Merge pull request #6985 from bashtage/remove-print

MAINT: Remove print statements

statsmodels/base/model.py

@@ -2383,7 +2383,6 @@ def bootstrap(self, nrep=100, method='nm', disp=0, store=1):
         distributed observations.
         """
         results = []
-        print(self.model.__class__)
         hascloneattr = True if hasattr(self.model, 'cloneattr') else False
         for i in range(nrep):
             rvsind = np.random.randint(self.nobs, size=self.nobs)

statsmodels/discrete/tests/test_constrained.py

@@ -553,12 +553,9 @@ def junk():  # FIXME: make this into a test, or move/remove
     # example without offset
     formula1a = 'deaths ~ logpyears + smokes + C(agecat)'
     mod1a = Poisson.from_formula(formula1a, data=data)
-    print(mod1a.exog.shape)
 
     mod1a.fit()
     lc_1a = patsy.DesignInfo(mod1a.exog_names).linear_constraint(
         'C(agecat)[T.4] = C(agecat)[T.5]')
-    resc1a = mod1a.fit_constrained(lc_1a.coefs, lc_1a.constants,
-                                   fit_kwds={'method': 'newton'})
-    print(resc1a[0])
-    print(resc1a[1])
+    mod1a.fit_constrained(lc_1a.coefs, lc_1a.constants,
+                          fit_kwds={'method': 'newton'})

statsmodels/nonparametric/kde.py

@@ -498,49 +498,3 @@ def kdensityfft(X, kernel="gau", bw="normal_reference", weights=None, gridsize=N
         return f, grid, bw
     else:
         return f, bw
-
-if __name__ == "__main__":
-    import numpy as np
-    np.random.seed(12345)
-    xi = np.random.randn(100)
-    f,grid, bw1 = kdensity(xi, kernel="gau", bw=.372735, retgrid=True)
-    f2, bw2 = kdensityfft(xi, kernel="gau", bw="silverman",retgrid=False)
-
-# do some checking vs. silverman algo.
-# you need denes.f, http://lib.stat.cmu.edu/apstat/176
-#NOTE: I (SS) made some changes to the Fortran
-# and the FFT stuff from Munro http://lib.stat.cmu.edu/apstat/97o
-# then compile everything and link to denest with f2py
-#Make pyf file as usual, then compile shared object
-#f2py denest.f -m denest2 -h denest.pyf
-#edit pyf
-#-c flag makes it available to other programs, fPIC builds a shared library
-#/usr/bin/gfortran -Wall -c -fPIC fft.f
-#f2py -c denest.pyf ./fft.o denest.f
-
-    try:
-        from denest2 import denest # @UnresolvedImport
-        a = -3.4884382032045504
-        b = 4.3671504686785605
-        RANGE = b - a
-        bw = bandwidths.bw_silverman(xi)
-
-        ft,smooth,ifault,weights,smooth1 = denest(xi,a,b,bw,np.zeros(512),np.zeros(512),0,
-                np.zeros(512), np.zeros(512))
-# We use a different binning algo, so only accurate up to 3 decimal places
-        np.testing.assert_almost_equal(f2, smooth, 3)
-#NOTE: for debugging
-#        y2 = forrt(weights)
-#        RJ = np.arange(512/2+1)
-#        FAC1 = 2*(np.pi*bw/RANGE)**2
-#        RJFAC = RJ**2*FAC1
-#        BC = 1 - RJFAC/(6*(bw/((b-a)/M))**2)
-#        FAC = np.exp(-RJFAC)/BC
-#        SMOOTH = np.r_[FAC,FAC[1:-1]] * y2
-
-#        dens = revrt(SMOOTH)
-
-    except:
-#        ft = np.loadtxt('./ft_silver.csv')
-#        smooth = np.loadtxt('./smooth_silver.csv')
-        print("Did not get the estimates from the Silverman algorithm")

statsmodels/nonparametric/smoothers_lowess.py

@@ -211,22 +211,19 @@ def lowess(endog, exog, frac=2.0/3.0, it=3, delta=0.0, xvals=None, is_sorted=Fal
 
     if not given_xvals:
         # Run LOWESS on the data points
-        print("Not giving xvalues")
         res, _ = _lowess(y, x, x, np.ones_like(x),
                         frac=frac, it=it, delta=delta, given_xvals=False)
     else:
         # First run LOWESS on the data points to get the weights of the data points
         # using it-1 iterations, last iter done next
         if it > 0:
-            print("Not giving xvalues")
             _, weights = _lowess(y, x, x, np.ones_like(x),
                                 frac=frac, it=it-1, delta=delta, given_xvals=False)
         else:
             weights = np.ones_like(x)
 
