def save_figure(fn, fig):
plotly.offline.plot(fig,
filename=fn + '.html',
auto_open=False,
show_link=True)
pio.write_image(fig, fn + '.png')
pio.write_image(fig, fn + '.pdf')
def plot_scatter_dynamicTraces(datasets, datasets_labels, datsets_colors,
title):
traces = []
for x in xrange(0, len(datasets)):
traces.append(
go.Scatter(x=datasets[x]['path_lenght_total_meters'],
y=datasets[x]['computation_time_millis'],
mode='markers',
marker=go.Marker(color=datsets_colors[x]),
name=datasets_labels[x]))
layout = go.Layout(
title=title,
xaxis=dict(title='Distance between points (in meters)',
titlefont=dict(family='Courier New, monospace',
size=18,
color='#7f7f7f')),
yaxis=dict(title='Computation time (seconds)',
titlefont=dict(family='Courier New, monospace',
size=18,
color='#7f7f7f')))
fig = dict(data=traces, layout=layout)
plotly.offline.iplot(fig,
filename='./scatter_time_vs_pathLength',
image='png')
pio.write_image(fig,
'/home/mfaria/Downloads/plot_scatter_dynamicTraces.svg',
scale=2)
def plot_bubble_area_hist(df, bSave=False, dirname=None):
"""Plots a histogram of the area of the bubbles
"""
import plotly
import plotly.graph_objs as go
import plotly.io as pio
data = []
for i in range(len(df['Frame'].unique())):
data.append(
go.Histogram(x=df[df['Frame'] == df['Frame'].unique()[i]]['Area'],
name=df['Frame'].unique()[i]))
fig = go.Figure({
"data":
data,
"layout":
go.Layout(width=800,
height=500,
xaxis=dict(title='Area (mm2)',
linecolor='black',
linewidth=2,
mirror=True),
showlegend=True)
})
plotly.offline.plot(fig, auto_open=True)
if bSave:
pio.write_image(fig, dirname + '/' + 'BubbleHist.pdf')
def printTable(data, outputPath):
headerColor, rowOddColor, rowEvenColor = '#CCE5FF', '#CCF3FF', 'white'
cellsData = {'values': [], 'align': ['left', 'center'], 'height': 30}
for i in range(len(data['header'])):
cellsData['values'].append([])
for i in range(len(data['values'])):
for j in range(len(data['header'])):
cellsData['values'][j].append(data['values'][i][j])
colors = [headerColor] + [
rowOddColor if i % 2 else rowEvenColor
for i in range(len(data['header']) - 1)
]
cellsData['fill'] = {'color': colors}
trace = go.Table(header={'values' : data['header'], 'fill': {'color':headerColor}}, cells=cellsData, \
columnwidth=[250, 180])
layout = dict(width=1800,
height=1000,
font=dict(family='"Open Sans", verdana, arial, sans-serif',
size=18,
color='#444'))
fig = dict(data=[trace], layout=layout)
plot(fig, filename=os.path.join(outputPath, HTML_OUTPUT_NAME))
pio.write_image(fig, os.path.join(outputPath, PNG_OUTPUT_NAME))
def plot_evolution_with_dimension(length_1, lenght_2, distance_for_cdist,
nb_try, min_dim, max_dim, name_fig):
evol_dist = []
for dim in range(min_dim, max_dim):
evol_dist.append(
random_dtw(dim, length_1, lenght_2, distance_for_cdist, nb_try))
print(evol_dist)
layout = go.Layout(
title=go.layout.Title(text='Evol',
font=dict(family='Times'),
xref='paper',
x=0),
xaxis=go.layout.XAxis(title=go.layout.xaxis.Title(
text='dimension',
font=dict(family='Times', size=18, color='#7f7f7f'))),
yaxis=go.layout.YAxis(title=go.layout.yaxis.Title(
text='random distance',
font=dict(family='Times', size=18, color='#7f7f7f')), ))
fig = go.Figure(layout=layout)
# you plot the correlation btwn x and y
fig.add_scatter(x=list(range(min_dim, max_dim)),
y=evol_dist,
mode='lines',
name='fitted line',
