def __init__(self, settingsFile, analysisId, genome=None, expType=None):
Analysis.__init__(self,
settingsFile,
analysisId=analysisId,
genome=genome)
if expType != None:
self.type = expType
self._resultsDir = None # Outside of galaxy and tmpDir struct. Same as inputFile location
self._stayWithinGalaxy = self._settings.getBoolean(
'stayWithinGalaxy', False)
self._galaxyInputs = {
} # May be in galaxy-dist/database/files or else symlinked lib
self._galaxyOutputs = {} # In galaxy-dist/database/files
self._nonGalaxyOutputs = {
} # In resultsDir (same as inputFiles location directory
self._fileSets = {
'galaxyInput': self._galaxyInputs,
'galaxyOutput': self._galaxyOutputs,
'nonGalaxyInput': self._inputFiles,
'nonGalaxyOutput': self._nonGalaxyOutputs,
'target': self._targetOutput,
'intermediate': self._interimFiles
}
self._deliverToGalaxyKeys = None
self.createAnalysisDir(
) # encode pipeline creates this via the manifest.
self.declareLogFile()
def __init__(self, verbose=False):
Analysis.__init__(self, verbose=verbose)
# Settings:
self.tries = 20 # number of repetitions for a certain signal height
self.min_percent = 0.001 # Quantiles
self.max_percent = 100 # Quantiles
self.binning = 100
self.minimum_statistics = 100
self.extremaconfiguration = "1.04 / 55 / 0.97 / 45"
def __init__(self, settingsFile,analysisId,genome=None,expType=None):
Analysis.__init__(self, settingsFile, analysisId=analysisId, genome=genome)
if expType != None:
self.type = expType
self._resultsDir = None # Outside of galaxy and tmpDir struct. Same as inputFile location
self._stayWithinGalaxy = self._settings.getBoolean('stayWithinGalaxy', False)
self._galaxyInputs = {} # May be in galaxy-dist/database/files or else symlinked lib
self._galaxyOutputs = {} # In galaxy-dist/database/files
self._nonGalaxyOutputs = {} # In resultsDir (same as inputFiles location directory
self._fileSets = { 'galaxyInput' : self._galaxyInputs,
'galaxyOutput': self._galaxyOutputs,
'nonGalaxyInput' : self._inputFiles,
'nonGalaxyOutput': self._nonGalaxyOutputs,
'target': self._targetOutput,
'intermediate': self._interimFiles }
self._deliverToGalaxyKeys = None
self.createAnalysisDir() # encode pipeline creates this via the manifest.
self.declareLogFile()
def __init__(self, settingsFile, manifestFile, resume=0):
manifest = Settings(manifestFile)
Analysis.__init__(self, settingsFile, manifest['expName'])
self.resume = resume
self.name = manifest['expName']
self.dataType = manifest['dataType']
self.readType = manifest['readType']
self.replicates = []
self.json = {}
if self.readType == 'single':
if 'fileRep1' in manifest:
self.replicates.append(1)
self.registerInputFile('tagsRep1.fastq', manifest['fileRep1'])
if 'fileRep2' in manifest:
self.replicates.append(2)
self.registerInputFile('tagsRep2.fastq', manifest['fileRep2'])
elif self.readType == 'paired':
if 'fileRd1Rep1' in manifest:
self.replicates.append(1)
self.registerInputFile('tagsRd1Rep1.fastq',
manifest['fileRd1Rep1'])
self.registerInputFile('tagsRd2Rep1.fastq',
manifest['fileRd2Rep1'])
if 'fileRd1Rep2' in manifest:
self.replicates.append(2)
self.registerInputFile('tagsRd1Rep2.fastq',
manifest['fileRd1Rep2'])
self.registerInputFile('tagsRd2Rep2.fastq',
manifest['fileRd2Rep2'])
self.interimDir = None
self.targetDir = None
self.pipeline = None
if self.dataType == 'DNAse':
self.pipeline = DnasePipeline(self)
else:
pass
def __init__(self, settingsFile, manifestFile, resume=0):
manifest = Settings(manifestFile)
Analysis.__init__(self, settingsFile, manifest['expName'])
self.resume = resume
self.name = manifest['expName']
self.dataType = manifest['dataType']
self.readType = manifest['readType']
self.replicates = []
self.json = {}
if self.readType == 'single':
if 'fileRep1' in manifest:
self.replicates.append(1)
self.registerInputFile('tagsRep1.fastq', manifest['fileRep1'])
if 'fileRep2' in manifest:
self.replicates.append(2)
self.registerInputFile('tagsRep2.fastq', manifest['fileRep2'])
