Repository: jrenaud90/CyRK (language: python)

Issue:
Min Step Size Bug
During the original implementation of CyRK I misunderstood how the minimum step size check was supposed to work. Scipy defines the minimum step size based on the actual value of time,
```python
import numpy as np
min_step = 10 * np.abs(np.nextafter(t, direction * np.inf) - t)
```

whereas CyRK originally had a constant step size based off of the floating point error,
defines the minimum step size based on the actual value of time,
```python
import numpy as np
min_step = 10 * np.finfo(np.float64).eps
```

This latter approach works for when t is close to zero, but is not accurate when t is far from zero since the number of floats between numbers decreases at higher values. 

Since `min_step` is used to set the step_size from becoming too small during integration - having an incorrect value could lead to incorrect or unexpected behavior. 

This bug affects both the `cyrk_ode` and `nbrk_ode`.

![image](https://user-images.githubusercontent.com/6547310/228333737-0533fd5e-ce42-4f12-ab01-c178c45dcdae.png)



Discussion / hints:

Fix patch:
diff --git a/Benchmarks/CyRK - SciPy Comparison.ipynb b/Benchmarks/CyRK - SciPy Comparison.ipynb
index 93927e3..09f091e 100644
--- a/Benchmarks/CyRK - SciPy Comparison.ipynb
+++ b/Benchmarks/CyRK - SciPy Comparison.ipynb
@@ -365,7 +365,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -394,7 +394,7 @@
     "ax.errorbar(end_times, nb_times, yerr=nb_errors, c='green', label=\"CyRK's Numba\", fmt='o')\n",
     "ax.errorbar(end_times, cy_times, yerr=cy_errors, c='orange', label=\"CyRK's Cython\", fmt='o')\n",
     "\n",
-    "ax.set(ylabel='Average Time [ms]', xlabel='Integration Domain Size', xscale='log', yscale='log',\n",
+    "ax.set(ylabel='Average Time [ms]\\n(Lower is Better)', xlabel='Integration Domain Size', xscale='log', yscale='log',\n",
     "       title=f'CyRK v{version} vs. SciPy')\n",
     "ax.set_xticks((1e-3, 1e-2, 1e-1, 1e0, 1e1, 1e2, 1e3, 1e4, 1e5))\n",
     "ax.grid(alpha=0.3)\n",
@@ -409,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "697a88bd",
    "metadata": {
     "scrolled": true
@@ -419,15 +419,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "end_time-1.0e-03: Numba=9.864x; Cython=3.852x; NbCy=2.561x\n",
-      "end_time-1.0e-02: Numba=9.637x; Cython=3.885x; NbCy=2.480x\n",
-      "end_time-1.0e-01: Numba=13.786x; Cython=5.466x; NbCy=2.522x\n",
-      "end_time-1.0e+00: Numba=27.818x; Cython=10.070x; NbCy=2.763x\n",
-      "end_time-1.0e+01: Numba=91.919x; Cython=24.243x; NbCy=3.792x\n",
-      "end_time-1.0e+02: Numba=120.299x; Cython=27.513x; NbCy=4.372x\n",
-      "end_time-1.0e+03: Numba=122.160x; Cython=27.331x; NbCy=4.470x\n",
-      "end_time-1.0e+04: Numba=104.658x; Cython=26.483x; NbCy=3.952x\n",
-      "end_time-1.0e+05: Numba=100.902x; Cython=27.963x; NbCy=3.608x\n"
+      "end_time-1.0e-03: Numba=9.509x; Cython=4.309x; NbCy=2.207x\n",
+      "end_time-1.0e-02: Numba=9.370x; Cython=4.302x; NbCy=2.178x\n",
+      "end_time-1.0e-01: Numba=13.604x; Cython=6.200x; NbCy=2.194x\n",
+      "end_time-1.0e+00: Numba=26.883x; Cython=10.880x; NbCy=2.471x\n",
+      "end_time-1.0e+01: Numba=90.134x; Cython=24.883x; NbCy=3.622x\n",
+      "end_time-1.0e+02: Numba=115.410x; Cython=27.104x; NbCy=4.258x\n",
+      "end_time-1.0e+03: Numba=120.006x; Cython=27.216x; NbCy=4.409x\n",
+      "end_time-1.0e+04: Numba=99.265x; Cython=25.542x; NbCy=3.886x\n",
+      "end_time-1.0e+05: Numba=95.779x; Cython=27.498x; NbCy=3.483x\n"
      ]
     }
    ],
diff --git a/CHANGES.md b/CHANGES.md
index f97de77..bfdb36a 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -12,6 +12,7 @@ Performance
 - Converted various ints to `short`s, `char`s, or `Py_ssize_t`. `Py_ssize_t` is recommended by Cython for loop integers to better support 64-bit architecture. 
 - Added custom interpolation functions which, depending on the size of the array, can be up to 10x faster than numpys.
 - Removed unnecessarily variables from `cyrk_ode`.
+- Had to turn off `fastmath` for `nbrk_ode`. See [issue 24](https://github.com/jrenaud90/CyRK/issues/24). This negatively impacted the numba integrator's performance by around 5%.
 