showlegend=False)
# save the figure
pio.write_image(fig, name_fig + '.pdf')
def save_static(self):
try:
pio.write_image(self.fig, self.path.replace('html', 'png'))
except ValueError as e:
logging.warning("Nanoplotter: orca not found, not creating static image of html. "
"See https://github.com/plotly/orca")
logging.warning(e, exc_info=True)
def make_chart(self, file_name='chart.png'):
data = dict(
type='sankey',
node = dict(
pad = 15,
thickness = 20,
line = dict(
color = "black",
width = 0.5
),
label = ["A1", "A2", "B1", "B2", "C1", "C2"],
color = ["blue", "blue", "blue", "blue", "blue", "blue"]
),
link = dict(
source = [0,1,0,2,3,3],
target = [2,3,3,4,4,5],
value = [8,4,2,8,4,2]
))
layout = dict(
title = "Basic Sankey Diagram",
font = dict(
size = 10
)
)
fig = dict(data=[data], layout=layout)
pio.write_image(fig, file_name)
def print_report(self, directory, plot_format='pdf'):
for plot_id in self._plots:
name = "_".join(plot_id)
for plot in self._plots[plot_id]:
figure = plot.figure
pio.write_image(
figure, os.path.join(directory, name + "." + plot_format))
def generate_candle_image_from_db_by_weeks(share_item, start_date, weeks=52, output_folder=''):
'''
Generates candle image using data from the database
'''
data = []
for monday, friday in generate_week_dates(start_date, weeks):
week_data = list(ShareDataItem.objects.filter(share=share_item).filter(date__range=[monday, friday]).order_by('-date'))
if week_data:
summary = {}
summary['open_price'] = week_data[-1].open_price
summary['close_price'] = week_data[0].close_price
summary['high_price'] = max(week_data, key=lambda item: item.high_price).high_price
summary['low_price'] = min(week_data, key= lambda item: item.low_price).low_price
summary['date'] = friday
data.append(summary)
df = pd.DataFrame(data)
trace = go.Candlestick(
x=df.date,
open=df.open_price,
high=df.high_price,
low=df.low_price,
close=df.close_price
)
layout = go.Layout(xaxis=dict(rangeslider=dict(visible=False)))
fig = go.Figure(data=[trace], layout=layout)
fig.layout.template = 'plotly_white'
base_name = share_item.ticker + '.png'
image_path = output_folder + '/' + base_name
pio.write_image(fig, image_path, width=500, height=335)
print('Successfully created image ' + image_path)
def id_3(data):
aux = 0
dado = []
z = 0
for i in data["data"]:
df = pd.DataFrame.from_dict(i)
eixo_x = str(df.columns[0])
eixo_y = str(df.columns[1])
trace = go.Scatter(x=df.loc[:, eixo_x],
y=df.loc[:, eixo_y],
mode='lines+markers+text',
text=df.loc[:, eixo_y],
textposition='bottom center',
name=data['names'][z])
dado.append(trace)
z = z + 1
layout = go.Layout(title=data['title'])
figure = go.Figure(data=dado, layout=layout)
pio.write_image(figure, 'images/fig.png')
def plot_evolution_with_len(length_min, lenght_max, distance_for_cdist, nb_try,
dim, name_fig):
evol_dist = []
for leng in range(length_min, lenght_max):
evol_dist.append(
random_dtw(dim, leng, leng, distance_for_cdist, nb_try))
