def TestOSTools():
path1 = "C://basin_data//Chile//lat26p0//"
path2 = "M:/Yo/ma/yoyo.ma"
path3 = "/home/smudd/devel_projects/LSDTopoTools/branches/LSDModel"
path4 = "C:\\basin_data\\Chile\\lat26p0\\heyJude_DEM.flt"
newpath1 = LSDost.ReformatSeperators(path1)
print "Old path: " + path1
print "New path: " + newpath1
newpath2 = LSDost.ReformatSeperators(path2)
print "Old path: " + path2
print "New path: " + newpath2
newpath3 = LSDost.ReformatSeperators(path3)
print "Old path: " + path3
print "New path: " + newpath3
newpath4 = LSDost.ReformatSeperators(path4)
print "Old path: " + path4
print "New path: " + newpath4
# test the directory adder
# test the directory adder
print "\n\n"
newpath = LSDost.AppendSepToDirectoryPath(path1)
print "Sep appended path is: " + newpath
print "\n\n"
newpath = LSDost.AppendSepToDirectoryPath(path3)
print "Sep appended path is: " + newpath
# Test the file prefix grabber
fprefix = LSDost.GetFilePrefix(path4)
print "\n\n"
print "File prefix is: " + fprefix
# Test the remove path level
print "\n\n"
print "Removing a directory level from: " + newpath
newnewpath = LSDost.RemoveDirectoryLevel(newpath)
print "The new directory is: " + newnewpath
# Test the last directory name function
print "\n\n"
print "The last directory name in: " + newnewpath
name = LSDost.GetLastDirectoryLevel(newnewpath)
print "is: " + name
def get_production_data(Directory):
#Directory = "T://Papers_LaTeX//crn_basinwide_paper//Compiled_results//"
Dirname = LSDost.ReformatSeperators(Directory)
Dirname = LSDost.AppendSepToDirectoryPath(Dirname)
FileName= Dirname+"Muon_production_comparison.csv"
# get rid of the first line of the file
this_file = open(FileName, 'r')
lines = this_file.readlines()
values = []
# get rid of the header
del lines[0:3]
# now go through the lines, extracting the data
for line in lines:
# get rid of the control characters
this_line = LSDost.RemoveEscapeCharacters(line)
# Split the data
split_line = this_line.split(',')
float_vec = []
for element in split_line:
float_vec.append(float(element))
values.append(float_vec)
return values
def GetListOfRasters(path, prefix):
#first get directory path into the correct format
fmt_path = LSDost.ReformatSeperators(path)
# add the trailing seperator
fmt_path = LSDost.AppendSepToDirectoryPath(fmt_path)
# now find the correct file
fname = fmt_path + prefix + "_CRNRasters.csv"
DEM_names = []
#See if the parameter files exist
if os.access(fname, os.F_OK):
this_file = open(fname, 'r')
lines = this_file.readlines()
# now get the list of DEM prefixes
for line in lines:
this_line = line.split(",")
DEM_prefix = this_line[0]
DEM_names.append(DEM_prefix)
else:
print "*_CRNRasters.csv file not found. Are you sure it is there and you have the correct path?"
return DEM_names
def SwathPlot(path, filename, axis):
# 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 = LSDMap_BM.SimpleSwath(
path, filename, axis)
print "Means shape is: "
print means.shape
x_vec, y_vec = LSDMap_IO.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
min_sd = np.subtract(means, std_deviations)
plus_sd = np.add(means, std_deviations)
ax.plot(dir_vec, means, linewidth=2, color="red")
#ax.fill_between(dir_vec, twentyfifth_percentile, seventyfifth_percentile, facecolor='green', alpha = 0.7, interpolate=True)
ax.fill_between(dir_vec,
min_sd,
plus_sd,
facecolor='blue',
alpha=0.5,
interpolate=True)
ax.set_xlim(dir_vec[0], dir_vec[-1])
plt.show()
def ConvertAllCSVToGeoJSON(path):
# make sure names are in correct format
NewPath = LSDOst.AppendSepToDirectoryPath(path)
print "The formatted path is: " + NewPath
for FileName in glob(NewPath + "*.csv"):
print "filename is: " + FileName
thisPointData = LSDMPD.LSDMap_PointData(FileName)
thisPointData.TranslateToReducedGeoJSON(FileName)
def SpawnFoldersFromDEMList(DEM_names):
# loop through the files checking if there are directories for the
# DEMs. If not, make them
for name in DEM_names:
path = LSDost.AppendSepToDirectoryPath(name)
if not os.access(path, os.F_OK):
print "Making path: "
os.mkdir(path)
print path
else:
print "Path: " + path + " already exists."
def RasterMeanValue(path, file1):
# make sure names are in correct format
NewPath = LSDOst.AppendSepToDirectoryPath(path)
raster_file1 = NewPath + file1
NPixels = LSDMap_IO.GetNPixelsInRaster(raster_file1)
Raster1 = LSDMap_IO.ReadRasterArrayBlocks(raster_file1, raster_band=1)
mean_value = np.sum(Raster1) / float(NPixels)
return mean_value
def GDALBatchConvert(DataDirectory, raster_format, target_format):
NewDataDirectory = LSDost.ReformatSeperators(DataDirectory)
DataDirectory = LSDost.AppendSepToDirectoryPath(NewDataDirectory)
# Check the target format
if target_format == "ENVI":
target_extension = ".bil"
elif target_format == "EHdr":
target_extension = ".bil"
elif target_format == "GTiff":
target_extension = ".tiff"
else:
print "You have not selcted a valid raster format!"
print "Options are ENVI, EHdr and GTiff"
target_extension = "NULL"
# now make a directory
if target_extension != "NULL":
target_directory = DataDirectory + target_format
if not os.access(target_directory, os.F_OK):
print "Making path: "
os.mkdir(target_directory)
print "I made a directory: " + target_directory
else:
print "Path: " + target_directory + " already exists."
# Now check the source format
if raster_format == "ENVI":
raster_extension = ".bil"
elif raster_format == "EHdr":
raster_extension = ".bil"
elif raster_format == "GTiff":
raster_extension = ".tif"
else:
print "You have not selcted a valid raster format!"
