def plotly(self):
"""
Returns:
"""
fieldmap = _pd.read_csv(self.FNAME,
skiprows=8,
names=['Y', 'Z', 'X', 'BY', 'BZ', 'BX'],
sep=r'\s+')
fieldmap['X'] = fieldmap['X'] + self.length.m_as('cm') / 2
fieldmap['Z_ABS'] = fieldmap['Z'].apply(_np.abs)
fieldmap = fieldmap[fieldmap['Z'] == fieldmap['Z_ABS'].min()]
rotation_matrix = _np.linalg.inv(
self.entry_patched.get_rotation_matrix())
origin = self.entry_patched.origin
u = _np.dot(fieldmap[['X', 'Y', 'Z']].values, rotation_matrix)
fieldmap['XG'] = (u[:, 0] + origin[0].m_as('cm')) / 100
fieldmap['YG'] = (u[:, 1] + origin[1].m_as('cm')) / 100
fieldmap['ZG'] = (u[:, 2] + origin[2].m_as('cm')) / 100
return _go.Histogram2d(
histfunc='avg',
nbinsx=100,
nbinsy=100,
x=fieldmap['XG'],
y=fieldmap['YG'],
z=fieldmap['BZ'],
opacity=1.0,
colorscale='Greys',
)
def plot_all2d(users, column):
fig = make_subplots(rows=2, cols=3,
shared_xaxes='all',
shared_yaxes='all',
subplot_titles=list(users.type.unique()))
for i, value in enumerate(users.groupby('type')):
key, df = value
fig.add_trace(go.Histogram2d(x=df[column_x], y=df[column_y]), row=int(i/3)+1, col=(i%3)+1)
fig.update_layout(height=600, width=800,
title_text=column_x+'/'+column_y,showlegend=False)
return fig
def histogram2d(self, x: List[float], y: List[float], title: str,
names: List[str]):
fig = go.Figure()
histogram = go.Histogram2d(x=x, y=y, colorbar=dict(title=names[2]))
fig.add_trace(histogram)
fig.update_layout(title=title,
xaxis_title=names[0],
yaxis_title=names[1])
fig.show(renderer='svg')
def plot_2D_Hist(samples,nbins,xlims=None,ylims=None): std = np.std(samples,axis=0) fig = go.Figure() if(xlims==None): xbin_dict = dict(size=std[0]*6/nbins) else: xbin_dict = dict(start=xlims[0],end=xlims[1],size=(xlims[1]-xlims[0])/nbins) if(ylims==None): ybin_dict = dict(size=std[1]*6/nbins) else: ybin_dict = dict(start=ylims[0],end=ylims[1],size=(ylims[1]-ylims[0])/nbins) fig.add_trace(go.Histogram2d( x=samples[:,0], y=samples[:,1], xbins=xbin_dict, ybins=ybin_dict, colorscale="hot", showscale=True)) fig = set_basic_layout(fig) return fig
def plotDataset(datasetFile, batch=True):
"""
Plot the dataset
Args:
datasetFile (str) : Path to dataset file
"""
logging.info("Got file: %s", datasetFile)
df = pd.read_csv(datasetFile)
# Let's plot all features
logging.info("Plotting features")
figures = []
for var in [x for x in df.columns if x not in ["time", "y", "x28", "x61"]]:
figures.append(go.Scatter(x=df.time, y=df[var], name=var))
if not batch:
fig = go.Figure(figures)
fig.show()
# PCA
logging.info("Plotting Scree-plot")
dfForPCA = df.copy()
dfForPCA = dfForPCA.drop(["time", "y", "x28", "x61"], axis=1)
logging.debug("N columns for PCA: %s", len(dfForPCA.columns))
logging.debug("n Samples for PCA: %s", len(dfForPCA))
dfForPCA = StandardScaler().fit_transform(dfForPCA)
pca = PCA()
pca.fit(dfForPCA)
logging.debug("nComponents of PCA: %s", pca.n_components_)
logging.debug("nFeatures of PCA: %s", pca.n_features_)
logging.debug("nSamples of PCA: %s", pca.n_samples_)
if not batch:
fig = px.bar(pca.explained_variance_ratio_)
fig.update_layout(title="Scree plot",
