pycharm最近遇到的一些问题
遇到的问题
use pycharm visit NCBI but the SDK is fault and report the ecoding is wrong
so I need to know how to config python and anacoda SDK again
1.SDK encoding问题
2.anaconda安装包失败
C:\Users\>conda list python
# packages in environment at D:\1_development\06 TOOL\anaconda:
#
# Name Version Build Channel
brotli-python 1.0.9 py312hd77b12b_8
gitpython 3.1.43 py312haa95532_0
ipython 8.27.0 py312haa95532_0
ipython_genutils 0.2.0 pyhd3eb1b0_1
msgpack-python 1.0.3 py312h59b6b97_0
python 3.12.3 h2628c8c_0_cpython conda-forge
python-dateutil 2.9.0post0 py312haa95532_2
python-dotenv 0.21.0 py312haa95532_0
python-fastjsonschema 2.16.2 py312haa95532_0
python-json-logger 2.0.7 py312haa95532_0
python-libarchive-c 5.1 pyhd3eb1b0_0
python-lmdb 1.4.1 py312hd77b12b_0
python-lsp-black 2.0.0 py312haa95532_0
python-lsp-jsonrpc 1.1.2 pyhd3eb1b0_0
python-lsp-server 1.10.0 py312haa95532_0
python-slugify 5.0.2 pyhd3eb1b0_0
python-tzdata 2023.3 pyhd3eb1b0_0
python_abi 3.12 5_cp312 conda-forge
C:\Users\>python -c "import encodings; print(encodings.__file__)"
D:\1_development\06 TOOL\anaconda\Lib\encodings\__init__.py
C:\Users\>
查看环境变量
版本
C:\Users>python --version
Python 3.12.3
C:\Users>pip --version
pip 24.2 from D:\1_development\06 TOOL\anaconda\Lib\site-packages\pip (python 3.12)
C:\Users>conda --version
conda 24.9.2
解决
卸载原来的anaconda和python
重新安装miniconda在D/1dev 目录下 配置python解释器
用python跑访问NCBI的代码
# 查看已安装的包
conda list
# 停用环境
conda deactivate
# 删除环境（不需要时）
conda env remove -n ncbi
# 使用 conda 安装 Biopython（推荐）
conda install -c conda-forge biopython
python -c "from Bio import Entrez; print('Biopython 已成功安装')"
访问NCBI数据库
from Bio import Entrez
from Bio import SeqIO
import time
# 设置你的邮箱（NCBI要求）
Entrez.email = "your_email@example.com" # 替换为你的真实邮箱
def search_ncbi(query, max_results=5):
"""在NCBI中搜索条目"""
print(f"\n搜索: {query}")
handle = Entrez.esearch(db="nucleotide", term=query, retmax=max_results)
record = Entrez.read(handle)
handle.close()
return record["IdList"]
def fetch_genbank_records(id_list):
"""获取GenBank记录"""
ids = ",".join(id_list)
handle = Entrez.efetch(db="nucleotide", id=ids, rettype="gb", retmode="text")
records = list(SeqIO.parse(handle, "gb"))
handle.close()
return records
def fetch_abstract(pubmed_id):
"""获取PubMed摘要"""
handle = Entrez.efetch(db="pubmed", id=pubmed_id, retmode="xml")
record = Entrez.read(handle)
handle.close()
return record
def main():
try:
# 示例1: 搜索核苷酸序列
query = "Homo sapiens[Organism] AND COX1[Gene]"
ids = search_ncbi(query)
print(f"找到的ID: {ids}")
# 示例2: 获取GenBank记录
if ids:
records = fetch_genbank_records(ids[:2]) # 只取前两个
for i, record in enumerate(records, 1):
print(f"\n记录 {i}:")
print(f"ID: {record.id}")
print(f"描述: {record.description}")
print(f"序列长度: {len(record.seq)} bp")
# print(f"序列: {record.seq}") # 取消注释查看完整序列
# 示例3: 搜索PubMed文章
pubmed_query = "COVID-19 vaccine"
pubmed_ids = search_ncbi(pubmed_query, db="pubmed")
if pubmed_ids:
abstract = fetch_abstract(pubmed_ids[0])
print("\nPubMed摘要示例:")
print(abstract[0]['MedlineCitation']['Article']['Abstract']['AbstractText'][0])
except Exception as e:
print(f"发生错误: {e}")
finally:
# NCBI要求不要频繁请求，每次请求后暂停
time.sleep(1)
if __name__ == "__main__":
