Beispiel #1
0
	def _draw_legend(self, data, lines, wordspacing, linespacing, *args, **kwargs):
		"""
		Draw a legend by iterating through all the lines.
		The labels are drawn on the same line as the corresponding token.

		:data: The text data as a dictionary.
			   This is used to look for `label` attributes.
		:type data: dict
		:param lines: The drawn lines.
		:type lines: list of list of :class:`matplotlib.text.Text`
		:param wordspacing: The space between tokens.
		:type wordspacing: float
		:param linespacing: The space between lines.
		:type linespacing: float

		:return: A list of lines, each containing a list of labels on that line.
		:rtype: list of list of :class:`matplotlib.text.Text`
		"""

		labels = []

		figure = self.drawable.figure
		axis = self.drawable.axis

		"""
		Iterate through each line, and then through each token in that line.
		"""
		drawn_labels = []
		i = 0
		for line, line_tokens in enumerate(lines):
			line_labels = []
			for token in line_tokens:
				label, style = data[i].get('label', ''), data[i].get('style', { })
				i += 1

				"""
				If the token has a label associated with it, draw it the first time it appears.
				"""
				if label and label not in drawn_labels:
					drawn_labels.append(label)
					token = text_util.draw_token(figure, axis, label, 0, line,
												  style, wordspacing, va='top',
												  *args, **kwargs)
					line_labels.append(token)

			"""
			After drawing the labels on each line, re-align the legend.
			The labels are aligned to the right.
			They are reversed so that the first label appears on the left.
			"""
			util.align(figure, axis, line_labels[::-1], 'right', wordspacing * 4,
					   (-1, - wordspacing * 4))

			labels.append(line_labels)

		return labels
Beispiel #2
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 def cmd_verify(self, ui, args):
   """verify memory against file"""
   x = util.file_mem_args(ui, args, self.cpu.device)
   if x is None:
     return
   (name, adr, size) = x
   # check the file
   filesize = util.file_arg(ui, name)
   if filesize is None:
     return
   # round up the filesize - the io object will return 0xff for any bytes beyond EOF
   filesize = util.roundup(filesize, 32)
   if size is None:
     # no length on the command line - verify the filesize
     size = filesize
   if size > filesize:
     # region is larger than file - just verify the filesize
     size = filesize
   # adjust the address and length
   adr = util.align(adr, 32)
   n = util.nbytes_to_nwords(size, 32)
   # read memory, verify against file object
   mf = iobuf.verify_file(ui, 'verify %s (%d bytes):' % (name, n * 4), name, n * 4)
   self.cpu.rdmem32(adr, n, mf)
   mf.close()
Beispiel #3
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 def cmd_verify(self, ui, args):
     """verify memory against file"""
     x = util.file_mem_args(ui, args, self.cpu.device)
     if x is None:
         return
     (name, adr, size) = x
     # check the file
     filesize = util.file_arg(ui, name)
     if filesize is None:
         return
     # round up the filesize - the io object will return 0xff for any bytes beyond EOF
     filesize = util.roundup(filesize, 32)
     if size is None:
         # no length on the command line - verify the filesize
         size = filesize
     if size > filesize:
         # region is larger than file - just verify the filesize
         size = filesize
     # adjust the address and length
     adr = util.align(adr, 32)
     n = util.nbytes_to_nwords(size, 32)
     # read memory, verify against file object
     mf = iobuf.verify_file(ui, 'verify %s (%d bytes):' % (name, n * 4),
                            name, n * 4)
     self.cpu.rdmem32(adr, n, mf)
     mf.close()
def seasonANNForecasting(ts, dataset, freq, lag):

    # 序列分解
    #ts.index = pd.date_range(start='19960318',periods=len(ts), freq='Q')
    trend, seasonal, residual = season_decompose.seasonDecompose(ts, freq=freq)
    # print trend.shape
    # print seasonal.shape
    # print residual.shape