elif self.readType == 'paired':
if 'fileRd1Rep1' in manifest:
self.replicates.append(1)
self.registerInputFile('tagsRd1Rep1.fastq', manifest['fileRd1Rep1'])
self.registerInputFile('tagsRd2Rep1.fastq', manifest['fileRd2Rep1'])
if 'fileRd1Rep2' in manifest:
self.replicates.append(2)
self.registerInputFile('tagsRd1Rep2.fastq', manifest['fileRd1Rep2'])
self.registerInputFile('tagsRd2Rep2.fastq', manifest['fileRd2Rep2'])
self.interimDir = None
self.targetDir = None
self.pipeline = None
if self.dataType == 'DNAse':
self.pipeline = DnasePipeline(self)
else:
pass
def view_waveform(self, start=0, end=None, precision=50):
"""
cat: info
desc: show the waveform of this audio
args:
[start: seconds/beats to begin view window. default beginning]
[end: seconds/beats to end view window. -1 selects end. default end]
[precision: percent of how detailed the plot should be. default 50]
"""
start = inpt_validate(start, 'beatsec')
if (end is None) or (end == "-1"):
end = self.size_samps()
else:
end = inpt_validate(end, 'beatsec')
if end.samples_value >= self.size_samps():
end = self.size_samps()
if end <= start:
err_mess("End cannot be before or equal to start")
return
precision = inpt_validate(precision, 'pcnt', allowed=[5, 10000])
info_block("Generating waveform at {0}%...".format(precision))
anlsys = Analysis(self, start=start, end=end)
frame_len = (end - start) / (precision * 2)
anlsys.set_frame_lengths(frame_len)
left = anlsys.arr[:, 0]
right = anlsys.arr[:, 1]
anlsys.plot(left, right, fill=True)
def __init__(self, analysis=None, test_campaign=None, dut=None, begin=None, end=None, averaging=None, verbose=None):
Analysis.__init__(self, test_campaign if analysis is None else analysis.TCString, verbose=verbose, sub_dir='currents')
# Settings
self.Averaging = averaging
self.TimeZone = timezone('Europe/Zurich')
self.DataDir = join(self.TCDir, 'hv')
# Config
self.Ana = analysis
self.IsCollection = hasattr(analysis, 'Runs')
self.Type = self.Ana.Type if analysis is not None and self.IsCollection else 'None'
self.RunSelection = RunSelector(testcampaign=self.TCString)
self.RunLogs = self.RunSelection.RunInfos
self.Run = self.RunSelection.Run if analysis is None else self.Ana.FirstAnalysis.Run if self.IsCollection else self.Ana.Run
if self.IsCollection:
self.Runs = self.Ana.Runs # required for plotting
self.RunPlan = self.load_run_plan() # required for plotting
self.HVConfig = self.load_parser()
self.Bias = self.Ana.Bias if hasattr(self.Ana, 'Bias') else None
self.Draw.ServerDir = analysis.Draw.ServerDir if analysis is not None else None
# Times
self.Begin, self.End = self.load_times(begin, end, dut)
# DUT
self.DUT = self.init_dut(dut)
# HV Device Info
self.Number = self.load_device_number()
self.Channel = self.load_device_channel()
self.Name = self.HVConfig.get('HV{}'.format(self.Number), 'name')
self.Brand = remove_digits(self.Name.split('-')[0])
self.Model = self.HVConfig.get('HV{}'.format(self.Number), 'model')
self.Precision = .005 if '237' in self.Name else .05
# data
self.IgnoreJumps = True
self.Data = self.load_data()
def onFail(self, step):
"""
Override Analysis.onFail() to include galaxy specific things
"""
#if self.dryRun:
self.printPaths(log=step.log) # For posterity
step.log.out('') # skip a lineline
self.runCmd('ls -l ' + step.dir, dryRun=False, log=step.log)
step.log.out('')
retVal = Analysis.onFail(self, step)
if retVal > 255: # This case has been returning 0 !!!
retVal = 55
if retVal == 0:
retVal = 1 # Must fail!
return retVal
def onFail(self, step):
"""
Override Analysis.onFail() to include galaxy specific things
"""
#if self.dryRun:
self.printPaths(log=step.log) # For posterity
step.log.out('') # skip a lineline
self.runCmd('ls -l ' + step.dir, dryRun=False, log=step.log)
step.log.out('')
retVal = Analysis.onFail(self,step)
if retVal > 255: # This case has been returning 0 !!!
retVal = 55
if retVal == 0:
retVal = 1 # Must fail!
