예제 #1
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 def __call__(self, ax, means, stds, mins, maxes, labels):
     if self.useLog:
         ax.set_yscale('log')
     numLayers = len(labels)
     ax.plot(range(numLayers), means, 'or', label="$\mu$")
     ax.errorbar(range(numLayers),
                 means,
                 stds,
                 label="$\sigma$",
                 fmt='.',
                 markersize=0,
                 ecolor="black",
                 lw=4)
     ax.errorbar(range(numLayers),
                 means, [means - mins, maxes - means],
                 label="range",
                 fmt=".",
                 markersize=0,
                 ecolor="grey",
                 lw=2)
     # ax.legend()
     ax.set_xticks(range(numLayers))
     ax.set_xticklabels(labels)
     plt.setp(ax.get_xticklabels(),
              rotation=30,
              horizontalalignment='right')
    def _init_plot_axis_1(self, gs):
        ax1 = plt.subplot(gs[0])

        ax1.grid('on')
        ax1.set_ylabel('meter')
        ax1.set_ylim(self.ylim1)

        #ax1.set_title('(a)')
        #ax1.spines['bottom'].set_visible(False)
        ax1.xaxis.tick_top()
        ax1.axhline(0,ls='..', color='red')

        plt.setp(ax1.get_xticklabels(), visible=False)
        ax1.axhline(0,ls='-.', color='red')
        self.ax1 = ax1
예제 #3
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    def _init_plot_axis_1(self, gs):
        ax1 = plt.subplot(gs[0])

        ax1.grid('on')
        ax1.set_ylabel('meter')
        ax1.set_ylim(self.ylim1)

        #ax1.set_title('(a)')
        #ax1.spines['bottom'].set_visible(False)
        ax1.xaxis.tick_top()
        ax1.axhline(0, ls='..', color='red')

        plt.setp(ax1.get_xticklabels(), visible=False)
        ax1.axhline(0, ls='-.', color='red')
        self.ax1 = ax1
예제 #4
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def plot_mat(data):
    # Three subplots sharing both x/y axes
    f, axarray = plt.subplots(16, sharex=True, sharey=True)
    for i, row in enumerate(data):
        axarray[i].plot(range(len(row)), row, 'g')
    # Fine-tune figure; make subplots close to each other and hide x ticks for
    # all but bottom plot.
    f.subplots_adjust(hspace=0)
    plt.setp([a.get_xticklabels() for a in f.axes], visible=False)
    plt.setp([a.get_yticklabels() for a in f.axes], visible=False)
    axarray[0].set_title('10 minute EEG Reading for Patient')
    axarray[int(len(axarray) / 2)].set_ylabel('Magnitude')
    axarray[-1].set_xlabel('Time')
    font = {'family': 'normal', 'weight': 'bold', 'size': 48}
    plt.rc('font', **font)
    plt.show()
예제 #5
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def plot_mat(data):
    # Three subplots sharing both x/y axes
    f, axarray = plt.subplots(16, sharex=True, sharey=True)
    for i, row in enumerate(data):
        axarray[i].plot(range(len(row)), row, 'g')
    # Fine-tune figure; make subplots close to each other and hide x ticks for
    # all but bottom plot.
    f.subplots_adjust(hspace=0)
    plt.setp([a.get_xticklabels() for a in f.axes], visible=False)
    plt.setp([a.get_yticklabels() for a in f.axes], visible=False)
    axarray[0].set_title('10 minute EEG Reading for Patient')
    axarray[int(len(axarray) / 2)].set_ylabel('Magnitude')
    axarray[-1].set_xlabel('Time')
    font = {'family': 'normal',
            'weight': 'bold',
            'size': 48}
    plt.rc('font', **font)
    plt.show()
예제 #6
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def _plot_base(dep, val, deplim_small, xlim_small, xlabel):
    plt.subplot(1,2,1)
    plt.plot(val, dep)
    plt.gca().invert_yaxis()
    plt.grid('on')
    plt.ylabel('depth/km')
    plt.xlabel(xlabel)
    locs, labels = plt.xticks()
    plt.setp(labels, rotation=-45)

    plt.subplot(1,2,2)
    plt.plot(val, dep)
    plt.gca().invert_yaxis()
    plt.grid('on')
    plt.ylim(deplim_small)
    plt.xlim(xlim_small)
    plt.xlabel(xlabel)
    locs, labels = plt.xticks()
    plt.setp(labels, rotation=-45)
예제 #7
0
def _plot_base(dep, val, deplim_small, xlim_small, xlabel):
    plt.subplot(1, 2, 1)
    plt.plot(val, dep)
    plt.gca().invert_yaxis()
    plt.grid('on')
    plt.ylabel('depth/km')
    plt.xlabel(xlabel)
    locs, labels = plt.xticks()
    plt.setp(labels, rotation=-45)

    plt.subplot(1, 2, 2)
    plt.plot(val, dep)
    plt.gca().invert_yaxis()
    plt.grid('on')
    plt.ylim(deplim_small)
    plt.xlim(xlim_small)
    plt.xlabel(xlabel)
    locs, labels = plt.xticks()
    plt.setp(labels, rotation=-45)
예제 #8
0
def show_chan_mpl(code,
                  start_date,
                  end_date,
                  stock_days,
                  resample,
                  show_mpl=True,
                  least_init=3,
                  chanK_flag=False,
                  windows=20):
    def get_least_khl_num(resample, idx=0, init_num=3):
        # init = 3
        if init_num - idx > 0:
            initw = init_num - idx
        else:
            initw = 0
        return init_num if resample == 'd' else initw if resample == 'w' else init_num-idx-1 if init_num-idx-1 >0 else 0\
                if resample == 'm' else 5

    stock_code = code  # 股票代码
    # stock_code = '002176' # 股票代码
    # start_date = '2017-09-05'
    # start_date = None
    # end_date = '2017-10-12 15:00:00'  # 最后生成k线日期
    # end_date = None
    # stock_days = 60  # 看几天/分钟前的k线
    # resample = 'd'
    # resample = 'w'
    x_jizhun = 3  # window 周期 x轴展示的时间距离  5:日,40:30分钟, 48: 5分钟
    least_khl_num = get_least_khl_num(resample, init_num=least_init)
    # stock_frequency = '5m' # 1d日线, 30m 30分钟, 5m 5分钟,1m 1分钟
    stock_frequency = resample  # 1d日线, 30m 30分钟, 5m 5分钟,1m 1分钟 w:week
    # chanK_flag = chanK  # True 看缠论K线, False 看k线
    # chanK_flag = True  # True 看缠论K线, False 看k线
    show_mpl = show_mpl

