def final_check(year=2006):
test_filename = os.path.join(DATA_SOURCES_DIR, "test.h5")
survey_filename = os.path.join(DATA_SOURCES_DIR, "survey.h5")
store = HDFStore(test_filename)
survey = HDFStore(survey_filename)
final2 = store.get('survey_2006')
print survey
finalT = survey.get('survey_2006')
varlist = [
'adeben',
'adfdap',
'amois',
'ancchom',
'ancentr',
'anciatm',
'ancrech',
'anref',
'contra',
'datant',
'dimtyp',
'ident',
'idfoy'
'noi',
'nondic',
'rabs',
'RABSP',
'RAISTP',
'raistp',
'rdem',
'retrai',
'sitant',
'sp10',
'sp11',
'stc',
'TXTPPB',
]
for i in range(0, 10):
varname = 'sp0' + str(i)
varlist.append(varname)
varlist = set(varlist)
columns = final2.columns
columns = set(columns)
print varlist.difference(columns)
print final2.loc[
final2.idfoy == 603018901,
['idfoy', 'quifoy', 'idfam', 'quifam', 'idmen', 'quimen', 'noi']
].to_string()
return
def final_check(year=2006):
test_filename = os.path.join(DATA_SOURCES_DIR,"test.h5")
survey_filename = os.path.join(DATA_SOURCES_DIR,"survey.h5")
store = HDFStore(test_filename)
survey = HDFStore(survey_filename)
final2 = store.get('survey_2006')
print survey
finalT = survey.get('survey_2006')
varlist = ['anref', 'sitant', 'adeben', 'stc', 'retrai', 'contra', 'datant', 'rabs', 'nondic', 'TXTPPB',
'ancrech', 'RAISTP', 'amois', 'adfdap', 'ancentr', 'anciatm', 'ancchom', 'ident', 'noi', 'dimtyp',
'RABSP', 'raistp', 'rdem', 'sp10', 'sp11', 'idfoy']
for i in range(0,10):
varname = 'sp0' + str(i)
varlist.append(varname)
varlist = set(varlist)
columns = final2.columns ;
columns = set(columns)
print varlist.difference(columns)
print final2.loc[final2.idfoy==603018901,
['idfoy', 'quifoy', 'idfam', 'quifam', 'idmen', 'quimen', 'noi']].to_string()
# print final2
# print finalT
# # control(final2, debug=True, verbose=True, verbose_columns=['idfam', 'quifam'])
# # control(finalT, debug=True, verbose=True, verbose_columns=['idfam', 'quifam'])
# print 'FAMILLE--------------'
# print final2.quifam.value_counts()
# print finalT.quifam.value_counts()
# print ''
# print 'FOYER------------------'
# print final2.quifoy.value_counts()
# print finalT.quifoy.value_counts()
# print ''
# print 'MENAGES-----------------'
# print final2.quimen.value_counts()
# print finalT.quimen.value_counts()
#
# print ''
# print final2.age.describe()
# print finalT.age.describe()
# # age_data = final2['age'].value_counts().reset_index()
# # age_data = age_data.sort_index(by='index', ascending='True')
# # print age_data.to_string()
# # print final2.loc[final2['quifam']==2, ['quifam', 'age']].describe()
return
def test():
'''
Validate check_consistency
'''
#===========================================================================
# from pandas import DataFrame
#res = DataFrame({af_col.name: simulation.output_table.get_value(af_col.name, af_col.entity)})
# print res
#===========================================================================
store = HDFStore(os.path.join(os.path.dirname(os.path.join(SRC_PATH,'countries','france','data','erf')),'fichiertest.h5'))
datatable = store.get('test12')
test_simu = store.get('test_simu')
print check_consistency(test_simu, datatable)
def load_df(path, default=None):
"""Load DataFrame for HDF5 store path '\logs' table"""
try:
store = HDFStore(path)
print store.keys()
df = store.get('logs')
store.close()
return df
except:
return default
def build_comparison():
directory = os.path.dirname(__file__)
fname = os.path.join(directory, H5_FILENAME)
store = HDFStore(fname)
openfisca = store.get("openfisca")
insee = store.get("insee")
print openfisca
print insee
# for year in range(2006,2010):
print openfisca.head()
openfisca.drop(0, axis=0, inplace=True)
openfisca.reset_index(inplace=True)
from pandas import DataFrame
print (openfisca.sum() - insee.sum())/insee.sum()
df = (openfisca-insee)/insee
print df
print df.to_string()
class HDFStoreDataFrame(BaseIO):
def setup(self):
N = 25000
index = tm.makeStringIndex(N)
self.df = DataFrame({'float1': np.random.randn(N),
'float2': np.random.randn(N)},
index=index)
self.df_mixed = DataFrame({'float1': np.random.randn(N),
'float2': np.random.randn(N),
'string1': ['foo'] * N,
'bool1': [True] * N,
'int1': np.random.randint(0, N, size=N)},
index=index)
self.df_wide = DataFrame(np.random.randn(N, 100))
self.start_wide = self.df_wide.index[10000]
self.stop_wide = self.df_wide.index[15000]
