def binomCheck(
ntpos
): #checking the nt position for both forward and reverse mate pairs
fordict = FORWARD_DICT[ntpos]
revdict = REVERSE_DICT[ntpos]
if not fordict or not revdict: #if either forward dict or reverse dict is empty..
accept = False
else:
topF = sorted(
fordict, key=fordict.get, reverse=True
)[:
2] #dictionaries can be in any particular order, where lists indexes start at 0
topR = sorted(revdict, key=revdict.get, reverse=True)[:2]
if len(topF) == 1 or len(topR) == 1:
accept = False
print 'unequal minor variant count in forward/reverse %d' % ntpos #%s= string %d = number variable ntpos has already been defined
print 'forward', fordict
print 'reverse', revdict
NOTESLIST.append('take a closer look at, only one minorvar %d' %
ntpos)
NOTESLIST.append(fordict)
NOTESLIST.append(revdict)
else:
f_majornt = topF[
0] #this will be the major because it will be the first highest
f_minornt = topF[
1] #this will be the minor variant because it will be the second highest, remember that python starts numbering at 0
r_majornt = topR[0]
r_minornt = topR[1]
if f_majornt != r_majornt or f_minornt != r_minornt:
print 'binom not equal'
NOTESLIST.append(
'take a closer look at %d' % ntpos
) #this will be added to the notelist for what went wrong exactly
NOTESLIST.append([f_majornt, r_majornt, f_minornt, r_minornt])
accept = False
else:
forwardMajorCount = fordict[f_majornt]
forwardMinorCount = fordict[f_minornt]
reverseMajorCount = revdict[r_majornt]
reverseMinorCount = revdict[r_minornt]
ALPHA = 0.05 #to check and make sure that it is signficiant, or in a significant number of reads
pforward = 1 - binom.cdf(
forwardMinorCount, (forwardMajorCount + forwardMinorCount),
args.cutoff) #calculating the p value
preverse = 1 - binom.cdf(
reverseMinorCount,
(reverseMajorCount + reverseMinorCount), args.cutoff)
if pforward <= ALPHA / 2 and preverse <= ALPHA / 2:
accept = True
else:
accept = False
return accept
def solve(N, X, Y):
X = abs(X)
z = xytotri(X + Y)
#print "%s, %s, %s, %s" % (N, X, Y, z)
if z >= N:
return 0
#print xytotri(X+Y+2)
if xytotri(X + Y + 2) <= N:
return 1
rem = N - z
#print "%s, %s, %s, %s, %s" % (N, X, Y, z, rem)
#print (((Y/2)+1)*2)+1
#if X == 0 and rem < (((Y/2)+1)*2)+1:
if X == 0 and xytotri(X + Y + 2) > N:
return 0
#print "n, z, x, y, rem", N, z, X, Y, rem
#return 1-normal_estimate((Y+1)-1, 0.5, rem)
if rem - (xytotri(X + Y + 2) - z - 1) / 2 > Y:
return 1
return 1 - binom.cdf((Y + 1) - 1, rem, 0.5)
def binomialcheck(majornt,minornt,fordict,revdict):
forwardMajorCount = fordict[majornt]
forwardMinorCount = fordict[minornt]
reverseMajorCount = revdict[majornt]
reverseMinorCount = revdict[minornt]
percentVariant = 0.03
ALPHA = 0.05
pforward = 1 - binom.cdf( forwardMinorCount, (forwardMajorCount + forwardMinorCount), percentVariant)
preverse = 1 - binom.cdf( reverseMinorCount, (reverseMajorCount + reverseMinorCount), percentVariant)
if pforward <= ALPHA/2 and preverse <= ALPHA/2:
accept = True
else:
accept = False
return accept
def WFMD(N,m,g,k):
'''
The probability that in a population of N diploid individuals initially
possessing m copies of a dominant allele, we will observe after g
generations at least k copies of a recessive allele. Assume the
Wright-Fisher model.
