def MergedWorkbookClassification(list_xls_path,num_head_rows,num_head_columns):
print('')
print('--Merged Workbook Classification')
plt.style.use('ggplot')
#construct output folder path
tables_output_folder=list_xls_path[0].split('input')[0]+'output\\颗分汇总\\分类\\'
figures_output_folder=list_xls_path[0].split('input')[0]+'output\\颗分汇总\\分类\\图\\总图\\'
#generate output folder
O_P.GenerateFolder(tables_output_folder)
O_P.GenerateFolder(figures_output_folder)
#DF channels
total_channels=[]
for this_xls_path in list_xls_path:
#open the excel sheet to be operated on
#formatting_info: keep the header format
workbook=xlrd.open_workbook(this_xls_path,formatting_info=True)
#construct map between sheet names and head rows
list_sheet_names=list(workbook.sheet_names())
#traverse all sheets
for this_sheet_name in list_sheet_names:
#Data Frame object
that_channel=pd.read_excel(this_xls_path,sheet_name=this_sheet_name)
#collect it
total_channels.append(that_channel)
title_list=['粉土密实度分类',
'粉土湿度分类',
'黏性土状态分类',
'土的分类',
'备注',
'砂类土分类（代号）',
'砾类土分类（代号）',
'砂类土分类（名称）',
'砾类土分类（名称）']
#classification result list
classification_ω0=[]
classification_e0=[]
classification_IL=[]
classification_GB=[]
classification_note=[]
classification_S_type=[]
classification_G_type=[]
classification_S_code=[]
classification_G_code=[]
#traverse all sheets
for channel in total_channels:
print('')
print('...')
print('......')
print('')
final_head_columns,unit_list=O_H_C.HeadColumnsGeneration(channel,num_head_rows)
#all info of dataframe
value_matrix=channel.values
#delete the repetition
index_valid=O_L.ValidIndexList(value_matrix[num_head_rows:,1])
print('-->Valid Samples:',len(index_valid))
for k in range(len(final_head_columns)):
this_head=final_head_columns[k]
#search for note and make statistics
if '备' in this_head or '注' in this_head:
list_note=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
head_note=this_head
print('-->head:'+head_note)
#search for type of silt
if '分类' in this_head:
list_GB=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
head_GB=this_head
print('-->head:'+head_GB)
#search for pore ratio
if 'e0' in this_head:
list_e0=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
head_e0=this_head
print('-->head:'+head_e0)
#search for moisture content
if 'ω0' in this_head:
list_ω0=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
head_ω0=this_head
print('-->head:'+head_ω0)
#search for liquidity index
if 'IL' in this_head:
list_IL=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
head_IL=this_head
print('-->head:'+head_IL)
#delete the repetition and remove label R
index_valid=O_L.ListWithR(value_matrix[num_head_rows:,1])
print('-->Total Samples:',len(value_matrix[num_head_rows:,1]))
print('-->Valid Samples:',len(index_valid))
#partition index list
list_partition_index=[]
for k in range(num_head_columns,np.shape(value_matrix)[1]):
#title str
title=final_head_columns[k]
# print(k,title)
if '颗' and '粒' and '分' and '析' in title:
print('-->',title)
list_partition_index.append(k)
if '不' and '均' and '匀' in title:
print('-->',title)
data_Cu=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
if '曲' and '率' in title:
print('-->',title)
data_Ce=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
if '分' and '类' in title:
print('-->',title)
data_GB=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
# print(list_partition_index)
#for partition
index_partition=O_L.GBIndexPartition(data_GB)
#matrix to contain grain partition proportion
data_partition=np.zeros((len(index_partition),len(list_partition_index)))
column=0
for this_index in list_partition_index:
data_partition[:,column]=O_L.CustomIndexList(list(value_matrix[num_head_rows:,this_index]),index_partition)
column+=1
#valid part
GB_partition=O_L.CustomIndexList(data_GB,index_partition)
Cu_partition=O_L.CustomIndexList(data_Cu,index_partition)
Ce_partition=O_L.CustomIndexList(data_Ce,index_partition)
# len(index_valid)
#classificaiotn result
S_classification_type=[]
G_classification_type=[]
S_classification_code=[]
G_classification_code=[]
for kk in range(len(index_partition)):
#construct new object
this_grain=C_F_V.grain()
this_grain.silt_type=GB_partition[kk]
this_grain.InitMap(list(data_partition[kk,:]))
this_grain.Partition()
this_grain.Classification(Cu_partition[kk],Ce_partition[kk])
if '砂' in this_grain.silt_type:
S_classification_type.append(this_grain.classification_type)
S_classification_code.append(this_grain.classification_code)
if '砾' in this_grain.silt_type:
G_classification_type.append(this_grain.classification_type)
G_classification_code.append(this_grain.classification_code)
#filter floury soil
index_floury_soil=O_L.GBIndexFlourySoil(list_GB)
ω0_valid=O_L.CustomIndexList(list_ω0,index_floury_soil)
e0_valid=O_L.CustomIndexList(list_e0,index_floury_soil)
#filter cohesive silt
index_cohesive_silt=O_L.GBIndexCohesiveSilt(list_GB)
IL_valid=O_L.CustomIndexList(list_IL,index_cohesive_silt)
#list of classification result
#floury soil
classification_ω0+=SiltMoistureClassification(ω0_valid,num_head_rows)
classification_e0+=SiltCompactnessClassification(e0_valid,num_head_rows)
#cohesive silt
classification_IL+=ClayeySiltStateClassification(IL_valid,num_head_rows)
#GB
classification_GB+=list_GB
#note
classification_note+=list_note
# print(len(classification_GB),len(classification_note))
#grain partition result
classification_S_type+=S_classification_type
classification_G_type+=G_classification_type
classification_S_code+=S_classification_code
classification_G_code+=G_classification_code
#collect them into list
classification_list=[classification_e0,
classification_ω0,
classification_IL,
classification_GB,
classification_note,
classification_S_type,
classification_G_type,
classification_S_code,
classification_G_code]
#delete blank list
real_title_list=O_L.CustomIndexList(title_list,O_L.DeleteBlankList(classification_list))
real_classification_list=O_L.CustomIndexList(classification_list,O_L.DeleteBlankList(classification_list))
#delete nan in classification list
new_classification_list=[]
for this_classification in real_classification_list:
new_classification=[]
for item in this_classification:
if not isinstance(item,str):
if np.isnan(item):
# print('nan')
continue
new_classification.append(item)
new_classification_list.append(new_classification)
#construct a map between title and classification result
map_title_classification=dict(zip(real_title_list,new_classification_list))
#statistics result tables of classification
TitleAndClassification2Table(map_title_classification,tables_output_folder)
#statistics result figures of classification
ClassificationStatistics(map_title_classification,figures_output_folder)
def SheetsClassification(xls_path,num_head_rows,num_head_columns,list_num_head_columns=None):
print('')
print('--Sheets Classification')
plt.style.use('ggplot')
#open the excel sheet to be operated on
#formatting_info: keep the header format
workbook=xlrd.open_workbook(xls_path,formatting_info=True)
#copy former workbook
new_workbook=copy(workbook)
#construct output folder path
tables_output_folder=xls_path.replace('.xls','').replace('input','output')+'\\分类\\'
#generate output folder
O_P.GenerateFolder(tables_output_folder)
#save as
new_workbook.save(tables_output_folder+'分类结果.xls')
#construct map between sheet names and head rows
list_sheet_names=list(workbook.sheet_names())
#default
if list_num_head_columns==None:
list_num_head_columns=[num_head_columns]*len(list_sheet_names)
map_sheet_names_num_head_columns=dict(zip(list_sheet_names,list_num_head_columns))
title_list=['粉土密实度分类',
'粉土湿度分类',
'黏性土状态分类',
'土的分类',
'备注']
#traverse all sheets
for this_sheet_name in workbook.sheet_names():
#open a sheet
this_sheet=new_workbook.get_sheet(this_sheet_name)
print('')
print('...')
