def plt_tab2():
tab2 = pd.read_csv("D:\\insight_project\\tabs\\refined_tab\\tab1_2.csv",
error_bad_lines=False)
df2 = tab2
#np.random.seed(1)
colors_a = n_colors('rgb(255, 200, 200)',
'rgb(200, 0, 0)',
2,
colortype='rgb')
colors_b = n_colors('rgb(255, 200, 200)',
'rgb(200, 0, 0)',
31407,
colortype='rgb')
colors_c = n_colors('rgb(255, 200, 200)',
'rgb(200, 0, 0)',
21000,
colortype='rgb')
colors_d = n_colors('rgb(255, 200, 200)',
'rgb(200, 0, 0)',
100,
colortype='rgb')
a = df2.name
b = df2.num_all_re
c = df2.num_neg_re
d = [round(x, 2) for x in df2.percent]
d_v = [round(x * 1) for x in df2.percent]
fig = go.Figure(data=[
go.Table(header=dict(values=[
'<b>Business Name</b>', '<b>Total Reviews</b>',
'<b>Negative Reviews</b>', '<b>Percentage</b>'
],
line_color='white',
fill_color='pink',
align='center',
font=dict(color='black', size=12)),
cells=dict(values=[a, b, c, d_v],
line_color=[
np.array(colors_a)[0],
np.array(colors_b)[b],
np.array(colors_c)[c],
np.array(colors_d)[d_v]
],
fill_color=[
np.array(colors_a)[0],
np.array(colors_b)[b],
np.array(colors_c)[c],
np.array(colors_d)[d_v]
],
align='center',
font=dict(color='black', size=11)))
])
#fig.show()
return fig
def plt_tab4():
df4 = pd.read_csv("D:\\insight_project\\tabs\\refined_tab\\tab1_4.csv",
error_bad_lines=False)
colors_a = n_colors('rgb(255, 200, 200)',
'rgb(200, 0, 0)',
2,
colortype='rgb')
colors_b = n_colors('rgb(255, 200, 200)',
'rgb(200, 0, 0)',
578,
colortype='rgb')
colors_c = n_colors('rgb(255, 200, 200)',
'rgb(200, 0, 0)',
4332,
colortype='rgb')
colors_d = n_colors('rgb(255, 200, 200)',
'rgb(200, 0, 0)',
100,
colortype='rgb')
a = df4.name
a_1 = df4.fans
b = df4.num_neg_re
c = df4.num_all_re
d = [round(x, 2) for x in df4.percent]
d_v = [round(x * 1) for x in df4.percent]
fig = go.Figure(data=[
go.Table(header=dict(values=[
'<b>Fisrt Name</b>', '<b>Fans</b>', '<b>Negative Reviews</b>',
'<b>Total Reviews</b>', '<b>Percentage</b>'
],
line_color='white',
fill_color='pink',
align='center',
font=dict(color='black', size=12)),
cells=dict(values=[a, a_1, b, c, d],
line_color=[
np.array(colors_a)[0],
np.array(colors_a)[0],
np.array(colors_b)[b],
np.array(colors_c)[c],
np.array(colors_d)[d_v]
],
fill_color=[
np.array(colors_a)[0],
np.array(colors_a)[0],
np.array(colors_b)[b],
np.array(colors_c)[c],
np.array(colors_d)[d_v]
],
align='center',
font=dict(color='black', size=11)))
])
#fig.show()
return fig
def get_color_palette(num, colors=DEFAULT_PLOTLY_COLORS):
"""Returns ``num`` of distinct RGB colors.
If ``num`` is less than or equal to the length of ``colors``, first ``num``
elements of ``colors`` are returned.
Else ``num`` elements of colors are interpolated between the first and the last
colors of ``colors``.
Parameters
----------
num : `int`
Number of colors required.
colors : [`str`, `list` [`str`]], default ``DEFAULT_PLOTLY_COLORS``
Which colors to use to build the color palette.
This can be a list of RGB colors or a `str` from ``PLOTLY_SCALES``.
Returns
-------
color_palette: List
A list consisting ``num`` of RGB colors.
"""
validate_colors(colors, colortype="rgb")
if len(colors) == 1:
return colors * num
elif len(colors) >= num:
color_palette = colors[0:num]
else:
color_palette = n_colors(
colors[0],
colors[-1],
num,
colortype="rgb")
return color_palette
def clf_table_report(self):
"""This function implements heatmap style pipeline cluster's model evaluation report(Dynamic Table) for classification output report.
Parameters
----------
Example
-------
.. [Example] https://optimal-flow.readthedocs.io/en/latest/demos.html#pipeline-cluster-model-evaluation-dynamic-table-using-autoviz
References
----------
"""
df = self.dyna_report
colors = n_colors('rgb(49, 130, 189)',
'rgb(239, 243, 255)',
15,
colortype='rgb')
bins = [-1, 2, 4, 6, 7, 8, 9, 11]
bins_latency = [0, 5, 10, 15, 20, 50, 80, 100]
labels = [1, 2, 3, 4, 5, 6, 7]
fig = go.Figure(data=[
go.Table(
header=dict(values=list(df.columns),
fill_color='paleturquoise',
font=dict(color='black', size=12),
align='center'),
cells=dict(
values=[
df.Dataset, df.Model_Name, df.Best_Parameters,
df.Accuracy, df.Precision, df.Recall, df.Latency
],
# fill_color='lavender',
fill_color=[
'lavender', 'lavender', 'lavender',
np.array(colors)[pd.cut(df.Accuracy.apply(
lambda x: x * 10),
bins=bins,
labels=labels).astype(int)],
np.array(colors)[pd.cut(df.Precision.apply(
lambda x: x * 10),
bins=bins,
labels=labels).astype(int)],
np.array(colors)[pd.cut(
df.Recall.apply(lambda x: x * 10),
bins=bins,
labels=labels).astype(int)], 'lavender'
],
align='left'))
# np.array(colors)[pd.cut(df.Latency,bins=bins_latency, labels=labels).astype(int)]],
])
fig.update_layout(
title=
f'Pipeline Cluster Model Classification Evaluation Report - autoViz <a href="https://www.linkedin.com/in/lei-tony-dong/"> ©Tony Dong</a>',
font_size=8)
plot(fig)
fig.show()
def build_table(uc_switch, val_idx):
table = table_array_for('f', 'q')
for k, v in results[interval_idx].items():
(f, uc, q) = k
if (uc_switch and uc) or (not uc_switch and not uc):
table[param['f'].index(f)][param['q'].index(
q)] = round(v[val_idx][1], 2)
min_val = pow(2, 31) - 1
max_val = 0
for row in table:
if min(row) < min_val:
min_val = min(row)
if max(row) > max_val:
max_val = max(row)
diff = max_val-min_val
cs = n_colors('rgb(0, 255, 0)', 'rgb(255, 0, 0)',
int(diff) + 1, colortype='rgb')
colors = []
for row in table:
c_row = []
for cell in row:
c_row.append(cs[int(cell-min_val)])
colors.append(c_row)
for idx, row in enumerate(colors):
for idx_r, cell in enumerate(row):
if(cell == 'rgb(255.00000000000003, -2.842170943040401e-14, 0.0)' or cell == 'rgb(254.99999999999997, 2.842170943040401e-14, 0.0)'):
colors[idx][idx_r] = 'rgb(255,0,0)'
return make_table('f', 'q', table, colors)
def _plotly_color_map(names: List[str]) -> Mapping[str, str]:
"""
Private plot helper method for generating colors
for similar groups across subplots.
Parameters
----------
names:
List of levels in a group.
Returns
-------
Dictionary assigning group names to a color.
