def display_value(group_chosen, compare_1_chosen, compare_2_chosen,
compare_3_chosen):
# df_fltrd = df[df['Genre'] == genre_chosen]
# df_fltrd = df_fltrd.nlargest(10, sales_chosen)
# fig = px.bar(df_fltrd, x=compare_1_chosen, y=compare_2_chosen, color='Platform')
# fig = fig.update_yaxes(tickprefix="$", ticksuffix="M")
print(group_chosen)
print(compare_1_chosen)
print(compare_2_chosen)
print(compare_3_chosen)
rootBranchesLeaves = [group_chosen, compare_1_chosen]
if compare_2_chosen.lower() != 'none':
rootBranchesLeaves.append(compare_2_chosen)
if compare_3_chosen.lower() != 'none':
rootBranchesLeaves.append(compare_3_chosen)
colorSequence = utilities.set_color_sequence(group_chosen)
# Produce a sunburst plot
fig = px.sunburst(
data_frame=df,
path=rootBranchesLeaves, # Root, branches, leaves
color=group_chosen,
color_discrete_sequence=colorSequence,
maxdepth=
-1, # set the sectors rendered. -1 will render all levels in the hierarchy
# color="Victim's age",
# color_continuous_scale=px.colors.sequential.BuGn,
# range_color=[10,100],
branchvalues="total", # or 'remainder'
hover_name=group_chosen,
# hover_data={'Unarmed': False}, # remove column name from tooltip (Plotly version >= 4.8.0)
title=documentName.capitalize() + ' Data Sunburst Plot',
template='ggplot2', # 'ggplot2', 'seaborn', 'simple_white', 'plotly',
# # 'plotly_white', 'plotly_dark', 'presentation',
# # 'xgridoff', 'ygridoff', 'gridon', 'none'
height=600,
)
fig.update_traces(textinfo='label+percent entry')
fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))
return [dcc.Graph(id='sunburst_plot' + uniqueComponentForApp, figure=fig)]
def update_line(all_rows_data, slctd_row_indices, slct_rows_names, slctd_rows,
order_of_rows_indices, order_of_rows_names, actv_cell,
slctd_cell, slctd_columns):
print(
'***************************************************************************'
)
print('Data across all pages pre or post filtering: {}'.format(
all_rows_data))
print('---------------------------------------------')
print(
"Indices of selected rows if part of table after filtering:{}".format(
slctd_row_indices))
print("Names of selected rows if part of table after filtering: {}".format(
slct_rows_names))
print(
"Indices of selected rows regardless of filtering results: {}".format(
slctd_rows))
print('---------------------------------------------')
print("Indices of all rows pre or post filtering: {}".format(
order_of_rows_indices))
print("Names of all rows pre or post filtering: {}".format(
order_of_rows_names))
print("---------------------------------------------")
print("Complete data of active cell: {}".format(actv_cell))
print("Complete data of all selected cells: {}".format(slctd_cell))
print("---------------------------------------------")
print("Indices of selected columns regardless of filtering results: {}".
format(slctd_columns))
if slctd_row_indices is None:
slctd_row_indices = []
dff_all = df if all_rows_data is None else pd.DataFrame(all_rows_data)
data = []
for index, row in dff_all.iterrows():
for col in slctd_columns:
dataRow = []
dataRow.append(row['sample_name'])
dataRow.append(col)
dataRow.append(row[col])
data.append(dataRow)
dff = pd.DataFrame(data, columns=['sample_name', 'column_name', 'value'])
colorSequence = utilities.set_color_sequence()
# used to highlight selected countries on bar chart
colors = [
'#7FDBFF' if i in slctd_row_indices else '#0074D9'
for i in range(len(dff))
]
return [
dcc.Graph(
id='line_plot',
figure=px.line(
data_frame=dff,
x='sample_name',
y='value',
color='column_name',
color_discrete_sequence=colorSequence).update_layout(
showlegend=True,
# xaxis={'categoryorder': 'total ascending'},
yaxis={
'title': 'Values'
},
title={
'text': 'scRNA-seq Summary Line Plot',
'font': {
'size': 28
},
'x': 0.5,
'xanchor': 'center'
}).update_traces(
marker_color=colors,
hovertemplate="<b>%{y}%</b><extra></extra>"))
]
def update_bar(all_rows_data, slctd_row_indices, slct_rows_names, slctd_rows,
order_of_rows_indices, order_of_rows_names, actv_cell,
slctd_cell, slctd_columns):
print(
'***************************************************************************'
)
print('Data across all pages pre or post filtering: {}'.format(
all_rows_data))
print('---------------------------------------------')
print(
"Indices of selected rows if part of table after filtering:{}".format(
slctd_row_indices))
print("Names of selected rows if part of table after filtering: {}".format(
slct_rows_names))
print(
"Indices of selected rows regardless of filtering results: {}".format(
slctd_rows))
print('---------------------------------------------')
print("Indices of all rows pre or post filtering: {}".format(
order_of_rows_indices))
print("Names of all rows pre or post filtering: {}".format(
order_of_rows_names))
print("---------------------------------------------")
print("Complete data of active cell: {}".format(actv_cell))
print("Complete data of all selected cells: {}".format(slctd_cell))
print("---------------------------------------------")
print("Indices of selected columns regardless of filtering results: {}".