 Other Changes:
 - Refactored, cleaned up, and added comments and docstrings to `cyrk_ode`.
@@ -20,6 +21,9 @@ Other Changes:
 - Added openmp dependence during compile time to allow for the use of `prange`.
 - Moved `cyrk_ode`'s Runge-Kutta constants to a separate module `CyRK.rk`. 
 
+Bug Fixes:
+- Fixed issue (for `nbrk_ode` and `cyrk_ode`) where incorrect step size could be used due to bad minimum step check (see [issue 20](https://github.com/jrenaud90/CyRK/issues/20)).
+
 ### v0.4.0
 
 New Features
diff --git a/CyRK/cy/_cyrk.pyx b/CyRK/cy/_cyrk.pyx
index 8550f54..19caef5 100644
--- a/CyRK/cy/_cyrk.pyx
+++ b/CyRK/cy/_cyrk.pyx
@@ -4,7 +4,7 @@ import numpy as np
 cimport numpy as np
 np.import_array()
 from libcpp cimport bool as bool_cpp_t
-from libc.math cimport sqrt, fabs
+from libc.math cimport sqrt, fabs, nextafter
 
 from CyRK.array.interp cimport interp_array, interp_complex_array
 from CyRK.rk.rk cimport (
@@ -184,7 +184,7 @@ def cyrk_ode(
         raise Exception('Unexpected type found for initial conditions (y0).')
 
     # Build time domain
-    cdef double t_start, t_end, t_delta, t_delta_abs, direction, t_old, t_new, time_
+    cdef double t_start, t_end, t_delta, t_delta_abs, direction, direction_inf, t_old, t_new, time_
     t_start = t_span[0]
     t_end   = t_span[1]
     t_delta = t_end - t_start
@@ -193,6 +193,7 @@ def cyrk_ode(
         direction = 1.
     else:
         direction = -1.
+    direction_inf = direction * INF
 
     # Pull out information on t-eval
     cdef unsigned int len_teval
@@ -502,7 +503,7 @@ def cyrk_ode(
             else:
                 h1 = (0.01 / max(d1, d2))**error_expo
 
-            step_size = min(100. * h0, h1)
+            step_size = max(10. * fabs(nextafter(t_old, direction_inf) - t_old), min(100. * h0, h1))
     else:
         if first_step <= 0.:
             raise Exception('Error in user-provided step size: Step size must be a positive number.')
@@ -531,7 +532,8 @@ def cyrk_ode(
 
         # Run RK integration step
         # Determine step size based on previous loop
-        min_step = EPS_10
+        # Find minimum step size based on the value of t (less floating point numbers between numbers when t is large)
+        min_step = 10. * fabs(nextafter(t_old, direction_inf) - t_old)
         # Look for over/undershoots in previous step size
         if step_size > max_step:
             step_size = max_step
@@ -740,7 +742,7 @@ def cyrk_ode(
         message = 'Integration finished with no issue.'
     elif status == -1:
         message = 'Error in step size calculation: Required step size is less than spacing between numbers.'
-    elif status < -2:
+    elif status < -1:
         message = 'Integration Failed.'
 
     # Create output arrays. To match the format that scipy follows, we will take the transpose of y.
diff --git a/CyRK/nb/nbrk.py b/CyRK/nb/nbrk.py
index 0db42f9..8834e10 100644
--- a/CyRK/nb/nbrk.py
+++ b/CyRK/nb/nbrk.py
@@ -1,4 +1,5 @@
 from typing import Tuple
+import platform
 
 import numpy as np
 from numba import njit
@@ -83,7 +84,7 @@ def _norm(x):
     return np.linalg.norm(x) / np.sqrt(x.size)
 
 
-@njit(cache=False, fastmath=True)
+@njit(cache=False, fastmath=False)
 def nbrk_ode(
         diffeq: callable, t_span: Tuple[float, float], y0: np.ndarray, args: tuple = tuple(),
         rtol: float = 1.e-6, atol: float = 1.e-8,
@@ -321,7 +322,6 @@ def nbrk_ode(
 
