save the image
ImageName = DataDirectory+"Xian_example1_hillshade.png"
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, Fig_dpi = 250)
def SimpleHillshadeForAnimation(DataDirectory,Base_file, cmap = "jet", cbar_loc = "right",
size_format = "ESURF", fig_format = "png",
dpi = 250, imgnumber = 0, full_basefile = [],
custom_cbar_min_max = [], out_fname_prefix = ""):
"""
This function make a hillshade image that is optimised for creating
an animation. Used with the MuddPILE model
Args:
DataDirectory (str): the data directory with the data files
Base_file (str): The prefix for the data files
cmap (str or colourmap): The colourmap to use for the plot
cbar_loc (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
imgnumber (int): the number of the image. Usually frames from model runs have integer numbers after them
full_basefile (str): The root name of the figures you want. If empty, it uses the data_directory+base_file
custom_min_max (list of int/float): if it contains two elements, recast the raster to [min,max] values for display.
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
Shaded relief plot. The elevation is also included in the plot.
Author: FJC, SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# Get the filenames you want
BackgroundRasterName = Base_file+"_hs.bil"
DrapeRasterName = Base_file+".bil"
# clear the plot
plt.clf()
# set up the base image and the map
MF = MapFigure(BackgroundRasterName, DataDirectory,coord_type="UTM_km",colourbar_location = cbar_loc)
MF.add_drape_image(DrapeRasterName,DataDirectory,colourmap = cmap, alpha = 0.6, colorbarlabel = "Elevation (m)",colour_min_max = custom_cbar_min_max)
# Save the image
if(full_basefile == []):
if len(out_fname_prefix) == 0:
ImageName = DataDirectory+Base_file+"_img"+"%004d" % (imgnumber)+"."+fig_format
else:
ImageName = DataDirectory+out_fname_prefix+"_img"+"%004d" % (imgnumber)+"."+fig_format
else:
ImageName = full_basefile+"_img"+"%004d" % (imgnumber)+"."+fig_format
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, FigFormat=fig_format, Fig_dpi = dpi, adjust_cbar_characters=False,
fixed_cbar_characters=4)
def SimpleDrape(DataDirectory,Base_file, Drape_prefix, cmap = "jet", cbar_loc = "right", cbar_label = "drape colourbar", size_format = "ESURF", fig_format = "png", dpi = 250, out_fname_prefix = ""):
"""
This function makes a simple drape plot. You can choose the colourbar in this one. Similar to the PlotHillshade routine but a bit more flexible.
Args:
DataDirectory (str): the data directory with the rasters
Base_file (str): The prefix for the rasters
cmap (str or colourmap): The colourmap to use for the plot
cbar_loc (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
Shaded relief plot. The elevation is also included in the plot.
Author: FJC, SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# Get the filenames you want
BackgroundRasterName = Base_file+"_hs.bil"
ElevationName = Base_file+".bil"
DrapeName = Drape_prefix+".bil"
# clear the plot
plt.clf()
# set up the base image and the map
MF = MapFigure(BackgroundRasterName, DataDirectory,coord_type="UTM_km",colourbar_location = cbar_loc)
#MF.add_drape_image(ElevationName,DataDirectory,colourmap = "gray", alpha = 0.6, colorbarlabel = None)
MF.add_drape_image(DrapeName,DataDirectory,colourmap = cmap, alpha = 0.6, colorbarlabel = cbar_label)
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory+Base_file+"_drape."+fig_format
else:
ImageName = DataDirectory+out_fname_prefix+"_drape."+fig_format
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, FigFormat=fig_format, Fig_dpi = dpi)
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 PrintChannelsAndBasins(DataDirectory,
fname_prefix,
add_basin_labels=True,
cmap="jet",
cbar_loc="right",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix=""):
"""
This function prints a channel map over a hillshade.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
cbar_lox (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
Shaded relief plot with the basins coloured by basin ID. Uses a colourbar to show each basin
Author: SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
print('Basin keys are: ')
print(basin_keys)
# going to make the basin plots - need to have bil extensions.
print(
"I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!"
)
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix + '_hs' + raster_ext
BasinsName = fname_prefix + '_AllBasins' + raster_ext
print(BasinsName)
Basins = LSDP.GetBasinOutlines(DataDirectory, BasinsName)
ChannelFileName = fname_prefix + "_chi_data_map.csv"
chi_csv_fname = DataDirectory + ChannelFileName
thisPointData = LSDMap_PD.LSDMap_PointData(chi_csv_fname)
# clear the plot
plt.clf()
# set up the base image and the map
print("I am showing the basins without text labels.")
MF = MapFigure(HillshadeName,
DataDirectory,
coord_type="UTM_km",
colourbar_location="None")
MF.plot_polygon_outlines(Basins, linewidth=0.8)
MF.add_drape_image(BasinsName,
DataDirectory,
colourmap=cmap,
alpha=0.1,
discrete_cmap=False,
n_colours=len(basin_keys),
show_colourbar=False,
modify_raster_values=True,
old_values=basin_junctions,
new_values=basin_keys,
cbar_type=int)
MF.add_point_data(thisPointData,
column_for_plotting="basin_key",
scale_points=True,
column_for_scaling="drainage_area",
this_colourmap=cmap,
scaled_data_in_log=True,
max_point_size=3,
min_point_size=1)
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + fname_prefix + "_channels_with_basins." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_channels_with_basins." + fig_format
MF.save_fig(fig_width_inches=fig_size_inches,
FigFileName=ImageName,
axis_style=ax_style,
FigFormat=fig_format,
Fig_dpi=dpi)
def PrintBasins_Complex(DataDirectory,
fname_prefix,
use_keys_not_junctions=True,
show_colourbar=False,
Remove_Basins=[],
Rename_Basins={},
Value_dict={},
cmap="jet",
colorbarlabel="colourbar",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix="",
include_channels=False,
label_basins=True):
"""
This function makes a shaded relief plot of the DEM with the basins coloured
by the basin ID.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
use_keys_not_junctions (bool): If true use basin keys to locate basins, otherwise use junction indices
show_colourbar (bool): if true show the colourbar
Remove_Basins (list): A lists containing either key or junction indices of basins you want to remove from plotting
Rename_Basins (dict): A dict where the key is either basin key or junction index, and the value is a new name for the basin denoted by the key
Value_dict (dict): A dict where the key is either basin key or junction index, and the value is a value of the basin that is used to colour the basins
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
cbar_loc (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
include_channels (bool): If true, adds a channel plot. It uses the chi_data_maps file
label_basins (bool): If true, the basins get labels
Returns:
Shaded relief plot with the basins coloured by basin ID. Uses a colourbar to show each basin. This allows more complex plotting with renamed and excluded basins.
Author: FJC, SMM
"""
#import modules
from LSDMapFigure.PlottingRaster import MapFigure
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
print('Basin keys are: ')
print(basin_keys)
# going to make the basin plots - need to have bil extensions.
print(
"I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!"
)
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix + '_hs' + raster_ext
BasinsName = fname_prefix + '_AllBasins' + raster_ext
# This initiates the figure
MF = MapFigure(HillshadeName,
DataDirectory,
coord_type="UTM_km",
colourbar_location="None")
# This adds the basins
MF.add_basin_plot(BasinsName,
fname_prefix,
DataDirectory,
mask_list=Remove_Basins,
rename_dict=Rename_Basins,
value_dict=Value_dict,
use_keys_not_junctions=use_keys_not_junctions,
show_colourbar=show_colourbar,
discrete_cmap=True,
n_colours=15,
colorbarlabel=colorbarlabel,
colourmap=cmap,
adjust_text=False,
label_basins=label_basins)
# See if you need the channels
if include_channels:
print("I am going to add some channels for you")
ChannelFileName = fname_prefix + "_chi_data_map.csv"
chi_csv_fname = DataDirectory + ChannelFileName
thisPointData = LSDMap_PD.LSDMap_PointData(chi_csv_fname)
MF.add_point_data(thisPointData,
column_for_plotting="basin_key",
scale_points=True,
column_for_scaling="drainage_area",
this_colourmap="Blues_r",
scaled_data_in_log=True,
max_point_size=3,
min_point_size=1,
discrete_colours=True,
NColours=1,
zorder=5)
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + fname_prefix + "_selected_basins." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_selected_basins." + fig_format
MF.save_fig(fig_width_inches=fig_width_inches,
FigFileName=ImageName,
FigFormat=fig_format,
Fig_dpi=dpi,
transparent=True) # Save the figure
def PlotTopoRaster(DataDirectory,
fname_prefix,
size_format='ESURF',
FigFormat='png',
colors="terrain"):
"""
Creates a basic Terrain topographic raster. Needs the Hillshade
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
size_format (str): Can be "big" (16 inches wide), "geomorphology" (6.25 inches wide), or "ESURF" (4.92 inches wide) (defualt esurf).
FigFormat (str): The format of the figure. Usually 'png' or 'pdf'. If "show" then it calls the matplotlib show() command.
Returns:
Shaded relief plot with the basins coloured by basin ID
Author: BG, FJC
"""
# check if a directory exists for the chi plots. If not then make it.
raster_directory = DataDirectory + 'raster_plots/'
if not os.path.isdir(raster_directory):
os.makedirs(raster_directory)
# Set up fonts for plots
label_size = 10
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['Liberation Sans']
rcParams['font.size'] = label_size
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# going to make the basin plots - need to have bil extensions.
