def knickpoint_plotter_by_basin(name ,DataDirectory, save_name = "kn_by_Basins", kp_type = "diff", FigFormat='pdf', processed = False):
"""
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
"""
# read in the raw csv file
PointData = load_Point_Tool(name)
# 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 = save_name+"_KP_elevations_%s.%s" %(str(basin_key),FigFormat)
KP.plot_knickpoint_elevations(PointData, DataDirectory, basin_key, kp_type, FileName, FigFormat)
def knickpoint_plotter_by_basin(name ,DataDirectory, save_name = "kn_by_Basins", kp_type = "diff", FigFormat='pdf', processed = False):
"""
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
"""
# read in the raw csv file
PointData = load_Point_Tool(name)
# 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 = save_name+"_KP_elevations_%s.%s" %(str(basin_key),FigFormat)
KP.plot_knickpoint_elevations(PointData, DataDirectory, basin_key, kp_type, FileName, 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)
#DataDirectory = 'B://GIS/Data/Santa_cruz/Smugglers/lidar/'
baseName = "smugglers_1"
dfp = read_MChi_file(DataDirectory,baseName+"_KsnKn.csv")
river_net = read_MChi_file(DataDirectory,baseName+"_MChiSegmented.csv")
dfp = select_main_basin(dfp)
#flat_values = sort_ratio_0_data(dfp, mode = "extract")
dfp = sort_ratio_0_data(dfp, mode = "delete")
PT = load_Point_Tool(dfp) # If you need actual pointdata
#PTflat = load_Point_Tool(flat_values)
PTriver = load_Point_Tool(river_net)
#KP.plot_outliers_x_vs_diff_ratio(PTZOUT,PT, DataDirectory,x_col = "elevation", saveName = "Outliers_bin_Z_ratio_int_"+str(interval), save_fmt = ".png", size_format = "ESURF", log_data = False, ylim_diff = [0,500])
######## binned by DA
# Binning
binned_by_DA = lst.binning_PD(dfp,column = "drainage area",values = "auto_power_10")
# Calculation of the outliers
binned_by_DA = lst.add_outlier_column_to_PD(binned_by_DA, column = ['diff','ratio'], threshold = [2,2])
plt.scatter(binned_by_DA['10000']['diff'],binned_by_DA['10000']['diff'])
plt.show()
#quit()
#print(binned_by_DA)
# plotting the figure
KP.plot_outliers_x_vs_diff_ratio(binned_by_DA, DataDirectory,x_col = "drainage area", saveName = "Basic_diff_ratio", save_fmt = ".png", size_format = "ESURF", log_data = True, ylim_diff =[-3,4])
#KP.map_knickpoint_sign(binned_by_DA, DataDirectory, 'smugglers_1', Time_in_name = False, river_network = PTriver, saveName = "Map_Knickpoint", size = 50000, outliers = 'diff_outlier')
KP.map_knickpoint_sign(binned_by_DA, DataDirectory, 'smugglers_1', Time_in_name = False, river_network = PTriver, saveName = "Map_Knickpoint", size = 50000)
dfp = read_MChi_file(DataDirectory,baseName+"_KsnKn.csv")
river_net = read_MChi_file(DataDirectory,baseName+"_MChiSegmented.csv")
dfp = select_main_basin(dfp)
#flat_values = sort_ratio_0_data(dfp, mode = "extract")
dfp = sort_ratio_0_data(dfp, mode = "delete")
dfp = lst.binning_PD(dfp, column = "source_key", values = "unique", log = False)
dfp = lst.add_outlier_column_to_PD(dfp, column = "diff", threshold = 2)
print dfp
quit()
PT = load_Point_Tool(dfp) # If you need actual pointdata
#PTflat = load_Point_Tool(flat_values)
PTriver = load_Point_Tool(river_net)
#KP.plot_outliers_x_vs_diff_ratio(PTZOUT,PT, DataDirectory,x_col = "elevation", saveName = "Outliers_bin_Z_ratio_int_"+str(interval), save_fmt = ".png", size_format = "ESURF", log_data = False, ylim_diff = [0,500])
KP.map_knickpoint_standard(PT, DataDirectory, baseName, HS_name = "none",Time_in_name = True, river_network = PTriver, saveName = "none", log = False)
quit()
######## binned by DA
# Binning
binned_by_DA = lst.binning_PD(dfp,column = "drainage area",values = "auto_power_10")
# Calculation of the outliers
binned_by_DA = lst.add_outlier_column_to_PD(binned_by_DA, column = ['diff','ratio'], threshold = [2,2])
plt.scatter(binned_by_DA['10000']['diff'],binned_by_DA['10000']['diff'])
plt.show()
#quit()
#print(binned_by_DA)
# plotting the figure
KP.plot_outliers_x_vs_diff_ratio(binned_by_DA, DataDirectory,x_col = "drainage area", saveName = "Basic_diff_ratio", save_fmt = ".png", size_format = "ESURF", log_data = True, ylim_diff =[-3,4])
#KP.map_knickpoint_sign(binned_by_DA, DataDirectory, 'smugglers_1', Time_in_name = False, river_network = PTriver, saveName = "Map_Knickpoint", size = 50000, outliers = 'diff_outlier')
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 that contains your data files. If this isn't defined I'll assume it's the same as the current directory."
