def comparing_stat_patch_list(pred, y):
patch1 = mpatches.Patch(color='red',
label='mean:' + ('%03.6f' % np.mean(pred)))
patch2 = mpatches.Patch(color='red',
label='std:' + ('%03.6f' % np.std(pred)))
patch3 = mpatches.Patch(color='red',
label='skewness:' +
('%03.3f' % stats.skewness(pred)))
patch4 = mpatches.Patch(color='red',
label='kurtosis:' +
('%03.3f' % stats.kurtosis(pred)))
patch5 = mpatches.Patch(color='blue',
label='mean:' + ('%03.6f' % np.mean(y)))
patch6 = mpatches.Patch(color='blue',
label='std:' + ('%03.6f' % np.std(y)))
patch7 = mpatches.Patch(color='blue',
label='skewness:' + ('%03.3f' % stats.skewness(y)))
patch8 = mpatches.Patch(color='blue',
label='kurtosis:' + ('%03.3f' % stats.kurtosis(y)))
#patch9 = mpatches.Patch(color='black', label= 'MAPE:'+ ('%03.3f' % stats.mape(pred, y)))
patch10 = mpatches.Patch(color='black',
label='RMSE:' + ('%03.6f' % stats.rmse(pred, y)))
#plt.text(.25,.5,str(np.mean(pred)))
return [
patch5, patch6, patch7, patch8, patch1, patch2, patch3, patch4, patch10
]
def get_seq_feature(seq, seq_name, user_id):
# total 11 features
if not seq:
print('seq is empty!')
return
df = pd.DataFrame()
df[seq_name + '_mean'] = [np.mean(seq)]
df[seq_name + '_median'] = [np.median(seq)]
df[seq_name + '_max'] = [np.max(seq)]
df[seq_name + '_min'] = [np.min(seq)]
df[seq_name + '_var'] = [np.var(seq)]
df[seq_name + '_std'] = [np.std(seq)]
if len(seq) == 1:
df[seq_name + '_upquantile'] = seq[0]
df[seq_name + '_downquantile'] = 0
else:
df[seq_name + '_upquantile'] = [sts.quantile(seq, p=0.75)]
df[seq_name + '_downquantile'] = [sts.quantile(seq, p=0.25)]
if np.mean(seq) != 0: df[seq_name + '_discrete'] = [np.std(seq) / np.mean(seq)]
else: df[seq_name + '_discrete'] = [np.NaN]
try: df[seq_name + 'skew'] = [sts.skewness(seq)]
except: df[seq_name + 'skew'] = [np.NaN]
try: df[seq_name + 'kurt'] = [sts.kurtosis(seq)]
except: df[seq_name + 'kurt'] = [np.NaN]
df['user_id'] = [user_id]
return df
def kusk(t_win, stockdata, indexes):
'''
:param t_win: 窗口长度
:param stockdata: 股票序列
:param indexes: 遍历下标
:return: 滑动窗口得到股票序列的峰度、偏度、波动率、收益率指标
'''
sk = []
ku = []
std = []
res = []
for j in indexes:
s = stockdata.iloc[-j - 2 * t_win:-j - t_win]
print("s_length: ", len(s))
sk.append(sts.skewness(s))
ku.append(sts.kurtosis(s))
std.append(np.std(s))
res.append((s.iloc[-1] - s.iloc[0]) / s.iloc[0])
df = pd.DataFrame({
"时间": pd.Series(s.date),
"峰度": pd.Series(sk),
"偏度": pd.Series(ku),
"波动率": pd.Series(std),
"收益率": pd.Series(res)
})
return df
def stat_patch_list(x):
patch0 = mpatches.Patch(label='data num:' + str((x.size)))
patch1 = mpatches.Patch(label='mean:' + ('%03.6f' % np.mean(x)))
patch2 = mpatches.Patch(label='std:' + ('%03.6f' % np.std(x)))
patch3 = mpatches.Patch(label='skewness:' + ('%03.3f' % stats.skewness(x)))
patch4 = mpatches.Patch(label='kurtosis:' + ('%03.3f' % stats.kurtosis(x)))
return [patch0, patch1, patch2, patch3, patch4]
def data_description(index, start, end):
returns = download_data.get_returns(index, start, end)
print('个数：', len(returns))
print('平均值:', np.mean(returns))
print('中位数:', np.median(returns))
