def curvelinear_test1(fig):
"""Grid for custom transform."""
def tr(x, y):
sgn = np.sign(x)
x, y = np.abs(np.asarray(x)), np.asarray(y)
return sgn * x**.5, y
def inv_tr(x, y):
sgn = np.sign(x)
x, y = np.asarray(x), np.asarray(y)
return sgn * x**2, y
grid_helper = GridHelperCurveLinear(
(tr, inv_tr),
extreme_finder=ExtremeFinderSimple(20, 20),
# better tick density
grid_locator1=MaxNLocator(nbins=6),
grid_locator2=MaxNLocator(nbins=6))
ax1 = Subplot(fig, 111, grid_helper=grid_helper)
# ax1 will have a ticks and gridlines defined by the given
# transform (+ transData of the Axes). Note that the transform of the Axes
# itself (i.e., transData) is not affected by the given transform.
fig.add_subplot(ax1)
ax1.imshow(np.arange(25).reshape(5, 5),
vmax=50,
cmap=plt.cm.gray_r,
origin="lower")
def test_axis_direction():
# Check that axis direction is propagated on a floating axis
fig = plt.figure()
ax = Subplot(fig, 111)
fig.add_subplot(ax)
ax.axis['y'] = ax.new_floating_axis(nth_coord=1, value=0,
axis_direction='left')
assert ax.axis['y']._axis_direction == 'left'
def test_Subplot():
fig = plt.figure()
ax = Subplot(fig, 1, 1, 1)
fig.add_subplot(ax)
xx = np.arange(0, 2 * np.pi, 0.01)
ax.plot(xx, np.sin(xx))
ax.set_ylabel("Test")
ax.axis["top"].major_ticks.set_tick_out(True)
ax.axis["bottom"].major_ticks.set_tick_out(True)
ax.axis["bottom"].set_label("Tk0")
def lineGraph(all_data, eps):
fig = plt.figure(1, (18 * 2, 9 * 2))
ax = Subplot(fig, 111)
fig.add_subplot(ax)
ax.plot(eps, all_data, 'ro-', label='POI聚类总数')
ax.axis["right"].set_visible(False)
ax.axis["top"].set_visible(False)
ax.set_xlabel('聚类距离', fontsize=30)
ax.set_ylabel('聚类总数', fontsize=30)
ax.tick_params(labelsize=20)
plt.legend()
plt.savefig(os.path.join(savingFig, "lineGraph"))
plt.show()
def curvelinear_test1(fig):
"""
grid for custom transform.
"""
def tr(x, y):
sgn = np.sign(x)
x, y = np.abs(np.asarray(x)), np.asarray(y)
return sgn * x**.5, y
def inv_tr(x, y):
sgn = np.sign(x)
x, y = np.asarray(x), np.asarray(y)
return sgn * x**2, y
extreme_finder = angle_helper.ExtremeFinderCycle(
20,
20,
lon_cycle=None,
lat_cycle=None,
# (0, np.inf),
lon_minmax=None,
lat_minmax=None,
)
grid_helper = GridHelperCurveLinear((tr, inv_tr),
extreme_finder=extreme_finder)
ax1 = Subplot(fig, 111, grid_helper=grid_helper)
# ax1 will have a ticks and gridlines defined by the given
# transform (+ transData of the Axes). Note that the transform of
# the Axes itself (i.e., transData) is not affected by the given
# transform.
fig.add_subplot(ax1)
ax1.imshow(np.arange(25).reshape(5, 5),
vmax=50,
cmap=plt.cm.gray_r,
interpolation="nearest",
origin="lower")
# tick density
grid_helper.grid_finder.grid_locator1._nbins = 6
grid_helper.grid_finder.grid_locator2._nbins = 6
def curvelinear_test1(fig):
"""
grid for custom transform.