print "Options are ENVI, EHdr and GTiff"
raster_extension = "NULL"
# find all the dataset of the source format
print "The data directory is: " + DataDirectory
print "The raster extension is: " + raster_extension
if raster_extension != "NULL":
for FileName in glob(DataDirectory + "*" + raster_extension):
print "found file: " + FileName
subprocess.call(['gdalinfo', FileName])
def SimpleSwath(path, file1, axis):
# make sure names are in correct format
NewPath = LSDOst.AppendSepToDirectoryPath(path)
raster_file1 = NewPath + file1
# get some information about the raster
NDV, xsize, ysize, GeoT, Projection, DataType = LSDMap_IO.GetGeoInfo(
raster_file1)
print "NDV is: "
print NDV
if NDV == None:
NDV = -9999
print "No NDV defined"
Raster1 = LSDMap_IO.ReadRasterArrayBlocks(raster_file1, raster_band=1)
#nan_raster = Raster1[Raster1==NDV]=np.nan
#print nan_raster
#now mask the nodata
masked_Raster1 = np.ma.masked_values(Raster1, NDV)
means = np.mean(masked_Raster1, axis)
medians = np.median(masked_Raster1, axis)
std_deviations = np.std(masked_Raster1, axis)
twentyfifth_percentile = np.percentile(masked_Raster1, 25, axis)
seventyfifth_percentile = np.percentile(masked_Raster1, 75, axis)
# This stuff only works with numpy 1.8 or later, wich we don't have
#means = np.nanmean(nan_raster, axis)
#medians = np.nanmedian(nan_raster, axis)
#std_deviations = np.nanstd(nan_raster, axis)
#twentyfifth_percentile = np.nanpercentile(nan_raster, 25, axis)
#seventyfifth_percentile = np.nanpercentile(nan_raster, 75, axis)
#print means
#print medians
#print std_deviations
#print twentyfifth_percentile
#print seventyfifth_percentile
return means, medians, std_deviations, twentyfifth_percentile, seventyfifth_percentile
def GetCRNData(path, prefix):
#first get directory path into the correct format
fmt_path = LSDost.ReformatSeperators(path)
# add the trailing seperator
fmt_path = LSDost.AppendSepToDirectoryPath(fmt_path)
# now find the correct file
fname = fmt_path + prefix + "_CRNData.csv"
Sample_names = []
SnowShield_values = []
#See if the parameter files exist
if os.access(fname, os.F_OK):
this_file = open(fname, 'r')
lines = this_file.readlines()
# get rid of the first line, since this has header information
lines.pop(0)
# now get the list of DEM prefixes
for line in lines:
this_line = line.split(",")
SampleName = this_line[0]
print "This line is: "
print this_line
# check to see if there is a snow shield value
N_entries = len(this_line)
if (N_entries == 8):
SnowShield = float(this_line[7])
Sample_names.append(SampleName)
SnowShield_values.append(SnowShield)
else:
print "there is no snow shielding on this line"
SnowShield_values.append(1)
Sample_names.append(SampleName)
else:
print "*_CRNRData.csv file not found. Are you sure it is there and you have the correct path?"
return Sample_names, SnowShield_values
def CopyDataAndParam(path, prefix, new_extension):
#first get directory path into the correct format
fmt_path = LSDost.ReformatSeperators(path)
# add the trailing seperator
fmt_path = LSDost.AppendSepToDirectoryPath(fmt_path)
# now find the correct file
Datafname = fmt_path + prefix + "_CRNData.csv"
Paramfname = fmt_path + prefix + ".CRNParam"
Datafname_out = fmt_path + prefix + "_" + new_extension + "_CRNData.csv"
Paramfname_out = fmt_path + prefix + "_" + new_extension + ".CRNParam"
# copy the files
shutil.copyfile(Datafname, Datafname_out)
shutil.copyfile(Paramfname, Paramfname_out)
def BasicMassBalance(path, file1, file2):
# make sure names are in correct format
NewPath = LSDOst.AppendSepToDirectoryPath(path)
raster_file1 = NewPath + file1
raster_file2 = NewPath + file2
PixelArea = LSDMap_IO.GetPixelArea(raster_file1)
print "PixelArea is: " + str(PixelArea)
print "The formatted path is: " + NewPath
Raster1 = LSDMap_IO.ReadRasterArrayBlocks(raster_file1, raster_band=1)
Raster2 = LSDMap_IO.ReadRasterArrayBlocks(raster_file2, raster_band=1)
NewRaster = np.subtract(Raster2, Raster1)
mass_balance = np.sum(NewRaster) * PixelArea
print "linear dif " + str(np.sum(NewRaster))
return mass_balance
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()
def ManageShieldingComputation(path, prefix, NJobs):
# Do some housekeeping with the path names
LSDost.ReformatSeperators(path)
LSDost.AppendSepToDirectoryPath(path)
# Now open the csv files
LSDRasters_fname = path + prefix + "_CRNRasters.csv"
LSDData_fname = path + prefix + "_CRNData.csv"
LSDParams_fname = path + prefix + ".CRNParam"
# And a command prompt fname this can be cut and paste into the command prompt
LSDCommandPromt_fname = path + prefix + "_ShieldCommandPrompt.txt"
# now go through the rasters listed, getting the n_nodes in each raster
if os.access(LSDRasters_fname, os.F_OK):
# this is the total lnumber of pixels to be analysed
Ntotal_pixels = 0
# open the file and get the data
Raster_file = open(LSDRasters_fname, 'r')
lines = Raster_file.readlines()
npixels_in_DEMs = []
n_cum_pixels = []
# loop through the file, collecting basin data
for line in lines:
split_line = line.split(",")
basin_fname = split_line[0]
# Add to the total number of pixels in the file
# IMPORTANT: it is assumed all files are in ENVI bil format
npixels_in_DEMs.append(
LSDmt.GetNPixelsInRaster(basin_fname + ".bil"))
Ntotal_pixels = Ntotal_pixels + npixels_in_DEMs[-1]
n_cum_pixels.append(Ntotal_pixels)
print "The total number of pixels are: " + str(Ntotal_pixels)
# now go back through the loop, setting the seperation
TargetPixels = Ntotal_pixels / NJobs
#Next_Target = TargetPixels
curr_line = 0
pixels_so_far = 0
breaks = [] # these are the indices into the breaks
for line in lines:
pixels_so_far = pixels_so_far + npixels_in_DEMs[curr_line]
# if the number of pixels exceeds the target pixels,
# this raster is not included
if pixels_so_far > TargetPixels:
breaks.append(curr_line + 1)
pixels_so_far = 0
curr_line = curr_line + 1
# now print out the details
print "The target pixels are: " + str(TargetPixels)
print "the pixels are: "
print npixels_in_DEMs
print "\nThe breaks are: "
if len(breaks) > NJobs:
breaks.pop()
if len(breaks) < NJobs:
breaks.append(len(lines))
print breaks
# now spawn the files
Param_file = open(LSDParams_fname, 'r')
Plines = Param_file.readlines()
Param_file.close()
Data_file = open(LSDData_fname, 'r')
Dlines = Data_file.readlines()
Data_file.close()
# make the command prompt file
CP_file = open(LSDCommandPromt_fname, 'w')
CP_file.write(
"Commands for running shielding calculations from command line.\n")
CP_file.write(
"To be used with the University of Edinburgh's basinwide CRN programs.\n"
)
CP_file.write(
"These are designed for use on a cluster without a job management system.\n"
)
CP_file.write(
"If you want to use qsub you will need to write your own script!\n"
)
CP_file.write("Copy and paste these into the command line.\n")
bb = 1
last_brk = 0
for brk in breaks:
new_param_name = path + prefix + "_brk" + str(bb) + ".CRNParam"
