def build_pies():
for series_key, series in data['data'].items():
for y2 in y:
negative_values = []
for x_val, y_val in zip(series['x'], series[y2]):
if y_val < 0:
negative_values.append('{} ({})'.format(x_val, y_val))
chart = wrapper(
dcc.Graph(
id='pie-{}-graph'.format(series_key),
figure={
'data': [
go.Pie(**dict_merge(
dict(labels=series['x'], values=series[y2]
), name_builder(y2, series_key)))
],
'layout':
build_layout(
build_title(x,
y2,
group=series_key,
agg=inputs.get('agg')))
}))
if len(negative_values):
error_title = (
'The following negative values could not be represented within the {}Pie chart'
).format('' if series_key ==
'all' else '{} '.format(series_key))
error_div = html.Div(
[
html.I(className='ico-error'),
html.Span(error_title),
html.Div(html.Pre(', '.join(negative_values)),
className='traceback')
],
className='dtale-alert alert alert-danger')
yield html.Div([
html.Div(error_div, className='col-md-12'),
html.Div(chart, className='col-md-12')
],
className='row')
else:
yield chart
def update_progress(n_intervals):
progress = rds.get_value(
f'{OPLSModel.redis_prefix}_image_save_progress')
progress_fraction = rds.get_value(
f'{OPLSModel.redis_prefix}_image_save_progress_fraction')
label_text = rds.get_value(
f'{OPLSModel.redis_prefix}_image_save_label')
job_id = rds.get_value(f'{OPLSModel.redis_prefix}_job_id').decode(
'utf-8')
job = Job.fetch(job_id, rds.get_redis())
job_status = job.get_status()
if isinstance(label_text, bytes):
label_text = label_text.decode('utf-8')
if isinstance(progress, bytes):
progress = int(float(progress))
if isinstance(progress_fraction, bytes):
progress_fraction = progress_fraction.decode('utf-8')
if job_status == 'finished':
message = dbc.Alert(f'Prepared plots file as {job.result}.',
color='success',
dismissable=True)
class_name = 'btn btn-success'
path = job.result
progress_label = dbc.FormText(label_text, color='success')
animated = False
elif job.get_status() == 'failed':
message = dbc.Alert(
[f'Error occurred.',
html.Pre(job.exc_info)],
color='danger',
dismissable=True)
class_name = 'btn btn-secondary disabled'
path = ''
progress_label = dbc.FormText(label_text, color='danger')
animated = False
else:
message = []
class_name = 'btn btn-secondary disabled'
path = ''
progress_label = dbc.FormText(label_text)
animated = True
return progress, animated, progress_fraction, progress_label, url_for(
'api.download_temporary_file', path=path), class_name, message
def layout():
return html.Div([
html.H3('Admin page'),
html.Div([
html.B("ABM Model Cache"),
html.Pre(f'{facade.rs.list(CompartmentalModel.ID)}',
id='cache_contents'),
html.Button('Clear', id='clear_button'),
]),
html.Div([
html.Button('Clear redis', id='clear_redis_button'),
html.Div(id='notification_div1')
]),
html.Div([
html.Button('Clear cache', id='clear_cache_button'),
html.Div(id='notification_div2'),
]),
],
style={'margin': 10})
def parse_contents(contents, filename, date):
return html.Div([
html.H5(filename),
html.H6(datetime.datetime.fromtimestamp(date)),
# HTML images accept base64 encoded strings in the same format
# that is supplied by the upload
html.Img(src=contents, style={
'width': 'auto',
'height': '400px'
}),
html.Hr(),
html.Div('Raw Content'),
html.Pre(contents[0:200] + '...',
style={
'whiteSpace': 'pre-wrap',
'wordBreak': 'break-all'
})
])
def display_click_data(clickData, DropValue, ValT1X1, ValT1Y1, ValT1X2,
ValT1Y2, ValT2X1, ValT2Y1, ValT2X2, ValT2Y2):
Val_Tangente = {
'T1X1': ValT1X1,
'T1Y1': ValT1Y1,
