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e3d/synth_dataset/params.py | ZiyunClaudeWang/e3d | 9 | 193681 | from dataclasses import dataclass, field
from utils.params import ParamsBase
shapenet_synset_map = {
'car': '02958343',
'chair': '02691156',
'plane': '03001627'
}
@dataclass
class Params(ParamsBase):
gpu_num: int
name: str
config_file: str = ""
# Dataset Generation Params
img_size: tuple = (280, 280)
sigma_hand: float = 0.15
mesh_translation: float = .1
variation: list = field(default_factory=lambda: ['dist', 'elev'])
pepper: list = field(default_factory=lambda: ['elev'])
random_start: list = field(default_factory=lambda: ['dist', 'azim'])
## Dataset Size
mini_batch: int = 72
batch_size: int = 360
data_batch_size: int = 45
mesh_iter: int = 4 #Iterations of data loop per mesh
show_frame: bool = False
#Mesh info
mesh_path: str = "../data/meshes/dolphin/dolphin.obj"
## Shapenet info
shapenet: bool = True
category: str = "car"
synsets: dict = field(default_factory=lambda:shapenet_synset_map)
shapenet_path: str = "../data/ShapeNetCorev2"
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tantipy/__init__.py | izihawa/tantipy | 0 | 1607789 | <reponame>izihawa/tantipy
from .tantivy_coder import (
TantivyCoder,
TantivyDocument,
)
from .tantivy_reader import TantivyReader
__all__ = ['TantivyCoder', 'TantivyDocument', 'TantivyReader']
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vnpy/extension/chart/item.py | asciili/vnpy | 0 | 166127 | <filename>vnpy/extension/chart/item.py
from datetime import datetime
from typing import List, Tuple, Dict
from vnpy.trader.ui import create_qapp, QtCore, QtGui, QtWidgets
from pyqtgraph import ScatterPlotItem
import pyqtgraph as pg
import numpy as np
import talib
import copy
from vnpy.chart import ChartWidget, VolumeItem, CandleItem
from vnpy.chart.item import ChartItem
from vnpy.chart.manager import BarManager
from vnpy.trader.utility import ArrayManager
from vnpy.trader.object import (
BarData,
OrderData,
TradeData
)
from vnpy.trader.object import Direction, Exchange, Interval, Offset, Status, Product, OptionType, OrderType
from collections import OrderedDict
import pytz
CHINA_TZ = pytz.timezone("Asia/Shanghai")
class ExCandleItem(CandleItem):
def __init__(self, manager: BarManager):
""""""
super().__init__(manager)
def get_info_text(self, ix: int) -> str:
"""
Get information text to show by cursor.
"""
bar = self._manager.get_bar(ix)
if bar:
words = [
"Open",
str(bar.open_price),
]
text = ":".join(words)
else:
text = ""
return text
class ExChartItem(ChartItem):
""""""
def __init__(self, manager: BarManager, am: ArrayManager):
""""""
super().__init__(manager)
self.am = am
self.values = None
def update_values(self):
"""
Update value.
"""
pass
def update_history(self, history: List[BarData]) -> BarData:
"""
Update a list of bar data.
"""
self.update_values()
super().update_history(history)
def update_bar(self, bar: BarData) -> None:
"""
Update single bar data.
"""
self.update_values()
super().update_bar(bar)
def boundingRect(self) -> QtCore.QRectF:
""""""
min_price, max_price = self._manager.get_price_range()
rect = QtCore.QRectF(
0,
min_price,
len(self._bar_picutures),
max_price - min_price
)
return rect
def get_y_range(self, min_ix: int = None, max_ix: int = None) -> Tuple[float, float]:
"""
Get range of y-axis with given x-axis range.
If min_ix and max_ix not specified, then return range with whole data set.
"""
min_price, max_price = self._manager.get_price_range(min_ix, max_ix)
return min_price, max_price
class LineItem(ExChartItem):
""""""
def __init__(self, manager: BarManager, am : ArrayManager):
""""""
super().__init__(manager, am)
self.white_pen: QtGui.QPen = pg.mkPen(color=(255, 255, 255), width=1)
def _draw_bar_picture(self, ix: int, bar: BarData) -> QtGui.QPicture:
""""""
last_bar = self._manager.get_bar(ix - 1)
# Create objects
picture = QtGui.QPicture()
painter = QtGui.QPainter(picture)
# Set painter color
painter.setPen(self.white_pen)
# Draw Line
end_point = QtCore.QPointF(ix, bar.close_price)
if last_bar:
start_point = QtCore.QPointF(ix - 1, last_bar.close_price)
else:
start_point = end_point
painter.drawLine(start_point, end_point)
# Finish
painter.end()
return picture
def get_info_text(self, ix: int) -> str:
""""""
text = ""
bar = self._manager.get_bar(ix)
if bar:
text = f"Close:{bar.close_price}"
return text
class SmaItem(ExChartItem):
""""""
def __init__(self, manager: BarManager, am : ArrayManager):
""""""
super().__init__(manager, am)
self.blue_pen: QtGui.QPen = pg.mkPen(color=(100, 100, 255), width=2)
self.window = 10
def update_values(self):
"""
Update value
"""
self.values = talib.SMA(self.am.close[-self.am.count:],self.window)
def _draw_bar_picture(self, ix: int, bar: BarData) -> QtGui.QPicture:
""""""
value = self.values[ix]
last_value = self.values[ix - 1]
# Create objects
picture = QtGui.QPicture()
painter = QtGui.QPainter(picture)
# Set painter color
painter.setPen(self.blue_pen)
# Draw Line
if np.isnan(last_value) or np.isnan(value):
# print(ix - 1, last_value,ix, value,)
pass
else:
start_point = QtCore.QPointF(ix-1, last_value)
end_point = QtCore.QPointF(ix, value)
painter.drawLine(start_point, end_point)
# Finish
painter.end()
return picture
def get_info_text(self, ix: int) -> str:
""""""
if ix > 0:
text = f"SMA {self.values[ix]:.1f}"
else:
text = "SMA -"
return text
class SarItem(ExChartItem):
""""""
def __init__(self, manager: BarManager, am : ArrayManager, acceleration:float = 0.02, maximum:float = 0.2):
""""""
super().__init__(manager, am)
self.yellow_pen: QtGui.QPen = pg.mkPen(color=(255, 255, 0), width=2)
self.yellow_brush: QtGui.QBrush = pg.mkBrush(color=(255, 255, 0))
self.acceleration = acceleration
self.maximum = maximum
def update_values(self):
"""
Update value
"""
self.values = talib.SAR(self.am.high[-self.am.count:], self.am.low[-self.am.count:], self.acceleration, self.maximum)
def _draw_bar_picture(self, ix: int, bar: BarData) -> QtGui.QPicture:
""""""
value = self.values[ix]
last_value = self.values[ix - 1]
# Create objects
picture = QtGui.QPicture()
painter = QtGui.QPainter(picture)
# Set painter color
painter.setPen(self.yellow_pen)
# painter.setBrush(self.yellow_brush)
# Draw Line
if np.isnan(last_value) or np.isnan(value):
# print(ix - 1, last_value,ix, value,)
pass
else:
start_point = QtCore.QPointF(ix-1, last_value)
end_point = QtCore.QPointF(ix, value)
painter.drawLine(start_point, end_point)
# Draw Circle
# painter.drawEllipse(QtCore.QPointF(ix, self.values[ix]), 2, 2)
# Finish
painter.end()
return picture
def get_info_text(self, ix: int) -> str:
""""""
if ix >= 0:
text = f"SAR {self.values[ix]:.1f}"
else:
text = "SAR -"
return text
class RsiItem(ExChartItem):
""""""
def __init__(self, manager: BarManager, am : ArrayManager):
""""""
super().__init__(manager, am)
self.white_pen: QtGui.QPen = pg.mkPen(color=(255, 255, 255), width=1)
self.yellow_pen: QtGui.QPen = pg.mkPen(color=(255, 255, 0), width=2)
self.rsi_window = 14
def update_values(self):
"""
Update value
"""
self.values = talib.RSI(self.am.close[-self.am.count:], self.rsi_window)
def _draw_bar_picture(self, ix: int, bar: BarData) -> QtGui.QPicture:
""""""
rsi_value = self.values[ix]
last_rsi_value = self.values[ix - 1]
# Create objects
picture = QtGui.QPicture()
painter = QtGui.QPainter(picture)
# Draw RSI line
painter.setPen(self.yellow_pen)
if np.isnan(last_rsi_value) or np.isnan(rsi_value):
# print(ix - 1, last_rsi_value,ix, rsi_value,)
pass
else:
end_point = QtCore.QPointF(ix, rsi_value)
start_point = QtCore.QPointF(ix - 1, last_rsi_value)
painter.drawLine(start_point, end_point)
# Draw oversold/overbought line
painter.setPen(self.white_pen)
painter.drawLine(
QtCore.QPointF(ix, 70),
QtCore.QPointF(ix - 1, 70),
)
painter.drawLine(
QtCore.QPointF(ix, 30),
QtCore.QPointF(ix - 1, 30),
)
# Finish
painter.end()
return picture
def boundingRect(self) -> QtCore.QRectF:
""""""
# min_price, max_price = self._manager.get_price_range()
rect = QtCore.QRectF(
0,
0,
len(self._bar_picutures),
100
)
return rect
def get_y_range( self, min_ix: int = None, max_ix: int = None) -> Tuple[float, float]:
""" """
return 0, 100
def get_info_text(self, ix: int) -> str:
""""""
if ix > 0:
rsi_value = self.values[ix]
text = f"RSI {rsi_value:.1f}"
# print(text)
else:
text = "RSI -"
return text
def to_int(value: float) -> int:
""""""
return int(round(value, 0))
""" 将y方向的显示范围扩大到1.1 """
def adjust_range(in_range:Tuple[float, float])->Tuple[float, float]:
ret_range:Tuple[float, float]
diff = abs(in_range[0] - in_range[1])
ret_range = (in_range[0]-diff*0.05,in_range[1]+diff*0.05)
return ret_range
class MacdItem(ExChartItem):
""""""
_values_ranges: Dict[Tuple[int, int], Tuple[float, float]] = {}
last_range:Tuple[int, int] = (-1,-1) # 最新显示K线索引范围
def __init__(self, manager: BarManager, am : ArrayManager):
""""""
super().__init__(manager, am)
self.white_pen: QtGui.QPen = pg.mkPen(color=(255, 255, 255), width=1)
self.yellow_pen: QtGui.QPen = pg.mkPen(color=(255, 255, 0), width=1)
self.red_pen: QtGui.QPen = pg.mkPen(color=(255, 0, 0), width=1)
self.green_pen: QtGui.QPen = pg.mkPen(color=(0, 255, 0), width=1)
self.short_window = 12
self.long_window = 26
self.M = 9
self.macd_data: Dict[int, Tuple[float,float,float]] = {}
def get_macd_value(self, ix: int) -> Tuple[float,float,float]:
""""""
if ix < 0:
return (0.0,0.0,0.0)
# When initialize, calculate all macd value
if not self.macd_data:
bars = self._manager.get_all_bars()
close_data = [bar.close_price for bar in bars]
diffs,deas,macds = talib.MACD(np.array(close_data),
fastperiod=self.short_window,
slowperiod=self.long_window,
signalperiod=self.M)
for n in range(0,len(diffs)):
self.macd_data[n] = (diffs[n],deas[n],macds[n])
# Return if already calcualted
if ix in self.macd_data:
return self.macd_data[ix]
# Else calculate new value
close_data = []
for n in range(ix-self.long_window-self.M+1, ix + 1):
bar = self._manager.get_bar(n)
close_data.append(bar.close_price)
diffs,deas,macds = talib.MACD(np.array(close_data),
fastperiod=self.short_window,
slowperiod=self.long_window,
signalperiod=self.M)
diff,dea,macd = diffs[-1],deas[-1],macds[-1]
self.macd_data[ix] = (diff,dea,macd)
return (diff,dea,macd)
def _draw_bar_picture(self, ix: int, bar: BarData) -> QtGui.QPicture:
""""""
macd_value = self.get_macd_value(ix)
last_macd_value = self.get_macd_value(ix - 1)
# # Create objects
picture = QtGui.QPicture()
painter = QtGui.QPainter(picture)
# # Draw macd lines
if np.isnan(macd_value[0]) or np.isnan(last_macd_value[0]):
# print("略过macd lines0")
pass
else:
end_point0 = QtCore.QPointF(ix, macd_value[0])
start_point0 = QtCore.QPointF(ix - 1, last_macd_value[0])
painter.setPen(self.white_pen)
painter.drawLine(start_point0, end_point0)
if np.isnan(macd_value[1]) or np.isnan(last_macd_value[1]):
# print("略过macd lines1")
pass
else:
end_point1 = QtCore.QPointF(ix, macd_value[1])
start_point1 = QtCore.QPointF(ix - 1, last_macd_value[1])
painter.setPen(self.yellow_pen)
painter.drawLine(start_point1, end_point1)
if not np.isnan(macd_value[2]):
if (macd_value[2]>0):
painter.setPen(self.red_pen)
painter.setBrush(pg.mkBrush(255,0,0))
else:
painter.setPen(self.green_pen)
painter.setBrush(pg.mkBrush(0,255,0))
painter.drawRect(QtCore.QRectF(ix-0.3,0,0.6,macd_value[2]))
else:
# print("略过macd lines2")
pass
painter.end()
return picture
def boundingRect(self) -> QtCore.QRectF:
""""""
min_y, max_y = self.get_y_range()
rect = QtCore.QRectF(
0,
min_y,
len(self._bar_picutures),
max_y
)
return rect
def get_y_range(self, min_ix: int = None, max_ix: int = None) -> Tuple[float, float]:
# 获得3个指标在y轴方向的范围
# hxxjava 修改，2020-6-29
# 当显示范围改变时，min_ix,max_ix的值不为None，当显示范围不变时，min_ix,max_ix的值不为None，
offset = max(self.short_window,self.long_window) + self.M - 1
if not self.macd_data or len(self.macd_data) < offset:
return 0.0, 1.0
# print("len of range dict:",len(self._values_ranges),",macd_data:",len(self.macd_data),(min_ix,max_ix))
if min_ix != None: # 调整最小K线索引
min_ix = max(min_ix,offset)
if max_ix != None: # 调整最大K线索引
max_ix = min(max_ix, len(self.macd_data)-1)
last_range = (min_ix,max_ix) # 请求的最新范围
if last_range == (None,None): # 当显示范围不变时
if self.last_range in self._values_ranges:
# 如果y方向范围已经保存
# 读取y方向范围
result = self._values_ranges[self.last_range]
# print("1:",self.last_range,result)
return adjust_range(result)
else:
# 如果y方向范围没有保存
# 从macd_data重新计算y方向范围
min_ix,max_ix = 0,len(self.macd_data)-1
macd_list = list(self.macd_data.values())[min_ix:max_ix + 1]
ndarray = np.array(macd_list)
max_price = np.nanmax(ndarray)
min_price = np.nanmin(ndarray)
# 保存y方向范围，同时返回结果
result = (min_price, max_price)
self.last_range = (min_ix,max_ix)
self._values_ranges[self.last_range] = result
# print("2:",self.last_range,result)
return adjust_range(result)
""" 以下为显示范围变化时 """
if last_range in self._values_ranges:
# 该范围已经保存过y方向范围
# 取得y方向范围，返回结果
result = self._values_ranges[last_range]
# print("3:",last_range,result)
return adjust_range(result)
# 该范围没有保存过y方向范围
# 从macd_data重新计算y方向范围
macd_list = list(self.macd_data.values())[min_ix:max_ix + 1]
ndarray = np.array(macd_list)
max_price = np.nanmax(ndarray)
min_price = np.nanmin(ndarray)
# 取得y方向范围，返回结果
result = (min_price, max_price)
self.last_range = last_range
self._values_ranges[self.last_range] = result
# print("4:",self.last_range,result)
return adjust_range(result)
def get_info_text(self, ix: int) -> str:
# """"""
if ix in self.macd_data:
diff,dea,macd = self.macd_data[ix]
words = [
f"diff {diff:.3f}"," ",
f"dea {dea:.3f}"," ",
f"macd {macd:.3f}"
]
text = "\n".join(words)
else:
text = "diff - \ndea - \nmacd -"
return text
class TradeItem(ScatterPlotItem,ExChartItem):
"""
成交单绘图部件
"""
def __init__(self, manager: BarManager, am : ArrayManager):
""""""
ScatterPlotItem.__init__(self)
# ExChartItem.__init__(self,manager)
# super(TradeItem,self).__init__(manager, am)
super(ExChartItem,self).__init__(manager, am)
self.blue_pen: QtGui.QPen = pg.mkPen(color=(100, 100, 255), width=2)
self.trades : Dict[int,Dict[str,TradeData]] = {} # {ix:{tradeid:trade}}
def add_trades(self,trades:List[TradeData]):
""" 增加成交单列表到TradeItem """
for trade in trades:
self.add_trade(trade)
self.set_scatter_data()
self.update()
def add_trade(self,trade:TradeData,draw:bool=False):
""" 增加一个成交单到TradeItem """
# 这里使用reverse=True，是考虑到实盘成交往往发生在最新的bar里，可以加快搜索速度
od = OrderedDict(sorted(self._manager._datetime_index_map.items(),key = lambda t:t[0],reverse=True))
idx = self._manager.get_count() - 1
for dt,ix in od.items():
# print(f"dt={dt}\ntrade.datetime {trade.datetime}")
dt1 = CHINA_TZ.localize(datetime.combine(dt.date(),dt.time()))
if dt1 <= trade.datetime:
# print(f"【dt={dt},dt1={dt1},dt2={trade.datetime} ix={ix}】")
idx = ix
break
# 注意：一个bar期间可能发生多个成交单
if idx in self.trades:
self.trades[idx][trade.tradeid] = trade
else:
self.trades[idx] = {trade.tradeid:trade}
if draw:
self.set_scatter_data()
self.update()
# print(f"add_trade idx={idx} trade={trade}")
def set_scatter_data(self):
""" 把成交单列表绘制到ScatterPlotItem上 """
scatter_datas = []
for ix in self.trades:
for trade in self.trades[ix].values():
scatter = {
"pos" : (ix, trade.price),
"data": 1,
"size": 14,
"pen": pg.mkPen((255, 255, 255)),
}
if trade.direction == Direction.LONG:
scatter_symbol = "t1" # Up arrow
else:
scatter_symbol = "t" # Down arrow
if trade.offset == Offset.OPEN:
scatter_brush = pg.mkBrush((255, 255, 0)) # Yellow
else:
scatter_brush = pg.mkBrush((0, 0, 255)) # Blue
scatter["symbol"] = scatter_symbol
scatter["brush"] = scatter_brush
scatter_datas.append(scatter)
self.setData(scatter_datas)
def get_info_text(self, ix: int) -> str:
""""""
if ix in self.trades:
text = "成交："
for tradeid,trade in self.trades[ix].items():
# TradeData
text += f"\n{trade.price}{trade.direction.value}{trade.offset.value}{trade.volume}手"
else:
text = "成交：-"
return text
class OrderItem(ScatterPlotItem,ExChartItem):
"""
委托单绘图部件
"""
def __init__(self, manager: BarManager, am : ArrayManager):
""""""
ScatterPlotItem.__init__(self)
super(ExChartItem,self).__init__(manager, am)
self.orders : Dict[int,Dict[str,Order]] = {} # {ix:{orderid:order}}
def add_orders(self,orders:List[OrderData]):
""" 增加委托单列表到OrderItem """
for order in orders:
if order.datetime:
self.add_order(order)
self.set_scatter_data()
self.update()
def add_order(self,order:OrderData,draw:bool=False):
""" 增加一个委托单到OrderItem """
# 这里使用reverse=True，是考虑到实盘成交往往发生在最新的bar里，可以加快搜索速度
od = OrderedDict(sorted(self._manager._datetime_index_map.items(),key = lambda t:t[0],reverse=True))
idx = self._manager.get_count() - 1
for dt,ix in od.items():
# print(f"dt={dt}\ntrade.datetime {trade.datetime}")
dt1 = CHINA_TZ.localize(datetime.combine(dt.date(),dt.time()))
if dt1 <= order.datetime:
# print(f"【dt={dt},dt1={dt1},dt2={order.datetime} ix={ix}】")
idx = ix
break
# 注意：一个bar期间可能发生多个委托单
if idx in self.orders:
self.orders[idx][order.orderid] = order
else:
self.orders[idx] = {order.orderid:order}
if draw:
self.set_scatter_data()
self.update()
def set_scatter_data(self):
""" 把委托单列表绘制到ScatterPlotItem上 """
scatter_datas = []
for ix in self.orders:
lowest,highest=self.get_y_range()
# print(f"range={lowest,highest}")
for order in self.orders[ix].values():
# 处理委托报价超出显示范围的问题
if order.price>highest:
show_price = highest - 7
elif order.price<lowest:
show_price = lowest + 7
else:
show_price = order.price
scatter = {
"pos" : (ix, show_price),
"data": 1,
"size": 14,
"pen": pg.mkPen((255, 255, 255)),
}
if order.direction == Direction.LONG:
scatter_symbol = "t1" # Up arrow
else:
scatter_symbol = "t" # Down arrow
if order.offset == Offset.OPEN:
scatter_brush = pg.mkBrush((0, 128, 128)) # Yellow
else:
scatter_brush = pg.mkBrush((128, 128, 0)) # Blue
scatter["symbol"] = scatter_symbol
scatter["brush"] = scatter_brush
scatter_datas.append(scatter)
self.setData(scatter_datas)
def get_info_text(self, ix: int) -> str:
""""""
if ix in self.orders:
text = "委托："
for orderid,order in self.orders[ix].items():
# OrderData
text += f"\n{order.price}{order.direction.value}{order.offset.value}{order.volume}手"
else:
text = "委托：-"
return text
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152,
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31532,
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152,
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31836,
31674,
13,
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861,
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7431,
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8628,
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13,
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3881,
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313,
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861,
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3317,
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861,
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31088,
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134,
232,
158,
183,
13,
13,
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565,
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259,
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235,
143,
134,
232,
158,
183,
30413,
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13,
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565,
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4230,
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3881,
297,
1583,
3032,
5975,
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13,
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396,
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30847,
30801,
29891,
30525,
31331,
235,
143,
134,
232,
158,
183,
31290,
31412,
30982,
30946,
13,
18884,
396,
29871,
235,
178,
190,
30683,
29891,
30525,
31331,
235,
143,
134,
232,
158,
183,
13,
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1121,
353,
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3032,
5975,
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1311,
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13,
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396,
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703,
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4230,
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2914,
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13,
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736,
10365,
29918,
3881,
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13,
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13,
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235,
143,
134,
232,
158,
183,
31423,
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13,
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396,
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31594,
8628,
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1272,
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31466,
31565,
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30525,
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235,
143,
134,
232,
158,
183,
13,
18884,
1375,
29918,
861,
29892,
3317,
29918,
861,
353,
29871,
29900,
29892,
2435,
29898,
1311,
29889,
8628,
29881,
29918,
1272,
6817,
29896,
13,
13,
18884,
5825,
29881,
29918,
1761,
353,
1051,
29898,
1311,
29889,
8628,
29881,
29918,
1272,
29889,
5975,
3101,
29961,
1195,
29918,
861,
29901,
3317,
29918,
861,
718,
29871,
29896,
29962,
13,
462,
299,
2378,
353,
7442,
29889,
2378,
29898,
8628,
29881,
29918,
1761,
29897,
13,
18884,
4236,
29918,
9175,
353,
7442,
29889,
13707,
3317,
29898,
299,
2378,
29897,
13,
18884,
1375,
29918,
9175,
353,
7442,
29889,
13707,
1195,
29898,
299,
2378,
29897,
13,
13,
18884,
396,
29871,
30982,
30946,
29891,
30525,
31331,
235,
143,
134,
232,
158,
183,
30214,
30980,
30594,
31086,
30742,
31320,
30801,
13,
18884,
1121,
353,
313,
1195,
29918,
9175,
29892,
4236,
29918,
9175,
29897,
13,
18884,
1583,
29889,
4230,
29918,
3881,
353,
313,
1195,
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861,
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3317,
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861,
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13,
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13,
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736,
10365,
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3881,
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2914,
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13,
13,
4706,
9995,
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30651,
30557,
30573,
31542,
30858,
235,
143,
134,
232,
158,
183,
31462,
30705,
30594,
9995,
13,
13,
4706,
565,
1833,
29918,
3881,
297,
1583,
3032,
5975,
29918,
29878,
6916,
29901,
13,
9651,
396,
29871,
31751,
235,
143,
134,
232,
158,
183,
31290,
31412,
30982,
30946,
31138,
29891,
30525,
31331,
235,
143,
134,
232,
158,
183,
13,
9651,
396,
29871,
30683,
31050,
29891,
30525,
31331,
235,
143,
134,
232,
158,
183,
30214,
31086,
30742,
31320,
30801,
13,
9651,
1121,
353,
1583,
3032,
5975,
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6916,
29961,
4230,
29918,
3881,
29962,
13,
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396,
1596,
703,
29941,
29901,
613,
4230,
29918,
3881,
29892,
2914,
29897,
13,
9651,
736,
10365,
29918,
3881,
29898,
2914,
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13,
13,
4706,
396,
29871,
31751,
235,
143,
134,
232,
158,
183,
31423,
30417,
30982,
30946,
31138,
29891,
30525,
31331,
235,
143,
134,
232,
158,
183,
13,
4706,
396,
29871,
31594,
8628,
29881,
29918,
1272,
30908,
30374,
31466,
31565,
29891,
30525,
31331,
235,
143,
134,
232,
158,
183,
13,
4706,
5825,
29881,
29918,
1761,
353,
1051,
29898,
1311,
29889,
8628,
29881,
29918,
1272,
29889,
5975,
3101,
29961,
1195,
29918,
861,
29901,
3317,
29918,
861,
718,
29871,
29896,
29962,
13,
308,
299,
2378,
353,
7442,
29889,
2378,
29898,
8628,
29881,
29918,
1761,
29897,
13,
4706,
4236,
29918,
9175,
353,
7442,
29889,
13707,
3317,
29898,
299,
2378,
29897,
13,
4706,
1375,
29918,
9175,
353,
7442,
29889,
13707,
1195,
29898,
299,
2378,
29897,
13,
13,
4706,
396,
29871,
30683,
31050,
29891,
30525,
31331,
235,
143,
134,
232,
158,
183,
30214,
31086,
30742,
31320,
30801,
13,
4706,
1121,
353,
313,
1195,
29918,
9175,
29892,
4236,
29918,
9175,
29897,
13,
4706,
1583,
29889,
4230,
29918,
3881,
353,
1833,
29918,
3881,
13,
4706,
1583,
3032,
5975,
29918,
29878,
6916,
29961,
1311,
29889,
4230,
29918,
3881,
29962,
353,
1121,
13,
4706,
396,
1596,
703,
29946,
29901,
613,
1311,
29889,
4230,
29918,
3881,
29892,
2914,
29897,
13,
4706,
736,
10365,
29918,
3881,
29898,
2914,
29897,
13,
13,
13,
1678,
822,
679,
29918,
3888,
29918,
726,
29898,
1311,
29892,
474,
29916,
29901,
938,
29897,
1599,
851,
29901,
13,
4706,
396,
9995,
15945,
29908,
13,
4706,
565,
474,
29916,
297,
1583,
29889,
8628,
29881,
29918,
1272,
29901,
13,
9651,
2923,
29892,
311,
29874,
29892,
8628,
29881,
353,
1583,
29889,
8628,
29881,
29918,
1272,
29961,
861,
29962,
13,
9651,
3838,
353,
518,
13,
18884,
285,
29908,
12765,
426,
12765,
29901,
29889,
29941,
29888,
29913,
3284,
9162,
13,
18884,
285,
29908,
311,
29874,
426,
311,
29874,
29901,
29889,
29941,
29888,
29913,
3284,
9162,
13,
18884,
285,
29908,
8628,
29881,
426,
8628,
29881,
29901,
29889,
29941,
29888,
5038,
13,
18884,
4514,
13,
9651,
1426,
353,
6634,
29876,
1642,
7122,
29898,
9303,
29897,
13,
4706,
1683,
29901,
13,
9651,
1426,
353,
376,
12765,
448,
320,
299,
11248,
448,
320,
29876,
8628,
29881,
448,
29908,
13,
13,
4706,
736,
1426,
13,
13,
13,
1990,
27226,
2001,
29898,
4421,
2620,
20867,
2001,
29892,
1252,
14732,
2001,
1125,
13,
1678,
9995,
13,
268,
30494,
31398,
31166,
234,
190,
155,
30861,
30636,
30631,
13,
1678,
9995,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
8455,
29901,
2261,
3260,
29892,
626,
584,
4398,
3260,
1125,
13,
4706,
9995,
15945,
29908,
13,
4706,
2522,
2620,
20867,
2001,
17255,
2344,
12035,
1311,
29897,
13,
4706,
396,
1222,
14732,
2001,
17255,
2344,
12035,
1311,
29892,
12847,
29897,
13,
4706,
396,
2428,
29898,
5323,
311,
2001,
29892,
1311,
467,
1649,
2344,
12035,
12847,
29892,
626,
29897,
13,
4706,
2428,
29898,
1252,
14732,
2001,
29892,
1311,
467,
1649,
2344,
12035,
12847,
29892,
626,
29897,
13,
13,
13,
4706,
1583,
29889,
9539,
29918,
2238,
29901,
14705,
28707,
29889,
29984,
29925,
264,
353,
23822,
29889,
11256,
29925,
264,
29898,
2780,
7607,
29896,
29900,
29900,
29892,
29871,
29896,
29900,
29900,
29892,
29871,
29906,
29945,
29945,
511,
2920,
29922,
29906,
29897,
13,
13,
4706,
1583,
29889,
509,
3076,
584,
360,
919,
29961,
524,
29892,
21533,
29961,
710,
29892,
5323,
311,
1469,
5262,
353,
6571,
396,
426,
861,
26254,
3018,
311,
333,
29901,
3018,
311,
930,
13,
13,
1678,
822,
788,
29918,
509,
3076,
29898,
1311,
29892,
509,
3076,
29901,
1293,
29961,
5323,
311,
1469,
29962,
1125,
13,
4706,
9995,
29871,
232,
165,
161,
30666,
30494,
31398,
31166,
31025,
30746,
30780,
5323,
311,
2001,
9995,
13,
4706,
363,
11302,
297,
534,
3076,
29901,
13,
9651,
1583,
29889,
1202,
29918,
3018,
311,
29898,
3018,
311,
29897,
13,
13,
4706,
1583,
29889,
842,
29918,
1557,
2620,
29918,
1272,
580,
13,
4706,
1583,
29889,
5504,
580,
13,
13,
1678,
822,
788,
29918,
3018,
311,
29898,
1311,
29892,
3018,
311,
29901,
5323,
311,
1469,
29892,
4012,
29901,
11227,
29922,
8824,
1125,
13,
4706,
9995,
29871,
232,
165,
161,
30666,
30287,
30502,
30494,
31398,
31166,
30780,
5323,
311,
2001,
9995,
13,
4706,
396,
29871,
30810,
30755,
30785,
30406,
24244,
29922,
5574,
30214,
30392,
235,
131,
134,
235,
156,
148,
30780,
31195,
234,
158,
155,
30494,
31398,
232,
193,
131,
232,
193,
131,
30910,
30486,
30505,
30878,
30374,
30210,
1646,
30755,
30214,
30682,
30651,
30666,
232,
194,
174,
233,
147,
159,
31836,
31859,
30898,
13,
4706,
2413,
353,
8170,
287,
21533,
29898,
24582,
29898,
1311,
3032,
12847,
3032,
12673,
29918,
2248,
29918,
1958,
29889,
7076,
3285,
1989,
353,
14013,
260,
29901,
29873,
29961,
29900,
1402,
24244,
29922,
5574,
876,
13,
4706,
22645,
353,
1583,
3032,
12847,
29889,
657,
29918,
2798,
580,
448,
29871,
29896,
13,
4706,
363,
11636,
29892,
861,
297,
2413,
29889,
7076,
7295,
13,
9651,
396,
1596,
29898,
29888,
29908,
6008,
3790,
6008,
1012,
593,
15464,
29889,
12673,
426,
3018,
311,
29889,
12673,
27195,
13,
9651,
11636,
29896,
353,
5868,
1177,
29909,
29918,
29911,
29999,
29889,
2997,
675,
29898,
12673,
29889,
17743,
457,
29898,
6008,
29889,
1256,
3285,
6008,
29889,
2230,
22130,
13,
9651,
565,
11636,
29896,
5277,
11302,
29889,
12673,
29901,
13,
18884,
396,
1596,
29898,
29888,
29908,
31478,
6008,
3790,
6008,
1118,
6008,
29896,
3790,
6008,
29896,
1118,
6008,
29906,
3790,
3018,
311,
29889,
12673,
29913,
474,
29916,
3790,
861,
29913,
31472,
1159,
13,
18884,
22645,
353,
474,
29916,
13,
18884,
2867,
13,
13,
4706,
396,
29871,
31368,
31474,
30383,
30287,
30502,
1646,
31117,
31016,
30682,
30815,
30910,
30486,
30923,
30502,
30494,
31398,
31166,
13,
4706,
565,
22645,
297,
1583,
29889,
509,
3076,
29901,
13,
9651,
1583,
29889,
509,
3076,
29961,
13140,
3816,
3018,
311,
29889,
3018,
311,
333,
29962,
353,
11302,
13,
4706,
1683,
29901,
13,
9651,
1583,
29889,
509,
3076,
29961,
13140,
29962,
353,
426,
3018,
311,
29889,
3018,
311,
333,
29901,
3018,
311,
29913,
13,
13,
4706,
565,
4216,
29901,
13,
9651,
1583,
29889,
842,
29918,
1557,
2620,
29918,
1272,
580,
13,
9651,
1583,
29889,
5504,
580,
13,
13,
4706,
396,
1596,
29898,
29888,
29908,
1202,
29918,
3018,
311,
22645,
3790,
13140,
29913,
11302,
3790,
3018,
311,
27195,
13,
13,
1678,
822,
731,
29918,
1557,
2620,
29918,
1272,
29898,
1311,
1125,
13,
4706,
9995,
29871,
233,
141,
141,
30494,
31398,
31166,
31025,
30746,
234,
190,
155,
31072,
30780,
4421,
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] |
BinaryTree/Node.py | Pedro29152/binary-search-tree-python | 0 | 23994 | <gh_stars>0
class Node():
def __init__(self, id: int, value = None, right: 'Node' = None, left: 'Node' = None):
self.id = id
self.value = value
self.right = right
self.left = left
self.parent: 'Node' = None
def add(self, node: 'Node'):
if not node:
raise ValueError('node value invalid')
if node.id == self.id:
raise ValueError('The id sent is alredy on the tree')
if node.id > self.id:
if not self.right:
node.parent = self
self.right = node
else:
self.right.add(node)
if node.id < self.id:
if not self.left:
node.parent = self
self.left = node
else:
self.left.add(node)
def get_size(self):
size_l = self.left.get_size() if self.left else 0
size_r = self.right.get_size() if self.right else 0
return 1 + size_l + size_r
def get_height(self):
h_l = self.left.get_height() if self.left else 0
h_r = self.right.get_height() if self.right else 0
if h_r > h_l:
return 1 + h_r
return 1 + h_l
def get_node(self, id: int):
if self.id == id:
return self
if id > self.id:
if self.right:
return self.right.get_node(id)
if id < self.id:
if self.left:
return self.left.get_node(id)
return None
def get_min_node(self):
if not self.left:
return self
return self.left.get_min_node()
def get_max_node(self):
if not self.right:
return self
return self.right.get_max_node()
def get_sorted_list(self, max_size: int=None, ascending: bool=True):
if max_size == None:
return self.__get_list(ascending)
return self.__get_list_by_size(max_size, ascending)
def __get_list(self, ascending: bool):
list_e = self.left.__get_list(ascending) if self.left else []
list_d = self.right.__get_list(ascending) if self.right else []
if ascending:
return list_e + [self.id] + list_d
return list_d + [self.id] + list_e
def __get_list_by_size(self, max_size: int, ascending: bool):
if ascending:
st = 'left'
fi = 'right'
else:
st = 'right'
fi = 'left'
list_st = self[st].__get_list_by_size(max_size=max_size, ascending=ascending) if self[st] else []
if max_size <= len(list_st):
return list_st
elif max_size <= len(list_st) + 1:
return list_st + [self.id]
else:
curr_size = len(list_st) + 1
list_fi = self[fi].__get_list_by_size(max_size=max_size-curr_size, ascending=ascending) if self[fi] else []
return list_st + [self.id] + list_fi
def __getitem__(self, name):
return getattr(self, name)
def __setitem__(self, name, value):
return setattr(self, name, value)
def __str__(self):
str_e = self.left.__str__() if self.left else None
str_d = self.right.__str__() if self.right else None
if not (str_e or str_d):
return f'[({self.id})]'
return f'[({self.id}) {str_e}, {str_d}]'
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] |
run.py | RecepVural/Eight-Queens | 0 | 1604614 | #libraries are imported
import numpy as np
import random
# Chess table is created
a=['|',' ','|',' ','|',' ','|',' ','|',' ','|',' ','|',' ','|',' ','|']
chart= np.array([a,a,a,a,a,a,a,a],dtype=object)
chart2= chart.copy()
list1=[]
# possible columns are listed and first random selection is performed
columns_list=[1,3,5,7,9,11,13,15]
n_column=random.choice(columns_list)
chart[0][n_column]='Q'
list1.append(n_column)
columns_list.remove(n_column)# used column is removed from column list
i=1
while i<8:
y,p=0,0
while True:
y=0
# column is randomly chosen from column list
n_column=random.choice(columns_list)
#Diagonals of randomly chosen column is checked if they match with other queens
for j in list1:
w=i-list1.index(j)
if n_column!=j-(2*w) and n_column!=j+(2*w):
y=y
else:
y=y+1
p=p+1
# if unsolvable situation is occured ,queens are take back and leaved from for loop with break
if p>4:
mask=chart=='Q'
chart[mask]=' '
comp1=chart==chart2
if comp1.all():
i=1
break
# leaved from while loop with break
if comp1.all():
break
# if conditions are proper. Q is placed. Placed colums is removed
if y==0 :
chart[i][n_column]='Q'
list1.append(n_column)
columns_list.remove(n_column)
i=i+1
break
# selection is start again here, with continue function go back to start of while loop.
if comp1.all():
mask=chart=='Q'
chart[mask]=' '
list1=[]
columns_list=[1,3,5,7,9,11,13,15]
n_column=random.choice(columns_list)
chart[0][n_column]='Q'
list1.append(n_column)
columns_list.remove(n_column)
i=1
continue
#cheess table is printed.
for i in range(8):
print(''.join(chart[i]))
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] |
scripts/migrations/versions/200e7ea0e64b_create_base_schema.py | hqhs/gosupport | 0 | 173685 | <gh_stars>0
"""Create base schema
Revision ID: 200e7ea0e64b
Revises:
Create Date: 2019-02-16 12:15:59.212678
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '2<PASSWORD>'
down_revision = None
branch_labels = None
depends_on = None
def upgrade():
op.create_table(
'admins',
sa.Column('id', sa.Integer, primary_key=True, index=True),
sa.Column('created_at', sa.DateTime),
sa.Column('updated_at', sa.DateTime),
sa.Column('email', sa.String(256), nullable=False, index=True, unique=True),
sa.Column('name', sa.String(256)),
sa.Column('hashed_password', sa.String(256), nullable=False),
sa.Column('is_superuser', sa.Boolean, nullable=False),
sa.Column('is_active', sa.Boolean, nullable=False),
sa.Column('email_confirmed', sa.Boolean, nullable=False),
sa.Column('auth_token', sa.String(64)),
sa.Column('password_reset_token', sa.String(64)),
)
# FIXME separate messangers should have separate tables
op.create_table(
'users',
sa.Column('user_id', sa.Integer, primary_key=True, nullable=False, index=True),
sa.Column('created_at', sa.DateTime),
sa.Column('updated_at', sa.DateTime),
sa.Column('chat_id', sa.BigInteger, nullable=False),
sa.Column('email', sa.String(256), index=True),
sa.Column('name', sa.String(256)),
sa.Column('username', sa.String(256)),
sa.Column('has_unread_messages', sa.Boolean, nullable=False),
)
op.create_table(
'messages',
sa.Column('id', sa.Integer, primary_key=True),
sa.Column('user_id', sa.Integer, sa.ForeignKey('users.user_id', ondelete="CASCADE"), nullable=False, index=True),
sa.Column('message_id', sa.Integer, index=True, unique=True),
sa.Column('is_broadcast', sa.Boolean, nullable=False),
sa.Column('from_admin', sa.Boolean, nullable=False),
sa.Column('created_at', sa.DateTime),
sa.Column('updated_at', sa.DateTime),
sa.Column('text', sa.String(4000), nullable=False),
sa.Column('reply_to_message', sa.Integer),
sa.Column('document_id', sa.String, nullable=False),
sa.Column('photo_id', sa.String, nullable=False),
)
op.add_column('users',
sa.Column('last_message_id', sa.Integer, sa.ForeignKey('messages.message_id', ondelete="CASCADE"), index=True),
)
def downgrade():
op.drop_table('admins')
op.drop_table('users')
op.drop_table('messages')
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] |
metrics/fid/fid_score.py | vfcosta/coegan-trained | 0 | 21965 | # Code apapted from https://github.com/mseitzer/pytorch-fid
"""Calculates the Frechet Inception Distance (FID) to evalulate GANs
The FID metric calculates the distance between two distributions of images.
Typically, we have summary statistics (mean & covariance matrix) of one
of these distributions, while the 2nd distribution is given by a GAN.
When run as a stand-alone program, it compares the distribution of
images that are stored as PNG/JPEG at a specified location with a
distribution given by summary statistics (in pickle format).
The FID is calculated by assuming that X_1 and X_2 are the activations of
the pool_3 layer of the inception net for generated samples and real world
samples respectively.
See --help to see further details.
Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead
of Tensorflow
Copyright 2018 Institute of Bioinformatics, JKU Linz
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 time
import numpy as np
import torch
from scipy import linalg
from torch.nn.functional import adaptive_avg_pool2d
from util import tools
def get_activations(dataset, model, size=1000, batch_size=50, dims=2048, device='cpu'):
"""Calculates the activations of the pool_3 layer for all images.
Params:
-- files : List of image files paths
-- model : Instance of inception model
-- batch_size : Batch size of images for the model to process at once.
Make sure that the number of samples is a multiple of
the batch size, otherwise some samples are ignored. This
behavior is retained to match the original FID score
implementation.
-- dims : Dimensionality of features returned by Inception
-- device : Device to run calculations
Returns:
-- A numpy array of dimension (num images, dims) that contains the
activations of the given tensor when feeding inception with the
query tensor.
"""
model.eval()
if batch_size > size:
print(('Warning: batch size is bigger than the data size. '
'Setting batch size to data size'))
batch_size = size
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False, drop_last=False)
pred_arr = np.empty((size, dims))
start_idx = 0
for batch, _ in dataloader:
if batch.shape[1] == 1:
batch = torch.cat((batch, batch, batch), 1)
batch = batch.to(device)
with torch.no_grad():
pred = model(batch)[0]
# If model output is not scalar, apply global spatial average pooling.
# This happens if you choose a dimensionality not equal 2048.
if pred.size(2) != 1 or pred.size(3) != 1:
pred = adaptive_avg_pool2d(pred, output_size=(1, 1))
pred = pred.squeeze(3).squeeze(2).cpu().numpy()
pred_arr[start_idx:start_idx + pred.shape[0]] = pred
start_idx = start_idx + pred.shape[0]
if start_idx >= size:
break
return pred_arr
def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
"""Numpy implementation of the Frechet Distance.
The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
and X_2 ~ N(mu_2, C_2) is
d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
Stable version by <NAME>.
Params:
-- mu1 : Numpy array containing the activations of a layer of the
inception net (like returned by the function 'get_predictions')
for generated samples.
-- mu2 : The sample mean over activations, precalculated on an
representative data set.
-- sigma1: The covariance matrix over activations for generated samples.
-- sigma2: The covariance matrix over activations, precalculated on an
representative data set.
Returns:
-- : The Frechet Distance.
"""
mu1 = np.atleast_1d(mu1)
mu2 = np.atleast_1d(mu2)
sigma1 = np.atleast_2d(sigma1)
sigma2 = np.atleast_2d(sigma2)
assert mu1.shape == mu2.shape, 'Training and test mean vectors have different lengths'
assert sigma1.shape == sigma2.shape, 'Training and test covariances have different dimensions'
diff = mu1 - mu2
# Product might be almost singular
start_time = time.time()
covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
print("FID: sqrtm --- %s seconds ---" % (time.time() - start_time))
if not np.isfinite(covmean).all():
msg = ('fid calculation produces singular product; '
'adding %s to diagonal of cov estimates') % eps
print(msg)
offset = np.eye(sigma1.shape[0]) * eps
covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
# Numerical error might give slight imaginary component
if np.iscomplexobj(covmean):
if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
m = np.max(np.abs(covmean.imag))
# raise ValueError('Imaginary component {}'.format(m))
print('Imaginary component {}'.format(m))
covmean = covmean.real
tr_covmean = np.trace(covmean)
return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean
def calculate_activation_statistics(dataset, model, size=1000, batch_size=50, dims=2048):
act = get_activations(dataset, model, size, batch_size, dims, tools.device_name())
mu = np.mean(act, axis=0)
sigma = np.cov(act, rowvar=False)
return mu, sigma
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13,
2
] |
molsysmt/tests/pbc/box_lengths_from_box_vectors/test_box_lengths_from_box_vectors.py | dprada/molsysmt | 0 | 195239 | <filename>molsysmt/tests/pbc/box_lengths_from_box_vectors/test_box_lengths_from_box_vectors.py
"""
Unit and regression test for the box_lengths_from_box_vectors module of the molsysmt package.