    # 分别预测
    trendWin = lag
    resWin = trendWin
    t1 = time.time()
    trTrain, trTest, mae1, mrse1, smape1 = ANNFORECAST.ANNforecasting(
        trend, inputDim=trendWin, epoch=100, hiddenNum=100)
    resTrain, resTest, mae2, mrse2, smape2 = ANNFORECAST.ANNforecasting(
        residual, inputDim=resWin, epoch=100, hiddenNum=100)
    t2 = time.time()
    print(t2 - t1)

    #'''
    # 数据对齐
    trendPred, resPred = util.align(trTrain, trTest, trendWin, resTrain,
                                    resTest, resWin)

    # 获取最终预测结果
    finalPred = trendPred + seasonal + resPred

    trainPred = trTrain + seasonal[trendWin:trendWin +
                                   trTrain.shape[0]] + resTrain
    testPred = trTest + seasonal[2 * resWin + resTrain.shape[0]:] + resTest

    # 获得ground-truth数据
    data = dataset[freq // 2:-(freq // 2)]
    trainY = data[trendWin:trendWin + trTrain.shape[0]]
    testY = data[2 * resWin + resTrain.shape[0]:]

    # 评估指标
    # MAE = eval.calcMAE(trainY, trainPred)
    # print ("train MAE",MAE)
    # MRSE = eval.calcRMSE(trainY, trainPred)
    # print ("train MRSE",MRSE)
    # MAPE = eval.calcMAPE(trainY, trainPred)
    # print ("train MAPE",MAPE)
    MAE = eval.calcMAE(testY, testPred)
    print("test MAE", MAE)
    MRSE = eval.calcRMSE(testY, testPred)
    print("test RMSE", MRSE)
    MAPE = eval.calcMAPE(testY, testPred)
    print("test MAPE", MAPE)
    SMAPE = eval.calcSMAPE(testY, testPred)
    print("test SMAPE", SMAPE)

    # plt.plot(data)
    # plt.plot(finalPred)
    # plt.show()
    #'''
    return trainPred, testPred, MAE, MRSE, SMAPE
def decompose_RNN_forecasting(ts, dataset, freq, lag, epoch=20, hidden_num=64,
                              batch_size=32, lr=1e-3, unit="GRU", varFlag=False, maxLen=48, minLen=24, step=8):

    # 序列分解
    trend, seasonal, residual = decompose.ts_decompose(ts, freq)
    print("trend shape:", trend.shape)
    print("peroid shape:", seasonal.shape)
    print("residual shape:", residual.shape)

    # 分别预测
    resWin = trendWin = lag
    t1 = time.time()
    trTrain, trTest, MAE1, MRSE1, SMAPE1 = RNN_forecasting(trend, lookBack=lag, epoch=epoch, batchSize=batch_size, hiddenNum=hidden_num,
                                            varFlag=varFlag, minLen=minLen, maxLen=maxLen, step=step, unit=unit, lr=lr)
    resTrain, resTest, MAE2, MRSE2, SMAPE2 = RNN_forecasting(residual, lookBack=lag, epoch=epoch, batchSize=batch_size, hiddenNum=hidden_num,
                                            varFlag=varFlag, minLen=minLen, maxLen=maxLen, step=step, unit=unit, lr=lr)
    t2 = time.time()
    print(t2-t1)

    print("trTrain shape:", trTrain.shape)
    print("resTrain shape:", resTrain.shape)

    # 数据对齐
    trendPred, resPred = util.align(trTrain, trTest, trendWin, resTrain, resTest, resWin)
    print("trendPred shape is", trendPred.shape)
    print("resPred shape is", resPred.shape)

    # 获取最终预测结果
    # finalPred = trendPred+seasonal+resPred

    trainPred = trTrain+seasonal[trendWin:trendWin+trTrain.shape[0]]+resTrain
    testPred = trTest+seasonal[2*resWin+resTrain.shape[0]:]+resTest

    # 获得ground-truth数据
    data = dataset[freq//2:-(freq//2)]
    trainY = data[trendWin:trendWin+trTrain.shape[0]]
    testY = data[2*resWin+resTrain.shape[0]:]