print('......')
print('->sheet name:',this_sheet_name)
#construct output folder path
figures_output_folder=xls_path.replace('.xls','').replace('input','output')+'\\分类\\图\\表 '+this_sheet_name+'\\'
#generate output folder
O_P.GenerateFolder(figures_output_folder)
O_P.GenerateFolder(tables_output_folder)
#Data Frame object
channel=pd.read_excel(xls_path,sheet_name=this_sheet_name)
final_head_columns,unit_list=O_H_C.HeadColumnsGeneration(channel,num_head_rows)
#all info of dataframe
value_matrix=channel.values
#delete the repetition
index_valid=O_L.ValidIndexList(value_matrix[num_head_rows:,1])
#index of line where info starts
start_info_row=num_head_rows+1
for k in range(len(final_head_columns)):
this_head=final_head_columns[k]
#search for note and make statistics
if '备' in this_head or '注' in this_head:
list_note=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
head_note=this_head
print('-->head:'+head_note)
#search for type of silt
if '分类' in this_head:
list_GB=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
head_GB=this_head
print('-->head:'+head_GB)
#search for pore ratio
if 'e0' in this_head:
list_e0=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
head_e0=this_head
print('-->head:'+head_e0)
#search for moisture content
if 'ω0' in this_head:
list_ω0=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
head_ω0=this_head
print('-->head:'+head_ω0)
#search for liquidity index
if 'IL' in this_head:
list_IL=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
head_IL=this_head
print('-->head:'+head_IL)
#list of classification result
classification_ω0=SiltMoistureClassification(list_ω0,num_head_rows)
classification_e0=SiltCompactnessClassification(list_e0,num_head_rows)
classification_IL=ClayeySiltStateClassification(list_IL,num_head_rows)
classification_GB=cp.deepcopy(list_GB)
classification_note=cp.deepcopy(list_note)
#collect them into list
classification_list=[classification_e0,
classification_ω0,
classification_IL,
classification_GB,
classification_note]
#frequency to save
list_frequency_map=[List2FrequencyMap(classification_list[ix]) for ix in range(len(title_list))]
#construct new workbook
new_workbook=xlwt.Workbook(encoding='utf-8')
#construct new sheet
new_sheet=new_workbook.add_sheet("总表")
#define the border style
borders = xlwt.Borders()
borders.left = 1
borders.right = 1
borders.top = 1
borders.bottom = 1
borders.bottom_colour=0x3A
style = xlwt.XFStyle()
style.borders = borders
#instant row
row=0
#title
for k in range(len(title_list)):
new_sheet.write(row,0,title_list[k],style)
row+=1
new_sheet.write(row,0,'总量',style)
new_sheet.write(row,1,len(classification_list[k]),style)
row+=1
# print(list_frequency_map[k])
for kk in range(len(list_frequency_map[k])):
if isinstance(list(list_frequency_map[k].keys())[kk],str):
new_sheet.write(row,0,list(list_frequency_map[k].keys())[kk],style)
else:
new_sheet.write(row,0,'其他',style)
new_sheet.write(row,1,list(list_frequency_map[k].values())[kk],style)
row+=1
row+=1
new_workbook.save(tables_output_folder+'统计总表.xls')
#plus columns
num_columns_plus=map_sheet_names_num_head_columns[this_sheet_name]-num_head_columns
#write table head
for this_title in title_list:
num_columns_plus+=1
this_sheet.write(num_head_rows,
np.shape(channel.values)[1]+num_columns_plus,
this_title,
style)
#plus columns
num_columns_plus=map_sheet_names_num_head_columns[this_sheet_name]-num_head_columns
#write classification result
for this_classification in classification_list:
num_columns_plus+=1
for i in range(len(this_classification)):
this_sheet.write(i+start_info_row,
np.shape(channel.values)[1]+num_columns_plus,
this_classification[i],
style)
#save as
new_workbook.save(tables_output_folder+'分类结果.xls')
#delete blank list
real_title_list=O_L.CustomIndexList(title_list,O_L.DeleteBlankList(classification_list))
real_classification_list=O_L.CustomIndexList(classification_list,O_L.DeleteBlankList(classification_list))
#delete nan in classification list
new_classification_list=[]
for this_classification in real_classification_list:
new_classification=[]
for item in this_classification:
if not isinstance(item,str):
if np.isnan(item):
# print('nan')
continue
new_classification.append(item)
new_classification_list.append(new_classification)
#construct a map between title and classification result
map_title_classification=dict(zip(real_title_list,new_classification_list))
#statistics result tables of classification
TitleAndClassification2Table(map_title_classification,tables_output_folder)
#statistics result figures of classification
ClassificationStatistics(map_title_classification,figures_output_folder)
def WorkbookClassification(xls_path,num_head_rows,num_head_columns):
print('')
print('--Workbook Classification')
plt.style.use('ggplot')
#open the excel sheet to be operated on
#formatting_info: keep the header format
workbook=xlrd.open_workbook(xls_path,formatting_info=True)
#construct output folder path
tables_output_folder=xls_path.replace('.xls','').replace('input','output')+'\\分类\\'
figures_output_folder=xls_path.replace('.xls','').replace('input','output')+'\\分类\\图\\总图\\'
#generate output folder
O_P.GenerateFolder(figures_output_folder)
#construct map between sheet names and head rows
list_sheet_names=list(workbook.sheet_names())
title_list=['粉土密实度分类',
'粉土湿度分类',
'黏性土状态分类',
'土的分类',
'备注']
#classification result list
classification_ω0=[]
classification_e0=[]
classification_IL=[]
classification_GB=[]
classification_note=[]
#classification result list
classification_ω0,classification_e0,classification_IL=[],[],[]
#traverse all sheets
for this_sheet_name in list_sheet_names[-1:]:
print('')
print('...')