new_data_name = path + prefix + "_brk" + str(bb) + "_CRNData.csv"
new_raster_name = path + prefix + "_brk" + str(
bb) + "_CRNRasters.csv"
New_Param_file = open(new_param_name, 'w')
New_Param_file.writelines(Plines)
New_Param_file.close()
New_Data_file = open(new_data_name, 'w')
New_Data_file.writelines(Dlines)
New_Data_file.close()
print "Last break: " + str(last_brk) + " and this break: " + str(
brk)
New_Raster_file = open(new_raster_name, 'w')
thispx = npixels_in_DEMs[last_brk:brk]
New_Raster_file.writelines(lines[last_brk:brk])
New_Raster_file.close()
last_brk = brk
print "these pixels are: "
print thispx
print "and sum is: " + str(sum(thispx))
CP_file.write("nohup nice ./Shielding_for_CRN.exe " + path + " " +
prefix + "_brk" + str(bb) + "\n")
bb = bb + 1
CP_file.close()
def UpdateRasterWithShielding(path, prefix, Sample_names, Snowshield_values):
#first get directory path into the correct format
fmt_path = LSDost.ReformatSeperators(path)
# add the trailing seperator
fmt_path = LSDost.AppendSepToDirectoryPath(fmt_path)
# now find the correct file
fname = fmt_path + prefix + "_CRNRasters.csv"
# also make the outfile
outfname = fmt_path + prefix + "_SS_CRNRasters.csv"
outfile = open(outfname, 'w')
new_lines = []
print "The sample names are"
print Sample_names
print "The snow shield values are: "
print Snowshield_values
#See if the parameter files exist
if os.access(fname, os.F_OK):
this_file = open(fname, 'r')
lines = this_file.readlines()
# now get the list of DEM prefixes
for line in lines:
this_line = line.split(",")
DEM_prefix = this_line[0]
print "The DEM prefix is: " + DEM_prefix
# Now get the sample name
split_dem_prefix = DEM_prefix.split("_")
sample_name = split_dem_prefix[-1]
print "The sample name is: " + sample_name
# get the index of the sample name to reference the shielding value
i = Sample_names.index(sample_name)
print "the index of the sample names is: " + str(i)
# calculate the effective depth. The 160 is the attenuation thickness in g/cm^2
this_snow_depth = -160 * np.log(Snowshield_values[i])
print "The shielding is: " + str(
Snowshield_values[i]) + " and eff_depth is: " + str(
this_snow_depth)
# update the snow effective depth
this_line[1] = str(this_snow_depth)
# update the line
this_new_line = ",".join(this_line)
new_lines.append(this_new_line)
# this will get printed to file
for line in new_lines:
# you have to get rid of the control characters
this_line = LSDost.RemoveEscapeCharacters(line)
outfile.write("%s\n" % this_line)
def plot_production_rates():
#Directory = "C://basin_data//CosmoPaper//Results//"
Directory = "T://Papers_LaTeX//crn_basinwide_paper//Compiled_results//"
Dirname = LSDost.ReformatSeperators(Directory)
Dirname = LSDost.AppendSepToDirectoryPath(Dirname)
# get the data
prod_data = get_production_data(Directory)
pdata = np.asarray(prod_data)
depth = pdata[:,0]
mu_CRONUS = pdata[:,1]
mu_Braucher = pdata[:,4]
mu_newCRONUS = pdata[:,5]
total_CRONUS = pdata[:,6]
total_Braucher = pdata[:,9]
total_newCRONUS = pdata[:,10]
# set up figure formatting
Fileformat = 'svg'
label_size = 10
axis_size = 12
# Set up fonts for plots
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
rcParams['xtick.major.size'] = 4
rcParams['ytick.major.size'] = 4
rcParams['legend.fontsize'] = label_size
rcParams['legend.handletextpad'] = 0.05
rcParams['legend.labelspacing'] =0.1
rcParams['legend.columnspacing'] =0.1
# 3.26 inches = 83 mm, the size of a 1 column figure
#Fig1 = plt.figure(1, facecolor='white',figsize=(3.26,3.26))
Fig1 = plt.figure(1, facecolor='white',figsize=(3.26,6))
# gendepth a grid.
gs = GridSpec(100,100,bottom=0.1,left=0.1,right=1.0,top=1.0)
ax = Fig1.add_subplot(gs[2:42,10:95])
ax.plot(mu_CRONUS,depth, "r-", label = "CRONUS",linewidth = 2)
ax.plot(mu_Braucher,depth, "k-", label = "Braucher",linewidth = 2)
ax.plot(mu_newCRONUS,depth, "b--", label = "new CRONUS",linewidth = 2)
ax.spines['top'].set_linewidth(1)
ax.spines['left'].set_linewidth(1)
ax.spines['right'].set_linewidth(1)
ax.spines['bottom'].set_linewidth(1)
#ax.tick_params(axis='both', width=2.5)
ax.set_yscale('log')
ax.set_ylim([0,10**5])
ax.invert_yaxis()
# This gets all the ticks, and pads them away from the axis so that the corners don't overlap
# the which command tells the program to get major and minor ticks
ax.tick_params(axis='both', width=1, pad = 1, which = 'both')
for tick in ax.xaxis.get_major_ticks():
tick.set_pad(3)
for tick in ax.yaxis.get_major_ticks():
tick.set_pad(3)
plt.xlabel('Muon production (atoms g$^{-1}$ yr$^{-1}$)', fontsize = axis_size)
plt.ylabel('Depth (g cm$^{-2}$)', fontsize = axis_size)
#plt.title('Cosmocalc / New_code',fontsize = label_size+6)
handles, labels = ax.get_legend_handles_labels()
#plt.legend(handles, labels, numpoints = 3, bbox_to_anchor=(0., 1.02, 1., .102),
# loc=4, ncol=1, mode="expand", borderaxespad=0.0)
#plt.legend(handles, labels, numpoints = 1, loc=1, ncol=3, mode="expand", borderaxespad=0.0)
plt.legend(handles, labels, numpoints = 3, loc=4, ncol=1, borderaxespad=0.5)
#plt.show()
# 3.26 inches = 83 mm, the size of a 1 column figure
#Fig2 = plt.figure(1, facecolor='white',figsize=(3.26,3.26))
# generate a grid.
#gs2 = GridSpec(100,75,bottom=0.13,left=0.13,right=0.90,top=0.95)
ax2 = Fig1.add_subplot(gs[57:97,10:95])
ax2.plot(total_CRONUS,depth, "r-", label = "CRONUS",linewidth = 2)
ax2.plot(total_Braucher,depth, "k-", label = "Braucher",linewidth = 2)
ax2.plot(total_newCRONUS,depth, "b--", label = "new CRONUS",linewidth = 2)
ax2.spines['top'].set_linewidth(1)
ax2.spines['left'].set_linewidth(1)
ax2.spines['right'].set_linewidth(1)
ax2.spines['bottom'].set_linewidth(1)
#ax.tick_params(axis='both', width=2.5)
ax2.set_yscale('log')
ax2.set_ylim([0,10**3])
ax2.invert_yaxis()
# This gets all the ticks, and pads them away from the axis so that the corners don't overlap
# the which command tells the program to get major and minor ticks
ax2.tick_params(axis='both', width=1, pad = 1, which = 'both')
for tick in ax2.xaxis.get_major_ticks():
tick.set_pad(3)
for tick in ax2.yaxis.get_major_ticks():
tick.set_pad(3)
plt.xlabel('Total production (atoms g$^{-1}$ yr$^{-1}$)', fontsize = axis_size)
plt.ylabel('Depth (g cm$^{-2}$)', fontsize = axis_size)
#plt.title('Cosmocalc / New_code',fontsize = label_size+6)
handles, labels = ax2.get_legend_handles_labels()
#plt.legend(handles, labels, numpoints = 3, bbox_to_anchor=(0., 1.02, 1., .102),
# loc=4, ncol=1, mode="expand", borderaxespad=0.2, fontsize = 8)
plt.legend(handles, labels, numpoints = 3, loc=4, ncol=1, borderaxespad=0.5)
plt.savefig(Dirname+"MuonProductionComparison.svg",format = Fileformat)
plt.show()
def prepare_CRNRasters_file(path, prefix):
print "\n\nHello! I am here to help you format your LSDRasters file."
print "I will look in your directories for DEMs and shielding rasters, and write them into your LSDRasters file."
print "It is my pleasure to perform this service for you!"
