def duplicate48Points(
x0, y0, z0
): #Find the equivalent points by permuating the indices and adding negative signs.
[x0, y0] = ary([x0, y0])
x, y, z = [], [], []
for n in range(8):
x.append((-1)**(n >> 2) * x0)
y.append((-1)**(n >> 1) * y0)
z.append((-1)**(n >> 0) * z0)
x.append((-1)**(n >> 2) * x0)
y.append((-1)**(n >> 1) * z0)
z.append((-1)**(n >> 0) * y0)
x.append((-1)**(n >> 2) * y0)
y.append((-1)**(n >> 1) * x0)
z.append((-1)**(n >> 0) * z0)
x.append((-1)**(n >> 2) * y0)
y.append((-1)**(n >> 1) * z0)
z.append((-1)**(n >> 0) * x0)
x.append((-1)**(n >> 2) * z0)
y.append((-1)**(n >> 1) * x0)
z.append((-1)**(n >> 0) * y0)
x.append((-1)**(n >> 2) * z0)
y.append((-1)**(n >> 1) * y0)
z.append((-1)**(n >> 0) * x0)
return ary([x, y, z]).T
def wall_repel(self):
force = ary([0., 0.])
# tangential walls' repulsion
if quadrature(
self.pos
) < radius_min + self.wall_effect_thickness: # close to the centre of the cable
direc = self.pos / quadrature(self.pos)
dist = quadrature(self.pos) - radius_min
force += direc * self.wall_kernel(
dist) #distance from the inner radius wall
elif quadrature(self.pos) > radius_max - self.wall_effect_thickness:
direc = -self.pos / quadrature(self.pos)
dist = radius_max - quadrature(self.pos)
force += direc * self.wall_kernel(
dist) #distance from the outer radius wall
# radial walls' repulsion
if self.pos[1] > 1 / sqrt(3) * self.pos[0] - 2 / sqrt(
3) * self.wall_effect_thickness:
direc = 0.5 * ary([1, -sqrt(3)])
dist = direc.dot(self.pos)
force += direc * self.wall_kernel(dist)
elif self.pos[1] < -1 / sqrt(3) * self.pos[0] + 2 / sqrt(
3) * self.wall_effect_thickness:
direc = 0.5 * ary([1, sqrt(3)])
dist = direc.dot(self.pos)
force += direc * self.wall_kernel(dist)
return force
def merge_identical_parent_products(
object_with_key
): # can I speed it up by reducing the number of (hidden) for-loops? (matching_reactions is a sort of for loop)
# I worry that it can only be sped up using Fortran. Not python, IMO.
# get the parent_product string and mt number string as two list, corresponding to each row in the sigma_df.
parent_product_list, mt_list = [], []
for parent_product_mt in object_with_key.index:
parent_product_list.append("-".join(parent_product_mt.split("-")[:2]))
mt_list.append(parent_product_mt.split("=")[1])
parent_product_list, mt_list = ary(parent_product_list), ary(
mt_list) # make them into array to make them indexible.
partial_reaction_array = object_with_key.values
parent_product_all = ordered_set(parent_product_list)
sigma_unique = {}
tprint(
"Condensing the sigma_xs dataframe to merge together reactions with identical (parent, product) pairs:"
)
for parent_product in tqdm(parent_product_all):
matching_reactions = parent_product_list == parent_product
mt_name = "-MT=({})".format(",".join(mt_list[matching_reactions]))
sigma_unique[parent_product +
mt_name] = partial_reaction_array[matching_reactions].sum(
axis=0)
del object_with_key
del partial_reaction_array
gc.collect()
return pd.DataFrame(sigma_unique).T
def __init__(self, x, y, interpolation):
assert np.shape(x) == np.shape(y)
assert np.ndim(x) == 1
assert len(interpolation) == len(x) - 1
self.x = ary(x)
self.y = ary(y)
self.interpolation = ary(interpolation)
def definite_integral(self, a, b):
"""
Definite integral that handles an array of (a, b) vs a scalar pair of (a, b) in different manners.
