def find_final_difference(datafile1, datafile2, print_values=True):
'''A function to find final absolute difference between mass fraction in two datafiles.
Inputs: datafile1 = ts file to be compared
datafile2 = second ts file to be compared
print_values = default to True, if True will print differences, if not will return lists of differences and corresponding nuclide names
Output: largest = list of n largest differences
names = list of nuclide names corresponding to items in largest
'''
import numpy as np
import read_ts_file as rtf
import heapq
#Read ts file, rename variables.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile1)
zz1, aa1, xmf1, time1, temperature1, density1, timestep1, edot1, flx_end1, flx1 = zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx
#Set certain parameters based on the data.
num_species_total = np.shape(xmf1)[1]
num_timesteps = np.shape(xmf1)[0]
#Read the second ts file, rename variables.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile2)
element = rtf.build_element_symbol()
nuc_name = rtf.build_isotope_symbol(zz, aa)
zz2, aa2, xmf2, time2, temperature2, density2, timestep2, edot2, flx_end2, flx2 = zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx
#Make empty list for maximum differences.
max_diff = []
for counter in np.arange(
0, num_species_total): #Count through number of species.
diff_list = [] #Make empty list for differences.
diff = float(xmf1[-1][counter]) - float(
xmf2[-1]
[counter]) #Find differences at end (accessed by -1), add to list.
diff = abs(diff)
diff_list.append(diff)
max_diff.append(
max(diff_list)) #Add largest difference to max_diff list.
largest = heapq.nlargest(
n, max_diff
) #list of final absolute differences, from largest to smallest
names = [] #Make empty list for names to fill in to.
for item in largest: #Assign relevant name to each difference.
foo = max_diff.index(item)
name = nuc_name[foo]
names.append(name)
#Either print or return largest and names.
if print_values == True:
for counter in np.arange(0, n):
print("%s %s %s" %
(counter + 1, names[counter], largest[counter]))
else:
return largest, names
def find_point_difference(datafile1, datafile2, t):
'''A function to find differences between mass fraction in two datafiles at a specific timestep.
Inputs: datafile1 = ts file to be compared
datafile2 = second ts file to be compared
t = timestep desired
Output: largest = list of n largest differences
names = list of nuclide names corresponding to items in largest
'''
import numpy as np
import read_ts_file as rtf
import heapq
#Read datafile, rename variables.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile1)
zz1, aa1, xmf1, time1, temperature1, density1, timestep1, edot1, flx_end1, flx1 = zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx
#Set certain parameters based on the data.
num_species_total = np.shape(xmf1)[1]
num_timesteps = np.shape(xmf1)[0]
#Read second ts file, rename and use variables.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile2)
element = rtf.build_element_symbol()
nuc_name = rtf.build_isotope_symbol(zz, aa)
zz2, aa2, xmf2, time2, temperature2, density2, timestep2, edot2, flx_end2, flx2 = zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx
diff_list = [] #Make empty list for differences.
for counter in np.arange(
0, num_species_total): #Count through number of species.
diff = float(xmf1[t][counter]) - float(
xmf2[t][counter]
) #Find each difference at specified timestep, add to list.
diff = abs(diff)
diff_list.append(diff)
largest = heapq.nlargest(
n, diff_list) #Rearrange diff_list into largest to smallest order.
names = [] #Make empty list for names to fill in to.
for item in largest: #Assign relevant name to each difference.
foo = diff_list.index(item)
name = nuc_name[foo]
names.append(name)
return largest, names #Return lists of differences and names.
#Print results.
for counter in np.arange(0, n):
print("%s %s %s" %
(counter + 1, names[counter], largest[counter]))
def massfractiont9rhovtime2(datafile1,
datafile2,
end,
num_plots=2,
num_species=14,
min_mf=.00000001,
time_spacing=.2,
h_ratio=[3, 1],
zz_wanted='None',
zz_wanted2='None',
aa_wanted='None',
nuc_names_wanted='None'):
'''
Inputs: datafile = must be a .txt file, ev file, any unnamed columns must be named with a single letter
end = k value at end of desired time
num_plots = number of plots to be shown simultaneously, default to 2
num_species = default to 14
min_mf = cutoff point below which mass fraction does not appear on the graph, default to .00000001
time_spacing = desired interval between x-axis ticks, default to .2
h_ratio = height ratio (if plotting several plots), default to 3:1
Outputs: plot of mass fraction, temperature, and density vs time
'''
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
import random
import string
import matplotlib.ticker as plticker
import matplotlib.gridspec as gridspec
import read_ts_file as rtf
#Create plot space
plt.figure(1)
#Read ts file, use variables.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile1)
zz1, aa1, xmf1, time1, temperature1, density1, timestep1, edot1, flx_end1, flx1 = zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx
element = rtf.build_element_symbol()
nuc_name = rtf.build_isotope_symbol(zz, aa)
#Set up grid layout
total_height = h_ratio[0] + h_ratio[1]
gs = gridspec.GridSpec(total_height, 1)
#Set parameter based on data.
num_species_total = np.shape(xmf1)[1]
#Assign each species a random color.
# colors = []
#for counter in np.arange(0, num_species_total):
# item = np.random.rand()
# colors.append(item)
colors = []
for counter in np.arange(1, num_species_total + 1):
item1 = np.random.rand(counter)[0]
item2 = np.random.rand(counter)[0]
item3 = np.random.rand(counter)[0]
colors.append(item1)
colors.append(item2)
colors.append(item3)
colors = np.asarray(colors)
colors = colors.reshape((num_species_total, 3))
#Create first plot according to grid structure.
ax1 = plt.subplot(gs[:h_ratio[0], :])
#Plot mf, add a legend.
for counter in np.arange(0, num_species_total):
if zz_wanted != 'None':
if zz1[counter] in zz_wanted and aa_wanted == 'None': #Plot all isotopes of an element.
if zz1[counter] == 1 and aa1[
counter] == 1: #Explicitly plots protons and hydrogen.
plt.plot(time1,
xmf1[:, counter],
color='r',
label=nuc_name[counter])
elif zz1[counter] == 1 and aa1[counter] == 2:
plt.plot(time1,
xmf1[:, counter],
color='b',
label=nuc_name[counter])
else:
plt.plot(time1,
xmf1[:, counter],
color=colors[counter],
label=nuc_name[counter])
box = ax1.get_position()
ax1.set_position(
[box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize=10)
elif zz1[counter] in zz_wanted and aa1[
counter] in aa_wanted: #Plot by atomic number and mass number.
plt.plot(time1,
xmf1[:, counter],
color=colors[counter],
label=nuc_name[counter])
box = ax1.get_position()
ax1.set_position(
[box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize=10)
zz_wanted.remove(zz1[counter])
elif zz_wanted == 'None' and nuc_names_wanted == 'None': #Plot all species above specified threshold.
if np.amax(xmf1[:, counter]) >= min_mf:
plt.plot(time1, xmf1[:, counter], color=colors[counter])
elif nuc_names_wanted != 'None': #Listed nuclear names switches plotting mechanism.
break
if nuc_names_wanted != 'None': #Sort through list to find mass fraction of named species, and plot.
for counter in np.arange(0, len(nuc_names_wanted)):
species_number = nuc_name.index(nuc_names_wanted[counter])
species_number = int(species_number)
plt.plot(time1,
xmf1[:, species_number],
color=colors[species_number],
label=nuc_name[species_number])
box = ax1.get_position()
ax1.set_position([box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=10)
#Start of datafile2.
