Project Six: Cloning

Java Python Project Six: Cloning (26 points)

In this two-part project you will explore the basic principles of cloning including primer design and finding compatible restriction enzyme sites between a cloning vector and an insert. These are widely used techniques in molecular biology labs.

Learning goals:

~Explain what cloning is and how it is done 

~Gain more practice designing primers

Please view the following videos to answer the first five questions before class:

DNA cloning and recombinant DNA

Primer design

I. Questions based on the introductory videos: (1 pt each; 4 total) + Quiz (5 pts)

1.  What is the ideal length of a primer (give a range)?

18-24 Nucleotides

2.  What is the maximum recommended difference in melting temperature between two primers?

5 degree celsius

3.  What is a “GC anchor”?

A GC anchor refers to the presence of guanine (G) or cytosine  bases at the 3' end of a PCR primer. Including these bases can improve the specificity and stability of the primer binding to the DNA template because G and C bases form. stronger hydrogen bonds compared to adenine (A) and thymine (T) bases

4.  How are bacteria prompted to take up a plasmid?

A heat shock is used to place the plasmid inside the body of the bacteria.

II. The role of mRNA and cDNA in cloning (10 pts)

Navigate to the NCBI website and use the search bar to locate the record for the human CFTR gene. As you did in Project One, part III, scroll down the page to the “NCBI Reference Sequences (RefSeq)” section. Look at the “mRNA and protein(s)” subsection. Recall that in the NCBI databases, specifically RefSeq, accession numbers that start with 'NM' refer to mRNA, and those starting with 'NP' refer to proteins.

Find the link to the CFTR mRNA record.

5. What is the accession number for the CFTR mRNA record?(1 pt)

NM_000492.4

6.  Which disease is a mutation version of this sequence associated with? (1 pt)

Cystic fibrosis

Notice that the sequence is written in DNA code, not RNA. This is typical. The information contained in the sequence is exactly the same as the DNA sequence. Furthermore, when mRNA is isolated from cells in the lab, we use the enzyme reverse transcriptase to convert it first into a strand of DNA complementary to the mRNA, then make the second DNA strand off of that, to make double-stranded DNA. DNA that has been transcribed from RNA is called cDNA. The "c" is for "complementary", but we just say "see DNA" so as not to confuse it with complementary DNA in the more general sense.

In the case of eukaryotic genes, the dsDNA will look exactly like the genomic DNA for the gene, except that it is missing the introns.  For cloning you use a cDNA version of the processed mRNA sequence so that the sequence will fit into the cloning vector (introns take up a lot of space!) and because splicing occurs in the nucleus, but your plasmid will be introduced into the cytoplasm.

On the left side of the GenBank record are the feature links. Clicking them highlights various regions of the sequence, such as specific exons.  Try clicking on "CDS", for "coding sequence". Notice that the transcript. has upstream and downstream sequence that is not translated (5'UTR, 3'UTR; UTR=untranslated region). Also notice that a box with more details pops up from the bottom of the page. If it's not there or you want to hide it, use the little up/down arrow next to "details".

7. How many nt in length is the coding sequence? (1 pt)

CCDS=4443

8.  What is the sequence of the start codon and its position for this transcript? (2 pts)

ATG position is 70-73

9.  What is the sequence of the stop codon and its position? (2 pts)

TAG position is 4510-4513

Find the feature link that corresponds to exon 10. To verify that it's the correct exon, make sure in include within it the sequence from the figure below question 12.

10.  Copy and paste the CFTR mRNA sequence for exon 10 below, in FASTA format (meaning, add a line that starts with the '>' symbol followed by a name). Give it a name that identifies it as wild-type (1 pt):

>Exon 10 [Organism = CFTR], Wild Type

acttcact tctaatggtg attatgggag aactggagcc

1501 ttcagagggt aaaattaagc acagtggaag aatttcattc tgttctcagt tttcctggat

1561 tatgcctggc accattaaag aaaatatcat ctttggtgtt tcctatgatg aatatagata

1621 cagaagcgtc atcaaagcat gccaactaga agag

Project Six: Cloning The most common mutation associated with this disease is a 3-bp deletion (CTT) affecting a codon in the tenth exon, resulting in the deletion of a phenylalanine (F).

