import ministat utility from freebsd
This commit is contained in:
Ben Gras
parent
cb7a3b0299
commit
d83af38877
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@ -2,6 +2,10 @@
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.include <bsd.own.mk>
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# NetBSD imports
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SUBDIR= indent m4 mkimage
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# Non-NetBSD imports
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SUBDIR+= ministat
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.include <bsd.subdir.mk>
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14
usr.bin/ministat/Makefile
Normal file
14
usr.bin/ministat/Makefile
Normal file
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@ -0,0 +1,14 @@
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# $FreeBSD$
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PROG= ministat
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DPADD= ${LIBM}
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LDADD= -lm
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.include <bsd.prog.mk>
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test: ${PROG}
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./${PROG} < ${.CURDIR}/chameleon
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./${PROG} ${.CURDIR}/chameleon
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./${PROG} ${.CURDIR}/iguana ${.CURDIR}/chameleon
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./${PROG} -c 80 ${.CURDIR}/iguana ${.CURDIR}/chameleon
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./${PROG} -s -c 80 ${.CURDIR}/chameleon ${.CURDIR}/iguana
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./${PROG} -s -c 80 ${.CURDIR}/chameleon ${.CURDIR}/iguana ${.CURDIR}/iguana
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50
usr.bin/ministat/README
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50
usr.bin/ministat/README
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$FreeBSD$
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A small tool to do the statistics legwork on benchmarks etc.
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Prepare your data into two files, one number per line
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run
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./ministat data_before data_after
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and see what it says.
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You need at least three data points in each data set, but the more
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you have the better your result generally gets.
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Here are two typical outputs:
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x _1
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+ _2
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+--------------------------------------------------------------------------+
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|x + x+ x x x + ++ |
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| |_________|______AM_______________|__A___________M_______________||
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+--------------------------------------------------------------------------+
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N Min Max Median Avg Stddev
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x 5 36060 36138 36107 36105.6 31.165686
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+ 5 36084 36187 36163 36142.6 49.952978
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No difference proven at 95.0% confidence
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Here nothing can be concluded from the numbers. It _may_ be possible to
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prove something if many more measurements are made, but with only five
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measurements, nothing is proven.
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x _1
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+ _2
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+--------------------------------------------------------------------------+
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| + |
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| x + +|
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|x x x x + +|
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| |_______________A_____M_________| |_M___A____| |
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+--------------------------------------------------------------------------+
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N Min Max Median Avg Stddev
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x 5 0.133 0.137 0.136 0.1354 0.0015165751
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+ 5 0.139 0.14 0.139 0.1394 0.00054772256
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Difference at 95.0% confidence
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0.004 +/- 0.00166288
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2.95421% +/- 1.22812%
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(Student's t, pooled s = 0.00114018)
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Here we have a clearcut difference, not very big, but clear and unambiguous.
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6
usr.bin/ministat/chameleon
Normal file
6
usr.bin/ministat/chameleon
Normal file
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# $FreeBSD$
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150
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400
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720
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500
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930
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8
usr.bin/ministat/iguana
Normal file
8
usr.bin/ministat/iguana
Normal file
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@ -0,0 +1,8 @@
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# $FreeBSD$
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50
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200
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150
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400
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750
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400
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150
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130
usr.bin/ministat/ministat.1
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130
usr.bin/ministat/ministat.1
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.\"
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.\" Copyright (c) 2007 Poul-Henning Kamp
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.\" All rights reserved.
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.\"
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.\" Redistribution and use in source and binary forms, with or without
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.\" modification, are permitted provided that the following conditions
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.\" are met:
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.\" 1. Redistributions of source code must retain the above copyright
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.\" notice, this list of conditions and the following disclaimer.
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.\" 2. Redistributions in binary form must reproduce the above copyright
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.\" notice, this list of conditions and the following disclaimer in the
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.\" documentation and/or other materials provided with the distribution.
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.\"
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.\" THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS ``AS IS'' AND
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.\" ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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.\" IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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.\" ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE
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.\" FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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.\" DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
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.\" OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
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.\" HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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.\" LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
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.\" OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
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.\" SUCH DAMAGE.
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.\"
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.\" $FreeBSD$
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.\"
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.Dd June 28, 2010
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.Dt MINISTAT 1
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.Os
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.Sh NAME
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.Nm ministat
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.Nd statistics utility
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.Sh SYNOPSIS
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.Nm
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.Op Fl ns
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.Op Fl C Ar column
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.Op Fl c Ar confidence_level
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.Op Fl d Ar delimiter
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.Op Fl w Op width
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.Op Ar
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.Sh DESCRIPTION
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The
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.Nm
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command calculates fundamental statistical properties of numeric data
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in the specified files or, if no file is specified, standard input.
