def print_certificate_info(buff, sock, cert):
cert_pem = ssl.DER_cert_to_PEM_cert(sock.getpeercert(True))
x509 = crypto.load_certificate(crypto.FILETYPE_PEM, cert_pem)
public_key = x509.get_pubkey()
key_type = ("RSA" if public_key.type() == crypto.TYPE_RSA else "DSA")
key_size = str(public_key.bits())
sha256_fingerprint = x509.digest(n(b"SHA256"))
sha1_fingerprint = x509.digest(n(b"SHA1"))
signature_algorithm = x509.get_signature_algorithm()
key_info = ("key info: {key_type} key {bits} bits, signed using "
"{algo}").format(key_type=key_type,
bits=key_size,
algo=n(signature_algorithm))
validity_info = (" Begins on: {before}\n"
" Expires on: {after}").format(
before=cert["notBefore"], after=cert["notAfter"])
rdns = chain(*cert["subject"])
subject = ", ".join(["{}={}".format(name, value) for name, value in rdns])
rdns = chain(*cert["issuer"])
issuer = ", ".join(["{}={}".format(name, value) for name, value in rdns])
subject = "subject: {sub}, serial number {serial}".format(
sub=subject, serial=cert["serialNumber"])
issuer = "issuer: {issuer}".format(issuer=issuer)
fingerprints = (" SHA1: {}\n"
" SHA256: {}").format(n(sha1_fingerprint),
n(sha256_fingerprint))
wrapper = textwrap.TextWrapper(initial_indent=" - ",
subsequent_indent=" ")
message = ("{prefix}matrix: received certificate\n"
" - certificate info:\n"
"{subject}\n"
"{issuer}\n"
"{key_info}\n"
" - period of validity:\n{validity_info}\n"
" - fingerprints:\n{fingerprints}").format(
prefix=W.prefix("network"),
subject=wrapper.fill(subject),
issuer=wrapper.fill(issuer),
key_info=wrapper.fill(key_info),
validity_info=validity_info,
fingerprints=fingerprints)
W.prnt(buff, message)
def plot_rose(data,
data_column,
dir_column,
title='',
var_name='',
var_unit='',
fig_filename='',
legend_position='upper left',
xlabels=('E', 'N-E', 'N', 'N-W', 'W', 'S-W', 'S', 'S-E')):
plot.get_default_plot_style(context='talk')
var_label = plot.get_var_label(var_name, var_unit)
ax = plot_windrose(data,
kind='bar',
var_name=data_column,
direction_name=dir_column,
normed=True,
opening=0.8,
edgecolor=n(b'white'))
plot.plot_title(ax, title)
ax.set_xlabel(var_label)
ax.legend(loc=legend_position, fontsize='small')
ax.set_xticklabels(xlabels)
plot.save_figure(fig_filename)
def decrypt(obj):
"""Decrypt the object.
It is an inner function because we must first configure our KMS
client. Then we call this recursively on the object.
"""
if isinstance(obj, list):
res_v = []
for item in obj:
res_v.append(decrypt(item))
return res_v
elif isinstance(obj, dict):
if '_kms' in obj:
try:
res = client.decrypt(CiphertextBlob=b64decode(obj['_kms']))
obj = n(res['Plaintext'])
except ClientError as err:
if 'AccessDeniedException' in err.args[0]:
log.warning(
'Unable to decrypt %s. Key does not exist or no access',
obj['_kms'])
else:
raise
else:
for k, v in obj.items():
obj[k] = decrypt(v)
else:
pass
return obj
def process_selectors(self):
tree = lxml.html.fromstring(self.html)
for process_selector in self.selectors:
for el in tree.xpath(process_selector):
self.add_attribute_to_element(el)
out = n(et.tostring(tree, pretty_print=False))
self.html = six.text_type(out)
def decrypt(obj):
"""Decrypt the object.
It is an inner function because we must first configure our KMS
client. Then we call this recursively on the object.
"""
if isinstance(obj, list):
res_v = []
for item in obj:
res_v.append(decrypt(item))
return res_v
elif isinstance(obj, dict):
if "_kms" in obj:
try:
res = client.decrypt(CiphertextBlob=b64decode(obj["_kms"]))
obj = n(res["Plaintext"])
except ClientError as err:
if "AccessDeniedException" in err.args[0]:
log.warning("Unable to decrypt %s. Key does not exist or no access", obj["_kms"])
else:
raise
else:
for k, v in obj.items():
obj[k] = decrypt(v)
else:
pass
return obj
def test_kms_encrypt(self):
key = 'alias/figgypy-test'
secret = 'test password 1234567890 !@#$%^&*()'
client = boto3.client('kms')
encrypted = kms_encrypt(secret, key)
dec_res = client.decrypt(CiphertextBlob=b64decode(encrypted))
decrypted = n(dec_res['Plaintext'])
assert decrypted == secret
def decrypt(obj):
"""Decrypt the object.
It is an inner function because we must first verify that gpg
is ready. If we did them in the same function we would end up
calling the gpg checks several times, potentially, since we are
calling this recursively.
"""
if isinstance(obj, list):
res_v = []
for item in obj:
res_v.append(decrypt(item))
return res_v
elif isinstance(obj, dict):
if '_gpg' in obj:
try:
decrypted = gpg.decrypt(obj['_gpg'])
if decrypted.ok:
obj = n(decrypted.data.decode('utf-8').encode())
else:
log.error("gpg error unpacking secrets %s",
decrypted.stderr)
except Exception as err:
log.error("error unpacking secrets %s", err)
else:
for k, v in obj.items():
obj[k] = decrypt(v)
else:
try:
if 'BEGIN PGP' in obj:
try:
decrypted = gpg.decrypt(obj)
if decrypted.ok:
obj = n(decrypted.data.decode('utf-8').encode())
else:
log.error("gpg error unpacking secrets %s",
decrypted.stderr)
except Exception as err:
log.error("error unpacking secrets %s", err)
except TypeError:
log.debug('Pass on decryption. Only decrypt strings')
return obj
def decrypt(obj):
"""Decrypt the object.
It is an inner function because we must first verify that gpg
is ready. If we did them in the same function we would end up
calling the gpg checks several times, potentially, since we are
calling this recursively.
"""
if isinstance(obj, list):
res_v = []
for item in obj:
res_v.append(decrypt(item))
return res_v
elif isinstance(obj, dict):
if "_gpg" in obj:
try:
decrypted = gpg.decrypt(obj["_gpg"])
if decrypted.ok:
obj = n(decrypted.data.decode("utf-8").encode())
else:
log.error("gpg error unpacking secrets %s", decrypted.stderr)
except Exception as err:
log.error("error unpacking secrets %s", err)
else:
for k, v in obj.items():
obj[k] = decrypt(v)
else:
try:
if "BEGIN PGP" in obj:
try:
decrypted = gpg.decrypt(obj)
if decrypted.ok:
obj = n(decrypted.data.decode("utf-8").encode())
else:
log.error("gpg error unpacking secrets %s", decrypted.stderr)
except Exception as err:
log.error("error unpacking secrets %s", err)
except TypeError:
log.debug("Pass on decryption. Only decrypt strings")
return obj
def kill_selectors(self):
tree = lxml.html.fromstring(self.html)
for kill_selector in self.kill:
for el in tree.xpath(kill_selector):
self.remove_keeping_tail(el)
for kill_empty_selector in self.kill_empty:
for el in tree.xpath(
'//{}['
'not(descendant-or-self::*/text()[normalize-space()])'
' and not(descendant-or-self::*/attribute::*)'
']'.format(kill_empty_selector)):
self.remove_keeping_tail(el)
out = n(et.tostring(tree, pretty_print=False))
self.html = six.text_type(out)
def s(value):
if compat.is_py2:
if isinstance(value, unicode):
return value.decode('ascii', 'ignore')
if isinstance(value, str):
return value
else:
if isinstance(value, bytes):
return n(value)
if isinstance(value, str):
return value
return value
def kms_encrypt(value, key, aws_config=None):
"""Encrypt and value with KMS key.
Args:
value (str): value to encrypt
key (str): key id or alias
aws_config (optional[dict]): aws credentials
dict of arguments passed into boto3 session
example:
aws_creds = {'aws_access_key_id': aws_access_key_id,
'aws_secret_access_key': aws_secret_access_key,
'region_name': 'us-east-1'}
Returns:
str: encrypted cipher text
"""
aws_config = aws_config or {}
aws = boto3.session.Session(**aws_config)
client = aws.client("kms")
enc_res = client.encrypt(KeyId=key, Plaintext=value)
return n(b64encode(enc_res["CiphertextBlob"]))
def kms_encrypt(value, key, aws_config=None):
"""Encrypt and value with KMS key.
Args:
value (str): value to encrypt
key (str): key id or alias
aws_config (optional[dict]): aws credentials
dict of arguments passed into boto3 session
example:
aws_creds = {'aws_access_key_id': aws_access_key_id,
'aws_secret_access_key': aws_secret_access_key,
'region_name': 'us-east-1'}
Returns:
str: encrypted cipher text
"""
aws_config = aws_config or {}
aws = boto3.session.Session(**aws_config)
client = aws.client('kms')
enc_res = client.encrypt(KeyId=key, Plaintext=value)
return n(b64encode(enc_res['CiphertextBlob']))
def do_POST(self):
self.logger.debug("Webhook triggered")
if self.path == self.server.webhook_path and \
'content-type' in self.headers and \
'content-length' in self.headers and \
self.headers['content-type'] == 'application/json':
json_string = \
n(self.rfile.read(int(self.headers['content-length'])))
self.send_response(200)
self.end_headers()
self.logger.debug("Webhook received data: " + json_string)
update = Update.de_json(json.loads(json_string))
self.logger.info("Received Update with ID %d on Webhook" %
update.update_id)
self.server.update_queue.put(update)
else:
self.send_error(403)
self.end_headers()
def do_POST(self):
self.logger.debug("Webhook triggered")
if self.path == self.server.webhook_path and \
'content-type' in self.headers and \
'content-length' in self.headers and \
self.headers['content-type'] == 'application/json':
json_string = \
n(self.rfile.read(int(self.headers['content-length'])))
self.send_response(200)
self.end_headers()
self.logger.debug("Webhook received data: " + json_string)
update = Update.de_json(json.loads(json_string))
self.logger.info("Received Update with ID %d on Webhook" %
update.update_id)
self.server.update_queue.put(update)
else:
self.send_error(403)
self.end_headers()
def MySQLInfo(command):
try:
# get sql status info
if (command == 'status'):
sql = mode(command)
status = show_update_status(sql)
return status
# get sql process list
if (command == 'process'):
sql = mode(command)
processlist = query(sql)
for process in processlist:
for key in process:
if (key == 'Info'):
if (process[key] is not None):
process[key] = n(process[key])
if (process[key] is None):
process[key] = "None"
return processlist
except Exception as err:
logging.exception(err)
print(err)
sys.exit()
self.delete_slice__2 = self.nop
self.delete_slice__3 = self.nop
super(FindFTrace, self).__init__(*args, **kwargs)
self._locals = AlmostReadOnlyDict(self._locals)
self._globals = AlmostReadOnlyDict(self._globals)
def store_attr(self):
"""STORE_ATTR opcode"""
if self.names[self.oparg] == "f_trace":
self._stop = True
self.result = self.stack.pop() if self.stack else True
FTraceExe = type(n(b"FTraceExe"), (FindFTrace, PyInterpreter), {}) # pylint: disable=invalid-name
def get_f_trace(code, loc, glob):
"""Get frame from frame.f_trace attribution"""
interpreter = FTraceExe(code, loc, glob)
interpreter.execute()
return interpreter.result
def find_f_trace(code, loc, glob, lasti):
"""Check if code has frame.f_trace attribution"""
if "f_trace" not in code.co_names:
return False
interpreter = FindFTrace(code, loc, glob)
interpreter.execute()
def main(args):
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
assert not cfg.MODEL.RPN_ONLY, \
'RPN models are not supported'
assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
'Models that require precomputed proposals are not supported'
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
if os.path.isdir(args.im_or_folder):
im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
else:
im_list = [args.im_or_folder]
"""
Add support for webcam
"""
# Set and get camera from OpenCV
cap = cv2.VideoCapture('/detectron/mypython/people-walking.mp4')
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH) + 0.5)
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT) + 0.5)
s = n(b'XVID')
fourcc = cv2.VideoWriter_fourcc(*s)
out = cv2.VideoWriter('output.avi', fourcc, 24.0, (width, height))
im_name = 'tmp_im'
count = 0
fileOut = open('people-walking.txt', 'w')
while True:
count += 1
# Fetch image from camera
_, im = cap.read()
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)')
box_list = [b for b in cls_boxes if len(b) > 0]
if len(box_list) > 0:
boxes = np.concatenate(box_list)
else:
boxes = None
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2,
ext='jpg' # default is PDF, but we want JPG.
)
time.sleep(0.05)
img = cv2.imread('/detectron/mypython/tmp_im.jpg')
cv2.putText(img, 'Frame: ', (5, 30), cv2.FONT_HERSHEY_SIMPLEX, 1,
(255, 255, 255), 2)
cv2.putText(img, str(count), (130, 30), cv2.FONT_HERSHEY_SIMPLEX, 1,
(255, 255, 255), 2)
cv2.putText(img, 'Model: e2e_mask_rcnn_R-101-FPN_2x.yaml', (200, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
cv2.putText(
img,
'WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl',
(5, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
for i in range(len(boxes)):
x1 = "{:.6f}".format(boxes[i][0] / width)
y1 = "{:.6f}".format(boxes[i][1] / height)
x2 = "{:.6f}".format(boxes[i][2] / width)
y2 = "{:.6f}".format(boxes[i][3] / height)
conf = "{:.6f}".format(boxes[i][4])
fileOut.write("Frame " + str(count).zfill(5) + ":" + " " +
str(x1) + " " + str(y1) + " " + str(x2) +
" " + str(y2) + " " + str(conf) + "\n")
#cv2.putText(img, str(x1),(5, 90+30*i), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
#cv2.putText(img, str(y1),(185, 90+30*i), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
#cv2.putText(img, str(x2),(365, 90+30*i), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
#cv2.putText(img, str(y2),(545, 90+30*i), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
#cv2.putText(img, str(conf),(725, 90+30*i), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
time.sleep(0.05)
out.write(img)
fileOut.close()
cap.release()
out.release()
cv2.destroyAllWindows()
class Emoji(object):
"""This object represents an Emoji."""
