def demo_fixed_size_axes():
fig1 = plt.figure(1, (6, 6))
h = [Size.Fixed(1.0), Size.Fixed(4.5)]
v = [Size.Fixed(0.7), Size.Fixed(5.)]
divider = Divider(fig1, (0.0, 0.0, 1., 1.), h, v, aspect=False)
ax = LocatableAxes(fig1, divider.get_position())
ax.set_axes_locator(divider.new_locator(nx=1, ny=1))
fig1.add_axes(ax)
ax.plot([1,2,3])
def demo_fixed_pad_axes():
fig = plt.figure(2, (6, 6))
h = [Size.Fixed(1.0), Size.Scaled(1.), Size.Fixed(.2),]
v = [Size.Fixed(0.7), Size.Scaled(1.), Size.Fixed(.5),]
divider = Divider(fig, (0.0, 0.0, 1., 1.), h, v, aspect=False)
ax = LocatableAxes(fig, divider.get_position())
ax.set_axes_locator(divider.new_locator(nx=1, ny=1))
fig.add_axes(ax)
ax.plot([1,2,3])
def demo_fixed_size_axes():
fig1 = plt.figure(1, (6, 6))
# The first items are for padding and the second items are for the axes.
# sizes are in inch.
h = [Size.Fixed(1.0), Size.Fixed(4.5)]
v = [Size.Fixed(0.7), Size.Fixed(5.)]
divider = Divider(fig1, (0.0, 0.0, 1., 1.), h, v, aspect=False)
# the width and height of the rectangle is ignored.
ax = LocatableAxes(fig1, divider.get_position())
ax.set_axes_locator(divider.new_locator(nx=1, ny=1))
fig1.add_axes(ax)
ax.plot([1,2,3])
def subplot_array( self, hsize, vsize=(1.0,), figsize=(10,10)):
""" Use the axes_divider module to make a single row of plots
hsize : list of floats
horizontal spacing: alternates Scaled for plot, Fixed for between plots
vsize : list of floats
vertical spacing
ref: http://matplotlib.org/mpl_toolkits/axes_grid/users/axes_divider.html
"""
nx = (len(hsize)+1)/2
ny = (len(vsize)+1)/2
fig, axx = plt.subplots(ny,nx,squeeze=False, figsize=figsize) # just to make the axes, will move them
sizer = lambda x,i: axes_size.Scaled(x) if i%2==0 else axes_size.Fixed(x)
horiz = [ sizer(h,i) for i,h in enumerate(hsize) ]
vert = [ sizer(v,i) for i,v in enumerate(vsize) ]
divider = Divider(fig, (0.1, 0.1, 0.8, 0.8), horiz, vert, aspect=False)
for i,ax in enumerate(axx.flatten()):
iy = i//nx; ix = i%nx
ax.set_axes_locator(divider.new_locator(nx=2*ix, ny=2*iy))
return fig, axx
def subplot_array(self, hsize, vsize=(1.0, ), figsize=(10, 10)):
""" Use the axes_divider module to make a single row of plots
hsize : list of floats
horizontal spacing: alternates Scaled for plot, Fixed for between plots
vsize : list of floats
vertical spacing
ref: http://matplotlib.org/mpl_toolkits/axes_grid/users/axes_divider.html
"""
nx = (len(hsize) + 1) / 2
ny = (len(vsize) + 1) / 2
fig, axx = plt.subplots(
ny, nx, squeeze=False,
figsize=figsize) # just to make the axes, will move them
sizer = lambda x, i: axes_size.Scaled(
x) if i % 2 == 0 else axes_size.Fixed(x)
horiz = [sizer(h, i) for i, h in enumerate(hsize)]
vert = [sizer(v, i) for i, v in enumerate(vsize)]
divider = Divider(fig, (0.1, 0.1, 0.8, 0.8), horiz, vert, aspect=False)
for i, ax in enumerate(axx.flatten()):
iy = i // nx
ix = i % nx
ax.set_axes_locator(divider.new_locator(nx=2 * ix, ny=2 * iy))
return fig, axx
import mpl_toolkits.axes_grid.axes_size as Size
from mpl_toolkits.axes_grid import Divider
import matplotlib.pyplot as plt
fig1 = plt.figure(1, (5.5, 4.))
# the rect parameter will be ignore as we will set axes_locator
rect = (0.1, 0.1, 0.8, 0.8)
ax = [fig1.add_axes(rect, label="%d"%i) for i in range(4)]
horiz = [Size.Scaled(1.5), Size.Fixed(.5), Size.Scaled(1.),
Size.Scaled(.5)]
vert = [Size.Scaled(1.), Size.Fixed(.5), Size.Scaled(1.5)]
# divide the axes rectangle into grid whose size is specified by horiz * vert
divider = Divider(fig1, rect, horiz, vert, aspect=False)
ax[0].set_axes_locator(divider.new_locator(nx=0, ny=0))
ax[1].set_axes_locator(divider.new_locator(nx=0, ny=2))
ax[2].set_axes_locator(divider.new_locator(nx=2, ny=2))
ax[3].set_axes_locator(divider.new_locator(nx=2, nx1=4, ny=0))
for ax1 in ax:
plt.setp(ax1.get_xticklabels()+ax1.get_yticklabels(),
visible=False)
plt.draw()
plt.show()
import mpl_toolkits.axes_grid.axes_size as Size
from mpl_toolkits.axes_grid import Divider
import matplotlib.pyplot as plt
fig1 = plt.figure(1, (5.5, 4.))
# the rect parameter will be ignore as we will set axes_locator
rect = (0.1, 0.1, 0.8, 0.8)
ax = [fig1.add_axes(rect, label="%d" % i) for i in range(4)]
horiz = [Size.Scaled(1.5), Size.Fixed(.5), Size.Scaled(1.), Size.Scaled(.5)]
vert = [Size.Scaled(1.), Size.Fixed(.5), Size.Scaled(1.5)]
# divide the axes rectangle into grid whose size is specified by horiz * vert
divider = Divider(fig1, rect, horiz, vert, aspect=False)
ax[0].set_axes_locator(divider.new_locator(nx=0, ny=0))
ax[1].set_axes_locator(divider.new_locator(nx=0, ny=2))
ax[2].set_axes_locator(divider.new_locator(nx=2, ny=2))
ax[3].set_axes_locator(divider.new_locator(nx=2, nx1=4, ny=0))
for ax1 in ax:
plt.setp(ax1.get_xticklabels() + ax1.get_yticklabels(), visible=False)
plt.draw()
plt.show()
from mpl_toolkits.axes_grid import Divider import matplotlib.pyplot as plt fig1 = plt.figure(1, (5.5, 4)) # the rect parameter will be ignore as we will set axes_locator rect = (0.1, 0.1, 0.8, 0.8) ax = [fig1.add_axes(rect, label="%d"%i) for i in range(4)] horiz = [Size.AxesX(ax[0]), Size.Fixed(.5), Size.AxesX(ax[1])] vert = [Size.AxesY(ax[0]), Size.Fixed(.5), Size.AxesY(ax[2])] # divide the axes rectangle into grid whose size is specified by horiz * vert divider = Divider(fig1, rect, horiz, vert, aspect=False) ax[0].set_axes_locator(divider.new_locator(nx=0, ny=0)) ax[1].set_axes_locator(divider.new_locator(nx=2, ny=0)) ax[2].set_axes_locator(divider.new_locator(nx=0, ny=2)) ax[3].set_axes_locator(divider.new_locator(nx=2, ny=2)) ax[0].set_xlim(0, 2) ax[1].set_xlim(0, 1) ax[0].set_ylim(0, 1) ax[2].set_ylim(0, 2) divider.set_aspect(1.)