#Read second ts file, use variables.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile2)
zz2, aa2, xmf2, time2, temperature2, density2, timestep2, edot2, flx_end2, flx2 = zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx
element = rtf.build_element_symbol()
#Set parameter based on data size.
nuc_name = rtf.build_isotope_symbol(zz, aa)
#Repeat plotting process above, using dashed lines and second set of provided information.
num_species_total = np.shape(xmf2)[1]
for counter in np.arange(0, num_species_total):
if zz_wanted2 != 'None':
if zz2[counter] in zz_wanted2 and aa_wanted == 'None':
if zz[counter] == 1 and aa2[counter] == 1:
plt.plot(time2,
xmf2[:, counter],
color='r',
label=nuc_name[counter],
linestyle='--')
elif zz2[counter] == 1 and aa2[counter] == 2:
plt.plot(time2,
xmf2[:, counter],
color='b',
label=nuc_name[counter],
linestyle='--')
else:
plt.plot(time2,
xmf2[:, counter],
color=colors[counter],
label=nuc_name[counter],
linestyle='--')
elif zz2[counter] in zz_wanted2 and aa2[counter] in aa_wanted:
plt.plot(time2,
xmf2[:, counter],
color=colors[counter],
label=nuc_name[counter],
linestyle='--')
zz_wanted2.remove(zz2[counter])
aa_wanted.remove(aa2[counter])
elif zz_wanted2 == 'None' and nuc_names_wanted == 'None':
if np.amax(xmf2[:, counter]) >= min_mf:
plt.plot(time2,
xmf2[:, counter],
color=colors[counter],
linestyle='--')
elif nuc_names_wanted != 'None':
break
if nuc_names_wanted != 'None':
for counter in np.arange(0, len(nuc_names_wanted)):
species_number = nuc_name.index(nuc_names_wanted[counter])
plt.plot(time2,
xmf2[:, species_number],
color=colors[species_number],
label=nuc_name[species_number],
linestyle='--')
#Format and label axes.
plt.yscale('log')
plt.ylim(min_mf, 1.5)
plt.ylabel("Mass Fraction")
plt.xscale('linear')
plt.xlim(time[0], time[end])
plt.xlabel("Time (s)")
#Remove superfluous ticks, show grid line instead.
plt.tick_params(axis='both',
which='both',
bottom='on',
top='on',
labelbottom='on',
left='off',
right='off',
labelleft='on')
plt.grid(True)
plt.title("%s (solid) and %s (dashed)" % (datafile1, datafile2))
#Create subplot.
ax2 = plt.subplot(gs[h_ratio[0], :])
plt.subplots_adjust(wspace=0, hspace=0)
#Plot temperature vs time, format axis.
temp_line = plt.plot(time1, temperature1, color='r', label="Temperature")
temp_line2 = plt.plot(time2,
temperature2,
color='r',
linestyle='--',
label="Temperature")
plt.yscale('linear')
plt.ylim(0, 10)
plt.ylabel("Temperature (GK)")
#Format x axis.
plt.xscale('linear')
plt.xlim(time[0], time[end])
plt.xlabel("Time (s)")
#Remove superfluous ticks, show grid line instead.
plt.tick_params(axis='both',
which='both',
bottom='on',
top='on',
labelbottom='on',
left='off',
right='off',
labelleft='on')
plt.grid(True)
#Plot density vs time, format axis.
ax3 = ax2.twinx()
dens_line = ax3.plot(time1, density1, color='b', label="Density")
dens_line2 = ax3.plot(time2,
density2,
color='b',
linestyle='--',
label="Density")
plt.yscale('log')
plt.ylim(10E0, 10E10)
plt.ylabel("Density g/$\mathregular{cm^3}$")
loc = plticker.LogLocator(base=100.0)
ax3.yaxis.set_major_locator(loc)
#Format x axis (repeated so that it does not go to default settings)
plt.xscale('linear')
plt.xlim(time[0], time[end])
plt.xlabel("Time (s)")
#Add x ticks at specified intervals.
x_labels = []
tick = time[0]
while tick <= time[end]:
tick = float("{0:.1f}".format(tick))
x_labels.append(tick)
tick += time_spacing
loc = plticker.MultipleLocator(base=time_spacing)
ax1.xaxis.set_major_locator(loc)
plt.xticks(x_labels, x_labels)
#Create a legend with both temperature and density.
lines = temp_line + dens_line
labs = [l.get_label() for l in lines]
ax2.legend(lines, labs, fontsize=10, loc='upper right')
lines2 = temp_line2 + dens_line2
labs2 = [l.get_label() for l in lines2]
#ax3.legend(lines2, labs2, fontsize = 10, loc = 'upper right')
#Show graph
plt.show()
def draw_nz_mf(datafile,
k,
num_species,
colors,
flux,
x_limit=40.0,
y_limit=40.0,
z_max=30,
figurename='None'):
'''
1. Scatter plot color coded by mass fractions at a specified timestep.
2. Draw vectors for the reaction flux on the NZ plane from reaction target
to product colored by relative flux intensity.
Inputs: datafile = must be a ts file
k = desired time for thermodynamic and mass fraction data
num_species = number of plotted species desired
colors = turns colorbar on or off (mass fraction coloring)
flux = if True, shows flux of nuclear reactions
x_limit = maximum of xaxis (neutron number)
y_limit = maximum of yaxis (proton number)
z_max = atomic number of heaviest element to plot
figurename = name of figure to be saved; if "None", the plot is not saved
Outputs: scatterplot of number of neutrons vs number of protons, color coded for mass fraction
thermodynamic data at specific time, optionally shows flux of nuclear reactions, color coded
'''
import read_ts_file as rtf
import numpy as numpy
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.ticker as plticker
import matplotlib.colors
#-------------------------------------------------------------------------------
# MASS FRACTION SCATTERPLOT
#Create plot space.
fig = plt.figure(1)
#Read ts file and use variables.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile)
print('Data read')
#Plot all species if 'all' is chosen
num_species_total = numpy.shape(xmf)[1]
if num_species == 'all':
num_species = num_species_total
#Create lists of variable to be plotted
zz_plot = []
nn_plot = []
xmf_plot = []
#Fill in lists with data from file
for counter in numpy.arange(0, num_species):
#If mass fraction is 0, skip data point, as colors.LogNorm requires values > 0
if xmf[k, counter] > 0:
zz_plot.append(zz[counter])
nn_plot.append(aa[counter] - zz[counter])
xmf_plot.append(xmf[k, counter])
#Plot proton number vs neutron number and color code for mass fraction (log).
if colors == True:
s = plt.scatter(nn_plot,
zz_plot,
c=xmf_plot,
norm=matplotlib.colors.LogNorm(vmin=min(xmf_plot),
vmax=max(xmf_plot)),
cmap='YlOrBr',
marker='s')
else:
s = plt.scatter(nn_plot, zz_plot, s=5, c='w')
print('Mass fraction plotted')
#Make nn back to an array
nn = aa - zz
#Plot colorbar as legend.
if colors == True:
cb = plt.colorbar(s)
cb.set_label('Log of Mass Fraction')
#Print thermodynamic data.
x_loc = 5
y_loc = 37
words = ('Time = %s sec') % time[k]
plt.text(x_loc, y_loc, words)
words = ('Temperature = %s GK') % temperature[k]
plt.text(x_loc, y_loc - 2, words)
words = (r'$\rho =$ %s g/$\mathregular{cm^3}$') % density[k]
plt.text(x_loc, y_loc - 4, words)
words = ('Timestep = %s') % k
plt.text(x_loc, y_loc - 6, words)
print('Thermodynamic data added')
#Format x and y axes.
