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
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
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()
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()
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)
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)
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)
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 $(g/cm^3)$')
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')
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()
# 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))
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])
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()
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()