"""
if len(names) > 1:
linear_purple_scale = n_colors(lowcolor='rgb(218,183,193)',
highcolor='rgb(64,19,98)',
n_colors=len(names),
colortype='rgb')
hex_scale = [
rgb_to_hex(*unlabel_rgb(rgb)) for rgb in linear_purple_scale
]
# From https://github.com/plotly/plotly.py/issues/1026
color_generator = cycle(hex_scale)
return dict(zip(names, color_generator))
else:
return dict(zip(names, ['rgb(64,19,98)']))
def get_ridgeplot_fig(df, distance='total_distance', nvals='all'):
clubs, xmin, xmax = utils.get_clubs(df)
colors = n_colors('rgb(242, 139, 0)', 'rgb(206, 0, 0)', 12, colortype='rgb')
fig = go.Figure()
for club, color in zip(clubs, colors):
name = utils.club_enum[club]
array = df.groupby('club').get_group(club)[distance].values
data = utils.get_values(array, nvals)
fig.add_trace(go.Violin(x=data, name=name, line_color=color))
fig.update_traces(
orientation='h',
side='positive',
width=3,
points=False,
)
fig.update_layout(
margin=dict(t=30, r=10, b=10, l=10),
xaxis_showgrid=True,
xaxis_zeroline=False,
showlegend=False,
xaxis=dict(
range=[xmin-10, xmax+20],
tickmode='linear',
tick0=0,
dtick=10,
)
)
return fig
def ridge_line(df_act, t_range='day', n=1000):
"""
https://plotly.com/python/violin/
for one day plot the activity distribution over the day
- sample uniform from each interval
"""
df = activities_dist(df_act.copy(), t_range, n)
colors = n_colors('rgb(5, 200, 200)',
'rgb(200, 10, 10)',
len(df.columns),
colortype='rgb')
data = df.values.T
fig = go.Figure()
i = 0
for data_line, color in zip(data, colors):
fig.add_trace(
go.Violin(x=data_line, line_color=color, name=df.columns[i]))
i += 1
fig.update_traces(orientation='h', side='positive', width=3, points=False)
fig.update_layout(xaxis_showgrid=False, xaxis_zeroline=False)
return fig
def violin_plot():
"""Get data for plot, return plot
Adds plotly.graph_objects charts for violin plot at initial loading page
Returns:
(dcc.Graph)
"""
np.random.seed(1)
points = (np.linspace(1, 2, 12)[:, None] * np.random.randn(12, 200) +
(np.arange(12) + 2 * np.random.random(12))[:, None])
points2 = np.array([
np.concatenate((point, [points.min(), points.max()]))
for point in points
])
colors = n_colors('rgb(32, 32, 41)',
'rgb(190, 155, 137)',
12,
colortype='rgb')
data = []
for data_line, color in zip(points2, colors):
trace = go.Violin(x=data_line,
line_color=color,
side='positive',
width=3,
points=False,
hoverinfo='skip')
data.append(trace)
layout = dict(title='u t i l s . p y',
xaxis={
'showgrid': False,
'zeroline': False,
'visible': False,
'fixedrange': True,
},
yaxis={
'showgrid': False,
'zeroline': False,
'visible': False,
'fixedrange': True,
},
showlegend=False,
margin=dict(l=0, r=0, t=80, b=0))
return dcc.Graph(figure=dict(data=data, layout=layout),
id='violin-plot',
config={
'modeBarButtonsToRemove': [
'zoom2d', 'pan2d', 'select2d', 'lasso2d',
'zoomIn2d', 'zoomOut2d', 'autoScale2d',
'resetScale2d', 'toggleSpikelines',
'hoverClosestCartesian', 'hoverCompareCartesian'
],
},
style={
'margin-top': '15vh',
'height': '60vh'
})
def code_couleur(tableau, mini, maxi):
tab_coul = n_colors('rgb(200, 100, 100)',
'rgb(100, 200, 100)',
100,
colortype='rgb')
tab_pourcent = [
np.int((v - mini) * 100 / (maxi - mini)) if not np.isnan(v) else 0
for v in tableau
]
return np.array(tab_coul)[tab_pourcent]
def plot_table(data_list):
from plotly.colors import n_colors
color_scales = n_colors('rgb(0, 255, 0)',
'rgb(255, 0, 0)',
100,
colortype='rgb')
stocks = [f"{x['TICKER']}: {x['STOCK']}" for x in data_list]
k9 = [f"{x['K9']:.2f}" for x in data_list]
d9 = [f"{x['D9']:.2f}" for x in data_list]
closes = [f"{x['CLOSE']:.3f}" for x in data_list]
change = [f"{x['CHANGE']:+.3f}" for x in data_list]
change_pct = [f"{x['CHANGE%']:+.2f}%" for x in data_list]
updated = [x['UPDATED'] for x in data_list]
# setup cell colors
headers = ['STOCK', 'K9', 'D9', 'CLOSE', 'CHANGE', 'CHANGE%', 'UPDATED']
row_num = len(stocks)
col_num = len(headers)
odd_even_colors = ('white', 'beige')
row_colors = [odd_even_colors[x % 2] for x in range(row_num)]
blacks = ['black'] * row_num
k9_colors = [color_scales[int(float(k))] for k in k9]
chg_colors = []
for x in data_list:
chg = x['CHANGE']
if chg < 0:
chg_colors.append('#db0000')
elif chg > 0:
chg_colors.append('#02cf4d')
else:
chg_colors.append('gray')
cell_colors = [
blacks, k9_colors, blacks, blacks, chg_colors, chg_colors, blacks
]
fig = go.Figure(data=[
go.Table(columnwidth=[200, 40, 40, 40, 40, 40, 80],
header=dict(values=headers,
line_color='darkslategray',
fill_color='lightskyblue',
align='left'),
cells=dict(values=[
stocks, k9, d9, closes, change, change_pct, updated
],
line_color='darkslategray',
fill_color=[row_colors * col_num],
font=dict(color=cell_colors),
align='left'))
])
fig.update_layout(height=250 + len(data_list) * 25)
return fig.to_html(full_html=False, include_plotlyjs='cdn')
def reg_table_report(self):
"""This function implements heatmap style pipeline cluster's model evaluation report(Dynamic Table) for regression output report.
Parameters
----------
Example
-------
.. [Example] https://optimal-flow.readthedocs.io/en/latest/demos.html#pipeline-cluster-model-evaluation-dynamic-table-using-autoviz
References
----------
"""
df = self.dyna_report
colors = n_colors('rgb(49, 130, 189)',
'rgb(239, 243, 255)',
15,
colortype='rgb')
bins = [-1, 2, 4, 6, 7, 8, 9, 11]
labels = [1, 2, 3, 4, 5, 6, 7]
fig = go.Figure(data=[
go.Table(
header=dict(values=list(df.columns),
fill_color='paleturquoise',
align='left'),
cells=dict(
values=[
df.Dataset, df.Model_Name, df.Best_Parameters, df.R2,
df.MAE, df.MSE, df.RMSE, df.Latency
],
# fill_color='lavender',
fill_color=[
'lavender', 'lavender', 'lavender',
np.array(colors)[pd.cut(df.R2.apply(lambda x: x * 10),
bins=bins,
labels=labels).astype(int)],
'lavender', 'lavender', 'lavender', 'lavender'
],
align='left'))
])
fig.update_layout(
title=
f'Pipeline Cluster Model Regression Evaluation Report - autoViz <a href="https://www.linkedin.com/in/lei-tony-dong/"> ©Tony Dong</a>',
font_size=8)
plot(fig,
filename='Pipeline Cluster Model Evaluation Report.html',
auto_open=False)
def monthly_distribution(sample_year, months, years, temps, monthly_path):
'''
Function to compute the monthly temperature distribution
Parameters
----------
sample_year: year of your choice (integer)
it is either 2007 or 2008
months : numpy.ndarray (integer)
1-dimensional numpy array with month identifiers
temps: numpy.ndarray (integer)
temperature (Celsius) of the measurement
years: numpy.ndarray (integer)
year of the measurement
monthly_path: string
path where to export the distribution in .html
'''
unique_months = np.unique(months)
names = [
'Jan. {y}'.format(y=sample_year), 'Feb. {y}'.format(y=sample_year),
'Mar. {y}'.format(y=sample_year), 'Apr. {y}'.format(y=sample_year),
'May {y}'.format(y=sample_year), 'Jun. {y}'.format(y=sample_year),
'Jul. {y}'.format(y=sample_year), 'Aug. {y}'.format(y=sample_year),
'Sept. {y}'.format(y=sample_year), 'Oct. {y}'.format(y=sample_year),
'Nov. {y}'.format(y=sample_year), 'Dec. {y}'.format(y=sample_year)
]
colors = n_colors('rgb(10, 200, 197)',
'rgb(10, 200, 197)',
12,
colortype='rgb')
fig = go.Figure()
for month, color, name in zip(unique_months, colors, names):
fig.add_trace(
go.Violin(x=temps[((months == month) &
(years == sample_year)).nonzero()[0]],
line=dict(color=color),
orientation='h',
side='positive',
points=False,
name=name))
fig.layout.update(
title=
'Distribution of Monthly Temperatures {y} (kernel density estimation plot)'
.format(y=sample_year),
xaxis=dict(title="Temperature (C°)"))
pyo.plot(fig, filename=monthly_path)
def createTable(keyword,username,bio,location,followers,following,isNew):
new=[]
for i in isNew:
if i[0]:
new.append(1)
else:
new.append(0)
colors = n_colors('rgb(255, 255, 255)', 'rgb(80, 220, 100)', 2, colortype='rgb')
fig = go.Figure(data=[go.Table(
columnorder = [1,2,3,4,5,6],
columnwidth = [20,30,20,20,20,20],
header=dict(
values=['<b>username</b>','<b>bio</b>','<b>location</b>','<b>followers</b>','<b>following</b>','<b>is new</b>'],
line_color='black', fill_color='white',
align='center',font=dict(color='black', size=12)
),
cells=dict(
values=[username,bio,location,followers,following,isNew],
line_color=['black'],
fill_color=[np.array(colors)[new]],
align='center', font=dict(color='black', size=11)
))
])
filePath = "/home/code/Desktop/twitterscraper/images/tables/{key}.png".format(key=(keyword + '_table'))
if (os.path.isfile(filePath)):
os.remove(filePath)
fig.write_image(filePath,width=800, height=len(username)*120)
print(keyword+' table created')
# fig = go.Figure(data=[go.Table(header=dict(values=['username','bio','location','followers','following','is new']),
# cells=dict(values=[username,bio,location,followers,following,isNew]))
# ])
# createTable(['exgoog'],['username','username2'],['bio','bio2'],['location','location2'],['followers','followers2'],['following','following2'],[True,False])
def table_report(self):
"""This function implements heatmap style pipeline cluster's model evaluation report.