)
parser.add_argument(
"-fname",
"--fname_prefix",
type=str,
help=
"The prefix of your DEM WITHOUT EXTENSION!!! This must be supplied or you will get an error."
)
# Basin and source selection
# Basins selection
parser.add_argument(
"-basin_keys",
"--basin_keys",
type=str,
default="",
help=
"This is a comma delimited string that gets the list of basins you want for the plotting. Default = all basins"
)
# Sources selection
parser.add_argument(
"-source_keys",
"--source_keys",
type=str,
default="",
help=
"This is a comma delimited string that gets the list of sources you want for the plotting. Default = all sources"
)
parser.add_argument(
"-min_sl",
"--min_source_length",
type=float,
default=0,
help=
"This is a minimum length for the river to plot, if you want to only plot the river profile of the main rivers for example. Default = 0 (no restrictions)"
)
parser.add_argument(
"-main_stem",
"--isolate_main_stem",
type=bool,
default=False,
help="set to True to only keep the main stem of each basins")
# These control the format of your figures
parser.add_argument(
"-fmt",
"--FigFormat",
type=str,
default='png',
help="Set the figure format for the plots. Default is png")
parser.add_argument(
"-size",
"--size_format",
type=str,
default='ESURF',
help=
"Set the size format for the figure. Can be 'big' (16 inches wide), 'geomorphology' (6.25 inches wide), or 'ESURF' (4.92 inches wide) (defualt esurf)."
)
# ALL
parser.add_argument("-all",
"--AllAnalysis",
type=bool,
default=False,
help="Turn on to have fun")
parser.add_argument("-allD",
"--AllAnalysisDebug",
type=bool,
default=False,
help="Turn on to have even more fun")
# Mchi_related
parser.add_argument(
"-mcstd",
"--mchi_map_std",
type=bool,
default=False,
help=
"Turn to True to plot a standart M_chi map on an HS. Small reminder, Mchi = Ksn if calculated with A0 = 1."
)
parser.add_argument(
"-mcbk",
"--mchi_map_black",
type=bool,
default=False,
help=
"Turn to True to plot a standart M_chi map on Black background. Small reminder, Mchi = Ksn if calculated with A0 = 1."
)
parser.add_argument(
"-minmc",
"--min_mchi_map",
type=float,
default=0,
help="mininum value for the scale of your m_chi maps, default 0")
parser.add_argument(
"-maxmc",
"--max_mchi_map",
type=float,
default=0,
help="maximum value for the scale of your m_chi maps, default auto")
parser.add_argument("-almc",
"--alpha_mchi",
type=float,
default=0,
help="alpha_value for the background mchi raster")
#knickpint related
parser.add_argument(
"-ksnPs",
"--ksn_per_source",
type=bool,
default=False,
help=
"Print one figure per source key selected, with ksn -> f(chi & flow_distance) in the folder .../river_plots/. it displays the ksn out of Mudd et al., 2014 method, and the TVD one out of the *insert algorithm name*"
)
parser.add_argument(
"-rivplot",
"--river_profile",
type=bool,
default=False,
help=
"Print one figure per source key selected, with elevation -> f(chi & flow_distance) in the folder .../river_plots/. it displays river profiles in a chi and distance spaces"
)
parser.add_argument(
"-basplot",
"--basin_plot",
type=bool,
default=False,
help=
"Print one figure per basins key selected, with elevation -> f(chi & flow_distance) in the folder .../river_plots/. it displays river profiles in a chi and distance spaces"
)
parser.add_argument(
"-rasplot",
"--raster_plots",
type=bool,
default=False,
help=
"Print raster plots with knickpoints on top of ksn in the folder .../raster_plots/"
)
parser.add_argument(
"-rasplot_ld",
"--raster_plots_large_dataset",
type=bool,
default=False,
help=
"Print raster plots with knickpoints on top of ksn in the folder .../raster_plots/"
)
parser.add_argument(
"-statplot",
"--statistical_plots",
type=bool,
default=False,
help=
"Print a bunch of statistics about the knickpoints in the folder .../raster_plots/"
)
parser.add_argument(
"-stradivarius",
"--multi_violin_plots",
type=bool,
default=False,
help=
"Print a bunch of statistical distribution against elevation, chi, ... for the selected knickpoints"
)
parser.add_argument(
"-clas",
"--classical_plot",
type=bool,
default=False,
help=
"Automatically print a set of basin-wide river profile and maps of knickpoints in their different expressions."
