def create_second_upload(match_title):
if match_title == 'uploaded':
return html.Div([
html.
H5('The match has already been uploaded. Please select another match.',
style={'textAlign': 'center'})
])
return html.Div([
html.H5('Next, please upload the position data for the game',
style={'textAlign': 'center'}),
html.H6(match_title.replace(":", " - "), style={'textAlign':
'center'}),
dash_resumable_upload.Upload(
id='upload_position',
maxFiles=1,
maxFileSize=1024 * 1024 * 1000, # 1000 MB
service="/upload_resumable",
textLabel="Drag and Drop here to upload!",
startButton=False,
pauseButton=False,
cancelButton=False,
defaultStyle={
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px'
},
activeStyle={
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'solid',
'borderRadius': '5px'
},
# Allow multiple files to be uploaded
#multiple=True
),
])
def test1(server):
app = dash.Dash(server=server,
routes_pathname_prefix='/dashapp/',
external_stylesheets=[
'https://codepen.io/dapraxis/pen/gOPGzPj.css',
'/static/dist/css/styles.css',
'https://fonts.googleapis.com/css?family=Lato'
])
dash_resumable_upload.decorate_server(app.server, "uploads")
app.index_string = html_layout
app.scripts.config.serve_locally = True # Uploaded to npm, this can work online now too.
app.layout = html.Div([
dash_resumable_upload.Upload(
id='upload-data',
maxFiles=1,
maxFileSize=1024*1024*1000, # 100 MB
service="/upload_resumable",
textLabel="Drag and Drop to upload",
# startButton=True
),
html.Div(id='output')
],style={
'width': '70%',
'margin': '5% 15%',
'text-align': 'center',
})
@app.callback(Output('output', 'children'),
[Input('upload-data', 'fileNames')])
def update_output(list_of_names):
if list_of_names is not None:
children = [parse_contents(filename) for filename in list_of_names]
return children
def parse_contents(filename):
try:
if 'csv' in filename:
# Assume that the user uploaded a CSV file
df = pd.read_csv("uploads/%s" % (filename))
elif 'xls' in filename:
# Assume that the user uploaded an excel file
df = pd.read_excel(io.BytesIO(decoded))
except Exception as e:
print(e)
return html.Div([
'There was an error processing this file.'
])
size = convert_unit(os.stat(str(os.getcwd())+'/uploads/'+str(filename)).st_size, SIZE_UNIT.MB)
return html.Div([
html.H5("Upload File: {}".format(filename)),
html.H5("File size: {:.3f}MB".format(size)),
dash_table.DataTable(
id='database-table',
columns=[{'name': i, 'id': i} for i in df.columns],
data=df[:1000].to_dict('records'),
sort_action="native",
sort_mode='native',
page_size=300,
fixed_rows = 100,
style_table={
'maxHeight': '80ex',
'overflowY': 'scroll',
'overflowX': 'scroll',
'width': '100%',
# 'minWidth': '100%',
# 'margin': '5% 15%'
},
),
html.Hr(), # horizontal line
html.A(html.Button('Next', id='btn'), href='/EDA')
])
style={'textAlign': 'center'}),
html.H2(
children=
'A Tool for Accelerating the Analysis of Cyclic Voltammetry Data',
style={
'textAlign': 'center',
'color': colors['text']
}),
html.Br(),
html.Div([
html.Link(rel='stylesheet',
href='https://codepen.io/rmarren1/pen/eMQKBW.css'),
dash_resumable_upload.Upload(
id='upload',
maxFiles=1,
maxFileSize=1024 * 1024 * 1000, # 100 MB
service="/upload_resumable",
textLabel="Upload Files",
startButton=False)
]),
html.Div(id='output_uploaded_file'),
html.Br(),
html.H2(children='Select File to Analyze',
style={
'textAlign': 'center',
'color': colors['text']
}),
html.Div(
[dcc.Dropdown(id='files_dropdown')],
style={
'width': '70%',
#ax.grid()
#plotly_fig = mpl_to_plotly(fig)
app.layout = html.Div([
dash_resumable_upload.Upload(
id='upload',
maxFiles=1,
maxFileSize=1024*1024*1000, # 100 MB
service="/upload_resumable",