Parameters
----------
Example
-------
.. []
References
----------
"""
colors = n_colors('rgb(255, 200, 200)',
'rgb(200, 0, 0)',
9,
colortype='rgb')
df = self.dyna_report
fig = go.Figure(data=[
go.Table(
header=dict(values=list(df.columns),
fill_color='paleturquoise',
align='left'),
cells=dict(
values=[
df.Dataset, df.Model_Name, df.Best_Parameters,
df.Accuracy, df.Precision, df.Recall, df.Latency
],
# fill_color='lavender',
fill_color=[
np.array(colors)[df.Accuracy],
np.array(colors)[df.Precision],
np.array(colors)[df.Recall]
],
align='left'))
])
fig.update_layout(
title=
f'Pipeline Cluster Model Evaluation Report - autoViz <a href="https://www.linkedin.com/in/lei-tony-dong/"> ©Tony Dong</a>',
font_size=8)
plot(fig)
fig.show()
def individuals_distribution(ids, temps, ind_path):
'''
Function to compute the distribution in temperature of each bird
Parameters
----------
ids: numpy.ndarray (integer)
bird identifiers
temps: numpy.ndarray (integer)
temperature (Celsius) of the measurement
ind_path: string
path where to export the distribution in .html
'''
unique_ids = np.unique(ids)
names = [
"Goose 72364", "Goose 72413", "Goose 72417", "Goose 73053",
"Goose 73054", "Goose 79694", "Goose 79698"
]
colors = n_colors('rgb(150, 150, 150)',
'rgb(150, 150, 150)',
7,
colortype='rgb')
fig = go.Figure()
for goose, color, name in zip(unique_ids, colors, names):
fig.add_trace(
go.Violin(x=temps[ids == goose],
line=dict(color=color),
orientation='h',
side='positive',
points=False,
name=name))
fig.layout.update(
title=
'Distribution of Individual Temperatures (kernel density estimation plot)',
xaxis=dict(title="Temperature (C°)"))
pyo.plot(fig, filename=ind_path)
def coloured_table(tab, name, colours_from, colours_to, MAIN_COLOUR,
HEADER_FONT_SIZE, BIG_HEADER_FONT_SIZE):
"""Стандартная функция для отображения таблицы с выделением градиента по значениям
Args:
tab (pd.DataFrame): Таблица для отображения
name (str): Title
colours_from (str, optionsl): Начальный цвет градиента
colours_to (str, optional): Конечный цвет градиента. Defaults to 'rgb(255, 255, 255)'.
Returns:
ОТображаемый объект
"""
month_names = [
'', 'Январь', 'Февраль', "Март", 'Апрель', 'Май', "Июнь", 'Июль',
'Август', "Сентябрь", 'Октябрь', 'Ноябрь', "Декабрь"
]
min_value = tab.drop(columns='Unnamed: 0').min().min()
quantity_colours = tab.drop(columns='Unnamed: 0').max().max() - min_value
if quantity_colours:
colors_c = np.array(
n_colors(colours_from,
colours_to,
quantity_colours,
colortype='rgb'))
colors_f = np.array(
n_colors(colours_to,
colours_from,
quantity_colours,
colortype='rgb'))
# Первая колонка всегда белая
colors_f[0] = 'rgb(255, 255, 255)'
else:
# Первая колонка все=гда белая
colors_c = np.array(['rgb(255, 255, 255)'])
colors_f = np.array(['rgb(255, 255, 255)'])
colors_f[0] = 'rgb(255, 255, 255)'
color_cells = [
colors_c[tab[c] - min_value - 1] if c != 'Unnamed: 0' else colours_to
for c in tab
]
color_fonts = [
colors_f[tab[c] - min_value - 1] if c != 'Unnamed: 0' else colours_from
for c in tab
]
qual_deficit_fig = go.Figure(data=[
go.Table(header=dict(
values=[month_names[c] for c in range(len(tab.columns))],
line_color='white',
fill_color='white',
align='center',
font=dict(color=MAIN_COLOUR, size=HEADER_FONT_SIZE)),
cells=dict(values=[tab[c] for c in tab],
line_color=color_cells,
fill_color=color_cells,
align='center',
font=dict(color=color_fonts, size=TABLE_FONT_SIZE),
height=CELL_HEIGHT))
],
layout=dict(title=dict(
text=name,
font=dict(color=MAIN_COLOUR,
size=BIG_HEADER_FONT_SIZE)),
height=500))
return qual_deficit_fig
def _bullet(df, markers, measures, ranges, subtitles, titles, orientation,
range_colors, measure_colors, horizontal_spacing, vertical_spacing,
scatter_options, layout_options):
num_of_lanes = len(df)
num_of_rows = num_of_lanes if orientation == 'h' else 1
num_of_cols = 1 if orientation == 'h' else num_of_lanes
if not horizontal_spacing:
horizontal_spacing = 1. / num_of_lanes
if not vertical_spacing:
vertical_spacing = 1. / num_of_lanes
fig = plotly.tools.make_subplots(num_of_rows,
num_of_cols,
print_grid=False,
horizontal_spacing=horizontal_spacing,
vertical_spacing=vertical_spacing)
# layout
fig['layout'].update(
dict(shapes=[]),
title='Bullet Chart',
height=600,
width=1000,
showlegend=False,
barmode='stack',
annotations=[],
margin=dict(l=120 if orientation == 'h' else 80),
)
# update layout
fig['layout'].update(layout_options)
if orientation == 'h':
width_axis = 'yaxis'
length_axis = 'xaxis'
else:
width_axis = 'xaxis'
length_axis = 'yaxis'
for key in fig['layout'].keys():
if 'axis' in key:
fig['layout'][key]['showgrid'] = False
fig['layout'][key]['zeroline'] = False
if length_axis in key:
fig['layout'][key]['tickwidth'] = 1
if width_axis in key:
fig['layout'][key]['showticklabels'] = False
fig['layout'][key]['range'] = [0, 1]
# narrow domain if 1 bar
if num_of_lanes <= 1:
fig['layout'][width_axis + '1']['domain'] = [0.4, 0.6]
if not range_colors:
range_colors = ['rgb(200, 200, 200)', 'rgb(245, 245, 245)']
if not measure_colors:
measure_colors = ['rgb(31, 119, 180)', 'rgb(176, 196, 221)']
for row in range(num_of_lanes):
# ranges bars
for idx in range(len(df.iloc[row]['ranges'])):
inter_colors = colors.n_colors(range_colors[0], range_colors[1],
len(df.iloc[row]['ranges']), 'rgb')
x = ([sorted(df.iloc[row]['ranges'])[-1 - idx]]
if orientation == 'h' else [0])
y = ([0] if orientation == 'h' else
[sorted(df.iloc[row]['ranges'])[-1 - idx]])
bar = go.Bar(x=x,
y=y,
marker=dict(color=inter_colors[-1 - idx]),
name='ranges',
hoverinfo='x' if orientation == 'h' else 'y',
orientation=orientation,
width=2,
base=0,
xaxis='x{}'.format(row + 1),
yaxis='y{}'.format(row + 1))
fig['data'].append(bar)
# measures bars
for idx in range(len(df.iloc[row]['measures'])):
inter_colors = colors.n_colors(measure_colors[0],
measure_colors[1],
len(df.iloc[row]['measures']),
'rgb')
x = ([sorted(df.iloc[row]['measures'])[-1 - idx]]
if orientation == 'h' else [0.5])
y = ([0.5] if orientation == 'h' else
[sorted(df.iloc[row]['measures'])[-1 - idx]])
bar = go.Bar(x=x,
y=y,
marker=dict(color=inter_colors[-1 - idx]),
name='measures',
hoverinfo='x' if orientation == 'h' else 'y',
orientation=orientation,
width=0.4,
base=0,
xaxis='x{}'.format(row + 1),
yaxis='y{}'.format(row + 1))
fig['data'].append(bar)
# markers
x = df.iloc[row]['markers'] if orientation == 'h' else [0.5]
y = [0.5] if orientation == 'h' else df.iloc[row]['markers']
markers = go.Scatter(x=x,
y=y,
name='markers',
hoverinfo='x' if orientation == 'h' else 'y',
xaxis='x{}'.format(row + 1),
