def BedPlotAutomator(Dirname):
# This is used to tell the model we want a profile perpendicular to shore
axis = 1
for fname in glob(Dirname+"*_BedElev.asc"):
# first we need the filename without the path
NoDirFname = LSDOst.GetFileNameNoPath(fname)
print "fname is: "+ NoDirFname
# Now get the prefix of the file
splitfname = NoDirFname.split('_BedElev.asc')
fprefix = splitfname[0]
ElevationSwaths(Dirname, NoDirFname, axis, fprefix)
# now do the bed thickness
for fname in glob(Dirname+"*_BedThick.asc"):
# first we need the filename without the path
NoDirFname = LSDOst.GetFileNameNoPath(fname)
print "fname is: "+ NoDirFname
# Now get the prefix of the file
splitfname = NoDirFname.split('_BedThick.asc')
fprefix = splitfname[0]
ElevationSwaths(Dirname, NoDirFname, axis, fprefix)
def CopyRequiredFilesToGitRepository(ObjectsDirectory,DriverDirectory,TargetDirectory):
# Ensure the directories exist
ObjectsDirectory,DriverDirectory,TargetDirectory,TargetDriverDirectory = CheckFileStructuresForCopy(ObjectsDirectory,DriverDirectory,TargetDirectory)
# Now get the required files
print "\n\n\n================================="
required_files_noduplicates = GetRequiredFilesFromFolder(DriverDirectory)
print "The required files are: "
print required_files_noduplicates
print "================================="
# loop through these files, collecting the filenames and directory names
# first you need to know what directory level the driver files are in
print "\n\n\n======================================"
n_level_of_driver_directory = LSDost.GetPathLevel(DriverDirectory)
for FileName in required_files_noduplicates:
# you need to know what level the file is
ThisPath = LSDost.GetPath(FileName)
ThisLevel = LSDost.GetPathLevel(ThisPath)
#if it is the same level as the driver directory, it is in the driver directory!
if ThisLevel == n_level_of_driver_directory:
CopyDirectory = TargetDriverDirectory
CopyFileName = LSDost.GetFileNameNoPath(FileName)
CopyFileNameWithPath = CopyDirectory+CopyFileName
else:
CopyDirectory = TargetDirectory
CopyFileName = LSDost.GetFileNameNoPath(FileName)
CopyFileNameWithPath = CopyDirectory+CopyFileName
print "The filename is: " + FileName
print "The copy filename is: " + CopyFileNameWithPath
shutil.copy(FileName, CopyFileNameWithPath)
# now copy the files over
print "=============================================="
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 CRBERCvsReported():
Dirname = "T://Papers_LaTeX//crn_basinwide_paper//Compiled_results//"
#Dirname = "C://basin_data//CosmoPaper//Results//Compiled//"
#Fname = "Palumbo*_CompareResults.csv"
SiteNames = []
SiteDicts = []
PaperNames = []
PaperColours = []
# loop through the directory, getting the results from the data
for fname in glob(Dirname + "*_CompareResults.csv"):
# get only the file without the data directory
NoDirFname = LSDost.GetFileNameNoPath(fname)
# Now get the prefix of the file
splitfname = NoDirFname.split('_CompareResults.csv')
fprefix = splitfname[0]
print "File prefix is: " + fprefix
# add to the sitenames list and create a holding dictionary
SiteNames.append(fprefix)
thisdict = {}
# now get the prefixes
if fprefix == "Bierman":
PaperNames.append("Bierman et al., 2005")
PaperColours.append("blue")
elif fprefix == "Dethier":
PaperNames.append("Dethier et al., 2014")
PaperColours.append("lawngreen")
elif fprefix == "Kirchner":
PaperNames.append("Kirchner et al., 2001")
PaperColours.append("yellow")
elif fprefix == "Munack":
PaperNames.append("Munack et al., 2014")
