float:\n if math.isnan(value):\n return value\n assert low <= high\n return max(low, min(high, value))","def normalise_modular_range(value, min, max):\n return numpy.mod(value-min, max-min)+min","def lt(value, limit):\n return value < limit","def _min_in_bounds(self, min):\n if min <= self.valmin:\n if not self.closedmin:\n return self.val[0]\n min = self.valmin\n\n if min > self.val[1]:\n min = self.val[1]\n return self._stepped_value(min)","def scale(value,rawmin=100941, rawmax=274919, rangemin=0, rangemax=100):\n\n # Convert the left range into a 0-1 range (float)\n valueScaled = float(value - rawmin) / float(rawmax - rawmin)\n\n # Convert the 0-1 range into a value in the right range.\n value = rangemin + (valueScaled * ((rangemax * 10) - rangemin))\n\n value = value // 10 * 10 // 10 # float to int\n\n return max(value, rangemin) # value must be greater or equal to rangemin","def intrange(value, name=\"\", value_min=None, value_max=None, zero=False):\n value = __integer(value, \"%s value\" % name, False)\n if value_min is not None:\n value_min = __integer(value_min, \"minimal %s value\" % name, True)\n intvalue(value_min, name, True, True, True)\n if value_max is not None:\n value_max = __integer(value_max, \"maximal %s value\" % name, True)\n intvalue(value_max, name, True, True, True)\n if not zero:\n if value == 0:\n __ex(\"The %s value must not be zero.\" % name, False)\n if (value_min is not None) and (value_max is not None):\n if value_min > value_max:\n __ex(\"The maximal %s value must be greater than the minimal \"\n \"value.\" % name, False)\n if (value_min == value_max) and (value != value_min):\n __ex(\"The %s value can only be %s (depending on further range \"\n \"further range arguments).\" % (name, value_min), False)\n if (value < value_min) or (value > value_max):\n __ex(\"The %s value must be between %s and %s (depending on \"\n \"further range arguments).\" % (name, value_min, value_max),\n False)\n elif value_min is not None:\n if value < value_min:\n __ex(\"The %s value must not be less than %s.\" % (name, value_min),\n False)\n elif value_max is not None:\n if value > value_max:\n __ex(\"The %s value must not be greater than %s.\" %\n (name, value_max), False)","def clamp(n: int, a: int, b: int):\n return min(max(n, a), b)","def apply_bound(x, var_min, var_max):\n x.position = np.maximum(x.position, var_min)\n x.position = np.minimum(x.position, var_max)","def pin_lim(cls, value):\n return cls.int_lim(lower=0, upper=27, value=value, less_than_lower_default=27, greater_than_upper_default=27)","def __verify_range(value, minimum, maximum):\n if value in range(minimum, maximum):\n return True\n else:\n return False","def test_threshold_range_a(self):\n code, out, err = self.t.runError(\"--threshold --max 3.1 --min 3.2\")\n self.assertIn(\"The min value must be lower than the max value.\", out)","def check_value(self, name, min_int, max_int):\n while True:\n numb = input(f\"-- {name} : Entrez une valeur comprise \"\n f\"entre {min_int} et {max_int} : \")\n try:\n check = int(numb)\n if check == 99 or min_int <= check <= max_int:\n break\n except ValueError:\n pass\n return check","def _check_value_range(self, key: str, value: Any):\n minValue = from_dot_notation(\n field=\".\".join([*self.parents, key]), obj=self.definition\n ).get(\"minValue\", None)\n maxValue = from_dot_notation(\n field=\".\".join([*self.parents, key]), obj=self.definition\n ).get(\"maxValue\", None)\n\n if minValue is not None and value < minValue:\n raise Exception(\n f\"Value for '{key}' is lower than the minimum value (value should be at least {minValue})\"\n )\n if maxValue is not None and value > maxValue:\n raise Exception(\n f\"Value for '{key}' is higher than the maximum value (value should not exceed {maxValue})\"\n )","def __init__(__self__, *,\n max: pulumi.Input[int],\n min: pulumi.Input[int]):\n pulumi.set(__self__, \"max\", max)\n pulumi.set(__self__, \"min\", min)","def __init__(__self__, *,\n max: pulumi.Input[int],\n min: pulumi.Input[int]):\n pulumi.set(__self__, \"max\", max)\n pulumi.set(__self__, \"min\", min)","def is_valid_range(parser, arg, minimum=0, maximum=100):\n if arg < minimum:\n parser.error(\"%s < %s\", arg, minimum)\n else:\n if arg > maximum:\n parser.error(\"%s > %s\", arg, maximum)\n\n return arg","def view_limits(self, vmin, vmax):\n return vmin, vmax\n # return nonsingular(vmin, vmax)","def clamp(self):\n self.threshold.data.clamp_(self.min_threshold)","def testMinMax(self, value):\n\t\tif value > self.oldmax:\n\t\t\tself.oldmax = value\n\t\t\tself.maxBox.SetValue(str(value).encode('utf-8'))\n\t\telif value < self.oldmin:\n\t\t\tself.oldmin = value\n\t\t\tself.minBox.SetValue(str(value).encode('utf-8'))","def between(x, val1, val2):\n\treturn max(val1, val2) > x > min(val1, val2)","def map_to_range(val, old_min, old_max, new_min, new_max):\n return new_max - (val - old_min) * (new_max - new_min) / (old_max - old_min)","def chkLimits(name, value, Min, Max, unit = 'V', Hex = False):\n\n #global Log\n if not Min < value < Max:\n if Hex:\n line = \"%s:0x%X OUT OF LIMITS (0x%X, 0x%X). Test Failed !\" %(name, value, Min, Max)\n else:\n line = \"%s:%F %s OUT OF LIMITS (%F, %f). Test Failed !\" %(name, value, unit, Min, Max)\n Log.logError(line)\n Err.bumpError()\n return False\n if Hex:\n Log.logText(' '+'%s:0x%X expected range from:0x%X To: 0x%X. Test PASS !'% (name, value, Min, Max))\n else:\n Log.logText(' '+'%s:%F %s expected range From:%F %s To: %F %s. Test PASS !'% (name, value, unit, Min,unit, Max, unit))\n return True","def check_range_value(array, min_=None, max_=None):\n # check lowest and highest bounds\n if min_ is not None and array.min() < min_:\n raise ValueError(\"The array should have a lower bound of {0}, but its \"\n \"minimum value is {1}.\".format(min_, array.min()))\n if max_ is not None and array.max() > max_:\n raise ValueError(\"The array should have an upper bound of {0}, but \"\n \"its maximum value is {1}.\".format(max_, array.max()))\n\n return True","def elevation_servo_set_min_max(self, min: int, max: int):\n self.elevation_servo.set_min_position(min)\n self.elevation_servo.set_max_position(max)","def le(value, limit):\n return value <= limit","def _validate_value(self, value):\n if self.limits[0] <= value <= self.limits[1]:\n return True\n else:\n return False","def test_min_max_limiting() -> None:\n d = {\n \"one\": [-1, 0, 1],\n \"two\": [2, 3, -1],\n }\n # Update a single column\n df = pd.DataFrame(d)\n #\n # .loc accepts a boolean mask and set of columns to return.\n #\n df.loc[df[\"one\"] < 0, [\"one\"]] = 0\n #\n # one two\n # 0 2\n # 0 3\n # 1 -1\n #\n assert df.iloc[0, 0] == 0\n assert df.iloc[2, 1] == -1\n\n # You can use `clip` to enforce minimum and maximum values for an entire df.\n df = df.clip(lower=0.0)\n assert df.iloc[0, 0] == 0.0\n assert df.iloc[2, 1] == 0.0","def __init__( # pylint: disable=too-many-arguments\n self,\n min: Optional[float] = None,\n max: Optional[float] = None,\n step: Optional[int] = None, # pylint: disable=redefined-outer-name\n include_min: bool = True,\n include_max: bool = True,\n ) -> None:\n #: The optional minimal allowed value.\n self.min = min\n\n #: The optional maximal allowed value.\n self.max = max\n\n #: The optional step between values.\n self.step = step\n\n #: Whether the minimal value is allowed.\n self.include_min = include_min\n\n #: Whether the maximal value is allowd.\n self.include_max = include_max","def above_threshold(self, value):\n # We use floating point number here so we have to take care\n return finf(value,self.min) or finf(self.max,value)","def _setBound(self, value):\n if self._colormap is not None:\n if self._index == 0:\n min_ = value\n max_ = self._colormap.getVMax()\n else: # self._index == 1\n min_ = self._colormap.getVMin()\n max_ = value\n\n if max_ is not None and min_ is not None and min_ > max_:\n min_, max_ = max_, min_\n self._colormap.setVRange(min_, max_)","def minmin_maxmax( *args ):\n rmin = min( [ mv.min() for mv in args ] )\n rmax = max( [ mv.max() for mv in args ] )\n rmv = cdms2.createVariable( [rmin,rmax] )\n return rmv","def __init__(self, min_val, max_val):\n self.values = (min_val, max_val)","def __init__(self, min_val, max_val):\n self.values = (min_val, max_val)","def __init__(self, min_val, max_val):\n self.values = (min_val, max_val)","def __init__(self, min_val, max_val):\n self.values = (min_val, max_val)"],"string":"[\n \"def _limit(value, min_value, max_value):\\n\\n if value < min_value:\\n return min_value\\n if value > max_value:\\n return max_value\\n return value\",\n \"def range_limit(val, minv, maxv):\\n\\tif (val < minv):\\n\\t\\tval = minv\\n\\telif (val > maxv):\\n\\t\\tval = maxv\\n\\treturn val\",\n \"def clamp(num, min, max): \\n if num < min:\\n num = min\\n elif num > max:\\n num = max\\n return num\",\n \"def clip(val, val_min, val_max):\\n return min(val_max, max(val_min, val))\",\n \"def check_valid_range(val, max_val):\\n if val < 0:\\n val = 0\\n elif val > max_val:\\n val = max_val\\n else:\\n pass\\n return val\",\n \"def clamp(min_value: float, max_value: float, value: float):\\n\\t\\tvalue = min(value, max_value)\\n\\t\\tvalue = max(value, min_value)\\n\\t\\treturn value\",\n \"def constrain(inputVal, lower_limit, upper_limit):\\n \\n if (inputVal < lower_limit):\\n return lower_limit\\n elif (inputVal > upper_limit):\\n return upper_limit\\n else:\\n return inputVal\",\n \"def clamp(self, value, minv, maxv):\\n if value > maxv:\\n return maxv\\n if value < minv:\\n return minv\\n return value\",\n \"def rangeLimit(val, minv, maxv):\\n\\treturn range_limit(val, minv, maxv)\",\n \"def clamp(minimum, value, maximum):\\n return max(minimum, min(maximum, value))\",\n \"def __limit_value(self, value, v_range):\\n if np.isnan(value):\\n print('Warning: trying to limit nan value in range {0}'.format(v_range))\\n return value\\n\\n return np.min([v_range[1], np.max([value, v_range[0]])])\",\n \"def limitValue(self, value, lowerLimit, upperLimit):\\n if value > upperLimit:\\n return upperLimit\\n elif value < lowerLimit:\\n return lowerLimit\\n else:\\n return value\",\n \"def clamp(self, value, minVal, maxVal):\\n if type(value) is type(\\\"string\\\"):\\n return value\\n if minVal != None and max != None:\\n return max(min(value, maxVal), minVal)\\n if minVal != None and maxVal == None:\\n return max(value, minVal)\\n if minVal == None and maxVal != None:\\n return min(value, maxVal)\\n return value\",\n \"def clamp(lower, value, upper):\\n if lower > value:\\n return lower\\n if upper < value:\\n return upper\\n return value\",\n \"def clamp(value, minval, maxval):\\n return sorted((minval, int(value), maxval))[1]\",\n \"def constrain(small, value, big):\\n return min(max(value, small), big)\",\n \"def __validate(self, value: int, extend_range: bool):\\n if extend_range:\\n bottom, top = self.getRange()\\n self.setRange(min(value, bottom), max(value, top))\\n return numpy.clip(value, *self.getRange())\",\n \"def simplebounds(cls, val, lower, upper):\\n if val < lower:\\n val = lower\\n if val > upper:\\n val = upper\\n return val\",\n \"def bounds(x, xMin, xMax):\\n if (x < xMin):\\n x = xMin\\n elif (x > xMax):\\n x = xMax\\n return(x)\",\n \"def ImposeLimits(Val, MinVal, MaxVal):\\n\\tif MinVal < Val < MaxVal:\\n\\t\\treturn Val\\n\\telif Val <= MinVal:\\n\\t\\treturn MinVal\\n\\telif Val >= MaxVal:\\n\\t\\treturn MaxVal\",\n \"def _value_in_bounds(self, val):\\n val = self._stepped_value(val)\\n\\n if val <= self.valmin:\\n if not self.closedmin:\\n return\\n val = self.valmin\\n elif val >= self.valmax:\\n if not self.closedmax:\\n return\\n val = self.valmax\\n\\n if self.slidermin is not None and val <= self.slidermin.val:\\n if not self.closedmin:\\n return\\n val = self.slidermin.val\\n\\n if self.slidermax is not None and val >= self.slidermax.val:\\n if not self.closedmax:\\n return\\n val = self.slidermax.val\\n return val\",\n \"def clip(x, min, max):\\r\\n # see decorator for function body\\r\\n # for grep: clamp, bound\\r\",\n \"def clamp(num,start,end):\\n if num >= start and num <= end: return num\\n elif num < start: return start\\n elif num > end: return end\",\n \"def clamp(value, mn, mx):\\n\\n return max(min(value, mx), mn)\",\n \"def _bound(x, min_value, max_value):\\n return np.maximum(min_value, np.minimum(x, max_value))\",\n \"def between(min, max):\\n def func(x):\\n return min <= x <= max\\n return func\",\n \"def clamp(value, mini, maxi):\\n if value < mini:\\n return mini\\n elif maxi < value:\\n return maxi\\n else:\\n return value\",\n \"def clamp(n, min_, max_):\\n return max(min(max_,n),min_)\",\n \"def min_max(obj, val, is_max):\\n n = getattr(obj, 'maximum' if is_max else 'minimum', None)\\n if n == None:\\n return\\n\\n _eq = getattr(obj, 'exclusiveMaximum' if is_max else 'exclusiveMinimum', False)\\n if is_max:\\n to_raise = val >= n if _eq else val > n\\n else:\\n to_raise = val <= n if _eq else val < n\\n\\n if to_raise:\\n raise ValidationError('condition failed: {0}, v:{1} compared to o:{2}'.format('maximum' if is_max else 'minimum', val, n))\",\n \"def isInRange(val, minv, maxv):\\n\\treturn val >= minv and val <= maxv\",\n \"def clamp(minimum, n, maximum):\\n return max(minimum, min(n, maximum))\",\n \"def check_range(number: object, min_r: float, max_r: float, name: str = \\\"\\\") -> float:\\n if not isinstance(number, (float, int)):\\n raise FFmpegNormalizeError(f\\\"{name} must be an int or float\\\")\\n if number < min_r or number > max_r:\\n raise FFmpegNormalizeError(f\\\"{name} must be within [{min_r},{max_r}]\\\")\\n return number\",\n \"def Clamp(val, min, max):\\n\\tval = float(val)\\n\\tmin = float(min)\\n\\tmax = float(max)\\n\\n\\tif val < min:\\n\\t\\treturn min\\n\\telif val > max:\\n\\t\\treturn max\\n\\telse:\\n\\t\\treturn val\",\n \"def is_valid(self, value: int) -> bool:\\n return value < self.min_value or value > self.max_value\",\n \"def sanitize(cls, value):\\n return cls._get_value_inside_range(value, cls.MIN_VALUE,\\n cls.MAX_VALUE, cls.DEFAULT_VALUE)\",\n \"def sanitize(cls, value):\\n return cls._get_value_inside_range(value, cls.MIN_VALUE,\\n cls.MAX_VALUE, cls.DEFAULT_VALUE)\",\n \"def sanitize(cls, value):\\n return cls._get_value_inside_range(value, cls.MIN_VALUE,\\n cls.MAX_VALUE, cls.DEFAULT_VALUE)\",\n \"def lim(cls, lower=PWM_MIN, upper=PWM_MAX, value=None, less_than_lower_default=None, greater_than_upper_default=None):\\n #Sanitise inputs\\n if less_than_lower_default is None:\\n less_than_lower_default = lower\\n if greater_than_upper_default is None:\\n greater_than_upper_default = upper\\n if not (less_than_lower_default >= lower and greater_than_upper_default <= upper):\\n raise Exception(\\\"LEDStrip.lim(): Defaults %s,%s are not within %s - %s\\\" % (less_than_lower_default, greater_than_upper_default, lower, upper)) \\n if value is None:\\n return less_than_lower_default\\n \\n #Test values\\n try:\\n if value < lower:\\n logging.warn(\\\" LEDStrip.lim(): Value %s is less than lower limit %s. Setting to %s.\\\" % (value, lower, less_than_lower_default))\\n return float(less_than_lower_default)\\n if value > upper:\\n logging.warn(\\\" LEDStrip.lim(): Value %s is greater than upper limit %s. Setting to %s\\\" % (value, upper, greater_than_upper_default))\\n return float(greater_than_upper_default)\\n except (ValueError, TypeError, AttributeError):\\n return float(less_than_lower_default)\\n return float(value)\",\n \"def clamp(x: float, min_x: float, max_x: float) -> float:\\n if x < min_x:\\n return min_x\\n elif x > max_x:\\n return max_x\\n return x\",\n \"def view_limits(self, dmin, dmax):\\n base = self._select_base(dmin, dmax)\\n if mpl.rcParams['axes.autolimit_mode'] == 'round_numbers':\\n vmin = base.le(dmin)\\n vmax = base.ge(dmax)\\n if vmin == vmax:\\n vmin -= 1\\n vmax += 1\\n else:\\n vmin = dmin\\n vmax = dmax\\n\\n return mtransforms.nonsingular(vmin, vmax)\",\n \"def clip(x, min_value, max_value):\\n if max_value is None:\\n max_value = np.inf\\n if min_value is None:\\n min_value = -np.inf\\n max_value = C.maximum(min_value, max_value)\\n return C.clip(x, min_value, max_value)\",\n \"def int_lim(cls, lower=PWM_MIN, upper=PWM_MAX, value=None, less_than_lower_default=None, greater_than_upper_default=None):\\n out_float = cls.lim(lower, upper, value, less_than_lower_default, greater_than_upper_default)\\n return int(round(out_float))\",\n \"def assert_between(value, minval, maxval):\\n assert_greater_equal(value, minval)\\n assert_less_equal(value, maxval)\",\n \"def constrain(amt,low,high):\\n if amt < low:\\n return low\\n elif amt > high:\\n return high\\n else:\\n return amt\",\n \"def clip(self, x):\\n return self.min_value if x<self.min_value else self.max_value if x > self.max_value else x\",\n \"def clamp(n, minn, maxn):\\n return max(min(maxn, n), minn)\",\n \"def clip_to_output_limits(self, value):\\n return max(self.out_min, min(self.out_max, value))\",\n \"def constrain(amt, low, high):\\n if amt < low:\\n return low\\n elif amt > high:\\n return high\\n else:\\n return amt\",\n \"def set_output_limits(self, min_value, max_value):\\n self.out_min = min_value\\n self.out_max = max_value\\n if self.out_min > self.out_max:\\n print(\\\"set_output_limits(): min must be smaller than max.\\\")\\n self.iterm = self.clip_to_output_limits(self.iterm)\\n self.output = self.clip_to_output_limits(self.output)\",\n \"def clamp(n, minn, maxn):\\n return max(min(maxn, n), minn)\",\n \"def limit(val, arr):\\n # Make copy\\n new = np.array(val)\\n extr = minmax(arr)\\n # Enforce lower bound\\n new = np.maximum(new, extr[0])\\n # Enforce upper bound\\n new = np.minimum(new, extr[1])\\n return new\",\n \"def ge(value, limit):\\n return value >= limit\",\n \"def rangeSample(val, minLim, maxLim):\\n\\tif val < minLim or val > maxLim:\\n\\t\\tval = randint(minLim, maxLim)\\n\\treturn val\",\n \"def check_out_range(value, lim_1, lim_2):\\n lo_lim = min(lim_1, lim_2)\\n hi_lim = max(lim_1, lim_2)\\n \\n if (abs(value) > abs(hi_lim)) or (abs(value) < abs(lo_lim)):\\n return True\\n else:\\n return False\",\n \"def pass_selection_val(self, val, val_min=None, val_max=None):\\n if (val_min is not None) and (val_max is not None):\\n return True if (val > val_min) and (val < val_max) else False\\n elif (val_min is None) and (val_max is not None):\\n # Check against val_max only. \\n return True if (val < val_max) else False\\n elif (val_min is not None) and (val_max is None):\\n # Check against val_min only. \\n return True if (val > val_min) else False\\n else:\\n msg = \\\"[WARNING] You performed a cut, but didn't specify any bounds.\\\"\\n raise RuntimeWarning(msg)\",\n \"def check_in_range(value, lim_1, lim_2):\\n lo_lim = min(lim_1, lim_2)\\n hi_lim = max(lim_1, lim_2)\\n \\n if (abs(value) > abs(lo_lim)) and (abs(value) < abs(hi_lim)):\\n return True\\n else:\\n return False\",\n \"def vc_clamp(x, lb, ub):\\n\\n y = min(x, ub)\\n y = max(y, lb)\\n\\n return y\",\n \"def clip_range(x, xlim):\\n return min([max([x, xlim[0]]), xlim[1]])\",\n \"def is_valid_paid_value_range(value):\\n\\n min_valid_payment = 1\\n max_valid_payment = 1_000_000\\n\\n if not min_valid_payment <= value <= max_valid_payment:\\n raise serializers.ValidationError(\\n 'Valor no permitido, debe ser estar entre 1 y 1000000'\\n )\\n return value\",\n \"def is_constrained(value, min_acceptable=None, max_acceptable=None):\\n if min_acceptable is not None and value < min_acceptable:\\n return False\\n if max_acceptable is not None and value > max_acceptable:\\n return False\\n return True\",\n \"def mapRange(num, min1, max1, min2, max2, clamp=True):\\n if(clamp and num < min1):\\n return min2\\n if(clamp and num > max1):\\n return max2\\n\\n num1 = (num - min1) / (max1 - min1)\\n num2 = (num1 * (max2 - min2)) + min2\\n return num2\",\n \"def in_range(data, minval=-np.inf, maxval=np.inf):\\n return (minval <= data) & (data <= maxval)\",\n \"def cpfclamp(f, min_, max_):\\n return min(max(f, min_), max_)\",\n \"def _get_min_max_value(min, max, value=None, step=None):\\n # Either min and max need to be given, or value needs to be given\\n if value is None:\\n if min is None or max is None:\\n raise ValueError('unable to infer range, value from: ({0}, {1}, {2})'.format(min, max, value))\\n diff = max - min\\n value = min + (diff / 2)\\n # Ensure that value has the same type as diff\\n if not isinstance(value, type(diff)):\\n value = min + (diff // 2)\\n else: # value is not None\\n if not isinstance(value, Real):\\n raise TypeError('expected a real number, got: %r' % value)\\n # Infer min/max from value\\n if value == 0:\\n # This gives (0, 1) of the correct type\\n vrange = (value, value + 1)\\n elif value > 0:\\n vrange = (-value, 3*value)\\n else:\\n vrange = (3*value, -value)\\n if min is None:\\n min = vrange[0]\\n if max is None:\\n max = vrange[1]\\n if step is not None:\\n # ensure value is on a step\\n tick = int((value - min) / step)\\n value = min + tick * step\\n if not min <= value <= max:\\n raise ValueError('value must be between min and max (min={0}, value={1}, max={2})'.format(min, value, max))\\n return min, max, value\",\n \"def _validate(self, value, **options):\\n\\n # this is a workaround to get the correct values of accepted min and max in\\n # case they are callable and producing different results on each call.\\n current_values = dict()\\n current_values[self.CURRENT_MAX_KEY] = None\\n current_values[self.CURRENT_MIN_KEY] = None\\n options[self.CURRENT_VALUE_KEY] = current_values\\n try:\\n super()._validate(value, **options)\\n except (self.maximum_value_error, self.minimum_value_error):\\n equality_min = ''\\n equality_max = ''\\n\\n inclusive_maximum = options.get('inclusive_maximum')\\n if inclusive_maximum is None:\\n inclusive_maximum = self.inclusive_maximum\\n\\n inclusive_minimum = options.get('inclusive_minimum')\\n if inclusive_minimum is None:\\n inclusive_minimum = self.inclusive_minimum\\n\\n if inclusive_minimum is not False:\\n equality_min = self.inclusive_minimum_value_message\\n\\n if inclusive_maximum is not False:\\n equality_max = self.inclusive_maximum_value_message\\n\\n current_min = current_values.get(self.CURRENT_MIN_KEY)\\n if current_min is None:\\n current_min = self.accepted_minimum\\n\\n current_max = current_values.get(self.CURRENT_MAX_KEY)\\n if current_max is None:\\n current_max = self.accepted_maximum\\n\\n raise self.range_value_error(self.range_value_message.format(\\n param_name=self._get_field_name(**options),\\n lower=self._get_representation(current_min),\\n upper=self._get_representation(current_max),\\n or_equal_min=equality_min, or_equal_max=equality_max))\",\n \"def clip(value: float, low: float, high: float) -> float:\\n if math.isnan(value):\\n return value\\n assert low <= high\\n return max(low, min(high, value))\",\n \"def normalise_modular_range(value, min, max):\\n return numpy.mod(value-min, max-min)+min\",\n \"def lt(value, limit):\\n return value < limit\",\n \"def _min_in_bounds(self, min):\\n if min <= self.valmin:\\n if not self.closedmin:\\n return self.val[0]\\n min = self.valmin\\n\\n if min > self.val[1]:\\n min = self.val[1]\\n return self._stepped_value(min)\",\n \"def scale(value,rawmin=100941, rawmax=274919, rangemin=0, rangemax=100):\\n\\n # Convert the left range into a 0-1 range (float)\\n valueScaled = float(value - rawmin) / float(rawmax - rawmin)\\n\\n # Convert the 0-1 range into a value in the right range.\\n value = rangemin + (valueScaled * ((rangemax * 10) - rangemin))\\n\\n value = value // 10 * 10 // 10 # float to int\\n\\n return max(value, rangemin) # value must be greater or equal to rangemin\",\n \"def intrange(value, name=\\\"\\\", value_min=None, value_max=None, zero=False):\\n value = __integer(value, \\\"%s value\\\" % name, False)\\n if value_min is not None:\\n value_min = __integer(value_min, \\\"minimal %s value\\\" % name, True)\\n intvalue(value_min, name, True, True, True)\\n if value_max is not None:\\n value_max = __integer(value_max, \\\"maximal %s value\\\" % name, True)\\n intvalue(value_max, name, True, True, True)\\n if not zero:\\n if value == 0:\\n __ex(\\\"The %s value must not be zero.\\\" % name, False)\\n if (value_min is not None) and (value_max is not None):\\n if value_min > value_max:\\n __ex(\\\"The maximal %s value must be greater than the minimal \\\"\\n \\\"value.\\\" % name, False)\\n if (value_min == value_max) and (value != value_min):\\n __ex(\\\"The %s value can only be %s (depending on further range \\\"\\n \\\"further range arguments).\\\" % (name, value_min), False)\\n if (value < value_min) or (value > value_max):\\n __ex(\\\"The %s value must be between %s and %s (depending on \\\"\\n \\\"further range arguments).\\\" % (name, value_min, value_max),\\n False)\\n elif value_min is not None:\\n if value < value_min:\\n __ex(\\\"The %s value must not be less than %s.\\\" % (name, value_min),\\n False)\\n elif value_max is not None:\\n if value > value_max:\\n __ex(\\\"The %s value must not be greater than %s.\\\" %\\n (name, value_max), False)\",\n \"def clamp(n: int, a: int, b: int):\\n return min(max(n, a), b)\",\n \"def apply_bound(x, var_min, var_max):\\n x.position = np.maximum(x.position, var_min)\\n x.position = np.minimum(x.position, var_max)\",\n \"def pin_lim(cls, value):\\n return cls.int_lim(lower=0, upper=27, value=value, less_than_lower_default=27, greater_than_upper_default=27)\",\n \"def __verify_range(value, minimum, maximum):\\n if value in range(minimum, maximum):\\n return True\\n else:\\n return False\",\n \"def test_threshold_range_a(self):\\n code, out, err = self.t.runError(\\\"--threshold --max 3.1 --min 3.2\\\")\\n self.assertIn(\\\"The min value must be lower than the max value.\\\", out)\",\n \"def check_value(self, name, min_int, max_int):\\n while True:\\n numb = input(f\\\"-- {name} : Entrez une valeur comprise \\\"\\n f\\\"entre {min_int} et {max_int} : \\\")\\n try:\\n check = int(numb)\\n if check == 99 or min_int <= check <= max_int:\\n break\\n except ValueError:\\n pass\\n return check\",\n \"def _check_value_range(self, key: str, value: Any):\\n minValue = from_dot_notation(\\n field=\\\".\\\".join([*self.parents, key]), obj=self.definition\\n ).get(\\\"minValue\\\", None)\\n maxValue = from_dot_notation(\\n field=\\\".\\\".join([*self.parents, key]), obj=self.definition\\n ).get(\\\"maxValue\\\", None)\\n\\n if minValue is not None and value < minValue:\\n raise Exception(\\n f\\\"Value for '{key}' is lower than the minimum value (value should be at least {minValue})\\\"\\n )\\n if maxValue is not None and value > maxValue:\\n raise Exception(\\n f\\\"Value for '{key}' is higher than the maximum value (value should not exceed {maxValue})\\\"\\n )\",\n \"def __init__(__self__, *,\\n max: pulumi.Input[int],\\n min: pulumi.Input[int]):\\n pulumi.set(__self__, \\\"max\\\", max)\\n pulumi.set(__self__, \\\"min\\\", min)\",\n \"def __init__(__self__, *,\\n max: pulumi.Input[int],\\n min: pulumi.Input[int]):\\n pulumi.set(__self__, \\\"max\\\", max)\\n pulumi.set(__self__, \\\"min\\\", min)\",\n \"def is_valid_range(parser, arg, minimum=0, maximum=100):\\n if arg < minimum:\\n parser.error(\\\"%s < %s\\\", arg, minimum)\\n else:\\n if arg > maximum:\\n parser.error(\\\"%s > %s\\\", arg, maximum)\\n\\n return arg\",\n \"def view_limits(self, vmin, vmax):\\n return vmin, vmax\\n # return nonsingular(vmin, vmax)\",\n \"def clamp(self):\\n self.threshold.data.clamp_(self.min_threshold)\",\n \"def testMinMax(self, value):\\n\\t\\tif value > self.oldmax:\\n\\t\\t\\tself.oldmax = value\\n\\t\\t\\tself.maxBox.SetValue(str(value).encode('utf-8'))\\n\\t\\telif value < self.oldmin:\\n\\t\\t\\tself.oldmin = value\\n\\t\\t\\tself.minBox.SetValue(str(value).encode('utf-8'))\",\n \"def between(x, val1, val2):\\n\\treturn max(val1, val2) > x > min(val1, val2)\",\n \"def map_to_range(val, old_min, old_max, new_min, new_max):\\n return new_max - (val - old_min) * (new_max - new_min) / (old_max - old_min)\",\n \"def chkLimits(name, value, Min, Max, unit = 'V', Hex = False):\\n\\n #global Log\\n if not Min < value < Max:\\n if Hex:\\n line = \\\"%s:0x%X OUT OF LIMITS (0x%X, 0x%X). Test Failed !\\\" %(name, value, Min, Max)\\n else:\\n line = \\\"%s:%F %s OUT OF LIMITS (%F, %f). Test Failed !\\\" %(name, value, unit, Min, Max)\\n Log.logError(line)\\n Err.bumpError()\\n return False\\n if Hex:\\n Log.logText(' '+'%s:0x%X expected range from:0x%X To: 0x%X. Test PASS !'% (name, value, Min, Max))\\n else:\\n Log.logText(' '+'%s:%F %s expected range From:%F %s To: %F %s. Test PASS !'% (name, value, unit, Min,unit, Max, unit))\\n return True\",\n \"def check_range_value(array, min_=None, max_=None):\\n # check lowest and highest bounds\\n if min_ is not None and array.min() < min_:\\n raise ValueError(\\\"The array should have a lower bound of {0}, but its \\\"\\n \\\"minimum value is {1}.\\\".format(min_, array.min()))\\n if max_ is not None and array.max() > max_:\\n raise ValueError(\\\"The array should have an upper bound of {0}, but \\\"\\n \\\"its maximum value is {1}.\\\".format(max_, array.max()))\\n\\n return True\",\n \"def elevation_servo_set_min_max(self, min: int, max: int):\\n self.elevation_servo.set_min_position(min)\\n self.elevation_servo.set_max_position(max)\",\n \"def le(value, limit):\\n return value <= limit\",\n \"def _validate_value(self, value):\\n if self.limits[0] <= value <= self.limits[1]:\\n return True\\n else:\\n return False\",\n \"def test_min_max_limiting() -> None:\\n d = {\\n \\\"one\\\": [-1, 0, 1],\\n \\\"two\\\": [2, 3, -1],\\n }\\n # Update a single column\\n df = pd.DataFrame(d)\\n #\\n # .loc accepts a boolean mask and set of columns to return.\\n #\\n df.loc[df[\\\"one\\\"] < 0, [\\\"one\\\"]] = 0\\n #\\n # one two\\n # 0 2\\n # 0 3\\n # 1 -1\\n #\\n assert df.iloc[0, 0] == 0\\n assert df.iloc[2, 1] == -1\\n\\n # You can use `clip` to enforce minimum and maximum values for an entire df.\\n df = df.clip(lower=0.0)\\n assert df.iloc[0, 0] == 0.0\\n assert df.iloc[2, 1] == 0.0\",\n \"def __init__( # pylint: disable=too-many-arguments\\n self,\\n min: Optional[float] = None,\\n max: Optional[float] = None,\\n step: Optional[int] = None, # pylint: disable=redefined-outer-name\\n include_min: bool = True,\\n include_max: bool = True,\\n ) -> None:\\n #: The optional minimal allowed value.\\n self.min = min\\n\\n #: The optional maximal allowed value.\\n self.max = max\\n\\n #: The optional step between values.\\n self.step = step\\n\\n #: Whether the minimal value is allowed.\\n self.include_min = include_min\\n\\n #: Whether the maximal value is allowd.\\n self.include_max = include_max\",\n \"def above_threshold(self, value):\\n # We use floating point number here so we have to take care\\n return finf(value,self.min) or finf(self.max,value)\",\n \"def _setBound(self, value):\\n if self._colormap is not None:\\n if self._index == 0:\\n min_ = value\\n max_ = self._colormap.getVMax()\\n else: # self._index == 1\\n min_ = self._colormap.getVMin()\\n max_ = value\\n\\n if max_ is not None and min_ is not None and min_ > max_:\\n min_, max_ = max_, min_\\n self._colormap.setVRange(min_, max_)\",\n \"def minmin_maxmax( *args ):\\n rmin = min( [ mv.min() for mv in args ] )\\n rmax = max( [ mv.max() for mv in args ] )\\n rmv = cdms2.createVariable( [rmin,rmax] )\\n return rmv\",\n \"def __init__(self, min_val, max_val):\\n self.values = (min_val, max_val)\",\n \"def __init__(self, min_val, max_val):\\n self.values = (min_val, max_val)\",\n \"def __init__(self, min_val, max_val):\\n self.values = (min_val, max_val)\",\n \"def __init__(self, min_val, max_val):\\n self.values = (min_val, max_val)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8242318","0.82057476","0.77939916","0.7717922","0.7658431","0.76336455","0.75694007","0.7560079","0.75334716","0.7511125","0.7503323","0.7490816","0.7443682","0.7372782","0.73508364","0.7305762","0.7283909","0.7263027","0.72585297","0.72390145","0.7189812","0.71267784","0.7126178","0.71082735","0.7078759","0.7069717","0.70644605","0.7064237","0.70349467","0.7011921","0.69800913","0.69423175","0.6918298","0.69106966","0.68916154","0.68916154","0.68916154","0.6887795","0.6869072","0.68459266","0.6845692","0.68345624","0.6811967","0.68031645","0.6791451","0.6766357","0.67646646","0.67577314","0.67263746","0.67201823","0.67013526","0.66945153","0.66791","0.6654271","0.664779","0.6638729","0.6633888","0.66188085","0.6605884","0.660448","0.65877527","0.65854883","0.6560133","0.65501595","0.654893","0.65393853","0.6531421","0.652693","0.6524714","0.6516082","0.65112925","0.65111977","0.65001523","0.6493067","0.6488919","0.6486601","0.64788646","0.64787716","0.647775","0.647775","0.64719975","0.6444289","0.6439807","0.6428986","0.64218354","0.6413216","0.64010924","0.6380481","0.63768035","0.63713497","0.6368426","0.6362485","0.6344793","0.6340574","0.6323823","0.63180697","0.63144886","0.63144886","0.63144886","0.63144886"],"string":"[\n \"0.8242318\",\n \"0.82057476\",\n \"0.77939916\",\n \"0.7717922\",\n \"0.7658431\",\n \"0.76336455\",\n \"0.75694007\",\n \"0.7560079\",\n \"0.75334716\",\n \"0.7511125\",\n \"0.7503323\",\n \"0.7490816\",\n \"0.7443682\",\n \"0.7372782\",\n \"0.73508364\",\n \"0.7305762\",\n \"0.7283909\",\n \"0.7263027\",\n \"0.72585297\",\n \"0.72390145\",\n \"0.7189812\",\n \"0.71267784\",\n \"0.7126178\",\n \"0.71082735\",\n \"0.7078759\",\n \"0.7069717\",\n \"0.70644605\",\n \"0.7064237\",\n \"0.70349467\",\n \"0.7011921\",\n \"0.69800913\",\n \"0.69423175\",\n \"0.6918298\",\n \"0.69106966\",\n \"0.68916154\",\n \"0.68916154\",\n \"0.68916154\",\n \"0.6887795\",\n \"0.6869072\",\n \"0.68459266\",\n \"0.6845692\",\n \"0.68345624\",\n \"0.6811967\",\n \"0.68031645\",\n \"0.6791451\",\n \"0.6766357\",\n \"0.67646646\",\n \"0.67577314\",\n \"0.67263746\",\n \"0.67201823\",\n \"0.67013526\",\n \"0.66945153\",\n \"0.66791\",\n \"0.6654271\",\n \"0.664779\",\n \"0.6638729\",\n \"0.6633888\",\n \"0.66188085\",\n \"0.6605884\",\n \"0.660448\",\n \"0.65877527\",\n \"0.65854883\",\n \"0.6560133\",\n \"0.65501595\",\n \"0.654893\",\n \"0.65393853\",\n \"0.6531421\",\n \"0.652693\",\n \"0.6524714\",\n \"0.6516082\",\n \"0.65112925\",\n \"0.65111977\",\n \"0.65001523\",\n \"0.6493067\",\n \"0.6488919\",\n \"0.6486601\",\n \"0.64788646\",\n \"0.64787716\",\n \"0.647775\",\n \"0.647775\",\n \"0.64719975\",\n \"0.6444289\",\n \"0.6439807\",\n \"0.6428986\",\n \"0.64218354\",\n \"0.6413216\",\n \"0.64010924\",\n \"0.6380481\",\n \"0.63768035\",\n \"0.63713497\",\n \"0.6368426\",\n \"0.6362485\",\n \"0.6344793\",\n \"0.6340574\",\n \"0.6323823\",\n \"0.63180697\",\n \"0.63144886\",\n \"0.63144886\",\n \"0.63144886\",\n \"0.63144886\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78470093"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":5292,"cells":{"query":{"kind":"string","value":"Create connection line constraint between item's handle and the port."},"document":{"kind":"string","value":"def constraint(self, item, handle, glue_item):\n start = MatrixProjection(self.start, glue_item.matrix_i2c)\n end = MatrixProjection(self.end, glue_item.matrix_i2c)\n point = MatrixProjection(handle.pos, item.matrix_i2c)\n\n cx = EqualsConstraint(point.x, start.x)\n cy = BetweenConstraint(point.y, start.y, end.y)\n\n return MultiConstraint(start, end, point, cx, cy)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_fixed_distance_to_line_constraint():\n return FixedDistanceToLineConstraint()","def test_connect(self):\n line, head = self._get_line()\n self.tool.connect(line, head, (120, 50))\n cinfo = self.canvas.get_connection(head)\n self.assertTrue(cinfo is not None)\n self.assertEquals(self.box1, cinfo.connected)\n self.assertTrue(cinfo.port is self.box1.ports()[0],\n 'port %s' % cinfo.port)\n self.assertTrue(isinstance(cinfo.constraint, LineConstraint))\n # No default callback defined:\n self.assertTrue(cinfo.callback is None)\n\n line, head = self._get_line()\n self.tool.connect(line, head, (90, 50))\n cinfo2 = self.canvas.get_connection(head)\n self.assertTrue(cinfo is not cinfo2, cinfo2)\n self.assertTrue(cinfo2 is None, cinfo2)","def test_item_and_port_glue(self):\n\n ports = self.box1.ports()\n\n # glue to port nw-ne\n sink = self.tool.glue(self.line, self.head, (120, 50))\n self.assertEquals(sink.item, self.box1)\n self.assertEquals(ports[0], sink.port)\n\n # glue to port ne-se\n sink = self.tool.glue(self.line, self.head, (140, 70))\n self.assertEquals(sink.item, self.box1)\n self.assertEquals(ports[1], sink.port)\n\n # glue to port se-sw\n sink = self.tool.glue(self.line, self.head, (120, 90))\n self.assertEquals(sink.item, self.box1)\n self.assertEquals(ports[2], sink.port)\n\n # glue to port sw-nw\n sink = self.tool.glue(self.line, self.head, (100, 70))\n self.assertEquals(sink.item, self.box1)\n self.assertEquals(ports[3], sink.port)","def ioLineDrag(self, startItem, pos0, pos1, done=False):\n assert isinstance(startItem, PortItem)\n assert isinstance(pos0, QPointF)\n assert isinstance(pos1, QPointF)\n assert isinstance(done, bool)\n\n if self._draggedLineItem is None:\n self._draggedLineItem = DraggedLineItem(pos0, pos1)\n self.addItem(self._draggedLineItem)\n else:\n self._draggedLineItem.setEndpoint(pos1)\n\n vaildItem = None\n\n if QLineF(pos0, pos1).length() > 5.0:\n # Check if line is over other PortItem\n for item in self.items(pos1):\n if isinstance(item, PortItem):\n vaildItem = item\n print item.name()\n break\n\n self._draggedLineItem.showEndpoint(vaildItem is not None)\n\n if done:\n self.removeItem(self._draggedLineItem)\n self._draggedLineItem = None\n\n if vaildItem is not None:\n # Request connection creation\n name1 = startItem.fullname()\n name2 = vaildItem.fullname()\n self.sigCreateConnection.emit(name1, name2)","def test_port_create_with_binding_information(self):\n network, segments, subnets = self._create_test_segments_with_subnets(3)\n\n # Map the host to the middle segment (by mocking host/segment mapping)\n self._setup_host_mappings([\n (segments[1]['segment']['id'], 'fakehost'),\n (segments[1]['segment']['id'], 'otherhost'),\n (segments[0]['segment']['id'], 'thirdhost')])\n\n response = self._create_port(self.fmt,\n net_id=network['network']['id'],\n tenant_id=network['network']['tenant_id'],\n is_admin=True,\n arg_list=(portbindings.HOST_ID,),\n **{portbindings.HOST_ID: 'fakehost'})\n res = self.deserialize(self.fmt, response)\n self._validate_immediate_ip_allocation(res['port']['id'])\n\n # Since host mapped to middle segment, IP must come from middle subnet\n self._assert_one_ip_in_subnet(response, subnets[1]['subnet']['cidr'])","def net_acl_iptables_rule(item):\n # defaults\n fmt = {\n 'chain': '-A INPUT',\n 'device': '',\n 'protocol': ' -p tcp',\n 'state': '',\n 'identifier': ' -m comment --comment \"20CACL {}\"'.format(item['name']),\n 'target': ' -j ACCEPT',\n }\n\n if item.get('device', None):\n fmt['device'] = ' -i {}'.format(item.device)\n if item.get('protocol', None):\n fmt['protocol'] = ' -p {}'.format(item.protocol)\n # FIXME parse for false\n if item.get('stateful', False) == True:\n fmt['state'] = ' -m state --state NEW'\n if not item.get('ports', None):\n raise ValueError(\"missing ports\")\n else:\n fmt['ports'] = ' -m multiport --dports={}'.format(','.join(map(str, item['ports'])))\n\n line = \"{chain}{device}{protocol}{state}{ports}{identifier}{target}\".format(**fmt)\n\n return line","def test_reconnect_same(self):\n line, head = self._get_line()\n self.tool.connect(line, head, (120, 50))\n cinfo = self.canvas.get_connection(head)\n assert cinfo is not None\n item = cinfo.connected\n port = cinfo.port\n constraint = cinfo.constraint\n\n assert item == self.box1\n assert item != self.box2\n\n # connect to box1 again, handle's connected item and port should be\n # the same but connection constraint will differ\n connected = self.tool.connect(line, head, (120, 50))\n cinfo = self.canvas.get_connection(head)\n assert cinfo is not None\n self.assertEqual(self.box1, cinfo.connected)\n self.assertEqual(self.box1.ports()[0], cinfo.port)\n self.assertNotEqual(constraint, cinfo.constraint)","def slot_constraint(self, item, role_spec):\n return self.kb.slot_value(\n logic.expr(item),\n CONSTRAINT_EXPR,\n logic.expr(role_spec))","def __init__(self, srcNode, destNode):\r\n super(NodeConnection, self).