def create_multipanel_plot(size, dpi, shape, layout, var_info, cmap, lims):
fig = plt.figure(figsize=size, dpi=dpi)
rings = []
# the rect parameter will be ignore as we will set axes_locator
rect = (0.08, 0.08, 0.9, 0.9)
nrow,ncol = shape
# divide the axes rectangle into grid whose size is specified
# by horiz * vert
horiz = [Scaled(1.)]
for i in range(ncol - 1):
horiz.extend([Fixed(.2), Scaled(1.)])
vert = [Scaled(.1), Fixed(.35), Scaled(1.)]
for i in range(nrow - 1):
vert.extend([Fixed(.1), Scaled(1.)])
divider = Divider(fig, rect, horiz, vert, aspect=False)
# ax0 = fig.add_axes(rect, label="0")
# ax0.set_aspect('equal', 'datalim')
# ax = [ax0] + [fig.add_axes(rect, label="%d"%i, sharex=ax0, sharey=ax0)
# for i in range(1,6)]
ax = [fig.add_axes(rect, label="%d"%i) for i in range(len(layout))]
cax = [fig.add_axes(rect, label='cb%d'%i) for i in range(ncol)]
for i,a in enumerate(ax):
# a.set_axes_locator(divider.new_locator(nx=(i // nrow) * 2,
# ny=((i%nrow) + 1) * 2))
a.set_axes_locator(divider.new_locator(nx=(i % ncol) * 2,
ny=(nrow - (i // ncol)) * 2))
a.set_aspect('equal', 'datalim')
for i,a in enumerate(cax):
a.set_axes_locator(divider.new_locator(nx=2 * i, ny=0))
for num,(a,(data, label, var)) in enumerate(zip(ax, layout)):
norm,ticks,units = var_info[var]
ppi_plot(init_data.xlocs, init_data.ylocs, data, norm=norm,
cmap=cmap, ax=a, rings=rings)
# a.set_title('%s (%s)' % (moment, units))
if num >= ncol:
a.set_xlabel('X Distance (km)')
cbar = ColorbarBase(ax=cax[num%ncol], norm=norm, cmap=cmap,
orientation='horizontal')
cbar.set_label('%s (%s)' % (label, units))
cbar.set_ticks(ticks)
else:
a.xaxis.set_major_formatter(plt.NullFormatter())
if num % ncol == 0:
a.set_ylabel('Y Distance (km)')
else:
a.yaxis.set_major_formatter(plt.NullFormatter())
if lims:
a.xaxis.set_major_locator(plt.MultipleLocator(lims[0]))
a.yaxis.set_major_locator(plt.MultipleLocator(lims[0]))
a.set_xlim(*lims[1:3])
a.set_ylim(*lims[3:])
# loc = 2 is upper left. TODO: Should patch matplotlib to use
# same strings as legend
at = AnchoredText("%s)" % chr(97 + num), loc=2, prop=dict(size='large'),
frameon=True)
# at.patch.set_boxstyle("round, pad=0., rounding_size=0.2")
a.add_artist(at)
return fig
def redraw(self):
if len(self.input) == 0:
self.warning("Empty input")
return
fast_redraw = self.name in self.pp.get_figlabels()
fig = self.pp.figure(self.name)
axes = fig.add_subplot(111)
if not fast_redraw:
self.draw_grid(axes)
else:
while len(axes.texts):
axes.texts[0].remove()
# Draw the inner hexagons with text
all_patches = [list() for _ in range(len(self.fitness_by_label))]
hits_max = numpy.max(self.input)
if hits_max == 0:
hits_max = 1
reversed_result = {}
for label, neurons in enumerate(self.result):
for neuron in neurons:
reversed_result[neuron] = label
# Add hexagons one by one
for y in range(self.height):
for x in range(self.width):
neuron = y * self.width + x
fitness = self.fitness_by_neuron[neuron]
number = self.input[y * self.width + x]
if numpy.sqrt(number / hits_max) <= \
KohonenHits.SIZE_TEXT_THRESHOLD:
fitness = 0
try:
label = reversed_result[neuron]
except KeyError:
continue
patches = all_patches[label]
self._add_hexagon(axes, patches, x, y, fitness)
if fast_redraw:
axes.collections = [axes.collections[-1]]
else:
legend_patches = []
cmap = getattr(self.cm, self.cmap)
for index, patches in enumerate(all_patches):
if len(patches) == 0:
continue
facecolor = cmap(index * cmap.N // len(all_patches))
col = self.matplotlib.collections.PatchCollection(
patches, edgecolors='none', facecolors=facecolor)
axes.add_collection(col)
if not fast_redraw:
legend_patches.append(
self.matplotlib.patches.Patch(
color=facecolor,
label="%d - %.2f" %
(index, self.fitness_by_label[index])))
# Make the grid to be drawn last
axes.collections = axes.collections[1:] + [axes.collections[0]]
if not fast_redraw:
legend = axes.legend(handles=legend_patches,
loc='upper left',
bbox_to_anchor=(1.05, 1),
borderaxespad=0.0,
title="Fitness: %.2f" % self.fitness)
fig.tight_layout()
# The legend becomes truncated, but it should not
# Measure the legend width to fix buggy matplotlib layout
# Without drawing, window extent equals to 1
if legend is not None:
legend.draw(fig.canvas.get_renderer())
bbox = legend.get_window_extent()
bbox = bbox.transformed(fig.dpi_scale_trans.inverted())
from mpl_toolkits.axes_grid import Divider
from mpl_toolkits.axes_grid.axes_size import Fixed, Scaled
divider = Divider(
fig, (0.05, 0.05, 0.9, 0.9),
(Scaled(1.0), Fixed(bbox.width), Scaled(0.05)),
(Scaled(1), Fixed(0)))
axes.set_axes_locator(divider.new_locator(0, 0))
else:
# Modify legend title and labels
legend = axes.get_legend()
if legend is not None:
legend.set_title("Fitness: %.2f" % self.fitness)
for index, text in enumerate(legend.get_texts()):
text.set_text("%d - %.2f" %
(index, self.fitness_by_label[index]))
self.show_figure(fig)
fig.canvas.draw()
return fig
from mpl_toolkits.axes_grid import Size, Divider
import matplotlib.pyplot as plt
fig1 = plt.figure(1, (6, 6))
horiz = [Size.Fixed(1.), Size.Fixed(.5), Size.Fixed(1.5),
Size.Fixed(.5)]
vert = [Size.Fixed(1.5), Size.Fixed(.5), Size.Fixed(1.)]
