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()
parser.add_argument("-bd", "--base_directory", type=str, help="The base directory with the MLE analyses. 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.")
parser.add_argument("-PR", "--plot_rasters", type=bool, default=False, help="If this is true, I'll make raster plots of the m/n value and basin keys")
parser.add_argument("-PC", "--plot_chi_profiles", type=bool, default=False, help="If this is true, I'll make chi-elevation plots for each basin coloured by the MLE")
parser.add_argument("-PO", "--plot_outliers", type=bool, default=False, help="If this is true, I'll make chi-elevation plots with the outliers removed")
parser.add_argument("-MLE", "--plot_MLE_movern", type=bool, default=False, help="If this is true, I'll make a plot of the MLE values for each m/n showing how the MLE values change as you remove the tributaries")
parser.add_argument("-start_movern", "--start_movern", type=float, default=0.2, help="Define the starting m/n value for testing, default = 0.2")
parser.add_argument("-d_movern", "--d_movern", type=float, default=0.1, help="Define the change in m/n value for testing, default = 0.1")
parser.add_argument("-n_movern", "--n_movern", type=int, default=7, help="Define the number of m/n values for testing, default = 7")
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).")
args = parser.parse_args()
if not args.fname_prefix:
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:
Directory = args.base_directory
else:
Directory = os.getcwd()
# loop through each sub-directory with the sensitivity results
MLE_str = "Chi_analysis_sigma_"
for subdir, dirs, files in os.walk(Directory):
for dir in dirs:
if MLE_str in dir:
this_dir = Directory+"/"+dir+'/'
# make the plots depending on your choices
if args.plot_rasters:
MN.MakeRasterPlotsBasins(this_dir, args.fname_prefix, args.size_format, args.FigFormat)
MN.MakeRasterPlotsMOverN(this_dir, args.fname_prefix, args.n_movern, args.d_movern, args.size_format, args.FigFormat)
if args.plot_chi_profiles:
MN.MakeChiPlotsMLE(this_dir, args.fname_prefix, basin_list=[], start_movern=args.start_movern, d_movern=args.d_movern, n_movern=args.n_movern, size_format=args.size_format, FigFormat = args.FigFormat)
if args.plot_outliers:
MN.PlotProfilesRemovingOutliers(this_dir, args.fname_prefix, basin_list=[], start_movern=args.start_movern, d_movern=args.d_movern, n_movern=args.n_movern)
if args.plot_MLE_movern:
MN.PlotMLEWithMOverN(this_dir, args.fname_prefix,basin_list=[], start_movern=args.start_movern, d_movern=args.d_movern, n_movern=args.n_movern, size_format=args.size_format, FigFormat = args.FigFormat)
def concavityCatcher(full_path,
write_name,
processed=False,
basins_not_glaciated=[]):
#returns the basin_key and median concavity
write_name = '/' + write_name
#reading in the basin info
BasinDF = Helper.ReadMCPointsCSV(full_path, write_name)
#Getting mn data
PointsDF = MN.GetMOverNRangeMCPoints(BasinDF,
start_movern=0.25,
d_movern=0.05,
n_movern=8)
#extract basin key and concavity as list
basin_series = PointsDF["basin_key"]
concavity_series = PointsDF["Median_MOverNs"]
basin_key = basin_series.tolist()
basin_keys = []
for x in basin_key:
x = int(x)
basin_keys.append(x)
concavities = concavity_series.tolist()
if not processed:
return basin_keys, concavities
if processed:
#processed_concavities = []
#for x in basins_not_glaciated:
processedDF = PointsDF[PointsDF.basin_key.isin(basins_not_glaciated)]
print("this is the processed DF")
print processedDF
if basin_key != basins_not_glaciated:
sys.exit()
def concavityCatcher(full_path, write_name, processed=False):
#returns the basin_key and median concavity
write_name = '/' + write_name
#reading in the basin info
BasinDF = Helper.ReadMCPointsCSV(full_path, write_name)
#Getting mn data
PointsDF = MN.GetMOverNRangeMCPoints(BasinDF,
start_movern=0.1,
d_movern=0.05,
n_movern=18)
#extract basin key and concavity as list
basin_series = PointsDF["basin_key"]
concavity_series = PointsDF["Median_MOverNs"]
basin_key = basin_series.tolist()
basin_keys = []
for x in basin_key:
x = int(x)
basin_keys.append(x)
concavities = concavity_series.tolist()
if not processed:
return basin_keys, concavities
if processed:
#processed_concavities = []
#print("this is the processed DF")
#print processedDF
processed_basin = processedDF["basin_key"]
processed_concavity = processedDF["Median_MOverNs"]
basin_list = processed_basin.tolist()
concavity_list = processed_concavity.tolist()
return basin_list, concavity_list
def concavityCatcher(full_path,write_name,processed=False,disorder_based=False,basins_not_glaciated=[]):
print("opened concavityCatcher")
#returns the basin_key and median concavity
write_name = '/'+write_name
#reading in the basin info
if not disorder_based:
BasinDF = Helper.ReadMCPointsCSV(full_path,write_name)
#Getting mn data
PointsDF = MN.GetMOverNRangeMCPoints(BasinDF,start_movern=0.25,d_movern=0.05,n_movern=8)
#extract basin key and concavity as list
basin_series = PointsDF["basin_key"]
concavity_series = PointsDF["Median_MOverNs"]
basin_key = basin_series.tolist()
basin_keys = []
for x in basin_key:
x = int(x)
basin_keys.append(x)
concavities = concavity_series.tolist()
if not processed:
return basin_keys,concavities
if processed:
#processed_concavities = []
#for x in basins_not_glaciated:
processedDF = PointsDF[PointsDF.basin_key.isin(basins_not_glaciated)]
#print("this is the processed DF")
#print processedDF
processed_basin = processedDF["basin_key"]
processed_concavity = processedDF["Median_MOverNs"]
basin_list = processed_basin.tolist()
concavity_list = processed_concavity.tolist()
return basin_list,concavity_list
if disorder_based:
print("getting basin keys")
infoDF = Helper.ReadBasinInfoCSV(full_path,write_name)
#print infoDF
basinSeries = infoDF["basin_key"]
basin_key = basinSeries.tolist()
basin_keys = []
for x in basin_key:
x = int(x)
basin_keys.append(x)
print("got keys, getting disorder")
disorder_concavity = getDisorderConcavity(full_path,write_name,basin_keys)
print("concavities:")
print disorder_concavity
return basin_keys,disorder_concavity
def basinsWithDesiredConcavity(full_path, write_name, concavity):
#returns a list of basins with the desired concavity
write_name = '/' + write_name
#reading in the basin info
BasinDF = Helper.ReadMCPointsCSV(full_path, write_name)
#Getting mn data
PointsDF = MN.GetMOverNRangeMCPoints(BasinDF,
start_movern=0.1,
d_movern=0.05,
n_movern=18)
#selecting by concavity
selectedDF = PointsDF[PointsDF["Median_MOverNs"] == concavity]
concavitySeries = selectedDF["basin_key"]
concavityList = concavitySeries.tolist()
return concavityList
def concavityCatcher(full_path,write_name):
#returns the basin_key and median concavity
write_name = '\\'+write_name
#reading in the basin info
BasinDF = Helper.ReadMCPointsCSV(full_path,write_name)
#Getting mn data
PointsDF = MN.GetMOverNRangeMCPoints(BasinDF,start_movern=0.25,d_movern=0.05,n_movern=8)
#extract basin key and concavity as list
basin_series = PointsDF["basin_key"]
concavity_series = PointsDF["Median_MOverNs"]
basin_key = basin_series.tolist()
basin_keys = []
for x in basin_key:
x = int(x)
basin_keys.append(x)
concavities = concavity_series.tolist()
return basin_keys,concavities
def main(argv):
# print("On some windows systems you need to set an environment variable GDAL_DATA")
# print("If the code crashes here it means the environment variable is not set")
# print("Let me check gdal enviroment for you. Currently is is:")
# print(os.environ['GDAL_DATA'])
#os.environ['GDAL_DATA'] = os.popen('gdal-config --datadir').read().rstrip()
#print("Now I am going to get the updated version:")
#print(os.environ['GDAL_DATA'])