print('......')
print('->sheet name:',this_sheet_name)
#Data Frame object
channel=pd.read_excel(xls_path,sheet_name=this_sheet_name)
final_head_columns,unit_list=O_H_C.HeadColumnsGeneration(channel,num_head_rows)
#all info of dataframe
value_matrix=channel.values
#delete the repetition
index_valid=O_L.ValidIndexList(value_matrix[num_head_rows:,1])
print('-->Valid Samples:',len(index_valid))
for k in range(len(final_head_columns)):
this_head=final_head_columns[k]
#search for note and make statistics
if '备' in this_head or '注' in this_head:
list_note=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
head_note=this_head
print('-->head:'+head_note)
#search for type of silt
if '分类' in this_head:
list_GB=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
head_GB=this_head
print('-->head:'+head_GB)
#search for pore ratio
if 'e0' in this_head:
list_e0=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
head_e0=this_head
print('-->head:'+head_e0)
#search for moisture content
if 'ω0' in this_head:
list_ω0=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
head_ω0=this_head
print('-->head:'+head_ω0)
#search for liquidity index
if 'IL' in this_head:
list_IL=O_L.CustomIndexList(list(value_matrix[num_head_rows:,k]),index_valid)
head_IL=this_head
print('-->head:'+head_IL)
#filter floury soil
index_floury_soil=O_L.GBIndexFlourySoil(list_GB)
ω0_valid=O_L.CustomIndexList(list_ω0,index_floury_soil)
e0_valid=O_L.CustomIndexList(list_e0,index_floury_soil)
#filter cohesive silt
index_cohesive_silt=O_L.GBIndexCohesiveSilt(list_GB)
IL_valid=O_L.CustomIndexList(list_IL,index_cohesive_silt)
#list of classification result
#floury soil
classification_ω0+=SiltMoistureClassification(ω0_valid,num_head_rows)
classification_e0+=SiltCompactnessClassification(e0_valid,num_head_rows)
#cohesive silt
classification_IL+=ClayeySiltStateClassification(IL_valid,num_head_rows)
#GB
classification_GB+=list_GB
#note
classification_note+=list_note
#collect them into list
classification_list=[classification_e0,
classification_ω0,
classification_IL,
classification_GB,
classification_note]
#frequency to save
list_frequency_map=[List2FrequencyMap(classification_list[ix]) for ix in range(len(title_list))]
#construct new workbook
new_workbook=xlwt.Workbook(encoding='utf-8')
#construct new sheet
new_sheet=new_workbook.add_sheet("总表")
#define the border style
borders = xlwt.Borders()
borders.left = 1
borders.right = 1
borders.top = 1
borders.bottom = 1
borders.bottom_colour=0x3A
style = xlwt.XFStyle()
style.borders = borders
#instant row
row=0
#title
for k in range(len(title_list)):
new_sheet.write(row,0,title_list[k],style)
row+=1
new_sheet.write(row,0,'总量',style)
new_sheet.write(row,1,len(classification_list[k]),style)
row+=1
# print(list_frequency_map[k])
for kk in range(len(list_frequency_map[k])):
if isinstance(list(list_frequency_map[k].keys())[kk],str):
new_sheet.write(row,0,list(list_frequency_map[k].keys())[kk],style)
else:
new_sheet.write(row,0,'其他',style)
new_sheet.write(row,1,list(list_frequency_map[k].values())[kk],style)
row+=1
row+=1
new_workbook.save(tables_output_folder+'统计总表.xls')
#delete blank list
real_title_list=O_L.CustomIndexList(title_list,O_L.DeleteBlankList(classification_list))
real_classification_list=O_L.CustomIndexList(classification_list,O_L.DeleteBlankList(classification_list))
#delete nan in classification list
new_classification_list=[]
for this_classification in real_classification_list:
new_classification=[]
for item in this_classification:
if not isinstance(item,str):
if np.isnan(item):
# print('nan')
continue
new_classification.append(item)
new_classification_list.append(new_classification)
#construct a map between title and classification result
map_title_classification=dict(zip(real_title_list,new_classification_list))
#statistics result tables of classification
TitleAndClassification2Table(map_title_classification,tables_output_folder)
#statistics result figures of classification
ClassificationStatistics(map_title_classification,figures_output_folder)
def WorkbookCondolidation(xls_path, num_head_rows, num_head_columns):
print('')
print('--Workbook Condolidation')
#plt.style.use('ggplot')
#construct output folder path
figures_output_folder = xls_path.replace('.xls', '').replace(
'input', 'output') + '\\'
#generate output folder
list_threshold = [
'0-100', '100-200', '200-400', '400-800', '800-1600', '1600-3200'
]
for this_threshold in list_threshold:
O_P.GenerateFolder(figures_output_folder + '先期固结压力\\' +
this_threshold + '\\')
#open the excel sheet to be operated on
#formatting_info: keep the header format
workbook = xlrd.open_workbook(xls_path, formatting_info=True)
#construct map between sheet names and head rows
list_sheet_names = list(workbook.sheet_names())
#data throughout workbook
Pc_high_pressure_workbook = []
#traverse all sheets
for this_sheet_name in list_sheet_names:
print('')
print('...')
print('......')
