def changelist_view(self, request, extra_context=None):
t = Timing('WorklogReport')
# выбранный диапазон месяцев
year = get_year_param(request)
month_list = [
date(year, 1 + i, 1) for i in range(BaseReportAdmin.COLUMNS)
]
stop_date = get_slice_param(request)
last_date = month_list[-1] + monthdelta(1)
if stop_date and stop_date > last_date:
# сброс параметра, если выбран набор данных в прошлом
stop_date = None
qs = self.get_queryset(request).filter(
startdate__range=(month_list[0], stop_date or last_date))
# загрузка и сохранение в request журнала работ за год
# (оптимизация для предотвращения необходимости повторной загрузки фрейма в фильтрах)
worklogframe = WorklogFrame().load(qs)
# ограничение набора данных только теми, по которым решал задачи пользователь
if not request.user.has_perm('jiradata.view_all'):
worklogframe = worklogframe.filter(author=request.user.username)
request.worklogframe = worklogframe
rows = worklogframe.rows()
# заполнение фильтров в базовом классе
response = super().changelist_view(request,
extra_context=extra_context)
try:
cl = response.context_data['cl']
qs = cl.queryset
except (AttributeError, KeyError):
return response
# фильтрация по выбранному сотруднику
user = get_user_param(request)
worklogframe = worklogframe.filter(author=user)
# список норм рабочего времени
month_norma = calc_month_norma(month_list, stop_date=stop_date)
seconds = t.step()
response.context_data['months'] = month_list
response.context_data['member'] = JiraUser.objects.filter(
user_name=user).first()
response.context_data['summary'], response.context_data[
'total'] = worklogframe.aggr_month_budget(month_list, month_norma)
response.context_data['norma'] = month_norma
response.context_data['slice'] = stop_date
response.context_data['year'] = year
response.context_data[
'stat'] = f'{rows} строк обработано за {seconds:.2} c'
return response
def getSatXYZ_m(self, prn, t_start, t_end):
time = Timing()
df_ephemeris = pd.DataFrame(self.nav_data, columns=['prn', 'year', 'month', 'day', 'hour', 'min', 'sec',
'sv_clock_bias', 'sv_clock_drift', 'sv_clock_drift_rate',
'IODE', 'Crs', 'Delta_n', 'M0', 'Cuc', 'e', 'Cus', 'sqrt_A',
'Toe', 'Cic', 'OMEGA', 'CIS', 'i0', 'Crc', 'omega',
'OMEGA_DOT', 'IDOT', 'Codes_L2_channel', 'GPS_week',
'L2_P_data_flag', 'SV_accuracy', 'SV_health', 'TGD',
'IODC', 'transmission_time', 'fit_interval'])
df_ephemeris["UnixGPSTime"] = time.iso_ToGPSUnixTime(df_ephemeris.year, df_ephemeris.month, df_ephemeris.day)
subDataFrame = df_ephemeris.query('prn == @prn')
def __init__(self, gridsize):
self._timing = Timing()
initt = self._timing.routine('simulation setup')
initt.start('building grid')
self._initgrid(gridsize)
self.tiles = {}
for v in self._grid.faces:
x, y, z = v
lat = 180/pi * atan2(z, sqrt(x*x + y*y))
lon = 180/pi * atan2(y, x)
self.tiles[v] = Tile(lat, lon)
for t in self.tiles.values():
t.emptyocean(self.seafloor())
t.climate = t.seasons = None
initt.start('building indexes')
self.shapes = []
self.adj = Adjacency(self._grid)
self._glaciationt = 0
self.initindexes()
self.populated = {}
self.agricultural = set()
self.fauna = []
self.plants = []
self.trees = []
self._species = None
initt.done()
def __init__(self):
self.log = LogWrapper("TradingBot")
self.tech_log = LogWrapper("TechnicalsBot")
self.trade_pairs = Settings.get_pairs()
self.settings = Settings.load_settings()
self.api = OandaAPI()
self.timings = { p: Timing(self.api.last_complete_candle(p, GRANULARITY)) for p in self.trade_pairs }
self.log_message(f"Bot started with\n{pprint.pformat(self.settings)}")
self.log_message(f"Bot Timings\n{pprint.pformat(self.timings)}")
def buildDoubleDifferences(self):
"""Construire les doubles differences"""
coord_sat_tmp = [0.0, 0.0, 0.0]
t = Timing()
flag = 0
# Il faut d'abord choisir le satellite pivot
for i in range(0, self.obs[0].rxm_raw.shape[0]):
for j in range(0, self.eph.nav_data.shape[0]):
year = int(self.eph.nav_data[j, 1]) + 2000
month = int(self.eph.nav_data[j, 2])
day = int(self.eph.nav_data[j, 3])
hour = int(self.eph.nav_data[j, 4])
minute = int(self.eph.nav_data[j, 5])
sec = int(self.eph.nav_data[j, 6])
if self.obs[0].rxm_raw[i,7] == self.eph.nav_data[j,0] and math.fabs(t.weekToW_ToGPSUnixTime(
self.obs[0].rxm_raw[i,1], self.obs[0].rxm_raw[i,0]) - t.iso_ToGPSUnixTime(
year, month, day, hour, minute, sec
)) < 7200000:
coord_sat_tmp = self.eph.getSatXYZ(int(self.obs[0].rxm_raw[i, 0] / 1000), self.eph.nav_data[j])
print(coord_sat_tmp)
if 10 < float(180 * self.getSatElevation(coord_sat_tmp[0], coord_sat_tmp[1], coord_sat_tmp[2],
self.coord_pivot[0],self.coord_pivot[1],self.coord_pivot[2])/math.pi):
print(float(180 * self.getSatElevation(coord_sat_tmp[0], coord_sat_tmp[1], coord_sat_tmp[2],
self.coord_pivot[0],self.coord_pivot[1],self.coord_pivot[2])/math.pi))
print(self.eph.nav_data[j,0])
print(str(year)+"/"+str(month)+"/"+str(day)+"/"+str(hour)+"/"+str(minute)+"/"+str(sec))
print(str(self.obs[0].rxm_raw[i,0])+"/"+str(self.obs[0].rxm_raw[i,1]))
flag = 1
break
if flag == 1:
break
for i in range(1, len(self.obs)):
# Le nombre des DD = Nbre de cubes fixes x Nbre de cube mobile
print("OK2")
def parse_timings(self, time_dict:dict):
timings = []
for key in time_dict.keys():
timing = Timing(
test_uuid=self.uuid,
function_name=key,
function_id=self.find_lambda_id(key),
total_time=time_dict[key]['total_time'],
exe_time=time_dict[key]['exe_time'],
latency=time_dict[key]['latency'],
memory_limit=time_dict[key]['memory'],
log_stream_name=self.parse_log_stream_name(time_dict[key]['log_stream_name'])
)
timings.append(timing)
return timings
class Log(object):
# Define logfile directory
log_dir = os.path.join(PROJECT_PATH, "logs")