# 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 (unless you're running the parallel plotting)."
)
# What sort of analyses you want
parser.add_argument(
"-PR",
"--plot_rasters",
type=bool,
default=False,
help=
"If this is true, I'll make raster plots of the m/n value and basin keys"
)
parser.add_argument(
"-chi",
"--plot_basic_chi",
type=bool,
default=False,
help=
"If this is true I'll make basin chi plots for each basin coloured by elevation."
)
parser.add_argument(
"-PC",
"--plot_chi_profiles",
type=bool,
default=False,
help=
"If this is true, I'll make chi-elevation plots for each basin coloured by the MLE"
)
parser.add_argument(
"-K",
"--plot_chi_by_K",
type=bool,
default=False,
help=
"If this is true, I'll make chi-elevation plots for each basin coloured by K. NOTE - you MUST have a column in your chi csv with the K value or this will break!"
)
parser.add_argument(
"-pcbl",
"--plot_chi_by_lith",
type=bool,
default=False,
help=
"If this is true, I'll make chi-elevation plots for each basin coloured by litho. NOTE - you MUST have a column in your chi csv with the K value or this will break!"
)
parser.add_argument(
"-PO",
"--plot_outliers",
type=bool,
default=False,
help=
"If this is true, I'll make chi-elevation plots with the outliers removed"
)
parser.add_argument(
"-MLE",
"--plot_MLE_movern",
type=bool,
default=False,
help=
"If this is true, I'll make a plot of the MLE values for each m/n showing how the MLE values change as you remove the tributaries"
)
parser.add_argument(
"-SA",
"--plot_SA_data",
type=bool,
default=False,
help=
"If this is true, I'll make a plot of the MLE values for each m/n showing how the MLE values change as you remove the tributaries"
)
parser.add_argument(
"-MCMC",
"--plot_MCMC",
type=bool,
default=False,
help=
"If this is true, I'll make a plot of the MCMC analysis. Specify which basins you want with the -basin_keys flag."
)
parser.add_argument(
"-pts",
"--point_uncertainty",
type=bool,
default=False,
help=
"If this is true, I'll make a plot of the range in m/n from the MC points analysis"
)
parser.add_argument(
"-hist",
"--plot_histogram",
type=bool,
default=False,
help=
"If this is true, I'll make plots of the pdfs of m/n values for each method."
)
parser.add_argument(
"-disorder",
"--plot_disorder",
type=bool,
default=False,
help="If this is true, I'll make plots of the chi disorder analysis.")
parser.add_argument(
"-SUM",
"--plot_summary",
type=bool,
default=False,
help=
"If this is true, I'll make the summary CSV file and plot of the best fit concavity from each of the methods."
)
parser.add_argument("-ALL",
"--all_movern_estimates",
type=bool,
default=False,
help="If this is true, I'll make all the plots")
parser.add_argument(
"-DisFxnDist",
"--disorder_function_of_distance",
type=bool,
default=False,
help=
"If this is true, I'll make a plot of the disorder metric as a function of position"
)
# Plotting options
parser.add_argument(
"-points",
"--point_analysis",
type=bool,
default=False,
help=
"If this is true then I'll assume that you're running the MLE analysis using the point method. Default = False"
)
parser.add_argument(
"-show_SA_raw",
"--show_SA_raw",
type=bool,
default=True,
help="Show the raw S-A data in background of SA plot. Default = True")
parser.add_argument(
"-show_SA_segments",
"--show_SA_segments",
type=bool,
default=False,
help="Show the segmented S-A data in SA plot. Default = False")
parser.add_argument(
"-test_SA_regression",
"--test_SA_regression",
type=bool,
default=False,
help=
"If this is true I'll print the regression stats for the slope area plots."
)
parser.add_argument(
"-show_legend",
"--show_legend",
type=bool,
default=True,
help="If this is true, I'll display the legend for plots.")
parser.add_argument(
"-no_legend",
"--no_legend",
dest="show_legend",
action="store_false",
help=
"Flag to not display legends, I'll not display the legend for plots, default is for legend to be displayed. Note taht setting show_legend False does not achieve this due to bool issues with python parsing"
)
# Options about basin 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 = no basins"
)
parser.add_argument(
"-basin_lists",
"--basin_lists",
type=str,
default="",
help=
"This is a string that initiates a list of a list for grouping basins. The object becomes a list of a list but the syntax is comma seperated lists, and each one is separated by a colon. Default = no dict"
)
parser.add_argument(
"-group_names",
"--group_names",
type=str,
default="",
help="Names of the groups provided by basin_lists. Used in legends")
# 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)."
)
parser.add_argument(
"-animate",
"--animate",
type=bool,
default=True,
help=
"If this is true I will create an animation of the chi plots. Must be used with the -PC flag set to True."
)
parser.add_argument(
"-keep_pngs",
"--keep_pngs",
type=bool,
default=False,
help=
"If this is true I will delete the png files when I animate the figures. Must be used with the -animate flag set to True."
)
parser.add_argument(
"-parallel",
"--parallel",
type=bool,
default=False,
help=
"If this is true I'll assume you ran the code in parallel and append all your CSVs together before plotting."
)
args = parser.parse_args()
print(argv)
print(args)
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("/"):
print(
"You forgot the '/' at the end of the directory, appending...")
this_dir = this_dir + "/"
else:
this_dir = os.getcwd()
# check the basins
print("You told me that the basin keys are: ")
print(args.basin_keys)
# See if a basin info file exists and if so get the basin list
print("Let me check if there is a basins info csv file.")
BasinInfoPrefix = args.fname_prefix + "_AllBasinsInfo.csv"
BasinInfoFileName = this_dir + BasinInfoPrefix
existing_basin_keys = []
if os.path.isfile(BasinInfoFileName):
print("There is a basins info csv file")
BasinInfoDF = Helper.ReadBasinInfoCSV(this_dir, args.fname_prefix)
existing_basin_keys = list(BasinInfoDF['basin_key'])
existing_basin_keys = [int(x) for x in existing_basin_keys]
else:
print(
"I didn't find a basins info csv file. Check directory or filename."