The main difference is that (a, b) will be clipped back into range if it exceeds the recorded x-values' range in the array case;
while such treatment won't happen in the scalar case.
We might change this later to remove the problem of havin g
"""
assert (np.diff([a, b], axis=0) >=
0).all(), "Can only integrate in the positive direction."
# assert False, "How the f**k are you not catching this shit?"
if np.not_equal(np.clip(a, self.func.x.min(), self.func.x.max()),
a).any():
if self.verbose:
print(
"Integration limit is below recorded range of x values! Clipping it back into range..."
)
a = np.clip(a, self.func.x.min(), self.func.x.max())
if np.not_equal(np.clip(b, self.func.x.min(), self.func.x.max()),
b).any():
if self.verbose:
print(
"Integration limit is above recorded range of x values! Clipping it back into range..."
)
b = np.clip(b, self.func.x.min(), self.func.x.max())
if isinstance(a, Iterable):
assert np.shape(a) == np.shape(
b), "The dimension of (a) must match that of (b)"
assert ary(a).ndim == 1, "Must be a flat 1D array"
return self._definite_integral_array(ary(a), ary(b))
else:
return self._definite_integral_array(ary([a]), ary([b]))[0]
def decay_mat_exp_num_decays(branching_ratios,
decay_constants,
a,
b,
c,
decay_constant_threshold=1E-23):
"""
decay_constants : a list of decay_constants
a : the end time of irradiation (irradiation time starts at t=0), a> 0
b : the start time of measurement, b> a
c : the end time of measurement, c> b
decay_constant_threshold : the threshold below which nuclides are considered as stable.
"""
if any(ary(decay_constants) <= decay_constant_threshold):
return 0
matrix = create_lambda_matrix(decay_constants)
iden = np.identity(len(decay_constants))
multiplier = 1 / a * (_expm(
matrix * (b - a))) @ (_expm(matrix *
(c - b)) - iden) @ (_expm(matrix *
(a)) - iden)
inv = np.linalg.inv(matrix)
initial_population_vector = ary(
[
1,
] + [0 for _ in decay_constants[1:]]
) # initial population of all nuclides = 0 except for the very first isotope, which has 1.0.
final_fractions = multiplier @ inv @ inv @ initial_population_vector # result of the (population * dt) integral
# total number of decays = branching_ratios * the integral * decay constant of that isotope .
return np.product(
branching_ratios[1:]
) * final_fractions[-1] * decay_constants[
-1] # multiplied by its own decay rate will give the number of decays over time period b to c.
def _definite_integral_array(self, a, b):
n = len(self._area)
x_array_2d = np.broadcast_to(self.func.x, [len(a), n + 1]).T
# finding the completely enveloped cells using l_bounds and u_bounds.
l_bounds = np.broadcast_to(
self.func.x[:-1],
[len(a), n
]).T # we don't care whether or not a is larger than the last x
u_bounds = np.broadcast_to(self.func.x[1:], [
len(b), n
]).T # we dont' care whether or not b is smaller than the first x.
# calculate area.
ge_a = np.greater_equal(
l_bounds, a).T # 2D array of bin upper bounds which are >= a.
le_b = np.less_equal(
u_bounds, b).T # 2D array of bin lower bounds which are <= b.
# use <= and >= instead of < and > to allow the left-edge and right-edge to have zero dx.
area_2d = np.broadcast_to(self._area, [len(a), n])
central_area = (area_2d * (-1 + ge_a + le_b)).sum(axis=1)
# -1 + False + False = -1 (cell envelope entire [a, b] interval);
# -1 + True + False = 0, -1 + False - True = 0 (cell to the right/left of the entire [a, b] interval respectively);
# -1 + True + True = +1 ([a, b] interval envelopes entire cell).
# left-edge half-cell
l_ind = n - ge_a.sum(axis=1)
l_edge_x = ary([a, self.func.x[l_ind]])
l_edge_y = ary([self.func(a), self.func.y[l_ind]])
l_edge_scheme = self._interpolation[np.clip(
l_ind - 1, 0, None, dtype=int
)] # make sure it doesn't go below zero when ge_a sums to equal n (i.e. a is less than the second x).