)
parser.add_argument(
"-clasriv",
"--classical_plot_per_river",
type=bool,
default=False,
help=
"Automatically print a set of river profiles with knickpoints in their different expressions. WARNINGS: can create a huge number of plot, you should sort your rivers before."
)
# Others
parser.add_argument(
"-nbh",
"--n_bin_hist",
type=int,
default=0,
help=
"Customization of the number of bin you want for the general histogram. Default is an automatic in-built selection from numpy"
)
parser.add_argument(
"-cov",
"--cut_off_val",
type=str,
default="0,0,0,0",
help=
"Cutoff value for the knickpoint magnitude (the drop/increase of ksn). Default is 0 (no cut)"
)
parser.add_argument("-kal",
"--kalib",
type=bool,
default=False,
help="Don't use that.")
parser.add_argument(
"-segelev",
"--print_segmented_elevation",
type=bool,
default=False,
help=
"This print the segmented elevation on the top of the river profiles, in transparent black. Useful to check segment boundaries and adjust target_nodes parameter. Default False."
)
parser.add_argument(
"-extent_rast_cmap",
"--manual_extent_colormap_knickpoint_raster",
type=str,
default="",
help=
"This print the segmented elevation on the top of the river profiles, in transparent black. Useful to check segment boundaries and adjust target_nodes parameter. Default False."
)
parser.add_argument(
"-size_kp_map",
"--size_kp_map",
type=bool,
default=True,
help=
"This print the segmented elevation on the top of the river profiles, in transparent black. Useful to check segment boundaries and adjust target_nodes parameter. Default False."
)
parser.add_argument(
"-max_hist",
"--maximum_extent_for_histogram",
type=int,
default=0,
help=
"This print the segmented elevation on the top of the river profiles, in transparent black. Useful to check segment boundaries and adjust target_nodes parameter. Default False."
)
parser.add_argument(
"-lith_rast",
"--lithologic_raster",
type=bool,
default=False,
help=
"switch on if you have a _LITHRAST raster, it will plot a hillshade colored by lithologic unit"
)
parser.add_argument(
"-save",
"--save_output",
type=bool,
default=False,
help=
"switch on if you have the willingness to save your selected knickpoints in a new csv file named prefix_output.csv"
)
parser.add_argument(
"-coeff_size_kp",
"--kp_coeff_size",
type=float,
default=30,
help=
"qualitative size of knickpoints on river profile. Default 10, increase or decrease to adapt the size of the triangles"
)
parser.add_argument(
"-fixed_size_kp_min_max_river",
"--fixed_size_kp_min_max_river",
type=str,
default="",
help=
"qualitative size of knickpoints on river profile. Default 50, increase or decrease to adapt the size of the triangles"
)
parser.add_argument(
"-normelev_rel",
"--normalise_elevation_to_outlet_relative",
type=bool,
default=False,
help=
"Normalize the elevation for each watershed, considering the outlet points as 0, but without changing the relief."
)
parser.add_argument(
"-normelev_abs",
"--normalise_elevation_to_outlet_absolute",
type=bool,
default=False,
help=
"Normalize the elevation for each watershed, considering the outlet points as 0 and the maximum to 1."
