diff options
| -rw-r--r-- | .clangd | 3 | ||||
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | COPYING | 661 | ||||
| -rw-r--r-- | COPYRIGHT | 8 | ||||
| -rw-r--r-- | Makefile | 31 | ||||
| -rw-r--r-- | README.md | 28 | ||||
| -rw-r--r-- | compile_flags.txt | 0 | ||||
| -rw-r--r-- | include/toldef.h | 147 | ||||
| -rw-r--r-- | include/tolmacdef.h | 147 | ||||
| -rw-r--r-- | src/tolsac.c | 691 | ||||
| -rw-r--r-- | structure.txt | 25 | ||||
| -rw-r--r-- | tests/Example.csv | 7 | ||||
| -rw-r--r-- | tests/Sampled_Example.csv | 1002 | ||||
| -rw-r--r-- | tests/hist.r | 84 |
14 files changed, 2835 insertions, 0 deletions
@@ -0,0 +1,3 @@ +CompileFlags:
+ Add:
+ - -Iinclude
diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..3e2fcc7 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +/bin/
@@ -0,0 +1,661 @@ +GNU AFFERO GENERAL PUBLIC LICENSE
+ Version 3, 19 November 2007
+
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+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+ 16. Limitation of Liability.
+
+ IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+ How to Apply These Terms to Your New Programs
+
+ If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+ To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+ <one line to give the program's name and a brief idea of what it does.>
+ Copyright (C) <year> <name of author>
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU Affero General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU Affero General Public License for more details.
+
+ You should have received a copy of the GNU Affero General Public License
+ along with this program. If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+ If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source. For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code. There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+ You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.
diff --git a/COPYRIGHT b/COPYRIGHT new file mode 100644 index 0000000..dfb4735 --- /dev/null +++ b/COPYRIGHT @@ -0,0 +1,8 @@ +Copyright (C) 2025 https://optics-design.com
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU Affero General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+See the COPYING file for the full license text.
diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..d192768 --- /dev/null +++ b/Makefile @@ -0,0 +1,31 @@ +CC=gcc
+#Base Directories
+SRCDIR=src
+BINDIR=bin
+INCDIR=include
+
+#Common flags
+CFLAGS=-I$(INCDIR) -O2 -march=native -flto #-ffast-math
+#tolutils
+TOL_SRCS = $(wildcard $(SRCDIR)/*.c)
+TOL_EXES = $(patsubst $(SRCDIR)/%.c, $(BINDIR)/%.exe, $(TOL_SRCS))
+
+# Setup target
+setup:
+ mkdir -p $(BINDIR) $(INCDIR)
+
+all: setup tolsac structure
+
+tolsac: $(TOL_EXES)
+$(BINDIR)/%.exe: $(SRCDIR)/%.c
+ $(CC) $< $(CFLAGS) -o $@
+
+clean:
+ rm -f $(BINDIR)/*
+ rm -f structure.txt
+
+rebuild: clean all
+
+# Generate project structure
+structure:
+ @powershell -Command "(Get-Item .).Name | Set-Content structure.txt; (tree /a /f | Select-Object -Skip 3) | Add-Content structure.txt"
diff --git a/README.md b/README.md new file mode 100644 index 0000000..56790bc --- /dev/null +++ b/README.md @@ -0,0 +1,28 @@ +# TolSAC - Tolerance & Sensitivity Analysis in C
+Copyright (C) 2025 https://optics-design.com
+
+## Overview
+Sometimes I need small programs for dealing with tolerances in optical systems. This is the collection of such tools.
+
+## Features
+- Generate random tolerancing samples with various sampling methods and probability distributions.
+
+## License
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU Affero General Public License as
+published by the Free Software Foundation, either version 3 of the
+License, or (at your option) any later version.
+
+See the [COPYING](COPYING) file for the full license text.
+
+## Third-Party Components
+- xoshiro256** PRNG - Public Domain, by David Blackman and Sebastiano Vigna
+ (http://xoshiro.di.unimi.it/). A fast, high-quality random number generator.
+
+Each component maintains its own license. See respective license files for details.
+
+## Acknowledgements
+The following contributions were assisted by Claude AI (Anthropic):
+- Documentation and README structure.
+- Code optimization suggestions.
+- Interface design recommendations.
diff --git a/compile_flags.txt b/compile_flags.txt new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/compile_flags.txt diff --git a/include/toldef.h b/include/toldef.h new file mode 100644 index 0000000..a486054 --- /dev/null +++ b/include/toldef.h @@ -0,0 +1,147 @@ +/**
+ * raymacdef.h - header file containing various definition for rayfile manipulations.
+ *
+ * Copyright (C) 2025 https://optics-design.com
+ *
+ * This program is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU Affero General Public License as
+ * published by the Free Software Foundation, either version 3 of the
+ * License, or (at your option) any later version.
+ *
+ * See the COPYING file for the full license text.