xaxis_title="PCs",
yaxis_title="Variance ratio")
fig.show()
logging.info("PLotting cumulative variance")
varSum = np.cumsum(
np.round(pca.explained_variance_ratio_, decimals=3) * 100)
if not batch:
fig = px.line(varSum)
fig.update_layout(title="Cummulative variance",
xaxis_title="PCs",
yaxis_title="% Variance explained")
fig.show()
# Correlation
logging.info("Plotting correlation matrix")
dfForCorr = df.copy()
dfForCorr = dfForCorr.drop(["time", "y", "x28", "x61"], axis=1)
corr = dfForCorr.corr()
if not batch:
fig = go.Figure(data=[
go.Heatmap(
z=corr,
x=corr.columns,
y=corr.columns,
colorscale=[[0, 'rgb(12,51,131)'], [0.25, 'rgb(10,136,186)'],
[0.5, 'rgb(242,211,56)'],
[0.75, 'rgb(242,143,56)'], [1, 'rgb(217,30,30)']])
])
fig.update_layout(title="Feature Correlation")
fig.show()
logging.info("Plotting 2D correlations")
dfFor2DCorr = df.copy()
dfFor2DCorr = dfFor2DCorr.drop(["time", "y", "x28", "x61"], axis=1)
dfFor2DCorr = pd.DataFrame(StandardScaler().fit_transform(dfFor2DCorr),
columns=dfFor2DCorr.columns)
figures = []
figures_titles = []
for var1, var2 in itertools.combinations(list(dfFor2DCorr.columns), 2):
logging.debug("Correlation for %s / %s", var1, var2)
figures.append(go.Histogram2d(x=dfFor2DCorr[var1],
y=dfFor2DCorr[var2]))
figures_titles.append((var1, var2))
SubplotsPerPlot = (3, 3)
nSubplotsPerPlot = SubplotsPerPlot[0] * SubplotsPerPlot[1]
nTotal = len(dfFor2DCorr.columns)
if round(nTotal / nSubplotsPerPlot) < nTotal / nSubplotsPerPlot:
nPlots = round(nTotal / nSubplotsPerPlot) + 1
else:
nPlots = round(nTotal / nSubplotsPerPlot)
logging.debug("Subplots : %s", SubplotsPerPlot)
logging.debug("nTotal : %s", nTotal)
logging.debug("nPlosts : %s", nPlots)
iPLotted = 0
for i in range(nPlots):
fig = make_subplots(SubplotsPerPlot[0], SubplotsPerPlot[1])
for ir in range(SubplotsPerPlot[0]):
for ic in range(SubplotsPerPlot[1]):
logging.debug(
"Plot %s -- iPLotted = %s / row = %s / column = %s", i,
iPLotted, ir + 1, ic + 1)
fig.add_trace(figures[iPLotted], row=ir + 1, col=ic + 1)
fig.update_xaxes(title_text=figures_titles[iPLotted][0],
row=ir + 1,
col=ic + 1)
fig.update_yaxes(title_text=figures_titles[iPLotted][1],
row=ir + 1,
col=ic + 1)
iPLotted += 1
if not batch:
fig.show()
def plot_obtained_pareto_front(art_type=None, problem_name=None):
art_names = dict(progressive='Progressive',
apriori='A priori',
aposteriori='A posteriori')
global save_path
load_path = '/home/kemal/Programming/Python/Articulation/data/pickles/' + art_type + '/' + problem_name + '/'
entries = os.listdir(load_path)
f1 = np.zeros(len(entries))
f2 = np.zeros(len(entries))
for i in range(len(entries)):
file = open(load_path + entries[i], 'rb')
data = pickle.load(file)
file.close()
f1[i] = data['global_best_sol'].get_y()[0]
f2[i] = data['global_best_sol'].get_y()[1]
fig = go.Figure()
fig.add_trace(
go.Histogram2d(name='Obtained Pareto front',
x=f1,
y=f2,
autobinx=False,
autobiny=False,
xbins=dict(start=-300, end=0, size=15),
ybins=dict(start=0, end=200, size=15),
colorscale='YlGnBu',
colorbar=dict(title='<b>Number of optimums:</b>',
titleside='top')))