# first, make sure path is working
path = LSDOst.ReformatSeperators(path)
path = LSDOst.AppendSepToDirectoryPath(path)
# initiate some empty lists
raster_names = []
toposhield_names = []
basin_names = []
self_shield_names = []
snow_shield_names = []
csv_strings = []
# now check to see if the path exists
if not os.access(path, os.F_OK):
print "The path you have chosen: " + path + " does not exist. Try with a new path."
else:
# now find all the rasters. These all have *.bil in them
for FileName in glob(path + "*.bil"):
# now remove the extension from the file
print "The filename is: " + FileName
print "Removing the bil extension"
Prefix = FileName[:-4]
print "new_filename is" + Prefix
# now we see if the files have shielding rasters
if Prefix[-3:] == "_SH":
print "This is a shielding raster"
toposhield_names.append(Prefix)
elif Prefix[-7:] == "_snowBL":
print "This is a snow raster"
snow_shield_names.append(Prefix)
elif Prefix[-7:] == "_SnowBL":
print "This is a snow raster"
snow_shield_names.append(Prefix)
elif Prefix[-9:] == "_snowclip":
print "This is a snow raster"
snow_shield_names.append(Prefix)
elif Prefix[-9:] == "_selfclip":
print "This is a self raster"
self_shield_names.append(Prefix)
elif Prefix[-7:] == "_BASINS":
print "This is a basins raster"
basin_names.append(Prefix)
elif Prefix[-7:] == "_HS":
print "This is a hillshade raster"
else:
print "No matching pattern for shielding (topo, self or snow) so I am assuming this is a DEM\n"
raster_names.append(Prefix)
# now print these to a rasters csv file
for raster_name in raster_names:
this_ts_name = "NULL"
this_sns_name = "NULL"
this_slfs_name = "NULL"
# search for toposhield
# There is probably a more efficient way to do this but it gets the job done
# Loop through all the toposhield names and find the one that contains the
# raster name
for ts in toposhield_names:
if raster_name in ts:
this_ts_name = ts
# now the snow shield
for sns in snow_shield_names:
if raster_name in sns:
this_sns_name = sns
# now the snow shield
for slfs in self_shield_names:
if raster_name in slfs:
this_slfs_name = slfs
if (this_ts_name == "NULL" and this_sns_name == "NULL"
and this_slfs_name == "NULL"):
print "I have a DEM: " + raster_name + " only (no snow, self or toposhield rasters)."
this_csv_line = raster_name
else:
this_csv_line = raster_name + ","
if this_sns_name == "NULL":
this_csv_line = this_csv_line + "0,"
else:
this_csv_line = this_csv_line + this_sns_name + ","
if this_slfs_name == "NULL":
this_csv_line = this_csv_line + "0"
else:
this_csv_line = this_csv_line + this_slfs_name
if not this_ts_name == "NULL":
this_csv_line = this_csv_line + "," + this_ts_name
# low append to the list
csv_strings.append(this_csv_line)
# now print the file
fname_csv = path + prefix + "_CRNRasters.csv"
f = open(fname_csv, 'w')
for item in csv_strings:
print >> f, item
f.close()
def CRONUS_diff_params():
#Directory = "C://basin_data//CosmoPaper//Results//Compiled//"
Directory = "T://Papers_LaTeX//crn_basinwide_paper//Compiled_results//"
Dirname = LSDost.ReformatSeperators(Directory)
Dirname = LSDost.AppendSepToDirectoryPath(Dirname)
Fileformat = 'svg'
label_size = 8
axis_size = 12
# Set up fonts for plots
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
rcParams['xtick.major.size'] = 4
rcParams['ytick.major.size'] = 4
rcParams['legend.fontsize'] = label_size
rcParams['legend.handletextpad'] = 0.05
rcParams['legend.labelspacing'] = 0.1
rcParams['legend.columnspacing'] = 0.1
fname = Dirname + "Scherler_CRNResults.csv"
# get only the file without the data directory
NoDirFname = LSDost.GetFileNameNoPath(fname)
# Now get the prefix of the file
splitfname = NoDirFname.split('_CRNResults.csv')
fprefix = splitfname[0]
# now produce the cronus name from this prefix
CRONUS_name1 = Dirname + fprefix + "_CRONUSEmulator.csv"
CRONUS_name2 = Dirname + fprefix + "_CRONUSEmulator_Modified.csv"
CRONUS_name3 = Dirname + fprefix + "_CRONUSEmulator_Modified2.csv"
print "File prefix is: " + fprefix
print "Cronus_name is: " + CRONUS_name1
# now read in the data. We need 3 objects for 3 different results
thisCRNData1 = CRNR.CRNResults(fname)
thisCRNData2 = CRNR.CRNResults(fname)
thisCRNData3 = CRNR.CRNResults(fname)
# read in the Cronus data and get the errors
thisCRNData1.ReadCRONUSData(CRONUS_name1)
thisCRNData1.GetErrorsBetweenMethods()
thisCRNData1.GetErrorsBetweenCRONUS()
thisCRNData2.ReadCRONUSData(CRONUS_name2)
thisCRNData2.GetErrorsBetweenMethods()
thisCRNData2.GetErrorsBetweenCRONUS()
thisCRNData3.ReadCRONUSData(CRONUS_name3)
thisCRNData3.GetErrorsBetweenMethods()
thisCRNData3.GetErrorsBetweenCRONUS()
#===========================================================================
# now make plots based on these data
# 3.26 inches = 83 mm, the size of a 1 column figure
Fig1 = plt.figure(1, facecolor='white', figsize=(3.26, 3.26))
# generate a 120,90 grid.
# gendepth a grid.
gs = GridSpec(100, 100, bottom=0.06, left=0.1, right=1.0, top=1.0)
ax = Fig1.add_subplot(gs[10:90, 10:95])
ax.plot(thisCRNData1.GetAverageCombinedScaling(),
thisCRNData1.GetError_CR(),
"o",
markersize=4,
color="black",
label="CRONUS2.2 default",
markeredgewidth=1)
ax.plot(thisCRNData2.GetAverageCombinedScaling(),
thisCRNData2.GetError_CR(),
"o",
markersize=4,
color="grey",
label="Updated spallation",
markeredgewidth=1)
ax.plot(thisCRNData3.GetAverageCombinedScaling(),
thisCRNData3.GetError_CR(),
"o",
markersize=4,
color="white",
label="Updated spallation, muons",
markeredgewidth=1)
ax.spines['top'].set_linewidth(1)
ax.spines['left'].set_linewidth(1)
ax.spines['right'].set_linewidth(1)
ax.spines['bottom'].set_linewidth(1)
ax.tick_params(axis='both', width=1)
plt.xlabel('Production factor ($S_{tot}$)', fontsize=axis_size)
plt.ylabel('($\epsilon_{CR2.2}$-$\epsilon_{CAIRN}$)/$\epsilon_{CAIRN}$',
fontsize=axis_size)
#plt.title('Cosmocalc / New_code',fontsize = label_size+6)
handles, labels = ax.get_legend_handles_labels()
plt.legend()
plt.legend(handles, labels, numpoints=1, loc=1, ncol=1, borderaxespad=1.)