"""
# Import package, test suite, and other packages as needed
import molsysmt as msm
import numpy as np
# Distance between atoms in space and time
def test_box_lengths_from_box_vectors_1():
molsys = msm.convert(msm.demo['Met-enkephalin']['vacuum.msmpk'], to_form='molsysmt.MolSys')
molsys = msm.build.solvate(molsys, box_geometry='cubic', clearance='14.0 angstroms', engine='PDBFixer')
box = msm.get(molsys, target='system', box=True)
lengths = msm.pbc.box_lengths_from_box_vectors(box)
check = np.allclose(msm.puw.get_value(lengths, to_unit='nm'), [[3.1236, 3.1236, 3.1236]])
assert check
def test_box_lengths_from_box_vectors_2():
molsys = msm.convert(msm.demo['Met-enkephalin']['vacuum.msmpk'], to_form='molsysmt.MolSys')
molsys = msm.build.solvate(molsys, box_geometry='truncated octahedral', clearance='14.0 angstroms', engine='PDBFixer')
box = msm.get(molsys, target='system', box=True)
lengths = msm.pbc.box_lengths_from_box_vectors(box)
check = np.allclose(msm.puw.get_value(lengths, to_unit='nm'), [[3.1236, 3.1236, 3.1236]])
assert check
def test_box_lengths_from_box_vectors_3():
molsys = msm.convert(msm.demo['Met-enkephalin']['vacuum.msmpk'], to_form='molsysmt.MolSys')
molsys = msm.build.solvate(molsys, box_geometry='rhombic dodecahedral', clearance='14.0 angstroms', engine='PDBFixer')
box = msm.get(molsys, target='system', box=True)
lengths = msm.pbc.box_lengths_from_box_vectors(box)
check = np.allclose(msm.puw.get_value(lengths, to_unit='nm'), [[3.1236, 3.1236, 3.1236]])
assert check
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source/evaluate_slt.py | faresbs/slrt | 2 | 71480 | #######################################################
#This script is for evaluating for the task of SLR #
#######################################################
import argparse
import time
import collections
import os
import sys
import torch
import torch.nn
from torch.autograd import Variable
import torch.nn as nn
import numpy as np
import datetime as dt
import _pickle as pickle
from collections import OrderedDict
from transformer_slt import make_model as TRANSFORMER
from dataloader_slt import loader
from utils import path_data, Batch, greedy_decode
#Progress bar to visualize training progress
import progressbar
import matplotlib.pyplot as plt
#Evaluation metrics
from bleu import compute_bleu
from rouge import rouge
from beam_search import beam_decode
#from nltk.translate.bleu_score import sentence_bleu, corpus_bleu
#https://pypi.org/project/py-rouge/
#import rouge
#Produce the translation using greedy decoding or beam search
def decoding(model, src, batch, hand_regions=None, start_symbol=1, max_len=20, device='cuda', method='greedy', n_beam=8):
src = Variable(src)
if(method=='greedy'):
translations = greedy_decode(model, src, hand_regions, batch.rel_mask, batch.src_mask, max_len=max_len, start_symbol=start_symbol, n_devices=1)
elif(method=='beam'):
translations = beam_decode(model, src, hand_regions, batch.rel_mask, batch.src_mask, max_len=max_len, start_symbol=start_symbol, n_beam=n_beam)
else:
print("Decoding method is not supported !")
quit(0)
return translations
###
# Arg parsing
##############
parser = argparse.ArgumentParser(description='Evaluation')
parser.add_argument('--data', type=str, default=os.path.join('data','PHOENIX-2014-T'),
help='location of the test data corpus')
parser.add_argument('--model_path', type=str, default=os.path.join("EXPERIMENTATIONS"),
help='location of the test data corpus')
parser.add_argument('--lookup_table', type=str, default=os.path.join('data','slt_lookup.txt'),
help='location of the words lookup table')
parser.add_argument('--lookup_table_gloss', type=str, default=os.path.join('data','slt_lookup_gloss.txt'),
help='location of the words gloss lookup table')
parser.add_argument('--rescale', type=int, default=224,
help='rescale data images. NOTE: use same image size as the training or else you get worse results.')
#Put to 0 to avoid memory segementation fault
parser.add_argument('--num_workers', type=int, default=4,
help='NOTE: put num of workers to 0 to avoid memory saturation.')
parser.add_argument('--show_sample', action='store_true',
help='Show a sample a preprocessed data.')
parser.add_argument('--batch_size', type=int, default=1,
help='size of one minibatch')
parser.add_argument('--save', action='store_true',
help='save the results of the evaluation')
parser.add_argument('--hand_query', type=bool, default=False,
help='Set hand cropped image as a query for transformer network.')
parser.add_argument('--emb_type', type=str, default='2d',
help='Type of image embeddings 2d or 3d.')
parser.add_argument('--emb_network', type=str, default='mb2',
help='Image embeddings network: mb2/resnet')
parser.add_argument('--decoding', type=str, default='greedy',
help='Decoding method (greedy/beam).')
parser.add_argument('--n_beam', type=int, default=4,
help='Beam width when using bean search for decoding.')
parser.add_argument('--decoding_length', type=int, default=20,
help='Set the maximum decoding length. NOTE: to get the same results as when training you need to use the same max_len')
parser.add_argument('--rel_window', type=int, default=None)
parser.add_argument('--bleu', action='store_true',
help='Use bleu for evaluation.')
parser.add_argument('--rouge', action='store_true',
help='Use rouge for evaluation.')
#----------------------------------------------------------------------------------------
#Same seed for reproducibility)
parser.add_argument('--seed', type=int, default=1111, help='random seed')
#Save folder with the date
start_date = dt.datetime.now().strftime("%Y-%m-%d-%H.%M")
print ("Start Time: "+start_date)
args = parser.parse_args()
#Set the random seed manually for reproducibility.
torch.manual_seed(args.seed)
#experiment_path = PureWindowsPath('EXPERIMENTATIONS\\' + start_date)
save_path = os.path.join('EVALUATION', start_date)
# Creates an experimental directory and dumps all the args to a text file
if(args.save):
if(os.path.exists(save_path)):
print('Evaluation already exists..')
else:
os.makedirs(save_path)
print ("\nPutting log in EVALUATION/%s"%start_date)
#Dump all configurations/hyperparameters in txt
with open (os.path.join(save_path,'eval_config.txt'), 'w') as f:
f.write('Experimentation done at: '+ str(start_date)+' with current configurations:\n')
for arg in vars(args):
f.write(arg+' : '+str(getattr(args, arg))+'\n')
#-------------------------------------------------------------------------------
#Run on GPU
if torch.cuda.is_available():
print("Using GPU")
device = torch.device("cuda:0")
else:
#Run on CPU
print("WARNING: You are about to run on cpu, and this will likely run out \
of memory. \n You can try setting batch_size=1 to reduce memory usage")
device = torch.device("cpu")
train_path, valid_path, test_path = path_data(data_path=args.data, task='SLT', hand_query=args.hand_query)
#No data augmentation for test data
test_dataloader, test_size = loader(csv_file=test_path[1],
root_dir=test_path[0],
lookup_gloss=args.lookup_table_gloss,
lookup=args.lookup_table,
rescale = args.rescale,
augmentation = False,
batch_size = args.batch_size,
num_workers = args.num_workers,
show_sample = args.show_sample,
istrain=False,
hand_dir=test_path[2]
)
#No data augmentation for test data
valid_dataloader, valid_size = loader(csv_file=valid_path[1],
root_dir=valid_path[0],
lookup=args.lookup_table,
lookup_gloss=args.lookup_table_gloss,
rescale = args.rescale,
augmentation = False,
batch_size = args.batch_size,
num_workers = args.num_workers,
show_sample = args.show_sample,
istrain=False,
hand_dir=valid_path[2]
)
print('Test dataset size: '+str(test_size))
print('Valid dataset size: '+str(valid_size))
#Retrieve size of target vocab
with open(args.lookup_table, 'rb') as pickle_file:
vocab = pickle.load(pickle_file)
vocab_size = len(vocab)
#Switch keys and values of vocab to easily look for words
vocab = {y:x for x,y in vocab.items()}
print('vocabulary size:' + str(vocab_size))
#Load entire model w/ weights
#NOTE: evaluate on single device
model = torch.load(args.model_path, map_location=device)
#Load params
#model.load_state_dict(n)
model = model.to(device)
print("Model successfully loaded")
model.eval() # Set model to evaluate mode
print ("Evaluating..")
start_time = time.time()
#Loop through test and val sets
dataloaders = [valid_dataloader, test_dataloader]
sizes = [valid_size, test_size]
dataset = ['Validation Set', 'Test Set']
for d in range(len(sizes)):
dataloader = dataloaders[d]
size = sizes[d]
print(dataset[d])
#For progress bar
bar = progressbar.ProgressBar(maxval=size, widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])
bar.start()
i = 0
count = 0
#Save translation and reference sentences
translation_corpus = []
reference_corpus = []
references_corpus = []
#Loop over minibatches
for step, (x, x_lengths, y, y_lengths, gloss, gloss_lengths) in enumerate(dataloader):
#Update progress bar with every iter
i += len(x)
bar.update(i)
y = torch.from_numpy(y).to(device)
x = x.to(device)
batch = Batch(x_lengths, y_lengths, y, DEVICE=device, emb_type='2d', fixed_padding=None, rel_window=args.rel_window)
with torch.no_grad():
#Return translation using our trained model
translations = decoding(model, x, batch, None, start_symbol=1, max_len=args.decoding_length, method=args.decoding, n_beam=args.n_beam, device=device)
#Loop over translations and references
#NOTE: discard special tokens
for j in range(y.shape[0]):
ys = y[j, :]
ys = ys[ys != 0]
#Keep <eos> token
ys = ys[1:]
translation = translations[j]
#Convert index tokens to words
translation_corpus.append([vocab[x.item()] for x in translation])
references_corpus.append([[vocab[x.item()] for x in ys]])
reference_corpus.append([vocab[x.item()] for x in ys])
#Free some memory
#NOTE: this helps alot in avoiding cuda out of memory
del x, y, batch
assert len(translation_corpus) == len(references_corpus)
if(args.save):
#Save results in txt files
str1=" "
with open(os.path.join(save_path, 'translations.txt') ,'a') as trans_file:
trans_file.write(str1.join(translation_corpus)+'\n')
str1=" "
with open(os.path.join(save_path, 'references.txt'), 'a') as ref_file:
ref_file.write(str1.join(reference_corpus)+'\n')
if(args.bleu):
#Default return
#NOTE: you can use the ntlk library to measure the bleu score
#bleu_4 = corpus_bleu(reference_corpus, translation_corpus)
bleu_4, _, _, _, _, _ = compute_bleu(references_corpus, translation_corpus, max_order=4)
#weights = (1.0/1.0, )
bleu_1, _, _, _, _, _ = compute_bleu(references_corpus, translation_corpus, max_order=1)
#weights = (1.0/2.0, 1.0/2.0, )
#bleu_2 = corpus_bleu(reference_corpus, translation_corpus, weights)
bleu_2, _, _, _, _, _ = compute_bleu(references_corpus, translation_corpus, max_order=2)
#weights = (1.0/3.0, 1.0/3.0, 1.0/3.0,)
#bleu_3 = corpus_bleu(reference_corpus, translation_corpus, weights)
bleu_3, _, _, _, _, _ = compute_bleu(references_corpus, translation_corpus, max_order=3)
log_str = 'Bleu Evaluation: ' + '\t' \
+ 'Bleu_1: ' + str(bleu_1) + '\t' \
+ 'Bleu_2: ' + str(bleu_2) + '\t' \
+ 'Bleu_3: ' + str(bleu_3) + '\t' \
+ 'Bleu_4: ' + str(bleu_4)
print(log_str)
if(args.save):
#Save evaluation results in a log file
with open(os.path.join(args.save_path, 'log.txt'), 'a') as f:
f.write(log_str+'\n')
if(args.rouge):
reference_corpus = [" ".join(reference) for reference in reference_corpus]
translation_corpus = [" ".join(hypothesis) for hypothesis in translation_corpus]
score = rouge(translation_corpus, reference_corpus)
print(score["rouge_l/f_score"])
log_str = 'Rouge Evaluation: ' + '\t'
print(log_str)
if(args.save):
#Save evaluation results in a log file
with open(os.path.join(args.save_path, 'log.txt'), 'a') as f:
f.write(log_str+'\n')
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origamicon/app.py | Mailea/individual-sha1-avatars | 14 | 97082 | from flask import Flask, render_template, request, send_file
import io
import base64 as b64
import generator
app = Flask(__name__)
def page(**kwargs):
return render_template('main.html', **kwargs)
@app.route("/")
def home():
return page()
@app.route("/<text>", methods=['GET'])
def load_origamicon(text):
buffer = io.BytesIO()
origamicon = generator.create_origamicon(text)
origamicon.save(buffer, format='PNG')
buffer.seek(0)
return send_file(buffer, mimetype='image/png')
@app.route("/", methods=['POST'])
def update_origamicon():
text = request.form['origamicon-text']
if not text:
return page()
buffer = io.BytesIO()
origamicon = generator.create_origamicon(text)
origamicon.save(buffer, format='PNG')
image_data = buffer.getvalue()
image_data = b64.b64encode(image_data).decode()
image = 'data:;base64, {}'.format(image_data)
return page(name=text, image=image)
if __name__ == '__main__':
app.run()
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polymorphic/tests/test_utils.py | likeanaxon/django-polymorphic | 1 | 20437 | <filename>polymorphic/tests/test_utils.py
from django.test import TransactionTestCase
from polymorphic.models import PolymorphicModel, PolymorphicTypeUndefined
from polymorphic.tests.models import (
Enhance_Base,
Enhance_Inherit,
Model2A,
Model2B,
Model2C,
Model2D,
)
from polymorphic.utils import (
get_base_polymorphic_model,
reset_polymorphic_ctype,
sort_by_subclass,
)
class UtilsTests(TransactionTestCase):
def test_sort_by_subclass(self):
self.assertEqual(
sort_by_subclass(Model2D, Model2B, Model2D, Model2A, Model2C),
[Model2A, Model2B, Model2C, Model2D, Model2D],
)
def test_reset_polymorphic_ctype(self):
"""
Test the the polymorphic_ctype_id can be restored.
"""
Model2A.objects.create(field1="A1")
Model2D.objects.create(field1="A1", field2="B2", field3="C3", field4="D4")
Model2B.objects.create(field1="A1", field2="B2")
Model2B.objects.create(field1="A1", field2="B2")
Model2A.objects.all().update(polymorphic_ctype_id=None)
with self.assertRaises(PolymorphicTypeUndefined):
list(Model2A.objects.all())
reset_polymorphic_ctype(Model2D, Model2B, Model2D, Model2A, Model2C)
self.assertQuerysetEqual(
Model2A.objects.order_by("pk"),
[Model2A, Model2D, Model2B, Model2B],
transform=lambda o: o.__class__,
)
def test_get_base_polymorphic_model(self):
"""
Test that finding the base polymorphic model works.
"""
# Finds the base from every level (including lowest)
self.assertIs(get_base_polymorphic_model(Model2D), Model2A)
self.assertIs(get_base_polymorphic_model(Model2C), Model2A)
self.assertIs(get_base_polymorphic_model(Model2B), Model2A)
self.assertIs(get_base_polymorphic_model(Model2A), Model2A)
# Properly handles multiple inheritance
self.assertIs(get_base_polymorphic_model(Enhance_Inherit), Enhance_Base)
# Ignores PolymorphicModel itself.
self.assertIs(get_base_polymorphic_model(PolymorphicModel), None)
def test_get_base_polymorphic_model_skip_abstract(self):
"""
Skipping abstract models that can't be used for querying.
"""
class A(PolymorphicModel):
class Meta:
abstract = True
class B(A):
pass
class C(B):
pass
self.assertIs(get_base_polymorphic_model(A), None)
self.assertIs(get_base_polymorphic_model(B), B)
self.assertIs(get_base_polymorphic_model(C), B)
self.assertIs(get_base_polymorphic_model(C, allow_abstract=True), A)
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] |
src/testing/postgresql.py | kmane-bbcock1/python_postgresql | 0 | 1613185 | <gh_stars>0
# -*- coding: utf-8 -*-
# Copyright 2013 <NAME>
#
# 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 os
import sys
import socket
import signal
import pg8000
import tempfile
import subprocess
from glob import glob
from time import sleep
from shutil import copytree, rmtree
from datetime import datetime
from contextlib import closing
__all__ = ['Postgresql', 'skipIfNotFound']
SEARCH_PATHS = (['/usr/local/pgsql', '/usr/local'] +
glob('/usr/lib/postgresql/*') + # for Debian/Ubuntu
glob('/opt/local/lib/postgresql*')) # for MacPorts
DEFAULT_SETTINGS = dict(auto_start=2,
base_dir=None,
initdb=None,
initdb_args='-U postgres -A trust',
postgres=None,
postgres_args='-h 127.0.0.1 -F -c logging_collector=off',
pid=None,
port=None,
copy_data_from=None)
class Postgresql(object):
def __init__(self, **kwargs):
self.settings = dict(DEFAULT_SETTINGS)
self.settings.update(kwargs)
self.pid = None
self._owner_pid = os.getpid()
self._use_tmpdir = False
if self.base_dir:
if self.base_dir[0] != '/':
self.settings['base_dir'] = os.path.join(os.getcwd(), self.base_dir)
else:
self.settings['base_dir'] = tempfile.mkdtemp()
self._use_tmpdir = True
if self.initdb is None:
self.settings['initdb'] = find_program('initdb', ['bin'])
if self.postgres is None:
self.settings['postgres'] = find_program('postgres', ['bin'])
if self.auto_start:
if self.auto_start >= 2:
self.setup()
self.start()
def __del__(self):
self.stop()
def __enter__(self):
return self
def __exit__(self, *args):
self.stop()
def __getattr__(self, name):
if name in self.settings:
return self.settings[name]
else:
raise AttributeError("'Postgresql' object has no attribute '%s'" % name)
def dsn(self, **kwargs):
# "database=test host=localhost user=postgres"
params = dict(kwargs)
params.setdefault('port', self.port)
params.setdefault('host', '127.0.0.1')
params.setdefault('user', 'postgres')
params.setdefault('database', 'test')
return params
def url(self, **kwargs):
params = self.dsn(**kwargs)
url = ('postgresql://%s@%s:%d/%s' %
(params['user'], params['host'], params['port'], params['database']))
return url
def setup(self):
# copy data files
if self.copy_data_from:
try:
copytree(self.copy_data_from, os.path.join(self.base_dir, 'data'))
os.chmod(os.path.join(self.base_dir, 'data'), 0o700)
except Exception as exc:
raise RuntimeError("could not copytree %s to %s: %r" %
(self.copy_data_from, os.path.join(self.base_dir, 'data'), exc))
# (re)create directory structure
for subdir in ['data', 'tmp']:
path = os.path.join(self.base_dir, subdir)
if not os.path.exists(path):
os.makedirs(path)
os.chmod(path, 0o700)
# initdb
if not os.path.exists(os.path.join(self.base_dir, 'data', 'PG_VERSION')):
args = ([self.initdb, '-D', os.path.join(self.base_dir, 'data'), '--lc-messages=C'] +
self.initdb_args.split())
try:
p = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
output, err = p.communicate()
if p.returncode != 0:
raise RuntimeError("initdb failed: %r" % err)
except OSError as exc:
raise RuntimeError("failed to spawn initdb: %s" % exc)
def start(self):
if self.pid:
return # already started
if self.port is None:
self.port = get_unused_port()
logger = open(os.path.join(self.base_dir, 'tmp', 'postgresql.log'), 'wt')
pid = os.fork()
if pid == 0:
os.dup2(logger.fileno(), sys.__stdout__.fileno())
os.dup2(logger.fileno(), sys.__stderr__.fileno())
try:
os.execl(self.postgres, self.postgres,
'-p', str(self.port),
'-D', os.path.join(self.base_dir, 'data'),
'-k', os.path.join(self.base_dir, 'tmp'),
*self.postgres_args.split())
except Exception as exc:
raise RuntimeError('failed to launch postgres: %r' % exc)
else:
logger.close()
self.pid = pid
exec_at = datetime.now()
while True:
if os.waitpid(pid, os.WNOHANG)[0] != 0:
raise RuntimeError("*** failed to launch postgres ***\n" + self.read_log())
if self.is_connection_available():
break
if (datetime.now() - exec_at).seconds > 10.0:
raise RuntimeError("*** failed to launch postgres (timeout) ***\n" + self.read_log())
sleep(0.1)
# create test database
with closing(pg8000.connect(**self.dsn(database='postgres'))) as conn:
conn.autocommit = True
with closing(conn.cursor()) as cursor:
cursor.execute("SELECT COUNT(*) FROM pg_database WHERE datname='test'")
if cursor.fetchone()[0] <= 0:
cursor.execute('CREATE DATABASE test')
def stop(self, _signal=signal.SIGINT):
self.terminate(_signal)
self.cleanup()
def terminate(self, _signal=signal.SIGINT):
if self.pid is None:
return # not started
if self._owner_pid != os.getpid():
return # could not stop in child process
try:
os.kill(self.pid, _signal)
killed_at = datetime.now()
while (os.waitpid(self.pid, os.WNOHANG)):
if (datetime.now() - killed_at).seconds > 10.0:
os.kill(self.pid, signal.SIGKILL)
raise RuntimeError("*** failed to shutdown postgres (timeout) ***\n" + self.read_log())
sleep(0.1)
except OSError:
pass
self.pid = None
def cleanup(self):
if self.pid is not None:
return
if self._use_tmpdir and os.path.exists(self.base_dir):
rmtree(self.base_dir, ignore_errors=True)
self._use_tmpdir = False
def read_log(self):
try:
with open(os.path.join(self.base_dir, 'tmp', 'postgresql.log')) as log:
return log.read()
except Exception as exc:
raise RuntimeError("failed to open file:tmp/postgresql.log: %r" % exc)
def is_connection_available(self):
try:
with closing(pg8000.connect(**self.dsn(database='template1'))):
pass
except pg8000.Error:
return False
else:
return True
def skipIfNotInstalled(arg=None):
if sys.version_info < (2, 7):
from unittest2 import skipIf
else:
from unittest import skipIf
def decorator(fn, path=arg):
if path:
cond = not os.path.exists(path)
else:
try:
find_program('postgres', ['bin']) # raise exception if not found
cond = False
except:
cond = True # not found
return skipIf(cond, "PostgreSQL not found")(fn)
if callable(arg): # execute as simple decorator
return decorator(arg, None)
else: # execute with path argument
return decorator
skipIfNotFound = skipIfNotInstalled
def find_program(name, subdirs):
path = get_path_of(name)
if path:
return path
for base_dir in SEARCH_PATHS:
for subdir in subdirs:
path = os.path.join(base_dir, subdir, name)
if os.path.exists(path):
return path
raise RuntimeError("command not found: %s" % name)
def get_path_of(name):
path = subprocess.Popen(['/usr/bin/which', name],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE).communicate()[0]
if path:
return path.rstrip().decode('utf-8')
else:
return None
def get_unused_port():
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.bind(('localhost', 0))
_, port = sock.getsockname()
sock.close()
return port
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tests/ut/test_core_files_public_link.py | CTERA-Networks/ctera-python-sdk | 5 | 163739 | import datetime
from unittest import mock
from cterasdk.core.enum import FileAccessMode
from cterasdk.common import Object
from tests.ut import base_core_services
class BaseCoreServicesFilesPublicLink(base_core_services.BaseCoreServicesTest):
def setUp(self):
super().setUp()
self._path = 'My Files/Documents'
self._public_link = 'https://cti.ctera.com/invitations/?share=d4db2b6054b7e447e07d&dl=0'
def test_create_public_link_defaults_args(self):
execute_response = self._create_public_link_response()
self._init_services(execute_response=execute_response)
public_link = self._services.files.mklink(self._path)
self._services.execute.assert_called_once_with('', 'createShare', mock.ANY)
expected_param = self._create_public_link_param(FileAccessMode.RO, 30)
actual_param = self._services.execute.call_args[0][2]
self._assert_equal_objects(actual_param, expected_param)
self.assertEqual(public_link, self._public_link)
def _create_public_link_response(self):
response = Object()
response.publicLink = self._public_link
return response
def _create_public_link_param(self, access_mode, expire_in):
param = Object()
param._classname = 'CreateShareParam' # pylint: disable=protected-access
param.url = self._services.cloud_drive_base_path + '/' + self._path
param.share = Object()
param.share._classname = 'ShareConfig' # pylint: disable=protected-access
param.share.accessMode = access_mode
param.share.protectionLevel = 'publicLink'
param.share.expiration = (datetime.date.today() + datetime.timedelta(days=expire_in)).strftime('%Y-%m-%d')
param.share.invitee = Object()
param.share.invitee._classname = 'Collaborator' # pylint: disable=protected-access
param.share.invitee.type = 'external'
return param
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tests/io/threading/serial/receiver.py | ethanjli/phyllo-python | 0 | 149910 | <filename>tests/io/threading/serial/receiver.py
"""Print all data received on the serial port."""
# Builtins
import logging
# Packages
from phylline.links.events import LinkException
from phyllo.io.cli.args.args import parse_args
from phyllo.io.cli.args.protocol import (
args_application_stack, args_transport_logical_substack, group_protocol_stack
)
from phyllo.io.polling.serial import connect_serial_retry
from phyllo.io.threading.serial import ThreadedSerialAdapter
from phyllo.io.threading.serial import make_stack as make_threading_stack
from phyllo.protocol.stacks import make_preset_stack
from phyllo.util.logging import config_logging
from tests.io.polling.serial.receiver import run_once, run_read
config_logging()
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
def receive_blocking(stack):
"""Repeatedly receive data on the stack."""
while stack.has_receive.acquire():
data = stack.receive()
if isinstance(data, LinkException):
logger.error('Unexpected error: {}'.format(data))
else:
yield data
if stack.receiver_disconnected:
raise ConnectionResetError
def main(transport_logical_preset, application_preset):
"""List the ports."""
connection = connect_serial_retry(baudrate=115200, timeout=1.0)
if connection is None:
logger.fatal('Failed to establish serial connection!')
return
protocol = make_preset_stack(
transport_logical=transport_logical_preset, application=application_preset
)
stack = ThreadedSerialAdapter(
connection, make_threading_stack(protocol_stack=protocol)
)
logger.info(stack)
run_once(run_read, stack, receive_blocking)
logger.info('Quitting!')
if __name__ == '__main__':
args = parse_args(
grouped_args={
group_protocol_stack: (
args_transport_logical_substack, args_application_stack
)
},
description='Print all data received on the serial port.'
)
main(args.logical, args.application)
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dedsecuritybot.py | dedsecurity/DedSecuritySearch | 0 | 34351 | <gh_stars>0
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
import pandas as pd
import json
import nltk
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.layers import Input, Embedding, LSTM , Dense,GlobalMaxPooling1D,Flatten
from tensorflow.keras.preprocessing.sequence import pad_sequences
from transformers import BertModel, BertTokenizer
from tensorflow.keras.models import Model
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import string
import torch
import random
with open('./links.json') as content:
datali = json.load(content)
tags = []
inputs = []
responses={}
for intent in datali['intents']:
responses[intent['tag']]=intent['responses']
for lines in intent['input']:
inputs.append(lines)
tags.append(intent['tag'])
data = pd.DataFrame({"inputs":inputs,
"tags":tags})
data
data = data.sample(frac=1)
data['inputs'] = data['inputs'].apply(lambda wrd:[ltrs.lower() for ltrs in wrd if ltrs not in string.punctuation])
data['inputs'] = data['inputs'].apply(lambda wrd: ''.join(wrd))
data
tokenizer = Tokenizer(num_words=2000)
tokenizer.fit_on_texts(data['inputs'])
train = tokenizer.texts_to_sequences(data['inputs'])
x_train = pad_sequences(train)
le = LabelEncoder()
y_train = le.fit_transform(data['tags'])
input_shape = x_train.shape[1]
print(input_shape)
vocabulary = len(tokenizer.word_index)
print("number of unique words : ",vocabulary)
output_length = le.classes_.shape[0]
print("output length: ",output_length)
i = Input(shape=(input_shape,))
x = Embedding(vocabulary+1,10)(i)
x = LSTM(10,return_sequences=True)(x)
x = Flatten()(x)
x = Dense(output_length,activation="softmax")(x)
model = Model(i,x)
model.compile(loss="sparse_categorical_crossentropy",optimizer='adam',metrics=['accuracy'])
train = model.fit(x_train,y_train,epochs=300)
model1 = BertModel.from_pretrained('bert-base-uncased')
tokenizer1 = BertTokenizer.from_pretrained('bert-base-uncased')
while True:
import random
print("\033[32mDed Security Search \033[m")
texts_p = []
prediction_input = input('\033[36mSearch: \033[m')
tokens = tokenizer1.tokenize(prediction_input)
tokens = ['[CLS]'] + tokens + ['[SEP]']
tokens = tokens + ['[PAD]'] + ['[PAD]']
attention_mask = [1 if i!= '[PAD]' else 0 for i in tokens]
token_ids = tokenizer1.convert_tokens_to_ids(tokens)
token_ids = torch.tensor(token_ids).unsqueeze(0)
attention_mask = torch.tensor(attention_mask).unsqueeze(0)
hidden_rep, cls_head = model1(token_ids, attention_mask = attention_mask)
prediction_input = [letters.lower() for letters in prediction_input if letters not in string.punctuation]
prediction_input = ''.join(prediction_input)
texts_p.append(prediction_input)
prediction_input = tokenizer.texts_to_sequences(texts_p)
prediction_input = np.array(prediction_input).reshape(-1)
prediction_input = pad_sequences([prediction_input],input_shape)
output = model.predict(prediction_input)
output = output.argmax()
response_tag = le.inverse_transform([output])[0]
print(random.choice(responses[response_tag])) | [
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2
] |
projects/fizzbuzz/python/test.py | jthompson22/methods | 0 | 106304 | <filename>projects/fizzbuzz/python/test.py
import pytest
from fizz_buzz import fizz_buzz
def test_fizzbuzz():
fizzbuzz_string5 = ' 1\n2\nfizz\n4\nbuzz\n'
fizzbuzz_string10 = ' 1\n2\nfizz\n4\nbuzz\nfizz\nbazz\n8\nfizz\nbuzz\n'
fizzbuzz_string21 =' 1\n2\nfizz\n4\nbuzz\nfizz\nbazz\n8\nfizz\nbuzz\n11\nfizz\n13\nbazz\nfizzbuzz\n16\n17\nfizz\n19\nbuzz\nfizzbazz\n'
assert fizz_buzz(10) == fizzbuzz_string10
assert fizz_buzz(5) == fizzbuzz_string5
assert fizz_buzz(21) == fizzbuzz_string21
| [
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] |
spexxy/weight/fromgridnearest.py | thusser/spexxy | 4 | 39239 | <gh_stars>1-10
import os
import numpy as np
import pandas as pd
from typing import List, Union
from .weight import Weight
from ..data import Spectrum
class WeightFromGridNearest(Weight):
"""
This class loads the weights from a grid depending on the initial values of the fit parameters by choosing the
nearest neighbour in the grid. It returns an array containing the weights.
"""
def __init__(self, filename, initial: float = 0., max_line_depth: float = 0.5, center_weight: float = 1.,
max_step: int = 1, mask_lines: Union[bool, str, List] = True, max_change=(300, 0.3), *args, **kwargs):
"""
Initializes a new weight.
Args:
filename: Name of grid file.
initial: Initial value for the whole weight array.
max_line_depth: Central pixel for lines with larger line depth are masked out.
center_weight: Factor that increases the weight of the central pixel of each line.
max_step: In iteration steps <= max_step new weights are loaded from the grid.
mask_lines: List of absorption lines that are always masked out in their centers.
"""
Weight.__init__(self, *args, **kwargs)
if 'objects' in kwargs:
self.objects = kwargs['objects']
self._initial = initial
self._max_line_depth = max_line_depth
self._center_weight = center_weight
self._max_step = max_step
self._max_change = sorted(max_change, reverse=True)
if mask_lines:
if isinstance(mask_lines, bool):
self._mask_lines = 'default'
elif isinstance(mask_lines, list):
self._mask_lines = []
for line in mask_lines:
if len(line) == 2:
self._mask_lines.append(line + [-0.5, 6.5])
else:
self._mask_lines.append(line)
elif isinstance(mask_lines, str):
df = pd.read_csv(os.path.expandvars(mask_lines))
df.loc[df['logg_min'].isna(), 'logg_min'] = -0.5
df.loc[df['logg_max'].isna(), 'logg_max'] = 6.5
self._mask_lines = df.to_numpy()
else:
self._mask_lines = mask_lines
# expand filename
filename = os.path.expandvars(filename)
# get grid's root path
self._root = os.path.dirname(filename)
# load CSV
self._data = pd.read_csv(filename, index_col=False)
# get all parameters, by removing 'Filename' from list of columns
self._parameters = list(self._data.columns)
self._parameters.remove('Filename')
# we assume that all parameters are floats, so treat them as such
for name in self._parameters:
self._data[name] = self._data[name].apply(lambda x: float(x))
# get grid axes
self._axes = [np.array(sorted(self._data[p].unique())) for p in self._parameters]
# remove axes that contain only a single value
for i, p in enumerate(self._parameters):
if len(self._axes[i]) <= 1:
del self._axes[i]
self._parameters.remove(p)
self._data.set_index(keys=self._parameters, inplace=True)
# initialize step counter
self._step = 1
# values of the fit parameter from previous iteration step
self._previous_values = None
self._filename = None
# weights will be stored for next iterations
self._weights = None
self._neighbour = None
# save initial parameters
self._initial_values = None
self._logg = None
def __call__(self, spectrum: Spectrum, filename: str) -> np.ndarray:
"""
Creates and returns weight array.
Args:
spectrum: Spectrum to create weight for.
filename: Name of spectrum file.
Returns:
Array containing the weight for given spectrum.
"""
# save initial values
if self._initial_values is None:
self._initial_values = {}
for cmp in self.objects['init_iter'].values():
for param_name in cmp.param_names:
self._initial_values[param_name] = cmp[param_name]
if param_name == 'logg' and self._logg is None:
self._logg = cmp[param_name]
break
# load new weights if the fit parameters changed significantly
new_weights = False
if self._previous_values is not None:
for param in self._parameters:
if new_weights:
break
for cmp in self.objects['init_iter'].values():
for param_name in cmp.param_names:
if param.lower() != param_name.lower():
continue
if param.lower() == 'teff':
# did Teff change by more than 300K?
new_weights = abs(
self._previous_values[self._parameters.index(param)] - cmp[param_name]) > self._max_change[0]
else:
# did FeH, Alpha or logg change by more than 0.3 dex?
new_weights = abs(
self._previous_values[self._parameters.index(param)] - cmp[param_name]) > self._max_change[1]
# are current parameter values identical with initial values?
if self._step > 1:
tmp = []
for cmp in self.objects['init_iter'].values():
for param_name in cmp.param_names:
tmp.append(cmp[param_name] == self._initial_values[param_name])
break
# component is reset to initial values if the fit restarts with a damping factor, in that case the iteration
# step needs to be reset as well
if np.all(tmp):
self._step = 1
# load new weights if max_step has not been reached or fit parameters changed significantly
if (self._step <= self._max_step) or new_weights:
if new_weights:
self._step = 1
# get parameters from component
params = []
for param in self._parameters:
for cmp in self.objects['init_iter'].values():
for param_name in cmp.param_names:
if param.lower() != param_name.lower():
continue
params.append(cmp[param_name])
break
# save current parameters for next step
self._previous_values = params.copy()
# find nearest neighbour
self._neighbour = []
for i, p in enumerate(params):
self._neighbour.append(self._axes[i][np.argmin(np.abs(self._axes[i] - p))])
# save filename of weight table
self._filename = self._data.loc[tuple(self._neighbour)].Filename
# load weights
w = self._load_weights(spectrum)
# increase step counter
self._step += 1
return w
def _load_weights(self, spectrum: Spectrum):
"""
Load CSV file from grid and create weight array.
Args:
filename: Filename of CSV file that contains the weights.
spectrum: Spectrum to create the weight for.
Returns:
Weight array.
"""
# load table containing the weights
df = pd.read_csv(os.path.join(self._root, self._filename))
# consider only weights for iteration steps lower/equal than the given one
df = df[df['step'] <= self._step]
# initialize weight array
weights = np.zeros(spectrum.wave.shape) + self._initial
# write weights to array
for i, row in df.iterrows():
if isinstance(self._mask_lines, list) or isinstance(self._mask_lines, np.ndarray):
if self._mask_centers(row, self._mask_lines, weights, spectrum):
continue
elif self._mask_lines == 'default':
if self._mask_default_lines(row, weights, spectrum):
continue
weights[(spectrum.wave >= row['wave_start']) & (spectrum.wave <= row['wave_end'])] += row['weights']
# if line depth larger than given threshold mask out the central region otherwise increase weight of
# central pixel by given factor
if row['line_depth'] > self._max_line_depth:
# if region spans more than 10 wavelength pixel mask out the 3 central pixel otherwise only the central
# one
if (row['wave_end'] - row['wave_start']) >= 12:
i = np.argmin(np.abs(spectrum.wave - row['wave_center']))
weights[i-1:i+2] = 0
else:
weights[np.argmin(np.abs(spectrum.wave - row['wave_center']))] = 0
else:
weights[np.argmin(np.abs(spectrum.wave - row['wave_center']))] *= self._center_weight
return weights
def _mask_default_lines(self, row: pd.Series, weights: np.ndarray, spectrum: Spectrum):
# Halpha
if (row['wave_center'] < 6566.) & (row['wave_center'] > 6557.) & (self._logg <= 3.5):
weights[(spectrum.wave >= row['wave_start']) & (spectrum.wave <= row['wave_end'])] += row['weights']
i = np.argmin(np.abs(spectrum.wave - row['wave_center']))
weights[i - 1:i + 2] = 0
return True
elif (row['wave_center'] < 6566.) & (row['wave_center'] > 6557.) & (self._logg > 3.5):
weights[(spectrum.wave >= row['wave_start']) & (spectrum.wave <= row['wave_end'])] += row['weights']
i = np.argmin(np.abs(spectrum.wave - row['wave_center']))
weights[i] = 0
return True
# Hbeta
if (row['wave_center'] < 4867.) & (row['wave_center'] > 4857.):
weights[(spectrum.wave >= row['wave_start']) & (spectrum.wave <= row['wave_end'])] += row['weights']
i = np.argmin(np.abs(spectrum.wave - row['wave_center']))
weights[i] = 0
return True
# FeI line
if (row['wave_center'] < 5272.) and (row['wave_center'] > 5267.):
weights[(spectrum.wave >= row['wave_start']) & (spectrum.wave <= row['wave_end'])] += row['weights']
i = np.argmin(np.abs(spectrum.wave - row['wave_center']))
weights[i - 1:i + 2] = 0
return True
# Ca triplet
if (row['wave_center'] < 8508.) and (row['wave_center'] > 8490.):
weights[(spectrum.wave >= row['wave_start']) & (spectrum.wave <= row['wave_end'])] += row['weights']
i = np.argmin(np.abs(spectrum.wave - row['wave_center']))
weights[i - 2:i + 3] = 0
return True
if (row['wave_center'] < 8553.) and (row['wave_center'] > 8530.):
weights[(spectrum.wave >= row['wave_start']) & (spectrum.wave <= row['wave_end'])] += row['weights']
i = np.argmin(np.abs(spectrum.wave - row['wave_center']))
weights[i - 2:i + 3] = 0
return True
if (row['wave_center'] < 8672.) and (row['wave_center'] > 8651.):
weights[(spectrum.wave >= row['wave_start']) & (spectrum.wave <= row['wave_end'])] += row['weights']
i = np.argmin(np.abs(spectrum.wave - row['wave_center']))
weights[i - 2:i + 3] = 0
return True
return False
def _mask_centers(self, row: pd.Series, lines: Union[list, np.ndarray], weights: np.ndarray, spectrum: Spectrum):
for center, npix, logg_min, logg_max in lines:
if (row['wave_start'] < center) and (row['wave_end'] > center) and (self._logg < logg_max) and (
self._logg >= logg_min):
weights[(spectrum.wave >= row['wave_start']) & (spectrum.wave <= row['wave_end'])] += row['weights']
i = np.argmin(np.abs(spectrum.wave - row['wave_center']))
if npix % 2 == 0:
weights[int(i-npix//2):int(i+npix//2)] = 0
else:
weights[int(i-npix//2):int(i+npix//2+1)] = 0
return True
return False
__all__ = ['WeightFromGridNearest']
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testing_app.py | michal090497/python-fruits-recognition | 1 | 28503 | # Import required libraries
import cv2
from os.path import os, dirname
import tensorflow as tf
import numpy as np
from tqdm import tqdm
import random
# List of categories (directories names)
CATEGORIES = ["bad_apple", "bad_grape", "bad_pear", "cherry", "good_apple", "good_avocado", "good_grape", "good_pear", "ripe_avocado"]
# Level 2 - display information about errors only
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# Commented line = use GPU
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
# Source folder path
main_dir = dirname(os.path.abspath(__file__))
# Paths to image database (train, test, all)
training_dir = os.path.join(main_dir, 'database', 'training')
testing_dir = os.path.join(main_dir, 'database', 'testing')
all_dir = os.path.join(main_dir, 'database', 'all')
# Currently used path
DATADIR = testing_dir
# Load all images and save them to array variable
for category in CATEGORIES:
path = os.path.join(DATADIR, category)
for img in os.listdir(path):
img_array = cv2.imread(os.path.join(path, img))
break
break
# Variable to store training data
testing_data = []
# Function that converts previously created data array to a test data array
def create_testing_data():
for category in CATEGORIES:
path = os.path.join(DATADIR, category)
class_num = CATEGORIES.index(category)
for img in tqdm(os.listdir(path)):
try:
img_array = cv2.imread(os.path.join(path, img))
testing_data.append([img_array, class_num])
except Exception as e:
pass
# Call the function
create_testing_data()
# Shuffle test data
random.shuffle(testing_data)
# Create array variables to store objects and labels
X = []
y = []
# Save objects and labels to arrays
for features, label in testing_data:
X.append(features)
y.append(label)
# Convert arrays to NumPy matrices
X = np.array(X)
y = np.array(y)
# Change the value range from 0-255 to 0-1
X = X / 255.0
# Load the trained model from given path
keras_model_path = os.path.join(main_dir, 'models', 'test')
model = tf.keras.models.load_model(keras_model_path)
# Display model summary
model.summary()
# Display information about the effectiveness of test data classification
loss, acc = model.evaluate(X, y, verbose=2)
print('Accuracy: {:5.2f}%'.format(100 * acc))
print('Loss: {:5.2f}'.format(loss)) | [
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Python/main.py | akulaarora/My-All-Weather-Strategy | 0 | 51157 | # For importing keys
import sys
sys.path.append("../")
sys.path.append("DONOTPUSH/")
import api_keys
# My modules
import database as db
# Libraries
import json
from time import sleep
# from github import Github
from alpha_vantage.timeseries import TimeSeries
import logging
# Constants
REPO_NAME = "My-All-Weather-Strategy"
STRATEGY = "../strategy.json"
LOG = "DONOTPUSH/errors.log"
# Global objects
client = None
def main(argv):
# Get values from JSON file
strategy_name = None
portfolio_json = None
band_threshold = None
assets = None
# Parse JSON into variables
with open(STRATEGY, 'r') as f:
strategy_json = json.loads(f.read())
strategy_name = strategy_json['Name']
band_threshold = strategy_json['Percent Band Threshold']
assets = strategy_json['Portfolio']
# Remove all invalid assets (no allocation or no ticker).
# Converts to dictionary of asset names.
temp = {}
for asset in assets:
if asset['Ticker'] != '' and asset['Percent Allocation'] != 0:
name = asset.pop('Name')
temp[name] = asset
assets = temp
# Get performance for all assets.
assets = get_asset_changes(assets)
# Push asset's performance to database
for name in assets.keys():
asset = assets[name]
ticker = asset['Ticker']
fields = {'Price': asset['Price'], 'Percent Change': asset['Percent Change']}
db.write_asset_price(name, ticker, fields)
# Updates portfolio
balances, balanced = update_portfolio(strategy_name, assets, band_threshold)
# Writes updated portfolio balances
db.write_balance(strategy_name, balances, balanced=balanced)
def get_asset_changes(assets):
"""
Calculate price changes on all assets
Returns dict {asset ->
{"price" -> price, "Percent Change" -> percent_change, ... (from original)} }
Mutates assets. Returns assets as well for ease of use.
"""
for name in assets.keys():
asset = assets[name]
ticker = asset['Ticker']
price = get_price_curr(ticker)
prev_price = db.get_price_prev(ticker)
if prev_price == None:
percent_change = 0
logging.error("Could not pull value for ticker " + ticker + " from database")
else:
percent_change = (price - prev_price) / prev_price * 100
asset['Price'] = price
asset['Percent Change'] = percent_change
return assets
def get_price_curr(ticker):
"""
Pulls data from Alphavantage using ticker.
Returns back close for today (latest value).
"""
ts = TimeSeries(api_keys.AV_KEY)
attempts = 1
while attempts <= 2:
try:
data = ts.get_quote_endpoint(symbol=ticker)
price = float(data[0]['05. price'])
return price
except ValueError:
sleep(61)
attempts += 1
def update_portfolio(strategy_name, assets, band_threshold):
"""
Handles all processing required to update the portfolio.
Returns back the balances for the updated portfolio
and whether the portfolio was created or rebalanced (balanced).
As a tuple.
