def hypsometry_tools_general():
# Here are the different parameters and their default value fr this script
default_param = AGPD.get_common_default_param()
default_param["hypsometry"] = False
default_param["absolute_elevation"] = False
default_param["normalise_to_outlets"] = False
default_param["X"] = None
default_param["Y"] = None
default_param = AGPD.ingest_param(default_param, sys.argv)
# Checking the param
if (isinstance(default_param["X"], str)):
X = [float(default_param["X"])]
Y = [float(default_param["Y"])]
else:
X = [float(i) for i in default_param["X"]]
Y = [float(i) for i in default_param["Y"]]
if (default_param["help"] or len(sys.argv) == 1):
print("""
This command-line tool run concavity analysis tools from LSDTopoTools.
Description of the algorithms in Mudd et al., 2018 -> https://www.earth-surf-dynam.net/6/505/2018/
To use, run the script with relevant options, for example:
lsdtt-concavity-tools.py file=myraster.tif quick_movern X=5432 Y=78546
option available:
file: name of the raster (file=name.tif)
path: path to the file (default = current folder)
hypsometry: run hypsometric calculations
absolute_elevation: hypsometric elevation will be the absolute value rather than the relavie/normalised one
X: X coordinate of the outlet (So far only single basin is supported as this is an alpha tool)
Y: Y coordinate of the outlet (So far only single basin is supported as this is an alpha tool)
help: if written, diplay this message. Documentation soon to be written.
""")
quit()
print(
"Welcome to the command-line tool to plot basin-wide hypsometry stuff!"
)
print("Let me first load the raster ...")
mydem = LSDDEM(file_name=default_param["file"],
path=default_param["path"],
already_preprocessed=False,
verbose=False)
print("Got it. Now dealing with the depressions ...")
mydem.PreProcessing(filling=True,
carving=True,
minimum_slope_for_filling=0.0001
) # Unecessary if already preprocessed of course.
print("Done! Extracting the river network")
mydem.ExtractRiverNetwork(method="area_threshold", area_threshold_min=1000)
print("I have some rivers for you! Defining the watershed of interest...")
mydem.DefineCatchment(method="from_XY",
X_coords=X,
Y_coords=Y,
test_edges=False,
coord_search_radius_nodes=25,
coord_threshold_stream_order=1)
print("I should have it now.")
print("I got all the common info, now I am running what you want!")
mydem.GenerateChi()
print(
"Producing a figure of your catchement just for you to check if their location is OK!"
)
qp.plot_check_catchments(mydem)
if default_param["hypsometry"]:
print(
"I am going to plot relative hypsometric curves for your different basins"
)
print(
"This is quite quickly coded (for Jorien) and will probably evolve in the future!"
)
fig, ax = plt.subplots()
# Basin Array
BAS = mydem.cppdem.get_chi_basin()
TOPT = mydem.cppdem.get_PP_raster()
DAD = mydem.cppdem.get_DA_raster()
for bas in np.unique(BAS):
if (bas != -9999):
# getting hte topo raster ONLY for current basin
this_topo = TOPT[BAS == bas].ravel()
# Now getting the DA
this_DA = DAD[BAS == bas].ravel()
A = np.nanmax(this_DA) # maximum area of that basin
# Normalising the topo
if (default_param["absolute_elevation"] == False):
this_topo = (this_topo - np.nanmin(this_topo))
this_topo = this_topo / np.nanmax(this_topo)
elif (default_param["normalise_to_outlets"]):
this_topo = (this_topo - np.nanmin(this_topo))
# Y is h/H, X is a/A
Y = []
X = []
for i in range(101):
if (default_param["absolute_elevation"] == False):
Y.append(i * 0.01)
else:
Y.append(np.percentile(this_topo, i))
X.append(np.nanmax(this_DA[this_topo >= Y[-1]]) / A)
ax.plot(X, Y, lw=1, label=bas, alpha=0.8)
ax.set_xlabel(r"$\frac{a}{A}$")
if (default_param["absolute_elevation"] == False):
ax.set_ylabel(r"$\frac{h}{H}$")
else:
ax.set_ylabel("Elevation (m)")
ax.legend()
if (default_param["absolute_elevation"] == False):
suffix = "_relative_hypsometry"
else:
suffix = "_absolute_hypsometry"
if (default_param["normalise_to_outlets"]):
suffix += "_norm2outlet"
plt.savefig(mydem.save_dir + mydem.prefix + suffix + "." + "png",
dpi=500)
plt.clf()
print("Finished!")