"""
def tr(x, y):
sgn = np.sign(x)
x, y = np.abs(np.asarray(x)), np.asarray(y)
return sgn*x**.5, y
def inv_tr(x, y):
sgn = np.sign(x)
x, y = np.asarray(x), np.asarray(y)
return sgn*x**2, y
extreme_finder = angle_helper.ExtremeFinderCycle(20, 20,
lon_cycle=None,
lat_cycle=None,
# (0, np.inf),
lon_minmax=None,
lat_minmax=None,
)
grid_helper = GridHelperCurveLinear((tr, inv_tr),
extreme_finder=extreme_finder)
ax1 = Subplot(fig, 111, grid_helper=grid_helper)
# ax1 will have a ticks and gridlines defined by the given
# transform (+ transData of the Axes). Note that the transform of
# the Axes itself (i.e., transData) is not affected by the given
# transform.
fig.add_subplot(ax1)
ax1.imshow(np.arange(25).reshape(5, 5),
vmax=50, cmap=plt.cm.gray_r,
interpolation="nearest",
origin="lower")
# tick density
grid_helper.grid_finder.grid_locator1._nbins = 6
grid_helper.grid_finder.grid_locator2._nbins = 6
def test_Subplot():
fig = plt.figure()
ax = Subplot(fig, 1, 1, 1)
fig.add_subplot(ax)
xx = np.arange(0, 2 * np.pi, 0.01)
ax.plot(xx, np.sin(xx))
ax.set_ylabel("Test")
ax.axis["top"].major_ticks.set_tick_out(True)
ax.axis["bottom"].major_ticks.set_tick_out(True)
ax.axis["bottom"].set_label("Tk0")
def test_Subplot():
# Remove this line when this test image is regenerated.
plt.rcParams['text.kerning_factor'] = 6
fig = plt.figure()
ax = Subplot(fig, 1, 1, 1)
fig.add_subplot(ax)
xx = np.arange(0, 2 * np.pi, 0.01)
ax.plot(xx, np.sin(xx))
ax.set_ylabel("Test")
ax.axis["top"].major_ticks.set_tick_out(True)
ax.axis["bottom"].major_ticks.set_tick_out(True)
ax.axis["bottom"].set_label("Tk0")
def test_subplot():
fig = plt.figure(figsize=(5, 5))
ax = Subplot(fig, 111)
fig.add_subplot(ax)
server_type_category = cursor.fetchall()
print(server_type_category)
first_image_frequency_x = [15, 16, 17, 19, 20, 21]
first_image_vcpu_count = [4, 6, 8, 10, 12, 14]
line_color = ['dimgrey', 'black', 'slategray']
line_marker = ['v', '^', 's', 'p']
figure = plt.figure(figsize=(10, 10))
# fig, axes = plt.subplots(int(len(first_image_vcpu_count) / 2), 2)
# ax=axisartist.Subplot(fig,2,2,1)
# fig.add_axes(ax)
# ax.axis["bottom"].set_axisline_style("-|>", size = 1.5)
# ax.axis["left"].set_axisline_style("->", size = 1.5)
for cpu_count_index in range(0, len(first_image_vcpu_count)):
ax = Subplot(figure, int(len(first_image_vcpu_count) / 2), 2,
cpu_count_index + 1)
figure.add_subplot(ax)
values = {}
values["memory_count"] = "4"
for server_type_index in range(0, len(server_type_category)):
values["server_type"] = server_type_category[server_type_index][0]
# ax = axes[int(cpu_count_index / 2), cpu_count_index % 2]
values["cpu_number"] = first_image_vcpu_count[cpu_count_index]
result = []
for cpu_frequency_index in range(0, len(first_image_frequency_x)):
values["cpu_frequency"] = first_image_frequency_x[
cpu_frequency_index]
cursor_DictCursor.execute(select_record, values)
print(values)
select_result = cursor_DictCursor.fetchall()
print(select_result)
standard_average_energy = np.nan*np.ones(shape=(len(accuracy_required),len(no_of_hidden_nodes)))
