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12b135466c5c850993528becc6050f17c4230012
| 1,906
|
py
|
Python
|
itests/client_fixtures.py
|
skivis/BlackSheep
|
486f04ba2045f31dd3e188f52c45a275eb150967
|
[
"MIT"
] | 1
|
2021-04-28T14:42:26.000Z
|
2021-04-28T14:42:26.000Z
|
itests/client_fixtures.py
|
skivis/BlackSheep
|
486f04ba2045f31dd3e188f52c45a275eb150967
|
[
"MIT"
] | null | null | null |
itests/client_fixtures.py
|
skivis/BlackSheep
|
486f04ba2045f31dd3e188f52c45a275eb150967
|
[
"MIT"
] | null | null | null |
from itests.utils import get_sleep_time
from blacksheep.client.pool import ClientConnectionPools
import os
import pathlib
import asyncio
from multiprocessing import Process
from time import sleep
import pytest
from blacksheep.client import ClientSession
from .flask_app import app
@pytest.fixture(scope="session")
def event_loop():
"""Create an instance of the default event loop for all test cases."""
loop = asyncio.get_event_loop_policy().new_event_loop()
yield loop
loop.close()
@pytest.fixture(scope="module")
@pytest.fixture(scope="module")
@pytest.fixture(scope="module")
@pytest.fixture(scope="module")
@pytest.fixture(scope="module", autouse=True)
| 24.753247
| 81
| 0.718258
|
from itests.utils import get_sleep_time
from blacksheep.client.pool import ClientConnectionPools
import os
import pathlib
import asyncio
from multiprocessing import Process
from time import sleep
import pytest
from blacksheep.client import ClientSession
from .flask_app import app
def get_static_path(file_name):
static_folder_path = pathlib.Path(__file__).parent.absolute() / "static"
return os.path.join(str(static_folder_path), file_name.lstrip("/"))
@pytest.fixture(scope="session")
def event_loop():
"""Create an instance of the default event loop for all test cases."""
loop = asyncio.get_event_loop_policy().new_event_loop()
yield loop
loop.close()
@pytest.fixture(scope="module")
def server_host():
return "127.0.0.1"
@pytest.fixture(scope="module")
def server_port():
return 44777
@pytest.fixture(scope="module")
def session(server_host, server_port, event_loop):
# It is important to pass the instance of ClientConnectionPools,
# to ensure that the connections are reused and closed
session = ClientSession(
loop=event_loop,
base_url=f"http://{server_host}:{server_port}",
pools=ClientConnectionPools(event_loop),
)
yield session
asyncio.run(session.close())
@pytest.fixture(scope="module")
def session_alt(event_loop):
session = ClientSession(
loop=event_loop,
default_headers=[(b"X-Default-One", b"AAA"), (b"X-Default-Two", b"BBB")],
)
yield session
event_loop.run_until_complete(session.close())
def start_server():
print(f"[*] Flask app listening on 0.0.0.0:44777")
app.run(host="127.0.0.1", port=44777)
@pytest.fixture(scope="module", autouse=True)
def server(server_host, server_port):
server_process = Process(target=start_server)
server_process.start()
sleep(get_sleep_time())
yield 1
sleep(1.2)
server_process.terminate()
| 1,060
| 0
| 156
|
9afbe23daffa1d6b64b319da9bb5fb508db62891
| 673
|
py
|
Python
|
2021/day6.py
|
astonshane/AdventOfCode
|
25c7380e73eede3f79287de6a9dedc8314ab7965
|
[
"MIT"
] | null | null | null |
2021/day6.py
|
astonshane/AdventOfCode
|
25c7380e73eede3f79287de6a9dedc8314ab7965
|
[
"MIT"
] | null | null | null |
2021/day6.py
|
astonshane/AdventOfCode
|
25c7380e73eede3f79287de6a9dedc8314ab7965
|
[
"MIT"
] | null | null | null |
print("part1:", iterate(80))
print("part2:", iterate(256))
| 29.26087
| 66
| 0.456166
|
def iterate(days):
with open("inputs/day6.txt") as f:
input = [int(x) for x in f.readline().strip().split(",")]
fish = {}
for f in input:
fish[f] = fish.get(f, 0) + 1
for day in range(1, days+1):
new_fish = {}
for x in fish:
if x == 0:
new_fish[6] = new_fish.get(6,0) + fish[x]
new_fish[8] = fish[x]
else:
new_fish[x-1] = new_fish.get(x-1, 0) + fish[x]
fish = new_fish
fish_count = sum(fish.values())
return fish_count
print("part1:", iterate(80))
print("part2:", iterate(256))
| 591
| 0
| 22
|
cfe8552d61b7b191dc8c08bf956d94364e70490c
| 7,322
|
py
|
Python
|
vivarium/core/repository.py
|
U8NWXD/vivarium
|
19c6a4096fe94e3342e40ce03e6708c24dd38fa3
|
[
"MIT"
] | null | null | null |
vivarium/core/repository.py
|
U8NWXD/vivarium
|
19c6a4096fe94e3342e40ce03e6708c24dd38fa3
|
[
"MIT"
] | null | null | null |
vivarium/core/repository.py
|
U8NWXD/vivarium
|
19c6a4096fe94e3342e40ce03e6708c24dd38fa3
|
[
"MIT"
] | null | null | null |
"""
==============================================
Repository of Updaters, Dividers, and Derivers
==============================================
You should interpret words and phrases that appear fully capitalized in
this document as described in :rfc:`2119`. Here is a brief summary of
the RFC:
* "MUST" indicates absolute requirements. Vivarium may not work
correctly if you don't follow these.
* "SHOULD" indicates strong suggestions. You might have a valid reason
for deviating from them, but be careful that you understand the
ramifications.
* "MAY" indicates truly optional features that you can include or
exclude as you wish.
--------
Updaters
--------
Each :term:`updater` is defined as a function whose name begins with
``update_``. Vivarium uses these functions to apply :term:`updates` to
:term:`variables`. Updater names are defined in
:py:data:`updater_library`, which maps these names to updater functions.
Updater API
===========
An updater function MUST have a name that begins with ``update_``. The
function MUST accept exactly two positional arguments: the first MUST be
the current value of the variable (i.e. before applying the update), and
the second MUST be the value associated with the variable in the update.
The function SHOULD not accept any other parameters. The function MUST
return the updated value of the variable only.
--------
Dividers
--------
Each :term:`divider` is defined by a function that follows the API we
describe below. Vivarium uses these dividers to generate daughter cell
states from the mother cell's state. Divider names are defined in
:py:data:`divider_library`, which maps these names to divider functions.
Divider API
===========
Each divider function MUST have a name prefixed with ``_divide``. The
function MUST accept a single positional argument, the value of the
variable in the mother cell. It SHOULD accept no other arguments. The
function MUST return a :py:class:`list` with two elements: the values of
the variables in each of the daughter cells.
.. note:: Dividers MAY not be deterministic and MAY not be symmetric.
For example, a divider splitting an odd, integer-valued value may
randomly decide which daughter cell receives the remainder.
--------
Derivers
--------
Each :term:`deriver` is defined as a separate :term:`process`, but here
deriver names are mapped to processes by :py:data:`deriver_library`. The
available derivers are:
* **mmol_to_counts**: :py:class:`vivarium.processes.derive_counts.DeriveCounts`
* **counts_to_mmol**:
:py:class:`vivarium.processes.derive_concentrations.DeriveConcentrations`
* **mass**: :py:class:`vivarium.processes.tree_mass.TreeMass`
* **globals**:
:py:class:`vivarium.processes.derive_globals.DeriveGlobals`
See the documentation for each :term:`process class` for more details on
that deriver.
"""
from __future__ import absolute_import, division, print_function
import copy
import random
import numpy as np
from vivarium.library.dict_utils import deep_merge
from vivarium.library.units import Quantity
# deriver processes
from vivarium.processes.derive_concentrations import DeriveConcentrations
from vivarium.processes.derive_counts import DeriveCounts
from vivarium.processes.derive_globals import DeriveGlobals
from vivarium.processes.tree_mass import TreeMass
## updater functions
def update_merge(current_value, new_value):
"""Merge Updater
Arguments:
current_value (dict):
new_value (dict):
Returns:
dict: The merger of ``current_value`` and ``new_value``. For any
shared keys, the value in ``new_value`` is used.
"""
update = current_value.copy()
for k, v in current_value.items():
new = new_value.get(k)
if isinstance(new, dict):
update[k] = deep_merge(dict(v), new)
else:
update[k] = new
return update
def update_set(current_value, new_value):
"""Set Updater
Returns:
The value provided in ``new_value``.
"""
return new_value
def update_accumulate(current_value, new_value):
"""Accumulate Updater
Returns:
The sum of ``current_value`` and ``new_value``.
"""
return current_value + new_value
#: Maps updater names to updater functions
updater_library = {
'accumulate': update_accumulate,
'set': update_set,
'merge': update_merge}
## divider functions
def divide_set(state):
"""Set Divider
Returns:
A list ``[state, state]``. No copying is performed.
"""
return [state, state]
def divide_split(state):
"""Split Divider
Arguments:
state: Must be an :py:class:`int`, a :py:class:`float`, or a
:py:class:`str` of value ``Infinity``.
Returns:
A list, each of whose elements contains half of ``state``. If
``state`` is an :py:class:`int`, the remainder is placed at
random in one of the two elements. If ``state`` is infinite, the
return value is ``[state, state]`` (no copying is done).
Raises:
Exception: if ``state`` is of an unrecognized type.
"""
if isinstance(state, int):
remainder = state % 2
half = int(state / 2)
if random.choice([True, False]):
return [half + remainder, half]
else:
return [half, half + remainder]
elif state == float('inf') or state == 'Infinity':
# some concentrations are considered infinite in the environment
# an alternative option is to not divide the local environment state
return [state, state]
elif isinstance(state, (float, Quantity)):
half = state/2
return [half, half]
else:
raise Exception('can not divide state {} of type {}'.format(state, type(state)))
def divide_zero(state):
"""Zero Divider
Returns:
``[0, 0]`` regardless of input
"""
return [0, 0]
def divide_split_dict(state):
"""Split-Dictionary Divider
Returns:
A list of two dictionaries. The first dictionary stores the
first half of the key-value pairs in ``state``, and the second
dictionary stores the rest of the key-value pairs.
.. note:: Since dictionaries are unordered, you should avoid
making any assumptions about which keys will be sent to
which daughter cell.
"""
if state is None:
state = {}
d1 = dict(list(state.items())[len(state) // 2:])
d2 = dict(list(state.items())[:len(state) // 2])
return [d1, d2]
#: Maps divider names to divider functions
divider_library = {
'set': divide_set,
'split': divide_split,
'split_dict': divide_split_dict,
'zero': divide_zero}
# Derivers
#: Maps deriver names to :term:`process classes`
deriver_library = {
'mmol_to_counts': DeriveCounts,
'counts_to_mmol': DeriveConcentrations,
'mass': TreeMass,
'globals': DeriveGlobals,
}
# Serializers
serializer_library = {
'numpy': NumpySerializer(),
}
| 29.643725
| 88
| 0.680688
|
"""
==============================================
Repository of Updaters, Dividers, and Derivers
==============================================
You should interpret words and phrases that appear fully capitalized in
this document as described in :rfc:`2119`. Here is a brief summary of
the RFC:
* "MUST" indicates absolute requirements. Vivarium may not work
correctly if you don't follow these.
* "SHOULD" indicates strong suggestions. You might have a valid reason
for deviating from them, but be careful that you understand the
ramifications.
* "MAY" indicates truly optional features that you can include or
exclude as you wish.
--------
Updaters
--------
Each :term:`updater` is defined as a function whose name begins with
``update_``. Vivarium uses these functions to apply :term:`updates` to
:term:`variables`. Updater names are defined in
:py:data:`updater_library`, which maps these names to updater functions.
Updater API
===========
An updater function MUST have a name that begins with ``update_``. The
function MUST accept exactly two positional arguments: the first MUST be
the current value of the variable (i.e. before applying the update), and
the second MUST be the value associated with the variable in the update.
The function SHOULD not accept any other parameters. The function MUST
return the updated value of the variable only.
--------
Dividers
--------
Each :term:`divider` is defined by a function that follows the API we
describe below. Vivarium uses these dividers to generate daughter cell
states from the mother cell's state. Divider names are defined in
:py:data:`divider_library`, which maps these names to divider functions.
Divider API
===========
Each divider function MUST have a name prefixed with ``_divide``. The
function MUST accept a single positional argument, the value of the
variable in the mother cell. It SHOULD accept no other arguments. The
function MUST return a :py:class:`list` with two elements: the values of
the variables in each of the daughter cells.
.. note:: Dividers MAY not be deterministic and MAY not be symmetric.
For example, a divider splitting an odd, integer-valued value may
randomly decide which daughter cell receives the remainder.
--------
Derivers
--------
Each :term:`deriver` is defined as a separate :term:`process`, but here
deriver names are mapped to processes by :py:data:`deriver_library`. The
available derivers are:
* **mmol_to_counts**: :py:class:`vivarium.processes.derive_counts.DeriveCounts`
* **counts_to_mmol**:
:py:class:`vivarium.processes.derive_concentrations.DeriveConcentrations`
* **mass**: :py:class:`vivarium.processes.tree_mass.TreeMass`
* **globals**:
:py:class:`vivarium.processes.derive_globals.DeriveGlobals`
See the documentation for each :term:`process class` for more details on
that deriver.
"""
from __future__ import absolute_import, division, print_function
import copy
import random
import numpy as np
from vivarium.library.dict_utils import deep_merge
from vivarium.library.units import Quantity
# deriver processes
from vivarium.processes.derive_concentrations import DeriveConcentrations
from vivarium.processes.derive_counts import DeriveCounts
from vivarium.processes.derive_globals import DeriveGlobals
from vivarium.processes.tree_mass import TreeMass
## updater functions
def update_merge(current_value, new_value):
"""Merge Updater
Arguments:
current_value (dict):
new_value (dict):
Returns:
dict: The merger of ``current_value`` and ``new_value``. For any
shared keys, the value in ``new_value`` is used.
"""
update = current_value.copy()
for k, v in current_value.items():
new = new_value.get(k)
if isinstance(new, dict):
update[k] = deep_merge(dict(v), new)
else:
update[k] = new
return update
def update_set(current_value, new_value):
"""Set Updater
Returns:
The value provided in ``new_value``.
"""
return new_value
def update_accumulate(current_value, new_value):
"""Accumulate Updater
Returns:
The sum of ``current_value`` and ``new_value``.
"""
return current_value + new_value
#: Maps updater names to updater functions
updater_library = {
'accumulate': update_accumulate,
'set': update_set,
'merge': update_merge}
## divider functions
def divide_set(state):
"""Set Divider
Returns:
A list ``[state, state]``. No copying is performed.
"""
return [state, state]
def divide_split(state):
"""Split Divider
Arguments:
state: Must be an :py:class:`int`, a :py:class:`float`, or a
:py:class:`str` of value ``Infinity``.
Returns:
A list, each of whose elements contains half of ``state``. If
``state`` is an :py:class:`int`, the remainder is placed at
random in one of the two elements. If ``state`` is infinite, the
return value is ``[state, state]`` (no copying is done).
Raises:
Exception: if ``state`` is of an unrecognized type.
"""
if isinstance(state, int):
remainder = state % 2
half = int(state / 2)
if random.choice([True, False]):
return [half + remainder, half]
else:
return [half, half + remainder]
elif state == float('inf') or state == 'Infinity':
# some concentrations are considered infinite in the environment
# an alternative option is to not divide the local environment state
return [state, state]
elif isinstance(state, (float, Quantity)):
half = state/2
return [half, half]
else:
raise Exception('can not divide state {} of type {}'.format(state, type(state)))
def divide_zero(state):
"""Zero Divider
Returns:
``[0, 0]`` regardless of input
"""
return [0, 0]
def divide_split_dict(state):
"""Split-Dictionary Divider
Returns:
A list of two dictionaries. The first dictionary stores the
first half of the key-value pairs in ``state``, and the second
dictionary stores the rest of the key-value pairs.
.. note:: Since dictionaries are unordered, you should avoid
making any assumptions about which keys will be sent to
which daughter cell.
"""
if state is None:
state = {}
d1 = dict(list(state.items())[len(state) // 2:])
d2 = dict(list(state.items())[:len(state) // 2])
return [d1, d2]
#: Maps divider names to divider functions
divider_library = {
'set': divide_set,
'split': divide_split,
'split_dict': divide_split_dict,
'zero': divide_zero}
def default_divide_condition(compartment):
return False
# Derivers
#: Maps deriver names to :term:`process classes`
deriver_library = {
'mmol_to_counts': DeriveCounts,
'counts_to_mmol': DeriveConcentrations,
'mass': TreeMass,
'globals': DeriveGlobals,
}
# Serializers
class Serializer(object):
def serialize(self, data):
return data
def deserialize(self, data):
return data
class NumpySerializer(Serializer):
def serialize(self, data):
return data.tolist()
def deserialize(self, data):
return np.array(data)
serializer_library = {
'numpy': NumpySerializer(),
}
| 161
| 17
| 174
|
c489189444952f919e3577efb4fb2967757abb02
| 591
|
py
|
Python
|
utils.py
|
doiken23/mccformers.pytorch
|
678bd9448e3a2f35bd408e8c8e510e0ea1f9a19f
|
[
"MIT"
] | 1
|
2021-11-26T12:08:41.000Z
|
2021-11-26T12:08:41.000Z
|
utils.py
|
doiken23/mccformers.pytorch
|
678bd9448e3a2f35bd408e8c8e510e0ea1f9a19f
|
[
"MIT"
] | null | null | null |
utils.py
|
doiken23/mccformers.pytorch
|
678bd9448e3a2f35bd408e8c8e510e0ea1f9a19f
|
[
"MIT"
] | null | null | null |
import torch
from torch import Tensor
def compute_accuracy(pred: Tensor, gt: Tensor, ignore: int = 0):
"""
pred (torch.Tensor): predicted words shape of [L, N]
gt (torch.Tensor): GT words shape of [L, N]
ignore (int): ignored label
"""
mask = gt != ignore
tp = torch.logical_and(pred == gt, mask)
return tp.sum() / mask.sum()
| 22.730769
| 64
| 0.563452
|
import torch
from torch import Tensor
def compute_accuracy(pred: Tensor, gt: Tensor, ignore: int = 0):
"""
pred (torch.Tensor): predicted words shape of [L, N]
gt (torch.Tensor): GT words shape of [L, N]
ignore (int): ignored label
"""
mask = gt != ignore
tp = torch.logical_and(pred == gt, mask)
return tp.sum() / mask.sum()
def decode_seq(seq, idx_to_word):
words = []
for s in seq:
if s == 2: # <START>
continue
if s == 3: # <END>
break
words.append(idx_to_word[s])
return ' '.join(words)
| 206
| 0
| 23
|
e1ef75ea90706b3fa5441badf9e317871b169654
| 96
|
py
|
Python
|
service-cfg-mgnt/ansible_app/apps.py
|
pfroelke/Confne
|
bd7771fdd3c6e59ec0f327ba2b9d72d31cb8e582
|
[
"MIT"
] | null | null | null |
service-cfg-mgnt/ansible_app/apps.py
|
pfroelke/Confne
|
bd7771fdd3c6e59ec0f327ba2b9d72d31cb8e582
|
[
"MIT"
] | 14
|
2021-03-30T14:26:53.000Z
|
2022-03-02T10:40:40.000Z
|
service-cfg-mgnt/ansible_app/apps.py
|
pfroelke/Confnetti
|
bd7771fdd3c6e59ec0f327ba2b9d72d31cb8e582
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
| 16
| 34
| 0.770833
|
from django.apps import AppConfig
class AnsibleAppConfig(AppConfig):
name = "ansible_app"
| 0
| 38
| 23
|
5749762850ea7ecd0b18114c1bb163d34428e1e5
| 6,295
|
py
|
Python
|
python-ref/python-make/cxx_sources_deps_rules.py
|
bogen/makeshells
|
a61ca2f9d35417d081a5c07c6c973d6039d39c38
|
[
"MIT"
] | 1
|
2019-10-16T12:15:53.000Z
|
2019-10-16T12:15:53.000Z
|
python-ref/python-make/cxx_sources_deps_rules.py
|
bogen/makeshells
|
a61ca2f9d35417d081a5c07c6c973d6039d39c38
|
[
"MIT"
] | null | null | null |
python-ref/python-make/cxx_sources_deps_rules.py
|
bogen/makeshells
|
a61ca2f9d35417d081a5c07c6c973d6039d39c38
|
[
"MIT"
] | null | null | null |
# this is a make/python hybrid file
# Normal make files are a make/sh hybrid.
# This makefile uses python instead of sh (or bash)
test_cxx_sources ?=
checkcxxsources $(cxxsources):$(out_init)
$(origin)
if (this == "checkcxxsources") and (not os.path.exists(env.cxxsources)):
leave()
caption()
if (this == env.cxxsources):
lib_cxx, main_cxx = [],[]
quote = "'"
else:
quote = ""
test_cxx=[]
for root, dirs, files in os.walk(env.cxxsrc) :
for file in files:
if file.endswith(".cxx"):
cxx = quote+os.path.join(root, file)+quote
if root.endswith("/main"):
if (this == env.cxxsources): lib_cxx.append(cxx); main_cxx.append(cxx)
test_cxx.append(cxx)
elif root.endswith("/test"):
test_cxx.append(cxx)
test_cxx.sort()
if (this == env.cxxsources):
lib_cxx.sort()
main_cxx.sort()
with open (this, "w") as f:
f.write("# === Generated by %s:%s ===\n\n" % (env.MAKEFILE_LIST, this))
f.write("lib_cxx_sources := %s\n\n" % (",".join(lib_cxx)))
f.write("main_cxx_sources := %s\n\n" % (",".join(main_cxx)))
f.write("test_cxx_sources := %s\n\n" % (",".join(test_cxx)))
leave()
before = set([$(test_cxx_sources)])
after = set(test_cxx)
removed = str(before-after).replace(root_prefix,"")
added = str(after-before).replace(root_prefix,"")
removals = removed != "set()"
additions = added != "set()"
if removals or additions:
print ("cxx source files were added or removed\n")
if removals: print("removals:", removed)
if additions: print("additions:", added)
print ("\nForcing dependency and rule regeneration and re-link.\n")
run(env.MAKE, "re-dep")
cxx_dep0 := $(CXX), "-E", "--trace-includes", $(DEP_CXX_FLAGS)
cxx_dep1 := "-I$(cxxinc)", cxx, "-o/dev/null"
cxx_dep := $(cxx_dep0), $(cxx_dep1)
$(cxxdeps): $(cxxsources);$(caption)
queues, process = [],[]
fd_a=types.SimpleNamespace()
fd_a.gorge = gorge
fd_a.root = root_prefix_len
prefix = "test_cxx_sources := "
prefix_len = len(prefix)
with open(first) as f:
lines = f.readlines()
for line in lines:
if line.startswith(prefix):
sources = line[prefix_len:].rstrip()
test_cxx = sources.split(",")
for _cxx in test_cxx:
cxx = _cxx.replace("'","")
q = multiprocessing.Queue()
p = multiprocessing.Process(target=find_dep, args=(cxx, q, fd_a))
process.append(p)
queues.append(q)
p.start()
break
with open (this, "w") as f:
f.write("# === Generated by %s:%s ===\n\n" % (env.MAKEFILE_LIST, this))
n = 1
for q in queues:
obj = q.get()
deps = q.get()
f.write("\n# %d\n%s := " % (n, obj))
f.write(" ".join(deps))
f.write("\n")
n+=1
for p in process: p.join()
$(objrules): $(cxxdeps); $(caption)
suffix = "_obj_deps"
prefix = "cxxsrc_"
main_prefix = prefix + "main_"
test_prefix = prefix + "test_"
sep = " := "
sep_len = len(sep)
prefix_len = len(prefix)
main_obj0, test_obj0, lib_obj0 = [],[],[]
main_objs, test_objs, lib_objs = {},{},{}
cxx_ext = ".cxx"
cxx_ext_len = len(cxx_ext)
with open(first) as f:
lines = f.readlines()
for line in lines:
if line.startswith(prefix):
i = line.find(sep)
if i == -1: raise RuntimeError("Source deps line not formatted correctly")
deps = line[:i]
sources = line [i+sep_len:]
j = sources.find(cxx_ext)+cxx_ext_len
if j == -1: raise RuntimeError("Source deps line not formatted correctly")
cxxfile = sources[root_prefix_len:j]
cxxfile_i = "need to work on ctfe wrapper..."
deps1 = deps.replace(suffix,".o")
deps1 = deps1.replace("cxxsrc","$$(obj)")
deps1 = deps1.replace("_","/",2)
obj = deps1
if deps.startswith(main_prefix):
lib_obj = obj.replace("$$(obj)/main","$$(obj)/lib")
test_obj = obj.replace("$$(obj)/main/","$$(obj)/test/main__")
main_objs[obj] = (cxxfile,deps,cxxfile_i)
lib_objs[lib_obj] = (cxxfile,deps,cxxfile_i)
test_objs[test_obj] = (cxxfile,deps,cxxfile_i)
main_obj0.append(obj)
lib_obj0.append(lib_obj)
test_obj0.append(test_obj)
continue
if deps.startswith(test_prefix):
test_objs[obj] = (cxxfile,deps,cxxfile_i)
test_obj0.append(obj)
with open (this, "w") as f:
f.write("# === Generated by %s:%s ===\n\n" % (env.MAKEFILE_LIST, this))
f.write("\nmain_exe_objects := %s\n" % (" ".join(main_obj0)))
f.write("\nlib_so_objects := %s\n" % (" ".join(lib_obj0)))
f.write("\ntest_exe_objects := %s\n" % (" ".join(test_obj0)))
f.write("\n__main_exe_objects__ := %s\n" % (make_quoted_list(main_obj0)))
f.write("\n__lib_so_objects__ := %s\n" % (make_quoted_list(lib_obj0)))
f.write("\n__test_exe_objects__ := %s\n" % (make_quoted_list(test_obj0)))
ipch = "'-include-pch',"
rule = "$$(__CXX_FLAGS), '-c', '$$<', '-o$$@'"
main = ipch + "'$$(main_sysheaders_pch)'," + rule + ", $$(MAIN_EXTRA)"
test = ipch + "'$$(test_sysheaders_pch)'," + rule + ", $$(TEST_EXTRA)"
lib = ipch + "'$$(lib_sysheaders_pch)'," + rule + ", $$(LIB_EXTRA)"
cxx = "$$(CXX)"
main_d = "$$(obj_main_init) $$(main_sysheaders_pch)"
lib_d = "$$(obj_lib_init) $$(lib_sysheaders_pch)"
test_d = "$$(obj_test_init) $$(test_sysheaders_pch)"
target("main", main_objs, main, cxx, main_d, f)
target("lib", lib_objs, lib, cxx, lib_d, f)
target("test", test_objs, test, cxx, test_d, f)
| 34.211957
| 82
| 0.58189
|
# this is a make/python hybrid file
# Normal make files are a make/sh hybrid.
# This makefile uses python instead of sh (or bash)
test_cxx_sources ?=
checkcxxsources $(cxxsources):$(out_init)
$(origin)
if (this == "checkcxxsources") and (not os.path.exists(env.cxxsources)):
leave()
caption()
if (this == env.cxxsources):
lib_cxx, main_cxx = [],[]
quote = "'"
else:
quote = ""
test_cxx=[]
for root, dirs, files in os.walk(env.cxxsrc) :
for file in files:
if file.endswith(".cxx"):
cxx = quote+os.path.join(root, file)+quote
if root.endswith("/main"):
if (this == env.cxxsources): lib_cxx.append(cxx); main_cxx.append(cxx)
test_cxx.append(cxx)
elif root.endswith("/test"):
test_cxx.append(cxx)
test_cxx.sort()
if (this == env.cxxsources):
lib_cxx.sort()
main_cxx.sort()
with open (this, "w") as f:
f.write("# === Generated by %s:%s ===\n\n" % (env.MAKEFILE_LIST, this))
f.write("lib_cxx_sources := %s\n\n" % (",".join(lib_cxx)))
f.write("main_cxx_sources := %s\n\n" % (",".join(main_cxx)))
f.write("test_cxx_sources := %s\n\n" % (",".join(test_cxx)))
leave()
before = set([$(test_cxx_sources)])
after = set(test_cxx)
removed = str(before-after).replace(root_prefix,"")
added = str(after-before).replace(root_prefix,"")
removals = removed != "set()"
additions = added != "set()"
if removals or additions:
print ("cxx source files were added or removed\n")
if removals: print("removals:", removed)
if additions: print("additions:", added)
print ("\nForcing dependency and rule regeneration and re-link.\n")
run(env.MAKE, "re-dep")
cxx_dep0 := $(CXX), "-E", "--trace-includes", $(DEP_CXX_FLAGS)
cxx_dep1 := "-I$(cxxinc)", cxx, "-o/dev/null"
cxx_dep := $(cxx_dep0), $(cxx_dep1)
$(cxxdeps): $(cxxsources);$(caption)
def find_dep(cxx, q, fd_a):
deps = []
lines = str(fd_a.gorge($(cxx_dep))).split(r"\n")
for line in lines:
i = line.find("$(cxxinc)")
if i != -1: deps.append(line[i:])
q.put(cxx[fd_a.root:].replace("/","_").replace(".cxx","_obj_deps"))
q.put([cxx] + sorted(deps))
queues, process = [],[]
fd_a=types.SimpleNamespace()
fd_a.gorge = gorge
fd_a.root = root_prefix_len
prefix = "test_cxx_sources := "
prefix_len = len(prefix)
with open(first) as f:
lines = f.readlines()
for line in lines:
if line.startswith(prefix):
sources = line[prefix_len:].rstrip()
test_cxx = sources.split(",")
for _cxx in test_cxx:
cxx = _cxx.replace("'","")
q = multiprocessing.Queue()
p = multiprocessing.Process(target=find_dep, args=(cxx, q, fd_a))
process.append(p)
queues.append(q)
p.start()
break
with open (this, "w") as f:
f.write("# === Generated by %s:%s ===\n\n" % (env.MAKEFILE_LIST, this))
n = 1
for q in queues:
obj = q.get()
deps = q.get()
f.write("\n# %d\n%s := " % (n, obj))
f.write(" ".join(deps))
f.write("\n")
n+=1
for p in process: p.join()
$(objrules): $(cxxdeps); $(caption)
suffix = "_obj_deps"
prefix = "cxxsrc_"
main_prefix = prefix + "main_"
test_prefix = prefix + "test_"
sep = " := "
sep_len = len(sep)
prefix_len = len(prefix)
main_obj0, test_obj0, lib_obj0 = [],[],[]
main_objs, test_objs, lib_objs = {},{},{}
cxx_ext = ".cxx"
cxx_ext_len = len(cxx_ext)
with open(first) as f:
lines = f.readlines()
for line in lines:
if line.startswith(prefix):
i = line.find(sep)
if i == -1: raise RuntimeError("Source deps line not formatted correctly")
deps = line[:i]
sources = line [i+sep_len:]
j = sources.find(cxx_ext)+cxx_ext_len
if j == -1: raise RuntimeError("Source deps line not formatted correctly")
cxxfile = sources[root_prefix_len:j]
cxxfile_i = "need to work on ctfe wrapper..."
deps1 = deps.replace(suffix,".o")
deps1 = deps1.replace("cxxsrc","$$(obj)")
deps1 = deps1.replace("_","/",2)
obj = deps1
if deps.startswith(main_prefix):
lib_obj = obj.replace("$$(obj)/main","$$(obj)/lib")
test_obj = obj.replace("$$(obj)/main/","$$(obj)/test/main__")
main_objs[obj] = (cxxfile,deps,cxxfile_i)
lib_objs[lib_obj] = (cxxfile,deps,cxxfile_i)
test_objs[test_obj] = (cxxfile,deps,cxxfile_i)
main_obj0.append(obj)
lib_obj0.append(lib_obj)
test_obj0.append(test_obj)
continue
if deps.startswith(test_prefix):
test_objs[obj] = (cxxfile,deps,cxxfile_i)
test_obj0.append(obj)
def target(label, objs, rule, cxx, dest, f):
f.write("\n\n# %s\n" % label)
for obj, pre in objs.items():
f.write("%s: $$(%s) %s\n $$(caption0);" % (obj, pre[1], dest))
f.write("""run(%s, '-D__CXX_SRCFILE__="%s"', %s)\n""" % (cxx, pre[0], rule))
f.write(" ## %s\n\n" % (pre[2]))
def make_quoted_list(obj): return ",".join(map(lambda x: "'"+x+"'", obj))
with open (this, "w") as f:
f.write("# === Generated by %s:%s ===\n\n" % (env.MAKEFILE_LIST, this))
f.write("\nmain_exe_objects := %s\n" % (" ".join(main_obj0)))
f.write("\nlib_so_objects := %s\n" % (" ".join(lib_obj0)))
f.write("\ntest_exe_objects := %s\n" % (" ".join(test_obj0)))
f.write("\n__main_exe_objects__ := %s\n" % (make_quoted_list(main_obj0)))
f.write("\n__lib_so_objects__ := %s\n" % (make_quoted_list(lib_obj0)))
f.write("\n__test_exe_objects__ := %s\n" % (make_quoted_list(test_obj0)))
ipch = "'-include-pch',"
rule = "$$(__CXX_FLAGS), '-c', '$$<', '-o$$@'"
main = ipch + "'$$(main_sysheaders_pch)'," + rule + ", $$(MAIN_EXTRA)"
test = ipch + "'$$(test_sysheaders_pch)'," + rule + ", $$(TEST_EXTRA)"
lib = ipch + "'$$(lib_sysheaders_pch)'," + rule + ", $$(LIB_EXTRA)"
cxx = "$$(CXX)"
main_d = "$$(obj_main_init) $$(main_sysheaders_pch)"
lib_d = "$$(obj_lib_init) $$(lib_sysheaders_pch)"
test_d = "$$(obj_test_init) $$(test_sysheaders_pch)"
target("main", main_objs, main, cxx, main_d, f)
target("lib", lib_objs, lib, cxx, lib_d, f)
target("test", test_objs, test, cxx, test_d, f)
| 609
| 0
| 74
|
6d18fbcf0c4128657606560451cbb6b3b8077b66
| 20,036
|
py
|
Python
|
binance/delivery/market.py
|
AlfonsoAgAr/binance-futures-connector-python
|
f0bd2c7b0576503bf526ce6be329ca2dae90fefe
|
[
"MIT"
] | 1
|
2022-01-29T14:37:47.000Z
|
2022-01-29T14:37:47.000Z
|
binance/delivery/market.py
|
sanjeevan121/binance-futures-connector-python
|
d820b73a15e9f64c80891a13694ca0c5d1693b90
|
[
"MIT"
] | null | null | null |
binance/delivery/market.py
|
sanjeevan121/binance-futures-connector-python
|
d820b73a15e9f64c80891a13694ca0c5d1693b90
|
[
"MIT"
] | 1
|
2022-02-25T16:23:41.000Z
|
2022-02-25T16:23:41.000Z
|
from binance.lib.utils import (
check_required_parameter,
)
from binance.lib.utils import check_required_parameters
def ping(self):
"""
|
| **Test Connectivity**
| *Test connectivity to the Rest API.*
:API endpoint: ``GET /dapi/v1/ping``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#test-connectivity
|
"""
url_path = "/dapi/v1/ping"
return self.query(url_path)
def time(self):
"""
|
| **Check Server Time**
| *Test connectivity to the Rest API and get the current server time.*
:API endpoint: ``GET /dapi/v1/time``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#check-server-time
|
"""
url_path = "/dapi/v1/time"
return self.query(url_path)
def exchange_info(self):
"""
|
| **Exchange Information**
| *Current exchange trading rules and symbol information*
:API endpoint: ``GET /dapi/v1/exchangeInfo``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#exchange-information
|
"""
url_path = "/dapi/v1/exchangeInfo"
return self.query(url_path)
def depth(self, symbol: str, **kwargs):
"""
|
| **Get Orderbook**
:API endpoint: ``GET /dapi/v1/depth``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#order-book
:parameter symbol: string; the trading pair
:parameter limit: optional int; limit the results. Default 500, valid limits: [5, 10, 20, 50, 100, 500, 1000].
|
"""
check_required_parameter(symbol, "symbol")
params = {"symbol": symbol, **kwargs}
return self.query("/dapi/v1/depth", params)
def trades(self, symbol: str, **kwargs):
"""
|
| **Get Recent Market Trades**
:API endpoint: ``GET /dapi/v1/trades``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#recent-trades-list
:parameter symbol: string; the trading pair
:parameter limit: optional int; limit the results. Default 500, max 1000.
|
"""
check_required_parameter(symbol, "symbol")
params = {"symbol": symbol, **kwargs}
return self.query("/dapi/v1/trades", params)
def historical_trades(self, symbol: str, **kwargs):
"""
|
| **Old Trade Lookup**
| *Get older market historical trades.*
:API endpoint: ``GET /dapi/v1/historicalTrades``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#old-trades-lookup-market_data
:parameter symbol: string; the trading pair
:parameter limit: optional int; limit the results. Default 500, max 1000.
:parameter formId: optional int; trade ID to fetch from. Default gets most recent trades.
|
"""
check_required_parameter(symbol, "symbol")
params = {"symbol": symbol, **kwargs}
return self.limit_request("GET", "/dapi/v1/historicalTrades", params)
def agg_trades(self, symbol: str, **kwargs):
"""
|
| **Compressed/Aggregate Trades List**
| *Get compressed, aggregate market trades. Market trades that fill at the time, from the same order, with the same price will have the quantity aggregated.*
:API endpoint: ``GET /dapi/v1/aggTrades``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#compressed-aggregate-trades-list
:parameter symbol: string; the trading pair
:parameter limit: optional int; limit the results. Default 500, max 1000.
:parameter formId: optional int; trade ID to fetch from. Default gets most recent trades.
:parameter startTime: optional int; Timestamp in ms to get aggregate trades from INCLUSIVE.
:parameter endTime: optional int; Timestamp in ms to get aggregate trades until INCLUSIVE.
|
"""
check_required_parameter(symbol, "symbol")
params = {"symbol": symbol, **kwargs}
return self.query("/dapi/v1/aggTrades", params)
def klines(self, symbol: str, interval: str, **kwargs):
"""
|
| **Kline/Candlestick Data**
| *Kline/candlestick bars for a symbol. Klines are uniquely identified by their open time.*
:API endpoint: ``GET /dapi/v1/klines``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#kline-candlestick-data
:parameter symbol: string; the trading pair
:parameter interval: string; the interval of kline, e.g 1m, 5m, 1h, 1d, etc. (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 500, max 1000.
:parameter startTime: optional int; Timestamp in ms to get aggregate trades from INCLUSIVE.
:parameter endTime: optional int; Timestamp in ms to get aggregate trades until INCLUSIVE.
|
"""
check_required_parameters([[symbol, "symbol"], [interval, "interval"]])
params = {"symbol": symbol, "interval": interval, **kwargs}
return self.query("/dapi/v1/klines", params)
def continuous_klines(self, pair: str, contractType: str, interval: str, **kwargs):
"""
|
| **Continuous Kline/Candlestick Data**
| *Kline/candlestick bars for a specific contract type. Klines are uniquely identified by their open time.*
:API endpoint: ``GET /dapi/v1/continuousKlines``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#continuous-contract-kline-candlestick-data
:parameter pair: string; the trading pair
:parameter contractType: string; PERPETUAL, CURRENT_MONTH, NEXT_MONTH, CURRENT_QUARTER, NEXT_QUARTER.
:parameter interval: string; the interval of kline, e.g 1m, 5m, 1h, 1d, etc. (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 500, max 1000.
:parameter startTime: optional int; Timestamp in ms to get aggregate trades from INCLUSIVE.
:parameter endTime: optional int; Timestamp in ms to get aggregate trades until INCLUSIVE.
|
"""
check_required_parameters([[pair, "pair"], [contractType,"contractType"], [interval, "interval"]])
params = {"pair": pair, "contractType":contractType, "interval": interval, **kwargs}
return self.query("/dapi/v1/continuousKlines", params)
def index_price_klines(self, pair: str, interval: str, **kwargs):
"""
|
| **Kline/Candlestick Data for the index price of a pair.**
| *Klines are uniquely identified by their open time.*
:API endpoint: ``GET /dapi/v1/indexPriceKlines``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#index-price-kline-candlestick-data
:parameter pair: string; the trading pair
:parameter interval: string; the interval of kline, e.g 1m, 5m, 1h, 1d, etc. (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 500, max 1000.
:parameter startTime: optional int; Timestamp in ms to get aggregate trades from INCLUSIVE.
:parameter endTime: optional int; Timestamp in ms to get aggregate trades until INCLUSIVE.
|
"""
check_required_parameters([[pair, "pair"], [interval, "interval"]])
params = {"pair": pair, "interval": interval, **kwargs}
return self.query("/dapi/v1/indexPriceKlines", params)
def mark_price_klines(self, symbol: str, interval: str, **kwargs):
"""
|
| **Kline/candlestick bars for the mark price of a symbol.**
| *Klines are uniquely identified by their open time.*
:API endpoint: ``GET /dapi/v1/markPriceKlines``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#mark-price-kline-candlestick-data
:parameter pair: string; the trading pair
:parameter interval: string; the interval of kline, e.g 1m, 5m, 1h, 1d, etc. (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 500, max 1000.
:parameter startTime: optional int; Timestamp in ms to get aggregate trades from INCLUSIVE.
:parameter endTime: optional int; Timestamp in ms to get aggregate trades until INCLUSIVE.
**Notes**
- The difference between startTime and endTime can only be up to 200 days
- Between startTime and endTime, the most recent limit data from endTime will be returned:
- If startTime and endTime are not sent, current timestamp will be set as endTime, and the most recent data will be returned.
- If startTime is sent only, the timestamp of 200 days after startTime will be set as endTime(up to the current time)
- If endTime is sent only, the timestamp of 200 days before endTime will be set as startTime
|
"""
check_required_parameters([[symbol, "symbol"], [interval, "interval"]])
params = {"symbol": symbol, "interval": interval, **kwargs}
return self.query("/dapi/v1/markPriceKlines", params)
def mark_price(self, symbol: str):
"""
|
| **Mark Price and Funding Rate**
:API endpoint: ``GET /dapi/v1/premiumIndex``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#index-price-and-mark-price
:parameter symbol: string; the trading pair
|
"""
check_required_parameter(symbol, "symbol")
params = {
"symbol": symbol,
}
return self.query("/dapi/v1/premiumIndex", params)
def funding_rate(self, symbol: str, **kwargs):
"""
|
| **Funding Rate History**
:API endpoint: ``GET /dapi/v1/fundingRate``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#get-funding-rate-history-of-perpetual-futures
:parameter symbol: string; the trading pair
:parameter limit: optional int; limit the results. Default 500, max 1000.
:parameter startTime: optional int; Timestamp in ms to get aggregate trades from INCLUSIVE.
:parameter endTime: optional int; Timestamp in ms to get aggregate trades until INCLUSIVE.
**Notes**
- Empty array will be returned for delivery symbols.
|
"""
params = {"symbol": symbol, **kwargs}
return self.query("/dapi/v1/fundingRate", params)
def ticker_24hr_price_change(self, symbol: str = None, pair: str = None):
"""
|
| **24 hour rolling window price change statistics.**
| *Careful when accessing this with no symbol.*
| *If the symbol is not sent, tickers for all symbols will be returned in an array.*
:API endpoint: ``GET /dapi/v1/ticker/24hr``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#24hr-ticker-price-change-statistics
:parameter symbol: optional string; the trading symbol
:parameter pair: optional string; the trading pair
**Notes**
- Symbol and pair cannot be sent together
- If a pair is sent, tickers for all symbols of the pair will be returned
- If either a pair or symbol is sent, tickers for all symbols of all pairs will be returned
|
"""
if (symbol is None) and (pair is None):
return self.query("/dapi/v1/ticker/24hr")
elif (symbol is None):
params = {"pair": pair}
else:
params = {"symbol": symbol}
return self.query("/dapi/v1/ticker/24hr", params)
def ticker_price(self, symbol: str = None, pair: str = None):
"""
|
| **Latest price for a symbol or symbols**
:API endpoint: ``GET /dapi/v1/ticker/price``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#symbol-price-ticker
:parameter symbol: optional string; the trading symbol
:parameter pair: optional string; the trading pair
**Notes**
- Symbol and pair cannot be sent together
- If a pair is sent,tickers for all symbols of the pair will be returned
- If either a pair or symbol is sent, tickers for all symbols of all pairs will be returned
|
"""
if (symbol is None) and (pair is None):
return self.query("/dapi/v1/ticker/price")
elif (symbol is None):
params = {"pair": pair}
else:
params = {"symbol": symbol}
return self.query("/dapi/v1/ticker/price", params)
def book_ticker(self, symbol: str = None, pair: str = None):
"""
|
| **Best price/qty on the order book for a symbol or symbols**
:API endpoint: ``GET /dapi/v1/ticker/bookTicker``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#symbol-order-book-ticker
:parameter symbol: optional string; the trading symbol
**Notes**
- If the symbol is not sent, bookTickers for all symbols will be returned in an array.
|
"""
if (symbol is None) and (pair is None):
return self.query("/dapi/v1/ticker/bookTicker")
elif (symbol is None):
params = {"pair": pair}
else:
params = {"symbol": symbol}
return self.query("/dapi/v1/ticker/bookTicker", params)
def open_interest(self, symbol: str):
"""
|
| **Get present open interest of a specific symbol**
:API endpoint: ``GET /dapi/v1/openInterest``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#open-interest
:parameter symbol: string; the trading symbol
|
"""
check_required_parameter(symbol, "symbol")
params = {"symbol": symbol}
return self.query("/dapi/v1/ticker/bookTicker", params)
def open_interest_hist(self, pair: str, contractType: str, period: str, **kwargs):
"""
|
| **Get historical open interest of a specific symbol**
:API endpoint: ``GET /futures/data/openInterestHist``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#open-interest-statistics-market-data
:parameter pair: string; the trading pair
:parameter contractType: string; ALL, CURRENT_QUARTER, NEXT_QUARTER, PERPETUAL.
:parameter period: string; the period of open interest, "5m", "15m", "30m", "1h", "2h", "4h", "6h", "12h", "1d". (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 30, max 500.
:parameter startTime: optional int
:parameter endTime: optional int
**Notes**
- If startTime and endTime are not sent, the most recent data is returned.
- Only the data of the latest 30 days is available.
|
"""
check_required_parameters([[pair, "pair"], [contractType, "contractType"], [period, "period"]])
params = {"pair": pair, "contractType": contractType, "period": period, **kwargs}
return self.query("/futures/data/openInterestHist", params)
def top_long_short_account_ratio(self, pair: str, period: str, **kwargs):
"""
|
| **Get top long short account ratio**
:API endpoint: `GET /futures/data/topLongShortAccountRatio`
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#top-trader-long-short-ratio-accounts-market-data
:parameter pair: string; the trading pair
:parameter period: string; the period of open interest, "5m", "15m", "30m", "1h", "2h", "4h", "6h", "12h", "1d". (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 30, max 500.
:parameter startTime: optional int
:parameter endTime: optional int
**Notes**
- If startTime and endTime are not sent, the most recent data is returned.
- Only the data of the latest 30 days is available.
|
"""
check_required_parameters([[pair, "pair"], [period, "period"]])
params = {"pair": pair, "period": period, **kwargs}
return self.query("/futures/data/topLongShortAccountRatio", params)
def top_long_short_position_ratio(self, pair: str, period: str, **kwargs):
"""
|
| **Get top long short position ratio**
:API endpoint: ``GET /futures/data/topLongShortPositionRatio``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#top-trader-long-short-ratio-positions-market-data
:parameter pair: string; the trading pair
:parameter period: string; the period of open interest, "5m", "15m", "30m", "1h", "2h", "4h", "6h", "12h", "1d". (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 30, max 500.
:parameter startTime: optional int
:parameter endTime: optional int
**Notes**
- If startTime and endTime are not sent, the most recent data is returned.
- Only the data of the latest 30 days is available.
|
"""
check_required_parameters([[pair, "pair"], [period, "period"]])
params = {"pair": pair, "period": period, **kwargs}
return self.query("/futures/data/topLongShortPositionRatio", params)
def long_short_account_ratio(self, pair: str, period: str, **kwargs):
"""
|
| **Get top long short account ratio**
:API endpoint: ``GET /futures/data/globalLongShortAccountRatio``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#top-trader-long-short-ratio-accounts-market-data
:parameter pair: string; the trading pair
:parameter period: string; the period of open interest, "5m", "15m", "30m", "1h", "2h", "4h", "6h", "12h", "1d". (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 30, max 500.
:parameter startTime: optional int
:parameter endTime: optional int
**Notes**
- If startTime and endTime are not sent, the most recent data is returned.
- Only the data of the latest 30 days is available.
|
"""
check_required_parameters([[pair, "pair"], [period, "period"]])
params = {"pair": pair, "period": period, **kwargs}
return self.query("/futures/data/globalLongShortAccountRatio", params)
def taker_long_short_ratio(self, pair: str, contractType: str, period: str, **kwargs):
"""
|
| **Get taker long short ratio**
:API endpoint: ``GET /futures/data/takerBuySellVol``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#taker-buy-sell-volume-market-data
:parameter pair: string; the trading pair
:parameter contractType: string; CURRENT_QUARTER, NEXT_QUARTER, PERPETUAL.
:parameter period: string; the period of open interest, "5m", "15m", "30m", "1h", "2h", "4h", "6h", "12h", "1d". (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 30, max 500.
:parameter startTime: optional int
:parameter endTime: optional int
**Notes**
- If startTime and endTime are not sent, the most recent data is returned.
- Only the data of the latest 30 days is available.
|
"""
check_required_parameters([[pair, "pair"], [contractType, "contractType"], [period, "period"]])
params = {"pair": pair, "contractType": contractType, "period": period, **kwargs}
return self.query("/futures/data/takerBuySellVol", params)
def basis(self, pair: str, contractType: str, period: str, **kwargs):
"""
|
| **Get Index Composite**
:API endpoint: ``GET /futures/data/basis``
:API doc: xshttps://binance-docs.github.io/apidocs/delivery/en/#basis-market-data
:parameter pair: string; the trading pair
:parameter contractType: string; CURRENT_QUARTER, NEXT_QUARTER, PERPETUAL.
:parameter period: string; the period of open interest, "5m", "15m", "30m", "1h", "2h", "4h", "6h", "12h", "1d". (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 30, max 500.
:parameter startTime: optional int
:parameter endTime: optional int
**Notes**
- If startTime and endTime are not sent, the most recent data is returned.
- Only the data of the latest 30 days is available.
|
"""
check_required_parameters([[pair, "pair"], [contractType, "contractType"], [period, "period"]])
params = {"pair": pair, "contractType": contractType, "period": period, **kwargs}
return self.query("/futures/data/basis", params)
| 38.679537
| 204
| 0.674636
|
from binance.lib.utils import (
check_required_parameter,
)
from binance.lib.utils import check_required_parameters
def ping(self):
"""
|
| **Test Connectivity**
| *Test connectivity to the Rest API.*
:API endpoint: ``GET /dapi/v1/ping``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#test-connectivity
|
"""
url_path = "/dapi/v1/ping"
return self.query(url_path)
def time(self):
"""
|
| **Check Server Time**
| *Test connectivity to the Rest API and get the current server time.*
:API endpoint: ``GET /dapi/v1/time``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#check-server-time
|
"""
url_path = "/dapi/v1/time"
return self.query(url_path)
def exchange_info(self):
"""
|
| **Exchange Information**
| *Current exchange trading rules and symbol information*
:API endpoint: ``GET /dapi/v1/exchangeInfo``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#exchange-information
|
"""
url_path = "/dapi/v1/exchangeInfo"
return self.query(url_path)
def depth(self, symbol: str, **kwargs):
"""
|
| **Get Orderbook**
:API endpoint: ``GET /dapi/v1/depth``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#order-book
:parameter symbol: string; the trading pair
:parameter limit: optional int; limit the results. Default 500, valid limits: [5, 10, 20, 50, 100, 500, 1000].
|
"""
check_required_parameter(symbol, "symbol")
params = {"symbol": symbol, **kwargs}
return self.query("/dapi/v1/depth", params)
def trades(self, symbol: str, **kwargs):
"""
|
| **Get Recent Market Trades**
:API endpoint: ``GET /dapi/v1/trades``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#recent-trades-list
:parameter symbol: string; the trading pair
:parameter limit: optional int; limit the results. Default 500, max 1000.
|
"""
check_required_parameter(symbol, "symbol")
params = {"symbol": symbol, **kwargs}
return self.query("/dapi/v1/trades", params)
def historical_trades(self, symbol: str, **kwargs):
"""
|
| **Old Trade Lookup**
| *Get older market historical trades.*
:API endpoint: ``GET /dapi/v1/historicalTrades``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#old-trades-lookup-market_data
:parameter symbol: string; the trading pair
:parameter limit: optional int; limit the results. Default 500, max 1000.
:parameter formId: optional int; trade ID to fetch from. Default gets most recent trades.
|
"""
check_required_parameter(symbol, "symbol")
params = {"symbol": symbol, **kwargs}
return self.limit_request("GET", "/dapi/v1/historicalTrades", params)
def agg_trades(self, symbol: str, **kwargs):
"""
|
| **Compressed/Aggregate Trades List**
| *Get compressed, aggregate market trades. Market trades that fill at the time, from the same order, with the same price will have the quantity aggregated.*
:API endpoint: ``GET /dapi/v1/aggTrades``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#compressed-aggregate-trades-list
:parameter symbol: string; the trading pair
:parameter limit: optional int; limit the results. Default 500, max 1000.
:parameter formId: optional int; trade ID to fetch from. Default gets most recent trades.
:parameter startTime: optional int; Timestamp in ms to get aggregate trades from INCLUSIVE.
:parameter endTime: optional int; Timestamp in ms to get aggregate trades until INCLUSIVE.
|
"""
check_required_parameter(symbol, "symbol")
params = {"symbol": symbol, **kwargs}
return self.query("/dapi/v1/aggTrades", params)
def klines(self, symbol: str, interval: str, **kwargs):
"""
|
| **Kline/Candlestick Data**
| *Kline/candlestick bars for a symbol. Klines are uniquely identified by their open time.*
:API endpoint: ``GET /dapi/v1/klines``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#kline-candlestick-data
:parameter symbol: string; the trading pair
:parameter interval: string; the interval of kline, e.g 1m, 5m, 1h, 1d, etc. (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 500, max 1000.
:parameter startTime: optional int; Timestamp in ms to get aggregate trades from INCLUSIVE.
:parameter endTime: optional int; Timestamp in ms to get aggregate trades until INCLUSIVE.
|
"""
check_required_parameters([[symbol, "symbol"], [interval, "interval"]])
params = {"symbol": symbol, "interval": interval, **kwargs}
return self.query("/dapi/v1/klines", params)
def continuous_klines(self, pair: str, contractType: str, interval: str, **kwargs):
"""
|
| **Continuous Kline/Candlestick Data**
| *Kline/candlestick bars for a specific contract type. Klines are uniquely identified by their open time.*
:API endpoint: ``GET /dapi/v1/continuousKlines``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#continuous-contract-kline-candlestick-data
:parameter pair: string; the trading pair
:parameter contractType: string; PERPETUAL, CURRENT_MONTH, NEXT_MONTH, CURRENT_QUARTER, NEXT_QUARTER.
:parameter interval: string; the interval of kline, e.g 1m, 5m, 1h, 1d, etc. (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 500, max 1000.
:parameter startTime: optional int; Timestamp in ms to get aggregate trades from INCLUSIVE.
:parameter endTime: optional int; Timestamp in ms to get aggregate trades until INCLUSIVE.
|
"""
check_required_parameters([[pair, "pair"], [contractType,"contractType"], [interval, "interval"]])
params = {"pair": pair, "contractType":contractType, "interval": interval, **kwargs}
return self.query("/dapi/v1/continuousKlines", params)
def index_price_klines(self, pair: str, interval: str, **kwargs):
"""
|
| **Kline/Candlestick Data for the index price of a pair.**
| *Klines are uniquely identified by their open time.*
:API endpoint: ``GET /dapi/v1/indexPriceKlines``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#index-price-kline-candlestick-data
:parameter pair: string; the trading pair
:parameter interval: string; the interval of kline, e.g 1m, 5m, 1h, 1d, etc. (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 500, max 1000.
:parameter startTime: optional int; Timestamp in ms to get aggregate trades from INCLUSIVE.
:parameter endTime: optional int; Timestamp in ms to get aggregate trades until INCLUSIVE.
|
"""
check_required_parameters([[pair, "pair"], [interval, "interval"]])
params = {"pair": pair, "interval": interval, **kwargs}
return self.query("/dapi/v1/indexPriceKlines", params)
def mark_price_klines(self, symbol: str, interval: str, **kwargs):
"""
|
| **Kline/candlestick bars for the mark price of a symbol.**
| *Klines are uniquely identified by their open time.*
:API endpoint: ``GET /dapi/v1/markPriceKlines``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#mark-price-kline-candlestick-data
:parameter pair: string; the trading pair
:parameter interval: string; the interval of kline, e.g 1m, 5m, 1h, 1d, etc. (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 500, max 1000.
:parameter startTime: optional int; Timestamp in ms to get aggregate trades from INCLUSIVE.
:parameter endTime: optional int; Timestamp in ms to get aggregate trades until INCLUSIVE.
**Notes**
- The difference between startTime and endTime can only be up to 200 days
- Between startTime and endTime, the most recent limit data from endTime will be returned:
- If startTime and endTime are not sent, current timestamp will be set as endTime, and the most recent data will be returned.
- If startTime is sent only, the timestamp of 200 days after startTime will be set as endTime(up to the current time)
- If endTime is sent only, the timestamp of 200 days before endTime will be set as startTime
|
"""
check_required_parameters([[symbol, "symbol"], [interval, "interval"]])
params = {"symbol": symbol, "interval": interval, **kwargs}
return self.query("/dapi/v1/markPriceKlines", params)
def mark_price(self, symbol: str):
"""
|
| **Mark Price and Funding Rate**
:API endpoint: ``GET /dapi/v1/premiumIndex``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#index-price-and-mark-price
:parameter symbol: string; the trading pair
|
"""
check_required_parameter(symbol, "symbol")
params = {
"symbol": symbol,
}
return self.query("/dapi/v1/premiumIndex", params)
def funding_rate(self, symbol: str, **kwargs):
"""
|
| **Funding Rate History**
:API endpoint: ``GET /dapi/v1/fundingRate``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#get-funding-rate-history-of-perpetual-futures
:parameter symbol: string; the trading pair
:parameter limit: optional int; limit the results. Default 500, max 1000.
:parameter startTime: optional int; Timestamp in ms to get aggregate trades from INCLUSIVE.
:parameter endTime: optional int; Timestamp in ms to get aggregate trades until INCLUSIVE.
**Notes**
- Empty array will be returned for delivery symbols.
|
"""
params = {"symbol": symbol, **kwargs}
return self.query("/dapi/v1/fundingRate", params)
def ticker_24hr_price_change(self, symbol: str = None, pair: str = None):
"""
|
| **24 hour rolling window price change statistics.**
| *Careful when accessing this with no symbol.*
| *If the symbol is not sent, tickers for all symbols will be returned in an array.*
:API endpoint: ``GET /dapi/v1/ticker/24hr``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#24hr-ticker-price-change-statistics
:parameter symbol: optional string; the trading symbol
:parameter pair: optional string; the trading pair
**Notes**
- Symbol and pair cannot be sent together
- If a pair is sent, tickers for all symbols of the pair will be returned
- If either a pair or symbol is sent, tickers for all symbols of all pairs will be returned
|
"""
if (symbol is None) and (pair is None):
return self.query("/dapi/v1/ticker/24hr")
elif (symbol is None):
params = {"pair": pair}
else:
params = {"symbol": symbol}
return self.query("/dapi/v1/ticker/24hr", params)
def ticker_price(self, symbol: str = None, pair: str = None):
"""
|
| **Latest price for a symbol or symbols**
:API endpoint: ``GET /dapi/v1/ticker/price``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#symbol-price-ticker
:parameter symbol: optional string; the trading symbol
:parameter pair: optional string; the trading pair
**Notes**
- Symbol and pair cannot be sent together
- If a pair is sent,tickers for all symbols of the pair will be returned
- If either a pair or symbol is sent, tickers for all symbols of all pairs will be returned
|
"""
if (symbol is None) and (pair is None):
return self.query("/dapi/v1/ticker/price")
elif (symbol is None):
params = {"pair": pair}
else:
params = {"symbol": symbol}
return self.query("/dapi/v1/ticker/price", params)
def book_ticker(self, symbol: str = None, pair: str = None):
"""
|
| **Best price/qty on the order book for a symbol or symbols**
:API endpoint: ``GET /dapi/v1/ticker/bookTicker``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#symbol-order-book-ticker
:parameter symbol: optional string; the trading symbol
**Notes**
- If the symbol is not sent, bookTickers for all symbols will be returned in an array.
|
"""
if (symbol is None) and (pair is None):
return self.query("/dapi/v1/ticker/bookTicker")
elif (symbol is None):
params = {"pair": pair}
else:
params = {"symbol": symbol}
return self.query("/dapi/v1/ticker/bookTicker", params)
def open_interest(self, symbol: str):
"""
|
| **Get present open interest of a specific symbol**
:API endpoint: ``GET /dapi/v1/openInterest``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#open-interest
:parameter symbol: string; the trading symbol
|
"""
check_required_parameter(symbol, "symbol")
params = {"symbol": symbol}
return self.query("/dapi/v1/ticker/bookTicker", params)
def open_interest_hist(self, pair: str, contractType: str, period: str, **kwargs):
"""
|
| **Get historical open interest of a specific symbol**
:API endpoint: ``GET /futures/data/openInterestHist``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#open-interest-statistics-market-data
:parameter pair: string; the trading pair
:parameter contractType: string; ALL, CURRENT_QUARTER, NEXT_QUARTER, PERPETUAL.
:parameter period: string; the period of open interest, "5m", "15m", "30m", "1h", "2h", "4h", "6h", "12h", "1d". (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 30, max 500.
:parameter startTime: optional int
:parameter endTime: optional int
**Notes**
- If startTime and endTime are not sent, the most recent data is returned.
- Only the data of the latest 30 days is available.
|
"""
check_required_parameters([[pair, "pair"], [contractType, "contractType"], [period, "period"]])
params = {"pair": pair, "contractType": contractType, "period": period, **kwargs}
return self.query("/futures/data/openInterestHist", params)
def top_long_short_account_ratio(self, pair: str, period: str, **kwargs):
"""
|
| **Get top long short account ratio**
:API endpoint: `GET /futures/data/topLongShortAccountRatio`
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#top-trader-long-short-ratio-accounts-market-data
:parameter pair: string; the trading pair
:parameter period: string; the period of open interest, "5m", "15m", "30m", "1h", "2h", "4h", "6h", "12h", "1d". (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 30, max 500.
:parameter startTime: optional int
:parameter endTime: optional int
**Notes**
- If startTime and endTime are not sent, the most recent data is returned.
- Only the data of the latest 30 days is available.
|
"""
check_required_parameters([[pair, "pair"], [period, "period"]])
params = {"pair": pair, "period": period, **kwargs}
return self.query("/futures/data/topLongShortAccountRatio", params)
def top_long_short_position_ratio(self, pair: str, period: str, **kwargs):
"""
|
| **Get top long short position ratio**
:API endpoint: ``GET /futures/data/topLongShortPositionRatio``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#top-trader-long-short-ratio-positions-market-data
:parameter pair: string; the trading pair
:parameter period: string; the period of open interest, "5m", "15m", "30m", "1h", "2h", "4h", "6h", "12h", "1d". (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 30, max 500.
:parameter startTime: optional int
:parameter endTime: optional int
**Notes**
- If startTime and endTime are not sent, the most recent data is returned.
- Only the data of the latest 30 days is available.
|
"""
check_required_parameters([[pair, "pair"], [period, "period"]])
params = {"pair": pair, "period": period, **kwargs}
return self.query("/futures/data/topLongShortPositionRatio", params)
def long_short_account_ratio(self, pair: str, period: str, **kwargs):
"""
|
| **Get top long short account ratio**
:API endpoint: ``GET /futures/data/globalLongShortAccountRatio``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#top-trader-long-short-ratio-accounts-market-data
:parameter pair: string; the trading pair
:parameter period: string; the period of open interest, "5m", "15m", "30m", "1h", "2h", "4h", "6h", "12h", "1d". (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 30, max 500.
:parameter startTime: optional int
:parameter endTime: optional int
**Notes**
- If startTime and endTime are not sent, the most recent data is returned.
- Only the data of the latest 30 days is available.
|
"""
check_required_parameters([[pair, "pair"], [period, "period"]])
params = {"pair": pair, "period": period, **kwargs}
return self.query("/futures/data/globalLongShortAccountRatio", params)
def taker_long_short_ratio(self, pair: str, contractType: str, period: str, **kwargs):
"""
|
| **Get taker long short ratio**
:API endpoint: ``GET /futures/data/takerBuySellVol``
:API doc: https://binance-docs.github.io/apidocs/delivery/en/#taker-buy-sell-volume-market-data
:parameter pair: string; the trading pair
:parameter contractType: string; CURRENT_QUARTER, NEXT_QUARTER, PERPETUAL.
:parameter period: string; the period of open interest, "5m", "15m", "30m", "1h", "2h", "4h", "6h", "12h", "1d". (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 30, max 500.
:parameter startTime: optional int
:parameter endTime: optional int
**Notes**
- If startTime and endTime are not sent, the most recent data is returned.
- Only the data of the latest 30 days is available.
|
"""
check_required_parameters([[pair, "pair"], [contractType, "contractType"], [period, "period"]])
params = {"pair": pair, "contractType": contractType, "period": period, **kwargs}
return self.query("/futures/data/takerBuySellVol", params)
def basis(self, pair: str, contractType: str, period: str, **kwargs):
"""
|
| **Get Index Composite**
:API endpoint: ``GET /futures/data/basis``
:API doc: xshttps://binance-docs.github.io/apidocs/delivery/en/#basis-market-data
:parameter pair: string; the trading pair
:parameter contractType: string; CURRENT_QUARTER, NEXT_QUARTER, PERPETUAL.
:parameter period: string; the period of open interest, "5m", "15m", "30m", "1h", "2h", "4h", "6h", "12h", "1d". (see more in https://binance-docs.github.io/apidocs/delivery/en/#public-endpoints-info)
:parameter limit: optional int; limit the results. Default 30, max 500.
:parameter startTime: optional int
:parameter endTime: optional int
**Notes**
- If startTime and endTime are not sent, the most recent data is returned.
- Only the data of the latest 30 days is available.
|
"""
check_required_parameters([[pair, "pair"], [contractType, "contractType"], [period, "period"]])
params = {"pair": pair, "contractType": contractType, "period": period, **kwargs}
return self.query("/futures/data/basis", params)
| 0
| 0
| 0
|
6561f371a02903143318b73cd46ae7e124479c61
| 279
|
py
|
Python
|
gitkit/commands/what.py
|
akx/git-kit
|
54948b57f201adecc810c4895b6712c1c8265cf3
|
[
"MIT"
] | 3
|
2017-02-16T09:04:09.000Z
|
2021-05-03T08:25:52.000Z
|
gitkit/commands/what.py
|
akx/git-kit
|
54948b57f201adecc810c4895b6712c1c8265cf3
|
[
"MIT"
] | 2
|
2017-02-16T08:54:15.000Z
|
2017-02-16T09:09:41.000Z
|
gitkit/commands/what.py
|
akx/git-kit
|
54948b57f201adecc810c4895b6712c1c8265cf3
|
[
"MIT"
] | 1
|
2022-02-07T09:07:39.000Z
|
2022-02-07T09:07:39.000Z
|
import click
from gitkit.util.shell import get_output
@click.command()
def what():
"""
What _is_ the current revision anyway?
"""
description = get_output("git describe")
revision = get_output("git rev-parse HEAD")
print(f"{description} ({revision})")
| 19.928571
| 47
| 0.670251
|
import click
from gitkit.util.shell import get_output
@click.command()
def what():
"""
What _is_ the current revision anyway?
