def plot_RECa124_tc(self):
self.get_args()
data = pd.read_csv(self.file_list[0])
data = data.dropna(how="any")
print(data)
fig, ax = plt.subplots(figsize=(7, 5))
norm = Normalize(vmin=data['ionic_radius'].min(), vmax=1.08)
cmap = get_cmap('seismic')
mappable = ScalarMappable(cmap=cmap, norm=norm)
mappable._A = []
re = ['Y', "Dy", "Gd", "Eu", "Sm"]
re_ir = [1.019, 1.027, 1.053, 1.066, 1.079]
re_ir = [0, 0.2, 0.6, 0.7, 0.9]
for r, rir in zip(re, re_ir):
print(rir)
data2 = data[data['RE'] == r]
print(data2)
x = data2['Ca_x'].values.reshape(-1, 1)
y = data2["Tc_onset"].values.reshape(-1, 1)
# lr = LinearRegression()
lr = GaussianProcessRegressor()
lr.fit(x, y)
# print(lr.coef_)
# print(lr.intercept_)
print(lr.score(x, y))
lrx = np.linspace(0, 0.15, 1000).reshape(-1, 1)
ax.plot(lrx,
lr.predict(lrx),
color=cmap(rir),
zorder=-1,
alpha=0.5,
lw=5)
# =======================================================
ax.scatter(data['Ca_x'],
data["Tc_onset"],
marker='o',
s=200,
c=data['ionic_radius'],
cmap=cmap)
fig.colorbar(mappable).set_label(r"$RE^{3+}$ ionic radius / $\rm\AA$")
ax.set_xlim(0, 0.1)
ax.set_ylim(70, 95)
ax.set_yticks(range(70, 96, 5))
ax.set_xticks([0, 0.05, 0.10])
ax.tick_params(length=5, pad=8)
ax.tick_params(right=True, top=True)
ax.set_xlabel(r"$\it{x}$")
ax.set_ylabel(r"$\it{T}_{\mathsf{c}}$ / K")
# plt.gca().spines['left'].set_visible(False)
# plt.gca().spines['right'].set_visible(False)
# plt.tick_range()
fig.tight_layout()
plt.show()
def w_value_2d_heatmap(f, w_store, _t_max, title="w value"):
w_store = np.array(w_store)
w_star = f.w_star
grid_x_min = min(w_store.T[0].min(), w_star[0]) - 1
grid_x_max = max(w_store.T[0].max(), w_star[0]) + 1
grid_y_min = min(w_store.T[1].min(), w_star[1]) - 1
grid_y_max = max(w_store.T[1].max(), w_star[1]) + 1
xvals = np.arange(grid_x_min, grid_x_max, 0.1)
yvals = np.arange(grid_y_min, grid_y_max, 0.1)
X, Y = np.meshgrid(xvals, yvals)
Z = f.f_opt([X, Y])
fig, axes = plt.subplots(1, 1, figsize=(6, 6))
axes.pcolor(X, Y, Z, cmap=plt.cm.rainbow)
# wの軌跡
axes.plot(w_store.T[0], w_store.T[1], c="k", alpha=0.2, linewidth=1)
c = np.linspace(0, _t_max, len(w_store))
axes.scatter(w_store.T[0],
w_store.T[1],
c=c,
cmap=plt.cm.hot,
linewidths=0.01,
alpha=1,
s=10)
# 始点(黄色)、終点（緑）、真値（赤）
axes.plot(*w_store[0], 'ks', markersize=5, label="start")
axes.plot(*w_store[-1], 'gs', markersize=5, label="finish")
axes.plot(*w_star, 'r*', markersize=8, label="true value")
axes.set_title(title)
# カラーバーの設定
axpos = axes.get_position()
cbar_ax = fig.add_axes([0.9, axpos.y0, 0.03, axpos.height])
norm = colors.Normalize(vmin=Z.min(), vmax=Z.max())
mappable = ScalarMappable(cmap=plt.cm.rainbow, norm=norm)
mappable._A = []
fig.colorbar(mappable, cax=cbar_ax)
# 余白の調整
plt.subplots_adjust(right=0.85)
plt.subplots_adjust(wspace=0.1)
plt.show()
def Make_animation(npy_file, PSO):
all_particle_data_with_cost = np.load(npy_file)
n_iterations = all_particle_data_with_cost.shape[0]
n_particles = all_particle_data_with_cost.shape[1]
all_particle_data_with_cost = all_particle_data_with_cost.T
range_max = [100, 100, 100, 100, 100000000]
