def load_Point_Tool(thing):
"""
Returns a PointTools object from a csv_file or a pandas dataframe (automatically detects the type)
Author:
PointTools object
"""
if(isinstance(thing, pandas.DataFrame)):
PointData = PointTools.LSDMap_PointData(thing,data_type ="pandas", PANDEX = True)
else:
if(isinstance(thing, str)):
if(thing[-3:] == "csv"):
tdf = read_MChi_file(thing)
PointData = PointTools.LSDMap_PointData(tdf,data_type ="pandas", PANDEX = True)
else:
if(isinstance(thing, dict)):
print("WARNING, I am concatenating your dictionnary of dataframe")
thing = pandas.concat(thing)
PointData = PointTools.LSDMap_PointData(thing,data_type ="pandas", PANDEX = True)
else:
print("Hum, your file does not exists or your pandas is not a pandas (then what is it???), anyway I am aborting")
quit()
return PointData
def MakeChannelsMap(DataDirectory,
DEM_prefix,
FigFormat='show',
size_format='ESURF'):
"""
Function to make a raster map with the channels
colour-coded by source node. The colours should match up between the slope-area plot and the map.
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).
Returns:
Figure with map and slope-area plot for each basin
Author: FJC
"""
from LSDPlottingTools import LSDMap_PointTools as PointTools
# read in the raw csv file
raw_csv_fname = DataDirectory + DEM_prefix + '_SAvertical.csv'
print("I'm reading in the csv file " + raw_csv_fname)
# read in the binned csv file
binned_csv_fname = DataDirectory + DEM_prefix + '_SAbinned.csv'
print("I'm reading in the csv file " + binned_csv_fname)
# get the point data objects
RawPointData = PointTools.LSDMap_PointData(raw_csv_fname)
BinnedPointData = PointTools.LSDMap_PointData(binned_csv_fname)
# get the basin keys
basin = BinnedPointData.QueryData('basin_key')
basin = [int(x) for x in basin]
Basin = np.asarray(basin)
basin_keys = np.unique(Basin)
print('There are %s basins') % (len(basin_keys))
# loop through each basin and make the figure
for basin_key in basin_keys:
# set up the figure
FileName = DEM_prefix + '_raster_SA_basin%s.%s' % (str(basin_key),
FigFormat)
# get the channels map
raster_fname = DataDirectory + DEM_prefix + ".bil"
hs_fname = DataDirectory + DEM_prefix + "_hs.bil"
LSDP.LSDMap_ChiPlotting.BasicChannelPlotByBasin(
raster_fname,
hs_fname,
raw_csv_fname,
size_format=size_format,
basin_key=basin,
FigFileName=DataDirectory + FileName,
FigFormat=FigFormat)
def knickpoint_plotter(DataDirectory = "none", DEM_prefix ="none" , kp_type = "diff", FigFormat='pdf', processed = False, pd_name = "none"):
"""
Function to test LSDMap_KnickpointPlotting
Args:
DataDirectory (str): the data directory of the chi csv file
DEM_prefix (str): DEM name without extension
kp_type (string): select the type of knickpoint data you want (diff vs ratio).
