def PrintChiChannels(DataDirectory,
fname_prefix,
ChannelFileName,
add_basin_labels=True,
cmap="jet",
cbar_loc="right",
size_format="ESURF",
fig_format="png",
dpi=250,
plotting_column="source_key",
discrete_colours=False,
NColours=10,
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
ChannelFileName (str): The name of the channel file (a csv) without path but with extension
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
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
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
Shaded relief plot with the channels coloured by a plotting column designated by the plotting_column keyword. 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"
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="gray",
alpha=0.6)
MF.add_point_data(thisPointData,
column_for_plotting=plotting_column,
this_colourmap=cmap,
scale_points=True,
column_for_scaling="drainage_area",
scaled_data_in_log=True,
max_point_size=5,
min_point_size=1,
discrete_colours=discrete_colours,
NColours=NColours)
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + fname_prefix + "_chi_channels." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_chi_channels." + fig_format
MF.save_fig(fig_width_inches=fig_size_inches,
FigFileName=ImageName,
axis_style=ax_style,
FigFormat=fig_format,
Fig_dpi=dpi)
def PlotSwath(swath_csv_name, FigFileName = 'Image.png', size_format = "geomorphology", fig_format = "png", dpi = 500, aspect_ratio = 2):
"""
This plots a swath profile
Args:
swath_csv_name (str): the name of the csv file (with path!)
Author: SMM
Date 20/02/2018
"""
print("STARTING swath plot.")
# Set up fonts for plots
label_size = 12
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_size_inches = 6.25
l_pad = -40
elif size_format == "big":
fig = plt.figure(1, facecolor='white',figsize=(16,9))
fig_size_inches = 16
l_pad = -50
else:
fig = plt.figure(1, facecolor='white',figsize=(4.92126,3.5))
fig_size_inches = 4.92126
l_pad = -35
# Note all the below parameters are overwritten by the figure sizer routine
gs = plt.GridSpec(100,100,bottom=0.15,left=0.1,right=1.0,top=1.0)
ax = fig.add_subplot(gs[25:100,10:95])
print("Getting data from the file: "+swath_csv_name)
thisPointData = LSDMap_PD.LSDMap_PointData(swath_csv_name)
distance = thisPointData.QueryData('Distance').values
mean_val = thisPointData.QueryData('Mean').values
min_val = thisPointData.QueryData('Min').values
max_val = thisPointData.QueryData('Max').values
# Get the minimum and maximum distances
X_axis_min = 0
X_axis_max = distance[-1]
n_target_tics = 5
xlocs,new_x_labels = LSDMap_BP.TickConverter(X_axis_min,X_axis_max,n_target_tics)
ax.fill_between(distance, min_val, max_val, facecolor='orange', alpha = 0.5, interpolate=True)
ax.plot(distance, mean_val,"b", linewidth = 1)
ax.plot(distance, min_val,"k",distance,max_val,"k",linewidth = 1)
ax.spines['top'].set_linewidth(1)
ax.spines['left'].set_linewidth(1)
ax.spines['right'].set_linewidth(1)
ax.spines['bottom'].set_linewidth(1)
ax.set_ylabel("Elevation (m)")
ax.set_xlabel("Distance along swath (km)")
ax.set_xticks(xlocs)
ax.set_xticklabels(new_x_labels,rotation=60)
# This gets all the ticks, and pads them away from the axis so that the corners don't overlap
ax.tick_params(axis='both', width=1, pad = 2)
for tick in ax.xaxis.get_major_ticks():
tick.set_pad(2)
# Lets try to size the figure
cbar_L = "None"
[fig_size_inches,map_axes,cbar_axes] = Helper.MapFigureSizer(fig_size_inches,aspect_ratio, cbar_loc = cbar_L, title = "None")
fig.set_size_inches(fig_size_inches[0], fig_size_inches[1])
ax.set_position(map_axes)
FigFormat = fig_format
print("The figure format is: " + FigFormat)
if FigFormat == 'show':
plt.show()
elif FigFormat == 'return':
return fig
else:
plt.savefig(FigFileName,format=FigFormat,dpi=dpi)
fig.clf()
def BinnedRegressionDriver(DataDirectory, DEM_prefix, basin_keys=[]):
"""
This function goes through a basin list and reports back the best fit
m/n values for mainstem data, all data, and both of these with outliers removed
Args:
DataDirectory (str): the path to the directory with the csv file
DEM_prefix (str): name of your DEM without extension
basin_keys (list): A list of the basin keys to plot. If empty, plot all the basins.