GRINNING_FACE_WITH_SMILING_EYES = n(b'\xF0\x9F\x98\x81')
FACE_WITH_TEARS_OF_JOY = n(b'\xF0\x9F\x98\x82')
SMILING_FACE_WITH_OPEN_MOUTH = n(b'\xF0\x9F\x98\x83')
SMILING_FACE_WITH_OPEN_MOUTH_AND_SMILING_EYES = n(b'\xF0\x9F\x98\x84')
SMILING_FACE_WITH_OPEN_MOUTH_AND_COLD_SWEAT = n(b'\xF0\x9F\x98\x85')
SMILING_FACE_WITH_OPEN_MOUTH_AND_TIGHTLY_CLOSED_EYES = n(
b'\xF0\x9F\x98\x86')
WINKING_FACE = n(b'\xF0\x9F\x98\x89')
SMILING_FACE_WITH_SMILING_EYES = n(b'\xF0\x9F\x98\x8A')
FACE_SAVOURING_DELICIOUS_FOOD = n(b'\xF0\x9F\x98\x8B')
RELIEVED_FACE = n(b'\xF0\x9F\x98\x8C')
SMILING_FACE_WITH_HEART_SHAPED_EYES = n(b'\xF0\x9F\x98\x8D')
SMIRKING_FACE = n(b'\xF0\x9F\x98\x8F')
UNAMUSED_FACE = n(b'\xF0\x9F\x98\x92')
FACE_WITH_COLD_SWEAT = n(b'\xF0\x9F\x98\x93')
PENSIVE_FACE = n(b'\xF0\x9F\x98\x94')
CONFOUNDED_FACE = n(b'\xF0\x9F\x98\x96')
FACE_THROWING_A_KISS = n(b'\xF0\x9F\x98\x98')
KISSING_FACE_WITH_CLOSED_EYES = n(b'\xF0\x9F\x98\x9A')
FACE_WITH_STUCK_OUT_TONGUE_AND_WINKING_EYE = n(b'\xF0\x9F\x98\x9C')
FACE_WITH_STUCK_OUT_TONGUE_AND_TIGHTLY_CLOSED_EYES = n(b'\xF0\x9F\x98\x9D')
DISAPPOINTED_FACE = n(b'\xF0\x9F\x98\x9E')
ANGRY_FACE = n(b'\xF0\x9F\x98\xA0')
POUTING_FACE = n(b'\xF0\x9F\x98\xA1')
CRYING_FACE = n(b'\xF0\x9F\x98\xA2')
PERSEVERING_FACE = n(b'\xF0\x9F\x98\xA3')
FACE_WITH_LOOK_OF_TRIUMPH = n(b'\xF0\x9F\x98\xA4')
DISAPPOINTED_BUT_RELIEVED_FACE = n(b'\xF0\x9F\x98\xA5')
FEARFUL_FACE = n(b'\xF0\x9F\x98\xA8')
WEARY_FACE = n(b'\xF0\x9F\x98\xA9')
SLEEPY_FACE = n(b'\xF0\x9F\x98\xAA')
TIRED_FACE = n(b'\xF0\x9F\x98\xAB')
LOUDLY_CRYING_FACE = n(b'\xF0\x9F\x98\xAD')
FACE_WITH_OPEN_MOUTH_AND_COLD_SWEAT = n(b'\xF0\x9F\x98\xB0')
FACE_SCREAMING_IN_FEAR = n(b'\xF0\x9F\x98\xB1')
ASTONISHED_FACE = n(b'\xF0\x9F\x98\xB2')
FLUSHED_FACE = n(b'\xF0\x9F\x98\xB3')
DIZZY_FACE = n(b'\xF0\x9F\x98\xB5')
FACE_WITH_MEDICAL_MASK = n(b'\xF0\x9F\x98\xB7')
GRINNING_CAT_FACE_WITH_SMILING_EYES = n(b'\xF0\x9F\x98\xB8')
CAT_FACE_WITH_TEARS_OF_JOY = n(b'\xF0\x9F\x98\xB9')
SMILING_CAT_FACE_WITH_OPEN_MOUTH = n(b'\xF0\x9F\x98\xBA')
SMILING_CAT_FACE_WITH_HEART_SHAPED_EYES = n(b'\xF0\x9F\x98\xBB')
CAT_FACE_WITH_WRY_SMILE = n(b'\xF0\x9F\x98\xBC')
KISSING_CAT_FACE_WITH_CLOSED_EYES = n(b'\xF0\x9F\x98\xBD')
POUTING_CAT_FACE = n(b'\xF0\x9F\x98\xBE')
CRYING_CAT_FACE = n(b'\xF0\x9F\x98\xBF')
WEARY_CAT_FACE = n(b'\xF0\x9F\x99\x80')
FACE_WITH_NO_GOOD_GESTURE = n(b'\xF0\x9F\x99\x85')
FACE_WITH_OK_GESTURE = n(b'\xF0\x9F\x99\x86')
PERSON_BOWING_DEEPLY = n(b'\xF0\x9F\x99\x87')
SEE_NO_EVIL_MONKEY = n(b'\xF0\x9F\x99\x88')
HEAR_NO_EVIL_MONKEY = n(b'\xF0\x9F\x99\x89')
SPEAK_NO_EVIL_MONKEY = n(b'\xF0\x9F\x99\x8A')
HAPPY_PERSON_RAISING_ONE_HAND = n(b'\xF0\x9F\x99\x8B')
PERSON_RAISING_BOTH_HANDS_IN_CELEBRATION = n(b'\xF0\x9F\x99\x8C')
PERSON_FROWNING = n(b'\xF0\x9F\x99\x8D')
PERSON_WITH_POUTING_FACE = n(b'\xF0\x9F\x99\x8E')
PERSON_WITH_FOLDED_HANDS = n(b'\xF0\x9F\x99\x8F')
BLACK_SCISSORS = n(b'\xE2\x9C\x82')
WHITE_HEAVY_CHECK_MARK = n(b'\xE2\x9C\x85')
AIRPLANE = n(b'\xE2\x9C\x88')
ENVELOPE = n(b'\xE2\x9C\x89')
RAISED_FIST = n(b'\xE2\x9C\x8A')
RAISED_HAND = n(b'\xE2\x9C\x8B')
VICTORY_HAND = n(b'\xE2\x9C\x8C')
PENCIL = n(b'\xE2\x9C\x8F')
BLACK_NIB = n(b'\xE2\x9C\x92')
HEAVY_CHECK_MARK = n(b'\xE2\x9C\x94')
HEAVY_MULTIPLICATION_X = n(b'\xE2\x9C\x96')
SPARKLES = n(b'\xE2\x9C\xA8')
EIGHT_SPOKED_ASTERISK = n(b'\xE2\x9C\xB3')
EIGHT_POINTED_BLACK_STAR = n(b'\xE2\x9C\xB4')
SNOWFLAKE = n(b'\xE2\x9D\x84')
SPARKLE = n(b'\xE2\x9D\x87')
CROSS_MARK = n(b'\xE2\x9D\x8C')
NEGATIVE_SQUARED_CROSS_MARK = n(b'\xE2\x9D\x8E')
BLACK_QUESTION_MARK_ORNAMENT = n(b'\xE2\x9D\x93')
WHITE_QUESTION_MARK_ORNAMENT = n(b'\xE2\x9D\x94')
WHITE_EXCLAMATION_MARK_ORNAMENT = n(b'\xE2\x9D\x95')
HEAVY_EXCLAMATION_MARK_SYMBOL = n(b'\xE2\x9D\x97')
HEAVY_BLACK_HEART = n(b'\xE2\x9D\xA4')
HEAVY_PLUS_SIGN = n(b'\xE2\x9E\x95')
HEAVY_MINUS_SIGN = n(b'\xE2\x9E\x96')
HEAVY_DIVISION_SIGN = n(b'\xE2\x9E\x97')
BLACK_RIGHTWARDS_ARROW = n(b'\xE2\x9E\xA1')
CURLY_LOOP = n(b'\xE2\x9E\xB0')
ROCKET = n(b'\xF0\x9F\x9A\x80')
RAILWAY_CAR = n(b'\xF0\x9F\x9A\x83')
HIGH_SPEED_TRAIN = n(b'\xF0\x9F\x9A\x84')
HIGH_SPEED_TRAIN_WITH_BULLET_NOSE = n(b'\xF0\x9F\x9A\x85')
METRO = n(b'\xF0\x9F\x9A\x87')
STATION = n(b'\xF0\x9F\x9A\x89')
BUS = n(b'\xF0\x9F\x9A\x8C')
BUS_STOP = n(b'\xF0\x9F\x9A\x8F')
AMBULANCE = n(b'\xF0\x9F\x9A\x91')
FIRE_ENGINE = n(b'\xF0\x9F\x9A\x92')
POLICE_CAR = n(b'\xF0\x9F\x9A\x93')
TAXI = n(b'\xF0\x9F\x9A\x95')
AUTOMOBILE = n(b'\xF0\x9F\x9A\x97')
RECREATIONAL_VEHICLE = n(b'\xF0\x9F\x9A\x99')
DELIVERY_TRUCK = n(b'\xF0\x9F\x9A\x9A')
SHIP = n(b'\xF0\x9F\x9A\xA2')
SPEEDBOAT = n(b'\xF0\x9F\x9A\xA4')
HORIZONTAL_TRAFFIC_LIGHT = n(b'\xF0\x9F\x9A\xA5')
CONSTRUCTION_SIGN = n(b'\xF0\x9F\x9A\xA7')
POLICE_CARS_REVOLVING_LIGHT = n(b'\xF0\x9F\x9A\xA8')
TRIANGULAR_FLAG_ON_POST = n(b'\xF0\x9F\x9A\xA9')
DOOR = n(b'\xF0\x9F\x9A\xAA')
NO_ENTRY_SIGN = n(b'\xF0\x9F\x9A\xAB')
SMOKING_SYMBOL = n(b'\xF0\x9F\x9A\xAC')
NO_SMOKING_SYMBOL = n(b'\xF0\x9F\x9A\xAD')
BICYCLE = n(b'\xF0\x9F\x9A\xB2')
PEDESTRIAN = n(b'\xF0\x9F\x9A\xB6')
MENS_SYMBOL = n(b'\xF0\x9F\x9A\xB9')
WOMENS_SYMBOL = n(b'\xF0\x9F\x9A\xBA')
RESTROOM = n(b'\xF0\x9F\x9A\xBB')
BABY_SYMBOL = n(b'\xF0\x9F\x9A\xBC')
TOILET = n(b'\xF0\x9F\x9A\xBD')
WATER_CLOSET = n(b'\xF0\x9F\x9A\xBE')
BATH = n(b'\xF0\x9F\x9B\x80')
CIRCLED_LATIN_CAPITAL_LETTER_M = n(b'\xE2\x93\x82')
NEGATIVE_SQUARED_LATIN_CAPITAL_LETTER_A = n(b'\xF0\x9F\x85\xB0')
NEGATIVE_SQUARED_LATIN_CAPITAL_LETTER_B = n(b'\xF0\x9F\x85\xB1')
NEGATIVE_SQUARED_LATIN_CAPITAL_LETTER_O = n(b'\xF0\x9F\x85\xBE')
NEGATIVE_SQUARED_LATIN_CAPITAL_LETTER_P = n(b'\xF0\x9F\x85\xBF')
NEGATIVE_SQUARED_AB = n(b'\xF0\x9F\x86\x8E')
SQUARED_CL = n(b'\xF0\x9F\x86\x91')
SQUARED_COOL = n(b'\xF0\x9F\x86\x92')
SQUARED_FREE = n(b'\xF0\x9F\x86\x93')
SQUARED_ID = n(b'\xF0\x9F\x86\x94')
SQUARED_NEW = n(b'\xF0\x9F\x86\x95')
SQUARED_NG = n(b'\xF0\x9F\x86\x96')
SQUARED_OK = n(b'\xF0\x9F\x86\x97')
SQUARED_SOS = n(b'\xF0\x9F\x86\x98')
SQUARED_UP_WITH_EXCLAMATION_MARK = n(b'\xF0\x9F\x86\x99')
SQUARED_VS = n(b'\xF0\x9F\x86\x9A')
REGIONAL_INDICATOR_SYMBOL_LETTER_D_PLUS_REGIONAL_INDICATOR_SYMBOL_LETTER_E\
= n(b'\xF0\x9F\x87\xA9\xF0\x9F\x87\xAA')
REGIONAL_INDICATOR_SYMBOL_LETTER_G_PLUS_REGIONAL_INDICATOR_SYMBOL_LETTER_B\
= n(b'\xF0\x9F\x87\xAC\xF0\x9F\x87\xA7')
REGIONAL_INDICATOR_SYMBOL_LETTER_C_PLUS_REGIONAL_INDICATOR_SYMBOL_LETTER_N\
= n(b'\xF0\x9F\x87\xA8\xF0\x9F\x87\xB3')
REGIONAL_INDICATOR_SYMBOL_LETTER_J_PLUS_REGIONAL_INDICATOR_SYMBOL_LETTER_P\
= n(b'\xF0\x9F\x87\xAF\xF0\x9F\x87\xB5')
REGIONAL_INDICATOR_SYMBOL_LETTER_K_PLUS_REGIONAL_INDICATOR_SYMBOL_LETTER_R\
= n(b'\xF0\x9F\x87\xB0\xF0\x9F\x87\xB7')
REGIONAL_INDICATOR_SYMBOL_LETTER_F_PLUS_REGIONAL_INDICATOR_SYMBOL_LETTER_R\
= n(b'\xF0\x9F\x87\xAB\xF0\x9F\x87\xB7')
REGIONAL_INDICATOR_SYMBOL_LETTER_E_PLUS_REGIONAL_INDICATOR_SYMBOL_LETTER_S\
= n(b'\xF0\x9F\x87\xAA\xF0\x9F\x87\xB8')
REGIONAL_INDICATOR_SYMBOL_LETTER_I_PLUS_REGIONAL_INDICATOR_SYMBOL_LETTER_T\
= n(b'\xF0\x9F\x87\xAE\xF0\x9F\x87\xB9')
REGIONAL_INDICATOR_SYMBOL_LETTER_U_PLUS_REGIONAL_INDICATOR_SYMBOL_LETTER_S\
= n(b'\xF0\x9F\x87\xBA\xF0\x9F\x87\xB8')
REGIONAL_INDICATOR_SYMBOL_LETTER_R_PLUS_REGIONAL_INDICATOR_SYMBOL_LETTER_U\
= n(b'\xF0\x9F\x87\xB7\xF0\x9F\x87\xBA')
SQUARED_KATAKANA_KOKO = n(b'\xF0\x9F\x88\x81')
SQUARED_KATAKANA_SA = n(b'\xF0\x9F\x88\x82')
SQUARED_CJK_UNIFIED_IDEOGRAPH_7121 = n(b'\xF0\x9F\x88\x9A')
SQUARED_CJK_UNIFIED_IDEOGRAPH_6307 = n(b'\xF0\x9F\x88\xAF')
SQUARED_CJK_UNIFIED_IDEOGRAPH_7981 = n(b'\xF0\x9F\x88\xB2')
SQUARED_CJK_UNIFIED_IDEOGRAPH_7A7A = n(b'\xF0\x9F\x88\xB3')
SQUARED_CJK_UNIFIED_IDEOGRAPH_5408 = n(b'\xF0\x9F\x88\xB4')
SQUARED_CJK_UNIFIED_IDEOGRAPH_6E80 = n(b'\xF0\x9F\x88\xB5')
SQUARED_CJK_UNIFIED_IDEOGRAPH_6709 = n(b'\xF0\x9F\x88\xB6')
SQUARED_CJK_UNIFIED_IDEOGRAPH_6708 = n(b'\xF0\x9F\x88\xB7')
SQUARED_CJK_UNIFIED_IDEOGRAPH_7533 = n(b'\xF0\x9F\x88\xB8')
SQUARED_CJK_UNIFIED_IDEOGRAPH_5272 = n(b'\xF0\x9F\x88\xB9')
SQUARED_CJK_UNIFIED_IDEOGRAPH_55B6 = n(b'\xF0\x9F\x88\xBA')
CIRCLED_IDEOGRAPH_ADVANTAGE = n(b'\xF0\x9F\x89\x90')
CIRCLED_IDEOGRAPH_ACCEPT = n(b'\xF0\x9F\x89\x91')
COPYRIGHT_SIGN = n(b'\xC2\xA9')
REGISTERED_SIGN = n(b'\xC2\xAE')
DOUBLE_EXCLAMATION_MARK = n(b'\xE2\x80\xBC')
EXCLAMATION_QUESTION_MARK = n(b'\xE2\x81\x89')
DIGIT_EIGHT_PLUS_COMBINING_ENCLOSING_KEYCAP = n(b'\x38\xE2\x83\xA3')
DIGIT_NINE_PLUS_COMBINING_ENCLOSING_KEYCAP = n(b'\x39\xE2\x83\xA3')
DIGIT_SEVEN_PLUS_COMBINING_ENCLOSING_KEYCAP = n(b'\x37\xE2\x83\xA3')
DIGIT_SIX_PLUS_COMBINING_ENCLOSING_KEYCAP = n(b'\x36\xE2\x83\xA3')
DIGIT_ONE_PLUS_COMBINING_ENCLOSING_KEYCAP = n(b'\x31\xE2\x83\xA3')
DIGIT_ZERO_PLUS_COMBINING_ENCLOSING_KEYCAP = n(b'\x30\xE2\x83\xA3')