# Define default logfile format.
file_name_format = Timing.get_current_time_for_log()
console_msg_format = '%(asctime)s %(levelname)-8s: %(message)s'
file_msg_format = '%(asctime)s %(levelname)-8s: %(message)s'
# Define the log level
log_level = logging.INFO
@staticmethod
def logger(logger_name=None):
# Create the root logger.
logger = logging.getLogger(logger_name)
logger.setLevel(Log.log_level)
# Validate the given directory.
Log.log_dir = os.path.normpath(Log.log_dir)
# Create a folder for the logfile.
TXT.make_dir(Log.log_dir)
# Build the logfile name
filename = Log.file_name_format + ".log"
filename = os.path.join(Log.log_dir, filename)
# Set up logging to the logfile
file_handler = RotatingFileHandler(
filename=filename
# ,maxBytes=Log.max_bytes, backupCount=Log.backup_count
)
file_handler.setLevel(Log.log_level)
file_formatter = logging.Formatter(Log.file_msg_format)
file_handler.setFormatter(file_formatter)
logger.addHandler(file_handler)
# Set up logging to console
stream_handler = logging.StreamHandler()
stream_handler.setLevel(Log.log_level)
stream_formatter = logging.Formatter(Log.console_msg_format)
stream_handler.setFormatter(stream_formatter)
logger.addHandler(stream_handler)
return logger
self.set_scene_coords_projection()
def finish_drag(self):
if not self._is_dragging:
return
self._viewport_fixed_center = self.viewport_center
self._is_dragging = False
self.set_scene_coords_projection()
def cancel_drag(self):
self._is_dragging = False
self.set_scene_coords_projection()
timing = Timing()
timing.set_value('main_opacity', 1.0)
timing.set_value('main_wireframe_color', Interface.HIDDEN_WIREFRAME_COLOR)
timing.set_value('point_border_color', Interface.HIDDEN_POINT_BORDER_COLOR)
timing.set_value('point_fill_color', Interface.HIDDEN_POINT_FILL_COLOR)
timing.set_value('target_wireframe_color', Interface.VISIBLE_TARGET_COLOR)
interface = Interface()
context = Context(config=config, interface=interface, timing=timing)
def display():
timing.update_time()
glClearColor(0.1, 0.1, 0.1, 1)
def main(plpy, args: str):
# some config parameter
# the map is passed down to the functions
config = {'src': '/benchmark/sql',
'dataset': 'tpch',
'query_types': ['bv', 'cv', 'dv', 'ev', 'fv'],
'queries': ['q1', 'q3', 'q6', 'q15', 'q20'],
'batch_size': 1000,
'max_batch_size': 10000}
# perform some basic argument parsing
args = args.split()
operation = args[0]
timing = Timing(config)
db = Database(plpy, timing)
# load the TPC-H relations
if operation == 'setup':
SetupPublic(db, config).execute()
db.commit()
# create auxiliary tables, views and functions for the given maintenance approach
elif operation == 'setup_query' and len(args) == 3:
clear_query(db, config, args[1], args[2])
setup_query(db, config, args[1], args[2])
# check for correctness of the given query
elif operation == 'compare' and len(args) == 2:
compare(db, config, args[1])
# check correctness of all available queries
elif operation == 'compare_all' and len(args) == 2:
config['batch_size'] = int(args[1])
for query in config['queries']:
compare(db, config, query)
# benchmark the given query for the obtained batch_size
elif operation == 'benchmark' and len(args) == 3:
benchmark(db, config, args[1], args[2], True)
timing.save(db)
# benchmark all queries and all maintenance approaches for the given batch size
elif operation == 'benchmark_all' and len(args) == 3:
config['batch_size'] = int(args[2])
for query_type in config['query_types']:
for query in config['queries']:
# warmup (discard first three iterations)
benchmark(db, config, query_type, query, False)
benchmark(db, config, query_type, query, False)
benchmark(db, config, query_type, query, False)
for i in range(int(args[1])):
benchmark(db, config, query_type, query, True)
# write execution times to the database
timing.save(db)
# clear everything, including TPC-H relations
elif operation == 'clear':
for query_type in config['query_types']:
for query in config['queries']:
clear_query(db, config, query_type, query)
ClearPublic(db, config).execute()
db.commit()
else:
raise RuntimeError('Missing arguments!')
# temperature from temperature_sensor import TemperatureSensor temperature_data = [] temperature = TemperatureSensor() # humidity from temperature_sensor import HumiditySensor humidity_data = [] humidity = HumiditySensor() # Timing set up time_data = [] t = Timing() # mqtt setup import paho.mqtt.client as mqtt piTopic = "IC.embedded/tEEEm/TO_PI" appTopic = "IC.embedded/tEEEm/TO_APP" # constants on piTopic SPEECH_TRIGGER = 0 TIME_SET = 1 SUNRISE = 0 AT = 1 ASK_RESULTS = 2 RECEIVED_START_ALARM = 3 RECEIVED_STOP_ALARM = 4
def __init__(self, r, dt):
"""Create a simulation for a planet of radius r km and timesteps of dt
million years.