    def con2Cxianduan(stock,
                      k_data,
                      chanK,
                      frsBiType,
                      biIdx,
                      end_date,
                      cur_ji=1,
                      recursion=False,
                      dl=None,
                      chanK_flag=False,
                      least_init=3):
        max_k_num = 4
        if cur_ji >= 6 or len(biIdx) == 0 or recursion:
            return biIdx
        idx = biIdx[len(biIdx) - 1]
        k_data_dts = list(k_data.index)
        st_data = chanK['enddate'][idx]
        if st_data not in k_data_dts:
            return biIdx
        # 重构次级别线段的点到本级别的chanK中

        def refactorXd(biIdx, xdIdxc, chanK, chanKc, cur_ji):
            new_biIdx = []
            biIdxB = biIdx[len(biIdx) - 1] if len(biIdx) > 0 else 0
            for xdIdxcn in xdIdxc:
                for chanKidx in range(len(chanK.index))[biIdxB:]:
                    if judge_day_bao(chanK, chanKidx, chanKc, xdIdxcn, cur_ji):
                        new_biIdx.append(chanKidx)
                        break
            return new_biIdx

        # 判断次级别日期是否被包含

        def judge_day_bao(chanK, chanKidx, chanKc, xdIdxcn, cur_ji):
            _end_date = chanK['enddate'][chanKidx] + datetime.timedelta(
                hours=15) if cur_ji == 1 else chanK['enddate'][chanKidx]
            _start_date = chanK.index[chanKidx] if chanKidx == 0\
                else chanK['enddate'][chanKidx - 1] + datetime.timedelta(minutes=1)
            return _start_date <= chanKc.index[xdIdxcn] <= _end_date

        # cur_ji = 1 #当前级别
        # 符合k线根数大于4根 1日级别, 2 30分钟, 3 5分钟, 4 一分钟
        if not recursion:
            resample = 'd' if cur_ji + 1 == 2 else '5m' if cur_ji + 1 == 3 else \
                'd' if cur_ji + 1 == 5 else 'w' if cur_ji + 1 == 6 else 'd'
        least_khl_num = get_least_khl_num(resample, 1, init_num=least_init)
        print "次级:%s st_data:%s k_data_dts:%s least_khl_num:%s" % (
            len(k_data_dts) - k_data_dts.index(st_data), str(st_data)[:10],
            len(k_data_dts), least_khl_num)
        if cur_ji + 1 != 2 and len(k_data_dts) - k_data_dts.index(
                st_data) >= least_khl_num + 1:
            frequency = '30m' if cur_ji + 1 == 2 else '5m' if cur_ji + 1 == 3 else '1m'
            # else:
            # frequency = 'd' if cur_ji+1==2 else '5m' if cur_ji+1==3 else \
            #                 'd' if cur_ji+1==5 else 'w' if cur_ji+1==6 else 'd'

            start_lastday = str(chanK.index[biIdx[-1]])[0:10]
            print "次级别为:%s cur_ji:%s %s" % (resample, cur_ji, start_lastday)
            # print [chanK.index[x] for x in biIdx]
            k_data_c, cname = get_quotes_tdx(stock,
                                             start=start_lastday,
                                             end=end_date,
                                             dl=dl,
                                             resample=resample)
            print k_data_c.index[0], k_data_c.index[-1]
            chanKc = chan.parse2ChanK(
                k_data_c, k_data_c.values) if chanK_flag else k_data_c
            fenTypesc, fenIdxc = chan.parse2ChanFen(chanKc, recursion=True)
            if len(fenTypesc) == 0:
                return biIdx
            biIdxc, frsBiTypec = chan.parse2ChanBi(
                fenTypesc, fenIdxc, chanKc, least_khl_num=least_khl_num - 1)
            if len(biIdxc) == 0:
                return biIdx
            print "biIdxc:", [round(k_data_c.high[x], 2) for x in biIdxc
                              ], [str(k_data_c.index[x])[:10] for x in biIdxc]
            xdIdxc, xdTypec = chan.parse2Xianduan(
                biIdxc, chanKc, least_windows=1 if least_khl_num > 0 else 0)
            biIdxc = con2Cxianduan(stock,
                                   k_data_c,
                                   chanKc,
                                   frsBiTypec,
                                   biIdxc,
                                   end_date,
                                   cur_ji + 1,
                                   recursion=True)
            print "xdIdxc:%s xdTypec:%s biIdxc:%s" % (xdIdxc, xdTypec, biIdxc)
            if len(xdIdxc) == 0:
                return biIdx
            # 连接线段位为上级别的bi
            lastBiType = frsBiType if len(biIdx) % 2 == 0 else -frsBiType
            if len(biIdx) == 0:
                return refactorXd(biIdx, xdIdxc, chanK, chanKc, cur_ji)
            lastbi = biIdx.pop()
            firstbic = xdIdxc.pop(0)
            # 同向连接
            if lastBiType == xdTypec:
                biIdx = biIdx + refactorXd(biIdx, xdIdxc, chanK, chanKc,
                                           cur_ji)
            # 逆向连接
            else:
                #             print '开始逆向连接'
                _mid = [lastbi] if (lastBiType == -1 and chanK['low'][lastbi] <= chanKc['low'][firstbic])\
                    or (lastBiType == 1 and chanK['high'][lastbi] >= chanKc['high'][firstbic]) else\
                    [chanKidx for chanKidx in range(len(chanK.index))[biIdx[len(biIdx) - 1]:]
                     if judge_day_bao(chanK, chanKidx, chanKc, firstbic, cur_ji)]
                biIdx = biIdx + [_mid[0]] + refactorXd(biIdx, xdIdxc, chanK,
                                                       chanKc, cur_ji)
            # print "次级:",len(biIdx),biIdx,[str(k_data_c.index[x])[:10] for x in biIdx]
        return biIdx

    def get_quotes_tdx(code,
                       start=None,
                       end=None,
                       dl=120,
                       resample='d',
                       show_name=True):