DIGIT_TWO_PLUS_COMBINING_ENCLOSING_KEYCAP = n(b'\x32\xE2\x83\xA3')
DIGIT_THREE_PLUS_COMBINING_ENCLOSING_KEYCAP = n(b'\x33\xE2\x83\xA3')
DIGIT_FIVE_PLUS_COMBINING_ENCLOSING_KEYCAP = n(b'\x35\xE2\x83\xA3')
DIGIT_FOUR_PLUS_COMBINING_ENCLOSING_KEYCAP = n(b'\x34\xE2\x83\xA3')
NUMBER_SIGN_PLUS_COMBINING_ENCLOSING_KEYCAP = n(b'\x23\xE2\x83\xA3')
TRADE_MARK_SIGN = n(b'\xE2\x84\xA2')
INFORMATION_SOURCE = n(b'\xE2\x84\xB9')
LEFT_RIGHT_ARROW = n(b'\xE2\x86\x94')
UP_DOWN_ARROW = n(b'\xE2\x86\x95')
NORTH_WEST_ARROW = n(b'\xE2\x86\x96')
NORTH_EAST_ARROW = n(b'\xE2\x86\x97')
SOUTH_EAST_ARROW = n(b'\xE2\x86\x98')
SOUTH_WEST_ARROW = n(b'\xE2\x86\x99')
LEFTWARDS_ARROW_WITH_HOOK = n(b'\xE2\x86\xA9')
RIGHTWARDS_ARROW_WITH_HOOK = n(b'\xE2\x86\xAA')
WATCH = n(b'\xE2\x8C\x9A')
HOURGLASS = n(b'\xE2\x8C\x9B')
BLACK_RIGHT_POINTING_DOUBLE_TRIANGLE = n(b'\xE2\x8F\xA9')
BLACK_LEFT_POINTING_DOUBLE_TRIANGLE = n(b'\xE2\x8F\xAA')
BLACK_UP_POINTING_DOUBLE_TRIANGLE = n(b'\xE2\x8F\xAB')
BLACK_DOWN_POINTING_DOUBLE_TRIANGLE = n(b'\xE2\x8F\xAC')
ALARM_CLOCK = n(b'\xE2\x8F\xB0')
HOURGLASS_WITH_FLOWING_SAND = n(b'\xE2\x8F\xB3')
BLACK_SMALL_SQUARE = n(b'\xE2\x96\xAA')
WHITE_SMALL_SQUARE = n(b'\xE2\x96\xAB')
BLACK_RIGHT_POINTING_TRIANGLE = n(b'\xE2\x96\xB6')
BLACK_LEFT_POINTING_TRIANGLE = n(b'\xE2\x97\x80')
WHITE_MEDIUM_SQUARE = n(b'\xE2\x97\xBB')
BLACK_MEDIUM_SQUARE = n(b'\xE2\x97\xBC')
WHITE_MEDIUM_SMALL_SQUARE = n(b'\xE2\x97\xBD')
BLACK_MEDIUM_SMALL_SQUARE = n(b'\xE2\x97\xBE')
BLACK_SUN_WITH_RAYS = n(b'\xE2\x98\x80')
CLOUD = n(b'\xE2\x98\x81')
BLACK_TELEPHONE = n(b'\xE2\x98\x8E')
BALLOT_BOX_WITH_CHECK = n(b'\xE2\x98\x91')
UMBRELLA_WITH_RAIN_DROPS = n(b'\xE2\x98\x94')
HOT_BEVERAGE = n(b'\xE2\x98\x95')
WHITE_UP_POINTING_INDEX = n(b'\xE2\x98\x9D')
WHITE_SMILING_FACE = n(b'\xE2\x98\xBA')
ARIES = n(b'\xE2\x99\x88')
TAURUS = n(b'\xE2\x99\x89')
GEMINI = n(b'\xE2\x99\x8A')
CANCER = n(b'\xE2\x99\x8B')
LEO = n(b'\xE2\x99\x8C')
VIRGO = n(b'\xE2\x99\x8D')
LIBRA = n(b'\xE2\x99\x8E')
SCORPIUS = n(b'\xE2\x99\x8F')
SAGITTARIUS = n(b'\xE2\x99\x90')
CAPRICORN = n(b'\xE2\x99\x91')
AQUARIUS = n(b'\xE2\x99\x92')
PISCES = n(b'\xE2\x99\x93')
BLACK_SPADE_SUIT = n(b'\xE2\x99\xA0')
BLACK_CLUB_SUIT = n(b'\xE2\x99\xA3')
BLACK_HEART_SUIT = n(b'\xE2\x99\xA5')
BLACK_DIAMOND_SUIT = n(b'\xE2\x99\xA6')
HOT_SPRINGS = n(b'\xE2\x99\xA8')
BLACK_UNIVERSAL_RECYCLING_SYMBOL = n(b'\xE2\x99\xBB')
WHEELCHAIR_SYMBOL = n(b'\xE2\x99\xBF')
ANCHOR = n(b'\xE2\x9A\x93')
WARNING_SIGN = n(b'\xE2\x9A\xA0')
HIGH_VOLTAGE_SIGN = n(b'\xE2\x9A\xA1')
MEDIUM_WHITE_CIRCLE = n(b'\xE2\x9A\xAA')
MEDIUM_BLACK_CIRCLE = n(b'\xE2\x9A\xAB')
SOCCER_BALL = n(b'\xE2\x9A\xBD')
BASEBALL = n(b'\xE2\x9A\xBE')
SNOWMAN_WITHOUT_SNOW = n(b'\xE2\x9B\x84')
SUN_BEHIND_CLOUD = n(b'\xE2\x9B\x85')
OPHIUCHUS = n(b'\xE2\x9B\x8E')
NO_ENTRY = n(b'\xE2\x9B\x94')
CHURCH = n(b'\xE2\x9B\xAA')
FOUNTAIN = n(b'\xE2\x9B\xB2')
FLAG_IN_HOLE = n(b'\xE2\x9B\xB3')
SAILBOAT = n(b'\xE2\x9B\xB5')
TENT = n(b'\xE2\x9B\xBA')
FUEL_PUMP = n(b'\xE2\x9B\xBD')
ARROW_POINTING_RIGHTWARDS_THEN_CURVING_UPWARDS = n(b'\xE2\xA4\xB4')
ARROW_POINTING_RIGHTWARDS_THEN_CURVING_DOWNWARDS = n(b'\xE2\xA4\xB5')
LEFTWARDS_BLACK_ARROW = n(b'\xE2\xAC\x85')
UPWARDS_BLACK_ARROW = n(b'\xE2\xAC\x86')
DOWNWARDS_BLACK_ARROW = n(b'\xE2\xAC\x87')
BLACK_LARGE_SQUARE = n(b'\xE2\xAC\x9B')
WHITE_LARGE_SQUARE = n(b'\xE2\xAC\x9C')
WHITE_MEDIUM_STAR = n(b'\xE2\xAD\x90')
HEAVY_LARGE_CIRCLE = n(b'\xE2\xAD\x95')
WAVY_DASH = n(b'\xE3\x80\xB0')
PART_ALTERNATION_MARK = n(b'\xE3\x80\xBD')
CIRCLED_IDEOGRAPH_CONGRATULATION = n(b'\xE3\x8A\x97')
CIRCLED_IDEOGRAPH_SECRET = n(b'\xE3\x8A\x99')
MAHJONG_TILE_RED_DRAGON = n(b'\xF0\x9F\x80\x84')
PLAYING_CARD_BLACK_JOKER = n(b'\xF0\x9F\x83\x8F')
CYCLONE = n(b'\xF0\x9F\x8C\x80')
FOGGY = n(b'\xF0\x9F\x8C\x81')
CLOSED_UMBRELLA = n(b'\xF0\x9F\x8C\x82')
NIGHT_WITH_STARS = n(b'\xF0\x9F\x8C\x83')
SUNRISE_OVER_MOUNTAINS = n(b'\xF0\x9F\x8C\x84')
SUNRISE = n(b'\xF0\x9F\x8C\x85')
CITYSCAPE_AT_DUSK = n(b'\xF0\x9F\x8C\x86')
SUNSET_OVER_BUILDINGS = n(b'\xF0\x9F\x8C\x87')
RAINBOW = n(b'\xF0\x9F\x8C\x88')
BRIDGE_AT_NIGHT = n(b'\xF0\x9F\x8C\x89')
WATER_WAVE = n(b'\xF0\x9F\x8C\x8A')
VOLCANO = n(b'\xF0\x9F\x8C\x8B')
MILKY_WAY = n(b'\xF0\x9F\x8C\x8C')
EARTH_GLOBE_ASIA_AUSTRALIA = n(b'\xF0\x9F\x8C\x8F')
NEW_MOON_SYMBOL = n(b'\xF0\x9F\x8C\x91')
FIRST_QUARTER_MOON_SYMBOL = n(b'\xF0\x9F\x8C\x93')
WAXING_GIBBOUS_MOON_SYMBOL = n(b'\xF0\x9F\x8C\x94')
FULL_MOON_SYMBOL = n(b'\xF0\x9F\x8C\x95')
CRESCENT_MOON = n(b'\xF0\x9F\x8C\x99')
FIRST_QUARTER_MOON_WITH_FACE = n(b'\xF0\x9F\x8C\x9B')
GLOWING_STAR = n(b'\xF0\x9F\x8C\x9F')
SHOOTING_STAR = n(b'\xF0\x9F\x8C\xA0')
CHESTNUT = n(b'\xF0\x9F\x8C\xB0')
SEEDLING = n(b'\xF0\x9F\x8C\xB1')
PALM_TREE = n(b'\xF0\x9F\x8C\xB4')
CACTUS = n(b'\xF0\x9F\x8C\xB5')
TULIP = n(b'\xF0\x9F\x8C\xB7')
CHERRY_BLOSSOM = n(b'\xF0\x9F\x8C\xB8')
ROSE = n(b'\xF0\x9F\x8C\xB9')
HIBISCUS = n(b'\xF0\x9F\x8C\xBA')
SUNFLOWER = n(b'\xF0\x9F\x8C\xBB')
BLOSSOM = n(b'\xF0\x9F\x8C\xBC')
EAR_OF_MAIZE = n(b'\xF0\x9F\x8C\xBD')
EAR_OF_RICE = n(b'\xF0\x9F\x8C\xBE')
HERB = n(b'\xF0\x9F\x8C\xBF')
FOUR_LEAF_CLOVER = n(b'\xF0\x9F\x8D\x80')
MAPLE_LEAF = n(b'\xF0\x9F\x8D\x81')
FALLEN_LEAF = n(b'\xF0\x9F\x8D\x82')
LEAF_FLUTTERING_IN_WIND = n(b'\xF0\x9F\x8D\x83')
MUSHROOM = n(b'\xF0\x9F\x8D\x84')
TOMATO = n(b'\xF0\x9F\x8D\x85')
AUBERGINE = n(b'\xF0\x9F\x8D\x86')
GRAPES = n(b'\xF0\x9F\x8D\x87')
MELON = n(b'\xF0\x9F\x8D\x88')
WATERMELON = n(b'\xF0\x9F\x8D\x89')
TANGERINE = n(b'\xF0\x9F\x8D\x8A')
BANANA = n(b'\xF0\x9F\x8D\x8C')
PINEAPPLE = n(b'\xF0\x9F\x8D\x8D')
RED_APPLE = n(b'\xF0\x9F\x8D\x8E')
GREEN_APPLE = n(b'\xF0\x9F\x8D\x8F')
PEACH = n(b'\xF0\x9F\x8D\x91')
CHERRIES = n(b'\xF0\x9F\x8D\x92')
STRAWBERRY = n(b'\xF0\x9F\x8D\x93')
HAMBURGER = n(b'\xF0\x9F\x8D\x94')
SLICE_OF_PIZZA = n(b'\xF0\x9F\x8D\x95')
MEAT_ON_BONE = n(b'\xF0\x9F\x8D\x96')
POULTRY_LEG = n(b'\xF0\x9F\x8D\x97')
RICE_CRACKER = n(b'\xF0\x9F\x8D\x98')
RICE_BALL = n(b'\xF0\x9F\x8D\x99')
COOKED_RICE = n(b'\xF0\x9F\x8D\x9A')
CURRY_AND_RICE = n(b'\xF0\x9F\x8D\x9B')
STEAMING_BOWL = n(b'\xF0\x9F\x8D\x9C')
SPAGHETTI = n(b'\xF0\x9F\x8D\x9D')
BREAD = n(b'\xF0\x9F\x8D\x9E')
FRENCH_FRIES = n(b'\xF0\x9F\x8D\x9F')
ROASTED_SWEET_POTATO = n(b'\xF0\x9F\x8D\xA0')
DANGO = n(b'\xF0\x9F\x8D\xA1')
ODEN = n(b'\xF0\x9F\x8D\xA2')
SUSHI = n(b'\xF0\x9F\x8D\xA3')
FRIED_SHRIMP = n(b'\xF0\x9F\x8D\xA4')
FISH_CAKE_WITH_SWIRL_DESIGN = n(b'\xF0\x9F\x8D\xA5')
SOFT_ICE_CREAM = n(b'\xF0\x9F\x8D\xA6')
SHAVED_ICE = n(b'\xF0\x9F\x8D\xA7')
ICE_CREAM = n(b'\xF0\x9F\x8D\xA8')
DOUGHNUT = n(b'\xF0\x9F\x8D\xA9')
COOKIE = n(b'\xF0\x9F\x8D\xAA')
CHOCOLATE_BAR = n(b'\xF0\x9F\x8D\xAB')
CANDY = n(b'\xF0\x9F\x8D\xAC')
LOLLIPOP = n(b'\xF0\x9F\x8D\xAD')
CUSTARD = n(b'\xF0\x9F\x8D\xAE')
HONEY_POT = n(b'\xF0\x9F\x8D\xAF')
SHORTCAKE = n(b'\xF0\x9F\x8D\xB0')
BENTO_BOX = n(b'\xF0\x9F\x8D\xB1')
POT_OF_FOOD = n(b'\xF0\x9F\x8D\xB2')
COOKING = n(b'\xF0\x9F\x8D\xB3')
FORK_AND_KNIFE = n(b'\xF0\x9F\x8D\xB4')
TEACUP_WITHOUT_HANDLE = n(b'\xF0\x9F\x8D\xB5')
SAKE_BOTTLE_AND_CUP = n(b'\xF0\x9F\x8D\xB6')
WINE_GLASS = n(b'\xF0\x9F\x8D\xB7')
COCKTAIL_GLASS = n(b'\xF0\x9F\x8D\xB8')
TROPICAL_DRINK = n(b'\xF0\x9F\x8D\xB9')
BEER_MUG = n(b'\xF0\x9F\x8D\xBA')
CLINKING_BEER_MUGS = n(b'\xF0\x9F\x8D\xBB')
RIBBON = n(b'\xF0\x9F\x8E\x80')
WRAPPED_PRESENT = n(b'\xF0\x9F\x8E\x81')
BIRTHDAY_CAKE = n(b'\xF0\x9F\x8E\x82')
JACK_O_LANTERN = n(b'\xF0\x9F\x8E\x83')
CHRISTMAS_TREE = n(b'\xF0\x9F\x8E\x84')
FATHER_CHRISTMAS = n(b'\xF0\x9F\x8E\x85')
FIREWORKS = n(b'\xF0\x9F\x8E\x86')
FIREWORK_SPARKLER = n(b'\xF0\x9F\x8E\x87')
BALLOON = n(b'\xF0\x9F\x8E\x88')
PARTY_POPPER = n(b'\xF0\x9F\x8E\x89')
CONFETTI_BALL = n(b'\xF0\x9F\x8E\x8A')
TANABATA_TREE = n(b'\xF0\x9F\x8E\x8B')
CROSSED_FLAGS = n(b'\xF0\x9F\x8E\x8C')
PINE_DECORATION = n(b'\xF0\x9F\x8E\x8D')
JAPANESE_DOLLS = n(b'\xF0\x9F\x8E\x8E')
CARP_STREAMER = n(b'\xF0\x9F\x8E\x8F')
WIND_CHIME = n(b'\xF0\x9F\x8E\x90')
MOON_VIEWING_CEREMONY = n(b'\xF0\x9F\x8E\x91')
SCHOOL_SATCHEL = n(b'\xF0\x9F\x8E\x92')
GRADUATION_CAP = n(b'\xF0\x9F\x8E\x93')
CAROUSEL_HORSE = n(b'\xF0\x9F\x8E\xA0')
FERRIS_WHEEL = n(b'\xF0\x9F\x8E\xA1')
ROLLER_COASTER = n(b'\xF0\x9F\x8E\xA2')
FISHING_POLE_AND_FISH = n(b'\xF0\x9F\x8E\xA3')
MICROPHONE = n(b'\xF0\x9F\x8E\xA4')
MOVIE_CAMERA = n(b'\xF0\x9F\x8E\xA5')
CINEMA = n(b'\xF0\x9F\x8E\xA6')
HEADPHONE = n(b'\xF0\x9F\x8E\xA7')
ARTIST_PALETTE = n(b'\xF0\x9F\x8E\xA8')
TOP_HAT = n(b'\xF0\x9F\x8E\xA9')
CIRCUS_TENT = n(b'\xF0\x9F\x8E\xAA')
TICKET = n(b'\xF0\x9F\x8E\xAB')
CLAPPER_BOARD = n(b'\xF0\x9F\x8E\xAC')
PERFORMING_ARTS = n(b'\xF0\x9F\x8E\xAD')
VIDEO_GAME = n(b'\xF0\x9F\x8E\xAE')
DIRECT_HIT = n(b'\xF0\x9F\x8E\xAF')
SLOT_MACHINE = n(b'\xF0\x9F\x8E\xB0')
BILLIARDS = n(b'\xF0\x9F\x8E\xB1')