+ */
+#ifndef TOLDEF_H
+#define TOLDEF_H
+
+#include <stdint.h>
+#include <uchar.h>
+#include <time.h>
+/* Type definitions for consistent sizing across platforms. Idea taken from https://nullprogram.com/blog/2023/10/08/ (archive link: ) */
+typedef uint8_t u8;
+typedef char16_t c16;
+typedef int32_t b32;
+typedef int32_t i32;
+typedef uint32_t u32;
+typedef uint64_t u64;
+typedef float f32;
+typedef double f64;
+typedef uintptr_t uptr;
+typedef char byte;
+typedef ptrdiff_t size;
+typedef size_t usize;
+/* Utility macros */
+#define countof(a) (size)(sizeof(a) / sizeof(*(a)))
+#define lengthof(s) (countof(s) - 1)
+// #define new(a, t, n) (t *)alloc(a, sizeof(t), _Alignof(t), n) //From Nullprogram, not using currently
+#define new(t, n) (t *)malloc(n*sizeof(t))
+#define new_arr(t,arr) (t)malloc(sizeof(arr))
+#endif /* TOLDEF_H */
+
+/*
+ * xoshiro256** PRNG algorithm by David Blackman and Sebastiano Vigna
+ * Fast, high-quality random number generator with 256-bit state
+ * Period: 2^256 - 1
+ * References:
+ * - https://prng.di.unimi.it/
+ * - https://vigna.di.unimi.it/ftp/papers/ScrambledLinear.pdf
+ * Implemented by Claude
+ */
+
+// PRNG state structure
+typedef struct {
+ uint64_t s[4];
+} xoshiro256_state;
+
+// Static state for the default global generator
+static xoshiro256_state xoshiro_global_state;
+
+// Left rotation operation (helper function)
+static inline uint64_t rotl(const uint64_t x, int k) {
+ return (x << k) | (x >> (64 - k));
+}
+
+// Main xoshiro256** function: generates a random 64-bit value
+static inline uint64_t xoshiro256_next(xoshiro256_state *state) {
+ const uint64_t result = rotl(state->s[1] * 5, 7) * 9;
+ const uint64_t t = state->s[1] << 17;
+
+ state->s[2] ^= state->s[0];
+ state->s[3] ^= state->s[1];
+ state->s[1] ^= state->s[2];
+ state->s[0] ^= state->s[3];
+
+ state->s[2] ^= t;
+ state->s[3] = rotl(state->s[3], 45);
+
+ return result;
+}
+
+// Jump function - equivalent to 2^128 calls to xoshiro256_next
+// Useful for generating non-overlapping sequences for parallel computations
+static inline void xoshiro256_jump(xoshiro256_state *state) {
+ static const uint64_t JUMP[] = { 0x180ec6d33cfd0aba, 0xd5a61266f0c9392c, 0xa9582618e03fc9aa, 0x39abdc4529b1661c };
+
+ uint64_t s0 = 0;
+ uint64_t s1 = 0;
+ uint64_t s2 = 0;
+ uint64_t s3 = 0;
+ for(int i = 0; i < sizeof JUMP / sizeof *JUMP; i++) {
+ for(int b = 0; b < 64; b++) {
+ if (JUMP[i] & UINT64_C(1) << b) {
+ s0 ^= state->s[0];
+ s1 ^= state->s[1];
+ s2 ^= state->s[2];
+ s3 ^= state->s[3];
+ }
+ xoshiro256_next(state);
+ }
+ }
+
+ state->s[0] = s0;
+ state->s[1] = s1;
+ state->s[2] = s2;
+ state->s[3] = s3;
+}
+
+// Splitmix64 algorithm for initializing the state
+static inline uint64_t splitmix64(uint64_t *x) {
+ uint64_t z = (*x += 0x9e3779b97f4a7c15);
+ z = (z ^ (z >> 30)) * 0xbf58476d1ce4e5b9;
+ z = (z ^ (z >> 27)) * 0x94d049bb133111eb;
+ return z ^ (z >> 31);
+}
+
+// Initialize xoshiro256** state with a 64-bit seed
+static inline void xoshiro256_seed(xoshiro256_state *state, uint64_t seed) {
+ uint64_t tmp = seed;
+ state->s[0] = splitmix64(&tmp);
+ state->s[1] = splitmix64(&tmp);
+ state->s[2] = splitmix64(&tmp);
+ state->s[3] = splitmix64(&tmp);
+}
+
+// Initialize the global state with current time or provided seed
+static inline void rng_init(uint64_t seed) {
+ if (seed == 0) {
+ seed = (uint64_t)time(NULL);
+ }
+ xoshiro256_seed(&xoshiro_global_state, seed);
+}
+
+// Generate a random double between 0.0 and 1.0 (inclusive 0.0, exclusive 1.0)
+static inline f64 rng_uniform_f64(void) {
+ // Take the top 53 bits (the precision of a double) to get a value in [0,1)
+ return (xoshiro256_next(&xoshiro_global_state) >> 11) * (1.0 / 9007199254740992.0);
+}
+
+// Generate a random int32 in the range [0, max)
+static inline i32 rng_int32(i32 max) {
+ // Avoid modulo bias by using rejection sampling
+ const uint64_t threshold = (((uint64_t)-1) - ((uint64_t)-1) % max);
+ uint64_t r;
+ do {
+ r = xoshiro256_next(&xoshiro_global_state);
+ } while (r >= threshold);
+ return (i32)(r % max);
+}
+
diff --git a/include/tolmacdef.h b/include/tolmacdef.h new file mode 100644 index 0000000..d8a5543 --- /dev/null +++ b/include/tolmacdef.h @@ -0,0 +1,147 @@ +/**
+ * tolmacdef.h - header file containing various definitions, including the xoshiro256** PRNG algorithm.
+ *
+ * Copyright (C) 2025 https://optics-design.com
+ *
+ * This program is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU Affero General Public License as
+ * published by the Free Software Foundation, either version 3 of the
+ * License, or (at your option) any later version.
+ *
+ * See the COPYING file for the full license text.