#textLabel="Drag and Drop Here to upload!",
startButton=False,
pauseButton=False,
cancelButton=False,
defaultStyle={
'width': '100%',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin': '10px'
}
# Allow multiple files to be uploaded
#multiple=True
),
#dcc.Graph(id='myGraph', figure=plotly_fig),
html.Div(id='output'),
def main_layout(self, tab_plots=[], tab_tables=[]):
'''Serve the main Dash layout '''
# ------------ Left panel features ------------
# File upload with organism selection
__upload_feature = self.__panel_feature(
element_id='upload-feature',
details_summary='File Upload',
open_details=True,
html_element_list = \
[
dash_resumable_upload.Upload(
id='upload-data',
maxFiles=1,
maxFileSize=1024*1024*1000, # 100 MB
service='/upload_resumable',
textLabel='Drag and Drop or Click Here to Upload',
startButton=False,
cancelButton=False,
pauseButton=False,
chunkSize=500_000,
defaultStyle={'color':'black', 'font-size':'1em', 'display':'inline-block'},
activeStyle={'color':'black', 'font-size':'1em', 'display':'inline-block'},
completeStyle={'color':'black', 'font-size':'1em', 'display':'inline-block', 'overflow-wrap':'break-word'}),
# Allow selection of organism for populating gene information
html.Summary('Select Organism', style={'margin-top':'5px'}),
dcc.Dropdown(
id='organism-select',
multi=False,
options=[
{'label':'Mouse', 'value':'mouse'},
{'label':'Human','value':'human'},
],
value='mouse'
)
])
# SC cluster selection dropdown menu
__cluster_feature = self.__panel_feature(
element_id='cluster-dropdown-div',
details_summary='Cluster',
open_details=True,
html_element_list = \
[
# html.Summary('Cluster'),
html.Div([
dcc.Dropdown(
id='cluster-dropdown',
multi=False)])])
# Gene highlighter dropdown menu
__gene_dropdown_feature = self.__panel_feature(
element_id='gene-dropdown-div',
details_summary='Highlight Genes',
open_details=True,
html_element_list=\
[
html.Div([
dcc.Dropdown(
id='gene-dropdown',
multi=True)])])
# Temporary GO filter menu.
__go_dropdown_feature = self.__panel_feature(
element_id='go-dropdown-div',
details_summary='Filter on Gene Ontology (GO)',
open_details=False,
html_element_list=\
[
html.Div([
dcc.Dropdown(
id='go-dropdown',
multi=True)])])
# log₁₀(adjusted p-value) filter sliders and buttons
__pvalue_slider_feature = self.__panel_feature(
element_id='pvalue-slider-div',
details_summary='Filter on Transformed p-value',
html_element_list=\
[
self.slider_layout(
slider_id='pvalue-slider',
input_min_id='pvalue-textbox-min',
input_max_id='pvalue-textbox-max',
submit_button_id='pvalue-submit-button',
reset_button_id='pvalue-reset-button'),
])
# Log2(foldchange) filter sliders and buttons
__foldchange_slider_feature = self.__panel_feature(
element_id='foldchange-slider-div',
details_summary='Filter on log₂(FoldChange)',
html_element_list=\
[
self.slider_layout(
slider_id='foldchange-slider',
input_min_id='foldchange-textbox-min',
input_max_id='foldchange-textbox-max',
submit_button_id='foldchange-submit-button',
reset_button_id='foldchange-reset-button'),
],)
# Base mean filter sliders and buttons
__basemean_slider_feature = self.__panel_feature(
element_id='basemean-slider-div',
details_summary='Filter on log₁₀(BaseMean)',
html_element_list=\
[
self.slider_layout(
slider_id='basemean-slider',
input_min_id='basemean-textbox-min',
input_max_id='basemean-textbox-max',
submit_button_id = 'basemean-submit-button',
reset_button_id='basemean-reset-button'),
])
def collapsible_tree(tree_data=None, id='dash-collapsible-tree'):
test_tree_data = {
'label':
'search me',
'value':
'searchme',
'children': [
{
'label':
'search me too',
'value':
'searchmetoo',
'children': [
{
'label': 'No one can get me',