"""
description = get_output("git describe")
revision = get_output("git rev-parse HEAD")
print(f"{description} ({revision})")
| 0
| 0
| 0
|
d7905df33f83738c4064bc686d449bb022aa90b4
| 1,281
|
py
|
Python
|
census/code.py
|
Balaji-Pa/greyatom-python-for-data-science
|
801905c377cbd0a573a9d5d8cc0b66972bffc4af
|
[
"MIT"
] | null | null | null |
census/code.py
|
Balaji-Pa/greyatom-python-for-data-science
|
801905c377cbd0a573a9d5d8cc0b66972bffc4af
|
[
"MIT"
] | null | null | null |
census/code.py
|
Balaji-Pa/greyatom-python-for-data-science
|
801905c377cbd0a573a9d5d8cc0b66972bffc4af
|
[
"MIT"
] | null | null | null |
# --------------
# Importing header files
import numpy as np
import warnings
warnings.filterwarnings('ignore')
#New record
new_record=[[50, 9, 4, 1, 0, 0, 40, 0]]
#Reading file
# data = np.genfromtxt(path, delimiter=",", skip_header=1)
#Code starts here
data = np.genfromtxt(path, delimiter = ",", skip_header = 1)
census = np.concatenate((new_record,data),axis = 0)
age = census[:,0]
max_age = np.max(age)
min_age = np.min(age)
age_mean = np.mean(age)
age_std = np.std(age)
race_0 = census[census[:,2]==0]
race_1 = census[census[:,2]==1]
race_2 = census[census[:,2]==2]
race_3 = census[census[:,3]==3]
race_4 = census[census[:,4]==4]
len_0 = len(race_0)
len_1 = len(race_1)
len_2 = len(race_2)
len_3 = len(race_3)
len_4 = len(race_4)
a = [len_0, len_1, len_2, len_3, len_4]
minority_race = min(a)
senior_citizens = census[census[:,0]>60]
working_hours_sum = senior_citizens.sum(axis=0)[6]
senior_citizens_len = len(senior_citizens)
avg_working_hours = working_hours_sum/senior_citizens_len
print(round(avg_working_hours,2))
high = census[census[:,1]>10]
low = census[census[:,1]<=10]
avg_pay_high = round(np.mean(high[:,7]),2)
avg_pay_low = round(np.mean(low[:,7]),2)
print(avg_pay_high)
print(avg_pay_low)
| 22.875
| 61
| 0.664325
|
# --------------
# Importing header files
import numpy as np
import warnings
warnings.filterwarnings('ignore')
#New record
new_record=[[50, 9, 4, 1, 0, 0, 40, 0]]
#Reading file
# data = np.genfromtxt(path, delimiter=",", skip_header=1)
#Code starts here
data = np.genfromtxt(path, delimiter = ",", skip_header = 1)
census = np.concatenate((new_record,data),axis = 0)
age = census[:,0]
max_age = np.max(age)
min_age = np.min(age)
age_mean = np.mean(age)
age_std = np.std(age)
race_0 = census[census[:,2]==0]
race_1 = census[census[:,2]==1]
race_2 = census[census[:,2]==2]
race_3 = census[census[:,3]==3]
race_4 = census[census[:,4]==4]
len_0 = len(race_0)
len_1 = len(race_1)
len_2 = len(race_2)
len_3 = len(race_3)
len_4 = len(race_4)
a = [len_0, len_1, len_2, len_3, len_4]
minority_race = min(a)
senior_citizens = census[census[:,0]>60]
working_hours_sum = senior_citizens.sum(axis=0)[6]
senior_citizens_len = len(senior_citizens)
avg_working_hours = working_hours_sum/senior_citizens_len
print(round(avg_working_hours,2))
high = census[census[:,1]>10]
low = census[census[:,1]<=10]
avg_pay_high = round(np.mean(high[:,7]),2)
avg_pay_low = round(np.mean(low[:,7]),2)
print(avg_pay_high)
print(avg_pay_low)
| 0
| 0
| 0
|
4165bf5b7d8a9d455b4c62503f72f9ecebf7e6ba
| 509
|
py
|
Python
|
CHAPTER 06 (stacks_queues_deques)/reverse_file_using_stack.py
|
ahammadshawki8/Data-Structures-Algorithms-in-Python-
|
fc18b54128cd5bc7639a14999d8f990190b524eb
|
[
"MIT"
] | null | null | null |
CHAPTER 06 (stacks_queues_deques)/reverse_file_using_stack.py
|
ahammadshawki8/Data-Structures-Algorithms-in-Python-
|
fc18b54128cd5bc7639a14999d8f990190b524eb
|
[
"MIT"
] | null | null | null |
CHAPTER 06 (stacks_queues_deques)/reverse_file_using_stack.py
|
ahammadshawki8/Data-Structures-Algorithms-in-Python-
|
fc18b54128cd5bc7639a14999d8f990190b524eb
|
[
"MIT"
] | null | null | null |
from stack_class import *
def reverse_file(path):
"""Overwrite given file using its context line-by-line reversed"""
s=ArrayStack()
with open(path,"r") as original:
for line in original:
s.push(line.rstrip("\n")) # removing newline characters
# overwrite the contents in LIFO order
with open(path,"w") as new:
while not s.is_empty():
new.write(s.pop()+"\n") # re-insert newline characters.
return "Reversed"
print(reverse_file("sample.txt"))
| 29.941176
| 70
| 0.642436
|
from stack_class import *
def reverse_file(path):
"""Overwrite given file using its context line-by-line reversed"""
s=ArrayStack()
with open(path,"r") as original:
for line in original:
s.push(line.rstrip("\n")) # removing newline characters
# overwrite the contents in LIFO order
with open(path,"w") as new:
while not s.is_empty():
new.write(s.pop()+"\n") # re-insert newline characters.
return "Reversed"
print(reverse_file("sample.txt"))
| 0
| 0
| 0
|
6500eec5e3f781f090b684bdb20725e228725ab7
| 816
|
py
|
Python
|
simulaqron/tests/performance/ring_teleport/configure_ring.py
|
Doomsk/SimulaQron
|
09bd81730e31c7642a0fece8ae7d518820fe57eb
|
[
"BSD-3-Clause"
] | 69
|
2018-10-14T10:32:34.000Z
|
2022-03-08T10:28:15.000Z
|
simulaqron/tests/performance/ring_teleport/configure_ring.py
|
Doomsk/SimulaQron
|
09bd81730e31c7642a0fece8ae7d518820fe57eb
|
[
"BSD-3-Clause"
] | 121
|
2018-10-03T13:57:44.000Z
|
2021-12-17T17:36:39.000Z
|
simulaqron/tests/performance/ring_teleport/configure_ring.py
|
Doomsk/SimulaQron
|
09bd81730e31c7642a0fece8ae7d518820fe57eb
|
[
"BSD-3-Clause"
] | 43
|
2018-10-10T15:53:28.000Z
|
2022-03-31T16:52:55.000Z
|
import sys
import os
from simulaqron.toolbox import get_simulaqron_path
# Get path to SimulaQron folder
simulaqron_path = get_simulaqron_path.main()
tot_nr = int(sys.argv[1])
# configure run files for nodes
with open("run.sh", "w") as f:
f.write("#!/bin/sh\n\n")
for i in range(tot_nr - 1):
f.write("python3 node.py {} {} &\n".format(i, tot_nr))
f.write("python3 node.py {} {}\n".format(tot_nr - 1, tot_nr))
with open("run_v2.sh", "w") as f:
f.write("#!/bin/sh\n\n")
for i in range(tot_nr - 1):
f.write("python3 node_v2.py {} {} &\n".format(i, tot_nr))
f.write("python3 node_v2.py {} {}\n".format(tot_nr - 1, tot_nr))
# configure network
nodes = "".join(["n" + str(i) + " " for i in range(tot_nr)])
os.system("python3 " + simulaqron_path + "configFiles.py " + nodes)
| 27.2
| 68
| 0.627451
|
import sys
import os
from simulaqron.toolbox import get_simulaqron_path
# Get path to SimulaQron folder
simulaqron_path = get_simulaqron_path.main()
tot_nr = int(sys.argv[1])
# configure run files for nodes
with open("run.sh", "w") as f:
f.write("#!/bin/sh\n\n")
for i in range(tot_nr - 1):
f.write("python3 node.py {} {} &\n".format(i, tot_nr))
f.write("python3 node.py {} {}\n".format(tot_nr - 1, tot_nr))
with open("run_v2.sh", "w") as f:
f.write("#!/bin/sh\n\n")
for i in range(tot_nr - 1):
f.write("python3 node_v2.py {} {} &\n".format(i, tot_nr))
f.write("python3 node_v2.py {} {}\n".format(tot_nr - 1, tot_nr))
# configure network
nodes = "".join(["n" + str(i) + " " for i in range(tot_nr)])
os.system("python3 " + simulaqron_path + "configFiles.py " + nodes)
| 0
| 0
| 0
|
3455b79313232342209d06b8958b01bacb4d6b24
| 181
|
py
|
Python
|
popupdict/util/__init__.py
|
hantaotaohan/popup-dict
|
9eb05fd9797a14323c9b1166f916778b32e933bc
|
[
"MIT"
] | 85
|
2018-02-23T07:16:27.000Z
|
2022-03-26T19:53:48.000Z
|
popupdict/util/__init__.py
|
glMa7/popup-dict
|
dbf9121aa63d65095bd848a582595e1b03327418
|
[
"MIT"
] | 12
|
2018-02-23T07:45:34.000Z
|
2020-03-10T03:20:03.000Z
|
popupdict/util/__init__.py
|
glMa7/popup-dict
|
dbf9121aa63d65095bd848a582595e1b03327418
|
[
"MIT"
] | 16
|
2018-01-02T02:07:50.000Z
|
2021-12-17T08:01:00.000Z
|
from .selection import Selection
from .logging import logger
from .dir import config_dir, cache_dir
__all__ = [
'Selection',
'logger',
'config_dir',
'cache_dir',
]
| 16.454545
| 38
| 0.685083
|
from .selection import Selection
from .logging import logger
from .dir import config_dir, cache_dir
__all__ = [
'Selection',
'logger',
'config_dir',
'cache_dir',
]
| 0
| 0
| 0
|
33fc243f69957eb951b7bee3d0b96740c540016f
| 13,978
|
py
|
Python
|
classification/CIFAR/gram_matrics.py
|
warner-benjamin/vos
|
1f6844caeb2985f875b446f284bcfcfb8f9bba0e
|
[
"Apache-2.0"
] | 174
|
2022-02-03T04:45:23.000Z
|
2022-03-31T06:04:23.000Z
|
classification/CIFAR/gram_matrics.py
|
warner-benjamin/vos
|
1f6844caeb2985f875b446f284bcfcfb8f9bba0e
|
[
"Apache-2.0"
] | 19
|
2022-02-08T14:48:43.000Z
|
2022-03-31T08:48:05.000Z
|
classification/CIFAR/gram_matrics.py
|
warner-benjamin/vos
|
1f6844caeb2985f875b446f284bcfcfb8f9bba0e
|
[
"Apache-2.0"
] | 24
|
2022-02-04T14:16:29.000Z
|
2022-03-26T12:13:06.000Z
|
from __future__ import division,print_function
#matplotlib inline
#load_ext autoreload
#autoreload 2
import sys
from tqdm import tqdm_notebook as tqdm
import random
import matplotlib.pyplot as plt
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.nn.init as init
from torch.autograd import Variable, grad
from torchvision import datasets, transforms
from torch.nn.parameter import Parameter
import calculate_log as callog
import warnings
warnings.filterwarnings('ignore')
torch.cuda.set_device(0) #Select the GPU
torch_model = ResNet(BasicBlock, [3, 4, 6, 3], num_classes=10)
torch_model.load('/nobackup-slow/dataset/my_xfdu/resnet_cifar10.pth')
torch_model.cuda()
torch_model.params = list(torch_model.parameters())
torch_model.eval()
print("Done")
batch_size = 128
mean = np.array([[0.4914, 0.4822, 0.4465]]).T
std = np.array([[0.2023, 0.1994, 0.2010]]).T
normalize = transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize
])
transform_test = transforms.Compose([
transforms.CenterCrop(size=(32, 32)),
transforms.ToTensor(),
normalize
])
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('/nobackup-slow/dataset/cifarpy', train=True, download=True,
transform=transform_train),
batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('/nobackup-slow/dataset/cifarpy', train=False, transform=transform_test),
batch_size=batch_size)
data_train = list(torch.utils.data.DataLoader(
datasets.CIFAR10('/nobackup-slow/dataset/cifarpy', train=True, download=True,
transform=transform_test),
batch_size=1, shuffle=False))
data = list(torch.utils.data.DataLoader(
datasets.CIFAR10('/nobackup-slow/dataset/cifarpy', train=False, download=True,
transform=transform_test),
batch_size=1, shuffle=False))
torch_model.eval()
# correct = 0
# total = 0
# for x,y in test_loader:
# x = x.cuda()
# y = y.numpy()
# correct += (y==np.argmax(torch_model(x).detach().cpu().numpy(),axis=1)).sum()
# total += y.shape[0]
# print("Accuracy: ",correct/total)
cifar100 = list(torch.utils.data.DataLoader(
datasets.CIFAR100('/nobackup-slow/dataset/cifarpy', train=False, download=True,
transform=transform_test),
batch_size=1, shuffle=True))
train_preds = []
train_confs = []
train_logits = []
for idx in range(0, len(data_train), 128):
batch = torch.squeeze(torch.stack([x[0] for x in data_train[idx:idx + 128]]), dim=1).cuda()
logits = torch_model(batch)
confs = F.softmax(logits, dim=1).cpu().detach().numpy()
preds = np.argmax(confs, axis=1)
logits = (logits.cpu().detach().numpy())
train_confs.extend(np.max(confs, axis=1))
train_preds.extend(preds)
train_logits.extend(logits)
print("Done")
test_preds = []
test_confs = []
test_logits = []
for idx in range(0, len(data), 128):
batch = torch.squeeze(torch.stack([x[0] for x in data[idx:idx + 128]]), dim=1).cuda()
logits = torch_model(batch)
confs = F.softmax(logits, dim=1).cpu().detach().numpy()
preds = np.argmax(confs, axis=1)
logits = (logits.cpu().detach().numpy())
test_confs.extend(np.max(confs, axis=1))
test_preds.extend(preds)
test_logits.extend(logits)
print("Done")
import calculate_log as callog
detector = Detector()
detector.compute_minmaxs(data_train, POWERS=range(1, 11))
detector.compute_test_deviations(POWERS=range(1, 11))
print("CIFAR-100")
c100_results = detector.compute_ood_deviations(cifar100,POWERS=range(1,11))
| 33.440191
| 136
| 0.609744
|
from __future__ import division,print_function
#matplotlib inline
#load_ext autoreload
#autoreload 2
import sys
from tqdm import tqdm_notebook as tqdm
import random
import matplotlib.pyplot as plt
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.nn.init as init
from torch.autograd import Variable, grad
from torchvision import datasets, transforms
from torch.nn.parameter import Parameter
import calculate_log as callog
import warnings
warnings.filterwarnings('ignore')
torch.cuda.set_device(0) #Select the GPU
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(in_planes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes)
)
def forward(self, x):
t = self.conv1(x)
out = F.relu(self.bn1(t))
torch_model.record(t)
torch_model.record(out)
t = self.conv2(out)
out = self.bn2(self.conv2(out))
torch_model.record(t)
torch_model.record(out)
t = self.shortcut(x)
out += t
torch_model.record(t)
out = F.relu(out)
torch_model.record(out)
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
super(ResNet, self).__init__()
self.in_planes = 64
self.conv1 = conv3x3(3, 64)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.linear = nn.Linear(512 * block.expansion, num_classes)
self.collecting = False
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
y = self.linear(out)
return y
def record(self, t):
if self.collecting:
self.gram_feats.append(t)
def gram_feature_list(self, x):
self.collecting = True
self.gram_feats = []
self.forward(x)
self.collecting = False
temp = self.gram_feats
self.gram_feats = []
return temp
def load(self, path="resnet_cifar10.pth"):
tm = torch.load(path, map_location="cpu")
self.load_state_dict(tm)
def get_min_max(self, data, power):
mins = []
maxs = []
for i in range(0, len(data), 128):
batch = data[i:i + 128].cuda()
feat_list = self.gram_feature_list(batch)
for L, feat_L in enumerate(feat_list):#96, x, x, x
if L == len(mins):
mins.append([None] * len(power))
maxs.append([None] * len(power))
for p, P in enumerate(power):
g_p = G_p(feat_L, P)
current_min = g_p.min(dim=0, keepdim=True)[0]
breakpoint()
current_max = g_p.max(dim=0, keepdim=True)[0]
if mins[L][p] is None:
mins[L][p] = current_min
maxs[L][p] = current_max
else:
mins[L][p] = torch.min(current_min, mins[L][p])
maxs[L][p] = torch.max(current_max, maxs[L][p])
# breakpoint()
return mins, maxs
def get_deviations(self, data, power, mins, maxs):
deviations = []
for i in range(0, len(data), 128):
batch = data[i:i + 128].cuda()
feat_list = self.gram_feature_list(batch)
batch_deviations = []
for L, feat_L in enumerate(feat_list):
dev = 0
for p, P in enumerate(power):
g_p = G_p(feat_L, P)
dev += (F.relu(mins[L][p] - g_p) / torch.abs(mins[L][p] + 10 ** -6)).sum(dim=1, keepdim=True)
dev += (F.relu(g_p - maxs[L][p]) / torch.abs(maxs[L][p] + 10 ** -6)).sum(dim=1, keepdim=True)
batch_deviations.append(dev.cpu().detach().numpy())
batch_deviations = np.concatenate(batch_deviations, axis=1)
deviations.append(batch_deviations)
deviations = np.concatenate(deviations, axis=0)
return deviations
torch_model = ResNet(BasicBlock, [3, 4, 6, 3], num_classes=10)
torch_model.load('/nobackup-slow/dataset/my_xfdu/resnet_cifar10.pth')
torch_model.cuda()
torch_model.params = list(torch_model.parameters())
torch_model.eval()
print("Done")
batch_size = 128
mean = np.array([[0.4914, 0.4822, 0.4465]]).T
std = np.array([[0.2023, 0.1994, 0.2010]]).T
normalize = transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize
])
transform_test = transforms.Compose([
transforms.CenterCrop(size=(32, 32)),
transforms.ToTensor(),
normalize
])
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('/nobackup-slow/dataset/cifarpy', train=True, download=True,
transform=transform_train),
batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('/nobackup-slow/dataset/cifarpy', train=False, transform=transform_test),
batch_size=batch_size)
data_train = list(torch.utils.data.DataLoader(
datasets.CIFAR10('/nobackup-slow/dataset/cifarpy', train=True, download=True,
transform=transform_test),
batch_size=1, shuffle=False))
data = list(torch.utils.data.DataLoader(
datasets.CIFAR10('/nobackup-slow/dataset/cifarpy', train=False, download=True,
transform=transform_test),
batch_size=1, shuffle=False))
torch_model.eval()
# correct = 0
# total = 0
# for x,y in test_loader:
# x = x.cuda()
# y = y.numpy()
# correct += (y==np.argmax(torch_model(x).detach().cpu().numpy(),axis=1)).sum()
# total += y.shape[0]
# print("Accuracy: ",correct/total)
cifar100 = list(torch.utils.data.DataLoader(
datasets.CIFAR100('/nobackup-slow/dataset/cifarpy', train=False, download=True,
transform=transform_test),
batch_size=1, shuffle=True))
train_preds = []
train_confs = []
train_logits = []
for idx in range(0, len(data_train), 128):
batch = torch.squeeze(torch.stack([x[0] for x in data_train[idx:idx + 128]]), dim=1).cuda()
logits = torch_model(batch)
confs = F.softmax(logits, dim=1).cpu().detach().numpy()
preds = np.argmax(confs, axis=1)
logits = (logits.cpu().detach().numpy())
train_confs.extend(np.max(confs, axis=1))
train_preds.extend(preds)
train_logits.extend(logits)
print("Done")
test_preds = []
test_confs = []
test_logits = []
for idx in range(0, len(data), 128):
batch = torch.squeeze(torch.stack([x[0] for x in data[idx:idx + 128]]), dim=1).cuda()
logits = torch_model(batch)
confs = F.softmax(logits, dim=1).cpu().detach().numpy()
preds = np.argmax(confs, axis=1)
logits = (logits.cpu().detach().numpy())
test_confs.extend(np.max(confs, axis=1))
test_preds.extend(preds)
test_logits.extend(logits)
print("Done")
import calculate_log as callog
def detect(all_test_deviations, all_ood_deviations, verbose=True, normalize=True):
average_results = {}
for i in range(1, 11):
random.seed(i)
validation_indices = random.sample(range(len(all_test_deviations)), int(0.1 * len(all_test_deviations)))
test_indices = sorted(list(set(range(len(all_test_deviations))) - set(validation_indices)))
validation = all_test_deviations[validation_indices]
test_deviations = all_test_deviations[test_indices]
t95 = validation.mean(axis=0) + 10 ** -7
if not normalize:
t95 = np.ones_like(t95)
test_deviations = (test_deviations / t95[np.newaxis, :]).sum(axis=1)
ood_deviations = (all_ood_deviations / t95[np.newaxis, :]).sum(axis=1)
results = callog.compute_metric(-test_deviations, -ood_deviations)
for m in results:
average_results[m] = average_results.get(m, 0) + results[m]
for m in average_results:
average_results[m] /= i
if verbose:
callog.print_results(average_results)
return average_results
def cpu(ob):
for i in range(len(ob)):
for j in range(len(ob[i])):
ob[i][j] = ob[i][j].cpu()
return ob
def cuda(ob):
for i in range(len(ob)):
for j in range(len(ob[i])):
ob[i][j] = ob[i][j].cuda()
return ob
class Detector:
def __init__(self):
self.all_test_deviations = None
self.mins = {}
self.maxs = {}
self.classes = range(10)
def compute_minmaxs(self, data_train, POWERS=[10]):
for PRED in tqdm(self.classes):
train_indices = np.where(np.array(train_preds) == PRED)[0]
train_PRED = torch.squeeze(torch.stack([data_train[i][0] for i in train_indices]), dim=1)
mins, maxs = torch_model.get_min_max(train_PRED, power=POWERS)
self.mins[PRED] = cpu(mins)
self.maxs[PRED] = cpu(maxs)
torch.cuda.empty_cache()
def compute_test_deviations(self, POWERS=[10]):
all_test_deviations = None
test_classes = []
for PRED in tqdm(self.classes):
test_indices = np.where(np.array(test_preds) == PRED)[0]
test_PRED = torch.squeeze(torch.stack([data[i][0] for i in test_indices]), dim=1)
test_confs_PRED = np.array([test_confs[i] for i in test_indices])
test_classes.extend([PRED] * len(test_indices))
mins = cuda(self.mins[PRED])
maxs = cuda(self.maxs[PRED])
test_deviations = torch_model.get_deviations(test_PRED, power=POWERS, mins=mins, maxs=maxs) / test_confs_PRED[:, np.newaxis]
cpu(mins)
cpu(maxs)
if all_test_deviations is None:
all_test_deviations = test_deviations
else:
all_test_deviations = np.concatenate([all_test_deviations, test_deviations], axis=0)
torch.cuda.empty_cache()
self.all_test_deviations = all_test_deviations
self.test_classes = np.array(test_classes)
def compute_ood_deviations(self, ood, POWERS=[10]):
ood_preds = []
ood_confs = []
for idx in range(0, len(ood), 128):
batch = torch.squeeze(torch.stack([x[0] for x in ood[idx:idx + 128]]), dim=1).cuda()
logits = torch_model(batch)
confs = F.softmax(logits, dim=1).cpu().detach().numpy()
preds = np.argmax(confs, axis=1)
ood_confs.extend(np.max(confs, axis=1))
ood_preds.extend(preds)
torch.cuda.empty_cache()
print("Done")
ood_classes = []
all_ood_deviations = None
for PRED in tqdm(self.classes):
ood_indices = np.where(np.array(ood_preds) == PRED)[0]
if len(ood_indices) == 0:
continue
ood_classes.extend([PRED] * len(ood_indices))
ood_PRED = torch.squeeze(torch.stack([ood[i][0] for i in ood_indices]), dim=1)
ood_confs_PRED = np.array([ood_confs[i] for i in ood_indices])
mins = cuda(self.mins[PRED])
maxs = cuda(self.maxs[PRED])
ood_deviations = torch_model.get_deviations(ood_PRED, power=POWERS, mins=mins, maxs=maxs) / ood_confs_PRED[
:, np.newaxis]
cpu(self.mins[PRED])
cpu(self.maxs[PRED])
if all_ood_deviations is None:
all_ood_deviations = ood_deviations
else:
all_ood_deviations = np.concatenate([all_ood_deviations, ood_deviations], axis=0)
torch.cuda.empty_cache()
self.ood_classes = np.array(ood_classes)
breakpoint()
average_results = detect(self.all_test_deviations, all_ood_deviations)
return average_results, self.all_test_deviations, all_ood_deviations
def G_p(ob, p):
temp = ob.detach()
temp = temp ** p
temp = temp.reshape(temp.shape[0], temp.shape[1], -1)
temp = ((torch.matmul(temp, temp.transpose(dim0=2, dim1=1)))).sum(dim=2)
temp = (temp.sign() * torch.abs(temp) ** (1 / p)).reshape(temp.shape[0], -1)
return temp
detector = Detector()
detector.compute_minmaxs(data_train, POWERS=range(1, 11))
detector.compute_test_deviations(POWERS=range(1, 11))
print("CIFAR-100")
c100_results = detector.compute_ood_deviations(cifar100,POWERS=range(1,11))
| 9,549
| 76
| 506
|
668413f0ce3cd197c88e2f927c1dbf7519e7a5cb
| 1,597
|
py
|
Python
|
LinkMeBot/utils.py
|
mdlss/PlayStoreLinks_Bot
|
3c4bec4594c9670c7a3b88848cdc59c988c7f454
|
[
"MIT"
] | null | null | null |
LinkMeBot/utils.py
|
mdlss/PlayStoreLinks_Bot
|
3c4bec4594c9670c7a3b88848cdc59c988c7f454
|
[
"MIT"
] | null | null | null |
LinkMeBot/utils.py
|
mdlss/PlayStoreLinks_Bot
|
3c4bec4594c9670c7a3b88848cdc59c988c7f454
|
[
"MIT"
] | null | null | null |
import logging
import math
from misaka import Markdown, HtmlRenderer
from lxml.html import fromstring
# https://stackoverflow.com/a/3155023
millnames = ['',' thousand',' million',' billion',' trillion']
| 33.270833
| 121
| 0.72511
|
import logging
import math
from misaka import Markdown, HtmlRenderer
from lxml.html import fromstring
def make_logger(logger_name, logfile, loggin_level=logging.DEBUG):
logger = logging.getLogger(logger_name)
logger.setLevel(loggin_level)
formatter = logging.Formatter('%(levelname)s - %(name)s - %(asctime)s - %(message)s', '%Y-%m-%d %H:%M:%S')
fh = logging.FileHandler(logfile)
fh.setLevel(loggin_level)
fh.setFormatter(formatter)
ch = logging.StreamHandler()
ch.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
return logger
def get_text_from_markdown(markdown_text):
renderer = HtmlRenderer()
markdown = Markdown(renderer, extensions=('tables', 'autolink', 'strikethrough', 'quote', 'superscript', 'fenced-code'))
html = markdown(markdown_text)
parsed_html = fromstring(html)
# remove quoted text
[x.getparent().remove(x) for x in parsed_html.xpath('//blockquote')]
# remove automatically added links
for link in parsed_html.xpath('//a'):
if link.text_content() == link.get('href'):
link.getparent().remove(link)
text = ''.join(parsed_html.text_content()).strip()
return text
# https://stackoverflow.com/a/3155023
millnames = ['',' thousand',' million',' billion',' trillion']
def human_readable_download_number(download_number_string):
download_number_string = download_number_string.split("-")[0].replace(',','').strip()
n = float(download_number_string)
millidx = max(0,min(len(millnames)-1,
int(math.floor(0 if n == 0 else math.log10(abs(n))/3))))
return '{:.0f}{}'.format(n / 10**(3 * millidx), millnames[millidx])
| 1,325
| 0
| 68
|
b88fd5088d5c6562e9e754916376f77c12018147
| 3,004
|
py
|
Python
|
handling.py
|
alnordst/address-book-api
|
4385512ea5d1fdfd153160f2de2e6874890acd70
|
[
"Apache-2.0"
] | null | null | null |
handling.py
|
alnordst/address-book-api
|
4385512ea5d1fdfd153160f2de2e6874890acd70
|
[
"Apache-2.0"
] | null | null | null |
handling.py
|
alnordst/address-book-api
|
4385512ea5d1fdfd153160f2de2e6874890acd70
|
[
"Apache-2.0"
] | null | null | null |
from flask import Flask
from elasticsearch import Elasticsearch
from contact import Contact
class Handler(object):
"""
Handles operations on elasticsearch.
"""
def list_contacts(self, arguments):
"""
Returns a list of contacts or False.
"""
try:
self.es.indices.refresh(index = self.index_name)
res = self.es.search(index = self.index_name, body = {
"from": arguments["page"] * arguments["pageSize"],
"size": arguments["pageSize"],
"query": arguments["query"]
})
return res['hits']['hits']
except:
return False
def create_contact(self, form):
"""
Creates contact from form data. Returns True if successful.
"""
try:
if self._get_contact(form['name']): #contact by that name exists
return False
else:
contact = Contact(form)
res = self.es.index(index = self.index_name, doc_type = '_doc',
body = str(contact))
return res['result'] == 'created'
except:
return False
def list_a_contact(self, name):
"""
Returns data on a single contact identified by name.
"""
try:
return self._get_contact(name)['_source']
except:
return False
def update_contact(self, form):
"""
Update a contact using form data. Returns True if successful.
"""
try:
if self.delete_contact(form['name']):
return self.create_contact(form)
else:
return False
except:
return False
def delete_contact(self, name):
"""
Delete a contact identified by name. Returns True if successful.
"""
try:
contact_id = self._get_contact(name)['_id']
res = self.es.delete(index = self.index_name, doc_type = '_doc',
id = contact_id)
return res['result'] == 'deleted'
except:
return False
| 29.165049
| 79
| 0.515313
|
from flask import Flask
from elasticsearch import Elasticsearch
from contact import Contact
class Handler(object):
"""
Handles operations on elasticsearch.
"""
def __init__(self, index_name, port = 9200, wipe_index = False):
self.index_name = index_name
self.es = Elasticsearch(port = port)
if wipe_index and self.es.indices.exists(self.index_name):
self.es.indices.delete(index = self.index_name)
if not self.es.indices.exists(self.index_name):
self.es.indices.create(index = self.index_name)
def list_contacts(self, arguments):
"""
Returns a list of contacts or False.
"""
try:
self.es.indices.refresh(index = self.index_name)
res = self.es.search(index = self.index_name, body = {
"from": arguments["page"] * arguments["pageSize"],
"size": arguments["pageSize"],
"query": arguments["query"]
})
return res['hits']['hits']
except:
return False
def create_contact(self, form):
"""
Creates contact from form data. Returns True if successful.
"""
try:
if self._get_contact(form['name']): #contact by that name exists
return False
else:
contact = Contact(form)
res = self.es.index(index = self.index_name, doc_type = '_doc',
body = str(contact))
return res['result'] == 'created'
except:
return False
def list_a_contact(self, name):
"""
Returns data on a single contact identified by name.
"""
try:
return self._get_contact(name)['_source']
except:
return False
def update_contact(self, form):
"""
Update a contact using form data. Returns True if successful.
"""
try:
if self.delete_contact(form['name']):
return self.create_contact(form)
else:
return False
except:
return False
def delete_contact(self, name):
"""
Delete a contact identified by name. Returns True if successful.
"""
try:
contact_id = self._get_contact(name)['_id']
res = self.es.delete(index = self.index_name, doc_type = '_doc',
id = contact_id)
return res['result'] == 'deleted'
except:
return False
def _get_contact(self, name):
try:
self.es.indices.refresh(index = self.index_name)
res = self.es.search(index = self.index_name, body = {
"query": {
"match": {
"name": name
}
}
})
return res['hits']['hits'][0]
except:
return False
| 752
| 0
| 54
|
3e1ac6e8a54a6c16c3f79943e80cd2d3572d84bc
| 1,671
|
py
|
Python
|
temp/models.py
|
oteejay/lms
|
be351c8ec7aee1f81dede6fcf4292c1ecad31c60
|
[
"MIT"
] | null | null | null |
temp/models.py
|
oteejay/lms
|
be351c8ec7aee1f81dede6fcf4292c1ecad31c60
|
[
"MIT"
] | 11
|
2020-06-05T22:33:23.000Z
|
2022-03-11T23:56:46.000Z
|
temp/models.py
|
oteejay/lms
|
be351c8ec7aee1f81dede6fcf4292c1ecad31c60
|
[
"MIT"
] | null | null | null |
from django.db import models
from django.contrib.auth.models import User
# Create your models here.
| 33.42
| 105
| 0.618791
|
from django.db import models
from django.contrib.auth.models import User
# Create your models here.
def upload_location(instance, filename):
return "%s/%s/%s" %('jtgreen', 'temp', filename)
class TempUser(models.Model):
user = models.ForeignKey(User, on_delete=models.CASCADE, related_name='tempusers')
designation = models.CharField(max_length=15,
default='Installer',
choices=[
('Installer', 'Installer'),
('Master', 'Master'),
('Personel', 'Personel'),
('Agent', 'Agent')
])
used = models.BooleanField(default=False)
cv = models.FileField(upload_to=upload_location, blank=True, null=True)
class Invitation(models.Model):
email = models.EmailField()
designation = models.CharField(max_length=15,
default='Installer',
choices=[
('Installer', 'Installer'),
('Master', 'Master'),
('Personel', 'Personel'),
('Agent', 'Agent')
])
used = models.BooleanField(default=False)
token = models.CharField(max_length=254)
inviter_id = models.IntegerField(blank=True)
date_created = models.DateTimeField(auto_now=False, auto_now_add=True)
created_by = models.ForeignKey(User, on_delete=models.DO_NOTHING, related_name='created_invitations')
class PasswordReset(models.Model):
email = models.EmailField()
used = models.BooleanField(default=False)
token = models.CharField(max_length=254)
date_created = models.DateTimeField(auto_now=False, auto_now_add=True)
| 72
| 1,391
| 92
|
0d89603cb6143b26a131f84180a888c6ee7dfc8b
| 279
|
py
|
Python
|
Early/copyField_offices.py
|
adambreznicky/smudge_python
|
af7ba221890253ac6fe7f38691b351861f8b3d96
|
[
"MIT"
] | 1
|
2017-05-24T02:05:20.000Z
|
2017-05-24T02:05:20.000Z
|
historic/copyField_offices.py
|
adambreznicky/smudge_python
|
af7ba221890253ac6fe7f38691b351861f8b3d96
|
[
"MIT"
] | null | null | null |
historic/copyField_offices.py
|
adambreznicky/smudge_python
|
af7ba221890253ac6fe7f38691b351861f8b3d96
|
[
"MIT"
] | null | null | null |
import arcpy
source = "C:\\TxDOT\\Shapefiles\\District_Offices.shp"
outputcopy = "T:\\DATAMGT\\MAPPING\\Personal Folders\\Adam\\District_Offices.shp"
copyPhone()
| 34.875
| 81
| 0.752688
|
import arcpy
source = "C:\\TxDOT\\Shapefiles\\District_Offices.shp"
outputcopy = "T:\\DATAMGT\\MAPPING\\Personal Folders\\Adam\\District_Offices.shp"
def copyPhone():
arcpy.JoinField_management(outputcopy, "Address", source, "Address", ["Phone"])
return "complete"
copyPhone()
| 95
| 0
| 22
|
0e5c3c46f6c668faf01c3b0e96efc84b5cd1661c
| 5,095
|
py
|
Python
|
novmpy/vm.py
|
Dy-Baby/NoVmpy
|
49b13f5c9e5f4d3d4931e52836ee526996cea557
|
[
"BSD-3-Clause"
] | null | null | null |
novmpy/vm.py
|
Dy-Baby/NoVmpy
|
49b13f5c9e5f4d3d4931e52836ee526996cea557
|
[
"BSD-3-Clause"
] | null | null | null |
novmpy/vm.py
|
Dy-Baby/NoVmpy
|
49b13f5c9e5f4d3d4931e52836ee526996cea557
|
[
"BSD-3-Clause"
] | null | null | null |
from novmpy.bridge import *
from capstone import *
from capstone.x86 import *
from novmpy.x86_deobf import *
from novmpy.match_helper import *
| 44.304348
| 138
| 0.479686
|
from novmpy.bridge import *
from capstone import *
from capstone.x86 import *
from novmpy.x86_deobf import *
from novmpy.match_helper import *
class VMConfig:
def __init__(self):
self.reg_key = X86_REG_INVALID
self.reg_ip = X86_REG_INVALID
self.reg_sp = X86_REG_INVALID
self.reg_regs = extend_reg(X86_REG_ESP)
self.reg_base = X86_REG_INVALID
self.dir = 0
self.rebase = 0
def __str__(self):
return 'VMConfig : r_key({}) r_ip({}) r_sp({}) r_regs({}) r_base({}) dir({}) rebase({})'.format(
bridge.reg_name(self.reg_key), bridge.reg_name(
self.reg_ip), bridge.reg_name(self.reg_sp),
bridge.reg_name(self.reg_regs), bridge.reg_name(self.reg_base), self.dir, hex(self.rebase))
def __repr__(self) -> str:
return self.__str__()
class VMState:
def __init__(self, **kwargs):
self.ip = kwargs.get('ip', 0)
self.key = kwargs.get('key', 0)
self.current_handler = kwargs.get('current_handler', 0)
self.config: VMConfig = kwargs.get('config', None)
def decode_emu(self, decoder, ct, reg, size):
mask = get_mask(size*8)
reg_key_op = X86_REG_INVALID
if size == 1:
reg_key_op = get_reg8(self.config.reg_key)
elif size == 2:
reg_key_op = get_reg16(self.config.reg_key)
elif size == 4:
reg_key_op = get_reg32(self.config.reg_key)
elif size == 8:
reg_key_op = get_reg64(self.config.reg_key)
else:
raise NotImplementedError('')
pt = ct & mask
for insn in decoder:
insn: CsInsn
regs_read, regs_write = insn.regs_access()
# xor r11d, imm regs_write = [??,r11d]
if reg in regs_write:
if insn.id == X86_INS_INC:
pt += 1
elif insn.id == X86_INS_DEC:
pt -= 1
elif insn.id == X86_INS_NOT:
pt = ~pt
elif insn.id == X86_INS_NEG:
pt = 0-pt
elif insn.id == X86_INS_BSWAP:
if insn.operands[0].size == 8:
pt = ((pt & 0xFF) << (7*8)) |\
((pt & 0xFF00) << (5*8)) |\
((pt & 0xFF0000) << (3*8)) | \
((pt & 0xFF000000) << (1*8)) |\
((pt & 0xFF00000000) >> (1*8)) | \
((pt & 0xFF0000000000) >> (3*8)) |\
((pt & 0xFF000000000000) >> (5*8)) | \
((pt & 0xFF00000000000000) >> (7*8))
elif insn.operands[0].size == 4:
pt = ((pt & 0xFF) << 24) | ((pt & 0xFF00) << 8) |\
((pt & 0xFF0000) >> 8) | ((pt & 0xFF000000) >> 24)
elif instr_match(insn, [X86_INS_XOR, X86_INS_ADD, X86_INS_SUB], [X86_OP_REG, X86_OP_IMM], [reg]):
if insn.id == X86_INS_XOR:
pt ^= insn.operands[1].imm
elif insn.id == X86_INS_ADD:
pt += insn.operands[1].imm
elif insn.id == X86_INS_SUB:
pt -= insn.operands[1].imm
elif instr_match(insn, [X86_INS_ROL, X86_INS_ROR], [X86_OP_REG, X86_OP_IMM], [reg]):
n = insn.operands[1].imm & 0x1F
if insn.id == X86_INS_ROL:
pt = ((pt & mask) << n) | (
(pt & mask) >> ((8 * size) - n))
elif insn.id == X86_INS_ROR:
pt = ((pt & mask) >> n) | (
(pt & mask) << ((8 * size) - n))
elif instr_match(insn, X86_INS_XOR, [X86_OP_REG, X86_OP_REG], [reg, reg_key_op]):
pt ^= self.key
elif instr_match(insn, X86_INS_ADD, [X86_OP_REG, X86_OP_REG]):
# add ip, rebase
pt += self.config.rebase
elif instr_match(insn, X86_INS_LEA, [X86_OP_REG, X86_OP_MEM], [reg, {'base': reg, 'index': X86_REG_INVALID, 'scale': 1}]):
pt += insn.operands[1].mem.disp
elif instr_match(insn, X86_INS_LEA, [X86_OP_REG, X86_OP_MEM], [reg, {'base': reg, 'disp': 0, 'scale': 1}]):
# fix lea ip, [ip+ecx]
pt += self.config.rebase
else:
print(decoder)
raise NotImplementedError(insn)
pt &= mask
# update key -> xor reg_key_op, reg
if instr_match(insn, X86_INS_XOR, [X86_OP_REG, X86_OP_REG], [reg_key_op, reg]):
self.key ^= pt & mask
if instr_match(insn, X86_INS_XOR, [X86_OP_MEM, X86_OP_REG], [None, reg]):
self.key ^= pt & mask
return pt & mask
def fetch(self, size) -> int:
i = bridge.read(self.ip, size, self.config.dir)
self.ip += self.config.dir*size
return i
| 4,757
| -13
| 206
|
2de0437e5ff66c828b518801f8dc21a58fbed809
| 952
|
py
|
Python
|
src/smach_tutorial/Introspection/Introspection.py
|
vishnuPra/state_machine_tutorial
|
e27ee3b91feba8da3389df921f1c4346bf8d4bc2
|
[
"Apache-2.0"
] | null | null | null |
src/smach_tutorial/Introspection/Introspection.py
|
vishnuPra/state_machine_tutorial
|
e27ee3b91feba8da3389df921f1c4346bf8d4bc2
|
[
"Apache-2.0"
] | null | null | null |
src/smach_tutorial/Introspection/Introspection.py
|
vishnuPra/state_machine_tutorial
|
e27ee3b91feba8da3389df921f1c4346bf8d4bc2
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python
import rospy
import smach_ros
from smach_tutorial.BasicStateMachine import BasicStateMachine_0,\
BasicStateMachine_1,\
BasicStateMachine_2
##-----------------------------------------------------------------------------------
# Example
##-----------------------------------------------------------------------------------
if __name__ == '__main__':
rospy.init_node('tutorial_node')
main() #Change to main1 to call your function
| 30.709677
| 85
| 0.548319
|
#!/usr/bin/env python
import rospy
import smach_ros
from smach_tutorial.BasicStateMachine import BasicStateMachine_0,\
BasicStateMachine_1,\
BasicStateMachine_2
##-----------------------------------------------------------------------------------
# Example
def main():
SimpleSM = BasicStateMachine_0.SetPrintStateMachine()
introspection_server = smach_ros.IntrospectionServer('SM', SimpleSM, '/SM_root')
introspection_server.start()
outcome = SimpleSM.execute()
rospy.loginfo("Result : " + outcome)
introspection_server.stop()
def main1():
#Create a main function that launch the BasicStateMachine_1.FooBarStateMachine()
pass
##-----------------------------------------------------------------------------------
if __name__ == '__main__':
rospy.init_node('tutorial_node')
main() #Change to main1 to call your function
| 356
| 0
| 45
|
922e212a09a16f5831aaa45b2164778b91ffe10a
| 8,448
|
py
|
Python
|
vendor/packages/translate-toolkit/translate/convert/test_xliff2po.py
|
jgmize/kitsune
|
8f23727a9c7fcdd05afc86886f0134fb08d9a2f0
|
[
"BSD-3-Clause"
] | 2
|
2019-08-19T17:08:47.000Z
|
2019-10-05T11:37:02.000Z
|
vendor/packages/translate-toolkit/translate/convert/test_xliff2po.py
|
jgmize/kitsune
|
8f23727a9c7fcdd05afc86886f0134fb08d9a2f0
|
[
"BSD-3-Clause"
] | null | null | null |
vendor/packages/translate-toolkit/translate/convert/test_xliff2po.py
|
jgmize/kitsune
|
8f23727a9c7fcdd05afc86886f0134fb08d9a2f0
|
[
"BSD-3-Clause"
] | null | null | null |
#!/usr/bin/env python
from translate.convert import xliff2po
from translate.misc import wStringIO
from translate.storage.test_base import headerless_len, first_translatable
class TestBasicXLIFF2PO(TestXLIFF2PO):
"""This tests a basic XLIFF file without xmlns attribute"""
xliffskeleton = '''<?xml version="1.0" ?>
<xliff version="1.1">
<file original="filename.po" source-language="en-US" datatype="po">
<body>
%s
</body>
</file>
</xliff>'''
| 38.054054
| 131
| 0.623816
|
#!/usr/bin/env python
from translate.convert import xliff2po
from translate.misc import wStringIO
from translate.storage.test_base import headerless_len, first_translatable
class TestXLIFF2PO:
xliffskeleton = '''<?xml version="1.0" ?>
<xliff version="1.1" xmlns="urn:oasis:names:tc:xliff:document:1.1">
<file original="filename.po" source-language="en-US" datatype="po">
<body>
%s
</body>
</file>
</xliff>'''
def xliff2po(self, xliffsource):
"""helper that converts xliff source to po source without requiring files"""
inputfile = wStringIO.StringIO(xliffsource)
convertor = xliff2po.xliff2po()
outputpo = convertor.convertstore(inputfile)
print "The generated po:"
print type(outputpo)
print str(outputpo)
return outputpo
def test_minimal(self):
minixlf = self.xliffskeleton % '''<trans-unit>
<source>red</source>
<target>rooi</target>
</trans-unit>'''
pofile = self.xliff2po(minixlf)
assert headerless_len(pofile.units) == 1
assert pofile.translate("red") == "rooi"
assert pofile.translate("bla") is None
def test_basic(self):
headertext = '''Project-Id-Version: program 2.1-branch
Report-Msgid-Bugs-To:
POT-Creation-Date: 2006-01-09 07:15+0100
PO-Revision-Date: 2004-03-30 17:02+0200
Last-Translator: Zuza Software Foundation <xxx@translate.org.za>
Language-Team: Afrikaans <translate-discuss-xxx@lists.sourceforge.net>
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit'''
minixlf = (self.xliffskeleton % '''<trans-unit id="1" restype="x-gettext-domain-header" approved="no" xml:space="preserve">
<source>%s</source>
<target>%s</target>
<note from="po-translator">Zulu translation of program ABC</note>
</trans-unit>
<trans-unit>
<source>gras</source>
<target>utshani</target>
</trans-unit>''') % (headertext, headertext)
print minixlf
pofile = self.xliff2po(minixlf)
assert pofile.translate("gras") == "utshani"
assert pofile.translate("bla") is None
potext = str(pofile)
assert potext.index('# Zulu translation of program ABC') == 0
assert potext.index('msgid "gras"\n')
assert potext.index('msgstr "utshani"\n')
assert potext.index('MIME-Version: 1.0\\n')
def test_translatorcomments(self):
"""Tests translator comments"""
minixlf = self.xliffskeleton % '''<trans-unit>
<source>nonsense</source>
<target>matlhapolosa</target>
<context-group name="po-entry" purpose="information">
<context context-type="x-po-trancomment">Couldn't do
it</context>
</context-group>
<note from="po-translator">Couldn't do
it</note>
</trans-unit>'''
pofile = self.xliff2po(minixlf)
assert pofile.translate("nonsense") == "matlhapolosa"
assert pofile.translate("bla") is None
unit = first_translatable(pofile)
assert unit.getnotes("translator") == "Couldn't do it"
potext = str(pofile)
assert potext.index("# Couldn't do it\n") >= 0
minixlf = self.xliffskeleton % '''<trans-unit xml:space="preserve">
<source>nonsense</source>
<target>matlhapolosa</target>
<context-group name="po-entry" purpose="information">
<context context-type="x-po-trancomment">Couldn't do
it</context>
</context-group>
<note from="po-translator">Couldn't do
it</note>
</trans-unit>'''
pofile = self.xliff2po(minixlf)
assert pofile.translate("nonsense") == "matlhapolosa"
assert pofile.translate("bla") is None
unit = first_translatable(pofile)
assert unit.getnotes("translator") == "Couldn't do\nit"
potext = str(pofile)
assert potext.index("# Couldn't do\n# it\n") >= 0
def test_autocomment(self):
"""Tests automatic comments"""
minixlf = self.xliffskeleton % '''<trans-unit>
<source>nonsense</source>
<target>matlhapolosa</target>
<context-group name="po-entry" purpose="information">
<context context-type="x-po-autocomment">Note that this is
garbage</context>
</context-group>
<note from="developer">Note that this is
garbage</note>
</trans-unit>'''
pofile = self.xliff2po(minixlf)
assert pofile.translate("nonsense") == "matlhapolosa"
assert pofile.translate("bla") is None
unit = first_translatable(pofile)
assert unit.getnotes("developer") == "Note that this is garbage"
potext = str(pofile)
assert potext.index("#. Note that this is garbage\n") >= 0
minixlf = self.xliffskeleton % '''<trans-unit xml:space="preserve">
<source>nonsense</source>
<target>matlhapolosa</target>
<context-group name="po-entry" purpose="information">
<context context-type="x-po-autocomment">Note that this is
garbage</context>
</context-group>
<note from="developer">Note that this is
garbage</note>
</trans-unit>'''
pofile = self.xliff2po(minixlf)
assert pofile.translate("nonsense") == "matlhapolosa"
assert pofile.translate("bla") is None
unit = first_translatable(pofile)
assert unit.getnotes("developer") == "Note that this is\ngarbage"
potext = str(pofile)
assert potext.index("#. Note that this is\n#. garbage\n") >= 0
def test_locations(self):
"""Tests location comments (#:)"""
minixlf = self.xliffskeleton % '''<trans-unit id="1">
<source>nonsense</source>
<target>matlhapolosa</target>
<context-group name="po-reference" purpose="location">
<context context-type="sourcefile">example.c</context>
<context context-type="linenumber">123</context>
</context-group>
<context-group name="po-reference" purpose="location">
<context context-type="sourcefile">place.py</context>
</context-group>
</trans-unit>'''
pofile = self.xliff2po(minixlf)
assert pofile.translate("nonsense") == "matlhapolosa"
assert pofile.translate("bla") is None
unit = first_translatable(pofile)
locations = unit.getlocations()
assert len(locations) == 2
assert "example.c:123" in locations
assert "place.py" in locations
def test_fuzzy(self):
"""Tests fuzzyness"""
minixlf = self.xliffskeleton % '''<trans-unit approved="no">
<source>book</source>
</trans-unit>
<trans-unit id="2" approved="yes">
<source>nonsense</source>
<target>matlhapolosa</target>
</trans-unit>
<trans-unit id="2" approved="no">
<source>verb</source>
<target state="needs-review-translation">lediri</target>
</trans-unit>'''
pofile = self.xliff2po(minixlf)
assert pofile.translate("nonsense") == "matlhapolosa"
assert pofile.translate("verb") == "lediri"
assert pofile.translate("book") is None
assert pofile.translate("bla") is None
assert headerless_len(pofile.units) == 3
#TODO: decide if this one should be fuzzy:
#assert pofile.units[0].isfuzzy()
assert not pofile.units[2].isfuzzy()
assert pofile.units[3].isfuzzy()
def test_plurals(self):
"""Tests fuzzyness"""
minixlf = self.xliffskeleton % '''<group id="1" restype="x-gettext-plurals">
<trans-unit id="1[0]" xml:space="preserve">
<source>cow</source>
<target>inkomo</target>
</trans-unit>
<trans-unit id="1[1]" xml:space="preserve">
<source>cows</source>
<target>iinkomo</target>
</trans-unit>
</group>'''
pofile = self.xliff2po(minixlf)
print str(pofile)
potext = str(pofile)
assert headerless_len(pofile.units) == 1
assert potext.index('msgid_plural "cows"')
assert potext.index('msgstr[0] "inkomo"')
assert potext.index('msgstr[1] "iinkomo"')
class TestBasicXLIFF2PO(TestXLIFF2PO):
"""This tests a basic XLIFF file without xmlns attribute"""
xliffskeleton = '''<?xml version="1.0" ?>
<xliff version="1.1">
<file original="filename.po" source-language="en-US" datatype="po">
<body>
%s
</body>
</file>
</xliff>'''
| 1,513
| 6,438
| 23
|
d8082c1ca4107fc4e8b66d4b8f63834432cb57a8
| 1,419
|
py
|
Python
|
Python/the_office_outed.py
|
ielvisd/CodeWar_Katas
|
3d95dd72332a81cc2bff1c7fd3b782d1f4658ca8
|
[
"MIT"
] | null | null | null |
Python/the_office_outed.py
|
ielvisd/CodeWar_Katas
|
3d95dd72332a81cc2bff1c7fd3b782d1f4658ca8
|
[
"MIT"
] | null | null | null |
Python/the_office_outed.py
|
ielvisd/CodeWar_Katas
|
3d95dd72332a81cc2bff1c7fd3b782d1f4658ca8
|
[
"MIT"
] | null | null | null |
""" Your colleagues have been looking over you shoulder. When you should have been doing your boring real job, you've been using the work computers to smash in endless hours of codewars.
In a team meeting, a terrible, awful person declares to the group that you aren't working. You're in trouble. You quickly have to gauge the feeling in the room to decide whether or not you should gather your things and leave.
Given an object (meet) containing team member names as keys, and their happiness rating out of 10 as the value, you need to assess the overall happiness rating of the group. If <= 5, return 'Get Out Now!'. Else return 'Nice Work Champ!'.
Happiness rating will be total score / number of people in the room.
Note that your boss is in the room (boss), their score is worth double it's face value (but they are still just one person!). """
"""
test.assert_equals(outed({'tim':0, 'jim':2, 'randy':0, 'sandy':7, 'andy':0, 'katie':5, 'laura':1, 'saajid':2, 'alex':3, 'john':2, 'mr':0}, 'laura'), 'Get Out Now!')
test.assert_equals(outed({'tim':1, 'jim':3, 'randy':9, 'sandy':6, 'andy':7, 'katie':6, 'laura':9, 'saajid':9, 'alex':9, 'john':9, 'mr':8}, 'katie'), 'Nice Work Champ!')
test.assert_equals(outed({'tim':2, 'jim':4, 'randy':0, 'sandy':5, 'andy':8, 'katie':6, 'laura':2, 'saajid':2, 'alex':3, 'john':2, 'mr':8}, 'john'), 'Get Out Now!') """
| 61.695652
| 237
| 0.680761
|
""" Your colleagues have been looking over you shoulder. When you should have been doing your boring real job, you've been using the work computers to smash in endless hours of codewars.
In a team meeting, a terrible, awful person declares to the group that you aren't working. You're in trouble. You quickly have to gauge the feeling in the room to decide whether or not you should gather your things and leave.
Given an object (meet) containing team member names as keys, and their happiness rating out of 10 as the value, you need to assess the overall happiness rating of the group. If <= 5, return 'Get Out Now!'. Else return 'Nice Work Champ!'.
Happiness rating will be total score / number of people in the room.
Note that your boss is in the room (boss), their score is worth double it's face value (but they are still just one person!). """
"""
test.assert_equals(outed({'tim':0, 'jim':2, 'randy':0, 'sandy':7, 'andy':0, 'katie':5, 'laura':1, 'saajid':2, 'alex':3, 'john':2, 'mr':0}, 'laura'), 'Get Out Now!')
test.assert_equals(outed({'tim':1, 'jim':3, 'randy':9, 'sandy':6, 'andy':7, 'katie':6, 'laura':9, 'saajid':9, 'alex':9, 'john':9, 'mr':8}, 'katie'), 'Nice Work Champ!')
test.assert_equals(outed({'tim':2, 'jim':4, 'randy':0, 'sandy':5, 'andy':8, 'katie':6, 'laura':2, 'saajid':2, 'alex':3, 'john':2, 'mr':8}, 'john'), 'Get Out Now!') """
def outed(meet, boss):
# Algorithm
#
return
| 34
| 0
| 23
|
421acd565a0e8398b36565449d99af0489f54b35
| 399
|
py
|
Python
|
app_orders/serializers.py
|
la5tway/candy_shop
|
c3bc5b958afe0c1066de98126b5cf5d96eb06a1b
|
[
"MIT"
] | null | null | null |
app_orders/serializers.py
|
la5tway/candy_shop
|
c3bc5b958afe0c1066de98126b5cf5d96eb06a1b
|
[
"MIT"
] | null | null | null |
app_orders/serializers.py
|
la5tway/candy_shop
|
c3bc5b958afe0c1066de98126b5cf5d96eb06a1b
|
[
"MIT"
] | null | null | null |
from app_couriers.serializers import CourierSerializer
from .models import Orders
| 22.166667
| 69
| 0.676692
|
from app_couriers.serializers import CourierSerializer
from .models import Orders
class OrderSerializer(CourierSerializer):
id_field_name = "order_id"
class Meta:
model = Orders
fields = ('order_id', 'weight', 'region', 'delivery_hours', )
class SingleOrderSerializer(OrderSerializer):
class Meta:
model = Orders
fields = '__all__'
depth = 1
| 0
| 270
| 46
|
383a19a0b2431ee34819fe0766e2d6b83c59586f
| 4,740
|
py
|
Python
|
Rake.py
|
beenotung/Rake_For_Chinese
|
07e8e570f7d35ec0b93f18d6a124eeb8529d7780
|
[
"MIT"
] | null | null | null |
Rake.py
|
beenotung/Rake_For_Chinese
|
07e8e570f7d35ec0b93f18d6a124eeb8529d7780
|
[
"MIT"
] | null | null | null |
Rake.py
|
beenotung/Rake_For_Chinese
|
07e8e570f7d35ec0b93f18d6a124eeb8529d7780
|
[
"MIT"
] | null | null | null |
'''
Implementation of Rapid Automatic Keyword Extraction (RAKE) algorithm for Chinese
Original algorithm described in: Rose, S., Engel, D., Cramer, N., & Cowley, W. (2010).
Automatic Keyword Extraction from Individual Documents. In M. W. Berry & J. Kogan
(Eds.), Text Mining: Theory and Applications: John Wiley & Sons.
'''
__author__ = "Ruoyang Xu"
import jieba
import jieba.posseg as pseg
import operator
import json
from collections import Counter
# Data structure for holding data
# Check if contains num
# Read Target Case if Json
if __name__ == '__main__':
with open('data/testCase/文本1.txt','r') as fp:
text = fp.read()
result = run(text)
print(result)
| 30.779221
| 126
| 0.587553
|
'''
Implementation of Rapid Automatic Keyword Extraction (RAKE) algorithm for Chinese
Original algorithm described in: Rose, S., Engel, D., Cramer, N., & Cowley, W. (2010).
Automatic Keyword Extraction from Individual Documents. In M. W. Berry & J. Kogan
(Eds.), Text Mining: Theory and Applications: John Wiley & Sons.
'''
__author__ = "Ruoyang Xu"
import jieba
import jieba.posseg as pseg
import operator
import json
from collections import Counter
# Data structure for holding data
class Word():
def __init__(self, char, freq = 0, deg = 0):
self.freq = freq
self.deg = deg
self.char = char
def returnScore(self):
return self.deg/self.freq
def updateOccur(self, phraseLength):
self.freq += 1
self.deg += phraseLength
def getChar(self):
return self.char
def updateFreq(self):
self.freq += 1
def getFreq(self):
return self.freq
# Check if contains num
def notNumStr(instr):
for item in instr:
if '\u0041' <= item <= '\u005a' or ('\u0061' <= item <='\u007a') or item.isdigit():
return False
return True
# Read Target Case if Json
def readSingleTestCases(testFile):
with open(testFile) as json_data:
try:
testData = json.load(json_data)
except:
# This try block deals with incorrect json format that has ' instead of "
data = json_data.read().replace("'",'"')
try:
testData = json.loads(data)
# This try block deals with empty transcript file
except:
return ""
returnString = ""
for item in testData:
try:
returnString += item['text']
except:
returnString += item['statement']
return returnString
def run(rawText):
# Construct Stopword Lib
swLibList = [line.rstrip('\n') for line in open("textlibs/中文停用词表(1208个).txt",'r')]
# Construct Phrase Deliminator Lib
conjLibList = [line.rstrip('\n') for line in open("textlibs/中文分隔词词库.txt",'r')]
# Cut Text
rawtextList = pseg.cut(rawText)
# Construct List of Phrases and Preliminary textList
textList = []
listofSingleWord = dict()
lastWord = ''
poSPrty = ['m','x','uj','ul','mq','u','v','f']
meaningfulCount = 0
checklist = []
for eachWord, flag in rawtextList:
checklist.append([eachWord,flag])
if eachWord in conjLibList or not notNumStr(eachWord) or eachWord in swLibList or flag in poSPrty or eachWord == '\n':
if lastWord != '|':
textList.append("|")
lastWord = "|"
elif eachWord not in swLibList and eachWord != '\n':
textList.append(eachWord)
meaningfulCount += 1
if eachWord not in listofSingleWord:
listofSingleWord[eachWord] = Word(eachWord)
lastWord = ''
# Construct List of list that has phrases as wrds
newList = []
tempList = []
for everyWord in textList:
if everyWord != '|':
tempList.append(everyWord)
else:
newList.append(tempList)
tempList = []
tempStr = ''
for everyWord in textList:
if everyWord != '|':
tempStr += everyWord + '|'
else:
if tempStr[:-1] not in listofSingleWord:
listofSingleWord[tempStr[:-1]] = Word(tempStr[:-1])
tempStr = ''
# Update the entire List
for everyPhrase in newList:
res = ''
for everyWord in everyPhrase:
listofSingleWord[everyWord].updateOccur(len(everyPhrase))
res += everyWord + '|'
phraseKey = res[:-1]
if phraseKey not in listofSingleWord:
listofSingleWord[phraseKey] = Word(phraseKey)
else:
listofSingleWord[phraseKey].updateFreq()
# Get score for entire Set
outputList = dict()
for everyPhrase in newList:
if len(everyPhrase) > 5:
continue
score = 0
phraseString = ''
outStr = ''
for everyWord in everyPhrase:
score += listofSingleWord[everyWord].returnScore()
phraseString += everyWord + '|'
outStr += everyWord
phraseKey = phraseString[:-1]
freq = listofSingleWord[phraseKey].getFreq()
if freq / meaningfulCount < 0.01 and freq < 3 :
continue
outputList[outStr] = score
sorted_list = sorted(outputList.items(), key = operator.itemgetter(1), reverse = True)
return sorted_list[:10]
if __name__ == '__main__':
with open('data/testCase/文本1.txt','r') as fp:
text = fp.read()
result = run(text)
print(result)
| 3,832
| -8
| 250
|
808fad95e5fb38e2f8830d881ee74dc3281332bf
| 249
|
py
|
Python
|
20211217_PartialExam/SaveTheWorld/solution.py
|
augustozanellato/Cybersec2021
|
466fd9db0e7c359a8afd5115eacb3fca2b439c28
|
[
"BSD-3-Clause"
] | 15
|
2021-10-01T16:10:48.000Z
|
2022-02-19T20:45:35.000Z
|
20211217_PartialExam/SaveTheWorld/solution.py
|
augustozanellato/Cybersec2021
|
466fd9db0e7c359a8afd5115eacb3fca2b439c28
|
[
"BSD-3-Clause"
] | null | null | null |
20211217_PartialExam/SaveTheWorld/solution.py
|
augustozanellato/Cybersec2021
|
466fd9db0e7c359a8afd5115eacb3fca2b439c28
|
[
"BSD-3-Clause"
] | 2
|
2021-11-06T08:32:41.000Z
|
2021-12-11T16:18:54.000Z
|
from pwn import * # type: ignore
context.binary = "./SaveTheWorld"
p = process()
p.sendline(b"A" * 72 + b"Jotaro!!" + b"Star Platinum!!!" + b"HORA" + b"9999")
p.recvuntil(b"Congratulation, you won!!!")
os.system("grep .*{.*}.* victory_recap.txt")
| 31.125
| 77
| 0.634538
|
from pwn import * # type: ignore
context.binary = "./SaveTheWorld"
p = process()
p.sendline(b"A" * 72 + b"Jotaro!!" + b"Star Platinum!!!" + b"HORA" + b"9999")
p.recvuntil(b"Congratulation, you won!!!")
os.system("grep .*{.*}.* victory_recap.txt")
| 0
| 0
| 0
|
e2a1f52aba416ba05637ca92a6566a1e403ae81d
| 654
|
py
|
Python
|
synlib/descriptions/CMPR32X1.py
|
vhnatyk/vlsistuff
|
0981097bd19a0c482728dcc5048a3615ac9a9a90
|
[
"MIT"
] | 26
|
2018-03-17T18:14:22.000Z
|
2022-03-14T07:23:13.000Z
|
synlib/descriptions/CMPR32X1.py
|
psumesh/vlsistuff
|
1fe64b093d0581d99c7d826b74c31b8655fa0b31
|
[
"MIT"
] | 1
|
2019-10-16T10:31:11.000Z
|
2019-10-17T04:14:53.000Z
|
synlib/descriptions/CMPR32X1.py
|
psumesh/vlsistuff
|
1fe64b093d0581d99c7d826b74c31b8655fa0b31
|
[
"MIT"
] | 7
|
2018-07-16T07:51:25.000Z
|
2022-02-15T14:22:54.000Z
|
Desc = cellDescClass("CMPR32X1")
Desc.properties["cell_leakage_power"] = "3632.359140"
Desc.properties["cell_footprint"] = "add32"
Desc.properties["area"] = "69.854400"
Desc.pinOrder = ['A', 'B', 'C', 'CO', 'S']
Desc.add_arc("A","S","combi")
Desc.add_arc("B","S","combi")
Desc.add_arc("C","S","combi")
Desc.add_arc("A","CO","combi")
Desc.add_arc("B","CO","combi")
Desc.add_arc("C","CO","combi")
Desc.add_param("area",69.854400);
Desc.add_pin("A","input")
Desc.add_pin("C","input")
Desc.add_pin("B","input")
Desc.add_pin("CO","output")
Desc.add_pin_func("CO","unknown")
Desc.add_pin("S","output")
Desc.add_pin_func("S","unknown")
CellLib["CMPR32X1"]=Desc
| 31.142857
| 53
| 0.666667
|
Desc = cellDescClass("CMPR32X1")
Desc.properties["cell_leakage_power"] = "3632.359140"
Desc.properties["cell_footprint"] = "add32"
Desc.properties["area"] = "69.854400"
Desc.pinOrder = ['A', 'B', 'C', 'CO', 'S']
Desc.add_arc("A","S","combi")
Desc.add_arc("B","S","combi")
Desc.add_arc("C","S","combi")
Desc.add_arc("A","CO","combi")
Desc.add_arc("B","CO","combi")
Desc.add_arc("C","CO","combi")
Desc.add_param("area",69.854400);
Desc.add_pin("A","input")
Desc.add_pin("C","input")
Desc.add_pin("B","input")
Desc.add_pin("CO","output")
Desc.add_pin_func("CO","unknown")
Desc.add_pin("S","output")
Desc.add_pin_func("S","unknown")
CellLib["CMPR32X1"]=Desc
| 0
| 0
| 0
|
fb949f558985982b6af7d2af1255dbe8dc314222
| 868
|
py
|
Python
|
965.py
|
OmangRawat/Leetcode
|
6fa696367ef9c5e6b08940b11e2202382d1afc07
|
[
"MIT"
] | null | null | null |
965.py
|
OmangRawat/Leetcode
|
6fa696367ef9c5e6b08940b11e2202382d1afc07
|
[
"MIT"
] | null | null | null |
965.py
|
OmangRawat/Leetcode
|
6fa696367ef9c5e6b08940b11e2202382d1afc07
|
[
"MIT"
] | null | null | null |
"""
---> Univalued Binary Tree
---> Easy
"""
from tree_func import *
in_array = [1, 1, 1, 1, 1, None, 1]
in_root = to_binary_tree(in_array)
pretty_print(in_root)
a = Solution()
print("Answer -", a.isUnivalTree(in_root))
# print("Answer -", a.isUnivalTree(in_root))
"""
Check if node is none or node.value should be equal to root value for that and every other node in its children
Reference - https://leetcode.com/problems/univalued-binary-tree/discuss/211397/JavaPython-3-BFS-and-DFS-clean-codes-w-brief-analysis.
"""
| 26.30303
| 133
| 0.673963
|
"""
---> Univalued Binary Tree
---> Easy
"""
from tree_func import *
class Solution:
def isUnivalTree(self, root) -> bool:
def dfs(node):
# pretty_print(node)
# print(node is None or node.value == root.value and dfs(node.left) and dfs(node.right))
return node is None or node.value == root.value and dfs(node.left) and dfs(node.right)
return dfs(root)
in_array = [1, 1, 1, 1, 1, None, 1]
in_root = to_binary_tree(in_array)
pretty_print(in_root)
a = Solution()
print("Answer -", a.isUnivalTree(in_root))
# print("Answer -", a.isUnivalTree(in_root))
"""
Check if node is none or node.value should be equal to root value for that and every other node in its children
Reference - https://leetcode.com/problems/univalued-binary-tree/discuss/211397/JavaPython-3-BFS-and-DFS-clean-codes-w-brief-analysis.
"""
| 297
| -6
| 49
|
13702b905917360de903277c86d31a8e4bc8103e
| 947
|
py
|
Python
|
BPNetWork/NeuralNetwork/mnist_data.py
|
Keneyr/MachineLearningMethods
|
9b15cce18c476f8b827ca5082ff119b6cba41198
|
[
"MIT"
] | 1
|
2021-07-02T15:01:30.000Z
|
2021-07-02T15:01:30.000Z
|
BPNetWork/NeuralNetwork/mnist_data.py
|
Keneyr/MachineLearningMethods
|
9b15cce18c476f8b827ca5082ff119b6cba41198
|
[
"MIT"
] | null | null | null |
BPNetWork/NeuralNetwork/mnist_data.py
|
Keneyr/MachineLearningMethods
|
9b15cce18c476f8b827ca5082ff119b6cba41198
|
[
"MIT"
] | 1
|
2021-07-02T15:01:30.000Z
|
2021-07-02T15:01:30.000Z
|
import os
import struct
import numpy as np
def load_mnist(path, kind='train'):
"""Load MNIST data from `path`"""
labels_path = os.path.join(path,
'%s-labels.idx1-ubyte'
% kind)
images_path = os.path.join(path,
'%s-images.idx3-ubyte'
% kind)
with open(labels_path, 'rb') as lbpath:
magic, n = struct.unpack('>II',
lbpath.read(8))
labels = np.fromfile(lbpath,
dtype=np.uint8)
labels = labels.reshape(labels.shape[0], 1)
with open(images_path, 'rb') as imgpath:
magic, num, rows, cols = struct.unpack('>IIII',
imgpath.read(16))
images = np.fromfile(imgpath,
dtype=np.uint8).reshape(len(labels), 784)
return images, labels
| 36.423077
| 70
| 0.469905
|
import os
import struct
import numpy as np
def load_mnist(path, kind='train'):
"""Load MNIST data from `path`"""
labels_path = os.path.join(path,
'%s-labels.idx1-ubyte'
% kind)
images_path = os.path.join(path,
'%s-images.idx3-ubyte'
% kind)
with open(labels_path, 'rb') as lbpath:
magic, n = struct.unpack('>II',
lbpath.read(8))
labels = np.fromfile(lbpath,
dtype=np.uint8)
labels = labels.reshape(labels.shape[0], 1)
with open(images_path, 'rb') as imgpath:
magic, num, rows, cols = struct.unpack('>IIII',
imgpath.read(16))
images = np.fromfile(imgpath,
dtype=np.uint8).reshape(len(labels), 784)
return images, labels
| 0
| 0
| 0
|
ab920960470b013ad4fbe77a3508e1b418275b48
| 295
|
py
|
Python
|
src/concepts/mode.py
|
Valeeswaran/tutorials
|
71b43cad46f4d7d2d67d3ff4be61bdaaade2a36a
|
[
"MIT"
] | null | null | null |
src/concepts/mode.py
|
Valeeswaran/tutorials
|
71b43cad46f4d7d2d67d3ff4be61bdaaade2a36a
|
[
"MIT"
] | null | null | null |
src/concepts/mode.py
|
Valeeswaran/tutorials
|
71b43cad46f4d7d2d67d3ff4be61bdaaade2a36a
|
[
"MIT"
] | null | null | null |
arr = [1, 2, 3, 4, 4, 4, 5, 6, 6, 7, 8, 9]
arr.sort()
my_dict = {i:arr.count(i) for i in arr}
# sorting the dictionary based on value
my_dict = {k: v for k, v in sorted(my_dict.items(), key=lambda item: item[1])}
print(len(my_dict))
print(my_dict)
list = list(my_dict.keys())
print(list[-1])
| 22.692308
| 78
| 0.637288
|
arr = [1, 2, 3, 4, 4, 4, 5, 6, 6, 7, 8, 9]
arr.sort()
my_dict = {i:arr.count(i) for i in arr}
# sorting the dictionary based on value
my_dict = {k: v for k, v in sorted(my_dict.items(), key=lambda item: item[1])}
print(len(my_dict))
print(my_dict)
list = list(my_dict.keys())
print(list[-1])
| 0
| 0
| 0
|
635dfca37db44428a5f283624c1effff583c2d39
| 1,125
|
py
|
Python
|
teamcat_service/doraemon/doraemon/auth_extend/user/templatetags/auth_required.py
|
zhangyin2088/Teamcat
|
be9be8d7c1e58c8d2d22ab78d25783d9aee4de71
|
[
"Apache-2.0"
] | 6
|
2018-11-26T08:42:52.000Z
|
2020-06-01T08:33:48.000Z
|
teamcat_service/doraemon/doraemon/auth_extend/user/templatetags/auth_required.py
|
zhangyin2088/Teamcat
|
be9be8d7c1e58c8d2d22ab78d25783d9aee4de71
|
[
"Apache-2.0"
] | null | null | null |
teamcat_service/doraemon/doraemon/auth_extend/user/templatetags/auth_required.py
|
zhangyin2088/Teamcat
|
be9be8d7c1e58c8d2d22ab78d25783d9aee4de71
|
[
"Apache-2.0"
] | 1
|
2019-01-22T06:45:36.000Z
|
2019-01-22T06:45:36.000Z
|
#coding=utf-8
'''
Created on 2016-1-18
@author: Devuser
'''
from django import template
from doraemon.auth_extend.user.templatetags.auth_required_node import LogoutRequiredNode,LoginRequiredNode,UserRequiredNode,ManagerRequiredNode,AdminRequiredNode
register = template.Library()
@register.tag()
@register.tag()
@register.tag()
@register.tag()
@register.tag()
| 26.162791
| 161
| 0.757333
|
#coding=utf-8
'''
Created on 2016-1-18
@author: Devuser
'''
from django import template
from doraemon.auth_extend.user.templatetags.auth_required_node import LogoutRequiredNode,LoginRequiredNode,UserRequiredNode,ManagerRequiredNode,AdminRequiredNode
register = template.Library()
@register.tag()
def admin_required(parser, token):
nodelist = parser.parse(('end_admin',))
parser.delete_first_token()
return AdminRequiredNode(nodelist)
@register.tag()
def manager_required(parser, token):
nodelist = parser.parse(('end_manager',))
parser.delete_first_token()
return ManagerRequiredNode(nodelist)
@register.tag()
def user_required(parser, token):
nodelist = parser.parse(('end_user',))
parser.delete_first_token()
return UserRequiredNode(nodelist)
@register.tag()
def login_required(parser, token):
nodelist = parser.parse(('end_login',))
parser.delete_first_token()
return LoginRequiredNode(nodelist)
@register.tag()
def logout_required(parser, token):
nodelist = parser.parse(('end_logout',))
parser.delete_first_token()
return LogoutRequiredNode(nodelist)
| 646
| 0
| 110
|
526c50830e718621f212e4da83405610854941d1
| 2,326
|
py
|
Python
|
schoolovy.py
|
N-l1/schoolovy
|
74ea456e04af6029b77cb8310915184a8467849e
|
[
"MIT"
] | null | null | null |
schoolovy.py
|
N-l1/schoolovy
|
74ea456e04af6029b77cb8310915184a8467849e
|
[
"MIT"
] | null | null | null |
schoolovy.py
|
N-l1/schoolovy
|
74ea456e04af6029b77cb8310915184a8467849e
|
[
"MIT"
] | null | null | null |
import yaml
import schoolopy
import sys
def err(msg):
"""
Prints out error message and exits with error.
"""
print(f"Error: {msg}")
exit(1)
def main(limit):
"""
Likes all the posts & comments
in your most recent feed (20 posts).
Args:
limit: How many posts to like.
Returns:
A message of the number of posts & comments that were newly liked.
"""
with open('config.yaml', 'r') as file:
config = yaml.load(file, Loader=yaml.FullLoader)
sc = schoolopy.Schoology(schoolopy.Auth(config['key'],
config['secret']))
post_liked = 0
comments_liked = 0
# Set the number of posts to check
try:
sc.limit = int(limit)
except ValueError:
err("The 'limit' argument must be a number")
# Get updates
try:
updates = sc.get_feed()
except KeyError:
err("The key or secret is incorrect")
print("Liking posts...")
# Go through all most recent 20 posts
for update in updates:
# Like post
try:
sc.like(update.id)
post_liked += 1
except schoolopy.NoDifferenceError:
pass
# Get comments if post is in a group
if update.realm == "group":
comments = sc.get_group_update_comments(update.id,
update.group_id)
# Else get comments if post is in a course
elif update.realm == "section":
comments = sc.get_section_update_comments(update.id,
update.section_id)
else:
continue
# Go through the comments inside the group
for comment in comments:
# Like each comment
try:
sc.like_comment(update.id, comment.id)
comments_liked += 1
except schoolopy.NoDifferenceError:
continue
return ("---------------\n"
f"Liked {post_liked} posts and {comments_liked} comments")
if __name__ == "__main__":
# Too many arguments are specified
if len(sys.argv) > 2:
err("Only the 'limit' argument is allowed")
# Default limit is 20
limit = 20 if len(sys.argv) == 1 else sys.argv[1]
print(main(limit))
| 26.735632
| 74
| 0.552021
|
import yaml
import schoolopy
import sys
def err(msg):
"""
Prints out error message and exits with error.
"""
print(f"Error: {msg}")
exit(1)
def main(limit):
"""
Likes all the posts & comments
in your most recent feed (20 posts).
Args:
limit: How many posts to like.
Returns:
A message of the number of posts & comments that were newly liked.
"""
with open('config.yaml', 'r') as file:
config = yaml.load(file, Loader=yaml.FullLoader)
sc = schoolopy.Schoology(schoolopy.Auth(config['key'],
config['secret']))
post_liked = 0
comments_liked = 0
# Set the number of posts to check
try:
sc.limit = int(limit)
except ValueError:
err("The 'limit' argument must be a number")
# Get updates
try:
updates = sc.get_feed()
except KeyError:
err("The key or secret is incorrect")
print("Liking posts...")
# Go through all most recent 20 posts
for update in updates:
# Like post
try:
sc.like(update.id)
post_liked += 1
except schoolopy.NoDifferenceError:
pass
# Get comments if post is in a group
if update.realm == "group":
comments = sc.get_group_update_comments(update.id,
update.group_id)
# Else get comments if post is in a course
elif update.realm == "section":
comments = sc.get_section_update_comments(update.id,
update.section_id)
else:
continue
# Go through the comments inside the group
for comment in comments:
# Like each comment
try:
sc.like_comment(update.id, comment.id)
comments_liked += 1
except schoolopy.NoDifferenceError:
continue
return ("---------------\n"
f"Liked {post_liked} posts and {comments_liked} comments")
if __name__ == "__main__":
# Too many arguments are specified
if len(sys.argv) > 2:
err("Only the 'limit' argument is allowed")
# Default limit is 20
limit = 20 if len(sys.argv) == 1 else sys.argv[1]
print(main(limit))
| 0
| 0
| 0
|
a057e34d83d549a6400f01ec669293543215e6ee
| 1,535
|
py
|
Python
|
examples/session2-fi/loops.py
|
futurice/PythonInBrowser
|
066ab28ffad265efc7968b87f33dab2c68216d9d
|
[
"MIT"
] | 4
|
2015-12-08T19:34:49.000Z
|
2019-09-08T22:11:05.000Z
|
examples/session2-fi/loops.py
|
futurice/PythonInBrowser
|
066ab28ffad265efc7968b87f33dab2c68216d9d
|
[
"MIT"
] | 18
|
2016-10-14T13:48:39.000Z
|
2019-10-11T12:14:21.000Z
|
examples/session2-fi/loops.py
|
futurice/PythonInBrowser
|
066ab28ffad265efc7968b87f33dab2c68216d9d
|
[
"MIT"
] | 4
|
2015-11-18T15:18:43.000Z
|
2018-03-02T09:36:23.000Z
|
# Loopit eli silmukat
##### INFO #####
#
# Joskus monimutkaisen kuvion piirtäminen vaatii samojen
# komentojen toistamista moneen kertaan. Loopilla eli silmukalla
# voit toistaa koodipalikoita eli pätkiä koodia
import turtle
t = turtle.Turtle()
# Seuraava on esimerkki silmukasta.
#
# "for" kertoo tietokoneelle että sen tulee toistaa jotakin
# monta kertaa
#
# "in range(2)" kertoo että komento tulee toistaa 2 kertaa
#
# "i" on muuttuja jonka arvo kasvaa yhdellä jokaisen toiston
# (eli iteraation) jälkeen. Muuttujaa i ei käytetä tässä
# tehtäväss, mutta näet myöhemmin esimerkkejä, joissa siitä
# on hyötyä.
for i in range(2):
# Seuraavilla riveillä on komennot jotka toistetaan.
# Nämä rivit ollaan sisennetty, eli ne alkavat kahdella välilyönnillä
# Sisennyksellä kerrotaan mitkä rivit kuuluvat toistettavaan koodipalikkaan.
t.forward(30)
t.left(120)
t.forward(30)
t.right(60)
##### TEHTÄVÄ 1 #####
#
# Klikkaa 'run' ja katso mitä tapahtuu.
#
# Kuinka monta kertaa silmukka tulisi toistaa että tähti olisi valmis?
# Laita oikea numero komennon range(...) sulkujen sisään.
# Vinkkin: voit kokeilla useita eri numeroita ja katsoa mikä toimii
##### TEHTÄVÄ 2 #####
#
# Mieti muita muotoja joissa on toistuva kaava.
# Esimerkiksi: neliö, rappuset, aallot
#
# Muuta silmukkaa niin että se piirtää valitsemasi kuvion.
#
# Vinkki: Aloita piirtämällä vain yksi toisto kirjoittamalla
# "range(1)" ja saa se piirtämään kuten haluat. Voit sitten
# toistaa kuvion niin monta kertaa kuin haluat muuttamalla
# range arvoa.
| 29.519231
| 78
| 0.755049
|
# Loopit eli silmukat
##### INFO #####
#
# Joskus monimutkaisen kuvion piirtäminen vaatii samojen
# komentojen toistamista moneen kertaan. Loopilla eli silmukalla
# voit toistaa koodipalikoita eli pätkiä koodia
import turtle
t = turtle.Turtle()
# Seuraava on esimerkki silmukasta.
#
# "for" kertoo tietokoneelle että sen tulee toistaa jotakin
# monta kertaa
#
# "in range(2)" kertoo että komento tulee toistaa 2 kertaa
#
# "i" on muuttuja jonka arvo kasvaa yhdellä jokaisen toiston
# (eli iteraation) jälkeen. Muuttujaa i ei käytetä tässä
# tehtäväss, mutta näet myöhemmin esimerkkejä, joissa siitä
# on hyötyä.
for i in range(2):
# Seuraavilla riveillä on komennot jotka toistetaan.
# Nämä rivit ollaan sisennetty, eli ne alkavat kahdella välilyönnillä
# Sisennyksellä kerrotaan mitkä rivit kuuluvat toistettavaan koodipalikkaan.
t.forward(30)
t.left(120)
t.forward(30)
t.right(60)
##### TEHTÄVÄ 1 #####
#
# Klikkaa 'run' ja katso mitä tapahtuu.
#
# Kuinka monta kertaa silmukka tulisi toistaa että tähti olisi valmis?
# Laita oikea numero komennon range(...) sulkujen sisään.
# Vinkkin: voit kokeilla useita eri numeroita ja katsoa mikä toimii
##### TEHTÄVÄ 2 #####
#
# Mieti muita muotoja joissa on toistuva kaava.
# Esimerkiksi: neliö, rappuset, aallot
#
# Muuta silmukkaa niin että se piirtää valitsemasi kuvion.
#
# Vinkki: Aloita piirtämällä vain yksi toisto kirjoittamalla
# "range(1)" ja saa se piirtämään kuten haluat. Voit sitten
# toistaa kuvion niin monta kertaa kuin haluat muuttamalla
# range arvoa.
| 0
| 0
| 0
|
7fc3e32ddb3a3537ba996d42f205b2e6ae14bd96
| 2,098
|
py
|
Python
|
bot_commands_test/exception.py
|
SimoneABNto/My-Code-Py
|
47276c1d69a92aa284685c9f148c1bd960147f7f
|
[
"MIT"
] | null | null | null |
bot_commands_test/exception.py
|
SimoneABNto/My-Code-Py
|
47276c1d69a92aa284685c9f148c1bd960147f7f
|
[
"MIT"
] | null | null | null |
bot_commands_test/exception.py
|
SimoneABNto/My-Code-Py
|
47276c1d69a92aa284685c9f148c1bd960147f7f
|
[
"MIT"
] | null | null | null |
from queue import Queue, Empty
from time import sleep
from threading import Timer
if __name__ == '__main__':
main()
| 28.739726
| 85
| 0.560534
|
from queue import Queue, Empty
from time import sleep
from threading import Timer
class CommandoLoader(object):
def __init__(self):
self.__bot = None
self.__error_callback_function = None
self.thread = None
self.error_queue_function = None
def start_demon(self, bot, error_queue_function, callback):
self.__bot = bot
self.error_queue_function = error_queue_function
self.__start_auto_runner(None, callback, 10)
print('Auto Run Demon Started')
def __start_auto_runner(self, action, callback, interval: int):
"""
:param action: a function that have to be executed
:param interval: a time in seconds
Run the function every interval seconds
"""
def func():
while True:
try:
# here will be the stuff to run
# action()
raise Exception('An error occurred here.')
except Exception as exc:
print(exc)
self.error_queue_function.put(exc)
callback(exc)
sleep(interval)
try:
self.thread = Timer(interval=interval, function=func) # wait in minutes
self.thread.start()
except Exception as e:
print(e)
def main():
error_queue_function = Queue()
def callback(exc):
# single error handling
print('Message in chat for exc: {}'.format(exc))
return
# multiple error handling, with queue
try:
print(error_queue_function.empty())
exc = error_queue_function.get(block=False)
print(error_queue_function.empty())
except Empty:
pass
else:
print('Message in chat for exc: {}'.format(exc))
cl = CommandoLoader()
# none is the bot command to send messages
cl.start_demon(None, error_queue_function, callback)
if __name__ == '__main__':
main()
| 1,375
| 540
| 50
|
77c55afce086a01fa8acafddadc82f59d1e34666
| 1,843
|
py
|
Python
|
core/common.py
|
MogooStudio/mogoopy
|
81d1bfc35fca46dd028f141fb59eb6d87d8396bc
|
[
"MIT"
] | null | null | null |
core/common.py
|
MogooStudio/mogoopy
|
81d1bfc35fca46dd028f141fb59eb6d87d8396bc
|
[
"MIT"
] | null | null | null |
core/common.py
|
MogooStudio/mogoopy
|
81d1bfc35fca46dd028f141fb59eb6d87d8396bc
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
import hashlib
import subprocess
import sys
import os
G_ZIP_SPLIT_LINE = 500
G_ZIP_SPLIT_UNIT = 100
| 23.628205
| 90
| 0.58166
|
# -*- coding: utf-8 -*-
import hashlib
import subprocess
import sys
import os
G_ZIP_SPLIT_LINE = 500
G_ZIP_SPLIT_UNIT = 100
def os_system(cmd, use_secure = False):
print("cmd", cmd)
os.system(cmd)
def os_popen(cmd):
print("cmd", cmd)
np = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)
while np.poll() is None:
ret = np.stdout.readline()[:-1]
if ret != "":
print(ret)
if np.poll():
print("cmd error:", cmd)
sys.exit(1)
def read_file(filename, mode="r"):
with open(filename, mode) as f_read:
buff = f_read.read()
return buff
def save_file(filename, content, mode="w"):
with open(filename, mode) as f_write:
f_write.write(content)
def _md5(data):
_hash = hashlib.md5(data).hexdigest()
return _hash
def md5(filepath):
path = filepath.replace("\\", "/")
print(path)
with open(path, "rb") as f_read:
buff = f_read.read()
if len(buff) > 0:
_hash = _md5(buff)
return _hash
else:
return ""
def zip_dir(name, dirpath):
assert isinstance(dirpath, str), "error: dirpath={0}".format(dirpath)
os.chdir(dirpath)
files = []
for name in os.listdir(".."):
files.append(os.path.join(dirpath, name))
zip_files(name, files)
def zip_files(name, filepath):
assert isinstance(filepath, list), "error: filepath={0}".format(filepath)
zip_len = len(filepath)
if zip_len <= 0:
return
if zip_len <= G_ZIP_SPLIT_LINE:
cmd = "zip -r %s ./ -i %s" % (name, " -i ".join(filepath))
os_popen(cmd)
else:
while zip_len > 0:
cmd = "zip -r %s ./ -i %s" % (name, " -i ".join(filepath[0:G_ZIP_SPLIT_UNIT]))
os_popen(cmd)
filepath = filepath[G_ZIP_SPLIT_UNIT:]
| 1,526
| 0
| 184
|
077095c547b884a222278093dfab78cec8fd69fa
| 3,839
|
py
|
Python
|
sonnet/src/parallel_linear.py
|
ScriptBox99/deepmind-sonnet
|
5cbfdc356962d9b6198d5b63f0826a80acfdf35b
|
[
"Apache-2.0"
] | 10,287
|
2017-04-07T12:33:37.000Z
|
2022-03-30T03:32:16.000Z
|
sonnet/src/parallel_linear.py
|
ScriptBox99/deepmind-sonnet
|
5cbfdc356962d9b6198d5b63f0826a80acfdf35b
|
[
"Apache-2.0"
] | 209
|
2017-04-07T15:57:11.000Z
|
2022-03-27T10:43:03.000Z
|
sonnet/src/parallel_linear.py
|
ScriptBox99/deepmind-sonnet
|
5cbfdc356962d9b6198d5b63f0826a80acfdf35b
|
[
"Apache-2.0"
] | 1,563
|
2017-04-07T13:15:06.000Z
|
2022-03-29T15:26:04.000Z
|
# Copyright 2019 The Sonnet Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Parallel linear module."""
import math
from typing import Optional
from sonnet.src import base
from sonnet.src import initializers
from sonnet.src import once
from sonnet.src import utils
import tensorflow as tf
class ParallelLinears(base.Module):
"""Parallel linear.
