def generatePDF():
gu, gd, gs = get_gSM()
si_modifier = get_SI_modifier()
global selected_datasets
#Will re-use the previously selected values for the dataset_type
#Optional: provide datasel file names in the POST parameters
if request.method == 'POST':
print('Use this are to parse to posted datasets')
datasets = selected_datasets
dfs = map(get_data, datasets)
#Filter, TODO: Apply filter to selected_datasets prior to conversion (attempt)
#get_data will return a 'None' object if the selected dataset could not be converted, (bad experiement string..)
dfs = [x for x in dfs if determine(x)]
metadata = map(get_metadata, datasets)
p1 = getSimplifiedPlot()
p2 = getDDPlot()
legendPlot = getLegendPlot()
legendItems = []
x = 1
y = 2 * x
for df, color in zip(dfs, colors):
label = df['label'].any()
df['color'] = color
p1.line(df['m_med'], df['m_DM'], line_width=2, color=color)
p2.line(df['m_DM'], df['sigma'], line_width=2, color=color)
line = legendPlot.line(x, y, line_width=2, color=color)
legendItems.append((label, [line]))
#Initialize all_data in the case that all datasets selected where invalid
all_data = pd.DataFrame()
if (len(dfs) > 0):
all_data = pd.concat(dfs)
legend = Legend(items=legendItems, location=(0, 0))
legendPlot.add_layout(legend, 'above')
script1, div1 = components(p1, CDN)
script2, div2 = components(p2, CDN)
script3, div3 = components(legendPlot, CDN)
html = render_template('pdf.html',
plot_script1=script1,
plot_div1=div1,
plot_script2=script2,
plot_div2=div2,
plot_script3=script3,
plot_div3=div3,
bokeh_version=bokeh.__version__,
metadata=metadata,
selected_datasets=selected_datasets,
si_modifier=si_modifier,
gSM_gSM=gu)
return html
def dmplotter():
known_datasets = get_datasets()
gu, gd, gs = get_gSM()
si_modifier = get_SI_modifier()
#Generate Array of options
options = list(getSavedPlots().keys())
dataset_selection = DatasetForm(gSM_input=gu)
dataset_selection.datasets.choices = zip(get_datasets(), get_datasets())
dataset_selection.savedPlots.choices = zip(options, options)
dataset_upload = UploadForm()
global selected_datasets
print(selected_datasets)
if request.method == 'POST':
# check if the post request has the file part
# print(request.values, request.data, request.form)
print(dataset_selection.datasets.data)
if dataset_selection.validate():
selected_datasets = dataset_selection.datasets.data
gSM = dataset_selection.gSM_input.data
set_gSM(gSM, gSM, gSM)
gu, gd, gs = get_gSM()
si_modifier = dataset_selection.radio_inputSI.data
set_SI_modifier(si_modifier)
datasets = selected_datasets
dfs = map(get_data, datasets)
#Filter, TODO: Apply filter to selected_datasets prior to conversion (attempt)
#get_data will return a 'None' object if the selected dataset could not be converted, (bad experiement string..)
dfs = [x for x in dfs if determine(x)]
metadata = map(get_metadata, datasets)
allmetadata = map(get_metadata, known_datasets)
p1 = getSimplifiedPlot()
p2 = getDDPlot()
legendPlot = getLegendPlot()
legendItems = []
x = 1
y = 2 * x
for df, color in zip(dfs, colors):
label = df['label'].any()
df['color'] = color
p1.line(df['m_med'], df['m_DM'], line_width=2, color=color)
p2.line(df['m_DM'], df['sigma'], line_width=2, color=color)
line = legendPlot.line(x, y, line_width=2, color=color)
legendItems.append((label, [line]))
#Initialize all_data in the case that all datasets selected where invalid
all_data = pd.DataFrame()
if (len(dfs) > 0):
all_data = pd.concat(dfs)
legend = Legend(items=legendItems, location=(0, 0))
legendPlot.add_layout(legend, 'above')
script1, div1 = components(p1, CDN)
script2, div2 = components(p2, CDN)
script3, div3 = components(legendPlot, CDN)
return render_template('dmplotter.html',
_debug=app.debug,
_signedIn=current_user.is_authenticated,
plot_script1=script1,
plot_div1=div1,
plot_script2=script2,
plot_div2=div2,
plot_script3=script3,
plot_div3=div3,
bokeh_version=bokeh.__version__,
data_table=all_data.to_html(),
datasets=known_datasets,
metadata=metadata,
dataset_selection=dataset_selection,
selected_datasets=selected_datasets,
allmetadata=allmetadata,
savedPlots=getSavedPlots(),
dataset_upload=dataset_upload,
si_modifier=si_modifier,
gSM_gSM=gu,
gSM_gU=gu,
gSM_gD=gd,
gSM_gS=gs)