processed (bool): Switch on to directly give a pandas dataframe to plots
pd_name (string or pandas dataframe): Name of this theoretical pandas dataframe
Returns:
Plot of knickpoint (diff or ratio) for each basin
Author: FJC
"""
from LSDPlottingTools import LSDMap_PointTools as PointTools
from LSDPlottingTools import LSDMap_KnickpointPlotting as KP
# read in the raw csv file
if(processed):
if(DEM_prefix !="none" and DataDirectory != "none"):
print("I will directly load your pandas dataframe, or at least try")
kp_csv_fname = read_MChi_file(DataDirectory,DEM_prefix+'_KsnKn.csv')
# get the point data objects
PointData = PointTools.LSDMap_PointData(kp_csv_fname,data_type ="pandas", PANDEX = True)
else:
if(isinstance(pd_name, pandas.DataFrame)):
PointData = PointTools.LSDMap_PointData(pd_name,data_type ="pandas", PANDEX = True)
else:
kp_csv_fname = DataDirectory+DEM_prefix+'_KsnKn.csv'
print("I'm reading in the csv file "+kp_csv_fname)
# get the point data objects
PointData = PointTools.LSDMap_PointData(+kp_csv_fname, data_type ="csv", PANDEX = True)
# get the basin keys
basin = PointData.QueryData('basin_key')
basin = [int(x) for x in basin]
Basin = np.asarray(basin)
basin_keys = np.unique(Basin)
print('There are %s basins') %(len(basin_keys))
# loop through each basin and make the figure
for basin_key in basin_keys:
FileName = DEM_prefix+"_KP_elevations_%s.%s" %(str(basin_key),FigFormat)
KP.plot_knickpoint_elevations(PointData, DataDirectory, DEM_prefix, basin_key, kp_type, FileName, FigFormat)
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 MakeSegmentedSlopeAreaPlot(DataDirectory,
DEM_prefix,
FigFormat='show',
size_format='ESURF',
x_param='midpoints',
y_param='mean'):
"""
This function makes a slope-area plot based on the raw data, using the
channel file generated from the chi mapping tool (ends in the extension
"_SAsegmented.csv".) It has a separate plot for each basin and colour codes
the points by source node so different tributaries can be identified.
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).
x_param (str): Key for which parameter to plot on the x axis, either 'midpoints' for the midpoints of the area data (default), or 'mean' for the mean of the area data.
y_param (str): Key for which parameter to plot on the y axis, either 'mean' for the mean of the slope data (default), or 'median', for the median of the slope data.
Returns:
Slope-area plot for each basin
Author: SMM
"""
from LSDPlottingTools import LSDMap_PointTools as PointTools
# read in the csv file
chi_csv_fname = DataDirectory + DEM_prefix + '_SAsegmented.csv'
print("I'm reading in the csv file " + chi_csv_fname)
# get the point data object
thisPointData = PointTools.LSDMap_PointData(chi_csv_fname)
# get the basin keys
basin = thisPointData.QueryData('basin_key')
basin = [int(x) for x in basin]
Basin = np.asarray(basin)
basin_keys = np.unique(Basin)
print('There are %s basins') % (len(basin_keys))
#basin_keys = []
#basin_keys.append(0)
FigFormat = "png"
for basin_key in basin_keys:
FileName = DEM_prefix + '_SA_plot_segmented_basin%s.%s' % (
str(basin_key), FigFormat)
LSDP.LSDMap_ChiPlotting.SegmentedSlopeAreaPlot(thisPointData,
DataDirectory,
FigFileName=FileName,
FigFormat=FigFormat,
size_format=size_format,
basin_key=basin_key)
def BinnedRegressionDriver(DataDirectory, DEM_prefix, basin_keys=[]):
"""
This function analyses slope-area data based on the binned data, using the
channel file generated from the chi mapping tool (ends in the extension
"_SAbinned.csv".) It has a separate plot for each basin and colour codes
the points by source node so different tributaries can be identified.
Args:
DataDirectory (str): the path to the directory with the csv file
DEM_prefix (str): name of your DEM without extension
basin_key (list): A list of the basin keys to plot. If empty, plot all the basins.