11.  Make a copy of the wild-type exon 10 sequence, but edit it to reflect the mutant version, clearing indicating where the mutation is. Make sure this sequence is also in FASTA format, and give it a name that identifies it as the mutant sequence. Paste below (1 pt):

>Exon 10 [Organism = CFTR], Mutant

ACTTCACTTCTAATGGTGATTATGGGAGAACTGGAGCCTTCAGAGGGTAAAATTAAGCA

CAGTGGAAGAATTTCATTCTGTTCTCAGTTTTCCTGGATTATGCCTGGCACCATTAAAGA

AAATATCATCTTTGGTGTTTCCTATGATGAATATAGA

TACAGAAGCGTCATCAAAGCATGCCAATTAGAAGAG#

Mutation: c to T deletion at position 1491 - 1466

12.  At what positions does exon 10 begin and end in the sequence? (1 pt)

1463..1654

In the next part you'll use the full CFTR mRNA sequence to design primers to amplify the gene sequence from cells from someone without the mutation and someone with the mutation. Then you can clone the mutant and wild-type DNA separately into cells in culture that will express each of the proteins so that you can do experiments comparing them.

III. Cloning plasmids (5 pts)

Cloning plasmids have been engineered to allow you to insert a gene sequence into the plasmid, then replicate the plasmid in bacteria. Some of these plasmids can also be introduced into mammalian cells (in culture) where the gene you inserted can be expressed. Such plasmids are known as mammalian expression plasmids, and this is the type of plasmid you will use.

The expression vector that you want to clone your gene into is called pFLAG-CMV-1. Plasmid vectors have what are called multiple cloning sites (MCSs): short stretches that have been designed to have many different restriction enzyme (RE) recognition sites, like multiple adaptors that can fit a wide range of possible inserts.

Look at the vector map (click on the pFLAG link above, and scroll down to the circular diagram). Mouse over the blue box representing the multiple cloning site.

13. What is the nucleotide position range of the MCS (mouse over it to see)? (For example, the SV40 site starts at position 1886 and ends at position 2021.)(1 pt)

991 - 1057

Now you'll want to see if the CFTR mRNA sequence happens to have any restriction enzyme (RE) sites that match in the upstream/downstream region from the coding sequence.

Copy and paste the full CFTR mRNA sequence into the NEB cutter site. Leave settings at default parameters and hit submit.

14. List two enzymes that cut upstream of the open reading frame. (represented by the first long gray arrow along the top of the linear map) and two that cut downstream. (4 pts):

Upstream - Aval and BsoBI

Downstream - BaeGI and BsaXI

IV. Primer Design (2 pts)

Since none of the RE cut sites match between the CFTR mRNA sequence and the plasmid vector (pFLAG-CMV-1), we'll design each primer to include the cut site we want at the 5' end. The extra nucleotides that make up the cut site will not hybridize to the template, but they'll be copied in the newly synthesized strands. After PCR, our amplicons will then include: RE cut site + primer sequence + sequence-of-interest + primer sequence + RE cut site. This type of PCR strategy is common, letting us add various useful motifs adjacent to the sequence-of-interest during amplification.

We'll use the same "Pick Primers" feature that we used in the previous Project to choose primers.

● Start at the NCBI GenBank record of the CFTR mRNA.

● On the right-hand side of the webpage select the “pick primers” link.

● On the primer page, specify that the minimum product size be 5000 bp, maximum 6000 bp.

● Leave everything else as default and hit "Get Primers" at the bottom of the page.

It might take a while to compute so be patient. The results will be displayed as a graphical summary and also detailed primer reports. Use the detailed primer reports to answer the next question.

 

15.  Referring back to questions 8 and 9, choose a primer pair whose product comes closest to including the coding region (start codon to stop 33333333codon). Screenshot the "Primer report" section for only that primer pair. (1 pt)