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.Pp
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The options are as follows:
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.Bl -tag -width Fl
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.It Fl n
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Just report the raw statistics of the input, suppress the ASCII-art plot
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and the relative comparisons.
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.It Fl s
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Print the average/median/stddev bars on separate lines in the ASCII-art
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plot, to avoid overlap.
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.It Fl C Ar column
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Specify which column of data to use.
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By default the first column in the input file(s) are used.
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.It Fl c Ar confidence_level
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Specify desired confidence level for Student's T analysis.
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Possible values are 80, 90, 95, 98, 99 and 99.5 %
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.It Fl d Ar delimiter
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Specifies the column delimiter characters, default is SPACE and TAB.
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See
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.Xr strtok 3
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for details.
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.It Fl w Ar width
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Width of ASCII-art plot in characters, default is 74.
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.El
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.Pp
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A sample output could look like this:
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.Bd -literal -offset indent
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$ ministat -s -w 60 iguana chameleon
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x iguana
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+ chameleon
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+------------------------------------------------------------+
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|x * x * + + x +|
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| |________M______A_______________| |
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| |________________M__A___________________| |
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+------------------------------------------------------------+
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N Min Max Median Avg Stddev
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x 7 50 750 200 300 238.04761
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+ 5 150 930 500 540 299.08193
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No difference proven at 95.0% confidence
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.Ed
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.Pp
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If
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.Nm
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tells you, as in the example above, that there is no difference
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proven at 95% confidence, the two data sets you gave it are for
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all statistical purposes identical.
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.Pp
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You have the option of lowering your standards by specifying a
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lower confidence level:
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.Bd -literal -offset indent
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$ ministat -s -w 60 -c 80 iguana chameleon
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x iguana
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+ chameleon
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+------------------------------------------------------------+
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|x * x * + + x +|
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| |________M______A_______________| |
