def retrospective(acct):
rtnstring = ""
subaccts = []
output = runledger(commands['retrospective'] + ["^"+acct])
for line in output.split('\n'):
subaccts += line.split(acct)[-1:]
#Remove the starting ":" from the subaccount name
subaccts = [subacct[1:] for subacct in subaccts]
#Determine which accounts are excluded before iterating
excluded = [subacct for ex in exclude['retrospective'] for subacct in subaccts if (unicode(acct + ":" + subacct).find(ex) != -1)]
subaccts = [subacct for subacct in subaccts if (subacct not in excluded)]
retro = []
for subacct in subaccts:
fullname = acct + ":" + subacct
#print retrospective of subaccts with at least 7 transactions when viewed weekly over the last 12 months
output = runledger(commands['last12months'] + ['-J', 'register'] + ["^" + fullname])
if(len(output.split('\n')) < 6): continue
safename = safe_name(fullname)
plot.main("./build/" + safename + "retro", commands['last12months'] + ['-J', 'register'] + ["^" + fullname] + ["-f", LEDGER_FILE])
retro.append({'name': subacct, 'plotfile': safename + "retro"})
return retro
def forecast(acct):
rtnstring = ""
#identify budgeted subaccts
subaccts = []
output = runledger(commands['acctbudget'] + ["^"+acct])
for line in output.split('\n'):
subaccts += line.split(acct)[-1:]
#Remove the starting ":" from the subaccount name
subaccts = [subacct[1:] for subacct in subaccts]
#Determine which accounts are excluded before iterating
excluded = [subacct for ex in exclude['forecast'] for subacct in subaccts if (unicode(acct + ":" + subacct).find(ex) != -1)]
subaccts = [subacct for subacct in subaccts if (subacct not in excluded)]
forecast = []
for subacct in subaccts:
fullname = acct + ":" + subacct
safename = safe_name(fullname)
file = "./build/" + safename + "forecast"
args = commands['next12months'] + ['-J', 'register'] + ["^" + fullname] + ["-f", LEDGER_FILE]
#print file, ' ', ' '.join(args)
plot.main(file, args)
#forecast.append({'name': subacct, 'plotfile': safename + "forecast"})
return forecast
def main(args):
"""
Main running script
"""
# Get the config file
config = util.get_config(args.config)
root_dir = config['ROOT_DIR']
# fill out initial folders
if not os.path.isdir('{}/metadata'.format(root_dir)):
os.mkdir('{}/metadata'.format(root_dir))
print('created metadata dir')
if not os.path.isdir('{}'.format(config['OBS_ROOT'])):
os.mkdir('{}'.format(config['OBS_ROOT']))
print('created OBS dir')
if not os.path.isdir('{}'.format(config['ESTIMATORS_ROOT'])):
os.mkdir('{}'.format(config['ESTIMATORS_ROOT']))
print('created ESTIMATORS dir')
if not os.path.isdir('{}'.format(config['PREDICTIONS_ROOT'])):
os.mkdir('{}'.format(config['PREDICTIONS_ROOT']))
print('created PREDICTIONS dir')
if not os.path.isdir('{}'.format(config['QAQC_ROOT'])):
os.mkdir('{}'.format(config['QAQC_ROOT']))
print('created QAQC dir')
if not os.path.isdir('{}'.format(config['PLOT_ROOT'])):
os.mkdir('{}'.format(config['PLOT_ROOT']))
print('created PLOT dir')
# --- download data ---
if args.clean:
clean.main(config)
else:
print('skipping database cleaning')
# --- download data ---
if args.download:
download.main(config)
else:
print('skipping download of new data')
# --- train models
if args.train:
train.main(config)
else:
print('skip training')
# --- make predictions ---
if args.predict:
predict.main(config)
else:
print('skipping download of new data')
# --- run qaqc checks ---
if args.qaqc:
qaqc.main(config)
else:
print('skipping qaqc')
# --- plot ---
if args.plot:
plot.main(config)
else:
print('skipping plots')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('mode', choices=['train', 'plot', 'eval', 'hogwild'])
# IO arguments.
parser.add_argument('--name', type=str, default='text8.10k',
help='name for model')
parser.add_argument('--vocab-dir', type=str, default='data/vocab',
help='input path for vocabulary')
parser.add_argument('--matrix-dir', type=str, default='data/cooccur',
help='input path for cooccurence matrix')
parser.add_argument('--out-dir', type=str, default='vec',
help='ouput directory to write vectors')
parser.add_argument('--model-dir', type=str, default='models',
help='directory to save model')
parser.add_argument('--log-dir', type=str, default='log',
help='directory log losses')
parser.add_argument('--vec-path', type=str, default='',
help='path to load vectors for plotting')
parser.add_argument('--gensim-format', action='store_true',
help='save vectors in gensim format')
# Model arguments.
parser.add_argument('--emb-dim', type=int, default=50,
help='dimension of vectors')