    # 评估指标
    MAE = eval.calcMAE(testY, testPred)
    print("test MAE", MAE)
    MRSE = eval.calcRMSE(testY, testPred)
    print("test RMSE", MRSE)
    MAPE = eval.calcMAPE(testY, testPred)
    print("test MAPE", MAPE)
    SMAPE = eval.calcSMAPE(testY, testPred)
    print("test SMAPE", SMAPE)

    plt.plot(testY, label='ground-truth')
    plt.plot(testPred, label='prediction')
    plt.xlabel("Time", fontsize=10)
    plt.ylabel("CPU Utilization(%)", fontsize=10)
    plt.legend()
    foo_fig = plt.gcf()
    foo_fig.savefig('M_1955_CPU.eps', format='eps', dpi=1000, bbox_inches='tight')
    plt.show()

    return trainPred, testPred, MAE, MRSE, SMAPE
Beispiel #6
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def seasonSVRForecasting(ts, dataset, freq, lag):

    # 序列分解
    trend, seasonal, residual = season_decompose.seasonDecompose(ts, freq=freq)
    # print trend.shape
    # print seasonal.shape
    # print residual.shape

    # 分别预测
    trendWin = lag
    resWin = trendWin
    t1 = time.time()
    trTrain, trTest, mae1, mrse1, mape1 = SVRFORECAST.SVRforecasting(
        trend, lookBack=trendWin)
    resTrain, resTest, mae2, mrse2, mape2 = SVRFORECAST.SVRforecasting(
        residual, lookBack=resWin)
    t2 = time.time()
    print(t2 - t1)

    #'''
    # 数据对齐
    trendPred, resPred = util.align(trTrain, trTest, trendWin, resTrain,
                                    resTest, resWin)

    # 获取最终预测结果
    finalPred = trendPred + seasonal + resPred

    trainPred = trTrain + seasonal[trendWin:trendWin +
                                   trTrain.shape[0]]  #+resTrain
    testPred = trTest + seasonal[2 * resWin + resTrain.shape[0]:]  #+resTest

    # 获得ground-truth数据
    data = dataset[freq // 2:-(freq // 2)]
    trainY = data[trendWin:trendWin + trTrain.shape[0]]
    testY = data[2 * resWin + resTrain.shape[0]:]

    # 评估指标
    # MAE = eval.calcMAE(trainY, trainPred)
    # print ("train MAE",MAE)
    # MRSE = eval.calcRMSE(trainY, trainPred)
    # print ("train MRSE",MRSE)
    # MAPE = eval.calcMAPE(trainY, trainPred)
    # print ("train MAPE",MAPE)
    MAE = eval.calcMAE(testY, testPred)
    print("test MAE", MAE)
    MRSE = eval.calcRMSE(testY, testPred)
    print("test RMSE", MRSE)
    MAPE = eval.calcMAPE(testY, testPred)
    print("test MAPE", MAPE)
    SMAPE = eval.calcSMAPE(testY, testPred)
    print("test SMAPE", SMAPE)

    # plt.plot(data)
    # plt.plot(finalPred)
    # plt.show()
    #'''
    return trainPred, testPred, MAE, MRSE, SMAPE
Beispiel #7
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 def cmd_rd(self, ui, args, n):
     """memory read command for n bits"""
     if util.wrong_argc(ui, args, (1, )):
         return
     adr = util.sex_arg(ui, args[0], self.cpu.width)
     if adr == None:
         return
     adr = util.align(adr, n)
     ui.put('[0x%08x] = ' % adr)
     ui.put('0x%%0%dx\n' % (n / 4) % self.cpu.rd(adr, n))
Beispiel #8
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 def wr(self, adr, val, n):
   """write to memory - n bits aligned"""
   adr = util.align(adr, n)
   if n == 32:
     return self.dbgio.wr32(adr, val)
   elif n == 16:
     return self.dbgio.wr16(adr, val)
   elif n == 8:
     return self.dbgio.wr8(adr, val)
   assert False
Beispiel #9
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 def rd(self, adr, n):
   """read from memory - n bits aligned"""
   adr = util.align(adr, n)
   if n == 32:
     return self.dbgio.rd32(adr)
   elif n == 16:
     return self.dbgio.rd16(adr)
   elif n == 8:
     return self.dbgio.rd8(adr)
   assert False
Beispiel #10
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 def cmd_rd(self, ui, args, n):
   """memory read command for n bits"""
   if util.wrong_argc(ui, args, (1,)):
     return
   adr = util.sex_arg(ui, args[0], self.cpu.width)
   if adr == None:
     return
   adr = util.align(adr, n)
   ui.put('[0x%08x] = ' % adr)
   ui.put('0x%%0%dx\n' % (n/4) % self.cpu.rd(adr, n))
def decompose_SVR_forecasting(ts, dataset, freq, lag, C=0.1, epsilon=0.01):