print("I'm going to make a cabic topographic plot")
# get the rasters
raster_ext = '.bil'
## Just checking if you have a PP version of it
if os.path.isfile(DataDirectory + fname_prefix + "_PP.bil"):
BackgroundRasterName = fname_prefix + "_PP" + raster_ext
else:
BackgroundRasterName = fname_prefix + raster_ext
HillshadeName = fname_prefix + '_hs' + raster_ext
# create the map figure
MF = MapFigure(BackgroundRasterName,
DataDirectory,
coord_type="UTM_km",
colourbar_location='None')
# Drape the hillshade and add the color
## Frist plot the terrain toporaster
MF.add_drape_image(
BackgroundRasterName,
DataDirectory, # Calling the function will add a drapped raster on the top of the background on
colourmap=
colors, # colormap used for this raster, see http://matplotlib.org/users/colormaps.html for examples, put _r at the end of a colormap to get the reversed version
alpha=
1, # transparency of this specific layer, 0 for fully transparent (why not) and 1 for fully opaque
show_colourbar=True, # Well, this one is explicit I think
colorbarlabel="None",
NFF_opti=True)
## Drape the Hillshade raster
MF.add_drape_image(
HillshadeName,
DataDirectory, # Calling the function will add a drapped raster on the top of the background on
colourmap=
"gray", # colormap used for this raster, see http://matplotlib.org/users/colormaps.html for examples, put _r at the end of a colormap to get the reversed version
alpha=
0.4, # transparency of this specific layer, 0 for fully transparent (why not) and 1 for fully opaque
show_colourbar=True, # Well, this one is explicit I think
colorbarlabel="None",
NFF_opti=True)
# Save the figure
ImageName = raster_directory + fname_prefix + '_Topo.' + FigFormat
MF.save_fig(fig_width_inches=fig_width_inches,
FigFileName=ImageName,
FigFormat=FigFormat,
Fig_dpi=300)
'#C8C864', '#9D78C1', '#6E9C6E', '#AF5A5A', '#A6835F', '#A7DBA7',
'#AF5A5A', '#F3C79B', '#AF5A5A', '#6E899B', '#B1DEDE'
]
CustomColorCombMap = matplotlib.colors.ListedColormap(CustomColorComb)
#%%======================CONVERT_CSV_FILE_TO_SHAPEFILES=====================%%#
thisPointData = LSDP.LSDMap_PointData(csv_file, PANDEX=True)
#%%============================LOAD_AND_PLOT_DATA===========================%%#
plt.clf()
MF = MapFigure(BaseRasterName=BaseRasterName,
Directory=Import_Directory,
coord_type="UTM",
colourbar_location="None",
basemap_colourmap="gray",
plot_title='None',
NFF_opti=False,
alpha=1) # load and display the background hillslope raster
MF.add_basin_plot(RasterName=RasterNameCatch,
BasinInfoPrefix=BasinInfoPrefix,
Directory=Import_Directory,
colourmap="gray",
alpha=1,
show_colourbar="False",
colorbarlabel="Colourbar",
discrete_cmap=False,
n_colours=10,
cbar_type=float,
use_keys_not_junctions=True,
#BR = BaseRaster(BackgroundRasterName, Directory)
#BR.set_raster_type("Terrain")
#print(BR._colourmap)
#BR.show_raster()
#BR.set_colourmap("RdYlGn")
#BR.show_raster()
#label_size = 100
#rcParams['font.family'] = 'sans-serif'
#rcParams['font.sans-serif'] = ['arial']
#rcParams['font.size'] = label_size
#rcParams['lines.linewidth'] = 1.5
plt.clf()
MF = MapFigure(DrapeRasterName, Directory,coord_type="UTM_km",colourbar_location='None')
# add the basins drape
cmap = cm.Set1
MF.add_drape_image(BasinsName, Directory, colourmap = cmap, alpha = 0.5, colorbarlabel='Basin ID', show_colourbar = False)
# add the basin outlines
Basins = LSDMap_VT.GetBasinOutlines(Directory, BasinsName)
MF.plot_polygon_outlines(Basins, linewidth=0.8)
#MF.add_drape_image(ChiRasterName,Directory,colourmap = "cubehelix",alpha = 0.4, show_colourbar = True)
#MF.show_plot()
MF.save_fig(fig_width_inches = 12, FigFileName=Directory+Base_file+'.png', FigFormat='png', Fig_dpi=300, transparent=True)
# Customise the DrapePlot
#dp.make_drape_colourbar(cbar_label=colourbar_label)
#dp.set_fig_axis_labels()
#dp.show_plot()
#label_size = 100 rcParams['font.family'] = 'sans-serif' rcParams['font.sans-serif'] = ['arial'] #rcParams['font.size'] = label_size #rcParams['lines.linewidth'] = 1.5 DataDirectory = "/home/smudd/SMMDataStore/analysis_for_papers/movern_testing/" Base_file = "Irian_Jaya_PP" #Directory = "/home/s1563094/Datastore/DATA/UK/LiDAR_DTM_1m/HIN/" #Base_file = "HIN_" #BackgroundRasterName = Base_file+".bil" BackgroundRasterName = Base_file+".bil" DrapeRasterName = Base_file+"_hs.bil" BasinRasterName = Base_file+"_AllBasins.bil" DischargeRasterName= Base_file+"_Q.bil" plt.clf() cbar_loc = "right" MF = MapFigure(BackgroundRasterName, DataDirectory,coord_type="UTM_km",colourbar_location = cbar_loc) #MF.add_drape_image(BackgroundRasterName,DataDirectory,alpha = 1) MF.add_drape_image(DischargeRasterName,DataDirectory,colourmap = "cubehelix", alpha = 0.6) ImageName = DataDirectory+"TestNewArtist.png" fig_size_inches = 12 ax_style = "Normal" MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, Fig_dpi = 250)
def ExampleOne_PartFour_MaskBasins(DataDirectory, fname_prefix):
"""
This function makes a shaded relief plot of the DEM with the basins coloured
by the basin ID. It shows how to mask certain basins.
Search the function for "Basins_to_mask".
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
Returns:
Shaded relief plot with the basins coloured by basin ID
Author: SMM
"""
import numpy as np
Basins_to_mask = [0,4,6]
FigFormat = "png"
size_format = "geomorphology"
#import modules
# from LSDMapFigure.PlottingRaster import MapFigure
# from LSDMapFigure.PlottingRaster import BaseRaster
# import LSDPlottingTools.LSDMap_VectorTools as LSDMap_VT
# import LSDPlottingTools.LSDMap_PointTools as LSDMap_PT
# Set up fonts for plots
label_size = 10
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
# get the junctions to mask
key_to_index_dict = dict(zip(basin_keys,basin_junctions))
junctions_to_mask = []
for basin in Basins_to_mask:
junctions_to_mask.append( key_to_index_dict[basin])
print("The junctions to mask are")
print(junctions_to_mask)
print ('Basin keys are: ')
print basin_keys
print("Let me mask those for you")
new_keys = []
for key in basin_keys:
if key in Basins_to_mask:
new_keys.append(np.nan)
else:
new_keys.append(key)
print("The new keys are: ")
print(new_keys)
basin_keys = new_keys
# get a discrete colormap
cmap = plt.cm.jet
# going to make the basin plots - need to have bil extensions.
print("I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!")
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix+'_hs'+raster_ext
BasinsName = fname_prefix+'_AllBasins'+raster_ext
print (BasinsName)
# create the map figure
# We set colourbar location to none since we are labelling the figures
MF = MapFigure(HillshadeName, DataDirectory,coord_type="UTM_km", colourbar_location='none')
# add the basins drape
MF.add_drape_image(BasinsName, DataDirectory, colourmap = cmap, alpha = 0.8, colorbarlabel='Basin ID', discrete_cmap=True, n_colours=len(basin_keys), show_colourbar = True, modify_raster_values=True, old_values=basin_junctions, new_values=basin_keys, cbar_type = int)
# add the basin outlines
Basins = LSDP.GetBasinOutlines(DataDirectory, BasinsName)
# get rid of the basins that are being masked
for junction in junctions_to_mask:
del Basins[junction]
# note that at this stage the Basins are keyed with the junction index
MF.plot_polygon_outlines(Basins, linewidth=0.8)
# add the basin labelling
label_dict = dict(zip(basin_junctions,basin_keys))
# this dict has the basin junction as the key and the basin_key as the value
Points = LSDP.GetPointWithinBasins(DataDirectory, BasinsName)
# get rid of points as well
for junction in junctions_to_mask:
del Points[junction]
del label_dict[junction]
MF.add_text_annotation_from_shapely_points(Points, text_colour='k', label_dict=label_dict)
# Save the figure
ImageName = DataDirectory+fname_prefix+'_labelled_basins.'+FigFormat
MF.save_fig(fig_width_inches = fig_width_inches, FigFileName = ImageName, FigFormat=FigFormat, Fig_dpi = 250)
def SimpleHillshadeForAnimation(DataDirectory,Base_file, cmap = "jet", cbar_loc = "right",
size_format = "ESURF", fig_format = "png",
dpi = 250, imgnumber = 0, full_basefile = [],
custom_cbar_min_max = [], out_fname_prefix = "", coord_type="UTM_km", hide_ticklabels=False):
"""
This function make a hillshade image that is optimised for creating
an animation. Used with the MuddPILE model
Args:
DataDirectory (str): the data directory with the data files
Base_file (str): The prefix for the data files
cmap (str or colourmap): The colourmap to use for the plot
cbar_loc (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
imgnumber (int): the number of the image. Usually frames from model runs have integer numbers after them
full_basefile (str): The root name of the figures you want. If empty, it uses the data_directory+base_file
custom_min_max (list of int/float): if it contains two elements, recast the raster to [min,max] values for display.
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
coord_type (str): either UTM or UTM_km
hide_ticklabels (bool): if true, hide the tick labels from the plot
Returns:
Shaded relief plot. The elevation is also included in the plot.