# right-edge half-cell.
r_ind = le_b.sum(axis=1)
r_edge_x = ary([self.func.x[r_ind], b])
r_edge_y = ary([self.func.y[r_ind], self.func(b)])
r_edge_scheme = self._interpolation[np.clip(r_ind,
None,
n - 1,
dtype=int)]
# calculate the left-edge half cell and right-edge half cell areas.
l_edge_area, r_edge_area = np.zeros(len(a)), np.zeros(len(a))
for scheme_number in INTERPOLATION_SCHEME.keys(
): # loop 5 times (x2 area calculations per loop) to get the l/r edges areas
matching_l = l_edge_scheme == scheme_number
matching_r = l_edge_scheme == scheme_number
l_edge_area[matching_l] = getattr(
self,
"area_scheme_" + str(scheme_number))(*l_edge_x.T[matching_l].T,
*l_edge_y.T[matching_l].T)
r_edge_area[matching_r] = getattr(
self,
"area_scheme_" + str(scheme_number))(*r_edge_x.T[matching_r].T,
*r_edge_y.T[matching_r].T)
return l_edge_area + central_area + r_edge_area
def cartesian_spherical(x, y, z): #cartesian unit vectors input spherical output. x,y,z = ary(np.clip([x,y,z],-1,1), dtype=float) #change the data type to the desired format Theta = arccos((z)) Phi = arctan(np.divide(y,x)) #This division is going to give nan if (x,y,z) = (0,0,1) Phi = np.nan_to_num(Phi) #Therefore assert phi = 0 if (x,y) = (0,0) Phi+= ary( (np.sign(x)-1), dtype=bool)*pi #if x is positive, then phi is on the RHS of the circle; vice versa. return ary([Theta, Phi])
def apply_mask_on_custom(vel_vec, mask):
"""
No docstring
"""
vel_vec_out = []
for this_slice, mask_line in zip(vel_vec, mask):
vel_vec_out.append([ vector if required else ary([[],[]]).T for vector, required in zip(this_slice, mask_line)])
return ary(vel_vec_out)
def picFast(picNum, x, y, xDim, yDim):
#xDim,yDim = np.shape(picNum)
if (x in range(xDim)) and (y in range(yDim)):
pixelValue = ary(picNum[x,
y][:3]) #take only the first three values
pixelValue = ary([255, 255, 255]) - pixelValue
return np.append(pixelValue, int(np.mean(pixelValue)))
else:
return transparent()
def append_axis(x, y):
if np.ndim(x) == 1: #assume
assert np.shape(x) == np.shape(y)
xy = ary([x, y]).T
if x.ndim == 2:
assert np.shape(x)[:1] == np.shape(y)
xy = [axis for axis in ary(x).T]
xy.append(ary(y))
xy = ary(xy).T
return xy
def top_n_sums_of_dict(un_sorted_dict, target_chosen_length, n, largest=True):
descending_sorted_dict = OrderedDict(sorted(un_sorted_dict.items(), key=itemgetter(1), reverse=True))
top_n_combos = top_n_sums(list(descending_sorted_dict.values()), target_chosen_length, n, largest)
selectable_keys = ary(list(descending_sorted_dict.keys()))
sort_result_as_return_dict = OrderedDict()
for boolean_mask, sum_value in top_n_combos.items():
sort_result_as_return_dict[tuple(selectable_keys[ary(boolean_mask)])] = sum_value
return sort_result_as_return_dict
def populate_with_vector(mask, vector):
"""
Fill in a vector if applicable ('True' in mask).
2x run of this function (which involves the copying operation)
using real_data (600x417 points) took only 0.8 seconds.
so it was deemed that the copying doesn't introduce much inefficency
and thus is not a point to be optimized.