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| |________________M__A___________________| |
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+------------------------------------------------------------+
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N Min Max Median Avg Stddev
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x 7 50 750 200 300 238.04761
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+ 5 150 930 500 540 299.08193
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Difference at 80.0% confidence
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240 +/- 212.215
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80% +/- 70.7384%
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(Student's t, pooled s = 264.159)
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.Ed
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.Pp
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But a lower standard does not make your data any better, and the
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example is only included here to show the format of the output when
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a statistical difference is proven according to Student's T method.
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.Sh SEE ALSO
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Any mathematics text on basic statistics, for instances Larry Gonicks
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excellent "Cartoon Guide to Statistics" which supplied the above example.
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.Sh HISTORY
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The
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.Nm
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command was written by Poul-Henning Kamp out of frustration
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over all the bogus benchmark claims made by people with no
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understanding of the importance of uncertainty and statistics.
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.Pp
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From
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.Fx 5.2
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it has lived in the source tree as a developer tool, graduating
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to the installed system from
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.Fx 8.0 .
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631
usr.bin/ministat/ministat.c
Normal file
631
usr.bin/ministat/ministat.c
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/*
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* ----------------------------------------------------------------------------
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* "THE BEER-WARE LICENSE" (Revision 42):
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* <phk@FreeBSD.ORG> wrote this file. As long as you retain this notice you
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* can do whatever you want with this stuff. If we meet some day, and you think
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* this stuff is worth it, you can buy me a beer in return. Poul-Henning Kamp
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* ----------------------------------------------------------------------------
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*
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*/
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#include <sys/cdefs.h>
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#if 0
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__FBSDID("$FreeBSD$");
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#endif
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#include <stdio.h>
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#include <math.h>
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#include <err.h>
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#include <string.h>
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#include <stdlib.h>
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#include <unistd.h>
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#include <sys/ioctl.h>
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#include <sys/queue.h>
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#include <sys/ttycom.h>
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#define NSTUDENT 100
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#define NCONF 6