format(slctd_columns))
if slctd_row_indices is None:
slctd_row_indices = []
dff = df if all_rows_data is None else pd.DataFrame(all_rows_data)
# dff = pd.DataFrame(all_rows_data)
colorSequence = utilities.set_color_sequence()
# used to highlight selected countries on bar chart
colors = [
'#7FDBFF' if i in slctd_row_indices else '#0074D9'
for i in range(len(dff))
]
return [
dcc.Graph(id=column,
figure=px.bar(
data_frame=dff,
x="sample_name",
y=dff[column],
labels={
"age": "Number of reads"
},
color_discrete_sequence=colorSequence).update_layout(
showlegend=False,
xaxis={
'categoryorder': 'total ascending'
},
title={
'text': 'scRNA-seq Summary Bar Plot',
'font': {
'size': 28
},
'x': 0.5,
'xanchor': 'center'
}).update_traces(
marker_color=colors,
hovertemplate="<b>%{y}%</b><extra></extra>"))
# check if column exists - user may have deleted it
# If `column.deletable=False`, then you don't
# need to do this check.
for column in slctd_columns if column in dff
]
def display_value(group_chosen, compare_1_chosen, compare_2_chosen,
compare_3_chosen, range_1):
# df_fltrd = df[df['Genre'] == genre_chosen]
# df_fltrd = df_fltrd.nlargest(10, sales_chosen)
# fig = px.bar(df_fltrd, x=compare_1_chosen, y=compare_2_chosen, color='Platform')
# fig = fig.update_yaxes(tickprefix="$", ticksuffix="M")
print(group_chosen)
print(compare_1_chosen)
print(compare_2_chosen)
print(compare_3_chosen)
print(range_1)
colorSequence = utilities.set_color_sequence(group_chosen)
# Filter based on range slider
df['numeric_age'] = pd.to_numeric(df['age'], errors='coerce')
dff = df[(df['numeric_age'] >= range_1[0])
& (df['numeric_age'] <= range_1[1])]
# Add age to title
title = documentName.capitalize() + ' Data Scatter Plot for Ages ' + str(
range_1[0]) + ' to ' + str(range_1[1])
# If the third compare choice is 'none' then produce a
# 2D scatter plot, otherwise produce a 3D scatter plot
if compare_3_chosen.lower() == 'none':
fig = px.scatter(
data_frame=dff,
x=compare_1_chosen,
y=compare_2_chosen,
color=group_chosen,
color_discrete_sequence=colorSequence,
template='ggplot2',
title=title,
# size='age', # size of bubble
# size_max=15, # set the maximum mark size when using size
hover_name='study_id', # values appear in bold in the hover tooltip
height=600)
else:
# Use for animation rotation at the end
x_eye = -1.25
y_eye = 2
z_eye = 0.5
fig = px.scatter_3d(
data_frame=dff,
x=compare_1_chosen,
y=compare_2_chosen,
z=compare_3_chosen,
color=group_chosen,
color_discrete_sequence=colorSequence,
template='ggplot2',
title=title,
size='age', # size of bubble
size_max=15, # set the maximum mark size when using size
hover_name='study_id', # values appear in bold in the hover tooltip
height=600).update_layout(
scene_camera_eye=dict(x=x_eye, y=y_eye, z=z_eye),
updatemenus=[
dict(type='buttons',
showactive=False,
y=1,
x=0.8,
xanchor='left',
yanchor='bottom',
pad=dict(t=45, r=10),
buttons=[
dict(label='Play',
method='animate',
args=[
None,
dict(frame=dict(duration=250,
redraw=True),
transition=dict(duration=0),
fromcurrent=True,
mode='immediate')
])
])
])
frames = []
for t in np.arange(0, 6.26, 0.1):
xe, ye, ze = rotate_z(x_eye, y_eye, z_eye, -t)
frames.append(
go.Frame(layout=dict(scene_camera_eye=dict(x=xe, y=ye, z=ze))))
fig.frames = frames
return [dcc.Graph(id='scatter_plot' + uniqueComponentForApp, figure=fig)]
def display_value(group_chosen, compare_1_chosen, compare_2_chosen,
compare_3_chosen):
print(group_chosen)
print(compare_1_chosen)
print(compare_2_chosen)
print(compare_3_chosen)
colorSequence = utilities.set_color_sequence(group_chosen)
# If the third compare choice is 'none' then produce a
# 2D scatter plot, otherwise produce a 3D scatter plot
if compare_3_chosen.lower() == 'none':