__init__()\r\n \r\n self.setSrcNode(srcNode)\r\n self.setDestNode(destNode)\r\n \r\n self._srcPt = None\r\n self._destPt = None\r\n self.setArrowSize(10)\r\n \r\n self.Adjust()\r\n self.setFlag(QtGui.QGraphicsItem.ItemIsSelectable, True)","def _set_constraint(self):\n pass","def create_connection(\n self,\n from_id: str,\n to_id: str\n ):\n raise NotImplementedError","def addConnection(self, port1Name, port2Name, connItem):\n assert isinstance(connItem, ConnectionItem)\n\n # Ensure port1Name and port2Name are str, not QString\n port1Name = str(port1Name)\n port2Name = str(port2Name)\n\n node1Name = port1Name.split(':')[0]\n node2Name = port2Name.split(':')[0]\n\n if node1Name == node2Name:\n return False\n\n node1 = self.nodeFromName(node1Name)\n node2 = self.nodeFromName(node2Name)\n\n if node1.isConnected(port1Name) or node2.isConnected(port2Name):\n return False\n\n self.addItem(connItem)\n node1.addConnection(port1Name, connItem)\n node2.addConnection(port2Name, connItem)\n\n assert connItem.startPortName() is not None\n assert connItem.endPortName() is not None\n return True","def _setup_create_firewall_rule_with_all_params(self, protocol='tcp'):\r\n resource = 'firewall_rule'\r\n cmd = firewallrule.CreateFirewallRule(test_cli20.MyApp(sys.stdout),\r\n None)\r\n name = 'my-name'\r\n description = 'my-desc'\r\n source_ip = '192.168.1.0/24'\r\n destination_ip = '192.168.2.0/24'\r\n source_port = '0:65535'\r\n destination_port = '0:65535'\r\n action = 'allow'\r\n tenant_id = 'my-tenant'\r\n my_id = 'myid'\r\n args = ['--description', description,\r\n '--shared',\r\n '--protocol', protocol,\r\n '--source-ip-address', source_ip,\r\n '--destination-ip-address', destination_ip,\r\n '--source-port', source_port,\r\n '--destination-port', destination_port,\r\n '--action', action,\r\n '--enabled',\r\n '--admin-state-up',\r\n '--tenant-id', tenant_id]\r\n position_names = []\r\n position_values = []\r\n if protocol == 'any':\r\n protocol = None\r\n self._test_create_resource(resource, cmd, name, my_id, args,\r\n position_names, position_values,\r\n description=description, shared=True,\r\n protocol=protocol,\r\n source_ip_address=source_ip,\r\n destination_ip_address=destination_ip,\r\n source_port=source_port,\r\n destination_port=destination_port,\r\n action=action, enabled=True,\r\n tenant_id=tenant_id)","def setup_rule(self, client):\n pass","def setup_rule(self, client):\n pass","def _add_line(self, key, info):\n info = copy.deepcopy(info)\n anticipated_bus = self._get_df_with_new_elements(\"bus\")\n new_lines = []\n required = {\"from_bus_id\", \"to_bus_id\"}\n xor_sets = {(\"capacity\", \"Pmax\"), (\"capacity\", \"Pmin\")}\n optional = {\"Pmin\"}\n for i, line in enumerate(info):\n self._check_entry_keys(line, i, key, required, xor_sets, optional)\n start = line[\"from_bus_id\"]\n end = line[\"to_bus_id\"]\n if start not in anticipated_bus.index:\n raise ValueError(\n \"No bus with the following id for line #%d: %d\" % (i + 1, start)\n )\n if end not in anticipated_bus.index:\n raise ValueError(\n \"No bus with the following id for line #%d: %d\" % (i + 1, end)\n )\n if start == end:\n raise ValueError(f\"to/from buses of line #{i + 1} must be different\")\n if \"capacity\" in line:\n if not isinstance(line[\"capacity\"], (int, float)):\n raise ValueError(\"'capacity' must be a number (int/float)\")\n if line[\"capacity\"] < 0:\n raise ValueError(\"capacity of line #%d must be positive\" % (i + 1))\n # Everything looks good, let's translate this to Pmin/Pmax\n line[\"Pmax\"] = line[\"capacity\"]\n line[\"Pmin\"] = -1 * line[\"capacity\"]\n del line[\"capacity\"]\n elif {\"Pmin\", \"Pmax\"} < set(line.keys()):\n if key == \"new_branch\":\n err_msg = \"Can't independently set Pmin & Pmax for AC branches\"\n raise ValueError(err_msg)\n for p in {\"Pmin\", \"Pmax\"}:\n if not isinstance(line[p], (int, float)):\n raise ValueError(f\"'{p}' must be a number (int/float)\")\n if line[\"Pmin\"] > line[\"Pmax\"]:\n raise ValueError(\"Pmin cannot be greater than Pmax\")\n else:\n raise ValueError(\"Must specify either 'capacity' or Pmin and Pmax\")\n if (\n key == \"new_branch\"\n and anticipated_bus.interconnect[start]\n != anticipated_bus.interconnect[end]\n ):\n raise ValueError(\n \"Buses of line #%d must be in same interconnect\" % (i + 1)\n )\n elif (\n anticipated_bus.lat[start] == anticipated_bus.lat[end]\n and anticipated_bus.lon[start] == anticipated_bus.lon[end]\n ):\n raise ValueError(\"Distance between buses of line #%d is 0\" % (i + 1))\n new_lines.append(line)\n\n if key not in self.ct:\n self.ct[key] = []\n self.ct[key] += new_lines","def cmd_CONNECTION(self, line):\r\n config = ConnectionOptions(self.terminal)\r\n\r\n try:\r\n config.parseOptions(line)\r\n cmd = config.subCommand\r\n opts = config.subOptions if hasattr(config, 'subOptions') else {}\r\n except usage.UsageError as errortext:\r\n self.terminal.write(\"BUG in usage: {0}\".format(errortext))\r\n else:\r\n if cmd == 'add':\r\n if opts['tag1'] and opts['tag2']:\r\n self.callToUser('addConnection', 'robot', opts['tag1'],\r\n opts['tag2'])\r\n elif cmd == 'remove':\r\n if opts['tag1'] and opts['tag2']:\r\n self.callToUser('removeConnection', 'robot', opts['tag1'],\r\n opts['tag2'])","def build_connection(self, src, tgt) -> NoReturn:\n # If src and tgt are the same node, src not in node_collection or\n # tgt not in node_collection,\n # then skip this edge.\n if src == tgt or src not in self._nodes_collection or tgt not in self._nodes_collection:\n if src.split(':')[0] not in self._nodes_collection:\n warnings.warn(f\"Graph construct a self-loop node {src}. Ignored.\")\n return\n\n if tgt not in self._nodes_collection[src.split(':')[0]].successor_nodes:\n self._nodes_collection[src.split(':')[0]].successor_nodes.append(tgt)\n if src not in self._nodes_collection[tgt].precursor_nodes:\n self._nodes_collection[tgt.split(':')[0]].precursor_nodes.append(src)","def cmd_port (self, line):\r\n info = line[1].split (',')\r\n ip = '.'.join (info[:4])\r\n port = int(info[4])*256 + int(info[5])\r\n # how many data connections at a time?\r\n # I'm assuming one for now...\r\n # TODO: we should (optionally) verify that the\r\n # ip number belongs to the client. [wu-ftpd does this?]\r\n self.client_addr = (ip, port)\r\n self.respond ('200 PORT command successful.')","def connect(self, handle: Handle, port: Port) -> bool:\n pin = self.pin\n if not pin.subject:\n pin.subject = pin.model.create(\n UML.InputPin if isinstance(pin, InputPinItem) else UML.OutputPin\n )\n\n assert isinstance(pin.subject, (UML.InputPin, UML.OutputPin))\n pin.subject.opaqueAction = self.action.subject\n\n # This raises the item in the item hierarchy\n pin.change_parent(self.action)\n\n return True","def test_port_create_with_binding_and_no_subnets(self):\n with self.network() as network:\n segment = self._test_create_segment(\n network_id=network['network']['id'],\n physical_network='physnet',\n network_type=constants.TYPE_VLAN)\n\n # Map the host to the segment\n self._setup_host_mappings([(segment['segment']['id'], 'fakehost')])\n\n response = self._create_port(self.fmt,\n net_id=network['network']['id'],\n tenant_id=network['network']['tenant_id'],\n is_admin=True,\n arg_list=(portbindings.HOST_ID,),\n **{portbindings.HOST_ID: 'fakehost'})\n res = self.deserialize(self.fmt, response)\n\n # No subnets, so no allocation. But, it shouldn't be an error.\n self.assertEqual(0, len(res['port']['fixed_ips']))","def _connection_maker(\n self,\n first_device,\n first_port,\n second_device,\n second_port):\n if first_port is None:\n return self.network.make_connection(\n first_device.id, None,\n second_device.id, second_port.id)\n else:\n return self.network.make_connection(\n first_device.id, first_port.id,\n second_device.id, second_port.id)","def allow(self, handle, port):\n assert self.canvas\n\n line = self.line\n element = self.element\n\n # Check if no other items are connected\n connections = self.canvas.get_connections(connected=element)\n connected_items = [\n c\n for c in connections\n if isinstance(c.item, TransitionItem) and c.item is not line\n ]\n if handle is line.head and not any(connected_items):\n return super().allow(handle, port)\n else:\n return None","def addConstraint(self, constraint: Constraint, /) -> None:\n ...","def add_connection(self, switch_name, port1, port2, bidir=False):\n raise NotImplementedError()","def createConstraint(*argv):","def create_port_item(self, port_spec, is_connected, is_optional,\n is_visible, is_editable, parent=None):\n return PortItem(port_spec, is_connected, True, False, False, parent)","def constraint(self, c):\n self.add_constraint(c)","def create_port_forward_rule(self, ipaddressid, protocol, virtualmachineid,\n privateport, privateendport,\n publicport, publicendport): \n params = {'command':'createPortForwardingRule',\n 'ipaddressid':ipaddressid,\n 'protocol':protocol,\n 'privateport':privateport,\n 'privateendport':privateendport,\n 'publicport':publicport,\n 'publicendport':publicendport,\n 'virtualmachineid':virtualmachineid,\n 'openfirewall':False} \n\n try:\n response = self.send_request(params)\n res = json.loads(response)\n clsk_job_id = res['createportforwardingruleresponse']['jobid']\n self.logger.debug('Start job - createPortForwardingRule: %s' % res)\n return clsk_job_id\n except KeyError as ex:\n raise ClskError('Error parsing json data: %s' % ex)\n except ApiError as ex:\n raise ClskError(ex)","def test_port_create_with_binding_information_fallback(self):\n with self.network() as network:\n with self.subnet(network=network,\n ip_version=constants.IP_VERSION_6,\n cidr='2001:db8:0:0::/64') as subnet:\n segment = self._test_create_segment(\n network_id=network['network']['id'],\n physical_network='physnet',\n network_type=constants.TYPE_VLAN)\n\n self._validate_l2_adjacency(network['network']['id'], is_adjacent=True)\n\n # Map the host to the segment\n self._setup_host_mappings([(segment['segment']['id'], 'fakehost')])\n\n response = self._create_port(self.fmt,\n net_id=network['network']['id'],\n tenant_id=network['network']['tenant_id'],\n is_admin=True,\n arg_list=(portbindings.HOST_ID,),\n **{portbindings.HOST_ID: 'fakehost'})\n\n res = self.deserialize(self.fmt, response)\n self._validate_immediate_ip_allocation(res['port']['id'])\n\n # Since the subnet is not on a segment, fall back to it\n self._assert_one_ip_in_subnet(response, subnet['subnet']['cidr'])","def creation_validation(openstack_resource):\n validate_resource_quota(openstack_resource, PORT_OPENSTACK_TYPE)\n ctx.logger.debug('OK: port configuration is valid')","def validate_rule(self, client):\n raise NotImplementedError(\"Please fix me.\")","def validate_rule(self, client):\n raise NotImplementedError(\"Please fix me.\")","def test_add_outgoing_connection():\n\n center = Coordinates(1 , 1)\n radius = 10\n speed_limit = 20\n\n i = Intersection(center, radius, speed_limit)\n i2 = Intersection(center, radius, speed_limit)\n i2.add_connection(10.0, 20, 2, 2, 40, 'test2')\n\n start = Coordinates(1,1)\n end = Coordinates(7, 9)\n len = 15\n out_ln = 2\n in_ln = 1\n ang = 3 * math.pi / 2\n\n road = Road(start, end, len, out_ln, in_ln, ang, 20, 'Test')\n\n l = i.get_connections()\n\n assert not l\n\n i.add_outgoing_connection(road)\n\n assert l\n assert l[0].get_length() == 15\n\n l2 = i2.get_connections()\n\n assert l2\n\n i2.add_outgoing_connection(road)\n\n assert l2\n assert l2[1].get_length() == 15","def add_link_capacity(self, path, bw):\n\n # PART 1, TASK 3.4 add bw to edges","def create_lines(self) -> None:\n res = []\n for connection in self.connections:\n start_component = self.components[connection.start_entity]\n end_component = self.components[connection.end_entity]\n start_pin_location = (\n start_component.location\n + start_component.pin_locations[connection.start_pin]\n )\n end_pin_location = (\n end_component.location + end_component.pin_locations[connection.end_pin]\n )\n\n x_midpoint = (start_pin_location.x + end_pin_location.x) / 2\n bend_start = Point(x_midpoint, start_pin_location.y)\n bend_end = Point(x_midpoint, end_pin_location.y)\n bends = [bend_start, bend_end]\n res.append(Line(connection, start_pin_location, *bends, end_pin_location))\n\n self.lines = res","def _create_port_ext(self, res_port, req_port, context):\n commit = self._get_port_attr(req_port, \"commit\")\n trunked = self._get_port_attr(req_port, \"trunked\")\n hardware_id = self._get_port_attr(req_port, \"switch:hardware_id\")\n if commit is None:\n commit = False\n port_ext = db.create_port_ext(\n port_id=res_port[\"id\"],\n commit=commit,\n trunked=trunked,\n hardware_id=hardware_id,\n session=context.session)\n return port_ext.as_dict()","def _make_connection(self, src_var, target_comp, target_vname):\n src_comp = src_var.component\n target_var = self._find_or_create_variable(target_comp.name, target_vname, src_var)\n # Sanity check the target variable\n if (target_var.get_type() == VarTypes.Mapped\n and target_var.get_source_variable(recurse=True) is src_var.get_source_variable(recurse=True)):\n# print \"Connection exists between\", src_var, \"and target\", target_var\n return target_var\n elif target_var.get_type() == VarTypes.Unknown:\n # We've created this variable, so should be ok, but check for gotchas\n assert not(hasattr(target_var, u'initial_value'))\n if src_comp is target_comp.parent():\n src_if = u'private'\n target_if = u'public'\n elif src_comp.parent() is target_comp:\n src_if = u'public'\n target_if = u'private'\n else:\n assert src_comp.parent() is target_comp.parent()\n src_if = u'public'\n target_if = u'public'\n # One special case: if the src_var is actually obtained from a different\n # component at this level or above, in which case we should use the real\n # source, not that given.\n if getattr(src_var, src_if + u'_interface', u'none') == u'in':\n src_var = src_var.get_source_variable()\n # Check and set the interface attributes\n# print \"Connecting source\", src_var, src_if, getattr(src_var, src_if + u'_interface', u'none'),\n# print \"to\", target_var, target_if, getattr(target_var, target_if + u'_interface', u'none')\n assert getattr(src_var, src_if + u'_interface', u'none') != u'in'\n assert getattr(target_var, target_if + u'_interface', u'none') != u'out'\n src_var.xml_set_attribute((src_if + u'_interface', None), u'out')\n target_var.xml_set_attribute((target_if + u'_interface', None), u'in')\n # Create the connection element\n self._create_connection_element(src_var, target_var)\n self.connections_made.add(frozenset([src_var, target_var]))\n # Ensure we handle a later connection attempt between these variables correctly\n target_var._set_source_variable(src_var)\n else:\n # Naming conflict; try again with a different target name\n return self._make_connection(src_var, target_comp, target_vname + u'_')\n return target_var","def generate_connection_i(self,N_e):\n raise NotImplementedError","def _createline(self):\n return self.cv.create_line(0, 0, 0, 0, fill=\"\", width=2,\n capstyle = TK.ROUND)","def connect(self, socket_item, connection):\n # Populate connection.\n connection.socketItem = socket_item\n connection.plugNode = self.parentItem().name\n connection.plugAttr = self.attribute\n\n # Add socket to connected slots.\n if socket_item in self.connected_slots:\n self.connected_slots.remove(socket_item)\n self.connected_slots.append(socket_item)\n\n # Add connection.\n if connection not in self.connections:\n self.connections.append(connection)\n\n # Emit signal.\n nodzInst = self.scene().views()[0]\n\n nodzInst.portConnected(self.port(), socket_item.port())\n nodzInst.signal_PlugConnected.emit(connection.plugNode, connection.plugAttr, connection.socketNode, connection.socketAttr)","def test_parseConnectionLine(self):\n fake_connection = \"5,3 2 4,3\"\n actual_result = rules.parseConnectionLine(fake_connection)\n self.assertEqual(actual_result.startX, 5)\n self.assertEqual(actual_result.startY, 3)\n self.assertEqual(actual_result.direction, 2)\n self.assertEqual(actual_result.endX, 4)\n self.assertEqual(actual_result.endY, 3)","def test_commentline_item_with_no_subject_connect(create, diagram):\n comment = create(CommentItem, Comment)\n line = create(CommentLineItem)\n gi = create(GeneralizationItem)\n\n connect(line, line.head, comment)\n connect(line, line.tail, gi)\n assert diagram.connections.get_connection(line.tail).connected is gi\n assert len(comment.subject.annotatedElement) == 0","def __init__(self, constraint: ConstraintExpr):\n self.constraint = constraint","def __init__(__self__, *,\n from_port: pulumi.Input[int],\n to_port: pulumi.Input[int]):\n pulumi.set(__self__, \"from_port\", from_port)\n pulumi.set(__self__, \"to_port\", to_port)","def _build_connection(self,\n node,\n source_port: PipelineNodeIO = None,\n target_port: PipelineNodeIO = None,\n filters: entities.Filters = None,\n action: str = None) -> PipelineConnection:\n if source_port is None and self.outputs:\n source_port = self.outputs[0]\n\n if target_port is None and node.inputs:\n target_port = node.inputs[0]\n\n if node.is_root():\n self._pipeline.set_start_node(self)\n\n source_connection = PipelineConnectionPort(node_id=self.node_id, port_id=source_port.port_id)\n target_connection = PipelineConnectionPort(node_id=node.node_id, port_id=target_port.port_id)\n if action is None and source_port.actions is not None and source_port.actions != []:\n action = source_port.actions[0]\n connection = PipelineConnection(source=source_connection, target=target_connection, filters=filters,\n action=action)\n return connection","def createConstraint(self):\n return _libsbml.Model_createConstraint(self)","def add_rule(self, ip_protocol, from_port, to_port,\r\n src_group_name, src_group_owner_id, cidr_ip):\r\n rule = IPPermissions(self)\r\n rule.ip_protocol = ip_protocol\r\n rule.from_port = from_port\r\n rule.to_port = to_port\r\n self.rules.append(rule)\r\n rule.add_grant(src_group_name, src_group_owner_id, cidr_ip)","def test_commentline_glie_to_item_with_no_subject(create, diagram):\n line = create(CommentLineItem)\n gi = create(GeneralizationItem)\n\n assert allow(line, line.tail, gi)","def ftp_PORT(self, line):\n # Parse PORT request for getting IP and PORT.\n # Request comes in as:\n # > h1,h2,h3,h4,p1,p2\n # ...where the client's IP address is h1.h2.h3.h4 and the TCP\n # port number is (p1 * 256) + p2.\n try:\n line = line.split(',')\n ip = \".\".join(line[:4]).replace(',','.')\n port = (int(line[4]) * 256) + int(line[5])\n except (ValueError, OverflowError):\n self.respond(\"501 Invalid PORT format.\")\n return\n\n # FTP bounce attacks protection: according to RFC-2577 it's\n # recommended to reject PORT if IP address specified in it\n # does not match client IP address.\n if not self.permit_foreign_addresses:\n if ip != self.remote_ip:\n self.log(\"Rejected data connection to foreign address %s:%s.\"\n %(ip, port))\n self.respond(\"501 Can't connect to a foreign address.\")\n return\n\n # ...another RFC-2577 recommendation is rejecting connections\n # to privileged ports (< 1024) for security reasons.\n if not self.permit_privileged_ports:\n if port < 1024:\n self.log('PORT against the privileged port \"%s\" refused.' %port)\n self.respond(\"501 Can't connect over a privileged port.\")\n return\n\n # close existent DTP-server instance, if any.\n if self.data_server:\n self.data_server.close()\n self.data_server = None\n\n if self.data_channel:\n self.data_channel.close()\n self.data_channel = None\n\n # make sure we are not hitting the max connections limit\n if self.ftpd_instance.max_cons:\n if len(self._map) >= self.ftpd_instance.max_cons:\n msg = \"Too many connections. Can't open data channel.\"\n self.respond(\"425 %s\" %msg)\n self.log(msg)\n return\n\n # open DTP channel\n self.active_dtp(ip, port, self)","def test_port_create_on_multiconnected_host(self):\n network, segments, subnets = self._create_test_segments_with_subnets(2)\n\n # This host is bound to multiple hosts\n self._setup_host_mappings([(segments[0]['segment']['id'], 'fakehost'),\n (segments[1]['segment']['id'], 'fakehost')])\n\n response = self._create_port(self.fmt,\n net_id=network['network']['id'],\n tenant_id=network['network']['tenant_id'],\n is_admin=True,\n arg_list=(portbindings.HOST_ID,),\n **{portbindings.HOST_ID: 'fakehost'})\n self.deserialize(self.fmt, response)\n\n # multi segments supported since Antelope.\n self.assertEqual(webob.exc.HTTPCreated.code, response.status_int)","def define_edge(self):\n\n self.canvas_edge = Line(\n points=[\n self.canvas_nodes[0].pos[0] + self.nodesize[0] / 2,\n self.canvas_nodes[0].pos[1] + self.nodesize[1] / 2,\n self.canvas_nodes[1].pos[0] + self.nodesize[0] / 2,\n self.canvas_nodes[1].pos[1] + self.nodesize[1] / 2\n ],\n joint='round',\n cap='round',\n width=3\n )","def test_glue_no_port_no_can_glue(self):\n\n class Tool(ConnectHandleTool):\n def __init__(self, *args):\n super(Tool, self).__init__(*args)\n self._calls = 0\n\n def can_glue(self, *args):\n self._calls += 1\n\n tool = Tool(self.view)\n # at 300, 50 there should be no item\n sink = tool.glue(self.line, self.head, (300, 50))\n assert sink is None\n self.assertEquals(0, tool._calls)","def _add_connection(self, con):\n # get connectors by the above specified labels\n start = self.connector_by_label(con[0])\n end = self.connector_by_label(con[1])\n if start.parent_type == 'box' and end.parent_type == 'box':\n # make sure, that not two inputs or two outputs are connected\n if start.connector_type == end.connector_type:\n raise ConnectorError(f\"Connection {con} connects \"\n f\"input to input or output to output.\")\n # make sure, that inputs are always first\n # and outputs are always second\n elif (start.connector_type == 'output'\n or end.connector_type == 'input'):\n start, end = end, start\n # make sure, that a switch does not connect to itself\n elif start.parent_type == 'switch' and end.parent_type == 'switch':\n if start.switch == end.switch:\n raise ConnectorError(f\"Connection {con} connects \"\n f\"a switch to itself.\")\n\n # create connection\n connection = ArduinoSwitchControlConnection(start, end)\n\n # add connection to attributes\n self.connections.append(connection)","def add_connection(\n self, port1: ryvencore.NodePort.NodeOutput, port2: ryvencore.NodePort.NodeInput\n ) -> ryvencore.Connection.DataConnection:\n ryven_connection = self.script.flow.connect_nodes(port1, port2)\n if not ryven_connection:\n return\n\n # Add connection in compas graph\n node1 = port1.node\n node2 = port2.node\n edge_key = (node1.GLOBAL_ID, node2.GLOBAL_ID)\n if not self.has_edge(*edge_key):\n self.add_edge(*edge_key, {\"connections\": []})\n connections = self.edge_attribute(edge_key, \"connections\")\n connections.append({\"port1\": self.get_port_info(port1), \"port2\": self.get_port_info(port2)})\n self.edge_attribute(edge_key, \"connections\", connections)\n\n return ryven_connection","def make_connection(self):\n if self._created_connections[self._pattern_idx] >= self.max_connections_per_pattern:\n raise ConnectionError(\"Too many connections\")\n self._created_connections[self._pattern_idx] += 1\n conn = self.connection_class(**self.patterns[self._pattern_idx])\n conn._pattern_idx = self._pattern_idx\n return conn","def _validate_port_can_commit(self, res_port, req_port,\n session=None):\n switchport_ids = [p[\"id\"] for p in res_port[\"switch:ports\"]]\n\n if not switchport_ids:\n msg = (\"Cannot attach, no switchports found\")\n raise exc.InvalidInput(error_message=msg)\n\n bound_port_ids = []\n if switchport_ids:\n # Fetch all existing networks we are attached to.\n portbindings = db.filter_switchport_bindings_by_switch_port_ids(\n switchport_ids, session=session)\n portbindings = list(portbindings)\n bound_port_ids = set([pb.port_id for pb in portbindings])\n\n # We can't attach to a non-trunked network if the port is already\n # attached to another network.\n if bound_port_ids and (res_port[\"trunked\"] is False):\n msg = (\"Cannot attach non-trunked network, port \"\n \"already bound to network(s) %s\" % (bound_port_ids))\n raise exc.InvalidInput(error_message=msg)\n\n for bound_port_id in bound_port_ids:\n # We can't attach a trunked network if we are already attached\n # to a non-trunked network.\n port_ext = db.get_port_ext(bound_port_id, session=session)\n if not port_ext.trunked:\n msg = (\"Already attached via non-trunked \"\n \"port %s\" % (bound_port_id))\n raise exc.InvalidInput(error_message=msg)","def add_link_reservation(self, node_id, tp_ofid, value):\n\n try:\n # Look for the port\n port = None\n for edge in self.neighbors[node_id]:\n if edge[\"src_port\"] == tp_ofid:\n port = edge\n break\n\n # Stop if the port is down\n if port is None:\n # fname = sys._getframe().f_code.co_name\n # print(\"{}: Port {} not found. Exiting.\".format(fname, tp_ofid))\n return\n\n # Adjust the link reservation amount\n port[\"bps_reserved\"] += value\n except KeyError:\n pass","def allocate_connection_on_interconnect(bandwidth=None, connectionName=None, ownerAccount=None, interconnectId=None, vlan=None):\n pass","def generate_connection_e(self,N_e):\n raise NotImplementedError","def add_constraint(self, constraint):\n self.constraints.append(constraint)","def publish_constraint(self):\n links = []\n for x, kf in enumerate(self.keyframes[1:], 1):\n p1 = self.keyframes[x - 1].pose.x(), self.keyframes[x - 1].pose.y()\n p2 = self.keyframes[x].pose.x(), self.keyframes[x].pose.y()\n links.append((p1, p2, \"green\"))\n\n for k, _ in self.keyframes[x].constraints:\n p0 = self.keyframes[k].pose.x(), self.keyframes[k].pose.y()\n links.append((p0, p2, \"red\"))\n\n if links:\n link_msg = ros_constraints(links)\n link_msg.header.stamp = self.current_keyframe.time\n self.constraint_pub.publish(link_msg)","def add_connector(self):\n \n no = len(self.connectors)\n state = {}\n state[\"s_pin\"] = no\n state[\"p_pin\"] = no\n state[\"s_label\"] = \"C%d\" % no\n \n if len(self.connectors)>0:\n state = self.connectors[-1].get_state()\n state[\"s_pin\"] = no\n state[\"p_pin\"] = no\n state[\"s_label\"] = \"C%d\" % (no)\n else:\n if self.mount == self.MOUNT_THT:\n state[\"p_shape\"] = Con.SHAPE_HOLE\n elif self.mount == self.MOUNT_SMD:\n state[\"p_shape\"] = Con.SHAPE_PAD\n \n c = Con(no)\n c.set_state(state) \n \n self.sch_layers[\"pins\"].add(c.s_svg)\n self.pcb_layers[\"copper1\"].add(c.p_svg)\n self.connectors.append(c)","def connector(midpoint = (0, 0), width = 1, orientation = 0):\n D = Device(name = 'connector')\n D.add_port(name = 1, midpoint = [midpoint[0], midpoint[1]],\n width = width, orientation = orientation)\n D.add_port(name = 2, midpoint = [midpoint[0], midpoint[1]],\n width = width, orientation = orientation - 180)\n return D","def create_link(self, node1, port1, node2, port2=None):\n if not port2:\n ports_in_use = set((node['adapter_number'], node['port_number']) for link in self.links() for node in link['nodes'] if node['node_id'] == node2['node_id'])\n available_ports = (port for port in node2['ports'] if (port['adapter_number'], port['port_number']) not in ports_in_use)\n next_available_port = next(available_ports)\n else:\n next_available_port = node2['ports'][port2]\n\n link_obj = {'nodes' : [{'adapter_number' : node1['ports'][port1]['adapter_number'],\n 'port_number' : node1['ports'][port1]['port_number'],\n 'label' : { 'text' : node1['ports'][port1]['name']},\n 'node_id' : node1['node_id']},\n {'adapter_number' : next_available_port['adapter_number'],\n 'port_number' : next_available_port['port_number'],\n 'label' : { 'text' : next_available_port['name']},\n 'node_id' : node2['node_id']}]}\n\n links_url = \"{}/links\".format(self.url)\n\n result = requests.post(links_url, auth=self.auth, data=json.dumps(link_obj))\n result.raise_for_status()\n #links.append(link_obj)","def test_commentline_element_connect(create, diagram):\n comment = create(CommentItem, Comment)\n line = create(CommentLineItem)\n ac = create(ActorItem, UML.Actor)\n\n connect(line, line.head, comment)\n connect(line, line.tail, ac)\n assert diagram.connections.get_connection(line.tail).connected is ac\n assert len(comment.subject.annotatedElement) == 1\n assert ac.subject in comment.subject.annotatedElement","def add_connection(self, ip, port, key):\n\n # Socket declaration\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n sock.bind((ip, port))\n\n # Adding connection to the list\n self.connections[key] = sock","def _check_connectionline(self):\n self.connection_first_device, \\\n self.connection_first_port \\\n = self._check_validconnectionoutput()\n if self._is_arrow(self.symbol):\n # Get next symbol\n self.symbol = self.scanner.get_symbol()\n self.connection_second_device, \\\n self.connection_second_port \\\n = self._check_validconnectioninput()\n if len(\n self.semantic_errors_list) == 0 and len(\n self.syntax_errors_list) == 0:\n # Only create connection if no previous errors\n connection_error = self._connection_maker(\n self.connection_first_device,\n self.connection_first_port,\n self.connection_second_device,\n self.connection_second_port)\n # Send the returned error ID for error reporting\n self._display_semantic_error(connection_error)\n # Run a while loop to check for possible multiple connections from\n # same output\n while (\n not self._is_semicolon(\n self.symbol)) and (\n not self._is_eof(\n self.symbol)):\n if self._is_comma(self.symbol):\n self.symbol = self.scanner.get_symbol()\n self.connection_second_device, \\\n self.connection_second_port \\\n = self._check_validconnectioninput()\n if len(\n self.semantic_errors_list) == 0 and len(\n self.syntax_errors_list) == 0:\n # Only create connection if no previous errors\n connection_error = self._connection_maker(\n self.connection_first_device,\n self.connection_first_port,\n self.connection_second_device,\n self.connection_second_port)\n # Send the returned error ID for error reporting\n self._display_semantic_error(connection_error)\n else:\n # No comma\n self._display_syntax_error(\"comma\")\n self._semicolon_skipper()\n self.symbol = self.scanner.get_symbol()\n elif self._is_semicolon(self.symbol):\n self.symbol = self.scanner.get_symbol()\n else:\n # No '->'\n self._display_syntax_error(\"arrow\")\n self._semicolon_skipper()\n self.symbol = self.scanner.get_symbol()\n return None","def is_constraint(self, line):\n constraints = ['PRIMARY', 'KEY', 'UNIQUE', 'CONSTRAINT']\n for constraint in constraints:\n if line.startswith(constraint): return True\n return False","def test_reconnect_another(self):\n line, head = self._get_line()\n self.tool.connect(line, head, (120, 50))\n cinfo = self.canvas.get_connection(head)\n assert cinfo is not None\n item = cinfo.connected\n port = cinfo.port\n constraint = cinfo.constraint\n\n assert item == self.box1\n assert port == self.box1.ports()[0]\n assert item != self.box2\n\n # connect to box2, handle's connected item and connection data\n # should differ\n self.tool.connect(line, head, (120, 150))\n cinfo = self.canvas.get_connection(head)\n assert cinfo is not None\n self.assertEqual(self.box2, cinfo.connected)\n self.assertEqual(self.box2.ports()[0], cinfo.port)\n\n # old connection does not exist\n self.assertNotEqual(item, cinfo.connected)\n self.assertNotEqual(constraint, cinfo.constraint)","def create_line(uniform = True, *args):\n axis = cmds.radioButtonGrp(widgets[\"lineAxisRBG\"], q=True, sl=True)\n length = cmds.floatFieldGrp(widgets[\"lineLenFFG\"], q=True, v1=True)\n density = cmds.floatFieldGrp(widgets[\"lineDenFFG\"], q=True, v1=True)\n\n numCvs = length * density\n if numCvs < 3.0: # curve needs 3 cvs (for 3 dg curve)\n numCvs = 3.0\n\n cvDist = length/numCvs\n\n # make a list of pt dist along some axis\n axisList = []\n for x in range(0,int(numCvs)+1):\n axisList.append(x)\n\n pts = []\n\n if axis == 1:\n for y in range(0, int(numCvs)+1):\n pt = [axisList[y]*cvDist, 0, 0]\n pts.append(pt)\n\n if axis == 2:\n for y in range(0, int(numCvs)+1):\n pt = [0, axisList[y]*cvDist, 0]\n pts.append(pt)\n\n if axis == 3:\n for y in range(0, int(numCvs)+1):\n pt = [0, 0, axisList[y]*cvDist]\n pts.append(pt)\t\t\t\n \n line = cmds.curve(name = \"line_01\", d=3, p=pts)\n shp = cmds.listRelatives(line, s=True)[0]\n cmds.rename(shp, \"{0}Shape\".format(line))\n if uniform:\n line = cmds.rebuildCurve(line, rebuildType = 0, spans = 0, keepRange = 0, replaceOriginal=True, end=1, keepControlPoints=0)[0]\n\n cmds.select(line, r=True)","def add_node():\n\ttry:\n\t\tif(check_entries()):\n\t\t\tserver_entry = [frame.entries[0].get(), str(frame.entries[1].get()+\".\"+frame.entries[2].get()+\".\"+frame.entries[3].get()+\".\"+frame.entries[4].get())]\n\t\t\tclient_entry = [frame.entries[5].get(), str(frame.entries[6].get()+\".\"+frame.entries[7].get()+\".\"+frame.entries[8].get()+\".\"+frame.entries[9].get())]\n\t\t\tconnection_lifetime = frame.entries[10].get()\n\t\t\tnetwork.add_connection(server_entry, client_entry, connection_lifetime);\n\n\texcept ValueError as err:\n\t\tfeedback.config(text=err)","def create_nodes_collinear_2D_constraint():\n return NodesCollinear2DConstraint()","def add_link(self, src_node_id, dst_node_id, src_port, dst_port, capacity=10000000):\n\n # Get function name\n fname = sys._getframe().f_code.co_name\n \n # Do not add the link if the nodes are not in the topology\n if src_node_id not in self.nodes or dst_node_id not in self.nodes:\n #print(\"{}: Nodes {} and/or {} not found - can't add link\".\n # format(fname, src_node_id, dst_node_id),\n # file=sys.stderr)\n return\n\n # Do not add the link if the link already exists\n for i in range(0, len(self.neighbors[src_node_id])):\n neighbor_id = self.neighbors[src_node_id][i][\"dst_node_id\"]\n if (neighbor_id == dst_node_id and\n self.neighbors[src_node_id][i][\"src_port\"] == src_port and\n self.neighbors[src_node_id][i][\"dst_port\"] == dst_port):\n # print(\"{}: link already exists - exiting\".format(fname))\n return\n\n # Do not add the link if the link already exists\n for i in range(0, len(self.neighbors[dst_node_id])):\n neighbor_id = self.neighbors[dst_node_id][i][\"dst_node_id\"]\n if (neighbor_id == src_node_id and\n self.neighbors[dst_node_id][i][\"src_port\"] == dst_port and\n self.neighbors[dst_node_id][i][\"dst_port\"] == src_port):\n # print(\"{}: link already exists - exiting\".format(fname))\n return\n\n # Derive src/dst interfaces\n # Want to make a version of this function in the Topology class. Not\n # good architecture\n src_int = self.mgr.get_interface(src_node_id, src_port)\n dst_int = self.mgr.get_interface(dst_node_id, dst_port)\n \n # Destination entry in the topology\n src_entry = {\n \"src_node_id\": src_node_id,\n \"dst_node_id\": dst_node_id,\n \"src_port\": src_port,\n \"dst_port\": dst_port,\n \"src_int\": src_int,\n \"dst_int\": dst_int,\n \"bps_reserved\": 0,\n \"bps_current\": 0,\n \"bps_capacity\": capacity,\n \"cur_bytes_sent\": 0,\n \"cur_bytes_recvd\": 0,\n \"prev_bytes_sent\": 0,\n \"prev_bytes_recvd\": 0,\n \"utilization_pct\": 0.0\n }\n self.neighbors[src_node_id].append(src_entry)\n\n # Destination entry in the topology\n dst_entry = {\n \"src_node_id\": dst_node_id,\n \"dst_node_id\": src_node_id,\n \"src_port\": dst_port,\n \"dst_port\": src_port,\n \"src_int\": dst_int,\n \"dst_int\": src_int,\n \"bps_reserved\": 0,\n \"bps_current\": 0,\n \"bps_capacity\": capacity,\n \"cur_bytes_sent\": 0,\n \"cur_bytes_recvd\": 0,\n \"prev_bytes_sent\": 0,\n \"prev_bytes_recvd\": 0,\n \"utilization_pct\": 0.0\n }\n self.neighbors[dst_node_id].append(dst_entry)\n\n # Add the link to self.links if it does not already exist\n # if ((src_port, dst_port) not in self.links and\n # (dst_port, src_port) not in self.links):\n # self.links[(src_port, dst_port)] = src_index\n # self.links[(dst_port, dst_port)] = dst_index\n \n self.l += 1","def get_connection(self, *args, **kwargs):\n conn = super(SSLHTTPAdapter, self).get_connection(*args, **kwargs)\n if conn.assert_hostname != self.assert_hostname:\n conn.assert_hostname = self.assert_hostname\n return conn","def plt_connecting_lines():\n\n for i in range(0, Molecule.connection_count):\n tmp1 = Molecule.right_endpt[Molecule.left_connection[i] - 1]\n tmp2 = Molecule.left_endpt[Molecule.right_connection[i] - 1]\n tmp3 = Molecule.energy[Molecule.left_connection[i] - 1]\n tmp4 = Molecule.energy[Molecule.right_connection[i] - 1]\n\n plt.plot([tmp1, tmp2], [tmp3, tmp4], color=PlotParameter.connection_line_color,\n lw=PlotParameter.connection_line_width, linestyle='--')\n\n return None","def add_connection_beginning(self, route, potential_solution):\n\n current_station = route.stations[0]\n\n # pick a random new station out of all connections of the current station\n new_station = random.choice(list(current_station.connections.keys()))\n\n # find the connection between the two stations \n link = current_station.connections[new_station] \n\n # find the time of the connection \n time = link.time \n \n\n # only accept the change if it wouldn't exceed the maximum time\n if time + route.total_time <= self.max_minutes:\n \n # insert the connection to the front of the connection list\n route.insert_connection(link, time, 0)\n\n # insert the station to the front of the station list\n route.insert_station(new_station, 0)","def constraints(self):\n ...","def setup_logical_port_connectivity(self, context, port_db):\n pass","def test_port_init_singelton(self):\n v1 = 23.0\n v2 = 29.0\n p1 = cn.Port()\n p1.value = v1\n # check setting of value\n p2 = cn.Port(v1)\n # check fixing\n p3 = cn.Port(\n value=v1,\n fixed=True\n )\n p4 = cn.Port(\n value=v1,\n fixed=False\n )\n # check linking\n p5 = cn.Port(v2)\n p5.link = p4\n\n self.assertEqual(\n np.allclose(\n p1.value, p2.value\n ),\n True\n )\n self.assertEqual(p3.fixed, True)\n self.assertEqual(p4.fixed, False)\n self.assertEqual(\n np.allclose(\n p5.value,\n p4.value\n ),\n True\n )\n\n # check bounds\n fixed = False\n is_output = False\n is_reactive = False\n is_bounded = True\n lower_bound = 2\n upper_bound = 5\n value = 0\n p6 = cn.Port(\n value=value,\n fixed=fixed,\n is_output=is_output,\n is_reactive=is_reactive,\n is_bounded=is_bounded,\n lb=lower_bound,\n ub=upper_bound\n )\n self.assertEqual(\n np.all(p6.value <= upper_bound),\n True\n )\n self.assertEqual(\n np.all(p6.value >= lower_bound),\n True\n )\n self.assertAlmostEqual(\n p6.value[0],\n lower_bound # the lower bound is not part of the\n )","def addPortOnSide(self, node: LNode, portSide: PortSide) -> LPort:\n side = node.getPortSideView(portSide)\n port = LPort(node, None, side=portSide, name=\"port%d\" % len(side))\n side.append(port)\n\n if not node.portConstraints.isSideFixed():\n node.portConstraints = PortConstraints.FIXED_SIDE\n\n return port","def setup_rule(self, client, *args, **keyword_args):\n pass","def setup_rule(self, client, *args, **keyword_args):\n pass","def create_outbound(self, addr, use_new_connection=False):","def _check_binding(self, value: t.Any) -> t.Any:\n client = getattr(value, \"CLIENT\", value)\n if isinstance(client, self.