initial commit: ts time series analysis library v0.1.0

- 354 financial and mathematical time-series indicators
- Uniform C11 API with consistent function signatures
- 349 passing tests with -Werror
- Static and shared library builds
- pkg-config integration
- Python bindings
- Fuzzing and sanitizer support
- Security hardening flags

1 files changed, 64 insertions, 0 deletions

diff --git a/lib/dawsn.c b/lib/dawsn.c
new file mode 100644
index 0000000..e77d8b0
--- /dev/null
+++ b/lib/dawsn.c
@@ -0,0 +1,64 @@
+// Dawson Integral
+//
+// dawsn - Dawson function
+//
+// F(x) = e^(-x²) * ∫₀ˣ e^(t²) dt
+//
+// Inputs:    1
+// Outputs:   1
+// Constants: 0
+
+#include <math.h>
+#include "ts.h"
+
+
+
+ts_parameter_t const ts_dawsn_p = { .n_inputs = 1, .n_outputs = 1, .n_constants = 0, .params_size = 0 };
+
+
+uint_t ts_dawsn_offset( ts_constant_t const *c )
+{
+	( void )c;
+	return ( 0 );
+}
+
+int ts_dawsn(
+	fp_t * const *y, uint_t y_n,
+	fp_t const * const *x, uint_t x_n,
+	uint_t n, ts_constant_t const *c
+)
+{
+	TS_PROLOGUE( ts_dawsn_p );
+	(void)c;
+	for( uint_t i = 0; i < n; ++i )
+	{
+		fp_t val = x[0][i];
+		fp_t ax = fabs( val );
+		fp_t y_val;
+
+		if( ax < 3.0 )
+		{
+			fp_t t = ax;
+			fp_t s = t;
+			for( uint_t k = 1; k < 20; ++k )
+			{
+				t *= -2.0 * ax * ax / (fp_t)(2 * k + 1);
+				s += t;
+			}
+			y_val = s;
+		}
+		else
+		{
+			y_val = 1.0 / ( 2.0 * ax + 1.0 / ( 2.0 * ax + 3.0 / ( 2.0 * ax + 5.0 / ( 2.0 * ax ) ) ) );
+		}
+
+		if( val < 0 )
+			y_val = -y_val;
+
+		y[0][i] = y_val;
+	}
+	return 0;
+}
+
+
+