GAME_DIE = n(b'\xF0\x9F\x8E\xB2')
BOWLING = n(b'\xF0\x9F\x8E\xB3')
FLOWER_PLAYING_CARDS = n(b'\xF0\x9F\x8E\xB4')
MUSICAL_NOTE = n(b'\xF0\x9F\x8E\xB5')
MULTIPLE_MUSICAL_NOTES = n(b'\xF0\x9F\x8E\xB6')
SAXOPHONE = n(b'\xF0\x9F\x8E\xB7')
GUITAR = n(b'\xF0\x9F\x8E\xB8')
MUSICAL_KEYBOARD = n(b'\xF0\x9F\x8E\xB9')
TRUMPET = n(b'\xF0\x9F\x8E\xBA')
VIOLIN = n(b'\xF0\x9F\x8E\xBB')
MUSICAL_SCORE = n(b'\xF0\x9F\x8E\xBC')
RUNNING_SHIRT_WITH_SASH = n(b'\xF0\x9F\x8E\xBD')
TENNIS_RACQUET_AND_BALL = n(b'\xF0\x9F\x8E\xBE')
SKI_AND_SKI_BOOT = n(b'\xF0\x9F\x8E\xBF')
BASKETBALL_AND_HOOP = n(b'\xF0\x9F\x8F\x80')
CHEQUERED_FLAG = n(b'\xF0\x9F\x8F\x81')
SNOWBOARDER = n(b'\xF0\x9F\x8F\x82')
RUNNER = n(b'\xF0\x9F\x8F\x83')
SURFER = n(b'\xF0\x9F\x8F\x84')
TROPHY = n(b'\xF0\x9F\x8F\x86')
AMERICAN_FOOTBALL = n(b'\xF0\x9F\x8F\x88')
SWIMMER = n(b'\xF0\x9F\x8F\x8A')
HOUSE_BUILDING = n(b'\xF0\x9F\x8F\xA0')
HOUSE_WITH_GARDEN = n(b'\xF0\x9F\x8F\xA1')
OFFICE_BUILDING = n(b'\xF0\x9F\x8F\xA2')
JAPANESE_POST_OFFICE = n(b'\xF0\x9F\x8F\xA3')
HOSPITAL = n(b'\xF0\x9F\x8F\xA5')
BANK = n(b'\xF0\x9F\x8F\xA6')
AUTOMATED_TELLER_MACHINE = n(b'\xF0\x9F\x8F\xA7')
HOTEL = n(b'\xF0\x9F\x8F\xA8')
LOVE_HOTEL = n(b'\xF0\x9F\x8F\xA9')
CONVENIENCE_STORE = n(b'\xF0\x9F\x8F\xAA')
SCHOOL = n(b'\xF0\x9F\x8F\xAB')
DEPARTMENT_STORE = n(b'\xF0\x9F\x8F\xAC')
FACTORY = n(b'\xF0\x9F\x8F\xAD')
IZAKAYA_LANTERN = n(b'\xF0\x9F\x8F\xAE')
JAPANESE_CASTLE = n(b'\xF0\x9F\x8F\xAF')
EUROPEAN_CASTLE = n(b'\xF0\x9F\x8F\xB0')
SNAIL = n(b'\xF0\x9F\x90\x8C')
SNAKE = n(b'\xF0\x9F\x90\x8D')
HORSE = n(b'\xF0\x9F\x90\x8E')
SHEEP = n(b'\xF0\x9F\x90\x91')
MONKEY = n(b'\xF0\x9F\x90\x92')
CHICKEN = n(b'\xF0\x9F\x90\x94')
BOAR = n(b'\xF0\x9F\x90\x97')
ELEPHANT = n(b'\xF0\x9F\x90\x98')
OCTOPUS = n(b'\xF0\x9F\x90\x99')
SPIRAL_SHELL = n(b'\xF0\x9F\x90\x9A')
BUG = n(b'\xF0\x9F\x90\x9B')
ANT = n(b'\xF0\x9F\x90\x9C')
HONEYBEE = n(b'\xF0\x9F\x90\x9D')
LADY_BEETLE = n(b'\xF0\x9F\x90\x9E')
FISH = n(b'\xF0\x9F\x90\x9F')
TROPICAL_FISH = n(b'\xF0\x9F\x90\xA0')
BLOWFISH = n(b'\xF0\x9F\x90\xA1')
TURTLE = n(b'\xF0\x9F\x90\xA2')
HATCHING_CHICK = n(b'\xF0\x9F\x90\xA3')
BABY_CHICK = n(b'\xF0\x9F\x90\xA4')
FRONT_FACING_BABY_CHICK = n(b'\xF0\x9F\x90\xA5')
BIRD = n(b'\xF0\x9F\x90\xA6')
PENGUIN = n(b'\xF0\x9F\x90\xA7')
KOALA = n(b'\xF0\x9F\x90\xA8')
POODLE = n(b'\xF0\x9F\x90\xA9')
BACTRIAN_CAMEL = n(b'\xF0\x9F\x90\xAB')
DOLPHIN = n(b'\xF0\x9F\x90\xAC')
MOUSE_FACE = n(b'\xF0\x9F\x90\xAD')
COW_FACE = n(b'\xF0\x9F\x90\xAE')
TIGER_FACE = n(b'\xF0\x9F\x90\xAF')
RABBIT_FACE = n(b'\xF0\x9F\x90\xB0')
CAT_FACE = n(b'\xF0\x9F\x90\xB1')
DRAGON_FACE = n(b'\xF0\x9F\x90\xB2')
SPOUTING_WHALE = n(b'\xF0\x9F\x90\xB3')
HORSE_FACE = n(b'\xF0\x9F\x90\xB4')
MONKEY_FACE = n(b'\xF0\x9F\x90\xB5')
DOG_FACE = n(b'\xF0\x9F\x90\xB6')
PIG_FACE = n(b'\xF0\x9F\x90\xB7')
FROG_FACE = n(b'\xF0\x9F\x90\xB8')
HAMSTER_FACE = n(b'\xF0\x9F\x90\xB9')
WOLF_FACE = n(b'\xF0\x9F\x90\xBA')
BEAR_FACE = n(b'\xF0\x9F\x90\xBB')
PANDA_FACE = n(b'\xF0\x9F\x90\xBC')
PIG_NOSE = n(b'\xF0\x9F\x90\xBD')
PAW_PRINTS = n(b'\xF0\x9F\x90\xBE')
EYES = n(b'\xF0\x9F\x91\x80')
EAR = n(b'\xF0\x9F\x91\x82')
NOSE = n(b'\xF0\x9F\x91\x83')
MOUTH = n(b'\xF0\x9F\x91\x84')
TONGUE = n(b'\xF0\x9F\x91\x85')
WHITE_UP_POINTING_BACKHAND_INDEX = n(b'\xF0\x9F\x91\x86')
WHITE_DOWN_POINTING_BACKHAND_INDEX = n(b'\xF0\x9F\x91\x87')
WHITE_LEFT_POINTING_BACKHAND_INDEX = n(b'\xF0\x9F\x91\x88')
WHITE_RIGHT_POINTING_BACKHAND_INDEX = n(b'\xF0\x9F\x91\x89')
FISTED_HAND_SIGN = n(b'\xF0\x9F\x91\x8A')
WAVING_HAND_SIGN = n(b'\xF0\x9F\x91\x8B')
OK_HAND_SIGN = n(b'\xF0\x9F\x91\x8C')
THUMBS_UP_SIGN = n(b'\xF0\x9F\x91\x8D')
THUMBS_DOWN_SIGN = n(b'\xF0\x9F\x91\x8E')
CLAPPING_HANDS_SIGN = n(b'\xF0\x9F\x91\x8F')
OPEN_HANDS_SIGN = n(b'\xF0\x9F\x91\x90')
CROWN = n(b'\xF0\x9F\x91\x91')
WOMANS_HAT = n(b'\xF0\x9F\x91\x92')
EYEGLASSES = n(b'\xF0\x9F\x91\x93')
NECKTIE = n(b'\xF0\x9F\x91\x94')
T_SHIRT = n(b'\xF0\x9F\x91\x95')
JEANS = n(b'\xF0\x9F\x91\x96')
DRESS = n(b'\xF0\x9F\x91\x97')
KIMONO = n(b'\xF0\x9F\x91\x98')
BIKINI = n(b'\xF0\x9F\x91\x99')
WOMANS_CLOTHES = n(b'\xF0\x9F\x91\x9A')
PURSE = n(b'\xF0\x9F\x91\x9B')
HANDBAG = n(b'\xF0\x9F\x91\x9C')
POUCH = n(b'\xF0\x9F\x91\x9D')
MANS_SHOE = n(b'\xF0\x9F\x91\x9E')
ATHLETIC_SHOE = n(b'\xF0\x9F\x91\x9F')
HIGH_HEELED_SHOE = n(b'\xF0\x9F\x91\xA0')
WOMANS_SANDAL = n(b'\xF0\x9F\x91\xA1')
WOMANS_BOOTS = n(b'\xF0\x9F\x91\xA2')
FOOTPRINTS = n(b'\xF0\x9F\x91\xA3')
BUST_IN_SILHOUETTE = n(b'\xF0\x9F\x91\xA4')
BOY = n(b'\xF0\x9F\x91\xA6')
GIRL = n(b'\xF0\x9F\x91\xA7')
MAN = n(b'\xF0\x9F\x91\xA8')
WOMAN = n(b'\xF0\x9F\x91\xA9')
FAMILY = n(b'\xF0\x9F\x91\xAA')
MAN_AND_WOMAN_HOLDING_HANDS = n(b'\xF0\x9F\x91\xAB')
POLICE_OFFICER = n(b'\xF0\x9F\x91\xAE')
WOMAN_WITH_BUNNY_EARS = n(b'\xF0\x9F\x91\xAF')
BRIDE_WITH_VEIL = n(b'\xF0\x9F\x91\xB0')
PERSON_WITH_BLOND_HAIR = n(b'\xF0\x9F\x91\xB1')
MAN_WITH_GUA_PI_MAO = n(b'\xF0\x9F\x91\xB2')
MAN_WITH_TURBAN = n(b'\xF0\x9F\x91\xB3')
OLDER_MAN = n(b'\xF0\x9F\x91\xB4')
OLDER_WOMAN = n(b'\xF0\x9F\x91\xB5')
BABY = n(b'\xF0\x9F\x91\xB6')
CONSTRUCTION_WORKER = n(b'\xF0\x9F\x91\xB7')
PRINCESS = n(b'\xF0\x9F\x91\xB8')
JAPANESE_OGRE = n(b'\xF0\x9F\x91\xB9')
JAPANESE_GOBLIN = n(b'\xF0\x9F\x91\xBA')
GHOST = n(b'\xF0\x9F\x91\xBB')
BABY_ANGEL = n(b'\xF0\x9F\x91\xBC')
EXTRATERRESTRIAL_ALIEN = n(b'\xF0\x9F\x91\xBD')
ALIEN_MONSTER = n(b'\xF0\x9F\x91\xBE')
IMP = n(b'\xF0\x9F\x91\xBF')
SKULL = n(b'\xF0\x9F\x92\x80')
INFORMATION_DESK_PERSON = n(b'\xF0\x9F\x92\x81')
GUARDSMAN = n(b'\xF0\x9F\x92\x82')
DANCER = n(b'\xF0\x9F\x92\x83')
LIPSTICK = n(b'\xF0\x9F\x92\x84')
NAIL_POLISH = n(b'\xF0\x9F\x92\x85')
FACE_MASSAGE = n(b'\xF0\x9F\x92\x86')
HAIRCUT = n(b'\xF0\x9F\x92\x87')
BARBER_POLE = n(b'\xF0\x9F\x92\x88')
SYRINGE = n(b'\xF0\x9F\x92\x89')
PILL = n(b'\xF0\x9F\x92\x8A')
KISS_MARK = n(b'\xF0\x9F\x92\x8B')
LOVE_LETTER = n(b'\xF0\x9F\x92\x8C')
RING = n(b'\xF0\x9F\x92\x8D')
GEM_STONE = n(b'\xF0\x9F\x92\x8E')
KISS = n(b'\xF0\x9F\x92\x8F')
BOUQUET = n(b'\xF0\x9F\x92\x90')
COUPLE_WITH_HEART = n(b'\xF0\x9F\x92\x91')
WEDDING = n(b'\xF0\x9F\x92\x92')
BEATING_HEART = n(b'\xF0\x9F\x92\x93')
BROKEN_HEART = n(b'\xF0\x9F\x92\x94')
TWO_HEARTS = n(b'\xF0\x9F\x92\x95')
SPARKLING_HEART = n(b'\xF0\x9F\x92\x96')
GROWING_HEART = n(b'\xF0\x9F\x92\x97')
HEART_WITH_ARROW = n(b'\xF0\x9F\x92\x98')
BLUE_HEART = n(b'\xF0\x9F\x92\x99')
GREEN_HEART = n(b'\xF0\x9F\x92\x9A')
YELLOW_HEART = n(b'\xF0\x9F\x92\x9B')
PURPLE_HEART = n(b'\xF0\x9F\x92\x9C')
HEART_WITH_RIBBON = n(b'\xF0\x9F\x92\x9D')
REVOLVING_HEARTS = n(b'\xF0\x9F\x92\x9E')
HEART_DECORATION = n(b'\xF0\x9F\x92\x9F')
DIAMOND_SHAPE_WITH_A_DOT_INSIDE = n(b'\xF0\x9F\x92\xA0')
ELECTRIC_LIGHT_BULB = n(b'\xF0\x9F\x92\xA1')
ANGER_SYMBOL = n(b'\xF0\x9F\x92\xA2')
BOMB = n(b'\xF0\x9F\x92\xA3')
SLEEPING_SYMBOL = n(b'\xF0\x9F\x92\xA4')
COLLISION_SYMBOL = n(b'\xF0\x9F\x92\xA5')
SPLASHING_SWEAT_SYMBOL = n(b'\xF0\x9F\x92\xA6')
DROPLET = n(b'\xF0\x9F\x92\xA7')
DASH_SYMBOL = n(b'\xF0\x9F\x92\xA8')
PILE_OF_POO = n(b'\xF0\x9F\x92\xA9')
FLEXED_BICEPS = n(b'\xF0\x9F\x92\xAA')
DIZZY_SYMBOL = n(b'\xF0\x9F\x92\xAB')
SPEECH_BALLOON = n(b'\xF0\x9F\x92\xAC')
WHITE_FLOWER = n(b'\xF0\x9F\x92\xAE')
HUNDRED_POINTS_SYMBOL = n(b'\xF0\x9F\x92\xAF')
MONEY_BAG = n(b'\xF0\x9F\x92\xB0')
CURRENCY_EXCHANGE = n(b'\xF0\x9F\x92\xB1')
HEAVY_DOLLAR_SIGN = n(b'\xF0\x9F\x92\xB2')
CREDIT_CARD = n(b'\xF0\x9F\x92\xB3')
BANKNOTE_WITH_YEN_SIGN = n(b'\xF0\x9F\x92\xB4')
BANKNOTE_WITH_DOLLAR_SIGN = n(b'\xF0\x9F\x92\xB5')
MONEY_WITH_WINGS = n(b'\xF0\x9F\x92\xB8')
CHART_WITH_UPWARDS_TREND_AND_YEN_SIGN = n(b'\xF0\x9F\x92\xB9')
SEAT = n(b'\xF0\x9F\x92\xBA')
PERSONAL_COMPUTER = n(b'\xF0\x9F\x92\xBB')
BRIEFCASE = n(b'\xF0\x9F\x92\xBC')
MINIDISC = n(b'\xF0\x9F\x92\xBD')
FLOPPY_DISK = n(b'\xF0\x9F\x92\xBE')
OPTICAL_DISC = n(b'\xF0\x9F\x92\xBF')
DVD = n(b'\xF0\x9F\x93\x80')
FILE_FOLDER = n(b'\xF0\x9F\x93\x81')
OPEN_FILE_FOLDER = n(b'\xF0\x9F\x93\x82')
PAGE_WITH_CURL = n(b'\xF0\x9F\x93\x83')
PAGE_FACING_UP = n(b'\xF0\x9F\x93\x84')
CALENDAR = n(b'\xF0\x9F\x93\x85')
TEAR_OFF_CALENDAR = n(b'\xF0\x9F\x93\x86')
CARD_INDEX = n(b'\xF0\x9F\x93\x87')
CHART_WITH_UPWARDS_TREND = n(b'\xF0\x9F\x93\x88')
CHART_WITH_DOWNWARDS_TREND = n(b'\xF0\x9F\x93\x89')
BAR_CHART = n(b'\xF0\x9F\x93\x8A')
CLIPBOARD = n(b'\xF0\x9F\x93\x8B')
PUSHPIN = n(b'\xF0\x9F\x93\x8C')
ROUND_PUSHPIN = n(b'\xF0\x9F\x93\x8D')
PAPERCLIP = n(b'\xF0\x9F\x93\x8E')
STRAIGHT_RULER = n(b'\xF0\x9F\x93\x8F')
TRIANGULAR_RULER = n(b'\xF0\x9F\x93\x90')
BOOKMARK_TABS = n(b'\xF0\x9F\x93\x91')
LEDGER = n(b'\xF0\x9F\x93\x92')
NOTEBOOK = n(b'\xF0\x9F\x93\x93')
NOTEBOOK_WITH_DECORATIVE_COVER = n(b'\xF0\x9F\x93\x94')
CLOSED_BOOK = n(b'\xF0\x9F\x93\x95')