         # Then run once more using those supplied weights at the points provided by xvals
         # No extra iterations are performed here since weights are fixed
-        print("giving xvalues")
         res, _ = _lowess(y, x, xvalues, weights,
                         frac=frac, it=0, delta=delta, given_xvals=True)
 

statsmodels/nonparametric/tests/test_kernels.py

@@ -85,19 +85,10 @@ def test_smoothconf(self):
             se_n_diff = self.se_n_diff
         assert_array_less(mask.sum(), se_n_diff + 1)  # at most 5 large diffs
 
-        if DEBUG:
-            # raises: RuntimeWarning: invalid value encountered in divide
-            print(fitted / res_fitted - 1)
-            print(se / res_se - 1)
         # Stata only displays ci, does not save it
         res_upp = res_fitted + crit * res_se
         res_low = res_fitted - crit * res_se
         self.res_fittedg = np.column_stack((res_low, res_fitted, res_upp))
-        if DEBUG:
-            print(fittedg[:, 2] / res_upp - 1)
-            print(fittedg[:, 2] - res_upp)
-            print(fittedg[:, 0] - res_low)
-            print(np.max(np.abs(fittedg[:, 2] / res_upp - 1)))
         assert_allclose(fittedg[se_valid, 2], res_upp[se_valid],
                         rtol=self.upp_rtol, atol=0.2)
         assert_allclose(fittedg[se_valid, 0], res_low[se_valid],
@@ -112,8 +103,6 @@ def test_smoothconf_data(self):
         crit = 1.9599639845400545  # norm.isf(0.05 / 2)
         # no reference results saved to csv yet
         fitted_x = np.array([kern.smoothconf(x, y, xi) for xi in x])
-        if DEBUG:
-            print(fitted_x[:, 2] - fitted_x[:, 1]) / crit
 
 
 class TestEpan(CheckKernelMixin):

statsmodels/stats/_adnorm.py

@@ -137,18 +137,3 @@ def normal_ad(x, axis=0):
             pval[mask] = pvalli[i](ad2a[mask])
 
     return ad2, pval
-
-
-if __name__ == '__main__':
-    x = np.array([-0.1184, -1.3403, 0.0063, -0.612, -0.3869, -0.2313,
-                  -2.8485, -0.2167, 0.4153, 1.8492, -0.3706, 0.9726,
-                  -0.1501, -0.0337, -1.4423, 1.2489, 0.9182, -0.2331,
-                  -0.6182, 0.1830])
-    r_res = np.array([0.58672353588821502, 0.1115380760041617])
-    ad2, pval = normal_ad(x)
-    print(ad2, pval)
-    print(r_res - [ad2, pval])
-
-    print(anderson_statistic((x - x.mean()) / x.std(), dist=stats.norm,
-                             fit=False))
-    print(anderson_statistic(x, dist=stats.norm, fit=True))

statsmodels/stats/descriptivestats.py

@@ -499,7 +499,6 @@ def _mode(ser):
             while dupe:
                 scale *= 10
                 idx = np.floor(scale * perc.index)
-                print(np.diff(idx))
                 if np.all(np.diff(idx) > 0):
                     dupe = False
             index = np.floor(scale * index) / (scale / 100)

statsmodels/stats/multivariate.py

@@ -579,7 +579,6 @@ def test_cov_oneway(cov_list, nobs_list):
     else:
         tmp = b2 * stat0
         statistic_f = a2 / a1 * tmp / (1 + tmp)
-        print("in branch 2")
     df_f = (a1, a2)
     pvalue_f = stats.f.sf(statistic_f, *df_f)
     return HolderTuple(statistic=statistic_f,  # name convention, using F here

statsmodels/tools/rootfinding.py

@@ -130,8 +130,6 @@ def brentq_expanding(func, low=None, upp=None, args=(), xtol=1e-5,
             sl = 1e-8
             f_low = func(sl, *args)
             increasing = (f_low < f_upp)
-            if DEBUG:
-                print('symm', sl, su, f_low, f_upp)
 
         # possibly func returns nan
         delta = su - sl
@@ -162,11 +160,6 @@ def brentq_expanding(func, low=None, upp=None, args=(), xtol=1e-5,
 
         increasing = (f_low < f_upp)
 
-    if DEBUG:
-        print('low, upp', low, upp, func(sl, *args), func(su, *args))
-        print('increasing', increasing)
-        print('sl, su', sl, su)
-
     if not increasing:
         sl, su = su, sl
         left, right = right, left