def get_fixation_dominant_disease(S):
sign_S = algopy.sign(S)
H = algopy.zeros_like(S)
for i in range(H.shape[0]):
for j in range(H.shape[1]):
H[i, j] = 1. / kimrecessive.denom_piecewise(
0.5*S[i, j], -sign_S[i, j])
return H
def get_fixation_unconstrained(S, d):
sign_S = algopy.sign(S)
D = d * sign_S
H = algopy.zeros_like(S)
for i in range(H.shape[0]):
for j in range(H.shape[1]):
H[i, j] = 1. / kimrecessive.denom_piecewise(0.5 * S[i, j], D[i, j])
return H
def get_fixation_dominant_disease(S):
sign_S = algopy.sign(S)
H = algopy.zeros_like(S)
for i in range(H.shape[0]):
for j in range(H.shape[1]):
H[i, j] = 1. / kimrecessive.denom_piecewise(
0.5*S[i, j], -sign_S[i, j])
return H
def get_fixation_unconstrained(S, d, k):
D = d * numpy.tanh(numpy.exp(k)*S)
H = numpy.zeros_like(S)
for i in range(H.shape[0]):
for j in range(H.shape[1]):
H[i, j] = 1. / kimrecessive.denom_piecewise(
0.5*S[i, j], D[i, j])
return H
def get_fixation_unconstrained(S, d):
sign_S = algopy.sign(S)
D = d * sign_S
H = algopy.zeros_like(S)
for i in range(H.shape[0]):
for j in range(H.shape[1]):
H[i, j] = 1. / kimrecessive.denom_piecewise(
0.5*S[i, j], D[i, j])
return H
def get_fixation_unconstrained_kb(S, d, log_kb):
"""
This uses the Kacser and Burns effect instead of the sign function.
"""
soft_sign_S = algopy.tanh(algopy.exp(log_kb) * S)
D = d * soft_sign_S
H = algopy.zeros_like(S)
for i in range(H.shape[0]):
for j in range(H.shape[1]):
H[i, j] = 1. / kimrecessive.denom_piecewise(0.5 * S[i, j], D[i, j])
return H
def get_fixation_unconstrained_kb(S, d, log_kb):
"""
This uses the Kacser and Burns effect instead of the sign function.
"""
soft_sign_S = algopy.tanh(algopy.exp(log_kb)*S)
D = d * soft_sign_S
H = algopy.zeros_like(S)
for i in range(H.shape[0]):
for j in range(H.shape[1]):
H[i, j] = 1. / kimrecessive.denom_piecewise(
0.5*S[i, j], D[i, j])
return H
def get_response_content(fs):
# create the R table string and scripts
headers = [
'z',
'c.neg2.0',
'c.neg0.5',
'c.0.5',
'c.2.0',
#'c.a',
#'c.b',
#'c.c',
#'c.d',
]
#C = numpy.array([-0.5, -0.2, 0.2, 0.5], dtype=float)
#C = numpy.array([-1.0, -0.4, 0.4, 1.0], dtype=float)
C = numpy.array([-2.0, -0.5, 0.5, 2.0], dtype=float)
Z = numpy.linspace(-5, 5, 101)
# get the data for the R table
arr = []
for z in Z:
row = [z]
for c in C:
rate = 1.0 / kimrecessive.denom_piecewise(c, z * numpy.sign(c))
row.append(rate)
arr.append(row)
# get the R table
table_string = RUtil.get_table_string(arr, headers)
# get the R script
script = get_ggplot()
# create the R plot image
device_name = Form.g_imageformat_to_r_function[fs.imageformat]
retcode, r_out, r_err, image_data = RUtil.run_plotter(
table_string, script, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def get_response_content(fs):
# create the R table string and scripts
headers = [
'z',
'c.neg2.0',
'c.neg0.5',
'c.0.5',
'c.2.0',
#'c.a',
#'c.b',
#'c.c',
#'c.d',
]
#C = numpy.array([-0.5, -0.2, 0.2, 0.5], dtype=float)
#C = numpy.array([-1.0, -0.4, 0.4, 1.0], dtype=float)
C = numpy.array([-2.0, -0.5, 0.5, 2.0], dtype=float)
Z = numpy.linspace(-5, 5, 101)
# get the data for the R table
arr = []
for z in Z:
row = [z]
for c in C:
rate = 1.0 / kimrecessive.denom_piecewise(c, z*numpy.sign(c))
row.append(rate)
arr.append(row)
# get the R table
table_string = RUtil.get_table_string(arr, headers)
# get the R script
script = get_ggplot()
# create the R plot image
device_name = Form.g_imageformat_to_r_function[fs.imageformat]
retcode, r_out, r_err, image_data = RUtil.run_plotter(
table_string, script, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def get_relative_error_c(c, d):
x = kimrecessive.denom_quad(c, d)
y = kimrecessive.denom_piecewise(c, d)
z = (y-x) / x
return refilter(z)