OPEN_BOOK = n(b'\xF0\x9F\x93\x96')
GREEN_BOOK = n(b'\xF0\x9F\x93\x97')
BLUE_BOOK = n(b'\xF0\x9F\x93\x98')
ORANGE_BOOK = n(b'\xF0\x9F\x93\x99')
BOOKS = n(b'\xF0\x9F\x93\x9A')
NAME_BADGE = n(b'\xF0\x9F\x93\x9B')
SCROLL = n(b'\xF0\x9F\x93\x9C')
MEMO = n(b'\xF0\x9F\x93\x9D')
TELEPHONE_RECEIVER = n(b'\xF0\x9F\x93\x9E')
PAGER = n(b'\xF0\x9F\x93\x9F')
FAX_MACHINE = n(b'\xF0\x9F\x93\xA0')
SATELLITE_ANTENNA = n(b'\xF0\x9F\x93\xA1')
PUBLIC_ADDRESS_LOUDSPEAKER = n(b'\xF0\x9F\x93\xA2')
CHEERING_MEGAPHONE = n(b'\xF0\x9F\x93\xA3')
OUTBOX_TRAY = n(b'\xF0\x9F\x93\xA4')
INBOX_TRAY = n(b'\xF0\x9F\x93\xA5')
PACKAGE = n(b'\xF0\x9F\x93\xA6')
E_MAIL_SYMBOL = n(b'\xF0\x9F\x93\xA7')
INCOMING_ENVELOPE = n(b'\xF0\x9F\x93\xA8')
ENVELOPE_WITH_DOWNWARDS_ARROW_ABOVE = n(b'\xF0\x9F\x93\xA9')
CLOSED_MAILBOX_WITH_LOWERED_FLAG = n(b'\xF0\x9F\x93\xAA')
CLOSED_MAILBOX_WITH_RAISED_FLAG = n(b'\xF0\x9F\x93\xAB')
POSTBOX = n(b'\xF0\x9F\x93\xAE')
NEWSPAPER = n(b'\xF0\x9F\x93\xB0')
MOBILE_PHONE = n(b'\xF0\x9F\x93\xB1')
MOBILE_PHONE_WITH_RIGHTWARDS_ARROW_AT_LEFT = n(b'\xF0\x9F\x93\xB2')
VIBRATION_MODE = n(b'\xF0\x9F\x93\xB3')
MOBILE_PHONE_OFF = n(b'\xF0\x9F\x93\xB4')
ANTENNA_WITH_BARS = n(b'\xF0\x9F\x93\xB6')
CAMERA = n(b'\xF0\x9F\x93\xB7')
VIDEO_CAMERA = n(b'\xF0\x9F\x93\xB9')
TELEVISION = n(b'\xF0\x9F\x93\xBA')
RADIO = n(b'\xF0\x9F\x93\xBB')
VIDEOCASSETTE = n(b'\xF0\x9F\x93\xBC')
CLOCKWISE_DOWNWARDS_AND_UPWARDS_OPEN_CIRCLE_ARROWS = n(b'\xF0\x9F\x94\x83')
SPEAKER_WITH_THREE_SOUND_WAVES = n(b'\xF0\x9F\x94\x8A')
BATTERY = n(b'\xF0\x9F\x94\x8B')
ELECTRIC_PLUG = n(b'\xF0\x9F\x94\x8C')
LEFT_POINTING_MAGNIFYING_GLASS = n(b'\xF0\x9F\x94\x8D')
RIGHT_POINTING_MAGNIFYING_GLASS = n(b'\xF0\x9F\x94\x8E')
LOCK_WITH_INK_PEN = n(b'\xF0\x9F\x94\x8F')
CLOSED_LOCK_WITH_KEY = n(b'\xF0\x9F\x94\x90')
KEY = n(b'\xF0\x9F\x94\x91')
LOCK = n(b'\xF0\x9F\x94\x92')
OPEN_LOCK = n(b'\xF0\x9F\x94\x93')
BELL = n(b'\xF0\x9F\x94\x94')
BOOKMARK = n(b'\xF0\x9F\x94\x96')
LINK_SYMBOL = n(b'\xF0\x9F\x94\x97')
RADIO_BUTTON = n(b'\xF0\x9F\x94\x98')
BACK_WITH_LEFTWARDS_ARROW_ABOVE = n(b'\xF0\x9F\x94\x99')
END_WITH_LEFTWARDS_ARROW_ABOVE = n(b'\xF0\x9F\x94\x9A')
ON_WITH_EXCLAMATION_MARK_WITH_LEFT_RIGHT_ARROW_ABOVE = n(
b'\xF0\x9F\x94\x9B')
SOON_WITH_RIGHTWARDS_ARROW_ABOVE = n(b'\xF0\x9F\x94\x9C')
TOP_WITH_UPWARDS_ARROW_ABOVE = n(b'\xF0\x9F\x94\x9D')
NO_ONE_UNDER_EIGHTEEN_SYMBOL = n(b'\xF0\x9F\x94\x9E')
KEYCAP_TEN = n(b'\xF0\x9F\x94\x9F')
INPUT_SYMBOL_FOR_LATIN_CAPITAL_LETTERS = n(b'\xF0\x9F\x94\xA0')
INPUT_SYMBOL_FOR_LATIN_SMALL_LETTERS = n(b'\xF0\x9F\x94\xA1')
INPUT_SYMBOL_FOR_NUMBERS = n(b'\xF0\x9F\x94\xA2')
INPUT_SYMBOL_FOR_SYMBOLS = n(b'\xF0\x9F\x94\xA3')
INPUT_SYMBOL_FOR_LATIN_LETTERS = n(b'\xF0\x9F\x94\xA4')
FIRE = n(b'\xF0\x9F\x94\xA5')
ELECTRIC_TORCH = n(b'\xF0\x9F\x94\xA6')
WRENCH = n(b'\xF0\x9F\x94\xA7')
HAMMER = n(b'\xF0\x9F\x94\xA8')
NUT_AND_BOLT = n(b'\xF0\x9F\x94\xA9')
HOCHO = n(b'\xF0\x9F\x94\xAA')
PISTOL = n(b'\xF0\x9F\x94\xAB')
CRYSTAL_BALL = n(b'\xF0\x9F\x94\xAE')
SIX_POINTED_STAR_WITH_MIDDLE_DOT = n(b'\xF0\x9F\x94\xAF')
JAPANESE_SYMBOL_FOR_BEGINNER = n(b'\xF0\x9F\x94\xB0')
TRIDENT_EMBLEM = n(b'\xF0\x9F\x94\xB1')
BLACK_SQUARE_BUTTON = n(b'\xF0\x9F\x94\xB2')
WHITE_SQUARE_BUTTON = n(b'\xF0\x9F\x94\xB3')
LARGE_RED_CIRCLE = n(b'\xF0\x9F\x94\xB4')
LARGE_BLUE_CIRCLE = n(b'\xF0\x9F\x94\xB5')
LARGE_ORANGE_DIAMOND = n(b'\xF0\x9F\x94\xB6')
LARGE_BLUE_DIAMOND = n(b'\xF0\x9F\x94\xB7')
SMALL_ORANGE_DIAMOND = n(b'\xF0\x9F\x94\xB8')
SMALL_BLUE_DIAMOND = n(b'\xF0\x9F\x94\xB9')
UP_POINTING_RED_TRIANGLE = n(b'\xF0\x9F\x94\xBA')
DOWN_POINTING_RED_TRIANGLE = n(b'\xF0\x9F\x94\xBB')
UP_POINTING_SMALL_RED_TRIANGLE = n(b'\xF0\x9F\x94\xBC')
DOWN_POINTING_SMALL_RED_TRIANGLE = n(b'\xF0\x9F\x94\xBD')
CLOCK_FACE_ONE_OCLOCK = n(b'\xF0\x9F\x95\x90')
CLOCK_FACE_TWO_OCLOCK = n(b'\xF0\x9F\x95\x91')
CLOCK_FACE_THREE_OCLOCK = n(b'\xF0\x9F\x95\x92')
CLOCK_FACE_FOUR_OCLOCK = n(b'\xF0\x9F\x95\x93')
CLOCK_FACE_FIVE_OCLOCK = n(b'\xF0\x9F\x95\x94')
CLOCK_FACE_SIX_OCLOCK = n(b'\xF0\x9F\x95\x95')
CLOCK_FACE_SEVEN_OCLOCK = n(b'\xF0\x9F\x95\x96')
CLOCK_FACE_EIGHT_OCLOCK = n(b'\xF0\x9F\x95\x97')
CLOCK_FACE_NINE_OCLOCK = n(b'\xF0\x9F\x95\x98')
CLOCK_FACE_TEN_OCLOCK = n(b'\xF0\x9F\x95\x99')
CLOCK_FACE_ELEVEN_OCLOCK = n(b'\xF0\x9F\x95\x9A')
CLOCK_FACE_TWELVE_OCLOCK = n(b'\xF0\x9F\x95\x9B')
MOUNT_FUJI = n(b'\xF0\x9F\x97\xBB')
TOKYO_TOWER = n(b'\xF0\x9F\x97\xBC')
STATUE_OF_LIBERTY = n(b'\xF0\x9F\x97\xBD')
SILHOUETTE_OF_JAPAN = n(b'\xF0\x9F\x97\xBE')
MOYAI = n(b'\xF0\x9F\x97\xBF')
GRINNING_FACE = n(b'\xF0\x9F\x98\x80')
SMILING_FACE_WITH_HALO = n(b'\xF0\x9F\x98\x87')
SMILING_FACE_WITH_HORNS = n(b'\xF0\x9F\x98\x88')
SMILING_FACE_WITH_SUNGLASSES = n(b'\xF0\x9F\x98\x8E')
NEUTRAL_FACE = n(b'\xF0\x9F\x98\x90')
EXPRESSIONLESS_FACE = n(b'\xF0\x9F\x98\x91')
CONFUSED_FACE = n(b'\xF0\x9F\x98\x95')
KISSING_FACE = n(b'\xF0\x9F\x98\x97')
KISSING_FACE_WITH_SMILING_EYES = n(b'\xF0\x9F\x98\x99')
FACE_WITH_STUCK_OUT_TONGUE = n(b'\xF0\x9F\x98\x9B')
WORRIED_FACE = n(b'\xF0\x9F\x98\x9F')
FROWNING_FACE_WITH_OPEN_MOUTH = n(b'\xF0\x9F\x98\xA6')
ANGUISHED_FACE = n(b'\xF0\x9F\x98\xA7')
GRIMACING_FACE = n(b'\xF0\x9F\x98\xAC')
FACE_WITH_OPEN_MOUTH = n(b'\xF0\x9F\x98\xAE')
HUSHED_FACE = n(b'\xF0\x9F\x98\xAF')
SLEEPING_FACE = n(b'\xF0\x9F\x98\xB4')
FACE_WITHOUT_MOUTH = n(b'\xF0\x9F\x98\xB6')
HELICOPTER = n(b'\xF0\x9F\x9A\x81')
STEAM_LOCOMOTIVE = n(b'\xF0\x9F\x9A\x82')
TRAIN = n(b'\xF0\x9F\x9A\x86')
LIGHT_RAIL = n(b'\xF0\x9F\x9A\x88')
TRAM = n(b'\xF0\x9F\x9A\x8A')
ONCOMING_BUS = n(b'\xF0\x9F\x9A\x8D')
TROLLEYBUS = n(b'\xF0\x9F\x9A\x8E')
MINIBUS = n(b'\xF0\x9F\x9A\x90')
ONCOMING_POLICE_CAR = n(b'\xF0\x9F\x9A\x94')
ONCOMING_TAXI = n(b'\xF0\x9F\x9A\x96')
ONCOMING_AUTOMOBILE = n(b'\xF0\x9F\x9A\x98')
ARTICULATED_LORRY = n(b'\xF0\x9F\x9A\x9B')
TRACTOR = n(b'\xF0\x9F\x9A\x9C')
MONORAIL = n(b'\xF0\x9F\x9A\x9D')
MOUNTAIN_RAILWAY = n(b'\xF0\x9F\x9A\x9E')
SUSPENSION_RAILWAY = n(b'\xF0\x9F\x9A\x9F')
MOUNTAIN_CABLEWAY = n(b'\xF0\x9F\x9A\xA0')
AERIAL_TRAMWAY = n(b'\xF0\x9F\x9A\xA1')
ROWBOAT = n(b'\xF0\x9F\x9A\xA3')
VERTICAL_TRAFFIC_LIGHT = n(b'\xF0\x9F\x9A\xA6')
PUT_LITTER_IN_ITS_PLACE_SYMBOL = n(b'\xF0\x9F\x9A\xAE')
DO_NOT_LITTER_SYMBOL = n(b'\xF0\x9F\x9A\xAF')
POTABLE_WATER_SYMBOL = n(b'\xF0\x9F\x9A\xB0')
NON_POTABLE_WATER_SYMBOL = n(b'\xF0\x9F\x9A\xB1')
NO_BICYCLES = n(b'\xF0\x9F\x9A\xB3')
BICYCLIST = n(b'\xF0\x9F\x9A\xB4')
MOUNTAIN_BICYCLIST = n(b'\xF0\x9F\x9A\xB5')
NO_PEDESTRIANS = n(b'\xF0\x9F\x9A\xB7')
CHILDREN_CROSSING = n(b'\xF0\x9F\x9A\xB8')
SHOWER = n(b'\xF0\x9F\x9A\xBF')
BATHTUB = n(b'\xF0\x9F\x9B\x81')
PASSPORT_CONTROL = n(b'\xF0\x9F\x9B\x82')
CUSTOMS = n(b'\xF0\x9F\x9B\x83')
BAGGAGE_CLAIM = n(b'\xF0\x9F\x9B\x84')
LEFT_LUGGAGE = n(b'\xF0\x9F\x9B\x85')
EARTH_GLOBE_EUROPE_AFRICA = n(b'\xF0\x9F\x8C\x8D')
EARTH_GLOBE_AMERICAS = n(b'\xF0\x9F\x8C\x8E')
GLOBE_WITH_MERIDIANS = n(b'\xF0\x9F\x8C\x90')
WAXING_CRESCENT_MOON_SYMBOL = n(b'\xF0\x9F\x8C\x92')
WANING_GIBBOUS_MOON_SYMBOL = n(b'\xF0\x9F\x8C\x96')
LAST_QUARTER_MOON_SYMBOL = n(b'\xF0\x9F\x8C\x97')
WANING_CRESCENT_MOON_SYMBOL = n(b'\xF0\x9F\x8C\x98')
NEW_MOON_WITH_FACE = n(b'\xF0\x9F\x8C\x9A')
LAST_QUARTER_MOON_WITH_FACE = n(b'\xF0\x9F\x8C\x9C')
FULL_MOON_WITH_FACE = n(b'\xF0\x9F\x8C\x9D')
SUN_WITH_FACE = n(b'\xF0\x9F\x8C\x9E')
EVERGREEN_TREE = n(b'\xF0\x9F\x8C\xB2')
DECIDUOUS_TREE = n(b'\xF0\x9F\x8C\xB3')
LEMON = n(b'\xF0\x9F\x8D\x8B')
PEAR = n(b'\xF0\x9F\x8D\x90')
BABY_BOTTLE = n(b'\xF0\x9F\x8D\xBC')
HORSE_RACING = n(b'\xF0\x9F\x8F\x87')
RUGBY_FOOTBALL = n(b'\xF0\x9F\x8F\x89')
EUROPEAN_POST_OFFICE = n(b'\xF0\x9F\x8F\xA4')
RAT = n(b'\xF0\x9F\x90\x80')
MOUSE = n(b'\xF0\x9F\x90\x81')
OX = n(b'\xF0\x9F\x90\x82')
WATER_BUFFALO = n(b'\xF0\x9F\x90\x83')
COW = n(b'\xF0\x9F\x90\x84')
TIGER = n(b'\xF0\x9F\x90\x85')
LEOPARD = n(b'\xF0\x9F\x90\x86')
RABBIT = n(b'\xF0\x9F\x90\x87')
CAT = n(b'\xF0\x9F\x90\x88')
DRAGON = n(b'\xF0\x9F\x90\x89')
CROCODILE = n(b'\xF0\x9F\x90\x8A')
WHALE = n(b'\xF0\x9F\x90\x8B')
RAM = n(b'\xF0\x9F\x90\x8F')
GOAT = n(b'\xF0\x9F\x90\x90')
ROOSTER = n(b'\xF0\x9F\x90\x93')
DOG = n(b'\xF0\x9F\x90\x95')
PIG = n(b'\xF0\x9F\x90\x96')
DROMEDARY_CAMEL = n(b'\xF0\x9F\x90\xAA')
BUSTS_IN_SILHOUETTE = n(b'\xF0\x9F\x91\xA5')
TWO_MEN_HOLDING_HANDS = n(b'\xF0\x9F\x91\xAC')
TWO_WOMEN_HOLDING_HANDS = n(b'\xF0\x9F\x91\xAD')
THOUGHT_BALLOON = n(b'\xF0\x9F\x92\xAD')
BANKNOTE_WITH_EURO_SIGN = n(b'\xF0\x9F\x92\xB6')
BANKNOTE_WITH_POUND_SIGN = n(b'\xF0\x9F\x92\xB7')
OPEN_MAILBOX_WITH_RAISED_FLAG = n(b'\xF0\x9F\x93\xAC')
OPEN_MAILBOX_WITH_LOWERED_FLAG = n(b'\xF0\x9F\x93\xAD')
POSTAL_HORN = n(b'\xF0\x9F\x93\xAF')
NO_MOBILE_PHONES = n(b'\xF0\x9F\x93\xB5')
TWISTED_RIGHTWARDS_ARROWS = n(b'\xF0\x9F\x94\x80')
CLOCKWISE_RIGHTWARDS_AND_LEFTWARDS_OPEN_CIRCLE_ARROWS = n(
