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@@ -14,7 +14,6 @@ The following plot shows results of a benchmark on a 9 GHz Macbook Pro. Random n
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[[hs_sd] [[classref boost::histogram::static_histogram static_histogram] with [classref boost::histogram::adaptive_storage adaptive_storage]]]
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[[hd_ss] [[classref boost::histogram::dynamic_histogram dynamic_histogram] with [classref boost::histogram::container_storage<std::vector<int>> container_storage<std::vector<int>>]]]
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[[hd_sd] [[classref boost::histogram::dynamic_histogram dynamic_histogram] with [classref boost::histogram::adaptive_storage adaptive_storage]]]
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]
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[classref boost::histogram::static_histogram static_histogram] is always faster than [classref boost::histogram::dynamic_histogram dynamic_histogram] and safer to use, as more checks are done at compile time. It is recommended when working in C++ only. [classref boost::histogram::adaptive_storage adaptive_storage] is faster than [classref boost::histogram::container_storage container_storage] for larger histograms, because it uses the cache more effectively due to its smaller memory consumption per bin.
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@@ -1,8 +1,6 @@
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[section Tutorial]
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[section Example 1: 1d-histogram in C++]
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How to make a 1d-histogram in C++ and to fill it:
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Example 1: How to make a 1d-histogram in C++ and to fill it.
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[c++]
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```
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@@ -55,7 +53,7 @@ How to make a 1d-histogram in C++ and to fill it:
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}
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```
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Example 2: Fill a 2d-histogram in Python with data in Numpy arrays
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Example 2: How to make, fill, and use a 2d-histogram in Python.
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[python]
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```
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