def test_dbcl007_hidden_labels(dash_duo):
app = dash.Dash(__name__)
data = _mtcars_data
row_labels = list(_mtcars_data.index)
col_labels = list(_mtcars_data.columns)
app.layout = html.Div(
nested_component_layout(
dash_bio.Clustergram(data=data,
row_labels=row_labels,
column_labels=col_labels)))
nested_component_app_callback(
app,
dash_duo,
component=dash_bio.Clustergram,
component_data=data,
test_prop_name="hidden_labels",
test_prop_value="row",
prop_value_type="string",
)
# ensure that row labels are hidden
assert len(dash_duo.find_elements("g.yaxislayer-above g.y5tick")) == 0
# ensure that column labels are displayed
assert len(dash_duo.find_elements("g.xaxislayer-above g.x5tick")) == len(
col_labels)
# create a new instance of the app to test hiding of column labels
app = dash.Dash(__name__)
app.layout = html.Div(
nested_component_layout(
dash_bio.Clustergram(data=data,
row_labels=row_labels,
column_labels=col_labels)))
nested_component_app_callback(
app,
dash_duo,
component=dash_bio.Clustergram,
component_data=data,
test_prop_name="hidden_labels",
test_prop_value="col",
prop_value_type="string",
)
# ensure that column labels are hidden
assert len(dash_duo.find_elements("g.xaxislayer-above g.x5tick")) == 0
# ensure that row labels are displayed
assert len(dash_duo.find_elements("g.yaxislayer-above g.y5tick")) == len(
row_labels)
def update_clustergram(type="Microarray"):
"""
"""
if type is "Microarray":
df = pd.read_excel(muArrayFile, index_col=0)
df = df.fillna(0)
else:
df = pd.read_csv(gcpFile, index_col=0)
df = df.fillna(0)
ccl = df.index.to_list()
feat = list(df.columns.values)
return {
dcc.Graph(figure=dashbio.Clustergram(
data=df.values
#row_labels=ccl,
#column_labels=feat,
#height=800,
#width=700
#col_group_marker=[
# {'group': 1, 'annotation': 'largest column cluster', 'color': '#EF553B'}
#],
#row_group_marker=[
# {'group': 2, 'annotation': 'cluster 2', 'color': '#AB63FA'},
# {'group': 1, 'annotation': '', 'color': '#19D3F3'}
#]
))
}
def test_dbcl003_row_col_thresholds(dash_duo):
app = dash.Dash(__name__)
app.layout = html.Div(
nested_component_layout(dash_bio.Clustergram(data=_data)))
nested_component_app_callback(
app,
dash_duo,
component=dash_bio.Clustergram,
component_data=_data,
test_prop_name="color_threshold",
test_prop_value=json.dumps({
"row": 250,
"col": 700
}),
prop_value_type="dict",
take_snapshot=True,
)
# there should be 9 traces for the column dendrogram
# plus one trace for the background
assert len(
dash_duo.find_elements(
"g.subplot.x2y2 > g.plot g.trace.scatter")) == 10
# 30 traces for the row dendrogram, plus one for the background
assert len(
dash_duo.find_elements(
"g.subplot.x4y4 > g.plot g.trace.scatter")) == 31
def update_clustergram(gnps_task):
quant_df = _get_quant_df(gnps_task)
filename_headers = [
header for header in quant_df.columns if "Peak area" in header
]
values_df = quant_df[filename_headers]
values_df = values_df.clip(1)
feature_labels = list(quant_df["row ID"])
color_palette = ['rgb(128, 0, 96)', 'rgb(230, 115, 0)', 'rgb(255, 191, 0)']
fig = dashbio.Clustergram(
data=values_df.values,
column_labels=filename_headers,
row_labels=feature_labels,
color_map=[[0.0, color_palette[0]], [0.5, color_palette[1]],
[1.0, color_palette[2]]],
color_list={
'row': [color_palette[0], color_palette[1], color_palette[2]],
'col': [color_palette[1], color_palette[2], color_palette[0]],
'bg': 'rgb(255,255,255)'
},
height=800,
width=700,
optimal_leaf_order=False,
center_values=True,
log_transform=True,
line_width=2)
fig.update_layout(yaxis2={'domain': [0.74, 1]})
return dcc.Graph(figure=fig)
def test_dbcl005_row_annotations(dash_duo):
app = dash.Dash(__name__)
app.layout = html.Div(nested_component_layout(
dash_bio.Clustergram(
data=_data
)
))
nested_component_app_callback(
app,
dash_duo,
component=dash_bio.Clustergram,
component_data=_data,
test_prop_name='row_group_marker',
test_prop_value=json.dumps(
[{'group': 2, 'annotation': 'cluster two',
'color': 'rgb(248, 62, 199)'}]),
extra_props={'color_threshold': {'row': 250, 'col': 700}},
prop_value_type='list',
take_snapshot=True
)
