def draw_china(sat1Nm, sat2Nm, ymd_s, ymd_e, drew_points, out_fig_file):
'''
画 中国区图
'''
fig = plt.figure(figsize=(8, 6), dpi=100) # china
ax = subplot(111)
plt.subplots_adjust(left=0.11, right=0.91, bottom=0.12, top=0.92)
drawFigs(fig, drew_points, False)
circle1 = mpatches.Circle((58, 36), 6, color=RED, ec=EDGE_GRAY, lw=0.3)
circle2 = mpatches.Circle((219, 36), 6, color=BLUE, ec=EDGE_GRAY, lw=0.3)
fig.patches.extend([circle1, circle2])
TEXT_Y = 0.05
fig.text(0.1, TEXT_Y, '%s' % sat1Nm, color=RED, fontproperties=FONT0)
fig.text(0.3, TEXT_Y, '%s' % sat2Nm, color=BLUE, fontproperties=FONT0)
if ymd_s != ymd_e:
fig.text(0.55, TEXT_Y, '%s-%s' % (ymd_s, ymd_e), fontproperties=FONT0)
else:
fig.text(0.55, TEXT_Y, '%s' % ymd_s, fontproperties=FONT0)
fig.text(0.83, TEXT_Y, ORG_NAME, fontproperties=FONT0)
# 设定Map边框粗细
spines = ax.spines
for eachspine in spines:
spines[eachspine].set_linewidth(0)
out_fig_dir = os.path.dirname(out_fig_file) # add 2015-10-16 10:30
if not os.path.exists(out_fig_dir):
os.makedirs(out_fig_dir)
fig.savefig(out_fig_file, dpi=100)
fig.clear()
plt.close()
def plot(cls, data, figure_folder, msg="", suffix=""):
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.2)
x = np.arange(len(data[:]))
ax.plot(x, data[:, 0], c="red", linewidth=2, label="agent 01")
ax.plot(x, data[:, 1], c="blue", linewidth=2, label="agent 12")
ax.plot(x, data[:, 2], c="green", linewidth=2, label="agent 20")
plt.ylim([-0.01, 1.01])
plt.text(0, -0.23, "PARAMETERS. {}".format(msg))
ax.legend(fontsize=12,
bbox_to_anchor=(1.1, 1.05)) # loc='upper center'
ax.set_xlabel("$t$")
ax.set_ylabel("Proportion of agents proceeding to indirect exchange")
ax.set_title("Money emergence with a basal ganglia model")
# Save fig
if not exists(figure_folder):
mkdir(figure_folder)
fig_name = "{}/figure_{}.pdf".format(figure_folder,
suffix.split(".p")[0])
plt.savefig(fig_name)
plt.close()
def draw_fixed(sat2Nm, ymd_s, ymd_e, drew_points, out_fig_file):
'''
画固定点图
'''
fig = plt.figure(figsize=(8, 4.5)) # world
ax = subplot(111)
plt.subplots_adjust(left=0.09, right=0.95, bottom=0.11, top=0.97)
drawFigs(fig, drew_points, True)
circle1 = mpatches.Circle((60, 24), 6, color=RED, ec=EDGE_GRAY, lw=0.3)
circle2 = mpatches.Circle((219, 24), 6, color=BLUE, ec=EDGE_GRAY, lw=0.3)
fig.patches.extend([circle1, circle2])
fig.text(0.1, 0.04, 'Fixed Points', color=RED, fontproperties=FONT0)
fig.text(0.3, 0.04, '%s' % sat2Nm, color=BLUE, fontproperties=FONT0)
if ymd_s != ymd_e:
fig.text(0.55, 0.04, '%s-%s' % (ymd_s, ymd_e), fontproperties=FONT0)
else:
fig.text(0.55, 0.04, '%s' % ymd_s, fontproperties=FONT0)
fig.text(0.83, 0.04, ORG_NAME, fontproperties=FONT0)
# 设定Map边框粗细
spines = ax.spines
for eachspine in spines:
spines[eachspine].set_linewidth(0)
out_fig_dir = os.path.dirname(out_fig_file) # add 2015-10-16 10:30
if not os.path.exists(out_fig_dir):
os.makedirs(out_fig_dir)
fig.savefig(out_fig_file, dpi=100)
fig.clear()
plt.close()
def draw_polar(sat1Nm, sat2Nm, ymd_s, ymd_e, drew_points, out_fig_file):
'''
画 两极图
'''
fig = plt.figure(figsize=(8, 4.5)) # polar
plt.subplots_adjust(left=0.09, right=0.93, bottom=0.11, top=0.94)
drew_polar_Figs(fig, drew_points)
circle1 = mpatches.Circle((58, 33), 6, color=RED, ec=EDGE_GRAY, lw=0.3)
circle2 = mpatches.Circle((219, 33), 6, color=BLUE, ec=EDGE_GRAY, lw=0.3)
fig.patches.extend([circle1, circle2])
TEXT_Y = 0.06
fig.text(0.1, TEXT_Y, '%s' % sat1Nm, color=RED, fontproperties=FONT0)
fig.text(0.3, TEXT_Y, '%s' % sat2Nm, color=BLUE, fontproperties=FONT0)
if ymd_s != ymd_e:
fig.text(0.55, TEXT_Y, '%s-%s' % (ymd_s, ymd_e), fontproperties=FONT0)
else:
fig.text(0.55, TEXT_Y, '%s' % ymd_s, fontproperties=FONT0)
fig.text(0.83, TEXT_Y, ORG_NAME, fontproperties=FONT0)
fig.subplots_adjust(wspace=0.1)
out_fig_dir = os.path.dirname(out_fig_file) # add 2015-10-16 10:30
if not os.path.exists(out_fig_dir):
os.makedirs(out_fig_dir)
fig.savefig(out_fig_file, dpi=100)
fig.clear()
plt.close()
def plot(cls, data, figure_folder, msg="", suffix=""):
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.2)
x = np.arange(len(data[:]))
ax.plot(x, data[:, 0], c="red", linewidth=2, label="agent 01")
ax.plot(x, data[:, 1], c="blue", linewidth=2, label="agent 12")
ax.plot(x, data[:, 2], c="green", linewidth=2, label="agent 20")
plt.ylim([-0.01, 1.01])
plt.text(0, -0.23, "PARAMETERS. {}".format(msg))
ax.legend(fontsize=12, bbox_to_anchor=(1.1, 1.05)) # loc='upper center'
ax.set_xlabel("$t$")
ax.set_ylabel("Proportion of agents proceeding to indirect exchange")
ax.set_title("Money emergence with a basal ganglia model")
# Save fig
if not exists(figure_folder):
mkdir(figure_folder)
fig_name = "{}/figure_{}.pdf".format(figure_folder, suffix.split(".p")[0])
plt.savefig(fig_name)
plt.close()
def __init__(self,
kernel,
xmin,
xmax,
ymin,
ymax,
base_accuracy,
max_iter,
interpolation='none',
splits=None,
*args):
fig, ax = plt.subplots()
self.base_accuracy = base_accuracy
self.height = base_accuracy
self.width = base_accuracy
self.max_iter = max_iter
self.kernel = kernel
self.x = np.linspace(xmin, xmax, self.width)
self.y = np.linspace(ymin, ymax, self.height)
self.splits = splits
self.args = args
self.ax = ax
self.fig = fig
self.interpolation = interpolation
self.image_array = None
class ImageSaver(ToolBase):
image_array = None
description = 'Save the image only'
def trigger(self, *args, **kwargs):
path = filedialog.asksaveasfilename(initialfile='Fractal_1',
defaultextension='png',
filetypes=[('PNG', ".png")
])
if path:
image = Image.fromarray(self.image_array, mode='RGB')
image.save(path, "PNG", quality=95, optimize=False)
tm = fig.canvas.manager.toolmanager
self.image_saver = tm.add_tool("Save Image", ImageSaver)
fig.canvas.manager.toolbar.add_tool(tm.get_tool("Save Image"),
"toolgroup")
self.slider_box = plt.axes([0.12, 0.02, 0.7, 0.03])
self.slider = Slider(self.slider_box,
'Max iter:',
100,
50000,
valinit=max_iter,
valstep=10)
self.slider.set_val(max_iter)
self.slider.on_changed(self.on_slider_change)
plt.subplots_adjust(bottom=0.1, top=0.95)
def drawHeatMap(dataDF, groupByVariableNames, subplotIndex, subplotIndexName, valueIndexName):
figure = plt.figure(figsize=(12, 10))
order = [7,8,9,4,5,6,1,2,3]
numOfplot = 0
for key, subDF in dataDF.groupby(groupByVariableNames):
subplot = figure.add_subplot(subplotIndex[0], subplotIndex[1], order[numOfplot])
plotDF = subDF.reset_index()
plotDF.plot.scatter(x=subplotIndexName[0], y=subplotIndexName[1], c=valueIndexName, colormap="jet", ax=subplot, vmin=0, vmax=9)
numOfplot = numOfplot + 1
plt.subplots_adjust(wspace=0.8, hspace=0.4)
plt.savefig("./Graphs/Factor3_fixed_50sample_actionDistribution_divergence_HeatMap.png")
def draw_predictions(self, data, predictions):
from pylab import plt
true, lstm, bp = [], [], []
for i in range(len(predictions)):
true.append(predictions[i][0])
bp.append(predictions[i][1])
lstm.append(predictions[i][2])
x = [1, 2, 3, 4, 5]
plt.plot(x, true, 'cx--', label='true')
plt.plot(x, bp, 'mo:', label='fusion')
plt.plot(x, lstm, 'kp-.', label='lstm')
plt.legend()
plt.margins(0)
plt.subplots_adjust(bottom=0.15)
plt.xlabel(u"days")
plt.ylabel("price")
plt.title("tendency predictions of different models")
plt.savefig('blog/static/blog/bootstrap/img/presult.jpg')
def show(filename=None, labels=False):
if not labels:
# fix everything if in 3D mode
plt.subplots_adjust(left=0.0, right=1.1, bottom=0.0, top=1.0)
# also do this if in 2d mode
if not is_3d:
frame1 = plt.gca()
frame1.axes.get_xaxis().set_visible(False)
frame1.axes.get_yaxis().set_visible(False)
if legend:
plt.legend(loc="upper left", fontsize=8, prop={'family': "Monaco", 'weight': "roman", 'size': "x-small"})
if filename is not None:
if '.' not in filename:
if not os.path.isdir(filename):
os.makedirs(filename)
filename = os.path.abspath(os.path.join(filename, "%s.png" % util.timestamp()))
figure.savefig(filename, dpi=150, facecolor=figure.get_facecolor(), edgecolor='none')
plt.show()
qval_min = min(qvals)
qval_max = max(qvals)
qval_n_bins = 40
hist(qvals,
bins=qval_n_bins,
range=(qval_min, qval_max),
facecolor='red',
alpha=0.5)
plt.title('QVal Histogram')
plt.xlabel('QVal')
plt.ylabel('Counts')
plt.savefig(output_dir + "/automatic/qval.tif")
plt.close()
plt.subplots_adjust(hspace=0.6)
if len(qvals_pertilt[0]) > 0:
plt.subplot(411)
hist(qvals_pertilt[0],
bins=qval_n_bins,
range=(qval_min, qval_max),
facecolor='red',
alpha=0.5)
plt.title('QVAL Histogram (0-10 degrees)')
plt.ylabel('Counts')
if len(qvals_pertilt[1]) == 0 and len(qvals_pertilt[2]) == 0 and len(
qvals_pertilt[3]) == 0:
plt.xlabel('QVAL')
if len(qvals_pertilt[1]) > 0:
plt.subplot(412)
hist(qvals_pertilt[1],
plt.subplot(241).set_title("无翻拍 : %s 分" % score_1)
plt.imshow(image_1)
plt.axis('off')
plt.subplot(242).set_title("翻拍 : %s 分" % score_2)
plt.imshow(image_2)
plt.axis('off')
plt.subplot(243).set_title("无翻拍 : %s 分" % score_3)
plt.imshow(image_3)
plt.axis('off')
plt.subplot(244).set_title("无翻拍-黑白 : %s 分" % score_4)
plt.imshow(image_4)
plt.axis('off')
plt.subplot(245).set_title("电脑拍摄 : %s 分" % score_5)
plt.imshow(image_5)
plt.axis('off')
plt.subplot(246).set_title("手机拍摄 : %s 分" % score_6)
plt.imshow(image_6)
plt.axis('off')
plt.subplot(247).set_title("电脑拍摄-翻拍 : %s 分" % score_7)
plt.imshow(image_7)
plt.axis('off')
plt.subplot(248).set_title("手机拍摄-翻拍 : %s 分" % score_8)
plt.imshow(image_8)
plt.axis('off')
plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)
plt.axis('off')
plt.suptitle("腾讯-活体检测")
plt.show()
def my_2p5d_plot_non_dominated_fronts(points,
marker='o',
comp=[0, 1],
up_to_rank_no=1,
no_text=True,
ax=None,
fig=None,
no_colorbar=False,
z_filter=None,
label=None,
bool_return_auto_optimal_design=False):
# this is adapted from pygmo package but there is a bug therein so I write my own function (I also initiated an issue at their Github page and they acknowledge the issue).
