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callbacks.py
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import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from app import app
from dash.dependencies import Input, Output, State
from results import layout_results
from organtox_backend import *
###################################################### Callbacks ######################################################
#### OrganTox Profile ####
@app.callback(Output('graph-profile', 'figure'),
[Input('data-source-profile', 'value'), Input('descriptor-type-profile', 'value'),
Input('slider-profile', 'value'), Input('similarity_df', 'data')])
def update_figure(source_profile, descriptor_profile, slider_value_profile, json_df):
try:
df = pd.read_json(json_df, orient='split')
df_0 = df[df.source.isin(source_profile)]
df_1 = df_0[df_0.descriptor == descriptor_profile]
df_2 = df_1[df_1.time == slider_value_profile]
df_3 = df_2.groupby(['compound_name', 'parameter'], as_index=False).agg(
{'outcome': 'max', 'similarity': 'max'})
df_4 = df_3.sort_values(by='similarity', ascending=False).head(1000)
df_pivot = df_4.groupby(['compound_name', 'parameter'],
sort=False,
as_index=True)['outcome'].median().unstack()
df_sim_name = df_4.groupby(['compound_name'], as_index=False).agg({'similarity': 'max'})
df_sim_name['compound_name_sim'] = df_sim_name['compound_name'].str.cat(
'(' + df_sim_name['similarity'].astype(str) + ')', sep=' ')
df_pivot = df_sim_name.merge(df_pivot, on='compound_name', how='right')
df_pivot.drop(columns=['compound_name', 'similarity'], inplace=True)
df_pivot.set_index('compound_name_sim', inplace=True)
sort_list = [k for k in param_list if k in df_pivot.columns.values]
df_pivot = df_pivot[sort_list]
df_to_plotly = {'x': df_pivot.columns.tolist(), 'y': df_pivot.index.tolist(), 'z': df_pivot.values}
hovertext = list()
z_text = np.where(df_to_plotly['z'] >= 0.5, 'Observed', df_to_plotly['z'])
z_text = np.where(z_text == '0.0', 'Not observed', z_text)
z_text = np.where(z_text == 'nan', 'No data', z_text)
for yi, yy in enumerate(df_to_plotly['y']):
hovertext.append(list())
for xi, xx in enumerate(df_to_plotly['x']):
hovertext[-1].append('Parameter: {}<br />Compound: {}<br />Effect: {}'.format(xx, yy, z_text[yi][xi]))
fig = go.Figure(data=go.Heatmap(df_to_plotly,
colorscale=[[0, '#f0e442'],
[0.25, '#f0e442'],
[0.5, '#f0e442'],
[0.5, '#093a75'],
[0.75, '#093a75'],
[1, '#093a75']],
hoverinfo='text',
text=hovertext))
fig.update_layout(height=700,
showlegend=False,
transition_duration=500,
xaxis_type='category',
xaxis=dict(
tickangle=30,
tickmode='array',
tickvals=[x for x in sorted(df_pivot.columns.tolist())]),
margin=dict(t=30, b=30),
yaxis=dict(
title='Nearest Neighbors',
tickmode='array',
tickvals=[y for y in sorted(df_pivot.index.tolist())]),
yaxis_nticks=len(df_pivot.index.tolist())
)
fig.update_traces(colorbar_title='Effect',
colorbar_tickmode='array',
colorbar_tickvals=[1, 0.75, 0.5, 0.25, 0],
colorbar_ticktext=['', 'Observed', '', 'Not observed', ''],
colorbar_len=0.15,
selector=dict(type='heatmap'))
return fig
except:
fig = go.Figure().add_annotation(text='No data to display', font=dict(color='#EAEAEA', size=25),
showarrow=False)
fig.update_layout(height=700,
xaxis={'showgrid': False, 'zeroline': False, 'showticklabels':False},
yaxis={'showgrid': False, 'zeroline': False, 'showticklabels':False},
margin=dict(t=30,b=30),
plot_bgcolor='white')
return fig
#### Tissue Profile ####
@app.callback(
Output('graph-weight', 'figure'),
