def TestNewMappingTools_asc():
DataDirectory = "T:\\analysis_for_papers\\Beaches\\"
#Filename1 = "BedThickness_050.asc"
#Filename2 = "BedThickness_100.asc"
Filename1 = "20m_bl.asc"
ThisFile = DataDirectory + Filename1
yo = LSDP.GetRasterExtent(ThisFile)
print "raster extent is: "
print yo
#MB = LSDP.BasicMassBalance(DataDirectory, Filename1, Filename2)
#print "Mass balance between these two time steps is: " + str(MB) + " cubic metres"
#Mean1 = LSDP.RasterMeanValue(DataDirectory, Filename1)
#Mean2 = LSDP.RasterMeanValue(DataDirectory, Filename2)
#print "The mean values of the two rasters are: " + str(Mean1) +" and "+ str(Mean2)
# now try the swath plotting
#axis = 0
#LSDP.SwathPlot(DataDirectory, Filename1, axis)
axis = 1
LSDP.SwathPlot(DataDirectory, Filename1, axis)
def make_ticks(self):
"""
This function makes the tick marks and the tick labels.
It has been optimised so you get nice looking ticks, so you shouldn't have to mess with them after this is called.
"""
if self._coord_type == "UTM":
self.tick_xlocs, self.tick_ylocs, self.tick_x_labels, self.tick_y_labels = LSDP.GetTicksForUTMNoInversion(
self._BaseRasterFullName, self._xmax, self._xmin, self._ymax,
self._ymin, self._n_target_ticks)
elif self._coord_type == "UTM_km":
self.tick_xlocs, self.tick_ylocs, self.tick_x_labels, self.tick_y_labels = LSDP.GetTicksForUTMNoInversion(
self._BaseRasterFullName, self._xmax, self._xmin, self._ymax,
self._ymin, self._n_target_ticks)
n_hacked_digits = 3
self.tick_x_labels = LSDP.TickLabelShortenizer(
self.tick_x_labels, n_hacked_digits)
self.tick_y_labels = LSDP.TickLabelShortenizer(
self.tick_y_labels, n_hacked_digits)
else:
raise ValueError("Sorry, the coordinate type: ", self._coord_type,
"is not yet supported")
print("I made the ticks.")
print("x labels are: ")
print(self.tick_x_labels)
print("x locations are:")
print(self.tick_xlocs)
print("y labels are: ")
print(self.tick_y_labels)
print("y locations are:")
print(self.tick_ylocs)
def Correct_Raterized_GLIM_map(tifname):
# And now for a hack that converts to
print("The raster name is: " + tifname)
[xsize, ysize, geotransform, geoproj, Z] = readFile(tifname)
print("Before data check")
print(Z)
print("Data type is: " + str(Z.dtype))
X = Z.astype(int)
# Set large negative values to -9999
X[X <= 0] = -9999
#Z[np.isnan(Z)]= -9999
print("After_data_check")
print(X)
#get path and filename seperately
filepath = LSDPT.GetPath(tifname)
#filename = LSDPT.GetFileNameNoPath(tifname)
fileshortname = LSDPT.GetFilePrefix(tifname)
outraster2 = filepath + fileshortname + '2.tif'
writeFile(outraster2, geotransform, geoproj, X)
def make_field_sites_figure(DataDirectory):
N_HSFiles = 4
NRows = 3
NCols = 2
n_target_ticks = 6
LSDP.field_sites(DataDirectory, N_HSFiles, NRows, NCols, n_target_ticks)
def geologic_maps_modify_shapefile(shapefile_name, geol_field="xx"):
# The shapefile to be rasterized:
print('Rasterize ' + shapefile_name)
#get path and filename seperately
shapefilefilepath = LSDPT.GetPath(shapefile_name)
#shapefilename = LSDPT.GetFileNameNoPath(shapefile_name)
shapefileshortname = LSDPT.GetFilePrefix(shapefile_name)
# get the new shapefile name
new_shapefile_name = shapefilefilepath + os.sep + shapefileshortname + "_new.shp"
# copy the shapefile into the new shapefile--we don't wwant to mess up the original data
print("The New Shapefile name is: " + new_shapefile_name)
Copy_Shapefile(shapefile_name, new_shapefile_name)
# New shapefile is opened for writing.
dataSource = ogr.Open(new_shapefile_name, 1)
daLayer = dataSource.GetLayer(0)
# add a new field
new_field = ogr.FieldDefn("GEOL_CODE", ogr.OFTInteger)
daLayer.CreateField(new_field)
# lets see what the layers are
print(
"Let me tell you what the names of the fields are after I added one!")