# from pylab import mpl
# mpl.rcParams['font.family'] = ['Times New Roman']
# mpl.rcParams['font.size'] = 16.0
full_comp = [0, 1, 2]
full_comp.remove(comp[0])
full_comp.remove(comp[1])
z_comp = full_comp[0]
# We plot
# fronts, dl, dc, ndr = pg.fast_non_dominated_sorting(points)
fronts, _, _, _ = pg.fast_non_dominated_sorting(points)
# We define the colors of the fronts (grayscale from black to white)
cl = list(
zip(np.linspace(0.9, 0.1, len(fronts)),
np.linspace(0.9, 0.1, len(fronts)),
np.linspace(0.9, 0.1, len(fronts))))
if ax is None:
fig, ax = plt.subplots(constrained_layout=False)
plt.subplots_adjust(left=None,
bottom=None,
right=0.85,
top=None,
wspace=None,
hspace=None)
count = 0
for ndr, front in enumerate(fronts):
count += 1
# Frist compute the points coordinates
x_scale = 1
y_scale = 1
z_scale = 1
if comp[0] == 1: # efficency
x_scale = 100
if comp[1] == 1: # efficency
y_scale = 100
if z_comp == 1: # efficency
z_scale = 100
x = [points[idx][comp[0]] * x_scale for idx in front]
y = [points[idx][comp[1]] * y_scale for idx in front]
z = [points[idx][z_comp] * z_scale for idx in front]
# # We plot the points
# for idx in front:
# ax.plot(points[idx][comp[0]], points[idx][comp[1]], marker=marker, color=cl[ndr])
# Then sort them by the first objective
tmp = [(a, b, c) for a, b, c in zip(x, y, z)]
tmp = sorted(tmp, key=lambda k: k[0])
# Now plot using step
ax.step([coords[0] for coords in tmp], [coords[1] for coords in tmp],
color=cl[ndr],
where='post')
# Now add color according to the value of the z-axis variable usign scatter
if z_filter is not None:
z = np.array(z)
x = np.array(x)[z < z_filter]
y = np.array(y)[z < z_filter]
z = z[z < z_filter]
print('Cost, -Efficency, Ripple Sum')
min_a_design = None
min_a_value = 99999999.0
min_b_design = None
min_b_value = 99999999.0
min_c_design = None
min_c_value = 99999999.0
for a, b, c in zip(x, y, z):
if a < min_a_value:
min_a_value = a
min_a_design = (a, b, c)
if b < min_b_value:
min_b_value = b
min_b_design = (a, b, c)
if c < min_c_value:
min_c_value = c
min_c_design = (a, b, c)
print(a, b, c)
auto_optimal_design = (min_a_design, min_b_design, min_c_design)
# scatter_handle = ax.scatter(x, y, c=z, edgecolor=None, alpha=0.5, cmap='Spectral', marker=marker, zorder=99, vmin=0, vmax=z_filter, label=label) #'viridis' Spectral
scatter_handle = ax.scatter(x,
y,
c=z,
edgecolor=None,
alpha=0.5,
cmap='plasma',
marker=marker,
zorder=99,
vmin=0,
vmax=z_filter,
label=label)
# ValueError: Colormap Option A is not recognized. Possible values are: Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r, winter, winter_r
# https://medium.com/better-programming/how-to-use-colormaps-with-matplotlib-to-create-colorful-plots-in-python-969b5a892f0c
# Perceptually Uniform Sequential
# ['viridis', 'plasma', 'inferno', 'magma']
# Sequential
# ['Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds', 'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu', 'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']
# Sequential (2)
# ['binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink', 'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia', 'hot', 'afmhot', 'gist_heat', 'copper']
# Diverging
# ['PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu', 'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']
# Qualitative
# ['Pastel1', 'Pastel2', 'Paired', 'Accent', 'Dark2', 'Set1', 'Set2', 'Set3', 'tab10', 'tab20', 'tab20b', 'tab20c']
# Miscellaneous
# ['flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern', 'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'hsv', 'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar']
else:
scatter_handle = ax.scatter(x,
y,
c=z,
edgecolor=None,
alpha=0.5,
cmap='Spectral',
marker=marker,
zorder=99) #'viridis'
if not no_colorbar:
# color bar
cbar_ax = fig.add_axes([0.875, 0.15, 0.02, 0.7])
cbar_ax.get_yaxis().labelpad = 10
clb = fig.colorbar(scatter_handle, cax=cbar_ax)
else:
pass # use vmin/vmax or set_clim
# if comp[0] == 0 and comp[1] == 1:
# min_, max_ = 0, 20 # Only implemented for Ripple Sum
# scatter_handle.set_clim(min_, max_) # https://stackoverflow.com/questions/3373256/set-colorbar-range-in-matplotlib
# # # # color bar
# # # cbar_ax = fig.add_axes([0.875, 0.15, 0.02, 0.7])
# # # cbar_ax.get_yaxis().labelpad = 10
# # # clb = fig.colorbar(scatter_handle, cax=cbar_ax)
# # # clb.ax.set_ylabel('Ripple Sum [1]', rotation=270)
# else:
# raise Exception('Not Implemented')
if z_comp == 0:
z_label = r'$\rm {Cost}$ [USD]'
z_text = '%.0f'
elif z_comp == 1:
z_label = r'$-\eta$ [%]'
z_text = '%.1f'
elif z_comp == 2:
z_label = r'$O_C$ [1]'
z_text = '%.1f'
if not no_colorbar:
clb.ax.set_ylabel(z_label, rotation=270)
if z_comp == 2: # when OC as z-axis
print('-----------------------------------------------------')
print('-----------------------------------------------------')
print('-----------------------------------------------------')
# Add index next to the points
for x_coord, y_coord, z_coord, idx in zip(x, y, z, front):
if no_text:
pass
else:
ax.annotate(z_text % (z_coord) + ' #%d' % (idx),
(x_coord, y_coord))
else:
# text next scatter showing the value of the 3rd objective
for i, val in enumerate(z):
if no_text:
pass
else:
ax.annotate(z_text % (val), (x[i], y[i]))
# refine the plotting
if comp[0] == 0:
ax.set_xlabel(r'$\rm {Cost}$ [USD]')
elif comp[0] == 1:
ax.set_xlabel(r'$-\eta$ [\%]')
elif comp[0] == 2:
ax.set_xlabel(r'$O_C$ [1]')
if comp[1] == 0:
ax.set_ylabel(r'$\rm {Cost}$ [USD]')
elif comp[1] == 1:
ax.set_ylabel(r'$-\eta$ [\%]')
elif comp[1] == 2:
ax.set_ylabel(r'$O_C$ [1]')
ax.grid()