#plt.show()
plt.savefig(Dirname + "CRONUS_update_spallation_and_muons.svg",
format=Fileformat)
def GetRequiredFilesFromFolder(DataDirectory):
#print "Current directory is: " + os.getcwd()
#Append a closing slash to the data directory if one is not already there
NewDataDirectory = LSDost.ReformatSeperators(DataDirectory)
DataDirectory = LSDost.AppendSepToDirectoryPath(NewDataDirectory)
#print "DataDirectory is (2): " + DataDirectory
# Start with a master list of required files
required_files = []
# find all the makefiles in the directory
for FileName in glob(DataDirectory+"*.make"):
#print "FileName is: " + FileName
# Now you need to find all the sources in the makefile
f = open(FileName,'r') # open file
lines = f.readlines() # read in the data
f.close()
# Initiate an empty list that will contain the filenames
cppfiles = []
# add the makefile to the files to be copied
required_files.append(FileName)
# loop through the lines, flag a start where SOURCES line starts
start_flag = 0
for line in lines:
if "SOURCE" in line:
start_flag = 1
# If there is OBJECTS in the line, stop looking for ".cpp"
if "OBJECTS" in line:
start_flag = 0
# Look for .cpp between SOURCES and OBJECTS
if start_flag == 1:
if ".cpp" in line:
# seperate the line using spaces
split_line = line.split(' ')
this_item = ""
# Go through the split line looking for .cpp
for item in split_line:
if ".cpp" in item:
this_item = item
# get rid of the SOURCES
new_this_item = this_item.replace("SOURCES=","")
#print "This cpp file is: " + new_this_item
# get rid of stupid escape characters
this_file = LSDost.RemoveEscapeCharacters(new_this_item)
cppfiles.append(this_file)
# now print to screen the files required for this makefile
#print "The files required for this makefile are: "
#print cppfiles
# now append to directory...this requires some logic because of the ../ seperators
for filename in cppfiles:
#print "Filename is: " + filename
# special logic for the ../ seperator
if "../" in filename:
#print "There is a lower level in this filename, this means it is an object"
thisfile = filename.replace("../","")
thisdirectory = LSDost.RemoveDirectoryLevel(DataDirectory)
fullfile = thisdirectory+thisfile
fullfile2 = fullfile.replace(".cpp",".hpp")
required_files.append(fullfile2)
else:
fullfile = DataDirectory+filename
# append to the required files list
required_files.append(fullfile)
# now thin out the required files to remove duplicates
nd = set(required_files)
required_files_noduplicates = list(nd)
#print "/n/n================================="
#print "Required files are: "
#print required_files
#print "--------"
#print "And removing duplicates:"
#print required_files_noduplicates
#print "====================================="
return required_files_noduplicates
def CheckFileStructuresForCopy(ObjectsDirectory,DriverDirectory,TargetDirectory):
# Format the target directories
Td = LSDost.ReformatSeperators(TargetDirectory)
TargetDirectory = LSDost.AppendSepToDirectoryPath(Td)
TDd = TargetDirectory + DriverDirectory
TargetDriverDirectory = LSDost.AppendSepToDirectoryPath(TDd)
# Format the source directories
Od = LSDost.ReformatSeperators(ObjectsDirectory)
ObjectsDirectory = LSDost.AppendSepToDirectoryPath(Od)
Dd = ObjectsDirectory+DriverDirectory
DriverDirectory = LSDost.AppendSepToDirectoryPath(Dd)
# Check if the source directories exist
if not os.access(ObjectsDirectory,os.F_OK):
print "The object directory for the code doesn't exist!"
print "You wanted this directory: " + ObjectsDirectory
return 0
if not os.access(ObjectsDirectory,os.F_OK):
print "The driver directory for the code doesn't exist!"
print "You wanted this directory: " + DriverDirectory
return 0
if not os.access(ObjectsDirectory+"TNT"+os.sep,os.F_OK):
print "The TNT directory for the code doesn't exist!"
print "You wanted this directory: " + ObjectsDirectory+"TNT"+os.sep
return 0
# check if the target object directory exists
if not os.access(TargetDirectory,os.F_OK):
print "The target directory for the code doesn't exist!"
print "You wanted this directory: " + TargetDirectory
print "I am making that now, along with the driver directory"
os.mkdir(TargetDirectory)
if not os.access(TargetDirectory,os.F_OK):
print "WTF the directory was not made??!"
os.mkdir(TargetDriverDirectory)
# check just the driver directory
if not os.access(TargetDriverDirectory,os.F_OK):
print "The target driver directory for the code doesn't exist!"
print "You wanted this directory: " + TargetDriverDirectory
print "I am making that now"
os.mkdir(TargetDriverDirectory)
# Check if the TNT directory exists. If it does, remove and replace it
# If it doesn't , just copy it across
TNTTargetDirectory = TargetDirectory+'TNT'+os.sep
TNTSourceDirectory = ObjectsDirectory+'TNT'+os.sep
if not os.access(TNTTargetDirectory,os.F_OK):
print "The target TNT directory for the code doesn't exist!"
print "You wanted this directory: " + TargetDriverDirectory
print "I am making that now"
shutil.copytree(TNTSourceDirectory,TNTTargetDirectory)
else:
print "There is a TNT directory here already. Removing and replacing"
shutil.rmtree(TNTTargetDirectory)
shutil.copytree(TNTSourceDirectory,TNTTargetDirectory)
print "========================="
print "DriverDirectory: " + DriverDirectory
print "ObjectsDirectory: " + ObjectsDirectory
print "TargetDirectory: " + TargetDirectory
print "TargetDriverDirectory: " + TargetDriverDirectory
print "========================="
return ObjectsDirectory,DriverDirectory,TargetDirectory,TargetDriverDirectory
def GDALBatchMerge(DataDirectory, merge_subfolder_name, merge_filename,
raster_format, target_format):
NewDataDirectory = LSDost.ReformatSeperators(DataDirectory)
DataDirectory = LSDost.AppendSepToDirectoryPath(NewDataDirectory)
# get the name of the data directory into which the file should be merged
merge_DataDirectory = DataDirectory + merge_subfolder_name
mDataDriectory = LSDost.AppendSepToDirectoryPath(merge_DataDirectory)
# make the directory
if not os.access(mDataDriectory, os.F_OK):
print "Making path: "
os.mkdir(mDataDriectory)
print "I made a directory: " + mDataDriectory
else:
print "Path: " + mDataDriectory + " already exists."
# Check the source format
if raster_format == "ENVI":
raster_extension = ".bil"
elif raster_format == "EHdr":
raster_extension = ".bil"
elif raster_format == "GTiff":
raster_extension = ".tif"
else:
print "You have not selcted a valid raster format!"
print "Options are ENVI, EHdr and GTiff"
raster_extension = "NULL"
# Check the target format. Default is geotiff
if target_format == "ENVI":
target_extension = ".bil"
elif target_format == "EHdr":
target_extension = ".bil"
elif target_format == "GTiff":
target_extension = ".tif"
else:
print "You have not selcted a valid raster format!"