b'\xF0\x9F\x94\x81')
CLOCKWISE_RIGHTWARDS_AND_LEFTWARDS_OPEN_CIRCLE_ARROWS_WITH_CIRCLED_ONE_OVERLAY = n(
b'\xF0\x9F\x94\x82')
ANTICLOCKWISE_DOWNWARDS_AND_UPWARDS_OPEN_CIRCLE_ARROWS = n(
b'\xF0\x9F\x94\x84')
LOW_BRIGHTNESS_SYMBOL = n(b'\xF0\x9F\x94\x85')
HIGH_BRIGHTNESS_SYMBOL = n(b'\xF0\x9F\x94\x86')
SPEAKER_WITH_CANCELLATION_STROKE = n(b'\xF0\x9F\x94\x87')
SPEAKER_WITH_ONE_SOUND_WAVE = n(b'\xF0\x9F\x94\x89')
BELL_WITH_CANCELLATION_STROKE = n(b'\xF0\x9F\x94\x95')
MICROSCOPE = n(b'\xF0\x9F\x94\xAC')
TELESCOPE = n(b'\xF0\x9F\x94\xAD')
CLOCK_FACE_ONE_THIRTY = n(b'\xF0\x9F\x95\x9C')
CLOCK_FACE_TWO_THIRTY = n(b'\xF0\x9F\x95\x9D')
CLOCK_FACE_THREE_THIRTY = n(b'\xF0\x9F\x95\x9E')
CLOCK_FACE_FOUR_THIRTY = n(b'\xF0\x9F\x95\x9F')
CLOCK_FACE_FIVE_THIRTY = n(b'\xF0\x9F\x95\xA0')
CLOCK_FACE_SIX_THIRTY = n(b'\xF0\x9F\x95\xA1')
CLOCK_FACE_SEVEN_THIRTY = n(b'\xF0\x9F\x95\xA2')
CLOCK_FACE_EIGHT_THIRTY = n(b'\xF0\x9F\x95\xA3')
CLOCK_FACE_NINE_THIRTY = n(b'\xF0\x9F\x95\xA4')
CLOCK_FACE_TEN_THIRTY = n(b'\xF0\x9F\x95\xA5')
CLOCK_FACE_ELEVEN_THIRTY = n(b'\xF0\x9F\x95\xA6')
CLOCK_FACE_TWELVE_THIRTY = n(b'\xF0\x9F\x95\xA7')
self.delete_slice__2 = self.nop
self.delete_slice__3 = self.nop
super(FindFTrace, self).__init__(*args, **kwargs)
self._locals = AlmostReadOnlyDict(self._locals)
self._globals = AlmostReadOnlyDict(self._globals)
def store_attr(self):
"""STORE_ATTR opcode"""
if self.names[self.oparg] == "f_trace":
self._stop = True
self.result = self.stack.pop() if self.stack else True
FTraceExe = type(n(b"FTraceExe"), (FindFTrace, PyInterpreter), {}) # pylint: disable=invalid-name
def get_f_trace(code, loc, glob):
"""Get frame from frame.f_trace attribution"""
interpreter = FTraceExe(code, loc, glob)
interpreter.execute()
return interpreter.result
def find_f_trace(code, loc, glob, lasti):
"""Check if code has frame.f_trace attribution"""
if "f_trace" not in code.co_names:
return False
interpreter = FindFTrace(code, loc, glob)
interpreter.execute()
def to_file(self, file_name, path='.'):
path_name = os.path.join(path, file_name)
metadata = self.get_properties()
packers.to_msgpack(path_name, metadata, encoding='utf-8')
packers.to_msgpack(path_name, self.get_dataframe(), encoding='utf-8', compress=n(b'zlib'), append=True)
def save_to_csv(data, file_name, path='.'):
path_name = os.path.join(path, file_name)
data.to_csv(path_name, sep=n(b'\t'))
from .variable_usage import VariableUsage
from .tag import Tag
from .trial import Trial
# Other models
from .history import History
from .diff import Diff
from .trial_prolog import TrialProlog
ORDER = [
Trial,
Head,
Tag,
GraphCache, # Trial
Module,
Dependency,
EnvironmentAttr, # Deployment
FunctionDef,
Object, # Definition
Activation,
ObjectValue,
FileAccess, # Execution
Variable,
VariableUsage,
VariableDependency # Slicing
]
__all__ = [n(x.__modelname__) for x in ORDER] + [
"History", "Diff", "TrialProlog", "MetaModel", "Model", "ORDER"
]
def save_to_msgpack(data, file_name, path='.'):
folder = os.path.join(path, file_name)
data.to_msgpack(folder, compress=n(b'zlib'))
return
def test_river_discharge_simulation():
# Modules activation and deactivation
# analysis = False
# cdf_pdf_representation = False
# temporal_dependency = False
# climatic_events_fitting = True
# threshold_checking_for_simulation = False
# simulation_cycles = True
analysis = True
cdf_pdf_representation = False
temporal_dependency = False
climatic_events_fitting = True
threshold_checking_for_simulation = False
simulation_cycles = True
#%% Input data
# Initial year, number of years, number of valid data in a year
anocomienzo, duracion, umbralano = (2018, 10, 0.8)
# Type of fit (0-GUI, 1-stationary, 2-nonstationary)
ant = [2]
# Fourier order for nonstationary analysis
no_ord_cycles = [2]
no_ord_calms = [2]
# Number of simulations
no_sim = 1
# Type of fit functions
fun_cycles = [st.exponweib]
fun_calms = [st.norm]
# Number of normals
no_norm_cycles = [False]
no_norm_calms = [False]
f_mix_cycles = [False]
mod_cycles = [[0, 0, 0, 0]]
# Cycles River discharge
threshold_cycles = 25
# minimum_interarrival_time = pd.Timedelta('250 days')
# minimum_cycle_length = pd.Timedelta('5 days')
minimum_interarrival_time = pd.Timedelta('7 days')
minimum_cycle_length = pd.Timedelta('2 days')
# Cycles SPEI
threshold_spei = 0
minimum_interarrival_time_spei = pd.Timedelta('150 days')
minimum_cycle_length_spei = pd.Timedelta('150 days')
interpolation = True
interpolation_method = 'linear'
interpolation_freq = '1min'
truncate = True
extra_info = True
#%% Read data
# Import river discharge data when all dams were active
data_path = os.path.join(tests.current_path, '..', '..', 'inputadapter',
'tests', 'output', 'modf')
modf_file_name = 'guadalete_estuary_river_discharge.modf'
path_name = os.path.join(data_path, modf_file_name)
modf_rd = MetOceanDF.read_file(path_name)
# Group into dataframe
river_discharge = pd.DataFrame(modf_rd)
# Delete rows where with no common values
river_discharge.dropna(how='any', inplace=True)
# Import complete rive discharge historic data
# All historic river discharge
data_path = os.path.join(tests.current_path, '..', '..', '..', '..',
'data', 'solar_flux_nao_index_spei')
modf_file_name = 'caudales.txt'
path_name = os.path.join(data_path, modf_file_name)
modf_all = pd.read_table(path_name, header=None, delim_whitespace=True)
date_col = dates.extract_date(modf_all.iloc[:, 0:4])
modf_all.index = date_col
modf_all.drop(modf_all.columns[0:4], axis=1, inplace=True)
modf_all.columns = ['Q']
#%% Preprocessing
t_step = missing_values.find_timestep(river_discharge) # Find tstep
data_gaps = missing_values.find_missing_values(river_discharge, t_step)
river_discharge = missing_values.fill_missing_values(
river_discharge,
t_step,
technique='interpolation',
method='nearest',
limit=16 * 24,
limit_direction='both')
data_gaps_after = missing_values.find_missing_values(
river_discharge, t_step)
# Add noise for VAR
noise = np.random.rand(river_discharge.shape[0],
river_discharge.shape[1]) * 1e-2
river_discharge = river_discharge + noise
# Save_to_pickle
river_discharge.to_pickle('river_discharge.p')
# Group into list of dataframes
df = list()
df.append(pd.DataFrame(river_discharge['Q']))
#%% Cycles and calms calculation
cycles, calm_periods, info = extremal.extreme_events(
river_discharge, 'Q', threshold_cycles, minimum_interarrival_time,
minimum_cycle_length, interpolation, interpolation_method,
interpolation_freq, truncate, extra_info)
# Calculate duration of the cycles
dur_cycles = extremal.events_duration(cycles)
dur_cycles_description = dur_cycles.describe()
sample_cycles = pd.DataFrame(info['data_cycles'].iloc[:, 0])
noise = np.random.rand(sample_cycles.shape[0],
sample_cycles.shape[1]) * 1e-2
sample_cycles = sample_cycles + noise
sample_calms = pd.DataFrame(info['data_calm_periods'])
noise = np.random.rand(sample_calms.shape[0], sample_calms.shape[1]) * 1e-2
sample_calms = sample_calms + noise
#%% CLIMATIC INDICES
# Sunspots
data_path = os.path.join(tests.current_path, '..', '..', '..', '..',
'data', 'solar_flux_nao_index_spei')
modf_file_name = 'sunspot.csv'
path_name = os.path.join(data_path, modf_file_name)
sunspot = pd.read_csv(path_name,
header=None,
delim_whitespace=True,
parse_dates=[[0, 1]],
index_col=0)
sunspot = sunspot.drop([2, 4, 5], axis=1)
# SPEI
data_path = os.path.join(tests.current_path, '..', '..', '..', '..',
'data', 'solar_flux_nao_index_spei')
modf_file_name = 'spei_cadiz.csv'
path_name = os.path.join(data_path, modf_file_name)
spei = pd.read_csv(path_name, sep=',')
spei.index = sunspot.index[2412:3233]
# Calculate cycles over SPEI
spei = pd.DataFrame(spei.loc[:, 'SPEI_12'] * 100).dropna()
cycles_spei, calm_periods_spei, info_spei = extremal.extreme_events(
spei, 'SPEI_12', threshold_spei, minimum_interarrival_time_spei,
minimum_cycle_length_spei, interpolation, interpolation_method,
interpolation_freq, truncate, extra_info)
peaks_over_thres_spei = extremal.events_max(cycles_spei)
# Plot peaks
peaks_over_thres = extremal.events_max(cycles)
# Represent cycles
fig1 = plt.figure(figsize=(20, 20))
ax = plt.axes()
ax.plot(river_discharge)
ax.axhline(threshold_cycles, color='lightgray')
ax.plot(spei.loc[:, 'SPEI_12'] * 100, color='0.75', linewidth=2)
# Plot cycles
# for cycle in cycles_all:
# ax.plot(cycle, 'sandybrown', marker='.', markersize=5)
# # ax.plot(cycle.index[0], cycle[0], 'gray', marker='.', markersize=10)
# # ax.plot(cycle.index[-1], cycle[-1], 'black', marker='.', markersize=10)
for cycle in cycles:
ax.plot(cycle, 'g', marker='.', markersize=5)
