def connect_structures_find_saddles(sequence, structure_list):
pairs = {}
#fc = RNA.fold_compound(sequence)
fp_pool = Pool(Max_Threads)
res_list=[]
for i, se_1 in enumerate(structure_list):
for j in range(i+1, len(structure_list)):
se_2 = structure_list[j]
a = fp_pool.apply_async(find_saddle, args=(se_1.Structure, se_2.Structure, i, j))
res_list.append(a)
#saddle_energy_dcal = fc.path_findpath_saddle(se_1.Structure, se_2.Structure)
#saddle_energy_kcal = saddle_energy_dcal / 100.0
#pairs[(i,j)] = saddle_energy_kcal
#pairs[(j,i)] = saddle_energy_kcal
fp_pool.close()
for a in res_list:
i,j, saddle_energy_kcal = a.get()
pairs[(i,j)] = saddle_energy_kcal
pairs[(j,i)] = saddle_energy_kcal
# get lowest saddle for each structure that ends in a structure with lower energy than the first structure.
minimal_saddle_list = []
for i in range(0, len(structure_list)):
se_1 = structure_list[i]
min_saddle_energy = sys.maxsize
tree_neighbor = None
for j in range(0, len(structure_list)):
if i == j:
continue
se_2 = structure_list[j]
saddle_energy = pairs[(i,j)]
if saddle_energy <= min_saddle_energy and se_2.Energy < se_1.Energy:
min_saddle_energy = saddle_energy
tree_neighbor = j
if tree_neighbor == None: # it could be the root.
tree_neighbor = -1
minimal_saddle_list.append((i, tree_neighbor, min_saddle_energy))
return minimal_saddle_list
print("Creating NEAT object")
# neat = NEAT(nrOfOrgamisms, 1054, 4)
neat = NEAT(nrOfOrgamisms, 1890, 4)
pyplot.show(block=False)
stillRunning = True
while stillRunning:
results = {}
# randomPhenotype = random.choice(neat.phenotypes)
# randomPhenotype.toDraw = True
pool = Pool(nrOfInstances)
finishedIndex = multiprocessing.Manager().Value('i', 0)
for i, phenotype in enumerate(neat.phenotypes):
results[i] = pool.apply_async(testOrganism, (phenotype, instances, finishedIndex, len(neat.phenotypes)))
# results[i] = pool.apply_async(LogExceptions(testOrganism), (phenotype, instances, finishedIndex, len(neat.phenotypes)))
pool.close()
pool.join()
distances = [result.get()[0] for func, result in results.items()]
fitnessScores = [result.get()[1] for func, result in results.items()]
for p, d in zip(neat.phenotypes, distances):
p.genome.distance = d
print("")
print("-----------------------------------------------------")
print("Running epoch")
neat.phenotypes = neat.epoch(fitnessScores)
print(distances)
def create_test_raw_data(self,
ticker_list=None,
start_date=None,
finish_date=None,
folder_prefix=None):
"""Downloads FX tick data from DukasCopy and then dumps each ticker in a separate HDF5 file if a folder is specified.
If no folder is specified returns a list of DataFrames (note: can be a very large list in memory)
Parameters
----------
ticker_list : str (list)
List of FX tickers to download
start_date : datetime/str
Start date of FX tick data download
finish_date : datetime/str
Finish date of FX tick data download
folder_prefix : str
Folder to dump everything
Returns
-------
DataFrame (list)
"""
from findatapy.market import MarketDataRequest, MarketDataGenerator, Market
if start_date is None and finish_date is None:
finish_date = datetime.datetime.utcnow().date() - timedelta(
days=30)
start_date = finish_date - timedelta(days=30 * 15)
start_date = self._compute_random_date(start_date, finish_date)
finish_date = start_date + timedelta(days=90)
df_list = []
result = []
# From multiprocessing.dummy import Pool # threading
from multiprocess.pool import Pool # actuall new processes
import time
# If we don't specify a folder
if folder_prefix is None:
mini_ticker_list = self._split_list(ticker_list, 2)
# Use multiprocess to speed up the download
for mini in mini_ticker_list:
pool = Pool(processes=2)
for ticker in mini:
time.sleep(1)
self.logger.info("Loading " + ticker)
md_request = MarketDataRequest(
start_date=start_date,
finish_date=finish_date,
category='fx',
tickers=ticker,
fields=['bid', 'ask', 'bidv', 'askv'],
data_source='dukascopy',
freq='tick')
# self._download(md_request)
result.append(
pool.apply_async(self._download,
args=(
md_request,
folder_prefix,
)))
pool.close()
pool.join()
else:
market = Market(market_data_generator=MarketDataGenerator())
for ticker in ticker_list:
md_request = MarketDataRequest(
start_date=start_date,
finish_date=finish_date,
category='fx',
tickers=ticker,
fields=['bid', 'ask', 'bidv', 'askv'],
data_source='dukascopy',
freq='tick')
df = market.fetch_market(md_request=md_request)
df.columns = ['bid', 'ask', 'bidv', 'askv']
df['venue'] = 'dukascopy'
df['ticker'] = ticker
# print(df)
if folder_prefix is not None:
self.dump_hdf5_file(df,
folder_prefix + "_" + ticker + ".h5")
# df.to_csv(folder_prefix + "_" + ticker + ".csv") # CSV files can be very large, so try to avoid
else:
df_list.append(df)
return df_list