     # Find first step size
     first_step_found = False
-    min_step = EPS_10
     if first_step is not None:
         step_size = max_step
         if first_step < 0.:
@@ -381,7 +381,8 @@ def nbrk_ode(
             else:
                 h1 = (0.01 / max(d1, d2))**error_expo
 
-            step_size = max(min_step, min(100. * h0, h1))
+            next_after = 10. * abs(np.nextafter(t_old, direction * np.inf) - t_old)
+            step_size  = max(next_after, min(100. * h0, h1))
 
     # Main integration loop
     # # Time Loop
@@ -395,6 +396,10 @@ def nbrk_ode(
 
         # Run RK integration step
         # Determine step size based on previous loop
+        # Find minimum step size based on the value of t (less floating point numbers between numbers when t is large)
+        next_after = 10. * abs(np.nextafter(t_old, direction * np.inf) - t_old)
+        min_step   = next_after
+
         # Look for over/undershoots in previous step size
         if step_size > max_step:
             step_size = max_step
diff --git a/Performance/cyrk_performance-DOP853.csv b/Performance/cyrk_performance-DOP853.csv
index b32d172..55d5366 100644
--- a/Performance/cyrk_performance-DOP853.csv
+++ b/Performance/cyrk_performance-DOP853.csv
@@ -7,3 +7,4 @@ CyRK Version, Run-on Date, cython (avg), cython (std), numba  (avg), numba  (std
 0.3.0, 05/01/2023 10:02:04,1.5628,0.0193,0.2212,0.0098,16.2939,1.0647,2.1032,0.1053,0.7873,0.0387,0.123,0.0016,7.3369,0.0999,1.1184,0.0366,2.7731,0.0207,0.3371,0.0034,38.5276,0.2702,4.4669,0.028,#N/A,#N/A,#N/A,#N/A,#N/A,#N/A,#N/A,#N/A
 0.4.0a0.dev7, 19/01/2023 00:17:19,1.282,0.009,0.2141,0.0004,12.9353,0.0725,2.0163,0.0234,0.6384,0.0009,0.1277,0.0007,6.065,0.0233,1.1282,0.0027,2.1301,0.0075,0.3451,0.0027,29.1336,0.1329,4.4394,0.0702,2.4673,0.006,0.6437,0.0034,33.9701,0.3546,8.4945,0.0814
 0.5.0a3.dev0, 28/03/2023 12:42:54, 1.1010, 0.0252, 0.1920, 0.0004, 10.5338, 0.0175, 1.7754, 0.0045, 0.5119, 0.0080, 0.1061, 0.0001, 4.7160, 0.0584, 0.8671, 0.0027, 1.5785, 0.0215, 0.3171, 0.0151, 24.3099, 2.8578, 4.0586, 0.0455, 1.9298, 0.0493, 0.5502, 0.0066, 25.5103, 0.0692, 7.3534, 0.0191
+0.5.0a4.dev1, 28/03/2023 15:03:36, 1.1174, 0.0288, 0.2091, 0.0126, 10.6654, 0.0716, 1.8152, 0.0304, 0.5364, 0.0278, 0.1082, 0.0024, 4.7510, 0.0480, 0.8862, 0.0099, 1.5926, 0.0258, 0.3166, 0.0222, 21.2931, 0.1835, 3.9808, 0.0297, 1.9513, 0.0068, 0.5477, 0.0008, 25.9224, 0.0799, 7.2492, 0.0525
diff --git a/Performance/cyrk_performance-RK23.csv b/Performance/cyrk_performance-RK23.csv
index ecc107a..e4cf2cd 100644
--- a/Performance/cyrk_performance-RK23.csv
+++ b/Performance/cyrk_performance-RK23.csv
@@ -7,3 +7,4 @@ CyRK Version, Run-on Date, cython (avg), cython (std), numba  (avg), numba  (std
 0.3.0, 05/01/2023 10:00:59,77.4853,121.2205,1.0396,0.0232,76.2795,1.2953,14.8855,1.8712,60.0498,95.4352,0.7488,0.0072,48.452,0.4612,7.7916,0.2563,61.9276,91.3916,1.1791,0.0227,161.7735,1.3318,29.561,1.7351,#N/A,#N/A,#N/A,#N/A,#N/A,#N/A,#N/A,#N/A
 0.4.0a0.dev7, 19/01/2023 00:15:45,6.9328,0.0419,1.0807,0.0225,70.2419,1.2946,12.7816,0.3986,4.5039,0.012,0.775,0.0066,45.4632,0.1552,7.4912,0.054,8.2474,0.0137,1.2234,0.0151,142.2633,0.4843,28.3285,0.4725,59.7768,86.0594,1231.1615,2128.4877,172.6841,0.9151,44.3725,0.7991