Author: SMM
"""
from LSDPlottingTools import LSDMap_PointTools as PointTools
print("These basin keys are: ")
print(basin_keys)
# read in binned data
binned_csv_fname = DataDirectory + DEM_prefix + '_SAbinned.csv'
print("I'm reading in the csv file " + binned_csv_fname)
binnedPointData = PointTools.LSDMap_PointData(binned_csv_fname)
# get the basin keys and check if the basins in the basin list exist
basin = binnedPointData.QueryData('basin_key')
basin = [int(x) for x in basin]
Basin = np.asarray(basin)
these_basin_keys = np.unique(Basin)
#print("The unique basin keys are: ")
#print(these_basin_keys)
final_basin_keys = []
# A bit of logic for checking keys
if (len(basin_keys) == 0):
final_basin_keys = these_basin_keys
else:
for basin in basin_keys:
if basin not in these_basin_keys:
print("You were looking for basin " + str(basin) +
" but it isn't in the basin keys.")
else:
final_basin_keys.append(basin)
#print("The final basin keys are:")
#print(final_basin_keys)
print("There are " + str(len(final_basin_keys)) +
"basins that I will plot")
mn_by_basin_dict = {}
#basin_keys.append(0)
# Loop through the basin keys, making a plot for each one
for basin_key in final_basin_keys:
(m1, m2, m3, m4) = BinnedRegression(binnedPointData, basin_key)
this_basin_SA_mn = []
this_basin_SA_mn.append(m1)
this_basin_SA_mn.append(m2)
this_basin_SA_mn.append(m3)
this_basin_SA_mn.append(m4)
mn_by_basin_dict[basin_key] = this_basin_SA_mn
return mn_by_basin_dict
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 and has the option of plooting over a raster of chi values. Similar to PrintChiChannelsAndBasins but adds the map of chi coordinate underneath
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
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
plot_chi_raster (bool): finds the chi raster and plots this underneath the chi points in the channels. It looks nice.
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 = LSDV.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)
def SAPlotDriver(DataDirectory,
DEM_prefix,
FigFormat='show',
size_format="ESURF",
show_raw=True,
show_segments=True,
cmap=plt.cm.Set1,
n_colours=10,
basin_keys=[],
parallel=False):
"""
This is a driver function that manages plotting of Slope-Area data
Args:
DataDirectory (str): the path to the directory with the csv file
DEM_prefix (str): name of your DEM without extension
FigFormat (str): The format of the figure. Usually 'png' or 'pdf'. If "show" then it calls the matplotlib show() command.
size_format (str): Can be "big" (16 inches wide), "geomorphology" (6.25 inches wide), or "ESURF" (4.92 inches wide) (defualt esurf).
show_raw (bool): If true show raw data in background
show_segments (bool): If true, show the segmented main stem,
cmap (string or colourmap): the colourmap use to colour tributaries
n_colours (int): The number of coulours used in plotting tributaries
basin_keys (list): A list of the basin keys to plot. If empty, plot all the basins.
parallel (bool): If true the data is in multiple files and must be merged
Returns:
Slope-area plot for each basin
Author: SMM
"""
from lsdviztools.lsdplottingtools import lsdmap_pointtools as PointTools
print("These basin keys are: ")
print(basin_keys)
# read in binned data
binned_csv_fname = DataDirectory + DEM_prefix + '_SAbinned.csv'
print("I'm reading in the csv file " + binned_csv_fname)
if not parallel:
binnedPointData = PointTools.LSDMap_PointData(binned_csv_fname)
else:
binnedPointData = Helper.AppendSABinnedCSVs(DataDirectory, DEM_prefix)
binnedPointData = PointTools.LSDMap_PointData(binned_csv_fname)
# Read in the raw data
if (show_raw):
print("I am going to show the raw data.")