    # 序列分解
    trend, seasonal, residual = decompose.ts_decompose(ts, freq=freq)
    print("trend shape:", trend.shape)
    print("peroid shape:", seasonal.shape)
    print("residual shape:", residual.shape)

    # 分别预测
    resWin = trendWin = lag
    t1 = time.time()
    trTrain, trTest, mae1, mrse1, mape1 = SVR_forecasting(trend,
                                                          lookBack=lag,
                                                          C=C,
                                                          epsilon=epsilon)
    resTrain, resTest, mae2, mrse2, mape2 = SVR_forecasting(residual,
                                                            lookBack=lag,
                                                            C=C,
                                                            epsilon=epsilon)
    t2 = time.time()
    print(t2 - t1)

    # 数据对齐
    trendPred, resPred = util.align(trTrain, trTest, trendWin, resTrain,
                                    resTest, resWin)

    # 获取最终预测结果
    finalPred = trendPred + seasonal + resPred

    trainPred = trTrain + seasonal[trendWin:trendWin +
                                   trTrain.shape[0]] + resTrain
    testPred = trTest + seasonal[2 * resWin + resTrain.shape[0]:] + resTest

    # 获得ground-truth数据
    data = dataset[freq // 2:-(freq // 2)]
    trainY = data[trendWin:trendWin + trTrain.shape[0]]
    testY = data[2 * resWin + resTrain.shape[0]:]

    # 评估指标
    MAE = eval.calcMAE(testY, testPred)
    print("test MAE", MAE)
    MRSE = eval.calcRMSE(testY, testPred)
    print("test RMSE", MRSE)
    MAPE = eval.calcMAPE(testY, testPred)
    print("test MAPE", MAPE)
    SMAPE = eval.calcSMAPE(testY, testPred)
    print("test SMAPE", SMAPE)