yaxis='y{}'.format(row + 1),
**scatter_options)
fig['data'].append(markers)
# titles and subtitles
title = df.iloc[row]['titles']
if 'subtitles' in df:
subtitle = '<br>{}'.format(df.iloc[row]['subtitles'])
else:
subtitle = ''
label = '<b>{}</b>'.format(title) + subtitle
annot = utils.annotation_dict_for_label(
label, (num_of_lanes - row if orientation == 'h' else row + 1),
num_of_lanes,
vertical_spacing if orientation == 'h' else horizontal_spacing,
'row' if orientation == 'h' else 'col',
True if orientation == 'h' else False, False)
fig['layout']['annotations'].append(annot)
return fig
def materiality_table_FEATURES_plotly(self, short = '', variable = 'mean', norm = '', std = True, n_areas = 2, selection = 'all', type = 'Feat'):
cat1 , cat2, cat3, catG = 0, 1, 2, 3
if type == 'Feat':
df = self.priorities_feats_df
else:
df = self.priorities_needs_df
if norm == True: norm = '_norm'
if short != '':
df = df.loc[short]
c1 = categories[cat1] + '_' + variable + norm
c2 = categories[cat2] + '_' + variable + norm
c3 = categories[cat3] + '_' + variable + norm
cG = categories[catG] + '_' + variable + norm
bord_low = min(df[c1].min(), df[c2].min(), df[c3].min(), df[cG].min())
bord_hig = max(df[c1].max(), df[c2].max(), df[c3].max(), df[cG].min())
d = (bord_hig-bord_low)/10
bord_low = bord_low - d
bord_hig = bord_hig + d
if selection != 'all' :
df = df.iloc[selection]
df = df.sort_values(by = cG )
hoverinfo = {3: 'Must have', 2: 'Key to discuss', 1: 'Potential added value', 0: 'To exclude'}
for c in [cG, c1, c2, c3]:
df[c] = df[c].map(lambda val: int(round((val - df[c].min()*0.9)*3/(df[c].max()*1.1 - df[c].min()*0.9))) )
df[c + 'hi'] = df[c].map(lambda val: hoverinfo[val])
colors = n_colors('rgb(255, 200, 200)', 'rgb(200, 0, 0)', 9, colortype='rgb')
fig = ff.create_annotated_heatmap(df[[cG, c1, c2, c3]].values,
#labels=dict(x=df_.index, y=[cG, c1, c2, c3], color="w"),
x=[categories[3], categories[0], categories[1], categories[2]],
y=df.index.array,
hovertemplate = "%{y}:<br>%{text}",
#hoverlabel=dict(font=dict(family='sans-serif', size=10)),
text = df[[cG + 'hi', c1+ 'hi', c2+ 'hi', c3+ 'hi']].values,
annotation_text=df[[cG + 'hi', c1+ 'hi', c2+ 'hi', c3+ 'hi']].values,
colorscale='YlGnBu',#px.colors.diverging.Portland,#px.colors.sequential.Viridis,
#reversescale=True,
xgap = 5,
ygap = 5,
)
fig.update_layout(
xaxis=dict(showgrid=False),
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
height = 38 * df.shape[0] + 60,
font=dict(size=10,)
)
fig.add_traces(self.add_score_bar(df, cat = 0))
fig.add_traces(self.add_score_bar(df, cat = 1))
fig.add_traces(self.add_score_bar(df, cat = 2))
#fig.add_traces(self.add_score_bar(df, cat = 1))
# initialize xaxis2 and yaxis2
fig['layout']['xaxis2'] = {}
fig['layout']['yaxis2'] = {}
# Edit layout for subplots
fig.layout.xaxis.update({'domain': [0, 0.64]})
fig.layout.xaxis2.update({'domain': [0.65, 1.]})
fig.layout.yaxis2.update({'anchor': 'x2', 'visible': False})
fig.layout.margin.update({'t':40, 'b':20})
return fig
tab = trade_df['netCash'].values.tolist()
tab = np.array(tab)
# print(np.min(tab))
redgreen = ['#df0101', '#f5f6ce', '#31b404']
fin_cmap, fin_cs = colorscale_from_list(redgreen, 'fin_cmap')
fin_asymm_cs = asymmetric_colorscale(tab,
fin_cmap,
ref_point=0.0,
step=0.05)
print(fin_asymm_cs)
# color scale
a = [4, 11, 0, 32]
blues = n_colors('rgb(200, 200, 255)',
'rgb(0, 0, 200)',
max(a) + 1,
colortype='rgb')
print(blues)
print([np.array(blues)[a]])
'''
for col in cols_to_show:
if col!='':
'''
print(trade_df[cols_to_show].values.T)
trace = dict(
type='table',
columnwidth=[20] + [50] + [50] + [20] + [20] + [20] + [20] + [20],
header=dict(height=50,
def create_evloution_table(category, algs, slider_val):
dev_func = np.std
mean_func = np.mean
alg_legend = " ".join([token.capitalize() for token in algs[0].split("_")[:2]])
category_1, category_2 = [
val.strip().upper() for val in re.sub("_", " ", category).split("vs")
]
title_category = " ".join([category_1.capitalize(), "Vs.", category_2.capitalize()])
if len(algs) == 1:
return html.H4(
f"No additional noise visualization data in Db found for {title_category} and {alg_legend}"
)
list_w_results = []
for alg in algs:
res = get_res_from_db(alg, "entropy", category_1, category_2)
if res is None:
return html.H4(
f"No additional noise visualization data in Db found for {title_category} and {alg_legend}"
)
list_w_results.append(
create_evolution_table_stats(res, mean_func, dev_func, slider_val)
)
list_w_results = list(map(list, zip(*list_w_results)))
noise_var = [0.1, 0.2, 0.3, 0.4, 0.6]
colors = n_colors("rgb(172, 193, 198)", "rgb(2, 52, 81)", 5, colortype="rgb")
table_to_vis = [noise_var] + [val for val in list_w_results]
slider_val -= 1
fig = go.Figure(
data=[
go.Table(
header=dict(
values=["<b>Noise Variance</b>", "<b>0</b>"]
+ [
f"<b>{int(i * 200 / (slider_val))}</b>"
for i in range(1, slider_val + 1)
],
font_size=13,
align="center",
height=40,
),
cells=dict(
values=table_to_vis,
fill_color=[
np.array(colors)[np.argsort(np.argsort(val))]
for val in table_to_vis
],
align="center",
font_size=13,
height=40,
),
)
]
)
fig.update_layout(
height=500,
width=1000,
title_text=f"{alg_legend} Algorithm with Respect to Different Additive Noise",
)
fig.layout.template = "plotly_dark"
return dcc.Graph(figure=fig)
import numpy as np
import plotly.colors as pc
import plotly.graph_objects as go
np.random.seed(1)
data = (np.linspace(1, 2, 12)[:, np.newaxis] * np.random.randn(12, 200) +
(np.arange(12) + 2 * np.random.random(12))[:, np.newaxis])
colors = pc.n_colors('rgb(5, 200, 200)',
'rgb(200, 10, 10)',
12,
colortype='rgb')
fig = go.Figure()
for data_line, color in zip(data, colors):
fig.add_trace(go.Violin(x=data_line, line_color=color))
fig.update_traces(orientation='h', side='positive', width=3, points=False)
fig.update_layout(xaxis_showgrid=False, xaxis_zeroline=False)
fig.show()
values_honest_borda = list(mas_assignment_1.outcome_borda.values())
keys_ascii_honest_plurality = util.number_to_ascii(keys_honest_plurality)
keys_ascii_honest_voting_for_two = util.number_to_ascii(keys_honest_voting_for_two)
keys_ascii_honest_antiplurality = util.number_to_ascii(keys_honest_antiplurality)
keys_ascii_honest_borda = util.number_to_ascii(keys_honest_borda)
text_honest_plurality = util.join_strings_for_graph(keys_honest_plurality,keys_ascii_honest_plurality,values_honest_plurality)
text_honest_voting_for_two = util.join_strings_for_graph(keys_honest_voting_for_two,keys_ascii_honest_voting_for_two,values_honest_voting_for_two)