return retVal
def createAnalysisDir(self):
Analysis.createAnalysisDir(self)
self.interimDir = self.dir + 'interim/'
os.mkdir(self.interimDir)
self.targetDir = self.dir + 'target/'
os.mkdir(self.targetDir)
def onFail(self, step):
self.pipeline.stop()
Analysis.onFail(self, step)
raise Exception('just failing')
'wmintaunu': {
'data_path': '../data/test_mc16a_wmintaunu/*/*.root',
# 'data_path': '/data/atlas/HighMassDrellYan/test_mc16a/wmintaunu_*/*.root',
'cutfile_path': '../options/cutfile_EXAMPLE.txt',
'TTree_name': 'truth',
'year': '2015+2016',
'hard_cut': r'Muon $|#eta|$',
'lepton': 'tau',
'label': r'$W^-\rightarrow\tau\nu\rightarrow\mu\nu$',
}
}
my_analysis = Analysis(datasets,
analysis_label='test_analysis',
force_rebuild=False,
log_level=10,
log_out='both',
timedatelog=False,
separate_loggers=False)
my_analysis.print_latex_table(['wminmunu', 'wmintaunu'])
# my_analysis.apply_cuts()
# my_analysis.merge_datasets('wminmunu', 'wmintaunu', apply_cuts=r'Muon $|#eta|$')
my_analysis.plot_hist(['wminmunu', 'wmintaunu'],
'MC_WZmu_el_eta_born',
bins=(30, -5, 5),
weight='truth_weight',
normalise='lumi',
lepton='muon',
yerr='rsumw2',
def run(self):
if not len(self.Runs):
return info('There are no runs to convert :-)')
self.multi() if self.Multi else self.auto_convert()
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('-m', action='store_true', help='turn parallel processing ON')
parser.add_argument('-tc', nargs='?', default=None)
parser.add_argument('s', nargs='?', default=None, help='run number where to start, default [None], = stop if no end is provided', type=int)
parser.add_argument('e', nargs='?', default=None, help='run number where to stop, default [None]')
parser.add_argument('-v', action='store_false', help='turn verbose OFF')
parser.add_argument('-t', action='store_true', help='turn test mode ON')
parser.add_argument('-pad', action='store_true', help='convert only pad runs')
parser.add_argument('-pixel', action='store_true', help='convert only pixel runs')
args = parser.parse_args()
from src.analysis import Analysis
z = AutoConvert(args.m, args.s, args.e, Analysis.find_testcampaign(args.tc), 'pad' if args.pad else 'pixel' if args.pixel else None, args.v)
if not args.t:
if len(z.Runs):
print_banner(f'Starting {"multi" if z.Multi else "auto"} conversion for runs {z.Runs[0]} - {z.Runs[-1]}', color='green')
z.run()
print_banner('Finished Conversion!', color='green')
else:
info('There is nothing to convert :-)\n', blank_lines=1)
def onFail(self, step):
self.pipeline.stop()
Analysis.onFail(self, step)
raise Exception('just failing')
def createAnalysisDir(self):
Analysis.createAnalysisDir(self)
self.interimDir = self.dir + 'interim/'
os.mkdir(self.interimDir)
self.targetDir = self.dir + 'target/'
os.mkdir(self.targetDir)
plot.efficiency(arange(e.size), e, make_bins(e.size, n=n))
def get_efficiencies():
return [get_raw_efficiency(i) for i in range(z.NPlanes)]
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('run')
parser.add_argument('-tc', nargs='?', default=None)
args = parser.parse_args()
from src.analysis import Analysis
tc = Analysis.find_testcampaign(args.tc)
if isint(args.run):
z = Run(int(args.run), tc)
t = z.Tree
else:
rootfile = TFile(args.run)
try:
run = int(argv[1].split('/')[-1].strip('.root').split('00')[-1])
except (IndexError, ValueError):
if argv[1].endswith('Tracks.root'):
run = int(argv[1].split('/')[-1].strip('_withTracks.roottest'))
elif 'Tracked' in argv[1]:
run = int(argv[1].split('/')[-1].strip('.root').strip('TrackedRun'))
tc = remove_letters(Path(args.run).absolute().parts[3]).replace('_', '') if 'psi' in args.run else tc
else:
def __lint_dir(directory: str) -> None:
'''
ディレクトリから`.md`を抽出し、それら全てを静的解析、整形します。
Args:
directory (str): `.md`ファイルが存在するディレクトリ。
Raises:
FileNotFoundError: `.md` file not found.
'''
save_file_dir = click.prompt('save direcry.', default=directory)
md_set_path = glob(os.path.join(directory, '*.md'))
for md_file in md_set_path:
old_file_name = os.path.splitext(os.path.basename(md_file))[0]
new_file_name = click.prompt(
f'save file name.(read file: {old_file_name}.md)',
default='lint_' + old_file_name)
analysis = Analysis(save_file_dir, md_file)
analysis.check_blank_line()
analysis.check_title()
analysis.check_header()
analysis.check_link(vaild_link=True)
analysis.check_image()
analysis.export_md(new_file_name)
else: # pylint: disable=W0120
raise FileNotFoundError('`.md`file not found.')
def __lint_file(file_path: str) -> None:
'''
ファイルを静的解析、整形します。
Args:
file_path (str): 静的解析、整形する`.md`ファイル。
Raises:
FileNotFounfError: It does not have the extension `.md`.
'''
if os.path.splitext(os.path.basename(file_path))[1] != '.md':
raise FileNotFoundError('It does not have the extension `.md`.')
