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libSUFR
a LIBrary of Some Useful Fortran Routines
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libSUFR
a LIBrary of Some Useful Fortran Routines
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Procedures to compute statistics. More...
Go to the source code of this file.
Modules | |
| module | sufr_statistics |
| Procedures to do statistics. | |
Functions/Subroutines | |
| real(double) function | sufr_statistics::median (data, mask) |
| Compute the median of a data array. | |
| real function | sufr_statistics::median_sp (data, mask) |
| Compute the median of a data array - single-precision wrapper for median() | |
| real(double) function | sufr_statistics::mean (data, mask) |
| Compute the mean of a data array. | |
| real function | sufr_statistics::mean_sp (data, mask) |
| Compute the mean of a data array - single-precision wrapper for mean() | |
| real(double) function | sufr_statistics::mean_weight (data, wgts, mask) |
| Compute the weighted mean of a data array. | |
| real(double) function | sufr_statistics::stdev (data, dmean, mask, var) |
| Compute the standard deviation of a data array with mean 'mean'. | |
| real function | sufr_statistics::stdev_sp (data, mean, mask) |
| Compute the standard deviation of a data array with mean 'mean' - single-precision wrapper for stDev() | |
| real(double) function | sufr_statistics::stdev_weight (data, wgts, wmean, mask) |
| Compute the weighted standard deviation of a data array with weighted mean 'wMean'. | |
| pure subroutine | sufr_statistics::mean_var_running (mean, var, data, num, stdev) |
| Compute a running mean and variance by adding a data point and the data point number to existing values. If num=1, initialise. Note that mean and var are I/O variables and cannot be dummy variables or values. Num must be accurate, and increased by one between calls by the user. Optionally, return the standard deviation. | |
| subroutine | sufr_statistics::prob_range (data, range, llim, ulim, mask) |
| Find a given probability range for a data array - the narrowest range that contains a given fraction of data points. | |
| subroutine | sufr_statistics::prob_range_sp (data, range, llim, ulim, mask) |
| Find a given probability range for a data array - the narrowest range that contains a given fraction of data points - single-precision wrapper for prob_range() | |
| pure integer function | sufr_statistics::determine_nbin_1d (npoints) |
| Roughly estimate the number of 1D bins needed, from the number of data points. | |
| subroutine | sufr_statistics::bin_data_1d (xdat, nbin, norm, mode, cumul, xmin, xmax, xbin, ybin) |
| Bin data in 1D bins by counting the number of data points in each bin. | |
| subroutine | sufr_statistics::bin_data_1d_sp (xdat, nbin, norm, mode, cumul, xmin, xmax, xbin, ybin) |
| Bin data in 1D bins by counting the number of data points in each bin - single-precision wrapper for bin_data_1d() | |
| pure subroutine | sufr_statistics::histogram_1d_onthefly (xdat, nbin, mode, cumul, xmin, xmax, xbin, ybin, init, weight) |
| Create a 1D histogram on the fly (point by point). Bin data points by computing the bin they should be in. | |
| subroutine | sufr_statistics::bin_data_2d (xdat, ydat, norm, nxbin, nybin, xmin, xmax, ymin, ymax, z, tr, weights) |
| Bin data in 2 dimensions - computing the bin number rather than searching for it is ~10x faster. | |
| subroutine | sufr_statistics::bin_data_2d_sp (xdat, ydat, norm, nxbin, nybin, xmin, xmax, ymin, ymax, z, tr, weights) |
| Bin data in 2 dimensions - single-precision wrapper for bin_data_2d() | |
| subroutine | sufr_statistics::histogram_2d_onthefly (xdat, ydat, nxbin, nybin, xmin, xmax, ymin, ymax, z, init, weight, tr) |
| Bin data in 2 dimensions - computing the bin number rather than searching for it is ~10x faster. | |
| pure integer(long) function | sufr_statistics::faculty_i (n) |
| Compute the faculty of an integer, returning a long integer. | |
| pure real(double) function | sufr_statistics::faculty (n) |
| Compute the faculty of an integer, returning a double-precision real. | |
| pure real(double) function | sufr_statistics::binom_coeff (n, k) |
| Compute the binomial coefficient of n and k. | |
| pure real(double) function | sufr_statistics::binom_prob (n, k, p) |
| Compute the binomial probability of n and k, and probability p. | |
| pure real(double) function | sufr_statistics::binom_cumul_prob (n, k, p) |
| Compute the cumulative binomial probability of n and k OR FEWER, and probability p. | |
| pure real(double) function | sufr_statistics::poisson_prob (k, lambda) |
| Compute the Poisson probability of EXACTLY k events occurring in a fixed interval for a known average rate lambda, and independently of the time since the last event: P = λ^k e^-λ / k! | |
| pure real(double) function | sufr_statistics::poisson_prob_cumul (k, lambda) |
| Compute the cumulative Poisson probability of k OR FEWER events occurring in a fixed interval for a known average rate lambda, and independently of the time since the last event: P = Σ(i=0,k) λ^i e^-λ / i! | |
| real(double) function | sufr_statistics::correlation (data1, data2) |
| Compute the normalised correlation between two data series. | |
Procedures to compute statistics.
Definition in file statistics.f90.
1.12.0