16         

PowerShell 7 环境已加载 (版本: 7.5.2) PowerShell 7 环境已加载 (版本: 7.5.2) PS C:\Users\Administrator\Desktop> cd E:\PyTorch_Build\pytorch PS E:\PyTorch_Build\pytorch> python -m venv rtx5070_env PS E:\PyTorch_Build\pytorch> .\rtx5070_env\Scripts\activate (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 修复之前的脚本错误 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $fixedActivation = @" >> try { >> & "$activatePath" >> Write-Host "✅ 虚拟环境激活成功" -ForegroundColor Green >> python -VV >> } >> catch [System.Exception] { >> Write-Host "❌ 激活失败: $($_.Exception.Message)" -ForegroundColor Red >> } >> "@ InvalidOperation: Line | 3 | & "$activatePath" | ~~~~~~~~~~~~~ | The variable '$activatePath' cannot be retrieved because it has not been set. 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installed certifi-2025.8.3 charset_normalizer-3.4.3 cmake-4.1.0 idna-3.10 ninja-1.13.0 numpy-2.2.6 packaging-25.0 pyyaml-6.0.2 requests-2.32.5 setuptools-79.0.1 six-1.17.0 typing-extensions-4.15.0 urllib3-2.5.0 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip install cmake ninja --upgrade Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: cmake in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (4.1.0) Requirement already satisfied: ninja in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (1.13.0) (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 清理旧编译产物 (rtx5070_env) PS E:\PyTorch_Build\pytorch> Remove-Item -Recurse -Force build, dist -ErrorAction SilentlyContinue (rtx5070_env) PS E:\PyTorch_Build\pytorch> Write-Host "`n==== 编译环境验证 ====" -ForegroundColor Cyan ==== 编译环境验证 ==== (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 1. 目录验证 (rtx5070_env) PS E:\PyTorch_Build\pytorch> Write-Host "当前目录: $pwd" 当前目录: E:\PyTorch_Build\pytorch (rtx5070_env) PS E:\PyTorch_Build\pytorch> if ($pwd -ne "E:\PyTorch_Build\pytorch") { >> Write-Host "⚠️ 错误: 需要切换到E:\PyTorch_Build\pytorch" -ForegroundColor Yellow >> cd E:\PyTorch_Build\pytorch >> } ⚠️ 错误: 需要切换到E:\PyTorch_Build\pytorch (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 2. CUDA工具链验证 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $cudaStatus = @( >> "nvcc --version", >> "nvidia-smi", >> "where cudnn64_8.dll" >> ) (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> foreach ($cmd in $cudaStatus) { >> Write-Host "`n执行: $cmd" -ForegroundColor Magenta >> try { >> Invoke-Expression $cmd >> } >> catch { >> Write-Host "❌ 命令失败: $_" -ForegroundColor Red >> } >> } 执行: nvcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2025 NVIDIA Corporation Built on Wed_Jul_16_20:06:48_Pacific_Daylight_Time_2025 Cuda compilation tools, release 13.0, V13.0.48 Build cuda_13.0.r13.0/compiler.36260728_0 执行: nvidia-smi Wed Sep 3 22:04:47 2025 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 580.97 Driver Version: 580.97 CUDA Version: 13.0 | +-----------------------------------------+------------------------+----------------------+ | GPU Name Driver-Model | Bus-Id Disp.A | Volatile Uncorr. 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Python环境验证 (rtx5070_env) PS E:\PyTorch_Build\pytorch> Write-Host "`nPython环境状态:" -ForegroundColor Magenta Python环境状态: (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip show torch | Select-String "Location" WARNING: Package(s) not found: torch (rtx5070_env) PS E:\PyTorch_Build\pytorch> python -c "import torch; print(f'PyTorch版本: {torch.__version__}')" Traceback (most recent call last): File "<string>", line 1, in <module> File "E:\PyTorch_Build\pytorch\torch\__init__.py", line 61, in <module> from torch.torch_version import __version__ as __version__ File "E:\PyTorch_Build\pytorch\torch\torch_version.py", line 5, in <module> from torch.version import __version__ as internal_version ModuleNotFoundError: No module named 'torch.version' (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 设置RTX 5070专属编译参数 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $cmakeArgs = @( >> "-B build", >> "-G Ninja", >> "-DUSE_CUDA=ON", >> "-DUSE_CUDNN=ON", >> "-DCUDA_TOOLKIT_ROOT_DIR=`"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0`"", >> "-DCUDNN_ROOT_DIR=`"E:\Program Files\NVIDIA\CUNND\v9.12`"", >> "-DCUDA_ARCH_LIST=`"8.9`"", # RTX 5070架构 >> "-DTORCH_CUDA_ARCH_LIST=`"8.9`"", >> "-DCMAKE_BUILD_TYPE=Release", >> "-DUSE_NCCL=OFF", >> "-DUSE_MKLDNN=ON", >> "-DTORCH_CUDA_VERSION=11.8" # 兼容旧驱动 >> ) (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 启动配置过程 (rtx5070_env) PS E:\PyTorch_Build\pytorch> cmake ($cmakeArgs -join " ") CMake Error: Unable to (re)create the private pkgRedirects directory: E:/PyTorch_Build/pytorch/build -G Ninja -DUSE_CUDA=ON -DUSE_CUDNN=ON -DCUDA_TOOLKIT_ROOT_DIR="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.0" -DCUDNN_ROOT_DIR="E:/Program Files/NVIDIA/CUNND/v9.12" -DCUDA_ARCH_LIST="8.9" -DTORCH_CUDA_ARCH_LIST="8.9" -DCMAKE_BUILD_TYPE=Release -DUSE_NCCL=OFF -DUSE_MKLDNN=ON -DTORCH_CUDA_VERSION=11.8/CMakeFiles/pkgRedirects This may be caused by not having read/write access to the build directory. Try specifying a location with read/write access like: cmake -B build If using a CMake presets file, ensure that preset parameter 'binaryDir' expands to a writable directory. (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 设置并行编译(根据CPU核心数调整) (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:MAX_JOBS = 8 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 启动编译并记录日志 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $logFile = "build_$(Get-Date -Format 'yyyyMMdd_HHmmss').log" (rtx5070_env) PS E:\PyTorch_Build\pytorch> Start-Transcript -Path $logFile Transcript started, output file is build_20250903_220514.log (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> try { >> cmake --build build --config Release --parallel $env:MAX_JOBS >> pip install -v --no-build-isolation . >> } >> catch { >> Write-Host "🔥 编译失败!错误详情: $_" -ForegroundColor Red >> } Error: E:/PyTorch_Build/pytorch/build is not a directory Using pip 25.2 from E:\PyTorch_Build\pytorch\rtx5070_env\lib\site-packages\pip (python 3.10) Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Processing e:\pytorch_build\pytorch Running command Preparing metadata (pyproject.toml) Building wheel torch-2.9.0a0+git2d31c3d E:\PyTorch_Build\pytorch\rtx5070_env\lib\site-packages\setuptools\config\_apply_pyprojecttoml.py:82: SetuptoolsDeprecationWarning: `project.license` as a TOML table is deprecated !! ******************************************************************************** Please use a simple string containing a SPDX expression for `project.license`. You can also use `project.license-files`. (Both options available on setuptools>=77.0.0). By 2026-Feb-18, you need to update your project and remove deprecated calls or your builds will no longer be supported. See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! corresp(dist, value, root_dir) running dist_info creating C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info writing C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info\PKG-INFO writing dependency_links to C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info\dependency_links.txt writing entry points to C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info\entry_points.txt writing requirements to C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info\requires.txt writing top-level names to C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info\top_level.txt writing manifest file 'C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info\SOURCES.txt' reading manifest file 'C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info\SOURCES.txt' reading manifest template 'MANIFEST.in' warning: no files found matching 'BUILD' warning: no files found matching '*.BUILD' warning: no files found matching 'BUCK' warning: no files found matching '[Mm]akefile.*' warning: no files found matching '*.[Dd]ockerfile' warning: no files found matching '[Dd]ockerfile.