Returns:
Slope-area plot for each basin
Author: SMM
"""
from LSDPlottingTools import LSDMap_PointTools as PointTools
# read in the csv file
binned_csv_fname = DataDirectory + DEM_prefix + '_SAbinned.csv'
print("I'm reading in the csv file " + binned_csv_fname)
# get the point data object
binnedPointData = PointTools.LSDMap_PointData(binned_csv_fname)
# get the basin keys
basin = binnedPointData.QueryData('basin_key')
basin = [int(x) for x in basin]
Basin = np.asarray(basin)
these_basin_keys = np.unique(Basin)
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)
# Loop through the basin keys, making a plot for each one
for basin_key in final_basin_keys:
LSDP.LSDMap_SAPlotting.BinnedRegression(binnedPointData,
basin_key=basin_key)
def BoxPlotByCluster(DataDirectory, OutDirectory, fname_prefix, raster_name, stream_order=1):
"""
Make a boxplot of the results of the clustering compared to the raster specified
by raster_name
"""
#df = pd.read_csv(OutDirectory+fname_prefix+'_profiles_clustered_SO{}.csv'.format(stream_order))
# read in the raster
raster_ext = '.bil'
this_raster = IO.ReadRasterArrayBlocks(DataDirectory+raster_name)
EPSG_string = IO.GetUTMEPSG(DataDirectory+raster_name)
NDV, xsize, ysize, GeoT, Projection, DataType = IO.GetGeoInfo(DataDirectory+raster_name)
CellSize,XMin,XMax,YMin,YMax = IO.GetUTMMaxMin(DataDirectory+raster_name)
pts = PT.LSDMap_PointData(OutDirectory+fname_prefix+'_profiles_clustered_SO{}.csv'.format(stream_order),data_type ='csv')
easting, northing = pts.GetUTMEastingNorthing(EPSG_string=EPSG_string)
cluster_id = pts.QueryData('cluster_id', PANDEX=True)
clusters = list(set(cluster_id))
# dict for the data
data = {k: [] for k in clusters}
for x, (i, j) in enumerate(zip(northing, easting)):
# convert to rows and cols
X_coordinate_shifted_origin = j - XMin;
Y_coordinate_shifted_origin = i - YMin;
col_point = int(X_coordinate_shifted_origin/CellSize);
row_point = (ysize - 1) - int(round(Y_coordinate_shifted_origin/CellSize));
# check for data at this cell
this_value = this_raster[row_point][col_point]
if not np.isnan(this_value):
if this_value < 10:
# get the cluster id
data[cluster_id[x]].append(this_value)
print(data)
# now make a boxplot
labels, these_data = [*zip(*data.items())] # 'transpose' items to parallel key, value lists
plt.boxplot(these_data)
plt.xticks(range(1, len(labels) + 1), labels)
plt.show()
def GetLithologyPercentages(DataDirectory, OutDirectory, fname_prefix, raster_name, stream_order=1):
"""
Get the percentage of the nodes in each cluster that drain each lithology
"""
from collections import Counter
# read in the raster
raster_ext = '.bil'
this_raster = IO.ReadRasterArrayBlocks(DataDirectory+raster_name)
EPSG_string = IO.GetUTMEPSG(DataDirectory+raster_name)
NDV, xsize, ysize, GeoT, Projection, DataType = IO.GetGeoInfo(DataDirectory+raster_name)
CellSize,XMin,XMax,YMin,YMax = IO.GetUTMMaxMin(DataDirectory+raster_name)
pts = PT.LSDMap_PointData(OutDirectory+fname_prefix+'_profiles_clustered_SO{}.csv'.format(stream_order),data_type ='csv')
easting, northing = pts.GetUTMEastingNorthing(EPSG_string=EPSG_string)
cluster_id = pts.QueryData('cluster_id', PANDEX=True)
clusters = list(set(cluster_id))
# dict for the data
data = {k: [] for k in clusters}
for x, (i, j) in enumerate(zip(northing, easting)):
# convert to rows and cols
X_coordinate_shifted_origin = j - XMin;
Y_coordinate_shifted_origin = i - YMin;
col_point = int(X_coordinate_shifted_origin/CellSize);
row_point = (ysize - 1) - int(round(Y_coordinate_shifted_origin/CellSize));
# check for data at this cell
this_value = this_raster[row_point][col_point]
if not np.isnan(this_value):
data[cluster_id[x]].append(this_value)
# you have the values. now what percentage are each?
for key, liths in data.items():
c = Counter(liths)
n_ndv = c[0.0]
print(c)
[print(x,": ",vals/len(liths) * 100) for x, vals in c.items()]
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 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 MakeBinnedWithRawSlopeAreaPlot(DataDirectory,
DEM_prefix,
FigFormat='show',
size_format='ESURF',
basin_keys=[]):
"""
This function makes a slope-area plot based on the raw data, using the
channel file generated from the chi mapping tool (ends in the extension
"_SAvertical.csv".) It has a separate plot for each basin and colour codes
the points by source node so different tributaries can be identified.