plt.ylim(-1, y_limit)
plt.ylabel('Z (Proton Number)')
plt.xlim(-2, x_limit)
plt.xlabel('N (Neutron Number)')
#Set axis ticks
special_elements = [2, 8, 15, 28]
loc = plticker.FixedLocator(special_elements)
plt.xticks(special_elements, special_elements)
plt.yticks(special_elements, special_elements)
print('Axes formatted')
#Label elements and mass numbers
z_min = min(zz)
n_max = max(nn)
n_min = min(nn)
leftn = numpy.empty([
0,
])
rightn = numpy.empty([
0,
])
alabel = numpy.empty([
0,
])
#Loop over all elements, including neutrons (z=0)
z_max = int(z_max) + 1 #33
for i in range(z_max): #[0,1,2,...,31,32]
zint = numpy.arange(z_max) #[0 1 2 ... 31 32]
zint[i] = i
iz = numpy.where(zz == i)[0]
#Find the lightest and heaviest isotope
if not iz.size == 0:
# returns True if the condition is considered False
# if proton no. array is not empty
nn = nn.astype(int)
leftn = numpy.append(leftn, min(nn[iz]))
leftn = leftn.astype(int)
rightn = numpy.append(rightn, max(nn[iz]))
rightn = rightn.astype(int)
alabel = numpy.append(alabel, rightn[i] + zint[i])
alabel = alabel.astype(int)
# max neutron no. + i in range(z_max)
#Load element names
element = rtf.build_element_symbol()
element = numpy.asarray(element, dtype=object)
for i in range(0, 113):
if i in zint:
#Label elements to the left
plt.text(leftn[i] - 1.25,
zint[i],
element[i],
fontsize=4,
horizontalalignment='left')
#Label mass numbers to the right
plt.text(rightn[i] + 1.25,
zint[i] - 1.25,
alabel[i],
fontsize=4,
horizontalalignment='right',
rotation=-45)
print('Species labels added')
#-------------------------------------------------------------------------------
# FLUX VECTORS
if flux == True:
#Identify starting and ending points for all fluxes
zt = zz[flx_end[:, 0] - 1]
zp = zz[flx_end[:, 1] - 1]
nt = nn[flx_end[:, 0] - 1]
np = nn[flx_end[:, 1] - 1]
#Choose cycle(timestep) to plot
flx_write = flx[k, :]
#Choose maximum flux for calculating bin ranges
flx_max = max(abs(flx_write))
#Calculate vector origin and lengths
aflx = abs(flx_write)
zo = zt # vector origin
no = nt # vector origin
zv = zp - zt # vector length (z-component)
nv = np - nt # vector length (n-component)
#Reverse arrows for negative flux
ineg = numpy.where(flx_write < 0.0)[0]
zo[ineg] = zp[ineg]
no[ineg] = np[ineg]
zv[ineg] = -zv[ineg]
nv[ineg] = -nv[ineg]
#set number of colors/levels
color_bins = numpy.array([1e-1, 1e-2, 1e-3])
#set number of arrays
colors = numpy.array(['k', 'r', 'g', 'b', 'c'])
ncolors = len(color_bins)
bin_upper = numpy.array([1])
bin_upper = numpy.append(bin_upper, color_bins[0:4])
bin_lower = color_bins
#Build Legend array
legend_array = numpy.array(range(ncolors), dtype=object)
#Reverse ncolors so that for loop runs through color_bins backwards
ncolors = range(ncolors)
#rev_ncolors = ncolors.reverse()
ncolors = numpy.asarray(ncolors)
# ultimately, heavier flux vectors will be plotted on top because
# they are calculated later than lighter fluxes
#Loop over colors
for i in ncolors:
#Plot Vectors
flx_top = flx_max * bin_upper[i] #scalar multiplication
flx_bot = flx_max * bin_lower[i]
ii = numpy.where((aflx <= flx_top) & (aflx > flx_bot))[0]
scale = 0.0 #prevents autoscaling
if i == 0:
lwidth = 0.002
elif i == 1:
lwidth = 0.0015
else:
lwidth = 0.001
#Assemble handles of legend
bin_lower[i] = str(bin_lower[i])
label = [' > ', '{:.1e}'.format(bin_lower[i]),
' of max'] #prints bin_lower[i] in scientific notation
label = ''.join(
map(str, label)
) #joins string elements of a sequence(i.e. label is a list)
legend_array[i] = label
#Draw vectors
h = fig.add_subplot(111)
h.quiver(no[ii],
zo[ii],
nv[ii],
zv[ii],
width=lwidth,
color=colors[i],
angles='xy',
scale_units='xy',
scale=1,
label=legend_array[i])
plt.grid()
plt.draw()
print('Flux plotted')
#Plot legend
plt.legend(loc=4, title='Reaction Fluxes')
# automatically pulls in label from quiver plot
# and label=legend_array[i]
#-------------------------------------------------------------------------------
# FINALIZE PLOT
#Add grid lines.
plt.grid(True)
#Add title.
plt.title(datafile)
#Show plot.
if figurename == 'None':
plt.show()
print('Plot shown')
else:
plt.savefig(figurename, bbox_inches="tight")
print('Plot saved')
plt.text(x_loc, y_loc - 1.5, words) #Writes temperature just below time.
words = (r'$\rho =$ %s g/$\mathregular{cm^3}$') % density[k] #Generates the density at the specified timestep
plt.text(x_loc, y_loc - 3.75, words) #Writes density just before temperature
#Format x and y axes.
plt.ylim(-1, 40) #Y axis will range from -1 to 40, and so will the x axis. They're labeled accordingly.
plt.ylabel('Z (Proton Number)')
plt.xlim(-2, 40)
plt.xlabel('N (Neutron Number)')
#Set axis ticks.
special_elements = [2, 8, 20, 28] #List of values where I want ticks
loc = plticker.FixedLocator(special_elements) #Says that I want fixed, specified locations for the ticks
plt.xticks(special_elements, special_elements) #Labels axes with the numerical values of the ticks at those positions (prints each item in the list at the value of that item)
plt.yticks(special_elements, special_elements)
#Add labels for each element.
element = rtf.build_element_symbol() #build_element_symbol returns a list of each element name, which you're using here
for item in range(0, 118): #counts through all elements, and if their proton numbers are in the list of proton numbers plotted, their name appears along the y axis.
if item in zz:
plt.text(-1.5, item - .25, element[item], fontsize = 7)
#Add grid lines,
plt.grid(True)
#Add title, just the name of the datafile input.
plt.title(datafile)
#Show plot, which makes the plot appear.
plt.show()
def massfractionedot(datafile1,
end,
num_plots=2,
num_species=14,
min_mf=.00000001,
time_spacing=10,
h_ratio=[3, 1],
zz_wanted='None',
zz_wanted2='None',
aa_wanted='None',
nuc_names_wanted='None'):
'''
Inputs: datafile1 = ts file
end = k value at end of desired time
num_plots = number of plots to be shown simultaneously, default to 2
num_species = default to 14
min_mf = cutoff point below which mass fraction does not appear on the graph, default to .00000001
time_spacing = desired interval between x-axis ticks, default to .2
h_ratio = height ratio (if plotting several plots), default to 3:1
Outputs: plot of mass fraction, energy vs time
'''
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
import random
import string
import matplotlib.ticker as plticker
import matplotlib.gridspec as gridspec
import read_ts_file as rtf
#Create plot space
plt.figure(1)
#Read ts file, use variables.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile1)
zz1, aa1, xmf1, time1, temperature1, density1, timestep1, edot1, flx_end1, flx1 = zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx
element = rtf.build_element_symbol()
nuc_name = rtf.build_isotope_symbol(zz, aa)
#Set up grid layout
total_height = h_ratio[0] + h_ratio[1]
gs = gridspec.GridSpec(total_height, 1)
#Set parameter based on data.