caching_average_energy = np.nan*np.ones(shape=(len(accuracy_required),len(no_of_hidden_nodes)))
for iAccu in range(len(accuracy_required)):
for iNode in range(len(no_of_hidden_nodes)):
tmp = ~np.isnan(standard_energy[iAccu][iNode])
standard_average_energy[iAccu][iNode] = np.mean(standard_energy[iAccu][iNode][tmp])
tmp = ~np.isnan(caching_energy[iAccu][iNode])
caching_average_energy[iAccu][iNode] = np.mean(caching_energy[iAccu][iNode][tmp])
arr_color = ['red','black']
arr_linestyle = ['-','--',':']
arr_label = ['No caching','Synaptic caching']
fig = plt.figure(facecolor="white",figsize=(4.5,4))
ax = Subplot(fig,111)
fig.add_subplot(ax)
for iAccu in range(len(accuracy_required)):
plt.plot(no_of_hidden_nodes,standard_average_energy[iAccu],color=arr_color[0],linewidth=3,linestyle=arr_linestyle[iAccu],label=arr_label[0]+', '+str(accuracy_required[iAccu]))
plt.plot(no_of_hidden_nodes,caching_average_energy[iAccu],color=arr_color[1],linewidth=3,linestyle=arr_linestyle[iAccu],label=arr_label[1]+', '+str(accuracy_required[iAccu]))
plt.xlabel('# hidden units',fontsize=24)
#plt.ylabel('Energy [arbitrary unit]',fontsize=28)
plt.yscale('log')
plt.xlim(0,no_of_hidden_nodes[0])
#plt.xticks(np.linspace(0,10,11))
plt.ylim(1e3,1e6)
plt.xticks([0,50,100,150,200])
plt.yticks([1e3,1e4,1e5,1e6])
ax.axis["right"].set_visible(False)
ax.axis["top"].set_visible(False)
#ax.axis["bottom"].label.set_text('# hidden units')
DFs = []
for root, dirs, files in os.walk(
dir_path): #第一个为起始路径，第二个为起始路径下的文件夹，第三个是起始路径下的文件。
for file in files:
file_path = os.path.join(root, file) #将路径名和文件名组合成一个完整路径
df = pd.read_excel(file_path) #excel转换成DataFrame
DFs.append(df)
df = pd.concat(DFs)
df = df.query('50>胸径>20')
"""
胸径树高频率频数分布拟合
"""
x = np.array(df['胸径'])
fig = plt.figure(figsize=(10, 6))
ax = Subplot(fig, 221)
fig.add_subplot(ax)
ax.axis["right"].set_visible(False)
ax.axis["top"].set_visible(False)
n, bins, c = ax.hist(df['胸径'], bins=range(23, 51, 2), edgecolor='black')
y = n
x = bins[:-1]
ztfb = get_curve_fit_param(x, y, func, p0=[5, 35, 6])
ztfbpre = func(x, *ztfb)
# ax.plot(x,y,'r*',ls='-')
ax.plot(x, ztfbpre, 'b+', ls='-', color='orange')
wbfb = get_curve_fit_param(x, y, weib, p0=[20, 20, 5])
wbfbpre = weib(x, *wbfb)
# plt.plot(x,y,'r*')
ax.plot(x, wbfbpre, 'b+', ls='-')
plt.xticks(range(24, 51, 2))
server_type_category = cursor.fetchall()
print(server_type_category)
first_image_frequency_x = [15, 16, 17, 19, 20, 21]
first_image_vcpu_count = [1, 2, 4, 6, 8, 10]
line_color = ['dimgrey', 'black', 'silver']
line_marker = ['v', '^', 's', 'p']
figure = plt.figure(figsize=(11, 11))
# fig, axes = plt.subplots(int(len(first_image_vcpu_count) / 2), 2)
# ax=axisartist.Subplot(fig,2,2,1)
# fig.add_axes(ax)
# ax.axis["bottom"].set_axisline_style("-|>", size = 1.5)
# ax.axis["left"].set_axisline_style("->", size = 1.5)
for cpu_count_index in range(0, len(first_image_vcpu_count)):
ax = Subplot(figure, int(len(first_image_vcpu_count) / 2), 2,
cpu_count_index + 1)
figure.add_subplot(ax)
values = {}
values["memory_count"] = "4"
line_list = []