# Train arguments.
parser.add_argument('--num-updates', type=int, default=10000,
help='number of parameter updates')
parser.add_argument('--batch-size', type=int, default=512,
help='size of minibatches')
parser.add_argument('--lr', type=float, default=1e-2,
help='learning rate')
parser.add_argument('--seed', type=int, default=42,
help='random seed')
parser.add_argument('--use-schedule', action='store_true',
help='using scheduler for optimizer')
parser.add_argument('--save-every', type=int, default=1000,
help='how often to save the model parameters')
parser.add_argument('--print-every', type=int, default=100,
help='how often to print loss to screen')
# Plot arguments.
parser.add_argument('--tsne', action='store_true',
help='plot tsne')
parser.add_argument('--matrices', action='store_true',
help='plot matrices and decomposition')
args = parser.parse_args()
if args.mode == 'train':
train.main(args)
if args.mode == 'plot':
plot.main(args)
if args.mode == 'hogwild':
hogwild.main(args)
def refreshPlot(f0, f1, c0, c1):
f0.clf()
f1.clf()
path = "/home/rba/Downloads/RBA-DAQ_multisensor/trunk"
os.chdir(path)
files = sorted(os.listdir(os.getcwd()), key=os.path.getctime)
newest = files[-1]
plot.main(path+"/"+newest, f0, f1)
canvas0.show()
canvas1.show()
def getTuneView(self):
global lb1
sel = lb1.curselection()
if len(sel) == 1:
actualDirToShow = lb1.get(sel)
plot.main(actualDirToShow)
elif len(sel) == 2:
actualDirToShow = lb1.get(sel[0])
actualDirToShow2 = lb1.get(sel[1])
plotTunesCompare.main(actualDirToShow, actualDirToShow2)
def main():
Createfolder = CreateFolders(absdir)
# Createfolder.create_folder(absdir)
Createfolder.__init__(absdir)
if deleteOldData == "on":
Createfolder.deleteOldData(absdir)
# This script plots the cpptraj created data-files
import qsub_hpc # If qsub is specified the ccptraj, R and plot scripts will be submitted to the hpc-queue.
import cpptraj_analysis # This script runs the cpptraj module for merging mdcrd files and making the analysis on the combined dcd file. The analysis includes rmsd, distance and clustering
import R_analysis # This script runs the "bio3d" package of "R" - which makes the principal component analysis
import plot
import CombinedPlots
import MMPBSA_analysis
if qsub == None:
CPPTRAJ = cpptraj_analysis.main()
if R_Analysis == "on":
RPlot = R_analysis.main()
if MMPBSA == "on":
mmpbsa = MMPBSA_analysis.main()
if makePlots == "on":
Plot = plot.main()
if makeHistPlots == "on":
HistPlot = CombinedPlots.main()
else:
submit = qsub_hpc.main()
def main():
print("Start word2vec")
print("Input source name")
project = input()
merge_source.sub(project)
print("is code? [y/n]")
iscode = input()
if iscode == "y":
replace.sub(project)
else:
mecab.sub(project)
model.sub(project)
result.sub(project)
print("plot? [y]")
iscode = input()
if iscode == "y":
plot.main(project)
def main():
# Process the command line arguments
args = processCommandLine(sys.argv)
if args["modulePaths"] != "":
paths = args["modulePaths"].split(';')
for path in paths:
sys.path.append(path)
import rig2py
import rig2pyHelper
import plot
# Create a rest pose
global restPose
restPose = rig2pyHelper.createRestPose()
print("Joint order: " + str(rig2pyHelper.jointOrder))
# Set our callbacks
rig2py.errorCallback(onError)
rig2py.frameCallback(onFrame)
# Load all data from the file (will invoke the callback many times)
try:
rig2py.read(args["inputJson"])
except RuntimeError as e:
print(e)
return
# Plot all the txt files we created
# TODO: use the frame number instead of assuming zero
files = []
for file in os.listdir("./"):
if file.endswith(".txt"):
files.append(file)
plot.main(files)
def main(exp_name, x_name, args, params):
print(f"Running exp with param {x_name}", params)
if args.debug != '':
exit()
title = "loss"
os.makedirs(exp_name, exist_ok=True)
with open(f'{exp_name}/args.json', 'w') as f:
dict = args.__dict__
dict["changed_parameter"] = x_name
dict["changed_parameter_values"] = params
json.dump(dict, f, indent=4)
g = graph_utils.create_fc_graph(args.h, args.w)
edges_num_dict = graph_utils.numerate_edges(g)
start_node = 0
finish_node = list(g.nodes)[-1]
solutions_hoef, solutions_dro, solutions_dro_cropped, c_worst_hoef, c_worst_dro, c_bar = run_experiments(
args, g, edges_num_dict, start_node, finish_node, x_name, params)
print(f"Finished exp, {x_name}")
np.save(f'{exp_name}/c_worst_dro.npy', c_worst_dro)
np.save(f'{exp_name}/c_worst_hoef.npy', c_worst_hoef)
np.save(f'{exp_name}/c_bar.npy', c_bar)
np.save(f'{exp_name}/solutions_hoef.npy', solutions_hoef)
np.save(f'{exp_name}/solutions_dro.npy', solutions_dro)
np.save(f'{exp_name}/solutions_dro_cropped.npy', solutions_dro_cropped)
np.save(f'{exp_name}/params.npy', params)
count_costs = args.costs == 'true'
count_percentage = args.percentage_mode == 'true'
plot.main(exp_name, x_name, title, args)
if count_costs:
plot.main(exp_name,
x_name,
title.replace("loss", "costs"),
args,
count_costs=count_costs)
if count_percentage:
plot.main(exp_name,
x_name,
title + ' percentage',
args,
count_percentage=count_percentage)
data = json.load(file)[1]
return data
except FileNotFoundError:
print("Data file not found.")
