k = np.asarray([K[i]]) # Strike Price
tau = np.asarray([T[i]]) # Time to Maturity
r = np.asarray(1.02) # CD91일물 금리 2020.5.21일 기준
q = np.asarray(0.00) # Dividend Rate
cp = np.asarray(-1) # Option Type
P = np.asarray([S[i]]) # Market Price
imp_vol = calcbsimpvol(dict(cp=cp, P=P, S=s, K=k, tau=tau, r=r, q=q))
implied_volatility.append(imp_vol[0][0])
print(implied_volatility)
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
trace = go.Surface(x=TM, y=KP, z=imp_vol, colorscale='Jet', opacity=0.8)
data = [trace]
layout = go.Layout(title='Call Theta Surface',
scene={
'xaxis': {
'title': 'Maturity'
},
'yaxis': {
'title': 'Spot Price'
},
'zaxis': {
'title': 'Theta'
}
},
width=800,
height=800,
def surface(x, y, z, opacity=1.0, colorscale=None):
trace = go.Surface(x=x, y=y, z=z, opacity=opacity, colorscale=colorscale)
return trace
def plot_betti_surfaces(betti_curves,
samplings=None,
homology_dimensions=None):
"""Plots the Betti surfaces (Betti numbers against time and filtration
parameter) by homology dimension.
Parameters
----------
betti_curves : ndarray of shape (n_samples, n_homology_dimensions, \
n_values)
``n_samples`` collections of discretised Betti curves. There are
``n_homology_dimension`` curves in each collection. Index i along axis
1 should yield all Betti curves in homology dimension i.
homology_dimensions : list of int or None, default: ``None``
Homology dimensions for which the Betti surfaces should be plotted.
If ``None``, all available dimensions will be used.
samplings : ndarray of shape (n_homology_dimension, n_values), \
default: ``None``
For each homology dimension, (filtration parameter) values to be used
on the x-axis against the corresponding values in `betti_curves` on the
y-axis. If ``None``, the samplings will start at 0 with step 1.
"""
if homology_dimensions is None:
homology_dimensions = np.arange(0, betti_curves.shape[1])
if samplings is None:
samplings = np.arange(0, betti_curves.shape[2])
scene = {
"xaxis": {
"title": "Epsilon",
"type": "linear",
"showexponent": "all",
"exponentformat": "e"
},
"yaxis": {
"title": "Time",
"type": "linear",
"showexponent": "all",
"exponentformat": "e"
},
"zaxis": {
"title": "Betti number",
"type": "linear",
"showexponent": "all",
"exponentformat": "e"
}
}
if betti_curves.shape[0] == 1:
plot_betti_curves(betti_curves[0], samplings, homology_dimensions)
else:
for i, dimension in enumerate(homology_dimensions):
fig = gobj.Figure()
fig.update_layout(scene=scene,
title="Betti surface for homology "
"dimension {}".format(int(dimension)))
fig.add_trace(
gobj.Surface(x=samplings,
y=np.arange(betti_curves.shape[0]),
z=betti_curves[:, i, :],
connectgaps=True,
hoverinfo='none'))
fig.show()
def interactive_3D_plot(post_equal_weights_file, trigdat_file, used_dets,
model, save_path):
# Plot 10 degree grid
trace_grid = []
phi_l = np.arange(-180, 181, 10) # file size!#
theta_l = np.arange(-90, 91, 10) # file size!#
scale_factor_grid = 1.02
b_side_angle = 45 # has to be <90
for phi in phi_l:
x, y, z = xyz(phi, theta_l)
trace_grid.append(
go.Scatter3d(
x=scale_factor_grid * x,
y=scale_factor_grid * y,
z=scale_factor_grid * z,
legendgroup="group",
showlegend=False,
mode="lines",
line=dict(color="black", width=1, dash="dash"),
hoverinfo=None,
))
for theta in theta_l:
theta_m = np.ones_like(phi_l) * theta
x, y, z = xyz(phi_l, theta_m)
trace_grid.append(
go.Scatter3d(
x=scale_factor_grid * x,
y=scale_factor_grid * y,
z=scale_factor_grid * z,
legendgroup="group",
showlegend=False,
mode="lines",
line=dict(color="black", width=1, dash="dash"),
hoverinfo=None,
))
# equator
theta_m = np.ones_like(phi_l) * 0
x, y, z = xyz(phi_l, theta_m)
trace_grid.append(
go.Scatter3d(
x=np.array(scale_factor_grid * x),
y=np.array(scale_factor_grid * y),
z=np.array(scale_factor_grid * z),
legendgroup="group",
name="Spherical Grid (10 deg steps)",
mode="lines",
line=dict(color="black", width=3),
hoverinfo=None,
))
# PLOT B0 and B1 Side and Solar panel
phi = np.concatenate([
np.arange(0, b_side_angle - 1, (b_side_angle - 1) / 10),
np.arange(b_side_angle - 1, b_side_angle + 1, 0.1),
np.arange(b_side_angle + 1, 180 - (b_side_angle + 1),
2 * b_side_angle / 4),
np.arange(180 - (b_side_angle + 1), 180 - (b_side_angle - 1), 0.1),
np.arange(180 - (b_side_angle - 1), 181, (b_side_angle - 1.0) / 10.0),
]) # file size!#
phi = np.concatenate([phi, -np.flip(phi[:-1], 0)])
theta = np.arange(-90, 91, 5) # file size!#
# phi, theta = np.mgrid[-180:180:720j, -90:90:18j]
phi, theta = np.meshgrid(phi, theta)
x, y, z = xyz(phi, theta)
points = np.array([x, y, z])
b0_zen = 0
b0_azi = 0
b1_zen = 0
b1_azi = 180
b0_x, b0_y, b0_z = xyz(b0_azi, b0_zen)
b1_x, b1_y, b1_z = xyz(b1_azi, b1_zen)
idx_b0 = np.abs(phi) < b_side_angle
idx_b1 = np.abs(phi) > 180 - b_side_angle
xin_b0, yin_b0, zin_b0 = xyz(phi, theta)
xin_b1, yin_b1, zin_b1 = xyz(phi, theta)
xin_s, yin_s, zin_s = xyz(phi, theta)
xin_b0[~idx_b0] = np.nan
yin_b0[~idx_b0] = np.nan
zin_b0[~idx_b0] = np.nan
xin_b1[~idx_b1] = np.nan
yin_b1[~idx_b1] = np.nan
zin_b1[~idx_b1] = np.nan
xin_s[idx_b1] = np.nan
yin_s[idx_b1] = np.nan
zin_s[idx_b1] = np.nan
xin_s[idx_b0] = np.nan
yin_s[idx_b0] = np.nan
zin_s[idx_b0] = np.nan
contours = go.surface.Contours(
x=go.surface.contours.X(highlight=False),
y=go.surface.contours.Y(highlight=False),
z=go.surface.contours.Z(highlight=False),
)
theta = np.arcsin(z) * 180 / np.pi
phi = np.arctan2(x, y) * 180 / np.pi
my_text = []
for i in range(len(phi)):
te = []
for j in range(len(phi[0])):
te.append("phi:{}<br>theta:{}".format(phi[i, j], theta[i, j]))
my_text.append(te)
my_text = np.array(my_text)
colorscale_b0 = [[0, "rgb(117,201,196)"], [1, "rgb(117,201,196)"]]
trace_b0 = go.Surface(
x=xin_b0,
y=yin_b0,
z=zin_b0,
name="b0-side",
showscale=False,
colorscale=colorscale_b0,
surfacecolor=np.ones_like(z),
opacity=1,
contours=contours,
text=my_text,
hoverinfo="text+name",
)
colorscale_b1 = [[0, "rgb(201,117,117)"], [1, "rgb(201,117,117)"]]
trace_b1 = go.Surface(
x=xin_b1,
y=yin_b1,
z=zin_b1,
name="b1-side",
showscale=False,
colorscale=colorscale_b1,
surfacecolor=np.ones_like(z),
opacity=1,
contours=contours,
text=my_text,
hoverinfo="text+name",
)
colorscale_s = [[0, "grey"], [1, "grey"]]
trace_s = go.Surface(
x=xin_s,
y=yin_s,
z=zin_s,
name="solar_panel side",
showscale=False,
colorscale=colorscale_s,
surfacecolor=np.ones_like(z),
opacity=1,
contours=contours,
text=my_text,
hoverinfo="text+name",
)
# PLOT DETS - dets in list used dets will be plotted solid all other dashed
trace_dets = []
color_dict = {
"n0": "blue",
"n1": "navy",
"n2": "crimson",
"n3": "lightgreen",
"n4": "orchid",
"n5": "brown",
"n6": "firebrick",
"n7": "plum",
"n8": "darkgreen",
"n9": "olive",
"na": "aqua",
"nb": "darkorange",
"b0": "darkmagenta",
"b1": "indigo",
}
det_pointing = {
"n0": [45.9, 90 - 20.6],
"n1": [45.1, 90 - 45.3],
"n2": [58.4, 90 - 90.2],
"n3": [314.9, 90 - 45.2],
"n4": [303.2, 90 - 90.3],
"n5": [3.4, 90 - 89.8],
"n6": [224.9, 90 - 20.4],
"n7": [224.6, 90 - 46.2],
"n8": [236.6, 90 - 90],
"n9": [135.2, 90 - 45.6],