# plot up to which domination rank?
if up_to_rank_no is None:
pass
else:
if count >= up_to_rank_no:
break
# Y730
# fig.savefig(r'C:\Users\horyc\Desktop/'+ '2p5D-%d%d.png'%(comp[0],comp[1]), dpi=300)
if bool_return_auto_optimal_design:
return scatter_handle, auto_optimal_design
else:
return scatter_handle
def plot_mean_curve(time_data, data, additions, plate_name, save_folder = r'Figures/'):
"""
"""
if not os.path.isdir(save_folder):
os.makedirs(save_folder)
for block in data:
#
group = group_similar(data[block].keys())
names = data[block].keys()
names.sort()
# -----------------
# Plot each group individualy
# -----------------
data_per_inch = 5.0
y_len = int(math.sqrt(len(names)))
x_len = int(np.ceil(len(names) / float(y_len)))
fig, axs = plt.subplots(y_len, x_len, sharex=True, sharey=True,
figsize=(x_len*data_per_inch,y_len*data_per_inch), dpi=300)
y_min = data[block][names[0]]['original_data'].min()
y_max = data[block][names[0]]['original_data'].max()
for i, name in enumerate(names):
if data[block][name]['original_data'].min() < y_min:
y_min = data[block][name]['original_data'].min()
if data[block][name]['original_data'].max() > y_max:
y_max = data[block][name]['original_data'].max()
y_range = y_max - y_min
for i, name in enumerate(names):
px = i / x_len
py = i % x_len
if y_len > 1:
position = (px, py)
else:
position = py
time = time_data[block][name]
mean = np.array(data[block][name]['mean'])
std = np.array(data[block][name]['std'])
all_curve = data[block][name]['original_data']
axs[position].plot(time, [1.0 for _ in time], 'k-')
for curve in all_curve:
axs[position].plot(time, curve, color = [0.7 for _ in xrange(3)])
axs[position].plot(time, mean, color = [0.2 for _ in xrange(3)], linewidth = 2.5, label = name)
axs[position].plot(time, mean+std, 'k--', color = [0.2 for _ in xrange(3)], linewidth = 2.5)
axs[position].plot(time, mean-std, 'k--', color = [0.2 for _ in xrange(3)], linewidth = 2.5)
for addition in additions[:block+1]:
axs[position].plot([addition[0] for _ in xrange(2)], [0.0,3.0], 'k-')
axs[position].text(addition[0], 0.98, addition[1], rotation=270)
if i % x_len == 0:
axs[position].set_ylabel('Normalized fluorescence', size = 14)
if i / x_len == y_len - 1:
axs[position].set_xlabel('Time / sec', size = 14)
axs[position].set_xlim(0, time[-1])
for j, text in enumerate(name.split(';')):
axs[position].text(10, y_max - y_range*j*0.1, text, size = 14)
axs[position].set_ylim(y_min-y_range*0.1, y_max+y_range*0.1)
# plt.suptitle(plate_name)
plt.subplots_adjust(wspace = 0.01, hspace = 0.01)
plt.tight_layout()
plt.savefig(save_folder + 'block_' + str(block + 1))
#
return None
def get_linear_model_histogram(code,
ptype='low',
dtype='d',
start=None,
end=None,
vtype='f',
filter='n',
df=None):
# 399001','cyb':'zs399006','zxb':'zs399005
# code = '999999'
# code = '601608'
# code = '000002'
# asset = get_kdate_data(code)['close'].sort_index(ascending=True)
# df = tdd.get_tdx_Exp_day_to_df(code, 'f').sort_index(ascending=True)
# ptype='close'
# if ptype == 'close' or ptype==''
# ptype=
if start is not None and filter == 'y':
if code not in ['999999', '399006', '399001']:
index_d, dl = tdd.get_duration_Index_date(dt=start)
log.debug("index_d:%s dl:%s" % (str(index_d), dl))
else:
index_d = cct.day8_to_day10(start)
log.debug("index_d:%s" % (index_d))
start = tdd.get_duration_price_date(code, ptype='low', dt=index_d)
log.debug("start:%s" % (start))
if df is None:
# df = tdd.get_tdx_append_now_df(code, ptype, start, end).sort_index(ascending=True)
df = tdd.get_tdx_append_now_df_api(code, start,
end).sort_index(ascending=True)
if not dtype == 'd':
df = tdd.get_tdx_stock_period_to_type(df,
dtype).sort_index(ascending=True)
asset = df[ptype]
log.info("df:%s" % asset[:1])
asset = asset.dropna()
dates = asset.index
if not code.startswith('999') and not code.startswith('399'):
# print "code:",code
if code[:1] in ['5', '6', '9']:
code2 = '999999'
elif code[:2] in ['30']:
# print "cyb"
code2 = '399006'
else:
code2 = '399001'
df1 = tdd.get_tdx_append_now_df_api(code2, start,
end).sort_index(ascending=True)
# df1 = tdd.get_tdx_append_now_df(code2, ptype, start, end).sort_index(ascending=True)
if not dtype == 'd':
df1 = tdd.get_tdx_stock_period_to_type(
df1, dtype).sort_index(ascending=True)
# if len(asset) < len(df1):
# asset1 = df1.loc[asset.index, ptype]
# else:
# asset1 = df1.loc[asset.index, ptype]
# startv = asset1[:1]
# asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# print asset[:1].index[0] , df1[:1].index[0]
if asset[:1].index[0] > df1[:1].index[0]:
asset1 = df1.loc[asset.index, ptype]
startv = asset1[:1]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
else:
df = df[df.index >= df1.index[0]]
asset = df[ptype]
asset = asset.dropna()
dates = asset.index
asset1 = df1.loc[df.index, ptype]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
else:
if code.startswith('399001'):
code2 = '999999'
elif code.startswith('399006'):
code2 = '399005'
else:
code2 = '399001'
df1 = tdd.get_tdx_append_now_df_api(code2, start,
end).sort_index(ascending=True)
# print df1[:1]
# df1 = tdd.get_tdx_append_now_df(code2, ptype, start, end).sort_index(ascending=True)
if not dtype == 'd':
df1 = tdd.get_tdx_stock_period_to_type(
df1, dtype).sort_index(ascending=True)
if len(asset) < len(df1):
asset1 = df1.loc[asset.index, ptype]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
else:
df = df[df.index >= df1.index[0]]
asset = df[ptype]
asset = asset.dropna()
dates = asset.index
asset1 = df1.loc[df.index, ptype]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# print len(df),len(asset),len(df1),len(asset1)
if end is not None:
# print asset[-1:]
asset = asset[:-1]
dates = asset.index
asset1 = asset1[:-1]
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# 画出价格随时间变化的图像
# _, ax = plt.subplots()
# fig = plt.figure()
fig = plt.figure(figsize=(16, 5))
# fig = plt.figure(figsize=(16, 10), dpi=72)
# fig.autofmt_xdate() #(no fact)
# plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
plt.subplots_adjust(left=0.05,
bottom=0.08,
right=0.95,
top=0.95,
wspace=0.15,
hspace=0.25)
# set (gca,'Position',[0,0,512,512])
# fig.set_size_inches(18.5, 10.5)
# fig=plt.fig(figsize=(14,8))
ax1 = fig.add_subplot(121)
# asset=asset.apply(lambda x:round( x/asset[:1],2))
ax1.plot(asset)
# ax1.plot(asset1,'-r', linewidth=2)
ticks = ax1.get_xticks()
# start, end = ax1.get_xlim()
# print start, end, len(asset)
# print ticks, ticks[:-1]
# (ticks[:-1] if len(asset) > end else np.append(ticks[:-1], len(asset) - 1))
ax1.set_xticklabels(
[dates[int(i)] for i in (np.append(ticks[:-1],
len(asset) - 1))],
rotation=15) # Label x-axis with dates
# 拟合
X = np.arange(len(asset))
x = sm.add_constant(X)
model = regression.linear_model.OLS(asset, x).fit()
a = model.params[0]
b = model.params[1]
# log.info("a:%s b:%s" % (a, b))
log.info("X:%s a:%s b:%s" % (len(asset), a, b))
Y_hat = X * b + a
# 真实值-拟合值，差值最大最小作为价值波动区间
# 向下平移
i = (asset.values.T - Y_hat).argmin()
c_low = X[i] * b + a - asset.values[i]
Y_hatlow = X * b + a - c_low
# 向上平移
i = (asset.values.T - Y_hat).argmax()
c_high = X[i] * b + a - asset.values[i]
Y_hathigh = X * b + a - c_high
plt.plot(X, Y_hat, 'k', alpha=0.9)
plt.plot(X, Y_hatlow, 'r', alpha=0.9)
plt.plot(X, Y_hathigh, 'r', alpha=0.9)
# plt.xlabel('Date', fontsize=12)
plt.ylabel('Price', fontsize=12)
plt.title(code + " | " + str(dates[-1])[:11], fontsize=14)
plt.legend([asset.iat[-1]], fontsize=12, loc=4)
plt.grid(True)
# plt.legend([code]);
# plt.legend([code, 'Value center line', 'Value interval line']);
# fig=plt.fig()
# fig.figsize = [14,8]
scale = 1.1
zp = zoompan.ZoomPan()
figZoom = zp.zoom_factory(ax1, base_scale=scale)
figPan = zp.pan_factory(ax1)
# 将Y-Y_hat股价偏离中枢线的距离单画出一张图显示，对其边界线之间的区域进行均分，大于0的区间为高估，小于0的区间为低估，0为价值中枢线。
ax3 = fig.add_subplot(122)
# distance = (asset.values.T - Y_hat)
distance = (asset.values.T - Y_hat)[0]
# if code.startswith('999') or code.startswith('399'):
if len(asset) > len(df1):
ax3.plot(asset)
plt.plot(distance)
ticks = ax3.get_xticks()
ax3.set_xticklabels(
[dates[int(i)] for i in (np.append(ticks[:-1],
len(asset) - 1))],
rotation=15)
n = 5
d = (-c_high + c_low) / n
c = c_high
while c <= c_low:
Y = X * b + a - c
plt.plot(X, Y - Y_hat, 'r', alpha=0.9)
c = c + d
ax3.plot(asset)
## plt.xlabel('Date', fontsize=12)
plt.ylabel('Price-center price', fontsize=14)
plt.grid(True)
else:
as3 = asset.apply(lambda x: round(x / asset[:1], 2))
ax3.plot(as3)
ax3.plot(asset1, '-r', linewidth=2)
# assvol = df.loc[asset.index]['vol']
# assvol = assvol.apply(lambda x: round(x / assvol[:1], 2))
# ax3.plot(assvol, '-g', linewidth=2)
plt.grid(True)
zp3 = zoompan.ZoomPan()
figZoom = zp3.zoom_factory(ax3, base_scale=scale)
figPan = zp3.pan_factory(ax3)
# plt.title(code, fontsize=14)
if 'name' in df.columns:
plt.legend([df.name[-1], df1.name[-1]], loc=0)
else:
dm = tdd.get_sina_data_df(code)
if 'name' in dm.columns:
cname = dm.name[0]
else:
cname = '-'
# plt.legend([code, code2], loc=0)
plt.legend([cname, code2], loc=0)
plt.show(block=False)
plt.ylabel('Counts')
plt.savefig(output_dir + "/automatic/defoci.tif")
plt.close()
qval_min = min(qvals)
qval_max = max(qvals)
qval_n_bins = 40
hist(qvals, bins=qval_n_bins, range=(qval_min,qval_max), facecolor='red', alpha=0.5)
plt.title('QVal Histogram')
plt.xlabel('QVal')
plt.ylabel('Counts')
plt.savefig(output_dir + "/automatic/qval.tif")
plt.close()
plt.subplots_adjust(hspace=0.6)
if len(qvals_pertilt[0])>0:
plt.subplot(411)
hist(qvals_pertilt[0], bins=qval_n_bins, range=(qval_min,qval_max), facecolor='red', alpha=0.5)
plt.title('QVAL Histogram (0-10 degrees)')
plt.ylabel('Counts')
if len(qvals_pertilt[1])==0 and len(qvals_pertilt[2])==0 and len(qvals_pertilt[3])==0:
plt.xlabel('QVAL')
if len(qvals_pertilt[1])>0:
plt.subplot(412)
hist(qvals_pertilt[1], bins=qval_n_bins, range=(qval_min,qval_max), facecolor='red', alpha=0.5)
plt.title('QVAL Histogram (10-20 degrees)')
plt.ylabel('Counts')
if len(qvals_pertilt[2])==0 and len(qvals_pertilt[3])==0:
plt.xlabel('QVAL')
if len(qvals_pertilt[2])>0:
def my_2p5d_plot_non_dominated_fronts(points,
marker='o',
comp=[0, 1],
up_to_rank_no=1,
no_text=True):
# from pylab import mpl
# mpl.rcParams['font.family'] = ['Times New Roman']
# mpl.rcParams['font.size'] = 16.0
full_comp = [0, 1, 2]
full_comp.remove(comp[0])
full_comp.remove(comp[1])
z_comp = full_comp[0]
# We plot
# fronts, dl, dc, ndr = pg.fast_non_dominated_sorting(points)
fronts, _, _, _ = pg.fast_non_dominated_sorting(points)
# We define the colors of the fronts (grayscale from black to white)
cl = list(
zip(np.linspace(0.9, 0.1, len(fronts)),
np.linspace(0.9, 0.1, len(fronts)),
np.linspace(0.9, 0.1, len(fronts))))
fig, ax = plt.subplots(constrained_layout=False)
plt.subplots_adjust(left=None,
bottom=None,
right=0.85,
top=None,
wspace=None,
hspace=None)
count = 0
for ndr, front in enumerate(fronts):
count += 1
# Frist compute the points coordinates
x_scale = 1
y_scale = 1
z_scale = 1
if comp[0] == 1: # efficency
x_scale = 100
if comp[1] == 1: # efficency
y_scale = 100
if z_comp == 1: # efficency
z_scale = 100
x = [points[idx][comp[0]] * x_scale for idx in front]
y = [points[idx][comp[1]] * y_scale for idx in front]
z = [points[idx][z_comp] * z_scale for idx in front]
# # We plot the points
# for idx in front:
# ax.plot(points[idx][comp[0]], points[idx][comp[1]], marker=marker, color=cl[ndr])
# Then sort them by the first objective
tmp = [(a, b, c) for a, b, c in zip(x, y, z)]
tmp = sorted(tmp, key=lambda k: k[0])
# Now plot using step
ax.step([coords[0] for coords in tmp], [coords[1] for coords in tmp],
color=cl[ndr],
where='post')
# Now add color according to the value of the z-axis variable usign scatter
scatter_handle = ax.scatter(x,
y,
c=z,
alpha=0.5,
cmap='Spectral',
marker=marker,
zorder=99) #'viridis'
# color bar
cbar_ax = fig.add_axes([0.875, 0.15, 0.02, 0.7])
cbar_ax.get_yaxis().labelpad = 10
clb = fig.colorbar(scatter_handle, cax=cbar_ax)
if z_comp == 0:
z_label = r'$-\rm {TRV}$ [Nm/m^3]'
z_text = '%.0f'
elif z_comp == 1:
z_label = r'$-\eta$ [%]'
z_text = '%.1f'
elif z_comp == 2:
z_label = r'$O_C$ [1]'
z_text = '%.1f'
clb.ax.set_ylabel(z_label, rotation=270)
if z_comp == 2: # when OC as z-axis
print('-----------------------------------------------------')
print('-----------------------------------------------------')
print('-----------------------------------------------------')
# Add index next to the points
for x_coord, y_coord, z_coord, idx in zip(x, y, z, front):
if no_text:
pass
else:
ax.annotate(z_text % (z_coord) + ' #%d' % (idx),
(x_coord, y_coord))
else:
# text next scatter showing the value of the 3rd objective
for i, val in enumerate(z):
if no_text:
pass
else:
ax.annotate(z_text % (val), (x[i], y[i]))
# refine the plotting
if comp[0] == 0:
ax.set_xlabel(r'$-\rm {TRV}$ [Nm/m^3]')
elif comp[0] == 1:
ax.set_xlabel(r'$-\eta$ [%]')
elif comp[0] == 2:
ax.set_xlabel(r'$O_C$ [1]')
if comp[1] == 0:
ax.set_ylabel(r'$-\rm {TRV}$ [Nm/m^3]')
elif comp[1] == 1:
ax.set_ylabel(r'$-\eta$ [%]')
elif comp[1] == 2:
ax.set_ylabel(r'$O_C$ [1]')
ax.grid()
