def results():
x, y, e = get_data()
reg = PolynomialRegressor(degree=1, xs=x, ys=y)
mi, ma = min(x), max(x)
pad = (ma - mi) * 0.1
fxs = linspace(mi - pad, ma + pad)
plt.errorbar(x, y, yerr=e,)
reg.calculate()
l, u = reg.calculate_error_envelope(fxs, error_calc='CI')
plt.plot(fxs, l, 'b')
plt.plot(fxs, u, 'b')
# plt.plot(fxs, reg.predict(fxs), 'b-')
print('Age={}, SD={} SEM={} CI={}'.format(reg.predict(328), reg.predict_error(328), reg.predict_error(328, 'SEM'),
reg.predict_error(328, 'CI')))
reg = WeightedPolynomialRegressor(degree=1, xs=x, ys=y, yserr=e)
reg.calculate()
plt.plot(fxs, reg.predict(fxs), 'g')
l,u=reg.calculate_error_envelope(fxs, error_calc='CI')
plt.plot(fxs, l, 'r')
plt.plot(fxs, u, 'r')
print('Weighted fit Age={}, SD={} SEM={} CI={}'.format(reg.predict(328),
reg.predict_error(328), reg.predict_error(328,'SEM'),
reg.predict_error(328, 'CI')))
plt.show()
def results():
x, y, e = get_data()
reg = PolynomialRegressor(degree=1, xs=x, ys=y)
mi, ma = min(x), max(x)
pad = (ma - mi) * 0.1
fxs = linspace(mi - pad, ma + pad)
plt.errorbar(
x,
y,
yerr=e,
)
reg.calculate()
l, u = reg.calculate_error_envelope(fxs, error_calc='CI')
plt.plot(fxs, l, 'b')
plt.plot(fxs, u, 'b')
# plt.plot(fxs, reg.predict(fxs), 'b-')
print('Age={}, SD={} SEM={} CI={}'.format(reg.predict(328),
reg.predict_error(328),
reg.predict_error(328, 'SEM'),
reg.predict_error(328, 'CI')))
reg = WeightedPolynomialRegressor(degree=1, xs=x, ys=y, yserr=e)
reg.calculate()
plt.plot(fxs, reg.predict(fxs), 'g')
l, u = reg.calculate_error_envelope(fxs, error_calc='CI')
plt.plot(fxs, l, 'r')
plt.plot(fxs, u, 'r')
print('Weighted fit Age={}, SD={} SEM={} CI={}'.format(
reg.predict(328), reg.predict_error(328),
reg.predict_error(328, 'SEM'), reg.predict_error(328, 'CI')))
plt.show()
def _test_fired(self):
import numpy as np
self.db.connect()
xbase = np.linspace(430, 580, 150)
# ybase = np.zeros(150)
# cddybase = np.zeros(150)
ybase = np.random.random(150)
cddybase = np.random.random(150) * 0.001
base = [
zip(xbase, ybase),
zip(xbase, ybase),
zip(xbase, ybase),
zip(xbase, ybase),
zip(xbase, cddybase),
]
xsig = np.linspace(20, 420, 410)
# y40 = np.ones(410) * 680
# y39 = np.ones(410) * 107
# y38 = np.zeros(410) * 1.36
# y37 = np.zeros(410) * 0.5
# y36 = np.ones(410) * 0.001
y40 = 680 - 0.1 * xsig
y39 = 107 - 0.1 * xsig
y38 = np.zeros(410) * 1.36
y37 = np.zeros(410) * 0.5
y36 = 1 + 0.1 * xsig
sig = [
zip(xsig, y40),
zip(xsig, y39),
zip(xsig, y38),
zip(xsig, y37),
zip(xsig, y36),
]
regbs = MeanRegressor(xs=xbase, ys=ybase)
cddregbs = MeanRegressor(xs=xbase, ys=cddybase)
reg = PolynomialRegressor(xs=xsig, ys=y40, fit='linear')
reg1 = PolynomialRegressor(xs=xsig, ys=y39, fit='linear')
reg2 = PolynomialRegressor(xs=xsig, ys=y38, fit='linear')
reg3 = PolynomialRegressor(xs=xsig, ys=y37, fit='linear')
reg4 = PolynomialRegressor(xs=xsig, ys=y36, fit='linear')
keys = [
('H1', 'Ar40'),
('AX', 'Ar39'),
('L1', 'Ar38'),
('L2', 'Ar37'),
('CDD', 'Ar36'),
]
regresults = (dict(
Ar40=ufloat(reg.predict(0), reg.predict_error(0)),
Ar39=ufloat(reg1.predict(0), reg1.predict_error(0)),
Ar38=ufloat(reg2.predict(0), reg2.predict_error(0)),
Ar37=ufloat(reg3.predict(0), reg3.predict_error(0)),
Ar36=ufloat(reg4.predict(0), reg4.predict_error(0)),
),
dict(Ar40=ufloat(regbs.predict(0),
regbs.predict_error(0)),
Ar39=ufloat(regbs.predict(0),
regbs.predict_error(0)),
Ar38=ufloat(regbs.predict(0),
regbs.predict_error(0)),
Ar37=ufloat(regbs.predict(0),
regbs.predict_error(0)),
Ar36=ufloat(cddregbs.predict(0),
cddregbs.predict_error(0))))
blanks = [
ufloat(0, 0.1),
ufloat(0.1, 0.001),