print('上四分位数', sts.quantile(returns, p=0.25))
print('下四分位数', sts.quantile(returns, p=0.75))
#离散趋势的度量
print('最大值:', np.max(returns))
print('最小值:', np.min(returns))
print('极差:', np.max(returns) - np.min(returns))
print('四分位差',
sts.quantile(returns, p=0.75) - sts.quantile(returns, p=0.25))
print('标准差:', np.std(returns))
print('方差:', np.var(returns))
print('离散系数:', np.std(returns) / np.mean(returns))
#偏度与峰度的度量
print('偏度:', sts.skewness(returns))
print('峰度:', sts.kurtosis(returns))
print(st.kstest(returns, 'norm'))
length = len(returns)
sns.distplot(returns, bins=100, label='Empirical')
sns.plt.legend()
sns.plt.title('Empirical')
sns.plt.show()
def get_seq_feature(seq, seq_name, user_id):
if not seq:
print('seq is empty! : %s'%seq_name)
return
df = pd.DataFrame()
df[seq_name + '_mean'] = [np.mean(seq)]
df[seq_name + '_median'] = [np.median(seq)]
df[seq_name + '_max'] = [np.max(seq)]
df[seq_name + '_min'] = [np.min(seq)]
df[seq_name + '_var'] = [np.var(seq)]
df[seq_name + '_std'] = [np.std(seq)]
if np.mean(seq) != 0:
df[seq_name + '_discrete'] = [np.std(seq) / np.mean(seq)]
else:
df[seq_name + '_discrete'] = [np.NaN]
try:
df[seq_name + '_skew'] = [sts.skewness(seq)]
except:
df[seq_name + '_skew'] = [np.NaN]
try:
df[seq_name + '_kurt'] = [sts.kurtosis(seq)]
except:
df[seq_name + '_kurt'] = [np.NaN]
df['user_id'] = [user_id]
return df
def extend_feature(scores):
"""
特征构造
Args:
scores: 原始滑动窗口获得的特征
Returns:
返回基于滑动窗口特征增加的统计特征
"""
features = scores
features.append(np.sum(scores)) #总数
features.append(np.mean(scores)) #平均数
features.append(np.median(scores)) #中位数
# features.append(sts.mode(scores)) #众数
features.append(sts.quantile(scores, p=0.25)) #上四分位
features.append(sts.quantile(scores, p=0.75)) #上七分位
features.append(np.max(scores)) #最大值
features.append(np.min(scores)) #最小值
features.append(np.max(scores) - np.min(scores)) #极差
features.append(
sts.quantile(scores, p=0.75) - sts.quantile(scores, p=0.25)) #四分位差
features.append(np.var(scores)) #方差
features.append(np.std(scores) / np.mean(scores)) #离散系数
features.append(sts.skewness(scores)) #偏度
features.append(sts.kurtosis(scores)) #峰度
return features
def waveCoeffs(data, wave='db4', levels=5):
w = pywt.wavedec(data, wavelet=wave, level=levels)
res_RSE = list()
res_kurtosis = list()
res_std = list()
for i in range(1, len(w)):
temp = 0
for j in range(0, len(w[i])):
temp += w[i][j]**2
res_RSE.append(np.sqrt(temp))
res_kurtosis.append(stats.kurtosis(w[i]))
res_std.append(np.std(w[i]))
return res_RSE, res_kurtosis, res_std
def waveCoeffs(data,wave='db4',levels=5):
w = pywt.wavedec(data,wavelet=wave,level=levels)
res_RSE = list()
res_kurtosis = list()
res_std = list()
for i in range(1,len(w)):
temp = 0
for j in range(0,len(w[i])):
temp += w[i][j]**2
res_RSE.append(np.sqrt(temp))
res_kurtosis.append(stats.kurtosis(w[i]))
res_std.append(np.std(w[i]))
return res_RSE, res_kurtosis, res_std
def calculate_stats_num(self, name, per = [5,25,50,75,95]):
# get columnt instance
Col = self.get_column(name)
# type validation
assert Col.type == 'numerical', 'only possible numerical columns.'