directory = os.path.dirname(file_path)
save_file_dir = click.prompt('save direcry.', default=directory)
old_file_name = os.path.splitext(os.path.basename(file_path))[0]
new_file_name = click.prompt(
f'save file name.(read file: {old_file_name}.md)',
default='lint_' + old_file_name)
analysis = Analysis(save_file_dir, file_path)
analysis.check_blank_line()
analysis.check_title()
analysis.check_header()
analysis.check_link(vaild_link=True)
analysis.check_image()
analysis.export_md(new_file_name)
# Open --------------------------------------------------------------------------------------------------------
if args.open:
pkg = open_pkl(args.open)
# set vd
vd = {'pkg': 'Content in .pkl'}
args.shell = True
# Analyze ------------------------------------------------------------------------------------------------------
if args.analyze:
from src.analysis import Analysis
if args.analyze == 'model':
# This analyze the training results stored with each model in the .pkl
if args.data:
analysis = Analysis(p_data=args.data)
else:
analysis = Analysis(p_data=_d_model_)
analysis.use_f1 = args.use_f1
data = analysis.compile_batch_train_results()
vd = {'data': 'Compiled training data (Pandas dataframe)'}
if args.analyze == 'precision' and args.pred_data and args.pred_data is not 'param':
# Generate a series of predictions with incremental rounding cutoff (greater precision)
Analysis().step_precisions(d_out=args.out,
model=m,
data=m.datas[-1],
predictions=res['pred'],
evaluations=res['eval'])
tc=None,
dut=None,
has_collection=True,
return_parser=True,
has_verbose=True)
p.add_argument('-s', '--show', action='store_true', help='activate show')
p.add_argument('-ms',
'--master_selection',
action='store_true',
help='run master selection')
p.add_argument('-d',
'--diamond',
nargs='?',
default=None,
help='diamond for show runplans')
args = p.parse_args()
from src.analysis import Analysis
z = RunSelector(Analysis.find_testcampaign(args.testcampaign),
args.runplan, args.dut, args.verbose)
if args.show:
if args.runplan is not None:
print_banner(z.TCString)
z.select_runs_from_runplan(args.runplan)
z.show_selected_runs()
else:
z.show_run_plans(diamond=args.diamond)
if args.master_selection:
z.master_selection()
x_tit='Column DUT',
y_tit='Row DUT',
canvas=c.cd(2))
# endregion DRAW
# ----------------------------------------
if __name__ == '__main__':
from pixel.run import PixelRun
from src.converter import Converter
from src.analysis import Analysis
# eg. (489/490, 201610), (147, 201810)
pp = init_argparser(return_parser=True)
pp.add_argument('dut', nargs='?', default=None, type=int)
pp.add_argument('-x', action='store_true')
pp.add_argument('-y', action='store_true')
pargs = pp.parse_args()
this_tc = Analysis.find_testcampaign(pargs.testcampaign)
zrun = PixelRun(pargs.run,
testcampaign=this_tc,
load_tree=False,
verbose=True)
z = PixAlignment(Converter(zrun),
dut_plane=pargs.dut,
mode='x' if pargs.x else 'y' if pargs.y else '')
z.reload()
def DoSignalHeightScan(self, heights=None, hits_per_height=300000):
gc.disable()
starttime = datetime.today()
# ROOT Logfile:
# path = "MC/Performance_Results/"+str(starttime)
path = "MC/Performance_Results/_" + str(
self.minimum_statistics) + "_" + str(
self.binning) + "_" + str(hits_per_height) + "_"
os.makedirs(path)
rootfile = TFile(path + '/MCPerformanceLog.root', 'RECREATE')
LogTree = TTree('LogTree', 'MC Log Tree')
RealSignalAmplitude = array('f', [0])
Repetition = array('i', [0])
TrueNPeaks = array('i', [0])
Ninjas = array('i', [0])
Ghosts = array('i', [0])
Minimas = array('i', [0])
RecSA_Quantiles = array('f', [0])
RecSA_MinMax = array('f', [0])
LogTree.Branch('RealSignalAmplitude', RealSignalAmplitude,
'RealSignalAmplitude/F')
LogTree.Branch('Repetition', Repetition, 'Repetition/I')
LogTree.Branch('TrueNPeaks', TrueNPeaks, 'TrueNPeaks/I')
LogTree.Branch('Ninjas', Ninjas, 'Ninjas/I')
LogTree.Branch('Ghosts', Ghosts, 'Ghosts/I')
LogTree.Branch('Minimas', Minimas, 'Minimas/I')
LogTree.Branch('RecSA_Quantiles', RecSA_Quantiles, 'RecSA_Quantiles/F')
LogTree.Branch('RecSA_MinMax', RecSA_MinMax, 'RecSA_MinMax/F')
# copy Config files:
shutil.copy("Configuration/MonteCarloConfig.cfg",
path + "/MonteCarloConfig.cfg")
shutil.copy("Configuration/AnalysisConfig.cfg",
path + "/AnalysisConfig.cfg")
if heights == None:
heights = [
0.0, 0.05, 0.08, 0.1, 0.125, 0.15, 0.175, 0.2, 0.3, 0.5, 0.8,
1.0
]
# infofile:
infofile = open(path + "/info.txt", "w")
infofile.write("DoSignalHeightScan\n\n")
infofile.write("Timestamp: " + str(starttime) + "\n\n")
infofile.write("Number of Repetitions for each Amplitude: " +
str(self.tries) + "\n")
infofile.write("Number of different Amplitudes: " +
str(len(heights)) + "\n")
infofile.write("Hits per Amplitude: " +
str(hits_per_height) + "\n")
infofile.write("Quantiles: " +
str(self.min_percent) + "/" + str(self.max_percent) +
"\n")
infofile.write("Binning: " +
str(self.binning) + "\n")
infofile.write("Minimum Statistics: " +
str(self.minimum_statistics) + "\n")
infofile.write("Extrema Configuration: " +
self.extremaconfiguration)
success_prob = []
ghost_prob = []
cycle_nr = 0
cycles = self.tries * len(heights)
for height in heights: # add more statistics for each height, not just one try..
fails = 0
tot_ghosts = 0
peaks_generated = 0
for repetition in range(self.tries):
cycle_nr += 1
print("\n{0}th repetition with Signal height set to: {1}\n".