num_species_total = np.shape(xmf1)[1]
#Assign each species a random color.
colors_array = []
for counter in np.arange(1, num_species_total + 1):
item1 = np.random.rand(counter)[0]
item2 = np.random.rand(counter)[0]
item3 = np.random.rand(counter)[0]
colors_array.append(item1)
colors_array.append(item2)
colors_array.append(item3)
colors_array = np.asarray(colors_array)
colors_array = colors_array.reshape((num_species_total, 3))
#Create upper subplot.
ax1 = plt.subplot(gs[:h_ratio[0], :])
#Plot mf, add a legend.
for counter in np.arange(0, num_species_total):
if zz_wanted != 'None':
if zz1[counter] in zz_wanted and aa_wanted == 'None': #Plot all isotopes of an element.
if zz1[counter] == 1 and aa1[
counter] == 1: #Explicitly plots protons and hydrogen.
plt.plot(time1,
xmf1[:, counter],
color='r',
label=nuc_name[counter])
elif zz1[counter] == 1 and aa1[counter] == 2:
plt.plot(time1,
xmf1[:, counter],
color='b',
label=nuc_name[counter])
else:
plt.plot(time1,
xmf1[:, counter],
color=colors_array[counter],
label=nuc_name[counter])
box = ax1.get_position()
ax1.set_position(
[box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize=10)
elif zz1[counter] in zz_wanted and aa1[
counter] in aa_wanted: #Plot by atomic number and mass number.
plt.plot(time1,
xmf1[:, counter],
color=colors_array[counter],
label=nuc_name[counter])
box = ax1.get_position()
ax1.set_position(
[box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize=10)
zz_wanted.remove(zz1[counter])
elif zz_wanted == 'None' and nuc_names_wanted == 'None': #Plot all species above specified threshold.
if np.amax(xmf1[:, counter]) >= min_mf:
plt.plot(time1, xmf1[:, counter], color=colors_array[counter])
elif nuc_names_wanted != 'None': #Listed nuclear names switch plotting mechanism.
break
if nuc_names_wanted != 'None': #Sort through list to find mass fraction of named species, and plot.
for counter in np.arange(0, len(nuc_names_wanted)):
species_number = nuc_name.index(nuc_names_wanted[counter])
species_number = int(species_number)
plt.plot(time1,
xmf1[:, species_number],
color=colors_array[species_number],
label=nuc_name[species_number])
box = ax1.get_position()
ax1.set_position([box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=10)
#Format and label axes.
plt.yscale('log')
plt.ylim(min_mf, 1.5)
plt.ylabel("Mass Fraction")
plt.xlim(time[0], time[end])
plt.xlabel("Time (s)")
#Remove superfluous ticks, show grid line instead.
plt.tick_params(axis='both',
which='both',
bottom='on',
top='on',
labelbottom='on',
left='off',
right='off',
labelleft='on')
plt.grid(True)
plt.title("%s" % (datafile1))
#Create subplot.
ax2 = plt.subplot(gs[h_ratio[0], :])
plt.subplots_adjust(wspace=0, hspace=0)
#Plot edot vs time, format axis.
edot_line = plt.plot(time1, edot1, color='r', label="dE/dt")
plt.yscale('log')
plt.ylim(10E2, 10E15)
plt.ylabel("Energy Production (erg/g/s)")
#Format x axis.
plt.xlim(time[0], time[end])
plt.xlabel("Time (s)")
#Remove superfluous ticks, show grid line instead.
plt.tick_params(axis='both',
which='both',
bottom='on',
top='on',
labelbottom='on',
left='off',
right='off',
labelleft='on')
plt.grid(True)
#Add x ticks at specified intervals.
x_labels = []
tick = time[0]
while tick <= time[end]:
tick = float("{0:.1f}".format(tick))
x_labels.append(tick)
tick += time_spacing
loc = plticker.MultipleLocator(base=time_spacing)
ax1.xaxis.set_major_locator(loc)
plt.xticks(x_labels, x_labels)
#Create a legend with both temperature and density.
lines = edot_line
labs = [l.get_label() for l in lines]
ax2.legend(lines, labs, fontsize=10, loc='upper right')
#Show graph
plt.show()
def draw_nz_mf(datafile, datafile2, k, num_species, colors=False):
'''
Colorbar is currently not working.
Inputs: datafile = must be a ts file
datafile2 = second datafile
k = desired timestep for thermodynamic and mass fraction data
num_species = number of plotted species desired
colors = turn colorbar on or off, defaults to False
Outputs: scatterplot of number of neutrons vs number of protons, color coded for mass fraction
thermodynamic data at specific time
'''
import read_ts_file as rtf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.ticker as plticker
import matplotlib.colors
fig = plt.figure(1)
#Read ts file, use variables.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile)
#For each species, determine neutron number by subtracting proton number from mass number, and plot neutron number by proton number. Apply a colormap, and normalize the data on a log scale.
ax1 = plt.subplot2grid((1, 2), (0, 0))
for counter in np.arange(0, num_species):
nn = aa[counter] - zz[counter]
nn = int(round(nn))
if colors:
s = ax1.scatter(nn,
zz[counter],
s=60,
c=xmf[k, counter],
cmap='YlGnBu_r',
norm=matplotlib.colors.LogNorm(vmin=xmf[k].min(),
vmax=xmf[k].max()),
marker='s')
else:
s = ax1.scatter(nn, zz[counter], s=60, marker='s')
#Configure and label axes, placing ticks at helium, oxygen, calcium, and nickel.
plt.ylim(-1, 40)
plt.ylabel('Z (Proton Number)')
plt.xlim(-2, 40)
plt.xlabel('N (Neutron Number)')
special_elements = [2, 8, 20, 28]
loc = plticker.FixedLocator(special_elements)
plt.xticks(special_elements, special_elements)
plt.yticks(special_elements, special_elements)
#Add labels for each element.
element = rtf.build_element_symbol()
for item in range(0, 113):
if item in zz:
plt.text(-1.5, item - .25, element[item], fontsize=7)
#Set up grid structure for adding second plot.
plt.grid(True)
#Read second ts file, use variables.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile2)
#Repeat plotting process from above, with data from the second ts file.
ax2 = plt.subplot2grid((1, 2),
(0, 1)) #positions the second plot on the grid
for counter in np.arange(0, num_species):
nn = aa[counter] - zz[counter]
nn = int(round(nn))
if colors:
s = ax2.scatter(nn,
zz[counter],
s=60,
c=xmf[k, counter],
cmap='YlGnBu_r',
norm=matplotlib.colors.LogNorm(vmin=xmf[k].min(),
vmax=xmf[k].max()),
marker='s')
else:
s = ax2.scatter(nn, zz[counter], s=60, marker='s')
#Set space between the two plots.
fig.subplots_adjust(wspace=0.1)
#Print thermodynamic data.
words = ('Time = %s sec') % time[k]
x_loc = 5
y_loc = 37
plt.text(x_loc, y_loc, words)
words = ('Temperature = %s GK') % temperature[k]
plt.text(x_loc, y_loc - 1.5, words)
words = (r'$\rho =$ %s g/$\mathregular{cm^3}$') % density[k]
plt.text(x_loc, y_loc - 3.75, words)
#Configure and label axes, placing ticks at helium, oxygen, calcium, and nickel.
plt.ylim(-1, 40)
plt.xlim(-2, 40)
special_elements = [2, 8, 20, 28]
loc = plticker.FixedLocator(special_elements)
plt.xticks(special_elements, special_elements)
plt.yticks(special_elements, special_elements)
#Add labels for each element.
element = rtf.build_element_symbol()
for item in range(0, 113):
if item in zz:
plt.text(-1.5, item - .25, element[item], fontsize=7)
plt.grid(True)
#Show colorbar as legend.
if colors:
cb = plt.colorbar(s)
cb.set_label('Log of Mass Fraction')
plt.show()
def draw_nz_mf(datafile, k, num_species, colors=False):
'''
Colorbar is currently not working.