# ax.plot(cycle.index[0], cycle[0], 'gray', marker='.', markersize=10)
# ax.plot(cycle.index[-1], cycle[-1], 'black', marker='.', markersize=10)
for cycle in cycles_spei:
ax.plot(cycle, 'k', marker='.', markersize=5, linewidth=2)
ax.plot(cycle.index[0], cycle[0], 'gray', marker='.', markersize=15)
ax.plot(cycle.index[-1], cycle[-1], 'black', marker='.', markersize=15)
ax.plot(peaks_over_thres, '.r', markersize=15)
ax.plot(peaks_over_thres_spei, '.c', markersize=15)
ax.grid()
ax.set_xlim([datetime.date(1970, 01, 01), datetime.date(2018, 04, 11)])
ax.set_ylim([-5, 500])
fig1.savefig(
os.path.join('output', 'analisis', 'graficas',
'ciclos_river_discharge_spei.png'))
#%% # ANALISIS CLIMATICO (0: PARA SALTARLO, 1: PARA HACERLO; LO MISMO PARA TODOS ESTOS IF)
if analysis:
if cdf_pdf_representation:
for i in range(len(df)):
# DIBUJO LAS CDF Y PDF DE LOS REGISTROS
plot_analisis.cdf_pdf_registro(df[i], df[i].columns[0])
plt.pause(0.5)
#%% THEORETICAL FIT CYCLES
data_cycles = sample_cycles['Q']
# Empirical cdf
ecdf = empirical_distributions.ecdf_histogram(data_cycles)
# Fit the variable to an extremal distribution
(param, x, cdf_expwbl, pdf_expwbl) = theoretical_fit.fit_distribution(
data_cycles,
fit_type=fun_cycles[0].name,
x_min=min(data_cycles),
x_max=2 * max(data_cycles),
n_points=1000)
par0_cycles = list()
par0_cycles.append(np.asarray(param))
# GUARDO LOS PARAMETROS
np.save(
os.path.join('output', 'analisis',
'parameter_river_discharge_cycles.npy'), par0_cycles)
# Check the goodness of the fit
fig1 = plt.figure(figsize=(20, 20))
ax = plt.axes()
ax.plot(ecdf.index, ecdf, '.')
ax.plot(x, cdf_expwbl)
ax.set_xlabel('Q (m3/s)')
ax.set_ylabel('CDF')
ax.legend([
'ECDF',
'Exponweib Fit',
])
ax.grid()
ax.set_xlim([0, 500])
fig1.savefig(
os.path.join('output', 'analisis', 'graficas',
'cdf_fit_ciclos_river_discharge.png'))
# PP - Plot values
(yppplot_emp,
yppplot_teo) = theoretical_fit.pp_plot(x, cdf_expwbl, ecdf)
# QQ - Plot values
(yqqplot_emp,
yqqplot_teo) = theoretical_fit.qq_plot(x, cdf_expwbl, ecdf)
# Plot Goodness of fit
theoretical_fit.plot_goodness_of_fit(cdf_expwbl, ecdf, river_discharge,
'Q', x, yppplot_emp, yqqplot_emp,
yppplot_teo, yqqplot_teo)
# Non-stationary fit for calms
par_cycles, mod_cycles, f_mix_cycles, data_graph_cycles = list(), list(
), list(), list()
df = list()
df.append(data_cycles)
for i in range(len(df)):
# SE HAN SELECCIONADO LOS ULTIMOS 7 ANOS PARA QUE EL ANALISIS SEA MAS RAPIDO
analisis_ = analisis.analisis(df[i],
fun_cycles[i],
ant[i],
ordg=no_ord_cycles[i],
nnorm=no_norm_cycles[i],
par0=par0_cycles[i])
par_cycles.append(analisis_[0])
mod_cycles.append(analisis_[1])
f_mix_cycles.append(analisis_[2])
aux = list(analisis_[3])
aux[5] = i
aux = tuple(aux)
data_graph_cycles.append(aux)
# DIBUJO LOS RESULTADOS (HAY UNA GRAN GAMA DE FUNCIONES DE DIBUJO; VER MANUAL)
plot_analisis.cuantiles_ne(*data_graph_cycles[i])
plt.pause(0.5)
fig2 = plt.figure(figsize=(20, 20))
plt.plot(x, pdf_expwbl)
_ = plt.hist(data_cycles,
bins=np.linspace(0, 500, 100),
normed=True,
alpha=0.5)
plt.xlim([0, 400])
fig2.savefig(
os.path.join('output', 'analisis', 'graficas',
'pdf_fit_ciclos_river_discharge.png'))
# %% THEORETICAL FIT CALMS
param0_calms = list()
data_calms = sample_calms['Q']
(param, x, cdf, pdf) = theoretical_fit.fit_distribution(
data_calms,
fit_type=fun_calms[0].name,
x_min=np.min(data_calms),
x_max=1.1 * np.max(data_calms),
n_points=1000)
param0_calms.append(np.asarray(param))
# Empirical cdf
ecdf = empirical_distributions.ecdf_histogram(data_calms)
epdf = empirical_distributions.epdf_histogram(data_calms, bins=0)
# PP - Plot values
(yppplot_emp, yppplot_teo) = theoretical_fit.pp_plot(x, cdf, ecdf)
# QQ - Plot values
(yqqplot_emp, yqqplot_teo) = theoretical_fit.qq_plot(x, cdf, ecdf)
# Plot Goodness of fit
theoretical_fit.plot_goodness_of_fit(cdf, ecdf, sample_calms, 'Q', x,
yppplot_emp, yqqplot_emp,
yppplot_teo, yqqplot_teo)
# Non-stationary fit for calms
par_calms, mod_calms, f_mix_calms, data_graph_calms = list(), list(
), list(), list()
df = list()
df.append(data_calms)
for i in range(len(df)):
# SE HAN SELECCIONADO LOS ULTIMOS 7 ANOS PARA QUE EL ANALISIS SEA MAS RAPIDO
analisis_ = analisis.analisis(df[i],
fun_calms[i],
ant[i],
ordg=no_ord_calms[i],
nnorm=no_norm_calms[i],
par0=param0_calms[i])
par_calms.append(analisis_[0])
mod_calms.append(analisis_[1])
f_mix_calms.append(analisis_[2])
data_graph_calms.append(analisis_[3])
# DIBUJO LOS RESULTADOS (HAY UNA GRAN GAMA DE FUNCIONES DE DIBUJO; VER MANUAL)
plot_analisis.cuantiles_ne(*data_graph_calms[i])
plt.pause(0.5)
# Guardo parametros
np.save(
os.path.join('output', 'analisis',
'parameter_river_discharge_calms.npy'), par_calms)
np.save(
os.path.join('output', 'analisis',
'mod_river_discharge_calms.npy'), mod_calms)
np.save(
os.path.join('output', 'analisis',
'f_mix_river_discharge_calms.npy'), f_mix_calms)
#%% TEMPORAL DEPENDENCY
if temporal_dependency:
# SE UTILIZAN LOS PARAMETROS DE SALIDA DEL ANÁLISIS PREVIO
# Lectura de datos
par_cycles = np.load(
os.path.join('output', 'analisis',
'parameter_river_discharge_cycles.npy'))
par_calms = np.load(
os.path.join('output', 'analisis',
'parameter_river_discharge_calms.npy'))
mod_calms = np.load(
os.path.join('output', 'analisis',
'mod_river_discharge_calms.npy'))
f_mix_calms = np.load(
os.path.join('output', 'analisis',
'f_mix_river_discharge_calms.npy'))
(df_dt_cycles,
cdf_) = analisis.dependencia_temporal(sample_cycles, par_cycles,
mod_cycles, no_norm_cycles,
f_mix_cycles, fun_cycles)
# SE GUARDAN LOS PARAMETROS DEL MODELO VAR
df_dt_cycles.to_pickle(
os.path.join('output', 'dependencia_temporal',
'df_dt_river_discharge_cycles.p'))
(df_dt_calms,
cdf_) = analisis.dependencia_temporal(sample_calms, par_calms,
mod_calms, no_norm_calms,
f_mix_calms, fun_calms)
# SE GUARDAN LOS PARAMETROS DEL MODELO VAR
df_dt_calms.to_pickle(
os.path.join('output', 'dependencia_temporal',
'df_dt_river_discharge_calms.p'))
if climatic_events_fitting:
#%% FIT NUMBER OF EVENTS DURING WET CYCLES
events_wet_cycle = pd.Series([5, 2, 1, 3, 2, 2, 0, 6, 1])
ecdf_events_wet_cycle = empirical_distributions.ecdf_histogram(
events_wet_cycle)
mu = np.mean(events_wet_cycle)
simulated_number_events = pd.Series(
poisson.rvs(mu, loc=0, size=100, random_state=None))
ecdf_simulated_events_wet_cycle = empirical_distributions.ecdf_histogram(
simulated_number_events)
x_poisson = np.linspace(0, 10, 100)
cdf_poisson = poisson.cdf(x_poisson, mu, loc=0)
plt.figure()
ax = plt.axes()
ax.plot(ecdf_events_wet_cycle.index, ecdf_events_wet_cycle, '.')
ax.plot(ecdf_simulated_events_wet_cycle.index,
ecdf_simulated_events_wet_cycle, '.')
ax.plot(x_poisson, cdf_poisson)
ax.legend(['ECDF', 'ECDF Sim', 'Poisson Fit'])
ax.grid()
#%% FIT TIME BETWEEN WET CYCLES
t_wet_cycles = peaks_over_thres_spei.index.to_series().diff().dropna(
).astype('m8[s]').astype(np.float32)
ecdf_t_wet_cycle = empirical_distributions.ecdf_histogram(t_wet_cycles)
norm_param = norm.fit(t_wet_cycles, loc=0)
simulated_t_wet_cycles = pd.Series(
norm.rvs(*norm_param, size=100, random_state=None))
ecdf_simulated_t_wet_cycles = empirical_distributions.ecdf_histogram(
simulated_t_wet_cycles)
x_norm = np.linspace(0, 2 * max(t_wet_cycles), 100)
cdf_norm = norm.cdf(x_norm, *norm_param)
plt.figure()
ax = plt.axes()
ax.plot(ecdf_t_wet_cycle.index, ecdf_t_wet_cycle, '.')
ax.plot(ecdf_simulated_t_wet_cycles.index, ecdf_simulated_t_wet_cycles,
'.')
ax.plot(x_norm, cdf_norm)
ax.legend(['ECDF', 'ECDF Sim', 'Exponential Fit'])
ax.grid()
simulated_t_wet_cycles_days = simulated_t_wet_cycles.astype('m8[s]')
# Elimino valores negativos
simulated_t_wet_cycles_days = simulated_t_wet_cycles_days[
simulated_t_wet_cycles_days.values > datetime.timedelta(days=1)]
#%% FIT TIME BETWEEN EVENTS DURING WET CYCLES
t_between_events = peaks_over_thres.index.to_series().diff().dropna()
t_between_events = t_between_events[
t_between_events < datetime.timedelta(days=400)]
t_between_events = t_between_events.astype('m8[s]').astype(np.float32)
ecdf_t_between_events = empirical_distributions.ecdf_histogram(
t_between_events)
lambda_par = expon.fit(t_between_events, loc=0)
simulated_t_between_events = pd.Series(
expon.rvs(scale=lambda_par[1], size=100, random_state=None))
ecdf_simulated_t_between_events = empirical_distributions.ecdf_histogram(
simulated_t_between_events)
x_expon = np.linspace(0, 2 * max(t_between_events), 100)
cdf_expon = expon.cdf(x_expon, scale=lambda_par[1], loc=0)
plt.figure()
ax = plt.axes()
ax.plot(ecdf_t_between_events.index, ecdf_t_between_events, '.')
ax.plot(ecdf_simulated_t_between_events.index,
ecdf_simulated_t_between_events, '.')