format(repetition, height))
run_object = MCRun(validate=False,
verbose=self.verbose,
run_number=364)
run_object.MCAttributes['PeakHeight'] = height
run_object.SetNumberOfHits(hits_per_height)
print("newAnalysis = Analysis(run_object)")
newAnalysis = Analysis(run_object, verbose=self.verbose)
print("newAnalysis.FindMaxima()")
newAnalysis.FindMaxima(
binning=self.binning,
minimum_bincontent=self.minimum_statistics)
print("newAnalysis.FindMinima()")
newAnalysis.FindMinima(
binning=self.binning,
minimum_bincontent=self.minimum_statistics)
npeaks = newAnalysis.ExtremeAnalysis.ExtremaResults[
'TrueNPeaks']
ninjas = newAnalysis.ExtremeAnalysis.ExtremaResults['Ninjas']
ghosts = newAnalysis.ExtremeAnalysis.ExtremaResults['Ghosts']
maxima = newAnalysis.ExtremeAnalysis.ExtremaResults[
'FoundMaxima']
minima = newAnalysis.ExtremeAnalysis.ExtremaResults[
'FoundMinima']
# Reconstruct Signal Amplitude:
if len(maxima) * len(minima) > 0:
maxbin = newAnalysis.ExtremeAnalysis.Pad.GetBinByCoordinates(
*(maxima[0]))
maxbin.FitLandau()
minbin = newAnalysis.ExtremeAnalysis.Pad.GetBinByCoordinates(
*(minima[0]))
minbin.FitLandau()
rec_sa_minmax = maxbin.Fit['MPV'] / minbin.Fit['MPV'] - 1.
else:
rec_sa_minmax = -99
q = array('d', [
1. * self.min_percent / 100., 1. * self.max_percent / 100.
])
y = array('d', [0, 0])
newAnalysis.ExtremeAnalysis.CreateMeanSignalHistogram()
newAnalysis.ExtremeAnalysis.MeanSignalHisto.GetQuantiles(
2, y, q)
rec_sa_quantiles = y[1] / y[0] - 1.
# Fill ROOT file:
RealSignalAmplitude[0] = height
Repetition[0] = repetition
TrueNPeaks[0] = npeaks
Ninjas[0] = ninjas
Ghosts[0] = ghosts
Minimas[0] = len(minima)
RecSA_Quantiles[0] = rec_sa_quantiles
RecSA_MinMax[0] = rec_sa_minmax
LogTree.Fill()
assert (npeaks > 0), 'no peak in MC created'
peaks_generated += npeaks
fails += ninjas
tot_ghosts += ghosts
# self.AddAnalysis(newAnalysis)
del newAnalysis
del run_object
elapsed_time = datetime.today() - starttime
estimated_time = elapsed_time / cycle_nr * cycles
remaining_time = estimated_time - elapsed_time
print("\n\nAPPROXIMATED TIME LEFT: " + str(remaining_time) +
"\n")
success = 1. * (peaks_generated - fails) / peaks_generated
ghost = 4. * ghosts / self.tries
success_prob.append(success)
ghost_prob.append(ghost)
print("Write ROOT-file")
rootfile.Write()
rootfile.Close()
print("ROOT File written. Write infofile")
infofile.write("\nTotal Time elapsed: " +
str(datetime.today() - starttime))
infofile.close()
print("infofile written")
# canvas = ROOT.TCanvas('canvas', 'canvas') # HERE IT CRASHES DUE TO MEMORY PROBLEMS
# canvas.cd()
# graph1 = ROOT.TGraph()
# graph1.SetNameTitle('graph1', 'success')
# graph1.SaveAs(path+"/SuccessGraph.root")
# graph2 = ROOT.TGraph()
# graph2.SetNameTitle('graph2', 'ghosts')
# graph2.SaveAs(path+"/GhostsGraph.root")
# for i in range(len(heights)):
# graph1.SetPoint(i, heights[i], success_prob[i])
# graph2.SetPoint(i, heights[i], ghost_prob[i])
# graph1.Draw('ALP*')
# graph2.Draw('SAME LP*')
# self.SavePlots("PerformanceResult", "png", path+"/")
answer = input('Wanna crash?')