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).
basin_key (list): A list of the basin keys to plot. If empty, plot all the basins.
Returns:
Slope-area plot for each basin
Author: SMM
"""
from LSDPlottingTools import LSDMap_PointTools as PointTools
# read in the csv file
binned_csv_fname = DataDirectory + DEM_prefix + '_SAbinned.csv'
print("I'm reading in the csv file " + binned_csv_fname)
all_csv_fname = DataDirectory + DEM_prefix + '_SAvertical.csv'
# get the point data object
binnedPointData = PointTools.LSDMap_PointData(binned_csv_fname)
allPointData = PointTools.LSDMap_PointData(all_csv_fname)
# get the basin keys
basin = binnedPointData.QueryData('basin_key')
basin = [int(x) for x in basin]
Basin = np.asarray(basin)
these_basin_keys = np.unique(Basin)
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)
this_cmap = plt.cm.Set1
print('There are %s basins') % (len(basin_keys))
#basin_keys.append(0)
# Loop through the basin keys, making a plot for each one
for basin_key in final_basin_keys:
FileName = DEM_prefix + '_SA_plot_raw_and_binned_basin%s.%s' % (
str(basin_key), FigFormat)
LSDP.LSDMap_SAPlotting.BinnedWithRawSlopeAreaPlot(
binnedPointData,
allPointData,
DataDirectory,
FigFileName=FileName,
FigFormat=FigFormat,
size_format=size_format,
basin_key=basin_key,
n_colours=10,
cmap=this_cmap)
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 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 MakeBoxPlotsKsnLithology(DataDirectory, fname_prefix, raster_name, theta=0.45, label_list=[]):
"""
Make boxplots of ksn compared to lithology raster. Lithology should have integer
values for the different rock types (rock type with 0 is excluded). Pass in list of
labels for the different units, which must be the same length as the number of lithology codes.
If none is passed then just use the integer values for labelling.
"""
from scipy import stats
# read in the raster
raster_ext = '.bil'
this_raster = IO.ReadRasterArrayBlocks(DataDirectory+raster_name)
#EPSG_string = IO.GetUTMEPSG(DataDirectory+raster_name)
EPSG_string='epsg:32611'
print(EPSG_string)
NDV, xsize, ysize, GeoT, Projection, DataType = IO.GetGeoInfo(DataDirectory+raster_name)
CellSize,XMin,XMax,YMin,YMax = IO.GetUTMMaxMin(DataDirectory+raster_name)
pts = PT.LSDMap_PointData(DataDirectory+fname_prefix+'_ksn.csv',data_type ='csv')
print(pts)
easting, northing = pts.GetUTMEastingNorthing(EPSG_string=EPSG_string)
ksn = pts.QueryData('ksn', PANDEX=True)
#print(ksn)
# get the unique values in the raster
raster_values = np.unique(this_raster)
raster_values = raster_values[1:]
# dict for the data
data = {k: [] for k in raster_values}
for x, (i, j) in enumerate(zip(northing, easting)):
# convert to rows and cols
X_coordinate_shifted_origin = j - XMin;
Y_coordinate_shifted_origin = i - YMin;
col_point = int(X_coordinate_shifted_origin/CellSize);
row_point = (ysize - 1) - int(round(Y_coordinate_shifted_origin/CellSize));
# check for data at this cell
this_raster_value = this_raster[row_point][col_point]
if not np.isnan(this_raster_value) and this_raster_value != 0:
data[this_raster_value].append(ksn[x])
# set up a figure
fig,ax = plt.subplots(nrows=1,ncols=1, figsize=(5,5), sharex=True, sharey=True)
labels, dict = [*zip(*data.items())] # 'transpose' items to parallel key, value lists
print(label_list)
if label_list:
labels = label_list
print(labels)
box = plt.boxplot(dict, patch_artist=True)
plt.xticks(range(1, len(labels) + 1), labels)