__class__) and client is not self:\n err = f\"{value} is already set to {client!r} and cannot be set to {self!r}\"\n raise ConnectError(err)\n value.CLIENT = self\n return value","def connect(self, address: Tuple[str, int]) -> None:\n ...","def item_create(\n item, item_id, item_type, create=\"create\", extra_args=None, cibfile=None\n):\n cmd = [\"pcs\"]\n if isinstance(cibfile, str):\n cmd += [\"-f\", cibfile]\n\n if isinstance(item, str):\n cmd += [item]\n elif isinstance(item, (list, tuple)):\n cmd += item\n\n # constraint command follows a different order\n if item in [\"constraint\"]:\n if isinstance(item_type, str):\n cmd += [item_type]\n\n if isinstance(create, str):\n cmd += [create]\n elif isinstance(create, (list, tuple)):\n cmd += create\n\n # constraint command needs item_id in format 'id=<id' after all params\n # constraint command follows a different order\n if item not in [\"constraint\"]:\n cmd += [item_id]\n if isinstance(item_type, str):\n cmd += [item_type]\n\n if isinstance(extra_args, (list, tuple)):\n # constraint command needs item_id in format 'id=<id' after all params\n if item in [\"constraint\"]:\n extra_args = extra_args + [\"id={}\".format(item_id)]\n cmd += extra_args\n\n return __salt__[\"cmd.run_all\"](cmd, output_loglevel=\"trace\", python_shell=False)","def __init__(self, source_node, source_gate_name, target_node, target_slot_name, weight=1):\n self.link(source_node, source_gate_name, target_node, target_slot_name, weight)","def serial_connection(self, value):\n if len(value) > 4:\n raise ValueUnsupportedError(\n 'serial port connection list', value,\n 'no more than 4 connections (ESXi limitation)')\n COTDeploy.serial_connection.fset(self, value)","def __init__(self, link, lineproto, interface_name):\n self.link = link\n self.lineproto = lineproto\n self.interface_name = interface_name","def __init__(self, ip, port, header):\n \n self.header = header\n self.ip = ip\n self.port = port\n try:\n self._connect_socket()\n except socket.error as e:\n print(e)\n self.close_and_exit()","def __init__(self, ip, port, automatic = True, control_buf_size = 32, data_buf_size = 128, \\\n m_to = 0.01, socket_to = 0.005):\n self.conn = Connector(ip, port, control_buf_size, data_buf_size, socket_to)\n self.conn.connect()\n self.m_to = m_to\n self.status = Modem.Status.IDLE\n self.node_status = 0\n self.automatic = automatic\n self.interpreter = Interpreter()\n self.mainPID = os.getpid()\n self.error_status = Modem.ErrorDict.NONE\n self.commands_queue = \"\".split(Interpreter.END_COMMAND)\n if automatic:\n thread.start_new_thread(self.run,())","def connectCurrentConnection(self, position):\n\n # get the item at the position\n itemAt = self.itemAt(position.toPoint(), self.getView().transform())\n\n # remove the connection (a new connection will get added if there is a valid connector)\n self.removeItem(self.currentlyConnecting)\n connection = self.currentlyConnecting\n self.currentlyConnecting = None\n\n\n \"\"\" if itemAt is a Connector (Top/Bottom) item (if you pull onto a Blob) \"\"\"\n if itemAt is not None and isinstance(itemAt, ConnectorItem) and not self.disabled:\n # check, whether the connection is already connected to connector of the given type (top/bottom)\n if connection.checkSameConnectorTypeRestriction(itemAt):\n # get the connectors\n if itemAt.isTopConnector():\n topConnector = itemAt\n bottomConnector = connection.getConnectorIfNotFullyConnected()\n else:\n topConnector = connection.getConnectorIfNotFullyConnected()\n bottomConnector = itemAt\n\n # get data needed to notify the underling data structure\n topLayerID = topConnector.getNodeItem().getLayerID()\n bottomLayerID = bottomConnector.getNodeItem().getLayerID()\n topBlobIndex = topConnector.getIndex()\n bottomBlobIndex = bottomConnector.getIndex()\n\n # notify to change the data\n self.__nodeEditor.tryToConnect(topLayerID, topBlobIndex, bottomLayerID, bottomBlobIndex)\n\n \"\"\" if itemAt is a Node Item (if you pull onto a layer) \"\"\"\n if itemAt is not None and isinstance(itemAt, NodeItem) and not self.disabled:\n # test if connector starts at a top Blob\n if connection.getConnectorIfNotFullyConnected().isTopConnector():\n\n # bottomNode is itemAt\n bottomNode = itemAt\n\n # get layer IDs\n topLayerID = connection.getConnectorIfNotFullyConnected().getNodeItem().getLayerID()\n bottomLayerID = bottomNode.getLayerID()\n topBlobIndex = connection.getConnectorIfNotFullyConnected().getIndex()\n\n # get the Index of the new Blob, should it be necessary to create one\n # (determined in the following for loop)\n bottomBlobIndex = bottomNode.getBottomConnectorCount()\n\n # current connection top name and phase\n topBlobName = connection.getConnectorIfNotFullyConnected().getBlobName()\n topBlobPhase = connection.getConnectorIfNotFullyConnected().getPhase()\n\n # check if there is a connected Node that has a different phase than the currently\n # connecting Node, but has a connection with the same top Blob Name\n topBlobFound = False\n for bottomBlob in bottomNode.getBottomConnectors():\n if len(bottomBlob.getConnectedNodes()) > 0:\n for topNode in bottomBlob.getConnectedNodes():\n for topBlob in topNode.getTopConnectors():\n if topBlob.getBlobName() == topBlobName and topBlob.getPhase() != topBlobPhase:\n bottomBlobIndex = bottomBlob.getIndex()\n topBlobFound = True\n break\n\n # otherwise (if no corresponding top Blob was found)\n # get Index of first empty bottom blob (if available)\n counter = -1\n emptyBlobAvailable = False\n if not topBlobFound:\n for blob in bottomNode.getBottomConnectors():\n counter += 1\n if len(blob.getConnectedNodes()) == 0:\n bottomBlobIndex = counter\n emptyBlobAvailable = True\n break\n\n # add empty bottom blob property\n if not emptyBlobAvailable and not topBlobFound:\n self.__nodeEditor.tryToAddBottomBlob(bottomLayerID, \"\")\n\n # connect nodes\n connected = self.__nodeEditor.tryToConnect(topLayerID, topBlobIndex, bottomLayerID, bottomBlobIndex)\n\n # if the connection did not work but a new blob was created, remove it\n if not connected and not emptyBlobAvailable and not topBlobFound:\n bottomNode.removeBottomConnector(bottomBlobIndex)","def add_constraint(self, constraint):\n self._ckey += 1\n self.constraints[self._ckey] = constraint","def drawHollowConstraints(fname, linewidth=2, rgb=\"(1,1,1)\"):\n constraints = read_parameters(fname)\n if not constraints.type in (\"box\", \"brick\"):\n raise NotImplementedError(\"sorry, constraint %(type)r not implemented\" % vars(constraints))\n c = constraints\n return drawBrick(c.res_num1, c.atom1, c.res_num2, c.atom2,\n chain1=c.chain1, chain2=c.chain2,\n offset1=c.offset1, offset2=c.offset2,\n boxname=c.type,\n linewidth=linewidth, rgb=rgb)","def removeConnection(self, connectionItem):\n assert isinstance(connectionItem, ConnectionItem)\n assert connectionItem.startPortName() is not None\n assert connectionItem.endPortName() is not None\n\n self.removeConnectionByPortNames(connectionItem.startPortName(),\n connectionItem.endPortName())","def _create_connection_element(self, var1, var2):\n conn, swap = self._find_connection_element(var1, var2)\n if conn:\n if swap:\n var1, var2 = var2, var1\n else:\n conn = var1.xml_create_element(u'connection', NSS[u'cml'])\n mapc = var1.xml_create_element(u'map_components', NSS[u'cml'],\n attributes={u'component_1': var1.component.name,\n u'component_2': var2.component.name})\n conn.xml_append(mapc)\n self.model.xml_append(conn)\n mapv = var1.xml_create_element(u'map_variables', NSS[u'cml'],\n attributes={u'variable_1': var1.name,\n u'variable_2': var2.name})\n conn.xml_append(mapv)","def validate_conn(self, solution):\r\n\r\n active_nodes = [idx for idx, value in enumerate(solution) # remove not included nodes in solution\r\n if value != 0 and idx not in self.dead_nodes and self.network.get_node(idx).energy >= cf.COMMUNICATION_ENERGY]\r\n active_nodes.append(-1) # add a sink node \r\n visited = self.DFS(self.network_graph, active_nodes[0], active_nodes)\r\n if len(visited) == len(active_nodes):\r\n return True\r\n else:\r\n return False","def rcv_addr(self, node_name, **kwargs):\n # Get keyword args\n auto_create = kwargs.get(\"auto_create\", True)\n auto_delete = kwargs.get(\"auto_delete\", False)\n link_name = kwargs.get(\"link_name\")\n durable = kwargs.get(\"durable\", False)\n browse = kwargs.get(\"browse\", False)\n exclusive = kwargs.get(\"exclusive\", False)\n binding_list = kwargs.get(\"binding_list\", [])\n ftd_count = kwargs.get(\"ftd_count\")\n ftd_size = kwargs.get(\"ftd_size\")\n policy = kwargs.get(\"policy\", \"flow-to-disk\")\n\n create_policy = None\n if auto_create:\n create_policy = \"always\"\n delete_policy = None\n if auto_delete:\n delete_policy = \"always\"\n mode = None\n if browse:\n mode = \"browse\"\n x_declare_list = [\"\\\"exclusive\\\": %s\" % exclusive]\n if ftd_count != None or ftd_size != None:\n queue_policy = [\"\\'qpid.policy_type\\': %s\" % policy]\n if ftd_count:\n queue_policy.append(\"\\'qpid.max_count\\': %d\" % ftd_count)\n if ftd_size:\n queue_policy.append(\"\\'qpid.max_size\\': %d\" % ftd_size)\n x_declare_list.append(\"arguments: %s\" % self._fmt_map(queue_policy))\n x_bindings_list = []\n for binding in binding_list:\n x_bindings_list.append(\"{exchange: %s, key: %s}\" % binding)\n if durable: reliability = 'at-least-once'\n else: reliability = None\n return self.addr_fmt(node_name, create_policy=create_policy, delete_policy=delete_policy, mode=mode, link=True,\n link_name=link_name, durable=durable, x_declare_list=x_declare_list,\n x_bindings_list=x_bindings_list, link_reliability=reliability)","def test_connection(self):\n gen = self.create(GeneralizationItem)\n c1 = self.create(ClassItem, UML.Class)\n c2 = self.create(ClassItem, UML.Class)\n\n self.connect(gen, gen.tail, c1)\n assert self.get_connected(gen.tail) is c1\n\n self.connect(gen, gen.head, c2)\n assert gen.subject is not None\n assert gen.subject.general is c2.subject\n assert gen.subject.specific is c1.subject"],"string":"[\n \"def create_fixed_distance_to_line_constraint():\\n return FixedDistanceToLineConstraint()\",\n \"def test_connect(self):\\n line, head = self._get_line()\\n self.tool.connect(line, head, (120, 50))\\n cinfo = self.canvas.get_connection(head)\\n self.assertTrue(cinfo is not None)\\n self.assertEquals(self.box1, cinfo.connected)\\n self.assertTrue(cinfo.port is self.box1.ports()[0],\\n 'port %s' % cinfo.port)\\n self.assertTrue(isinstance(cinfo.constraint, LineConstraint))\\n # No default callback defined:\\n self.assertTrue(cinfo.callback is None)\\n\\n line, head = self._get_line()\\n self.tool.connect(line, head, (90, 50))\\n cinfo2 = self.canvas.get_connection(head)\\n self.assertTrue(cinfo is not cinfo2, cinfo2)\\n self.assertTrue(cinfo2 is None, cinfo2)\",\n \"def test_item_and_port_glue(self):\\n\\n ports = self.box1.ports()\\n\\n # glue to port nw-ne\\n sink = self.tool.glue(self.line, self.head, (120, 50))\\n self.assertEquals(sink.item, self.box1)\\n self.assertEquals(ports[0], sink.port)\\n\\n # glue to port ne-se\\n sink = self.tool.glue(self.line, self.head, (140, 70))\\n self.assertEquals(sink.item, self.box1)\\n self.assertEquals(ports[1], sink.port)\\n\\n # glue to port se-sw\\n sink = self.tool.glue(self.line, self.head, (120, 90))\\n self.assertEquals(sink.item, self.box1)\\n self.assertEquals(ports[2], sink.port)\\n\\n # glue to port sw-nw\\n sink = self.tool.glue(self.line, self.head, (100, 70))\\n self.assertEquals(sink.item, self.box1)\\n self.assertEquals(ports[3], sink.port)\",\n \"def ioLineDrag(self, startItem, pos0, pos1, done=False):\\n assert isinstance(startItem, PortItem)\\n assert isinstance(pos0, QPointF)\\n assert isinstance(pos1, QPointF)\\n assert isinstance(done, bool)\\n\\n if self._draggedLineItem is None:\\n self._draggedLineItem = DraggedLineItem(pos0, pos1)\\n self.addItem(self._draggedLineItem)\\n else:\\n self._draggedLineItem.setEndpoint(pos1)\\n\\n vaildItem = None\\n\\n if QLineF(pos0, pos1).length() > 5.0:\\n # Check if line is over other PortItem\\n for item in self.items(pos1):\\n if isinstance(item, PortItem):\\n vaildItem = item\\n print item.name()\\n break\\n\\n self._draggedLineItem.showEndpoint(vaildItem is not None)\\n\\n if done:\\n self.removeItem(self._draggedLineItem)\\n self._draggedLineItem = None\\n\\n if vaildItem is not None:\\n # Request connection creation\\n name1 = startItem.fullname()\\n name2 = vaildItem.fullname()\\n self.sigCreateConnection.emit(name1, name2)\",\n \"def test_port_create_with_binding_information(self):\\n network, segments, subnets = self._create_test_segments_with_subnets(3)\\n\\n # Map the host to the middle segment (by mocking host/segment mapping)\\n self._setup_host_mappings([\\n (segments[1]['segment']['id'], 'fakehost'),\\n (segments[1]['segment']['id'], 'otherhost'),\\n (segments[0]['segment']['id'], 'thirdhost')])\\n\\n response = self._create_port(self.fmt,\\n net_id=network['network']['id'],\\n tenant_id=network['network']['tenant_id'],\\n is_admin=True,\\n arg_list=(portbindings.HOST_ID,),\\n **{portbindings.HOST_ID: 'fakehost'})\\n res = self.deserialize(self.fmt, response)\\n self._validate_immediate_ip_allocation(res['port']['id'])\\n\\n # Since host mapped to middle segment, IP must come from middle subnet\\n self._assert_one_ip_in_subnet(response, subnets[1]['subnet']['cidr'])\",\n \"def net_acl_iptables_rule(item):\\n # defaults\\n fmt = {\\n 'chain': '-A INPUT',\\n 'device': '',\\n 'protocol': ' -p tcp',\\n 'state': '',\\n 'identifier': ' -m comment --comment \\\"20CACL {}\\\"'.format(item['name']),\\n 'target': ' -j ACCEPT',\\n }\\n\\n if item.get('device', None):\\n fmt['device'] = ' -i {}'.format(item.device)\\n if item.get('protocol', None):\\n fmt['protocol'] = ' -p {}'.format(item.protocol)\\n # FIXME parse for false\\n if item.get('stateful', False) == True:\\n fmt['state'] = ' -m state --state NEW'\\n if not item.get('ports', None):\\n raise ValueError(\\\"missing ports\\\")\\n else:\\n fmt['ports'] = ' -m multiport --dports={}'.format(','.join(map(str, item['ports'])))\\n\\n line = \\\"{chain}{device}{protocol}{state}{ports}{identifier}{target}\\\".format(**fmt)\\n\\n return line\",\n \"def test_reconnect_same(self):\\n line, head = self._get_line()\\n self.tool.connect(line, head, (120, 50))\\n cinfo = self.canvas.get_connection(head)\\n assert cinfo is not None\\n item = cinfo.connected\\n port = cinfo.port\\n constraint = cinfo.constraint\\n\\n assert item == self.box1\\n assert item != self.box2\\n\\n # connect to box1 again, handle's connected item and port should be\\n # the same but connection constraint will differ\\n connected = self.tool.connect(line, head, (120, 50))\\n cinfo = self.canvas.get_connection(head)\\n assert cinfo is not None\\n self.assertEqual(self.box1, cinfo.connected)\\n self.assertEqual(self.box1.ports()[0], cinfo.port)\\n self.assertNotEqual(constraint, cinfo.constraint)\",\n \"def slot_constraint(self, item, role_spec):\\n return self.kb.slot_value(\\n logic.expr(item),\\n CONSTRAINT_EXPR,\\n logic.expr(role_spec))\",\n \"def __init__(self, srcNode, destNode):\\r\\n super(NodeConnection, self).__init__()\\r\\n \\r\\n self.setSrcNode(srcNode)\\r\\n self.setDestNode(destNode)\\r\\n \\r\\n self._srcPt = None\\r\\n self._destPt = None\\r\\n self.setArrowSize(10)\\r\\n \\r\\n self.Adjust()\\r\\n self.setFlag(QtGui.QGraphicsItem.ItemIsSelectable, True)\",\n \"def _set_constraint(self):\\n pass\",\n \"def create_connection(\\n self,\\n from_id: str,\\n to_id: str\\n ):\\n raise NotImplementedError\",\n \"def addConnection(self, port1Name, port2Name, connItem):\\n assert isinstance(connItem, ConnectionItem)\\n\\n # Ensure port1Name and port2Name are str, not QString\\n port1Name = str(port1Name)\\n port2Name = str(port2Name)\\n\\n node1Name = port1Name.split(':')[0]\\n node2Name = port2Name.split(':')[0]\\n\\n if node1Name == node2Name:\\n return False\\n\\n node1 = self.nodeFromName(node1Name)\\n node2 = self.nodeFromName(node2Name)\\n\\n if node1.isConnected(port1Name) or node2.isConnected(port2Name):\\n return False\\n\\n self.addItem(connItem)\\n node1.addConnection(port1Name, connItem)\\n node2.addConnection(port2Name, connItem)\\n\\n assert connItem.startPortName() is not None\\n assert connItem.endPortName() is not None\\n return True\",\n \"def _setup_create_firewall_rule_with_all_params(self, protocol='tcp'):\\r\\n resource = 'firewall_rule'\\r\\n cmd = firewallrule.CreateFirewallRule(test_cli20.MyApp(sys.stdout),\\r\\n None)\\r\\n name = 'my-name'\\r\\n description = 'my-desc'\\r\\n source_ip = '192.168.1.0/24'\\r\\n destination_ip = '192.168.2.0/24'\\r\\n source_port = '0:65535'\\r\\n destination_port = '0:65535'\\r\\n action = 'allow'\\r\\n tenant_id = 'my-tenant'\\r\\n my_id = 'myid'\\r\\n args = ['--description', description,\\r\\n '--shared',\\r\\n '--protocol', protocol,\\r\\n '--source-ip-address', source_ip,\\r\\n '--destination-ip-address', destination_ip,\\r\\n '--source-port', source_port,\\r\\n '--destination-port', destination_port,\\r\\n '--action', action,\\r\\n '--enabled',\\r\\n '--admin-state-up',\\r\\n '--tenant-id', tenant_id]\\r\\n position_names = []\\r\\n position_values = []\\r\\n if protocol == 'any':\\r\\n protocol = None\\r\\n self._test_create_resource(resource, cmd, name, my_id, args,\\r\\n position_names, position_values,\\r\\n description=description, shared=True,\\r\\n protocol=protocol,\\r\\n source_ip_address=source_ip,\\r\\n destination_ip_address=destination_ip,\\r\\n source_port=source_port,\\r\\n destination_port=destination_port,\\r\\n action=action, enabled=True,\\r\\n tenant_id=tenant_id)\",\n \"def setup_rule(self, client):\\n pass\",\n \"def setup_rule(self, client):\\n pass\",\n \"def _add_line(self, key, info):\\n info = copy.deepcopy(info)\\n anticipated_bus = self._get_df_with_new_elements(\\\"bus\\\")\\n new_lines = []\\n required = {\\\"from_bus_id\\\", \\\"to_bus_id\\\"}\\n xor_sets = {(\\\"capacity\\\", \\\"Pmax\\\"), (\\\"capacity\\\", \\\"Pmin\\\")}\\n optional = {\\\"Pmin\\\"}\\n for i, line in enumerate(info):\\n self._check_entry_keys(line, i, key, required, xor_sets, optional)\\n start = line[\\\"from_bus_id\\\"]\\n end = line[\\\"to_bus_id\\\"]\\n if start not in anticipated_bus.index:\\n raise ValueError(\\n \\\"No bus with the following id for line #%d: %d\\\" % (i + 1, start)\\n )\\n if end not in anticipated_bus.index:\\n raise ValueError(\\n \\\"No bus with the following id for line #%d: %d\\\" % (i + 1, end)\\n )\\n if start == end:\\n raise ValueError(f\\\"to/from buses of line #{i + 1} must be different\\\")\\n if \\\"capacity\\\" in line:\\n if not isinstance(line[\\\"capacity\\\"], (int, float)):\\n raise ValueError(\\\"'capacity' must be a number (int/float)\\\")\\n if line[\\\"capacity\\\"] < 0:\\n raise ValueError(\\\"capacity of line #%d must be positive\\\" % (i + 1))\\n # Everything looks good, let's translate this to Pmin/Pmax\\n line[\\\"Pmax\\\"] = line[\\\"capacity\\\"]\\n line[\\\"Pmin\\\"] = -1 * line[\\\"capacity\\\"]\\n del line[\\\"capacity\\\"]\\n elif {\\\"Pmin\\\", \\\"Pmax\\\"} < set(line.keys()):\\n if key == \\\"new_branch\\\":\\n err_msg = \\\"Can't independently set Pmin & Pmax for AC branches\\\"\\n raise ValueError(err_msg)\\n for p in {\\\"Pmin\\\", \\\"Pmax\\\"}:\\n if not isinstance(line[p], (int, float)):\\n raise ValueError(f\\\"'{p}' must be a number (int/float)\\\")\\n if line[\\\"Pmin\\\"] > line[\\\"Pmax\\\"]:\\n raise ValueError(\\\"Pmin cannot be greater than Pmax\\\")\\n else:\\n raise ValueError(\\\"Must specify either 'capacity' or Pmin and Pmax\\\")\\n if (\\n key == \\\"new_branch\\\"\\n and anticipated_bus.interconnect[start]\\n != anticipated_bus.interconnect[end]\\n ):\\n raise ValueError(\\n \\\"Buses of line #%d must be in same interconnect\\\" % (i + 1)\\n )\\n elif (\\n anticipated_bus.lat[start] == anticipated_bus.lat[end]\\n and anticipated_bus.lon[start] == anticipated_bus.lon[end]\\n ):\\n raise ValueError(\\\"Distance between buses of line #%d is 0\\\" % (i + 1))\\n new_lines.append(line)\\n\\n if key not in self.ct:\\n self.ct[key] = []\\n self.ct[key] += new_lines\",\n \"def cmd_CONNECTION(self, line):\\r\\n config = ConnectionOptions(self.terminal)\\r\\n\\r\\n try:\\r\\n config.parseOptions(line)\\r\\n cmd = config.subCommand\\r\\n opts = config.subOptions if hasattr(config, 'subOptions') else {}\\r\\n except usage.UsageError as errortext:\\r\\n self.terminal.write(\\\"BUG in usage: {0}\\\".format(errortext))\\r\\n else:\\r\\n if cmd == 'add':\\r\\n if opts['tag1'] and opts['tag2']:\\r\\n self.callToUser('addConnection', 'robot', opts['tag1'],\\r\\n opts['tag2'])\\r\\n elif cmd == 'remove':\\r\\n if opts['tag1'] and opts['tag2']:\\r\\n self.callToUser('removeConnection', 'robot', opts['tag1'],\\r\\n opts['tag2'])\",\n \"def build_connection(self, src, tgt) -> NoReturn:\\n # If src and tgt are the same node, src not in node_collection or\\n # tgt not in node_collection,\\n # then skip this edge.\\n if src == tgt or src not in self._nodes_collection or tgt not in self._nodes_collection:\\n if src.split(':')[0] not in self._nodes_collection:\\n warnings.warn(f\\\"Graph construct a self-loop node {src}. Ignored.\\\")\\n return\\n\\n if tgt not in self._nodes_collection[src.split(':')[0]].successor_nodes:\\n self._nodes_collection[src.split(':')[0]].successor_nodes.append(tgt)\\n if src not in self._nodes_collection[tgt].precursor_nodes:\\n self._nodes_collection[tgt.split(':')[0]].precursor_nodes.append(src)\",\n \"def cmd_port (self, line):\\r\\n info = line[1].split (',')\\r\\n ip = '.'.join (info[:4])\\r\\n port = int(info[4])*256 + int(info[5])\\r\\n # how many data connections at a time?\\r\\n # I'm assuming one for now...\\r\\n # TODO: we should (optionally) verify that the\\r\\n # ip number belongs to the client. [wu-ftpd does this?]\\r\\n self.client_addr = (ip, port)\\r\\n self.respond ('200 PORT command successful.')\",\n \"def connect(self, handle: Handle, port: Port) -> bool:\\n pin = self.pin\\n if not pin.subject:\\n pin.subject = pin.model.create(\\n UML.InputPin if isinstance(pin, InputPinItem) else UML.OutputPin\\n )\\n\\n assert isinstance(pin.subject, (UML.InputPin, UML.OutputPin))\\n pin.subject.opaqueAction = self.action.subject\\n\\n # This raises the item in the item hierarchy\\n pin.change_parent(self.action)\\n\\n return True\",\n \"def test_port_create_with_binding_and_no_subnets(self):\\n with self.network() as network:\\n segment = self._test_create_segment(\\n network_id=network['network']['id'],\\n physical_network='physnet',\\n network_type=constants.TYPE_VLAN)\\n\\n # Map the host to the segment\\n self._setup_host_mappings([(segment['segment']['id'], 'fakehost')])\\n\\n response = self._create_port(self.fmt,\\n net_id=network['network']['id'],\\n tenant_id=network['network']['tenant_id'],\\n is_admin=True,\\n arg_list=(portbindings.HOST_ID,),\\n **{portbindings.HOST_ID: 'fakehost'})\\n res = self.deserialize(self.fmt, response)\\n\\n # No subnets, so no allocation. But, it shouldn't be an error.\\n self.assertEqual(0, len(res['port']['fixed_ips']))\",\n \"def _connection_maker(\\n self,\\n first_device,\\n first_port,\\n second_device,\\n second_port):\\n if first_port is None:\\n return self.network.make_connection(\\n first_device.id, None,\\n second_device.id, second_port.id)\\n else:\\n return self.network.make_connection(\\n first_device.id, first_port.id,\\n second_device.id, second_port.id)\",\n \"def allow(self, handle, port):\\n assert self.canvas\\n\\n line = self.line\\n element = self.element\\n\\n # Check if no other items are connected\\n connections = self.canvas.get_connections(connected=element)\\n connected_items = [\\n c\\n for c in connections\\n if isinstance(c.item, TransitionItem) and c.item is not line\\n ]\\n if handle is line.head and not any(connected_items):\\n return super().allow(handle, port)\\n else:\\n return None\",\n \"def addConstraint(self, constraint: Constraint, /) -> None:\\n ...\",\n \"def add_connection(self, switch_name, port1, port2, bidir=False):\\n raise NotImplementedError()\",\n \"def createConstraint(*argv):\",\n \"def create_port_item(self, port_spec, is_connected, is_optional,\\n is_visible, is_editable, parent=None):\\n return PortItem(port_spec, is_connected, True, False, False, parent)\",\n \"def constraint(self, c):\\n self.add_constraint(c)\",\n \"def create_port_forward_rule(self, ipaddressid, protocol, virtualmachineid,\\n privateport, privateendport,\\n publicport, publicendport): \\n params = {'command':'createPortForwardingRule',\\n 'ipaddressid':ipaddressid,\\n 'protocol':protocol,\\n 'privateport':privateport,\\n 'privateendport':privateendport,\\n 'publicport':publicport,\\n 'publicendport':publicendport,\\n 'virtualmachineid':virtualmachineid,\\n 'openfirewall':False} \\n\\n try:\\n response = self.send_request(params)\\n res = json.loads(response)\\n clsk_job_id = res['createportforwardingruleresponse']['jobid']\\n self.logger.debug('Start job - createPortForwardingRule: %s' % res)\\n return clsk_job_id\\n except KeyError as ex:\\n raise ClskError('Error parsing json data: %s' % ex)\\n except ApiError as ex:\\n raise ClskError(ex)\",\n \"def test_port_create_with_binding_information_fallback(self):\\n with self.network() as network:\\n with self.subnet(network=network,\\n ip_version=constants.IP_VERSION_6,\\n cidr='2001:db8:0:0::/64') as subnet:\\n segment = self._test_create_segment(\\n network_id=network['network']['id'],\\n physical_network='physnet',\\n network_type=constants.TYPE_VLAN)\\n\\n self._validate_l2_adjacency(network['network']['id'], is_adjacent=True)\\n\\n # Map the host to the segment\\n self._setup_host_mappings([(segment['segment']['id'], 'fakehost')])\\n\\n response = self._create_port(self.fmt,\\n net_id=network['network']['id'],\\n tenant_id=network['network']['tenant_id'],\\n is_admin=True,\\n arg_list=(portbindings.HOST_ID,),\\n **{portbindings.HOST_ID: 'fakehost'})\\n\\n res = self.deserialize(self.fmt, response)\\n self._validate_immediate_ip_allocation(res['port']['id'])\\n\\n # Since the subnet is not on a segment, fall back to it\\n self._assert_one_ip_in_subnet(response, subnet['subnet']['cidr'])\",\n \"def creation_validation(openstack_resource):\\n validate_resource_quota(openstack_resource, PORT_OPENSTACK_TYPE)\\n ctx.logger.debug('OK: port configuration is valid')\",\n \"def validate_rule(self, client):\\n raise NotImplementedError(\\\"Please fix me.\\\")\",\n \"def validate_rule(self, client):\\n raise NotImplementedError(\\\"Please fix me.\\\")\",\n \"def test_add_outgoing_connection():\\n\\n center = Coordinates(1 , 1)\\n radius = 10\\n speed_limit = 20\\n\\n i = Intersection(center, radius, speed_limit)\\n i2 = Intersection(center, radius, speed_limit)\\n i2.add_connection(10.0, 20, 2, 2, 40, 'test2')\\n\\n start = Coordinates(1,1)\\n end = Coordinates(7, 9)\\n len = 15\\n out_ln = 2\\n in_ln = 1\\n ang = 3 * math.pi / 2\\n\\n road = Road(start, end, len, out_ln, in_ln, ang, 20, 'Test')\\n\\n l = i.get_connections()\\n\\n assert not l\\n\\n i.add_outgoing_connection(road)\\n\\n assert l\\n assert l[0].get_length() == 15\\n\\n l2 = i2.get_connections()\\n\\n assert l2\\n\\n i2.add_outgoing_connection(road)\\n\\n assert l2\\n assert l2[1].get_length() == 15\",\n \"def add_link_capacity(self, path, bw):\\n\\n # PART 1, TASK 3.4 add bw to edges\",\n \"def create_lines(self) -> None:\\n res = []\\n for connection in self.connections:\\n start_component = self.components[connection.start_entity]\\n end_component = self.components[connection.end_entity]\\n start_pin_location = (\\n start_component.location\\n + start_component.pin_locations[connection.start_pin]\\n )\\n end_pin_location = (\\n end_component.location + end_component.pin_locations[connection.end_pin]\\n )\\n\\n x_midpoint = (start_pin_location.x + end_pin_location.x) / 2\\n bend_start = Point(x_midpoint, start_pin_location.y)\\n bend_end = Point(x_midpoint, end_pin_location.y)\\n bends = [bend_start, bend_end]\\n res.append(Line(connection, start_pin_location, *bends, end_pin_location))\\n\\n self.lines = res\",\n \"def _create_port_ext(self, res_port, req_port, context):\\n commit = self._get_port_attr(req_port, \\\"commit\\\")\\n trunked = self._get_port_attr(req_port, \\\"trunked\\\")\\n hardware_id = self._get_port_attr(req_port, \\\"switch:hardware_id\\\")\\n if commit is None:\\n commit = False\\n port_ext = db.create_port_ext(\\n port_id=res_port[\\\"id\\\"],\\n commit=commit,\\n trunked=trunked,\\n hardware_id=hardware_id,\\n session=context.session)\\n return port_ext.as_dict()\",\n \"def _make_connection(self, src_var, target_comp, target_vname):\\n src_comp = src_var.component\\n target_var = self._find_or_create_variable(target_comp.name, target_vname, src_var)\\n # Sanity check the target variable\\n if (target_var.get_type() == VarTypes.Mapped\\n and target_var.get_source_variable(recurse=True) is src_var.get_source_variable(recurse=True)):\\n# print \\\"Connection exists between\\\", src_var, \\\"and target\\\", target_var\\n return target_var\\n elif target_var.get_type() == VarTypes.Unknown:\\n # We've created this variable, so should be ok, but check for gotchas\\n assert not(hasattr(target_var, u'initial_value'))\\n if src_comp is target_comp.parent():\\n src_if = u'private'\\n target_if = u'public'\\n elif src_comp.parent() is target_comp:\\n src_if = u'public'\\n target_if = u'private'\\n else:\\n assert src_comp.parent() is target_comp.parent()\\n src_if = u'public'\\n target_if = u'public'\\n # One special case: if the src_var is actually obtained from a different\\n # component at this level or above, in which case we should use the real\\n # source, not that given.\\n if getattr(src_var, src_if + u'_interface', u'none') == u'in':\\n src_var = src_var.get_source_variable()\\n # Check and set the interface attributes\\n# print \\\"Connecting source\\\", src_var, src_if, getattr(src_var, src_if + u'_interface', u'none'),\\n# print \\\"to\\\", target_var, target_if, getattr(target_var, target_if + u'_interface', u'none')\\n assert getattr(src_var, src_if + u'_interface', u'none') != u'in'\\n assert getattr(target_var, target_if + u'_interface', u'none') != u'out'\\n src_var.xml_set_attribute((src_if + u'_interface', None), u'out')\\n target_var.xml_set_attribute((target_if + u'_interface', None), u'in')\\n # Create the connection element\\n self._create_connection_element(src_var, target_var)\\n self.connections_made.add(frozenset([src_var, target_var]))\\n # Ensure we handle a later connection attempt between these variables correctly\\n target_var._set_source_variable(src_var)\\n else:\\n # Naming conflict; try again with a different target name\\n return self._make_connection(src_var, target_comp, target_vname + u'_')\\n return target_var\",\n \"def generate_connection_i(self,N_e):\\n raise NotImplementedError\",\n \"def _createline(self):\\n return self.cv.create_line(0, 0, 0, 0, fill=\\\"\\\", width=2,\\n capstyle = TK.ROUND)\",\n \"def connect(self, socket_item, connection):\\n # Populate connection.\\n connection.socketItem = socket_item\\n connection.plugNode = self.parentItem().name\\n connection.plugAttr = self.attribute\\n\\n # Add socket to connected slots.\\n if socket_item in self.connected_slots:\\n self.connected_slots.remove(socket_item)\\n self.connected_slots.append(socket_item)\\n\\n # Add connection.\\n if connection not in self.connections:\\n self.connections.append(connection)\\n\\n # Emit signal.\\n nodzInst = self.scene().views()[0]\\n\\n nodzInst.portConnected(self.port(), socket_item.port())\\n nodzInst.signal_PlugConnected.emit(connection.plugNode, connection.plugAttr, connection.socketNode, connection.socketAttr)\",\n \"def test_parseConnectionLine(self):\\n fake_connection = \\\"5,3 2 4,3\\\"\\n actual_result = rules.parseConnectionLine(fake_connection)\\n self.assertEqual(actual_result.startX, 5)\\n self.assertEqual(actual_result.startY, 3)\\n self.assertEqual(actual_result.direction, 2)\\n self.assertEqual(actual_result.endX, 4)\\n self.assertEqual(actual_result.endY, 3)\",\n \"def test_commentline_item_with_no_subject_connect(create, diagram):\\n comment = create(CommentItem, Comment)\\n line = create(CommentLineItem)\\n gi = create(GeneralizationItem)\\n\\n connect(line, line.head, comment)\\n connect(line, line.tail, gi)\\n assert diagram.connections.get_connection(line.tail).connected is gi\\n assert len(comment.subject.annotatedElement) == 0\",\n \"def __init__(self, constraint: ConstraintExpr):\\n self.constraint = constraint\",\n \"def __init__(__self__, *,\\n from_port: pulumi.Input[int],\\n to_port: pulumi.Input[int]):\\n pulumi.set(__self__, \\\"from_port\\\", from_port)\\n pulumi.set(__self__, \\\"to_port\\\", to_port)\",\n \"def _build_connection(self,\\n node,\\n source_port: PipelineNodeIO = None,\\n target_port: PipelineNodeIO = None,\\n filters: entities.Filters = None,\\n action: str = None) -> PipelineConnection:\\n if source_port is None and self.outputs:\\n source_port = self.outputs[0]\\n\\n if target_port is None and node.inputs:\\n target_port = node.inputs[0]\\n\\n if node.is_root():\\n self._pipeline.set_start_node(self)\\n\\n source_connection = PipelineConnectionPort(node_id=self.node_id, port_id=source_port.port_id)\\n target_connection = PipelineConnectionPort(node_id=node.node_id, port_id=target_port.port_id)\\n if action is None and source_port.actions is not None and source_port.actions != []:\\n action = source_port.actions[0]\\n connection = PipelineConnection(source=source_connection, target=target_connection, filters=filters,\\n action=action)\\n return connection\",\n \"def createConstraint(self):\\n return _libsbml.Model_createConstraint(self)\",\n \"def add_rule(self, ip_protocol, from_port, to_port,\\r\\n src_group_name, src_group_owner_id, cidr_ip):\\r\\n rule = IPPermissions(self)\\r\\n rule.ip_protocol = ip_protocol\\r\\n rule.from_port = from_port\\r\\n rule.to_port = to_port\\r\\n self.rules.append(rule)\\r\\n rule.add_grant(src_group_name, src_group_owner_id, cidr_ip)\",\n \"def test_commentline_glie_to_item_with_no_subject(create, diagram):\\n line = create(CommentLineItem)\\n gi = create(GeneralizationItem)\\n\\n assert allow(line, line.tail, gi)\",\n \"def ftp_PORT(self, line):\\n # Parse PORT request for getting IP and PORT.\\n # Request comes in as:\\n # > h1,h2,h3,h4,p1,p2\\n # ...where the client's IP address is h1.h2.h3.h4 and the TCP\\n # port number is (p1 * 256) + p2.\\n try:\\n line = line.split(',')\\n ip = \\\".\\\".join(line[:4]).replace(',','.')\\n port = (int(line[4]) * 256) + int(line[5])\\n except (ValueError, OverflowError):\\n self.respond(\\\"501 Invalid PORT format.\\\")\\n return\\n\\n # FTP bounce attacks protection: according to RFC-2577 it's\\n # recommended to reject PORT if IP address specified in it\\n # does not match client IP address.\\n if not self.permit_foreign_addresses:\\n if ip != self.remote_ip:\\n self.log(\\\"Rejected data connection to foreign address %s:%s.\\\"\\n %(ip, port))\\n self.respond(\\\"501 Can't connect to a foreign address.\\\")\\n return\\n\\n # ...another RFC-2577 recommendation is rejecting connections\\n # to privileged ports (< 1024) for security reasons.\\n if not self.permit_privileged_ports:\\n if port < 1024:\\n self.log('PORT against the privileged port \\\"%s\\\" refused.' %port)\\n self.respond(\\\"501 Can't connect over a privileged port.\\\")\\n return\\n\\n # close existent DTP-server instance, if any.\\n if self.data_server:\\n self.data_server.close()\\n self.data_server = None\\n\\n if self.data_channel:\\n self.data_channel.close()\\n self.data_channel = None\\n\\n # make sure we are not hitting the max connections limit\\n if self.ftpd_instance.max_cons:\\n if len(self._map) >= self.ftpd_instance.max_cons:\\n msg = \\\"Too many connections. Can't open data channel.\\\"\\n self.respond(\\\"425 %s\\\" %msg)\\n self.log(msg)\\n return\\n\\n # open DTP channel\\n self.active_dtp(ip, port, self)\",\n \"def test_port_create_on_multiconnected_host(self):\\n network, segments, subnets = self._create_test_segments_with_subnets(2)\\n\\n # This host is bound to multiple hosts\\n self._setup_host_mappings([(segments[0]['segment']['id'], 'fakehost'),\\n (segments[1]['segment']['id'], 'fakehost')])\\n\\n response = self._create_port(self.fmt,\\n net_id=network['network']['id'],\\n tenant_id=network['network']['tenant_id'],\\n is_admin=True,\\n arg_list=(portbindings.HOST_ID,),\\n **{portbindings.HOST_ID: 'fakehost'})\\n self.deserialize(self.fmt, response)\\n\\n # multi segments supported since Antelope.