        quotes = tdd.get_tdx_append_now_df_api(
            code=stock_code, start=start, end=end,
            dl=dl).sort_index(ascending=True)
        if not resample == 'd' and resample in tdd.resample_dtype:
            quotes = tdd.get_tdx_stock_period_to_type(quotes,
                                                      period_day=resample)
        quotes.index = quotes.index.astype('datetime64')
        if show_name:
            if 'name' in quotes.columns:
                cname = quotes.name[0]
                # cname_g =cname
            else:
                dm = tdd.get_sina_data_df(code)
                if 'name' in dm.columns:
                    cname = dm.name[0]
                else:
                    cname = '-'
        else:
            cname = '-'
        if quotes is not None and len(quotes) > 0:
            quotes = quotes.loc[:, [
                'open', 'close', 'high', 'low', 'vol', 'amount'
            ]]
        else:
            # log.error("quotes is None check:%s"%(code))
            raise Exception("Code:%s error, df is None%s" % (code))
        return quotes, cname

    quotes, cname = get_quotes_tdx(stock_code,
                                   start_date,
                                   end_date,
                                   dl=stock_days,
                                   resample=resample,
                                   show_name=show_mpl)
    # quotes.rename(columns={'amount': 'money'}, inplace=True)
    # quotes.rename(columns={'vol': 'vol'}, inplace=True)
    # print quotes[-2:]
    # print quotes[:1]
    # 缠论k线
    #         open  close   high    low    volume      money
    # 2017-05-03  15.69  15.66  15.73  15.53  10557743  165075887
    # 2017-05-04  15.66  15.63  15.70  15.52   8343270  130330396
    # 2017-05-05  15.56  15.65  15.68  15.41  18384031  285966842
    # 2017-05-08  15.62  15.75  15.76  15.54  12598891  197310688
    quotes = chan.parse2ChanK(quotes, quotes.values) if chanK_flag else quotes
    # print quotes[:1].index
    # print quotes[-1:].index

    quotes[quotes['vol'] == 0] = np.nan
    quotes = quotes.dropna()
    Close = quotes['close']
    Open = quotes['open']
    High = quotes['high']
    Low = quotes['low']
    T0 = quotes.index.values
    # T0 =  mdates.date2num(T0)
    length = len(Close)

    initial_trend = "down"
    cur_ji = 1 if stock_frequency == 'd' else \
        2 if stock_frequency == '30m' else \
        3 if stock_frequency == '5m' else \
        4 if stock_frequency == 'w' else \
        5 if stock_frequency == 'm' else 6

    log.debug('======笔形成最后一段未完成段判断是否是次级别的走势形成笔=======:%s %s' %
              (stock_frequency, cur_ji))

    x_date_list = quotes.index.values.tolist()
    # for x_date in x_date_list:
    #     d = datetime.datetime.fromtimestamp(x_date/1000000000)
    #     print d.strftime("%Y-%m-%d %H:%M:%S.%f")
    # print x_date_list
    k_data = quotes
    k_values = k_data.values
    # 缠论k线
    chanK = quotes if chanK_flag else chan.parse2ChanK(
        k_data, k_values, chan_kdf=chanK_flag)

    fenTypes, fenIdx = chan.parse2ChanFen(chanK)
    # log.debug("code:%s fenTypes:%s fenIdx:%s k_data:%s" % (stock_code,fenTypes, fenIdx, len(k_data)))
    biIdx, frsBiType = chan.parse2ChanBi(fenTypes,
                                         fenIdx,
                                         chanK,
                                         least_khl_num=least_khl_num)
    # log.debug("biIdx1:%s chanK:%s" % (biIdx, len(chanK)))
    print("biIdx1:%s %s chanK:%s" %
          (biIdx, str(chanK.index.values[biIdx[-1]])[:10], len(chanK)))

    biIdx = con2Cxianduan(stock_code,
                          k_data,
                          chanK,
                          frsBiType,
                          biIdx,
                          end_date,
                          cur_ji,
                          least_init=least_init)
    # log.debug("biIdx2:%s chanK:%s" % (biIdx, len(biIdx)))
    chanKIdx = [(chanK.index[x]) for x in biIdx]

    if len(biIdx) == 0 and len(chanKIdx) == 0:
        print "BiIdx is None and chanKidx is None:%s" % (code)
        return None

    log.debug("con2Cxianduan:%s chanK:%s %s" %
              (biIdx, len(chanK), chanKIdx[-1] if len(chanKIdx) > 0 else None))

    # print quotes['close'].apply(lambda x:round(x,2))

    # print '股票代码', get_security_info(stock_code).display_name
    # print '股票代码', (stock_code), resample, least_khl_num
    #  3.得到分笔结果,计算坐标显示

    def plot_fenbi_seq(biIdx, frsBiType, plt=None, color=None):
        x_fenbi_seq = []
        y_fenbi_seq = []
        for i in range(len(biIdx)):
            if biIdx[i] is not None:
                fenType = -frsBiType if i % 2 == 0 else frsBiType
                #         dt = chanK['enddate'][biIdx[i]]
                # 缠论k线
                dt = chanK.index[biIdx[i]] if chanK_flag else chanK['enddate'][
                    biIdx[i]]
                # print i,k_data['high'][dt], k_data['low'][dt]
                time_long = long(
                    time.mktime(
                        (dt + datetime.timedelta(hours=8)).timetuple()) *
                    1000000000)
                # print x_date_list.index(time_long) if time_long in x_date_list else 0
                if fenType == 1:
                    if plt is not None:
                        if color is None:
                            plt.text(x_date_list.index(time_long),
                                     k_data['high'][dt],
                                     str(k_data['high'][dt]),
                                     ha='left',
                                     fontsize=12)
                        else:
                            col_v = color[0] if fenType > 0 else color[1]
                            plt.text(x_date_list.index(time_long),
                                     k_data['high'][dt],
                                     str(k_data['high'][dt]),
                                     ha='left',
                                     fontsize=12,
                                     bbox=dict(facecolor=col_v, alpha=0.5))