rect = (0.1, 0.1, 0.8, 0.8)
divider = Divider(fig1, rect, horiz, vert, aspect=False)
ax1 = fig1.add_axes(rect, label="1")
ax2 = fig1.add_axes(rect, label="2")
ax3 = fig1.add_axes(rect, label="3")
ax4 = fig1.add_axes(rect, label="4")
ax1.set_axes_locator(divider.new_locator(nx=0, ny=0))
ax2.set_axes_locator(divider.new_locator(nx=0, ny=2))
ax3.set_axes_locator(divider.new_locator(nx=2, ny=2))
ax4.set_axes_locator(divider.new_locator(nx=2, nx1=4, ny=0))
plt.draw()
plt.show()
def main(path):
adsdata = ads_data.AdsData(path)
all_structs = adsdata.get_all_structs()
print(all_structs)
all_temps = adsdata.get_all_temps()
print(all_temps)
all_num_comps = adsdata.get_all_num_comps()
print(all_num_comps)
all_comps = adsdata.get_all_comps()
print(all_comps)
all_possible_ratios = adsdata.get_all_possible_ratios()
print(all_possible_ratios)
num_grids = len(all_temps)
# dubug grid indexing
#num_grids = 6
with PdfPages(os.getcwd() + "/delH_asfcn_n.pdf") as pdf:
#print(adsdata.master_dict[all_structs[-1]][all_temps[-1]][all_num_comps[-1]][all_comps[-1]])
curr_fig = 0
# Max num of columns of grid is 2
if(num_grids == 1):
colnum = 1
else:
colnum = 2
# all the rest of the grids get stacked in the rows underneath
rownum = int(math.ceil(float(num_grids)/colnum))
# one special object is buffered in the first row to the right of the two grids
special_obj_buffer = 2.6
grid_x = 3.5
grid_y = 2.9
if(rownum == 1):
figsize = (colnum*grid_x+special_obj_buffer, rownum*grid_y)
else:
figsize = (colnum*grid_x+special_obj_buffer, rownum*grid_y+1.2)
fig = plt.figure(curr_fig, figsize=figsize)
# the location of the axes in each grid
#rect = (0.1,0.1,0.8,0.8)
rect = (0.06,0.16,0.2,0.2)
#rect = (0.0,0.0,0.2,0.2)
#ax = [fig.add_axes(rect, label="%d"%i) for i in range((colnum+1)*rownum)]
ax = [fig.add_axes(rect, label="%d"%i) for i in range(num_grids+1)]
horiz = []
vert = []
all_rect = []
#for i in range(colnum):
# horiz += [Size.Fixed(3.5)]
#for j in range(rownum):
# vert += [Size.Fixed(2.8)]
# all_rect.append(rect)
#print(Size.Scaled(1).get_size())
for i in range(colnum):
horiz += [Size.Fixed(2.9), Size.Fixed(0.6)]
for j in range(rownum):
vert += [Size.Fixed(2.2), Size.Fixed(0.6)]
horiz.pop()
horiz += [Size.Fixed(0.2)]
vert.pop()
horiz += [Size.Fixed(special_obj_buffer - 1.0),Size.Fixed(0.2)]
divider = Divider(fig, rect, horiz, vert, aspect=False)
this_rect = divider.get_position()
print('num_grids' + str(num_grids))
print('num_rows' + str(rownum))
print('num_rows' + str(colnum))
# set all access, i counts across rows and wraps to the next row after reaching max of columns
for i in range(num_grids):
print(rownum)
print(int(np.floor(i/colnum)))
if(num_grids <= colnum):
row_ind = 0
else:
row_ind = 2*((rownum-1) - int(np.floor(i/colnum)))
col_ind = 2*(np.mod(i,colnum))
print(str(i) + ": " + " nx: " + str(col_ind) + " ny: " + str(row_ind))
ax[i].set_axes_locator(divider.new_locator(nx = col_ind, ny = row_ind))
# set special object access to last column in first row
special_col_ind = 2*(colnum)
special_row_ind = 2*(rownum-1)
print(str("s") + ": " + " nx: " + str(special_col_ind) + " ny: " + str(special_row_ind))
ax[num_grids].set_axes_locator(divider.new_locator(nx = special_col_ind, ny=0, ny1 = special_row_ind+1))
#plt.show()
# for keeping track of the 95% drop
summary1 = []
summary2 = []
ax_iter = -1
plot_max_max = -1
for this_temp in all_temps:
ax_iter += 1
print("\n\nPREPARING TEMP = " + str(this_temp) + " K\n----------------------------------")
temp = this_temp
temp_ind = all_temps.index(temp)
xkey = "water"
plot_xs = []
plot_ys = []
plot_ys_err = []
percentage_drop = []
labels = []
colors = []
markers = []
linestyles = []
facecolors = []
edgecolors = []
mfcs = []
plot_max = 0.0
for i in range(len(all_structs)):
for j in range(len(all_num_comps)):
comp = all_comps[j]
if(all_structs[i] == "0_all_conf_0_0_full_symm"):
this_struct = "Mg$_2$(DOBDC)"
markers += ["o" for k in range(int(all_num_comps[j]))]
labels.append(this_struct + ": " + comp)
linestyles += ["--" for k in range(int(all_num_comps[j]))]
mfcs.append('white')
facecolors.append('white')
edgecolors.append('blue')
colors.append('blue')
print(markers)
elif(all_structs[i] == "1000_all_conf_0_0_full_symm"):
this_struct = "Mg$_2$(DHFUMA)"
labels.append(this_struct + ": " + comp)
markers += ["^" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
mfcs.append('blue')
facecolors.append('blue')
edgecolors.append('blue')
colors.append('blue')
print(markers)
elif(all_structs[i] == "Fe_dobdc"):
this_struct = "Fe$_2$(DOBDC)"
labels.append(this_struct + ": " + comp)
markers += ["o" for k in range(int(all_num_comps[j]))]
linestyles += ["--" for k in range(int(all_num_comps[j]))]
mfcs.append('white')
facecolors.append('white')
edgecolors.append('firebrick')
colors.append('firebrick')
print(markers)
elif(all_structs[i] == "Fe_dhfuma"):
this_struct = "Fe$_2$(DHFUMA)"
labels.append(this_struct + ": " + comp)
markers += ["^" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
mfcs.append('firebrick')
facecolors.append('firebrick')
edgecolors.append('firebrick')
colors.append('firebrick')
print(markers)
elif(all_structs[i] == "Co_dobdc"):
this_struct = "Co$_2$(DOBDC)"
labels.append(this_struct + ": " + comp)
markers += ["o" for k in range(int(all_num_comps[j]))]
linestyles += ["--" for k in range(int(all_num_comps[j]))]
mfcs.append('white')
facecolors.append('white')
edgecolors.append('dimgray')
colors.append('dimgray')
print(markers)
elif(all_structs[i] == "Co_dhfuma"):
this_struct = "Co$_2$(DHFUMA)"
labels.append(this_struct + ": " + comp)
markers += ["^" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
mfcs.append('dimgray')
facecolors.append('dimgray')
edgecolors.append('dimgray')
colors.append('dimgray')
print(markers)
elif(all_structs[i] == "Ni_dobdc"):
this_struct = "Ni$_2$(DOBDC)"
labels.append(this_struct + ": " + comp)
markers += ["o" for k in range(int(all_num_comps[j]))]
linestyles += ["--" for k in range(int(all_num_comps[j]))]
mfcs.append('white')