self.df2 = DataFrame({'float1': np.random.randn(N),
'float2': np.random.randn(N)},
index=date_range('1/1/2000', periods=N))
self.start = self.df2.index[10000]
self.stop = self.df2.index[15000]
self.df_wide2 = DataFrame(np.random.randn(N, 100),
index=date_range('1/1/2000', periods=N))
self.df_dc = DataFrame(np.random.randn(N, 10),
columns=['C%03d' % i for i in range(10)])
self.fname = '__test__.h5'
self.store = HDFStore(self.fname)
self.store.put('fixed', self.df)
self.store.put('fixed_mixed', self.df_mixed)
self.store.append('table', self.df2)
self.store.append('table_mixed', self.df_mixed)
self.store.append('table_wide', self.df_wide)
self.store.append('table_wide2', self.df_wide2)
def teardown(self):
self.store.close()
self.remove(self.fname)
def time_read_store(self):
self.store.get('fixed')
def time_read_store_mixed(self):
self.store.get('fixed_mixed')
def time_write_store(self):
self.store.put('fixed_write', self.df)
def time_write_store_mixed(self):
self.store.put('fixed_mixed_write', self.df_mixed)
def time_read_store_table_mixed(self):
self.store.select('table_mixed')
def time_write_store_table_mixed(self):
self.store.append('table_mixed_write', self.df_mixed)
def time_read_store_table(self):
self.store.select('table')
def time_write_store_table(self):
self.store.append('table_write', self.df)
def time_read_store_table_wide(self):
self.store.select('table_wide')
def time_write_store_table_wide(self):
self.store.append('table_wide_write', self.df_wide)
def time_write_store_table_dc(self):
self.store.append('table_dc_write', self.df_dc, data_columns=True)
def time_query_store_table_wide(self):
self.store.select('table_wide', where="index > self.start_wide and "
"index < self.stop_wide")
def time_query_store_table(self):
self.store.select('table', where="index > self.start and "
"index < self.stop")
def time_store_repr(self):
repr(self.store)
def time_store_str(self):
str(self.store)
def time_store_info(self):
self.store.info()
def PlotTestLogsAll(myfile):
""" Reads the specifed TestLogsAll.HDF5 file and calculates and plots relationships.
Input: File name with the results, e.g.: "aLabView2\\TestLogsAll.h5"
Output: Plots in the same directory
"""
import pandas as pd # multidimensional data analysis
import numpy as np # python numerical library
# from os import listdir
# from os.path import isdir, isfile, join
import matplotlib.pyplot as plt
# from matplotlib.backends.backend_pdf import PdfPages
# Wei's advice ===
import matplotlib.backends.backend_pdf as dpdf
from pandas import read_hdf, HDFStore, ExcelWriter
CONFIG = {'PlotIslDurHist':True, # Island durations histogram
'PlotCorDur2Pen':True, # Correlation of duration to penetration
'PlotCorDur2fStd':True, # Correlation of duration to standard deviation of frequency
}
if myfile.endswith(".h5"):
mypdffile = "".join(myfile.split(".")[0:-1] + ['.pdf'])
else:
mypdffile = myfile + '.pdf'
print "Opening: " + myfile
h5store = HDFStore(myfile)
TestLog = h5store.get('TestLogsAll')
print "Opening: " + mypdffile
pltPdf = dpdf.PdfPages(mypdffile)
# Filtering TestLog into df1
df1 = TestLog[(TestLog['tIslDur'] > 0.) &
(TestLog['NrmlFlg'] == 'y') &
(TestLog['FileName'] != 'TestLogMotorBr.xlsx') &
(TestLog['FileName'] != 'TestLogSummer01.xlsx')]
# Adding details to df1
df1['QCload0']=df1['QCload']
df1.loc[(df1[df1['QCload0']<0].index), ('QCload0')] =0.0 # df1['QCload0'][df1['QCload0']<0] = 0.0
df1['PFact']=df1['LabViewP']/(df1['LabViewP']**2 + (-df1['GEAmpQ']+df1['QCload0'])**2).apply(np.sqrt)
df1['PFactsign']='ind'
df1.loc[(df1[df1['QCload0']>df1['GEAmpQ']].index), ('PFactsign')]='cap' # df1['PFactsign'][df1['QCload0']>df1['GEAmpQ']]='cap'
df1s = df1[df1['FileName'].str.contains('Summer')]
df1w = df1[df1['FileName'].str.contains('Winter')]
if CONFIG['PlotIslDurHist']: # Island duration histogram
# Fig: Island duration histogram
fig, (ax0)= plt.subplots(nrows=1, ncols=1, figsize=(8,6))
# provision for a label
# fig.suptitle(myfile) # This titles the figure
# File info output to page top
# label= file_info[file_info.index==fname][['fiComment']].values[0][0]
# label = myfile
# ax0.annotate(label,
# xy=(0.2/6.4, 4.6/4.8), # (0.2,-0.2)inch from top left corner
# xycoords='figure fraction',
# horizontalalignment='left',
# verticalalignment='top',
# fontsize=10)
# subplots_adjust(top=4./4.8)
df2a = TestLog['tIslDur'][TestLog['tIslDur'] > 0.]