main()
安装Django
conda comaand always use
命令说明
conda list查看已安装的包
conda deactivate退出当前环境
conda env list列出所有环境
conda remove -n django_env --all删除环境
conda install -c conda-forge django
安装pytorch
conda install pytorch torchvision torchaudio cpuonly -c pytorch
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
import torch
# 检查PyTorch版本
print(torch.__version__)
# 检查CUDA是否可用（GPU版本）
print(torch.cuda.is_available())
# 尝试简单张量运算
x = torch.rand(5, 3)
print(x)
#delete
conda remove pytorch torchvision torchaudio
conda clean --all
code example
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from tqdm import tqdm # 进度条工具
# 1. 设置随机种子保证可重复性
torch.manual_seed(42)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
# 2. 数据准备
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
# 加载MNIST数据集
train_set = torchvision.datasets.MNIST(
root='./data',
train=True,
download=True,
transform=transform
)
test_set = torchvision.datasets.MNIST(
root='./data',
train=False,
download=True,
transform=transform
)
train_loader = DataLoader(train_set, batch_size=64, shuffle=True)
test_loader = DataLoader(test_set, batch_size=64, shuffle=False)
# 3. 定义神经网络模型
class SimpleCNN(nn.Module):
def __init__(self):
super(SimpleCNN, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(64*7*7, 128)
self.fc2 = nn.Linear(128, 10)
self.dropout = nn.Dropout(0.25)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = self.pool(torch.relu(self.conv2(x)))
x = x.view(-1, 64*7*7)
x = self.dropout(x)
x = torch.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
model = SimpleCNN().to(device)
# 4. 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 5. 训练函数
def train(model, loader, criterion, optimizer, epoch):
model.train()
running_loss = 0.0
correct = 0
total = 0
pbar = tqdm(loader, desc=f'Epoch {epoch}')
for images, labels in pbar:
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
pbar.set_postfix({
'loss': running_loss/(pbar.n+1),
'acc': 100.*correct/total
})
return running_loss/len(loader), correct/total
# 6. 测试函数
def test(model, loader, criterion):
model.eval()
running_loss = 0.0
correct = 0
total = 0
with torch.no_grad():
for images, labels in loader:
images, labels = images.to(device), labels.to(device)
outputs = model(images)
loss = criterion(outputs, labels)
running_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
return running_loss/len(loader), correct/total
# 7. 训练循环
num_epochs = 5
for epoch in range(1, num_epochs+1):
train_loss, train_acc = train(model, train_loader, criterion, optimizer, epoch)
test_loss, test_acc = test(model, test_loader, criterion)
print(f'\nEpoch {epoch}:')
print(f'Train Loss: {train_loss:.4f}, Acc: {train_acc:.2f}%')
print(f'Test Loss: {test_loss:.4f}, Acc: {test_acc:.2f}%')
# 8. 保存模型
torch.save(model.state_dict(), 'mnist_cnn.pth')
print("Model saved to mnist_cnn.pth")
# 9. 示例预测
sample_image, _ = test_set[0]
model.eval()
with torch.no_grad():
output = model(sample_image.unsqueeze(0).to(device))
_, predicted = torch.max(output, 1)
print(f"\nSample prediction: {predicted.item()}")
原文地址：https://blog.csdn.net/m0_46179147/article/details/148568551
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