This is equivalent to n separate linears applied in parallel to n inputs. It
takes an input of shape [num_linears, batch_size, input_size] and returns an
output of shape [num_linears, batch_size, output_size].
It uses a single batched matmul which is more efficient than stacking separate
snt.Linear layers. This is implemented using `num_linear`s first to avoid the
need for transposes in order to make it efficient when stacking these.
"""
def __init__(self,
output_size: int,
with_bias: bool = True,
w_init: Optional[initializers.Initializer] = None,
b_init: Optional[initializers.Initializer] = None,
name: Optional[str] = None):
"""Constructs a `ParallelLinear` module.
Args:
output_size: Output dimensionality.
with_bias: Whether to include bias parameters. Default `True`.
w_init: Optional initializer for the weights. By default the weights are
initialized truncated random normal values with a standard deviation of
`1 / sqrt(input_feature_size)`, which is commonly used when the inputs
are zero centered (see https://arxiv.org/abs/1502.03167v3).
b_init: Optional initializer for the bias. By default the bias is
initialized to zero.
name: Name of the module.
"""
super().__init__(name=name)
self.output_size = output_size
self.with_bias = with_bias
self.w_init = w_init
if with_bias:
self.b_init = b_init if b_init is not None else initializers.Zeros()
elif b_init is not None:
raise ValueError("When not using a bias the b_init must be None.")
@once.once
def _initialize(self, inputs: tf.Tensor):
"""Constructs parameters used by this module."""
utils.assert_rank(inputs, 3)
self.input_size = inputs.shape[2]
if self.input_size is None: # Can happen inside an @tf.function.
raise ValueError("Input size must be specified at module build time.")
num_linears = inputs.shape[0]
if num_linears is None: # Can happen inside an @tf.function.
raise ValueError(
"The number of linears must be specified at module build time.")
if self.w_init is None:
# See https://arxiv.org/abs/1502.03167v3.
stddev = 1. / math.sqrt(self.input_size)
self.w_init = initializers.TruncatedNormal(stddev=stddev)
self.w = tf.Variable(
self.w_init([num_linears, self.input_size, self.output_size],
inputs.dtype),
name="w")
if self.with_bias:
self.b = tf.Variable(
self.b_init([num_linears, 1, self.output_size], inputs.dtype),
name="b")
| 37.637255
| 80
| 0.68273
|
# Copyright 2019 The Sonnet Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Parallel linear module."""
import math
from typing import Optional
from sonnet.src import base
from sonnet.src import initializers
from sonnet.src import once
from sonnet.src import utils
import tensorflow as tf
class ParallelLinears(base.Module):
"""Parallel linear.
This is equivalent to n separate linears applied in parallel to n inputs. It
takes an input of shape [num_linears, batch_size, input_size] and returns an
output of shape [num_linears, batch_size, output_size].
It uses a single batched matmul which is more efficient than stacking separate
snt.Linear layers. This is implemented using `num_linear`s first to avoid the
need for transposes in order to make it efficient when stacking these.
"""
def __init__(self,
output_size: int,
with_bias: bool = True,
w_init: Optional[initializers.Initializer] = None,
b_init: Optional[initializers.Initializer] = None,
name: Optional[str] = None):
"""Constructs a `ParallelLinear` module.
Args:
output_size: Output dimensionality.
with_bias: Whether to include bias parameters. Default `True`.
w_init: Optional initializer for the weights. By default the weights are
initialized truncated random normal values with a standard deviation of
`1 / sqrt(input_feature_size)`, which is commonly used when the inputs
are zero centered (see https://arxiv.org/abs/1502.03167v3).
b_init: Optional initializer for the bias. By default the bias is
initialized to zero.
name: Name of the module.
"""
super().__init__(name=name)
self.output_size = output_size
self.with_bias = with_bias
self.w_init = w_init
if with_bias:
self.b_init = b_init if b_init is not None else initializers.Zeros()
elif b_init is not None:
raise ValueError("When not using a bias the b_init must be None.")
@once.once
def _initialize(self, inputs: tf.Tensor):
"""Constructs parameters used by this module."""
utils.assert_rank(inputs, 3)
self.input_size = inputs.shape[2]
if self.input_size is None: # Can happen inside an @tf.function.
raise ValueError("Input size must be specified at module build time.")
num_linears = inputs.shape[0]
if num_linears is None: # Can happen inside an @tf.function.
raise ValueError(
"The number of linears must be specified at module build time.")
if self.w_init is None:
# See https://arxiv.org/abs/1502.03167v3.
stddev = 1. / math.sqrt(self.input_size)
self.w_init = initializers.TruncatedNormal(stddev=stddev)
self.w = tf.Variable(
self.w_init([num_linears, self.input_size, self.output_size],
inputs.dtype),
name="w")
if self.with_bias:
self.b = tf.Variable(
self.b_init([num_linears, 1, self.output_size], inputs.dtype),
name="b")
def __call__(self, inputs: tf.Tensor) -> tf.Tensor:
self._initialize(inputs)
outputs = tf.matmul(inputs, self.w)
if self.with_bias:
outputs = tf.add(outputs, self.b)
return outputs
| 182
| 0
| 25
|
c4035174e5609822f5176b25618ba665b6bcb7bd
| 1,431
|
py
|
Python
|
tests/spot/futures/test_futures_loan_borrow.py
|
fossabot/binance-connector-python
|
bab18df22ba57b407b15dd0a9147cd75e6389b9e
|
[
"MIT"
] | 1
|
2021-08-05T03:36:24.000Z
|
2021-08-05T03:36:24.000Z
|
tests/spot/futures/test_futures_loan_borrow.py
|
fossabot/binance-connector-python
|
bab18df22ba57b407b15dd0a9147cd75e6389b9e
|
[
"MIT"
] | 2
|
2021-07-12T11:18:55.000Z
|
2021-07-12T11:28:19.000Z
|
tests/spot/futures/test_futures_loan_borrow.py
|
fossabot/binance-connector-python
|
bab18df22ba57b407b15dd0a9147cd75e6389b9e
|
[
"MIT"
] | 1
|
2021-07-10T20:50:04.000Z
|
2021-07-10T20:50:04.000Z
|
import responses
from urllib.parse import urlencode
from tests.util import random_str
from tests.util import mock_http_response
from binance.spot import Spot as Client
from binance.error import ParameterRequiredError, ClientError
mock_item = {"key_1": "value_1", "key_2": "value_2"}
mock_exception = {"code": -1105, "msg": "error message."}
key = random_str()
secret = random_str()
params = {"coin": "USDT", "collateralCoin": "BTC", "amount": "1"}
def test_futures_loan_borrow_without_coin():
"""Tests the API endpoint to borrow cross funds without coin"""
params = {"coin": "", "collateralCoin": "BTC"}
client = Client(key, secret)
client.futures_loan_borrow.when.called_with(**params).should.throw(
ParameterRequiredError
)
def test_futures_loan_borrow_without_collateralCoin():
"""Tests the API endpoint to borrow cross funds without collateralCoin"""
params = {"coin": "USDT", "collateralCoin": ""}
client = Client(key, secret)
client.futures_loan_borrow.when.called_with(**params).should.throw(
ParameterRequiredError
)
@mock_http_response(
responses.POST,
"/sapi/v1/futures/loan/borrow\\?" + urlencode(params),
mock_item,
200,
)
def test_futures_loan_borrow():
"""Tests the API endpoint to borrow cross funds"""
client = Client(key, secret)
response = client.futures_loan_borrow(**params)
response.should.equal(mock_item)
| 27.519231
| 77
| 0.712788
|
import responses
from urllib.parse import urlencode
from tests.util import random_str
from tests.util import mock_http_response
from binance.spot import Spot as Client
from binance.error import ParameterRequiredError, ClientError
mock_item = {"key_1": "value_1", "key_2": "value_2"}
mock_exception = {"code": -1105, "msg": "error message."}
key = random_str()
secret = random_str()
params = {"coin": "USDT", "collateralCoin": "BTC", "amount": "1"}
def test_futures_loan_borrow_without_coin():
"""Tests the API endpoint to borrow cross funds without coin"""
params = {"coin": "", "collateralCoin": "BTC"}
client = Client(key, secret)
client.futures_loan_borrow.when.called_with(**params).should.throw(
ParameterRequiredError
)
def test_futures_loan_borrow_without_collateralCoin():
"""Tests the API endpoint to borrow cross funds without collateralCoin"""
params = {"coin": "USDT", "collateralCoin": ""}
client = Client(key, secret)
client.futures_loan_borrow.when.called_with(**params).should.throw(
ParameterRequiredError
)
@mock_http_response(
responses.POST,
"/sapi/v1/futures/loan/borrow\\?" + urlencode(params),
mock_item,
200,
)
def test_futures_loan_borrow():
"""Tests the API endpoint to borrow cross funds"""
client = Client(key, secret)
response = client.futures_loan_borrow(**params)
response.should.equal(mock_item)
| 0
| 0
| 0
|
e531ec686052a49b40461d2d3da353e84817d346
| 24,729
|
py
|
Python
|
conary_test/cvctest/buildtest/expansiontest.py
|
sassoftware/conary
|
d418968acd5e11ee17ed6d91ca395ea10a040222
|
[
"Apache-2.0"
] | 43
|
2015-03-31T01:37:10.000Z
|
2021-11-14T16:26:48.000Z
|
conary_test/cvctest/buildtest/expansiontest.py
|
sassoftware/conary
|
d418968acd5e11ee17ed6d91ca395ea10a040222
|
[
"Apache-2.0"
] | 9
|
2015-06-10T16:39:41.000Z
|
2020-01-27T16:35:01.000Z
|
conary_test/cvctest/buildtest/expansiontest.py
|
sassoftware/conary
|
d418968acd5e11ee17ed6d91ca395ea10a040222
|
[
"Apache-2.0"
] | 9
|
2015-04-07T08:12:37.000Z
|
2020-01-26T09:54:18.000Z
|
#
# Copyright (c) SAS Institute Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from testrunner import testhelp
from conary_test import rephelp
import os
from conary_test.cvctest.buildtest import policytest
from conary import versions
from conary.build import action, trovefilter
from conary.conaryclient import cmdline
from conary.deps import deps
from conary.lib import util
| 42.709845
| 89
| 0.597921
|
#
# Copyright (c) SAS Institute Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from testrunner import testhelp
from conary_test import rephelp
import os
from conary_test.cvctest.buildtest import policytest
from conary import versions
from conary.build import action, trovefilter
from conary.conaryclient import cmdline
from conary.deps import deps
from conary.lib import util
class PackageRecipeTest(rephelp.RepositoryHelper):
def touch(self, fn):
d = os.path.dirname(fn)
if not os.path.exists(fn):
util.mkdirChain(d)
f = open(fn, 'w')
f.write('')
f.close()
def getRecipe(self):
return policytest.DummyRecipe(self.cfg)
def getGlob(self, pattern, extraMacros = {}):
recipe = self.getRecipe()
recipe.macros.update(extraMacros)
return action.Glob(recipe, pattern)
def getRegexp(self, pattern):
return action.Regexp(pattern)
def testGlobBasics(self):
ref = '^foo$'
glob = self.getGlob('foo')
self.assertEquals(glob(), ref)
self.assertEquals(repr(glob), "Glob('foo')")
self.assertEquals(str(glob), "Glob('foo')")
self.assertEquals(ref, glob)
self.assertEquals(hash(ref), hash(glob))
self.assertEquals(glob, self.getGlob('foo'))
self.assertEquals(glob, self.getRegexp(ref))
glob = self.getGlob('foo*')
self.assertEquals(glob(), '^foo[^/]*$')
self.assertEquals(repr(glob), "Glob('foo*')")
self.assertEquals(str(glob), "Glob('foo*')")
glob = self.getGlob('f?o')
self.assertEquals(glob(), '^f[^/]o$')
glob = self.getGlob('foo.*')
self.assertEquals(glob(), '^foo\\.[^/]*$')
glob = self.getGlob('%(datadir)s/foo', {'datadir': '/usr/share'})
self.assertEquals(glob(), '^\\/usr\\/share\\/foo$')
self.assertEquals(repr(glob), "Glob('%(datadir)s/foo')")
self.assertEquals(str(glob), "Glob('%%(datadir)s/foo')")
glob = self.getGlob('%(datadir)s/?[!foo|bar][^baz][]', {'datadir': '/usr/share'})
self.assertEquals(glob(), \
'^\\/usr\\/share\\/[^/][^foo|bar][\\^baz]\\[\\]$')
def testRegexpBasics(self):
exp = '^foo$'
reg = self.getRegexp(exp)
self.assertEquals(reg, exp)
self.assertEquals(reg, self.getRegexp(exp))
self.assertEquals(reg, self.getGlob('foo'))
self.assertEquals(str(reg), "Regexp('^foo$')")
self.assertEquals(repr(reg), "Regexp('^foo$')")
def testExpandPaths(self):
recipe = self.getRecipe()
tmpDir = recipe.macros.destdir
fn = os.path.join(tmpDir, 'foo')
self.touch(fn)
reg = self.getRegexp('/foo')
paths = action._expandPaths([reg], recipe.macros)
self.assertEquals([fn], paths)
paths = action._expandPaths(['/*'], recipe.macros)
self.assertEquals([fn], paths)
glob = self.getGlob('/f?o')
paths = action._expandPaths([glob], recipe.macros)
self.assertEquals([fn], paths)
def testExpandGlobsWithDirs(self):
recipe = self.getRecipe()
tmpDir = recipe.macros.destdir
goodFn = os.path.join(tmpDir, 'foo.bar')
badFn = os.path.join(tmpDir, 'foo', 'bar')
self.touch(goodFn)
self.touch(badFn)
glob = self.getGlob('/foo*')
paths = action._expandPaths([glob], recipe.macros)
self.assertEquals(sorted([goodFn, os.path.dirname(badFn)]), sorted(paths))
glob = self.getGlob('/foo?*')
paths = action._expandPaths([glob], recipe.macros)
self.assertEquals([goodFn], paths)
glob = self.getGlob('/foo?bar')
paths = action._expandPaths([glob], recipe.macros)
self.assertEquals([goodFn], paths)
def testGlobExcludeDirs(self):
recipeStr = """\
class TestGlob(PackageRecipe):
name = 'testglob'
version = '1.0.0'
clearBuildRequires()
def setup(r):
r.MakeDirs('%(datadir)s/foo')
r.ExcludeDirectories(exceptions = r.glob('%(datadir)s/*'))
"""
built, d = self.buildRecipe(recipeStr, "TestGlob")
self.assertEquals(len(built), 1)
self.assertEquals(built[0][0], 'testglob:data')
def testRegexpConfig(self):
recipeStr = """\
class TestRegexp(PackageRecipe):
name = 'testregexp'
version = '1.0.0'
clearBuildRequires()
def setup(r):
r.Create('/var/foo')
r.Config(r.regexp('/var/.*'))
r.Create('%(datadir)s/foo')
r.Config(r.regexp('%(datadir)s/.*'))
"""
built, d = self.buildRecipe(recipeStr, "TestRegexp")
self.assertEquals(len(built), 2)
self.assertEquals(sorted(x[0] for x in built),
['testregexp:data', 'testregexp:runtime'])
repos = self.openRepository()
for nvf in built:
trvNVF = repos.findTrove(None, nvf)
trv = repos.getTrove(*trvNVF[0])
for fileInfo in trv.iterFileList():
fileObj = repos.getFileVersion(fileInfo[0],
fileInfo[2], fileInfo[3])
self.assertEquals(fileObj.flags.isConfig(), 1)
def testGlobDoc(self):
recipeStr = """\
class TestRegexp(PackageRecipe):
name = 'testregexp'
version = '1.0.0'
clearBuildRequires()
def setup(r):
r.Create('foo', contents = 'foo')
r.Doc(r.glob('f?o'))
"""
built, d = self.buildRecipe(recipeStr, "TestRegexp")
self.assertEquals(len(built), 1)
self.assertEquals(built[0][0], 'testregexp:supdoc')
def testRegexpDoc(self):
recipeStr = """\
class TestRegexp(PackageRecipe):
name = 'testregexp'
version = '1.0.0'
clearBuildRequires()
def setup(r):
r.Create('foo', contents = 'foo')
r.Doc(r.regexp('f.o'))
"""
built, d = self.buildRecipe(recipeStr, "TestRegexp")
self.assertEquals(len(built), 1)
self.assertEquals(built[0][0], 'testregexp:supdoc')
class TroveFilterTest(rephelp.RepositoryHelper):
def setUp(self):
rephelp.RepositoryHelper.setUp(self)
def getRecipe(self):
return policytest.DummyRecipe(self.cfg)
@testhelp.context('trove-filter')
def testTroveFilterBasics(self):
recipe = self.getRecipe()
filt = trovefilter.TroveFilter(recipe,
'foo', version = 'test.rpath.local@rpl:devel')
nvf = cmdline.parseTroveSpec('foo=test.rpath.local@rpl:devel')
self.assertEquals(filt.match((nvf,)), True)
nvf = cmdline.parseTroveSpec('foo=foo.rpath.local@rpl:devel')
self.assertEquals(filt.match((nvf,)), False)
filt = trovefilter.TroveFilter(recipe,
'foo', version = '/test.rpath.local@rpl:devel')
nvf = cmdline.parseTroveSpec('foo=/test.rpath.local@rpl:devel')
self.assertEquals(filt.match((nvf,)), True)
nvf = cmdline.parseTroveSpec('foo=/foo.rpath.local@rpl:devel')
self.assertEquals(filt.match((nvf,)), False)
filt = trovefilter.TroveFilter(recipe,
'foo', version = '/test.rpath.local@rpl:devel/1-1-1')
nvf = cmdline.parseTroveSpec('foo=/test.rpath.local@rpl:devel/1-1-1')
self.assertEquals(filt.match((nvf,)), True)
nvf = cmdline.parseTroveSpec('foo=/foo.rpath.local@rpl:devel/1-1-1')
self.assertEquals(filt.match((nvf,)), False)
@testhelp.context('trove-filter')
def testBadTroveFilters(self):
recipe = self.getRecipe()
filt = trovefilter.AbstractFilter()
self.assertRaises(NotImplementedError, filt.match)
try:
filt = trovefilter.TroveFilter(recipe, 'foo(')
except RuntimeError, e:
self.assertEquals(str(e), "Bad Regexp: 'foo(' for name")
else:
self.fail("Expected RuntimeError")
nvf = cmdline.parseTroveSpec('foo=/test.rpath.local@rpl:devel')
filt = trovefilter.TroveFilter(recipe, 'foo')
self.assertEquals(filt.match((nvf,)), True)
filt.compile()
filt.versionType = True
filt.version = 'foo'
self.assertEquals(filt.match((nvf,)), False)
@testhelp.context('trove-filter')
def testTroveFilterVersion(self):
recipe = self.getRecipe()
filt = trovefilter.TroveFilter(recipe,
'foo', version = 'test.rpath.local@rpl:linux')
filt2 = trovefilter.TroveFilter(recipe,
'bar', version = 'test.rpath.local@rpl:linux')
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), True)
self.assertEquals(filt2.match((nvf,)), False)
filt = trovefilter.TroveFilter(recipe,
'foo', version = '/test.rpath.local@rpl:linux')
self.assertEquals(filt.match((nvf,)), True)
filt = trovefilter.TroveFilter(recipe,
'foo', version = '/test.rpath.local@rpl:linux/1-1-1')
self.assertEquals(filt.match((nvf,)), True)
filt = trovefilter.TroveFilter(recipe,
'foo', version = 'test.rpath.local@rpl:linux')
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:devel/1-1-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), False)
filt = trovefilter.TroveFilter(recipe,
'foo', version = '/test.rpath.local@rpl:linux')
self.assertEquals(filt.match((nvf,)), False)
filt = trovefilter.TroveFilter(recipe,
'foo', version = '/test.rpath.local@rpl:linux/1-1-1')
self.assertEquals(filt.match((nvf,)), False)
@testhelp.context('trove-filter')
def testTroveFilterMacros(self):
recipe = self.getRecipe()
recipe.macros.name = 'foo'
# test a macro in the name element
filt = trovefilter.TroveFilter(recipe,
'%(name)s', version = 'test.rpath.local@rpl:linux')
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), True)
# test a macro in the version element
filt = trovefilter.TroveFilter(recipe,
'%(name)s', version = '%(name)s.rpath.local@rpl:linux')
nvf = ('foo', versions.VersionFromString( \
'/foo.rpath.local@rpl:linux/1-1-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), True)
@testhelp.context('trove-filter')
def testTroveFilterRegexps(self):
recipe = self.getRecipe()
recipe.macros.name = 'foo'
# test a regexp in the name element
filt = trovefilter.TroveFilter(recipe,
'%(name)s1+', version = 'test.rpath.local@rpl:linux')
nvf = ('foo11', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), True)
# test that name regexp is anchored
nvf = ('foo113', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), False)
@testhelp.context('trove-filter')
def testTroveFilterFlavors(self):
recipe = self.getRecipe()
filt = trovefilter.TroveFilter(recipe, flavor = 'xen,domU')
filt2 = trovefilter.TroveFilter(recipe, name = 'bar',
flavor = 'xen,domU')
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), False)
self.assertEquals(filt2.match((nvf,)), False)
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'),
deps.parseFlavor('xen,domU is: x86'))
self.assertEquals(filt.match((nvf,)), True)
self.assertEquals(filt2.match((nvf,)), False)
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'),
deps.parseFlavor('xen,domU is: x86_64'))
self.assertEquals(filt.match((nvf,)), True)
self.assertEquals(filt2.match((nvf,)), False)
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'),
'xen,domU is: x86_64')
self.assertEquals(filt.match((nvf,)), True)
self.assertEquals(filt2.match((nvf,)), False)
@testhelp.context('trove-filter')
def testTroveFilterFlavors2(self):
recipe = self.getRecipe()
filt1 = trovefilter.TroveFilter(recipe, flavor = 'xen,domU is: x86')
filt2 = trovefilter.TroveFilter(recipe, flavor = 'xen,domU is: x86_64')
filt3 = trovefilter.TroveFilter(recipe,
flavor = 'xen,domU is: x86_64 x86')
nvf1 = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'),
deps.parseFlavor('is: x86'))
nvf2 = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'),
deps.parseFlavor('xen,domU is: x86'))
nvf3 = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'),
deps.parseFlavor('xen,domU is: x86_64'))
nvf4 = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'),
deps.parseFlavor('xen,domU is: x86 x86_64'))
self.assertEquals(filt1.match((nvf1,)), False)
self.assertEquals(filt1.match((nvf2,)), True)
self.assertEquals(filt2.match((nvf2,)), False)
self.assertEquals(filt2.match((nvf3,)), True)
self.assertEquals(filt2.match((nvf4,)), False)
self.assertEquals(filt3.match((nvf4,)), True)
self.assertEquals(filt3.match((nvf1,)), False)
self.assertEquals(filt3.match((nvf2,)), False)
self.assertEquals(filt3.match((nvf3,)), False)
@testhelp.context('trove-filter')
def testTroveFilterFlavors3(self):
recipe = self.getRecipe()
filt1 = trovefilter.TroveFilter(recipe, flavor = 'xen,domU is: x86')
filt2 = trovefilter.TroveFilter(recipe,
flavor = 'xen,domU is: x86(sse, sse2, 486, 586, 686)')
nvf1 = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'),
deps.parseFlavor('xen,domU is: x86'))
nvf2 = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'),
deps.parseFlavor('xen,domU is: x86(sse, sse2, 486, 586, 686)'))
self.assertEquals(filt1.match((nvf1,)), True)
self.assertEquals(filt2.match((nvf2,)), True)
# most important test. x86 filter matches x86(sse)
self.assertEquals(filt1.match((nvf2,)), True)
self.assertEquals(filt2.match((nvf1,)), False)
@testhelp.context('trove-filter')
def testTroveFilterFlavors4(self):
recipe = self.getRecipe()
filt1 = trovefilter.TroveFilter(recipe, flavor = 'xen,domU is: sparc')
filt2 = trovefilter.TroveFilter(recipe,
flavor = 'xen,domU is: x86(sse, sse2, 486, 586, 686)')
filt3 = trovefilter.TroveFilter(recipe, flavor = 'xen,domU')
nvf1 = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'),
deps.parseFlavor('xen,domU is: sparc'))
nvf2 = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'),
deps.parseFlavor('xen,domU is: x86(sse, sse2, 486, 586, 686)'))
self.assertEquals(filt1.match((nvf1,)), True)
self.assertEquals(filt2.match((nvf2,)), True)
self.assertEquals(filt1.match((nvf2,)), False)
self.assertEquals(filt2.match((nvf1,)), False)
self.assertEquals(filt3.match((nvf1,)), True)
@testhelp.context('trove-filter')
def testTroveFilterNoVersion(self):
recipe = self.getRecipe()
filt = trovefilter.TroveFilter(recipe, name = 'foo')
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), True)
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:devel/1-1-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), True)
@testhelp.context('trove-filter')
def testTroveFilterRevision(self):
recipe = self.getRecipe()
filt = trovefilter.TroveFilter(recipe, version = '1.1-1-1')
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1-1-2'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), False)
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.1-1-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), True)
filt = trovefilter.TroveFilter(recipe, version = '1.1-1')
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.1-1-2'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), True)
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.1-2-2'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), False)
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.1-1-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), True)
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.2-1-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), False)
filt = trovefilter.TroveFilter(recipe, version = '1.1')
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.1-1-2'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), True)
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.1-2-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), True)
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.2-1-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), False)
filt = trovefilter.TroveFilter(recipe, version = '')
self.assertEquals(filt.match((nvf,)), True)
@testhelp.context('trove-filter')
def testTroveFilterBlank(self):
recipe = self.getRecipe()
filt = trovefilter.TroveFilter(recipe)
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.1-2-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), True)
nvf = ('bar', versions.VersionFromString( \
'/test.rpath.local@rpl:devel/1.0-6-4'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), True)
@testhelp.context('trove-filter')
def testTroveFilterNot(self):
recipe = self.getRecipe()
filt = -trovefilter.TroveFilter(recipe, name = 'foo')
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.1-2-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), False)
nvf = ('bar', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.1-2-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), True)
filt = ~trovefilter.TroveFilter(recipe, name = 'foo')
nvf = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.1-2-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), False)
nvf = ('bar', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.1-2-1'), deps.parseFlavor(''))
self.assertEquals(filt.match((nvf,)), True)
@testhelp.context('trove-filter')
def testTroveFilterOr(self):
recipe = self.getRecipe()
filt1 = trovefilter.TroveFilter(recipe, name = 'foo')
filt2 = trovefilter.TroveFilter(recipe, name = 'group-foo')
filt3 = filt1 - filt2
filt4 = filt1 + - filt2
filt5 = filt2 - filt1
filt6 = filt2 | filt1
nvf1 = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.1-2-1'), deps.parseFlavor(''))
nvf2 = ('group-foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.1-2-1'), deps.parseFlavor(''))
self.assertEquals(filt3.match((nvf1, nvf2)), True)
self.assertEquals(filt3.match((nvf2,)), False)
self.assertEquals(filt4.match((nvf1, nvf2)), True)
self.assertEquals(filt5.match((nvf1, nvf2)), True)
self.assertEquals(filt5.match((nvf1,)), False)
self.assertEquals(filt6.match((nvf1, nvf2)), True)
@testhelp.context('trove-filter')
def testTroveFilterAnd(self):
recipe = self.getRecipe()
filt1 = trovefilter.TroveFilter(recipe, name = 'foo')
filt2 = trovefilter.TroveFilter(recipe, name = 'group-foo')
filt3 = filt1 * -filt2
filt4 = filt2 * -filt1
filt5 = filt2 & filt1
filt6 = filt2 & ~filt1
nvf1 = ('foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.1-2-1'), deps.parseFlavor(''))
nvf2 = ('group-foo', versions.VersionFromString( \
'/test.rpath.local@rpl:linux/1.1-2-1'), deps.parseFlavor(''))
self.assertEquals(filt3.match((nvf1, nvf2)), False)
self.assertEquals(filt3.match((nvf2,)), False)
self.assertEquals(filt3.match((nvf1,)), True)
self.assertEquals(filt4.match((nvf1, nvf2)), False)
self.assertEquals(filt4.match((nvf1,)), False)
self.assertEquals(filt4.match((nvf2,)), True)
self.assertEquals(filt5.match((nvf1, nvf2)), True)
self.assertEquals(filt5.match((nvf1,)), False)
self.assertEquals(filt5.match((nvf2,)), False)
self.assertEquals(filt6.match((nvf1, nvf2)), False)
@testhelp.context('trove-filter')
def testTroveFilterEquality(self):
recipe = self.getRecipe()
filt1 = trovefilter.TroveFilter(recipe, name = 'foo')
filt2 = trovefilter.TroveFilter(recipe, name = 'group-foo')
self.assertNotEquals(filt1, filt2)
filt1 = trovefilter.TroveFilter(recipe, name = 'foo')
filt2 = trovefilter.TroveFilter(recipe, name = 'foo')
self.assertEquals(filt1, filt2)
filt1 = trovefilter.TroveFilter(recipe, name = 'foo',
version = 'c.r.c@rpl:linux')
filt2 = trovefilter.TroveFilter(recipe, name = 'foo')
self.assertNotEquals(filt1, filt2)
filt1 = trovefilter.TroveFilter(recipe, name = 'foo',
version = 'c.r.c@rpl:linux')
filt2 = trovefilter.TroveFilter(recipe, name = 'foo',
version = 'c.r.c@rpl:linux')
self.assertEquals(filt1, filt2)
filt1 = trovefilter.TroveFilter(recipe, name = 'foo',
version = 'c.r.c@rpl:linux')
filt2 = trovefilter.TroveFilter(recipe, name = 'foo',
version = '/c.r.c@rpl:linux')
self.assertNotEquals(filt1, filt2)
filt1 = trovefilter.TroveFilter(recipe, name = 'foo',
version = 'c.r.c@rpl:linux', flavor = 'is: x86')
filt2 = trovefilter.TroveFilter(recipe, name = 'foo',
version = 'c.r.c@rpl:linux')
self.assertNotEquals(filt1, filt2)
filt1 = trovefilter.TroveFilter(recipe, name = 'foo',
flavor = 'is: x86')
filt2 = trovefilter.TroveFilter(recipe, name = 'foo',
flavor = 'is: x86 x86_64')
self.assertNotEquals(filt1, filt2)
filt1 = trovefilter.TroveFilter(recipe, name = 'foo',
version = 'c.r.c@rpl:linux', flavor = 'is: x86')
filt2 = trovefilter.TroveFilter(recipe, name = 'foo',
version = 'c.r.c@rpl:linux', flavor = 'is: x86')
self.assertEquals(filt1, filt2)
| 22,321
| 1,149
| 369
|
be31d4bb7630a7fc2c1993bb09d802aee8af78d3
| 6,647
|
py
|
Python
|
Project/RE4017_proj1.py
|
Ciaran-Carroll/college
|
46052aa177280f7900e04e0e828247d7097eb07b
|
[
"MIT"
] | null | null | null |
Project/RE4017_proj1.py
|
Ciaran-Carroll/college
|
46052aa177280f7900e04e0e828247d7097eb07b
|
[
"MIT"
] | null | null | null |
Project/RE4017_proj1.py
|
Ciaran-Carroll/college
|
46052aa177280f7900e04e0e828247d7097eb07b
|
[
"MIT"
] | null | null | null |
'''
#Students Name's: Ciaran Carroll
# Student Id Number's: 13113259
#
# Project 1:
# Implement image reconstruction from parallel-projection sinograms using Python.
#
# CAT Scanners (or CT scan) - Computer Axial Tomography
# CT scan: is a special X-ray tests that produce cross-sectional images of the body using X-rays and
# a computer
# FFTs - Fast Fourieris Transform
# FFT: is an algorithm that samples a signal over a period of time (or space) and divides it
# into its frequency components
# Laminogram: Reconstruct the sum of the backprojections (i.e. sum of the f(x,y))
# Coplanar rotational laminography (CRL) is a special case of laminography which is a
# tomographic technique used to image cross-sectional views through solid objects.
#
# Aim:
# (1) Reconstruct an image from the sinogram image (sinogram.png)
# (2) Investigate the behaviour of backprojection reconstruction with ramp-filtering
# (3) Investigate the behaviour of backprojection reconstruction without ramp-filtering
# (4) Investigate the behaviour of backprojection reconstruction with Hamming-windowed ramp-filtering
#
# A display of all the projections for all X-ray angles is called a Sinogram
#
# Rebuild the image from a sum of the 'Backprojections' of the 1-d projection data
Step 1 - Backprojection reconstruction of the sinogram without filtering:
When all the projection angles are combined the projection, the resulting image will
be blurred. This is due to the fact that the resulting image is concentrated towards the
center. (concentrated samples of the image towards the center, and more sparse samples near
the edges). To compensate for this we will need to apply a filter to the output image of the
backprojection such as the ramp filter or the Hamming-windowed ramp-filter
New Steps
(1) - Form the image projections and translate into the frequency domain using the FFT
'''
import numpy as np
import matplotlib.pylab as plt
from PIL import Image
from scipy.ndimage.filters import gaussian_filter
from skimage.transform import rotate
import scipy.fftpack as fft
#from skimage.transform import iradon
def imread(filename,greyscale=True):
"""Load an image, return as a Numpy array."""
if greyscale:
pil_im = Image.open(filename).convert('L')
else:
pil_im = Image.open(filename)
return np.array(pil_im)
def imshow(im, autoscale=False,colourmap='gray', newfig=True, title=None):
"""Display an image, turning off autoscaling (unless explicitly required)
and interpolation.
(1) 8-bit greyscale images and 24-bit RGB are scaled in 0..255.
(2) 0-1 binary images are scaled in 0..1.
(3) Float images are scaled in 0.0..1.0 if their min values are >= 0
and their max values <= 1.0
(4) Float images are scaled in 0.0..255.0 if their min values are >= 0
and their max values are > 1 and <= 255.0
(5) Any image not covered by the above cases is autoscaled. If
autoscaling is explicitly requested, it is always turned on.
A new figure is created by default. "newfig=False" turns off this
behaviour.
Interpolation is always off (unless the backend stops this).
"""
if newfig:
if title != None: fig = plt.figure(title)
else: fig = plt.figure()
if autoscale:
plt.imshow(im,interpolation='nearest',cmap=colourmap)
else:
maxval = im.max()
if im.dtype == 'uint8': ## 8-bit greyscale or 24-bit RGB
if maxval > 1: maxval = 255
plt.imshow(im,interpolation='nearest',vmin=0,vmax=maxval,cmap=colourmap)
elif im.dtype == 'float32' or im.dtype == 'float64':
minval = im.min()
if minval >= 0.0:
if maxval <= 1.0: ## Looks like 0..1 float greyscale
minval, maxval = 0.0, 1.0
elif maxval <= 255.0: ## Looks like a float 0 .. 255 image.
minval, maxval = 0.0, 255.0
plt.imshow(im,interpolation='nearest',vmin=minval,vmax=maxval,cmap=colourmap)
else:
plt.imshow(im,interpolation='nearest',cmap=colourmap)
plt.axis('image')
## plt.axis('off')
plt.show()
##return fig
def build_proj_ffts(projs):
"Build 1-d FFTs of an array of projections, each projection 1 row fo the array."
return fft.rfft(projs, axis=1)
def build_proj_iffts(projs):
"Build 1-d iFFTs of an array of projections, each projection 1 row fo the array."
return fft.irfft(projs, axis=1)
def build_laminogram(radonT):
"Generate a laminogram by simple backprojection using the Radon Transform of an image, 'radonT'."
laminogram = np.zeros((radonT.shape[1],radonT.shape[1]))
dTheta = 180.0 / radonT.shape[0]
for i in range(radonT.shape[0]):
temp = np.tile(radonT[i],(radonT.shape[1],1))
temp = rotate(temp, dTheta*i)
laminogram += temp
return laminogram
def ramp_filter_ffts(ffts):
"Ramp filter a 2-d array of 1-d FFTs (1-d FFTs along the rows)."
ramp = np.floor(np.arange(0.5, ffts.shape[1]//2 + 0.1, 0.5))
return ffts * ramp
def radon(image, steps):
"Build the Radon Transform using 'steps' projections of 'image’."
projections = [] # Accumulate projections in a list.
dTheta = -180.0 / steps # Angle increment for rotations.
for i in range(steps):
projections.append(rotate(image, i*dTheta).sum(axis=0))
return np.vstack(projections)
# Original Sinogram Image
sinogram = imread('sinogram.png')
imshow(sinogram, title="Original Sinogram Image")
# Backprojection reconstruction without ramp filtering
sinogram_laminogram = build_laminogram(sinogram)
imshow(sinogram_laminogram, title="Sinogram reconstruction by backprojection")
# Backprojection reconstruction with ramp filtering
# Apply an infinite ramp filter to the reconstruction
# Maybe apply a ramp filter with a cutoff at half the max frwquency
# But most likely no point
# Get the FFT of the image (Frequency Domain)
fourier = build_proj_ffts(sinogram)
# Filter the fourier transform by the ramp filter
ramp_filtered = ramp_filter_ffts(fourier)
# Take the inverse FFT of the image to convert it back to Special Domain
inverse_fourier_ramp_filtered = build_proj_iffts(ramp_filtered)
#imshow(iffts_projection_sinogram, title="Test ramp filter")
#test1 = radon(iffts_projection_sinogram, 180)
#imshow(test1, title="Test ramp filter")
# Build the filtered image by pbackprojecting the filtered projections
filtered_reconstrution = build_laminogram(inverse_fourier_ramp_filtered)
imshow(filtered_reconstrution, title="Test ramp filter")
| 38.871345
| 101
| 0.70844
|
'''
#Students Name's: Ciaran Carroll
# Student Id Number's: 13113259
#
# Project 1:
# Implement image reconstruction from parallel-projection sinograms using Python.
#
# CAT Scanners (or CT scan) - Computer Axial Tomography
# CT scan: is a special X-ray tests that produce cross-sectional images of the body using X-rays and
# a computer
# FFTs - Fast Fourieris Transform
# FFT: is an algorithm that samples a signal over a period of time (or space) and divides it
# into its frequency components
# Laminogram: Reconstruct the sum of the backprojections (i.e. sum of the f(x,y))
# Coplanar rotational laminography (CRL) is a special case of laminography which is a
# tomographic technique used to image cross-sectional views through solid objects.
#
# Aim:
# (1) Reconstruct an image from the sinogram image (sinogram.png)
# (2) Investigate the behaviour of backprojection reconstruction with ramp-filtering
# (3) Investigate the behaviour of backprojection reconstruction without ramp-filtering
# (4) Investigate the behaviour of backprojection reconstruction with Hamming-windowed ramp-filtering
#
# A display of all the projections for all X-ray angles is called a Sinogram
#
# Rebuild the image from a sum of the 'Backprojections' of the 1-d projection data
Step 1 - Backprojection reconstruction of the sinogram without filtering:
When all the projection angles are combined the projection, the resulting image will
be blurred. This is due to the fact that the resulting image is concentrated towards the
center. (concentrated samples of the image towards the center, and more sparse samples near
the edges). To compensate for this we will need to apply a filter to the output image of the
backprojection such as the ramp filter or the Hamming-windowed ramp-filter
New Steps
(1) - Form the image projections and translate into the frequency domain using the FFT
'''
import numpy as np
import matplotlib.pylab as plt
from PIL import Image
from scipy.ndimage.filters import gaussian_filter
from skimage.transform import rotate
import scipy.fftpack as fft
#from skimage.transform import iradon
def imread(filename,greyscale=True):
"""Load an image, return as a Numpy array."""
if greyscale:
pil_im = Image.open(filename).convert('L')
else:
pil_im = Image.open(filename)
return np.array(pil_im)
def imshow(im, autoscale=False,colourmap='gray', newfig=True, title=None):
"""Display an image, turning off autoscaling (unless explicitly required)
and interpolation.
(1) 8-bit greyscale images and 24-bit RGB are scaled in 0..255.
(2) 0-1 binary images are scaled in 0..1.
(3) Float images are scaled in 0.0..1.0 if their min values are >= 0
and their max values <= 1.0
(4) Float images are scaled in 0.0..255.0 if their min values are >= 0
and their max values are > 1 and <= 255.0
(5) Any image not covered by the above cases is autoscaled. If
autoscaling is explicitly requested, it is always turned on.
A new figure is created by default. "newfig=False" turns off this
behaviour.
Interpolation is always off (unless the backend stops this).
"""
if newfig:
if title != None: fig = plt.figure(title)
else: fig = plt.figure()
if autoscale:
plt.imshow(im,interpolation='nearest',cmap=colourmap)
else:
maxval = im.max()
if im.dtype == 'uint8': ## 8-bit greyscale or 24-bit RGB
if maxval > 1: maxval = 255
plt.imshow(im,interpolation='nearest',vmin=0,vmax=maxval,cmap=colourmap)
elif im.dtype == 'float32' or im.dtype == 'float64':
minval = im.min()
if minval >= 0.0:
if maxval <= 1.0: ## Looks like 0..1 float greyscale
minval, maxval = 0.0, 1.0
elif maxval <= 255.0: ## Looks like a float 0 .. 255 image.
minval, maxval = 0.0, 255.0
plt.imshow(im,interpolation='nearest',vmin=minval,vmax=maxval,cmap=colourmap)
else:
plt.imshow(im,interpolation='nearest',cmap=colourmap)
plt.axis('image')
## plt.axis('off')
plt.show()
##return fig
def build_proj_ffts(projs):
"Build 1-d FFTs of an array of projections, each projection 1 row fo the array."
return fft.rfft(projs, axis=1)
def build_proj_iffts(projs):
"Build 1-d iFFTs of an array of projections, each projection 1 row fo the array."
return fft.irfft(projs, axis=1)
def build_laminogram(radonT):
"Generate a laminogram by simple backprojection using the Radon Transform of an image, 'radonT'."
laminogram = np.zeros((radonT.shape[1],radonT.shape[1]))
dTheta = 180.0 / radonT.shape[0]
for i in range(radonT.shape[0]):
temp = np.tile(radonT[i],(radonT.shape[1],1))
temp = rotate(temp, dTheta*i)
laminogram += temp
return laminogram
def ramp_filter_ffts(ffts):
"Ramp filter a 2-d array of 1-d FFTs (1-d FFTs along the rows)."
ramp = np.floor(np.arange(0.5, ffts.shape[1]//2 + 0.1, 0.5))
return ffts * ramp
def radon(image, steps):
"Build the Radon Transform using 'steps' projections of 'image’."
projections = [] # Accumulate projections in a list.
dTheta = -180.0 / steps # Angle increment for rotations.
for i in range(steps):
projections.append(rotate(image, i*dTheta).sum(axis=0))
return np.vstack(projections)
# Original Sinogram Image
sinogram = imread('sinogram.png')
imshow(sinogram, title="Original Sinogram Image")
# Backprojection reconstruction without ramp filtering
sinogram_laminogram = build_laminogram(sinogram)
imshow(sinogram_laminogram, title="Sinogram reconstruction by backprojection")
# Backprojection reconstruction with ramp filtering
# Apply an infinite ramp filter to the reconstruction
# Maybe apply a ramp filter with a cutoff at half the max frwquency
# But most likely no point
# Get the FFT of the image (Frequency Domain)
fourier = build_proj_ffts(sinogram)
# Filter the fourier transform by the ramp filter
ramp_filtered = ramp_filter_ffts(fourier)
# Take the inverse FFT of the image to convert it back to Special Domain
inverse_fourier_ramp_filtered = build_proj_iffts(ramp_filtered)
#imshow(iffts_projection_sinogram, title="Test ramp filter")
#test1 = radon(iffts_projection_sinogram, 180)
#imshow(test1, title="Test ramp filter")
# Build the filtered image by pbackprojecting the filtered projections
filtered_reconstrution = build_laminogram(inverse_fourier_ramp_filtered)
imshow(filtered_reconstrution, title="Test ramp filter")
| 0
| 0
| 0
|
16ed837df429a96c0cdbd562240040a729a666ee
| 7,343
|
py
|
Python
|
messyger.py
|
raxod502/messyger
|
fbfe286d2448bc8ec112e7f3063f71dfe3bf2c27
|
[
"MIT"
] | 4
|
2021-12-06T17:06:20.000Z
|
2022-02-24T21:10:02.000Z
|
messyger.py
|
raxod502/messyger
|
fbfe286d2448bc8ec112e7f3063f71dfe3bf2c27
|
[
"MIT"
] | null | null | null |
messyger.py
|
raxod502/messyger
|
fbfe286d2448bc8ec112e7f3063f71dfe3bf2c27
|
[
"MIT"
] | null | null | null |
import argparse
import collections
import datetime
import json
import random
import re
import esprima
import requests
## Get the email and password
parser = argparse.ArgumentParser("messyger")
parser.add_argument("-u", "--email", required=True)
parser.add_argument("-p", "--password", required=True)
parser.add_argument("-m", "--message")
parser.add_argument("-r", "--recipient", type=int)
args = parser.parse_args()
## Parse the HTML response
html_resp = requests.get("https://www.messenger.com")
html_resp.raise_for_status()
html_page = html_resp.text
initial_request_id = re.search(
r'name="initial_request_id" value="([^"]+)"', html_page
).group(1)
lsd = re.search(r'name="lsd" value="([^"]+)"', html_page).group(1)
datr = re.search(r'"_js_datr","([^"]+)"', html_page).group(1)
## Make the login request
login = requests.post(
"https://www.messenger.com/login/password/",
cookies={"datr": datr},
data={
"lsd": lsd,
"initial_request_id": initial_request_id,
"email": args.email,
"pass": args.password,
},
allow_redirects=False,
)
assert login.status_code == 302
## Extract the inbox query parameters
inbox_html_resp = requests.get("https://www.messenger.com", cookies=login.cookies)
inbox_html_resp.raise_for_status()
inbox_html_page = inbox_html_resp.text
dtsg = re.search(r'"DTSGInitialData",\[\],\{"token":"([^"]+)"', inbox_html_page).group(
1
)
device_id = re.search(r'"deviceId":"([^"]+)"', inbox_html_page).group(1)
schema_version = re.search(r'"schemaVersion":"([0-9]+)"', inbox_html_page).group(1)
script_urls = re.findall(r'"([^"]+rsrc\.php/[^"]+\.js[^"]+)"', inbox_html_page)
scripts = []
for url in script_urls:
resp = requests.get(url)
resp.raise_for_status()
scripts.append(resp.text)
for script in scripts:
if "LSPlatformGraphQLLightspeedRequestQuery" not in script:
continue
doc_id = re.search(
r'id:"([0-9]+)",metadata:\{\},name:"LSPlatformGraphQLLightspeedRequestQuery"',
script,
).group(1)
break
if not args.message:
inbox_resp = requests.post(
"https://www.messenger.com/api/graphql/",
cookies=login.cookies,
data={
"fb_dtsg": dtsg,
"doc_id": doc_id,
"variables": json.dumps(
{
"deviceId": device_id,
"requestId": 0,
"requestPayload": json.dumps(
{
"database": 1,
"version": schema_version,
"sync_params": json.dumps({}),
}
),
"requestType": 1,
}
),
},
)
inbox_resp.raise_for_status()
## Parse the inbox data response
inbox_json = inbox_resp.json()
inbox_js = inbox_json["data"]["viewer"]["lightspeed_web_request"]["payload"]
ast = esprima.parseScript(inbox_js)
fn_calls = collections.defaultdict(list)
esprima.parseScript(inbox_js, delegate=handle_node)
conversations = collections.defaultdict(dict)
for args in fn_calls["deleteThenInsertThread"]:
last_sent_ts, last_read_ts, last_msg, *rest = args
user_id, last_msg_author = [
arg for arg in rest if isinstance(arg, int) and arg > 1e14
]
conversations[user_id]["unread"] = last_sent_ts != last_read_ts
conversations[user_id]["last_message"] = last_msg
conversations[user_id]["last_message_author"] = last_msg_author
for args in fn_calls["verifyContactRowExists"]:
user_id, _, _, name, *rest = args
conversations[user_id]["name"] = name
print(json.dumps(conversations, indent=2))
else:
## Replicate the send-message request
timestamp = int(datetime.datetime.now().timestamp() * 1000)
epoch = timestamp << 22
otid = epoch + random.randrange(2 ** 22)
send_message_resp = requests.post(
"https://www.messenger.com/api/graphql/",
cookies=login.cookies,
data={
"fb_dtsg": dtsg,
"doc_id": doc_id,
"variables": json.dumps(
{
"deviceId": device_id,
"requestId": 0,
"requestPayload": json.dumps(
{
"version_id": str(schema_version),
"tasks": [
{
"label": "46",
"payload": json.dumps(
{
"thread_id": args.recipient,
"otid": "6870463702739115830",
"source": 0,
"send_type": 1,
"text": args.message,
"initiating_source": 1,
}
),
"queue_name": str(args.recipient),
"task_id": 0,
"failure_count": None,
},
{
"label": "21",
"payload": json.dumps(
{
"thread_id": args.recipient,
"last_read_watermark_ts": timestamp,
"sync_group": 1,
}
),
"queue_name": str(args.recipient),
"task_id": 1,
"failure_count": None,
},
],
"epoch_id": 6870463702858032000,
}
),
"requestType": 3,
}
),
},
)
print(send_message_resp.text)
| 32.635556
| 87
| 0.493395
|
import argparse
import collections
import datetime
import json
import random
import re
import esprima
import requests
## Get the email and password
parser = argparse.ArgumentParser("messyger")
parser.add_argument("-u", "--email", required=True)
parser.add_argument("-p", "--password", required=True)
parser.add_argument("-m", "--message")
parser.add_argument("-r", "--recipient", type=int)
args = parser.parse_args()
## Parse the HTML response
html_resp = requests.get("https://www.messenger.com")
html_resp.raise_for_status()
html_page = html_resp.text
initial_request_id = re.search(
r'name="initial_request_id" value="([^"]+)"', html_page
).group(1)
lsd = re.search(r'name="lsd" value="([^"]+)"', html_page).group(1)
datr = re.search(r'"_js_datr","([^"]+)"', html_page).group(1)
## Make the login request
login = requests.post(
"https://www.messenger.com/login/password/",
cookies={"datr": datr},
data={
"lsd": lsd,
"initial_request_id": initial_request_id,
"email": args.email,
"pass": args.password,
},
allow_redirects=False,
)
assert login.status_code == 302
## Extract the inbox query parameters
inbox_html_resp = requests.get("https://www.messenger.com", cookies=login.cookies)
inbox_html_resp.raise_for_status()
inbox_html_page = inbox_html_resp.text
dtsg = re.search(r'"DTSGInitialData",\[\],\{"token":"([^"]+)"', inbox_html_page).group(
1
)
device_id = re.search(r'"deviceId":"([^"]+)"', inbox_html_page).group(1)
schema_version = re.search(r'"schemaVersion":"([0-9]+)"', inbox_html_page).group(1)
script_urls = re.findall(r'"([^"]+rsrc\.php/[^"]+\.js[^"]+)"', inbox_html_page)
scripts = []
for url in script_urls:
resp = requests.get(url)
resp.raise_for_status()
scripts.append(resp.text)
for script in scripts:
if "LSPlatformGraphQLLightspeedRequestQuery" not in script:
continue
doc_id = re.search(
r'id:"([0-9]+)",metadata:\{\},name:"LSPlatformGraphQLLightspeedRequestQuery"',
script,
).group(1)
break
if not args.message:
inbox_resp = requests.post(
"https://www.messenger.com/api/graphql/",
cookies=login.cookies,
data={
"fb_dtsg": dtsg,
"doc_id": doc_id,
"variables": json.dumps(
{
"deviceId": device_id,
"requestId": 0,
"requestPayload": json.dumps(
{
"database": 1,
"version": schema_version,
"sync_params": json.dumps({}),
}
),
"requestType": 1,
}
),
},
)
inbox_resp.raise_for_status()
## Parse the inbox data response
inbox_json = inbox_resp.json()
inbox_js = inbox_json["data"]["viewer"]["lightspeed_web_request"]["payload"]
ast = esprima.parseScript(inbox_js)
def is_lightspeed_call(node):
return (
node.type == "CallExpression"
and node.callee.type == "MemberExpression"
and node.callee.object.type == "Identifier"
and node.callee.object.name == "LS"
and node.callee.property.type == "Identifier"
and node.callee.property.name == "sp"
)
def parse_argument(node):
if node.type == "Literal":
return node.value
if node.type == "ArrayExpression":
assert len(node.elements) == 2
high_bits, low_bits = map(parse_argument, node.elements)
return (high_bits << 32) + low_bits
if node.type == "UnaryExpression" and node.prefix and node.operator == "-":
return -parse_argument(node.argument)
fn_calls = collections.defaultdict(list)
def handle_node(node, meta):
if not is_lightspeed_call(node):
return
args = [parse_argument(arg) for arg in node.arguments]
(fn_name, *fn_args) = args
fn_calls[fn_name].append(fn_args)
esprima.parseScript(inbox_js, delegate=handle_node)
conversations = collections.defaultdict(dict)
for args in fn_calls["deleteThenInsertThread"]:
last_sent_ts, last_read_ts, last_msg, *rest = args
user_id, last_msg_author = [
arg for arg in rest if isinstance(arg, int) and arg > 1e14
]
conversations[user_id]["unread"] = last_sent_ts != last_read_ts
conversations[user_id]["last_message"] = last_msg
conversations[user_id]["last_message_author"] = last_msg_author
for args in fn_calls["verifyContactRowExists"]:
user_id, _, _, name, *rest = args
conversations[user_id]["name"] = name
print(json.dumps(conversations, indent=2))
else:
## Replicate the send-message request
timestamp = int(datetime.datetime.now().timestamp() * 1000)
epoch = timestamp << 22
otid = epoch + random.randrange(2 ** 22)
send_message_resp = requests.post(
"https://www.messenger.com/api/graphql/",
cookies=login.cookies,
data={
"fb_dtsg": dtsg,
"doc_id": doc_id,
"variables": json.dumps(
{
"deviceId": device_id,
"requestId": 0,
"requestPayload": json.dumps(
{
"version_id": str(schema_version),
"tasks": [
{
"label": "46",
"payload": json.dumps(
{
"thread_id": args.recipient,
"otid": "6870463702739115830",
"source": 0,
"send_type": 1,
"text": args.message,
"initiating_source": 1,
}
),
"queue_name": str(args.recipient),
"task_id": 0,
"failure_count": None,
},
{
"label": "21",
"payload": json.dumps(
{
"thread_id": args.recipient,
"last_read_watermark_ts": timestamp,
"sync_group": 1,
}
),
"queue_name": str(args.recipient),
"task_id": 1,
"failure_count": None,
},
],
"epoch_id": 6870463702858032000,
}
),
"requestType": 3,
}
),
},
)
print(send_message_resp.text)
| 959
| 0
| 81
|
1711dbc6d5f8418cbf2d6ff20883e3525a9a462a
| 1,540
|
py
|
Python
|
.github/actions/update-version/update-version.py
|
nihaals/visual-studio-code-insiders-arch
|
bbd4c26f4766ccab1b9a15608bd84cccaae51341
|
[
"MIT"
] | 3
|
2020-09-19T19:26:11.000Z
|
2021-08-18T18:30:45.000Z
|
.github/actions/update-version/update-version.py
|
nihaals/visual-studio-code-insiders-arch
|
bbd4c26f4766ccab1b9a15608bd84cccaae51341
|
[
"MIT"
] | null | null | null |
.github/actions/update-version/update-version.py
|
nihaals/visual-studio-code-insiders-arch
|
bbd4c26f4766ccab1b9a15608bd84cccaae51341
|
[
"MIT"
] | 2
|
2020-08-17T02:29:14.000Z
|
2020-08-18T01:28:49.000Z
|
import os
import re
with open('PKGBUILD') as fp:
for line in fp.readlines():
line = line.strip()
current_build_number = re.search(r"^_pkgbuildnumber=(.+)$", line)
if current_build_number is None:
continue
current_build_number = current_build_number.group(1)
break
else:
raise ValueError("_pkgbuildnumber not found")
latest_version = os.environ['INPUT_VERSION']
latest_build_number = os.environ['INPUT_BUILD_NUMBER']
latest_hash_x86_64 = os.environ['INPUT_SHA256_x86_64']
print(f'Current build number: {current_build_number}')
print(f'Latest build number: {latest_build_number}')
print(f'Latest version: {latest_version}')
print(f'{latest_version}+{latest_build_number} x86_64 SHA256: {latest_hash_x86_64}')
if latest_build_number.isdigit() is False:
print('Latest build number is invalid')
exit(1)
if ' ' in latest_version or '-' in latest_version:
print('Latest version is invalid')
exit(1)
with open('PKGBUILD') as fp:
contents = fp.read()
if current_build_number != latest_build_number:
contents = re.sub(r"^pkgrel=.+$", 'pkgrel=1', contents, flags=re.MULTILINE)
contents = re.sub(r"^_pkgbuildnumber=.+$", f'_pkgbuildnumber={latest_build_number}', contents, flags=re.MULTILINE)
contents = re.sub(r"^_pkgversion=.+$", f'_pkgversion={latest_version}', contents, flags=re.MULTILINE)
contents = re.sub(r"(sha256sums_x86_64=\(\n ').+'\n", f"\g<1>{latest_hash_x86_64}'\n", contents)
with open('PKGBUILD', 'w') as fp:
fp.write(contents)
| 34.222222
| 114
| 0.707143
|
import os
import re
with open('PKGBUILD') as fp:
for line in fp.readlines():
line = line.strip()
current_build_number = re.search(r"^_pkgbuildnumber=(.+)$", line)
if current_build_number is None:
continue
current_build_number = current_build_number.group(1)
break
else:
raise ValueError("_pkgbuildnumber not found")
latest_version = os.environ['INPUT_VERSION']
latest_build_number = os.environ['INPUT_BUILD_NUMBER']
latest_hash_x86_64 = os.environ['INPUT_SHA256_x86_64']
print(f'Current build number: {current_build_number}')
print(f'Latest build number: {latest_build_number}')
print(f'Latest version: {latest_version}')
print(f'{latest_version}+{latest_build_number} x86_64 SHA256: {latest_hash_x86_64}')
if latest_build_number.isdigit() is False:
print('Latest build number is invalid')
exit(1)
if ' ' in latest_version or '-' in latest_version:
print('Latest version is invalid')
exit(1)
with open('PKGBUILD') as fp:
contents = fp.read()
if current_build_number != latest_build_number:
contents = re.sub(r"^pkgrel=.+$", 'pkgrel=1', contents, flags=re.MULTILINE)
contents = re.sub(r"^_pkgbuildnumber=.+$", f'_pkgbuildnumber={latest_build_number}', contents, flags=re.MULTILINE)
contents = re.sub(r"^_pkgversion=.+$", f'_pkgversion={latest_version}', contents, flags=re.MULTILINE)
contents = re.sub(r"(sha256sums_x86_64=\(\n ').+'\n", f"\g<1>{latest_hash_x86_64}'\n", contents)
with open('PKGBUILD', 'w') as fp:
fp.write(contents)
| 0
| 0
| 0
|
cbd4baa2dbcce74135081efb545a3df0f0b369a9
| 10,085
|
py
|
Python
|
hottbox/utils/generation/tests/test_basic.py
|
adamurban98/hottbox
|
26580018ec6d38a1b08266c04ce4408c9e276130
|
[
"Apache-2.0"
] | 167
|
2018-05-07T10:31:00.000Z
|
2022-02-24T19:20:31.000Z
|
hottbox/utils/generation/tests/test_basic.py
|
adamurban98/hottbox
|
26580018ec6d38a1b08266c04ce4408c9e276130
|
[
"Apache-2.0"
] | 19
|
2018-05-10T13:26:39.000Z
|
2020-01-31T12:49:27.000Z
|
hottbox/utils/generation/tests/test_basic.py
|
adamurban98/hottbox
|
26580018ec6d38a1b08266c04ce4408c9e276130
|
[
"Apache-2.0"
] | 24
|
2018-04-02T17:16:50.000Z
|
2021-12-07T06:21:40.000Z
|
import pytest
import numpy as np
from functools import reduce
from hottbox.core.structures import Tensor,TensorCPD, TensorTKD, TensorTT
from hottbox.utils.validation.checks import is_super_symmetric
from ..basic import dense_tensor, sparse_tensor, super_diagonal_tensor, \
super_diag_tensor, super_symmetric_tensor, residual_tensor
def test_super_diag_tensor():
""" Tests for creating super-diagonal tensor"""
order = 3
rank = 2
correct_shape = (rank, ) * order
true_default_data = np.array([[[1., 0.],
[0., 0.]],
[[0., 0.],
[0., 1.]]])
true_default_mode_names = ['mode-0', 'mode-1', 'mode-2']
correct_values = np.arange(rank)
true_data = np.array([[[0., 0.],
[0., 0.]],
[[0., 0.],
[0., 1.]]])
# ------ tests for default super diagonal tensor
tensor = super_diag_tensor(correct_shape)
assert isinstance(tensor, Tensor)
np.testing.assert_array_equal(tensor.data, true_default_data)
assert (tensor.mode_names == true_default_mode_names)
# ------ tests for super diagonal tensor with custom values on the main diagonal
tensor = super_diag_tensor(correct_shape, values=correct_values)
assert isinstance(tensor, Tensor)
np.testing.assert_array_equal(tensor.data, true_data)
assert (tensor.mode_names == true_default_mode_names)
# ------ tests that should Fail
with pytest.raises(TypeError):
# shape should be passed as tuple
super_diag_tensor(shape=list(correct_shape))
with pytest.raises(ValueError):
# all values in shape should be the same
incorrect_shape = [rank] * order
incorrect_shape[1] = order+1
super_diag_tensor(shape=tuple(incorrect_shape))
with pytest.raises(ValueError):
# values should be an one dimensional numpy array
incorrect_values = np.ones([rank, rank])
super_diag_tensor(shape=correct_shape, values=incorrect_values)
with pytest.raises(ValueError):
# too many values for the specified shape
incorrect_values = np.ones(correct_shape[0]+1)
super_diag_tensor(shape=correct_shape, values=incorrect_values)
with pytest.raises(TypeError):
# values should be a numpy array
incorrect_values = [1] * correct_shape[0]
super_diag_tensor(shape=correct_shape, values=incorrect_values)
def test_residual_tensor():
""" Tests for computing/creating a residual tensor """
true_default_mode_names = ['mode-0', 'mode-1', 'mode-2']
# ------ tests for residual tensor with the Tensor
array_3d = np.array([[[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
true_residual_data = np.zeros(array_3d.shape)
tensor_1 = Tensor(array=array_3d)
tensor_2 = Tensor(array=array_3d)
residual = residual_tensor(tensor_orig=tensor_1, tensor_approx=tensor_2)
assert isinstance(residual, Tensor)
assert (residual.mode_names == true_default_mode_names)
np.testing.assert_array_equal(residual.data, true_residual_data)
# ------ tests for residual tensor with the TensorCPD
array_3d = np.array([[[100., 250., 400., 550.],
[250., 650., 1050., 1450.],
[400., 1050., 1700., 2350.]],
[[250., 650., 1050., 1450.],
[650., 1925., 3200., 4475.],
[1050., 3200., 5350., 7500.]]]
)
true_residual_data = np.zeros(array_3d.shape)
tensor = Tensor(array=array_3d)
ft_shape = (2, 3, 4) # define shape of the tensor in full form
R = 5 # define Kryskal rank of a tensor in CP form
core_values = np.ones(R)
fmat = [np.arange(orig_dim * R).reshape(orig_dim, R)
for orig_dim in ft_shape]
tensor_cpd = TensorCPD(fmat=fmat, core_values=core_values)
residual = residual_tensor(tensor_orig=tensor, tensor_approx=tensor_cpd)
assert isinstance(residual, Tensor)
assert (residual.mode_names == true_default_mode_names)
np.testing.assert_array_equal(residual.data, true_residual_data)
# ------ tests for residual tensor with the TensorTKD
array_3d = np.array([[[378, 1346, 2314, 3282, 4250],
[1368, 4856, 8344, 11832, 15320],
[2358, 8366, 14374, 20382, 26390],
[3348, 11876, 20404, 28932, 37460]],
[[1458, 5146, 8834, 12522, 16210],
[5112, 17944, 30776, 43608, 56440],
[8766, 30742, 52718, 74694, 96670],
[12420, 43540, 74660, 105780, 136900]],
[[2538, 8946, 15354, 21762, 28170],
[8856, 31032, 53208, 75384, 97560],
[15174, 53118, 91062, 129006, 166950],
[21492, 75204, 128916, 182628, 236340]]])
true_residual_data = np.zeros(array_3d.shape)
tensor = Tensor(array=array_3d)
ft_shape = (3, 4, 5) # define shape of the tensor in full form
ml_rank = (2, 3, 4) # define multi-linear rank of a tensor in Tucker form
core_size = reduce(lambda x, y: x * y, ml_rank)
core_values = np.arange(core_size).reshape(ml_rank)
fmat = [np.arange(ft_shape[mode] * ml_rank[mode]).reshape(ft_shape[mode],
ml_rank[mode]) for mode in range(len(ft_shape))]
tensor_tkd = TensorTKD(fmat=fmat, core_values=core_values)
residual = residual_tensor(tensor_orig=tensor, tensor_approx=tensor_tkd)
assert isinstance(residual, Tensor)
assert (residual.mode_names == true_default_mode_names)
np.testing.assert_array_equal(residual.data, true_residual_data)
# ------ tests for residual tensor with the TensorTT
array_3d = np.array([[[300, 348, 396, 444, 492, 540],
[354, 411, 468, 525, 582, 639],
[408, 474, 540, 606, 672, 738],
[462, 537, 612, 687, 762, 837],
[516, 600, 684, 768, 852, 936]],
[[960, 1110, 1260, 1410, 1560, 1710],
[1230, 1425, 1620, 1815, 2010, 2205],
[1500, 1740, 1980, 2220, 2460, 2700],
[1770, 2055, 2340, 2625, 2910, 3195],
[2040, 2370, 2700, 3030, 3360, 3690]],
[[1620, 1872, 2124, 2376, 2628, 2880],
[2106, 2439, 2772, 3105, 3438, 3771],
[2592, 3006, 3420, 3834, 4248, 4662],
[3078, 3573, 4068, 4563, 5058, 5553],
[3564, 4140, 4716, 5292, 5868, 6444]],
[[2280, 2634, 2988, 3342, 3696, 4050],
[2982, 3453, 3924, 4395, 4866, 5337],
[3684, 4272, 4860, 5448, 6036, 6624],
[4386, 5091, 5796, 6501, 7206, 7911],
[5088, 5910, 6732, 7554, 8376, 9198]]])
true_residual_data = np.zeros(array_3d.shape)
tensor = Tensor(array=array_3d)
r1, r2 = 2, 3
I, J, K = 4, 5, 6
core_1 = np.arange(I * r1).reshape(I, r1)
core_2 = np.arange(r1 * J * r2).reshape(r1, J, r2)
core_3 = np.arange(r2 * K).reshape(r2, K)
core_values = [core_1, core_2, core_3]
ft_shape = (I, J, K)
tensor_tt = TensorTT(core_values=core_values)
residual = residual_tensor(tensor_orig=tensor, tensor_approx=tensor_tt)
assert isinstance(residual, Tensor)
assert (residual.mode_names == true_default_mode_names)
np.testing.assert_array_equal(residual.data, true_residual_data)
# ------ tests that should FAIL for residual tensor due to wrong input type
array_3d = np.array([[[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
tensor_1 = Tensor(array=array_3d)
tensor_2 = array_3d
with pytest.raises(TypeError):
residual_tensor(tensor_orig=tensor_1, tensor_approx=tensor_2)
tensor_1 = array_3d
tensor_2 = Tensor(array=array_3d)
with pytest.raises(TypeError):
residual_tensor(tensor_orig=tensor_1, tensor_approx=tensor_2)
| 41.846473
| 110
| 0.578681
|
import pytest
import numpy as np
from functools import reduce
from hottbox.core.structures import Tensor,TensorCPD, TensorTKD, TensorTT
from hottbox.utils.validation.checks import is_super_symmetric
from ..basic import dense_tensor, sparse_tensor, super_diagonal_tensor, \
super_diag_tensor, super_symmetric_tensor, residual_tensor
def test_dense():
# TODO: test distribution
true_shape = (4,3,2,3)
true_distribution = 'normal'
true_type = 0
tensor = dense_tensor(true_shape, true_distribution, true_type)
assert isinstance(tensor, Tensor)
assert true_shape == tensor.shape
pct_nonzero = np.count_nonzero(tensor.data) / tensor.data.size
assert pct_nonzero > 0.8
def test_sparse():
true_shape = (4,3,2,3)
true_distribution = 'normal'
true_type = 0
tensor = sparse_tensor(true_shape, true_distribution, true_type)
assert isinstance(tensor, Tensor)
assert true_shape == tensor.shape
pct_nonzero = np.count_nonzero(tensor.data) / tensor.data.size
assert pct_nonzero < 0.08 and pct_nonzero > 0.02
def test_superdiagonal():
true_shape = (4,4,4)
true_distribution = 'ones'
true_type = 0
tensor = super_diagonal_tensor(true_shape, true_distribution)
assert true_shape == tensor.shape
assert isinstance(tensor, Tensor)
tensor = tensor.data
trace = 0
for i in range(true_shape[0]):
trace += tensor[i,i,i]
assert trace == true_shape[0]
def test_super_diag_tensor():
""" Tests for creating super-diagonal tensor"""
order = 3
rank = 2
correct_shape = (rank, ) * order
true_default_data = np.array([[[1., 0.],
[0., 0.]],
[[0., 0.],
[0., 1.]]])
true_default_mode_names = ['mode-0', 'mode-1', 'mode-2']
correct_values = np.arange(rank)
true_data = np.array([[[0., 0.],
[0., 0.]],
[[0., 0.],
[0., 1.]]])
# ------ tests for default super diagonal tensor
tensor = super_diag_tensor(correct_shape)
assert isinstance(tensor, Tensor)
np.testing.assert_array_equal(tensor.data, true_default_data)
assert (tensor.mode_names == true_default_mode_names)
# ------ tests for super diagonal tensor with custom values on the main diagonal
tensor = super_diag_tensor(correct_shape, values=correct_values)
assert isinstance(tensor, Tensor)
np.testing.assert_array_equal(tensor.data, true_data)
assert (tensor.mode_names == true_default_mode_names)
# ------ tests that should Fail
with pytest.raises(TypeError):
# shape should be passed as tuple
super_diag_tensor(shape=list(correct_shape))
with pytest.raises(ValueError):
# all values in shape should be the same
incorrect_shape = [rank] * order
incorrect_shape[1] = order+1
super_diag_tensor(shape=tuple(incorrect_shape))
with pytest.raises(ValueError):
# values should be an one dimensional numpy array
incorrect_values = np.ones([rank, rank])
super_diag_tensor(shape=correct_shape, values=incorrect_values)
with pytest.raises(ValueError):
# too many values for the specified shape
incorrect_values = np.ones(correct_shape[0]+1)
super_diag_tensor(shape=correct_shape, values=incorrect_values)
with pytest.raises(TypeError):
# values should be a numpy array
incorrect_values = [1] * correct_shape[0]
super_diag_tensor(shape=correct_shape, values=incorrect_values)
def test_supersymmetric():
true_shape = (4,4,4)
tensor = super_symmetric_tensor(true_shape)
assert true_shape == tensor.shape
assert isinstance(tensor, Tensor)
assert is_super_symmetric(tensor)
def test_residual_tensor():
""" Tests for computing/creating a residual tensor """
true_default_mode_names = ['mode-0', 'mode-1', 'mode-2']
# ------ tests for residual tensor with the Tensor
array_3d = np.array([[[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
true_residual_data = np.zeros(array_3d.shape)
tensor_1 = Tensor(array=array_3d)
tensor_2 = Tensor(array=array_3d)
residual = residual_tensor(tensor_orig=tensor_1, tensor_approx=tensor_2)
assert isinstance(residual, Tensor)
assert (residual.mode_names == true_default_mode_names)
np.testing.assert_array_equal(residual.data, true_residual_data)
# ------ tests for residual tensor with the TensorCPD
array_3d = np.array([[[100., 250., 400., 550.],
[250., 650., 1050., 1450.],
[400., 1050., 1700., 2350.]],
[[250., 650., 1050., 1450.],
[650., 1925., 3200., 4475.],
[1050., 3200., 5350., 7500.]]]