)
parser.add_argument(
"-unicol_kp",
"--unicolor_for_knickpoint_map",
type=str,
default=None,
help=
"use this flag to force a uni color for knickpoints on map. It has to be a matplotlib color code (simplest way is to use html colorcode like #AABB47)"
)
parser.add_argument(
"-GS",
"--general_size",
type=str,
default="",
help=
"This is a comma delimited string used to scale your knickpoints relatively: min_value,min_size,max_value,max_size. see documentation for explanations"
)
args = parser.parse_args()
# Housekeeping for filenames and directory names
if not args.fname_prefix:
if not args.parallel:
print(
"WARNING! You haven't supplied your DEM name. Please specify this with the flag '-fname'"
)
sys.exit()
# get the base directory
if args.base_directory:
this_dir = args.base_directory
# check if you remembered a / at the end of your path_name
if not this_dir.endswith(os.sep):
print(
"You forgot the separator at the end of the directory, appending..."
)
this_dir = this_dir + os.sep
else:
this_dir = os.getcwd()
# Processing the basin/source keys selection
print(
"I am reading the basin/source key selection and checking your parameters..."
)
if len(args.basin_keys) == 0:
print("No basins found, I will plot all of them")
these_basin_keys = []
else:
these_basin_keys = [int(item) for item in args.basin_keys.split(',')]
print("The basins I will plot are:")
print(these_basin_keys)
if len(args.source_keys) == 0:
print("No sources found, I will plot all of them")
these_source_keys = []
else:
these_source_keys = [int(item) for item in args.source_keys.split(',')]
print("The sources preselected are:")
print(these_source_keys)
if len(args.fixed_size_kp_min_max_river) != 2:
min_max_kp_river = []
else:
min_max_kp_river = [
float(item) for item in args.min_max_kp_river.split(',')
]
print(
"You are recasting the size for all your knickpoints triangles plotted on the river_plots:"
)
print(min_max_kp_river)
if len(args.manual_extent_colormap_knickpoint_raster) > 0:
manual_cmap_extent_raster_plot = [
float(item) for item in
args.manual_extent_colormap_knickpoint_raster.split(',')
]
print("You choose a manual colorbar for plotting:")
print(manual_cmap_extent_raster_plot)
else:
manual_cmap_extent_raster_plot = []
if (args.maximum_extent_for_histogram > 0):
ext_dseg_hist = [0, args.maximum_extent_for_histogram]
ext_dksn_hist = [
-args.maximum_extent_for_histogram,
args.maximum_extent_for_histogram
]
else:
ext_dseg_hist = []
ext_dksn_hist = []
if len(args.general_size) == 0:
print("Sizing the knickpoints automatically")
general_size_kp = []
else:
general_size_kp = [
float(item) for item in args.general_size.split(',')
]
print(general_size_kp)
print("Getting your cut off values...")
if (args.cut_off_val != "auto"):
try:
covfefe = [
float(item)
for item in args.cut_off_val.replace(" ", "").split(',')
]
print("ok.")
covfefe_t = [-covfefe[0], covfefe[1], -10000, covfefe[2]]
covfefe = covfefe_t
except ValueError:
print("Something went wrong - I am defaulting the values")
covfefe = [0, 0, -10000, 0]
print("cut off values:")
print(covfefe)
else:
covfefe = "auto"
print(
"I will choose automatically the cut-off values, based on the quartiles"
)
# Processing the size choice
try:
size = [float(item) for item in args.size_format.split(',')]
except ValueError:
size = args.size_format
if not args.fname_prefix:
print(
"WARNING! You haven't supplied your DEM name. Please specify this with the flag '-fname'"
)
sys.exit()
print("Done")
# Normalisation???
if (args.normalise_elevation_to_outlet_absolute
and args.normalise_elevation_to_outlet_relative):
print(
"Erm, you cannot normaliSe your elevation in relatively absolute or absolutely relative way! you have to choose."