text_honest_antiplurality = util.join_strings_for_graph(keys_honest_antiplurality,keys_ascii_honest_antiplurality,values_honest_antiplurality)
text_honest_borda = util.join_strings_for_graph(keys_honest_borda,keys_ascii_honest_borda,values_honest_borda)
#----------Outcome of honest voting for all schemes------------
colors = n_colors('rgb(255, 200, 200)', 'rgb(200, 0, 0)', max(values_honest_borda)+1, colortype='rgb')
fig_outcome = go.Figure(data=[go.Table(
header=dict(
values=['<b>Plurality<b>','<b>Voting for two<b>', '<b>Antiplurality<b>', '<b>Borda<b>'],
line_color='white', fill_color='white',
align='center',font=dict(color='black', size=12)
),
cells=dict(
values=[text_honest_plurality, text_honest_voting_for_two, text_honest_antiplurality, text_honest_borda],
line_color=[np.array(colors)[values_honest_plurality],np.array(colors)[values_honest_voting_for_two],np.array(colors)[values_honest_antiplurality], np.array(colors)[values_honest_borda]],
fill_color=[np.array(colors)[values_honest_plurality],np.array(colors)[values_honest_voting_for_two],np.array(colors)[values_honest_antiplurality], np.array(colors)[values_honest_borda]],
align='center', font=dict(color='black', size=12)
))
])
fig_outcome.update_layout(
def scatterplot_theme(dataframe, headers, diag, size, height, width, title,
index, index_vals, endpts, colormap, colormap_type,
**kwargs):
"""
Refer to FigureFactory.create_scatterplotmatrix() for docstring
Returns fig for scatterplotmatrix with both index and colormap picked
"""
# Check if index is made of string values
if isinstance(index_vals[0], str):
unique_index_vals = []
for name in index_vals:
if name not in unique_index_vals:
unique_index_vals.append(name)
n_colors_len = len(unique_index_vals)
# Convert colormap to list of n RGB tuples
if colormap_type == 'seq':
foo = clrs.color_parser(colormap, clrs.unlabel_rgb)
foo = clrs.n_colors(foo[0], foo[1], n_colors_len)
theme = clrs.color_parser(foo, clrs.label_rgb)
if colormap_type == 'cat':
# leave list of colors the same way
theme = colormap
dim = len(dataframe)
fig = make_subplots(rows=dim, cols=dim, print_grid=False)
trace_list = []
legend_param = 0
# Work over all permutations of list pairs
for listy in dataframe:
for listx in dataframe:
# create a dictionary for index_vals
unique_index_vals = {}
for name in index_vals:
if name not in unique_index_vals:
unique_index_vals[name] = []
c_indx = 0 # color index
# Fill all the rest of the names into the dictionary
for name in sorted(unique_index_vals.keys()):
new_listx = []
new_listy = []
for j in range(len(index_vals)):
if index_vals[j] == name:
new_listx.append(listx[j])
new_listy.append(listy[j])
# Generate trace with VISIBLE icon
if legend_param == 1:
if (listx == listy) and (diag == 'histogram'):
trace = graph_objs.Histogram(
x=new_listx,
marker=dict(
color=theme[c_indx]),
showlegend=True
)
elif (listx == listy) and (diag == 'box'):
trace = graph_objs.Box(
y=new_listx,
name=None,
marker=dict(
color=theme[c_indx]),
showlegend=True
)
else:
if 'marker' in kwargs:
kwargs['marker']['size'] = size
kwargs['marker']['color'] = theme[c_indx]
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=name,
showlegend=True,
**kwargs
)
else:
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=name,
marker=dict(
size=size,
color=theme[c_indx]),
showlegend=True,
**kwargs
)
# Generate trace with INVISIBLE icon
else:
if (listx == listy) and (diag == 'histogram'):
trace = graph_objs.Histogram(
x=new_listx,
marker=dict(
color=theme[c_indx]),
showlegend=False
)
elif (listx == listy) and (diag == 'box'):
trace = graph_objs.Box(
y=new_listx,
name=None,
marker=dict(
color=theme[c_indx]),
showlegend=False
)
else:
if 'marker' in kwargs:
kwargs['marker']['size'] = size
kwargs['marker']['color'] = theme[c_indx]
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=name,
showlegend=False,
**kwargs
)
else:
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=name,
marker=dict(
size=size,
color=theme[c_indx]),
showlegend=False,
**kwargs
)
# Push the trace into dictionary
unique_index_vals[name] = trace
if c_indx >= (len(theme) - 1):
c_indx = -1
c_indx += 1
trace_list.append(unique_index_vals)
legend_param += 1
trace_index = 0
indices = range(1, dim + 1)
for y_index in indices:
for x_index in indices:
for name in sorted(trace_list[trace_index].keys()):
fig.append_trace(
trace_list[trace_index][name],
y_index,
x_index)
trace_index += 1
# Insert headers into the figure
for j in range(dim):
xaxis_key = 'xaxis{}'.format((dim * dim) - dim + 1 + j)
fig['layout'][xaxis_key].update(title=headers[j])
for j in range(dim):
yaxis_key = 'yaxis{}'.format(1 + (dim * j))
fig['layout'][yaxis_key].update(title=headers[j])
hide_tick_labels_from_box_subplots(fig)
if diag == 'histogram':
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True,
barmode='stack')
return fig
elif diag == 'box':
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True)
return fig
else:
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True)
return fig
else:
if endpts:
intervals = utils.endpts_to_intervals(endpts)
# Convert colormap to list of n RGB tuples
if colormap_type == 'seq':
foo = clrs.color_parser(colormap, clrs.unlabel_rgb)
foo = clrs.n_colors(foo[0], foo[1], len(intervals))
theme = clrs.color_parser(foo, clrs.label_rgb)
if colormap_type == 'cat':
# leave list of colors the same way
theme = colormap
dim = len(dataframe)
fig = make_subplots(rows=dim, cols=dim, print_grid=False)
trace_list = []
legend_param = 0
# Work over all permutations of list pairs
for listy in dataframe:
for listx in dataframe:
interval_labels = {}
for interval in intervals:
interval_labels[str(interval)] = []
c_indx = 0 # color index
# Fill all the rest of the names into the dictionary
for interval in intervals:
new_listx = []
new_listy = []
for j in range(len(index_vals)):
if interval[0] < index_vals[j] <= interval[1]:
new_listx.append(listx[j])
new_listy.append(listy[j])
# Generate trace with VISIBLE icon
if legend_param == 1:
if (listx == listy) and (diag == 'histogram'):
trace = graph_objs.Histogram(
x=new_listx,
marker=dict(
color=theme[c_indx]),
showlegend=True
)
elif (listx == listy) and (diag == 'box'):
trace = graph_objs.Box(
y=new_listx,
name=None,
marker=dict(
color=theme[c_indx]),
showlegend=True
)
else:
if 'marker' in kwargs:
kwargs['marker']['size'] = size
(kwargs['marker']
['color']) = theme[c_indx]
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=str(interval),
showlegend=True,
**kwargs
)
else:
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=str(interval),
