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Description: Remove code from Stack Overflow and wasteful loop
Stack Overflow content is CC-BY-SA licensed,
which this package is not supposed to be. This snippet may be
too small to be copyrightable, but removing it to be safe.
Author: Rebecca N. Palmer <rebecca_palmer@zoho.com>
Forwarded: no
--- a/statsmodels/examples/ex_generic_mle_tdist.py
+++ b/statsmodels/examples/ex_generic_mle_tdist.py
@@ -198,6 +198,9 @@ class MyPareto(GenericLikelihoodModel):
this does not trim lower values during ks optimization
'''
+ # all 3 of these are based on
+ # https://stackoverflow.com/questions/3242326/fitting-a-pareto-distribution-with-python-scipy
+ # validly licensed because the same person added it there and here
rvs = self.endog
rvsmin = rvs.min()
fixdf = np.nan * np.ones(3)
--- a/statsmodels/graphics/regressionplots.py
+++ b/statsmodels/graphics/regressionplots.py
@@ -434,11 +434,12 @@ def plot_partregress(endog, exog_i, exog
ax.set_ylabel("e(%s | X)" % y_axis_endog_name)
ax.set_title('Partial Regression Plot', **title_kwargs)
- # NOTE: if we want to get super fancy, we could annotate if a point is
- # clicked using this widget
+ # NOTE: it is possible to annotate if a point is clicked using
# http://stackoverflow.com/questions/4652439/
# is-there-a-matplotlib-equivalent-of-matlabs-datacursormode/
# 4674445#4674445
+ # but for licensing reasons this is _not_ to be directly copied
+ # into statsmodels itself
if obs_labels is True:
if data is not None:
obs_labels = data.index
--- a/statsmodels/tools/sequences.py
+++ b/statsmodels/tools/sequences.py
@@ -61,24 +61,22 @@ def primes_from_2_to(n):
Parameters
----------
n : int
- Sup bound with ``n >= 6``.
+ Upper limit (exclusive).
Returns
-------
primes : list(int)
Primes in ``2 <= p < n``.
-
- References
- ----------
- [1] `StackOverflow <https://stackoverflow.com/questions/2068372>`_.
"""
- sieve = np.ones(n // 3 + (n % 6 == 2), dtype=bool)
- for i in range(1, int(n ** 0.5) // 3 + 1):
- if sieve[i]:
- k = 3 * i + 1 | 1
- sieve[k * k // 3::2 * k] = False
- sieve[k * (k - 2 * (i & 1) + 4) // 3::2 * k] = False
- return np.r_[2, 3, ((3 * np.nonzero(sieve)[0][1:] + 1) | 1)]
+ isprime = np.ones((n - 2,)) # 2...n-1, so indices offset by 2 from values
+ endidx = int(np.sqrt(n)) - 1
+ nextprime = 2
+ while True:
+ isprime[nextprime * nextprime - 2::nextprime] = 0
+ nextps = np.nonzero(isprime[nextprime - 1:endidx])[0]
+ if len(nextps) == 0:
+ return np.nonzero(isprime)[0] + 2
+ nextprime = nextprime + nextps[0] + 1
def n_primes(n):
@@ -109,12 +107,13 @@ def n_primes(n):
953, 967, 971, 977, 983, 991, 997][:n]
if len(primes) < n:
- big_number = 10
- while 'Not enought primes':
+ # this should always be enough - Rosser (1941)
+ big_number = int(n * (np.log(n) + np.log(np.log(n))))
+ while True: # not enough primes
primes = primes_from_2_to(big_number)[:n]
if len(primes) == n:
break
- big_number += 1000
+ big_number = int(big_number * 1.5)
return primes
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