def chi_mapping_tools():
# Here are the different parameters and their default value fr this script
default_param = AGPD.get_common_default_param()
default_param["map"] = False
default_param["theta_ref"] = 0.45
default_param["A_0"] = 1
default_param["area_threshold"] = 500
default_param["X"] = None
default_param["Y"] = None
default_param = AGPD.ingest_param(default_param, sys.argv)
# Checking the param
if (isinstance(default_param["X"], str)):
X = [float(default_param["X"])]
Y = [float(default_param["Y"])]
else:
X = [float(i) for i in default_param["X"]]
Y = [float(i) for i in default_param["Y"]]
if (default_param["help"] or len(sys.argv) == 1):
print("""
This command-line tool run chi analysis tools from LSDTopoTools. This tool is focused on extracting chi, ksn or things like that.
Chi -> DOI: 10.1002/esp.3302 An integral approach to bedrock river profile analysis, Perron and Royden 2013
ksn -> (calculated from a robust statistical method based on chi): https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/2013JF002981, Mudd et al., 2014
To use, run the script with relevant options, for example:
lsdtt-chi-tools file=myraster.tif map X=531586 Y=6189787 theta_ref=0.55 A_0=10
option available:
file: name of the raster (file=name.tif)
path: path to the file (default = current folder)
map: generate map of chi
theta_ref: theta ref on the main equation
A_0: A_0 on the chi equation
area_threshold: the threshold of pixel to initiate the river network
X: X coordinate of the outlet (So far only single basin is supported as this is an alpha tool)
Y: Y coordinate of the outlet (So far only single basin is supported as this is an alpha tool)
help: if written, diplay this message. Documentation soon to be written.
""")
quit()
print(
"Welcome to the command-line tool to plot basin-wide hypsometry stuff!"
)
print("Let me first load the raster ...")
mydem = LSDDEM(file_name=default_param["file"],
path=default_param["path"],
already_preprocessed=False,
verbose=False)
print("Got it. Now dealing with the depressions ...")
mydem.PreProcessing(filling=True,
carving=True,
minimum_slope_for_filling=0.0001
) # Unecessary if already preprocessed of course.
print("Done! Extracting the river network")
mydem.ExtractRiverNetwork(method="area_threshold", area_threshold_min=1000)
print("I have some rivers for you! Defining the watershed of interest...")
mydem.DefineCatchment(method="from_XY",
X_coords=X,
Y_coords=Y,
test_edges=False,
coord_search_radius_nodes=25,
coord_threshold_stream_order=1)
print("I should have it now.")
print("I got all the common info, now I am running what you want!")
mydem.GenerateChi(theta=float(default_param["theta_ref"]),
A_0=float(default_param["A_0"]))
print(
"Producing a figure of your catchement just for you to check if their location is OK!"
)
qp.plot_check_catchments(mydem)
if default_param["map"]:
print("I am going to plot chi maps")
fig, ax = qp.plot_nice_topography(mydem,
figure_width=4,
figure_width_units="inches",
cmap="gist_earth",
hillshade=True,
alpha_hillshade=1,
color_min=None,
color_max=None,
dpi=300,
output="return",
format_figure="png",
fontsize_ticks=6,
fontsize_label=8,
hillshade_cmin=0,
hillshade_cmax=250,
colorbar=False,
colorbar_label=None,
colorbar_ax=None)
normalize = matplotlib.colors.Normalize(
vmin=mydem.df_base_river["drainage_area"].min(),
vmax=mydem.df_base_river["drainage_area"].max())
mydem.df_base_river = mydem.df_base_river[
mydem.df_base_river["chi"] >= 0]
cb = ax.scatter(mydem.df_base_river["x"],
mydem.df_base_river["y"],
s=4 *
normalize(mydem.df_base_river["drainage_area"].values),
c=mydem.df_base_river["chi"],
vmin=mydem.df_base_river["chi"].quantile(0.1),
vmax=mydem.df_base_river["chi"].quantile(0.9),
zorder=5,
lw=0)
plt.colorbar(cb)
plt.savefig(mydem.save_dir + mydem.prefix + "_chi_map" + "." + "png",
dpi=500)
plt.clf()
print("Finished!")