exit()
def get_values(data):
"""Get a list of all first chromosomes' objective values."""
values = [population[0]["fitness"] for population in data]
return values
def get_new_values(values):
"""Record any changes higher. Its size is the same as its argument's."""
new_value = values[0]
for index, value in enumerate(values):
if value < new_value:
print(index)
if __name__ == "__main__":
# get_new_values(get_values(get_data()))
things = [thing["fitness"] for thing in get_data()[2]]
# for thing in things:
# thing["genes"] = 0
plot.main(things)
# things = get_data()[29][0]
# print(beautifier(things))
import plot import train import util from config import Config from logger import Logger # main function if __name__ == '__main__': args = Config.getArgs () util.printHeader () if args.plot: plot.main () elif args.image is None: Logger.log ( "Going to train network!" ) train.main () else: plot.plotOutput ()
def main(dataset,
expert_cost=100,
rloss=10,
total_cost=100000,
runs=1,
y_len=2500,
suf=""):
expert_cost = int(expert_cost)
rloss = int(rloss)
total_cost = int(total_cost)
runs = int(runs)
#print dataset, expert_cost
directory = 'exp_' + dataset + suf + '/'
os.makedirs(directory)
logging.basicConfig(filename=directory + '/log.log', level=logging.DEBUG)
start.main(dataset)
(je, adata) = active.experi_money(start.mat,
start.rel,
start.turk_data,
start.turk_data_uncer,
runs, (total_cost, 1, expert_cost),
stra='je',
rloss=rloss)
save_disk(directory, 'je.pkl', je)
(jc, adata) = active.experi_money(start.mat,
start.rel,
start.turk_data,
start.turk_data_uncer,
runs, (total_cost, 1, expert_cost),
stra='jc',
rloss=rloss)
save_disk(directory, 'jc.pkl', jc)
(cde, adata) = active.experi_money(start.mat,
start.rel,
start.turk_data,
start.turk_data_uncer,
runs, (total_cost, 1, expert_cost),
stra='cde',
rloss=rloss)
save_disk(directory, 'cde.pkl', cde)
#(dec5, adata) = active.experi_money(start.mat, start.rel, start.turk_data, start.turk_data_uncer, 5, (100000, 1, expert_cost), stra = 'dec5', rloss = rloss)
#save_disk('dec5.pkl', dec5)
(dec5_ip, adata) = active.experi_money(start.mat,
start.rel,
start.turk_data,
start.turk_data_uncer,
runs, (total_cost, 1, expert_cost),
stra='dec5_ip',
rloss=rloss)
save_disk(directory, 'dec5_ip.pkl', dec5_ip)
#PLOT
plot.main(directory, y_len)
if b1<b2:
s = b1+'_'+b2
else:
s = b2 + '_' + b1
# print(s)
new_topos.append(s)
result = []
for nt in new_topos:
if nt not in topo_names:
result.append(nt)
return result
if __name__ == '__main__':
in_path = "/Users/rafalmaselek/Projects/CheckMateCalculations/results/fastlim/FASTLIM_OUT"
slha_path = "/Users/rafalmaselek/Projects/CheckMateCalculations/results/fastlim/SLHA_FIX"
points = mplt.main(in_path, slha_path)[0]
procs = list(chain.from_iterable([p.procs for p in points]))
topo = mplt.parse_topo('topologies.txt')
# analyze processes to extract particles
for p in procs+topo:
p.analyze_process(None, True)
if not validate_topo(p):
cmssm.drawFullTree(p.decayTree)
# print('#'*40)
procs = list(set(procs))
topo = set(topo)
print('Looking for new topologies...')
new_topos = []
def test_one(self):
plot.main()
p.add_argument("cities", nargs="*", help="list of cities to plot")
p.add_argument("--destructive",
"--delete",
"-d",
action="store_true",
dest="destructive",
help="delete files after plotting them")
p.add_argument("--no-pressure",
"-np",
dest="pressure",
action="store_false",
help="do not plot pressure data")
p.add_argument("--no-humidity",
"-nh",
dest="humidity",
action="store_false",
help="do not plot humidity data")
args = p.parse_args()
categories = ["temp", "feels_like"]
if args.pressure:
categories.append("pressure")
if args.humidity:
categories.append("humidity")
load.get_weather(args.cities)
plot.main(categories)
if args.destructive:
for f in os.listdir("data/"):
os.remove("data/" + f)