Inputs: datafile = must be a ts file, formatted as 'datafile'
k = desired timestep for thermodynamic and mass fraction data
num_species = number of plotted species desired
colors = turns colorbar on or off, defaults to False
Outputs: scatterplot of number of neutrons vs number of protons, color coded for mass fraction
thermodynamic data at specific time
'''
import read_ts_file as rtf #module to read ts file
import numpy as np #module to provide calculation support
import matplotlib.pyplot as plt #plotting module
import matplotlib.cm as cm #colormap module
import matplotlib.ticker as plticker #module to make and assign tick marks on axes
import matplotlib.colors #colors module
#Create plot space.
fig = plt.figure(1)
#Read ts file and use variables.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile
) #Each of these variables is returned by the function read_ts_file in the module rtf, so you just assign them to the same names here
#Plot proton number vs neutron number and color code for mass fraction (log).
for counter in np.arange(
0, num_species
): #generates a counter that goes from 0 to the number of species
nn = aa[counter] - zz[
counter] #Determine number of neutrons for each species. aa is atomic weight, and zz is proton number.
nn = int(
round(nn)
) #Round the number of neutrons, and cast it to an integer so that it can be used for plotting
#If colors has been set as true, plot a scatterplot of nn vs zz.
#the first two terms are the x and y of your plot
#nn = neutron number
#zz[counter] = proton number, counted through for every species
#s = a size determiner, not all that important
#marker = lets you choose the point shape, 's' means square. It should default to round.
#c = sets color. Here, I have it do a gradient based on mass fraction.
#cmap = lets you choose colormap (there's a list of them on the matplotlib website)
#norm = normalizes the data to fit between 0 and 1, here I use a log scale with largest and smallest mass fractions at timestep as parameters
if colors == True:
s = plt.scatter(nn,
zz[counter],
s=50,
c=xmf[k, counter],
cmap='YlOrBr',
norm=matplotlib.colors.LogNorm(vmin=xmf[k].min(),
vmax=xmf[k].max()),
marker='s')
else:
s = plt.scatter(nn, zz[counter], s=50, marker='s')
if colors == True:
cb = plt.colorbar(s) #Plot colorbar as legend.
cb.set_label('Log of Mass Fraction')
#Print thermodynamic data.
words = ('Time = %s sec'
) % time[k] #Generates the time at the specified timestep
x_loc = 5 #Arbitrary x and y coordinates
y_loc = 37
plt.text(x_loc, y_loc, words) #Writes the time at specified x and y.
words = ('Temperature = %s GK') % temperature[
k] #Generates the temperature at the specified timestep
plt.text(x_loc, y_loc - 1.5, words) #Writes temperature just below time.
words = (r'$\rho =$ %s g/$\mathregular{cm^3}$'
) % density[k] #Generates the density at the specified timestep
plt.text(x_loc, y_loc - 3.75,
words) #Writes density just before temperature
#Format x and y axes.
plt.ylim(
-1, 40
) #Y axis will range from -1 to 40, and so will the x axis. They're labeled accordingly.
plt.ylabel('Z (Proton Number)')
plt.xlim(-2, 40)
plt.xlabel('N (Neutron Number)')
#Set axis ticks.
special_elements = [2, 8, 20, 28] #List of values where I want ticks
loc = plticker.FixedLocator(
special_elements
) #Says that I want fixed, specified locations for the ticks
plt.xticks(
special_elements, special_elements
) #Labels axes with the numerical values of the ticks at those positions (prints each item in the list at the value of that item)
plt.yticks(special_elements, special_elements)
#Add labels for each element.
element = rtf.build_element_symbol(
) #build_element_symbol returns a list of each element name, which you're using here
for item in range(
0, 118
): #counts through all elements, and if their proton numbers are in the list of proton numbers plotted, their name appears along the y axis.
if item in zz:
plt.text(-1.5, item - .25, element[item], fontsize=7)
#Add grid lines,
plt.grid(True)
#Add title, just the name of the datafile input.
plt.title(datafile)
#Show plot, which makes the plot appear.
plt.show()
def massfractionvtime(datafile,
end,
num_plots=1,
min_mf=.00000001,
time_spacing=.2,
h_ratio=[3, 1],
zz_wanted='None',
aa_wanted='None',
nuc_names_wanted='None'):
'''
Inputs: datafile = either an ev or a ts file
end = k value at end of desired time
num_plots = number of plots to be shown simultaneously, default to 1
min_mf = cutoff point below which mass fraction does not appear on the graph, default to .00000001
time_spacing = desired interval between x-axis ticks, default to .2
h_ratio = height ratio (if plotting multiple plots), default to 3:1
zz_wanted = list of atomic numbers of desired isotopes (if multiple isotopes of the same element are desired, their zz values must be added multiple times)
aa_wanted = list of atomic masses of desired isotopes (used in conjunction with zz_wanted)
nuc_names_wanted = list of desired species names, formatted as '$^{aa}$Symbol' (best option when plotting specific isotopes)
Outputs: plot of mass fraction vs time
'''
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
import matplotlib.ticker as plticker
import matplotlib.gridspec as gridspec
import find_file_type as fft
import read_ev_file as ref
import read_ts_file as rtf
#Create plot space
plt.figure(1)
file_type = fft.find_file_type(datafile)
#Set up grid layout
total_height = h_ratio[0] + h_ratio[1]
gs = gridspec.GridSpec(total_height, 1)
if file_type == 'ev':
#Read ev file, use variables.
time, density, temperature, edot, timestep, species, data, datafile = ref.read_ev_file(
datafile)
#If there is only one plot, take up entire space, plot mf, and add a legend
if num_plots == 1:
ax1 = plt.subplot(1, 1, 1)
for isotope in species:
if data[isotope][end] <= min_mf:
plt.plot(time, data[isotope], label=isotope)
box = ax1.get_position()
ax1.set_position(
[box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize=10)
else:
plt.plot(time, data[isotope], label=isotope)
box = ax1.get_position()
ax1.set_position(
[box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize=10)
#Alternatively, follow given sizing guidelines, plot mf, and add a legend
else:
ax1 = plt.subplot(gs[:h_ratio[0], :])
for isotope in species:
if data[isotope][end] <= min_mf:
plt.plot(time, data[isotope], label=isotope)
box = ax1.get_position()
ax1.set_position(
[box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize=10)
else:
plt.plot(time, data[isotope], label=isotope)
box = ax1.get_position()
ax1.set_position(
[box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize=10)
elif file_type == 'ts':
#Read ts file, use variables.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile)
print("File read.")
element = rtf.build_element_symbol()
nuc_name = rtf.build_isotope_symbol(zz, aa)
num_species_total = np.shape(xmf)[1]
#Assign each species a random color.
colors_array = []
for counter in np.arange(1, num_species_total + 1):
item1 = np.random.rand(counter)[0]
item2 = np.random.rand(counter)[0]
item3 = np.random.rand(counter)[0]
colors_array.append(item1)
colors_array.append(item2)
colors_array.append(item3)
colors_array = np.asarray(colors_array)
colors_array = colors_array.reshape((num_species_total, 3))
print("Colors assigned.")