'value': 'anonymous',
},
],
},
],
}
if tree_data == None:
tree_data = test_tree_data
return html.Div([
dash_collapsible_tree.DashCollapsibleTree(id=id,
data=tree_data)
])
__collapsible_tree_feature = self.__panel_feature(
element_id='go-dropdown-div',
details_summary='Filter on GO Terms',
# !! Keep invisible for now
visibility=False,
html_element_list=\
[
collapsible_tree()
])
# ------------ /Left panel features ------------
tabs_styles = {
'height': '38px',
'display': 'inline-block',
'white-space': 'nowrap',
}
return html.Div(children=[
# Hidden Div to store session
html.Div(id='session-id', style={'display': 'none'}),
# Hidden Div to store file type
html.Div(id='file-type', style={'display': 'none'}),
# Hidden Div as store for organism type
html.Div(id='organism-div', style={'display': 'none'}),
# Store timestamps of plot clicks help determine last plot clicked
html.Div(id='volcano-plot-timediv', style={'display': 'none'}),
html.Div(id='ma-plot-timediv', style={'display': 'none'}),
html.Div(id='maxvolc-plot-timediv', style={'display': 'none'}),
# Hidden div for subset_data callback
html.Div(id='data-subset-sink', style={'display': 'none'}),
# Keep track of the last clicked gene for highlighting and metadata retrieval/display
html.Div(id='last-selected-gene', style={'display': 'none'}),
# App title header
html.A(
children=[
html.Img(
src='assets/lavaruins_logo.png',
style={
'width': '60px',
'display': 'inline',
'vertical-align': 'middle'
},
),
],
href=
'https://github.com/tubuliferous/lavaruins', # Link to GitHub page
target='_blank',
),
# style={'text-decoration':'none', 'color':'black'},),
html.H3('LavaRuins Differential Gene Expression Explorer',
style={
'display': 'inline',
}),
# Plots and side bars (top part of interface)
html.Div(
children=[
html.Div(
children=[
# File upload with organism selection
__upload_feature,
# SC cluster selection dropdown menu
__cluster_feature,
# Gene highlighter dropdown menu
__gene_dropdown_feature,
# GO filter dropdown menu
__go_dropdown_feature,
# log₁₀(adjusted p-value) filter sliders and buttons
__pvalue_slider_feature,
# Log2(foldchange) filter sliders and buttons
__foldchange_slider_feature,
# Log₁₀(basemean) filter sliders and buttons
__basemean_slider_feature,
# GO Tree filter menu
# __collapsible_tree_feature,
],
style={
'width': '20%',
'display': 'inline-block',
'vertical-align': 'top',
'padding-top': '0px'
},
),
# Tab-accessed plots in the center of the layout
html.Div(
children=[
dcc.Tabs(
id='plot-tabs',
children=tab_plots,
style=tabs_styles,
),
],
style={
'width': '60%',
'display': 'inline-block',
'vertical-align': 'top',
'padding-top': '0px'
},
),
html.Div(id='gene-info-markdown',
style={
'width': '20%',
'display': 'inline-block',
'vertical-align': 'top',
'padding-top': '35px'
})
],
style={'margin-bottom': '20px'}),
# DataTables (bottom part of interface)
dcc.Tabs(id='table-tabs', children=tab_tables, style=tabs_styles),
])
import base64
app = dash.Dash('')
dash_resumable_upload.decorate_server(app.server, "uploads")
app.scripts.config.serve_locally = True # Uploaded to npm, this can work online now too.
app.css.append_css(
{"external_url": "https://codepen.io/rmarren1/pen/eMQKBW.css"})
app.layout = html.Div([
dash_resumable_upload.Upload(
id='upload',
maxFiles=1,
maxFileSize=1024 * 1024 * 1000, # 100 MB
service="/upload_resumable",
textLabel="Drag and Drop Here to upload!",
startButton=False),
html.Div(id='output')
])
def get_img(x):
return x
#encode = base64.b64encode(
# open("uploads/%s" % (x), 'rb').read()).decode('ascii')
#return "data:image/jpg;base64,{}".format(encode)
@app.callback(Output('output', 'children'), [Input('upload', 'fileNames')])