ROOT.gDirectory.GetList().ls()
ROOT.gROOT.GetListOfFiles().ls()
if answer == 'yes':
gc.collect()
SLACK_URL = args.url
DATA_FILE = f"last_post_data_{args.script_name}.json"
# endregion
# Get data and convert accordingly
cb = Coinbase(Currencies.default(), args.interval)
prices = cb.price_list()
cur_price = prices[0]
# Get history from last runs, use it to work out what test to make
history = History(DATA_FILE)
# Get stats from coinbase data
# If change isn't large enough, then update history and exit
stats = HourData(prices, EMA_NUM_HOURS)
if Analysis.ema_checks(stats, history, EMA_THRESHOLD_PERCENT,
EMA_RESET_PERCENT):
sys.exit(1)
if not Analysis.should_post(history, stats, prices, EMA_THRESHOLD_PERCENT):
sys.exit(1)
logging.info("Message should be posted, generating attachment")
attachments = Slack.generate_post(prices, stats, Currencies.default())
image_url = SlackImages.get_image(stats.is_diff_positive)
logging.info("Posting to slack")
Slack.post_to_slack(BOT_NAME, image_url, "", attachments, SLACK_URL,
SLACK_CHANNEL)
history.price = stats.cur_price
history.rising = stats.is_diff_positive
history.ema_reset = False
def main(argv = None):
# hardcode defaults
RESULT_DIR = '%s%sresult' % (sys.path[0], os.sep)
PARAM_FILE = '%s%sparameter.conf' % (sys.path[0], os.sep)
STEPS = ['preprocessing', 'annotate', 'assembly', 'analysis']
# Get the starting time
starting_time = time.time()
# setup Argument Parser for stdin arguments
parser = argparse.ArgumentParser(add_help = True)
# define arguments
parser.add_argument('input', nargs = '+', action = 'store',
help = 'single or paired input files in <fastq> format')
parser.add_argument('--version', action = 'version', version = '%(prog)s 0.5')
parser.add_argument('-v', dest = 'verbose', action = 'store_true', default = False,
help = 'more detailed output (default = False)')
parser.add_argument('-t', dest = 'threads', type = int, action = 'store',
default = multiprocessing.cpu_count() - 1,
help = 'number of threads to use (default = %d)'
% (multiprocessing.cpu_count() - 1))
parser.add_argument('-p', dest = 'param', action = 'store', default = PARAM_FILE,
help = 'use alternative config file (default = parameter.conf)')
parser.add_argument('-s', dest = 'skip', action = 'store', default = '',
choices = ['preprocessing', 'assembly', 'annotation','analysis'],
help = 'skip steps in the pipeline (default = None)')
parser.add_argument('-o', dest = 'output', action = 'store', default = RESULT_DIR,
help = 'use alternative output folder')
parser.add_argument('-a', dest = 'assembler', default = 'MetaVelvet',
choices = ['metavelvet', 'flash','both'],
help = 'assembling program to use (default = MetaVelvet)')
parser.add_argument('-c', dest = 'annotation', default = 'both',
choices = ['metacv', 'blastn', 'both'],
help = 'classifier to use for annotation (default = both)')
parser.add_argument('--use_contigs', dest = 'use_contigs', action = 'store_true',
default = 'False',
help = 'should MetaCV use assembled Reads or RAW Reads (default = RAW')
parser.add_argument('--notrimming', dest = 'trim', action = 'store_false', default = True,
help = 'trim and filter input reads? (default = True)')
parser.add_argument('--noquality', dest = 'quality', action = 'store_false', default = True,
help = 'create no quality report (default = True)')
parser.add_argument('--noreport', dest = 'krona', action = 'store_false', default = True,
help = 'create no pie chart with the annotated taxonomical data (default = True)')
parser.add_argument('--merge', dest = 'merge_uncombined', action = 'store_true', default = False,
help = 'merge concatinated reads with not concatinated (default = False)')
args = parser.parse_args()
# init the Pipeline
RESULT_DIR = args.output if args.output else RESULT_DIR
# check if param File exists
if os.path.isfile(args.param):
PARAM_FILE = args.param
else:
if os.path.isfile(PARAM_FILE):
sys.stderr.write('ERROR 3: Parameter File could not be found!\n')
sys.stderr.write('Use standard Parameter File:\n%s\n\n' % (PARAM_FILE))
else:
raise ParamFileNotFound(args.param)
# check if input exists
if not all(os.path.isfile(file) for file in args.input):
raise InputNotFound(to_string(args.input))
if __name__ == '__main__':
# create outputdir and log folder
create_outputdir(RESULT_DIR)
create_outputdir(RESULT_DIR + os.sep +'log')
# create the global settings object
settings = General(args.threads, args.verbose, args.skip, starting_time, args.trim,
args.quality, args.krona, args.use_contigs, args.merge_uncombined, args.assembler,
args.annotation, 1)
# setup the input, outputs and important files
files = FileSettings(absolute_path(args.input), os.path.normpath(RESULT_DIR), PARAM_FILE)
exe = Executables(PARAM_FILE)
# get the all skipped steps
skip = to_string(settings.get_skip())
try:
print "hello"