"""
self._timing = Timing()
initt = self._timing.routine('simulation setup')
# max speed is 100km per million years
self._dp = 100.0/r * dt
self._build = dt/5.0
self._erode = dt
tilearea = 4 * pi * r**2
initt.start('building grid')
grid = Grid()
while grid.size < 6:
grid = Grid(grid)
grid.populate()
self._grid = grid
self.tiles = {}
for v in self._grid.faces:
x, y, z = v
lat = 180/pi * atan2(y, sqrt(x*x + z*z))
lon = 180/pi * atan2(-x, z)
self.tiles[v] = Tile(lat, lon)
initt.start('building indexes')
self.initindexes()
initt.start('creating initial landmass')
tilearea /= len(self._indexedtiles)
# the numerator of the split probability, where
# the number of tiles in the shape is the denomenator:
# a 50M km^2 continent has a 50/50 chance of splitting in a given step
self._splitnum = 25e6/tilearea
# initial location
p = (0, 1, 0)
# 0 velocity vector
v = (0, 0, 0)
# orienting point
o = (1, 0, 0)
r = 1.145
shape = [(r*random.uniform(0.9,1.1)*cos(th),
r*random.uniform(0.9,1.1)*sin(th))
for th in [i*pi/8 for i in range(16)]]
shape = Shape(shape, p, o, v).projection()
self._shapes = [Group([t for t in self.tiles.itervalues() if shape.contains(t.vector)], v)]
# initial landmass starts at elevation based on distance from center
c = self._indexedtiles[self._index.nearest(p)[0]]
r2 = r*r
# on land, one random tile is the center of a felsic chunk
f = random.choice(self._shapes[0].tiles)
for t in self._indexedtiles:
if t in self._shapes[0].tiles:
dc = t.distance(c)
df = t.distance(f)
r = igneous.extrusive(max(0.5, 1 - df*df/r2))
h = 1 - dc*dc/r2
t.emptyland(r, h)
else:
t.emptyocean(self.seafloor())
for t in self.tiles.itervalues():
t.climate = None
initt.done()
self._atmosphere = self._life = False
self._climatemappings = {}
self._climateprof = None
self.dirty = True
"""
Example usage of module timing
"""
from timing import Timing
import time
my_outer_process = Timing("My outer process")
my_inner_process = Timing("My inner process")
my_outer_process.start()
time.sleep(2)
my_inner_process.start()
time.sleep(2)
my_inner_process.stop()
time.sleep(1)
my_outer_process.stop()
#Output:
# 'My outer process' started.
# 'My inner process' started.
# 'My inner process' finished.
# ++++++++++++SUMMARY+EXECUTION+TIME+'My inner process'+++++++++++++
# Elapsed time 'My inner process':0 min, 2.000 sec
# 'My outer process' finished.
# ++++++++++++SUMMARY+EXECUTION+TIME+'My outer process'+++++++++++++
# Elapsed time 'My outer process':0 min, 5.000 sec
def app(unused_argv):
tf.logging.debug("Starting app")
# Start action server
action_server = ActionServer()
action_server.start()
# Init midi ports, keep direct references to output_ports for
# direct sending without the hub player
if platform.system() == "Windows":
input_ports = [
port for port in midi_hub.get_available_input_ports()
if MIDI_INPUT_PORT in port
]
output_ports = [
port for port in midi_hub.get_available_output_ports()
if MIDI_OUTPUT_PORT in port
]
if len(input_ports) is not 1 or len(output_ports) is not 1:
raise Exception(f"Need exactly 1 midi input ({input_ports}) "
f"matching {MIDI_INPUT_PORT}"
f"and 1 midi output port ({output_ports}) "
f"matching {MIDI_OUTPUT_PORT},"
f"you can use LoopMIDI for that")
else:
input_ports = [MIDI_INPUT_PORT]
output_ports = [MIDI_OUTPUT_PORT]
hub = midi_hub.MidiHub(input_ports, output_ports, None)
output_port = hub._outport.ports[0]
# Panic to stop all current messages (note off everywhere)
[output_port.send(message) for message in mido.ports.panic_messages()]
# Synchronise event for all the loopers, controlled by the metronome
bar_start_event = threading.Event()
# Common stuff
qpm = 80
timing = Timing(qpm)
loopers = []
try:
# Init and start the loopers, they block on the event
drum_looper = SequenceLooper("drums",
bar_start_event,
action_server,
hub,
"drum_kit_rnn",
"drum_kit",
timing,
midi_channel=9,
bar_per_loop=2)
melody_looper = SequenceLooper("melody",
bar_start_event,
action_server,
hub,
"attention_rnn",
"attention_rnn",
timing,
midi_channel=0,
bar_per_loop=4)
loopers.append(drum_looper)
loopers.append(melody_looper)
[looper.start() for looper in loopers]
tf.logging.debug("Loopers started " +
str([("drum_looper", drum_looper),
("melody_looper", melody_looper)]))
# Start metronome (wait to make sure everything is started)
time.sleep(1)
metronome = Metronome(bar_start_event, timing)
loopers.append(metronome)
metronome.start()
tf.logging.debug("Metronome started " +
str([("metronome", metronome)]))
# Wait for the loopers
[looper.join() for looper in loopers]
except KeyboardInterrupt:
print("SIGINT received, stopping action server, loopers and stuff")
action_server.stop()
[looper.stop() for looper in loopers]
return 1
return 0
if confidence > args["confidence"]:
# compute the coordinates of the bounding box for the detection
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# draw the bounding box and display the confidence
text = "{:.2f}%".format(confidence * 100)
y = startY - 10 if startY - 10 > 10 else startY + 10
cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 255),
2)
cv2.putText(image, text, (startX, y), cv2.FONT_HERSHEY_DUPLEX,
0.45, (0, 0, 255), 2)
# cv2.imshow("Output", image)
cv2.imwrite(f"./outC/detected_{os.path.splitext(image_path)[0]}.jpg",
image)
# cv2.imwrite(f"./outC/detected_{image_path}", image)
timer = Timing("1 image test")
detect_faces(images1)
timer.end_log()
timer = Timing("10 image test")