'T1X2': ValT1X2,
'T1Y2': ValT1Y2,
'T2X1': ValT2X1,
'T2Y1': ValT2Y1,
'T2X2': ValT2X2,
'T2Y2': ValT2Y2
}
#logging.debug(Val_Tangente)
#logging.debug(clickData)
Val_Tangente[DropValue] = 'Test'
return html.Div([
html.Pre(json.dumps(clickData, indent=2), style=styles['pre']),
html.Div([value])
])
def generate_html(n, backgroundcolor='yellow', textcolor='black'):
data_n = data.get(n)
if not data_n:
return [html.H3("No data")]
text = data_n.get('text', "Somehow no text key")
header = data_n.get('header', "Somehow no header key")
new_text = []
for line in text:
phrases = get_phrases(line)
new_line = []
for phrase in phrases:
color = phrase[0][1:-1]
new_line.append(
html.Span(phrase[1], style={'color': color}
) if color else phrase[1])
new_text.extend(new_line)
new_text.append(html.Br())
span_style = {'fontSize': '20px', 'color': textcolor}
div_style = {
'fontSize': '16px',
'backgroundColor': backgroundcolor,
'borderWidth': 'medium',
'borderColor': 'black',
'borderStyle': 'solid'
}
# complete kluge to present table (infobox=11) with monospaced font
if n != 11:
return [
html.Div([html.H3(header),
html.Span(new_text, style=span_style)],
style=div_style)
]
return [
html.Div(
[html.H3(header),
html.Pre(new_text, style={'fontSize': '20px'})],
style=div_style)
]
def test_surface_selector(dash_duo):
app = dash.Dash(__name__)
app.config.suppress_callback_exceptions = True
realizations = pd.read_csv("tests/data/realizations.csv")
s = SurfaceSelector(app, surface_context, realizations)
app.layout = html.Div(
children=[s.layout, html.Pre(id="pre", children="ok")])
@app.callback(Output("pre", "children"), [Input(s.storage_id, "children")])
def _test(data):
return json.dumps(json.loads(data))
dash_duo.start_server(app)
dash_duo.wait_for_contains_text("#pre",
json.dumps(return_value),
timeout=4)
def parse_contents(contents, filename, date):
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
try:
if 'csv' in filename:
# Assume that the user uploaded a CSV file
df = pd.read_csv(
io.StringIO(decoded.decode('utf-8')))
elif 'xls' in filename:
# Assume that the user uploaded an excel file
df = pd.read_excel(io.BytesIO(decoded))
else:
raise Exception('unknown extension')
except Exception:
return html.Div([
'There was an error processing this file.'
])
# print([{'name': i, 'id': i} for i in df.columns])
df2 = pd.DataFrame(df.dtypes).reset_index()
df2.columns = ['Column', 'Type']
return html.Div([
html.H5(filename),
#html.Div([
# ColumnChecklist,
# ColumnSubmitButton,
#]),
html.Div(dash_table.DataTable(
data=df2.to_dict('records'),
columns=[{'name': i, 'id': i} for i in df.columns]
)),
html.Hr(), # horizontal line
# For debugging, display the raw contents provided by the web browser
html.Div('Raw Content'),
html.Pre(contents[0:200] + '...', style={
'whiteSpace': 'pre-wrap',
'wordBreak': 'break-all'
})
])
def execute_code(n_clicks, code):
if n_clicks < 1 or code is None:
raise PreventUpdate
try:
resp = requests.post("http://127.0.0.1:5000/code/kostas",
json={"code": code})
code_output = resp.json()["code_output"]
console_output = str(resp.json()["console_output"])
return [[
html.Div([html.H4(k),
html.Pre("Output: " + str(v)),
html.Hr()]) for (k, v) in code_output.items()
],
str(console_output)]
except Exception as e:
return no_update, str(e)
def update_gs_input(spin_mode, kppra, gs_type, json_structure):
if not json_structure: return structure_undefined_error()
structure = abilab.mjson_loads(json_structure)
pseudos = os.path.join(abidata.pseudo_dir, "14si.pspnc")