    # plt.plot(data)
    # plt.plot(finalPred)
    # plt.show()

    return trainPred, testPred, MAE, MRSE, SMAPE
Beispiel #12
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 def cmd_mem2file(self, ui, args):
     """read from memory, write to file"""
     x = util.file_mem_args(ui, args, self.cpu.device)
     if x is None:
         return
     (name, adr, size) = x
     if size is None:
         ui.put('invalid length')
         return
     # adjust the address and length
     adr = util.align(adr, 32)
     n = util.nbytes_to_nwords(size, 32)
     # read memory, write to file object
     mf = iobuf.write_file(ui, 'writing to %s' % name, name, n * 4)
     self.cpu.rdmem32(adr, n, mf)
     mf.close()
Beispiel #13
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 def cmd_mem2file(self, ui, args):
   """read from memory, write to file"""
   x = util.file_mem_args(ui, args, self.cpu.device)
   if x is None:
     return
   (name, adr, size) = x
   if size is None:
     ui.put('invalid length')
     return
   # adjust the address and length
   adr = util.align(adr, 32)
   n = util.nbytes_to_nwords(size, 32)
   # read memory, write to file object
   mf = iobuf.write_file(ui, 'writing to %s' % name, name, n * 4)
   self.cpu.rdmem32(adr, n, mf)
   mf.close()
Beispiel #14
0
 def cmd_wr(self, ui, args, n):
     """memory write command for n bits"""
     if util.wrong_argc(ui, args, (1, 2)):
         return
     adr = util.sex_arg(ui, args[0], self.cpu.width)
     if adr == None:
         return
     adr = util.align(adr, n)
     val = 0
     if len(args) == 2:
         val = util.int_arg(ui, args[1], util.limit_32, 16)
         if val == None:
             return
     val = util.mask_val(val, n)
     self.cpu.wr(adr, val, n)
     ui.put('[0x%08x] = ' % adr)
     ui.put('0x%%0%dx\n' % (n / 4) % val)
Beispiel #15
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 def cmd_wr(self, ui, args, n):
   """memory write command for n bits"""
   if util.wrong_argc(ui, args, (1,2)):
     return
   adr = util.sex_arg(ui, args[0], self.cpu.width)
   if adr == None:
     return
   adr = util.align(adr, n)
   val = 0
   if len(args) == 2:
     val = util.int_arg(ui, args[1], util.limit_32, 16)
     if val == None:
       return
   val = util.mask_val(val, n)
   self.cpu.wr(adr, val, n)
   ui.put('[0x%08x] = ' % adr)
   ui.put('0x%%0%dx\n' % (n/4) % val)
 def align_from_file(self, reference_image_obj,
                     align_method='cv2.TM_CCOEFF_NORMED',
                     roi_size=0.5):
     """Align an image taking by reference another image. roi_size
     is entered as input parameter as tant per one of the original
     image size"""
     image_ref = reference_image_obj.image
     image_to_align = self.image
     aligned_image, mv_vector = align(image_ref, image_to_align,
                                      align_method=align_method,
                                      roi_size=roi_size)
     ref_fn = reference_image_obj.h5_image_filename
     ref_dataset_name = reference_image_obj.image_dataset
     description = ("Image " + self.image_dataset +
                    " has been aligned taking as reference image " +
                    ref_dataset_name + "@" + path.basename(ref_fn))
     return aligned_image, mv_vector, description
Beispiel #17
0
 def cmd_disassemble(self, ui, args):
   """disassemble memory"""
   if util.wrong_argc(ui, args, (0, 1, 2)):
     return
   n = 16
   if len(args) == 0:
     # read the pc
     self.halt()
     adr = self.dbgio.rdreg('pc')
   if len(args) >= 1:
     adr = util.sex_arg(ui, args[0], 32)
     if adr is None:
       return
   if len(args) == 2:
     n = util.int_arg(ui, args[1], (1, 2048), 16)
     if n is None:
       return
   # align the address to 32 bits
   adr = util.align(adr, 32)
   # disassemble
   md = iobuf.arm_disassemble(ui, adr)
   self.rdmem32(adr, n, md)
def seasonRNNForecasting(ts, dataset, freq, lag, unit="GRU"):

    # 序列分解
    #ts.index = pd.date_range(start='19960318',periods=len(ts), freq='Q')
    trend, seasonal, residual = season_decompose.seasonDecompose(ts, freq)
    print(trend.shape)
    print(seasonal.shape)
    print(residual.shape)

    # 分别预测
    trendWin = lag
    resWin = trendWin
    t1 = time.time()
    trTrain, trTest, MAE1, MRSE1, SMAPE1 = RNNFORECAST.RNNforecasting(
        trend, lookBack=trendWin, epoch=50, unit=unit)
    resTrain, resTest, MAE2, MRSE2, SMAPE2 = RNNFORECAST.RNNforecasting(
        residual, lookBack=resWin, epoch=60, unit=unit, hiddenNum=100)
    # trTrain, trTest, MAE1, MRSE1, SMAPE1= RNNFORECAST.RNNforecasting(trend, lookBack=resWin, epoch=30, unit=unit,
    #                                                                     varFlag=True, minLen=20, maxLen=lag, step=4,
    #                                                                     hiddenNum=100)
    # resTrain, resTest, MAE2, MRSE2, SMAPE2 = RNNFORECAST.RNNforecasting(residual, lookBack=resWin, epoch=30, unit=unit,
    #                                                                     varFlag=True, minLen=20, maxLen=lag, step=4, hiddenNum=100)
    t2 = time.time()
    print(t2 - t1)

    print("trTrain shape is", trTrain.shape)
    print("resTrain shape is", resTrain.shape)