                    x_fenbi_seq.append(x_date_list.index(time_long))
                    y_fenbi_seq.append(k_data['high'][dt])
                if fenType == -1:
                    if plt is not None:
                        if color is None:
                            plt.text(x_date_list.index(time_long),
                                     k_data['low'][dt],
                                     str(k_data['low'][dt]),
                                     va='bottom',
                                     fontsize=12)
                        else:
                            col_v = color[0] if fenType > 0 else color[1]
                            plt.text(x_date_list.index(time_long),
                                     k_data['low'][dt],
                                     str(k_data['low'][dt]),
                                     va='bottom',
                                     fontsize=12,
                                     bbox=dict(facecolor=col_v, alpha=0.5))

                    x_fenbi_seq.append(x_date_list.index(time_long))
                    y_fenbi_seq.append(k_data['low'][dt])
    #             bottom_time = None
    #             for k_line_dto in m_line_dto.member_list[::-1]:
    #                 if k_line_dto.low == m_line_dto.low:
    #                     # get_price返回的日期,默认时间是08:00:00
    #                     bottom_time = k_line_dto.begin_time.strftime('%Y-%m-%d') +' 08:00:00'
    #                     break
    #             x_fenbi_seq.append(x_date_list.index(long(time.mktime(datetime.strptime(bottom_time, "%Y-%m-%d %H:%M:%S").timetuple())*1000000000)))
    #             y_fenbi_seq.append(m_line_dto.low)
        return x_fenbi_seq, y_fenbi_seq

    # print  T0[-len(T0):].astype(dt.date)
    T1 = T0[-len(T0):].astype(datetime.date) / 1000000000
    Ti = []
    if len(T0) / x_jizhun > 12:
        x_jizhun = len(T0) / 12
    for i in range(len(T0) / x_jizhun):
        # print "len(T0)/x_jizhun:",len(T0)/x_jizhun
        a = i * x_jizhun
        d = datetime.date.fromtimestamp(T1[a])
        # print d
        T2 = d.strftime('$%Y-%m-%d$')
        Ti.append(T2)
        # print tab
    d1 = datetime.date.fromtimestamp(T1[len(T0) - 1])
    d2 = (d1 + datetime.timedelta(days=1)).strftime('$%Y-%m-%d$')
    Ti.append(d2)

    ll = Low.min() * 0.97
    hh = High.max() * 1.03

    # ht = HoverTool(tooltips=[
    #             ("date", "@date"),
    #             ("open", "@open"),
    #             ("close", "@close"),
    #             ("high", "@high"),
    #             ("low", "@low"),
    #             ("volume", "@volume"),
    #             ("money", "@money"),])
    # TOOLS = [ht, WheelZoomTool(dimensions=['width']),\
    #          ResizeTool(), ResetTool(),\
    #          PanTool(dimensions=['width']), PreviewSaveTool()]
    if show_mpl:
        fig = plt.figure(figsize=(10, 6))
        ax1 = plt.subplot2grid((10, 1), (0, 0), rowspan=8, colspan=1)
        # ax1 = fig.add_subplot(2,1,1)
        #fig = plt.figure()
        #ax1 = plt.axes([0,0,3,2])

        X = np.array(range(0, length))
        pad_nan = X + nan

        # 计算上 下影线
        max_clop = Close.copy()
        max_clop[Close < Open] = Open[Close < Open]
        min_clop = Close.copy()
        min_clop[Close > Open] = Open[Close > Open]

        # 上影线
        line_up = np.array([High, max_clop, pad_nan])
        line_up = np.ravel(line_up, 'F')
        # 下影线
        line_down = np.array([Low, min_clop, pad_nan])
        line_down = np.ravel(line_down, 'F')

        # 计算上下影线对应的X坐标
        pad_nan = nan + X
        pad_X = np.array([X, X, X])
        pad_X = np.ravel(pad_X, 'F')

        # 画出实体部分,先画收盘价在上的部分
        up_cl = Close.copy()
        up_cl[Close <= Open] = nan
        up_op = Open.copy()
        up_op[Close <= Open] = nan

        down_cl = Close.copy()
        down_cl[Open <= Close] = nan
        down_op = Open.copy()
        down_op[Open <= Close] = nan

        even = Close.copy()
        even[Close != Open] = nan

        # 画出收红的实体部分
        pad_box_up = np.array([up_op, up_op, up_cl, up_cl, pad_nan])
        pad_box_up = np.ravel(pad_box_up, 'F')
        pad_box_down = np.array([down_cl, down_cl, down_op, down_op, pad_nan])
        pad_box_down = np.ravel(pad_box_down, 'F')
        pad_box_even = np.array([even, even, even, even, pad_nan])
        pad_box_even = np.ravel(pad_box_even, 'F')

        # X的nan可以不用与y一一对应
        X_left = X - 0.25
        X_right = X + 0.25
        box_X = np.array([X_left, X_right, X_right, X_left, pad_nan])
        # print box_X
        box_X = np.ravel(box_X, 'F')
        # print box_X
        # Close_handle=plt.plot(pad_X,line_up,color='k')

        vertices_up = np.array([box_X, pad_box_up]).T
        vertices_down = np.array([box_X, pad_box_down]).T
        vertices_even = np.array([box_X, pad_box_even]).T

        handle_box_up = mat.patches.Polygon(vertices_up, color='r', zorder=1)
        handle_box_down = mat.patches.Polygon(vertices_down,
                                              color='g',
                                              zorder=1)
        handle_box_even = mat.patches.Polygon(vertices_even,
                                              color='k',
                                              zorder=1)

        ax1.add_patch(handle_box_up)
        ax1.add_patch(handle_box_down)
        ax1.add_patch(handle_box_even)

        handle_line_up = mat.lines.Line2D(pad_X,
                                          line_up,
                                          color='k',
                                          linestyle='solid',
                                          zorder=0)
        handle_line_down = mat.lines.Line2D(pad_X,
                                            line_down,
                                            color='k',
                                            linestyle='solid',
                                            zorder=0)

        ax1.add_line(handle_line_up)
        ax1.add_line(handle_line_down)

        v = [0, length, Open.min() - 0.5, Open.max() + 0.5]
        plt.axis(v)

        ax1.set_xticks(np.linspace(-2, len(Close) + 2, len(Ti)))

        ax1.set_ylim(ll, hh)

        ax1.set_xticklabels(Ti)

        plt.grid(True)
        plt.setp(plt.gca().get_xticklabels(),
                 rotation=30,
                 horizontalalignment='right')
    '''
    以上代码拷贝自https://www.joinquant.com/post/1756
    感谢alpha-smart-dog