)
true_residual_data = np.zeros(array_3d.shape)
tensor = Tensor(array=array_3d)
ft_shape = (2, 3, 4) # define shape of the tensor in full form
R = 5 # define Kryskal rank of a tensor in CP form
core_values = np.ones(R)
fmat = [np.arange(orig_dim * R).reshape(orig_dim, R)
for orig_dim in ft_shape]
tensor_cpd = TensorCPD(fmat=fmat, core_values=core_values)
residual = residual_tensor(tensor_orig=tensor, tensor_approx=tensor_cpd)
assert isinstance(residual, Tensor)
assert (residual.mode_names == true_default_mode_names)
np.testing.assert_array_equal(residual.data, true_residual_data)
# ------ tests for residual tensor with the TensorTKD
array_3d = np.array([[[378, 1346, 2314, 3282, 4250],
[1368, 4856, 8344, 11832, 15320],
[2358, 8366, 14374, 20382, 26390],
[3348, 11876, 20404, 28932, 37460]],
[[1458, 5146, 8834, 12522, 16210],
[5112, 17944, 30776, 43608, 56440],
[8766, 30742, 52718, 74694, 96670],
[12420, 43540, 74660, 105780, 136900]],
[[2538, 8946, 15354, 21762, 28170],
[8856, 31032, 53208, 75384, 97560],
[15174, 53118, 91062, 129006, 166950],
[21492, 75204, 128916, 182628, 236340]]])
true_residual_data = np.zeros(array_3d.shape)
tensor = Tensor(array=array_3d)
ft_shape = (3, 4, 5) # define shape of the tensor in full form
ml_rank = (2, 3, 4) # define multi-linear rank of a tensor in Tucker form
core_size = reduce(lambda x, y: x * y, ml_rank)
core_values = np.arange(core_size).reshape(ml_rank)
fmat = [np.arange(ft_shape[mode] * ml_rank[mode]).reshape(ft_shape[mode],
ml_rank[mode]) for mode in range(len(ft_shape))]
tensor_tkd = TensorTKD(fmat=fmat, core_values=core_values)
residual = residual_tensor(tensor_orig=tensor, tensor_approx=tensor_tkd)
assert isinstance(residual, Tensor)
assert (residual.mode_names == true_default_mode_names)
np.testing.assert_array_equal(residual.data, true_residual_data)
# ------ tests for residual tensor with the TensorTT
array_3d = np.array([[[300, 348, 396, 444, 492, 540],
[354, 411, 468, 525, 582, 639],
[408, 474, 540, 606, 672, 738],
[462, 537, 612, 687, 762, 837],
[516, 600, 684, 768, 852, 936]],
[[960, 1110, 1260, 1410, 1560, 1710],
[1230, 1425, 1620, 1815, 2010, 2205],
[1500, 1740, 1980, 2220, 2460, 2700],
[1770, 2055, 2340, 2625, 2910, 3195],
[2040, 2370, 2700, 3030, 3360, 3690]],
[[1620, 1872, 2124, 2376, 2628, 2880],
[2106, 2439, 2772, 3105, 3438, 3771],
[2592, 3006, 3420, 3834, 4248, 4662],
[3078, 3573, 4068, 4563, 5058, 5553],
[3564, 4140, 4716, 5292, 5868, 6444]],
[[2280, 2634, 2988, 3342, 3696, 4050],
[2982, 3453, 3924, 4395, 4866, 5337],
[3684, 4272, 4860, 5448, 6036, 6624],
[4386, 5091, 5796, 6501, 7206, 7911],
[5088, 5910, 6732, 7554, 8376, 9198]]])
true_residual_data = np.zeros(array_3d.shape)
tensor = Tensor(array=array_3d)
r1, r2 = 2, 3
I, J, K = 4, 5, 6
core_1 = np.arange(I * r1).reshape(I, r1)
core_2 = np.arange(r1 * J * r2).reshape(r1, J, r2)
core_3 = np.arange(r2 * K).reshape(r2, K)
core_values = [core_1, core_2, core_3]
ft_shape = (I, J, K)
tensor_tt = TensorTT(core_values=core_values)
residual = residual_tensor(tensor_orig=tensor, tensor_approx=tensor_tt)
assert isinstance(residual, Tensor)
assert (residual.mode_names == true_default_mode_names)
np.testing.assert_array_equal(residual.data, true_residual_data)
# ------ tests that should FAIL for residual tensor due to wrong input type
array_3d = np.array([[[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
tensor_1 = Tensor(array=array_3d)
tensor_2 = array_3d
with pytest.raises(TypeError):
residual_tensor(tensor_orig=tensor_1, tensor_approx=tensor_2)
tensor_1 = array_3d
tensor_2 = Tensor(array=array_3d)
with pytest.raises(TypeError):
residual_tensor(tensor_orig=tensor_1, tensor_approx=tensor_2)
| 1,236
| 0
| 92
|
3157151c02d05ab116eb33bad54d5906f6b8d1d5
| 2,045
|
py
|
Python
|
src/appengine/model.py
|
tomwilkie/awesomation
|
708a0ff2ffd431f24ed3f942cafd24882dc89620
|
[
"MIT"
] | 28
|
2015-01-12T15:34:37.000Z
|
2021-06-17T14:27:49.000Z
|
src/appengine/model.py
|
tomwilkie/awesomation
|
708a0ff2ffd431f24ed3f942cafd24882dc89620
|
[
"MIT"
] | 16
|
2015-01-11T21:46:08.000Z
|
2015-02-06T17:01:50.000Z
|
src/appengine/model.py
|
tomwilkie/awesomation
|
708a0ff2ffd431f24ed3f942cafd24882dc89620
|
[
"MIT"
] | 2
|
2015-01-10T17:34:23.000Z
|
2015-01-10T18:38:01.000Z
|
"""Base classes for my data model."""
import decimal
from google.appengine.ext import ndb
from google.appengine.ext.ndb import polymodel
from appengine import history, rest, user
# From http://stackoverflow.com/questions/10035133/ndb-decimal-property
class DecimalProperty(ndb.IntegerProperty):
"""Decimal property ideal to store currency values, such as $20.34."""
# See https://developers.google.com/appengine/docs/python/ndb/subclassprop
class Base(polymodel.PolyModel):
"""Base for all objects."""
def to_dict(self):
"""Convert this object to a python dict."""
result = super(Base, self).to_dict()
result['id'] = self.key.id()
result['class'] = result['class_'][-1]
del result['class_']
# Should move this into detector mixin when I figure out how
if 'detector' in result:
del result['detector']
return result
@classmethod
def _put_async(self, **ctx_options):
"""Overrides _put_async and sends event to UI."""
classname = self._event_classname()
if classname is not None:
values = self.to_dict()
user.send_event(cls=classname, id=self.key.string_id(),
event='update', obj=values)
history.store_version(values)
return super(Base, self)._put_async(**ctx_options)
put_async = _put_async
@rest.command
def sync(self):
"""Called when fields on the object are updated
through the API."""
pass
| 30.984848
| 76
| 0.672372
|
"""Base classes for my data model."""
import decimal
from google.appengine.ext import ndb
from google.appengine.ext.ndb import polymodel
from appengine import history, rest, user
# From http://stackoverflow.com/questions/10035133/ndb-decimal-property
class DecimalProperty(ndb.IntegerProperty):
"""Decimal property ideal to store currency values, such as $20.34."""
# See https://developers.google.com/appengine/docs/python/ndb/subclassprop
def _validate(self, value):
if not isinstance(value, (decimal.Decimal, str, unicode, int, long)):
raise TypeError('Expected a Decimal, str, unicode, int '
'or long an got instead %s' % repr(value))
def _to_base_type(self, value):
return int(decimal.Decimal(value) * 100)
def _from_base_type(self, value):
return decimal.Decimal(value)/decimal.Decimal(100)
class Base(polymodel.PolyModel):
"""Base for all objects."""
def to_dict(self):
"""Convert this object to a python dict."""
result = super(Base, self).to_dict()
result['id'] = self.key.id()
result['class'] = result['class_'][-1]
del result['class_']
# Should move this into detector mixin when I figure out how
if 'detector' in result:
del result['detector']
return result
@classmethod
def _event_classname(cls):
return None
def _put_async(self, **ctx_options):
"""Overrides _put_async and sends event to UI."""
classname = self._event_classname()
if classname is not None:
values = self.to_dict()
user.send_event(cls=classname, id=self.key.string_id(),
event='update', obj=values)
history.store_version(values)
return super(Base, self)._put_async(**ctx_options)
put_async = _put_async
@rest.command
def get_history(self, start, end):
values = self.to_dict()
return history.get_range(values['class'], values['id'],
start, end)
def sync(self):
"""Called when fields on the object are updated
through the API."""
pass
| 493
| 0
| 122
|
cedbd4d63dbf752123f11e31471ca8fd234d5f07
| 1,909
|
py
|
Python
|
Blender 2.91/2.91/scripts/addons/power_sequencer/operators/scene_cycle.py
|
calculusrobotics/RNNs-for-Bayesian-State-Estimation
|
2aacf86d2e447e10c840b4926d4de7bc5e46d9bc
|
[
"MIT"
] | 1
|
2021-06-30T00:39:40.000Z
|
2021-06-30T00:39:40.000Z
|
release/scripts/addons/power_sequencer/operators/scene_cycle.py
|
ringsce/Rings3D
|
8059d1e2460fc8d6f101eff8e695f68a99f6671d
|
[
"Naumen",
"Condor-1.1",
"MS-PL"
] | null | null | null |
release/scripts/addons/power_sequencer/operators/scene_cycle.py
|
ringsce/Rings3D
|
8059d1e2460fc8d6f101eff8e695f68a99f6671d
|
[
"Naumen",
"Condor-1.1",
"MS-PL"
] | null | null | null |
#
# Copyright (C) 2016-2020 by Nathan Lovato, Daniel Oakey, Razvan Radulescu, and contributors
#
# This file is part of Power Sequencer.
#
# Power Sequencer is free software: you can redistribute it and/or modify it under the terms of the
# GNU General Public License as published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# Power Sequencer is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
# without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along with Power Sequencer. If
# not, see <https://www.gnu.org/licenses/>.
#
import bpy
from .utils.doc import doc_name, doc_idname, doc_brief, doc_description
class POWER_SEQUENCER_OT_scene_cycle(bpy.types.Operator):
"""
Cycle through scenes
"""
doc = {
"name": doc_name(__qualname__),
"demo": "https://i.imgur.com/7zhq8Tg.gif",
"description": doc_description(__doc__),
"shortcuts": [({"type": "TAB", "value": "PRESS", "shift": True}, {}, "Cycle Scenes")],
"keymap": "Sequencer",
}
bl_idname = doc_idname(__qualname__)
bl_label = doc["name"]
bl_description = doc_brief(doc["description"])
bl_options = {"REGISTER", "UNDO"}
@classmethod
| 34.709091
| 99
| 0.675746
|
#
# Copyright (C) 2016-2020 by Nathan Lovato, Daniel Oakey, Razvan Radulescu, and contributors
#
# This file is part of Power Sequencer.
#
# Power Sequencer is free software: you can redistribute it and/or modify it under the terms of the
# GNU General Public License as published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# Power Sequencer is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
# without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along with Power Sequencer. If
# not, see <https://www.gnu.org/licenses/>.
#
import bpy
from .utils.doc import doc_name, doc_idname, doc_brief, doc_description
class POWER_SEQUENCER_OT_scene_cycle(bpy.types.Operator):
"""
Cycle through scenes
"""
doc = {
"name": doc_name(__qualname__),
"demo": "https://i.imgur.com/7zhq8Tg.gif",
"description": doc_description(__doc__),
"shortcuts": [({"type": "TAB", "value": "PRESS", "shift": True}, {}, "Cycle Scenes")],
"keymap": "Sequencer",
}
bl_idname = doc_idname(__qualname__)
bl_label = doc["name"]
bl_description = doc_brief(doc["description"])
bl_options = {"REGISTER", "UNDO"}
@classmethod
def poll(cls, context):
return bpy.data.scenes
def execute(self, context):
scenes = bpy.data.scenes
scene_count = len(scenes)
if context.screen.is_animation_playing:
bpy.ops.screen.animation_cancel(restore_frame=False)
for index in range(scene_count):
if context.scene == scenes[index]:
context.window.scene = scenes[(index + 1) % scene_count]
break
return {"FINISHED"}
| 432
| 0
| 53
|
df341bd8c114208ae94cc128e9c72342d72f8af5
| 8,094
|
py
|
Python
|
demo/demo_guess_count_file.py
|
UChicagoSUPERgroup/analytic-password-cracking
|
1c30153e852af36ffdc0566c949f63d9736105f8
|
[
"MIT"
] | 22
|
2019-05-20T16:43:16.000Z
|
2021-04-23T09:32:11.000Z
|
demo/demo_guess_count_file.py
|
UChicagoSUPERgroup/analytic-password-cracking
|
1c30153e852af36ffdc0566c949f63d9736105f8
|
[
"MIT"
] | null | null | null |
demo/demo_guess_count_file.py
|
UChicagoSUPERgroup/analytic-password-cracking
|
1c30153e852af36ffdc0566c949f63d9736105f8
|
[
"MIT"
] | 6
|
2019-05-20T18:01:05.000Z
|
2020-11-12T07:54:33.000Z
|
from sys import path as sys_path
from os import path as os_path
from subprocess import Popen, PIPE
import time
import logging
import warnings
import numpy as np
sys_path.append(os_path.abspath('../src'))
from config import RUNTIME_CONFIG
from config import john_nick_names, hc_nick_names
from common import PasswordPolicyConf, FilePath
from argparsing import setup_args, parse_args
from guess_count import GuessCount
from tokenstr import TokenString
from utility import read_passwords,read_wordlist,read_rulelist,get_look_cmd,build_trie_from_wordlist
from utility import filter_passwords_with_password_policy
from preprocess import precomputation
from invert_rule import invert_one_rule
from demo_common import match_inversion_result, search_exist_data, search_trie, estimate_guess_number
def start_processing():
""" Take in a wordlist, rulelist and test set, outputs the guessability and guess number of each pwd in the test set.
Steps:
1. read rulelist and do precomputation (detect invertibility)
2. read wordlist/pwlist, and get count for each rule
3. Rule Inversion (for each rule, invert all pwds)
"""
stime = time.perf_counter()
##################### Precomputation and Other Preparation #####################
# initialize a bash exe for communication
external_bash_process = Popen(['/bin/bash'], stdin=PIPE, stdout=PIPE)
# Logging Basic Info
logging.basicConfig(filename=RUNTIME_CONFIG.get_log_addr(),level=logging.DEBUG)
logging.info("Starting Time: {}\n\nConfigurations: {}\n".format(time.strftime("%Y-%m-%d %H:%M"), RUNTIME_CONFIG.short_config_string()))
logging.info("PasswordPolicy: {}\n".format(RUNTIME_CONFIG['password_policy'].to_debug_string()))
print("Reading Rulelist\n")
rulelist = read_rulelist(RUNTIME_CONFIG['rulelist_path']['name'], RUNTIME_CONFIG['rulelist_path']['prefix'])
print("Start Precomputation\n")
rulelist = precomputation(rulelist)
print("Reading Wordlist and Password Set\n")
wordlist = read_wordlist(RUNTIME_CONFIG['wordlist_path']['name'], RUNTIME_CONFIG['wordlist_path']['prefix'])
# Computing Guess Count
counts, cumsum = GuessCount.get_counts(wordlist, rulelist, RUNTIME_CONFIG['preprocess_path'])
# read other things
pwlist = read_passwords(RUNTIME_CONFIG['pwlist_path']['addr'])
# filter out pwds not consistent with the policy
not_filtered_pwds, filtered_pwds = filter_passwords_with_password_policy(pwlist)
trie = build_trie_from_wordlist(wordlist)
##################### Start Inversion #####################
print("Start Inverting Rules\n")
i_time = time.perf_counter()
# guessability of pwds
is_guessable = [False] * len(pwlist)
is_enable_regex = RUNTIME_CONFIG['enable_regex']
is_debug = RUNTIME_CONFIG['debug']
lookup_threshold = RUNTIME_CONFIG['lookup_threshold']
# tokenize pwds once.
tokenized_pwds = [TokenString(pwd) for pw_idx, pwd in not_filtered_pwds]
# invert rules (with special memory handling and other staff)
for r_idx, r in enumerate(rulelist):
if is_debug == True:
print(r.raw)
if r.feasibility.is_invertible(): # invertible, if blow up, use trie
for token_pwd, (pw_idx, pwd) in zip(tokenized_pwds,not_filtered_pwds):
result = invert_one_rule(token_pwd,r,is_enable_regex,r.feasibility.special_idx)
if result.is_normal():
if result.get_number_of_strings() <= lookup_threshold:
ret_vals = match_inversion_result(result, wordlist)
else:
ret_vals = search_trie(result, trie)
if len(ret_vals) != 0:
is_guessable[pw_idx] = True
for v in ret_vals:
logging.info("\nPasswordIdx:{}\nPassword:{}\nRule:{}\nWord:{}\nGuess:{} ( {} - {} )\n".format(pw_idx, pwd, r.raw, v, *estimate_guess_number(counts, cumsum, v, r_idx, wordlist)))
elif result.is_out_of_scope():
ret_vals = []
logging.info("Inversion error for {}(RL) {}(pw), error msg: {}\n".format(r.raw, pwd, "out_of_scope"))
print("Inversion error for {}(RL) {}(pw), error msg: {}".format(r.raw, pwd, "out_of_scope"))
else:
ret_vals = []
logging.info("Inversion error for {}(RL) {}(pw), error msg: {}\n".format(r.raw, pwd, result.error_msg))
print("Inversion error for {}(RL) {}(pw), error msg: {}".format(r.raw, pwd, result.error_msg))
elif r.feasibility.is_optimizable(): # uninvertible, if cannot handle, binary
# where the binary file is stored
enumerated_data_addr = "{}/enumerated/rule{}.txt".format(RUNTIME_CONFIG['preprocess_path'],r_idx)
for token_pwd, (pw_idx, pwd) in zip(tokenized_pwds,not_filtered_pwds):
result = invert_one_rule(token_pwd,r,is_enable_regex)
if result.is_normal():
if result.get_number_of_strings() <= lookup_threshold:
ret_vals = match_inversion_result(result, wordlist)
else:
ret_vals = search_exist_data(pwd,enumerated_data_addr,external_bash_process)
if len(ret_vals) != 0:
is_guessable[pw_idx] = True
for v in ret_vals:
logging.info("\nPasswordIdx:{}\nPassword:{}\nRule:{}\nWord:{}\nGuess:{} ( {} - {} )\n".format(pw_idx, pwd, r.raw, v, *estimate_guess_number(counts, cumsum, v, r_idx, wordlist)))
elif result.is_out_of_scope():
ret_vals = search_exist_data(pwd,enumerated_data_addr,external_bash_process)
if len(ret_vals) != 0:
is_guessable[pw_idx] = True
for v in ret_vals:
logging.info("\nPasswordIdx:{}\nPassword:{}\nRule:{}\nWord:{}\nGuess:{} ( {} - {} )\n".format(pw_idx, pwd, r.raw, v, *estimate_guess_number(counts, cumsum, v, r_idx, wordlist)))
else:
ret_vals = []
logging.info("Inversion error for {}(RL) {}(pw), error msg: {}\n".format(r.raw, pwd, result.error_msg))
print("Inversion error for {}(RL) {}(pw), error msg: {}".format(r.raw, pwd, result.error_msg))
else: # binary
# where the binary file is stored
enumerated_data_addr = "{}/enumerated/rule{}.txt".format(RUNTIME_CONFIG['preprocess_path'],r_idx)
for token_pwd, (pw_idx, pwd) in zip(tokenized_pwds,not_filtered_pwds):
ret_vals = search_exist_data(pwd,enumerated_data_addr,external_bash_process)
if len(ret_vals) != 0:
is_guessable[pw_idx] = True
for v in ret_vals:
logging.info("\nPasswordIdx:{}\nPassword:{}\nRule:{}\nWord:{}\nGuess:{} ( {} - {} )\n".format(pw_idx, pwd, r.raw, v, *estimate_guess_number(counts, cumsum, v, r_idx, wordlist)))
##################### End of Inversion #####################
# Write Not Guessable Data
for pw_idx, pwd in filtered_pwds:
logging.info("\nPasswordIdx:{}\nPassword:{}\nNot Guessable\n".format(pw_idx, pwd))
for is_guessed, (pw_idx, pwd) in zip(is_guessable, not_filtered_pwds):
if is_guessed == False:
logging.info("\nPasswordIdx:{}\nPassword:{}\nNot Guessable\n".format(pw_idx, pwd))
logging.info("Total guesses made by this configuration: {}\n".format(np.sum(counts)))
print("Finished Inverting Rules, Total Time: {}".format(time.perf_counter()-i_time))
if __name__ == "__main__":
main()
| 47.611765
| 205
| 0.627255
|
from sys import path as sys_path
from os import path as os_path
from subprocess import Popen, PIPE
import time
import logging
import warnings
import numpy as np
sys_path.append(os_path.abspath('../src'))
from config import RUNTIME_CONFIG
from config import john_nick_names, hc_nick_names
from common import PasswordPolicyConf, FilePath
from argparsing import setup_args, parse_args
from guess_count import GuessCount
from tokenstr import TokenString
from utility import read_passwords,read_wordlist,read_rulelist,get_look_cmd,build_trie_from_wordlist
from utility import filter_passwords_with_password_policy
from preprocess import precomputation
from invert_rule import invert_one_rule
from demo_common import match_inversion_result, search_exist_data, search_trie, estimate_guess_number
def start_processing():
""" Take in a wordlist, rulelist and test set, outputs the guessability and guess number of each pwd in the test set.
Steps:
1. read rulelist and do precomputation (detect invertibility)
2. read wordlist/pwlist, and get count for each rule
3. Rule Inversion (for each rule, invert all pwds)
"""
stime = time.perf_counter()
##################### Precomputation and Other Preparation #####################
# initialize a bash exe for communication
external_bash_process = Popen(['/bin/bash'], stdin=PIPE, stdout=PIPE)
# Logging Basic Info
logging.basicConfig(filename=RUNTIME_CONFIG.get_log_addr(),level=logging.DEBUG)
logging.info("Starting Time: {}\n\nConfigurations: {}\n".format(time.strftime("%Y-%m-%d %H:%M"), RUNTIME_CONFIG.short_config_string()))
logging.info("PasswordPolicy: {}\n".format(RUNTIME_CONFIG['password_policy'].to_debug_string()))
print("Reading Rulelist\n")
rulelist = read_rulelist(RUNTIME_CONFIG['rulelist_path']['name'], RUNTIME_CONFIG['rulelist_path']['prefix'])
print("Start Precomputation\n")
rulelist = precomputation(rulelist)
print("Reading Wordlist and Password Set\n")
wordlist = read_wordlist(RUNTIME_CONFIG['wordlist_path']['name'], RUNTIME_CONFIG['wordlist_path']['prefix'])
# Computing Guess Count
counts, cumsum = GuessCount.get_counts(wordlist, rulelist, RUNTIME_CONFIG['preprocess_path'])
# read other things
pwlist = read_passwords(RUNTIME_CONFIG['pwlist_path']['addr'])
# filter out pwds not consistent with the policy
not_filtered_pwds, filtered_pwds = filter_passwords_with_password_policy(pwlist)
trie = build_trie_from_wordlist(wordlist)
##################### Start Inversion #####################
print("Start Inverting Rules\n")
i_time = time.perf_counter()
# guessability of pwds
is_guessable = [False] * len(pwlist)
is_enable_regex = RUNTIME_CONFIG['enable_regex']
is_debug = RUNTIME_CONFIG['debug']
lookup_threshold = RUNTIME_CONFIG['lookup_threshold']
# tokenize pwds once.
tokenized_pwds = [TokenString(pwd) for pw_idx, pwd in not_filtered_pwds]
# invert rules (with special memory handling and other staff)
for r_idx, r in enumerate(rulelist):
if is_debug == True:
print(r.raw)
if r.feasibility.is_invertible(): # invertible, if blow up, use trie
for token_pwd, (pw_idx, pwd) in zip(tokenized_pwds,not_filtered_pwds):
result = invert_one_rule(token_pwd,r,is_enable_regex,r.feasibility.special_idx)
if result.is_normal():
if result.get_number_of_strings() <= lookup_threshold:
ret_vals = match_inversion_result(result, wordlist)
else:
ret_vals = search_trie(result, trie)
if len(ret_vals) != 0:
is_guessable[pw_idx] = True
for v in ret_vals:
logging.info("\nPasswordIdx:{}\nPassword:{}\nRule:{}\nWord:{}\nGuess:{} ( {} - {} )\n".format(pw_idx, pwd, r.raw, v, *estimate_guess_number(counts, cumsum, v, r_idx, wordlist)))
elif result.is_out_of_scope():
ret_vals = []
logging.info("Inversion error for {}(RL) {}(pw), error msg: {}\n".format(r.raw, pwd, "out_of_scope"))
print("Inversion error for {}(RL) {}(pw), error msg: {}".format(r.raw, pwd, "out_of_scope"))
else:
ret_vals = []
logging.info("Inversion error for {}(RL) {}(pw), error msg: {}\n".format(r.raw, pwd, result.error_msg))
print("Inversion error for {}(RL) {}(pw), error msg: {}".format(r.raw, pwd, result.error_msg))
elif r.feasibility.is_optimizable(): # uninvertible, if cannot handle, binary
# where the binary file is stored
enumerated_data_addr = "{}/enumerated/rule{}.txt".format(RUNTIME_CONFIG['preprocess_path'],r_idx)
for token_pwd, (pw_idx, pwd) in zip(tokenized_pwds,not_filtered_pwds):
result = invert_one_rule(token_pwd,r,is_enable_regex)
if result.is_normal():
if result.get_number_of_strings() <= lookup_threshold:
ret_vals = match_inversion_result(result, wordlist)
else:
ret_vals = search_exist_data(pwd,enumerated_data_addr,external_bash_process)
if len(ret_vals) != 0:
is_guessable[pw_idx] = True
for v in ret_vals:
logging.info("\nPasswordIdx:{}\nPassword:{}\nRule:{}\nWord:{}\nGuess:{} ( {} - {} )\n".format(pw_idx, pwd, r.raw, v, *estimate_guess_number(counts, cumsum, v, r_idx, wordlist)))
elif result.is_out_of_scope():
ret_vals = search_exist_data(pwd,enumerated_data_addr,external_bash_process)
if len(ret_vals) != 0:
is_guessable[pw_idx] = True
for v in ret_vals:
logging.info("\nPasswordIdx:{}\nPassword:{}\nRule:{}\nWord:{}\nGuess:{} ( {} - {} )\n".format(pw_idx, pwd, r.raw, v, *estimate_guess_number(counts, cumsum, v, r_idx, wordlist)))
else:
ret_vals = []
logging.info("Inversion error for {}(RL) {}(pw), error msg: {}\n".format(r.raw, pwd, result.error_msg))
print("Inversion error for {}(RL) {}(pw), error msg: {}".format(r.raw, pwd, result.error_msg))
else: # binary
# where the binary file is stored
enumerated_data_addr = "{}/enumerated/rule{}.txt".format(RUNTIME_CONFIG['preprocess_path'],r_idx)
for token_pwd, (pw_idx, pwd) in zip(tokenized_pwds,not_filtered_pwds):
ret_vals = search_exist_data(pwd,enumerated_data_addr,external_bash_process)
if len(ret_vals) != 0:
is_guessable[pw_idx] = True
for v in ret_vals:
logging.info("\nPasswordIdx:{}\nPassword:{}\nRule:{}\nWord:{}\nGuess:{} ( {} - {} )\n".format(pw_idx, pwd, r.raw, v, *estimate_guess_number(counts, cumsum, v, r_idx, wordlist)))
##################### End of Inversion #####################
# Write Not Guessable Data
for pw_idx, pwd in filtered_pwds:
logging.info("\nPasswordIdx:{}\nPassword:{}\nNot Guessable\n".format(pw_idx, pwd))
for is_guessed, (pw_idx, pwd) in zip(is_guessable, not_filtered_pwds):
if is_guessed == False:
logging.info("\nPasswordIdx:{}\nPassword:{}\nNot Guessable\n".format(pw_idx, pwd))
logging.info("Total guesses made by this configuration: {}\n".format(np.sum(counts)))
print("Finished Inverting Rules, Total Time: {}".format(time.perf_counter()-i_time))
def main():
args = setup_args() # set up args
try:
parse_args(args) # parse args
except:
raise
print("Your Running Configuration: {}\n".format(RUNTIME_CONFIG.short_config_string()))
start_processing()
if __name__ == "__main__":
main()
| 220
| 0
| 23
|
333a2f2f140534cc1d6698425fb54de5fcbfec93
| 642
|
py
|
Python
|
utils/env.py
|
Omarzintan/bumblebee-ai
|
0b8c5cecf032730e23b1b710a88538f5e4ea70c9
|
[
"MIT"
] | 3
|
2021-05-06T16:29:26.000Z
|
2022-01-09T03:32:40.000Z
|
utils/env.py
|
Omarzintan/bumblebee-ai
|
0b8c5cecf032730e23b1b710a88538f5e4ea70c9
|
[
"MIT"
] | 1
|
2021-05-20T17:59:12.000Z
|
2021-05-20T17:59:12.000Z
|
utils/env.py
|
Omarzintan/bumblebee-ai
|
0b8c5cecf032730e23b1b710a88538f5e4ea70c9
|
[
"MIT"
] | null | null | null |
'''
This file is for retrieving system environment variables and helper
variables directly derived from them.
In decreasing order of precedence, environment variables can be set by:
1. adding them to .env file at root of this project
2. exporting and then running bumblebee in then same terminal.
E.g. export BUMBLEBEE_ENV=local; bumblebee
3. prefixing 'bumblebee' command with the environment variable when running.
E.g. BUMBLEBEE_ENV=local bumblebee
'''
from dotenv import load_dotenv
import os
load_dotenv()
bumblebee_environment = os.environ.get('BUMBLEBEE_ENV', 'production').lower()
is_local = bumblebee_environment == 'local'
| 33.789474
| 77
| 0.78972
|
'''
This file is for retrieving system environment variables and helper
variables directly derived from them.
In decreasing order of precedence, environment variables can be set by:
1. adding them to .env file at root of this project
2. exporting and then running bumblebee in then same terminal.
E.g. export BUMBLEBEE_ENV=local; bumblebee
3. prefixing 'bumblebee' command with the environment variable when running.
E.g. BUMBLEBEE_ENV=local bumblebee
'''
from dotenv import load_dotenv
import os
load_dotenv()
bumblebee_environment = os.environ.get('BUMBLEBEE_ENV', 'production').lower()
is_local = bumblebee_environment == 'local'
| 0
| 0
| 0
|
248e74a35b18bd30f4803c6ed6fcc98efb31c3af
| 1,701
|
py
|
Python
|
pagination.py
|
billaanil3/Paginization
|
4556634517841bc1104fd1a015beda04c16d5322
|
[
"MIT"
] | 11
|
2017-11-28T22:26:55.000Z
|
2022-03-21T15:42:41.000Z
|
pagination.py
|
billaanil3/Paginization
|
4556634517841bc1104fd1a015beda04c16d5322
|
[
"MIT"
] | 3
|
2017-11-28T21:05:48.000Z
|
2019-04-02T22:38:48.000Z
|
pagination.py
|
billaanil3/Paginization
|
4556634517841bc1104fd1a015beda04c16d5322
|
[
"MIT"
] | 8
|
2017-11-28T17:23:39.000Z
|
2021-11-19T15:41:18.000Z
|
"""Pagination sample for Microsoft Graph."""
# Copyright (c) Microsoft. All rights reserved. Licensed under the MIT license.
# See LICENSE in the project root for license information.
import os
import bottle
import graphrest
import config
MSGRAPH = graphrest.GraphSession(client_id=config.CLIENT_ID,
client_secret=config.CLIENT_SECRET,
redirect_uri=config.REDIRECT_URI,
scopes=['User.Read', 'Mail.Read'])
bottle.TEMPLATE_PATH = ['./static/templates']
@bottle.route('/')
@bottle.view('homepage.html')
def homepage():
"""Render the home page."""
return {'title': 'Pagination Basics'}
@bottle.route('/login')
def login():
"""Prompt user to authenticate."""
endpoint = MSGRAPH.api_endpoint('me/messages')
MSGRAPH.login(login_redirect=f'/pagination?endpoint={endpoint}')
@bottle.route('/login/authorized')
def authorized():
"""Handler for the application's Redirect URI."""
MSGRAPH.redirect_uri_handler()
@bottle.route('/pagination')
@bottle.view('pagination.html')
def pagination():
"""Example of paginated response from Microsoft Graph."""
endpoint = bottle.request.query.endpoint
graphdata = MSGRAPH.get(endpoint).json()
return {'graphdata': graphdata}
@bottle.route('/static/<filepath:path>')
def server_static(filepath):
"""Handler for static files, used with the development server."""
root_folder = os.path.abspath(os.path.dirname(__file__))
return bottle.static_file(filepath, root=os.path.join(root_folder, 'static'))
if __name__ == '__main__':
bottle.run(app=bottle.app(), server='wsgiref', host='localhost', port=5000)
| 29.327586
| 81
| 0.681952
|
"""Pagination sample for Microsoft Graph."""
# Copyright (c) Microsoft. All rights reserved. Licensed under the MIT license.
# See LICENSE in the project root for license information.
import os
import bottle
import graphrest
import config
MSGRAPH = graphrest.GraphSession(client_id=config.CLIENT_ID,
client_secret=config.CLIENT_SECRET,
redirect_uri=config.REDIRECT_URI,
scopes=['User.Read', 'Mail.Read'])
bottle.TEMPLATE_PATH = ['./static/templates']
@bottle.route('/')
@bottle.view('homepage.html')
def homepage():
"""Render the home page."""
return {'title': 'Pagination Basics'}
@bottle.route('/login')
def login():
"""Prompt user to authenticate."""
endpoint = MSGRAPH.api_endpoint('me/messages')
MSGRAPH.login(login_redirect=f'/pagination?endpoint={endpoint}')
@bottle.route('/login/authorized')
def authorized():
"""Handler for the application's Redirect URI."""
MSGRAPH.redirect_uri_handler()
@bottle.route('/pagination')
@bottle.view('pagination.html')
def pagination():
"""Example of paginated response from Microsoft Graph."""
endpoint = bottle.request.query.endpoint
graphdata = MSGRAPH.get(endpoint).json()
return {'graphdata': graphdata}
@bottle.route('/static/<filepath:path>')
def server_static(filepath):
"""Handler for static files, used with the development server."""
root_folder = os.path.abspath(os.path.dirname(__file__))
return bottle.static_file(filepath, root=os.path.join(root_folder, 'static'))
if __name__ == '__main__':
bottle.run(app=bottle.app(), server='wsgiref', host='localhost', port=5000)
| 0
| 0
| 0
|
d7ba520906466c429eb7e964cc54b23a09f4bd0c
| 27,726
|
py
|
Python
|
numbas_lti/models.py
|
pbh4/numbas_leicester
|
bac5c66c0c7809ee9588a69b3bced4244cee08e5
|
[
"Apache-2.0"
] | null | null | null |
numbas_lti/models.py
|
pbh4/numbas_leicester
|
bac5c66c0c7809ee9588a69b3bced4244cee08e5
|
[
"Apache-2.0"
] | null | null | null |
numbas_lti/models.py
|
pbh4/numbas_leicester
|
bac5c66c0c7809ee9588a69b3bced4244cee08e5
|
[
"Apache-2.0"
] | null | null | null |
from django.conf import settings
from django.db import models
from django.dispatch import receiver
from django.contrib.auth.models import User
import requests
from django.utils.text import slugify
from django.utils.translation import ugettext_lazy as _, ugettext
from django.core import validators
from channels import Group, Channel
from django.utils import timezone
from datetime import timedelta,datetime
from django_auth_lti.patch_reverse import reverse
from .groups import group_for_attempt
from .report_outcome import report_outcome_for_attempt, ReportOutcomeFailure, ReportOutcomeConnectionError
import os
import shutil
from zipfile import ZipFile
from lxml import etree
import re
import json
from collections import defaultdict
@receiver(models.signals.post_save)
# Create your models here.
@receiver(models.signals.pre_save, sender=Exam)
GRADING_METHODS = [
('highest',_('Highest score')),
('last',_('Last attempt')),
]
REPORT_TIMES = [
('immediately',_('Immediately')),
('oncompletion',_('On completion')),
('manually',_('Manually, by instructor')),
]
REPORTING_STATUSES = [
('reporting',_('Reporting scores')),
('error',_('Error encountered')),
('complete',_('All scores reported')),
]
SHOW_SCORES_MODES = [
('always',_('Always')),
('complete',_('When attempt is complete')),
('never',_('Never')),
]
COMPLETION_STATUSES = [
('not attempted',_('Not attempted')),
('incomplete',_('Incomplete')),
('completed',_('Complete')),
]
models.signals.post_save.connect(remark_update_scaled_score,sender=RemarkPart)
models.signals.post_delete.connect(remark_update_scaled_score,sender=RemarkPart)
DISCOUNT_BEHAVIOURS = [
('remove','Remove from total'),
('fullmarks','Award everyone full credit'),
]
models.signals.post_save.connect(discount_update_scaled_score,sender=DiscountPart)
models.signals.post_delete.connect(discount_update_scaled_score,sender=DiscountPart)
@receiver(models.signals.post_save,sender=ScormElement)
@receiver(models.signals.post_save,sender=ScormElement)
@receiver(models.signals.post_save,sender=ScormElement)
@receiver(models.signals.post_save,sender=ScormElement)
def scorm_set_num_questions(sender,instance,created,**kwargs):
""" Set the number of questions for this resource - can only work this out once the exam has been run! """
if not re.match(r'^cmi.objectives.([0-9]+).id$',instance.key) or not created:
return
number = int(re.match(r'q(\d+)',instance.value).group(1))+1
resource = instance.attempt.resource
if number>resource.num_questions:
resource.num_questions = number
resource.save()
@receiver(models.signals.pre_save,sender=EditorLink)
| 40.773529
| 197
| 0.68351
|
from django.conf import settings
from django.db import models
from django.dispatch import receiver
from django.contrib.auth.models import User
import requests
from django.utils.text import slugify
from django.utils.translation import ugettext_lazy as _, ugettext
from django.core import validators
from channels import Group, Channel
from django.utils import timezone
from datetime import timedelta,datetime
from django_auth_lti.patch_reverse import reverse
from .groups import group_for_attempt
from .report_outcome import report_outcome_for_attempt, ReportOutcomeFailure, ReportOutcomeConnectionError
import os
import shutil
from zipfile import ZipFile
from lxml import etree
import re
import json
from collections import defaultdict
class NotDeletedManager(models.Manager):
def get_queryset(self):
return super(NotDeletedManager,self).get_queryset().filter(deleted=False)
class LTIConsumer(models.Model):
url = models.URLField(blank=True,default='',verbose_name='Home URL of consumer')
key = models.CharField(max_length=100,unique=True,verbose_name=_('Consumer key'),help_text=_('The key should be human-readable, and uniquely identify this consumer.'))
secret = models.CharField(max_length=100,verbose_name=_('Shared secret'))
deleted = models.BooleanField(default=False)
objects = NotDeletedManager()
def __str__(self):
return self.key
@property
def resources(self):
return Resource.objects.filter(context__consumer=self)
class ExtractPackageMixin(object):
extract_folder = 'extracted_zips'
@property
def extracted_path(self):
return os.path.join(settings.MEDIA_ROOT,self.extract_folder,self.__class__.__name__,str(self.pk))
@property
def extracted_url(self):
return '{}{}/{}/{}'.format(settings.MEDIA_URL,self.extract_folder,self.__class__.__name__,str(self.pk))
@receiver(models.signals.post_save)
def extract_package(sender,instance,**kwargs):
if not issubclass(sender,ExtractPackageMixin):
return
if os.path.exists(instance.extracted_path):
shutil.rmtree(instance.extracted_path)
os.makedirs(instance.extracted_path)
z = ZipFile(instance.package.file,'r')
z.extractall(instance.extracted_path)
# Create your models here.
class Exam(ExtractPackageMixin,models.Model):
title = models.CharField(max_length=300)
package = models.FileField(upload_to='exams/',verbose_name='Package file')
retrieve_url = models.URLField(blank=True,default='',verbose_name='URL used to retrieve the exam package')
rest_url = models.URLField(blank=True,default='',verbose_name='URL of the exam on the editor\'s REST API')
creation_time = models.DateTimeField(auto_now_add=True, verbose_name=_('Time this exam was created'))
def __str__(self):
return self.title
@receiver(models.signals.pre_save, sender=Exam)
def set_exam_name_from_package(sender,instance,**kwargs):
z = ZipFile(instance.package.file,'r')
with z.open('imsmanifest.xml','r') as manifest_file:
manifest = etree.parse(manifest_file)
instance.title = manifest.find('.//ims:title',namespaces={'ims':'http://www.imsglobal.org/xsd/imscp_v1p1'}).text
GRADING_METHODS = [
('highest',_('Highest score')),
('last',_('Last attempt')),
]
REPORT_TIMES = [
('immediately',_('Immediately')),
('oncompletion',_('On completion')),
('manually',_('Manually, by instructor')),
]
REPORTING_STATUSES = [
('reporting',_('Reporting scores')),
('error',_('Error encountered')),
('complete',_('All scores reported')),
]
SHOW_SCORES_MODES = [
('always',_('Always')),
('complete',_('When attempt is complete')),
('never',_('Never')),
]
class LTIContext(models.Model):
consumer = models.ForeignKey(LTIConsumer,related_name='contexts', on_delete=models.CASCADE)
context_id = models.CharField(max_length=300)
name = models.CharField(max_length=300)
label = models.CharField(max_length=300)
instance_guid = models.CharField(max_length=300)
def __str__(self):
if self.name == self.label:
return self.name
else:
return '{} ({})'.format(self.name, self.label)
class Resource(models.Model):
resource_link_id = models.CharField(max_length=300)
exam = models.ForeignKey(Exam,blank=True,null=True,on_delete=models.SET_NULL)
context = models.ForeignKey(LTIContext,blank=True,null=True,on_delete=models.SET_NULL,related_name='resources')
title = models.CharField(max_length=300,default='')
description = models.TextField(default='')
creation_time = models.DateTimeField(auto_now_add=True, verbose_name=_('Time this resource was created'))
grading_method = models.CharField(max_length=20,choices=GRADING_METHODS,default='highest',verbose_name=_('Grading method'))
include_incomplete_attempts = models.BooleanField(default=True,verbose_name=_('Include incomplete attempts in grading?'))
show_marks_when = models.CharField(max_length=20, default='always', choices=SHOW_SCORES_MODES, verbose_name=_('When to show scores to students'))
report_mark_time = models.CharField(max_length=20,choices=REPORT_TIMES,default='immediately',verbose_name=_('When to report scores back'))
max_attempts = models.PositiveIntegerField(default=0,verbose_name=_('Maximum attempts per user'))
num_questions = models.PositiveIntegerField(default=0)
class Meta:
ordering = ['-creation_time','title']
def __str__(self):
if self.exam:
return str(self.exam)
elif self.context:
return _('Resource in "{}" - no exam uploaded').format(self.context.name)
else:
return ugettext('Resource with no context')
@property
def slug(self):
if self.exam:
return slugify(self.exam.title)
else:
return 'resource'
def grade_user(self,user):
methods = {
'highest': self.grade_highest,
'last': self.grade_last,
}
attempts = self.attempts.filter(user=user)
if not self.include_incomplete_attempts:
attempts = attempts.filter(completion_status='completed')
if not attempts.exists():
return 0
return methods[self.grading_method](user,attempts)
def grade_highest(self,user,attempts):
return attempts.aggregate(highest_score=models.Max('scaled_score'))['highest_score']
def grade_last(self,user,attempts):
return attempts.order_by('-start_time').first()
def students(self):
return User.objects.filter(attempts__resource=self).distinct().order_by('last_name','first_name')
def can_start_new_attempt(self,user):
if self.max_attempts==0:
return True
return self.attempts.filter(user=user).count()<self.max_attempts or AccessToken.objects.filter(resource=self,user=user).exists()
def user_data(self,user):
return LTIUserData.objects.filter(resource=self,user=user).last()
def part_hierarchy(self):
"""
Returns an object
{
question_num: {
part_num: {
gaps: [list of gap indices],
steps: [list of step indices]
}
}
}
"""
paths = sorted(set(e['value'] for e in ScormElement.objects.filter(attempt__resource=self,key__regex=r'cmi.interactions.[0-9]+.id').values('value')),key=lambda x:(len(x),x))
re_path = re.compile(r'q([0-9]+)p([0-9]+)(?:g([0-9]+)|s([0-9]+))?')
out = defaultdict(lambda: defaultdict(lambda: {'gaps':[],'steps':[]}))
for path in paths:
m = re_path.match(path)
question_index = m.group(1)
part_index = m.group(2)
gap_index = m.group(3)
step_index = m.group(4)
p = out[question_index][part_index]
if m.group(3):
p['gaps'].append(gap_index)
elif m.group(4):
p['steps'].append(step_index)
return out
def last_activity(self):
if self.attempts.exists():
return self.attempts.order_by('-start_time').first().start_time
else:
return self.creation_time
def time_since_last_activity(self):
now = timezone.now()
diff = now - self.last_activity()
return diff
def is_new(self):
return self.time_since_last_activity().days < 7
def is_old(self):
return self.time_since_last_activity().days > 14
class ReportProcess(models.Model):
resource = models.ForeignKey(Resource,on_delete=models.CASCADE,related_name='report_processes')
status = models.CharField(max_length=10,choices=REPORTING_STATUSES,default='reporting',verbose_name=_("Current status of the process"))
time = models.DateTimeField(auto_now_add=True,verbose_name=_("Time the reporting process started"))
response = models.TextField(blank=True,verbose_name=_("Description of any error"))
dismissed = models.BooleanField(default=False,verbose_name=_('Has the result of this process been dismissed by the instructor?'))
class Meta:
ordering = ['-time',]
COMPLETION_STATUSES = [
('not attempted',_('Not attempted')),
('incomplete',_('Incomplete')),
('completed',_('Complete')),
]
class AccessToken(models.Model):
user = models.ForeignKey(User,on_delete=models.CASCADE,related_name='access_tokens')
resource = models.ForeignKey(Resource,on_delete=models.CASCADE,related_name='access_tokens')
class LTIUserData(models.Model):
consumer = models.ForeignKey(LTIConsumer,on_delete=models.CASCADE,null=True)
user = models.ForeignKey(User,on_delete=models.CASCADE,related_name='lti_data')
resource = models.ForeignKey(Resource,on_delete=models.CASCADE)
lis_result_sourcedid = models.CharField(max_length=200,default='',blank=True,null=True)
lis_outcome_service_url = models.TextField(default='',blank=True,null=True)
last_reported_score = models.FloatField(default=0)
consumer_user_id = models.TextField(default='',blank=True,null=True)
class Attempt(models.Model):
resource = models.ForeignKey(Resource,on_delete=models.CASCADE,related_name='attempts')
exam = models.ForeignKey(Exam,on_delete=models.CASCADE,related_name='attempts',null=True) # need to keep track of both resource and exam in case the exam later gets overwritten
user = models.ForeignKey(User,on_delete=models.CASCADE,related_name='attempts')
start_time = models.DateTimeField(auto_now_add=True)
end_time = models.DateTimeField(blank=True,null=True)
completion_status = models.CharField(max_length=20,choices=COMPLETION_STATUSES,default='not attempted')
completion_status_element = models.ForeignKey("ScormElement", on_delete=models.SET_NULL, related_name="current_completion_status_of", null=True)
scaled_score = models.FloatField(default=0)
scaled_score_element = models.ForeignKey("ScormElement", on_delete=models.SET_NULL, related_name="current_scaled_score_of", null=True)
deleted = models.BooleanField(default=False)
broken = models.BooleanField(default=False)
objects = NotDeletedManager()
class Meta:
ordering = ['-start_time',]
def __str__(self):
return 'Attempt by "{}" on "{}"'.format(self.user,self.resource)
def get_element_default(self,key,default=None):
try:
return self.scormelements.current(key).value
except ScormElement.DoesNotExist:
return default
def completed(self):
return self.completion_status=='completed'
@property
def raw_score(self):
if self.remarked_parts.exists() or self.resource.discounted_parts.exists():
total = 0
for i in range(self.resource.num_questions):
total += self.question_raw_score(i)
return total
return float(self.get_element_default('cmi.score.raw',0))
@property
def max_score(self):
if self.resource.discounted_parts.exists():
total = 0
for i in range(self.resource.num_questions):
total += self.question_max_score(i)
return total
return float(self.get_element_default('cmi.score.max',sum(self.question_max_score(i) for i in range(self.resource.num_questions))))
def part_discount(self,part):
return self.resource.discounted_parts.filter(part=part).first()
def part_paths(self):
return set(e['value'] for e in self.scormelements.filter(key__regex='cmi.interactions.[0-9]+.id').values('value').distinct())
def part_hierarchy(self):
"""
Returns an object
{
question_num: {
part_num: {
gaps: [list of gap indices],
steps: [list of step indices]
}
}
}
"""
paths = sorted(self.part_paths(),key=lambda x:(len(x),x))
re_path = re.compile('q(\d+)p(\d+)(?:g(\d+)|s(\d+))?')
out = defaultdict(lambda: defaultdict(lambda: {'gaps':[],'steps':[]}))
for path in paths:
m = re_path.match(path)
p = out[m.group(1)][m.group(2)]
if m.group(3):
p['gaps'].append(m.group(3))
elif m.group(4):
p['steps'].append(m.group(4))
return out
def part_gaps(self,part):
if not re.match(r'q\d+p\d+$',part):
return None
gaps = [g for g in self.part_paths() if g.startswith(part+'g')]
return gaps
def part_interaction_id(self,part):
id_element = self.scormelements.filter(key__regex='cmi.interactions.[0-9]+.id',value=part).first()
n = re.match(r'cmi.interactions.(\d+).id',id_element.key).group(1)
return n
def part_raw_score(self,part):
discounted = self.part_discount(part)
if discounted:
return self.part_max_score(part)
remarked = self.remarked_parts.filter(part=part)
if remarked.exists():
return remarked.get().score
if self.remarked_parts.filter(part__startswith=part+'g').exists() or self.resource.discounted_parts.filter(part__startswith=part+'g').exists():
gaps = self.part_gaps(part)
return sum(self.part_raw_score(g) for g in gaps)
try:
id = self.part_interaction_id(part)
except ScormElement.DoesNotExist:
return 0
score = self.get_element_default('cmi.interactions.{}.result'.format(id),0)
return float(score)
def part_max_score(self,part):
discounted = self.part_discount(part)
if discounted:
if discounted.behaviour == 'remove':
return 0
if DiscountPart.objects.filter(part__startswith=part+'g').exists():
gaps = self.part_gaps(part)
return sum(self.part_max_score(g) for g in gaps)
try:
id = self.part_interaction_id(part)
except ScormElement.DoesNotExist:
return 0
return float(self.get_element_default('cmi.interactions.{}.weighting'.format(id),0))
def question_raw_score(self,n):
_,raw,_,_ = self.calculate_question_score_info(n)
return raw
def calculate_question_score_info(self,n):
qid = 'q{}'.format(n)
if self.remarked_parts.filter(part__startswith=qid).exists() or self.resource.discounted_parts.filter(part__startswith=qid).exists():
question_parts = [p for p in self.part_paths() if p.startswith(qid)]
total_raw = 0.0
total_max = 0.0
for part in question_parts:
if re.match(r'^q{}p\d+$'.format(n),part):
total_raw += self.part_raw_score(part)
total_max += self.part_max_score(part)
raw_score = total_raw
scaled_score = total_raw/total_max if total_max>0 else 0.0
max_score = total_max
else:
raw_score = float(self.get_element_default('cmi.objectives.{}.score.raw'.format(n),0))
scaled_score = float(self.get_element_default('cmi.objectives.{}.score.scaled'.format(n),0))
max_score = float(self.get_element_default('cmi.objectives.{}.score.max'.format(n),0))
completion_status = self.get_element_default('cmi.objectives.{}.completion_status'.format(n),'not attempted')
return (scaled_score, raw_score, max_score, completion_status)
def update_question_score_info(self,n):
scaled_score,raw_score,max_score,completion_status = self.calculate_question_score_info(n)
AttemptQuestionScore.objects.update_or_create(attempt=self,number=n,defaults={'scaled_score':scaled_score,'raw_score':raw_score,'max_score':max_score,'completion_status':completion_status})
def question_score_info(self,n):
try:
return self.cached_question_scores.get(number=n)
except AttemptQuestionScore.DoesNotExist:
scaled_score, raw_score, max_score, completion_status = self.calculate_question_score_info(n)
aqs = AttemptQuestionScore.objects.create(attempt = self, number = n, raw_score = raw_score, scaled_score = scaled_score, max_score = max_score, completion_status = completion_status)
return aqs
except AttemptQuestionScore.MultipleObjectsReturned:
aqs = self.cached_question_scores.filter(number=n)
n = aqs.count()
aq = aqs[n]
aqs[:n].delete()
return aq
def question_numbers(self):
questions = self.scormelements.filter(key__regex='cmi.objectives.[0-9]+.id').values('key').distinct()
re_number = re.compile(r'cmi.objectives.([0-9]+).id')
numbers = sorted(set([re_number.match(q['key']).group(1) for q in questions]))
return numbers
def question_scores(self):
return sorted([self.question_score_info(n) for n in self.question_numbers()],key=lambda x:int(x.number))
def question_max_score(self,n):
_,_,max_score,_ = self.calculate_question_score_info(n)
return max_score
def channels_group(self):
return 'attempt-{}'.format(self.pk)
def should_show_scores(self):
return self.resource.show_marks_when=='always' or (self.resource.show_marks_when=='complete' and self.completed())
class AttemptQuestionScore(models.Model):
attempt = models.ForeignKey(Attempt,related_name='cached_question_scores', on_delete=models.CASCADE)
number = models.IntegerField()
raw_score = models.FloatField()
scaled_score = models.FloatField()
max_score = models.FloatField()
completion_status = models.CharField(default='not attempted',max_length=20)
class Meta:
unique_together = (('attempt','number'),)
def __str__(self):
return '{}/{} on question {} of {}'.format(self.raw_score,self.max_score,self.number,self.attempt)
class RemarkPart(models.Model):
attempt = models.ForeignKey(Attempt,related_name='remarked_parts', on_delete=models.CASCADE)
part = models.CharField(max_length=20)
score = models.FloatField()
def __str__(self):
return '{} on part {} in {}'.format(self.score, self.part, self.attempt)
def remark_update_scaled_score(sender,instance,**kwargs):
attempt = instance.attempt
question = int(re.match(r'^q(\d+)p\d+$',instance.part).group(1))
attempt.update_question_score_info(question)
if attempt.max_score>0:
scaled_score = attempt.raw_score/attempt.max_score
else:
scaled_score = 0
if scaled_score != attempt.scaled_score:
attempt.scaled_score = scaled_score
attempt.save()
models.signals.post_save.connect(remark_update_scaled_score,sender=RemarkPart)
models.signals.post_delete.connect(remark_update_scaled_score,sender=RemarkPart)
DISCOUNT_BEHAVIOURS = [
('remove','Remove from total'),
('fullmarks','Award everyone full credit'),
]
class DiscountPart(models.Model):
resource = models.ForeignKey(Resource,related_name='discounted_parts', on_delete=models.CASCADE)
part = models.CharField(max_length=20)
behaviour = models.CharField(max_length=10,choices=DISCOUNT_BEHAVIOURS,default='remove')
def discount_update_scaled_score(sender,instance,**kwargs):
for attempt in instance.resource.attempts.all():
question = int(re.match(r'^q(\d+)p\d+$',instance.part).group(1))
attempt.update_question_score_info(question)
scaled_score = attempt.raw_score/attempt.max_score
if scaled_score != attempt.scaled_score:
attempt.scaled_score = scaled_score
attempt.save()
models.signals.post_save.connect(discount_update_scaled_score,sender=DiscountPart)
models.signals.post_delete.connect(discount_update_scaled_score,sender=DiscountPart)
class ScormElementQuerySet(models.QuerySet):
def current(self,key):
""" Return the last value of this field """
elements = self.filter(key=key).order_by('-time','-counter')
if not elements.exists():
raise ScormElement.DoesNotExist()
else:
return elements.first()
class ScormElementManager(models.Manager):
use_for_related_fields = True
def get_queryset(self):
return ScormElementQuerySet(self.model, using=self.db)
def current(self,key):
return self.get_queryset().current(key)
class ScormElement(models.Model):
objects = ScormElementManager()
attempt = models.ForeignKey(Attempt,on_delete=models.CASCADE,related_name='scormelements')
key = models.CharField(max_length=200)
value = models.TextField()
time = models.DateTimeField()
counter = models.IntegerField(default=0,verbose_name='Element counter to disambiguate elements with the same timestamp')
current = models.BooleanField(default=True) # is this the latest version?
class Meta:
ordering = ['-time','-counter']
def __str__(self):
return '{}: {}'.format(self.key,self.value[:50]+(self.value[50:] and '...'))
def newer_than(self, other):
return self.time>other.time or (self.time==other.time and self.counter>other.counter)
@receiver(models.signals.post_save,sender=ScormElement)
def send_scorm_element_to_dashboard(sender,instance,created,**kwargs):
Group(instance.attempt.channels_group()).send({
"text": json.dumps({
'key': instance.key,
'value': instance.value,
'time': instance.time.strftime('%Y-%m-%d %H:%M:%S'),
})
})
@receiver(models.signals.post_save,sender=ScormElement)
def scorm_set_score(sender,instance,created,**kwargs):
if instance.key!='cmi.score.scaled' or not created:
return
if not (instance.attempt.scaled_score_element is None or instance.newer_than(instance.attempt.scaled_score_element)):
return
instance.attempt.scaled_score = float(instance.value)
instance.attempt.scaled_score_element = instance
instance.attempt.save()
if instance.attempt.resource.report_mark_time == 'immediately':
try:
report_outcome_for_attempt(instance.attempt)
except (ReportOutcomeFailure, ReportOutcomeConnectionError):
pass
@receiver(models.signals.post_save,sender=ScormElement)
def scorm_set_completion_status(sender,instance,created,**kwargs):
if instance.key!='cmi.completion_status' or not created:
return
if not (instance.attempt.completion_status_element is None or instance.newer_than(instance.attempt.completion_status_element)):
return
instance.attempt.completion_status = instance.value
instance.attempt.completion_status_element = instance
if instance.value=='completed' and instance.attempt.end_time is None:
instance.attempt.end_time = timezone.now()
group_for_attempt(instance.attempt).send({'text':json.dumps({
'completion_status':'completed',
})})
instance.attempt.save()
if instance.attempt.resource.report_mark_time == 'oncompletion' and instance.value=='completed':
try:
report_outcome_for_attempt(instance.attempt)
except (ReportOutcomeFailure, ReportOutcomeConnectionError):
pass
@receiver(models.signals.post_save,sender=ScormElement)
def scorm_set_num_questions(sender,instance,created,**kwargs):
""" Set the number of questions for this resource - can only work this out once the exam has been run! """
if not re.match(r'^cmi.objectives.([0-9]+).id$',instance.key) or not created:
return
number = int(re.match(r'q(\d+)',instance.value).group(1))+1
resource = instance.attempt.resource
if number>resource.num_questions:
resource.num_questions = number
resource.save()
class EditorLink(models.Model):
name = models.CharField(max_length=200,verbose_name='Editor name')
url = models.URLField(verbose_name='Base URL of the editor',unique=True)
cached_available_exams = models.TextField(blank=True,editable=False,verbose_name='Cached JSON list of available exams from this editor')
last_cache_update = models.DateTimeField(blank=True,editable=False,verbose_name='Time of last cache update')
def __str__(self):
return self.name
def update_cache(self,bounce=True):
if bounce and self.time_since_last_update().seconds<30:
return
if self.projects.exists():
project_pks = [str(p.remote_id) for p in self.projects.all()]
r = requests.get('{}/api/available-exams'.format(self.url),{'projects':project_pks})
self.cached_available_exams = r.text
else:
self.cached_available_exams = '[]'
self.last_cache_update = timezone.now()
def time_since_last_update(self):
if self.last_cache_update is None:
return timedelta.max
return timezone.now() - self.last_cache_update
@property
def available_exams(self):
if self.time_since_last_update().seconds> 30:
Channel("editorlink.update_cache").send({'pk':self.pk})
if self.cached_available_exams:
return json.loads(self.cached_available_exams)
else:
return []
class EditorLinkProject(models.Model):
editor = models.ForeignKey(EditorLink,on_delete=models.CASCADE,related_name='projects',verbose_name='Editor that this project belongs to')
name = models.CharField(max_length=200,verbose_name='Name of the project')
description = models.TextField(blank=True,verbose_name='Description of the project')
remote_id = models.IntegerField(verbose_name='ID of the project on the editor')
homepage = models.URLField(verbose_name='URL of the project\'s homepage on the editor')
rest_url = models.URLField(verbose_name='URL of the project on the editor\'s REST API')
class Meta:
ordering = ['name']
def __str__(self):
return self.name
@receiver(models.signals.pre_save,sender=EditorLink)
def update_editor_cache_before_save(sender,instance,**kwargs):
instance.update_cache()
class StressTest(models.Model):
resource = models.OneToOneField(Resource,on_delete=models.CASCADE,primary_key=True)
def __str__(self):
return self.resource.creation_time.strftime('%B %d, %Y %H:%M')
def get_absolute_url(self):
return reverse('view_stresstest',args=(self.pk,))
class StressTestNote(models.Model):
stresstest = models.ForeignKey(StressTest,on_delete=models.CASCADE,related_name='notes')
text = models.TextField()
time = models.DateTimeField(auto_now_add=True)
| 12,860
| 11,495
| 663
|
c2dec3dcd5aba4c26930d56a78814b05201b9fd5
| 2,763
|
py
|
Python
|
nb_compress.py
|
tanakatsu/nb_compress
|
a1fe923c4b271ce399e1550e51e6bfa354681c06
|
[
"MIT"
] | null | null | null |
nb_compress.py
|
tanakatsu/nb_compress
|
a1fe923c4b271ce399e1550e51e6bfa354681c06
|
[
"MIT"
] | null | null | null |
nb_compress.py
|
tanakatsu/nb_compress
|
a1fe923c4b271ce399e1550e51e6bfa354681c06
|
[
"MIT"
] | null | null | null |
import json
import re
import argparse
import sys
if __name__ == '__main__':
main()
| 30.7
| 106
| 0.579081
|
import json
import re
import argparse
import sys
def eprint(*args, **kwargs):
print(*args, file=sys.stderr, **kwargs)
def compress_output(output, mode):
if "text" in output and mode['first_last_epoch']:
text = output["text"]
mask = False
_text = []
for line in text:
m = re.search('^Epoch (\d+)/(\d+)', line)
if m:
total = m.group(2)
cur = m.group(1)
if cur == "2":
mask = True
elif cur == total:
mask = False
if not mask:
_text.append(line)
output["text"] = _text
if "traceback" in output and mode['no_traceback']:
output["traceback"] = []
if "data" in output and mode['no_image']:
output_data = output["data"]
if "image/png" in output_data:
output_data["image/png"] = ""
return output
def main():
parser = argparse.ArgumentParser()
parser.add_argument('file', type=str, help='input ipython notebook file')
parser.add_argument('-o', '--output', type=str, help='output filename')
parser.add_argument('--first-last-epoch', action='store_true', help='show first and last epochs only')
parser.add_argument('--no-image', action='store_true', help='cut image code')
parser.add_argument('--no-traceback', action='store_true', help='cut traceback code')
parser.add_argument('--no-execution-count', action='store_true', help='clear execution count')
args = parser.parse_args()
mode = {}
mode['first_last_epoch'] = args.first_last_epoch
mode['no_image'] = args.no_image
mode['no_traceback'] = args.no_traceback
mode['no_execution_count'] = args.no_execution_count
if mode['first_last_epoch']:
eprint('Apply filter: first_last_epoch')
if mode['no_image']:
eprint('Apply filter: no_image')
if mode['no_traceback']:
eprint('Apply filter: no_tracekback')
if mode['no_execution_count']:
eprint('Apply filter: no_execution_count')
with open(args.file) as f:
data = json.load(f)
cells = []
for cell in data["cells"]:
if cell["cell_type"] == "code":
outputs = cell["outputs"]
if len(outputs) > 0:
cell["outputs"] = [compress_output(o, mode) for o in outputs]
if mode['no_execution_count']:
cell["execution_count"] = None
cells.append(cell)
data["cells"] = cells
if args.output:
with open(args.output, "w") as f:
jsonStr = json.dumps(data)
f.write(jsonStr)
eprint('Finished.')
else:
print(json.dumps(data))
if __name__ == '__main__':
main()
| 2,603
| 0
| 69
|
d43678988ad4c195277e0b62eb974c64a085ba2d
| 827
|
py
|
Python
|
src/utils/payloadHelper.py
|
gertschreuder/kinesis-consumer
|
cfd0dc4fb2ae98f4b54838d390cea0488bbbb975
|
[
"MIT"
] | null | null | null |
src/utils/payloadHelper.py
|
gertschreuder/kinesis-consumer
|
cfd0dc4fb2ae98f4b54838d390cea0488bbbb975
|
[
"MIT"
] | 1
|
2021-02-10T11:06:38.000Z
|
2021-02-10T11:06:38.000Z
|
src/utils/payloadHelper.py
|
gertschreuder/kinesis-consumer
|
cfd0dc4fb2ae98f4b54838d390cea0488bbbb975
|
[
"MIT"
] | null | null | null |
import json
from src.mappers.heartbeatMapper import Heartbeat
| 24.323529
| 59
| 0.61185
|
import json
from src.mappers.heartbeatMapper import Heartbeat
class PayloadHelper(object):
def __init__(self):
self.heartbeat = None
self.messageTimeStamp = None
def map(self, data):
self.meta(data)
self.resolveStatus(data)
return self
def meta(self, data):
if "MessageId" in data:
k, t = data["MessageId"].split("_TS")
self.resolveMessageId(k)
self.resolveTimeStamp(t)
def resolveMessageId(self, data):
self.messageId = data.split(":")[1]
def resolveTimeStamp(self, data):
self.messageTimeStamp = data.split(":")[1]
def resolveStatus(self, data):
if "Status" in data and data["Status"] is not None:
self.heartbeat = Heartbeat(data["Status"])
return self.heartbeat
| 572
| 7
| 184
|
70deaa73a8457f76f76ea1cbe027b3e157405f0d
| 4,002
|
py
|
Python
|
python/h5/_h5py_desc.py
|
phdum/h5
|
d5f1b02354fd93da55cd09dc6a83218d98adab76
|
[
"Apache-2.0"
] | null | null | null |
python/h5/_h5py_desc.py
|
phdum/h5
|
d5f1b02354fd93da55cd09dc6a83218d98adab76
|
[
"Apache-2.0"
] | null | null | null |
python/h5/_h5py_desc.py
|
phdum/h5
|
d5f1b02354fd93da55cd09dc6a83218d98adab76
|
[
"Apache-2.0"
] | null | null | null |
# Generated automatically using the command :
# c++2py h5py_io.hpp --members_read_only -N h5 -a _h5py -m _h5py -o _h5py --moduledoc="A lightweight hdf5 python interface" --cxxflags="-std=c++20" --includes=./../../c++ --only="object file group h5_read_bare h5_write_bare"
from cpp2py.wrap_generator import *
# The module
module = module_(full_name = "_h5py", doc = r"A lightweight hdf5 python interface", app_name = "_h5py")
# Imports
# Add here all includes
module.add_include("<h5py_io.hpp>")
# Add here anything to add in the C++ code at the start, e.g. namespace using
module.add_preamble("""
#include <cpp2py/converters/span.hpp>
#include <cpp2py/converters/string.hpp>
#include <cpp2py/converters/vector.hpp>
using namespace h5;
""")
# The class file
c = class_(
py_type = "File", # name of the python class
c_type = "file", # name of the C++ class
doc = r"""A little handler for the HDF5 file
The class is basically a pointer to the file.""", # doc of the C++ class
hdf5 = False,
)
c.add_constructor("""()""", doc = r"""Open a file in memory""")
c.add_constructor("""(std::string name, char mode)""", doc = r"""""")
c.add_constructor("""(std::span<std::byte> buf)""", doc = r"""Create a file in memory from a byte buffer""")
c.add_property(name = "name", getter = cfunction("""std::string name ()"""),
doc = r"""Name of the file""")
c.add_method("""void flush ()""",
doc = r"""Flush the file""")
c.add_method("""std::vector<std::byte> as_buffer ()""",
doc = r"""Get a copy of the associated byte buffer""")
module.add_class(c)
# The class group
c = class_(
py_type = "Group", # name of the python class
c_type = "group", # name of the C++ class
doc = r"""HDF5 group""", # doc of the C++ class
hdf5 = False,
)
c.add_constructor("""(file f)""", doc = r"""Takes the "/" group at the top of the file""")
c.add_property(name = "name", getter = cfunction("""std::string name ()"""),
doc = r"""Name of the group""")
c.add_method("""group open_group (std::string key)""",
doc = r"""Open a subgroup.
Throws std::runtime_error if it does not exist.
Parameters
----------
key
The name of the subgroup. If empty, return this group""")
c.add_method("""group create_group (std::string key, bool delete_if_exists = true)""",
doc = r"""Create a subgroup in this group
Parameters
----------
key
The name of the subgroup. If empty, return this group.
delete_if_exists
Unlink the group if it exists""")
c.add_method("""std::vector<std::string> get_all_subgroup_dataset_names ()""", name='keys',
doc = r"""Returns all names of dataset of G""")
c.add_property(name = "file", getter = cfunction("""file get_file ()"""),
doc = r"""The parent file""")
c.add_method("""bool has_subgroup (std::string key)""",
doc = r"""True iff key is a subgroup of this.
Parameters
----------
key""")
c.add_method("""bool has_dataset (std::string key)""",
doc = r"""True iff key is a dataset of this.
Parameters
----------
key""")
c.add_method("void write_attribute(std::string key, std::string val)", calling_pattern = "h5_write_attribute(self_c, key, val)", doc = "Write an attribute")
c.add_method("std::string read_attribute(std::string name)", calling_pattern = "std::string result = h5_read_attribute<std::string>(self_c, name)", doc = "Read an attribute")
c.add_method("std::string read_hdf5_format_from_key(std::string key)", calling_pattern = "std::string result; read_hdf5_format_from_key(self_c, key, result);", doc = "Read the format string from the key in the group")
module.add_class(c)
module.add_function (name = "h5_write", signature = "void h5_write_bare (group g, std::string name, PyObject * ob)", doc = r"""""")
module.add_function (name = "h5_read", signature = "PyObject * h5_read_bare (group g, std::string name)", doc = r"""""")
module.generate_code()
| 33.915254
| 224
| 0.643428
|
# Generated automatically using the command :
# c++2py h5py_io.hpp --members_read_only -N h5 -a _h5py -m _h5py -o _h5py --moduledoc="A lightweight hdf5 python interface" --cxxflags="-std=c++20" --includes=./../../c++ --only="object file group h5_read_bare h5_write_bare"
from cpp2py.wrap_generator import *
# The module
module = module_(full_name = "_h5py", doc = r"A lightweight hdf5 python interface", app_name = "_h5py")
# Imports
# Add here all includes
module.add_include("<h5py_io.hpp>")
# Add here anything to add in the C++ code at the start, e.g. namespace using
module.add_preamble("""
#include <cpp2py/converters/span.hpp>
#include <cpp2py/converters/string.hpp>
#include <cpp2py/converters/vector.hpp>
using namespace h5;
""")
# The class file
c = class_(
py_type = "File", # name of the python class
c_type = "file", # name of the C++ class
doc = r"""A little handler for the HDF5 file
The class is basically a pointer to the file.""", # doc of the C++ class
hdf5 = False,
)
c.add_constructor("""()""", doc = r"""Open a file in memory""")
c.add_constructor("""(std::string name, char mode)""", doc = r"""""")
c.add_constructor("""(std::span<std::byte> buf)""", doc = r"""Create a file in memory from a byte buffer""")
c.add_property(name = "name", getter = cfunction("""std::string name ()"""),
doc = r"""Name of the file""")
c.add_method("""void flush ()""",
doc = r"""Flush the file""")
c.add_method("""std::vector<std::byte> as_buffer ()""",
doc = r"""Get a copy of the associated byte buffer""")
module.add_class(c)
# The class group
c = class_(
py_type = "Group", # name of the python class
c_type = "group", # name of the C++ class
doc = r"""HDF5 group""", # doc of the C++ class
hdf5 = False,
)
c.add_constructor("""(file f)""", doc = r"""Takes the "/" group at the top of the file""")
c.add_property(name = "name", getter = cfunction("""std::string name ()"""),
doc = r"""Name of the group""")
c.add_method("""group open_group (std::string key)""",
doc = r"""Open a subgroup.
Throws std::runtime_error if it does not exist.
Parameters
----------
key
The name of the subgroup. If empty, return this group""")
c.add_method("""group create_group (std::string key, bool delete_if_exists = true)""",
doc = r"""Create a subgroup in this group
Parameters
----------
key
The name of the subgroup. If empty, return this group.
delete_if_exists
Unlink the group if it exists""")
c.add_method("""std::vector<std::string> get_all_subgroup_dataset_names ()""", name='keys',
doc = r"""Returns all names of dataset of G""")
c.add_property(name = "file", getter = cfunction("""file get_file ()"""),
doc = r"""The parent file""")
c.add_method("""bool has_subgroup (std::string key)""",
doc = r"""True iff key is a subgroup of this.
Parameters
----------
key""")
c.add_method("""bool has_dataset (std::string key)""",
doc = r"""True iff key is a dataset of this.
Parameters
----------
key""")
c.add_method("void write_attribute(std::string key, std::string val)", calling_pattern = "h5_write_attribute(self_c, key, val)", doc = "Write an attribute")
c.add_method("std::string read_attribute(std::string name)", calling_pattern = "std::string result = h5_read_attribute<std::string>(self_c, name)", doc = "Read an attribute")
c.add_method("std::string read_hdf5_format_from_key(std::string key)", calling_pattern = "std::string result; read_hdf5_format_from_key(self_c, key, result);", doc = "Read the format string from the key in the group")
module.add_class(c)
module.add_function (name = "h5_write", signature = "void h5_write_bare (group g, std::string name, PyObject * ob)", doc = r"""""")
module.add_function (name = "h5_read", signature = "PyObject * h5_read_bare (group g, std::string name)", doc = r"""""")
module.generate_code()
| 0
| 0
| 0
|
467ad5dbc7ae14468d001e76f9f66437da261324
| 13,803
|
py
|
Python
|
internals/detect_intent_test.py
|
www-business-com/chromium-dashboard
|
f7b9c5136f4cfee4adbfca872335eb9c455b071b
|
[
"Apache-2.0"
] | 1
|
2022-03-25T14:40:37.000Z
|
2022-03-25T14:40:37.000Z
|
internals/detect_intent_test.py
|
BossNetworking/chromium-dashboard
|
9f0d76e70ef5e6169552407e728b3d3205517da8
|
[
"Apache-2.0"
] | null | null | null |
internals/detect_intent_test.py
|
BossNetworking/chromium-dashboard
|
9f0d76e70ef5e6169552407e728b3d3205517da8
|
[
"Apache-2.0"
] | 1
|
2021-11-15T06:49:12.000Z
|
2021-11-15T06:49:12.000Z
|
# Copyright 2021 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License")
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import testing_config # Must be imported first
import flask
from unittest import mock
import werkzeug
from internals import models
from internals import approval_defs
from internals import detect_intent
test_app = flask.Flask(__name__)
| 40.716814
| 80
| 0.701152
|
# Copyright 2021 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License")
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import testing_config # Must be imported first
import flask
from unittest import mock
import werkzeug
from internals import models
from internals import approval_defs
from internals import detect_intent
test_app = flask.Flask(__name__)
class FunctionTest(testing_config.CustomTestCase):
def setUp(self):
self.feature_1 = models.Feature(
name='feature one', summary='detailed sum', category=1, visibility=1,
standardization=1, web_dev_views=1, impl_status_chrome=1,
intent_stage=models.INTENT_IMPLEMENT)
self.feature_1.put()
def tearDown(self):
self.feature_1.key.delete()
def test_detect_field(self):
"""We can detect intent thread type by subject line."""
test_data = {
approval_defs.PrototypeApproval: [
'Intent to Prototype: Something cool',
'Re: Re:Intent to Prototype: Something cool',
'intent to prototype: something cool',
'Intent to Prototype request for Something cool',
],
approval_defs.ExperimentApproval: [
'Intent to Experiment: Something cool',
'intent to experiment: something cool',
'Intent to experiment on Something cool',
],
approval_defs.ExtendExperimentApproval: [
'Intent to Continue Experiment: Something cool',
'Intent to Extend Experiment: Something cool',
'Intent to Continue Experiment: Something cool',
],
approval_defs.ShipApproval: [
'Intent to Ship: Something cool',
'intent to ship: something cool',
'Intent to ship request for Something cool',
],
None: [
'Status of something cool',
'[meta] Are Intent to Prototype threads too long?',
'PSA: We are making changes',
'Why is feature so cool?',
'Save the date for BlinkOn',
],
}
for expected, subjects in test_data.items():
for subject in subjects:
with self.subTest(subject=subject):
actual = detect_intent.detect_field(subject)
self.assertEqual(expected, actual)
def test_detect_feature_id__generated(self):
"""We can parse the feature ID from a link in the generated body."""
body = (
'blah blah blah\n'
'Link to entry on the Chrome Platform Status\n'
'https://www.chromestatus.com/feature/5144822362931200\n'
'blah blah blah')
self.assertEqual(
5144822362931200,
detect_intent.detect_feature_id(body))
def test_detect_feature_id__generated_no_www(self):
"""We can parse the feature ID from a link in the generated body."""
body = (
'blah blah blah\n'
'Link to entry on the Chrome Platform Status\n'
'http://chromestatus.com/feature/5144822362931200\n'
'blah blah blah')
self.assertEqual(
5144822362931200,
detect_intent.detect_feature_id(body))
def test_detect_feature_id__alternative(self):
"""We can parse the feature ID from another common link."""
body = (
'blah blah blah\n'
'Entry on the feature dashboard\n'
'https://www.chromestatus.com/feature/5144822362931200\n'
'blah blah blah')
self.assertEqual(
5144822362931200,
detect_intent.detect_feature_id(body))
def test_detect_feature_id__alternative_no_www(self):
"""We can parse the feature ID from another common link."""
body = (
'blah blah blah\n'
'Entry on the feature dashboard\n'
'http://chromestatus.com/feature/5144822362931200\n'
'blah blah blah')
self.assertEqual(
5144822362931200,
detect_intent.detect_feature_id(body))
def test_detect_feature_id__quoted(self):
"""We can parse the feature ID from link in quoted body text."""
body = (
'I have something more to add\n'
'\n'
'On Monday, November 29, 2021 at 3:49:24 PM UTC-8 a user wrote:\n'
'>>> Entry on the feature dashboard\n'
'>>> http://chromestatus.com/feature/5144822362931200\n'
'>>> blah blah blah')
self.assertEqual(
5144822362931200,
detect_intent.detect_feature_id(body))
def test_detect_thread_url(self):
"""We can parse the thread archive link from the body footer."""
footer = (
'You received this message because you are subscribed to the Google '
'Groups "blink-dev" group.\n'
'To unsubscribe from this group and stop receiving emails from it,'
'send an email to blink-dev+unsubscribe@chromium.org.\n'
'To view this discussion on the web visit https://groups.google.com'
'/a/chromium.org/d/msgid/blink-dev/CAMO6jDPGfXfE5z6hJcWO112zX3We'
'-oNTb%2BZjiJk%2B6RNb9%2Bv05w%40mail.gmail.com.')
self.assertEqual(
('https://groups.google.com'
'/a/chromium.org/d/msgid/blink-dev/CAMO6jDPGfXfE5z6hJcWO112zX3We'
'-oNTb%2BZjiJk%2B6RNb9%2Bv05w%40mail.gmail.com'),
detect_intent.detect_thread_url(footer))
def test_detect_thread_url__staging(self):
"""We can parse the staging thread archive link from the body footer."""
footer = (
'You received this message because you are subscribed to the Google '
'Groups "jrobbins-test" group.\n'
'To unsubscribe from this group and stop receiving emails from it,'
'send an email to jrobbins-test+unsubscribe@googlegroups.com.\n'
'To view this discussion on the web visit https://groups.google.com'
'/d/msgid/jrobbins-test/CAMO6jDPGfXfE5z6hJcWO112zX3We'
'-oNTb%2BZjiJk%2B6RNb9%2Bv05w%40mail.gmail.com.')
self.assertEqual(
('https://groups.google.com'
'/d/msgid/jrobbins-test/CAMO6jDPGfXfE5z6hJcWO112zX3We'
'-oNTb%2BZjiJk%2B6RNb9%2Bv05w%40mail.gmail.com'),
detect_intent.detect_thread_url(footer))
def test_detect_lgtm__good(self):
"""We can find an LGTM in the email body text."""
self.assertTrue(detect_intent.detect_lgtm('LGTM'))
self.assertTrue(detect_intent.detect_lgtm('Lgtm'))
self.assertTrue(detect_intent.detect_lgtm('lgtm'))
self.assertTrue(detect_intent.detect_lgtm('LGTM1'))
self.assertTrue(detect_intent.detect_lgtm('LGTM2'))
self.assertTrue(detect_intent.detect_lgtm('LGTM3'))
self.assertTrue(detect_intent.detect_lgtm('LGTM with nits'))
self.assertTrue(detect_intent.detect_lgtm('This LGTM!'))
self.assertTrue(detect_intent.detect_lgtm('Sounds good! LGTM2'))
self.assertTrue(detect_intent.detect_lgtm('LGTM to extend M94-M97'))
self.assertTrue(detect_intent.detect_lgtm('''
LGTM
Thanks for all your work.