plt.ylabel('$k_{sn}$', fontsize=14)
# get the medians for plotting as an upper label
medians = []
print("========SOME KSN STATISTICS=========")
for key, value in data.items():
print("Key {}, median ksn = {}".format(key, np.median(value)))
medians.append(np.median(value))
print("Key {}, IQR = {}".format(key, stats.iqr(value)))
print("========================================")
pos = np.arange(len(labels)) + 1
upperLabels = [str(np.round(s, 2)) for s in medians]
# change the colours for each lithology
colors=['#60609fff', '#fdbb7fff', '#935353ff', '#f07b72ff']
for patch, color in zip(box['boxes'], colors):
patch.set_facecolor(color)
patch.set_alpha(0.9)
patch.set_edgecolor('k')
for cap in box['caps']:
cap.set(color='k')
for wh in box['whiskers']:
wh.set(color='k')
for med in box['medians']:
med.set(color='k')
for flier, color in zip(box['fliers'], colors):
flier.set_markeredgecolor(color)
flier.set_markerfacecolor(color)
flier.set_markersize(2)
# for tick, label in zip(range(len(labels)), ax.get_xticklabels()):
# k = tick % 2
# ax.text(pos[tick], top - (top*0.05), upperLabels[tick],
# horizontalalignment='center', color=colors[k])
ax.grid(color='0.8', linestyle='--', which='major', zorder=1)
plt.title('Boxplots of $k_{sn}$ by lithology', fontsize=14)
plt.savefig(DataDirectory+fname_prefix+'_boxplot_lith_ksn.png', dpi=300, transparent=True)
plt.clf()
# Do some stats, yo
# KS test to see if we can distinguish the distributions at a confidence level of p = 0.05
# https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.kstest.html
# relief
keys = list(data.keys())
values = list(data.values())
k=0
for i in range(len(keys)-1):
for j in range(i+1, len(keys)):
print("KS test between {} and {}".format(keys[i], keys[j]))
d, p = stats.ks_2samp(values[i], values[j])
print(d, p)
k += 1
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'] = ['arial']
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 plot_outliers_x_vs_diff_ratio(PointData, DataDirectory,x_col = "elevation", saveName = "Outliers", save_fmt = ".png", size_format = "ESURF", log_data = False, ylim_ratio = [], ylim_diff = []):
"""
Basic plot to have a general view of the knickpoints: flow distance against ratio and diff colored by elevation
Args:
PointData: A PointData object
DataDirectory: Where the data is saved
saveName: save name
returns:
Nothing, sorry.
Author: BG
"""
# Merging the dictionnary
if(isinstance(PointData, dict)):
print("Your data is a dictionnary of dataframes, let me create a PointTool object that contains all of these.")
PointData = pd.concat(PointData)
PointData = LSDMap_PD.LSDMap_PointData(PointData,data_type ="pandas", PANDEX = True)
plt.clf()
label_size = 10
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
# make a figure
if size_format == "geomorphology":
fig = plt.figure(2, facecolor='white',figsize=(6.25,3.5))
l_pad = -40
elif size_format == "big":
fig = plt.figure(2, facecolor='white',figsize=(16,9))
l_pad = -50
else:
fig = plt.figure(2, facecolor='white',figsize=(4.92126,3.5))
l_pad = -35
gs = plt.GridSpec(100,100,bottom=0.15,left=0.1,right=1.0,top=1.0)
ax1 = fig.add_subplot(gs[5:45,10:95])
ax2 = fig.add_subplot(gs[55:100,10:95])
diff = PointData.QueryData("diff")
ratio = PointData.QueryData("ratio")
Z = PointData.QueryData(x_col)
PointData.selectValue('diff_outlier',value = True, operator = "==")
PointData.selectValue('ratio_outlier',value = True, operator = "==")
diffout = PointData.QueryData("diff")
ratioout = PointData.QueryData("ratio")