layerDefinition = daLayer.GetLayerDefn()
for i in range(layerDefinition.GetFieldCount()):
print(layerDefinition.GetFieldDefn(i).GetName())
# Make a key for the bedrock
geol_dict = dict()
geol_iterator = 0
geol_field = geol_field #
for feature in daLayer:
GEOL = feature.GetField(geol_field)
if GEOL not in geol_dict:
geol_iterator = geol_iterator + 1
print("I found a new rock type, GEOL: " + str(GEOL) +
" and rock type: " + str(geol_iterator))
geol_dict[GEOL] = geol_iterator
# now get the geol code
this_geol_code = geol_dict[GEOL]
# set the feature
feature.SetField("GEOL_CODE", this_geol_code)
# need to update the layer
daLayer.SetFeature(feature)
print("The rocks are: ")
print(geol_dict)
print("All done")
return new_shapefile_name, geol_dict
def __init__(self, RasterName, Directory):
self._RasterFileName = RasterName
self._RasterDirectory = Directory
self._FullPathRaster = self._RasterDirectory + self._RasterFileName
# I think the BaseRaster should contain a numpy array of the Raster
self._RasterArray = LSDP.ReadRasterArrayBlocks(self._FullPathRaster)
# Get the extents as a list
self._RasterExtents = LSDP.GetRasterExtent(self._FullPathRaster)
def ResetErosionRaster():
DataDirectory = "T://analysis_for_papers//Manny_Idaho//Revised//nested//"
#DataDirectory = "C://basin_data//Manny_Idaho//nested//"
ConstFname = "ConstEros.bil"
NewErateName = "HarringCreek_ERKnown.bil"
ThisFile = DataDirectory + ConstFname
NewFilename = DataDirectory + NewErateName
LSDP.CheckNoData(ThisFile)
# now print the constant value file
constant_value = 0.0092
LSDP.SetToConstantValue(ThisFile, NewFilename, constant_value)
LSDP.CheckNoData(NewFilename)
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()
else:
# Get the arguments
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-pf", "--parameter_file",type=str, default="Params.param",
help="The name, with extension and path, of your parameter file.")
args = parser.parse_args()
full_paramfile = args.parameter_file
print("The full parameter file is: "+full_paramfile)
# Now make a plotting driver object
PD = LSDPT.LSDMap_PlottingDriver(full_paramfile)
print(PD.FilePrefix)
print(PD.FilePath)
print("The plotting switches are: ")
print(PD.plotting_switches)
# Now make some plots!!
PD.plot_data()
def ChiMappingToolsTest():
DataDirectory = "/home/smudd/SMMDataStore/analysis_for_papers/Meghalaya/chi_analysis/"
#DataDirectory = "T:\\analysis_for_papers\\Meghalaya/chi_analysis\\"
#DataDirectory = "C:\\Vagrantboxes\\LSDTopoTools\\Topographic_projects\\Meghalaya\\"
Filename = "Mega_clip.bil"
HSFilename = "Mega_clip_hs.bil"
DEMname = DataDirectory+Filename
HSname = DataDirectory+HSFilename
FigName = DataDirectory+'TestChiFull2.png'
ChiName = DataDirectory+'Mega_clip_MChiSegmented.csv'
#FigFormat = 'svg'
#FigFileN= 'Sorbas_chi.svg'
#FigFileName= DataDirectory+FigFileN
elevation_threshold = 1
#LSDP.BasicChiPlotGridPlot(DEMname,HSname,ChiName, 'gray','gray',
# '$k_{sn}$',(0,0),
# 0.4,FigName,'png',elevation_threshold)
#FigName2 = DataDirectory+'TestChannelMap.png'
#LSDP.BasicChannelPlotGridPlotCategories(DEMname,HSname,ChiName, 'gray','gray',
# '$Channel$',(0,0),
# 0.4,FigName2,'png',elevation_threshold,'source_key')
FigName3 = DataDirectory+'ChiProfiles.png'
LSDP.ChiProfiles(ChiName, FigName3,'png',elevation_threshold)
def PlotBasinsWithHillshade(DataDirectory, OutDirectory, fname_prefix, stream_order=1):
"""
Read in the basins and plot them over a hillshade coloured by their cluster ID
"""
df = pd.read_csv(OutDirectory+fname_prefix+'_profiles_clustered_SO{}.csv'.format(stream_order))
clusters = df.cluster_id.unique()
# make a figure
fig = plt.figure(1, facecolor='white')
gs = plt.GridSpec(100,100,bottom=0.1,left=0.1,right=0.9,top=0.9)
ax = fig.add_subplot(gs[5:100,5:100])
# plot the raster
hs_raster = IO.ReadRasterArrayBlocks(DataDirectory+fname_prefix+'_hs.bil')
extent = LSDP.GetRasterExtent(DataDirectory+fname_prefix+'_hs.bil')