for i in images10:
detect_faces(i)
timer.end_log()
timer = Timing("100 image test")
for i in images:
detect_faces(i)
timer.end_log()
# -*- coding: utf-8 -*-
import pytz
from datetime import datetime
import redis
from timing import Timing
from .models import TimingTask
from . import models
time_task = Timing()
# 计算优先级
class TimingPriority():
def calculate(self, name, type, priority, setime, timing_number,
timing_type, url, start_minutes, end_minutes):
# 计算优先级
level = 0
# 为电商类网站
if type == "jd_products" or type == "tb_products":
level += 2
else:
level += 0
# 计算本身的优先级
level = level + int(priority)
#计算时间
tz = pytz.timezone('Asia/Shanghai')
now = datetime.now(tz)
now_time = now.strftime("%m/%d/%Y")
class LifeformsSimulation(object):
mean_temprange = (-25.0, 50.0)
seasons = [-1, -0.5, 0, 0.5, 1, 0.5, 0, -0.5]
def __init__(self, gridsize):
self._timing = Timing()
initt = self._timing.routine('simulation setup')
initt.start('building grid')
self._initgrid(gridsize)
self.tiles = {}
for v in self._grid.faces:
x, y, z = v
lat = 180/pi * atan2(z, sqrt(x*x + y*y))
lon = 180/pi * atan2(y, x)
self.tiles[v] = Tile(lat, lon)
for t in self.tiles.values():
t.emptyocean(self.seafloor())
t.climate = t.seasons = None
initt.start('building indexes')
self.shapes = []
self.adj = Adjacency(self._grid)
self._glaciationt = 0
self.initindexes()
self.populated = {}
self.agricultural = set()
self.fauna = []
self.plants = []
self.trees = []
self._species = None
initt.done()
def _initgrid(self, gridsize):
grid = Grid()
while grid.size < gridsize:
grid = Grid(grid)
grid.populate()
self._grid = grid
def initindexes(self):
self._indexedtiles = []
for t in self.tiles.values():
self._indexedtiles.append(t)
self._tileadj = dict()
for v in self._grid.faces:
self._tileadj[self.tiles[v]] = set([self.tiles[nv] for nv in self.adj[v]])
self._index = PointTree(dict([[self._indexedtiles[i].vector, i]
for i in range(len(self._indexedtiles))]))
def nearest(self, loc):
return self._indexedtiles[self._index.nearest(loc)[0]]
@property
def grid(self):
return self._grid
def classify(self):
c = climate(self.tiles, self.adj, self.seasons, self.cells, self.spin, self.tilt, temprange(self.mean_temprange, self.glaciation), self.glaciation, True, {})
for v, tile in self.tiles.items():
tile.climate = c[v]['classification']
tile.seasons = c[v]['seasons']
def settle(self):
timing = self._timing.routine('settling species')
timing.start('classifying climate')
self.classify()
self.fauna = []
self.plants = []
self.trees = []
lifeformsmethod.settle(self.fauna, self.plants, self.trees, self.tiles, self.adj, timing)
self._species = None
timing.done()
def species(self):
if not self._species:
timing = self._timing.routine('indexing species')
types = self.fauna, self.plants, self.trees
pops = [{} for _ in types]
for t in range(len(types)):
p = pops[t]
for s in types[t]:
for f, ss in s.seasonalrange(len(self.seasons)).items():
for i in ss:
if f not in p:
p[f] = [set() for _ in self.seasons]
p[f][i].add(s)
self._species = pops
timing.done()
return self._species
@property
def glaciation(self):
return self._glaciation if hasattr(self, '_glaciation') else 0.5
@glaciation.setter
def glaciation(self, value):
self._glaciation = value
self.settle()
@staticmethod
def seafloor():
return igneous.extrusive(0.5)
def loaddata(self, data):
random.setstate(data['random'])
self._initgrid(data['gridsize'])
self.spin, self.cells, self.tilt = [data[k] for k in ['spin', 'cells', 'tilt']]
self.tiles = data['tiles']
self.shapes = data['shapes']
self.populated = data['population']
self.agricultural = data['agricultural']
self._glaciationt = data['glaciationtime']
self.initindexes()
self.settle()
def load(self, filename):
self.loaddata(Data.load(filename))
def savedata(self):
return Data.savedata(random.getstate(), self._grid.size, 0, self.spin, self.cells, self.tilt, None, None, None, self.tiles, self.shapes, self._glaciationt, self.populated, self.agricultural, True, True, False, [], {}, {}, [], {}, {}, [], [])
def save(self, filename):
Data.save(filename, self.savedata())
class PrehistorySimulation(object):
coastprox = 2
range = 6
seasons = [-1, -0.5, 0, 0.5, 1, 0.5, 0, -0.5]
mean_temprange = (-25.0, 50.0)
minriverelev = 5
minriverprecip = 0.5
glaciationstep = 16
anthroglacial = 6
def __init__(self, gridsize, spin, cells, tilt):
self._timing = Timing()
initt = self._timing.routine('simulation setup')
self.spin, self.cells, self.tilt = spin, cells, tilt
initt.start('building grid')
self._initgrid(gridsize)
self.tiles = {}
for v in self._grid.faces:
x, y, z = v
lat = 180/pi * atan2(z, sqrt(x*x + y*y))
lon = 180/pi * atan2(y, x)
self.tiles[v] = Tile(lat, lon)
for t in self.tiles.values():
t.emptyocean(self.seafloor())
t.climate = t.seasons = None
t.candidate = False
initt.start('building indexes')
self.shapes = []
self.adj = Adjacency(self._grid)
self._glaciationt = 0
self.initindexes()
self.populated = {}
self.agricultural = set()
initt.done()
def _initgrid(self, gridsize):
grid = Grid()
while grid.size < gridsize:
grid = Grid(grid)
grid.populate()
self._grid = grid
def initindexes(self):
self._indexedtiles = []
for t in self.tiles.values():
self._indexedtiles.append(t)
self._tileadj = dict()
for v in self._grid.faces:
self._tileadj[self.tiles[v]] = set([self.tiles[nv] for nv in self.adj[v]])
self._index = PointTree(dict([[self._indexedtiles[i].vector, i]
for i in range(len(self._indexedtiles))]))
