git-svn-id: https://lxsd.femto-st.fr/svn/fvn@29 b657c933-2333-4658-acf2-d3c7c2708721

daniau
1 parent b40f93a8d7
Showing 2 changed files with 17 additions and 17 deletions Side-by-side Diff
doc/fvn.pdf
doc/fvn.tex
@@ -49,9 +49,9 @@
 \section{Naming scheme and convention}
 The naming scheme of the routines is as follow :
 \begin{verbatim}
-    fvn_x_name()
+    fvn_*_name()
 \end{verbatim} 
-where x can be s,d,c or z. 
+where * can be s,d,c or z. 
 \begin{itemize}
  \item s is for single precision real (real,real*4,real(4),real(kind=4))
  \item d for double precision real (double precision,real*8,real(8),real(kind=8))
@@ -64,7 +64,7 @@
 The linear algebra routines of fvn are an interface to lapack, which make it easier to use.
 \subsection{Matrix inversion}
 \begin{verbatim}
-call fvn_x_matinv(d,a,inva,status)
+call fvn_*_matinv(d,a,inva,status)      (*=s,d,c,z)
 \end{verbatim}
 \begin{itemize}
  \item d (in) is an integer equal to the matrix rank
@@ -98,7 +98,7 @@
  
 \subsection{Matrix determinants}
 \begin{verbatim}
-det=fvn_x_det(d,a,status)
+det=fvn_*_det(d,a,status)      (*=s,d,c,z)
 \end{verbatim}
 \begin{itemize}
  \item d (in) is an integer equal to the matrix rank
@@ -131,7 +131,7 @@
  
 \subsection{Matrix condition}
 \begin{verbatim}
-call fvn_x_matcon(d,a,rcond,status)
+call fvn_*_matcon(d,a,rcond,status)      (*=s,d,c,z)
 \end{verbatim}
 \begin{itemize}
  \item d (in) is an integer equal to the matrix rank
@@ -176,7 +176,7 @@
  
 \subsection{Eigenvalues/Eigenvectors}
 \begin{verbatim}
-call fvn_x_matev(d,a,evala,eveca,status)
+call fvn_*_matev(d,a,evala,eveca,status)      (*=s,d,c,z)
 \end{verbatim}
 \begin{itemize}
  \item d (in) is an integer equal to the matrix rank
@@ -223,7 +223,7 @@
 The provided routines solves the equation $Ax=B$ where A is sparse and given in its triplet form.
  
 \begin{verbatim}
-call fvn_*_sparse_solve(n,nz,T,Ti,Tj,B,x,status)  where * is either zl, zi, dl or di
+call fvn_*_sparse_solve(n,nz,T,Ti,Tj,B,x,status)      (*=zl,zi,dl,di)
 \end{verbatim}
 \begin{itemize}
  \item For this family of subroutine the two letters (zl,zi,dl,di) decribe the arguments's type. z is for complex(8), d for real(8), l for integer(8) and i for integer(4)
@@ -290,7 +290,7 @@
  
 \subsection{Identity matrix}
 \begin{verbatim}
- I=fvn_x_ident(n)
+ I=fvn_*_ident(n)      (*=s,d,c,z)
 \end{verbatim}
 \begin{itemize}
  \item n (in) is an integer equal to the matrix rank
@@ -305,7 +305,7 @@
 fvn provide function for interpolating values of a tabulated function of 1, 2 or 3 variables, for both single and double precision.
 \subsubsection{One variable function}
 \begin{verbatim}
- value=fvn_x_quad_interpol(x,n,xdata,ydata)
+ value=fvn_*_quad_interpol(x,n,xdata,ydata)      (*=s,d)
 \end{verbatim}
 \begin{itemize}
  \item x is the real where we want to evaluate the function
@@ -356,7 +356,7 @@
  
 \subsubsection{Two variables function}
 \begin{verbatim}
-value=fvn_x_quad_2d_interpol(x,y,nx,xdata,ny,ydata,zdata)
+value=fvn_*_quad_2d_interpol(x,y,nx,xdata,ny,ydata,zdata)      (*=s,d)
 \end{verbatim}
 \begin{itemize}
  \item x,y are the real coordinates where we want to evaluate the function
@@ -415,7 +415,7 @@
  
 \subsubsection{Three variables function}
 \begin{verbatim}
-value=fvn_x_quad_3d_interpol(x,y,z,nx,xdata,ny,ydata,nz,zdata,tdata)
+value=fvn_*_quad_3d_interpol(x,y,z,nx,xdata,ny,ydata,nz,zdata,tdata)      (*=s,d)
 \end{verbatim}
 \begin{itemize}
  \item x,y,z are the real coordinates where we want to evaluate the function
@@ -481,7 +481,7 @@
 \subsubsection{Utility procedure}
 fvn provides a simple utility procedure to locate the interval in which a value is located in an increasingly ordered array.
 \begin{verbatim}
-call fvn_x_find_interval(x,i,xdata,n)
+call fvn_*_find_interval(x,i,xdata,n)      (*=s,d)
 \end{verbatim}
 \begin{itemize}
  \item x (in) the real value to locate
@@ -497,7 +497,7 @@
 fvn provides Akima spline interpolation and evaluation for both single and double precision real.
 \subsubsection{Interpolation}
 \begin{verbatim}
-call fvn_x_akima(n,x,y,br,co)
+call fvn_*_akima(n,x,y,br,co)      (*=s,d)
 \end{verbatim}
 \begin{itemize}
  \item n (in) is an integer equal to the number of points
@@ -508,7 +508,7 @@
  
