def __init__(self, sub_group ='M03A'):
#
import Lily.ctao.hostmetadata as chmd
import Lily.ctao.database as cdb
self.sub_group = sub_group
self.hostmetadata = chmd.hostmetadata()
self.database = cdb.database(self.hostmetadata.database)
self.sub_warehouse = '{0}/crawler_ETC_{1}'.format(self.hostmetadata.warehouse, self.sub_group)
self.excel_filename = '{0}/data_clawler_ETC_{1}_list.xlsx'.format(self.hostmetadata.warehouse , self.sub_group)
self.sqlite_tablename = 'data_crawler_ETC_{0}_list'.format(self.sub_group)
self.sqlite_tablepull = 'data_crawler_ETC_{0}_pull'.format(self.sub_group)
#check/create if not exists directory
if not os.path.exists(self.sub_warehouse) :
os.mkdir(self.sub_warehouse)
#date regular expresstion YYYYMMDD
date_YYYYMMDD_pattern = '''([12]\d{3}(0[1-9]|1[0-2])(0[1-9]|[12]\d|3[01]))'''
self.url = 'http://tisvcloud.freeway.gov.tw/history/TDCS/{0}/'.format(self.sub_group) #
self.cloud_archive_pattern = 'href=\"({0}_{1}\.tar\.gz)\"' .format(self.sub_group, date_YYYYMMDD_pattern)
self.local_archive_pattern = '({0}_{1}\.tar\.gz)' .format(self.sub_group , date_YYYYMMDD_pattern)
self.check_archive_list()
def cwb_melt2():
db = cdb.database('data_crawler.sqlite')
df = db.to_dataframe('data_rdset_pylily_cwb_sensible_earthquake')
station = []
for ind, row in df.iterrows():
for st in df.at[ind, 'Stations'].split(';'):
if u'''地區最大震度''' not in st and st != '':
rdset = [
df.at[ind, 'id'], df.at[ind, 'time'],
float(df.at[ind, 'px'][4:-2]),
float(df.at[ind, 'py'][4:-2]),
float(df.at[ind, 'depth'][:-3]),
float(df.at[ind, 'ML']), df.at[ind, 'Location'],
''.join(st.split('\u3000')[:-1]),
float(st.split('\u3000')[-1:][0])
]
station.append(rdset)
df2 = pandas.DataFrame(
station, columns=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i'])
df2.to_sql(
'data_rdset_pylily_cwb_sensible_earthquake_LocalSeismicIntensity',
db.connect,
if_exists='replace',
index=False)
return
def check_docx(docx_file_name):
from Lily.ctao.database import database
from Lily.ctao.nsgstring import alnum
from Lily.ctao.hostmetadata import hostmetadata
from Lily.blacksmith.file_feature import get_feature
host = hostmetadata()
db = database(host.database)
doc = Document(docx_file_name)
feature = get_feature(docx_file_name)
excelfile = feature['path'] + '/' + feature['name'] + '.xlsx'
tablename = (feature['name'] + '_{0}')
writer = pandas.ExcelWriter( excelfile , engine = 'xlsxwriter')
counter = 1
sheetlist = []
for tab in doc.tables:
data1=[]
for row in tab.rows:
data1.append( [cell.text for cell in row.cells] )
df = pandas.DataFrame(data1)
counter = counter + 1
table_name = tablename.format( str(counter).zfill(3) )
sheetlist.append(table_name)
df.to_sql(table_name, db.connect, if_exists='replace')
df.to_excel(writer, sheet_name=table_name)
writer.save()
writer.close()
return sheetlist
def __init__(self):
self.ctaohost = hmd.hostmetadata()
today = datetime.datetime.today()