)
# Parse any lists, dicts, or list of lists from the arguments
these_basin_keys = parse_list_from_string(args.basin_keys)
basin_stack_list = parse_list_of_list_from_string(args.basin_lists)
basin_stack_names = parse_string_list_from_string(args.group_names)
# If the basin keys are not supplied then assume all basins are used.
if these_basin_keys == []:
these_basin_keys = existing_basin_keys
# Python is so amazing. Look at the line below.
Mask_basin_keys = [
i for i in existing_basin_keys if i not in these_basin_keys
]
print("All basins are: ")
print(existing_basin_keys)
print("The basins to keep are:")
print(these_basin_keys)
print("The basins to mask are:")
print(Mask_basin_keys)
# This is an old version. It passes empty strings to the plotting functions.
#if len(args.basin_keys) == 0:
# print("No basins found, I will plot all of them")
# # Note that if you pass an empty list to the plotting functions, they will plot all the basins
# 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)
# This checks to see if chi points method is being used.
# If not, assumes only the disorder metric has been calculated
Using_disorder_metric_only = check_if_disorder_metric_only(
this_dir, args.fname_prefix)
if not args.parallel:
BasinDF = Helper.ReadBasinStatsCSV(this_dir, args.fname_prefix)
else:
BasinDF = Helper.AppendBasinCSVs(this_dir, args.fname_prefix)
# if parallel, get the fname from the data directory. This assumes that your directory is called
# something sensible that relates to the DEM name.
split_fname = this_dir.split("/")
split_fname = split_fname[len(split_fname) - 2]
# get the range of moverns, needed for plotting
# we need the column headers
columns = BasinDF.columns[BasinDF.columns.str.contains(
'm_over_n')].tolist()
moverns = [float(x.split("=")[-1]) for x in columns]
start_movern = moverns[0]
n_movern = len(moverns)
x = Decimal((moverns[-1] - moverns[0]) / (n_movern - 1))
d_movern = round(x, 2)
print('Start movern, n_movern, d_movern: ')
print(start_movern, n_movern, d_movern)
# some formatting for the figures
if args.FigFormat == "manuscipt_svg":
print(
"You chose the manuscript svg option. This only works with the -ALL flag. For other flags it will default to simple svg"
)
simple_format = "svg"
elif args.FigFormat == "manuscript_png":
print(
"You chose the manuscript png option. This only works with the -ALL flag. For other flags it will default to simple png"
)
simple_format = "png"
else:
simple_format = args.FigFormat
# make the plots depending on your choices
if args.plot_rasters:
MN.MakeRasterPlotsBasins(this_dir,
args.fname_prefix,
args.size_format,
simple_format,
parallel=args.parallel)
if args.plot_basic_chi:
MN.MakePlotsWithMLEStats(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
parallel=args.parallel)
if args.plot_chi_profiles:
if Using_disorder_metric_only:
MN.MakeChiPlotsChi(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
size_format=args.size_format,
FigFormat=args.FigFormat,
animate=True,
keep_pngs=True,
parallel=args.parallel)
else:
MN.MakeChiPlotsMLE(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
size_format=args.size_format,
FigFormat=args.FigFormat,
animate=True,
keep_pngs=True,
parallel=args.parallel)
if args.plot_chi_by_K:
MN.MakeChiPlotsColouredByK(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
size_format=args.size_format,
FigFormat=simple_format,
animate=args.animate,
keep_pngs=args.keep_pngs,
parallel=args.parallel)
if args.plot_chi_by_lith:
MN.MakeChiPlotsColouredByLith(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
size_format=args.size_format,
FigFormat=simple_format,
animate=args.animate,
keep_pngs=args.keep_pngs,
parallel=args.parallel)
if args.plot_outliers:
MN.PlotProfilesRemovingOutliers(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
parallel=args.parallel)
if args.plot_MLE_movern:
MN.PlotMLEWithMOverN(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
size_format=args.size_format,
FigFormat=simple_format,
parallel=args.parallel)
if args.plot_SA_data:
SA.SAPlotDriver(this_dir,
args.fname_prefix,
FigFormat=simple_format,
size_format=args.size_format,
show_raw=args.show_SA_raw,
show_segments=args.show_SA_segments,
basin_keys=these_basin_keys,
parallel=args.parallel)
if args.test_SA_regression:
#SA.TestSARegression(this_dir, args.fname_prefix)
SA.LinearRegressionRawDataByChannel(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
parallel=args.parallel)
#SA.LinearRegressionSegmentedData(this_dir, args.fname_prefix, basin_list=these_basin_keys)
if args.plot_MCMC:
MN.plot_MCMC_analysis(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
FigFormat=simple_format,
size_format=args.size_format,
parallel=args.parallel)
if args.point_uncertainty:
MN.PlotMCPointsUncertainty(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
FigFormat=simple_format,
size_format=args.size_format,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
parallel=args.parallel)
if args.plot_histogram:
MN.MakeMOverNSummaryHistogram(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
FigFormat=simple_format,
size_format=args.size_format,
show_legend=args.show_legend,
Chi_disorder=True)
if args.plot_summary:
# This function creates a csv that has the concavity statistics in it
MN.CompareMOverNEstimatesAllMethods(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
parallel=args.parallel,
Chi_disorder=True)
MN.MakeMOverNSummaryPlot(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
FigFormat=simple_format,
size_format=args.size_format,
show_legend=args.show_legend,
parallel=args.parallel,
Chi_disorder=True)
# This only prints the summary plots for bootstrap and disorder metrics
MN.MakeMOverNSummaryPlot(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
FigFormat=simple_format,
size_format=args.size_format,
show_legend=args.show_legend,
parallel=args.parallel,
Chi_all=False,
SA_raw=False,
SA_segmented=False,
SA_channels=False,
Chi_bootstrap=True,
Chi_disorder=True)
MN.MakeMOverNSummaryHistogram(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
FigFormat=args.FigFormat,
size_format=args.size_format,
show_legend=args.show_legend,
Chi_disorder=True)
if args.plot_disorder:
MN.MakeRasterPlotsMOverN(this_dir,
args.fname_prefix,
start_movern,
n_movern,
d_movern,
movern_method="Chi_disorder",
size_format=args.size_format,
FigFormat=args.FigFormat,
parallel=args.parallel)
# This function creates a csv that has the concavity statistics in it
MN.CompareMOverNEstimatesAllMethods(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
parallel=args.parallel,
Chi_disorder=True)
MN.MakeMOverNSummaryPlot(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
FigFormat=simple_format,
size_format=args.size_format,
show_legend=args.show_legend,
parallel=args.parallel,
Chi_disorder=True)
MN.MakeMOverNSummaryHistogram(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
FigFormat=args.FigFormat,
size_format=args.size_format,
show_legend=args.show_legend,
Chi_disorder=True)
if args.all_movern_estimates:
print("I am going to print out loads and loads of figures for you.")