 0.5.0a3.dev0, 28/03/2023 12:41:14, 56.6555, 87.5021, 0.8593, 0.0037, 60.7470, 0.6072, 9.5173, 0.0707, 54.8074, 87.2917, 0.6171, 0.0381, 37.7550, 0.2152, 5.8001, 0.0471, 59.0615, 89.1476, 1.1360, 0.0656, 119.4322, 6.3822, 21.4468, 0.8531, 56.7765, 84.3508, 1150.4060, 1989.3127, 140.5121, 0.2122, 38.2787, 0.2288
+0.5.0a4.dev1, 28/03/2023 15:01:57, 59.3399, 91.9592, 0.8934, 0.0096, 63.7064, 1.3348, 12.8172, 0.4395, 53.3725, 85.1252, 0.6898, 0.0364, 40.9606, 0.8638, 6.0094, 0.0111, 6.9182, 0.0561, 1.0516, 0.0078, 119.1599, 0.1658, 24.0576, 0.5495, 57.3195, 84.8271, 1236.1289, 2137.0688, 147.8034, 2.0147, 40.1323, 0.5919
diff --git a/Performance/cyrk_performance-RK45.csv b/Performance/cyrk_performance-RK45.csv
index 60a4dfd..19245fb 100644
--- a/Performance/cyrk_performance-RK45.csv
+++ b/Performance/cyrk_performance-RK45.csv
@@ -7,3 +7,4 @@ CyRK Version, Run-on Date, cython (avg), cython (std), numba  (avg), numba  (std
 0.3.0, 05/01/2023 10:01:39,1.798,0.0179,0.2488,0.007,18.3591,0.4265,2.275,0.0356,1.3278,0.0082,0.2179,0.003,13.2111,0.2056,1.9998,0.1186,2.9175,0.0569,0.4892,0.0545,43.7249,0.7168,5.2899,0.0926,#N/A,#N/A,#N/A,#N/A,#N/A,#N/A,#N/A,#N/A
 0.4.0a0.dev7, 19/01/2023 00:16:47,1.6215,0.007,0.2658,0.0012,15.9618,0.0617,2.4543,0.0102,1.2047,0.0078,0.2206,0.0013,11.5133,0.032,1.9925,0.0192,2.4698,0.0055,0.4073,0.0049,36.6541,0.1025,5.4567,0.0308,2.9369,0.0051,0.7251,0.0146,43.2334,0.1386,10.229,0.0706
 0.5.0a3.dev0, 28/03/2023 12:42:18, 1.4355, 0.0038, 0.2273, 0.0006, 13.8784, 0.0545, 2.0894, 0.0244, 1.0099, 0.0026, 0.1802, 0.0007, 9.5253, 0.0467, 1.5740, 0.0116, 1.9879, 0.0359, 0.3394, 0.0033, 28.5659, 0.1013, 4.7250, 0.0673, 2.3823, 0.0177, 0.6134, 0.0056, 34.5347, 0.1064, 8.6572, 0.0694
+0.5.0a4.dev1, 28/03/2023 15:03:00, 1.6564, 0.0976, 0.2369, 0.0042, 14.2637, 0.2702, 2.4129, 0.2290, 1.0572, 0.0308, 0.1929, 0.0102, 9.8867, 0.2293, 1.6442, 0.0303, 2.0550, 0.0588, 0.3415, 0.0025, 29.0485, 0.0882, 4.6925, 0.0758, 2.4380, 0.0456, 0.6565, 0.0238, 35.9287, 0.5693, 9.5987, 0.8587
diff --git a/pyproject.toml b/pyproject.toml
index 35562ab..6087036 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name='CyRK'
-version = '0.5.0a3.dev0'
+version = '0.5.0a4.dev2'
 description='Runge-Kutta ODE Integrator Implemented in Cython and Numba.'
 authors= [
     {name = 'Joe P. Renaud', email = 'joe.p.renaud@gmail.com'}


Test patch:
diff --git a/.github/workflows/push_tests_mac.yml b/.github/workflows/push_tests_mac.yml
index 39baae5..bbb3be4 100644
--- a/.github/workflows/push_tests_mac.yml
+++ b/.github/workflows/push_tests_mac.yml
@@ -52,4 +52,4 @@ jobs:
           python -m pip install . -v
       - name: Run pytest
         shell: bash -el {0}
-        run: pytest -v Tests/
+        run: pytest --capture=sys -v Tests/
diff --git a/.github/workflows/push_tests_ubun.yml b/.github/workflows/push_tests_ubun.yml
index 33875fc..508ceb7 100644
--- a/.github/workflows/push_tests_ubun.yml
+++ b/.github/workflows/push_tests_ubun.yml
@@ -47,7 +47,7 @@ jobs:
           python -m pip install . -v
 