# get data
data = self.get_data(name)
# initialize
dstats = dict()
# calculate statistics
dstats['mean'] = stats.mean(data)
dstats['median'] = stats.median(data)
dstats['std'] = stats.std(data)
dstats['min'] = stats.min(data)
dstats['max'] = stats.max(data)
dstats['skew'] = stats.skew(data)
dstats['kurtosis'] = stats.kurtosis(data)
for ip in per:
dstats['per%s'%ip] = stats.percentile(data, ip)
# return
Col.stats = dstats
import pandas as pd
import numpy as np
import stats as sts
#建立一個15*5的二維陣列
post = np.zeros((15, 5))
#post
for i in range(15):
path = 'data/post/' + str(i + 1) + '.csv'
#將V前1萬5千筆讀出來並分析,skiprows為跳過第一個row
V = pd.read_csv(path, skiprows=1)['V'][0:20004]
post[i, 0] = np.std(V) / np.mean(V) #離散係數
post[i, 1] = np.max(V) - np.min(V) #極差
post[i, 2] = sts.skewness(V) #偏度
post[i, 3] = sts.kurtosis(V) #峰度
#post 為1
post[i, 4] = '1'
pre = np.zeros((15, 5))
#pre
for i in range(15):
path = 'data/pre/' + str(i + 1) + '.csv'
#將V前1萬5千筆讀出來並分析,skiprows為跳過第一個row
V = pd.read_csv(path, skiprows=1)['V'][0:20004]
pre[i, 0] = np.std(V) / np.mean(V) #離散係數
pre[i, 1] = np.max(V) - np.min(V) #極差
pre[i, 2] = sts.skewness(V) #偏度
pre[i, 3] = sts.kurtosis(V) #峰度
#pre 為0
pre[i, 4] = '0'
import numpy as np
import stats as sts
a = [31, 24, 23, 25, 14, 25, 13, 12, 14, 23,
32, 34, 43, 41, 21, 23, 26, 26, 34, 42,
43, 25, 24, 23, 24, 44, 23, 14, 52,32,
42, 44, 35, 28, 17, 21, 32, 42, 12, 34]
scores=np.array(a)
print('總合為：',np.sum(scores))
print('筆數為：',len(scores))
print('平均值為:',np.mean(scores))
print('中位數為:',np.median(scores))
print('眾數為:',sts.mode(scores))
print('上四分位數為',sts.quantile(scores,p=0.25))
print('下四分位數為',sts.quantile(scores,p=0.75))
print('最大值:',np.max(scores))
print('最小值:',np.min(scores))
print('全距:',np.ptp(scores))
print('標準差:',np.std(scores))
print('變異數:',np.var(scores))
print('離散係數:',np.std(scores)/np.mean(scores))
print('偏態係數:',sts.skewness(scores))
print('峰態係數:',sts.kurtosis(scores))
print 'harmonicmean:', stats.harmonicmean(l), stats.harmonicmean(
lf), stats.harmonicmean(a), stats.harmonicmean(af)
print 'mean:', stats.mean(l), stats.mean(lf), stats.mean(a), stats.mean(af)
print 'median:', stats.median(l), stats.median(lf), stats.median(
a), stats.median(af)
print 'medianscore:', stats.medianscore(l), stats.medianscore(
lf), stats.medianscore(a), stats.medianscore(af)
print 'mode:', stats.mode(l), stats.mode(a)
print '\nMOMENTS'
print 'moment:', stats.moment(l), stats.moment(lf), stats.moment(
a), stats.moment(af)
print 'variation:', stats.variation(l), stats.variation(a), stats.variation(
lf), stats.variation(af)
print 'skew:', stats.skew(l), stats.skew(lf), stats.skew(a), stats.skew(af)
print 'kurtosis:', stats.kurtosis(l), stats.kurtosis(lf), stats.kurtosis(
a), stats.kurtosis(af)
print 'mean:', stats.mean(a), stats.mean(af)
print 'var:', stats.var(a), stats.var(af)
print 'stdev:', stats.stdev(a), stats.stdev(af)
print 'sem:', stats.sem(a), stats.sem(af)
print 'describe:'
