def getDF():
site = sys.argv[1]
fdate = sys.argv[2]
tdate = sys.argv[3]
nid = sys.argv[4]
# site = 'agbsb'
# fdate = '2016-04-10'
# tdate = '2016-04-11'
# nid = '2'
df = CSR.getsomsrawdata(site + 'm', nid, fdate, tdate)
df = pd.DataFrame(df, columns=['raw'])
#df.index = df.ts
#print df
dfajson = df.reset_index().to_json(orient="records", date_format='iso')
dfajson = dfajson.replace("T", " ").replace("Z", "").replace(".000", "")
print dfajson
def getDF():
site = sys.argv[1]
fdate = sys.argv[2]
tdate = sys.argv[3]
nid = sys.argv[4]
#site = 'agbsb'
#fdate = '2016-04-10'
#tdate = '2016-04-11'
#nid = '2'
df= CSR.getsomsrawdata(site+'m',nid,fdate,tdate)
df = pd.DataFrame(df,columns=['raw'])
#df.index = df.ts
#print df
dfajson = df.reset_index().to_json(orient="records",date_format='iso')
dfajson = dfajson.replace("T"," ").replace("Z","").replace(".000","")
print dfajson
print '>> Error parsing database'
columns = cur.fetchall()
print columns
for column in columns:
columnName = column[0]
if len(columnName) <= 6:
#Get list of nodes for column
queryNodes = 'SELECT DISTINCT id FROM %s WHERE id > 0 AND id < 40 ORDER BY id' % (columnName)
cur.execute(queryNodes)
nodes = cur.fetchall()
print nodes
for nodeData in nodes:
node = nodeData[0]
print "%s: %s" % (columnName, node)
# test = soms.getsomscaldata(columnName, node, fdate, tdate)
test = soms.getsomsrawdata(columnName, node, fdate, tdate)
#print test
print "row count: %s" % (len(test.index))
pass
except IndexError:
print '>> Error in writing extracting database data to files..'
#test = soms.getsomsrawdata(column, gid, fdate, tdate)
#print test
v_a2.rename(columns={'v':'v2'}, inplace=True)
v_a2=v_a2.resample('30Min',base=0)
x=pd.concat([df,v_a1.v1,v_a2.v2],axis=1,ignore_index=True)
x.columns=['mval1','v1','v2']
x=x.resample('30Min',base=0)
df=x.mval1[((x.v1>3.2) & (x.v1<3.4) & (x.v2>3.2) & (x.v2<3.4)) | (x.v1.isnull() & x.v2.isnull())]
df = df.resample('30Min',base=0)
return df
column = 'gaasb'
node = 2
mode = 0
fdate='2016-04-01'
if mode==0:
df = CSR.getsomsrawdata(column+'m',node,fdate)
else:
df = CSR.getsomscaldata(column+'m',node,fdate)
f,ax=plt.subplots(4,sharex=True)
ax[0].plot(df,color='b')
out=seek_outlier(df,column,node,mode)
#df[out].plot(style='ro')
#plt.subplot(312)
filtered=f_outlier(df,column,node,mode)
ax[1].plot(filtered,color='m')
#filtered.plot(color='m')
filtered2= f_undervoltage(filtered,column,node,mode)
ax[2].plot(filtered2,color='g')
def heatmap(col, t_timestamp, t_win='1d'):
df_merge = pd.DataFrame()
smin = 0
smax = 255
mini = 0
maxi = 1300
if (t_win == '1d'):
for_base = 0
timew = 24
interval = '30T'
elif (t_win == '3d'):
if (int(t_timestamp[14] + t_timestamp[15]) == 30):
# print int(t_timestamp[14]+t_timestamp[15])
for_base = 90
timew = 72
interval = '120T'
else:
for_base = 60
timew = 72
interval = '120T'
elif (t_win == '30d'):
for_base = 30
timew = 720
interval = 'D'
else:
print "invalid monitoring window"
f_timestamp = pd.to_datetime(
pd.to_datetime(t_timestamp) - timedelta(hours=timew))
t_timestamp = pd.to_datetime(
pd.to_datetime(t_timestamp) + timedelta(minutes=30))
if (len(col) > 4):
query = "select num_nodes from senslopedb.site_column_props where name = '%s'" % col
node = qs.GetDBDataFrame(query)
for node_num in range(1, int(node.num_nodes[0]) + 1):
df = CSR.getsomscaldata(col,
node_num,
f_timestamp,