nfr = n_iterations # Number of frames
fps = 3 # Frame per sec
xs = []
ys = []
zs = []
ws = []
vs = []
us = []
for iteration in range(n_iterations):
xs.append(all_particle_data_with_cost[0].T[iteration] / 1000)
ys.append(all_particle_data_with_cost[1].T[iteration])
zs.append(all_particle_data_with_cost[2].T[iteration])
ws.append(all_particle_data_with_cost[3].T[iteration])
vs.append(all_particle_data_with_cost[4].T[iteration])
us.append(all_particle_data_with_cost[5].T[iteration])
def Convert_int(ndarray):
return ndarray.astype(np.int32)
vs = list(map(Convert_int, vs))
us = list(map(Convert_int, us))
# %% Create Color Map
cmap = get_cmap('jet')
# y_tick_max = ceil(np.max(vs), 1e+7)
y_tick_max = ceil(np.min(us) + (1e+7), 1e+7)
y_tick_min = floor(np.min(us), 1e+7) + (5e+6)
# norm = matplotlib.colors.LogNorm(vmin=y_tick_min, vmax=y_tick_max)
norm = matplotlib.colors.Normalize(vmin=y_tick_min, vmax=y_tick_max)
mappable = ScalarMappable(cmap=cmap, norm=norm)
mappable._A = []
fig = plt.figure(figsize=(24, 13))
ax1 = fig.add_subplot(232, projection=Axes3D.name)
ax2 = fig.add_subplot(233, projection=Axes3D.name)
ax3 = fig.add_subplot(235, projection=Axes3D.name)
ax4 = fig.add_subplot(236, projection=Axes3D.name)
def update(ifrm, xa, ya, za, wa, va, ua):
ax1.cla()
ax2.cla()
ax3.cla()
ax4.cla()
for particle in range(n_particles):
# ax1
ax1.scatter(xa[ifrm][particle],
ya[ifrm][particle],
za[ifrm][particle],
s=100,
color=cmap(norm(va[ifrm][particle])),
marker='o')
ax1.set_title('Particle XYZ iterations = ' + str(ifrm + 1) +
" Global best =" + str(np.max(ua[ifrm])),
y=1.0)
ax1.set_xlim(0, range_max[0])
ax1.set_ylim(0, range_max[1])
ax1.set_zlim(0, range_max[2])
ax1.set_xlabel('PV capacity[kW]')
ax1.set_ylabel('Wind capacity[kW]')
ax1.set_zlabel('Battery capacity[kW]')
# ax2
ax2.scatter(xa[ifrm][particle],
ya[ifrm][particle],
wa[ifrm][particle],
s=100,
color=cmap(norm(va[ifrm][particle])),
marker='o')
ax2.set_title('Particle movement XYW iterations = ' +
str(ifrm + 1) + " Global best =" +
str(np.max(ua[ifrm])),
y=1.0)
ax2.set_xlim(0, range_max[0])
ax2.set_ylim(0, range_max[1])
ax2.set_zlim(0, range_max[3])
ax2.set_xlabel('PV capacity[kW]')
ax2.set_ylabel('Wind capacity[kW]')
ax2.set_zlabel('Diesel capacity[kW]')
# ax3
ax3.scatter(xa[ifrm][particle],
za[ifrm][particle],
wa[ifrm][particle],
s=100,
color=cmap(norm(va[ifrm][particle])),
marker='o')
ax3.set_title('Particle movement XZW iterations = ' +
str(ifrm + 1) + " Global best =" +
str(np.max(ua[ifrm])),
y=1.0)
ax3.set_xlim(0, range_max[0])
ax3.set_ylim(0, range_max[2])
ax3.set_zlim(0, range_max[3])
ax3.set_xlabel('PV capacity[kW]')
ax3.set_ylabel('Battery capacity[kW]')
ax3.set_zlabel('Diesel capacity[kW]')
# ax4
ax4.scatter(ya[ifrm][particle],
za[ifrm][particle],
wa[ifrm][particle],
s=100,
color=cmap(norm(va[ifrm][particle])),
marker='o')
ax4.set_title('Particle movement YZW iterations = ' +
str(ifrm + 1) + " Global best =" +
str(np.max(ua[ifrm])),
y=1.0)
ax4.set_xlim(0, range_max[1])
ax4.set_ylim(0, range_max[2])
ax4.set_zlim(0, range_max[3])