self.database_filename = self.ctaohost.warehouse + '/ctao_data_crawler_vehicledetect_{0}.sqlite'.format(
today.strftime('%Y%m'))
self.database = cdb.database(self.database_filename)
self.sub_group = 'data_crawler_vd'
self.dict_data = {
'tpec_vddata': [
'https://tcgbusfs.blob.core.windows.net/blobtisv/GetVDDATA.xml.gz',
'<ExchangeTime>(.*)</ExchangeTime>', '%Y/%m/%dT%H:%M:%S'
],
'tpec_vd': [
'https://tcgbusfs.blob.core.windows.net/blobtisv/GetVD.xml.gz',
'<vd:ExchangeTime>(.*)</vd:ExchangeTime>', '%Y/%m/%dT%H:%M:%S'
],
'nfbx_1968': [
'http://tisvcloud.freeway.gov.tw/xml/1min_incident_data_1968.xml',
'time="([^"]*)"', '%Y-%m-%d %H:%M:%S'
],
'nfbx_rlx1': [
'http://tisvcloud.freeway.gov.tw/roadlevel_value.xml.gz',
'updatetime="([^"]*)"', '%Y/%m/%d %H:%M:%S'
],
'nfbx_rlx5': [
'http://tisvcloud.freeway.gov.tw/roadlevel_value5.xml.gz',
'updatetime="([^"]*)"', '%Y/%m/%d %H:%M:%S'
],
'nfbx_vdx1': [
'http://tisvcloud.freeway.gov.tw/vd_value.xml.gz',
'updatetime="([^"]*)"', '%Y/%m/%d %H:%M:%S'
],
'nfbx_vdx5': [
'http://tisvcloud.freeway.gov.tw/vd_value5.xml.gz',
'updatetime="([^"]*)"', '%Y/%m/%d %H:%M:%S'
]
}
#all opendata source
self.list_df = pandas.DataFrame.from_dict(
self.dict_data,
orient='index',
columns=[
'url', 'exchange_time_repattern',
'exchange_time_datetimepattern'
])
self.list_df['gzip_context'] = numpy.random.bytes(1)
self.list_df['download_datetime'] = numpy.datetime64(
datetime.datetime.now())
self.list_df['exchange_datetime'] = numpy.datetime64(
datetime.datetime.now())
def cwb_melt1():
import lzma
db = cdb.database('data_crawler_cwb_earthquake_list.sqlite')
sql = '''
select max(rowid) rowid, id, lzma_html from data_crawler_cwb_sensible_earthquake_download group by id
'''
df = pandas.read_sql(sql, db.connect, index_col=['rowid'])
df = df.reindex(columns=[
'id', 'lzma_html', 'time', 'py', 'px', 'depth', 'ML', 'Location',
'Stations'
],
fill_value='')
for ind, row in df.iterrows():
# print ('melt', row[0])
html_tables = pandas.read_html(lzma.decompress(
sqlite3.Binary(row['lzma_html'])),
encoding='utf-8')
arg2 = html_tables[2]
df.at[ind, 'time'] = arg2.iat[0, 1]
df.at[ind, 'py'] = arg2.iat[1, 1]
df.at[ind, 'px'] = arg2.iat[2, 1]
df.at[ind, 'depth'] = arg2.iat[3, 1]
df.at[ind, 'ML'] = arg2.iat[4, 1]
df.at[ind, 'Location'] = arg2.iat[5, 1]
for ind2, row2 in html_tables[3].iterrows():
if isinstance(row2, pandas.core.series.Series):
for elem in row2:
if isinstance(elem, str):
df.at[ind,
'Stations'] = df.at[ind, 'Stations'] + ';' + elem
else:
if isinstance(elem, str):
df.at[ind,
'Stations'] = df.at[ind, 'Stations'] + ';' + row2
df = df.drop(columns=['lzma_html'])
df.to_sql('data_rdset_pylily_cwb_sensible_earthquake',
db.connect,
if_exists='append',
index=False)
db.connect.execute('''delete from {0} where rowid not in
(select max (rowid) from {0} group by id)'''.format(
'data_rdset_pylily_cwb_sensible_earthquake'))
db.connect.commit()
return