plt.imshow(hs_raster, cmap=cm.gray, extent=extent)
means = {}
for i,cl in enumerate(clusters):
this_df = df[df.cluster_id == cl]
# get the polygons
polygons = ReadBasinPolygons(DataDirectory, OutDirectory, fname_prefix+'_basins_SO{}_CL{}'.format(stream_order,int(cl)))
for p in polygons:
#print(list(p.exterior.coords))
patch = PolygonPatch(p, facecolor=this_df.iloc[0]['colour'], alpha=1.0, zorder=2, lw=0.2)
ax.add_patch(patch)
#print(polygons)
# for each
plt.savefig(OutDirectory+'polygons.png', FigFormat='png', dpi=500)
def ShieldPlots_fine():
DataDirectory = "T://analysis_for_papers//Cosmo_paper//Palumbo_shield_plots//"
Filename = "SpawnedBasin_07C13-fine_SH.bil"
ThisFile = DataDirectory+Filename
LSDP.BasicDensityPlotGridPlot(ThisFile, thiscmap='summer',colorbarlabel='Topographic shielding fine',
clim_val = (0.85,1),FigFileName = 'Shield_fine.pdf', FigFormat = 'pdf')
def _render_background(self, fullpath_to_raster):
"""
Renders the background image that
will form the drape plot, e.g. a hillshade
"""
if self._backgroundtype == "Hillshade":
self.Hillshade = LSDP.Hillshade(self.fullpath_to_raster)
self.colourmap = "gray"
elif self._backgroundtype == "Terrain":
self.Hillshade = LSDP.ReadRasterArrayBlocks(
self.fullpath_to_raster)
self.colourmap = LSDP.colours.UsefulColourmaps.niceterrain
#self.colourmap = LSDP.colours.UsefulColourmaps.darkearth
#self.colourmap = "terrain"
else:
print ("That background style is not yet supported. Currently only " \
" 'Hillshade' and 'Terrain' are supported.")
def TestNewMappingTools2():
DataDirectory = "T://analysis_for_papers//Manny_idaho//"
Filename = "TestIdaho.bil"
NewFilename = "TestIdaho_after2.bil"
ThreshFname = "ThreshIdaho.bil"
ConstFname = "ConstEros.bil"
ThisFile = DataDirectory+Filename
NewFilename = DataDirectory+NewFilename
ThreshFname = DataDirectory+ThreshFname
ConstFname = DataDirectory+ConstFname
#FigFormat = 'svg'
#FigFileN= 'Sorbas_chi.svg'
#FigFileName= DataDirectory+FigFileN
#Plot the basin over the elevation
#tcmapcolorbarlabel = "Elevation (m)"
#tcmap = 'jet'
#tcmapcolorbarlabel='Chi'
#clim_val = (50,300)
#LSDP.BasicDensityPlotGridPlot(ThisFile,tcmap,tcmapcolorbarlabel,clim_val)
#get the nodata values
NData = LSDP.getNoDataValue(ThisFile)
print "NoData is: " + str(NData)
# get the data as an array
array = LSDP.ReadRasterArrayBlocks(ThisFile)
driver_name = "ENVI"
LSDP.array2raster(ThisFile,NewFilename,array,driver_name, -9999)
#get the nodata values
NData2 = LSDP.getNoDataValue(NewFilename)
#print "NoData 2 is: " + str(NData2)
LSDP.SetNoDataBelowThreshold(ThisFile,ThreshFname)
# now print the constant value file
constant_value = 0.001
LSDP.SetToConstantValue(ThreshFname,ConstFname,constant_value)
def map_custom():
"""
Testing function to plot custom maps before creating real function for mapping routines
Args:
Yes.
returns:
No.
Author:
BG
"""
###### Parameters ######
Directory = "/home/s1675537/PhD/DataStoreBoris/GIS/Data/Carpathian/knickpoint/" # reading directory
wDirectory = Directory # writing directory
Base_file = "Buzau" # It will be the cabkground raster. Each other raster you want to drap on it will be cropped to its extents including nodata
csv_file = Directory + "test_sign_m_chi.csv" # Name of your point file, add a similar line with different name if you have more than one point file
DrapeRasterName = "Buzau_hs.bil" # if you want to drap a raster on your background one. Just add a similar line in case you want another raster to drap and so on
wname = "sign_test" # name of your output file
dpi = 500 # Quality of your output image, don't exceed 900
fig_size_inches = 7 # Figure size in Inches
##### Now we can load and plot the data
BackgroundRasterName = Base_file + ".bil" # Ignore this line
thisPointData = LSDP.LSDMap_PointData(csv_file, PANDEX = True) # Load the point file #1, add a similar line with different name if you have more than one point file.