all_csv_fname = DataDirectory + DEM_prefix + '_SAvertical.csv'
if not parallel:
allPointData = PointTools.LSDMap_PointData(all_csv_fname)
else:
allPointData = Helper.AppendSAVerticalCSVs(DataDirectory,
DEM_prefix)
allPointData = PointTools.LSDMap_PointData(all_csv_fname)
# Read in the segmented data
if (show_segments):
print("I am going to show segments on the main stem.")
segmented_csv_fname = DataDirectory + DEM_prefix + '_SAsegmented.csv'
if not parallel:
segmentedPointData = PointTools.LSDMap_PointData(
segmented_csv_fname)
else:
segmentedPointData = Helper.AppendSASegmentedCSVs(
DataDirectory, DEM_prefix)
segmentedPointData = PointTools.LSDMap_PointData(
segmented_csv_fname)
# get the basin keys and check if the basins in the basin list exist
basin = binnedPointData.QueryData('basin_key')
basin = [int(x) for x in basin]
Basin = np.asarray(basin)
these_basin_keys = np.unique(Basin)
print("The unique basin keys are: ")
print(these_basin_keys)
final_basin_keys = []
# A bit of logic for checking keys
if (len(basin_keys) == 0):
final_basin_keys = these_basin_keys
else:
for basin in basin_keys:
if basin not in these_basin_keys:
print("You were looking for basin " + str(basin) +
" but it isn't in the basin keys.")
else:
final_basin_keys.append(basin)
print("The final basin keys are:")
print(final_basin_keys)
this_cmap = cmap
print("There are " + str(len(final_basin_keys)) +
"basins that I will plot")
#basin_keys.append(0)
# Loop through the basin keys, making a plot for each one
for basin_key in final_basin_keys:
print("I am making a plot for basin: " + str(basin_key))
if (show_segments):
if (show_raw):
FileName = DEM_prefix + '_SA_plot_raw_and_segmented_basin%s.%s' % (
str(basin_key), FigFormat)
SegmentedWithRawSlopeAreaPlot(segmentedPointData,
allPointData,
DataDirectory,
FigFileName=FileName,
FigFormat=FigFormat,
size_format=size_format,
basin_key=basin_key,
cmap=this_cmap,
n_colours=n_colours)
else:
FileName = DEM_prefix + '_SA_plot_segmented_basin%s.%s' % (
str(basin_key), FigFormat)
SegmentedSlopeAreaPlot(segmentedPointData,
DataDirectory,
FigFileName=FileName,
FigFormat=FigFormat,
size_format=size_format,
basin_key=basin_key)
else:
if (show_raw):
FileName = DEM_prefix + '_SA_plot_raw_and_binned_basin%s.%s' % (
str(basin_key), FigFormat)
BinnedWithRawSlopeAreaPlot(DataDirectory,
DEM_prefix,
FigFileName=FileName,
FigFormat=FigFormat,
size_format=size_format,
basin_key=basin_key,
n_colours=n_colours,
cmap=this_cmap)
else:
print(
"You selected an option that doesn't produce any plots. Turn either show raw or show segments to True."
)
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,
save_fig=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:
A string with the name of the image (printed to file): 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
"""
