|  | #ifndef Stats_DEFINED | 
|  | #define Stats_DEFINED | 
|  |  | 
|  | #include "SkString.h" | 
|  | #include "SkTSort.h" | 
|  |  | 
|  | #ifdef SK_BUILD_FOR_WIN | 
|  | static const char* kBars[] = { ".", "o", "O" }; | 
|  | #else | 
|  | static const char* kBars[] = { "▁", "▂", "▃", "▄", "▅", "▆", "▇", "█" }; | 
|  | #endif | 
|  |  | 
|  | struct Stats { | 
|  | Stats(const double samples[], int n) { | 
|  | min = samples[0]; | 
|  | max = samples[0]; | 
|  | for (int i = 0; i < n; i++) { | 
|  | if (samples[i] < min) { min = samples[i]; } | 
|  | if (samples[i] > max) { max = samples[i]; } | 
|  | } | 
|  |  | 
|  | double sum = 0.0; | 
|  | for (int i = 0 ; i < n; i++) { | 
|  | sum += samples[i]; | 
|  | } | 
|  | mean = sum / n; | 
|  |  | 
|  | double err = 0.0; | 
|  | for (int i = 0 ; i < n; i++) { | 
|  | err += (samples[i] - mean) * (samples[i] - mean); | 
|  | } | 
|  | var = err / (n-1); | 
|  |  | 
|  | SkAutoTMalloc<double> sorted(n); | 
|  | memcpy(sorted.get(), samples, n * sizeof(double)); | 
|  | SkTQSort(sorted.get(), sorted.get() + n - 1); | 
|  | median = sorted[n/2]; | 
|  |  | 
|  | // Normalize samples to [min, max] in as many quanta as we have distinct bars to print. | 
|  | for (int i = 0; i < n; i++) { | 
|  | if (min == max) { | 
|  | // All samples are the same value.  Don't divide by zero. | 
|  | plot.append(kBars[0]); | 
|  | continue; | 
|  | } | 
|  |  | 
|  | double s = samples[i]; | 
|  | s -= min; | 
|  | s /= (max - min); | 
|  | s *= (SK_ARRAY_COUNT(kBars) - 1); | 
|  | const size_t bar = (size_t)(s + 0.5); | 
|  | SK_ALWAYSBREAK(bar < SK_ARRAY_COUNT(kBars)); | 
|  | plot.append(kBars[bar]); | 
|  | } | 
|  | } | 
|  |  | 
|  | double min; | 
|  | double max; | 
|  | double mean;    // Estimate of population mean. | 
|  | double var;     // Estimate of population variance. | 
|  | double median; | 
|  | SkString plot;  // A single-line bar chart (_not_ histogram) of the samples. | 
|  | }; | 
|  |  | 
|  | #endif//Stats_DEFINED |