PaperColours.append("orange")
elif fprefix == "Scherler":
PaperNames.append("Scherler et al., 2014")
PaperColours.append("black")
elif fprefix == "Safran":
PaperNames.append("Safran et al., 2005")
PaperColours.append("powderblue")
elif fprefix == "Palumbo":
PaperNames.append("Palumbo et al., 2010")
PaperColours.append("maroon")
#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
BERC_erate = []
BERC_uncert = []
Report_erate = []
Report_uncert = []
BERC_shield = []
Report_shield = []
# now get the data into the dict
for line in lines:
this_line = LSDost.RemoveEscapeCharacters(line)
split_line = this_line.split(',')
BERC_erate.append(float(split_line[4]))
BERC_uncert.append(float(split_line[5]))
Report_erate.append(float(split_line[6]))
Report_uncert.append(float(split_line[7]))
BERC_shield.append(float(split_line[2]))
Report_shield.append(float(split_line[3]))
thisdict["BERC_erate"] = BERC_erate
thisdict["BERC_uncert"] = BERC_uncert
thisdict["Report_erate"] = Report_erate
thisdict["Report_uncert"] = Report_uncert
thisdict["BERC_shield"] = BERC_shield
thisdict["Report_shield"] = Report_shield
SiteDicts.append(thisdict)
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'] = 0.02
rcParams['legend.labelspacing'] = 0.1
rcParams['legend.columnspacing'] = 0.05
# now make plots based on these data
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])
#cmap = plt.cm.jet
#colo = 0
for index, thisdict in enumerate(SiteDicts):
#colo = colo + (1.000/len(SiteDicts))
plt.plot(thisdict['BERC_shield'],
thisdict['Report_shield'],
"o",
markersize=4,
color=PaperColours[index],
label=PaperNames[index],
markeredgewidth=1)
#plt.plot(self.CRNData['AvgProdScaling'],self.CRNData['Error_CR'],color=cmap(self.CRNData['basin_relief']),"o", markersize=8 )
#plt.errorbar(datazz['erate_cosmocalc']*10, datazz['erate_cmperkyr']*10, xerr=datazz['error_cosmocalc'], yerr=datazz['error_newcode'], fmt='o',color = cmap(colo))
ax.spines['top'].set_linewidth(1)
ax.spines['left'].set_linewidth(1)
ax.spines['right'].set_linewidth(1)
ax.spines['bottom'].set_linewidth(1)
# 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=1, pad=2)
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('CAIRN topographic shielding', fontsize=axis_size)
plt.ylabel('Reported topographic shielding', fontsize=axis_size)
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()
Fileformat = "svg"
plt.savefig(Dirname + "CAIRN_vs_Reported_toposhield.svg",
format=Fileformat)
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 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()
def CRBERCvsReported_erates():
#Dirname = "C://basin_data//CosmoPaper//Results//Compiled//"
Dirname = "T://Papers_LaTeX//crn_basinwide_paper//Compiled_results//"
#Fname = "Palumbo*_CompareResults.csv"
SiteNames = []
SiteDicts = []
PaperNames = []
PaperColours = []
# loop through the directory, getting the results from the data
for fname in glob(Dirname+"*_ErateComparisonNew.csv"):
# get only the file without the data directory
NoDirFname = LSDost.GetFileNameNoPath(fname)
# Now get the prefix of the file
splitfname = NoDirFname.split('_ErateComparisonNew.csv')
fprefix = splitfname[0]
print "File prefix is: " + fprefix
# add to the sitenames list and create a holding dictionary
SiteNames.append(fprefix)
thisdict = {}
# now get the prefixes
if fprefix == "Bierman":
PaperNames.append("Bierman et al., 2005")
PaperColours.append("blue")
elif fprefix == "Dethier":
PaperNames.append("Dethier et al., 2014")