#If there is only one plot, take up entire space, plot mf
if num_plots == 1:
ax1 = plt.subplot(1, 1, 1)
num_species_total = np.shape(xmf)[1]
for counter in np.arange(0, num_species_total):
if zz_wanted != 'None':
if zz[counter] in zz_wanted and aa_wanted == 'None': #Plot all isotopes of an element.
plt.plot(time,
xmf[:, counter],
label=nuc_name[counter],
linestyle='--')
box = ax1.get_position()
ax1.set_position(
[box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize=10)
elif zz[counter] in zz_wanted and aa[
counter] in aa_wanted: #Plot by atomic number and mass number.
plt.plot(time,
xmf[:, counter],
label=nuc_name[counter])
box = ax1.get_position()
ax1.set_position(
[box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize=10)
zz_wanted.remove(zz[counter])
else:
if np.amax(
xmf[:, counter]
) >= min_mf: #Plot all species over specified threshold.
plt.plot(time, xmf[:, counter])
if nuc_names_wanted != 'None': #Sort through list to find mass fraction of named species, and plot.
for counter in np.arange(0, len(nuc_names_wanted)):
species_number = nuc_name.index(nuc_names_wanted[counter])
species_number = int(species_number)
plt.plot(time,
xmf[:, species_number],
color=colors[species_number],
label=nuc_name[species_number])
box = ax1.get_position()
ax1.set_position(
[box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize=10)
print("Data assigned to plot.")
#Alternatively, follow given sizing guidelines, plot mf
else:
ax1 = plt.subplot(gs[:h_ratio[0], :])
num_species_total = np.shape(xmf)[1]
for counter in np.arange(0, num_species_total):
if zz_wanted != 'None': #If atomic numbers are specified
if zz[counter] in zz_wanted and aa[
counter] in aa_wanted: #Plot by atomic number and mass number.
plt.plot(time,
xmf[:, counter],
label=nuc_name[counter])
box = ax1.get_position()
ax1.set_position(
[box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize=10)
zz_wanted.remove(zz[counter])
elif zz[counter] in zz_wanted and aa_wanted == 'None': #Plot all isotopes of an element.
plt.plot(time,
xmf[:, counter],
label=nuc_name[counter])
box = ax1.get_position()
ax1.set_position(
[box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize=10)
elif zz_wanted == 'None' and nuc_names_wanted == 'None': #Plot all species above a specified threshold.
if np.amax(xmf[:, counter]) >= min_mf:
plt.plot(time, xmf[:, counter], color=colors_array)
elif nuc_names_wanted != 'None': #Listed nuclear names switch plotting mechanism.
break
if nuc_names_wanted != 'None': #Sort through list to find mass fraction of named species, and plot.
for counter in np.arange(0, len(nuc_names_wanted)):
species_number = nuc_name.index(nuc_names_wanted[counter])
species_number = int(species_number)
plt.plot(time,
xmf[:, species_number],
color=colors_array[species_number],
label=nuc_name[species_number])
box = ax1.get_position()
ax1.set_position(
[box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize=10)
print("Data assigned to plot.")
#Format and label axes
plt.yscale('log')
plt.ylim(min_mf, 1.5)
plt.ylabel("Mass Fraction")
plt.xscale('linear')
plt.xlim(time[0], time[end])
plt.xlabel("Time (s)")
print("Axes formatted.")
#Add x ticks at specified intervals
x_labels = []
tick = time[0]
while tick <= time[end]:
tick = float("{0:.1f}".format(tick))
x_labels.append(tick)
tick += time_spacing
loc = plticker.MultipleLocator(base=time_spacing)
ax1.xaxis.set_major_locator(loc)
plt.xticks(x_labels, x_labels)
print("Axes labeled.")
#Remove superfluous ticks, show grid line instead
plt.tick_params(axis='both',
which='both',
bottom='on',
top='on',
labelbottom='on',
left='off',
right='off',
labelleft='on')
plt.grid(True)
print("Grid line.")
#Show graph
plt.show()
print("Plot shown.")
def massfractionvtime(datafile,
nuc_names_wanted='None',
ymin_max=[10e-10, 10e-3],
figurename='None',
time_spacing=10,
end='default'):
'''
Inputs: datafile = ts files, formatted as a list of strings
nuc_names_wanted2 = list of desired species names from file, formatted as '$^{aa}$Symbol'
ymin_max = y range of data to be plotted, formatted as a list
figure_name = name of figure that plot is saved as, if "None", then plot is not saved
time_spacing = desired interval between x-axis ticks, default to 10
end = k value at end of desired time, default is number of timesteps of file with smallest number of timesteps
Outputs: plot of mass fraction vs time
'''
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as plticker
import matplotlib.gridspec as gridspec
import read_ts_file as rtf
#Create Figure
plt.figure(1)
#set up grid layout
h_ratio = [3, 1]
total_height = h_ratio[0] + h_ratio[1]
gs = gridspec.GridSpec(total_height, 1)
ax1 = plt.subplot(1, 1, 1)
#Create list of file lengths to find xaxis maximum
file_length_list = []
#Loop through all files given in list of files
for file in datafile:
#Read ts file, use variables.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
file)
print(file + ": File read.")
#Build lis of elements
element = rtf.build_element_symbol()
nuc_name = rtf.build_isotope_symbol(zz, aa)
num_species_total = np.shape(xmf)[1]
file_length_list.append(len(time))
#Loop through list of species to be plotted
for counter in nuc_names_wanted:
if counter not in nuc_name:
counter += " "
species_number = nuc_name.index(counter)
species_number = int(species_number)
#Plot Data
if len(datafile) == 1:
plt.plot(time,
xmf[:, species_number],
label=nuc_name[species_number])
else:
plt.plot(time,
xmf[:, species_number],
label=file + ": " + nuc_name[species_number])
print(file + ": " + counter + ": Data assigned to plot.")
#Format axes
box = ax1.get_position()
ax1.set_position([box.x0, box.y0, box.width * 0.995, box.height])
#Create legend:
ax1.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=10)
#Format and label axes
plt.yscale('log')
plt.ylim(ymin_max)
plt.ylabel("Mass Fraction")
if end == 'default':
end = min(file_length_list) - 1
print("end: " + str(end))
plt.xscale('linear')
plt.xlim(time[0], time[end])
plt.xlabel("Time (s)")
print("Axes formatted.")
#Add x ticks at specified intervals
x_labels = []
tick = time[0]
while tick <= time[end]:
tick = float("{0:.1f}".format(tick))
x_labels.append(tick)
tick += time_spacing
loc = plticker.MultipleLocator(base=time_spacing)
ax1.xaxis.set_major_locator(loc)
plt.xticks(x_labels, x_labels)
print("Axes labeled.")
#Remove superfluous ticks, show grid line instead
plt.tick_params(axis='both',
which='both',
bottom='on',
top='on',
labelbottom='on',
left='off',
right='off',
labelleft='on')
plt.grid(True)
print("Grid line.")
