def target(self, xVars, xParaCombos, targetVars, target_fx):
'''
Method that builds an overview table for the jobs that were run and then returns the values.
'''
logger.debug(
'entering idRiskParaSearchParallel.target. Computing target for xVar = %s, xVals = \n%s, targetVars = %s'
% (xVars, xParaCombos, targetVars))
# Each line in the resulting table (overviewSimu) represents one paraCombo
overviewTable = createOverviewTable(resultDir=self.optimFolder,
outFile='simulation.out',
exportFileName='overviewSimu',
sortCols=[],
orderCols=[],
verb='INFO')
if overviewTable == None:
logger.critical('overviewTable empty')
return None
xVars = copy.deepcopy(xVars)
for ixVar, xVar in enumerate(xVars):
if xVar == 'beta':
xVars[ixVar] = 'beta_disc'
else:
xVars[ixVar] = xVar
# print table
#overviewTable.order(['dy'] + xVars)
#overviewTable.sort(['dy'] + xVars)
logger.info('table for job: %s' % self.jobname)
logger.info(overviewTable)
# Could replace this with a check that every xVar value is in the table, then output the two relevant columns.
result = np.array([])
for ixParaCombo, xParaCombo in enumerate(xParaCombos):
overviewTableSub = copy.deepcopy(overviewTable)
for xVar, xVal in zip(xVars, xParaCombo):
overviewTableSub = overviewTableSub.getSubset(
np.abs(overviewTableSub[xVar] - xVal) < 1.e-8)
if len(overviewTableSub) == 0:
logger.critical(
'Cannot find value for xVal %s, i.e. overviewTableSub empty. Did you set the pythonPath?'
% xVal)
result = np.append(result, None)
# return None
elif len(overviewTableSub) == 1:
#result.append(np.linalg.norm(np.array([ overviewTableSub[targetVars[ixVar]][0] for ixVar, var in enumerate(xVars) ]) - target_fx))
result = np.append(result, [
overviewTableSub[targetVars[ixVar]][0]
for ixVar, var in enumerate(xVars)
])
logger.info('found xParaCombo = %s, result = %s' %
(xParaCombo, result[ixParaCombo]))
else:
logger.critical('Cannot find unique value for xVal %s' % xVal)
os._exit(1)
logger.info('result for variables %s: xParaCombos = %s. Values = %s' %
(xVars, xParaCombos, result))
logger.info('done target')
logger.info('')
return result
def target(self, xVars, xParaCombos, targetVars, target_fx):
"""
Method that builds an overview table for the jobs that were run and then returns the values.
"""
logger.debug(
"entering idRiskParaSearchParallel.target. Computing target for xVar = %s, xVals = \n%s, targetVars = %s"
% (xVars, xParaCombos, targetVars)
)
# Each line in the resulting table (overviewSimu) represents one paraCombo
overviewTable = createOverviewTable(
resultDir=self.optimFolder,
outFile="simulation.out",
exportFileName="overviewSimu",
sortCols=[],
orderCols=[],
verb="INFO",
)
if overviewTable == None:
logger.critical("overviewTable empty")
return None
xVars = copy.deepcopy(xVars)
for ixVar, xVar in enumerate(xVars):
if xVar == "beta":
xVars[ixVar] = "beta_disc"
else:
xVars[ixVar] = xVar
# print table
# overviewTable.order(['dy'] + xVars)
# overviewTable.sort(['dy'] + xVars)
logger.info("table for job: %s" % self.jobname)
logger.info(overviewTable)
# Could replace this with a check that every xVar value is in the table, then output the two relevant columns.
result = np.array([])
for ixParaCombo, xParaCombo in enumerate(xParaCombos):
overviewTableSub = copy.deepcopy(overviewTable)
for xVar, xVal in zip(xVars, xParaCombo):
overviewTableSub = overviewTableSub.getSubset(np.abs(overviewTableSub[xVar] - xVal) < 1.0e-8)
if len(overviewTableSub) == 0:
logger.critical(
"Cannot find value for xVal %s, i.e. overviewTableSub empty. Did you set the pythonPath?" % xVal
)
result = np.append(result, None)
# return None
elif len(overviewTableSub) == 1:
# result.append(np.linalg.norm(np.array([ overviewTableSub[targetVars[ixVar]][0] for ixVar, var in enumerate(xVars) ]) - target_fx))
result = np.append(result, [overviewTableSub[targetVars[ixVar]][0] for ixVar, var in enumerate(xVars)])
logger.info("found xParaCombo = %s, result = %s" % (xParaCombo, result[ixParaCombo]))
else:
logger.critical("Cannot find unique value for xVal %s" % xVal)
os._exit(1)
logger.info("result for variables %s: xParaCombos = %s. Values = %s" % (xVars, xParaCombos, result))
logger.info("done target")
logger.info("")
return result
def target(self, xVars, xParaCombos, targetVars, target_fx):
'''
Method that builds an overview table for the jobs that were run and then returns the values.