load_path = '/home/kemal/Programming/Python/Articulation/data/precomputed_data/'
file = open(load_path + problem_name + '_data.pickle', 'rb')
data = pickle.load(file)
file.close()
f1 = data['pareto_f1']
f2 = data['pareto_f2']
fig.add_trace(
go.Scatter(name='Actual Pareto front',
x=f1,
y=f2,
mode='markers',
line=dict(color='red'),
marker_size=7))
fig.update_layout(title={
'text':
str('<b>' + art_names[art_type] + ' ' + problem_name +
': actual and obtained Pareto front' + '</b>'),
'y':
0.93,
'x':
0.5,
'xanchor':
'center',
'yanchor':
'top'
},
xaxis_title='<b>f1</b>',
yaxis_title='<b>f2</b>',
legend_title='<b>Legend:</b>')
fig.show()
fig.write_image(save_path + art_type + '_' + problem_name +
'_front_density.png')
import plotly.graph_objects as go
import numpy as np
np.random.seed(1)
x = np.random.randn(500)
y = np.random.randn(500)+1
fig = go.Figure(go.Histogram2d(
x=x,
y=y
))
fig.show()
)
),
row=1, col=2)
bin_size = (max_lwd-min_lwd)/32
fig.add_trace(
go.Histogram2d(
x=obs_lwd,
y=fov_lwd,
colorscale='BuPu',
zauto=False,
zmax=15,
xbins=dict(
start=min_lwd,
end=max_lwd,
size=bin_size
),
ybins=dict(
start=min_lwd,
end=max_lwd,
size=bin_size
),
),
row=1, col=2)
fig.add_trace(
go.Scatter(
x=[min_lwd+75, min_lwd+75, min_lwd+75, min_lwd+75, min_lwd+75, min_lwd+75],
y=[max_lwd-25, max_lwd-45, max_lwd-65, max_lwd-85, max_lwd-105, max_lwd-125],
mode="text",
def Histogram2D_tseng(filename, data, SNR=0, EVM=0, bercount=(0, 0, 0)):
x = np.real(data)
y = np.imag(data)
miny = y.min()
fig = go.Figure()
filename = str(filename)
# fig.add_trace(go.Histogram2dContour(
# x=x,
# y=y,
# colorscale='Hot',
# reversescale=True,
# xaxis='x',
# yaxis='y'
# ))
# fig.add_trace(go.Scatter(
# x=x,
# y=y,
# xaxis='x',
# yaxis='y',
# mode='markers',
# marker=dict(
# color='rgba(255,156,0,1)',
# size=3)
# ))
# fig.add_trace(go.Histogram(
# y=y,
# xaxis='x2',
# marker=dict(
# color='#F58518'
# )
# ))
# fig.add_trace(go.Histogram(
# x=x,
# yaxis='y2',
# marker=dict(
# color='#F58518'
# )
# ))
# if SNR != 0:
# fig.add_annotation(
# text='SNR:{:.2f}(dB)<br>EVM:{:.2f}(%)<br>Bercount:{:.2E} [{}/{}]'.format(SNR, EVM * 100, bercount[0],
# bercount[1], bercount[2]),
# align='left',
# showarrow=False,
# font_family='Arial',
# font_size=17,
# font_color='white',
# bgcolor='black',
# # xref='x2',
# x=0,
# y=miny - 0.3,
# bordercolor='orange',
# borderwidth=5
# )
# fig.add_annotation(
# x=0,
# y=miny-0.3,
# text="SNR = {:.2f}(dB)".format(SNR),
# showarrow=False)
fig.add_trace(
go.Histogram2d(
x=x,
y=y,
colorscale=[[0.0, "rgb(255, 255, 255)"],
[0.1111111111111111, "rgb(255,230,0)"],
[0.2222222222222222, "rgb(255,150,0)"],
[0.3333333333333333, "rgb(255,102,0)"],
[0.4444444444444444, "rgb(254,051,0)"],
[0.5555555555555556, "rgb(255,0,0)"],
[0.6666666666666666, "rgb(255,0,51)"],
[0.7777777777777778, "rgb(153,0,51)"],
[0.8888888888888888, "rgb(60,0,30)"],
[1.0, "rgb(35,0, 0)"]],
nbinsx=256,
nbinsy=256,
zauto=True,
))
fig.update_layout(
autosize=False,
xaxis=dict(zeroline=False,
domain=[0, 1],
fixedrange=True,
mirror=True,
ticks='inside',
showline=True,
linewidth=2,