#evol_dist = [0.9244184515300428, 1.3507927079775819, 1.8953683994705877, 2.4164033165307894, 3.078017969791574, 3.5451338898270066, 3.8834481672845627, 4.419339504421014, 4.929312150282599, 5.295734833813404, 5.677188914851658, 6.3604791638624345, 6.401814264195343, 7.04105355729374, 7.677628549582872, 8.167956434624223, 8.69908235446326, 8.995387920683337, 9.174546910183219, 10.08512368328054, 10.451475420024673, 10.636857194244064, 11.393734396557317, 11.98788877714612, 11.99260450811431, 12.777608772282964, 12.778001918199413, 13.423174160537071, 13.780544588074179, 14.392920760371453, 14.96848321383208, 15.46319106461876, 16.22948537989721, 16.425017514508678, 16.878242066406663, 17.17548436842818, 17.7774467130331, 18.20496954585564, 18.3381123333226, 19.255758106963853, 19.84398311128651, 19.84627547720434, 20.571929630668553, 20.910664435519838, 21.487493599042047, 21.78891178691278, 22.210448540716584, 23.359410022663074, 23.00198941136967, 23.390665909858498, 24.02559960143591, 24.867349020481875, 24.88244134695304, 25.98445817885347, 25.883857617614797, 26.406262437726692, 27.363585641640036, 27.62783716052433, 27.65636942183851, 28.36497986903198, 28.916510784921552, 29.13178885907545, 30.03558322310999, 30.024455252759488, 30.642342470720575, 30.87941129481068, 31.695468603126137, 31.555577077386122, 32.147240484857534, 33.14356474543625, 33.4930579623546, 33.845940385257016, 34.4251507329126, 34.6541119223024, 35.13020073716517, 35.435646100605105, 36.02059806027671, 35.878206059824414, 36.86396012833017, 37.139630699988516, 37.3972232842984, 38.064723046402975, 38.53765721451534, 39.238206941313805, 39.693134424957485, 39.85683533797666, 39.893693308593406, 40.99388383417763, 41.40841115956261, 41.80706268864515, 42.71695395325237, 42.36003200993532, 43.57041069241617, 43.72312624494503, 44.69736247639636, 44.3356386196287, 45.10948612403638, 45.87740286605979, 46.65415117978885, 46.42349349750527, 46.96884013373549, 46.986471459677155, 47.52170575028901, 48.63862899894237, 48.85977743480101, 49.42741050967709, 49.342940471237974, 49.71675807954058, 50.521044498722546, 51.41492390536246, 50.94726062485392, 51.764335272511666, 52.167366927752816, 52.481607295842885, 53.17333474877533, 53.4459672975531, 53.3550557453019, 54.92344608482172, 55.119760391444395, 55.12788907631439, 56.23660645259143, 56.95121330374064, 56.656807256110596, 56.963609698749856, 57.74024863755208, 58.581524222094906, 58.89197602684423, 59.102798593629046, 59.6928693318405, 60.24153671106531, 60.453148503910725, 61.1788992255016, 61.44209093999771, 61.664544443794355, 61.87036410811632, 62.63445407877545, 63.66630810846576, 63.79297960904965, 64.02874725539911, 65.05092869335913, 65.48925129176443, 65.28811453398272, 66.42282525343099, 66.60478814902703, 66.8905539933685, 67.44827964742701, 68.3705186658108, 67.95891234840168, 68.38101822826553, 68.91923283829, 69.29291644966138, 70.18665647244956, 69.91892372840867, 71.67342463090085, 71.40155550341318, 71.37516822196531, 71.89652606969257, 72.77351215753846, 72.38440462804617, 73.48218216041623, 74.57367645793732, 74.40755938022447, 75.61079871652382, 75.85369864185125, 75.91256417476934, 76.53561566996765, 77.08702184858045, 77.32665757110153, 78.16365920812619, 78.07940240422725, 78.89869906584177, 79.06192474679548, 79.89111660521982, 80.03806939455772, 80.40198602423798, 81.12453313119207, 81.08450539657146, 81.87680654261554, 82.16043702976306, 83.04337513323824, 82.91431388724827, 83.7666027701332, 83.58246288994455, 85.10454035814158, 85.22545986451196, 85.80602193488214, 85.80543781933584, 86.45845772078516, 86.66503187772597, 87.35958349187968, 87.21248332860984, 88.2721550004548, 88.52823547130133, 89.06569201993662, 89.28519705802732, 90.1514118047247, 90.75702366611661, 90.84336486934026]