plt.clf() # Ignore this line
MF = MapFigure(BackgroundRasterName, Directory,coord_type="UTM_km") # load the background raster
MF.add_drape_image(DrapeRasterName,Directory, # Calling the function will add a drapped raster on the top of the background one
colourmap = "gray", # colormap used for this raster, see http://matplotlib.org/users/colormaps.html for examples, put _r at the end of a colormap to get the reversed version
alpha=0.5, # transparency of this specific layer, 0 for fully transparent (why not) and 1 for fully opaque
show_colourbar = False, # Well, this one is explicit I think
colorbarlabel = "Colourbar") # Name of your Colourbar, it might bug though
MF.add_point_data( thisPointData, # this function plot the requested point file using the lat/long column in the csv file
column_for_plotting = "m_chi_sign", # Column used to color the data
this_colourmap = "cubehelix", # Colormap used, see http://matplotlib.org/users/colormaps.html for examples, put _r at the end of a colormap to get the reversed version
colorbarlabel = "Colourbar", # Label
scale_points = False, # All the point will have the same size if False
column_for_scaling = "None", # If scale point True, you can scale the size of your points using one of the columns
scaled_data_in_log = False, # If scale point True, you can log the scaling
max_point_size = 5, # max size if scale point True again
min_point_size = 0.5, # You should be able to guess that one now
coulor_log = False, # do you want a log scale for your colorbar ?
coulor_manual_scale = [], #Do you want to manually limit the scale of your colorbar? if not let is false
manual_size = 0.5, # If none of above is choosen but you want to put another value than 0.5 to scale your point
alpha = 1, # transparency of this specific layer, 0 for fully transparent (why not) and 1 for fully opaque
minimum_log_scale_cut_off = -10) # you probably won't need this
ImageName = wDirectory+str(int(clock.time()))+wname+".png" # Ignore this
ax_style = "Normal" # Ignore this
MF.save_fig(fig_width_inches = fig_size_inches, FigFileName = ImageName, axis_style = ax_style, Fig_dpi = dpi) # Save the figure
def __init__(self, RasterName, Directory):
self._RasterFileName = RasterName
self._RasterDirectory = Directory
self._FullPathRaster = self._RasterDirectory + self._RasterFileName
# I think the BaseRaster should contain a numpy array of the Raster
self._RasterArray = LSDP.ReadRasterArrayBlocks(self._FullPathRaster)
# Get the extents as a list
self._RasterExtents = LSDP.GetRasterExtent(self._FullPathRaster)
self._RasterAspectRatio = (
self._RasterExtents[1] - self._RasterExtents[0]) / (
self._RasterExtents[3] - self._RasterExtents[2])
# set the default colourmap
self._colourmap = "gray"
# get the EPSG string
self._EPSGString = LSDP.LSDMap_IO.GetUTMEPSG(self._FullPathRaster)
def PalumboPlots():
DataDirectory = "T://analysis_for_papers//Cosmo_paper//Palumbo_shield_plots//"
Filename = "SpawnedBasin_07C13-(Q8).bil"
Drapename = "SpawnedBasin_07C13-(Q8)_BASINS.bil"
ThisFile = DataDirectory+Filename
DrapeFile = DataDirectory+Drapename
LSDP.BasicDrapedPlotGridPlot(ThisFile, DrapeFile, thiscmap='terrain',drape_cmap='gray',
colorbarlabel='Elevation in meters',clim_val = (2000,5000),
drape_alpha = 0.4,FigFileName = 'Basin.pdf',FigFormat = 'pdf')
def FloodThenHillshade():
DataDirectory = "M:\\students\\kunkelova\\"
DEMName = "bk_10m_dem.bil"
newDEMName = "bk_10m_dem_updated.bil"
HillshadeName = "bk_10m_dem_HS.bil"
RasterFilename = DataDirectory + DEMName
NewRasterFilename = DataDirectory + newDEMName
HillshadeNameFile = DataDirectory + HillshadeName
LSDP.SetNoDataBelowThreshold(RasterFilename,
NewRasterFilename,
threshold=0,
driver_name="ENVI",
NoDataValue=-9999)
LSDP.GetHillshade(NewRasterFilename,
HillshadeNameFile,
azimuth=315,
angle_altitude=45,
driver_name="ENVI",
NoDataValue=-9999)
def FixStupidNoData():
DataDirectory = "T://analysis_for_papers//Indus//"
DEMName = "indus_utm44.bil"
newDEMName = "Indus_ND.bil"
RasterFilename = DataDirectory + DEMName
NewRasterFilename = DataDirectory + newDEMName
LSDP.SetNoDataBelowThreshold(RasterFilename,
NewRasterFilename,
threshold=0,
driver_name="ENVI",
NoDataValue=-9999)
def findmaxval_multirasters(FileList):
"""
Loops through a list or array of rasters (np arrays)
and finds the maximum single value in the set of arrays.