print stats.describe(l)
print stats.describe(lf)
print stats.describe(a)
print stats.describe(af)
print '\nFREQUENCY'
print 'freqtable:'
print 'itemfreq:'
print stats.itemfreq(l)
mode1 = stats.mode(list1)
mode2 = stats.mode(list2)
median1 = stats.median(list1)
median2 = stats.median(list2)
variance1 = stats.variance(list1)
variance2 = stats.variance(list2)
standard_deviation1 = stats.SD(list1)
standard_deviation2 = stats.SD(list2)
covariance_pop = stats.covariance(list1, list2)
covariance_sample = stats.covariance(list1, list2, True)
correlation = stats.correlation(list1, list2)
skewness_pop1 = stats.skewness(list1)
skewness_pop2 = stats.skewness(list2)
skewness_sample1 = stats.skewness(list1, True)
skewness_sample2 = stats.skewness(list2, True)
kurtosis_pop1 = stats.kurtosis(list1)
kurtosis_pop2 = stats.kurtosis(list2)
kurtosis_sample1 = stats.kurtosis(list1, True)
kurtosis_sample2 = stats.kurtosis(list2, True)
print("Total1:", total1)
print("Total2:", total2)
print("Mean1:", mean1)
print("Mean2", mean2)
print("Mode1:", mode1)
print("Mode2:", mode2)
print("Median1:", median1)
print("Median2:", median2)
print("Variance1:", variance1)
print("Variance2:", variance2)
print("Standard Deviation1:", standard_deviation1)
import numpy as np
import stats as sts
scares = [
31, 24, 23, 25, 14, 25, 13, 12, 14, 23, 32, 34, 43, 41, 21, 23, 26, 26, 34,
42, 43, 25, 24, 23, 24, 44, 23, 14, 52, 32, 42, 44, 35, 28, 17, 21, 32, 42,
12, 34
]
print('求和：', np.sum(scares))
print('個數：', len(scares))
print('平均值：', np.mean(scares))
print('中位數：', np.median(scares))
print('眾數：', sts.mode(scares))
print('上四分位數：', sts.quantile(scares, p=0.25))
print('下四分位數：', sts.quantile(scares, p=0.75))
print('最大值：', np.max(scares))
print('最小值：', np.min(scares))
print('極差：', np.std(scares))
print('四分位數：', sts.quantile(scares, p=0.75), sts.quantile(scares, p=0.25))
print('標準差：', np.std(scares))
print('方差', np.var(scares))
print('離散係數', np.std(scares) / np.mean(scares))
print('遍度：', sts.skewness(scares))
print('峰度：', sts.kurtosis(scares))
def _fill_moment_results(self):
"""
Fills the result arrays used for storing the calculated moments
common format: time, mean, mode, var, skewness, kurtosis,
95% confidence lower limit, 95% upper limit
"""
toprocess = [('stock_tom', self.c_stock, 2),
('stock_woody', self.c_stock, 3),
('stock_non_woody', self.c_stock, 4),
('stock_acid', self.c_stock, 5),
('stock_water', self.c_stock, 6),
('stock_ethanol', self.c_stock, 7),
('stock_non_soluble', self.c_stock, 8),
('stock_humus', self.c_stock, 9),
('change_tom', self.c_change, 2),
('change_woody', self.c_change, 3),
('change_non_woody', self.c_change, 4),
('change_acid', self.c_change, 5),
('change_water', self.c_change, 6),
('change_ethanol', self.c_change, 7),
('change_non_soluble', self.c_change, 8),
('change_humus', self.c_change, 9),
('co2', self.co2_yield, 2)]
for (resto, dataarr, dataind) in toprocess:
# filter time steps
ts = numpy.unique(dataarr[:,1])