Author: FJC, SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# Get the filenames you want
BackgroundRasterName = Base_file+"_hs.bil"
DrapeRasterName = Base_file+".bil"
# clear the plot
plt.clf()
# set up the base image and the map
MF = MapFigure(BackgroundRasterName, DataDirectory,coord_type=coord_type,colourbar_location = cbar_loc)
MF.add_drape_image(DrapeRasterName,DataDirectory,colourmap = cmap, alpha = 0.6, colorbarlabel = "Elevation (m)",colour_min_max = custom_cbar_min_max)
# Save the image
if(full_basefile == []):
if len(out_fname_prefix) == 0:
ImageName = DataDirectory+Base_file+"_img"+"%004d" % (imgnumber)+"."+fig_format
else:
ImageName = DataDirectory+out_fname_prefix+"_img"+"%004d" % (imgnumber)+"."+fig_format
else:
ImageName = full_basefile+"_img"+"%004d" % (imgnumber)+"."+fig_format
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, FigFormat=fig_format, Fig_dpi = dpi, adjust_cbar_characters=False,
fixed_cbar_characters=4, hide_ticklabels=hide_ticklabels)
Base_file = "area"
BackgroundRasterName = Base_file + ".bil"
DrapeRasterName = Base_file + "_hs.bil"
ChiRasterName = Base_file + "_curvature.bil"
#BR = BaseRaster(BackgroundRasterName, Directory)
#BR.set_raster_type("Terrain")
#print(BR._colourmap)
#BR.show_raster()
#BR.set_colourmap("RdYlGn")
#BR.show_raster()
plt.clf()
MF = MapFigure(BackgroundRasterName, Directory, coord_type="UTM_km")
MF.add_drape_image(DrapeRasterName, Directory, alpha=0.4)
MF.add_drape_image(ChiRasterName,
Directory,
colourmap="cubehelix",
alpha=0.4,
show_colourbar=True)
#MF.show_plot()
ImageName = Directory + "boris.png"
fig_size_inches = 6
ax_style = "Madhouse"
MF.save_fig(fig_width_inches=fig_size_inches,
FigFileName=ImageName,
axis_style=ax_style)
# Customise the DrapePlot
RasterName_Seg = 'Extract_Basin_Segre_AllBasins.bil'
BasinInfoPrefix_Sal = 'Extract_Basin_Salat2'
RasterName_Sal = 'Extract_Basin_Salat2_AllBasins.bil'
BasinInfoPrefix_Sai = 'Extract_Basin_Saison'
RasterName_Sai = 'Extract_Basin_Saison_AllBasins.bil'
csv_file1 = Import_Directory + 'AHe_Data_used.csv'
csv_file2 = Import_Directory + 'AHe_Data_not_used.csv'
#%%============================LOAD_AND_PLOT_DATA===========================%%#
plt.clf()
MF = MapFigure(BaseRasterName=BaseRasterName,
Directory=Import_Directory,
coord_type="UTM",
colourbar_location="None",
basemap_colourmap="gray",
plot_title='None',
NFF_opti=False,
alpha=1,
zorder=1)
MF.add_drape_image(RasterName=HSRaster,
Directory=Import_Directory,
colourmap="gray",
alpha=0.25,
colorbarlabel="Colourbar",
discrete_cmap=False,
n_colours=10,
norm="None",
colour_min_max=[],
modify_raster_values=False,
#BR.set_raster_type("Terrain")
#print(BR._colourmap)
#BR.show_raster()
#BR.set_colourmap("RdYlGn")
#BR.show_raster()
#label_size = 100
#rcParams['font.family'] = 'sans-serif'
#rcParams['font.sans-serif'] = ['arial']
#rcParams['font.size'] = label_size
#rcParams['lines.linewidth'] = 1.5
plt.clf()
MF = MapFigure(DrapeRasterName,
Directory,
coord_type="UTM_km",
colourbar_location='None')
# add the basins drape
cmap = cm.Set1
MF.add_drape_image(BasinsName,
Directory,
colourmap=cmap,
alpha=0.5,
colorbarlabel='Basin ID',
show_colourbar=False)
# add the basin outlines
Basins = LSDMap_VT.GetBasinOutlines(Directory, BasinsName)
MF.plot_polygon_outlines(Basins, linewidth=0.8)
#MF.add_drape_image(ChiRasterName,Directory,colourmap = "cubehelix",alpha = 0.4, show_colourbar = True)
#MF.show_plot()
MF.save_fig(fig_width_inches=12,
rcParams['text.usetex'] = False
# define filenames and relative workspace
fname_prefix = "bolinas"
HillshadeName = "bolinas_hs.bil"
Directory = "/home/mhurst/bolinas_paper/"
DataDirectory = Directory + "data/"
ChannelDataDirectory = DataDirectory + "channel_data/"
HillslopeDataDirectory = DataDirectory + "hillslope_data/"
HilltopPointsData = HillslopeDataDirectory + "bolinas_HilltopData.csv"
ChannelPointsDat = ChannelDataDirectory + "bolinas_MChiSegmented.csv"
ChannelHeadPointsData = ChannelDataDirectory + "bolinas_CH_wiener_nodeindices_for_Arc.csv"
# create the map figure
MF = MapFigure(HillshadeName,
DataDirectory,
coord_type="UTM_km",
colourbar_location='None')
# add hilltops
HilltopPointsDF = pd.read_csv(HilltopPointsData)
HilltopPoints = LSDP.LSDMap_PointData(HilltopPointsDF,
data_type="pandas",
PANDEX=True)
MF.add_point_data(HilltopPoints,
alpha=0.5,
zorder=100,
unicolor="blue",
manual_size=5)
# add channel heads
#ChannelHeadsDF = pd.read_csv(ChannelHeadPointsData)
liste = ["autumn"]
for i in range(len(liste)) :
color = random.choice(liste)
##### Now we can load and plot the data
BackgroundRasterName = Base_file + ".bil" # Ignore this line
thisPointData = LSDP.LSDMap_PointData(df, data_type = "pandas", PANDEX = True) # Load the point file #1, add a similar line with different name if you have more than one point file.
plt.clf() # Ignore this line
MF = MapFigure(BackgroundRasterName, Directory,coord_type="UTM_km", NFF_opti = True, colourbar_location = 'bottom') # load the background raster
MF.add_drape_image(BackgroundRasterName,Directory, # Calling the function will add a drapped raster on the top of the background one
colourmap = "gray", # colormap used for this raster, see http://matplotlib.org/users/colormaps.html for examples, put _r at the end of a colormap to get the reversed version
alpha = 0.5, # transparency of this specific layer, 0 for fully transparent (why not) and 1 for fully opaque
show_colourbar = False, # Well, this one is explicit I think
colorbarlabel = "Colourbar", # Name of your Colourbar, it might bug though
NFF_opti = True)
MF.add_drape_image(DrapeRasterName,Directory, # Calling the function will add a drapped raster on the top of the background one
colourmap = "gray", # colormap used for this raster, see http://matplotlib.org/users/colormaps.html for examples, put _r at the end of a colormap to get the reversed version
alpha = 0.5, # transparency of this specific layer, 0 for fully transparent (why not) and 1 for fully opaque
show_colourbar = False, # Well, this one is explicit I think
colorbarlabel = "Colourbar", # Name of your Colourbar, it might bug though
NFF_opti = True)
#BR = BaseRaster(BackgroundRasterName, Directory)
#BR.set_raster_type("Terrain")
#print(BR._colourmap)
#BR.show_raster()
#BR.set_colourmap("RdYlGn")
#BR.show_raster()
#PD_file = Base_file+"_chi_coord_basins.csv"
PD_file = Base_file + "_MChiSegmented.csv"
PointData = LSDMap_PointTools.LSDMap_PointData(Directory + PD_file)
plt.clf()
cbar_loc = "Bottom"
MF = MapFigure(BackgroundRasterName,
Directory,
coord_type="UTM_km",
colourbar_location=cbar_loc)
MF.add_drape_image(DrapeRasterName, Directory, alpha=0.4)
#MF.add_drape_image(ChiRasterName,Directory,colourmap = "cubehelix",alpha = 0.4)
MF.add_point_data(PointData,
column_for_plotting="source_key",
colorbarlabel="I am point data",
scale_points=True,
column_for_scaling="drainage area",
scaled_data_in_log=False)
#MF.show_plot()
ImageName = Directory + "TestNewArtist.png"
fig_size_inches = 6
ax_style = "Normal"
MF.save_fig(fig_width_inches=fig_size_inches,
FigFileName=ImageName,
def ExampleOne_PartFive_MaskBasinsMF(DataDirectory, fname_prefix):
"""
This function makes a shaded relief plot of the DEM with the basins coloured
by the basin ID.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
Returns:
Shaded relief plot with the basins coloured by basin ID
Author: SMM
"""
FigFormat = "png"
size_format = "geomorphology"
# Set up fonts for plots
label_size = 10
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# going to make the basin plots - need to have bil extensions.
print("I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!")
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix+'_hs'+raster_ext
BasinsName = fname_prefix+'_AllBasins'+raster_ext
print (BasinsName)
# create the map figure
# We set colourbar location to none since we are labelling the figures
MF = MapFigure(HillshadeName, DataDirectory,coord_type="UTM_km", colourbar_location='bottom',basemap_colourmap = "gray")
# add the basins drape
#MF.add_drape_image(HillshadeName, DataDirectory, colourmap = cmap, alpha = 0.8, colorbarlabel='Basin ID', discrete_cmap=True, n_colours=len(basin_keys), show_colourbar = False)
Remove_Basins = [4,8]
Rename_Basins = { 12: 'chumbox', 14: 'zeppo'}
Value_dict= { 1: 0.2, 2:0.3, 3:0.4, 5:0.9,6:0.7, 7:0.3, 9:0.5, 10:0.5}
MF.add_basin_plot(BasinsName,fname_prefix,DataDirectory, mask_list = Remove_Basins,
rename_dict = Rename_Basins, value_dict = Value_dict,
use_keys_not_junctions = True, show_colourbar = True,
discrete_cmap=True, n_colours=8, colorbarlabel = "$m/n$",
colourmap = plt.cm.jet, adjust_text = False)
# Save the figure
ImageName = DataDirectory+fname_prefix+'_test_Coloured_basins.'+FigFormat
MF.save_fig(fig_width_inches = fig_width_inches, FigFileName = ImageName, FigFormat=FigFormat, Fig_dpi = 250)
def MakeRasterPlotTerraceDips(DataDirectory,fname_prefix,min_size=5000,FigFormat='png',size_format='ESURF'):
"""
This function makes a raster plot of terrace locations with arrows showing the terrace
dip and dip directions.
Dip and dip direction are calculated by fitting a plane to each terrace using least-squares
regression.
Args:
DataDirectory (str): the data directory
fname_prefix (str): the name of the DEM without extension.
min_size (int): minimum number of pixels for a terrace, smaller ones will be removed
FigFormat (str): the figure format, default='png'
size_format (str): Can be "big" (16 inches wide), "geomorphology" (6.25 inches wide), or "ESURF" (4.92 inches wide) (defualt esurf).
Returns:
plot of terrace locations and dip/dip directions
Author: FJC
"""
from LSDMapFigure.PlottingRaster import BaseRaster
from LSDMapFigure.PlottingRaster import MapFigure
# Set up fonts for plots
label_size = 10
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['Liberation Sans']
rcParams['font.size'] = label_size
# make a figure
if size_format == "geomorphology":
#fig = plt.figure(1, facecolor='white',figsize=(6.25,3.5))
fig_width_inches=6.25
#l_pad = -40
elif size_format == "big":
#fig = plt.figure(1, facecolor='white',figsize=(16,9))
fig_width_inches=16
#l_pad = -50
else:
fig_width_inches = 4.92126
#fig = plt.figure(1, facecolor='white',figsize=(4.92126,3.2))
#l_pad = -35
# going to make the terrace plots - need to have bil extensions.
print("I'm going to make a raster plot of terrace elevations. Your topographic data must be in ENVI bil format or I'll break!!")
# get the rasters
raster_ext = '.bil'
BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix+'_hs'+raster_ext
TerraceElevName = fname_prefix+'_terrace_relief_final'+raster_ext
# get the terrace csv
terraces = H.read_terrace_csv(DataDirectory,fname_prefix)
filter_terraces(terraces)
# get the terrace IDs
terraceIDs = terraces.TerraceID.unique()
n_colours=len(terraceIDs)
# get the terrace dip and dip dirs
terrace_dips = get_terrace_dip_and_dipdir(terraces)
# create the map figure
MF = MapFigure(HillshadeName, DataDirectory, coord_type='UTM_km', colourbar_location='right')
# add the terrace drape
terrace_cmap = plt.cm.Reds
#terrace_cmap = colours.cmap_discretize(n_colours,terrace_cmap)
MF.add_drape_image(TerraceElevName, DataDirectory, colourmap = terrace_cmap, colorbarlabel="Elevation above channel (m)", alpha=0.8)
# add arrows oriented in the direction of dip. We might want to colour these by the dip angle?
# MF.add_arrows_from_points(terrace_dips,azimuth_header='dip_azimuth', arrow_length=100)
MF.add_strike_and_dip_symbols(terrace_dips,symbol_length=100,linewidth=0.5)
ImageName = DataDirectory+fname_prefix+'_terrace_dips_raster_plot.'+FigFormat
MF.save_fig(fig_width_inches = fig_width_inches, FigFileName = ImageName, FigFormat=FigFormat, Fig_dpi = 300) # Save the figure
def MakeRasterLithoBasinMap(DataDirectory, fname_prefix, lname_prefix, lithodict, size_format='ESURF', FigFormat='png', basins = True, m_chi = False, mancol = [], log_scale_river = False, minmax_m_chi = []):
"""
This function makes a shaded relief plot of the DEM with lithologic map on the top and basin outline
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
size_format (str): Can be "big" (16 inches wide), "geomorphology" (6.25 inches wide), or "ESURF" (4.92 inches wide) (defualt esurf).