"na": [123.7, 90 - 90.4],
"nb": [183.7, 90 - 90.3],
"b0": [0.01, 90 - 90.01],
"b1": [180.01, 90 - 90.01],
}
for keys in det_pointing:
det_opening = 40 # in deg
pointing = det_pointing[keys]
ra_d = pointing[0] * np.pi / 180
dec_d = pointing[1] * np.pi / 180
scale_factor_d = 1.01
theta_l = np.linspace(-np.pi / 2, np.pi / 2, 720) # file size!#
phi_res_0 = []
phi_res_1 = []
for theta in theta_l:
phi_res_0.append(
phi_0(theta, ra_d, dec_d, det_opening * np.pi / 180))
phi_res_1.append(
phi_1(theta, ra_d, dec_d, det_opening * np.pi / 180))
phi_res_0 = np.array(phi_res_0)
phi_res_1 = np.array(phi_res_1)
theta_all = np.concatenate([theta_l, np.flip(theta_l, 0)])
phi_all = np.concatenate([phi_res_0, np.flip(phi_res_1, 0)])
mask = phi_all < 100
theta_all = theta_all[mask]
phi_all = phi_all[mask]
theta_all = np.concatenate([theta_all, theta_all[0:1]])
phi_all = np.concatenate([phi_all, phi_all[:1]])
x = np.cos(theta_all) * np.cos(phi_all)
y = np.cos(theta_all) * np.sin(phi_all)
z = np.sin(theta_all)
# plot earth
name = str(keys)
color = str(color_dict[keys])
if name in used_dets:
trace_dets.append(
go.Scatter3d(
x=scale_factor_d * x,
y=scale_factor_d * y,
z=scale_factor_d * z,
name=name,
legendgroup="used detectors",
mode="lines",
line=dict(color=color, width=5, dash="solid"),
hoverinfo="name",
))
else:
trace_dets.append(
go.Scatter3d(
x=scale_factor_d * x,
y=scale_factor_d * y,
z=scale_factor_d * z,
name=name,
mode="lines",
legendgroup="unused detectors",
line=dict(color=color, width=5, dash="dash"),
hoverinfo="name",
))
with fits.open(trigdat_file) as f:
quat = f["TRIGRATE"].data["SCATTITD"][0]
sc_pos = f["TRIGRATE"].data["EIC"][0]
times = f["TRIGRATE"].data["TIME"][0]
# Plot Earth Shadow
det = gbm_detector_list["n0"](quaternion=quat,
sc_pos=sc_pos,
time=astro_time.Time(utc(times)))
earth_pos_sat = det.earth_position
ra_earth_sat = earth_pos_sat.lon.deg
dec_earth_sat = earth_pos_sat.lat.deg
# earth_pos
xe, ye, ze = xyz(ra_earth_sat, dec_earth_sat)
earth_vec = np.array([xe, ye, ze])
opening_angle = 67
# points on sphere
theta_l = np.concatenate([
np.linspace(-np.pi / 2, -np.pi / 2 + 0.1, 30),
np.linspace(-np.pi / 2 + 0.1, np.pi / 2 - 0.1, 400),
np.linspace(np.pi / 2 - 0.1, np.pi / 2, 30),
]) # file size!#
theta_final = []
phi_final = []
phi_l = np.arange(-np.pi, np.pi + 0.1, 0.1)
for theta in theta_l:
for phi in phi_l:
x, y, z = xyz(phi * 180 / np.pi, theta * 180 / np.pi)
angle = np.arccos(np.dot(np.array([x, y, z]), earth_vec))
if angle < opening_angle * np.pi / 180:
theta_final.append(theta)
phi_final.append(phi)
theta_final = np.array(theta_final)
phi_final = np.array(phi_final)
x = np.cos(theta_final) * np.cos(phi_final)
y = np.cos(theta_final) * np.sin(phi_final)
z = np.sin(theta_final)
scale_factor_earth = 1.005
colorscale = [[0, "navy"], [1, "navy"]]
theta = np.arcsin(z) * 180 / np.pi
phi = np.arctan2(x, y) * 180 / np.pi
my_text = []
for i in range(len(phi)):
my_text.append("phi:{}<br>theta:{}".format(phi[i], theta[i]))
my_text = np.array(my_text)
trace_earth = go.Mesh3d(
x=scale_factor_earth * x,
y=scale_factor_earth * y,
z=scale_factor_earth * z,
showscale=False,
name="earth",
color="navy",
alphahull=0,
text=my_text,
hoverinfo="text+name",
)
# Plot Balrog ERROR CONTOURS
# Load data from chain with chain consumer
chain = loadtxt2d(post_equal_weights_file)
# Get parameter for model
parameter = model_param_lookup[model]
c1 = ChainConsumer()
c1.add_chain(chain[:, :-1][:, :2],
parameters=parameter[:2]).configure(contour_labels="sigma",
colors="#cd5c5c",
label_font_size=20)
# ra_contour, dec_contour, val_contour = c1.plotter.get_contours_list('ra', 'dec') # ra, dec in deg here
chains, parameters, truth, extents, blind, log_scales = c1.plotter._sanitise(
None, None, None, None, color_p=True, blind=None)
hist, ra_contour, dec_contour = c1.plotter._get_smoothed_histogram2d(
chains[0], "ra (deg)", "dec (deg)") # ra, dec in deg here
hist[hist == 0] = 1e-16
val_contour = c1.plotter._convert_to_stdev(hist.T)
ra_con, dec_con = np.meshgrid(ra_contour, dec_contour)
a = np.array([ra_con, dec_con]).T
res = []
q1, q2, q3, q4 = quat
scx, scy, scz = sc_pos
for a_inter in a:
loc_icrs = SkyCoord(ra=a_inter[:, 0],
dec=a_inter[:, 1],
unit="deg",
frame="icrs")
loc_sat = loc_icrs.transform_to(
GBMFrame(
quaternion_1=q1,
quaternion_2=q2,
quaternion_3=q3,
quaternion_4=q4,
sc_pos_X=scx,
sc_pos_Y=scy,
sc_pos_Z=scz,
))
ra_sat = Angle(loc_sat.lon.deg * unit.degree).value
dec_sat = Angle(loc_sat.lat.deg * unit.degree).value
res.append(np.stack((ra_sat, dec_sat), axis=-1))
res = np.array(res)
scale_factor_con = 1.02
x, y, z = xyz(res[:, :, 0], res[:, :, 1])
x = scale_factor_con * x
y = y * scale_factor_con
z = z * scale_factor_con
colorscale = [[0, "green"], [1.0 / 3.0, "orange"], [2.0 / 3.0, "red"],
[1, "grey"]]
conf_levels = [0.68, 0.95, 0.99]
trace_conf_l = []
theta = np.arcsin(z) * 180 / np.pi
phi = np.arctan2(x, y) * 180 / np.pi
my_text = []
for i in range(len(phi)):
te = []
for j in range(len(phi[0])):
te.append("phi:{}<br>theta:{}".format(phi[i, j], theta[i, j]))
my_text.append(te)
my_text = np.array(my_text)
for conf in conf_levels:
x2n, y2n, z2n = (
np.where(val_contour < conf, x, None),
np.where(val_contour < conf, y, None),
np.where(val_contour < conf, z, None),
)
trace_conf = go.Surface(
x=x2n,
y=y2n,
z=z2n,
cmin=0,
cmax=3,
showscale=False,
colorscale=colorscale,
surfacecolor=z2n,
name="Balrog {} confidence level".format(conf),
text=my_text,
hoverinfo="text+name",
)
lx = len(trace_conf["z"])
ly = len(trace_conf["z"][0])
out = []
x_sigma1 = []
for i in range(lx):
temp = []
for j in range(ly):
if val_contour[i, j] < 0.68:
temp.append(0)
elif val_contour[i, j] < 0.95:
temp.append(1)
elif val_contour[i, j] < 0.99:
temp.append(2)
else:
temp.append(3)
out.append(temp)
# PLOT BESTFIT and SWIFT (if given)
trace_conf["surfacecolor"] = out
trace_conf_l.append(trace_conf)
# TODO add swift position
# add data together
data = ([trace_b0, trace_b1, trace_s, trace_earth] + trace_grid +
trace_dets + trace_conf_l)
# change layout
layout = go.Layout(
dict(
hovermode="closest",
autosize=True,
# width=1000,
height=800,
scene=dict(
xaxis=dict(
title="",
autorange=True,
showgrid=False,
zeroline=False,
showline=False,
ticks="",
showticklabels=False,
showspikes=False,
),
yaxis=dict(
title="",
autorange=True,
showgrid=False,
zeroline=False,
showline=False,
ticks="",
showticklabels=False,
showspikes=False,
),
zaxis=dict(
title="",
autorange=True,
showgrid=False,
zeroline=False,
showline=False,
ticks="",
showticklabels=False,
showspikes=False,
),
),
))
# create figure
fig = go.Figure(data=data, layout=layout)
output = plotly.offline.plot(fig,
auto_open=False,
output_type="div",
include_plotlyjs=False,
show_link=False)
file_utils.if_dir_containing_file_not_existing_then_make(save_path)
with open(save_path, "w") as text_file:
text_file.write(output)
def bandplot3d(bands, selector, indaxis=0, title='', fname='', **kwargs):
""" Make 3D band structure (a number of electronic bands) surface plot.