"""
vec = ary(vector.copy())
return ary( [[vec.copy() if required else ary([[],[]]).T for required in mask_line] for mask_line in mask] )
def RemoveData(xz, Flux, FluxErr, z_llim, z_ulim=200):
xz = ary(xz)
Flux = ary(Flux)
FluxErr = ary(FluxErr)
assert xz.ndim == 2 and xz.shape[1] == 2
assert xz.shape[0] == Flux.shape[0]
assert xz.shape[0] == FluxErr.shape[0]
x, z = xz.T
ind_ary = ary([n for n in range(len(xz)) if z_llim <= z[n] < z_ulim])
assert len(ind_ary) > 0, "no z fit these limit!"
return xz[ind_ary], Flux[ind_ary], FluxErr[ind_ary]
def ToDataFrame(xz, Flux, FluxErr):
xz = ary(xz)
Flux = ary(Flux)
FluxErr = ary(FluxErr)
assert xz.ndim == 2 and xz.shape[1] == 2
assert xz.shape[0] == Flux.shape[0]
assert xz.shape[0] == FluxErr.shape[0]
x, z = xz.T
df = pd.DataFrame(ary([x, z, Flux, FluxErr]).T,
columns=["x", "z", "flux", "err"])
return df
def turn_white_to_color(image_array, target_color):
"""
Takes in a png with RGBA format, keep the transparency of each pixel the same,
Next, rescale the "white" (FF, FF, FF) into the target_color
This function works best when the image is only black and white, where all elements that you want to highlight are white.
"""
decimal_array = ary(image_array, dtype='float')
decimal_array[:,:,0] *= target_color[0]
decimal_array[:,:,1] *= target_color[1]
decimal_array[:,:,2] *= target_color[2]
return ary(decimal_array, dtype='int64')
def RemoveDatax(xz, Flux, FluxErr, x_llim, x_ulim):
xz = ary(xz)
Flux = ary(Flux)
FluxErr = ary(FluxErr)
assert xz.ndim == 2 and xz.shape[1] == 2
assert xz.shape[0] == Flux.shape[0]
assert xz.shape[0] == FluxErr.shape[0]
x, z = xz.T
ind_ary = ary([n for n in range(len(xz)) if x_llim <= x[n] < x_ulim])
assert len(ind_ary) > 0, "no x fits these limits!"
#print(ind_ary)
return xz[ind_ary], Flux[ind_ary], FluxErr[ind_ary]
def area_between_2_pts(xy1, xy2, xi, scheme):
x1, y1, x2, y2, xi = np.hstack([xy1, xy2, xi]).flatten()
assert x1 <= xi <= x2, "xi must be between x1 and x2"
x_ = xi - x1
if x1 == x2:
return 0.0 #catch all cases with zero-size width bins
if y1 == y2 or scheme == 1: # histogramic/ flat interpolation
return y1 * x_ #will cause problems in all log(y) interpolation schemes if not caught
dy, dx = y2 - y1, x2 - x1
logy, logx = [bool(int(i)) for i in bin(scheme - 2)[2:].zfill(2)]
if logx:
assert (all(ary([x1, x2, xi]) > 0)
), "Must use non-zero x values for these interpolation schemes"
lnx1, lnx2, lnxi = ln(x1), ln(x2), ln(xi)
dlnx = lnx2 - lnx1
if logy:
assert (all(ary([y1, y2]) > 0)
), "Must use non-zero y values for these interpolation schemes"
lny1, lny2 = ln(y1), ln(y2)
dlny = lny2 - lny1
if scheme == 2: # linx, liny
m = dy / dx
if xi == x2:
return dy * dx / 2 + y1 * dx
else:
return y1 * x_ + m * x_**2 / 2
elif scheme == 3: # logx, liny
m = dy / dlnx
if xi == x2:
return y1 * dx + m * (x2 * dlnx - dx)
else:
return y1 * x_ + m * (xi * (lnxi - lnx1) - x_)
return (y1 - m * lnx1) * x_ + m * (-x_ + xi * lnxi - x1 * lnx1)
elif scheme == 4: # linx, logy
m = dlny / dx
if xi == x2:
return 1 / m * dy
else:
return 1 / m * y1 * (exp(x_ * m) - 1)
elif scheme == 5:
m = dlny / dlnx
if m == -1:
return y1 * x1 * (lnxi - lnx1)
if xi == x2:
return y1 / (m + 1) * (x2 * (x2 / x1)**m - x1)
else:
return y1 / (m + 1) * (xi * (1 + x_ / x1)**m - x1)
else:
raise AssertionError(
"a wrong interpolation scheme {0} is provided".format(scheme))
def rotateColorSpace(theta):
#given a vector pointing at 1,0,0; we want to rotate it around the (1/sqrt(3))*[1,1,1] axis by theta degrees.