facecolors.append('white')
edgecolors.append('gold')
colors.append('gold')
print(markers)
elif(all_structs[i] == "Ni_dhfuma"):
this_struct = "Ni$_2$(DHFUMA)"
labels.append(this_struct + ": " + comp)
markers += ["^" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
mfcs.append('gold')
facecolors.append('gold')
edgecolors.append('gold')
colors.append('gold')
print(markers)
elif(all_structs[i] == "Zn_dobdc"):
this_struct = "Zn$_2$(DOBDC)"
labels.append(this_struct + ": " + comp)
markers += ["o" for k in range(int(all_num_comps[j]))]
linestyles += ["--" for k in range(int(all_num_comps[j]))]
mfcs.append('white')
facecolors.append('white')
edgecolors.append('green')
colors.append('green')
print(markers)
elif(all_structs[i] == "Zn_dhfuma"):
this_struct = "Zn$_2$(DHFUMA)"
labels.append(this_struct + ": " + comp)
markers += ["^" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
mfcs.append('green')
facecolors.append('green')
edgecolors.append('green')
colors.append('green')
print(markers)
elif(all_structs[i] == "1001_all_conf_0_0_full_symm"):
this_struct = "MW_INV"
labels.append(this_struct + ": " + comp)
markers += ["o" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
mfcs.append('black')
facecolors.append('black')
edgecolors.append('black')
colors.append('black')
else:
this_struct = all_structs[i]
labels.append(this_struct + ": " + comp)
markers += ["o" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
mfcs.append('black')
facecolors.append('black')
edgecolors.append('black')
colors.append('black')
# NOTE all keys
#temp comp partial_p partial_f fugacity_coeff avg_load[molec/unit_cell] avg_err[molec/unit_ce ll] avg_load[mol/kg] avg_err[mol/kg] avg_load[mg/g] avg_err[mg/g] exs_load[molec/unit_cell] exs_err[molec/unit_cell] exs_load [mol/kg] exs_err[mol/kg] exs_load[mg/g] exc_err[mg/g] heat_of_des[K] heat_of_des_err[K] heat_of_des[kJ/mol] heat_of_des_err[k J/mol]
# Some structs/temps may have different ratio_keys
x, y = adsdata.extract_data(all_structs[i], all_temps[temp_ind],"1", comp, len(all_possible_ratios), yheader = "heat_of_des[kJ/mol]")
x_err, y_err = adsdata.extract_data(all_structs[i], all_temps[temp_ind],"1", comp, len(all_possible_ratios), yheader = "heat_of_des_err[kJ/mol]")
x = np.array(x)/100000
y = np.array(y)
y_err = np.array(y_err)
perm = x.argsort()
x = x[perm]
y = y[perm]
y_err = y_err[perm]
# Print debug info to terminal
print(this_struct + ": " + comp)
print("x: " + str(x))
print("y: " + str(y))
plot_xs.append(x)
plot_ys.append(y)
plot_ys_err.append(y_err)
try:
if(max(y) > plot_max):
plot_max = max(y)
except:
plot_max = plot_max
if(plot_max > plot_max_max):
plot_max_max = float(plot_max)
print("Data max:" + str(plot_max))
print("Overall max:" + str(plot_max_max))
#fig = plt.figure(curr_fig, figsize = (7,5.6))
#ax = fig.add_subplot(1, 1, 1)
ax[ax_iter].set_xlabel(r'CO$_2$ Pressure [bar]')
ax[ax_iter].set_ylabel(r'Avg. Heat of Des. [kJ/mol]')
ax[ax_iter].set_xscale('log')
#ax[ax_iter].set_xlim((0, 1))
ax[ax_iter].set_ylim(0,plot_max_max*1.2)
label = this_temp
ax[ax_iter].legend(label, title='T=' + str(this_temp) + ' K', loc='upper left', frameon = False, framealpha=1.0, prop={'size':10})
#axtwin = ax[ax_iter].twinx()
#axtwin.set_ylabel(r'% CO$_2$ Capacity Lost')
#axtwin.set_ylim(0,100)
merge_legends = []
#for i in range(len(all_structs)*int(all_num_comps[0]) + int(all_num_comps[0])):
# if((i+1)%(int(all_num_comps[0])+1)==0):
# print("twin axis: " + str(i))
# #merge_legends += axtwin.plot(plot_xs[i], plot_ys[i], label=labels[i], color = colors[i], marker = markers[i], linestyle = linestyles[i], fillstyle='none')
# else:
for i in range(len(markers)):
merge_legends += ax[ax_iter].plot(plot_xs[i], plot_ys[i], label=labels[i], ms = 3, marker = markers[i], linestyle = linestyles[i], color = colors[i], mfc = mfcs[i], mec = edgecolors[i], markeredgewidth = 1)
#ax[ax_iter].errorbar(plot_xs[i], plot_ys[i], yerr = plot_ys_err[i], label=labels[i], marker = markers[i], linestyle = linestyles[i],color = colors[i], mfc = mfcs[i], mec = edgecolors[i], markeredgewidth = 1)
ax[ax_iter].scatter(plot_xs[i], plot_ys[i], label=labels[i], marker = markers[i], s = 3, facecolors = facecolors[i], edgecolors = edgecolors[i], linestyle = linestyles[i])
print(type(merge_legends))
merge_labels = [l.get_label() for l in merge_legends]
#merge_legends = np.array(merge_legends)
#merge_labels = np.array(merge_labels)
#perm = merge_labels.argsort(key=lambda x:x[0])
#merge_legends = merge_legends[perm]
lab_leg = zip(merge_labels, merge_legends)
lab_leg.sort(key=lambda x:x[0:6][0])
merge_labels = [lab_leg[i][0] for i in range(len(lab_leg))]
merge_legends = [lab_leg[i][1] for i in range(len(lab_leg))]
#merge_labels, merge_legends
#merge_labels.sort(key=lambda x:x[0])
# ax num_grids is the axis allocated for the legend
ax[num_grids].legend(merge_legends, merge_labels, loc='center left', prop={'size':12})
ax[num_grids].get_xaxis().set_visible(False)
ax[num_grids].get_yaxis().set_visible(False)
ax[num_grids].set_frame_on(False)
#ax.legend(merge_legends, merge_labels, loc='best', prop={'size':12})
#plt.tight_layout()
#pdf.savefig(fig)
fig.savefig(os.getcwd() + "/" + all_comps[0] + "_heat_of_des_ALL_T.png", dpi = 300)
#curr_fig += 1
print("Summary1:")
for i in range(len(summary1)):
print(summary1[i])
print("Summary2:")
for i in range(len(summary2)):
print(summary2[i])
def static_plot_grid_hist_selections(lst_det_left, lst_det_right, selection,
fname_det):
"""
:param lst_det_left:
:param lst_det_right:
:param selection:
:param fname_det:
"""
# Plot starts here:
cms = matplotlib.cm
color_map_left = cms.Blues
color_map_right = cms.RdPu
if fname_det:
f1 = plt.figure(1, (23, 13), dpi=1200)
else:
f1 = plt.figure(1, (15, 8))
vel_range = range(-90, 70)
f1.clf()
# the rect parameter is ignored as we set the axes_locator
rect = (0.05, 0.07, 0.9, 0.87)
f1ax = [f1.add_axes(rect, label="%d" % i) for i in range(7)]
horiz = [
Size.Scaled(5.),
Size.Fixed(1.0),
Size.Scaled(1.5),
Size.Fixed(1.0),
Size.Scaled(2.)