)
quit()
elif (args.normalise_elevation_to_outlet_absolute):
normalisation = "absolute"
elif (args.normalise_elevation_to_outlet_relative):
normalisation = "relative"
else:
normalisation = None
print("Loading the dataset:")
KI = KP.KP_plotting(this_dir,
args.fname_prefix,
basin_key=these_basin_keys,
source_key=these_source_keys,
min_length=args.min_source_length,
cut_off_val=covfefe,
main_stem=args.isolate_main_stem,
normalisation=normalisation,
size_kp=general_size_kp,
coeff_size=args.kp_coeff_size)
if (args.AllAnalysisDebug):
args.AllAnalysis = True
args.ksn_per_source = True
if (args.AllAnalysis):
args.statistical_plots = True
args.river_profile = True
args.raster_plots = True
# args.raster_plots_large_dataset = True
args.basin_plot = True
# Plotting hte knickpoints
if (args.statistical_plots):
if (int(args.n_bin_hist) == 0):
n_b = "auto"
else:
n_b = int(args.n_bin_hist)
KI.print_histogram(size=size,
format=args.FigFormat,
n_bin=n_b,
x_extents=ext_dksn_hist)
KI.print_histogram(size=size,
format=args.FigFormat,
n_bin=n_b,
data="delta_segelev",
x_extents=ext_dseg_hist)
KI.print_box_and_whisker(size=size,
format=args.FigFormat,
label_size=8,
binning='source_key',
facecolor="white",
grid=True)
KI.print_box_and_whisker(size=size,
format=args.FigFormat,
label_size=8,
binning='basin_key',
facecolor="white",
grid=True)
if (args.ksn_per_source):
print(
"Printing a set of ksn values with the knickpoints and their magnitude in a Chi distance"
)
KI.print_ksn_profile(size=size,
format=args.FigFormat,
x_axis="chi",
knickpoint=True,
title="auto",
label_size=8,
facecolor='white',
legend=True)
# KI.print_ksn_profile(size = size, format = args.FigFormat, x_axis = "chi",y_axis = "segmented_elevation", knickpoint = True, title = "auto", label_size = 8, facecolor = 'white', legend = True)
# print("Printing a set of ksn values with the knickpoints and their magnitude in a Flow distance")
# KI.print_ksn_profile(size = size, format = args.FigFormat, x_axis = "flow_distance", knickpoint = True, title = "auto", label_size = 8, facecolor = 'white', legend = True)
if (args.river_profile):
print("Printing river profiles in chi spaces")
KI.print_river_profile(size=size,
format=args.FigFormat,
x_axis="chi",
knickpoint=True,
title="auto",
label_size=8,
facecolor='white',
kalib=args.kalib,
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river)
print("Printing river profiles in flow distance")
KI.print_river_profile(size=size,
format=args.FigFormat,
x_axis="flow_distance",
knickpoint=True,
title="auto",
label_size=8,
facecolor='white',
kalib=args.kalib,
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river)
print("Printing river profiles for the entire basins")
if (args.classical_plot):
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="chi",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="basin_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=True,
pos=False,
step=False)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="flow_distance",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="basin_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=True,
pos=False,
step=False)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="chi",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="basin_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=False,
pos=True,
step=False)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="flow_distance",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="basin_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=False,
pos=True,
step=False)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="chi",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="basin_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=False,
pos=False,
step=True)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="flow_distance",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="basin_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=False,
pos=False,
step=True)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="chi",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="basin_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=True,
pos=True,
step=True)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="flow_distance",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="basin_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=True,
pos=True,
step=True)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="chi",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="basin_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=True,
pos=True,