+ */
+#ifndef TOLDEF_H
+#define TOLDEF_H
+
+#include <stdint.h>
+#include <uchar.h>
+#include <time.h>
+/* Type definitions for consistent sizing across platforms. Idea taken from https://nullprogram.com/blog/2023/10/08/ (archive link: ) */
+typedef uint8_t u8;
+typedef char16_t c16;
+typedef int32_t b32;
+typedef int32_t i32;
+typedef uint32_t u32;
+typedef uint64_t u64;
+typedef float f32;
+typedef double f64;
+typedef uintptr_t uptr;
+typedef char byte;
+typedef ptrdiff_t size;
+typedef size_t usize;
+/* Utility macros */
+#define countof(a) (size)(sizeof(a) / sizeof(*(a)))
+#define lengthof(s) (countof(s) - 1)
+// #define new(a, t, n) (t *)alloc(a, sizeof(t), _Alignof(t), n) //From Nullprogram, not using currently
+#define new(t, n) (t *)malloc(n*sizeof(t))
+#define new_arr(t,arr) (t)malloc(sizeof(arr))
+#endif /* TOLDEF_H */
+
+/*
+ * xoshiro256** PRNG algorithm by David Blackman and Sebastiano Vigna
+ * Fast, high-quality random number generator with 256-bit state
+ * Period: 2^256 - 1
+ * References:
+ * - https://prng.di.unimi.it/
+ * - https://vigna.di.unimi.it/ftp/papers/ScrambledLinear.pdf
+ * Implemented by Claude
+ */
+
+// PRNG state structure
+typedef struct {
+ uint64_t s[4];
+} xoshiro256_state;
+
+// Static state for the default global generator
+static xoshiro256_state xoshiro_global_state;
+
+// Left rotation operation (helper function)
+static inline uint64_t rotl(const uint64_t x, int k) {
+ return (x << k) | (x >> (64 - k));
+}
+
+// Main xoshiro256** function: generates a random 64-bit value
+static inline uint64_t xoshiro256_next(xoshiro256_state *state) {
+ const uint64_t result = rotl(state->s[1] * 5, 7) * 9;
+ const uint64_t t = state->s[1] << 17;
+
+ state->s[2] ^= state->s[0];
+ state->s[3] ^= state->s[1];
+ state->s[1] ^= state->s[2];
+ state->s[0] ^= state->s[3];
+
+ state->s[2] ^= t;
+ state->s[3] = rotl(state->s[3], 45);
+
+ return result;
+}
+
+// Jump function - equivalent to 2^128 calls to xoshiro256_next
+// Useful for generating non-overlapping sequences for parallel computations
+static inline void xoshiro256_jump(xoshiro256_state *state) {
+ static const uint64_t JUMP[] = { 0x180ec6d33cfd0aba, 0xd5a61266f0c9392c, 0xa9582618e03fc9aa, 0x39abdc4529b1661c };
+
+ uint64_t s0 = 0;
+ uint64_t s1 = 0;
+ uint64_t s2 = 0;
+ uint64_t s3 = 0;
+ for(int i = 0; i < sizeof JUMP / sizeof *JUMP; i++) {
+ for(int b = 0; b < 64; b++) {
+ if (JUMP[i] & UINT64_C(1) << b) {
+ s0 ^= state->s[0];
+ s1 ^= state->s[1];
+ s2 ^= state->s[2];
+ s3 ^= state->s[3];
+ }
+ xoshiro256_next(state);
+ }
+ }
+
+ state->s[0] = s0;
+ state->s[1] = s1;
+ state->s[2] = s2;
+ state->s[3] = s3;
+}
+
+// Splitmix64 algorithm for initializing the state
+static inline uint64_t splitmix64(uint64_t *x) {
+ uint64_t z = (*x += 0x9e3779b97f4a7c15);
+ z = (z ^ (z >> 30)) * 0xbf58476d1ce4e5b9;
+ z = (z ^ (z >> 27)) * 0x94d049bb133111eb;
+ return z ^ (z >> 31);
+}
+
+// Initialize xoshiro256** state with a 64-bit seed
+static inline void xoshiro256_seed(xoshiro256_state *state, uint64_t seed) {
+ uint64_t tmp = seed;
+ state->s[0] = splitmix64(&tmp);
+ state->s[1] = splitmix64(&tmp);
+ state->s[2] = splitmix64(&tmp);
+ state->s[3] = splitmix64(&tmp);
+}
+
+// Initialize the global state with current time or provided seed
+static inline void rng_init(uint64_t seed) {
+ if (seed == 0) {
+ seed = (uint64_t)time(NULL);
+ }
+ xoshiro256_seed(&xoshiro_global_state, seed);
+}
+
+// Generate a random double between 0.0 and 1.0 (inclusive 0.0, exclusive 1.0)
+static inline f64 rng_uniform_f64(void) {
+ // Take the top 53 bits (the precision of a double) to get a value in [0,1)
+ return (xoshiro256_next(&xoshiro_global_state) >> 11) * (1.0 / 9007199254740992.0);
+}
+
+// Generate a random int32 in the range [0, max)
+static inline i32 rng_int32(i32 max) {
+ // Avoid modulo bias by using rejection sampling
+ const uint64_t threshold = (((uint64_t)-1) - ((uint64_t)-1) % max);
+ uint64_t r;
+ do {
+ r = xoshiro256_next(&xoshiro_global_state);
+ } while (r >= threshold);
+ return (i32)(r % max);
+}
+
diff --git a/src/tolsac.c b/src/tolsac.c new file mode 100644 index 0000000..4a3a0bc --- /dev/null +++ b/src/tolsac.c @@ -0,0 +1,691 @@ +/**
+ * tolsac.c - source file containing creation of samples with various distributions.
+ *
+ * Copyright (C) 2025 https://optics-design.com
+ *
+ * This program is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU Affero General Public License as
+ * published by the Free Software Foundation, either version 3 of the
+ * License, or (at your option) any later version.
+ *
+ * See the COPYING file for the full license text.
+ */
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <math.h>
+#include "tolmacdef.h"
+
+// Enum for sampling methods
+typedef enum {
+ SAMPLING_LHS, // Latin Hypercube Sampling
+ SAMPLING_MCS, // Monte Carlo Sampling
+ SAMPLING_GRID, // Grid Sampling
+ SAMPLING_SENS // Sensitivity Analysis
+} SamplingMethod;
+
+// Enum for probability distribution functions
+typedef enum {
+ PDF_UNIFORM = 'U',
+ PDF_NORMAL = 'N',
+ PDF_TRUNCNORMAL = 'T' // Truncated normal
+} PdfType;
+
+// Parameter data structure for improved memory locality
+typedef struct {
+ byte* pdf_types; // Probability distribution types
