def getSavedPlots():
globalPlots = json.load(open("savedPlots.json"))
userPlots = {}
if (os.path.isfile('data/' + getUserPath() + '/userPlots.json')):
userPlots = json.load(open('data/' + getUserPath() +
'/userPlots.json'))
allPlots = {
key: value
for (key, value) in ({
"": ""
}.items() + globalPlots.items() + userPlots.items())
}
return allPlots
def savePlot():
savedPlotName = request.form['name']
selected_datasets = request.form['data'].split(",")
userPlots = {}
#Create the User directory if not exists- TODO: do at time of account setup
directory = 'data/' + getUserPath()
if not os.path.exists(directory):
os.makedirs(directory)
if (os.path.isfile(directory + '/userPlots.json')):
userPlots = json.load(open('data/' + getUserPath() +
'/userPlots.json'))
userPlots[savedPlotName] = selected_datasets
with open('data/' + getUserPath() + '/userPlots.json', 'w') as f:
json.dump(userPlots, f)
return redirect(url_for('main.dmplotter'))
def dataset_names():
datasets = glob('data/*.xml')
datasets.extend(glob('data/' + getUserPath() + '/*.xml'))
for dataset in datasets:
dataset = ntpath.basename(dataset)
dataset = dataset.replace(DATA_FILE_EXT, '')
yield dataset
def upload():
form = UploadForm(CombinedMultiDict((request.files, request.form)))
if form.validate_on_submit():
f = form.data_file.data
filename = secure_filename(f.filename)
directory = os.path.join(app.config['UPLOAD_FOLDER'], getUserPath())
#Validate Data here
if validateFile(f.read()):
if not os.path.exists(directory):
os.makedirs(directory)
f.seek(0)
f.save(os.path.join(directory, filename))
flash(u'File uploaded.', 'info')
else:
flash(u'Could not parse input file- check required fields.',
'error')
return redirect(url_for('main.dmplotter'))
def get_metadata(dataset):
input_file = os.path.join(DATA_LOCATION, dataset + DATA_FILE_EXT)
if (os.path.isfile(input_file) == False):
input_file = os.path.join(DATA_LOCATION + getUserPath(),
dataset + DATA_FILE_EXT)
#XML Parsing Test
result = untangle.parse(input_file)
dataComment = result.limit.data_comment.cdata
dataLabel = result.limit.data_label.cdata
dataReference = result.limit.data_reference.cdata
dateOfAnnouncement = result.limit.date_of_announcement.cdata
experiment = result.limit.experiment.cdata
dataformat = result.limit.dataformat.cdata
measurementType = result.limit.measurement_type.cdata
resultType = result.limit.result_type.cdata
spinDependency = result.limit.spin_dependency.cdata
xRescale = result.limit.x_rescale.cdata
xUnits = result.limit.x_units.cdata
yRescale = result.limit.y_rescale.cdata
yUnits = result.limit.y_units.cdata
metadata = {
'fileName': dataset,
'dataComment': dataComment,
'dataLabel': dataLabel,
'dataReference': dataReference,
'dateOfAnnouncement': dateOfAnnouncement,
'experiment': experiment,
'dataformat': dataformat,
'measurementType': measurementType,
'resultType': resultType,
'spinDependency': spinDependency,
'xRescale': xRescale,
'xUnits': xUnits,
'yRescale': yRescale,
'yUnits': yUnits
}
#print (metadata)
return metadata
def get_data(dataset, modifier=''):
input_file = os.path.join(DATA_LOCATION, dataset + DATA_FILE_EXT)
if (os.path.isfile(input_file) == False):
input_file = os.path.join(DATA_LOCATION + getUserPath(),
dataset + DATA_FILE_EXT)
if (os.path.isfile(input_file) == False):
return None
#XML Parsing
result = untangle.parse(input_file)
dataValues = result.limit.data_values.cdata
experiment = result.limit.experiment.cdata
dataformat = result.limit.dataformat.cdata
spinDependency = result.limit.spin_dependency.cdata
yRescale = result.limit.y_rescale.cdata
#print (dataValues)
#remove leading {[, trailing ]}
rawData = dataValues.replace("{[", "").replace("]}", "").replace("\n", "")
data = StringIO(rawData)
#dataset_type, names = parseExperimentType(experiment)
dataset_type, names = parseDataformat(dataformat)
if (dataset_type == ''):
return None
#parse
df = pd.read_csv(data,
delim_whitespace=True,
lineterminator=';',
names=names)
#adjust the yRescale, ie (multiply every value in column index 1 with the yRescale)
df.iloc[:, 1] = df.iloc[:, 1].apply(lambda x: x * float(yRescale))
#add a coloum of labels
label = os.path.basename(input_file).split('.')[0]
df.insert(0, 'label', label)
#convert
if dataset_type == 'DD':
df['type'] = 'DD'
if spinDependency == 'SD': #BP
dd2lhc_SD(df)
elif spinDependency == 'SI':
dd2lhc_SI(df, modifier if modifier else si_modifier)
else:
return None
elif dataset_type == 'LHC':
df['type'] = 'LHC'
if spinDependency == 'SD':
lhc2dd_SD(df, modifier if modifier else sd_modifier)
elif spinDependency == 'SI':
lhc2dd_SI(df, modifier if modifier else si_modifier)
else:
return None
#extrapolate LHC Data
#note, not sure if appending in this fashion is the best way to extrapolate and expand the dataframe
extrap_mdm = range(1, int(min(df['m_DM'])))
extrap_sigma = np.repeat(min(df['sigma']), len(extrap_mdm))
extrap_mMed = np.repeat(max(df['m_med']), len(extrap_mdm))
extrap_df = pd.DataFrame({
'label': label,
'm_DM': extrap_mdm,
'm_med': extrap_mMed,
'sigma': extrap_sigma,
'type': 'LHC'
})
df = df.append(extrap_df)
else:
return None
#Data has be read from file, converted, extrapolated, and returned in a DataFrame. None is returned if error converting.
return df