"""
overall_max_val = 0
for i in range (len(FileList)):
raster_as_array = LSDP.ReadRasterArrayBlocks(FileList[i])
this_max_val = np.max(raster_as_array)
if this_max_val > overall_max_val:
overall_max_val = this_max_val
print(overall_max_val)
return overall_max_val
def TestNewMappingTools2():
DataDirectory = "T://analysis_for_papers//Manny_idaho//"
Filename = "TestIdaho.bil"
NewFilename = "TestIdaho_after2.bil"
ThreshFname = "ThreshIdaho.bil"
ConstFname = "ConstEros.bil"
ThisFile = DataDirectory + Filename
NewFilename = DataDirectory + NewFilename
ThreshFname = DataDirectory + ThreshFname
ConstFname = DataDirectory + ConstFname
#FigFormat = 'svg'
#FigFileN= 'Sorbas_chi.svg'
#FigFileName= DataDirectory+FigFileN
#Plot the basin over the elevation
#tcmapcolorbarlabel = "Elevation (m)"
#tcmap = 'jet'
#tcmapcolorbarlabel='Chi'
#clim_val = (50,300)
#LSDP.BasicDensityPlotGridPlot(ThisFile,tcmap,tcmapcolorbarlabel,clim_val)
#get the nodata values
NData = LSDP.getNoDataValue(ThisFile)
print "NoData is: " + str(NData)
# get the data as an array
array = LSDP.ReadRasterArrayBlocks(ThisFile)
driver_name = "ENVI"
LSDP.array2raster(ThisFile, NewFilename, array, driver_name, -9999)
#get the nodata values
NData2 = LSDP.getNoDataValue(NewFilename)
#print "NoData 2 is: " + str(NData2)
LSDP.SetNoDataBelowThreshold(ThisFile, ThreshFname)
# now print the constant value file
constant_value = 0.001
LSDP.SetToConstantValue(ThreshFname, ConstFname, constant_value)
def TestMappingToolsPoints():
#DataDirectory = "T://analysis_for_papers//Test_map_chi_gradient//results//"
DataDirectory = "T://students//Wainwright//"
#Filename = "Mandakini_OutletList.csv"
Filename = "Bids_DD.csv"
#ExportName = "ShapeTest"
fname = DataDirectory + Filename
#Exp_fname = DataDirectory+ExportName
thisPointData = LSDP.LSDMap_PointData(fname)
thisPointData.GetParameterNames(True)
thisPointData.GetLongitude(True)
print "Hey buddy, the province is: "
thisPointData.QueryData("province", True)
print "Hey buddy, the gma is: "
thisPointData.QueryData("gma", True)
thisPointData.TranslateToReducedShapefile(fname)
thisPointData.TranslateToReducedGeoJSON(fname)
def ElevationSwaths(path, filename, axis, fprefix):
Fileformat = 'png'
# get the path to the raster file
NewPath = LSDOst.AppendSepToDirectoryPath(path)
FileName = NewPath+filename
# get the data vectors
means,medians,std_deviations,twentyfifth_percentile,seventyfifth_percentile = LSDP.SimpleSwath(path, filename, axis)
print "Means shape is: "
print means.shape
x_vec,y_vec = LSDP.GetLocationVectors(FileName)
print "X shape is: "
print x_vec.shape
print "Y shape is: "
print y_vec.shape
import matplotlib.pyplot as plt
import matplotlib.lines as mpllines
from mpl_toolkits.axes_grid1 import AxesGrid
label_size = 20
#title_size = 30
axis_size = 28
# Set up fonts for plots
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
# make a figure, sized for a ppt slide
fig = plt.figure(1, facecolor='white',figsize=(10,7.5))
gs = plt.GridSpec(100,75,bottom=0.1,left=0.1,right=0.9,top=1.0)
ax = fig.add_subplot(gs[10:100,10:75])
if axis == 0:
dir_vec = x_vec
else:
dir_vec = y_vec
# get the distance from shore
dist_from_shore = np.subtract(dir_vec[-1],dir_vec)
min_sd = np.subtract(means,std_deviations)
plus_sd = np.add(means,std_deviations)
ax.plot(dist_from_shore,means, linewidth = 2.5, color = "black")
#ax.fill_between(dist_from_shore, twentyfifth_percentile, seventyfifth_percentile, facecolor='green', alpha = 0.7, interpolate=True)
ax.fill_between(dist_from_shore, min_sd, plus_sd, facecolor='blue', alpha = 0.25, interpolate=True)
ax.set_xlim(dist_from_shore[0],dist_from_shore[-1])
ax.annotate('Standard deviation envelope', xy=(dist_from_shore[10],plus_sd[10]), xycoords='data',
xytext=(0.1, 0.8), textcoords='axes fraction',
size=label_size,
# bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="simple",
fc="0.6", ec="none",
connectionstyle="arc3,rad=0.3"),
)
ax.spines['top'].set_linewidth(2)
ax.spines['left'].set_linewidth(2)
ax.spines['right'].set_linewidth(2)
ax.spines['bottom'].set_linewidth(2)
#ax.tick_params(axis='both', width=1)
plt.xlabel('Distance from shore (m)', fontsize = axis_size)