print(evol_dist)
layout = go.Layout(
title=go.layout.Title(text='Evol',
font=dict(family='Times'),
xref='paper',
x=0),
xaxis=go.layout.XAxis(title=go.layout.xaxis.Title(
text='lenght x and y',
font=dict(family='Times', size=18, color='#7f7f7f'))),
yaxis=go.layout.YAxis(title=go.layout.yaxis.Title(
text='random distance',
font=dict(family='Times', size=18, color='#7f7f7f')), ))
fig = go.Figure(layout=layout)
# you plot the correlation btwn x and y
fig.add_scatter(x=list(range(length_min, lenght_max)),
y=evol_dist,
mode='lines',
name='fitted line',
showlegend=False)
# save the figure
pio.write_image(fig, name_fig + '.pdf')
def create_graph(freq):
# Add credentials... f**k plotly
init_ploty()
months = [f[0] for f in freq]
data = [f[1] for f in freq]
# Create and style traces
line = go.Scatter(x=months,
y=data,
name='Dream frequency by month',
line=dict(color=('rgb(205, 12, 24)'), width=4))
data = [line]
# Edit the layout
layout = dict(
title='Number of dreams per month',
xaxis=dict(title='Month'),
yaxis=dict(title='Number of dreams'),
autosize=False,
width=2000,
height=700,
)
fig = dict(data=data, layout=layout)
pio.write_image(fig, 'frequency.png')
print('run "open frequency.png" to see the graph!')
def plot_output(outfile_name):
"""
Plot the output dataset using Plotly. Linear regression line is
calculated Scipy linregress function.
Args:
outfile_name:
Returns:
"""
df = pd.read_csv(os.path.join('output_dataset/', outfile_name))
x = df['Unemployment rate(%)']
y = df['Death rate(deaths/1000 population)']
# Generated linear fit
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
line = slope * x + intercept
p1 = go.Scatter(x=x, y=y, mode='markers', name='Value')
p2 = go.Scatter(x=x, y=line, mode='lines', name='Fit')
layout = go.Layout(
title='Death Rates per Country in relation to Pop. Unemployment %',
xaxis={'title': x.name},
yaxis={'title': y.name},
)
fig = dict(data=[p1, p2], layout=layout)
pio.write_image(fig, f'output_dataset/{outfile_name}.png')
def plot_flight_network(df, base_dir, subset):
dfC = df.set_index('FL_DATE')
dfJuly = dfC.loc['2018-07', :].reset_index()
dfRoutes = (dfJuly.groupby(['ORIGIN', 'DEST']).agg({
'FL_DATE': 'count'
}).rename(columns={
'FL_DATE': 'ct'
}).reset_index())
dfPosO = (dfJuly.groupby('ORIGIN').agg({
'ORIGIN_LAT': 'mean',
'ORIGIN_LONG': 'mean'
}).reset_index())
dfPosD = (dfJuly.groupby('DEST').agg({
'DEST_LAT': 'mean',
'DEST_LONG': 'mean'
}).reset_index())
newRoutes = dfRoutes.merge(dfPosO, on='ORIGIN',
how='left').merge(dfPosD, on='DEST', how='left')
fig = go.Figure()
fig.add_trace(
go.Scattergeo(locationmode='USA-states',
lon=dfPosO['ORIGIN_LONG'],
lat=dfPosO['ORIGIN_LAT'],
mode='markers',
marker=dict(size=2,
color='rgb(255, 0, 0)',
line=dict(width=3,
color='rgba(68, 68, 68, 0)'))))
for i in range(len(newRoutes)):
fig.add_trace(
go.Scattergeo(
locationmode='USA-states',
lon=[newRoutes['ORIGIN_LONG'][i], newRoutes['DEST_LONG'][i]],
lat=[newRoutes['ORIGIN_LAT'][i], newRoutes['DEST_LAT'][i]],
mode='lines',
line=dict(width=1, color='rgb(252, 32, 128)'),
opacity=float(newRoutes['ct'][i] / newRoutes['ct'].max())))
fig.update_layout(
title_text='July 2018 Flight Paths<br>(Hover for airport names)',
showlegend=False,
geo=dict(
