小编jjd*_*ast的帖子

我使用闪亮的服务器在端口3838上构建一个web-app,当我在我的服务器中使用nginx它运行良好.但当我在我的服务器上停止nginx并尝试使用docker nginx时,我发现该网站出现'502-Bad Gate Way'错误,nginx日志显示:

2016/04/28 18:51:15 [error] 8#8: *1 connect() failed (111: Connection refused) while connecting to upstream, ...

我通过这个命令安装docker-nginx:

sudo docker pull nginx

我的docker命令行是这样的(为了清除我添加一些缩进):

sudo docker run --name docker-nginx -p 80:80 
    -v ~/docker-nginx/default.conf:/etc/nginx/conf.d/default.conf
    -v  /usr/share/nginx/html:/usr/share/nginx/html nginx

我在我的家庭目录中创建了一个文件夹名称'docker-nginx',将我的nginx conf文件移到此文件夹中,然后在etc/nginx目录中删除我原来的conf以防万一.

我的nginx conf文件如下所示:

server {
    listen 80 default_server;
    # listen [::]:80 default_server ipv6only=on;

    root /usr/share/nginx/html;
    index index.html index.htm;

    # Make site accessible from http://localhost/
    server_name localhost;

    location / {
            proxy_pass http://127.0.0.1:3838/;
            proxy_redirect http://127.0.0.1:3838/ $scheme://$host/;
            auth_basic "Username and Password are required";
            auth_basic_user_file /etc/nginx/.htpasswd; …

我有一个文件夹trip_data包含许多带有日期的 csv 文件，如下所示：

trip_data/
??? df_trip_20140803_1.csv
??? df_trip_20140803_2.csv
??? df_trip_20140803_3.csv
??? df_trip_20140803_4.csv
??? df_trip_20140803_5.csv
??? df_trip_20140803_6.csv
??? df_trip_20140804_1.csv
??? df_trip_20140804_2.csv
??? df_trip_20140804_3.csv
??? df_trip_20140804_4.csv
??? df_trip_20140804_5.csv
??? df_trip_20140804_6.csv
??? df_trip_20140805_1.csv
??? df_trip_20140805_2.csv
??? df_trip_20140805_3.csv
??? df_trip_20140805_4.csv
??? df_trip_20140805_5.csv
??? df_trip_20140805_6.csv
??? df_trip_20140806_1.csv
??? df_trip_20140806_2.csv
??? df_trip_20140806_3.csv
??? df_trip_20140806_4.csv

现在我想用python pandas按日期分别加载所有这些文件，意味着4个DataFrame df_traip_20140803, df_traip_20140804, df_traip_20140805, df_traip_20140806

我的代码如下所示：

days = [20140803,20140804,20140805,20140806]

for day in days:
    ## Locate to the path
    path ='./trip_data/df_trip_%d*.csv' % day
    df = pd.read_csv(path, header=None, nrows=10,
                        names=['ID','lat','lon','status','timestamp']) 

哪个不能得到正确的结果。我怎样才能做到这一点？

假设我有一个这样的矩阵:

A = matrix( 
  c(2, 4, 3, 1, 5, 7, 4, 5, 1), # the data elements 
  nrow=3,              # number of rows 
  ncol=3,              # number of columns 
  byrow = TRUE
)     

> A
     [,1] [,2] [,3]
[1,]    2    4    3
[2,]    1    5    7
[3,]    4    5    1

现在我想计算这个矩阵中对称数据的平均值,如下所示:

> A.mean
     [,1] [,2] [,3]
[1,]  2.0  2.5  3.5
[2,]  2.5  5.0  6.0
[3,]  3.5  6.0  1.0

如何在不使用循环的情况下执行此操作？

我尝试使用docker构建一个启用了ffmpeg的Python3 + OpenCV3环境.

由于我也想使用GPU加速模型,我使用NVIDIA-docker图像构建.

这是我的Dockerfile:

FROM nvidia/cuda:8.0-cudnn5-devel

...
...

#############################################
# OpenCV 3 w/ Python 2.7 from Anaconda
#############################################

RUN cd ~/ &&\
    git clone https://github.com/opencv/opencv.git &&\
    git clone https://github.com/opencv/opencv_contrib.git &&\
    cd opencv && mkdir build && cd build && \
    cmake -D CMAKE_BUILD_TYPE=RELEASE \
          -D CMAKE_INSTALL_PREFIX=/opt/opencv \
          -D INSTALL_C_EXAMPLES=ON \
          -D INSTALL_PYTHON_EXAMPLES=ON \
          -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \
          -D BUILD_EXAMPLES=ON \
          -D PYTHON_DEFAULT_EXECUTABLE=/opt/conda/bin/python2.7  BUILD_opencv_python2=True \
          -D PYTHON_LIBRARY=/opt/conda/lib/libpython2.7.so \
          -D PYTHON_INCLUDE_DIR=/opt/conda/include/python2.7 \
          -D PYTHON2_NUMPY_INCLUDE_DIRS=/opt/conda/lib/python2.7/site-packages/numpy/core/include \
          -D PYTHON_EXECUTABLE=/opt/conda/bin/python2.7 -DWITH_FFMPEG=ON \
          -D BUILD_SHARED_LIBS=ON .. …