Python 带有JSON文件的Matplotlib

我试图合并两个源代码，但没有效果。我正在使用默认的finance2.py matplotlib示例（如下所列）和json文件（如下所列）。代码的“finance.fetch\u historical\u yahoo”部分通过yahoo从.csv中提取数据，并将其放入numpy数组中。问题是，我不需要翻译yahoo的数据，我需要以matplotlib库可读的方式翻译JSON数据

财务2.py：

import datetime
import numpy as np
import matplotlib.colors as colors
import matplotlib.finance as finance
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager
import json #to load json libraries for json data

startdate = datetime.date(2006,1,1)
today = enddate = datetime.date.today()
ticker = 'SPY'


fh = finance.fetch_historical_yahoo(ticker, startdate, enddate) #would like this call to be json
# a numpy record array with fields: date, open, high, low, close, volume, adj_close)

r = mlab.csv2rec(fh); fh.close()
r.sort()


def moving_average(x, n, type='simple'):
    """
    compute an n period moving average.

    type is 'simple' | 'exponential'

    """
    x = np.asarray(x)
    if type=='simple':
        weights = np.ones(n)
    else:
        weights = np.exp(np.linspace(-1., 0., n))

    weights /= weights.sum()


    a =  np.convolve(x, weights, mode='full')[:len(x)]
    a[:n] = a[n]
    return a

def relative_strength(prices, n=14):
    """
    compute the n period relative strength indicator
    http://stockcharts.com/school/doku.php?id=chart_school:glossary_r#relativestrengthindex
    http://www.investopedia.com/terms/r/rsi.asp
    """

    deltas = np.diff(prices)
    seed = deltas[:n+1]
    up = seed[seed>=0].sum()/n
    down = -seed[seed<0].sum()/n
    rs = up/down
    rsi = np.zeros_like(prices)
    rsi[:n] = 100. - 100./(1.+rs)

    for i in range(n, len(prices)):
        delta = deltas[i-1] # cause the diff is 1 shorter

        if delta>0:
            upval = delta
            downval = 0.
        else:
            upval = 0.
            downval = -delta

        up = (up*(n-1) + upval)/n
        down = (down*(n-1) + downval)/n

        rs = up/down
        rsi[i] = 100. - 100./(1.+rs)

    return rsi

def moving_average_convergence(x, nslow=26, nfast=12):
    """
    compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'
    return value is emaslow, emafast, macd which are len(x) arrays
    """
    emaslow = moving_average(x, nslow, type='exponential')
    emafast = moving_average(x, nfast, type='exponential')
    return emaslow, emafast, emafast - emaslow


plt.rc('axes', grid=True)
plt.rc('grid', color='0.75', linestyle='-', linewidth=0.5)

textsize = 9
left, width = 0.1, 0.8
rect1 = [left, 0.7, width, 0.2]
rect2 = [left, 0.3, width, 0.4]
rect3 = [left, 0.1, width, 0.2]


fig = plt.figure(facecolor='white')
axescolor  = '#f6f6f6'  # the axes background color

ax1 = fig.add_axes(rect1, axisbg=axescolor)  #left, bottom, width, height
ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1)
ax2t = ax2.twinx()
ax3  = fig.add_axes(rect3, axisbg=axescolor, sharex=ax1)



### plot the relative strength indicator
prices = r.adj_close
rsi = relative_strength(prices)
fillcolor = 'darkgoldenrod'

ax1.plot(r.date, rsi, color=fillcolor)
ax1.axhline(70, color=fillcolor)
ax1.axhline(30, color=fillcolor)
ax1.fill_between(r.date, rsi, 70, where=(rsi>=70), facecolor=fillcolor, edgecolor=fillcolor)
ax1.fill_between(r.date, rsi, 30, where=(rsi<=30), facecolor=fillcolor, edgecolor=fillcolor)
ax1.text(0.6, 0.9, '>70 = overbought', va='top', transform=ax1.transAxes, fontsize=textsize)
ax1.text(0.6, 0.1, '<30 = oversold', transform=ax1.transAxes, fontsize=textsize)
ax1.set_ylim(0, 100)
ax1.set_yticks([30,70])
ax1.text(0.025, 0.95, 'RSI (14)', va='top', transform=ax1.transAxes, fontsize=textsize)
ax1.set_title('%s daily'%ticker)

