def processAlgorithm(self, parameters, context, feedback):
source = self.parameterAsSource(parameters, self.INPUT, context)
namefieldname = self.parameterAsString(parameters, self.NAME_FIELD,
context)
valuefieldname = self.parameterAsString(parameters, self.VALUE_FIELD,
context)
output = self.parameterAsFileOutput(parameters, self.OUTPUT, context)
values = vector.values(source, namefieldname, valuefieldname)
d = {}
for i in range(len(values[namefieldname])):
v = values[namefieldname][i]
if v not in d:
d[v] = [values[valuefieldname][i]]
else:
d[v].append(values[valuefieldname][i])
data = []
for k, v in d.items():
data.append(go.Box(y=list(v), boxmean='sd', name=k))
plt.offline.plot(data, filename=output, auto_open=False)
return {self.OUTPUT: output}
def processAlgorithm(self, parameters, context, feedback):
source = self.parameterAsSource(parameters, self.INPUT, context)
namefieldname = self.parameterAsString(parameters, self.NAME_FIELD, context)
valuefieldname = self.parameterAsString(parameters, self.VALUE_FIELD, context)
output = self.parameterAsFileOutput(parameters, self.OUTPUT, context)
values = vector.values(source, valuefieldname)
x_index = source.fields().lookupField(namefieldname)
x_var = [i[namefieldname] for i in source.getFeatures(QgsFeatureRequest().setFlags(QgsFeatureRequest.NoGeometry).setSubsetOfAttributes([x_index]))]
msdIndex = self.parameterAsEnum(parameters, self.MSD, context)
msd = True
if msdIndex == 1:
msd = 'sd'
elif msdIndex == 2:
msd = False
data = [go.Box(
x=x_var,
y=values[valuefieldname],
boxmean=msd)]
plt.offline.plot(data, filename=output, auto_open=False)
return {self.OUTPUT: output}
def processAlgorithm(self, parameters, context, feedback):
source = self.parameterAsSource(parameters, self.INPUT, context)
fieldname = self.parameterAsString(parameters, self.FIELD, context)
bins = self.parameterAsInt(parameters, self.BINS, context)
output = self.parameterAsFileOutput(parameters, self.OUTPUT, context)
values = vector.values(source, fieldname)
data = [go.Histogram(x=values[fieldname], nbinsx=bins)]
plt.offline.plot(data, filename=output, auto_open=False)
return {self.OUTPUT: output}
def processAlgorithm(self, parameters, context, feedback):
source = self.parameterAsSource(parameters, self.INPUT, context)
xfieldname = self.parameterAsString(parameters, self.XFIELD, context)
yfieldname = self.parameterAsString(parameters, self.YFIELD, context)
output = self.parameterAsFileOutput(parameters, self.OUTPUT, context)
values = vector.values(source, xfieldname, yfieldname)
data = [go.Scatter(x=values[xfieldname],
y=values[yfieldname],
mode='markers')]
plt.offline.plot(data, filename=output, auto_open=False)
return {self.OUTPUT: output}
def processAlgorithm(self, parameters, context, feedback):
source = self.parameterAsSource(parameters, self.INPUT, context)
namefieldname = self.parameterAsString(parameters, self.NAME_FIELD, context) # NOQA FIXME unused?
valuefieldname = self.parameterAsString(parameters, self.VALUE_FIELD, context)
output = self.parameterAsFileOutput(parameters, self.OUTPUT, context)
values = vector.values(source, valuefieldname)
data = [go.Area(r=values[valuefieldname],
t=np.degrees(np.arange(0.0, 2 * np.pi, 2 * np.pi / len(values[valuefieldname]))))]
plt.offline.plot(data, filename=output, auto_open=False)
return {self.OUTPUT: output}
def processAlgorithm(self, parameters, context, feedback):
source = self.parameterAsSource(parameters, self.INPUT, context)
namefieldname = self.parameterAsString(parameters, self.NAME_FIELD, context)
valuefieldname = self.parameterAsString(parameters, self.VALUE_FIELD, context)
output = self.parameterAsFileOutput(parameters, self.OUTPUT, context)
values = vector.values(source, valuefieldname)
x_var = [i[namefieldname] for i in source.getFeatures()]
data = [go.Bar(x=x_var,
y=values[valuefieldname])]
plt.offline.plot(data, filename=output, auto_open=False)
return {self.OUTPUT: output}