ufloat(0.01, 0.001),
ufloat(0.01, 0.001),
ufloat(0.00001, 0.0001),
]
fits = (dict(
zip(['Ar40', 'Ar39', 'Ar38', 'Ar37', 'Ar36'],
['Linear', 'Linear', 'Linear', 'Linear', 'Linear'])),
dict(
zip(['Ar40', 'Ar39', 'Ar38', 'Ar37', 'Ar36'], [
'Average Y', 'Average Y', 'Average Y', 'Average Y',
'Average Y'
])))
mass_spectrometer = 'obama'
extract_device = 'Laser Furnace'
extract_value = 10
position = 1
duration = 10
first_stage_delay = 0
second_stage_delay = 30
tray = '100-hole'
runscript_name = 'Foo'
runscript_text = 'this is a test script'
self.add_analysis(
'4318',
'500',
'',
'4318',
base,
sig,
blanks,
keys,
regresults,
fits,
mass_spectrometer,
extract_device,
tray,
position,
extract_value, # power requested
extract_value, # power achieved
duration, # total extraction
duration, # time at extract_value
first_stage_delay,
second_stage_delay,
runscript_name,
runscript_text,
)
def _test_fired(self):
import numpy as np
self.db.connect()
xbase = np.linspace(430, 580, 150)
# ybase = np.zeros(150)
# cddybase = np.zeros(150)
ybase = np.random.random(150)
cddybase = np.random.random(150) * 0.001
base = [zip(xbase, ybase), zip(xbase, ybase), zip(xbase, ybase), zip(xbase, ybase), zip(xbase, cddybase)]
xsig = np.linspace(20, 420, 410)
# y40 = np.ones(410) * 680
# y39 = np.ones(410) * 107
# y38 = np.zeros(410) * 1.36
# y37 = np.zeros(410) * 0.5
# y36 = np.ones(410) * 0.001
y40 = 680 - 0.1 * xsig
y39 = 107 - 0.1 * xsig
y38 = np.zeros(410) * 1.36
y37 = np.zeros(410) * 0.5
y36 = 1 + 0.1 * xsig
sig = [zip(xsig, y40), zip(xsig, y39), zip(xsig, y38), zip(xsig, y37), zip(xsig, y36)]
regbs = MeanRegressor(xs=xbase, ys=ybase)
cddregbs = MeanRegressor(xs=xbase, ys=cddybase)
reg = PolynomialRegressor(xs=xsig, ys=y40, fit="linear")
reg1 = PolynomialRegressor(xs=xsig, ys=y39, fit="linear")
reg2 = PolynomialRegressor(xs=xsig, ys=y38, fit="linear")
reg3 = PolynomialRegressor(xs=xsig, ys=y37, fit="linear")
reg4 = PolynomialRegressor(xs=xsig, ys=y36, fit="linear")
keys = [("H1", "Ar40"), ("AX", "Ar39"), ("L1", "Ar38"), ("L2", "Ar37"), ("CDD", "Ar36")]
regresults = (
dict(
Ar40=ufloat(reg.predict(0), reg.predict_error(0)),
Ar39=ufloat(reg1.predict(0), reg1.predict_error(0)),
Ar38=ufloat(reg2.predict(0), reg2.predict_error(0)),
Ar37=ufloat(reg3.predict(0), reg3.predict_error(0)),
Ar36=ufloat(reg4.predict(0), reg4.predict_error(0)),
),
dict(
Ar40=ufloat(regbs.predict(0), regbs.predict_error(0)),
Ar39=ufloat(regbs.predict(0), regbs.predict_error(0)),
Ar38=ufloat(regbs.predict(0), regbs.predict_error(0)),
Ar37=ufloat(regbs.predict(0), regbs.predict_error(0)),
Ar36=ufloat(cddregbs.predict(0), cddregbs.predict_error(0)),
),
)
blanks = [ufloat(0, 0.1), ufloat(0.1, 0.001), ufloat(0.01, 0.001), ufloat(0.01, 0.001), ufloat(0.00001, 0.0001)]
fits = (
dict(zip(["Ar40", "Ar39", "Ar38", "Ar37", "Ar36"], ["Linear", "Linear", "Linear", "Linear", "Linear"])),
dict(
zip(
["Ar40", "Ar39", "Ar38", "Ar37", "Ar36"],
["Average Y", "Average Y", "Average Y", "Average Y", "Average Y"],
)
),
)
mass_spectrometer = "obama"
extract_device = "Laser Furnace"
extract_value = 10
position = 1
duration = 10
first_stage_delay = 0
second_stage_delay = 30
tray = "100-hole"
runscript_name = "Foo"
runscript_text = "this is a test script"
self.add_analysis(
"4318",
"500",
"",
"4318",
base,
sig,
blanks,
keys,
regresults,
fits,
mass_spectrometer,
extract_device,
tray,
position,
extract_value, # power requested
extract_value, # power achieved
duration, # total extraction
duration, # time at extract_value
first_stage_delay,
second_stage_delay,
runscript_name,
runscript_text,
)