# Build input for GS calculation
# ecut must be specified because this pseudopotential does not provide hints for ecut.
try:
gs_inp = abilab.gs_input(structure,
pseudos,
kppa=kppra,
ecut=8,
spin_mode=spin_mode,
smearing=None)
gs_inp.pop_vars(("charge", "chksymbreak"))
if gs_type == "relax":
gs_inp.set_vars(optcell=2, ionmov=2, ecutsm=0.5, dilatmx=1.05)
#multi = ebands_input(structure, pseudos,
# kppa=kppra, nscf_nband=None, ndivsm=15,
# ecut=8, pawecutdg=None, scf_nband=None, accuracy="normal", spin_mode=spin_mode,
# smearing="fermi_dirac:0.1 eV", charge=None, dos_kppa=None):
gs_inp.set_mnemonics(False)
except Exception as exc:
return html.Div([
dbc.Jumbotron([
html.H2("There was an error processing this file. %s" %
str(exc),
className="text-danger")
])
])
s = DangerouslySetInnerHTML(gs_inp._repr_html_())
return html.Div([
copy_to_clipboard_button(id="copy_gsinput"),
html.Hr(),
html.Pre(s, id="copy_gsinput"), # className="text-sm-left",
])
def parse_contents(contents, filename, date):
print matimg[:100]
print fcodes[:100]
print contents[:100]
return html.Div([
html.H5(filename),
html.H6(datetime.datetime.fromtimestamp(date)),
# HTML images accept base64 encoded strings in the same format
# that is supplied by the upload
html.Img(src='data:image/png;base64,{}'.format(matimg)),
html.Img(src='data:image/png;base64,{}'.format(fcodes)),
html.Img(src=contents),
html.Hr(),
html.Div('Raw Content'),
html.Pre(contents[0:200] + '...', style={
'whiteSpace': 'pre-wrap',
'wordBreak': 'break-all'
})
])
def mask_content(self):
return [
dcc.Markdown("""
To mask a region, click on the **Select region button** then select your region on the graph.
To mask one of the trace, click on it (selected trace are highlihted. Confirmation will be asked before saving the
masks.
""",
className='markdown'), # roi x limits
dbc.InputGroup([
dbc.InputGroupAddon("Masks", id='masks', addon_type="prepend"),
dbc.Button("Select region",
color="secondary",
outline=True,
size='sm',
id='select-mask')
],
size='sm'),
html.Pre(children="No masks selected", id='text-data'),
dcc.ConfirmDialog(id='confirm-mask', ),
]
def modify_text():
"""
Text describing current operations
with record of previous operations as tooltip
"""
return html.Div([
# Current operations
html.Pre(id='show_operations',
style={
'fontWeight': 'bold',
'fontSize': 14,
'textAlign': 'center',
'marginLeft': '3%'
}),
# Tooltip explaining previous operations
dbc.Tooltip(id='prev_operations',
target='show_operations',
placement='bottom',
style={'fontSize': 12})
])
def node_info(self, state):
# [html.P(atd_rec.graph_info()) for atd_rec in self.model.uid_state_map[n_uid].atd_records] +\
# TODO for presentation purposes use image_atd_paths
ui_elem = [html.P(f"Node: {state.unique_id}")] + \
[
html.Img(id=f"node_img_{state.unique_id}", src=get_state_img(image_path), style={'width': '600px'})
for image_path in state.image_paths
] +\
[html.P(f"Merged states:")] +\
[
html.Pre(f"{merged_state}") for merged_state in state.merged_states
] +\
[html.P(f"ATDs:")] +\
[html.P(atd_rec.graph_info()) for atd_rec in state.atd_records] +\
[
html.P(f"Features (pruned): {state.unique_features}"),
html.P(f"Features: {state.features}"),