"""
old = db.get_balance_prev(strategy_name)
new = {}
balanced = False
if old == None:
new = create_portfolio(assets)
balanced = True
else:
total_balance = 0
for name in old.keys():
if name != 'Balance':
balance = old[name]
change = assets[name]['Percent Change'] / 100
new_balance = balance * (1 + change)
new[name] = new_balance
total_balance += new_balance
new['Balance'] = total_balance
balanced = rebalance_portfolio(new, assets, band_threshold)
return (new, balanced)
def create_portfolio(assets, size = 1.0):
# Checks that portfolio allocation sums to 100%.
sum = 0
for name in assets.keys():
allocation = assets[name]['Percent Allocation']
sum += allocation
if sum != 100:
raise ValueError("Portfolio allocation does not sum to 100%")
portfolio = {}
portfolio['Balance'] = size
for name in assets.keys():
allocation = (assets[name]['Percent Allocation']) / 100
portfolio[name] = size * allocation
return portfolio
def rebalance_portfolio(portfolio, assets, band_threshold):
rebalanced = False
total_balance = portfolio['Balance']
for name in portfolio.keys():
if name != 'Balance':
asset_balance = portfolio[name]
allocation = assets[name]['Percent Allocation']
curr_allocation = asset_balance/total_balance * 100
if abs(curr_allocation - allocation) >= band_threshold:
create_portfolio(assets, size = total_balance)
rebalanced = True
break
return rebalanced
if __name__ == "__main__":
try:
logging.basicConfig(filename=LOG, level=logging.DEBUG, \
format='%(asctime)s %(levelname)s %(name)s %(message)s')
main(sys.argv[1:])
except Exception as e:
logging.error(e) | [
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contents/MyExperiment/Exp3_test/cluster_env.py | Feng-XiaoYue/Reinforcement-learning-with-tensorflow-master | 0 | 16726 | <reponame>Feng-XiaoYue/Reinforcement-learning-with-tensorflow-master
import numpy as np
import pandas as pd
import random
import time
import sys
if sys.version_info.major == 2:
import Tkinter as tk
else:
import tkinter as tk
class Cluster(tk.Tk, object):
def __init__(self, state_init, server_attribute):
super(Cluster, self).__init__()
self.action_space = np.array([[0,0],[0,1],[0,2],[0,3],
[1,0],[1,1],[1,2],[1,3],
[2,0],[2,1],[2,2],[2,3],
[3,0],[3,1],[3,2],[3,3],
[4,0],[4,1],[4,2],[4,3],
[5,0],[5,1],[5,2],[5,3],
[6,0],[6,1],[6,2],[6,3],
[7,0],[7,1],[7,2],[7,3],
[8,0],[8,1],[8,2],[8,3],
[9,0],[9,1],[9,2],[9,3],
[10,0],[10,1],[10,2],[10,3],
[11,0],[11,1],[11,2],[11,3]])
self.n_actions = len(self.action_space)
self.cost_matrix = pd.DataFrame(np.array([[0,1,5,12],
[1,0,4,2],
[5,4,0,3],
[12,2,3,0]]),
columns=[0, 1, 2, 3])
self.server_attribute = server_attribute
self.QSs = self.read_file()
self.state_init = state_init
self.cost_init = self.cost_init()
def step(self, action, state, costs):
s = state.copy()
#action_real[查询，移动到的服务器]
action_real = self.action_space[action]
q = action_real[0]
index_server = action_real[1]
s.iloc[q, :] = 0
s.iloc[q, index_server] = 1
cost_new = self.cost_caculate(q, index_server)
if cost_new > costs[q]:
is_better = True
else:
is_better = False
# costs[action_real[0]] = cost_new
costs[q] = cost_new
cost_all = self.cost_all(costs)
reward = self.reward(cost_all, s)
s_ = s
return s_, costs, reward, cost_all, is_better
#判断结束的条件 选择的action在执行之后状态仍然没有变 or 判断状态是否在处与某种情况下，例如负载不平衡
def is_finish(self):
# TODO
return True
# read the file and store in an array[query,[server1,server2,......]]
def read_file(self):
server_attribute = self.server_attribute
with open("D:\SynologyDrive\Reinforcement-learning-with-tensorflow-master\contents\MyExperiment\Exp3_test\QueryAttribute_test",'r') as f:
content = f.readlines()
QSs = []
for item in content:
QS = []
item = item.strip("\n")
q = item.split(",")[0]
targetAttribute = item.split(",")[1:]
targetAttribute = list(map(int, targetAttribute))
servers = []
for attribute in targetAttribute:
server = server_attribute[server_attribute.loc[:, attribute] == 1].index[0]
servers.append(server)
QS.append(int(q))
QS.append(servers)
QSs.append(QS)
return QSs
# compute the initial costs array based on the initial state matrix. every element represent the total cost of the query
def cost_init(self):
state_init = self.state_init
# print(len(state_init))
states = self.state_array(state_init)
# print(len(states))
costs = []
# print(len(state_init))
for i in range(len(state_init)):
index_server = states[i][1]
cost = self.cost_caculate(i, index_server)
costs.append(cost)
return costs
def cost_caculate(self,q,index_server):
cost = 0
for j in range(len(self.QSs[q][1])):
target_server = self.QSs[q][1][j]
cost += self.cost_matrix.iloc[index_server, target_server]
return cost
# create the initial state matrix（random）
# compute the total reward based on the costs array
def cost_all(self, costs):
cost_all = 0
for i in range(len(costs)):
cost_all += costs[i]
return cost_all
def reward(self, cost_all, state):
list = []
for i in state.columns:
list.append(state[i].sum())
load_weight_var = np.var(list)
reward = (len(state)/cost_all) * self.function(1.1, load_weight_var)
return reward
def function(self, a, x):
y = 100/(a**x)
return y
# transform the state matrix into array
def state_array(self, state):
states = []
for i in range(len(state)):
for j in range(len(state.columns)):
state_arr = []
if state.iloc[i, j] == 1:
state_arr.append(i)
state_arr.append(j)
states.append(state_arr)
return states
def state_init():
init_state = pd.DataFrame(np.zeros(327*8).reshape(327, 8), columns=[0, 1, 2, 3, 4, 5, 6, 7])
for i in range(len(init_state)):
j = random.randint(0, 7)
init_state.iloc[i][j] = 1
return init_state
# if __name__ == '__main__':
# server_attribute = pd.DataFrame(np.array([0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
# 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
# 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
# 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
# 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]).
# reshape(8, 24),
# columns=np.arange(24))
# env = Cluster(state_init(), server_attribute)
# Qss = env.QSs
# print(Qss)
# for i in range(len(Qss)):
# q = i
# for j in range(len(server_attribute)):
# index_server = j
# print(env.cost_init)
# print("The reward of initial state is:")
# print(env.reward(env.cost_all(env.cost_init), env.state_init))
# print(env.state_init)
# actions=list(range(env.n_actions))
# print(actions)
# env.after(100, update)
# env.mainloop() | [
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] |
seqtools/get_gene_info.py | NeilHumps/SequenceTools | 0 | 92378 | from Bio import SeqIO
import gzip
import os
import sys
from seqtools.general import rc, translate
def parse_gtf_dict(gtf_str):
return {i.split(' "')[0]:i.split(' "')[1] for i in gtf_str.split('"; ')}
def gtf_to_gene_length(gtf_file, outfile, sum_type='transcript_longest'):
"""
Get Gene length, 3 options:
1. transcript_longest: Length of longest transcript (default)
2. gene_unique: Length of all unique 'expressed DNA' i.e. count every base that is expressed once
3. gene_all: Total length of all 'expressed DNA' i.e. if transcripts contain overlapping exons then count exon length multiple times
"""
if not sum_type == 'transcript_longest':
gene_dict = {}
gene_count = 0
with open(gtf_file) as input_fh:
for line in input_fh:
if not line.startswith('#'):
fields = line.strip().split('\t')
if fields[2] == 'exon':
gtf_dict = parse_gtf_dict(fields[8])
if not gtf_dict['gene_name'] in gene_dict:
gene_count += 1
if gene_count % 1000 == 0:
print(' - {0} genes processed'.format(gene_count))
gene_dict[gtf_dict['gene_name']] = []
gene_dict[gtf_dict['gene_name']] += [i for i in range(int(fields[3])-1, int(fields[4]))]
gene_lengths = []
for gene in gene_dict:
if sum_type == 'gene_all':
gene_lengths.append('{0},{1}\n'.format(gene, len(gene_dict[gene])))
elif sum_type == 'gene_unique':
gene_lengths.append('{0},{1}\n'.format(gene, len(set(gene_dict[gene]))))
else:
raise IOError('sum_type must be gene_all, gene_unique or transcript_longest')
else:
gene_dict = {}
gene_count = 0
with open(gtf_file) as input_fh:
for line in input_fh:
if not line.startswith('#'):
fields = line.strip().split('\t')
if fields[2] == 'exon':
gtf_dict = parse_gtf_dict(fields[8])
if not gtf_dict['gene_name'] in gene_dict:
gene_count += 1
if gene_count % 1000 == 0:
print(' - {0} genes processed'.format(gene_count))
gene_dict[gtf_dict['gene_name']] = {}
if not gtf_dict['transcript_id'] in gene_dict[gtf_dict['gene_name']]:
gene_dict[gtf_dict['gene_name']][gtf_dict['transcript_id']] = 0
gene_dict[gtf_dict['gene_name']][gtf_dict['transcript_id']] += int(fields[4]) - int(fields[3]) + 1
gene_lengths = []
for gene in gene_dict:
transcript_lengths = gene_dict[gene].values()
gene_lengths.append('{0},{1}\n'.format(gene, max(transcript_lengths)))
with open(outfile, 'w') as output_fh:
for gene in gene_lengths:
output_fh.write(gene)
def get_transcripts(ens_gene_id, output_dir, gtf_file, fasta_str,
protein=False):
exons = {}
if protein == True:
feature_type = 'CDS'
else:
feature_type = 'exon'
with open(gtf_file) as input_fh:
for line in input_fh:
if not line.startswith('#'):
fields = line.strip().split('\t')
if fields[2] == feature_type:
gtf_dict = parse_gtf_dict(fields[8])
if gtf_dict['gene_id'] == ens_gene_id:
if not gtf_dict['transcript_id'] in exons:
exons[gtf_dict['transcript_id']] = []
exons[gtf_dict['transcript_id']].append({
'chrom': fields[0],
'start': int(fields[3]),
'end': int(fields[4]),
'strand': fields[6],
})
for transcript in exons:
if set([i['strand'] for i in exons[transcript]]) == set('+'):
strand = '+'
sorted_exons = sorted(exons[transcript], key=lambda i:i['start'])
elif set([i['strand'] for i in exons[transcript]]) == set('-'):
strand = '-'
sorted_exons = sorted(exons[transcript], key=lambda i:i['start'], reverse=True)
sequence = ''
with gzip.open(fasta_str.format(exons[transcript][0]['chrom']), 'rt') as input_fh:
for record in SeqIO.parse(input_fh, 'fasta'):
chrom_seq = str(record.seq)
for exon in sorted_exons:
if strand == '+':
sequence += chrom_seq[exon['start']-1:exon['end']]
elif strand == '-':
sequence += rc(chrom_seq[exon['start']-1:exon['end']])
if protein == True:
sequence = translate(sequence)
with open(os.path.join(output_dir, transcript + '.fa'), 'w') as output_fh:
output_fh.write('>{0}\n{1}\n'.format(transcript, sequence))
print('fasta file written: {0}/{1}.fa'.format(output_dir, transcript))
if __name__ == '__main__':
if len(sys.argv) == 3:
# gtf wget from ensembl (wget ftp://ftp.ensembl.org/pub/release-95/gtf/mus_musculus/Mus_musculus.GRCm38.95.gtf.gz)
#gtf_to_gene_length(gtf_file='Mus_musculus.GRCm38.95.gtf',
# outfile='mm10_gene_lengths.txt')
gtf_to_gene_length(gtf_file=sys.argv[1],
outfile=sys.argv[2])
elif len(sys.argv) == 4:
gtf_to_gene_length(gtf_file=sys.argv[1],
outfile=sys.argv[2],
include_all=sys.argv[3])
else:
pass
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] |
src/transformers/models/layoutlmv3/configuration_layoutlmv3.py | bugface/transformers | 5 | 113461 | # coding=utf-8
# Copyright 2022 Microsoft Research and The HuggingFace Inc. team. 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.
""" LayoutLMv3 model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}
class LayoutLMv3Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LayoutLMv3Model`]. It is used to instantiate an
LayoutLMv3 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the LayoutLMv3
[microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the LayoutLMv3 model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`LayoutLMv3Model`].
hidden_size (`int`, *optional*, defaults to 768):
Dimension of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`LayoutLMv3Model`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum value that the 2D position embedding might ever be used with. Typically set this to something
large just in case (e.g., 1024).
coordinate_size (`int`, *optional*, defaults to `128`):
Dimension of the coordinate embeddings.
shape_size (`int`, *optional*, defaults to `128`):
Dimension of the width and height embeddings.
has_relative_attention_bias (`bool`, *optional*, defaults to `True`):
Whether or not to use a relative attention bias in the self-attention mechanism.
rel_pos_bins (`int`, *optional*, defaults to 32):
The number of relative position bins to be used in the self-attention mechanism.
max_rel_pos (`int`, *optional*, defaults to 128):
The maximum number of relative positions to be used in the self-attention mechanism.
max_rel_2d_pos (`int`, *optional*, defaults to 256):
The maximum number of relative 2D positions in the self-attention mechanism.
rel_2d_pos_bins (`int`, *optional*, defaults to 64):
The number of 2D relative position bins in the self-attention mechanism.
has_spatial_attention_bias (`bool`, *optional*, defaults to `True`):
Whether or not to use a spatial attention bias in the self-attention mechanism.
visual_embed (`bool`, *optional*, defaults to `True`):
Whether or not to add patch embeddings.
input_size (`int`, *optional*, defaults to `224`):
The size (resolution) of the images.
num_channels (`int`, *optional*, defaults to `3`):
The number of channels of the images.
patch_size (`int`, *optional*, defaults to `16`)
The size (resolution) of the patches.
classifier_dropout (`float`, *optional*):
The dropout ratio for the classification head.
Example:
```python
>>> from transformers import LayoutLMv3Model, LayoutLMv3Config
>>> # Initializing a LayoutLMv3 microsoft/layoutlmv3-base style configuration
>>> configuration = LayoutLMv3Config()
>>> # Initializing a model from the microsoft/layoutlmv3-base style configuration
>>> model = LayoutLMv3Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "layoutlmv3"
def __init__(
self,
vocab_size=50265,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-5,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
max_2d_position_embeddings=1024,
coordinate_size=128,
shape_size=128,
has_relative_attention_bias=True,
rel_pos_bins=32,
max_rel_pos=128,
rel_2d_pos_bins=64,
max_rel_2d_pos=256,
has_spatial_attention_bias=True,
text_embed=True,
visual_embed=True,
input_size=224,
num_channels=3,
patch_size=16,
classifier_dropout=None,
**kwargs
):
super().__init__(
vocab_size=vocab_size,
hidden_size=hidden_size,
num_hidden_layers=num_hidden_layers,
num_attention_heads=num_attention_heads,
intermediate_size=intermediate_size,
hidden_act=hidden_act,
hidden_dropout_prob=hidden_dropout_prob,
attention_probs_dropout_prob=attention_probs_dropout_prob,
max_position_embeddings=max_position_embeddings,
type_vocab_size=type_vocab_size,
initializer_range=initializer_range,
layer_norm_eps=layer_norm_eps,
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
self.max_2d_position_embeddings = max_2d_position_embeddings
self.coordinate_size = coordinate_size
self.shape_size = shape_size
self.has_relative_attention_bias = has_relative_attention_bias
self.rel_pos_bins = rel_pos_bins
self.max_rel_pos = max_rel_pos
self.has_spatial_attention_bias = has_spatial_attention_bias
self.rel_2d_pos_bins = rel_2d_pos_bins
self.max_rel_2d_pos = max_rel_2d_pos
self.text_embed = text_embed
self.visual_embed = visual_embed
self.input_size = input_size
self.num_channels = num_channels
self.patch_size = patch_size
self.classifier_dropout = classifier_dropout
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] |
ml4sts/summary_statistics.py | aguirre-lab/sts-ml | 0 | 103781 | <gh_stars>0
# Imports: standard library
import os
import logging
# Imports: third party
import pandas as pd
from tableone import TableOne
# Imports: ml4sts
# Imports: first party
from ml4sts.definitions import (
OUTCOME_MAP,
OPERATIVE_FEATURES,
CONTINUOUS_FEATURES,
DESCRIPTIVE_FEATURE_NAMES,
)
def summary_statistics(args):
df = pd.read_csv(args.path_to_csv, low_memory=False)
# Determine categorical features
categorical_features = set(args.features) - set(CONTINUOUS_FEATURES)
categorical_features -= set(OPERATIVE_FEATURES)
mytable = TableOne(
data=df,
columns=args.features,
categorical=list(categorical_features),
groupby=OUTCOME_MAP[args.outcome],
rename=DESCRIPTIVE_FEATURE_NAMES,
label_suffix=True,
missing=False,
decimals={"age": 0, "platelets": 0},
pval=True,
)
print(mytable.tabulate(tablefmt=args.tablefmt))
fpath = os.path.join(args.output_directory, args.id, "summary_statistics.csv")
mytable.to_csv(fpath)
logging.info(f"Saved {fpath}")
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DjangoSite/apps/tones/migrations/0001_initial.py | abec/getabe.com | 0 | 60209 | <reponame>abec/getabe.com<filename>DjangoSite/apps/tones/migrations/0001_initial.py<gh_stars>0
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
]
operations = [
migrations.CreateModel(
name='ChineseMetadata',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('native', models.BooleanField(verbose_name='Native speaker')),
('gender', models.CharField(max_length=255, verbose_name='Gender')),
],
),
migrations.CreateModel(
name='ChineseWord',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('word', models.CharField(max_length=255, verbose_name='Word')),
('wave', models.BinaryField(verbose_name='Wave', null=True, blank=True)),
('metadata', models.ForeignKey(related_name='words', to='tones.ChineseMetadata')),
],
),
]
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ch05/challenge.py | laszlokiraly/LearningAlgorithms | 74 | 100034 | <filename>ch05/challenge.py
"""
Challenge Exercises for Chapter 5.
"""
import timeit
from algs.table import DataTable, ExerciseNum, caption
from algs.modeling import n_log_n_model, quadratic_model, numpy_error
from ch01.book import Order
numRecursiveImproved = [0]
def num_swaps(A):
"""Given an array of integers from 0 to N-1, return number of swaps to sort."""
N = len(A)
seen = [False] * N
ct = 0
for i in range(N):
if not seen[i]:
idx = i
num = 0
while not seen[idx]:
num += 1
seen[idx] = True
idx = A[idx]
ct += (num - 1) # number of swaps is one less than size
return ct
def num_swaps_hashable(A):
"""Given an array of distinct strings, return minimum number of swaps."""
N = len(A)
seen = {}
final = {}
original = list(A)
# Selection sort and remember locations
for i in range(N-1):
min_index = i
for j in range(i+1, N):
if A[j] < A[min_index]:
min_index = j
final[A[min_index]] = i
A[i],A[min_index] = A[min_index],A[i]
final[A[N-1]] = N-1 # TRICKY can't forget this one
ct = 0
for i in range(N):
if not original[i] in seen:
idx = i
num = 0
while original[idx] not in seen:
num += 1
seen[original[idx]] = True
idx = final[original[idx]] # move to where final location will be
ct += (num - 1) # number of swaps is one less than size
return ct
def slice_merge_sort(A):
"""Merge Sort where merge uses Python slice."""
aux = [None] * len(A)
def rsort(lo, hi):
if hi <= lo:
return
mid = (lo+hi) // 2
rsort(lo, mid)
rsort(mid+1, hi)
merge(lo, mid, hi)
def merge(lo, mid, hi):
# copy results of sorted sub-problems into auxiliary storage
aux[lo:hi+1] = A[lo:hi+1]
i = lo # starting index into left sorted sub-array
j = mid+1 # starting index into right sorted sub-array
for k in range(lo, hi+1):
if i > mid:
A[k:hi+1] = aux[j:j+hi+1-k]
return
if j > hi:
A[k:hi+1] = aux[i:i+hi+1-k]
return
if aux[j] < aux[i]:
A[k] = aux[j]
j += 1
else:
A[k] = aux[i]
i += 1
rsort(0, len(A)-1)
def recursive_two(A):
"""Return two largest values in A, using recursive approach."""
def rtwo(lo, hi):
# Base case: 1 or two values
if lo == hi: return (A[lo], None)
if lo+1 == hi:
if A[lo] < A[hi]:
return (A[hi], A[lo])
return (A[lo], A[hi])
mid = (lo+hi) // 2
L = rtwo(lo, mid)
R = rtwo(mid+1, hi)
# Recursive case: Find largest of the possible four values. Note
# That L[1] can never be None since that would have been handled
# By the special case above where lo+1 == hi
if L[0] < R[0]:
if R[1] is None:
return (R[0], L[0])
return (R[0], R[1]) if L[0] < R[1] else (R[0], L[0])
return (L[0], L[1]) if R[0] < L[1] else (L[0], R[0])
return rtwo(0, len(A)-1)
def run_largest_two_trials_with_recursive(mode, max_k=22, output=True, decimals=2):
"""Mode is either Order.REVERSED or Order.SHUFFLED for 2**k up to (but not including) max_k."""
tbl = DataTable([10,15,15,10,10,15,15],
['N','double_two','mutable_two','largest_two','sorting_two','tournament_two','recursive_two'],
output=output, decimals=decimals)
if mode is Order.REVERSED:
prepare = 'list(reversed(x))'
if mode is Order.SHUFFLED:
prepare = 'random.shuffle(x)'
trials = [2**k for k in range(10,max_k)]
num = 100
for n in trials:
if mode is Order.ALTERNATING:
prepare = '''
up_down = zip(range(0,{0},2),range({0}-1,0,-2))
x=[i for i in itertools.chain(*up_down)]
'''.format(n)
m_dt = timeit.timeit(stmt='double_two(x)', setup='''
import random
from ch01.largest_two import double_two
x=list(range({}))
{}'''.format(n,prepare), number=num)
m_mt = timeit.timeit(stmt='mutable_two(x)', setup='''
import random
from ch01.largest_two import mutable_two
x=list(range({}))
{}'''.format(n,prepare), number=num)
m_lt = timeit.timeit(stmt='largest_two(x)', setup='''
import random
from ch01.largest_two import largest_two
x=list(range({}))
{}'''.format(n,prepare), number=num)
# hard-code these values since take too long to compute...
if n > 1048576:
m_tt = None
else:
m_tt = timeit.timeit(stmt='tournament_two(x)', setup='''
import random
from ch01.largest_two import tournament_two
x=list(range({}))
{}'''.format(n,prepare), number=num)
m_st = timeit.timeit(stmt='sorting_two(x)', setup='''
import random
from ch01.largest_two import sorting_two
x=list(range({}))
{}'''.format(n,prepare), number=num)
m_rt = timeit.timeit(stmt='recursive_two(x)', setup='''
import random
from ch05.challenge import recursive_two
x=list(range({}))
{}'''.format(n,prepare), number=num)
# Skip runs that are going to be too expensive
if m_tt:
tbl.row([n, m_dt, m_mt, m_lt, m_st, m_tt, m_rt])
else:
tbl.row([n, m_dt, m_mt, m_lt, m_st ])
return tbl
def fib(n):
"""Inefficient Fibonacci recurive implementation."""
if n <= 0: return 0
if n == 1: return 1
return fib(n-1) + fib(n-2)
fib_profile_count = [0]
def fib_profile(n):
"""Inefficient Fibonacci recurive implementation."""
fib_profile_count[0] += 1
if n <= 0: return 0
if n <= 1: return 1
return fib_profile(n-1) + fib_profile(n-2)
def fib_with_lucas(n):
"""
F_(x+y) = 1/2 * (F_x * L_y) + (F_y * L_x)
Improved efficiency using Lucas numbers.
"""
numRecursiveImproved[0] += 1
if n == 0: return 0
if n <= 2: return 1
if n == 3: return 2 # Needs to be here to prevent infinite recursion
remainder = n - n//2
return (fib_with_lucas(n//2)*lucas_with_fib(remainder) + fib_with_lucas(remainder)*lucas_with_fib(n//2)) / 2
def lucas_with_fib(n):
"""
Improved recursive implementation that takes advantage of identify that:
Ln = Fn-1 + Fn+1 for n > 1
"""
numRecursiveImproved[0] += 1
if n == 0: return 2
if n == 1: return 1
return fib_with_lucas(n-1) + fib_with_lucas(n+1)
def fib_table(output=True, decimals=3):
"""Generate table showing reduced recursive invocations of fibonacci."""
import math
tbl = DataTable([8,12,12],['N', 'FiRec', 'Model'], output=output, decimals=decimals)
tbl.format('FiRec', 'd')
def exp_model(n, a, b):
"""Formula for A*N^B ."""
return a*math.pow(n, b)
for n in range(3, 100):
old = numRecursiveImproved[0]
fib_with_lucas(n)
model = exp_model(n, 0.28711343, 2.58031481)
tbl.row([n, (numRecursiveImproved[0] - old), model])
if numpy_error:
pass
else:
import numpy as np
from scipy.optimize import curve_fit
x_arr = np.array(tbl.column(tbl.labels[0]))
y_arr = np.array(tbl.column(tbl.labels[1]))
def np_exp_model(n, a, b):
"""Formula for A*N^B ."""
return a*np.power(n, b)
if output:
[exp_coeffs, _] = curve_fit(np_exp_model, x_arr, y_arr)
print('A*N^B = {:.12f}*N^{:f} '.format(exp_coeffs[0], exp_coeffs[1]))
return tbl
def rediscover_heap(num_trials=10000000):
"""
Given a partial heap, rediscover original input probabilistically.
This might not work the first time, but after repeated attempts, the following
possible starting points were found:
[15, 12, 7, 4, 13, 8, 11, 14, 2, 1, 10, 9, 6, 9, 12, 8, 5, 14]
[8, 2, 15, 5, 1, 14, 11, 4, 12, 12, 10, 13, 6, 9, 7, 14, 9, 8] found in 2,380,433 tries
[13, 14, 12, 5, 10, 6, 14, 11, 9, 1, 12, 8, 15, 9, 7, 4, 8, 2]
[9, 10, 6, 13, 15, 8, 9, 4, 2, 1, 11, 14, 14, 12, 7, 8, 5, 12] found in 8394544 attempts.
[5, 9, 6, 14, 10, 13, 12, 11, 15, 1, 12, 8, 14, 9, 7, 4, 8, 2] found in 3234220 attempts
with seed 14
"""
from ch05.heapsort import HeapSort
from random import shuffle, seed
A = [15, 13, 14, 12, 11, 12, 14, 8, 9, 1, 10, 8, 6, 9, 7, 4, 5, 2]
N = len(A)
more = 2
seed(14)
for i in range(num_trials):
copy = list(A)
shuffle(copy)
copy1 = list(copy)
hs = HeapSort(copy)
one = hs.A[N//2 - more:]
two = A[N//2 - more:]
if one == two:
return '{} found in {} attempts.'.format(copy1, i)
return 'none found in {} attempts'.format(i)
def insertion_sort_bas(max_k=18, output=True, decimals=3):
"""Generate Table for Insertion Sort."""
# Evaluate prototype execution
x = []
y = []
for n in [2**k for k in range(8, 12)]:
m_insert_bas = 1000*min(timeit.repeat(stmt='insertion_sort_bas(A)', setup='''
import random
from ch05.sorting import insertion_sort_bas
A=list(range({}))
random.shuffle(A)'''.format(n), repeat=10, number=10))
x.append(n)
y.append(m_insert_bas)
# Coefficients are returned as first argument
if numpy_error:
log_coeffs = quadratic_coeffs = [0, 0]
else:
import numpy as np
from scipy.optimize import curve_fit
[log_coeffs, _] = curve_fit(n_log_n_model, np.array(x), np.array(y))
[quadratic_coeffs, _] = curve_fit(quadratic_model, np.array(x), np.array(y))
if output:
print('Quadratic = {}*N*N + {}*N'.format(quadratic_coeffs[0], quadratic_coeffs[1]))
print('Log = {:.12f}*N*log2(N)'.format(log_coeffs[0]))
print()
tbl = DataTable([12,10,10,10],['N','Time','Quad','Log'], output=output, decimals=decimals)
for n,p in zip(x,y):
tbl.row([n, p, quadratic_model(n,
quadratic_coeffs[0], quadratic_coeffs[1]), n_log_n_model(n, log_coeffs[0])])
for n in [2**k for k in range(12, max_k)]:
m_insert_bas = 1000*min(timeit.repeat(stmt='insertion_sort_bas(A)', setup='''
import random
from ch05.sorting import insertion_sort_bas
A=list(range({}))
random.shuffle(A)'''.format(n), repeat=10, number=10))
tbl.row([n, m_insert_bas,
quadratic_model(n, quadratic_coeffs[0], quadratic_coeffs[1]),
n_log_n_model(n, log_coeffs[0])])
return tbl
def trial_merge_sort_python_style(max_k=15, output=True, decimals=3):
"""Empirical trial for merge sort using slicing."""
tbl = DataTable([8, 8, 8], ['N', 'merge', 'mergeSlice'], output=output, decimals=decimals)
for n in [2**k for k in range(8, max_k)]:
m_slice = 1000*min(timeit.repeat(stmt='slice_merge_sort(A)', setup='''
import random
from ch05.challenge import slice_merge_sort
A=list(range({}))
random.shuffle(A)'''.format(n), repeat=10, number=10))
m_merge = 1000*min(timeit.repeat(stmt='merge_sort(A)', setup='''
import random
from ch05.merge import merge_sort
A=list(range({}))
random.shuffle(A)'''.format(n), repeat=10, number=10))
tbl.row([n, m_merge, m_slice])
return tbl
#######################################################################
if __name__ == '__main__':
chapter = 5
with ExerciseNum(1) as exercise_number:
print('find count() in ch05.recursion')
print(caption(chapter, exercise_number), 'Recursive count method')
print()
with ExerciseNum(2) as exercise_number:
print('find num_swaps() in ch05.challenge')
print(caption(chapter, exercise_number), 'Compute number of swaps in array with 0 .. N-1')
print()
with ExerciseNum(3) as exercise_number:
from ch05.recursion import find_max_with_count
print('how many comparisons to find max in array of size N=15:', find_max_with_count(list(range(15)))[0], 'or N-1')
print()
with ExerciseNum(4) as exercise_number:
trial_merge_sort_python_style()
print('Merge Sort with Python slice improves performance of MergeSort.')
print()
with ExerciseNum(5) as exercise_number:
run_largest_two_trials_with_recursive(Order.REVERSED)
print('find recursive_two() in ch05.challenge.')
print()
with ExerciseNum(6) as exercise_number:
fib_table()
print('find recursive_two() in ch05.challenge.')
print()
print('Attempting to rediscover heap. This might take unusually long time')
print(rediscover_heap())
| [
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] |
tests/test_bin.py | CiscoSystems/ceilometer | 1 | 61810 | <reponame>CiscoSystems/ceilometer
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
#
# Copyright © 2012 eNovance <<EMAIL>>
#
# Author: <NAME> <<EMAIL>>
#
# 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 httplib2
import json
import os
import random
import socket
import subprocess
import tempfile
import time
import unittest
class BinDbsyncTestCase(unittest.TestCase):
def setUp(self):
self.tempfile = tempfile.mktemp()
with open(self.tempfile, 'w') as tmp:
tmp.write("[DEFAULT]\n")
tmp.write("database_connection=log://localhost\n")
def test_dbsync_run(self):
subp = subprocess.Popen(["../bin/ceilometer-dbsync",
"--config-file=%s" % self.tempfile])
self.assertEqual(subp.wait(), 0)
def tearDown(self):
os.unlink(self.tempfile)
class BinSendCounterTestCase(unittest.TestCase):
def setUp(self):
self.tempfile = tempfile.mktemp()
with open(self.tempfile, 'w') as tmp:
tmp.write("[DEFAULT]\n")
tmp.write(
"rpc_backend=ceilometer.openstack.common.rpc.impl_fake\n")
tmp.write(
"pipeline_cfg_file=../etc/ceilometer/pipeline.yaml\n")
def test_send_counter_run(self):
subp = subprocess.Popen(["../bin/ceilometer-send-counter",
"--config-file=%s" % self.tempfile,
"--counter-resource=someuuid",
"--counter-name=mycounter"])
self.assertEqual(subp.wait(), 0)
def tearDown(self):
os.unlink(self.tempfile)
class BinApiTestCase(unittest.TestCase):
def setUp(self):
self.api_port = random.randint(10000, 11000)
self.http = httplib2.Http()
self.tempfile = tempfile.mktemp()
with open(self.tempfile, 'w') as tmp:
tmp.write("[DEFAULT]\n")
tmp.write(
"metering_api_port=%s\n" % self.api_port)
tmp.write(
"rpc_backend=ceilometer.openstack.common.rpc.impl_fake\n")
tmp.write("database_connection=log://localhost\n")
tmp.write(
"auth_strategy=noauth\n")
tmp.write(
"debug=true\n")
self.subp = subprocess.Popen(["../bin/ceilometer-api",
"--config-file=%s" % self.tempfile])
def tearDown(self):
os.unlink(self.tempfile)
self.subp.kill()
self.subp.wait()
def get_response(self, path):
url = 'http://%s:%d/%s' % ('127.0.0.1', self.api_port, path)
for x in range(10):
try:
r, c = self.http.request(url, 'GET')
except socket.error:
time.sleep(.3)
self.assertEqual(self.subp.poll(), None)
else:
return r, c
def test_v1(self):
response, content = self.get_response('v1/meters')
self.assertEqual(response.status, 200)
self.assertEqual(json.loads(content), {'meters': []})
def test_v2(self):
response, content = self.get_response('v2/meters')
self.assertEqual(response.status, 200)
self.assertEqual(json.loads(content), [])
| [
1,
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configs/production.py | syz247179876/Flask-Sports | 2 | 14634 | <reponame>syz247179876/Flask-Sports
# -*- coding: utf-8 -*-
# @Time : 2020/12/1 下午11:24
# @Author : 司云中
# @File : production.py
# @Software: Pycharm
from configs.default import DefaultConfig
class ProductionConfig(DefaultConfig):
"""the config of production env"""
DEBUG = False
TESTING = False
MONGODB_DB = ''
MONGODB_HOST = ''
MONGODB_PORT = ''
MONGODB_USERNAME = ''
MONGODB_PASSWORD = ''
production_config = ProductionConfig()
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Python/insert-delete-getrandom-o1-duplicates-allowed.py | RideGreg/LeetCode | 1 | 109726 | <reponame>RideGreg/LeetCode
# Time: O(1)
# Space: O(n)
# 381
# Design a data structure that supports all following operations in average O(1) time.
#
# Note: Duplicate elements are allowed.
# insert(val): Inserts an item val to the collection.
# remove(val): Removes an item val from the collection if present.
# getRandom: Returns a random element from current collection of elements.
# The probability of each element being returned is linearly related to
# the number of same value the collection contains.
# Example:
#
# // Init an empty collection.
# RandomizedCollection collection = new RandomizedCollection();
#
# // Inserts 1 to the collection. Returns true as the collection did not contain 1.
# collection.insert(1);
#
# // Inserts another 1 to the collection. Returns false as the collection contained 1. Collection now contains [1,1].
# collection.insert(1);
#
# // Inserts 2 to the collection, returns true. Collection now contains [1,1,2].
# collection.insert(2);
#
# // getRandom should return 1 with the probability 2/3, and returns 2 with the probability 1/3.
# collection.getRandom();
#
# // Removes 1 from the collection, returns true. Collection now contains [1,2].
# collection.remove(1);
#
# // getRandom should return 1 and 2 both equally likely.
# collection.getRandom();
# 动态数组 + 哈希表：简单的解决方案将所有值包括重复值存储在一个数组中，牺牲空间换时间。
import random, bisect, collections
class RandomizedCollection(object): # USE THIS
def __init__(self):
"""
Initialize your data structure here.
"""
self.__list = []
self.__v2ids = collections.defaultdict(set)
def insert(self, val):
"""
Inserts a value to the collection. Returns true if the collection did not already contain the specified element.
:type val: int
:rtype: bool
"""
self.__v2ids[val].add(len(self.__list))
self.__list.append(val)
return len(self.__v2ids[val]) == 1
def remove(self, val):
"""
Removes a value from the collection. Returns true if the collection contained the specified element.
:type val: int
:rtype: bool
"""
if val not in self.__v2ids or not self.__v2ids[val]:
return False
pos = self.__v2ids[val].pop() # pop an arbitrary elem from set
self.__v2ids[self.__list[-1]].add(pos) # add MUST done before discard, otherwise wrong for single item!!
self.__v2ids[self.__list[-1]].discard(len(self.__list)-1)
self.__list[-1], self.__list[pos] = self.__list[pos], self.__list[-1]
self.__list.pop()
return True
def getRandom(self):
"""
Get a random element from the collection.
:rtype: int
"""
return random.choice(self.__list)
# OR return self.__list[randint(0, len(self.__list)-1)]
class RandomizedCollection__CDF: # Cumulative Density Function: getRandom not O(1)
def __init__(self):
self.d = {}
def insert(self, val: int) -> bool:
if val not in self.d:
self.d[val] = 1
return True
self.d[val] += 1
return False
def remove(self, val: int) -> bool:
if val not in self.d:
return False
self.d[val] -= 1
if self.d[val] == 0:
self.d.pop(val)
return True
def getRandom(self) -> int:
psum = list(self.d.values())
for i in range(1, len(psum)):
psum[i] += psum[i - 1]
id = random.randint(1, psum[-1])
keys = list(self.d.keys())
return keys[bisect.bisect_left(psum, id)]
# Your RandomizedCollection object will be instantiated and called as such:
obj = RandomizedCollection()
print(obj.insert(1)) # True
print(obj.remove(1)) # True
print(obj.insert(3)) # True
print(obj.insert(1)) # True **
print(obj.insert(2)) # True
print(obj.insert(2)) # False
print(obj.insert(2)) # False
print(obj.remove(1)) # True
print(obj.remove(1)) # False **
print(obj.remove(3)) # True
print(obj.getRandom()) # 2
print(obj.remove(2)) # True
print(obj.insert(1)) # True
print(obj.remove(2)) # True
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] |
middleware.py | wjayesh/flask-prometheus | 0 | 46345 | <filename>middleware.py
from flask import request
import time
import sys
from prometheus_client import Counter, Histogram
APP_REQUEST_COUNT_TOTAL = Counter(
'app_request_count_total', 'App Request Count Total',
['app_name', 'method', 'endpoint']
)
APP_REQUEST_COUNT_FAILED = Counter(
'app_request_count_failed', 'App Request Count Failed',
['app_name', 'method', 'endpoint']
)
APP_REQUEST_COUNT_SUCCESS = Counter(
'app_request_count_success', 'App Request Count Succeeded',
['app_name', 'method', 'endpoint']
)
APP_REQUEST_LATENCY = Histogram('app_request_latency_seconds', 'Request latency',
['app_name', 'endpoint']
)
def start_timer():
request.start_time = time.time()
def stop_timer(response):
resp_time = time.time() - request.start_time
APP_REQUEST_LATENCY.labels('hw_app', request.path).observe(resp_time)
sys.stderr.write("Response time: %ss\n" % resp_time)
return response
def record_request_data(response):
APP_REQUEST_COUNT_TOTAL.labels('hw_app', request.method, request.path).inc()
if response.status_code is 200:
APP_REQUEST_COUNT_SUCCESS.labels('hw_app', request.method, request.path).inc()
else:
APP_REQUEST_COUNT_FAILED.labels('hw_app', request.method, request.path).inc()
sys.stderr.write("Request path: %s Request method: %s Response status: %s\n" %
(request.path, request.method, response.status_code))
return response
def setup_metrics(app):
app.before_request(start_timer)
# The order here matters since we want stop_timer
# to be executed first
app.after_request(record_request_data)
app.after_request(stop_timer) | [
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] |
setup.py | adriangb/EasyAnimation | 1 | 121460 | <reponame>adriangb/EasyAnimation
from setuptools import setup
setup(name='easyanimation',
version='0.1',
description='Wrapper for live plotting in matplotlib and Qt',
author='<NAME>',
author_email='<EMAIL>',
url='https://github.com/adriangb/easyanimation',
package_dir={'easyanimation': 'src'},
packages=['easyanimation'],
install_requires=['numpy>=1.16', 'matplotlib>=3.1', 'pyside2>5.12',
'pyqtgraph@git+ssh://[email protected]/pyqtgraph/pyqtgraph@develop#egg=pyqtgraph'],
python_requires='>=3.7',
)
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] |
app/BaseHandler.py | Trupal00p/polymer-template | 0 | 116044 | <gh_stars>0
from AuthHandler import AuthHandler
import json
class BaseHandler(AuthHandler):
def serve_json(self, dict):
self.response.write(json.dumps(dict))
| [
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tests/extractors/test_etrade.py | mkazin/StatementRenamer | 0 | 7819 | <filename>tests/extractors/test_etrade.py
from datetime import datetime
from statement_renamer.extractors.etrade import ETradeDateExtractor as EXTRACTOR_UNDER_TEST
from statement_renamer.extractors.factory import ExtractorFactory
TESTDATA = (
"""
PAGE 1 OF 6 February 1, 2019 - March 31, 2019AccountNumber:####-####AccountType:ROTH IRA
PAGE 5 OF 6Account Number: ####-####Statement Period : February 1, 2019 - March 31, 2019Account Type
TolearnmoreabouttheRSDAProgram,pleasereviewyourRSDAProgramCustomerAgreement,visitwww.etrade.com,orcallusat1-800-387-2331
"""
)
def test_monthly_statement():
extractor = EXTRACTOR_UNDER_TEST()
data = extractor.extract(TESTDATA)
new_name = extractor.rename(data)
assert data.get_start_date() == datetime(2019, 2, 1)
assert data.get_end_date() == datetime(2019, 3, 31)
assert new_name == '2019-03 E-Trade Statement.pdf'
def test_factory():
extractor = ExtractorFactory.get_matching_extractor(TESTDATA)
assert isinstance(extractor, EXTRACTOR_UNDER_TEST)
| [
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python/utils.py | fpbfabio/newsgroups1000s | 0 | 61704 | import os
from const import *
class Utils:
@staticmethod
def string_find(to_find_string, target_string):
result = str(target_string).find(to_find_string)
if (result != -1):
return result
return None
@staticmethod
def string_replace(to_find_string, replace_string, target_string):
index = Utils.string_find(to_find_string, target_string)
if (index is None):
return target_string
to_find_string_length = len(to_find_string)
beginning = EMPTY_STRING
end = EMPTY_STRING
if (index > 0):
beginning = target_string[0:index]
if (index + to_find_string_length < len(target_string)):
end = target_string[index + to_find_string_length:]
new_string = beginning + replace_string + end
return new_string
@staticmethod
def extract_file_parent_folder(path):
name = path
for i in range(len(path) - 1, -1, -1):
if (path[i] == os.path.sep):
most_right_slash_index = i
break
second_most_right_slash_index = -1
for i in range(most_right_slash_index - 1, -1, -1):
if (path[i] == os.path.sep):
second_most_right_slash_index = i
break
parent_folder_name = path[second_most_right_slash_index + 1:most_right_slash_index]
return parent_folder_name
@staticmethod
def extract_file_name(path):
name = path
for i in range(len(path) - 1, -1, -1):
if (path[i] == os.path.sep):
most_right_slash_index = i
name = path[most_right_slash_index + 1:]
break
if (FILE_EXTENSION_SEPARATOR in name):
dot_index = Utils.string_find(FILE_EXTENSION_SEPARATOR, name)
name = name[0:dot_index]
return name
@staticmethod
def extract_file_extension(path):
separator_index = None
file_extension = None
for i in range(len(path) - 1, -1, -1):
if (path[i] == os.path.sep):
break
if (path[i] == FILE_EXTENSION_SEPARATOR):
separator_index = i
if (separator_index is not None):
file_extension = path[separator_index:]
return file_extension
@staticmethod
def is_backup_file(path):
file_name = Utils.extract_file_name(path)
file_extension = Utils.extract_file_extension(path)
if (file_extension is not None):
last_char_index = len(file_extension) - 1
return file_extension[last_char_index] == BACKUP_SYMBOL
last_char_index = len(file_name) - 1
return file_name[last_char_index] == BACKUP_SYMBOL
@staticmethod
def is_integer(s):
try:
int(s)
return True
except ValueError:
return False | [
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tests/test_glove.py | muxgt/pycadl | 398 | 184112 | <reponame>muxgt/pycadl<gh_stars>100-1000
from cadl import glove
from cadl.utils import exists
from numpy.testing import run_module_suite
def test_glove_exists():
assert(exists('http://nlp.stanford.edu/data/wordvecs/glove.6B.zip'))
def test_glove_model():
model = glove.get_model()
assert(model[0].shape == (400001, 300))
assert(len(model[1]) == 400001)
if __name__ == "__main__":
run_module_suite()
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notebooks/deprecated/notebooks/nets/_seq2vec.py | grchristensen/avpd | 0 | 179026 | import torch
from torch import Tensor
from torch.nn import Module
from torch.nn.utils.rnn import PackedSequence, pad_packed_sequence
class Seq2Vec(Module):
"""A sequence-to-vector RNN."""
def __init__(self, rnn, attn=None):
"""
Initialize a Seq2Vec module.
:param rnn: The RNN module to use for encoding.
:param attn: The attention module to use after encoding.
"""
super().__init__()
if attn is None:
self._impl = NoAttnSeq2Vec(rnn)
else:
self._impl = AttnSeq2Vec(rnn, attn)
def forward(self, x):
"""
Pass :param x through the network.
:param x: The input for the network, should be of shape (seq_len, batch_size, num_features). If attention is
used, this should not be a packed sequence.
:return: The output of the network.
"""
return self._impl(x)
class NoAttnSeq2Vec(Module):
"""A sequence-to-vector RNN, without attention."""
def __init__(self, rnn):
"""
Initialize a NoAttnSeq2Vec module.
:param rnn: The RNN module to use in encoding.
"""
super().__init__()
self.rnn = rnn
def forward(self, x: Tensor) -> Tensor:
"""
Pass :param x through the network.
:param x: The input to the network, should be of shape (seq_len, batch_size, num_features).
:return: The output of the network.
"""
encoding, _ = self.rnn(x)
# The way that we retrieve the last encoding depends on whether the sequence is packed.
if isinstance(encoding, PackedSequence):
encoding, seq_lens = pad_packed_sequence(encoding)
# If the sequence is packed, then each encoding is at the position of the last item in the sequence
# (seq_len - 1).
encoding = encoding[seq_lens - 1, torch.arange(encoding.shape[1])]
else:
# If the sequence is not packed, then we know each encoding is simply located at the last position in the
# sequence.
encoding = encoding[-1]
return encoding
class AttnSeq2Vec(Module):
"""A sequence-to-vector RNN, with attention. Does not support packed tensors"""
def __init__(self, rnn, attn):
"""
Initialize an AttnSeq2Vec module.
:param rnn: The RNN module to use in encoding.
:param attn: The attention module to use after encoding.
"""
super().__init__()
self.rnn = rnn
self.attn = attn
def forward(self, x):
"""
Pass :param x through the network.
:param x: The input to the network, should be of shape (seq_len, batch_size, num_features). Packed tensors are
not supported.
:return: The output of the network.
"""
seq_outputs, _ = self.rnn(x)
encoding = self.attn(seq_outputs)
return encoding
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] |
src/sas/sasgui/perspectives/invariant/invariant_widgets.py | opendatafit/sasview | 0 | 169305 | ################################################################################
# This software was developed by the University of Tennessee as part of the
# Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
# project funded by the US National Science Foundation.
#
# See the license text in license.txt
#
# copyright 2009, University of Tennessee
################################################################################
import wx
from wx.lib.scrolledpanel import ScrolledPanel
WIDTH = 400
HEIGHT = 350
class DialogPanel(ScrolledPanel):
def __init__(self, *args, **kwds):
ScrolledPanel.__init__(self, *args, **kwds)
self.SetupScrolling()
class InvTextCtrl(wx.TextCtrl):
"""
Text control for model and fit parameters.
Binds the appropriate events for user interactions.
"""
def __init__(self, *args, **kwds):
wx.TextCtrl.__init__(self, *args, **kwds)
# # Set to True when the mouse is clicked while
# the whole string is selected
self.full_selection = False
# # Call back for EVT_SET_FOCUS events
_on_set_focus_callback = None
# Bind appropriate events
self.Bind(wx.EVT_LEFT_UP, self._highlight_text)
self.Bind(wx.EVT_SET_FOCUS, self._on_set_focus)
def _on_set_focus(self, event):
"""
Catch when the text control is set in focus to highlight the whole
text if necessary
:param event: mouse event
"""
event.Skip()
self.full_selection = True
def _highlight_text(self, event):
"""
Highlight text of a TextCtrl only of no text has be selected
:param event: mouse event
"""
# Make sure the mouse event is available to other listeners
event.Skip()
control = event.GetEventObject()
if self.full_selection:
self.full_selection = False
# Check that we have a TextCtrl
if issubclass(control.__class__, wx.TextCtrl):
# Check whether text has been selected,
# if not, select the whole string
(start, end) = control.GetSelection()
if start == end:
control.SetSelection(-1, -1)
class OutputTextCtrl(wx.TextCtrl):
"""
Text control used to display outputs.
No editing allowed. The background is
grayed out. User can't select text.
"""
def __init__(self, *args, **kwds):
wx.TextCtrl.__init__(self, *args, **kwds)
self.SetEditable(False)
self.SetBackgroundColour(self.GetParent().GetBackgroundColour())
# Bind to mouse event to avoid text highlighting
# The event will be skipped once the call-back
# is called.
self.Bind(wx.EVT_MOUSE_EVENTS, self._click)
def _click(self, event):
"""
Prevent further handling of the mouse event
by not calling Skip().