### plot the price and volume data
dx = r.adj_close - r.close
low = r.low + dx
high = r.high + dx

deltas = np.zeros_like(prices)
deltas[1:] = np.diff(prices)
up = deltas>0
ax2.vlines(r.date[up], low[up], high[up], color='black', label='_nolegend_')
ax2.vlines(r.date[~up], low[~up], high[~up], color='black', label='_nolegend_')
ma20 = moving_average(prices, 20, type='simple')
ma200 = moving_average(prices, 200, type='simple')

linema20, = ax2.plot(r.date, ma20, color='blue', lw=2, label='MA (20)')
linema200, = ax2.plot(r.date, ma200, color='red', lw=2, label='MA (200)')


last = r[-1]
s = '%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f' % (
    today.strftime('%d-%b-%Y'),
    last.open, last.high,
    last.low, last.close,
    last.volume*1e-6,
    last.close-last.open )
t4 = ax2.text(0.3, 0.9, s, transform=ax2.transAxes, fontsize=textsize)

props = font_manager.FontProperties(size=10)
leg = ax2.legend(loc='center left', shadow=True, fancybox=True, prop=props)
leg.get_frame().set_alpha(0.5)


volume = (r.close*r.volume)/1e6  # dollar volume in millions
vmax = volume.max()
poly = ax2t.fill_between(r.date, volume, 0, label='Volume', facecolor=fillcolor, edgecolor=fillcolor)
ax2t.set_ylim(0, 5*vmax)
ax2t.set_yticks([])


### compute the MACD indicator
fillcolor = 'darkslategrey'
nslow = 26
nfast = 12
nema = 9
emaslow, emafast, macd = moving_average_convergence(prices, nslow=nslow, nfast=nfast)
ema9 = moving_average(macd, nema, type='exponential')
ax3.plot(r.date, macd, color='black', lw=2)
ax3.plot(r.date, ema9, color='blue', lw=1)
ax3.fill_between(r.date, macd-ema9, 0, alpha=0.5, facecolor=fillcolor, edgecolor=fillcolor)


ax3.text(0.025, 0.95, 'MACD (%d, %d, %d)'%(nfast, nslow, nema), va='top',
         transform=ax3.transAxes, fontsize=textsize)

#ax3.set_yticks([])
# turn off upper axis tick labels, rotate the lower ones, etc
for ax in ax1, ax2, ax2t, ax3:
    if ax!=ax3:
        for label in ax.get_xticklabels():
            label.set_visible(False)
    else:
        for label in ax.get_xticklabels():
            label.set_rotation(30)
            label.set_horizontalalignment('right')

    ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')



class MyLocator(mticker.MaxNLocator):
    def __init__(self, *args, **kwargs):
        mticker.MaxNLocator.__init__(self, *args, **kwargs)

    def __call__(self, *args, **kwargs):
        return mticker.MaxNLocator.__call__(self, *args, **kwargs)

# at most 5 ticks, pruning the upper and lower so they don't overlap
# with other ticks
#ax2.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))
#ax3.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))

ax2.yaxis.set_major_locator(MyLocator(5, prune='both'))
ax3.yaxis.set_major_locator(MyLocator(5, prune='both'))

plt.show()