html.P(f"Use cases: {[uc.to_string_short() for uc in state.use_cases]}"),
]
return ui_elem
def __init__(self, contents: str, filename: str, time_stamp: int,
df: pd.DataFrame):
self.component = html.Div([
html.H5(filename),
html.H6(datetime.datetime.fromtimestamp(time_stamp)),
dash_table.DataTable(data=df.to_dict('rows'),
columns=[{
'name': i,
'id': i
} for i in df.columns]),
html.Hr(), # horizontal line
# For debugging, display the raw contents provided by the web browser
html.Div('Raw Content'),
html.Pre(contents[0:200] + '...',
style={
'whiteSpace': 'pre-wrap',
'wordBreak': 'break-all'
})
])
def launch_manual_collector(self) -> None:
self.get_new_candlesticks()
fig = self.get_fig()
app = dash.Dash()
app.layout = html.Div([
dcc.Graph(id='graph', figure=fig),
dcc.Interval(id='graph-update', interval=1000),
html.Pre(id='click-data', style={'display': 'none'}),
html.Button('Get new data', id='retrieve-data-button', n_clicks=0),
html.Div(id='retrieve-data-output', style={'display': 'none'}),
])
@app.callback(Output('click-data', 'children'),
[Input('graph', 'clickData')])
def display_click_data(click_data):
if click_data is not None:
self.click_candlestick(click_data['points'][0])
@app.callback(Output(component_id='graph',
component_property='figure'), [
Input(component_id='graph-update',
component_property='n_intervals')
])
def update_graph(_):
return self.get_fig()
@app.callback(
Output(component_id='retrieve-data-output',
component_property='children'), [
Input(component_id='retrieve-data-button',
component_property='n_clicks')
])
def retrieve_data(n_clicks):
if n_clicks <= 0:
return
log.info('Processing collected data and getting new candlesticks')
self.process_collected_data()
self.get_new_candlesticks()
return
app.run_server(debug=True, use_reloader=False)
def update_graph(contents, filename):
fig = {
'layout':
go.Layout(plot_bgcolor=colors["graphBackground"],
paper_bgcolor=colors["graphBackground"])
}
if contents:
contents = contents[0]
filename = filename[0]
df = parse_data(contents, filename)
print(df)
# trace1 = go.Scatter(
# x=df.iloc[:,0],
# y=df.iloc[:,1],
# mode='lines+markers',
# )
# fig['data'] = [traces1]
df = df.set_index(df.columns[0])
fig['data'] = df.iplot(asFigure=True,
kind='scatter',
mode='lines+markers',
size=1)
table = html.Div([
html.H5(filename),
dash_table.DataTable(data=df.to_dict('rows'),
columns=[{
'name': i,
'id': i
} for i in df.columns]),
html.Hr(),
html.Div('Raw Content'),
html.Pre(contents[0:200] + '...',
style={
'whiteSpace': 'pre-wrap',
'wordBreak': 'break-all'
})
])
return fig
def parse_contents(contents, filename, date):
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
try:
if 'csv' in filename:
# Assume that the user uploaded a CSV file
df = pd.read_csv(
io.StringIO(decoded.decode('utf-8')))
elif 'xls' in filename:
# Assume that the user uploaded an excel file
df = pd.read_excel(io.BytesIO(decoded))
elif 'tsv' in filename:
# Assume that the user uploaded a TSV file
# this doesn't seem to work: when TSV is uploaded, table does not display
df = pd.read_csv(
io.StringIO(decoded.decode('utf-8')), sep = '\t')
except Exception as e:
print(e)
return html.Div([
'There was an error processing this file.'