    #'''
    # 数据对齐
    trendPred, resPred = util.align(trTrain, trTest, trendWin, resTrain,
                                    resTest, resWin)

    print("trendPred shape is", trendPred.shape)
    print("resPred shape is", resPred.shape)

    # 获取最终预测结果
    finalPred = trendPred + seasonal + resPred

    trainPred = trTrain + seasonal[trendWin:trendWin +
                                   trTrain.shape[0]] + resTrain
    testPred = trTest + seasonal[2 * resWin + resTrain.shape[0]:] + resTest

    # 获得ground-truth数据
    data = dataset[freq // 2:-(freq // 2)]
    trainY = data[trendWin:trendWin + trTrain.shape[0]]
    testY = data[2 * resWin + resTrain.shape[0]:]

    # 评估指标
    MAE = eval.calcMAE(trainY, trainPred)
    print("train MAE", MAE)
    MRSE = eval.calcRMSE(trainY, trainPred)
    print("train MRSE", MRSE)
    MAPE = eval.calcMAPE(trainY, trainPred)
    print("train MAPE", MAPE)
    MAE = eval.calcMAE(testY, testPred)
    print("test MAE", MAE)
    MRSE = eval.calcRMSE(testY, testPred)
    print("test RMSE", MRSE)
    MAPE = eval.calcMAPE(testY, testPred)
    print("test MAPE", MAPE)
    SMAPE = eval.calcSMAPE(testY, testPred)
    print("test SMAPE", SMAPE)

    # plt.plot(data)
    # plt.plot(finalPred)
    # plt.show()
    #'''
    return trainPred, testPred, MAE, MRSE, SMAPE
resTrain = resTest = None
regNum = 4
for i in range(regNum):
    tmpresTrain, tmpresTest = RNNFORECAST.RNNforecasting(residual,lookBack=resWin,epoch=50,varFlag=True,maxLen=20,inputNum=1500)
    if i == 0:
        resTrain = tmpresTrain
        resTest = tmpresTest
    else:
        resTrain += tmpresTrain
        resTest += tmpresTest
resTrain /= regNum
resTest /= regNum

#'''
# 数据对齐
trendPred,resPred = util.align(trTrain,trTest,trendWin,resTrain,resTest,resWin)

# 获取最终预测结果
finalPred = trendPred+resPred+seasonal

# 分别获得训练集测试集结果
trainPred = trTrain+resTrain+seasonal[trendWin:trendWin+trTrain.shape[0]]
testPred = trTest+resTest+seasonal[2*resWin+resTrain.shape[0]:]

# 获得ground-truth数据
data = dataset[2:-2]
trainY = data[trendWin:trendWin+trTrain.shape[0]]
testY = data[2*resWin+resTrain.shape[0]:]

# 评估指标
MAE = eval.calcMAE(trainY, trainPred)
Beispiel #20
0
para_dct['__EVENTTARGET'] = 'ddl_ywyl'
para_dct['__EVENTARGUMENT'] = ''
para_dct['__LASTFOCUS'] = ''
para_dct['__VIEWSTATE'] = viewstate
para_dct['__EVENTVALIDATION'] = eventvali
para_data = urllib.urlencode(para_dct)

response = opener.open(baseurl + paramters, para_data)
content = read(response).decode('gbk').encode('utf-8')

classes = re.compile('(<tr[\s\S]*?<\/tr>)').findall(content)
regnum = re.compile('kcmcGrid_ctl(\d+)')
regname = re.compile('target=\'_blank\'>(.*?)<\/a>')
reginfo = re.compile('<td>(.*?)<\/td>')

for i in range(1, len(classes) - 1):
    sclass = classes[i]
    codeg = regnum.findall(sclass)
    if codeg:
        code = codeg[0]
    else:
        break
    group1 = regname.findall(sclass)
    name = group1[0]
    teacher = group1[1]
    group2 = reginfo.findall(sclass)
    print(
        align(code, 4) + align(name, 35) + align(teacher, 18) +
        align(group2[4], 20) + align(group2[5], 5) + align(group2[6], 10) +
        align(group2[7], 7) + group2[9] + '/' + group2[8])
Beispiel #21
0
from util import align

align(['Afr1953','ESV','DutSVV'], ['Afr1953_short_translated.csv','ESV_short_translated.csv','DutSVV_short_translated.csv'], 'test_aligned.csv')
Beispiel #22
0
    def _draw_tokens(self,
                     tokens,
                     x,
                     y,
                     wordspacing,
                     lineheight,
                     align,
                     va,
                     transform=None,
                     *args,
                     **kwargs):
        """
		Draw the tokens on the plot.

