def compute_group(cls, data, scales, **params):
sample = data['sample'].sort_values()
alpha, beta = params['alpha_beta']
quantiles = params['quantiles']
if quantiles is None:
quantiles = plotting_positions(sample, alpha, beta)
elif len(quantiles) != len(sample):
raise PlotnineError(
"The number of quantile values is not the same as "
"the number of sample values.")
quantiles = np.asarray(quantiles)
cdist = get_continuous_distribution(params['distribution'])
theoretical = cdist.ppf(quantiles, *params['dparams'])
return pd.DataFrame({'sample': sample, 'theoretical': theoretical})
def qqcalc(data, distrib=ssd.norm, alpha=.4, beta=.4):
"""
Returns the theoretical quantiles from an empirical distribution.
Parameters
----------
data : array
Input data
distrib : {norm, function}, optional
Theoretical distribution used to compute the expected quantiles.
If None, use a normal distribution.
Otherwise, ``distrib`` must have a :meth:`.ppf` method.
alpha : {float}, optional
Coefficient for the computation of plotting positions
beta : {float}, optional
Coefficient for the computation of plotting positions.
"""
pp = mstats.plotting_positions(data, alpha=alpha, beta=beta)
qq = ma.fix_invalid(distrib.ppf(pp))
qq._mask = pp._mask
return qq
def test_plotting_positions():
# Regression test for #1256
pos = mstats.plotting_positions(np.arange(3), 0, 0)
assert_array_almost_equal(pos.data, np.array([0.25, 0.5, 0.75]))
def test_plotting_positions():
# Regression test for #1256
pos = mstats.plotting_positions(np.arange(3), 0, 0)
assert_array_almost_equal(pos.data, np.array([0.25, 0.5, 0.75]))
def returnForecastDist(fileLoc, forecastFileName):
forecast = pd.DataFrame(
pd.read_csv(os.path.join(fileLoc, forecastFileName)))
forecast = forecast[['EVENT', 'FCST.VOL']]
forecast['PROB'] = plotting_positions(forecast['FCST.VOL'], 0, 0)
return forecast
#data for interpolating damage
dataHistoric = pd.read_csv(
r"X:\CRT2014\PDT\WAT\FRA-Results\Dataprocessing_tools\Post Processing Scripts - R and Python\TiltonPython\data\flowDamageData.csv"
)
#data for location->reach
ccpData = pd.DataFrame(
pd.read_table(
r"X:\CRT2014\PDT\WAT\FRA-Results\Dataprocessing_tools\Post Processing Scripts - R and Python\TiltonPython\data\damage_ccp_classifications.txt"
))
#Curent forecast data
forecastData = pd.DataFrame(
pd.read_csv(
r"X:\CRT2014\PDT\WAT\FRA-Results\Dataprocessing_tools\Post Processing Scripts - R and Python\TiltonPython\data\forecast.csv"
))[['EVENT', 'FCST.VOL']]
forecastData['PROB'] = plotting_positions(forecastData['FCST.VOL'], 0, 0)
fcstVol = dict(zip(forecastData['EVENT'], forecastData['FCST.VOL']))
fcstProb = dict(zip(forecastData['EVENT'], forecastData['PROB']))
def returnForecastDist(fileLoc, forecastFileName):
forecast = pd.DataFrame(
pd.read_csv(os.path.join(fileLoc, forecastFileName)))
forecast = forecast[['EVENT', 'FCST.VOL']]
forecast['PROB'] = plotting_positions(forecast['FCST.VOL'], 0, 0)
return forecast
ex = "SELECT Path_ID, Part_A, Part_B, Part_C, Part_D, Part_E, Part_F, Label FROM master_plot WHERE "
paramDict = {
'maxOutFlow': {
def func(df, x):
df['plotting_positions'] = (1- plotting_positions(df[x], 0, 0)) *100
return df