step=False)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="flow_distance",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="basin_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=True,
pos=True,
step=False)
KI.print_classic_map(unicolor_kp=args.unicolor_for_knickpoint_map,
size=size,
format=args.FigFormat,
black_bg=False,
scale_points=True,
label_size=6,
size_kp=args.size_kp_map,
extent_cmap=manual_cmap_extent_raster_plot,
kalib=args.kalib,
neg=True,
pos=False,
step=False)
KI.print_classic_map(unicolor_kp=args.unicolor_for_knickpoint_map,
size=size,
format=args.FigFormat,
black_bg=False,
scale_points=True,
label_size=6,
size_kp=args.size_kp_map,
extent_cmap=manual_cmap_extent_raster_plot,
kalib=args.kalib,
neg=False,
pos=True,
step=False)
KI.print_classic_map(unicolor_kp=args.unicolor_for_knickpoint_map,
size=size,
format=args.FigFormat,
black_bg=False,
scale_points=True,
label_size=6,
size_kp=args.size_kp_map,
extent_cmap=manual_cmap_extent_raster_plot,
kalib=args.kalib,
neg=False,
pos=False,
step=True)
KI.print_classic_map(unicolor_kp=args.unicolor_for_knickpoint_map,
size=size,
format=args.FigFormat,
black_bg=False,
scale_points=True,
label_size=6,
size_kp=args.size_kp_map,
extent_cmap=manual_cmap_extent_raster_plot,
kalib=args.kalib,
neg=True,
pos=True,
step=False)
KI.print_classic_map(unicolor_kp=args.unicolor_for_knickpoint_map,
size=size,
format=args.FigFormat,
black_bg=False,
scale_points=True,
label_size=6,
size_kp=args.size_kp_map,
extent_cmap=manual_cmap_extent_raster_plot,
kalib=args.kalib,
neg=True,
pos=True,
step=True)
if (args.classical_plot_per_river):
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="chi",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="source_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=True,
pos=False,
step=False)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="flow_distance",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="source_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=True,
pos=False,
step=False)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="chi",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="source_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=False,
pos=True,
step=False)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="flow_distance",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="source_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=False,
pos=True,
step=False)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="chi",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="source_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=False,
pos=False,
step=True)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="flow_distance",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="source_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=False,
pos=False,
step=True)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="chi",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="source_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=True,
pos=True,
step=True)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="flow_distance",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="source_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=True,
pos=True,
step=True)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="chi",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="source_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=True,
pos=True,
step=False)
KI.print_classic_basin_profile(
size=size,
format=args.FigFormat,
x_axis="flow_distance",
knickpoint=True,
label_size=8,
facecolor='white',
kalib=args.kalib,
binning="source_key",
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
neg=True,
pos=True,
step=False)
if (args.basin_plot):
KI.print_river_profile(size=size,
format=args.FigFormat,
x_axis="flow_distance",
knickpoint=True,
title="auto",
label_size=8,
facecolor='white',
binning="basin_key",
kalib=args.kalib,
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
up_set=15)
KI.print_river_profile(size=size,
format=args.FigFormat,
x_axis="chi",
knickpoint=True,
title="auto",
label_size=8,
facecolor='white',
binning="basin_key",
kalib=args.kalib,
print_seg_elev=args.print_segmented_elevation,
size_recasting=min_max_kp_river,
up_set=15)
if (args.raster_plots):
KI.print_map_topo(size=size,
format=args.FigFormat,
label_size=8,
return_fig=False,
extent_cmap=[],
kalib=False)
KI.print_map_of_kp(unicolor_kp=args.unicolor_for_knickpoint_map,
size=size,
format=args.FigFormat,
black_bg=False,
scale_points=True,
label_size=6,
size_kp=args.size_kp_map,
extent_cmap=manual_cmap_extent_raster_plot,
kalib=args.kalib)
KI.print_map_of_kp(unicolor_kp=args.unicolor_for_knickpoint_map,
size=size,
format=args.FigFormat,