"""
pass
class DataDialog(wx.Dialog):
"""
Allow file selection at loading time
"""
def __init__(self, data_list, parent=None, text='', *args, **kwds):
kwds['size'] = (WIDTH, HEIGHT)
kwds['title'] = "Data Selection"
wx.Dialog.__init__(self, parent, *args, **kwds)
self.list_of_ctrl = []
self._sizer_main = wx.BoxSizer(wx.VERTICAL)
self._sizer_txt = wx.BoxSizer(wx.VERTICAL)
self._sizer_button = wx.BoxSizer(wx.HORIZONTAL)
self._choice_sizer = wx.GridBagSizer(5, 5)
self._panel = DialogPanel(self, style=wx.RAISED_BORDER,
size=(WIDTH - 20, HEIGHT / 3))
self.SetSizer(self._sizer_main)
self.__do_layout(data_list, text=text)
self.Layout()
def __do_layout(self, data_list, text=''):
"""
layout the dialog
"""
# add text
if text.strip() == "":
text = "This Perspective does not allow multiple data !\n"
text += "Please select only one Data.\n"
text_ctrl = wx.TextCtrl(self, -1, str(text), style=wx.TE_MULTILINE,
size=(-1, HEIGHT / 3))
text_ctrl.SetEditable(False)
self._sizer_txt.Add(text_ctrl, 1, wx.EXPAND | wx.ALL, 10)
iy = 0
ix = 0
rbox = wx.RadioButton(self._panel, -1, str(data_list[0].name),
(10, 10), style=wx.RB_GROUP)
rbox.SetValue(True)
self.list_of_ctrl.append((rbox, data_list[0]))
self._choice_sizer.Add(rbox, (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
for i in range(1, len(data_list)):
iy += 1
rbox = wx.RadioButton(self._panel, -1,
str(data_list[i].name), (10, 10))
rbox.SetValue(False)
self.list_of_ctrl.append((rbox, data_list[i]))
self._choice_sizer.Add(rbox, (iy, ix),
(1, 1), wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
self._panel.SetSizer(self._choice_sizer)
# add sizer
self._sizer_button.Add((20, 20), 1, wx.EXPAND | wx.ADJUST_MINSIZE, 0)
button_cancel = wx.Button(self, wx.ID_CANCEL, "Cancel")
self._sizer_button.Add(button_cancel, 0,
wx.LEFT | wx.RIGHT | wx.ADJUST_MINSIZE, 10)
button_OK = wx.Button(self, wx.ID_OK, "Ok")
button_OK.SetFocus()
self._sizer_button.Add(button_OK, 0,
wx.LEFT | wx.RIGHT | wx.ADJUST_MINSIZE, 10)
static_line = wx.StaticLine(self, -1)
self._sizer_txt.Add(self._panel, 0, wx.EXPAND | wx.ALL, 5)
self._sizer_main.Add(self._sizer_txt, 0, wx.EXPAND | wx.ALL, 5)
self._sizer_main.Add(static_line, 0, wx.EXPAND, 0)
self._sizer_main.Add(self._sizer_button, 0, wx.EXPAND | wx.ALL, 10)
def get_data(self):
"""
return the selected data
"""
for item in self.list_of_ctrl:
rbox, data = item
if rbox.GetValue():
return data
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diff-operations.py | suhasksv/py-ground | 3 | 152713 | <reponame>suhasksv/py-ground
import time
print("Hi, Welcome to Quantum Tricks")
time.sleep(1.0)
print("This a different operations calculator using same numbers")
time.sleep(2.0)
print("Lets start calculating")
time.sleep(1.0)
num1 = int(input("Enter an integer:"))
num2 = int(input("Enter an integer:"))
# Calculating addition
def add(a, b):
return a + b
# Calculating subtraction
def sub(a, b):
return a - b
# Calculating multiplication
def mul(a, b):
return a * b
# Calculating division
def div(a, b):
return a / b
# Calculating exponent
def power(a, b):
return a ** b
# Calculating mod
def mod(a, b):
return a % b
ops = {"+": add, "add": add, "addition": add, "sum": add, "-": sub, "sub": sub, "subtraction": sub, "difference": sub, "x": mul,
"multiply": mul, "*": mul, "multiplication": mul, "div": div, "division": div, "/": div, "power": power, "^": power,
"**": power, "mod": mod, "%": mod, "||": mod, "| |": mod, }
while True:
op = input("Enter an operation:")
if op.lower() in ops:
print(eval("ops[op](num1, num2)"))
else:
print("Sorry operation does not exists. Please try again")
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Fetch_contents/youtube_search.py | tshe777/WebSearch | 9 | 1606428 | import time
import webbrowser
import selenium
from selenium import webdriver
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.by import By
from selenium.webdriver.chrome.options import Options
from selenium.webdriver import ActionChains
def youtube_topic(yt_search):
print('============\nYOUTUBE\n============')
PATH = "C:\Program Files (x86)\chromedriver.exe" ## chrome executable on Windows
options_chrome = Options()
options_chrome.add_argument('headless') ## no UI
#yt_search = input('What are you looking for in YouTube? ')
## initialise the driver to youtube and open filters.
DRIVER = webdriver.Chrome(PATH, options=options_chrome)
DRIVER.get(f'https://www.youtube.com/results?search_query={yt_search}')
time.sleep(3)
filters = DRIVER.find_element_by_xpath('//*[@id="container"]/ytd-toggle-button-renderer')
ActionChains(DRIVER).click(filters).perform()
## try to locate the "last hour" button and click it.
try:
filter_box = WebDriverWait(DRIVER, 10).until(EC.presence_of_element_located((By.XPATH,'//*[@id="collapse-content"]/ytd-search-filter-group-renderer[1]')))
news = filter_box.find_elements_by_xpath('//*[@id="collapse-content"]/ytd-search-filter-group-renderer[1]')
DRIVER.find_element_by_link_text('Last hour').click()
finally:
pass
## find into the page all vids and print title and url.
main = WebDriverWait(DRIVER,10).until(EC.presence_of_element_located((By.ID, 'contents')))
time.sleep(5)
videos = main.find_elements_by_tag_name('ytd-video-renderer')
##==========================
container_titles = []
container_urls = []
vids = {}
##==========================
for video in videos[:10]:
video_title = video.find_element_by_id('video-title')
container_titles.append(video_title.text)
url = video.find_element_by_tag_name('a').get_attribute('href')
container_urls.append(url)
vids = dict(zip(container_titles,container_urls))
return vids
if __name__ == "__main__":
topic = input("what is your topic?: ")
print(youtube_topic(topic))
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] |
src/crl/devutils/_tmpindex.py | petrieh/crl-devutils | 0 | 69996 | <gh_stars>0
import os
from contextlib import contextmanager
import virtualenvrunner.runner
from crl.devutils.utils import get_randomstring
from crl.devutils.doccreator import DocCreator
from crl.devutils.devpiindex import DevpiIndex
__copyright__ = 'Copyright (C) 2019, Nokia'
class _TmpIndex(DevpiIndex):
def __init__(self,
run,
packagehandler,
baseindex=None,
baseurl=None,
index_name=None,
username=None,
clientarg=None):
super(_TmpIndex, self).__init__(self)
self.run = run
self.packagehandler = packagehandler
self.baseindex = baseindex
self.baseurl = baseurl
self.username = username
self.clientarg = clientarg
self._index = index_name
self._set_default_cleanup(not bool(index_name))
@property
def index(self):
if not self._index:
self._index = 'tmp_{random}'.format(random=get_randomstring(10))
return self._index
def _set_default_cleanup(self, cleanup):
self._default_cleanup = cleanup
def __enter__(self):
if not self.index_exists():
self.create_index()
self.use_index()
return self
def __exit__(self, *args):
if self._default_cleanup:
self.delete_index()
def test(self):
self._upload()
self.run('devpi test {spec}{clientarg} --detox'.format(
spec=self.spec,
clientarg=self.clientarg))
def _upload(self):
self.run('devpi upload{clientarg}'.format(
clientarg=self.clientarg))
if self._is_docs:
with self._docsrunnercontext() as runner:
self._generate_robotdocs(runner)
self._upload_only_docs(runner)
def publish(self, index):
self.push(self.spec, index)
def _docsrunnercontext(self):
@contextmanager
def _runner():
yield self
return (_runner()
if self.packagehandler.novirtualenv else
virtualenvrunner.runner.Runner(pip_index_url=self.pypiurl,
run=self.run))
@property
def _is_docs(self):
return os.path.isdir('sphinxdocs')
def _generate_robotdocs(self, runner):
if not self.packagehandler.novirtualenv:
self._install_docs_requirements(runner)
DocCreator(robotdocs_root_folders='robotdocs',
run=runner.run).create_robotdocs()
def _install_docs_requirements(self, runner):
runner.run('pip install crl.devutils -i {pypiurl}'.format(
pypiurl=self.pypiurl))
runner.run('pip install {name}=={version} -i {pypiurl}'.format(
name=self.packagehandler.name,
version=self.packagehandler.version,
pypiurl=self.pypiurl))
def _upload_only_docs(self, runner):
runner.run('devpi upload --no-vcs --only-docs{clientarg}'.format(
clientarg=self.clientarg))
@property
def spec(self):
return '{name}=={version}'.format(
name=self.packagehandler.name,
version=self.packagehandler.version)
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] |
cytoskeleton_analyser/database/sqlite_alchemy_orm/containers/cell_elements.py | vsukhor/cytoskeleton-analyser | 0 | 29222 | <reponame>vsukhor/cytoskeleton-analyser
# Copyright (c) 2021 <NAME>. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# 3. Neither the name of the copyright holder nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDER ''AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# -----------------------------------------------------------------------------
"""Subclasses of container base class for tailored to cell components.
"""
from collections import namedtuple
from . import container
from .. import models
class MembraneNucleus(container.Optional):
"""Container class for configuration of cell nuclear membrane.
"""
model = models.ConfigNucleus
class MembranePlasma(container.Optional):
"""Container class for configuration of cell plasma membrane.
"""
model = models.ConfigPlasma
class InSpace(container.Base):
"""Container class for configuration of unanchored microtubule MTOC.
"""
model = models.ConfigMtocInSpace
class Golgi(container.Base):
"""Container class for configuration of Golgi-type MTOC.
"""
model = models.ConfigMtocGolgi
class Centrosome(container.Base):
"""Container class for configuration of centrosome-type MTOC.
"""
model = models.ConfigMtocCentrosome
class Nucleus(container.Base):
"""Container class for configuration of Nucleus-type MTOC.
"""
model = models.ConfigMtocNucleus
#: Types of Microtubule Organizing Centers (MTOCs).
Mtoc = namedtuple('Mtoc', 'InSpace Golgi Centrosome Nucleus')
# Microtubule Organizing Centers (MTOCs).
mtoc = Mtoc(InSpace, Golgi, Centrosome, Nucleus)
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handybeam/opencl_wrappers/propagator_wrappers.py | ultraleap/HandyBeam | 2 | 160564 | ## Imports
import handybeam
import handybeam.opencl_wrappers.abstract_wrapper
import handybeam.propagator_mixins
import handybeam.propagator_mixins.clist_propagator
import handybeam.propagator_mixins.rect_propagator
import handybeam.propagator_mixins.hex_propagator
import handybeam.propagator_mixins.lamb_propagator
import handybeam.tx_array
import handybeam.cl_system
## Global variables
## Class
class Propagator(
handybeam.opencl_wrappers.abstract_wrapper.Wrapper,
handybeam.propagator_mixins.clist_propagator.ClistPropMixin,
handybeam.propagator_mixins.rect_propagator.RectPropMixin,
handybeam.propagator_mixins.hex_propagator.HexPropMixin,
handybeam.propagator_mixins.lamb_propagator.LambPropMixin
):
'''This is a wrapper class which inherits from the template wrapper class Wrapper and the
OpenCL propagator mixin classes. An instance of this class is initialised when a world
object is initialised.
'''
def __init__(self, parent=None, use_device = 0, use_platform = 0):
'''This method intialises an instance of the Propagator class. During the initialisation process,
the compiled OpenCL propagator kernels are assigned to the appropriate propagator mixin classes.
.. TODO::
Provide description and type for the handybeam world object.
Parameters
----------
parent : handybeam world object
DESCRIPTION
'''
# Inherits the OpenCL wrappers - i.e. the mixin classes
super(Propagator, self).__init__()
self.parent = parent
self.cl_system = handybeam.cl_system.OpenCLSystem(parent = self.parent,use_device = self.parent.device, use_platform=self.parent.platform)
# Run the _register methods for each of mixin classes to initialise the high-performance opencl kernels.
self._register_clist_propagator()
self._register_rect_propagator()
self._register_hex_propagator()
self._register_lamb_propagator()
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a10_octavia/controller/housekeeping/house_keeping.py | sridharsrinivas/a10-octavia | 1 | 182821 | <gh_stars>1-10
# Copyright 2019, A10 Networks
#
# 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 concurrent import futures
import datetime
from oslo_config import cfg
from oslo_log import log as logging
from octavia.db import api as db_api
from octavia.db import repositories as repo
from a10_octavia.controller.worker import controller_worker as cw
from a10_octavia.db import repositories as a10repo
LOG = logging.getLogger(__name__)
CONF = cfg.CONF
class SpareAmphora(object):
def __init__(self):
self.amp_repo = repo.AmphoraRepository()
self.vthunder_repo = a10repo.VThunderRepository()
self.cw = cw.A10ControllerWorker()
def spare_check(self):
"""Checks the DB for the Spare amphora count.
If it's less than the requirement, starts new amphora.
"""
session = db_api.get_session()
conf_spare_cnt = CONF.a10_house_keeping.spare_amphora_pool_size
curr_spare_cnt = self.vthunder_repo.get_spare_vthunder_count(session)
LOG.debug("Required Spare vThunder count : %d", conf_spare_cnt)
LOG.debug("Current Spare vThunder count : %d", curr_spare_cnt)
diff_count = conf_spare_cnt - curr_spare_cnt
# When the current spare amphora is less than required
if diff_count > 0:
LOG.info("Initiating creation of %d spare amphora.", diff_count)
# Call Amphora Create Flow diff_count times
with futures.ThreadPoolExecutor(
max_workers=CONF.a10_house_keeping.spare_amphora_pool_size
) as executor:
for i in range(1, diff_count + 1):
LOG.debug("Starting amphorae number %d ...", i)
executor.submit(self.cw.create_amphora)
else:
LOG.debug("Current spare vThunder count satisfies the requirement")
class DatabaseCleanup(object):
def __init__(self):
self.vthunder_repo = a10repo.VThunderRepository()
self.lb_repo = a10repo.LoadBalancerRepository()
def delete_old_amphorae(self):
"""Checks the DB for old amphora and deletes them based on its age."""
exp_age = datetime.timedelta(
seconds=CONF.a10_house_keeping.amphora_expiry_age)
session = db_api.get_session()
amp_ids = self.vthunder_repo.get_all_deleted_expiring(session,
exp_age=exp_age)
LOG.info('VThunder ids: %s', amp_ids)
for amp_id in amp_ids:
LOG.info('Attempting to purge db record for VThunder ID: %s',
amp_id)
self.vthunder_repo.delete(session, id=amp_id)
LOG.info('Purged db record for Amphora ID: %s', amp_id)
def cleanup_load_balancers(self):
"""Checks the DB for old load balancers and triggers their removal."""
exp_age = datetime.timedelta(
seconds=CONF.a10_house_keeping.load_balancer_expiry_age)
session = db_api.get_session()
lb_ids = self.lb_repo.get_all_deleted_expiring(session,
exp_age=exp_age)
LOG.info('Load balancer ids: %s', lb_ids)
for lb_id in lb_ids:
LOG.info('Attempting to delete load balancer id : %s', lb_id)
self.lb_repo.delete(session, id=lb_id)
LOG.info('Deleted load balancer id : %s', lb_id)
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] |
zeus/networks/pytorch/backbones/getter.py | shaido987/vega | 1 | 12142 | <filename>zeus/networks/pytorch/backbones/getter.py
# -*- coding: utf-8 -*-
# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
# This program is free software; you can redistribute it and/or modify
# it under the terms of the MIT License.
# 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
# MIT License for more details.
"""ResNetVariant for Detection."""
from zeus.common import ClassType, ClassFactory
from zeus.modules.connections.connections import MultiOutputGetter
@ClassFactory.register(ClassType.NETWORK)
class BackboneGetter(MultiOutputGetter):
"""Backbone Getter form torchvision ResNet."""
def __init__(self, backbone_name, layer_names=None, **kwargs):
backbone = ClassFactory.get_cls(ClassType.NETWORK, backbone_name)
backbone = backbone(**kwargs) if kwargs else backbone()
if hasattr(backbone, "layers_name"):
layer_names = backbone.layers_name()
layer_names = layer_names or ['layer1', 'layer2', 'layer3', 'layer4']
super(BackboneGetter, self).__init__(backbone, layer_names)
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] |
mydocpy/types.py | R1tschY/mydocpy | 0 | 189923 | <filename>mydocpy/types.py
# -*- coding=utf-8 -*-
#
# Copyright 2017 <NAME>
#
# 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 re
from collections import namedtuple
from typing import Text, Tuple, Sequence
ImportedType = namedtuple("ImportedType", ["module", "name"])
class TypeInterpreter(object):
# options
# imports.prevered_import = typing | collections
def interpret(self, type_expr):
# type: (Text) -> Tuple[Text, Sequence[ImportedType]]
"""
Interpret a user type expression to a python type hint
:param type_expr: type expression (for example:
``List[int]`` or ``list<int>``)
:return: Type hint expression and needed imports
"""
re.sub(r"(\S+)\s*->\s*(\S+)", "Callable[[\1], \2]")
re.sub(r"(\S+)\s*|\s*(\S+)+", "Union[\1, \2]")
re.sub(r"[<(]", "[")
re.sub(r"[>)]", "]")
return (type_expr, ())
typing_replacements = {
"dict": "Dict",
"list": "List",
"set": "Set",
"tuple": "Tuple",
"defaultdict": "DefaultDict",
"frozenset": "FrozenSet",
"namedtuple": "NamedTuple",
"deque": "Deque",
"str": "ByteString",
"unicode": "Text",
"string": "Text",
"object": "Any",
"function": "Callable"
}
builtin_replacements = {
"integer": "int",
"long": "int",
"double": "float",
"real": "float",
"floating point": "float"
}
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] |
Chapter39.Decorators/3-timerdeco1.py | mindnhand/Learning-Python-5th | 0 | 177319 | #!/usr/bin/env python3
#encoding=utf-8
#-----------------------------------------------
# Usage: python3 3-timerdeco1.py
# Description: timing function decorators
#-----------------------------------------------
import sys
import time
force = list if sys.version_info[0] == 3 else lambda X: X
class Timer:
def __init__(self, func):
self.func = func
self.alltime = 0
def __call__(self, *args, **kwargs):
start = time.time()
result = self.func(*args, **kwargs)
elapsed = time.time() - start
self.alltime += elapsed
print('%s: %.5f, %.5f' % (self.func.__name__, elapsed, self.alltime))
return result
@Timer
def listcomp(n):
return [x * 2 for x in range(n)]
@Timer
def mapcall(n):
return force(map((lambda x: x * 2), range(n)))
if __name__ == '__main__':
print('\n\033[1;35mEntrance\033[0m\n')
print('\033[1;37mlistcomp function\033[0m')
result = listcomp(5)
listcomp(50000)
listcomp(500000)
listcomp(1000000)
print(result)
print('allTime = %s' % listcomp.alltime)
print('\n')
print('\033[1;37mmapcall function\033[0m')
result = mapcall(5)
mapcall(50000)
mapcall(500000)
mapcall(1000000)
print(result)
print('allTime = %s' % mapcall.alltime)
print('\nmapcall = %s' % round(mapcall.alltime / listcomp.alltime, 3))
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] |
yt_dlp/downloader/parallel.py | evolution-ant/local-youtube-dl | 0 | 198009 | import os
import threading
import time
import socket
import json
import re
from .common import FileDownloader
from .http import HttpFD
from ..compat import (
compat_str,
compat_urllib_error,
)
from ..utils import (
encodeFilename,
int_or_none,
sanitize_open,
sanitized_Request,
)
class ParallelFD(FileDownloader):
def real_download(self, filename, info_dict):
class DownloadContext(dict):
__getattr__ = dict.get
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
def start_parallel_download(thread_id, range_start, range_end, retries = 5):
if range_start >= range_end:
self.report_warning("range end longer than range start.")
return
request = sanitized_Request(url, None, headers)
set_range(request, range_start, range_end)
try:
response = self.ydl.urlopen(request)
content_range = response.headers.get('Content-Range')
if content_range:
content_range_m = re.search(r'bytes (\d+)-(\d+)?(?:/(\d+))?', content_range)
content_range_start = int(content_range_m.group(1))
self.to_screen("thread: {}, current: {}, tototal: {}, start: {}, end {}, content start:{} content end:{}".format(thread_id, ctx.resume_cfg[thread_id].get('current', 0), (range_end - range_start), range_start, range_end, content_range_start, content_range_m.group(2)))
if range_start == content_range_start:
content_range_end = int_or_none(content_range_m.group(2))
offset = range_start
start_time = time.time()
while True:
while True:
try:
content = response.read(ctx.block_size)
break
except socket.timeout as e:
self.report_warning(e.strerror + ' try times: ' + retries)
start_parallel_download(thread_id, offset, range_end, retries-1)
break
except socket.error as e:
self.report_warning(e.strerror + (' try times: ' + retries))
start_parallel_download(thread_id, offset, range_end, retries-1)
break
if not content:
if abs(content_range_end - offset) > 1:
start_parallel_download(thread_id, offset, content_range_end)
self.report_warning("expect end in %d,but offset: %d not end" % (content_range_end, offset))
break
else:
break
self.slow_down(start_time, time.time(), offset - content_range_start)
self._hook_progress(None)
with ctx.lock:
ctx.stream.seek(offset)
ctx.stream.write(content)
offset = offset + len(content)
ctx.resume_cfg[thread_id]['start'] = offset
ctx.resume_cfg[thread_id]['end'] = range_end
if ctx.resume_cfg[thread_id].get("current", 0) == 0:
ctx.resume_cfg[thread_id]['current'] = len(content)
else:
ctx.resume_cfg[thread_id]['current'] += len(content)
ctx.downloaded_bytes += len(content)
else:
start_parallel_download(thread_id, content_range_start, range_end, retries)
self.report_warning("not suppport start:%s should be %%s" % (range_start, content_range_start))
else:
self.report_warning('Not found content-range')
raise ValueError
except (compat_urllib_error.HTTPError, ) as err:
self.report_warning("connect error.")
if retries == 0:
return
start_parallel_download(thread_id, range_start, range_end, retries - 1)
def speed_calc():
start = time.time()
now = time.time()
ctx.before_byte_counter = ctx.downloaded_bytes
while True:
now = time.time()
ctx.resume_cfg_stream.seek(0)
ctx.resume_cfg_stream.truncate(0)
ctx.resume_cfg_stream.flush()
ctx.resume_cfg_stream.write(json.dumps(ctx.resume_cfg))
ctx.resume_cfg_stream.flush()
speed = self.calc_speed(start, now, ctx.downloaded_bytes - ctx.before_byte_counter)
eta = self.calc_eta(start, time.time(), ctx.data_len - ctx.before_byte_counter, ctx.downloaded_bytes - ctx.before_byte_counter)
self._hook_progress({
'status': 'downloading',
'downloaded_bytes': ctx.downloaded_bytes,
'total_bytes': ctx.data_len,
'filename': ctx.filename,
'eta': eta,
'speed': speed,
'elapsed': now - ctx.start_time,
})
start = time.time()
ctx.before_byte_counter = ctx.downloaded_bytes
time.sleep(1)
if ctx.downloaded_bytes >= ctx.data_len:
self._hook_progress({
'downloaded_bytes': ctx.data_len,
'total_bytes': ctx.data_len,
'filename': ctx.filename,
'status': 'finished',
'elapsed': time.time() - ctx.start_time,
})
break
def set_range(req, start, end):
range_header = 'bytes=%d-' % start
if end:
range_header += compat_str(end)
req.add_header('Range', range_header)
url = info_dict['url']
ctx = DownloadContext()
ctx.filename = filename
ctx.tmpfilename = self.temp_name(filename)
ctx.resume_file = ctx.tmpfilename + '.json'
ctx.stream = None
ctx.start_time = time.time()
ctx.block_size = self.params.get('buffersize', 1024)
headers = {'Youtubedl-no-compression': 'True'}
add_headers = info_dict.get('http_headers')
if add_headers:
headers.update(add_headers)
max_try_num = int_or_none(self.params.get('retries', 10))
request = sanitized_Request(url, headers = headers)
ctx.data = self.ydl.urlopen(request)
data_len = int_or_none(ctx.data.info().get('Content-length', None))
if data_len is not None and data_len > 0:
# Not support multiple thread download.
if ctx.data.info().get('Accept-Ranges', None) is None:
return HttpFD.real_download(self , filename, info_dict)
ctx.resume_cfg = []
#region Init file streams
# Create files if not exist
if os.path.isfile(encodeFilename(ctx.tmpfilename)) is False:
f, ctx.tmpfilename = sanitize_open(ctx.tmpfilename, 'wb')
f.close()
if os.path.isfile(ctx.resume_file) is False:
f, ctx.resume_file = sanitize_open(ctx.resume_file, 'w')
f.close()
ctx.stream, ctx.tmpfilename = sanitize_open(ctx.tmpfilename, 'rb+')
ctx.resume_cfg_stream, ctx.resume_file = sanitize_open(ctx.resume_file, 'r+')
assert ctx.stream is not None
assert ctx.resume_cfg_stream is not None
ctx.filename = self.undo_temp_name(ctx.tmpfilename)
self.report_destination(ctx.filename)
#endregion
ctx.lock = threading.Lock()
ctx.data_len = data_len
ranges = []
ctx.thread_count = int(self.params.get('thread_count', 5))
ctx.range_size = int(data_len / ctx.thread_count)
ctx.diff_size = 0
real_size = (ctx.range_size * ctx.thread_count)
if ctx.data_len > real_size:
ctx.diff_size = ctx.data_len - real_size
elif ctx.data_len < real_size:
ctx.diff_size = ctx.data_len - real_size
try:
cfg = json.load(ctx.resume_cfg_stream)
except ValueError as e:
cfg = None
need_download_size = 0
rate_limit = self.params.get('ratelimit')
if rate_limit:
self.params['ratelimit'] = float(rate_limit) / float(ctx.thread_count)
for i in range(ctx.thread_count):
if cfg is not None:
start = cfg[i]['start']
end = cfg[i]['end']
need_download_size = need_download_size + (end - start)
ranges.append((start, end))
else:
need_download_size = data_len
if i == ctx.thread_count - 1:
ranges.append((i * ctx.range_size + 1, data_len))
elif i == 0:
ranges.append((i * ctx.range_size, (i + 1) * ctx.range_size + ctx.diff_size))
else:
ranges.append((i * ctx.range_size + 1, (i + 1) * ctx.range_size))
ctx.downloaded_bytes = data_len - need_download_size
calc_thread = threading.Thread(target=speed_calc)
thread_list = [calc_thread]
thread_id = 0
for ran in ranges:
start, end = ran
ctx.resume_cfg.append({"start": start, "end": end})
thread = threading.Thread(target=start_parallel_download, args=(thread_id, start, end, max_try_num), daemon=True)
thread.start()
thread_list.append(thread)
thread_id = thread_id + 1
calc_thread.start()
for thread in thread_list:
thread.join()
ctx.stream.close()
ctx.resume_cfg_stream.close()
try:
os.remove(ctx.resume_file)
self.try_rename(ctx.tmpfilename, ctx.filename)
except OSError as e:
self.report_warning(e.strerror)
return True
else:
self.report_error('download fail, get content length error.')
return False
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] |
projects/day024/main.py | gabrielmdr/100-days-of-code | 0 | 81953 | <reponame>gabrielmdr/100-days-of-code
names = []
startingletter = ""
# Open file and getting all names from it
with open("./Input/Names/invited_names.txt") as file:
for line in file:
names.append(line.strip())
# Getting the text from the starting letter
with open("./Input/Letters/starting_letter.txt") as file:
startingletter = file.read()
# Looping through the list of names and writing the final version of the letter for each person
for name in names:
with open(f"./Output/ReadyToSend/letter_for_{name}", "w") as readytosend:
readytosend.write(startingletter.replace("[name]", name))
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] |
commands/network.py | prometheanfire/openstack-guest-agents-unix | 1 | 78502 | # vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (c) 2011 Openstack, LLC.
# 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.
#
"""
JSON misc commands plugin
"""
try:
import anyjson
except ImportError:
try:
import simplejson as json
except ImportError:
import json
class anyjson(object):
"""Fake anyjson module as a class"""
@staticmethod
def serialize(buf):
return json.dumps(buf)
@staticmethod
def deserialize(buf):
return json.loads(buf)
from cStringIO import StringIO
import fcntl
import logging
import os
import platform
import pyxenstore
import re
import socket
import time
from ctypes import *
import agentlib
import commands
import debian.network
import redhat.network
import arch.network
import suse.network
import gentoo.network
import freebsd.network
XENSTORE_INTERFACE_PATH = "vm-data/networking"
XENSTORE_HOSTNAME_PATH = "vm-data/hostname"
DEFAULT_HOSTNAME = ''
HOSTS_FILE = '/etc/hosts'
RESOLV_CONF_FILE = '/etc/resolv.conf'
if os.uname()[0].lower() == 'freebsd':
INTERFACE_LABELS = {"public": "xn0",
"private": "xn1"}
else:
INTERFACE_LABELS = {"public": "eth0",
"private": "eth1"}
NETMASK_TO_PREFIXLEN = {
'255.255.255.255': 32, '255.255.255.254': 31,
'255.255.255.252': 30, '255.255.255.248': 29,
'255.255.255.240': 28, '255.255.255.224': 27,
'255.255.255.192': 26, '255.255.255.128': 25,
'255.255.255.0': 24, '255.255.254.0': 23,
'255.255.252.0': 22, '255.255.248.0': 21,
'255.255.240.0': 20, '255.255.224.0': 19,
'255.255.192.0': 18, '255.255.128.0': 17,
'255.255.0.0': 16, '255.254.0.0': 15,
'255.252.0.0': 14, '255.248.0.0': 13,
'255.240.0.0': 12, '255.224.0.0': 11,
'255.192.0.0': 10, '255.128.0.0': 9,
'255.0.0.0': 8, '254.0.0.0': 7,
'252.0.0.0': 6, '248.0.0.0': 5,
'240.0.0.0': 4, '172.16.31.10': 3,
'192.0.0.0': 2, '172.16.17.32': 1,
'0.0.0.0': 0,
}
class NetworkCommands(commands.CommandBase):
def __init__(self, *args, **kwargs):
pass
@staticmethod
def detect_os():
"""
Return the Linux Distribution or other OS name
"""
translations = {"debian": debian,
"ubuntu": debian,
"redhat": redhat,
"centos": redhat,
"fedora": redhat,
"oracle": redhat,
"arch": arch,
"opensuse": suse,
"suse": suse,
"gentoo": gentoo,
"freebsd": freebsd}
system = os.uname()[0]
if system == "Linux":
system = platform.linux_distribution(full_distribution_name=0)[0]
# Arch Linux returns None for platform.linux_distribution()
if not system and os.path.exists('/etc/arch-release'):
system = 'arch'
if not system:
return None
system = system.lower()
global DEFAULT_HOSTNAME
DEFAULT_HOSTNAME = system
return translations.get(system)
@commands.command_add('resetnetwork')
def resetnetwork_cmd(self, data):
os_mod = self.detect_os()
if not os_mod:
raise SystemError("Couldn't figure out my OS")
xs_handle = pyxenstore.Handle()
try:
hostname = xs_handle.read(XENSTORE_HOSTNAME_PATH)
logging.info('hostname: %r (from xenstore)' % hostname)
except pyxenstore.NotFoundError:
hostname = DEFAULT_HOSTNAME
logging.info('hostname: %r (default)' % hostname)
interfaces = []
try:
entries = xs_handle.entries(XENSTORE_INTERFACE_PATH)
except pyxenstore.NotFoundError:
entries = []
for entry in entries:
data = xs_handle.read(XENSTORE_INTERFACE_PATH + '/' + entry)
data = anyjson.deserialize(data)
interfaces.append(data)
logging.info('interface %s: %r' % (entry, data))
del xs_handle
# Normalize interfaces data. It can come in a couple of different
# (similar) formats, none of which are convenient.
by_macaddr = dict([(mac, (up, name))
for name, up, mac in agentlib.get_interfaces()])
config = {}
for interface in interfaces:
ifconfig = {}
mac = interface.get('mac')
if not mac:
raise RuntimeError('No MAC found in config')
# by_macaddr is keyed using lower case hexadecimal
mac = mac.lower()
ifconfig['mac'] = mac
# 'label' used to be the method to determine which interface
# this configuration applies to, but 'mac' is safer to use.
# 'label' is being phased out now.
if mac not in by_macaddr:
raise RuntimeError('Unknown interface MAC %s' % mac)
ifconfig['label'] = interface.get('label')
up, ifname = by_macaddr[mac]
# Record if the interface is up already
ifconfig['up'] = up
# List of IPv4 and IPv6 addresses
ip4s = interface.get('ips', [])
ip6s = interface.get('ip6s', [])
if not ip4s and not ip6s:
raise RuntimeError('No IPs found for interface')
# Gateway (especially IPv6) can be tied to an interface
gateway4 = interface.get('gateway')
gateway6 = interface.get('gateway6')
# Filter out any IPs that aren't enabled
for ip in ip4s + ip6s:
try:
ip['enabled'] = int(ip.get('enabled', 0))
except ValueError:
raise RuntimeError("Invalid value %r for 'enabled' key" %
ip.get('enabled'))
ip4s = filter(lambda i: i['enabled'], ip4s)
ip6s = filter(lambda i: i['enabled'], ip6s)
# Validate and normalize IPv4 and IPv6 addresses
for ip in ip4s:
if 'ip' not in ip:
raise RuntimeError("Missing 'ip' key for IPv4 address")
if 'netmask' not in ip:
raise RuntimeError("Missing 'netmask' key for IPv4 address")
# Rename 'ip' to 'address' to be more specific
ip['address'] = ip.pop('ip')
ip['prefixlen'] = NETMASK_TO_PREFIXLEN[ip['netmask']]
for ip in ip6s:
if 'ip' not in ip and 'address' not in ip:
raise RuntimeError("Missing 'ip' or 'address' key for IPv6 address")
if 'netmask' not in ip:
raise RuntimeError("Missing 'netmask' key for IPv6 address")
if 'gateway' in ip:
# FIXME: Should we fail if gateway6 is already set?
gateway6 = ip.pop('gateway')
# FIXME: Should we fail if both 'ip' and 'address' are
# specified but differ?
# Rename 'ip' to 'address' to be more specific
if 'address' not in ip:
ip['address'] = ip.pop('ip')
# Rename 'netmask' to 'prefixlen' to be more accurate
ip['prefixlen'] = ip.pop('netmask')
ifconfig['ip4s'] = ip4s
ifconfig['ip6s'] = ip6s
ifconfig['gateway4'] = gateway4
ifconfig['gateway6'] = gateway6
# Routes are optional
routes = interface.get('routes', [])
# Validate and normalize routes
for route in routes:
if 'route' not in route:
raise RuntimeError("Missing 'route' key for route")
if 'netmask' not in route:
raise RuntimeError("Missing 'netmask' key for route")
if 'gateway' not in route:
raise RuntimeError("Missing 'gateway' key for route")
# Rename 'route' to 'network' to be more specific
route['network'] = route.pop('route')
route['prefixlen'] = NETMASK_TO_PREFIXLEN[route['netmask']]
ifconfig['routes'] = routes
ifconfig['dns'] = interface.get('dns', [])
config[ifname] = ifconfig
# TODO: Should we fail if there isn't at least one gateway specified?
#if not gateway4 and not gateway6:
# raise RuntimeError('No gateway found for public interface')
return os_mod.network.configure_network(hostname, config)
def _get_etc_hosts(infile, interfaces, hostname):
ips = set()
for interface in interfaces.itervalues():
ip4s = interface['ip4s']
if ip4s:
ips.add(ip4s[0]['address'])
ip6s = interface['ip6s']
if ip6s:
ips.add(ip6s[0]['address'])
outfile = StringIO()
for line in infile:
line = line.strip()
if '#' in line:
config, comment = line.split('#', 1)
config = config.strip()
comment = '\t#' + comment
else:
config, comment = line, ''
parts = re.split('\s+', config)
if parts:
if parts[0] in ips:
confip = parts.pop(0)
if len(parts) == 1 and parts[0] != hostname:
# Single hostname that differs, we replace that one
print >> outfile, '# %s\t# Removed by nova-agent' % line
print >> outfile, '%s\t%s%s' % (confip, hostname, comment)
elif len(parts) == 2 and len(
filter(lambda h: '.' in h, parts)) == 1:
# Two hostnames, one a hostname, one a domain name. Replace
# the hostname
hostnames = map(
lambda h: ('.' in h) and h or hostname, parts)
print >> outfile, '# %s\t# Removed by nova-agent' % line
print >> outfile, '%s\t%s%s' % (confip,
' '.join(hostnames), comment)
else:
# Don't know how to handle this line, so skip it
print >> outfile, line
ips.remove(confip)
else:
print >> outfile, line
else:
print >> outfile, line
# Add public IPs we didn't manage to patch
for ip in ips:
print >> outfile, '%s\t%s' % (ip, hostname)
outfile.seek(0)
return outfile.read()
def get_etc_hosts(interfaces, hostname):
if os.path.exists(HOSTS_FILE):
infile = open(HOSTS_FILE)
else:
infile = StringIO()
return HOSTS_FILE, _get_etc_hosts(infile, interfaces, hostname)
def get_gateways(interfaces):
gateway4s = []
gateway6s = []
for interface in interfaces.itervalues():
gateway = interface.get('gateway4')
if gateway:
gateway4s.append(gateway)
gateway = interface.get('gateway6')
if gateway:
gateway6s.append(gateway)
if len(gateway4s) > 1:
raise RuntimeError("Multiple IPv4 default routes specified")
if len(gateway6s) > 1:
raise RuntimeError("Multiple IPv6 default routes specified")
gateway4 = gateway4s and gateway4s[0] or None
gateway6 = gateway6s and gateway6s[0] or None
return gateway4, gateway6
def get_nameservers(interfaces):
for interface in interfaces.itervalues():
for nameserver in interface['dns']:
yield nameserver
def get_resolv_conf(interfaces):
resolv_data = ''
for nameserver in get_nameservers(interfaces):
resolv_data += 'nameserver %s\n' % nameserver
if not resolv_data:
return None, None
return RESOLV_CONF_FILE, '# Automatically generated, do not edit\n' + \
resolv_data
def sethostname(hostname):
agentlib.sethostname(hostname)
def stage_files(update_files):
tmp_suffix = '%d.tmp~' % os.getpid()
for filepath, data in update_files.items():
if os.path.exists(filepath):
# If the data is the same, skip it, nothing to do
if data == open(filepath).read():
logging.info("skipping %s (no changes)" % filepath)
del update_files[filepath]
continue
logging.info("staging %s (%s)" % (filepath, tmp_suffix))
tmp_file = '%s.%s' % (filepath, tmp_suffix)
f = open(tmp_file, 'w')
try:
f.write(data)
f.close()
os.chown(tmp_file, 0, 0)
os.chmod(tmp_file, 0644)
except:
os.unlink(tmp_file)
raise
def move_files(update_files, remove_files=None):
if not remove_files:
remove_files = set()
tmp_suffix = '%d.tmp~' % os.getpid()
bak_suffix = '%d.bak~' % time.time()
for filepath in update_files.iterkeys():
if os.path.exists(filepath):
# Move previous version to a backup
logging.info("backing up %s (%s)" % (filepath, bak_suffix))
os.rename(filepath, '%s.%s' % (filepath, bak_suffix))
logging.info("updating %s" % filepath)
try:
os.rename('%s.%s' % (filepath, tmp_suffix), filepath)
except:
# Move backup file back so there's some sort of configuration
os.rename('%s.%s' % (filepath, bak_suffix), filepath)
raise
for filepath in remove_files:
logging.info("moving %s (%s)" % (filepath, bak_suffix))
os.rename(filepath, '%s.%s' % (filepath, bak_suffix))
def update_files(update_files, remove_files=None):
stage_files(update_files)
move_files(update_files, remove_files)
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CNN_classify.py | chunnuanhuakai/chuanganqi_classify | 2 | 133868 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 3 17:14:53 2019
@author: liuhongbing
"""
import pandas as pd
import numpy as np
from scipy import stats
from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix, roc_curve, auc
import tensorflow as tf
# 加载数据集
def read_data(file_path):
column_names = ['user-id', 'activity', 'timestamp', 'x-axis', 'y-axis', 'z-axis']
data = pd.read_csv(file_path, header=None, names=column_names)
data['z-axis'] = dataset['z-axis'].apply(lambda x : str(x).split(";")[0])
data['z-axis'] = data['z-axis'].astype('float32')
return data
# 数据标准化
def feature_normalize(dataset):
mu = np.mean(dataset, axis=0)
print('mu:',mu)
sigma = np.std(dataset, axis=0)
print('sigma:',sigma)
return (dataset - mu) / sigma
# 创建时间窗口，90 × 50ms，也就是 4.5 秒，每次前进 45 条记录，半重叠的方式。
def windows(data, size):
start = 0
while start < data.count():
yield start, start + size
start += (size / 2)
# 创建输入数据，每一组数据包含 x, y, z 三个轴的 90 条连续记录，
# 用 `stats.mode` 方法获取这 90 条记录中出现次数最多的行为
# 作为该组行为的标签，这里有待商榷，其实可以完全使用同一种行为的数据记录
# 来创建一组数据用于输入的。
def segment_signal(data, window_size=90):
segments = np.empty((0, window_size, 3))
labels = np.empty((0))
print (len(data['timestamp']))
count = 0
for (start, end) in windows(data['timestamp'], window_size):
print (count)
start = int(start)
end = int(end)
count += 1
x = data["x-axis"][start:end]
y = data["y-axis"][start:end]
z = data["z-axis"][start:end]
if (len(dataset['timestamp'][start:end]) == window_size):
segments = np.vstack([segments, np.dstack([x, y, z])])
labels = np.append(labels, stats.mode(data["activity"][start:end])[0][0])
return segments, labels
# 初始化神经网络参数
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
# 初始化神经网络参数
def bias_variable(shape):
initial = tf.constant(0.0, shape=shape)
return tf.Variable(initial)
# 执行卷积操作
def depthwise_conv2d(x, W):
return tf.nn.depthwise_conv2d(x, W, [1, 1, 1, 1], padding='VALID')
# 为输入数据的每个 channel 执行一维卷积，并输出到 ReLU 激活函数
def apply_depthwise_conv(x, kernel_size, num_channels, depth):
weights = weight_variable([1, kernel_size, num_channels, depth])
biases = bias_variable([depth * num_channels])
return tf.nn.relu(tf.add(depthwise_conv2d(x, weights), biases))
# 在卷积层输出进行一维 max pooling
def apply_max_pool(x, kernel_size, stride_size):
return tf.nn.max_pool(x, ksize=[1, 1, kernel_size, 1],
strides=[1, 1, stride_size, 1], padding='VALID')
root = "/Users/liuhongbing/Documents/tensorflow/data/WISDM_ar_v1.1/"
dataset2 = read_data(root +'WISDM_ar_v1.1_raw.txt')
dataset2.fillna(0, inplace=True)
dataset = dataset2[:200000]
dataset['x-axis'] = feature_normalize(dataset['x-axis'])
dataset['y-axis'] = feature_normalize(dataset['y-axis'])
dataset['z-axis'] = feature_normalize(dataset['z-axis'])
segments, labels = segment_signal(dataset)
labels = np.asarray(pd.get_dummies(labels), dtype = np.int8)
# 创建输入
## [batch_size, height, width, chanles]
reshaped_segments = segments.reshape(len(segments), 1, 90, 3)
# 在准备好的输入数据中，分别抽取训练数据和测试数据，按照 70/30 原则来做。
train_test_split = np.random.rand(len(reshaped_segments)) < 0.70
train_x = reshaped_segments[train_test_split]
train_y = labels[train_test_split]
test_x = reshaped_segments[~train_test_split]
test_y = labels[~train_test_split]
# 定义输入数据的维度和标签个数
input_height = 1
input_width = 90
num_labels = 4 # 6
num_channels = 3
batch_size = 10
kernel_size = 60
depth = 60
# 隐藏层神经元个数
num_hidden = 1000
learning_rate = 0.0001
# 降低 cost 的迭代次数
training_epochs = 8
total_batchs = reshaped_segments.shape[0] // batch_size
# 下面是使用 Tensorflow 创建神经网络的过程。
X = tf.placeholder(tf.float32, shape=[None,input_height,input_width,num_channels])
Y = tf.placeholder(tf.float32, shape=[None,num_labels])
c = apply_depthwise_conv(X,kernel_size,num_channels,depth)
p = apply_max_pool(c,20,2)
c = apply_depthwise_conv(p,6,depth*num_channels,depth//10)
shape = c.get_shape().as_list()
c_flat = tf.reshape(c, [-1, shape[1] * shape[2] * shape[3]])
f_weights_l1 = weight_variable([shape[1] * shape[2] * depth * num_channels * (depth//10), num_hidden])
f_biases_l1 = bias_variable([num_hidden])
f = tf.nn.tanh(tf.add(tf.matmul(c_flat, f_weights_l1),f_biases_l1))
out_weights = weight_variable([num_hidden, num_labels])
out_biases = bias_variable([num_labels])
y_ = tf.nn.softmax(tf.matmul(f, out_weights) + out_biases)
loss = -tf.reduce_sum(Y * tf.log(y_))
optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(loss)
correct_prediction = tf.equal(tf.argmax(y_,1), tf.argmax(Y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
cost_history = np.empty(shape=[1], dtype=float)
# 开始训练
with tf.Session() as session:
tf.initialize_all_variables().run()
# 开始迭代
for epoch in range(training_epochs):
for b in range(total_batchs):
offset = (b * batch_size) % (train_y.shape[0] - batch_size)
batch_x = train_x[offset:(offset + batch_size), :, :, :]
batch_y = train_y[offset:(offset + batch_size), :]
_, c = session.run([optimizer, loss], feed_dict={X: batch_x, Y: batch_y})
cost_history = np.append(cost_history, c)
print("Epoch {}: Training Loss = {}, Training Accuracy = {}".format(
epoch, c, session.run(accuracy, feed_dict={X: train_x, Y: train_y})))
y_p = tf.argmax(y_, 1)
y_true = np.argmax(test_y, 1)
final_acc, y_pred = session.run([accuracy, y_p], feed_dict={X: test_x, Y: test_y})
print("Testing Accuracy: {}".format(final_acc))
temp_y_true = np.unique(y_true)
temp_y_pred = np.unique(y_pred)
np.save("y_true", y_true)
np.save("y_pred", y_pred)
print("temp_y_true", temp_y_true)
print( "temp_y_pred", temp_y_pred)
# 计算模型的 metrics
print( "Precision", precision_score(y_true.tolist(), y_pred.tolist(), average='weighted'))
print( "Recall", recall_score(y_true, y_pred, average='weighted'))
print( "f1_score", f1_score(y_true, y_pred, average='weighted'))
print( "confusion_matrix")
print( confusion_matrix(y_true, y_pred)) | [
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gpslocation/migrations/0001_initial.py | VekotinVerstas/DjangoHttpBroker-GpsLocation | 0 | 178581 | # Generated by Django 2.2 on 2019-04-27 12:49
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('broker', '0007_auto_20190427_1224'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Trackpoint',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('status', models.IntegerField(default=1)),
('time', models.DateTimeField(db_index=True)),
('lat', models.FloatField()),
('lon', models.FloatField()),
('speed', models.FloatField(blank=True, null=True)),
('course', models.FloatField(blank=True, null=True)),
('ele', models.FloatField(blank=True, null=True)),
('hacc', models.FloatField(blank=True, null=True)),
('vacc', models.FloatField(blank=True, null=True)),
('hdop', models.FloatField(blank=True, null=True)),
('vdop', models.FloatField(blank=True, null=True)),
('pdop', models.FloatField(blank=True, null=True)),
('tdop', models.FloatField(blank=True, null=True)),
('sat', models.IntegerField(blank=True, null=True)),
('satavail', models.IntegerField(blank=True, null=True)),
('created_at', models.DateTimeField(auto_now_add=True)),
('datalogger', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='broker.Datalogger')),
('user', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
options={
'unique_together': {('datalogger', 'time')},
},
),
]
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lemur/plugins/lemur_linuxdst/plugin.py | prdonahue/lemur | 0 | 73648 | #!/usr/bin/python
from lemur.plugins.bases import DestinationPlugin
from lemur.plugins.lemur_linuxdst import remote_host
class LinuxDstPlugin(DestinationPlugin):
title = 'Linux Destination Plugin'
slug = 'linux-destination'
description = 'Allow the distribution of certificates to a Linux host'
version = 1
author = '<NAME> '
author_url = 'https://github.com/RickB17/'
options = [
{
'name': 'dstHost',
'type': 'str',
'required': True,
'helpMessage': 'This is the host you will be sending the certificate to',
},
{
'name': 'dstPort',
'type': 'int',
'required': True,
'helpMessage': 'This is the port SSHD is running on',
'default': '22'
},
{
'name': 'dstUser',
'type': 'str',
'required': True,
'helpMessage': 'The user name to use on the remote host. Hopefully not root.',
'default': 'root',
},
{
'name': 'dstPriv',
'type': 'str',
'required': True,
'helpMessage': 'The private key to use for auth',
'default': '/root/.ssh/id_rsa',
},
{
'name': 'dstPrivKey',
'type': 'str',
'required': False,
'helpMessage': 'The password for the destination private key',
'default': 'somethingsecret',
},
{
'name': 'dstDir',
'type': 'str',
'required': True,
'helpMessage': 'This is the directory on the host you want to send the certificate to',
'default': '/etc/nginx/certs/'
},
{
"available": [
"NGINX",
"3File"
],
"name": "exportType",
"required": True,
"value": "NGINX",
"helpMessage": "Reference the docs for an explanation of each export type",
"type": "select"
}
]
requires_key = False
def upload(self, name, body, private_key, cert_chain, options, **kwargs):
export_type = self.get_option('exportType', options)
dst_host = self.get_option('dstHost', options)
dst_host_port = self.get_option('dstPort', options)
dst_user = self.get_option('dstUser', options)
dst_priv = self.get_option('dstPriv', options)
dst_priv_key = self.get_option('dstPrivKey', options)
if dst_priv_key:
dst_priv_key = None
dst_dir = self.get_option('dstDir', options)
remote_host.create_cert(name, dst_dir, export_type, dst_user, dst_priv, dst_priv_key, dst_host, int(dst_host_port))
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] |
src/utils/plots.py | Light4Code/tensorflow-research | 5 | 33045 | <filename>src/utils/plots.py
import matplotlib.pyplot as plt
import numpy as np
import numpy.ma as ma
import utils.image_util as iu
from utils.custom_types import Vector
def plot_history(loss, acc, val_loss, val_acc):
plt.figure(figsize=(20, 10))
plt.subplot(2, 1, 1)
plt.title("Loss")
plt.grid()
plt.plot(loss)
plt.plot(val_loss)
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend(["Train", "Test"], loc="upper left")
plt.subplot(2, 1, 2)
plt.title("Accuracy")
plt.grid()
plt.plot(acc)
plt.plot(val_acc)
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.legend(["Train", "Test"], loc="upper left")
plt.show()
def plot_difference(
predictions, test_images, input_shape: Vector, threshold: float = 0.0
):
plt.figure(figsize=(20, 10))
pred_count = len(predictions)
plt_shape = (input_shape[0], input_shape[1])
plt_cmap = "gray"
if input_shape[2] > 1:
plt_shape = (
input_shape[0],
input_shape[1],
input_shape[2],
)
index = 1
plt_index = 0
for test_image in test_images:
original_image = test_image.reshape(plt_shape)
pred_image = predictions[plt_index].reshape(plt_shape)
diff, se = iu.create_diff(original_image, pred_image, threshold)
mask = ma.masked_where(diff == False, diff)
plt.subplot(pred_count, 4, index)
plt.title("Original")
plt.imshow(original_image, interpolation="none", cmap=plt_cmap)
index += 1
plt.subplot(pred_count, 4, index)
plt.title("Prediction")
plt.imshow(pred_image, interpolation="none", cmap=plt_cmap)
index += 1
plt.subplot(pred_count, 4, index)
plt.title("Diff (SE: {0})".format(round(se, 2)))
plt.imshow(diff, interpolation="none", cmap=plt_cmap)
index += 1
plt.subplot(pred_count, 4, index)
plt.title("Overlay")
plt.imshow(original_image, interpolation="none", cmap=plt_cmap)
plt.imshow(mask, cmap="jet", interpolation="none", alpha=0.7)
index += 1
plt_index += 1
plt.show()
def plot_prediction(
predictions, test_images, input_shape: Vector, threshold: float = 0.4
):
plt.figure(figsize=(20, 10))
pred_count = len(predictions)
plt_shape = (input_shape[0], input_shape[1])
plt_cmap = "gray"
if input_shape[2] > 1:
plt_shape = (
input_shape[0],
input_shape[1],
input_shape[2],
)
index = 1
plt_index = 0
for test_image in test_images:
original_image = test_image.reshape(plt_shape)
pred_image = predictions[plt_index].reshape(plt_shape)
mask = ma.masked_where(pred_image < threshold, pred_image)
plt.subplot(pred_count, 3, index)
plt.title("Original")
plt.imshow(original_image, interpolation="none", cmap=plt_cmap)
index += 1
plt.subplot(pred_count, 3, index)
plt.title("Prediction")
plt.imshow(pred_image, interpolation="none", cmap=plt_cmap)
index += 1
plt.subplot(pred_count, 3, index)
plt.title("Overlay")
plt.imshow(original_image, interpolation="none", cmap=plt_cmap)
plt.imshow(mask, cmap="jet", interpolation="none", alpha=0.7)
index += 1
plt_index += 1
plt.show()
def plot_classification(predictions, test_images, input_shape: Vector, classes: [], threshold: float = 0.4):
plt.figure(figsize=(20, 10))
pred_count = len(predictions)
plt_shape = (input_shape[0], input_shape[1])
plt_cmap = "gray"
if input_shape[2] > 1:
plt_shape = (
input_shape[0],
input_shape[1],
input_shape[2],
)
index = 1
plt_index = 0
for test_image in test_images:
original_image = test_image.reshape(plt_shape)
pred = predictions[plt_index]
c_idx = np.argmax(pred)
plt.subplot(pred_count, 1, index)
value = pred[c_idx]
if (value >= threshold):
plt.title("{0} ({1})".format(classes[c_idx], value))
else:
plt.title("{0} ({1})".format("Unknown", value))
plt.imshow(original_image, interpolation="none", cmap=plt_cmap)
index += 1
plt_index += 1
plt.show()
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xmodaler/engine/build.py | cclauss/xmodaler | 830 | 68589 | # Copyright 2021 JD.com, Inc., JD AI
"""
@author: <NAME>
@contact: <EMAIL>
"""
from xmodaler.utils.registry import Registry
ENGINE_REGISTRY = Registry("ENGINE")
ENGINE_REGISTRY.__doc__ = """
Registry for engine
"""
def build_engine(cfg):
engine = ENGINE_REGISTRY.get(cfg.ENGINE.NAME)(cfg)
return engine | [
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inlet_data.py | cyborgsphinx/ios-inlets | 0 | 162199 | from dataclasses import dataclass
import datetime
import logging
import os
import sqlite3
from typing import List, Tuple
sqlite3.paramstyle = "named"
DB_NAME = os.path.join("data", "inlet_data.db")
def _table_name(inlet_name: str) -> str:
return inlet_name.lower().replace(" ", "_")
@dataclass(frozen=True)
class InletData:
time: datetime.datetime
depth: float
value: float
quality: int
longitude: float
latitude: float
source: str
computed: bool = False
assumed_density: bool = False
def as_dict(self):
return {
"time": self.time.isoformat(timespec="microseconds"),
"depth": self.depth,
"value": self.value,
"quality": self.quality,
"longitude": self.longitude,
"latitude": self.latitude,
"source": self.source,
"computed": self.computed,
"assumed_density": self.assumed_density,
}
def _averaged(data: List[InletData]) -> List[InletData]:
"""Perform daily vertical averaging inside depth categories."""