 \subsubsection{Evaluation}
 \begin{verbatim}
-y=fvn_x_spline_eval(x,n,br,co)
+y=fvn_*_spline_eval(x,n,br,co)      (*=s,d)
 \end{verbatim}
 \begin{itemize}
  \item x (in) is the point where we want to evaluate
@@ -583,7 +583,7 @@
 fvn provide a function to find a least square polynomial of a given degree, for real in single or double precision. It is performed using Lapack subroutine sgelss (dgelss), which solve this problem using singular value decomposition.
  
 \begin{verbatim}
-call fvn_x_lspoly(np,x,y,deg,coeff,status)
+call fvn_*_lspoly(np,x,y,deg,coeff,status)      (*=s,d)
 \end{verbatim}
 \begin{itemize}
  \item np (in) is an integer equal to the number of points
@@ -880,7 +880,7 @@
 \begin{itemize}
  \item x (in) is a real(kind=8) 
 \end{itemize}
-This function return the natural logarithm of gamma(x) : $ln(\Gamma(x)$
+This function return the natural logarithm of gamma(x) : $ln(\Gamma(x))$
  
 \subsection{factorial}
 Only double precision real
@@ -895,7 +895,7 @@
 \subsection{Chebyshev series evaluation}
 Single and double precision real.
 \begin{verbatim}
-s=fvn_x_csevl(x,a,n)
+s=fvn_*_csevl(x,a,n)      (*=s,d)
 \end{verbatim}
 \begin{itemize}
  \item x is a real
...	...	@@ -49,9 +49,9 @@
49	49	\section{Naming scheme and convention}
50	50	The naming scheme of the routines is as follow :
51	51	\begin{verbatim}
52		- fvn_x_name()
	52	+ fvn_*_name()
53	53	\end{verbatim}
54		-where x can be s,d,c or z.
	54	+where * can be s,d,c or z.
55	55	\begin{itemize}
56	56	\item s is for single precision real (real,real*4,real(4),real(kind=4))
57	57	\item d for double precision real (double precision,real*8,real(8),real(kind=8))
...	...	@@ -64,7 +64,7 @@
64	64	The linear algebra routines of fvn are an interface to lapack, which make it easier to use.
65	65	\subsection{Matrix inversion}
66	66	\begin{verbatim}
67		-call fvn_x_matinv(d,a,inva,status)
	67	+call fvn__matinv(d,a,inva,status) (=s,d,c,z)
68	68	\end{verbatim}
69	69	\begin{itemize}
70	70	\item d (in) is an integer equal to the matrix rank
...	...	@@ -98,7 +98,7 @@
98	98
99	99	\subsection{Matrix determinants}
100	100	\begin{verbatim}
101		-det=fvn_x_det(d,a,status)
	101	+det=fvn__det(d,a,status) (=s,d,c,z)
102	102	\end{verbatim}
103	103	\begin{itemize}
104	104	\item d (in) is an integer equal to the matrix rank
...	...	@@ -131,7 +131,7 @@
131	131
132	132	\subsection{Matrix condition}
133	133	\begin{verbatim}
134		-call fvn_x_matcon(d,a,rcond,status)
	134	+call fvn__matcon(d,a,rcond,status) (=s,d,c,z)
135	135	\end{verbatim}
136	136	\begin{itemize}
137	137	\item d (in) is an integer equal to the matrix rank
...	...	@@ -176,7 +176,7 @@
176	176
177	177	\subsection{Eigenvalues/Eigenvectors}
178	178	\begin{verbatim}
179		-call fvn_x_matev(d,a,evala,eveca,status)
	179	+call fvn__matev(d,a,evala,eveca,status) (=s,d,c,z)
180	180	\end{verbatim}
181	181	\begin{itemize}
182	182	\item d (in) is an integer equal to the matrix rank
...	...	@@ -223,7 +223,7 @@
223	223	The provided routines solves the equation $Ax=B$ where A is sparse and given in its triplet form.
224	224
225	225	\begin{verbatim}
226		-call fvn__sparse_solve(n,nz,T,Ti,Tj,B,x,status) where is either zl, zi, dl or di
	226	+call fvn__sparse_solve(n,nz,T,Ti,Tj,B,x,status) (=zl,zi,dl,di)
227	227	\end{verbatim}
228	228	\begin{itemize}
229	229	\item For this family of subroutine the two letters (zl,zi,dl,di) decribe the arguments's type. z is for complex(8), d for real(8), l for integer(8) and i for integer(4)
...	...	@@ -290,7 +290,7 @@
290	290
291	291	\subsection{Identity matrix}
292	292	\begin{verbatim}
293		- I=fvn_x_ident(n)