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double const studentpct[] = { 80, 90, 95, 98, 99, 99.5 };
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double student [NSTUDENT + 1][NCONF] = {
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/* inf */ { 1.282, 1.645, 1.960, 2.326, 2.576, 3.090 },
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/* 1. */ { 3.078, 6.314, 12.706, 31.821, 63.657, 318.313 },
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/* 2. */ { 1.886, 2.920, 4.303, 6.965, 9.925, 22.327 },
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/* 3. */ { 1.638, 2.353, 3.182, 4.541, 5.841, 10.215 },
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/* 4. */ { 1.533, 2.132, 2.776, 3.747, 4.604, 7.173 },
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/* 5. */ { 1.476, 2.015, 2.571, 3.365, 4.032, 5.893 },
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/* 6. */ { 1.440, 1.943, 2.447, 3.143, 3.707, 5.208 },
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/* 7. */ { 1.415, 1.895, 2.365, 2.998, 3.499, 4.782 },
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/* 8. */ { 1.397, 1.860, 2.306, 2.896, 3.355, 4.499 },
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/* 9. */ { 1.383, 1.833, 2.262, 2.821, 3.250, 4.296 },
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/* 10. */ { 1.372, 1.812, 2.228, 2.764, 3.169, 4.143 },
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/* 11. */ { 1.363, 1.796, 2.201, 2.718, 3.106, 4.024 },
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/* 12. */ { 1.356, 1.782, 2.179, 2.681, 3.055, 3.929 },
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/* 13. */ { 1.350, 1.771, 2.160, 2.650, 3.012, 3.852 },
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/* 14. */ { 1.345, 1.761, 2.145, 2.624, 2.977, 3.787 },
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/* 15. */ { 1.341, 1.753, 2.131, 2.602, 2.947, 3.733 },
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/* 16. */ { 1.337, 1.746, 2.120, 2.583, 2.921, 3.686 },
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/* 17. */ { 1.333, 1.740, 2.110, 2.567, 2.898, 3.646 },
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/* 18. */ { 1.330, 1.734, 2.101, 2.552, 2.878, 3.610 },
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/* 19. */ { 1.328, 1.729, 2.093, 2.539, 2.861, 3.579 },
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/* 20. */ { 1.325, 1.725, 2.086, 2.528, 2.845, 3.552 },
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/* 21. */ { 1.323, 1.721, 2.080, 2.518, 2.831, 3.527 },
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/* 22. */ { 1.321, 1.717, 2.074, 2.508, 2.819, 3.505 },
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/* 23. */ { 1.319, 1.714, 2.069, 2.500, 2.807, 3.485 },
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/* 24. */ { 1.318, 1.711, 2.064, 2.492, 2.797, 3.467 },
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/* 25. */ { 1.316, 1.708, 2.060, 2.485, 2.787, 3.450 },
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/* 26. */ { 1.315, 1.706, 2.056, 2.479, 2.779, 3.435 },
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/* 27. */ { 1.314, 1.703, 2.052, 2.473, 2.771, 3.421 },
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/* 28. */ { 1.313, 1.701, 2.048, 2.467, 2.763, 3.408 },
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/* 29. */ { 1.311, 1.699, 2.045, 2.462, 2.756, 3.396 },
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/* 30. */ { 1.310, 1.697, 2.042, 2.457, 2.750, 3.385 },
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/* 31. */ { 1.309, 1.696, 2.040, 2.453, 2.744, 3.375 },
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/* 32. */ { 1.309, 1.694, 2.037, 2.449, 2.738, 3.365 },
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/* 33. */ { 1.308, 1.692, 2.035, 2.445, 2.733, 3.356 },
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/* 34. */ { 1.307, 1.691, 2.032, 2.441, 2.728, 3.348 },
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/* 35. */ { 1.306, 1.690, 2.030, 2.438, 2.724, 3.340 },
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/* 36. */ { 1.306, 1.688, 2.028, 2.434, 2.719, 3.333 },
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/* 37. */ { 1.305, 1.687, 2.026, 2.431, 2.715, 3.326 },
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/* 38. */ { 1.304, 1.686, 2.024, 2.429, 2.712, 3.319 },
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/* 39. */ { 1.304, 1.685, 2.023, 2.426, 2.708, 3.313 },
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/* 40. */ { 1.303, 1.684, 2.021, 2.423, 2.704, 3.307 },