'''
logger.debug('entering idRiskParaSearchParallel.target. Computing target for xVar = %s, xVals = %s' % (xVars, xParaCombos))
# Each line in the resulting table (overviewSimu) represents one paraCombo
## if xVar == 'wBarLower' and xVals[0] == -0.10085714:
## print 'here'
overviewTable = createOverviewTable(resultDir = self.optimFolder, outFile = 'simulation.out', exportFileName = 'overviewSimu', sortCols = [], orderCols = [], verb = 'INFO')
if overviewTable == None:
logger.critical('overviewTable empty')
return None
# os._exit(1)
xVars = copy.deepcopy(xVars)
for ixVar, xVar in enumerate(xVars):
if xVar == 'beta':
xVars[ixVar] = 'beta_disc'
else:
xVars[ixVar] = xVar
# print table
#overviewTable.order(['dy'] + xVars)
#overviewTable.sort(['dy'] + xVars)
logger.info('table for job: %s' % self.jobname)
logger.info(overviewTable)
# Could replace this with a check that every xVar value is in the table, then output the two relevant columns.
result = np.array([])
for xParaCombo in xParaCombos:
overviewTableSub = copy.deepcopy(overviewTable)
for xVar, xVal in zip(xVars, xParaCombo):
overviewTableSub = overviewTableSub.getSubset( np.abs( overviewTableSub[xVar] - xVal ) < 1.e-8 )
if len(overviewTableSub) == 0:
logger.critical('Cannot find value for xVal %s, i.e. overviewTableSub empty. Did you set the pythonPath?' % xVal)
return None
elif len(overviewTableSub) == 1:
#result.append(np.linalg.norm(np.array([ overviewTableSub[targetVars[ixVar]][0] for ixVar, var in enumerate(xVars) ]) - target_fx))
result = np.append(result, [ overviewTableSub[targetVars[ixVar]][0] for ixVar, var in enumerate(xVars) ])
else:
logger.critical('Cannot find unique value for xVal %s' % xVal)
os._exit(1)
logger.info('Computed target: Returning result to solver\n')
return result
def momentPlots(baseName, path, xVar, overlay, conditions = {}, xVarRange = None, figureFile = None, type = 'presentation'):
if os.path.isdir(path):
# if not tableFile:
tableFile = os.path.join(path, 'overviewSimu')
elif os.path.isfile(path):
tableFile = path
else:
logger.critical('path is not a directory or a table file.')
logger.info('\nCreating plot for path %s with conditions %s' % (path, conditions))
# make sure xVar is not conditioned upon -> we want to use it for xaxis.
if xVar in conditions.keys():
conditions.pop(xVar)
# create a file name for emerging plots
moments = ['EP', 'e_rs', 'e_rb', 'sigma_rs', 'sigma_rb']
if not figureFile:
paraMoments = moments + conditions.keys()
# paraMomentVals = map(str, [EP, e_rs, e_rb, sigma_rs, sigma_rb] + conditions.values())
paraMomentVals = map(str, overlay.values() + conditions.values())
paraString = '_'.join(map(''.join, zip(paraMoments, paraMomentVals)))
fileName = baseName + 'xVar' + xVar + '__' + paraString
fileName = fileName.replace('.', '') # kill all dots for latex
figureFile = os.path.join('/mnt/resultsLarge/idRisk/plots', fileName + '.eps')
# create table if not existent already
if not os.path.isfile(tableFile) or not os.path.getsize(tableFile):
tablePath = os.path.dirname(tableFile)
createOverviewTable(tablePath)
# read and adjust table
overviewTab = tableDict.fromTextFile(tableFile, None, 20, 5)
logger.debug('Read the following table: ')
# Rename variables
logger.debug(overviewTab)
overviewTab.rename('eR_a', 'e_rs')
overviewTab.rename('std_R_a(quar)', 'sigma_rs')
overviewTab.rename('eR_b', 'e_rb')
overviewTab.rename('std_R_b(quar)', 'sigma_rb')