]
vert = [
Size.Scaled(1.),
Size.Fixed(0.5),
Size.Scaled(1.),
Size.Fixed(0.5),
Size.Scaled(1.),
Size.Fixed(0.5),
Size.Scaled(1.)
]
# divide the axes rectangle into grid whose size is specified by horiz * vert
divider = Divider(f1, rect, horiz, vert, aspect=False)
f1ax[0].set_axes_locator(divider.new_locator(nx=0, ny=0, ny1=7))
f1ax[1].set_axes_locator(divider.new_locator(nx=2, ny=0))
f1ax[2].set_axes_locator(divider.new_locator(nx=2, ny=2))
f1ax[3].set_axes_locator(divider.new_locator(nx=2, ny=4))
f1ax[4].set_axes_locator(divider.new_locator(nx=2, ny=6))
f1ax[5].set_axes_locator(divider.new_locator(nx=4, ny=0, ny1=3))
f1ax[6].set_axes_locator(divider.new_locator(nx=4, ny=4, ny1=7))
f1ax[0].axis([-40, 100, -80, 80])
f1ax[5].axis([-140, 140, 0, 100])
f1ax[6].axis([-140, 140, -100, 80])
f1ax[0].grid(True)
f1ax[1].grid(True)
f1ax[2].grid(True)
f1ax[3].grid(True)
f1ax[4].grid(True)
f1ax[5].grid(True)
f1ax[6].grid(True)
number_of_dets_left = 0
number_of_dets_left_processed = 0
number_of_dets_right = 0
number_of_dets_right_processed = 0
# Left radar plot
if lst_det_left:
LR_data = lst_det_left.get_array_detections_selected(
selection=selection)
if LR_data["mcc"].any():
LR_data_exists = True
f1ax[2].hist(LR_data["rvelocity"],
vel_range,
color=color_map_left(0.4),
normed=1)
number_of_dets_left = np.size(LR_data["mcc"])
if selection["beam_tp"].count(0):
selection_tp = copy.deepcopy(selection)
selection_tp["beam_tp"] = [0]
LR0_data = lst_det_left.get_array_detections_selected(
selection=selection_tp)
f1ax[1].hist(LR0_data["rvelocity"],
vel_range,
color=color_map_left(0.2),
normed=1,
label='beam 0')
f1ax[0].plot(LR0_data["x"],
LR0_data["y"],
color=color_map_left(0.2),
marker='o',
ls='None',
label='Left RDR, beam 0')
f1ax[6].plot(-180 * LR0_data["razimuth"] / np.pi,
LR0_data["rvelocity"],
color=color_map_left(0.2),
marker='o',
ls='None',
label='Left RDR, beam 0')
f1ax[5].plot(-180 * LR0_data["razimuth"] / np.pi,
LR0_data["range"],
color=color_map_left(0.2),
marker='o',
ls='None',
label='Left RDR, beam 0')
number_of_dets_left_processed += np.size(LR0_data["mcc"])
if selection["beam_tp"].count(1):
selection_tp = copy.deepcopy(selection)
selection_tp["beam_tp"] = [1]
LR1_data = lst_det_left.get_array_detections_selected(
selection=selection_tp)
f1ax[1].hist(LR1_data["rvelocity"],
vel_range,
color=color_map_left(0.4),
normed=1,
label='beam 1')
f1ax[0].plot(LR1_data["x"],
LR1_data["y"],
color=color_map_left(0.4),
marker='o',
ls='None',
label='Left RDR, beam 1')
f1ax[6].plot(-180 * LR1_data["razimuth"] / np.pi,
LR1_data["rvelocity"],
color=color_map_left(0.4),
marker='o',
ls='None',
label='Left RDR, beam 1')
f1ax[5].plot(-180 * LR1_data["razimuth"] / np.pi,
LR1_data["range"],
color=color_map_left(0.4),
marker='o',
ls='None',
label='Left RDR, beam 1')
number_of_dets_left_processed += np.size(LR1_data["mcc"])
if selection["beam_tp"].count(2):
selection_tp = copy.deepcopy(selection)
selection_tp["beam_tp"] = [2]
LR2_data = lst_det_left.get_array_detections_selected(
selection=selection_tp)
f1ax[1].hist(LR2_data["velocity"],
vel_range,
color=color_map_left(0.6),
normed=1,
label='beam 2')
f1ax[0].plot(LR2_data["x"],
LR2_data["y"],
color=color_map_left(0.6),
marker='o',
ls='None',
label='Left RDR, beam 2')
f1ax[6].plot(-180 * LR2_data["azimuth"] / np.pi,
LR2_data["velocity"],
color=color_map_left(0.6),
marker='o',
ls='None',
label='Left RDR, beam 2')
f1ax[5].plot(-180 * LR2_data["azimuth"] / np.pi,
LR2_data["range"],
color=color_map_left(0.6),
marker='o',
ls='None',
label='Left RDR, beam 2')
number_of_dets_left_processed += np.size(LR2_data["mcc"])
if selection["beam_tp"].count(3):
selection_tp = copy.deepcopy(selection)
selection_tp["beam_tp"] = [3]
LR3_data = lst_det_left.get_array_detections_selected(
selection=selection_tp)
f1ax[1].hist(LR3_data["rvelocity"],
vel_range,
color=color_map_left(0.8),
normed=1,
label='beam 3')
f1ax[0].plot(LR3_data["x"],
LR3_data["y"],
color=color_map_left(0.8),
marker='o',
ls='None',
label='Left RDR, beam 3')
f1ax[6].plot(-180 * LR3_data["razimuth"] / np.pi,
LR3_data["rvelocity"],
color=color_map_left(0.8),
marker='o',
ls='None',
label='Left RDR, beam 3')
f1ax[5].plot(-180 * LR3_data["razimuth"] / np.pi,
LR3_data["range"],
color=color_map_left(0.8),
marker='o',
ls='None',
label='Left RDR, beam 3')
number_of_dets_left_processed += np.size(LR3_data["mcc"])
plt.draw()
else:
LR_data_exists = False
# Right radar plot
if lst_det_right:
RR_data = lst_det_right.get_array_detections_selected(
mcc=selection['mcc_tp'])
if RR_data["mcc"].any():
RR_data_exists = True
f1ax[4].hist(RR_data["velocity"],
vel_range,
color=color_map_left(0.4),
normed=1)
number_of_dets_right = np.size(RR_data["mcc"])
if selection["beam_tp"].count(0):
selection_tp = copy.deepcopy(selection)
selection_tp["beam_tp"] = [0]
RR0_data = lst_det_right.get_array_detections_selected(
selection=selection_tp)
f1ax[3].hist(RR0_data["velocity"],
vel_range,
color=color_map_right(0.2),
normed=1,
label='beam 0')
f1ax[0].plot(RR0_data["x"],
RR0_data["y"],
color=color_map_right(0.2),
marker='o',
ls='None',
label='Right RDR, beam 0')