FigFormat (str): The format of the figure. Usually 'png' or 'pdf'. If "show" then it calls the matplotlib show() command.
Basins (bool): Do you want the basin on top
minmax_m_chi (list): define a minimum/maximum for plotting m_chi on the top of litho (at the moment this plot is generated from knickpoint dataset)
Returns:
Shaded relief plot with the basins coloured by basin ID
Author: BG, FJC
"""
# check if a directory exists for the chi plots. If not then make it.
raster_directory = DataDirectory+'raster_plots/'
if not os.path.isdir(raster_directory):
os.makedirs(raster_directory)
# Set up fonts for plots
label_size = 10
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
raster_ext = '.bil'
# get the basin IDs to make a discrete colourmap for each ID
if(basins):
BasinInfoDF = Helper.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
print ('Basin keys are: ')
print (basin_keys)
BasinsName = fname_prefix+'_AllBasins.bil'
# going to make the basin plots - need to have bil extensions.
print("I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!")
# get the rasters
raster_ext = '.bil'
BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix+'_hs'+raster_ext
LithoMap = lname_prefix+raster_ext
# create the map figure
MF = MapFigure(HillshadeName, DataDirectory,coord_type="UTM_km", colourbar_location='None')
MF.add_drape_image(HillshadeName,DataDirectory,NFF_opti = True, custom_min_max = [90,240], alpha = 1)
# add the geology drape
# MF.add_basin_plot(BasinsName,fname_prefix,DataDirectory,
# use_keys_not_junctions = True, show_colourbar = True,
# discrete_cmap=True, n_colours=len(basin_keys), colorbarlabel = "Basin ID",
# colourmap = cmap, adjust_text = False)
# getting the right color now
color_map_litho = getLithoColorMap(fname_prefix, DataDirectory)
df_litho_size = pd.read_csv(DataDirectory+fname_prefix+"_lithokey.csv")
MF.add_drape_image(LithoMap,DataDirectory,colourmap = color_map_litho,
alpha=0.6,
show_colourbar = False,
colorbarlabel = "Colourbar", discrete_cmap=False,
norm = "None",
colour_min_max = [0,df_litho_size["rocktype"].max()-1],
modify_raster_values=False,
old_values=[], new_values=[], cbar_type=int,
NFF_opti = True, custom_min_max = [])
if(basins):
# add the basin outlines
Basins = LSDP.GetBasinOutlines(DataDirectory, BasinsName)
MF.plot_polygon_outlines(Basins, linewidth=0.8)
# knickpoints!
if(m_chi):
ChannelDF = pd.read_csv(DataDirectory+fname_prefix+"_ksnkp_mchi.csv")
ChannelPoints = LSDP.LSDMap_PointData(ChannelDF, data_type = "pandas", PANDEX = True)
MF.add_point_data(ChannelPoints,column_for_plotting = 'm_chi',show_colourbar = True, scale_points=True, column_for_scaling='drainage_area',alpha=0.5,zorder=100,this_colourmap = "RdBu_r" ,colour_manual_scale = mancol, scaled_data_in_log = log_scale_river,max_point_size = minmax_m_chi[1], min_point_size = minmax_m_chi[0])
else:
# add the channel network
ChannelDF = Helper.ReadChiDataMapCSV(DataDirectory,fname_prefix)
ChannelPoints = LSDP.LSDMap_PointData(ChannelDF, data_type = "pandas", PANDEX = True)
MF.add_point_data(ChannelPoints,show_colourbar="False", scale_points=True, column_for_scaling='drainage_area',alpha=0.5,zorder=100)
if(basins):
# add the basin labelling
label_dict = dict(zip(basin_junctions,basin_keys))
Points = LSDP.GetPointWithinBasins(DataDirectory, BasinsName)
MF.add_text_annotation_from_shapely_points(Points, text_colour='k', label_dict=label_dict,zorder=200)
if(basins):
# Save the figure
ImageName = raster_directory+fname_prefix+'_basin_keys_litho.'+FigFormat
else:
ImageName = raster_directory+fname_prefix+'_litho.'+FigFormat
MF.save_fig(fig_width_inches = fig_width_inches, FigFileName = ImageName, FigFormat=FigFormat, Fig_dpi = 500)
def PrintChannelsAndBasins(DataDirectory,fname_prefix, add_basin_labels = True, cmap = "jet", cbar_loc = "right", size_format = "ESURF", fig_format = "png", dpi = 250, out_fname_prefix = ""):
"""
This function prints a channel map over a hillshade.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
cbar_lox (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
Shaded relief plot with the basins coloured by basin ID. Uses a colourbar to show each basin
Author: SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
print ('Basin keys are: ')
print (basin_keys)
# going to make the basin plots - need to have bil extensions.
print("I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!")
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix+'_hs'+raster_ext
BasinsName = fname_prefix+'_AllBasins'+raster_ext
print (BasinsName)
Basins = LSDP.GetBasinOutlines(DataDirectory, BasinsName)
ChannelFileName = fname_prefix+"_chi_data_map.csv"
chi_csv_fname = DataDirectory+ChannelFileName
thisPointData = LSDMap_PD.LSDMap_PointData(chi_csv_fname)
# clear the plot
plt.clf()
# set up the base image and the map
print("I am showing the basins without text labels.")
MF = MapFigure(HillshadeName, DataDirectory,coord_type="UTM_km", colourbar_location="None")
MF.plot_polygon_outlines(Basins, linewidth=0.8)
MF.add_drape_image(BasinsName, DataDirectory, colourmap = cmap, alpha = 0.1, discrete_cmap=False, n_colours=len(basin_keys), show_colourbar = False, modify_raster_values=True, old_values=basin_junctions, new_values=basin_keys, cbar_type = int)
MF.add_point_data(thisPointData,column_for_plotting = "basin_key",
scale_points = True,column_for_scaling = "drainage_area",
this_colourmap = cmap, scaled_data_in_log = True,
max_point_size = 3, min_point_size = 1)
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory+fname_prefix+"_channels_with_basins."+fig_format
else:
ImageName = DataDirectory+out_fname_prefix+"_channels_with_basins."+fig_format
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, FigFormat=fig_format, Fig_dpi = dpi)
def ExampleOne_PartTwo_PrintBasins(DataDirectory,fname_prefix):
"""
This function makes a shaded relief plot of the DEM with the basins coloured
by the basin ID.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
Returns:
Shaded relief plot with the basins coloured by basin ID
Author: FJC
"""
#import modules
from LSDMapFigure.PlottingRaster import MapFigure
# Set up fonts for plots
label_size = 10
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
size_format = "geomorphology"
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
print ('Basin keys are: ')
print basin_keys
# get a discrete colormap
cmap = plt.cm.jet
# going to make the basin plots - need to have bil extensions.
print("I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!")
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix+'_hs'+raster_ext
BasinsName = fname_prefix+'_AllBasins'+raster_ext
print (BasinsName)
# create the map figure
MF = MapFigure(HillshadeName, DataDirectory,coord_type="UTM_km", colourbar_location='bottom')
# add the basins drape
MF.add_drape_image(BasinsName, DataDirectory, colourmap = cmap, alpha = 0.8, colorbarlabel='Basin ID', discrete_cmap=True, n_colours=len(basin_keys), show_colourbar = True, modify_raster_values=True, old_values=basin_junctions, new_values=basin_keys, cbar_type = int)
# add the basin outlines
Basins = LSDP.GetBasinOutlines(DataDirectory, BasinsName)
MF.plot_polygon_outlines(Basins, linewidth=0.8)
FigFormat = "png"
ImageName = DataDirectory+fname_prefix+'_coloured_basins.'+FigFormat
MF.save_fig(fig_width_inches = fig_width_inches, FigFileName = ImageName, FigFormat=FigFormat, Fig_dpi = 250) # Save the figure
def ExampleOne_PartFive_MaskBasinsMF(DataDirectory, fname_prefix):
"""
This function makes a shaded relief plot of the DEM with the basins coloured
by the basin ID.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
Returns:
Shaded relief plot with the basins coloured by basin ID
Author: SMM
"""
FigFormat = "png"
size_format = "geomorphology"
# Set up fonts for plots
label_size = 10
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['Liberation Sans']
rcParams['font.size'] = label_size
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# going to make the basin plots - need to have bil extensions.
print("I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!")
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix+'_hs'+raster_ext
BasinsName = fname_prefix+'_AllBasins'+raster_ext
print (BasinsName)
# create the map figure
# We set colourbar location to none since we are labelling the figures
MF = MapFigure(HillshadeName, DataDirectory,coord_type="UTM_km", colourbar_location='bottom',basemap_colourmap = "gray")
# add the basins drape
#MF.add_drape_image(HillshadeName, DataDirectory, colourmap = cmap, alpha = 0.8, colorbarlabel='Basin ID', discrete_cmap=True, n_colours=len(basin_keys), show_colourbar = False)
Remove_Basins = [4,8]
Rename_Basins = { 12: 'chumbox', 14: 'zeppo'}
Value_dict= { 1: 0.2, 2:0.3, 3:0.4, 5:0.9,6:0.7, 7:0.3, 9:0.5, 10:0.5}
MF.add_basin_plot(BasinsName,fname_prefix,DataDirectory, mask_list = Remove_Basins,
rename_dict = Rename_Basins, value_dict = Value_dict,
use_keys_not_junctions = True, show_colourbar = True,
discrete_cmap=True, n_colours=8, colorbarlabel = "$m/n$",
colourmap = plt.cm.jet, adjust_text = False)
# Save the figure
ImageName = DataDirectory+fname_prefix+'_test_Coloured_basins.'+FigFormat
MF.save_fig(fig_width_inches = fig_width_inches, FigFileName = ImageName, FigFormat=FigFormat, Fig_dpi = 250)
CustomColorMap = matplotlib.colors.ListedColormap(CustomColor)
CustomColorComb=['#C8C864','#9D78C1','#6E9C6E','#AF5A5A','#A6835F','#A7DBA7','#AF5A5A','#F3C79B','#AF5A5A','#6E899B','#B1DEDE']
CustomColorCombMap = matplotlib.colors.ListedColormap(CustomColorComb)
#%%======================CONVERT_CSV_FILE_TO_SHAPEFILES=====================%%#
thisPointData=LSDP.LSDMap_PointData(csv_file, PANDEX=True)
#%%============================LOAD_AND_PLOT_DATA===========================%%#
plt.clf()
MF = MapFigure(BaseRasterName=BaseRasterName,
Directory=Import_Directory,
coord_type="UTM",
colourbar_location="Top",
basemap_colourmap="gray",
plot_title='None',
NFF_opti=False,alpha=1) # load and display the background hillslope raster
MF.add_drape_image(RasterName=RasterName,
Directory=Import_Directory,
colourmap=CustomColorCombMap,
alpha=0.6,
show_colourbar=True,
colorbarlabel='Lithology',
modify_raster_values=False,
NFF_opti=False)
MF.add_basin_plot(RasterName=RasterNameCatch,BasinInfoPrefix=BasinInfoPrefix,Directory=Import_Directory,
colourmap = "gray",alpha=1,
def ExampleOne_PartTwo_PrintBasins(DataDirectory,fname_prefix):
"""
This function makes a shaded relief plot of the DEM with the basins coloured
by the basin ID.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
Returns:
Shaded relief plot with the basins coloured by basin ID
Author: FJC
"""
#import modules
from LSDMapFigure.PlottingRaster import MapFigure
# Set up fonts for plots
label_size = 10
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['Liberation Sans']
rcParams['font.size'] = label_size
size_format = "geomorphology"
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
print ('Basin keys are: ')
print basin_keys
# get a discrete colormap
cmap = plt.cm.jet
# going to make the basin plots - need to have bil extensions.
print("I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!")