def concavity_FFS_down_to_top():
# Here are the different parameters and their default value fr this script
default_param = AGPD.get_common_default_param()
default_param["already_preprocessed"] = False
default_param["save_XY_array"] = False
default_param["X_source"] = None
default_param["Y_source"] = None
default_param["min_elevation"] = None
default_param["area_threshold"] = None
default_param["flow_distance_step"] = None
default_param["min_DA"] = None
default_param["prefix"] = ""
default_param = AGPD.ingest_param(default_param, sys.argv)
if (default_param["help"] or len(sys.argv) == 1 or "help" in sys.argv):
print("""
This command-line tool run concavity analysis tools from LSDTopoTools.
Description of the algorithms in Mudd et al., 2018 -> https://www.earth-surf-dynam.net/6/505/2018/
To use, run the script with relevant options, for example:
lsdtt-concavity-down-to-top file=myraster.tif ---
option available:
file: name of the raster (file=name.tif)
path: path to the file (default = current folder)
todo
help: if written, diplay this message. Documentation soon to be written.
""")
quit()
try:
default_param["X_source"] = float(default_param["X_source"])
default_param["Y_source"] = float(default_param["Y_source"])
default_param["min_elevation"] = float(default_param["min_elevation"])
default_param["area_threshold"] = float(
default_param["area_threshold"])
default_param["flow_distance_step"] = float(
default_param["flow_distance_step"])
default_param["min_DA"] = float(default_param["min_DA"])
except:
print(default_param)
print(
"I struggle to understand your input parameters: make sure I can convert them to number"
)
raise SystemExit("I need to quit now")
mydem = LSDDEM(file_name=default_param["file"],
path=default_param["path"],
already_preprocessed=default_param["already_preprocessed"],
remove_seas=True,
sea_level=default_param["min_elevation"])
if (default_param["already_preprocessed"] == False):
print(
"I am preprocessing your raster with default options, see lsdtt-depressions for extended options."
)
print(
"Command line tools can load already processed rasters with the keyword already_preprocessed"
)
mydem.PreProcessing()
# flwo routines
print("Processing flow routines with d8 algorithm")
mydem.CommonFlowRoutines()
print("Extracting the river")
river = mydem.ExtractSingleRiverFromSource(default_param["X_source"],
default_param["Y_source"])
river = pd.DataFrame(river)
print("Saving teh river to csv file: river_for_concavity.csv")
river.to_csv("%sriver_for_concavity.csv" % (default_param["prefix"]),
index=False)
print("Initialising the concavity analysis")
river.sort_values("elevation", inplace=True)
river.reset_index(drop=True, inplace=True)
# OUtputs
global_results = {}
outlet_info = {
"X": [],
"Y": [],
"elevation": [],
"flow_distance": [],
"drainage_area": []
}
# potential saving of basins
XY_basins = {}
flow_distance_this = river["flow_distance"].min()
index_this = river["flow_distance"].idxmin()
while (flow_distance_this < river["flow_distance"].max()
and river["drainage_area"][index_this] > default_param["min_DA"]):
print("processing flow distance =", flow_distance_this)
del mydem
mydem = LSDDEM(file_name="%sdem_for_concavity.tif" %
(default_param["prefix"]),
already_preprocessed=True,
remove_seas=True,
sea_level=river["elevation"][index_this] - 5)
mydem.CommonFlowRoutines()
# mydem.save_array_to_raster_extent(mydem.cppdem.get_DA_raster(), name = "DA", save_directory = "./")
mydem.ExtractRiverNetwork(
area_threshold_min=default_param["area_threshold"])
# print(river.iloc[index_this])
mydem.DefineCatchment(method="from_XY",
X_coords=[river["X"][index_this]],
Y_coords=[river["Y"][index_this]],
coord_search_radius_nodes=0)
mydem.GenerateChi()
print(mydem.df_base_river["source_key"].unique())
mydem.cppdem.calculate_movern_disorder(0.05, 0.05, 19, 1,
default_param["area_threshold"])
all_disorder = mydem.cppdem.get_best_fits_movern_per_BK()
global_results[str(round(
river["flow_distance"][index_this], 2))] = np.array(
all_disorder[0]
) # [0] means basin 0, which is normal as I only have one
outlet_info["X"].append(river["X"][index_this])
outlet_info["Y"].append(river["Y"][index_this])
outlet_info["elevation"].append(river["elevation"][index_this])
outlet_info["flow_distance"].append(
round(river["flow_distance"][index_this], 2))
outlet_info["drainage_area"].append(river["drainage_area"][index_this])
# New index
flow_distance_this += default_param["flow_distance_step"]
index_this = river.index[
river["flow_distance"] >= flow_distance_this].values[0]
np.savez("%sdown_to_top_conc.npz" % (default_param["prefix"]),
**global_results)
pd.DataFrame(outlet_info).to_csv("%sdown_to_top_conc_basin_info.csv" %
(default_param["prefix"]),
index=False)
if (default_param["save_XY_array"]):
this_basin = mydem.cppdem.query_xy_for_each_basin()[
0] # [0] because I want a single basin there
print(this_basin)
XY_basins[str(round(river["flow_distance"][index_this],
2))] = this_basin
np.savez(default_param["prefix"] + "down_to_top_basins_XY.npz",
**XY_basins)
def spawn_XY_outlet():
"""
Command-line tool to prechoose the basins used for other analysis. Outputs a file with outlet coordinates readable from other command-line tools and a basin perimeter csv readable by GISs to if the basins corresponds to your needs.