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/* 41. */ { 1.303, 1.683, 2.020, 2.421, 2.701, 3.301 },
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/* 42. */ { 1.302, 1.682, 2.018, 2.418, 2.698, 3.296 },
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/* 43. */ { 1.302, 1.681, 2.017, 2.416, 2.695, 3.291 },
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/* 44. */ { 1.301, 1.680, 2.015, 2.414, 2.692, 3.286 },
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/* 45. */ { 1.301, 1.679, 2.014, 2.412, 2.690, 3.281 },
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/* 46. */ { 1.300, 1.679, 2.013, 2.410, 2.687, 3.277 },
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/* 47. */ { 1.300, 1.678, 2.012, 2.408, 2.685, 3.273 },
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/* 48. */ { 1.299, 1.677, 2.011, 2.407, 2.682, 3.269 },
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/* 49. */ { 1.299, 1.677, 2.010, 2.405, 2.680, 3.265 },
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/* 50. */ { 1.299, 1.676, 2.009, 2.403, 2.678, 3.261 },
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/* 51. */ { 1.298, 1.675, 2.008, 2.402, 2.676, 3.258 },
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/* 52. */ { 1.298, 1.675, 2.007, 2.400, 2.674, 3.255 },
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/* 53. */ { 1.298, 1.674, 2.006, 2.399, 2.672, 3.251 },
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/* 54. */ { 1.297, 1.674, 2.005, 2.397, 2.670, 3.248 },
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/* 55. */ { 1.297, 1.673, 2.004, 2.396, 2.668, 3.245 },
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/* 56. */ { 1.297, 1.673, 2.003, 2.395, 2.667, 3.242 },
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/* 57. */ { 1.297, 1.672, 2.002, 2.394, 2.665, 3.239 },
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/* 58. */ { 1.296, 1.672, 2.002, 2.392, 2.663, 3.237 },
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/* 59. */ { 1.296, 1.671, 2.001, 2.391, 2.662, 3.234 },
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/* 60. */ { 1.296, 1.671, 2.000, 2.390, 2.660, 3.232 },
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/* 61. */ { 1.296, 1.670, 2.000, 2.389, 2.659, 3.229 },
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/* 62. */ { 1.295, 1.670, 1.999, 2.388, 2.657, 3.227 },
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/* 63. */ { 1.295, 1.669, 1.998, 2.387, 2.656, 3.225 },
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/* 64. */ { 1.295, 1.669, 1.998, 2.386, 2.655, 3.223 },
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/* 65. */ { 1.295, 1.669, 1.997, 2.385, 2.654, 3.220 },
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/* 66. */ { 1.295, 1.668, 1.997, 2.384, 2.652, 3.218 },
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/* 67. */ { 1.294, 1.668, 1.996, 2.383, 2.651, 3.216 },
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/* 68. */ { 1.294, 1.668, 1.995, 2.382, 2.650, 3.214 },
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/* 69. */ { 1.294, 1.667, 1.995, 2.382, 2.649, 3.213 },
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/* 70. */ { 1.294, 1.667, 1.994, 2.381, 2.648, 3.211 },
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/* 71. */ { 1.294, 1.667, 1.994, 2.380, 2.647, 3.209 },
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/* 72. */ { 1.293, 1.666, 1.993, 2.379, 2.646, 3.207 },
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/* 73. */ { 1.293, 1.666, 1.993, 2.379, 2.645, 3.206 },
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/* 74. */ { 1.293, 1.666, 1.993, 2.378, 2.644, 3.204 },
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/* 75. */ { 1.293, 1.665, 1.992, 2.377, 2.643, 3.202 },
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/* 76. */ { 1.293, 1.665, 1.992, 2.376, 2.642, 3.201 },
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/* 77. */ { 1.293, 1.665, 1.991, 2.376, 2.641, 3.199 },
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/* 78. */ { 1.292, 1.665, 1.991, 2.375, 2.640, 3.198 },
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/* 79. */ { 1.292, 1.664, 1.990, 2.374, 2.640, 3.197 },
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/* 80. */ { 1.292, 1.664, 1.990, 2.374, 2.639, 3.195 },
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/* 81. */ { 1.292, 1.664, 1.990, 2.373, 2.638, 3.194 },
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/* 82. */ { 1.292, 1.664, 1.989, 2.373, 2.637, 3.193 },