f1ax[6].plot(-180 * RR0_data["azimuth"] / np.pi,
RR0_data["velocity"],
color=color_map_right(0.2),
marker='o',
ls='None',
label='Right RDR, beam 0')
f1ax[5].plot(-180 * RR0_data["azimuth"] / np.pi,
RR0_data["range"],
color=color_map_right(0.2),
marker='o',
ls='None',
label='Right RDR, beam 0')
number_of_dets_right_processed += np.size(RR0_data["mcc"])
if selection["beam_tp"].count(1):
selection_tp = copy.deepcopy(selection)
selection_tp["beam_tp"] = [1]
RR1_data = lst_det_right.get_array_detections_selected(
selection=selection_tp)
f1ax[3].hist(RR1_data["velocity"],
vel_range,
color=color_map_right(0.4),
normed=1,
label='beam 1')
f1ax[0].plot(RR1_data["x"],
RR1_data["y"],
color=color_map_right(0.4),
marker='o',
ls='None',
label='Right RDR, beam 1')
f1ax[6].plot(-180 * RR1_data["azimuth"] / np.pi,
RR1_data["velocity"],
color=color_map_right(0.4),
marker='o',
ls='None',
label='Right RDR, beam 1')
f1ax[5].plot(-180 * RR1_data["azimuth"] / np.pi,
RR1_data["range"],
color=color_map_right(0.4),
marker='o',
ls='None',
label='Right RDR, beam 1')
number_of_dets_right_processed += np.size(RR1_data["mcc"])
if selection["beam_tp"].count(2):
selection_tp = copy.deepcopy(selection)
selection_tp["beam_tp"] = [2]
RR2_data = lst_det_right.get_array_detections_selected(
selection=selection_tp)
f1ax[3].hist(RR2_data["velocity"],
vel_range,
color=color_map_right(0.6),
normed=1,
label='beam 2')
f1ax[0].plot(RR2_data["x"],
RR2_data["y"],
color=color_map_right(0.6),
marker='o',
ls='None',
label='Right RDR, beam 2')
f1ax[6].plot(-180 * RR2_data["azimuth"] / np.pi,
RR2_data["velocity"],
color=color_map_right(0.6),
marker='o',
ls='None',
label='Right RDR, beam 2')
f1ax[5].plot(-180 * RR2_data["azimuth"] / np.pi,
RR2_data["range"],
color=color_map_right(0.6),
marker='o',
ls='None',
label='Right RDR, beam 2')
number_of_dets_right_processed += np.size(RR2_data["mcc"])
if selection["beam_tp"].count(3):
selection_tp = copy.deepcopy(selection)
selection_tp["beam_tp"] = [3]
RR3_data = lst_det_right.get_array_detections_selected(
selection=selection_tp)
f1ax[3].hist(RR3_data["velocity"],
vel_range,
color=color_map_right(0.8),
normed=1,
label='beam 3')
f1ax[0].plot(RR3_data["x"],
RR3_data["y"],
color=color_map_right(0.8),
marker='o',
ls='None',
label='Right RDR, beam 3')
f1ax[6].plot(-180 * RR3_data["azimuth"] / np.pi,
RR3_data["velocity"],
color=color_map_right(0.8),
marker='o',
ls='None',
label='Right RDR, beam 3')
f1ax[5].plot(-180 * RR3_data["azimuth"] / np.pi,
RR3_data["range"],
color=color_map_right(0.8),
marker='o',
ls='None',
label='Right RDR, beam 3')
number_of_dets_right_processed += np.size(RR3_data["mcc"])
plt.draw()
else:
RR_data_exists = False
if LR_data_exists and RR_data_exists:
lgd2 = f1ax[0].legend(loc='upper right', bbox_to_anchor=(1.0, 1.0))
f1ax[0].set_xlabel('x [meters]')
f1ax[0].set_ylabel('y [meters]')
f1ax[1].set_ylabel('Left RDR, separate')
f1ax[1].set_xlabel('Velocity $v(mcc)$ [m/s]')
f1ax[2].set_ylabel('Left RDR, all')
f1ax[3].set_ylabel('Right RDR, separate')
f1ax[4].set_ylabel('Right RDR, all')
f1ax[5].set_xlabel('Azimuth [deg]')
f1ax[5].set_ylabel('Range [meters]')
f1ax[6].set_ylabel('Velocity [km h$^{-1}$]')
tit = "MCC: %08d Num of Det: L=%d R=%d In Selected Beams: L=%d R=%d" % (
selection["mcc_tp"][0], number_of_dets_left, number_of_dets_right,
number_of_dets_left_processed, number_of_dets_right_processed)
f1.suptitle(tit, fontsize=14, fontweight='bold')
if fname_det:
f1.savefig(fname_det, format='eps', dpi=1200)
else:
plt.show()
else:
print("Nothing to plot for MCC:", selection["mcc_tp"])
def main(path):
adsdata = AdsData(path)
all_structs = adsdata.get_all_structs()
print(all_structs)
all_temps = adsdata.get_all_temps()
print(all_temps)
all_num_comps = adsdata.get_all_num_comps()
print(all_num_comps)
all_comps = adsdata.get_all_comps()
print(all_comps)
all_possible_ratios = adsdata.get_all_possible_ratios()
print(all_possible_ratios)
num_grids = len(all_temps)
# dubug grid indexing
#num_grids = 6
with PdfPages(os.getcwd() + "/H20_CO2_reduction.pdf") as pdf:
#print(adsdata.master_dict[all_structs[-1]][all_temps[-1]][all_num_comps[-1]][all_comps[-1]])
curr_fig = 0
# Max num of columns of grid is 2
if(num_grids == 1):
colnum = 1
else:
colnum = 2
# all the rest of the grids get stacked in the rows underneath
rownum = int(math.ceil(float(num_grids)/colnum))
# one special object is buffered in the first row to the right of the two grids
special_obj_buffer = 2.6
grid_x = 3.5
grid_y = 2.8
if(rownum == 1):
figsize = (colnum*3.5+special_obj_buffer, rownum*2.8)
else:
figsize = (colnum*3.5+special_obj_buffer, rownum*2.8+1.2)
fig = plt.figure(curr_fig, figsize=figsize)
# the location of the axes in each grid
#rect = (0.1,0.1,0.8,0.8)
rect = (0.06,0.16,0.2,0.2)
#rect = (0.0,0.0,0.2,0.2)
#ax = [fig.add_axes(rect, label="%d"%i) for i in range((colnum+1)*rownum)]
ax = [fig.add_axes(rect, label="%d"%i) for i in range(num_grids+1)]
horiz = []
vert = []
all_rect = []
#for i in range(colnum):
# horiz += [Size.Fixed(3.5)]
#for j in range(rownum):
# vert += [Size.Fixed(2.8)]
# all_rect.append(rect)
#print(Size.Scaled(1).get_size())