+ f64* nominal_values; // Nominal values
+ f64* min_values; // Minimum values
+ f64* max_values; // Maximum values
+ f64* mean_values; // Mean values
+ f64* stddev_values; // Standard deviation values
+} ParameterData;
+
+// Allocate memory for parameter data structure
+ParameterData* allocate_parameter_data(i32 param_count) {
+ ParameterData* data = (ParameterData*)malloc(sizeof(ParameterData));
+ if (!data) return NULL;
+
+ // Single contiguous allocation for all arrays for better cache locality
+ void* buffer = malloc(
+ param_count * sizeof(byte) + // pdf_types
+ param_count * sizeof(f64) * 5 // nominal, min, max, mean, stddev
+ );
+
+ if (!buffer) {
+ free(data);
+ return NULL;
+ }
+
+ // Assign pointers to appropriate sections of the buffer
+ data->pdf_types = (byte*)buffer;
+ data->nominal_values = (f64*)(data->pdf_types + param_count);
+ data->min_values = data->nominal_values + param_count;
+ data->max_values = data->min_values + param_count;
+ data->mean_values = data->max_values + param_count;
+ data->stddev_values = data->mean_values + param_count;
+
+ return data;
+}
+
+// Free parameter data structure
+void free_parameter_data(ParameterData* data) {
+ if (data) {
+ // Only need to free the buffer pointed to by pdf_types (first allocation)
+ free(data->pdf_types);
+ free(data);
+ }
+}
+
+// Normal CDF implementation (cumulative distribution function)
+static inline f64 normal_cdf(f64 x) {
+ // Constants for approximation
+ const f64 a1 = 0.254829592;
+ const f64 a2 = -0.284496736;
+ const f64 a3 = 1.421413741;
+ const f64 a4 = -1.453152027;
+ const f64 a5 = 1.061405429;
+ const f64 p = 0.3275911;
+
+ // Save the sign of x
+ int sign = 1;
+ if (x < 0) {
+ sign = -1;
+ x = -x;
+ }
+
+ // A&S formula 7.1.26
+ f64 t = 1.0 / (1.0 + p * x);
+ f64 y = 1.0 - (((((a5 * t + a4) * t + a3) * t + a2) * t + a1) * t * exp(-x * x));
+
+ return 0.5 * (1 + sign * y);
+}
+
+static inline f64 inverse_normal_cdf(f64 u) {
+ // Use the approximation of the inverse CDF of the normal distribution
+ const f64 a1 = -3.969683028665376e+01;
+ const f64 a2 = 2.209460984245205e+02;
+ const f64 a3 = -2.759285104469687e+02;
+ const f64 a4 = 1.383577518672690e+02;
+ const f64 a5 = -3.066479806614716e+01;
+ const f64 a6 = 2.506628277459239e+00;
+
+ const f64 b1 = -5.447609879822406e+01;
+ const f64 b2 = 1.615858368580409e+02;
+ const f64 b3 = -1.556989798598866e+02;
+ const f64 b4 = 6.680131188771972e+01;
+ const f64 b5 = -1.328068155288572e+01;
+
+ const f64 c1 = -7.784894002430293e-03;
+ const f64 c2 = -3.223964580411365e-01;
+ const f64 c3 = -2.400758277161838e+00;
+ const f64 c4 = -2.549732539343734e+00;
+ const f64 c5 = 4.374664141464968e+00;
+ const f64 c6 = 2.938163982698783e+00;
+
+ const f64 d1 = 7.784695709041462e-03;
+ const f64 d2 = 3.224671290700398e-01;
+ const f64 d3 = 2.445134137142996e+00;
+ const f64 d4 = 3.754408661907416e+00;
+
+ const f64 p_low = 0.02425;
+ const f64 p_high = 1.0 - p_low;
+
+ f64 q, r;
+
+ if (u < p_low) {
+ q = sqrt(-2.0 * log(u));
+ return (((((c1 * q + c2) * q + c3) * q + c4) * q + c5) * q + c6) /
+ ((((d1 * q + d2) * q + d3) * q + d4) * q + 1.0);
+ } else if (u <= p_high) {
+ q = u - 0.5;
+ r = q * q;
+ return (((((a1 * r + a2) * r + a3) * r + a4) * r + a5) * r + a6) * q /
+ (((((b1 * r + b2) * r + b3) * r + b4) * r + b5) * r + 1.0);
+ } else {
+ q = sqrt(-2.0 * log(1.0 - u));
+ return -(((((c1 * q + c2) * q + c3) * q + c4) * q + c5) * q + c6) /
+ ((((d1 * q + d2) * q + d3) * q + d4) * q + 1.0);
+ }
+}
+
+// Generate sample based on distribution type
+static inline f64 generate_sample(byte pdf_type, f64 u, f64 min, f64 max, f64 mean, f64 stddev) {
+ switch (pdf_type) {
+ case PDF_NORMAL: // 'N'
+ // For normal: mean + stddev * inverse_normal_cdf(u)
+ return mean + stddev * inverse_normal_cdf(u);
+
+ case PDF_TRUNCNORMAL: // 'T'
+ // For truncated normal:
+ {
+ // 1. Calculate normalized min and max values
+ f64 z_min = (min - mean) / stddev;
+ f64 z_max = (max - mean) / stddev;
+
+ // 2. Calculate CDF at min and max
+ f64 cdf_min = normal_cdf(z_min);
+ f64 cdf_max = normal_cdf(z_max);
+
+ // 3. Scale u to be between cdf_min and cdf_max
+ f64 u_scaled = cdf_min + u * (cdf_max - cdf_min);
+
+ // 4. Apply inverse CDF to get value in correct range
+ return mean + stddev * inverse_normal_cdf(u_scaled);
+ }
+
+ case PDF_UNIFORM: // 'U'
+ // For uniform: min + u * (max - min)
+ return min + u * (max - min);
+
+ default:
+ // Default to uniform if unknown
+ return min + u * (max - min);
+ }
+}
+
+// Function to display help information
+void display_help(void) {
+ printf("Description:\n"
+ "The program generates random values for parameters based on the "
+ "given range and the probability distribution. "
+ "It is expected that the output is used in another program "
+ "to perform tolerancing.\n\n"
+ "Usage:\n"
+ ".\\tolsamp.exe Input.csv 50 [-o Output.csv]\n"
+ "Where:\n"
+ "Input.csv is a csv input file of a specific form.\n"
+ "50 is the number of iterations produced (not required for the "
+ "grid and sensitivity samplings)\n"
+ "-o Output.csv specifies the output path and filename.\n"
+ "If not provided, output will be named Sampled_[Input.csv] in the current directory.\n"
+ "If you are unsure how the input should look like, "