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/* 83. */ { 1.292, 1.663, 1.989, 2.372, 2.636, 3.191 },
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/* 84. */ { 1.292, 1.663, 1.989, 2.372, 2.636, 3.190 },
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/* 85. */ { 1.292, 1.663, 1.988, 2.371, 2.635, 3.189 },
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/* 86. */ { 1.291, 1.663, 1.988, 2.370, 2.634, 3.188 },
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/* 87. */ { 1.291, 1.663, 1.988, 2.370, 2.634, 3.187 },
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/* 88. */ { 1.291, 1.662, 1.987, 2.369, 2.633, 3.185 },
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/* 89. */ { 1.291, 1.662, 1.987, 2.369, 2.632, 3.184 },
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/* 90. */ { 1.291, 1.662, 1.987, 2.368, 2.632, 3.183 },
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/* 91. */ { 1.291, 1.662, 1.986, 2.368, 2.631, 3.182 },
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/* 92. */ { 1.291, 1.662, 1.986, 2.368, 2.630, 3.181 },
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/* 93. */ { 1.291, 1.661, 1.986, 2.367, 2.630, 3.180 },
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/* 94. */ { 1.291, 1.661, 1.986, 2.367, 2.629, 3.179 },
|
||||
/* 95. */ { 1.291, 1.661, 1.985, 2.366, 2.629, 3.178 },
|
||||
/* 96. */ { 1.290, 1.661, 1.985, 2.366, 2.628, 3.177 },
|
||||
/* 97. */ { 1.290, 1.661, 1.985, 2.365, 2.627, 3.176 },
|
||||
/* 98. */ { 1.290, 1.661, 1.984, 2.365, 2.627, 3.175 },
|
||||
/* 99. */ { 1.290, 1.660, 1.984, 2.365, 2.626, 3.175 },
|
||||
/* 100. */ { 1.290, 1.660, 1.984, 2.364, 2.626, 3.174 }
|
||||
};
|
||||
|
||||
#define MAX_DS 8
|
||||
static char symbol[MAX_DS] = { ' ', 'x', '+', '*', '%', '#', '@', 'O' };
|
||||
|
||||
struct dataset {
|
||||
char *name;
|
||||
double *points;
|
||||
unsigned lpoints;
|
||||
double sy, syy;
|
||||
unsigned n;
|
||||
};
|
||||
|
||||
static struct dataset *
|
||||
NewSet(void)
|
||||
{
|
||||
struct dataset *ds;
|
||||
|
||||
ds = calloc(1, sizeof *ds);
|
||||
ds->lpoints = 100000;
|
||||
ds->points = calloc(sizeof *ds->points, ds->lpoints);
|
||||
return(ds);
|
||||
}
|
||||
|
||||
static void
|
||||
AddPoint(struct dataset *ds, double a)
|
||||
{
|
||||
double *dp;
|
||||
|
||||
if (ds->n >= ds->lpoints) {
|
||||
dp = ds->points;
|
||||
ds->lpoints *= 4;
|
||||
ds->points = calloc(sizeof *ds->points, ds->lpoints);
|
||||
memcpy(ds->points, dp, sizeof *dp * ds->n);
|
||||
free(dp);
|
||||
}
|
||||
ds->points[ds->n++] = a;
|
||||
ds->sy += a;
|
||||
ds->syy += a * a;
|
||||
}
|
||||
|
||||
static double
|
||||
Min(struct dataset *ds)
|
||||
{
|
||||
|
||||
return (ds->points[0]);
|
||||
}
|
||||
|
||||
static double
|
||||
Max(struct dataset *ds)
|
||||
{
|
||||
|
||||
return (ds->points[ds->n -1]);
|
||||
}
|
||||
|
||||
static double
|
||||
Avg(struct dataset *ds)
|
||||
{
|
||||
|
||||
return(ds->sy / ds->n);
|
||||
}
|
||||
|
||||
static double
|
||||
Median(struct dataset *ds)
|
||||
{
|
||||
|
||||
return (ds->points[ds->n / 2]);
|
||||
}
|
||||
|
||||
static double
|
||||
Var(struct dataset *ds)
|
||||
{
|
||||
|
||||
return (ds->syy - ds->sy * ds->sy / ds->n) / (ds->n - 1.0);
|
||||
}
|
||||
|
||||
static double
|
||||
Stddev(struct dataset *ds)
|
||||
{
|
||||
|
||||
return sqrt(Var(ds));
|
||||
}
|
||||
|
||||
static void
|
||||
VitalsHead(void)
|
||||
{
|
||||
|
||||
printf(" N Min Max Median Avg Stddev\n");
|
||||
}
|
||||
|
||||
static void
|
||||
Vitals(struct dataset *ds, int flag)
|
||||
{
|
||||
|
||||
printf("%c %3d %13.8g %13.8g %13.8g %13.8g %13.8g", symbol[flag],
|
||||
ds->n, Min(ds), Max(ds), Median(ds), Avg(ds), Stddev(ds));
|
||||
printf("\n");
|
||||
}
|
||||
|
||||
static void
|
||||
Relative(struct dataset *ds, struct dataset *rs, int confidx)
|
||||
{
|
||||
double spool, s, d, e, t;
|
||||
int i;
|
||||
|
||||
i = ds->n + rs->n - 2;
|
||||
if (i > NSTUDENT)
|
||||
t = student[0][confidx];
|
||||
else
|
||||
t = student[i][confidx];
|
||||
spool = (ds->n - 1) * Var(ds) + (rs->n - 1) * Var(rs);
|
||||
spool /= ds->n + rs->n - 2;
|
||||
spool = sqrt(spool);
|
||||
s = spool * sqrt(1.0 / ds->n + 1.0 / rs->n);
|
||||
d = Avg(ds) - Avg(rs);
|
||||
e = t * s;
|
||||
|
||||
if (fabs(d) > e) {
|
||||
|
||||
printf("Difference at %.1f%% confidence\n", studentpct[confidx]);
|
||||
printf(" %g +/- %g\n", d, e);
|
||||
printf(" %g%% +/- %g%%\n", d * 100 / Avg(rs), e * 100 / Avg(rs));
|
||||
printf(" (Student's t, pooled s = %g)\n", spool);
|
||||
} else {
|
||||
printf("No difference proven at %.1f%% confidence\n",
|
||||
studentpct[confidx]);
|
||||
}
|
||||
}
|
||||
|