ax.plot(x_expon, cdf_expon)
ax.legend(['ECDF', 'ECDF Sim', 'Exponential Fit'])
ax.grid()
simulated_t_between_events_days = simulated_t_between_events.astype(
'm8[s]')
#%% FIT TIME BETWEEN ALL EVENTS
# Fit time between events (without considering wet cycles) 2 method
t_between_events_2method = peaks_over_thres.index.to_series().diff(
).dropna()
t_between_events_2method = t_between_events_2method.astype(
'm8[s]').astype(np.float32)
ecdf_t_between_events_2method = empirical_distributions.ecdf_histogram(
t_between_events_2method)
lambda_par = expon.fit(t_between_events_2method, loc=0)
simulated_t_between_events_2method = pd.Series(
expon.rvs(scale=lambda_par[1], size=100, random_state=None))
ecdf_simulated_t_between_events_2method = empirical_distributions.ecdf_histogram(
simulated_t_between_events_2method)
x_expon = np.linspace(0, 2 * np.max(t_between_events_2method), 100)
cdf_expon = expon.cdf(x_expon, scale=lambda_par[1], loc=0)
plt.figure()
ax = plt.axes()
ax.plot(ecdf_t_between_events_2method.index,
ecdf_t_between_events_2method, '.')
ax.plot(ecdf_simulated_t_between_events_2method.index,
ecdf_simulated_t_between_events_2method, '.')
ax.plot(x_expon, cdf_expon)
ax.legend(['ECDF', 'ECDF Sim', 'Exponential Fit'])
ax.grid()
simulated_t_between_events_2method_days = simulated_t_between_events.astype(
'm8[s]')
# nul_values = simulated_t_between_events_2method_days.values > datetime.timedelta(days=2000)
#%% SIMULACION CLIMÁTICA CHEQUEO UMBRAL OPTIMO PARA AJUSTAR DURACIONES
if threshold_checking_for_simulation:
# CARGO PARÁMETROS
par_cycles = np.load(
os.path.join('output', 'analisis',
'parameter_river_discharge_cycles.npy'))
df_dt_cycles = pd.read_pickle(
os.path.join('output', 'dependencia_temporal',
'df_dt_river_discharge_cycles.p'))
vars_ = ['Q']
# Cargo el SPEI Index para ajustar tiempo entre ciclos humedos, numero de eventos por ciclo humedo
# tiempo entre eventos dentro de ciclo humedo
# Figura de las cdf y pdf empiricas
fig1, axes1 = plt.subplots(1, 2, figsize=(20, 7))
cont = 0
iter = 0
while cont < no_sim:
df_sim = simulacion.simulacion(anocomienzo,
duracion,
par_cycles,
mod_cycles,
no_norm_cycles,
f_mix_cycles,
fun_cycles,
vars_,
sample_cycles,
df_dt_cycles, [0, 0, 0, 0, 0],
semilla=int(
np.random.rand(1) * 1e6))
iter += 1
# Primero filtro si hay valores mayores que el umbral,en cuyo caso descarto la serie
if np.max(df_sim).values <= np.max(sample_cycles['Q']) * 1.25:
# Representacion de la serie
plt.figure()
ax = plt.axes()
ax.plot(df_sim)
ax.plot(sample_cycles, '.')
ax.plot(df_sim * 0 + max(sample_cycles['Q']), 'r')
ax.grid()
# Cdf Pdf
data = df_sim['Q']
ecdf = empirical_distributions.ecdf_histogram(data)
epdf = empirical_distributions.epdf_histogram(data, bins=0)
axes1[0].plot(epdf.index, epdf, '--', color='0.75')
axes1[1].plot(ecdf.index, ecdf, '--', color='0.75')
# Extract cycles from data for different thresholds to fix the duration
fig2, axes2 = plt.subplots(1, 2, figsize=(20, 7))
if cont == 0:
dur_cycles = dur_cycles.astype('m8[s]').astype(
np.float32) # Convierto a segundos y flotante
ecdf_dur = empirical_distributions.ecdf_histogram(dur_cycles)
epdf_dur = empirical_distributions.epdf_histogram(dur_cycles,
bins=0)
axes2[0].plot(epdf_dur.index, epdf_dur, 'r', lw=2)
axes2[1].plot(ecdf_dur.index, ecdf_dur, 'r', lw=2)
threshold = np.arange(20, 110, 10)
color_sequence = [
'#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c',
'#98df8a', '#d62728', '#ff9896', '#9467bd', '#c5b0d5',
'#8c564b', '#c49c94', '#e377c2', '#f7b6d2', '#7f7f7f',
'#c7c7c7', '#bcbd22', '#dbdb8d', '#17becf', '#9edae5'
]
for j, th in enumerate(threshold):
minimum_interarrival_time = pd.Timedelta('1 hour')
minimum_cycle_length = pd.Timedelta('2 days')
cycles, calm_periods, info = extremal.extreme_events(
df_sim, 'Q', th, minimum_interarrival_time,
minimum_cycle_length, interpolation,
interpolation_method, interpolation_freq, truncate,
extra_info)
# Calculate duration of the cycles
dur_cycles_sim = extremal.events_duration(cycles)
dur_cycles_sim_description = dur_cycles_sim.describe()
# Represent cycles
fig3 = plt.figure(figsize=(20, 20))
ax = plt.axes()
ax.plot(df_sim)
ax.axhline(th, color='lightgray')
ax.grid()
ax.legend([
'Threshold: ' + str(th) + ' (m3/s)' + '/ Dur_min ' +
str(dur_cycles_description['min']) + ' - ' +
str(dur_cycles_sim_description['min']) +
'/ Dur_mean ' + str(dur_cycles_description['mean']) +
' - ' + str(dur_cycles_sim_description['mean']) +
'/ Dur_max ' + str(dur_cycles_description['max']) +
' - ' + str(dur_cycles_sim_description['max'])
])
for cycle in cycles:
ax.plot(cycle, 'g', marker='.', markersize=5)
ax.plot(cycle.index[0],
cycle[0],
'gray',
marker='.',
markersize=10)
ax.plot(cycle.index[-1],
cycle[-1],
'black',
marker='.',
markersize=10)
ax.set_xlim([
datetime.date(2018, 04, 01),
datetime.date(2030, 01, 01)
])
ax.set_ylim([0, 600])
fig_name = 'ciclos_sim_' + str(cont) + '_threshold_' + str(
th) + '.png'
fig3.savefig(
os.path.join('output', 'simulacion', 'graficas',
'descarga_fluvial', 'umbral_optimo',
fig_name))
# Calculate the cdf and pdf of the cycle duration
dur_cycles_sim = dur_cycles_sim.astype('m8[s]').astype(
np.float32)
ecdf_dur_sim = empirical_distributions.ecdf_histogram(
dur_cycles_sim)
epdf_dur_sim = empirical_distributions.epdf_histogram(
dur_cycles_sim, bins=0)
axes2[0].plot(epdf_dur_sim.index,
epdf_dur_sim,
'--',
color=color_sequence[j],
label=['Threshold: ' + str(threshold[j])])
axes2[1].plot(ecdf_dur_sim.index,
ecdf_dur_sim,
'--',
color=color_sequence[j],
label=['Threshold: ' + str(threshold[j])])
axes2[0].legend()
axes2[1].set_xlim([0, 5000000])
axes2[0].set_xlim([0, 5000000])
fig_name = 'ciclos_dur_sim_' + str(cont) + '.png'
fig2.savefig(
os.path.join('output', 'simulacion', 'graficas',
'descarga_fluvial', 'umbral_optimo',
fig_name))
cont += 1
data = sample_cycles['Q']
ecdf = empirical_distributions.ecdf_histogram(data)
epdf = empirical_distributions.epdf_histogram(data, bins=0)
axes1[0].plot(epdf.index, epdf, 'r', lw=2)
axes1[1].plot(ecdf.index, ecdf, 'r', lw=2)
fig_name = 'pdf_cdf_descarga_fluvial.png'
fig1.savefig(
os.path.join('output', 'simulacion', 'graficas',
'descarga_fluvial', 'umbral_optimo', fig_name))
#%% SIMULACION CLIMATICA
threshold = 50
minimum_interarrival_time = pd.Timedelta('1 hour')
minimum_cycle_length = pd.Timedelta('2 days')
if simulation_cycles:
# CARGO PARÁMETROS
par_cycles = np.load(
os.path.join('output', 'analisis',
'parameter_river_discharge_cycles.npy'))
par_calms = np.load(
os.path.join('output', 'analisis',
'parameter_river_discharge_calms.npy'))
mod_calms = np.load(
os.path.join('output', 'analisis',
'mod_river_discharge_calms.npy'))
f_mix_calms = np.load(
os.path.join('output', 'analisis',
'f_mix_river_discharge_calms.npy'))
df_dt_cycles = pd.read_pickle(
os.path.join('output', 'dependencia_temporal',
'df_dt_river_discharge_cycles.p'))
df_dt_calms = pd.read_pickle(
os.path.join('output', 'dependencia_temporal',
'df_dt_river_discharge_calms.p'))
vars_ = ['Q']
# Figura de las cdf y pdf empiricas
fig2, axes1 = plt.subplots(1, 2, figsize=(20, 7))
cont = 0
iter = 0
while cont < no_sim:
df_sim = simulacion.simulacion(anocomienzo,
duracion,
par_cycles,
mod_cycles,
no_norm_cycles,
f_mix_cycles,
fun_cycles,
vars_,
sample_cycles,
df_dt_cycles, [0, 0, 0, 0, 0],
semilla=int(
np.random.rand(1) * 1e6))
iter += 1
# Primero filtro si hay valores mayores que el umbral,en cuyo caso descarto la serie
if np.max(df_sim).values <= np.max(sample_cycles['Q']) * 1.25:
df_sim = df_sim.resample('1H').interpolate()
# Extract cycles from data for different thresholds to fix the duration
if cont == 0:
dur_cycles = dur_cycles.astype('m8[s]').astype(
np.float32) # Convierto a segundos y flotante
# Calculate cycles
cycles, calm_periods, info = extremal.extreme_events(
df_sim, 'Q', threshold, minimum_interarrival_time,
minimum_cycle_length, interpolation, interpolation_method,
interpolation_freq, truncate, extra_info)
# # Represent cycles
# fig3 = plt.figure(figsize=(20, 20))
# ax = plt.axes()
# ax.plot(df_sim)
# ax.axhline(threshold, color='lightgray')
# ax.grid()
#
# for cycle in cycles:
# ax.plot(cycle, 'g', marker='.', markersize=5)
# ax.plot(cycle.index[0], cycle[0], 'gray', marker='.', markersize=10)
# ax.plot(cycle.index[-1], cycle[-1], 'black', marker='.', markersize=10)
# ax.set_xlim([datetime.date(2018, 01, 01), datetime.date(2021, 01, 01)])
# ax.set_ylim([0, 600])
# fig3.savefig(os.path.join('output', 'simulacion', 'graficas', 'descarga_fluvial',
# 'ciclos_cadiz_simulado_' + str(cont).zfill(4) + '.png'))
# Start to construct the time series
indices = pd.date_range(start='2018', end='2100', freq='1H')
df_simulate = pd.DataFrame(np.zeros((len(indices), 1)) + 25,
dtype=float,
index=indices,
columns=['Q'])
# The start is in wet cycles
cont_wet_cicles = 0
cont_df_events = 1
t_ini = datetime.datetime(2018, 01, 01)
t_end = datetime.datetime(2018, 01, 01)
while t_end < datetime.datetime(2090, 01, 01):
if cont_wet_cicles != 0:
t_ini = t_end + simulated_t_wet_cycles_days[
cont_wet_cicles]
year = t_ini.year
else:
year = 2018
# Select the number of events during wet cycle
n_events = simulated_number_events[cont_wet_cicles] - 1
cont_wet_cicles += 1
if n_events != 0:
# for j in range(0, n_events):
cont_df_events_in_wet_cycles = 0
while cont_df_events_in_wet_cycles <= n_events:
if cont_df_events_in_wet_cycles != 0:
# Time between events
year = year + 1
# Select the event
cycle = cycles[cont_df_events]
if np.max(cycle) >= 150:
# Simulate date
month1 = [
random.randint(1, 3),
random.randint(10, 12)
]
rand_pos = random.randint(0, 1)
month = month1[rand_pos]
day = random.randint(1, 28)
hour = random.randint(0, 23)
else:
# Simulate date
month = random.randint(1, 12)
day = random.randint(1, 28)
hour = random.randint(0, 23)
t_ini = datetime.datetime(year, month, day, hour)
pos_ini = np.where(
df_simulate.index == t_ini)[0][0]
pos_end = pos_ini + cycle.shape[0]
# Insert cycle
df_simulate.iloc[pos_ini:pos_end, 0] = cycle.values
t_end = df_simulate.index[pos_end]
year = df_simulate.index[pos_end].to_datetime(
).year
cont_df_events += 1
cont_df_events_in_wet_cycles += 1
else:
t_end = t_ini
# Simulation of calm periods
df_sim_calms = simulacion.simulacion(
anocomienzo,
85,
par_calms,
mod_calms,
no_norm_calms,
f_mix_calms,
fun_calms,
vars_,
sample_calms,
df_dt_calms, [0, 0, 0, 0, 0],
semilla=int(np.random.rand(1) * 1e6))
# Remove negative values
df_sim_calms[df_sim_calms < 0] = np.random.randint(1, 5)
# Combine both dataframes with cycles and calms
pos_cycles = df_simulate >= 50
df_river_discharge = df_sim_calms
df_river_discharge[pos_cycles] = df_simulate
# Hourly interpolation
df_river_discharge = df_river_discharge.resample(
'H').interpolate()
# Representation of results
fig1 = plt.figure(figsize=(20, 10))
ax = plt.axes()
ax.plot(river_discharge)
ax.plot(df_river_discharge)
ax.legend('Hindcast', 'Forecast')
ax.grid()
ax.set_ylim([-5, 500])
fig1.savefig(
os.path.join(
'output', 'simulacion', 'graficas', 'descarga_fluvial',
'descarga_fluvial_cadiz_simulado_' +
str(cont).zfill(4) + '.png'))
# Cdf Pdf
data = df_river_discharge['Q']
ecdf = empirical_distributions.ecdf_histogram(data)
epdf = empirical_distributions.epdf_histogram(data, bins=0)
axes1[0].plot(epdf.index, epdf, '--', color='0.75')
axes1[1].plot(ecdf.index, ecdf, '--', color='0.75')
# Guardado de ficheros
df_river_discharge.to_csv(os.path.join(
'output', 'simulacion', 'series_temporales',
'descarga_fluvial_500', 'descarga_fluvial_guadalete_sim_' +
str(cont).zfill(4) + '.txt'),
sep=n(b'\t'))
cont += 1
data = river_discharge['Q']
ecdf = empirical_distributions.ecdf_histogram(data)
epdf = empirical_distributions.epdf_histogram(data, bins=0)
axes1[0].plot(epdf.index, epdf, 'r', lw=2)
axes1[1].plot(ecdf.index, ecdf, 'r', lw=2)
fig_name = 'pdf_cdf_descarga_fluvial.png'
fig2.savefig(
os.path.join('output', 'simulacion', 'graficas',
'descarga_fluvial', fig_name))
from .slicing_variable import SlicingVariable
from .tag import Tag
from .trial import Trial
# Other models
from .history import History
from .diff import Diff
from .trial_prolog import TrialProlog
ORDER = [
Trial, Head, Tag, GraphCache, # Trial
Module, Dependency, EnvironmentAttr, # Deployment
FunctionDef, Object, # Definition
Activation, ObjectValue, FileAccess, # Execution
SlicingVariable, SlicingUsage, SlicingDependency # Slicing
]
__all__ = [
n(x.__modelname__) for x in ORDER
] + [
"History",
"Diff",
"TrialProlog",
"MetaModel",
"Model",
"ORDER"
]