# plot up to which domination rank?
if up_to_rank_no is None:
pass
else:
if count >= up_to_rank_no:
break
# Y730
# fig.savefig(r'C:\Users\horyc\Desktop/'+ '2p5D-%d%d.png'%(comp[0],comp[1]), dpi=300)
return ax
def show_chan_mpl(code,
start_date,
end_date,
stock_days,
resample,
show_mpl=True,
least_init=3,
chanK_flag=False,
windows=20):
def get_least_khl_num(resample, idx=0, init_num=3):
# init = 3
if init_num - idx > 0:
initw = init_num - idx
else:
initw = 0
return init_num if resample == 'd' else initw if resample == 'w' else init_num-idx-1 if init_num-idx-1 >0 else 0\
if resample == 'm' else 5
stock_code = code # 股票代码
# stock_code = '002176' # 股票代码
# start_date = '2017-09-05'
# start_date = None
# end_date = '2017-10-12 15:00:00' # 最后生成k线日期
# end_date = None
# stock_days = 60 # 看几天/分钟前的k线
# resample = 'd'
# resample = 'w'
x_jizhun = 3 # window 周期 x轴展示的时间距离 5：日，40:30分钟， 48： 5分钟
least_khl_num = get_least_khl_num(resample, init_num=least_init)
# stock_frequency = '5m' # 1d日线， 30m 30分钟， 5m 5分钟，1m 1分钟
stock_frequency = resample # 1d日线， 30m 30分钟， 5m 5分钟，1m 1分钟 w:week
# chanK_flag = chanK # True 看缠论K线， False 看k线
# chanK_flag = True # True 看缠论K线， False 看k线
show_mpl = show_mpl
def con2Cxianduan(stock,
k_data,
chanK,
frsBiType,
biIdx,
end_date,
cur_ji=1,
recursion=False,
dl=None,
chanK_flag=False,
least_init=3):
max_k_num = 4
if cur_ji >= 6 or len(biIdx) == 0 or recursion:
return biIdx
idx = biIdx[len(biIdx) - 1]
k_data_dts = list(k_data.index)
st_data = chanK['enddate'][idx]
if st_data not in k_data_dts:
return biIdx
# 重构次级别线段的点到本级别的chanK中
def refactorXd(biIdx, xdIdxc, chanK, chanKc, cur_ji):
new_biIdx = []
biIdxB = biIdx[len(biIdx) - 1] if len(biIdx) > 0 else 0
for xdIdxcn in xdIdxc:
for chanKidx in range(len(chanK.index))[biIdxB:]:
if judge_day_bao(chanK, chanKidx, chanKc, xdIdxcn, cur_ji):
new_biIdx.append(chanKidx)
break
return new_biIdx
# 判断次级别日期是否被包含
def judge_day_bao(chanK, chanKidx, chanKc, xdIdxcn, cur_ji):
_end_date = chanK['enddate'][chanKidx] + datetime.timedelta(
hours=15) if cur_ji == 1 else chanK['enddate'][chanKidx]
_start_date = chanK.index[chanKidx] if chanKidx == 0\
else chanK['enddate'][chanKidx - 1] + datetime.timedelta(minutes=1)
return _start_date <= chanKc.index[xdIdxcn] <= _end_date
# cur_ji = 1 #当前级别
# 符合k线根数大于4根 1日级别， 2 30分钟， 3 5分钟， 4 一分钟
if not recursion:
resample = 'd' if cur_ji + 1 == 2 else '5m' if cur_ji + 1 == 3 else \
'd' if cur_ji + 1 == 5 else 'w' if cur_ji + 1 == 6 else 'd'
least_khl_num = get_least_khl_num(resample, 1, init_num=least_init)
print "次级:%s st_data:%s k_data_dts:%s least_khl_num:%s" % (
len(k_data_dts) - k_data_dts.index(st_data), str(st_data)[:10],
len(k_data_dts), least_khl_num)
if cur_ji + 1 != 2 and len(k_data_dts) - k_data_dts.index(
st_data) >= least_khl_num + 1:
frequency = '30m' if cur_ji + 1 == 2 else '5m' if cur_ji + 1 == 3 else '1m'
# else:
# frequency = 'd' if cur_ji+1==2 else '5m' if cur_ji+1==3 else \
# 'd' if cur_ji+1==5 else 'w' if cur_ji+1==6 else 'd'
start_lastday = str(chanK.index[biIdx[-1]])[0:10]
print "次级别为:%s cur_ji:%s %s" % (resample, cur_ji, start_lastday)
# print [chanK.index[x] for x in biIdx]
k_data_c, cname = get_quotes_tdx(stock,
start=start_lastday,
end=end_date,
dl=dl,
resample=resample)
print k_data_c.index[0], k_data_c.index[-1]
chanKc = chan.parse2ChanK(
k_data_c, k_data_c.values) if chanK_flag else k_data_c
fenTypesc, fenIdxc = chan.parse2ChanFen(chanKc, recursion=True)
if len(fenTypesc) == 0:
return biIdx
biIdxc, frsBiTypec = chan.parse2ChanBi(
fenTypesc, fenIdxc, chanKc, least_khl_num=least_khl_num - 1)
if len(biIdxc) == 0:
return biIdx
print "biIdxc:", [round(k_data_c.high[x], 2) for x in biIdxc
], [str(k_data_c.index[x])[:10] for x in biIdxc]
xdIdxc, xdTypec = chan.parse2Xianduan(
biIdxc, chanKc, least_windows=1 if least_khl_num > 0 else 0)
biIdxc = con2Cxianduan(stock,
k_data_c,
chanKc,
frsBiTypec,
biIdxc,
end_date,
cur_ji + 1,
recursion=True)
print "xdIdxc:%s xdTypec:%s biIdxc:%s" % (xdIdxc, xdTypec, biIdxc)
if len(xdIdxc) == 0:
return biIdx
# 连接线段位为上级别的bi
lastBiType = frsBiType if len(biIdx) % 2 == 0 else -frsBiType
if len(biIdx) == 0:
return refactorXd(biIdx, xdIdxc, chanK, chanKc, cur_ji)
lastbi = biIdx.pop()
firstbic = xdIdxc.pop(0)
# 同向连接
if lastBiType == xdTypec:
biIdx = biIdx + refactorXd(biIdx, xdIdxc, chanK, chanKc,
cur_ji)
# 逆向连接
else:
# print '开始逆向连接'
_mid = [lastbi] if (lastBiType == -1 and chanK['low'][lastbi] <= chanKc['low'][firstbic])\
or (lastBiType == 1 and chanK['high'][lastbi] >= chanKc['high'][firstbic]) else\
[chanKidx for chanKidx in range(len(chanK.index))[biIdx[len(biIdx) - 1]:]
if judge_day_bao(chanK, chanKidx, chanKc, firstbic, cur_ji)]
biIdx = biIdx + [_mid[0]] + refactorXd(biIdx, xdIdxc, chanK,
chanKc, cur_ji)
# print "次级:",len(biIdx),biIdx,[str(k_data_c.index[x])[:10] for x in biIdx]
return biIdx
def get_quotes_tdx(code,
start=None,
end=None,
dl=120,
resample='d',
show_name=True):
quotes = tdd.get_tdx_append_now_df_api(
code=stock_code, start=start, end=end,
dl=dl).sort_index(ascending=True)
if not resample == 'd' and resample in tdd.resample_dtype:
quotes = tdd.get_tdx_stock_period_to_type(quotes,
period_day=resample)
quotes.index = quotes.index.astype('datetime64')
if show_name:
if 'name' in quotes.columns:
cname = quotes.name[0]
# cname_g =cname
else:
dm = tdd.get_sina_data_df(code)
if 'name' in dm.columns:
cname = dm.name[0]
else:
cname = '-'
else:
cname = '-'
if quotes is not None and len(quotes) > 0:
quotes = quotes.loc[:, [
'open', 'close', 'high', 'low', 'vol', 'amount'
]]
else:
# log.error("quotes is None check:%s"%(code))
raise Exception("Code:%s error, df is None%s" % (code))
return quotes, cname
quotes, cname = get_quotes_tdx(stock_code,
start_date,
end_date,
dl=stock_days,
resample=resample,
show_name=show_mpl)
# quotes.rename(columns={'amount': 'money'}, inplace=True)
# quotes.rename(columns={'vol': 'vol'}, inplace=True)
# print quotes[-2:]
# print quotes[:1]
# 缠论k线
# open close high low volume money
# 2017-05-03 15.69 15.66 15.73 15.53 10557743 165075887
# 2017-05-04 15.66 15.63 15.70 15.52 8343270 130330396
# 2017-05-05 15.56 15.65 15.68 15.41 18384031 285966842
# 2017-05-08 15.62 15.75 15.76 15.54 12598891 197310688
quotes = chan.parse2ChanK(quotes, quotes.values) if chanK_flag else quotes
# print quotes[:1].index
# print quotes[-1:].index
quotes[quotes['vol'] == 0] = np.nan
quotes = quotes.dropna()
Close = quotes['close']
Open = quotes['open']
High = quotes['high']
Low = quotes['low']
T0 = quotes.index.values
# T0 = mdates.date2num(T0)
length = len(Close)
initial_trend = "down"
cur_ji = 1 if stock_frequency == 'd' else \
2 if stock_frequency == '30m' else \
3 if stock_frequency == '5m' else \
4 if stock_frequency == 'w' else \
5 if stock_frequency == 'm' else 6
log.debug('======笔形成最后一段未完成段判断是否是次级别的走势形成笔=======:%s %s' %
(stock_frequency, cur_ji))
x_date_list = quotes.index.values.tolist()
# for x_date in x_date_list:
# d = datetime.datetime.fromtimestamp(x_date/1000000000)
# print d.strftime("%Y-%m-%d %H:%M:%S.%f")
# print x_date_list
k_data = quotes
k_values = k_data.values
# 缠论k线
chanK = quotes if chanK_flag else chan.parse2ChanK(
k_data, k_values, chan_kdf=chanK_flag)
fenTypes, fenIdx = chan.parse2ChanFen(chanK)
# log.debug("code:%s fenTypes:%s fenIdx:%s k_data:%s" % (stock_code,fenTypes, fenIdx, len(k_data)))
biIdx, frsBiType = chan.parse2ChanBi(fenTypes,
fenIdx,
chanK,
least_khl_num=least_khl_num)