marker=dict(
size=size,
color=theme[c_indx]),
showlegend=True,
**kwargs
)
# Generate trace with INVISIBLE icon
else:
if (listx == listy) and (diag == 'histogram'):
trace = graph_objs.Histogram(
x=new_listx,
marker=dict(
color=theme[c_indx]),
showlegend=False
)
elif (listx == listy) and (diag == 'box'):
trace = graph_objs.Box(
y=new_listx,
name=None,
marker=dict(
color=theme[c_indx]),
showlegend=False
)
else:
if 'marker' in kwargs:
kwargs['marker']['size'] = size
(kwargs['marker']
['color']) = theme[c_indx]
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=str(interval),
showlegend=False,
**kwargs
)
else:
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=str(interval),
marker=dict(
size=size,
color=theme[c_indx]),
showlegend=False,
**kwargs
)
# Push the trace into dictionary
interval_labels[str(interval)] = trace
if c_indx >= (len(theme) - 1):
c_indx = -1
c_indx += 1
trace_list.append(interval_labels)
legend_param += 1
trace_index = 0
indices = range(1, dim + 1)
for y_index in indices:
for x_index in indices:
for interval in intervals:
fig.append_trace(
trace_list[trace_index][str(interval)],
y_index,
x_index)
trace_index += 1
# Insert headers into the figure
for j in range(dim):
xaxis_key = 'xaxis{}'.format((dim * dim) - dim + 1 + j)
fig['layout'][xaxis_key].update(title=headers[j])
for j in range(dim):
yaxis_key = 'yaxis{}'.format(1 + (dim * j))
fig['layout'][yaxis_key].update(title=headers[j])
hide_tick_labels_from_box_subplots(fig)
if diag == 'histogram':
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True,
barmode='stack')
return fig
elif diag == 'box':
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True)
return fig
else:
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True)
return fig
else:
theme = colormap
# add a copy of rgb color to theme if it contains one color
if len(theme) <= 1:
theme.append(theme[0])
color = []
for incr in range(len(theme)):
color.append([1. / (len(theme) - 1) * incr, theme[incr]])
dim = len(dataframe)
fig = make_subplots(rows=dim, cols=dim, print_grid=False)
trace_list = []
legend_param = 0
# Run through all permutations of list pairs
for listy in dataframe:
for listx in dataframe:
# Generate trace with VISIBLE icon
if legend_param == 1:
if (listx == listy) and (diag == 'histogram'):
trace = graph_objs.Histogram(
x=listx,
marker=dict(
color=theme[0]),
showlegend=False
)
elif (listx == listy) and (diag == 'box'):
trace = graph_objs.Box(
y=listx,
marker=dict(
color=theme[0]),
showlegend=False
)
else:
if 'marker' in kwargs:
kwargs['marker']['size'] = size
kwargs['marker']['color'] = index_vals
kwargs['marker']['colorscale'] = color
kwargs['marker']['showscale'] = True
trace = graph_objs.Scatter(
x=listx,
y=listy,
mode='markers',
showlegend=False,
**kwargs
)
else:
trace = graph_objs.Scatter(
x=listx,
y=listy,
mode='markers',
marker=dict(
size=size,
color=index_vals,
colorscale=color,
showscale=True),
showlegend=False,
**kwargs
)
# Generate trace with INVISIBLE icon
else:
if (listx == listy) and (diag == 'histogram'):
trace = graph_objs.Histogram(
x=listx,
marker=dict(
color=theme[0]),
showlegend=False
)
elif (listx == listy) and (diag == 'box'):
trace = graph_objs.Box(
y=listx,
marker=dict(
color=theme[0]),
showlegend=False
)
else:
if 'marker' in kwargs:
kwargs['marker']['size'] = size
kwargs['marker']['color'] = index_vals
kwargs['marker']['colorscale'] = color
kwargs['marker']['showscale'] = False
trace = graph_objs.Scatter(
x=listx,
y=listy,
mode='markers',
showlegend=False,
**kwargs
)
else:
trace = graph_objs.Scatter(
x=listx,
y=listy,
mode='markers',
marker=dict(
size=size,
color=index_vals,
colorscale=color,
showscale=False),
showlegend=False,
**kwargs
)
# Push the trace into list
trace_list.append(trace)
legend_param += 1
trace_index = 0
indices = range(1, dim + 1)
for y_index in indices:
for x_index in indices:
fig.append_trace(trace_list[trace_index],
y_index,
x_index)
trace_index += 1
# Insert headers into the figure
for j in range(dim):
xaxis_key = 'xaxis{}'.format((dim * dim) - dim + 1 + j)
fig['layout'][xaxis_key].update(title=headers[j])
for j in range(dim):
yaxis_key = 'yaxis{}'.format(1 + (dim * j))
fig['layout'][yaxis_key].update(title=headers[j])
hide_tick_labels_from_box_subplots(fig)
if diag == 'histogram':
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True,
barmode='stack')
return fig
elif diag == 'box':
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True)
return fig
else:
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True)
return fig
def _bullet(
df,
markers,
measures,
ranges,
subtitles,
titles,
orientation,
range_colors,
measure_colors,
horizontal_spacing,
vertical_spacing,
scatter_options,
layout_options,
):
num_of_lanes = len(df)
num_of_rows = num_of_lanes if orientation == "h" else 1
num_of_cols = 1 if orientation == "h" else num_of_lanes
if not horizontal_spacing:
horizontal_spacing = 1.0 / num_of_lanes
if not vertical_spacing:
vertical_spacing = 1.0 / num_of_lanes
fig = plotly.subplots.make_subplots(
num_of_rows,
num_of_cols,
print_grid=False,
horizontal_spacing=horizontal_spacing,
vertical_spacing=vertical_spacing,
)
# layout
fig["layout"].update(
dict(shapes=[]),
title="Bullet Chart",
height=600,
width=1000,
showlegend=False,
barmode="stack",
annotations=[],
margin=dict(l=120 if orientation == "h" else 80),
)
# update layout
fig["layout"].update(layout_options)
if orientation == "h":
width_axis = "yaxis"
length_axis = "xaxis"
else:
width_axis = "xaxis"
length_axis = "yaxis"
for key in fig["layout"]:
if "xaxis" in key or "yaxis" in key:
fig["layout"][key]["showgrid"] = False
fig["layout"][key]["zeroline"] = False
if length_axis in key:
fig["layout"][key]["tickwidth"] = 1
if width_axis in key:
fig["layout"][key]["showticklabels"] = False
fig["layout"][key]["range"] = [0, 1]
# narrow domain if 1 bar
if num_of_lanes <= 1:
fig["layout"][width_axis + "1"]["domain"] = [0.4, 0.6]
if not range_colors:
range_colors = ["rgb(200, 200, 200)", "rgb(245, 245, 245)"]
if not measure_colors:
measure_colors = ["rgb(31, 119, 180)", "rgb(176, 196, 221)"]
for row in range(num_of_lanes):
# ranges bars
for idx in range(len(df.iloc[row]["ranges"])):
inter_colors = clrs.n_colors(range_colors[0], range_colors[1],
len(df.iloc[row]["ranges"]), "rgb")
x = ([sorted(df.iloc[row]["ranges"])[-1 - idx]]
if orientation == "h" else [0])
y = ([0] if orientation == "h" else
[sorted(df.iloc[row]["ranges"])[-1 - idx]])
bar = go.Bar(
x=x,
y=y,
marker=dict(color=inter_colors[-1 - idx]),
name="ranges",