'''))
def test_detect_lgtm__bad(self):
"""We don't mistakenly count a message as an LGTM ."""
self.assertFalse(detect_intent.detect_lgtm("> LGTM from other approver"))
self.assertFalse(detect_intent.detect_lgtm('LG'))
self.assertFalse(detect_intent.detect_lgtm('Looks good to me'))
self.assertFalse(detect_intent.detect_lgtm('Almost LGTM'))
self.assertFalse(detect_intent.detect_lgtm('This is not an LGTM'))
self.assertFalse(detect_intent.detect_lgtm('Not LGTM yet'))
self.assertFalse(detect_intent.detect_lgtm('You still need LGTM'))
self.assertFalse(detect_intent.detect_lgtm("You're missing LGTM"))
self.assertFalse(detect_intent.detect_lgtm("You're missing a LGTM"))
self.assertFalse(detect_intent.detect_lgtm("You're missing an LGTM"))
self.assertFalse(detect_intent.detect_lgtm('''
Any discussion whatsoever that might even include the word
LGTM on any line other than the first line.
'''))
@mock.patch('internals.approval_defs.get_approvers')
def test_is_lgtm_allowed__approver(self, mock_get_approvers):
"""A user who is in the list of approvers can LGTM."""
mock_get_approvers.return_value = ['owner@example.com']
self.assertTrue(detect_intent.is_lgtm_allowed(
'owner@example.com', self.feature_1, approval_defs.ShipApproval))
mock_get_approvers.assert_called_once_with(
approval_defs.ShipApproval.field_id)
@mock.patch('framework.permissions.can_admin_site')
@mock.patch('internals.approval_defs.get_approvers')
def test_is_lgtm_allowed__admin(
self, mock_get_approvers, mock_can_admin_site):
"""A site admin can LGTM."""
mock_get_approvers.return_value = ['owner@example.com']
mock_can_admin_site.return_value = True
self.assertTrue(detect_intent.is_lgtm_allowed(
'admin@example.com', self.feature_1, approval_defs.ShipApproval))
@mock.patch('internals.approval_defs.get_approvers')
def test_is_lgtm_allowed__other(self, mock_get_approvers):
"""An average user cannot LGTM."""
mock_get_approvers.return_value = ['owner@example.com']
self.assertFalse(detect_intent.is_lgtm_allowed(
'other@example.com', self.feature_1, approval_defs.ShipApproval))
@mock.patch('internals.models.Approval.get_approvals')
def test_detect_new_thread(self, mock_get_approvals):
"""A thread is new if there are no previous approval values."""
mock_get_approvals.return_value = []
self.assertTrue(detect_intent.detect_new_thread(
self.feature_1.key.integer_id(), approval_defs.ShipApproval))
mock_get_approvals.return_value = ['fake approval value']
self.assertFalse(detect_intent.detect_new_thread(
self.feature_1.key.integer_id(), approval_defs.ShipApproval))
class IntentEmailHandlerTest(testing_config.CustomTestCase):
def setUp(self):
self.feature_1 = models.Feature(
name='feature one', summary='detailed sum', category=1, visibility=1,
standardization=1, web_dev_views=1, impl_status_chrome=1,
intent_stage=models.INTENT_IMPLEMENT)
self.feature_1.put()
self.feature_id = self.feature_1.key.integer_id()
self.request_path = '/tasks/detect-intent'
self.thread_url = (
'https://groups.google.com/a/chromium.org/d/msgid/blink-dev/fake')
self.entry_link = (
'\n*Link to entry on the Chrome Platform Status*\n'
'https://www.chromestatus.com/feature/%d\n' % self.feature_id)
self.footer = (
'\n--\n'
'instructions...\n'
'---\n'
'To view this discussion on the web visit ' +
self.thread_url + '.')
self.review_json_data = {
'from_addr': 'user@example.com',
'subject': 'Intent to Ship: Featurename',
'body': 'Please review. ' + self.entry_link + self.footer,
}
self.lgtm_json_data = {
'from_addr': 'user@example.com',
'subject': 'Intent to Ship: Featurename',
'body': 'LGTM. ' + self.footer,
}
self.handler = detect_intent.IntentEmailHandler()
def tearDown(self):
self.feature_1.key.delete()
for appr in models.Approval.query().fetch(None):
appr.key.delete()
def test_process_post_data__new_thread(self):
"""When we detect a new thread, we record it as the intent thread."""
with test_app.test_request_context(
self.request_path, json=self.review_json_data):
actual = self.handler.process_post_data()
self.assertEqual(actual, {'message': 'Done'})
created_approvals = list(models.Approval.query().fetch(None))
self.assertEqual(1, len(created_approvals))
appr = created_approvals[0]
self.assertEqual(self.feature_id, appr.feature_id)
self.assertEqual(approval_defs.ShipApproval.field_id, appr.field_id)
self.assertEqual(models.Approval.REVIEW_REQUESTED, appr.state)
self.assertEqual('user@example.com', appr.set_by)
self.assertEqual(self.feature_1.intent_to_ship_url, self.thread_url)
def test_process_post_data__new_thread_just_FYI(self):
"""When we detect a new thread, it might not require a review."""
self.review_json_data['subject'] = 'Intent to Prototype: featurename'
with test_app.test_request_context(
self.request_path, json=self.review_json_data):
actual = self.handler.process_post_data()
self.assertEqual(actual, {'message': 'Done'})
created_approvals = list(models.Approval.query().fetch(None))
self.assertEqual(0, len(created_approvals))
self.assertEqual(self.feature_1.intent_to_implement_url, self.thread_url)
@mock.patch('internals.detect_intent.is_lgtm_allowed')
def test_process_post_data__lgtm(self, mock_is_lgtm_allowed):
"""If we get an LGTM, we store the approval value and update the feature."""
mock_is_lgtm_allowed.return_value = True
self.feature_1.intent_to_ship_url = self.thread_url
self.feature_1.put()
with test_app.test_request_context(
self.request_path, json=self.lgtm_json_data):
actual = self.handler.process_post_data()
self.assertEqual(actual, {'message': 'Done'})
created_approvals = list(models.Approval.query().fetch(None))
self.assertEqual(1, len(created_approvals))
appr = created_approvals[0]
self.assertEqual(self.feature_id, appr.feature_id)
self.assertEqual(approval_defs.ShipApproval.field_id, appr.field_id)
self.assertEqual(models.Approval.APPROVED, appr.state)
self.assertEqual('user@example.com', appr.set_by)
self.assertEqual(self.feature_1.intent_to_ship_url, self.thread_url)
self.assertEqual(self.feature_1.i2s_lgtms, ['user@example.com'])
| 1,575
| 11,365
| 46
|
1c741e6bc69fc8671df5a15c26f40ce7a3bf09f3
| 2,839
|
py
|
Python
|
paranuara/citizens/models/citizens.py
|
SPLAYER-HD/Paranuara
|
5a42f23d761e16e3b486ba04d9185551614f06a5
|
[
"MIT"
] | null | null | null |
paranuara/citizens/models/citizens.py
|
SPLAYER-HD/Paranuara
|
5a42f23d761e16e3b486ba04d9185551614f06a5
|
[
"MIT"
] | 4
|
2021-06-08T20:53:43.000Z
|
2022-03-12T00:13:51.000Z
|
paranuara/citizens/models/citizens.py
|
SPLAYER-HD/RestServiceDjango
|
5a42f23d761e16e3b486ba04d9185551614f06a5
|
[
"MIT"
] | null | null | null |
"""Citizens model."""
# Django
from django.db import models
from django.contrib.auth.models import AbstractUser
from django.core.validators import RegexValidator
# models
from paranuara.companies.models import Company
# PostgreSQL fields
from django.contrib.postgres.fields import JSONField
# Utilities
from paranuara.utils.models import ParanuaraModel
class Citizen(ParanuaraModel, AbstractUser):
"""Citizen model.
Extend from Django's Abstract User, change the username field
to email and add some extra fields.
"""
index = models.IntegerField(
unique=True,
default=-1
)
favorite_food = models.ManyToManyField(
'foods.Food',
related_name='favorite_food'
)
has_died = models.BooleanField(
'died',
default=False,
help_text=(
'Help easily distinguish citizens died or alive. '
)
)
balance = models.DecimalField(
max_digits=15,
decimal_places=2,
default=None
)
picture = models.ImageField(
'profile picture',
upload_to='paranuara/citizens/pictures/',
blank=True,
null=True
)
age = models.IntegerField(
default=-1
)
eyeColor = models.CharField(
max_length=50,
blank=False
)
gender = models.CharField(
max_length=6,
blank=True
)
email = models.EmailField(
'email address',
unique=True,
error_messages={
'unique': 'A user with that email already exists.'
}
)
phone_regex = RegexValidator(
regex=r'\+?1?\d{9,15}$',
message="Phone number must be entered in the format: +999999999. Up to 15 digits allowed."
)
phone = models.CharField(
validators=[phone_regex],
max_length=20,
blank=True
)
address = models.CharField(
max_length=100,
blank=True
)
company = models.ForeignKey(
Company,
related_name='employees_company',
on_delete=models.SET_NULL,
null=True
)
about = models.CharField(
max_length=1000,
blank=True,
null=True
)
greeting = models.CharField(
max_length=1000,
blank=True,
null=True
)
tags = JSONField(
default=None,
blank=True,
null=True
)
REQUIRED_FIELDS = ['has_died', 'eyeColor', 'index']
class Relationship(models.Model):
"""Class to represent many to many relation between Ctizens"""
from_people = models.ForeignKey(Citizen, related_name='from_people', on_delete=models.CASCADE)
to_people = models.ForeignKey(Citizen, related_name='to_people', on_delete=models.CASCADE)
| 22.007752
| 98
| 0.62205
|
"""Citizens model."""
# Django
from django.db import models
from django.contrib.auth.models import AbstractUser
from django.core.validators import RegexValidator
# models
from paranuara.companies.models import Company
# PostgreSQL fields
from django.contrib.postgres.fields import JSONField
# Utilities
from paranuara.utils.models import ParanuaraModel
class Citizen(ParanuaraModel, AbstractUser):
"""Citizen model.
Extend from Django's Abstract User, change the username field
to email and add some extra fields.
"""
index = models.IntegerField(
unique=True,
default=-1
)
favorite_food = models.ManyToManyField(
'foods.Food',
related_name='favorite_food'
)
has_died = models.BooleanField(
'died',
default=False,
help_text=(
'Help easily distinguish citizens died or alive. '
)
)
balance = models.DecimalField(
max_digits=15,
decimal_places=2,
default=None
)
picture = models.ImageField(
'profile picture',
upload_to='paranuara/citizens/pictures/',
blank=True,
null=True
)
age = models.IntegerField(
default=-1
)
eyeColor = models.CharField(
max_length=50,
blank=False
)
gender = models.CharField(
max_length=6,
blank=True
)
email = models.EmailField(
'email address',
unique=True,
error_messages={
'unique': 'A user with that email already exists.'
}
)
phone_regex = RegexValidator(
regex=r'\+?1?\d{9,15}$',
message="Phone number must be entered in the format: +999999999. Up to 15 digits allowed."
)
phone = models.CharField(
validators=[phone_regex],
max_length=20,
blank=True
)
address = models.CharField(
max_length=100,
blank=True
)
company = models.ForeignKey(
Company,
related_name='employees_company',
on_delete=models.SET_NULL,
null=True
)
about = models.CharField(
max_length=1000,
blank=True,
null=True
)
greeting = models.CharField(
max_length=1000,
blank=True,
null=True
)
tags = JSONField(
default=None,
blank=True,
null=True
)
REQUIRED_FIELDS = ['has_died', 'eyeColor', 'index']
def get_relations(self):
return models.Relationship.objects.get(from_person=self)
class Relationship(models.Model):
"""Class to represent many to many relation between Ctizens"""
from_people = models.ForeignKey(Citizen, related_name='from_people', on_delete=models.CASCADE)
to_people = models.ForeignKey(Citizen, related_name='to_people', on_delete=models.CASCADE)
| 68
| 0
| 27
|
e340c1025aff6d53bab2a99990b56f88b3b6f369
| 1,001
|
py
|
Python
|
aocpo_backend/aocpo_api/urls.py
|
CoderChen01/aocpo
|
279bfae910a30be762e1954df1a53a6217a6e300
|
[
"Apache-2.0"
] | 7
|
2020-02-17T12:20:26.000Z
|
2021-03-15T01:02:34.000Z
|
aocpo_backend/aocpo_api/urls.py
|
CoderChen01/aocpo
|
279bfae910a30be762e1954df1a53a6217a6e300
|
[
"Apache-2.0"
] | 3
|
2020-04-19T03:01:41.000Z
|
2020-04-19T03:02:09.000Z
|
aocpo_backend/aocpo_api/urls.py
|
CoderChen01/aocpo
|
279bfae910a30be762e1954df1a53a6217a6e300
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from django.urls import path
from .views import login_register, task_manage, analysis_page
urlpatterns = [
path('login/', login_register.Login.as_view()),
path('register/', login_register.SignIn.as_view()),
path('register/check_username', login_register.SignIn.as_view()),
path('task_manager/addition/', task_manage.TaskManage.as_view()),
path('task_manager/removing/', task_manage.TaskManage.as_view()),
path('task_manager/recovering/', task_manage.Recover.as_view()),
path('task_manager/upgrade/', task_manage.TaskManage.as_view()),
path('task_manager/tasks', task_manage.TaskManage.as_view()),
path('task_manager/schools', task_manage.SearchSchool.as_view()),
path('analysis_page/posts_data', analysis_page.GetData.as_view()),
path('analysis_page/users_analysis_data', analysis_page.GetUserAnalyseData.as_view()),
path('analysis_page/posts_analysis_data', analysis_page.GetPostsAnalysisData.as_view())
]
| 52.684211
| 91
| 0.751249
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from django.urls import path
from .views import login_register, task_manage, analysis_page
urlpatterns = [
path('login/', login_register.Login.as_view()),
path('register/', login_register.SignIn.as_view()),
path('register/check_username', login_register.SignIn.as_view()),
path('task_manager/addition/', task_manage.TaskManage.as_view()),
path('task_manager/removing/', task_manage.TaskManage.as_view()),
path('task_manager/recovering/', task_manage.Recover.as_view()),
path('task_manager/upgrade/', task_manage.TaskManage.as_view()),
path('task_manager/tasks', task_manage.TaskManage.as_view()),
path('task_manager/schools', task_manage.SearchSchool.as_view()),
path('analysis_page/posts_data', analysis_page.GetData.as_view()),
path('analysis_page/users_analysis_data', analysis_page.GetUserAnalyseData.as_view()),
path('analysis_page/posts_analysis_data', analysis_page.GetPostsAnalysisData.as_view())
]
| 0
| 0
| 0
|
45719d34a81e10187b4b4005d07f676e2396fd1d
| 905
|
py
|
Python
|
example_project/urls.py
|
amarandon/django-audiotracks
|
e2480ebe555b07cc3c3c60b075a7caed462ed96d
|
[
"MIT"
] | 30
|
2015-04-16T04:56:30.000Z
|
2021-02-26T05:28:54.000Z
|
example_project/urls.py
|
amarandon/django-audiotracks
|
e2480ebe555b07cc3c3c60b075a7caed462ed96d
|
[
"MIT"
] | 2
|
2016-05-29T09:41:40.000Z
|
2016-07-12T17:47:06.000Z
|
example_project/urls.py
|
amarandon/django-audiotracks
|
e2480ebe555b07cc3c3c60b075a7caed462ed96d
|
[
"MIT"
] | 10
|
2015-07-16T12:57:41.000Z
|
2021-12-05T22:06:22.000Z
|
from django.conf import settings
from django.conf.urls import url, include
from django.contrib.staticfiles.urls import staticfiles_urlpatterns
from main import views
from django.contrib.auth import views as auth_views
from django.views.static import serve
# Uncomment the next two lines to enable the admin:
from django.contrib import admin
admin.autodiscover()
urlpatterns = [
url(r'^$', views.index, name="home"),
url("^music/", include("audiotracks.urls")),
url("^(?P<username>[\w\._-]+)/music/", include("audiotracks.urls")),
url(r'^login$', auth_views.login, name="login"),
url(r'^logout$', auth_views.logout, name="logout"),
url(r'^admin/', include(admin.site.urls)),
]
if settings.DEBUG:
urlpatterns += [
url(r'^site_media/(?P<path>.*)$', serve, {
'document_root': settings.MEDIA_ROOT
})
]
urlpatterns += staticfiles_urlpatterns()
| 32.321429
| 72
| 0.685083
|
from django.conf import settings
from django.conf.urls import url, include
from django.contrib.staticfiles.urls import staticfiles_urlpatterns
from main import views
from django.contrib.auth import views as auth_views
from django.views.static import serve
# Uncomment the next two lines to enable the admin:
from django.contrib import admin
admin.autodiscover()
urlpatterns = [
url(r'^$', views.index, name="home"),
url("^music/", include("audiotracks.urls")),
url("^(?P<username>[\w\._-]+)/music/", include("audiotracks.urls")),
url(r'^login$', auth_views.login, name="login"),
url(r'^logout$', auth_views.logout, name="logout"),
url(r'^admin/', include(admin.site.urls)),
]
if settings.DEBUG:
urlpatterns += [
url(r'^site_media/(?P<path>.*)$', serve, {
'document_root': settings.MEDIA_ROOT
})
]
urlpatterns += staticfiles_urlpatterns()
| 0
| 0
| 0
|
85ec8b6d8d12f7b35f2c1f28a6864da735f40a62
| 864
|
py
|
Python
|
mathbox/app/signal/outlier.py
|
freedeaths/mathbox-py
|
e294dc1b916bb634807378883b1ba941a924bec5
|
[
"MIT"
] | 7
|
2021-12-23T07:03:12.000Z
|
2021-12-31T06:35:34.000Z
|
mathbox/app/signal/outlier.py
|
freedeaths/mathbox-py
|
e294dc1b916bb634807378883b1ba941a924bec5
|
[
"MIT"
] | 8
|
2021-12-23T06:12:19.000Z
|
2022-01-07T15:01:47.000Z
|
mathbox/app/signal/outlier.py
|
freedeaths/mathbox-py
|
e294dc1b916bb634807378883b1ba941a924bec5
|
[
"MIT"
] | null | null | null |
from mathbox.statistics.estimator import mean, std
# Generalized ESD Test for Outliers
# https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h3.htm
| 39.272727
| 89
| 0.675926
|
from mathbox.statistics.estimator import mean, std
def noise_outlier(noise, bias=3):
noise_mean = mean(noise)
noise_std = std(noise)
outlier_lo = [(i,x) for i,x in enumerate(noise) if x < noise_mean - bias * noise_std]
outlier_hi = [(i,x) for i,x in enumerate(noise) if x > noise_mean + bias * noise_std]
return outlier_lo, outlier_hi
def simple_outlier(series, bias=3):
sorted_series = sorted(series)
length = len(series)
q3 = sorted_series[int(length * 0.75)]
q1 = sorted_series[int(length * 0.25)]
outlier_lo = [(i,x) for i,x in enumerate(series) if x < q1 - bias * (q3 - q1)]
outlier_hi = [(i,x) for i,x in enumerate(series) if x > q3 + bias * (q3 - q1)]
return outlier_lo, outlier_hi
# Generalized ESD Test for Outliers
# https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h3.htm
def gesd():
pass
| 641
| 0
| 68
|
9070ee332d8938903159cc96d4620a2bd3b5401c
| 397
|
py
|
Python
|
OS/Syncronization/main.py
|
prtx/What-I-learned-in-college
|
914f6e69beafdf66f53410bc7cd2e5344bf43308
|
[
"MIT"
] | null | null | null |
OS/Syncronization/main.py
|
prtx/What-I-learned-in-college
|
914f6e69beafdf66f53410bc7cd2e5344bf43308
|
[
"MIT"
] | null | null | null |
OS/Syncronization/main.py
|
prtx/What-I-learned-in-college
|
914f6e69beafdf66f53410bc7cd2e5344bf43308
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python
from requirement import *
from producer import producer
from scheduler import fcfs
from teller import teller
txt = open('result/processes','w')
txt.write('Processes\n\n')
#Thread(target = producer).start()
producer()
for process in processes:
txt.write(str(process)+'\n')
for i in range(teller_count):
tellers.append( teller() )
a = fcfs(processes,tellers)
txt.close()
| 16.541667
| 34
| 0.730479
|
#!/usr/bin/python
from requirement import *
from producer import producer
from scheduler import fcfs
from teller import teller
txt = open('result/processes','w')
txt.write('Processes\n\n')
#Thread(target = producer).start()
producer()
for process in processes:
txt.write(str(process)+'\n')
for i in range(teller_count):
tellers.append( teller() )
a = fcfs(processes,tellers)
txt.close()
| 0
| 0
| 0
|
33ee54f7c6793f6de7032a70a6e0460c0d4a6957
| 1,674
|
py
|
Python
|
floem/programs/queue_custom.py
|
mangpo/floem
|
2ff53dc601237597b299ebf93607d51b82cb8f4c
|
[
"BSD-2-Clause"
] | 21
|
2018-10-10T18:52:32.000Z
|
2022-02-16T12:23:51.000Z
|
floem/programs/queue_custom.py
|
mangpo/floem
|
2ff53dc601237597b299ebf93607d51b82cb8f4c
|
[
"BSD-2-Clause"
] | null | null | null |
floem/programs/queue_custom.py
|
mangpo/floem
|
2ff53dc601237597b299ebf93607d51b82cb8f4c
|
[
"BSD-2-Clause"
] | 3
|
2020-04-22T23:09:26.000Z
|
2021-09-30T01:35:34.000Z
|
from floem import *
n_cores = 2
Enq, Deq, Release = queue.queue_custom('queue', Tuple, 4, n_cores, Tuple.task, enq_output=True)
RxWrite('mysend')
RxPrint('process')
c = Compiler()
c.testing = r'''
Tuple tuples[5];
for(int i=0; i<5;i++) {
tuples[i].task = 10;
tuples[i].val = i;
}
for(int i=0; i<5;i++) {
mysend(&tuples[i], 0);
process(0);
}
for(int i=0; i<5;i++) {
tuples[i].val = 100 + i;
mysend(&tuples[i], 1);
tuples[i].task = 0;
}
for(int i=0; i<5;i++) {
process(1);
}
'''
c.generate_code_and_run([0,0,-1,1,-2,2,-3,3,-4,4,-100,-101,-102,-103,-104,100,101,102,103])
| 20.666667
| 95
| 0.549582
|
from floem import *
n_cores = 2
class Tuple(State):
val = Field(Int)
task = Field(Uint(8))
layout = [val, task]
class Display(Element):
def configure(self):
self.inp = Input(queue.q_buffer)
self.out = Output(queue.q_buffer)
def impl(self):
self.run_c(r'''
q_buffer buff = inp();
Tuple* t = (Tuple*) buff.entry;
if(t) printf("%d\n", t->val);
output switch { case t: out(buff); }
''')
class EnqConfirm(Element):
def configure(self):
self.inp = Input(Pointer(Tuple))
def impl(self):
self.run_c(r'''
Tuple* t = inp();
printf("%d\n", -t->val);
''')
Enq, Deq, Release = queue.queue_custom('queue', Tuple, 4, n_cores, Tuple.task, enq_output=True)
class RxWrite(CallableSegment):
def configure(self):
self.inp = Input(Pointer(Tuple), Int)
def impl(self):
enq = Enq()
self.inp >> enq >> EnqConfirm()
class RxPrint(CallableSegment):
def configure(self):
self.inp = Input(Int)
def impl(self):
deq = Deq()
release = Release()
display = Display()
self.inp >> deq >> display >> release
RxWrite('mysend')
RxPrint('process')
c = Compiler()
c.testing = r'''
Tuple tuples[5];
for(int i=0; i<5;i++) {
tuples[i].task = 10;
tuples[i].val = i;
}
for(int i=0; i<5;i++) {
mysend(&tuples[i], 0);
process(0);
}
for(int i=0; i<5;i++) {
tuples[i].val = 100 + i;
mysend(&tuples[i], 1);
tuples[i].task = 0;
}
for(int i=0; i<5;i++) {
process(1);
}
'''
c.generate_code_and_run([0,0,-1,1,-2,2,-3,3,-4,4,-100,-101,-102,-103,-104,100,101,102,103])
| 641
| 97
| 327
|
539ef8f7d8675478ff779ecc18fb39f706cede98
| 14,871
|
py
|
Python
|
electrum/auxpow.py
|
ZenyattaAbosom/AbosomElectrum
|
02748b0b14e37385d6e77591d122e592740222bf
|
[
"MIT"
] | 4
|
2020-06-27T22:43:34.000Z
|
2021-04-12T02:29:30.000Z
|
electrum/auxpow.py
|
ZenyattaAbosom/AbosomElectrum
|
02748b0b14e37385d6e77591d122e592740222bf
|
[
"MIT"
] | 21
|
2020-06-20T15:02:50.000Z
|
2021-04-07T10:14:59.000Z
|
electrum/auxpow.py
|
ZenyattaAbosom/AbosomElectrum
|
02748b0b14e37385d6e77591d122e592740222bf
|
[
"MIT"
] | 13
|
2020-06-28T08:13:28.000Z
|
2021-12-28T00:11:56.000Z
|
# -*- coding: utf-8 -*-
#
# Electrum-NMC - lightweight Namecoin client
# Copyright (C) 2018 The Namecoin developers
#
# License for all components not part of Electrum-DOGE:
#
# Permission is hereby granted, free of charge, to any person
# obtaining a copy of this software and associated documentation files
# (the "Software"), to deal in the Software without restriction,
# including without limitation the rights to use, copy, modify, merge,
# publish, distribute, sublicense, and/or sell copies of the Software,
# and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
#
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# Based on Electrum-DOGE - lightweight Dogecoin client
# Copyright (C) 2014 The Electrum-DOGE contributors
#
# License for the Electrum-DOGE components:
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import binascii
from .bitcoin import hash_encode, hash_decode
from .crypto import sha256d
from . import blockchain, constants, transaction
from .transaction import BCDataStream, Transaction, TxOutput, TYPE_SCRIPT
from .util import bfh, bh2u
# Maximum index of the merkle root hash in the coinbase transaction script,
# where no merged mining header is present.
MAX_INDEX_PC_BACKWARDS_COMPATIBILITY = 20
# Header for merge-mining data in the coinbase.
COINBASE_MERGED_MINING_HEADER = bfh('fabe') + b'mm'
def deserialize_auxpow_header(base_header, s, start_position=0) -> (dict, int):
"""Deserialises an AuxPoW instance.
Returns the deserialised AuxPoW dict and the end position in the byte
array as a pair."""
auxpow_header = {}
# Chain ID is the top 16 bits of the 32-bit version.
auxpow_header['chain_id'] = get_chain_id(base_header)
# The parent coinbase transaction is first.
# Deserialize it and save the trailing data.
parent_coinbase_tx = Transaction(s, expect_trailing_data=True, copy_input=False, start_position=start_position)
parent_coinbase_tx._allow_zero_outputs = True
start_position = fast_tx_deserialize(parent_coinbase_tx)
auxpow_header['parent_coinbase_tx'] = parent_coinbase_tx
# Next is the parent block hash. According to the Bitcoin.it wiki,
# this field is not actually consensus-critical. So we don't save it.
start_position = start_position + 32
# The coinbase and chain merkle branches/indices are next.
# Deserialize them and save the trailing data.
auxpow_header['coinbase_merkle_branch'], auxpow_header['coinbase_merkle_index'], start_position = deserialize_merkle_branch(s, start_position=start_position)
auxpow_header['chain_merkle_branch'], auxpow_header['chain_merkle_index'], start_position = deserialize_merkle_branch(s, start_position=start_position)
# Finally there's the parent header. Deserialize it.
parent_header_bytes = s[start_position : start_position + constants.net.HEADER_SIZE]
auxpow_header['parent_header'] = blockchain.deserialize_pure_header(parent_header_bytes, None)
start_position += constants.net.HEADER_SIZE
# The parent block header doesn't have any block height,
# so delete that field. (We used None as a dummy value above.)
del auxpow_header['parent_header']['block_height']
return auxpow_header, start_position
# Copied from merkle_branch_from_string in https://github.com/electrumalt/electrum-doge/blob/f74312822a14f59aa8d50186baff74cade449ccd/lib/blockchain.py#L622
# Returns list of hashes, merkle index, and position of trailing data in s
# TODO: Audit this function carefully.
# Reimplementation of btcutils.check_merkle_branch from Electrum-DOGE.
# btcutils seems to have an unclear license and no obvious Git repo, so it
# seemed wiser to re-implement.
# This re-implementation is roughly based on libdohj's calculateMerkleRoot.
# Copied from Electrum-DOGE
# TODO: Audit this function carefully.
# https://github.com/kR105/i0coin/compare/bitcoin:master...master#diff-610df86e65fce009eb271c2a4f7394ccR262
# Copied from Electrum-DOGE
# TODO: Audit this function carefully.
# This is calculated the same as the Transaction.txid() method, but doesn't
# reserialize it.
# Used by fast_tx_deserialize
# This is equivalent to (tx.deserialize(), ), but doesn't parse outputs.
| 39.134211
| 161
| 0.721404
|
# -*- coding: utf-8 -*-
#
# Electrum-NMC - lightweight Namecoin client
# Copyright (C) 2018 The Namecoin developers
#
# License for all components not part of Electrum-DOGE:
#
# Permission is hereby granted, free of charge, to any person
# obtaining a copy of this software and associated documentation files
# (the "Software"), to deal in the Software without restriction,
# including without limitation the rights to use, copy, modify, merge,
# publish, distribute, sublicense, and/or sell copies of the Software,
# and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
#
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# Based on Electrum-DOGE - lightweight Dogecoin client
# Copyright (C) 2014 The Electrum-DOGE contributors
#
# License for the Electrum-DOGE components:
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import binascii
from .bitcoin import hash_encode, hash_decode
from .crypto import sha256d
from . import blockchain, constants, transaction
from .transaction import BCDataStream, Transaction, TxOutput, TYPE_SCRIPT
from .util import bfh, bh2u
# Maximum index of the merkle root hash in the coinbase transaction script,
# where no merged mining header is present.
MAX_INDEX_PC_BACKWARDS_COMPATIBILITY = 20
# Header for merge-mining data in the coinbase.
COINBASE_MERGED_MINING_HEADER = bfh('fabe') + b'mm'
class AuxPowVerifyError(Exception):
pass
class AuxPoWNotGenerateError(AuxPowVerifyError):
pass
class AuxPoWOwnChainIDError(AuxPowVerifyError):
pass
class AuxPoWChainMerkleTooLongError(AuxPowVerifyError):
pass
class AuxPoWBadCoinbaseMerkleBranchError(AuxPowVerifyError):
pass
class AuxPoWCoinbaseNoInputsError(AuxPowVerifyError):
pass
class AuxPoWCoinbaseRootTooLate(AuxPowVerifyError):
pass
class AuxPoWCoinbaseRootMissingError(AuxPowVerifyError):
pass
class AuxPoWCoinbaseRootDuplicatedError(AuxPowVerifyError):
pass
class AuxPoWCoinbaseRootWrongOffset(AuxPowVerifyError):
pass
def get_chain_id(base_header):
return base_header['version'] >> 16
def deserialize_auxpow_header(base_header, s, start_position=0) -> (dict, int):
"""Deserialises an AuxPoW instance.
Returns the deserialised AuxPoW dict and the end position in the byte
array as a pair."""
auxpow_header = {}
# Chain ID is the top 16 bits of the 32-bit version.
auxpow_header['chain_id'] = get_chain_id(base_header)
# The parent coinbase transaction is first.
# Deserialize it and save the trailing data.
parent_coinbase_tx = Transaction(s, expect_trailing_data=True, copy_input=False, start_position=start_position)
parent_coinbase_tx._allow_zero_outputs = True
start_position = fast_tx_deserialize(parent_coinbase_tx)
auxpow_header['parent_coinbase_tx'] = parent_coinbase_tx
# Next is the parent block hash. According to the Bitcoin.it wiki,
# this field is not actually consensus-critical. So we don't save it.
start_position = start_position + 32
# The coinbase and chain merkle branches/indices are next.
# Deserialize them and save the trailing data.
auxpow_header['coinbase_merkle_branch'], auxpow_header['coinbase_merkle_index'], start_position = deserialize_merkle_branch(s, start_position=start_position)
auxpow_header['chain_merkle_branch'], auxpow_header['chain_merkle_index'], start_position = deserialize_merkle_branch(s, start_position=start_position)
# Finally there's the parent header. Deserialize it.
parent_header_bytes = s[start_position : start_position + constants.net.HEADER_SIZE]
auxpow_header['parent_header'] = blockchain.deserialize_pure_header(parent_header_bytes, None)
start_position += constants.net.HEADER_SIZE
# The parent block header doesn't have any block height,
# so delete that field. (We used None as a dummy value above.)
del auxpow_header['parent_header']['block_height']
return auxpow_header, start_position
# Copied from merkle_branch_from_string in https://github.com/electrumalt/electrum-doge/blob/f74312822a14f59aa8d50186baff74cade449ccd/lib/blockchain.py#L622
# Returns list of hashes, merkle index, and position of trailing data in s
# TODO: Audit this function carefully.
def deserialize_merkle_branch(s, start_position=0):
vds = BCDataStream()
vds.input = s
vds.read_cursor = start_position
hashes = []
n_hashes = vds.read_compact_size()
for i in range(n_hashes):
_hash = vds.read_bytes(32)
hashes.append(hash_encode(_hash))
index = vds.read_int32()
return hashes, index, vds.read_cursor
def hash_parent_header(header):
if not constants.net.is_auxpow_active(header):
return blockchain.hash_header(header)
verify_auxpow(header)
return blockchain.hash_header(header['auxpow']['parent_header'])
# Reimplementation of btcutils.check_merkle_branch from Electrum-DOGE.
# btcutils seems to have an unclear license and no obvious Git repo, so it
# seemed wiser to re-implement.
# This re-implementation is roughly based on libdohj's calculateMerkleRoot.
def calculate_merkle_root(leaf, merkle_branch, index):
target = hash_decode(leaf)
mask = index
for merkle_step in merkle_branch:
if mask & 1 == 0: # 0 means it goes on the right
data_to_hash = target + hash_decode(merkle_step)
else:
data_to_hash = hash_decode(merkle_step) + target
target = sha256d(data_to_hash)
mask = mask >> 1
return hash_encode(target)
# Copied from Electrum-DOGE
# TODO: Audit this function carefully.
# https://github.com/kR105/i0coin/compare/bitcoin:master...master#diff-610df86e65fce009eb271c2a4f7394ccR262
def calc_merkle_index(chain_id, nonce, merkle_size):
rand = nonce
rand = (rand * 1103515245 + 12345) & 0xffffffff
rand += chain_id
rand = (rand * 1103515245 + 12345) & 0xffffffff
return rand % merkle_size
# Copied from Electrum-DOGE
# TODO: Audit this function carefully.
def verify_auxpow(header):
auxhash = blockchain.hash_header(header)
auxpow = header['auxpow']
parent_block = auxpow['parent_header']
coinbase = auxpow['parent_coinbase_tx']
coinbase_hash = fast_txid(coinbase)
chain_merkle_branch = auxpow['chain_merkle_branch']
chain_index = auxpow['chain_merkle_index']
coinbase_merkle_branch = auxpow['coinbase_merkle_branch']
coinbase_index = auxpow['coinbase_merkle_index']
#if (coinbaseTx.nIndex != 0)
# return error("AuxPow is not a generate");
if (coinbase_index != 0):
raise AuxPoWNotGenerateError("AuxPow is not a generate")
#if (get_chain_id(parent_block) == chain_id)
# return error("Aux POW parent has our chain ID");
if (get_chain_id(parent_block) == constants.net.AUXPOW_CHAIN_ID):
raise AuxPoWOwnChainIDError("Aux POW parent has our chain ID")
#if (vChainMerkleBranch.size() > 30)
# return error("Aux POW chain merkle branch too long");
if (len(chain_merkle_branch) > 30):
raise AuxPoWChainMerkleTooLongError("Aux POW chain merkle branch too long")
#// Check that the chain merkle root is in the coinbase
#uint256 nRootHash = CBlock::CheckMerkleBranch(hashAuxBlock, vChainMerkleBranch, nChainIndex);
#vector<unsigned char> vchRootHash(nRootHash.begin(), nRootHash.end());
#std::reverse(vchRootHash.begin(), vchRootHash.end()); // correct endian
# Check that the chain merkle root is in the coinbase
root_hash_bytes = bfh(calculate_merkle_root(auxhash, chain_merkle_branch, chain_index))
# Check that we are in the parent block merkle tree
# if (CBlock::CheckMerkleBranch(GetHash(), vMerkleBranch, nIndex) != parentBlock.hashMerkleRoot)
# return error("Aux POW merkle root incorrect");
if (calculate_merkle_root(coinbase_hash, coinbase_merkle_branch, coinbase_index) != parent_block['merkle_root']):
raise AuxPoWBadCoinbaseMerkleBranchError("Aux POW merkle root incorrect")
#// Check that there is at least one input.
#if (coinbaseTx->vin.empty())
# return error("Aux POW coinbase has no inputs");
# Check that there is at least one input.
if (len(coinbase.inputs()) == 0):
raise AuxPoWCoinbaseNoInputsError("Aux POW coinbase has no inputs")
# const CScript script = coinbaseTx->vin[0].scriptSig;
script_bytes = coinbase.inputs()[0].script_sig
#// Check that the same work is not submitted twice to our chain.
#//
# const unsigned char* const mmHeaderBegin = pchMergedMiningHeader;
# const unsigned char* const mmHeaderEnd
# = mmHeaderBegin + sizeof (pchMergedMiningHeader);
# CScript::const_iterator pcHead =
# std::search(script.begin(), script.end(), mmHeaderBegin, mmHeaderEnd);
pos_header = script_bytes.find(COINBASE_MERGED_MINING_HEADER)
#CScript::const_iterator pc =
# std::search(script.begin(), script.end(), vchRootHash.begin(), vchRootHash.end());
pos = script_bytes.find(root_hash_bytes)
#if (pc == script.end())
if pos == -1:
#return error("Aux POW missing chain merkle root in parent coinbase");
raise AuxPoWCoinbaseRootMissingError('Aux POW missing chain merkle root in parent coinbase')
#if (pcHead != script.end())
#{
if pos_header != -1:
#// Enforce only one chain merkle root by checking that a single instance of the merged
#// mining header exists just before.
#if (script.end() != std::search(pcHead + 1, script.end(), UBEGIN(pchMergedMiningHeader), UEND(pchMergedMiningHeader)))
#return error("Multiple merged mining headers in coinbase");
#if (pcHead + sizeof(pchMergedMiningHeader) != pc)
#return error("Merged mining header is not just before chain merkle root");
if -1 != script_bytes.find(COINBASE_MERGED_MINING_HEADER, pos_header + 1):
raise AuxPoWCoinbaseRootDuplicatedError('Multiple merged mining headers in coinbase')
if pos_header + len(COINBASE_MERGED_MINING_HEADER) != pos:
raise AuxPoWCoinbaseRootWrongOffset('Merged mining header is not just before chain merkle root')
#}
#else
#{
else:
#// For backward compatibility.
#// Enforce only one chain merkle root by checking that it starts early in the coinbase.
#// 8-12 bytes are enough to encode extraNonce and nBits.
#if (pc - script.begin() > 20)
#return error("Aux POW chain merkle root must start in the first 20 bytes of the parent coinbase");
# For backward compatibility.
# Enforce only one chain merkle root by checking that it starts early in the coinbase.
# 8-12 bytes are enough to encode extraNonce and nBits.
if pos > 20:
raise AuxPoWCoinbaseRootTooLate("Aux POW chain merkle root must start in the first 20 bytes of the parent coinbase")
#}
#// Ensure we are at a deterministic point in the merkle leaves by hashing
#// a nonce and our chain ID and comparing to the index.
#pc += vchRootHash.size();
#if (script.end() - pc < 8)
#return error("Aux POW missing chain merkle tree size and nonce in parent coinbase");
pos = pos + len(root_hash_bytes)
if (len(script_bytes) - pos < 8):
raise Exception('Aux POW missing chain merkle tree size and nonce in parent coinbase')
#int nSize;
#memcpy(&nSize, &pc[0], 4);
#if (nSize != (1 << vChainMerkleBranch.size()))
#return error("Aux POW merkle branch size does not match parent coinbase");
def bytes_to_int(b):
return int.from_bytes(b, byteorder='little')
size = bytes_to_int(script_bytes[pos:pos+4])
nonce = bytes_to_int(script_bytes[pos+4:pos+8])
#print 'size',size
#print 'nonce',nonce
#print '(1 << len(chain_merkle_branch)))', (1 << len(chain_merkle_branch))
#size = hex_to_int(script[pos:pos+4])
#nonce = hex_to_int(script[pos+4:pos+8])
if (size != (1 << len(chain_merkle_branch))):
raise Exception('Aux POW merkle branch size does not match parent coinbase')
#int nNonce;
#memcpy(&nNonce, &pc[4], 4);
#// Choose a pseudo-random slot in the chain merkle tree
#// but have it be fixed for a size/nonce/chain combination.
#//
#// This prevents the same work from being used twice for the
#// same chain while reducing the chance that two chains clash
#// for the same slot.
#unsigned int rand = nNonce;
#rand = rand * 1103515245 + 12345;
#rand += nChainID;
#rand = rand * 1103515245 + 12345;
#if (nChainIndex != (rand % nSize))
#return error("Aux POW wrong index");
index = calc_merkle_index(constants.net.AUXPOW_CHAIN_ID, nonce, size)
#print 'index', index
if (chain_index != index):
raise Exception('Aux POW wrong index')
# This is calculated the same as the Transaction.txid() method, but doesn't
# reserialize it.
def fast_txid(tx):
return bh2u(sha256d(tx._cached_network_ser_bytes)[::-1])
# Used by fast_tx_deserialize
def stub_parse_output(vds):
vds.read_int64() # value
vds.read_bytes(vds.read_compact_size()) # scriptpubkey
return TxOutput(value=0, scriptpubkey=b'')
# This is equivalent to (tx.deserialize(), ), but doesn't parse outputs.
def fast_tx_deserialize(tx):
# Monkeypatch output address parsing with a stub, since we only care about
# inputs.
real_parse_output, transaction.parse_output = transaction.parse_output, stub_parse_output
try:
result = tx.deserialize()
except Exception as exc:
print(exc)
finally:
# Restore the real output address parser.
transaction.parse_output = real_parse_output
return result
| 8,649
| 399
| 430
|
2d8bed25eba5b2d701f79d132f9bbf690b6b08da
| 545
|
py
|
Python
|
src/readset.py
|
Neko250/readset
|
22edf00320244be77455e3937e01dd405e478dea
|
[
"MIT"
] | null | null | null |
src/readset.py
|
Neko250/readset
|
22edf00320244be77455e3937e01dd405e478dea
|
[
"MIT"
] | null | null | null |
src/readset.py
|
Neko250/readset
|
22edf00320244be77455e3937e01dd405e478dea
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
def extract(file=None, path=None):
"""
Extract all of the YouTube links within a Headset user-made list.
:param file: headset json export file path
:param path: json path to extract, you can use [JSON Columns](http://json-columns.com) to get it
:return: `list` containing all of the links in the list
"""
if not file or not path:
print('error: file or json path not provided...')
return None
# todo: implement
pass
if __name__ == '__main__':
extract()
| 21.8
| 98
| 0.67156
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
def extract(file=None, path=None):
"""
Extract all of the YouTube links within a Headset user-made list.
:param file: headset json export file path
:param path: json path to extract, you can use [JSON Columns](http://json-columns.com) to get it
:return: `list` containing all of the links in the list
"""
if not file or not path:
print('error: file or json path not provided...')
return None
# todo: implement
pass
if __name__ == '__main__':
extract()
| 0
| 0
| 0
|
f8ed8a42efb7fd5e2ac5ef6c2ba9004eee3a0b6f
| 6,180
|
py
|
Python
|
svision/viewer/views.py
|
artkulak/ads-eye-tracking
|
693a87600362417361dc2725c3577dcbebf3925e
|
[
"Unlicense"
] | null | null | null |
svision/viewer/views.py
|
artkulak/ads-eye-tracking
|
693a87600362417361dc2725c3577dcbebf3925e
|
[
"Unlicense"
] | null | null | null |
svision/viewer/views.py
|
artkulak/ads-eye-tracking
|
693a87600362417361dc2725c3577dcbebf3925e
|
[
"Unlicense"
] | 1
|
2022-02-10T11:19:04.000Z
|
2022-02-10T11:19:04.000Z
|
from django.shortcuts import render
from django.http import HttpResponse
from django.contrib.auth.forms import UserCreationForm
from django.contrib.auth import login, authenticate
from django.contrib.auth.models import User
from django.http import JsonResponse
####################
# IMPORT OTHER LIBS
####################
import os
import numpy as np
import seaborn as sns
import cv2
from heatmappy import Heatmapper
from heatmappy.video import VideoHeatmapper
from PIL import Image
import moviepy.editor as mp
import urllib
import glob
import pandas as pd
from pathlib import Path
import shutil
import vimeo_dl as vimeo
import plotly.express as px
import plotly
import plotly.graph_objects as go
from .models import Video, VideoStat
EMOTIONS = [
'angry',
'disgusted',
'fearful',
'happy',
'neutral',
'sad',
'surprised'
]
# # Create your views here.
# def index(request):
# return render(request, 'index.html')
heatmap_points = []
def index(request):
'''
Renders login + main page
'''
global user
if request.method == 'POST':
username = request.POST['username']
password = request.POST['password']
user = authenticate(username=username, password=password)
if user is not None:
# if user is authentificated
data = Video.objects.all()
response_data = {
"video_data": data,
"name" : username,
"is_staff": user.is_staff,
}
return render(request, 'main.html', response_data)
return render(request, 'index.html')
else:
form = UserCreationForm()
return render(request, 'index.html', {'form': form})
def video(request, video_id):
'''
Renders video page
'''
global video
video = list(Video.objects.all())[video_id-1]
VideoStat.objects.filter(video_link= video.video_link, user_id= user.username).delete()
response_data = {
"name" : user.username,
"video_name": video.video_name,
"video_link": video.video_link,
"is_staff": user.is_staff
}
return render(request, 'video.html', response_data)
def recievePoints(request):
'''
Recieves gaze points via ajax request
'''
x, y = request.GET['x'], request.GET['y']
time = request.GET['time']
width, height = request.GET['width'], request.GET['height']
username = request.GET['username']
try:
expressions = urllib.parse.unquote(request.GET['expressions']).split(';')
expressions = list(map(float, expressions))
except:
expressions = []
try:
emotion = EMOTIONS[np.argmax(expressions)]
except:
emotion = 'None'
try:
x, y, time = int(float(x)), int(float(y)), int(float(time))
except:
x, y = 0, 0
try:
width, height = int(width), int(height)
except:
width, height = 0, 0
VideoStat.objects.create(video_link= video.video_link, user_id= user.username, timestamp = time, emotions=emotion, coordinates=f'{x}:{y}', screen_width=width, screen_height=height)
return JsonResponse({'ok': True})
def exportStats(request):
'''
Recieves export request via ajax
'''
# get video data
entries = VideoStat.objects.filter(video_link=video.video_link)
DOWNLOAD_PATH = Path('viewer/static/downloads') / video.video_link
try:
os.mkdir(DOWNLOAD_PATH)
except:
pass
video_data = vimeo.new(f'https://vimeo.com/{video.video_link}')
video_data.streams[0].download(quiet=False)
video_width, video_height = str(video_data.streams[0]).split('@')[-1].split('x')
video_width, video_height = int(video_width), int(video_height)
# get video db entries
heatmap_points = []
emotion_points = []
for e in entries:
x,y = list(map(int, e.coordinates.split(':')))
time = int(e.timestamp)
x *= video_width / int(e.screen_width)
y *= video_height / int(e.screen_height)
heatmap_points.append([x,y, time])
emotion_points.append([e.user_id, time//5000, e.emotions])
emotions = pd.DataFrame(emotion_points)
emotions.columns = ['user_name', 'timestamp', 'emotion']
emotion_counts = []
for (ts, item) in emotions.groupby('timestamp'):
COUNTER = {
'timestamp': item['timestamp'].iloc[0] * 5,
'angry': 0,
'disgusted': 0,
'fearful': 0,
'happy': 0,
'neutral': 0,
'sad': 0,
'surprised': 0,
'None': 0
}
for index, count in item['emotion'].value_counts().items():
COUNTER[index] = count
emotion_counts.append(COUNTER.values())
emotion_counts = pd.DataFrame(emotion_counts)
emotion_counts.columns = COUNTER.keys()
emotion_counts.to_csv(DOWNLOAD_PATH / 'out.csv', index = None)
heatmapper = Heatmapper(point_strength=0.6, opacity=0.8)
video_heatmapper = VideoHeatmapper(heatmapper)
heatmap_video = video_heatmapper.heatmap_on_video_path(
video_path=f'{video_data.title}.mp4',
points=heatmap_points
)
heatmap_video.write_videofile(str(DOWNLOAD_PATH / 'out.mp4'), bitrate="500k", fps=24)
mp4_files = glob.glob(str('*.mp4'))
for f in mp4_files:
if f != 'out.mp4':
os.remove(f)
shutil.make_archive(str(DOWNLOAD_PATH), 'zip', str(DOWNLOAD_PATH))
shutil.rmtree(str(DOWNLOAD_PATH))
# time based graph
fig = px.line(emotion_counts, x="timestamp", y=emotion_counts.columns[1:])
fig = plotly.graph_objs.Figure(fig.data, fig.layout)
fig_json_1 = fig.to_json()
# pie chart
labels, counts = list(emotions['emotion'].value_counts().index), list(emotions['emotion'].value_counts().values)
fig = go.Figure(data=[go.Pie(labels=labels, values=counts)])
fig_json_2 = fig.to_json()
return JsonResponse({'ok': True, 'plotly_graph_1': fig_json_1, 'plotly_graph_2': fig_json_2})
| 27.713004
| 184
| 0.625405
|
from django.shortcuts import render
from django.http import HttpResponse
from django.contrib.auth.forms import UserCreationForm
from django.contrib.auth import login, authenticate
from django.contrib.auth.models import User
from django.http import JsonResponse
####################
# IMPORT OTHER LIBS
####################
import os
import numpy as np
import seaborn as sns
import cv2
from heatmappy import Heatmapper
from heatmappy.video import VideoHeatmapper
from PIL import Image
import moviepy.editor as mp
import urllib
import glob
import pandas as pd
from pathlib import Path
import shutil
import vimeo_dl as vimeo
import plotly.express as px
import plotly
import plotly.graph_objects as go
from .models import Video, VideoStat
EMOTIONS = [
'angry',
'disgusted',
'fearful',
'happy',
'neutral',
'sad',
'surprised'
]
# # Create your views here.
# def index(request):
# return render(request, 'index.html')
heatmap_points = []
def index(request):
'''
Renders login + main page
'''
global user
if request.method == 'POST':
username = request.POST['username']
password = request.POST['password']
user = authenticate(username=username, password=password)
if user is not None:
# if user is authentificated
data = Video.objects.all()
response_data = {
"video_data": data,
"name" : username,
"is_staff": user.is_staff,
}
return render(request, 'main.html', response_data)
return render(request, 'index.html')
else:
form = UserCreationForm()
return render(request, 'index.html', {'form': form})
def video(request, video_id):
'''
Renders video page
'''
global video
video = list(Video.objects.all())[video_id-1]
VideoStat.objects.filter(video_link= video.video_link, user_id= user.username).delete()
response_data = {
"name" : user.username,
"video_name": video.video_name,
"video_link": video.video_link,
"is_staff": user.is_staff
}
return render(request, 'video.html', response_data)
def calibrate(request):
return render(request, 'calibration.html')
def recievePoints(request):
'''
Recieves gaze points via ajax request
'''
x, y = request.GET['x'], request.GET['y']
time = request.GET['time']
width, height = request.GET['width'], request.GET['height']
username = request.GET['username']
try:
expressions = urllib.parse.unquote(request.GET['expressions']).split(';')
expressions = list(map(float, expressions))
except:
expressions = []
try:
emotion = EMOTIONS[np.argmax(expressions)]
except:
emotion = 'None'
try:
x, y, time = int(float(x)), int(float(y)), int(float(time))
except:
x, y = 0, 0
try:
width, height = int(width), int(height)
except:
width, height = 0, 0
VideoStat.objects.create(video_link= video.video_link, user_id= user.username, timestamp = time, emotions=emotion, coordinates=f'{x}:{y}', screen_width=width, screen_height=height)
return JsonResponse({'ok': True})
def exportStats(request):
'''
Recieves export request via ajax
'''
# get video data
entries = VideoStat.objects.filter(video_link=video.video_link)
DOWNLOAD_PATH = Path('viewer/static/downloads') / video.video_link
try:
os.mkdir(DOWNLOAD_PATH)
except:
pass
video_data = vimeo.new(f'https://vimeo.com/{video.video_link}')
video_data.streams[0].download(quiet=False)
video_width, video_height = str(video_data.streams[0]).split('@')[-1].split('x')
video_width, video_height = int(video_width), int(video_height)
# get video db entries
heatmap_points = []
emotion_points = []
for e in entries:
x,y = list(map(int, e.coordinates.split(':')))
time = int(e.timestamp)
x *= video_width / int(e.screen_width)
y *= video_height / int(e.screen_height)
heatmap_points.append([x,y, time])
emotion_points.append([e.user_id, time//5000, e.emotions])
emotions = pd.DataFrame(emotion_points)
emotions.columns = ['user_name', 'timestamp', 'emotion']
emotion_counts = []
for (ts, item) in emotions.groupby('timestamp'):
COUNTER = {
'timestamp': item['timestamp'].iloc[0] * 5,
'angry': 0,
'disgusted': 0,
'fearful': 0,
'happy': 0,
'neutral': 0,
'sad': 0,
'surprised': 0,
'None': 0
}
for index, count in item['emotion'].value_counts().items():
COUNTER[index] = count
emotion_counts.append(COUNTER.values())
emotion_counts = pd.DataFrame(emotion_counts)
emotion_counts.columns = COUNTER.keys()
emotion_counts.to_csv(DOWNLOAD_PATH / 'out.csv', index = None)
heatmapper = Heatmapper(point_strength=0.6, opacity=0.8)
video_heatmapper = VideoHeatmapper(heatmapper)
heatmap_video = video_heatmapper.heatmap_on_video_path(
video_path=f'{video_data.title}.mp4',
points=heatmap_points
)
heatmap_video.write_videofile(str(DOWNLOAD_PATH / 'out.mp4'), bitrate="500k", fps=24)
mp4_files = glob.glob(str('*.mp4'))
for f in mp4_files:
if f != 'out.mp4':
os.remove(f)
shutil.make_archive(str(DOWNLOAD_PATH), 'zip', str(DOWNLOAD_PATH))
shutil.rmtree(str(DOWNLOAD_PATH))
# time based graph
fig = px.line(emotion_counts, x="timestamp", y=emotion_counts.columns[1:])
fig = plotly.graph_objs.Figure(fig.data, fig.layout)
fig_json_1 = fig.to_json()
# pie chart
labels, counts = list(emotions['emotion'].value_counts().index), list(emotions['emotion'].value_counts().values)
fig = go.Figure(data=[go.Pie(labels=labels, values=counts)])
fig_json_2 = fig.to_json()
return JsonResponse({'ok': True, 'plotly_graph_1': fig_json_1, 'plotly_graph_2': fig_json_2})
| 49
| 0
| 23
|
4c2dfc45b6d9a010d7e553bb3578d11ead92b7c0
| 3,079
|
py
|
Python
|
py_module_complete.py
|
Saevon/sublime_pymodule_complete
|
92d32dbe341c8278a5665bfa6311d3d4439b8537
|
[
"MIT"
] | null | null | null |
py_module_complete.py
|
Saevon/sublime_pymodule_complete
|
92d32dbe341c8278a5665bfa6311d3d4439b8537
|
[
"MIT"
] | null | null | null |
py_module_complete.py
|
Saevon/sublime_pymodule_complete
|
92d32dbe341c8278a5665bfa6311d3d4439b8537
|
[
"MIT"
] | null | null | null |
import re
from importlib import import_module
import inspect
import sublime_plugin
import sublime
SCOPE_RE = re.compile(r'\bsource\.python\b')
LIB_MODULE_RE = re.compile(r'\bsupport\.module\.python\b')
def grab_module(view, cursor):
''' Grabs the entire module path under the cursor '''
word_sel = view.word(cursor)
pos = None
# Are we on a dot right now?
if view.substr(cursor.begin() - 1) == '.':
pos = cursor.begin() - 1
# Are we on a word?
elif view.substr(word_sel.begin() - 1) == '.':
pos = word_sel.begin() - 1
# Not a module
else:
return False
path_parts = []
while view.substr(pos) == '.':
# Expand prefix to a word
word_sel = view.word(pos - 1)
word = view.substr(word_sel)
path_parts.append(word)
pos = word_sel.begin() - 1
# Format the module path
path = '.'.join(reversed(path_parts))
return path
| 24.830645
| 93
| 0.602793
|
import re
from importlib import import_module
import inspect
import sublime_plugin
import sublime
SCOPE_RE = re.compile(r'\bsource\.python\b')
LIB_MODULE_RE = re.compile(r'\bsupport\.module\.python\b')
def format_attr(attr, module):
module_name = module.__name__
pretty_attr = attr
snippet_attr = attr
obj = getattr(module, attr)
# if inspect.isclass(attr):
if isinstance(attr, type):
pretty_attr = 'class {}()'.format(attr)
elif callable(obj):
pretty_attr = '{}()'.format(attr)
snippet_attr = '{}'.format(attr)
return (
pretty_attr + '\t' + module_name,
snippet_attr,
)
def grab_module(view, cursor):
''' Grabs the entire module path under the cursor '''
word_sel = view.word(cursor)
pos = None
# Are we on a dot right now?
if view.substr(cursor.begin() - 1) == '.':
pos = cursor.begin() - 1
# Are we on a word?
elif view.substr(word_sel.begin() - 1) == '.':
pos = word_sel.begin() - 1
# Not a module
else:
return False
path_parts = []
while view.substr(pos) == '.':
# Expand prefix to a word
word_sel = view.word(pos - 1)
word = view.substr(word_sel)
path_parts.append(word)
pos = word_sel.begin() - 1
# Format the module path
path = '.'.join(reversed(path_parts))
return path
class PythonAutoCompletion(sublime_plugin.EventListener):
def __init__(self, *args, **kwargs):
self.on_settings_update()
self.watch_settings()
super(PythonAutoCompletion, self).__init__(*args, **kwargs)
def watch_settings(self):
"""Observe changes."""
self.unwatch_settings()
self._settings.add_on_change('PyComplete-settings-listener', self.on_settings_update)
def unwatch_settings(self):
self._settings.clear_on_change('PyComplete-settings-listener')
def on_settings_update(self):
self._settings = sublime.load_settings('PyComplete.sublime-settings')
def on_query_completions(self, view, prefix, locations):
cursor = view.sel()[-1]
scopes = view.scope_name(cursor.begin())
# Skip unknown languages
if not SCOPE_RE.match(scopes):
return
# Grab the current path
module_name = grab_module(view, cursor)
if module_name not in self._settings.get('modules'):
return
module = import_module(module_name)
properties = dir(module)
completions = [
# Convert to completions format
format_attr(prop, module)
for prop in properties
# Filter out private properties
if not prop.startswith('_')
]
return (
# Completions
completions,
# Flags:
(
# Disable document-word completions
sublime.INHIBIT_WORD_COMPLETIONS
# Disable .sublime-completions
| sublime.INHIBIT_EXPLICIT_COMPLETIONS
),
)
| 1,755
| 332
| 46
|
e586b33c1f0991a69d385f1562e8356586b2257e
| 13,430
|
py
|
Python
|
layeredGraphLayouter/crossing/abstractBarycenterPortDistributor.py
|
Nic30/layeredGraphLayouter
|
fa792fa8f4b3a781adfbd7756015fbf4b067315b
|
[
"MIT"
] | 1
|
2020-02-07T15:07:15.000Z
|
2020-02-07T15:07:15.000Z
|
layeredGraphLayouter/crossing/abstractBarycenterPortDistributor.py
|
Nic30/layeredGraphLayouter
|
fa792fa8f4b3a781adfbd7756015fbf4b067315b
|
[
"MIT"
] | null | null | null |
layeredGraphLayouter/crossing/abstractBarycenterPortDistributor.py
|
Nic30/layeredGraphLayouter
|
fa792fa8f4b3a781adfbd7756015fbf4b067315b
|
[
"MIT"
] | null | null | null |
"""
Calculates port ranks and distributes ports.
The rank of a port is a floating point number that represents its position
inside the containing layer. This depends on the node order of that layer and on the
port constraints of the nodes. Port ranks are used by {@link ICrossingMinimizationHeuristics
for calculating barycenter or median values for nodes. Furthermore, they are used in this
class for distributing the ports of nodes where the order of ports is not fixed,
which has to be done as the last step of each crossing minimization processor.
There are different ways to determine port ranks, therefore that is done in concrete subclasses.
"""
from collections import defaultdict
from math import inf
from typing import List
from layeredGraphLayouter.containers.constants import PortType, PortSide
from layeredGraphLayouter.containers.lNode import LNode
from layeredGraphLayouter.containers.lPort import LPort
class AbstractBarycenterPortDistributor():
"""
Constructs a port distributor for the given array of port ranks.
All ports are required to be assigned ids in the range of the given array.
:ivar portRanks: port ranks dict {port: rank} in which the results of ranks calculation are stored.
"""
# ######################################/
# Port Rank Assignment
def calculatePortRanks_many(self, layer: List[LNode], portType: PortType):
"""
Determine ranks for all ports of specific type in the given layer.
The ranks are written to the {@link #getPortRanks() array.
:param layer: a layer as node array
:param portType: the port type to consider
"""
#assert isinstance(layer, LNodeLayer), (layer, layer.__class__)
calculatePortRanks = self.calculatePortRanks
consumedRank = 0
for node in layer:
consumedRank += calculatePortRanks(node, consumedRank, portType)
def calculatePortRanks(self, node: LNode, rankSum: float, type_: PortType):
"""
Assign port ranks for the input or output ports of the given node. If the node's port
constraints imply a fixed order, the ports are assumed to be pre-ordered in the usual way,
i.e. in clockwise order north - east - south - west.
The ranks are written to the {@link #getPortRanks() array.
:param node: a node
:param rankSum: the sum of ranks of preceding nodes in the same layer
:param type: the port type to consider
:return the rank consumed by the given node the following node's ranks start at
{@code rankSum + consumedRank
:see: {@link org.eclipse.alg.layered.intermediate.PortListSorter
"""
raise NotImplementedError("Implement on child class")
# ######################################/
# Port Distribution
def distributePorts(self, node, ports):
"""
* Distribute the ports of the given node by their sides, connected ports, and input or output
* type.
*
* :param node
* node whose ports shall be sorted
"""
self.inLayerPorts.clear()
if ports:
self.iteratePortsAndCollectInLayerPorts(node, ports)
if self.inLayerPorts:
self.calculateInLayerPortsBarycenterValues(node)
def sortPorts(self, node):
"""
Sort the ports of a node using the given relative position values.
These values are interpreted as a hint for the clockwise order of ports.
:param node: a node
"""
portBarycenter = self.portBarycenter
for side in node.iterSides():
side.sort(key=lambda p: portBarycenter[p])
| 42.365931
| 108
| 0.606925
|
"""
Calculates port ranks and distributes ports.
The rank of a port is a floating point number that represents its position
inside the containing layer. This depends on the node order of that layer and on the
port constraints of the nodes. Port ranks are used by {@link ICrossingMinimizationHeuristics
for calculating barycenter or median values for nodes. Furthermore, they are used in this
class for distributing the ports of nodes where the order of ports is not fixed,
which has to be done as the last step of each crossing minimization processor.
There are different ways to determine port ranks, therefore that is done in concrete subclasses.
"""
from collections import defaultdict
from math import inf
from typing import List
from layeredGraphLayouter.containers.constants import PortType, PortSide
from layeredGraphLayouter.containers.lNode import LNode
from layeredGraphLayouter.containers.lPort import LPort
def hasNestedGraph(node):
return node.nestedLgraph is not None
def isNotFirstLayer(length: int, currentIndex: int, isForwardSweep: bool):
return currentIndex != 0 if isForwardSweep else currentIndex != length - 1
def portTypeFor(isForwardSweep: bool):
return PortType.OUTPUT if isForwardSweep else PortType.INPUT
class AbstractBarycenterPortDistributor():
"""
Constructs a port distributor for the given array of port ranks.
All ports are required to be assigned ids in the range of the given array.
:ivar portRanks: port ranks dict {port: rank} in which the results of ranks calculation are stored.
"""
def __init__(self, random, graph):
self.random = random
r = self.portRanks = {}
self.minBarycenter = inf
self.maxBarycenter = 0.0
np = self.nodePositions = {}
for i, la in enumerate(graph.layers):
for node in la:
np[node] = i
for p in node.iterPorts():
r[p] = 0
self.portBarycenter = defaultdict(int)
self.inLayerPorts = []
# ######################################/
# Port Rank Assignment
def distributePortsWhileSweeping(self, nodeOrder, currentIndex: int, isForwardSweep: bool):
self.updateNodePositions(nodeOrder, currentIndex)
freeLayer = nodeOrder[currentIndex]
side = PortSide.WEST if isForwardSweep else PortSide.EAST
distributePorts_side = self.distributePorts_side
if isNotFirstLayer(len(nodeOrder), currentIndex, isForwardSweep):
if isForwardSweep:
fixedLayer = nodeOrder[currentIndex - 1]
else:
fixedLayer = nodeOrder[currentIndex + 1]
self.calculatePortRanks_many(
fixedLayer, portTypeFor(isForwardSweep))
for node in freeLayer:
distributePorts_side(node, side)
self.calculatePortRanks_many(
freeLayer, portTypeFor(not isForwardSweep))
for node in fixedLayer:
if not hasNestedGraph(node):
distributePorts_side(node, PortSide.opposite(side))
else:
for node in freeLayer:
distributePorts_side(node, side)
# Barycenter port distributor can not be used with always improving crossing minimization heuristics
# which do not need to count.
return False
def calculatePortRanks_many(self, layer: List[LNode], portType: PortType):
"""
Determine ranks for all ports of specific type in the given layer.
The ranks are written to the {@link #getPortRanks() array.
:param layer: a layer as node array
:param portType: the port type to consider
"""
#assert isinstance(layer, LNodeLayer), (layer, layer.__class__)
calculatePortRanks = self.calculatePortRanks
consumedRank = 0
for node in layer:
consumedRank += calculatePortRanks(node, consumedRank, portType)
def calculatePortRanks(self, node: LNode, rankSum: float, type_: PortType):
"""
Assign port ranks for the input or output ports of the given node. If the node's port
constraints imply a fixed order, the ports are assumed to be pre-ordered in the usual way,
i.e. in clockwise order north - east - south - west.
The ranks are written to the {@link #getPortRanks() array.
:param node: a node
:param rankSum: the sum of ranks of preceding nodes in the same layer
:param type: the port type to consider
:return the rank consumed by the given node the following node's ranks start at
{@code rankSum + consumedRank
:see: {@link org.eclipse.alg.layered.intermediate.PortListSorter
"""
raise NotImplementedError("Implement on child class")
# ######################################/
# Port Distribution
def distributePorts_side(self, node: LNode, side: PortSide):
if not node.portConstraints.isOrderFixed():
# distribute ports in sweep direction and on north south side of
# node.
self.distributePorts(node, node.getPortSideView(side))
self.distributePorts(node, node.getPortSideView(PortSide.SOUTH))
self.distributePorts(node, node.getPortSideView(PortSide.NORTH))
# sort the ports by considering the side, type, and barycenter
# values
self.sortPorts(node)
def distributePorts(self, node, ports):
"""
* Distribute the ports of the given node by their sides, connected ports, and input or output
* type.
*
* :param node
* node whose ports shall be sorted
"""
self.inLayerPorts.clear()
if ports:
self.iteratePortsAndCollectInLayerPorts(node, ports)
if self.inLayerPorts:
self.calculateInLayerPortsBarycenterValues(node)
def iteratePortsAndCollectInLayerPorts(self, node, ports):
minBarycenter = 0.0
maxBarycenter = 0.0
# a float value large enough to ensure that barycenters of south ports
# work fine
absurdlyLargeFloat = 2 * len(node.layer) + 1
# calculate barycenter values for the ports of the node
dealWithNorthSouthPorts = self.dealWithNorthSouthPorts
continueOnPortIteration = False
inLayerPorts = self.inLayerPorts
portRanks = self.portRanks
portBarycenter = self.portBarycenter
for port in ports:
northSouthPort = port.side == PortSide.NORTH or port.side == PortSide.SOUTH
sum_ = 0
if northSouthPort:
# Find the dummy node created for the port
portDummy = port.portDummy
if (portDummy is None):
continue
sum_ += dealWithNorthSouthPorts(absurdlyLargeFloat,
port, portDummy)
else:
# add up all ranks of connected ports
for outgoingEdge in port.outgoingEdges:
if outgoingEdge.dstNode.layer is node.layer:
inLayerPorts.append(port)
continueOnPortIteration = True
break
else:
# outgoing edges go to the subsequent layer and are
# seen clockwise
connectedPort = outgoingEdge.dst
sum_ += portRanks[connectedPort]
if continueOnPortIteration:
continueOnPortIteration = False
continue
for incomingEdge in port.incomingEdges:
if incomingEdge.srcNode.layer is node.layer:
inLayerPorts.append(port)
continueOnPortIteration = True
break
else:
# incoming edges go to the preceding layer and are seen
# counter-clockwise
connectedPort = incomingEdge.src
sum_ -= portRanks[connectedPort]
if continueOnPortIteration:
continueOnPortIteration = False
continue
if port.getDegree() > 0:
portBarycenter[port] = sum_ / port.getDegree()
minBarycenter = min(minBarycenter, portBarycenter[port])
maxBarycenter = max(maxBarycenter, portBarycenter[port])
elif northSouthPort:
# For northern and southern ports, the sum directly corresponds to the
# barycenter value to be used.
portBarycenter[port] = sum_
def calculateInLayerPortsBarycenterValues(self, node):
# go through the list of in-layer ports and calculate their barycenter
# values
nodePositions = self.nodePositions
nodeIndexInLayer = nodePositions[node] + 1
layerSize = len(node.layer) + 1
minBarycenter = self.minBarycenter
maxBarycenter = self.maxBarycenter
portBarycenter = self.portBarycenter
for inLayerPort in self.inLayerPorts:
# add the indices of all connected in-layer ports
sum_ = 0
inLayerConnections = 0
for connectedPort in inLayerPort.getConnectedPorts():
if connectedPort.getNode().layer is node.layer:
sum_ += nodePositions[connectedPort.getNode()] + 1
inLayerConnections += 1
# The port's barycenter value is the mean index of connected nodes. If that
# value is lower than the node's index, most in-layer edges point upwards, so we want
# the port to be placed at the top of the side. If the value is higher than the
# nodes's index, most in-layer edges point downwards, so we want the port to be
# placed at the bottom of the side.
barycenter = sum_ / inLayerConnections
portSide = inLayerPort.side
if portSide == PortSide.EAST:
if barycenter < nodeIndexInLayer:
# take a low value in order to have the port above
portBarycenter[inLayerPort] = minBarycenter - barycenter
else:
# take a high value in order to have the port below
portBarycenter[inLayerPort] = maxBarycenter + \
(layerSize - barycenter)
elif portSide == PortSide.WEST:
if barycenter < nodeIndexInLayer:
# take a high value in order to have the port above
portBarycenter[inLayerPort] = maxBarycenter + barycenter
else:
# take a low value in order to have the port below
portBarycenter[inLayerPort] = minBarycenter - \
(layerSize - barycenter)
def dealWithNorthSouthPorts(self, absurdlyLargeFloat: float,
port: LPort, portDummy: LNode):
# Find out if it's an input port, an output port, or both
input_ = False
output = False
for portDummyPort in portDummy.iterPorts():
if portDummyPort.origin == port:
if portDummyPort.outgoingEdges:
output = True
elif portDummyPort.incomingEdges:
input_ = True
sum_ = 0.0
if input_ and input_ ^ output:
# It's an input port the index of its dummy node is its inverted sortkey
# (for southern input ports, the key must be larger than the ones
# assigned to output ports or inputandoutput ports)
if port.side == PortSide.NORTH:
sum_ = -self.nodePositions[portDummy]
else:
sum_ = absurdlyLargeFloat - self.nodePositions[portDummy]
elif output and input_ ^ output:
# It's an output port the index of its dummy node is its sort key
# (for northern output ports, the key must be larger than the ones assigned
# to input ports or inputandoutput ports, which are negative and 0,
# respectively)
sum_ = self.nodePositions[portDummy] + 1.0
elif input_ and output:
# It's both, an input and an output port it must sit between input and
# output ports
# North: input ports < 0.0, output ports > 0.0
# South: input ports > FLOAT_MAX / 2, output ports near zero
if port.side == PortSide.NORTH:
sum_ = 0.0
else:
sum_ = absurdlyLargeFloat / 2
return sum_
def updateNodePositions(self, nodeOrder, currentIndex: int):
layer = nodeOrder[currentIndex]
nodePositions = self.nodePositions
for i, node in enumerate(layer):
nodePositions[node] = i
def sortPorts(self, node):
"""
Sort the ports of a node using the given relative position values.
These values are interpreted as a hint for the clockwise order of ports.
:param node: a node
"""
portBarycenter = self.portBarycenter
for side in node.iterSides():
side.sort(key=lambda p: portBarycenter[p])
| 9,466
| 0
| 258
|
b9bd1928a2c1d5f03cdbd0cf4d58af342f9134b6
| 592
|
py
|
Python
|
setup.py
|
villarrealas/deltasigma
|
b1c2e9f307d37064ed4163a2682be825b3a44bf2
|
[
"BSD-3-Clause"
] | null | null | null |
setup.py
|
villarrealas/deltasigma
|
b1c2e9f307d37064ed4163a2682be825b3a44bf2
|
[
"BSD-3-Clause"
] | null | null | null |
setup.py
|
villarrealas/deltasigma
|
b1c2e9f307d37064ed4163a2682be825b3a44bf2
|
[
"BSD-3-Clause"
] | null | null | null |
from setuptools import setup, find_packages
PACKAGENAME = "deltasigma"
VERSION = "0.0.dev"
setup(
name=PACKAGENAME,
version=VERSION,
author="Antonio Villarreal",
author_email="avillarreal@anl.gov",
description="Source code for chopper / halotools implementation to calculate delta sigma.",
long_description="Source code for chopper / halotools implementation to calculate delta sigma.",
install_requires=["numpy", "halotools", "colossus", "yaml", "pyyaml", "psutil", "six"],
packages=find_packages(),
url="https://github.com/villarrealas/deltasigma"
)
| 31.157895
| 100
| 0.724662
|
from setuptools import setup, find_packages
PACKAGENAME = "deltasigma"
VERSION = "0.0.dev"
setup(
name=PACKAGENAME,
version=VERSION,
author="Antonio Villarreal",
author_email="avillarreal@anl.gov",
description="Source code for chopper / halotools implementation to calculate delta sigma.",
long_description="Source code for chopper / halotools implementation to calculate delta sigma.",
install_requires=["numpy", "halotools", "colossus", "yaml", "pyyaml", "psutil", "six"],
packages=find_packages(),
url="https://github.com/villarrealas/deltasigma"
)
| 0
| 0
| 0
|
9df0a549c8d74fd28425f7e25b04aa46f91804bb
| 3,055
|
py
|
Python
|
Python/utils/persistence.py
|
RemkoPr/smart_textile_public
|
ffb53b2f5d039f0ca0d84770f2775688f237da1a
|
[
"Apache-2.0",
"MIT"
] | null | null | null |
Python/utils/persistence.py
|
RemkoPr/smart_textile_public
|
ffb53b2f5d039f0ca0d84770f2775688f237da1a
|
[
"Apache-2.0",
"MIT"
] | 1
|
2021-12-21T22:15:04.000Z
|
2021-12-21T22:15:04.000Z
|
Python/utils/persistence.py
|
RemkoPr/smart_textile_public
|
ffb53b2f5d039f0ca0d84770f2775688f237da1a
|
[
"Apache-2.0",
"MIT"
] | null | null | null |
import itertools
import os
import csv
from loguru import logger
from datetime import *
class SensorPersistence(Persistence):
"""
Writes sensor data to a buffer and periodically flushes to file system.