w = cos(theta / 2)
x = sin(theta / 2) / sqrt(3)
y = sin(theta / 2) / sqrt(3)
z = sin(theta / 2) / sqrt(3)
A = ary([[y * y + z * z, w * z - x * y, -w * y - x * z],
[-w * z - x * y, x * x + z * z, w * x - y * z],
[w * y - x * z, -w * x - y * z, x * x + y * y]])
R = np.identity(3) # = identity matrix.
R -= 2 * A
return (np.round(R @ ary([1, 0, 0])))
def calculate_convoled_population(t):
"""
Calculates the population at any given time t when a non-flash irradiation schedule is used,
generated using irradiation duration a={} seconds
""".format(a)
vector_uncollapsed = ary([
+unpy.exp(
-ary([l * np.clip(t - a, 0, None) for l in decay_constants])),
-unpy.exp(-ary([l * np.clip(t, 0, None) for l in decay_constants]))
],
dtype=object)
vector = np.sum(vector_uncollapsed, axis=0)
return premultiplying_factor * (multiplying_factors @ vector)
def readData(inFile, y_val):
df = pd.read_csv(inFile, delimiter="\t", header=1)
dfxlen = len(df["x"]) #shortening the variable
x = ary([df["x"][n] for n in range(dfxlen) if df["y"][n] == y_val])
z = ary([df["z"][n] for n in range(dfxlen) if df["y"][n] == y_val])
count = ary(
[df["counts"][n] for n in range(dfxlen) if df["y"][n] == y_val])
time = ary([df["time"][n] for n in range(dfxlen) if df["y"][n] == y_val])
count_err = ary([sqrt(cnt) for cnt in count])
CntRate = count / time
CntRateErr = count_err / time
return x, z, CntRate, CntRateErr
def plot_chi2_line(ax, anchor, singular_dir, shift=[], chi2_mark=[1], sigma_N_meas=None, R=None):
"""
Plot the singular directions within the viewing cube, fixed at an anchor
"""
lims = ary(ax.get_w_lims())
lower_bounds, upper_bounds = lims[::2], lims[1::2]
min_lambda_point, max_lambda_point = parametric_within_bounds(anchor, unit_vec(singular_dir), lower_bounds, upper_bounds)
ax.plot(*ary([min_lambda_point, max_lambda_point]).T, label='chi^2=0')
ax.set_xlim3d(lims[0], lims[1]); ax.set_ylim3d(lims[2], lims[3]); ax.set_zlim3d(lims[4], lims[5])
for chi2_value in chi2_mark:
equichi_circle = rotate_around_axis(singular_dir, sigma_N_meas, R, num_points=120)
ax.plot(*(equichi_circle*sqrt(chi2_value) + anchor).T, label='chi^2='+str(chi2_value)+' circle')
return ax
def RemoveDataPoint(xz, Flux, FluxErr, x_rm, z_rm):
xz = ary(xz)
Flux = ary(Flux)
FluxErr = ary(FluxErr)
assert xz.ndim == 2 and xz.shape[1] == 2
assert xz.shape[0] == Flux.shape[0]
assert xz.shape[0] == FluxErr.shape[0]
x, z = xz.T
ind_ary = ary(
[n for n in range(len(xz)) if (x[n] != x_rm or z[n] != z_rm)])
assert len(ind_ary) == (len(xz) -
1), "more than 1 point removed from the xzdata!!"