		:param tokens: The text tokens to draw.
					   The method expects a `list` of tokens, each one a `dict`.
		:type tokens: list of str
		:param x: The start and end x-position of the annotation.
		:type x: tuple
		:param y: The starting y-position of the annotation.
		:type y: float
		:param wordspacing: The space between words.
							If `None` is given, the space is calculated based on the height of the line.
		:type wordspacing: float or None
		:param lineheight: The space between lines.
		:type lineheight: float
		:param align: The text's alignment.
					  Possible values:

					    - left
					    - center
					    - right
					    - justify
					    - justify-start (or justify-left)
					    - justify-center
					    - justify-end or (justify-right)
		:type align: str
		:param va: The vertical alignment, can be one of `top` or `bottom`.
				   If the vertical alignment is `bottom`, the annotation grows up.
				   If the vertical alignment is `top`, the annotation grows down.
		:type va: str
		:param transform: The bounding box transformation.
						  If `None` is given, the data transformation is used.
		:type transform: None or :class:`matplotlib.transforms.TransformNode`

		:return: The drawn lines.
				 Each line is made up of the text tokens.
		:rtype: list of list of :class:`matplotlib.text.Text`
		"""

        figure = self.drawable.figure
        axis = self.drawable.axis
        transform = transform if transform is not None else axis.transData

        linespacing = util.get_linespacing(
            figure, axis, wordspacing, transform=transform, *args, **
            kwargs) * lineheight
        if wordspacing is None:
            wordspacing = linespacing / 10.
        """
		Go through each token and draw it on the axis.
		"""
        drawn_lines, line_tokens = [], []
        offset = x[0]
        for token in tokens:
            """
			Draw the text token.
			If the vertical alignment is top, the annotation grows downwards: one line after the other.
			If the vertical alignment is bottom, the annotation grows upwards.
			When the vertical alignment is bottom, new text is always added to the same place.
			New lines push previous lines up.

			Note that the center alignment is not considered here.
			There is no way of knowing how many lines there will be in advance.
			Therefore lines are centered at a later stage.
			"""
            va = 'top' if va == 'center' else va
            text = text_util.draw_token(
                figure,
                axis,
                token.get('text'),
                offset,
                y - len(drawn_lines) * linespacing if va == 'top' else y,
                token.get('style', {}),
                wordspacing,
                va=va,
                transform=transform,
                *args,
                **kwargs)
            line_tokens.append(text)
            """
			If the token exceeds the x-limit, break it into a new line.
			The offset is reset to the left, and a new line is added.
			The token is moved to this new line.
			Lines do not break on punctuation marks.

			Note that lists are passed by reference.
			Therefore when the last token is removed from drawn lines when create a new line, the change is reflected here.
			"""
            bb = util.get_bb(figure, axis, text, transform=transform)
            if bb.x1 > x[1] and token.get('text') not in string.punctuation:
                self._newline(line_tokens,
                              drawn_lines,
                              linespacing,
                              x[0],
                              y,
                              va,
                              transform=transform)
                util.align(figure,
                           axis,
                           line_tokens,
                           xpad=wordspacing,
                           align=util.get_alignment(align),
                           xlim=x,
                           va=va,
                           transform=transform)
                offset = x[0]
                line_tokens = [text]

            offset += bb.width + wordspacing
        """
		Align the last line.
		"""
        drawn_lines.append(line_tokens)
        util.align(figure,
                   axis,
                   line_tokens,
                   xpad=wordspacing,
                   align=util.get_alignment(align, end=True),
                   xlim=x,
                   va=va,
                   transform=transform)

        return drawn_lines