# START the modules of Pipeline and wait until completion
if skip in 'preprocessing' and skip:
skip_msg(skip)
else:
# init the preprocessing module
pre = Preprocess(settings.get_threads(),
settings.get_step_number(),
settings.get_verbose(),
settings.get_actual_time(),
files.get_input(),
files.get_logdir(),
exe.get_FastQC(),
settings.get_quality(),
files.get_quality_dir(),
parse_parameter(FastQC_Parameter(PARAM_FILE)),
exe.get_TrimGalore(),
settings.get_trim(),
files.get_trim_dir(),
parse_parameter(TrimGalore_Parameter(PARAM_FILE)))
# run preprocessing functions
results = pre.manage_preprocessing()
# update pipeline variables with results
settings.set_step_number(results[0])
if len(results) > 1:
files.set_input(absolute_path(results[1]))
files.set_preprocessed_output(absolute_path(results[1]))
if skip in 'assembly' and skip:
skip_msg(skip)
else:
# init the assembly module
assembly = Assembly(settings.get_threads(),
settings.get_step_number(),
settings.get_verbose(),
settings.get_actual_time(),
files.get_logdir(),
files.get_input(),
settings.get_assembler(),
exe.get_Flash(),
files.get_concat_dir(),
parse_parameter(FLASH_Parameter(PARAM_FILE)),
settings.get_merge_uncombined(),
exe.get_Velveth(),
exe.get_Velvetg(),
exe.get_MetaVelvet(),
files.get_assembly_dir(),
Velveth_Parameter(PARAM_FILE).get_kmer(PARAM_FILE),
parse_parameter(Velveth_Parameter(PARAM_FILE)),
parse_parameter(Velvetg_Parameter(PARAM_FILE)),
parse_parameter(MetaVelvet_Parameter(PARAM_FILE)))
# run assembly functions
results = assembly.manage_assembly()
# update pipeline variables with results
settings.set_step_number(results[0])
files.set_input(absolute_path(results[1]))
files.set_concatinated_output(absolute_path(results[2]))
files.set_assembled_output(absolute_path(results[3]))
if skip in 'annotation'and skip:
skip_msg(skip)
else:
# init the annotation module
anno = Annotation(settings.get_threads(),
settings.get_step_number(),
settings.get_verbose(),
settings.get_actual_time(),
files.get_logdir(),
files.get_input(),
files.get_raw(),
settings.get_annotation(),
settings.get_use_contigs(),
exe.get_Blastn(),
exe.get_Blastn_DB(),
exe.get_Converter(),
files.get_blastn_dir(),
Blastn_Parameter(PARAM_FILE).outfmt,
parse_parameter(Blastn_Parameter(PARAM_FILE)),
exe.get_MetaCV(),
exe.get_MetaCV_DB(),
files.get_metacv_dir(),
MetaCV_Parameter(PARAM_FILE).get_seq(),
MetaCV_Parameter(PARAM_FILE).get_mode(),
MetaCV_Parameter(PARAM_FILE).get_orf(),
MetaCV_Parameter(PARAM_FILE).get_total_reads(),
MetaCV_Parameter(PARAM_FILE).get_min_qual(),
MetaCV_Parameter(PARAM_FILE).get_taxon(),
MetaCV_Parameter(PARAM_FILE).get_name())
# run the annotation functions
results = anno.manage_annotation()
settings.set_step_number(results[0])
files.set_blastn_output(absolute_path(results[1]))
files.set_metacv_output(absolute_path(results[2]))
if skip in 'analysis' and skip:
skip_msg(skip)
else:
# init the analysis module
analysis = Analysis(settings.get_threads(),
settings.get_step_number(),
settings.get_verbose(),
settings.get_actual_time(),
files.get_logdir(),
settings.get_annotation(),
files.get_output(),
files.get_parsed_db_dir(),
files.get_annotated_db_dir(),
files.get_subseted_db_dir(),
files.get_krona_report_dir(),
files.get_blastn_output(),
files.get_metacv_output(),
exe.get_Parser(),
parse_parameter(blastParser_Parameter(PARAM_FILE)),
blastParser_Parameter(PARAM_FILE).get_name(),
exe.get_Annotate(),
parse_parameter(Rannotate_Parameter(PARAM_FILE)),
Rannotate_Parameter(PARAM_FILE).get_name(),
Rannotate_Parameter(PARAM_FILE).get_taxon_db(),
exe.get_Subset(),
subsetDB_Parameter(PARAM_FILE).get_bitscore(),
subsetDB_Parameter(PARAM_FILE).get_classifier(),
subsetDB_Parameter(PARAM_FILE).get_rank(),
subsetDB_Parameter(PARAM_FILE).get_taxon_db(),
exe.get_Krona_Blast(),
parse_parameter(Krona_Parameter(PARAM_FILE)),
Krona_Parameter(PARAM_FILE).get_name(),
settings.get_krona(),
exe.get_Perl_lib())
# run the analysis function
results = analysis.manage_analysis()
files.set_parser_output(absolute_path(results[0]))
files.set_annotated_output(absolute_path(results[1]))
except KeyboardInterrupt:
sys.stdout.write('\nERROR 1 : Operation cancelled by User!\n')
sys.exit(1)
# print ending message
print_verbose('\nPIPELINE COMPLETE!\n\n')
print_running_time(settings.get_actual_time())
import src.persistence.article_service as articles_service
from kafka import KafkaConsumer
import src.config as config
import src.constants as constants
from json import loads
from src.producer import Producer
import src.api.service as api
from threading import Thread
from src.analysis import Analysis
TFIDF_TOPIC = 'tfidf-input'
UNIQUE_TOPIC = 'unique-articles-input'
analysis = Analysis()
unique_consumer = KafkaConsumer(
UNIQUE_TOPIC,
bootstrap_servers=[
config.CONNECTION['host'] + ':' + config.CONNECTION['port']
],
auto_offset_reset='earliest',
enable_auto_commit=True,
group_id='articles_consumer',