# extract data for the timestep
for timestep in ts:
ind = numpy.where(dataarr[:,1]==timestep)
mean = stats.mean(dataarr[ind[0], dataind])
mode_res = stats.mode(dataarr[ind[0], dataind])
mode = mode_res[0]
var = stats.var(dataarr[ind[0], dataind])
skew = stats.skew(dataarr[ind[0], dataind])
kurtosis = stats.kurtosis(dataarr[ind[0], dataind])
if var>0.0:
sd2 = 2 * math.sqrt(var)
else:
sd2 = var
res = [[timestep, mean, mode[0], var, skew, kurtosis,
mean - sd2, mean + sd2]]
if resto=='stock_tom':
self.md.stock_tom = numpy.append(self.md.stock_tom,
res, axis=0)
elif resto=='stock_woody':
self.md.stock_woody = numpy.append(self.md.stock_woody,
res, axis=0)
elif resto=='stock_non_woody':
self.md.stock_non_woody = numpy.append(\
self.md.stock_non_woody, res, axis=0)
elif resto=='stock_acid':
self.md.stock_acid = numpy.append(self.md.stock_acid,
res, axis=0)
elif resto=='stock_water':
self.md.stock_water = numpy.append(self.md.stock_water,
res, axis=0)
elif resto=='stock_ethanol':
self.md.stock_ethanol = numpy.append(self.md.stock_ethanol,
res, axis=0)
elif resto=='stock_non_soluble':
self.md.stock_non_soluble= numpy.append(
self.md.stock_non_soluble, res, axis=0)
elif resto=='stock_humus':
self.md.stock_humus = numpy.append(self.md.stock_humus,
res, axis=0)
elif resto=='change_tom':
self.md.change_tom = numpy.append(self.md.change_tom,
res, axis=0)
elif resto=='change_woody':
self.md.change_woody = numpy.append(self.md.change_woody,
res, axis=0)
elif resto=='change_non_woody':
self.md.change_non_woody = numpy.append(\
self.md.change_non_woody, res, axis=0)
elif resto=='change_acid':
self.md.change_acid = numpy.append(self.md.change_acid,
res, axis=0)
elif resto=='change_water':
self.md.change_water = numpy.append(self.md.change_water,
res, axis=0)
elif resto=='change_ethanol':
self.md.change_ethanol = numpy.append(
self.md.change_ethanol, res, axis=0)
elif resto=='change_non_soluble':
self.md.change_non_soluble=numpy.append(
self.md.change_non_soluble, res, axis=0)
elif resto=='change_humus':
self.md.change_humus = numpy.append(self.md.change_humus,
res, axis=0)
elif resto=='co2':
self.md.co2 = numpy.append(self.md.co2, res, axis=0)
def kurtosis(self, data):
print('峰度:', sts.kurtosis(data))
def G2(vals) :
"""Calculates the adjusted and corrected kurtosis (G2) as
defined on pg 115 of Sokal and Rohlf"""
n = len(vals)
if n < 4 : return -99 # hack
return ((n+1)*n*stats.kurtosis(vals)) / ((n-1) * (n-2)) - ((3*(n-1)*(n-1))/((n-2)*(n-3)))
def Ts_Kurtosis(x, n):
return stats.kurtosis(x[-n:])
print('方差', df['身高'].var())
print('标准差', df['身高'].std())
print('极差', df['身高'].max() - df['身高'].min())
print('偏度', df['身高'].skew())
print('峰度', df['身高'].kurt())
import numpy as np
import stats as sts
scores = [1, 2, 2, 2, 5]
#集中趋势的度量
print('求和：', np.sum(scores))
print('个数：', len(scores))
print('平均值:', np.mean(scores))
print('中位数:', np.median(scores))
print('众数:', sts.mode(scores))