	293	+ I=fvn__ident(n) (=s,d,c,z)
294	294	\end{verbatim}
295	295	\begin{itemize}
296	296	\item n (in) is an integer equal to the matrix rank
...	...	@@ -305,7 +305,7 @@
305	305	fvn provide function for interpolating values of a tabulated function of 1, 2 or 3 variables, for both single and double precision.
306	306	\subsubsection{One variable function}
307	307	\begin{verbatim}
308		- value=fvn_x_quad_interpol(x,n,xdata,ydata)
	308	+ value=fvn__quad_interpol(x,n,xdata,ydata) (=s,d)
309	309	\end{verbatim}
310	310	\begin{itemize}
311	311	\item x is the real where we want to evaluate the function
...	...	@@ -356,7 +356,7 @@
356	356
357	357	\subsubsection{Two variables function}
358	358	\begin{verbatim}
359		-value=fvn_x_quad_2d_interpol(x,y,nx,xdata,ny,ydata,zdata)
	359	+value=fvn__quad_2d_interpol(x,y,nx,xdata,ny,ydata,zdata) (=s,d)
360	360	\end{verbatim}
361	361	\begin{itemize}
362	362	\item x,y are the real coordinates where we want to evaluate the function
...	...	@@ -415,7 +415,7 @@
415	415
416	416	\subsubsection{Three variables function}
417	417	\begin{verbatim}
418		-value=fvn_x_quad_3d_interpol(x,y,z,nx,xdata,ny,ydata,nz,zdata,tdata)
	418	+value=fvn__quad_3d_interpol(x,y,z,nx,xdata,ny,ydata,nz,zdata,tdata) (=s,d)
419	419	\end{verbatim}
420	420	\begin{itemize}
421	421	\item x,y,z are the real coordinates where we want to evaluate the function
...	...	@@ -481,7 +481,7 @@
481	481	\subsubsection{Utility procedure}
482	482	fvn provides a simple utility procedure to locate the interval in which a value is located in an increasingly ordered array.
483	483	\begin{verbatim}
484		-call fvn_x_find_interval(x,i,xdata,n)
	484	+call fvn__find_interval(x,i,xdata,n) (=s,d)
485	485	\end{verbatim}
486	486	\begin{itemize}
487	487	\item x (in) the real value to locate
...	...	@@ -497,7 +497,7 @@
497	497	fvn provides Akima spline interpolation and evaluation for both single and double precision real.
498	498	\subsubsection{Interpolation}
499	499	\begin{verbatim}
500		-call fvn_x_akima(n,x,y,br,co)
	500	+call fvn__akima(n,x,y,br,co) (=s,d)
501	501	\end{verbatim}
502	502	\begin{itemize}
503	503	\item n (in) is an integer equal to the number of points
...	...	@@ -508,7 +508,7 @@
508	508
509	509	\subsubsection{Evaluation}
510	510	\begin{verbatim}
511		-y=fvn_x_spline_eval(x,n,br,co)
	511	+y=fvn__spline_eval(x,n,br,co) (=s,d)
512	512	\end{verbatim}
513	513	\begin{itemize}
514	514	\item x (in) is the point where we want to evaluate
...	...	@@ -583,7 +583,7 @@
583	583	fvn provide a function to find a least square polynomial of a given degree, for real in single or double precision. It is performed using Lapack subroutine sgelss (dgelss), which solve this problem using singular value decomposition.
584	584
585	585	\begin{verbatim}
586		-call fvn_x_lspoly(np,x,y,deg,coeff,status)
	586	+call fvn__lspoly(np,x,y,deg,coeff,status) (=s,d)
587	587	\end{verbatim}
588	588	\begin{itemize}
589	589	\item np (in) is an integer equal to the number of points
...	...	@@ -880,7 +880,7 @@
880	880	\begin{itemize}
881	881	\item x (in) is a real(kind=8)
882	882	\end{itemize}
883		-This function return the natural logarithm of gamma(x) : $ln(\Gamma(x)$
	883	+This function return the natural logarithm of gamma(x) : $ln(\Gamma(x))$
884	884
885	885	\subsection{factorial}
886	886	Only double precision real
...	...	@@ -895,7 +895,7 @@
895	895	\subsection{Chebyshev series evaluation}
896	896	Single and double precision real.
897	897	\begin{verbatim}
898		-s=fvn_x_csevl(x,a,n)
	898	+s=fvn__csevl(x,a,n) (=s,d)
899	899	\end{verbatim}
900	900	\begin{itemize}
901	901	\item x is a real