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix+'_hs'+raster_ext
BasinsName = fname_prefix+'_AllBasins'+raster_ext
print (BasinsName)
# create the map figure
MF = MapFigure(HillshadeName, DataDirectory,coord_type="UTM_km", colourbar_location='bottom')
# add the basins drape
MF.add_drape_image(BasinsName, DataDirectory, colourmap = cmap, alpha = 0.8, colorbarlabel='Basin ID', discrete_cmap=True, n_colours=len(basin_keys), show_colourbar = True, modify_raster_values=True, old_values=basin_junctions, new_values=basin_keys, cbar_type = int)
# add the basin outlines
Basins = LSDP.GetBasinOutlines(DataDirectory, BasinsName)
MF.plot_polygon_outlines(Basins, linewidth=0.8)
FigFormat = "png"
ImageName = DataDirectory+fname_prefix+'_coloured_basins.'+FigFormat
MF.save_fig(fig_width_inches = fig_width_inches, FigFileName = ImageName, FigFormat=FigFormat, Fig_dpi = 250) # 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
#BR = BaseRaster(BackgroundRasterName, Directory)
#BR.set_raster_type("Terrain")
#print(BR._colourmap)
#BR.show_raster()
#BR.set_colourmap("RdYlGn")
#BR.show_raster()
#PD_file = Base_file+"_chi_coord_basins.csv"
#PointData = LSDMap_PointTools.LSDMap_PointData(Directory+PD_file)
plt.clf()
cbar_loc = "bottom"
MF = MapFigure(BackgroundRasterName,
DataDirectory,
coord_type="UTM_km",
colourbar_location=cbar_loc)
#MF.add_drape_image(DrapeRasterName,Directory,alpha = 0.4)
#MF.add_drape_image(ChiRasterName,Directory,colourmap = "cubehelix",alpha = 0.4)
#MF.add_point_data(PointData)
#MF.show_plot()
ImageName = DataDirectory + "TestNewArtist.png"
fig_size_inches = 12
ax_style = "Normal"
MF.save_fig(fig_width_inches=fig_size_inches,
FigFileName=ImageName,
axis_style=ax_style,
Fig_dpi=250)
# Customise the DrapePlot
#dp.make_drape_colourbar(cbar_label=colourbar_label)
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
DrapeRasterName = "Betics_UTM30clip_hs.bil" # if you want to drap a raster on your background one. Just add a similar line in case you want another raster to drap and so on
wname = "output" # name of your output file
dpi = 900 # Quality of your output image, don't exceed 900
fig_size_inches = 24 # Figure size in Inches
##### Now we can load and plot the data
BackgroundRasterName = Base_file + ".bil" # Ignore this line
thisPointData = LSDP.LSDMap_PointData(
csv_file, PANDEX=True
) # Load the point file #1, add a similar line with different name if you have more than one point file.
plt.clf() # Ignore this line
MF = MapFigure(BackgroundRasterName,
Directory,
coord_type="UTM_km",
NFF_opti=True) # load the background raster
MF.add_drape_image(
DrapeRasterName,
Directory, # Calling the function will add a drapped raster on the top of the background one
colourmap=
"gray", # colormap used for this raster, see http://matplotlib.org/users/colormaps.html for examples, put _r at the end of a colormap to get the reversed version
alpha=
0.5, # transparency of this specific layer, 0 for fully transparent (why not) and 1 for fully opaque
show_colourbar=False, # Well, this one is explicit I think
colorbarlabel="Colourbar", # Name of your Colourbar, it might bug though
NFF_opti=True)
MF.add_point_data(
thisPointData, # this function plot the requested point file using the lat/long column in the csv file
def PrintBasins(DataDirectory,fname_prefix, add_basin_labels = True, cmap = "jet", cbar_loc = "right", size_format = "ESURF", fig_format = "png", dpi = 250, out_fname_prefix = ""):
"""
This function makes a shaded relief plot of the DEM with the basins coloured
by the basin ID.
IMPORTANT: To get this to run you need to set the flags in chi mapping tool to:
write_hillshade: true
print_basin_raster: true
print_chi_data_maps: true
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
cbar_lox (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
Shaded relief plot with the basins coloured by basin ID. Uses a colourbar to show each basin
Author: FJC, SMM
"""
#import modules
from LSDMapFigure.PlottingRaster import MapFigure
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
print ('Basin keys are: ')
print (basin_keys)
# going to make the basin plots - need to have bil extensions.
print("I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!")
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix+'_hs'+raster_ext
BasinsName = fname_prefix+'_AllBasins'+raster_ext
print (BasinsName)
Basins = LSDP.GetBasinOutlines(DataDirectory, BasinsName)
# If wanted, add the labels
if add_basin_labels:
print("I am going to add basin labels, there will be no colourbar.")
MF = MapFigure(HillshadeName, DataDirectory,coord_type="UTM_km", colourbar_location="None")
MF.plot_polygon_outlines(Basins, linewidth=0.8)
MF.add_drape_image(BasinsName, DataDirectory, colourmap = cmap, alpha = 0.8, colorbarlabel='Basin ID', discrete_cmap=True, n_colours=len(basin_keys), show_colourbar = False, modify_raster_values=True, old_values=basin_junctions, new_values=basin_keys, cbar_type = int)
# This is used to label the basins
label_dict = dict(zip(basin_junctions,basin_keys))
# this dict has the basin junction as the key and the basin_key as the value
Points = LSDP.GetPointWithinBasins(DataDirectory, BasinsName)
MF.add_text_annotation_from_shapely_points(Points, text_colour='k', label_dict=label_dict)
else:
print("I am showing the basins without text labels.")
MF = MapFigure(HillshadeName, DataDirectory,coord_type="UTM_km", colourbar_location=cbar_loc)
MF.plot_polygon_outlines(Basins, linewidth=0.8)
MF.add_drape_image(BasinsName, DataDirectory, colourmap = cmap, alpha = 0.8, colorbarlabel='Basin ID', discrete_cmap=True, n_colours=len(basin_keys), show_colourbar = True, modify_raster_values=True, old_values=basin_junctions, new_values=basin_keys, cbar_type = int)
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory+fname_prefix+"_basins."+fig_format
else:
ImageName = DataDirectory+out_fname_prefix+"_basins."+fig_format
MF.save_fig(fig_width_inches = fig_width_inches, FigFileName = ImageName, FigFormat=fig_format, Fig_dpi = dpi) # Save the figure
def PrintBasins(DataDirectory,
fname_prefix,
add_basin_labels=True,
cmap="jet",
cbar_loc="right",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix=""):
"""
This function makes a shaded relief plot of the DEM with the basins coloured
by the basin ID.
IMPORTANT: To get this to run you need to set the flags in chi mapping tool to:
write_hillshade: true
print_basin_raster: true
print_chi_data_maps: true
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
cbar_lox (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
Shaded relief plot with the basins coloured by basin ID. Uses a colourbar to show each basin
Author: FJC, SMM
"""
#import modules
from LSDMapFigure.PlottingRaster import MapFigure
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
print('Basin keys are: ')
print(basin_keys)
# going to make the basin plots - need to have bil extensions.
print(
"I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!"
)
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix + '_hs' + raster_ext
BasinsName = fname_prefix + '_AllBasins' + raster_ext
print(BasinsName)
Basins = LSDP.GetBasinOutlines(DataDirectory, BasinsName)
# If wanted, add the labels
if add_basin_labels:
print("I am going to add basin labels, there will be no colourbar.")
MF = MapFigure(HillshadeName,
DataDirectory,
coord_type="UTM_km",
colourbar_location="None")
MF.plot_polygon_outlines(Basins, linewidth=0.8)
MF.add_drape_image(BasinsName,
DataDirectory,
colourmap=cmap,
alpha=0.8,
colorbarlabel='Basin ID',
discrete_cmap=True,
n_colours=len(basin_keys),
show_colourbar=False,
modify_raster_values=True,
old_values=basin_junctions,
new_values=basin_keys,
cbar_type=int)
# This is used to label the basins
label_dict = dict(zip(basin_junctions, basin_keys))
# this dict has the basin junction as the key and the basin_key as the value
Points = LSDP.GetPointWithinBasins(DataDirectory, BasinsName)
MF.add_text_annotation_from_shapely_points(Points,
text_colour='k',
label_dict=label_dict)
else:
print("I am showing the basins without text labels.")
MF = MapFigure(HillshadeName,
DataDirectory,
coord_type="UTM_km",
colourbar_location=cbar_loc)
MF.plot_polygon_outlines(Basins, linewidth=0.8)
MF.add_drape_image(BasinsName,
DataDirectory,
colourmap=cmap,
alpha=0.8,
colorbarlabel='Basin ID',
discrete_cmap=True,
n_colours=len(basin_keys),
show_colourbar=True,
modify_raster_values=True,
old_values=basin_junctions,
new_values=basin_keys,
cbar_type=int)
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + fname_prefix + "_basins." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_basins." + fig_format
MF.save_fig(fig_width_inches=fig_width_inches,
FigFileName=ImageName,
FigFormat=fig_format,
Fig_dpi=dpi) # Save the figure
#BR = BaseRaster(BackgroundRasterName, Directory)
#BR.set_raster_type("Terrain")
#print(BR._colourmap)
#BR.show_raster()
#BR.set_colourmap("RdYlGn")
#BR.show_raster()
#PD_file = Base_file+"_chi_coord_basins.csv"
#PointData = LSDMap_PointTools.LSDMap_PointData(Directory+PD_file)
plt.clf()
cbar_loc = "bottom"
MF = MapFigure(BackgroundRasterName, DataDirectory,coord_type="UTM_km",colourbar_location = cbar_loc)
#MF.add_drape_image(DrapeRasterName,Directory,alpha = 0.4)
#MF.add_drape_image(ChiRasterName,Directory,colourmap = "cubehelix",alpha = 0.4)
#MF.add_point_data(PointData)
#MF.show_plot()
ImageName = DataDirectory+"TestNewArtist.png"
fig_size_inches = 12
ax_style = "Normal"
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, Fig_dpi = 250)
# Customise the DrapePlot
#dp.make_drape_colourbar(cbar_label=colourbar_label)
#dp.set_fig_axis_labels()
def main(argv):
# If there are no arguments, send to the welcome screen
if not len(sys.argv) > 1:
full_paramfile = print_welcome()
sys.exit()
# Get the arguments
import argparse
parser = argparse.ArgumentParser()
# The location of the data files
parser.add_argument(
"-dir",
"--base_directory",
type=str,
help="The base directory. If not defined, current directory.")
parser.add_argument("-fname",
"--fname_prefix",
type=str,
help="The prefix of your DEM WITHOUT EXTENSION!")
parser.add_argument(
"-fmt",
"--FigFormat",
type=str,
default='png',
help="Set the figure format for the plots. Default is png")
args = parser.parse_args()
# get the base directory
if args.base_directory:
DataDirectory = args.base_directory
# check if you remembered a / at the end of your path_name
if not DataDirectory.endswith("/"):
print(
"You forgot the '/' at the end of the directory, appending...")