\\n self.assertEqual(webob.exc.HTTPCreated.code, response.status_int)\",\n \"def define_edge(self):\\n\\n self.canvas_edge = Line(\\n points=[\\n self.canvas_nodes[0].pos[0] + self.nodesize[0] / 2,\\n self.canvas_nodes[0].pos[1] + self.nodesize[1] / 2,\\n self.canvas_nodes[1].pos[0] + self.nodesize[0] / 2,\\n self.canvas_nodes[1].pos[1] + self.nodesize[1] / 2\\n ],\\n joint='round',\\n cap='round',\\n width=3\\n )\",\n \"def test_glue_no_port_no_can_glue(self):\\n\\n class Tool(ConnectHandleTool):\\n def __init__(self, *args):\\n super(Tool, self).__init__(*args)\\n self._calls = 0\\n\\n def can_glue(self, *args):\\n self._calls += 1\\n\\n tool = Tool(self.view)\\n # at 300, 50 there should be no item\\n sink = tool.glue(self.line, self.head, (300, 50))\\n assert sink is None\\n self.assertEquals(0, tool._calls)\",\n \"def _add_connection(self, con):\\n # get connectors by the above specified labels\\n start = self.connector_by_label(con[0])\\n end = self.connector_by_label(con[1])\\n if start.parent_type == 'box' and end.parent_type == 'box':\\n # make sure, that not two inputs or two outputs are connected\\n if start.connector_type == end.connector_type:\\n raise ConnectorError(f\\\"Connection {con} connects \\\"\\n f\\\"input to input or output to output.\\\")\\n # make sure, that inputs are always first\\n # and outputs are always second\\n elif (start.connector_type == 'output'\\n or end.connector_type == 'input'):\\n start, end = end, start\\n # make sure, that a switch does not connect to itself\\n elif start.parent_type == 'switch' and end.parent_type == 'switch':\\n if start.switch == end.switch:\\n raise ConnectorError(f\\\"Connection {con} connects \\\"\\n f\\\"a switch to itself.\\\")\\n\\n # create connection\\n connection = ArduinoSwitchControlConnection(start, end)\\n\\n # add connection to attributes\\n self.connections.append(connection)\",\n \"def add_connection(\\n self, port1: ryvencore.NodePort.NodeOutput, port2: ryvencore.NodePort.NodeInput\\n ) -> ryvencore.Connection.DataConnection:\\n ryven_connection = self.script.flow.connect_nodes(port1, port2)\\n if not ryven_connection:\\n return\\n\\n # Add connection in compas graph\\n node1 = port1.node\\n node2 = port2.node\\n edge_key = (node1.GLOBAL_ID, node2.GLOBAL_ID)\\n if not self.has_edge(*edge_key):\\n self.add_edge(*edge_key, {\\\"connections\\\": []})\\n connections = self.edge_attribute(edge_key, \\\"connections\\\")\\n connections.append({\\\"port1\\\": self.get_port_info(port1), \\\"port2\\\": self.get_port_info(port2)})\\n self.edge_attribute(edge_key, \\\"connections\\\", connections)\\n\\n return ryven_connection\",\n \"def make_connection(self):\\n if self._created_connections[self._pattern_idx] >= self.max_connections_per_pattern:\\n raise ConnectionError(\\\"Too many connections\\\")\\n self._created_connections[self._pattern_idx] += 1\\n conn = self.connection_class(**self.patterns[self._pattern_idx])\\n conn._pattern_idx = self._pattern_idx\\n return conn\",\n \"def _validate_port_can_commit(self, res_port, req_port,\\n session=None):\\n switchport_ids = [p[\\\"id\\\"] for p in res_port[\\\"switch:ports\\\"]]\\n\\n if not switchport_ids:\\n msg = (\\\"Cannot attach, no switchports found\\\")\\n raise exc.InvalidInput(error_message=msg)\\n\\n bound_port_ids = []\\n if switchport_ids:\\n # Fetch all existing networks we are attached to.\\n portbindings = db.filter_switchport_bindings_by_switch_port_ids(\\n switchport_ids, session=session)\\n portbindings = list(portbindings)\\n bound_port_ids = set([pb.port_id for pb in portbindings])\\n\\n # We can't attach to a non-trunked network if the port is already\\n # attached to another network.\\n if bound_port_ids and (res_port[\\\"trunked\\\"] is False):\\n msg = (\\\"Cannot attach non-trunked network, port \\\"\\n \\\"already bound to network(s) %s\\\" % (bound_port_ids))\\n raise exc.InvalidInput(error_message=msg)\\n\\n for bound_port_id in bound_port_ids:\\n # We can't attach a trunked network if we are already attached\\n # to a non-trunked network.\\n port_ext = db.get_port_ext(bound_port_id, session=session)\\n if not port_ext.trunked:\\n msg = (\\\"Already attached via non-trunked \\\"\\n \\\"port %s\\\" % (bound_port_id))\\n raise exc.InvalidInput(error_message=msg)\",\n \"def add_link_reservation(self, node_id, tp_ofid, value):\\n\\n try:\\n # Look for the port\\n port = None\\n for edge in self.neighbors[node_id]:\\n if edge[\\\"src_port\\\"] == tp_ofid:\\n port = edge\\n break\\n\\n # Stop if the port is down\\n if port is None:\\n # fname = sys._getframe().f_code.co_name\\n # print(\\\"{}: Port {} not found. Exiting.\\\".format(fname, tp_ofid))\\n return\\n\\n # Adjust the link reservation amount\\n port[\\\"bps_reserved\\\"] += value\\n except KeyError:\\n pass\",\n \"def allocate_connection_on_interconnect(bandwidth=None, connectionName=None, ownerAccount=None, interconnectId=None, vlan=None):\\n pass\",\n \"def generate_connection_e(self,N_e):\\n raise NotImplementedError\",\n \"def add_constraint(self, constraint):\\n self.constraints.append(constraint)\",\n \"def publish_constraint(self):\\n links = []\\n for x, kf in enumerate(self.keyframes[1:], 1):\\n p1 = self.keyframes[x - 1].pose.x(), self.keyframes[x - 1].pose.y()\\n p2 = self.keyframes[x].pose.x(), self.keyframes[x].pose.y()\\n links.append((p1, p2, \\\"green\\\"))\\n\\n for k, _ in self.keyframes[x].constraints:\\n p0 = self.keyframes[k].pose.x(), self.keyframes[k].pose.y()\\n links.append((p0, p2, \\\"red\\\"))\\n\\n if links:\\n link_msg = ros_constraints(links)\\n link_msg.header.stamp = self.current_keyframe.time\\n self.constraint_pub.publish(link_msg)\",\n \"def add_connector(self):\\n \\n no = len(self.connectors)\\n state = {}\\n state[\\\"s_pin\\\"] = no\\n state[\\\"p_pin\\\"] = no\\n state[\\\"s_label\\\"] = \\\"C%d\\\" % no\\n \\n if len(self.connectors)>0:\\n state = self.connectors[-1].get_state()\\n state[\\\"s_pin\\\"] = no\\n state[\\\"p_pin\\\"] = no\\n state[\\\"s_label\\\"] = \\\"C%d\\\" % (no)\\n else:\\n if self.mount == self.MOUNT_THT:\\n state[\\\"p_shape\\\"] = Con.SHAPE_HOLE\\n elif self.mount == self.MOUNT_SMD:\\n state[\\\"p_shape\\\"] = Con.SHAPE_PAD\\n \\n c = Con(no)\\n c.set_state(state) \\n \\n self.sch_layers[\\\"pins\\\"].add(c.s_svg)\\n self.pcb_layers[\\\"copper1\\\"].add(c.p_svg)\\n self.connectors.append(c)\",\n \"def connector(midpoint = (0, 0), width = 1, orientation = 0):\\n D = Device(name = 'connector')\\n D.add_port(name = 1, midpoint = [midpoint[0], midpoint[1]],\\n width = width, orientation = orientation)\\n D.add_port(name = 2, midpoint = [midpoint[0], midpoint[1]],\\n width = width, orientation = orientation - 180)\\n return D\",\n \"def create_link(self, node1, port1, node2, port2=None):\\n if not port2:\\n ports_in_use = set((node['adapter_number'], node['port_number']) for link in self.links() for node in link['nodes'] if node['node_id'] == node2['node_id'])\\n available_ports = (port for port in node2['ports'] if (port['adapter_number'], port['port_number']) not in ports_in_use)\\n next_available_port = next(available_ports)\\n else:\\n next_available_port = node2['ports'][port2]\\n\\n link_obj = {'nodes' : [{'adapter_number' : node1['ports'][port1]['adapter_number'],\\n 'port_number' : node1['ports'][port1]['port_number'],\\n 'label' : { 'text' : node1['ports'][port1]['name']},\\n 'node_id' : node1['node_id']},\\n {'adapter_number' : next_available_port['adapter_number'],\\n 'port_number' : next_available_port['port_number'],\\n 'label' : { 'text' : next_available_port['name']},\\n 'node_id' : node2['node_id']}]}\\n\\n links_url = \\\"{}/links\\\".format(self.url)\\n\\n result = requests.post(links_url, auth=self.auth, data=json.dumps(link_obj))\\n result.raise_for_status()\\n #links.append(link_obj)\",\n \"def test_commentline_element_connect(create, diagram):\\n comment = create(CommentItem, Comment)\\n line = create(CommentLineItem)\\n ac = create(ActorItem, UML.Actor)\\n\\n connect(line, line.head, comment)\\n connect(line, line.tail, ac)\\n assert diagram.connections.get_connection(line.tail).connected is ac\\n assert len(comment.subject.annotatedElement) == 1\\n assert ac.subject in comment.subject.annotatedElement\",\n \"def add_connection(self, ip, port, key):\\n\\n # Socket declaration\\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n sock.bind((ip, port))\\n\\n # Adding connection to the list\\n self.connections[key] = sock\",\n \"def _check_connectionline(self):\\n self.connection_first_device, \\\\\\n self.connection_first_port \\\\\\n = self._check_validconnectionoutput()\\n if self._is_arrow(self.symbol):\\n # Get next symbol\\n self.symbol = self.scanner.get_symbol()\\n self.connection_second_device, \\\\\\n self.connection_second_port \\\\\\n = self._check_validconnectioninput()\\n if len(\\n self.semantic_errors_list) == 0 and len(\\n self.syntax_errors_list) == 0:\\n # Only create connection if no previous errors\\n connection_error = self._connection_maker(\\n self.connection_first_device,\\n self.connection_first_port,\\n self.connection_second_device,\\n self.connection_second_port)\\n # Send the returned error ID for error reporting\\n self._display_semantic_error(connection_error)\\n # Run a while loop to check for possible multiple connections from\\n # same output\\n while (\\n not self._is_semicolon(\\n self.symbol)) and (\\n not self._is_eof(\\n self.symbol)):\\n if self._is_comma(self.symbol):\\n self.symbol = self.scanner.get_symbol()\\n self.connection_second_device, \\\\\\n self.connection_second_port \\\\\\n = self._check_validconnectioninput()\\n if len(\\n self.semantic_errors_list) == 0 and len(\\n self.syntax_errors_list) == 0:\\n # Only create connection if no previous errors\\n connection_error = self._connection_maker(\\n self.connection_first_device,\\n self.connection_first_port,\\n self.connection_second_device,\\n self.connection_second_port)\\n # Send the returned error ID for error reporting\\n self._display_semantic_error(connection_error)\\n else:\\n # No comma\\n self._display_syntax_error(\\\"comma\\\")\\n self._semicolon_skipper()\\n self.symbol = self.scanner.get_symbol()\\n elif self._is_semicolon(self.symbol):\\n self.symbol = self.scanner.get_symbol()\\n else:\\n # No '->'\\n self._display_syntax_error(\\\"arrow\\\")\\n self._semicolon_skipper()\\n self.symbol = self.scanner.get_symbol()\\n return None\",\n \"def is_constraint(self, line):\\n constraints = ['PRIMARY', 'KEY', 'UNIQUE', 'CONSTRAINT']\\n for constraint in constraints:\\n if line.startswith(constraint): return True\\n return False\",\n \"def test_reconnect_another(self):\\n line, head = self._get_line()\\n self.tool.connect(line, head, (120, 50))\\n cinfo = self.canvas.get_connection(head)\\n assert cinfo is not None\\n item = cinfo.connected\\n port = cinfo.port\\n constraint = cinfo.constraint\\n\\n assert item == self.box1\\n assert port == self.box1.ports()[0]\\n assert item != self.box2\\n\\n # connect to box2, handle's connected item and connection data\\n # should differ\\n self.tool.connect(line, head, (120, 150))\\n cinfo = self.canvas.get_connection(head)\\n assert cinfo is not None\\n self.assertEqual(self.box2, cinfo.connected)\\n self.assertEqual(self.box2.ports()[0], cinfo.port)\\n\\n # old connection does not exist\\n self.assertNotEqual(item, cinfo.connected)\\n self.assertNotEqual(constraint, cinfo.constraint)\",\n \"def create_line(uniform = True, *args):\\n axis = cmds.radioButtonGrp(widgets[\\\"lineAxisRBG\\\"], q=True, sl=True)\\n length = cmds.floatFieldGrp(widgets[\\\"lineLenFFG\\\"], q=True, v1=True)\\n density = cmds.floatFieldGrp(widgets[\\\"lineDenFFG\\\"], q=True, v1=True)\\n\\n numCvs = length * density\\n if numCvs < 3.0: # curve needs 3 cvs (for 3 dg curve)\\n numCvs = 3.0\\n\\n cvDist = length/numCvs\\n\\n # make a list of pt dist along some axis\\n axisList = []\\n for x in range(0,int(numCvs)+1):\\n axisList.append(x)\\n\\n pts = []\\n\\n if axis == 1:\\n for y in range(0, int(numCvs)+1):\\n pt = [axisList[y]*cvDist, 0, 0]\\n pts.append(pt)\\n\\n if axis == 2:\\n for y in range(0, int(numCvs)+1):\\n pt = [0, axisList[y]*cvDist, 0]\\n pts.append(pt)\\n\\n if axis == 3:\\n for y in range(0, int(numCvs)+1):\\n pt = [0, 0, axisList[y]*cvDist]\\n pts.append(pt)\\t\\t\\t\\n \\n line = cmds.curve(name = \\\"line_01\\\", d=3, p=pts)\\n shp = cmds.listRelatives(line, s=True)[0]\\n cmds.rename(shp, \\\"{0}Shape\\\".format(line))\\n if uniform:\\n line = cmds.rebuildCurve(line, rebuildType = 0, spans = 0, keepRange = 0, replaceOriginal=True, end=1, keepControlPoints=0)[0]\\n\\n cmds.select(line, r=True)\",\n \"def add_node():\\n\\ttry:\\n\\t\\tif(check_entries()):\\n\\t\\t\\tserver_entry = [frame.entries[0].get(), str(frame.entries[1].get()+\\\".\\\"+frame.entries[2].get()+\\\".\\\"+frame.entries[3].get()+\\\".\\\"+frame.entries[4].get())]\\n\\t\\t\\tclient_entry = [frame.entries[5].get(), str(frame.entries[6].get()+\\\".\\\"+frame.entries[7].get()+\\\".\\\"+frame.entries[8].get()+\\\".\\\"+frame.entries[9].get())]\\n\\t\\t\\tconnection_lifetime = frame.entries[10].get()\\n\\t\\t\\tnetwork.add_connection(server_entry, client_entry, connection_lifetime);\\n\\n\\texcept ValueError as err:\\n\\t\\tfeedback.config(text=err)\",\n \"def create_nodes_collinear_2D_constraint():\\n return NodesCollinear2DConstraint()\",\n \"def add_link(self, src_node_id, dst_node_id, src_port, dst_port, capacity=10000000):\\n\\n # Get function name\\n fname = sys._getframe().f_code.co_name\\n \\n # Do not add the link if the nodes are not in the topology\\n if src_node_id not in self.nodes or dst_node_id not in self.nodes:\\n #print(\\\"{}: Nodes {} and/or {} not found - can't add link\\\".\\n # format(fname, src_node_id, dst_node_id),\\n # file=sys.stderr)\\n return\\n\\n # Do not add the link if the link already exists\\n for i in range(0, len(self.neighbors[src_node_id])):\\n neighbor_id = self.neighbors[src_node_id][i][\\\"dst_node_id\\\"]\\n if (neighbor_id == dst_node_id and\\n self.neighbors[src_node_id][i][\\\"src_port\\\"] == src_port and\\n self.neighbors[src_node_id][i][\\\"dst_port\\\"] == dst_port):\\n # print(\\\"{}: link already exists - exiting\\\".format(fname))\\n return\\n\\n # Do not add the link if the link already exists\\n for i in range(0, len(self.neighbors[dst_node_id])):\\n neighbor_id = self.neighbors[dst_node_id][i][\\\"dst_node_id\\\"]\\n if (neighbor_id == src_node_id and\\n self.neighbors[dst_node_id][i][\\\"src_port\\\"] == dst_port and\\n self.neighbors[dst_node_id][i][\\\"dst_port\\\"] == src_port):\\n # print(\\\"{}: link already exists - exiting\\\".format(fname))\\n return\\n\\n # Derive src/dst interfaces\\n # Want to make a version of this function in the Topology class. Not\\n # good architecture\\n src_int = self.mgr.get_interface(src_node_id, src_port)\\n dst_int = self.mgr.get_interface(dst_node_id, dst_port)\\n \\n # Destination entry in the topology\\n src_entry = {\\n \\\"src_node_id\\\": src_node_id,\\n \\\"dst_node_id\\\": dst_node_id,\\n \\\"src_port\\\": src_port,\\n \\\"dst_port\\\": dst_port,\\n \\\"src_int\\\": src_int,\\n \\\"dst_int\\\": dst_int,\\n \\\"bps_reserved\\\": 0,\\n \\\"bps_current\\\": 0,\\n \\\"bps_capacity\\\": capacity,\\n \\\"cur_bytes_sent\\\": 0,\\n \\\"cur_bytes_recvd\\\": 0,\\n \\\"prev_bytes_sent\\\": 0,\\n \\\"prev_bytes_recvd\\\": 0,\\n \\\"utilization_pct\\\": 0.0\\n }\\n self.neighbors[src_node_id].append(src_entry)\\n\\n # Destination entry in the topology\\n dst_entry = {\\n \\\"src_node_id\\\": dst_node_id,\\n \\\"dst_node_id\\\": src_node_id,\\n \\\"src_port\\\": dst_port,\\n \\\"dst_port\\\": src_port,\\n \\\"src_int\\\": dst_int,\\n \\\"dst_int\\\": src_int,\\n \\\"bps_reserved\\\": 0,\\n \\\"bps_current\\\": 0,\\n \\\"bps_capacity\\\": capacity,\\n \\\"cur_bytes_sent\\\": 0,\\n \\\"cur_bytes_recvd\\\": 0,\\n \\\"prev_bytes_sent\\\": 0,\\n \\\"prev_bytes_recvd\\\": 0,\\n \\\"utilization_pct\\\": 0.0\\n }\\n self.neighbors[dst_node_id].append(dst_entry)\\n\\n # Add the link to self.links if it does not already exist\\n # if ((src_port, dst_port) not in self.links and\\n # (dst_port, src_port) not in self.links):\\n # self.links[(src_port, dst_port)] = src_index\\n # self.links[(dst_port, dst_port)] = dst_index\\n \\n self.l += 1\",\n \"def get_connection(self, *args, **kwargs):\\n conn = super(SSLHTTPAdapter, self).get_connection(*args, **kwargs)\\n if conn.assert_hostname != self.assert_hostname:\\n conn.assert_hostname = self.assert_hostname\\n return conn\",\n \"def plt_connecting_lines():\\n\\n for i in range(0, Molecule.connection_count):\\n tmp1 = Molecule.right_endpt[Molecule.left_connection[i] - 1]\\n tmp2 = Molecule.left_endpt[Molecule.right_connection[i] - 1]\\n tmp3 = Molecule.energy[Molecule.left_connection[i] - 1]\\n tmp4 = Molecule.energy[Molecule.right_connection[i] - 1]\\n\\n plt.plot([tmp1, tmp2], [tmp3, tmp4], color=PlotParameter.connection_line_color,\\n lw=PlotParameter.connection_line_width, linestyle='--')\\n\\n return None\",\n \"def add_connection_beginning(self, route, potential_solution):\\n\\n current_station = route.stations[0]\\n\\n # pick a random new station out of all connections of the current station\\n new_station = random.choice(list(current_station.connections.keys()))\\n\\n # find the connection between the two stations \\n link = current_station.connections[new_station] \\n\\n # find the time of the connection \\n time = link.time \\n \\n\\n # only accept the change if it wouldn't exceed the maximum time\\n if time + route.total_time <= self.max_minutes:\\n \\n # insert the connection to the front of the connection list\\n route.insert_connection(link, time, 0)\\n\\n # insert the station to the front of the station list\\n route.insert_station(new_station, 0)\",\n \"def constraints(self):\\n ...\",\n \"def setup_logical_port_connectivity(self, context, port_db):\\n pass\",\n \"def test_port_init_singelton(self):\\n v1 = 23.0\\n v2 = 29.0\\n p1 = cn.Port()\\n p1.value = v1\\n # check setting of value\\n p2 = cn.Port(v1)\\n # check fixing\\n p3 = cn.Port(\\n value=v1,\\n fixed=True\\n )\\n p4 = cn.Port(\\n value=v1,\\n fixed=False\\n )\\n # check linking\\n p5 = cn.Port(v2)\\n p5.link = p4\\n\\n self.assertEqual(\\n np.allclose(\\n p1.value, p2.value\\n ),\\n True\\n )\\n self.assertEqual(p3.fixed, True)\\n self.assertEqual(p4.fixed, False)\\n self.assertEqual(\\n np.allclose(\\n p5.value,\\n p4.value\\n ),\\n True\\n )\\n\\n # check bounds\\n fixed = False\\n is_output = False\\n is_reactive = False\\n is_bounded = True\\n lower_bound = 2\\n upper_bound = 5\\n value = 0\\n p6 = cn.Port(\\n value=value,\\n fixed=fixed,\\n is_output=is_output,\\n is_reactive=is_reactive,\\n is_bounded=is_bounded,\\n lb=lower_bound,\\n ub=upper_bound\\n )\\n self.assertEqual(\\n np.all(p6.value <= upper_bound),\\n True\\n )\\n self.assertEqual(\\n np.all(p6.value >= lower_bound),\\n True\\n )\\n self.assertAlmostEqual(\\n p6.value[0],\\n lower_bound # the lower bound is not part of the\\n )\",\n \"def addPortOnSide(self, node: LNode, portSide: PortSide) -> LPort:\\n side = node.getPortSideView(portSide)\\n port = LPort(node, None, side=portSide, name=\\\"port%d\\\" % len(side))\\n side.append(port)\\n\\n if not node.portConstraints.isSideFixed():\\n node.portConstraints = PortConstraints.FIXED_SIDE\\n\\n return port\",\n \"def setup_rule(self, client, *args, **keyword_args):\\n pass\",\n \"def setup_rule(self, client, *args, **keyword_args):\\n pass\",\n \"def create_outbound(self, addr, use_new_connection=False):\",\n \"def _check_binding(self, value: t.Any) -> t.Any:\\n client = getattr(value, \\\"CLIENT\\\", value)\\n if isinstance(client, self.__class__) and client is not self:\\n err = f\\\"{value} is already set to {client!r} and cannot be set to {self!r}\\\"\\n raise ConnectError(err)\\n value.CLIENT = self\\n return value\",\n \"def connect(self, address: Tuple[str, int]) -> None:\\n ...\",\n \"def item_create(\\n item, item_id, item_type, create=\\\"create\\\", extra_args=None, cibfile=None\\n):\\n cmd = [\\\"pcs\\\"]\\n if isinstance(cibfile, str):\\n cmd += [\\\"-f\\\", cibfile]\\n\\n if isinstance(item, str):\\n cmd += [item]\\n elif isinstance(item, (list, tuple)):\\n cmd += item\\n\\n # constraint command follows a different order\\n if item in [\\\"constraint\\\"]:\\n if isinstance(item_type, str):\\n cmd += [item_type]\\n\\n if isinstance(create, str):\\n cmd += [create]\\n elif isinstance(create, (list, tuple)):\\n cmd += create\\n\\n # constraint command needs item_id in format 'id=<id' after all params\\n # constraint command follows a different order\\n if item not in [\\\"constraint\\\"]:\\n cmd += [item_id]\\n if isinstance(item_type, str):\\n cmd += [item_type]\\n\\n if isinstance(extra_args, (list, tuple)):\\n # constraint command needs item_id in format 'id=<id' after all params\\n if item in [\\\"constraint\\\"]:\\n extra_args = extra_args + [\\\"id={}\\\".format(item_id)]\\n cmd += extra_args\\n\\n return __salt__[\\\"cmd.run_all\\\"](cmd, output_loglevel=\\\"trace\\\", python_shell=False)\",\n \"def __init__(self, source_node, source_gate_name, target_node, target_slot_name, weight=1):\\n self.link(source_node, source_gate_name, target_node, target_slot_name, weight)\",\n \"def serial_connection(self, value):\\n if len(value) > 4:\\n raise ValueUnsupportedError(\\n 'serial port connection list', value,\\n 'no more than 4 connections (ESXi limitation)')\\n COTDeploy.serial_connection.fset(self, value)\",\n \"def __init__(self, link, lineproto, interface_name):\\n self.link = link\\n self.lineproto = lineproto\\n self.interface_name = interface_name\",\n \"def __init__(self, ip, port, header):\\n \\n self.header = header\\n self.ip = ip\\n self.port = port\\n try:\\n self._connect_socket()\\n except socket.error as e:\\n print(e)\\n self.close_and_exit()\",\n \"def __init__(self, ip, port, automatic = True, control_buf_size = 32, data_buf_size = 128, \\\\\\n m_to = 0.01, socket_to = 0.005):\\n self.conn = Connector(ip, port, control_buf_size, data_buf_size, socket_to)\\n self.conn.connect()\\n self.m_to = m_to\\n self.status = Modem.Status.IDLE\\n self.node_status = 0\\n self.automatic = automatic\\n self.interpreter = Interpreter()\\n self.mainPID = os.getpid()\\n self.error_status = Modem.ErrorDict.NONE\\n self.commands_queue = \\\"\\\".split(Interpreter.END_COMMAND)\\n if automatic:\\n thread.start_new_thread(self.run,())\",\n \"def connectCurrentConnection(self, position):\\n\\n # get the item at the position\\n itemAt = self.itemAt(position.toPoint(), self.getView().transform())\\n\\n # remove the connection (a new connection will get added if there is a valid connector)\\n self.removeItem(self.currentlyConnecting)\\n connection = self.currentlyConnecting\\n self.currentlyConnecting = None\\n\\n\\n \\\"\\\"\\\" if itemAt is a Connector (Top/Bottom) item (if you pull onto a Blob) \\\"\\\"\\\"\\n if itemAt is not None and isinstance(itemAt, ConnectorItem) and not self.disabled:\\n # check, whether the connection is already connected to connector of the given type (top/bottom)\\n if connection.checkSameConnectorTypeRestriction(itemAt):\\n # get the connectors\\n if itemAt.isTopConnector():\\n topConnector = itemAt\\n bottomConnector = connection.getConnectorIfNotFullyConnected()\\n else:\\n topConnector = connection.getConnectorIfNotFullyConnected()\\n bottomConnector = itemAt\\n\\n # get data needed to notify the underling data structure\\n topLayerID = topConnector.getNodeItem().getLayerID()\\n bottomLayerID = bottomConnector.getNodeItem().getLayerID()\\n topBlobIndex = topConnector.getIndex()\\n bottomBlobIndex = bottomConnector.getIndex()\\n\\n # notify to change the data\\n self.__nodeEditor.tryToConnect(topLayerID, topBlobIndex, bottomLayerID, bottomBlobIndex)\\n\\n \\\"\\\"\\\" if itemAt is a Node Item (if you pull onto a layer) \\\"\\\"\\\"\\n if itemAt is not None and isinstance(itemAt, NodeItem) and not self.disabled:\\n # test if connector starts at a top Blob\\n if connection.getConnectorIfNotFullyConnected().isTopConnector():\\n\\n # bottomNode is itemAt\\n bottomNode = itemAt\\n\\n # get layer IDs\\n topLayerID = connection.getConnectorIfNotFullyConnected().getNodeItem().getLayerID()\\n bottomLayerID = bottomNode.getLayerID()\\n topBlobIndex = connection.getConnectorIfNotFullyConnected().getIndex()\\n\\n # get the Index of the new Blob, should it be necessary to create one\\n # (determined in the following for loop)\\n bottomBlobIndex = bottomNode.getBottomConnectorCount()\\n\\n # current connection top name and phase\\n topBlobName = connection.getConnectorIfNotFullyConnected().getBlobName()\\n topBlobPhase = connection.getConnectorIfNotFullyConnected().getPhase()\\n\\n # check if there is a connected Node that has a different phase than the currently\\n # connecting Node, but has a connection with the same top Blob Name\\n topBlobFound = False\\n for bottomBlob in bottomNode.getBottomConnectors():\\n if len(bottomBlob.getConnectedNodes()) > 0:\\n for topNode in bottomBlob.getConnectedNodes():\\n for topBlob in topNode.getTopConnectors():\\n if topBlob.getBlobName() == topBlobName and topBlob.getPhase() != topBlobPhase:\\n bottomBlobIndex = bottomBlob.getIndex()\\n topBlobFound = True\\n break\\n\\n # otherwise (if no corresponding top Blob was found)\\n # get Index of first empty bottom blob (if available)\\n counter = -1\\n emptyBlobAvailable = False\\n if not topBlobFound:\\n for blob in bottomNode.getBottomConnectors():\\n counter += 1\\n if len(blob.getConnectedNodes()) == 0:\\n bottomBlobIndex = counter\\n emptyBlobAvailable = True\\n break\\n\\n # add empty bottom blob property\\n if not emptyBlobAvailable and not topBlobFound:\\n self.__nodeEditor.tryToAddBottomBlob(bottomLayerID, \\\"\\\")\\n\\n # connect nodes\\n connected = self.__nodeEditor.tryToConnect(topLayerID, topBlobIndex, bottomLayerID, bottomBlobIndex)\\n\\n # if the connection did not work but a new blob was created, remove it\\n if not connected and not emptyBlobAvailable and not topBlobFound:\\n bottomNode.removeBottomConnector(bottomBlobIndex)\",\n \"def add_constraint(self, constraint):\\n self._ckey += 1\\n self.constraints[self._ckey] = constraint\",\n \"def drawHollowConstraints(fname, linewidth=2, rgb=\\\"(1,1,1)\\\"):\\n constraints = read_parameters(fname)\\n if not constraints.type in (\\\"box\\\", \\\"brick\\\"):\\n raise NotImplementedError(\\\"sorry, constraint %(type)r not implemented\\\" % vars(constraints))\\n c = constraints\\n return drawBrick(c.res_num1, c.atom1, c.res_num2, c.atom2,\\n chain1=c.chain1, chain2=c.chain2,\\n offset1=c.offset1, offset2=c.offset2,\\n boxname=c.type,\\n linewidth=linewidth, rgb=rgb)\",\n \"def removeConnection(self, connectionItem):\\n assert isinstance(connectionItem, ConnectionItem)\\n assert connectionItem.startPortName() is not None\\n assert connectionItem.endPortName() is not None\\n\\n self.removeConnectionByPortNames(connectionItem.startPortName(),\\n connectionItem.endPortName())\",\n \"def _create_connection_element(self, var1, var2):\\n conn, swap = self._find_connection_element(var1, var2)\\n if conn:\\n if swap:\\n var1, var2 = var2, var1\\n else:\\n conn = var1.xml_create_element(u'connection', NSS[u'cml'])\\n mapc = var1.xml_create_element(u'map_components', NSS[u'cml'],\\n attributes={u'component_1': var1.component.name,\\n u'component_2': var2.component.name})\\n conn.xml_append(mapc)\\n self.model.xml_append(conn)\\n mapv = var1.xml_create_element(u'map_variables', NSS[u'cml'],\\n attributes={u'variable_1': var1.name,\\n u'variable_2': var2.name})\\n conn.xml_append(mapv)\",\n \"def validate_conn(self, solution):\\r\\n\\r\\n active_nodes = [idx for idx, value in enumerate(solution) # remove not included nodes in solution\\r\\n if value != 0 and idx not in self.dead_nodes and self.network.get_node(idx).energy >= cf.COMMUNICATION_ENERGY]\\r\\n active_nodes.append(-1) # add a sink node \\r\\n visited = self.DFS(self.network_graph, active_nodes[0], active_nodes)\\r\\n if len(visited) == len(active_nodes):\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def rcv_addr(self, node_name, **kwargs):\\n # Get keyword args\\n auto_create = kwargs.get(\\\"auto_create\\\", True)\\n auto_delete = kwargs.get(\\\"auto_delete\\\", False)\\n link_name = kwargs.get(\\\"link_name\\\")\\n durable = kwargs.get(\\\"durable\\\", False)\\n browse = kwargs.get(\\\"browse\\\", False)\\n exclusive = kwargs.get(\\\"exclusive\\\", False)\\n binding_list = kwargs.get(\\\"binding_list\\\", [])\\n ftd_count = kwargs.get(\\\"ftd_count\\\")\\n ftd_size = kwargs.get(\\\"ftd_size\\\")\\n policy = kwargs.get(\\\"policy\\\", \\\"flow-to-disk\\\")\\n\\n create_policy = None\\n if auto_create:\\n create_policy = \\\"always\\\"\\n delete_policy = None\\n if auto_delete:\\n delete_policy = \\\"always\\\"\\n mode = None\\n if browse:\\n mode = \\\"browse\\\"\\n x_declare_list = [\\\"\\\\\\\"exclusive\\\\\\\": %s\\\" % exclusive]\\n if ftd_count != None or ftd_size != None:\\n queue_policy = [\\\"\\\\'qpid.policy_type\\\\': %s\\\" % policy]\\n if ftd_count:\\n queue_policy.append(\\\"\\\\'qpid.max_count\\\\': %d\\\" % ftd_count)\\n if ftd_size:\\n queue_policy.append(\\\"\\\\'qpid.max_size\\\\': %d\\\" % ftd_size)\\n x_declare_list.append(\\\"arguments: %s\\\" % self._fmt_map(queue_policy))\\n x_bindings_list = []\\n for binding in binding_list:\\n x_bindings_list.append(\\\"{exchange: %s, key: %s}\\\" % binding)\\n if durable: reliability = 'at-least-once'\\n else: reliability = None\\n return self.addr_fmt(node_name, create_policy=create_policy, delete_policy=delete_policy, mode=mode, link=True,\\n link_name=link_name, durable=durable, x_declare_list=x_declare_list,\\n x_bindings_list=x_bindings_list, link_reliability=reliability)\",\n \"def test_connection(self):\\n gen = self.create(GeneralizationItem)\\n c1 = self.create(ClassItem, UML.Class)\\n c2 = self.create(ClassItem, UML.Class)\\n\\n self.connect(gen, gen.tail, c1)\\n assert self.get_connected(gen.tail) is c1\\n\\n self.connect(gen, gen.head, c2)\\n assert gen.subject is not None\\n assert gen.subject.general is c2.subject\\n assert gen.subject.specific is c1.subject\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5818294","0.5687332","0.5500008","0.52568966","0.5220818","0.5207515","0.52057487","0.505119","0.5035412","0.5020907","0.49766943","0.4966835","0.49285123","0.492827","0.492827","0.49215764","0.49072984","0.49011382","0.4897819","0.48977235","0.4889161","0.4876178","0.4873932","0.48451567","0.48292992","0.48227188","0.47957954","0.47898212","0.47815078","0.4751471","0.4746205","0.474032","0.474032","0.47365743","0.47233927","0.47203544","0.471862","0.4715587","0.47126704","0.47111252","0.468648","0.46840006","0.46773657","0.4669509","0.4651717","0.46445978","0.46434456","0.46392262","0.46280602","0.46247777","0.46145654","0.4612712","0.45941883","0.45859197","0.45857245","0.4585033","0.45808288","0.45802006","0.45787632","0.4570262","0.45510253","0.4546734","0.45457652","0.4534617","0.45344183","0.45342407","0.45341218","0.45339024","0.45333508","0.45322156","0.45227775","0.4521536","0.4518277","0.45113742","0.45056388","0.44979978","0.44920564","0.44907925","0.44855762","0.44813353","0.44770795","0.44743526","0.44743526","0.44734544","0.4470202","0.44700396","0.44622612","0.44390792","0.4438595","0.44341296","0.44333005","0.44264913","0.4422834","0.44226348","0.44122314","0.4410221","0.4405696","0.44054008","0.43961388","0.43886468"],"string":"[\n \"0.5818294\",\n \"0.5687332\",\n \"0.5500008\",\n \"0.52568966\",\n \"0.5220818\",\n \"0.5207515\",\n \"0.52057487\",\n \"0.505119\",\n \"0.5035412\",\n \"0.5020907\",\n \"0.49766943\",\n \"0.4966835\",\n \"0.49285123\",\n \"0.492827\",\n \"0.492827\",\n \"0.49215764\",\n \"0.49072984\",\n \"0.49011382\",\n \"0.4897819\",\n \"0.48977235\",\n \"0.4889161\",\n \"0.4876178\",\n \"0.4873932\",\n \"0.48451567\",\n \"0.48292992\",\n \"0.48227188\",\n \"0.47957954\",\n \"0.47898212\",\n \"0.47815078\",\n \"0.4751471\",\n \"0.4746205\",\n \"0.474032\",\n \"0.474032\",\n \"0.47365743\",\n \"0.47233927\",\n \"0.47203544\",\n \"0.471862\",\n \"0.4715587\",\n \"0.47126704\",\n \"0.47111252\",\n \"0.468648\",\n \"0.46840006\",\n \"0.46773657\",\n \"0.4669509\",\n \"0.4651717\",\n \"0.46445978\",\n \"0.46434456\",\n \"0.46392262\",\n \"0.46280602\",\n \"0.46247777\",\n \"0.46145654\",\n \"0.4612712\",\n \"0.45941883\",\n \"0.45859197\",\n \"0.45857245\",\n \"0.4585033\",\n \"0.45808288\",\n \"0.45802006\",\n \"0.45787632\",\n \"0.4570262\",\n \"0.45510253\",\n \"0.4546734\",\n \"0.45457652\",\n \"0.4534617\",\n \"0.45344183\",\n \"0.45342407\",\n \"0.45341218\",\n \"0.45339024\",\n \"0.45333508\",\n \"0.45322156\",\n \"0.45227775\",\n \"0.4521536\",\n \"0.4518277\",\n \"0.45113742\",\n \"0.45056388\",\n \"0.44979978\",\n \"0.44920564\",\n \"0.44907925\",\n \"0.44855762\",\n \"0.44813353\",\n \"0.44770795\",\n \"0.44743526\",\n \"0.44743526\",\n \"0.44734544\",\n \"0.4470202\",\n \"0.44700396\",\n \"0.44622612\",\n \"0.44390792\",\n \"0.4438595\",\n \"0.44341296\",\n \"0.44333005\",\n \"0.44264913\",\n \"0.4422834\",\n \"0.44226348\",\n \"0.44122314\",\n \"0.4410221\",\n \"0.4405696\",\n \"0.44054008\",\n \"0.43961388\",\n \"0.43886468\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6178213"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":5293,"cells":{"query":{"kind":"string","value":"Set lifeline's lifetime length."},"document":{"kind":"string","value":"def _set_length(self, length):\n self.bottom.pos.y = self.top.pos.y + length"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def change_tail_length(self, value):\n self.layer.tail_length = value","def setLength(self, new_length):\n\n self.length = new_length","def set_lifetime(self, lifetime):\n self.__lifetime = lifetime","def length(self, length):\n\n self._length = length","def set_last_segment_length(self, length):\n prior_length = self.segments[-1].get_length()\n if prior_length != -1:\n self.end_time -= prior_length\n\n self.segments[-1].set_length(length)\n self.end_time += length","def set_line_width(self, val):\n self.lwidth = val","def set_length(self, new_length):\n if(new_length == None):\n self._logger.write(\"Error! new_length cannot be a NoneType\")\n elif(type(new_length) != float):\n self._logger.write(\"Error! new_length must be of type float\")\n else:\n try:\n self._length = new_length\n except Exception as e:\n self._logger.write(\"Error! Could not set the new length:\\n %s\" % e)","def set_length(self, ak_tpl: BKT, newLength: float): # -> None:\n ...","def _set_packet_len(self, packet_len):\n self._packet_len = packet_len","def set_length(self, length):\n if length < 0:\n raise AttributeError('length should be positive')\n self.progress_char_length = length","def token_length(self, token_length):\n\n self._token_length = token_length","def setLength(self, length):\n self.vector.norm = length","def length(self, length: Union[int, float]):\n self._length = length\n self._update_length()\n self.events.length()\n\n self.refresh()","def set_part_length(self, seconds):\n self._part_length = seconds","def setGoalLength(self, length):\n assert isinstance(length, int)\n self.goal_length = length","async def gpt2_set_length(self, ctx, *, arg=None):\n print('Command gpt2_set_length triggered')\n if arg:\n try:\n i = int(arg)\n assert (i > 0) and (i < 1024)\n except ValueError or AssertionError:\n ctx.send(\"ERROR: Argument must be a positive integer number\")\n self.update_config(length=arg)\n else:\n await ctx.send(\"ERROR: Argument required\")","def _on_tail_length_change(self, event=None):\n with self.layer.events.tail_length.blocker():\n value = self.layer.tail_length\n value = np.clip(value, 1, MAX_TAIL_LENGTH)\n self.tail_length_slider.setValue(value)","def _set_length(self):\n if self.nb_points <= 1:\n self.length = 0\n else:\n ldiff_degree = self.coord_list[1:] - self.coord_list[:-1]\n ldiff_meter = ldiff_degree * np.pi * EQUATORIAL_EARTH_RADIUS / 180\n ldiff_meter[:, 0] *= np.cos(self.mean_pos[1] * np.pi / 180)\n self.length = np.sum(\n np.sqrt(ldiff_meter[:, 0] ** 2 + ldiff_meter[:, 1] ** 2)\n )","def extendSequenceLength(self, timeLength):\n timeLength = self.secToStep(timeLength)\n self._addNewSwitch(timeLength,0,0)","def set_lives(self, new_number_of_lives):\n self.__lives = new_number_of_lives","def change_length(self, value):\n self.layer.length = value\n self.lengthSpinBox.clearFocus()\n self.setFocus()","def set_max_lines(self, n):\n\t\tself._maxLines = n\n\t\tself._trunc_lines()","def __init__(self, lineLen):\n if lineLen < 10:\n raise ValueError('lineLen cannot be less than 10')\n self.len = lineLen\n self.currentSpace = lineLen\n self.beginningOfLine = True","def setTickLength(major=24,minor=16):\n dislin.ticlen(major,minor)","def set_base_length_entry(self, base_length):\n self.entries[\"ent_base_length\"].delete(0, END)\n self.entries[\"ent_base_length\"].insert(\n 0, str(base_length))","def set_body_size(self, length: int) -> None:\n self._body = [Coord2D(0, 0) for _ in range(length)]\n self._tail_visited = set()\n self.record_tail_location()","def _on_len_change(self, event=None):\n with self.layer.events.length.blocker():\n self.lengthSpinBox.setValue(self.layer.length)","def fl_set_object_lsize(ptr_flobject, size):\n _fl_set_object_lsize = library.cfuncproto(\n library.load_so_libforms(), \"fl_set_object_lsize\", \\\n None, [cty.POINTER(xfdata.FL_OBJECT), cty.c_int], \\\n \"\"\"void fl_set_object_lsize(FL_OBJECT * ob, int lsize) \"\"\")\n library.check_if_flinitialized()\n library.verify_flobjectptr_type(ptr_flobject)\n i_size = library.convert_to_intc(size)\n library.keep_elem_refs(ptr_flobject, size, i_size)\n _fl_set_object_lsize(ptr_flobject, i_size)","def grr_set_flow_timeout(line: Text) -> None:\n args = grr_set_flow_timeout.parser.parse_args(shlex.split(line))\n magics_impl.grr_set_flow_timeout_impl(args.timeout)","def setLSLimits(*args):\n args[0].Limit.LSLimit.ls_limit = args[1]","def setMaxWindowLen(self, length):\n return self._set(maxWindowLen=length)","def setMaxWindowLen(self, length):\n return self._set(maxWindowLen=length)","def setLives(self,life):\n self._lives = life","def __padlen(self,l):\n return Utils.padlen(l,self.com.granularity)","def setSplitLength(self, value):\n return self._set(splitLength=value)","def set_line_end(self, line_nr):\n self._line_end = line_nr","def change_log_length(self, log_length):\n len_diff = abs(self.log_length - log_length)\n if log_length > self.log_length:\n for log_group in self.log_names.values():\n for log_array in log_group:\n tmparr = numpy.full(log_length, self.log_arrays[log_array][0]) # generate tmparr with first value from array\n tmparr[-self.log_arrays[log_array].size:] = self.log_arrays[log_array] # fill end with current array\n self.log_arrays[log_array] = tmparr\n tmparr = numpy.zeros(log_length)\n tmparr[:len_diff] = numpy.linspace(self.log_time[0] - len_diff/self.frequency,\n self.log_time[0], len_diff)\n tmparr[-self.log_time.size:] = self.log_time\n self.log_time = tmparr\n else:\n for log_group in self.log_names.values():\n for log_array in log_group:\n tmparr = numpy.zeros(log_length)\n tmparr[:] = self.log_arrays[log_array][-log_length:]\n self.log_arrays[log_array] = tmparr\n tmparr = numpy.zeros(log_length)\n tmparr[:] = self.log_time[-log_length:]\n self.log_time = tmparr\n self.log_length = log_length","def Resize(self):\n\n self.history_length = int( round( self.owner['time_span']/self.owner['sample_speed']))\n self.FreshStart()","def set_trailing_sl(self, n_atr: float = 6):\n self.