def cwb_crawler():
import Lily.crawler.url_string as curlstr
db = cdb.database('data_crawler_cwb_earthquake_list.sqlite')
db_target_tab = 'data_crawler_cwb_sensible_earthquake_download'
eq_dir_pa = '''https://scweb.cwb.gov.tw/earthquake/Page.aspx?ItemId=20&Date={0}'''
sub_url = '''https://scweb.cwb.gov.tw/earthquake/Page.aspx{0}'''
days = pandas.date_range(
datetime.datetime.today() - datetime.timedelta(days=90),
datetime.datetime.today() + datetime.timedelta(days=31),
freq='M')
for mon in days:
eq_list = pandas.DataFrame(
columns=['id', 'lzma_html', 'download_time'])
time.sleep(90 / 1000.0)
day_url = eq_dir_pa.format(mon.strftime('%Y%m'))
cur1 = curlstr.url_string(day_url)
arg1 = LH.fromstring(cur1.to_str())
arg2 = arg1.xpath('//tr/td/a/@href')
arg3 = {}
for elem in arg2:
if elem not in arg3 and elem[1:7] == 'ItemId':
print('download html', elem)
cur2 = curlstr.url_string(sub_url.format(elem))
arg3[elem] = True
eq_list.loc[len(eq_list)] = [
elem[22:],
cur2.to_lzma_xz(),
datetime.datetime.now()
]
eq_list.to_sql(db_target_tab,
db.connect,
if_exists='append',
index=False)
db.connect.execute('''delete from {0} where rowid not in
(select max (rowid) from {0} group by id, lzma_html)'''
.format(db_target_tab))
db.connect.commit()
return
def to_database( target_dir ):
import Lily.ctao.database as cdb
import Lily.ctao.nsgstring as nstr
import Lily.ctao.hostmetadata as chmd
host = chmd.hostmetadata()
p1 = nstr.alnum(host.platform)
h1 = nstr.alnum(host.hostname)
d1 = nstr.alnum(target_dir)
db = cdb.database(host.database)
dflist = get_all_filefeature_with_md5sum(target_dir)
table_name = '''data_rdset_filemd5_{0}_{1}_hhot_{2}'''.format(p1, h1, d1)
dflist.to_sql(table_name, db.connect, if_exists='replace', index=False)
def cwb_melt1():
import lzma
db = cdb.database('data_crawler.sqlite')
sql = '''
select Id, routeId, nameZh, nameEn, seqNo, pgp, longitude, showLon, showLat, vector from group by id
'''
df = pandas.read_sql(sql, db.connect, index_col=['rowid'])
df = df.reindex(columns=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i'],
fill_value='')
for ind, row in df.iterrows():
# print ('melt', row[0])
json_tables = pandas.read_html(lzma.decompress(
sqlite3.Binary(row['lzma_html'])),
encoding='utf-8')
arg2 = json_tables[2]
df.at[ind, 'routeId'] = arg2.iat[0, 1]
df.at[ind, 'nameZh'] = arg2.iat[1, 1]
df.at[ind, 'nameEn'] = arg2.iat[2, 1]
df.at[ind, 'seqNo'] = arg2.iat[3, 1]
df.at[ind, 'pgp'] = arg2.iat[4, 1]
df.at[ind, 'longitude'] = arg2.iat[5, 1]
for ind2, row2 in json_tables[3].iterrows():
if isinstance(row2, pandas.core.series.Series):
for elem in row2:
if isinstance(elem, str):
df.at[ind, 'i'] = df.at[ind, 'i'] + ',' + elem
else:
if isinstance(elem, str):
df.at[ind, 'i'] = df.at[ind, 'i'] + ',' + row2
#df = df.drop(columns=['lzma_html'])
df.to_sql('data_rdset_pylily', db.connect, if_exists='append', index=False)
db.connect.execute('''delete from {0} where rowid not in