DataDirectory = this_dir + "/"
else:
this_dir = os.getcwd()
if not args.fname_prefix:
print(
"WARNING! You haven't supplied your DEM name. Please specify this with the flag '-fname'"
)
sys.exit()
else:
fname_prefix = args.fname_prefix
# set to not parallel
parallel = False
faults = True
FigFormat = args.FigFormat
# check if a directory exists for the chi plots. If not then make it.
raster_directory = DataDirectory + 'raster_plots/'
if not os.path.isdir(raster_directory):
os.makedirs(raster_directory)
# Set up fonts for plots
label_size = 8
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
# set figure sizes based on format
size_format = ""
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = Helper.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [int(x) for x in basin_junctions]
# get a discrete colormap
cmap = plt.cm.viridis
# going to make the basin plots - need to have bil extensions.
print(
"I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!"
)
# get the rasters
raster_ext = '.bil'
BackgroundRasterName = fname_prefix + raster_ext
HillshadeName = fname_prefix + '_hs' + raster_ext
BasinsName = fname_prefix + '_AllBasins' + raster_ext
# create the map figure
MF = MapFigure(HillshadeName,
DataDirectory,
coord_type="UTM_km",
colourbar_location='none')
# add the basins drape
BasinsDict = dict(zip(basin_keys, basin_keys))
# MF.add_basin_plot(BasinsName,fname_prefix,DataDirectory, label_basins=False,
# use_keys_not_junctions = True, show_colourbar = False,
# value_dict = BasinsDict, discrete_cmap=True, n_colours=len(basin_keys),
# colorbarlabel = "Basin ID", cbar_type=int, tickspacefactor=2,
# colourmap = cmap, edgecolour='none', adjust_text = True, parallel=parallel)
# add the channel network
if not parallel:
ChannelDF = Helper.ReadChiDataMapCSV(DataDirectory, fname_prefix)
else:
ChannelDF = Helper.AppendChiDataMapCSVs(DataDirectory)
# remove chi no data values
ChannelDF = ChannelDF[ChannelDF.chi != -9999]
ChannelPoints = LSDP.LSDMap_PointData(ChannelDF,
data_type="pandas",
PANDEX=True)
# add chi map
MF.add_point_data(ChannelPoints,
column_for_plotting="chi",
column_for_scaling="chi",
colorbarlabel="$\chi$ (m)",
show_colourbar=True,
this_colourmap=cmap,
colourbar_location="top")
# add the faults
if faults:
LineFileName = DataDirectory + fname_prefix + "_faults.shp"
MF.add_line_data(LineFileName,
linestyle="-",
linewidth=1.5,
zorder=99,
legend=True,
label="Fault Segments")
# add the basin outlines ### need to parallelise
if not parallel:
Basins = LSDP.GetBasinOutlines(DataDirectory, BasinsName)
else:
Basins = LSDP.GetMultipleBasinOutlines(DataDirectory)
# Find the relay basins and plot separately
RelayBasinIDs = [1248, 4788, 4995, 5185, 6187, 6758, 6805]
RelayBasins = {
key: value
for key, value in Basins.items() if key in RelayBasinIDs
}
# Plot all basins
MF.plot_polygon_outlines(Basins,
colour='k',
linewidth=0.5,
alpha=1,
legend=True,
label="Catchments")
MF.plot_polygon_outlines(RelayBasins,
colour='r',
linewidth=0.5,
alpha=1,
legend=True,
label="Relay Catchments")
# Add the legend
MF.add_legend()
# Save the figure
ImageName = raster_directory + fname_prefix + '_chi_map.' + FigFormat
MF.save_fig(fig_width_inches=fig_width_inches,
FigFileName=ImageName,
FigFormat=FigFormat,
Fig_dpi=300)
rcParams['font.size'] = 8 rcParams['text.usetex'] = False # define filenames and relative workspace fname_prefix = "bolinas" HillshadeName = "bolinas_hs.bil" Directory = "/home/mhurst/bolinas_paper/" DataDirectory = Directory+"data/" ChannelDataDirectory = DataDirectory+"channel_data/" HillslopeDataDirectory = DataDirectory+"hillslope_data/" HilltopPointsData = HillslopeDataDirectory+"bolinas_HilltopData.csv" ChannelPointsDat = ChannelDataDirectory+"bolinas_MChiSegmented.csv" ChannelHeadPointsData = ChannelDataDirectory+"bolinas_CH_wiener_nodeindices_for_Arc.csv" # create the map figure MF = MapFigure(HillshadeName, DataDirectory, coord_type="UTM_km", colourbar_location='None') # add hilltops HilltopPointsDF = pd.read_csv(HilltopPointsData) HilltopPoints = LSDP.LSDMap_PointData(HilltopPointsDF, data_type = "pandas", PANDEX = True) MF.add_point_data(HilltopPoints,alpha=0.5,zorder=100,unicolor="blue",manual_size=5) # add channel heads #ChannelHeadsDF = pd.read_csv(ChannelHeadPointsData) #ChannelHeadPoints = LSDP.LSDMap_PointData(ChannelHeadsDF, data_type = "pandas", PANDEX = True) #MF.add_point_data(ChannelHeadPoints,alpha=0.5,zorder=100,unicolor="blue",manual_size=5) # add channels #ChannelDF = Helper.ReadChiDataMapCSV(ChannelDataDirectory,fname_prefix) #ChannelPoints = LSDP.LSDMap_PointData(ChannelDF, data_type = "pandas", PANDEX = True) #MF.add_point_data(ChannelPoints,show_colourbar="False", scale_points=True, column_for_scaling='drainage_area',alpha=0.5,zorder=90)
def main(argv):
# If there are no arguments, send to the welcome screen
if not len(sys.argv) > 1:
full_paramfile = print_welcome()
sys.exit()
# Get the arguments
import argparse
parser = argparse.ArgumentParser()
# The location of the data files
parser.add_argument("-dir", "--base_directory", type=str, help="The base directory. If not defined, current directory.")
parser.add_argument("-fname", "--fname_prefix", type=str, help="The prefix of your DEM WITHOUT EXTENSION!")
parser.add_argument("-fmt", "--FigFormat", type=str, default='png', help="Set the figure format for the plots. Default is png")
args = parser.parse_args()
# get the base directory
if args.base_directory:
DataDirectory = args.base_directory
# check if you remembered a / at the end of your path_name
if not DataDirectory.endswith("/"):
print("You forgot the '/' at the end of the directory, appending...")
DataDirectory = this_dir+"/"
else:
this_dir = os.getcwd()
if not args.fname_prefix:
print("WARNING! You haven't supplied your DEM name. Please specify this with the flag '-fname'")
sys.exit()
else:
fname_prefix = args.fname_prefix
# set to not parallel
parallel = False
faults = True
FigFormat = args.FigFormat
# check if a directory exists for the chi plots. If not then make it.
raster_directory = DataDirectory+'raster_plots/'
if not os.path.isdir(raster_directory):
os.makedirs(raster_directory)
# Set up fonts for plots
label_size = 8
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
# set figure sizes based on format
size_format = ""
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = Helper.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [int(x) for x in basin_junctions]
# get a discrete colormap
cmap = plt.cm.viridis
# going to make the basin plots - need to have bil extensions.
print("I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!")
# get the rasters
raster_ext = '.bil'
BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix+'_hs'+raster_ext
BasinsName = fname_prefix+'_AllBasins'+raster_ext
# create the map figure
MF = MapFigure(HillshadeName, DataDirectory,coord_type="UTM_km", colourbar_location='none')
# add the basins drape
BasinsDict = dict(zip(basin_keys,basin_keys))
# MF.add_basin_plot(BasinsName,fname_prefix,DataDirectory, label_basins=False,
# use_keys_not_junctions = True, show_colourbar = False,
# value_dict = BasinsDict, discrete_cmap=True, n_colours=len(basin_keys),
# colorbarlabel = "Basin ID", cbar_type=int, tickspacefactor=2,
# colourmap = cmap, edgecolour='none', adjust_text = True, parallel=parallel)
# add the channel network
if not parallel:
ChannelDF = Helper.ReadChiDataMapCSV(DataDirectory,fname_prefix)
else:
ChannelDF = Helper.AppendChiDataMapCSVs(DataDirectory)
# remove chi no data values
ChannelDF = ChannelDF[ChannelDF.chi != -9999]
ChannelPoints = LSDP.LSDMap_PointData(ChannelDF, data_type = "pandas", PANDEX = True)
# add chi map
MF.add_point_data(ChannelPoints, column_for_plotting = "chi", column_for_scaling = "chi", colorbarlabel = "$\chi$ (m)", show_colourbar=True, this_colourmap = cmap, colourbar_location="top")
# add the faults
if faults:
LineFileName = DataDirectory + fname_prefix + "_faults.shp"
MF.add_line_data(LineFileName, linestyle="-", linewidth=1.5, zorder=99, legend=True, label="Fault Segments")
# add the basin outlines ### need to parallelise
if not parallel:
Basins = LSDP.GetBasinOutlines(DataDirectory, BasinsName)
else:
Basins = LSDP.GetMultipleBasinOutlines(DataDirectory)
# Find the relay basins and plot separately
RelayBasinIDs = [1248, 4788, 4995, 5185, 6187, 6758, 6805]
RelayBasins = {key:value for key, value in Basins.items() if key in RelayBasinIDs}
# Plot all basins
MF.plot_polygon_outlines(Basins, colour='k', linewidth=0.5, alpha = 1, legend=True, label="Catchments")
MF.plot_polygon_outlines(RelayBasins, colour='r', linewidth=0.5, alpha = 1,legend=True, label="Relay Catchments")
# Add the legend
MF.add_legend()
# Save the figure
ImageName = raster_directory+fname_prefix+'_chi_map.'+FigFormat
MF.save_fig(fig_width_inches = fig_width_inches, FigFileName = ImageName, FigFormat=FigFormat, Fig_dpi = 300)
def PrintCategorised(DataDirectory,
fname_prefix,
Drape_prefix,
show_colourbar=False,
cmap="jet",
cbar_loc="right",
cbar_label="drape colourbar",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix=""):
"""
This function makes a shaded relief plot of the DEM a drape plot that has categorised colours.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
Drape_prefix (str): The prefix of the drape name
show_colourbar (bool): if true show the colourbar
cmap (str or colourmap): The colourmap to use for the plot
cbar_loc (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
cbar_label (str): The text on the colourbar label
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
Shaded relief plot with categorised data.