hoverinfo="x" if orientation == "h" else "y",
orientation=orientation,
width=2,
base=0,
xaxis="x{}".format(row + 1),
yaxis="y{}".format(row + 1),
)
fig.add_trace(bar)
# measures bars
for idx in range(len(df.iloc[row]["measures"])):
inter_colors = clrs.n_colors(
measure_colors[0],
measure_colors[1],
len(df.iloc[row]["measures"]),
"rgb",
)
x = ([sorted(df.iloc[row]["measures"])[-1 - idx]]
if orientation == "h" else [0.5])
y = ([0.5] if orientation == "h" else
[sorted(df.iloc[row]["measures"])[-1 - idx]])
bar = go.Bar(
x=x,
y=y,
marker=dict(color=inter_colors[-1 - idx]),
name="measures",
hoverinfo="x" if orientation == "h" else "y",
orientation=orientation,
width=0.4,
base=0,
xaxis="x{}".format(row + 1),
yaxis="y{}".format(row + 1),
)
fig.add_trace(bar)
# markers
x = df.iloc[row]["markers"] if orientation == "h" else [0.5]
y = [0.5] if orientation == "h" else df.iloc[row]["markers"]
markers = go.Scatter(x=x,
y=y,
name="markers",
hoverinfo="x" if orientation == "h" else "y",
xaxis="x{}".format(row + 1),
yaxis="y{}".format(row + 1),
**scatter_options)
fig.add_trace(markers)
# titles and subtitles
title = df.iloc[row]["titles"]
if "subtitles" in df:
subtitle = "<br>{}".format(df.iloc[row]["subtitles"])
else:
subtitle = ""
label = "<b>{}</b>".format(title) + subtitle
annot = utils.annotation_dict_for_label(
label,
(num_of_lanes - row if orientation == "h" else row + 1),
num_of_lanes,
vertical_spacing if orientation == "h" else horizontal_spacing,
"row" if orientation == "h" else "col",
True if orientation == "h" else False,
False,
)
fig["layout"]["annotations"] += (annot, )
return fig
title_text="Number of songs per year"
)
fig.update_layout(
xaxis=dict(
rangeslider=dict(
visible=True
),
type="date"
)
)
st.write(fig)
st.markdown('This Violin chart helps to investigate how the mean music tempo has been changing')
value = st.slider("Year range", 1920, 2014, (1920, 2014), 1)
colors = n_colors('rgb(135, 251, 44)', 'rgb(75, 144, 22)', value[1]-value[0], colortype='rgb')
fig = go.Figure()
for data_line, color, year in zip(dist_per_year[(value[0]-1920):(value[1]-1920)], colors, data['year'].unique()[(value[0]-1920):(value[1]-1920)]):
fig.add_trace(go.Violin(x=data_line, line_color=color, name=str(year)))
fig.update_traces(orientation='h', side='positive', width=3, points=False)
fig.update_layout(xaxis_showgrid=False, xaxis_zeroline=False)
st.write(fig)
if select_event == 'The artist':
st.markdown("### **Select Artist:**")
select_artist = []
select_artist.append(st.selectbox('', sorted(soloists['artists'].unique())))
artist = soloists[soloists['artists'].str.contains(select_artist[0])]
st.markdown("### **Summary statistics:**")
st.markdown("Here there is aggregated values of artist's songs characteristics")
fig = go.Figure(go.Bar(
x=artist[['danceability', 'energy', 'instrumentalness', 'speechiness', "liveness"]].mean(),
def plot_heatmap_week(request):
# label
timeslot = [x for x in range(7, 23)]
weekdays = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
# data
frequency = [[0 for x in range(1, 8)] for y in range(1, 17)]
# get all workouts from user
if request.user.is_authenticated:
query = Training.objects.all().filter(user_name=request.user).exclude(
training_time__isnull=True)
else:
query = Training.objects.all().filter(user_name='test_user').exclude(
training_time__isnull=True)
# 2d array of days - timeslots
timeslots = pd.date_range("07:30", "22:30", freq="60min").time
for i in range(len(query)):
ind_day = query.values()[i]['training_date'].weekday(
) # index tag first element of query
for j in range(len(timeslots)):
if query.values()[i]['training_time'] <= timeslots[j]:
frequency[j][ind_day] += 1
break
# create hover text - time | frequency
text = [[0 for x in range(1, 8)] for y in range(1, 13)]
for i in range(len(text)):
for j in range(len(text[i])):
if frequency[i][j] == 0:
text[i][j] = ''
else:
text[i][j] = f'{timeslot[i]}:00 | {frequency[i][j]}'
# color of circles
range_frequency = range(0, np.max(frequency) + 1)
greys = n_colors('rgba(255,255,255)',
'rgb(0,0,0)',
np.max(range_frequency) + 1,
colortype='rgb')
greys[0] = 'rgba(0,0,0,0)' # zero is transparent
colorscale = np.array(greys)[range_frequency]
# colorscale legend
color = []
for i in range(np.max(frequency) + 1):
color.append([i / (np.max(frequency) + 1), greys[i]])
color.append([(i + 1) / (np.max(frequency) + 1), greys[i]])
# plot invisible data to get hover data at the right spot
# this plots rectangles in the plot, I want circles/ellipses
fig = go.Figure()
fig.add_trace(
go.Heatmap(
x=weekdays,
y=timeslot,
z=frequency,
colorscale=color, #px.colors.sequential.Blugrn,
text=text,
hoverinfo='text',
opacity=0,
colorbar=dict(tick0=0, dtick=1),
name='Frequency'))
# legend with categorical data - otherwise the legend will be continuous
text2 = [['' for x in range(1, 8)] for y in range(1, 13)]
for i in range(1, np.max(frequency) + 1):
fig.add_trace(
go.Heatmap(
x=weekdays,
y=timeslot,
z=[None for x in range(1, 8)],
name=i,
colorscale=[
np.array(greys)[range_frequency][i] for x in range(2)
],
text=text2,
hoverinfo='text',
))
fig.update_traces(showlegend=True, showscale=False)
fig.update_layout(yaxis_title="Hour of Day",
title='Workout Days & Times:',
xaxis={'showgrid': False},
yaxis={'showgrid': False},
font=dict(family="Courier New, monospace", ))
fig.update_layout({
'plot_bgcolor': 'rgba(0, 0, 0, 0)',
'paper_bgcolor': 'rgba(0, 0, 0, 0)',
})
fig.update_layout(hovermode='x')
# plot circles at the right spots to coincide with data
for i in range(len(frequency)):
for j in range(len(frequency[0])):
if frequency[i][j] > 0:
fig.add_shape(
type="circle",
xref="x",
yref="y",
x0=j - 0.3,
y0=timeslot[i] - 0.5,
x1=j + 0.3,
y1=timeslot[i] + 0.5,
line_color=colorscale[frequency[i][j]],
fillcolor=colorscale[frequency[i][j]],
)
fig.update_traces(hoverlabel=dict(bgcolor="darkslategrey"))
fig['layout']['yaxis']['autorange'] = "reversed"
fig.update_xaxes(side="top")
fig.layout.xaxis.fixedrange = True
fig.layout.yaxis.fixedrange = True
config = {'displayModeBar': False}
fig.update_layout(margin=dict(l=0, r=0))
fig.update_layout(yaxis=dict(tickmode='linear', tick0=8, dtick=2))
plot_div = plot(fig, output_type='div', config=config)
return plot_div
from dash.dependencies import Input, Output
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objects as go
from plotly.colors import n_colors
import numpy as np
from inf import app
colorsf = n_colors('rgb(255, 200, 200)', 'rgb(200, 0, 0)', 90, colortype='rgb')
colorsd = n_colors('rgb(255, 200, 200)', 'rgb(200, 0, 0)', 41, colortype='rgb')