ax0.set_title('Island Duration Histogram')
df2a.plot(kind='hist', bins=20, ax=ax0, alpha=0.5) # legend=True
# df2.plot(kind='hist', bins=20, ax=ax0, alpha=0.5, legend=True)
# ax0.set_xlim([-1.5,1.5])
# ax0.set_ylim([-1.2,1.2])
ax0.grid(True, which='both')
ax0.set_xlabel('Island duration (sec)')
ax0.set_ylabel('Number of observations')
# ax0.set_aspect('equal')
# ax1.set_title('Currents Al/Be')
# ax1.plot(df2['pvIal']/1.5, df2['pvIbe']/1.5)
# ax1.set_xlim([-300,300])
# ax1.set_ylim([-240,240])
# ax1.grid(True, which='both')
# ax1.set_aspect('equal')
# ax1.set_title('Island Voltage Al/Be')
# ax1.plot(df2['Time'], df2['Island Val']/1.5/sqrt(2)/BASE['Vln'])
# ax1.plot(df2['Time'], df2['Island Vbe']/1.5/sqrt(2)/BASE['Vln'])
# ax1.set_ylim([-1.2,1.2])
# ax1.grid(True, which='both')
pltPdf.savefig() # saves fig to pdf
plt.close() # Closes fig to clean up memory
# Fig: PF actual histogram
fig, (ax0)= plt.subplots(nrows=1, ncols=1, figsize=(8,6))
df2a = df1['PFact']
ax0.set_title('Load PF actual')
df2a.plot(kind='hist', bins=20, ax=ax0, alpha=0.5) # legend=True
ax0.grid(True, which='both')
ax0.set_xlabel('PF actual')
ax0.set_ylabel('Number of observations')
pltPdf.savefig() # saves fig to pdf
plt.close() # Closes fig to clean up memory
if CONFIG['PlotCorDur2Pen']: # Island duration corelations
# Fig: duration to penetration
fig, (ax0)= plt.subplots(nrows=1, ncols=1, figsize=(8,6))
ax0.set_title('Island Duration vs. Penetration')
df2a = TestLog[['tIslDur','PrcntPen']][(TestLog['tIslDur'] > 0.)&(TestLog['NrmlFlg'] == 'y') & (TestLog['FileName'] != 'TestLogMotorBr.xlsx')]
df2b = TestLog[['tIslDur','PrcntPen']][(TestLog['tIslDur'] > 0.) & (TestLog['NrmlFlg'] == 'y') & (TestLog['FileName'] == 'TestLogMotorBr.xlsx')]
df2a1 = df2a[['tIslDur','PrcntPen']][(df2a['PrcntPen'] > 0.95) & (df2a['PrcntPen'] < 1.05)]
df2a2 = df2a[['tIslDur','PrcntPen']][(df2a['PrcntPen'] < 0.95) | (df2a['PrcntPen'] > 1.05)]
ax0.plot(df2a2['PrcntPen'], df2a2['tIslDur'], 'bo', markersize=4, markeredgecolor='none', label='CMPLDs')
ax0.plot(df2a1['PrcntPen']-df2a1['PrcntPen'].mean() + 1.0, df2a1['tIslDur'], 'bo', markersize=4, markeredgecolor='none', label='')
ax0.plot(df2b['PrcntPen'], df2b['tIslDur'], 'ro', markersize=4, markeredgecolor='none', label='MotorB')
ax0.grid(True, which='both')
ax0.set_xlabel('PV Penetration (pu)')
ax0.set_ylabel('Island Duration (sec)')
