小编Dal*_*lek的帖子

我正在使用ssh命令在另一台机器上运行我的python脚本linux.我也运行了这个命令:

source ~/.bashrc 

登录其他计算机后,为了在新计算机中定义正确的路径.我收到运行以下python代码行的错误消息,即使我已经尝试通过定义后端来遵循此问题中的指令.

>>> import matplotlib
>>> import pylab as plt
>>> matplotlib.use('Agg')
>>> import numpy as np
>>> x=np.arange(0,2,0.001)
>>> y=np.sin(x)**2+4*np.cos(x)
>>> fig = plt.figure()
>>> plt.plot(x,y,'r.')     

错误消息

This probably means that Tcl wasn't installed properly.
Traceback (most recent call last):
  File "Systematic_Optimised.py", line 513, in <module>
    fig = plt.figure()
  File "/vol/anaconda/lib/python2.7/site-packages/matplotlib/pyplot.py", line 435, in figure
    **kwargs)
  File "/vol/anaconda/lib/python2.7/site-packages/matplotlib/backends/backend_qt4agg.py", line 47, in new_figure_manager
    return new_figure_manager_given_figure(num, thisFig)
  File "/vol/anaconda/lib/python2.7/site-packages/matplotlib/backends/backend_qt4agg.py", line 54, in new_figure_manager_given_figure …

我是新手spark和pyspark.如果有人解释SparkContext参数到底做了什么,我将不胜感激？我怎么能设置spark_contextpython应用程序？

我对c ++的了解有限.我尝试编译一个c ++库,当我运行以下头文件的make文件时

mcmc_dhs.h

#include <algorithm>
#include <map>

// intrinsic shape and (reduced) shear just add?
//#define WLNOISE

// use shear instead of reduced shear for model
//#define NOREDSHEAR

/// parameters for the M200-concentration relation
const number mcreal[2] = {9.59,-0.102}; // Dolag et al. (2004)
//const number mcreal[2] = {5.26,-0.100}; // Neto et al. (2007) [Millenium Run]

/// critical density at z=0 (h100=1) in [Msun/Mpc^3]
const number rhocrit = exp(log(rhoCrit)+3.*log(Mpc)-log(Msun)); 

/// two extra halo parameters: r200 …

我已经编写了以下函数cython来估计对数似然

@cython.boundscheck(False)
@cython.wraparound(False)
def likelihood(double m,
               double c,
               np.ndarray[np.double_t, ndim=1, mode='c'] r_mpc not None,
               np.ndarray[np.double_t, ndim=1, mode='c'] gtan not None,
               np.ndarray[np.double_t, ndim=1, mode='c'] gcrs not None,
               np.ndarray[np.double_t, ndim=1, mode='c'] shear_err not None,
               np.ndarray[np.double_t, ndim=1, mode='c'] beta not None,
               double rho_c,
               np.ndarray[np.double_t, ndim=1, mode='c'] rho_c_sigma not None):
    cdef double rscale = rscaleConstM(m, c,rho_c, 200)

    cdef Py_ssize_t ngals = r_mpc.shape[0]

    cdef np.ndarray[DTYPE_T, ndim=1, mode='c'] gamma_inf = Sh(r_mpc, c, rscale, rho_c_sigma)
    cdef np.ndarray[DTYPE_T, ndim=1, mode='c'] kappa_inf = Kap(r_mpc, c, rscale, …

我有一个三列的数据目录,我想制作一个3D图,加上每个轴的投影作为另外两个轴平面中的投影轮廓.到目前为止,我可以使用3D图matplotlib,但仍然没有显示数据属性的任何内容.

from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from numpy import * 
data=loadtxt('test.cat')
X=data[:,0]
Y=data[:,1]
Z=data[:,2]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X, Y, Z, c='r', marker='.')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

plt.show()

我怎样才能绘制每个平面中数据的投影colorbar？

我在一个显示2D图像的示例ASCII文件中有一组点. 我想估计这些点填充的总面积.在这个平面内有一些地方没有被任何点填满,因为这些区域已经被掩盖了.我认为估计该区域可能是实用的将是应用凹形船体 或alpha形状.我尝试了这种方法来找到合适的alpha值,并因此估计面积.