return xz[ind_ary], Flux[ind_ary], FluxErr[ind_ary]
def ReadR(fileName): #Rotation matrix reader.
f = open(str(fileName))
Matrices = f.readlines()
f.close()
Matrices = np.reshape(Matrices, [-1, 3])
Matrix = []
for n in range(len(Matrices)):
Matrix.append([
ary(Matrices[n][0].split(), dtype=float),
ary(Matrices[n][1].split(), dtype=float),
ary(Matrices[n][2].split(), dtype=float)
])
#np.shape(Matrix) ==(n,3,3)
return Matrix
def decay_mat_exp_population_convolved(
branching_ratios,
decay_constants,
a,
t,
decay_constant_threshold: float = 1E-23):
"""
Separated cases out that that will cause singular matrix or 1/0's.
"""
if any(ary(decay_constants[:-1]) <= decay_constant_threshold
): # any stable isotope in the chain:
return 0
elif len(decay_constants) == 1 and decay_constants[
0] <= decay_constant_threshold: # single stable isotope in chain:
return 1.0
elif decay_constants[
-1] <= decay_constant_threshold: # last isotope is stable; rest of the chain is unstable:
if t < a:
raise NotImplementedError(
"The formula for population during irradiation hasn't been properly derived yet."
)
matrix, iden = create_lambda_matrix(
decay_constants[:-1]), np.identity(len(decay_constants) - 1)
inv = np.linalg.inv(matrix)
initial_population_vector = ary([
1.0,
] + [0.0 for _ in decay_constants[1:-1]])
# during_irradiation_production_matrix = -1/a * inv @ ( a*iden - inv @ (_expm(-matrix*a) - iden) )
during_irradiation_production_matrix = -inv + 1 / a * (
_expm(matrix * a) - iden) @ inv @ inv
during_irradiation_production = (
during_irradiation_production_matrix @ initial_population_vector
)[-1] * decay_constants[-2] * np.product(branching_ratios)
post_irradiation_production = decay_mat_exp_num_decays(
branching_ratios, decay_constants[:-1], a, a, t)
return during_irradiation_production + post_irradiation_production
else: # all unstable:
matrix, iden = create_lambda_matrix(decay_constants), np.identity(
len(decay_constants))
inv = np.linalg.inv(matrix)
initial_population_vector = ary([
1.0,
] + [0.0 for _ in decay_constants[1:]])
transformation = 1 / a * _expm(matrix * np.clip(t - a, 0, None)) @ (
_expm(matrix * np.clip(t, 0, a)) - iden) @ inv
final_fractions = transformation @ initial_population_vector
return np.product(branching_ratios[1:]) * final_fractions[-1]
def test_exclude_transect_with_dataset_2(self):
"""
test that _exclude_transect excludes only the specified transects
"""
excludeA = ('Jantang', 1)
excludeB = [('Jantang', 1), ('Kuala Merisi', 2)]
slKey = {1: "Jantang", 2: "Kuala Merisi"}
t2A = tdb.Transect(self.x2, self.slc2, self.tsc2, self.dts2,
exclude=excludeA, slKey=slKey)
t2B = tdb.Transect(self.x2, self.slc2, self.tsc2, self.dts2,
exclude=excludeB, slKey=slKey)
assert_array_equal(t2A.sds, ary([10, 2, 3, 3, 0, 20]))
assert_array_equal(t2A.x, ary([3., 4, 6, 7, 8, 9]))
assert_array_equal(t2B.sds, ary([10, 2, 3, 3]))
assert_array_equal(t2B.ind, ary([5, 4, 7, 8]))
def tessellate_circle(num_sample):
#create hexagonal packing of circles:
r = get_radius_given_number(num_sample)
xspace = 2 * r
yspace = 2 * sqrt(3) * r
full_rows = ary(
np.meshgrid(np.arange(-1 + r, 1, xspace),
np.arange(-1 + r, 1, yspace))).T.reshape([-1, 2])