# plot the rasters
MN.MakeRasterPlotsBasins(this_dir,
args.fname_prefix,
args.size_format,
args.FigFormat,
parallel=args.parallel)
if not Using_disorder_metric_only:
MN.MakeRasterPlotsMOverN(this_dir,
args.fname_prefix,
start_movern,
n_movern,
d_movern,
movern_method="Chi_full",
size_format=args.size_format,
FigFormat=args.FigFormat,
parallel=args.parallel)
MN.MakeRasterPlotsMOverN(this_dir,
args.fname_prefix,
start_movern,
n_movern,
d_movern,
movern_method="Chi_points",
size_format=args.size_format,
FigFormat=args.FigFormat,
parallel=args.parallel)
MN.MakeRasterPlotsMOverN(this_dir,
args.fname_prefix,
start_movern,
n_movern,
d_movern,
movern_method="SA",
size_format=args.size_format,
FigFormat=args.FigFormat,
parallel=args.parallel)
MN.MakeRasterPlotsMOverN(this_dir,
args.fname_prefix,
start_movern,
n_movern,
d_movern,
movern_method="Chi_disorder",
size_format=args.size_format,
FigFormat=args.FigFormat,
parallel=args.parallel)
# make the chi plots
if Using_disorder_metric_only:
MN.MakeChiPlotsChi(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
size_format=args.size_format,
FigFormat=args.FigFormat,
animate=True,
keep_pngs=True,
parallel=args.parallel)
else:
MN.MakeChiPlotsMLE(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
size_format=args.size_format,
FigFormat=args.FigFormat,
animate=True,
keep_pngs=True,
parallel=args.parallel)
# make the SA plots
SA.SAPlotDriver(this_dir,
args.fname_prefix,
FigFormat=args.FigFormat,
size_format=args.size_format,
show_raw=args.show_SA_raw,
show_segments=True,
basin_keys=these_basin_keys,
parallel=args.parallel)
SA.SAPlotDriver(this_dir,
args.fname_prefix,
FigFormat=args.FigFormat,
size_format=args.size_format,
show_raw=args.show_SA_raw,
show_segments=False,
basin_keys=these_basin_keys,
parallel=args.parallel)
#summary plots
# This function creates a csv that has the concavity statistics in it
MN.CompareMOverNEstimatesAllMethods(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
parallel=args.parallel,
Chi_disorder=True)
MN.MakeMOverNSummaryPlot(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
FigFormat=simple_format,
size_format=args.size_format,
show_legend=args.show_legend,
parallel=args.parallel,
Chi_disorder=True)
# This only prints the summary plots for bootstrap and disorder metrics
MN.MakeMOverNSummaryPlot(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
FigFormat=simple_format,
size_format=args.size_format,
show_legend=args.show_legend,
parallel=args.parallel,
Chi_all=False,
SA_raw=False,
SA_segmented=False,
SA_channels=False,
Chi_bootstrap=True,
Chi_disorder=True)
MN.MakeMOverNSummaryHistogram(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
FigFormat=args.FigFormat,
size_format=args.size_format,
show_legend=args.show_legend,
Chi_disorder=True)
if args.disorder_function_of_distance:
# This function creates a csv that has the concavity statistics in it
print("=====================================================")
print("=====================================================")
print("\n\n\n\nI am going to get the summary information.")
# See if the summary already exists
print("Let me check if there is a concavity summary csv file.")
SummaryPrefix = args.fname_prefix + "_movern_summary.csv"
SummaryFileName = this_dir + "summary_plots/" + SummaryPrefix
print("The summary filename is: " + SummaryFileName)
if os.path.isfile(SummaryFileName):
print("There is already a summary file")
else:
print("No summray csv found. I will calculate a new one.")
MN.CompareMOverNEstimatesAllMethods(this_dir,
args.fname_prefix,
basin_list=these_basin_keys,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
parallel=args.parallel,
Chi_disorder=True)
# Okay, now we plot the metrics as a function of distance
print("I am going to print the following lists of basins: ")
print(basin_stack_list)
MN.MakeMOverNDisorderDistancePlot(this_dir,
args.fname_prefix,
basin_list_list=basin_stack_list,
start_movern=start_movern,
d_movern=d_movern,
n_movern=n_movern,
FigFormat=simple_format,
size_format=args.size_format,
show_legend=args.show_legend,
parallel=args.parallel,
group_names=basin_stack_names)
def concavityCatcher(full_path,
write_name,
processed=False,
basins_not_glaciated=[],
alter_ID=True):
#returns the basin_key and median concavity
write_name = '/' + write_name
#reading in the basin info
BasinDF = Helper.ReadMCPointsCSV(full_path, write_name)
#Getting mn data
PointsDF = MN.GetMOverNRangeMCPoints(BasinDF,
start_movern=0.1,
d_movern=0.05,
n_movern=18)
#extract basin key and concavity as list
basin_series = PointsDF["basin_key"]
concavity_series = PointsDF["Median_MOverNs"]
if not disorder:
basin_key = basin_series.tolist()
basin_keys = []
for x in basin_key:
x = int(x)
basin_keys.append(x)
concavities = concavity_series.tolist()
if disorder:
print "got to disorder"
print full_path, write_name
if not alter_ID:
basin_keys, concavities = getDisorderConcavity(
full_path,
write_name,
fromConcavityCatcher=True,
alter_ID=False)
else:
basin_keys, concavities, new_IDs = getDisorderConcavity(
full_path, write_name, fromConcavityCatcher=True)
if not processed:
print concavities
return basin_keys, concavities, new_IDs
if processed:
print "got to processed"
#processed_concavities = []
#for x in basins_not_glaciated:
try:
processedDF = PointsDF[PointsDF.basin_key.isin(
basins_not_glaciated)]
except Exception as e:
print e
print "error in processed section of concavity catcher"
#print("this is the processed DF")
processed_basin = processedDF["basin_key"]
processed_concavity = processedDF["Median_MOverNs"]
basin_list = processed_basin.tolist()
concavity_list = processed_concavity.tolist()
return basin_list, concavity_list
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()
parser.add_argument("-dir", "--base_directory", type=str, help="The base directory with the MLE analyses. 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.")