"""
| 37.716049
| 115
| 0.594763
|
import itertools
import os
import csv
from loguru import logger
from datetime import *
class Persistence:
def __init__(self, directory):
self.directory = directory
self.init_directory(self.directory)
self.file_name = self.create_unique_file_name(directory)
def init_directory(self, directory):
"""
Checks if directory exists, if not, creates it
:param directory: directory to check
"""
if not os.path.exists(directory):
ans = input("Make new directory (" + directory + ")? [Y/N] ")
if ans.lower() == "y":
os.makedirs(directory)
elif ans.lower() == "n":
raise NotADirectoryError("Data directory doesn't exist, creation cancelled by user.")
else:
raise ValueError("Invalid answer given, enter [Y] or [N].")
def create_unique_file_name(self, directory):
# Build the file name so that each experiment (a.k.a. each run of the code) saves data to a different file
file_name = str(date.today())
file_number = 0
for file_in_dir in os.listdir(directory):
if file_in_dir.startswith(file_name):
val = int(file_in_dir[file_in_dir.find("[") + 1:file_in_dir.find("]")])
if val > file_number:
file_number = val
file_name = file_name + "[" + str(file_number + 1) + "].csv"
return file_name
class SensorPersistence(Persistence):
"""
Writes sensor data to a buffer and periodically flushes to file system.
"""
def __init__(self, devices, directory="./data/", buffer_size=10):
super().__init__(directory)
self.devices = devices
self.buffer = []
self.max_buffer_size = buffer_size
self.init_csv_file(self.directory, self.file_name)
def init_csv_file(self, directory, file_name):
# TODO: haal grid size van smart textile
grid_width = grid_height = 7
header = ['PCB addr', 'timestamp', 'LowBattery'] + \
[f'sensor_value_{digital_pin}_{analog_pin}'
for digital_pin, analog_pin in itertools.product(range(grid_width), range(grid_height))]
with open(os.path.join(directory, file_name), 'w', newline='') as csv_file:
csv_writer = csv.writer(csv_file, delimiter=';')
csv_writer.writerow(header)
def persist(self, data):
self.buffer.append(data)
if len(self.buffer) > self.max_buffer_size:
self._persist_to_file()
self.buffer = []
def _persist_to_file(self):
with open(os.path.join(self.directory, self.file_name), 'a+', newline='') as csv_file:
csv_writer = csv.writer(csv_file, delimiter=';')
for all_pcb_sensor_values in self.buffer:
for row in all_pcb_sensor_values:
csv_writer.writerow(row)
logger.info("Wrote to file")
| 2,020
| 657
| 140
|
83ed5ef1449300aba87baa249f1d49549d0f1227
| 318
|
py
|
Python
|
Python/CopyPasta/animation.py
|
escharf72/Engineering_4_Notebook
|
bcfd7edf74791cedc2519be66ac79246670116f3
|
[
"BSD-Source-Code"
] | 1
|
2020-11-04T15:27:34.000Z
|
2020-11-04T15:27:34.000Z
|
Python/CopyPasta/animation.py
|
escharf72/Engineering_4_Notebook
|
bcfd7edf74791cedc2519be66ac79246670116f3
|
[
"BSD-Source-Code"
] | null | null | null |
Python/CopyPasta/animation.py
|
escharf72/Engineering_4_Notebook
|
bcfd7edf74791cedc2519be66ac79246670116f3
|
[
"BSD-Source-Code"
] | null | null | null |
from picamera import PiCamera
from time import sleep
from gpiozero import Button
import keyboard
button = keyboard.is_pressed('h')
camera = PiCamera()
while True:
camera.start_preview()
button.wait_for_press()
print("Button has been pressed!")
sleep(3)
camera.capture('animateImage.jpg')
camera.stop_preview()
| 19.875
| 35
| 0.77673
|
from picamera import PiCamera
from time import sleep
from gpiozero import Button
import keyboard
button = keyboard.is_pressed('h')
camera = PiCamera()
while True:
camera.start_preview()
button.wait_for_press()
print("Button has been pressed!")
sleep(3)
camera.capture('animateImage.jpg')
camera.stop_preview()
| 0
| 0
| 0
|
bcead883cb43a65442ccf599bda0b4064946fb5a
| 4,716
|
py
|
Python
|
bin/train-model.py
|
giuscri/thesis
|
d7aa0a8476f53ad304495b437841af1a8d6c87d4
|
[
"MIT"
] | null | null | null |
bin/train-model.py
|
giuscri/thesis
|
d7aa0a8476f53ad304495b437841af1a8d6c87d4
|
[
"MIT"
] | 10
|
2018-05-11T08:40:48.000Z
|
2018-06-29T16:14:27.000Z
|
bin/train-model.py
|
giuscri/thesis
|
d7aa0a8476f53ad304495b437841af1a8d6c87d4
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
import os, sys, pickle
import keras.backend as K
import tensorflow as tf
import numpy as np
from argparse import ArgumentParser
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from datasets import mnist
from models import (train, accuracy, save_to_file, fc_100_100_10,
pca_filtered_model, fastica_filtered_model,
incrementalpca_filtered_model, nmf_filtered_model,
truncatedsvd_filtered_model, kernelpca_filtered_model)
argument_parser = ArgumentParser()
argument_parser.add_argument("--pca", action="store_true",
help="use PCA image filter defense")
argument_parser.add_argument("--fastica", action="store_true",
help="use FastICA image filter defense")
argument_parser.add_argument("--incrementalpca", action="store_true",
help="use IncrementalPCA image filter defense")
argument_parser.add_argument("--nmf", action="store_true",
help="use IncrementalPCA image filter defense")
argument_parser.add_argument("--truncatedsvd", action="store_true",
help="use TruncatedSVD image filter defense")
argument_parser.add_argument("--kernelpca", action="store_true",
help="use KernelPCA image filter defense")
argument_parser.add_argument("--n-components", type=int, nargs="+", default=[],
help="number of components for image filters")
argument_parser.add_argument("--epochs", type=int, default=-1,
help="default: let the model choose")
argument_parser.add_argument("--random-seed", action="store_true",
help="initialize model with random seed")
args = argument_parser.parse_args()
PREFIX = os.environ.get('PREFIX', '.')
X_train, y_train, X_test, y_test = mnist()
if not args.random_seed:
K.clear_session()
tf.set_random_seed(1234)
np.random.seed(1234)
no_defense_model = fc_100_100_10()
print(f"Training {no_defense_model.name}...")
train(no_defense_model, X_train, y_train, args.epochs, verbose=True,
stop_on_stable_weights=True, reduce_lr_on_plateau=True,
stop_on_stable_weights_patience=60, reduce_lr_on_plateau_patience=30)
print(f"Saving {no_defense_model.name}...")
save_to_file(no_defense_model, PREFIX)
for n_components in args.n_components:
if args.pca:
pca = cached(f"pca-{n_components}")
filtered_model = pca_filtered_model(no_defense_model, X_train,
n_components, pca=pca)
print(f"Saving {filtered_model.name}...")
save_to_file(filtered_model, PREFIX)
if args.fastica:
fastica = cached(f"fastica-{n_components}")
filtered_model = fastica_filtered_model(no_defense_model, X_train,
n_components, fastica=fastica)
print(f"Saving {filtered_model.name}...")
save_to_file(filtered_model, PREFIX)
if args.incrementalpca:
incrementalpca = cached(f"incrementalpca-{n_components}")
filtered_model = incrementalpca_filtered_model(no_defense_model, X_train,
n_components,
incrementalpca=incrementalpca)
print(f"Saving {filtered_model.name}...")
save_to_file(filtered_model, PREFIX)
if args.nmf:
nmf = cached(f"nmf-{n_components}")
filtered_model = nmf_filtered_model(no_defense_model, X_train,
n_components, nmf=nmf)
print(f"Saving {filtered_model.name}...")
save_to_file(filtered_model, PREFIX)
if args.truncatedsvd:
truncatedsvd = cached(f"truncatedsvd-{n_components}")
filtered_model = truncatedsvd_filtered_model(no_defense_model, X_train,
n_components,
truncatedsvd=truncatedsvd)
print(f"Saving {filtered_model.name}...")
save_to_file(filtered_model, PREFIX)
if args.kernelpca:
kernelpca = cached(f"kernelpca-{n_components}")
filtered_model = kernelpca_filtered_model(no_defense_model, X_train,
n_components, kernelpca=kernelpca)
print(f"Saving {filtered_model.name}...")
save_to_file(filtered_model, PREFIX)
| 41.734513
| 85
| 0.632103
|
#!/usr/bin/env python
import os, sys, pickle
import keras.backend as K
import tensorflow as tf
import numpy as np
from argparse import ArgumentParser
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from datasets import mnist
from models import (train, accuracy, save_to_file, fc_100_100_10,
pca_filtered_model, fastica_filtered_model,
incrementalpca_filtered_model, nmf_filtered_model,
truncatedsvd_filtered_model, kernelpca_filtered_model)
def cached(name):
filename = f"cache/{name}.pkl"
if not os.path.exists(filename):
return None
with open(filename, "rb") as f:
return pickle.load(f)
argument_parser = ArgumentParser()
argument_parser.add_argument("--pca", action="store_true",
help="use PCA image filter defense")
argument_parser.add_argument("--fastica", action="store_true",
help="use FastICA image filter defense")
argument_parser.add_argument("--incrementalpca", action="store_true",
help="use IncrementalPCA image filter defense")
argument_parser.add_argument("--nmf", action="store_true",
help="use IncrementalPCA image filter defense")
argument_parser.add_argument("--truncatedsvd", action="store_true",
help="use TruncatedSVD image filter defense")
argument_parser.add_argument("--kernelpca", action="store_true",
help="use KernelPCA image filter defense")
argument_parser.add_argument("--n-components", type=int, nargs="+", default=[],
help="number of components for image filters")
argument_parser.add_argument("--epochs", type=int, default=-1,
help="default: let the model choose")
argument_parser.add_argument("--random-seed", action="store_true",
help="initialize model with random seed")
args = argument_parser.parse_args()
PREFIX = os.environ.get('PREFIX', '.')
X_train, y_train, X_test, y_test = mnist()
if not args.random_seed:
K.clear_session()
tf.set_random_seed(1234)
np.random.seed(1234)
no_defense_model = fc_100_100_10()
print(f"Training {no_defense_model.name}...")
train(no_defense_model, X_train, y_train, args.epochs, verbose=True,
stop_on_stable_weights=True, reduce_lr_on_plateau=True,
stop_on_stable_weights_patience=60, reduce_lr_on_plateau_patience=30)
print(f"Saving {no_defense_model.name}...")
save_to_file(no_defense_model, PREFIX)
for n_components in args.n_components:
if args.pca:
pca = cached(f"pca-{n_components}")
filtered_model = pca_filtered_model(no_defense_model, X_train,
n_components, pca=pca)
print(f"Saving {filtered_model.name}...")
save_to_file(filtered_model, PREFIX)
if args.fastica:
fastica = cached(f"fastica-{n_components}")
filtered_model = fastica_filtered_model(no_defense_model, X_train,
n_components, fastica=fastica)
print(f"Saving {filtered_model.name}...")
save_to_file(filtered_model, PREFIX)
if args.incrementalpca:
incrementalpca = cached(f"incrementalpca-{n_components}")
filtered_model = incrementalpca_filtered_model(no_defense_model, X_train,
n_components,
incrementalpca=incrementalpca)
print(f"Saving {filtered_model.name}...")
save_to_file(filtered_model, PREFIX)
if args.nmf:
nmf = cached(f"nmf-{n_components}")
filtered_model = nmf_filtered_model(no_defense_model, X_train,
n_components, nmf=nmf)
print(f"Saving {filtered_model.name}...")
save_to_file(filtered_model, PREFIX)
if args.truncatedsvd:
truncatedsvd = cached(f"truncatedsvd-{n_components}")
filtered_model = truncatedsvd_filtered_model(no_defense_model, X_train,
n_components,
truncatedsvd=truncatedsvd)
print(f"Saving {filtered_model.name}...")
save_to_file(filtered_model, PREFIX)
if args.kernelpca:
kernelpca = cached(f"kernelpca-{n_components}")
filtered_model = kernelpca_filtered_model(no_defense_model, X_train,
n_components, kernelpca=kernelpca)
print(f"Saving {filtered_model.name}...")
save_to_file(filtered_model, PREFIX)
| 155
| 0
| 23
|
7ae50bdf807009d9b67b7cff99403c699f653f93
| 3,048
|
py
|
Python
|
plugin/vimtern.py
|
shivaghose/vimtern
|
2f74ee41cd34106d14362d373ef279501f9b1b5b
|
[
"MIT"
] | null | null | null |
plugin/vimtern.py
|
shivaghose/vimtern
|
2f74ee41cd34106d14362d373ef279501f9b1b5b
|
[
"MIT"
] | null | null | null |
plugin/vimtern.py
|
shivaghose/vimtern
|
2f74ee41cd34106d14362d373ef279501f9b1b5b
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
'''
VIMTern.py dispatch work to your intern via Slack from the command line.
'''
from random import randint
from sys import exit, argv
import argparse
import json
import yaml # To load the intrn file
VERBOSE = False
try:
import requests
except ImportError:
print "Unable to import requests. Run `pip install requests`."
exit(1)
def _load_intrn(intrn_file="default.intrn"):
'''
Load the config file.
'''
config = None
with open(intrn_file, 'r') as stream:
try:
config = yaml.load(stream)
except yaml.YAMLError as ex:
print str(ex)
exit(1)
return config
def vimtern_do(msg, intrn_file):
'''
Issue commands to 1ntern.
'''
global VERBOSE
if not intrn_file:
raise AttributeError("Path to .intrn file required.")
config = _load_intrn(intrn_file)
if not msg or msg == '':
num = len(config["default_msgs"])
msg = config["default_msgs"][randint(0, num - 1)]
if not isinstance(msg, basestring):
print "vimtern_do: msg is not a string."
print "msg: ", msg
exit(1)
# Build JSON message payload
msg = msg.replace('"', '').strip()
channel = config["Slack"]["channel"]
username = config["Slack"]["username"]
icon_emoji = config["Slack"]["icon_emoji"]
payload = json.dumps({
"text": msg,
"channel": channel,
"username": username,
"icon_emoji": icon_emoji,
"parse": "full"
})
# Create and send POST request to Slack webhook
slack_uri = config['Slack']['uri']
try:
r = requests.post(slack_uri, data=payload, headers={
'Content-type': 'application/json'})
r.raise_for_status()
except requests.exceptions.ConnectionError:
print "Could not establish connection to Slack."
exit(1)
except requests.exceptions.HTTPError as err:
print "Slack API request was not successful."
print err.message
exit(1)
except requests.exceptions.Timeout:
print "Slack API request timed out."
exit(1)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-f",
"--config",
dest='config',
help="Path to the .intrn config file.")
parser.add_argument("-m",
"--msg",
dest='msg',
help="Message to send.",
default="")
parser.add_argument('-v',
'--verbose',
dest='verbose',
action='store_true',
help='Verbose mode to help debug.')
parser.set_defaults(verbose=False)
args = parser.parse_args()
VERBOSE = args.verbose
if VERBOSE:
print "ARGS: ", argv
try:
vimtern_do(args.msg, args.config)
except Exception, e:
print str(e)
parser.print_help()
| 27.963303
| 72
| 0.562992
|
#!/usr/bin/env python
'''
VIMTern.py dispatch work to your intern via Slack from the command line.
'''
from random import randint
from sys import exit, argv
import argparse
import json
import yaml # To load the intrn file
VERBOSE = False
try:
import requests
except ImportError:
print "Unable to import requests. Run `pip install requests`."
exit(1)
def _load_intrn(intrn_file="default.intrn"):
'''
Load the config file.
'''
config = None
with open(intrn_file, 'r') as stream:
try:
config = yaml.load(stream)
except yaml.YAMLError as ex:
print str(ex)
exit(1)
return config
def vimtern_do(msg, intrn_file):
'''
Issue commands to 1ntern.
'''
global VERBOSE
if not intrn_file:
raise AttributeError("Path to .intrn file required.")
config = _load_intrn(intrn_file)
if not msg or msg == '':
num = len(config["default_msgs"])
msg = config["default_msgs"][randint(0, num - 1)]
if not isinstance(msg, basestring):
print "vimtern_do: msg is not a string."
print "msg: ", msg
exit(1)
# Build JSON message payload
msg = msg.replace('"', '').strip()
channel = config["Slack"]["channel"]
username = config["Slack"]["username"]
icon_emoji = config["Slack"]["icon_emoji"]
payload = json.dumps({
"text": msg,
"channel": channel,
"username": username,
"icon_emoji": icon_emoji,
"parse": "full"
})
# Create and send POST request to Slack webhook
slack_uri = config['Slack']['uri']
try:
r = requests.post(slack_uri, data=payload, headers={
'Content-type': 'application/json'})
r.raise_for_status()
except requests.exceptions.ConnectionError:
print "Could not establish connection to Slack."
exit(1)
except requests.exceptions.HTTPError as err:
print "Slack API request was not successful."
print err.message
exit(1)
except requests.exceptions.Timeout:
print "Slack API request timed out."
exit(1)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-f",
"--config",
dest='config',
help="Path to the .intrn config file.")
parser.add_argument("-m",
"--msg",
dest='msg',
help="Message to send.",
default="")
parser.add_argument('-v',
'--verbose',
dest='verbose',
action='store_true',
help='Verbose mode to help debug.')
parser.set_defaults(verbose=False)
args = parser.parse_args()
VERBOSE = args.verbose
if VERBOSE:
print "ARGS: ", argv
try:
vimtern_do(args.msg, args.config)
except Exception, e:
print str(e)
parser.print_help()
| 0
| 0
| 0
|
643c6bc30b01b37a7a4941a389cd64e1f9090dc2
| 398,739
|
py
|
Python
|
Data/scigrid-de/pypower/scigrid_2011_01_08_01.py
|
thanever/SOC
|
9f30d1a9c7610a68de9c178a1170bdf1c8ca11d4
|
[
"MIT"
] | null | null | null |
Data/scigrid-de/pypower/scigrid_2011_01_08_01.py
|
thanever/SOC
|
9f30d1a9c7610a68de9c178a1170bdf1c8ca11d4
|
[
"MIT"
] | null | null | null |
Data/scigrid-de/pypower/scigrid_2011_01_08_01.py
|
thanever/SOC
|
9f30d1a9c7610a68de9c178a1170bdf1c8ca11d4
|
[
"MIT"
] | null | null | null |
from numpy import array
| 70.962627
| 137
| 0.463647
|
from numpy import array
def scigrid_2011_01_08_01():
ppc = {"version": '2'}
ppc["baseMVA"] = 100.0
ppc["bus"] = array([
[586, 3, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[589, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[590, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[593, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[595, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[598, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[599, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[601, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[602, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[603, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[607, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[608, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[609, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[612, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[614, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[616, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[617, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[618, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[619, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[624, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[629, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[632, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[637, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[638, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[640, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[641, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[642, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[643, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[647, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[652, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[655, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[661, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[663, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[666, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[668, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[670, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[672, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[681, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[683, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[687, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[694, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[695, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[696, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[697, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[698, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[702, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[704, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[705, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[707, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[713, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[714, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[716, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[717, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[719, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[724, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[730, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[732, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[735, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[738, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[741, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[742, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[743, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[747, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[748, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[749, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[750, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[753, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[758, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[761, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[762, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[763, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[765, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[767, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[769, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[771, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[772, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[774, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[777, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[778, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[781, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[784, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[785, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
[787, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[788, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ],
[789, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ],
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[401, 580, 0 ],
[402, 581, 0 ],
[409, 582, 0 ],
[415, 583, 0 ],
[444, 584, 0 ],
[452, 585, 0 ]
])
ppc["parameters"] = {
"x_trans_sg": 0.003,
"x_trans_fm": 0.001,
"x_trans_fl": 0.001,
"d_l": 1e-3,
"d_l_perturb": 1e-5,
"w_1_ij": 1,
"w_2_ij": 1,
"w_3_ij": 1,
"w_4_ij": 1,
"b_r": 238,
"b_c": 248 }
return ppc
| 398,694
| 0
| 22
|
5fce4f32c59cbf2710e6f68e6f01a2641933d1a1
| 564
|
py
|
Python
|
env/lib/python2.7/site-packages/stripe/test/resources/test_payouts.py
|
imran1234567/plutus
|
c964f18beb139de2645e052eb4c75a6bc0677029
|
[
"MIT"
] | null | null | null |
env/lib/python2.7/site-packages/stripe/test/resources/test_payouts.py
|
imran1234567/plutus
|
c964f18beb139de2645e052eb4c75a6bc0677029
|
[
"MIT"
] | 8
|
2019-06-10T19:43:54.000Z
|
2021-11-15T17:48:16.000Z
|
Lib/site-packages/stripe/test/resources/test_payouts.py
|
JulioCantu/IndiStore
|
723c4ced800d43ffbfd34dc0ff7649b628008416
|
[
"bzip2-1.0.6"
] | null | null | null |
import stripe
from stripe.test.helper import StripeResourceTest
| 22.56
| 55
| 0.576241
|
import stripe
from stripe.test.helper import StripeResourceTest
class PayoutTest(StripeResourceTest):
def test_list_payouts(self):
stripe.Payout.list()
self.requestor_mock.request.assert_called_with(
'get',
'/v1/payouts',
{}
)
def test_cancel_payout(self):
payout = stripe.Payout(id='po_cancel')
payout.cancel()
self.requestor_mock.request.assert_called_with(
'post',
'/v1/payouts/po_cancel/cancel',
{},
None
)
| 406
| 16
| 77
|
9470da2d0635021627dca44b44a547d259bd8a63
| 1,783
|
py
|
Python
|
src/scs_dfe/display/led_state.py
|
open-seneca/alphasense_aq_sensor
|
12f37f19b4e2fe6f159b127261130d8d3bc48196
|
[
"MIT"
] | 1
|
2021-05-10T09:12:13.000Z
|
2021-05-10T09:12:13.000Z
|
src/scs_dfe/display/led_state.py
|
open-seneca/alphasense_aq_sensor
|
12f37f19b4e2fe6f159b127261130d8d3bc48196
|
[
"MIT"
] | null | null | null |
src/scs_dfe/display/led_state.py
|
open-seneca/alphasense_aq_sensor
|
12f37f19b4e2fe6f159b127261130d8d3bc48196
|
[
"MIT"
] | 2
|
2019-03-07T00:25:11.000Z
|
2020-02-28T13:45:55.000Z
|
"""
Created on 10 Nov 2018
@author: Bruno Beloff (bruno.beloff@southcoastscience.com)
a dummy LED state, to maintain compatibility with the DFE Eng package
"""
from collections import OrderedDict
from scs_core.data.json import JSONable
# --------------------------------------------------------------------------------------------------------------------
class LEDState(JSONable):
"""
classdocs
"""
# ----------------------------------------------------------------------------------------------------------------
@classmethod
# ----------------------------------------------------------------------------------------------------------------
# noinspection PyUnusedLocal
def __init__(self, colour0, colour1):
"""
Constructor
"""
pass
# ----------------------------------------------------------------------------------------------------------------
@classmethod
# ----------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------
@property
@property
# ----------------------------------------------------------------------------------------------------------------
| 24.763889
| 118
| 0.325294
|
"""
Created on 10 Nov 2018
@author: Bruno Beloff (bruno.beloff@southcoastscience.com)
a dummy LED state, to maintain compatibility with the DFE Eng package
"""
from collections import OrderedDict
from scs_core.data.json import JSONable
# --------------------------------------------------------------------------------------------------------------------
class LEDState(JSONable):
"""
classdocs
"""
# ----------------------------------------------------------------------------------------------------------------
@classmethod
def construct_from_jdict(cls, _):
return LEDState(None, None)
# ----------------------------------------------------------------------------------------------------------------
# noinspection PyUnusedLocal
def __init__(self, colour0, colour1):
"""
Constructor
"""
pass
# ----------------------------------------------------------------------------------------------------------------
@classmethod
def is_valid(cls):
return None
# ----------------------------------------------------------------------------------------------------------------
def as_json(self):
jdict = OrderedDict()
jdict['colour0'] = None
jdict['colour1'] = None
return jdict
# ----------------------------------------------------------------------------------------------------------------
@property
def colour0(self):
return None
@property
def colour1(self):
return None
# ----------------------------------------------------------------------------------------------------------------
def __str__(self, *args, **kwargs):
return "LEDState:{colour0:None, colour1:None}"
| 282
| 0
| 158
|
90fdb9009ee72b8b1db38f9c34195a42d71de57f
| 2,344
|
py
|
Python
|
Scripts/001_le_tutoriel_python/s004_les_instructions_de_controle.py
|
OrangePeelFX/Python-Tutorial
|
0d47f194553666304765f5bbc928374b7aec8a48
|
[
"MIT"
] | null | null | null |
Scripts/001_le_tutoriel_python/s004_les_instructions_de_controle.py
|
OrangePeelFX/Python-Tutorial
|
0d47f194553666304765f5bbc928374b7aec8a48
|
[
"MIT"
] | 1
|
2021-06-02T00:28:17.000Z
|
2021-06-02T00:28:17.000Z
|
Scripts/001_le_tutoriel_python/s004_les_instructions_de_controle.py
|
florianwns/python-scripts
|
0d47f194553666304765f5bbc928374b7aec8a48
|
[
"MIT"
] | 1
|
2020-01-13T11:08:18.000Z
|
2020-01-13T11:08:18.000Z
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Les boucles et les instruction de contrôle
Quelques exemples de manipulations des boucles et des instructions
"""
# la suite de fibonnaci
a, b = 0, 1
while a < 20:
print(a, end=",") # on idente de 4 espace l'instruction suivante
a, b = b, a+b
print()
if a == 21:
print("_")
elif a == 13: # 'else if' se note 'elif' en python
print("°")
else:
print(")")
# Un peu d'unicode ;) et des boucles for
words = ["Bonjour", "Jeune", "Padawan"]
for w in words:
if w == "Yoda":
break # le 'break' permet de sortie de la boucle,
else: # par contre on passe dans le 'else' si le break
# n'est jamais appelé dans la boucle for'
# ici on utilise le r de raw_string
st = r"""
____
(xXXXX|xx======---(-
/ |
/ XX|
/xxx XXX|
/xxx X |
/ ________|
__ ____/_|_|_______\_
###|=||________|_________|_
~~ |==| __ _ __ /|~~~~~~~~~-------------_______
|==| ||(( ||()| | |XXXXXXXX| >
__ |==| ~~__~__~~__ \|_________-------------~~~~~~~
###|=||~~~~~~~~|_______ |"
~~ ~~~~\~|~| /~
\ ~~~~~~~~~
\xxx X |
\xxx XXX|
\ XX| Incom's T-65B X-wing Space
\ | Superiority Starfighter (4)
(xXXXX|xx======---(-
~~~~"""
print(st)
# on peut aussi utiliser range dans la même idée
# que la boucle for(i = 0; i < words.length; i++) dans d'autres langage
for i in range(len(words)):
print(words[i], len(words[i]))
# exemple de range qui est objet iterable,
# et pas une liste à proprement parlée
range(5) # 0, 1, 2, 3, 4
range(5, 10) # 5, 6, 7, 8, 9
range(0, 10, 3) # 0, 3, 6, 9
range(-10, -100, -30) # -10, -40, -70
# mot clé 'pass'
a = 9
if a < 10:
pass # 'pass' ne fait rien, mais est parfois nécessaire après une instruction
# TODO : Afficher un message d'erreur...
else:
print("a supérieur a 10")
| 30.051282
| 88
| 0.433447
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Les boucles et les instruction de contrôle
Quelques exemples de manipulations des boucles et des instructions
"""
# la suite de fibonnaci
a, b = 0, 1
while a < 20:
print(a, end=",") # on idente de 4 espace l'instruction suivante
a, b = b, a+b
print()
if a == 21:
print("_")
elif a == 13: # 'else if' se note 'elif' en python
print("°")
else:
print(")")
# Un peu d'unicode ;) et des boucles for
words = ["Bonjour", "Jeune", "Padawan"]
for w in words:
if w == "Yoda":
break # le 'break' permet de sortie de la boucle,
else: # par contre on passe dans le 'else' si le break
# n'est jamais appelé dans la boucle for'
# ici on utilise le r de raw_string
st = r"""
____
(xXXXX|xx======---(-
/ |
/ XX|
/xxx XXX|
/xxx X |
/ ________|
__ ____/_|_|_______\_
###|=||________|_________|_
~~ |==| __ _ __ /|~~~~~~~~~-------------_______
|==| ||(( ||()| | |XXXXXXXX| >
__ |==| ~~__~__~~__ \|_________-------------~~~~~~~
###|=||~~~~~~~~|_______ |"
~~ ~~~~\~|~| /~
\ ~~~~~~~~~
\xxx X |
\xxx XXX|
\ XX| Incom's T-65B X-wing Space
\ | Superiority Starfighter (4)
(xXXXX|xx======---(-
~~~~"""
print(st)
# on peut aussi utiliser range dans la même idée
# que la boucle for(i = 0; i < words.length; i++) dans d'autres langage
for i in range(len(words)):
print(words[i], len(words[i]))
# exemple de range qui est objet iterable,
# et pas une liste à proprement parlée
range(5) # 0, 1, 2, 3, 4
range(5, 10) # 5, 6, 7, 8, 9
range(0, 10, 3) # 0, 3, 6, 9
range(-10, -100, -30) # -10, -40, -70
# mot clé 'pass'
a = 9
if a < 10:
pass # 'pass' ne fait rien, mais est parfois nécessaire après une instruction
# TODO : Afficher un message d'erreur...
else:
print("a supérieur a 10")
| 0
| 0
| 0
|
3ceaf757a4a9ba8ec3f0e7a41102e00bf16e21bf
| 228
|
py
|
Python
|
optimal-road-trip/runtime_timer.py
|
YoungMaker/Machine-Learning-Analysis-With-CUDA
|
f67cdce6d47a341bab55c20057bb939929c98dc3
|
[
"Unlicense",
"CC-BY-4.0",
"MIT"
] | null | null | null |
optimal-road-trip/runtime_timer.py
|
YoungMaker/Machine-Learning-Analysis-With-CUDA
|
f67cdce6d47a341bab55c20057bb939929c98dc3
|
[
"Unlicense",
"CC-BY-4.0",
"MIT"
] | null | null | null |
optimal-road-trip/runtime_timer.py
|
YoungMaker/Machine-Learning-Analysis-With-CUDA
|
f67cdce6d47a341bab55c20057bb939929c98dc3
|
[
"Unlicense",
"CC-BY-4.0",
"MIT"
] | null | null | null |
import time
| 16.285714
| 43
| 0.614035
|
import time
class runtimeTimer(object):
def __init__(self):
self.starttime = time.time()
def start(self):
self.starttime = time.time()
def stop(self):
return time.time() - self.starttime
| 105
| 6
| 104
|
e1e1c9c9b3b1d2f9ea5e172e14b36b3bf582c0be
| 652
|
py
|
Python
|
lambda/setup.py
|
ingalls/ml-enabler
|
efda973cb3fa9954cbe24cd0963a7b8f5be5ad6f
|
[
"BSD-2-Clause"
] | null | null | null |
lambda/setup.py
|
ingalls/ml-enabler
|
efda973cb3fa9954cbe24cd0963a7b8f5be5ad6f
|
[
"BSD-2-Clause"
] | 6
|
2021-06-08T22:14:52.000Z
|
2022-03-12T00:45:58.000Z
|
lambda/setup.py
|
ingalls/ml-enabler
|
efda973cb3fa9954cbe24cd0963a7b8f5be5ad6f
|
[
"BSD-2-Clause"
] | null | null | null |
"""Setup."""
from setuptools import setup, find_packages
inst_reqs = [
"mercantile == 1.1.5",
"requests",
"geojson",
"pillow",
"gdal == 2.4.2",
"shapely == 1.6.4",
"affine == 2.3.0",
"numpy == 1.19.0",
"rasterio == 1.1.5"
]
extra_reqs = {"test": ["pytest", "pytest-cov"]}
setup(
name="app",
version="0.5.0",
description=u"Lambda Download and Predict",
python_requires=">=3",
keywords="AWS-Lambda Python",
packages=find_packages(exclude=["ez_setup", "examples", "tests"]),
include_package_data=True,
zip_safe=False,
install_requires=inst_reqs,
extras_require=extra_reqs,
)
| 21.733333
| 70
| 0.602761
|
"""Setup."""
from setuptools import setup, find_packages
inst_reqs = [
"mercantile == 1.1.5",
"requests",
"geojson",
"pillow",
"gdal == 2.4.2",
"shapely == 1.6.4",
"affine == 2.3.0",
"numpy == 1.19.0",
"rasterio == 1.1.5"
]
extra_reqs = {"test": ["pytest", "pytest-cov"]}
setup(
name="app",
version="0.5.0",
description=u"Lambda Download and Predict",
python_requires=">=3",
keywords="AWS-Lambda Python",
packages=find_packages(exclude=["ez_setup", "examples", "tests"]),
include_package_data=True,
zip_safe=False,
install_requires=inst_reqs,
extras_require=extra_reqs,
)
| 0
| 0
| 0
|
a51f153c8663eebe0dd7b69da625b02703f7e870
| 950
|
py
|
Python
|
cumm/tensorview/gemm.py
|
FindDefinition/cumm
|
3d58e85b660afa05c20514afe65b8aa3a4995953
|
[
"Apache-2.0"
] | 20
|
2021-10-13T03:41:59.000Z
|
2022-03-31T07:23:14.000Z
|
cumm/tensorview/gemm.py
|
FindDefinition/cumm
|
3d58e85b660afa05c20514afe65b8aa3a4995953
|
[
"Apache-2.0"
] | 3
|
2021-11-21T11:25:55.000Z
|
2022-03-08T06:12:35.000Z
|
cumm/tensorview/gemm.py
|
FindDefinition/cumm
|
3d58e85b660afa05c20514afe65b8aa3a4995953
|
[
"Apache-2.0"
] | 4
|
2021-10-13T03:42:01.000Z
|
2022-03-21T13:07:56.000Z
|
# Copyright 2022 Yan Yan
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from cumm.core_cc.tensorview_bind import (NVRTCParams, GemmAlgoDesp,
ConvAlgoDesp, ConvParams, ConvOpType,
ConvLayoutType, ShuffleStrideType,
ConvMode, run_nvrtc_conv_kernel,
GemmParams, run_nvrtc_gemm_kernel)
| 47.5
| 79
| 0.649474
|
# Copyright 2022 Yan Yan
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from cumm.core_cc.tensorview_bind import (NVRTCParams, GemmAlgoDesp,
ConvAlgoDesp, ConvParams, ConvOpType,
ConvLayoutType, ShuffleStrideType,
ConvMode, run_nvrtc_conv_kernel,
GemmParams, run_nvrtc_gemm_kernel)
| 0
| 0
| 0
|
0a6eebd1d93127e3126b0ebdfd4693d4c9e4d3a9
| 2,036
|
py
|
Python
|
src/cihpc/www/rest/__init__.py
|
janhybs/ci-hpi
|
293740c7af62ecada5744ff663266de2e3d37445
|
[
"MIT"
] | 1
|
2020-01-09T13:00:18.000Z
|
2020-01-09T13:00:18.000Z
|
src/cihpc/www/rest/__init__.py
|
janhybs/ci-hpi
|
293740c7af62ecada5744ff663266de2e3d37445
|
[
"MIT"
] | null | null | null |
src/cihpc/www/rest/__init__.py
|
janhybs/ci-hpi
|
293740c7af62ecada5744ff663266de2e3d37445
|
[
"MIT"
] | 2
|
2018-08-12T01:13:28.000Z
|
2018-08-13T14:37:28.000Z
|
#!/bin/python3
# author: Jan Hybs
from loguru import logger
from flask_restful import Resource
from cihpc.common.utils import strings
from cihpc.common.utils import datautils as du
| 29.507246
| 118
| 0.543222
|
#!/bin/python3
# author: Jan Hybs
from loguru import logger
from flask_restful import Resource
from cihpc.common.utils import strings
from cihpc.common.utils import datautils as du
class ConfigurableView(Resource):
def __init__(self):
super(ConfigurableView, self).__init__()
@classmethod
def _process_list_args(cls, value):
result: dict = dict([tuple(v.split('=')) for v in value])
for k, v in result.items():
if v.lower() == 'true':
result[k] = True
elif v.lower() == 'false':
result[k] = False
else:
try:
result[k] = int(v)
except:
result[k] = v
return result
@classmethod
def error_empty_df(cls, filters):
logger.info(f'empty result')
return cls.show_error(
status=300,
message='No results found',
description='\n'.join([
'<p>This usually means provided filters filtered out everything.</p>',
'<p>DEBUG: The following filters were applied:</p>',
'<pre><code>%s</code></pre>' % strings.to_json(filters)
])
)
@classmethod
def show_error(cls, message, status=400, description=''):
logger.info(f'error [{status}] {message}')
return dict(
status=status,
message=message,
description=description
)
@classmethod
def group_by(cls, df, groupby):
"""
:rtype: list[(list[str], list[str], list[str], pd.DataFrame)]
"""
if not groupby:
yield ('', '', '', df)
else:
keys = du.ensure_iterable(list(groupby.keys()))
names = du.ensure_iterable(list(groupby.values()))
for group_values, group_data in df.groupby(keys):
yield (
du.ensure_iterable(group_values), du.ensure_iterable(keys), du.ensure_iterable(names), group_data)
| 1,107
| 721
| 23
|
28bc94dd5ea00429c9ff3030fa52184e52e1be49
| 3,624
|
py
|
Python
|
src/spinnaker_ros_lsm/venv/lib/python2.7/site-packages/spinn_front_end_common/interface/interface_functions/front_end_common_partitionable_graph_data_specification_writer.py
|
Roboy/LSM_SpiNNaker_MyoArm
|
04fa1eaf78778edea3ba3afa4c527d20c491718e
|
[
"BSD-3-Clause"
] | 2
|
2020-11-01T13:22:11.000Z
|
2020-11-01T13:22:20.000Z
|
src/spinnaker_ros_lsm/venv/lib/python2.7/site-packages/spinn_front_end_common/interface/interface_functions/front_end_common_partitionable_graph_data_specification_writer.py
|
Roboy/LSM_SpiNNaker_MyoArm
|
04fa1eaf78778edea3ba3afa4c527d20c491718e
|
[
"BSD-3-Clause"
] | null | null | null |
src/spinnaker_ros_lsm/venv/lib/python2.7/site-packages/spinn_front_end_common/interface/interface_functions/front_end_common_partitionable_graph_data_specification_writer.py
|
Roboy/LSM_SpiNNaker_MyoArm
|
04fa1eaf78778edea3ba3afa4c527d20c491718e
|
[
"BSD-3-Clause"
] | null | null | null |
from spinn_machine.utilities.progress_bar import ProgressBar
from spinn_front_end_common.abstract_models.\
abstract_data_specable_vertex import AbstractDataSpecableVertex
from spinn_front_end_common.utilities.utility_objs.executable_targets import \
ExecutableTargets
from spinn_front_end_common.utilities import exceptions
class FrontEndCommonPartitionableGraphDataSpecificationWriter(object):
""" Executes a partitionable graph data specification generation
"""
def __call__(
self, placements, graph_mapper, tags, executable_finder,
partitioned_graph, partitionable_graph, routing_infos, hostname,
report_default_directory, write_text_specs,
app_data_runtime_folder):
""" generates the dsg for the graph.
:return:
"""
# iterate though subvertices and call generate_data_spec for each
# vertex
executable_targets = ExecutableTargets()
dsg_targets = dict()
# create a progress bar for end users
progress_bar = ProgressBar(len(list(placements.placements)),
"Generating data specifications")
for placement in placements.placements:
associated_vertex = graph_mapper.get_vertex_from_subvertex(
placement.subvertex)
self._generate_data_spec_for_subvertices(
placement, associated_vertex, executable_targets, dsg_targets,
graph_mapper, tags, executable_finder, partitioned_graph,
partitionable_graph, routing_infos, hostname,
report_default_directory, write_text_specs,
app_data_runtime_folder)
progress_bar.update()
# finish the progress bar
progress_bar.end()
return {'executable_targets': executable_targets,
'dsg_targets': dsg_targets}
| 41.655172
| 78
| 0.678532
|
from spinn_machine.utilities.progress_bar import ProgressBar
from spinn_front_end_common.abstract_models.\
abstract_data_specable_vertex import AbstractDataSpecableVertex
from spinn_front_end_common.utilities.utility_objs.executable_targets import \
ExecutableTargets
from spinn_front_end_common.utilities import exceptions
class FrontEndCommonPartitionableGraphDataSpecificationWriter(object):
""" Executes a partitionable graph data specification generation
"""
def __call__(
self, placements, graph_mapper, tags, executable_finder,
partitioned_graph, partitionable_graph, routing_infos, hostname,
report_default_directory, write_text_specs,
app_data_runtime_folder):
""" generates the dsg for the graph.
:return:
"""
# iterate though subvertices and call generate_data_spec for each
# vertex
executable_targets = ExecutableTargets()
dsg_targets = dict()
# create a progress bar for end users
progress_bar = ProgressBar(len(list(placements.placements)),
"Generating data specifications")
for placement in placements.placements:
associated_vertex = graph_mapper.get_vertex_from_subvertex(
placement.subvertex)
self._generate_data_spec_for_subvertices(
placement, associated_vertex, executable_targets, dsg_targets,
graph_mapper, tags, executable_finder, partitioned_graph,
partitionable_graph, routing_infos, hostname,
report_default_directory, write_text_specs,
app_data_runtime_folder)
progress_bar.update()
# finish the progress bar
progress_bar.end()
return {'executable_targets': executable_targets,
'dsg_targets': dsg_targets}
def _generate_data_spec_for_subvertices(
self, placement, associated_vertex, executable_targets,
dsg_targets, graph_mapper, tags, executable_finder,
partitioned_graph, partitionable_graph, routing_infos, hostname,
report_default_directory, write_text_specs,
app_data_runtime_folder):
# if the vertex can generate a DSG, call it
if isinstance(associated_vertex, AbstractDataSpecableVertex):
ip_tags = tags.get_ip_tags_for_vertex(
placement.subvertex)
reverse_ip_tags = tags.get_reverse_ip_tags_for_vertex(
placement.subvertex)
file_path = associated_vertex.generate_data_spec(
placement.subvertex, placement, partitioned_graph,
partitionable_graph, routing_infos, hostname, graph_mapper,
report_default_directory, ip_tags, reverse_ip_tags,
write_text_specs, app_data_runtime_folder)
# link dsg file to subvertex
dsg_targets[placement.x, placement.y, placement.p] = file_path
# Get name of binary from vertex
binary_name = associated_vertex.get_binary_file_name()
# Attempt to find this within search paths
binary_path = executable_finder.get_executable_path(
binary_name)
if binary_path is None:
raise exceptions.ExecutableNotFoundException(binary_name)
if not executable_targets.has_binary(binary_path):
executable_targets.add_binary(binary_path)
executable_targets.add_processor(
binary_path, placement.x, placement.y, placement.p)
| 1,697
| 0
| 27
|
9d9964d264e68dcb80b3c95e97441214bc9d5263
| 1,719
|
py
|
Python
|
sveetoy_cli/colors/registry.py
|
sveetch/sveetoy-cli
|
b73159e657b9d23e2cffc70869b82c2024439ae1
|
[
"MIT"
] | null | null | null |
sveetoy_cli/colors/registry.py
|
sveetch/sveetoy-cli
|
b73159e657b9d23e2cffc70869b82c2024439ae1
|
[
"MIT"
] | 3
|
2017-11-16T00:35:13.000Z
|
2017-11-24T23:59:29.000Z
|
sveetoy_cli/colors/registry.py
|
sveetch/sveetoy-cli
|
b73159e657b9d23e2cffc70869b82c2024439ae1
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
import io, json
from pathlib import Path
class ColorRegistry:
"""
Open, read and store color names maps
Default shipped color registry is used on loading if no specific path is
given to ``load`` method.
"""
def load(self, path=None):
"""
Load registry and set maps
Keyword args:
path (pathlib.Path): Optionnal path object to open instead of
default of from ``ColorRegistry.map_path``.
"""
names = self.get_registry_file(path or self.map_path)
self.name_map, self.hexa_map = self.get_registry_maps(names)
def get_registry_file(self, path):
"""
Open registry file from given path
Args:
path (pathlib.Path): Path object to open.
Returns:
list: List of map items from registry.
"""
with io.open(str(path), 'r') as fp:
registry_map = json.load(fp)
return registry_map
def get_registry_maps(self, items):
"""
From registry items build maps, one indexed on name, another
one indexed on color.
Args:
items (list): Registry items
Returns:
tuple: First item is the names map, second item is the colors map.
Both are list object.
"""
name_map = items
# Reverse keys/values so map is indexed on hexa
hexa_map = list(zip([v for k,v in items], [k for k,v in items]))
return name_map, hexa_map
| 26.446154
| 78
| 0.585224
|
# -*- coding: utf-8 -*-
import io, json
from pathlib import Path
class ColorRegistry:
"""
Open, read and store color names maps
Default shipped color registry is used on loading if no specific path is
given to ``load`` method.
"""
def __init__(self):
datas_dirpath = Path(__file__).parent / "datas"
self.map_path = datas_dirpath / "names.json"
self.name_map, self.hexa_map = {}, {}
def load(self, path=None):
"""
Load registry and set maps
Keyword args:
path (pathlib.Path): Optionnal path object to open instead of
default of from ``ColorRegistry.map_path``.
"""
names = self.get_registry_file(path or self.map_path)
self.name_map, self.hexa_map = self.get_registry_maps(names)
def get_registry_file(self, path):
"""
Open registry file from given path
Args:
path (pathlib.Path): Path object to open.
Returns:
list: List of map items from registry.
"""
with io.open(str(path), 'r') as fp:
registry_map = json.load(fp)
return registry_map
def get_registry_maps(self, items):
"""
From registry items build maps, one indexed on name, another
one indexed on color.
Args:
items (list): Registry items
Returns:
tuple: First item is the names map, second item is the colors map.
Both are list object.
"""
name_map = items
# Reverse keys/values so map is indexed on hexa
hexa_map = list(zip([v for k,v in items], [k for k,v in items]))
return name_map, hexa_map
| 155
| 0
| 26
|
19ec1ada2edcd3626071d17e705fddd15c6842f3
| 705
|
py
|
Python
|
futuquant/testcase/person/eva/quote/test_get_multiple_history_kline.py
|
hxhxhx88/futuquant
|
a1b4a875604f1de451ddde4bfa3e713452482b0a
|
[
"Apache-2.0"
] | null | null | null |
futuquant/testcase/person/eva/quote/test_get_multiple_history_kline.py
|
hxhxhx88/futuquant
|
a1b4a875604f1de451ddde4bfa3e713452482b0a
|
[
"Apache-2.0"
] | null | null | null |
futuquant/testcase/person/eva/quote/test_get_multiple_history_kline.py
|
hxhxhx88/futuquant
|
a1b4a875604f1de451ddde4bfa3e713452482b0a
|
[
"Apache-2.0"
] | null | null | null |
#-*-coding:utf-8-*-
from futuquant import *
import pandas
if __name__ == '__main__':
GetMulHtryKl().test1()
| 27.115385
| 140
| 0.62695
|
#-*-coding:utf-8-*-
from futuquant import *
import pandas
class GetMulHtryKl(object):
def test1(self):
pandas.set_option('display.width',1000)
pandas.set_option('max_columns',1000)
quote_ctx = OpenQuoteContext(host='127.0.0.1',port=11111)
codelist = ['HK.999011']
start = '2018-06-29' #'2018-07-01'
end = '2018-07-13'
ktype = KLType.K_30M
autype = AuType.QFQ
ret_code, ret_data = quote_ctx.get_multiple_history_kline(codelist = codelist,start = start,end = end,ktype = ktype,autype = autype)
print(ret_code)
print(ret_data)
quote_ctx.close()
if __name__ == '__main__':
GetMulHtryKl().test1()
| 534
| 6
| 50
|
9473573f60bf4ff2c4fdba5a8b551bc8fd0d7cd6
| 308
|
py
|
Python
|
_archive/ren2tan/ren2tan.py
|
oatsu-gh/utau-plugins
|
c742ed09f6dae3b52d2d1679890194add56f0fd9
|
[
"Beerware"
] | 1
|
2021-08-31T00:51:48.000Z
|
2021-08-31T00:51:48.000Z
|
_archive/ren2tan/ren2tan.py
|
oatsu-gh/utau_plugins
|
82f2145fc044a6028f7f7a88a74689797a4b83df
|
[
"Beerware"
] | null | null | null |
_archive/ren2tan/ren2tan.py
|
oatsu-gh/utau_plugins
|
82f2145fc044a6028f7f7a88a74689797a4b83df
|
[
"Beerware"
] | null | null | null |
#!/usr/bin/env python3
# Copyright (c) 2021 oatsu
"""
連続音歌詞を空白で区切って単独音にするUTAUプラグイン
"""
import utaupy
def ren2tan(plugin):
"""
歌詞を空白で区切って、空白より後ろ側だけ残す。
"""
for note in plugin.notes:
note.lyric = note.lyric.split()[-1]
if __name__ == '__main__':
utaupy.utauplugin.run(ren2tan)
| 14.666667
| 43
| 0.649351
|
#!/usr/bin/env python3
# Copyright (c) 2021 oatsu
"""
連続音歌詞を空白で区切って単独音にするUTAUプラグイン
"""
import utaupy
def ren2tan(plugin):
"""
歌詞を空白で区切って、空白より後ろ側だけ残す。
"""
for note in plugin.notes:
note.lyric = note.lyric.split()[-1]
if __name__ == '__main__':
utaupy.utauplugin.run(ren2tan)
| 0
| 0
| 0
|
8a58b436d68c59696458a2751cff5fe08edd9a40
| 5,422
|
py
|
Python
|
cupy/creation/basic.py
|
fukuta0614/Chainer
|
337fe78e1c27924c1195b8b677a9b2cd3ea68828
|
[
"MIT"
] | null | null | null |
cupy/creation/basic.py
|
fukuta0614/Chainer
|
337fe78e1c27924c1195b8b677a9b2cd3ea68828
|
[
"MIT"
] | 1
|
2016-11-09T06:32:32.000Z
|
2016-11-09T10:20:04.000Z
|
cupy/creation/basic.py
|
fukuta0614/Chainer
|
337fe78e1c27924c1195b8b677a9b2cd3ea68828
|
[
"MIT"
] | 1
|
2021-05-27T16:52:11.000Z
|
2021-05-27T16:52:11.000Z
|
import cupy
def empty(shape, dtype=float):
"""Returns an array without initializing the elements.
This function currently does not support ``order`` option.
Args:
shape (tuple of ints): Dimensionalities of the array.
dtype: Data type specifier.
Returns:
cupy.ndarray: A new array with elements not initialized.
.. seealso:: :func:`numpy.empty`
"""
# TODO(beam2d): Support ordering option
return cupy.ndarray(shape, dtype=dtype)
def empty_like(a, dtype=None):
"""Returns a new array with same shape and dtype of a given array.
This function currently does not support ``order`` and ``subok`` options.
Args:
a (cupy.ndarray): Base array.
dtype: Data type specifier. The data type of ``a`` is used by default.
Returns:
cupy.ndarray: A new array with same shape and dtype of ``a`` with
elements not initialized.
.. seealso:: :func:`numpy.empty_like`
"""
# TODO(beam2d): Support ordering option
if dtype is None:
dtype = a.dtype
return empty(a.shape, dtype=dtype)
def eye(N, M=None, k=0, dtype=float):
"""Returns a 2-D array with ones on the diagonals and zeros elsewhere.
Args:
N (int): Number of rows.
M (int): Number of columns. M == N by default.
k (int): Index of the diagonal. Zero indicates the main diagonal,
a positive index an upper diagonal, and a negative index a lower
diagonal.
dtype: Data type specifier.
Returns:
cupy.ndarray: A 2-D array with given diagonals filled with ones and
zeros elsewhere.
.. seealso:: :func:`numpy.eye`
"""
if M is None:
M = N
ret = zeros((N, M), dtype)
ret.diagonal(k)[:] = 1
return ret
def identity(n, dtype=float):
"""Returns a 2-D identity array.
It is equivalent to ``eye(n, n, dtype)``.
Args:
n (int): Number of rows and columns.
dtype: Data type specifier.
Returns:
cupy.ndarray: A 2-D identity array.
.. seealso:: :func:`numpy.identity`
"""
return eye(n, dtype=dtype)
def ones(shape, dtype=float):
"""Returns a new array of given shape and dtype, filled with ones.
This function currently does not support ``order`` option.
Args:
shape (tuple of ints): Dimensionalities of the array.
dtype: Data type specifier.
Returns:
cupy.ndarray: An array filled with ones.
.. seealso:: :func:`numpy.ones`
"""
# TODO(beam2d): Support ordering option
return full(shape, 1, dtype)
def ones_like(a, dtype=None):
"""Returns an array of ones with same shape and dtype as a given array.
This function currently does not support ``order`` and ``subok`` options.
Args:
a (cupy.ndarray): Base array.
dtype: Data type specifier. The dtype of ``a`` is used by default.
Returns:
cupy.ndarray: An array filled with ones.
.. seealso:: :func:`numpy.ones_like`
"""
# TODO(beam2d): Support ordering option
if dtype is None:
dtype = a.dtype
return ones(a.shape, dtype)
def zeros(shape, dtype=float):
"""Returns a new array of given shape and dtype, filled with zeros.
This function currently does not support ``order`` option.
Args:
shape (tuple of ints): Dimensionalities of the array.
dtype: Data type specifier.
Returns:
cupy.ndarray: An array filled with ones.
.. seealso:: :func:`numpy.zeros`
"""
# TODO(beam2d): Support ordering option
a = empty(shape, dtype)
a.data.memset(0, a.nbytes)
return a
def zeros_like(a, dtype=None):
"""Returns an array of zeros with same shape and dtype as a given array.
This function currently does not support ``order`` and ``subok`` options.
Args:
a (cupy.ndarray): Base array.
dtype: Data type specifier. The dtype of ``a`` is used by default.
Returns:
cupy.ndarray: An array filled with ones.
.. seealso:: :func:`numpy.zeros_like`
"""
# TODO(beam2d): Support ordering option
if dtype is None:
dtype = a.dtype
return zeros(a.shape, dtype=dtype)
def full(shape, fill_value, dtype=None):
"""Returns a new array of given shape and dtype, filled with a given value.
This function currently does not support ``order`` option.
Args:
shape (tuple of ints): Dimensionalities of the array.
fill_value: A scalar value to fill a new array.
dtype: Data type specifier.
Returns:
cupy.ndarray: An array filled with ``fill_value``.
.. seealso:: :func:`numpy.full`
"""
# TODO(beam2d): Support ordering option
a = empty(shape, dtype)
a.fill(fill_value)
return a
def full_like(a, fill_value, dtype=None):
"""Returns a full array with same shape and dtype as a given array.
This function currently does not support ``order`` and ``subok`` options.
Args:
a (cupy.ndarray): Base array.
fill_value: A scalar value to fill a new array.
dtype: Data type specifier. The dtype of ``a`` is used by default.
Returns:
cupy.ndarray: An array filled with ``fill_value``.
.. seealso:: :func:`numpy.full_like`
"""
# TODO(beam2d): Support ordering option
if dtype is None:
dtype = a.dtype
return full(a.shape, fill_value, dtype)
| 25.575472
| 79
| 0.629288
|
import cupy
def empty(shape, dtype=float):
"""Returns an array without initializing the elements.
This function currently does not support ``order`` option.
Args:
shape (tuple of ints): Dimensionalities of the array.
dtype: Data type specifier.
Returns:
cupy.ndarray: A new array with elements not initialized.
.. seealso:: :func:`numpy.empty`
"""
# TODO(beam2d): Support ordering option
return cupy.ndarray(shape, dtype=dtype)
def empty_like(a, dtype=None):
"""Returns a new array with same shape and dtype of a given array.
This function currently does not support ``order`` and ``subok`` options.
Args:
a (cupy.ndarray): Base array.
dtype: Data type specifier. The data type of ``a`` is used by default.
Returns:
cupy.ndarray: A new array with same shape and dtype of ``a`` with
elements not initialized.
.. seealso:: :func:`numpy.empty_like`
"""
# TODO(beam2d): Support ordering option
if dtype is None:
dtype = a.dtype
return empty(a.shape, dtype=dtype)
def eye(N, M=None, k=0, dtype=float):
"""Returns a 2-D array with ones on the diagonals and zeros elsewhere.
Args:
N (int): Number of rows.
M (int): Number of columns. M == N by default.
k (int): Index of the diagonal. Zero indicates the main diagonal,
a positive index an upper diagonal, and a negative index a lower
diagonal.
dtype: Data type specifier.
Returns:
cupy.ndarray: A 2-D array with given diagonals filled with ones and
zeros elsewhere.
.. seealso:: :func:`numpy.eye`
"""
if M is None:
M = N
ret = zeros((N, M), dtype)
ret.diagonal(k)[:] = 1
return ret
def identity(n, dtype=float):
"""Returns a 2-D identity array.
It is equivalent to ``eye(n, n, dtype)``.
Args:
n (int): Number of rows and columns.
dtype: Data type specifier.
Returns:
cupy.ndarray: A 2-D identity array.
.. seealso:: :func:`numpy.identity`
"""
return eye(n, dtype=dtype)
def ones(shape, dtype=float):
"""Returns a new array of given shape and dtype, filled with ones.
This function currently does not support ``order`` option.
Args:
shape (tuple of ints): Dimensionalities of the array.
dtype: Data type specifier.
Returns:
cupy.ndarray: An array filled with ones.
.. seealso:: :func:`numpy.ones`
"""
# TODO(beam2d): Support ordering option
return full(shape, 1, dtype)
def ones_like(a, dtype=None):
"""Returns an array of ones with same shape and dtype as a given array.
This function currently does not support ``order`` and ``subok`` options.
Args:
a (cupy.ndarray): Base array.
dtype: Data type specifier. The dtype of ``a`` is used by default.
Returns:
cupy.ndarray: An array filled with ones.
.. seealso:: :func:`numpy.ones_like`
"""
# TODO(beam2d): Support ordering option
if dtype is None:
dtype = a.dtype
return ones(a.shape, dtype)
def zeros(shape, dtype=float):
"""Returns a new array of given shape and dtype, filled with zeros.
This function currently does not support ``order`` option.
Args:
shape (tuple of ints): Dimensionalities of the array.
dtype: Data type specifier.
Returns:
cupy.ndarray: An array filled with ones.
.. seealso:: :func:`numpy.zeros`
"""
# TODO(beam2d): Support ordering option
a = empty(shape, dtype)
a.data.memset(0, a.nbytes)
return a
def zeros_like(a, dtype=None):
"""Returns an array of zeros with same shape and dtype as a given array.
This function currently does not support ``order`` and ``subok`` options.
Args:
a (cupy.ndarray): Base array.
dtype: Data type specifier. The dtype of ``a`` is used by default.
Returns:
cupy.ndarray: An array filled with ones.
.. seealso:: :func:`numpy.zeros_like`
"""
# TODO(beam2d): Support ordering option
if dtype is None:
dtype = a.dtype
return zeros(a.shape, dtype=dtype)
def full(shape, fill_value, dtype=None):
"""Returns a new array of given shape and dtype, filled with a given value.
This function currently does not support ``order`` option.
Args:
shape (tuple of ints): Dimensionalities of the array.
fill_value: A scalar value to fill a new array.
dtype: Data type specifier.
Returns:
cupy.ndarray: An array filled with ``fill_value``.
.. seealso:: :func:`numpy.full`
"""
# TODO(beam2d): Support ordering option
a = empty(shape, dtype)
a.fill(fill_value)
return a
def full_like(a, fill_value, dtype=None):
"""Returns a full array with same shape and dtype as a given array.
This function currently does not support ``order`` and ``subok`` options.
Args:
a (cupy.ndarray): Base array.
fill_value: A scalar value to fill a new array.
dtype: Data type specifier. The dtype of ``a`` is used by default.
Returns:
cupy.ndarray: An array filled with ``fill_value``.
.. seealso:: :func:`numpy.full_like`
"""
# TODO(beam2d): Support ordering option
if dtype is None:
dtype = a.dtype
return full(a.shape, fill_value, dtype)
| 0
| 0
| 0
|
606ef29647dfca7025dc7a7bcba6b2c14ce348cc
| 24,399
|
py
|
Python
|
sciencebeam_gym/trainer/models/pix2pix/pix2pix_model.py
|
elifesciences/sciencebeam-gym
|
3ad654e08775e0c0cdd256753e14093bb5a42d44
|
[
"MIT"
] | 25
|
2017-07-25T12:44:55.000Z
|
2020-09-30T22:16:50.000Z
|
sciencebeam_gym/trainer/models/pix2pix/pix2pix_model.py
|
elifesciences/sciencebeam-gym
|
3ad654e08775e0c0cdd256753e14093bb5a42d44
|
[
"MIT"
] | 192
|
2017-11-29T08:57:03.000Z
|
2022-03-29T18:44:41.000Z
|
sciencebeam_gym/trainer/models/pix2pix/pix2pix_model.py
|
elifesciences/sciencebeam-gym
|
3ad654e08775e0c0cdd256753e14093bb5a42d44
|
[
"MIT"
] | 6
|
2019-02-01T18:49:33.000Z
|
2020-07-26T08:18:46.000Z
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import argparse
import json
from functools import reduce
import tensorflow as tf
from tensorflow.python.lib.io.file_io import FileIO # pylint: disable=E0611
from sciencebeam_gym.trainer.data.examples import (
get_matching_files,
read_examples
)
from sciencebeam_gym.preprocess.color_map import (
parse_color_map_from_file
)
from sciencebeam_gym.tools.calculate_class_weights import (
tf_calculate_efnet_weights_for_frequency_by_label
)
from sciencebeam_gym.trainer.models.pix2pix.tf_utils import (
find_nearest_centroid_indices
)
from sciencebeam_gym.preprocess.preprocessing_utils import (
parse_page_range
)
from sciencebeam_gym.trainer.models.pix2pix.pix2pix_core import (
BaseLoss,
ALL_BASE_LOSS,
create_pix2pix_model,
create_other_summaries
)
from sciencebeam_gym.trainer.models.pix2pix.evaluate import (
evaluate_separate_channels,
evaluate_predictions,
evaluation_summary
)
from sciencebeam_gym.model_utils.channels import (
calculate_color_masks
)
UNKNOWN_COLOR = (255, 255, 255)
UNKNOWN_LABEL = 'unknown'
DEFAULT_UNKNOWN_CLASS_WEIGHT = 0.1
class GraphReferences(object):
"""Holder of base tensors used for training model using common task."""
def create_model(argv=None):
"""Factory method that creates model to be used by generic task.py."""
parser = model_args_parser()
args, task_args = parser.parse_known_args(argv)
return Model(args), task_args
| 33.653793
| 100
| 0.612115
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import argparse
import json
from functools import reduce
import tensorflow as tf
from tensorflow.python.lib.io.file_io import FileIO # pylint: disable=E0611
from sciencebeam_gym.trainer.data.examples import (
get_matching_files,
read_examples
)
from sciencebeam_gym.preprocess.color_map import (
parse_color_map_from_file
)
from sciencebeam_gym.tools.calculate_class_weights import (
tf_calculate_efnet_weights_for_frequency_by_label
)
from sciencebeam_gym.trainer.models.pix2pix.tf_utils import (
find_nearest_centroid_indices
)
from sciencebeam_gym.preprocess.preprocessing_utils import (
parse_page_range
)
from sciencebeam_gym.trainer.models.pix2pix.pix2pix_core import (
BaseLoss,
ALL_BASE_LOSS,
create_pix2pix_model,
create_other_summaries
)
from sciencebeam_gym.trainer.models.pix2pix.evaluate import (
evaluate_separate_channels,
evaluate_predictions,
evaluation_summary
)
from sciencebeam_gym.model_utils.channels import (
calculate_color_masks
)
UNKNOWN_COLOR = (255, 255, 255)
UNKNOWN_LABEL = 'unknown'
DEFAULT_UNKNOWN_CLASS_WEIGHT = 0.1
class GraphMode(object):
TRAIN = 1
EVALUATE = 2
PREDICT = 3
def get_logger():
return logging.getLogger(__name__)
class GraphReferences(object):
"""Holder of base tensors used for training model using common task."""
def __init__(self):
self.is_training = None
self.inputs = {}
self.examples = None
self.train = None
self.global_step = None
self.metric_updates = []
self.metric_values = []
self.predictions = []
self.input_jpeg = None
self.input_uri = None
self.image_tensor = None
self.annotation_uri = None
self.annotation_tensor = None
self.separate_channel_annotation_tensor = None
self.class_labels_tensor = None
self.pred = None
self.probabilities = None
self.summary = None
self.summaries = None
self.image_tensors = None
self.targets_class_indices = None
self.outputs_class_indices = None
self.output_layer_labels = None
self.evaluation_result = None
self.pos_weight = None
def batch_dimensions_to_colors_list(image_tensor, colors):
batch_images = []
for i, single_label_color in enumerate(colors):
batch_images.append(
tf.expand_dims(
image_tensor[:, :, :, i],
axis=-1
) * ([x / 255.0 for x in single_label_color])
)
return batch_images
def batch_dimensions_to_most_likely_colors_list(image_tensor, colors):
with tf.variable_scope("batch_dimensions_to_most_likely_colors_list"):
colors_tensor = tf.constant(colors, dtype=tf.uint8, name='colors')
most_likely_class_index = tf.argmax(image_tensor, 3)
return tf.gather(params=colors_tensor, indices=most_likely_class_index)
def add_summary_image(tensors, name, image):
tensors.image_tensors[name] = image
tf.summary.image(name, image)
def convert_image(image_tensor):
return tf.image.convert_image_dtype(
image_tensor,
dtype=tf.uint8,
saturate=True
)
def add_simple_summary_image(tensors, name, image_tensor):
with tf.name_scope(name):
add_summary_image(
tensors,
name,
convert_image(image_tensor)
)
def replace_black_with_white_color(image_tensor):
is_black = tf.reduce_all(
tf.equal(image_tensor, (0, 0, 0)),
axis=-1
)
is_black = tf.stack([is_black] * 3, axis=-1)
return tf.where(
is_black,
255 * tf.ones_like(image_tensor),
image_tensor
)
def combine_image(batch_images, replace_black_with_white=False):
clipped_batch_images = [
tf.clip_by_value(batch_image, 0.0, 1.0)
for batch_image in batch_images
]
combined_image = convert_image(
reduce(
lambda a, b: a + b,
clipped_batch_images
)
)
if replace_black_with_white:
combined_image = replace_black_with_white_color(combined_image)
return combined_image
def remove_last(a):
return a[:-1]
def add_model_summary_images(
tensors, dimension_colors, dimension_labels,
use_separate_channels=False,
has_unknown_class=False):
tensors.summaries = {}
add_simple_summary_image(
tensors, 'input', tensors.image_tensor
)
add_simple_summary_image(
tensors, 'target', tensors.annotation_tensor
)
if (has_unknown_class or not use_separate_channels) and dimension_labels is not None:
dimension_labels_with_unknown = dimension_labels + [UNKNOWN_LABEL]
dimension_colors_with_unknown = dimension_colors + [(255, 255, 255)]
else:
dimension_labels_with_unknown = dimension_labels
dimension_colors_with_unknown = dimension_colors
if use_separate_channels:
for name, outputs in [
('targets', tensors.separate_channel_annotation_tensor),
('outputs', tensors.pred)
]:
batch_images = batch_dimensions_to_colors_list(
outputs,
dimension_colors_with_unknown
)
batch_images_excluding_unknown = (
remove_last(batch_images)
if has_unknown_class
else batch_images
)
for i, (batch_image, dimension_label) in enumerate(zip(
batch_images, dimension_labels_with_unknown)):
suffix = "_{}_{}".format(
i, dimension_label if dimension_label else 'unknown_label'
)
add_simple_summary_image(
tensors, name + suffix, batch_image
)
with tf.name_scope(name + "_combined"):
combined_image = combine_image(batch_images_excluding_unknown)
if name == 'outputs':
tensors.summaries['output_image'] = combined_image
add_summary_image(
tensors,
name + "_combined",
combined_image
)
if name == 'outputs':
with tf.name_scope(name + "_most_likely"):
add_summary_image(
tensors,
name + "_most_likely",
batch_dimensions_to_most_likely_colors_list(
outputs,
dimension_colors_with_unknown)
)
else:
add_simple_summary_image(
tensors,
"output",
tensors.pred
)
if tensors.outputs_class_indices is not None:
outputs = tensors.pred
with tf.name_scope("outputs_most_likely"):
colors_tensor = tf.constant(
dimension_colors_with_unknown,
dtype=tf.uint8, name='colors'
)
add_summary_image(
tensors,
"outputs_most_likely",
tf.gather(
params=colors_tensor,
indices=tensors.outputs_class_indices
)
)
tensors.summaries['output_image'] = tensors.image_tensors['output']
def parse_json_file(filename):
with FileIO(filename, 'r') as f:
return json.load(f)
def class_weights_to_pos_weight(
class_weights, labels,
use_unknown_class, unknown_class_weight=DEFAULT_UNKNOWN_CLASS_WEIGHT):
pos_weight = [class_weights[k] for k in labels]
return pos_weight + [unknown_class_weight] if use_unknown_class else pos_weight
def parse_color_map(color_map_filename):
with FileIO(color_map_filename, 'r') as config_f:
return parse_color_map_from_file(
config_f
)
def color_map_to_labels(color_map, labels=None):
if labels:
if not all(k in color_map for k in labels):
raise ValueError(
'not all lables found in color map, labels=%s, available keys=%s' %
(labels, color_map.keys())
)
return labels
return sorted(color_map.keys())
def color_map_to_colors(color_map, labels):
return [color_map[k] for k in labels]
def colors_and_labels_with_unknown_class(colors, labels, use_unknown_class):
if use_unknown_class or not colors:
return (
colors + [UNKNOWN_COLOR],
labels + [UNKNOWN_LABEL]
)
else:
return colors, labels
def remove_none_from_dict(d: dict):
return {k: v for k, v in d.items() if v is not None}
def _create_pos_weights_tensor(
base_loss,
separate_channel_annotation_tensor,
pos_weight_values,
input_uri,
debug):
frequency_by_label = tf.reduce_sum(
separate_channel_annotation_tensor,
axis=[0, 1],
keep_dims=True,
name='frequency_by_channel'
)
pos_weight_sample = tf_calculate_efnet_weights_for_frequency_by_label(
frequency_by_label
)
pos_weight = (
pos_weight_sample * pos_weight_values
if base_loss == BaseLoss.WEIGHTED_SAMPLE_WEIGHTED_CROSS_ENTROPY
else pos_weight_sample
)
if debug:
pos_weight = tf.Print(
pos_weight, [
pos_weight,
pos_weight_sample,
frequency_by_label,
input_uri
],
'pos weights, sample, frequency, uri: ',
summarize=1000
)
get_logger().debug(
'pos_weight before batch: %s (frequency_by_label: %s)',
pos_weight, frequency_by_label
)
return pos_weight
class Model(object):
def __init__(self, args):
self.args = args
self.image_width = 256
self.image_height = 256
self.color_map = None
self.pos_weight = None
self.dimension_colors = None
self.dimension_labels = None
self.use_unknown_class = args.use_unknown_class
self.use_separate_channels = args.use_separate_channels and self.args.color_map is not None
logger = get_logger()
logger.info('use_separate_channels: %s', self.use_separate_channels)
if self.args.color_map:
color_map = parse_color_map(args.color_map)
class_weights = (
parse_json_file(self.args.class_weights)
if (
self.args.class_weights and
self.args.base_loss in {
BaseLoss.WEIGHTED_CROSS_ENTROPY,
BaseLoss.WEIGHTED_SAMPLE_WEIGHTED_CROSS_ENTROPY
}
)
else None
)
available_labels = color_map_to_labels(color_map)
if class_weights:
# remove labels with zero class weights
available_labels = [k for k in available_labels if class_weights.get(k, 0.0) != 0.0]
self.dimension_labels = args.channels if args.channels else available_labels
self.dimension_colors = color_map_to_colors(color_map, self.dimension_labels)
self.dimension_colors_with_unknown, self.dimension_labels_with_unknown = (
colors_and_labels_with_unknown_class(
self.dimension_colors,
self.dimension_labels,
self.use_unknown_class
)
)
logger.debug("dimension_colors: %s", self.dimension_colors)
logger.debug("dimension_labels: %s", self.dimension_labels)
if class_weights:
self.pos_weight = class_weights_to_pos_weight(
class_weights,
self.dimension_labels,
self.use_separate_channels,
class_weights.get(UNKNOWN_LABEL, DEFAULT_UNKNOWN_CLASS_WEIGHT)
)
logger.info("pos_weight: %s", self.pos_weight)
def _build_predict_graph(self):
tensors = GraphReferences()
input_image_tensor = tf.placeholder(
tf.uint8, (None, None, None, 3),
name='inputs_image'
)
tensors.inputs = dict(
image=input_image_tensor
)
tensors.image_tensor = tf.image.resize_images(
tf.image.convert_image_dtype(input_image_tensor, tf.float32),
(self.image_height, self.image_width),
method=tf.image.ResizeMethod.BILINEAR
)
if self.use_separate_channels:
n_output_channels = len(self.dimension_labels_with_unknown)
else:
n_output_channels = 3
pix2pix_model = create_pix2pix_model(
tensors.image_tensor,
None,
self.args,
is_training=False,
pos_weight=tensors.pos_weight,
n_output_channels=n_output_channels
)
tensors.pred = pix2pix_model.outputs
return tensors
def build_graph(self, data_paths, batch_size, graph_mode):
if graph_mode == GraphMode.PREDICT:
return self._build_predict_graph()
logger = get_logger()
logger.debug('batch_size: %s', batch_size)
tensors = GraphReferences()
tensors.is_training = tf.constant(graph_mode == GraphMode.TRAIN)
is_training = (
graph_mode == GraphMode.TRAIN or
graph_mode == GraphMode.EVALUATE
)
if not data_paths:
raise ValueError('data_paths required')
get_logger().info('reading examples from %s', data_paths)
tensors.examples = read_examples(
get_matching_files(data_paths),
shuffle=(graph_mode == GraphMode.TRAIN),
num_epochs=None if is_training else 2,
page_range=self.args.pages,
channel_colors=(
self.dimension_colors if self.args.filter_annotated
else None
)
)
parsed = tensors.examples
tensors.image_tensors = {}
tensors.input_uri = tf.squeeze(parsed['input_uri'])
tensors.annotation_uri = tf.squeeze(parsed['annotation_uri'])
raw_input_image = tf.squeeze(parsed['input_image'])
logging.info('raw_input_image: %s', raw_input_image)
raw_annotation_image = tf.squeeze(parsed['annotation_image'])
tensors.image_tensor = tf.image.decode_png(raw_input_image, channels=3)
tensors.annotation_tensor = tf.image.decode_png(raw_annotation_image, channels=3)
# TODO resize_images and tf.cast did not work on input image
# but did work on annotation image
tensors.image_tensor = tf.image.resize_image_with_crop_or_pad(
tensors.image_tensor, self.image_height, self.image_width
)
tensors.image_tensor = tf.image.convert_image_dtype(tensors.image_tensor, tf.float32)
tensors.annotation_tensor = tf.image.resize_image_with_crop_or_pad(
tensors.annotation_tensor, self.image_height, self.image_width
)
if self.use_separate_channels:
with tf.variable_scope('channels'):
color_masks = calculate_color_masks(
tensors.annotation_tensor,
self.dimension_colors,
use_unknown_class=self.use_unknown_class
)
tensors.separate_channel_annotation_tensor = tf.stack(color_masks, axis=-1)
if self.args.base_loss == BaseLoss.SAMPLE_WEIGHTED_CROSS_ENTROPY:
with tf.variable_scope('class_weights'):
tensors.pos_weight = _create_pos_weights_tensor(
base_loss=self.args.base_loss,
separate_channel_annotation_tensor=(
tensors.separate_channel_annotation_tensor
),
pos_weight_values=self.pos_weight,
input_uri=tensors.input_uri,
debug=self.args.debug
)
else:
tensors.annotation_tensor = tf.image.convert_image_dtype(
tensors.annotation_tensor, tf.float32
)
tensors.separate_channel_annotation_tensor = tensors.annotation_tensor
batched_tensors: dict = tf.train.batch(
remove_none_from_dict({
k: getattr(tensors, k)
for k in [
'input_uri',
'annotation_uri',
'image_tensor',
'annotation_tensor',
'separate_channel_annotation_tensor',
'pos_weight'
]
}),
batch_size=batch_size
)
for k, v in batched_tensors.items():
setattr(tensors, k, v)
if tensors.pos_weight is None:
tensors.pos_weight = self.pos_weight
pix2pix_model = create_pix2pix_model(
tensors.image_tensor,
tensors.separate_channel_annotation_tensor,
self.args,
is_training=tensors.is_training,
pos_weight=tensors.pos_weight
)
if self.use_separate_channels:
with tf.name_scope("evaluation"):
tensors.output_layer_labels = tf.constant(self.dimension_labels_with_unknown)
evaluation_result = evaluate_separate_channels(
targets=pix2pix_model.targets,
outputs=pix2pix_model.outputs
)
tensors.evaluation_result = evaluation_result
evaluation_summary(evaluation_result, self.dimension_labels_with_unknown)
else:
with tf.name_scope('evaluation'):
if self.dimension_colors:
tensors.output_layer_labels = tf.constant(self.dimension_labels)
colors_tensor = tf.constant(
self.dimension_colors_with_unknown,
dtype=tf.float32
) / 255.0
tensors.outputs_class_indices = find_nearest_centroid_indices(
predictions=pix2pix_model.outputs,
centroids=colors_tensor
)
tensors.targets_class_indices = find_nearest_centroid_indices(
predictions=pix2pix_model.targets,
centroids=colors_tensor
)
evaluation_result = evaluate_predictions(
labels=tensors.targets_class_indices,
predictions=tensors.outputs_class_indices,
n_classes=len(self.dimension_colors_with_unknown)
)
tensors.evaluation_result = evaluation_result
evaluation_summary(evaluation_result, self.dimension_labels)
tensors.global_step = pix2pix_model.global_step
tensors.train = pix2pix_model.train
tensors.class_labels_tensor = tensors.annotation_tensor
tensors.pred = pix2pix_model.outputs
tensors.probabilities = pix2pix_model.outputs
tensors.metric_values = [pix2pix_model.discrim_loss]
add_model_summary_images(
tensors,
self.dimension_colors,
self.dimension_labels,
use_separate_channels=self.use_separate_channels,
has_unknown_class=self.use_unknown_class
)
# tensors.summaries = create_summaries(pix2pix_model)
create_other_summaries(pix2pix_model)
if (
self.args.base_loss == BaseLoss.SAMPLE_WEIGHTED_CROSS_ENTROPY and
tensors.pos_weight is not None
):
with tf.variable_scope('pos_weight_summary'):
tf.summary.text('pos_weight', tf.as_string(tf.reshape(
tensors.pos_weight, [-1, int(tensors.pos_weight.shape[-1])]
)))
tensors.summary = tf.summary.merge_all()
return tensors
def build_train_graph(self, data_paths, batch_size):
return self.build_graph(data_paths, batch_size, GraphMode.TRAIN)
def build_eval_graph(self, data_paths, batch_size):
return self.build_graph(data_paths, batch_size, GraphMode.EVALUATE)
def build_predict_graph(self):
return self.build_graph(None, None, GraphMode.PREDICT)
def initialize(self, session):
pass
def format_metric_values(self, metric_values):
"""Formats metric values - used for logging purpose."""