print "Defaulting to GTiff"
target_format == "GTiff"
target_extension = ".tif"
# set the name of the target file
target_FileName = mDataDriectory + merge_filename + target_extension
# find all the dataset of the source format
print "The data directory is: " + DataDirectory
print "The raster extension is: " + raster_extension
if raster_extension != "NULL":
# Set up the list for holding command prompt commands
command_prompt = []
command_prompt.append("gdal_merge.py")
command_prompt.append("-of")
command_prompt.append(target_format)
command_prompt.append("-o")
command_prompt.append(target_FileName)
for FileName in glob(DataDirectory + "*" + raster_extension):
print "found file: " + FileName
command_prompt.append(FileName)
print "The subprocess call is: "
print command_prompt
subprocess.call(command_prompt)
def CollatenewCRONUScomparisonCRNData():
#Directory = "C://basin_data//CosmoPaper//Results//Compiled//Brauch_vs_newCRONUS//"
Directory = "T://Papers_LaTeX//crn_basinwide_paper//Compiled_results//Brauch_vs_newCRONUS//"
Dirname = LSDost.ReformatSeperators(Directory)
Dirname = LSDost.AppendSepToDirectoryPath(Dirname)
Fileformat = 'svg'
# This list will store the crn data
CRNDataList = []
CRNprefixes = []
PaperNames = []
PaperColours = []
ScalingNames = []
label_size = 8
axis_size = 12
# Set up fonts for plots
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
rcParams['xtick.major.size'] = 4
rcParams['ytick.major.size'] = 4
rcParams['legend.fontsize'] = label_size
rcParams['legend.handletextpad'] = 0.05
rcParams['legend.labelspacing'] = 0.1
rcParams['legend.columnspacing'] = 0.1
# loop through the directory, getting the results from the data
for fname in glob(Dirname + "*_CRNResults.csv"):
# get only the file without the data directory
NoDirFname = LSDost.GetFileNameNoPath(fname)
# Now get the prefix of the file
splitfname = NoDirFname.split('_CRNResults.csv')
fprefix = splitfname[0]
print "File prefix is: " + fprefix
# now read in the data
thisCRNData = CRNR.CRNResults(fname)
CRNDataList.append(thisCRNData)
CRNprefixes.append(fprefix)
# now get the prefixes
if "Dethier" in fprefix:
PaperNames.append("Dethier et al., 2014")
elif "Palumbo" in fprefix:
PaperNames.append("Palumbo et al., 2010")
# now get lists based on this data and place into a dictionary
if "newCRONUS" in fprefix:
#print "I found newCRONUS in the file prefix: " + fprefix
ScalingNames.append("newCRONUS")
else:
#print "Sorry, I didn't find the file prefix newCRONUS in " + fprefix
ScalingNames.append("Braucher")
# now get the errors
for index, CRNObj in enumerate(CRNDataList):
print "Looking for scaling and paper names"
print "paper name is: " + PaperNames[index]
print "scaling name is: " + ScalingNames[index]
if "Dethier" in PaperNames[index]:
if "newCRONUS" in ScalingNames[index]:
dethier_index_newCRONUS = index
else:
dethier_index_braucher = index
elif "Palumbo" in PaperNames[index]:
if "newCRONUS" in ScalingNames[index]:
palumbo_index_newCRONUS = index
#print "I got the palumbo newcronus index"
else:
palumbo_index_braucher = index
P_erate_brauch = CRNDataList[palumbo_index_braucher].GetErosionRates()
print "Braucher erate palumbo: "
print P_erate_brauch
P_erate_newCRONUS = CRNDataList[palumbo_index_newCRONUS].GetErosionRates()
print "newCRONUS erate palumbo: "
print P_erate_newCRONUS
P_erate_newCRONUS
P_B = np.asarray(P_erate_brauch)
P_nC = np.asarray(P_erate_newCRONUS)
P_err = np.divide(np.subtract(P_nC, P_B), P_B)
print "P_err: "
print P_err
D_erate_brauch = CRNDataList[dethier_index_braucher].GetErosionRates()
D_erate_newCRONUS = CRNDataList[dethier_index_newCRONUS].GetErosionRates()
D_B = np.asarray(D_erate_brauch)
D_nC = np.asarray(D_erate_newCRONUS)
D_err = np.divide(np.subtract(D_nC, D_B), D_B)
print "D_err: "
print D_err
#print "The palumbo error is: "
#print P_err
#print "The dethier error is: "
#print D_err
#===========================================================================
# now make plots based on these data
# 3.26 inches = 83 mm, the size of a 1 column figure
Fig1 = plt.figure(1, facecolor='white', figsize=(3.26, 3.26))
# generate a 120,90 grid.
gs = GridSpec(100, 75, bottom=0.13, left=0.13, right=0.98, top=0.85)
ax = Fig1.add_subplot(gs[10:100, 5:75])
ax.plot(P_B,
P_err,
"o",
markersize=4,
color="magenta",
label="Palumbo et al., 2010",
markeredgewidth=1)
ax.plot(D_B,
D_err,
"ro",
markersize=4,
label="Dethier et al., 2014",
markeredgewidth=1)
ax.spines['top'].set_linewidth(1)
ax.spines['left'].set_linewidth(1)
ax.spines['right'].set_linewidth(1)
ax.spines['bottom'].set_linewidth(1)
ax.tick_params(axis='both', width=1)
#ax.set_ylim([0.02,0.06])
plt.title(
'We are not using this in the paper!! Use CRNCAIRNvsnewCRONUS_erates.py instead!'
)
plt.xlabel('CAIRN denudation rate (g cm$^{-2}$ yr$^{-1}$)',
fontsize=axis_size)
plt.ylabel('($\epsilon_{CRCalc}$-$\epsilon_{CAIRN}$)/$\epsilon_{CAIRN}$',
fontsize=axis_size)
#plt.title('Cosmocalc / New_code',fontsize = label_size+6)
handles, labels = ax.get_legend_handles_labels()
plt.legend()
plt.legend(handles,
labels,
numpoints=1,
bbox_to_anchor=(0., 1.02, 1., .102),
loc=3,
ncol=2,
mode="expand",
borderaxespad=0.)
plt.savefig(Dirname + "CAIRNvsnewCRONUSapprox_erate.svg",
format=Fileformat)
Fig1.show()
def CollatenewCRONUScomparisonCRNData():
#Directory = "C://code//git_papers//crn_basinwide_paper//Compiled_results//Brauch_vs_newCRONUS//"
Directory = "T://Papers_LaTeX//crn_basinwide_paper//Compiled_results//Brauch_vs_newCRONUS//"
Dirname = LSDost.ReformatSeperators(Directory)
Dirname = LSDost.AppendSepToDirectoryPath(Dirname)
Fileformat = 'svg'
label_size = 8
axis_size = 12
# Set up fonts for plots
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
rcParams['xtick.major.size'] = 4
rcParams['ytick.major.size'] = 4
rcParams['legend.fontsize'] = label_size
rcParams['legend.handletextpad'] = 0.05
rcParams['legend.labelspacing'] = 0.1
rcParams['legend.columnspacing'] = 0.1
P_erate_newCRONUS = []
D_erate_newCRONUS = []
P_newCRONUS = []
D_newCRONUS = []
# loop through the directory, getting the results from the data
for fname in glob(Dirname + "*_CRNResults.csv"):
# get only the file without the data directory
NoDirFname = LSDost.GetFileNameNoPath(fname)
# Now get the prefix of the file
splitfname = NoDirFname.split('_CRNResults.csv')
fprefix = splitfname[0]
print "File prefix is: " + fprefix
# now read in the data
thisCRNData = CRNR.CRNResults(fname)
# Only get the newCRONUS dethier and Palumbo data
# now get the prefixes
if "Dethier" in fprefix:
if "newCRONUS" in fprefix:
D_erate_newCRONUS = thisCRNData.GetErosionRates_mmperkyr_rho2650(
)
elif "Palumbo" in fprefix:
if "newCRONUS" in fprefix:
P_erate_newCRONUS = thisCRNData.GetErosionRates_mmperkyr_rho2650(