overviewTab.rename('rP_ana', 'EP')
# Scale to yearly values
overviewTab['e_rs'] = overviewTab['e_rs'] ** 4
overviewTab['e_rb'] = overviewTab['e_rb'] ** 4
overviewTab['EP'] = overviewTab['e_rs'] - overviewTab['e_rb']
overviewTab['sigma_rs'] = overviewTab['sigma_rs'] * 2
overviewTab['sigma_rb'] = overviewTab['sigma_rb'] * 2
# make consistency check
if not xVar in overviewTab.cols:
print 'xVar %s not found in table' % xVar
sys.exit()
overviewTab.order([xVar] + conditions.keys() + moments)
overviewTab.sort(conditions.keys() + [xVar])
logger.debug('table after order and sort')
logger.debug(overviewTab)
# Copy table and impose conditions
overviewTabSub = copy.deepcopy(overviewTab)
logger.debug('keep only relevant variables')
varsToKeep = ['EP', 'e_rs', 'e_rb', 'sigma_rs', 'sigma_rb']
if not varsToKeep.count(xVar): varsToKeep.insert(0, xVar)
overviewTabSub.keep(varsToKeep)
# Subset y series
for cond in conditions:
overviewTabSub = overviewTabSub.getSubset( (overviewTabSub[cond] == conditions[cond] ))
# Subset x range
if xVarRange:
overviewTabSub.subset( (overviewTabSub[xVar] >= xVarRange[0]) & (overviewTabSub[xVar] <= xVarRange[1]) )
# make sure the table is not empty
if len(overviewTabSub) == 0:
logger.critical('table empty. Returning')
return 1
logger.info('table after imposing conditions:')
logger.info(overviewTabSub)
x = overviewTabSub[xVar]
# Scale variables to percentage points
overviewTabSub['EP'] = overviewTabSub['EP'] * 100
overviewTabSub['e_rs'] = (overviewTabSub['e_rs'] -1 ) * 100
overviewTabSub['e_rb'] = (overviewTabSub['e_rb'] -1 ) * 100
overviewTabSub['sigma_rs'] = overviewTabSub['sigma_rs'] * 100
overviewTabSub['sigma_rb'] = overviewTabSub['sigma_rb'] * 100
logger.info('Final plot data')
logger.info(overviewTabSub)
# make plot
fig = plt.figure()
ax = fig.add_subplot(111)
# set formatting for plots
if type == 'presentation':
colors = {'EP': 'k', 'e_rs': 'b', 'e_rb': 'g', 'sigma_rs': 'r', 'sigma_rb': 'c'}
linewidths = { 'EP': 3, 'e_rs': 3, 'e_rb': 3, 'sigma_rs': 3, 'sigma_rb': 3 }
linestyles = { 'EP': '-', 'e_rs': '-', 'e_rb': '-', 'sigma_rs': '-', 'sigma_rb': '-' }
elif type == 'paper':
colors = {'EP': 'k', 'e_rs': 'b', 'e_rb': 'g', 'sigma_rs': 'r', 'sigma_rb': 'c'}
linewidths = { 'EP': 3, 'e_rs': 3, 'e_rb': 3, 'sigma_rs': 3, 'sigma_rb': 3 }
linestyles = { 'EP': '-', 'e_rs': '-', 'e_rb': '-', 'sigma_rs': '-', 'sigma_rb': '-' }
else:
logger.critical('unknown plot type to create. ')
labels = { 'EP': '$E(R_m-R_f)$', 'e_rs': '$E(R_m)$', 'e_rb': '$E(R_f)$', 'sigma_rs': '$\sigma(R_m)$', 'sigma_rb': '$\sigma(R_f)$' }
yVars = list(varsToKeep)
yVars.remove(xVar)
for yvar in yVars:
ax.plot(x, overviewTabSub[yvar], linewidth = linewidths[yvar], linestyle = linestyles[yvar], color = colors[yvar], antialiased = True, label = labels[yvar])
## if overlay['EP']:
## ax.plot(x, overviewTabSub['EP'] * 100 , linewidths = 3, linestyle = '-', color = 'k', antialiased = True, label = '$E(R_m-R_f)$')
## if overlay['e_rs']:
## ax.plot(x, (overviewTabSub['e_rs'] -1 ) * 100 , linewidth = 3, linestyle = '-', color = 'b', antialiased = True, label = '$E(R_m)$')
## if overlay['e_rb']:
## ax.plot(x, (overviewTabSub['e_rb'] -1 ) * 100 , linewidth = 3, linestyle = '-', color = 'g', antialiased = True, label = '$E(R_f)$')
## if overlay['sigma_rs']:
## ax.plot(x, overviewTabSub['sigma_rs'] * 100 , linewidth = 3, linestyle = '-', color = 'r', antialiased = True, label = '$\sigma(R_m)$')
## if overlay['sigma_rb']:
## ax.plot(x, overviewTabSub['sigma_rb'] * 100 , linewidth = 3, linestyle = '-', marker = '', color = 'c', antialiased = True, label = '$\sigma(R_f)$')
## #ax.plot(x, overviewTabSub['iBar_Shock0Agent0'] * 10 , linewidth = 3, linestyle = '-', marker = '', color = 'c', antialiased = True, label = 'iBar_Shock0Agent0')
if type == 'presentation':