Takes several arguments (the values after = are example values to adapt):
file=NameOfFile.tif -> The code NEEDS the neame of the raster to process.
already_preprocessed -> OPTIONAL Tell the code your raster does not need preprocessing, otherwise carve the DEM (see lsdtt-depressions for more options)
test_edges -> OPTIONAL will test if the basin extracted are potentially influenced by nodata and threfore uncomplete. WARNING, will take out ANY basin potentially cut, if you know what you are doing, you can turn off.
prefix=test -> OPTIONAL Add a prefix to each outputted file (handy for automation)
method=from_range -> DEFAULT from_range: determine the method to select basin. Can be
from_range -> select largest basins bigger than min_DA but smaller than max_DA (in m^2)
min_area -> select largest basins bigger than min_DA
main_basin -> select the largest basin
Other methods to come.
min_elevation=45 -> DEFAULT 0. Ignore any basin bellow that elevation
area_threshold=3500 -> DEFAULT 5000. River network area threshold in number of pixels (part of the basin selection is based on river junctions HIGHLY sensitive to that variable).
Example:
lsdtt-concFFS-spawn-outlets file=DEM.tif already_preprocessed min_DA=1e7 max_DA=1e9 area_threshold=3500
"""
default_param = AGPD.get_common_default_param()
default_param["already_preprocessed"] = False
default_param["test_edges"] = False
default_param["area_threshold"] = 5000
default_param["method"] = "from_range"
default_param["min_DA"] = 1e6
default_param["max_DA"] = 1e9
default_param["min_elevation"] = 0
default_param["prefix"] = ""
default_param = AGPD.ingest_param(default_param, sys.argv)
choice_of_method = ["min_area", "main_basin", "from_range"]
if (default_param["help"] or len(sys.argv) == 1 or "help" in sys.argv):
print("""
Command-line tool to prechoose the basins used for other analysis. Outputs a file with outlet coordinates readable from other command-line tools and a basin perimeter csv readable by GISs to if the basins corresponds to your needs.
Takes several arguments (the values after = are example values to adapt):
file=NameOfFile.tif -> The code NEEDS the neame of the raster to process.
already_preprocessed -> OPTIONAL Tell the code your raster does not need preprocessing, otherwise carve the DEM (see lsdtt-depressions for more options)
test_edges -> OPTIONAL will test if the basin extracted are potentially influenced by nodata and threfore uncomplete. WARNING, will take out ANY basin potentially cut, if you know what you are doing, you can turn off.
prefix=test -> OPTIONAL Add a prefix to each outputted file (handy for automation)
method=from_range -> DEFAULT from_range: determine the method to select basin. Can be
from_range -> select largest basins bigger than min_DA but smaller than max_DA (in m^2)
min_area -> select largest basins bigger than min_DA
main_basin -> select the largest basin
Other methods to come.
min_elevation=45 -> DEFAULT 0. Ignore any basin bellow that elevation
area_threshold=3500 -> DEFAULT 5000. River network area threshold in number of pixels (part of the basin selection is based on river junctions HIGHLY sensitive to that variable).