(select max (rowid) from {0} group by id)'''.format(
'data_rdset_pylily'))
db.connect.commit()
return
def cwb_melt1():
import lzma
db = cdb.database('data_crawler.sqlite')
sql = '''
select Id, routeId, nameZh, nameEn, seqNo, pgp, longitude, showLon, showLat, vector from 'd:/0702/臺北市站牌.gz' group by id
'''
df = pandas.read_sql(sql, db.connect , index_col=['routeId'])
df = df.reindex ( columns=[ 'Id','routeId','nameZh', 'nameEn', 'seqNo', 'pgp', 'longitude', 'showLon','showLat','vector' ], fill_value='' )
for ind, row in df.iterrows():
# print ('melt', row[0])
arg2 = json_tables[0]
df.at[ind,'routeId'] = arg2.iat[0,1]
df.at[ind,'nameZh'] = arg2.iat[1,1]
df.at[ind,'nameEn'] = arg2.iat[2,1]
df.at[ind,'seqNo'] = arg2.iat[3,1]
df.at[ind,'pgp'] = arg2.iat[4,1]
df.at[ind,'longitude'] = arg2.iat[5,1]
df.at[ind,'showLon'] = arg2.iat[6,1]
df.at[ind,'showLat'] = arg2.iat[7,1]
df.at[ind,'vector'] = arg2.iat[8,1]
for ind2, row2 in json_tables[3].iterrows():
if isinstance(row2, pandas.core.series.Series):
for elem in row2:
if isinstance(elem,str):
df.at[ind, 'i'] = df.at[ind, 'i'] + ',' + elem
else:
if isinstance(elem,str):
df.at[ind, 'i'] = df.at[ind, 'i'] + ',' + row2
df.to_sql('data_rdset_pylily', db.connect, if_exists='append', index=False)
db.connect.execute('''delete from {0} where rowid not in
(select max (rowid) from {0} group by id)'''.format('data_rdset_pylily') )
db.connect.commit()
return
def check_time():
import Lily.ctao.database as cdb
import Lily.ctao.nsgstring as nstr
import Lily.ctao.hostmetadata as chmd
import re
host = chmd.hostmetadata()
db = cdb.database(host.database)
#^\d\d\d\d-(0?[1-9]|1[0-2])-(0?[1-9]|[12][0-9]|3[01]) (00|[0-9]|1[0-9]|2[0-3]):([0-9]|[0-5][0-9]):([0-9]|[0-5][0-9])$
patern0 = r'''(0?[1-9]|1[0-2])/(0[1-9]|[12][0-9]|3[01])'''
patern1 = r'''([0-2][0-9]):([0-5][0-9])'''
patern2 = r'''^(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])|(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])$'''
df = db.to_dataframe('hln_0206_3')
df = df.iloc[1:]
for ind, row in df.iterrows():
twoday = [day for day in re.findall(patern0, row[1])]
twotim = [tim for tim in re.findall(patern1, row[2])]
if len(twoday) == 0:
twoday = [('01', '01'), ('01', '01')]
if len(twoday) == 1:
twoday = [twoday[0], twoday[0]]
if len(twotim) == 0:
twotim = [('00', '00'), ('00', '00')]
if len(twotim) == 1:
twotim = [twotim[0], twotim[0]]
date1 = '2018-{0}-{1} {2}:{3}'.format(twoday[0][0], twoday[0][1],
twotim[0][0], twotim[0][1])
date2 = '2018-{0}-{1} {2}:{3}'.format(twoday[1][0], twoday[1][1],
twotim[1][0], twotim[1][1])
df.iloc[ind]['beg'] = datetime.datetime.strptime(date1, '%Y%m%d %H%M')
df.iloc[ind]['end'] = datetime.datetime.strptime(date2, '%Y%m%d %H%M')
def __init__(self):
import Lily.ctao.database as cdb
import Lily.ctao.hostmetadata as chmd
self.this_host = chmd.hostmetadata()
self.log_database = cdb.database(self.this_host.database)