[Input('data-source-tissue', 'value'), Input('descriptor-type-tissue', 'value'),
Input('tissue-type', 'value'), Input('slider-similarity-tissue', 'value'),
Input('similarity_df', 'data')])
def update_figure(source_tissue, descriptor_tissue, tissue, slider_value_tissue, json_df):
try:
df = pd.read_json(json_df, orient='split')
df_0 = df[df.parameter_type == 'Weight']
df_1 = df_0[df_0.source.isin(source_tissue)]
df_2 = df_1[df_1.descriptor == descriptor_tissue]
df_3 = df_2[df_2.parameter == tissue]
df_4 = df_3[df_3.similarity.between(slider_value_tissue[0], slider_value_tissue[1])]
df_5 = df_4.round(2)
fig = px.scatter(df_5,
x='time',
y='value',
color='similarity',
color_continuous_scale='RdYlBu',
symbol=f'compound_name',
range_color=[slider_value_tissue[0], slider_value_tissue[1]],
hover_data=['value', 'value_unit', 'dose'],
labels={'compound_name': 'Compound Name',
'time': 'Days',
'dose': 'Dose (mg/kg/day)',
'value': f'Experiment Value',
'value_unit': 'Value Unit',
'similarity': 'Tanimoto'}
)
fig.update_layout(showlegend=False,
transition_duration=500,
xaxis = dict(
tickmode = 'array',
tickvals = [x for x in sorted(df_4['time'].unique())]),
margin=dict(t=30,b=30)
)
fig.update_coloraxes(colorbar_tickmode='array',
colorbar_tickvals=np.arange(0,1.1,0.1),
colorbar_ticktext=['0.0',
'0.1',
'0.2',
'0.3',
'0.4',
'0.5',
'0.6',
'0.7',
'0.8',
'0.9',
'1.0']
)
return fig
except:
fig = go.Figure().add_annotation(x=2.5, y=1.5, text='No data to display', font=dict(color='#EAEAEA', size=25),
showarrow=False)
fig.update_layout(xaxis={'showgrid': False, 'zeroline': False, 'showticklabels':False},
yaxis={'showgrid': False, 'zeroline': False, 'showticklabels':False},
margin=dict(t=30,b=30),
plot_bgcolor='white')
return fig
#### Tissue MuDRA plot ####
@app.callback(
Output('graph-mudra-tissue', 'figure'),
[Input('data-source-tissue', 'value'), Input('tissue-type', 'value'),
Input('slider-mudra-tissue', 'value'), Input('similarity_df', 'data')])
def update_mudra(source, tissue, slider_value, json_df):
try:
df = pd.read_json(json_df, orient='split')
df0 = df[df.parameter_type == 'Weight']
df1 = df0[df0.source.isin(source)]
df2 = df1[df1.parameter == tissue]
df2 = df2.sort_values(by='similarity', ascending=False)
df_morgan = df2[df2.descriptor == 'morgan'].groupby('InChIKey').first().sort_values(by='similarity',
ascending=False).head(slider_value)
df_maccs = df2[df2.descriptor == 'maccs'].groupby('InChIKey').first().sort_values(by='similarity',
ascending=False).head(slider_value)
df_atom_pair = df2[df2.descriptor == 'atom_pair'].groupby('InChIKey').first().sort_values(by='similarity',
ascending=False).head(slider_value)
df_avalon = df2[df2.descriptor == 'avalon'].groupby('InChIKey').first().sort_values(by='similarity',
ascending=False).head(slider_value)
fig = go.Figure()
fig.add_trace(go.Scatterpolargl(r = df_morgan.similarity,
theta = np.arange(92, 179, 87/df_morgan.shape[0]),
name = 'Morgan',
marker=dict(size=10, color='mediumseagreen'),
customdata=df_morgan['compound_name']
)
)
fig.add_trace(go.Scatterpolargl(r= df_maccs.similarity,
theta = np.arange(2, 89, 87/df_maccs.shape[0]),
name = 'MACCS',
marker=dict(size=10, color='darkorange'),
customdata=df_maccs['compound_name']
)
)
fig.add_trace(go.Scatterpolargl(r = df_atom_pair.similarity,
theta = np.arange(182, 269, 87/df_atom_pair.shape[0]),
name = 'AtomPair',
marker=dict(size=10, color='mediumpurple'),
customdata=df_atom_pair['compound_name']
)
)
fig.add_trace(go.Scatterpolargl(r = df_avalon.similarity,