Example:
lsdtt-concFFS-spawn-outlets file=DEM.tif already_preprocessed min_DA=1e7 max_DA=1e9 area_threshold=3500
""")
return 0
# Checks if the method requested is valid or not
if (default_param["method"].lower() not in choice_of_method):
print("I cannot recognise the method! Please choose from:")
print(choice_of_method)
return 0
# Formatting parameters
area_threshold = int(default_param["area_threshold"])
min_DA = float(default_param["min_DA"])
max_DA = float(default_param["max_DA"])
min_elevation = float(default_param["min_elevation"])
# Reading DEM
mydem = LSDDEM(file_name=default_param["file"],
path=default_param["path"],
already_preprocessed=default_param["already_preprocessed"],
remove_seas=True,
sea_level=min_elevation)
if (default_param["already_preprocessed"] == False):
mydem.PreProcessing()
# Extracting basins
mydem.CommonFlowRoutines()
print("Done with flow routines")
mydem.ExtractRiverNetwork(method="area_threshold",
area_threshold_min=area_threshold)
# Get the outlet coordinates of all the extracted basins
print("Extracted rivers")
df_outlet = mydem.DefineCatchment(
method=default_param["method"],
min_area=min_DA,
max_area=max_DA,
test_edges=default_param["test_edges"]
) #, X_coords = [X_coordinates_outlets[7]], Y_coords = [Y_coordinates_outlets[7]])
print("Extracted")
for key, val in df_outlet.items():
df_outlet[key] = np.array(df_outlet[key])
# Getting the rivers
mydem.GenerateChi(theta=0.4, A_0=1)
# Saing the rivers to csv
mydem.df_base_river.to_csv(default_param["prefix"] + "rivers.csv",
index=False)
#Saving the outlet
df_outlet["area_threshold"] = np.full(df_outlet["X"].shape[0],
area_threshold)
# print(df_outlet)
pd.DataFrame(df_outlet).to_csv(default_param["prefix"] + "outlets.csv",
index=False)
# Getting the perimeter of basins
this = mydem.cppdem.extract_perimeter_of_basins()
df_perimeter = {"X": [], "Y": [], "Z": [], "IDs": []}
for key, val in this.items():
df_perimeter["X"].append(np.array(val["X"]))
df_perimeter["Y"].append(np.array(val["Y"]))
df_perimeter["Z"].append(np.array(val["Z"]))
df_perimeter["IDs"].append(np.full(np.array(val["Z"]).shape[0], key))
for key, val in df_perimeter.items():
df_perimeter[key] = np.concatenate(val)
pd.DataFrame(df_perimeter).to_csv(default_param["prefix"] +
"perimeters.csv",
index=False)
def main_concavity():
# Here are the different parameters and their default value fr this script
default_param = AGPD.get_common_default_param()
default_param["quick_movern"] = False
default_param["X"] = None
default_param["Y"] = None
default_param = AGPD.ingest_param(default_param, sys.argv)
# Checking the param
if (isinstance(default_param["X"], str)):
X = [float(default_param["X"])]
Y = [float(default_param["Y"])]
else:
try:
X = [float(i) for i in default_param["X"]]
Y = [float(i) for i in default_param["Y"]]
except:
pass
if (default_param["help"] or len(sys.argv) == 2 or "help" in sys.argv):
print("""
This command-line tool run concavity analysis tools from LSDTopoTools.
Description of the algorithms in Mudd et al., 2018 -> https://www.earth-surf-dynam.net/6/505/2018/
To use, run the script with relevant options, for example:
lsdtt-concavity-tools.py file=myraster.tif quick_movern X=5432 Y=78546
option available:
file: name of the raster (file=name.tif)
path: path to the file (default = current folder)
quick_movern: run disorder metrics and plot a result figure (you jsut need to write it)
X: X coordinate of the outlet (So far only single basin is supported as this is an alpha tool)
Y: Y coordinate of the outlet (So far only single basin is supported as this is an alpha tool)
help: if written, diplay this message. Documentation soon to be written.
""")
quit()
print(
"Welcome to the command-line tool to constrain your river network concavity. Refer to Mudd et al., 2018 -> https://www.earth-surf-dynam.net/6/505/2018/ for details about these algorithms."