linecolor='black',
showticklabels=False),
yaxis=dict(zeroline=False,
domain=[0, 1],
fixedrange=True,
mirror=True,
ticks='inside',
showline=True,
linewidth=2,
linecolor='black',
showticklabels=False),
xaxis2=dict(zeroline=False,
domain=[0.905, 1],
fixedrange=True,
mirror=True,
ticks='inside',
showline=True,
linewidth=2,
linecolor='black',
showticklabels=False),
yaxis2=dict(zeroline=False,
domain=[0.905, 1],
fixedrange=True,
mirror=True,
ticks='inside',
showline=True,
linewidth=2,
linecolor='black',
showticklabels=False),
height=800,
width=800,
bargap=0,
hovermode='closest',
showlegend=False,
title=go.layout.Title(text="Color Histogram---" + filename),
font=dict(family="Arial", size=20, color="Black"),
# yaxis_range=[-4.3, 4.3],
# xaxis_range=[-4.3, 4.3]
)
if SNR != 0:
fig.update_layout(
xaxis=dict(
zeroline=False,
domain=[0, 0.9],
showgrid=True,
fixedrange=True,
title=
'In-Phase<br>SNR:{:.2f}(dB) || EVM:{:.2f}(%) || Bercount:{:.2E} [{}/{}]'
.format(SNR, EVM * 100, bercount[0], bercount[1], bercount[2]),
),
font=dict(family="Arial", size=20, color="Black"),
)
fig.write_image(
"G:\KENG\pycharm\pycharm_coherent_16QAM\data\KENG_optsim_py\{}.pdf".
format(filename))
# +
list(nx.cluster._directed_triangles_and_degree_iter(G.subgraph(['albamoraleda','antonialaborde','EduLorenGarcia'])))
# -
all_profiles_with_network['clustering_network'] = all_profiles_with_network.apply(lambda x: sum([t[3]/2 for t in nx.cluster._directed_triangles_and_degree_iter(x.ego_network) ])/3, axis=1) #if t[0]!=x.screen_name
compare_groups_plot(all_profiles_with_network, 'clustering_network','Outlier connectness')
from plotly.subplots import make_subplots
import plotly.graph_objects as go
fig = make_subplots(rows=4, cols=1, shared_xaxes=True)
for i, g in enumerate(all_profiles[selected].groupby('type_profile')):
g_id, g = g
fig.add_trace(go.Histogram2d(x=g.edges_reciprocal_ego, y=g.ego_network.apply(len), name=g_id),col=1, row=i+1, )
fig.update_layout(title='Degree of ego-network', height=700)
from plotly.subplots import make_subplots
import plotly.graph_objects as go
fig = make_subplots(rows=4, cols=1, shared_xaxes=True)
for i, g in enumerate(all_profiles[has_network].groupby('type_profile')):
g_id, g = g
fig.add_trace(go.Histogram(x=g['edges_between_outliers']/g.ego_network.apply(lambda x: (len(x)-1)*(len(x)-2)), name=g_id),col=1, row=i+1, )
fig.update_layout(title='Edges between outliers', height=700)
from plotly.subplots import make_subplots
import plotly.graph_objects as go
fig = make_subplots(rows=4, cols=1, shared_xaxes=True)
for i, g in enumerate(all_profiles[has_network].groupby('type_profile')):
g_id, g = g
def Histogram2D(filename, data, Image_Address, SNR=0, EVM=0, BERcount=0):
x = np.real(data)
y = np.imag(data)
miny = y.min()
fig = go.Figure()
filename = str(filename)
# fig.add_trace(go.Histogram2dContour(
# x=x,
# y=y,
# colorscale='Hot',
# reversescale=True,
# xaxis='x',
# yaxis='y'
# ))
# fig.add_trace(go.Scatter(
# x=x,
# y=y,
# xaxis='x',
# yaxis='y',
# mode='markers',
# marker=dict(
# color='rgba(255,156,0,1)',
# size=3)
# ))
fig.add_trace(go.Histogram(y=y, xaxis='x2', marker=dict(color='#F58518')))