ax.legend(loc = 'lower left')
if xVar == 'dy':
ax.set_xlabel('$\Delta y$', fontsize = 18)
else:
ax.set_xlabel(xVar, fontsize = 18)
ax.set_ylabel(r'%', fontsize = 18)
elif type == 'paper':
ax.legend(loc = 'lower left')
if xVar == 'dy':
ax.set_xlabel('$\Delta y$', fontsize = 10)
else:
ax.set_xlabel(xVar, fontsize = 10)
ax.set_ylabel(r'%', fontsize = 10)
else:
logger.critical('unknown plot type to create. ')
fig.savefig(figureFile)
os.system('chmod 660 ' + figureFile)
logger.info('figure saved to %s' % figureFile)
def momentPlots(baseName,
path,
xVar,
overlay,
conditions={},
xVarRange=None,
figureFile=None,
type='presentation'):
if os.path.isdir(path):
# if not tableFile:
tableFile = os.path.join(path, 'overviewSimu')
elif os.path.isfile(path):
tableFile = path
else:
logger.critical('path is not a directory or a table file.')
logger.info('\nCreating plot for path %s with conditions %s' %
(path, conditions))
# make sure xVar is not conditioned upon -> we want to use it for xaxis.
if xVar in conditions.keys():
conditions.pop(xVar)
# create a file name for emerging plots
moments = ['EP', 'e_rs', 'e_rb', 'sigma_rs', 'sigma_rb']
if not figureFile:
paraMoments = moments + conditions.keys()
# paraMomentVals = map(str, [EP, e_rs, e_rb, sigma_rs, sigma_rb] + conditions.values())
paraMomentVals = map(str, overlay.values() + conditions.values())
paraString = '_'.join(map(''.join, zip(paraMoments, paraMomentVals)))
fileName = baseName + 'xVar' + xVar + '__' + paraString
fileName = fileName.replace('.', '') # kill all dots for latex
figureFile = os.path.join('/mnt/resultsLarge/idRisk/plots',
fileName + '.eps')
# create table if not existent already
if not os.path.isfile(tableFile) or not os.path.getsize(tableFile):
tablePath = os.path.dirname(tableFile)
createOverviewTable(tablePath)
# read and adjust table
overviewTab = tableDict.fromTextFile(tableFile, None, 20, 5)
logger.debug('Read the following table: ')
# Rename variables
logger.debug(overviewTab)
overviewTab.rename('eR_a', 'e_rs')
overviewTab.rename('std_R_a(quar)', 'sigma_rs')
overviewTab.rename('eR_b', 'e_rb')
overviewTab.rename('std_R_b(quar)', 'sigma_rb')
overviewTab.rename('rP_ana', 'EP')
# Scale to yearly values
overviewTab['e_rs'] = overviewTab['e_rs']**4
overviewTab['e_rb'] = overviewTab['e_rb']**4
overviewTab['EP'] = overviewTab['e_rs'] - overviewTab['e_rb']
overviewTab['sigma_rs'] = overviewTab['sigma_rs'] * 2
overviewTab['sigma_rb'] = overviewTab['sigma_rb'] * 2
# make consistency check
if not xVar in overviewTab.cols:
print 'xVar %s not found in table' % xVar
sys.exit()
overviewTab.order([xVar] + conditions.keys() + moments)
overviewTab.sort(conditions.keys() + [xVar])
logger.debug('table after order and sort')
logger.debug(overviewTab)
# Copy table and impose conditions
overviewTabSub = copy.deepcopy(overviewTab)
logger.debug('keep only relevant variables')
varsToKeep = ['EP', 'e_rs', 'e_rb', 'sigma_rs', 'sigma_rb']
if not varsToKeep.count(xVar): varsToKeep.insert(0, xVar)
overviewTabSub.keep(varsToKeep)