'''
p = float(m)/(2*N)
q = 1-p
bc = binom.cdf(k,n,p)
return
def WFMD(N, m, g, k):
'''
The probability that in a population of N diploid individuals initially
possessing m copies of a dominant allele, we will observe after g
generations at least k copies of a recessive allele. Assume the
Wright-Fisher model.
'''
p = float(m) / (2 * N)
q = 1 - p
bc = binom.cdf(k, n, p)
return
def _calculate_ci(p,sigma,n):
"""Return all indices j and k that correspond to confidence interval
of level sigma for percentile p*100 along with the respective
confidence levels
Arguments:
p : p-quantile e.g. F(m_p) = P(X < m_p) = p for 0 < p < 1
sigma: confidence interval level
n : number of samples
Returns:
(j_selection,k_selection,confidence_levels)
We need to calculate
B_{n,p}(k-1)-B_{n,p}(j-1) \leq \sigma
Therefore, we do an exhaustive search for all values of k and j
and then filter out
See Jean-Yves Le Boudec, Performance Evaluation of Computer and
Communication Systems, EPFL Press, 2010
"""
j_k_range = np.arange(0,n) # Already j-1 and k-1
J = np.tile(j_k_range,(n,1)).T
K = np.tile(j_k_range,(n,1))
# print(J)
# print(K)
diff_Bk_Bj = binom.cdf(K,n,p)-binom.cdf(J,n,p)
j_all,k_all = np.where(diff_Bk_Bj >= sigma) # We get too many of them
if len(j_all) == 0:
return None
diff_k_j = k_all-j_all # Hence, find the minimum interval
index_min_int = np.where(diff_k_j == diff_k_j.min()) # There might be several of them
j_selection = j_all[index_min_int]+1 # j and k can range from 1 to n
k_selection = k_all[index_min_int]+1
confidence_levels = diff_Bk_Bj[j_selection-1,k_selection-1]
# All confidence intervals and their confidence level
return (j_selection,k_selection,confidence_levels)
def Compute_Binomial_Prob(Topic_List,Global_Topic_Count):
"""Commutes pValues from a binomial probility distribution given a list of events
and a dictonary that descirbes the freqeuncy those events are expected to be
observed at. The values in the Topic_List must be the keys is in the Global_Topic_Count
Keywords:
Topic_List -- List of all the topics that are being test for disbution, each value should have a labled topic and thats what this list is
Global_Topic_Count -- dictonary containing the expected distrbution of topics
returns:
List_of_Topics_Dict -- List of Dicts with keys as ['names','obs','expected','pval'] sorted by obs
"""
List_of_Topic_Dict =[]
Global_Keys =Global_Topic_Count.keys()
i = 0
for key,val in dict(Counter(Topic_List)).items():
List_of_Topic_Dict.append({'name':key,'obs':val,'exp':int(len(Topic_List)*Global_Topic_Count[Global_Keys[i]])})
if List_of_Topic_Dict[-1]['exp']>=List_of_Topic_Dict[-1]['obs']:
List_of_Topic_Dict[-1]['pVal']=binom.cdf(List_of_Topic_Dict[-1]['obs'], len(Topic_List),Global_Topic_Count[Global_Keys[i]] )
else:
List_of_Topic_Dict[-1]['pVal']=1-binom.cdf(List_of_Topic_Dict[-1]['obs'], len(Topic_List), Global_Topic_Count[Global_Keys[i]])
i +=1
return sorted(List_of_Topic_Dict, key=lambda x: x['obs'], reverse=True)
def _cihs_1D(data, alpha):
data = np.sort(data.compressed())
n = len(data)
alpha = min(alpha, 1 - alpha)
k = int(binom._ppf(alpha / 2.0, n, 0.5))