)
# Convert the data to arrays (to calculate errors)
P_nC_CAIRN = np.asarray(P_erate_newCRONUS)
D_nC_CAIRN = np.asarray(D_erate_newCRONUS)
#print "P CAIRN is: "
#print P_nC_CAIRN
#print "D CAIRN is: "
#print D_nC_CAIRN
# Now get the CRONUScalc data
print "Entering second glob loop"
for fname in glob(Dirname + "*.csv"):
print "I found comparison data! Name is " + fname
# Now get the CRONUScalc data
print "Entering third glob loop"
for fname in glob(Dirname + "*Comparison.csv*"):
# get only the file without the data directory
NoDirFname = LSDost.GetFileNameNoPath(fname)
print "I found comparison data! Name is " + NoDirFname
# Now get the prefix of the file
splitfname = NoDirFname.split('_newCRONUSCAIRNComparison.csv')
fprefix = splitfname[0]
print "File prefix is: " + fprefix
print "I am woking with the dataset: " + fprefix
#See if the parameter files exist
if os.access(fname, os.F_OK):
this_file = open(fname, 'r')
lines = this_file.readlines()
# get rid fo the first line
lines.pop(0)
# create the lists for populating with data
CAIRN_erate = []
CAIRN_uncert = []
Report_erate = []
Report_uncert = []
newCRONUS_erate = []
newCRONUS_uncert = []
# now get the data into the dict
for line in lines:
this_line = LSDost.RemoveEscapeCharacters(line)
split_line = this_line.split(',')
#print split_line[22]+" " +split_line[23]
#print split_line
CAIRN_erate.append(float(split_line[16]))
CAIRN_uncert.append(float(split_line[17]))
Report_erate.append(float(split_line[20]))
Report_uncert.append(float(split_line[21]))
newCRONUS_erate.append(float(split_line[22]))
newCRONUS_uncert.append(float(split_line[23]))
# now get the prefixes
if fprefix == "Dethier":
D_newCRONUS = newCRONUS_erate
elif fprefix == "Palumbo":
P_newCRONUS = newCRONUS_erate
#print "P_newCRONUS is: "
#print P_newCRONUS
#print "D_newCRONUS is: "
#print D_newCRONUS
P_nC = np.asarray(P_newCRONUS)
D_nC = np.asarray(D_newCRONUS)
Perr = np.divide(np.subtract(P_nC_CAIRN, P_nC), P_nC)
Derr = np.divide(np.subtract(D_nC_CAIRN, D_nC), D_nC)
print "The errors are: "
print Perr
print Derr
print P_nC
print P_nC_CAIRN
# okay, now you should have the errors
#===========================================================================
# now make plots based on these data
# 3.26 inches = 83 mm, the size of a 1 column figure
Fig1 = plt.figure(1, facecolor='white', figsize=(3.26, 3.26))
# generate a 120,90 grid.
gs = GridSpec(100, 75, bottom=0.13, left=0.13, right=0.95, top=0.95)
ax = Fig1.add_subplot(gs[10:100, 5:95])
ax.plot(P_nC,
Perr,
"o",
markersize=5,
color="maroon",
label="Palumbo et al., 2010",
markeredgewidth=1)
ax.plot(D_nC,
Derr,
"ro",
markersize=5,
color="lawngreen",
label="Dethier et al., 2014",
markeredgewidth=1)
ax.spines['top'].set_linewidth(1)
ax.spines['left'].set_linewidth(1)
ax.spines['right'].set_linewidth(1)
ax.spines['bottom'].set_linewidth(1)
ax.tick_params(axis='both', width=1)
#ax.set_ylim([0.02,0.06])
#plt.title('We are not using this in the paper!! Use CRNCAIRNvsnewCRONUS_erates.py instead!')
plt.xlabel('$\epsilon_{CRCalc}$ (g cm$^{-2}$ yr$^{-1}$)',
fontsize=axis_size)
plt.ylabel(
'($\epsilon_{CAIRN-CRCalc}$-$\epsilon_{CRCalc}$)/$\epsilon_{CRCalc}$',
fontsize=axis_size)
#plt.title('Cosmocalc / New_code',fontsize = label_size+6)
handles, labels = ax.get_legend_handles_labels()
plt.legend()
plt.legend(handles, labels, numpoints=1, loc=4, ncol=1, borderaxespad=1.)
plt.savefig(Dirname + "CAIRN_newCRONUS_emulator.svg", format=Fileformat)
def CollateCRNData():
#Directory = "C://basin_data//CosmoPaper//Results//Compiled//"
Directory = "T://Papers_LaTeX//crn_basinwide_paper//Compiled_results//"
Dirname = LSDost.ReformatSeperators(Directory)
Dirname = LSDost.AppendSepToDirectoryPath(Dirname)
Fileformat = 'svg'
# This list will store the crn data
CRNDataList = []
CRNprefixes = []
PaperNames = []
label_size = 8
axis_size = 12
# Set up fonts for plots
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['arial']
rcParams['font.size'] = label_size
rcParams['xtick.major.size'] = 4
rcParams['ytick.major.size'] = 4
rcParams['legend.fontsize'] = label_size
rcParams['legend.handletextpad'] = 0.05
rcParams['legend.labelspacing'] =0.1
rcParams['legend.columnspacing'] =0.1
# loop through the directory, getting the results from the data
for fname in glob(Dirname+"*_CRNResults.csv"):
# get only the file without the data directory
NoDirFname = LSDost.GetFileNameNoPath(fname)
# Now get the prefix of the file
splitfname = NoDirFname.split('_CRNResults.csv')
fprefix = splitfname[0]
# now produce the cronus name from this prefix
CRONUS_name = Dirname+fprefix+"_CRONUS.csv"
print "File prefix is: " + fprefix
print "Cronus_name is: " + CRONUS_name
# now read in the data
thisCRNData = CRNR.CRNResults(fname)
# read in the Cronus data and get the errors
thisCRNData.ReadCRONUSData(CRONUS_name)
thisCRNData.GetErrorsBetweenMethods()
thisCRNData.GetErrorsBetweenCRONUS()
CRNDataList.append(thisCRNData)
CRNprefixes.append(fprefix)
# now get the prefixes
if fprefix == "Bierman":
PaperNames.append("Bierman et al., 2005")
elif fprefix == "Dethier":
PaperNames.append("Dethier et al., 2014")
elif fprefix == "Kirchner":
PaperNames.append("Kirchner et al., 2001")
elif fprefix == "Munack":
PaperNames.append("Munack et al., 2014")
elif fprefix == "Scherler":
PaperNames.append("Scherler et al., 2014")
elif fprefix == "Safran":
PaperNames.append("Safran et al., 2005")
elif fprefix == "Palumbo":
PaperNames.append("Palumbo et al., 2010")
#===========================================================================
# now make plots based on these data
# 3.26 inches = 83 mm, the size of a 1 column figure
Fig1 = plt.figure(1, facecolor='white',figsize=(3.26,3.26))
# generate a 120,90 grid.
gs = GridSpec(100,75,bottom=0.13,left=0.13,right=0.98,top=0.85)
ax = Fig1.add_subplot(gs[10:100,5:75])
# this gets the colors to map to specific sites
cmap = plt.cm.jet
colo = 0
for index,CRNObj in enumerate( CRNDataList):
colo = colo + (1.000/len(CRNprefixes))
ax.plot(CRNObj.GetAverageCombinedScaling(),CRNObj.GetError_CR(), "o",
markersize=4, color=cmap(colo), label = PaperNames[index],markeredgewidth=1)
ax.spines['top'].set_linewidth(1)
ax.spines['left'].set_linewidth(1)
ax.spines['right'].set_linewidth(1)
ax.spines['bottom'].set_linewidth(1)
ax.tick_params(axis='both', width=1)
plt.xlabel('Production factor ($S_{tot}$)', fontsize = axis_size)
plt.ylabel('($\epsilon_{CR2.2}$-$\epsilon_{CAIRN}$)/$\epsilon_{CAIRN}$', fontsize = axis_size)
#plt.title('Cosmocalc / New_code',fontsize = label_size+6)
handles, labels = ax.get_legend_handles_labels()
plt.legend()
plt.legend(handles, labels, numpoints = 1, bbox_to_anchor=(0., 1.02, 1., .102),
loc=3, ncol=2, mode="expand", borderaxespad=0.)