:Parameters:
bands : 2D/3D matrix
Electronic band structure ordered by band index.
selector : iterable
Index selector for the bands to visualize.
indaxis : int | 0
Axis of the band index.
title : str | ''
Title to appear on the plot.
fname : str | ''
User-defined filename for the plotted figure.
**kwargs : keyword arguments
Additional keyword arguments for `plotly.graph_objs.Layout()`.
:width, height: numeric
Width and height of the plotted figure.
:plot_margin: dictionary
Margins of the figure upon initialization, such as dict(l=65, r=50, b=65, t=90).
:labelsize: int
Size of the axes label.
:aspect: list/tuple
Aspect ratio of the x, y and z directions.
"""
nb = bands.ndim
if nb == 2: # Amend an axis in case only one band is used as the input
bands = bands[None, :, :]
elif nb == 3:
bands = np.moveaxis(bands, indaxis, 0)
# Retrieve figure attributes
plotmargin = kwargs.pop('plot_margin', dict(l=65, r=50, b=65, t=90))
width = kwargs.pop('width', 600)
height = kwargs.pop('height', 600)
labelsize = kwargs.pop('labelsize', 30)
asp = kwargs.pop('aspect', [1, 1, 1])
data = [
go.Surface(z=bands[i, :, :], showscale=False) for i in selector
]
layout = go.Layout(scene=dict(
xaxis=dict(ticks='',
showticklabels=False,
title='kx',
linewidth=3,
titlefont=dict(size=labelsize)),
aspectratio=dict(x=asp[0], y=asp[1], z=asp[2]),
yaxis=dict(ticks='',
showticklabels=False,
title='ky',
linewidth=3,
titlefont=dict(size=labelsize)),
zaxis=dict(ticks='',
showticklabels=False,
title='Energy',
linewidth=3,
titlefont=dict(size=labelsize))),
title=title,
autosize=False,
width=width,
height=height,
margin=plotmargin,
**kwargs)
f = go.Figure(data=data, layout=layout)
plot = py.iplot(f, filename=fname)
return plot
# ---------------------------------------------------------------------
print("Graphing results for cap w/ R0 = ", R0[r])
totCapSurf = go.Figure()
# Graph GTotCap (GCap + GPore) surface
print("Graphing total surface")
totCapSurf.add_trace(
go.Surface(
#coloraxis=go.layout.Coloraxis(dict(
# title="Neg G", "coloraxis1")),
colorbar=go.surface.ColorBar(title='Net G'),
colorscale='matter',
contours={
"z": {
"show": True,
"start": np.max(GTotCap[r, :, :]) / 20,
"end": np.max(GTotCap[r, :, :]) * 0.95,
"size": np.max(GTotCap[r, :, :]) / 20
}
},
name='Net G',
x=rEffCap,
y=rt,
z=GTotCap[r, :, :],
opacity=0.9))
# Graph curve of min GTotCap for expanding rEffCap
print("Graphing minimal G path")
totCapSurf.add_trace(
go.Scatter3d(marker=dict(color='blue', size=2),
mode='lines',
name='Min G path',
x=GMinPathCap[r, :, 0],
new_policy = policy_improvement(episode, player_policy, action_values)
log.debug('Changes made to policy: {}'.format((new_policy != player_policy).sum()))
player_policy = new_policy
state_values = to_state_value(action_values)
player_policy = to_policy(action_values)
x = np.arange(0, state_values.shape[0]) + DEALER_MIN
y = np.arange(0, state_values.shape[1]) + PLAYER_MIN
label_dealer_sum = {'title': 'dealer sum', 'dtick': 1}
label_player_sum = {'title': 'player sum', 'dtick': 1}
kwargs = dict(x=x, y=y, hoverinfo="x+y+z")
layout_surf = {'xaxis': label_dealer_sum, 'yaxis': label_player_sum, 'aspectratio': {'x': 1, 'y': 1, 'z': 0.5}}
single_contour_config = {'show': True, 'highlight': False, 'project': {'z': True}}
contours = {'x': single_contour_config, 'y': single_contour_config, 'z': {'highlight': False}}
surface_no_ace = go.Surface(z=state_values[:, :, NO_ACE_LAYER].T, contours=contours, **kwargs)
surface_ace = go.Surface(z=state_values[:, :, ACE_LAYER].T, contours=contours, **kwargs)
heatmap_policy_no_ace = go.Heatmap(z=player_policy[:, :, NO_ACE_LAYER].T, **kwargs)
heatmap_policy_ace = go.Heatmap(z=player_policy[:, :, ACE_LAYER].T, **kwargs)
fig = tools.make_subplots(rows=2, cols=2, shared_xaxes=True,
specs=[[{'is_3d': True}, {'is_3d': True}],
[{'is_3d': False}, {'is_3d': False}]],
subplot_titles=('No usable ace', 'Usable ace',
'No usable ace', 'Usable ace'))
fig.append_trace(surface_no_ace, 1, 1)
fig.append_trace(surface_ace, 1, 2)
fig.append_trace(heatmap_policy_no_ace, 2, 1)
fig.append_trace(heatmap_policy_ace, 2, 2)
fig['layout']['scene1'].update(layout_surf)
plt.imshow(images[0])
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
iplot([{"x": [1, 2, 3], "y": [3, 1, 6]}])
data = [go.Surface(z=img[0])]
data
layout = go.Layout(
title='Mt Bruno Elevation',
autosize=False,
width=500,
height=500,
margin=dict(
l=65,
r=50,
b=65,
t=90)
)
fig = go.Figure(data=data)
def update_figure(invar, invar_2, invar_3, outvar, invar1_log, invar2_log, invar3_log, outvar_log, param_slider,
graph_type, color_use, color_dd, error_use, error_dd, filter_active, fit_use, fit_dd, fit_num, fit_conf, add_noise_var, fit_color,
fit_opacity, fit_sampling, id_type, param_center, param_log):
for i in range(len(param_slider)):
if param_log[i] == ['log']:
param_slider[i] = [10**val for val in param_slider[i]]
param_center[i] = 10**param_center[i]
if invar is None:
return go.Figure()
sel_y = np.full((len(outdata),), True)
dds_value = []
for iteration, values in enumerate(param_slider):
dds_value.append(invars[id_type[iteration]['index']])
# filter for minimum
sel_y_min = np.array(indata[dds_value[iteration]] >= param_slider[iteration][0])
# filter for maximum
sel_y_max = np.array(indata[dds_value[iteration]] <= param_slider[iteration][1])
# print('iter ', iteration, 'filer', filter_active[iteration][0])
if filter_active != [[]]:
if filter_active[iteration] == ['act']:
sel_y = sel_y_min & sel_y_max & sel_y
if graph_type == '1D':
fig = go.Figure(
data=[go.Scatter(
x=indata[invar][sel_y],
y=outdata[outvar][sel_y],
mode='markers',
name='data',
error_y=dict(type='data', array=outdata[error_dd][sel_y], visible= error_use == ['true']),
# text=[(invar, outvar) for i in range(len(indata[invar][sel_y]))],
# hovertemplate=" %{text} <br> %{x} <br> %{y}",
)],
layout=go.Layout(xaxis=dict(title=invar, rangeslider=dict(visible=True)), yaxis=dict(title=outvar))
)
if fit_use == ['show']:
mesh_in, mesh_out, mesh_out_std, fit_dd_values = mesh_fit(param_slider, id_type, fit_dd, fit_num,
param_center, [invar], [invar1_log],
outvar, fit_sampling, add_noise_var)
for i in range(len(fit_dd_values)):
fig.add_trace(go.Scatter(
x=mesh_in[i][invars.index(invar)],
y=mesh_out[i],
mode='lines',
name=f'fit: {fit_dd}={fit_dd_values[i]:.1e}',
line_color=colormap(indata[fit_dd].min(), indata[fit_dd].max(), fit_dd_values[i]),
marker_line=dict(coloraxis="coloraxis2"),
))
fig.add_trace(go.Scatter(
x=np.hstack((mesh_in[i][invars.index(invar)], mesh_in[i][invars.index(invar)][::-1])),
y=np.hstack((mesh_out[i] + fit_conf * mesh_out_std[i], mesh_out[i][::-1] - fit_conf * mesh_out_std[i][::-1])),
showlegend=False,
fill='toself',
line_color=colormap(indata[fit_dd].min(), indata[fit_dd].max(), fit_dd_values[i]),
marker_line=dict(coloraxis="coloraxis2"),
opacity=fit_opacity,
))
elif graph_type == '2D':
fig = go.Figure(
data=[go.Scatter3d(
x=indata[invar][sel_y],
y=indata[invar_2][sel_y],
z=outdata[outvar][sel_y],
mode='markers',
name='Data',
error_z=dict(type='data', array=outdata[error_dd][sel_y], visible=error_use == ['true'], width= 10)
)],
layout=go.Layout(scene=dict(xaxis_title=invar, yaxis_title=invar_2, zaxis_title=outvar))
)
if fit_use == ['show'] and invar != invar_2:
mesh_in, mesh_out, mesh_out_std, fit_dd_values = mesh_fit(param_slider, id_type, fit_dd, fit_num,
param_center, [invar, invar_2],
[invar1_log, invar2_log], outvar,
fit_sampling, add_noise_var)
for i in range(len(fit_dd_values)):