    K线图绘制完毕
    '''

    # print "biIdx:%s chankIdx:%s"%(biIdx,str(chanKIdx[-1])[:10])
    if show_mpl:
        x_fenbi_seq, y_fenbi_seq = plot_fenbi_seq(biIdx, frsBiType, plt)
        # plot_fenbi_seq(fenIdx,fenTypes[0], plt,color=['red','green'])
        plot_fenbi_seq(fenIdx, frsBiType, plt, color=['red', 'green'])
    else:
        x_fenbi_seq, y_fenbi_seq = plot_fenbi_seq(biIdx, frsBiType, plt=None)
        plot_fenbi_seq(fenIdx, frsBiType, plt=None, color=['red', 'green'])
    #  在原图基础上添加分笔蓝线
    inx_value = chanK.high.values
    inx_va = [round(inx_value[x], 2) for x in biIdx]
    log.debug("inx_va:%s count:%s" % (inx_va, len(quotes.high)))
    log.debug("yfenbi:%s count:%s" % ([round(y, 2)
                                       for y in y_fenbi_seq], len(chanK)))
    j_BiType = [
        -frsBiType if i % 2 == 0 else frsBiType for i in range(len(biIdx))
    ]
    BiType_s = j_BiType[-1] if len(j_BiType) > 0 else -2
    # bi_price = [str(chanK.low[idx]) if i % 2 == 0 else str(chanK.high[idx])  for i,idx in enumerate(biIdx)]
    # print ("笔     :%s %s"%(biIdx,bi_price))
    # fen_dt = [str(chanK.index[fenIdx[i]])[:10] if chanK_flag else str(chanK['enddate'][fenIdx[i]])[:10]for i in range(len(fenIdx))]
    fen_dt = [(chanK.index[fenIdx[i]]) if chanK_flag else
              (chanK['enddate'][fenIdx[i]]) for i in range(len(fenIdx))]
    if len(fenTypes) > 0:
        if fenTypes[0] == -1:
            # fen_price = [str(k_data.low[idx]) if i % 2 == 0 else str(k_data.high[idx])  for i,idx in enumerate(fen_dt)]
            low_fen = [idx for i, idx in enumerate(fen_dt) if i % 2 == 0]
            high_fen = [idx for i, idx in enumerate(fen_dt) if i % 2 <> 0]
        else:
            # fen_price = [str(k_data.high[idx]) if i % 2 == 0 else str(k_data.low[idx])  for i,idx in enumerate(fen_dt)]
            high_fen = [idx for i, idx in enumerate(fen_dt) if i % 2 == 0]
            low_fen = [idx for i, idx in enumerate(fen_dt) if i % 2 <> 0]
        # fen_duration =[fenIdx[i] - fenIdx[i -1 ] if i >0 else 0 for i,idx in enumerate(fenIdx)]
    else:
        # fen_price = fenTypes
        # fen_duration = fenTypes
        low_fen = []
        high_fen = []
    # fen_dt = [str(k_data.index[idx])[:10] for i,idx in enumerate(fenIdx)]
    # print low_fen,high_fen
    def dataframe_mode_round(df):
        roundlist = [1, 0]
        df_mode = []
        # df.high.cummin().value_counts()
        for i in roundlist:
            df_mode = df.apply(lambda x: round(x, i)).mode()
            if len(df_mode) > 0:
                break
        return df_mode

    kdl = k_data.loc[low_fen].low
    kdl_mode = dataframe_mode_round(kdl)
    kdh = k_data.loc[high_fen].high
    kdh_mode = dataframe_mode_round(kdh)

    print("kdl:%s" % (kdl.values))
    print("kdh:%s" % (kdh.values))
    print("kdl_mode:%s kdh_mode%s chanKidx:%s" %
          (kdl_mode.values, kdh_mode.values, str(chanKIdx[-1])[:10]))

    lastdf = k_data[k_data.index >= chanKIdx[-1]]
    if BiType_s == -1:
        keydf = lastdf[((lastdf.close >= kdl_mode.max()) &
                        (lastdf.low >= kdl_mode.max()))]
    elif BiType_s == 1:
        keydf = lastdf[((lastdf.close >= kdh_mode.max()) &
                        (lastdf.high >= kdh_mode.min()))]
    else:
        keydf = lastdf[((lastdf.close >= kdh_mode.max()) &
                        (lastdf.high >= kdh_mode.min())) |
                       ((lastdf.close <= kdl_mode.min()) &
                        (lastdf.low <= kdl_mode.min()))]
    print("BiType_s:%s keydf:%s key:%s" %
          (BiType_s, None if len(keydf) == 0 else str(
              keydf.index.values[0])[:10], len(keydf)))

    # return BiType_s,None if len(keydf) == 0 else str(keydf.index.values[0])[:10],len(keydf)
    # import ipdb;ipdb.set_trace()

    log.debug("Fentype:%s " % (fenTypes))
    log.debug("fenIdx:%s " % (fenIdx))
    # print ("fen_duration:%s "%(fen_duration))
    # print ("fen_price:%s "%(fen_price))
    # print ("fendt:%s "%(fen_dt))

    print("BiType :%s frsBiType:%s" % (j_BiType, frsBiType))

    if len(j_BiType) > 0:
        if j_BiType[0] == -1:
            tb_price = [
                str(quotes.low[idx]) if i % 2 == 0 else str(quotes.high[idx])
                for i, idx in enumerate(x_fenbi_seq)
            ]
        else:
            tb_price = [
                str(quotes.high[idx]) if i % 2 == 0 else str(quotes.low[idx])
                for i, idx in enumerate(x_fenbi_seq)
            ]
        tb_duration = [
            x_fenbi_seq[i] - x_fenbi_seq[i - 1] if i > 0 else 0
            for i, idx in enumerate(x_fenbi_seq)
        ]

    else:
        tb_price = j_BiType
        tb_duration = j_BiType
    print "图笔 :", x_fenbi_seq, tb_price
    print "图笔dura :", tb_duration