plt.savefig(Dirname+"Production_vs_error.svg",format = Fileformat)
#Fig1.show()
Fig1.clf()
# These don't work in my version of matplotlib...I must update (!)
# have found a workaround below
#plt.rcParams['xtick.major.linewidth'] = 4
#plt.rcParams['xtick.minor.width'] = 2
#===========================================================================
# now make plots based on these data
# 3.26 inches = 83 mm, the size of a 1 column figure
Fig2 = plt.figure(1, facecolor='white',figsize=(3.26,3.5))
# generate a 120,90 grid.
gs = GridSpec(100,75,bottom=0.14,left=0.13,right=0.98,top=0.85)
ax = Fig2.add_subplot(gs[10:100,5:75])
plt.rcParams['xtick.major.size'] = 4
plt.rcParams['xtick.minor.size'] = 3
plt.rcParams['ytick.major.size'] = 4
# this gets the colors to map to specific sites
cmap = plt.cm.jet
colo = 0
for index,CRNObj in enumerate( CRNDataList):
colo = colo + (1.000/len(CRNprefixes))
ax.plot(CRNObj.GetErosionRates(),CRNObj.GetError_CR(), "o", markersize=4,
color=cmap(colo), label = PaperNames[index],markeredgewidth=1)
ax.spines['top'].set_linewidth(1)
ax.spines['left'].set_linewidth(1)
ax.spines['right'].set_linewidth(1)
ax.spines['bottom'].set_linewidth(1)
#ax.tick_params(axis='both', width=2.5)
ax.set_xscale('log')
# This gets all the ticks, and pads them away from the axis so that the corners don't overlap
# the which command tells the program to get major and minor ticks
ax.tick_params(axis='both', width=1, pad = 1, which = 'both')
for tick in ax.xaxis.get_major_ticks():
tick.set_pad(3)
for tick in ax.yaxis.get_major_ticks():
tick.set_pad(3)
#for tick in ax.xaxis.get_minor_ticks():
# tick.tick_params(width = 2.5)
plt.xlabel('$\epsilon_{CAIRN}$ (g cm$^{-2}$ yr$^{-1}$)', fontsize = axis_size)
plt.ylabel('($\epsilon_{CR2.2}$-$\epsilon_{CAIRN}$)/$\epsilon_{CAIRN}$', fontsize = axis_size)
#plt.title('Cosmocalc / New_code',fontsize = label_size+6)
handles, labels = ax.get_legend_handles_labels()
plt.legend()
plt.legend(handles, labels, numpoints = 1, bbox_to_anchor=(0., 1.02, 1., .102),
loc=3, ncol=2, mode="expand", borderaxespad=0.)
#plt.show()
plt.savefig(Dirname+"Erosion_vs_error.svg",format = Fileformat)
Fig2.clf()
#===========================================================================
# now make plots based on these data
Fig3 = plt.figure(1, facecolor='white',figsize=(3.26,3.5))
# generate a 120,90 grid.
gs = GridSpec(100,75,bottom=0.14,left=0.14,right=0.97,top=0.85)
ax = Fig3.add_subplot(gs[10:100,5:75])
plt.rcParams['xtick.major.size'] = 4
plt.rcParams['ytick.major.size'] = 4
# this gets the colors to map to specific sites
cmap = plt.cm.jet
colo = 0
for index,CRNObj in enumerate( CRNDataList):
colo = colo + (1.000/len(CRNprefixes))
ax.plot(CRNObj.GetAverageCombinedScaling(),CRNObj.GetError_CC(), "o",
markersize=4, color=cmap(colo), label = PaperNames[index],markeredgewidth=1)
ax.spines['top'].set_linewidth(1)
ax.spines['left'].set_linewidth(1)
ax.spines['right'].set_linewidth(1)
ax.spines['bottom'].set_linewidth(1)
#ax.tick_params(axis='both', width=2.5)
# This gets all the ticks, and pads them away from the axis so that the corners don't overlap
# the which command tells the program to get major and minor ticks
ax.tick_params(axis='both', width=1, pad = 1, which = 'both')
for tick in ax.xaxis.get_major_ticks():
tick.set_pad(3)
for tick in ax.yaxis.get_major_ticks():
tick.set_pad(3)
#for tick in ax.xaxis.get_minor_ticks():
# tick.tick_params(width = 2.5)
plt.xlabel('Production factor ($S_{CCtot}$)', fontsize = axis_size)
plt.ylabel('($\epsilon_{CC}$-$\epsilon_{CAIRN}$)/$\epsilon_{CAIRN}$', fontsize = axis_size)
#plt.title('Cosmocalc / New_code',fontsize = label_size+6)
handles, labels = ax.get_legend_handles_labels()
plt.legend(handles, labels, numpoints = 1, bbox_to_anchor=(0., 1.02, 1., .102),
loc=3, ncol=2, mode="expand", borderaxespad=0.)
#plt.show()
plt.savefig(Dirname+"COSMOCALC_vs_error.svg",format = Fileformat)
Fig3.clf()
#===========================================================================
# now make plots based on these data
Fig4 = plt.figure(1, facecolor='white',figsize=(3.26,3.5))
# generate a 120,90 grid.
gs = GridSpec(100,75,bottom=0.14,left=0.14,right=0.97,top=0.85)
ax = Fig4.add_subplot(gs[10:100,5:75])
plt.rcParams['xtick.major.size'] = 4
# this gets the colors to map to specific sites
cmap = plt.cm.jet
colo = 0
for index,CRNObj in enumerate( CRNDataList):
colo = colo + (1.000/len(CRNprefixes))
ax.plot(CRNObj.GetAverageCombinedScaling(),CRNObj.GetError_CR_em(), "o",
markersize=4, color=cmap(colo), label = PaperNames[index],markeredgewidth=1)
ax.spines['top'].set_linewidth(1)
ax.spines['left'].set_linewidth(1)
ax.spines['right'].set_linewidth(1)
ax.spines['bottom'].set_linewidth(1)
#ax.tick_params(axis='both', width=2.5)
# This gets all the ticks, and pads them away from the axis so that the corners don't overlap
# the which command tells the program to get major and minor ticks
ax.tick_params(axis='both', width=1, pad = 1, which = 'both')
for tick in ax.xaxis.get_major_ticks():
tick.set_pad(3)
for tick in ax.yaxis.get_major_ticks():
tick.set_pad(3)
#for tick in ax.xaxis.get_minor_ticks():
# tick.tick_params(width = 2.5)
plt.xlabel('Production factor ($S_{CRShield}$*$S_{effp}$)', fontsize = axis_size)
plt.ylabel('($\epsilon_{CC-CR}$-$\epsilon_{CAIRN}$)/$\epsilon_{CAIRN}$', fontsize = axis_size)
#plt.title('Cosmocalc / New_code',fontsize = label_size+6)
handles, labels = ax.get_legend_handles_labels()
plt.legend(handles, labels, numpoints = 1, bbox_to_anchor=(0., 1.02, 1., .102),
loc=3, ncol=2, mode="expand", borderaxespad=0.)
#plt.show()
plt.savefig(Dirname+"COSMOCALC_CRem_vs_error.svg",format = Fileformat)
Fig4.clf()