# Now add the legend with some customizations.
legend = ax0.legend(loc='upper left', shadow=False)
for label in legend.get_texts():
label.set_fontsize('small')
# plt.legend()
pltPdf.savefig() # saves fig to pdf
plt.close() # Closes fig to clean up memory
# Fig: duration to penetration with PF as a parameter
fig, (ax0)= plt.subplots(nrows=1, ncols=1, figsize=(8,6))
ax0.set_title('Island Duration vs. Penetration')
df2a = df1[['tIslDur','PrcntPen']][(df1['PFact'] < 0.97) & (df1['PFactsign'] == 'ind')]
df2a1 = df2a[['tIslDur','PrcntPen']][(df2a['PrcntPen'] > 0.95) & (df2a['PrcntPen'] < 1.05)]
df2a2 = df2a[['tIslDur','PrcntPen']][(df2a['PrcntPen'] < 0.95) | (df2a['PrcntPen'] > 1.05)]
df2b = df1[['tIslDur','PrcntPen']][(df1['PFact'] < 0.99) & (df1['PFact'] >= 0.97) & (df1['PFactsign'] == 'ind')]
df2b1 = df2b[['tIslDur','PrcntPen']][(df2b['PrcntPen'] > 0.95) & (df2b['PrcntPen'] < 1.05)]
df2b2 = df2b[['tIslDur','PrcntPen']][(df2b['PrcntPen'] < 0.95) | (df2b['PrcntPen'] > 1.05)]
df2c = df1[['tIslDur','PrcntPen']][(df1['PFact'] > 0.99)]
df2c1 = df2c[['tIslDur','PrcntPen']][(df2c['PrcntPen'] > 0.95) & (df2c['PrcntPen'] < 1.05)]
df2c2 = df2c[['tIslDur','PrcntPen']][(df2c['PrcntPen'] < 0.95) | (df2c['PrcntPen'] > 1.05)]
df2d = df1[['tIslDur','PrcntPen']][(df1['PFact'] < 0.99) & (df1['PFactsign'] == 'cap')]
df2d1 = df2d[['tIslDur','PrcntPen']][(df2d['PrcntPen'] > 0.95) & (df2d['PrcntPen'] < 1.05)]
df2d2 = df2d[['tIslDur','PrcntPen']][(df2d['PrcntPen'] < 0.95) | (df2d['PrcntPen'] > 1.05)]
print df2a['tIslDur'].count()
print df2b['tIslDur'].count()
print df2c['tIslDur'].count()
print df2d['tIslDur'].count()
ax0.plot(df2d1['PrcntPen']-df2d1['PrcntPen'].mean()+1.0, df2d1['tIslDur'], 'ko', markersize=4, markeredgecolor='none', label='')
ax0.plot(df2d2['PrcntPen'], df2d2['tIslDur'], 'ko', markersize=4, markeredgecolor='none', label='PF ~ 0.98cap')
ax0.plot(df2c1['PrcntPen']-df2c1['PrcntPen'].mean()+1.0, df2c1['tIslDur'], 'ro', markersize=4, markeredgecolor='none', label='')
ax0.plot(df2c2['PrcntPen'], df2c2['tIslDur'], 'ro', markersize=4, markeredgecolor='none', label='PF ~ 1.0')
ax0.plot(df2b1['PrcntPen']-df2b1['PrcntPen'].mean()+1.0, df2b1['tIslDur'], 'go', markersize=4, markeredgecolor='none', label='')
ax0.plot(df2b2['PrcntPen'], df2b2['tIslDur'], 'go', markersize=4, markeredgecolor='none', label='PF ~ 0.98ind')
ax0.plot(df2a1['PrcntPen']-df2a1['PrcntPen'].mean()+1.0, df2a1['tIslDur'], 'bo', markersize=4, markeredgecolor='none', label='')
ax0.plot(df2a2['PrcntPen'], df2a2['tIslDur'], 'bo', markersize=4, markeredgecolor='none', label='PF ~ 0.95ind')
ax0.grid(True, which='both')
ax0.set_xlabel('PV Penetration (pu)')
ax0.set_ylabel('Island Duration (sec)')
ax0.set_ylim([0,0.6])
# Now add the legend with some customizations.
legend = ax0.legend(loc='upper left', shadow=False)
for label in legend.get_texts():
label.set_fontsize('small')
# plt.legend()
pltPdf.savefig() # saves fig to pdf
plt.close() # Closes fig to clean up memory
# Fig: duration to penetration at PF with season as parameter
fig, (ax0)= plt.subplots(nrows=1, ncols=1, figsize=(8,6))
ax0.set_title('Island Duration vs. Penetration at PF ~ 0.95ind')
df2a = df1s[['tIslDur','PrcntPen']][(df1s['PFact'] < 0.97) & (df1s['PFactsign'] == 'ind')]
df2a1 = df2a[['tIslDur','PrcntPen']][(df2a['PrcntPen'] > 0.95) & (df2a['PrcntPen'] < 1.05)]
df2a2 = df2a[['tIslDur','PrcntPen']][(df2a['PrcntPen'] < 0.95) | (df2a['PrcntPen'] > 1.05)]
df2b = df1w[['tIslDur','PrcntPen']][(df1w['PFact'] < 0.97) & (df1w['PFactsign'] == 'ind')]
df2b1 = df2b[['tIslDur','PrcntPen']][(df2b['PrcntPen'] > 0.95) & (df2b['PrcntPen'] < 1.05)]
df2b2 = df2b[['tIslDur','PrcntPen']][(df2b['PrcntPen'] < 0.95) | (df2b['PrcntPen'] > 1.05)]
print "Summer: " + str(df2a['tIslDur'].count())
print "Winter: " + str(df2b['tIslDur'].count())
ax0.plot(df2b1['PrcntPen']-df2b1['PrcntPen'].mean()+1.0, df2b1['tIslDur'], 'bo', markersize=4, markeredgecolor='none', label='')
ax0.plot(df2b2['PrcntPen'], df2b2['tIslDur'], 'bo', markersize=4, markeredgecolor='none', label='winter')
ax0.plot(df2a1['PrcntPen']-df2a1['PrcntPen'].mean()+1.0, df2a1['tIslDur'], 'ro', markersize=4, markeredgecolor='none', label='')
ax0.plot(df2a2['PrcntPen'], df2a2['tIslDur'], 'ro', markersize=4, markeredgecolor='none', label='summer')
ax0.grid(True, which='both')
ax0.set_xlabel('PV Penetration (pu)')
ax0.set_ylabel('Island Duration (sec)')
ax0.set_ylim([0,0.6])