)
print("Let me first load the raster ...")
try:
mydem = LSDDEM(file_name=default_param["file"],
path=default_param["path"],
already_preprocessed=False,
verbose=False)
except:
print("Testing data still to build")
print("Got it. Now dealing with the depressions ...")
mydem.PreProcessing(filling=True,
carving=True,
minimum_slope_for_filling=0.0001
) # Unecessary if already preprocessed of course.
print("Done! Extracting the river network")
mydem.ExtractRiverNetwork(method="area_threshold", area_threshold_min=1000)
print("I have some rivers for you! Defining the watershed of interest...")
mydem.DefineCatchment(method="from_XY",
X_coords=X,
Y_coords=Y,
test_edges=False,
coord_search_radius_nodes=25,
coord_threshold_stream_order=1)
print("I should have it now.")
print("I got all the common info, now I am running what you want!")
if (default_param["quick_movern"]):
print("Initialising Chi-culations (lol)")
mydem.GenerateChi()
print(
"Alright, getting the disorder metrics for each chi values. LSDTopoTools can split a lot of messages, sns."
)
mydem.cppdem.calculate_movern_disorder(
0.15, 0.05, 17, 1, 1000) # start theta, delta, n, A0, threashold
print("I am done, plotting the results now")
qmn.plot_disorder_results(mydem,
legend=False,
normalise=True,
cumulative_best_fit=False)
qp.plot_check_catchments(mydem)
qmn.plot_disorder_map(mydem, cmap="RdBu_r")
print("FInished with quick disorder metric")
print("Finished!")
## Now we can load the dem into LSDTopytools:
### already_preprocessed can be turn to True if you are 100% sure that your dem does not need preprocessing before flow routines
mydem = LSDDEM(path = path_to_dem, file_name = dem_name, already_preprocessed = False)
# Alright the dem is in the system and now needs to be preprocessed (if not done yet)
mydem.PreProcessing(filling = True, carving = True, minimum_slope_for_filling = 0.0001) # Unecessary if already preprocessed of course.
#Need to pregenerate a number of routines, it calculates flow direction, flow accumulation, drainage area , ...
mydem.CommonFlowRoutines()
# This define the river network, it is required to actually calculate other metrics
mydem.ExtractRiverNetwork( method = "area_threshold", area_threshold_min = 1500)
# Defining catchment of interest: it extracts the catchments by outlet coordinates. You also need to adpat these obviously!!
## they need to be in the same coordinate system than the raster.
mydem.DefineCatchment( method="from_XY", X_coords = [532297,521028], Y_coords = [6188085,6196305])
# Calculates chi coordinate with an according theta
mydem.GenerateChi(theta = 0.4, A_0 = 1)
# At that stage you can get the basic river characteristics as follow:
# my_rivers = mydem.df_base_river
# my_rivers.to_csv("name_of_my_file_containing_base_river.csv", index = False) # This saves the base rivers to csv
##############################
# Find below the range of specific chi related analysis you can run with lsdtopytools
# Note that this script contains a list of analysis as exhaustive as possible, but you do not need to run all of them at the same time !!
# It is rather adviced to run them separately when possible
##############################
my_raster_path = "./" # I am telling saving my path to a variable. In this case, I assume the rasters is in the same folder than my script
file_name = "WAWater.bil" # The name of your raster with extension. RasterIO then takes care internally of decoding it. Good guy rasterio!
# I am now telling lsdtopytools where is my raster, and What do I want to do with it. No worries It will deal with the detail internally
mydem = LSDDEM(
path=my_raster_path, file_name=file_name
) # If your dem is already preprocessed: filled or carved basically, add: , is_preprocessed = True
## Loaded in the system, now preprocessing: I want to carve it and imposing a minimal slope on remaining flat surfaces: 0.0001
mydem.PreProcessing(filling=True,
carving=True,
minimum_slope_for_filling=0.0001
) # Unecessary if already preprocessed of course.
mydem.ExtractRiverNetwork(method="area_threshold", area_threshold_min=500)
mydem.DefineCatchment(method="from_XY",
X_coords=[527107, 527033, 530832],
Y_coords=[6190656, 6191745, 6191015])
mydem.GenerateChi(theta=0.45, A_0=1)
print("Starting movern extraction")
mydem.cppdem.calculate_movern_disorder(
0.1, 0.05, 18, 1, 1000) # start theta, delta, n, A0, threashold
print("movern done, getting the data")
quickplot_movern.plot_disorder_results(mydem,
normalise=True,
figsize=(4, 3),
dpi=300,
output="save",
format_figure="png",
legend=True,