*' warning: no previously-included files matching '*.o' found anywhere in distribution warning: no previously-included files matching '*.obj' found anywhere in distribution warning: no previously-included files matching '*.so' found anywhere in distribution warning: no previously-included files matching '*.a' found anywhere in distribution warning: no previously-included files matching '*.dylib' found anywhere in distribution no previously-included directories found matching '*\.git' warning: no previously-included files matching '*~' found anywhere in distribution warning: no previously-included files matching '*.swp' found anywhere in distribution adding license file 'LICENSE' adding license file 'NOTICE' writing manifest file 'C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info\SOURCES.txt' creating 'C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch-2.9.0a0+git2d31c3d.dist-info' Preparing metadata (pyproject.toml) ... done Collecting filelock (from torch==2.9.0a0+git2d31c3d) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/42/14/42b2651a2f46b022ccd948bca9f2d5af0fd8929c4eec235b8d6d844fbe67/filelock-3.19.1-py3-none-any.whl (15 kB) Requirement already satisfied: typing-extensions>=4.10.0 in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (from torch==2.9.0a0+git2d31c3d) (4.15.0) Collecting sympy>=1.13.3 (from torch==2.9.0a0+git2d31c3d) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl (6.3 MB) Link requires a different Python (3.10.10 not in: '>=3.11'): https://pypi.tuna.tsinghua.edu.cn/packages/eb/8d/776adee7bbf76365fdd7f2552710282c79a4ead5d2a46408c9043a2b70ba/networkx-3.5-py3-none-any.whl (from https://pypi.tuna.tsinghua.edu.cn/simple/networkx/) (requires-python:>=3.11) Link requires a different Python (3.10.10 not in: '>=3.11'): https://pypi.tuna.tsinghua.edu.cn/packages/6c/4f/ccdb8ad3a38e583f214547fd2f7ff1fc160c43a75af88e6aec213404b96a/networkx-3.5.tar.gz (from https://pypi.tuna.tsinghua.edu.cn/simple/networkx/) (requires-python:>=3.11) Link requires a different Python (3.10.10 not in: '>=3.11'): https://pypi.tuna.tsinghua.edu.cn/packages/3f/a1/46c1b6e202e3109d2a035b21a7e5534c5bb233ee30752d7f16a0bd4c3989/networkx-3.5rc0-py3-none-any.whl (from https://pypi.tuna.tsinghua.edu.cn/simple/networkx/) (requires-python:>=3.11) Link requires a different Python (3.10.10 not in: '>=3.11'): https://pypi.tuna.tsinghua.edu.cn/packages/90/7e/0319606a20ced20730806b9f7fe91d8a92f7da63d76a5c388f87d3f7d294/networkx-3.5rc0.tar.gz (from https://pypi.tuna.tsinghua.edu.cn/simple/networkx/) (requires-python:>=3.11) Collecting networkx>=2.5.1 (from torch==2.9.0a0+git2d31c3d) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b9/54/dd730b32ea14ea797530a4479b2ed46a6fb250f682a9cfb997e968bf0261/networkx-3.4.2-py3-none-any.whl (1.7 MB) Collecting jinja2 (from torch==2.9.0a0+git2d31c3d) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl (134 kB) Collecting fsspec>=0.8.5 (from torch==2.9.0a0+git2d31c3d) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/47/71/70db47e4f6ce3e5c37a607355f80da8860a33226be640226ac52cb05ef2e/fsspec-2025.9.0-py3-none-any.whl (199 kB) Collecting mpmath<1.4,>=1.1.0 (from sympy>=1.13.3->torch==2.9.0a0+git2d31c3d) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl (536 kB) Collecting MarkupSafe>=2.0 (from jinja2->torch==2.9.0a0+git2d31c3d) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/44/06/e7175d06dd6e9172d4a69a72592cb3f7a996a9c396eee29082826449bbc3/MarkupSafe-3.0.2-cp310-cp310-win_amd64.whl (15 kB) Building wheels for collected packages: torch Running command Building wheel for torch (pyproject.toml) Building wheel torch-2.9.0a0+git2d31c3d -- Building version 2.9.0a0+git2d31c3d E:\PyTorch_Build\pytorch\rtx5070_env\lib\site-packages\setuptools\_distutils\_msvccompiler.py:12: UserWarning: _get_vc_env is private; find an alternative (pypa/distutils#340) warnings.warn( Cloning into 'nccl'... Note: switching to '3ea7eedf3b9b94f1d9f99f4e55536dfcbd23c1ca'. You are in 'detached HEAD' state. You can look around, make experimental changes and commit them, and you can discard any commits you make in this state without impacting any branches by switching back to a branch. If you want to create a new branch to retain commits you create, you may do so (now or later) by using -c with the switch command. Example: git switch -c <new-branch-name> Or undo this operation with: git switch - Turn off this advice by setting config variable advice.detachedHead to false cmake -GNinja -DBUILD_PYTHON=True -DBUILD_TEST=True -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=E:\PyTorch_Build\pytorch\torch -DCMAKE_PREFIX_PATH=E:\PyTorch_Build\pytorch\rtx5070_env\Lib\site-packages -DCUDNN_INCLUDE_DIR=E:\Program