fig.add_trace(go.Surface(
x=mesh_in[i][invars.index(invar)].reshape((fit_sampling, fit_sampling)),
y=mesh_in[i][invars.index(invar_2)].reshape((fit_sampling, fit_sampling)),
z=mesh_out[i].reshape((fit_sampling, fit_sampling)),
name=f'fit: {fit_dd}={fit_dd_values[i]:.2f}',
surfacecolor=fit_dd_values[i] * np.ones([fit_sampling, fit_sampling])
if fit_color == 'multi-fit' else
(mesh_in[i][invars.index(color_dd)].reshape((fit_sampling, fit_sampling))
if (fit_color == 'marker-color' and color_dd in invars) else
mesh_out[i].reshape((fit_sampling, fit_sampling))),
opacity=fit_opacity,
coloraxis="coloraxis2" if (fit_color == 'multi-fit' or
(fit_color == 'output' and (color_dd != outvar and color_dd != 'OUTPUT'))) else "coloraxis",
showlegend=True if len(invars) > 2 else False,
))
if fit_conf > 0:
fig.add_trace(go.Surface(
x=mesh_in[i][invars.index(invar)].reshape((fit_sampling, fit_sampling)),
y=mesh_in[i][invars.index(invar_2)].reshape((fit_sampling, fit_sampling)),
z=mesh_out[i].reshape((fit_sampling, fit_sampling)) + fit_conf * mesh_out_std[i].reshape((fit_sampling, fit_sampling)),
showlegend=False,
name=f'fit+v: {fit_dd}={fit_dd_values[i]:.2f}',
surfacecolor=fit_dd_values[i] * np.ones([fit_sampling, fit_sampling])
if fit_color == 'multi-fit' else
(mesh_in[i][invars.index(color_dd)].reshape((fit_sampling, fit_sampling))
if (fit_color == 'marker-color' and color_dd in invars) else
mesh_out[i].reshape((fit_sampling, fit_sampling))),
opacity=fit_opacity,
coloraxis="coloraxis2" if (fit_color == 'multi-fit' or
(fit_color == 'output' and (color_dd != outvar and color_dd != 'OUTPUT')))
else "coloraxis",
))
fig.add_trace(go.Surface(
x=mesh_in[i][invars.index(invar)].reshape((fit_sampling, fit_sampling)),
y=mesh_in[i][invars.index(invar_2)].reshape((fit_sampling, fit_sampling)),
z=mesh_out[i].reshape((fit_sampling, fit_sampling)) - fit_conf * mesh_out_std[i].reshape((fit_sampling, fit_sampling)),
showlegend=False,
name=f'fit-v: {fit_dd}={fit_dd_values[i]:.2f}',
surfacecolor=fit_dd_values[i] * np.ones([fit_sampling, fit_sampling])
if fit_color == 'multi-fit' else
(mesh_in[i][invars.index(color_dd)].reshape((fit_sampling, fit_sampling))
if (fit_color == 'marker-color' and color_dd in invars) else
mesh_out[i].reshape((fit_sampling, fit_sampling))),
opacity=fit_opacity,
coloraxis="coloraxis2" if (fit_color == 'multi-fit' or
(fit_color == 'output' and (color_dd != outvar and color_dd != 'OUTPUT')))
else "coloraxis",
))
fig.update_layout(coloraxis2=dict(
colorbar=dict(title=outvar if fit_color == 'output' else fit_dd),
cmin=min(fit_dd_values) if fit_color == 'multi-fit' else None,
cmax=max(fit_dd_values) if fit_color == 'multi-fit' else None,
))
elif graph_type == '2D contour':
mesh_in, mesh_out, mesh_out_std, fit_dd_values = mesh_fit(param_slider, id_type, fit_dd, fit_num,
param_center, [invar, invar_2],
[invar1_log, invar2_log], outvar,
fit_sampling, add_noise_var)
data_x = mesh_in[0][invars.index(invar)]
data_y = mesh_in[0][invars.index(invar_2)]
fig = go.Figure()
if min(data_x) != max(data_x):
if min(data_y) != max(data_y):
fig.add_trace(go.Scatter(
x=indata[invar][sel_y],
y=indata[invar_2][sel_y],
mode='markers',
name='Data',
))
fig.add_trace(go.Contour(
x=mesh_in[0][invars.index(invar)],
y=mesh_in[0][invars.index(invar_2)],
z=mesh_out[0],
contours_coloring='heatmap',
contours_showlabels=True,
coloraxis='coloraxis2',
name='fit',
))
fig.update_xaxes(
range=[log10(min(fig.data[1]['x'])), log10(max(fig.data[1]['x']))] if invar1_log == ['log']
else [min(fig.data[1]['x']), max(fig.data[1]['x'])])
fig.update_yaxes(
range=[log10(min(fig.data[1]['y'])), log10(max(fig.data[1]['y']))] if invar2_log == ['log']
else [min(fig.data[1]['y']), max(fig.data[1]['y'])])
fig.update_layout(xaxis_title=invar,
yaxis_title=invar_2,
coloraxis2=dict(colorbar=dict(title=outvar),
colorscale='solar',
cmin=min(fig.data[1]['z']),
cmax=max(fig.data[1]['z'])))
else:
fig.update_layout(title="y-data is constant, no contour-plot possible")
else:
fig.update_layout(title="x-data is constant, no contour-plot possible")
elif graph_type == '3D':
fig = go.Figure(
data=go.Scatter3d(
x=indata[invar][sel_y],
y=indata[invar_2][sel_y],
z=indata[invar_3][sel_y],
mode='markers',
marker=dict(
color=outdata[outvar][sel_y],
coloraxis="coloraxis2",
),
name='Data',
),
layout=go.Layout(scene=dict(xaxis_title=invar, yaxis_title=invar_2, zaxis_title=invar_3)),
)
fig.update_layout(coloraxis2=dict(
colorbar=dict(title=outvar),
))
if fit_use == ['show'] and len({invar, invar_2, invar_3}) == 3:
mesh_in, mesh_out, mesh_out_std, fit_dd_values = mesh_fit(param_slider, id_type, fit_dd, fit_num,
param_center, [invar, invar_2, invar_3],
[invar1_log, invar2_log, invar3_log], outvar,
fit_sampling, add_noise_var)
for i in range(len(fit_dd_values)):
fig.add_trace(
go.Isosurface(
x=mesh_in[i][invars.index(invar)],
y=mesh_in[i][invars.index(invar_2)],
z=mesh_in[i][invars.index(invar_3)],
value=mesh_out[i],
surface_count=fit_num,
coloraxis="coloraxis2",
isomin=mesh_out[i].min() * 1.1,
isomax=mesh_out[i].max() * 0.9,
caps=dict(x_show=False, y_show=False, z_show=False),
opacity=fit_opacity,
),
)
else:
fig = go.Figure()
fig.update_layout(legend=dict(xanchor="left", x=0.01))
# log scale
log_dict = {'1D': (invar1_log, outvar_log),
'2D': (invar1_log, invar2_log, outvar_log),
'2D contour': (invar1_log, invar2_log),
'3D': (invar1_log, invar2_log, invar3_log),}
log_list = ['linear' if log is None or len(log) == 0 else log[0] for log in log_dict[graph_type]]
log_key = ['xaxis', 'yaxis', 'zaxis']
comb_dict = dict(zip(log_key, [{'type': log} for log in log_list]))
if len(log_list) < 3 :
fig.update_layout(**comb_dict)
else:
fig.update_scenes(**comb_dict)
# color
if color_use == ['true']: # TODO: trigger-detection no new fig just update
if fit_use == ['show'] and (graph_type=='2D' and (fit_color=='multi-fit' and color_dd==fit_dd)):
fig.update_traces(
marker=dict(
coloraxis="coloraxis2",
color=indata[color_dd][sel_y] if color_dd in indata.dtype.names else outdata[color_dd][sel_y],
),
selector=dict(mode='markers'),
)
elif graph_type == '3D':
fig.update_traces(
marker=dict(
coloraxis="coloraxis2",
color=outdata[outvar][sel_y],
),
selector=dict(mode='markers'),
)
elif graph_type=='1D':
fig.update_traces(
marker=dict(
coloraxis="coloraxis2",
color=outdata[outvar][sel_y] if color_dd == 'OUTPUT' else
(indata[color_dd][sel_y] if color_dd in indata.dtype.names else outdata[color_dd][sel_y]),
),
selector=dict(mode='markers'),
)
if color_dd==fit_dd:
fig.update_layout(coloraxis2=dict(colorscale='cividis', colorbar=dict(title=fit_dd)))
elif color_dd == 'OUTPUT':
fig.update_layout(coloraxis2=dict(colorscale='plasma', colorbar=dict(title=outvar)))
else:
fig.update_layout(coloraxis2=dict(colorscale='plasma', colorbar=dict(title=color_dd)))
elif graph_type =='2D contour':
fig.update_traces(
marker=dict(
coloraxis="coloraxis",
color=outdata[outvar][sel_y] if color_dd == 'OUTPUT' else
(indata[color_dd][sel_y] if color_dd in indata.dtype.names else outdata[color_dd][sel_y]),
),
selector=dict(mode='markers'),
)
if color_dd == outvar or color_dd == 'OUTPUT':
fig.update_traces(marker_coloraxis="coloraxis2", selector=dict(mode='markers'))
else:
fig.update_layout(coloraxis=dict(colorbar=dict(title=color_dd, x=1.1),
colorscale='ice'))
else:
fig.update_traces(
marker=dict(
coloraxis="coloraxis",
color=outdata[outvar][sel_y] if color_dd == 'OUTPUT' else
(indata[color_dd][sel_y] if color_dd in indata.dtype.names else outdata[color_dd][sel_y]),