# the annotation has shown up
assert len(dash_duo.find_elements('g.subplot.x6y6')) == 1
# the annotation is the correct color
dash_duo.wait_for_style_to_equal(
'g.subplot.x6y6 g.plot g.lines > path', 'stroke', 'rgb(248, 62, 199)')
def test_dbcl005_row_annotations(dash_duo):
app = dash.Dash(__name__)
app.layout = html.Div(
nested_component_layout(dash_bio.Clustergram(data=_data)))
nested_component_app_callback(
app,
dash_duo,
component=dash_bio.Clustergram,
component_data=_data,
test_prop_name="row_group_marker",
test_prop_value=json.dumps([{
"group": 2,
"annotation": "cluster two",
"color": "rgb(248, 62, 199)"
}]),
extra_props={"color_threshold": {
"row": 250,
"col": 700
}},
prop_value_type="list",
take_snapshot=True,
)
# the annotation has shown up
assert len(dash_duo.find_elements("g.subplot.x6y6")) == 1
# the annotation is the correct color
dash_duo.wait_for_style_to_equal("g.subplot.x6y6 g.plot g.lines > path",
"stroke", "rgb(248, 62, 199)")
def test_dbcl003_row_col_thresholds(dash_duo):
app = dash.Dash(__name__)
app.layout = html.Div(nested_component_layout(
dash_bio.Clustergram(
data=_data
)
))
nested_component_app_callback(
app,
dash_duo,
component=dash_bio.Clustergram,
component_data=_data,
test_prop_name='color_threshold',
test_prop_value=json.dumps({'row': 250, 'col': 700}),
prop_value_type='dict',
take_snapshot=True
)
# there should be two clusters, i.e., two traces, for the column dendrogram
# plus one trace for the background
assert len(dash_duo.find_elements(
'g.subplot.x2y2 > g.plot g.trace.scatter')) == 3
# three clusters for the row dendrogram, plus one for the background
assert len(dash_duo.find_elements(
'g.subplot.x4y4 > g.plot g.trace.scatter')) == 4
def test_dbcl002_cluster_by_row_or_col(dash_duo):
app = dash.Dash(__name__)
app.layout = html.Div(nested_component_layout(
dash_bio.Clustergram(
data=_data
)
))
nested_component_app_callback(
app,
dash_duo,
component=dash_bio.Clustergram,
component_data=_data,
test_prop_name='cluster',
test_prop_value='row',
prop_value_type='string'
)
assert len(dash_duo.find_elements('g.subplot.x2y2')) == 0
assert len(dash_duo.find_elements('g.subplot.x4y4')) == 1
# create a new instance of the app to test column clustering
app = dash.Dash(__name__)
app.layout = html.Div(nested_component_layout(
dash_bio.Clustergram(
data=_data
)
))
nested_component_app_callback(
app,
dash_duo,
component=dash_bio.Clustergram,
component_data=_data,
test_prop_name='cluster',
test_prop_value='col',
prop_value_type='string',
take_snapshot=True
)
assert len(dash_duo.find_elements('g.subplot.x4y4')) == 0
assert len(dash_duo.find_elements('g.subplot.x2y2')) == 1
def display_clustergram(_, group_markers, selRows, selCols, fig_opts,
dataset_name, contents, filename, rowLabelsSource):
if (len(selRows) < 2 or len(selCols) < 2 or fig_opts is None):
return html.Div(
'No data have been selected to display. Please upload a file \
or select a preloaded file from the dropdown, then select at \
least two columns and two rows.',
style={
'padding': '30px',
'font-size': '20pt'
})
if dataset_name is not None:
dataset = datasets[dataset_name]
data, _, _, _ = \
geneExpressionReader.parse_tsv(
filepath=dataset['file'],
rowLabelsSource=dataset['rowLabelsSource'],
headerRows=dataset['headerRows'],
headerCols=dataset['headerCols'],
rows=selRows,
columns=selCols
)
elif contents is not None and dataset_name is None:
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string).decode('UTF-8')
if rowLabelsSource is None:
rowLabelsSource = 'Gene Name'
data, _, _, _ = \
geneExpressionReader.parse_tsv(
contents=decoded,
rowLabelsSource=rowLabelsSource,
rows=selRows,
columns=selCols
)
if group_markers is not None:
fig_opts['rowGroupMarker'] = group_markers['rowGroupMarker']
fig_opts['colGroupMarker'] = group_markers['colGroupMarker']
try:
fig, _ = dash_bio.Clustergram(computed_traces=None,
data=data,
**fig_opts)
return dcc.Graph(id='clustergram', figure=fig)
except Exception as e:
return "There was an error: {}.".format(e)
def update_clustergram(rows):
if len(rows) < 2:
return "Please select at least two rows to display."