Files\NVIDIA\CUNND\v9.12\include\12.9 -DCUDNN_LIBRARY=E:\Program Files\NVIDIA\CUNND\v9.12\lib\12.9\x64 -DCUDNN_ROOT=E:\Program Files\NVIDIA\CUNND\v9.12 -DPython_EXECUTABLE=E:\PyTorch_Build\pytorch\rtx5070_env\Scripts\python.exe -DPython_NumPy_INCLUDE_DIR=E:\PyTorch_Build\pytorch\rtx5070_env\lib\site-packages\numpy\_core\include -DTORCH_BUILD_VERSION=2.9.0a0+git2d31c3d -DTORCH_CUDA_ARCH_LIST=8.9 -DUSE_NUMPY=True -DUSE_OPENBLAS=1 E:\PyTorch_Build\pytorch CMake Deprecation Warning at CMakeLists.txt:9 (cmake_policy): The OLD behavior for policy CMP0126 will be removed from a future version of CMake. The cmake-policies(7) manual explains that the OLD behaviors of all policies are deprecated and that a policy should be set to OLD only under specific short-term circumstances. Projects should be ported to the NEW behavior and not rely on setting a policy to OLD. -- The CXX compiler identification is MSVC 19.44.35215.0 -- The C compiler identification is MSVC 19.44.35215.0 -- Detecting CXX compiler ABI info -- Detecting CXX compiler ABI info - done -- Check for working CXX compiler: C:/Program Files (x86)/Microsoft Visual Studio/2022/BuildTools/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/cl.exe - skipped -- Detecting CXX compile features -- Detecting CXX compile features - done -- Detecting C compiler ABI info -- Detecting C compiler ABI info - done -- Check for working C compiler: C:/Program Files (x86)/Microsoft Visual Studio/2022/BuildTools/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/cl.exe - skipped -- Detecting C compile features -- Detecting C compile features - done -- Not forcing any particular BLAS to be found CMake Warning at CMakeLists.txt:421 (message): TensorPipe cannot be used on Windows. Set it to OFF CMake Warning at CMakeLists.txt:423 (message): KleidiAI cannot be used on Windows. Set it to OFF CMake Warning at CMakeLists.txt:435 (message): Libuv is not installed in current conda env. Set USE_DISTRIBUTED to OFF. Please run command 'conda install -c conda-forge libuv=1.39' to install libuv. -- Performing Test C_HAS_AVX_1 -- Performing Test C_HAS_AVX_1 - Success -- Performing Test C_HAS_AVX2_1 -- Performing Test C_HAS_AVX2_1 - Success -- Performing Test C_HAS_AVX512_1 -- Performing Test C_HAS_AVX512_1 - Success -- Performing Test CXX_HAS_AVX_1 -- Performing Test CXX_HAS_AVX_1 - Success -- Performing Test CXX_HAS_AVX2_1 -- Performing Test CXX_HAS_AVX2_1 - Success -- Performing Test CXX_HAS_AVX512_1 -- Performing Test CXX_HAS_AVX512_1 - Success -- Current compiler supports avx2 extension. Will build perfkernels. -- Performing Test COMPILER_SUPPORTS_HIDDEN_VISIBILITY -- Performing Test COMPILER_SUPPORTS_HIDDEN_VISIBILITY - Failed -- Performing Test COMPILER_SUPPORTS_HIDDEN_INLINE_VISIBILITY -- Performing Test COMPILER_SUPPORTS_HIDDEN_INLINE_VISIBILITY - Failed -- Could not find hardware support for NEON on this machine. -- No OMAP3 processor on this machine. -- No OMAP4 processor on this machine. -- Compiler does not support SVE extension. Will not build perfkernels. CMake Warning at CMakeLists.txt:841 (message): x64 operating system is required for FBGEMM. Not compiling with FBGEMM. Turn this warning off by USE_FBGEMM=OFF. -- Performing Test HAS/UTF_8 -- Performing Test HAS/UTF_8 - Success -- Found CUDA: E:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.0 (found version "13.0") -- The CUDA compiler identification is NVIDIA 13.0.48 with host compiler MSVC 19.44.35215.0 -- Detecting CUDA compiler ABI info -- Detecting CUDA compiler ABI info - done -- Check for working CUDA compiler: E:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.0/bin/nvcc.exe - skipped -- Detecting CUDA compile features -- Detecting CUDA compile features - done -- Found CUDAToolkit: E:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.0/include (found version "13.0.48") -- PyTorch: CUDA detected: 13.0 -- PyTorch: CUDA nvcc is: E:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.0/bin/nvcc.exe -- PyTorch: CUDA toolkit directory: E:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.0 -- PyTorch: Header version is: 13.0 -- Found Python: E:\PyTorch_Build\pytorch\rtx5070_env\Scripts\python.exe (found version "3.10.10") found components: Interpreter CMake Warning at cmake/public/cuda.cmake:140 (message): Failed to compute shorthash for libnvrtc.so Call Stack (most recent call first): cmake/Dependencies.cmake:44 (include) CMakeLists.txt:869 (include) -- Found CUDNN: E:/Program Files/NVIDIA/CUNND/v9.12/lib/13.0/x64/cudnn.lib -- Could NOT find CUSPARSELT (missing: CUSPARSELT_LIBRARY_PATH CUSPARSELT_INCLUDE_PATH) CMake Warning at cmake/public/cuda.cmake:226 (message): Cannot find cuSPARSELt library. Turning the option off Call Stack (most recent call first): cmake/Dependencies.cmake:44 (include) CMakeLists.txt:869 (include) -- Could NOT find CUDSS (missing: CUDSS_LIBRARY_PATH CUDSS_INCLUDE_PATH) CMake Warning at cmake/public/cuda.cmake:242 (message): Cannot find CUDSS library. Turning the option off Call Stack (most recent call first): cmake/Dependencies.cmake:44 (include) CMakeLists.txt:869 (include) -- USE_CUFILE is set to 0. Compiling without cuFile support CMake Warning at cmake/public/cuda.cmake:317 (message): pytorch is not compatible with `CMAKE_CUDA_ARCHITECTURES` and will ignore its value. Please configure `TORCH_CUDA_ARCH_LIST` instead. Call Stack (most recent call first): cmake/Dependencies.cmake:44 (include) CMakeLists.txt:869 (include) -- Added CUDA NVCC flags for: -gencode;arch=compute_89,code=sm_89 CMake Warning at cmake/Dependencies.cmake:95 (message): Not compiling with XPU. Could NOT find SYCL. Suppress this warning with -DUSE_XPU=OFF. Call Stack (most recent call first): CMakeLists.txt:869 (include) -- Building using own protobuf under third_party per request. -- Use custom protobuf build. CMake Warning at cmake/ProtoBuf.cmake:37 (message): Ancient protobuf forces CMake compatibility Call Stack (most recent call first): cmake/ProtoBuf.cmake:87 (custom_protobuf_find) cmake/Dependencies.cmake:107 (include) CMakeLists.txt:869 (include) CMake Deprecation Warning at third_party/protobuf/cmake/CMakeLists.txt:2 (cmake_minimum_required): Compatibility with CMake < 3.10 will be removed from a future version of CMake. Update the VERSION argument <min> value. Or, use the <min>...<max> syntax to tell CMake that the project requires at least <min> but has been updated to work with policies introduced by <max> or earlier. -- -- 3.13.0.0 -- Performing Test CMAKE_HAVE_LIBC_PTHREAD -- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Failed -- Looking for pthread_create in pthreads -- Looking for pthread_create in pthreads - not found -- Looking for pthread_create in pthread -- Looking for pthread_create in pthread - not found -- Found Threads: TRUE -- Caffe2 protobuf include directory: $<BUILD_INTERFACE:E:/PyTorch_Build/pytorch/third_party/protobuf/src>$<INSTALL_INTERFACE:include> -- Trying to find preferred BLAS backend of choice: MKL -- MKL_THREADING = OMP -- Looking for sys/types.h -- Looking for sys/types.h - found -- Looking for stdint.h -- Looking for stdint.h - found -- Looking for stddef.h -- Looking for stddef.h - found -- Check size of void* -- Check size of void* - done -- MKL_THREADING = OMP CMake Warning at cmake/Dependencies.cmake:213 (message): MKL could not be found. Defaulting to Eigen Call Stack (most recent call first): CMakeLists.txt:869 (include) CMake Warning at cmake/Dependencies.cmake:279 (message): Preferred BLAS (MKL) cannot be found, now searching for a general BLAS library Call Stack (most recent call first): CMakeLists.txt:869 (include) -- MKL_THREADING = OMP -- Checking for [mkl_intel_lp64 - mkl_intel_thread - mkl_core - libiomp5md] -- Library mkl_intel_lp64: not found -- Checking for [mkl_intel - mkl_intel_thread - mkl_core - libiomp5md] -- Library mkl_intel: not found -- Checking for [mkl_intel_lp64 - mkl_intel_thread - mkl_core] -- Library mkl_intel_lp64: not found -- Checking for [mkl_intel - mkl_intel_thread - mkl_core] -- Library mkl_intel: not found -- Checking for [mkl_intel_lp64 - mkl_sequential - mkl_core] -- Library mkl_intel_lp64: not found -- Checking for [mkl_intel - mkl_sequential - mkl_core] -- Library mkl_intel: not found -- Checking for [mkl_intel_lp64 - mkl_core - libiomp5md - pthread] -- Library mkl_intel_lp64: not found -- Checking for [mkl_intel - mkl_core - libiomp5md - pthread] -- Library mkl_intel: not found -- Checking for [mkl_intel_lp64 - mkl_core - pthread] -- Library mkl_intel_lp64: not found -- Checking for [mkl_intel - mkl_core - pthread] -- Library mkl_intel: not found -- Checking for [mkl - guide - pthread - m] -- Library mkl: not found -- MKL library not found -- Checking for [blis] -- Library blis: BLAS_blis_LIBRARY-NOTFOUND -- Checking for [Accelerate] -- Library Accelerate: BLAS_Accelerate_LIBRARY-NOTFOUND -- Checking for [vecLib] -- Library vecLib: BLAS_vecLib_LIBRARY-NOTFOUND -- Checking for [flexiblas] -- Library flexiblas: BLAS_flexiblas_LIBRARY-NOTFOUND -- Checking for [openblas] -- Library openblas: BLAS_openblas_LIBRARY-NOTFOUND -- Checking for [openblas - pthread - m] -- Library openblas: BLAS_openblas_LIBRARY-NOTFOUND -- Checking for [openblas - pthread - m - gomp] -- Library openblas: BLAS_openblas_LIBRARY-NOTFOUND -- Checking for [libopenblas] -- Library libopenblas: BLAS_libopenblas_LIBRARY-NOTFOUND -- Checking for [goto2 - gfortran] -- Library goto2: BLAS_goto2_LIBRARY-NOTFOUND -- Checking for [goto2 - gfortran - pthread] -- Library goto2: BLAS_goto2_LIBRARY-NOTFOUND -- Checking for [acml - gfortran] -- Library acml: BLAS_acml_LIBRARY-NOTFOUND -- Checking for [blis] -- Library blis: BLAS_blis_LIBRARY-NOTFOUND -- Could NOT find Atlas (missing: Atlas_CBLAS_INCLUDE_DIR Atlas_CLAPACK_INCLUDE_DIR Atlas_CBLAS_LIBRARY Atlas_BLAS_LIBRARY Atlas_LAPACK_LIBRARY) -- Checking for [ptf77blas - atlas - gfortran] -- Library ptf77blas: BLAS_ptf77blas_LIBRARY-NOTFOUND -- Checking for [] -- Looking for sgemm_ -- Looking for sgemm_ - not found -- Cannot find a library with BLAS API. Not using BLAS. -- Using pocketfft in directory: E:/PyTorch_Build/pytorch/third_party/pocketfft/ CMake Deprecation Warning at third_party/pthreadpool/CMakeLists.txt:1 (CMAKE_MINIMUM_REQUIRED): Compatibility with CMake < 3.10 will be removed from a future version of CMake. Update the VERSION argument <min> value. Or, use the <min>...<max> syntax to tell CMake that the project requires at least <min> but has been updated to work with policies introduced by <max> or earlier. CMake Deprecation Warning at third_party/FXdiv/CMakeLists.txt:1 (CMAKE_MINIMUM_REQUIRED): Compatibility with CMake < 3.10 will be removed from a future version of CMake. Update the VERSION argument <min> value. Or, use the <min>...<max> syntax to tell CMake that the project requires at least <min> but has been updated to work with policies introduced by <max> or earlier. CMake Deprecation Warning at third_party/cpuinfo/CMakeLists.txt:1 (CMAKE_MINIMUM_REQUIRED): Compatibility with CMake < 3.10 will be removed from a future version of CMake. Update the VERSION argument <min> value. Or, use the <min>...<max> syntax to tell CMake that the project requires at least <min> but has been updated to work with policies introduced by <max> or earlier. -- The ASM compiler identification is MSVC CMake Warning (dev) at rtx5070_env/Lib/site-packages/cmake/data/share/cmake-4.1/Modules/CMakeDetermineASMCompiler.cmake:234 (message): Policy CMP194 is not set: MSVC is not an assembler for language ASM. Run "cmake --help-policy CMP194" for policy details. Use the cmake_policy command to set the policy and suppress this warning. Call Stack (most recent call first): third_party/XNNPACK/CMakeLists.txt:18 (PROJECT) This warning is for project developers. Use -Wno-dev to suppress it. -- Found assembler: C:/Program Files (x86)/Microsoft Visual Studio/2022/BuildTools/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/cl.exe -- Building for XNNPACK_TARGET_PROCESSOR: x86_64 -- Generating microkernels.cmake
09-04
基于可靠性评估序贯蒙特卡洛模拟法的配电网可靠性评估研究(Matlab代码实现)内容概要:本文围绕“基于可靠性评估序贯蒙特卡洛模拟法的配电网可靠性评估研究”,介绍了利用Matlab代码实现配电网可靠性的仿真分析方法。重点采用序贯蒙特卡洛模拟法对配电网进行长时间段的状态抽样与统计,通过模拟系统元件的故障与修复过程,评估配电网的关键可靠性指标,如系统停电频率、停电持续时间、负荷点可靠性等。该方法能够有效处理复杂网络结构与设备时序特性,提升评估精度,适用于含分布式电源、电动汽车等新型负荷接入的现代配电网。文中提供了完整的Matlab实现代码与案例分析,便于复现和扩展应用。; 适合人群:具备电力系统基础知识和Matlab编程能力的高校研究生、科研人员及电力行业技术人员,尤其适合从事配电网规划、运行与可靠性分析相关工作的人员; 使用场景及目标:①掌握序贯蒙特卡洛模拟法在电力系统可靠性评估中的基本原理与实现流程;②学习如何通过Matlab构建配电网仿真模型并进行状态转移模拟;③应用于含新能源接入的复杂配电网可靠性定量评估与优化设计; 阅读建议:建议结合文中提供的Matlab代码逐段调试运行,理解状态抽样、故障判断、修复逻辑及指标统计的具体实现方式,同时可扩展至不同网络结构或加入更多不确定性因素进行深化研究。
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