def test_simple(self):
grid = ag.EnergyGrid(bounds=ag.linspace(1, 2, 2))
self.assertEqual(1.0, grid.minEnergy, "Assert default min energy")
self.assertEqual(2.0, grid.maxEnergy, "Assert default max energy")
self.assertEqual("eV", grid.units, "Assert units")
self.assertEqual(2, len(grid), "Assert length")
self.assertEqual(1.0, grid[0], "Assert first")
self.assertEqual(2.0, grid[-1], "Assert last")
self.assertEqual(1, grid.nrofbins, "Assert nrofbins")
self.assertEqual([1.5], list(grid.midpoints), "Assert mid points")
self.assertEqual([1.0, 2.0], list(grid.bounds), "Assert bounds")
def getmaxcount(nrofbins):
grid = ag.EnergyGrid(bounds=ag.linspace(MIN_ENERGY, MAX_ENERGY,
math.floor(nrofbins) + 1))
# bin the lines appropriately
lc = BinaryLineAggregator(db, grid)
hist = np.zeros(grid.nrofbins)
bin_edges = grid.bounds
for nuclide in tqdm(db.allnuclidesoftype(spectype=SPECTYPE)):
inv = ag.UnstablesInventory(data=[(db.getzai(nuclide), 1)])
h, _ = lc(inv, spectype=SPECTYPE)
hist += h
return np.max(hist)
bins = ag.logspace(0, 7, 30)
y = [
1707.0, 1707.0, 1680.0, 1625.0, 1499.0, 1300.0, 1116.0, 954.0, 780.0,
611.0, 448.0, 316.0, 214.0, 143.0, 101.0, 64.0, 44.0, 31.0, 26.0, 15.0,
14.0, 13.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0
]
plt.plot(bins, y, 'ko')
plt.xscale('log')
plt.yscale('log')
plt.xlabel('Number of bins', fontsize=18)
plt.ylabel('Max nr. unique nuclides', fontsize=18)
# look at the lines for one type of binning.
# bin the lines appropriately
grid = ag.EnergyGrid(bounds=ag.linspace(MIN_ENERGY, MAX_ENERGY, 1000))
lc = BinaryLineAggregator(db, grid)
hist = np.zeros(grid.nrofbins)
bin_edges = grid.bounds
for nuclide in tqdm(db.allnuclidesoftype(spectype=SPECTYPE)):
inv = ag.UnstablesInventory(data=[(db.getzai(nuclide), 1)])
h, _ = lc(inv, spectype=SPECTYPE, include_same_nuclide=False)
hist += h
# make a plot of cumulative
cumsum_hist = np.cumsum(hist)
X, Y = ag.getplotvalues(bin_edges, cumsum_hist)
fig = plt.figure(figsize=(12, 7))
plt.plot(X, Y, 'k')
plt.xlabel("Energy ({})".format(grid.units), fontsize=18)
import matplotlib.pyplot as plt
import actigamma as ag
# normally gamma but could be something else - "alpha", "beta" if data exists!
SPECTYPES = ["gamma", "x-ray"]
# setup the DB
db = ag.Decay2012Database()
# define my unstable inventory by activities (Bq)
inv = ag.UnstablesInventory(data=[
(db.getzai("Co60"), 9.87e13),
])
# define an energy grid between 0 and 4 MeV with 10,000 bins
grid = ag.EnergyGrid(bounds=ag.linspace(0.0, 4e6, 10000))
# or we can do logspace between 1e3 eV and 1e7 eV with 10,000 bins
# grid = ag.EnergyGrid(bounds=ag.logspace(3, 7, 10000))
# plt.xscale('log')
# bin the lines appropriately
lc = ag.MultiTypeLineAggregator(db, grid)
hist, bin_edges = lc(inv, types=SPECTYPES)
# make a plot
X, Y = ag.getplotvalues(bin_edges, hist)
fig = plt.figure(figsize=(12, 7))
plt.plot(X, Y, 'k')
plt.xlabel("Energy ({})".format(grid.units), fontsize=18)
plt.ylabel("{} per unit time (s-1)".format("+".join(SPECTYPES)), fontsize=18)
from sklearn.ensemble import RandomForestClassifier
import actigamma as ag
classifier = MultiOutputClassifier(RandomForestClassifier(n_estimators=100, max_features='auto', verbose=False))
# generate a dataset based on 100 bins from 0 to 4 MeV
MINENERGY = 0.0
MAXENERGY = 4e6
NBINS = 1000
SPECTYPE = "gamma"
# setup the DB
db = ag.Decay2012Database()
grid = ag.EnergyGrid(bounds=ag.linspace(MINENERGY, MAXENERGY, NBINS+1))
lc = ag.LineAggregator(db, grid)
NSAMPLES = 1000
NNUCLIDES = 10
# randomly generate 100 samples based on 10 radionuclides
radionuclides = db.allnuclidesoftype(spectype=SPECTYPE)[:NNUCLIDES]
# ignore activity - set to 1
ACTIVITY = 1
datasetX, datasetY = [], []
for _ in range(NSAMPLES):
# randomly pick between 1 and NNUCLIDIES to generate data
nrofnuclides = random.randrange(1, NNUCLIDES+1)
# pick nrofnuclides - must be unique
nuclides = []