freqs = {}
for datum in data:
source = datum.source
date = datum.time.date()
key = (source, date)
if key not in freqs:
freqs[key] = (0, 0)
total, count = freqs[key]
freqs[key] = (total + datum.value, count + 1)
return [
InletData(d, 0, total / count, 0, 0, 0, fn)
for (fn, d), (total, count) in freqs.items()
]
class InletDb:
def __init__(self, inlet_name: str, clear: bool = False, db_name: str = DB_NAME):
self.name = _table_name(inlet_name)
self.connection = sqlite3.connect(db_name)
self.connection.row_factory = sqlite3.Row
if clear:
self.__clear_data_table()
self.__ensure_data_table()
def __del__(self):
self.connection.close()
def clear(self):
self.__clear_data_table()
def add_temperature_value(self, value: InletData):
try:
self.__add_value(value, "temperature")
except sqlite3.IntegrityError:
logging.exception(
f"Integrity error inserting temperature data ({value}) into database for {self.name}"
)
def add_temperature_data(self, data: List[InletData]):
try:
self.__add_data(data, "temperature")
except sqlite3.IntegrityError:
source = data[0].source
logging.exception(
f"Integrity error inserting temperature data from {source} into database for {self.name}"
)
def get_temperature_data(
self, bucket: Tuple[float, float], average: bool = False
) -> List[InletData]:
data = self.__get_data("temperature", bucket)
if average:
return _averaged(data)
else:
return data
def add_salinity_value(self, value: InletData):
try:
self.__add_value(value, "salinity")
except sqlite3.IntegrityError:
logging.exception(
f"Integrity error inserting salinity data ({value}) into database for {self.name}"
)
def add_salinity_data(self, data: List[InletData]):
try:
self.__add_data(data, "salinity")
except sqlite3.IntegrityError:
source = data[0].source
logging.exception(
f"Integrity error inserting salinity data from {source} into database for {self.name}"
)
def get_salinity_data(
self, bucket: Tuple[float, float], average: bool = False
) -> List[InletData]:
data = self.__get_data("salinity", bucket)
if average:
return _averaged(data)
else:
return data
def add_oxygen_value(self, value: InletData):
try:
self.__add_value(value, "oxygen")
except sqlite3.IntegrityError:
logging.exception(
f"Integrity error inserting oxygen data ({value}) into database for {self.name}"
)
def add_oxygen_data(self, data: List[InletData]):
try:
self.__add_data(data, "oxygen")
except sqlite3.IntegrityError:
source = data[0].source
logging.exception(
f"Integrity error inserting oxygen data from {source} into database for {self.name}"
)
def get_oxygen_data(
self, bucket: Tuple[float, float], average: bool = False
) -> List[InletData]:
data = self.__get_data("oxygen", bucket)
if average:
return _averaged(data)
else:
return data
def __add_value(self, value: InletData, kind: str):
with self.connection:
self.connection.execute(
f"""
insert into {self.name}
values (
:kind,
:source,
:latitude,
:longitude,
:time,
:depth,
:value,
:quality,
:computed,
:assumed_density
)""",
{"kind": kind, **value.as_dict()},
)
def __add_data(self, data: List[InletData], kind: str):
with self.connection:
self.connection.executemany(
f"""
insert into {self.name}
values (
:kind,
:source,
:latitude,
:longitude,
:time,
:depth,
:value,
:quality,
:computed,
:assumed_density
)""",
({"kind": kind, **datum.as_dict()} for datum in data),
)
def __get_data(self, kind: str, bucket: Tuple[float, float]) -> List[InletData]:
min_depth, max_depth = bucket
if min_depth is None and max_depth is None:
cursor = self.connection.execute(
f"""select * from {self.name}
where kind=:kind
""",
{"kind": kind},
)
elif min_depth is None:
cursor = self.connection.execute(
f"""select * from {self.name}
where kind=:kind and depth<=:max
""",
{"kind": kind, "max": max_depth},
)
elif max_depth is None:
cursor = self.connection.execute(
f"""select * from {self.name}
where kind=:kind and depth>=:min
""",
{"kind": kind, "min": min_depth},
)
else:
cursor = self.connection.execute(
f"""select * from {self.name}
where kind=:kind and depth>=:min and depth<=:max
""",
{"kind": kind, "min": min_depth, "max": max_depth},
)
return [
InletData(
source=row["source"],
latitude=row["latitude"],
longitude=row["longitude"],
time=datetime.datetime.fromisoformat(row["time"]),
depth=row["depth"],
value=row["value"],
quality=row["quality"],
computed=(row["computed"] > 0),
assumed_density=(row["assumed_density"] > 0),
)
for row in cursor
]
def __ensure_data_table(self):
if not self.__has_data_table():
with self.connection:
self.connection.execute(
f"""
create table {self.name} (
kind text not null,
source text not null,
latitude real not null,
longitude real not null,
time text not null,
depth real not null,
value real not null,
quality integer not null,
computed integer not null,
assumed_density integer not null
)"""
)
def __clear_data_table(self):
if self.__has_data_table():
with self.connection:
self.connection.execute(f"""drop table {self.name}""")
def __has_data_table(self):
cursor = self.connection.execute(
f"""
select count(name)
from sqlite_master
where type='table' and name='{self.name}'
"""
)
return cursor.fetchone()[0] > 0
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] |
tools/bk7231u/gen_firmware_img.py | Ai-Thinker-Open/Ai-Thinker-Open_ALIOS_DEV | 12 | 192247 | <filename>tools/bk7231u/gen_firmware_img.py
import os
import sys
import struct
import zlib
import time
def pack_image(fw_file, boot_file):
print 'app file_name:', fw_file
print 'boot file_name:', boot_file
fname = os.path.splitext(fw_file)
f = file(boot_file, 'rb')
bootloader = f.read()
f.close()
f = file(fw_file, 'rb')
data_fw = f.read()
f.close()
f = file(fname[0] + '_all.bin', 'wb')
i = len(bootloader)
f.write(bootloader)
j = 0x11000 - i
i = 0
while i < j:
i += 1
f.write("\xff")
f.write(data_fw)
f.close();
if __name__ == "__main__":
if len(sys.argv) < 2:
print sys.argv[0], "firmware file"
exit(0)
pack_image(sys.argv[1], sys.argv[2])
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] |
examples/2-benchmark/fock_multigrid.py | QuESt-Calculator/pyscf | 8 | 196588 | #!/usr/bin/env python
import os
import time
import numpy as np
import pyscf
from pyscf.pbc.dft import multigrid
log = pyscf.lib.logger.Logger(verbose=5)
with open('/proc/cpuinfo') as f:
for line in f:
if 'model name' in line:
log.note(line[:-1])
break
with open('/proc/meminfo') as f:
log.note(f.readline()[:-1])
log.note('OMP_NUM_THREADS=%s\n', os.environ.get('OMP_NUM_THREADS', None))
boxlen = 12.4138
cell0 = pyscf.M(a = np.eye(3) * boxlen,
atom = """
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H 11.281750 5.653082 9.374494
H 12.103020 8.841164 10.006916
H 11.491592 8.576221 8.647557
""",
basis = 'gth-tzv2p',
pseudo = 'gth-pade',
max_memory = 50000,
precision = 1e-6)
for xc in ('lsda', 'pbe'):
for images in ([1,1,1], [2,1,1], [2,2,1], [2,2,2]):
cell = pbc.tools.super_cell(cell0, images)
nao = cell.nao
log.note('nao = %d', nao)
dm = np.random.random((nao,nao))
dm = dm + dm.T
mf = cell.RKS().set(xc=xc)
mf.with_df = multigrid.MultiGridFFTDF(cell)
cpu0 = time.clock(), time.time()
v = mf.get_veff(cell, dm)
log.timer('Fock build (xc=%s, nao=%d)' % (xc, nao), *cpu0)
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scitbx/math/tests/tst_gaussian.py | rimmartin/cctbx_project | 0 | 36645 | <reponame>rimmartin/cctbx_project
from __future__ import division
from scitbx.examples import immoptibox_ports
from scitbx.math import gaussian
from scitbx.array_family import flex
from libtbx.test_utils import approx_equal, eps_eq
from libtbx.utils import format_cpu_times
try:
import cPickle as pickle
except ImportError:
import pickle
from cStringIO import StringIO
import math
import sys
def finite_gradient_dx_at_x(gaussian, x, eps=1.e-5):
if (x == 0): return 0
assert x >= eps
tm = gaussian.at_x(x-eps)
tp = gaussian.at_x(x+eps)
return (tp-tm)/(2*eps)
def exercise_gradient_dx(gaussian, x_max=1., n_points=50):
for i in xrange(n_points+1):
x = x_max * i / n_points
grad_finite = finite_gradient_dx_at_x(gaussian, x)
grad_analytical = gaussian.gradient_dx_at_x(x)
assert eps_eq(grad_finite, grad_analytical)
def exercise_integral_dx(gaussian, x_max=1., n_points=1000):
numerical_integral = 0
x_step = x_max / n_points
for i in xrange(n_points+1):
x = x_max * i / n_points
new_value = gaussian.at_x(x)
if (i):
numerical_integral += (prev_value + new_value) * .5
prev_value = new_value
analytical_integral = gaussian.integral_dx_at_x(x, 1.e-3)
assert eps_eq(analytical_integral, gaussian.integral_dx_at_x(x))
assert eps_eq(numerical_integral*x_step, analytical_integral, eps=1.e-5)
def term_finite_gradient_d_ab_at_x(term, x, eps=1.e-5):
tm = gaussian.term(term.a-eps,term.b).at_x(x)
tp = gaussian.term(term.a+eps,term.b).at_x(x)
gr_a = (tp-tm)/(2*eps)
tm = gaussian.term(term.a,term.b-eps).at_x(x)
tp = gaussian.term(term.a,term.b+eps).at_x(x)
gr_b = (tp-tm)/(2*eps)
return gaussian.term(gr_a, gr_b)
def exercise_term_gradients_d_ab(term, x_max=1., n_points=50):
for i in xrange(n_points+1):
x = x_max * i / n_points
grad_finite = term_finite_gradient_d_ab_at_x(term, x)
grad_analytical = term.gradients_d_ab_at_x_sq(x*x)
assert eps_eq(grad_finite.a, grad_analytical.a)
assert eps_eq(grad_finite.b, grad_analytical.b)
def exercise_term():
t = gaussian.term(2,3)
assert approx_equal(t.a, 2)
assert approx_equal(t.b, 3)
assert approx_equal(t.at_x_sq(4), 2*math.exp(-3*4))
assert approx_equal(t.at_x(2), 2*math.exp(-3*4))
eps = 1.e-5
for ix in (xrange(10)):
x = ix/10.
assert eps_eq((t.at_x(x+eps)-t.at_x(x-eps))/(2*eps), t.gradient_dx_at_x(x))
for f in [1,-1]:
for t in [gaussian.term(f*2,3),
gaussian.term(f*3,0),
gaussian.term(f*4,1.e-4),
gaussian.term(f*5,-1)]:
exercise_gradient_dx(t)
exercise_integral_dx(t)
exercise_term_gradients_d_ab(t)
def exercise_sum():
g = gaussian.sum(0)
assert g.n_terms() == 0
assert g.array_of_a() == ()
assert g.array_of_b() == ()
assert approx_equal(g.c(), 0)
assert g.use_c()
assert g.n_parameters() == 1
assert approx_equal(g.parameters(), [0])
g = gaussian.sum(0, True)
assert g.use_c()
g = gaussian.sum(0, False)
assert not g.use_c()
g = gaussian.sum(1)
assert g.n_terms() == 0
assert g.array_of_a() == ()
assert g.array_of_b() == ()
assert approx_equal(g.c(), 1)
assert g.use_c()
assert g.n_parameters() == 1
assert approx_equal(g.parameters(), [1])
g = gaussian.sum((), ())
assert g.n_terms() == 0
assert g.array_of_a() == ()
assert g.array_of_b() == ()
assert g.c() == 0
assert not g.use_c()
assert g.n_parameters() == 0
assert g.parameters().size() == 0
g = gaussian.sum((), (), -2)
assert g.n_terms() == 0
assert g.array_of_a() == ()
assert g.array_of_b() == ()
assert approx_equal(g.c(), -2)
g = gaussian.sum(flex.double((1,2,3,4)))
assert approx_equal(g.array_of_a(), (1,3))
assert approx_equal(g.array_of_b(), (2,4))
assert approx_equal(g.c(), 0)
assert not g.use_c()
assert approx_equal(g.parameters(), [1,2,3,4])
g = gaussian.sum(flex.double((1,2,3,4)), 0, True)
assert approx_equal(g.c(), 0)
assert g.use_c()
g = gaussian.sum(flex.double((1,2,3,4)), 5)
assert approx_equal(g.c(), 5)
assert g.use_c()
assert approx_equal(g.parameters(), [1,2,3,4,5])
g = gaussian.sum(flex.double((1,2,3,4,5)))
assert approx_equal(g.c(), 5)
assert g.use_c()
assert approx_equal(g.parameters(), [1,2,3,4,5])
g = gaussian.sum((1,-2,3,-4,5), (-.1,.2,-.3,.4,-.5), 6)
assert g.n_terms() == 5
assert approx_equal(g.array_of_a(),(1,-2,3,-4,5))
assert approx_equal(g.array_of_b(),(-.1,.2,-.3,.4,-.5))
assert approx_equal(g.c(), 6)
assert approx_equal(g.at_x_sq(3/4.), 13.4251206)
assert approx_equal(g.at_x_sq(flex.double([2/4.,3/4.])),
[11.8723031, 13.4251206])
assert approx_equal(g.at_x(math.sqrt(3/4.)), 13.4251206)
assert approx_equal(g.at_x(flex.sqrt(flex.double([2/4.,3/4.]))),
[11.8723031, 13.4251206])
s = pickle.dumps(g)
l = pickle.loads(s)
assert l.n_terms() == g.n_terms()
assert approx_equal(l.array_of_a(), g.array_of_a())
assert approx_equal(l.array_of_b(), g.array_of_b())
assert approx_equal(l.c(), g.c())
assert l.use_c()
s = pickle.dumps(gaussian.sum((),()))
l = pickle.loads(s)
assert not l.use_c()
exercise_gradient_dx(gaussian.sum(
[5.5480], [10.4241], 0))
exercise_gradient_dx(gaussian.sum(
[2.657506,1.078079,1.490909,-4.241070,0.713791],
[14.780758,0.776775,42.086842,-0.000294,0.239535],
4.297983))
exercise_integral_dx(gaussian.sum([5.5480], [10.4241]))
exercise_integral_dx(gaussian.sum([5.5480], [10.4241], 3))
exercise_integral_dx(gaussian.sum([5.5480], [0], 0))
exercise_integral_dx(gaussian.sum([5.5480], [-0.01]))
exercise_integral_dx(gaussian.sum(
[2.657506,1.078079,1.490909,-4.241070,0.713791],
[14.780758,0.776775,42.086842,-0.000294,0.239535],
4.297983))
g = gaussian.sum((1,-2,3,-4,5), (-.1,.2,-.3,.4,-.5), 6)
s = StringIO()
g.show(s)
assert len(s.getvalue().split()) == 14
g = gaussian.sum((3,-2,1,-4,5), (-.3,.2,-.1,.4,-.5))
s = StringIO()
g.show(s)
assert len(s.getvalue().split()) == 12
assert isinstance(g.sort(), gaussian.sum)
assert approx_equal(g.sort().array_of_a(), (5,-4,3,-2,1))
assert approx_equal(g.sort().array_of_b(), (-.5,.4,-.3,.2,-.1))
assert not g.sort().use_c()
g = gaussian.sum((1,2),(3,4),5)
assert approx_equal(g.sort().array_of_a(), (2,1))
assert approx_equal(g.sort().array_of_b(), (4,3))
assert approx_equal(g.sort().c(), 5)
assert g.sort().use_c()
def fit_finite_diff_gradients(gfit, x, eps=1.e-2):
gr = flex.double()
c = gfit.c()
use_c = gfit.use_c()
for i in xrange(gfit.n_terms()):
t = []
for seps in (eps, -eps):
a = list(gfit.array_of_a())
a[i] += seps
t.append(
gaussian.sum(a, gfit.array_of_b(), c, use_c).at_x(x))
gr.append((t[0]-t[1])/(2*eps))
t = []
for seps in (eps, -eps):
b = list(gfit.array_of_b())
b[i] += seps
t.append(
gaussian.sum(gfit.array_of_a(), b, c, use_c).at_x(x))
gr.append((t[0]-t[1])/(2*eps))
if (use_c):
t = []
for seps in (eps, -eps):
t.append(
gaussian.sum(
gfit.array_of_a(), gfit.array_of_b(), c+seps, use_c).at_x(x))
gr.append((t[0]-t[1])/(2*eps))
return gr
def fit_finite_diff_target_gradients(gfit, power, use_sigmas, eps=1.e-2):
assert gfit.table_x().size() == 1
weight = 1/gfit.table_sigmas()[0]**2
gr = flex.double()
c = gfit.c()
use_c = gfit.use_c()
for i in xrange(gfit.n_terms()):
t = []
for seps in (eps, -eps):
a = list(gfit.array_of_a())
a[i] += seps
gf = gaussian.fit(
gfit.table_x(),
gfit.table_y(),
gfit.table_sigmas(),
gaussian.sum(a, gfit.array_of_b(), c, use_c))
t.append(gf.target_function(power, use_sigmas, gf.differences()))
gr.append((t[0]-t[1])/(2*eps))
t = []
for seps in (eps, -eps):
b = list(gfit.array_of_b())
b[i] += seps
gf = gaussian.fit(
gfit.table_x(),
gfit.table_y(),
gfit.table_sigmas(),
gaussian.sum(gfit.array_of_a(), b, c, use_c))
t.append(gf.target_function(power, use_sigmas, gf.differences()))
gr.append((t[0]-t[1])/(2*eps))
if (use_c):
t = []
for seps in (eps, -eps):
gf = gaussian.fit(
gfit.table_x(),
gfit.table_y(),
gfit.table_sigmas(),
gaussian.sum(gfit.array_of_a(), gfit.array_of_b(), c+seps, use_c))
t.append(gf.target_function(power, use_sigmas, gf.differences()))
gr.append((t[0]-t[1])/(2*eps))
return gr
def exercise_fit():
x = flex.double((0.1, 0.2, 0.5))
y = flex.double((3,2,1))
sigmas = flex.double((0.04,0.02,0.01))
gf = gaussian.fit(
x, y, sigmas,
gaussian.sum((1,2), (4,5)))
assert approx_equal(gf.array_of_a(), (1,2))
assert approx_equal(gf.array_of_b(), (4,5))
assert approx_equal(gf.c(), 0)
assert not gf.use_c()
assert approx_equal(gf.table_x(), x)
assert approx_equal(gf.table_y(), y)
assert approx_equal(gf.table_sigmas(), sigmas)
assert approx_equal(gf.fitted_values(),
[2.8632482881537511, 2.4896052951221748, 0.94088903489182252])
reference_gaussian = gaussian.sum((1,2,3), (4,5,6))
gf = gaussian.fit(
x, reference_gaussian, sigmas,
gaussian.sum((1,2), (4,5)))
assert approx_equal(gf.array_of_a(), (1,2))
assert approx_equal(gf.array_of_b(), (4,5))
assert approx_equal(gf.c(), 0)
assert approx_equal(gf.table_x(), x)
assert approx_equal(gf.table_y(), reference_gaussian.at_x(x))
assert approx_equal(gf.table_sigmas(), sigmas)
assert isinstance(gf.sort(), gaussian.fit)
assert gf.sort().table_x() == gf.table_x()
assert gf.sort().table_y() == gf.table_y()
assert gf.sort().table_sigmas() == gf.table_sigmas()
assert approx_equal(gf.differences(), gf.at_x(x)-reference_gaussian.at_x(x))
c_fit = gaussian.fit(
flex.double([0.0, 0.066666666666666666, 0.13333333333333333,
0.2, 0.26666666666666666]),
gaussian.sum(
(2.657506, 1.078079, 1.490909, -4.2410698, 0.71379101),
(14.780758, 0.776775, 42.086842, -0.000294, 0.239535),
4.2979832),
flex.double(5, 0.0005),
gaussian.sum(
(1.1423916, 4.1728425, 0.61716694),
(0.50733125, 14.002512, 41.978928)))
differences = flex.double([-0.064797341823577881, 0.003608505180995536,
0.098159179757290715, 0.060724224581695019, -0.10766283796372011])
assert approx_equal(c_fit.differences(), differences)
assert approx_equal(c_fit.significant_relative_errors(),
[0.0107212, 0.0005581, 0.0213236, 0.0169304, 0.0385142])
gf = gaussian.fit(
x, reference_gaussian, flex.double(x.size(), 1),
gaussian.sum((1,2), (4,5)))
assert list(gf.bound_flags(False, False)) == [False,False,False,False]
assert list(gf.bound_flags(True, False)) == [True,False,True,False]
assert list(gf.bound_flags(False, True)) == [False,True,False,True]
sgf = gf.apply_shifts(flex.double((3,-3,4,6)), True)
assert approx_equal(sgf.array_of_a(), (1+3,2+4))
assert approx_equal(sgf.array_of_b(),
((math.sqrt(4)-3)**2,(math.sqrt(5)+6)**2))
assert approx_equal(sgf.c(), 0)
assert not sgf.use_c()
sgf = gf.apply_shifts(flex.double((3,-3,4,6)), False)
assert approx_equal(sgf.array_of_a(), (1+3,2+4))
assert approx_equal(sgf.array_of_b(), (4-3,5+6))
assert approx_equal(sgf.c(), 0)
assert not sgf.use_c()
differences = sgf.differences()
for use_sigmas in [False, True]:
assert approx_equal(sgf.target_function(2, use_sigmas, differences),
25.0320634)
assert approx_equal(sgf.target_function(4, use_sigmas, differences),
256.2682575)
assert approx_equal(
sgf.gradients_d_abc(2, use_sigmas, differences),
[15.6539271, -4.1090114, 10.4562306, -1.6376781])
gfc = gaussian.fit(
x, reference_gaussian, flex.double(x.size(), 1),
gaussian.sum((1,2), (4,5), 6))
assert list(gfc.bound_flags(False, False)) == [False,False,False,False,False]
assert list(gfc.bound_flags(True, False)) == [True,False,True,False,True]
assert list(gfc.bound_flags(False, True)) == [False,True,False,True,False]
sgfc = gfc.apply_shifts(flex.double((3,-3,4,6,-5)), True)
assert approx_equal(sgfc.array_of_a(), (1+3,2+4))
assert approx_equal(sgfc.array_of_b(),
((math.sqrt(4)-3)**2,(math.sqrt(5)+6)**2))
assert approx_equal(sgfc.c(), 6-5)
assert sgfc.use_c()
sgfc = gfc.apply_shifts(flex.double((3,-3,4,6,-5)), False)
assert approx_equal(sgfc.array_of_a(), (1+3,2+4))
assert approx_equal(sgfc.array_of_b(), (4-3,5+6))
assert approx_equal(sgfc.c(), 6-5)
assert sgfc.use_c()
differences = sgfc.differences()
for use_sigmas in [False, True]:
assert approx_equal(sgfc.target_function(2, use_sigmas, differences),
44.8181444)
assert approx_equal(sgfc.target_function(4, use_sigmas, differences),
757.3160329)
assert approx_equal(
sgfc.gradients_d_abc(2, use_sigmas, differences),
[21.1132071, -6.0532695, 13.6638274, -2.2460994, 22.7860809])
differences = c_fit.differences()
gabc = c_fit.gradients_d_abc(2, False, differences)
assert approx_equal(
gabc,
[-0.016525391425206391, 0.0074465239375589107, 0.020055876723667564,
0.00054794635257838251, -0.018754011379726425, -0.0011194004809549143])
assert approx_equal(
c_fit.gradients_d_shifts(flex.double((0.1,0.4,0.2,0.5,0.3,0.6)), gabc),
[-0.0165254, 0.01656512, 0.0200559, 0.0046488, -0.0187540, -0.0158487])
g5c = gaussian.sum(
(2.657505989074707, 1.0780789852142334, 1.4909089803695679,
-4.2410697937011719, 0.71379101276397705),
(14.780757904052734, 0.77677500247955322, 42.086841583251953,
-0.00029399999766610563, 0.23953500390052795),
4.2979831695556641)
for include_constant_term in (False, True):
a = flex.double(g5c.array_of_a())
b = flex.double(g5c.array_of_b())
permutation = flex.sort_permutation(data=flex.abs(a), reverse=True)[:4]
gf = gaussian.fit(
flex.double([0]),
g5c,
flex.double(1, 1),
gaussian.sum(
iter(a.select(permutation)),
iter(b.select(permutation)), 0, include_constant_term))
assert approx_equal(gf.differences(), [-5.01177418232])
shifts = flex.double(8,-1)
if (include_constant_term): shifts.append(-.2)
sgf = gf.apply_shifts(shifts, False)
assert approx_equal(sgf.array_of_a(),
[-5.2410698, 1.657506, 0.49090898, 0.078078985])
assert approx_equal(sgf.array_of_b(),
[-1.0002940, 13.780758, 41.086842, -0.223225])
if (include_constant_term):
assert approx_equal(sgf.c(), -.2)
expected_gradients = [1,0,1,0,1,0,1,0]
if (include_constant_term): expected_gradients.append(1)
assert approx_equal(
fit_finite_diff_gradients(sgf, 0),
expected_gradients,
eps=1.e-4)
for i in xrange(10):
gf = gaussian.fit(
flex.double([i / 10.]),
g5c,
flex.double(1, 1),
sgf)
differences = flex.double([0.5])
assert approx_equal(
gf.gradients_d_abc(2, False, differences),
fit_finite_diff_gradients(gf, gf.table_x()[0]),
eps=1.e-3)
for sigma in [0.04,0.02,0.01]:
gf = gaussian.fit(
flex.double([i / 20.]),
g5c,
flex.double([sigma]),
sgf)
for power in [2,4]:
for use_sigmas in [False, True]:
differences = gf.differences()
an=gf.gradients_d_abc(power, use_sigmas, differences)
fi=fit_finite_diff_target_gradients(gf, power, use_sigmas)
assert eps_eq(an, fi, eps=1.e-3)
carbon_s_y_table = [
0.00, 6.000, 0.01, 5.990, 0.02, 5.958, 0.03, 5.907, 0.04, 5.837, 0.05, 5.749,
0.06, 5.645, 0.07, 5.526, 0.08, 5.396, 0.09, 5.255, 0.10, 5.107, 0.11, 4.952,
0.12, 4.794, 0.13, 4.633, 0.14, 4.472, 0.15, 4.311, 0.16, 4.153, 0.17, 3.998,
0.18, 3.847, 0.19, 3.701, 0.20, 3.560, 0.22, 3.297, 0.24, 3.058, 0.25, 2.949,
0.26, 2.846, 0.28, 2.658, 0.30, 2.494, 0.32, 2.351, 0.34, 2.227, 0.35, 2.171,
0.36, 2.120, 0.38, 2.028, 0.40, 1.948, 0.42, 1.880, 0.44, 1.821, 0.45, 1.794,
0.46, 1.770, 0.48, 1.725, 0.50, 1.685, 0.55, 1.603, 0.60, 1.537, 0.65, 1.479,
0.70, 1.426, 0.80, 1.322, 0.90, 1.219, 1.00, 1.114, 1.10, 1.012, 1.20, 0.914,
1.30, 0.822, 1.40, 0.736, 1.50, 0.659, 1.60, 0.588, 1.70, 0.525, 1.80, 0.468,
1.90, 0.418, 2.00, 0.373, 2.50, 0.216, 3.00, 0.130, 3.50, 0.081, 4.00, 0.053,
5.00, 0.025, 6.00, 0.013]
class tabulated_fit:
def __init__(self, limit, coefficients):
self.limit = limit
self.coefficients = coefficients
carbon_fit_6 = tabulated_fit(6.0, [
2.18188567686, 13.4533708328,
1.77612377639, 32.5790123523,
1.08772011297, 0.747293264573,
0.641460989931, 0.251251498175,
0.207885994451, 80.9799313275,
0.105219184507, 0.0587297979816])
carbon_fit_5 = tabulated_fit(6.0, [
2.65463431663, 14.7665037505,
1.49420264709, 42.0409767208,
1.05563210943, 0.780856499884,
0.688021531597, 0.258963998784,
0.104681246572, 0.0579465611728])
carbon_fit_4 = tabulated_fit(3.0, [
2.21557580709, 12.7523000206,
1.98306066831, 36.4905110196,
1.31636728472, 0.632825354093,
0.480812064621, 0.148079120135])
carbon_fit_3 = tabulated_fit(1.4, [
2.51340127252, 31.8053433708,
1.74867019409, 0.445605499982,
1.72398202356, 10.5831679451])
carbon_fit_2 = tabulated_fit(0.5, [
3.54355550695, 25.6239838191,
2.42579673735, 1.50364460774])
carbon_fit_1 = tabulated_fit(0.15, [
5.96792806111, 14.8957682987])
carbon_it1992 = tabulated_fit(2.0, [
2.31000, 20.8439,
1.02000, 10.2075,
1.58860, 0.568700,
0.865000, 51.6512,
0.215600])
carbon_wk1995 = tabulated_fit(6.0, [
2.657506, 14.780758,
1.078079, 0.776775,
1.490909, 42.086842,
-4.241070, -0.000294,
0.713791, 0.239535,
4.297983])
class carbon_fit(immoptibox_ports.test_function):
def __init__(self, tab_fit, perturb, verbose):
self.tab_fit = tab_fit
self.perturb = perturb
self.verbose = verbose
carbon_ss = flex.double(carbon_s_y_table)[0::2]
carbon_ys = flex.double(carbon_s_y_table)[1::2]
selection = carbon_ss <= tab_fit.limit + 1.e-3
self.fit = gaussian.fit(
carbon_ss.select(selection),
carbon_ys.select(selection),
flex.double(selection.count(True), 1),
gaussian.sum(flex.double(tab_fit.coefficients)))
n = self.fit.n_parameters()
immoptibox_ports.test_function.__init__(self,
m=self.fit.table_x().size(),
n=n,
check_with_finite_differences=(n <= 6 or n == 9),
verbose=verbose)
def initialization(self):
self.x0 = self.fit.parameters()
self.capital_f_x_star = 0.5*self.f(x=self.x0).norm()**2
if (self.perturb):
mersenne_twister = flex.mersenne_twister(seed=0)
self.x0 *= 1 + mersenne_twister.random_double(
size=self.x0.size(), factor=0.01)
self.tau0 = 1e-8
self.delta0 = 10
self.x_star = None
def label(self):
return "carbon_fit(n=%d, perturb=%s)" % (
self.fit.n_parameters(), str(self.perturb))
def check_minimized_capital_f_x_star(self, f_x_star, tolerance=1.e-3):
capital_f_x_star = 0.5*f_x_star.norm()**2
if (capital_f_x_star > self.capital_f_x_star):
assert capital_f_x_star < tolerance, (
capital_f_x_star, self.capital_f_x_star)
if (self.verbose):
print " WARNING: minimization converged to larger residual", \
"than original solution:"
print " original:", self.capital_f_x_star
assert self.perturb
def f(self, x):
fit = gaussian.fit(
self.fit.table_x(), self.fit.table_y(), self.fit.table_sigmas(),
gaussian.sum(x))
return fit.differences()
def jacobian_analytical(self, x):
fit = gaussian.fit(
self.fit.table_x(), self.fit.table_y(), self.fit.table_sigmas(),
gaussian.sum(x))
return fit.least_squares_jacobian_abc()
def hessian_analytical(self, x):
j = self.jacobian_analytical(x=x)
fit = gaussian.fit(
self.fit.table_x(), self.fit.table_y(), self.fit.table_sigmas(),
gaussian.sum(x))
return fit.least_squares_hessian_abc_as_packed_u() \
.matrix_packed_u_as_symmetric()
def exercise_fit_jacobian_and_hessian(verbose):
for tab_fit in [carbon_fit_1, carbon_fit_2, carbon_fit_3,
carbon_fit_4, carbon_fit_5, carbon_fit_6,
carbon_it1992, carbon_wk1995]:
for perturb in [False, True]:
carbon_fit(tab_fit=tab_fit, perturb=perturb, verbose=verbose)
def run():
exercise_term()
exercise_sum()
exercise_fit()
exercise_fit_jacobian_and_hessian(verbose="--verbose" in sys.argv[1:])
print format_cpu_times()
if (__name__ == "__main__"):
run()
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Python/surface-area-of-3d-shapes.py | RideGreg/LeetCode | 1 | 71304 | <gh_stars>1-10
# Time: O(n^2)
# Space: O(1)
# 892
# On a N * N grid, we place some 1 * 1 * 1 cubes.
#
# Each value v = grid[i][j] represents a tower of v cubes
# placed on top of grid cell (i, j).
#
# Return the total surface area of the resulting shapes.
#
# Example 1:
#
# Input: [[2]]
# Output: 10
# Example 2:
#
# Input: [[1,2],[3,4]]
# Output: 34
# Example 3:
#
# Input: [[1,0],[0,2]]
# Output: 16
# Example 4:
#
# Input: [[1,1,1],[1,0,1],[1,1,1]]
# Output: 32
# Example 5:
#
# Input: [[2,2,2],[2,1,2],[2,2,2]]
# Output: 46
#
# Note:
# - 1 <= N <= 50
# - 0 <= grid[i][j] <= 50
class Solution(object):
def surfaceArea(self, grid): # USE THIS: minus joint surface, 36 ms
"""
:type grid: List[List[int]]
:rtype: int
"""
result = 0
for i in xrange(len(grid)):
for j in xrange(len(grid)):
if grid[i][j]:
result += 2 + grid[i][j]*4
if i:
result -= min(grid[i][j], grid[i-1][j])*2
if j:
result -= min(grid[i][j], grid[i][j-1])*2
return result
# look at 4 neighbors of each cell, add contribution if higher than neighbor. Avoid double count
# 56 ms
def surfaceArea2(self, grid):
n, ans = len(grid), 0
for i in xrange(n):
for j in xrange(n):
if grid[i][j]:
ans += 2
for nx, ny in [(i-1,j),(i,j-1),(i+1,j),(i,j+1)]:
if 0<=nx<n and 0<=ny<n:
nei_val = grid[nx][ny]
else:
nei_val = 0
ans += max(0, grid[i][j]-nei_val)
return ans
# add difference between neighboring cells: iterate more times
def surfaceArea3(self, grid):
ans = 0
for row in grid:
ans += 2*sum(c>0 for c in row)
rrow = [0]+row+[0]
ans += sum(abs(rrow[i]-rrow[i+1]) for i in xrange(len(rrow)-1))
for col in zip(*grid):
col = (0,)+col+(0,) # KENG: zip's result is tuple which cannot concatenate to list
ans += sum(abs(col[i]-col[i+1]) for i in xrange(len(col)-1))
return ans
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weekly-context-133/two-city-scheduling.py | elfgzp/leetCode | 3 | 157072 | <filename>weekly-context-133/two-city-scheduling.py<gh_stars>1-10
from typing import List
class Solution:
def twoCitySchedCost(self, costs: List[List[int]]) -> int:
# costs.sort(key=lambda x: (-(abs(x[0] - x[1])), x[0] + x[1]))
costs.sort(key=lambda x: -(abs(x[0] - x[1])))
half = len(costs) // 2
count_a = 0
count_b = 0
res = 0
for a, b in costs:
if (a < b and count_a < half) or count_b == half:
res += a
count_a += 1
else:
res += b
count_b += 1
return res | [
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Linked-in Job scrapper.py | MMallah/Linked-in-Job-scrapper | 0 | 32377 | <reponame>MMallah/Linked-in-Job-scrapper