# Now add the legend with some customizations.
legend = ax0.legend(loc='upper left', shadow=False)
for label in legend.get_texts():
label.set_fontsize('small')
# plt.legend()
pltPdf.savefig() # saves fig to pdf
plt.close() # Closes fig to clean up memory
# Fig: duration to penetration at PF with season as parameter
fig, (ax0)= plt.subplots(nrows=1, ncols=1, figsize=(8,6))
ax0.set_title('Island Duration vs. Penetration at PF ~ 0.98ind')
df2a = df1s[['tIslDur','PrcntPen']][(df1s['PFact'] > 0.97) & (df1s['PFact'] < 0.99) & (df1s['PFactsign'] == 'ind')]
df2a1 = df2a[['tIslDur','PrcntPen']][(df2a['PrcntPen'] > 0.95) & (df2a['PrcntPen'] < 1.05)]
df2a2 = df2a[['tIslDur','PrcntPen']][(df2a['PrcntPen'] < 0.95) | (df2a['PrcntPen'] > 1.05)]
df2b = df1w[['tIslDur','PrcntPen']][(df1w['PFact'] > 0.97) & (df1w['PFact'] < 0.99) & (df1w['PFactsign'] == 'ind')]
df2b1 = df2b[['tIslDur','PrcntPen']][(df2b['PrcntPen'] > 0.95) & (df2b['PrcntPen'] < 1.05)]
df2b2 = df2b[['tIslDur','PrcntPen']][(df2b['PrcntPen'] < 0.95) | (df2b['PrcntPen'] > 1.05)]
print "Summer: " + str(df2a['tIslDur'].count())
print "Winter: " + str(df2b['tIslDur'].count())
ax0.plot(df2b1['PrcntPen']-df2b1['PrcntPen'].mean()+1.0, df2b1['tIslDur'], 'bo', markersize=4, markeredgecolor='none', label='')
ax0.plot(df2b2['PrcntPen'], df2b2['tIslDur'], 'bo', markersize=4, markeredgecolor='none', label='winter')
ax0.plot(df2a1['PrcntPen']-df2a1['PrcntPen'].mean()+1.0, df2a1['tIslDur'], 'ro', markersize=4, markeredgecolor='none', label='')
ax0.plot(df2a2['PrcntPen'], df2a2['tIslDur'], 'ro', markersize=4, markeredgecolor='none', label='summer')
ax0.grid(True, which='both')
ax0.set_xlabel('PV Penetration (pu)')
ax0.set_ylabel('Island Duration (sec)')
ax0.set_ylim([0,0.6])
# Now add the legend with some customizations.
legend = ax0.legend(loc='upper left', shadow=False)
for label in legend.get_texts():
label.set_fontsize('small')
# plt.legend()
pltPdf.savefig() # saves fig to pdf
plt.close() # Closes fig to clean up memory
# Fig: duration to penetration at PF with season as parameter
fig, (ax0)= plt.subplots(nrows=1, ncols=1, figsize=(8,6))
ax0.set_title('Island Duration vs. Penetration at PF ~ 1.0')
df2a = df1s[['tIslDur','PrcntPen']][(df1s['PFact'] > 0.99)]
df2a1 = df2a[['tIslDur','PrcntPen']][(df2a['PrcntPen'] > 0.95) & (df2a['PrcntPen'] < 1.05)]
df2a2 = df2a[['tIslDur','PrcntPen']][(df2a['PrcntPen'] < 0.95) | (df2a['PrcntPen'] > 1.05)]
df2b = df1w[['tIslDur','PrcntPen']][(df1w['PFact'] > 0.99)]
df2b1 = df2b[['tIslDur','PrcntPen']][(df2b['PrcntPen'] > 0.95) & (df2b['PrcntPen'] < 1.05)]
df2b2 = df2b[['tIslDur','PrcntPen']][(df2b['PrcntPen'] < 0.95) | (df2b['PrcntPen'] > 1.05)]
print "Summer: " + str(df2a['tIslDur'].count())
print "Winter: " + str(df2b['tIslDur'].count())
ax0.plot(df2b1['PrcntPen']-df2b1['PrcntPen'].mean()+1.0, df2b1['tIslDur'], 'bo', markersize=4, markeredgecolor='none', label='')
ax0.plot(df2b2['PrcntPen'], df2b2['tIslDur'], 'bo', markersize=4, markeredgecolor='none', label='winter')
ax0.plot(df2a1['PrcntPen']-df2a1['PrcntPen'].mean()+1.0, df2a1['tIslDur'], 'ro', markersize=4, markeredgecolor='none', label='')
ax0.plot(df2a2['PrcntPen'], df2a2['tIslDur'], 'ro', markersize=4, markeredgecolor='none', label='summer')
ax0.grid(True, which='both')
ax0.set_xlabel('PV Penetration (pu)')
ax0.set_ylabel('Island Duration (sec)')
ax0.set_ylim([0,0.6])