print('->sheet name:', this_sheet_name)
print('')
#Data Frame object
channel = pd.read_excel(xls_path, sheet_name=this_sheet_name)
final_head_columns, unit_list = O_H_C.HeadColumnsGeneration(
channel, num_head_rows)
#print(final_head_columns)
#all info of dataframe
value_matrix = channel.values
#delete the repetition
index_valid = O_L.ValidIndexList(value_matrix[num_head_rows:, 1])
#fetch the id of P and e
index_e_high = []
#pressure
P_high = []
index_list = [1, 2, 3]
#hole id, start depth, end depth
list_hole_id,\
list_start_depth,\
list_end_depth=[O_L.CustomIndexList(list(value_matrix[num_head_rows:,this_index]),index_valid) for this_index in index_list]
for k in range(num_head_columns, np.shape(value_matrix)[1]):
#title str
title = final_head_columns[k]
if '各级压力下的孔隙比' in title and '高压固结' in title:
print(k, title)
index_e_high.append(k)
P_high.append(float(title.strip().split(' ')[1]))
#matrix to contain grain partition proportion
data_e_high = np.zeros((len(index_valid), len(index_e_high)))
column = 0
for this_index in index_e_high:
data_e_high[:, column] = O_L.CustomIndexList(
list(value_matrix[num_head_rows:, this_index]), index_valid)
column += 1
Pc_high_pressure = []
#high pressure
for i in range(np.shape(data_e_high)[0]):
expire_nan_index_list = O_L.ExpireNanIndexList(data_e_high[i])
this_e = O_L.CustomIndexList(list(data_e_high[i]),
expire_nan_index_list)
this_P = O_L.CustomIndexList(P_high, expire_nan_index_list)
#construct new data object
that_data = data()
that_data.hole_id = list_hole_id[i]
that_data.end_depth = list_end_depth[i]
that_data.start_depth = list_start_depth[i]
that_data.porosity_compression = this_e
that_data.pressure_compression = this_P
Pc_high_pressure.append(
that_data.ConsolidationCurve(figures_output_folder +
'先期固结压力\\'))
Pc_high_pressure_sheet = []
for j in range(len(index_valid)):
if Pc_high_pressure[j] is not None:
Pc_high_pressure_sheet.append(Pc_high_pressure[j])
Pc_high_pressure_workbook += Pc_high_pressure_sheet
fig, ax = plt.subplots(figsize=(8, 8))
#for iteration
list_Pc_worbook = [Pc_high_pressure_workbook]
list_title = ['高压固结']
for k in range(len(list_title)):
#Pc, list title, folder name
Pc_workbook = list_Pc_worbook[k]
Pc_title = list_title[k]
if Pc_title == '':
continue
group = np.linspace(min(Pc_workbook), max(Pc_workbook), 20)
title_font = FontProperties(fname=r"C:\Windows\Fonts\simhei.ttf",
size=16)
label_font = FontProperties(fname=r"C:\Windows\Fonts\simhei.ttf",
size=13)
#plot histogram
plt.hist(Pc_workbook, group, histtype='bar', rwidth=0.95)
plt.title(Pc_title + '先期固结压力频数分布直方图\n样本总量:' +
str(int(len(Pc_workbook))),
FontProperties=title_font)
plt.xlabel('先期固结压力(kPa)', FontProperties=label_font)
#list of frequency
frequency = [0] * (len(group) - 1)
#mannual histogram
for this_valid_data in Pc_workbook:
for g in range(len(group) - 1):
if group[g] <= this_valid_data <= group[g + 1]:
frequency[g] += 1
break
ax.yaxis.set_major_locator(
MultipleLocator(
int(np.ceil((max(frequency) - min(frequency)) / 20))))
#set ticks
plt.tick_params(labelsize=15)
labels = ax.get_xticklabels() + ax.get_yticklabels()
#label fonts
for this_label in labels:
this_label.set_fontname('Times New Roman')
fig_path = figures_output_folder + '先期固结压力\\先期固结压力值分布.png'
plt.savefig(fig_path, dpi=300, bbox_inches='tight')
plt.close()
def MergedWorkbookStatistics(list_xls_path, num_head_rows, num_head_columns):
print('')
print('--Merged Workbook Statistics')
plt.style.use('ggplot')
#construct output folder path
tables_output_folder = list_xls_path[0].split(
'input')[0] + 'output\\颗分汇总\\统计\\'
#construct output folder path
figures_output_folder = list_xls_path[0].split(
'input')[0] + 'output\\颗分汇总\\统计\\图\\总图\\'
#generate output folder
O_P.GenerateFolder(tables_output_folder)
O_P.GenerateFolder(figures_output_folder)
#DF channels
total_channels = []
for this_xls_path in list_xls_path:
#open the excel sheet to be operated on
#formatting_info: keep the header format
workbook = xlrd.open_workbook(this_xls_path, formatting_info=True)
#construct map between sheet names and head rows
list_sheet_names = list(workbook.sheet_names())
#traverse all sheets
for this_sheet_name in list_sheet_names:
#Data Frame object
that_channel = pd.read_excel(this_xls_path,
sheet_name=this_sheet_name)
#collect it
total_channels.append(that_channel)
'''title and data throughout whole workbook'''
list_title = []
list_data = []
list_unit = []
#check if the repetition exists
total_id = []
#traverse all sheets
for channel in total_channels:
print('')
print('...')
print('......')
print('')
final_head_columns, unit_list = O_H_C.HeadColumnsGeneration(
channel, num_head_rows)
#print(final_head_columns)
#all info of dataframe
value_matrix = channel.values
title_font = FontProperties(fname=r"C:\Windows\Fonts\simhei.ttf",
size=16)
label_font = FontProperties(fname=r"C:\Windows\Fonts\simhei.ttf",
size=13)
'''complete info of statistics'''
#item names of statistics
statistic_items = ['数据量', '最大值', '最小值', '平均值', '标准差', '变异系数', '标准值']
#columns to delete
columns_to_delete = []
#no valid data
columns_void = []
#delete the repetition and remove label R
index_valid = O_L.ValidIndexList(value_matrix[num_head_rows:, 1])
total_id += (list(value_matrix[num_head_rows:, 1]))
print('-->Total Samples:', len(value_matrix[num_head_rows:, 1]))
print('-->Valid Samples:', len(index_valid))
for k in range(num_head_columns, np.shape(value_matrix)[1]):
#num of steps
n_step = 20
#fetch the data
data = list(value_matrix[num_head_rows:, k])
#unit str
unit = unit_list[k]
#title str
title = final_head_columns[k]
#valid data
list_data.append(O_L.CustomIndexList(data, index_valid))
list_title.append(title)
list_unit.append(unit)
print('')
print('...')
print('......')