])
return html.Div([
html.H5(filename),
html.H6(datetime.datetime.fromtimestamp(date)),
dash_table.DataTable(
data=df.round(1).to_dict('records'),
columns=[{'name': i, 'id': i} for i in df.columns]
),
html.Hr(), # horizontal line
# For debugging, display the raw contents provided by the web browser
html.Div('Raw Content'),
html.Pre(contents[0:200] + '...', style={
'whiteSpace': 'pre-wrap',
'wordBreak': 'break-all'
})
])
def parse_contents(contents, filename, date):
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
try:
if 'csv' in filename:
# Assume that the user uploaded a CSV file
df = pd.read_csv(io.StringIO(decoded.decode('utf-8')))
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.'])
return html.Div([
html.H5(filename),
html.H6(datetime.datetime.fromtimestamp(date)),
dash_table.DataTable(data=df.to_dict('rows'),
columns=[{
'name': i,
'id': i
} for i in df.columns],
style_data_conditional=[{
"if": {
"row_index": 4
},
"backgroundColor": "#3D9970",
'color': 'white'
}],
n_fixed_rows=1),
html.Hr(), # horizontal line
# For debugging, display the raw contents provided by the web browser
html.Div('Raw Content'),
html.Pre(contents[0:200] + '...',
style={
'whiteSpace': 'pre-wrap',
'wordBreak': 'break-all'
})
])
def parse_contents(contents, filename, date):
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
try:
if 'csv' in filename:
# Assume that the user uploaded a CSV file
newdf = pd.read_csv(io.StringIO(decoded.decode('utf-8')))
updatedf = df.append(newdf)
updatedf.to_excel('Data/crime against women 2001-2020.xlsx',
index=False)
elif 'xls' in filename:
# Assume that the user uploaded an excel file
newdf = pd.read_excel(io.BytesIO(decoded))
updatedf = df.append(newdf)
updatedf.to_excel('Data/crime against women 2001-2020.xlsx',
index=False)
except Exception as e:
print(e)
return html.Div(['There was an error processing this file.'])
return html.Div([
html.H5(filename),
html.H6(datetime.datetime.fromtimestamp(date)),
html.H4("Dataframe added!"),
# dash_table.DataTable(
# data=updatedf.to_dict('rows'),
# columns=[{'name': i, 'id': i} for i in updatedf.columns]
# ),
html.Hr(), # horizontal line
# For debugging, display the raw contents provided by the web browser
html.Div('Raw Content'),
html.Pre(contents[0:200] + '...',
style={
'whiteSpace': 'pre-wrap',
'wordBreak': 'break-all'
})
])
def build_error(error, tb):
"""
Returns error/traceback information in standard component with styling
:param error: execption message
:type error: str
:param tb: traceback
:type tb: str
:return: error component
:rtype: :dash:`dash_html_components.Div <dash-html-components/div>`
"""
if isinstance(error, ChartBuildingError):
if error.details:
tb = error.details
error = error.error
return html.Div([
html.I(className='ico-error'),
html.Span(str(error)),
html.Div(html.Pre(str(tb)), className='traceback')
],
className='dtale-alert alert alert-danger')
def summary_window(id, text, component_theme):
return html.Div(
id=id,
children=[
html.H5(children=["Summary"], style={
"margin-left": "5px",
}),
html.Pre(id='group_summary',
style={
"height": "150px",
"border": f"5px solid {component_theme['bg_color']}",
'background-color': "black",
'color': component_theme['text_color'],
'overflowX': 'auto',
'overflowY': 'scroll',
}),
],
style={
'background-color': component_theme['bg_color'],
'color': component_theme['text_color']
})
def parse_contents(contents, filename, date):
import qmin
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
try:
if 'csv' in filename:
# Assume that the user uploaded a CSV file is CPRM style (evandro)
df = qmin.test_cprm_datasets_web(
io.StringIO(decoded.decode('ISO-8859-1')))
elif 'xls' in filename:
# Assume that the user uploaded an excel file
#This excel is format of Microssonda!!!!