def nearest(self, loc):
return self._indexedtiles[self._index.nearest(loc)[0]]
def newrace(self):
# TODO make unique
vs, cs = phonemes()
name = output.write(random.choice(list(lexicon(vs, cs, round(random.gauss(-0.5, 1)), 0.5, 0.5, None, 1000))))
return name[0].upper() + name[1:]
def update(self):
stept = self._timing.routine('simulation step')
stept.start('identifying glaciers')
gs = [sum([1 for t in s.tiles if t.climate and t.climate.koeppen[0] == u'E']) for s in self.shapes]
stept.start('iterating climate')
glaciation = 0.5 - math.cos(self._glaciationt*math.pi/self.glaciationstep)/2
c = climate(self.tiles, self.adj, self.seasons, self.cells, self.spin, self.tilt, temprange(self.mean_temprange, glaciation), glaciation, True, {})
for v, tile in self.tiles.items():
tile.climate = c[v]['classification']
tile.seasons = c[v]['seasons']
if not habitable(tile) and tile in self.populated:
del self.populated[tile]
stept.start('applying isostasy')
for s, g in zip(self.shapes, gs):
dg = sum([1 for t in s.tiles if t.climate and t.climate.koeppen[0] == u'E']) - g
dh = 0.6 * dg / len(s.tiles)
for t in s.tiles:
t.isostasize(dh)
self._glaciationt += 1
if not self.populated:
stept.start('genesis')
self.populated = eden(self.tiles, self._tileadj, self.newrace())
stept.start('running rivers')
rivers = run(self.tiles.values(), self._tileadj, self.minriverelev, self.minriverprecip)
stept.start('sparking agriculture')
gfactor = math.pow(glaciation, 2) # Agriculture more likely in interglacial period
for r in rivers:
for t in r:
if t in self.populated and t not in self.agricultural and random.random() < gfactor * agprob(t.climate.koeppen):
self.agricultural.add(self.populated[t])
popcache = {}
for i in range(self.anthroglacial):
stept.start('migration {}'.format(i))
if not expandpopulation(rivers, self._tileadj, self.populated, self.agricultural, self.range, self.coastprox, popcache):
break
stept.start('identifying distinct populations')
racinate(self.tiles.values(), self._tileadj, self.populated, self.newrace, self.agricultural, self.range)
stept.done()
@property
def grid(self):
return self._grid
@property
def peoples(self):
return len({p for p in self.populated.values()})
@staticmethod
def seafloor():
return igneous.extrusive(0.5)
def loaddata(self, data):
random.setstate(data['random'])
self._initgrid(data['gridsize'])
self.spin, self.cells, self.tilt = [data[k] for k in ['spin', 'cells', 'tilt']]
self.tiles = data['tiles']
self.shapes = data['shapes']
self.populated = data['population']
self.agricultural = data['agricultural']
self._glaciationt = data['glaciationtime']
self.initindexes()
def load(self, filename):
self.loaddata(Data.load(filename))
def savedata(self):
return Data.savedata(random.getstate(), self._grid.size, 0, self.spin, self.cells, self.tilt, None, None, None, self.tiles, self.shapes, self._glaciationt, self.populated, self.agricultural, True, True, False, [], {}, {}, [], {}, {}, [], [])
def save(self, filename):
Data.save(filename, self.savedata())
import pygame
from pygame.locals import *
from kernels import *
from vector import Vec
#from timing import print_timing
from timing import Timing
timings = Timing()
#@print_timing
@timings
def density_update(sphp, particles):
#brute force
for pi in particles:
pi.dens = 0.
for pj in particles:
r = pi.pos - pj.pos
#print r
if mag(r) > pi.h: continue
#pi.dens += pj.mass*Wpoly6(pi.h, r)
pi.dens += pj.mass * sphp.kernels.poly6(r)
#@print_timing
@timings
def force_update(sphp, particles):
#brute force
rho0 = sphp.rho0
K = sphp.K
from light.http_light_writer import HttpLightWriter
from light.light_timing import LightStepListener
from light.priority_light_writer import PriorityLightWriterFactory
from timing import Timing
from midi.midi_output import MidiOutputBpm, MidiOutputTime
import time
import midi.midi_bindings as bindings
from midi.midi_input import *
DEFAULT_IP = "192.168.1.72"
DEFAULT_PORT = 80
DEFAULT_GPIO = 12
if __name__ == "__main__":
timing = Timing(4 * 8, 1.0 / 4.0)
midi_binding = bindings.APC_KEY_25
light_writer = HttpLightWriter(DEFAULT_IP, DEFAULT_PORT, DEFAULT_GPIO)
priority_light_writer_factory = PriorityLightWriterFactory(light_writer)
light_step_listener = LightStepListener(timing, priority_light_writer_factory.low())
output_steps = MidiOutputTime(timing, midi_binding)
output_bpm = MidiOutputBpm(timing, midi_binding)
output_bpm.start_bpm_thread()
input_steps = MidiInputSteps(timing, midi_binding)
input_steps.start_listening()
generic_midi_input = MidiGenericInputListener(midi_binding)
class PlanetSimulation(object):
temprange = (-25.0, 50.0)
def __init__(self, r, gridsize, spin, cells, tilt, landr, dt, atmdt, lifedt):
"""Create a simulation for a planet with the given characteristics. """
self._timing = Timing()
initt = self._timing.routine('simulation setup')
self.spin, self.cells, self.tilt = spin, cells, tilt
# max speed is 100km per million years
self._dp = 100.0/r * dt
self._build = dt/5.0
self._erode = dt
tilearea = 4 * pi * r**2
initt.start('building grid')
self._initgrid(gridsize)
self.tiles = {}
for v in self._grid.faces:
x, y, z = v
lat = 180/pi * atan2(z, sqrt(x*x + y*y))
lon = 180/pi * atan2(y, x)
self.tiles[v] = Tile(lat, lon)
initt.start('building indexes')
self.initindexes()
initt.start('creating initial landmass')
tilearea /= len(self._indexedtiles)