scope='north america',
projection_type='azimuthal equal area',
showland=True,
landcolor='rgb(0, 0, 0)',
countrycolor='rgb(100, 100, 100)',
),
)
path = os.path.join(base_dir, 'network.png')
fig.show()
pio.write_image(fig, path, format='png')
def write_graph_image(self):
basic_path = self.write_path
if not os.path.exists(os.path.join(basic_path, 'graph_images')):
os.mkdir(os.path.join(basic_path, 'graph_images'))
for i, f in zip(self.graphs_ids, self.graphs_output):
output_path = os.path.join(basic_path, 'graph_images',
'{}.png'.format(i))
pio.write_image(f, output_path)
def save_image(figure, filepath):
if os.environ['PY_ENV'] == 'test':
return
filepath = util.smart_path(filepath)
try:
pio.write_image(figure, filepath)
except Exception as e:
orca_warn_once(e)
def get_im(fig):
buf = io.BytesIO()
pio.orca.config.server_url = os.getenv(
'SMART_SPACE_TRACKING_ORCA_PORT_9091_TCP_ADDR') + ":9091"
pio.orca.config.use_xvfb = False
pio.write_image(fig, buf)
img = Image.open(buf)
return np.array(img)
def save_image(fig, id, sub, sub_filter):
date = dt.datetime.now().date()
if not os.getcwd().split(os.sep)[-1] == str(id):
change_directory(sub=sub, id=str(id), date=str(date.month) + "." + str(date.day), filter=sub_filter)
try:
pio.write_image(fig, str(id) + ".png")
except PermissionError:
print('unable to write to file')
def show(fig): import io import plotly.io as pio from PIL import Image buf = io.BytesIO() pio.write_image(fig, buf) img = Image.open(buf) img.show()
def draw_plot(self):
# Draw the plot
plot_figure = go.Figure(data=self.create_traces(),
layout=self.define_layout())
if self.out_file != '':
pio.write_image(fig=plot_figure, file=self.out_file)
plotly.offline.iplot(plot_figure)
def visualize_result(self, result_dir, output_type, **kwargs):
info = read_table_file(os.path.join(result_dir, "files", "info.csv"))
info = info.set_index("filename")
classes = read_table_file(
os.path.join(result_dir, "files", "classes.csv"))
embeddings = ["PCA", "ICA", "Truncated SVD", "tSNE", "UMAP"]
if output_type == "pdf":
from plotly import graph_objs as go, io as pio
for class_label in classes.index.values:
class_names = classes.loc[class_label].astype("str").values
for embedding_name in embeddings:
embedding_slug = embedding_name.replace(" ", "_").lower()
filename = "%s_%s.csv" % (class_label, embedding_slug)
embedded_df = read_table_file(
os.path.join(result_dir, "files", filename))
fig = go.Figure(layout=go.Layout(title=info.loc[filename]
["plot_description"],
font=dict(size=8)))
color_scale = self._get_color_scales(class_names)
clusters = embedded_df["k_means_clusters"].values
X = embedded_df["X"].values
Y = embedded_df["Y"].values
for i, class_name in enumerate(
list(sorted(set(class_names)))):
indices = [
j for j, c in enumerate(class_names)
if c == class_name
]
color = color_scale[i]
fig.add_scatter(x=X[indices],
y=Y[indices],
mode='markers',
marker=dict(
color=color,
opacity=0.5,
symbol=[
ployly_symbols[c]
for c in clusters[indices]
]),
name=class_name)
pio.write_image(fig,
os.path.join(
result_dir, "plot_%s_%s.pdf" %
(class_label, embedding_slug)),
width=800,
height=600)
elif output_type == "html":
raise NotImplementedError()
else:
raise NotImplementedError()
def test_write_image_string_bad_extension_failure(fig1):
# Bad extension
file_path = os.path.join(tmp_dir, "fig1.bogus")