plt.ylabel('Bed elevation relative to MSL (m)', fontsize = axis_size)
plt.title(fprefix)
# This gets all the ticks, and pads them away from the axis so that the corners don't overlap
ax.tick_params(axis='both', width=2, pad = 10)
for tick in ax.xaxis.get_major_ticks():
tick.set_pad(10)
#plt.show()
plt.savefig(NewPath+fprefix+"_BedElev.png",format = Fileformat)
plt.clf()
#DataDirectory = '/home/s0923330/DEMs_for_analysis/mid_bailey_run_10m/'
#DEM_prefix = 'bailey_dem_10m'
DataDirectory = 'C:\\VagrantBoxes\\LSDTopoTools\\Topographic_projects\\Meghalaya\\Paper\\'
DEM_prefix = 'Meghalaya'
FigFormat = 'png'
x_param = 'mean'
#MakeRawSlopeAreaPlot(DataDirectory, DEM_prefix, FigFormat='png', size_format = 'ESURF')
#MakeBinnedSlopeAreaPlot(DataDirectory, DEM_prefix, FigFormat, x_param)
#MakeChannelsMap(DataDirectory, DEM_prefix, FigFormat=FigFormat)
#MakeSegmentedSlopeAreaPlot(DataDirectory, DEM_prefix, FigFormat)
#these_basin_keys = [0,1,2]
these_basin_keys = []
#MakeSegmentedWithRawSlopeAreaPlot(DataDirectory, DEM_prefix, FigFormat, basin_keys = these_basin_keys)
#MakeBinnedWithRawSlopeAreaPlot(DataDirectory, DEM_prefix, FigFormat, basin_keys = these_basin_keys)
#BinnedRegressionDriver(DataDirectory, DEM_prefix, basin_keys = these_basin_keys)
LSDP.SAPlotDriver(DataDirectory,
DEM_prefix,
FigFormat=FigFormat,
show_raw=True,
show_segments=False,
cmap=plt.cm.Set1,
n_colours=5,
basin_keys=these_basin_keys)
#LSDP.TestSARegression(DataDirectory, DEM_prefix)
#adict = LSDP.BinnedRegressionDriver(DataDirectory, DEM_prefix, basin_keys = these_basin_keys)
#from LSDPlottingTools import LSDMap_MOverNPlotting as MN
#MN.CompareChiAndSAMOverN(DataDirectory, DEM_prefix, basin_list=these_basin_keys, start_movern=0.1, d_movern=0.1, n_movern=8)
def flood_maps_shapefile_zoom(DataDirectory):
LSDP.flood_maps_shapefile_with_zoom(DataDirectory)
def flood_maps_with_shapefile(DataDirectory):
LSDP.flood_maps_with_shapefile(DataDirectory)
def make_flood_maps(DataDirectory):
LSDP.multiple_flood_maps(DataDirectory)
Created on Tue May 05 14:08:16 2015
@author: dav
"""
import glob as glob
import os.path
import numpy as np
import LSDPlottingTools as LSDP
import matplotlib.cm as cm
#import lsdmatplotlibextensions as mplext
import matplotlib.pyplot as plt
from matplotlib import ticker
# Get favourite plotting fonts and sizes
LSDP.init_plotting_DV()
# Truncate the colour map
#trunc_cmap = LSDP.colours.truncate_colormap("Blues", 0.4, 1.0)
# Option for getting a discrete colour map
#discreet_cmap = mplext.colours.discrete_colourmap(8, "Blues")
#discreet_cmap = mplext.colours.cmap_discretize(8, trunc_cmap)
# Non-linear colourmap
levels = [-2.5, -1.5 -0.5, -0.25, 0, 0.25, 0.5, 1.5, 2.5 ]
#levels = levels[levels <= tmax]
print (levels)
levels.sort()
print (levels)
cmap_lin = cm.jet
Created on Tue May 05 14:08:16 2015
@author: dav
"""
import glob as glob
import os.path
import numpy as np
import LSDPlottingTools as LSDP
import matplotlib.cm as cm
#import lsdmatplotlibextensions as mplext
import matplotlib.pyplot as plt
from matplotlib import ticker
# Get favourite plotting fonts and sizes
LSDP.init_plotting_DV()
# Truncate the colour map
#trunc_cmap = LSDP.colours.truncate_colormap("Blues", 0.4, 1.0)
# Option for getting a discrete colour map
#discreet_cmap = mplext.colours.discrete_colourmap(8, "Blues")
#discreet_cmap = mplext.colours.cmap_discretize(8, trunc_cmap)
# Non-linear colourmap
levels = [-2.5, -1.5 - 0.5, -0.25, 0, 0.25, 0.5, 1.5, 2.5]
#levels = levels[levels <= tmax]
print(levels)
levels.sort()
print(levels)
cmap_lin = cm.jet
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. If not defined, current directory.")
parser.add_argument("-fname",
"--fname_prefix",
type=str,
help="The prefix of your DEM WITHOUT EXTENSION!")
parser.add_argument(
"-fmt",
"--FigFormat",
type=str,
default='png',
help="Set the figure format for the plots. Default is png")
args = parser.parse_args()
# get the base directory
if args.base_directory:
DataDirectory = args.base_directory
# check if you remembered a / at the end of your path_name
if not DataDirectory.endswith("/"):
print(
"You forgot the '/' at the end of the directory, appending...")