print('Merged Workbook')
print('-->Total Samples:', len(total_id))
print('-->Valid Samples:', len(O_L.ValidIndexList(total_id)))
print('')
#map between title and data
map_title_data = {}
for k in range(len(list_title)):
this_title = list_title[k]
this_data = list_data[k]
if this_title in list(map_title_data.keys()):
map_title_data[this_title] += this_data
else:
map_title_data[this_title] = this_data
#new matrix to contain statistic result
statistic = np.zeros((len(map_title_data), len(statistic_items)))
#traverse the dictionary
for k in range(len(map_title_data)):
title = list(map_title_data.keys())[k]
data = list(map_title_data.values())[k]
unit = '(' + list_unit[k] + ')'
#expire particular conditions
if '分类' in title or '备' in title or '注' in title:
columns_to_delete.append(title)
continue
#expire nan
valid_data = [
float(this_data) for this_data in data
if not np.isnan(float(this_data))
]
print(k, title, unit)
if valid_data == []:
columns_void.append(title)
continue
#x coordinates
group = np.linspace(min(valid_data), max(valid_data), n_step)
#whether to process
scaled_flag = False
#exception processing
for this_tick in group:
if 'e' in str(this_tick):
factor = str(min(group)).split('e')[-1]
scaled_flag = True
break
fig, ax = plt.subplots(figsize=(8, 8))
if scaled_flag:
#mutiply a factor
valid_data = np.array(valid_data) / 10**(int(factor))
group = np.linspace(min(valid_data), max(valid_data), n_step)
#plot histogram
ax.hist(valid_data, group, histtype='bar', rwidth=0.95)
plt.title(title + ' 频数分布直方图\n样本总量:' + str(int(len(valid_data))),
FontProperties=title_font)
plt.xlabel(title + ' e' + factor + ' ' + unit,
FontProperties=label_font)
else:
#plot histogram
ax.hist(valid_data, group, histtype='bar', rwidth=0.95)
plt.title(title + ' 频数分布直方图\n样本总量:' + str(int(len(valid_data))),
FontProperties=title_font)
plt.xlabel(title + ' ' + unit, FontProperties=label_font)
#list of frequency
frequency = [0] * (len(group) - 1)
#mannual histogram
for this_valid_data in valid_data:
for g in range(len(group) - 1):
if group[g] <= this_valid_data <= group[g + 1]:
frequency[g] += 1
break
ax.yaxis.set_major_locator(
MultipleLocator(
int(np.ceil((max(frequency) - min(frequency)) / n_step))))
#set ticks
plt.tick_params(labelsize=15)
labels = ax.get_xticklabels() + ax.get_yticklabels()
#label fonts
for this_label in labels:
this_label.set_fontname('Times New Roman')
#amount
data_amount = len(valid_data)
#maximum
data_maximum = np.max(valid_data)
#minimum
data_minimum = np.min(valid_data)
#average
data_average = np.mean(valid_data)
#standard deviation
data_standard_deviation = C_F_V.StandardDeviation(valid_data)
#variable coefficient
data_variable_coefficient = C_F_V.VariableCoefficient(valid_data)
#standard value
data_standard_value = C_F_V.StandardValue(valid_data)
#give the value
statistic[k, 0] = round(data_amount, 3)
statistic[k, 1] = round(data_maximum, 3)
statistic[k, 2] = round(data_minimum, 3)
statistic[k, 3] = round(data_average, 3)
if statistic[k, 0] != 1:
statistic[k, 4] = round(data_standard_deviation, 3)
statistic[k, 5] = round(data_variable_coefficient, 3)
statistic[k, 6] = round(data_standard_value, 3)
#valid file name
if '<' in title:
title = title.replace('<', '小于')
if '>' in title:
title = title.replace('>', '大于')
fig_path = figures_output_folder + title + '.png'
#save the fig
plt.savefig(fig_path, dpi=300, bbox_inches='tight')
plt.close()
#construct new workbook
new_workbook = xlwt.Workbook(encoding='utf-8')
#construct new sheet
new_sheet = new_workbook.add_sheet("总表")
#define the border style
borders = xlwt.Borders()
borders.left = 1
borders.right = 1
borders.top = 1
borders.bottom = 1
borders.bottom_colour = 0x3A
style = xlwt.XFStyle()
style.borders = borders
#decoration
plus = 1
#title
new_sheet.write(0, 0, '特征值', style)
#header
for kk in range(len(map_title_data)):
this_title = list(map_title_data.keys())[kk]
new_sheet.write(kk + 1, 0, this_title, style)
#items
for kk in range(len(statistic_items)):
this_item = statistic_items[kk]
new_sheet.write(0, kk + plus, this_item, style)
for i in range(len(statistic_items)):
for j in range(len(map_title_data)):
if statistic[j][i] == 0:
new_sheet.write(j + 1, i + plus, '', style)
else:
try:
new_sheet.write(j + 1, i + plus, statistic[j][i], style)
#transform int to float
except:
new_sheet.write(j + plus, i + plus, float(statistic[j][i]),
style)
new_workbook.save(tables_output_folder + '统计总表.xls')
def SheetsStatistics(xls_path,
num_head_rows,
num_head_columns,
list_num_head_columns=None):
print('')
print('--Sheets Statistics')
plt.style.use('ggplot')
#open the excel sheet to be operated on
#formatting_info: keep the header format
workbook = xlrd.open_workbook(xls_path, formatting_info=True)
#copy former workbook
new_workbook = copy(workbook)
#construct output folder path
tables_output_folder = xls_path.replace('.xls', '').replace(
'input', 'output') + '\\统计\\'
#generate output folder
O_P.GenerateFolder(tables_output_folder)
#save as
new_workbook.save(tables_output_folder + '统计结果.xls')
#construct map between sheet names and head rows
list_sheet_names = list(workbook.sheet_names())
#default
if list_num_head_columns == None:
list_num_head_columns = [num_head_columns] * len(list_sheet_names)
map_sheet_names_num_head_columns = dict(
zip(list_sheet_names, list_num_head_columns))
#traverse all sheets
for this_sheet_name in workbook.sheet_names():
print('')
print('...')
print('......')
print('->sheet name:', this_sheet_name)
print('')
#construct output folder path
figures_output_folder = xls_path.replace('.xls', '').replace(
'input', 'output') + '\\统计\\图\\表 ' + this_sheet_name + '\\'
#generate output folder
O_P.GenerateFolder(figures_output_folder)
#Data Frame object
channel = pd.read_excel(xls_path, sheet_name=this_sheet_name)
final_head_columns, unit_list = O_H_C.HeadColumnsGeneration(
channel, num_head_rows)
#print(final_head_columns)
#all info of dataframe
value_matrix = channel.values
title_font = FontProperties(fname=r"C:\Windows\Fonts\simhei.ttf",
size=16)
label_font = FontProperties(fname=r"C:\Windows\Fonts\simhei.ttf",
size=13)
'''complete info of statistics'''
#item names of statistics
statistic_items = ['数据量', '最大值', '最小值', '平均值', '标准差', '变异系数', '标准值']