    # 线段画到笔上
    xdIdxs, xfenTypes = chan.parse2ChanXD(frsBiType, biIdx, chanK)
    print '线段', xdIdxs, xfenTypes
    x_xd_seq = []
    y_xd_seq = []
    for i in range(len(xdIdxs)):
        if xdIdxs[i] is not None:
            fenType = xfenTypes[i]
            #         dt = chanK['enddate'][biIdx[i]]
            # 缠论k线
            dt = chanK.index[xdIdxs[i]] if chanK_flag else chanK['enddate'][
                xdIdxs[i]]
            #         print k_data['high'][dt], k_data['low'][dt]
            time_long = long(
                time.mktime((dt + datetime.timedelta(hours=8)).timetuple()) *
                1000000000)
            #         print x_date_list.index(time_long) if time_long in x_date_list else 0
            if fenType == 1:
                x_xd_seq.append(x_date_list.index(time_long))
                y_xd_seq.append(k_data['high'][dt])
            if fenType == -1:
                x_xd_seq.append(x_date_list.index(time_long))
                y_xd_seq.append(k_data['low'][dt])
    #             bottom_time = None
    #             for k_line_dto in m_line_dto.member_list[::-1]:
    #                 if k_line_dto.low == m_line_dto.low:
    #                     # get_price返回的日期,默认时间是08:00:00
    #                     bottom_time = k_line_dto.begin_time.strftime('%Y-%m-%d') +' 08:00:00'
    #                     break
    #             x_fenbi_seq.append(x_date_list.index(long(time.mktime(datetime.strptime(bottom_time, "%Y-%m-%d %H:%M:%S").timetuple())*1000000000)))
    #             y_fenbi_seq.append(m_line_dto.low)

    #  在原图基础上添加分笔蓝线
    print("线段   :%s" % (x_xd_seq))
    print("笔值  :%s" % ([str(x) for x in (y_xd_seq)]))
    # Y_hat = X * b + a

    if show_mpl:
        plt.plot(x_fenbi_seq, y_fenbi_seq)
        plt.legend([stock_code, cname], loc=0)
        plt.title(stock_code + " | " + cname + " | " +
                  str(quotes.index[-1])[:10],
                  fontsize=14)

        plt.plot(x_xd_seq, y_xd_seq)
        if len(quotes) > windows:
            roll_mean = pd.rolling_mean(quotes.close, window=windows)
            plt.plot(roll_mean, 'r')
        zp = zoompan.ZoomPan()
        figZoom = zp.zoom_factory(ax1, base_scale=1.1)
        figPan = zp.pan_factory(ax1)
        '''#subplot2 bar
        ax2 = plt.subplot2grid((10, 1), (8, 0), rowspan=2, colspan=1)
        # ax2.plot(quotes.vol)
        # ax2.set_xticks(np.linspace(-2, len(quotes) + 2, len(Ti)))
        ll = min(quotes.vol.values.tolist()) * 0.97
        hh = max(quotes.vol.values.tolist()) * 1.03
        ax2.set_ylim(ll, hh)
        # ax2.set_xticklabels(Ti)
        # plt.hist(quotes.vol, histtype='bar', rwidth=0.8)
        plt.bar(x_date_list,quotes.vol, label="Volume", color='b')
        '''

        #画Volume no tight_layout()
        '''
        pad = 0.25
        yl = ax1.get_ylim()
        ax1.set_ylim(yl[0]-(yl[1]-yl[0])*pad,yl[1])
        ax2 = ax1.twinx()
        ax2.set_position(mat.transforms.Bbox([[0.125,0.1],[0.9,0.32]]))
        volume = np.asarray(quotes.amount)
        pos = quotes['open']-quotes['close']<0
        neg = quotes['open']-quotes['close']>=0
        idx = quotes.reset_index().index
        ax2.bar(idx[pos],volume[pos],color='red',width=1,align='center')
        ax2.bar(idx[neg],volume[neg],color='green',width=1,align='center')
        yticks = ax2.get_yticks()
        ax2.set_yticks(yticks[::3])        
        '''

        # same sharex
        plt.subplots_adjust(left=0.05,
                            bottom=0.08,
                            right=0.95,
                            top=0.95,
                            wspace=0.15,
                            hspace=0.00)
        plt.setp(ax1.get_xticklabels(), visible=False)
        yl = ax1.get_ylim()
        # ax2 = plt.subplot(212, sharex=ax1)
        ax2 = plt.subplot2grid((10, 1), (8, 0),
                               rowspan=2,
                               colspan=1,
                               sharex=ax1)
        # ax2.set_position(mat.transforms.Bbox([[0.125,0.1],[0.9,0.32]]))
        volume = np.asarray(quotes.amount)
        pos = quotes['open'] - quotes['close'] < 0
        neg = quotes['open'] - quotes['close'] >= 0
        idx = quotes.reset_index().index
        ax2.bar(idx[pos], volume[pos], color='red', width=1, align='center')
        ax2.bar(idx[neg], volume[neg], color='green', width=1, align='center')
        yticks = ax2.get_yticks()
        ax2.set_yticks(yticks[::3])
        # plt.tight_layout()
        # plt.subplots_adjust(hspace=0.00, bottom=0.08)
        plt.xticks(rotation=15, horizontalalignment='center')
        # plt.bar(x_date_list,quotes.vol, label="Volume", color='b')

        # quotes['vol'].plot(kind='bar', ax=ax2, color='g', alpha=0.1)
        # ax2.set_ylim([0, ax2.get_ylim()[1] * 2])
        # plt.gcf().subplots_adjust(bottom=0.15)
        # fig.subplots_adjust(left=0.05, bottom=0.08, right=0.95, top=0.95, wspace=0.15, hspace=0.25)
        #scale the x-axis tight
        # ax2.set_xlim(min(x_date_list),max(x_date_list))
        # the y-ticks for the bar were too dense, keep only every third one
        # plt.grid(True)
        # plt.xticks(rotation=30, horizontalalignment='center')
        # plt.setp( axs[1].xaxis.get_majorticklabels(), rotation=70 )
        # plt.legend()
        # plt.tight_layout()
        # plt.draw()
        # plt.show()
        plt.show(block=False)
예제 #9
0
ax1.text(0.2, -40, s='crust  $H_e=?$', fontsize=12)
ax1.text(0.2, -170, s='asthenosphere \n$ \\eta = ?$', fontsize=12)
ax1.text(0.2,
         -450,
         s='uppermantle \n$ \\eta = 1 \\times 10^{20}$',
         fontsize=12)
ax1.text(0.2,
         -850,
         s='lowermantle \n$ \\eta = 1 \\times 10^{21}$',
         fontsize=12)
ax1.set_ylabel('depth (km)')