__n_atr = n_atr","def setNewLen(self):\n self.wordLen = randint(3, 31)","def reference_nusselt_number_length(self, reference_nusselt_number_length):\n\n self._reference_nusselt_number_length = reference_nusselt_number_length","def height_lt(self, height_lt):\n\n self._height_lt = height_lt","def setPacketLength(self):\n self.packetLength = len(self) - PRIMARY_HEADER_BYTE_SIZE - 1","def setLong(self, name: unicode, value: long) -> None:\n ...","def _setVals(self, cmd_length=0):\n self.cmd_length = cmd_length","def set_life(self, value):\n self._life = value","def set_life(self):\n self.life -= 1","def setLives(self, lives):\n assert type(lives) == int\n self._lives = lives","def setMaxLength(self, value):\n return self._set(maxLength=value)","def setMaxLength(self, value):\n return self._set(maxLength=value)","def setMaxLength(self, value):\n return self._set(maxLength=value)","def setMaxLength(self, value):\n return self._set(maxLength=value)","def llen(self, name):\n self.connect()\n self._write('LLEN %s\\r\\n' % name)\n return self._get_numeric_response()","def initializeLegend(nlines, maxlen):\n dislin.legini(' ',nlines, maxlen)","def length(self, value):\n raise TypeError(\"Cannot delete {class-name} length property.\")","def lmp(self, lmp):\n\n self.logger.debug(\"In 'lmp' setter.\")\n\n self._lmp = lmp","def __init__(self, length):\r\n self.length = length\r\n self.start_time = int(time.time())","def set_length(vec, length):\n return normalized(vec) * length","def setLCLimits(*args):\n args[0].Limit.LCLimit.lc_limit = args[1]","def random_password_length(self, random_password_length):\n\n self._random_password_length = random_password_length","def line_length(self, dLine = 0):\n return self.buffer.line_length(self.line + dLine)","def reset_line_count(self):\n self._line_count = 0","def extend(self, L):\n self[len(self):] = L","def length_changed(self, value):\n self.message.dlc = value\n self.validate_data_input(value)","def set_end(self, end_line):\n self.__end_line = end_line","def set_alive(self, a, line_number=0):\n self.alive = a\n self._alive_line = line_number","def len23(self, len): # -> None:\n ...","def sequence_length(self, new_seq_length):\n self._sequence_length = new_seq_length\n self.tensors = [torch.tensor(\n generate_batches(x, self._sequence_length, self._sequence_stride)).float()\n for x in [self.inputs, self.outputs]]","def set_width(self, *args):\n return _ida_hexrays.lvar_t_set_width(self, *args)","def set_length(self, ak_spec: Union[str, BKT], val: float) -> None:\n ...","def as_length(self, value):\n new_vec = self.copy()\n new_vec.length = value\n return new_vec","def length(self):\n ...","def grr_set_default_flow_timeout(line: Text) -> None:\n del line # Unused.\n magics_impl.grr_set_default_flow_timeout_impl()","def set_size(self, new_size: int):\n self.__tab_size = new_size\n self.__check_interpreter()\n self.__vals = [0 for _ in range(self.__tab_size)]","def length(self):\n pass","def set_endline(self, line_no):\n self.set_attribute(\"endline\", line_no)","def set_linked_lmax(\n self,\n val=None\n ):\n if val != None:\n self.linked_lmax = val","def set_lives(self, lives):\n self._lives = lives","def setLong(self, addr: ghidra.program.model.address.Address, value: long) -> None:\n ...","def adjust_detector_length(requested_detector_length,\n requested_distance_to_tls,\n lane_length):\n\n if requested_detector_length == -1:\n return lane_length - requested_distance_to_tls\n\n return min(lane_length - requested_distance_to_tls,\n requested_detector_length)","def set_auto_throats_length(self):\n\n for n1, n2 in self.graph.edges:\n self.graph[n1][n2]['length'] = self._compute_auto_throat_length(\n n1, n2)","def _update_end_lineno():\n if origin:\n record.origin.line_end = lineno","def __init__(self, length):\n super().__init__([length] * 3)","def lifetime(self) -> str:\n return pulumi.get(self, \"lifetime\")","def set_max_log_lines(self, value):\n\t\tself.spinMaxLogLines.set_value(value)","def lun_count(self, lun_count):\n\n self._lun_count = lun_count","def _fixHeaderLength(self):\n self.header.seek(0)\n lines = self.header.readlines()\n headlength = len(lines)\n lines[0] = wrapLine(\"NLHEAD_FFI\", self.annotation, self.delimiter, \"%d%s%d\\n\" % (headlength, self.delimiter, self.FFI))\n self.header = StringIO(\"\".join(lines))\n self.header.seek(0)","def liver(self, liver):\n\n self.logger.debug(\"In 'liver' setter.\")\n\n self._liver = liver","def change_tail_width(self, value):\n self.layer.tail_width = float(value) / 2.0","def set_max_sentence_length(self):\n new_max = int(self.set_max_sentence.get())\n cur_min = self.min_sentence_length\n\n if new_max > cur_min:\n self.max_sentence_length = new_max\n else:\n old_max = self.max_sentence_length\n old_max_var = tk.StringVar(self.master)\n old_max_var.set(str(old_max))\n self.set_max_sentence.config(textvariable=old_max_var)","def __init__(self, *args, **kwargs):\n super(LinlLis, self).__init__(\n ('linl', Bits(maxlen=4)),\n ('lis', Bits(maxlen=4)),\n *args, **kwargs\n )","def length_minutes(self, length_minutes):\n \n self._length_minutes = length_minutes","def setLong(self, address: ghidra.program.model.address.Address, value: long) -> None:\n ...","def set_timeout(self, new_timeout):\n self.timeout = new_timeout\n self._update_timestamp()","def lineLengthExceeded(self, line):\n segments = self._breakLineIntoSegments(line)\n for segment in segments:\n self.lineReceived(segment)","def __init__(self, keep_last_n_lines=5) :\r\n self.contextLines_ = keep_last_n_lines\r\n self.data_ = CircularBuffer(1024)\r\n self.lineNumber_ = 1\r\n self.charNumber_ = 0","def len_literal(self):\n if hasattr(self, '_m_len_literal'):\n return self._m_len_literal if hasattr(self, '_m_len_literal') else None\n\n self._m_len_literal = (self.len_literal_div2 * 2)\n return self._m_len_literal if hasattr(self, '_m_len_literal') else None","def set_size(self, length, width=None):\n\n length = float(length)\n try:\n width = float(width)\n except:\n pass\n if width is not None:\n self.ang_size = np.sqrt(length * width)\n else:\n self.ang_size = length\n\n ang_size_in_rad = self.ang_size / 60 * np.pi / 180\n self.sr = ct.angle_to_solid_angle(ang_size_in_rad)","def __init__(self) -> None:\n self.length = 0","def setMaxSentenceLength(self, value):\n return self._set(maxSentenceLength=value)"],"string":"[\n \"def change_tail_length(self, value):\\n self.layer.tail_length = value\",\n \"def setLength(self, new_length):\\n\\n self.length = new_length\",\n \"def set_lifetime(self, lifetime):\\n self.__lifetime = lifetime\",\n \"def length(self, length):\\n\\n self._length = length\",\n \"def set_last_segment_length(self, length):\\n prior_length = self.segments[-1].get_length()\\n if prior_length != -1:\\n self.end_time -= prior_length\\n\\n self.segments[-1].set_length(length)\\n self.end_time += length\",\n \"def set_line_width(self, val):\\n self.lwidth = val\",\n \"def set_length(self, new_length):\\n if(new_length == None):\\n self._logger.write(\\\"Error! new_length cannot be a NoneType\\\")\\n elif(type(new_length) != float):\\n self._logger.write(\\\"Error! new_length must be of type float\\\")\\n else:\\n try:\\n self._length = new_length\\n except Exception as e:\\n self._logger.write(\\\"Error! Could not set the new length:\\\\n %s\\\" % e)\",\n \"def set_length(self, ak_tpl: BKT, newLength: float): # -> None:\\n ...\",\n \"def _set_packet_len(self, packet_len):\\n self._packet_len = packet_len\",\n \"def set_length(self, length):\\n if length < 0:\\n raise AttributeError('length should be positive')\\n self.progress_char_length = length\",\n \"def token_length(self, token_length):\\n\\n self._token_length = token_length\",\n \"def setLength(self, length):\\n self.vector.norm = length\",\n \"def length(self, length: Union[int, float]):\\n self._length = length\\n self._update_length()\\n self.events.length()\\n\\n self.refresh()\",\n \"def set_part_length(self, seconds):\\n self._part_length = seconds\",\n \"def setGoalLength(self, length):\\n assert isinstance(length, int)\\n self.goal_length = length\",\n \"async def gpt2_set_length(self, ctx, *, arg=None):\\n print('Command gpt2_set_length triggered')\\n if arg:\\n try:\\n i = int(arg)\\n assert (i > 0) and (i < 1024)\\n except ValueError or AssertionError:\\n ctx.send(\\\"ERROR: Argument must be a positive integer number\\\")\\n self.update_config(length=arg)\\n else:\\n await ctx.send(\\\"ERROR: Argument required\\\")\",\n \"def _on_tail_length_change(self, event=None):\\n with self.layer.events.tail_length.blocker():\\n value = self.layer.tail_length\\n value = np.clip(value, 1, MAX_TAIL_LENGTH)\\n self.tail_length_slider.setValue(value)\",\n \"def _set_length(self):\\n if self.nb_points <= 1:\\n self.length = 0\\n else:\\n ldiff_degree = self.coord_list[1:] - self.coord_list[:-1]\\n ldiff_meter = ldiff_degree * np.pi * EQUATORIAL_EARTH_RADIUS / 180\\n ldiff_meter[:, 0] *= np.cos(self.mean_pos[1] * np.pi / 180)\\n self.length = np.sum(\\n np.sqrt(ldiff_meter[:, 0] ** 2 + ldiff_meter[:, 1] ** 2)\\n )\",\n \"def extendSequenceLength(self, timeLength):\\n timeLength = self.secToStep(timeLength)\\n self._addNewSwitch(timeLength,0,0)\",\n \"def set_lives(self, new_number_of_lives):\\n self.__lives = new_number_of_lives\",\n \"def change_length(self, value):\\n self.layer.length = value\\n self.lengthSpinBox.clearFocus()\\n self.setFocus()\",\n \"def set_max_lines(self, n):\\n\\t\\tself._maxLines = n\\n\\t\\tself._trunc_lines()\",\n \"def __init__(self, lineLen):\\n if lineLen < 10:\\n raise ValueError('lineLen cannot be less than 10')\\n self.len = lineLen\\n self.currentSpace = lineLen\\n self.beginningOfLine = True\",\n \"def setTickLength(major=24,minor=16):\\n dislin.ticlen(major,minor)\",\n \"def set_base_length_entry(self, base_length):\\n self.entries[\\\"ent_base_length\\\"].delete(0, END)\\n self.entries[\\\"ent_base_length\\\"].insert(\\n 0, str(base_length))\",\n \"def set_body_size(self, length: int) -> None:\\n self._body = [Coord2D(0, 0) for _ in range(length)]\\n self._tail_visited = set()\\n self.record_tail_location()\",\n \"def _on_len_change(self, event=None):\\n with self.layer.events.length.blocker():\\n self.lengthSpinBox.setValue(self.layer.length)\",\n \"def fl_set_object_lsize(ptr_flobject, size):\\n _fl_set_object_lsize = library.cfuncproto(\\n library.load_so_libforms(), \\\"fl_set_object_lsize\\\", \\\\\\n None, [cty.POINTER(xfdata.FL_OBJECT), cty.c_int], \\\\\\n \\\"\\\"\\\"void fl_set_object_lsize(FL_OBJECT * ob, int lsize) \\\"\\\"\\\")\\n library.check_if_flinitialized()\\n library.verify_flobjectptr_type(ptr_flobject)\\n i_size = library.convert_to_intc(size)\\n library.keep_elem_refs(ptr_flobject, size, i_size)\\n _fl_set_object_lsize(ptr_flobject, i_size)\",\n \"def grr_set_flow_timeout(line: Text) -> None:\\n args = grr_set_flow_timeout.parser.parse_args(shlex.split(line))\\n magics_impl.grr_set_flow_timeout_impl(args.timeout)\",\n \"def setLSLimits(*args):\\n args[0].Limit.LSLimit.ls_limit = args[1]\",\n \"def setMaxWindowLen(self, length):\\n return self._set(maxWindowLen=length)\",\n \"def setMaxWindowLen(self, length):\\n return self._set(maxWindowLen=length)\",\n \"def setLives(self,life):\\n self._lives = life\",\n \"def __padlen(self,l):\\n return Utils.padlen(l,self.com.granularity)\",\n \"def setSplitLength(self, value):\\n return self._set(splitLength=value)\",\n \"def set_line_end(self, line_nr):\\n self._line_end = line_nr\",\n \"def change_log_length(self, log_length):\\n len_diff = abs(self.log_length - log_length)\\n if log_length > self.log_length:\\n for log_group in self.log_names.values():\\n for log_array in log_group:\\n tmparr = numpy.full(log_length, self.log_arrays[log_array][0]) # generate tmparr with first value from array\\n tmparr[-self.log_arrays[log_array].size:] = self.log_arrays[log_array] # fill end with current array\\n self.log_arrays[log_array] = tmparr\\n tmparr = numpy.zeros(log_length)\\n tmparr[:len_diff] = numpy.linspace(self.log_time[0] - len_diff/self.frequency,\\n self.log_time[0], len_diff)\\n tmparr[-self.log_time.size:] = self.log_time\\n self.log_time = tmparr\\n else:\\n for log_group in self.log_names.values():\\n for log_array in log_group:\\n tmparr = numpy.zeros(log_length)\\n tmparr[:] = self.log_arrays[log_array][-log_length:]\\n self.log_arrays[log_array] = tmparr\\n tmparr = numpy.zeros(log_length)\\n tmparr[:] = self.log_time[-log_length:]\\n self.log_time = tmparr\\n self.log_length = log_length\",\n \"def Resize(self):\\n\\n self.history_length = int( round( self.owner['time_span']/self.owner['sample_speed']))\\n self.FreshStart()\",\n \"def set_trailing_sl(self, n_atr: float = 6):\\n self.__n_atr = n_atr\",\n \"def setNewLen(self):\\n self.wordLen = randint(3, 31)\",\n \"def reference_nusselt_number_length(self, reference_nusselt_number_length):\\n\\n self._reference_nusselt_number_length = reference_nusselt_number_length\",\n \"def height_lt(self, height_lt):\\n\\n self._height_lt = height_lt\",\n \"def setPacketLength(self):\\n self.packetLength = len(self) - PRIMARY_HEADER_BYTE_SIZE - 1\",\n \"def setLong(self, name: unicode, value: long) -> None:\\n ...\",\n \"def _setVals(self, cmd_length=0):\\n self.cmd_length = cmd_length\",\n \"def set_life(self, value):\\n self._life = value\",\n \"def set_life(self):\\n self.life -= 1\",\n \"def setLives(self, lives):\\n assert type(lives) == int\\n self._lives = lives\",\n \"def setMaxLength(self, value):\\n return self._set(maxLength=value)\",\n \"def setMaxLength(self, value):\\n return self._set(maxLength=value)\",\n \"def setMaxLength(self, value):\\n return self._set(maxLength=value)\",\n \"def setMaxLength(self, value):\\n return self._set(maxLength=value)\",\n \"def llen(self, name):\\n self.connect()\\n self._write('LLEN %s\\\\r\\\\n' % name)\\n return self._get_numeric_response()\",\n \"def initializeLegend(nlines, maxlen):\\n dislin.legini(' ',nlines, maxlen)\",\n \"def length(self, value):\\n raise TypeError(\\\"Cannot delete {class-name} length property.\\\")\",\n \"def lmp(self, lmp):\\n\\n self.logger.debug(\\\"In 'lmp' setter.\\\")\\n\\n self._lmp = lmp\",\n \"def __init__(self, length):\\r\\n self.length = length\\r\\n self.start_time = int(time.time())\",\n \"def set_length(vec, length):\\n return normalized(vec) * length\",\n \"def setLCLimits(*args):\\n args[0].Limit.LCLimit.lc_limit = args[1]\",\n \"def random_password_length(self, random_password_length):\\n\\n self._random_password_length = random_password_length\",\n \"def line_length(self, dLine = 0):\\n return self.buffer.line_length(self.line + dLine)\",\n \"def reset_line_count(self):\\n self._line_count = 0\",\n \"def extend(self, L):\\n self[len(self):] = L\",\n \"def length_changed(self, value):\\n self.message.dlc = value\\n self.validate_data_input(value)\",\n \"def set_end(self, end_line):\\n self.__end_line = end_line\",\n \"def set_alive(self, a, line_number=0):\\n self.alive = a\\n self._alive_line = line_number\",\n \"def len23(self, len): # -> None:\\n ...\",\n \"def sequence_length(self, new_seq_length):\\n self._sequence_length = new_seq_length\\n self.tensors = [torch.tensor(\\n generate_batches(x, self._sequence_length, self._sequence_stride)).float()\\n for x in [self.inputs, self.outputs]]\",\n \"def set_width(self, *args):\\n return _ida_hexrays.lvar_t_set_width(self, *args)\",\n \"def set_length(self, ak_spec: Union[str, BKT], val: float) -> None:\\n ...\",\n \"def as_length(self, value):\\n new_vec = self.copy()\\n new_vec.length = value\\n return new_vec\",\n \"def length(self):\\n ...\",\n \"def grr_set_default_flow_timeout(line: Text) -> None:\\n del line # Unused.\\n magics_impl.grr_set_default_flow_timeout_impl()\",\n \"def set_size(self, new_size: int):\\n self.__tab_size = new_size\\n self.__check_interpreter()\\n self.__vals = [0 for _ in range(self.__tab_size)]\",\n \"def length(self):\\n pass\",\n \"def set_endline(self, line_no):\\n self.set_attribute(\\\"endline\\\", line_no)\",\n \"def set_linked_lmax(\\n self,\\n val=None\\n ):\\n if val != None:\\n self.linked_lmax = val\",\n \"def set_lives(self, lives):\\n self._lives = lives\",\n \"def setLong(self, addr: ghidra.program.model.address.Address, value: long) -> None:\\n ...\",\n \"def adjust_detector_length(requested_detector_length,\\n requested_distance_to_tls,\\n lane_length):\\n\\n if requested_detector_length == -1:\\n return lane_length - requested_distance_to_tls\\n\\n return min(lane_length - requested_distance_to_tls,\\n requested_detector_length)\",\n \"def set_auto_throats_length(self):\\n\\n for n1, n2 in self.graph.edges:\\n self.graph[n1][n2]['length'] = self._compute_auto_throat_length(\\n n1, n2)\",\n \"def _update_end_lineno():\\n if origin:\\n record.origin.line_end = lineno\",\n \"def __init__(self, length):\\n super().__init__([length] * 3)\",\n \"def lifetime(self) -> str:\\n return pulumi.get(self, \\\"lifetime\\\")\",\n \"def set_max_log_lines(self, value):\\n\\t\\tself.spinMaxLogLines.set_value(value)\",\n \"def lun_count(self, lun_count):\\n\\n self._lun_count = lun_count\",\n \"def _fixHeaderLength(self):\\n self.header.seek(0)\\n lines = self.header.readlines()\\n headlength = len(lines)\\n lines[0] = wrapLine(\\\"NLHEAD_FFI\\\", self.annotation, self.delimiter, \\\"%d%s%d\\\\n\\\" % (headlength, self.delimiter, self.FFI))\\n self.header = StringIO(\\\"\\\".join(lines))\\n self.header.seek(0)\",\n \"def liver(self, liver):\\n\\n self.logger.debug(\\\"In 'liver' setter.\\\")\\n\\n self._liver = liver\",\n \"def change_tail_width(self, value):\\n self.layer.tail_width = float(value) / 2.0\",\n \"def set_max_sentence_length(self):\\n new_max = int(self.set_max_sentence.get())\\n cur_min = self.min_sentence_length\\n\\n if new_max > cur_min:\\n self.max_sentence_length = new_max\\n else:\\n old_max = self.max_sentence_length\\n old_max_var = tk.StringVar(self.master)\\n old_max_var.set(str(old_max))\\n self.set_max_sentence.config(textvariable=old_max_var)\",\n \"def __init__(self, *args, **kwargs):\\n super(LinlLis, self).__init__(\\n ('linl', Bits(maxlen=4)),\\n ('lis', Bits(maxlen=4)),\\n *args, **kwargs\\n )\",\n \"def length_minutes(self, length_minutes):\\n \\n self._length_minutes = length_minutes\",\n \"def setLong(self, address: ghidra.program.model.address.Address, value: long) -> None:\\n ...\",\n \"def set_timeout(self, new_timeout):\\n self.timeout = new_timeout\\n self._update_timestamp()\",\n \"def lineLengthExceeded(self, line):\\n segments = self._breakLineIntoSegments(line)\\n for segment in segments:\\n self.lineReceived(segment)\",\n \"def __init__(self, keep_last_n_lines=5) :\\r\\n self.contextLines_ = keep_last_n_lines\\r\\n self.data_ = CircularBuffer(1024)\\r\\n self.lineNumber_ = 1\\r\\n self.charNumber_ = 0\",\n \"def len_literal(self):\\n if hasattr(self, '_m_len_literal'):\\n return self._m_len_literal if hasattr(self, '_m_len_literal') else None\\n\\n self._m_len_literal = (self.len_literal_div2 * 2)\\n return self._m_len_literal if hasattr(self, '_m_len_literal') else None\",\n \"def set_size(self, length, width=None):\\n\\n length = float(length)\\n try:\\n width = float(width)\\n except:\\n pass\\n if width is not None:\\n self.ang_size = np.sqrt(length * width)\\n else:\\n self.ang_size = length\\n\\n ang_size_in_rad = self.ang_size / 60 * np.pi / 180\\n self.sr = ct.angle_to_solid_angle(ang_size_in_rad)\",\n \"def __init__(self) -> None:\\n self.length = 0\",\n \"def setMaxSentenceLength(self, value):\\n return self._set(maxSentenceLength=value)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6628996","0.6548008","0.6527316","0.6292543","0.61618847","0.60966504","0.605155","0.59509474","0.59412754","0.59316313","0.59247273","0.5702406","0.56750685","0.5643853","0.5622231","0.55569696","0.55228","0.5480967","0.54594636","0.5426649","0.5416591","0.5415061","0.5395622","0.5348917","0.5322101","0.5316463","0.53009427","0.5281208","0.5280256","0.52669144","0.5234339","0.5234339","0.5201205","0.5148295","0.5084994","0.50651485","0.5059319","0.50544727","0.50509447","0.50501007","0.50469553","0.5044129","0.5040274","0.50318587","0.5029048","0.5024301","0.501612","0.49987596","0.4997782","0.4997782","0.4997782","0.4997782","0.49809036","0.49637124","0.49553275","0.4951865","0.4947657","0.49470782","0.49403775","0.49171615","0.49137568","0.4910371","0.4899165","0.48912367","0.4879797","0.48691186","0.48671076","0.48600674","0.48416224","0.4830541","0.482549","0.4821683","0.48200938","0.48129222","0.48065355","0.4804439","0.47883922","0.4786316","0.4780585","0.47727942","0.47692135","0.47626528","0.47599858","0.47591552","0.47506195","0.47467607","0.4734122","0.47341177","0.47317293","0.4723978","0.47239318","0.47206935","0.47068396","0.4702105","0.47020692","0.4699961","0.46933934","0.46830922","0.4680476","0.46767548"],"string":"[\n \"0.6628996\",\n \"0.6548008\",\n \"0.6527316\",\n \"0.6292543\",\n \"0.61618847\",\n \"0.60966504\",\n \"0.605155\",\n \"0.59509474\",\n \"0.59412754\",\n \"0.59316313\",\n \"0.59247273\",\n \"0.5702406\",\n \"0.56750685\",\n \"0.5643853\",\n \"0.5622231\",\n \"0.55569696\",\n \"0.55228\",\n \"0.5480967\",\n \"0.54594636\",\n \"0.5426649\",\n \"0.5416591\",\n \"0.5415061\",\n \"0.5395622\",\n \"0.5348917\",\n \"0.5322101\",\n \"0.5316463\",\n \"0.53009427\",\n \"0.5281208\",\n \"0.5280256\",\n \"0.52669144\",\n \"0.5234339\",\n \"0.5234339\",\n \"0.5201205\",\n \"0.5148295\",\n \"0.5084994\",\n \"0.50651485\",\n \"0.5059319\",\n \"0.50544727\",\n \"0.50509447\",\n \"0.50501007\",\n \"0.50469553\",\n \"0.5044129\",\n \"0.5040274\",\n \"0.50318587\",\n \"0.5029048\",\n \"0.5024301\",\n \"0.501612\",\n \"0.49987596\",\n \"0.4997782\",\n \"0.4997782\",\n \"0.4997782\",\n \"0.4997782\",\n \"0.49809036\",\n \"0.49637124\",\n \"0.49553275\",\n \"0.4951865\",\n \"0.4947657\",\n \"0.49470782\",\n \"0.49403775\",\n \"0.49171615\",\n \"0.49137568\",\n \"0.4910371\",\n \"0.4899165\",\n \"0.48912367\",\n \"0.4879797\",\n \"0.48691186\",\n \"0.48671076\",\n \"0.48600674\",\n \"0.48416224\",\n \"0.4830541\",\n \"0.482549\",\n \"0.4821683\",\n \"0.48200938\",\n \"0.48129222\",\n \"0.48065355\",\n \"0.4804439\",\n \"0.47883922\",\n \"0.4786316\",\n \"0.4780585\",\n \"0.47727942\",\n \"0.47692135\",\n \"0.47626528\",\n \"0.47599858\",\n \"0.47591552\",\n \"0.47506195\",\n \"0.47467607\",\n \"0.4734122\",\n \"0.47341177\",\n \"0.47317293\",\n \"0.4723978\",\n \"0.47239318\",\n \"0.47206935\",\n \"0.47068396\",\n \"0.4702105\",\n \"0.47020692\",\n \"0.4699961\",\n \"0.46933934\",\n \"0.46830922\",\n \"0.4680476\",\n \"0.46767548\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5764367"},"document_rank":{"kind":"string","value":"11"}}},{"rowIdx":5294,"cells":{"query":{"kind":"string","value":"Draw lifeline. We always draw the lifeline's head. We only draw the lifeline's lifetime when the lifetime is visible."},"document":{"kind":"string","value":"def draw_lifeline(self, box, context, bounding_box):\n cr = context.cairo\n cr.rectangle(0, 0, self.width, self.height)\n stroke(context)\n\n if (\n context.hovered\n or context.focused\n or context.dropzone\n or self._lifetime.visible\n ):\n bottom = self._lifetime.bottom\n cr = context.cairo\n with cairo_state(cr):\n cr.set_dash((7.0, 5.0), 0)\n x = self._handles[SW].pos.x\n top = self._lifetime.top\n cr.move_to(top.pos.x - x, top.pos.y)\n cr.line_to(bottom.pos.x - x, bottom.pos.y)\n stroke(context, dash=False)\n\n # draw destruction event\n if self.is_destroyed:\n d1 = 8\n d2 = d1 * 2\n cr.move_to(bottom.pos.x - d1, bottom.pos.y - d2)\n cr.line_to(bottom.pos.x + d1, bottom.pos.y)\n cr.move_to(bottom.pos.x - d1, bottom.pos.y)\n cr.line_to(bottom.pos.x + d1, bottom.pos.y - d2)\n cr.stroke()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def draw_line():\n\n # Small Size Line\n glLineWidth(0.1)\n glColor3f(0.5, 1.0, 0.9)\n wid = 0\n while wid <= width:\n length = 0\n while length <= height:\n glBegin(GL_LINES)\n glVertex3f(0.0, length, 0.0)\n glVertex3f(wid, length, 0)\n glEnd()\n glBegin(GL_LINES)\n glVertex3f(length, 0, 0.0)\n glVertex3f(length, wid, 0)\n glEnd()\n length += 10\n wid += 50\n # Medium Size Line\n glLineWidth(2.0)\n wid = 0\n while wid <= width:\n length = 0\n while length <= height:\n glBegin(GL_LINES)\n glVertex3f(0.0, length, 0.0)\n glVertex3f(wid, length, 0)\n glEnd()\n length += 50\n glBegin(GL_LINES)\n glVertex3f(length, 0, 0.0)\n glVertex3f(length, wid, 0)\n glEnd()\n wid += 50\n # Main Line\n # ordinat\n glLineWidth(1.5)\n glColor3f(0.5, 0.4, 0.8)\n glBegin(GL_LINES)\n glVertex3f(height / 2, 0, 0.0)\n glVertex3f(height / 2, width, 0)\n glEnd()\n # absis\n glBegin(GL_LINES)\n glVertex3f(0, width / 2, 0.0)\n glVertex3f(height, width / 2, 0)\n glEnd()","def draw_reticle(self):\n glColor3d(0, 0, 0)\n self.reticle.draw(GL_LINES)","def __draw_line(display, color, ball_pos, dx, dy):\n pygame.draw.line(display, color, ball_pos, (ball_pos[0] + dx, ball_pos[1] + dy), 2)","def display_line_map(self):\n lh_count = len(flatten(self.lh_data))\n print('{} horizontal line mapping: {} hline draw calls. {} bytes'.format(\n self.char,\n lh_count,\n len(list(self._stream_lhmap()))\n ))\n print('v' * len(''.join([str(i) for i in range(self.width)])), ' y [(x, length)]')\n for y in range(self.height):\n for x in range(self.width):\n space = ' ' if x < 10 else ' '\n char = space if self.pixels[y * self.width + x] else x\n print(char, end='')\n print(' ', '%2d' % y, self.lh_data[y])\n print()\n\n lv_count = len(flatten(self.lv_data))\n print('{} vertical line mapping: {} vline draw calls. {} bytes'.format(\n self.char,\n lv_count,\n len(list(self._stream_lvmap()))\n ))\n print('>' * len(''.join([str(i) for i in range(self.height)])), ' x [(y, length)]')\n for x in range(self.width)[::-1]:\n for y in range(self.height):\n space = ' ' if y < 10 else ' '\n char = space if self.pixels[y * self.width + x] else y\n print(char, end='')\n print(' ', '%2d' % x, self.lv_data[x])\n print()\n\n print('selecting {} mapping for {} char\\n'.format(\n 'lhmap horizontal' if self.is_char_lhmap() else 'lvmap vertical',\n self.char\n ))","def do_draw_network(self, line):\n self.fibbing.root.lsdb.graph.draw(line)","def _draw_line(self, event):\n if not self.obstacle_creation_mode:\n return\n\n if self.previous_coordinates is None:\n self.previous_coordinates = event.x, event.y\n self.new_obstacle.append([event.x, event.y])\n return\n\n x1, y1 = event.x, event.y\n\n if self._is_closing_shape(x1, y1, self.new_obstacle):\n x1, y1 = self.new_obstacle[0]\n else:\n self.new_obstacle.append([x1, y1])\n\n x0, y0 = self.previous_coordinates\n self.canvas.create_line(x0, y0, x1, y1, **self.LINE_OPTIONS)\n self.previous_coordinates = x1, y1","def drawLines(self):\n\t\tintersections = [[], []]\n\t\tfor l in self.lines:\n\t\t\tif l.direction == 'v':\n\t\t\t\tif l.rtc:\n\t\t\t\t\tposition = l.coordinate + int((self.width - 1) / 2)\n\t\t\t\telse:\n\t\t\t\t\tposition = int((l.coordinate * self.width / 100) if type(l.coordinate) == float else l.coordinate)\n\t\t\t\tintersections[0].append(position)\n\t\t\t\tfor yPos in range(1, self.height - 2):\n\t\t\t\t\tself.wts(yPos, position, '│', self._borderColor)\n\t\t\t\t# endpoints\n\t\t\t\tself.wts(0, position, '┬',self._borderColor)\n\t\t\t\tself.wts(self.height - 2, position, '┴', self._borderColor)\n\t\t\telif l.direction == 'h':\n\t\t\t\tif l.rtc:\n\t\t\t\t\tposition = l.coordinate + ((self.height - 1) / 2)\n\t\t\t\telse:\n\t\t\t\t\tposition = int((l.coordinate * self.height / 100) - 1 if type(l.coordinate) == float else l.coordinate)\n\t\t\t\tintersections[1].append(position)\n\t\t\t\tself.wts(position, 1, '─' * (self.width - 2), self._borderColor)\n\t\t\t\t# endpoints\n\t\t\t\tself.wts(position, 0, '├', self._borderColor)\n\t\t\t\tself.wts(position, self.width - 1, '┤', self._borderColor)\n\t\t# draw intersections\n\t\tfor x in intersections[1]:\n\t\t\tfor y in intersections[0]:\n\t\t\t\tself.wts(x, y, '┼', self._borderColor)\n\t\tself.verticalBoundaries = intersections[0]\n\t\tif self.screenBorder:\n\t\t\tself.verticalBoundaries.append(self.width)","def draw_line(color, start_pos, end_pos, width=1):\n pygame.draw.line(screen, color, start_pos, end_pos, width)","def draw_lines(self):\n for x_cord in range(0, Dimension.SCREEN_WIDTH.value, Dimension.SQUARE_WIDTH.value):\n pg.draw.line(self.window, Colors.BLACK.value, (x_cord, 0), (x_cord, Dimension.SCREEN_HEIGHT.value))\n\n for y_cord in range(0, Dimension.SCREEN_HEIGHT.value, Dimension.SQUARE_HEIGHT.value):\n pg.draw.line(self.window, Colors.BLACK.value, (0, y_cord), (Dimension.SCREEN_WIDTH.value, y_cord))\n\n pg.display.update()","def draw(self):\n glColor3f(1.0, 0.0, 0.0)\n glBegin(GL_LINES)\n for vertex in self.edges[0]:\n glVertex3fv(self.vertices[vertex])\n glColor3f(0.0, 1.0, 0.0)\n for vertex in self.edges[1]:\n glVertex3fv(self.vertices[vertex])\n glColor3f(0.0, 0.0, 1.0)\n for vertex in self.edges[2]:\n glVertex3fv(self.vertices[vertex])\n glEnd()","def draw(self):\n # s1 = ShowPoint(self.cnv, self.p1.xpt, self.p1.ypt)\n # s2 = ShowPoint(self.cnv, self.p2.xpt, self.p2.ypt)\n # s1.draw()\n # # s2.draw()\n self.cnv.create_line(self.p1.xpt, self.p1.ypt, self.p2.xpt, self.p2.ypt)","def update_line(self):\n self._draw_line_text()\n self._draw_status()\n self._line_listbox.set_focus(self.model.l_index)","def _render_horizontal(self, gc, lx, ly, rx, ry, mx, my):\n\n with gc:\n gc.set_line_width(20)\n gc.set_stroke_color(self._get_border_color())\n tee_h(gc, lx, ly, mx, my, ry)\n\n gc.set_line_width(10)\n self.set_fill_color(gc)\n tee_h(gc, lx, ly, mx, my, ry)","def _draw_line_text(self):\n self._line_text.set_text(self.model.get_current_line())","def display(self, screen: pygame.Surface, line_thickness=3):\n\t\tfor p1, p2 in self.__calculate_points():\n\t\t\tpygame.draw.line(screen, Color(255).get(), p1.get_int(), p2.get_int(), line_thickness)","def draw_lines(self):\n # draw x lines\n y = self.step_y\n while y <= self.height:\n x = 0\n while x <= self.width:\n self.canvas.create_line(x, y, x+3.5, y)\n self.canvas.update()\n x += 3.5\n y += self.step_y\n \n # draw y lines\n x = self.step_x\n while x <= self.width:\n y = 0\n while y <= self.height:\n self.canvas.create_line(x, y, x, y+3.5)\n self.canvas.update()\n y += 3.5\n x += self.step_x\n \n self.is_operating = False","def draw(self, camera):\n for line in self._polyline.lines:\n camera.draw_line(line.begin, line.end, self.color, self.width)","def hline(self, x, y, length, color):\n self.fill_rect(x, y, length, 1, color)","def draw_bullet(self):\n pygame.draw.rect(self.__screen, self.__color, self.rect)","def create_line(self):\n if self.hosts and self.line:\n self.msg(\"There is a line here already.\")\n self.display_line()\n return\n self.line = []\n other_hosts = [self.caller.search(arg) for arg in self.lhslist]\n other_hosts = [ob for ob in other_hosts if ob and ob.player]\n other_hosts.append(self.caller)\n self.hosts = other_hosts\n if \"loop\" in self.switches:\n self.toggle_loop()\n self.display_line()","def _newLine(self, usePos=True):\n if len(self.currentLine) > 1:\n self.screen._drawline(self.currentLineItem, self.currentLine,\n self._pencolor, self._pensize)\n self.currentLineItem = self.screen._createline()\n self.items.append(self.currentLineItem)\n else:\n self.screen._drawline(self.currentLineItem, top=True)\n self.currentLine = []\n if usePos:\n self.currentLine = [self._position]","def draw_line(self, x0, y0, x1, y1, color=Color['white']):\n pygame.draw.line(self.display, color, (x0, y0), (x1, y1))","def draw_laser(self):\n pygame.draw.rect(self.screen, self.color, self.rect)","def drawReference(x, y, z, l):\r\n\r\n glPushMatrix()\r\n\r\n glColor3f(1.0, 0.0, 0.0)\r\n\r\n glBegin(GL_LINES)\r\n glNormal3f(0.0, 0.0, 1.0)\r\n glVertex3f(x, y, z)\r\n glVertex3f(x + l, y, z)\r\n glEnd()\r\n\r\n glColor3f(0.0, 1.0, 0.0)\r\n\r\n glBegin(GL_LINES)\r\n glNormal3f(0.0, 0.0, 1.0)\r\n glVertex3f(x, y, z)\r\n glVertex3f(x, y + l, z)\r\n glEnd()\r\n\r\n glColor3f(0.0, 0.0, 1.0)\r\n\r\n glBegin(GL_LINES)\r\n glNormal3f(0.0, 0.0, 1.0)\r\n glVertex3f(x, y, z)\r\n glVertex3f(x, y, z + l)\r\n glEnd()\r\n\r\n glPopMatrix()","def draw_bullet(self):\r\n pygame.draw.rect(self.screen, self.color, self.rect)","def draw_line(self, DISP, side:str, indizes:tuple, pink = False):\r\n offset = 1 #< Just to draw the line nicely\r\n pos = (indizes[0] - 1) * self.grid_size, indizes[1] * self.grid_size\r\n # Check if it's a pink line\r\n if pink:\r\n start_pos = pos[0], pos[1] + self.grid_size // 2\r\n end_pos = pos[0] + self.grid_size, pos[1] + self.grid_size // 2\r\n # Check if the line should be vertically. u for up\r\n elif side == 'u':\r\n start_pos = pos[0] + self.width - offset + self.grid_size // 2, pos[1] + self.grid_size // 2\r\n end_pos = pos[0] + self.grid_size + offset + self.grid_size // 2 - self.width, pos[1] + self.grid_size // 2\r\n # Check if the line should be horizontally. l for left\r\n elif side == 'l':\r\n start_pos = pos[0] + self.grid_size // 2, pos[1] + self.width - offset + self.grid_size // 2\r\n end_pos = pos[0] + self.grid_size // 2, pos[1] - self.width + self.grid_size + offset + self.grid_size // 2\r\n if not pink:\r\n pg.draw.line(DISP, Colors.colors['BLACK'], start_pos,end_pos, self.width + 2 * offset) \r\n else:\r\n pg.draw.line(DISP, Colors.colors['PINK'], start_pos,end_pos, self.width + 2 * offset)","def add_line(self, text):\n\t\twidth, height = self.font.size(text)\n\t\tpos = (self.rect.left + 10, self.rect.bottom - height- 5)\n\t\trend = self.font.render(text, True, BLACK)\n\t\t# Move all already existing lines up\n\t\tfor i in range(len(self.all_lines)):\n\t\t\toldsurf, oldpos = self.all_lines[i]\n\t\t\tself.all_lines[i] = self.lift_line(oldsurf, height, oldpos)\n\t\t\tcopy = oldsurf.copy()\n\t\t\tcopy.fill(BG_COLOR)\n\t\t\tself.image.blit(copy, oldpos)\n\t\tself.all_lines.append([rend, pos])\n\t\tself.image.blit(rend, pos)","def draw(self):\r\n pygame.draw.rect(self.screen, self.background_color, self.bounds)\r\n line_window = self.lines[self.scroll_window_top:self.scroll_window_bottom]\r\n for idx,line in enumerate(line_window):\r\n text = self.font.render(line, True, self.foreground_color)\r\n x,y = self._get_x_y_from_pos(self.position[0], self.position[1]+idx)\r\n self.screen.blit(text,(x,y))\r\n \r\n if self.cursor_visible and self.scroll_window_bottom == len(self.lines):\r\n x,y = self._get_x_y_from_pos(len(line_window[-1]), len(line_window))\r\n cursor_rect = pygame.Rect(x,y,\r\n self.text_width,self.text_height)\r\n pygame.draw.rect(self.screen, self.foreground_color, cursor_rect)","def draw_line(tick_length, tick_label=''):\n line = \"_\" * tick_length\n if tick_label:\n line += ' ' + tick_label\n print(line)","def draw(self):\n if len(self.__points) >= 2:\n self._total_length = 0\n for i in range(len(self.__points) - 1):\n p1 = self.__points[i]\n p2 = self.__points[i + 1]\n coords = self.__line_segment(p1, p2)\n if not coords is None:\n pyglet.graphics.draw_indexed(4, pyglet.gl.GL_TRIANGLES,\n [0, 1, 2, 1, 2, 3],\n ('v2i', coords),\n ('c4b', self.color * 4)\n )\n coords = self.__line_cap(p2)\n pyglet.graphics.draw_indexed(4, pyglet.gl.GL_TRIANGLES,\n [0, 1, 2, 0, 2, 3],\n ('v2i', coords),\n ('c4b', self.color * 4)\n )","def draw_bullet(self):\n\t\tpygame.draw.rect(self.screen, self.colour, self.rect)","def hline(self, x, y, width, color):\n self.rect(x, y, width, 1, color, fill=True)","def _defLine(self):\n self._dline=GPath(points = [0,100,GAME_WIDTH,100], linewidth = 1.5,\n linecolor = 'cyan')","def _DrawLineList(*args, **kwargs):\n return _gdi_.DC__DrawLineList(*args, **kwargs)","def draw_line(self, x1, y1, x2, y2, color):\n painter = QPainter()\n painter.begin(self.lbFFmpeg.pixmap())\n painter.setPen(QColor(color))\n painter.drawLine(x1, y1, x2, y2)\n painter.end()\n self.lbFFmpeg.update()","def startLineDrawing(self, startPos):\n self.line = LineNodePath(render2d, thickness=2, colorVec=(0.8,0.8,0.8,1))\n self.line.moveTo(startPos)\n t = taskMgr.add(self.drawLineTask, \"drawLineTask\")\n t.startPos = startPos","def _draw_line(plot, hori, vert, color, text):\n plot.plot(hori, vert, '-o'+color)\n plot.text(hori[-1]-3, vert[-1]+2, text, color=color)","def DrawLine(*args, **kwargs):\n return _gdi_.PseudoDC_DrawLine(*args, **kwargs)","def line_layer(self):\n screen_origin = self.ids.mapview.get_window_xy_from(lat1, lon1, self.ids.mapview.zoom)\n screen_destination = self.ids.mapview.get_window_xy_from(lat2, lon2, self.ids.mapview.zoom)\n point_list = [screen_origin[0], screen_origin[1], screen_destination[0], screen_destination[1]]\n\n with self.ids.line.canvas:\n self.ids.line.canvas.clear()\n\n Color(0, 0, 0, .6)\n Line(points=point_list, width=3, joint=\"bevel\")","def draw_lines(display, coord, box_size, color, bg_color):\n left, top = coord\n stroke = 6\n half_stroke = int(stroke / 2)\n left = left + half_stroke\n top = top + half_stroke\n box_size = box_size - stroke\n for i in range(0, box_size, int(stroke + 2)):\n pygame.draw.line(\n display, color,\n (left, top + i),\n (left + i, top),\n stroke,\n )\n pygame.draw.line(\n display, color,\n (left + i, top + box_size - 1),\n (left + box_size - 1, top + i),\n stroke,\n )\n return","def draw(self, img):\n self._erase_last_line(self.img)\n\n idxs = np.argwhere(img[:, self._pos] == 0)\n self.prev_y = (idxs.min(), idxs.max())\n\n cv.line(img, (self._pos, 0), (self._pos, self.h), (0, 0, 0), 1)","def draw_asc_diagonal(self, player):\n if player == 1:\n color = GameData.circle_color\n elif player == 2:\n color = GameData.cross_color\n\n pygame.draw.line(\n self.game_screen,\n color,\n (15, GameData.screen_dim - 15),\n (GameData.screen_dim - 15, 15), GameData.win_line_width)","def line(self, start, end, color=(255, 255, 255), width=1):\n start = self._transform(start)\n end = self._transform(end)\n\n pygame.draw.line(self.screen, color, start, end, width)","def draw_line(self, coords, smooth=False, **options):\n # NOTE: Outline does not work because uses paths instead of normal line method.