),
selector=dict(mode='markers'),
)
fig.update_layout(coloraxis=dict(
colorbar=dict(title=outvar if color_dd == 'OUTPUT' else color_dd, x=1.1),
colorscale='viridis',
))
fig.update_layout(height=graph_height)
return fig
import plotly.graph_objs as go
import numpy as np
r = np.linspace(0, 10, 100)
t = np.linspace(0, 2 * np.pi, 240)
rGrid, tGrid = np.meshgrid(r, t)
x = rGrid * np.cos(tGrid)
y = rGrid * np.sin(tGrid)
z1 = np.maximum(1 - rGrid / 5, rGrid * 0)
z2 = np.maximum(1 - rGrid / 3, rGrid * 0)
surface1 = go.Surface(x=x,
y=y,
z=z1,
opacity=0.7,
colorscale='Viridis',
colorbar=dict(showticklabels=False))
surface2 = go.Surface(x=x, y=y, z=z2, opacity=1, colorscale='Viridis')
data = [surface1, surface2]
layout = go.Layout(title='2D IT2 Fuzzy FMF',
scene=dict(xaxis=dict(
title='y₁',
gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)',
range=[-5, 5],
),
yaxis=dict(
def get_fit_trace(result,
x,
n_points=None,
log_x=False,
flip_xy=False,
showlegend=False,
fit_x_range=None):
if isinstance(result, np.ndarray):
fit_type = result["fit_type"]
# params = result["params"]
x_range = result["x_range"]
y_range = result["y_range"]
is_multivariate = result["is_multivariate"]
else:
fit_type, params, _, _, _, x_range, y_range, *_, is_multivariate = result
x = np.asarray(x)
if x.ndim == 1:
x = x[np.isfinite(x)]
elif x.ndim == 2:
x = x[np.isfinite(x).any(axis=-1)]
else:
raise NotImplementedError
if is_multivariate:
if n_points is None:
n_points = int(np.power(len(x) * 20, 1.5))
assert log_x is False
n_variables = x.shape[1]
if 2 < n_variables:
raise NotImplementedError("2 < n_variables")
# x_margin = 0.1 * (x.max(axis=0) - x.min(axis=0))
# print(x_margin)
x_margin = [0, 0]
if fit_x_range is None:
fit_x_range = [(max(x_range[0][0], x[:, 0].min() - x_margin[0]),
min(x_range[0][1], x[:, 0].max() + x_margin[0])),
(max(x_range[1][0], x[:, 1].min() - x_margin[1]),
min(x_range[1][1], x[:, 1].max() + x_margin[1]))]
# fit_x = np.stack([
# e.flatten()
# for e in np.meshgrid(
# np.linspace(fit_x_range[0][0], fit_x_range[0][1], int(np.sqrt(n_points))),
# np.linspace(fit_x_range[1][0], fit_x_range[1][1], int(np.sqrt(n_points)))
# )
# ], axis=-1)
fit_x = (np.linspace(fit_x_range[0][0], fit_x_range[0][1],
int(np.sqrt(n_points))),
np.linspace(fit_x_range[1][0], fit_x_range[1][1],
int(np.sqrt(n_points))))
else:
if n_points is None:
n_points = min(len(x) * 20, 300_000)
if log_x is True:
x_margin = 0.1 * (np.log10(np.max(x)) - np.log10(np.min(x)))
if fit_x_range is None:
fit_x_range = (max(x_range[0],
10**(np.log10(np.min(x)) - x_margin)),
min(x_range[1],
10**(np.log10(np.max(x)) + x_margin)))
fit_x = np.logspace(*np.log10(fit_x_range), n_points)
else:
x_margin = 0.1 * (np.max(x) - np.min(x))
if fit_x_range is None:
fit_x_range = (max(x_range[0],
np.min(x) - x_margin),
min(x_range[1],
np.max(x) + x_margin))
fit_x = np.linspace(*fit_x_range, n_points)
if is_multivariate:
fit_y = regression.eval(
np.stack([e.flatten() for e in np.meshgrid(fit_x[0], fit_x[1])],
axis=-1), result)
else:
fit_y = regression.eval(fit_x, result)
matched_on_y = (y_range[0] <= fit_y) & (fit_y <= y_range[1])
fit_y[~matched_on_y] = np.nan
# if is_multivariate:
# fit_x[0][~matched_on_y] = fit_x[1][~matched_on_y] = np.nan
# else:
# fit_x[~matched_on_y] = np.nan
plot_kwargs = dict(name=f"{fit_type} fit", showlegend=showlegend)
if is_multivariate:
assert flip_xy is False
import plotly.express as px
# plot_kwargs.update(color="#EF553B", opacity=0.3)
# return go.Mesh3d(x=fit_x[:, 0], y=fit_x[:, 1], z=fit_y, **plot_kwargs)
# plot_kwargs.update(colorscale=px.colors.sequential.Reds[2:], opacity=0.5, showscale=False)
plot_kwargs.update(colorscale=["#EF553B"] * 2,
opacity=0.3,
showscale=False)
return go.Surface(
# x=np.linspace(x[:, 0].min(), x[:, 0].max(), int(np.sqrt(n_points))),
# y=np.linspace(x[:, 1].min(), x[:, 1].max(), int(np.sqrt(n_points))),
x=fit_x[0],
y=fit_x[1],
z=fit_y.reshape((int(np.sqrt(n_points))), int(np.sqrt(n_points))),
**plot_kwargs)
else:
if flip_xy:
fit_x, fit_y = fit_y, fit_x
plot_kwargs.update(line=dict(color="red"))
return go.Scattergl(mode="lines", x=fit_x, y=fit_y, **plot_kwargs)
def __init__(self, z, **kwargs):
super().__init__(data=go.Surface(z=z, **kwargs))
distance = math.sqrt(math.pow(i-charge[0],2.0)+math.pow(j-charge[1],2.0))
if distance != 0:
total +=charge[2] / distance
else:
total += charge[2]/0.8
zlist.append(total)
masterlist.append(zlist)
## y value is index of z
## x value is index of inner list
## z value is number at that point
## indexing list z [y][x]
data = [
go.Surface(
z = masterlist)
]
layout = go.Layout(
title='Electric Potential',
autosize=True,
width=500,
height=500,
margin=dict(
l=65,
r=50,
b=65,
t=90
)
)
fig = go.Figure(data=data, layout=layout)
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
from joblib import load
import pandas as pd
import plotly.graph_objs as go
# Imports from this application
from app import app
pdp = load('assets/pdp.joblib')
pdp2 = load('assets/pdp2.joblib')
pdp3 = load('assets/pdp3.joblib')
surface = go.Surface(x=pdp.columns, y=pdp.index, z=pdp.values)
fig = go.Figure(surface)
fig.update_layout(scene=dict(
xaxis_title='Spore Print Color',
yaxis_title='Ring Type',
zaxis_title='Predicted Probability of Poisonous Mushroom'),
width=700,
margin=dict(r=20, b=10, l=10, t=10))
surface2 = go.Surface(x=pdp2.columns, y=pdp2.index, z=pdp2.values)
fig2 = go.Figure(surface2)
fig2.update_layout(scene=dict(
'title': 'Hit = f(X,Y)',
}
}))
]),
html.H3('Eit visualization'),
html.Hr(),
html.Div(className='row',
children=[
html.Div(className='col-6',
children=dcc.Graph(
id='map3d-graph',
figure={
'data': [
go.Surface(
z=Z_Eit,
x=xi,
y=yi,
colorscale='Viridis',
)
],
'layout': {
'title': 'Eit = f(X,Y)',
}
})),
html.Div(className='col-5',
children=dcc.Graph(
id='map2-graph',
figure={
'data': [
go.Scatter(
x=X_list,
y=Y_list,
plt.tight_layout()
mfcc = librosa.feature.mfcc(S=log_S, n_mfcc=13)
# Let's pad on the first and second deltas while we're at it
delta2_mfcc = librosa.feature.delta(mfcc, order=2)
plt.figure(figsize=(12, 4))
librosa.display.specshow(delta2_mfcc)
plt.ylabel('MFCC coeffs')
plt.xlabel('Time')
plt.title('MFCC')
plt.colorbar()
plt.tight_layout()
data = [go.Surface(z=spectrogram.T)]
layout = go.Layout(
title='Specgtrogram of "yes" in 3d',
scene=dict(
yaxis=dict(title='Frequencies', range=freqs),
xaxis=dict(title='Time', range=times),
zaxis=dict(title='Log amplitude'),
),
)
fig = go.Figure(data=data, layout=layout)
py.iplot(fig)
#%matplotlib inline
ipd.Audio(samples, rate=sample_rate)
import IPython
import plotly import plotly.graph_objs as go import numpy as np df = 'data/mt_bruno_elevation.csv' Z = np.loadtxt(df, delimiter=',', skiprows=1, usecols=range(1, 25)) data = [go.Surface(z=Z)] fig = go.Figure(data=data) plotly.offline.plot(fig, filename='../templates/mt-3d-surface.html')
width=0.5
),
opacity=0.8
)
)
data = [trace1]
layout = go.Layout(
margin=dict(
l=0,
r=0,
b=0,
t=0
)
)
fig = go.Figure(data=data, layout=layout)
"""
data = [go.Surface(z=A)]
layout = go.Layout(
title='CNN Output/Input Sizes',
autosize=True, # we want this to fill the screen
# width=500,
# height=500,
margin=dict(l=65, r=50, b=65, t=90),
xaxis=dict(title='Filter Size'),
yaxis=dict(title='Stride Length'))
fig = go.Figure(data=data, layout=layout)
plot(fig)
def PyPlot3D(title,
z_csv,
x_csv,
y_csv,
t_coeff=1,
online=True,
path=".",
filename="wt2",
xaxis="states",
yaxis="time",
to_file="",
y_scale=1):
print("Making plot...")