print('上四分位数', sts.quantile(scores, p=0.25))
print('下四分位数', sts.quantile(scores, p=0.75))
#离散趋势的度量
print('最大值:', np.max(scores))
print('最小值:', np.min(scores))
print('极差:', np.max(scores) - np.min(scores))
print('四分位差', sts.quantile(scores, p=0.75) - sts.quantile(scores, p=0.25))
print('标准差:', np.std(scores))
print('方差:', np.var(scores))
print('离散系数:', np.std(scores) / np.mean(scores))
#偏度与峰度的度量
print('偏度:', sts.skewness(scores))
print('峰度:', sts.kurtosis(scores))
print('平均值:', np.mean(data))
print('中位数:', np.median(data))
print('众数:', sts.mode(data))
print('上四分位数', sts.quantile(data, p=0.25))
print('下四分位数', sts.quantile(data, p=0.75))
#离散趋势的度量
print('最大值:', np.max(data))
print('最小值:', np.min(data))
print('极差:', np.max(data) - np.min(data))
print('四分位差', sts.quantile(data, p=0.75) - sts.quantile(data, p=0.25))
print('标准差:', np.std(data))
print('方差:', np.var(data))
print('变异系数:', np.std(data) / np.mean(data))
#偏度与峰度的度量
print('偏度:', sts.skewness(data))
print('峰度:', sts.kurtosis(data))
# 随机生成两个样本
x = np.random.randint(0, 9, 1000)
y = np.random.randint(0, 9, 1000)
# 计算平均值
mx = x.mean()
my = y.mean()
# 计算标准差
stdx = x.std()
stdy = y.std()
# 计算协方差矩阵
covxy = np.cov(x, y)
af = N.array(lf)
ll = [l]*5
aa = N.array(ll)
print('\nCENTRAL TENDENCY')
print('geometricmean:',stats.geometricmean(l), stats.geometricmean(lf), stats.geometricmean(a), stats.geometricmean(af))
print('harmonicmean:',stats.harmonicmean(l), stats.harmonicmean(lf), stats.harmonicmean(a), stats.harmonicmean(af))
print('mean:',stats.mean(l), stats.mean(lf), stats.mean(a), stats.mean(af))
print('median:',stats.median(l),stats.median(lf),stats.median(a),stats.median(af))
print('medianscore:',stats.medianscore(l),stats.medianscore(lf),stats.medianscore(a),stats.medianscore(af))
print('mode:',stats.mode(l),stats.mode(a))
print('\nMOMENTS')
print('moment:',stats.moment(l),stats.moment(lf),stats.moment(a),stats.moment(af))
print('variation:',stats.variation(l),stats.variation(a),stats.variation(lf),stats.variation(af))
print('skew:',stats.skew(l),stats.skew(lf),stats.skew(a),stats.skew(af))
print('kurtosis:',stats.kurtosis(l),stats.kurtosis(lf),stats.kurtosis(a),stats.kurtosis(af))
print('mean:',stats.mean(a),stats.mean(af))
print('var:',stats.var(a),stats.var(af))
print('stdev:',stats.stdev(a),stats.stdev(af))
print('sem:',stats.sem(a),stats.sem(af))
print('describe:')
print(stats.describe(l))
print(stats.describe(lf))
print(stats.describe(a))
print(stats.describe(af))
print('\nFREQUENCY')
print('freqtable:')
print('itemfreq:')
print(stats.itemfreq(l))
print(stats.itemfreq(a))
print('scoreatpercentile:',stats.scoreatpercentile(l,40),stats.scoreatpercentile(lf,40),stats.scoreatpercentile(a,40),stats.scoreatpercentile(af,40))
def _fill_moment_results(self):
"""
Fills the result arrays used for storing the calculated moments
common format: time, mean, mode, var, skewness, kurtosis,
95% confidence lower limit, 95% upper limit