我甚至没有确切地寻找一个明确的答案，即使是在正确的方向上的一点，在这个时候也会非常有帮助

---

提前感谢

这是一个部分答案-它展示了如何将json解压到行（行）或列（列）列表中

---

剩下的是构造一个numpy重新排列：这就是mlab.csv2rec（）产生的结果。这是留给numpy gurus的练习（我不是！；）

你可以用熊猫更简洁地解决这个问题。以下是解决您问题的相关信息。抱歉，我没有时间根据您的特定用例自定义所有代码。如果您再次访问，请告诉我，并希望我这样做

从

现在，你可以使用一个

[1]中的

：将matplotlib.pyplot作为plt导入
在[2]中：ts=pandas.Series（np.random.randn（1000），index=pd.date\u range（'1/1/2000'，句点=1000））
[3]中：ts=ts.cumsum（）
在[4]：ts.plot（）中
出[4]：

fetch\u historical\u yahoo下面的一行告诉您数据应该是什么样子。那么，你试过什么？向我们展示一些试图将json输入转换为numpy记录数组的代码（仅相关部分）。阅读json时，应该从json.load（）开始。现在这里的代码太多了。很少有人愿意费力阅读与您的问题无关的代码。请将此减少到重现问题所需的最小代码量。

{
        "instrument" : "EUR_USD",
        "granularity" : "D",
        "candles" : [
                {
                        "time" : "2014-02-17T22:00:00Z",
                        "openMid" : 1.259445,
                        "highMid" : 1.259955,
                        "lowMid" : 1.251825,
                        "closeMid" : 1.257955,
                        "volume" : 61184,
                        "complete" : true
                },
                {
                        "time" : "2014-02-18T22:00:00Z",
                        "openMid" : 1.257975,
                        "highMid" : 1.259955,
                        "lowMid" : 1.251825,
                        "closeMid" : 1.252945,
                        "volume" : 67528,
                        "complete" : false
                }
        ]
}

import json
import numpy as np
# json embedded here, but could be read in from text file
json_string = """{
        "instrument" : "EUR_USD",
        "granularity" : "D",
        "candles" : [
                {
                        "time" : "2014-02-17T22:00:00Z",
                        "openMid" : 1.259445,
                        "highMid" : 1.259955,
                        "lowMid" : 1.251825,
                        "closeMid" : 1.257955,
                        "volume" : 61184,
                        "complete" : true
                },
                {
                        "time" : "2014-02-18T22:00:00Z",
                        "openMid" : 1.257975,
                        "highMid" : 1.259955,
                        "lowMid" : 1.251825,
                        "closeMid" : 1.252945,
                        "volume" : 67528,
                        "complete" : false
                }
        ]
       }"""

candles = json.loads(json_string)['candles']
col_heads = ['time', 'openMid', 'highMid', 'lowMid', 'closeMid', 'volume']
f = lambda c: [c[col] for col in col_heads]
row_wise = [col_heads[:]]
row_wise.extend([f(candle) for candle in candles])
for row in row_wise: print row
print
col_wise = zip(*row_wise)
for col in col_wise: print col

>>> data = [{'state': 'Florida',
...          'shortname': 'FL',
...          'info': {
...               'governor': 'Rick Scott'
...          },
...          'counties': [{'name': 'Dade', 'population': 12345},
...                      {'name': 'Broward', 'population': 40000},
...                      {'name': 'Palm Beach', 'population': 60000}]},
...         {'state': 'Ohio',
...          'shortname': 'OH',
...          'info': {
...               'governor': 'John Kasich'
...          },
...          'counties': [{'name': 'Summit', 'population': 1234},
...                       {'name': 'Cuyahoga', 'population': 1337}]}]
>>> from pandas.io.json import json_normalize
>>> result = json_normalize(data, 'counties', ['state', 'shortname',
...                                           ['info', 'governor']])
>>> result
         name  population info.governor    state shortname
0        Dade       12345    Rick Scott  Florida        FL
1     Broward       40000    Rick Scott  Florida        FL
2  Palm Beach       60000    Rick Scott  Florida        FL
3      Summit        1234   John Kasich     Ohio        OH
4    Cuyahoga        1337   John Kasich     Ohio        OH

In [1]: import matplotlib.pyplot as plt

In [2]: ts = pandas.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))

In [3]: ts = ts.cumsum()

In [4]: ts.plot()
Out[4]: <matplotlib.axes._subplots.AxesSubplot at 0x1120939d0>