#Show graph
if figurname == 'None':
plt.show()
print("Plot shown.")
else:
plt.savefig(figurename, bbox_inches="tight")
print("plot saved.")
def massfractionvtime2(datafile1,
datafile2,
end,
num_plots=1,
min_mf=.00000001,
time_spacing=.2,
h_ratio=[3, 1],
zz_wanted='None',
zz_wanted2='None',
aa_wanted='None',
nuc_names_wanted='None'):
''' Inputs: datafile = a ts file
datafile2 = a second ts file to be plotted simultaneously
end = k value at end of desired time
num_plots = number of plots to be shown simultaneously, default to 1
min_mf = cutoff point below which mass fraction does not appear on the graph, default to .00000001
time_spacing = desired interval between x-axis ticks, default to .2
h_ratio = height ratio (if plotting multiple plots), default to 3:1
zz_wanted = list of atomic numbers of desired isotopes (if multiple isotopes of the same element are desired, their zz values must be added multiple times)
zz_wanted2 = zz_wanted for the second datafile
aa_wanted = list of atomic masses of desired isotopes (used in conjunction with zz_wanted)
nuc_names_wanted = list of desired species names, formatted as '$^{aa}$Symbol' (best option when plotting specific isotopes)
Outputs: plot of mass fraction vs time
'''
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
import matplotlib.ticker as plticker
import matplotlib.gridspec as gridspec
import find_file_type as fft
import read_ts_file as rtf
import random
#Create plot space
plt.figure(1)
#Read ts file, use variables.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile1)
element = rtf.build_element_symbol()
nuc_name = rtf.build_isotope_symbol(zz, aa)
#Create plot.
ax1 = plt.subplot(1, 1, 1)
#Set parameter based on data.
num_species_total = np.shape(xmf)[1]
#Assign each species a random color.
colors_array = []
for counter in np.arange(1, num_species_total + 1):
item1 = np.random.rand(counter)[0]
item2 = np.random.rand(counter)[0]
item3 = np.random.rand(counter)[0]
colors_array.append(item1)
colors_array.append(item2)
colors_array.append(item3)
colors_array = np.asarray(colors_array)
colors_array = colors_array.reshape((num_species_total, 3))
colors = []
for counter in np.arange(1, num_species_total + 1):
item1 = np.random.rand(counter)[0]
item2 = np.random.rand(counter)[0]
item3 = np.random.rand(counter)[0]
colors.append(item1)
colors.append(item2)
colors.append(item3)
colors = np.asarray(colors)
colors = colors.reshape((num_species_total, 3))
#Plot mf, add a legend.
for counter in np.arange(0, num_species_total):
if zz_wanted != 'None':
if zz[counter] in zz_wanted and aa_wanted == 'None': #Plot all isotopes of an element.
if zz[counter] == 1 and aa[
counter] == 1: #Explicitly plots protons and hydrogen.
plt.plot(time,
xmf[:, counter],
color='r',
label=nuc_name[counter])
elif zz[counter] == 1 and aa[counter] == 2:
plt.plot(time,
xmf[:, counter],
color='b',
label=nuc_name[counter])
else:
plt.plot(time,
xmf[:, counter],
color=colors_array[counter],
label=nuc_name[counter])
box = ax1.get_position()
ax1.set_position(
[box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize=10)
elif zz[counter] in zz_wanted and aa[
counter] in aa_wanted: #Plot by atomic number and mass number.
plt.plot(time,
xmf[:, counter],
color=colors_array[counter],
label=nuc_name[counter])
box = ax1.get_position()
ax1.set_position(
[box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize=10)
zz_wanted.remove(zz[counter])
elif zz_wanted == 'None' and nuc_names_wanted == 'None': #Plot all species above specified threshold.
if np.amax(xmf[:, counter]) >= min_mf:
plt.plot(time, xmf[:, counter], color=colors_array[counter])
elif nuc_names_wanted != 'None': #Listed nuclear names switches plotting mechanism.
break
if nuc_names_wanted != 'None': #Sort through list to find mass fraction of named species, and plot.
for counter in np.arange(0, len(nuc_names_wanted)):
species_number = nuc_name.index(nuc_names_wanted[counter])
species_number = int(species_number)
plt.plot(time,
xmf[:, species_number],
color=colors_array[species_number],
label=nuc_name[species_number])
box = ax1.get_position()
ax1.set_position([box.x0, box.y0, box.width * 0.995, box.height])
ax1.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=10)
#Start of datafile2.
#Read second ts file, use variables.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile2)
element = rtf.build_element_symbol()
nuc_name = rtf.build_isotope_symbol(zz, aa)
#Set parameter based on data size.
num_species_total = np.shape(xmf)[1]
#Repeat plotting process above, using dashed lines and second set of provided information.
for counter in np.arange(0, num_species_total):
if zz_wanted2 != 'None':
if zz[counter] in zz_wanted2 and aa_wanted == 'None':
if zz[counter] == 1 and aa[counter] == 1:
plt.plot(time,
xmf[:, counter],
color='r',
label=nuc_name[counter],
linestyle='--')
elif zz[counter] == 1 and aa[counter] == 2:
plt.plot(time,
xmf[:, counter],
color='b',
label=nuc_name[counter],
linestyle='--')
else:
plt.plot(time,
xmf[:, counter],
color=colors_array[counter],
label=nuc_name[counter],
linestyle='--')
elif zz[counter] in zz_wanted2 and aa[counter] in aa_wanted:
plt.plot(time,
xmf[:, counter],
color=colors_array[counter],
label=nuc_name[counter],
linestyle='--')
zz_wanted2.remove(zz[counter])
aa_wanted.remove(aa[counter])
elif zz_wanted2 == 'None' and nuc_names_wanted == 'None':
if np.amax(xmf[:, counter]) >= min_mf:
plt.plot(time,
xmf[:, counter],
color=colors_array[counter],
linestyle='--')
elif nuc_names_wanted != 'None':
break
if nuc_names_wanted != 'None':
for counter in np.arange(0, len(nuc_names_wanted)):
species_number = nuc_name.index(nuc_names_wanted[counter])
plt.plot(time,
xmf[:, species_number],
color=colors_array[species_number],
label=nuc_name[species_number],
linestyle='--')
#Format and label axes
plt.yscale('log')
plt.ylim(min_mf, 1.5)
plt.ylabel("Mass Fraction")
plt.xscale('linear')
plt.xlim(time[0], time[end])
plt.xlabel("Time (s)")
#Add x ticks at specified intervals
x_labels = []
tick = time[0]
while tick <= time[end]:
tick = float("{0:.1f}".format(tick))
x_labels.append(tick)
tick += time_spacing
loc = plticker.MultipleLocator(base=time_spacing)
ax1.xaxis.set_major_locator(loc)
plt.xticks(x_labels, x_labels)
#Remove superfluous ticks, show grid line instead
plt.tick_params(axis='both',
which='both',
bottom='on',
top='on',
labelbottom='on',
left='off',
right='off',
labelleft='on')
plt.grid(True)
plt.title("%s (solid) and %s (dashed)" % (datafile1, datafile2))
#Show graph
plt.show()
def compare_final(datafile1, datafile2):
'''A function to compare one datafile relative to another
Inputs: datafile1 = ts file to be compared (reported errors are relative to this file)
datafile2 = second ts file to be compared
Output: rerr_sort = list of relative errors from smallest to largest
names = list of nuclide names corresponding to items in rerr_sort
'''
import numpy as np
import read_ts_file as rtf
import heapq
#Take in data from the first file, give it variable names.