Author: SMM
"""
#import modules
from LSDMapFigure.PlottingRaster import MapFigure
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# going to make the basin plots - need to have bil extensions.
print(
"I'm going to make a plot of categorised data. Your topographic data must be in ENVI bil format or I'll break!!"
)
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
BackgroundRasterName = fname_prefix + '_hs' + raster_ext
CatName = Drape_prefix + raster_ext
# clear the plot
plt.clf()
# set up the base image and the map
MF = MapFigure(BackgroundRasterName,
DataDirectory,
coord_type="UTM_km",
colourbar_location=cbar_loc)
#MF.add_drape_image(ElevationName,DataDirectory,colourmap = "gray", alpha = 0.6, colorbarlabel = None)
MF.add_categorised_drape_image(CatName,
DataDirectory,
colourmap=cmap,
alpha=0.7,
colorbarlabel=cbar_label,
norm="none")
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + fname_prefix + "_categorised." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_categorised." + fig_format
MF.save_fig(fig_width_inches=fig_width_inches,
FigFileName=ImageName,
FigFormat=fig_format,
Fig_dpi=dpi,
transparent=True) # Save the figure
def MakeRasterPlotTerraceElev(DataDirectory,fname_prefix, FigFormat='png', size_format='ESURF'):
"""
This function makes a hillshade of the DEM with the terraces
plotted onto it coloured by their elevation
Args:
DataDirectory (str): the data directory
fname_prefix (str): the name of the DEM without extension.
FigFormat (str): the figure format, default='png'
size_format (str): Can be "big" (16 inches wide), "geomorphology" (6.25 inches wide), or "ESURF" (4.92 inches wide) (defualt esurf).
Returns:
Raster plot of terrace IDs
Author: FJC
"""
from LSDMapFigure.PlottingRaster import BaseRaster
from LSDMapFigure.PlottingRaster import MapFigure
# Set up fonts for plots
label_size = 10
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['Liberation Sans']
rcParams['font.size'] = label_size
# make a figure
if size_format == "geomorphology":
#fig = plt.figure(1, facecolor='white',figsize=(6.25,3.5))
fig_width_inches=6.25
#l_pad = -40
elif size_format == "big":
#fig = plt.figure(1, facecolor='white',figsize=(16,9))
fig_width_inches=16
#l_pad = -50
else:
fig_width_inches = 4.92126
#fig = plt.figure(1, facecolor='white',figsize=(4.92126,3.2))
#l_pad = -35
# going to make the terrace plots - need to have bil extensions.
print("I'm going to make a raster plot of terrace elevations. Your topographic data must be in ENVI bil format or I'll break!!")
# get the rasters
raster_ext = '.bil'
BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix+'_hs'+raster_ext
TerraceElevName = fname_prefix+'_terrace_relief_final'+raster_ext
# get the terrace csv
terraces = H.read_terrace_csv(DataDirectory,fname_prefix)
terraceIDs = sorted(list(set(list(terraces.TerraceID))))
xTerraces = []
zTerraces = []
yTerraces = []
newIDs = []
# loop through the terrace IDs and get the x, y, and z values
for terraceID in terraceIDs:
_terrace_subset = (terraces.TerraceID.values == terraceID)
_x = terraces['DistAlongBaseline'].values[_terrace_subset]
_y = terraces['DistToBaseline'].values[_terrace_subset]
_z = terraces['Elevation'].values[_terrace_subset]
_x_unique = sorted(list(set(list(_x))))
_z_unique = []
# Filter
if len(_x) > 50 and len(_x_unique) > 1 and len(_x_unique) < 1000:
#print np.max(np.diff(_x_unique))
#if len(_x_unique) > 10 and len(_x_unique) < 1000 \
#and :
for _x_unique_i in _x_unique:
#_y_unique_i = np.min(np.array(_y)[_x == _x_unique_i])
#_z_unique.append(np.min(_z[_y == _y_unique_i]))
_z_unique.append(np.min(_z[_x == _x_unique_i]))
if np.mean(np.diff(_z_unique)/np.diff(_x_unique)) < 10:
xTerraces.append(_x_unique)
zTerraces.append(_z_unique)
newIDs.append(terraceID)
n_colours=len(newIDs)
# create the map figure
MF = MapFigure(HillshadeName, DataDirectory, coord_type='UTM_km', colourbar_location='right')
# add the terrace drape
terrace_cmap = plt.cm.Reds
#terrace_cmap = colours.cmap_discretize(n_colours,terrace_cmap)
MF.add_drape_image(TerraceElevName, DataDirectory, colourmap = terrace_cmap, colorbarlabel="Elevation above channel (m)", alpha=0.8)
ImageName = DataDirectory+fname_prefix+'_terrace_elev_raster_plot.'+FigFormat
MF.save_fig(fig_width_inches = fig_width_inches, FigFileName = ImageName, FigFormat=FigFormat, Fig_dpi = 300) # Save the figure
#BR = BaseRaster(BackgroundRasterName, Directory)
#BR.set_raster_type("Terrain")
#print(BR._colourmap)
#BR.show_raster()
#BR.set_colourmap("RdYlGn")
#BR.show_raster()
thisPointData = LSDP.LSDMap_PointData(csv_file)
river_network = LSDP.LSDMap_PointData("/home/s1675537/PhD/DataStoreBoris/Emma/rv.csv")
#names = ['cubehelix','CMRmap','RdBu']
#names = ['spring_r', 'autumn_r','YlOrRd','YlOrRd_r']
names = ['autumn_r']
for nami in names:
plt.clf()
MF = MapFigure(BackgroundRasterName, Directory,coord_type="UTM_km", alpha = 1)
MF.add_drape_image(DrapeRasterName,Directory,alpha = 0.5)
#MF.add_drape_image(BlackRaster, BlackRasterD, alpha = alpha_black)
MF.add_point_data(river_network,scale_points = True, max_point_size = 1, min_point_size = 0.1, column_for_scaling ="drainage area", scaled_data_in_log = True)
MF.add_point_data( thisPointData,column_for_plotting = "elevation",
this_colourmap = nami, colorbarlabel = "knickpoint sign", scale_points = True, max_point_size = 100, min_point_size = 0, column_for_scaling ="knickpoints", scaled_data_in_log = True, minimum_log_scale_cut_off = 2 )
#MF.add_drape_image(CurveRasterName,Directory,colourmap = "cubehelix",alpha = 0.4, show_colourbar = True, colorbarlabel= "Colourbar")
#MF.show_plot()
ImageName = wDirectory+str(int(clock.time()))+nami+".png"
fig_size_inches = 12
ax_style = "Normal"
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, Fig_dpi = 500)
DrapeRasterName = Base_file+"_hs.bil"
ChiRasterName = Base_file+"_curvature.bil"
#BR = BaseRaster(BackgroundRasterName, Directory)
#BR.set_raster_type("Terrain")
#print(BR._colourmap)
#BR.show_raster()
#BR.set_colourmap("RdYlGn")
#BR.show_raster()
plt.clf()
MF = MapFigure(BackgroundRasterName, Directory,coord_type="UTM_km")
MF.add_drape_image(DrapeRasterName,Directory,alpha = 0.4)
MF.add_drape_image(ChiRasterName,Directory,colourmap = "cubehelix",alpha = 0.4, show_colourbar = True)
#MF.show_plot()
ImageName = Directory+"boris.png"
fig_size_inches = 6
ax_style = "Madhouse"
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style)
# Customise the DrapePlot
#dp.make_drape_colourbar(cbar_label=colourbar_label)
#dp.set_fig_axis_labels()
#dp.show_plot()
def ExampleOne_PartFour_MaskBasins(DataDirectory, fname_prefix):
"""
This function makes a shaded relief plot of the DEM with the basins coloured
by the basin ID. It shows how to mask certain basins.
Search the function for "Basins_to_mask".
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
Returns:
Shaded relief plot with the basins coloured by basin ID
Author: SMM
"""
import numpy as np
Basins_to_mask = [0,4,6]
FigFormat = "png"
size_format = "geomorphology"
#import modules
# from LSDMapFigure.PlottingRaster import MapFigure
# from LSDMapFigure.PlottingRaster import BaseRaster
# import LSDPlottingTools.LSDMap_VectorTools as LSDMap_VT
# import LSDPlottingTools.LSDMap_PointTools as LSDMap_PT
# Set up fonts for plots
label_size = 10
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['Liberation Sans']
rcParams['font.size'] = label_size
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
# get the junctions to mask
key_to_index_dict = dict(zip(basin_keys,basin_junctions))
junctions_to_mask = []
for basin in Basins_to_mask:
junctions_to_mask.append( key_to_index_dict[basin])
print("The junctions to mask are")
print(junctions_to_mask)
print ('Basin keys are: ')
print basin_keys
print("Let me mask those for you")
new_keys = []
for key in basin_keys:
if key in Basins_to_mask:
new_keys.append(np.nan)
else:
new_keys.append(key)
print("The new keys are: ")
print(new_keys)
basin_keys = new_keys
# get a discrete colormap
cmap = plt.cm.jet
# going to make the basin plots - need to have bil extensions.
print("I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!")