#new dataframe to store statistic data
statistic = cp.deepcopy(channel.iloc[:len(statistic_items)])
#columns to delete
columns_to_delete = []
#no valid data
columns_void = []
#delete the repetition
index_valid = O_L.ListWithoutRepetition(value_matrix[num_head_rows:,
1])
print('-->Total Samples:', len(value_matrix[num_head_rows:, 1]))
print('-->Valid Samples:', len(index_valid))
for k in range(num_head_columns, np.shape(value_matrix)[1]):
#num of steps
n_step = 20
#fetch the data
data = O_L.CustomIndexList(list(value_matrix[num_head_rows:, k]),
index_valid)
#unit str
unit = '(' + unit_list[k] + ')'
#title str
title = final_head_columns[k]
#expire particular conditions
if '分类' in title or '备' in title or '注' in title:
#give the value
statistic.iloc[0, k] = ''
statistic.iloc[1, k] = ''
statistic.iloc[2, k] = ''
statistic.iloc[3, k] = ''
statistic.iloc[4, k] = ''
statistic.iloc[5, k] = ''
statistic.iloc[6, k] = ''
columns_to_delete.append(title)
continue
#expire nan
valid_data = [
float(this_data) for this_data in data
if not np.isnan(float(this_data))
]
print(k, title, unit)
if valid_data == []:
#give the value
statistic.iloc[0, k] = ''
statistic.iloc[1, k] = ''
statistic.iloc[2, k] = ''
statistic.iloc[3, k] = ''
statistic.iloc[4, k] = ''
statistic.iloc[5, k] = ''
statistic.iloc[6, k] = ''
columns_void.append(title)
continue
#x coordinates
group = np.linspace(min(valid_data), max(valid_data), n_step)
#whether to process
scaled_flag = False
#exception processing
for this_tick in group:
if 'e' in str(this_tick):
factor = str(min(group)).split('e')[-1]
scaled_flag = True
break
fig, ax = plt.subplots(figsize=(8, 8))
if scaled_flag:
#mutiply a factor
valid_data = np.array(valid_data) / 10**(int(factor))
group = np.linspace(min(valid_data), max(valid_data), n_step)
#plot histogram
plt.hist(valid_data, group, histtype='bar', rwidth=0.95)
plt.title(title + ' 频数分布直方图\n样本总量:' +
str(int(len(valid_data))),
FontProperties=title_font)
plt.xlabel(title + ' e' + factor + ' ' + unit,
FontProperties=label_font)
else:
#plot histogram
plt.hist(valid_data, group, histtype='bar', rwidth=0.95)
plt.title(title + ' 频数分布直方图\n样本总量:' +
str(int(len(valid_data))),
FontProperties=title_font)
plt.xlabel(title + ' ' + unit, FontProperties=label_font)
#list of frequency
frequency = [0] * (len(group) - 1)
#mannual histogram
for this_valid_data in valid_data:
for g in range(len(group) - 1):
if group[g] <= this_valid_data <= group[g + 1]:
frequency[g] += 1
break
ax.yaxis.set_major_locator(
MultipleLocator(
int(np.ceil((max(frequency) - min(frequency)) / n_step))))
#set ticks
plt.tick_params(labelsize=15)
labels = ax.get_xticklabels() + ax.get_yticklabels()
#label fonts
for this_label in labels:
this_label.set_fontname('Times New Roman')
#amount
data_amount = len(valid_data)
#maximum
data_maximum = np.max(valid_data)
#minimum
data_minimum = np.min(valid_data)
#average
data_average = np.mean(valid_data)
#standard deviation
data_standard_deviation = C_F_V.StandardDeviation(valid_data)
#variable coefficient
data_variable_coefficient = C_F_V.VariableCoefficient(valid_data)
#standard value
data_standard_value = C_F_V.StandardValue(valid_data, '-')
#give the value
statistic.iloc[0, k] = data_amount
statistic.iloc[1, k] = data_maximum
statistic.iloc[2, k] = data_minimum
statistic.iloc[3, k] = data_average
statistic.iloc[4, k] = data_standard_deviation
statistic.iloc[5, k] = data_variable_coefficient
statistic.iloc[6, k] = data_standard_value
#valid file name
if '<' in title:
title = title.replace('<', '小于')
if '>' in title:
title = title.replace('>', '大于')
fig_path = figures_output_folder + title + '.png'
#save the fig
plt.savefig(fig_path, dpi=300, bbox_inches='tight')
plt.close()
#statistics decoration
for k in range(len(statistic_items)):
statistic.iloc[k, 1] = statistic_items[k]
#delete one column
statistic = statistic.drop(statistic.columns[0], axis=1, index=None)
#rename column
statistic = statistic.rename(columns={statistic.columns[1]: '特征值'})
#index of line where info starts
start_info_row = num_head_rows + 1
#open a sheet
this_sheet = new_workbook.get_sheet(this_sheet_name)
#total lines
num_info_rows = len(this_sheet.rows)
#blank row
one_list = [''] * (len(channel.iloc[:1].columns) + 2)
#define the border style
borders = xlwt.Borders()
borders.left = 1
borders.right = 1
borders.top = 1
borders.bottom = 1
borders.bottom_colour = 0x3A
style = xlwt.XFStyle()
style.borders = borders
#fill with blank lines
for ii in range(num_info_rows):
for jj in range(len(one_list)):
this_sheet.write(ii + start_info_row, jj, one_list[jj])
'''Data frame reads data and automatically ignores empty rows and columns'''
for i in range(statistic.shape[0]):
for j in range(statistic.shape[1]):
try:
this_sheet.write(
i + start_info_row,
j + map_sheet_names_num_head_columns[this_sheet_name],
statistic.iloc[i, j], style)
#transform int to float
except:
this_sheet.write(
i + start_info_row,
j + map_sheet_names_num_head_columns[this_sheet_name],
float(statistic.iloc[i, j]), style)
new_workbook.save(tables_output_folder + '统计结果.xls')
def WorkbookDiameter(xls_path, num_head_rows, num_head_columns):
print('')
print('-- Workbook Diameter')
#plt.style.use('ggplot')
#construct output folder path
output_folder = xls_path.replace('.xls', '').replace('input',
'output') + '\\粒径曲线\\'
#generate output folder
O_P.GenerateFolder(output_folder)
#open the excel sheet to be operated on
#formatting_info: keep the header format
workbook = xlrd.open_workbook(xls_path, formatting_info=True)
#construct map between sheet names and head rows
list_sheet_names = list(workbook.sheet_names())
#traverse all sheets
for this_sheet_name in list_sheet_names[:-1]:
O_P.GenerateFolder(output_folder + this_sheet_name + '\层划分集合\\')
O_P.GenerateFolder(output_folder + this_sheet_name + '\孔划分集合\\')
O_P.GenerateFolder(output_folder + this_sheet_name + '\层划分\\')
O_P.GenerateFolder(output_folder + this_sheet_name + '\孔划分\\')
O_P.GenerateFolder(output_folder + this_sheet_name + '\孔集合\\')
O_P.GenerateFolder(output_folder + this_sheet_name + '\孔\\')
O_P.GenerateFolder(output_folder + this_sheet_name + '\层\\')
print('')
print('...')
print('......')