# Now add the legend with some customizations.
legend = ax0.legend(loc='upper left', shadow=False)
for label in legend.get_texts():
label.set_fontsize('small')
# plt.legend()
pltPdf.savefig() # saves fig to pdf
plt.close() # Closes fig to clean up memory
# Fig: duration to penetration at PF with season as parameter
fig, (ax0)= plt.subplots(nrows=1, ncols=1, figsize=(8,6))
ax0.set_title('Island Duration vs. Penetration at PF ~ 0.98cap')
df2a = df1s[['tIslDur','PrcntPen']][(df1s['PFact'] > 0.97) & (df1s['PFact'] < 0.99) & (df1s['PFactsign'] == 'cap')]
df2a1 = df2a[['tIslDur','PrcntPen']][(df2a['PrcntPen'] > 0.95) & (df2a['PrcntPen'] < 1.05)]
df2a2 = df2a[['tIslDur','PrcntPen']][(df2a['PrcntPen'] < 0.95) | (df2a['PrcntPen'] > 1.05)]
df2b = df1w[['tIslDur','PrcntPen']][(df1w['PFact'] > 0.97) & (df1w['PFact'] < 0.99) & (df1w['PFactsign'] == 'cap')]
df2b1 = df2b[['tIslDur','PrcntPen']][(df2b['PrcntPen'] > 0.95) & (df2b['PrcntPen'] < 1.05)]
df2b2 = df2b[['tIslDur','PrcntPen']][(df2b['PrcntPen'] < 0.95) | (df2b['PrcntPen'] > 1.05)]
print "Summer: " + str(df2a['tIslDur'].count())
print "Winter: " + str(df2b['tIslDur'].count())
ax0.plot(df2b1['PrcntPen']-df2b1['PrcntPen'].mean()+1.0, df2b1['tIslDur'], 'bo', markersize=4, markeredgecolor='none', label='')
ax0.plot(df2b2['PrcntPen'], df2b2['tIslDur'], 'bo', markersize=4, markeredgecolor='none', label='winter')
ax0.plot(df2a1['PrcntPen']-df2a1['PrcntPen'].mean()+1.0, df2a1['tIslDur'], 'ro', markersize=4, markeredgecolor='none', label='')
ax0.plot(df2a2['PrcntPen'], df2a2['tIslDur'], 'ro', markersize=4, markeredgecolor='none', label='summer')
ax0.grid(True, which='both')
ax0.set_xlabel('PV Penetration (pu)')
ax0.set_ylabel('Island Duration (sec)')
ax0.set_ylim([0,0.6])