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix+'_hs'+raster_ext
BasinsName = fname_prefix+'_AllBasins'+raster_ext
print (BasinsName)
# create the map figure
# We set colourbar location to none since we are labelling the figures
MF = MapFigure(HillshadeName, DataDirectory,coord_type="UTM_km", colourbar_location='none')
# add the basins drape
MF.add_drape_image(BasinsName, DataDirectory, colourmap = cmap, alpha = 0.8, colorbarlabel='Basin ID', discrete_cmap=True, n_colours=len(basin_keys), show_colourbar = True, modify_raster_values=True, old_values=basin_junctions, new_values=basin_keys, cbar_type = int)
# add the basin outlines
Basins = LSDP.GetBasinOutlines(DataDirectory, BasinsName)
# get rid of the basins that are being masked
for junction in junctions_to_mask:
del Basins[junction]
# note that at this stage the Basins are keyed with the junction index
MF.plot_polygon_outlines(Basins, linewidth=0.8)
# add the basin labelling
label_dict = dict(zip(basin_junctions,basin_keys))
# this dict has the basin junction as the key and the basin_key as the value
Points = LSDP.GetPointWithinBasins(DataDirectory, BasinsName)
# get rid of points as well
for junction in junctions_to_mask:
del Points[junction]
del label_dict[junction]
MF.add_text_annotation_from_shapely_points(Points, text_colour='k', label_dict=label_dict)
# Save the figure
ImageName = DataDirectory+fname_prefix+'_labelled_basins.'+FigFormat
MF.save_fig(fig_width_inches = fig_width_inches, FigFileName = ImageName, FigFormat=FigFormat, Fig_dpi = 250)
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
#BR = BaseRaster(BackgroundRasterName, Directory)
#BR.set_raster_type("Terrain")
#print(BR._colourmap)
#BR.show_raster()
#BR.set_colourmap("RdYlGn")
#BR.show_raster()
#PD_file = Base_file+"_chi_coord_basins.csv"
PD_file = Base_file+"_MChiSegmented.csv"
PointData = LSDMap_PointTools.LSDMap_PointData(Directory+PD_file)
plt.clf()
cbar_loc = "Bottom"
MF = MapFigure(BackgroundRasterName, Directory,coord_type="UTM_km",colourbar_location = cbar_loc)
MF.add_drape_image(DrapeRasterName,Directory,alpha = 0.4)
#MF.add_drape_image(ChiRasterName,Directory,colourmap = "cubehelix",alpha = 0.4)
MF.add_point_data(PointData,column_for_plotting = "source_key",colorbarlabel = "I am point data",
scale_points = True,column_for_scaling = "drainage area",
scaled_data_in_log = False)
#MF.show_plot()
ImageName = Directory+"TestNewArtist.png"
fig_size_inches = 6
ax_style = "Normal"
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, Fig_dpi = 250)
# Customise the DrapePlot
#dp.make_drape_colourbar(cbar_label=colourbar_label)
def PrintBasins_Complex(DataDirectory,fname_prefix,
use_keys_not_junctions = True, show_colourbar = False,
Remove_Basins = [], Rename_Basins = {}, Value_dict= {},
cmap = "jet", colorbarlabel = "colourbar", size_format = "ESURF",
fig_format = "png", dpi = 250, out_fname_prefix = "", include_channels = False, label_basins = True):
"""
This function makes a shaded relief plot of the DEM with the basins coloured
by the basin ID.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
use_keys_not_junctions (bool): If true use basin keys to locate basins, otherwise use junction indices
show_colourbar (bool): if true show the colourbar
Remove_Basins (list): A lists containing either key or junction indices of basins you want to remove from plotting
Rename_Basins (dict): A dict where the key is either basin key or junction index, and the value is a new name for the basin denoted by the key
Value_dict (dict): A dict where the key is either basin key or junction index, and the value is a value of the basin that is used to colour the basins
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
cbar_loc (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
include_channels (bool): If true, adds a channel plot. It uses the chi_data_maps file
label_basins (bool): If true, the basins get labels
Returns:
Shaded relief plot with the basins coloured by basin ID. Uses a colourbar to show each basin. This allows more complex plotting with renamed and excluded basins.
Author: FJC, SMM
"""
#import modules
from LSDMapFigure.PlottingRaster import MapFigure
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
print ('Basin keys are: ')
print (basin_keys)
# going to make the basin plots - need to have bil extensions.
print("I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!")
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix+'_hs'+raster_ext
BasinsName = fname_prefix+'_AllBasins'+raster_ext
# This initiates the figure
MF = MapFigure(HillshadeName, DataDirectory,coord_type="UTM_km", colourbar_location="None")
# This adds the basins
MF.add_basin_plot(BasinsName,fname_prefix,DataDirectory, mask_list = Remove_Basins,
rename_dict = Rename_Basins, value_dict = Value_dict,
use_keys_not_junctions = use_keys_not_junctions, show_colourbar = show_colourbar,
discrete_cmap=True, n_colours=15, colorbarlabel = colorbarlabel,
colourmap = cmap, adjust_text = False, label_basins = label_basins)
# See if you need the channels
if include_channels:
print("I am going to add some channels for you")
ChannelFileName = fname_prefix+"_chi_data_map.csv"
chi_csv_fname = DataDirectory+ChannelFileName
thisPointData = LSDMap_PD.LSDMap_PointData(chi_csv_fname)
MF.add_point_data(thisPointData,column_for_plotting = "basin_key",
scale_points = True,column_for_scaling = "drainage_area",
this_colourmap = "Blues_r", scaled_data_in_log = True,
max_point_size = 3, min_point_size = 1, discrete_colours = True, NColours = 1, zorder = 5)
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory+fname_prefix+"_selected_basins."+fig_format
else:
ImageName = DataDirectory+out_fname_prefix+"_selected_basins."+fig_format
MF.save_fig(fig_width_inches = fig_width_inches, FigFileName = ImageName, FigFormat=fig_format, Fig_dpi = dpi, 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 PlotTopoRaster(DataDirectory, fname_prefix, size_format='ESURF', FigFormat='png', colors = "terrain"):
"""
Creates a basic Terrain topographic raster. Needs the Hillshade
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
size_format (str): Can be "big" (16 inches wide), "geomorphology" (6.25 inches wide), or "ESURF" (4.92 inches wide) (defualt esurf).
FigFormat (str): The format of the figure. Usually 'png' or 'pdf'. If "show" then it calls the matplotlib show() command.
Returns:
Shaded relief plot with the basins coloured by basin ID
Author: BG, FJC
"""
# check if a directory exists for the chi plots. If not then make it.
raster_directory = DataDirectory+'raster_plots/'
if not os.path.isdir(raster_directory):
os.makedirs(raster_directory)
# Set up fonts for plots
label_size = 10
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# going to make the basin plots - need to have bil extensions.
print("I'm going to make a cabic topographic plot")
# get the rasters
raster_ext = '.bil'
## Just checking if you have a PP version of it
if os.path.isfile(DataDirectory + fname_prefix +"_PP.bil"):
BackgroundRasterName = fname_prefix+"_PP"+raster_ext
else:
BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix+'_hs'+raster_ext
# create the map figure
MF = MapFigure(BackgroundRasterName, DataDirectory,coord_type="UTM_km", colourbar_location='None')
# Drape the hillshade and add the color
## Frist plot the terrain toporaster
MF.add_drape_image(BackgroundRasterName,DataDirectory, # Calling the function will add a drapped raster on the top of the background on
colourmap = colors, # colormap used for this raster, see http://matplotlib.org/users/colormaps.html for examples, put _r at the end of a colormap to get the reversed version
alpha=1, # transparency of this specific layer, 0 for fully transparent (why not) and 1 for fully opaque
show_colourbar = True, # Well, this one is explicit I think
colorbarlabel = "None",
NFF_opti = True)
## Drape the Hillshade raster
MF.add_drape_image(HillshadeName,DataDirectory, # Calling the function will add a drapped raster on the top of the background on
colourmap = "gray", # colormap used for this raster, see http://matplotlib.org/users/colormaps.html for examples, put _r at the end of a colormap to get the reversed version
alpha=0.4, # transparency of this specific layer, 0 for fully transparent (why not) and 1 for fully opaque
show_colourbar = True, # Well, this one is explicit I think
colorbarlabel = "None",
NFF_opti = True)
# Save the figure
ImageName = raster_directory+fname_prefix+'_Topo.'+FigFormat
MF.save_fig(fig_width_inches = fig_width_inches, FigFileName = ImageName, FigFormat=FigFormat, Fig_dpi = 300)
def PrintChiCoordChannelsAndBasins(DataDirectory,fname_prefix, ChannelFileName, add_basin_labels = True, cmap = "cubehelix", cbar_loc = "right", size_format = "ESURF", fig_format = "png", dpi = 250,plotting_column = "source_key",discrete_colours = False, NColours = 10, colour_log = True, colorbarlabel = "Colourbar", Basin_remove_list = [], Basin_rename_dict = {} , value_dict = {}, plot_chi_raster = False, out_fname_prefix = "", show_basins = True, min_channel_point_size = 0.5, max_channel_point_size = 2):
"""
This function prints a channel map over a hillshade.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
cbar_loc (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
plotting_column (str): the name of the column to plot
discrete_colours (bool): if true use a discrete colourmap
NColours (int): the number of colours to cycle through when making the colourmap
colour_log (bool): If true the colours are in log scale
Basin_remove_list (list): A lists containing either key or junction indices of basins you want to remove from plotting
Basin_rename_dict (dict): A dict where the key is either basin key or junction index, and the value is a new name for the basin denoted by the key
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
show_basins (bool): If true, plot the basins
min_channel_point_size (float): The minimum size of a channel point in points
max_channel_point_size (float): The maximum size of a channel point in points
Returns:
Shaded relief plot with the basins coloured by basin ID. Includes channels. These can be plotted by various metrics denoted but the plotting_column parameter.
Author: SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
print ('Basin keys are: ')
print (basin_keys)
# going to make the basin plots - need to have bil extensions.
print("I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!")
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix+'_hs'+raster_ext
BasinsName = fname_prefix+'_AllBasins'+raster_ext
ChiCoordName = fname_prefix+'_Maskedchi'+raster_ext
print (BasinsName)
Basins = LSDP.GetBasinOutlines(DataDirectory, BasinsName)
chi_csv_fname = DataDirectory+ChannelFileName
chi_csv_fname = DataDirectory+ChannelFileName
thisPointData = LSDMap_PD.LSDMap_PointData(chi_csv_fname)
# Remove data that has nodata values
thisPointData.selectValue(plotting_column,value = -9999, operator = "!=")
thisPointData.selectValue("basin_key",value = Basin_remove_list, operator = "!=")
#print("The new point data is:")
#print(thisPointData.GetLongitude())
# clear the plot
plt.clf()
# set up the base image and the map
print("I am showing the basins without text labels.")
MF = MapFigure(HillshadeName, DataDirectory,coord_type="UTM_km", colourbar_location="None")
# This adds the basins
if plot_chi_raster:
if show_basins:
MF.add_basin_plot(BasinsName,fname_prefix,DataDirectory, mask_list = Basin_remove_list, rename_dict = Basin_rename_dict, value_dict = value_dict, label_basins = add_basin_labels, show_colourbar = False, colourmap = "gray", alpha = 1, outlines_only = True)
MF.add_drape_image(ChiCoordName,DataDirectory,colourmap = "cubehelix",alpha=0.6,zorder = 0.5)
MF.add_point_data(thisPointData,column_for_plotting = plotting_column,scale_points = True,column_for_scaling = "drainage_area", show_colourbar = True, colourbar_location = cbar_loc,colorbarlabel = colorbarlabel, this_colourmap = cmap,scaled_data_in_log = True,max_point_size = max_channel_point_size, min_point_size = min_channel_point_size,zorder=0.4, colour_log = colour_log, discrete_colours = discrete_colours, NColours = NColours)
else:
if show_basins:
MF.add_basin_plot(BasinsName,fname_prefix,DataDirectory, mask_list = Basin_remove_list, rename_dict = Basin_rename_dict, value_dict = value_dict, label_basins = add_basin_labels, show_colourbar = False, colourmap = "gray", alpha = 0.7, outlines_only = False)
MF.add_point_data(thisPointData,column_for_plotting = plotting_column,scale_points = True,column_for_scaling = "drainage_area", show_colourbar = True, colourbar_location = cbar_loc,colorbarlabel = colorbarlabel, this_colourmap = cmap,scaled_data_in_log = True,max_point_size = 2, min_point_size = 0.5,zorder=10, colour_log = colour_log, discrete_colours = discrete_colours, NColours = NColours)
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory+fname_prefix+"_chicoord_and_basins."+fig_format
else:
ImageName = DataDirectory+out_fname_prefix+"_chicoord_and_basins."+fig_format
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, FigFormat=fig_format, Fig_dpi = dpi)