gk = binom.cdf(n - k, n, 0.5) - binom.cdf(k - 1, n, 0.5)
if gk < 1 - alpha:
k -= 1
gk = binom.cdf(n - k, n, 0.5) - binom.cdf(k - 1, n, 0.5)
gkk = binom.cdf(n - k - 1, n, 0.5) - binom.cdf(k, n, 0.5)
I = (gk - 1 + alpha) / (gk - gkk)
lambd = (n - k) * I / float(k + (n - 2 * k) * I)
lims = (lambd * data[k] + (1 - lambd) * data[k - 1], lambd * data[n - k - 1] + (1 - lambd) * data[n - k])
return lims
def _cihs_1D(data, alpha):
data = np.sort(data.compressed())
n = len(data)
alpha = min(alpha, 1 - alpha)
k = int(binom._ppf(alpha / 2., n, 0.5))
gk = binom.cdf(n - k, n, 0.5) - binom.cdf(k - 1, n, 0.5)
if gk < 1 - alpha:
k -= 1
gk = binom.cdf(n - k, n, 0.5) - binom.cdf(k - 1, n, 0.5)
gkk = binom.cdf(n - k - 1, n, 0.5) - binom.cdf(k, n, 0.5)
I = (gk - 1 + alpha) / (gk - gkk)
lambd = (n - k) * I / float(k + (n - 2 * k) * I)
lims = (lambd * data[k] + (1 - lambd) * data[k - 1],
lambd * data[n - k - 1] + (1 - lambd) * data[n - k])
return lims
train = int(line.replace('(', '').replace(',', '').split()[0])
continue
test = int(line.replace('(', '').replace(',', '').split()[0])
elif line.startswith('Test score:'):
if firstTestScore is None:
firstTestScore = float(line.replace('Test score: ', ''))
continue
secondTestScore = float(line.replace('Test score: ', ''))
assert (train is not None)
assert (test is not None)
assert (testScoreAll is not None)
a_cdf = 1 - binom.cdf(secondTestScore * test, test, firstTestScore)
if firstTestScore > secondTestScore:
a_cdf = -a_cdf
b_cdf = 1 - binom.cdf(secondTestScore * test, test, testScoreAll)
if testScoreAll > secondTestScore:
b_cdf = -b_cdf
#print(a_cdf, b_cdf)
arr.append([
name, train, test, firstTestScore, secondTestScore,
testScoreAll, a_cdf, b_cdf
])
name = None
train = None
test = None
firstTestScore = None
def INDC(n, k, p):
'''
the probability of observating at least k 'heads' in 2n trials
'''
bc = binom.cdf(k, n, p)
return math.log(bc, 10)
def INDC(n, k, p):
bc = binom.cdf(k, n, p)
return math.log(bc, 10)
def binomial(n, c, p):
return binom.cdf(c, n, p)
def binom_test_low(n, N, p):
return binom.cdf(n, N, p)
def INDC(n,k,p):
'''
the probability of observating at least k 'heads' in 2n trials
'''
bc = binom.cdf(k,n,p)
return math.log(bc,10)
def pbinom(successes, fail, prob):
"""
Returns cumulative binomial probability given number of 'successes' and 'failures'.
"""
total = successes + fail
return binom.cdf(successes, total, prob)
def calculate_p1_index(n_success, n_attempts, chance_of_success):
return 1 - binom.cdf(n_success-1, n_attempts, chance_of_success)
def result(a, b):
if alpha < binom.cdf(b, a + b + 1, 0.5) < 1- alpha:
return '-'
else:
return '+'
def calculate_p2_index(n_success, n_attempts, chance_of_success):
return 2 * min(calculate_p1_index(n_success, n_attempts, chance_of_success), binom.cdf(n_success, n_attempts, chance_of_success))
def binom_test_low(n, N, p):
return binom.cdf(n, N, p)
def INDC(n,k,p):
bc = binom.cdf(k,n,p)
return math.log(bc,10)
def pbinom(successes, fail, prob):
"""
Returns cumulative binomial probability given number of 'successes' and 'failures'.
"""
total = successes + fail
return binom.cdf(successes, total, prob)
