def pceManifold(json, title, xtype, ytype, xlabel, ylabel, lxrange, uxrange,
lyrange, uyrange, trace_type, x_trace, y_trace, trace_name,
queue):
engine = PlotControlEngine(json)
xtrace = []
ytrace = []
if x_trace is not None and y_trace is not None and trace_type is not None:
try:
xtrace = array([float(y) for y in x_trace if extractor(y)])
ytrace = array([float(x) for x in y_trace if extractor(x)])
except ValueError:
print(
'Exception raised in pceManifold() when attempting to cast types'
)
engine.addTrace(xtrace, ytrace, trace_name, trace_type)
if lxrange is not None and uxrange is not None:
engine.changeXRange(lxrange, uxrange)
if lyrange is not None and uyrange is not None:
engine.changeYRange(lyrange, uyrange)
if xtype is not None:
engine.changeXType(xtype)
if ytype is not None:
engine.changeYType(ytype)
if xlabel is not None:
engine.changeXLabel(xlabel)
if ylabel is not None:
engine.changeYLabel(ylabel)
if title is not None:
engine.changeTitle(title)
queue.put(engine.render())
def __init__(self, _sample, _svgr=None):
self.rock = _sample
self.tortuosidade = []
self.svgr = None
self.rock.setPhi([p / 100 for p in _sample.getPhi()]) # convert porosity from % to decimal
if _svgr is None:
_svgr = []
else:
self.svgr = [float(y) for y in _svgr if extractor(y)]
def histogramManifold(data, _nbins, queue):
if _nbins == '':
_nbins=0
data = np.array([float(y) for y in data if extractor(y)])
try:
ndata = pd.DataFrame(data)
nbins = int(_nbins)
except ValueError:
return 'Error'
hst = Histogram(data, nbins)
hst.plot()
queue.put([hst.plot_path, hst.json])
def boxplotDataCleaner(_data):
#flatten, remove strs and then convert to df
ndata = pd.DataFrame(_data).transpose()
rows = ndata.shape[1]
columns = ndata.shape[0]
auxdata = []
for c in range(columns):
d = [float(x) for x in list(ndata.iloc[c]) if extractor(x)]
auxdata.append(d)
return pd.DataFrame(auxdata).transpose()
def statisticsManifold(_xdata: list, method: str, quartile, percentile, queue):
xdata = np.array([float(x) for x in _xdata if extractor(x)])
st = Statistics(xdata)
methods = [
'Média aritmética', 'Média Ponderada', 'Mediana', 'Moda',
'Variância Pop.', 'Variância Am.', 'Desvio Padrão Pop.',
'Desvio Padrão Am.', 'Máximo', 'Mínimo', 'Amplitude', 'Quartil',
'Percentil'
]
container = []
if method == methods[0]:
container.append(st.average())
if method == methods[1]:
container.append(st.average())
if method == methods[2]:
container.append(st.median())
if method == methods[3]:
container.append(st.mode())
if method == methods[4]:
container.append(st.varP())
if method == methods[5]:
container.append(st.varS())
if method == methods[6]:
container.append(st.stdevP())
if method == methods[7]:
container.append(st.stdevS())
if method == methods[8]:
container.append(st.max())
if method == methods[9]:
container.append(st.min())
if method == methods[10]:
container.append(st.range())
if method == methods[11]:
container.append(st.quartile(quartile))
if method == methods[12]:
container.append(st.percentile(percentile))
queue.put(container)