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Narrow param grid for serotypes

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sidneymbell committed Jan 27, 2019
1 parent c6f6c47 commit 388fe9664dce2fb008b1ce4424977cdd64935a67
Showing with 7 additions and 7 deletions.
  1. +4 −4 fitness_model/antigenic_fitness.py
  2. +3 −3 fitness_model/helper_scripts/sbatch_param_wrapper.py
@@ -54,7 +54,7 @@ def get_Dij(j, i):
return af.titers[(j, i)]
except KeyError:
return af.titers[(serotype_of(j), serotype_of(i))]

def sum_over_j(i, past_timepoint):
''' Return a frequency-weighted sum of the probability of protection from i given prior exposure to j '''
antigenic_distance = [ get_Dij(j, i) if i != j else 0. for j in af.clades] # Pull precomputed antigenic distance between virus i and serum j
@@ -540,9 +540,9 @@ def plot_trajectory(af, trajectory, ax=None):

elif args.mode == 'fit':

d1_vals = np.linspace(0,7,8)
d2_vals = np.linspace(0,7,8)
d3_vals = np.linspace(0,7,8)
d1_vals = np.linspace(1,3,8)
d2_vals = np.linspace(1,3,8)
d3_vals = np.linspace(0,2,8)

output = []
for (d1,d2,d3) in product(d1_vals, d2_vals, d3_vals):
@@ -14,9 +14,9 @@
if not os.path.isdir(out_path):
os.mkdir(out_path)

beta_vals = np.linspace(0,3,8)
gamma_vals = np.linspace(0,3,8)
sigma_vals = np.linspace(0,3,8)
beta_vals = np.linspace(2,3,8)
gamma_vals = np.linspace(0.5,1.5,8)
sigma_vals = np.linspace(0,1,8)

for (b,g,s) in product(beta_vals, gamma_vals, sigma_vals):
name = ''.join(random.choice('0123456789abcdef') for n in xrange(30))

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