for i in range(colnum):
horiz += [Size.Fixed(2.9), Size.Fixed(0.6)]
for j in range(rownum):
vert += [Size.Fixed(2.2), Size.Fixed(0.6)]
horiz.pop()
horiz += [Size.Fixed(0.2)]
vert.pop()
horiz += [Size.Fixed(special_obj_buffer - 1.0),Size.Fixed(0.2)]
divider = Divider(fig, rect, horiz, vert, aspect=False)
this_rect = divider.get_position()
print('num_grids' + str(num_grids))
print('num_rows' + str(rownum))
print('num_rows' + str(colnum))
# set all access, i counts across rows and wraps to the next row after reaching max of columns
for i in range(num_grids):
print(rownum)
print(int(np.floor(i/colnum)))
if(num_grids <= colnum):
row_ind = 0
else:
row_ind = 2*((rownum-1) - int(np.floor(i/colnum)))
col_ind = 2*(np.mod(i,colnum))
print(str(i) + ": " + " nx: " + str(col_ind) + " ny: " + str(row_ind))
ax[i].set_axes_locator(divider.new_locator(nx = col_ind, ny = row_ind))
# set special object access to last column in first row
special_col_ind = 2*(colnum)
special_row_ind = 2*(rownum-1)
print(str("s") + ": " + " nx: " + str(special_col_ind) + " ny: " + str(special_row_ind))
ax[num_grids].set_axes_locator(divider.new_locator(nx = special_col_ind, ny=0, ny1 = special_row_ind+1))
#plt.show()
# for keeping track of the 95% drop
summary1 = []
summary2 = []
ax_iter = -1
plot_max_max = -1
for this_temp in all_temps:
ax_iter += 1
print("\n\nPREPARING TEMP = " + str(this_temp) + " K\n----------------------------------")
temp = this_temp
temp_ind = all_temps.index(temp)
xkey = "water"
plot_xs = []
plot_ys = []
percentage_drop = []
labels = []
colors = []
markers = []
linestyles = []
plot_max = 0.0
for i in range(len(all_structs)):
for j in range(len(all_num_comps)):
comp = all_comps[0]
if(all_structs[i] == "0_all_conf_0_0_full_symm"):
this_struct = "Mg$_2$(DOBDC)"
markers += ["o" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
print(markers)
elif(all_structs[i] == "1000_all_conf_0_0_full_symm"):
this_struct = "Mg$_2$(DHFUMA)"
markers += ["^" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
print(markers)
elif(all_structs[i] == "Fe_dobdc"):
this_struct = "Fe$_2$(DOBDC)"
markers += ["o" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
print(markers)
elif(all_structs[i] == "Fe_dhfuma"):
this_struct = "Fe$_2$(DHFUMA)"
markers += ["^" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
print(markers)
elif(all_structs[i] == "Co_dobdc"):
this_struct = "Co$_2$(DOBDC)"
markers += ["o" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
print(markers)
elif(all_structs[i] == "Co_dhfuma"):
this_struct = "Co$_2$(DHFUMA)"
markers += ["^" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
print(markers)
elif(all_structs[i] == "Ni_dobdc"):
this_struct = "Ni$_2$(DOBDC)"
markers += ["o" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
print(markers)
elif(all_structs[i] == "Ni_dhfuma"):
this_struct = "Ni$_2$(DHFUMA)"
markers += ["^" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
print(markers)
elif(all_structs[i] == "Zn_dobdc"):
this_struct = "Zn$_2$(DOBDC)"
markers += ["o" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
print(markers)
elif(all_structs[i] == "Zn_dhfuma"):
this_struct = "Zn$_2$(DHFUMA)"
markers += ["^" for k in range(int(all_num_comps[j]))]
linestyles += ["-" for k in range(int(all_num_comps[j]))]
print(markers)
elif(all_structs[i] == "1001_all_conf_0_0_full_symm"):
this_struct = "MW_INV"
else:
this_struct = all_structs[i]
labels.append(this_struct + ": " + comp)
# Some structs/temps may have different ratio_keys
ratio_keys = adsdata.master_dict[all_structs[i]][all_temps[temp_ind]]["2"][comp].keys()
x, y = adsdata.extract_data(all_structs[i], all_temps[temp_ind],"2", comp, len(ratio_keys), xkey = "water")
# Print debug info to terminal
print(this_struct + ": " + comp)
print("x: " + str(x))
print("y: " + str(y))
plot_xs.append(x)
plot_ys.append(y)
try:
if(max(y) > plot_max):
plot_max = max(y)
except:
plot_max = plot_max
colors.append("black")
comp = all_comps[1]
labels.append(this_struct + ": " + comp)
# Some structures may have diff ratio keys
ratio_keys = adsdata.master_dict[all_structs[i]][all_temps[temp_ind]]["2"][comp].keys()
x1, y1 = adsdata.extract_data(all_structs[i], all_temps[temp_ind], "2", comp, len(ratio_keys), xkey = "water")
# Print debug info to terminal
print(this_struct + ": " + comp)
print("x1: " + str(x1))
print("y1: " + str(y1))
plot_xs.append(x1)
plot_ys.append(y1)
colors.append("red")
#try:
# if(max(y1) > plot_max):
# plot_max = max(y1)
#except:
# plot_max = plot_max
# NOTE for now kind of irrelevant to calculate % loss, it's easily determined visually
percent = []
for k in range(len(x)):
percent.append((y[0] - y[k])/y[0]*100)
print(this_struct + " percent")
print("x: " + str(x))
print("y: " + str(percent))
#plot_xs.append(x)
#plot_ys.append(percent)
#colors.append("black")
#labels.append(this_struct + ": % loss")
# Calculate critical water fraction
compile_summary = False
for k in range(len(percent)):
if(k != len(percent)-1):
if(10.0 > percent[k] and 10.0 < percent[k+1]):
ind1 = int(k)
ind2 = int(k+1)
h2Omolfrac=(x[k+1]-x[k])/(percent[k+1]-percent[k]) + x[k]