DataDirectory = this_dir + "/"
else:
this_dir = os.getcwd()
if not args.fname_prefix:
print(
"WARNING! You haven't supplied your DEM name. Please specify this with the flag '-fname'"
)
sys.exit()
else:
fname_prefix = args.fname_prefix
# set to not parallel
parallel = False
faults = True
FigFormat = args.FigFormat
# check if a directory exists for the chi plots. If not then make it.
raster_directory = DataDirectory + 'raster_plots/'
if not os.path.isdir(raster_directory):
os.makedirs(raster_directory)
# Set up fonts for plots
label_size = 8
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
# set figure sizes based on format
size_format = ""
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = Helper.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [int(x) for x in basin_junctions]
# get a discrete colormap
cmap = plt.cm.viridis
# going to make the basin plots - need to have bil extensions.
print(
"I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!"
)
# get the rasters
raster_ext = '.bil'
BackgroundRasterName = fname_prefix + raster_ext
HillshadeName = fname_prefix + '_hs' + raster_ext
BasinsName = fname_prefix + '_AllBasins' + raster_ext
# create the map figure
MF = MapFigure(HillshadeName,
DataDirectory,
coord_type="UTM_km",
colourbar_location='none')
# add the basins drape
BasinsDict = dict(zip(basin_keys, basin_keys))
# MF.add_basin_plot(BasinsName,fname_prefix,DataDirectory, label_basins=False,
# use_keys_not_junctions = True, show_colourbar = False,
# value_dict = BasinsDict, discrete_cmap=True, n_colours=len(basin_keys),
# colorbarlabel = "Basin ID", cbar_type=int, tickspacefactor=2,
# colourmap = cmap, edgecolour='none', adjust_text = True, parallel=parallel)
# add the channel network
if not parallel:
ChannelDF = Helper.ReadChiDataMapCSV(DataDirectory, fname_prefix)
else:
ChannelDF = Helper.AppendChiDataMapCSVs(DataDirectory)
# remove chi no data values
ChannelDF = ChannelDF[ChannelDF.chi != -9999]
ChannelPoints = LSDP.LSDMap_PointData(ChannelDF,
data_type="pandas",
PANDEX=True)
# add chi map
MF.add_point_data(ChannelPoints,
column_for_plotting="chi",
column_for_scaling="chi",
colorbarlabel="$\chi$ (m)",
show_colourbar=True,
this_colourmap=cmap,
colourbar_location="top")
# add the faults
if faults:
LineFileName = DataDirectory + fname_prefix + "_faults.shp"
MF.add_line_data(LineFileName,
linestyle="-",
linewidth=1.5,
zorder=99,
legend=True,
label="Fault Segments")
# add the basin outlines ### need to parallelise
if not parallel:
Basins = LSDP.GetBasinOutlines(DataDirectory, BasinsName)
else:
Basins = LSDP.GetMultipleBasinOutlines(DataDirectory)
# Find the relay basins and plot separately
RelayBasinIDs = [1248, 4788, 4995, 5185, 6187, 6758, 6805]
RelayBasins = {
key: value
for key, value in Basins.items() if key in RelayBasinIDs
}
# Plot all basins
MF.plot_polygon_outlines(Basins,
colour='k',
linewidth=0.5,
alpha=1,
legend=True,
label="Catchments")
MF.plot_polygon_outlines(RelayBasins,
colour='r',
linewidth=0.5,
alpha=1,
legend=True,
label="Relay Catchments")
# Add the legend
MF.add_legend()
# Save the figure
ImageName = raster_directory + fname_prefix + '_chi_map.' + FigFormat
MF.save_fig(fig_width_inches=fig_width_inches,
FigFileName=ImageName,
FigFormat=FigFormat,
Fig_dpi=300)
def PrintBasins(DataDirectory,
fname_prefix,
add_basin_labels=True,
cmap="jet",
cbar_loc="right",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix=""):
"""
This function makes a shaded relief plot of the DEM with the basins coloured
by the basin ID.
IMPORTANT: To get this to run you need to set the flags in chi mapping tool to:
write_hillshade: true
print_basin_raster: true
print_chi_data_maps: true
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
cbar_lox (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
Shaded relief plot with the basins coloured by basin ID. Uses a colourbar to show each basin
Author: FJC, SMM
"""
#import modules
from LSDMapFigure.PlottingRaster import MapFigure
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
print('Basin keys are: ')
print(basin_keys)
# going to make the basin plots - need to have bil extensions.
print(
"I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!"
)
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix + '_hs' + raster_ext
BasinsName = fname_prefix + '_AllBasins' + raster_ext
print(BasinsName)
Basins = LSDP.GetBasinOutlines(DataDirectory, BasinsName)
# If wanted, add the labels
if add_basin_labels:
print("I am going to add basin labels, there will be no colourbar.")