fig = px.scatter(
data_frame=df,
x=compare_1_chosen,
y=compare_2_chosen,
color=group_chosen,
color_discrete_sequence=colorSequence,
template='ggplot2',
title=documentName + ' Data Scatter Plot',
# size='size', # size of bubble
# size_max=30, # set the maximum mark size when using size
hover_name='study_id', # values appear in bold in the hover tooltip
height=600)
else:
# Use for animation rotation at the end
x_eye = -1.25
y_eye = 2
z_eye = 0.5
fig = px.scatter_3d(
data_frame=df,
x=compare_1_chosen,
y=compare_2_chosen,
z=compare_3_chosen,
color=group_chosen,
color_discrete_sequence=colorSequence,
template='ggplot2',
title=documentName + ' Data 3D Scatter Plot',
size='age', # size of bubble
size_max=15, # set the maximum mark size when using size
hover_name='study_id', # values appear in bold in the hover tooltip
height=600).update_layout(
scene_camera_eye=dict(x=x_eye, y=y_eye, z=z_eye),
updatemenus=[
dict(type='buttons',
showactive=False,
y=1,
x=0.8,
xanchor='left',
yanchor='bottom',
pad=dict(t=45, r=10),
buttons=[
dict(label='Play',
method='animate',
args=[
None,
dict(frame=dict(duration=250,
redraw=True),
transition=dict(duration=0),
fromcurrent=True,
mode='immediate')
])
])
])
frames = []
for t in np.arange(0, 6.26, 0.1):
xe, ye, ze = rotate_z(x_eye, y_eye, z_eye, -t)
frames.append(
go.Frame(layout=dict(scene_camera_eye=dict(x=xe, y=ye, z=ze))))
fig.frames = frames
return [dcc.Graph(id='scatter_plot' + uniqueComponentForApp, figure=fig)]
def display_value(group_chosen, compare_1_chosen, compare_2_chosen, radioitems_1, range_1):
# df_fltrd = df[df['Genre'] == genre_chosen]
# df_fltrd = df_fltrd.nlargest(10, sales_chosen)
# fig = px.bar(df_fltrd, x=compare_1_chosen, y=compare_2_chosen, color='Platform')
# fig = fig.update_yaxes(tickprefix="$", ticksuffix="M")
print(group_chosen)
print(compare_1_chosen)
print(compare_2_chosen)
print(radioitems_1)
print(range_1)
colorSequence = utilities.set_color_sequence(group_chosen)
# Filter based on range slider
df['numeric_age'] = pd.to_numeric(df['age'], errors='coerce')
dff = df[(df['numeric_age'] >= range_1[0]) & (df['numeric_age'] <= range_1[1])]
# Filter based on radio items
if radioitems_1 != 'BOTH':
dff2 = dff[dff['phenotype'] == radioitems_1]
else:
dff2 = dff
# Add age to title
title = documentName.capitalize() + ' Violin Plot for Ages ' + str(range_1[0]) + ' to ' + str(range_1[1])
fig = px.violin(
# data_frame=df.query("State == ['{}','{}']".format('ALABAMA','NEW YORK')),
data_frame=dff2,
x=compare_1_chosen,
y=compare_2_chosen,
category_orders={
'timepoint': ['D1-PRE', 'D1-POST', 'D2-PRE', 'D2-POST']},
orientation="v", # vertical 'v' or horizontal 'h'
points='all', # 'outliers','suspectedoutliers', 'all', or False
box=True, # draw box inside the violins
color=group_chosen, # differentiate markers by color
violinmode="group", # 'overlay' or 'group'
color_discrete_sequence=colorSequence,
# color_discrete_map={"ALABAMA": "blue" ,"NEW YORK":"magenta"}, # map your chosen colors
hover_name='study_id', # values appear in bold in the hover tooltip
hover_data=[group_chosen, 'timepoint'], # values appear as extra data in the hover tooltip
# custom_data=['Program'], # values are extra data to be used in Dash callbacks
# facet_row='State', # assign marks to subplots in the vertical direction
# facet_col='Period', # assign marks to subplots in the horizontal direction
# facet_col_wrap=2, # maximum number of subplot columns. Do not set facet_row
# log_x=True, # x-axis is log-scaled
# log_y=True, # y-axis is log-scaled
# labels={"State": "STATE"}, # map the labels
title=title,