# What sort of analyses you want
parser.add_argument("-PR", "--plot_rasters", type=bool, default=False, help="If this is true, I'll make raster plots of the m/n value and basin keys")
parser.add_argument("-chi", "--plot_basic_chi", type=bool, default=False, help="If this is true I'll make basin chi plots for each basin coloured by elevation.")
parser.add_argument("-PC", "--plot_chi_profiles", type=bool, default=False, help="If this is true, I'll make chi-elevation plots for each basin coloured by the MLE")
parser.add_argument("-K", "--plot_chi_by_K", type=bool, default=False, help="If this is true, I'll make chi-elevation plots for each basin coloured by K. NOTE - you MUST have a column in your chi csv with the K value or this will break!")
parser.add_argument("-pcbl", "--plot_chi_by_lith", type=bool, default=False, help="If this is true, I'll make chi-elevation plots for each basin coloured by litho. NOTE - you MUST have a column in your chi csv with the K value or this will break!")
parser.add_argument("-PO", "--plot_outliers", type=bool, default=False, help="If this is true, I'll make chi-elevation plots with the outliers removed")
parser.add_argument("-MLE", "--plot_MLE_movern", type=bool, default=False, help="If this is true, I'll make a plot of the MLE values for each m/n showing how the MLE values change as you remove the tributaries")
parser.add_argument("-SA", "--plot_SA_data", type=bool, default=False, help="If this is true, I'll make a plot of the MLE values for each m/n showing how the MLE values change as you remove the tributaries")
parser.add_argument("-MCMC", "--plot_MCMC", type=bool, default=False, help="If this is true, I'll make a plot of the MCMC analysis. Specify which basins you want with the -basin_keys flag.")
parser.add_argument("-pts", "--point_uncertainty", type=bool, default=False, help="If this is true, I'll make a plot of the range in m/n from the MC points analysis")
parser.add_argument("-hist", "--plot_histogram", type=bool, default=False, help="If this is true, I'll make plots of the pdfs of m/n values for each method.")
# parser.add_argument("-basin_joyplot", "--basin_joyplot", type=bool, default=False, help="If this is true, I'll make a joyplot showing m/n for each basin from the chi points")
parser.add_argument("-SUM", "--plot_summary", type=bool, default=False, help="If this is true, I'll make the summary CSV file and plot of the best fit m/n from each of the methods.")
parser.add_argument("-ALL", "--all_movern_estimates", type=bool, default=False, help="If this is true, I'll make all the plots")
# Plotting options
parser.add_argument("-points", "--point_analysis", type=bool, default=False, help="If this is true then I'll assume that you're running the MLE analysis using the point method. Default = False")
parser.add_argument("-show_SA_raw", "--show_SA_raw", type=bool, default=True, help="Show the raw S-A data in background of SA plot. Default = True")
parser.add_argument("-show_SA_segments", "--show_SA_segments", type=bool, default=False, help="Show the segmented S-A data in SA plot. Default = False")
parser.add_argument("-test_SA_regression", "--test_SA_regression", type=bool, default=False, help="If this is true I'll print the regression stats for the slope area plots.")
parser.add_argument("-show_legend", "--show_legend", type=bool, default=True, help="If this is true, I'll display the legend for the SA plots.")
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 = no 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).")
parser.add_argument("-animate", "--animate", type=bool, default=True, help="If this is true I will create an animation of the chi plots. Must be used with the -PC flag set to True.")
parser.add_argument("-keep_pngs", "--keep_pngs", type=bool, default=False, help="If this is true I will delete the png files when I animate the figures. Must be used with the -animate flag set to True.")
args = parser.parse_args()
if not args.fname_prefix:
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:
Directory = args.base_directory
else:
Directory = os.getcwd()
# check the basins
print("You told me that the basin keys are: ")
print(args.basin_keys)
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)
# get the range of moverns, needed for plotting
BasinDF = Helper.ReadBasinStatsCSV(Directory+"/sigma_10/", args.fname_prefix)
# we need the column headers
columns = BasinDF.columns[BasinDF.columns.str.contains('m_over_n')].tolist()
moverns = [float(x.split("=")[-1]) for x in columns]
start_movern = moverns[0]
n_movern = len(moverns)
d_movern = (moverns[-1] - moverns[0])/(n_movern-1)
# loop through each sub-directory with the sensitivity results
MLE_str = "sigma_"
for subdir, dirs, files in os.walk(Directory):
for dir in dirs:
if MLE_str in dir:
this_dir = Directory+"/"+dir+'/'
# make the plots depending on your choices
# make the plots depending on your choices
if args.plot_rasters:
MN.MakeRasterPlotsBasins(this_dir, args.fname_prefix, args.size_format, simple_format)
MN.MakeRasterPlotsMOverN(this_dir, args.fname_prefix, start_movern, n_movern, d_movern, size_format=args.size_format, FigFormat=simple_format)
MN.MakeRasterPlotsMOverN(this_dir, args.fname_prefix, start_movern, n_movern, d_movern, movern_method="Chi_points", size_format=args.size_format, FigFormat=simple_format)
MN.MakeRasterPlotsMOverN(this_dir, args.fname_prefix, start_movern, n_movern, d_movern, movern_method="SA", size_format=args.size_format, FigFormat=simple_format)
if args.plot_basic_chi:
MN.MakePlotsWithMLEStats(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern)
if args.plot_chi_profiles:
MN.MakeChiPlotsMLE(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, size_format=args.size_format, FigFormat = simple_format, animate=args.animate, keep_pngs=args.keep_pngs)
if args.plot_chi_by_K:
MN.MakeChiPlotsColouredByK(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, size_format=args.size_format, FigFormat=simple_format, animate=args.animate, keep_pngs=args.keep_pngs)
if args.plot_chi_by_lith:
MN.MakeChiPlotsColouredByLith(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, size_format=args.size_format, FigFormat=simple_format, animate=args.animate, keep_pngs=args.keep_pngs)
if args.plot_outliers:
MN.PlotProfilesRemovingOutliers(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern)
if args.plot_MLE_movern:
MN.PlotMLEWithMOverN(this_dir, args.fname_prefix,basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, size_format=args.size_format, FigFormat =simple_format)
if args.plot_SA_data:
SA.SAPlotDriver(this_dir, args.fname_prefix, FigFormat = simple_format,size_format=args.size_format,
show_raw = args.show_SA_raw, show_segments = args.show_SA_segments,basin_keys = these_basin_keys)
if args.test_SA_regression:
#SA.TestSARegression(this_dir, args.fname_prefix)
SA.LinearRegressionRawDataByChannel(this_dir,args.fname_prefix, basin_list=these_basin_keys)
#SA.LinearRegressionSegmentedData(this_dir, args.fname_prefix, basin_list=these_basin_keys)
if args.plot_MCMC:
MN.plot_MCMC_analysis(this_dir, args.fname_prefix,basin_list=these_basin_keys, FigFormat= simple_format, size_format=args.size_format)
if args.point_uncertainty:
MN.PlotMCPointsUncertainty(this_dir, args.fname_prefix,basin_list=these_basin_keys, FigFormat=simple_format, size_format=args.size_format,start_movern=start_movern, d_movern=d_movern, n_movern=n_movern)
if args.plot_histogram:
MN.MakeMOverNSummaryHistogram(this_dir, args.fname_prefix,basin_list=these_basin_keys,start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, FigFormat=simple_format, size_format=args.size_format, show_legend=args.show_legend)
if args.plot_summary:
MN.CompareMOverNEstimatesAllMethods(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern)
MN.MakeMOverNSummaryPlot(this_dir, args.fname_prefix, basin_list=these_basin_keys,start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, FigFormat = simple_format,size_format=args.size_format, show_legend=args.show_legend)
MN.MakeMOverNPlotOneMethod(this_dir,args.fname_prefix,basin_list=these_basin_keys,start_movern=start_movern,d_movern=d_movern,n_movern=n_movern,FigFormat=args.FigFormat,size_format=args.size_format)
# if args.basin_joyplot:
# MN.CompareMOverNEstimatesAllMethods(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern)
# MN.MakeBasinJoyplot(this_dir, args.fname_prefix, basin_list=these_basin_keys, FigFormat=simple_format, size_format=args.size_format)
if args.all_movern_estimates:
# plot the rasters
MN.MakeRasterPlotsBasins(this_dir, args.fname_prefix, args.size_format, args.FigFormat)
MN.MakeRasterPlotsMOverN(this_dir, args.fname_prefix, start_movern, n_movern, d_movern, movern_method="Chi_full", size_format=args.size_format, FigFormat=args.FigFormat)
MN.MakeRasterPlotsMOverN(this_dir, args.fname_prefix, start_movern, n_movern, d_movern, movern_method="Chi_points", size_format=args.size_format, FigFormat=args.FigFormat)
MN.MakeRasterPlotsMOverN(this_dir, args.fname_prefix, start_movern, n_movern, d_movern, movern_method="SA", size_format=args.size_format, FigFormat=args.FigFormat)
# make the chi plots
MN.MakeChiPlotsMLE(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, size_format=args.size_format, FigFormat = args.FigFormat, animate=True, keep_pngs=True)
# make the SA plots
SA.SAPlotDriver(this_dir, args.fname_prefix, FigFormat = args.FigFormat,size_format=args.size_format,
show_raw = args.show_SA_raw, show_segments = True, basin_keys = these_basin_keys)
SA.SAPlotDriver(this_dir, args.fname_prefix, FigFormat = args.FigFormat,size_format=args.size_format,
show_raw = args.show_SA_raw, show_segments = False, basin_keys = these_basin_keys)
#summary plots
MN.CompareMOverNEstimatesAllMethods(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern)
MN.MakeMOverNSummaryPlot(this_dir, args.fname_prefix, basin_list=these_basin_keys,start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, FigFormat = args.FigFormat,size_format=args.size_format, show_legend=args.show_legend)
#joyplot
MN.MakeMOverNPlotOneMethod(this_dir,args.fname_prefix,basin_list=these_basin_keys,start_movern=start_movern,d_movern=d_movern,n_movern=n_movern,FigFormat=args.FigFormat,size_format=args.size_format)
#MN.MakeMOverNSummaryHistogram(this_dir, args.fname_prefix,basin_list=these_basin_keys,start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, FigFormat=args.FigFormat, size_format=args.size_format, show_legend=args.show_legend)
# collate all the results to get the final figure
MN.PlotSensitivityResultsSigma(Directory, args.fname_prefix,FigFormat=args.FigFormat,size_format=args.size_format,movern_method='points')
import sys
import os
import pandas as pd
import csv
#LSDTopoTools specific imports
#Loading the LSDTT setup configuration
setup_file = open('chi_automation.config','r')
LSDMT_PT = setup_file.readline().rstrip()
LSDMT_MF = setup_file.readline().rstrip()
Iguanodon = setup_file.readline().rstrip()
setup_file.close()
sys.path.append(LSDMT_PT)
sys.path.append(LSDMT_MF)
sys.path.append(Iguanodon)
from LSDPlottingTools import LSDMap_MOverNPlotting as MN
from LSDMapFigure import PlottingHelpers as Helper
current_path = '/exports/csce/datastore/geos/users/s1134744/LSDTopoTools/Topographic_projects/precip_test/27.00_90.35_himalaya_27.0_13/100000/'
writing_prefix = '13100000_120000'
BasinDF = Helper.ReadMCPointsCSV(current_path,writing_prefix)
PointsDF = MN.GetMOverNRangeMCPoints(BasinDF,start_movern=0.1,d_movern=0.1,n_movern=9)
PointsDF.to_csv(current_path+writing_prefix+'mn_stats.csv', mode="w",header=True,index=False)
#matplotlib pyplot hist2D
#windows concavity catcher test
import pandas as pd
import csv
import os
import sys
import matplotlib
matplotlib.use('Agg')
#LSDTopoTools specific imports
#Loading the LSDTT setup configuration
setup_file = open('chi_automation.config','r')
LSDMT_PT = setup_file.readline().rstrip()
LSDMT_MF = setup_file.readline().rstrip()
Iguanodon = setup_file.readline().rstrip()
setup_file.close()
sys.path.append(LSDMT_PT)
sys.path.append(LSDMT_MF)
sys.path.append(Iguanodon)
from LSDPlottingTools import LSDMap_MOverNPlotting as MN
from LSDMapFigure import PlottingHelpers as Helper
target = '/exports/csce/datastore/geos/users/s1134744/LSDTopoTools/Topographic_projects/full_himalaya/'
MN.MakeChiPlotsMLE(target,'0.1',basin_list=[299], start_movern=0.1, d_movern=0.1, n_movern=1,size_format='ESURF', FigFormat='png',keep_pngs=True)
for x, y in zip(new_basin_list, fullstats_node_counter):
new_list = [x] * y
fullstats_new_list.extend(new_list)
print fullstats_new_list[-1]
print movern_new_list
print len(movern_new_list)
movernDF = openPandas(target, '/' + c + '_burned_movern')
basinstatsDF = openPandas(target, '/' + c + '_movernstats_basinstats')
fullstatsDF = openPandas(target, '/' + c + '_movernstats_0.35_fullstats')
addNewBasinKey(movernDF, movern_new_list, target + '/' + c + '_burned_movern')
addNewBasinKey(basinstatsDF, new_basin_list,
target + '/' + c + '_movernstats_basinstats')
addNewBasinKey(fullstatsDF, fullstats_new_list,
target + '/' + c + '_movernstats_0.35_fullstats')
#for x in new_basin_list:
MN.MakeChiPlotsMLE(target,
'0.35',
basin_list=new_basin_list,
start_movern=0.35,
d_movern=0.05,
n_movern=1,
size_format='ESURF',
FigFormat='png',
keep_pngs=True,
animate=True)
def main(argv):
# print("On some windows systems you need to set an environment variable GDAL_DATA")
# print("If the code crashes here it means the environment variable is not set")
# print("Let me check gdal enviroment for you. Currently is is:")
# print(os.environ['GDAL_DATA'])
#os.environ['GDAL_DATA'] = os.popen('gdal-config --datadir').read().rstrip()
#print("Now I am going to get the updated version:")
#print(os.environ['GDAL_DATA'])
# 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 (unless you're running the parallel plotting).")
# What sort of analyses you want
parser.add_argument("-PR", "--plot_rasters", type=bool, default=False, help="If this is true, I'll make raster plots of the m/n value and basin keys")
parser.add_argument("-chi", "--plot_basic_chi", type=bool, default=False, help="If this is true I'll make basin chi plots for each basin coloured by elevation.")