ax4.set_xlabel('Wind capacity[kW]')
ax4.set_ylabel('Battery capacity[kW]')
ax4.set_zlabel('Diesel capacity[kW]')
colorbar_ax = fig.add_axes([0.3, 0.1, 0.05, 0.7])
colorbar = fig.colorbar(mappable, cax=colorbar_ax, shrink=0.5)
colorbar.set_label('Objective function[yen]')
ani = animation.FuncAnimation(fig,
update,
nfr,
fargs=(xs, ys, zs, ws, vs, us),
interval=1000 / fps)
# Set up formatting for the movie files
Writer = animation.writers['ffmpeg']
writer = Writer(fps=fps / 2, metadata=dict(artist='Yuichiro'))
ani.save("Result/" + str(PSO.Multiprocessing_parameters["state_name"]) +
'plot_3d_scatter_funcanimation.mp4',
writer=writer)
ani.save("Result/" + str(PSO.Multiprocessing_parameters["state_name"]) +
'plot_3d_scatter_funcanimation.gif',
writer="imagemagick")
s = ani.to_jshtml()
with open(
"Result/" + str(PSO.Multiprocessing_parameters["state_name"]) +
'plot_3d_scatter_funcanimation.html', 'w') as f:
f.write(s)
marker=marks[num],alpha = alptmp,label=regionsE[num])
else:
if depthc == 'on':
ax1.scatter(tmpXY[xptype],tmpXY[yptype],c=tmpXY['Depth'],\
marker=marks[num],label=regionsE[num],vmin=vmin,vmax=vmax,cmap=cm.jet,alpha = alptmp)
else:
ax1.scatter(tmpXY[xptype],tmpXY[yptype],\
marker=marks[num],alpha = alptmp,label=regionsE[num])
ax1.set_xlim(xlim)
ax1.set_ylim(ylim)
ax1.set_xscale(xscale)
ax1.set_yscale(yscale)
num = num + 1
ax1.set_title(xptype + '(X) vs ' + yptype + '(Y)')
mappable = ScalarMappable(cmap='jet', norm=Normalize(vmin=vmin, vmax=vmax))
mappable._A = []
if depthc == 'on':
fig1.colorbar(mappable).set_label('Depth (km)')
plt.tight_layout()
plt.grid()
plt.legend()
fig1.savefig(path + 'Figs/ken' + str(numi) + '.png')
plt.show()
def plot_tree(tree, feature, outputpath, font_size=14, line_type="-", vt_line_width=0.5, hz_line_width=0.2,
max_circle_size=20, min_circle_size=4):
node_list = tree.iter_descendants(strategy='postorder')
node_list = chain(node_list, [tree])
print(feature + "\n" + outputpath)
vlinec, vlines, vblankline, hblankline, nodes, nodex, nodey = [], [], [], [], [], [], []
if len(tree) < 50:
fig = plt.figure(figsize=(16, 9))
elif len(tree) < 70:
fig = plt.figure(figsize=(16, 12))
else:
fig = plt.figure(figsize=(16, 16))
ax = fig.add_subplot(111)
min_annot = min(get_annot(n, feature) for n in tree.iter_leaves() if feature in n.features)
max_annot = max(get_annot(n, feature) for n in tree.iter_leaves() if feature in n.features)
cmap = plt.get_cmap("inferno")
norm = colors.LogNorm(vmin=min_annot, vmax=max_annot)
color_map = ScalarMappable(norm=norm, cmap=cmap)
node_pos = dict((n2, i) for i, n2 in enumerate(tree.get_leaves()[::-1]))
max_name_size = max(len(n.name) for n in tree)
# draw tree
rows = []
for n in node_list:
x = sum(n2.dist for n2 in n.iter_ancestors()) + n.dist
min_node_annot, max_node_annot = False, False
if (feature + "_min" in n.features) and (feature + "_max" in n.features):
min_node_annot = get_annot(n, feature + "_min")
max_node_annot = get_annot(n, feature + "_max")
if n.is_leaf():
y = node_pos[n]
node_name = " " + n.name
row = {"Taxon": n.name}
if len(n.name) != max_name_size:
node_name += " " * (max_name_size - len(n.name))
if feature in n.features:
node_name += " " + format_float(get_annot(n, feature))
row[feature] = get_annot(n, feature)
if min_node_annot and max_node_annot:
node_name += " [{0},{1}]".format(format_float(min_node_annot), format_float(max_node_annot))
row[feature + "Lower"] = min_node_annot
row[feature + "Upper"] = max_node_annot
ax.text(x, y, node_name, va='center', size=font_size, name="Latin Modern Mono")
rows.append(row)
else:
y = np.mean([node_pos[n2] for n2 in n.children])
node_pos[n] = y
if feature in n.features:
node_annot = get_annot(n, feature)
# draw vertical line
vlinec.append(((x, node_pos[n.children[0]]), (x, node_pos[n.children[-1]])))
vlines.append(node_annot)
# draw horizontal lines
for child in n.children:
child_annot = get_annot(child, feature)
h = node_pos[child]
xs = [[x, x], [x + child.dist, x + child.dist]]
ys = [[h - hz_line_width, h + hz_line_width], [h - hz_line_width, h + hz_line_width]]
zs = [[node_annot, node_annot], [child_annot, child_annot]]
ax.pcolormesh(xs, ys, zs, cmap=cmap, norm=norm, shading="gouraud")
else:
vblankline.append(((x, node_pos[n.children[0]]), (x, node_pos[n.children[-1]])))
for child in n.children:
h = node_pos[child]
hblankline.append(((x, h), (x + child.dist, h)))
nodex.append(x)
nodey.append(y)
pd.DataFrame(rows).to_csv(outputpath[:-4] + ".tsv", index=None, header=rows[0].keys(), sep='\t')
vline_col = LineCollection(vlinec, colors=[color_map.to_rgba(l) for l in vlines],
linestyle=line_type,
linewidth=vt_line_width * 2)
ax.add_collection(LineCollection(hblankline, colors='black', linestyle=line_type, linewidth=hz_line_width * 2))
ax.add_collection(LineCollection(vblankline, colors='black', linestyle=line_type, linewidth=vt_line_width * 2))
ax.add_collection(vline_col)
# scale line
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
diffy = ymax - ymin
dist = round((xmax - xmin) / 4, 1)
padding = 200.
ymin -= diffy / padding
ax.plot([xmin, xmin + dist], [ymin, ymin], color='k')
ax.plot([xmin, xmin], [ymin - diffy / padding, ymin + diffy / padding], color='k')
ax.plot([xmin + dist, xmin + dist], [ymin - diffy / padding, ymin + diffy / padding],
color='k')
ax.text((xmin + xmin + dist) / 2, ymin - diffy / padding, dist, va='top',
ha='center', size=font_size)
ax.set_axis_off()
# plt.tight_layout()
color_map._A = []
ticks = "PopulationSize" in feature or "MutationRatePerTime" in feature or "Omega" in feature
cbar = fig.colorbar(color_map, ticks=(
[0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20,
50] if ticks else None),
orientation='horizontal', pad=0, shrink=0.6)
cbar.ax.xaxis.set_tick_params('major', labelsize=font_size * 1.8)
cbar.ax.xaxis.set_tick_params('minor', labelsize=font_size)
if ticks:
cbar.ax.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
cbar.ax.set_xlabel(label_transform(feature), labelpad=5, size=font_size * 1.8)
plt.tight_layout()
for o in fig.findobj():
o.set_clip_on(False)
plt.savefig(outputpath, format=outputpath[outputpath.rfind('.') + 1:], bbox_inches='tight')
plt.close("all")