+ "run the program with the -e option to generate an example "
+ "input file.\n\n"
+ "Options:\n"
+ "-l, -g, -m, -s are used for latin hypercube, grid, Monte Carlo "
+ "(random), and sensitivity samplings.\n"
+ "Use -m only if there are some issues with the LHS. "
+ "LHS usually is the best.\n"
+ "-o specifies the output filename (can include path).\n"
+ "-e is used for example csv generation.\n\n"
+ "Defaults:\n"
+ "In the default case the program runs with -l.\n\n"
+ "Input CSV file info:\n"
+ "CSV file also contains possible probability density functions "
+ "(U-Uniform, N-Normal, T-Truncated Normal) and the associated mean "
+ "and standard deviation parameters.\n"
+ "This program can produce symmetric normal and truncated normal distributions.");
+}
+
+// Function to generate example file
+void generate_example(void) {
+ FILE *writefile = fopen("Example.csv", "w");
+ if (!writefile) {
+ printf("Error creating example file!\n");
+ return;
+ }
+
+ fprintf(writefile, "Iteration Number,Parameter 1 Name,Parameter 2 Name,Parameter 3 Name,Parameter 4 Name\n");
+ fprintf(writefile, "Probability Density Function (U=Uniform; N=Normal/Gaussian; T=Truncated Normal),U,U,N,T\n");
+ fprintf(writefile, "Nominal, 0, 0, 6, 5\n");
+ fprintf(writefile, "Max, 1, 0.1, 10, 7\n");
+ fprintf(writefile, "Min, -1, -0.1, 2, 3\n");
+ fprintf(writefile, "Mean,0.0, 0.0, 6.0, 5.0\n");
+ fprintf(writefile, "Standard Deviation, 0, 0, 1.3, 1.0\n");
+ fclose(writefile);
+
+ printf("Example.csv file generated!\n");
+}
+
+// Optimized LHS interval generation that transposes for better cache locality
+void generate_lhs_intervals(i32 iterations, i32 param_count, i32* intervals) {
+ // Initialize intervals for LHS in column-major order for better cache locality
+ for (i32 i = 0; i < iterations; i++) {
+ for (i32 j = 0; j < param_count; j++) {
+ // Store in transposed form: intervals[i * param_count + j] = i
+ intervals[i * param_count + j] = i;
+ }
+ }
+
+ // Fisher-Yates shuffle for each parameter (each column)
+ for (i32 j = 0; j < param_count; j++) {
+ for (i32 i = iterations - 1; i > 0; i--) {
+ i32 k = rng_int32(i + 1);
+ // Swap intervals[i * param_count + j] and intervals[k * param_count + j]
+ i32 temp = intervals[i * param_count + j];
+ intervals[i * param_count + j] = intervals[k * param_count + j];
+ intervals[k * param_count + j] = temp;
+ }
+ }
+}
+
+i32 main(i32 argc, char *argv[]) {
+ // Initialize variables
+ SamplingMethod sampling_method = SAMPLING_LHS; // Default to LHS
+ i32 iterations = 0;
+ char rfilename[FILENAME_MAX] = {0};
+ char wfilename[FILENAME_MAX] = {0};
+ i32 input_specified = 0;
+ i32 output_specified = 0;
+ i32 expect_output_file = 0;
+
+ // Check for no arguments
+ if (argc == 1) {
+ printf("No input provided. Use -h for help.\n");
+ return 1;
+ }
+
+ // Process all arguments
+ for (i32 i = 1; i < argc; i++) {
+ if (argv[i][0] == '-') {
+ // Process flag arguments
+ switch (argv[i][1]) {
+ case 'h':
+ display_help();
+ return 0;
+ case 'e':
+ generate_example();
+ return 0;
+ case 'm':
+ sampling_method = SAMPLING_MCS;
+ break;
+ case 'g':
+ sampling_method = SAMPLING_GRID;
+ break;
+ case 'l':
+ sampling_method = SAMPLING_LHS;
+ break;
+ case 's':
+ sampling_method = SAMPLING_SENS;
+ break;
+ case 'o':
+ // Next argument should be the output file
+ expect_output_file = 1;
+ break;
+ default:
+ printf("Unknown option: -%c\n", argv[i][1]);
+ break;
+ }
+ } else if (expect_output_file) {
+ // This is the output file specified after -o
+ strcpy(wfilename, argv[i]);
+ output_specified = 1;
+ expect_output_file = 0;
+ } else if (strstr(argv[i], ".csv")) {
+ // This is an input CSV file (we now only allow one)
+ if (!input_specified) {
+ strcpy(rfilename, argv[i]);
+ input_specified = 1;
+ } else {
+ printf("Warning: Multiple input files specified. Using only the first one: %s\n", rfilename);
+ }
+ } else {
+ // Parse iteration count
+ i32 tmp = atoi(argv[i]);
+ if (tmp > 0) {
+ iterations = tmp;
+ }
+ }
+ }
+
+ // Error checks
+ if (!input_specified) {
+ printf("Error! No input CSV provided. Use -e if you want to see how it looks like.\n");
+ return 1;
+ }
+
+ if (expect_output_file) {
+ printf("Error! Expected an output file after -o option.\n");
+ return 1;
+ }
+
+ if (iterations == 0 && sampling_method != SAMPLING_GRID && sampling_method != SAMPLING_SENS) {
+ printf("Error! You didn't provide an integer number of iterations.\n");
+ return 1;
+ }
+
+ // Generate default output filename if not specified
+ if (!output_specified) {
+ // Generate default output filename - handle both Windows and Unix paths
+ char *file_ptr = strrchr(rfilename, '\\');
+ if (!file_ptr) {
+ file_ptr = strrchr(rfilename, '/');
+ }
+
+ strcpy(wfilename, "Sampled_");
+ if (file_ptr) {
+ strcat(wfilename, file_ptr + 1);
+ } else {
+ strcat(wfilename, rfilename);
+ }
+ }
+
+ // Check if input file exists
+ FILE *readfile = fopen(rfilename, "r");
+ if (!readfile) {
+ printf("Error! Input file not found: %s\n", rfilename);
+ return 1;
+ }
+
+ // Open output file
+ FILE *writefile = fopen(wfilename, "w");
+ if (!writefile) {
+ printf("Error! Could not create output file: %s\n", wfilename);
+ fclose(readfile);
+ return 1;
+ }
+
+ // Read the header line
+ char header_line[1024] = {0};