# Now add the legend with some customizations.
legend = ax0.legend(loc='upper left', shadow=False)
for label in legend.get_texts():
label.set_fontsize('small')
# plt.legend()
pltPdf.savefig() # saves fig to pdf
plt.close() # Closes fig to clean up memory
if False: #
# Fig2:
fig, (ax0,ax1,ax2,ax3,ax4) = plt.subplots(nrows=5, ncols=1,
figsize=(8.5,11),
sharex=True)
fig.suptitle(fname) # This titles the figure
# ax0.set_title('Utility Bus Vabc')
# ax0.plot(df2['Time'], df2[u'Utility Bus V A'])
# ax0.plot(df2['Time'], df2[u'Utility Bus V B'])
# ax0.plot(df2['Time'], df2[u'Utility Bus V C'])
# ax0.set_ylim([-500,500])
# ax0.grid(True, which='both')
ax0.set_title('Island Bus Vabc')
ax0.plot(df2['Time'], df2[u'Island Bus V A'])
ax0.plot(df2['Time'], df2[u'Island Bus V B'])
ax0.plot(df2['Time'], df2[u'Island Bus V C'])
ax0.plot(df2['Time'], df2[u'Island Vmag'])
# ax0.set_ylim([-500,500])
ax0.grid(True, which='both')
ax1.set_title('Island Bus Frequency')
# ax1.plot(df2['Time'], df2[u'Island Wpll']/(2*pi))
ax1.plot(df2['Time'], df2[u'Island freq'])
# ax1.set_ylim([50, 70])
ax1.grid(True, which='both')
ax2.set_title('Total Load Current Iabc')
ax2.plot(df2['Time'], df2[u'RLC Passive Load I A']+df2[u'GE Load I A'])
ax2.plot(df2['Time'], df2[u'RLC Passive Load I B']+df2[u'GE Load I B'])
ax2.plot(df2['Time'], df2[u'RLC Passive Load I C']+df2[u'GE Load I C'])
# ax2.set_ylim([-100,100])
ax2.grid(True, which='both')
ax3.set_title('B1+B2 Iabc')
ax3.plot(df2['Time'], df2[u'pvIa'])
ax3.plot(df2['Time'], df2[u'pvIb'])
ax3.plot(df2['Time'], df2[u'pvIc'])
# ax3.set_ylim([-100,100])
ax3.grid(True, which='both')
ax4.set_title('Utility Iabc')
ax4.plot(df2['Time'], df2[u'Utility I A'])
ax4.plot(df2['Time'], df2[u'Utility I B'])
ax4.plot(df2['Time'], df2[u'Utility I C'])
# ax4.set_ylim([-100,100])
ax4.grid(True, which='both')
pltPdf.savefig() # Saves fig to pdf
plt.close() # Closes fig to clean up memory
# Fig4:
fig, (ax0,ax1,ax2,ax3,ax4) = plt.subplots(nrows=5, ncols=1,
figsize=(8.5,11),
sharex=True)
fig.suptitle(fname) # This titles the figure
ax0.set_title('P[kW]: Utility, Load, PV')
ax0.plot(df2['Time'], df2[u'P Utility'])
ax0.plot(df2['Time'], df2[u'P RLC']+df2[u'P AMP'])
ax0.plot(df2['Time'], df2[u'P B1']+df2[u'P B2'])
# ax0.set_ylim([-50,250])
ax0.grid(True, which='both')
ax1.set_title('Q[kVAr]: Utility, Load, PV')
ax1.plot(df2['Time'], df2[u'Q Utility'])
ax1.plot(df2['Time'], df2[u'Q RLC']+df2[u'Q AMP'])
ax1.plot(df2['Time'], df2[u'Q B1']+df2[u'Q B2'])
# ax1.set_ylim([-80,80])
ax1.grid(True, which='both')
ax2.set_title('Island Vpos, pu penetration')
ax2.plot(df2['Time'], df2[u'Island Vpos']/BASE['Vln'])
ax2.plot(df2['Time'], df2[u'B1+B2 pen'])
ax2.set_ylim([0,1.5])
ax2.grid(True, which='both')
ax3.set_title('Island Vneg, Vzero')
ax3.plot(df2['Time'], df2[u'Island Vneg']/BASE['Vln'])
ax3.plot(df2['Time'], df2[u'Island Vzer']/BASE['Vln'])
# ax3.set_ylim([0,0.25])
ax3.grid(True, which='both')
ax4.set_title('Island Vrms abc')
ax4.plot(df2['Time'], df2[u'Island Varms']/BASE['Vln'])
ax4.plot(df2['Time'], df2[u'Island Vbrms']/BASE['Vln'])
ax4.plot(df2['Time'], df2[u'Island Vcrms']/BASE['Vln'])
# ax4.set_ylim([0,1.25])
ax4.grid(True, which='both')
pltPdf.savefig() # Saves fig to pdf
plt.close() # Closes fig to clean up memory
print "Closing: " + mypdffile
pltPdf.close() # Close the pdf file
h5store.close()
return
from pandas import HDFStore
store = HDFStore('store.h5', complevel=9)
fmap = wrap_monitor(wrap_write(partial(fetch_safe, rse=args.rse), store, overwrite=args.overwrite),
monitor)
p.map(fmap, datelist)
monitor.close()
logging.info("closing file")
store.close()
logging.info("trying to open output")
store = HDFStore('store.h5')
data = []
for k in store.keys():
try:
d = store.get(k)
d['timestamp'] = pd.to_datetime(k.split("_")[1], format='%d%m%Y')
data.append(d)
except Exception as e:
print "Problem reading", k
print e
store.close()
data = pd.concat(data)
data = data.set_index(['timestamp', 'owner'])
data_to_plot = data['size'].unstack().fillna(0)
dataplot = data_to_plot.iplot(kind='area', fill=True, asFigure=True)
for d in dataplot['data']:
d['hoverinfo'] = 'text+x+name'
d['text'] = ["%.2f Gb" % xx for xx in data_to_plot[d['name']].tolist()]
data.iplot(data=dataplot['data'])
class LogSaver:
"""
self.directory : Directory structure for temp and saved files
self.log_list : List of server.log files to process
self.extra : True if log messages and thread ids are to be saved too