# the numerator of the split probability, where
# the number of tiles in the shape is the denomenator:
# a 50M km^2 continent has a 50/50 chance of splitting in a given step
self._splitnum = 25e6/tilearea
# initial location
p = [random.uniform(-1, 1) for i in range(3)]
p /= norm(p)
# 0 velocity vector
v = (0, 0, 0)
# orienting point
o = [0, 0, 0]
mini = min(range(len(p)), key=lambda i: abs(p[i]))
o[mini] = 1 if p[mini] < 0 else -1
shape = SphericalPolygon([rotate(rotate(p, o, landr*random.uniform(0.9,1.1)), p, th)
for th in [i*pi/8 for i in range(16)]])
self._shapes = [Group([t for t in self.tiles.values() if shape.contains(t.vector)], v)]
# initial landmass starts at elevation based on distance from center
c = self._indexedtiles[self._index.nearest(p)[0]]
r2 = landr*landr
# on land, one random tile is the center of a felsic chunk
f = random.choice(self._shapes[0].tiles)
for t in self._indexedtiles:
if t in self._shapes[0].tiles:
dc = t.distance(c)
df = t.distance(f)
r = igneous.extrusive(max(0.5, 1 - df*df/r2))
h = 1 - dc*dc/r2
t.emptyland(r, h)
else:
t.emptyocean(self.seafloor())
for t in self.tiles.values():
t.climate = t.seasons = None
initt.done()
self._atmosphereticks = atmdt / dt
self._lifeticks = lifedt / dt
self._climatemappings = {}
self._climateprof = None
self.dirty = True
def _initgrid(self, gridsize):
grid = Grid()
while grid.size < gridsize:
grid = Grid(grid)
grid.populate()
self._grid = grid
@property
def grid(self):
return self._grid
@property
def hasatmosphere(self):
return self._atmosphereticks == 0
@property
def haslife(self):
return self._lifeticks == 0
@staticmethod
def seafloor():
return igneous.extrusive(0.5)
def initindexes(self):
self._indexedtiles = []
for t in self.tiles.values():
self._indexedtiles.append(t)
self.adj = Adjacency(self._grid)
self._tileadj = dict()
for v in self._grid.faces:
self._tileadj[self.tiles[v]] = set([self.tiles[nv] for nv in self.adj[v]])
self._index = PointTree(dict([[self._indexedtiles[i].vector, i]
for i in range(len(self._indexedtiles))]))
def nearest(self, loc):
return self._indexedtiles[self._index.nearest(loc)[0]]
@property
def continents(self):
return len(self._shapes)
@property
def land(self):
return int(100.0*sum([len(s.tiles) for s in self._shapes])/len(self._indexedtiles) + 0.5)
def loaddata(self, data):
random.setstate(data['random'])
self._initgrid(data['gridsize'])
self.spin, self.cells, self.tilt = [data[k] for k in ['spin', 'cells', 'tilt']]
self._dp = data['dp']
self._build = data['build']
self._splitnum = data['splitnum']
self.tiles = data['tiles']
self._shapes = data['shapes']
self._atmosphereticks = data['atmt']
self._lifeticks = data['lifet']
self.initindexes()
self.dirty = True
def load(self, filename):
self.loaddata(Data.load(filename))
def savedata(self):
return Data.savedata(random.getstate(), self._grid.size, 0, self.spin, self.cells, self.tilt, self._dp, self._build, self._splitnum, self.tiles, self._shapes, 0, {}, set(), self._atmosphereticks, self._lifeticks, False, [], {}, {}, [], {}, {}, [], [])
def save(self, filename):
Data.save(filename, self.savedata())
def update(self):
"""Update the simulation by one timestep."""
stept = self._timing.routine('simulation step')
stept.start('determining tile movements')
old = set([t for shape in self._shapes for t in shape.tiles])
new = dict()
overlapping = {}
for t in self._indexedtiles:
overlapping[t] = []
for i in range(len(self._shapes)):
speed = norm(self._shapes[i].v)
group, v = move(self._indexedtiles,
self._shapes[i].tiles,
self._shapes[i].v,
self._tileadj,
self._index)
self._shapes[i] = Group(list(group.keys()), v)
for dest, sources in group.items():
if dest in new:
new[dest].append(TileMovement(sources, speed))
else:
new[dest] = [TileMovement(sources, speed)]
overlapping[dest].append(i)
stept.start('applying tile movements')
collisions = {}
newe = {}
seen = set()
for dest, movements in new.items():
# get all the source tiles contributing to this one
newe[dest] = NextTileValue(movements)
if not dest in seen:
try:
old.remove(dest)
except KeyError:
# calculate the amount to build up the leading edge
newe[dest].build(self._build * sum([m.speed for m in movements])/len(movements))
seen.add(dest)
for pair in combinations(overlapping[dest], 2):
if pair in collisions:
collisions[pair] += 1
else:
collisions[pair] = 1
# apply the new values
for t, e in newe.items():
e.apply(t)
# clear out abandoned tiles
for t in old:
t.emptyocean(self.seafloor())
# record each continent's total pre-erosion above-sea size
heights = [sum([t.elevation for t in s.tiles]) for s in self._shapes]
if self.hasatmosphere:
stept.start('"simulating" climate')
seasons = [0.1*v for v in list(range(-10,10,5)) + list(range(10,-10,-5))]
c = climate(self.tiles, self.adj, seasons, self.cells, self.spin, self.tilt, self.temprange, 0.5, self.haslife, self._climatemappings, self._climateprof)
if self._climateprof:
self._climateprof.dump_stats('climate.profile')
for v, tile in self.tiles.items():
tile.climate = c[v]['classification']
tile.seasons = c[v]['seasons']
stept.start('determining erosion')
erosion = erode(self.tiles, self.adj)
for t in self.tiles.values():
t.erode(erosion, self._erode)
for t in self.tiles.values():
# if the tile is in at least one shape, apply the erosion materials
if len(overlapping[t]) > 0:
if len(erosion[t].materials) > 0:
t.deposit(sedimentary.deposit(erosion[t].materials, self.haslife, False, t.climate))
# otherwise, require a certain threshold
elif sum([m.amount for m in erosion[t].materials]) > 1.5:
t.deposit(sedimentary.deposit(erosion[t].materials, self.haslife, True, t.climate))
sourceshapes = set()
for e in erosion[t].sources:
for shape in overlapping[e]:
sourceshapes.add(shape)
for s in sourceshapes:
if not t in self._shapes[s].tiles:
self._shapes[s].tiles.append(t)
overlapping[t] = list(sourceshapes)
if self._lifeticks:
self._lifeticks -= 1
else:
self._atmosphereticks -= 1
stept.start('applying isostatic effects')
for s, h in zip(self._shapes, heights):
dh = (h - sum([t.elevation for t in s.tiles]))/float(len(s.tiles))
for t in s.tiles:
t.isostasize(dh)
stept.start('performing random intrusions')
for t in self.tiles.values():
if t.subduction > 0:
if random.random() < 0.1:
t.intrude(igneous.intrusive(max(0, min(1, random.gauss(0.85, 0.15)))))
t.transform(metamorphic.contact(t.substance[-1], t.intrusion))
stept.start('applying regional metamorphism')
for t in self.tiles.values():
t.transform(metamorphic.regional(t.substance[-1], t.subduction > 0))
for t in self.tiles.values():
t.cleartemp()
stept.start('merging overlapping shapes')
# merge shapes that overlap a lot
groups = []
for pair, count in collisions.items():
if count > min([len(self._shapes[i].tiles) for i in pair])/10:
for group in groups:
if pair[0] in group:
group.add(pair[1])
break
elif pair[1] in group:
group.add(pair[0])
break
else:
groups.append(set(pair))
gone = []
for group in groups:
largest = max(group, key=lambda i: len(self._shapes[i].tiles))
tiles = list(self._shapes[largest].tiles)
v = array(self._shapes[largest].v) * len(tiles)
for other in group:
if other != largest:
v += array(self._shapes[other].v) * len(self._shapes[other].tiles)
tiles += self._shapes[other].tiles
gone.append(self._shapes[other])
self._shapes[largest].tiles = list(set(tiles))
v /= len(tiles)
self._shapes[largest].v = v
for s in gone:
self._shapes.remove(s)
stept.start('randomly splitting shapes')
# occaisionally split big shapes
for i in range(len(self._shapes)):
if random.uniform(0,1) > self._splitnum / len(self._shapes[i].tiles):
self._shapes[i:i+1] = [Group(ts, self._shapes[i].v + v * self._dp)
for ts, v in split(self._shapes[i].tiles)]
stept.done()
self.dirty = True
from imutils.video import VideoStream
import imutils
import time
import cv2
from timing import Timing
print("[INFO] loading cascade file stream...")
haarcascade_path = cv2.data.haarcascades + "/haarcascade_frontalface_default.xml"
face_cascade = cv2.CascadeClassifier(haarcascade_path)
print("[INFO] starting video stream...")
vs = VideoStream(usePiCamera=True).start()
time.sleep(2.0)
frame_count = 0
while True:
timer = Timing(f"Frame {frame_count} timing")
# grab the current frame of video and resize it to 400px wide
frame = vs.read()
frame = imutils.resize(frame, width=400)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
roi_gray = gray[y:y + h, x:x + w]
roi_color = frame[y:y + h, x:x + w]
cv2.imshow("Frame", frame)
timer.end_log()
frame_count = frame_count + 1
# if the `q` key was pressed, break from the loop
def __init__(self, r, gridsize, spin, cells, tilt, landr, dt, atmdt, lifedt):
"""Create a simulation for a planet with the given characteristics. """
self._timing = Timing()
initt = self._timing.routine('simulation setup')
self.spin, self.cells, self.tilt = spin, cells, tilt
# max speed is 100km per million years
self._dp = 100.0/r * dt
self._build = dt/5.0
self._erode = dt
tilearea = 4 * pi * r**2
initt.start('building grid')
self._initgrid(gridsize)
self.tiles = {}
for v in self._grid.faces:
x, y, z = v
lat = 180/pi * atan2(z, sqrt(x*x + y*y))
lon = 180/pi * atan2(y, x)
self.tiles[v] = Tile(lat, lon)
initt.start('building indexes')
self.initindexes()
initt.start('creating initial landmass')
tilearea /= len(self._indexedtiles)
# the numerator of the split probability, where
# the number of tiles in the shape is the denomenator:
# a 50M km^2 continent has a 50/50 chance of splitting in a given step
self._splitnum = 25e6/tilearea
# initial location
p = [random.uniform(-1, 1) for i in range(3)]
p /= norm(p)
# 0 velocity vector
v = (0, 0, 0)
# orienting point
o = [0, 0, 0]
mini = min(range(len(p)), key=lambda i: abs(p[i]))
o[mini] = 1 if p[mini] < 0 else -1
shape = SphericalPolygon([rotate(rotate(p, o, landr*random.uniform(0.9,1.1)), p, th)
for th in [i*pi/8 for i in range(16)]])
self._shapes = [Group([t for t in self.tiles.values() if shape.contains(t.vector)], v)]
# initial landmass starts at elevation based on distance from center
c = self._indexedtiles[self._index.nearest(p)[0]]
r2 = landr*landr
# on land, one random tile is the center of a felsic chunk
f = random.choice(self._shapes[0].tiles)
for t in self._indexedtiles:
if t in self._shapes[0].tiles:
dc = t.distance(c)
df = t.distance(f)
r = igneous.extrusive(max(0.5, 1 - df*df/r2))
h = 1 - dc*dc/r2
t.emptyland(r, h)
else:
t.emptyocean(self.seafloor())
for t in self.tiles.values():
t.climate = t.seasons = None
initt.done()
self._atmosphereticks = atmdt / dt
self._lifeticks = lifedt / dt
self._climatemappings = {}
self._climateprof = None
self.dirty = True
def timing_test(Container=SortedList, t=None):
tk = Toolkit(rs, Container=Container)
note = f"(Using {Container.__name__} as container type)"
Timing.test(brute_force, tk, note=note, t=t)
print()
# coding=utf-8
"""
Demonstrates how to use the background scheduler to schedule a job that executes on 3 second
intervals.
