小编Dan*_* Tz的帖子

这是一个简单的情节:

1)如何禁用滴答？2)如何减少他们的数量？

这是一个示例代码:

from pylab import *
import numpy as np

x = [5e-05, 5e-06, 5e-07, 5e-08, 5e-09, 5e-10]
y = [-13, 14, 100, 120, 105, 93]

def myfunc(x,p):
    sl,yt,yb,ec=p    
    y = yb + (yt-yb)/(1+np.power(10, sl*(np.log10(x)-np.log10(ec))))
    return y

xp = np.power(10, np.linspace(np.log10(min(x)/10), np.log10(max(x)*10), 100))

pxp=myfunc(xp, [1,100,0,1e-6])
subplot(111,axisbg="#dfdfdf")
plt.plot(x, y, '.', xp, pxp, 'g-', linewidth=1)   
plt.xscale('log')

plt.grid(True,ls="-", linewidth=0.4, color="#ffffff", alpha=0.5)


plt.draw()
plt.show()

哪个产生:

我正在尝试使用多处理来计算和生成绘图.在Linux上,下面的代码运行正常,但在Mac(ML)上它没有运行,给出以下错误:

import multiprocessing
import matplotlib.pyplot as plt
import numpy as np
import rpy2.robjects as robjects

def main():
    pool = multiprocessing.Pool()
    num_figs = 2
    # generate some random numbers
    input = zip(np.random.randint(10,1000,num_figs), 
                range(num_figs))

    pool.map(plot, input)

def plot(args):
    num, i = args
    fig = plt.figure()
    data = np.random.randn(num).cumsum()
    plt.plot(data)

main()

Rpy2是rpy2 == 2.3.1,R是2.13.2(我无法在任何mac上安装R 3.0和rpy2最新版本而不会出现分段错误).

错误是:

The process has forked and you cannot use this CoreFoundation functionality safely. You MUST exec().
Break on __THE_PROCESS_HAS_FORKED_AND_YOU_CANNOT_USE_THIS_COREFOUNDATION_FUNCTIONALITY___YOU_MUST_EXEC__() to debug.
The process has forked and you cannot …

我正在使用django-parler(django-hvad的衍生物)进行翻译.在管理员显示具有多种关系的Foreignkey字段时,django为每个字段运行一个查询:

因此,当有300个服务时,会有尽可能多的查询.

我认为get_queryset上的prefetch_related不适用于mantomany过滤器/列表,如果我错了,请纠正我:

def get_queryset(self, request):
    return super(DoctorAdmin, self).get_queryset(request).prefetch_related('translations', 'services__translations')

对查询数量没有影响.启用缓存上parler(如笔者建议在这里)也于事无补,因为相同的查询不重复,但这些过滤器的每一项被称为在翻译项目查询(IDS每一次都是不同的).所以,我要找的是内部过滤器上的select_related/prefetch_related.如果您已经解决了这个问题,我也会同时审核您的应用程序.

这是我读取csv文件时的输入文件:

Sample Info     D3S1358 1       D3S1358 2       TH01 1      TH01 2      D21S11 1        D21S11 2        D21S11 3
TEST_646            17          17                  9       9.3         28                  28          nan
TEST_647            18          18                  7       7           29                  30          30.2
TEST_648            16          16                  9       9           31.2                31.2        nan

我想将其转换为这样的形式:

Sample_name  Marker     mrk     value
TEST_646     D3S1358     1      17
TEST_646     D3S1358     2      17
TEST_646     TH01        1      9
TEST_646     TH01        2      9.3
TEST_646     D21S11      1      28.0
TEST_646     D21S11      2      28.0
TEST_646     D21S11      3      nan

PS.以下是逗号分隔形式的值,以方便您:

Sample Info, D3S1358 1, D3S1358 …

我想根据其范围的有效性来选择我的原始数据。有一个仪器，最敏感的设置是C，然后是B，然后是A。所以从C开始，看看所有的值是否都小于阈值，如果是，那么完美，把这个灵敏度中的所有数据设置为best= 1.

from StringIO import StringIO

a = """category,val,sensitivity_level
x,20,A
x,31,B
x,60,C
x,20,A
x,25,B
x,60,C
y,20,A
y,40,B
y,60,C
y,20,A
y,24,B
y,30,C"""

df = pd.read_csv(StringIO(a))

def grp_1evel_1(x):
    """ 
    return if all the elements are less than threshold
    """

    return x<=30

def grp_1evel_0(x):
"""
Input: data grouped by category. Here I want to go through this categories, in an descending order, 
that is C, B and then A, and wherever one of this categories has x<=30 valid for all elements select 
that …