+ if (!fgets(header_line, sizeof(header_line), readfile)) {
+ printf("Error reading header line!\n");
+ fclose(readfile);
+ fclose(writefile);
+ return 1;
+ }
+
+ // Write the header line to output
+ fputs(header_line, writefile);
+
+ // Count parameters (commas)
+ i32 param_count = 0;
+ for (char *p = header_line; *p; p++) {
+ if (*p == ',') param_count++;
+ }
+
+ printf("1. Total of %d parameters detected in %s. Output file is named %s.\n", param_count, rfilename, wfilename);
+
+ // Initialize RNG with current time as seed
+ rng_init(0);
+
+ // Allocate parameter data structure
+ ParameterData* params = allocate_parameter_data(param_count);
+ if (!params) {
+ printf("Error! Memory allocation failed for parameter data.\n");
+ fclose(readfile);
+ fclose(writefile);
+ return 1;
+ }
+
+ // Parse input file - using larger fixed size buffers to avoid potential overflows
+ char buffer[32768]; // 32KB buffer for reading lines
+ char *token, *err_ptr;
+ i32 k = 0;
+
+ // PDFs
+ if (!fgets(buffer, sizeof(buffer), readfile)) {
+ printf("Error reading PDF line from input file!\n");
+ free_parameter_data(params);
+ fclose(readfile);
+ fclose(writefile);
+ return 1;
+ }
+
+ token = strtok(buffer, ",");
+ while ((token = strtok(NULL, ","))) {
+ params->pdf_types[k++] = *token;
+ }
+
+ // Nominal
+ k = 0;
+ if (!fgets(buffer, sizeof(buffer), readfile)) {
+ printf("Error reading nominal values from input file!\n");
+ free_parameter_data(params);
+ fclose(readfile);
+ fclose(writefile);
+ return 1;
+ }
+
+ token = strtok(buffer, ",");
+ while ((token = strtok(NULL, ","))) {
+ params->nominal_values[k++] = strtof(token, &err_ptr);
+ }
+
+ // Maxima
+ k = 0;
+ if (!fgets(buffer, sizeof(buffer), readfile)) {
+ printf("Error reading maximum values from input file!\n");
+ free_parameter_data(params);
+ fclose(readfile);
+ fclose(writefile);
+ return 1;
+ }
+
+ token = strtok(buffer, ",");
+ while ((token = strtok(NULL, ","))) {
+ params->max_values[k++] = strtof(token, &err_ptr);
+ }
+
+ // Minima
+ k = 0;
+ if (!fgets(buffer, sizeof(buffer), readfile)) {
+ printf("Error reading minimum values from input file!\n");
+ free_parameter_data(params);
+ fclose(readfile);
+ fclose(writefile);
+ return 1;
+ }
+
+ token = strtok(buffer, ",");
+ while ((token = strtok(NULL, ","))) {
+ params->min_values[k++] = strtof(token, &err_ptr);
+ }
+
+ // Means
+ k = 0;
+ if (!fgets(buffer, sizeof(buffer), readfile)) {
+ printf("Error reading mean values from input file!\n");
+ free_parameter_data(params);
+ fclose(readfile);
+ fclose(writefile);
+ return 1;
+ }
+
+ token = strtok(buffer, ",");
+ while ((token = strtok(NULL, ","))) {
+ params->mean_values[k++] = strtof(token, &err_ptr);
+ }
+
+ // Standard Deviations
+ k = 0;
+ if (!fgets(buffer, sizeof(buffer), readfile)) {
+ printf("Error reading standard deviation values from input file!\n");
+ free_parameter_data(params);
+ fclose(readfile);
+ fclose(writefile);
+ return 1;
+ }
+
+ token = strtok(buffer, ",");
+ while ((token = strtok(NULL, ","))) {
+ params->stddev_values[k++] = strtof(token, &err_ptr);
+ }
+
+ fclose(readfile);
+ printf("2. Input CSV successfully parsed.\n");
+
+ // Write initial reference row
+ fprintf(writefile, "1");
+ for (i32 j = 0; j < param_count; j++) {
+ fprintf(writefile, ",%g", params->nominal_values[j]);
+ }
+ fprintf(writefile, "\n");
+
+ // Generate and write samples based on sampling method
+ switch (sampling_method) {
+ case SAMPLING_MCS:
+ // Monte Carlo sampling
+ for (i32 i = 0; i < iterations; i++) {
+ // Write row number
+ fprintf(writefile, "%d", i + 2);
+
+ // Generate samples for each parameter
+ for (i32 j = 0; j < param_count; j++) {
+ f64 u = rng_uniform_f64();
+ f64 sample = generate_sample(params->pdf_types[j], u,
+ params->min_values[j], params->max_values[j],
+ params->mean_values[j], params->stddev_values[j]);
+
+ // If truncated normal and out of bounds, use uniform random between min and max
+ if (params->pdf_types[j] == PDF_TRUNCNORMAL &&
+ (sample < params->min_values[j] || sample > params->max_values[j])) {
+ f64 u_uniform = rng_uniform_f64();
+ sample = generate_sample('U', u_uniform,
+ params->min_values[j], params->max_values[j],
+ params->mean_values[j], params->stddev_values[j]);
+ }
+
+ fprintf(writefile, ",%g", sample);
+ }
+
+ fprintf(writefile, "\n");
+ }
+ break;
+
+ case SAMPLING_GRID:
+ // Handle grid sampling iterations for large parameter counts
+ if (param_count >= 30) { // 2^30 would be over a billion iterations
+ printf("Error: Grid sampling with %d parameters would result in too many iterations (2^%d).\n",
+ param_count, param_count);
+ free_parameter_data(params);
+ fclose(writefile);
+ return 1;
+ } else {
+ iterations = 1 << param_count;
+ if (param_count > 17) {
+ char response;
+ printf("You have %d parameters, which corresponds to %d iterations. Are you sure this is what you want? (y/n): ",
+ param_count, iterations);
+ scanf("%c", &response);
+ if (response != 'y') {
+ printf("Quitting.\n");
+ free_parameter_data(params);
+ fclose(writefile);
+ return 1;
+ }
+ }
+ }
+
+ // Grid sampling
+ for (i32 i = 0; i < iterations; i++) {
+ // Write row number
+ fprintf(writefile, "%d", i + 2);
+
+ // Generate samples for each parameter
+ for (i32 j = 0; j < param_count; j++) {