||||
struct plot {
|
||||
double min;
|
||||
double max;
|
||||
double span;
|
||||
int width;
|
||||
|
||||
double x0, dx;
|
||||
int height;
|
||||
char *data;
|
||||
char **bar;
|
||||
int separate_bars;
|
||||
int num_datasets;
|
||||
};
|
||||
|
||||
static struct plot plot;
|
||||
|
||||
static void
|
||||
SetupPlot(int width, int separate, int num_datasets)
|
||||
{
|
||||
struct plot *pl;
|
||||
|
||||
pl = &plot;
|
||||
pl->width = width;
|
||||
pl->height = 0;
|
||||
pl->data = NULL;
|
||||
pl->bar = NULL;
|
||||
pl->separate_bars = separate;
|
||||
pl->num_datasets = num_datasets;
|
||||
pl->min = 999e99;
|
||||
pl->max = -999e99;
|
||||
}
|
||||
|
||||
static void
|
||||
AdjPlot(double a)
|
||||
{
|
||||
struct plot *pl;
|
||||
|
||||
pl = &plot;
|
||||
if (a < pl->min)
|
||||
pl->min = a;
|
||||
if (a > pl->max)
|
||||
pl->max = a;
|
||||
pl->span = pl->max - pl->min;
|
||||
pl->dx = pl->span / (pl->width - 1.0);
|
||||
pl->x0 = pl->min - .5 * pl->dx;
|
||||
}
|
||||
|
||||
static void
|
||||
DimPlot(struct dataset *ds)
|
||||
{
|
||||
AdjPlot(Min(ds));
|
||||
AdjPlot(Max(ds));
|
||||
AdjPlot(Avg(ds) - Stddev(ds));
|
||||
AdjPlot(Avg(ds) + Stddev(ds));
|
||||
}
|
||||
|
||||
static void
|
||||
PlotSet(struct dataset *ds, int val)
|
||||
{
|
||||
struct plot *pl;
|
||||
int i, j, m, x;
|
||||
unsigned n;
|
||||
int bar;
|
||||
|
||||
pl = &plot;
|
||||
if (pl->span == 0)
|
||||
return;
|
||||
|
||||
if (pl->separate_bars)
|
||||
bar = val-1;
|
||||
else
|
||||
bar = 0;
|
||||
|
||||
if (pl->bar == NULL) {
|
||||
pl->bar = malloc(sizeof(char *) * pl->num_datasets);
|
||||
memset(pl->bar, 0, sizeof(char*) * pl->num_datasets);
|
||||
}
|
||||
if (pl->bar[bar] == NULL) {
|
||||
pl->bar[bar] = malloc(pl->width);
|
||||
memset(pl->bar[bar], 0, pl->width);
|
||||
}
|
||||
|
||||
m = 1;
|
||||
i = -1;
|
||||
j = 0;
|
||||
for (n = 0; n < ds->n; n++) {
|
||||
x = (ds->points[n] - pl->x0) / pl->dx;
|
||||
if (x == i) {
|
||||
j++;
|
||||
if (j > m)
|
||||
m = j;
|
||||
} else {
|
||||
j = 1;
|
||||
i = x;
|
||||
}
|
||||
}
|
||||
m += 1;
|
||||
if (m > pl->height) {
|
||||
pl->data = realloc(pl->data, pl->width * m);
|
||||
memset(pl->data + pl->height * pl->width, 0,
|
||||
(m - pl->height) * pl->width);
|
||||
}
|
||||
pl->height = m;
|
||||
i = -1;
|
||||
for (n = 0; n < ds->n; n++) {
|
||||
x = (ds->points[n] - pl->x0) / pl->dx;
|
||||
if (x == i) {
|
||||
j++;
|
||||
} else {
|
||||
j = 1;
|
||||
i = x;
|
||||
}
|
||||
pl->data[j * pl->width + x] |= val;
|
||||
}
|
||||
if (!isnan(Stddev(ds))) {
|
||||
x = ((Avg(ds) - Stddev(ds)) - pl->x0) / pl->dx;
|
||||
m = ((Avg(ds) + Stddev(ds)) - pl->x0) / pl->dx;
|
||||
pl->bar[bar][m] = '|';
|
||||
pl->bar[bar][x] = '|';
|
||||
for (i = x + 1; i < m; i++)
|
||||
if (pl->bar[bar][i] == 0)
|
||||
pl->bar[bar][i] = '_';
|
||||
}
|
||||
x = (Median(ds) - pl->x0) / pl->dx;
|
||||
pl->bar[bar][x] = 'M';
|
||||
x = (Avg(ds) - pl->x0) / pl->dx;
|
||||
pl->bar[bar][x] = 'A';
|
||||
}
|
||||
|
||||
static void
|
||||
DumpPlot(void)
|
||||
{
|
||||
struct plot *pl;
|
||||
int i, j, k;
|
||||
|
||||
pl = &plot;
|
||||
if (pl->span == 0) {
|
||||
printf("[no plot, span is zero width]\n");
|
||||
return;
|
||||
}
|
||||
|
||||
putchar('+');
|
||||
for (i = 0; i < pl->width; i++)
|
||||
putchar('-');
|
||||
putchar('+');
|
||||
putchar('\n');
|
||||
for (i = 1; i < pl->height; i++) {
|
||||
putchar('|');
|
||||
for (j = 0; j < pl->width; j++) {
|
||||
k = pl->data[(pl->height - i) * pl->width + j];
|
||||
if (k >= 0 && k < MAX_DS)
|
||||
putchar(symbol[k]);
|
||||
else
|
||||
printf("[%02x]", k);
|
||||
}
|
||||
putchar('|');
|
||||
putchar('\n');
|
||||
}
|
||||
for (i = 0; i < pl->num_datasets; i++) {
|
||||
if (pl->bar[i] == NULL)
|
||||
continue;
|
||||
putchar('|');
|
||||
for (j = 0; j < pl->width; j++) {
|
||||
k = pl->bar[i][j];
|
||||
if (k == 0)
|
||||
k = ' ';
|
||||
putchar(k);
|
||||
}
|
||||
putchar('|');
|
||||
putchar('\n');
|
||||
}
|
||||
putchar('+');
|
||||
for (i = 0; i < pl->width; i++)
|
||||
putchar('-');
|
||||
putchar('+');
|
||||
putchar('\n');
|
||||
}
|
||||
|
||||
static int
|
||||
dbl_cmp(const void *a, const void *b)
|
||||
{
|
||||
const double *aa = a;
|
||||
const double *bb = b;
|
||||
|
||||
if (*aa < *bb)
|
||||
return (-1);
|
||||
else if (*aa > *bb)
|
||||
return (1);
|
||||
else
|
||||
return (0);
|
||||
}
|
||||
|
||||
static struct dataset *
|
||||
ReadSet(const char *n, int column, const char *delim)
|
||||
{
|
||||
FILE *f;
|
||||
char buf[BUFSIZ], *p, *t;
|
||||
struct dataset *s;
|
||||
double d;
|
||||
int line;
|
||||
int i;
|
||||
|
||||
if (n == NULL) {
|
||||