#!/usr/bin/env python
# coding: utf-8
# # Project Data Science Desired Skills
#
# this project will Answer the question of what are the most desired skills for Data Science in Egypt and UAE.
# * Extract data from Linked in Via webscrapping or Manually.
# * Perform data wrangling ( image recognition or NLP).
# * Segment the the job market based on skills.
# * Analyze the skill growth over time.
# Import libraries
# In[134]:
from selenium import webdriver
from selenium.webdriver.common.by import By
import pandas as pd
from scrapy import selector
import time
import operator
from functools import reduce
# ## step 1 Importing Data
#
# #### Selenium driver for chrome
# In[78]:
# instantiate a webdriver
driver = webdriver.Chrome('C:\\Users\\mosta\\Downloads\\chromedriver_win32\\chromedriver.exe')
# In[79]:
# use chrome to get the desired url
driver.get('https://www.linkedin.com/jobs/search/?geoId=102007122&keywords=data%20scientist&location=Cairo%2C%20Egypt')
# In[61]:
# the job card container(link for job)
full_card_link= driver.find_elements_by_class_name("result-card__full-card-link")
# In[62]:
# view the Selenium elment count
len(full_card_link)
# In[66]:
link_list=[]
for e in full_card_link:
link_list.append(e.get_attribute('href'))
# In[67]:
len(link_list)
# In[166]:
# In[187]:
for l in range(len(link_list)):
print(link_list[l])
# In[122]:
# create list place holder for data
job_title=[]
company=[]
location=[]
posting_date=[]
applicants_count=[]
job_description=[]
job_seniority_level=[]
employment_type=[]
industry=[]
job_function=[]
# In[237]:
# create a function to iterate over job links
def get_job(links):
''' function to import the required data (10 specifications) from the search url'''
for link in range(len(links)):
driver.get(links[link])
job_title.append(driver.find_element_by_class_name("topcard__title").text)
company.append(driver.find_element_by_class_name('topcard__flavor').text)
location.append(driver.find_element_by_xpath('//*[@id="main-content"]/section[1]/section[2]/div[1]/div[1]/h3[1]/span[2]').text)
posting_date.append(driver.find_element_by_xpath('//*[@id="main-content"]/section[1]/section[2]/div[1]/div[1]/h3[2]/span[1]').text)
try:
applicants_count.append(driver.find_element_by_xpath('//*[@id="main-content"]/section[1]/section[2]/div[1]/div[1]/h3[2]/span[2]').text)
except:
applicants_count.append('')
job_description.append(driver.find_element_by_xpath('//*[@id="main-content"]/section[1]/section[3]/div/section/div').text)
try:
job_seniority_level.append(driver.find_element_by_xpath('//*[@id="main-content"]/section[1]/section[3]/ul/li[1]/span').text)
except:
job_seniority_level.append('')
employment_type.append(driver.find_element_by_xpath('//*[@id="main-content"]/section[1]/section[3]/ul/li[2]/span').text)
industry.append(driver.find_element_by_xpath('//*[@id="main-content"]/section[1]/section[3]/ul/li[4]/span').text)
job_function.append(reduce(operator.concat,
[(e.text+ ", ") for e in driver.find_elements_by_xpath('//*[@id="main-content"]/section[1]/section[3]/ul/li[3]/span')]))
time.sleep(5)
# In[241]:
#company
# In[239]:
#use the function to import the data
get_job(link_list)
# In[243]:
# convert lists acqquired to a dataframe
data= pd.DataFrame(list(zip(job_title, company, location, posting_date, applicants_count, job_description, job_seniority_level,
employment_type, industry, job_function)),
columns=['job_title', 'company', 'location', 'posting_date', 'applicants_count', 'job_description', 'job_seniority_level',
'employment_type', 'industry', 'job_function'])
# In[245]:
data.info()
# In[200]:
# job title
#print(driver.find_element_by_class_name("topcard__title").text)
# In[98]:
# company
#print(driver.find_element_by_class_name('topcard__flavor').text)
# In[107]:
# job location
#print(driver.find_element_by_xpath('//*[@id="main-content"]/section[1]/section[2]/div[1]/div[1]/h3[1]/span[2]').text)
# In[110]:
# posting date
#print(driver.find_element_by_xpath('//*[@id="main-content"]/section[1]/section[2]/div[1]/div[1]/h3[2]/span[1]').text)
# In[112]:
# number of applicants
#rint(driver.find_element_by_xpath('//*[@id="main-content"]/section[1]/section[2]/div[1]/div[1]/h3[2]/span[2]').text)
# In[113]:
# description
#print(driver.find_element_by_xpath('//*[@id="main-content"]/section[1]/section[3]/div/section/div').text)
# In[206]:
# job_seniority_level
#print(driver.find_element_by_xpath('//*[@id="main-content"]/section[1]/section[3]/ul/li[1]/span').text)
# In[114]:
# employment_type
#print(driver.find_element_by_xpath('//*[@id="main-content"]/section[1]/section[3]/ul/li[2]/span').text)
# In[119]:
# job_function
for e in(driver.find_elements_by_xpath('//*[@id="main-content"]/section[1]/section[3]/ul/li[3]/span')):
print(e.text)
# In[145]:
#test=[]
#test.append(reduce(operator.concat,
# [(e.text+ ", ") for e in driver.find_elements_by_xpath('//*[@id="main-content"]/section[1]/section[3]/ul/li[3]/span')]))
# In[146]:
test
# In[120]:
# industry
#print(driver.find_element_by_xpath('//*[@id="main-content"]/section[1]/section[3]/ul/li[4]/span').text)
# In[10]:
#job_card = driver.find_elements_by_class_name("results__detail-view")
# In[147]:
#for e in job_card:
# print(e.text)
# In[ ]:
# the right rail, jobs-search job details container
#body > div.application-outlet > div.authentication-outlet > div.job-search-ext > div > div > section.jobs-search__right-rail > div
# In[ ]:
# the path for the header
#/html/body/main/div/section[2]/ul/li[1]/a
# In[ ]:
# the show more tab where body is located
#/html/body/main/section/div[2]/section[2]/div/section/div
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] |
appengine/components/tests/datastore_utils_properties_test.py | pombreda/swarming | 0 | 3870 | <filename>appengine/components/tests/datastore_utils_properties_test.py
#!/usr/bin/env python
# Copyright 2014 The Swarming Authors. All rights reserved.
# Use of this source code is governed by the Apache v2.0 license that can be
# found in the LICENSE file.
import sys
import unittest
import test_env
test_env.setup_test_env()
from google.appengine.ext import ndb
from components.datastore_utils import properties
from support import test_case
class BP(ndb.Model):
prop = properties.BytesComputedProperty(lambda _: '\x00')
class DJP(ndb.Model):
prop = properties.DeterministicJsonProperty(json_type=dict)
class PropertiesTest(test_case.TestCase):
def test_DeterministicJsonProperty(self):
self.assertEqual({'a': 1}, DJP(prop={'a': 1}).prop)
DJP(prop={'a': 1}).put()
self.assertEqual({'a': 1}, DJP.query().get().prop)
with self.assertRaises(TypeError):
DJP(prop=[])
def test_BytesComputedProperty(self):
self.assertEqual('\x00', BP().prop)
BP().put()
self.assertEqual('\x00', BP.query().get().prop)
if __name__ == '__main__':
if '-v' in sys.argv:
unittest.TestCase.maxDiff = None
unittest.main()
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day08.py | Pil0u/adventofcode2020 | 0 | 323 | <filename>day08.py
from copy import deepcopy
def boot(seq):
index = 0
played_indices = set()
acc = 0
while True:
if index == len(seq):
return True, acc
if index in played_indices:
return False, acc
played_indices.add(index)
line = seq[index].split()
op = line[0]
value = int(line[1])
if op == 'nop':
index += 1
if op == 'acc':
acc += value
index += 1
if op == 'jmp':
index += value
def generate_sequences(list_):
all_seqs = []
for idx, value in enumerate(list_):
if value[:3] == 'nop':
seq = deepcopy(list_)
seq[idx] = 'jmp' + value[3:]
all_seqs.append(seq)
if value[:3] == 'jmp':
seq = deepcopy(list_)
seq[idx] = 'nop' + value[3:]
all_seqs.append(seq)
return all_seqs
def result(input_):
# Part 1
part_one = boot(input_)[1]
# Part 2
all_sequences = generate_sequences(input_)
for sequence in all_sequences:
result = boot(sequence)
if result[0] is not False:
part_two = result[1]
break
return part_one, part_two
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] |
data_collection/gazette/spiders/sc_laguna.py | kaiocp/querido-diario | 454 | 1616433 | from gazette.spiders.base.fecam import FecamGazetteSpider
class ScLagunaSpider(FecamGazetteSpider):
name = "sc_laguna"
FECAM_QUERY = "cod_entidade:146"
TERRITORY_ID = "4209409"
| [
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] |
finder.py | giuseppebrb/Pynder | 3 | 23389 | <gh_stars>1-10
import os
import fnmatch
import smtplib
import email.mime.application
import sys
import subprocess
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from pathlib import Path
home = str(Path.home()) # Return a string representing the user’s home directory
fileFound = 0 # Number of files found while discovering
fileScanned = 0 # Number of the already processed files
maxSize = 23068672 # Attachments bytes limit for the mail host (22MB in byte, but it can be changed)
actualSizeCounter = 0 # Bytes count for files already attached to the email
paths = [] # List of files directories, matching the pattern, that will be print into the email body
# Following values need to be changed
email_user = "SENDER-ADDRESS-HERE"
email_pwd = "<PASSWORD>"
recipient = "RECIPIENT-ADDRESS-HERE"
"""
This function will return a list of strings which represents the files path with the specified extension inside the
specified path
"""
def find(pattern, path):
result = []
for root, dirs, files in os.walk(path):
for name in files:
if fnmatch.fnmatch(name, pattern):
result.append(os.path.join(root, name))
return result
"""
__________ START - It may NOT work on MacOS __________
| |
"""
injecting_folder = home+'\\script' # 'Injecting' folder
if not os.path.exists(injecting_folder):
os.system("mkdir %s" % injecting_folder)
executableLocation = find('EXECUTABLE-NAME-HERE.exe', os.path.dirname(os.path.abspath(__file__)))
# Create a new 'injecting' folder where software will copy itself
if not os.path.isfile(injecting_folder + "\\EXECUTABLE-NAME-HERE.exe"):
os.system("xcopy {!s} {!s} /R".format(executableLocation[0], injecting_folder))
# If current working directory is not the 'injecting' folder opens a new instance from there and close this one.
if os.getcwd() != injecting_folder:
os.chdir(injecting_folder)
subprocess.Popen([injecting_folder+'\\EXECUTABLE-NAME-HERE.exe'], stdin=None, stdout=None, stderr=None)
sys.exit()
"""
|__________ END - It may NOT work on MacOS __________|
"""
filesFound = find("*.pdf", home) # List of every pdf file found in every folder starting from the user's home directory
# Building the email structure
msg = MIMEMultipart()
msg['From'] = email_user
msg['To'] = recipient
msg['Subject'] = "Files Found"
for f in filesFound:
fp = open(r'%s' % f, 'rb')
att = email.mime.application.MIMEApplication(fp.read())
fp.close()
paths.append("Directory: " + f)
att.add_header('Content-Disposition', 'attachment; filename="%s"' % f)
msg.attach(att)
for p in paths:
msg.attach(MIMEText(p, 'plain'))
# Open the connection with mail host with specified credentials
server = smtplib.SMTP('smtp.gmail.com', 587) # These values are just an example working with Gmail, you need to change
# them with your own host's SMTP address and port
server.ehlo()
server.starttls() # Starts a secure tls connection
server.login(email_user, email_pwd)
email_body = msg.as_string()
server.sendmail(email_user, recipient, email_body) # Send the email
server.quit() # Close the connection with host
sys.exit() # Quit program
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BaseTest/click_button_chrome.py | lloydtawanda/AzurePriceListWebScrapper | 2 | 18314 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 16 14:36:46 2019
@author: Tawanda
"""
import sys
import argparse
from selenium import webdriver
from selenium.common.exceptions import NoSuchElementException
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--driver", help="path to chrome driver")
args = parser.parse_args()
if not args.driver:
print("Please enter a valid path to the chrome driver ( --driver argument )")
sys.exit(1)
browser = webdriver.Chrome(executable_path=args.driver)
browser.implicitly_wait(10)
browser.maximize_window()
try:
browser.get('https://www.oursky.com/')
button = browser.find_element_by_class_name('btn-header')
button.click()
print('=======Button Click test was successful=======')
except NoSuchElementException as ex:
print(f'Error :: No such element : {ex}') | [
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PEC4-Oscar_Rojo.py | zumaia/PEC_4 | 0 | 178502 | <reponame>zumaia/PEC_4
# # Ejercicio
#
# Primero, será necesario que leáis los archivos facilitados de la forma más óptima teniendo en cuenta las tareas pedidas. Tendréis que justificar vuestra decisión.
# Importo los módulos necesarios
import pandas as pd
import glob
import matplotlib.pyplot as plt
import os
import re
# Pandas me da el siguiente error:
# A value is trying to be set on
# a copy of a slice from a DataFrame.
# Try using .loc[row_indexer,col_indexer] = value instead
# disable chained assignments
pd.options.mode.chained_assignment = None
# ## Carga ficherox "txt"
#
# En este apartado cargo el fichero txt de la carpeta data/orfs leyendo linea a lines, filtrando por lineas, generando sublista, realizando unas transformaciones y generando el dataframe **txt_data**
# In[3]:
# obtengo una lista con todos los txt contenidos en un directorio
txt_files = glob.glob("data/orfs/*.txt")
def read_lists():
"""
Parsear un texto dado por un archivo
Parameters
----------
Returns
-------
data : lista de distas
Parsed data
"""
for i in txt_files:
with open(i) as file:
sublist = []
previous_line = ''
# Genero un patron donde solo me busque la lineas que contengan
# las palabras "begin, funciton, tb_protein, tb_to_tb_evalue, end"
pattern = re.compile(
r'\bbegin\b | \bfunction\b | \btb_protein\b | \btb_to_tb_evalue\b | \bend\b',
flags=re.I | re.X)
for line in file:
# realizo transformaciones
line = line.replace('.', '')
line = line.replace('(', ";(")
line = line.replace('begin;(model', "begin_model")
line = line.replace('end;(model', "end_model")
line = line.replace('))', ")")
line = line.replace(')', "")
line = line.replace('(', "")
line = line.strip()
# Le indico donde comienza y termina cada sublista
if line.startswith(
'begin_model'
) and previous_line.startswith(
'end_model'
):
yield sublist
sublist = []
# Le informo que debe buscar las palabras del patros para
# añadir ilas a la sublista
if pattern.search(line) != None:
# voy añadiendo los datos a la sublista
sublist.append(line.split(';'))
# sublist.append(line)
previous_line = line
# obtengo la sublista
return sublist
# creo una lista vacia para recolectar los datos
data = []
# Realizo una iteración para generar una lista de listas donde en
# cada lista contiene la sublista generadas anteriormente
for sublist in read_lists():
data.append(sublist)
# convierto la lista en un dataframe
def appen_data(data):
"""
Con esta función lo que hago es convertir las listas de la función
anterior en un dataframe.
:param data:
:return:
"""
def countList(data):
"""
Función destinada a obtener el número de listas generadas
:param data: la lista generada anteriormente
:return: el número de listas
"""
return len(data)
# Genero una función para unir las columnas con
# mismo nombre se unan y se separen por ';'
def sjoin(x):
"""
Función generada para fusionar las columnas con mismo nombre
se unan incorporando el detalle de cada una de ellas
y se separen por ";"
:param x: columna a unir
:return: los valores de las columnas unidas
"""
return ';'.join(x[x.notnull()].astype(str))
# Creo un dataframe vacio para agrupar los dataframes
# que generamos con las listas
df_a = pd.DataFrame()
# genero una iteración donde convierto las listas en un dataframe
for i in range(countList(data)):
df_n = pd.DataFrame(data[i]).set_index(0)
# pivoto el dataframe
df_n = df_n.transpose()
# uno las columnas con el mismo nombre
df_n = df_n.groupby(level=0, axis=1).apply(
lambda x: x.apply(sjoin, axis=1))
df_a = df_a.append(df_n)
return df_a
txt_data = appen_data(data)
# selecciono las columnas que me interesab
data_relac_txt = txt_data.iloc[:, [1, 2, 0]]
# las renombro
data_relac_txt.columns = ['ORF', 'relacion_ORF', 'function']
# Separo la columna función en otras
data_relac_txt[['clase',
'name_gem',
'descripcion']] = data_relac_txt['function'].str.split(
",'", expand=True)
# Genero nuevo dataframe seleccionando las columnas que me interesan
df_total = data_relac_txt[['clase',
'name_gem',
'descripcion',
'ORF',
'relacion_ORF']]
# Elimino valores na
df_total = df_total.dropna()
# genero nueva columna donde indico el número de relaciones
# sumando el número de "tb" que hay
df_total['numero de relaciones'] = df_total['relacion_ORF'].str.findall(
r'tb').str.len()
# In[4]:
df_total.head()
# ## Carga fichero "tb_functions.pl"
#
# En este apartado cargo el fichero tb_functions.pl leyendo linea a lines, realizando unas transformaciones y generando el dataframe **df**
# In[5]:
file = 'data/tb_functions.pl'
def parse_file(file):
"""
Parse text at given filepath
Parameters
----------
filepath : str
Filepath for file_object to be parsed
Returns
-------
data : pd.DataFrame
Parsed data
"""
# Cargo el archivo de texto dentro de "content"
with open(file) as f:
content = f.readlines()
content = [x.strip() for x in content]
# creo la lista vaciacreate an empty list to collect the data
events_cl = []
# create an empty list to collect the data
events_fc = []
# this is to filter the lines in the data
for line in content:
# extracto la clase
if 'class([' in line:
line = line.replace('.', '')
line = line.replace('"', '')
line = line.replace(')', '')
line = line.replace('],', '];')
events_cl.append(line.split('('))
# extracto la función
if 'function(' in line:
line = line.replace('").', '"')
line = line.replace(',[', ';[')
line = line.replace("],'", '];"')
line = line.replace("',", '";')
events_fc.append(line.split('('))
# para el caso de las clases
# convierto la lista en dataframe
data_class = pd.DataFrame(events_cl)
# divido la columna en 2 columnas nuevas
data_class[['clase',
'desc']] = data_class[1].str.split(';',
1,
expand=True)
# Selecciono las columnas necesarias
data_class = data_class[['clase', 'desc']]
# Para el caso de las funciones
# convierto la lista en dataframe
data_fc = pd.DataFrame(events_fc)
# divido la columna en 2 columnas nuevas
data_fc[['ORF',
'clase',
'name_gem',
'desc_ORF']] = data_fc[1].str.split(';',
3,
expand=True)
# Selecciono las columnas necesarias
data_fc = data_fc[['ORF', 'clase', 'name_gem', 'desc_ORF']]
# los 2 dataframes
df = pd.merge(data_class, data_fc, on='clase', how='outer')
return df
# Genero el dataframe con el fichero 'tb_fuctions.plt'
df = parse_file('data/tb_functions.pl')
# 1.1 Calcular cuántos ORFs pertenecen a cada clase.
# In[6]:
def grafico(x):
"""
Función destinada a generar la gráfica para reproducir los
ORFs que pertenecen a cada clase
:param x: introducimos el valor
:returns obtenemos la gráfica
"""
ax = x.plot(kind='barh', figsize=(8, 20), color='#86bf91',
zorder=2, width=0.85)
# Despine
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
# Switch off ticks
ax.tick_params(axis="both", which="both",
bottom="off", top="off", labelbottom="on",
left="off", right="off", labelleft="on")
# Draw vertical axis lines
vals = ax.get_xticks()
for tick in vals:
ax.axvline(x=tick, linestyle='dashed', alpha=0.3,
color='#eeeeee', zorder=1)
[ax.text(v, i, '{}'.format(v)) for i, v in enumerate(x)]
# Set x-axis labe
ax.set_xlabel("Número de ORF", labelpad=20, weight='bold', size=12)
# Set y-axis label
ax.set_ylabel("Clase", labelpad=20, weight='bold', size=12)
plt.savefig(f"./graficos/numero_ORF.png")
plt.show()
plt.close()
# In[7]:
def primera_grafica():
# Agrupo las clases y cuento el número de ORF
group_clase_count = df.groupby(['clase'])['ORF'].count()
# Aplico la función al resultado.
grafico(group_clase_count)
primera_grafica()
# 1.2 Dado que el B<NAME> Koch afecta sobre todo a los pulmones, queremos que mostréis por pantalla cuántos ORFs pertenecen a la clase que tiene Respiration como descripción. Mostrad el resultado por pantalla debidamente formateado (utilizando el método format() u otro similar), incluyendo un mensaje explicativo de los valores que enseñáis.
# In[8]:
def result_respir():
result_respiration = df[df.desc.str.contains(
'Respiration', case=False)].sum()
print(
"El Nº de ORF incluidos en la clase con descripción "
"'Respiration' es: {} \n\n".
format(result_respiration['ORF']))
result_respir()
# 2.1 El número de clases que contienen como mínimo un ORF con el patrón indicado en su descripción.
# In[9]:
# Genero una función para mostrar en pantalla el
# esultado de este ejercicio
def plot_pie(x, names):
"""
Genero una función donde obtengo en formato tarta el
número de clases
:param x_protein:
:param names: el patrón correspondiente
:return:
"""
# eliminio el index
x = x.reset_index()
# convierto un dataframe solo con el campo seleccionado
x = pd.DataFrame(x)['desc_ORF']
x.index += 1
x.plot.pie(figsize=(8, 8))
plt.title(f"El Nº de clases con mínimo de 1 ORF con patron "
f"${names}$", fontsize=14, weight="bold")
plt.ylabel("Porcion clase sobre total")
plt.xlabel("Total clases: {}".format(len(x)))
# guardo la gráfoca en un lugar adecuado
plt.savefig(f"./graficos/grafica_patron_{names}.png")
# muestro en pantalla
plt.show()
plt.close()
return plot_pie
# In[10]:
def num_clases_protein():
des_ORF_protein = df[df['desc_ORF'].str.contains(r'protein', re.IGNORECASE,
regex=False, na=False)]
protein = des_ORF_protein.groupby(['clase'])['desc_ORF'].count()
numero = len(protein)
print(
"El número de clases que contienen como mínimo un ORF con el patron "
"'que contiene el término protein'. {} \n\n". format(numero))
# aplico la función al resultado anterior
plot_pie(protein, 'proteina')
num_clases_protein()
# In[11]:
def num_clases_hydro():
"""
:return:
"""
# Busco las palabras que contengan 13 caracteres
ORF_hydro_13 = df[df['desc_ORF'].str.contains(r'\b\w{13}\b') == True]
# dentro de ese resultado, que me ofrezca los que contienen
# la palabra 'hydro'
ORF_hydro_13 = ORF_hydro_13[ORF_hydro_13['desc_ORF'].str.contains(
r'hydro') == True]
hydro = ORF_hydro_13.groupby(['clase'])['desc_ORF'].count()
numero = len(hydro)
print(
"El número de clases según el segundo patron es {} \n\n".format(numero))
plot_pie(hydro, 'hydro')
num_clases_hydro()
# 2.2 El número promedio de ORFs con los cuales se relacionan los ORFs con el patrón indicado en su descripción.
# In[12]:
# Genero la función para las gráficas de los promedios
def graf_relac(x, names):
"""
Función destinada a generar las gráficas de los promedios de relaciones
según el patrón correspondiente
:param x: el datafame de los ORF con el número de relaciones
:param names: Indicamos el patron
:return: la gráfica
"""
x.plot.barh('ORF', 'numero de relaciones', figsize=(8, 10))
# muestro en patalla la media
plt.axvline(x['numero de relaciones'].mean(),
color='r', linestyle='--', label='media')
# la leyenda en la parte superior derecha
plt.legend(loc='upper right')
# titulo
plt.title(f"Nº de ORF relacionados con otro ORF y su promedio ${names}$",
fontsize=14,
weight="bold")
# etiquetas
plt.xlabel("Nº de ORF")
plt.ylabel("ORF")
# guardo en fichero png la gŕafica
plt.savefig(f"./graficos/grafica_relaciones_patron_{names}.png")
# muestro en pantalla
plt.show()
# cierro
plt.close()
return plot_pie
# In[13]:
def promedio_patron_protein():
"""
:return:
"""
# filtro los valores que contengan la palabra 'protein'
df_total_protein = df_total[df_total['descripcion'].str.contains(
r'protein', regex=False, na=False)]
# selecciono 2 columnas
prome_relac_protein = df_total_protein[['ORF', 'numero de relaciones']]
# obtengo la media
n_prome_relac_protein = float(
df_total_protein[['numero de relaciones']].mean())
# muestro el resultado
print("El número promedio de ORFs con los cuales se relacionan los"
"ORFs con el patron 'que contiene el término protein'"
" {} \n\n".format(n_prome_relac_protein))
# aplico la función a los datos obtenidos
graf_relac(prome_relac_protein, ' proteina')
promedio_patron_protein()
# In[14]:
def promedio_patron_hydro():
"""
:return:
"""
df_total_hydro = df_total[df_total['descripcion'].str.contains(
r'\b\w{13}\b') == True]
df_total_hydro = df_total_hydro[df_total_hydro['descripcion'].str.contains(
r'hydro') == True]
n_prome_relac_hydro = float(df_total_hydro[['numero de relaciones']].mean())
print("El número promedio de ORFs con los cuales se relacionan"
"los ORFs según el segundo patron es: {} \n\n".format(n_prome_relac_hydro))
graf_relac(df_total_hydro, 'hydro')
promedio_patron_hydro()
#
# 3. Para cada entero M entre 2 y 9 (ambos incluidos), calcula el número de clases que tienen como mínimo una *dimensión* mayor estricta (>) que 0 y a la vez múltiple de M. Con el término dimensión nos referimos a cada uno de los 4 números que forman el identificador de la clase (explicado en la sección anterior). Este cálculo tendrá que resultar en algo como:
# ```
# M=2: ? clases
# M=3: ? clases
# ...
# M=9: ? clases
# ```
# donde ? representa un entero.
def clases_condicional():
"""
Función destinada a obtener el número de clases
:return:
número de clases que tienen como mínimo una dimensión
mayor estricta (>) que 0 y a la vez múltiple de M entre 2 y 9.
"""
# Selecciono la columna clase del dataframe y realizo unas modificaciones
df['clase'] = df['clase'].str.replace('[', '').str.replace(']', '').str.replace("''", "")
# obtengo una lista de las cláses únicas
clase_uniq = df['clase'].unique().tolist()
# Genero una lista vacia
lista = []
print("Para cada entero M entre 2 y 9 (ambos incluidos),"
"calcula el número de clases que tienen como mínimo"
"una *dimensión* mayor estricta (>) que 0 y a la vez"
"múltiple de M.")
# Realizo una iteración para generar una lista de listas
# selecciono cada uno de los valores de las lista
for j in range(len(clase_uniq)):
# divido el elemento correspondiente
x = clase_uniq[j].split(',')
# convierto el str en int
x = [int(i) for i in x]
# añado a la nueva lista
lista.append(x)
# genero una iteración para obtener el resultado solicitado
for i in range(2, 10):
# genero una lista vacia
res = []
# any() usado para filtrar elementos
# selecciono las que tienen al menos un 0
lista_zero = [sub for sub in lista if(any(ele > 0 for ele in sub))]
# para evitar que a la hora de ejecutar el siguiente condicionante
# me selecciono el valor 0 ya que '0%i==0' y me lo contaría
# sustituyo su valor por -1
lista_zeros = [[-1 if x == 0 else x for x in y] for y in lista_zero]
# seleccion las listas que es multiple por M
res = [subs for subs in lista_zeros if(any(
ele % i == 0 for ele in subs))]
# imprimo el resultado según el formato solicitado
print("M = {}: ".format(i) + str(len(res)) + " clases")
# Ejecuto la función
clases_condicional()
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trivial.py | nvlass/pongctrl | 0 | 121551 | #!/usr/bin/env python
import sys
import random
if __name__ == '__main__':
try:
y0 = 0
while True:
fmax = 0
b = sys.stdin.readline()
width = int(b.split(" ")[0])
height = int(b.split(" ")[1])
for q in range(height):
a = sys.stdin.readline().strip()
bq = a[8:50] + a[-50:-8]
t = sum(map(lambda x: int(x), list(bq)))
if t > fmax:
y0 = q
fmax = t
sys.stdout.write(str(y0) + "\n")
sys.stdout.flush()
except Exception as ex:
sys.stderr.write(str(ex))
sys.stderr.flush()
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] |
queridodiario_toolbox/process/text_process.py | jvanz/querido-diario-toolbox | 0 | 156809 | <gh_stars>0
from subprocess import check_output, DEVNULL
import re
import string
def remove_breaks(text: str) -> str:
"""
Remove breaks, multiple whitespaces (including trimming leading
and trailing spaces), and return single text.
"""
text = _remove_new_line_splitting_word(text)
text = _remove_repeating_whitespaces_and_new_lines(text)
text = _remove_whitespaces_beginning_end_text(text)
return text
def _remove_new_line_splitting_word(text: str) -> str:
""" Removes new lines splitting words."""
return re.sub(r"(\w)-(\n|\r){1,2}(\w)", r"\1\3", text)
def _remove_repeating_whitespaces_and_new_lines(text: str) -> str:
"""Removes repeating new lines and tabular char."""
return re.sub(r"(\n|\r|\t| ){1,}", " ", text)
def _remove_whitespaces_beginning_end_text(text: str) -> str:
"""Removes whitespace from the beginning and end of the given text."""
return text.strip()
def remove_duplicate_punctuation(text: str) -> str:
"""
Remove duplicate punctuation, which may have been a feature of
gazette design.
"""
pattern = f"([{string.punctuation}])" + "{1,}"
pattern = re.compile(pattern)
text = re.sub(pattern, r"\1", text)
return text
def execute_tabula(
filepath: str, tabula_jar: str, *args: str, **kwargs: str
) -> str:
"""
Extract table and text from gazette. pass additional args to
tabula following its documentation
"""
command = ["java", "-jar", tabula_jar, filepath]
if args:
for arg in args:
command.extend([arg])
if kwargs:
for k, v in kwargs.items():
command.extend([k, v])
page = check_output(command, stderr=DEVNULL)
page = page.decode("utf-8")
return page
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] |
codes/scripts/extract_subimgs_single.py | audzms2776/BasicSR | 0 | 1607810 | <reponame>audzms2776/BasicSR
import os
import os.path as osp
import sys
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import cv2
from tqdm import tqdm
from glob import glob
import time
pbar = None
crop_sz = 96
step = crop_sz // 2
thres_sz = 48
def main():
global pbar
"""A multi-thread tool to crop sub imags."""
input_folder = 'D:\\tmp\\data\\sample_res'
save_folder = 'D:\\tmp\\data\\sample_py'
# input_folder = 'D:\\tmp\\BasicSR\\data_samples\\samples'
# save_folder = 'D:\\tmp\\BasicSR\\data_samples\\test_sample'
compression_level = 3 # 3 is the default value in cv2
# CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
# compression time. If read raw images during training, use 0 for faster IO speed.
if not os.path.exists(save_folder):
os.makedirs(save_folder)
print('mkdir [{:s}] ...'.format(save_folder))
img_list = glob(input_folder + '/*')
pbar = tqdm(total=len(img_list))
start = time.time()
# with ThreadPoolExecutor() as executor:
# future_worker = {executor.submit(worker, path, save_folder, compression_level): path for path in img_list}
# for future in as_completed(future_worker):
# try:
# future.result()
# except Exception as exc:
# print(exc)
for path in img_list:
print(path)
worker(path, save_folder, compression_level)
print('result time {}'.format(time.time() - start))
# print('All subprocesses done.')
def worker(path, save_folder, compression_level):
img_name = os.path.basename(path)
names = img_name.split('.')
img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
n_channels = len(img.shape)
if n_channels == 2:
h, w = img.shape
elif n_channels == 3:
h, w, c = img.shape
else:
raise ValueError('Wrong image shape - {}'.format(n_channels))
h_space = np.arange(0, h - crop_sz + 1, step)
if h - (h_space[-1] + crop_sz) > thres_sz:
h_space = np.append(h_space, h - crop_sz)
w_space = np.arange(0, w - crop_sz + 1, step)
if w - (w_space[-1] + crop_sz) > thres_sz:
w_space = np.append(w_space, w - crop_sz)
xy_arr = []
for x in h_space:
for y in w_space:
xy_arr.append((x, y))
def sub_f(img, x, y, idx):
if n_channels == 2:
crop_img = img[x:x + crop_sz, y:y + crop_sz]
else:
crop_img = img[x:x + crop_sz, y:y + crop_sz, :]
crop_img = np.ascontiguousarray(crop_img)
cv2.imwrite(
os.path.join(save_folder, '{}-{}.{}'.format(names[0], idx, names[1])),
crop_img, [cv2.IMWRITE_PNG_COMPRESSION, compression_level])
with ThreadPoolExecutor() as executor:
future_worker = {executor.submit(sub_f, img, xy[0], xy[1], idx): (idx, xy) for idx, xy in enumerate(xy_arr)}
for future in as_completed(future_worker):
try:
future.result()
except Exception as exc:
print(exc)
pbar.update(1)
if __name__ == '__main__':
main()
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525,
1649,
3396,
1649,
2396,
13,
1678,
1667,
580,
13,
2
] |
labs/lab-09/checkpoint5.py | AayushSriram/oss-repo | 0 | 47053 | from pymongo import MongoClient
from bson.objectid import ObjectId
from datetime import datetime as dt
import pprint
client = MongoClient()
db = client['mongo_db_lab']
defs = db['definitions']
def random_word_requester():
'''
This function should return a random word and its definition and also
log in the MongoDB database the timestamp that it was accessed.
'''
# perform a sample data aggregation
rando = list(defs.aggregate([{'$sample': {'size': 1 }}]))[0]
# update the list of definitions with the last accessed timestamp
query = {'_id': rando.get('_id')}
date = { "$push": { "dates": dt.utcnow().isoformat() } }
defs.update_one(query, date)
return rando
if __name__ == '__main__':
# create a temporary collection to check for duplicates
dupes = db['dupes']
item = random_word_requester()
# we keep getting random words until we find a dupe
while dupes.count_documents({'_id': item.get('_id')}) <= 0:
dupes.insert_one(item)
item = random_word_requester()
# get the dupe in the original collection
duped = defs.find_one({'_id': item.get('_id')})
pprint.pprint(duped)
# remove the duplicate collection and remove
# the dates field before exiting
dupes.drop()
defs.update_many({}, {'$unset': {'dates': ''}})
client.close() | [
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tests/factories.py | seik/django-belt | 0 | 199606 | from factory import DjangoModelFactory
from factory.fuzzy import FuzzyText
from tests.app.models import Post, Category, Blog
from factory.declarations import SubFactory
class BlogFactory(DjangoModelFactory):
class Meta:
model = Blog
class PostFactory(DjangoModelFactory):
blog = SubFactory(BlogFactory)
title = FuzzyText()
content = FuzzyText()
class Meta:
model = Post
class CategoryFactory(DjangoModelFactory):
class Meta:
model = Category
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wlmrt/review.py | jayrumi/walmart_pars_lib | 3 | 106767 | from random import randint
import requests
from lxml import html
import datetime
import time
def reviews(in_product_id, *arg):
product_id = str(in_product_id)
try:
if str(arg[0]).lower() == 'log':
log = 1
else:
log = 0
except:
log = 0
gen_headers = str(
'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US) AppleWebKit/525.19 (KHTML, like Gecko) Chrome/' + str(
randint(1, 42)) + '.0.' + str(randint(1, 2311)) + '.' + str(randint(1, 90)) + ' Safari/' + str(
randint(100, 537)) + '.' + str(randint(1, 36)))
in_headers = {'User-Agent': gen_headers}
walmart_url = str('https://www.walmart.com/reviews/product/' + product_id)
page = requests.get(walmart_url, headers=in_headers).text
if log == 1:
print("Product link:", walmart_url)
parser = html.fromstring(page)
XPATH_PRODUCT_NAME = '//div[@class="Grid review-product-header hide-content display-inline-block-m"]/div/div/div/h2/a/text()'
product_name = parser.xpath(XPATH_PRODUCT_NAME)[0]
countPagesList = []
for cnt in parser.xpath('//div[@class="js-review-pagination-bottom"]/div[@class ="review-pagination js-review-pagination hide-content display-inline-block-m"]/div/ul/li/a/text()'):
countPagesList.append(cnt)
try:
pageCounter = int(countPagesList[-1])
except:
pageCounter = 1
if log == 1:
print("Count of page(s) with review(s)", str(pageCounter))
reviews_list = []
for pg_counter in range(pageCounter):
walmart_url = str('https://www.walmart.com/reviews/product/' + product_id + '?limit=20&page=' + str(pg_counter + 1) + '&sort=relevancy')
page = requests.get(walmart_url, headers=in_headers).text
parser = html.fromstring(page)
XPATH_REVIEW_LIST = '//div[@class="js-review-list"]/div'
page_review_list = parser.xpath(XPATH_REVIEW_LIST)
for review in page_review_list:
try:
review_title = review.xpath(
'.//div[@class="Grid-col u-size-8-12-m js-customer-review-body customer-review-body"]/div[@class="Grid"]/div/text()')[0]
except:
review_title = 'None'
#print('review_title:', review_title)
try:
review_date = review.xpath(
'.//div[@class="Grid-col u-size-8-12-m js-customer-review-body customer-review-body"]/div[@class="Grid"]/span/text()')[0]
except:
review_date = 'None'
#print('review_date:', review_date)
try:
review_rating = str(review.xpath(
'.//div[@class="Grid-col u-size-8-12-m js-customer-review-body customer-review-body"]/div[@class="stars customer-stars"]/span[@class="visuallyhidden"]/text()')[0]).replace(' stars', '')
except:
review_rating = 'None'
#print('review_rating:', review_rating)
try:
reviewer_name = review.xpath(
'.//div[@class="Grid-col u-size-8-12-m js-customer-review-body customer-review-body"]/div[@class="stars customer-stars"]/span[@class="customer-name hide-content display-inline-block-m"]/span/text()')[0]
except:
reviewer_name = 'None'
#print('reviewer_name:', reviewer_name)
try:
review_text = review.xpath(
'.//div[@class="Grid-col u-size-8-12-m js-customer-review-body customer-review-body"]/div[@class="customer-review-text"]/p/text()')[0]
except:
review_text = 'None'
#print('review_text:', review_text)
try:
review_helpful_yes = review.xpath(
'.//div[@class="Grid-col u-size-8-12-m js-customer-review-body customer-review-body"]/div[@class="btn-vote-group js-btn-vote-group customer-review-voting hide-content display-inline-block-m"]/span/span[@class="js-vote-positive-count"]/text()')[0]
except:
review_helpful_yes = 'None'
#print('review_helpful_yes:', review_helpful_yes)
try:
review_helpful_no = review.xpath(
'.//div[@class="Grid-col u-size-8-12-m js-customer-review-body customer-review-body"]/div[@class="btn-vote-group js-btn-vote-group customer-review-voting hide-content display-inline-block-m"]/span/span[@class="js-vote-negative-count"]/text()')[0]
except:
review_helpful_no = 'None'
#print('review_helpful_no:', review_helpful_no)
try:
reviewer_location = review.xpath(
'.//div[@class="Grid-col u-size-3-12-m customer-info hide-content display-inline-block-m"]/div[@class="customer-location"]/text()')[0]
except:
reviewer_location = 'None'
#print('reviewer_location:', reviewer_location)
reviewer_attributes = []
for attr in review.xpath(
'.//div[@class="Grid-col u-size-3-12-m customer-info hide-content display-inline-block-m"]/div[@class="customer-attributes"]/div'):
#print(attr.xpath('text()'), attr.xpath('span/text()'))
attr_dict = {}
attr_dict[str(attr.xpath('text()')[0]).replace('Would recommend to a friend?\n ', 'Recommend').replace(':', '')] = attr.xpath('span/text()')[0]
reviewer_attributes.append(attr_dict)
#print(reviewer_attributes)
if len(review.xpath(
'.//div[@class="Grid-col u-size-8-12-m js-customer-review-body customer-review-body"]/div[@class="hide-content-m"]/div/div/div/a/text()')) > 0:
is_reviewer_verified = 'Yes'
else:
is_reviewer_verified = 'No'
#print('is_reviewer_verified:', is_reviewer_verified)
reviewer_top_list = []
reviewer_top_list_raw = review.xpath(
'.//div[@class="Grid-col u-size-8-12-m js-customer-review-body customer-review-body"]/div[@class="hide-content-m"]/div/div/div/text()')
try:
for i in reviewer_top_list_raw:
if i.find(' ') > 0:
None
else:
reviewer_top_list.append(i)
except:
None
#print(is_reviewer_verified, reviewer_top_list)
review_dictionary = {
'review_title': review_title,
'review_date': review_date,
'review_rating': review_rating,
'review_text': review_text,
'review_helpful_yes': review_helpful_yes,
'review_helpful_no': review_helpful_no,
'reviewer_name': reviewer_name,
'reviewer_location': reviewer_location,
'reviewer_attributes': reviewer_attributes,
'is_reviewer_verified': is_reviewer_verified,
'reviewer_top_list': reviewer_top_list
}
reviews_list.append(review_dictionary)
if log == 1:
print("Page #" + str(pg_counter + 1) + ". Count of parsed reviews in overall:", str(len(reviews_list)))
#print(reviews_list)
product_dictionary = {
'product_id': product_id,
'product_name': product_name,
'product_reviews_list': reviews_list
}
ts = time.time()
if log == 1:
print("System date: ", str(datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')), "\n")
return(product_dictionary)
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] |
doc/samples/pos.py | m4ta1l/doit | 1,390 | 6821 | def task_pos_args():
def show_params(param1, pos):
print('param1 is: {0}'.format(param1))
for index, pos_arg in enumerate(pos):
print('positional-{0}: {1}'.format(index, pos_arg))
return {'actions':[(show_params,)],
'params':[{'name':'param1',
'short':'p',
'default':'default value'},
],
'pos_arg': 'pos',
'verbosity': 2,
}
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setup.py | egrp365/python-egrp365-api | 0 | 124096 | <reponame>egrp365/python-egrp365-api
import setuptools
with open('README.rst', 'r') as fh:
long_description = fh.read()
setuptools.setup(
name='egrp365',
version='0.0.4',
packages=['egrp365'],
url='https://github.com/egrp365/python-egrp365-api',
license='MIT License (MIT)',
long_description=long_description,
author='egrp365',
author_email='<EMAIL>',
description='This module implements the egrp365.ru HTTP API.',
install_requires=[
"requests",
],
)
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Pr_test.py | mohaEs/Train-Predict-Landmarks-by-dlib | 3 | 91451 | #!/usr/bin/python
# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
#
# This example program shows how to use dlib's implementation of the paper:
# One Millisecond Face Alignment with an Ensemble of Regression Trees by
# <NAME> and <NAME>, CVPR 2014
#
# In particular, we will train a face landmarking model based on a small
# dataset and then evaluate it. If you want to visualize the output of the
# trained model on some images then you can run the
# face_landmark_detection.py example program with predictor.dat as the input
# model.
#
# It should also be noted that this kind of model, while often used for face
# landmarking, is quite general and can be used for a variety of shape
# prediction tasks. But here we demonstrate it only on a simple face
# landmarking task.
#
# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
# You can install dlib using the command:
# pip install dlib
#
# Alternatively, if you want to compile dlib yourself then go into the dlib
# root folder and run:
# python setup.py install
#
# Compiling dlib should work on any operating system so long as you have
# CMake installed. On Ubuntu, this can be done easily by running the
# command:
# sudo apt-get install cmake
#
# Also note that this example requires Numpy which can be installed
# via the command:
# pip install numpy
import os
import sys
import glob
import matplotlib
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
import matplotlib.pyplot as plt
import dlib
# In this example we are going to train a face detector based on the small
# faces dataset in the examples/faces directory. This means you need to supply
# the path to this faces folder as a command line argument so we will know
# where it is.
if len(sys.argv) != 5:
print(
"Give the path to the examples/faces directory as the argument to this "
"program. For example, if you are in the python_examples folder then "
"execute this program by running:\n"
" ./train_shape_predictor.py ../faces")
exit()
image_folder = sys.argv[1]
checkpoint_folder = sys.argv[2]
result_folder = sys.argv[3]
Num_landmarks = int(sys.argv[4])
# Now let's use it as you would in a normal application. First we will load it
# from disk. We also need to load a face detector to provide the initial
# estimate of the facial location.
predictor = dlib.shape_predictor(checkpoint_folder+"/predictor.dat")
from skimage import io as ioSK
import io
from contextlib import redirect_stdout
import numpy as np
if not os.path.exists(result_folder):
os.makedirs(result_folder)
# Now let's run the detector and shape_predictor over the images in the faces
# folder and display the results.
print("Showing detections and predictions on the images in the faces folder...")
for f in glob.glob(os.path.join(image_folder, "*.jpg")):
print("Processing file: {}".format(f))
#img = dlib.load_rgb_image(f)
img = ioSK.imread(f)
# Ask the detector to find the bounding boxes of each face. The 1 in the
# second argument indicates that we should upsample the image 1 time. This
# will make everything bigger and allow us to detect more faces.
dets=dlib.rectangle(left=1, top=1, right=255, bottom=255)
### My comment: dets would be tuple of dlib.rectangles object contain
## multi rectagls and corresponing points
newLandmarks=np.zeros((Num_landmarks,3),dtype=np.float16)
shape = predictor(img, dets)
for k in range(Num_landmarks):
# print('-',k)
# print(shape.part(k))
h = io.StringIO()
with redirect_stdout(h):
print(shape.part(k) )
out = h.getvalue()
# print(out)
ind_n=out.find('\n', 0, len(out))
ind_comma=out.find(',', 0, len(out))
# print(ind_comma,ind_n)
# print(out[1:ind_comma])
# print(out[ind_comma+2:ind_n-1])
newLandmarks[k,0]=k+1
newLandmarks[k,1]=int(out[1:ind_comma])
newLandmarks[k,2]=int(out[ind_comma+2:ind_n-1])
# Draw the face landmarks on the screen.
ind=f.rfind('\\')
FileName=f[ind:]
fig=plt.figure()
plt.imshow(img)
plt.scatter(newLandmarks[:,1],newLandmarks[:,2], marker='x',color='blue')
fig.savefig(result_folder+'/'+FileName)
plt.close()
np.savetxt(result_folder+'/'+FileName[:-4]+'_pred.csv',
newLandmarks , delimiter=",", fmt='%i')
# np.savetxt(result_folder+FileName+'_true.csv',
# newLandmarks_true , delimiter=",", fmt='%i')
#dlib.hit_enter_to_continue()
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profiles/models.py | geoanalytic/geopap-rest | 0 | 159142 | <filename>profiles/models.py
from django.db import models
from django.contrib.postgres.fields import ArrayField
# Geopaparrazi profiles model
class Project(models.Model):
path = models.CharField(max_length=100, blank=True, default='')
modifieddate = models.DateTimeField(auto_now_add=True)
url = models.URLField(blank=True)
uploadurl = models.URLField(blank=True)
def __str__(self):
return self.path
class Tag(models.Model):
path = models.CharField(max_length=100, blank=True, default='')
modifieddate = models.DateTimeField(auto_now_add=True)
url = models.URLField(blank=True)
def __str__(self):
return self.path
class Basemap(models.Model):
path = models.CharField(max_length=100, blank=True, default='')
modifieddate = models.DateTimeField(auto_now_add=True)
url = models.URLField(blank=True)
size = models.CharField(max_length=30)
def __str__(self):
return self.path
class Spatialitedbs(models.Model):
path = models.CharField(max_length=100, blank=True, default='')
modifieddate = models.DateTimeField(auto_now_add=True)
url = models.URLField(blank=True)
size = models.CharField(max_length=30)
uploadurl = models.URLField(blank=True)
visible = ArrayField(models.CharField(max_length=30))
def __str__(self):
return self.path
class Otherfiles(models.Model):
path = models.CharField(max_length=100, blank=True, default='')
modifieddate = models.DateTimeField(auto_now_add=True)
url = models.URLField(blank=True)
def __str__(self):
return self.path
class Profile(models.Model):
name = models.CharField(max_length=100, blank=True, default='')
description = models.TextField()
creationdate = models.DateTimeField(auto_now_add=True)
modifieddate = models.DateTimeField(auto_now_add=True)
color = models.CharField(max_length=30, default="#FBC02D")
active = models.BooleanField(default=False)
sdcardPath = models.CharField(max_length=100, default="MAINSTORAGE")
mapView = models.CharField(max_length=100, default="52.02025604248047,-115.70208740234375,10.0")
project = models.ForeignKey(Project, on_delete = models.SET_NULL, blank=True, null=True)
tags = models.ForeignKey(Tag, on_delete = models.SET_NULL, blank=True, null=True)
basemaps = models.ManyToManyField(Basemap, blank=True)
spatialitedbs = models.ManyToManyField(Spatialitedbs, blank=True)
otherfiles = models.ManyToManyField(Otherfiles, blank=True)
def __str__(self):
return self.name
class Meta:
ordering = ('modifieddate', 'name', )
class ProfileSet(models.Model):
owner = models.OneToOneField('users.user', related_name='profilesets', on_delete=models.CASCADE)
profiles = models.ManyToManyField(Profile, blank=True)
formatVersion = models.FloatField(default=1.1)
def __str__(self):
return self.owner.username
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f3dasm/abaqus/post_processing/get_data.py | bessagroup/F3DASM | 26 | 64390 | <filename>f3dasm/abaqus/post_processing/get_data.py
'''
Created on 2020-04-15 14:50:02
Last modified on 2020-05-07 21:21:10
Python 2.7.16
v0.1
@author: <NAME> (<EMAIL>)
Main goal
---------
Develop functions to get data from odb files.