# plot ax2 - shear and bulk modulus
axhspan_for_viscosity(ax2)
plt.setp(ax2.get_yticklabels(), visible=False)
ax2.plot(shear, dep, '--', label='shear modulus', dashes=(3, 3), lw=2)
ax2.plot(bulk, dep, label='bulk modulus', lw=2)
plt.setp(ax2.get_xticklabels(), rotation=90)
ax2.set_xlabel('Shear/bulk modulus (GPa)')
ax2.legend(prop={'size': 8})
ax2.set_xlim([0, 460])

# plot ax3 - density
axhspan_for_viscosity(ax3)
plt.setp(ax3.get_yticklabels(), visible=False)
ax3.plot(den, dep, lw=2)
ax3.set_xlim([2.2, 5])
plt.setp(ax3.get_xticklabels(), rotation=90)
ax3.set_xlabel(r'Density $(g/cm^3)$')
예제 #10
0
def bars(scheme, verbose=None, norm='load'):
    """
    Figure to compare link proportional and usage proportional for a single
    scheme and put them in ./sensitivity/figures/scheme/
    """
    # Load data and results
    F = abs(np.load('./results/' + scheme + '-flows.npy'))
    quantiles = np.load('./results/quantiles_' + scheme + '_' + str(lapse) + '.npy')
    nNodes = 30

    names = node_namer(N)  # array of node labels
    links = range(len(F))
    nodes = np.linspace(0.5, 2 * nNodes - 1.5, nNodes)
    nodes_shift = nodes + .5

    for direction in directions:
        N_usages = np.load('./results/Node_contrib_' + scheme + '_' + direction + '_' + str(lapse) + '.npy')

        # Compare node transmission to mean load
        if verbose:
            print('Plotting node comparison - ' + scheme + ' - ' + direction)
        # sort node names for x-axis
        Total_usage = np.sum(N_usages, 1)
        node_ids = np.array(range(len(N))).reshape((len(N), 1))
        node_mean_load = [n.mean for n in N]

        # Vector for normalisation
        if norm == 'cap':
            normVec = np.ones(nNodes) * sum(quantiles)
        else:
            normVec = node_mean_load

        # Calculate node proportional
        EU_load = np.sum(node_mean_load)
        Total_caps = sum(quantiles)
        Node_proportional = node_mean_load / EU_load * Total_caps / normVec
        Node_proportional = np.reshape(Node_proportional, (len(Node_proportional), 1))

        # Calculate link proportional
        link_proportional = linkProportional(N, link_dic, quantiles)
        link_proportional = [link_proportional[i] / normVec[i] for i in range(nNodes)]

        # Calculate old usage proportional
        if direction == 'combined':
            old_usages = np.load('./linkcolouring/old_' + scheme + '_copper_link_mix_import_all_alpha=same.npy')
            old_usages += np.load('./linkcolouring/old_' + scheme + '_copper_link_mix_export_all_alpha=same.npy')
        else:
            old_usages = np.load('./linkcolouring/old_' + scheme + '_copper_link_mix_' + direction + '_all_alpha=same.npy')
        avg_node_usage = np.sum(np.sum(old_usages, axis=2), axis=0) / 70128.
        avg_EU_usage = np.sum(np.sum(np.sum(old_usages, axis=2), axis=0)) / 70128.
        avg_node_usage /= avg_EU_usage
        avg_node_usage /= normVec
        avg_node_usage *= 500000

        # Calculate usage and sort countries by mean load
        normed_usage = Total_usage / normVec
        normed_usage = np.reshape(normed_usage, (len(normed_usage), 1))
        node_mean_load = np.reshape(node_mean_load, (len(node_mean_load), 1))
        data = np.hstack([normed_usage, node_ids, node_mean_load, link_proportional, Node_proportional])
        data_sort = data[data[:, 2].argsort()]
        names_sort = [names[int(i)] for i in data_sort[:, 1]]
        # flip order so largest is first
        names_sort = names_sort[::-1]
        link_proportional = data_sort[:, 3][::-1]
        Node_proportional = data_sort[:, 4][::-1]
        data_sort = data_sort[:, 0][::-1]

        plt.figure(figsize=(10, 4), facecolor='w', edgecolor='k')
        ax = plt.subplot(111)
        green = '#009900'
        blue = '#000099'

        # Plot node proportional
        plt.rc('lines', lw=2)
        plt.rc('lines', dash_capstyle='round')
        plt.plot(np.linspace(0, len(N) * 2 + 2, len(N)), Node_proportional, '--k')
        # Plot link proportional
        #plt.bar(nodes, link_proportional, width=1, color=green, edgecolor='none')
        # Plot old usage proportional
        plt.bar(nodes, avg_node_usage[loadOrder], width=1, color=green, edgecolor='none')
        # Plot usage proportional
        plt.bar(nodes_shift, data_sort, width=1, color=blue, edgecolor='none')

        # Magic with ticks and labels
        ax.set_xticks(np.linspace(2, len(N) * 2 + 2, len(N) + 1))
        ax.set_xticklabels(names_sort, rotation=60, ha="right", va="top", fontsize=10.5)

        ax.xaxis.grid(False)
        ax.xaxis.set_tick_params(width=0)
        if norm == 'cap':
            ax.set_ylabel(r'$M_n/ \mathcal{K}^T$')
        else:
            # ax.set_ylabel(r'Network usage [MW$_T$/MW$_L$]')
            ax.set_ylabel(r'$M_n/\left\langle L_n \right\rangle$')
        maxes = [max(avg_node_usage), max(data_sort)]
        plt.axis([0, nNodes * 2 + .5, 0, 1.15 * max(maxes)])