MF = MapFigure(HillshadeName,
DataDirectory,
coord_type="UTM_km",
colourbar_location="None")
MF.plot_polygon_outlines(Basins, linewidth=0.8)
MF.add_drape_image(BasinsName,
DataDirectory,
colourmap=cmap,
alpha=0.8,
colorbarlabel='Basin ID',
discrete_cmap=True,
n_colours=len(basin_keys),
show_colourbar=False,
modify_raster_values=True,
old_values=basin_junctions,
new_values=basin_keys,
cbar_type=int)
# This is used to label the basins
label_dict = dict(zip(basin_junctions, basin_keys))
# this dict has the basin junction as the key and the basin_key as the value
Points = LSDP.GetPointWithinBasins(DataDirectory, BasinsName)
MF.add_text_annotation_from_shapely_points(Points,
text_colour='k',
label_dict=label_dict)
else:
print("I am showing the basins without text labels.")
MF = MapFigure(HillshadeName,
DataDirectory,
coord_type="UTM_km",
colourbar_location=cbar_loc)
MF.plot_polygon_outlines(Basins, linewidth=0.8)
MF.add_drape_image(BasinsName,
DataDirectory,
colourmap=cmap,
alpha=0.8,
colorbarlabel='Basin ID',
discrete_cmap=True,
n_colours=len(basin_keys),
show_colourbar=True,
modify_raster_values=True,
old_values=basin_junctions,
new_values=basin_keys,
cbar_type=int)
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + fname_prefix + "_basins." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_basins." + fig_format
MF.save_fig(fig_width_inches=fig_width_inches,
FigFileName=ImageName,
FigFormat=fig_format,
Fig_dpi=dpi) # Save the figure
def PrintChannelsAndBasins(DataDirectory,
fname_prefix,
add_basin_labels=True,
cmap="jet",
cbar_loc="right",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix=""):
"""
This function prints a channel map over a hillshade.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
cbar_lox (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
Shaded relief plot with the basins coloured by basin ID. Uses a colourbar to show each basin
Author: SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
print('Basin keys are: ')
print(basin_keys)
# going to make the basin plots - need to have bil extensions.
print(
"I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!"
)
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix + '_hs' + raster_ext
BasinsName = fname_prefix + '_AllBasins' + raster_ext
print(BasinsName)
Basins = LSDP.GetBasinOutlines(DataDirectory, BasinsName)
ChannelFileName = fname_prefix + "_chi_data_map.csv"
chi_csv_fname = DataDirectory + ChannelFileName
thisPointData = LSDMap_PD.LSDMap_PointData(chi_csv_fname)
# clear the plot
plt.clf()
# set up the base image and the map
print("I am showing the basins without text labels.")
MF = MapFigure(HillshadeName,
DataDirectory,
coord_type="UTM_km",
colourbar_location="None")
MF.plot_polygon_outlines(Basins, linewidth=0.8)
MF.add_drape_image(BasinsName,
DataDirectory,
colourmap=cmap,
alpha=0.1,
discrete_cmap=False,
n_colours=len(basin_keys),
show_colourbar=False,
modify_raster_values=True,
old_values=basin_junctions,
new_values=basin_keys,
cbar_type=int)
MF.add_point_data(thisPointData,
column_for_plotting="basin_key",
scale_points=True,
column_for_scaling="drainage_area",
this_colourmap=cmap,
scaled_data_in_log=True,
max_point_size=3,
min_point_size=1)
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + fname_prefix + "_channels_with_basins." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_channels_with_basins." + fig_format
MF.save_fig(fig_width_inches=fig_size_inches,
FigFileName=ImageName,
axis_style=ax_style,
FigFormat=fig_format,
Fig_dpi=dpi)
"""
Created on Tue May 05 14:08:16 2015
@author: dav
"""
import glob as glob
import os.path
import numpy as np
import LSDPlottingTools as LSDP
import lsdmatplotlibextensions as mplext
import matplotlib.pyplot as plt
from matplotlib import ticker
# Get favourite plotting fonts and sizes
LSDP.init_plotting_DV()
# Truncate the colour map
trunc_cmap = mplext.colours.truncate_colormap("Blues", 0.4, 1.0)
# Option for getting a discrete colour map
#discreet_cmap = mplext.colours.discrete_colourmap(8, "Blues")
#discreet_cmap = mplext.colours.cmap_discretize(8, trunc_cmap)
#def RasterDifference(OriginalRaster, FinalRaster):
# """
# Subtracts FinalRaster from OriginalRaster
# to create a 'DEM of difference'
# """
# Raster1 = LSDP.ReadRasterArrayBlocks(OriginalRaster,raster_band=1)
# Raster2 = LSDP.ReadRasterArrayBlocks(FinalRaster,raster_band=1)