theta = np.arange(272, 359, 87/df_avalon.shape[0]),
name = 'Avalon',
marker=dict(size=10, color = 'magenta'),
customdata=df_avalon['compound_name']
)
)
fig.update_traces(mode='markers',
marker=dict(line_color='white', opacity=0.7),
hovertemplate='<br>'.join(['Compound Name: %{customdata}',
'Similarity: %{r}',
])
)
fig.update_layout(
transition_duration=500,
font_size = 16,
showlegend = True,
polar = dict(angularaxis = dict(showticklabels=True,
showgrid=True,
ticks='',
direction = 'counterclockwise',
tickmode='array',
tickvals=[0, 90, 180, 270],
ticktext=['','', '', ''],
linewidth=1,
showline=True,
linecolor='black'
),
radialaxis = dict(tickfont_size=12,
tickangle=30,
ticks='outside',
showline=True,
range=[0, 1]
)
), margin=dict(t=30,b=30),
)
return fig
except:
fig = go.Figure().add_annotation(x=2.5, y=1.5, text='No data to display', font=dict(color='#EAEAEA', size=25),
showarrow=False)
fig.update_layout(xaxis={'showgrid': False, 'zeroline': False, 'showticklabels':False},
yaxis={'showgrid': False, 'zeroline': False, 'showticklabels':False},
margin=dict(t=30,b=30),
plot_bgcolor='white')
return fig
#### Clinical Hemistry Profile ####
@app.callback(
Output('graph-clin-chem', 'figure'),
[Input('data-source-clin-chem', 'value'), Input('descriptor-type-clin-chem', 'value'),
Input('clin-chem-type', 'value'), Input('slider-similarity-clin-chem', 'value'),
Input('similarity_df', 'data')])
def update_figure(source_tissue, descriptor_tissue, tissue, slider_value_tissue, json_df):
try:
df = pd.read_json(json_df, orient='split')
df_0 = df[df.parameter_type == 'Clinical chemistry']
df_1 = df_0[df_0.source.isin(source_tissue)]
df_2 = df_1[df_1.descriptor == descriptor_tissue]
df_3 = df_2[df_2.parameter == tissue]
df_4 = df_3[df_3.similarity.between(slider_value_tissue[0], slider_value_tissue[1])]
fig = px.scatter(df_4,
x='time',
y='value',
color='similarity',
color_continuous_scale='RdYlBu',
symbol=f'compound_name',
range_color=[slider_value_tissue[0], slider_value_tissue[1]],
hover_data=['value', 'value_unit', 'dose'],
labels={'compound_name': 'Compound Name',
'time': 'Days',
'dose': 'Dose (mg/kg/day)',
'value': 'Experiment Value',
'value_unit': 'Value Unit',
'similarity': 'Tanimoto'}
)
fig.update_layout(showlegend=False,
transition_duration=500,
xaxis = dict(
tickmode = 'array',
tickvals = [x for x in sorted(df_4['time'].unique())]),
margin=dict(t=30,b=30)
)
fig.update_coloraxes(colorbar_tickmode='array',
colorbar_tickvals=np.arange(0,1.1,0.1),
colorbar_ticktext=['0.0',
'0.1',
'0.2',
'0.3',
'0.4',
'0.5',
'0.6',
'0.7',
'0.8',
'0.9',
'1.0']
)
return fig
except:
fig = go.Figure().add_annotation(x=2.5, y=1.5, text='No data to display', font=dict(color='#EAEAEA', size=25),
showarrow=False)
fig.update_layout(xaxis={'showgrid': False, 'zeroline': False, 'showticklabels':False},
yaxis={'showgrid': False, 'zeroline': False, 'showticklabels':False},
margin=dict(t=30,b=30),
plot_bgcolor='white')
return fig
#### Clinical Chemistry MuDRA plot ####
@app.callback(
Output('graph-mudra-clin-chem', 'figure'),
[Input('data-source-clin-chem', 'value'), Input('clin-chem-type', 'value'),
Input('slider-mudra-clin-chem', 'value'), Input('similarity_df', 'data')])
def update_mudra(source, clinchem, slider_value, json_df):
try:
df = pd.read_json(json_df, orient='split')
df0 = df[df.parameter_type == 'Clinical chemistry']
df1 = df0[df0.source.isin(source)]
df2 = df1[df1.parameter == clinchem]
df2 = df2.sort_values(by='similarity', ascending=False)