\n # TODO: Add volume param, containing a list of linewidths same length as line\n # or as a function that calculates the width at each node\n # Result is a flow line with varying thickness at each node\n # Have to calculate left/right xy at each node, and use symbol curveto()\n # Easy and really cool...DO IT!\n options = self._check_options(options)\n \n if not hasattr(coords[0], \"__iter__\"):\n coords = _grouper(coords, 2)\n else: coords = (point for point in coords)\n \n # get drawing tools from options\n args = []\n if options[\"fillcolor\"]:\n pen = aggdraw.Pen(options[\"fillcolor\"], options[\"fillsize\"])\n args.append(pen)\n\n if smooth:\n\n # Note: Creation of the aggdraw.Symbol object here can be\n # very slow for long lines; Path is much faster but due\n # to a bug it does not correctly render curves, hence the use\n # of Symbol\n \n pathstring = \"\"\n \n # begin\n coords = _pairwise(coords)\n (startx,starty),(endx,endy) = next(coords)\n pathstring += \" M%s,%s\" %(startx, starty)\n \n # draw straight line to first line midpoint\n midx,midy = (endx + startx) / 2.0, (endy + starty) / 2.0\n pathstring += \" L%s,%s\" %(midx, midy)\n oldmidx,oldmidy = midx,midy\n \n # for each line\n for line in coords:\n # curve from midpoint of first to midpoint of second\n (startx,starty),(endx,endy) = line\n midx,midy = (endx + startx) / 2.0, (endy + starty) / 2.0\n pathstring += \" Q%s,%s,%s,%s\" %(startx, starty, midx, midy)\n oldmidx,oldmidy = midx,midy\n \n # draw straight line to endpoint of last line\n pathstring += \" L%s,%s\" %(endx, endy)\n\n # make into symbol object\n symbol = aggdraw.Symbol(pathstring)\n\n # draw the constructed symbol\n self.drawer.symbol((0,0), symbol, *args)\n\n else:\n\n path = aggdraw.Path()\n \n # begin\n startx,starty = next(coords)\n path.moveto(startx, starty)\n \n # connect to each successive point\n for nextx,nexty in coords:\n path.lineto(nextx, nexty)\n\n # draw the constructed path\n self.drawer.path((0,0), path, *args)","def draw_line(self, frame, rect):\n print(\"x0, y0, x1, y1\", self.x0, self.y0, self.x1, self.y1)\n print(\"cross_x, cross_y\", self.cross_x, self.cross_y)\n left, top, right, bottom = rect\n # 枠内では線を表示しないようにしてやる\n if top<self.y1<bottom and left<self.x1<right:\n return\n # フレームと線の交点\n if (self.x1 >= right or self.x1 <= left or self.y1 <= top or self.y1 >= bottom) and self.cross_x == 0:\n self.cross_x = self.x1\n self.cross_y = self.y1\n return\n draw = ImageDraw.Draw(frame)\n draw.line((self.cross_x, self.cross_y, self.x1, self.y1), fill=(255, 255, 255), width=3)","def draw_lines(self, color, points, width = 1, closed = False):\n color = spyral.color._determine(color)\n pygame.draw.aalines(self._surf, color, closed, points)","def draw_line(self, color, p1: Point, p2: Point, width):\n _p1 = self.T.itrans(p1)\n _p2 = self.T.itrans(p2)\n pg.draw.line(self.screen, color, _p1(), _p2(), 2)","def paint_axes(self, l=1):\n GL.glBegin(GL.GL_LINES)\n GL.glColor3f(1, 0, 0) # red x axis\n GL.glVertex3f(0, 0, 0)\n GL.glVertex3f(l, 0, 0)\n GL.glColor3f(0, 1, 0) # green y axis\n GL.glVertex3f(0, 0, 0)\n GL.glVertex3f(0, l, 0)\n GL.glColor3f(0, 0, 1) # blue z axis\n GL.glVertex3f(0, 0, 0)\n GL.glVertex3f(0, 0, l)\n GL.glEnd()","def paint_axes(self, l=1):\n GL.glBegin(GL.GL_LINES)\n GL.glColor3f(1, 0, 0) # red x axis\n GL.glVertex3f(0, 0, 0)\n GL.glVertex3f(l, 0, 0)\n GL.glColor3f(0, 1, 0) # green y axis\n GL.glVertex3f(0, 0, 0)\n GL.glVertex3f(0, l, 0)\n GL.glColor3f(0, 0, 1) # blue z axis\n GL.glVertex3f(0, 0, 0)\n GL.glVertex3f(0, 0, l)\n GL.glEnd()","def on_draw(event):\n # First, we clear the window in white\n # (it is necessary to do that at every frame)\n gloo.set_clear_color((1.0, 1.0, 1.0, 1.0))\n gloo.clear()\n program.draw(\"line_strip\")","def _createline(self):\n return self.cv.create_line(0, 0, 0, 0, fill=\"\", width=2,\n capstyle = TK.ROUND)","def OnDraw(self):\r\n self.SetCurrent()\r\n\r\n glClear(GL_COLOR_BUFFER_BIT)\r\n\r\n glBegin(GL_LINES)\r\n\r\n glColor3f(1.0, 1.0, 1.0)\r\n \r\n \r\n #Just in case these aren't set back to their starting place yet...\r\n self.currentpoint = self.startingpoint\r\n self.currentheading = 0 \r\n \r\n for element in self.finalstring:\r\n if element == '+':\r\n self.currentheading += self.angle\r\n elif element == '-':\r\n self.currentheading -= self.angle\r\n elif element == 'F':\r\n glVertex2i(self.currentpoint[0], self.currentpoint[1])\r\n self.currentpoint = self.NextPoint(self.currentpoint, self.length, self.currentheading)\r\n glVertex2i(self.currentpoint[0], self.currentpoint[1])\r\n elif element == '[':\r\n self.stack.append([self.currentpoint[0], self.currentpoint[1], self.currentheading])\r\n elif element == ']':\r\n popped = self.stack.pop()\r\n self.currentheading = popped.pop()\r\n self.currentpoint = popped\r\n \r\n \r\n glEnd()\r\n self.currentpoint = self.startingpoint\r\n self.currentheading = 0\r\n \r\n \r\n self.SwapBuffers() \r\n\r\n return","def dline(x, y):\n glClear(GL_COLOR_BUFFER_BIT)\n glColor3f(0.0, 0.0, 1.0)\n glPointSize(10.0)\n glBegin(GL_POINTS)\n while (x <= y):\n glVertex2f(x, x)\n x += 0.05\n glEnd()\n glFlush()","def line(self, clear_screen=True, x1=10, y1=10, x2=50, y2=50, line_color='black', width=1):\n\n if clear_screen:\n self.clear()\n\n return self.draw.line((x1, y1, x2, y2), fill=line_color, width=width)","def redraw(self): \r\n\r\n self.arrow.undraw() \r\n pt2 = Point(self.vel*cos(self.angle), self.vel*sin(self.angle))\r\n self.arrow = Line(Point(0,0), pt2).draw(self.win) \r\n self.arrow.setArrow('last')\r\n self.arrow.setWidth(3)","def redraw(self):\n \n self.arrow.undraw()\n pt2 = Point(self.vel*cos(self.angle), self.vel*sin(self.angle))\n self.arrow = Line(Point(0,0), pt2).draw(self.win)\n self.arrow.setArrow(\"last\")\n self.arrow.setWidth(3)","def draw_lh_lines(data):\n #hnd = extract_left_hand(data);\n hnd = np.array(data['crop']);\n hand.draw_hand_lines(hnd,data['lhkpss'][data['i']]);\n return hnd;","def drawPath(self):\r\n bgl.glColor4f(0.8,0.8,0.9,0.01)\r\n bgl.glLineWidth(0.01)\r\n\r\n bgl.glBegin(bgl.GL_LINES)\r\n bgl.glVertex3f(self.p1[0],self.p1[1],self.p1[2])\r\n bgl.glVertex3f(self.p2[0],self.p2[1],self.p2[2])\r\n bgl.glEnd()\r\n\r\n bgl.glNormal3f(0.0,0.0,1.0)\r\n bgl.glShadeModel(bgl.GL_SMOOTH);","def draw(self, base, level):\n\n a = base.a\n b = base.b\n\n if level > 0:\n delta = base.b - base.a\n px = a.x + delta.x / 3\n py = a.y + delta.y / 3\n rx = a.x + 2 * delta.x / 3\n ry = a.y + 2 * delta.y / 3\n p = Point(px, py)\n r = Point(rx, ry)\n q = Point(rx, ry)\n q.rotate_deg(60, p)\n self.draw(Line(a,p), level-1)\n self.draw(Line(p,q), level-1)\n self.draw(Line(q,r), level-1)\n self.draw(Line(r,b), level-1)\n else:\n self.container.window.create_line(a.x, a.y, b.x, b.y)","def draw(cls, lcfg, parent):\n axes = parent.getAxes()\n props = {}\n line = None\n label = None\n idx = int(lcfg.key)\n for child in lcfg.children:\n key = child.key\n val = child.val\n if key == cls.XY:\n xvals = []\n yvals = []\n for xy in val:\n x, y = xy\n xvals.append(x)\n yvals.append(y)\n line = axes.plot(xvals, yvals)\n elif key in cls.KEYS:\n props[key] = val\n if line == None:\n raise ValueError(\"No xy data found in %s.%s\" %(parent, idx))\n for key, val in props.iteritems():\n if key == LABEL:\n label = val\n if key == MARKER:\n plt.setp(line, marker=val)\n elif key == MARKERSIZE:\n plt.setp(line, markersize=val)\n elif key == CONN:\n plt.setp(line, linestyle=val)\n elif key == WIDTH:\n plt.setp(line, linewidth=val)\n elif key == COLOR:\n plt.setp(line, color=val)\n newline = Line2D(line, parent, label)\n parent.addLine(newline)\n return newline","def draw():","def draw_line(self, gray=0, nextline=0):\n\n self.fontsize = 4\n if nextline:\n self.nextline()\n else:\n self.linespace(8)\n self.resetx()\n c = self.canvas\n c.setStrokeGray(gray)\n c.setLineWidth(1)\n #self.y = self.y + self.linespacing + (self.fontsize/2)\n c.line(self.x, self.y, self.width - self.x, self.y)\n self.y = self.y + (self.linespacing)","def draw():\n screen.fill((0, 0, 0))\n alien.draw()","def wdraw_line(self, wx0, wy0, wx1, wy1, color, arrow):\r\n dx0, dy0 = self.w_to_d(wx0, wy0)\r\n dx1, dy1 = self.w_to_d(wx1, wy1)\r\n self.canvas.create_line(dx0, dy0, dx1, dy1, fill=color, arrow=arrow)","def draw(self):\n arcade.draw_xywh_rectangle_filled(\n self.x, self.y, self.width, self.height, self.fill.color\n )\n arcade.draw_xywh_rectangle_outline(\n self.x, self.y, self.width, self.height, self.pen.color, 3\n )","def draw_horizontal_winning_line(self, row, player):\n posY = row * GameData.square_size + GameData.square_size // 2\n\n if player == 1:\n color = GameData.circle_color\n elif player == 2:\n color = GameData.cross_color\n\n pygame.draw.line(\n self.game_screen,\n color, (15, posY),\n (GameData.screen_dim - 15, posY),\n GameData.win_line_width\n )","def my_simple_line(master, name, r, c, rsp, csp, px, py) -> object:\n line = tk.Label(master=master, text=name, anchor='w')\n line.grid(row=r, column=c, rowspan=rsp, columnspan=csp, padx=px, pady=py)\n return line","def DrawLine(*args, **kwargs):\n return _gdi_.DC_DrawLine(*args, **kwargs)","def draw(self):\n self.drawLine()\n\n for l in range(0, self.height):\n print(\"|\", end='', flush=True)\n for c in range(0, self.width):\n print(\" \" + str(self.grid[l][c]) + \" |\", end='', flush=True)\n print(\"\\n\", end='', flush=True)\n\n self.drawLine()","def draw(x,y,x1,y1,d,color=1):\n d.add(dxf.line((x,y),(x1,y1),color=color, layer='LINES',thickness=0.01))","def draw(self, screen):\n lines = self.text.strip().split('\\n')\n y = self.y\n for line in lines:\n self.ui.show_text(line, (self.x, y), 30)\n y += 32","def draw_bullet(self):\n self.screen.blit(self.image, self.rect)","def draw_bullet(self):\n self.screen.blit(self.image, self.rect)","def _plot_line(self, image, x1, y1, x2, y2, width, color):\n\n draw = ImageDraw.Draw(image, \"RGBA\")\n draw.line([x1, y1, x2, y2], fill=color, width=width)\n del draw\n\n return image","def draw_lines(self, color, points, width=1, closed=False):\n if width == 1:\n pygame.draw.aalines(self._surf, color, closed, points)\n else:\n pygame.draw.lines(self._surf, color, closed, points, width)\n self._version += 1\n spyral.util.scale_surface.clear(self._surf)\n return self","def _render_vertical(self, gc, lx, ly, rx, ry, mx, my):\n mx = lx + (rx - lx) / 2.\n with gc:\n gc.set_line_width(20)\n gc.set_stroke_color(self._get_border_color())\n tee_v(gc, lx, ly, rx, mx, my)\n\n gc.set_line_width(10)\n self.set_fill_color(gc)\n tee_v(gc, lx, ly, rx, mx, my)","def line(self, drawer, canvas):\n start_width = random.randint(\n self._width / 8, self._width / 4)\n start_height = random.randint(\n self._height / 4, self._height * 3 / 4)\n stop_width = random.randint(\n self._width * 3 / 4, self._width * 7 / 8)\n stop_height = random.randint(\n self._height / 4, self._height * 3 / 4)\n drawer.line(\n (start_width,\n start_height,\n stop_width,\n stop_height),\n fill=random.randint(128, 155),\n width=3\n )","def draw_lines_slow(orbit_pos, factor):","def draw_desc_diagonal(self, player):\n if player == 1:\n color = GameData.circle_color\n elif player == 2:\n color = GameData.cross_color\n\n pygame.draw.line(self.game_screen, color, (15, 15), (GameData.screen_dim - 15, GameData.screen_dim - 15),\n GameData.win_line_width)","def line(l, color='k', **kwargs):\n ax.plot(wfl(nth(l, 0)), hfl(nth(l, 1)), color=color, **kwargs)","def plot(self, **kwargs):\n base.plot_homline(self.line, **kwargs)","def _draw_line(event, x, y, flags, params):\n global img, source_img\n global p1, p2\n if event == cv2.EVENT_LBUTTONDOWN:\n img = source_img.copy()\n p1 = (x, y)\n elif event == cv2.EVENT_LBUTTONUP:\n p2 = (x, y)\n img = source_img.copy()\n text = 'position: %d' % p2[0]\n cv2.putText(img, text, (100, 100),\n cv2.FONT_HERSHEY_SIMPLEX, 3, DrawingShapeUtils.COLOR, \n DrawingShapeUtils.LINE_THICKNESS)\n cv2.line(img, (x, y+100), (x, y-100), DrawingShapeUtils.COLOR,\n DrawingShapeUtils.LINE_THICKNESS)","def line(self, points, ls=\"--\", draw=\"black\", lw=None, options=None, kwoptions=None):\n\n draw = norm_colour(draw)\n self.use_colour(draw)\n\n if kwoptions is None:\n kwoptions = {}\n kwopts = {'draw': draw, **kwoptions}\n if lw:\n kwopts['line width'] = lw\n\n self._commands.append(rf\"\\draw{wrap(fmt_options(options,kwopts))} \" +\n f\" {ls} \".join(map(fmt_point, points))+\";\")","def hline(self, xi: int, yi: int, length: int, color: int):\n for x in range(length):\n self.pixel(xi + x, yi, color)","def draw_log(self):\n if self.logwin == None:\n return\n self.logwin.clear()\n max_y, max_x = self.logwin.getmaxyx()\n lines = self.list_prev_lines(max_y)\n k = 0\n n = max_y - 1\n while n >= 0:\n if k < len(lines):\n line = lines[k]\n k += 1\n else:\n break\n if len(line) == 0:\n line = '~'\n line = line.rstrip('\\n\\r').expandtabs(4)\n blocks = self.split_string(line, max_x-1)\n blocks.reverse()\n for block in blocks:\n try:\n self.logwin.addstr(n, 0, block)\n except Exception as e:\n logging.warning('An error occured in the curses library.')\n logging.debug(e, exc_info=1)\n n -= 1\n if n < 0:\n break\n self.logwin.noutrefresh()\n return","def plotLines( self ):\n \n ## plot tree in dfs manner\n def plotLines( node_id ):\n\n node = self.mTree.node( node_id )\n\n left = self.mNodeWidthsStart[node_id]\n right = self.mNodeWidthsEnd[node_id]\n height = self.mNodeHeights[node_id] \n\n if right != left and node_id != self.mTree.root:\n self.addElements( self.mDecoratorHorizontalBranches.getElements(\n node_id,\n self.getHeaderWidth() + left,\n self.getHeaderWidth() + right,\n self.getHeaderHeight() + height ))\n \n\n for s in node.succ:\n\n new_height = self.mNodeHeights[s]\n self.addElements( self.mDecoratorVerticalBranches.getElements(\n node_id,\n self.getHeaderWidth() + right,\n self.getHeaderHeight() + height,\n self.getHeaderHeight() + new_height ))\n \n TreeTools.TreeDFS( self.mTree, self.mTree.root,\n pre_function = plotLines )","def Draw_Node( self, node, xstart, ystart):\r\n xdist = node.data.length * cb.xtick\r\n handle = self.canvas_one.create_line( xstart, ystart, xstart+xdist, ystart,width = 3, fill=self.branch_color )\r\n #Attach a handle to a node and place in the handle_list of all LineSegments\r\n ls = LineSegment( handle, node )\r\n self.handle_list.append(ls)\r\n return ystart","def HorizLine(self, parent, depth=3):\n line = sppasStaticLine(parent, orient=wx.LI_HORIZONTAL)\n line.SetMinSize(wx.Size(-1, depth))\n line.SetSize(wx.Size(-1, depth))\n line.SetPenStyle(wx.PENSTYLE_SOLID)\n line.SetDepth(depth)\n line.SetForegroundColour(self.GetForegroundColour())\n return line","def on_draw(self):\n # Clearing the buffers\n self.clear()\n self.set3d()\n # Makes it so color can be added\n glColor3d(1, 1, 1)\n\n self.push(self.player.pos, self.player.rot)\n self.model.draw()\n glPopMatrix()\n self.model.process_queue_slowly()\n\n # Draws the crosshairs on the screen\n self.set2d()\n self.draw_position_label()\n self.draw_reticle()","def draw_line():\n global y1, y2\n canvas.create_line(x1, y1, x2, y2, width=2, fill=color)\n y1 -= 10\n y2 += 10","def __draw(self, display, color, size):\n\t\tif self.walls[0]: # up\n\t\t\tpygame.draw.line(display, color, (self.col * size , self.row * size) , (self.col * size + size, self.row * size))\n\t\tif self.walls[3]: # down\n\t\t\tpygame.draw.line(display, color, (self.col * size + size, self.row * size + size), (self.col * size , self.row * size + size))\n\t\tif self.walls[1]: #left\n\t\t\tpygame.draw.line(display, color, (self.col * size + size, self.row * size) , (self.col * size + size, self.row * size + size))\n\t\tif self.walls[2]: #right\n\t\t\tpygame.draw.line(display, color, (self.col * size , self.row * size + size), (self.col * size , self.row * size))\n\n\t\tif self.current:\n\t\t\tdraw_rect_with_alpha(display, self.CURRENT_COLOR, Vector((self.col, self.row)) * size, (size, size))\n\n\t\telif self.backtracked and self.SHOW_BACKTRACK:\n\t\t\tdraw_rect_with_alpha(display, self.BACKTRACKED_COLOR, Vector((self.col, self.row)) * size, (size, size))\n\n\t\telif self.visited:\n\t\t\tdraw_rect_with_alpha(display, self.VISITED_COLOR, Vector((self.col, self.row)) * size, (size, size))","def drawSlope(self):\n length = sqrt(1 + self.slope**2) # Length of the line segment over 1 x-unit\n xOffset = (segmentLength / length) / 2 # Figures out how many times the length of the 1 unit length fits into the desired length\n # then divides by 2 becuase half is on the left and half on the right of the center\n\n\n # Left end point\n xLeft = self.x - xOffset\n yLeft = (self.slope * (xLeft - self.x)) + self.y\n\n # Right end point\n xRight = self.x + xOffset\n yRight = (self.slope * (xRight - self.x)) + self.y\n\n\n # Converts the left and right end points from cartesian coordinates to screen coordinates\n left = cartesianToScreen(xLeft , yLeft)\n right = cartesianToScreen(xRight, yRight)\n\n\n pygame.draw.aaline(display, self.color, left, right, 1) # DRAWS THE LINE AHHHHHHHHHHHHHHHHHH :P","def display_hline():\n for i in range(12):\n print(\"-\", end=\"\")\n print()","def drawLine(x0,y0,x1,y1,ucoords=1):\n if ucoords:\n dislin.rline(x0,y0,x1,y1)\n else:\n dislin.line(x0,y0,x1,y1)","def draw_line(self, x):\n self.PDF.setStrokeColor(black01)\n self.PDF.setLineWidth(1)\n self.PDF.line(75, x, 550, x)\n self.PDF.setStrokeColor(\"black\")","def draw_line(self, x0, y0, x1, y1, color=None, colorFunc=None, aa=False):\n if aa:\n self._draw_wu_line(x0, y0, x1, y1, color, colorFunc)\n else:\n self._draw_bresenham_line(x0, y0, x1, y1, color, colorFunc)","def draw_lines(point_list, color, border_width=1):\n GL.glEnable(GL.GL_BLEND)\n GL.glBlendFunc(GL.GL_SRC_ALPHA, GL.GL_ONE_MINUS_SRC_ALPHA)\n GL.glEnable(GL.GL_LINE_SMOOTH)\n GL.glHint(GL.GL_LINE_SMOOTH_HINT, GL.GL_NICEST)\n GL.glHint(GL.GL_POLYGON_SMOOTH_HINT, GL.GL_NICEST)\n\n GL.glLoadIdentity()\n\n # Set line width\n GL.glLineWidth(border_width)\n\n # Set color\n if len(color) == 4:\n GL.glColor4ub(color[0], color[1], color[2], color[3])\n elif len(color) == 3:\n GL.glColor4ub(color[0], color[1], color[2], 255)\n\n GL.glBegin(GL.GL_LINES)\n for point in point_list:\n GL.glVertex3f(point[0], point[1], 0.5)\n GL.glEnd()","def drawLine(self,start,stop):\n startX = int(self.vert[start][0]*self.scale + self.size/2)\n startY = int(self.vert[start][1]*self.scale + self.size/2)\n endX = int(self.vert[stop][0]*self.scale + self.size/2)\n endY = int(self.vert[stop][1]*self.scale + self.size/2)\n \n self.canvas.create_line(startX,startY,endX,endY,fill='white')","def vline(self, x, y, height, color):\n self.rect(x, y, 1, height, color, fill=True)","def draw(self):\n if self.state == 'alive':\n for i in range(len(self.tail)):\n pygame.draw.rect(display, black, (squareToXPix(self.tail[-(i + 1)][0], objectSize), squareToYPix(self.tail[-(i + 1)][1], objectSize), objectSize, objectSize))\n\n pygame.draw.rect(display, black, (squareToXPix(self.x, objectSize), squareToYPix(self.y, objectSize), objectSize, objectSize))\n\n else:\n for i in range(len(self.tail)):\n pygame.draw.rect(display, red, (squareToXPix(self.tail[-(i + 1)][0], objectSize), squareToYPix(self.tail[-(i + 1)][1], objectSize), objectSize, objectSize))\n\n pygame.draw.rect(display, red, (squareToXPix(self.x, objectSize), squareToYPix(self.y, objectSize), objectSize, objectSize))"],"string":"[\n \"def draw_line():\\n\\n # Small Size Line\\n glLineWidth(0.1)\\n glColor3f(0.5, 1.0, 0.9)\\n wid = 0\\n while wid <= width:\\n length = 0\\n while length <= height:\\n glBegin(GL_LINES)\\n glVertex3f(0.0, length, 0.0)\\n glVertex3f(wid, length, 0)\\n glEnd()\\n glBegin(GL_LINES)\\n glVertex3f(length, 0, 0.0)\\n glVertex3f(length, wid, 0)\\n glEnd()\\n length += 10\\n wid += 50\\n # Medium Size Line\\n glLineWidth(2.0)\\n wid = 0\\n while wid <= width:\\n length = 0\\n while length <= height:\\n glBegin(GL_LINES)\\n glVertex3f(0.0, length, 0.0)\\n glVertex3f(wid, length, 0)\\n glEnd()\\n length += 50\\n glBegin(GL_LINES)\\n glVertex3f(length, 0, 0.0)\\n glVertex3f(length, wid, 0)\\n glEnd()\\n wid += 50\\n # Main Line\\n # ordinat\\n glLineWidth(1.5)\\n glColor3f(0.5, 0.4, 0.8)\\n glBegin(GL_LINES)\\n glVertex3f(height / 2, 0, 0.0)\\n glVertex3f(height / 2, width, 0)\\n glEnd()\\n # absis\\n glBegin(GL_LINES)\\n glVertex3f(0, width / 2, 0.0)\\n glVertex3f(height, width / 2, 0)\\n glEnd()\",\n \"def draw_reticle(self):\\n glColor3d(0, 0, 0)\\n self.reticle.draw(GL_LINES)\",\n \"def __draw_line(display, color, ball_pos, dx, dy):\\n pygame.draw.line(display, color, ball_pos, (ball_pos[0] + dx, ball_pos[1] + dy), 2)\",\n \"def display_line_map(self):\\n lh_count = len(flatten(self.lh_data))\\n print('{} horizontal line mapping: {} hline draw calls. {} bytes'.format(\\n self.char,\\n lh_count,\\n len(list(self._stream_lhmap()))\\n ))\\n print('v' * len(''.join([str(i) for i in range(self.width)])), ' y [(x, length)]')\\n for y in range(self.height):\\n for x in range(self.width):\\n space = ' ' if x < 10 else ' '\\n char = space if self.pixels[y * self.width + x] else x\\n print(char, end='')\\n print(' ', '%2d' % y, self.lh_data[y])\\n print()\\n\\n lv_count = len(flatten(self.lv_data))\\n print('{} vertical line mapping: {} vline draw calls. {} bytes'.format(\\n self.char,\\n lv_count,\\n len(list(self._stream_lvmap()))\\n ))\\n print('>' * len(''.join([str(i) for i in range(self.height)])), ' x [(y, length)]')\\n for x in range(self.width)[::-1]:\\n for y in range(self.height):\\n space = ' ' if y < 10 else ' '\\n char = space if self.pixels[y * self.width + x] else y\\n print(char, end='')\\n print(' ', '%2d' % x, self.lv_data[x])\\n print()\\n\\n print('selecting {} mapping for {} char\\\\n'.format(\\n 'lhmap horizontal' if self.is_char_lhmap() else 'lvmap vertical',\\n self.char\\n ))\",\n \"def do_draw_network(self, line):\\n self.fibbing.root.lsdb.graph.draw(line)\",\n \"def _draw_line(self, event):\\n if not self.obstacle_creation_mode:\\n return\\n\\n if self.previous_coordinates is None:\\n self.previous_coordinates = event.x, event.y\\n self.new_obstacle.append([event.x, event.y])\\n return\\n\\n x1, y1 = event.x, event.y\\n\\n if self._is_closing_shape(x1, y1, self.new_obstacle):\\n x1, y1 = self.new_obstacle[0]\\n else:\\n self.new_obstacle.append([x1, y1])\\n\\n x0, y0 = self.previous_coordinates\\n self.canvas.create_line(x0, y0, x1, y1, **self.LINE_OPTIONS)\\n self.previous_coordinates = x1, y1\",\n \"def drawLines(self):\\n\\t\\tintersections = [[], []]\\n\\t\\tfor l in self.lines:\\n\\t\\t\\tif l.direction == 'v':\\n\\t\\t\\t\\tif l.rtc:\\n\\t\\t\\t\\t\\tposition = l.coordinate + int((self.width - 1) / 2)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tposition = int((l.coordinate * self.width / 100) if type(l.coordinate) == float else l.coordinate)\\n\\t\\t\\t\\tintersections[0].append(position)\\n\\t\\t\\t\\tfor yPos in range(1, self.height - 2):\\n\\t\\t\\t\\t\\tself.wts(yPos, position, '│', self._borderColor)\\n\\t\\t\\t\\t# endpoints\\n\\t\\t\\t\\tself.wts(0, position, '┬',self._borderColor)\\n\\t\\t\\t\\tself.wts(self.height - 2, position, '┴', self._borderColor)\\n\\t\\t\\telif l.direction == 'h':\\n\\t\\t\\t\\tif l.rtc:\\n\\t\\t\\t\\t\\tposition = l.coordinate + ((self.height - 1) / 2)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tposition = int((l.coordinate * self.height / 100) - 1 if type(l.coordinate) == float else l.coordinate)\\n\\t\\t\\t\\tintersections[1].append(position)\\n\\t\\t\\t\\tself.wts(position, 1, '─' * (self.width - 2), self._borderColor)\\n\\t\\t\\t\\t# endpoints\\n\\t\\t\\t\\tself.wts(position, 0, '├', self._borderColor)\\n\\t\\t\\t\\tself.wts(position, self.width - 1, '┤', self._borderColor)\\n\\t\\t# draw intersections\\n\\t\\tfor x in intersections[1]:\\n\\t\\t\\tfor y in intersections[0]:\\n\\t\\t\\t\\tself.wts(x, y, '┼', self._borderColor)\\n\\t\\tself.verticalBoundaries = intersections[0]\\n\\t\\tif self.screenBorder:\\n\\t\\t\\tself.verticalBoundaries.append(self.width)\",\n \"def draw_line(color, start_pos, end_pos, width=1):\\n pygame.draw.line(screen, color, start_pos, end_pos, width)\",\n \"def draw_lines(self):\\n for x_cord in range(0, Dimension.SCREEN_WIDTH.value, Dimension.SQUARE_WIDTH.value):\\n pg.draw.line(self.window, Colors.BLACK.value, (x_cord, 0), (x_cord, Dimension.SCREEN_HEIGHT.value))\\n\\n for y_cord in range(0, Dimension.SCREEN_HEIGHT.value, Dimension.SQUARE_HEIGHT.value):\\n pg.draw.line(self.window, Colors.BLACK.value, (0, y_cord), (Dimension.SCREEN_WIDTH.value, y_cord))\\n\\n pg.display.update()\",\n \"def draw(self):\\n glColor3f(1.0, 0.0, 0.0)\\n glBegin(GL_LINES)\\n for vertex in self.edges[0]:\\n glVertex3fv(self.vertices[vertex])\\n glColor3f(0.0, 1.0, 0.0)\\n for vertex in self.edges[1]:\\n glVertex3fv(self.vertices[vertex])\\n glColor3f(0.0, 0.0, 1.0)\\n for vertex in self.edges[2]:\\n glVertex3fv(self.vertices[vertex])\\n glEnd()\",\n \"def draw(self):\\n # s1 = ShowPoint(self.cnv, self.p1.xpt, self.p1.ypt)\\n # s2 = ShowPoint(self.cnv, self.p2.xpt, self.p2.ypt)\\n # s1.draw()\\n # # s2.draw()\\n self.cnv.create_line(self.p1.xpt, self.p1.ypt, self.p2.xpt, self.p2.ypt)\",\n \"def update_line(self):\\n self._draw_line_text()\\n self._draw_status()\\n self._line_listbox.set_focus(self.model.l_index)\",\n \"def _render_horizontal(self, gc, lx, ly, rx, ry, mx, my):\\n\\n with gc:\\n gc.set_line_width(20)\\n gc.set_stroke_color(self._get_border_color())\\n tee_h(gc, lx, ly, mx, my, ry)\\n\\n gc.set_line_width(10)\\n self.set_fill_color(gc)\\n tee_h(gc, lx, ly, mx, my, ry)\",\n \"def _draw_line_text(self):\\n self._line_text.set_text(self.model.get_current_line())\",\n \"def display(self, screen: pygame.Surface, line_thickness=3):\\n\\t\\tfor p1, p2 in self.__calculate_points():\\n\\t\\t\\tpygame.draw.line(screen, Color(255).get(), p1.get_int(), p2.get_int(), line_thickness)\",\n \"def draw_lines(self):\\n # draw x lines\\n y = self.step_y\\n while y <= self.height:\\n x = 0\\n while x <= self.width:\\n self.canvas.create_line(x, y, x+3.5, y)\\n self.canvas.update()\\n x += 3.5\\n y += self.step_y\\n \\n # draw y lines\\n x = self.step_x\\n while x <= self.width:\\n y = 0\\n while y <= self.height:\\n self.canvas.create_line(x, y, x, y+3.5)\\n self.canvas.update()\\n y += 3.5\\n x += self.step_x\\n \\n self.is_operating = False\",\n \"def draw(self, camera):\\n for line in self._polyline.lines:\\n camera.draw_line(line.begin, line.end, self.color, self.width)\",\n \"def hline(self, x, y, length, color):\\n self.fill_rect(x, y, length, 1, color)\",\n \"def draw_bullet(self):\\n pygame.draw.rect(self.__screen, self.__color, self.rect)\",\n \"def create_line(self):\\n if self.hosts and self.line:\\n self.msg(\\\"There is a line here already.\\\")\\n self.display_line()\\n return\\n self.line = []\\n other_hosts = [self.caller.search(arg) for arg in self.lhslist]\\n other_hosts = [ob for ob in other_hosts if ob and ob.player]\\n other_hosts.append(self.caller)\\n self.hosts = other_hosts\\n if \\\"loop\\\" in self.switches:\\n self.toggle_loop()\\n self.display_line()\",\n \"def _newLine(self, usePos=True):\\n if len(self.currentLine) > 1:\\n self.screen._drawline(self.currentLineItem, self.currentLine,\\n self._pencolor, self._pensize)\\n self.currentLineItem = self.screen._createline()\\n self.items.append(self.currentLineItem)\\n else:\\n self.screen._drawline(self.currentLineItem, top=True)\\n self.currentLine = []\\n if usePos:\\n self.currentLine = [self._position]\",\n \"def draw_line(self, x0, y0, x1, y1, color=Color['white']):\\n pygame.draw.line(self.display, color, (x0, y0), (x1, y1))\",\n \"def draw_laser(self):\\n pygame.draw.rect(self.screen, self.color, self.rect)\",\n \"def drawReference(x, y, z, l):\\r\\n\\r\\n glPushMatrix()\\r\\n\\r\\n glColor3f(1.0, 0.0, 0.0)\\r\\n\\r\\n glBegin(GL_LINES)\\r\\n glNormal3f(0.0, 0.0, 1.0)\\r\\n glVertex3f(x, y, z)\\r\\n glVertex3f(x + l, y, z)\\r\\n glEnd()\\r\\n\\r\\n glColor3f(0.0, 1.0, 0.0)\\r\\n\\r\\n glBegin(GL_LINES)\\r\\n glNormal3f(0.0, 0.0, 1.0)\\r\\n glVertex3f(x, y, z)\\r\\n glVertex3f(x, y + l, z)\\r\\n glEnd()\\r\\n\\r\\n glColor3f(0.0, 0.0, 1.0)\\r\\n\\r\\n glBegin(GL_LINES)\\r\\n glNormal3f(0.0, 0.0, 1.0)\\r\\n glVertex3f(x, y, z)\\r\\n glVertex3f(x, y, z + l)\\r\\n glEnd()\\r\\n\\r\\n glPopMatrix()\",\n \"def draw_bullet(self):\\r\\n pygame.draw.rect(self.screen, self.color, self.rect)\",\n \"def draw_line(self, DISP, side:str, indizes:tuple, pink = False):\\r\\n offset = 1 #< Just to draw the line nicely\\r\\n pos = (indizes[0] - 1) * self.grid_size, indizes[1] * self.grid_size\\r\\n # Check if it's a pink line\\r\\n if pink:\\r\\n start_pos = pos[0], pos[1] + self.grid_size // 2\\r\\n end_pos = pos[0] + self.grid_size, pos[1] + self.grid_size // 2\\r\\n # Check if the line should be vertically. u for up\\r\\n elif side == 'u':\\r\\n start_pos = pos[0] + self.width - offset + self.grid_size // 2, pos[1] + self.grid_size // 2\\r\\n end_pos = pos[0] + self.grid_size + offset + self.grid_size // 2 - self.width, pos[1] + self.grid_size // 2\\r\\n # Check if the line should be horizontally. l for left\\r\\n elif side == 'l':\\r\\n start_pos = pos[0] + self.grid_size // 2, pos[1] + self.width - offset + self.grid_size // 2\\r\\n end_pos = pos[0] + self.grid_size // 2, pos[1] - self.width + self.grid_size + offset + self.grid_size // 2\\r\\n if not pink:\\r\\n pg.draw.line(DISP, Colors.colors['BLACK'], start_pos,end_pos, self.width + 2 * offset) \\r\\n else:\\r\\n pg.draw.line(DISP, Colors.colors['PINK'], start_pos,end_pos, self.width + 2 * offset)\",\n \"def add_line(self, text):\\n\\t\\twidth, height = self.font.size(text)\\n\\t\\tpos = (self.rect.left + 10, self.rect.bottom - height- 5)\\n\\t\\trend = self.font.render(text, True, BLACK)\\n\\t\\t# Move all already existing lines up\\n\\t\\tfor i in range(len(self.all_lines)):\\n\\t\\t\\toldsurf, oldpos = self.all_lines[i]\\n\\t\\t\\tself.all_lines[i] = self.lift_line(oldsurf, height, oldpos)\\n\\t\\t\\tcopy = oldsurf.copy()\\n\\t\\t\\tcopy.fill(BG_COLOR)\\n\\t\\t\\tself.image.blit(copy, oldpos)\\n\\t\\tself.all_lines.append([rend, pos])\\n\\t\\tself.image.blit(rend, pos)\",\n \"def draw(self):\\r\\n pygame.draw.rect(self.screen, self.background_color, self.bounds)\\r\\n line_window = self.lines[self.scroll_window_top:self.scroll_window_bottom]\\r\\n for idx,line in enumerate(line_window):\\r\\n text = self.font.render(line, True, self.foreground_color)\\r\\n x,y = self._get_x_y_from_pos(self.position[0], self.position[1]+idx)\\r\\n self.screen.blit(text,(x,y))\\r\\n \\r\\n if self.cursor_visible and self.scroll_window_bottom == len(self.lines):\\r\\n x,y = self._get_x_y_from_pos(len(line_window[-1]), len(line_window))\\r\\n cursor_rect = pygame.Rect(x,y,\\r\\n self.text_width,self.text_height)\\r\\n pygame.draw.rect(self.screen, self.foreground_color, cursor_rect)\",\n \"def draw_line(tick_length, tick_label=''):\\n line = \\\"_\\\" * tick_length\\n if tick_label:\\n line += ' ' + tick_label\\n print(line)\",\n \"def draw(self):\\n if len(self.__points) >= 2:\\n self._total_length = 0\\n for i in range(len(self.__points) - 1):\\n p1 = self.__points[i]\\n p2 = self.__points[i + 1]\\n coords = self.__line_segment(p1, p2)\\n if not coords is None:\\n pyglet.graphics.draw_indexed(4, pyglet.gl.GL_TRIANGLES,\\n [0, 1, 2, 1, 2, 3],\\n ('v2i', coords),\\n ('c4b', self.color * 4)\\n )\\n coords = self.__line_cap(p2)\\n pyglet.graphics.draw_indexed(4, pyglet.gl.GL_TRIANGLES,\\n [0, 1, 2, 0, 2, 3],\\n ('v2i', coords),\\n ('c4b', self.color * 4)\\n )\",\n \"def draw_bullet(self):\\n\\t\\tpygame.draw.rect(self.screen, self.colour, self.rect)\",\n \"def hline(self, x, y, width, color):\\n self.rect(x, y, width, 1, color, fill=True)\",\n \"def _defLine(self):\\n self._dline=GPath(points = [0,100,GAME_WIDTH,100], linewidth = 1.5,\\n linecolor = 'cyan')\",\n \"def _DrawLineList(*args, **kwargs):\\n return _gdi_.DC__DrawLineList(*args, **kwargs)\",\n \"def draw_line(self, x1, y1, x2, y2, color):\\n painter = QPainter()\\n painter.begin(self.lbFFmpeg.pixmap())\\n painter.setPen(QColor(color))\\n painter.drawLine(x1, y1, x2, y2)\\n painter.end()\\n self.lbFFmpeg.update()\",\n \"def startLineDrawing(self, startPos):\\n self.line = LineNodePath(render2d, thickness=2, colorVec=(0.8,0.8,0.8,1))\\n self.line.moveTo(startPos)\\n t = taskMgr.add(self.drawLineTask, \\\"drawLineTask\\\")\\n t.startPos = startPos\",\n \"def _draw_line(plot, hori, vert, color, text):\\n plot.plot(hori, vert, '-o'+color)\\n plot.text(hori[-1]-3, vert[-1]+2, text, color=color)\",\n \"def DrawLine(*args, **kwargs):\\n return _gdi_.PseudoDC_DrawLine(*args, **kwargs)\",\n \"def line_layer(self):\\n screen_origin = self.ids.mapview.get_window_xy_from(lat1, lon1, self.ids.mapview.zoom)\\n screen_destination = self.ids.mapview.get_window_xy_from(lat2, lon2, self.ids.mapview.zoom)\\n point_list = [screen_origin[0], screen_origin[1], screen_destination[0], screen_destination[1]]\\n\\n with self.ids.line.canvas:\\n self.ids.line.canvas.clear()\\n\\n Color(0, 0, 0, .6)\\n Line(points=point_list, width=3, joint=\\\"bevel\\\")\",\n \"def draw_lines(display, coord, box_size, color, bg_color):\\n left, top = coord\\n stroke = 6\\n half_stroke = int(stroke / 2)\\n left = left + half_stroke\\n top = top + half_stroke\\n box_size = box_size - stroke\\n for i in range(0, box_size, int(stroke + 2)):\\n pygame.draw.line(\\n display, color,\\n (left, top + i),\\n (left + i, top),\\n stroke,\\n )\\n pygame.draw.line(\\n display, color,\\n (left + i, top + box_size - 1),\\n (left + box_size - 1, top + i),\\n stroke,\\n )\\n return\",\n \"def draw(self, img):\\n self._erase_last_line(self.img)\\n\\n idxs = np.argwhere(img[:, self._pos] == 0)\\n self.prev_y = (idxs.min(), idxs.max())\\n\\n cv.line(img, (self._pos, 0), (self._pos, self.h), (0, 0, 0), 1)\",\n \"def draw_asc_diagonal(self, player):\\n if player == 1:\\n color = GameData.circle_color\\n elif player == 2:\\n color = GameData.cross_color\\n\\n pygame.draw.line(\\n self.game_screen,\\n color,\\n (15, GameData.screen_dim - 15),\\n (GameData.screen_dim - 15, 15), GameData.win_line_width)\",\n \"def line(self, start, end, color=(255, 255, 255), width=1):\\n start = self._transform(start)\\n end = self._transform(end)\\n\\n pygame.draw.line(self.screen, color, start, end, width)\",\n \"def draw_line(self, coords, smooth=False, **options):\\n # NOTE: Outline does not work because uses paths instead of normal line method.\\n # TODO: Add volume param, containing a list of linewidths same length as line\\n # or as a function that calculates the width at each node\\n # Result is a flow line with varying thickness at each node\\n # Have to calculate left/right xy at each node, and use symbol curveto()\\n # Easy and really cool...DO IT!\\n options = self._check_options(options)\\n \\n if not hasattr(coords[0], \\\"__iter__\\\"):\\n coords = _grouper(coords, 2)\\n else: coords = (point for point in coords)\\n \\n # get drawing tools from options\\n args = []\\n if options[\\\"fillcolor\\\"]:\\n pen = aggdraw.Pen(options[\\\"fillcolor\\\"], options[\\\"fillsize\\\"])\\n args.append(pen)\\n\\n if smooth:\\n\\n # Note: Creation of the aggdraw.Symbol object here can be\\n # very slow for long lines; Path is much faster but due\\n # to a bug it does not correctly render curves, hence the use\\n # of Symbol\\n \\n pathstring = \\\"\\\"\\n \\n # begin\\n coords = _pairwise(coords)\\n (startx,starty),(endx,endy) = next(coords)\\n pathstring += \\\" M%s,%s\\\" %(startx, starty)\\n \\n # draw straight line to first line midpoint\\n midx,midy = (endx + startx) / 2.0, (endy + starty) / 2.0\\n pathstring += \\\" L%s,%s\\\" %(midx, midy)\\n oldmidx,oldmidy = midx,midy\\n \\n # for each line\\n for line in coords:\\n # curve from midpoint of first to midpoint of second\\n (startx,starty),(endx,endy) = line\\n midx,midy = (endx + startx) / 2.0, (endy + starty) / 2.0\\n pathstring += \\\" Q%s,%s,%s,%s\\\" %(startx, starty, midx, midy)\\n oldmidx,oldmidy = midx,midy\\n \\n # draw straight line to endpoint of last line\\n pathstring += \\\" L%s,%s\\\" %(endx, endy)\\n\\n # make into symbol object\\n symbol = aggdraw.Symbol(pathstring)\\n\\n # draw the constructed symbol\\n self.drawer.symbol((0,0), symbol, *args)\\n\\n else:\\n\\n path = aggdraw.Path()\\n \\n # begin\\n startx,starty = next(coords)\\n path.moveto(startx, starty)\\n \\n # connect to each successive point\\n for nextx,nexty in coords:\\n path.lineto(nextx, nexty)\\n\\n # draw the constructed path\\n self.drawer.path((0,0), path, *args)\",\n \"def draw_line(self, frame, rect):\\n print(\\\"x0, y0, x1, y1\\\", self.x0, self.y0, self.x1, self.y1)\\n print(\\\"cross_x, cross_y\\\", self.cross_x, self.cross_y)\\n left, top, right, bottom = rect\\n # 枠内では線を表示しないようにしてやる\\n if top<self.y1<bottom and left<self.x1<right:\\n return\\n # フレームと線の交点\\n if (self.x1 >= right or self.x1 <= left or self.y1 <= top or self.y1 >= bottom) and self.cross_x == 0:\\n self.cross_x = self.x1\\n self.cross_y = self.y1\\n return\\n draw = ImageDraw.Draw(frame)\\n draw.line((self.cross_x, self.cross_y, self.x1, self.y1), fill=(255, 255, 255), width=3)\",\n \"def draw_lines(self, color, points, width = 1, closed = False):\\n color = spyral.color._determine(color)\\n pygame.draw.aalines(self._surf, color, closed, points)\",\n \"def draw_line(self, color, p1: Point, p2: Point, width):\\n _p1 = self.T.itrans(p1)\\n _p2 = self.T.itrans(p2)\\n pg.draw.line(self.screen, color, _p1(), _p2(), 2)\",\n \"def paint_axes(self, l=1):\\n GL.glBegin(GL.GL_LINES)\\n GL.glColor3f(1, 0, 0) # red x axis\\n GL.glVertex3f(0, 0, 0)\\n GL.glVertex3f(l, 0, 0)\\n GL.glColor3f(0, 1, 0) # green y axis\\n GL.glVertex3f(0, 0, 0)\\n GL.glVertex3f(0, l, 0)\\n GL.glColor3f(0, 0, 1) # blue z axis\\n GL.glVertex3f(0, 0, 0)\\n GL.glVertex3f(0, 0, l)\\n GL.glEnd()\",\n \"def paint_axes(self, l=1):\\n GL.glBegin(GL.GL_LINES)\\n GL.glColor3f(1, 0, 0) # red x axis\\n GL.glVertex3f(0, 0, 0)\\n GL.glVertex3f(l, 0, 0)\\n GL.glColor3f(0, 1, 0) # green y axis\\n GL.glVertex3f(0, 0, 0)\\n GL.glVertex3f(0, l, 0)\\n GL.glColor3f(0, 0, 1) # blue z axis\\n GL.glVertex3f(0, 0, 0)\\n GL.glVertex3f(0, 0, l)\\n GL.glEnd()\",\n \"def on_draw(event):\\n # First, we clear the window in white\\n # (it is necessary to do that at every frame)\\n gloo.set_clear_color((1.0, 1.0, 1.0, 1.0))\\n gloo.clear()\\n program.draw(\\\"line_strip\\\")\",\n \"def _createline(self):\\n return self.cv.create_line(0, 0, 0, 0, fill=\\\"\\\", width=2,\\n capstyle = TK.ROUND)\",\n \"def OnDraw(self):\\r\\n self.SetCurrent()\\r\\n\\r\\n glClear(GL_COLOR_BUFFER_BIT)\\r\\n\\r\\n glBegin(GL_LINES)\\r\\n\\r\\n glColor3f(1.0, 1.0, 1.0)\\r\\n \\r\\n \\r\\n #Just in case these aren't set back to their starting place yet...