in_between_rows = ary(
np.meshgrid(np.arange(-1 + 2 * r, 1, xspace),
np.arange(-1 + (1 + sqrt(3)) * r, 1,
yspace))).T.reshape([-1, 2])
tessellated_square = np.concatenate([full_rows, in_between_rows])
mask = [quadrature(point) < 1 - 0.8 * r for point in tessellated_square]
return tessellated_square[mask]
def fit(x, y, yerr, cov=False, verbose=False):
(m, c), residual, _rank, _sv, _rcond = np.polyfit(x,
y,
w=1 / ary(yerr),
deg=1,
full=True)
if verbose:
print("rank=", _rank)
print("singular values =", _sv)
print("condition number=", _rcond)
if cov:
m_c, cov_matr = np.polyfit(x, y, w=1 / ary(yerr), deg=1, cov=cov)
return m_c, cov_matr
fit_func = lambda x: m * ary(x) + c
return fit_func, get_rms_residuals(fit_func, x, y, yerr)
def ReadSurfTall(NameOfTally):
#read from file
TallyStats = ReadBlockOfText(
fname,
trigger1="1tally fluc",
length=12,
ScrollMore=1,
)
#Separate the name of the first row from the body
tallynames = TallyStats[0][
1::2] #Get the names of the tallies in the first row
TallyStats = TallyStats[1:] #Cut out the first row
#Separate the first row from the rest.
namelist = TallyStats[0][1:] #ignore the first element "nps"
TallyStats = ary(TallyStats[1:]).T #reshape it by transposing it.
#create a namelist without duplicates
cutlist = []
[cutlist.append(s) for s in namelist
if s not in cutlist] #Implicit for loop without an explicit output
#separate x values (nps) from the y-values (everything else.)
nps, TallyStats = [
ary(TallyStats[0], dtype=int),
ary(TallyStats[1:], dtype=float)
] #cut out the first row
NumTallies = len(namelist) / len(
cutlist) #Check how many times does namelist repeat itself
assert int(
NumTallies
) == NumTallies, "I suck at programming because there are more columns outputted by this tally table than I expected"
TallyStats = np.reshape(
TallyStats,
[int(NumTallies), -1, np.shape(TallyStats)[1]
]) #reshaping it, while preserving the last dimension
#Dictionary
ListOfTallyDict = []
for tal in TallyStats:
ListOfTallyDict.append(ConvToDict(tal, cutlist))
for i in range(len(ListOfTallyDict)):
ListOfTallyDict[i]["nps"] = nps
ListOfTallyDict[i]["tally"] = tallynames[i]
for D in ListOfTallyDict:
if D["tally"] == NameOfTally:
return D
def uglyAverage(qList): #Simply take the renomalized average.
qList = ary(qList) #Turn into array if not already one.
average = np.zeros(4)
for n in range(4):
average[n] = np.average(qList[:, n]) #find the average in each column.
return normalize(average)
def read_tab2(in_file_path):
"""takes in a block of text
and sort according to the activities
"""
# sniff the file for the point where the transition happens
nuclides, activities, doses = [], [], []
with open(in_file_path) as tab2:
skiprows=0
for line in tab2:
if line.startswith(" TIME"):
skiprows += 1
else:
nuclides.append(line[2:8])
activities.append(float(line[8:22]))
doses.append(float(line[22:36]))
table = pd.DataFrame(
ary([activities, doses]).T,
columns=["activities(Bq)", "doses(Sv/hr)"],
index=nuclides,
)
table.sort_values("activities(Bq)", inplace=True, ascending=False)
total = table.sum(axis=0)
total.name = "total"
table = table.append(total)
return table