compile_summary = True
else:
break
# Calculate CO2 capacity at that point
# For now we know x,y contains CO2 stats and x1,y1 contains water
if(compile_summary):
CO290percent = y[ind1] + (y[ind2]-y[ind1])/(x[ind2]-x[ind1])*(h2Omolfrac-x[ind1])
print("<H2Omolfrac> <90%CO2capacity> <temp>")
print(str(h2Omolfrac) + " " + str(CO290percent) + " " + str(this_temp))
else:
print("Not a high enough H20 fraction to see 90% CO2 capacity")
if(i==0):
summary1 += [str(h2Omolfrac) + " " + str(CO290percent) + " " + str(this_temp)]
elif(i==1):
summary2 += [str(h2Omolfrac) + " " + str(CO290percent) + " " + str(this_temp)]
if(plot_max > plot_max_max):
plot_max_max = float(plot_max)
print("Data max:" + str(plot_max))
print("Overall max:" + str(plot_max_max))
#fig = plt.figure(curr_fig, figsize = (7,5.6))
#ax = fig.add_subplot(1, 1, 1)
ax[ax_iter].set_xlabel(r'H$_2$O Mol Fraction')
ax[ax_iter].set_ylabel(r'Avg. Loading [mol/kg]')
ax[ax_iter].set_xscale('log')
ax[ax_iter].set_xlim((1e-5, 1))
ax[ax_iter].set_ylim(0,plot_max_max*1.2)
label = this_temp
ax[ax_iter].legend(label, title='T=' + str(this_temp) + ' K', loc='upper left', frameon = False, framealpha=1.0, prop={'size':10})
#axtwin = ax[ax_iter].twinx()
#axtwin.set_ylabel(r'% CO$_2$ Capacity Lost')
#axtwin.set_ylim(0,100)
merge_legends = []
#for i in range(len(all_structs)*int(all_num_comps[0]) + int(all_num_comps[0])):
# if((i+1)%(int(all_num_comps[0])+1)==0):
# print("twin axis: " + str(i))
# #merge_legends += axtwin.plot(plot_xs[i], plot_ys[i], label=labels[i], color = colors[i], marker = markers[i], linestyle = linestyles[i], fillstyle='none')
# else:
for i in range(len(markers)):
merge_legends += ax[ax_iter].plot(plot_xs[i], plot_ys[i], label=labels[i], color = colors[i], marker = markers[i], linestyle = linestyles[i])
merge_labels = [l.get_label() for l in merge_legends]
ax[num_grids].legend(merge_legends, merge_labels, loc='center left', prop={'size':12})
ax[num_grids].get_xaxis().set_visible(False)
ax[num_grids].get_yaxis().set_visible(False)
ax[num_grids].set_frame_on(False)
#ax.legend(merge_legends, merge_labels, loc='best', prop={'size':12})
#plt.tight_layout()
#pdf.savefig(fig)
fig.savefig(os.getcwd() + "/H2O_CO2_reduction_ALL_T.png", dpi = 300)
#curr_fig += 1
print("Summary1:")
for i in range(len(summary1)):
print(summary1[i])
print("Summary2:")
for i in range(len(summary2)):
print(summary2[i])
def redraw(self):
if len(self.input) == 0:
self.warning("Empty input")
return
fast_redraw = self.name in self.pp.get_figlabels()
fig = self.pp.figure(self.name)
axes = fig.add_subplot(111)
if not fast_redraw:
self.draw_grid(axes)
else:
while len(axes.texts):
axes.texts[0].remove()
# Draw the inner hexagons with text
all_patches = [list() for _ in range(len(self.fitness_by_label))]
hits_max = numpy.max(self.input)
if hits_max == 0:
hits_max = 1
reversed_result = {}
for label, neurons in enumerate(self.result):
for neuron in neurons:
reversed_result[neuron] = label
# Add hexagons one by one
for y in range(self.height):
for x in range(self.width):
neuron = y * self.width + x
fitness = self.fitness_by_neuron[neuron]
number = self.input[y * self.width + x]
if numpy.sqrt(number / hits_max) <= \
KohonenHits.SIZE_TEXT_THRESHOLD:
fitness = 0
try:
label = reversed_result[neuron]
except KeyError:
continue
patches = all_patches[label]
self._add_hexagon(axes, patches, x, y, fitness)
if fast_redraw:
axes.collections = [axes.collections[-1]]
else:
legend_patches = []
cmap = getattr(self.cm, self.cmap)
for index, patches in enumerate(all_patches):
if len(patches) == 0:
continue
facecolor = cmap(index * cmap.N // len(all_patches))
col = self.matplotlib.collections.PatchCollection(
patches, edgecolors='none',
facecolors=facecolor)
axes.add_collection(col)
if not fast_redraw:
legend_patches.append(self.matplotlib.patches.Patch(
color=facecolor,
label="%d - %.2f" % (index, self.fitness_by_label[index])))
# Make the grid to be drawn last
axes.collections = axes.collections[1:] + [axes.collections[0]]
if not fast_redraw:
legend = axes.legend(handles=legend_patches, loc='upper left',
bbox_to_anchor=(1.05, 1), borderaxespad=0.0,
title="Fitness: %.2f" % self.fitness)
fig.tight_layout()
# The legend becomes truncated, but it should not
# Measure the legend width to fix buggy matplotlib layout
# Without drawing, window extent equals to 1
if legend is not None:
legend.draw(fig.canvas.get_renderer())
bbox = legend.get_window_extent()
bbox = bbox.transformed(fig.dpi_scale_trans.inverted())
from mpl_toolkits.axes_grid import Divider
from mpl_toolkits.axes_grid.axes_size import Fixed, Scaled
divider = Divider(fig, (0.05, 0.05, 0.9, 0.9),
(Scaled(1.0), Fixed(bbox.width),
Scaled(0.05)), (Scaled(1), Fixed(0)))
axes.set_axes_locator(divider.new_locator(0, 0))
else:
# Modify legend title and labels
legend = axes.get_legend()
if legend is not None:
legend.set_title("Fitness: %.2f" % self.fitness)
for index, text in enumerate(legend.get_texts()):
text.set_text("%d - %.2f" %
(index, self.fitness_by_label[index]))
self.show_figure(fig)
fig.canvas.draw()
return fig