black_bg=True,
scale_points=True,
label_size=6,
size_kp=args.size_kp_map,
extent_cmap=manual_cmap_extent_raster_plot,
kalib=args.kalib)
if (args.raster_plots_large_dataset):
KI.print_map_of_kp(unicolor_kp=args.unicolor_for_knickpoint_map,
size=size,
format=args.FigFormat,
black_bg=False,
scale_points=False,
label_size=6,
size_kp=args.size_kp_map,
extent_cmap=manual_cmap_extent_raster_plot,
kalib=args.kalib)
KI.print_map_of_kp(unicolor_kp=args.unicolor_for_knickpoint_map,
size=size,
format=args.FigFormat,
black_bg=True,
scale_points=False,
label_size=6,
size_kp=args.size_kp_map,
extent_cmap=manual_cmap_extent_raster_plot,
kalib=args.kalib)
if (args.multi_violin_plots):
KI.stradivarius_analysis(size=size, format=args.FigFormat)
# Preparing the min_max color for mchi maps
if (args.max_mchi_map <= args.min_mchi_map):
colo = []
else:
colo = [args.min_mchi_map, args.max_mchi_map]
if (args.lithologic_raster):
dict_file = LP.litho_pre_check(this_dir, "", fname=args.fname_prefix)
LP.MakeRasterLithoBasinMap(this_dir,
args.fname_prefix,
args.fname_prefix + "_LITHRAST",
dict_file["lithodict"],
size_format=size,
FigFormat=args.FigFormat,
m_chi=True,
mancol=colo,
log_scale_river=True,
minmax_m_chi=[0.01, 1])
cml = LP.getLithoColorMap(args.fname_prefix, this_dir)
KI.print_map_of_kp(unicolor_kp=args.unicolor_for_knickpoint_map,
size=size,
format=args.FigFormat,
black_bg=False,
scale_points=False,
label_size=6,
size_kp=args.size_kp_map,
extent_cmap=manual_cmap_extent_raster_plot,
kalib=args.kalib,
cml=cml,
lith_raster=True)
if (args.save_output):
KI.save_output_csv()
# Preparing the min_max color for mchi maps
if (args.max_mchi_map <= args.min_mchi_map):
colo = []
else:
colo = [args.min_mchi_map, args.max_mchi_map]
if args.mchi_map_std:
CP.map_Mchi_standard(this_dir,
args.fname_prefix,
size_format=args.size_format,
FigFormat=args.FigFormat,
source_list=these_source_keys,
basin_list=these_basin_keys,
log=False,
colmanscal=colo,
knickpoint=True,
alpha_background=args.alpha_mchi)
if args.mchi_map_black:
CP.map_Mchi_standard(this_dir,
args.fname_prefix,
size_format=args.size_format,
FigFormat=args.FigFormat,
source_list=these_source_keys,
basin_list=these_basin_keys,
log=False,
colmanscal=colo,
bkbg=True,
knickpoint=True,
alpha_background=args.alpha_mchi)
# This part saves the different parameters you used, in case you want to reproduce your results and do not remember what you've used.
print("log of your parameters into the file %s" %
(this_dir + "log_PlotKnickpoint_Analysis.txt"))
f = open(this_dir + "log_PlotKnickpoint_Analysis.txt", "a+")
f.write(
"#########################################################################"
)
f.write(os.linesep)
f.write(os.linesep)
f.write("Ran on the %s " % (datetime.datetime.now()))
f.write(os.linesep)
for arg in vars(args):
f.write("%s : %s " % (arg, getattr(args, arg)))
f.write(os.linesep)
f.write(os.linesep)
f.write(os.linesep)
f.write(os.linesep)
f.close()
print("Done!")
if os.name == 'nt':
print(
"You're running this on windows, note that (i) Linux is increadibly faster and (ii) native windows text editor will misread your log text file, open it with another software (e.g. Brackets or SublimeText)"
)
# # Smugglers.map_of_knickpoint(method = "deriv_ksn", color_extent_Mx = [],color_extent_kp = [], cut_off = 0)
# # Smugglers.plot_mchi_segments(method = "deriv_ksn", group = "basin_key")
# # Smugglers.plot_chi_profiles( method = "deriv_ksn", group = "basin_key")
# # Smugglers.plot_KDE( method = "deriv_ksn", group = "source_key")
# # Smugglers.map_of_knickpoint(method = "deriv_ksn", color_extent_Mx = [],color_extent_kp = [], cut_off = 0)
# # Smugglers.plot_mchi_segments(method = "deriv_ksn", group = "source_key")
# # Smugglers.plot_chi_profiles( method = "deriv_ksn", group = "source_key")
# # print("done")
# df = pd.read_csv("/home/s1675537/PhD/LSDTopoData/knickpoint/model/movern_0p65_n_is_one94/movern_0p65_n_is_one94_MChiSegmented_Ks.csv")
# df = df[df["chi"]>=0]
# for source in df["source_key"].unique():
# print("source %s out of %s"%(source,df["source_key"].unique().shape[0] ))
# this_df = df[df["source_key"] == source]
# plt.plot(this_df["chi"], this_df["elevation"], c = "b")
# plt.plot(this_df["chi"], this_df["segmented_elevation"], c = "r")
# plt.savefig("/home/s1675537/PhD/LSDTopoData/knickpoint/model/movern_0p65_n_is_one94/source_%s.png"%source)
# plt.clf()
path = "/home/s1675537/PhD/LSDTopoData/knickpoint/puerto_rico/"
prefix = "puerto_rico"
Jaguar = KP.KP_dev_v2(path, prefix, min_length=20000)
# Jaguar.DEBUG_print_ksn_filters()
# Jaguar.DEBUG_print_KDE()
Jaguar.DEBUG_print_ksn_outliers()
# Jaguar.DEBUG_print_ksn_dksndchi()
Jaguar.DEBUG_print_ksn_comparison()