# log.debug("biIdx1:%s chanK:%s" % (biIdx, len(chanK)))
print("biIdx1:%s %s chanK:%s" %
(biIdx, str(chanK.index.values[biIdx[-1]])[:10], len(chanK)))
biIdx = con2Cxianduan(stock_code,
k_data,
chanK,
frsBiType,
biIdx,
end_date,
cur_ji,
least_init=least_init)
# log.debug("biIdx2:%s chanK:%s" % (biIdx, len(biIdx)))
chanKIdx = [(chanK.index[x]) for x in biIdx]
if len(biIdx) == 0 and len(chanKIdx) == 0:
print "BiIdx is None and chanKidx is None:%s" % (code)
return None
log.debug("con2Cxianduan:%s chanK:%s %s" %
(biIdx, len(chanK), chanKIdx[-1] if len(chanKIdx) > 0 else None))
# print quotes['close'].apply(lambda x:round(x,2))
# print '股票代码', get_security_info(stock_code).display_name
# print '股票代码', (stock_code), resample, least_khl_num
# 3.得到分笔结果，计算坐标显示
def plot_fenbi_seq(biIdx, frsBiType, plt=None, color=None):
x_fenbi_seq = []
y_fenbi_seq = []
for i in range(len(biIdx)):
if biIdx[i] is not None:
fenType = -frsBiType if i % 2 == 0 else frsBiType
# dt = chanK['enddate'][biIdx[i]]
# 缠论k线
dt = chanK.index[biIdx[i]] if chanK_flag else chanK['enddate'][
biIdx[i]]
# print i,k_data['high'][dt], k_data['low'][dt]
time_long = long(
time.mktime(
(dt + datetime.timedelta(hours=8)).timetuple()) *
1000000000)
# print x_date_list.index(time_long) if time_long in x_date_list else 0
if fenType == 1:
if plt is not None:
if color is None:
plt.text(x_date_list.index(time_long),
k_data['high'][dt],
str(k_data['high'][dt]),
ha='left',
fontsize=12)
else:
col_v = color[0] if fenType > 0 else color[1]
plt.text(x_date_list.index(time_long),
k_data['high'][dt],
str(k_data['high'][dt]),
ha='left',
fontsize=12,
bbox=dict(facecolor=col_v, alpha=0.5))
x_fenbi_seq.append(x_date_list.index(time_long))
y_fenbi_seq.append(k_data['high'][dt])
if fenType == -1:
if plt is not None:
if color is None:
plt.text(x_date_list.index(time_long),
k_data['low'][dt],
str(k_data['low'][dt]),
va='bottom',
fontsize=12)
else:
col_v = color[0] if fenType > 0 else color[1]
plt.text(x_date_list.index(time_long),
k_data['low'][dt],
str(k_data['low'][dt]),
va='bottom',
fontsize=12,
bbox=dict(facecolor=col_v, alpha=0.5))
x_fenbi_seq.append(x_date_list.index(time_long))
y_fenbi_seq.append(k_data['low'][dt])
# bottom_time = None
# for k_line_dto in m_line_dto.member_list[::-1]:
# if k_line_dto.low == m_line_dto.low:
# # get_price返回的日期，默认时间是08:00:00
# bottom_time = k_line_dto.begin_time.strftime('%Y-%m-%d') +' 08:00:00'
# break
# x_fenbi_seq.append(x_date_list.index(long(time.mktime(datetime.strptime(bottom_time, "%Y-%m-%d %H:%M:%S").timetuple())*1000000000)))
# y_fenbi_seq.append(m_line_dto.low)
return x_fenbi_seq, y_fenbi_seq
# print T0[-len(T0):].astype(dt.date)
T1 = T0[-len(T0):].astype(datetime.date) / 1000000000
Ti = []
if len(T0) / x_jizhun > 12:
x_jizhun = len(T0) / 12
for i in range(len(T0) / x_jizhun):
# print "len(T0)/x_jizhun:",len(T0)/x_jizhun
a = i * x_jizhun
d = datetime.date.fromtimestamp(T1[a])
# print d
T2 = d.strftime('$%Y-%m-%d$')
Ti.append(T2)
# print tab
d1 = datetime.date.fromtimestamp(T1[len(T0) - 1])
d2 = (d1 + datetime.timedelta(days=1)).strftime('$%Y-%m-%d$')
Ti.append(d2)
ll = Low.min() * 0.97
hh = High.max() * 1.03
# ht = HoverTool(tooltips=[
# ("date", "@date"),
# ("open", "@open"),
# ("close", "@close"),
# ("high", "@high"),
# ("low", "@low"),
# ("volume", "@volume"),
# ("money", "@money"),])
# TOOLS = [ht, WheelZoomTool(dimensions=['width']),\
# ResizeTool(), ResetTool(),\
# PanTool(dimensions=['width']), PreviewSaveTool()]
if show_mpl:
fig = plt.figure(figsize=(10, 6))
ax1 = plt.subplot2grid((10, 1), (0, 0), rowspan=8, colspan=1)
# ax1 = fig.add_subplot(2,1,1)
#fig = plt.figure()
#ax1 = plt.axes([0,0,3,2])
X = np.array(range(0, length))
pad_nan = X + nan
# 计算上 下影线
max_clop = Close.copy()
max_clop[Close < Open] = Open[Close < Open]
min_clop = Close.copy()
min_clop[Close > Open] = Open[Close > Open]
# 上影线
line_up = np.array([High, max_clop, pad_nan])
line_up = np.ravel(line_up, 'F')
# 下影线
line_down = np.array([Low, min_clop, pad_nan])
line_down = np.ravel(line_down, 'F')
# 计算上下影线对应的X坐标
pad_nan = nan + X
pad_X = np.array([X, X, X])
pad_X = np.ravel(pad_X, 'F')
# 画出实体部分,先画收盘价在上的部分
up_cl = Close.copy()
up_cl[Close <= Open] = nan
up_op = Open.copy()
up_op[Close <= Open] = nan
down_cl = Close.copy()
down_cl[Open <= Close] = nan
down_op = Open.copy()
down_op[Open <= Close] = nan
even = Close.copy()
even[Close != Open] = nan
# 画出收红的实体部分
pad_box_up = np.array([up_op, up_op, up_cl, up_cl, pad_nan])
pad_box_up = np.ravel(pad_box_up, 'F')
pad_box_down = np.array([down_cl, down_cl, down_op, down_op, pad_nan])
pad_box_down = np.ravel(pad_box_down, 'F')
pad_box_even = np.array([even, even, even, even, pad_nan])
pad_box_even = np.ravel(pad_box_even, 'F')
# X的nan可以不用与y一一对应
X_left = X - 0.25
X_right = X + 0.25
box_X = np.array([X_left, X_right, X_right, X_left, pad_nan])
# print box_X
box_X = np.ravel(box_X, 'F')
# print box_X
# Close_handle=plt.plot(pad_X,line_up,color='k')
vertices_up = np.array([box_X, pad_box_up]).T
vertices_down = np.array([box_X, pad_box_down]).T
vertices_even = np.array([box_X, pad_box_even]).T
handle_box_up = mat.patches.Polygon(vertices_up, color='r', zorder=1)
handle_box_down = mat.patches.Polygon(vertices_down,
color='g',
zorder=1)
handle_box_even = mat.patches.Polygon(vertices_even,
color='k',
zorder=1)
ax1.add_patch(handle_box_up)
ax1.add_patch(handle_box_down)
ax1.add_patch(handle_box_even)
handle_line_up = mat.lines.Line2D(pad_X,
line_up,
color='k',
linestyle='solid',
zorder=0)
handle_line_down = mat.lines.Line2D(pad_X,
line_down,
color='k',
linestyle='solid',
zorder=0)
ax1.add_line(handle_line_up)
ax1.add_line(handle_line_down)
v = [0, length, Open.min() - 0.5, Open.max() + 0.5]
plt.axis(v)
ax1.set_xticks(np.linspace(-2, len(Close) + 2, len(Ti)))
ax1.set_ylim(ll, hh)
ax1.set_xticklabels(Ti)
plt.grid(True)
plt.setp(plt.gca().get_xticklabels(),
rotation=30,
horizontalalignment='right')
'''
以上代码拷贝自https://www.joinquant.com/post/1756
感谢alpha-smart-dog
K线图绘制完毕
'''
# print "biIdx:%s chankIdx:%s"%(biIdx,str(chanKIdx[-1])[:10])
if show_mpl:
x_fenbi_seq, y_fenbi_seq = plot_fenbi_seq(biIdx, frsBiType, plt)
# plot_fenbi_seq(fenIdx,fenTypes[0], plt,color=['red','green'])
plot_fenbi_seq(fenIdx, frsBiType, plt, color=['red', 'green'])
else:
x_fenbi_seq, y_fenbi_seq = plot_fenbi_seq(biIdx, frsBiType, plt=None)
plot_fenbi_seq(fenIdx, frsBiType, plt=None, color=['red', 'green'])
# 在原图基础上添加分笔蓝线
inx_value = chanK.high.values
inx_va = [round(inx_value[x], 2) for x in biIdx]
log.debug("inx_va:%s count:%s" % (inx_va, len(quotes.high)))
log.debug("yfenbi:%s count:%s" % ([round(y, 2)
for y in y_fenbi_seq], len(chanK)))
j_BiType = [
-frsBiType if i % 2 == 0 else frsBiType for i in range(len(biIdx))
]
BiType_s = j_BiType[-1] if len(j_BiType) > 0 else -2
# bi_price = [str(chanK.low[idx]) if i % 2 == 0 else str(chanK.high[idx]) for i,idx in enumerate(biIdx)]
# print ("笔 :%s %s"%(biIdx,bi_price))
# fen_dt = [str(chanK.index[fenIdx[i]])[:10] if chanK_flag else str(chanK['enddate'][fenIdx[i]])[:10]for i in range(len(fenIdx))]
fen_dt = [(chanK.index[fenIdx[i]]) if chanK_flag else
(chanK['enddate'][fenIdx[i]]) for i in range(len(fenIdx))]
if len(fenTypes) > 0:
if fenTypes[0] == -1:
# fen_price = [str(k_data.low[idx]) if i % 2 == 0 else str(k_data.high[idx]) for i,idx in enumerate(fen_dt)]
low_fen = [idx for i, idx in enumerate(fen_dt) if i % 2 == 0]
high_fen = [idx for i, idx in enumerate(fen_dt) if i % 2 <> 0]
else:
# fen_price = [str(k_data.high[idx]) if i % 2 == 0 else str(k_data.low[idx]) for i,idx in enumerate(fen_dt)]
high_fen = [idx for i, idx in enumerate(fen_dt) if i % 2 == 0]
low_fen = [idx for i, idx in enumerate(fen_dt) if i % 2 <> 0]
# fen_duration =[fenIdx[i] - fenIdx[i -1 ] if i >0 else 0 for i,idx in enumerate(fenIdx)]
else:
# fen_price = fenTypes
# fen_duration = fenTypes
low_fen = []
high_fen = []
# fen_dt = [str(k_data.index[idx])[:10] for i,idx in enumerate(fenIdx)]
# print low_fen,high_fen
def dataframe_mode_round(df):
roundlist = [1, 0]
df_mode = []
# df.high.cummin().value_counts()
for i in roundlist:
df_mode = df.apply(lambda x: round(x, i)).mode()
if len(df_mode) > 0:
break
return df_mode
kdl = k_data.loc[low_fen].low
kdl_mode = dataframe_mode_round(kdl)
kdh = k_data.loc[high_fen].high
kdh_mode = dataframe_mode_round(kdh)
print("kdl:%s" % (kdl.values))
print("kdh:%s" % (kdh.values))
print("kdl_mode:%s kdh_mode%s chanKidx:%s" %
(kdl_mode.values, kdh_mode.values, str(chanKIdx[-1])[:10]))
lastdf = k_data[k_data.index >= chanKIdx[-1]]
if BiType_s == -1:
keydf = lastdf[((lastdf.close >= kdl_mode.max()) &
(lastdf.low >= kdl_mode.max()))]
elif BiType_s == 1:
keydf = lastdf[((lastdf.close >= kdh_mode.max()) &
(lastdf.high >= kdh_mode.min()))]
else:
keydf = lastdf[((lastdf.close >= kdh_mode.max()) &
(lastdf.high >= kdh_mode.min())) |
((lastdf.close <= kdl_mode.min()) &
(lastdf.low <= kdl_mode.min()))]
print("BiType_s:%s keydf:%s key:%s" %
(BiType_s, None if len(keydf) == 0 else str(
keydf.index.values[0])[:10], len(keydf)))
# return BiType_s,None if len(keydf) == 0 else str(keydf.index.values[0])[:10],len(keydf)
# import ipdb;ipdb.set_trace()
log.debug("Fentype:%s " % (fenTypes))
log.debug("fenIdx:%s " % (fenIdx))
# print ("fen_duration:%s "%(fen_duration))
# print ("fen_price:%s "%(fen_price))
# print ("fendt:%s "%(fen_dt))
print("BiType :%s frsBiType:%s" % (j_BiType, frsBiType))
if len(j_BiType) > 0:
if j_BiType[0] == -1:
tb_price = [
str(quotes.low[idx]) if i % 2 == 0 else str(quotes.high[idx])
for i, idx in enumerate(x_fenbi_seq)
]
else:
tb_price = [
str(quotes.high[idx]) if i % 2 == 0 else str(quotes.low[idx])
for i, idx in enumerate(x_fenbi_seq)
]
tb_duration = [
x_fenbi_seq[i] - x_fenbi_seq[i - 1] if i > 0 else 0
for i, idx in enumerate(x_fenbi_seq)
]
else:
tb_price = j_BiType
tb_duration = j_BiType
print "图笔 :", x_fenbi_seq, tb_price
print "图笔dura :", tb_duration
# 线段画到笔上
xdIdxs, xfenTypes = chan.parse2ChanXD(frsBiType, biIdx, chanK)
print '线段', xdIdxs, xfenTypes
x_xd_seq = []
y_xd_seq = []
for i in range(len(xdIdxs)):
if xdIdxs[i] is not None:
fenType = xfenTypes[i]
# dt = chanK['enddate'][biIdx[i]]
# 缠论k线
dt = chanK.index[xdIdxs[i]] if chanK_flag else chanK['enddate'][
xdIdxs[i]]
# print k_data['high'][dt], k_data['low'][dt]
time_long = long(
time.mktime((dt + datetime.timedelta(hours=8)).timetuple()) *
1000000000)
# print x_date_list.index(time_long) if time_long in x_date_list else 0
if fenType == 1:
x_xd_seq.append(x_date_list.index(time_long))
y_xd_seq.append(k_data['high'][dt])
if fenType == -1:
x_xd_seq.append(x_date_list.index(time_long))
y_xd_seq.append(k_data['low'][dt])
# bottom_time = None
# for k_line_dto in m_line_dto.member_list[::-1]:
# if k_line_dto.low == m_line_dto.low:
# # get_price返回的日期，默认时间是08:00:00
# bottom_time = k_line_dto.begin_time.strftime('%Y-%m-%d') +' 08:00:00'
# break
# x_fenbi_seq.append(x_date_list.index(long(time.mktime(datetime.strptime(bottom_time, "%Y-%m-%d %H:%M:%S").timetuple())*1000000000)))
# y_fenbi_seq.append(m_line_dto.low)
# 在原图基础上添加分笔蓝线
print("线段 :%s" % (x_xd_seq))
print("笔值 :%s" % ([str(x) for x in (y_xd_seq)]))
# Y_hat = X * b + a
if show_mpl:
plt.plot(x_fenbi_seq, y_fenbi_seq)
plt.legend([stock_code, cname], loc=0)
plt.title(stock_code + " | " + cname + " | " +
str(quotes.index[-1])[:10],
fontsize=14)
plt.plot(x_xd_seq, y_xd_seq)
if len(quotes) > windows:
roll_mean = pd.rolling_mean(quotes.close, window=windows)
plt.plot(roll_mean, 'r')
zp = zoompan.ZoomPan()
figZoom = zp.zoom_factory(ax1, base_scale=1.1)
figPan = zp.pan_factory(ax1)
'''#subplot2 bar
ax2 = plt.subplot2grid((10, 1), (8, 0), rowspan=2, colspan=1)
# ax2.plot(quotes.vol)
# ax2.set_xticks(np.linspace(-2, len(quotes) + 2, len(Ti)))
ll = min(quotes.vol.values.tolist()) * 0.97
hh = max(quotes.vol.values.tolist()) * 1.03
ax2.set_ylim(ll, hh)
# ax2.set_xticklabels(Ti)
# plt.hist(quotes.vol, histtype='bar', rwidth=0.8)
plt.bar(x_date_list,quotes.vol, label="Volume", color='b')
'''
#画Volume no tight_layout()
'''
pad = 0.25
yl = ax1.get_ylim()
ax1.set_ylim(yl[0]-(yl[1]-yl[0])*pad,yl[1])
ax2 = ax1.twinx()
ax2.set_position(mat.transforms.Bbox([[0.125,0.1],[0.9,0.32]]))
volume = np.asarray(quotes.amount)
pos = quotes['open']-quotes['close']<0
neg = quotes['open']-quotes['close']>=0
idx = quotes.reset_index().index
ax2.bar(idx[pos],volume[pos],color='red',width=1,align='center')
ax2.bar(idx[neg],volume[neg],color='green',width=1,align='center')
yticks = ax2.get_yticks()
ax2.set_yticks(yticks[::3])
'''
# same sharex
plt.subplots_adjust(left=0.05,
bottom=0.08,
right=0.95,
top=0.95,
wspace=0.15,
hspace=0.00)
plt.setp(ax1.get_xticklabels(), visible=False)
yl = ax1.get_ylim()
# ax2 = plt.subplot(212, sharex=ax1)
ax2 = plt.subplot2grid((10, 1), (8, 0),
rowspan=2,
colspan=1,
sharex=ax1)
# ax2.set_position(mat.transforms.Bbox([[0.125,0.1],[0.9,0.32]]))
volume = np.asarray(quotes.amount)
pos = quotes['open'] - quotes['close'] < 0
neg = quotes['open'] - quotes['close'] >= 0
idx = quotes.reset_index().index
ax2.bar(idx[pos], volume[pos], color='red', width=1, align='center')
ax2.bar(idx[neg], volume[neg], color='green', width=1, align='center')
yticks = ax2.get_yticks()
ax2.set_yticks(yticks[::3])
# plt.tight_layout()
# plt.subplots_adjust(hspace=0.00, bottom=0.08)
plt.xticks(rotation=15, horizontalalignment='center')
# plt.bar(x_date_list,quotes.vol, label="Volume", color='b')
# quotes['vol'].plot(kind='bar', ax=ax2, color='g', alpha=0.1)
# ax2.set_ylim([0, ax2.get_ylim()[1] * 2])
# plt.gcf().subplots_adjust(bottom=0.15)
# fig.subplots_adjust(left=0.05, bottom=0.08, right=0.95, top=0.95, wspace=0.15, hspace=0.25)
#scale the x-axis tight
# ax2.set_xlim(min(x_date_list),max(x_date_list))
# the y-ticks for the bar were too dense, keep only every third one
# plt.grid(True)
# plt.xticks(rotation=30, horizontalalignment='center')
# plt.setp( axs[1].xaxis.get_majorticklabels(), rotation=70 )
# plt.legend()
# plt.tight_layout()
# plt.draw()
# plt.show()
plt.show(block=False)
def show_patches():
dirname = C.TRAIN.SAMPLES.DIR
labels = EasyDict(pos=EasyDict(), neg=EasyDict())
labels.pos.samples = pd.read_csv(os.path.join(dirname, 'positives.csv'))
labels.pos.index = 0
labels.neg.samples = pd.read_csv(os.path.join(dirname, 'negatives.csv'))
labels.neg.index = 0
labels.label = 'pos'
fig = plt.figure()
ax1 = plt.subplot(131)
ax2 = plt.subplot(132)
ax3 = plt.subplot(133)
plt.subplots_adjust(bottom=0.15)
# Slider to choose the sample
ax_slider = plt.axes([0.25, 0.05, 0.65, 0.05],
facecolor='lightgoldenrodyellow')
slider = Slider(ax_slider,
'Index',
0,
len(labels[labels.label].samples),
valinit=labels[labels.label].index,
valstep=1,
closedmax=False)
# Radio buttons to choose the label
ax_label = plt.axes([0.025, 0.05, 0.1, 0.1])
radio_buttons = RadioButtons(ax_label, ['pos', 'neg'], active=0)
ax_help = plt.axes([0.25, 0.10, 0.65, 0.05])
ax_help.axis('off')
ax_help.text(0, 0.5,
'Press [j,k] to change the index, [p,n] to set the label.')