self.history_path : History of server.log conversions saved here
self.progress_store_path : HDF5 file that holds one DataFrame for each server.log file
self.store_path : Final DataFrame of all server.log entries saved here
self.history : History of server.log conversions
"""
FINAL = 'logs'
PROGRESS = 'progress'
HISTORY = 'history'
@staticmethod
def normalize(name):
return re.sub(r'[^a-zA-Z0-9]', '_', name)
@staticmethod
def make_name(base_name, extra):
if extra:
return base_name + '.extra'
else:
return base_name
#@staticmethod
#def temp_name(log_list, extra):
# hsh = hash(log_list)
# sgn = 'n' if hsh < 0 else 'p'
# temp = 'temp_%s%08X' % (sgn, abs(hsh))
# return LogSaver.make_name(temp, extra)
def __init__(self, store_path, log_list, extra):
self.directory = ObjectDirectory(store_path)
self.log_list = tuple(sorted(log_list))
self.extra = extra
self.history_path = self.directory.get_path(LogSaver.HISTORY, temp=True)
self.progress_store_path = self.directory.get_path(LogSaver.PROGRESS, temp=True, is_df=True)
self.store_path = self.directory.get_path(LogSaver.make_name(LogSaver.FINAL, extra),
is_df=True)
self.history = ObjectDirectory.load_object(self.history_path, {})
self.saved = False
def __repr__(self):
return '\n'.join('%s: %s' % (k,v) for k,v in self.__dict__.items())
def __str__(self):
return '\n'.join([repr(self), '%d log files' % len(self.log_list)])
def save_all_logs(self, force=False):
if os.path.exists(self.store_path):
final_store = HDFStore(self.store_path)
print 'Keys: %s' % final_store
final_store.close()
return
if not force:
assert not os.path.exists(self.history_path), '''
%s exists but %s does not.
There appears to be a conversion in progress.
-f forces conversion to complete.
''' % (self.history_path, self.store_path)
self.directory.make_dir_if_necessary(self.progress_store_path)
self.progress_store = HDFStore(self.progress_store_path)
for path in self.log_list:
self.save_log(path)
self.check()
print '--------'
print 'All tables in %s' % self.progress_store_path
print self.progress_store.keys()
print '--------'
def get_log(path):
try:
return self.progress_store.get(LogSaver.normalize(path))
except Exception as e:
print
print path
raise e
df_list = [get_log(path) for path in self.log_list]
self.progress_store.close()
print 'Closed %s' % self.progress_store_path
df_all = pd.concat(df_list)
print 'Final list has %d entries' % len(df_all)
final_store = HDFStore(self.store_path)
final_store.put('logs', df_all)
print 'Keys: %s' % final_store
final_store.close()
print 'Closed %s' % self.store_path
# Save the history in a corresponding file
self.directory.save('history', self.history)
print 'Saved history'
self.saved = True
def test_store(self):
final_store = HDFStore(self.store_path)
print '----'
print final_store.keys()
print '-' * 80
logs = final_store['/logs']
print type(logs)
print len(logs)
print logs.columns
final_store.close()
def cleanup(self):
os.remove(self.progress_store_path)
os.remove(self.history_path)
def delete(self):
os.remove(self.store_path)
def save_log(self, path):
"""Return a pandas DataFrame for all the valid log entry lines in log_file
The index of the DataFrame are the uniqufied timestamps of the log entries
"""
if path in self.history:
return
print 'Processing %s' % path,
start = time.time()
header, df = load_log(path, extra=self.extra)
if df is None:
print 'Could not process %s' % path
return
self.progress_store.put(LogSaver.normalize(path), df)
load_time = time.time() - start
self.history[path] = {
'start': df.index[0],
'end': df.index[-1],
'load_time': int(load_time),
'num': len(df),
'header': header
}
ObjectDirectory.save_object(self.history_path, self.history)
del df
print { k:v for k,v in self.history[path].items() if k != 'header' },
print '%d of %d' % (len(self.history), len(self.log_list))
def check(self):
history = ObjectDirectory.load_object(self.history_path, {})
sorted_keys = history.keys()
sorted_keys.sort(key=lambda k: history[k]['start'])
print '-' * 80
print 'Time range by log file'
for i, path in enumerate(sorted_keys):
hist = history[path]
print '%2d: %s --- %s : %s' % (i, hist['start'], hist['end'], path)
path0 = sorted_keys[0]
for path1 in sorted_keys[1:]:
hist0,hist1 = history[path0],history[path1]
assert hist0['end'] < hist1['start'], '''
-----------
%s %s
start: %s
end : %s
-----------
%s %s
hist1['start']
start: %s
end : %s
''' % (
path0, hist0, hist0['start'], hist0['end'],
path1, hist1, hist1['start'], hist1['end'])