return dcc.Graph(figure=dashbio.Clustergram(data=df.loc[rows].values,
column_labels=columns,
row_labels=rows,
color_threshold={
'row': 250,
'col': 700
},
hidden_labels='row',
height=800,
width=700))
def test_dbcl001_colorscale(dash_duo):
app = dash.Dash(__name__)
app.layout = html.Div(
nested_component_layout(dash_bio.Clustergram(data=_data)))
nested_component_app_callback(
app,
dash_duo,
component=dash_bio.Clustergram,
component_data=_data,
test_prop_name='color_map',
test_prop_value=json.dumps([[0, 'blue'], [0.5, 'yellow'], [1,
'pink']]),
prop_value_type='list',
path_to_test_prop='["data"][2]["colorscale"]',
take_snapshot=True)
def test_dbcl001_colorscale(dash_duo):
app = dash.Dash(__name__)
app.layout = html.Div(
nested_component_layout(dash_bio.Clustergram(data=_data)))
nested_component_app_callback(
app,
dash_duo,
component=dash_bio.Clustergram,
component_data=_data,
test_prop_name="color_map",
test_prop_value=json.dumps([[0, "blue"], [0.5, "yellow"], [1,
"pink"]]),
prop_value_type="list",
path_to_test_prop='["data"][41]["colorscale"]',
take_snapshot=True,
)
def test_dbcl006_df_input_row_cluster(dash_duo):
app = dash.Dash(__name__)
# run the same test as dbcl002 (row clustering) where table of
# observations (data argument) is left as a DataFrame
assert isinstance(_mtcars_data, pd.DataFrame)
app.layout = html.Div(
nested_component_layout(dash_bio.Clustergram(data=_mtcars_data)))
nested_component_app_callback(app,
dash_duo,
component=dash_bio.Clustergram,
component_data=_data,
test_prop_name='cluster',
test_prop_value='row',
prop_value_type='string')
assert len(dash_duo.find_elements('g.subplot.x2y2')) == 0
assert len(dash_duo.find_elements('g.subplot.x4y4')) == 1
import dash_html_components as html
import dash_core_components as dcc
import dash_bio as dashbio
import base64
import pandas as pd
# copy docs code here
df = pd.read_csv('https://raw.githubusercontent.com/plotly/dash-bio/master/tests/dashbio_demos/dash-clustergram/data/mtcars.tsv', sep=' ', skiprows=4).set_index('model')
data = df.values
component = dcc.Graph(figure=dashbio.Clustergram(
data=data,
column_labels=list(df.columns.values),
color_threshold={'row': 150, 'col': 700},
row_labels=list(df.index),
hidden_labels=['row'],
height=800,
width=600
))
component_image = html.Img(
src='data:image/png;base64,{}'.format(
base64.b64encode(
open(
'./images/pic_clustergram.png', 'rb'
).read()
).decode()
),
style={'width': '600px'}
)
import dash
import dash_bio as dashbio
import dash_core_components as dcc
import dash_html_components as html
import plotly
iris = plotly.data.iris()
iris = iris.drop("species", 1)
app = dash.Dash(__name__)
app.layout = html.Div([
dcc.Graph(figure=dashbio.Clustergram(
data=iris.values, # ➊ グラフ作成に用いる値の設定
column_labels=list(iris.columns.values), # ➋ 列ラベルの設定
width=800, # ➋ 横幅の設定
height=800, # ➋ 高さの設定
# ➋ ヒートマップの配色
color_map=[[0.0, "white"], [0.5, "gray"], [1.0, "black"]],
# ➋ デントログラムの高さの設定（行側、列側、ヒートマップ比）
display_ratio=[0.3, 0.1],
))
])
if __name__ == "__main__":
app.run_server(debug=True)
def display_clustergram(_, group_markers, sel_rows, sel_cols, fig_opts,
dataset_name, contents, filename, computed_traces):
ctx = dash.callback_context
adding_grp_marker = ctx.triggered[0]['prop_id'].split(
'.')[0] == 'group-markers'
wrapper_content = ''
curves = None
comp_traces = computed_traces
if len(sel_rows) < 2 or len(sel_cols) < 2 and \
dataset_name is None and contents is None:
wrapper_content = html.Div(
'No data have been selected to display. Please upload a file \
or select a preloaded file from the dropdown, then select at \
least two columns and two rows.',
style={
'padding': '40px',
'font-size': '20pt'
})
return wrapper_content, curves, comp_traces
if fig_opts['cluster'] is None or len(fig_opts['cluster']) == 0:
wrapper_content = html.Div(
'No dimension has been selected along which to perform \
clustering. \
Please select at least one option from the dropdown.',
style={
'padding': '30px',
'font-size': '20pt'
})
return wrapper_content, curves, comp_traces
if dataset_name is not None:
dataset = datasets[dataset_name]
if dataset['file'].endswith('.tsv'):
data = gene_expression_reader.read_tsv_file(
filepath=dataset['file'],
rows=sel_rows,
columns=sel_cols,
ignore_columns=dataset['ignore_columns'],
index_column=dataset['row_labels_source'],
return_filtered_data=True)
elif dataset['file'].endswith('.soft'):
data = gene_expression_reader.read_soft_file(
filepath=dataset['file'],
rows=sel_rows,
columns=sel_cols,
return_filtered_data=True)
elif contents is not None and dataset_name is None:
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string).decode('UTF-8')
try:
data = gene_expression_reader.read_soft_file(
contents=decoded,
rows=sel_rows,
columns=sel_cols,
return_filtered_data=True)
except Exception:
data = None
if group_markers is not None:
fig_opts['row_group_marker'] = group_markers['row_group_marker']
fig_opts['col_group_marker'] = group_markers['col_group_marker']
try:
# don't recompute the dendrogram traces if we're just adding a group
# marker
if adding_grp_marker and computed_traces is not None:
fig, curves = dash_bio.Clustergram(
generate_curves_dict=True,
computed_traces=computed_traces,
data=data,
**fig_opts)
else:
fig, curves, comp_traces = dash_bio.Clustergram(
generate_curves_dict=True,
return_computed_traces=True,
data=data,
**fig_opts)
wrapper_content = dcc.Graph(id='clustergram', figure=fig)
except IndexError:
wrapper_content = "Loading data..."
except ValueError:
wrapper_content = "Loading data..."
except Exception as e:
wrapper_content = "There was an error: {}".format(e)
return wrapper_content, curves, comp_traces
#! -*- utf-8 -*-
import pandas as pd
import dash_core_components as dcc
import dash_bio as dashbio
import plotly.subplots
df = pd.read_csv(
'https://raw.githubusercontent.com/plotly/dash-bio/master/tests/dashbio_demos/sample_data/clustergram_mtcars.tsv',
sep='\t',
skiprows=4).set_index('model')
columns = list(df.columns.values)
rows = list(df.index)
clustergram = dashbio.Clustergram(data=df.loc[rows].values,
row_labels=rows,
column_labels=columns,
color_threshold={
'row': 250,
'col': 700
},
height=800,
width=700,
display_ratio=[0.1, 0.7])
print(dcc.Graph(figure=clustergram))
import pandas as pd
import dash_core_components as dcc
import dash_bio as dashbio
import warnings
df = pd.read_csv(
'https://raw.githubusercontent.com/plotly/dash-bio/master/tests/dashbio_demos/sample_data/clustergram_mtcars.tsv',
sep='\t',
skiprows=4).set_index('model')
columns = list(df.columns.values)
rows = list(df.index)
clustergram = dashbio.Clustergram(data=df.loc[rows].values,
row_labels=rows,
column_labels=columns,
color_threshold={
'row': 250,
'col': 700
},
height=800,
width=700,
color_list={
'row': ['#636EFA', '#00CC96', '#19D3F3'],
'col': ['#AB63FA', '#EF553B'],
'bg': '#506784'
},
line_width=2)
print(dcc.Graph(figure=clustergram))
# dcc.Graph(figure=clustergram)
]),
label="3D protein",
),
dbc.Tab(
dcc.Graph(figure=dashbio.Clustergram(
data=data,
hidden_labels=["row"],
height=900,
width=1400,
column_labels=list(df.columns.values),
row_labels=list(df.index),
color_threshold={
"row": 150,
"col": 700
},
color_map=[
[0.0, color_palette[0]],
[0.5, color_palette[1]],
[1.0, color_palette[2]],
],
color_list={
"row": [color_palette[0], color_palette[1], color_palette[2]],
"col": [color_palette[1], color_palette[2], color_palette[0]],
"bg": "rgb(255,255,255)",
},
annotation_font=dict(color="white", size=10),
tick_font=dict(size=15, color="rgb(200,200,200)"),
)),
label="Microarray Heatmap",
),
])