# width=1050, # figure width in pixels
height=600, # igure height in pixels
template='ggplot2', # 'ggplot2', 'seaborn', 'simple_white', 'plotly',
# 'plotly_white', 'plotly_dark', 'presentation',
# 'xgridoff', 'ygridoff', 'gridon', 'none'
# animation_frame='Year', # assign marks to animation frames
# animation_group='', # use only when df has multiple rows with same object
# range_x=[5,50], # set range of x-axis
# range_y=[-5,100], # set range of y-axis
# category_orders={'Year':[2015,2016,2017,2018,2019]}, # set a specific ordering of values per column
)
return [
dcc.Graph(id='violin_plot' + uniqueComponentForApp, figure=fig)
]
def update_3d_scatter(all_rows_data, slctd_row_indices, slct_rows_names, slctd_rows,
order_of_rows_indices, order_of_rows_names, actv_cell, slctd_cell, slctd_columns):
print('***************************************************************************')
print('Data across all pages pre or post filtering: {}'.format(all_rows_data))
print('---------------------------------------------')
print("Indices of selected rows if part of table after filtering:{}".format(slctd_row_indices))
print("Names of selected rows if part of table after filtering: {}".format(slct_rows_names))
print("Indices of selected rows regardless of filtering results: {}".format(slctd_rows))
print('---------------------------------------------')
print("Indices of all rows pre or post filtering: {}".format(order_of_rows_indices))
print("Names of all rows pre or post filtering: {}".format(order_of_rows_names))
print("---------------------------------------------")
print("Complete data of active cell: {}".format(actv_cell))
print("Complete data of all selected cells: {}".format(slctd_cell))
print("---------------------------------------------")
print("Indices of selected columns regardless of filtering results: {}".format(slctd_columns))
if slctd_row_indices is None:
slctd_row_indices = []
dff = df if all_rows_data is None else pd.DataFrame(all_rows_data)
dff['size'] = dff['age'] / 4
# data = []
# for index, row in dff_all.iterrows():
# for col in slctd_columns:
# dataRow = []
# dataRow.append(row['sample_name'])
# dataRow.append(col)
# dataRow.append(row[col])
# data.append(dataRow)
# dff = pd.DataFrame(data, columns=['sample_name', 'column_name', 'value'])
# used to highlight selected countries on bar chart
# colors = ['#7FDBFF' if i in slctd_row_indices else '#0074D9'
# for i in range(len(dff))]
if len(slctd_columns) < 3:
slctd_columns = ['valid_barcodes', 'sequencing_saturation', 'q30_bases_in_barcode']
# Use for animation rotation at the end
x_eye = -1.25
y_eye = 2
z_eye = 0.5
group_chosen = 'phenotype'
colorSequence = utilities.set_color_sequence(group_chosen)
fig = px.scatter_3d(
data_frame=dff,
x=slctd_columns[0],
y=slctd_columns[1],
z=slctd_columns[2],
color=group_chosen,
color_discrete_sequence=colorSequence,
template='ggplot2',
title='scRNA-seq Summary 3D Scatter Plot',
size='size', # size of bubble
size_max=30, # set the maximum mark size when using size
hover_name='study_id', # values appear in bold in the hover tooltip
height=600
).update_layout(scene_camera_eye=dict(x=x_eye, y=y_eye, z=z_eye),
updatemenus=[dict(type='buttons',
showactive=False,
y=1,
x=0.8,
xanchor='left',
yanchor='bottom',
pad=dict(t=45, r=10),
buttons=[dict(label='Play',
method='animate',
args=[None, dict(frame=dict(duration=250, redraw=True),
transition=dict(duration=0),
fromcurrent=True,
mode='immediate'
)]
)
]
)
]
)
frames = []
for t in np.arange(0, 6.26, 0.1):
xe, ye, ze = rotate_z(x_eye, y_eye, z_eye, -t)
frames.append(go.Frame(layout=dict(scene_camera_eye=dict(x=xe, y=ye, z=ze))))
fig.frames = frames
return [
dcc.Graph(id='3d_scatter_plot', figure=fig)
]