# Z----------------------------------------------
z_data = pd.read_csv(z_csv, header=None)
# Z----------------------------------------------
# X----------------------------------------------
x = pd.read_csv(x_csv, keep_default_na=False)
x.replace(r'\[(.+)\]', r'≺\1≻', regex=True, inplace=True)
x_header = list(x)[0]
x["x"] = list(x["x"])
print(x["x"])
print(x["vals"])
# x_ticktext = list(x["x"])
x_tickvals = np.linspace(list(x["x"])[0], list(x["x"])[-1], 10)
# x_ticktext = np.linspace(0, len(x["x"]), 5)
x_ticktext = np.linspace(list(x["vals"])[0], list(x["vals"])[-1], 10)
x_ticktext /= 100
x_ticktext = np.round(x_ticktext, 4)
# print(list(x["x"])[-1])
# for i in range(len(x_ticktext)):
# x_ticktext[i] = x_ticktext[i]
# x_ticktext[i] = str(x_ticktext[i])
print(x["x"])
print('x_ticktext:', x_ticktext)
print('x_tickvals:', x_tickvals)
# exit(0)
# X----------------------------------------------
# Y----------------------------------------------
y = pd.read_csv(y_csv, keep_default_na=False)
y_header = list(y)[0]
# print(list(y["y"]))
# exit(0)
y_y = list(y["y"])
y_v = list(y["vals"])
y["vals"] = list(y["vals"])
y_tickvals = list(np.arange(0, y_y[-1] + 1, 2))
# y_tickvals = y_y
# y_tickvals = list(np.arange(0, y_v[-1] + 1, 10))
y_ticktext = list(np.arange(y_v[0], y_v[-1] + 1, 10))
# y_tickvals = np.linspace(
# list(y["y"])[0], list(y["y"])[-1], 5)
# y_ticktext = np.linspace(
# list(y["vals"])[0], list(y["vals"])[-1], 5)
# y_ticktext = np.round(y_ticktext, 2)
# y_ticktext = list(y["y"])
# y_tickvals = list(y["vals"])
# y_tickvals = np.array(y_tickvals) / t_coeff
print('y_ticktext:', y_ticktext)
print('y_tickvals:', y_tickvals)
# Y----------------------------------------------
data = [
go.Surface(
showlegend=False,
showscale=False,
lighting=dict(diffuse=0.5, specular=.2, fresnel=0.2),
z=z_data.as_matrix(),
colorscale="Portland",
)
]
scale = int(y_ticktext[-1])
layout = go.Layout(
# plot_bgcolor="#000000",
# pap_bgcolor="#000000",
title=title,
titlefont=dict(
# family="Courier New, monospace",
# family='Open Sans, sans-serif',
family='Lato',
size=20,
color="#222"),
margin=go.Margin(
l=0,
r=0,
b=0,
t=35,
pad=50,
),
xaxis=dict(
# linecolor="black",
# linewidth=2,
autotick=False,
# dtick=1,
ticks='outside',
tickfont=dict(
# size=20,
size=200, ),
),
yaxis=dict(
# tickangle=45,
title="y Axis",
titlefont=dict(
family="Courier New, monospace",
# family='Old Standard TT, serif',
size=40,
# size=14,
color="#FFFFFF"),
autotick=False,
# dtick=1,
ticks='outside',
tickfont=dict(
# size=20,
size=200, ),
),
# zaxis=dict(
# tickangle=90
# ),
autosize=False,
# autosize=True,
width=1200,
height=650,
plot_bgcolor="#AAA",
# paper_bgcolor="#AAA",
scene=go.Scene(
camera=dict(up=dict(x=0, y=0, z=1),
center=dict(x=0, y=0, z=0.2),
eye=dict(x=3.75, y=3.75, z=3.75)),
# aspectratio={"x": 1, "y": 1, "z": 1},
aspectratio={
"x": 2,
"y": 2,
"z": 1
},
xaxis={
"title": xaxis,
"showgrid": False,
"showline": False,
# "showline":True,
# "ticks": "outside",
# "showticklabels": True,
# "linewidth": 1,
# "tickvals": list(range(len(x_tickvals))),
# "ticktext": list(range(len(x_tickvals))),
"tickvals": x_tickvals,
"ticktext": x_ticktext,
'titlefont': dict(size=18, ),
'tickfont': dict(size=14, ),
'autorange': True,
# "tickangle": 45,
# "linecolor": "black",
# "linewidth": 2,
},
yaxis={
'autorange': True,
"title": yaxis + "\t\t\t\t.",
"ticktext": y_ticktext,
"tickvals": y_tickvals,
# "linecolor": "black",
"linewidth": 1,
'titlefont': dict(size=18, ),
'tickfont': dict(size=14, )
},
zaxis={
'autorange': True,
"range": [0, 1],
"title": "",
# "title": "prob.\t\t\t\t\t\t.",
"title": "T, ns .",
'nticks': 5,
# "title": "max(hp) / max(p(t)) .",
# 'dtick': -20,
# "tickangle": 45,
# "linecolor": "black",
# "ticktext": y_ticktext,
# "tickvals": y_tickvals,
"linewidth": 1,
'titlefont': dict(size=18, ),
'tickfont': dict(size=14, )
# "transform": {"rotate": '0'}
},
),
showlegend=False)
fig = go.Figure(data=data, layout=layout)
if to_file:
py.image.save_as(fig, filename=to_file)
return
# fig["layout"].update(scene=dict(aspectmode="data"))
# online=False
if online:
py.plot(fig, filename=filename)
# plotly.offline.init_notebook_mode()
# plotly.offline.iplot(fig, filename="wt.html")
# plotly.
# py.offline.iplot(fig, filename="wt")
else:
# plotly.offline.init_notebook_mode()
plotly.offline.plot(fig, filename=path + filename + ".html")
return
else:
effective_order = h_order
if effective_order < 3:
rE_min = 0.5
else:
rE_min = 0.7
rE_plot = go.Surface(
name='Energy Model Localization Vector (r<sub>E</sub>)',
x=0.9 * np.min(spkr_rr) * np.reshape(xyz[0, :], T.shape),
y=0.9 * np.min(spkr_rr) * np.reshape(xyz[1, :], T.shape),
z=0.9 * np.min(spkr_rr) * np.reshape(xyz[2, :], T.shape),
cmin=rE_min,
cmax=np.ceil(np.max(rEr) * 10) / 10,
surfacecolor=c,
colorscale='Portland',
hoverinfo='text',
visible=False,
opacity=1.0,
text=np.vectorize(lambda u, v, c: "rE: %.2f<br>a: %.1f<br>e: %.1f" %
(c, u, v))(T.az * 180 / pi, T.el * 180 / pi,
np.reshape(r, T.shape)),
contours=dict(z=dict(show=True), y=dict(show=True), x=dict(show=True)))
# https://plot.ly/python/reference/#scatter3d
spkr_locs = go.Scatter3d(
name='Loudspeakers',
x=spkr_x,
y=spkr_y,
z=spkr_z,
mode='markers',
def prepare(self):
# ---------------------
print("Making plot...")