fig.update_yaxes(range=[-9, 9])
fig.update_layout(yaxis=dict(
tickmode='array',
tickvals=[-7, -5, -3, -1, 1, 3, 5, 7],
))
fig.add_trace(go.Histogram(x=x, yaxis='y2', marker=dict(color='#F58518')))
fig.update_xaxes(range=[-9, 9])
fig.update_layout(xaxis=dict(
tickmode='array',
tickvals=[-7, -5, -3, -1, 1, 3, 5, 7],
))
# if SNR != 0:
# fig.add_annotation(
# x=0,
# y=miny-0.2,
# text="SNR = {}(dB)".format(SNR),
# showarrow=False)
# fig.add_annotation(
# x=0,
# y=miny-0.35,
# text="EVM = {}(%)".format(EVM * 100),
# showarrow=False)
fig.add_trace(
go.Histogram2d(x=x,
y=y,
colorscale='Hot',
nbinsx=256,
nbinsy=256,
zauto=True))
fig.update_layout(autosize=False,
xaxis=dict(zeroline=False,
domain=[0, 0.85],
showgrid=False,
fixedrange=True),
yaxis=dict(zeroline=False,
domain=[0, 0.85],
showgrid=False,
fixedrange=True),
xaxis2=dict(zeroline=False,
domain=[0.86, 1],
showgrid=False,
fixedrange=True),
yaxis2=dict(zeroline=False,
domain=[0.86, 1],
showgrid=False,
fixedrange=True),
height=800,
width=800,
bargap=0,
hovermode='closest',
showlegend=False,
title=go.layout.Title(text="Color Histogram---" +
filename),
xaxis_title="In-Phase",
yaxis_title="Quadrature-Phase",
font=dict(family="Time New Roman",
size=16,
color="Black"))
if SNR != 0:
fig.update_layout(xaxis_title="SNR : {}_EVM : {}_BERcount : {}".format(
SNR, EVM, BERcount))
# fig.write_image(r"data\KENG_optsim_py\20201130_DATA_2Laser_final\noLW_1GFO_noNoise_80KM_initialphase225\image\{}.png".format(filename))
fig.write_image(Image_Address + "\{}.png".format(filename))
# fig = go.Figure(go.Histogram2d(x=X_sorted_org_scale[:, 0].flatten(),
# y=Y_pred_ordered_org_scale[:, 0].flatten()-Y_pred_reduced_org_scale[:, 0].flatten()))
# seasons_No = 6
# fig = go.Figure()
# for i in range(seasons_No):
# start_hour = int((i/seasons_No)*8760)
# end_hour = int(((i+1)/seasons_No)*8760)
# fig.add_trace(go.Scatter(x=X[start_hour:end_hour, 0].flatten(),
# y=Y_pred_org_scale[start_hour:end_hour, 0].flatten()-
# scaler_Y.inverse_transform(Y_pred_reduced)[start_hour:end_hour,0], mode='markers', name=str(i)))
# fig.update_layout(title="Residual Demand vs. DPrice in seasons")
# # fig.update_yaxes(range=[-2, 0])
# # fig.update_xaxes(range=[20000, 80000])
# fig.show()
fig = go.Figure(go.Histogram2d(x=res_gen[orders],
y=Y_pred_ordered_org_scale[:, 0].flatten() - Y_pred_reduced_org_scale[:, 0].flatten()))
# fig = go.Figure(go.Scatter(x=wind_data_year_tech[orders],
# y=Y_pred_ordered_org_scale[:, 0].flatten()-Y_pred_reduced_org_scale[:, 0].flatten(), mode='markers', name='Original'))
fig.update_layout(title="RES vs. DPrice")
fig.show()
xx = X_sorted_org_scale.sum(axis=1)
fig = go.Figure(
go.Histogram2d(x=xx, y=Y_pred_ordered_org_scale[:, 0].flatten() - Y_pred_reduced_org_scale[:, 0].flatten()))
# fig = go.Figure(go.Scatter(x=xx, y=Y_pred_ordered_org_scale[:, 0].flatten()-Y_pred_reduced_org_scale[:, 0].flatten(),
# mode='markers', name='Original'))
fig.update_layout(title="Sum neighbours vs. DPrice")
fig.show()
print(clf.cv_results_)
print(clf.best_estimator_)