parser.add_argument("-PC", "--plot_chi_profiles", type=bool, default=False, help="If this is true, I'll make chi-elevation plots for each basin coloured by the MLE")
parser.add_argument("-K", "--plot_chi_by_K", type=bool, default=False, help="If this is true, I'll make chi-elevation plots for each basin coloured by K. NOTE - you MUST have a column in your chi csv with the K value or this will break!")
parser.add_argument("-pcbl", "--plot_chi_by_lith", type=bool, default=False, help="If this is true, I'll make chi-elevation plots for each basin coloured by litho. NOTE - you MUST have a column in your chi csv with the K value or this will break!")
parser.add_argument("-PO", "--plot_outliers", type=bool, default=False, help="If this is true, I'll make chi-elevation plots with the outliers removed")
parser.add_argument("-MLE", "--plot_MLE_movern", type=bool, default=False, help="If this is true, I'll make a plot of the MLE values for each m/n showing how the MLE values change as you remove the tributaries")
parser.add_argument("-SA", "--plot_SA_data", type=bool, default=False, help="If this is true, I'll make a plot of the MLE values for each m/n showing how the MLE values change as you remove the tributaries")
parser.add_argument("-MCMC", "--plot_MCMC", type=bool, default=False, help="If this is true, I'll make a plot of the MCMC analysis. Specify which basins you want with the -basin_keys flag.")
parser.add_argument("-pts", "--point_uncertainty", type=bool, default=False, help="If this is true, I'll make a plot of the range in m/n from the MC points analysis")
parser.add_argument("-hist", "--plot_histogram", type=bool, default=False, help="If this is true, I'll make plots of the pdfs of m/n values for each method.")
parser.add_argument("-disorder", "--plot_disorder", type=bool, default=False, help="If this is true, I'll make plots of the chi disorder analysis.")
# parser.add_argument("-basin_joyplot", "--basin_joyplot", type=bool, default=False, help="If this is true, I'll make a joyplot showing m/n for each basin from the chi points")
parser.add_argument("-SUM", "--plot_summary", type=bool, default=False, help="If this is true, I'll make the summary CSV file and plot of the best fit concavity from each of the methods.")
parser.add_argument("-ALL", "--all_movern_estimates", type=bool, default=False, help="If this is true, I'll make all the plots")
# Plotting options
parser.add_argument("-points", "--point_analysis", type=bool, default=False, help="If this is true then I'll assume that you're running the MLE analysis using the point method. Default = False")
parser.add_argument("-show_SA_raw", "--show_SA_raw", type=bool, default=True, help="Show the raw S-A data in background of SA plot. Default = True")
parser.add_argument("-show_SA_segments", "--show_SA_segments", type=bool, default=False, help="Show the segmented S-A data in SA plot. Default = False")
parser.add_argument("-test_SA_regression", "--test_SA_regression", type=bool, default=False, help="If this is true I'll print the regression stats for the slope area plots.")
parser.add_argument("-show_legend", "--show_legend", type=bool, default=True, help="If this is true, I'll display the legend for plots.")
parser.add_argument("-no_legend", "--no_legend", dest="show_legend", action="store_false", help="Flag to not display legends, I'll not display the legend for plots, default is for legend to be displayed. Note taht setting show_legend False does not achieve this due to bool issues with python parsing")
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 = no 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).")
parser.add_argument("-animate", "--animate", type=bool, default=True, help="If this is true I will create an animation of the chi plots. Must be used with the -PC flag set to True.")
parser.add_argument("-keep_pngs", "--keep_pngs", type=bool, default=False, help="If this is true I will delete the png files when I animate the figures. Must be used with the -animate flag set to True.")
parser.add_argument("-parallel", "--parallel", type=bool, default=False, help="If this is true I'll assume you ran the code in parallel and append all your CSVs together before plotting.")
args = parser.parse_args()
print(argv)
print(args)
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()
# check the basins
print("You told me that the basin keys are: ")
print(args.basin_keys)
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)
# 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("/"):
print("You forgot the '/' at the end of the directory, appending...")
this_dir = this_dir+"/"
else:
this_dir = os.getcwd()
# get the range of moverns, needed for plotting
if not args.parallel:
BasinDF = Helper.ReadBasinStatsCSV(this_dir, args.fname_prefix)
else:
BasinDF = Helper.AppendBasinCSVs(this_dir, args.fname_prefix)
# if parallel, get the fname from the data directory. This assumes that your directory is called
# something sensible that relates to the DEM name.
split_fname = this_dir.split("/")
split_fname = split_fname[len(split_fname)-2]
#args.fname_prefix = split_fname # commented out for now since base fname given, basins will always have basinX fname_prefix
# # we need the column headers
columns = BasinDF.columns[BasinDF.columns.str.contains('m_over_n')].tolist()
moverns = [float(x.split("=")[-1]) for x in columns]
start_movern = moverns[0]
n_movern = len(moverns)
x = Decimal((moverns[-1] - moverns[0])/(n_movern-1))
d_movern = round(x,2)
print ('Start movern, n_movern, d_movern: ')
print (start_movern, n_movern, d_movern)
# some formatting for the figures
if args.FigFormat == "manuscipt_svg":
print("You chose the manuscript svg option. This only works with the -ALL flag. For other flags it will default to simple svg")
simple_format = "svg"
elif args.FigFormat == "manuscript_png":
print("You chose the manuscript png option. This only works with the -ALL flag. For other flags it will default to simple png")
simple_format = "png"
else:
simple_format = args.FigFormat
# make the plots depending on your choices
if args.plot_rasters:
MN.MakeRasterPlotsBasins(this_dir, args.fname_prefix, args.size_format, simple_format, parallel=args.parallel)
# MN.MakeRasterPlotsMOverN(this_dir, args.fname_prefix, start_movern, n_movern, d_movern, size_format=args.size_format, FigFormat=simple_format, parallel=args.parallel)
# MN.MakeRasterPlotsMOverN(this_dir, args.fname_prefix, start_movern, n_movern, d_movern, movern_method="Chi_points", size_format=args.size_format, FigFormat=simple_format,parallel=args.parallel)
# MN.MakeRasterPlotsMOverN(this_dir, args.fname_prefix, start_movern, n_movern, d_movern, movern_method="SA", size_format=args.size_format, FigFormat=simple_format,parallel=args.parallel)
if args.plot_basic_chi:
MN.MakePlotsWithMLEStats(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern,parallel=args.parallel)
if args.plot_chi_profiles:
MN.MakeChiPlotsMLE(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, size_format=args.size_format, FigFormat = simple_format, animate=args.animate, keep_pngs=args.keep_pngs, parallel=args.parallel)
if args.plot_chi_by_K:
MN.MakeChiPlotsColouredByK(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, size_format=args.size_format, FigFormat=simple_format, animate=args.animate, keep_pngs=args.keep_pngs, parallel=args.parallel)
if args.plot_chi_by_lith:
MN.MakeChiPlotsColouredByLith(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, size_format=args.size_format, FigFormat=simple_format, animate=args.animate, keep_pngs=args.keep_pngs,parallel=args.parallel)
if args.plot_outliers:
MN.PlotProfilesRemovingOutliers(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern,parallel=args.parallel)
if args.plot_MLE_movern:
MN.PlotMLEWithMOverN(this_dir, args.fname_prefix,basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, size_format=args.size_format, FigFormat =simple_format,parallel=args.parallel)
if args.plot_SA_data:
SA.SAPlotDriver(this_dir, args.fname_prefix, FigFormat = simple_format,size_format=args.size_format,
show_raw = args.show_SA_raw, show_segments = args.show_SA_segments,basin_keys = these_basin_keys, parallel=args.parallel)
if args.test_SA_regression:
#SA.TestSARegression(this_dir, args.fname_prefix)
SA.LinearRegressionRawDataByChannel(this_dir,args.fname_prefix, basin_list=these_basin_keys, parallel=args.parallel)
#SA.LinearRegressionSegmentedData(this_dir, args.fname_prefix, basin_list=these_basin_keys)
if args.plot_MCMC:
MN.plot_MCMC_analysis(this_dir, args.fname_prefix,basin_list=these_basin_keys, FigFormat= simple_format, size_format=args.size_format,parallel=args.parallel)
if args.point_uncertainty:
MN.PlotMCPointsUncertainty(this_dir, args.fname_prefix,basin_list=these_basin_keys, FigFormat=simple_format, size_format=args.size_format,start_movern=start_movern, d_movern=d_movern, n_movern=n_movern,parallel=args.parallel)
if args.plot_histogram:
MN.MakeMOverNSummaryHistogram(this_dir, args.fname_prefix,basin_list=these_basin_keys,start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, FigFormat=simple_format, size_format=args.size_format, show_legend=args.show_legend)
if args.plot_summary:
MN.CompareMOverNEstimatesAllMethods(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern,
n_movern=n_movern, parallel=args.parallel, Chi_disorder=True)
MN.MakeMOverNSummaryPlot(this_dir, args.fname_prefix, basin_list=these_basin_keys,start_movern=start_movern, d_movern=d_movern,
n_movern=n_movern, FigFormat = simple_format,size_format=args.size_format, show_legend=args.show_legend,parallel=args.parallel, Chi_disorder=True)
# This only prints the summary plots for bootstrap and disorder metrics
MN.MakeMOverNSummaryPlot(this_dir, args.fname_prefix, basin_list=these_basin_keys,start_movern=start_movern, d_movern=d_movern,
n_movern=n_movern, FigFormat = simple_format,size_format=args.size_format,
show_legend=args.show_legend,parallel=args.parallel,
Chi_all = False, SA_raw = False, SA_segmented = False,
SA_channels = False, Chi_bootstrap = True, Chi_disorder=True)
MN.MakeMOverNSummaryHistogram(this_dir, args.fname_prefix,basin_list=these_basin_keys,start_movern=start_movern, d_movern=d_movern,
n_movern=n_movern, FigFormat=args.FigFormat, size_format=args.size_format, show_legend=args.show_legend, Chi_disorder=True)
if args.plot_disorder:
MN.MakeRasterPlotsMOverN(this_dir, args.fname_prefix, start_movern, n_movern, d_movern, movern_method="Chi_disorder", size_format=args.size_format, FigFormat=args.FigFormat,parallel=args.parallel)
MN.CompareMOverNEstimatesAllMethods(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, parallel=args.parallel, Chi_disorder=True)
MN.MakeMOverNSummaryPlot(this_dir, args.fname_prefix, basin_list=these_basin_keys,start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, FigFormat = simple_format,size_format=args.size_format, show_legend=args.show_legend,parallel=args.parallel, Chi_disorder=True)
MN.MakeMOverNSummaryHistogram(this_dir, args.fname_prefix,basin_list=these_basin_keys,start_movern=start_movern, d_movern=d_movern, n_movern=n_movern, FigFormat=args.FigFormat, size_format=args.size_format, show_legend=args.show_legend, Chi_disorder=True)
if args.all_movern_estimates:
# plot the rasters
MN.MakeRasterPlotsBasins(this_dir, args.fname_prefix, args.size_format, args.FigFormat,parallel=args.parallel)
MN.MakeRasterPlotsMOverN(this_dir, args.fname_prefix, start_movern, n_movern, d_movern, movern_method="Chi_full", size_format=args.size_format,
FigFormat=args.FigFormat,parallel=args.parallel)
MN.MakeRasterPlotsMOverN(this_dir, args.fname_prefix, start_movern, n_movern, d_movern, movern_method="Chi_points", size_format=args.size_format,
FigFormat=args.FigFormat,parallel=args.parallel)
MN.MakeRasterPlotsMOverN(this_dir, args.fname_prefix, start_movern, n_movern, d_movern, movern_method="SA", size_format=args.size_format,
FigFormat=args.FigFormat,parallel=args.parallel)
MN.MakeRasterPlotsMOverN(this_dir, args.fname_prefix, start_movern, n_movern, d_movern, movern_method="Chi_disorder",
size_format=args.size_format, FigFormat=args.FigFormat,parallel=args.parallel)
# make the chi plots
MN.MakeChiPlotsMLE(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern, n_movern=n_movern,
size_format=args.size_format, FigFormat = args.FigFormat, animate=True, keep_pngs=True,parallel=args.parallel)
# make the SA plots
SA.SAPlotDriver(this_dir, args.fname_prefix, FigFormat = args.FigFormat,size_format=args.size_format,
show_raw = args.show_SA_raw, show_segments = True, basin_keys = these_basin_keys, parallel=args.parallel)
SA.SAPlotDriver(this_dir, args.fname_prefix, FigFormat = args.FigFormat,size_format=args.size_format,
show_raw = args.show_SA_raw, show_segments = False, basin_keys = these_basin_keys, parallel=args.parallel)
#summary plots
MN.CompareMOverNEstimatesAllMethods(this_dir, args.fname_prefix, basin_list=these_basin_keys, start_movern=start_movern, d_movern=d_movern,
n_movern=n_movern, parallel=args.parallel, Chi_disorder=True)
MN.MakeMOverNSummaryPlot(this_dir, args.fname_prefix, basin_list=these_basin_keys,start_movern=start_movern, d_movern=d_movern,
n_movern=n_movern, FigFormat = simple_format,size_format=args.size_format, show_legend=args.show_legend,parallel=args.parallel, Chi_disorder=True)
# This only prints the summary plots for bootstrap and disorder metrics
MN.MakeMOverNSummaryPlot(this_dir, args.fname_prefix, basin_list=these_basin_keys,start_movern=start_movern, d_movern=d_movern,
n_movern=n_movern, FigFormat = simple_format,size_format=args.size_format,
show_legend=args.show_legend,parallel=args.parallel,
Chi_all = False, SA_raw = False, SA_segmented = False,
SA_channels = False, Chi_bootstrap = True, Chi_disorder=True)
MN.MakeMOverNSummaryHistogram(this_dir, args.fname_prefix,basin_list=these_basin_keys,start_movern=start_movern, d_movern=d_movern,
n_movern=n_movern, FigFormat=args.FigFormat, size_format=args.size_format, show_legend=args.show_legend, Chi_disorder=True)