zz1, aa1, xmf1, time1, temperature1, density1, timestep1, edot1, flx_end1, flx1 = rtf.read_ts_file(
datafile1)
en1 = np.multiply(edot1, timestep1)
enuc1 = np.cumsum(en1)
#Set certain constraints based on the input data. (How many species, how many timesteps, etc)
num_species_total = np.shape(xmf1)[1]
num_timesteps = np.shape(xmf1)[0]
#Take in data from the second file.
zz2, aa2, xmf2, time2, temperature2, density2, timestep2, edot2, flx_end2, flx2 = rtf.read_ts_file(
datafile2)
en2 = np.multiply(edot2, timestep2)
enuc2 = np.cumsum(en2)
#Make lists of elements and nuclear names for later use.
element = rtf.build_element_symbol()
nuc_name = rtf.build_isotope_symbol(zz2, aa2)
#Set certain constraints based on the new set of input data.
num_species_total2 = np.shape(xmf2)[1]
#Create lists for the differences
aerr = np.abs(np.subtract(xmf2[-1][:], xmf1[-1][:]))
rerr = np.divide(aerr, np.abs(xmf1[-1][:]))
isort = np.argsort(rerr)
aerr_sort = aerr[isort]
rerr_sort = rerr[isort]
xmf1_sort = xmf1[-1][isort]
xmf2_sort = xmf2[-1][isort]
name_sort = [nuc_name[i] for i in isort]
print("%5s %5s\t%10s\t%16s\t%16s\t%16s\t%16s" %
('i', 'isort', 'name', 'X1', 'X2', '|dX|', '|dX| / |X1|'))
fmt = "%5s %5s\t%10s\t%16.8e\t%16.8e\t%16.8e\t%16.8e"
for i in np.arange(0, num_species_total):
print(fmt % (i + 1, isort[i] + 1, name_sort[i], xmf1_sort[i],
xmf2_sort[i], aerr_sort[i], rerr_sort[i]))
print("")
fmt = "%5s %5s\t%10s\t%16s\t%16s\t%16.8e\t%16.8e"
aerr_norm = np.linalg.norm(aerr, ord=2)
rerr_norm = np.divide(aerr_norm, np.linalg.norm(xmf1[-1][:], ord=2))
print(fmt % ('', '', '2-norm', '', '', aerr_norm, rerr_norm))
fmt = "%5s %5s\t%10s\t%16.8e\t%16.8e\t%16.8e\t%16.8e"
aerr = np.abs(np.subtract(temperature2[-1], temperature1[-1]))
rerr = np.divide(aerr, np.abs(temperature1[-1]))
print(fmt % ('', '', 'T', temperature1[-1], temperature2[-1], aerr, rerr))
aerr = np.abs(np.subtract(enuc2[-1], enuc1[-1]))
rerr = np.divide(aerr, np.abs(enuc1[-1]))
print(fmt % ('', '', 'E_nuc', enuc1[-1], enuc2[-1], aerr, rerr))
def per_diff(datafile1, datafile2):
'''A function to find final percent difference between two data sets of mass fraction.
Inputs: datafile1 = ts file to be compared
datafile2 = second ts file to be compared
Output: smallest = list of differences from smallest to largest
names = list of nuclide names corresponding to items in largest
'''
import numpy as np
import read_ts_file as rtf
import heapq
#Take in data from the first file, give it variable names.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile1)
zz1, aa1, xmf1, time1, temperature1, density1, timestep1, edot1, flx_end1, flx1 = zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx
#Set certain constraints based on the input data. (How many species, how many timesteps, etc)
num_species_total = np.shape(xmf1)[1]
num_timesteps = np.shape(xmf1)[0]
n = num_species_total
#Take in data from the second file.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile2)
#Make lists of elements and nuclear names for later use.
element = rtf.build_element_symbol()
nuc_name = rtf.build_isotope_symbol(zz, aa)
#Change variable names.
zz2, aa2, xmf2, time2, temperature2, density2, timestep2, edot2, flx_end2, flx2 = zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx
#Set certain constraints based on the new set of input data.
num_species_total2 = np.shape(xmf2)[1]
num_timesteps2 = np.shape(xmf2)[0]
#Create an empty list for the maximum percentage differences.
max_per_diff = []
for counter in np.arange(
0, num_species_total): #count through each column (species)
avg = (
float((xmf1[-1][counter]) + float(xmf2[-1][counter])) / 2
) #take the average of each item, -1 as an index accesses the last entry, so it's end time
if avg == 0.0: #if average is zero, make it something calculable
avg = 10E-20
diff_list = [] #make empty list for differences
diff = float(xmf1[-1][counter]) - float(
xmf2[-1][counter]) #calculate differences and add to list
diff = abs(diff)
diff_list.append(diff)
per_diff_list = [] #create list of percent differences
for item in diff_list: #calculate percent differences, add to list
per_diff = item / avg
per_diff_list.append(per_diff)
max_per_diff.append(
max(per_diff_list
)) #find largest percentage differences and add to list
smallest = heapq.nsmallest(
n, max_per_diff
) #make list of smallest percentage differences (remember that n = num_species_total, so this is a list of all perdiffs in ascending order)
names = [] #make empty list of names
for item in smallest: #assign relevant name to each percentage difference
foo = max_per_diff.index(item)
name = nuc_name[foo]
names.append(name)
for counter in np.arange(0, n):
print("%s %s %s" %
(counter + 1, names[counter], smallest[counter]))
def find_max_difference(datafile1, datafile2):
'''A function to find absolute differences between mass fraction in two datafiles.
Inputs: datafile1 = ts file to be compared
datafile2 = second ts file to be compared
Output: largest = list of n largest differences
names = list of nuclide names corresponding to items in largest
times = list of times corresponding to items in largest (list of times where largest difference occurs
'''
import numpy as np
import read_ts_file as rtf
import plot_time_mf_2files as plt2
import heapq
#Read data file, change variable names.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile1)
zz1, aa1, xmf1, time1, temperature1, density1, timestep1, edot1, flx_end1, flx1 = zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx
#Set certain constraints based on the size and shape of the mass fraction array.
num_species_total = np.shape(xmf1)[1]
num_timesteps = np.shape(xmf1)[0]
n = num_species_total
#Read the second data file.
zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx = rtf.read_ts_file(
datafile2)
#Make lists of elements and nuclear names for later use.
element = rtf.build_element_symbol()
nuc_name = rtf.build_isotope_symbol(zz, aa)
#Change variable names.
zz2, aa2, xmf2, time2, temperature2, density2, timestep2, edot2, flx_end2, flx2 = zz, aa, xmf, time, temperature, density, timestep, edot, flx_end, flx
#Make empty list for maximum differences.
max_diff = []
for counter in np.arange(
0, num_species_total): #Count through number of species.
for counter2 in np.arange(
0, num_timesteps): #Count through number of timesteps.
diff_list = [] #Make empty list for differences.
diff = float(xmf1[counter2][counter]) - float(
xmf2[counter2][counter]) #Find differences, add to list
diff = abs(diff)
diff_list.append(diff)
max_diff.append(
max(diff_list)) #Add largest difference to max_diff list
largest = heapq.nlargest(
n, max_diff
) #Make list of all maximum differences, from smallest to largest. (max_diff but sorted)
names = [] #Make empty list for names to fill into
times = [] #Make empty list for times to fill into
for item in largest: #Assign relevant name and time to each difference.
foo = max_diff.index(item)
name = nuc_name[foo]
times.append(time[foo])
names.append(name)
#Print results.
for counter in np.arange(0, n):
print("%s %s %s %s" %
(counter + 1, names[counter], largest[counter], times[counter]))