t_timestamp,
if_multi=True)
if (df.empty == False):
df = df.reset_index()
df.ts = pd.to_datetime(df.ts)
df.index = df.ts
df.drop('ts', axis=1, inplace=True)
df = df[((df < 1300) == True) & ((df > 0) == True)]
df['cval'] = df['mval1'].apply(lambda x: (x - mini) * smax /
(maxi) + smin)
dfrs = pd.rolling_mean(
df.resample(interval, base=for_base),
window=3,
min_periods=1) #mean for one day (dataframe)
if 'mval1' in df.columns:
dfrs = dfrs.drop('mval1', axis=1)
# n=len(dfrs)-1
dfrs = dfrs.reset_index(0)
# dfp=dfrs[n-timew:n]
# dfp = dfp.reset_index()
df_merge = pd.concat([df_merge, dfrs], axis=0)
df_merge['ts'] = df_merge.ts.astype(object).astype(str)
dfjson = df_merge.to_json(orient='records', double_precision=0)
print dfjson
else:
return 'v1'
def heatmap(col, t_timestamp, t_win = '1d'):
df_merge = pd.DataFrame()
smin=0; smax=255;mini = 0; maxi = 1300
if (t_win == '1d'):
for_base = 0
timew = 24
interval = '30T'
elif (t_win == '3d'):
if (int(t_timestamp[14]+t_timestamp[15]) == 30):
# print int(t_timestamp[14]+t_timestamp[15])
for_base = 90
timew = 72
interval = '120T'
else:
for_base = 60
timew = 72
interval = '120T'
elif (t_win == '30d'):
for_base = 30
timew = 720
interval = 'D'
else:
print "invalid monitoring window"
f_timestamp = pd.to_datetime(pd.to_datetime(t_timestamp) - timedelta(hours = timew))
t_timestamp = pd.to_datetime(pd.to_datetime(t_timestamp) + timedelta(minutes = 30))
if(len(col)>4):
query = "select num_nodes from senslopedb.site_column_props where name = '%s'" %col
node = qs.GetDBDataFrame(query)
for node_num in range (1,int(node.num_nodes[0])+1):
df = CSR.getsomscaldata(col,node_num,f_timestamp,t_timestamp, if_multi = True)
if (df.empty == False):
df = df.reset_index()
df.ts=pd.to_datetime(df.ts)
df.index=df.ts
df.drop('ts', axis=1, inplace=True)
df=df[((df<1300) == True) & ((df>0)==True)]
df['cval'] = df['mval1'].apply(lambda x:(x- mini) * smax / (maxi) + smin)
dfrs =pd.rolling_mean(df.resample(interval, base= for_base), window=3, min_periods=1) #mean for one day (dataframe)
if 'mval1' in df.columns:
dfrs = dfrs.drop('mval1', axis=1)
# n=len(dfrs)-1
dfrs = dfrs.reset_index(0)
# dfp=dfrs[n-timew:n]
# dfp = dfp.reset_index()
df_merge = pd.concat([df_merge, dfrs], axis = 0)
df_merge['ts'] = df_merge.ts.astype(object).astype(str)
dfjson = df_merge.to_json(orient='records' , double_precision=0)
print dfjson
else:
return 'v1'
from datetime import datetime as dt
import sqlalchemy
from sqlalchemy import create_engine
import requests
path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../updews-pycodes/Analysis/'))
if not path in sys.path:
sys.path.insert(1, path)
del path
import SomsRangeFilter
import ConvertSomsRaw as CSR
site = sys.argv[1]
fdate = sys.argv[2]
tdate = sys.argv[3]
nid = sys.argv[4]
mode = sys.argv[5]
# site = 'gaasb'
# nid = 2
# mode = 1
# fdate = '2016-04-01'
# tdate ='2016-04-05'
if mode == 0:
df = CSR.getsomsrawdata(site,int(nid),fdate,tdate)
else:
df = CSR.getsomscaldata(site,int(nid),fdate,tdate)
df_filt = SomsRangeFilter.f_outlier(df,site,int(nid),int(mode))
dfajson = df_filt.reset_index().to_json(orient='records',date_format='iso')
dfajson = dfajson.replace("T"," ").replace("Z","").replace(".000","")
print dfajson
# print type(fdate)
from datetime import datetime as dt
import sqlalchemy
from sqlalchemy import create_engine
import requests