\\r\\n self.currentpoint = self.startingpoint\\r\\n self.currentheading = 0 \\r\\n \\r\\n for element in self.finalstring:\\r\\n if element == '+':\\r\\n self.currentheading += self.angle\\r\\n elif element == '-':\\r\\n self.currentheading -= self.angle\\r\\n elif element == 'F':\\r\\n glVertex2i(self.currentpoint[0], self.currentpoint[1])\\r\\n self.currentpoint = self.NextPoint(self.currentpoint, self.length, self.currentheading)\\r\\n glVertex2i(self.currentpoint[0], self.currentpoint[1])\\r\\n elif element == '[':\\r\\n self.stack.append([self.currentpoint[0], self.currentpoint[1], self.currentheading])\\r\\n elif element == ']':\\r\\n popped = self.stack.pop()\\r\\n self.currentheading = popped.pop()\\r\\n self.currentpoint = popped\\r\\n \\r\\n \\r\\n glEnd()\\r\\n self.currentpoint = self.startingpoint\\r\\n self.currentheading = 0\\r\\n \\r\\n \\r\\n self.SwapBuffers() \\r\\n\\r\\n return\",\n \"def dline(x, y):\\n glClear(GL_COLOR_BUFFER_BIT)\\n glColor3f(0.0, 0.0, 1.0)\\n glPointSize(10.0)\\n glBegin(GL_POINTS)\\n while (x <= y):\\n glVertex2f(x, x)\\n x += 0.05\\n glEnd()\\n glFlush()\",\n \"def line(self, clear_screen=True, x1=10, y1=10, x2=50, y2=50, line_color='black', width=1):\\n\\n if clear_screen:\\n self.clear()\\n\\n return self.draw.line((x1, y1, x2, y2), fill=line_color, width=width)\",\n \"def redraw(self): \\r\\n\\r\\n self.arrow.undraw() \\r\\n pt2 = Point(self.vel*cos(self.angle), self.vel*sin(self.angle))\\r\\n self.arrow = Line(Point(0,0), pt2).draw(self.win) \\r\\n self.arrow.setArrow('last')\\r\\n self.arrow.setWidth(3)\",\n \"def redraw(self):\\n \\n self.arrow.undraw()\\n pt2 = Point(self.vel*cos(self.angle), self.vel*sin(self.angle))\\n self.arrow = Line(Point(0,0), pt2).draw(self.win)\\n self.arrow.setArrow(\\\"last\\\")\\n self.arrow.setWidth(3)\",\n \"def draw_lh_lines(data):\\n #hnd = extract_left_hand(data);\\n hnd = np.array(data['crop']);\\n hand.draw_hand_lines(hnd,data['lhkpss'][data['i']]);\\n return hnd;\",\n \"def drawPath(self):\\r\\n bgl.glColor4f(0.8,0.8,0.9,0.01)\\r\\n bgl.glLineWidth(0.01)\\r\\n\\r\\n bgl.glBegin(bgl.GL_LINES)\\r\\n bgl.glVertex3f(self.p1[0],self.p1[1],self.p1[2])\\r\\n bgl.glVertex3f(self.p2[0],self.p2[1],self.p2[2])\\r\\n bgl.glEnd()\\r\\n\\r\\n bgl.glNormal3f(0.0,0.0,1.0)\\r\\n bgl.glShadeModel(bgl.GL_SMOOTH);\",\n \"def draw(self, base, level):\\n\\n a = base.a\\n b = base.b\\n\\n if level > 0:\\n delta = base.b - base.a\\n px = a.x + delta.x / 3\\n py = a.y + delta.y / 3\\n rx = a.x + 2 * delta.x / 3\\n ry = a.y + 2 * delta.y / 3\\n p = Point(px, py)\\n r = Point(rx, ry)\\n q = Point(rx, ry)\\n q.rotate_deg(60, p)\\n self.draw(Line(a,p), level-1)\\n self.draw(Line(p,q), level-1)\\n self.draw(Line(q,r), level-1)\\n self.draw(Line(r,b), level-1)\\n else:\\n self.container.window.create_line(a.x, a.y, b.x, b.y)\",\n \"def draw(cls, lcfg, parent):\\n axes = parent.getAxes()\\n props = {}\\n line = None\\n label = None\\n idx = int(lcfg.key)\\n for child in lcfg.children:\\n key = child.key\\n val = child.val\\n if key == cls.XY:\\n xvals = []\\n yvals = []\\n for xy in val:\\n x, y = xy\\n xvals.append(x)\\n yvals.append(y)\\n line = axes.plot(xvals, yvals)\\n elif key in cls.KEYS:\\n props[key] = val\\n if line == None:\\n raise ValueError(\\\"No xy data found in %s.%s\\\" %(parent, idx))\\n for key, val in props.iteritems():\\n if key == LABEL:\\n label = val\\n if key == MARKER:\\n plt.setp(line, marker=val)\\n elif key == MARKERSIZE:\\n plt.setp(line, markersize=val)\\n elif key == CONN:\\n plt.setp(line, linestyle=val)\\n elif key == WIDTH:\\n plt.setp(line, linewidth=val)\\n elif key == COLOR:\\n plt.setp(line, color=val)\\n newline = Line2D(line, parent, label)\\n parent.addLine(newline)\\n return newline\",\n \"def draw():\",\n \"def draw_line(self, gray=0, nextline=0):\\n\\n self.fontsize = 4\\n if nextline:\\n self.nextline()\\n else:\\n self.linespace(8)\\n self.resetx()\\n c = self.canvas\\n c.setStrokeGray(gray)\\n c.setLineWidth(1)\\n #self.y = self.y + self.linespacing + (self.fontsize/2)\\n c.line(self.x, self.y, self.width - self.x, self.y)\\n self.y = self.y + (self.linespacing)\",\n \"def draw():\\n screen.fill((0, 0, 0))\\n alien.draw()\",\n \"def wdraw_line(self, wx0, wy0, wx1, wy1, color, arrow):\\r\\n dx0, dy0 = self.w_to_d(wx0, wy0)\\r\\n dx1, dy1 = self.w_to_d(wx1, wy1)\\r\\n self.canvas.create_line(dx0, dy0, dx1, dy1, fill=color, arrow=arrow)\",\n \"def draw(self):\\n arcade.draw_xywh_rectangle_filled(\\n self.x, self.y, self.width, self.height, self.fill.color\\n )\\n arcade.draw_xywh_rectangle_outline(\\n self.x, self.y, self.width, self.height, self.pen.color, 3\\n )\",\n \"def draw_horizontal_winning_line(self, row, player):\\n posY = row * GameData.square_size + GameData.square_size // 2\\n\\n if player == 1:\\n color = GameData.circle_color\\n elif player == 2:\\n color = GameData.cross_color\\n\\n pygame.draw.line(\\n self.game_screen,\\n color, (15, posY),\\n (GameData.screen_dim - 15, posY),\\n GameData.win_line_width\\n )\",\n \"def my_simple_line(master, name, r, c, rsp, csp, px, py) -> object:\\n line = tk.Label(master=master, text=name, anchor='w')\\n line.grid(row=r, column=c, rowspan=rsp, columnspan=csp, padx=px, pady=py)\\n return line\",\n \"def DrawLine(*args, **kwargs):\\n return _gdi_.DC_DrawLine(*args, **kwargs)\",\n \"def draw(self):\\n self.drawLine()\\n\\n for l in range(0, self.height):\\n print(\\\"|\\\", end='', flush=True)\\n for c in range(0, self.width):\\n print(\\\" \\\" + str(self.grid[l][c]) + \\\" |\\\", end='', flush=True)\\n print(\\\"\\\\n\\\", end='', flush=True)\\n\\n self.drawLine()\",\n \"def draw(x,y,x1,y1,d,color=1):\\n d.add(dxf.line((x,y),(x1,y1),color=color, layer='LINES',thickness=0.01))\",\n \"def draw(self, screen):\\n lines = self.text.strip().split('\\\\n')\\n y = self.y\\n for line in lines:\\n self.ui.show_text(line, (self.x, y), 30)\\n y += 32\",\n \"def draw_bullet(self):\\n self.screen.blit(self.image, self.rect)\",\n \"def draw_bullet(self):\\n self.screen.blit(self.image, self.rect)\",\n \"def _plot_line(self, image, x1, y1, x2, y2, width, color):\\n\\n draw = ImageDraw.Draw(image, \\\"RGBA\\\")\\n draw.line([x1, y1, x2, y2], fill=color, width=width)\\n del draw\\n\\n return image\",\n \"def draw_lines(self, color, points, width=1, closed=False):\\n if width == 1:\\n pygame.draw.aalines(self._surf, color, closed, points)\\n else:\\n pygame.draw.lines(self._surf, color, closed, points, width)\\n self._version += 1\\n spyral.util.scale_surface.clear(self._surf)\\n return self\",\n \"def _render_vertical(self, gc, lx, ly, rx, ry, mx, my):\\n mx = lx + (rx - lx) / 2.\\n with gc:\\n gc.set_line_width(20)\\n gc.set_stroke_color(self._get_border_color())\\n tee_v(gc, lx, ly, rx, mx, my)\\n\\n gc.set_line_width(10)\\n self.set_fill_color(gc)\\n tee_v(gc, lx, ly, rx, mx, my)\",\n \"def line(self, drawer, canvas):\\n start_width = random.randint(\\n self._width / 8, self._width / 4)\\n start_height = random.randint(\\n self._height / 4, self._height * 3 / 4)\\n stop_width = random.randint(\\n self._width * 3 / 4, self._width * 7 / 8)\\n stop_height = random.randint(\\n self._height / 4, self._height * 3 / 4)\\n drawer.line(\\n (start_width,\\n start_height,\\n stop_width,\\n stop_height),\\n fill=random.randint(128, 155),\\n width=3\\n )\",\n \"def draw_lines_slow(orbit_pos, factor):\",\n \"def draw_desc_diagonal(self, player):\\n if player == 1:\\n color = GameData.circle_color\\n elif player == 2:\\n color = GameData.cross_color\\n\\n pygame.draw.line(self.game_screen, color, (15, 15), (GameData.screen_dim - 15, GameData.screen_dim - 15),\\n GameData.win_line_width)\",\n \"def line(l, color='k', **kwargs):\\n ax.plot(wfl(nth(l, 0)), hfl(nth(l, 1)), color=color, **kwargs)\",\n \"def plot(self, **kwargs):\\n base.plot_homline(self.line, **kwargs)\",\n \"def _draw_line(event, x, y, flags, params):\\n global img, source_img\\n global p1, p2\\n if event == cv2.EVENT_LBUTTONDOWN:\\n img = source_img.copy()\\n p1 = (x, y)\\n elif event == cv2.EVENT_LBUTTONUP:\\n p2 = (x, y)\\n img = source_img.copy()\\n text = 'position: %d' % p2[0]\\n cv2.putText(img, text, (100, 100),\\n cv2.FONT_HERSHEY_SIMPLEX, 3, DrawingShapeUtils.COLOR, \\n DrawingShapeUtils.LINE_THICKNESS)\\n cv2.line(img, (x, y+100), (x, y-100), DrawingShapeUtils.COLOR,\\n DrawingShapeUtils.LINE_THICKNESS)\",\n \"def line(self, points, ls=\\\"--\\\", draw=\\\"black\\\", lw=None, options=None, kwoptions=None):\\n\\n draw = norm_colour(draw)\\n self.use_colour(draw)\\n\\n if kwoptions is None:\\n kwoptions = {}\\n kwopts = {'draw': draw, **kwoptions}\\n if lw:\\n kwopts['line width'] = lw\\n\\n self._commands.append(rf\\\"\\\\draw{wrap(fmt_options(options,kwopts))} \\\" +\\n f\\\" {ls} \\\".join(map(fmt_point, points))+\\\";\\\")\",\n \"def hline(self, xi: int, yi: int, length: int, color: int):\\n for x in range(length):\\n self.pixel(xi + x, yi, color)\",\n \"def draw_log(self):\\n if self.logwin == None:\\n return\\n self.logwin.clear()\\n max_y, max_x = self.logwin.getmaxyx()\\n lines = self.list_prev_lines(max_y)\\n k = 0\\n n = max_y - 1\\n while n >= 0:\\n if k < len(lines):\\n line = lines[k]\\n k += 1\\n else:\\n break\\n if len(line) == 0:\\n line = '~'\\n line = line.rstrip('\\\\n\\\\r').expandtabs(4)\\n blocks = self.split_string(line, max_x-1)\\n blocks.reverse()\\n for block in blocks:\\n try:\\n self.logwin.addstr(n, 0, block)\\n except Exception as e:\\n logging.warning('An error occured in the curses library.')\\n logging.debug(e, exc_info=1)\\n n -= 1\\n if n < 0:\\n break\\n self.logwin.noutrefresh()\\n return\",\n \"def plotLines( self ):\\n \\n ## plot tree in dfs manner\\n def plotLines( node_id ):\\n\\n node = self.mTree.node( node_id )\\n\\n left = self.mNodeWidthsStart[node_id]\\n right = self.mNodeWidthsEnd[node_id]\\n height = self.mNodeHeights[node_id] \\n\\n if right != left and node_id != self.mTree.root:\\n self.addElements( self.mDecoratorHorizontalBranches.getElements(\\n node_id,\\n self.getHeaderWidth() + left,\\n self.getHeaderWidth() + right,\\n self.getHeaderHeight() + height ))\\n \\n\\n for s in node.succ:\\n\\n new_height = self.mNodeHeights[s]\\n self.addElements( self.mDecoratorVerticalBranches.getElements(\\n node_id,\\n self.getHeaderWidth() + right,\\n self.getHeaderHeight() + height,\\n self.getHeaderHeight() + new_height ))\\n \\n TreeTools.TreeDFS( self.mTree, self.mTree.root,\\n pre_function = plotLines )\",\n \"def Draw_Node( self, node, xstart, ystart):\\r\\n xdist = node.data.length * cb.xtick\\r\\n handle = self.canvas_one.create_line( xstart, ystart, xstart+xdist, ystart,width = 3, fill=self.branch_color )\\r\\n #Attach a handle to a node and place in the handle_list of all LineSegments\\r\\n ls = LineSegment( handle, node )\\r\\n self.handle_list.append(ls)\\r\\n return ystart\",\n \"def HorizLine(self, parent, depth=3):\\n line = sppasStaticLine(parent, orient=wx.LI_HORIZONTAL)\\n line.SetMinSize(wx.Size(-1, depth))\\n line.SetSize(wx.Size(-1, depth))\\n line.SetPenStyle(wx.PENSTYLE_SOLID)\\n line.SetDepth(depth)\\n line.SetForegroundColour(self.GetForegroundColour())\\n return line\",\n \"def on_draw(self):\\n # Clearing the buffers\\n self.clear()\\n self.set3d()\\n # Makes it so color can be added\\n glColor3d(1, 1, 1)\\n\\n self.push(self.player.pos, self.player.rot)\\n self.model.draw()\\n glPopMatrix()\\n self.model.process_queue_slowly()\\n\\n # Draws the crosshairs on the screen\\n self.set2d()\\n self.draw_position_label()\\n self.draw_reticle()\",\n \"def draw_line():\\n global y1, y2\\n canvas.create_line(x1, y1, x2, y2, width=2, fill=color)\\n y1 -= 10\\n y2 += 10\",\n \"def __draw(self, display, color, size):\\n\\t\\tif self.walls[0]: # up\\n\\t\\t\\tpygame.draw.line(display, color, (self.col * size , self.row * size) , (self.col * size + size, self.row * size))\\n\\t\\tif self.walls[3]: # down\\n\\t\\t\\tpygame.draw.line(display, color, (self.col * size + size, self.row * size + size), (self.col * size , self.row * size + size))\\n\\t\\tif self.walls[1]: #left\\n\\t\\t\\tpygame.draw.line(display, color, (self.col * size + size, self.row * size) , (self.col * size + size, self.row * size + size))\\n\\t\\tif self.walls[2]: #right\\n\\t\\t\\tpygame.draw.line(display, color, (self.col * size , self.row * size + size), (self.col * size , self.row * size))\\n\\n\\t\\tif self.current:\\n\\t\\t\\tdraw_rect_with_alpha(display, self.CURRENT_COLOR, Vector((self.col, self.row)) * size, (size, size))\\n\\n\\t\\telif self.backtracked and self.SHOW_BACKTRACK:\\n\\t\\t\\tdraw_rect_with_alpha(display, self.BACKTRACKED_COLOR, Vector((self.col, self.row)) * size, (size, size))\\n\\n\\t\\telif self.visited:\\n\\t\\t\\tdraw_rect_with_alpha(display, self.VISITED_COLOR, Vector((self.col, self.row)) * size, (size, size))\",\n \"def drawSlope(self):\\n length = sqrt(1 + self.slope**2) # Length of the line segment over 1 x-unit\\n xOffset = (segmentLength / length) / 2 # Figures out how many times the length of the 1 unit length fits into the desired length\\n # then divides by 2 becuase half is on the left and half on the right of the center\\n\\n\\n # Left end point\\n xLeft = self.x - xOffset\\n yLeft = (self.slope * (xLeft - self.x)) + self.y\\n\\n # Right end point\\n xRight = self.x + xOffset\\n yRight = (self.slope * (xRight - self.x)) + self.y\\n\\n\\n # Converts the left and right end points from cartesian coordinates to screen coordinates\\n left = cartesianToScreen(xLeft , yLeft)\\n right = cartesianToScreen(xRight, yRight)\\n\\n\\n pygame.draw.aaline(display, self.color, left, right, 1) # DRAWS THE LINE AHHHHHHHHHHHHHHHHHH :P\",\n \"def display_hline():\\n for i in range(12):\\n print(\\\"-\\\", end=\\\"\\\")\\n print()\",\n \"def drawLine(x0,y0,x1,y1,ucoords=1):\\n if ucoords:\\n dislin.rline(x0,y0,x1,y1)\\n else:\\n dislin.line(x0,y0,x1,y1)\",\n \"def draw_line(self, x):\\n self.PDF.setStrokeColor(black01)\\n self.PDF.setLineWidth(1)\\n self.PDF.line(75, x, 550, x)\\n self.PDF.setStrokeColor(\\\"black\\\")\",\n \"def draw_line(self, x0, y0, x1, y1, color=None, colorFunc=None, aa=False):\\n if aa:\\n self._draw_wu_line(x0, y0, x1, y1, color, colorFunc)\\n else:\\n self._draw_bresenham_line(x0, y0, x1, y1, color, colorFunc)\",\n \"def draw_lines(point_list, color, border_width=1):\\n GL.glEnable(GL.GL_BLEND)\\n GL.glBlendFunc(GL.GL_SRC_ALPHA, GL.GL_ONE_MINUS_SRC_ALPHA)\\n GL.glEnable(GL.GL_LINE_SMOOTH)\\n GL.glHint(GL.GL_LINE_SMOOTH_HINT, GL.GL_NICEST)\\n GL.glHint(GL.GL_POLYGON_SMOOTH_HINT, GL.GL_NICEST)\\n\\n GL.glLoadIdentity()\\n\\n # Set line width\\n GL.glLineWidth(border_width)\\n\\n # Set color\\n if len(color) == 4:\\n GL.glColor4ub(color[0], color[1], color[2], color[3])\\n elif len(color) == 3:\\n GL.glColor4ub(color[0], color[1], color[2], 255)\\n\\n GL.glBegin(GL.GL_LINES)\\n for point in point_list:\\n GL.glVertex3f(point[0], point[1], 0.5)\\n GL.glEnd()\",\n \"def drawLine(self,start,stop):\\n startX = int(self.vert[start][0]*self.scale + self.size/2)\\n startY = int(self.vert[start][1]*self.scale + self.size/2)\\n endX = int(self.vert[stop][0]*self.scale + self.size/2)\\n endY = int(self.vert[stop][1]*self.scale + self.size/2)\\n \\n self.canvas.create_line(startX,startY,endX,endY,fill='white')\",\n \"def vline(self, x, y, height, color):\\n self.rect(x, y, 1, height, color, fill=True)\",\n \"def draw(self):\\n if self.state == 'alive':\\n for i in range(len(self.tail)):\\n pygame.draw.rect(display, black, (squareToXPix(self.tail[-(i + 1)][0], objectSize), squareToYPix(self.tail[-(i + 1)][1], objectSize), objectSize, objectSize))\\n\\n pygame.draw.rect(display, black, (squareToXPix(self.x, objectSize), squareToYPix(self.y, objectSize), objectSize, objectSize))\\n\\n else:\\n for i in range(len(self.tail)):\\n pygame.draw.rect(display, red, (squareToXPix(self.tail[-(i + 1)][0], objectSize), squareToYPix(self.tail[-(i + 1)][1], objectSize), objectSize, objectSize))\\n\\n pygame.draw.rect(display, red, (squareToXPix(self.x, objectSize), squareToYPix(self.y, objectSize), objectSize, objectSize))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6687818","0.6577214","0.65438086","0.64897895","0.63934726","0.63052434","0.6266094","0.6217813","0.61868715","0.61665386","0.6159552","0.6142421","0.6132305","0.60893184","0.60786086","0.60565233","0.60377926","0.60295296","0.6012153","0.59879833","0.59606403","0.595874","0.59582007","0.59281206","0.5923167","0.59134054","0.5903696","0.5900028","0.5886914","0.5865786","0.5841419","0.5841355","0.58362246","0.5829959","0.582558","0.581513","0.5806804","0.58012825","0.57934916","0.5790543","0.57851416","0.5783564","0.5770053","0.57697934","0.5767894","0.576559","0.5764961","0.57517326","0.57517326","0.574389","0.5731478","0.5723073","0.5717405","0.57008255","0.56977636","0.569275","0.56790674","0.5670691","0.5657995","0.5657743","0.5647384","0.56401104","0.56198317","0.5616447","0.5606434","0.56054157","0.5601878","0.5597472","0.5595401","0.5595292","0.5593592","0.55933535","0.55933535","0.5585213","0.5583836","0.55806226","0.5575466","0.55726594","0.55712384","0.55709654","0.55666095","0.55665445","0.5565574","0.5559607","0.5556027","0.555597","0.55487484","0.5542114","0.55406755","0.5534779","0.5534259","0.55331475","0.5522396","0.5522202","0.55215156","0.5515157","0.55084217","0.55066544","0.54928094","0.5489795"],"string":"[\n \"0.6687818\",\n \"0.6577214\",\n \"0.65438086\",\n \"0.64897895\",\n \"0.63934726\",\n \"0.63052434\",\n \"0.6266094\",\n \"0.6217813\",\n \"0.61868715\",\n \"0.61665386\",\n \"0.6159552\",\n \"0.6142421\",\n \"0.6132305\",\n \"0.60893184\",\n \"0.60786086\",\n \"0.60565233\",\n \"0.60377926\",\n \"0.60295296\",\n \"0.6012153\",\n \"0.59879833\",\n \"0.59606403\",\n \"0.595874\",\n \"0.59582007\",\n \"0.59281206\",\n \"0.5923167\",\n \"0.59134054\",\n \"0.5903696\",\n \"0.5900028\",\n \"0.5886914\",\n \"0.5865786\",\n \"0.5841419\",\n \"0.5841355\",\n \"0.58362246\",\n \"0.5829959\",\n \"0.582558\",\n \"0.581513\",\n \"0.5806804\",\n \"0.58012825\",\n \"0.57934916\",\n \"0.5790543\",\n \"0.57851416\",\n \"0.5783564\",\n \"0.5770053\",\n \"0.57697934\",\n \"0.5767894\",\n \"0.576559\",\n \"0.5764961\",\n \"0.57517326\",\n \"0.57517326\",\n \"0.574389\",\n \"0.5731478\",\n \"0.5723073\",\n \"0.5717405\",\n \"0.57008255\",\n \"0.56977636\",\n \"0.569275\",\n \"0.56790674\",\n \"0.5670691\",\n \"0.5657995\",\n \"0.5657743\",\n \"0.5647384\",\n \"0.56401104\",\n \"0.56198317\",\n \"0.5616447\",\n \"0.5606434\",\n \"0.56054157\",\n \"0.5601878\",\n \"0.5597472\",\n \"0.5595401\",\n \"0.5595292\",\n \"0.5593592\",\n \"0.55933535\",\n \"0.55933535\",\n \"0.5585213\",\n \"0.5583836\",\n \"0.55806226\",\n \"0.5575466\",\n \"0.55726594\",\n \"0.55712384\",\n \"0.55709654\",\n \"0.55666095\",\n \"0.55665445\",\n \"0.5565574\",\n \"0.5559607\",\n \"0.5556027\",\n \"0.555597\",\n \"0.55487484\",\n \"0.5542114\",\n \"0.55406755\",\n \"0.5534779\",\n \"0.5534259\",\n \"0.55331475\",\n \"0.5522396\",\n \"0.5522202\",\n \"0.55215156\",\n \"0.5515157\",\n \"0.55084217\",\n \"0.55066544\",\n \"0.54928094\",\n \"0.5489795\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6821605"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":5295,"cells":{"query":{"kind":"string","value":"Find distance to lifeline item. We calculate the distance to the lifeline's head, and then we calculate the lifetime. We return the minimum."},"document":{"kind":"string","value":"def point(self, x, y):\n d1 = super().point(x, y)\n top = self._lifetime.top\n bottom = self._lifetime.bottom\n d2 = distance_line_point(top.pos, bottom.pos, (x, y))[0]\n return min(d1, d2)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _calc_min_distance(self, walker):\n\n cell_lengths, cell_angles = box_vectors_to_lengths_angles(walker.state['box_vectors'])\n\n t2 = time.time()\n # make a traj out of it so we can calculate distances through\n # the periodic boundary conditions\n walker_traj = mdj.Trajectory(walker.state['positions'],\n topology=self._mdj_top,\n unitcell_lengths=cell_lengths,\n unitcell_angles=cell_angles)\n\n t3 = time.time()\n # calculate the distances through periodic boundary conditions\n # and get hte minimum distance\n min_distance = np.min(mdj.compute_distances(walker_traj,\n it.product(self.ligand_idxs,\n self.receptor_idxs),\n periodic=self._periodic)\n )\n t4 = time.time()\n logging.info(\"Make a traj: {0}; Calc dists: {1}\".format(t3-t2,t4-t3))\n\n return min_distance","def get_min_distance(self, node):\r\n if self.have_min_distance(node):\r\n return self.table[node][\"dist\"]\r\n return None","def _minimum_distance(self,arg):\n return min([abs(arg-e) for e in self if not e is arg])","def lidar_relative(self):\n return self.distance","def min_distance(distance, spt_set, self_nodes):\n minimum = sys.maxsize\n minimum_node = None\n for curr_node in self_nodes.values():\n if distance[curr_node.id] < minimum and not spt_set[curr_node.id]:\n minimum = distance[curr_node.id]\n minimum_node = curr_node\n return minimum_node","def linelength(l):\n return dist(l[0],l[1])","def get_min_distance(self):\n return round(min(self.combined_euclidian_distance))","def getMinNode(self):\n currentNode = self.openList[0]\n for node in self.openList:\n if node.g + node.h < currentNode.g + currentNode.h:\n currentNode = node\n return currentNode","def _distance_next(self):\n\n self.distance = 10\n\n # Here a set index to 0 if the car is finishing a lap\n # Also reset the farthest\n if self.index > (len(self.x_trajectory) - 6) and self.closed:\n self.index = 0\n self.farthest = -1\n self.laps += 1\n\n for w in range(self.index, self.index + 20):\n\n self.dist_point = math.sqrt((self.x_trajectory[w] - self.x)**2\n + (self.y_trajectory[w] - self.y)**2)\n\n if self.dist_point < self.distance:\n self.distance = self.dist_point\n self.index = w\n\n if w >= (len(self.x_trajectory) - 1):\n break\n\n self._calc_side()\n\n self.distance = self.distance * self.sign\n\n return self.distance","def minDist(l, a, b):\n pre = 0\n rt = float('INF')\n for i in range(len(l)):\n if l[i] == a or l[i] == b:\n pre = i\n break\n\n for i in range(pre+1, len(l)):\n if l[i] == a or l[i] == b:\n if l[i] != l[pre] and i - pre < rt:\n rt = i - pre\n pre = i\n return rt","def top_of_climb_distance(self):\n return self.distances[self.top_of_climb_index()]","def top_of_descent_distance(self):\n return self.distances[self.top_of_descent_index()]","def previous_min(L):\n\n return itertoolsextra.max_diff(L)","def minimal_distance(me):\n smallest_d = 101 # given length of edge <= 100\n ismallest = -1 # index of the edge in the list, me\n for i, e in enumerate(me):\n if e[0] < smallest_d:\n smallest_d = e[0]\n ismallest = i\n\n d = me[ismallest][0]\n v1 = me[ismallest][1]\n v2 = me[ismallest][2]\n me.pop(ismallest)\n\n smallest_d = 101\n for i, e in enumerate(me):\n if (e[1] == v1 or e[2] == v1 or e[1] == v2 or e[2] == v2) and e[0] < smallest_d:\n smallest_d = e[0]\n\n d += smallest_d\n return d","def LineMinDistanceTo(line, point_or_line):\n line = rhutil.coerceline(line, True)\n test = rhutil.coerceline(point_or_line)\n if test is None: test = rhutil.coerce3dpoint(point_or_line, True)\n return line.MinimumDistanceTo(test)","def find_min_distance():\n return np.argmin(d)","def min_distance(self, target):\n difference = self.pivot - target\n return max(math.sqrt(np.dot(difference, difference)) - self.radius, 0)","def longest_flight(self):\r\n distance = 0\r\n for code, _list in self.edges.items():\r\n for edge in _list:\r\n if edge.distance > distance:\r\n distance = edge.distance\r\n start = edge.start\r\n destination = edge.destination\r\n return start, destination, distance","def _findMinNode(self, s):\n\n minNode = None\n minVal = self.inf\n for vertex in s:\n if self.dist[vertex] < minVal:\n minVal = self.dist[vertex]\n minNode = vertex\n return minNode","def find_min(ls):\n\n if len(ls) == 1:\n return ls[0]\n elif len(ls) == 2:\n return ls[0] if ls[0] < ls[1] else ls[1]\n else:\n mid = len(ls) // 2\n m1 = find_min(ls[0:mid])\n m2 = find_min(ls[mid:])\n return m1 if m1 < m2 else m2","def shortest_flight(self):\r\n distance = sys.maxsize\r\n for code, _list in self.edges.items():\r\n for edge in _list:\r\n if edge.distance < distance:\r\n distance = edge.distance\r\n start = edge.start\r\n destination = edge.destination\r\n return start, destination, distance","def get_closest_distance_to_path(self, path):\n min_distance_to_line = float(\"inf\")\n for p in path:\n game_path = p[:]\n\n game_path.sort(key = lambda coord: calculate_distance(self, coord))\n point_A = game_path[0] # Closest point out of all the points on the path to to the tower\n\n try:\n point_after_A = p[p.index(point_A) + 1]\n point_before_A = p[p.index(point_A) - 1]\n closest_to_A = min(point_after_A, point_before_A, key = lambda point: calculate_distance(point_A, point))\n except:\n if p.index(point_A) == 0:\n closest_to_A = p[p.index(point_A) + 1]\n \n elif p.index(point_A) == len(p) - 1:\n closest_to_A = p[p.index(point_A) - 1]\n finally:\n if closest_to_A[0] != point_A[0]:\n m = (closest_to_A[1] - point_A[1]) / (closest_to_A[0] - point_A[0])\n else:\n m = 2\n\n b = point_A[1] - m * point_A[0]\n\n closest_distance = abs(-m * self.x + self.y - b) / math.sqrt((-m) ** 2 + 1)\n min_distance_to_line = min(closest_distance, min_distance_to_line)\n \n return min_distance_to_line","def closest(self, x):\n # http://www.ahinson.com/algorithms_general/Sections/Geometry/PluckerLine.pdf\n # has different equation for moment, the negative\n\n x = arg.getvector(x, 3)\n\n lam = np.dot(x - self.pp, self.uw)\n p = self.point(lam) # is the closest point on the line\n d = np.linalg.norm( x - p)\n \n return namedtuple('closest', 'p d lam')(p, d, lam)","def _find_min(self):\n if self.is_empty(): # is_empty inherited from base class\n raise Empty('Priority queue is empty')\n small = self._data.first()\n walk = self._data.after(small)\n while walk is not None:\n if walk.element() < small.element():\n small = walk\n walk = self._data.after(walk)\n return small","def distance_to_current_waypoint(self):\n next_waypoint = self.vehicle.commands.next\n if next_waypoint == 1:\n return None\n mission_item = self.vehicle.commands[next_waypoint]\n lat = mission_item.x\n lon = mission_item.y\n alt = mission_item.z\n waypoint_location = Location(lat, lon, alt, is_relative=True)\n distance = get_distance_meters(self.vehicle.location, waypoint_location)\n return distance","def h(self,node):\n \"*** YOUR CODE HERE ***\"\n dist_arr = [] #Initialize Array\n for goal in self.goals: # Iterate through Goals\n dist_arr.append(manhattan_distance_with_heading(node.state, goal)) # Add distance between node and goal\n return min(dist_arr) # Return minimum","def _get_distance(reindeer, race_time):\n interval = reindeer.flight_time + reindeer.rest_time\n cycles = race_time // interval\n flight_time = min(reindeer.flight_time, race_time - interval * cycles)\n total_flying_time = reindeer.flight_time * cycles + flight_time\n return total_flying_time * reindeer.flight_speed","def closest_distance(node_a, node_b):\n min_distance = 999999\n for loc_a in node_a.locations:\n for loc_b in node_b.locations:\n distance = abs(loc_a - loc_b)\n if distance < min_distance:\n min_distance = distance\n return min_distance","def nn(x, S, dist):\n\n # note that there might be more than on minimal item. min will return the\n # first one ecountered\n return min(S, key=lambda y: dist(x, y[:-1]))","def get_distance(self):\n values = self.speakers.values()\n values.sort(reverse=True)\n try:\n return abs(values[1]) - abs(values[0])\n except (IndexError, ValueError):\n return -1","def get_closest_lane(self, car: Car) -> AbstractLane:\n pos = car.position\n lanes = self.upstream_lane_list if car.lane.upstream else self.downstream_lane_list\n return min(lanes, key=lambda l: l.distance(pos))","def calculate_distance_edge(self):\n mu_star = -np.sqrt(1. - (self.cell_xl / self.x)**2)\n\n if self.mu <= mu_star:\n\n l_edge = (-self.mu * self.x -\n np.sqrt(self.mu**2 * self.x**2 -\n self.x**2 + self.cell_xl**2))\n self.next_cell_index = self.cell_index - 1\n\n else:\n\n l_edge = (-self.mu * self.x +\n np.sqrt(self.mu**2 * self.x**2 -\n self.x**2 + self.cell_xr**2))\n self.next_cell_index = self.cell_index + 1\n\n return l_edge","def shortest_distance_to(self, pt):\n return self._nearest_to_point(pt)[0]","def _get_distance_betweenitems(self, item_no1, item_no2):\n\n try:\n if item_no1 >= 0 and item_no2 >= 0:\n loc_current = self.page_current.item_onscreenlocs[item_no1]\n loc_potential = self.page_current.item_onscreenlocs[item_no2]\n distance = abs(loc_potential - loc_current)\n else:\n distance = 0\n\n except IndexError:\n distance = 0\n\n return distance","def distance_to_current_waypoint():\n nextwaypoint = vehicle.commands.next\n if nextwaypoint == 0:\n return None\n missionitem = vehicle.commands[nextwaypoint -\n 1] #commands are zero indexed\n lat = missionitem.x\n lon = missionitem.y\n alt = missionitem.z\n targetWaypointLocation = LocationGlobalRelative(lat, lon, alt)\n distancetopoint = get_distance_metres(vehicle.location.global_frame,\n targetWaypointLocation)\n return distancetopoint","def find_min(self):\n return self.min","def find_min(self):\n return self.min","def distancetoline(p, l1, l2):\n vx = l1.x-p.x \n vy = l1.y-p.y\n ux = l2.x-l1.x\n uy = l2.y-l1.y\n\n length = ux*ux+uy*uy;\n\n det = (-vx*ux)+(-vy*uy); \n # if this is < 0 or > length then its outside the line segment\n if det<0 or det>length:\n ux=l2.x-p.x\n uy=l2.y-p.y\n return sqrt(min(vx*vx+vy*vy, ux*ux+uy*uy))\n\n det = ux*vy-uy*vx\n if length == 0.0:\n return 0.0\n else:\n return sqrt((det*det)/length)","def closest_point(self, l):\n cos = np.dot(self.direction, l.direction)\n n = 1 - cos ** 2\n if n < sys.float_info.epsilon:\n # Lines are parallel.\n return self.zero\n\n d0 = l.zero - self.zero\n a = np.dot(d0, self.direction)\n b = np.dot(d0, l.direction)\n return self.zero + self.direction * ( a - b * cos) / n","def minimum_distance(object_1, object_2):\n\n # package import\n import numpy as np\n\n # main algorithm\n minimum_distance = 100000\n\n for coord_1 in object_1:\n for coord_2 in object_2:\n distance_btwn_coords = np.linalg.norm(coord_1 - coord_2)\n if distance_btwn_coords == 0:\n minimum_distance = distance_btwn_coords\n return float(minimum_distance)\n elif distance_btwn_coords < minimum_distance:\n minimum_distance = distance_btwn_coords\n\n return float(minimum_distance)","def get_distance(self, point):\n if not isinstance(point, Point):\n point = Point(*point)\n\n distances = [(point.distance_to_point(p), p) for p in self.points]\n sortpoints = sorted(distances, key=lambda x: x[0])\n closest = sortpoints[0][1]\n\n vc = Vector(*closest)\n d1 = vc.dot(vc)\n\n secondc = sortpoints[1][1]\n vs = Vector(*secondc)\n v1 = Vector(*point) - (vc+vs)/2\n v2 = vs-vc\n v2.unitize()\n d2 = v1.dot(v2)\n\n return abs(min(d1, d2)) - self.thickness/2","def __find_max_distance(self):\n return utils.find_max_distance(self.__game)","def find_min(self):\n return min(self.nodes, key=int)","def calculate_distance_line(\n r_packet, comov_nu, is_last_line, nu_line, time_explosion\n):\n\n nu = r_packet.nu\n\n if is_last_line:\n return MISS_DISTANCE\n\n nu_diff = comov_nu - nu_line\n\n # for numerical reasons, if line is too close, we set the distance to 0.\n if r_packet.is_close_line:\n nu_diff = 0.0\n r_packet.is_close_line = False\n\n if nu_diff >= 0:\n distance = (nu_diff / nu) * C_SPEED_OF_LIGHT * time_explosion\n else:\n print(\"WARNING: nu difference is less than 0.0\")\n raise MonteCarloException(\n \"nu difference is less than 0.0; for more\"\n \" information, see print statement beforehand\"\n )\n\n if numba_config.ENABLE_FULL_RELATIVITY:\n return calculate_distance_line_full_relativity(\n nu_line, nu, time_explosion, r_packet\n )\n return distance","def _compute_smallest_step_len_for_candidate_vector(x_candidate, z_min):\n ta, tb = _solve_scalar_quadratic_equation(x_candidate, z_min)\n step_len = min([ta, tb], key=abs)\n\n return step_len","def get_closest_node(data, loc):\n min_dist = None\n closest = None\n for i in data:\n # Standard min-value search loop\n dist = great_circle_distance(get_coords(data, i), loc)\n if closest is None or dist < min_dist:\n closest = i\n min_dist = dist\n return closest","def extract_minOld(H):\n minDist = approxInf\n u = None\n for v in H:\n if v[1] <= minDist:\n minDist = v[1]\n u = v\n return(H.pop(u))","def closest_waypoint(self, location: pylot.utils.Location):\n min_dist = np.infty\n min_index = 0\n for index, waypoint in enumerate(self.waypoints):\n dist = waypoint.location.distance(location)\n if dist < min_dist:\n min_dist = dist\n min_index = index\n return min_index","def find_min(list):\n return find_value_at(list, -1)","def smallest (self):\n return self.pointers[0].smallest()","def find_middle_point(self):\n leaf1, longest_dist = None, 0.0\n for leaf in self.leaves:\n dist = sum(self.path_dists[leaf])\n if dist > longest_dist:\n leaf1 = leaf\n longest_dist = dist\n leaf2, longest_dist = None, 0.0\n for leaf in self.leaves:\n dist = self.node_distance(leaf1, leaf)\n if dist > longest_dist:\n leaf2 = leaf\n longest_dist = dist\n for ind, (n1, n2) in enumerate(zip(self.paths[leaf1], self.paths[leaf2])):\n if n1 != n2:\n break\n rev_ind = ind - len(self.paths[leaf1]) - 1\n nodes = self.paths[leaf1][-1:rev_ind-1:-1] + self.paths[leaf2][ind:]\n dists = self.path_dists[leaf1][-1:rev_ind:-1] + self.path_dists[leaf2][ind:]\n mid_dist, cur_dist = longest_dist / 2.0, 0.0\n for i in range(len(nodes)-1):\n dist = dists[i]\n if cur_dist + dist >= mid_dist:\n node1, node2 = nodes[i], nodes[i+1]\n if cur_dist + dist == mid_dist:\n distance = dist\n else:\n distance = mid_dist - cur_dist\n break\n else:\n cur_dist += dist\n return node1, node2, distance","def nodeAtMinimumDistance(self, notFoundYet, distances):\n # found minimal\n minimal = None\n for node in notFoundYet:\n if (distances[node] >= 0): \n if minimal == None or (distances[minimal] > distances[node]):\n minimal = node\n\n # return\n if minimal == -1: return None\n else: return minimal","def extractmin(self):\n if len(self.heap) == 0: \n return None\n i = self.heap[0]\n last = self.heap[-1]\n del self.heap[-1]\n if len(self.heap) > 0:\n self.siftdown(last, 0)\n return i","def min(self):\n least = self.data[0]\n \n for i in range(len(self.data)):\n if self.data[i] < least:\n least = self.data[i]\n return least","def distance(self) -> int:\n return 0","def _distance_to_line(begin, end, point):\n return _vec_distance(point, _nearest_point_on_line(begin, end, point))","def nearest(items, pivot):\n return min(items, key=lambda x: abs(x - pivot))","def DRFindMinimalEdge(self, flow, edge_lst):\n\t\tarr_time = flow[3]\n\t\tend_time = flow[3] + flow[4]\n\t\tmin_cum_size = float('inf')\n\t\tmin_edge = (-1, -1)\n\t\tfor e in edge_lst:\n\t\t\tprev_time = arr_time\n\t\t\tprev_rate = 0\n\t\t\tcum_size = 0\n\t\t\tfor time, rate in sorted(self.rate_lst[e].items()):\n\t\t\t\tif time > arr_time:\n\t\t\t\t\tif time < end_time:\n\t\t\t\t\t\tcum_size += prev_rate * (time - prev_time)\n\t\t\t\t\telse:\n\t\t\t\t\t\tcum_size += prev_rate * (end_time - prev_time)\n\t\t\t\t\t\tbreak\n\t\t\t\tif cum_size >= min_cum_size:\n\t\t\t\t\t# Break if already >= minimum\n\t\t\t\t\tbreak\n\t\t\t\telif time >= end_time:\n\t\t\t\t\t# Break if time exceeds deadline\n\t\t\t\t\tbreak\n\n\t\t\t\tprev_time = time\n\t\t\t\tprev_rate = rate\n\n\t\t\tif cum_size < min_cum_size:\n\t\t\t\tmin_cum_size = cum_size\n\t\t\t\tmin_edge = e\n\n\t\treturn min_edge, min_cum_size","def h(self,node):\n \"*** YOUR CODE HERE ***\"\n dist_arr = []\n for goal in self.goals:\n dist_arr.append(manhattan_distance_with_heading(node.state, goal))\n return min(dist_arr)","def get_closest_slot(self):\n if not self.__available_slots__:\n return None\n return min(self.