# Early in training, metric_values may actually be None.
loss_str = 'N/A'
accuracy_str = 'N/A'
try:
loss_str = '%.3f' % metric_values[0]
accuracy_str = '%.3f' % metric_values[1]
except (TypeError, IndexError):
pass
return '%s, %s' % (loss_str, accuracy_str)
def str_to_bool(s):
return s.lower() in ('yes', 'true', '1')
def str_to_list(s):
s = s.strip()
if not s:
return []
return [x.strip() for x in s.split(',')]
def model_args_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"--ngf", type=int, default=64, help="number of generator filters in first conv layer"
)
parser.add_argument(
"--ndf", type=int, default=64, help="number of discriminator filters in first conv layer"
)
parser.add_argument(
"--lr", type=float, default=0.0002, help="initial learning rate for adam"
)
parser.add_argument(
"--beta1", type=float, default=0.5, help="momentum term of adam"
)
parser.add_argument(
"--l1_weight", type=float, default=100.0, help="weight on L1 term for generator gradient"
)
parser.add_argument(
"--gan_weight", type=float, default=1.0, help="weight on GAN term for generator gradient"
)
parser.add_argument(
'--pages', type=parse_page_range, default=None,
help='only processes the selected pages'
)
parser.add_argument(
'--color_map',
type=str,
help='The path to the color map configuration.'
)
parser.add_argument(
'--class_weights',
type=str,
help='The path to the class weights configuration.'
)
parser.add_argument(
'--channels',
type=str_to_list,
help='The channels to use (subset of color map), otherwise all of the labels will be used'
)
parser.add_argument(
'--filter_annotated',
type=str_to_bool,
default=False,
help='Only include pages that have annotations for the selected channels'
' (if color map is provided)'
)
parser.add_argument(
'--use_unknown_class',
type=str_to_bool,
default=True,
help='Use unknown class channel (if color map is provided)'
)
parser.add_argument(
'--use_separate_channels',
type=str_to_bool,
default=False,
help='The separate output channels per annotation (if color map is provided)'
)
parser.add_argument(
'--use_separate_discriminator_channels',
type=str_to_bool,
default=False,
help='The separate discriminator channels per annotation (if color map is provided)'
)
parser.add_argument(
'--use_separate_discriminators',
type=str_to_bool,
default=False,
help='The separate discriminators per annotation (if color map is provided)'
)
parser.add_argument(
'--base_loss',
type=str,
default=BaseLoss.L1,
choices=ALL_BASE_LOSS,
help='The base loss function to use'
)
parser.add_argument(
'--debug',
type=str_to_bool,
default=True,
help='Enable debug mode'
)
return parser
def create_model(argv=None):
"""Factory method that creates model to be used by generic task.py."""
parser = model_args_parser()
args, task_args = parser.parse_known_args(argv)
return Model(args), task_args
| 21,544
| 697
| 556
|
a59878ebd625530edf2818a79e847570d7a43bc3
| 3,985
|
py
|
Python
|
dashboard/modules/job/job_head.py
|
sungho-joo/ray
|
7a18d90a2527ba603f3e0444346389c3136bf50e
|
[
"Apache-2.0"
] | null | null | null |
dashboard/modules/job/job_head.py
|
sungho-joo/ray
|
7a18d90a2527ba603f3e0444346389c3136bf50e
|
[
"Apache-2.0"
] | 30
|
2021-11-05T06:54:54.000Z
|
2022-03-19T07:10:33.000Z
|
dashboard/modules/job/job_head.py
|
RuofanKong/ray
|
60e9737679d93e7e8902dcea0720addb506ddf0a
|
[
"Apache-2.0"
] | null | null | null |
import aiohttp.web
from functools import wraps
import logging
from typing import Callable
import json
import dataclasses
import ray
import ray.dashboard.utils as dashboard_utils
from ray._private.job_manager import JobManager
from ray._private.runtime_env.packaging import (package_exists,
upload_package_to_gcs)
from ray.dashboard.modules.job.data_types import (
GetPackageResponse, JobStatus, JobSubmitRequest, JobSubmitResponse,
JobStatusResponse, JobLogsResponse)
logger = logging.getLogger(__name__)
routes = dashboard_utils.ClassMethodRouteTable
RAY_INTERNAL_JOBS_NAMESPACE = "_ray_internal_jobs_"
JOBS_API_PREFIX = "/api/jobs/"
JOBS_API_ROUTE_LOGS = JOBS_API_PREFIX + "logs"
JOBS_API_ROUTE_SUBMIT = JOBS_API_PREFIX + "submit"
JOBS_API_ROUTE_STATUS = JOBS_API_PREFIX + "status"
JOBS_API_ROUTE_PACKAGE = JOBS_API_PREFIX + "package"
| 36.898148
| 77
| 0.697867
|
import aiohttp.web
from functools import wraps
import logging
from typing import Callable
import json
import dataclasses
import ray
import ray.dashboard.utils as dashboard_utils
from ray._private.job_manager import JobManager
from ray._private.runtime_env.packaging import (package_exists,
upload_package_to_gcs)
from ray.dashboard.modules.job.data_types import (
GetPackageResponse, JobStatus, JobSubmitRequest, JobSubmitResponse,
JobStatusResponse, JobLogsResponse)
logger = logging.getLogger(__name__)
routes = dashboard_utils.ClassMethodRouteTable
RAY_INTERNAL_JOBS_NAMESPACE = "_ray_internal_jobs_"
JOBS_API_PREFIX = "/api/jobs/"
JOBS_API_ROUTE_LOGS = JOBS_API_PREFIX + "logs"
JOBS_API_ROUTE_SUBMIT = JOBS_API_PREFIX + "submit"
JOBS_API_ROUTE_STATUS = JOBS_API_PREFIX + "status"
JOBS_API_ROUTE_PACKAGE = JOBS_API_PREFIX + "package"
def _ensure_ray_initialized(f: Callable) -> Callable:
@wraps(f)
def check(self, *args, **kwargs):
if not ray.is_initialized():
ray.init(address="auto", namespace=RAY_INTERNAL_JOBS_NAMESPACE)
return f(self, *args, **kwargs)
return check
class JobHead(dashboard_utils.DashboardHeadModule):
def __init__(self, dashboard_head):
super().__init__(dashboard_head)
self._job_manager = None
@routes.get(JOBS_API_ROUTE_PACKAGE)
@_ensure_ray_initialized
async def get_package(self,
req: aiohttp.web.Request) -> aiohttp.web.Response:
package_uri = req.query["package_uri"]
resp = GetPackageResponse(package_exists=package_exists(package_uri))
return aiohttp.web.Response(
text=json.dumps(dataclasses.asdict(resp)),
content_type="application/json")
@routes.put(JOBS_API_ROUTE_PACKAGE)
@_ensure_ray_initialized
async def upload_package(self, req: aiohttp.web.Request):
package_uri = req.query["package_uri"]
logger.info(f"Uploading package {package_uri} to the GCS.")
upload_package_to_gcs(package_uri, await req.read())
return aiohttp.web.Response()
@routes.post(JOBS_API_ROUTE_SUBMIT)
@_ensure_ray_initialized
async def submit(self, req: aiohttp.web.Request) -> aiohttp.web.Response:
# TODO: (jiaodong) Validate if job request is valid without using
# pydantic.
submit_request = JobSubmitRequest(**(await req.json()))
job_id = self._job_manager.submit_job(
entrypoint=submit_request.entrypoint,
runtime_env=submit_request.runtime_env,
metadata=submit_request.metadata)
resp = JobSubmitResponse(job_id=job_id)
return aiohttp.web.Response(
text=json.dumps(dataclasses.asdict(resp)),
content_type="application/json")
@routes.get(JOBS_API_ROUTE_STATUS)
@_ensure_ray_initialized
async def status(self, req: aiohttp.web.Request) -> aiohttp.web.Response:
job_id = req.query["job_id"]
status: JobStatus = self._job_manager.get_job_status(job_id)
resp = JobStatusResponse(job_status=status)
return aiohttp.web.Response(
text=json.dumps(dataclasses.asdict(resp)),
content_type="application/json")
@routes.get(JOBS_API_ROUTE_LOGS)
@_ensure_ray_initialized
async def logs(self, req: aiohttp.web.Request) -> aiohttp.web.Response:
job_id = req.query["job_id"]
stdout: bytes = self._job_manager.get_job_stdout(job_id)
stderr: bytes = self._job_manager.get_job_stderr(job_id)
# TODO(jiaodong): Support log streaming #19415
resp = JobLogsResponse(
stdout=stdout.decode("utf-8"), stderr=stderr.decode("utf-8"))
return aiohttp.web.Response(
text=json.dumps(dataclasses.asdict(resp)),
content_type="application/json")
async def run(self, server):
if not self._job_manager:
self._job_manager = JobManager()
| 2,481
| 559
| 46
|
c24074ac9a3cdf8728bde947483d6f521b704136
| 7,589
|
py
|
Python
|
minispider/__main__.py
|
leepxrk/scrapy_learn
|
7c0aea1c1e312aa16d11926f2421ed00aa92d97f
|
[
"MIT"
] | null | null | null |
minispider/__main__.py
|
leepxrk/scrapy_learn
|
7c0aea1c1e312aa16d11926f2421ed00aa92d97f
|
[
"MIT"
] | null | null | null |
minispider/__main__.py
|
leepxrk/scrapy_learn
|
7c0aea1c1e312aa16d11926f2421ed00aa92d97f
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
import argparse
from .sql import MiniSpiderSQL
from .scheduler import MiniSpider
from .extractor import Extractor
from .downloader import MiniSpiderDownloader
__version__ = '0.0.3'
if __name__ == '__main__':
main()
| 35.966825
| 112
| 0.58598
|
#!/usr/bin/env python
import argparse
from .sql import MiniSpiderSQL
from .scheduler import MiniSpider
from .extractor import Extractor
from .downloader import MiniSpiderDownloader
__version__ = '0.0.3'
def main():
# Make parser for terminal.
description = 'MiniSpider makes it easy to create user-friendly spider.'
usage = 'mini-spider [OPTION]... [URL]...'
parser = argparse.ArgumentParser(prog='MiniSpider', description=description, usage=usage,
epilog='Powered by ZYunH. Version:%s' % __version__)
# Add arguments.
analysis_help = 'Analysis a URL.'
parser.add_argument('-a', help=analysis_help, nargs='+', dest='analysis_url', metavar='[URL]')
similarity_threshold_help = 'Set similarity_threshold,default = 0.6'
parser.add_argument('-st', help=similarity_threshold_help, nargs=1, dest='similarity', type=float,
metavar='[float]')
choose_help = 'Choose block make extractor.'
parser.add_argument('-c', help=choose_help, nargs='+', dest='choose_block', type=int, metavar='[num]')
timeout_help = 'Set timeout.(default: 2)'
parser.add_argument('-time', help=timeout_help, nargs=1, dest='time_out', type=float, metavar='[float]')
to_help = 'Choose match data.(default: u)'
parser.add_argument('-to', help=to_help, nargs='?', dest='to', const='u', choices=['u', 'r'])
name_help = 'Name your extractor.it can be ignored.'
parser.add_argument('-n', help=name_help, nargs=1, dest='name', metavar='[name]')
start_help = 'Start spider to get url and resource.'
parser.add_argument('-start', help=start_help, nargs='?', dest='start', const=True, metavar='URL')
download_help = 'Download all url from database.'
parser.add_argument('-download', help=download_help, nargs='?', dest='download', const=True, metavar='Path')
make_help = 'Make extractor by user.'
parser.add_argument('-m', help=make_help, nargs='+', dest='make', metavar='[RE]')
export_help = 'Export url from database.'
parser.add_argument('-export', help=export_help, nargs=1, dest='export_url', metavar='[FileName]')
import_help = 'Import url into database.'
parser.add_argument('-import', help=import_help, nargs=1, dest='import_url', metavar='[FileName]')
list_help = 'List url in url_list or resource.options: "u" or "r"'
parser.add_argument('-list', help=list_help, nargs='+', dest='list_url', metavar='')
false_help = 'Disable classification function in -download.'
parser.add_argument('-false', help=false_help, nargs='?', dest='false_set', const=True, metavar='')
reset_help = 'Reset database stats = 1.(default: u)'
parser.add_argument('-reset', help=reset_help, nargs='?', dest='reset', const='u', choices=['u', 'r'])
# Parse arguments.
args = parser.parse_args()
# Parse analysis url.
if args.analysis_url:
if len(args.analysis_url) == 1:
print('Error: Please input what resource you are looking for!')
return False
timeout = 2.0
if args.time_out:
timeout = args.time_out[0]
if args.similarity:
spider = MiniSpider(args.analysis_url[0], search=args.analysis_url[1:],
similarity_threshold=args.similarity[0], timeout=timeout)
spider.analysis_url()
else:
spider = MiniSpider(args.analysis_url[0], search=args.analysis_url[1:], similarity_threshold=0.6,
timeout=timeout)
spider.analysis_url()
# Choose block make regular expression.
elif args.choose_block:
num = args.choose_block[0]
start = None
end = None
if len(args.choose_block) == 2:
start = args.choose_block[1]
elif len(args.choose_block) == 3:
start = args.choose_block[1]
end = args.choose_block[2]
pattern = MiniSpider().choose_block(num, start, end)
# Print pattern.
if len(pattern) == 2:
print('Host:' + pattern[1])
print(pattern[0])
else:
print(pattern)
# Choose database.
if args.to:
name = None
if args.name:
name = args.name[0]
if args.to[0] == 'u':
Extractor().make_extractor(name, pattern=pattern, mode='url')
elif args.to[0] == 'r':
Extractor().make_extractor(name, pattern=pattern, mode='resource')
print('The extractor was created successfully！')
else:
print("Error: Please input '-to u' or '-to r'")
return False
# Make pattern by user.
elif args.make:
pattern_user = args.make[0]
# Get host, if possible.
if len(args.make) == 2:
pattern_user = pattern_user, args.make[1]
# Choose database.
if args.to:
name = None
if args.name:
name = args.name[0]
if args.to[0] == 'u':
Extractor().make_extractor(name, pattern=pattern_user, mode='url')
elif args.to[0] == 'r':
Extractor().make_extractor(name, pattern=pattern_user, mode='resource')
print('The extractor was created successfully！')
else:
print("Error: Please input '-to u' or '-to r'")
return False
# Start project.
elif args.start:
if args.start is True:
MiniSpider().start()
else:
MiniSpider().start(args.start)
# Start downloading.
elif args.download:
classify = True
timeout = 2.0
if args.false_set:
classify = False
if args.time_out:
timeout = args.time_out[0]
if args.download is True:
MiniSpiderDownloader().start(classify=classify, timeout=timeout)
else:
MiniSpiderDownloader().start(args.download, classify=classify, timeout=timeout)
# Import txt.
elif args.import_url:
# Choose database.
if args.to:
if args.to[0] == 'u':
MiniSpiderSQL().import_txt(args.import_url[0], 'url_list')
elif args.to[0] == 'r':
MiniSpiderSQL().import_txt(args.import_url[0], 'resource')
print('Import success!')
else:
print("Error: Please input '-to u' or '-to r'")
return False
# Export txt.
elif args.export_url:
# Choose database.
if args.to:
if args.to[0] == 'u':
MiniSpiderSQL().export_txt(args.export_url[0], 'url_list')
elif args.to[0] == 'r':
MiniSpiderSQL().export_txt(args.export_url[0], 'resource')
print('Export success!')
else:
print("Error: Please input '-to u' or '-to r'")
return False
# List database.
elif args.list_url:
num = 50
if len(args.list_url) == 2:
num = int(args.list_url[1])
if args.list_url[0] == 'u':
MiniSpiderSQL().list_url(table_name='url_list', num=num)
elif args.list_url[0] == 'r':
MiniSpiderSQL().list_url(table_name='resource', num=num)
else:
print("Error: Please input '-list u' or '-list r'")
return False
# print(parser.print_help())
# Reset database stats.
elif args.reset:
MiniSpiderSQL().reset(args.reset[0])
else:
parser.print_help()
if __name__ == '__main__':
main()
| 7,324
| 0
| 23
|
cecda11e8240a03768dc24d5ae13e7860657aad1
| 1,345
|
py
|
Python
|
invenio_app_ils/ill/mail/tasks.py
|
masonproffitt/invenio-app-ils
|
81dd12aa774d7d70096de77cc526d9b4ca614437
|
[
"MIT"
] | null | null | null |
invenio_app_ils/ill/mail/tasks.py
|
masonproffitt/invenio-app-ils
|
81dd12aa774d7d70096de77cc526d9b4ca614437
|
[
"MIT"
] | null | null | null |
invenio_app_ils/ill/mail/tasks.py
|
masonproffitt/invenio-app-ils
|
81dd12aa774d7d70096de77cc526d9b4ca614437
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
#
# Copyright (C) 2020 CERN.
#
# invenio-app-ils is free software; you can redistribute it and/or modify it
# under the terms of the MIT License; see LICENSE file for more details.
"""ILL mail tasks."""
from invenio_app_ils.ill.errors import ILLError
from invenio_app_ils.ill.mail.factory import ill_message_creator_factory
from invenio_app_ils.mail.messages import get_common_message_ctx
from invenio_app_ils.mail.tasks import send_ils_email
def send_ill_mail(brw_req, action=None, message_ctx={}, **kwargs):
"""Send an ILL email.
:param brw_req: the borrowing request record.
:param action: the action performed, if any.
:param message_ctx: any other parameter to be passed as ctx in the msg.
"""
creator = ill_message_creator_factory()
message_ctx.update(get_common_message_ctx(record=brw_req))
try:
# fetch and inject in the email template the patron loan if available
loan = brw_req.patron_loan.get()
message_ctx["patron_loan"] = loan
except ILLError:
# no loan in the borrowin request
message_ctx["patron_loan"] = dict()
patron = message_ctx["patron"]
msg = creator(
brw_req,
action=action,
message_ctx=message_ctx,
recipients=[patron.email],
**kwargs,
)
send_ils_email(msg)
| 30.568182
| 77
| 0.700372
|
# -*- coding: utf-8 -*-
#
# Copyright (C) 2020 CERN.
#
# invenio-app-ils is free software; you can redistribute it and/or modify it
# under the terms of the MIT License; see LICENSE file for more details.
"""ILL mail tasks."""
from invenio_app_ils.ill.errors import ILLError
from invenio_app_ils.ill.mail.factory import ill_message_creator_factory
from invenio_app_ils.mail.messages import get_common_message_ctx
from invenio_app_ils.mail.tasks import send_ils_email
def send_ill_mail(brw_req, action=None, message_ctx={}, **kwargs):
"""Send an ILL email.
:param brw_req: the borrowing request record.
:param action: the action performed, if any.
:param message_ctx: any other parameter to be passed as ctx in the msg.
"""
creator = ill_message_creator_factory()
message_ctx.update(get_common_message_ctx(record=brw_req))
try:
# fetch and inject in the email template the patron loan if available
loan = brw_req.patron_loan.get()
message_ctx["patron_loan"] = loan
except ILLError:
# no loan in the borrowin request
message_ctx["patron_loan"] = dict()
patron = message_ctx["patron"]
msg = creator(
brw_req,
action=action,
message_ctx=message_ctx,
recipients=[patron.email],
**kwargs,
)
send_ils_email(msg)
| 0
| 0
| 0
|
e490594eb909224b9de0516fb7d5021a6e745b8b
| 980
|
py
|
Python
|
csp/propagators/propagator.py
|
abeccaro/csp-solver
|
a761dee02a4dd12162eb55ef34cc0989c79567cc
|
[
"MIT"
] | null | null | null |
csp/propagators/propagator.py
|
abeccaro/csp-solver
|
a761dee02a4dd12162eb55ef34cc0989c79567cc
|
[
"MIT"
] | null | null | null |
csp/propagators/propagator.py
|
abeccaro/csp-solver
|
a761dee02a4dd12162eb55ef34cc0989c79567cc
|
[
"MIT"
] | null | null | null |
from abc import abstractmethod
from csp.observer import Observer
class Propagator(Observer):
"""Abstract class for a constraint propagator."""
@abstractmethod
def on_domain_change(self, var):
"""Called when a variable domain has changed.
:param var: The variable that changed
:type var: Variable
"""
pass
def setup(self, problem):
"""Called to initialize this propagator with problem data
:param problem: The csp
:type problem: Problem
"""
for v in problem.variables:
v.add_observer(self)
self.map[v] = []
for c in problem.constraints:
for v in c.get_vars():
self.map[v].append(c)
| 24.5
| 66
| 0.544898
|
from abc import abstractmethod
from csp.observer import Observer
class Propagator(Observer):
"""Abstract class for a constraint propagator."""
def __init__(self):
super().__init__()
self.enabled = True
self.map = {}
def on_event(self, var):
self.on_domain_change(var)
@abstractmethod
def on_domain_change(self, var):
"""Called when a variable domain has changed.
:param var: The variable that changed
:type var: Variable
"""
pass
def setup(self, problem):
"""Called to initialize this propagator with problem data
:param problem: The csp
:type problem: Problem
"""
for v in problem.variables:
v.add_observer(self)
self.map[v] = []
for c in problem.constraints:
for v in c.get_vars():
self.map[v].append(c)
| 117
| 0
| 66
|
6fec7946842257e2587af351dffd6c086fd072c2
| 413
|
py
|
Python
|
publications/migrations/0025_publication_is_from_systematic_search.py
|
gormshackelford/metadataset
|
ab8a1c0c70a508da37b3a64906ba85c69dd74b6b
|
[
"MIT"
] | 2
|
2019-12-18T12:00:02.000Z
|
2020-03-11T01:15:45.000Z
|
publications/migrations/0025_publication_is_from_systematic_search.py
|
gormshackelford/metadataset
|
ab8a1c0c70a508da37b3a64906ba85c69dd74b6b
|
[
"MIT"
] | 2
|
2020-06-06T00:01:13.000Z
|
2021-06-10T22:09:30.000Z
|
publications/migrations/0025_publication_is_from_systematic_search.py
|
gormshackelford/metadataset
|
ab8a1c0c70a508da37b3a64906ba85c69dd74b6b
|
[
"MIT"
] | 1
|
2020-01-07T12:28:43.000Z
|
2020-01-07T12:28:43.000Z
|
# Generated by Django 2.0 on 2019-04-02 09:57
from django.db import migrations, models
| 21.736842
| 52
| 0.627119
|
# Generated by Django 2.0 on 2019-04-02 09:57
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('publications', '0024_auto_20190401_1522'),
]
operations = [
migrations.AddField(
model_name='publication',
name='is_from_systematic_search',
field=models.BooleanField(default=True),
),
]
| 0
| 301
| 23
|
8c62f4298524bab607b4dbc094a650eb75802544
| 2,046
|
py
|
Python
|
spider_package/config.py
|
hjjia/spider
|
ae52414b608487523e235d69b6404d9d0c06a931
|
[
"MIT"
] | null | null | null |
spider_package/config.py
|
hjjia/spider
|
ae52414b608487523e235d69b6404d9d0c06a931
|
[
"MIT"
] | null | null | null |
spider_package/config.py
|
hjjia/spider
|
ae52414b608487523e235d69b6404d9d0c06a931
|
[
"MIT"
] | null | null | null |
# coding:utf8
import re
options = {
'root_url': 'http://www.juooo.com',
'max_count': 1000,
'urlReg': {
'urlRegType': 1,
'urlFull': '',
'urlStr': 'http://(\w+).juooo.com/\w+'
},
'urlData': []
}
| 26.230769
| 75
| 0.529326
|
# coding:utf8
import re
options = {
'root_url': 'http://www.juooo.com',
'max_count': 1000,
'urlReg': {
'urlRegType': 1,
'urlFull': '',
'urlStr': 'http://(\w+).juooo.com/\w+'
},
'urlData': []
}
def initOptions():
print '请输入入口url:'
root_url = raw_input('入口url：')
if root_url:
print 'aaa'
options['root_url'] = root_url
print '====================================='
print '请输入最大收集条数，大于0的正整数, 0默认收集%d条' % options['max_count']
max_count = raw_input('最大收集条数:')
if max_count and int(max_count) > 1:
options['max_count'] = int(max_count)
print '\n'
print '================================='
print '请输入需要收集的url格式'
print '1. 完整url格式, 默认模式'
print '2. 域名+部分url格式'
urlType = raw_input('请选择url格式：')
print urlType
urlStr = ''
urlFull = ''
urlRe = r'^http(s?)://(\w+.+)\w'
if urlType and int(urlType) == 2:
urlFull = raw_input('请输入带完整域名的urlFull:')
urlTest = re.match(urlRe, urlFull)
if not (urlTest) is None:
options['urlReg']['urlFull'] = urlFull
urlStr = raw_input('请输入需要查找的urlStr:')
options['urlReg']['urlStr'] = urlStr
options['urlReg']['urlRegType'] = int(urlType)
elif urlType and int(urlType) == 1 :
while True:
urlStr = raw_input('请输入带完整域名的urlStr:')
urlTest = re.match(urlRe, urlStr)
if not (urlTest) is None:
options['urlReg']['urlStr'] = urlStr
options['urlReg']['urlRegType'] = int(urlType)
break
# 输入需要配置的数据项
print '请输入配置项，每一个配置项标签名和class名'
num = 1
while True:
itemTag = raw_input('请输入数据项' + num +'的标签名 ')
itemClass = raw_input('请输入数据项' + num +'的class ')
options.urlData[num].itemTag = itemTag
options.urlData[num].itemName = itemClass
if (not (itemTag) is None or not (itemClass) is None) and num != 1:
break
num = num + 1
return options
| 2,055
| 0
| 23
|
033ec08de916e40e952cee70655dddeca4bcde74
| 31,594
|
py
|
Python
|
code/src/features.py
|
bsm8734/BC_stage2_Tabular_data_Classification
|
e421360f3f6f9016c58bfff2dd20485206e4a365
|
[
"MIT"
] | null | null | null |
code/src/features.py
|
bsm8734/BC_stage2_Tabular_data_Classification
|
e421360f3f6f9016c58bfff2dd20485206e4a365
|
[
"MIT"
] | null | null | null |
code/src/features.py
|
bsm8734/BC_stage2_Tabular_data_Classification
|
e421360f3f6f9016c58bfff2dd20485206e4a365
|
[
"MIT"
] | null | null | null |
import pandas as pd
import numpy as np
import os, sys, gc, random
import datetime
import dateutil.relativedelta
# Machine learning
from sklearn.preprocessing import LabelEncoder
from sklearn.impute import SimpleImputer
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import roc_auc_score
# Custom library
from utils import seed_everything, print_score
TOTAL_THRES = 300 # 구매액 임계값
SEED = 42 # 랜덤 시드
seed_everything(SEED) # 시드 고정
data_dir = '../input/train.csv' # os.environ['SM_CHANNEL_TRAIN']
model_dir = '../model' # os.environ['SM_MODEL_DIR']
'''
입력인자로 받는 year_month에 대해 고객 ID별로 총 구매액이
구매액 임계값을 넘는지 여부의 binary label을 생성하는 함수
'''
# def get_year_month_list(df, year_month):
# df = df.copy()
#
# df['year_month-mode'] = df['order_date'].dt.strftime('%Y-%m')
# dd = df.groupby(['year_month-mode', 'customer_id'])['total'].sum()
# cust_ids = df['customer_id'].unique()
#
# # year_month 이전 월 계산
# bef_12_d = datetime.datetime.strptime(year_month, "%Y-%m")
# bef_12_prev_ym = bef_12_d - dateutil.relativedelta.relativedelta(months=12)
# bef_12_prev_ym = bef_12_prev_ym.strftime('%Y-%m')
#
# # ddt = df[df['year_month-mode'] == bef_12_prev_ym]
#
# first_bef = []
# for id in cust_ids:
# dd[:, bef_12_prev_ym]
# # first_bef.append(dd.xs((id, bef_12_prev_ym)))
#
# # df['cycle_month'] = pd.Series(first_bef)
#
# print(df)
if __name__ == '__main__':
print('data_dir', data_dir)
| 40.298469
| 161
| 0.620877
|
import pandas as pd
import numpy as np
import os, sys, gc, random
import datetime
import dateutil.relativedelta
# Machine learning
from sklearn.preprocessing import LabelEncoder
from sklearn.impute import SimpleImputer
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import roc_auc_score
# Custom library
from utils import seed_everything, print_score
TOTAL_THRES = 300 # 구매액 임계값
SEED = 42 # 랜덤 시드
seed_everything(SEED) # 시드 고정
data_dir = '../input/train.csv' # os.environ['SM_CHANNEL_TRAIN']
model_dir = '../model' # os.environ['SM_MODEL_DIR']
'''
입력인자로 받는 year_month에 대해 고객 ID별로 총 구매액이
구매액 임계값을 넘는지 여부의 binary label을 생성하는 함수
'''
def generate_label(df, year_month, total_thres=TOTAL_THRES, print_log=False):
df = df.copy()
# year_month에 해당하는 label 데이터 생성
df['year_month'] = df['order_date'].dt.strftime('%Y-%m')
df.reset_index(drop=True, inplace=True)
# year_month 이전 월의 고객 ID 추출
cust = df[df['year_month']<year_month]['customer_id'].unique()
# year_month에 해당하는 데이터 선택
df = df[df['year_month']==year_month]
# label 데이터프레임 생성
label = pd.DataFrame({'customer_id':cust})
label['year_month'] = year_month
# year_month에 해당하는 고객 ID의 구매액의 합 계산
grped = df.groupby(['customer_id','year_month'], as_index=False)[['total']].sum()
# label 데이터프레임과 merge하고 구매액 임계값을 넘었는지 여부로 label 생성
label = label.merge(grped, on=['customer_id','year_month'], how='left')
label['total'].fillna(0.0, inplace=True)
label['label'] = (label['total'] > total_thres).astype(int)
# 고객 ID로 정렬
label = label.sort_values('customer_id').reset_index(drop=True)
if print_log: print(f'{year_month} - final label shape: {label.shape}')
return label
def feature_preprocessing(train, test, features, do_imputing=True):
x_tr = train.copy()
x_te = test.copy()
# 범주형 피처 이름을 저장할 변수
cate_cols = []
# 레이블 인코딩
for f in features:
if x_tr[f].dtype.name == 'object': # 데이터 타입이 object(str)이면 레이블 인코딩
cate_cols.append(f)
le = LabelEncoder()
# train + test 데이터를 합쳐서 레이블 인코딩 함수에 fit
le.fit(list(x_tr[f].values) + list(x_te[f].values))
# train 데이터 레이블 인코딩 변환 수행
x_tr[f] = le.transform(list(x_tr[f].values))
# test 데이터 레이블 인코딩 변환 수행
x_te[f] = le.transform(list(x_te[f].values))
print('categorical feature:', cate_cols)
if do_imputing:
# 중위값으로 결측치 채우기
imputer = SimpleImputer(strategy='median')
x_tr[features] = imputer.fit_transform(x_tr[features])
x_te[features] = imputer.transform(x_te[features])
return x_tr, x_te
def feature_engineering(df, year_month):
df = df.copy()
# year_month 이전 월 계산
d = datetime.datetime.strptime(year_month, "%Y-%m")
prev_ym = d - dateutil.relativedelta.relativedelta(months=1)
prev_ym = prev_ym.strftime('%Y-%m')
# train, test 데이터 선택
train = df[df['order_date'] < prev_ym]
test = df[df['order_date'] < year_month]
# train, test 레이블 데이터 생성
train_label = generate_label(df, prev_ym)[['customer_id','year_month','label']]
test_label = generate_label(df, year_month)[['customer_id','year_month','label']]
# group by aggregation 함수 선언
agg_func = ['mean','max','min','sum','count','std','skew']
all_train_data = pd.DataFrame()
for i, tr_ym in enumerate(train_label['year_month'].unique()):
# group by aggretation 함수로 train 데이터 피처 생성
train_agg = train.loc[train['order_date'] < tr_ym].groupby(['customer_id']).agg(agg_func)
# 멀티 레벨 컬럼을 사용하기 쉽게 1 레벨 컬럼명으로 변경
new_cols = []
for col in train_agg.columns.levels[0]:
for stat in train_agg.columns.levels[1]:
new_cols.append(f'{col}-{stat}')
train_agg.columns = new_cols
train_agg.reset_index(inplace = True)
train_agg['year_month'] = tr_ym
all_train_data = all_train_data.append(train_agg)
all_train_data = train_label.merge(all_train_data, on=['customer_id', 'year_month'], how='left')
features = all_train_data.drop(columns=['customer_id', 'label', 'year_month']).columns
# group by aggretation 함수로 test 데이터 피처 생성
test_agg = test.groupby(['customer_id']).agg(agg_func)
test_agg.columns = new_cols
test_data = test_label.merge(test_agg, on=['customer_id'], how='left')
# train, test 데이터 전처리
x_tr, x_te = feature_preprocessing(all_train_data, test_data, features)
print('x_tr.shape', x_tr.shape, ', x_te.shape', x_te.shape)
return x_tr, x_te, all_train_data['label'], features
def feature_engineering1(df, year_month):
df = df.copy()
# customer_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산
df['cumsum_total_by_cust_id'] = df.groupby(['customer_id'])['total'].cumsum()
df['cumsum_quantity_by_cust_id'] = df.groupby(['customer_id'])['quantity'].cumsum()
df['cumsum_price_by_cust_id'] = df.groupby(['customer_id'])['price'].cumsum()
# product_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산
df['cumsum_total_by_prod_id'] = df.groupby(['product_id'])['total'].cumsum()
df['cumsum_quantity_by_prod_id'] = df.groupby(['product_id'])['quantity'].cumsum()
df['cumsum_price_by_prod_id'] = df.groupby(['product_id'])['price'].cumsum()
# order_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산
df['cumsum_total_by_order_id'] = df.groupby(['order_id'])['total'].cumsum()
df['cumsum_quantity_by_order_id'] = df.groupby(['order_id'])['quantity'].cumsum()
df['cumsum_price_by_order_id'] = df.groupby(['order_id'])['price'].cumsum()
# year_month 이전 월 계산
d = datetime.datetime.strptime(year_month, "%Y-%m")
prev_ym = d - dateutil.relativedelta.relativedelta(months=1)
prev_ym = prev_ym.strftime('%Y-%m')
# train, test 데이터 선택
train = df[df['order_date'] < prev_ym]
test = df[df['order_date'] < year_month]
# train, test 레이블 데이터 생성
train_label = generate_label(df, prev_ym)[['customer_id', 'year_month', 'label']]
test_label = generate_label(df, year_month)[['customer_id', 'year_month', 'label']]
# group by aggregation 함수 선언
agg_func = ['mean', 'max', 'min', 'sum', 'count', 'std', 'skew']
agg_dict = {
'quantity': agg_func,
'price': agg_func,
'total': agg_func,
'cumsum_total_by_cust_id': agg_func,
'cumsum_quantity_by_cust_id': agg_func,
'cumsum_price_by_cust_id': agg_func,
'cumsum_total_by_prod_id': agg_func,
'cumsum_quantity_by_prod_id': agg_func,
'cumsum_price_by_prod_id': agg_func,
'cumsum_total_by_order_id': agg_func,
'cumsum_quantity_by_order_id': agg_func,
'cumsum_price_by_order_id': agg_func,
'order_id': ['nunique'],
'product_id': ['nunique'],
}
all_train_data = pd.DataFrame()
for i, tr_ym in enumerate(train_label['year_month'].unique()):
# group by aggretation 함수로 train 데이터 피처 생성
train_agg = train.loc[train['order_date'] < tr_ym].groupby(['customer_id']).agg(agg_dict)
new_cols = []
for col in agg_dict.keys():
for stat in agg_dict[col]:
if type(stat) is str:
new_cols.append(f'{col}-{stat}')
else:
new_cols.append(f'{col}-mode')
train_agg.columns = new_cols
train_agg.reset_index(inplace=True)
train_agg['year_month'] = tr_ym
all_train_data = all_train_data.append(train_agg)
all_train_data = train_label.merge(all_train_data, on=['customer_id', 'year_month'], how='left')
features = all_train_data.drop(columns=['customer_id', 'label', 'year_month']).columns
# group by aggretation 함수로 test 데이터 피처 생성
test_agg = test.groupby(['customer_id']).agg(agg_dict)
test_agg.columns = new_cols
test_data = test_label.merge(test_agg, on=['customer_id'], how='left')
# train, test 데이터 전처리
x_tr, x_te = feature_preprocessing(all_train_data, test_data, features)
print('x_tr.shape', x_tr.shape, ', x_te.shape', x_te.shape)
return x_tr, x_te, all_train_data['label'], features
# def get_year_month_list(df, year_month):
# df = df.copy()
#
# df['year_month-mode'] = df['order_date'].dt.strftime('%Y-%m')
# dd = df.groupby(['year_month-mode', 'customer_id'])['total'].sum()
# cust_ids = df['customer_id'].unique()
#
# # year_month 이전 월 계산
# bef_12_d = datetime.datetime.strptime(year_month, "%Y-%m")
# bef_12_prev_ym = bef_12_d - dateutil.relativedelta.relativedelta(months=12)
# bef_12_prev_ym = bef_12_prev_ym.strftime('%Y-%m')
#
# # ddt = df[df['year_month-mode'] == bef_12_prev_ym]
#
# first_bef = []
# for id in cust_ids:
# dd[:, bef_12_prev_ym]
# # first_bef.append(dd.xs((id, bef_12_prev_ym)))
#
# # df['cycle_month'] = pd.Series(first_bef)
#
# print(df)
def make_time_series_data(df, Input, year_month, stand):
# 기준을 잡습니다. 기준은 여기서 %Y-%m 입니다.
standard = ['customer_id'] + [stand]
data = Input.copy()
df = df.copy()
data[stand] = pd.to_datetime(df['order_date']).dt.strftime(stand)
data.order_date = pd.to_datetime(data['order_date'])
# 월단위의 틀을 만들어주고, 기준으로 aggregation을 해준 다음에 merge를 해줄 것입니다
times = pd.date_range('2009-12-01', periods=(data.order_date.max() - data.order_date.min()).days + 1, freq='1d')
customerid_frame = np.repeat(data.customer_id.unique(), len(times))
date_frame = np.tile(times, len(data.customer_id.unique()))
frame = pd.DataFrame({'customer_id': customerid_frame, 'order_date': date_frame})
frame[stand] = pd.to_datetime(frame.order_date).dt.strftime(stand)
# group by
data_group = data.groupby(standard).sum().reset_index()
frame_group = frame.groupby(standard).count().reset_index().drop(['order_date'], axis=1)
# merge
merge = pd.merge(frame_group, data_group, on=standard, how='left').fillna(0)
merge = merge.rename(columns={stand: 'standard'})
merge_test = merge[merge['standard'] == year_month].drop(columns=['standard', 'quantity', 'price']) #.drop(merge.columns.tolist() - ['customer_id', 'total'])
return merge_test
def add_trend(df, year_month):
df = df.copy()
df['year_month'] = df['order_date'].dt.strftime('%Y-%m')
# year_month 이전 월 계산
d = datetime.datetime.strptime(year_month, "%Y-%m")
prev_ym = d - dateutil.relativedelta.relativedelta(months=1)
# train과 test 데이터 생성
train = df[df['order_date'] < prev_ym] # 2009-12부터 2011-10 데이터 추출
test = df[df['order_date'] < year_month] # 2009-12부터 2011-11 데이터 추출
train_window_ym = []
test_window_ym = []
for month_back in [1, 2, 3, 5, 7, 12, 20, 23]: # 1개월, 2개월, ... 20개월, 23개월 전 year_month 파악
train_window_ym.append((prev_ym - dateutil.relativedelta.relativedelta(months=month_back)).strftime('%Y-%m'))
test_window_ym.append((d - dateutil.relativedelta.relativedelta(months=month_back)).strftime('%Y-%m'))
# aggregation 함수 선언
agg_func = ['max', 'min', 'sum', 'mean', 'count', 'std', 'skew']
# group by aggregation with Dictionary
agg_dict = {
'quantity': agg_func,
'price': agg_func,
'total': agg_func,
}
# general statistics for train data with time series trend
for i, tr_ym in enumerate(train_window_ym):
# group by aggretation 함수로 train 데이터 피처 생성
train_agg = train.loc[train['year_month'] >= tr_ym].groupby(['customer_id']).agg(
agg_dict) # 해당 year_month 이후부터 모든 데이터에 대한 aggregation을 실시
# 멀티 레벨 컬럼을 사용하기 쉽게 1 레벨 컬럼명으로 변경
new_cols = []
for level1, level2 in train_agg.columns:
new_cols.append(f'{level1}-{level2}-{i}')
train_agg.columns = new_cols
train_agg.reset_index(inplace=True)
if i == 0:
train_data = train_agg
else:
train_data = train_data.merge(train_agg, on=['customer_id'], how='right')
# general statistics for test data with time series trend
for i, tr_ym in enumerate(test_window_ym):
# group by aggretation 함수로 test 데이터 피처 생성
test_agg = test.loc[test['year_month'] >= tr_ym].groupby(['customer_id']).agg(agg_dict)
# 멀티 레벨 컬럼을 사용하기 쉽게 1 레벨 컬럼명으로 변경
new_cols = []
for level1, level2 in test_agg.columns:
new_cols.append(f'{level1}-{level2}-{i}')
test_agg.columns = new_cols
test_agg.reset_index(inplace=True)
if i == 0:
test_data = test_agg
else:
test_data = test_data.merge(test_agg, on=['customer_id'], how='right')
return train_data, test_data
def add_seasonality(df, year_month):
df = df.copy()
df['year_month'] = df['order_date'].dt.strftime('%Y-%m')
# year_month 이전 월 계산
d = datetime.datetime.strptime(year_month, "%Y-%m")
prev_ym = d - dateutil.relativedelta.relativedelta(months=1)
# train과 test 데이터 생성
train = df[df['order_date'] < prev_ym] # 2009-12부터 2011-10 데이터 추출
test = df[df['order_date'] < year_month] # 2009-12부터 2011-11 데이터 추출
train_window_ym = []
test_window_ym = []
for month_back in [1, 6, 12, 18]: # 각 주기성을 파악하고 싶은 구간을 생성
train_window_ym.append(
(
(prev_ym - dateutil.relativedelta.relativedelta(months=month_back)).strftime('%Y-%m'),
(prev_ym - dateutil.relativedelta.relativedelta(months=month_back + 2)).strftime('%Y-%m')
# 1~3, 6~8, 12~14, 18~20 Pair를 만들어준다
)
)
test_window_ym.append(
(
(d - dateutil.relativedelta.relativedelta(months=month_back)).strftime('%Y-%m'),
(d - dateutil.relativedelta.relativedelta(months=month_back + 2)).strftime('%Y-%m')
)
)
# aggregation 함수 선언
agg_func = ['max', 'min', 'sum', 'mean', 'count', 'std', 'skew']
# group by aggregation with Dictionary
agg_dict = {
'quantity': agg_func,
'price': agg_func,
'total': agg_func,
}
# seasonality for train data with time series
for i, (tr_ym, tr_ym_3) in enumerate(train_window_ym):
# group by aggretation 함수로 train 데이터 피처 생성
# 구간 사이에 존재하는 월들에 대해서 aggregation을 진행
train_agg = train.loc[(train['year_month'] >= tr_ym_3) & (train['year_month'] <= tr_ym)].groupby(
['customer_id']).agg(agg_dict)
# 멀티 레벨 컬럼을 사용하기 쉽게 1 레벨 컬럼명으로 변경
new_cols = []
for level1, level2 in train_agg.columns:
new_cols.append(f'{level1}-{level2}-season{i}')
train_agg.columns = new_cols
train_agg.reset_index(inplace=True)
if i == 0:
train_data = train_agg
else:
train_data = train_data.merge(train_agg, on=['customer_id'], how='right')
# seasonality for test data with time series
for i, (tr_ym, tr_ym_3) in enumerate(test_window_ym):
# group by aggretation 함수로 train 데이터 피처 생성
test_agg = test.loc[(test['year_month'] >= tr_ym_3) & (test['year_month'] <= tr_ym)].groupby(
['customer_id']).agg(agg_dict)
# 멀티 레벨 컬럼을 사용하기 쉽게 1 레벨 컬럼명으로 변경
new_cols = []
for level1, level2 in test_agg.columns:
new_cols.append(f'{level1}-{level2}-season{i}')
test_agg.columns = new_cols
test_agg.reset_index(inplace=True)
if i == 0:
test_data = test_agg
else:
test_data = test_data.merge(test_agg, on=['customer_id'], how='right')
return train_data, test_data
def feature_engineering2(df, year_month):
df = df.copy()
# customer_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산
df['cumsum_total_by_cust_id'] = df.groupby(['customer_id'])['total'].cumsum()
df['cumsum_quantity_by_cust_id'] = df.groupby(['customer_id'])['quantity'].cumsum()
df['cumsum_price_by_cust_id'] = df.groupby(['customer_id'])['price'].cumsum()
# product_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산
df['cumsum_total_by_prod_id'] = df.groupby(['product_id'])['total'].cumsum()
df['cumsum_quantity_by_prod_id'] = df.groupby(['product_id'])['quantity'].cumsum()
df['cumsum_price_by_prod_id'] = df.groupby(['product_id'])['price'].cumsum()
# order_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산
df['cumsum_total_by_order_id'] = df.groupby(['order_id'])['total'].cumsum()
df['cumsum_quantity_by_order_id'] = df.groupby(['order_id'])['quantity'].cumsum()
df['cumsum_price_by_order_id'] = df.groupby(['order_id'])['price'].cumsum()
# oredr_ts
df['order_ts'] = df['order_date'].astype(np.int64)//1e9
df['order_ts_diff'] = df.groupby(['customer_id'])['order_ts'].diff()
df['quantity_diff'] = df.groupby(['customer_id'])['quantity'].diff()
df['price_diff'] = df.groupby(['customer_id'])['price'].diff()
df['total_diff'] = df.groupby(['customer_id'])['total'].diff()
# mode
df['month-mode'] = df['order_date'].dt.month
df['year_month-mode'] = df['order_date'].dt.strftime('%Y-%m')
# oredr_ts_plus ===
df['order_ts_plus'] = df[df['total'] > 0]['order_date'].astype(np.int64) // 1e9
df['order_ts_plus_diff'] = df[df['total'] > 0].groupby(['customer_id'])['order_ts'].diff()
df['order_ts_plus'] = df['order_ts_plus'].fillna(0)
df['order_ts_plus_diff'] = df['order_ts_plus_diff'].fillna(0)
# df[~(df.order_id.str.contains('C'))].groupby(['customer_id'])['order_date'].last().astype(np.int64) // 1e9
# ================================================================================================
# year_month 이전 월 계산
d = datetime.datetime.strptime(year_month, "%Y-%m")
prev_ym = d - dateutil.relativedelta.relativedelta(months=1)
prev_ym = prev_ym.strftime('%Y-%m')
# train, test 데이터 선택
train = df[df['order_date'] < prev_ym]
test = df[df['order_date'] < year_month]
# train, test 레이블 데이터 생성
train_label = generate_label(df, prev_ym)[['customer_id', 'year_month', 'label']]
test_label = generate_label(df, year_month)[['customer_id', 'year_month', 'label']]
# ================================================================================================
# 연월 피처 생성
target = datetime.datetime.strptime('2011-11', "%Y-%m") # 타겟 연월
prev = target - dateutil.relativedelta.relativedelta(years=1) # 전년 연월
prev = prev.strftime('%Y-%m') # 문자열로 변환
groupby = train.groupby(['customer_id', 'year_month-mode'])['total'].sum() # 고객별, 월별 total 합
groupby = groupby.unstack() # 월별을 컬럼으로 변환
prev_pprev_total = groupby.loc[:, [prev]] # 전년, 전전년 데이터만 추출
prev_pprev_total = prev_pprev_total.fillna(0)
train_1224 = (prev_pprev_total['2010-11']) / 2
target = datetime.datetime.strptime('2011-12', "%Y-%m") # 타겟 연월
prev = target - dateutil.relativedelta.relativedelta(years=1) # 전년 연월
pprev = prev - dateutil.relativedelta.relativedelta(years=1) # 전전년 연월
prev, pprev = prev.strftime('%Y-%m'), pprev.strftime('%Y-%m') # 문자열로 변환
groupby = test.groupby(['customer_id', 'year_month-mode'])['total'].sum() # 고객별, 월별 total 합
groupby = groupby.unstack() # 월별을 컬럼으로 변환
prev_pprev_total = groupby.loc[:, [prev, pprev]] # 전년, 전전년 데이터만 추출
prev_pprev_total = prev_pprev_total.fillna(0)
test_1224 = (prev_pprev_total['2010-12'] + prev_pprev_total['2009-12']) / 2
# ================================================================================================
# lambda 식
mode_f = lambda x: x.value_counts().index[0]
# group by aggregation 함수 선언
agg_func = ['mean', 'max', 'min', 'sum', 'count', 'std', 'skew']
# agg_func = ['mean', 'max'] # , 'min', 'sum', 'count', 'std', 'skew']
agg_dict = {
'order_ts': ['first', 'last'],
'order_ts_diff': agg_func,
'order_ts_plus': ['first', 'last'],
'order_ts_plus_diff': agg_func,
'quantity_diff': agg_func,
'price_diff': agg_func,
'total_diff': agg_func,
'quantity': agg_func,
'price': agg_func,
'total': agg_func,
'cumsum_total_by_cust_id': agg_func,
'cumsum_quantity_by_cust_id': agg_func,
'cumsum_price_by_cust_id': agg_func,
'cumsum_total_by_prod_id': agg_func,
'cumsum_quantity_by_prod_id': agg_func,
'cumsum_price_by_prod_id': agg_func,
'cumsum_total_by_order_id': agg_func,
'cumsum_quantity_by_order_id': agg_func,
'cumsum_price_by_order_id': agg_func,
'order_id': ['nunique'],
'product_id': ['nunique'],
'month-mode': [mode_f],
'year_month-mode': [mode_f],
}
all_train_data = pd.DataFrame()
for i, tr_ym in enumerate(train_label['year_month'].unique()):
# group by aggretation 함수로 train 데이터 피처 생성
train_agg = train.loc[train['order_date'] < tr_ym].groupby(['customer_id']).agg(agg_dict)
new_cols = []
for col in agg_dict.keys():
for stat in agg_dict[col]:
if type(stat) is str:
new_cols.append(f'{col}-{stat}')
else:
new_cols.append(f'{col}-mode')
train_agg.columns = new_cols
train_agg.reset_index(inplace=True)
train_agg['year_month'] = tr_ym
all_train_data = all_train_data.append(train_agg)
all_train_data = train_label.merge(all_train_data, on=['customer_id', 'year_month'], how='left')
all_train_data['cycle_1224'] = train_1224.to_numpy()
# ================================================================================================
data = pd.read_csv("/opt/ml/code/input/train.csv", parse_dates=["order_date"])
# # baseline feature engineering
# train, test, y, features = feature_engineering(data, '2011-12')
# trend
train_t, test_t = add_trend(data, year_month='2011-12')
# seasonality
train_s, test_s = add_seasonality(data, year_month='2011-12')
# train 데이터 병합
all_train_data = all_train_data.merge(train_t, on=['customer_id'], how='left')
all_train_data = all_train_data.merge(train_s, on=['customer_id'], how='left')
all_train_data = all_train_data.fillna(0)
# ================================================================================================
features = all_train_data.drop(columns=['customer_id', 'label', 'year_month']).columns
print(features.shape)
import csv
with open("../output/feature.csv", 'w', newline='') as f:
writer = csv.writer(f)
for items in features.tolist():
print(items)
writer.writerow([items])
test_agg = test.groupby(['customer_id']).agg(agg_dict)
test_agg.columns = new_cols
test_agg['cycle_1224'] = test_1224
test_data = test_label.merge(test_agg, on=['customer_id'], how='left')
# test 데이터 병합 ===================================================================================
test_data = test_data.merge(test_t, on=['customer_id'], how='left')
test_data = test_data.merge(test_s, on=['customer_id'], how='left')
test_data = test_data.fillna(0)
# train, test 데이터 전처리
print(all_train_data.shape)
print(test_data.shape)
x_tr, x_te = feature_preprocessing(all_train_data, test_data, features)
print('x_tr.shape', x_tr.shape, ', x_te.shape', x_te.shape)
return x_tr, x_te, all_train_data['label'], features
def feature_engineering3(df, year_month):
my_pick = [
'order_ts-last',
'order_ts-first',
'price_diff-skew',
'price-skew',
'order_ts_diff-max',
'quantity_diff-skew',
'cumsum_total_by_prod_id-skew',
'cumsum_price_by_prod_id-skew',
'cumsum_total_by_cust_id-skew',
'cumsum_quantity_by_prod_id-sum',
'quantity-skew',
'cumsum_total_by_order_id-skew',
'cumsum_price_by_cust_id-skew',
'cumsum_price_by_order_id-skew',
'year_month-mode',
'total_diff-skew',
'price-mean',
'cumsum_quantity_by_order_id-skew',
'cumsum_quantity_by_prod_id-skew',
'price_diff-mean',
]
df = df.copy()
# customer_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산
df['cumsum_total_by_cust_id'] = df.groupby(['customer_id'])['total'].cumsum()
df['cumsum_quantity_by_cust_id'] = df.groupby(['customer_id'])['quantity'].cumsum()
df['cumsum_price_by_cust_id'] = df.groupby(['customer_id'])['price'].cumsum()
# product_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산
df['cumsum_total_by_prod_id'] = df.groupby(['product_id'])['total'].cumsum()
df['cumsum_quantity_by_prod_id'] = df.groupby(['product_id'])['quantity'].cumsum()
df['cumsum_price_by_prod_id'] = df.groupby(['product_id'])['price'].cumsum()
# order_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산
df['cumsum_total_by_order_id'] = df.groupby(['order_id'])['total'].cumsum()
df['cumsum_quantity_by_order_id'] = df.groupby(['order_id'])['quantity'].cumsum()
df['cumsum_price_by_order_id'] = df.groupby(['order_id'])['price'].cumsum()
# oredr_ts
df['order_ts'] = df['order_date'].astype(np.int64)//1e9
df['order_ts_diff'] = df.groupby(['customer_id'])['order_ts'].diff()
df['quantity_diff'] = df.groupby(['customer_id'])['quantity'].diff()
df['price_diff'] = df.groupby(['customer_id'])['price'].diff()
df['total_diff'] = df.groupby(['customer_id'])['total'].diff()
# mode
df['month-mode'] = df['order_date'].dt.month
df['year_month-mode'] = df['order_date'].dt.strftime('%Y-%m')
# oredr_ts_plus ===
df['order_ts_plus'] = df[df['total'] > 0]['order_date'].astype(np.int64) // 1e9
df['order_ts_plus_diff'] = df[df['total'] > 0].groupby(['customer_id'])['order_ts'].diff()
df['order_ts_plus'] = df['order_ts_plus'].fillna(0)
df['order_ts_plus_diff'] = df['order_ts_plus_diff'].fillna(0)
# df[~(df.order_id.str.contains('C'))].groupby(['customer_id'])['order_date'].last().astype(np.int64) // 1e9
# ================================================================================================
# year_month 이전 월 계산
d = datetime.datetime.strptime(year_month, "%Y-%m")
prev_ym = d - dateutil.relativedelta.relativedelta(months=1)
prev_ym = prev_ym.strftime('%Y-%m')
# train, test 데이터 선택
train = df[df['order_date'] < prev_ym]
test = df[df['order_date'] < year_month]
# train, test 레이블 데이터 생성
train_label = generate_label(df, prev_ym)[['customer_id', 'year_month', 'label']]
test_label = generate_label(df, year_month)[['customer_id', 'year_month', 'label']]
####################################################################################
# year_month 이전 월 계산
bef_12_d1 = datetime.datetime.strptime(year_month, "%Y-%m")
bef_12_prev_ym1 = bef_12_d1 - dateutil.relativedelta.relativedelta(months=12)
bef_12_prev_ym1 = bef_12_prev_ym1.strftime('%Y-%m')
merge_df_12_train = make_time_series_data(train, train, bef_12_prev_ym1, "%Y-%m")
print(bef_12_prev_ym1)
bef_24_d1 = datetime.datetime.strptime(year_month, "%Y-%m")
bef_24_prev_ym1 = bef_24_d1 - dateutil.relativedelta.relativedelta(months=24)
bef_24_prev_ym1 = bef_24_prev_ym1.strftime('%Y-%m')
merge_df_24_train = make_time_series_data(train, train, bef_24_prev_ym1, "%Y-%m")
print(bef_24_prev_ym1)
merge_1224_train = merge_df_24_train.merge(merge_df_12_train, on=['customer_id'], how='left')
series_1224_train = (merge_1224_train['total_x'] + merge_1224_train['total_y']) / 2
####################################################################################
# year_month 이전 월 계산
bef_12_d2 = datetime.datetime.strptime(prev_ym, "%Y-%m")
bef_12_prev_ym2 = bef_12_d2 - dateutil.relativedelta.relativedelta(months=12)
bef_12_prev_ym2 = bef_12_prev_ym2.strftime('%Y-%m')
merge_df_12_test = make_time_series_data(test, test, bef_12_prev_ym2, "%Y-%m")
print(bef_12_prev_ym2)
bef_24_d2 = datetime.datetime.strptime(prev_ym, "%Y-%m")
bef_24_prev_ym2 = bef_24_d2 - dateutil.relativedelta.relativedelta(months=24)
bef_24_prev_ym2 = bef_24_prev_ym2.strftime('%Y-%m')
merge_df_24_test = make_time_series_data(test, test, bef_24_prev_ym2, "%Y-%m")
print(bef_24_prev_ym2)
merge_1224_test = merge_df_24_test.merge(merge_df_12_test, on=['customer_id'], how='left')
series_1224_test = (merge_1224_test['total_x'] + merge_1224_test['total_y']) / 2
####################################################################################
# lambda 식
mode_f = lambda x: x.value_counts().index[0]
# group by aggregation 함수 선언
# agg_func = ['mean', 'max', 'min', 'sum', 'count', 'std', 'skew']
agg_func = ['mean', 'max'] # , 'min', 'sum', 'count', 'std', 'skew']
agg_dict = {
'order_ts': ['first', 'last'],
'order_ts_diff': agg_func,
# 'order_ts_plus': ['first', 'last'],
# 'order_ts_plus_diff': agg_func,
# 'quantity_diff': agg_func,
# 'price_diff': agg_func,
# 'total_diff': agg_func,
# 'quantity': agg_func,
# 'price': agg_func,
# 'total': agg_func,
# 'cumsum_total_by_cust_id': agg_func,
# 'cumsum_quantity_by_cust_id': agg_func,
# 'cumsum_price_by_cust_id': agg_func,
# 'cumsum_total_by_prod_id': agg_func,
# 'cumsum_quantity_by_prod_id': agg_func,
# 'cumsum_price_by_prod_id': agg_func,
# 'cumsum_total_by_order_id': agg_func,
# 'cumsum_quantity_by_order_id': agg_func,
# 'cumsum_price_by_order_id': agg_func,
# 'order_id': ['nunique'],
# 'product_id': ['nunique'],
# 'month-mode': [mode_f],
# 'year_month-mode': [mode_f],
}
all_train_data = pd.DataFrame()
for i, tr_ym in enumerate(train_label['year_month'].unique()):
# group by aggretation 함수로 train 데이터 피처 생성
train_agg = train.loc[train['order_date'] < tr_ym].groupby(['customer_id']).agg(agg_dict)
new_cols = []
for col in agg_dict.keys():
for stat in agg_dict[col]:
if type(stat) is str:
new_cols.append(f'{col}-{stat}')
else:
new_cols.append(f'{col}-mode')
train_agg.columns = new_cols
train_agg.reset_index(inplace=True)
train_agg['year_month'] = tr_ym
all_train_data = all_train_data.append(train_agg)
all_train_data = train_label.merge(all_train_data, on=['customer_id', 'year_month'], how='left')
all_train_data['cycle_1224'] = series_1224_train
features = all_train_data.drop(columns=['customer_id', 'label', 'year_month']).columns
import csv
with open("../output/feature.csv", 'w', newline='') as f:
writer = csv.writer(f)
for items in features.tolist():
print(items)
writer.writerow([items])
test_agg = test.groupby(['customer_id']).agg(agg_dict)
test_agg.columns = new_cols
test_agg['cycle_1224'] = series_1224_test
test_data = test_label.merge(test_agg, on=['customer_id'], how='left')
# train, test 데이터 전처리
x_tr, x_te = feature_preprocessing(all_train_data, test_data, features)
# x_tr = x_tr[my_pick]
# x_te = x_te[my_pick]
# features = pd.Index(my_pick)
print('x_tr.shape', x_tr.shape, ', x_te.shape', x_te.shape)
return x_tr, x_te, all_train_data['label'], features
if __name__ == '__main__':
print('data_dir', data_dir)
| 31,616
| 0
| 206
|
6987eae1d20eb0280e52f15424abd63287a36aae
| 1,576
|
py
|
Python
|
nlp.py
|
ktrnka/helpers
|
b52102d81007301ed576c908c8f9fa5df386abbb
|
[
"MIT"
] | null | null | null |
nlp.py
|
ktrnka/helpers
|
b52102d81007301ed576c908c8f9fa5df386abbb
|
[
"MIT"
] | null | null | null |
nlp.py
|
ktrnka/helpers
|
b52102d81007301ed576c908c8f9fa5df386abbb
|
[
"MIT"
] | null | null | null |
from __future__ import unicode_literals
from __future__ import print_function
import unicodedata
import unittest
"""
Very simple assorted helpers for natural language processing that I've used a few times.
"""
_CHAR_TRANSLATIONS = {
# chars to remove
"\u00ae": None,
"\u2122": None,
# chars to normalize that aren't handled by combining char stripping
"\u2018": "'",
"\u2019": "'",
"\u201c": '"',
"\u201d": '"',
"\u2013": "-",
"\u2014": "-",
"\u00bd": "1/2"
}
_CODEPOINT_TRANSLATIONS = {ord(k): v for k, v in _CHAR_TRANSLATIONS.items()}
def strip_diacritics(s):
"""Remove accents and other diacritics"""
return "".join(c for c in unicodedata.normalize("NFD", s) if unicodedata.category(c) != "Mn")
def normalize_unicode(s):
"""Remove trademark sign, normalize smart quotes, etc"""
return s.translate(_CODEPOINT_TRANSLATIONS)
| 25.419355
| 117
| 0.616117
|
from __future__ import unicode_literals
from __future__ import print_function
import unicodedata
import unittest
"""
Very simple assorted helpers for natural language processing that I've used a few times.
"""
_CHAR_TRANSLATIONS = {
# chars to remove
"\u00ae": None,
"\u2122": None,
# chars to normalize that aren't handled by combining char stripping
"\u2018": "'",
"\u2019": "'",
"\u201c": '"',
"\u201d": '"',
"\u2013": "-",
"\u2014": "-",
"\u00bd": "1/2"
}
_CODEPOINT_TRANSLATIONS = {ord(k): v for k, v in _CHAR_TRANSLATIONS.items()}
def strip_diacritics(s):
"""Remove accents and other diacritics"""
return "".join(c for c in unicodedata.normalize("NFD", s) if unicodedata.category(c) != "Mn")
def normalize_unicode(s):
"""Remove trademark sign, normalize smart quotes, etc"""
return s.translate(_CODEPOINT_TRANSLATIONS)
def ngramify(sequence, n=3, start="^", end="$"):
if start:
sequence = [start] + sequence
if end:
sequence = sequence + [end]
for i in range(n, len(sequence) + 1):
yield tuple(sequence[i - n:i])
def get_hash_indicators(items, num_features):
features = [0 for _ in range(num_features)]
for item in items:
features[hash(item) % num_features] += 1
return features
class NlpTests(unittest.TestCase):
def test_ngramify(self):
self.assertSequenceEqual([("^", "This", "is"), ("This", "is", "a"), ("is", "a", "test"), ("a", "test", "$")],
list(ngramify("This is a test".split())))
| 571
| 13
| 95
|
8c1a3c2529398f838d4805204cccfb8bf42655bf
| 2,471
|
py
|
Python
|
animations/measure_scene.py
|
61smiling/algorithm-stone
|
625bcef514a82ad93871987e81c6ec18b34f27cb
|
[
"MIT"
] | 693
|
2021-02-22T03:52:10.000Z
|
2022-03-31T15:54:46.000Z
|
animations/measure_scene.py
|
Karen4tree/algorithm-stone
|
12e46463bb57929dfb4ab142f5cf0e8e69d460a8
|
[
"MIT"
] | 5
|
2021-09-14T07:06:20.000Z
|
2022-01-04T02:49:11.000Z
|
animations/measure_scene.py
|
Karen4tree/algorithm-stone
|
12e46463bb57929dfb4ab142f5cf0e8e69d460a8
|
[
"MIT"
] | 265
|
2021-02-28T02:35:44.000Z
|
2022-03-31T13:21:31.000Z
|
from manim_imports_ext import *
| 35.3
| 124
| 0.566977
|
from manim_imports_ext import *
class MeasureScene(AlgoScene):
def construct(self):
shape = self.camera.frame.get_shape()
font_size = 30
t = Text("1 width %.2f height %.2f delta 0.00"%(shape[0], shape[1]), color=GREEN, font_size=font_size).shift(LEFT*3)
self.add(t)
hw = shape[0]/2
hh = shape[1]/2
left = Line(start=LEFT*hw+UP*hh, end=LEFT*hw+DOWN*hh, color=BLUE)
right = Line(start=RIGHT*hw+UP*hh, end=RIGHT*hw+DOWN*hh, color=BLUE_A)
top = Line(start=LEFT*hw+UP*hh, end=RIGHT*hw+UP*hh, color=BLUE_B)
bottom = Line(start=LEFT*hw+DOWN*hh, end=RIGHT*hw+DOWN*hh, color=BLUE_C)
horizon = Line(start=LEFT*shape[0]/2, end=RIGHT*shape[0]/2, color=RED)
verticle = Line(start=UP*shape[1]/2, end=DOWN*shape[1]/2, color=YELLOW)
self.play(ShowCreation(horizon), ShowCreation(verticle), ShowCreation(left),
ShowCreation(right), ShowCreation(top), ShowCreation(bottom))
t.next_to(horizon, direction=UP, buff=0)
count = 2
delta = 0.0
lastcentery = t.get_center()[1]
print("center:", t.get_center())
while True:
nt = Text("%d width %.2f height %.2f delta %.2f"%(count, shape[0], shape[1], delta),
color=GREEN, font_size=font_size).shift(LEFT*3)
nt.next_to(t, direction=UP, buff=0)
p = nt.get_center()
print("center:", p)
delta = p[1] - lastcentery
t = nt
lastcentery = p[1]
if p[1] > shape[1]/2:
break
count += 1
self.play(ShowCreation(nt))
self.camera.frame.shift(OUT*10)
self.snapshot()
self.wait()
class MeasureScene2(AlgoScene):
def construct(self):
shape = self.camera.frame.get_shape()
horizon = Line(start=LEFT*shape[0]/2, end=RIGHT*shape[0]/2, color=RED)
verticle = Line(start=UP*shape[1]/2, end=DOWN*shape[1]/2, color=BLUE)
self.play(ShowCreation(horizon), ShowCreation(verticle))
delta = 0.0
v = VGroup()
for i in range(12):
nt = Text("%d width %.2f height %.2f delta %.2f"%(i, shape[0], shape[1], delta),
color=GREEN).shift(LEFT*3)
v.add(nt)
v.arrange(direction=UP, aligned_edge=LEFT, buff=0.0)
self.add(v)
self.camera.frame.shift(OUT*10)
self.snapshot()
self.wait()
| 2,322
| 19
| 98
|
f730ed7aa9f9349f15c6cbfc5165fbc8a60781f3
| 1,755
|
py
|
Python
|
MGSIM/Commands/Genome_download.py
|
nick-youngblut/MGSIM
|
9edae3c170cf5100b3408a853a87e1205e70dd1b
|
[
"MIT"
] | 3
|
2019-09-02T11:03:40.000Z
|
2021-12-13T15:59:06.000Z
|
MGSIM/Commands/Genome_download.py
|
nick-youngblut/MGSIM
|
9edae3c170cf5100b3408a853a87e1205e70dd1b
|
[
"MIT"
] | 2
|
2020-11-13T13:04:47.000Z
|
2022-02-03T14:58:13.000Z
|
MGSIM/Commands/Genome_download.py
|
nick-youngblut/MGSIM
|
9edae3c170cf5100b3408a853a87e1205e70dd1b
|
[
"MIT"
] | 1
|
2020-08-13T12:40:39.000Z
|
2020-08-13T12:40:39.000Z
|
#!/usr/bin/env python
"""
genome_download: downloading genomes
Usage:
genome_download [options] <accession_table>
genome_download -h | --help
genome_download --version
Options:
<accessin_table> Taxon-accession table (see Description).
Use '-' if from STDIN.
-d=<d> Output directory. [Default: .]
-e=<e> Email to use for NCBI queries. [Default: blank@gmail.com]
-a=<a> Number of ambiguous nucleotides allowed in a genome. [Default: 0]
-n=<n> Number of cpus. [Default: 1]
-t=<t> Number of tries to download genomes. [Default: 10]
-r Rename genome sequences based on taxon name?
--debug Debug mode (no multiprocessing).
-h --help Show this screen.
--version Show version.
Description:
Taxon-accession table
---------------------
* tab-delimited
* must contain 2 columns
* "Taxon" = taxon name
* "Accession" = NCBI accession used for downloading
* Possible accessions:
* ncbi nucleotide db
* ncbi assembly db
* ftp url to genome (direct download)
* other columns are allowed
Output
------
* Genome fasta files written to the specified output directory
* A table mapping taxa to the download genome fasta file is written to STDOUT
"""
# import
import sys,os
import logging
## batteries
from docopt import docopt
from MGSIM import Genome_Download
## logging
logging.basicConfig(format='%(asctime)s - %(message)s', level=logging.DEBUG)
# opt parse
| 28.770492
| 85
| 0.631339
|
#!/usr/bin/env python
"""
genome_download: downloading genomes
Usage:
genome_download [options] <accession_table>
genome_download -h | --help
genome_download --version
Options:
<accessin_table> Taxon-accession table (see Description).
Use '-' if from STDIN.
-d=<d> Output directory. [Default: .]
-e=<e> Email to use for NCBI queries. [Default: blank@gmail.com]
-a=<a> Number of ambiguous nucleotides allowed in a genome. [Default: 0]
-n=<n> Number of cpus. [Default: 1]
-t=<t> Number of tries to download genomes. [Default: 10]
-r Rename genome sequences based on taxon name?
--debug Debug mode (no multiprocessing).
-h --help Show this screen.
--version Show version.
