def contour(self, ax, X, Y, C, levels=20, label=None, **kwargs):
return Contour(
x=X,
y=Y,
z=C,
#ncontours=levels, contours=Contours(start=C.min(), end=C.max(), size=(C.max()-C.min())/levels),
name=label,
**kwargs)
def make_Contour(sbplt_in, x_in, y_in, Z_in):
return Contour(
x=x_in,
y=y_in,
z=Z_in,
scl=cm_name,
showscale=False,
reversescl=False,
contours=Contours(showlines=False),
#opacity=0.8,
xaxis='x{}'.format(sbplt_in),
yaxis='y{}'.format(sbplt_in))
def result_plotly_contour(self,
min_duration=1.0,
max_duration=8640,
xscale="linear"):
# duration_steps = np.arange(min_duration, max_duration, 1)
# duration_steps = np.arange(5, 60, 1)
duration_steps = np.logspace(0, 3, num=300)
result_table = pd.DataFrame(index=duration_steps)
heights = np.logspace(0, 2, num=300)
# print(heights)
# exit()
# heights = range(1, 180, 1)
# heights = range(0, 60, 1)
for h in heights:
result_table[h] = self.get_return_period(h, result_table.index)
result_table[result_table < 0.1] = 0
result_table[result_table > 200] = 200
s = result_table.stack()
z = s.values.tolist()
y = s.index.get_level_values(1).tolist()
x = s.index.get_level_values(0).tolist()
axes = PlotlyAxes()
axes.append(
Ax(row=1,
traces=Contour(z=z,
x=x,
y=y,
contours=dict(coloring='heatmap',
showlabels=True,
labelfont=dict(family='Raleway',
size=12,
color='white')),
name='Wiederkehrperiode in [Jahre]',
colorscale='Rainbow',
colorbar=dict(
title='Wiederkehrperiode in [Jahre]',
titleside='right'),
hoverinfo='x+y+z+name'),
ylabel='Regenhöhe in [mm]',
xlabel='Dauerstufe in [min]'))
fig = axes.get_figure()
fig.set_size(w=1800, h=1000)
fig.set_title('Regenhöhenlinien')
file = self.output_filename + '_countour_plot'
fig.save(file)
def make_2d_surface_trace(gp_mu_folded, x_gp, y_gp, z_axis_title):
trace = Contour(
z=gp_mu_folded,
x=x_gp,
y=y_gp,
colorscale='Viridis',
colorbar={
'title': z_axis_title,
'titleside': 'right'
},
opacity=0.9,
line=dict(width=1, smoothing=0.85),
contours=dict(
# showlines=False,
# showlabels=False,
coloring="heatmap",
start=min(gp_mu),
end=max(gp_mu),
size=0.05,
labelfont=dict(size=15, ),
))
return trace
"color": "lime"
}
},
])
real_price = get_history("bitcoinity_data.csv",
tcs[-1],
delta_t=0,
length=len(tcs),
col="kraken")
plots = list()
plots.append(
Contour(z=lm_all,
y=sample_sizes,
x=tcs,
colorscale=my_color,
showscale=False))
plots.append(
Contour(z=keep_all,
y=sample_sizes,
x=tcs,
colorscale=filter_color,
showscale=False,
opacity=0.5,
contours=dict(showlines=False)))
plots.append(
Scatter(y=real_price.price,
x=tcs,
yaxis="y2",
name="Bitcoin",
def test_nested_list():
z = [[
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21
]]
print(Contour(z=z).to_string())
#mean: 0.98048, std: 0.00063, params: {'C': 5}
#mean: 0.98029, std: 0.00068, params: {'C': 10}
#mean: 0.98019, std: 0.00067, params: {'C': 20}
#mean: 0.98017, std: 0.00064, params: {'C': 50}
#mean: 0.98017, std: 0.00064, params: {'C': 100}
pca85p_svc_best_estimator = pca85p_svc_model_gsearch.best_estimator_
pd.DataFrame({'ImageId': np.arange(28000) + 1, 'Label': pca85p_svc_best_estimator.predict(test_pca85p_x)}).to_csv("D:/Users/perry/Downloads/pca85p_svc_gridsearch.csv", index = False)
plotly.offline.plot(
[Contour(
z = [[0.97600, 0.97848, 0.97955, 0.97924, 0.97914, 0.97845, 0.97855],
[0.97881, 0.98048, 0.98079, 0.98060, 0.98017, 0.98017, 0.98017],
[0.97767, 0.98026, 0.98048, 0.98029, 0.98019, 0.98017, 0.98017]],
x = [1, 2, 5, 10, 20, 50, 100],
y = [0.7, 0.8, 0.85]
)]
)