# Subset y series
for cond in conditions:
overviewTabSub = overviewTabSub.getSubset(
(overviewTabSub[cond] == conditions[cond]))
# Subset x range
if xVarRange:
overviewTabSub.subset((overviewTabSub[xVar] >= xVarRange[0])
& (overviewTabSub[xVar] <= xVarRange[1]))
# make sure the table is not empty
if len(overviewTabSub) == 0:
logger.critical('table empty. Returning')
return 1
logger.info('table after imposing conditions:')
logger.info(overviewTabSub)
x = overviewTabSub[xVar]
# Scale variables to percentage points
overviewTabSub['EP'] = overviewTabSub['EP'] * 100
overviewTabSub['e_rs'] = (overviewTabSub['e_rs'] - 1) * 100
overviewTabSub['e_rb'] = (overviewTabSub['e_rb'] - 1) * 100
overviewTabSub['sigma_rs'] = overviewTabSub['sigma_rs'] * 100
overviewTabSub['sigma_rb'] = overviewTabSub['sigma_rb'] * 100
logger.info('Final plot data')
logger.info(overviewTabSub)
# make plot
fig = plt.figure()
ax = fig.add_subplot(111)
# set formatting for plots
if type == 'presentation':
colors = {
'EP': 'k',
'e_rs': 'b',
'e_rb': 'g',
'sigma_rs': 'r',
'sigma_rb': 'c'
}
linewidths = {
'EP': 3,
'e_rs': 3,
'e_rb': 3,
'sigma_rs': 3,
'sigma_rb': 3
}
linestyles = {
'EP': '-',
'e_rs': '-',
'e_rb': '-',
'sigma_rs': '-',
'sigma_rb': '-'
}
elif type == 'paper':
colors = {
'EP': 'k',
'e_rs': 'b',
'e_rb': 'g',
'sigma_rs': 'r',
'sigma_rb': 'c'
}
linewidths = {
'EP': 3,
'e_rs': 3,
'e_rb': 3,
'sigma_rs': 3,
'sigma_rb': 3
}
linestyles = {
'EP': '-',
'e_rs': '-',
'e_rb': '-',
'sigma_rs': '-',
'sigma_rb': '-'
}
else:
logger.critical('unknown plot type to create. ')
labels = {
'EP': '$E(R_m-R_f)$',
'e_rs': '$E(R_m)$',
'e_rb': '$E(R_f)$',
'sigma_rs': '$\sigma(R_m)$',
'sigma_rb': '$\sigma(R_f)$'
}
yVars = list(varsToKeep)
yVars.remove(xVar)
for yvar in yVars:
ax.plot(x,
overviewTabSub[yvar],
linewidth=linewidths[yvar],
linestyle=linestyles[yvar],
color=colors[yvar],
antialiased=True,
label=labels[yvar])
## if overlay['EP']:
## ax.plot(x, overviewTabSub['EP'] * 100 , linewidths = 3, linestyle = '-', color = 'k', antialiased = True, label = '$E(R_m-R_f)$')
## if overlay['e_rs']:
## ax.plot(x, (overviewTabSub['e_rs'] -1 ) * 100 , linewidth = 3, linestyle = '-', color = 'b', antialiased = True, label = '$E(R_m)$')
## if overlay['e_rb']:
## ax.plot(x, (overviewTabSub['e_rb'] -1 ) * 100 , linewidth = 3, linestyle = '-', color = 'g', antialiased = True, label = '$E(R_f)$')
## if overlay['sigma_rs']:
## ax.plot(x, overviewTabSub['sigma_rs'] * 100 , linewidth = 3, linestyle = '-', color = 'r', antialiased = True, label = '$\sigma(R_m)$')
## if overlay['sigma_rb']:
## ax.plot(x, overviewTabSub['sigma_rb'] * 100 , linewidth = 3, linestyle = '-', marker = '', color = 'c', antialiased = True, label = '$\sigma(R_f)$')
## #ax.plot(x, overviewTabSub['iBar_Shock0Agent0'] * 10 , linewidth = 3, linestyle = '-', marker = '', color = 'c', antialiased = True, label = 'iBar_Shock0Agent0')
if type == 'presentation':
ax.legend(loc='lower left')
if xVar == 'dy':
ax.set_xlabel('$\Delta y$', fontsize=18)
else:
ax.set_xlabel(xVar, fontsize=18)
ax.set_ylabel(r'%', fontsize=18)
elif type == 'paper':
ax.legend(loc='lower left')
if xVar == 'dy':
ax.set_xlabel('$\Delta y$', fontsize=10)
else:
ax.set_xlabel(xVar, fontsize=10)
ax.set_ylabel(r'%', fontsize=10)
else:
logger.critical('unknown plot type to create. ')
fig.savefig(figureFile)
os.system('chmod 660 ' + figureFile)
logger.info('figure saved to %s' % figureFile)