df = qmin.load_data_ms_web(io.BytesIO(decoded))
# csv_string = df.to_csv(index=False, encoding='utf-8')
#csv_string = "data:text/csv;charset=utf-8," + urllib.quote(csv_string)
#update_download_link(df)
except Exception as e:
print(e)
return html.Div(['There was an error processing this file.'])
return html.Div([
html.H5(filename),
html.H6(datetime.datetime.fromtimestamp(date)),
dash_table.DataTable(data=df.to_dict('records'),
columns=[{
'name': i,
'id': i
} for i in df.columns]),
html.Hr(), # horizontal line
# For debugging, display the raw contents provided by the web browser
html.Div('Raw Content'),
html.Pre(contents[0:200] + '...',
style={
'whiteSpace': 'pre-wrap',
'wordBreak': 'break-all'
})
])
def parse_contents(contents, filename, date):
# upload time
date_u = datetime.datetime.fromtimestamp(date)
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
image_path = BytesIO(decoded)
# color extraction
get_colors(infile = image_path)
image_filename = 'outfile.png' # replace with your own image
encoded_image = base64.b64encode(open(image_filename, 'rb').read())
# return pal
return html.Div([
html.Div([
html.Div([
html.H5(['File Name : ' + filename], style={'margin-top': '5%'}),
html.H5(['Upload date : ' + str(date_u.year) + "/" + str(date_u.month) + "/" + str(date_u.day)], style={'margin-bottom': '5%'} ),
html.Img(src=contents, style={'width': '200px'}, className="animated bounceInDown")
], style={'float': 'left', 'width':'50%'}),
html.Div([
# extracted image
html.H5(['Result']),
html.Img(src='data:image/png;base64,{}'.format(encoded_image.decode()),
style={'width': '200px'})
], style={'float': 'left', 'width':'50%'})
]),
html.Div([
html.Hr(),
html.Div('Raw Content'),
html.Pre(contents[0:30] + '...', style={
'whiteSpace': 'pre-wrap',
'wordBreak': 'break-all'
}),
html.Hr(),
])
])
def display_click_point(clickData, dataset, iterations, perplexity,
pca_dim, learning_rate):
if not clickData:
return None
try:
data_url = [
"embeddings",
str(dataset),
"iterations_" + str(iterations),
"perplexity_" + str(perplexity),
"pca_" + str(pca_dim),
"learning_rate_" + str(learning_rate),
"data.csv",
]
full_path = PATH.joinpath(*data_url)
embedding_df = pd.read_csv(full_path, encoding="ISO-8859-1")
except FileNotFoundError as error:
print(
error,
"\nThe dataset was not found. Please generate it using generate_embeddings.py",
)
return
# Convert the point clicked into float64 numpy array
click_point_np = np.array([
clickData["points"][0][i] for i in ["x", "y", "z"]
]).astype(np.float64)
# Create a boolean mask of the point clicked, truth value exists at only one row
bool_mask_click = (embedding_df.loc[:, "x":"z"].eq(click_point_np).all(
axis=1))
# Retrieve the index of the point clicked, given it is present in the set
if bool_mask_click.any():
clicked_idx = embedding_df[bool_mask_click].index[0]
# Retrieve the data corresponding to the index (Dimension reduction 이전의 원래 vector)
origin_vector = origin_datas[dataset].iloc[clicked_idx]
return html.Pre(children=pprint.pformat(origin_vector.to_dict()))
def parse_contents(contents, filename, date):
global res
res = main('data/resized2014/' + filename)
text_list.append(res[7:-5])
return html.Div([
html.H5("File Name:" + filename),
html.H6("Time Stamp:" + str(datetime.datetime.fromtimestamp(date))),
# HTML images accept base64 encoded strings in the same format
# that is supplied by the upload
html.Img(src=contents,
style={
'position': 'bottom-left',
"width": "25%",
"height": "30%",
"margin-left": "500px",
"margin-bottom": "0px",
"padding": "0px",
'text-align': "center",
'horizontal-align': 'middle',
'position': 'relative'
}),
html.Hr(),
html.Div('Raw Content:',
style={
'color': "Blue",
'textAlign': 'center'
}),
# html.P( res+ '...', style={
# 'whiteSpace': 'pre-wrap',
# 'wordBreak': 'break-all', 'margin-left':"380px",'color':"Black"
# }),
dcc.Textarea(placeholder='Enter a value...',
value=res[7:-5],
style={'width': '100%'}),
html.Hr(),
html.Pre(id='hover-data', style=styles1['pre']),
])
def parse_contents(contents, filename, date):
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
try:
if 'csv' in filename:
# Assume that the user uploaded a CSV file
df = prepared_csv(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.'