# ---------------------
# -------------------------------------------
x_data = self.x_data
y_data = self.y_data
z_data = self.z_data
# -------------------------------------------
# ------------------------------------------------
# x_ticktext = x_data
# x_tickvals = x_data
# x_tickvals = list(range(len(x_data)))
# x_ticktext = [str(i) for i in x_ticktext]
# x_ticktext = x_ticktext[::50]
# x_tickvals = x_tickvals[::50]
# ------------------------------
# print('x_tickvals:', x_tickvals)
# print('x_ticktext:', x_ticktext)
# ------------------------------------------------
# ------------------------------------------------
# y_ticktext = y_data
# y_ticktext = [str(i) for i in y_ticktext]
# y_tickvals = list(range(len(y_data)))
# y_tickvals = y_data
# y_tickvals = np.array(y_tickvals) / self.t_coeff
# y_ticktext = y_ticktext[::50]
# y_tickvals = y_tickvals[::50]
# y_tickvals = y_tickvals[::50]
# ------------------------------
# print('y_tickvals:', y_tickvals)
# print('y_ticktext:', y_ticktext)
# ------------------------------------------------
data = [
# go.Surface(
# showlegend=False,
# showscale=False,
# lighting=dict(diffuse=0.5, specular=.2, fresnel=0.2),
# z=z_data,
# # colorscale="Portland",
# # colorscale='Viridis',
# )
go.Surface(
z=z_data,
colorscale="Portland",
contours=go.surface.Contours(z=go.surface.contours.Z(
show=True,
usecolormap=True,
# highlightcolor="#42f462",
project=dict(z=True),
# colorscale="Portland",
)))
]
scale = 1
# scale = int(y_ticktext[-1])
layout = go.Layout(
# needed
# ---------------
title='<b>' + self.title + '</b>',
# ---------------
# -----------------
width=self.width,
height=self.height,
# -----------------
titlefont=dict(
family='Lato',
color="#222",
size=20,
),
xaxis=dict(title=r'$\sqrt{(n_\text{c}(t|{T_\text{early}}))}$'),
# margin=go.Margin(
# l=0,
# r=0,
# b=0,
# t=35,
# pad=50,
# ),
# zaxis=dict(
# tickangle=90
# ),
# --------------
# autosize=False,
autosize=True,
# --------------
plot_bgcolor="#AAA",
# paper_bgcolor="#AAA",
scene=go.Scene(
camera=dict(up=dict(x=0, y=0, z=1),
center=dict(x=0, y=0, z=0.2),
eye=dict(x=3.75, y=3.75, z=3.75)),
xaxis={
# --------------------
"title":
self.x_title,
# --------------------
# ---------------------
# ---------------------
"linewidth":
2,
"showgrid":
False,
"showline":
False,
# -----------------------------------
'titlefont':
dict(
family=self.ticks['family'],
color=self.ticks['color'],
size=self.ticks['title']['size'],
),
'tickfont':
dict(
family=self.ticks['family'],
color=self.ticks['color'],
size=self.ticks['size'],
),
# -----------------------------------
# 'nticks': 5,
'tickangle':
0,
# 'orientation': 'h',
# 'autorange': True,
# "ticktext": self.x_tickvals,
"tickvals":
self.x_tickvals,
"ticktext":
self.x_ticktext,
# "showline":True,
# "ticks": "outside",
# "showticklabels": True,
# "tickvals": list(range(len(x_tickvals))),
# "ticktext": list(range(len(x_tickvals))),
# "tickangle": 45,
# "linecolor": "black",
# "linewidth": 2,
},
yaxis={
# --------------------------------
"title":
self.y_title,
# "title": self.y_title+"\t\t\t\t.",
# --------------------------------
# ---------------------
# "ticktext": self.y_tickvals,
"tickvals":
self.y_tickvals,
"ticktext":
self.y_ticktext,
# ---------------------
"linewidth":
2,
# 'nticks': 5,
# 'autotick': False,
# 'tick0': 0.001,
# 'dtick': 0.5,
# -----------------------------------
'titlefont':
dict(
family=self.ticks['family'],
color=self.ticks['color'],
size=self.ticks['title']['size'],
),
'tickfont':
dict(
family=self.ticks['family'],
color=self.ticks['color'],
size=self.ticks['size'],
),
# -----------------------------------
'tickangle':
0,
# 'autorange': True,
# "linecolor": "black",
},
zaxis={
# --------------------
"title":
self.z_title,
# --------------------
"linewidth":
2,
# 'nticks': 5,
# -----------------------------------
'titlefont':
dict(
family=self.ticks['family'],
color=self.ticks['color'],
size=self.ticks['title']['size'],
),
'tickfont':
dict(
family=self.ticks['family'],
color=self.ticks['color'],
size=self.ticks['size'],
),
# -----------------------------------
'tickangle':
0,
'autorange':
True,
# 'usecolormap': True,
# 'highlightcolor': "#42f462",
# 'dtick': -20,
# "tickangle": 45,
# "linecolor": "black",
# "range": self.z_range,
# "transform": {"rotate": '0'}
},
aspectratio={
"x": self.scale['x'],
"y": self.scale['y'],
"z": self.scale['z'],
},
),
# showlegend=False
)
self.fig = go.Figure(data=data, layout=layout)
if self.to_file:
py.image.save_as(self.fig, filename=self.to_file)
return
return
x=list(corrs.columns),
y=list(corrs.index),
annotation_text=corrs.round(2).values,
showscale=True)
py.iplot(figure, filename='heatmap')
df1 = dataset.drop(axis=1,
columns=["Axial load(kN)", "Tensile elongation (%)"])
df2 = dataset.drop(
axis=1, columns=["Axial load(kN)", "ultimate tensile strength(MPa)"])
#3D surface plots
#1
data = [
go.Surface(x=dataset['Rotational speed(RPM)'],
y=dataset['Weilding speed(mm/min)'],
z=df1.values.tolist(),
colorscale='Viridis')
]
layout = go.Layout(width=800,
height=700,
autosize=False,
title='Friction Stir Weilding dataset',
scene=dict(xaxis=dict(gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)'),
yaxis=dict(gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)'),
def data_graph(
df,
filename,
chart_type,
xaxis_type,
yaxis_type,
):
"""アップロードされたデータのグラフを描画"""
basename = os.path.splitext(filename)[0]
# ファイル名の1つ目の'_'で区切って、グラフタイトルとY軸名に分ける
if '_' in basename:
title, yaxis_name = basename.split('_', 1)
# ファイル名に'_'がなければグラフタイトル、Y軸名ともにファイル名
else:
title, yaxis_name = basename, basename
def args(i):
"""graph_objs helper func"""
return {'x': df.index, 'y': df[i], 'name': i}
# チャートの種類をディクショナリで分岐
# 内包表記でdfの列の数だけトレース
data = {
'Line': [go.Scatter(args(i)) for i in df.columns],
'Bar': [go.Bar(args(i)) for i in df.columns],
'Histogram':
[go.Histogram(x=df[i], name=i, opacity=.5) for i in df.columns],
'Pie': [
go.Pie(labels=df.index,
values=df[i],
name=i,
domain={'column': list(df.columns).index(i)})
for i in df.columns
],
'Polar': [
go.Scatterpolar(
r=df[i],
theta=df.index,
name=i,
) for i in df.columns
],
'Heatmap': [go.Heatmap(x=df.index, y=df.columns, z=df.values)],
'Box': [go.Box(y=df[i], name=i) for i in df.columns],
# 'Contour': [go.Contour(x=df.index, y=df.columns, z=df.values)]
'3D Scatter': [
go.Scatter3d(x=df.index, y=df.columns, z=df[i], name=i)
for i in df.columns
],
'3D Surface': [
go.Surface(x=df.index,
y=df.columns,
z=df.values,
name=yaxis_name,
contours=go.surface.Contours(
z=go.surface.contours.Z(show=True,
usecolormap=True,
highlightcolor="#42f462",
project=dict(z=True)))),
],
'2D Histogram': [go.Histogram2d(x=df.iloc[:, 0], y=df.iloc[:, 1])]
}
# チャートの種類でレイアウトを分岐
# 分岐にはdefaultdictを使い、デフォルトはlambda式で返す
layout = defaultdict(
# default layout
lambda: go.Layout(title=go.layout.Title(text=title),
xaxis={
'type': xaxis_type,
'title': df.index.name,
'rangeslider': dict(visible=False),
},
yaxis={
'type': yaxis_type,
'title': yaxis_name,
},
margin={
'l': 60,
'b': 50
},
hovermode='closest'),
# other layout
{
'Histogram':
go.Layout(title=title,
xaxis={'title': 'Value'},
yaxis={'title': 'Count'},
barmode='overlay',
hovermode='closest'),
'Pie':
go.Layout(title=go.layout.Title(text=title),
grid={
'columns': len(df.columns) - 1,
'rows': 1
},
hovermode='closest')
})
return dcc.Graph(id='the_graph',
figure={
'data': data[chart_type],
'layout': layout[chart_type]
})
# HiMCM 2017 Model Calculation
# Team # 7680
import plotly.plotly as py
import plotly.graph_objs as go
import pandas as pd
# Read data from a csv
z_data = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv'
)
data = [go.Surface(z=z_data.as_matrix())]
layout = go.Layout(title='Mt Bruno Elevation',
autosize=False,
width=500,
height=500,
margin=dict(l=65, r=50, b=65, t=90))
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='elevations-3d-surface')
init_notebook_mode(connected=True)
np.random.seed(1)
x = (np.random.rand(1000) - .5) * 10
y = -2 * x + np.random.normal(0, 2, x.shape) + 5
def cost(w, b):
y_hat = w * x + b
return np.mean((y - y_hat)**2)
cost = np.vectorize(cost)
w = np.linspace(-5, 1, 100)
b = np.linspace(2, 8, 100)
W, B = np.meshgrid(w, b)
Z = cost(W, B)
surface = go.Surface(x=W, y=B, z=Z, colorscale='Viridis')
layout = go.Layout(scene=dict(
xaxis=dict(title='w'),
yaxis=dict(title='b'),
zaxis=dict(title='cost'),
))
figure = go.Figure(data=[surface], layout=layout)
iplot(figure)
def __init__(self, layout, input_data, axes, value_name, cb,
show_variances, volume, volume_sampling):
super().__init__(input_data,
axes,
value_name,
cb,
show_variances,
button_options=['X', 'Y', 'Z'],
volume=volume)
self.cube = None
self.volume = volume
# Initialise Figure and VBox objects
self.fig = None
params = {
"values": {
"cbmin": "min",
"cbmax": "max"
},
"variances": None
}
if self.show_variances:
params["variances"] = {"cbmin": "min_var", "cbmax": "max_var"}
# Set colorbar limits once to keep them constant for slicer
# TODO: should there be auto scaling as slider value is changed?
for i, (key, val) in enumerate(sorted(params.items())):
if val is not None:
arr = getattr(self.input_data, key)
if self.cb[val["cbmin"]] is not None:
val["cmin"] = self.cb[val["cbmin"]]
else:
val["cmin"] = np.amin(arr[np.where(np.isfinite(arr))])
if self.cb[val["cbmax"]] is not None:
val["cmax"] = self.cb[val["cbmax"]]
else:
val["cmax"] = np.amax(arr[np.where(np.isfinite(arr))])
colorbars = [{
"x": 1.0,
"title": value_name,
"thicknessmode": 'fraction',
"thickness": 0.02
}]
# Store min/max for each dimension for invisible scatter
self.xminmax = dict()
for key, var in self.slider_x.items():
self.xminmax[key] = [var.values[0], var.values[-1]]
scatter_x, scatter_y, scatter_z = self.get_outline_as_scatter()
# Make a generic volume trace
if self.volume:
vol_trace = go.Volume(x=[0],
y=[0],
z=[0],
value=[0],
opacity=0.1,
surface_count=volume_sampling,
colorscale=self.cb["name"],
showscale=True)
xyz = "xyz"
if self.show_variances:
self.fig = go.FigureWidget(
make_subplots(rows=1,
cols=2,
horizontal_spacing=0.16,
specs=[[{
"type": "scene"
}, {
"type": "scene"
}]]))
colorbars.append({
"x": 1.0,
"title": "Variances",
"thicknessmode": 'fraction',
"thickness": 0.02
})
colorbars[0]["x"] = -0.1
for i, (key, val) in enumerate(sorted(params.items())):
if self.volume:
vol_trace["isomin"] = val["cmin"]
vol_trace["isomax"] = val["cmax"]
vol_trace["meta"] = key
vol_trace["colorbar"] = colorbars[i]
self.fig.add_trace(vol_trace, row=1, col=i + 1)
else:
for j in range(3):
self.fig.add_trace(go.Surface(
cmin=val["cmin"],
cmax=val["cmax"],
showscale=False,
colorscale=self.cb["name"],
colorbar=colorbars[i],
meta=key,
name="slice_{}".format(xyz[j])),
row=1,
col=i + 1)
self.fig.add_trace(go.Scatter3d(
x=scatter_x,
y=scatter_y,
z=scatter_z,
marker=dict(cmin=val["cmin"],
cmax=val["cmax"],
color=np.linspace(val["cmin"], val["cmax"],
8),
colorbar=colorbars[i],
colorscale=self.cb["name"],
showscale=True,
opacity=1.0e-6),
mode="markers",
hoverinfo="none",
meta=key,
name="scatter"),
row=1,
col=i + 1)
self.fig.update_layout(**layout)
else:
if self.volume:
vol_trace["isomin"] = params["values"]["cmin"]
vol_trace["isomax"] = params["values"]["cmax"]
vol_trace["meta"] = "values"
vol_trace["colorbar"] = colorbars[0]
data = [vol_trace]
else:
data = [
go.Surface(cmin=params["values"]["cmin"],
cmax=params["values"]["cmax"],
colorscale=self.cb["name"],
colorbar=colorbars[0],
showscale=False,
meta="values",
name="slice_{}".format(xyz[j]))
for j in range(3)
]
data += [
go.Scatter3d(x=scatter_x,
y=scatter_y,
z=scatter_z,
marker=dict(cmin=params["values"]["cmin"],
cmax=params["values"]["cmax"],
color=np.linspace(
params["values"]["cmin"],
params["values"]["cmax"], 8),
colorbar=colorbars[0],
colorscale=self.cb["name"],
showscale=True,
opacity=1.0e-6),
mode="markers",
hoverinfo="none",
meta="values",
name="scatter")
]
self.fig = go.FigureWidget(data=data, layout=layout)
# Call update_slice once to make the initial image
self.update_axes()
self.vbox = [self.fig] + self.vbox
self.vbox = widgets.VBox(self.vbox)
self.vbox.layout.align_items = 'center'
return
def update1(value1, value2, value3):
print('----------------------------------------')
print(volume.shape)
print(value1, value2, value3)
print(dim)
xx = np.array(range(dim[0]))
yy = np.array(range(dim[1]))
zz = np.array(range(dim[2]))
# trace X
z, y = np.meshgrid(zz, yy)
x = z * 0 + value1
surf = volume[value1, :, :]
tracex = go.Surface(
x=x,
y=y,
z=z,
surfacecolor=surf,
colorscale='Gray'
)
# trace Y
z, x = np.meshgrid(zz, xx)
y = x * 0 + value2
surf = volume[:, value2, :]
tracey = go.Surface(
x=x,
y=y,
z=z,
surfacecolor=surf,
colorscale='Gray'
)
# trace Z
y, x = np.meshgrid(yy, xx)
z = y * 0 + value3
surf = volume[:, :, value3]
print(z.shape, surf.shape)
tracez = go.Surface(
x=x,
y=y,
z=z,
surfacecolor=surf,
colorscale='Gray'
)
data = [
tracex,
tracey,
tracez,
]
fig = go.Figure(data=data)
m = max(dim)
fig.update_layout(
title='Volume',
scene=dict(
xaxis={'range': [-1, dim[0]]},
yaxis={'range': [-1, dim[1]]},
zaxis={'range': [-1, dim[2]]},
aspectratio=dict(x=dim[0]/m,
y=dim[1]/m,
z=dim[2]/m),
)
)
return fig
data6000 = nouvellesdonnees[:6000, :]
print(data6000.shape)
for pays in np.unique(pays_index):
pays_sbvts[pays] = data6000[data6000[:, 2] == pays,
-2].astype(np.float).sum()
##########################################
# trace1 = go.Pie(labels=list(pays_sbvts.keys()), values=list(pays_sbvts.values()))
# plot([trace1], filename="PieChartSubv1.html")
#
# randomdata2 = np.random.randn(500)
# trace2 = go.Histogram(x=randomdata2)
# plot([trace2], filename="HistoSubv1.html")
size3 = 100000
# randomdata3 = np.zeros(3 * size3)
# randomdata3[0::3] = (np.random.rand(size3) * 2) - float(size3)
# randomdata3[1::3] = (np.random.rand(size3) * 2) - float(size3)
# randomdata3[2::3] = np.multiply((np.square(randomdata3[0::3]) + np.square(randomdata3[1::3])),
# (np.exp(1 / (randomdata3[0::3] + randomdata3[1::3]))))
# randomdata3 = np.array(list(zip(randomdata3[0::3], randomdata3[1::3], randomdata3[2::3])))
x3 = ((np.random.rand(size3) * 2) - 1) * 30
y3 = ((np.random.rand(size3) * 2) - 1) * 30
z3 = np.multiply((np.square(x3) + np.square(y3)),
(np.log2(np.abs(1 / (x3 + y3)))))
# randomdata3 = np.array(list(zip(randomdata3[0::3], randomdata3[1::3], randomdata3[2::3])))
trace3 = go.Surface(x=x3, y=y3, z=z3)
layout3 = go.Layout(title='Test surface', autosize=True)
# fig3 = go.Figure(data=trace3, layout=layout3)
plot([trace3], filename="Surface1.html")
# In[5]:
# Input value
x = lon_topo
y = lat_topo
z = topo
# Import color scale
reload(Pcode)
name = "topo"
Ctopo = Pcode.Colorscale_Plotly(name)
cmin = -8000
cmax = 8000
topo3D = go.Surface(x=x, y=y, z=z, colorscale=Ctopo, cmin=cmin, cmax=cmax)
# The position of z-axis
z_offset = depmax * np.ones(z.shape) # Plot at the bottom
#z_offset=0*np.ones(z.shape) # Plot at 0 level
topo_surf = go.Surface(z=z_offset,
x=x,
y=y,
colorscale=Ctopo,
cmin=cmin,
cmax=cmax,
showlegend=False,
showscale=False,
surfacecolor=topo,
hoverinfo='text')
import plotly.plotly as py
import plotly.graph_objs as go
import numpy as np
import pandas as pd
sparse_matrix = np.load(
r"C:\Users\Isaiah Nields\Documents\GitHub\brown_datathon_v2\tripadvisor\topo_map.npz"
)['sparse_matrix']
data = [go.Surface(z=sparse_matrix[:1000, :])]
layout = go.Layout(
title='Sparse Matrix Topography',
autosize=True
# margin=dict(
# l=65,
# r=50,
# b=65,
# t=90
# )
)
fig = go.Figure(data=data, layout=layout)