# Use file extension to infer image type.
with pytest.raises(ValueError) as err:
pio.write_image(fig1, file_path)
assert "must be specified as one of the following" in str(err.value)
def test_write_image_string_no_extension_failure(fig1):
# No extension
file_path = os.path.join(tmp_dir, "fig1")
# Use file extension to infer image type.
with pytest.raises(ValueError) as err:
pio.write_image(fig1, file_path)
assert "add a file extension or specify the type" in str(err.value)
def test_write_image_string_bad_extension_failure(fig1):
# Bad extension
file_path = tmp_dir + 'fig1.bogus'
# Use file extension to infer image type.
with pytest.raises(ValueError) as err:
pio.write_image(fig1, file_path + 'fig1')
assert 'must be specified as one of the following' in str(err.value)
def test_write_image_string_no_extension_failure(fig1):
# No extension
file_path = tmp_dir + 'fig1'
# Use file extension to infer image type.
with pytest.raises(ValueError) as err:
pio.write_image(fig1, file_path + 'fig1')
assert 'add a file extension or specify the type' in str(err.value)
def plot_error_vs_iterations(self, a_solver: SolverGaussSeidelPsi):
df = pd.DataFrame(data={'Iteration':a_solver.solution_iterations, 'Error':a_solver.solution_error})
fig = px.line(df, x="Iteration", y="Error", title='Solution convergence')
fig.update_xaxes(title_text="Iteration")
fig.update_yaxes(title_text="Error")
fig.update_layout(font={'size':28})
fig.show()
pio.write_image(fig, files_output_path + self.output_files_name + 'Error_vs_Iteration.png', width=2864, height=1356)
def write(self, path, **kwargs):
"""writes static image file, suffix dictates format"""
from plotly.io import write_image
fig = self.figure
kwargs["width"] = kwargs.get("width", fig.layout.width)
kwargs["height"] = kwargs.get("height", fig.layout.height)
write_image(fig, path, **kwargs)
def TS_plotly(self,df,idx_target,idx_feature,filename_out=None):
import plotly.express as px
import plotly.io as pio
fig = px.line(data_frame=df,x=df.columns[idx_feature], y=df.columns[idx_target])
if filename_out is not None:
pio.write_image(fig, self.folder_out+filename_out, width=1080, height=720)
#fig.show()
return
def download(click, figure):
if click > 0:
download_df, str_name = load_results(figure)
print(download_df)
download_df.to_csv(str_name + '.csv')
pio.write_image(figure, r'fig1.svg', format='svg')
return ['Downloaded csv as ' + str_name + '.csv']
else:
return []
def make_bar_plot_color_size(df1, df2, titles, title, ylabel, saveFig):
data1 = np.nanmean(df1, axis=0)
error1 = confidence_interval(df1)[1] - data1
data2 = np.nanmean(df2, axis=0)
error2 = confidence_interval(df2)[1] - data2
short = False
if not (type(error1) == np.ndarray):
short = True
error1 = [error1]
error2 = [error2]
data1 = [data1]
data2 = [data2]
fig = go.Figure()
fig.add_trace(
go.Bar(name='Color',
marker_color='#AE3209',
x=titles,
y=data1,
error_y=dict(type='data', array=error1)))
fig.add_trace(
go.Bar(name='Size',
marker_color='#f3511b',
x=titles,
y=data2,
error_y=dict(type='data', array=error2)))
fig.update_layout(
barmode='group',
title={
'text': title,
'y': 0.9,
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'
},
font=dict(size=15),
yaxis_title=ylabel,
)
fig.update_layout()
fig.show()
if saveFig:
fig.write_html(title + ".html")
if short:
pio.write_image(fig,
title + ".png",
width=500,
format="png",
engine="kaleido",
scale=3)
else:
pio.write_image(fig,
title + ".png",
width=1000,
format="png",
engine="kaleido",
scale=3)
def test_write_image_string_bad_extension_failure(fig1):
# Bad extension
file_path = tmp_dir + 'fig1.bogus'