'''
#%% history outputs
def get_xydata_from_nodes_history_output(odb, nodes, variable,
directions=(1, 2, 3),
step_name=None):
'''
Given a node array, returns the values for time and the given variable.
Assumes the variable was requested in history outputs.
Parameters
----------
odb : Abaqus odb object
nodes : array-like of Abaqus OdbMeshNode objects
variable : str
It only works for vector-like variables. e.g. 'U' and 'RF'
directions : array
Directions for which to extract values.
step_name : str
If None, it uses the last step.
Returns
-------
y : array-like, shape = [n_nodes x [n_increments x n_directions]]
Notes
-----
-Assumes all the nodes have history output and the array contains each node
only once.
'''
# initialization
if not step_name:
step_name = odb.steps.keys()[-1]
step = odb.steps[step_name]
# collect data
y = []
for node in nodes:
instance_name = node.instanceName if node.instanceName else 'ASSEMBLY'
name = 'Node ' + instance_name + '.' + str(node.label)
historyOutputs = step.historyRegions[name].historyOutputs
node_data = []
for direction in directions:
node_data.append([data[1] for data in historyOutputs['%s%i' % (variable, direction)].data])
y.append(node_data)
return y
#%% field outputs
def get_ydata_from_nodeSets_field_output(odb, nodeSet, variable,
directions=(1, 2, 3), step_name=None,
frames=None):
'''
Given a node set, returns the values for the given variable.
It may take a while to run.
Parameters
----------
odb : Abaqus odb object
nodeSet : Abaqus nodeSet object
variable : str
It only works for vector-like variables. e.g. 'U' and 'RF'
directions : array
Directions for which to extract values. The value will be subtracted
by 1 when accessing abaqus data.
frames : array-like of Abaqus OdbFrame objects
If frames are available from outside, use it because it may significantly
decreases the computational time.
step_name : str
If None, it uses the last step. Only required if frames=None.
Returns
-------
values : array-like, shape = [n_increments x n_directions x n_nodes]]
'''
# TODO: extend to scalar and tensor like variables
# TODO: change name to accept also elements (position should be an input)
# access frames
if not frames:
if not step_name:
step_name = odb.steps.keys()[-1]
frames = [frame for frame in odb.steps[step_name].frames]
# collect data
values = []
for frame in frames:
varFieldOutputs = frame.fieldOutputs[variable]
outputs = varFieldOutputs.getSubset(region=nodeSet).values
output_frame = []
for direction in directions:
output_frame.append([output.data[direction - 1] for output in outputs])
values.append(output_frame)
return values
#%% other
def get_eigenvalues(odb, frames=None, step_name=None):
'''
Parameters
----------
odb : Abaqus odb object
frames : array-like of Abaqus OdbFrame objects
step_name : str
If None, it uses the last step. Only required if frames=None.
'''
# access frames
if not frames:
if not step_name:
step_name = odb.steps.keys()[-1]
frames = [frame for frame in odb.steps[step_name].frames]
# get eigenvalues
eigenvalues = [float(frame.description.split('EigenValue =')[1]) for frame in list(frames)[1:]]
return eigenvalues
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detect_fraud_email_enron/tools/k_best_selector.py | gotamist/other_machine_learning | 0 | 22349 | <gh_stars>0
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 25 18:26:45 2018
@author: gotamist
"""
import sys
sys.path.append("../tools/")
from feature_format import featureFormat, targetFeatureSplit
from sklearn.feature_selection import SelectKBest
def KBestSelector(data_dict, features_list, k):
data_array = featureFormat(data_dict, features_list)
labels, features = targetFeatureSplit(data_array)
kbest = SelectKBest(k=k)
kbest.fit(features, labels)
scores = kbest.scores_
tuples = zip(features_list[1:], scores)
top_k_features = sorted(tuples, key=lambda x: x[1], reverse=True)
return top_k_features[:k] | [
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] |
locationsharinglib/locationsharinglib.py | lufton/locationsharinglib | 0 | 165303 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: locationsharinglib.py
#
# Copyright 2017 <NAME>
#
# 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.
#
"""
Main code for locationsharinglib
.. _Google Python Style Guide:
http://google.github.io/styleguide/pyguide.html
"""
from __future__ import unicode_literals
import json
import logging
import pickle
from datetime import datetime
from bs4 import BeautifulSoup as Bfs
from cachetools import TTLCache, cached
from requests import Session
from .locationsharinglibexceptions import (InvalidCredentials,
InvalidData,
InvalidUser,
InvalidCookies,
TooManyFailedAuthenticationAttempts,
NoExpectedFormOption,
Unexpected2FAResponse)
__author__ = '''<NAME> <<EMAIL>>'''
__docformat__ = '''google'''
__date__ = '''2017-12-24'''
__copyright__ = '''Copyright 2017, Costas Tyfoxylos'''
__credits__ = ["<NAME>", "<NAME>", "<NAME>",
"<NAME>", "<NAME>"]
__license__ = '''MIT'''
__maintainer__ = '''<NAME>'''
__email__ = '''<<EMAIL>>'''
__status__ = '''Development''' # "Prototype", "Development", "Production".
# This is the main prefix used for logging
LOGGER_BASENAME = '''locationsharinglib'''
LOGGER = logging.getLogger(LOGGER_BASENAME)
LOGGER.addHandler(logging.NullHandler())
REPLACE_PATTERN = ")]}'\n"
SIGN_IN_MESSAGE = 'Sign in - Google Accounts'
INVALID_PASSWORD_TOKEN = '<PASSWORD>'
TOO_MANY_ATTEMPTS = 'Unavailable because of too many failed attempts'
TWO_STEP_VERIFICATION = 'TWO_STEP_VERIFICATION'
STATE_CACHING_SECONDS = 30
DEBUG = not True
STATE_CACHE = TTLCache(maxsize=1, ttl=STATE_CACHING_SECONDS)
class Person: # pylint: disable=too-many-instance-attributes
"""A person sharing its location as coordinates"""
def __init__(self, data):
logger_name = u'{base}.{suffix}'.format(base=LOGGER_BASENAME,
suffix=self.__class__.__name__)
self._logger = logging.getLogger(logger_name)
self._id = None
self._picture_url = None
self._full_name = None
self._nickname = None
self._latitude = None
self._longitude = None
self._timestamp = None
self._accuracy = None
self._address = None
self._country_code = None
self._charging = None
self._battery_level = None
self._populate(data)
def _populate(self, data):
try:
self._id = data[6][0]
self._picture_url = data[6][1]
self._full_name = data[6][2]
self._nickname = data[6][3]
self._latitude = data[1][1][2]
self._longitude = data[1][1][1]
self._timestamp = data[1][2]
self._accuracy = data[1][3]
self._address = data[1][4]
self._country_code = data[1][6]
self._charging = data[13][0]
self._battery_level = data[13][1]
except (IndexError, TypeError):
self._logger.debug(data)
raise InvalidData
def __str__(self):
text = (u'Full name :{}'.format(self.full_name),
u'Nickname :{}'.format(self.nickname),
u'Current location :{}'.format(self.address),
u'Latitute :{}'.format(self.latitude),
u'Longitude :{}'.format(self.longitude),
u'Datetime :{}'.format(self.datetime),
u'Charging :{}'.format(self.charging),
u'Battery % :{}'.format(self.battery_level),
u'Accuracy :{}'.format(self._accuracy))
return '\n'.join(text)
@property
def id(self): # pylint: disable=invalid-name
"""The internal google id of the account"""
return self._id or self.full_name
@property
def picture_url(self):
"""The url of the person's avatar"""
return self._picture_url
@property
def full_name(self):
"""The full name of the user as set in google"""
return self._full_name
@property
def nickname(self):
"""The nickname as set in google"""
return self._nickname
@property
def latitude(self):
"""The latitude of the person's current location"""
return self._latitude
@property
def longitude(self):
"""The longitude of the person's current location"""
return self._longitude
@property
def timestamp(self):
"""The timestamp of the location retrieval"""
return self._timestamp
@property
def datetime(self):
"""A datetime representation of the location retrieval"""
return datetime.fromtimestamp(int(self.timestamp) / 1000)
@property
def address(self):
"""The address as reported by google for the current location"""
return self._address
@property
def country_code(self):
"""The location's country code"""
return self._country_code
@property
def accuracy(self):
"""The accuracy of the gps"""
return self._accuracy
@property
def charging(self):
"""Whether or not the user's device is charging"""
return bool(self._charging)
@property
def battery_level(self):
"""The battery level of the user's device"""
return self._battery_level
class Authenticator: # pylint: disable=too-few-public-methods
"""Handles the authentication with google"""
def __init__(self, email, password, cookies_file=None):
logger_name = u'{base}.{suffix}'.format(base=LOGGER_BASENAME,
suffix=self.__class__.__name__)
self._logger = logging.getLogger(logger_name)
self._session = Session()
agent = ('Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:59.0) '
'Gecko/20100101 Firefox/59.0')
self._session.headers.update({'User-Agent': agent,
'Referer': 'https://www.google.com'})
self.email = email
self.password = password
self._login_url = 'https://accounts.google.com'
# Try login with cookie
if cookies_file and self._validate_cookie(cookies_file):
return
# Try login based on username/password
self._authenticate()
if cookies_file:
self._export_cookie(cookies_file)
def _validate_cookie(self, cookies_file):
try:
cfile = open(cookies_file, 'rb')
self._session.cookies = pickle.load(cfile)
except KeyError:
self._logger.error(('Could not load pickle file, '
'probably invalid pickle.'))
return False
except IOError:
self._logger.error(('Could not open pickle file, either file '
'does not exist or no read access.'))
return False
# Check if cookies are valid
response = self._session.get(self._login_url, verify=not DEBUG)
if SIGN_IN_MESSAGE in response.text:
message = ('The cookies provided do not provide a valid session.'
'Please authenticate normally and save a valid session '
'again')
raise InvalidCookies(message)
return True
def _export_cookie(self, cookies_file):
try:
cfile = open(cookies_file, 'wb')
pickle.dump(self._session.cookies, cfile)
except IOError:
message = 'Could not export cookies to {}.'.format(cookies_file)
raise InvalidCookies(message)
def _authenticate(self):
self._initialize()
self._submit_email()
self._submit_password()
def _initialize(self):
url = '{login_url}/ServiceLogin'.format(login_url=self._login_url)
response = self._session.get(url, verify=not DEBUG)
soap = Bfs(response.text, 'html.parser')
try:
data = soap.find('div', {'data-initial-sign-in-data': True})\
.get('data-initial-sign-in-data').replace('%.@.', '[')
self.req_id = json.loads(data)[30]
except (TypeError, KeyError):
self._logger.exception('Unable to parse response :%s', response.text)
raise InvalidData
except (ValueError, IndexError):
self._logger.exception('Unable to parse response :%s', data)
raise InvalidData
# Alternative method
# self.req_id = re.search(' data-initial-sign-in-data="%.*?"([\w-]{100,})"', response.text, re.S).group(1)
def _submit_email(self):
self._session.headers.update({'Google-Accounts-XSRF': '1'})
url = '{login_url}/_/signin/sl/lookup'.format(login_url=self._login_url)
req = '["{email}","{request_id}"]'.format(email=self.email, request_id=self.req_id)
response = self._session.post(url, data={'f.req': req}, verify=not DEBUG)
body = response.text.replace(REPLACE_PATTERN, '')
if self.email not in body:
raise InvalidUser(self.email)
try:
self.req_id = json.loads(body)[0][2]
except (ValueError, IndexError):
self._logger.exception('Unable to parse response :%s', body)
raise InvalidData
# Alternative method
# self.req_id = re.search('"([\w-]{100,})"', response.text, re.S).group(1)
def _submit_password(self):
url = '{login_url}/_/signin/sl/challenge'.format(login_url=self._login_url)
# Todo: I really don't know how to make this sturdier
req = '["{req_id}",null,null,null,[1,null,null,null,["{password}"]]]'\
.format(req_id=self.req_id, password=self.password)
response = self._session.post(url, data={'f.req': req}, verify=not DEBUG)
body = response.text.replace(REPLACE_PATTERN, '')
if INVALID_PASSWORD_TOKEN in body:
raise InvalidCredentials(body)
elif TOO_MANY_ATTEMPTS in body: # Todo: update TOO_MANY_ATTEMPTS constant?
raise TooManyFailedAuthenticationAttempts(body)
elif TWO_STEP_VERIFICATION in body:
self._handle_prompt(body)
def _handle_prompt(self, body):
# Borrowed with slight modification from https://git.io/vxu1A
try:
data = json.loads(body)
# Todo: I really don't know how to make this sturdier
data_key = data[0][10][0][0][23]['5004'][12]
data_tx_id = data[0][10][0][0][23]['5004'][1]
data_tl = data[1][2]
except (AttributeError, IndexError, KeyError):
message = 'Unexpected response received :{}'.format(body)
raise Unexpected2FAResponse(message)
await_url = ('https://content.googleapis.com/cryptauth/v1/'
'authzen/awaittx?alt=json&key={}').format(data_key)
await_body = {'txId': data_tx_id}
print("Open the Google App, and tap 'Yes' on the prompt to sign in ...")
self._session.headers['Referer'] = '{login_url}/signin/v2/challenge/az'.format(login_url=self._login_url)
response = self._session.post(await_url, json=await_body, verify=not DEBUG)
data = json.loads(response.text)
url = '{login_url}/_/signin/challenge?TL={tl}'.format(login_url=self._login_url, tl=data_tl)
req = '["{req_id}",null,5,null,[4,null,null,null,null,null,null,null,null,["{tx_token}",1,true]]]' \
.format(req_id=self.req_id, tx_token=data['txToken'])
response = self._session.post(url, data={'f.req': req}, verify=not DEBUG)
response.raise_for_status()
return response
def logout(self):
"""Logs the session out, invalidating the cookies
Returns:
True on success, False otherwise
"""
url = '{login_url}/Logout'.format(login_url=self._login_url)
response = self._session.get(url)
return response.ok
class CookieGetter(Authenticator): # pylint: disable=too-few-public-methods
"""Object to interactively get the cookies"""
def __init__(self, email, password, cookies_file='authenticated.cookies'):
super(CookieGetter, self).__init__(email,
password,
cookies_file=cookies_file)
class Service(Authenticator):
"""An object modeling the service to retrieve locations"""
def __init__(self, email, password, cookies_file=None):
super(Service, self).__init__(email,
password,
cookies_file=cookies_file)
@cached(STATE_CACHE)
def _get_data(self):
payload = {'authuser': 0,
'hl': 'en',
'gl': 'us',
# pd holds the information about the rendering of the map and
# it is irrelevant with the location sharing capabilities.
# the below info points to google's headquarters.
'pb': ('!1m7!8m6!1m3!1i14!2i8413!3i5385!2i6!3x4095'
'!2m3!1e0!2sm!3i407105169!3m7!2sen!5e1105!12m4'
'!1e68!2m2!1sset!2sRoadmap!4e1!5m4!1e4!8m2!1e0!'
'1e1!6m9!1e12!2i2!26m1!4b1!30m1!'
'1f1.3953487873077393!39b1!44e1!50e0!23i4111425')}
url = 'https://www.google.com/maps/preview/locationsharing/read'
response = self._session.get(url, params=payload, verify=not DEBUG)
self._logger.debug(response.text)
if response.ok:
try:
data = json.loads(response.text.split("'", 1)[1])
except (ValueError, IndexError, TypeError):
self._logger.exception('Unable to parse response :%s',
response.text)
data = ['']
else:
self._logger.warning('Received response code:%s', response.status_code)
data = ['']
return data
def get_shared_people(self):
"""Retrieves all people that share their location with this account"""
people = []
output = self._get_data()
for info in output[0]:
try:
people.append(Person(info))
except InvalidData:
self._logger.debug('Missing location or other info, dropping person with info: %s', info)
return people
def get_authenticated_person(self):
"""Retrieves the person associated with this account"""
try:
output = self._get_data()
person = Person([
self.email,
output[9][1],
None,
None,
None,
None,
[
None,
None,
self.email,
self.email
],
None,
None,
None,
None,
None,
None,
None,
])
except (IndexError, TypeError, InvalidData):
self._logger.debug('Missing essential info, cannot instantiate authenticated person')
return None
return person
def get_all_people(self):
"""Retrieves all people sharing their location"""
people = self.get_shared_people() + [self.get_authenticated_person()]
return filter(None, people)
def get_person_by_nickname(self, nickname):
"""Retrieves a person by nickname"""
return next((person for person in self.get_all_people()
if person.nickname.lower() == nickname.lower()), None)
def get_person_by_full_name(self, name):
"""Retrieves a person by full name"""
return next((person for person in self.get_all_people()
if person.full_name.lower() == name.lower()), None)
def get_coordinates_by_nickname(self, nickname):
"""Retrieves a person's coordinates by nickname"""
person = self.get_person_by_nickname(nickname)
if not person:
return '', ''
return person.latitude, person.longitude
def get_coordinates_by_full_name(self, name):
"""Retrieves a person's coordinates by full name"""
person = self.get_person_by_full_name(name)
if not person:
return '', ''
return person.latitude, person.longitude
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events/handler/views.py | totaki/learn-python-pnz-00 | 0 | 97230 | from rest_framework import viewsets
from handler.serializers import EventsSerializer, PlaceSerializer
from handler.models import Event, Place
class EventViewSet(viewsets.ModelViewSet):
"""
API endpoint that allows users to be viewed or edited.
"""
queryset = Event.objects.all()
serializer_class = EventsSerializer
class PlaceViewSet(viewsets.ModelViewSet):
"""
API endpoint that allows groups to be viewed or edited.
"""
queryset = Place.objects.all()
serializer_class = PlaceSerializer
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58/main.py | pauvrepetit/leetcode | 0 | 63515 | <filename>58/main.py
class Solution:
def lengthOfLastWord(self, s: str) -> int:
s = s.strip()
wordList = s.split(' ')
try:
lastWord = wordList[-1]
return len(lastWord)
except IndexError:
return 0
sol = Solution()
print(sol.lengthOfLastWord("hello world"))
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python/bulk_ignore_alerts.py | uptime-com/rest-api-examples | 0 | 1608463 | #!/usr/bin/env python3
import argparse
import datetime as dt
import time
import requests
def date(d):
return dt.datetime.strptime(d, '%Y-%m-%d').date()
def parse_args():
parser = argparse.ArgumentParser(description='Ignore alerts between date ranges for '
'one or more checks.')
parser.add_argument('--token', required=True,
help='Your Uptime.com API Token')
parser.add_argument('--api', default='https://uptime.com/api/v1/',
help='(optional) The Uptime.com API endpoint to use, eg. '
'https://uptime.com/api/v1/')
parser.add_argument('--from', required=True, type=date,
help='The earliest date to ignore alerts from, in YYYY-MM-DD format.')
parser.add_argument('--to', required=True, type=date,
help='The latest date to ignore alerts from, in YYYY-MM-DD format.')
parser.add_argument('--prefix', required=True,
help='The check name or check name prefix to ignore alerts for.')
parser.add_argument('--subaccount', type=int,
help='(optional) A subaccount to process instead of the main account.')
return parser.parse_args()
opts = parse_args()
headers = {'Authorization': 'token ' + opts.token}
if opts.subaccount:
headers['X-Subaccount'] = str(opts.subaccount)
def ignore_alert(outage):
"""
Call the API to ignore the alert for this outage.
"""
print('Ignoring: {} @ {}'.format(outage['check_name'], outage['created_at']))
r = requests.post(outage['ignore_alert_url'], headers=headers)
r.raise_for_status()
# Delay to keep within rate limits
time.sleep(2.2)
page = 0
while True:
# Read through each page of outages between the given dates
page += 1
r = requests.get(opts.api + 'outages/',
headers=headers,
params={
'start_date': str(getattr(opts, 'from')),
'end_date': str(opts.to),
'page_size': 250,
'page': page,
})
r.raise_for_status()
outages = r.json()['results']
if not outages:
break
# For each outage matching the check name prefix, ignore the outage
for outage in outages:
if outage['check_name'].startswith(opts.prefix) and not outage['ignored']:
ignore_alert(outage)
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] |
MAModule/networks/predict.py | MrReochen/MultiAgentModule | 0 | 15996 | import torch
import torch.nn as nn
from ..networks.basic.util import check
from ..networks.basic.predict import PredictNet, PredictLayer, OutLayer
from ..utils.util import get_shape_from_obs_space
class OneHot:
def __init__(self, out_dim):
self.out_dim = out_dim
def transform(self, tensor):
y_onehot = tensor.new(*tensor.shape[:-1], self.out_dim).zero_()
y_onehot.scatter_(-1, tensor.long(), 1)
return y_onehot.float()
class Predict(nn.Module):
def __init__(self, args, obs_space, action_space, cent_obs_space, device=torch.device("cpu")):
super(Predict, self).__init__()
self.hidden_size = args.hidden_size
self._gain = args.gain
self._use_orthogonal = args.use_orthogonal
self.tpdv = dict(dtype=torch.float32, device=device)
obs_shape = get_shape_from_obs_space(obs_space)
cent_obs_shape = get_shape_from_obs_space(cent_obs_space)
self.net = PredictNet(args, obs_shape, action_space, cent_obs_shape, use_projector=True)
self.onehot = OneHot(action_space.n)
self.to(device)
def forward(self, obs, actions):
obs = check(obs).to(**self.tpdv)
actions = self.onehot.transform(check(actions)).to(**self.tpdv)
x = torch.cat((obs, actions), dim=-1)
out = self.net(x)
return out
class Projector(nn.Module):
def __init__(self, args, cent_obs_space, device=torch.device("cpu")):
super(Projector, self).__init__()
self.args = args
self.hidden_size = args.hidden_size
self._gain = args.gain
self._use_orthogonal = args.use_orthogonal
self.tpdv = dict(dtype=torch.float32, device=device)
cent_obs_shape = get_shape_from_obs_space(cent_obs_space)
if args.predict_dim:
self.mlp = PredictLayer(cent_obs_shape[0], args.predict_dim,
1, self._use_orthogonal, False)
# self.out = OutLayer(cent_obs_shape[0] // 4, self.hidden_size, self._use_orthogonal, False)
self.to(device)
def forward(self, cent_obs):
x = check(cent_obs).to(**self.tpdv)
if self.args.predict_dim:
out = self.mlp(x)
# out = self.out(out)
else:
out = x
return out | [
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] |
phigaro/const.py | bobeobibo/phigaro | 31 | 53666 | <gh_stars>10-100
DEFAULT_WINDOW_SIZE = 32
DEFAULT_THRESHOLD_MIN_BASIC = 45.39
DEFAULT_THRESHOLD_MAX_BASIC = 46.0
DEFAULT_THRESHOLD_MIN_ABS = 50.32
DEFAULT_THRESHOLD_MAX_ABS = 52.96
DEFAULT_THRESHOLD_MIN_WITHOUT_GC = 11.28
DEFAULT_THRESHOLD_MAX_WITHOUT_GC = 11.42
DEFAULT_MEAN_GC = 0.46354823199323626
DEFAULT_MAX_EVALUE = 0.00445
DEFAULT_PENALTY_BLACK = 2.2
DEFAULT_PENALTY_WHITE = 0.7
DEFAULT_BLACK_LIST = [
'VOG0496',
'VOG5264',
'VOG4730',
'VOG5818',
'VOG7281',
'VOG6063',
'VOG6030',
'VOG1710',
'VOG0996',
'VOG4524',
'VOG8021',
'VOG8536',
'VOG2368',
'VOG1850',
'VOG1031',
'VOG0985',
'VOG0274',
'VOG4344',
'VOG1844',
'VOG0088',
'VOG8607',
'VOG4615',
'VOG8992',
'VOG3235',
'VOG0092',
'VOG4155',
'VOG3532',
'VOG1045',
'VOG4149',
'VOG8062',
'VOG4562',
'VOG7442',
'VOG7446',
'VOG0419',
'VOG4319',
'VOG8429',
'VOG4409',
'VOG1422',
'VOG10018',
'VOG3101',
'VOG5441',
'VOG4469',
'VOG6988',
'VOG4678',
'VOG3722',
]
DEFAULT_WHITE_LIST = [
"VOG0568",
"VOG0569",
"VOG0565",
"VOG0566",
"VOG0567",
"VOG0562",
"VOG2703",
"VOG0968",
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"VOG5046",
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"VOG0559",
"VOG0557",
"VOG2779",
"VOG4545",
"VOG0953",
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"VOG0602",
"VOG5254",
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] |
envs/env_base.py | imdeep2905/Turing-Q-Learning | 0 | 195203 | import logging
import numpy as np
import tensorflow as tf
from tf_agents.specs import TensorSpec
from envs.utils import Epsilon, TFUniformReplayBufferWrapper
class BaseEnvWrapper:
def __init__(
self,
env,
eval_env,
name,
in_interactor,
out_interactor,
replay_buffer_size=10_000,
replay_buffer_initialization_size=1000,
epsilon_initial_value=1.0,
epsilon_end_value=0.1,
epsilon_decay_steps=10_000,
epsilon_power=1,
):
self.name = name
self.env = env
self.eval_env = eval_env
self.number_of_actions = self.env.action_spec().maximum + 1
self._epislon = Epsilon(
initial_value=epsilon_initial_value,
end_value=epsilon_end_value,
decay_steps=epsilon_decay_steps,
power=epsilon_power,
identifier=f"Epsilon ({self.name})",
)
self._replay_buffer_size = replay_buffer_size
self._replay_buffer_batch_size = self.env.batch_size
self._replay_buffer = TFUniformReplayBufferWrapper(
self._replay_buffer_size,
self._replay_buffer_batch_size,
(
self.env.observation_spec(),
TensorSpec(shape=(), dtype=tf.int32),
TensorSpec(shape=(), dtype=tf.float32),
self.env.observation_spec(),
TensorSpec(shape=(), dtype=tf.bool),
),
self.name,
)
self.in_interactor = in_interactor
self.out_interactor = out_interactor
logging.debug(
f"{self.__class__.__name__}, {self.name}: Object created."
)
self.init_replay_buffer(replay_buffer_initialization_size)
def init_replay_buffer(self, size):
logging.info(
f"Initializing replay buffer of {self.name} with size {size}."
)
for _ in range(size):
cur_obs = self.env.current_time_step()
action = np.random.randint(0, self.number_of_actions)
action = tf.convert_to_tensor(action, dtype=tf.int32)
next_obs = self.env.step(action)
self._replay_buffer.add_batch(
(
cur_obs.observation,
tf.expand_dims(action, axis=0),
next_obs.reward,
next_obs.observation,
next_obs.is_last(),
)
)
if next_obs.is_last():
self.env.reset()
def get_batch_from_replay_buffer(self, batch_size):
return self._replay_buffer.sample_batch(batch_size)
def _step_driver(self, action):
cur_obs = self.env.current_time_step()
if np.random.rand() < self._epislon.epsilon:
action = tf.convert_to_tensor(
np.random.randint(0, self.number_of_actions),
dtype=tf.int32,
)
else:
action = tf.convert_to_tensor(action, dtype=tf.int32)
next_obs = self.env.step(action)
self._replay_buffer.add_batch(
(
cur_obs.observation,
tf.expand_dims(action, axis=0),
next_obs.reward,
next_obs.observation,
next_obs.is_last(),
)
)
if next_obs.is_last():
self.env.reset()
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] |
tests/test_rpc.py | tzoiker/aio-pika | 0 | 14228 | <reponame>tzoiker/aio-pika
import asyncio
import logging
import pytest
from aio_pika import Message, connect_robust
from aio_pika.exceptions import DeliveryError
from aio_pika.patterns.rpc import RPC, log as rpc_logger
from tests import AMQP_URL
from tests.test_amqp import BaseTestCase
pytestmark = pytest.mark.asyncio
def rpc_func(*, foo, bar):
assert not foo
assert not bar
return {'foo': 'bar'}
class TestCase(BaseTestCase):
async def test_simple(self):
channel = await self.create_channel()
rpc = await RPC.create(channel, auto_delete=True)
await rpc.register('test.rpc', rpc_func, auto_delete=True)
result = await rpc.proxy.test.rpc(foo=None, bar=None)
self.assertDictEqual(result, {'foo': 'bar'})
await rpc.unregister(rpc_func)
await rpc.close()
# Close already closed
await rpc.close()
async def test_error(self):
channel = await self.create_channel()
rpc = await RPC.create(channel, auto_delete=True)
await rpc.register('test.rpc', rpc_func, auto_delete=True)
with pytest.raises(AssertionError):
await rpc.proxy.test.rpc(foo=True, bar=None)
await rpc.unregister(rpc_func)
await rpc.close()
async def test_unroutable(self):
channel = await self.create_channel()
rpc = await RPC.create(channel, auto_delete=True)
await rpc.register('test.rpc', rpc_func, auto_delete=True)
with pytest.raises(DeliveryError):
await rpc.proxy.unroutable()
await rpc.unregister(rpc_func)
await rpc.close()
async def test_timed_out(self):
channel = await self.create_channel()
rpc = await RPC.create(channel, auto_delete=True)
await rpc.register('test.rpc', rpc_func, auto_delete=True)
await channel.declare_queue(
'test.timed_out', auto_delete=True, arguments={
'x-dead-letter-exchange': RPC.DLX_NAME,
}
)
with pytest.raises(asyncio.TimeoutError):
await rpc.call('test.timed_out', expiration=1)
await rpc.unregister(rpc_func)
await rpc.close()
async def test_close_twice(self):
channel = await self.create_channel()
rpc = await RPC.create(channel, auto_delete=True)
await rpc.close()
await rpc.close()
async def test_init_twice(self):
channel = await self.create_channel()
rpc = await RPC.create(channel, auto_delete=True)
await rpc.initialize()
await rpc.close()
async def test_send_unknown_message(self):
channel = await self.create_channel()
rpc = await RPC.create(channel, auto_delete=True)
with self.assertLogs(rpc_logger, logging.WARNING) as capture:
await channel.default_exchange.publish(
Message(b''), routing_key=rpc.result_queue.name
)
await asyncio.sleep(0.5, loop=self.loop)
self.assertIn('Unknown message: ', capture.output[0])
with self.assertLogs(rpc_logger, logging.WARNING) as capture:
await channel.default_exchange.publish(
Message(b''), routing_key='should-returned'
)
await asyncio.sleep(0.5, loop=self.loop)
self.assertIn('Unknown message was returned: ', capture.output[0])
await rpc.close()
async def test_close_cancelling(self):
channel = await self.create_channel()
rpc = await RPC.create(channel, auto_delete=True)
async def sleeper():
await asyncio.sleep(60, loop=self.loop)
method_name = self.get_random_name('test', 'sleeper')
await rpc.register(method_name, sleeper, auto_delete=True)
tasks = set()
for _ in range(10):
tasks.add(self.loop.create_task(rpc.call(method_name)))
await rpc.close()
logging.info("Waiting for results")
for task in tasks:
with pytest.raises(asyncio.CancelledError):
await task
async def test_register_twice(self):
channel = await self.create_channel()
rpc = await RPC.create(channel, auto_delete=True)
await rpc.register('test.sleeper', lambda x: None, auto_delete=True)
with pytest.raises(RuntimeError):
await rpc.register(
'test.sleeper', lambda x: None, auto_delete=True
)
await rpc.register(
'test.one', rpc_func, auto_delete=True
)
with pytest.raises(RuntimeError):
await rpc.register(
'test.two', rpc_func, auto_delete=True
)
await rpc.unregister(rpc_func)
await rpc.unregister(rpc_func)
await rpc.close()
class TestCaseRobust(TestCase):
async def create_connection(self, cleanup=True):
client = await connect_robust(str(AMQP_URL), loop=self.loop)
if cleanup:
self.addCleanup(client.close)
return client
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server/app/views.py | Bryan-0/Python-Web-Interpreter | 0 | 1616639 | <filename>server/app/views.py<gh_stars>0
from app import app, datetime, request, Path, subprocess, os, uuid4
'''
@app.route('/')
def index():
return app.send_static_file('index.html')
'''
@app.route('/api/python/interprete', methods=['POST'])
def get_python_code():
code = request.json['pyCode']
fixed_Code = clean_Code(code)
print(fixed_Code)
return run_Code(fixed_Code)
@app.route('/api/python/remove', methods=['POST'])
def remove_python_file():
file_to_delete = request.json['filename']
os.remove(file_to_delete)
return {'operation': 'done'}
# Utility
def clean_Code(code) -> str:
return str(code).replace("</div>", "\n").replace("<div>", "\n").replace("<br>", "\n").replace(">", ">").replace("<", "<")
def run_Code(code) -> dict:
tempfile = str(uuid4()) + ".py"
with open(tempfile, 'w') as f:
f.write(code)
cmd = subprocess.run(["python", tempfile], capture_output=True)
output = cmd.stdout.decode()
return {'output': output, 'filename': tempfile} | [
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allennlp/commands/test_install.py | MaxMotovilov/allennlp | 21 | 129028 | <filename>allennlp/commands/test_install.py
"""
The ``test-install`` subcommand verifies
an installation by running the unit tests.
.. code-block:: bash
$ allennlp test-install --help
usage: allennlp test-install [-h] [--run-all]
[--include-package INCLUDE_PACKAGE]
Test that installation works by running the unit tests.
optional arguments:
-h, --help show this help message and exit
--run-all By default, we skip tests that are slow or download
large files. This flag will run all tests.
--include-package INCLUDE_PACKAGE
additional packages to include
"""
import argparse
import logging
import os
import pathlib
import pytest
import allennlp
from allennlp.commands.subcommand import Subcommand
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
class TestInstall(Subcommand):
def add_subparser(self, name: str, parser: argparse._SubParsersAction) -> argparse.ArgumentParser:
# pylint: disable=protected-access
description = '''Test that installation works by running the unit tests.'''
subparser = parser.add_parser(
name, description=description, help='Run the unit tests.')
subparser.add_argument('--run-all', action="store_true",
help="By default, we skip tests that are slow "
"or download large files. This flag will run all tests.")
subparser.set_defaults(func=_run_test)
return subparser
def _get_module_root():
return pathlib.Path(allennlp.__file__).parent
def _run_test(args: argparse.Namespace):
initial_working_dir = os.getcwd()
module_root = _get_module_root()
logger.info("Changing directory to %s", module_root)
os.chdir(module_root)
test_dir = os.path.join(module_root, "tests")
logger.info("Running tests at %s", test_dir)
if args.run_all:
# TODO(nfliu): remove this when notebooks have been rewritten as markdown.
pytest.main([test_dir, '-k', 'not notebooks_test'])
else:
pytest.main([test_dir, '-k', 'not sniff_test and not notebooks_test'])
# Change back to original working directory after running tests
os.chdir(initial_working_dir)
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better-bus-buffers/BBB_Polygons_Step1.py | d-wasserman/public-transit-tools | 130 | 83686 | <reponame>d-wasserman/public-transit-tools<filename>better-bus-buffers/BBB_Polygons_Step1.py
####################################################
## Tool name: BetterBusBuffers
## Created by: <NAME>, Esri, <EMAIL>
## Last updated: 5 December 2017
####################################################
''' BetterBusBuffers Polygon Tool: Step 1 - Preprocess Buffers
BetterBusBuffers provides a quantitative measure of access to public transit
in your city. It creates buffers around the transit stops and weights them by
the number of trips that pass that stop during the time window you select,
accounting for areas served by more than one stop.
Output can be shown as the total number of trips or the average number of trips
per hour during the time window. You can use the symbology settings of the
resulting feature class to highlight the frequency of service in different
areas of town. Note that the tool tells you nothing about the destination of
the buses that pass by the stops, only how many of them there are.
BetterBusBuffers uses GTFS public transit data and ArcGIS Network Analyst.
Step 1 does the following:
- Creates service areas around your transit stops
- Runs some post-processing on those service areas to prepare them for further
analysis
You should only have to run Step 1 once for the geography and buffer size you
are analyzing. Step 1 will take a while to run for larger transit systems.
'''
################################################################################
'''Copyright 2017 Esri
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 os, sqlite3
from shutil import copyfile
import arcpy
import BBB_SharedFunctions
def runTool(outDir, outGDB, inSQLDbase, inNetworkDataset, imp, BufferSize, restrictions, TrimSettings):
try:
# ----- Set up the run -----
try:
BBB_SharedFunctions.CheckArcVersion(min_version_pro="1.2")
BBB_SharedFunctions.CheckArcInfoLicense()
BBB_SharedFunctions.CheckOutNALicense()
BBB_SharedFunctions.CheckWorkspace()
# It's okay to overwrite in-memory stuff.
OverwriteOutput = arcpy.env.overwriteOutput # Get the orignal value so we can reset it.
arcpy.env.overwriteOutput = True
# Append .gdb to geodatabase name.
if not outGDB.lower().endswith(".gdb"):
outGDB += ".gdb"
outGDBwPath = os.path.join(outDir, outGDB)
# Create a file geodatabase for the results.
arcpy.management.CreateFileGDB(outDir, outGDB)
# Make a copy of the input SQL file in the Step 1 output so we can modify it.
SQLDbase = os.path.join(outGDBwPath, "Step1_GTFS.sql")
copyfile(inSQLDbase, SQLDbase)
# Connect to or create the SQL file.
conn = sqlite3.connect(SQLDbase)
c = BBB_SharedFunctions.c = conn.cursor()
impedanceAttribute = BBB_SharedFunctions.CleanUpImpedance(imp)
TrimPolys, TrimPolysValue = BBB_SharedFunctions.CleanUpTrimSettings(TrimSettings)
except:
arcpy.AddError("Error setting up run.")
raise
#----- Make a feature class of GTFS stops that we can use for buffers -----
try:
# Create a feature class of transit stops
arcpy.AddMessage("Creating a feature class of GTFS stops...")
StopsLayer, StopIDList = BBB_SharedFunctions.MakeStopsFeatureClass(os.path.join(outGDBwPath, "Step1_Stops"))
except:
arcpy.AddError("Error creating a feature class of GTFS stops.")
raise
#----- Create Service Areas around all stops in the system -----
try:
arcpy.AddMessage("Creating service areas around stops...")
arcpy.AddMessage("(This step will take a while for large networks.)")
polygons = BBB_SharedFunctions.MakeServiceAreasAroundStops(StopsLayer,
inNetworkDataset, impedanceAttribute, BufferSize,
restrictions, TrimPolys, TrimPolysValue)
except:
arcpy.AddError("Error creating service areas around stops.")
raise
#----- Post-process the polygons to prepare for Step 2 -----
try:
arcpy.AddMessage("Reformatting polygons for further analysis...")
arcpy.AddMessage("(This step will take a while for large networks.)")
# ----- Flatten the overlapping service area polygons -----
# Use World Cylindrical Equal Area (WKID 54034) to ensure proper use of cluster tolerance in meters
arcpy.env.outputCoordinateSystem = BBB_SharedFunctions.WorldCylindrical
# Flatten the overlapping polygons. This will ultimately be our output.
# Dummy points to use in FeatureToPolygon to get rid of unnecessary fields.
dummypoints = arcpy.management.CreateFeatureclass("in_memory",
"DummyPoints", "POINT")
# The flattened polygons will be our ultimate output in the end (final
# output of step 2).
FlatPolys = os.path.join(outGDBwPath, "Step1_FlatPolys")
# FeatureToPolygon flattens overalpping polys.
# Set a large cluster tolerance to eliminate small sliver polygons and to
# keep the output file size down. Boundaries may move up to the distance
# specified in the cluster tolerance, but some amount of movement is
# acceptable, as service area polygons are inexact anyway.
# The large cluster tolerance may cause some geometry issues with the output
# later, but this is the best solution I've found so far that doesn't eat
# up too much analysis time and memory
clusTol = "5 meters"
arcpy.management.FeatureToPolygon(polygons, FlatPolys, clusTol, "", dummypoints)
arcpy.management.Delete(dummypoints)
# Add a field to the output file for number of trips and num trips / hour.
# Also create a polygon id field so we can keep track of them.
arcpy.management.AddField(FlatPolys, "PolyID", "LONG")
arcpy.management.AddField(FlatPolys, "NumTrips", "LONG")
arcpy.management.AddField(FlatPolys, "NumTripsPerHr", "DOUBLE")
arcpy.management.AddField(FlatPolys, "NumStopsInRange", "LONG")
arcpy.management.AddField(FlatPolys, "MaxWaitTime", "DOUBLE")
# ----- Create stacked points, one for each original SA polygon -----
# Create points for use in the Identity tool (one point per poly)
FlattenedPoints = os.path.join(outGDBwPath, "Step1_FlattenedPoints")
arcpy.management.FeatureToPoint(FlatPolys, FlattenedPoints, "INSIDE")
# Use Identity to stack points and keep the stop_ids from the original SAs.
# Results in a points layer with fields ORIG_FID for the IDs of the
# flattened polygons and a stop_id column with the stop ids.
# Points are stacked, and each has only one stop_id.
StackedPoints = os.path.join(outGDBwPath, "Step1_StackedPoints")
arcpy.analysis.Identity(FlattenedPoints, polygons, StackedPoints)
arcpy.management.Delete(FlattenedPoints)
# ----- Read the Stacked Points into an SQL table -----
# Create a SQL table associating the Polygon FID with the stop_ids that serve it.
c.execute("DROP TABLE IF EXISTS StackedPoints;")
schema = "Polygon_FID LONG, stop_id TEXT"
create_stmt = "CREATE TABLE StackedPoints (%s);" % schema
c.execute(create_stmt)
# Add data to the table. Track Polygon IDs with no associated stop_ids so we can delete them.
FIDsToDelete = []
AddToStackedPts = []
with arcpy.da.SearchCursor(StackedPoints, ["ORIG_FID", "stop_id"]) as StackedPtCursor:
for row in StackedPtCursor:
if not row[1]:
FIDsToDelete.append(row[0])
else:
AddToStackedPts.append((row[0], row[1],))
# Add the OD items to the SQL table
c.executemany('''INSERT INTO StackedPoints \
(Polygon_FID, stop_id) \
VALUES (?, ?);''', AddToStackedPts)
conn.commit()
arcpy.management.Delete(StackedPoints)
FIDsToDelete = set(FIDsToDelete)
# ----- Delete polygons not associated with any stop_ids -----
# These were generated by the FeatureToPolygon tool in areas completely
# surrounded by other polygons and aren't associated with any stops.
# Make feature layer containing only the polygons we want to delete.
desc2 = arcpy.Describe(FlatPolys)
OutputOIDName = desc2.OIDFieldName
# Anything with 0 area will just cause problems later.
WhereClause = '"Shape_Area" = 0'
if FIDsToDelete:
WhereClause += ' OR "' + OutputOIDName + '" IN ('
for FID in FIDsToDelete:
WhereClause += str(FID) + ", "
WhereClause = WhereClause[:-2] + ")"
arcpy.management.MakeFeatureLayer(FlatPolys, "FlatPolysLayer", WhereClause)
# Delete the polygons that don't correspond to any stop_ids.
arcpy.management.DeleteFeatures("FlatPolysLayer")
# ----- Populate the PolyID field -----
# Set PolyID equal to the OID.
expression = "!" + OutputOIDName + "!"
arcpy.management.CalculateField(FlatPolys, "PolyID", expression, "PYTHON")
except:
arcpy.AddError("Error post-processing polygons")
raise
arcpy.AddMessage("Done!")
arcpy.AddMessage("Files written to output geodatabase " + outGDBwPath + ":")
arcpy.AddMessage("- Step1_Stops")
arcpy.AddMessage("- Step1_FlatPolys")
arcpy.AddMessage("- Step1_GTFS.sql")
# Tell the tool that this is output. This will add the output to the map.
arcpy.SetParameterAsText(8, os.path.join(outGDBwPath, "Step1_Stops"))
arcpy.SetParameterAsText(9, os.path.join(outGDBwPath, "Step1_FlatPolys"))
arcpy.SetParameterAsText(10, os.path.join(outGDBwPath, "Step1_GTFS.sql"))
except BBB_SharedFunctions.CustomError:
arcpy.AddError("Failed to create BetterBusBuffers polygons.")
pass
except:
arcpy.AddError("Failed to create BetterBusBuffers polygons.")