"""
from timing import Timing
if __name__ == '__main__':
task = Timing()
#print task
task.start()
system.at['part_solver'].updateMeshValues(
old_system.at['electrons'], extent=2)
system.at['part_solver'].updateMeshValues(
old_system.at['photoelectrons'], extent=2)
system.at['part_solver'].updateMeshValues(old_system.at['protons'],
extent=2)
out.saveVTK(system.at['mesh'], old_system.at, system.arrangeVTK())
if system.at['ts'] % 10000 == 0:
out.saveParticlesTXT(old_system.at, system.arrangeParticlesTXT())
if system.at['ts'] % 10000 == 0:
out.particleTracker(old_system.at['ts'], old_system.at['protons'],
old_system.at['electrons'],
old_system.at['photoelectrons'])
#Updating previous state
deepcopy = Timing(copy.deepcopy)
old_system = deepcopy(system)
#Execution time of loop step and storage
t1 = time.perf_counter()
getattr(Timing, 'time_dict')['Global'] = t1 - t0
if system.at['ts'] % 10 == 0:
out.saveTimes(system.at['ts'], getattr(Timing, 'time_dict'))
Timing.reset_dict()
#Advance in timestep
system.at['ts'] += 1
except KeyboardInterrupt:
out.savePickle(old_system.at, old_system.arrangePickle())
print('Process aborted')
def run(self):
def send_controls(timing, solver):
control = Control(timing=timing, previous_solver=solver)
control.apply()
def solve_short_term_problem(timing, previous_solver, queue_st):
ambient = Ambient(timing=timing)
ambient.update()
state = State()
state.update()
predictor = Predictor(timing=timing, ambient=ambient, \
state=state, previous_solver=previous_solver, \
solver_name="predictor_bin_" \
+ str(timing.grid_position_cursor))
predictor.solve()
timing.increment_grid_position_cursor()
if timing.grid_position_cursor >= timing.N_short_term:
timing.shift_time_grid()
ambient = Ambient(timing=timing)
ambient.update()
nlpsolver_bin = NLPSolverBin( \
timing=timing, ambient=ambient, \
previous_solver=previous_solver, predictor=predictor, \
solver_name = "nlpsolver_bin_" \
+ str(timing.grid_position_cursor))
nlpsolver_bin.set_solver_max_cpu_time(time_point_to_finish=\
timing.time_points[timing.grid_position_cursor])
nlpsolver_bin.solve()
nlpsolver_bin.reduce_object_memory_size()
timing.sleep_until_time_grid_point("solve_short_term_problem", \
timing.grid_position_cursor)
queue_st.put(nlpsolver_bin)
send_controls(timing, nlpsolver_bin)
nlpsolver_bin.save_results()
def generate_initial_controls(timing, queue_st):
ambient = Ambient(timing=timing)
ambient.update()
state = State()
state.update()
simulator = Simulator( \
timing=timing, ambient=ambient, state=state)
simulator.solve()
queue_st.put(simulator)
simulator.save_results()
def solve_long_term_problem(timing, previous_solver, queue_lt):
ambient = Ambient(timing=timing)
ambient.update()
state = State()
state.update()
predictor = Predictor(timing=timing, ambient=ambient, \
state=state, previous_solver=previous_solver, \
solver_name="predictor_rel")
predictor.solve(n_steps=timing.N_short_term)
timing_next_interval = copy.deepcopy(timing)
timing_next_interval.shift_time_grid()
ambient = Ambient(timing=timing_next_interval)
ambient.update()
nlpsolver_rel = NLPSolverRel( \
timing=timing_next_interval, ambient=ambient, \
previous_solver=previous_solver, predictor=predictor, \
solver_name="nlpsolver_rel")
nlpsolver_rel.solve()
nlpsolver_rel.save_results()
binapprox = BinaryApproximation( \
timing=timing_next_interval, previous_solver=nlpsolver_rel, \
predictor=predictor, solver_name="binapprox")
binapprox.set_solver_max_cpu_time(time_point_to_finish=\
timing_next_interval.time_points[0])
binapprox.solve()
binapprox.save_results()
timing.increment_grid_position_cursor(n_steps=timing.N_short_term -
1)
timing.sleep_until_grid_position_cursor_time_grid_point(
"solve_long_term_problem")
queue_lt.put((timing_next_interval, binapprox))
timing = Timing(startup_time=time.time())
queue_lt = mp.Queue()
queue_st = mp.Queue()
p = mp.Process(target=generate_initial_controls,
args=(timing, queue_st))
p.start()
while timing.mpc_iteration_count < self._MAX_MPC_ITERATIONS:
previous_solver_st = queue_st.get()
timing.define_initial_switch_positions(previous_solver_st)
p = mp.Process(target=solve_long_term_problem, \
args=(timing, previous_solver_st, queue_lt))
p.start()
for k in range(timing.N_short_term - 1):
p = mp.Process(target=solve_short_term_problem, \
args=(timing, previous_solver_st, queue_st))
p.start()
timing.increment_grid_position_cursor()
previous_solver_st = queue_st.get()
# Retrieve results of long-term optimization
timing_next_interval, previous_solver_lt = queue_lt.get()
p = mp.Process(target=solve_short_term_problem, \
args=(timing, previous_solver_lt, queue_st))
p.start()
timing = copy.deepcopy(timing_next_interval)
timing.increment_mpc_iteration_count()