Z_out = PointData.QueryData(x_col)
if(log_data):
print("I am logging the data")
diff = np.log10(diff)
ratio = np.log10(ratio)
if(isinstance(diffout, list) == False):
diffout = [diffout]
ratioout = [ ratioout]
for i in range(len(diffout)):
diffout[i]= np.log10(diffout[i])
ratioout[i]= np.log10(ratioout[i])
sign = PointData.QueryData("sign")
ax1.scatter(Z,ratio, s=1.5, lw = 0, c = "gray")
ax1.scatter(Z_out,ratioout, s=1.5, lw = 0, c = sign)
ax1.set_ylabel("Ratio")
ax1.tick_params(axis = 'x', length = 0, width = 0, labelsize = 0)
ax1.spines['bottom'].set_visible(False)
ax2.scatter(Z,diff,s=1.5, lw = 0, c = "gray")
ax2.scatter(Z_out,diffout,s=1.5, lw = 0, c = sign)
ax2.set_ylabel("Diff")
ax2.set_xlabel(x_col)
if(ylim_diff != []):
ax2.set_ylim(ylim_diff[0],ylim_diff[1])
if ylim_ratio != []:
ax.set_ylim(ylim_ratio[0],ylim_ratio[1])
#ax2.tick_params(axis = 'x', labelsize = 6)
#ax1.set_xticks([4,5,6,7,8,9,10])
#ax2.set_xticks([4,5,6,7,8,9,10])
#ax2.set_xticklabels([ur"$10^{4}$",ur"$10^{5}$",ur"$10^{6}$",ur"$10^{7}$",ur"$10^{8}$",ur"$10^{9}$",ur"$10^{10}$"])
plt.savefig(DataDirectory+saveName+save_fmt,dpi=500)
def plot_outliers_vs_others(PointData, DataDirectory, saveName = "Basic_diff_ratio", save_fmt = ".png", size_format = "ESURF", log_data = False):
"""
Basic plot to have a general view of the knickpoints: diff against ratio colored by elevation
Args:
PointData: A PointData object or a dictionnary of dataframe containing outliers and none outliers
DataDirectory: Where the data is saved
saveName: save name
returns:
Nothing, sorry.
Author: BG
"""
# Merging the dictionnary
if(isinstance(PointData, dict)):
print("Your data is a dictionnary of dataframes, let me create a PointTool object that contains all of these.")
PointData = pd.concat(PointData)
PointData = LSDMap_PD.LSDMap_PointData(PointData,data_type ="pandas", PANDEX = True)
plt.clf()
label_size = 10
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
# make a figure
if size_format == "geomorphology":
fig = plt.figure(1, facecolor='white',figsize=(6.25,3.5))
l_pad = -40
elif size_format == "big":
fig = plt.figure(1, facecolor='white',figsize=(16,9))
l_pad = -50
else:
fig = plt.figure(1, facecolor='white',figsize=(4.92126,3.5))
l_pad = -35
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])
diff = PointData.QueryData("diff")
ratio = PointData.QueryData("ratio")
PointData.selectValue('diff_outlier',value = True, operator = "==")
PointData.selectValue('ratio_outlier',value = True, operator = "==")
diffout = PointData.QueryData("diff")
ratioout = PointData.QueryData("ratio")
elevation = PointData.QueryData("elevation")
if(log_data):
print("I am logging the data")
diff = np.log10(diff)
ratio = np.log10(ratio)
if(isinstance(diffout, list) == False):
diffout = [diffout]
ratioout = [ ratioout]
for i in range(len(diffout)):
diffout[i]= np.log10(diffout[i])
ratioout[i]= np.log10(ratioout[i])
print("Now plotting the outliers vs the non-outliers")
ax.scatter(diff,ratio, s=0.5, lw = 0, c = 'gray')
ax.scatter(diffout,ratioout, s = 0.5,c = elevation,lw = 0)
ax.set_xlabel("Diff")
ax.set_ylabel("Ratio")
plt.savefig(DataDirectory+saveName+save_fmt,dpi=500)
print("Your figure is " +DataDirectory+saveName+save_fmt)
def map_knickpoint_sign(PointData, DataDirectory, Raster_base_name, HS_name = "none",Time_in_name = False, river_network = "none", saveName = "none", size = 2, outliers = 'none'):
"""
Will create a map of the knickpoint simply colored by sign.