raise
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notebooks/project_jupyter_notebooks/RINGO_T1.3/modules/STILT_modules_plus.py | ICOS-Carbon-Portal/jupyter | 6 | 147861 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import os
import numpy as np
import pandas as pd
import datetime as dt
import netCDF4 as cdf
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import cartopy
cartopy.config['data_dir'] = '/data/project/cartopy/'
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.feature import NaturalEarthFeature, LAND, COASTLINE, LAKES
import json
import requests
#Import ICOS tools:
from icoscp.cpb.dobj import Dobj
from icoscp.sparql.runsparql import RunSparql
from icoscp.sparql import sparqls
my_home = os.getenv('HOME')
sys.path.insert(0,my_home+'/new_jupyter/modules')
from extra_sparqls import get_station_class, get_icos_stations_atc_samplingheight #, atc_station_tracer_query
# paths --- changed for new JupyterHub instance
path_stiltweb = '/data/stiltweb/'
path_stilt = '/data/stilt/'
path_edgar = '/data/stilt/Emissions/'
#path_plots = './plots/'
#------------------------------------------------------------------------------------------------------------------
def read_icos_data(f_icos,tracer,flag=True):
if (len(f_icos)>0):
df = Dobj(f_icos.dobj.iloc[0]).getColumns()
df.set_index('TIMESTAMP',inplace=True)
if flag:
if (tracer.lower() == 'mto'):
value_cols =[x.replace('-Flag','') for x in df.columns[df.columns.str.contains('Flag')]]
for x in value_cols:
df[x].loc[df[x+'-Flag']!='O']=np.nan
else:
df[tracer.lower()].loc[df['Flag']!='O']=np.nan
df['Stdev'].loc[df['Flag']!='O']=np.nan
else:
if (tracer.lower() != 'mto'):
df[tracer.lower()].loc[df[tracer.lower()]<0.0]=np.nan
df['Stdev'].loc[df['Stdev']<0.0]=np.nan
else:
df = pd.DataFrame(None)
return df
#------------------------------------------------------------------------------------
# function to convert station longitude and latitude (slat, slon) to indices of STILT model grid (ix,jy)
def lonlat_2_ixjy(slon,slat,mlon,mlat):
#slon, slat: longitude and latitude of station
#mlon, mlat: 1-dim. longitude and latitude of model grid
ix = (np.abs(mlon-slon)).argmin()
jy = (np.abs(mlat-slat)).argmin()
return ix,jy
#------------------------------------------------------------------------------------
# function to read annual mean EDGAR emissions
def read_emissions(filename):
# read EDAGR anthropogenic emissions
# latitude and longitude are for lower left corner of grid cell
f = cdf.Dataset(filename)
#print(f)
emis=f.variables["emission"][:,:,:]
lon_ll=f.variables["lon"][:]
lat_ll=f.variables["lat"][:]
time=f.variables["time"][:]
#shift lat and lon to cell center
dlon=np.abs(lon_ll[2]-lon_ll[1])
dlat=np.abs(lat_ll[2]-lat_ll[1])
emis_lon=lon_ll+0.5*dlon
emis_lat=lat_ll+0.5*dlat
unit="["+f.variables["emission"].units+"]"
datetime=cdf.num2date(f.variables["time"][:],units=f.variables["time"].units)
f.close()
return emis, emis_lon, emis_lat, datetime, unit
#------------------------------------------------------------------------------------
def create_STILT_dictionary():
# store all STILT station information in a dictionary
#
# Dictionary contains information on
# STILT station id
# Station coordinates (latitude, longitude)
# Altitude of tracer release in STILT simultation
# STILT location identifier
# Station name - if available
#
# get all ICOS station IDs by listing subdirectories in stiltweb
# extract location from filename of link
path_stations = path_stiltweb+'/stations/'
all_stations = os.listdir(path_stations)
# empty dictionary
stations = {}
# fill dictionary with ICOS station id, latitude, longitude and altitude
for ist in sorted(list(set(all_stations))):
stations[ist] = {}
# get filename of link (original stiltweb directory structure)
# and extract location information
if os.path.exists(path_stations+ist):
loc_ident = os.readlink(path_stations+ist)
clon = loc_ident[-13:-6]
lon = np.float(clon[:-1])
if clon[-1:] == 'W':
lon = -lon
clat = loc_ident[-20:-14]
lat = np.float(clat[:-1])
if clat[-1:] == 'S':
lat = -lat
alt = np.int(loc_ident[-5:])
stations[ist]['lat']=lat
stations[ist]['lon']=lon
stations[ist]['alt']=alt
stations[ist]['locIdent']=os.path.split(loc_ident)[-1]
# add information on station name (and new STILT station id) from stations.csv file used in stiltweb
url="https://stilt.icos-cp.eu/viewer/stationinfo"
df = pd.read_csv(url)
for ist in sorted(list(set(stations))):
stationName = df.loc[df['STILT id'] == ist]['STILT name']
if len(stationName.value_counts()) > 0:
stations[ist]['name'] = stationName.item()
else:
stations[ist]['name'] = ''
# Get list of ICOS class 1 and class 2 stations from Carbon Portal
#query = sparqls.get_station_class()
query = get_station_class()
fmt = 'pandas'
df_datatable = RunSparql(query, fmt).run()
# add information if ICOS class 1 or class 2 site
# add ICOS station name
for ist in sorted(list(set(stations))):
stations[ist]['stationClass'] = np.nan
stations[ist]['icosName'] = ''
stations[ist]['icosId'] = ''
stations[ist]['icosLon'] = np.nan
stations[ist]['icosLat'] = np.nan
for istICOS in df_datatable['stationId']:
ic = int(df_datatable[df_datatable['stationId']==istICOS].index.values)
if istICOS in ist:
stations[ist]['stationClass'] = int(df_datatable['stationClass'][ic])
stations[ist]['icosName'] = df_datatable['longName'][ic]
stations[ist]['icosId'] = df_datatable['stationId'][ic]
stations[ist]['icosLon'] = float(df_datatable['lon'][ic])
stations[ist]['icosLat'] = float(df_datatable['lat'][ic])
# add available sampling heights
# find all co2 data files for station
query = get_icos_stations_atc_samplingheight()
fmt = 'pandas'
df_datatable_hgt = RunSparql(query, fmt).run()
for ist in sorted(list(set(stations))):
stations[ist]['icosHeight'] = np.nan
if (len(stations[ist]['icosName']) > 0):
heights = df_datatable_hgt[df_datatable_hgt['stationId']==ist[0:3]]['height'].unique()
ih = np.nan
if (len(heights) > 0):
if (len(ist[3:])==0):
ih = int(float(heights[0]))
else:
ih = [int(float(el)) for el in heights if (abs(int(float(el))-int(ist[3:]))<6)]
if isinstance(ih, list):
if (len(ih) == 0):
stations[ist]['icosHeight'] = np.nan
else:
stations[ist]['icosHeight'] = ih[0]
else:
stations[ist]['icosHeight'] = ih
# print dictionary
#for ist in sorted(stations):
# print ('station:', ist)
# for k in stations[ist]:
# print (k,':', stations[ist][k])
# write dictionary to json file for further use
if os.access('./', os.W_OK):
with open("stationsDict", "w") as outfile:
json.dump(stations, outfile)
return stations
#---------------------------------------------------------------------------------------------
def read_STILT_dictionary():
# read STILT station dictionary from json file
if os.path.exists("stationsDict"):
with open("stationsDict") as jsonfile:
stations = json.load(jsonfile)
else:
print("no STILT station dictionary found")
stations={}
return stations
#------------------------------------------------------------------------------------------------
def print_STILT_dictionary(stilt_stations):
if not stilt_stations:
# read STILT station dictionary from json file
stilt_stations = read_STILT_dictionary()
# print dictionary
for ist in sorted(stilt_stations):
print ('station:', ist)
for k in stilt_stations[ist]:
print (k,':', stilt_stations[ist][k])
print(' ')
#------------------------------------------------------------------------------
# function to read STILT time series with hourly concentrations for RINGO T1.3
def read_stilt_timeseries_RINGO_T13(station,year,loc_ident):
filename=path_stilt+'Results_RINGO_T1.3_test/'+station+'/stiltresult'+str(year)+'x'+loc_ident+'.csv'
df= pd.read_csv(filename,delim_whitespace=True)
df.date = pd.to_datetime(df[['year', 'month', 'day', 'hour']])
df.name = station
df.model = 'STILT'
df['wind.speed']=np.sqrt((df['wind.u']**2)+(df['wind.v']**2))
df['co2.ff']=df['co2.fuel.coal']+df['co2.fuel.oil']+df['co2.fuel.gas']
df.set_index(['date'],inplace=True)
#print(df)
return df
#------------------------------------------------------------------------------
# function to read STILT time series with hourly concentrations for RINGO T1.3, updated with more STILT runs
def read_stilt_timeseries_RINGO_T13_update(station,year,loc_ident):
filename=path_stilt+'Results_RINGO_T1.3_test/'+station+'/stiltresult'+str(year)+'x'+loc_ident+'.csv'
df= pd.read_csv(filename,delim_whitespace=True)
df.date = pd.to_datetime(df[['year', 'month', 'day', 'hour']])
df.name = station
df.model = 'STILT'
df['wind.speed']=np.sqrt((df['wind.u']**2)+(df['wind.v']**2))
df['co2.ff']=df['co2.fuel.coal']+df['co2.fuel.oil']+df['co2.fuel.gas']
df.set_index(['date'],inplace=True)
#print(df)
return df | [
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bring_in_stt.py | ICTD-IITD/Voice_App_Automated_QnA | 1 | 151454 | <filename>bring_in_stt.py
import pandas as pd
from bs4 import BeautifulSoup
import requests
def get_unique_index(url):
if url.endswith('.mp3'):
return url
r = requests.get(url)
soup = BeautifulSoup(r.content,'html.parser')
links = soup.findAll('a')
mp3_links = [link['href'] for link in links if link['href'].endswith('mp3')]
if len(mp3_links) == 0:
print(url)
return url
link=mp3_links[0]
return 'http://voice.gramvaani.org' + link
df_stt = pd.read_excel('data/Q_A_STT_Transcribed.xlsx', sheet_name=0)
df = pd.read_excel('data/jeevika_final.xlsx', sheet_name=0).fillna('')
df_stt2 = pd.read_excel('data/jeevika_stt.xlsx', sheet_name=0)
df['STT Transcript'] = df['Caller query']
need_stt = []
for i,r in df.iterrows():
df_ = df_stt[df_stt['Caller query'] == r['Caller query']]
df_2 = df_stt2[(df_stt2['Public Link'] == r['Caller query'])|(df_stt2['Recording audio link'] == r['Caller query'])]
if len(df_) > 0:
stt = list(df_['STT Transcript'])[0]
df.loc[i, 'STT Transcript'] = stt
elif len(df_2) > 0:
stt = list(df_2['ML transcript'])[0]
df.loc[i, 'STT Transcript'] = stt
elif df.loc[i, 'Caller query']!='':
df.loc[i, 'STT Transcript'] = df.loc[i, 'Caller query transcription']
need_stt.append(get_unique_index(df.loc[i, 'Caller query']))
df.to_excel('data/jeevika_final_stt.xlsx', index = False)
df_ = pd.DataFrame({'Caller query': need_stt})
df_.to_excel('data/need_jee_stt.xlsx', index = False)
df_stt = pd.read_excel('data/paj_stt.xlsx', sheet_name=0)
df = pd.read_excel('data/paj_final.xlsx', sheet_name=0).fillna('')
df['STT Transcript'] = df['Caller query']
need_stt = []
for i,r in df.iterrows():
df_ = df_stt[(df_stt['Public Link'] == r['Caller query'])|(df_stt['Recording audio link'] == r['Caller query'])]
if len(df_) > 0:
stt = list(df_['ML transcript'])[0]
df.loc[i, 'STT Transcript'] = stt
elif df.loc[i, 'Caller query']!='':
df.loc[i, 'STT Transcript'] = df.loc[i, 'Caller query transcription']
need_stt.append(get_unique_index(df.loc[i, 'Caller query']))
df.to_excel('data/paj_final_stt.xlsx', index = False)
df_ = pd.DataFrame({'Caller query': need_stt})
df_.to_excel('data/need_stt_paj.xlsx', index = False)
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scripts/data_extract.py | amichalski2/WBC-SHAP | 0 | 9760 | import os
import cv2
import random
import numpy as np
from tensorflow.keras.utils import to_categorical
from scripts.consts import class_dict
def get_data(path, split=0.2):
X, y = [], []
for directory in os.listdir(path):
dirpath = os.path.join(path, directory)
print(directory, len(os.listdir(dirpath)))
for file in os.listdir(dirpath):
filepath = os.path.join(dirpath, file)
img = cv2.imread(filepath, cv2.IMREAD_UNCHANGED)
if img.shape != (360, 363, 3):
img = cv2.resize(img, (360, 363), cv2.INTER_CUBIC)
X.append(img)
y.append(class_dict[directory])
data = list(zip(X, y))
random.shuffle(data)
X, y = zip(*data)
num_train = int((1.0 - split) * len(y))
X_train, X_valid = np.array(X[:num_train]).astype(
'float32'), np.array(X[num_train:]).astype('float32')
y_train, y_valid = np.array(
y[:num_train]).reshape(-1, 1), np.array(y[num_train:]).reshape((-1, 1))
X_train = X_train / 255.0
X_valid = X_valid / 255.0
y_train, y_valid = to_categorical(y_train), to_categorical(y_valid)
print(X_train.shape, y_train.shape)
print(X_valid.shape, y_valid.shape)
return X_train, y_train, X_valid, y_valid
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] |
tools/train_net.py | achalddave/maskrcnn-benchmark | 6 | 188448 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
r"""
Basic training script for PyTorch
"""
# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:skip
import argparse
import logging
import os
import uuid
from pathlib import Path
from script_utils.common import common_setup
from script_utils.log import add_time_to_path
import torch
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.data import make_data_loader
from maskrcnn_benchmark.engine.inference import inference
from maskrcnn_benchmark.engine.trainer import do_train
from maskrcnn_benchmark.modeling.detector import build_detection_model
from maskrcnn_benchmark.solver import make_lr_scheduler
from maskrcnn_benchmark.solver import make_optimizer
from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer
from maskrcnn_benchmark.utils.collect_env import collect_env_info
from maskrcnn_benchmark.utils.comm import synchronize, get_rank
from maskrcnn_benchmark.utils.imports import import_file
from maskrcnn_benchmark.utils.metric_logger import (
MetricLogger, TensorboardLogger)
from maskrcnn_benchmark.utils.miscellaneous import mkdir, save_config
# See if we can use apex.DistributedDataParallel instead of the torch default,
# and enable mixed-precision via apex.amp
try:
from apex import amp
except ImportError:
raise ImportError('Use APEX for multi-precision via apex.amp')
def _safe_int(x):
if isinstance(x, float):
assert x.is_integer(), ('%s is not an integer.' % x)
return int(x)
elif isinstance(x, int):
return int(x)
else:
raise ValueError('Unknown type: %s (%s)' % (x, type(x)))
def merge_keys(cfg, opts_list, keys):
"""Update specific keys in cfg if they are in opts_list.
Additionally removes updates corresponding to the keys from opts_list."""
selected_updates = []
for key in keys:
try:
key_index = opts_list[0::2].index(key) * 2
except ValueError: # key not found
continue
value = opts_list[key_index + 1]
del opts_list[key_index:key_index + 2]
logging.info('Updating %s to %s in merge_keys' % (key, value))
selected_updates.extend([key, value])
if selected_updates:
cfg.merge_from_list(selected_updates)
def train(cfg, local_rank, distributed, use_tensorboard=False):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
# Initialize mixed-precision training
use_mixed_precision = cfg.DTYPE == "float16"
amp_opt_level = 'O1' if use_mixed_precision else 'O0'
model, optimizer = amp.initialize(model, optimizer, opt_level=amp_opt_level)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
# load_scheduler_only_epoch will prefer the scheduler specified in the
# config rather than the one in the checkpoint, and will load only the
# last_epoch from the checkpoint.
extra_checkpoint_data = checkpointer.load(
cfg.MODEL.WEIGHT,
load_model_only=cfg.MODEL.LOAD_ONLY_WEIGHTS,
load_scheduler_only_epoch=True)
if not cfg.MODEL.LOAD_ONLY_WEIGHTS:
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
if use_tensorboard:
meters = TensorboardLogger(
log_dir=output_dir,
exp_name=cfg.TENSORBOARD_EXP_NAME,
start_iter=arguments['iteration'],
delimiter=" ")
else:
meters = MetricLogger(delimiter=" ")
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
meters
)
return model
def run_test(cfg, model, distributed):
if distributed:
model = model.module
torch.cuda.empty_cache() # TODO check if it helps
iou_types = ("bbox",)
if cfg.MODEL.MASK_ON:
iou_types = iou_types + ("segm",)
if cfg.MODEL.KEYPOINT_ON:
iou_types = iou_types + ("keypoints",)
output_folders = [None] * len(cfg.DATASETS.TEST)
dataset_names = cfg.DATASETS.TEST
if cfg.OUTPUT_DIR:
for idx, dataset_name in enumerate(dataset_names):
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
mkdir(output_folder)
output_folders[idx] = output_folder
data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed)
for output_folder, dataset_name, data_loader_val in zip(output_folders, dataset_names, data_loaders_val):
inference(
model,
data_loader_val,
dataset_name=dataset_name,
iou_types=iou_types,
box_only=False if cfg.MODEL.RETINANET_ON else cfg.MODEL.RPN_ONLY,
device=cfg.MODEL.DEVICE,
expected_results=cfg.TEST.EXPECTED_RESULTS,
expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
output_folder=output_folder,
)
synchronize()
def main():
parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")
parser.add_argument(
"--config-file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument(
'--reduce-batch',
type=int,
help=('Divide IMS_PER_BATCH by this amount. This appropriately '
'updates the learning rate, number of iterations, and so on.'))
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument(
"--skip-test",
dest="skip_test",
help="Do not test the final model",
action="store_true",
)
parser.add_argument(
"--use-tensorboard",
dest="use_tensorboard",
help="Use tensorboardX logger (Requires tensorboardX installed)",
action="store_true",
default=False
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
# Generate a unique experiment ID for this run.
# Note: uuid generation relies on os.urandom, so it is not affected by,
# e.g., random.seed.
experiment_id = uuid.uuid4()
num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
args.distributed = num_gpus > 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(
backend="nccl", init_method="env://"
)
synchronize()
cfg.merge_from_file(args.config_file)
# We want to get the OUTPUT_DIR from the args immediately, if it exists, so
# we can setup logging. We will merge the rest of the config in a few
# lines.
merge_keys(cfg, args.opts, ['OUTPUT_DIR'])
output_dir = cfg.OUTPUT_DIR
if output_dir:
mkdir(output_dir)
if get_rank() == 0:
file_logger = common_setup(__file__, output_dir, args)
else:
file_logger = common_setup(
__file__ + '-worker%s' % get_rank(),
output_dir,
args,
log_console_level=logging.CRITICAL,
save_git_state=False)
# Automatically handle config changes as required by
# https://github.com/facebookresearch/maskrcnn-benchmark/tree/327bc29bcc4924e35bd61c59877d5a1d25bb75af#single-gpu-training
if args.reduce_batch:
# Update using --opts first, then override.
merge_keys(cfg, args.opts, [
'SOLVER.IMS_PER_BATCH', 'SOLVER.BASE_LR', 'SOLVER.MAX_ITER',
'SOLVER.STEPS', 'SOLVER.CHECKPOINT_PERIOD'
])
assert num_gpus in (1, 2, 4)
scale = args.reduce_batch
logging.info('Updating config for # GPUs = %s', num_gpus)
def update_config(key, new_value):
key_list = key.split('.')
d = cfg
for subkey in key_list[:-1]:
d = cfg[subkey]
subkey = key_list[-1]
old_value = d[subkey]
logging.info('Updating cfg.%s: %s -> %s', key, old_value,
new_value)
d[subkey] = new_value
update_config('SOLVER.IMS_PER_BATCH',
_safe_int(cfg.SOLVER.IMS_PER_BATCH / scale))
update_config('SOLVER.BASE_LR', cfg.SOLVER.BASE_LR / scale)
update_config('SOLVER.MAX_ITER',
_safe_int(cfg.SOLVER.MAX_ITER * scale))
update_config('SOLVER.CHECKPOINT_PERIOD',
_safe_int(cfg.SOLVER.CHECKPOINT_PERIOD * scale))
update_config('SOLVER.STEPS',
tuple(_safe_int(x * scale) for x in cfg.SOLVER.STEPS))
logging.info('Updating config from arguments')
cfg.merge_from_list(args.opts)
cfg.freeze()
logger = logging.getLogger("maskrcnn_benchmark")
logger.info("Using {} GPUs".format(num_gpus))
logger.info(args)
logger.info("Collecting env info (might take some time)")
logger.info("\n" + collect_env_info())
logger.info("Loaded configuration file {}".format(args.config_file))
with open(args.config_file, "r") as cf:
config_str = "\n" + cf.read()
file_logger.info(config_str)
file_logger.info("Running with config:\n{}".format(cfg))
if get_rank() == 0:
config_output = add_time_to_path(Path(output_dir) / 'config.yaml')
with open(config_output, 'w') as f:
f.write(cfg.dump())
logging.info('Experiment id: %s', experiment_id)
with open(os.path.join(output_dir, 'experiment_id.txt'), 'w') as f:
f.write('%s\n' % experiment_id)
output_config_path = os.path.join(cfg.OUTPUT_DIR, 'config.yml')
logger.info("Saving config into: {}".format(output_config_path))
# save overloaded model config in the output directory
save_config(cfg, output_config_path)
model = train(cfg, args.local_rank, args.distributed, use_tensorboard=args.use_tensorboard)
if not args.skip_test:
run_test(cfg, model, args.distributed)
if __name__ == "__main__":
main()
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] |
tests/test_app.py | MrHenryD/serverless-lambda | 0 | 1602515 | <reponame>MrHenryD/serverless-lambda
from fastapi.testclient import TestClient
from main import app
def test_search__success(mock_response_api__jikan_v4_anime__success):
client = TestClient(app)
response = client.get("/anime", params=[("q", "some-text")])
assert response.status_code == 200
assert response.json() == {
"count": 2,
"results": [
{"title": "Title A", "url": "https://example.com/title-a"},
{"title": "Title B", "url": "https://example.com/title-b"}
],
}
def test_search__fail(mock_response_api__jikan_v4_anime__fail):
client = TestClient(app)
response = client.get("/anime", params=[("q", "some-text")])
assert response.status_code == 200
assert response.json() == {
"count": 0,
"results": [],
}
def test_search__invalid_parameters():
client = TestClient(app)
response = client.get("/anime", params=[("q", "")])
assert response.status_code == 422
| [
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] |
project_V1/core/models.py | arnabtarwani/UCD-MSc-Project | 1 | 85755 | <reponame>arnabtarwani/UCD-MSc-Project
from django.db import models
# Create your models here.
# model for following table
class following(models.Model):
class Meta:
unique_together=(("user_id","twitter_handle"),)
user_id=models.CharField("user_id",max_length=40)
twitter_handle=models.CharField("twitter_handle",max_length=40,default="<EMAIL>")
friend_Email=models.CharField("friend_Email", max_length=40)
url=models.CharField("url",max_length=100,default="")
isActive=models.BooleanField(default=False)
# model for tweets_data table
class tweets_data(models.Model):
twitter_handle=models.CharField("twitter_handle",max_length=40)
tweet_id=models.BigIntegerField("tweets_id",primary_key=True)
tweet_data=models.TextField("tweet_data",max_length=1000)
tweet_date=models.DateTimeField("date")
is_notification=models.BooleanField(default=False)
score=models.FloatField("score",default=0)
sum_score=models.IntegerField("sum_score",default=0)
counter=models.IntegerField("counter",default=0)
# model for notification_data table
class notification_data(models.Model):
twitter_handle=models.CharField("twitter_handle",max_length=40)
tweet_data=models.TextField("tweet_data",max_length=1000)
noti_date=models.DateTimeField("noti_date")
is_notified=models.BooleanField(default=False) | [
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Hitalent/UI/migrations/0001_initial.py | iliyas01/Django | 0 | 1613194 | # Generated by Django 4.0.3 on 2022-03-25 07:33
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Profile',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('first_name', models.CharField(max_length=100)),
('last_name', models.CharField(max_length=100)),
('avatar', models.ImageField(default='default.jpg', upload_to='avatars')),
('bio', models.TextField()),
('gender', models.CharField(choices=[('M', 'Male'), ('F', 'Female')], max_length=1)),
('educational_details', models.TextField()),
('git_profile', models.CharField(max_length=200)),
('phone', models.IntegerField(null=True)),
('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
]
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source/utils/augmentations.py | dovietchinh/multi-task-classification | 0 | 3490 | <reponame>dovietchinh/multi-task-classification<filename>source/utils/augmentations.py<gh_stars>0
import numpy as np
import cv2
import random
def preprocess(img,img_size,padding=True):
"""[summary]
Args:
img (np.ndarray): images
img_size (int,list,tuple): target size. eg: 224 , (224,224) or [224,224]
padding (bool): padding img before resize. Prevent from image distortion. Defaults to True.
Returns:
images (np.ndarray): images in target size
"""
if padding:
height,width,_ = img.shape
delta = height - width
if delta > 0:
img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255)
else:
img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255)
if isinstance(img_size,int):
img_size = (img_size,img_size)
return cv2.resize(img,img_size)
class RandAugment:
def __init__(self, augment_params):
self.num_layers = augment_params['num_layers']
self.AUGMENT_FUNCTION = {
'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None,
'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None,
'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None,
'solarize' : RandAugment.solarize if augment_params.get('solarize') else None,
'posterize': RandAugment.posterize if augment_params.get('posterize') else None,
'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None,
'invert' : RandAugment.invert if augment_params.get('invert') else None,
'contrast': RandAugment.contrast if augment_params.get('contrast') else None,
'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None,
'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None,
'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None,
'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None,
'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None,
'cutout' : RandAugment.cutout if augment_params.get('cutout') else None,
'rotate' : RandAugment.rotate if augment_params.get('rotate') else None,
'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None,
'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None,
'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None,
'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None,
# 'random_crop':random_crop
}
self.ARGS_LIMIT = {
'fliplr' : augment_params.get('fliplr'),
'augment_hsv': augment_params.get('augment_hsv'),
'hist_equalize' : augment_params.get('hist_equalize'),
'solarize' : augment_params.get('solarize'),
'posterize': augment_params.get('posterize'),
'adjust_brightness': augment_params.get('adjust_brightness'),
'invert' : augment_params.get('invert'),
'contrast': augment_params.get('contrast'),
'shearX' : augment_params.get('shearX'),
'shearY' : augment_params.get('shearY'),
'translateX' : augment_params.get('translateX'),
'translateY' : augment_params.get('translateY'),
'sharpness' : augment_params.get('sharpness'),
'cutout' : augment_params.get('cutout'),
'rotate' : augment_params.get('rotate'),
'cut_25_left' : augment_params.get('cut_25_left'),
'cut_25_right': augment_params.get('cut_25_right'),
'cut_25_above': augment_params.get('cut_25_above'),
'cut_25_under': augment_params.get('cut_25_under')
# 'random_crop':random_crop
}
self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if v)
# print(self.policy)
def mixup(img1,img2,factor):
img = img1.astype('float')* factor + img2.astype('float') * (1-factor)
img = np.clip(img, 0,255)
img = img.astype('uint8')
return img
def augment_fliplr(img,level):
if random.random() < level:
return np.fliplr(img)
return img
def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4):
im = im.copy()
# HSV color-space augmentation
if hgain or sgain or vgain:
r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains
hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))
dtype = im.dtype # uint8
x = np.arange(0, 256, dtype=r.dtype)
lut_hue = ((x * r[0]) % 180).astype(dtype)
lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed
return im_hsv
def hist_equalize(im, level=None,clahe=True, bgr=True):
im = im.copy()
# Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255
yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
if clahe:
c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
yuv[:, :, 0] = c.apply(yuv[:, :, 0])
else:
yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram
return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB
def solarize(image, level=128):
threshold = level
image = image.copy()
# For each pixel in the image, select the pixel
# if the value is less than the threshold.
# Otherwise, subtract 255 from the pixel.
return np.where(image <= threshold, image, 255 - image)
def posterize(img, level=3):
bits = level
shift = 8 - bits
# img = img >> shift
img = np.left_shift(img,shift)
img = np.right_shift(img,shift)
return img.astype('uint8')
def adjust_brightness(img,level=0.5):
factor = level
degenerate = np.zeros(img.shape,dtype='uint8')
img = RandAugment.mixup(img,degenerate,factor)
return img
def invert(img,level=None):
return 255-img
def contrast(img,factor=0.5):
degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR)
return RandAugment.mixup(img,degenerate,factor)
def shear_x(img,level=0.4,mode='reflect'):
M = np.array([[1, level, 0],
[0, 1 , 0],
[0, 0 , 1]],dtype='float')
height,width,_ = img.shape
option_mode ={
'reflect' : cv2.BORDER_REPLICATE,
'constant' : cv2.BORDER_CONSTANT
}
mode = option_mode[mode]
sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode)
return sheared_img
def shear_y(img,level=0.4,mode='reflect'):
M = np.array([[1, 0 , 0],
[level, 1 , 0],
[0, 0 , 1]],dtype='float')
height,width,_ = img.shape
option_mode ={
'reflect' : cv2.BORDER_REPLICATE,
'constant' : cv2.BORDER_CONSTANT
}
mode = option_mode[mode]
sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode)
return sheared_img
def translate_x(img,level,mode='reflect'):
height,width,_ = img.shape
option_mode ={
'reflect' : cv2.BORDER_REPLICATE,
'constant' : cv2.BORDER_CONSTANT
}
mode = option_mode[mode]
translate_pixel = int(width * level)
M = np.array([[1, 0 , translate_pixel],
[level, 1 , 0],
[0, 0 , 1]],dtype='float')
translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode)
return translate_img
def translate_y(img,level,mode='reflect'):
height,width,_ = img.shape
option_mode ={
'reflect' : cv2.BORDER_REPLICATE,
'constant' : cv2.BORDER_CONSTANT
}
mode = option_mode[mode]
translate_pixel = int(width * level)
M = np.array([[1, 0 , 0],
[level, 1 , translate_pixel],
[0, 0 , 1]],dtype='float')
translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode)
return translate_img
# def sharpness(img,):
# kernel = np.array(
# [[1, 1, 1],
# [1, 5, 1],
# [1, 1, 1]], dtype=tf.float32,
# shape=[3, 3, 1, 1]) / 13.
# cv2.
def cutout(img,level,**kwargs):
img = img.copy()
height,width ,_ = img.shape
padding_size = int(height*level),int(width*level)
value = kwargs.get('value')
cordinate_h = np.random.randint(0,height-padding_size[0])
cordinate_w = np.random.randint(0,width-padding_size[1])
img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255
return img
def rotate(image, level=45, center = None, scale = 1.0):
angle=level
(h, w) = image.shape[:2]
if center is None:
center = (w / 2, h / 2)
# Perform the rotation
M = cv2.getRotationMatrix2D(center, angle, scale)
rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE)
return rotated
def cut_25_under(img,level=0.25):
ratio = level
height,width,_ = img.shape
new_height = int((1-ratio)*height)
img_ = img[:new_height,:,:]
height,width,_ = img_.shape
if height > width :
img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255)
else:
img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255)
img2 = cv2.resize(img2,(224,224))
return img2
def cut_25_above(img,level=0.25):
ratio = level
height,width,_ = img.shape
new_height = int(ratio*height)
img_ = img[new_height:,:,:]
height,width,_ = img_.shape
if height > width :
img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255)
else:
img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255)
img2 = cv2.resize(img2,(224,224))
return img2
def cut_25_right(img,level=0.25):
ratio = level
height,width,_ = img.shape
new_width = int((1-ratio)*width)
img_ = img[:,:new_width,:]
height,width,_ = img_.shape
if height > width :
img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255)
else:
img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255)
img2 = cv2.resize(img2,(224,224))
return img2
def cut_25_left(img,level=0.25):
ratio = level
height,width,_ = img.shape
new_width = int(ratio*width)
img_ = img[:,new_width:,:]
height,width,_ = img_.shape
if height > width :
img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255)
else:
img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255)
img2 = cv2.resize(img2,(224,224))
return img2
def __call__(self,img):
augmenters = random.choices(self.policy, k=self.num_layers)
for augmenter in augmenters:
level = random.random()
# try:
min_arg,max_arg = self.ARGS_LIMIT[augmenter]
level = min_arg + (max_arg - min_arg) * level
img = self.AUGMENT_FUNCTION[augmenter](img,level=level)
# except:
# print(augmenter)
return img
def augmentation_test():
img_org = cv2.imread('test.jpg')
import yaml
augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params')
augmenter = RandAugment(augment_params=augment_params)#(num_layers=1)
for _ in range(10000):
img_aug = augmenter(img_org)
img_pad = preprocess(img_aug,224)
# cv2.imshow('a',img_org)
# cv2.imshow('b',img_aug)
# cv2.imshow('c',img_pad)
# if cv2.waitKey(0)==ord('q'):
# exit()
if __name__ =='__main__':
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packages/labs/webpack/index.bzl | benlesh/rules_nodejs | 1 | 1606960 | # Public API surface re-exports
# Users shouldn't import under src/
load("//webpack/src:webpack_bundle.bzl", _webpack_bundle = "webpack_bundle")
webpack_bundle = _webpack_bundle
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] |
python/MDSplus/tests/connectionUnitTest.py | WhoBrokeTheBuild/mdsplus | 0 | 159677 | <filename>python/MDSplus/tests/connectionUnitTest.py
#!/usr/bin/env python
# Copyright (c) 2017, Massachusetts Institute of Technology All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# Redistributions in binary form must reproduce the above copyright notice, this
# list of conditions and the following disclaimer in the documentation and/or
# other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
import os
import shutil
import subprocess
import tempfile
import time
from MDSplus import Connection, GetMany, Float32, Range, setenv, Tree, TreeNNF, ADD
def _mimport(name, level=1):
try:
return __import__(name, globals(), level=level)
except:
return __import__(name, globals())
_UnitTest = _mimport("_UnitTest")
class Tests(_UnitTest.TreeTests, _UnitTest.MdsIp):
index = 0
trees = ["pysub"]
tree = "pytree"
def thick(self):
def testnci(thick, local, con, nci):
l = local.S.__getattribute__(nci)
t = thick.S.__getattribute__(nci)
if nci.endswith("_nids"):
l, t = str(l), str(t)
try:
c = str(con.get("getnci(getnci(S,$),'nid_number')", nci))
except TreeNNF:
c = '[]'
else:
c = con.get("getnci(S,$)", nci)
try:
self.assertEqual(t, c)
self.assertEqual(t, l)
except:
print(nci, t, l, c)
raise
server, server_port = self._setup_mdsip(
'ACTION_SERVER', 'ACTION_PORT', 7100+self.index, True)
svr = svr_log = None
try:
svr, svr_log = self._start_mdsip(server, server_port, 'thick')
try:
con = Connection(server)
self.assertEqual(con.get("zero([1,1,1,1,1,1,1,1],1)").tolist(), [
[[[[[[[0]]]]]]]])
with Tree(self.tree, -1, "new") as local:
local.addNode("pysub", "SUBTREE")
s = local.addNode("S", "SIGNAL")
s.addTag("tagS")
s.record = ADD(Float32(1), Float32(2))
t = local.addNode("T", "TEXT")
t.addNode("TT", "TEXT").addTag("tagTT")
t.record = t.TT
t.TT = "recTT"
local.write()
with Tree(self.trees[0], -1, "new") as sub:
sub.addNode("OK")
sub.write()
local.normal()
Tree.setCurrent(self.tree, 7)
setenv("pytree_path", "%s::" % server)
print(con.get("getenv($//'_path')", self.tree))
con.get("TreeShr->TreeOpen(ref($),val($),val(1))", self.tree, -1)
thick = Tree(self.tree, -1)
thick.createPulse(1)
thick1 = Tree(self.tree, 1)
self.assertEqual(local.PYSUB.OK.nid, thick1.PYSUB.OK.nid)
self.assertEqual(local.getFileName(),
thick.getFileName().split("::", 2)[1])
""" TreeTurnOff / TreeTurnOn """
thick.S.on = False
self.assertEqual(local.S.on, False)
thick.S.on = True
self.assertEqual(local.S.on, True)
""" TreeSetCurrentShotId / TreeGetCurrentShotId """
Tree.setCurrent(self.tree, 1)
self.assertEqual(Tree.getCurrent(self.tree), 1)
""" TreeGetRecord / TreeSetRecord """
self.assertEqual(str(local.S.record), "1. + 2.")
self.assertEqual(str(thick.S.record), "1. + 2.")
thick.S.record = ADD(Float32(2), Float32(4))
self.assertEqual(str(local.S.record), "2. + 4.")
self.assertEqual(str(thick.S.record), "2. + 4.")
self.assertEqual(str(local.T.record), str(thick.T.record))
""" GetDefaultNid / SetDefaultNid """
self.assertEqual(thick.getDefault(), thick.top)
thick.setDefault(thick.S)
self.assertEqual(thick.getDefault(), thick.top.S)
thick.setDefault(thick.top)
""" FindNodeWildRemote """
self.assertEqual(str(thick.getNodeWild("T*")),
str(local.getNodeWild("T*")))
""" FindTagWildRemote """
self.assertEqual(thick.findTags("*"), local.findTags("*"))
""" nci """
thick.S.write_once = True
self.assertEqual(thick.S.write_once, True)
for nci in ('on', 'depth', 'usage_str', 'dtype', 'length', 'rlength', 'fullpath', 'minpath', 'member_nids', 'children_nids', 'rfa', 'write_once'):
testnci(thick, local, con, nci)
""" new stuff """
self.assertEqual(local.getFileName(), con.get(
"treefilename($,-1)", self.tree))
finally:
self._stop_mdsip((svr, server))
finally:
if svr_log:
svr_log.close()
def threadsTcp(self):
server, server_port = self._setup_mdsip(
'ACTION_SERVER', 'ACTION_PORT', 7100+self.index, True)
svr = svr_log = None
try:
svr, svr_log = self._start_mdsip(server, server_port, 'tcp')
try:
if svr is not None:
time.sleep(1)
def requests(c, idx):
args = [Float32(i/10+idx) for i in range(10)]
for _ in range(10):
self.assertEqual(
c.get("[$,$,$,$,$,$,$,$,$,$]", *args).tolist(), args)
c = Connection(server)
""" mdsconnect """
self.assertEqual(c.get('_a=1').tolist(), 1)
self.assertEqual(c.get('_a').tolist(), 1)
self.assertEqual(c.getObject('1:3:1').__class__, Range)
g = GetMany(c)
g.append('a', '1')
g.append('b', '$', 2)
g.append('c', '$+$', 1, 2)
g.execute()
self.assertEqual(g.get('a'), 1)
self.assertEqual(g.get('b'), 2)
self.assertEqual(g.get('c'), 3)
_UnitTest.TestThread.assertRun(*(
_UnitTest.TestThread("C%d" % i, requests, c, i)
for i in range(10)
))
finally:
if svr and svr.poll() is None:
svr.terminate()
svr.wait()
finally:
if svr_log:
svr_log.close()
def threadsLocal(self):
connection = Connection('local://gub')
def test(s, c):
for i in range(1000):
s.assertEqual(int(c.get('%d' % i)), i)
_UnitTest.TestThread.assertRun(*(
_UnitTest.TestThread("C%d" % i, test, self, connection)
for i in range(10)
))
def fast_remote_write(self):
count = 100
def mdsip(hostfile):
with open(hostfile, "w") as f:
f.write("multi|SELF\n*")
process = subprocess.Popen((
'mdsip', '-m',
'-p', '8000',
'-P', 'TCP',
'-h', hostfile,
))
time.sleep(1)
return process
def thread(test, name, node, count):
start = time.time()
i = -1
for i in range(count):
data = Float32([i*10+1])
now = Float32([time.time()])
node.makeSegment(now[0], now[0], data, now)
end = time.time()
i += 1
test.assertEqual(i, count)
print("%s: rate %f" % (name, i / (end-start)))
tempdir = tempfile.mkdtemp()
try:
server = mdsip(os.path.join(tempdir, "mdsip.hosts"))
try:
con = Connection('127.0.0.1')
def check(line, *args):
sts = con.get(line, *args)
self.assertTrue(sts & 1, "error %d in '%s'" % (sts, line))
check("setenv('test_path='//$)", tempdir)
for line in (
"TreeOpenNew('test', 1)",
"TreeAddNode('EV1', _, 6)",
"TreeAddNode('EV2', _, 6)",
"TreeWrite()",
"TreeClose()",
):
check(line)
setenv("test_path", "127.0.0.1::" + tempdir)
tree = Tree("test", 1)
_UnitTest.TestThread.assertRun(
_UnitTest.TestThread('EV1', thread, self, 'EV1', tree.EV1.copy(), count),
_UnitTest.TestThread('EV2', thread, self, 'EV2', tree.EV2.copy(), count),
)
finally:
if server:
server.kill()
server.wait()
finally:
shutil.rmtree(tempdir, ignore_errors=False, onerror=None)
@staticmethod
def getTests():
return ['threadsTcp', 'threadsLocal', 'thick']
Tests.main(__name__)
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] |
slicer_module/comm/asyncsock.py | dentsoft-foundation/linkSlicerBlender | 6 | 148985 | """
@author: <NAME>
"""
# REFERENCES:
# https://pymotw.com/2/asynchat/
# tuple unpacking https://stackoverflow.com/questions/1993727/expanding-tuples-into-arguments
# https://pymotw.com/2/asyncore/
import asyncore
import logging
import socket
import threading #would multiprocessing be better?
import time
packet_terminator = '\nEND_TRANSMISSION\n\n'
socket_obj = None
thread = None
address = ('127.0.0.1', 5959)
class SlicerComm():
# https://github.com/pieper/SlicerWeb/blob/master/WebServer/WebServer.py#L1479 adapted from, using QSocketNotifier class
# https://stackoverflow.com/questions/55494422/python-qtcpsocket-and-qtcpserver-receiving-message QtTcpSocket example
# https://python.hotexamples.com/examples/PyQt4.QtNetwork/QTcpSocket/-/python-qtcpsocket-class-examples.html
class EchoClient():
"""3D Slicer send and receive/handle data from TCP server via Qt event loop.
"""
def __init__(self, host, port, handle = None):
from __main__ import qt
self.received_data = [] #socket buffer
self.write_buffer = ""
self.connected = False
self.cmd_ops = {"TERM" : [self.handle_close,[]]} #dict stores packet command, corresponding function call, and the number of arguments needed to be passed
self.socket = qt.QTcpSocket()
try:
self.socket.connectToHost(host, port)
except:
return
self.socket.readyRead.connect(self.handle_read)
self.socket.connected.connect(self.handle_connected)
self.socket.disconnected.connect(self.handle_close)
if handle is not None:
for CMD, handler in handle:
self.cmd_ops[CMD] = handler
return
def handle_connected(self):
self.connected = True
def handle_close(self):
self.connected = False
self.socket.close()
print("DISCONNECTED")
def handle_read(self):
data = self.socket.readAll()
#print(data)
self.received_data.append(data)
for i in range(0, len(self.received_data)):
try: self.received_data[i] = self.received_data[i].data().decode()
except: pass
data = ''.join(self.received_data)
if packet_terminator in data:
self._process_data()
self.received_data = []
def _process_data(self):
"""We have the full ECHO command"""
data = ''.join(self.received_data)
#print(data)
data = data[:-len(packet_terminator)]
data = data.split(' net_packet: ')
self.received_data = [] #empty buffer
if data[0] in self.cmd_ops: self.cmd_ops[data[0]](data[1]) #call stored function, pass stored arguments from tuple
elif data[0] in self.cmd_ops and len(data) > 2: self.cmd_ops[data[0]][0](data[1], *self.cmd_ops[data[0]][1]) # call stored function this way if more args exist - not tested
else: pass
return
def send_data(self, cmd, data):
self.write_buffer = str.encode(cmd.upper() + " net_packet: " + data + packet_terminator)
self.socket.write(self.write_buffer)
self.write_buffer = ""
class BlenderComm():
#blender_main_thread = None
def start():
try:
asyncore.loop()
except asyncore.ExitNow as e:
#print(e)
pass
#asyncore.loop()
def init_thread(server_instance, socket_obj):
blender_thread = threading.current_thread()
new_thread = threading.Thread()
new_thread.run = server_instance
new_thread.start()
check_thread = threading.Thread(target=BlenderComm.check_main_thread, args=(blender_thread, new_thread, socket_obj,))
check_thread.start()
return new_thread
def stop_thread(my_thread):
my_thread.join()
#raise asyncore.ExitNow('Server is quitting!')
def check_main_thread (main_thread, server_thread, socket_obj):
while main_thread.is_alive() and server_thread.is_alive():
time.sleep(5)
socket_obj.stop_server(socket_obj)
BlenderComm.stop_thread(server_thread)
exit()
class EchoClient(asyncore.dispatcher):
"""Sends messages to the server and receives responses.
"""
# Artificially reduce buffer sizes to illustrate
# sending and receiving partial messages.
#ac_in_buffer_size = 64
#ac_out_buffer_size = 64
def __init__(self, host, port):
asyncore.dispatcher.__init__(self)
self.received_data = [] #socket buffer
self.connected = False
self.cmd_ops = {"TERM" : [self.handle_close,[]]} #dict stores packet command, corresponding function call, and the number of arguments needed to be passed
self.create_socket(socket.AF_INET, socket.SOCK_STREAM)
self.connect((host, port))
def handle_connect(self):
self.connected = True
print("client connected!")
def handle_close(self):
self.connected = False
self.close()
def handle_read(self):
data = self.recv(8192)
print("INCOMING RAW DATA:")
print(data)
self.received_data.append(data)
for i in range(0, len(self.received_data)):
try: self.received_data[i] = self.received_data[i].decode()
except: pass
data = ''.join(self.received_data)
if packet_terminator in data:
self._process_data()
self.received_data = []
def _process_data(self):
"""We have the full ECHO command"""
data = ''.join(self.received_data)
data = data[:-len(packet_terminator)]
data = data.split(' net_packet: ')
self.received_data = [] #empty buffer
if data[0] in self.cmd_ops: self.cmd_ops[data[0]](data[1]) #call stored function, pass stored arguments from tuple
elif data[0] in self.cmd_ops and len(data) > 2: self.cmd_ops[data[0]][0](data[1], *self.cmd_ops[data[0]][1])
else: pass
def send_data(self, cmd, data):
self.send(str.encode(cmd.upper() + " net_packet: " + data + packet_terminator))
class EchoHandler(asyncore.dispatcher_with_send):
def init(self, instance, cmd_handle = None):
self.instance = instance
self.received_data = [] #socket buffer
self.write_buffer = ""
self.connected = False
self.cmd_ops_client = {"TERM" : [self.handle_close,[]]}
if cmd_handle is not None:
self.cmd_ops_client.update(cmd_handle)
def handle_connect(self):
self.connected = True
def handle_close(self):
self.connected = False
self.close()
for client in self.instance.sock_handler:
if client == self:
del self.instance.sock_handler[self.instance.sock_handler.index(self)]
print("client instance deleted")
print("disconnected")
def handle_read(self):
data = self.recv(8192)
#print(data)
#self.logger.debug('handle_read() -> %d bytes', len(data))
self.received_data.append(data)
for i in range(0, len(self.received_data)):
try: self.received_data[i] = self.received_data[i].decode()
except: pass
data = ''.join(self.received_data)
if packet_terminator in data:
self._process_data()
self.received_data = []
def _process_data(self):
"""We have the full ECHO command"""
data = ''.join(self.received_data)
data = data[:-len(packet_terminator)]
#print(data)
data = data.split(' net_packet: ')
#print(data)
self.received_data = [] #empty buffer
#try:
if data[0] in self.cmd_ops_client: self.cmd_ops_client[data[0]](data[1]) #call stored function, pass stored arguments from tuple
elif data[0] in self.cmd_ops_client and len(data) > 2: self.cmd_ops_client[data[0]][0](data[1], *self.cmd_ops_client[data[0]][1])
else: pass
#except: pass
def send_data(self, cmd, data):
self.send(str.encode(cmd.upper() + " net_packet: " + data + packet_terminator))
class EchoServer(asyncore.dispatcher):
def __init__(self, host, port, cmd_handle = None):
asyncore.dispatcher.__init__(self)
self.instance = self
self.create_socket(socket.AF_INET, socket.SOCK_STREAM)
#self.set_reuse_addr()
self.bind((host, port))
self.listen(5) #max number of connected clients
self.sock_handler = []
self.cmd_ops = {}
if cmd_handle is not None:
for CMD, handler in cmd_handle:
self.cmd_ops[CMD] = handler
def handle_accepted(self, sock, addr):
print('Incoming connection from %s' % repr(addr))
self.sock_handler.append(BlenderComm.EchoHandler(sock))
self.sock_handler[-1].init(self.instance, self.cmd_ops)
self.sock_handler[-1].connected = True
def stop_server(self, socket_obj):
for connected_client in socket_obj.sock_handler:
connected_client.handle_close()
self.close()
#socket_obj = None
print("server stopped")
if __name__ == "__main__":
#socket_obj = EchoClient(address[0], address[1])
#init_thread(start)
socket_obj = BlenderComm.EchoClient(address[0], address[1])
BlenderComm.init_thread(BlenderComm.start)
#BlenderComm.start()
time.sleep(10)
socket_obj.send_data("TEST", "bogus string from GIL CLIENT")
time.sleep(10)
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29918,
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580,
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396,
11514,
29918,
5415,
353,
6213,
13,
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703,
2974,
11084,
1159,
13,
308,
13,
13,
361,
4770,
978,
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376,
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1115,
13,
1678,
396,
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382,
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29918,
5415,
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29918,
1272,
703,
18267,
613,
376,
29890,
468,
375,
1347,
515,
402,
6227,
24492,
3919,
1159,
13,
1678,
931,
29889,
17059,
29898,
29896,
29900,
29897,
13,
268,
13,
2
] |
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