"""
toprocess = [('stock_tom', self.c_stock, 2),
('stock_woody', self.c_stock, 3),
('stock_non_woody', self.c_stock, 4),
('stock_acid', self.c_stock, 5),
('stock_water', self.c_stock, 6),
('stock_ethanol', self.c_stock, 7),
('stock_non_soluble', self.c_stock, 8),
('stock_humus', self.c_stock, 9),
('change_tom', self.c_change, 2),
('change_woody', self.c_change, 3),
('change_non_woody', self.c_change, 4),
('change_acid', self.c_change, 5),
('change_water', self.c_change, 6),
('change_ethanol', self.c_change, 7),
('change_non_soluble', self.c_change, 8),
('change_humus', self.c_change, 9),
('co2', self.co2_yield, 2)]
for (resto, dataarr, dataind) in toprocess:
# filter time steps
ts = numpy.unique(dataarr[:, 1])
# extract data for the timestep
for timestep in ts:
ind = numpy.where(dataarr[:, 1] == timestep)
mean = stats.mean(dataarr[ind[0], dataind])
mode_res = stats.mode(dataarr[ind[0], dataind])
mode = mode_res[0]
var = stats.var(dataarr[ind[0], dataind])
skew = stats.skew(dataarr[ind[0], dataind])
kurtosis = stats.kurtosis(dataarr[ind[0], dataind])
if var > 0.0:
sd2 = 2 * math.sqrt(var)
else:
sd2 = var
res = [[
timestep, mean, mode[0], var, skew, kurtosis, mean - sd2,
mean + sd2
]]
if resto == 'stock_tom':
self.md.stock_tom = numpy.append(self.md.stock_tom,
res,
axis=0)
elif resto == 'stock_woody':
self.md.stock_woody = numpy.append(self.md.stock_woody,
res,
axis=0)
elif resto == 'stock_non_woody':
self.md.stock_non_woody = numpy.append(\
self.md.stock_non_woody, res, axis=0)
elif resto == 'stock_acid':
self.md.stock_acid = numpy.append(self.md.stock_acid,
res,
axis=0)
elif resto == 'stock_water':
self.md.stock_water = numpy.append(self.md.stock_water,
res,
axis=0)
elif resto == 'stock_ethanol':
self.md.stock_ethanol = numpy.append(self.md.stock_ethanol,
res,
axis=0)
elif resto == 'stock_non_soluble':
self.md.stock_non_soluble = numpy.append(
self.md.stock_non_soluble, res, axis=0)
elif resto == 'stock_humus':
self.md.stock_humus = numpy.append(self.md.stock_humus,
res,
axis=0)
elif resto == 'change_tom':
self.md.change_tom = numpy.append(self.md.change_tom,
res,
axis=0)
elif resto == 'change_woody':
self.md.change_woody = numpy.append(self.md.change_woody,
res,
axis=0)
elif resto == 'change_non_woody':
self.md.change_non_woody = numpy.append(\
self.md.change_non_woody, res, axis=0)
elif resto == 'change_acid':
self.md.change_acid = numpy.append(self.md.change_acid,
res,
axis=0)
elif resto == 'change_water':
self.md.change_water = numpy.append(self.md.change_water,
res,
axis=0)
elif resto == 'change_ethanol':
self.md.change_ethanol = numpy.append(
self.md.change_ethanol, res, axis=0)
elif resto == 'change_non_soluble':
self.md.change_non_soluble = numpy.append(
self.md.change_non_soluble, res, axis=0)
elif resto == 'change_humus':
self.md.change_humus = numpy.append(self.md.change_humus,
res,
axis=0)
elif resto == 'co2':
self.md.co2 = numpy.append(self.md.co2, res, axis=0)