Args:
PointData (PointTools object)
DataDirectory (str): directory where the data will be saved and loaded.
Raster_base_name (str): Base name of your files without the .bil
HS_name (str): name of your Hillshade file, by default baseName + _hs.bil like LSDTT create it
Time_in_name (bool): Option to add timing info in the nae of the figure. Can be useful if you test loads of parameters and you want to be sure that your files names are different (but awful).
returns:
No, but creates a map named map_knickpoint_sign.png
Author:
BG
"""
###### Parameters ######
if(isinstance(PointData, dict)):
print("Your data is a dictionnary of dataframes, let me create a PointTool object that contains all of these.")
PointData = pd.concat(PointData)
PointData = LSDMap_PD.LSDMap_PointData(PointData,data_type ="pandas", PANDEX = False)
if(outliers != 'none' ):
PointData.selectValue(outliers, operator = "==", value = True)
print PointData
Directory = DataDirectory # reading directory
wDirectory = Directory # writing directory
Base_file = Raster_base_name # It will be the cabkground raster. Each other raster you want to drap on it will be cropped to its extents including nodata
if(saveName == "none"):
wname = "map_knickpoint_sign" # name of your output file
else:
wname = saveName
dpi = 500 # Quality of your output image, don't exceed 900
fig_size_inches = 7 # Figure size in Inches
if(HS_name == "none"):
HS_name = Raster_base_name+("_hs.bil")
DrapeRasterName = HS_name # 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
##### Now we can load and plot the data
BackgroundRasterName = Base_file + ".bil" # Ignore this line
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)
if(isinstance(river_network,LSDP.LSDMap_PointData)):
MF.add_point_data( river_network, # this function plot the requested point file using the lat/long column in the csv file
column_for_plotting = "none", # 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
MF.add_point_data( PointData, # this function plot the requested point file using the lat/long column in the csv file
column_for_plotting = "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 = size, # 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
)
if(Time_in_name):
ImageName = wDirectory+str(int(clock.time()))+wname+".png" # Ignore this
else:
ImageName = wDirectory+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 plot_data(self):
"""This is the bit that actually plots the data.
"""
import LSDPlottingTools.LSDMap_PointTools as LSDMap_PD
if self.plotting_switches["BasicDensityPlot"]:
# Check to see if there is a filename. If not set a default file name
if self.plotting_parameters["FigFileName"] == "None":
self.plotting_parameters[
"FigFileName"] = self.FilePath + os.sep + self.FilePrefix + "BDP." + self.plotting_parameters[
"FigFormat"]
print("Hey there partner, I am making a grid plot.")
LSDMap_BP.BasicDensityPlot(self.base_faster_fname,
self.plotting_parameters["base_cmap"],
self.plotting_parameters["cbar_label"],
self.plotting_parameters["clim_val"],
self.plotting_parameters["FigFileName"],
self.plotting_parameters["FigFormat"],
self.plotting_parameters["size_format"],
self.plotting_parameters["is_log"])
if self.plotting_switches["BasicDrapedPlotGridPlot"]:
# Check to see if there is a filename. If not set a default file name
if self.plotting_parameters["FigFileName"] == "None":
self.plotting_parameters[
"FigFileName"] = self.FilePath + os.sep + self.FilePrefix + "BDPDPG." + self.plotting_parameters[
"FigFormat"]
LSDMap_BP.BasicDrapedPlotGridPlot(
self.base_faster_fname, self.plotting_parameters["DrapeName"],
self.plotting_parameters["base_cmap"],
self.plotting_parameters["drape_cmap"],
self.plotting_parameters["cbar_label"],
self.plotting_parameters["clim_val"],
self.plotting_parameters["drape_alpha"],
self.plotting_parameters["FigFileName"],
self.plotting_parameters["FigFormat"])