server_type_list = []
for server_type_index in range(0, len(server_type_category) - 1):
values["server_type"] = server_type_category[server_type_index][0]
server_type_list.append(server_type_category[server_type_index][0])
# ax = axes[int(cpu_count_index / 2), cpu_count_index % 2]
values["cpu_number"] = first_image_vcpu_count[cpu_count_index]
result = []
for cpu_frequency_index in range(0, len(first_image_frequency_x)):
values["cpu_frequency"] = first_image_frequency_x[
cpu_frequency_index]
cursor_DictCursor.execute(select_record, values)
def tbme_plot(r_name, bg_ham, curr_ham, vlc_num):
# I want a TBME scatter plot for fitted TBME
a = bg_ham.strong.copy()
a = a[a['group'] != 'other']
b = curr_ham.strong.copy()
b = b[b['group'] != 'other']
c = a.merge(b,
left_on=['k1', 'k2', 'k3', 'k4', 'j', 't'],
right_on=['k1', 'k2', 'k3', 'k4', 'j', 't'])
fig = plt.figure(figsize=(6, 6))
matplotlib.rcParams.update({'font.size': 12})
ax = Subplot(fig, 111)
fig.add_subplot(ax)
ax.set_xlabel('TBME (MeV) starting')
ax.set_ylabel('TBME (MeV) with {} vlc'.format(vlc_num))
ax.set_aspect('equal')
x, y = c['v_x'].tolist(), c['v_y'].tolist()
min, max = -3, 1.
plt.xticks(np.arange(min, max, 0.5))
plt.yticks(np.arange(min, max, 0.5))
plt.plot([min, max], [min, max], ls='--', color='black')
ax.scatter(x, y)
ax.axhline(0., ls='-', color='black')
ax.axvline(0., ls='-', color='black')
plt_name = r_name + '\\tbme-vlc-{}.png'.format(vlc_num)
plt.savefig(plt_name)
plt.close(fig)
return
""" ================ Simple Axisline3 ================ """ import matplotlib.pyplot as plt from mpl_toolkits.axisartist.axislines import Subplot fig = plt.figure(1, (3,3)) ax = Subplot(fig, 111) fig.add_subplot(ax) ax.axis["right"].set_visible(False) ax.axis["top"].set_visible(False) plt.show()
select_record = "select * from pi5000 where server_type=%(server_type)s and cpu_number=%(cpu_number)s and cpu_frequency=%(cpu_frequency)s and memory_count=%(memory_count)s"
select_server_type_category = "select distinct server_type from pi5000 "
cursor.execute(select_server_type_category)
server_type_category = cursor.fetchall()
print(server_type_category)
first_image_frequency_x = [12, 13, 15, 16, 17, 19, 20, 21]
first_image_vcpu_count = [8]
line_color = ['dimgrey', 'black', 'silver']
line_marker = ['v', '^', 's', 'p']
figure = plt.figure(figsize=(5, 5))
for cpu_count_index in range(0, len(first_image_vcpu_count)):
ax = Subplot(figure, 1, 1, 1)
figure.add_subplot(ax)
values = {}
values["memory_count"] = "4"
line_list = []
server_type_list = []
for server_type_index in range(0, len(server_type_category) - 1):
values["server_type"] = server_type_category[server_type_index][0]
server_type_list.append(server_type_category[server_type_index][0])
# ax = axes[int(cpu_count_index / 2), cpu_count_index % 2]
values["cpu_number"] = first_image_vcpu_count[cpu_count_index]
result = []
for cpu_frequency_index in range(0, len(first_image_frequency_x)):
values["cpu_frequency"] = first_image_frequency_x[
cpu_frequency_index]
cursor_DictCursor.execute(select_record, values)