# 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!!"
)
# clear the plot
plt.clf()
# 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 = LSDP.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
thing_to_return = []
if (save_fig):
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)
thing_to_return = ImageName
else:
fig = MF.save_fig(fig_width_inches=fig_width_inches,
transparent=True,
return_fig=True)
thing_to_return = fig
print("I'm returning:")
print(thing_to_return)
return thing_to_return
def PrintChannelsAndBasins(DataDirectory,
fname_prefix,
add_basin_labels=True,
cmap="jet",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix="",
save_fig=True):
"""
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
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:
A string with the name of the image (printed to file): 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 = LSDV.GetBasinOutlines(DataDirectory, BasinsName)
ChannelFileName = fname_prefix + "_chi_data_map.csv"
chi_csv_fname = DataDirectory + ChannelFileName
thisPointData = LSDP.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
thing_to_return = []
if (save_fig):
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,
FigFormat=fig_format,
Fig_dpi=dpi,
transparent=True)
thing_to_return = ImageName
else:
fig = MF.save_fig(fig_width_inches=fig_size_inches,
transparent=True,
return_fig=True)
thing_to_return = fig
print("I'm returning:")
print(thing_to_return)
return thing_to_return
def PrintChannels(DataDirectory,
fname_prefix,
ChannelFileName,
cmap="jet",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix="",
plotting_column="basin_key",
save_fig=True):
"""
This function prints a channel map over a hillshade. It is more flexible than PrintAllChannels since you can choose the channel csv and you can also choose the column in the csv to plot
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
ChannelFileName (str): The name of the channel file. Doesn't need path but needs extension
cmap (str or colourmap): The colourmap to use for the plot
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
plotting_column (str): the column to plot from the csv file
save_fig (bool): If true, saves the fig, else, returns the filename
Returns:
If save_fig is true, return a string with the name of the image (printed to file). If save_fig is false, returns the figure handle to the shaded relief plot with the channels.
Returns:
A string with the name of the image (printed to file): A string with the name of the image (printed to file): 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"
chi_csv_fname = DataDirectory + ChannelFileName
print("Let me get the point data")
print("The filename is: " + chi_csv_fname)
thisPointData = LSDP.LSDMap_PointData(chi_csv_fname)
print("The parameter names are")
thisPointData.GetParameterNames(True)
# 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="gray",
alpha=0.6)
MF.add_point_data(thisPointData,
column_for_plotting=plotting_column,
scale_points=True,
column_for_scaling="drainage_area",
this_colourmap=cmap,
scaled_data_in_log=True,
max_point_size=5,
min_point_size=1)
# Save the image
thing_to_return = []
if (save_fig):
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,
FigFormat=fig_format,
Fig_dpi=dpi,
transparent=True)
thing_to_return = ImageName
else:
fig = MF.save_fig(fig_width_inches=fig_size_inches,
transparent=True,
return_fig=True)
thing_to_return = fig
print("I'm returning:")
print(thing_to_return)
return thing_to_return
def PrintAllChannels(DataDirectory,
fname_prefix,
add_basin_labels=True,
cmap="jet",
cbar_loc="right",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix="",
channel_colourmap="Blues",
save_fig=True):
"""
This function prints a channel map over a hillshade. It gets ALL the channels within the DEM: it automatically selects the _CN csv file that is obtained by the print channel network tool in lsdtopotools. Channels are coloured by the stream order.
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
channel_colourmap (str or cmap): the colourmap of the point data
save_fig (bool): If true, saves the fig, else, returns the filename
Returns:
If save_fig is true, return a string with the name of the image (printed to file). If save_fig is false, returns the figure handle to the shaded relief plot with the channels.
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 + "_CN.csv"
chi_csv_fname = DataDirectory + ChannelFileName
thisPointData = LSDP.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="Stream Order",
this_colourmap=channel_colourmap,
scale_points=True,
column_for_scaling="Stream Order",
scaled_data_in_log=False,
max_point_size=5,
min_point_size=1,
zorder=10,
alpha=1)
# Save the image
thing_to_return = []
if (save_fig):
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + fname_prefix + "_channels." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_channels." + fig_format
MF.save_fig(fig_width_inches=fig_size_inches,
FigFileName=ImageName,
FigFormat=fig_format,
Fig_dpi=dpi,
transparent=True)
thing_to_return = ImageName
else:
fig = MF.save_fig(fig_width_inches=fig_size_inches,
transparent=True,
return_fig=True)
thing_to_return = fig
print("I'm returning:")
print(thing_to_return)
return thing_to_return