def update(dummy):
if str(radio_buttons.value_selected) != labels.label:
labels.label = radio_buttons.value_selected
slider.valmax = len(labels[labels.label].samples)
slider.val = labels[labels.label].index
ax_slider.set_xlim(0, slider.valmax)
labels[labels.label].index = int(slider.val)
current = labels[labels.label].samples.iloc[labels[labels.label].index]
with open(os.path.join(dirname, current['name']), 'rb') as f:
patch = pickle.load(f)
print(f.name)
plt.suptitle(patch.name)
vol = patch.volumetric[2:2 + 3].transpose(1, 2, 0)
display_vol = 2 * np.arctan(vol) / np.pi
radius = patch.rgb.shape[1] / 2
extent = (-radius, radius, -radius, radius)
ax1.clear()
ax2.clear()
ax3.clear()
ax1.imshow(patch.rgb.transpose(1, 2, 0), extent=extent)
ax1.set_title('rgb')
ax2.imshow(patch.rgb.transpose(1, 2, 0), extent=extent)
ax2.imshow(display_vol, extent=extent, alpha=0.5)
if patch.obb is not None:
patch.obb.plot(ax2, lw=4, color='yellow')
patch.obb.plot(ax2, lw=3, ls='--', color='red')
ax2.set_title('both')
ax3.imshow(display_vol, extent=extent)
ax3.set_title('vol:max={:.1f}'.format(vol.max()))
fig.canvas.draw_idle()
# First plot
update(0)
radio_buttons.on_clicked(update)
slider.on_changed(update)
def keypress(event):
if event.key == 'j':
slider.set_val(slider.val - 1)
elif event.key == 'k':
slider.set_val(slider.val + 1)
elif event.key == 'p':
radio_buttons.set_active(0)
elif event.key == 'n':
radio_buttons.set_active(1)
fig.canvas.mpl_connect('key_press_event', keypress)
plt.show()
def get_linear_model_histogramDouble(code, ptype='f', dtype='d', start=None, end=None, vtype='close', filter='n',
df=None):
# 399001','cyb':'zs399006','zxb':'zs399005
# code = '999999'
# code = '601608'
# code = '000002'
# asset = ts.get_hist_data(code)['close'].sort_index(ascending=True)
# df = tdd.get_tdx_Exp_day_to_df(code, 'f').sort_index(ascending=True)
# vtype='close'
# if vtype == 'close' or vtype==''
# ptype=
if start is not None and filter == 'y':
if code not in ['999999', '399006', '399001']:
index_d, dl = tdd.get_duration_Index_date(dt=start)
log.debug("index_d:%s dl:%s" % (str(index_d), dl))
else:
index_d = cct.day8_to_day10(start)
log.debug("index_d:%s" % (index_d))
start = tdd.get_duration_price_date(code, ptype='low', dt=index_d)
log.debug("start:%s" % (start))
if df is None:
# df = tdd.get_tdx_append_now_df(code, ptype, start, end).sort_index(ascending=True)
df = tdd.get_tdx_append_now_df_api(code, ptype, start, end).sort_index(ascending=True)
if not dtype == 'd':
df = tdd.get_tdx_stock_period_to_type(df, dtype).sort_index(ascending=True)
asset = df[vtype]
log.info("df:%s" % asset[:1])
asset = asset.dropna()
dates = asset.index
if not code.startswith('999') or not code.startswith('399'):
if code[:1] in ['5', '6', '9']:
code2 = '999999'
elif code[:1] in ['3']:
code2 = '399006'
else:
code2 = '399001'
df1 = tdd.get_tdx_append_now_df_api(code2, ptype, start, end).sort_index(ascending=True)
# df1 = tdd.get_tdx_append_now_df(code2, ptype, start, end).sort_index(ascending=True)
if not dtype == 'd':
df1 = tdd.get_tdx_stock_period_to_type(df1, dtype).sort_index(ascending=True)
asset1 = df1.loc[asset.index, vtype]
startv = asset1[:1]
# asset_v=asset[:1]
# print startv,asset_v
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# print asset1[:4]
# 画出价格随时间变化的图像
# _, ax = plt.subplots()
# fig = plt.figure()
fig = plt.figure(figsize=(16, 10))
# fig = plt.figure(figsize=(16, 10), dpi=72)
# fig.autofmt_xdate() #(no fact)
# plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
plt.subplots_adjust(left=0.05, bottom=0.08, right=0.95, top=0.95, wspace=0.15, hspace=0.25)
# set (gca,'Position',[0,0,512,512])
# fig.set_size_inches(18.5, 10.5)
# fig=plt.fig(figsize=(14,8))
ax1 = fig.add_subplot(321)
# asset=asset.apply(lambda x:round( x/asset[:1],2))
ax1.plot(asset)
# ax1.plot(asset1,'-r', linewidth=2)
ticks = ax1.get_xticks()
# start, end = ax1.get_xlim()
# print start, end, len(asset)
# print ticks, ticks[:-1]
# (ticks[:-1] if len(asset) > end else np.append(ticks[:-1], len(asset) - 1))
ax1.set_xticklabels([dates[i] for i in (np.append(ticks[:-1], len(asset) - 1))],
rotation=15) # Label x-axis with dates
# 拟合
X = np.arange(len(asset))
x = sm.add_constant(X)
model = regression.linear_model.OLS(asset, x).fit()
a = model.params[0]
b = model.params[1]
# log.info("a:%s b:%s" % (a, b))
log.info("X:%s a:%s b:%s" % (len(asset), a, b))
Y_hat = X * b + a
# 真实值-拟合值，差值最大最小作为价值波动区间
# 向下平移
i = (asset.values.T - Y_hat).argmin()
c_low = X[i] * b + a - asset.values[i]
Y_hatlow = X * b + a - c_low
# 向上平移
i = (asset.values.T - Y_hat).argmax()
c_high = X[i] * b + a - asset.values[i]
Y_hathigh = X * b + a - c_high
plt.plot(X, Y_hat, 'k', alpha=0.9);
plt.plot(X, Y_hatlow, 'r', alpha=0.9);
plt.plot(X, Y_hathigh, 'r', alpha=0.9);
# plt.xlabel('Date', fontsize=12)
plt.ylabel('Price', fontsize=12)
plt.title(code + " | " + str(dates[-1])[:11], fontsize=14)
plt.legend([asset.iat[-1]], fontsize=12, loc=4)
plt.grid(True)
# plt.legend([code]);
# plt.legend([code, 'Value center line', 'Value interval line']);
# fig=plt.fig()
# fig.figsize = [14,8]
scale = 1.1
zp = zoompan.ZoomPan()
figZoom = zp.zoom_factory(ax1, base_scale=scale)
figPan = zp.pan_factory(ax1)
ax2 = fig.add_subplot(323)
# ax2.plot(asset)
# ticks = ax2.get_xticks()
ax2.set_xticklabels([dates[i] for i in (np.append(ticks[:-1], len(asset) - 1))], rotation=15)
# plt.plot(X, Y_hat, 'k', alpha=0.9)
n = 5
d = (-c_high + c_low) / n
c = c_high
while c <= c_low:
Y = X * b + a - c
plt.plot(X, Y, 'r', alpha=0.9);
c = c + d
# asset=asset.apply(lambda x:round(x/asset[:1],2))
ax2.plot(asset)
# ax2.plot(asset1,'-r', linewidth=2)
# plt.xlabel('Date', fontsize=12)
plt.ylabel('Price', fontsize=12)
plt.grid(True)
# plt.title(code, fontsize=14)
# plt.legend([code])
# 将Y-Y_hat股价偏离中枢线的距离单画出一张图显示，对其边界线之间的区域进行均分，大于0的区间为高估，小于0的区间为低估，0为价值中枢线。
ax3 = fig.add_subplot(322)
# distance = (asset.values.T - Y_hat)
distance = (asset.values.T - Y_hat)[0]
if code.startswith('999') or code.startswith('399'):
ax3.plot(asset)
plt.plot(distance)
ticks = ax3.get_xticks()
ax3.set_xticklabels([dates[i] for i in (np.append(ticks[:-1], len(asset) - 1))], rotation=15)
n = 5
d = (-c_high + c_low) / n
c = c_high
while c <= c_low:
Y = X * b + a - c
plt.plot(X, Y - Y_hat, 'r', alpha=0.9);
c = c + d
ax3.plot(asset)
# plt.xlabel('Date', fontsize=12)
plt.ylabel('Price-center price', fontsize=14)
plt.grid(True)
else:
as3 = asset.apply(lambda x: round(x / asset[:1], 2))
ax3.plot(as3)
ax3.plot(asset1, '-r', linewidth=2)
plt.grid(True)
zp3 = zoompan.ZoomPan()
figZoom = zp3.zoom_factory(ax3, base_scale=scale)
figPan = zp3.pan_factory(ax3)
# plt.title(code, fontsize=14)
# plt.legend([code])
# 统计出每个区域内各股价的频数，得到直方图，为了更精细的显示各个区域的频数，这里将整个边界区间分成100份。
ax4 = fig.add_subplot(325)
log.info("assert:len:%s %s" % (len(asset.values.T - Y_hat), (asset.values.T - Y_hat)[0]))
# distance = map(lambda x:int(x),(asset.values.T - Y_hat)/Y_hat*100)
# now_distanse=int((asset.iat[-1]-Y_hat[-1])/Y_hat[-1]*100)
# log.debug("dis:%s now:%s"%(distance[:2],now_distanse))
# log.debug("now_distanse:%s"%now_distanse)
distance = (asset.values.T - Y_hat)
now_distanse = asset.iat[-1] - Y_hat[-1]
# distance = (asset.values.T-Y_hat)[0]
pd.Series(distance).plot(kind='hist', stacked=True, bins=100)
# plt.plot((asset.iat[-1].T-Y_hat),'b',alpha=0.9)
plt.axvline(now_distanse, hold=None, label="1", color='red')
# plt.axhline(now_distanse,hold=None,label="1",color='red')
# plt.axvline(asset.iat[0],hold=None,label="1",color='red',linestyle="--")
plt.xlabel('Undervalue ------------------------------------------> Overvalue', fontsize=12)
plt.ylabel('Frequency', fontsize=14)
# plt.title('Undervalue & Overvalue Statistical Chart', fontsize=14)
plt.legend([code, asset.iat[-1], str(dates[-1])[5:11]], fontsize=12)
plt.grid(True)
# plt.show()
# import os
# print(os.path.abspath(os.path.curdir))
ax5 = fig.add_subplot(326)
# fig.figsize=(5, 10)
log.info("assert:len:%s %s" % (len(asset.values.T - Y_hat), (asset.values.T - Y_hat)[0]))
# distance = map(lambda x:int(x),(asset.values.T - Y_hat)/Y_hat*100)
distance = (asset.values.T - Y_hat) / Y_hat * 100
now_distanse = ((asset.iat[-1] - Y_hat[-1]) / Y_hat[-1] * 100)
log.debug("dis:%s now:%s" % (distance[:2], now_distanse))
log.debug("now_distanse:%s" % now_distanse)
# n, bins = np.histogram(distance, 50)
# print n, bins[:2]
pd.Series(distance).plot(kind='hist', stacked=True, bins=100)
# plt.plot((asset.iat[-1].T-Y_hat),'b',alpha=0.9)
plt.axvline(now_distanse, hold=None, label="1", color='red')
# plt.axhline(now_distanse,hold=None,label="1",color='red')
# plt.axvline(asset.iat[0],hold=None,label="1",color='red',linestyle="--")
plt.xlabel('Undervalue ------------------------------------------> Overvalue', fontsize=14)