df_morgan = df2[df2.descriptor == 'morgan'].groupby('InChIKey').first().sort_values(by='similarity',
ascending=False).head(slider_value)
df_maccs = df2[df2.descriptor == 'maccs'].groupby('InChIKey').first().sort_values(by='similarity',
ascending=False).head(slider_value)
df_atom_pair = df2[df2.descriptor == 'atom_pair'].groupby('InChIKey').first().sort_values(by='similarity',
ascending=False).head(slider_value)
df_avalon = df2[df2.descriptor == 'avalon'].groupby('InChIKey').first().sort_values(by='similarity',
ascending=False).head(slider_value)
fig = go.Figure()
fig.add_trace(go.Scatterpolargl(r = df_morgan.similarity,
theta = np.arange(92, 179, 87/df_morgan.shape[0]),
name = 'Morgan',
marker=dict(size=10, color='mediumseagreen'),
customdata=df_morgan['compound_name']
)
)
fig.add_trace(go.Scatterpolargl(r= df_maccs.similarity,
theta = np.arange(2, 89, 87/df_maccs.shape[0]),
name = 'MACCS',
marker=dict(size=10, color='darkorange'),
customdata=df_maccs['compound_name']
)
)
fig.add_trace(go.Scatterpolargl(r = df_atom_pair.similarity,
theta = np.arange(182, 269, 87/df_atom_pair.shape[0]),
name = 'AtomPair',
marker=dict(size=10, color='mediumpurple'),
customdata=df_atom_pair['compound_name']
)
)
fig.add_trace(go.Scatterpolargl(r = df_avalon.similarity,
theta = np.arange(272, 359, 87/df_avalon.shape[0]),
name = 'Avalon',
marker=dict(size=10, color = 'magenta'),
customdata=df_avalon['compound_name']
)
)
fig.update_traces(mode='markers',
marker=dict(line_color='white', opacity=0.7),
hovertemplate='<br>'.join(['Compound Name: %{customdata}',
'Similarity: %{r}',
])
)
fig.update_layout(
transition_duration=500,
font_size = 16,
showlegend = True,
polar = dict(angularaxis = dict(showticklabels=True,
showgrid=True,
ticks='',
direction = 'counterclockwise',
tickmode='array',
tickvals=[0, 90, 180, 270],
ticktext=['','', '', ''],
linewidth=1,
showline=True,
linecolor='black'
),
radialaxis = dict(tickfont_size=12,
tickangle=30,
ticks='outside',
showline=True,
range=[0, 1]
)
), margin=dict(t=30,b=30),
)
return fig
except:
fig = go.Figure().add_annotation(x=2.5, y=1.5, text='No data to display', font=dict(color='#EAEAEA', size=25),
showarrow=False)
fig.update_layout(xaxis={'showgrid': False, 'zeroline': False, 'showticklabels':False},
yaxis={'showgrid': False, 'zeroline': False, 'showticklabels':False},
margin=dict(t=30,b=30),
plot_bgcolor='white')
return fig
#### Hematology Profile ####
@app.callback(
Output('graph-hemato', 'figure'),
[Input('data-source-hemato', 'value'), Input('descriptor-type-hemato', 'value'), Input('hemato-type', 'value'),
Input('slider-similarity-hemato', 'value'), Input('similarity_df', 'data')])
def update_figure(source_tissue, descriptor_tissue, tissue, slider_value_tissue, json_df):
try:
df = pd.read_json(json_df, orient='split')
df_0 = df[df.parameter_type == 'Hematology']
df_1 = df_0[df_0.source.isin(source_tissue)]
df_2 = df_1[df_1.descriptor == descriptor_tissue]
df_3 = df_2[df_2.parameter == tissue]
df_4 = df_3[df_3.similarity.between(slider_value_tissue[0], slider_value_tissue[1])]
fig = px.scatter(df_4,
x='time',
y='value',
color='similarity',
color_continuous_scale='RdYlBu',
symbol=f'compound_name',
range_color=[slider_value_tissue[0], slider_value_tissue[1]],
hover_data=['value', 'value_unit', 'dose'],
labels={'compound_name': 'Compound Name',
'time': 'Days',
'dose': 'Dose (mg/kg/day)',
'value': 'Experiment Value',
'value_unit': 'Value Unit',
'similarity': 'Tanimoto'}
)
fig.update_layout(showlegend=False,
transition_duration=500,
xaxis = dict(
tickmode = 'array',