+ f64 sample;
+
+ // Safe bit manipulation even for large parameter counts
+ if ((i >> j) & 1) {
+ sample = params->max_values[j];
+ } else {
+ sample = params->min_values[j];
+ }
+
+ fprintf(writefile, ",%g", sample);
+ }
+
+ fprintf(writefile, "\n");
+ }
+ break;
+
+ case SAMPLING_SENS:
+ // Calculate number of iterations needed: 2 * number of parameters
+ iterations = 2 * param_count;
+
+ // For each parameter
+ for (i32 j = 0; j < param_count; j++) {
+ // Parameter at minimum, others at nominal
+ fprintf(writefile, "%d", j*2 + 2);
+ for (i32 k = 0; k < param_count; k++) {
+ if (k == j) {
+ // This parameter at minimum
+ fprintf(writefile, ",%g", params->min_values[k]);
+ } else {
+ // Others at nominal
+ fprintf(writefile, ",%g", params->nominal_values[k]);
+ }
+ }
+ fprintf(writefile, "\n");
+
+ // Parameter at maximum, others at nominal
+ fprintf(writefile, "%d", j*2 + 3);
+ for (i32 k = 0; k < param_count; k++) {
+ if (k == j) {
+ // This parameter at maximum
+ fprintf(writefile, ",%g", params->max_values[k]);
+ } else {
+ // Others at nominal
+ fprintf(writefile, ",%g", params->nominal_values[k]);
+ }
+ }
+ fprintf(writefile, "\n");
+ }
+ break;
+
+ case SAMPLING_LHS:
+ // Allocate intervals for LHS (transposed for better cache locality)
+ i32 *intervals = (i32*)malloc(iterations * param_count * sizeof(i32));
+ if (!intervals) {
+ printf("Error! Memory allocation failed for intervals.\n");
+ free_parameter_data(params);
+ fclose(writefile);
+ return 1;
+ }
+
+ // Generate LHS intervals with improved cache locality
+ generate_lhs_intervals(iterations, param_count, intervals);
+
+ // Latin Hypercube sampling
+ for (i32 i = 0; i < iterations; i++) {
+ // Write row number
+ fprintf(writefile, "%d", i + 2);
+
+ // Generate samples for each parameter
+ for (i32 j = 0; j < param_count; j++) {
+ // Get interval for this parameter (accessing in transposed form)
+ i32 interval = intervals[i * param_count + j];
+ f64 interval_start = (f64)interval / iterations;
+ f64 interval_end = (f64)(interval + 1) / iterations;
+ f64 u = interval_start + rng_uniform_f64() * (interval_end - interval_start);
+
+ f64 sample = generate_sample(params->pdf_types[j], u,
+ params->min_values[j], params->max_values[j],
+ params->mean_values[j], params->stddev_values[j]);
+
+ // Handle truncated normal out-of-bounds samples with improved approach
+ while (params->pdf_types[j] == PDF_TRUNCNORMAL &&
+ (sample < params->min_values[j] || sample > params->max_values[j])) {
+ // Try a new sample within the same interval for better statistical properties
+ i32 i_rej = rng_int32(iterations);
+ i32 j_rej = rng_int32(param_count);
+ i32 interval_rej = intervals[i_rej * param_count + j_rej];
+ f64 interval_start_rej = (f64)interval_rej / iterations;
+ f64 interval_end_rej = (f64)(interval_rej + 1) / iterations;
+ f64 u_rej = interval_start_rej + rng_uniform_f64() * (interval_end_rej - interval_start_rej);
+ sample = generate_sample(params->pdf_types[j], u_rej,
+ params->min_values[j], params->max_values[j],
+ params->mean_values[j], params->stddev_values[j]);
+ }
+
+ fprintf(writefile, ",%g", sample);
+ }
+
+ fprintf(writefile, "\n");
+ }
+
+ // Free LHS intervals
+ free(intervals);
+ break;
+ }
+
+ // Clean up
+ free_parameter_data(params);
+
+ printf("3. %d rows and %d columns have been written to the file.\nProgram finished! Check the output!\n",
+ iterations + 1, param_count + 1);
+ fclose(writefile);
+
+ return 0;
+}
diff --git a/structure.txt b/structure.txt new file mode 100644 index 0000000..3b7407e --- /dev/null +++ b/structure.txt @@ -0,0 +1,25 @@ +tolsac
+| .clangd
+| .gitignore
+| compile_flags.txt
+| COPYING
+| COPYRIGHT
+| Makefile
+| README.md
+| structure.txt
+|
++---bin
+| tolsac.exe
+|
++---include
+| toldef.h
+| tolmacdef.h
+|
++---src
+| tolsac.c
+|
+\---tests
+ Example.csv
+ hist.r
+ Sampled_Example.csv
+
diff --git a/tests/Example.csv b/tests/Example.csv new file mode 100644 index 0000000..2c6ed66 --- /dev/null +++ b/tests/Example.csv @@ -0,0 +1,7 @@ +Iteration Number,Parameter 1 Name,Parameter 2 Name,Parameter 3 Name,Parameter 4 Name
+Probability Density Function (U=Uniform; N=Normal/Gaussian; T=Truncated Normal),U,U,N,T
+Nominal, 0, 0, 6, 5
+Max, 1, 0.1, 10, 7
+Min, -1, -0.1, 2, 3
+Mean,0.0, 0.0, 6.0, 5.0
+Standard Deviation, 0, 0, 1.3, 1.5
diff --git a/tests/Sampled_Example.csv b/tests/Sampled_Example.csv new file mode 100644 index 0000000..4357a72 --- /dev/null +++ b/tests/Sampled_Example.csv @@ -0,0 +1,1002 @@ +Iteration Number,Parameter 1 Name,Parameter 2 Name,Parameter 3 Name,Parameter 4 Name
+1,0,0,6,5
+2,-0.447882,-0.0167241,2.57527,4.37054
+3,0.968682,-0.0586998,7.45248,5.06988
+4,-0.842439,-0.0561725,6.52784,5.01692
+5,-0.395612,0.0442927,4.92421,5.38399
+6,0.0420414,0.0584518,8.34442,3.96429
+7,-0.730353,0.00440439,7.07359,4.5874
+8,-0.464467,-0.00851533,6.55851,4.45161
+9,-0.996313,0.0883977,5.48266,3.83068
+10,-0.716747,0.0174173,7.06949,5.65414
+11,-0.218966,0.0199446,7.29352,6.10357
+12,0.716854,-0.00492169,5.5187,4.27839
+13,0.654153,-0.00251044,5.53421,5.51787
+14,0.265945,-0.0634619,8.16951,4.17465
+15,0.736711,-0.0784835,4.87941,4.75171
+16,-0.732259,-0.0571093,5.59827,4.18606
+17,0.826133,-0.0293251,6.51912,6.4991
+18,0.986128,0.014704,5.47865,3.89358