a = [
"CCSD", "M06-2X-COMB7", "PBE0", "M06-2X-PBE25-OPT1", "M06-2X-COMB14",
"TPSS", "M06-2X-OPT6", "M06-2X_S", "M06-2X"
]
b = [0, 1.808, 1.818, 1.867, 1.910, 2.043, 2.941, 4.028, 4.028]
c = [0, 24.9783, 83.4887, 89.1403, 19.5589, 0, 16.7704, 19.2325, 18.9840]
bc = list(map(lambda x: round(x * 10), b))
cc = list(map(lambda x: round(x), c))
c = ["?", 24.9783, 83.4887, 89.1403, 19.5589, "?", 16.7704, 19.2325, 18.9840]
hf = ["-", 0.540, 0.250, 0.250, 0.432, "-", 0.510, 0.540, 0.540]
fig = go.Figure(data=[
go.Table(header=dict(values=[
'<b>Функционал</b>', '<b>maxNE</b>', '<b>Ferr</b>', '<b>pHF</b>'
],
line_color='white',
dtick='M1',
color=MAIN_COLOUR,
showgrid=True,
gridcolor=MAIN_COLOUR2),
yaxis=dict(color=MAIN_COLOUR),
plot_bgcolor='rgb(255,255,255)'))
return fig
if __name__ == '__main__':
# Initialise the app
app = dash.Dash(__name__)
MAIN_COLOUR = 'rgb(156, 60, 56)'
MAIN_COLOUR2 = n_colors(MAIN_COLOUR,
'rgb(255, 255, 255)',
3,
colortype='rgb')[1]
BIG_HEADER_FONT_SIZE = 30
HEADER_FONT_SIZE = 18
TABLE_FONT_SIZE = 14
CELL_HEIGHT = 28
MAIN_PARAMETERS_PLUS = 5
MAIN_PARAMETERS_ADDITIONAL_PLUS = 20
HISTOGRAM_BINS_NUM = 12
YEAR = 2021
GUYS_CODES = [11842, 12230, 12455, 33717, 93898]
def _bullet(df, markers, measures, ranges, subtitles, titles, orientation,
range_colors, measure_colors, horizontal_spacing,
vertical_spacing, scatter_options, layout_options):
num_of_lanes = len(df)
num_of_rows = num_of_lanes if orientation == 'h' else 1
num_of_cols = 1 if orientation == 'h' else num_of_lanes
if not horizontal_spacing:
horizontal_spacing = 1./num_of_lanes
if not vertical_spacing:
vertical_spacing = 1./num_of_lanes
fig = plotly.tools.make_subplots(
num_of_rows, num_of_cols, print_grid=False,
horizontal_spacing=horizontal_spacing,
vertical_spacing=vertical_spacing
)
# layout
fig['layout'].update(
dict(shapes=[]),
title='Bullet Chart',
height=600,
width=1000,
showlegend=False,
barmode='stack',
annotations=[],
margin=dict(l=120 if orientation == 'h' else 80),
)
# update layout
fig['layout'].update(layout_options)
if orientation == 'h':
width_axis = 'yaxis'
length_axis = 'xaxis'
else:
width_axis = 'xaxis'
length_axis = 'yaxis'
for key in fig['layout']:
if 'xaxis' in key or 'yaxis' in key:
fig['layout'][key]['showgrid'] = False
fig['layout'][key]['zeroline'] = False
if length_axis in key:
fig['layout'][key]['tickwidth'] = 1
if width_axis in key:
fig['layout'][key]['showticklabels'] = False
fig['layout'][key]['range'] = [0, 1]
# narrow domain if 1 bar
if num_of_lanes <= 1:
fig['layout'][width_axis + '1']['domain'] = [0.4, 0.6]
if not range_colors:
range_colors = ['rgb(200, 200, 200)', 'rgb(245, 245, 245)']
if not measure_colors:
measure_colors = ['rgb(31, 119, 180)', 'rgb(176, 196, 221)']
for row in range(num_of_lanes):
# ranges bars
for idx in range(len(df.iloc[row]['ranges'])):
inter_colors = colors.n_colors(
range_colors[0], range_colors[1],
len(df.iloc[row]['ranges']), 'rgb'
)
x = ([sorted(df.iloc[row]['ranges'])[-1 - idx]] if
orientation == 'h' else [0])
y = ([0] if orientation == 'h' else
[sorted(df.iloc[row]['ranges'])[-1 - idx]])
bar = go.Bar(
x=x,
y=y,
marker=dict(
color=inter_colors[-1 - idx]
),
name='ranges',
hoverinfo='x' if orientation == 'h' else 'y',
orientation=orientation,
width=2,
base=0,
xaxis='x{}'.format(row + 1),
yaxis='y{}'.format(row + 1)
)
fig.add_trace(bar)
# measures bars
for idx in range(len(df.iloc[row]['measures'])):
inter_colors = colors.n_colors(
measure_colors[0], measure_colors[1],
len(df.iloc[row]['measures']), 'rgb'
)
x = ([sorted(df.iloc[row]['measures'])[-1 - idx]] if
orientation == 'h' else [0.5])
y = ([0.5] if orientation == 'h'
else [sorted(df.iloc[row]['measures'])[-1 - idx]])
bar = go.Bar(
x=x,
y=y,
marker=dict(
color=inter_colors[-1 - idx]
),
name='measures',
hoverinfo='x' if orientation == 'h' else 'y',
orientation=orientation,
width=0.4,
base=0,
xaxis='x{}'.format(row + 1),
yaxis='y{}'.format(row + 1)
)
fig.add_trace(bar)
# markers
x = df.iloc[row]['markers'] if orientation == 'h' else [0.5]
y = [0.5] if orientation == 'h' else df.iloc[row]['markers']
markers = go.Scatter(
x=x,
y=y,
name='markers',
hoverinfo='x' if orientation == 'h' else 'y',
xaxis='x{}'.format(row + 1),
yaxis='y{}'.format(row + 1),
**scatter_options
)
fig.add_trace(markers)
# titles and subtitles
title = df.iloc[row]['titles']
if 'subtitles' in df:
subtitle = '<br>{}'.format(df.iloc[row]['subtitles'])
else:
subtitle = ''
label = '<b>{}</b>'.format(title) + subtitle
annot = utils.annotation_dict_for_label(
label,
(num_of_lanes - row if orientation == 'h' else row + 1),
num_of_lanes,
vertical_spacing if orientation == 'h' else horizontal_spacing,
'row' if orientation == 'h' else 'col',
True if orientation == 'h' else False,
False
)
fig['layout']['annotations'] += (annot,)
return fig
def plot_table_color(col_labels, values, title='', font_size=12):
color_list = ['paleturquoise']
light_color = 'rgb(200, 200, 255)'
dark_color = 'rgb(150, 100, 200)'
for i in range(1, len(values)):
vals = values[i]
if isinstance(vals[0], np.int64):
if max(vals) - min(vals) == 0:
color_list.append(light_color)
else:
colors = np.array(
n_colors(light_color,
dark_color,
max(vals) - min(vals) + 1,
colortype='rgb'))
color_list.append(colors[vals - min(vals)])
elif isinstance(vals[0], str):
time_units = vals[0].split(':')
if len(time_units) == 2:
vals = [pd.Timedelta('00:' + x).seconds for x in vals]
if max(vals) - min(vals) == 0:
color_list.append(light_color)
else:
colors = np.array(
n_colors(light_color,
dark_color,
max(vals) - min(vals) + 1,
colortype='rgb'))
color_list.append(colors[[(x - min(vals)) for x in vals]])
elif len(time_units) == 3:
vals = [pd.Timedelta(x).seconds for x in vals]
if max(vals) - min(vals) == 0:
color_list.append(light_color)
else:
colors = np.array(
n_colors(light_color,
dark_color,
max(vals) - min(vals) + 1,
colortype='rgb'))
color_list.append(colors[[(x - min(vals)) for x in vals]])
else:
color_list.append('white')
else:
color_list.append('white')
fig = go.Figure(data=[
go.Table(
# columnorder = [1,2,3],
# columnwidth = [80,400],
header=dict(values=col_labels,
line_color='darkslategray',
fill_color='royalblue',
align=['center', 'center'],
font=dict(color='white', size=12),
height=40),
cells=dict(
values=values,
line_color='darkslategray',
# fill=dict(color=['paleturquoise', 'white']),
fill_color=color_list,
align=['left', 'center'],
font_size=font_size,
height=30))
])
fig.update_layout(
title=title,
title_x=0.5,
font=dict(family="Times New Roman", size=20, color="Black"),
)
return fig