])
view = html.Div([
html.H5(filename),
html.H6(datetime.datetime.fromtimestamp(date)),
dash_table.DataTable(
data=df.to_dict('records'),
columns=[{'name': i, 'id': i} for i in df.columns]
),
html.Hr(), # horizontal line
# For debugging, display the raw contents provided by the web browser
html.Div('Raw Content'),
html.Pre(contents[0:200] + '...', style={
'whiteSpace': 'pre-wrap',
'wordBreak': 'break-all'
})
])
return_component = (view, df) # use a tuple to return multiple values
return return_component
def post_results(n_clicks, name, analysis_ids):
if not n_clicks:
raise PreventUpdate('Callback triggered with no action.')
pca_data = PCAModel()
try:
iter(analysis_ids)
except TypeError:
analysis_ids = [analysis_ids]
try:
return pca_data.post_results(name, analysis_ids)
except Exception as e:
return [
dbc.Alert([
html.P([html.Strong('Error: '), f'{e}']),
html.Strong('Traceback:'),
html.P(
html.Pre(traceback.format_exc(),
className='text-white'))
],
color='danger',
dismissable=True)
]
def parse_contents(contents, filename, dates):
if contents is not None:
content_type, content_string = contents.split(',')
print(filename)
decoded = base64.b64decode(content_string)
try:
if 'csv' in filename:
# Assume that the user uploaded a CSV file
df = pd.read_csv(
io.StringIO(decoded.decode('utf-8')))
elif 'xls' in filename:
# Assume that the user uploaded an excel file
df = pd.read_excel(io.BytesIO(decoded), sheet_name='Données brutes', index_col=None, header=None)
df.columns = ['Temps', 'Température', 'Dilatation']
except Exception as e:
print(e)
return html.Div([
'There was an error processing this file.'
])
return html.Div([
html.H5(filename),
#html.H6(datetime.datetime.fromtimestamp(date)),
# HTML images accept base64 encoded strings in the same format
# that is supplied by the upload
html.Hr(),
dcc.Graph(id='trc_graph', figure={'data': [{'x': df.Température, "y": df.Dilatation, 'type': 'Scatter', 'name': 'Test'}],
'layout': {'title': 'Test Titre'
}}),
html.Hr(),
html.Div('Raw Content'),
html.Pre(contents[0:200] + '...', style={
'whiteSpace': 'pre-wrap',
'wordBreak': 'break-all'
})
])
def update_table(contents, filename):
table = html.Div()
if contents:
contents = contents[0]
filename = filename[0]
df = parse_data(contents, filename)
table = html.Div([
html.H5(filename),
dash_table.DataTable(data=df.to_dict('rows'),
columns=[{
'name': i,
'id': i
} for i in df.columns]),
html.Hr(),
html.Div('Raw Content'),
html.Pre(contents[0:200] + '...',
style={
'whiteSpace': 'pre-wrap',
'wordBreak': 'break-all'
})
])
return table