from shapely.ops import cascaded_union, polygonize
import shapely.geometry as geometry
from scipy.spatial import Delaunay
import numpy as np
import pylab as pl
from descartes import PolygonPatch
from matplotlib.collections import LineCollection
def plot_polygon(polygon):
    fig = pl.figure(figsize=(10,10))
    ax = fig.add_subplot(111)
    margin = .3
    x_min, y_min, x_max, y_max = polygon.bounds
    ax.set_xlim([x_min-margin, x_max+margin])
    ax.set_ylim([y_min-margin, y_max+margin])
    patch = PolygonPatch(polygon, fc='#999999',
                         ec='#000000', fill=True,
                         zorder=-1)
    ax.add_patch(patch)
    return fig
def alpha_shape(points, alpha):
    if len(points) < 4:
        # …

我想删除一列gsl_matrix(i第 - 列),然后将其内容复制到另一列gsl_matrix.这是我的代码:

#include <stdio.h>
#include <iostream>
#include <gsl/gsl_matrix.h>
#include <gsl/gsl_vector.h>

int remove_col (int K,
                int N,
                int i,//between range 1 to N
                gsl_matrix *Sn, //Kx(N-1)
                gsl_matrix *Z)
{
    gsl_matrix_view Z_view;

    gsl_matrix_view Sn_view;
    if (i==0){
       Z_view  = gsl_matrix_submatrix (Z, 0, 1, K, N-1);
       gsl_matrix_memcpy (Sn, &Z_view.matrix);
    }else{
       Z_view  = gsl_matrix_submatrix (Z, 0, 0, K, i);
       Sn_view = gsl_matrix_submatrix (Sn, 0, 0, K, i);
       gsl_matrix_memcpy (&Sn_view.matrix, &Z_view.matrix);
       Z_view  = gsl_matrix_submatrix (Z, 0, i, K, N-i); …

我有一组真实数据,我想使用这些数据来查找概率分布,然后根据它们的pdf使用它们的属性生成一些随机点.我的数据集示例如下:

#Mag Weight
21.9786 3.6782
24.0305 6.1120
21.9544 4.2225
23.9383 5.1375
23.9352 4.6499
23.0261 5.1355
23.8682 5.9932
24.8052 4.1765
22.8976 5.1901
23.9679 4.3190
25.3362 4.1519
24.9079 4.2090
23.9851 5.1951
22.2094 5.1570
22.3452 5.6159
24.0953 6.2697
24.3901 6.9299
24.1789 4.0222
24.2648 4.4997
25.3931 3.3920
25.8406 3.9587
23.1427 6.9398
21.2985 7.7582
25.4807 3.1112
25.1935 5.0913
25.2136 4.0578
24.6990 3.9899
23.5299 4.6788
24.0880 7.0576
24.7931 5.7088
25.1860 3.4825
24.4757 5.8500
24.1398 4.9842
23.4947 4.4730
20.9806 5.2717
25.9470 3.4706
25.0324 3.3879
24.7186 3.8443
24.3350 …

我希望在点击submit输入字段中包含新值的按钮后,我的网络d3.js图将根据新输入值生成的新图表进行更新.在下面,您可以找到我的示例代码:

GenerateGraph.js此文件包含一组函数,这些函数根据提交的输入值生成graph(randomGraph).然后需要在浏览器中刷新图表.

function degree(node,list){
  var deg=new Array();
  for (var i=0; i<node.length; i++){
    var count=0;
    for (var j=0; j<list.length; j++){
        if (node[i]==list[j][0] || node[i]==list[j][1]){
          count++;
        }
    }
    deg.push(count);
  }
  return deg;
}
function randomGraph (n, m) { //creates a random graph on n nodes and m links
  var graph={};
  var nodes = d3.range(n).map(Object),
      list  = randomChoose(unorderedPairs(d3.range(n)), m),
      links = list.map(function (a) { return {source: a[0], target: a[1]} }); …

我在班级中实例化了内存视图，如下所示：

from __future__ import division
import numpy as np
import pylab as plt
cimport numpy as np
cimport cython
cdef class fit(object):
     cdef public double[::1] shear_g1, shear_g2, shear_z, halo_pos_arcsec
     cdef public double[:,::1] shear_pos_arcsec, source_zpdf

     cdef char* path
     cdef double omega_m, omega_l, h, sigma_g

     @cython.boundscheck(False)
     @cython.cdivision(True)
     @cython.wraparound(False)
     @cython.nonecheck(False)
     def __init__(self, shear_g1, shear_g2, shear_pos_arcsec, shear_z, halo_pos_arcsec, double halo_z, source_zpdf, sigma_g, path=None, omega_m=None, omega_l=None, h=None ):

         self.shear_g1 = shear_g1
         self.shear_g2 = shear_g2
         self.shear_pos_arcsec = shear_pos_arcsec
         self.shear_z  = shear_z
         self.halo_pos_arcsec  = halo_pos_arcsec
         self.halo_z    = …