# Use file extension to infer image type.
with pytest.raises(ValueError) as err:
pio.write_image(fig1, file_path + 'fig1')
assert 'must be specified as one of the following' in str(err.value)
def test_write_image_string_no_extension_failure(fig1):
# No extension
file_path = tmp_dir + 'fig1'
# Use file extension to infer image type.
with pytest.raises(ValueError) as err:
pio.write_image(fig1, file_path + 'fig1')
assert 'add a file extension or specify the type' in str(err.value)
def test_write_image_writeable(fig1, format):
file_name = 'fig1.' + format
with open(images_dir + file_name, 'rb') as f:
expected_bytes = f.read()
mock_file = MagicMock()
pio.write_image(fig1, mock_file, format=format,
width=700, height=500)
mock_file.write.assert_called_once_with(expected_bytes)
def test_write_image_string_bad_extension_override(fig1):
# Bad extension
file_name = 'fig1.bogus'
tmp_path = tmp_dir + file_name
pio.write_image(fig1, tmp_path, format='eps', width=700, height=500)
with open(tmp_path, 'rb') as f:
written_bytes = f.read()
with open(images_dir + 'fig1.eps', 'rb') as f:
expected_bytes = f.read()
assert written_bytes == expected_bytes
def test_write_image_string_format_inference(fig1, format):
# Build file paths
file_name = 'fig1.' + format
file_path = tmp_dir + file_name
# Use file extension to infer image type.
pio.write_image(fig1, tmp_dir + file_name,
width=700, height=500)
with open(file_path, 'rb') as f:
written_bytes = f.read()
with open(images_dir + file_name, 'rb') as f:
expected_bytes = f.read()
assert written_bytes == expected_bytes
def test_write_image_string(fig1, format):
# Build file paths
file_name = 'fig1.' + format
file_path = tmp_dir + file_name
pio.write_image(fig1, tmp_dir + file_name,
format=format, width=700, height=500)
with open(file_path, 'rb') as f:
written_bytes = f.read()
with open(images_dir + file_name, 'rb') as f:
expected_bytes = f.read()
assert written_bytes == expected_bytes
def bar_chart(self, members, is_terminal, is_root, p, score, split):
fig = {
"data": [
{
"values": [v for v in members.values()],
"labels": [k for k in members.keys()],
"domain": {"x": [0, 1], "y":[0.4, 1.0]},
"hole": .4,
"type": "pie",
'showlegend': True if is_root else False,
'marker': {'colors': cl.scales['5']['qual']['Set1']}
}],
"layout": {
"margin": dict(t=50),
"annotations": [
{
"y": {'domain': [0, 0.2]},
"font": {
"size": 18
},
"showarrow": False,
"text": '',
"x": 0.5,
"y": 0.5
},
{
"y": {'domain': [0, 0.2]},
}
]
}
}
if is_terminal is False: self.append_table(fig, [p, score, split])
file = '/tmp/' + ("%.20f" % time.time()).replace('.', '') + '.png'
pio.write_image(fig, file=file, format='png')
self.files.append(file)
return file