print('-> sheet name:', this_sheet_name)
print('')
#Data Frame object
channel = pd.read_excel(xls_path, sheet_name=this_sheet_name)
final_head_columns, unit_list = O_H_C.HeadColumnsGeneration(
channel, num_head_rows)
# print(final_head_columns)
#all info of dataframe
value_matrix = channel.values
# '''special condition'''
# num_head_rows-=1
#delete the repetition
index_valid = O_L.ValidIndexList(value_matrix[num_head_rows:, 1])
index_diameter = []
for k in range(num_head_columns, np.shape(value_matrix)[1]):
#title str
title = final_head_columns[k]
if '颗' in title\
and '粒' in title\
and '分' in title\
and '析' in title\
and 'mm' in title:
print(k, title)
index_diameter.append(k)
index_list = [0, 1, 2, 3]
#indoor id, hole id, start depth, end depth,
#pore aperture, consolidation pressure, compression index, resilience index
list_indoor_id,\
list_hole_id,\
list_start_depth,\
list_end_depth=[O_L.CustomIndexList(list(value_matrix[num_head_rows:,this_index]),index_valid) for this_index in index_list]
#matrix to contain grain partition proportion
layers_diameter = np.zeros((len(index_valid), len(index_diameter)))
column = 0
for this_index in index_diameter:
layers_diameter[:, column] = O_L.CustomIndexList(
list(value_matrix[num_head_rows:, this_index]), index_valid)
column += 1
#construct data objects
list_layer = []
for i in range(np.shape(layers_diameter)[0]):
new_layer = data()
new_layer.hole_id = list_hole_id[i]
new_layer.indoor_id = list_indoor_id[i]
new_layer.end_depth = list_end_depth[i]
new_layer.start_depth = list_start_depth[i]
new_layer.list_diameter = cp.deepcopy(diameter_range)
new_layer.list_diameter_percentage = list(layers_diameter[i, :])
list_bool = [
np.isnan(this_percentage)
for this_percentage in new_layer.list_diameter_percentage
]
#expire list with all nan
if list_bool == len(list_bool) * [True]:
continue
#calculate the cumulative percentage
new_layer.list_diameter_percentage_cumulative = []
for s in range(len(new_layer.list_diameter_percentage)):
this_cumulative_percentage = np.sum([
this_percentage for this_percentage in
new_layer.list_diameter_percentage[s:]
if not np.isnan(this_percentage)
])
new_layer.list_diameter_percentage_cumulative.append(
this_cumulative_percentage)
list_layer.append(new_layer)
#Generate hole list
list_hole = Layer2Hole(list_layer)
#Generate range layer list
list_range_layer = Data2RangeData(list_layer)
#Generate range hole list
list_range_hole = Data2RangeData(list_hole)
#output the visualization
for this_layer in list_layer:
this_layer.DiameterCurve(output_folder + this_sheet_name + '\层\\')
for this_hole in list_hole:
this_hole.DiameterCurve(output_folder + this_sheet_name + '\孔\\')
this_hole.DiameterCurveBatch(output_folder + this_sheet_name +
'\孔集合\\')
for this_range_layer in list_range_layer:
this_range_layer.DiameterCurve(output_folder + this_sheet_name +
'\层划分\\')
this_range_layer.DiameterCurveBatch(output_folder +
this_sheet_name + '\层划分集合\\')
for this_range_hole in list_range_hole:
this_range_hole.DiameterCurve(output_folder + this_sheet_name +
'\孔划分\\')
this_range_hole.DiameterCurveBatch(output_folder +
this_sheet_name + '\孔划分集合\\')
def WorkbookResilience(xls_path, num_head_rows, num_head_columns):
print('')
print('--Workbook Resilience')
#plt.style.use('ggplot')
#construct output folder path
output_folder = xls_path.replace('.xls', '').replace(
'input', 'output') + '\\先期固结压力\\回弹\\'
#generate output folder
O_P.GenerateFolder(output_folder)
#open the excel sheet to be operated on
#formatting_info: keep the header format
workbook = xlrd.open_workbook(xls_path, formatting_info=True)
#construct map between sheet names and head rows
list_sheet_names = list(workbook.sheet_names())
#construct new workbook
new_workbook = xlwt.Workbook(encoding='utf-8')
#construct new sheet
new_sheet = new_workbook.add_sheet("总表")
#define the border style
borders = xlwt.Borders()
borders.left = 1
borders.right = 1
borders.top = 1
borders.bottom = 1
borders.bottom_colour = 0x3A
style = xlwt.XFStyle()
style.borders = borders
#traverse all sheets
for this_sheet_name in list_sheet_names:
print('')
print('...')
print('......')
print('->sheet name:', this_sheet_name)
print('')
#Data Frame object
channel = pd.read_excel(xls_path, sheet_name=this_sheet_name)
final_head_columns, unit_list = O_H_C.HeadColumnsGeneration(
channel, num_head_rows)
# print(final_head_columns)
#all info of dataframe
value_matrix = channel.values
'''special condition'''
num_head_rows -= 1
#delete the repetition
index_valid = O_L.ValidIndexList(value_matrix[num_head_rows:, 1])
#fetch the id of P and e
index_settlement_compression = []
index_settlement_resilience = []
index_settlement_recompression = []
#pressure
pressure_compression = []
pressure_resilience = []
pressure_recompression = []
for k in range(num_head_columns, np.shape(value_matrix)[1]):
#title str
title = final_head_columns[k]
if 'PC' in title:
print(k, title)
index_pressure_consolidation = k
if '压缩指数' in title:
print(k, title)
index_index_compression = k
if '回弹指数' in title:
print(k, title)
index_index_resilience = k
if '孔隙比' in title:
print(k, title)
index_porosity_original = k
if '一定压力固结沉降量' in title:
print(k, title)
index_settlement_compression.append(k)
pressure_compression.append(
float(title.strip().split(' ')[1].replace('kPa', '')))
if '回弹固结沉降量' in title:
print(k, title)
index_settlement_resilience.append(k)
pressure_resilience.append(float(title.strip().split(' ')[1]))
if '再压缩固结沉降量' in title:
if 'PC' in title or '指数' in title:
continue
print(k, title)
index_settlement_recompression.append(k)
pressure_recompression.append(
float(title.strip().split(' ')[1]))
index_list = [
0, 1, 2, 3, index_index_compression, index_index_resilience,
index_porosity_original, index_pressure_consolidation
]
#indoor id, hole id, start depth, end depth,
#pore aperture, consolidation pressure, compression index, resilience index
list_indoor_id,\
list_hole_id,\
list_start_depth,\
list_end_depth,\
list_index_compression,\
list_index_resilience,\
list_porosity_original,\
list_pressure_consolidation=[O_L.CustomIndexList(list(value_matrix[num_head_rows:,this_index]),index_valid) for this_index in index_list]
#settlement volume
list_index = [
index_settlement_compression, index_settlement_resilience,
index_settlement_recompression
]
list_data = []
for this_index_list in list_index:
#matrix to contain grain partition proportion
this_data = np.zeros((len(index_valid), len(this_index_list)))
column = 0
for this_index in this_index_list:
this_data[:, column] = O_L.CustomIndexList(
list(value_matrix[num_head_rows:, this_index]),
index_valid)
column += 1
list_data.append(this_data)
data_settlement_compression,\
data_settlement_resilience,\
data_settlement_recompression=list_data
#construct data object
for i in range(len(index_valid)):
that_data = data()
that_data.hole_id = list_hole_id[i]
that_data.indoor_id = list_indoor_id[i]
that_data.end_depth = list_end_depth[i]
that_data.start_depth = list_start_depth[i]
that_data.porosity_original = list_porosity_original[i]
that_data.pressure_consolidation = list_pressure_consolidation[i]
that_data.index_compression = list_index_compression[i]
that_data.index_resilience = list_index_resilience[i]
print('')
print('...')
