Revert "Pre release v1.0.0"

docs.doxygen

@@ -38,7 +38,7 @@ PROJECT_NAME           = "Cytnx"
 # could be handy for archiving the generated documentation or if some version
 # control system is used.
 
-PROJECT_NUMBER         = "v1.0.0"
+PROJECT_NUMBER         = "v0.9.7"
 
 # Using the PROJECT_BRIEF tag one can provide an optional one line description
 # for a project that appears at the top of each page and should give viewer a
@@ -51,7 +51,7 @@ PROJECT_BRIEF          =
 # pixels and the maximum width should not exceed 200 pixels. Doxygen will copy
 # the logo to the output directory.
 
-PROJECT_LOGO           = "./Icons/Icon_small.png"
+PROJECT_LOGO           = "./Icon_small.png"
 
 
 # The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path
@@ -793,7 +793,7 @@ WARN_LOGFILE           =
 # spaces. See also FILE_PATTERNS and EXTENSION_MAPPING
 # Note: If this tag is empty the current directory is searched.
 
-INPUT                  = "./python_doc" "./dox.md" "./include" "./misc_doc"
+INPUT                  = "./python_doc" "./dox.md" "./include" "./include/tn_algo" "./include/linalg.hpp" "./include/utils/utils.hpp" "./include/UniTensor.hpp" "./misc_doc" #"./src/linalg" "./src/utils"
 
 # This tag can be used to specify the character encoding of the source files
 # that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
@@ -877,7 +877,7 @@ RECURSIVE              = NO
 # Note that relative paths are relative to the directory from which doxygen is
 # run.
 
-EXCLUDE                = "./include/Gncon.hpp" "./include/contraction_tree" "./include/search_tree.hpp""./include/stat.hpp" "./include/sp.py"
+EXCLUDE                = "./include/Gncon.hpp"
 
 # The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
 # directories that are symbolic links (a Unix file system feature) are excluded

example/TDVP/tdvp1_dense.py

@@ -263,14 +263,14 @@ def Local_meas(A, B, Op, site):
 def prepare_rand_init_MPS(Nsites, chi, d):
     lbls = []
     A = [None for i in range(Nsites)]
-    A[0] = cytnx.UniTensor(cytnx.random.normal([1, d, min(chi, d)], 0., 1., seed=0), rowrank = 2)
+    A[0] = cytnx.UniTensor(cytnx.random.normal([1, d, min(chi, d)], 0., 1.), rowrank = 2)
     A[0].relabels_(["0","1","2"])
     lbls.append(["0","1","2"]) # store the labels for later convinience.
 
     for k in range(1,Nsites):
         dim1 = A[k-1].shape()[2]; dim2 = d
         dim3 = min(min(chi, A[k-1].shape()[2] * d), d ** (Nsites - k - 1))
-        A[k] = cytnx.UniTensor(cytnx.random.normal([dim1, dim2, dim3],0.,1., seed=0), rowrank = 2)
+        A[k] = cytnx.UniTensor(cytnx.random.normal([dim1, dim2, dim3],0.,1.), rowrank = 2)
 
         lbl = [str(2*k),str(2*k+1),str(2*k+2)]
         A[k].relabels_(lbl)

example/Tensor/at.cpp

@@ -4,24 +4,24 @@
 using namespace cytnx;
 using namespace std;
 int main() {
-  Tensor A = arange(30).reshape(2, 3, 5);
+  Tensor A = arange(30, Type.Float).reshape(2, 3, 5);
   cout << A << endl;
 
   // note that type resolver should be consist with the dtype
-  cout << A.at<cytnx_double>(0, 0, 2) << endl;
+  cout << A.at<cytnx_float>(0, 0, 2) << endl;
 
   // the return is a ref., can be modify directly.
-  A.at<cytnx_double>(0, 0, 2) = 999;
+  A.at<cytnx_float>(0, 0, 2) = 999;
 
-  cout << A.at<cytnx_double>(0, 0, 2) << endl;
+  cout << A.at<cytnx_float>(0, 0, 2) << endl;
 
   // [Note] there are two way to give argument:
   // Method 1: more like 'c++' way:
   //           (alternatively, you can also simply give a std::vector)
-  A.at<cytnx_double>({0, 0, 2});  // note the braket{}
+  A.at<cytnx_float>({0, 0, 2});  // note the braket{}
 
   // Method 2: more like 'python' way:
-  A.at<cytnx_double>(0, 0, 2);
+  A.at<cytnx_float>(0, 0, 2);
 
   return 0;
 }

example/Tensor/at.cpp.out

@@ -1,14 +0,0 @@
-Total elem: 30
-type  : Double (Float64)
-cytnx device: CPU
-Shape : (2,3,5)
-[[[0.00000e+00 1.00000e+00 2.00000e+00 3.00000e+00 4.00000e+00 ]
-  [5.00000e+00 6.00000e+00 7.00000e+00 8.00000e+00 9.00000e+00 ]
-  [1.00000e+01 1.10000e+01 1.20000e+01 1.30000e+01 1.40000e+01 ]]
- [[1.50000e+01 1.60000e+01 1.70000e+01 1.80000e+01 1.90000e+01 ]
-  [2.00000e+01 2.10000e+01 2.20000e+01 2.30000e+01 2.40000e+01 ]
-  [2.50000e+01 2.60000e+01 2.70000e+01 2.80000e+01 2.90000e+01 ]]]
-
-
-2
-999

example/Tensor/to.cpp.out

@@ -1,2 +0,0 @@
-cytnx device: CUDA/GPU-id:0
-cytnx device: CPU

example/Tensor/to_.cpp.out

@@ -1 +0,0 @@
-cytnx device: CUDA/GPU-id:0

include/LinOp.hpp

@@ -62,6 +62,10 @@ namespace cytnx {
     examples for how to use them.
 
     ## Example:
+    ### c++ API:
+    \include example/LinOp/init.cpp
+    #### output>
+    \verbinclude example/LinOp/init.cpp.out
     ### python API:
     \include example/LinOp/init.py
     #### output>

include/Network.hpp

@@ -278,10 +278,10 @@ namespace cytnx {
            Currently, only Regular Network is support!
 
 
-    @note
+    ##note:
         1. each network file cannot have more than 1024 lines.
 
-    @details
+    ##detail:
         Format of a network file:
 
         - each line defines a UniTensor, that takes the format '[name] : [Labels]'
@@ -336,10 +336,10 @@ namespace cytnx {
            Currently, only Regular Network is support!
 
 
-    @note
+    ##note:
         1. contents cannot have more than 1024 lines/strings.
 
-    @details
+    ##detail:
         Format of each string follows the same policy as Fromfile.
 
 

include/Tensor.hpp

@@ -495,8 +495,6 @@ namespace cytnx {
 
     // This mechanism is to remove the 'void' type from Type_list. Taking advantage of it
     // appearing first ...
-
-    /// @cond
     struct internal {
       template <typename Variant>
       struct exclude_first;
@@ -506,7 +504,6 @@ namespace cytnx {
         using type = std::variant<Rest...>;
       };
     };  // internal
-        /// @endcond
 
     // std::variant of pointers to Type_list, without void ....
     using pointer_types =