+19,-0.672543,-0.0179533,7.21728,4.71736
+20,0.339959,0.0226431,6.15126,5.12981
+21,0.712746,-0.0704099,5.71231,4.53799
+22,-0.713658,-0.0568181,5.76801,5.43618
+23,0.697891,-0.0548865,5.5665,5.29645
+24,-0.746132,0.0634282,4.62583,4.83363
+25,0.781298,-0.0111269,4.26713,6.79052
+26,0.795403,0.0501854,5.22077,3.26088
+27,0.657083,-0.0880077,3.84039,3.87686
+28,-0.710088,-0.0532829,7.09584,4.47555
+29,0.701074,-0.0691546,8.04346,4.01534
+30,0.650317,-0.0203927,5.82273,3.84334
+31,0.0681637,0.0888699,6.53559,3.81846
+32,-0.900036,0.0988853,5.56396,3.23206
+33,-0.939905,-0.0604353,5.63373,5.726
+34,-0.285908,0.0605226,7.77042,4.78695
+35,0.359812,-0.0106921,6.72127,6.19754
+36,-0.971847,0.0587401,5.84393,5.93756
+37,-0.192236,0.0783495,4.46394,6.07152
+38,0.527827,0.0127592,5.68722,3.761
+39,-0.629812,-0.0360936,4.54711,3.87198
+40,0.955132,0.0494486,5.80461,4.66938
+41,-0.502164,-0.0948897,6.96044,4.406
+42,-0.8638,-0.0364034,5.39141,4.11295
+43,0.456398,-0.0667134,5.74804,5.84887
+44,0.0230029,-0.0822804,6.51306,3.99537
+45,0.678608,0.0979709,6.01203,5.78782
+46,0.52493,-0.0645964,4.84491,3.88287
+47,-0.314555,-0.096328,5.68084,3.67162
+48,-0.57384,0.0377082,5.45966,4.52394
+49,0.964836,0.0856935,8.93762,3.62629
+50,-0.985586,-0.0135407,5.94306,5.71236
+51,-0.17499,0.0588124,4.73971,3.16714
+52,0.453448,-0.0366041,7.23516,6.32631
+53,0.575641,-0.0452107,4.80583,6.09766
+54,-0.561616,0.0203688,6.20688,4.93558
+55,0.413243,0.00117644,5.38056,4.96387
+56,-0.119488,-0.0208496,7.11087,4.70102
+57,0.908748,-0.0554151,6.94175,5.46428
+58,0.436673,-0.076233,6.95583,4.72678
+59,-0.812224,-0.0741759,4.00006,6.54116
+60,-0.213875,0.0815374,3.43701,6.11375
+61,0.0997154,-0.0959217,6.10218,5.54485
+62,-0.182745,-0.0713067,5.2254,4.8262
+63,0.643063,-0.0777787,6.29343,4.97055
+64,-0.657443,-0.0261338,7.44153,5.81302
+65,0.907553,0.0529798,1.21361,4.73027
+66,-0.727405,-0.000964708,6.71294,4.59887
+67,0.991171,0.0410835,4.09214,5.75676
+68,-0.403396,0.0474208,5.73934,3.92465
+69,0.369762,-0.0263021,7.06172,5.55773
+70,-0.535768,0.0908397,6.74394,6.24678
+71,0.470341,0.0117787,5.72393,6.94032
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diff --git a/tests/hist.r b/tests/hist.r new file mode 100644 index 0000000..ee20d09 --- /dev/null +++ b/tests/hist.r @@ -0,0 +1,84 @@ +#!/usr/bin/env Rscript
+
+# Parse arguments (filename, column number, min, max, bins)
+args <- commandArgs(trailingOnly = TRUE)
+filename <- args[1]
+# start_row <- as.numeric(args[2])
+start_row <- 2
+col_num <- as.numeric(args[2])
+
+# Optional min and max values (positions 3 and 4)
+user_min <- ifelse(length(args) >= 3, as.numeric(args[3]), NA)
+user_max <- ifelse(length(args) >= 4, as.numeric(args[4]), NA)
+
+# Number of bins now at the end (position 5)
+num_bins <- ifelse(length(args) >= 5, as.numeric(args[5]), 20) # Default to 20 bins if not specified
+
+# Read the CSV file (assuming it has a header)
+data <- read.csv(filename, header = TRUE)
+
+# Extract the data
+plot_data <- data[start_row:nrow(data), col_num]
+
+# Convert to numeric
+plot_data <- as.numeric(as.character(plot_data))
+plot_data <- plot_data[!is.na(plot_data)]
+
+# Determine range bounds
+data_min <- min(plot_data)
+data_max <- max(plot_data)
+
+range_min <- ifelse(is.na(user_min), data_min, user_min)
+range_max <- ifelse(is.na(user_max), data_max, user_max)
+
+# Calculate percentage of samples within the range
+count_in_range <- sum(plot_data >= range_min & plot_data <= range_max)
+total_count <- length(plot_data)
+percent_in_range <- (count_in_range / total_count) * 100
+
+# Output to command line
+cat(sprintf("%.2f%% of samples are within the range [%.2f, %.2f]\n",
+ percent_in_range, range_min, range_max))
+
+# Set up graphics
+windows(width = 8, height = 6)
+
+# Create the histogram
+hist(plot_data,
+ breaks = num_bins,
+ main = paste("Histogram of Column", col_num),
+ xlab = "Value",
+ col = "lightblue",
+ border = "black")
+
+# Add vertical lines for min and max if provided by user
+if(!is.na(user_min)) {
+ abline(v = user_min, col = "red", lwd = 2, lty = 2)
+}
+if(!is.na(user_max)) {
+ abline(v = user_max, col = "red", lwd = 2, lty = 2)
+}
+
+# Add basic statistics including standard deviation
+stats_text <- paste0("n = ", length(plot_data), "\n",
+ "min = ", sprintf("%.2f", data_min), "\n",
+ "max = ", sprintf("%.2f", data_max), "\n",
+ "mean = ", sprintf("%.2f", mean(plot_data)), "\n",
+ "median = ", sprintf("%.2f", median(plot_data)), "\n",
+ "sd = ", sprintf("%.2f", sd(plot_data)), "\n",
+ sprintf("%.2f%% in [%.2f, %.2f]",
+ percent_in_range, range_min, range_max))
+
+text_x <- par("usr")[1] + 0.05 * (par("usr")[2] - par("usr")[1])
+text_y <- par("usr")[4] - 0.1 * (par("usr")[4] - par("usr")[3])
+text(text_x, text_y, stats_text, adj = c(0, 1))
+
+# Add text to instruct user
+mtext("Close this window using the X button when done", side = 1, line = 4, col = "red")
+
+# Tell user what to do
+cat("Histogram displayed. Close the window manually when done viewing.\n")
+cat("Usage: Rscript hist.r filename.csv column_number [min] [max] [bins]\n")
+
+# Keep the script running until the window is closed
+Sys.sleep(1000) # Keep script running for a long time
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