one more example, and now using showgood

Author: edgarcosta

src/flint/types/acb_theta.pyx

@@ -21,12 +21,11 @@ def acb_theta(acb_mat z, acb_mat tau, ulong square=False):
         >>> t0, t1, t2, t3 = acb_mat([[tau]]).theta(acb_mat([[z]])).entries()
         >>> sum([abs(x) for x in acb_mat([z.modular_theta(tau)]) - acb_mat([[-t3,t2,t0,t1]])])
         [+/- 3.82e-14]
-        >>> for i in range(4):showgood(lambda: acb_mat([[tau]]).theta(acb_mat([[z]])).entries()[i], dps=25)
-        ...
-        0.9694430387796704100046143 - 0.03055696120816803328582847j
-        1.030556961196006476576271 + 0.03055696120816803328582847j
-        -1.220790267576967690128359 - 1.827055516791154669091679j
-        -1.820235910124989594900076 + 1.216251950154477951760042j
+        >>> showgood(lambda: acb_mat([[tau]]).theta(acb_mat([[z]])).transpose(), dps=25)
+        [0.9694430387796704100046143 - 0.03055696120816803328582847j]
+        [ 1.030556961196006476576271 + 0.03055696120816803328582847j]
+        [  -1.220790267576967690128359 - 1.827055516791154669091679j]
+        [  -1.820235910124989594900076 + 1.216251950154477951760042j]
         >>> acb_mat([[1j,0],[0,2*1j]]).theta(acb_mat([[0],[0]])).transpose()
         [ [1.09049252082308 +/- 3.59e-15] + [+/- 2.43e-16]j]
         [ [1.08237710165638 +/- 4.15e-15] + [+/- 2.43e-16]j]
@@ -44,6 +43,24 @@ def acb_theta(acb_mat z, acb_mat tau, ulong square=False):
         [                  [+/- 2.43e-16] + [+/- 2.43e-16]j]
         [                  [+/- 2.43e-16] + [+/- 2.43e-16]j]
         [                  [+/- 2.43e-16] + [+/- 2.43e-16]j]
+        >>> ctx.prec = 10000
+        >>> print(acb_mat([[1j, 0],[0,1j]]).theta(acb_mat([[0],[0]])).transpose().str(25))
+        [ [1.180340599016096226045338 +/- 5.95e-26] + [+/- 1.23e-3010]j]
+        [[0.9925441784910574194770081 +/- 3.15e-26] + [+/- 1.23e-3010]j]
+        [[0.9925441784910574194770081 +/- 3.15e-26] + [+/- 1.23e-3010]j]
+        [[0.8346268416740731862814297 +/- 3.29e-26] + [+/- 1.23e-3010]j]
+        [[0.9925441784910574194770081 +/- 3.15e-26] + [+/- 1.23e-3010]j]
+        [                          [+/- 1.23e-3010] + [+/- 1.23e-3010]j]
+        [[0.8346268416740731862814297 +/- 3.29e-26] + [+/- 1.23e-3010]j]
+        [                          [+/- 1.23e-3010] + [+/- 1.23e-3010]j]
+        [[0.9925441784910574194770081 +/- 3.15e-26] + [+/- 1.23e-3010]j]
+        [[0.8346268416740731862814297 +/- 3.29e-26] + [+/- 1.23e-3010]j]
+        [                          [+/- 1.23e-3010] + [+/- 1.23e-3010]j]
+        [                          [+/- 1.23e-3010] + [+/- 1.23e-3010]j]
+        [[0.8346268416740731862814297 +/- 3.29e-26] + [+/- 1.23e-3010]j]
+        [                          [+/- 1.23e-3010] + [+/- 1.23e-3010]j]
+        [                          [+/- 1.23e-3010] + [+/- 1.23e-3010]j]
+        [                          [+/- 1.23e-3010] + [+/- 1.23e-3010]j]
 
     """
     g = tau.nrows()