Description:
Taxon-accession table
---------------------
* tab-delimited
* must contain 2 columns
* "Taxon" = taxon name
* "Accession" = NCBI accession used for downloading
* Possible accessions:
* ncbi nucleotide db
* ncbi assembly db
* ftp url to genome (direct download)
* other columns are allowed
Output
------
* Genome fasta files written to the specified output directory
* A table mapping taxa to the download genome fasta file is written to STDOUT
"""
# import
import sys,os
import logging
## batteries
from docopt import docopt
from MGSIM import Genome_Download
## logging
logging.basicConfig(format='%(asctime)s - %(message)s', level=logging.DEBUG)
# opt parse
def opt_parse(args=None):
if args is None:
args = docopt(__doc__, version='0.1')
else:
args = docopt(__doc__, version='0.1', argv=args)
Genome_Download.main(args)
| 177
| 0
| 22
|
6c2d37e74fbca6fb979828a56b8838b574e1ca73
| 6,460
|
py
|
Python
|
Interface/app.py
|
BrandonLawler/Office-365-Connector
|
2421c560cfbc517a1074c7fdbda916c842e2995d
|
[
"MIT"
] | null | null | null |
Interface/app.py
|
BrandonLawler/Office-365-Connector
|
2421c560cfbc517a1074c7fdbda916c842e2995d
|
[
"MIT"
] | null | null | null |
Interface/app.py
|
BrandonLawler/Office-365-Connector
|
2421c560cfbc517a1074c7fdbda916c842e2995d
|
[
"MIT"
] | null | null | null |
import logging
import multiprocessing
from typing import MutableMapping
from PyQt6.QtCore import *
from PyQt6.QtWidgets import *
from Core.messages import Courier, Message
from .widgets import *
import os, sys
| 35.108696
| 140
| 0.636223
|
import logging
import multiprocessing
from typing import MutableMapping
from PyQt6.QtCore import *
from PyQt6.QtWidgets import *
from Core.messages import Courier, Message
from .widgets import *
import os, sys
class App:
_TITLE = 'Office 365 Connector'
_MEDIA_PATH = f"{os.getcwd()}\\Interface\\media"
_WINDOW_HEIGHT = 600
_WINDOW_WIDTH = 860
_LOADING_WINDOW_HEIGHT = 80
_LOADING_WINDOW_WIDTH = 60
_INITIALISATION_CHECKS = [
"Initialising Exchange Online",
"Initialising Microsoft 365 Online"
]
_STAGE_INITIALISATION = 0
_STAGE_DISCONNECTED = 1
def __init__(self, process_event: multiprocessing.Event, shutdown_event: multiprocessing.Event, courier: Courier):
self._logger = multiprocessing.get_logger()
# self._logger.setLevel(logging.DEBUG)
self._logger.addHandler(logging.StreamHandler())
self._process_event = process_event
self._shutdown_event = shutdown_event
self._courier = courier
self._courier.send("Core", "PID", os.getpid())
self._mainframe = None
self._body = None
self._body_frame = None
self._customization = None
self._mode = 0
self._movie = None
self._loading_label = None
self._loading_bar = None
self._shutdown_timer = QTimer()
self._shutdown_timer.timeout.connect(self._shutdown_application)
self._shutdown_timer.setInterval(1000)
self._message_timer = QTimer()
self._message_timer.timeout.connect(self._message_process)
self._message_timer.setInterval(100)
self._initialise()
self._build()
self._courier.send("O365", "Initialise")
self.start()
def _shutdown(self):
self._logger.info("PyQt Application Shutting Down")
self._shutdown_event.set()
while not self._process_event.is_set():
pass
def _shutdown_application(self):
if self._process_event.is_set():
self._logger.info("Externally Shutting Down Application")
self._application.quit()
def _message_process(self):
message = self._courier.receive()
if message is not None:
if message.type == "Initalise":
if type(message.content) is int:
self._loading_bar.setValue(message.content)
if message.content == self._INITIALISATION_CHECKS.__len__():
self._mode = self._STAGE_DISCONNECTED
self._window.resize(self._WINDOW_WIDTH, self._WINDOW_HEIGHT)
else:
self._loading_label.setText(self._INITIALISATION_CHECKS[message.content-1])
else:
raise Exception("Unable to Initialise PowerShell Modules")
elif message.type == "Check Customization":
self._customization = message.content
def _initialise(self):
self._application = QApplication([])
self._window = MainWindow(self._shutdown)
self._window.setWindowTitle(self._TITLE)
self._window.setMinimumSize(self._WINDOW_WIDTH, self._WINDOW_HEIGHT)
self._window.setMaximumSize(self._WINDOW_WIDTH, self._WINDOW_HEIGHT)
self._mainframe = QFrame()
self._window.setCentralWidget(self._mainframe)
def _build_variable_display(self, *args, horizontal=False, vertical=False, grid=False):
frame_container = Widget()
if horizontal:
frame = QHBoxLayout(frame_container)
elif vertical:
frame = QVBoxLayout(frame_container)
frame.setAlignment(Qt.AlignmentFlag.AlignCenter)
elif grid:
frame = QGridLayout(frame_container)
else:
raise ValueError("Invalid Layout Type: Horizontal, Vertical or Grid input must be specified")
frame.setSpacing(0)
frame.setContentsMargins(10, 0, 10, 10)
for arg in args:
if grid:
try:
widget, row, column, rsize, csize = arg
frame.addWidget(widget, row, column, rsize, csize)
except:
raise ValueError("Invalid Grid Layout: Must be (Widget, row, column, row_size, column_size)")
else:
if vertical:
arg.setAlignment(Qt.AlignmentFlag.AlignCenter)
frame.addWidget(arg)
return frame_container
def _build(self):
self._mainframe.setLayout(QVBoxLayout())
self._mainframe.layout().addWidget(self._build_header())
self._mainframe.layout().addWidget(self._build_body())
self._mainframe.layout().addWidget(self._build_footer())
def _build_loading_frame(self, message):
self._logger.debug("Building Loading Frame")
self._movie = Movie(f"{self._MEDIA_PATH}\\loading_icon.gif", 120)
loading_movie = Label("")
loading_movie.setMovie(self._movie)
loading_label = Label(message)
self._loading_bar = ProgressBar(0, self._INITIALISATION_CHECKS.__len__())
return self._build_variable_display(loading_movie, loading_label, self._loading_bar, vertical=True, horizontal=False, grid=False)
def _build_initialisation(self):
self._window.resize(self._LOADING_WINDOW_WIDTH, self._LOADING_WINDOW_HEIGHT)
return self._build_loading_frame("Initialising PowerShell Modules")
def _build_header(self):
header = QFrame()
header.setLayout(QHBoxLayout())
return header
def _build_body(self):
self._body = QFrame()
self._body.setLayout(QVBoxLayout())
if self._body_frame is not None:
self._body_frame.hide()
if self._mode == self._STAGE_INITIALISATION:
self._body_frame = self._build_initialisation()
self._body.layout().addWidget(self._body_frame)
return self._body
def _build_footer(self):
footer = QFrame()
footer.setLayout(QHBoxLayout())
# footer.layout().addWidget(self._build_version())
# footer.layout().addWidget(self._build_copyright())
return footer
def start(self):
self._window.show()
if self._movie is not None:
self._movie.start()
self._shutdown_timer.start()
sys.exit(self._application.exec())
| 5,460
| 766
| 23
|
69cc9a1f6a639563622f0c9fe9107b2a2258bf9f
| 6,079
|
py
|
Python
|
img4.py
|
clayfreeman/img4
|
e2d8f05ca5ba4c9d445dff5f8a6b3b4cf48d3bf2
|
[
"MIT"
] | null | null | null |
img4.py
|
clayfreeman/img4
|
e2d8f05ca5ba4c9d445dff5f8a6b3b4cf48d3bf2
|
[
"MIT"
] | null | null | null |
img4.py
|
clayfreeman/img4
|
e2d8f05ca5ba4c9d445dff5f8a6b3b4cf48d3bf2
|
[
"MIT"
] | null | null | null |
# SPDX-License-Identifier: MIT
# Greetings to:
# - https://www.theiphonewiki.com/wiki/IMG4_File_Format
# - https://github.com/tihmstar/img4tool/
# - https://lapo.it/asn1js/
# - hexdump tool of choice
import functools
from asn1crypto.core import (
Enumerated, Choice, Sequence, SequenceOf, SetOf,
Integer, IA5String, OctetString, ParsableOctetString, Integer,
Any
)
from asn1crypto.x509 import Certificate
import restruct
class any_tag(tuple):
""" highly cursed tuple subtype to bully asn1crypto into accepting any tag """
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-r', '--raw', action='store_true', help='print raw parsed data')
parser.add_argument('infile', type=argparse.FileType('rb'), help='input .img4/.im4m/.im4p file')
parser.add_argument('outfile', type=argparse.FileType('wb'), nargs='?', help='output data file for payload')
args = parser.parse_args()
contents = args.infile.read()
errors = {}
for p in (IMG4, IMG4Manifest, IMG4Payload):
try:
img4 = p.load(contents)
img4.native # trigger parsing
break
except Exception as e:
errors[p] = e
else:
print('Could not parse file {}:'.format(args.infile.name))
for (p, e) in errors.items():
print(' - As {}: {}'.format(p.__name__, e))
sys.exit(1)
if isinstance(img4, IMG4):
payload = img4['payload']
manifest = img4['manifest']
elif isinstance(img4, IMG4Manifest):
payload = None
manifest = img4
elif isinstance(img4, IMG4Payload):
payload = img4
manifest = None
if payload:
p = payload.native
if args.raw:
print(restruct.format_value(p, str))
else:
print('payload:')
print(' type:', p['type'])
print(' desc:', p['description'])
if p['keybags']:
print(' keybags:')
keybags = payload['keybags'].parse(IMG4KeyBagSequence).native
for kb in keybags:
print(' id: ', kb['id'])
print(' iv: ', restruct.format_value(kb['iv'], str))
print(' key:', restruct.format_value(kb['key'], str))
print()
if p['compression']:
print(' compression:')
print(' algo:', p['compression']['algorithm'])
print(' size:', p['compression']['original_size'])
algo = p['compression']['algorithm']
else:
algo = None
print()
if args.outfile:
if algo == 'lzfse':
import lzfse
data = lzfse.decompress(p['data'])
elif algo:
raise ValueError('unknown algorithm: {}'.format(algo))
else:
data = p['data']
args.outfile.write(data)
if manifest:
m = manifest.native
if args.raw:
print(restruct.format_value(m, str))
else:
print('manifest:')
for p in m['contents']:
print(' body:')
if p['type'] == 'MANB':
for c in p['categories']:
cname = c['category']['type']
for v in c['category']['values']:
print(' {}.{}: {}'.format(cname, v['value']['key'], restruct.format_value(v['value']['value'], str)))
print()
| 29.225962
| 132
| 0.550584
|
# SPDX-License-Identifier: MIT
# Greetings to:
# - https://www.theiphonewiki.com/wiki/IMG4_File_Format
# - https://github.com/tihmstar/img4tool/
# - https://lapo.it/asn1js/
# - hexdump tool of choice
import functools
from asn1crypto.core import (
Enumerated, Choice, Sequence, SequenceOf, SetOf,
Integer, IA5String, OctetString, ParsableOctetString, Integer,
Any
)
from asn1crypto.x509 import Certificate
import restruct
def ascii2int(s):
return int.from_bytes(s.encode('ascii'), byteorder='big')
class any_tag(tuple):
""" highly cursed tuple subtype to bully asn1crypto into accepting any tag """
def __contains__(self, o):
return True
class IMG4KeyBag(Sequence):
_fields = [
('id', Integer),
('iv', OctetString),
('key', OctetString),
]
class IMG4KeyBagSequence(SequenceOf):
_child_spec = IMG4KeyBag
class IMG4CompressionAlgorithm(Integer):
_map = {
1: 'lzfse',
}
class IMG4Compression(Sequence):
_fields = [
('algorithm', IMG4CompressionAlgorithm),
('original_size', Integer),
]
class IMG4Payload(Sequence):
_fields = [
('magic', IA5String), # "IM4P"
('type', IA5String),
('description', IA5String),
('data', OctetString, {'optional': True}),
('keybags', ParsableOctetString, {'optional': True}),
('compression', IMG4Compression, {'optional': True}),
]
class AnyValueInner(Sequence):
_fields = [
('key', IA5String),
('value', Any, {'optional': True}),
]
class AnyValue(Sequence):
_fields = [
('value', AnyValueInner),
]
class_ = 3
_bad_tag = any_tag()
class AnySet(SetOf):
_child_spec = AnyValue
class IMG4ManifestProperties(Sequence):
_fields = [
('type', IA5String), # "MANP",
('values', AnySet)
]
class IMG4ManifestCategory(Sequence):
_fields = [
('category', IMG4ManifestProperties)
]
class_ = 3
_bad_tag = any_tag()
class IMG4ManifestCategorySet(SetOf):
_child_spec = IMG4ManifestCategory
class IMG4ManifestBody(Sequence):
_fields = [
('type', IA5String), # "MANB"
('categories', IMG4ManifestCategorySet),
]
class IMG4ManifestContent(Choice):
_alternatives = [
('category', IMG4ManifestBody, {'explicit': ('private', ascii2int('MANB'))}),
]
class IMG4ManifestContentSet(SetOf):
_child_spec = IMG4ManifestContent
class IMG4CertificateSequence(SequenceOf):
_child_spec = Certificate
class IMG4Manifest(Sequence):
_fields = [
('magic', IA5String), # "IM4M"
('version', Integer),
('contents', IMG4ManifestContentSet),
('signature', OctetString),
('certificates', IMG4CertificateSequence),
]
class IMG4(Sequence):
_fields = [
('magic', IA5String), # "IMG4",
('payload', IMG4Payload),
('manifest', IMG4Manifest, {'explicit': 0}),
]
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-r', '--raw', action='store_true', help='print raw parsed data')
parser.add_argument('infile', type=argparse.FileType('rb'), help='input .img4/.im4m/.im4p file')
parser.add_argument('outfile', type=argparse.FileType('wb'), nargs='?', help='output data file for payload')
args = parser.parse_args()
contents = args.infile.read()
errors = {}
for p in (IMG4, IMG4Manifest, IMG4Payload):
try:
img4 = p.load(contents)
img4.native # trigger parsing
break
except Exception as e:
errors[p] = e
else:
print('Could not parse file {}:'.format(args.infile.name))
for (p, e) in errors.items():
print(' - As {}: {}'.format(p.__name__, e))
sys.exit(1)
if isinstance(img4, IMG4):
payload = img4['payload']
manifest = img4['manifest']
elif isinstance(img4, IMG4Manifest):
payload = None
manifest = img4
elif isinstance(img4, IMG4Payload):
payload = img4
manifest = None
if payload:
p = payload.native
if args.raw:
print(restruct.format_value(p, str))
else:
print('payload:')
print(' type:', p['type'])
print(' desc:', p['description'])
if p['keybags']:
print(' keybags:')
keybags = payload['keybags'].parse(IMG4KeyBagSequence).native
for kb in keybags:
print(' id: ', kb['id'])
print(' iv: ', restruct.format_value(kb['iv'], str))
print(' key:', restruct.format_value(kb['key'], str))
print()
if p['compression']:
print(' compression:')
print(' algo:', p['compression']['algorithm'])
print(' size:', p['compression']['original_size'])
algo = p['compression']['algorithm']
else:
algo = None
print()
if args.outfile:
if algo == 'lzfse':
import lzfse
data = lzfse.decompress(p['data'])
elif algo:
raise ValueError('unknown algorithm: {}'.format(algo))
else:
data = p['data']
args.outfile.write(data)
if manifest:
m = manifest.native
if args.raw:
print(restruct.format_value(m, str))
else:
print('manifest:')
for p in m['contents']:
print(' body:')
if p['type'] == 'MANB':
for c in p['categories']:
cname = c['category']['type']
for v in c['category']['values']:
print(' {}.{}: {}'.format(cname, v['value']['key'], restruct.format_value(v['value']['value'], str)))
print()
| 83
| 1,958
| 440
|
608146772a2f2ce45897b6339cbe785cf26ad687
| 2,548
|
py
|
Python
|
test/programytest/storage/stores/nosql/mongo/dao/test_rdf.py
|
cdoebler1/AIML2
|
ee692ec5ea3794cd1bc4cc8ec2a6b5e5c20a0d6a
|
[
"MIT"
] | 345
|
2016-11-23T22:37:04.000Z
|
2022-03-30T20:44:44.000Z
|
test/programytest/storage/stores/nosql/mongo/dao/test_rdf.py
|
MikeyBeez/program-y
|
00d7a0c7d50062f18f0ab6f4a041068e119ef7f0
|
[
"MIT"
] | 275
|
2016-12-07T10:30:28.000Z
|
2022-02-08T21:28:33.000Z
|
test/programytest/storage/stores/nosql/mongo/dao/test_rdf.py
|
VProgramMist/modified-program-y
|
f32efcafafd773683b3fe30054d5485fe9002b7d
|
[
"MIT"
] | 159
|
2016-11-28T18:59:30.000Z
|
2022-03-20T18:02:44.000Z
|
import unittest
from programy.storage.stores.nosql.mongo.dao.rdf import RDF
| 43.931034
| 132
| 0.619702
|
import unittest
from programy.storage.stores.nosql.mongo.dao.rdf import RDF
class RDFTests(unittest.TestCase):
def test_init_no_id(self):
rdf = RDF(name="TEST", subject="subj", predicate="pred", obj="obj")
self.assertIsNotNone(rdf)
self.assertIsNone(rdf.id)
self.assertEqual("TEST", rdf.name)
self.assertEqual("subj", rdf.subject)
self.assertEqual("pred", rdf.predicate)
self.assertEqual("obj", rdf.object)
self.assertEqual({'name': 'TEST', 'object': 'obj', 'predicate': 'pred', 'subject': 'subj'}, rdf.to_document())
def test_init_with_id(self):
rdf = RDF(name="TEST", subject="subj", predicate="pred", obj="obj")
rdf.id = '666'
self.assertIsNotNone(rdf)
self.assertIsNotNone(rdf.id)
self.assertEqual('666', rdf.id)
self.assertEqual("TEST", rdf.name)
self.assertEqual("subj", rdf.subject)
self.assertEqual("pred", rdf.predicate)
self.assertEqual("obj", rdf.object)
self.assertEqual({'_id': '666', 'name': 'TEST', 'object': 'obj', 'predicate': 'pred', 'subject': 'subj'}, rdf.to_document())
def test_from_document_no_id(self):
rdf1 = RDF.from_document({'name': 'TEST', 'object': 'obj', 'predicate': 'pred', 'subject': 'subj'})
self.assertIsNotNone(rdf1)
self.assertIsNone(rdf1.id)
self.assertEqual("TEST", rdf1.name)
self.assertEqual("subj", rdf1.subject)
self.assertEqual("pred", rdf1.predicate)
self.assertEqual("obj", rdf1.object)
def test_from_document_with_id(self):
rdf2 = RDF.from_document({'_id': '666', 'name': 'TEST', 'object': 'obj', 'predicate': 'pred', 'subject': 'subj'})
self.assertIsNotNone(rdf2)
self.assertIsNotNone(rdf2.id)
self.assertEqual('666', rdf2.id)
self.assertEqual("TEST", rdf2.name)
self.assertEqual("subj", rdf2.subject)
self.assertEqual("pred", rdf2.predicate)
self.assertEqual("obj", rdf2.object)
def test_repr_no_id(self):
rdf1 = RDF.from_document({'name': 'TEST', 'object': 'obj', 'predicate': 'pred', 'subject': 'subj'})
self.assertEquals("<RDF(id='n/a', name='TEST', subject='subj', predicate='pred', object='obj')>", str(rdf1))
def test_repr_with_id(self):
rdf2 = RDF.from_document({'_id': '666', 'name': 'TEST', 'object': 'obj', 'predicate': 'pred', 'subject': 'subj'})
self.assertEquals("<RDF(id='666', name='TEST', subject='subj', predicate='pred', object='obj')>", str(rdf2))
| 2,272
| 13
| 185
|
9f9edf6016950ee307ec6bab15c4f6306ef09f36
| 2,328
|
py
|
Python
|
scripts/pughpore/plot_diff_ahem.py
|
jhwnkim/nanopores
|
98b3dbb5d36464fbdc03f59d224d38e4255324ce
|
[
"MIT"
] | null | null | null |
scripts/pughpore/plot_diff_ahem.py
|
jhwnkim/nanopores
|
98b3dbb5d36464fbdc03f59d224d38e4255324ce
|
[
"MIT"
] | null | null | null |
scripts/pughpore/plot_diff_ahem.py
|
jhwnkim/nanopores
|
98b3dbb5d36464fbdc03f59d224d38e4255324ce
|
[
"MIT"
] | null | null | null |
# (c) 2017 Gregor Mitscha-Baude
from matplotlib import pyplot as plt
import numpy as np
import dolfin
from nanopores.tools import fields
fields.set_dir_dropbox()
from nanopores.models.nanopore import Setup
from nanopores.geometries.alphahempoly import poly
from nanopores.geometries.alphahem import default
from nanopores.geometries.cylpore import Pore, get_geo
from nanopores.models.diffusion_ahem import diff_profile_z_ahem, get_diffusivity
# params for precomputed diffusivity
params = dict(dim=2, Nmax=1e5, h=.5, ahemqsuniform=True, rMolecule=0.11)
#ap1 = 18
#ap2 = 49
#x0 = poly[18]
#x1 = poly[49]
#
#zmem = .5*(x0[1] + x1[1])
#print zmem
#
#poly = [[x[0], x[1] - zmem] for x in poly]
#proteincs = [z - zmem for z in default["proteincs"]]
#cs = [z - zmem for z in default["cs"]]
#default.update(zmem=0., hmem=2.82, Htop=10, Hbot=6, R=6, proteincs=proteincs, cs=cs)
#print default
#
#def new_get_geo(**params):
# return get_geo(poly, **params)
#
#p = Pore(poly, **default)
#p.build(h=.5)
#
#p.polygons["alphahem"].plot("ok")
#p.polygons["membrane"].plot()
#p.polygons["bulkfluid_top"].plot()
#p.polygons["bulkfluid_bottom"].plot()
#plt.show()
#setup = Setup(get_geo=new_get_geo, geop=default, h=.5)
#setup = Setup(h=.5)
#setup.geo.plot_boundaries()
functions, mesh = fields.get_functions(name="Dalphahem-coupled", **params)
dist = functions["dist"]
#dolfin.plot(dist, interactive=True)
# construct D fit from Noskov2004 and plot tabulated D values
A = 0.64309
B = 0.00044
C = 0.06894
D = 0.35647
E = 0.19409
z, D = diff_profile_fit(a=-12, b=2, N=100)
plt.plot(z, D, "-b", label="Tabulated (infinite cylinder)")
data = diff_profile_z_ahem(a=-12, b=2, N=100, **params)
z = [x0[2] for x0 in data["x"]]
Dz = data["D"]
plt.plot(z, Dz, "og", label="Full hydrodynamic model")
plt.ylabel("Rel. diffusivity")
plt.xlabel("z [nm]")
plt.xlim(-10, 0)
ax = plt.gca()
#ax.yaxis.tick_right()
#ax.yaxis.set_label_position("right")
plt.legend(loc="upper left", frameon=False)
from nanopores import savefigs
from folders import FIGDIR
savefigs("Dz", FIGDIR + "/ahem", (6, 4.5))
#print results
| 27.388235
| 85
| 0.693299
|
# (c) 2017 Gregor Mitscha-Baude
from matplotlib import pyplot as plt
import numpy as np
import dolfin
from nanopores.tools import fields
fields.set_dir_dropbox()
from nanopores.models.nanopore import Setup
from nanopores.geometries.alphahempoly import poly
from nanopores.geometries.alphahem import default
from nanopores.geometries.cylpore import Pore, get_geo
from nanopores.models.diffusion_ahem import diff_profile_z_ahem, get_diffusivity
# params for precomputed diffusivity
params = dict(dim=2, Nmax=1e5, h=.5, ahemqsuniform=True, rMolecule=0.11)
#ap1 = 18
#ap2 = 49
#x0 = poly[18]
#x1 = poly[49]
#
#zmem = .5*(x0[1] + x1[1])
#print zmem
#
#poly = [[x[0], x[1] - zmem] for x in poly]
#proteincs = [z - zmem for z in default["proteincs"]]
#cs = [z - zmem for z in default["cs"]]
#default.update(zmem=0., hmem=2.82, Htop=10, Hbot=6, R=6, proteincs=proteincs, cs=cs)
#print default
#
#def new_get_geo(**params):
# return get_geo(poly, **params)
#
#p = Pore(poly, **default)
#p.build(h=.5)
#
#p.polygons["alphahem"].plot("ok")
#p.polygons["membrane"].plot()
#p.polygons["bulkfluid_top"].plot()
#p.polygons["bulkfluid_bottom"].plot()
#plt.show()
#setup = Setup(get_geo=new_get_geo, geop=default, h=.5)
#setup = Setup(h=.5)
#setup.geo.plot_boundaries()
functions, mesh = fields.get_functions(name="Dalphahem-coupled", **params)
dist = functions["dist"]
#dolfin.plot(dist, interactive=True)
# construct D fit from Noskov2004 and plot tabulated D values
A = 0.64309
B = 0.00044
C = 0.06894
D = 0.35647
E = 0.19409
def Dfit(z, rion=0.11):
rpore = dist([0., z])
beta = rion/rpore
return 1./(A + B*np.exp(beta/C) + D*np.exp(beta/E))
def diff_profile_fit(a=-10.3, b=0.05, N=20):
Z = np.linspace(a, b, N)
return Z, [Dfit(z) for z in Z]
z, D = diff_profile_fit(a=-12, b=2, N=100)
plt.plot(z, D, "-b", label="Tabulated (infinite cylinder)")
data = diff_profile_z_ahem(a=-12, b=2, N=100, **params)
z = [x0[2] for x0 in data["x"]]
Dz = data["D"]
plt.plot(z, Dz, "og", label="Full hydrodynamic model")
plt.ylabel("Rel. diffusivity")
plt.xlabel("z [nm]")
plt.xlim(-10, 0)
ax = plt.gca()
#ax.yaxis.tick_right()
#ax.yaxis.set_label_position("right")
plt.legend(loc="upper left", frameon=False)
from nanopores import savefigs
from folders import FIGDIR
savefigs("Dz", FIGDIR + "/ahem", (6, 4.5))
#print results
| 193
| 0
| 46
|
a417f4cdb25dac12c25d28f2f1b5f38a475ca13e
| 320
|
py
|
Python
|
codewars/src/multiples_digits_sum.py
|
wenima/math-series
|
60aa0b0c845bd859a1bf936c7e200f60ecfce026
|
[
"MIT"
] | null | null | null |
codewars/src/multiples_digits_sum.py
|
wenima/math-series
|
60aa0b0c845bd859a1bf936c7e200f60ecfce026
|
[
"MIT"
] | null | null | null |
codewars/src/multiples_digits_sum.py
|
wenima/math-series
|
60aa0b0c845bd859a1bf936c7e200f60ecfce026
|
[
"MIT"
] | 1
|
2019-10-03T07:38:06.000Z
|
2019-10-03T07:38:06.000Z
|
"""This module solves kata https://www.codewars.com/kata/multiples-and-digit-sums/train/python."""
def procedure(i):
"""Return an integer derived by first finding all multiples of i up to 100,
then summing all up digit sums of all multiples."""
return sum(int(d) for i in range(n, 101, n) for d in str(i))
| 40
| 98
| 0.7
|
"""This module solves kata https://www.codewars.com/kata/multiples-and-digit-sums/train/python."""
def procedure(i):
"""Return an integer derived by first finding all multiples of i up to 100,
then summing all up digit sums of all multiples."""
return sum(int(d) for i in range(n, 101, n) for d in str(i))
| 0
| 0
| 0
|
36e541780526915a358b17b1f29530895478a06a
| 1,869
|
py
|
Python
|
tests/show_drive.py
|
jmscslgroup/privpurge
|
f21fa1e05c5e15ea9ed3b3f720aabb151afcc51e
|
[
"BSD-2-Clause"
] | null | null | null |
tests/show_drive.py
|
jmscslgroup/privpurge
|
f21fa1e05c5e15ea9ed3b3f720aabb151afcc51e
|
[
"BSD-2-Clause"
] | 4
|
2021-04-12T17:58:41.000Z
|
2021-08-01T12:35:57.000Z
|
tests/show_drive.py
|
jmscslgroup/privpurge
|
f21fa1e05c5e15ea9ed3b3f720aabb151afcc51e
|
[
"BSD-2-Clause"
] | null | null | null |
import json
import folium
import folium.plugins
import tempfile
import os
import re
import argparse
if __name__ == "__main__":
cwd = os.getcwd()
args = get_args()
plot_privpurge(
os.path.join(cwd, args.zonefile),
os.path.join(cwd, args.directory),
filename=args.output,
)
| 26.7
| 86
| 0.579989
|
import json
import folium
import folium.plugins
import tempfile
import os
import re
def plot_privpurge(message, outdir, filename=None):
if filename is None:
filename = "~map_" + next(tempfile._get_candidate_names()) + ".html"
my_map = folium.Map()
for fl in [
f for f in os.listdir(outdir) if os.path.isfile(os.path.join(outdir, f))
]:
if re.search(r".{37}_GPS_Messages.csv", fl):
with open(os.path.join(os.getcwd(), os.path.join(outdir, fl))) as f:
f.readline()
points = [
tuple(map(float, l.split(",")[2:4][::-1])) for l in f.readlines()
]
folium.vector_layers.PolyLine(points).add_to(my_map)
data = json.load(open(os.path.join(os.getcwd(), message)))
for region in data["regions"]:
if region["type"] == "circle":
lon, lat = region["data"]["center"]
rad = region["data"]["radius"]
folium.vector_layers.Circle(
location=[lat, lon], radius=rad, color="#3186cc", fill_color="#3186cc"
).add_to(my_map)
elif region["type"] == "polygon":
dat = [(lat, lon) for lon, lat in region["data"]]
folium.vector_layers.Polygon(
locations=dat, color="#3186cc", fill_color="#3186cc"
).add_to(my_map)
my_map.save(filename)
print(f"Map saved to: {filename}")
import argparse
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("directory")
parser.add_argument("zonefile")
parser.add_argument("-o", "--output")
return parser.parse_args()
if __name__ == "__main__":
cwd = os.getcwd()
args = get_args()
plot_privpurge(
os.path.join(cwd, args.zonefile),
os.path.join(cwd, args.directory),
filename=args.output,
)
| 1,503
| 0
| 46
|
b5646fff9a5543547a5f966f926aa2fd063a36b3
| 8,249
|
py
|
Python
|
autopycoin/utils/data_utils_test.py
|
AutocoinLab/autopycoin
|
d091292517c3429d5cc2dfeb513c644b506af9cf
|
[
"Apache-2.0"
] | 3
|
2021-09-26T14:04:25.000Z
|
2022-02-10T09:52:00.000Z
|
autopycoin/utils/data_utils_test.py
|
AutocoinLab/autopycoin
|
d091292517c3429d5cc2dfeb513c644b506af9cf
|
[
"Apache-2.0"
] | 24
|
2021-09-26T14:04:27.000Z
|
2022-02-11T13:32:06.000Z
|
autopycoin/utils/data_utils_test.py
|
AutocoinLab/autopycoin
|
d091292517c3429d5cc2dfeb513c644b506af9cf
|
[
"Apache-2.0"
] | null | null | null |
# pylint: skip-file
"""
Unit test for data utils functions.
"""
import numpy as np
import pandas as pd
import pytest
import tensorflow as tf
from tensorflow import test
from .data_utils import quantiles_handler, example_handler, fill_none
from ..data import random_ts
from ..dataset import WindowGenerator
@pytest.fixture(scope="class")
@pytest.mark.usefixtures("prepare_data")
@pytest.mark.usefixtures("prepare_data")
| 30.439114
| 88
| 0.534731
|
# pylint: skip-file
"""
Unit test for data utils functions.
"""
import numpy as np
import pandas as pd
import pytest
import tensorflow as tf
from tensorflow import test
from .data_utils import quantiles_handler, example_handler, fill_none
from ..data import random_ts
from ..dataset import WindowGenerator
class CheckQuantileTest(test.TestCase):
def test_nested_quantiles(self):
quantiles = [[0.1, 0.1], [3.0, 0.7]]
self.assertEqual([[0.1], [0.03, 0.7]], quantiles_handler(quantiles))
quantiles = [0.1, 0.1]
self.assertEqual([[0.1]], quantiles_handler(quantiles))
def test_float_quantile(self):
quantiles = 0.1
self.assertEqual([[0.1]], quantiles_handler(quantiles))
def test_int_quantile(self):
quantiles = 1
self.assertEqual([[0.01]], quantiles_handler(quantiles))
def test_none_quantile(self):
quantiles = [1, None]
with self.assertRaisesRegexp(
ValueError, "None value or empty list are not supported"
):
self.assertEqual([[0.1]], quantiles_handler(quantiles))
@pytest.fixture(scope="class")
def prepare_data(request):
data = random_ts(
n_steps=400,
trend_degree=2,
periods=[10],
fourier_orders=[10],
trend_mean=0,
trend_std=1,
seasonality_mean=0,
seasonality_std=1,
batch_size=1,
n_variables=1,
noise=True,
seed=42,
)
request.cls.data = pd.DataFrame(data[0].numpy(), columns=["test"])
request.cls.w = WindowGenerator(
input_width=50,
label_width=20,
shift=20,
test_size=10,
valid_size=10,
flat=True,
batch_size=32,
)
request.cls.w = request.cls.w.from_array(
data=request.cls.data, input_columns=["test"], label_columns=["test"],
)
request.cls.w2 = WindowGenerator(
input_width=50,
label_width=20,
shift=20,
test_size=10,
valid_size=10,
flat=True,
batch_size=32,
)
request.cls.w2 = request.cls.w2.from_array(
data=request.cls.data,
input_columns=["test"],
label_columns=["test"],
date_columns=["test"],
)
@pytest.mark.usefixtures("prepare_data")
class ExampleHandlerTest(test.TestCase):
def test_shape(self):
"""
Dataset and tuple shapes are not handled.
"""
outputs = example_handler(self.w.train, self.w)
self.assertEqual(
(
[output if output is None else output.shape for output in outputs[0]]
+ [outputs[1].shape]
),
[tf.TensorShape([32, 50]), None, None, None, tf.TensorShape([32, 20]),],
)
outputs = example_handler(self.w.test, self.w)
self.assertEqual(
(
[output if output is None else output.shape for output in outputs[0]]
+ [outputs[1].shape]
),
[tf.TensorShape([10, 50]), None, None, None, tf.TensorShape([10, 20]),],
)
outputs = example_handler(self.w.valid, self.w)
self.assertEqual(
(
[output if output is None else output.shape for output in outputs[0]]
+ [outputs[1].shape]
),
[tf.TensorShape([10, 50]), None, None, None, tf.TensorShape([10, 20]),],
)
outputs = example_handler(self.w.production(self.data, None), self.w)
self.assertEqual(
(
[output if output is None else output.shape for output in outputs[0]]
+ [outputs[1].shape]
),
[tf.TensorShape([1, 50]), None, None, None, tf.TensorShape([1, 20]),],
)
# example_handler doesn't affect tuple inputs with good shape
tensor = (tf.constant([0.0]), tf.constant([0.0]))
outputs = example_handler(tensor, self.w)
self.assertEqual(
(
[output if output is None else output.shape for output in outputs[0]]
+ [outputs[1].shape]
),
[tf.TensorShape([1]), None, None, None, tf.TensorShape([1])],
)
def test_dtype(self):
"""
Dataset dtypes are effectively turned into desired dtypes.
tuple with good types is not handled.
"""
outputs = example_handler(self.w2.train, self.w2)
self.assertEqual(
(
[output if output is None else output.dtype for output in outputs[0]]
+ [outputs[1].dtype]
),
[tf.float32, None, np.dtype("<U3"), np.dtype("<U3"), tf.float32],
)
outputs = example_handler(self.w2.test, self.w2)
self.assertEqual(
(
[output if output is None else output.dtype for output in outputs[0]]
+ [outputs[1].dtype]
),
[tf.float32, None, np.dtype("<U3"), np.dtype("<U3"), tf.float32],
)
outputs = example_handler(self.w2.valid, self.w2)
self.assertEqual(
(
[output if output is None else output.dtype for output in outputs[0]]
+ [outputs[1].dtype]
),
[tf.float32, None, np.dtype("<U3"), np.dtype("<U3"), tf.float32],
)
outputs = example_handler(self.w2.production(self.data, None), self.w2)
self.assertEqual(
(
[output if output is None else output.dtype for output in outputs[0]]
+ [outputs[1].dtype]
),
[tf.float32, None, np.dtype("<U3"), np.dtype("<U3"), tf.float32],
)
# example_handler doesn't affect tuple inputs with good -types
tensor = (
(
tf.constant([0.0]),
np.array(["0"], dtype="<U2"),
np.array(["0"], dtype="<U2"),
),
tf.constant([0.0]),
)
outputs = example_handler(tensor, self.w2)
self.assertEqual(
(
[output if output is None else output.dtype for output in outputs[0]]
+ [outputs[1].dtype]
),
[tf.float32, None, np.dtype("<U2"), np.dtype("<U2"), tf.float32],
)
def test_raise(self):
"""
inputs shape or type is not respected.
"""
# list and not tuple
with self.assertRaises(ValueError):
tensor = [tf.constant([0.0]), tf.constant([0.0])]
example_handler(tensor, self.w)
# bad inputs shape
with self.assertRaises(ValueError):
tensor = (
(
tf.constant([0]),
tf.constant([0]),
tf.constant([0]),
tf.constant([0]),
),
tf.constant([0]),
)
example_handler(tensor, self.w)
# good inputs shape but bad dtypes
with self.assertRaises(ValueError):
tensor = (tf.constant(["0"]), tf.constant([0]))
example_handler(tensor, self.w)
def test_none(self):
"""Test if None value fill the input of example_handler."""
w = WindowGenerator(
input_width=50,
label_width=20,
shift=20,
test_size=10,
valid_size=10,
flat=True,
batch_size=32,
)
w = w.from_array(data=self.data, input_columns=["test"], label_columns=["test"])
tensor = (tf.constant([0.0]), tf.constant([0.0]))
np.testing.assert_equal(
example_handler(tensor, w),
((tf.constant([0.0]), None, None, None), tf.constant([0.0])),
)
@pytest.mark.usefixtures("prepare_data")
class FillNoneTest(test.TestCase):
def test_max_value(self):
tensor = (tf.constant([0.0]), tf.constant([0.0]))
np.testing.assert_equal(
fill_none(tensor, max_value=4),
(tf.constant([0.0]), tf.constant([0.0]), None, None),
)
np.testing.assert_equal(
fill_none(tensor, max_value=5),
(tf.constant([0.0]), tf.constant([0.0]), None, None, None),
)
| 2,109
| 5,488
| 222
|
462a6cefdccfc40c9c840424ec8ddc04070744bd
| 12,923
|
py
|
Python
|
packages/adminrouter/extra/src/test-harness/modules/mocker/endpoints/generic.py
|
makkes/dcos
|
a6df70f3f58ead134c8c49af8fa1387b4f81c19c
|
[
"Apache-2.0"
] | 1
|
2019-04-26T17:46:37.000Z
|
2019-04-26T17:46:37.000Z
|
packages/adminrouter/extra/src/test-harness/modules/mocker/endpoints/generic.py
|
makkes/dcos
|
a6df70f3f58ead134c8c49af8fa1387b4f81c19c
|
[
"Apache-2.0"
] | 720
|
2017-02-08T04:04:19.000Z
|
2021-09-14T14:04:56.000Z
|
packages/adminrouter/extra/src/test-harness/modules/mocker/endpoints/generic.py
|
makkes/dcos
|
a6df70f3f58ead134c8c49af8fa1387b4f81c19c
|
[
"Apache-2.0"
] | 14
|
2017-02-08T03:57:24.000Z
|
2019-10-28T12:14:49.000Z
|
# Copyright (C) Mesosphere, Inc. See LICENSE file for details.
"""
Shared code for DC/OS endpoints mocks used by AR instances, both EE and Open.
"""
import abc
import http.server
import logging
import os
import socket
import socketserver
import ssl
import threading
# pylint: disable=C0103
log = logging.getLogger(__name__)
# Just a dict would be no good as we want to have threading lock initialization
# as well.
# pylint: disable=R0903
class EndpointContext:
"""An endpoint context that holds all the endpoint data together with
threading lock that protects it."""
data = None
lock = None
def __init__(self, initial_data=None):
"""Initialize EndpointContext object.
This data is often manipulated by methods nested across
inheritance chains, so we need to use RLock() instead of Lock().
The need for the lock itself stems from the fact that very often certain
keys of the context need to be manipulated at the same time/in synchronized
manner.
In some of the places, code relies on thread safety/atomicity of
some of Python's expressions/statements:
https://docs.python.org/3.6/faq/library.html#what-kinds-of-global-value-mutation-are-thread-safe
This is why some of the operations on the EndpointContext dictionary
are not protected by locks, esp. in case when it's only about fetching
a single value from context dict or storing/appending one there.
Args:
initial_data (dict): initial data to initialize context with
"""
self.lock = threading.RLock()
if initial_data is not None:
self.data = initial_data
else:
self.data = {}
class Endpoint(abc.ABC):
"""Endpoint base class, from which all Endpoints must inherit
This class represents common behaviour shared across all endpoints,
no matter the function or repository flavour (ee/open).
Ever endpoint must by default serve GOOD/expected data, and only after
changing it's state using it's methods, it may start serving something
else and/or simulate error conditions.
The state of the endpoint may be changed by tests/fixtures by executing
Mocker's .send_command() method which in turn redirect the call to the
correct endpoint call. For the sake of simplicity it is assumed that each
such method will have well-defined interface:
def do_something(self, aux_data=None):
return result
`aux_data` is a python dictionary that must provide all data required
by function to execute. It can be None if such data is not required
`result` can be anything that makes sense in particular function's case.
"""
_context = None
_httpd_thread = None
_httpd = None
def __init__(self, endpoint_id):
"""Initialize new Endpoint object
Args:
endpoint_id (str): ID of the endpoint that it should identify itself
with
"""
initial_data = {"always_bork": False,
"endpoint_id": endpoint_id,
"always_redirect": False,
"redirect_target": None,
"always_stall": False,
"response_headers": {},
"stall_time": 0,
}
self._context = EndpointContext(initial_data)
@property
def id(self):
"""Return ID of the endpoint"""
return self._context.data['endpoint_id']
def start(self):
"""Start endpoint's threaded httpd server"""
log.debug("Starting endpoint `%s`", self.id)
self._httpd_thread.start()
self._httpd.startup_done.wait()
def stop(self):
"""Perform cleanup of the endpoint threads
This method should be used right before destroying the Endpoint object.
It takes care of stopping internal httpd server.
"""
log.debug("Stopping endpoint `%s`", self.id)
self._httpd.shutdown()
self._httpd_thread.join()
self._httpd.server_close()
def reset(self, aux_data=None):
"""Reset endpoint to the default/good state
Args:
aux_data (dict): unused, present only to satisfy the endpoint's
method interface. See class description for details.
"""
del aux_data
log.debug("Resetting endpoint `%s`", self.id)
# Locking is not really needed here as it is atomic op anyway,
# but let's be consistent
with self._context.lock:
self._context.data['always_bork'] = False
self._context.data['always_stall'] = False
self._context.data['stall_time'] = 0
self._context.data["always_redirect"] = False
self._context.data["redirect_target"] = None
def set_response_headers(self, aux_data):
"""Make endpoint sent custom headers in the response
Args:
aux_data: a dict with header's name/content as keys/vals
"""
with self._context.lock:
self._context.data["response_headers"].update(aux_data)
def always_stall(self, aux_data=None):
"""Make endpoint always wait given time before answering the request
Args:
aux_data (numeric): time in seconds, as acepted by time.sleep()
function
"""
with self._context.lock:
self._context.data["always_stall"] = True
self._context.data["stall_time"] = aux_data
def always_bork(self, aux_data=True):
"""Make endpoint always respond with an error
Args:
aux_data (dict): True or False, depending whether endpoint should
always respond with errors or not.
"""
self._context.data["always_bork"] = aux_data
def always_redirect(self, aux_data=None):
"""Make endpoint always respond with a redirect
Args:
aux_data (str): target location for the redirect
"""
with self._context.lock:
self._context.data["always_redirect"] = True
self._context.data["redirect_target"] = aux_data
class StatefullHTTPServer(socketserver.ThreadingMixIn, http.server.HTTPServer):
"""Base class for all endpoint-internal httpd servers.
This class serves as a base for all internal httpd server, it's role is
to pull in Threading mix-in and link Endpoint context to httpd itself,
so that it's available in the httpd request handler through request's
.server.context attribute.
Worth noting that this is by default a TCP/IP server.
It's based on:
https://mail.python.org/pipermail/python-list/2012-March/621727.html
"""
class TcpIpHttpEndpoint(Endpoint):
"""Base class for all endpoints that serve TCP/IP requests
This class binds together HTTPd server code, http request handler and
endpoint context to form a base class for all endpoints that serve
TCP/IP traffic.
"""
def __init__(self, handler_class, port, ip='', keyfile=None, certfile=None):
"""Initialize new TcpIpHttpEndpoint object
Args:
handler_class (obj): a request handler class that will be handling
requests received by internal httpd server
port (int): tcp port that httpd server will listen on
ip (str): ip address that httpd server will listen on, by default
listen on all addresses
"""
if certfile is not None and keyfile is not None:
endpoint_id = "https://{}:{}".format(ip, port)
else:
endpoint_id = "http://{}:{}".format(ip, port)
super().__init__(endpoint_id)
self._context.data['listen_ip'] = ip
self._context.data['listen_port'] = port
self._context.data['certfile'] = certfile
self._context.data['keyfile'] = keyfile
self._handler_class = handler_class
self.__setup_httpd_thread(ip, port)
def __setup_httpd_thread(self, ip, port):
"""Setup internal HTTPd server that this endpoints relies on to serve
requests.
"""
self._httpd = StatefullHTTPServer(self._context,
(ip, port),
self._handler_class)
httpd_thread_name = "TcpIpHttpdThread-{}".format(self.id)
self._httpd_thread = threading.Thread(target=self._httpd.serve_forever,
name=httpd_thread_name)
class UnixSocketStatefulHTTPServer(StatefullHTTPServer):
"""Base class for all endpoint-internal httpd servers that listen on
Unix socket.
This class inherits from StatefullHTTPServer and mofies it's behaviour
so that it's able to listen on Unix socket.
Attributes:
address_family: set only to override default value of the variable set
in the http.server.HTTPServer class, must not be modified.
"""
address_family = socket.AF_UNIX
def server_bind(self):
"""Override default server socket bind behaviour to adapt it to
serving on Unix socket.
Please check the documentation of http.server.HTTPServer class for more
details.
"""
socketserver.TCPServer.server_bind(self)
self.server_name = self.context.data['socket_path']
self.server_port = 0
def client_address(self):
"""Override default client_address method to adapt it to serving on Unix
socket. Without it logging will break as Unix socket has no notion of
the client's IP address.
Please check the documentation of http.server.HTTPServer class for more
details.
"""
return (self.context.data['socket_path'], 0)
# http://stackoverflow.com/questions/21650370/setting-up-an-http-server-that-listens-over-a-file-socket
# https://docs.python.org/3.3/library/socketserver.html
class UnixSocketHTTPEndpoint(Endpoint):
"""Base class for all endpoints that serve requests on the Unix socket
This class binds together HTTPd server code, http request handler and
endpoint context to form a base class for all endpoints that serve
Unix socket traffic.
"""
def __init__(self, handler_class, path, keyfile=None, certfile=None):
"""Initialize new UnixSocketHTTPEndpoint object
Args:
handler_class (obj): a request handler class that will be handling
requests received by internal httpd server
path (str): Unix socket path, that internal httpd server will listen
on
"""
if certfile is not None and keyfile is not None:
endpoint_id = "https://{}".format(path)
else:
endpoint_id = "http://{}".format(path)
super().__init__(endpoint_id)
self._context.data['socket_path'] = path
self._context.data['certfile'] = certfile
self._context.data['keyfile'] = keyfile
self._handler_class = handler_class
self.__cleanup_stale_socket(path)
self.__setup_httpd_thread(path)
@staticmethod
def __setup_httpd_thread(self, socket_path):
"""Setup internal HTTPd server that this endpoints relies on to serve
requests.
Args:
path (str): Unix socket path, that internal httpd server will listen
on
"""
self._httpd = UnixSocketStatefulHTTPServer(self._context,
socket_path,
self._handler_class)
httpd_thread_name = "UnixSocketHttpdThread-{}".format(self.id)
self._httpd_thread = threading.Thread(target=self._httpd.serve_forever,
name=httpd_thread_name)
# nginx spawns worker processes as 'nobody/nogroup', so we need to
# make the socket available to it.
os.chmod(socket_path, 0o777)
| 36.922857
| 108
| 0.630349
|
# Copyright (C) Mesosphere, Inc. See LICENSE file for details.
"""
Shared code for DC/OS endpoints mocks used by AR instances, both EE and Open.
"""
import abc
import http.server
import logging
import os
import socket
import socketserver
import ssl
import threading
# pylint: disable=C0103
log = logging.getLogger(__name__)
# Just a dict would be no good as we want to have threading lock initialization
# as well.
# pylint: disable=R0903
class EndpointContext:
"""An endpoint context that holds all the endpoint data together with
threading lock that protects it."""
data = None
lock = None
def __init__(self, initial_data=None):
"""Initialize EndpointContext object.
This data is often manipulated by methods nested across
inheritance chains, so we need to use RLock() instead of Lock().
The need for the lock itself stems from the fact that very often certain
keys of the context need to be manipulated at the same time/in synchronized
manner.
In some of the places, code relies on thread safety/atomicity of
some of Python's expressions/statements:
https://docs.python.org/3.6/faq/library.html#what-kinds-of-global-value-mutation-are-thread-safe
This is why some of the operations on the EndpointContext dictionary
are not protected by locks, esp. in case when it's only about fetching
a single value from context dict or storing/appending one there.
Args:
initial_data (dict): initial data to initialize context with
"""
self.lock = threading.RLock()
if initial_data is not None:
self.data = initial_data
else:
self.data = {}
class Endpoint(abc.ABC):
"""Endpoint base class, from which all Endpoints must inherit
This class represents common behaviour shared across all endpoints,
no matter the function or repository flavour (ee/open).
Ever endpoint must by default serve GOOD/expected data, and only after
changing it's state using it's methods, it may start serving something
else and/or simulate error conditions.
The state of the endpoint may be changed by tests/fixtures by executing
Mocker's .send_command() method which in turn redirect the call to the
correct endpoint call. For the sake of simplicity it is assumed that each
such method will have well-defined interface:
def do_something(self, aux_data=None):
return result
`aux_data` is a python dictionary that must provide all data required
by function to execute. It can be None if such data is not required
`result` can be anything that makes sense in particular function's case.
"""
_context = None
_httpd_thread = None
_httpd = None
def __init__(self, endpoint_id):
"""Initialize new Endpoint object
Args:
endpoint_id (str): ID of the endpoint that it should identify itself
with
"""
initial_data = {"always_bork": False,
"endpoint_id": endpoint_id,
"always_redirect": False,
"redirect_target": None,
"always_stall": False,
"response_headers": {},
"stall_time": 0,
}
self._context = EndpointContext(initial_data)
@property
def id(self):
"""Return ID of the endpoint"""
return self._context.data['endpoint_id']
def start(self):
"""Start endpoint's threaded httpd server"""
log.debug("Starting endpoint `%s`", self.id)
self._httpd_thread.start()
self._httpd.startup_done.wait()
def stop(self):
"""Perform cleanup of the endpoint threads
This method should be used right before destroying the Endpoint object.
It takes care of stopping internal httpd server.
"""
log.debug("Stopping endpoint `%s`", self.id)
self._httpd.shutdown()
self._httpd_thread.join()
self._httpd.server_close()
def reset(self, aux_data=None):
"""Reset endpoint to the default/good state
Args:
aux_data (dict): unused, present only to satisfy the endpoint's
method interface. See class description for details.
"""
del aux_data
log.debug("Resetting endpoint `%s`", self.id)
# Locking is not really needed here as it is atomic op anyway,
# but let's be consistent
with self._context.lock:
self._context.data['always_bork'] = False
self._context.data['always_stall'] = False
self._context.data['stall_time'] = 0
self._context.data["always_redirect"] = False
self._context.data["redirect_target"] = None
def set_response_headers(self, aux_data):
"""Make endpoint sent custom headers in the response
Args:
aux_data: a dict with header's name/content as keys/vals
"""
with self._context.lock:
self._context.data["response_headers"].update(aux_data)
def always_stall(self, aux_data=None):
"""Make endpoint always wait given time before answering the request
Args:
aux_data (numeric): time in seconds, as acepted by time.sleep()
function
"""
with self._context.lock:
self._context.data["always_stall"] = True
self._context.data["stall_time"] = aux_data
def always_bork(self, aux_data=True):
"""Make endpoint always respond with an error
Args:
aux_data (dict): True or False, depending whether endpoint should
always respond with errors or not.
"""
self._context.data["always_bork"] = aux_data
def always_redirect(self, aux_data=None):
"""Make endpoint always respond with a redirect
Args:
aux_data (str): target location for the redirect
"""
with self._context.lock:
self._context.data["always_redirect"] = True
self._context.data["redirect_target"] = aux_data
class StatefullHTTPServer(socketserver.ThreadingMixIn, http.server.HTTPServer):
"""Base class for all endpoint-internal httpd servers.
This class serves as a base for all internal httpd server, it's role is
to pull in Threading mix-in and link Endpoint context to httpd itself,
so that it's available in the httpd request handler through request's
.server.context attribute.
Worth noting that this is by default a TCP/IP server.
It's based on:
https://mail.python.org/pipermail/python-list/2012-March/621727.html
"""
def __init__(self, context, *args, **kw):
self.context = context
self.startup_done = threading.Event()
http.server.HTTPServer.__init__(self, *args, **kw)
certfile = self.context.data['certfile']
keyfile = self.context.data['keyfile']
if certfile is not None and keyfile is not None:
self.socket = ssl.wrap_socket(self.socket,
keyfile=keyfile,
certfile=certfile,
server_side=True)
def server_activate(self):
super().server_activate()
self.startup_done.set()
class TcpIpHttpEndpoint(Endpoint):
"""Base class for all endpoints that serve TCP/IP requests
This class binds together HTTPd server code, http request handler and
endpoint context to form a base class for all endpoints that serve
TCP/IP traffic.
"""
def __init__(self, handler_class, port, ip='', keyfile=None, certfile=None):
"""Initialize new TcpIpHttpEndpoint object
Args:
handler_class (obj): a request handler class that will be handling
requests received by internal httpd server
port (int): tcp port that httpd server will listen on
ip (str): ip address that httpd server will listen on, by default
listen on all addresses
"""
if certfile is not None and keyfile is not None:
endpoint_id = "https://{}:{}".format(ip, port)
else:
endpoint_id = "http://{}:{}".format(ip, port)
super().__init__(endpoint_id)
self._context.data['listen_ip'] = ip
self._context.data['listen_port'] = port
self._context.data['certfile'] = certfile
self._context.data['keyfile'] = keyfile
self._handler_class = handler_class
self.__setup_httpd_thread(ip, port)
def __setup_httpd_thread(self, ip, port):
"""Setup internal HTTPd server that this endpoints relies on to serve
requests.
"""
self._httpd = StatefullHTTPServer(self._context,
(ip, port),
self._handler_class)
httpd_thread_name = "TcpIpHttpdThread-{}".format(self.id)
self._httpd_thread = threading.Thread(target=self._httpd.serve_forever,
name=httpd_thread_name)
class UnixSocketStatefulHTTPServer(StatefullHTTPServer):
"""Base class for all endpoint-internal httpd servers that listen on
Unix socket.
This class inherits from StatefullHTTPServer and mofies it's behaviour
so that it's able to listen on Unix socket.
Attributes:
address_family: set only to override default value of the variable set
in the http.server.HTTPServer class, must not be modified.
"""
address_family = socket.AF_UNIX
def server_bind(self):
"""Override default server socket bind behaviour to adapt it to
serving on Unix socket.
Please check the documentation of http.server.HTTPServer class for more
details.
"""
socketserver.TCPServer.server_bind(self)
self.server_name = self.context.data['socket_path']
self.server_port = 0
def client_address(self):
"""Override default client_address method to adapt it to serving on Unix
socket. Without it logging will break as Unix socket has no notion of
the client's IP address.
Please check the documentation of http.server.HTTPServer class for more
details.
"""
return (self.context.data['socket_path'], 0)
# http://stackoverflow.com/questions/21650370/setting-up-an-http-server-that-listens-over-a-file-socket
# https://docs.python.org/3.3/library/socketserver.html
class UnixSocketHTTPEndpoint(Endpoint):
"""Base class for all endpoints that serve requests on the Unix socket
This class binds together HTTPd server code, http request handler and
endpoint context to form a base class for all endpoints that serve
Unix socket traffic.
"""
def __init__(self, handler_class, path, keyfile=None, certfile=None):
"""Initialize new UnixSocketHTTPEndpoint object
Args:
handler_class (obj): a request handler class that will be handling
requests received by internal httpd server
path (str): Unix socket path, that internal httpd server will listen
on
"""
if certfile is not None and keyfile is not None:
endpoint_id = "https://{}".format(path)
else:
endpoint_id = "http://{}".format(path)
super().__init__(endpoint_id)
self._context.data['socket_path'] = path
self._context.data['certfile'] = certfile
self._context.data['keyfile'] = keyfile
self._handler_class = handler_class
self.__cleanup_stale_socket(path)
self.__setup_httpd_thread(path)
@staticmethod
def __cleanup_stale_socket(socket_path):
if os.path.exists(socket_path):
os.remove(socket_path)
def __setup_httpd_thread(self, socket_path):
"""Setup internal HTTPd server that this endpoints relies on to serve
requests.
Args:
path (str): Unix socket path, that internal httpd server will listen
on
"""
self._httpd = UnixSocketStatefulHTTPServer(self._context,
socket_path,
self._handler_class)
httpd_thread_name = "UnixSocketHttpdThread-{}".format(self.id)
self._httpd_thread = threading.Thread(target=self._httpd.serve_forever,
name=httpd_thread_name)
# nginx spawns worker processes as 'nobody/nogroup', so we need to
# make the socket available to it.
os.chmod(socket_path, 0o777)
| 710
| 0
| 79
|
9ec35e51357b9666792505fb9f52a9e1ad10e223
| 24,247
|
py
|
Python
|
dspn/train.py
|
FlorianSchroevers/dspn
|
8bd2fe9b336099da8c18c9ce716c00e7cf1ca0b6
|
[
"MIT"
] | null | null | null |
dspn/train.py
|
FlorianSchroevers/dspn
|
8bd2fe9b336099da8c18c9ce716c00e7cf1ca0b6
|
[
"MIT"
] | null | null | null |
dspn/train.py
|
FlorianSchroevers/dspn
|
8bd2fe9b336099da8c18c9ce716c00e7cf1ca0b6
|
[
"MIT"
] | null | null | null |
import os
import argparse
from datetime import datetime
import time
import torch
import torch.nn.functional as F
import torch.multiprocessing as mp
import numpy as np
import pandas as pd
from tqdm import tqdm
import matplotlib
import matplotlib.pyplot as plt
from tensorboardX import SummaryWriter
import data
import track
import model
import utils
matplotlib.use("Qt5Agg")
if __name__ == "__main__":
try:
main()
except KeyboardInterrupt:
print("Process interrupted by user, emptying cache...")
torch.cuda.empty_cache()
| 33.352132
| 79
| 0.479894
|
import os
import argparse
from datetime import datetime
import time
import torch
import torch.nn.functional as F
import torch.multiprocessing as mp
import numpy as np
import pandas as pd
from tqdm import tqdm
import matplotlib
import matplotlib.pyplot as plt
from tensorboardX import SummaryWriter
import data
import track
import model
import utils
matplotlib.use("Qt5Agg")
def main():
global net
global test_loader
global scatter
parser = argparse.ArgumentParser()
# generic params
parser.add_argument(
"--name",
default=datetime.now().strftime("%Y-%m-%d_%H:%M:%S"),
help="Name to store the log file as",
)
parser.add_argument("--resume", help="Path to log file to resume from")
parser.add_argument("--encoder", default="FSEncoder", help="Encoder")
parser.add_argument("--decoder", default="DSPN", help="Decoder")
parser.add_argument(
"--epochs", type=int, default=10, help="Number of epochs to train with"
)
parser.add_argument(
"--latent", type=int, default=32, help="Dimensionality of latent space"
)
parser.add_argument(
"--dim", type=int, default=64, help="Dimensionality of hidden layers"
)
parser.add_argument(
"--lr", type=float, default=1e-2, help="Outer learning rate of model"
)
parser.add_argument(
"--batch-size", type=int, default=12, help="Batch size to train with"
)
parser.add_argument(
"--num-workers",
type=int,
default=0,
help="Number of threads for data loader"
)
parser.add_argument(
"--dataset",
choices=[
"mnist", "clevr-box", "clevr-state", "cats", "merged", "wflw"
],
help="Which dataset to use",
)
parser.add_argument(
"--no-cuda",
action="store_true",
help="Run on CPU instead of GPU (not recommended)",
)
parser.add_argument(
"--train-only",
action="store_true",
help="Only run training, no evaluation"
)
parser.add_argument(
"--eval-only",
action="store_true",
help="Only run evaluation, no training"
)
parser.add_argument(
"--multi-gpu",
action="store_true",
help="Use multiple GPUs"
)
parser.add_argument(
"--show",
action="store_true",
help="Plot generated samples in Tensorboard"
)
parser.add_argument(
"--show-skip",
type=int,
default=1,
help="Number of epochs to skip before exporting to Tensorboard"
)
parser.add_argument(
"--infer-name",
action="store_true",
help="Automatically name run based on dataset/run number"
)
parser.add_argument("--supervised", action="store_true", help="")
parser.add_argument(
"--baseline",
action="store_true",
help="Use baseline model"
)
parser.add_argument(
"--export-dir", type=str, help="Directory to output samples to")
parser.add_argument(
"--export-n",
type=int,
default=10 ** 9,
help="How many samples to output"
)
parser.add_argument(
"--export-progress",
action="store_true",
help="Output intermediate set predictions for DSPN?",
)
parser.add_argument(
"--full-eval",
action="store_true",
help="Use full evaluation set (default: 1/10 of evaluation data)",
# don't need full evaluation when training to save some time
)
parser.add_argument(
"--mask-feature",
action="store_true",
help="Treat mask as a feature to compute loss with",
)
parser.add_argument(
"--inner-lr",
type=float,
default=800,
help="Learning rate of DSPN inner optimisation",
)
parser.add_argument(
"--iters",
type=int,
default=10,
help="How many DSPN inner optimisation iteration to take",
)
parser.add_argument(
"--huber-repr",
type=float,
default=1,
help="Scaling of repr loss term for DSPN supervised learning",
)
parser.add_argument(
"--loss",
choices=["hungarian", "chamfer", "emd"],
default="emd",
help="Type of loss used",
)
parser.add_argument(
"--export-csv",
action="store_true",
help="Only perform predictions, don't evaluate in any way"
)
parser.add_argument(
"--eval-split",
help="Overwrite split on test set"
)
args = parser.parse_args()
if args.infer_name:
if args.baseline:
prefix = "base"
else:
prefix = "dspn"
used_nums = []
if not os.path.exists("runs"):
os.makedirs("runs")
runs = os.listdir("runs")
for run in runs:
if args.dataset in run:
used_nums.append(int(run.split("-")[-1]))
num = 1
while num in used_nums:
num += 1
name = f"{prefix}-{args.dataset}-{num}"
else:
name = args.name
print(f"Saving run to runs/{name}")
train_writer = SummaryWriter(f"runs/{name}", purge_step=0)
net = model.build_net(args)
if not args.no_cuda:
net = net.cuda()
if args.multi_gpu:
net = torch.nn.DataParallel(net)
optimizer = torch.optim.Adam(
[p for p in net.parameters() if p.requires_grad], lr=args.lr
)
print("Building dataloader")
if args.dataset == "mnist":
dataset_train = data.MNISTSet(train=True, full=args.full_eval)
dataset_test = data.MNISTSet(train=False, full=args.full_eval)
elif args.dataset in ["clevr-box", "clevr-state"]:
dataset_train = data.CLEVR(
"clevr",
"train",
box=args.dataset == "clevr-box",
full=args.full_eval
)
dataset_test = data.CLEVR(
"clevr",
"val",
box=args.dataset == "clevr-box",
full=args.full_eval
)
elif args.dataset == "cats":
dataset_train = data.Cats("cats", "train", 9, full=args.full_eval)
dataset_test = data.Cats("cats", "val", 9, full=args.full_eval)
elif args.dataset == "faces":
dataset_train = data.Faces("faces", "train", 4, full=args.full_eval)
dataset_test = data.Faces("faces", "val", 4, full=args.full_eval)
elif args.dataset == "wflw":
if args.eval_split:
eval_split = f"test_{args.eval_split}"
else:
eval_split = "test"
dataset_train = data.WFLW("wflw", "train", 7, full=args.full_eval)
dataset_test = data.WFLW("wflw", eval_split, 7, full=args.full_eval)
elif args.dataset == "merged":
# merged cats and human faces
dataset_train_cats = data.Cats("cats", "train", 9, full=args.full_eval)
dataset_train_wflw = data.WFLW("wflw", "train", 9, full=args.full_eval)
dataset_test_cats = data.Cats("cats", "val", 9, full=args.full_eval)
dataset_test_wflw = data.WFLW("wflw", "test", 9, full=args.full_eval)
dataset_train = data.MergedDataset(
dataset_train_cats,
dataset_train_wflw
)
dataset_test = data.MergedDataset(
dataset_test_cats,
dataset_test_wflw
)
if not args.eval_only:
train_loader = data.get_loader(
dataset_train,
batch_size=args.batch_size,
num_workers=args.num_workers
)
if not args.train_only:
test_loader = data.get_loader(
dataset_test,
batch_size=args.batch_size,
num_workers=args.num_workers,
shuffle=False
)
tracker = track.Tracker(
train_mae=track.ExpMean(),
train_last=track.ExpMean(),
train_loss=track.ExpMean(),
test_mae=track.Mean(),
test_last=track.Mean(),
test_loss=track.Mean(),
)
if args.resume:
log = torch.load(args.resume)
weights = log["weights"]
n = net
if args.multi_gpu:
n = n.module
n.load_state_dict(weights, strict=True)
if args.export_csv:
names = []
predictions = []
export_targets = []
def run(net, loader, optimizer, train=False, epoch=0, pool=None):
writer = train_writer
if train:
net.train()
prefix = "train"
torch.set_grad_enabled(True)
else:
net.eval()
prefix = "test"
torch.set_grad_enabled(False)
if args.export_dir:
true_export = []
pred_export = []
iters_per_epoch = len(loader)
loader = tqdm(
loader,
ncols=0,
desc="{1} E{0:02d}".format(epoch, "train" if train else "test "),
)
for i, sample in enumerate(loader, start=epoch * iters_per_epoch):
# input is either a set or an image
input, target_set, target_mask = map(lambda x: x.cuda(), sample)
# forward evaluation through the network
(progress, masks, evals, gradn), (y_enc, y_label) = net(
input, target_set, target_mask
)
progress_only = progress
# if using mask as feature, concat mask feature into progress
if args.mask_feature:
target_set = torch.cat(
[target_set, target_mask.unsqueeze(dim=1)], dim=1
)
progress = [
torch.cat([p, m.unsqueeze(dim=1)], dim=1)
for p, m in zip(progress, masks)
]
if args.loss == "chamfer":
# dim 0 is over the inner iteration steps
# target set is broadcasted over dim 0
set_loss = utils.chamfer_loss(
torch.stack(progress), target_set.unsqueeze(0)
)
elif args.loss == "hungarian":
set_loss = utils.hungarian_loss(
progress[-1], target_set, thread_pool=pool
).unsqueeze(0)
elif args.loss == "emd":
set_loss = utils.emd(progress[-1], target_set).unsqueeze(0)
# Only use representation loss with DSPN and when doing general
# supervised prediction, not when auto-encoding
if args.supervised and not args.baseline:
repr_loss = args.huber_repr * F.smooth_l1_loss(y_enc, y_label)
loss = set_loss.mean() + repr_loss.mean()
else:
loss = set_loss.mean()
# restore progress variable to not contain masks for correct
# exporting
progress = progress_only
# Outer optim step
if train:
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Tensorboard tracking of metrics for debugging
tracked_last = tracker.update(
f"{prefix}_last", set_loss[-1].item()
)
tracked_loss = tracker.update(f"{prefix}_loss", loss.item())
if train:
writer.add_scalar(
"metric/set-loss",
loss.item(),
global_step=i
)
writer.add_scalar(
"metric/set-last",
set_loss[-1].mean().item(),
global_step=i
)
if not args.baseline:
writer.add_scalar(
"metric/eval-first",
evals[0].mean().item(),
global_step=i
)
writer.add_scalar(
"metric/eval-last",
evals[-1].mean().item(),
global_step=i
)
writer.add_scalar(
"metric/max-inner-grad-norm",
max(g.item() for g in gradn),
global_step=i
)
writer.add_scalar(
"metric/mean-inner-grad-norm",
sum(g.item() for g in gradn)/len(gradn),
global_step=i
)
if args.supervised:
writer.add_scalar(
"metric/repr_loss",
repr_loss.item(),
global_step=i
)
# Print current progress to progress bar
fmt = "{:.6f}".format
loader.set_postfix(
last=fmt(tracked_last),
loss=fmt(tracked_loss),
bad=fmt(evals[-1].detach().cpu().item() * 1000)
if not args.baseline
else 0
)
if args.export_dir:
# export last inner optim of each input as csv
# (one input per row)
if args.export_csv:
# the second to last element are the last of the
# inner optim
for batch_i, p in enumerate(progress[-2]):
img_id = i * args.batch_size + batch_i
names.append(loader.iterable.dataset.get_fname(img_id))
m = masks[-2][batch_i]
m = m.cpu().detach().numpy().astype(bool)
p = p.cpu().detach().numpy()
p = p[:, m]
sample_preds = [
p[k % 2, k // 2] for k in range(p.shape[1] * 2)
]
# remove values according to mask and add zeros to the
# end in stead
sample_preds += [0] * (len(m) * 2 - len(sample_preds))
predictions.append(sample_preds)
true_mask = target_set[batch_i, 2, :].cpu().detach()
true_mask = true_mask.numpy().astype(bool)
trues = target_set[batch_i, :2, :]
trues = trues.cpu().detach().numpy()
t = trues[:, true_mask]
t = [t[k % 2, k // 2] for k in range(t.shape[1] * 2)]
t += [0] * (len(true_mask) * 2 - len(t))
export_targets.append(t)
# Store predictions to be exported
else:
if len(true_export) < args.export_n:
for p, m in zip(target_set, target_mask):
true_export.append(p.detach().cpu())
progress_steps = []
for pro, ms in zip(progress, masks):
# pro and ms are one step of the inner optim
# score boxes contains the list of predicted
# elements for one step
score_boxes = []
for p, m in zip(
pro.cpu().detach(),
ms.cpu().detach()):
score_box = torch.cat(
[m.unsqueeze(0), p],
dim=0
)
score_boxes.append(score_box)
progress_steps.append(score_boxes)
for b in zip(*progress_steps):
pred_export.append(b)
# Plot predictions in Tensorboard
if args.show and epoch % args.show_skip == 0 and not train:
name = f"set/epoch-{epoch}/img-{i}"
# thresholded set
progress.append(progress[-1])
masks.append((masks[-1] > 0.5).float())
# target set
if args.mask_feature:
# target set is augmented with masks, so remove them
progress.append(target_set[:, :-1])
else:
progress.append(target_set)
masks.append(target_mask)
# intermediate sets
for j, (s, ms) in enumerate(zip(progress, masks)):
if args.dataset == "clevr-state":
continue
if args.dataset.startswith("clevr"):
threshold = 0.5
else:
threshold = None
s, ms = utils.scatter_masked(
s,
ms,
binned=args.dataset.startswith("clevr"),
threshold=threshold
)
if j != len(progress) - 1:
tag_name = f"{name}"
else:
tag_name = f"{name}-target"
if args.dataset == "clevr-box":
img = input[0].detach().cpu()
writer.add_image_with_boxes(
tag_name,
img,
s.transpose(0, 1),
global_step=j
)
elif args.dataset == "cats" \
or args.dataset == "wflw" \
or args.dataset == "merged":
img = input[0].detach().cpu()
fig = plt.figure()
plt.scatter(s[0, :]*128, s[1, :]*128)
plt.imshow(np.transpose(img, (1, 2, 0)))
writer.add_figure(tag_name, fig, global_step=j)
else: # mnist
fig = plt.figure()
y, x = s
y = 1 - y
ms = ms.numpy()
rgba_colors = np.zeros((ms.size, 4))
rgba_colors[:, 2] = 1.0
rgba_colors[:, 3] = ms
plt.scatter(x, y, color=rgba_colors)
plt.axes().set_aspect("equal")
plt.xlim(0, 1)
plt.ylim(0, 1)
writer.add_figure(tag_name, fig, global_step=j)
# Export predictions
if args.export_dir and not args.export_csv:
os.makedirs(f"{args.export_dir}/groundtruths", exist_ok=True)
os.makedirs(f"{args.export_dir}/detections", exist_ok=True)
for i, (gt, dets) in enumerate(zip(true_export, pred_export)):
export_groundtruths_path = os.path.join(
args.export_dir,
"groundtruths",
f"{i}.txt"
)
with open(export_groundtruths_path, "w") as fd:
for box in gt.transpose(0, 1):
if (box == 0).all():
continue
s = "box " + " ".join(map(str, box.tolist()))
fd.write(s + "\n")
if args.export_progress:
for step, det in enumerate(dets):
export_progress_path = os.path.join(
args.export_dir,
"detections",
f"{i}-step{step}.txt"
)
with open(export_progress_path, "w") as fd:
for sbox in det.transpose(0, 1):
s = f"box " + " ".join(map(str, sbox.tolist()))
fd.write(s + "\n")
export_path = os.path.join(
args.export_dir,
"detections",
f"{i}.txt"
)
with open(export_path, "w") as fd:
for sbox in dets[-1].transpose(0, 1):
s = f"box " + " ".join(map(str, sbox.tolist()))
fd.write(s + "\n")
import subprocess
git_hash = subprocess.check_output(["git", "rev-parse", "HEAD"])
# git_hash = "483igtrfiuey46"
torch.backends.cudnn.benchmark = True
metrics = {}
start = time.time()
if args.eval_only:
tracker.new_epoch()
with mp.Pool(10) as pool:
run(
net,
test_loader,
optimizer,
train=False,
epoch=0,
pool=pool
)
metrics["test_loss"] = np.mean(tracker.data["test_loss"][-1])
metrics["test_set_loss"] = np.mean(tracker.data["test_last"][-1])
else:
best_test_loss = float("inf")
for epoch in range(args.epochs):
tracker.new_epoch()
with mp.Pool(10) as pool:
run(
net,
train_loader,
optimizer,
train=True,
epoch=epoch,
pool=pool
)
if not args.train_only:
run(
net,
test_loader,
optimizer,
train=False,
epoch=epoch,
pool=pool
)
epoch_test_loss = np.mean(tracker.data["test_loss"][-1])
if epoch_test_loss < best_test_loss:
print("new best loss")
best_test_loss = epoch_test_loss
# only save if the epoch has lower loss
metrics["test_loss"] = epoch_test_loss
metrics["train_loss"] = np.mean(tracker.data["train_loss"][-1])
metrics["train_set_loss"] = np.mean(
tracker.data["train_last"][-1])
metrics["test_set_loss"] = np.mean(
tracker.data["test_last"][-1])
metrics["best_epoch"] = epoch
results = {
"name": name + "-best",
"tracker": tracker.data,
"weights": net.state_dict()
if not args.multi_gpu
else net.module.state_dict(),
"args": vars(args),
"hash": git_hash,
}
torch.save(results, os.path.join("logs", name + "-best"))
results = {
"name": name + "-final",
"tracker": tracker.data,
"weights": net.state_dict()
if not args.multi_gpu
else net.module.state_dict(),
"args": vars(args),
"hash": git_hash,
}
torch.save(results, os.path.join("logs", name + "-final"))
if args.export_csv and args.export_dir:
path = os.path.join(args.export_dir, f'{args.name}-predictions.csv')
pd.DataFrame(np.array(predictions), index=names).to_csv(
path, sep=',', index=names, header=False
)
path = os.path.join(args.export_dir, f'{args.name}-targets.csv')
pd.DataFrame(np.array(export_targets), index=names).to_csv(
path, sep=',', index=names, header=False
)
took = time.time() - start
print(f"Process took {took:.1f}s, avg {took/args.epochs:.1f} s/epoch.")
# save hyper parameters to tensorboard for reference
hparams = {k: v for k, v in vars(args).items() if v is not None}
print(metrics)
metrics = {
"total_time": took,
"avg_time_per_epoch": took/args.epochs
}
print("writing hparams")
train_writer.add_hparams(hparams, metric_dict=metrics, name="hparams")
if __name__ == "__main__":
try:
main()
except KeyboardInterrupt:
print("Process interrupted by user, emptying cache...")
torch.cuda.empty_cache()
| 23,664
| 0
| 23
|
8dce960a7b6703eb2654296841d98428d993d7fa
| 6,284
|
py
|
Python
|
classes/Math.py
|
lekevin42/discord_music_single
|
fcf30cf33f8bd5b8f2a16dba7f32600024700213
|
[
"MIT"
] | null | null | null |
classes/Math.py
|
lekevin42/discord_music_single
|
fcf30cf33f8bd5b8f2a16dba7f32600024700213
|
[
"MIT"
] | 1
|
2017-07-28T14:51:13.000Z
|
2017-07-28T20:27:46.000Z
|
classes/Math.py
|
lekevin42/discord_music_single
|
fcf30cf33f8bd5b8f2a16dba7f32600024700213
|
[
"MIT"
] | null | null | null |
import math
#def find_par(self):
if __name__ == "__main__":
main()
| 20.739274
| 106
| 0.60837
|
import math
class Math:
def __init__(self, eq):
self.equation = eq
self.equation_list = None
def print_equation(self):
print(self.equation)
def remove_spaces(self):
self.equation = self.equation.replace(" ", "")
def split_equation(self):
self.equation_list = self.equation.split(" ")
def is_operator(self, char):
if char is "+" or char is "-" or char is "*" or char is "/":
return True
else:
return False
def remove_spaces(self):
counter = 0
eq_list = []
#equation = ""
length = len(self.equation)
for char in self.equation:
eq_list.append(char)
while eq_list.count(" ") is not 0:
if counter is length:
counter = 0
if eq_list[counter] is " ":
eq_list.pop(counter )
length -= 1
counter += 1
self.equation = ""
for char in eq_list:
self.equation += char
#return equation
def check_num(self, num):
check = False
if num is "(" or num is ")" or num is "+" or num is "-" or num is "*" or num is "/" or num is "^":
return False
for n in range(0, 10):
if num == str(n) or num is ".":
return True
return False
def prep_equation(self):
eq_list = []
#equation = ""
#print(self.equation)
for char in self.equation:
eq_list.append(char)
counter = 0
#eq_list[counter] is not " " and eq_list[counter + 1] is not " " and
# and self.check_num(eq_list[counter]) is False and self.check_num(eq_list[counter + 1]) is not False
#print(self.check_num((".")))
#print(self.check_num("1"))
while counter is not len(eq_list) - 1:
if eq_list[counter] is not " " and eq_list[counter + 1] is not " ":
if self.check_num(eq_list[counter]) is True and self.check_num(eq_list[counter + 1]) is True:
pass
else:
eq_list.insert(counter + 1, " ")
counter += 1
self.equation = ""
for char in eq_list:
self.equation += char
#print(self.equation)
#return equation
def find_operators(self, equation_list):
add = []
sub = []
mul = []
div = []
left_par = []
right_par = []
pows = []
operator_count = 0
counter = 0
for char in equation_list:
if char is "+":
add.append(counter)
operator_count += 1
elif char is "-":
sub.append(counter)
operator_count += 1
elif char is "*":
mul.append(counter)
operator_count += 1
elif char is "/":
div.append(counter)
operator_count += 1
elif char is "(":
left_par.append(counter)
elif char is ")":
right_par.append(counter)
elif char is "^":
pows.append(counter)
operator_count += 1
counter += 1
#print(add)
#print(sub)
#print(mul)
#print(div)
#print(left_par)
#print(right_par)
#print(equation_list)
return add, sub, mul, div, left_par, right_par, pows, operator_count
#def find_par(self):
def solve_equation(self, equation):
add, sub, mul, div, left_par, right_par, pows, operator_count = self.find_operators(equation)
#print(add)
#print(sub)
#print(mul)
#print(div)
#print(left_par)
#print(right_par)
#print(operator_count)
solved = self.solve(add, sub, mul, div, pows, operator_count, equation)
return solved
def solve(self, add, sub, mul, div, pows, operator_count, equation):
#print(equation)
counter = 0
while operator_count is not 0:
if len(pows) is not 0:
char = "^"
counter = pows.pop(0)
elif len(mul) is not 0:
char = "*"
counter = mul.pop(0)
elif len(div) is not 0:
char = "/"
counter = div.pop(0)
elif len(add) is not 0:
char = "+"
counter = add.pop(0)
elif len(sub) is not 0:
char = "-"
counter = sub.pop(0)
#print(char)
x = equation[counter - 1]
y = equation[counter + 1]
equation.pop(counter - 1)
equation.pop(counter - 1)
equation.pop(counter - 1)
if char is "+":
equation.insert(counter -1, float(x) + float(y))
elif char is "-":
equation.insert(counter -1, float(x) - float(y))
elif char is "*":
equation.insert(counter -1, float(x) * float(y))
elif char is "/":
equation.insert(counter -1, float(x) / float(y))
elif char is "^":
equation.insert(counter - 1, pow(float(x), float(y)))
while len(add) is not 0:
add.pop(0)
while len(sub) is not 0:
sub.pop(0)
while len(mul) is not 0:
mul.pop(0)
while len(div) is not 0:
div.pop(0)
while len(pows) is not 0:
pows.pop(0)
operator_count -= 1
add, sub, mul, div, left_par, right_par, pows, operator_count = self.find_operators(equation)
return equation[0]
def parse_equation(self):
sub_eq = []
in_par = False
try:
self.remove_spaces()
self.prep_equation()
self.split_equation()
#print(self.equation_list)
add, sub, mul, div, left_par, right_par, pows, operator_count = self.find_operators(self.equation_list)
while len(left_par) is not 0:
for char in self.equation_list:
if char is ")":
in_par = False
break
if in_par:
sub_eq.append(char)
if char is "(":
in_par = True
solved_sub = self.solve_equation(sub_eq)
sub_eq = []
#print(solved_sub)
placed = left_par_pos = left_par.pop(0)
right_par_pos = right_par.pop(0)
while left_par_pos < right_par_pos + 1:
#print(self.equation_list)
self.equation_list.pop(placed)
left_par_pos += 1
self.equation_list.insert(placed, solved_sub)
#print(self.equation_list)
while len(left_par) is not 0:
left_par.pop(0)
while len(right_par) is not 0:
right_par.pop(0)
add, sub, mul, div, left_par, right_par, pow, operator_count = self.find_operators(self.equation_list)
#print(self.equation_list)
value = self.solve_equation(self.equation_list)
if isinstance(value, (int, float)):
return(value)
#print(equation)
# return equation
except Exception as error:
#print("Please check your equation.")
pass
def main():
eq = "2.4 + 2.6"
#eq = remove_spaces(eq, len(eq))
#equation = prep_equation(eq)
math = Math(eq)
value = math.parse_equation()
print(value)
if __name__ == "__main__":
main()
| 5,852
| -10
| 352
|