path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../updews-pycodes/Analysis/Soms'))
if not path in sys.path:
sys.path.insert(1, path)
del path
import SomsRangeFilter
import ConvertSomsRaw as CSR
#site = "agbsb"
#fdate = "2016-01-28"
#tdate = "2017-12-29"
#mode = '0'
site = sys.argv[1]
fdate = sys.argv[2]
tdate = sys.argv[3]
mode = sys.argv[4]
if mode == '0':
df = CSR.getsomsrawdata(column=site, fdate=fdate, tdate=tdate, if_multi=True )
else:
df = CSR.getsomscaldata(column=site, fdate=fdate, tdate=tdate ,if_multi = True)
df_filt = SomsRangeFilter.f_outlier(df,site,int(mode))
dfajson = df_filt.reset_index().to_json(orient='records',date_format='iso')
dfajson = dfajson.replace("T"," ").replace("Z","").replace(".000","")
print dfajson
from datetime import datetime as dt
import sqlalchemy
from sqlalchemy import create_engine
import requests
path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../updews-pycodes/Analysis/'))
if not path in sys.path:
sys.path.insert(1, path)
del path
import SomsRangeFilter
import ConvertSomsRaw as CSR
site = sys.argv[1]
fdate = sys.argv[2]
tdate = sys.argv[3]
nid = sys.argv[4]
mode = sys.argv[5]
#site = 'laysb'
#nid = '2'
#mode = '0'
#fdate = '2016-01-15'
#tdate ='2016-01-18'
if mode == '0':
df = CSR.getsomsrawdata(site,int(nid),fdate,tdate)
else:
df = CSR.getsomscaldata(site,int(nid),fdate,tdate)
df_filt = SomsRangeFilter.f_outlier(df,site,int(nid),int(mode))
dfajson = df_filt.reset_index().to_json(orient='records',date_format='iso')
dfajson = dfajson.replace("T"," ").replace("Z","").replace(".000","")
print dfajson
# print type(fdate)
from datetime import timedelta as td
from datetime import datetime as dt
import sqlalchemy
from sqlalchemy import create_engine
path = os.path.abspath(
os.path.join(os.path.dirname(__file__),
'../../updews-pycodes/Analysis/Soms'))
if not path in sys.path:
sys.path.insert(1, path)
del path
import ConvertSomsRaw as CSR
import SomsRangeFilter
site = sys.argv[1]
fdate = sys.argv[2]
tdate = sys.argv[3]
nid = sys.argv[4]
mode = sys.argv[5]
#site = 'laysa'
#fdate = '2013-04-02'
#tdate = '2016-04-10'
#nid = 1
df = CSR.getsomsrawdata(column=site,
gid=nid,
fdate=fdate,
tdate=tdate,
if_multi=False)
dfajson = df.reset_index().to_json(orient="records", date_format='iso')
dfajson = dfajson.replace("T", " ").replace("Z", "").replace(".000", "")
print dfajson
column = "laysa"
fdate='2013-01-1'
tdate='2016-08-1'
#for a in range(1,17,1):
#def heatmap(column,fdate,tdate ):
df_merge = pd.DataFrame()
days = 30
query = "select num_nodes from senslopedb.site_column_props where name = '%s'" %column
node = qs.GetDBDataFrame(query)
for node_num in range (1,int(node.num_nodes[0])):
df = CSR.getsomscaldata(column,node_num,fdate,tdate)
# df=pd.read_csv('C:/Users/JosephRyan/Desktop/SENSLOPE/FILTERS/tester/output/'+ str(columns[a])+'_CAL.csv')
# print df
df = df.reset_index()
df.ts=pd.to_datetime(df.ts)
#df.drop('ts', axis=1, inplace=True)
df.index=df.ts
df.drop('ts', axis=1, inplace=True)
#pd.to_datetime(df.index)
#df=df[((df<5000) == True) & ((df>2000)==True)]
df=df[((df<1300) == True) & ((df>0)==True)]
dfrs =pd.rolling_mean(df.resample('1D'), window=3, min_periods=1) #mean for one day (dataframe)
dfrs.rename(columns={'mval1':node_num}, inplace=True)
#wmean=pd.ewma(df,span=48,min_periods=1)
#ncols=range(1,int(nodecount[6])+1)