       - name: Run pytest
-        run: pytest -v --cov=CyRK Tests/
+        run: pytest --capture=sys -v --cov=CyRK Tests/
         env:
           # Disable numba so that njited functions can have their coverage checked.
           NUMBA_DISABLE_JIT: 1
diff --git a/.github/workflows/push_tests_win.yml b/.github/workflows/push_tests_win.yml
index 2a73cd6..07e2d94 100644
--- a/.github/workflows/push_tests_win.yml
+++ b/.github/workflows/push_tests_win.yml
@@ -30,4 +30,4 @@ jobs:
           python -m pip install . -v
 
       - name: Run pytest
-        run: pytest -v .\Tests\
\ No newline at end of file
+        run: pytest --capture=sys -v .\Tests\
\ No newline at end of file
diff --git a/Tests/B_Cython_Tests/test_a_cython.py b/Tests/B_Cython_Tests/test_a_cython.py
index ec20c50..54279a6 100644
--- a/Tests/B_Cython_Tests/test_a_cython.py
+++ b/Tests/B_Cython_Tests/test_a_cython.py
@@ -103,7 +103,7 @@ def test_max_step(rk_method, complex_valued):
         initial_conds_to_use = initial_conds
 
     time_domain, y_results, success, message = \
-        cyrk_ode(diffeq, time_span, initial_conds_to_use, rk_method=rk_method, max_step=1.0e5)
+        cyrk_ode(diffeq, time_span, initial_conds_to_use, rk_method=rk_method, max_step=time_span[1] / 2.)
 
     # Check that the ndarrays make sense
     assert type(time_domain) == np.ndarray
diff --git a/Tests/C_Numba_Tests/test_a_numba.py b/Tests/C_Numba_Tests/test_a_numba.py
index 380e761..690e63f 100644
--- a/Tests/C_Numba_Tests/test_a_numba.py
+++ b/Tests/C_Numba_Tests/test_a_numba.py
@@ -107,7 +107,7 @@ def test_max_step(rk_method, complex_valued):
         initial_conds_to_use = initial_conds
 
     time_domain, y_results, success, message = \
-        nbrk_ode(diffeq, time_span, initial_conds_to_use, rk_method=rk_method, max_step=1.0e5)
+        nbrk_ode(diffeq, time_span, initial_conds_to_use, rk_method=rk_method, max_step=time_span[1] / 2.)
 
     # Check that the ndarrays make sense
     assert type(time_domain) == np.ndarray
@@ -159,7 +159,7 @@ def test_first_step(rk_method, complex_valued):
 @pytest.mark.parametrize('complex_valued', (True, False))
 @pytest.mark.parametrize('rk_method', (0, 1, 2))
 def test_large_end_value(rk_method, complex_valued):
-    """Check that the numba solver is able to run using the RK23 method. Using a larger ending time value """
+    """Check that the numba solver is able to run using a larger ending time value """
 
     if complex_valued:
         initial_conds_to_use = initial_conds_complex
@@ -245,6 +245,7 @@ def test_args(rk_method, complex_valued):
     assert y_results[0].size == y_results[1].size
 
     # Check that the other output makes sense
+    print(message)
     assert type(success) == bool
     assert success
     assert type(message) == str
diff --git a/Tests/D_Other_Tests/test_helpers.py b/Tests/D_Other_Tests/test_helpers.py
index aa7b00c..c2df180 100644
--- a/Tests/D_Other_Tests/test_helpers.py
+++ b/Tests/D_Other_Tests/test_helpers.py
@@ -48,6 +48,7 @@ def test_nb2cy_noargs():
     # First calculate the result of an integration using nbrk.
     time_domain_nb, y_results_nb, success_nb, message_nb = \
         nbrk_ode(diffeq_scipy, time_span, initial_conds, t_eval=t_eval)
+    print(message_nb)
     assert success_nb
 
     # Perform a cyrk integration using the diffeq that was written for cyrk
@@ -124,6 +125,7 @@ def test_cy2nb_noargs():
     # First calculate the result of an integration using nbrk.
     time_domain_nb, y_results_nb, success_nb, message_nb = \
         nbrk_ode(diffeq_scipy, time_span, initial_conds, t_eval=t_eval)
+    print(message_nb)
     assert success_nb
 
     # Perform a cyrk integration using the diffeq that was written for cyrk