tickvals = [x for x in sorted(df_4['time'].unique())]),
margin=dict(t=30,b=30)
)
fig.update_coloraxes(colorbar_tickmode='array',
colorbar_tickvals=np.arange(0,1.1,0.1),
colorbar_ticktext=['0.0',
'0.1',
'0.2',
'0.3',
'0.4',
'0.5',
'0.6',
'0.7',
'0.8',
'0.9',
'1.0']
)
return fig
except:
fig = go.Figure().add_annotation(x=2.5, y=1.5, text='No data to display', font=dict(color='#EAEAEA', size=25),
showarrow=False)
fig.update_layout(xaxis={'showgrid': False, 'zeroline': False, 'showticklabels':False},
yaxis={'showgrid': False, 'zeroline': False, 'showticklabels':False},
margin=dict(t=30,b=30),
plot_bgcolor='white')
return fig
#### Hematology MuDRA plot ####
@app.callback(
Output('graph-mudra-hemato', 'figure'),
[Input('data-source-hemato', 'value'), Input('hemato-type', 'value'),
Input('slider-mudra-hemato', 'value'), Input('similarity_df', 'data')])
def update_mudra(source, hemato, slider_value, json_df):
try:
df = pd.read_json(json_df, orient='split')
df0 = df[df.parameter_type == 'Hematology']
df1 = df0[df0.source.isin(source)]
df2 = df1[df1.parameter == hemato]
df2 = df2.sort_values(by='similarity', ascending=False)
df_morgan = df2[df2.descriptor == 'morgan'].groupby('InChIKey').first().sort_values(by='similarity',
ascending=False).head(slider_value)
df_maccs = df2[df2.descriptor == 'maccs'].groupby('InChIKey').first().sort_values(by='similarity',
ascending=False).head(slider_value)
df_atom_pair = df2[df2.descriptor == 'atom_pair'].groupby('InChIKey').first().sort_values(by='similarity',
ascending=False).head(slider_value)
df_avalon = df2[df2.descriptor == 'avalon'].groupby('InChIKey').first().sort_values(by='similarity',
ascending=False).head(slider_value)
fig = go.Figure()
fig.add_trace(go.Scatterpolargl(r = df_morgan.similarity,
theta = np.arange(92, 179, 87/df_morgan.shape[0]),
name = 'Morgan',
marker=dict(size=10, color='mediumseagreen'),
customdata=df_morgan['compound_name']
)
)
fig.add_trace(go.Scatterpolargl(r= df_maccs.similarity,
theta = np.arange(2, 89, 87/df_maccs.shape[0]),
name = 'MACCS',
marker=dict(size=10, color='darkorange'),
customdata=df_maccs['compound_name']
)
)
fig.add_trace(go.Scatterpolargl(r = df_atom_pair.similarity,
theta = np.arange(182, 269, 87/df_atom_pair.shape[0]),
name = 'AtomPair',
marker=dict(size=10, color='mediumpurple'),
customdata=df_atom_pair['compound_name']
)
)
fig.add_trace(go.Scatterpolargl(r = df_avalon.similarity,
theta = np.arange(272, 359, 87/df_avalon.shape[0]),
name = 'Avalon',
marker=dict(size=10, color = 'magenta'),
customdata=df_avalon['compound_name']
)
)
fig.update_traces(mode='markers',
marker=dict(line_color='white', opacity=0.7),
hovertemplate='<br>'.join(['Compound Name: %{customdata}',
'Similarity: %{r}',
])
)
fig.update_layout(
transition_duration=500,
font_size = 16,
showlegend = True,
polar = dict(angularaxis = dict(showticklabels=True,
showgrid=True,
ticks='',
direction = 'counterclockwise',
tickmode='array',
tickvals=[0, 90, 180, 270],
ticktext=['','', '', ''],
linewidth=1,
showline=True,
linecolor='black'
),
radialaxis = dict(tickfont_size=12,
tickangle=30,
ticks='outside',
showline=True,
range=[0, 1]
)
), margin=dict(t=30,b=30),
)
return fig
except:
fig = go.Figure().add_annotation(x=2.5, y=1.5, text='No data to display', font=dict(color='#EAEAEA', size=25),
showarrow=False)
fig.update_layout(xaxis={'showgrid': False, 'zeroline': False, 'showticklabels':False},
yaxis={'showgrid': False, 'zeroline': False, 'showticklabels':False},
margin=dict(t=30,b=30),
plot_bgcolor='white')
return fig