value_deserializer=lambda x: loads(x.decode(constants.UTF_ENCODING)))
tfidf_producer = Producer(TFIDF_TOPIC)
def format_message(message):
message['title'] = ' '.join(message['title'])
message['text'] = ' '.join(message['text'])
return message
# 'datapath': '/data/atlas/HighMassDrellYan/test_mc16a/zzllnunu/*.root',
# 'cutfile': 'options/jesal_cutflow/cutfile_jesal.txt',
# 'TTree_name': 'nominal_Loose',
# 'is_slices': False,
# },
# 'zzqqll': {
# 'datapath': '/data/atlas/HighMassDrellYan/test_mc16a/zzqqll/*.root',
# 'cutfile': 'options/jesal_cutflow/cutfile_jesal.txt',
# 'TTree_name': 'nominal_Loose',
# 'is_slices': False,
# },
}
my_analysis = Analysis(datasets,
analysis_label='jesal_cutflow',
force_rebuild=False,
log_level=10,
log_out='console')
my_analysis.plot_1d(x='mu_mt',
bins=(50, 120, 4000),
title=r'$W\rightarrow\tau\nu$ (13 TeV)',
scaling='xs',
log_y=True)
# pipeline
# my_analysis.plot_mass_slices(ds_name='wmintaunu_slices', xvar='MC_WZ_m',
# inclusive_dataset='wmintaunu_inclusive', logx=True, to_pkl=True)
# my_analysis.plot_mass_slices(ds_name='wplustaunu_slices', xvar='MC_WZ_m',
# inclusive_dataset='wplustaunu_inclusive', logx=True, to_pkl=True)
# my_analysis.plot_mass_slices(ds_name='wminmunu_slices', xvar='MC_WZ_m',
# inclusive_dataset='wminmunu_inclusive', logx=True, to_pkl=True)
'lepton': 'muon',
'label': r'$W^+\rightarrow\mu\nu$',
},
'wplustaunu': {
'data_path':
'/data/atlas/HighMassDrellYan/test_mc16a/wplustaunu/*.root',
'cutfile_path': '../options/jesal_cutflow/DY_peak.txt',
'lepton': 'tau',
'label': r'$W^+\rightarrow\tau\nu\rightarrow\mu\nu$',
}
}
analysis = Analysis(datasets,
'mutau_compare',
log_level=10,
log_out='both',
timedatelog=False,
year='2015+2016',
force_rebuild=False,
TTree_name='nominal_Loose')
analysis.merge_datasets("wminmunu", "wminmunu_hm", verify=True)
analysis.merge_datasets("wmintaunu", "wmintaunu_hm", verify=True)
analysis.merge_datasets("wplusmunu", "wplusmunu_hm", verify=True)
analysis.merge_datasets("wplustaunu", "wplustaunu_hm", verify=True)
# normalised
analysis.plot_hist(['wminmunu', 'wmintaunu'],
'met_met',
weight='reco_weight',
title='reco 139fb$^{-1}$',
bins=(30, 150, 5000),
# Open --------------------------------------------------------------------------------------------------------
if args.open:
pkg = open_pkl(args.open)
# set vd
vd = {'pkg': 'Content in .pkl'}
args.shell = True
# Analyze ------------------------------------------------------------------------------------------------------
if args.analyze:
from src.analysis import Analysis
if args.analyze == 'model':
# This analyze the training results stored with each model in the .pkl
if args.data:
analysis = Analysis(p_data=args.data)
else:
analysis = Analysis(p_data=_d_model_)
analysis.use_f1 = args.use_f1
data = analysis.compile_batch_train_results()
vd = {'data': 'Compiled training data (Pandas dataframe)'}
# Enable interaction \______________________________________________________________________________________________
if args.shell:
interact(var_desc=vd, local=locals())
# For testing new code \____________________________________________________________________________________________
if args.test:
d = Dummy().init_networks()
print(' Generated pure network: %s' %
out=d.joinpath(step),
geo=self.toml_name(self.Steps[i - 1]) if i else None,
section=step,
cfg='align')
else:
warning('geo file already exists!')
print_elapsed_time(t)
def recon(self, raw=False):
""" step 3: based on the alignment generate the tracks with proteus. """
self.Out.parent.mkdir(exist_ok=True)
self.run('pt-recon',
out=self.Out,
cfg=None if raw else self.toml_name())
# endregion RUN
# ----------------------------------------
if __name__ == '__main__':
from src.analysis import Analysis, Dir
a = Analysis()
sdir = Path(a.Config.get('SOFTWARE', 'dir')).expanduser().joinpath(
a.Config.get('SOFTWARE', 'proteus'))
f_ = a.BeamTest.Path.joinpath('data', f'run{11:06d}.root')
z = Proteus(sdir, a.BeamTest.Path.joinpath('proteus'),
Dir.joinpath('proteus'), f_,
a.Config.getint('align', 'max events'),
a.Config.getint('align', 'skip events'))
},
'wplustaunu': {
'data_path':
'/data/atlas/HighMassDrellYan/mc16a/wplustaunu/*.root',
'cutfile_path': '../options/joanna_cutflow/DY_peak.txt',
'lepton': 'tau',
'label': r'$W^+\rightarrow\tau\nu\rightarrow\mu\nu$',
}
}
analysis = Analysis(
datasets,
'mutau_compare_full',
data_dir='/data/keanu/framework_outputs/mutau_compare_full/',
log_level=10,
log_out='both',
timedatelog=True,
year='2015+2016',
force_rebuild=False,
TTree_name='truth',
hard_cut='M_W')
analysis.print_latex_table()
analysis.merge_datasets("wminmunu", "wminmunu_hm")
analysis.merge_datasets("wmintaunu", "wmintaunu_hm")
analysis.merge_datasets("wplusmunu", "wplusmunu_hm")
analysis.merge_datasets("wplustaunu", "wplustaunu_hm")
# =========================
# ===== TRUTH - UNCUT =====