if self.plotting_switches["BasinsOverFancyHillshade"]:
# Check to see if there is a filename. If not set a default file name
if self.plotting_parameters["FigFileName"] == "None":
self.plotting_parameters[
"FigFileName"] = self.FilePath + os.sep + self.FilePrefix + "Basins." + self.plotting_parameters[
"FigFormat"]
print("Type of base raster is: " +
str(type(self.base_faster_fname)))
print("The chan net csv is: " +
str(self.plotting_parameters["chan_net_csv"]))
print("The drape cmap is: " +
self.plotting_parameters["drape_cmap"])
thisPointData = LSDMap_PD.LSDMap_PointData(self.basin_csv_fname)
if self.plotting_parameters["chan_net_csv"] == "None":
chanPointData = "None"
else:
chanPointData = LSDMap_PD.LSDMap_PointData(
self.plotting_parameters["chan_net_csv"])
LSDMap_BP.BasinsOverFancyHillshade(
self.base_faster_fname, self.hs_fname, self.basin_fname,
self.basin_csv_fname, thisPointData,
self.plotting_parameters["base_cmap"],
self.plotting_parameters["drape_cmap"],
self.plotting_parameters["clim_val"],
self.plotting_parameters["drape_alpha"],
self.plotting_parameters["FigFileName"],
self.plotting_parameters["FigFormat"],
self.plotting_parameters["elevation_threshold"],
self.plotting_parameters["grouped_basin_list"],
self.plotting_parameters["basin_rename_list"],
self.plotting_parameters["spread"], chanPointData,
self.plotting_parameters["label_sources"],
self.plotting_parameters["source_chi_threshold"],
self.plotting_parameters["size_format"])
if self.plotting_switches["BasicChiCoordinatePlot"]:
print("I am plotting a basic chi plot!")
# Check to see if there is a filename. If not set a default file name
if self.plotting_parameters["FigFileName"] == "None":
self.plotting_parameters[
"FigFileName"] = self.FilePath + os.sep + self.FilePrefix + "Chi." + self.plotting_parameters[
"FigFormat"]
thisBasinData = LSDMap_PD.LSDMap_PointData(self.basin_csv_fname)
chi_drape_cname = 'CMRmap_r'
#chi_drape_cname = 'brg_r'
cbar_lablel = "$\chi$ (m)"
LSDMap_CP.BasicChiCoordinatePlot(
self.hs_fname, self.chi_raster_fname, self.basic_chi_csv_fname,
self.plotting_parameters["base_cmap"], chi_drape_cname,
cbar_lablel, self.plotting_parameters["clim_val"],
self.plotting_parameters["basin_order_list"], thisBasinData,
self.basin_fname, self.plotting_parameters["drape_alpha"],
self.plotting_parameters["FigFileName"],
self.plotting_parameters["FigFormat"],
self.plotting_parameters["size_format"])
if self.plotting_switches["ChiProfiles"]:
print("I am plotting a basic chi profile plot!")
# Check to see if there is a filename. If not set a default file name
if self.plotting_parameters["FigFileName"] == "None":
self.plotting_parameters[
"FigFileName"] = self.FilePath + os.sep + self.FilePrefix + "ChiProfile." + self.plotting_parameters[
"FigFormat"]
thisBasinData = LSDMap_PD.LSDMap_PointData(self.basin_csv_fname)
chi_drape_cname = 'CMRmap_r'
#chi_drape_cname = 'brg_r'
cbar_lablel = "$\chi$ (m)"
print("The csv filename is: " + self.chi_csv_fname)
LSDMap_CP.ChiProfiles(
self.chi_csv_fname, self.plotting_parameters["FigFileName"],
self.plotting_parameters["FigFormat"],
self.plotting_parameters["basin_order_list"],
self.plotting_parameters["basin_rename_list"],
self.plotting_parameters["label_sources"],
self.plotting_parameters["elevation_threshold"],
self.plotting_parameters["source_thinning_threshold"],
self.plotting_parameters["plot_M_chi"],
self.plotting_parameters["plot_segments"],
self.plotting_parameters["size_format"])
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)
#thisPointData.ThinDataSelection("basin_key",[10])
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)
BackgroundRasterName = Base_file + ".bil"
DrapeRasterName = Base_file + "_hs.bil"
ChiRasterName = Base_file + "_chi_coord.bil"
#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)
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