f = stdin;
|
||||
n = "<stdin>";
|
||||
} else if (!strcmp(n, "-")) {
|
||||
f = stdin;
|
||||
n = "<stdin>";
|
||||
} else {
|
||||
f = fopen(n, "r");
|
||||
}
|
||||
if (f == NULL)
|
||||
err(1, "Cannot open %s", n);
|
||||
s = NewSet();
|
||||
s->name = strdup(n);
|
||||
line = 0;
|
||||
while (fgets(buf, sizeof buf, f) != NULL) {
|
||||
line++;
|
||||
|
||||
i = strlen(buf);
|
||||
if (buf[i-1] == '\n')
|
||||
buf[i-1] = '\0';
|
||||
for (i = 1, t = strtok(buf, delim);
|
||||
t != NULL && *t != '#';
|
||||
i++, t = strtok(NULL, delim)) {
|
||||
if (i == column)
|
||||
break;
|
||||
}
|
||||
if (t == NULL || *t == '#')
|
||||
continue;
|
||||
|
||||
d = strtod(t, &p);
|
||||
if (p != NULL && *p != '\0')
|
||||
err(2, "Invalid data on line %d in %s\n", line, n);
|
||||
if (*buf != '\0')
|
||||
AddPoint(s, d);
|
||||
}
|
||||
fclose(f);
|
||||
if (s->n < 3) {
|
||||
fprintf(stderr,
|
||||
"Dataset %s must contain at least 3 data points\n", n);
|
||||
exit (2);
|
||||
}
|
||||
qsort(s->points, s->n, sizeof *s->points, dbl_cmp);
|
||||
return (s);
|
||||
}
|
||||
|
||||
static void
|
||||
usage(char const *whine)
|
||||
{
|
||||
int i;
|
||||
|
||||
fprintf(stderr, "%s\n", whine);
|
||||
fprintf(stderr,
|
||||
"Usage: ministat [-C column] [-c confidence] [-d delimiter(s)] [-ns] [-w width] [file [file ...]]\n");
|
||||
fprintf(stderr, "\tconfidence = {");
|
||||
for (i = 0; i < NCONF; i++) {
|
||||
fprintf(stderr, "%s%g%%",
|
||||
i ? ", " : "",
|
||||
studentpct[i]);
|
||||
}
|
||||
fprintf(stderr, "}\n");
|
||||
fprintf(stderr, "\t-C : column number to extract (starts and defaults to 1)\n");
|
||||
fprintf(stderr, "\t-d : delimiter(s) string, default to \" \\t\"\n");
|
||||
fprintf(stderr, "\t-n : print summary statistics only, no graph/test\n");
|
||||
fprintf(stderr, "\t-s : print avg/median/stddev bars on separate lines\n");
|
||||
fprintf(stderr, "\t-w : width of graph/test output (default 74 or terminal width)\n");
|
||||
exit (2);
|
||||
}
|
||||
|
||||
int
|
||||
main(int argc, char **argv)
|
||||
{
|
||||
struct dataset *ds[7];
|
||||
int nds;
|
||||
double a;
|
||||
const char *delim = " \t";
|
||||
char *p;
|
||||
int c, i, ci;
|
||||
int column = 1;
|
||||
int flag_s = 0;
|
||||
int flag_n = 0;
|
||||
int termwidth = 74;
|
||||
|
||||
if (isatty(STDOUT_FILENO)) {
|
||||
struct winsize wsz;
|
||||
|
||||
if ((p = getenv("COLUMNS")) != NULL && *p != '\0')
|
||||
termwidth = atoi(p);
|
||||
else if (ioctl(STDOUT_FILENO, TIOCGWINSZ, &wsz) != -1 &&
|
||||
wsz.ws_col > 0)
|
||||
termwidth = wsz.ws_col - 2;
|
||||
}
|
||||
|
||||
ci = -1;
|
||||
while ((c = getopt(argc, argv, "C:c:d:snw:")) != -1)
|
||||
switch (c) {
|
||||
case 'C':
|
||||
column = strtol(optarg, &p, 10);
|
||||
if (p != NULL && *p != '\0')
|
||||
usage("Invalid column number.");
|
||||
if (column <= 0)
|
||||
usage("Column number should be positive.");
|
||||
break;
|
||||
case 'c':
|
||||
a = strtod(optarg, &p);
|
||||
if (p != NULL && *p != '\0')
|
||||
usage("Not a floating point number");
|
||||
for (i = 0; i < NCONF; i++)
|
||||
if (a == studentpct[i])
|
||||
ci = i;
|
||||
if (ci == -1)
|
||||
usage("No support for confidence level");
|
||||
break;
|
||||
case 'd':
|
||||
if (*optarg == '\0')
|
||||
usage("Can't use empty delimiter string");
|
||||
delim = optarg;
|
||||
break;
|
||||
case 'n':
|
||||
flag_n = 1;
|
||||
break;
|
||||
case 's':
|
||||
flag_s = 1;
|
||||
break;
|
||||
case 'w':
|
||||
termwidth = strtol(optarg, &p, 10);
|
||||
if (p != NULL && *p != '\0')
|
||||
usage("Invalid width, not a number.");
|
||||
if (termwidth < 0)
|
||||
usage("Unable to move beyond left margin.");
|
||||
break;
|
||||
default:
|
||||
usage("Unknown option");
|
||||
break;
|
||||
}
|
||||
if (ci == -1)
|
||||
ci = 2;
|
||||
argc -= optind;
|
||||
argv += optind;
|
||||
|
||||
if (argc == 0) {
|
||||
ds[0] = ReadSet("-", column, delim);
|
||||
nds = 1;
|
||||
} else {
|
||||
if (argc > (MAX_DS - 1))
|
||||
usage("Too many datasets.");
|
||||
nds = argc;
|
||||
for (i = 0; i < nds; i++)
|
||||
ds[i] = ReadSet(argv[i], column, delim);
|
||||
}
|
||||
|
||||
for (i = 0; i < nds; i++)
|
||||
printf("%c %s\n", symbol[i+1], ds[i]->name);
|
||||
|
||||
if (!flag_n) {
|
||||
SetupPlot(termwidth, flag_s, nds);
|
||||
for (i = 0; i < nds; i++)
|
||||
DimPlot(ds[i]);
|
||||
for (i = 0; i < nds; i++)
|
||||
PlotSet(ds[i], i + 1);
|
||||
DumpPlot();
|
||||
}
|
||||
VitalsHead();
|
||||
Vitals(ds[0], 1);
|
||||
for (i = 1; i < nds; i++) {
|
||||
Vitals(ds[i], i + 1);
|
||||
if (!flag_n)
|
||||
Relative(ds[i], ds[0], ci);
|
||||
}
|
||||
exit(0);
|
||||
}
|
||||
Loading…
Reference in a new issue