        # Legend
        artists = [plt.Line2D([0, 0], [0, 0], ls='dashed', lw=2.0, c='k'), plt.Rectangle((0, 0), 0, 0, ec=green, fc=green), plt.Rectangle((0, 0), 0, 0, ec=blue, fc=blue)]
        LABS = ['$M^1$', '$M^{3}_{old}$', '$M^{3}_{new}$']
        leg = plt.legend(artists, LABS, loc='upper left', ncol=len(artists), columnspacing=0.6, borderpad=0.4, borderaxespad=0.0, handletextpad=0.2, handleheight=1.2)
        leg.get_frame().set_alpha(0)
        leg.get_frame().set_edgecolor('white')
        ltext = leg.get_texts()
        plt.setp(ltext, fontsize=12)    # the legend text fontsize

        plt.savefig(figPath + scheme + '/network-usage-' + direction + '-' + norm + '.png', bbox_inches='tight')
        if verbose:
            print('Saved figures to ./figures/compareUsage/' + scheme + '/network-usage-' + direction + '-' + norm + '.png')
예제 #11
0
파일: plot.py 프로젝트: zy31415/viscojapan
Hes = []
for f in files:
    nth_epochs = int(f.split('_')[-5])
    print(nth_epochs)
    reader = vj.inv.ResultFileReader(f)
    log_vis = reader.get_nlin_par_solved_value('log10(visM)')
    log_He = reader.get_nlin_par_solved_value('log10(He)')
    vis = 10**log_vis
    He = 10**log_He
    
    vises.append(vis)
    Hes.append(He)

max_time = [max(epochs) for epochs in epochs_list]

ax1 = plt.subplot(211)
plt.plot(max_time, vises, 'x-')
plt.grid('on')
plt.ylabel(r'viscosity $(Pa \cdot s)$')
plt.setp(ax1.get_xticklabels(), visible=False)

plt.subplot(212, sharex=ax1)
plt.plot(max_time, Hes, '^-')
plt.ylabel(r'He (km)')
plt.grid('on')
plt.xlabel('days of data used')

plt.savefig('diff_days_span.png')
plt.show()

예제 #12
0
    # Pick some colors
    palegreen = matplotlib.colors.colorConverter.to_rgb('#8CFF6F')
    paleblue = matplotlib.colors.colorConverter.to_rgb('#708DFF')

    # Plot response time
    ax1 = fig.add_subplot(111)
    # ax1.set_yscale('log')
    bp = boxplot(plot_data,
                 notch=0,
                 sym='+',
                 vert=1,
                 whis=1.5,
                 showfliers=False)

    # Tweak colors on the boxplot
    plt.setp(bp['boxes'], color='g')
    plt.setp(bp['whiskers'], color='g')
    plt.setp(bp['medians'], color='black')
    plt.setp(bp['fliers'], color=palegreen, marker='x')

    # Now fill the boxes with desired colors
    numBoxes = len(plot_data)
    medians = range(numBoxes)
    for i in range(numBoxes):
        box = bp['boxes'][i]
        boxX = []
        boxY = []
        for j in range(5):
            boxX.append(box.get_xdata()[j])
            boxY.append(box.get_ydata()[j])
        boxCoords = list(zip(boxX, boxY))
예제 #13
0
         s='crust  $H_e=?$',
         fontsize=12)
ax1.text(0.2, -170,
         s='asthenosphere \n$ \\eta = ?$',
         fontsize=12)
ax1.text(0.2, -450,
         s='uppermantle \n$ \\eta = 1 \\times 10^{20}$',
         fontsize=12)
ax1.text(0.2, -850,
         s='lowermantle \n$ \\eta = 1 \\times 10^{21}$',
         fontsize=12)
ax1.set_ylabel('depth (km)')

# plot ax2 - shear and bulk modulus
axhspan_for_viscosity(ax2)
plt.setp(ax2.get_yticklabels(), visible=False)
ax2.plot(shear, dep, '--', label='shear modulus',
         dashes=(3,3),
         lw=2)
ax2.plot(bulk, dep, label='bulk modulus',
         lw=2)
plt.setp(ax2.get_xticklabels(), rotation=90)
ax2.set_xlabel('Shear/bulk modulus (GPa)')
ax2.legend(prop={'size':8})
ax2.set_xlim([0, 460])
    
# plot ax3 - density
axhspan_for_viscosity(ax3)
plt.setp(ax3.get_yticklabels(), visible=False)
ax3.plot(den, dep, lw=2)
ax3.set_xlim([2.2, 5])
예제 #14
0
vises = []
Hes = []
for f in files:
    nth_epochs = int(f.split('_')[-5])
    print(nth_epochs)
    reader = vj.inv.ResultFileReader(f)
    log_vis = reader.get_nlin_par_solved_value('log10(visM)')
    log_He = reader.get_nlin_par_solved_value('log10(He)')
    vis = 10**log_vis
    He = 10**log_He

    vises.append(vis)
    Hes.append(He)

max_time = [max(epochs) for epochs in epochs_list]

ax1 = plt.subplot(211)
plt.plot(max_time, vises, 'x-')
plt.grid('on')
plt.ylabel(r'viscosity $(Pa \cdot s)$')
plt.setp(ax1.get_xticklabels(), visible=False)

plt.subplot(212, sharex=ax1)
plt.plot(max_time, Hes, '^-')
plt.ylabel(r'He (km)')
plt.grid('on')
plt.xlabel('days of data used')

plt.savefig('diff_days_span.png')
plt.show()
예제 #15
0
ax2.set_title('(b)')

############# 
ax3 = plt.subplot(224, sharex=ax2)

pplt = vj.inv.PredictedTimeSeriesPlotter(
    partition_file = 'partition.h5',
    result_file = 'nrough_05_naslip_11.h5'
    )
pplt.plot_post_disp_decomposition(site, cmpt,
                    marker_for_obs='.')
plt.title('')
plt.ylim(ylim)
ax3.set_title('(c)')

def ajust_xaxis_tick_labels(ax):
    for tick in ax.xaxis.get_major_ticks():
        tick.label.set_fontsize(8)
        # specify integer or one of preset strings, e.g.
        #tick.label.set_fontsize('x-small')
        tick.label.set_rotation('vertical')

ajust_xaxis_tick_labels(ax3)
plt.setp(ax1.get_xticklabels(), visible=True)
ajust_xaxis_tick_labels(ax1)


plt.savefig('2EXPs_EXP_pred_%s-%s.png'%(site, cmpt))
plt.savefig('2EXPs_EXP_pred_%s-%s.pdf'%(site, cmpt))
plt.show()