PaperColours.append("lawngreen")
elif fprefix == "Kirchner":
PaperNames.append("Kirchner et al., 2001")
PaperColours.append("yellow")
elif fprefix == "Munack":
PaperNames.append("Munack et al., 2014")
PaperColours.append("orange")
elif fprefix == "Scherler":
PaperNames.append("Scherler et al., 2014")
PaperColours.append("black")
elif fprefix == "Safran":
PaperNames.append("Safran et al., 2005")
PaperColours.append("powderblue")
elif fprefix == "Palumbo":
PaperNames.append("Palumbo et al., 2010")
PaperColours.append("maroon")
print "I am woking with the dataset: " + fprefix
min_erate = 5
max_erate = 5000
#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
BERC_erate = []
BERC_uncert = []
Report_erate = []
Report_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
BERC_erate.append(float(split_line[16]))
BERC_uncert.append(float(split_line[17]))
Report_erate.append(float(split_line[20]))
Report_uncert.append(float(split_line[21]))
# get the maximum and minimum erosion rates
if (float(split_line[16]) > max_erate):
max_erate = float(split_line[16])
if (float(split_line[20]) > max_erate):
max_erate = float(split_line[20])
if (float(split_line[16]) < min_erate):
min_erate = float(split_line[16])
if (float(split_line[20]) < min_erate):
min_erate = float(split_line[20])
thisdict["BERC_erate"] = BERC_erate
thisdict["BERC_uncert"] = BERC_uncert
thisdict["Report_erate"] = Report_erate
thisdict["Report_uncert"] = Report_uncert
SiteDicts.append(thisdict)
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
# make a line between the maximum and minimum erate
one_line = np.linspace(min_erate,max_erate,1000)
# now make plots based on these data
Fig1 = plt.figure(1, facecolor='white',figsize=(4.72,4.72))
# 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])
#cmap = plt.cm.jet
#colo = 0
# plot the 1:1 line
plt.plot(one_line,one_line,'k-',linewidth = 2)
for index,thisdict in enumerate(SiteDicts):
#colo = colo + (1.000/len(SiteDicts))
plt.errorbar(thisdict['BERC_erate'], thisdict['Report_erate'],
thisdict['BERC_uncert'], thisdict['Report_uncert'], fmt='.',color = PaperColours[index], linewidth = 1.5)
plt.plot(thisdict['BERC_erate'], thisdict['Report_erate'], "o", markersize=4,
color=PaperColours[index], label = PaperNames[index],markeredgewidth=1.)
ax.annotate("1:1 line",
xy=(3000, 3000), xycoords='data',
xytext=(200, 4000), textcoords='data',
arrowprops=dict(arrowstyle="fancy", #linestyle="dashed",
color="0.5",
shrinkB=5,
connectionstyle="arc3,rad=-0.3",
),
)
#plt.plot(self.CRNData['AvgProdScaling'],self.CRNData['Error_CR'],color=cmap(self.CRNData['basin_relief']),"o", markersize=8 )
#plt.errorbar(datazz['erate_cosmocalc']*10, datazz['erate_cmperkyr']*10, xerr=datazz['error_cosmocalc'], yerr=datazz['error_newcode'], fmt='o',color = cmap(colo))
ax.spines['top'].set_linewidth(1.5)
ax.spines['left'].set_linewidth(1.5)
ax.spines['right'].set_linewidth(1.5)
ax.spines['bottom'].set_linewidth(1.5)
# 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=1.5, pad = 2)
for tick in ax.xaxis.get_major_ticks():
tick.set_pad(3)
for tick in ax.yaxis.get_major_ticks():
tick.set_pad(3)
# logarithmic axes
ax.set_yscale('log')
ax.set_xscale('log')
plt.xlabel('CAIRN denudation rate (mm/kyr)', fontsize = axis_size)
plt.ylabel('Reported denudation rate (mm/kyr)', fontsize = axis_size)
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()
Fileformat = "svg"
plt.savefig(Dirname+"CAIRN_vs_Reported_erates.svg",format = Fileformat)
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()
