def download(_):
# For csv and html, the index must be removed to preserve the structure.
if fmt in ["csv", "html", "excel"]:
return dcc.send_data_frame(writer, filename, index=False)
# For csv and html, the index must be removed to preserve the structure.
if fmt in ["stata"]:
a = dcc.send_data_frame(writer, filename, write_index=False)
return a
# For other formats, no modifications are needed.
return dcc.send_data_frame(writer, filename)
def _export_table_data(
_data_requested: list,
table_data: list,
table_columns: list,
button_ids: list,
table_ids: list,
) -> Callable:
ctx = dash.callback_context.triggered[0]
export_clicks = {
id_value["table_id"]: n_clicks
for id_value, n_clicks in zip(button_ids, _data_requested)
}
table_to_extract = [
x for x in export_clicks.keys() if x in ctx["prop_id"]
]
if not table_to_extract or export_clicks[table_to_extract[0]] is None:
raise PreventUpdate
index = [x["table_id"] for x in table_ids].index(table_to_extract[0])
table_data = table_data[index]
table_columns = [x["name"] for x in table_columns[index]]
return dcc.send_data_frame(
pd.DataFrame(data=table_data, columns=table_columns).to_excel,
"VolumetricAnalysis.xlsx",
index=False,
)
def FUNCTION_DOWNLOAD_TABLE_COMPONENT_TAB1_BODY_CONTENT2(n, data):
if n != 0 and data is not None:
result = [
dcc.send_data_frame(pd.DataFrame.from_dict(data).to_excel, "DataTable.xlsx", sheet_name="Sheet1", index=False),
0
]
return result
else:
raise PreventUpdate
def func(n_clicks):
global df2
df2 = pd.DataFrame()
header_list = ['expected', 'Predicted', 'F1Score']
df2 = df2.reindex(columns=header_list)
n = df.index[df['F1Score'] > float(0.8)]
df2 = (df.iloc[n])
df2['Predicted'] = df2['expected']
return dcc.send_data_frame(df2.to_csv, "mydf_csv.csv")
def download_filtered_xlsx(n_clicks, jsonified_filtered_data):
## make sure that the button was clicked (we ignore the trigger event from altered data)
changed_id = [p['prop_id'] for p in dash.callback_context.triggered][0]
if 'btn_filtered_xlsx' in changed_id:
df_filter = pd.read_json(jsonified_filtered_data, orient='split')
df_filter = df_filter.drop(columns = ['tree_dbh_vis'])
return dcc.send_data_frame(df_filter.to_excel, "StreetTreesOfNYC_filtered.xlsx", sheet_name="Sheet_1")
return
def download_graph_select_xlsx(n_clicks, tree_ids):
## make sure that the button was clicked (we ignore the trigger event from altered data)
changed_id = [p['prop_id'] for p in dash.callback_context.triggered][0]
if 'btn_graph_select_xlsx' in changed_id and tree_ids:
df_filter = df[df['tree_id'].isin(tree_ids)]
df_filter = df_filter.drop(columns = ['tree_dbh_vis'])
return dcc.send_data_frame(df_filter.to_excel, "StreetTreesOfNYC_graph_select.xlsx", sheet_name="Sheet_1")
return
def download_csv(n_clicks, subspace_data, qoi):
# Parse data
results = jsonpickle.decode(subspace_data)
subspace = results['subspace']
X_train = subspace.sample_points
y_train = subspace.sample_outputs
subdim = subspace.subspace_dimension
W = subspace.get_subspace()[:, :subdim]
u_train = X_train @ W
# Build dataframe
data = np.hstack([u_train, y_train.reshape(-1, 1)])
cols = ['active dim. %d' % j for j in range(subdim)]
cols.append(qoi)
df = pd.DataFrame(data=data, columns=cols)
return dcc.send_data_frame(df.to_csv, "reduced_results.csv")
def func(n_clicks):
return dcc.send_data_frame(df.to_excel,
"mydf.xlxs",
sheet_name="Sheet_name_1")
def func(n_clicks):
return dcc.send_data_frame(df.to_csv, "mydf.csv")
def download(n_clicks):
return dcc.send_data_frame(df.to_csv, 'mydf.csv')
def download_all_xlsx(n_clicks):
df_download = df.drop(columns = ['tree_dbh_vis'])
return dcc.send_data_frame(df_download.to_excel, "StreetTreesOfNYC.xlsx", sheet_name="Sheet_1")
def download_all_csv(n_clicks):
df_download = df.drop(columns = ['tree_dbh_vis'])
return dcc.send_data_frame(df_download.to_csv, "StreetTreesOfNYC.csv")