plt.ylabel('Frequency', fontsize=12)
# plt.title('Undervalue & Overvalue Statistical Chart', fontsize=14)
plt.legend([code, asset.iat[-1]], fontsize=12)
plt.grid(True)
ax6 = fig.add_subplot(324)
h = df.loc[:, ['open', 'close', 'high', 'low']]
highp = h['high'].values
lowp = h['low'].values
openp = h['open'].values
closep = h['close'].values
lr = LinearRegression()
x = np.atleast_2d(np.linspace(0, len(closep), len(closep))).T
lr.fit(x, closep)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
xt = np.atleast_2d(np.linspace(0, len(closep) + 200, len(closep) + 200)).T
yt = lr.predict(xt)
bV = []
bP = []
for i in range(1, len(highp) - 1):
if highp[i] <= highp[i - 1] and highp[i] < highp[i + 1] and lowp[i] <= lowp[i - 1] and lowp[i] < lowp[i + 1]:
bV.append(lowp[i])
bP.append(i)
d, p = LIS(bV)
idx = []
for i in range(len(p)):
idx.append(bP[p[i]])
lr = LinearRegression()
X = np.atleast_2d(np.array(idx)).T
Y = np.array(d)
lr.fit(X, Y)
estV = lr.predict(xt)
ax6.plot(closep, linewidth=2)
ax6.plot(idx, d, 'ko')
ax6.plot(xt, estV, '-r', linewidth=3)
ax6.plot(xt, yt, '-g', linewidth=3)
plt.grid(True)
# plt.tight_layout()
zp2 = zoompan.ZoomPan()
figZoom = zp2.zoom_factory(ax6, base_scale=scale)
figPan = zp2.pan_factory(ax6)
# plt.ion()
plt.show(block=False)
def get_linear_model_histogram(code, ptype="f", dtype="d", start=None, end=None):
# 399001','cyb':'zs399006','zxb':'zs399005
# code = '999999'
# code = '601608'
# code = '000002'
# asset = ts.get_hist_data(code)['close'].sort_index(ascending=True)
# df = tdd.get_tdx_Exp_day_to_df(code, 'f').sort_index(ascending=True)
df = tdd.get_tdx_append_now_df(code, ptype, start, end).sort_index(ascending=True)
if not dtype == "d":
df = tdd.get_tdx_stock_period_to_type(df, dtype).sort_index(ascending=True)
asset = df["close"]
log.info("df:%s" % asset[:1])
asset = asset.dropna()
dates = asset.index
if not code.startswith("999") or not code.startswith("399"):
if code[:1] in ["5", "6", "9"]:
code2 = "999999"
elif code[:1] in ["3"]:
code2 = "399006"
else:
code2 = "399001"
df1 = tdd.get_tdx_append_now_df(code2, ptype, start, end).sort_index(ascending=True)
if not dtype == "d":
df1 = tdd.get_tdx_stock_period_to_type(df1, dtype).sort_index(ascending=True)
asset1 = df1.loc[asset.index, "close"]
startv = asset1[:1]
# asset_v=asset[:1]
# print startv,asset_v
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# print asset1[:4]
# 画出价格随时间变化的图像
# _, ax = plt.subplots()
# fig = plt.figure()
fig = plt.figure(figsize=(16, 10))
# fig = plt.figure(figsize=(16, 10), dpi=72)
# plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
plt.subplots_adjust(left=0.05, bottom=0.08, right=0.95, top=0.95, wspace=0.15, hspace=0.25)
# set (gca,'Position',[0,0,512,512])
# fig.set_size_inches(18.5, 10.5)
# fig=plt.fig(figsize=(14,8))
ax1 = fig.add_subplot(321)
# asset=asset.apply(lambda x:round( x/asset[:1],2))
ax1.plot(asset)
# ax1.plot(asset1,'-r', linewidth=2)
ticks = ax1.get_xticks()
ax1.set_xticklabels([dates[i] for i in ticks[:-1]]) # Label x-axis with dates
# 拟合
X = np.arange(len(asset))
x = sm.add_constant(X)
model = regression.linear_model.OLS(asset, x).fit()
a = model.params[0]
b = model.params[1]
# log.info("a:%s b:%s" % (a, b))
log.info("X:%s a:%s b:%s" % (len(asset), a, b))
Y_hat = X * b + a
# 真实值-拟合值，差值最大最小作为价值波动区间
# 向下平移
i = (asset.values.T - Y_hat).argmin()
c_low = X[i] * b + a - asset.values[i]
Y_hatlow = X * b + a - c_low
# 向上平移
i = (asset.values.T - Y_hat).argmax()
c_high = X[i] * b + a - asset.values[i]
Y_hathigh = X * b + a - c_high
plt.plot(X, Y_hat, "k", alpha=0.9)
plt.plot(X, Y_hatlow, "r", alpha=0.9)
plt.plot(X, Y_hathigh, "r", alpha=0.9)
plt.xlabel("Date", fontsize=14)
plt.ylabel("Price", fontsize=14)
plt.title(code, fontsize=14)
plt.grid(True)
# plt.legend([code]);
# plt.legend([code, 'Value center line', 'Value interval line']);
# fig=plt.fig()
# fig.figsize = [14,8]
scale = 1.1
zp = zoompan.ZoomPan()
figZoom = zp.zoom_factory(ax1, base_scale=scale)
figPan = zp.pan_factory(ax1)
ax2 = fig.add_subplot(323)
ticks = ax2.get_xticks()
ax2.set_xticklabels([dates[i] for i in ticks[:-1]])
# plt.plot(X, Y_hat, 'k', alpha=0.9)
n = 5
d = (-c_high + c_low) / n
c = c_high
while c <= c_low:
Y = X * b + a - c
plt.plot(X, Y, "r", alpha=0.9)
c = c + d
# asset=asset.apply(lambda x:round(x/asset[:1],2))
ax2.plot(asset)
# ax2.plot(asset1,'-r', linewidth=2)
plt.xlabel("Date", fontsize=14)
plt.ylabel("Price", fontsize=14)
plt.grid(True)
# plt.title(code, fontsize=14)
# plt.legend([code])
# 将Y-Y_hat股价偏离中枢线的距离单画出一张图显示，对其边界线之间的区域进行均分，大于0的区间为高估，小于0的区间为低估，0为价值中枢线。
ax3 = fig.add_subplot(322)
# distance = (asset.values.T - Y_hat)
distance = (asset.values.T - Y_hat)[0]
if code.startswith("999") or code.startswith("399"):
ax3.plot(asset)
plt.plot(distance)
ticks = ax3.get_xticks()
ax3.set_xticklabels([dates[i] for i in ticks[:-1]])
n = 5
d = (-c_high + c_low) / n
c = c_high
while c <= c_low:
Y = X * b + a - c
plt.plot(X, Y - Y_hat, "r", alpha=0.9)
c = c + d
ax3.plot(asset)
plt.xlabel("Date", fontsize=14)
plt.ylabel("Price-center price", fontsize=14)
plt.grid(True)
else:
as3 = asset.apply(lambda x: round(x / asset[:1], 2))
ax3.plot(as3)
ax3.plot(asset1, "-r", linewidth=2)
plt.grid(True)
zp3 = zoompan.ZoomPan()
figZoom = zp3.zoom_factory(ax3, base_scale=scale)
figPan = zp3.pan_factory(ax3)
# plt.title(code, fontsize=14)
# plt.legend([code])
# 统计出每个区域内各股价的频数，得到直方图，为了更精细的显示各个区域的频数，这里将整个边界区间分成100份。
ax4 = fig.add_subplot(325)
log.info("assert:len:%s %s" % (len(asset.values.T - Y_hat), (asset.values.T - Y_hat)[0]))
# distance = map(lambda x:int(x),(asset.values.T - Y_hat)/Y_hat*100)
# now_distanse=int((asset.iat[-1]-Y_hat[-1])/Y_hat[-1]*100)
# log.debug("dis:%s now:%s"%(distance[:2],now_distanse))
# log.debug("now_distanse:%s"%now_distanse)
distance = asset.values.T - Y_hat
now_distanse = asset.iat[-1] - Y_hat[-1]
# distance = (asset.values.T-Y_hat)[0]
pd.Series(distance).plot(kind="hist", stacked=True, bins=100)
# plt.plot((asset.iat[-1].T-Y_hat),'b',alpha=0.9)
plt.axvline(now_distanse, hold=None, label="1", color="red")
# plt.axhline(now_distanse,hold=None,label="1",color='red')
# plt.axvline(asset.iat[0],hold=None,label="1",color='red',linestyle="--")
plt.xlabel("Undervalue ------------------------------------------> Overvalue", fontsize=14)
plt.ylabel("Frequency", fontsize=14)
# plt.title('Undervalue & Overvalue Statistical Chart', fontsize=14)
plt.legend([code, asset.iat[-1]])
plt.grid(True)
# plt.show()
# import os
# print(os.path.abspath(os.path.curdir))
ax5 = fig.add_subplot(326)
# fig.figsize=(5, 10)
log.info("assert:len:%s %s" % (len(asset.values.T - Y_hat), (asset.values.T - Y_hat)[0]))
# distance = map(lambda x:int(x),(asset.values.T - Y_hat)/Y_hat*100)
distance = (asset.values.T - Y_hat) / Y_hat * 100
now_distanse = (asset.iat[-1] - Y_hat[-1]) / Y_hat[-1] * 100
log.debug("dis:%s now:%s" % (distance[:2], now_distanse))
log.debug("now_distanse:%s" % now_distanse)
# n, bins = np.histogram(distance, 50)
# print n, bins[:2]
pd.Series(distance).plot(kind="hist", stacked=True, bins=100)
# plt.plot((asset.iat[-1].T-Y_hat),'b',alpha=0.9)
plt.axvline(now_distanse, hold=None, label="1", color="red")
# plt.axhline(now_distanse,hold=None,label="1",color='red')
# plt.axvline(asset.iat[0],hold=None,label="1",color='red',linestyle="--")
plt.xlabel("Undervalue ------------------------------------------> Overvalue", fontsize=14)
plt.ylabel("Frequency", fontsize=14)
# plt.title('Undervalue & Overvalue Statistical Chart', fontsize=14)
plt.legend([code, asset.iat[-1]])
plt.grid(True)
ax6 = fig.add_subplot(324)
h = df.loc[:, ["open", "close", "high", "low"]]
highp = h["high"].values
lowp = h["low"].values
openp = h["open"].values
closep = h["close"].values
lr = LinearRegression()
x = np.atleast_2d(np.linspace(0, len(closep), len(closep))).T
lr.fit(x, closep)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
xt = np.atleast_2d(np.linspace(0, len(closep) + 200, len(closep) + 200)).T
yt = lr.predict(xt)
bV = []
bP = []
for i in range(1, len(highp) - 1):
if highp[i] <= highp[i - 1] and highp[i] < highp[i + 1] and lowp[i] <= lowp[i - 1] and lowp[i] < lowp[i + 1]:
bV.append(lowp[i])
bP.append(i)
d, p = LIS(bV)
idx = []
for i in range(len(p)):
idx.append(bP[p[i]])
lr = LinearRegression()
X = np.atleast_2d(np.array(idx)).T
Y = np.array(d)
lr.fit(X, Y)
estV = lr.predict(xt)
ax6.plot(closep, linewidth=2)
ax6.plot(idx, d, "ko")
ax6.plot(xt, estV, "-r", linewidth=3)
ax6.plot(xt, yt, "-g", linewidth=3)
plt.grid(True)
# plt.tight_layout()
zp2 = zoompan.ZoomPan()
figZoom = zp2.zoom_factory(ax6, base_scale=scale)
figPan = zp2.pan_factory(ax6)
show()