print('......')
print('Hole ID:', that_data.hole_id)
'''calculate a and e of compression'''
that_data.pressure_compression = pressure_compression
that_data.settlement_compression = data_settlement_compression[i]
#difference of s and p
diff_p=np.array(that_data.pressure_compression[1:])\
-np.array(that_data.pressure_compression[:-1])
diff_s=np.array(that_data.settlement_compression[1:])\
-np.array(that_data.settlement_compression[:-1])
#first value
s_0 = that_data.settlement_compression[0]
p_0 = that_data.pressure_compression[0]
e_0 = that_data.porosity_original
'''unit of compression coeffient is 1/MPa'''
a_0 = (s_0 / p_0) * 1000 / 20 * (1 + e_0)
list_a = [a_0] + list((diff_s / diff_p) * 1000 / 20 * (1 + e_0))
list_diff_p = [p_0] + list(diff_p.ravel())
#porosity list
list_e = [e_0]
for j in range(len(list_a)):
e_next = list_e[j] - list_a[j] * list_diff_p[j] / 1000
# print('...')
# print('last e:',list_e[j])
# print('a:',list_a[j])
# print('diff p:',list_diff_p[j])
# print('next e:',e_next)
#
list_e.append(e_next)
that_data.coefficient_compression = list_a
that_data.porosity_compression = list_e
# print(that_data.pressure_compression)
# print(that_data.porosity_compression)
#compression modulus calculation
a = that_data.coefficient_compression[
that_data.pressure_compression.index(200)]
that_data.modulus_compression = (1 + e_0) / a
"""e=e0-(1+e0)ΔH/20"""
'''calculate a and e of resilience'''
that_data.pressure_resilience = [800] + pressure_resilience
that_data.settlement_resilience = data_settlement_resilience[i]
that_data.porosity_resilience = list(
e_0 -
(1 + e_0) * np.array(that_data.settlement_resilience) / 20)
#tail: add an element whose pressure is 800
e_800 = that_data.porosity_compression[
that_data.pressure_compression.index(800) + 1]
that_data.porosity_resilience.insert(0, e_800)
# print(that_data.pressure_resilience)
# print(that_data.porosity_resilience)
#resilience modulus calculation
s_100 = that_data.settlement_resilience[
that_data.pressure_resilience.index(100)]
s_200 = that_data.settlement_resilience[
that_data.pressure_resilience.index(200)]
that_data.modulus_resilience = (200 - 100) / (s_200 -
s_100) / 1000 * 20
'''calculate a and e of recompression'''
that_data.pressure_recompression = [
50
] + pressure_recompression + [1600]
that_data.settlement_recompression = data_settlement_recompression[
i]
that_data.porosity_recompression = list(
e_0 -
(1 + e_0) * np.array(that_data.settlement_recompression) / 20)
#head: add an element whose pressure is 50
e_50 = that_data.porosity_resilience[-1]
#
that_data.porosity_recompression.insert(0, e_50)
#tail: add an element whose pressure is 1600
e_1600 = that_data.porosity_compression[
that_data.pressure_compression.index(1600) + 1]
that_data.porosity_recompression.append(e_1600)
# print(that_data.pressure_recompression)
# print(that_data.porosity_recompression)
print('Pc: %dkPa' % (that_data.pressure_consolidation))
print('Cc: %.3f' % (that_data.index_compression))
print('Cs: %.3f' % (that_data.index_resilience))
print('Es1-2: %.2fMPa' % (that_data.modulus_compression))
print('Eo2-1: %.2fMPa' % (that_data.modulus_resilience))
'''print all data in sample paper'''
new_sheet.write(i * 7 + 1, 0, '室内编号', style)
new_sheet.write(i * 7 + 1, 1, that_data.indoor_id, style)
new_sheet.write(i * 7 + 1, 2, '野外编号', style)
new_sheet.write(i * 7 + 1, 3, that_data.hole_id, style)
new_sheet.write(i * 7 + 1, 4, '起始深度', style)
new_sheet.write(i * 7 + 1, 5,
str(that_data.start_depth) + 'm', style)
new_sheet.write(i * 7 + 1, 6, '终止深度', style)
new_sheet.write(i * 7 + 1, 7,
str(that_data.end_depth) + 'm', style)
new_sheet.write(i * 7 + 2, 0, '先期固结压力', style)
new_sheet.write(i * 7 + 2, 1,
'Pc=%dkPa' % (that_data.pressure_consolidation),
style)
new_sheet.write(i * 7 + 2, 2, '压缩指数', style)
new_sheet.write(i * 7 + 2, 3,
'Cc=%.3f' % (that_data.index_compression), style)
new_sheet.write(i * 7 + 2, 4, '回弹指数', style)
new_sheet.write(i * 7 + 2, 5,
'Cs=%.3f' % (that_data.index_resilience), style)
new_sheet.write(i * 7 + 2, 6, '压缩模量', style)
new_sheet.write(i * 7 + 2, 7,
'Es1-2=%.2fMPa' % (that_data.modulus_compression),
style)
new_sheet.write(i * 7 + 2, 8, '回弹模量', style)
new_sheet.write(i * 7 + 2, 9,
'Eo2-1=%.2fMPa' % (that_data.modulus_resilience),
style)
new_sheet.write(i * 7 + 3, 0, 'P (kPa)', style)
new_sheet.write(i * 7 + 3, 1, '0', style)
for j in range(len(that_data.pressure_compression)):
new_sheet.write(i * 7 + 3, j + 2,
'%d' % (that_data.pressure_compression[j]),
style)
new_sheet.write(i * 7 + 4, 0, 'ΔH (mm)', style)
new_sheet.write(i * 7 + 4, 1, '', style)
for j in range(len(that_data.settlement_compression)):
new_sheet.write(i * 7 + 4, j + 2,
'%.3f' % (that_data.settlement_compression[j]),
style)
new_sheet.write(i * 7 + 5, 0, 'e', style)
for j in range(len(that_data.porosity_compression)):
new_sheet.write(i * 7 + 5, j + 1,
'%.3f' % (that_data.porosity_compression[j]),
style)
new_sheet.write(i * 7 + 6, 0, 'a (1/MPa)', style)
new_sheet.write(i * 7 + 6, 1, '', style)
for j in range(len(that_data.coefficient_compression)):
new_sheet.write(
i * 7 + 6, j + 2,
'%.3f' % (that_data.coefficient_compression[j]), style)
that_data.ResilienceCurve(output_folder)
new_workbook.save(output_folder + '数据输出.xls')