__available_slots__)","def find_min_node(self):\n min_energy = 10 ** 10\n min_id = -1\n for node in self.node:\n if node.energy < min_energy:\n min_energy = node.energy\n min_id = node.id\n return min_id","def get_nearest_row(self):\n return (self.rect.top - (self.screen.get_height() // 12)) // self.maze.block_size","def compute_min_refills(distance: int, tank: int, stops: List[int]):\n location: int = 0\n n_stops = 0\n last_stop = 0\n max_drive = location + tank\n\n while max_drive < distance:\n counter = 0\n\n # Handle the case that stops are depleted before we reach distance\n if len(stops) == 0:\n return -1\n for s in stops:\n if s <= max_drive:\n counter += 1\n last_stop = s\n max_drive = last_stop + tank\n\n # Handle the case that wi did not reach the next stop\n if counter == 0:\n return -1\n else:\n del stops[0:counter]\n n_stops += 1\n\n return n_stops","async def distance(self):\n return round(await self._rpc.distance(), 2)","def _get_nearest_slot(self):\n available_slots = [pslot for pslot in self.slots.values() if pslot.available]\n if not available_slots:\n return None\n\n return sorted(available_slots, key=lambda x: x.slot_no)[0]","def get_distance(self) -> int:\n return self.get_measurement_data().distance","def min_diff_return(self, data_useable):\n\n min_diff = abs(data_useable[0][1] - data_useable[0][2])\n\n for data_row in data_useable:\n if abs(float(data_row[2]) - float(data_row[1])) < min_diff:\n min_diff = abs(float(data_row[2]) - float(data_row[1]))\n self.data = data_row\n\n return self.data","def get_distance(self, node):\n return np.sqrt(\n (self.x - node.x) ** 2 +\n (self.y - node.y) ** 2\n )","def find_closest_interior_node(arc, hull, nodes):\n lengths = [float(\"inf\") for x in range(len(nodes))]\n for (j, node) in enumerate(nodes):\n if not node.nid in hull:\n i = hull.index(arc[1])\n hull.insert(i, node.nid)\n lengths[j] = (tsputil.get_path_length(nodes, 100, hull))\n hull.pop(i)\n return lengths.index(min(lengths))","def nearest(node):\n count = 0\n distance = 100000\n while count != node_count[0]:\n city = d_list[node.value - 1]\n if city != []:\n if city[0][1] < distance:\n distance = city[0][1]\n new_city = city[0][0]\n closest_city = node.value\n node = node.left\n count = count + 1\n return (closest_city, new_city, distance)","def find_move_from_line(\n x,\n data,\n overlap_penalty,\n norm_penalty,\n offdiagonal_energy_penalty,\n lagrange_multiplier,\n energy_weights=None,\n max_norm_deviation=0.2,\n):\n N = np.abs(data[\"overlap\"].diagonal(axis1=1, axis2=2))\n Nij = np.asarray([np.sqrt(np.outer(a, a)) for a in N])\n nwf = data[\"energy\"].shape[-1]\n if energy_weights is None:\n energy_weights = np.ones(nwf) / nwf\n\n energy = data[\"energy\"] / Nij\n overlap = data[\"overlap\"]\n # print(\"energy cost\", np.sum(energy.diagonal(axis1=1,axis2=2),axis=1))\n # print(\"overlap cost\",np.sum(np.triu(overlap**2,1),axis=(1,2)) )\n # print(\"offdiagonal_energy\", energy)\n # print(\"norm\",np.einsum('ijj->i', (overlap-1)**2 ))\n cost = (\n np.einsum(\"i,nii->n\", energy_weights, energy)\n + overlap_penalty * np.sum(np.triu(overlap**2, 1), axis=(1, 2))\n + np.sum(lagrange_multiplier * np.triu(overlap, 1), axis=(1, 2))\n + offdiagonal_energy_penalty * np.sum(np.triu(energy**2, 1), axis=(1, 2))\n + norm_penalty * np.einsum(\"ijj->i\", (overlap - 1) ** 2)\n )\n\n # good_norms = np.prod(np.einsum('ijj->ij',np.abs(overlap-1) < max_norm_deviation),axis=1)\n # print(\"good norms\", good_norms, 'cost', cost[good_norms])\n xmin = linemin.stable_fit(x, cost)\n return xmin, cost","def new_distance(self, prev_len, prev, sol, i, l):\n swapped1 = i\n swapped2 = l\n if swapped2 == self.file_size-1:\n remove = self.dist_matrix[prev[swapped1 - 1]][prev[swapped1]] \\\n + self.dist_matrix[prev[swapped2]][prev[0]]\n add = self.dist_matrix[sol[swapped1 - 1]][sol[swapped1]] + self.dist_matrix[sol[swapped2]][sol[0]]\n attempt = prev_len - remove + add\n else:\n remove = self.dist_matrix[prev[swapped1 - 1]][prev[swapped1]] \\\n + self.dist_matrix[prev[swapped2+1]][prev[swapped2]]\n add = self.dist_matrix[sol[swapped1 - 1]][sol[swapped1]] + self.dist_matrix[sol[swapped2+1]][sol[swapped2]]\n attempt = prev_len - remove + add\n return attempt","def _get_max_dist_from_tail(self, snake, board, food_evaluation):\n\n if snake.health_points == self.MAX_HEALTH:\n return 1000\n elif food_evaluation == CANT_FIND:\n return 2\n else:\n return 2 + (self.MAX_HEALTH - snake.health_points) / (\n self.MAX_HEALTH) * (board.width + board.height)","def get_min_distance(distances, unvisited_nodes):\n min_value = None\n node = None\n for city, distance in distances.items():\n if city not in unvisited_nodes:\n continue\n if min_value is None:\n node = city\n min_value = distance\n elif distance < min_value:\n node = city\n min_value = distance\n return node","def getnearest(iterable, value):\n return min(enumerate(iterable), key=lambda i: abs(i[1] - value))","def smallest_ellapsed(login):\n df = login\n df[\"Time\"] = pd.to_datetime(df[\"Time\"])\n\n\n return df.groupby(\"Login Id\").agg(lambda group: group.diff().min()).dropna()[\"Time\"].to_frame()","def find_min(self):\n\n if self.left:\n return self.left.find_min()\n\n return self.data","def findMin(self):\n curr = self\n while curr.hasLeftChild():\n curr = curr.leftChild\n return curr","def _get_closest(self, x, y, clients):\n target = min(\n clients,\n key=lambda c: math.hypot(c.x - x, c.y - y),\n default=self.clients.current_client,\n )\n return target","def calc_dist(L, Seff):\n return (L / Seff)**0.5","def derivative_of_dist_to_obstacle(min_dist, x_jnt0, y_jnt0, x_jnt1, y_jnt1,\n dx_jnt0, dy_jnt0, dx_jnt1, dy_jnt1,\n link_slope, x_obs, y_obs):\n dist, point_type = min_dist\n if point_type == 0:\n dist_der = ((x_jnt0 - x_obs) * dx_jnt0 + (y_jnt0 - y_obs) * dy_jnt0)\n dist_der /= dist\n elif point_type == 1:\n dist_der = ((x_jnt1 - x_obs) * dx_jnt1 + (y_jnt1 - y_obs) * dy_jnt1)\n dist_der /= dist\n elif point_type == 2:\n if link_slope is None:\n dist_der = dx_jnt0 if x_jnt0 > x_obs else -dx_jnt0\n elif link_slope == 0:\n dist_der = dy_jnt0 if y_jnt0 > y_obs else -dy_jnt0\n else:\n x_intersect = (\n x_obs / link_slope + y_obs + link_slope * x_jnt0 - y_jnt0\n ) / (link_slope + 1 / link_slope)\n y_intersect = link_slope * (x_intersect - x_jnt0) + y_jnt0\n dlink_slope = (\n (1 / (x_jnt1 - x_jnt0))\n * (dy_jnt1 - dy_jnt0 + link_slope * (dx_jnt1 - dx_jnt0))\n )\n dx_intersect = (\n link_slope**4 * dx_jnt0\n + link_slope**2 * dlink_slope * (y_jnt0 - y_obs)\n - link_slope**3 * dy_jnt0\n + dlink_slope * (y_obs - y_jnt0)\n + 2 * link_slope * dlink_slope * (x_jnt0 - x_obs)\n + link_slope**2 * dx_jnt0\n - link_slope * dy_jnt0\n ) / (1 + link_slope**2) ** 2\n dy_intersect = (link_slope * (dx_intersect - dx_jnt0)\n + dlink_slope * (x_intersect - x_jnt0)\n + dy_jnt0)\n dist_der = (\n (x_intersect - x_obs) * dx_intersect\n + (y_intersect - y_obs) * dy_intersect\n ) / dist\n return dist_der","def closest_fruit(maze, currX, currY, fruit_list):\n curr_min = sys.maxsize\n for position in fruit_list:\n distance = Astar(maze, currX, currY, position[0], position[1])\n if distance < curr_min:\n curr_min = distance\n return curr_min","def displacement(self):\n return self[0].distance(self[-1])","def distance(self, p=None, l=None):\n if l is None:\n d = p - self.zero\n n = np.zeros(3)\n # try:\n # n = d - np.dot(d, self.direction) * self.direction\n # except RuntimeWarning:\n # print(d, self.direction)\n # return norm(n)\n with warnings.catch_warnings(record=True) as w:\n # Cause all warnings to always be triggered.\n warnings.simplefilter(\"always\")\n n = d - np.dot(d, self.direction) * self.direction\n # print(n, norm(n))\n if len(w) > 0 and issubclass(w[-1].category, RuntimeWarning):\n # Todo: check w/ Ram if this is what he meant to do when catch a warning: n = np.zeros(3)\n # n = np.zeros(3)\n # print(d, self.direction)\n pass\n return norm(n)\n else:\n normal = np.cross(self.direction, l.direction)\n n = norm(normal)\n if n < sys.float_info.min:\n # Lines are parallel.\n return self.distance(p=l.zero)\n offset = np.dot(l.zero - self.zero, normal) / n\n return np.abs(offset)","def get_head_distance(head_pixels, frame_shape):\n head_center = ((head_pixels[0] + head_pixels[2]) / 2 / frame_shape[1],\n (head_pixels[1] + head_pixels[3]) / 2 / frame_shape[0])\n center = (0.5, 0.5)\n return np.linalg.norm(np.subtract(head_center, center))","def _get_distance_diff(self, input):\n nbatch = input.shape[0]\n in1 = input.unsqueeze(1).expand(\n nbatch, self.nelec, self.nelec, self.ndim)\n in2 = input.unsqueeze(2).expand(\n nbatch, self.nelec, self.nelec, self.ndim)\n dist = torch.pow(in1 - in2, 2).sum(3)\n return dist","def item_t(data_alt, item, min_t):\n for t in data_alt[item]:\n if t >= min_t:\n return t\n return None","def get_min(h: Heap) -> Node:\n prev, curr = _min(h)\n return curr","def takeClosest(myList, myNumber):\n pos = bisect_left(myList, myNumber)\n if pos == 0:\n return 0 #myList[0]\n if pos == len(myList):\n return len(myList)-1 #myList[-1]\n\n before = myList[pos - 1]\n after = myList[pos]\n\n if after - myNumber < myNumber - before:\n return pos #after\n else:\n return pos-1 #before","def get_closest_waypoint(self, pose):\n wpclosestDist = sys.maxint\n for index in range(len(self.waypoints.waypoints)):\n wp = self.waypoints.waypoints[index]\n wpdist = self.calcDistance_PoseStamped(pose, wp.pose)\n if(wpclosestDist > wpdist):\n wpclosestDist = wpdist\n wpindex = index\n return wpindex","def left_distance(self):\n return self.x","def lmin(scape, start):\n i = start\n while scape[i - 1] < scape[i] - 0.06:\n i -= 1\n while scape[i + 1] < scape[i] - 0.06:\n i += 1\n return i","def min(self):\n return self.get_first()","def get_distance_between_checkpoint(self, i1, i2):\n ma = max(i1, i2)\n mi = min(i1, i2)\n dist = 0\n while mi != ma:\n dist += get_distance(self.cp[mi], self.cp[mi + 1])\n mi += 1\n return dist","def closest(start, incoming_angle, timeleft):\n visited = set()\n frontier = [ (0, 0, 0, incoming_angle, start) ]\n distances = {}\n while frontier:\n (cost, difficulty, count, in_angle, n) = heappop(frontier)\n if n in visited:\n continue\n distances[n] = cost\n if cost > timeleft:\n # cannot reach a non visited edge on time\n return None\n edges = sorted(n.edges, key=priority(in_angle))\n for edge in edges:\n if cost + edge.cost <= timeleft:\n # we can take this edge\n if edge.distance > 0:\n return compute_path(distances, cost, start, n) + [ edge ]\n else:\n if edge.stop not in visited:\n difficulty = max(e2.difficulty for e2 in edge.stop.edges)\n candidate = (cost + edge.cost, difficulty, count + edge.visits, edge.angle, edge.stop)\n # print candidate\n heappush(frontier, candidate)\n visited.add(n)\n return None","def distance(self) -> float:\n return self._dist_two_wire() # at this time we only support 2-wire meausre","def min_time(self):\n return self._ll_tree_sequence.get_min_time()","def find_minimum_path_cost(cls, start_point: Coordination, current_point: Coordination) -> int:\n return abs(start_point.x - current_point.x) + abs(start_point.y - current_point.y)","def diameter(self):\n\n v = self.vertices()\n pairs = [ (v[i],v[j]) for i in range(len(v)-1) for j in range(i+1, len(v))]\n smallest_paths = []\n for (s,e) in pairs:\n paths = self.find_all_path(s,e)\n smallest = sorted(paths, key=len)[0]\n smallest_paths.append(smallest)\n\n smallest_paths.sort(key=len)\n\n # Print the list smallest_paths\n\n # Longest path is at the end of list\n # ie diameter corresponds to length of this path\n\n diameter = len(smallest_paths[-1]) -1\n return diameter","def distance(self, other_pt, is_lla=True):\n return 0.0"],"string":"[\n \"def _calc_min_distance(self, walker):\\n\\n cell_lengths, cell_angles = box_vectors_to_lengths_angles(walker.state['box_vectors'])\\n\\n t2 = time.time()\\n # make a traj out of it so we can calculate distances through\\n # the periodic boundary conditions\\n walker_traj = mdj.Trajectory(walker.state['positions'],\\n topology=self._mdj_top,\\n unitcell_lengths=cell_lengths,\\n unitcell_angles=cell_angles)\\n\\n t3 = time.time()\\n # calculate the distances through periodic boundary conditions\\n # and get hte minimum distance\\n min_distance = np.min(mdj.compute_distances(walker_traj,\\n it.product(self.ligand_idxs,\\n self.receptor_idxs),\\n periodic=self._periodic)\\n )\\n t4 = time.time()\\n logging.info(\\\"Make a traj: {0}; Calc dists: {1}\\\".format(t3-t2,t4-t3))\\n\\n return min_distance\",\n \"def get_min_distance(self, node):\\r\\n if self.have_min_distance(node):\\r\\n return self.table[node][\\\"dist\\\"]\\r\\n return None\",\n \"def _minimum_distance(self,arg):\\n return min([abs(arg-e) for e in self if not e is arg])\",\n \"def lidar_relative(self):\\n return self.distance\",\n \"def min_distance(distance, spt_set, self_nodes):\\n minimum = sys.maxsize\\n minimum_node = None\\n for curr_node in self_nodes.values():\\n if distance[curr_node.id] < minimum and not spt_set[curr_node.id]:\\n minimum = distance[curr_node.id]\\n minimum_node = curr_node\\n return minimum_node\",\n \"def linelength(l):\\n return dist(l[0],l[1])\",\n \"def get_min_distance(self):\\n return round(min(self.combined_euclidian_distance))\",\n \"def getMinNode(self):\\n currentNode = self.openList[0]\\n for node in self.openList:\\n if node.g + node.h < currentNode.g + currentNode.h:\\n currentNode = node\\n return currentNode\",\n \"def _distance_next(self):\\n\\n self.distance = 10\\n\\n # Here a set index to 0 if the car is finishing a lap\\n # Also reset the farthest\\n if self.index > (len(self.x_trajectory) - 6) and self.closed:\\n self.index = 0\\n self.farthest = -1\\n self.laps += 1\\n\\n for w in range(self.index, self.index + 20):\\n\\n self.dist_point = math.sqrt((self.x_trajectory[w] - self.x)**2\\n + (self.y_trajectory[w] - self.y)**2)\\n\\n if self.dist_point < self.distance:\\n self.distance = self.dist_point\\n self.index = w\\n\\n if w >= (len(self.x_trajectory) - 1):\\n break\\n\\n self._calc_side()\\n\\n self.distance = self.distance * self.sign\\n\\n return self.distance\",\n \"def minDist(l, a, b):\\n pre = 0\\n rt = float('INF')\\n for i in range(len(l)):\\n if l[i] == a or l[i] == b:\\n pre = i\\n break\\n\\n for i in range(pre+1, len(l)):\\n if l[i] == a or l[i] == b:\\n if l[i] != l[pre] and i - pre < rt:\\n rt = i - pre\\n pre = i\\n return rt\",\n \"def top_of_climb_distance(self):\\n return self.distances[self.top_of_climb_index()]\",\n \"def top_of_descent_distance(self):\\n return self.distances[self.top_of_descent_index()]\",\n \"def previous_min(L):\\n\\n return itertoolsextra.max_diff(L)\",\n \"def minimal_distance(me):\\n smallest_d = 101 # given length of edge <= 100\\n ismallest = -1 # index of the edge in the list, me\\n for i, e in enumerate(me):\\n if e[0] < smallest_d:\\n smallest_d = e[0]\\n ismallest = i\\n\\n d = me[ismallest][0]\\n v1 = me[ismallest][1]\\n v2 = me[ismallest][2]\\n me.pop(ismallest)\\n\\n smallest_d = 101\\n for i, e in enumerate(me):\\n if (e[1] == v1 or e[2] == v1 or e[1] == v2 or e[2] == v2) and e[0] < smallest_d:\\n smallest_d = e[0]\\n\\n d += smallest_d\\n return d\",\n \"def LineMinDistanceTo(line, point_or_line):\\n line = rhutil.coerceline(line, True)\\n test = rhutil.coerceline(point_or_line)\\n if test is None: test = rhutil.coerce3dpoint(point_or_line, True)\\n return line.MinimumDistanceTo(test)\",\n \"def find_min_distance():\\n return np.argmin(d)\",\n \"def min_distance(self, target):\\n difference = self.pivot - target\\n return max(math.sqrt(np.dot(difference, difference)) - self.radius, 0)\",\n \"def longest_flight(self):\\r\\n distance = 0\\r\\n for code, _list in self.edges.items():\\r\\n for edge in _list:\\r\\n if edge.distance > distance:\\r\\n distance = edge.distance\\r\\n start = edge.start\\r\\n destination = edge.destination\\r\\n return start, destination, distance\",\n \"def _findMinNode(self, s):\\n\\n minNode = None\\n minVal = self.inf\\n for vertex in s:\\n if self.dist[vertex] < minVal:\\n minVal = self.dist[vertex]\\n minNode = vertex\\n return minNode\",\n \"def find_min(ls):\\n\\n if len(ls) == 1:\\n return ls[0]\\n elif len(ls) == 2:\\n return ls[0] if ls[0] < ls[1] else ls[1]\\n else:\\n mid = len(ls) // 2\\n m1 = find_min(ls[0:mid])\\n m2 = find_min(ls[mid:])\\n return m1 if m1 < m2 else m2\",\n \"def shortest_flight(self):\\r\\n distance = sys.maxsize\\r\\n for code, _list in self.edges.items():\\r\\n for edge in _list:\\r\\n if edge.distance < distance:\\r\\n distance = edge.distance\\r\\n start = edge.start\\r\\n destination = edge.destination\\r\\n return start, destination, distance\",\n \"def get_closest_distance_to_path(self, path):\\n min_distance_to_line = float(\\\"inf\\\")\\n for p in path:\\n game_path = p[:]\\n\\n game_path.sort(key = lambda coord: calculate_distance(self, coord))\\n point_A = game_path[0] # Closest point out of all the points on the path to to the tower\\n\\n try:\\n point_after_A = p[p.index(point_A) + 1]\\n point_before_A = p[p.index(point_A) - 1]\\n closest_to_A = min(point_after_A, point_before_A, key = lambda point: calculate_distance(point_A, point))\\n except:\\n if p.index(point_A) == 0:\\n closest_to_A = p[p.index(point_A) + 1]\\n \\n elif p.index(point_A) == len(p) - 1:\\n closest_to_A = p[p.index(point_A) - 1]\\n finally:\\n if closest_to_A[0] != point_A[0]:\\n m = (closest_to_A[1] - point_A[1]) / (closest_to_A[0] - point_A[0])\\n else:\\n m = 2\\n\\n b = point_A[1] - m * point_A[0]\\n\\n closest_distance = abs(-m * self.x + self.y - b) / math.sqrt((-m) ** 2 + 1)\\n min_distance_to_line = min(closest_distance, min_distance_to_line)\\n \\n return min_distance_to_line\",\n \"def closest(self, x):\\n # http://www.ahinson.com/algorithms_general/Sections/Geometry/PluckerLine.pdf\\n # has different equation for moment, the negative\\n\\n x = arg.getvector(x, 3)\\n\\n lam = np.dot(x - self.pp, self.uw)\\n p = self.point(lam) # is the closest point on the line\\n d = np.linalg.norm( x - p)\\n \\n return namedtuple('closest', 'p d lam')(p, d, lam)\",\n \"def _find_min(self):\\n if self.is_empty(): # is_empty inherited from base class\\n raise Empty('Priority queue is empty')\\n small = self._data.first()\\n walk = self._data.after(small)\\n while walk is not None:\\n if walk.element() < small.element():\\n small = walk\\n walk = self._data.after(walk)\\n return small\",\n \"def distance_to_current_waypoint(self):\\n next_waypoint = self.vehicle.commands.next\\n if next_waypoint == 1:\\n return None\\n mission_item = self.vehicle.commands[next_waypoint]\\n lat = mission_item.x\\n lon = mission_item.y\\n alt = mission_item.z\\n waypoint_location = Location(lat, lon, alt, is_relative=True)\\n distance = get_distance_meters(self.vehicle.location, waypoint_location)\\n return distance\",\n \"def h(self,node):\\n \\\"*** YOUR CODE HERE ***\\\"\\n dist_arr = [] #Initialize Array\\n for goal in self.goals: # Iterate through Goals\\n dist_arr.append(manhattan_distance_with_heading(node.state, goal)) # Add distance between node and goal\\n return min(dist_arr) # Return minimum\",\n \"def _get_distance(reindeer, race_time):\\n interval = reindeer.flight_time + reindeer.rest_time\\n cycles = race_time // interval\\n flight_time = min(reindeer.flight_time, race_time - interval * cycles)\\n total_flying_time = reindeer.flight_time * cycles + flight_time\\n return total_flying_time * reindeer.flight_speed\",\n \"def closest_distance(node_a, node_b):\\n min_distance = 999999\\n for loc_a in node_a.locations:\\n for loc_b in node_b.locations:\\n distance = abs(loc_a - loc_b)\\n if distance < min_distance:\\n min_distance = distance\\n return min_distance\",\n \"def nn(x, S, dist):\\n\\n # note that there might be more than on minimal item. min will return the\\n # first one ecountered\\n return min(S, key=lambda y: dist(x, y[:-1]))\",\n \"def get_distance(self):\\n values = self.speakers.values()\\n values.sort(reverse=True)\\n try:\\n return abs(values[1]) - abs(values[0])\\n except (IndexError, ValueError):\\n return -1\",\n \"def get_closest_lane(self, car: Car) -> AbstractLane:\\n pos = car.position\\n lanes = self.upstream_lane_list if car.lane.upstream else self.downstream_lane_list\\n return min(lanes, key=lambda l: l.distance(pos))\",\n \"def calculate_distance_edge(self):\\n mu_star = -np.sqrt(1. - (self.cell_xl / self.x)**2)\\n\\n if self.mu <= mu_star:\\n\\n l_edge = (-self.mu * self.x -\\n np.sqrt(self.mu**2 * self.x**2 -\\n self.x**2 + self.cell_xl**2))\\n self.next_cell_index = self.cell_index - 1\\n\\n else:\\n\\n l_edge = (-self.mu * self.x +\\n np.sqrt(self.mu**2 * self.x**2 -\\n self.x**2 + self.cell_xr**2))\\n self.next_cell_index = self.cell_index + 1\\n\\n return l_edge\",\n \"def shortest_distance_to(self, pt):\\n return self._nearest_to_point(pt)[0]\",\n \"def _get_distance_betweenitems(self, item_no1, item_no2):\\n\\n try:\\n if item_no1 >= 0 and item_no2 >= 0:\\n loc_current = self.page_current.item_onscreenlocs[item_no1]\\n loc_potential = self.page_current.item_onscreenlocs[item_no2]\\n distance = abs(loc_potential - loc_current)\\n else:\\n distance = 0\\n\\n except IndexError:\\n distance = 0\\n\\n return distance\",\n \"def distance_to_current_waypoint():\\n nextwaypoint = vehicle.commands.next\\n if nextwaypoint == 0:\\n return None\\n missionitem = vehicle.commands[nextwaypoint -\\n 1] #commands are zero indexed\\n lat = missionitem.x\\n lon = missionitem.y\\n alt = missionitem.z\\n targetWaypointLocation = LocationGlobalRelative(lat, lon, alt)\\n distancetopoint = get_distance_metres(vehicle.location.global_frame,\\n targetWaypointLocation)\\n return distancetopoint\",\n \"def find_min(self):\\n return self.min\",\n \"def find_min(self):\\n return self.min\",\n \"def distancetoline(p, l1, l2):\\n vx = l1.x-p.x \\n vy = l1.y-p.y\\n ux = l2.x-l1.x\\n uy = l2.y-l1.y\\n\\n length = ux*ux+uy*uy;\\n\\n det = (-vx*ux)+(-vy*uy); \\n # if this is < 0 or > length then its outside the line segment\\n if det<0 or det>length:\\n ux=l2.x-p.x\\n uy=l2.y-p.y\\n return sqrt(min(vx*vx+vy*vy, ux*ux+uy*uy))\\n\\n det = ux*vy-uy*vx\\n if length == 0.0:\\n return 0.0\\n else:\\n return sqrt((det*det)/length)\",\n \"def closest_point(self, l):\\n cos = np.dot(self.direction, l.direction)\\n n = 1 - cos ** 2\\n if n < sys.float_info.epsilon:\\n # Lines are parallel.\\n return self.zero\\n\\n d0 = l.zero - self.zero\\n a = np.dot(d0, self.direction)\\n b = np.dot(d0, l.direction)\\n return self.zero + self.direction * ( a - b * cos) / n\",\n \"def minimum_distance(object_1, object_2):\\n\\n # package import\\n import numpy as np\\n\\n # main algorithm\\n minimum_distance = 100000\\n\\n for coord_1 in object_1:\\n for coord_2 in object_2:\\n distance_btwn_coords = np.linalg.norm(coord_1 - coord_2)\\n if distance_btwn_coords == 0:\\n minimum_distance = distance_btwn_coords\\n return float(minimum_distance)\\n elif distance_btwn_coords < minimum_distance:\\n minimum_distance = distance_btwn_coords\\n\\n return float(minimum_distance)\",\n \"def get_distance(self, point):\\n if not isinstance(point, Point):\\n point = Point(*point)\\n\\n distances = [(point.distance_to_point(p), p) for p in self.points]\\n sortpoints = sorted(distances, key=lambda x: x[0])\\n closest = sortpoints[0][1]\\n\\n vc = Vector(*closest)\\n d1 = vc.dot(vc)\\n\\n secondc = sortpoints[1][1]\\n vs = Vector(*secondc)\\n v1 = Vector(*point) - (vc+vs)/2\\n v2 = vs-vc\\n v2.unitize()\\n d2 = v1.dot(v2)\\n\\n return abs(min(d1, d2)) - self.thickness/2\",\n \"def __find_max_distance(self):\\n return utils.find_max_distance(self.__game)\",\n \"def find_min(self):\\n return min(self.nodes, key=int)\",\n \"def calculate_distance_line(\\n r_packet, comov_nu, is_last_line, nu_line, time_explosion\\n):\\n\\n nu = r_packet.nu\\n\\n if is_last_line:\\n return MISS_DISTANCE\\n\\n nu_diff = comov_nu - nu_line\\n\\n # for numerical reasons, if line is too close, we set the distance to 0.\\n if r_packet.is_close_line:\\n nu_diff = 0.0\\n r_packet.is_close_line = False\\n\\n if nu_diff >= 0:\\n distance = (nu_diff / nu) * C_SPEED_OF_LIGHT * time_explosion\\n else:\\n print(\\\"WARNING: nu difference is less than 0.0\\\")\\n raise MonteCarloException(\\n \\\"nu difference is less than 0.0; for more\\\"\\n \\\" information, see print statement beforehand\\\"\\n )\\n\\n if numba_config.ENABLE_FULL_RELATIVITY:\\n return calculate_distance_line_full_relativity(\\n nu_line, nu, time_explosion, r_packet\\n )\\n return distance\",\n \"def _compute_smallest_step_len_for_candidate_vector(x_candidate, z_min):\\n ta, tb = _solve_scalar_quadratic_equation(x_candidate, z_min)\\n step_len = min([ta, tb], key=abs)\\n\\n return step_len\",\n \"def get_closest_node(data, loc):\\n min_dist = None\\n closest = None\\n for i in data:\\n # Standard min-value search loop\\n dist = great_circle_distance(get_coords(data, i), loc)\\n if closest is None or dist < min_dist:\\n closest = i\\n min_dist = dist\\n return closest\",\n \"def extract_minOld(H):\\n minDist = approxInf\\n u = None\\n for v in H:\\n if v[1] <= minDist:\\n minDist = v[1]\\n u = v\\n return(H.pop(u))\",\n \"def closest_waypoint(self, location: pylot.utils.Location):\\n min_dist = np.infty\\n min_index = 0\\n for index, waypoint in enumerate(self.waypoints):\\n dist = waypoint.location.distance(location)\\n if dist < min_dist:\\n min_dist = dist\\n min_index = index\\n return min_index\",\n \"def find_min(list):\\n return find_value_at(list, -1)\",\n \"def smallest (self):\\n return self.pointers[0].smallest()\",\n \"def find_middle_point(self):\\n leaf1, longest_dist = None, 0.0\\n for leaf in self.leaves:\\n dist = sum(self.path_dists[leaf])\\n if dist > longest_dist:\\n leaf1 = leaf\\n longest_dist = dist\\n leaf2, longest_dist = None, 0.0\\n for leaf in self.leaves:\\n dist = self.node_distance(leaf1, leaf)\\n if dist > longest_dist:\\n leaf2 = leaf\\n longest_dist = dist\\n for ind, (n1, n2) in enumerate(zip(self.paths[leaf1], self.paths[leaf2])):\\n if n1 != n2:\\n break\\n rev_ind = ind - len(self.paths[leaf1]) - 1\\n nodes = self.paths[leaf1][-1:rev_ind-1:-1] + self.paths[leaf2][ind:]\\n dists = self.path_dists[leaf1][-1:rev_ind:-1] + self.path_dists[leaf2][ind:]\\n mid_dist, cur_dist = longest_dist / 2.0, 0.0\\n for i in range(len(nodes)-1):\\n dist = dists[i]\\n if cur_dist + dist >= mid_dist:\\n node1, node2 = nodes[i], nodes[i+1]\\n if cur_dist + dist == mid_dist:\\n distance = dist\\n else:\\n distance = mid_dist - cur_dist\\n break\\n else:\\n cur_dist += dist\\n return node1, node2, distance\",\n \"def nodeAtMinimumDistance(self, notFoundYet, distances):\\n # found minimal\\n minimal = None\\n for node in notFoundYet:\\n if (distances[node] >= 0): \\n if minimal == None or (distances[minimal] > distances[node]):\\n minimal = node\\n\\n # return\\n if minimal == -1: return None\\n else: return minimal\",\n \"def extractmin(self):\\n if len(self.heap) == 0: \\n return None\\n i = self.heap[0]\\n last = self.heap[-1]\\n del self.heap[-1]\\n if len(self.heap) > 0:\\n self.siftdown(last, 0)\\n return i\",\n \"def min(self):\\n least = self.data[0]\\n \\n for i in range(len(self.data)):\\n if self.data[i] < least:\\n least = self.data[i]\\n return least\",\n \"def distance(self) -> int:\\n return 0\",\n \"def _distance_to_line(begin, end, point):\\n return _vec_distance(point, _nearest_point_on_line(begin, end, point))\",\n \"def nearest(items, pivot):\\n return min(items, key=lambda x: abs(x - pivot))\",\n \"def DRFindMinimalEdge(self, flow, edge_lst):\\n\\t\\tarr_time = flow[3]\\n\\t\\tend_time = flow[3] + flow[4]\\n\\t\\tmin_cum_size = float('inf')\\n\\t\\tmin_edge = (-1, -1)\\n\\t\\tfor e in edge_lst:\\n\\t\\t\\tprev_time = arr_time\\n\\t\\t\\tprev_rate = 0\\n\\t\\t\\tcum_size = 0\\n\\t\\t\\tfor time, rate in sorted(self.rate_lst[e].items()):\\n\\t\\t\\t\\tif time > arr_time:\\n\\t\\t\\t\\t\\tif time < end_time:\\n\\t\\t\\t\\t\\t\\tcum_size += prev_rate * (time - prev_time)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tcum_size += prev_rate * (end_time - prev_time)\\n\\t\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\tif cum_size >= min_cum_size:\\n\\t\\t\\t\\t\\t# Break if already >= minimum\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\telif time >= end_time:\\n\\t\\t\\t\\t\\t# Break if time exceeds deadline\\n\\t\\t\\t\\t\\tbreak\\n\\n\\t\\t\\t\\tprev_time = time\\n\\t\\t\\t\\tprev_rate = rate\\n\\n\\t\\t\\tif cum_size < min_cum_size:\\n\\t\\t\\t\\tmin_cum_size = cum_size\\n\\t\\t\\t\\tmin_edge = e\\n\\n\\t\\treturn min_edge, min_cum_size\",\n \"def h(self,node):\\n \\\"*** YOUR CODE HERE ***\\\"\\n dist_arr = []\\n for goal in self.goals:\\n dist_arr.append(manhattan_distance_with_heading(node.state, goal))\\n return min(dist_arr)\",\n \"def get_closest_slot(self):\\n if not self.__available_slots__:\\n return None\\n return min(self.__available_slots__)\",\n \"def find_min_node(self):\\n min_energy = 10 ** 10\\n min_id = -1\\n for node in self.node:\\n if node.energy < min_energy:\\n min_energy = node.energy\\n min_id = node.id\\n return min_id\",\n \"def get_nearest_row(self):\\n return (self.rect.top - (self.screen.get_height() // 12)) // self.maze.block_size\",\n \"def compute_min_refills(distance: int, tank: int, stops: List[int]):\\n location: int = 0\\n n_stops = 0\\n last_stop = 0\\n max_drive = location + tank\\n\\n while max_drive < distance:\\n counter = 0\\n\\n # Handle the case that stops are depleted before we reach distance\\n if len(stops) == 0:\\n return -1\\n for s in stops:\\n if s <= max_drive:\\n counter += 1\\n last_stop = s\\n max_drive = last_stop + tank\\n\\n # Handle the case that wi did not reach the next stop\\n if counter == 0:\\n return -1\\n else:\\n del stops[0:counter]\\n n_stops += 1\\n\\n return n_stops\",\n \"async def distance(self):\\n return round(await self._rpc.distance(), 2)\",\n \"def _get_nearest_slot(self):\\n available_slots = [pslot for pslot in self.slots.values() if pslot.available]\\n if not available_slots:\\n return None\\n\\n return sorted(available_slots, key=lambda x: x.slot_no)[0]\",\n \"def get_distance(self) -> int:\\n return self.get_measurement_data().distance\",\n \"def min_diff_return(self, data_useable):\\n\\n min_diff = abs(data_useable[0][1] - data_useable[0][2])\\n\\n for data_row in data_useable:\\n if abs(float(data_row[2]) - float(data_row[1])) < min_diff:\\n min_diff = abs(float(data_row[2]) - float(data_row[1]))\\n self.data = data_row\\n\\n return self.data\",\n \"def get_distance(self, node):\\n return np.sqrt(\\n (self.x - node.x) ** 2 +\\n (self.y - node.y) ** 2\\n )\",\n \"def find_closest_interior_node(arc, hull, nodes):\\n lengths = [float(\\\"inf\\\") for x in range(len(nodes))]\\n for (j, node) in enumerate(nodes):\\n if not node.nid in hull:\\n i = hull.index(arc[1])\\n hull.insert(i, node.nid)\\n lengths[j] = (tsputil.get_path_length(nodes, 100, hull))\\n hull.pop(i)\\n return lengths.index(min(lengths))\",\n \"def nearest(node):\\n count = 0\\n distance = 100000\\n while count != node_count[0]:\\n city = d_list[node.value - 1]\\n if city != []:\\n if city[0][1] < distance:\\n distance = city[0][1]\\n new_city = city[0][0]\\n closest_city = node.value\\n node = node.left\\n count = count + 1\\n return (closest_city, new_city, distance)\",\n \"def find_move_from_line(\\n x,\\n data,\\n overlap_penalty,\\n norm_penalty,\\n offdiagonal_energy_penalty,\\n lagrange_multiplier,\\n energy_weights=None,\\n max_norm_deviation=0.2,\\n):\\n N = np.abs(data[\\\"overlap\\\"].diagonal(axis1=1, axis2=2))\\n Nij = np.asarray([np.sqrt(np.outer(a, a)) for a in N])\\n nwf = data[\\\"energy\\\"].shape[-1]\\n if energy_weights is None:\\n energy_weights = np.ones(nwf) / nwf\\n\\n energy = data[\\\"energy\\\"] / Nij\\n overlap = data[\\\"overlap\\\"]\\n # print(\\\"energy cost\\\", np.sum(energy.diagonal(axis1=1,axis2=2),axis=1))\\n # print(\\\"overlap cost\\\",np.sum(np.triu(overlap**2,1),axis=(1,2)) )\\n # print(\\\"offdiagonal_energy\\\", energy)\\n # print(\\\"norm\\\",np.einsum('ijj->i', (overlap-1)**2 ))\\n cost = (\\n np.einsum(\\\"i,nii->n\\\", energy_weights, energy)\\n + overlap_penalty * np.sum(np.triu(overlap**2, 1), axis=(1, 2))\\n + np.sum(lagrange_multiplier * np.triu(overlap, 1), axis=(1, 2))\\n + offdiagonal_energy_penalty * np.sum(np.triu(energy**2, 1), axis=(1, 2))\\n + norm_penalty * np.einsum(\\\"ijj->i\\\", (overlap - 1) ** 2)\\n )\\n\\n # good_norms = np.prod(np.einsum('ijj->ij',np.abs(overlap-1) < max_norm_deviation),axis=1)\\n # print(\\\"good norms\\\", good_norms, 'cost', cost[good_norms])\\n xmin = linemin.stable_fit(x, cost)\\n return xmin, cost\",\n \"def new_distance(self, prev_len, prev, sol, i, l):\\n swapped1 = i\\n swapped2 = l\\n if swapped2 == self.file_size-1:\\n remove = self.dist_matrix[prev[swapped1 - 1]][prev[swapped1]] \\\\\\n + self.dist_matrix[prev[swapped2]][prev[0]]\\n add = self.dist_matrix[sol[swapped1 - 1]][sol[swapped1]] + self.dist_matrix[sol[swapped2]][sol[0]]\\n attempt = prev_len - remove + add\\n else:\\n remove = self.dist_matrix[prev[swapped1 - 1]][prev[swapped1]] \\\\\\n + self.dist_matrix[prev[swapped2+1]][prev[swapped2]]\\n add = self.dist_matrix[sol[swapped1 - 1]][sol[swapped1]] + self.dist_matrix[sol[swapped2+1]][sol[swapped2]]\\n attempt = prev_len - remove + add\\n return attempt\",\n \"def _get_max_dist_from_tail(self, snake, board, food_evaluation):\\n\\n if snake.health_points == self.MAX_HEALTH:\\n return 1000\\n elif food_evaluation == CANT_FIND:\\n return 2\\n else:\\n return 2 + (self.MAX_HEALTH - snake.health_points) / (\\n self.MAX_HEALTH) * (board.width + board.height)\",\n \"def get_min_distance(distances, unvisited_nodes):\\n min_value = None\\n node = None\\n for city, distance in distances.items():\\n if city not in unvisited_nodes:\\n continue\\n if min_value is None:\\n node = city\\n min_value = distance\\n elif distance < min_value:\\n node = city\\n min_value = distance\\n return node\",\n \"def getnearest(iterable, value):\\n return min(enumerate(iterable), key=lambda i: abs(i[1] - value))\",\n \"def smallest_ellapsed(login):\\n df = login\\n df[\\\"Time\\\"] = pd.to_datetime(df[\\\"Time\\\"])\\n\\n\\n return df.groupby(\\\"Login Id\\\").agg(lambda group: group.diff().min()).dropna()[\\\"Time\\\"].to_frame()\",\n \"def find_min(self):\\n\\n if self.left:\\n return self.left.find_min()\\n\\n return self.data\",\n \"def findMin(self):\\n curr = self\\n while curr.hasLeftChild():\\n curr = curr.leftChild\\n return curr\",\n \"def _get_closest(self, x, y, clients):\\n target = min(\\n clients,\\n key=lambda c: math.hypot(c.x - x, c.y - y),\\n default=self.clients.current_client,\\n )\\n return target\",\n \"def calc_dist(L, Seff):\\n return (L / Seff)**0.5\",\n \"def derivative_of_dist_to_obstacle(min_dist, x_jnt0, y_jnt0, x_jnt1, y_jnt1,\\n dx_jnt0, dy_jnt0, dx_jnt1, dy_jnt1,\\n link_slope, x_obs, y_obs):\\n dist, point_type = min_dist\\n if point_type == 0:\\n dist_der = ((x_jnt0 - x_obs) * dx_jnt0 + (y_jnt0 - y_obs) * dy_jnt0)\\n dist_der /= dist\\n elif point_type == 1:\\n dist_der = ((x_jnt1 - x_obs) * dx_jnt1 + (y_jnt1 - y_obs) * dy_jnt1)\\n dist_der /= dist\\n elif point_type == 2:\\n if link_slope is None:\\n dist_der = dx_jnt0 if x_jnt0 > x_obs else -dx_jnt0\\n elif link_slope == 0:\\n dist_der = dy_jnt0 if y_jnt0 > y_obs else -dy_jnt0\\n else:\\n x_intersect = (\\n x_obs / link_slope + y_obs + link_slope * x_jnt0 - y_jnt0\\n ) / (link_slope + 1 / link_slope)\\n y_intersect = link_slope * (x_intersect - x_jnt0) + y_jnt0\\n dlink_slope = (\\n (1 / (x_jnt1 - x_jnt0))\\n * (dy_jnt1 - dy_jnt0 + link_slope * (dx_jnt1 - dx_jnt0))\\n )\\n dx_intersect = (\\n link_slope**4 * dx_jnt0\\n + link_slope**2 * dlink_slope * (y_jnt0 - y_obs)\\n - link_slope**3 * dy_jnt0\\n + dlink_slope * (y_obs - y_jnt0)\\n + 2 * link_slope * dlink_slope * (x_jnt0 - x_obs)\\n + link_slope**2 * dx_jnt0\\n - link_slope * dy_jnt0\\n ) / (1 + link_slope**2) ** 2\\n dy_intersect = (link_slope * (dx_intersect - dx_jnt0)\\n + dlink_slope * (x_intersect - x_jnt0)\\n + dy_jnt0)\\n dist_der = (\\n (x_intersect - x_obs) * dx_intersect\\n + (y_intersect - y_obs) * dy_intersect\\n ) / dist\\n return dist_der\",\n \"def closest_fruit(maze, currX, currY, fruit_list):\\n curr_min = sys.maxsize\\n for position in fruit_list:\\n distance = Astar(maze, currX, currY, position[0], position[1])\\n if distance < curr_min:\\n curr_min = distance\\n return curr_min\",\n \"def displacement(self):\\n return self[0].distance(self[-1])\",\n \"def distance(self, p=None, l=None):\\n if l is None:\\n d = p - self.zero\\n n = np.zeros(3)\\n # try:\\n # n = d - np.dot(d, self.direction) * self.direction\\n # except RuntimeWarning:\\n # print(d, self.direction)\\n # return norm(n)\\n with warnings.catch_warnings(record=True) as w:\\n # Cause all warnings to always be triggered.\\n warnings.simplefilter(\\\"always\\\")\\n n = d - np.dot(d, self.direction) * self.direction\\n # print(n, norm(n))\\n if len(w) > 0 and issubclass(w[-1].category, RuntimeWarning):\\n # Todo: check w/ Ram if this is what he meant to do when catch a warning: n = np.zeros(3)\\n # n = np.zeros(3)\\n # print(d, self.direction)\\n pass\\n return norm(n)\\n else:\\n normal = np.cross(self.direction, l.direction)\\n n = norm(normal)\\n if n < sys.float_info.min:\\n # Lines are parallel.\\n return self.distance(p=l.zero)\\n offset = np.dot(l.zero - self.zero, normal) / n\\n return np.abs(offset)\",\n \"def get_head_distance(head_pixels, frame_shape):\\n head_center = ((head_pixels[0] + head_pixels[2]) / 2 / frame_shape[1],\\n (head_pixels[1] + head_pixels[3]) / 2 / frame_shape[0])\\n center = (0.5, 0.5)\\n return np.linalg.norm(np.subtract(head_center, center))\",\n \"def _get_distance_diff(self, input):\\n nbatch = input.shape[0]\\n in1 = input.unsqueeze(1).expand(\\n nbatch, self.nelec, self.nelec, self.ndim)\\n in2 = input.unsqueeze(2).expand(\\n nbatch, self.nelec, self.nelec, self.ndim)\\n dist = torch.pow(in1 - in2, 2).sum(3)\\n return dist\",\n \"def item_t(data_alt, item, min_t):\\n for t in data_alt[item]:\\n if t >= min_t:\\n return t\\n return None\",\n \"def get_min(h: Heap) -> Node:\\n prev, curr = _min(h)\\n return curr\",\n \"def takeClosest(myList, myNumber):\\n pos = bisect_left(myList, myNumber)\\n if pos == 0:\\n return 0 #myList[0]\\n if pos == len(myList):\\n return len(myList)-1 #myList[-1]\\n\\n before = myList[pos - 1]\\n after = myList[pos]\\n\\n if after - myNumber < myNumber - before:\\n return pos #after\\n else:\\n return pos-1 #before\",\n \"def get_closest_waypoint(self, pose):\\n wpclosestDist = sys.maxint\\n for index in range(len(self.waypoints.waypoints)):\\n wp = self.waypoints.waypoints[index]\\n wpdist = self.calcDistance_PoseStamped(pose, wp.pose)\\n if(wpclosestDist > wpdist):\\n wpclosestDist = wpdist\\n wpindex = index\\n return wpindex\",\n \"def left_distance(self):\\n return self.x\",\n \"def lmin(scape, start):\\n i = start\\n while scape[i - 1] < scape[i] - 0.06:\\n i -= 1\\n while scape[i + 1] < scape[i] - 0.06:\\n i += 1\\n return i\",\n \"def min(self):\\n return self.get_first()\",\n \"def get_distance_between_checkpoint(self, i1, i2):\\n ma = max(i1, i2)\\n mi = min(i1, i2)\\n dist = 0\\n while mi != ma:\\n dist += get_distance(self.cp[mi], self.cp[mi + 1])\\n mi += 1\\n return dist\",\n \"def closest(start, incoming_angle, timeleft):\\n visited = set()\\n frontier = [ (0, 0, 0, incoming_angle, start) ]\\n distances = {}\\n while frontier:\\n (cost, difficulty, count, in_angle, n) = heappop(frontier)\\n if n in visited:\\n continue\\n distances[n] = cost\\n if cost > timeleft:\\n # cannot reach a non visited edge on time\\n return None\\n edges = sorted(n.edges, key=priority(in_angle))\\n for edge in edges:\\n if cost + edge.cost <= timeleft:\\n # we can take this edge\\n if edge.distance > 0:\\n return compute_path(distances, cost, start, n) + [ edge ]\\n else:\\n if edge.stop not in visited:\\n difficulty = max(e2.difficulty for e2 in edge.stop.edges)\\n candidate = (cost + edge.cost, difficulty, count + edge.visits, edge.angle, edge.stop)\\n # print candidate\\n heappush(frontier, candidate)\\n visited.add(n)\\n return None\",\n \"def distance(self) -> float:\\n return self._dist_two_wire() # at this time we only support 2-wire meausre\",\n \"def min_time(self):\\n return self._ll_tree_sequence.get_min_time()\",\n \"def find_minimum_path_cost(cls, start_point: Coordination, current_point: Coordination) -> int:\\n return abs(start_point.x - current_point.x) + abs(start_point.y - current_point.y)\",\n \"def diameter(self):\\n\\n v = self.vertices()\\n pairs = [ (v[i],v[j]) for i in range(len(v)-1) for j in range(i+1, len(v))]\\n smallest_paths = []\\n for (s,e) in pairs:\\n paths = self.find_all_path(s,e)\\n smallest = sorted(paths, key=len)[0]\\n smallest_paths.append(smallest)\\n\\n smallest_paths.sort(key=len)\\n\\n # Print the list smallest_paths\\n\\n # Longest path is at the end of list\\n # ie diameter corresponds to length of this path\\n\\n diameter = len(smallest_paths[-1]) -1\\n return diameter\",\n \"def distance(self, other_pt, is_lla=True):\\n return 0.0\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64288473","0.6343981","0.629061","0.6034535","0.602224","0.6003383","0.5981095","0.593175","0.5914017","0.5857995","0.5832663","0.5826591","0.5805714","0.5800851","0.57828003","0.5737947","0.5734673","0.56870914","0.56647193","0.56186366","0.55836093","0.5577574","0.55390465","0.55263233","0.55238676","0.5523338","0.55123746","0.5509846","0.5486163","0.5485987","0.5485585","0.54688555","0.5467318","0.546515","0.546386","0.5456844","0.5456844","0.54343045","0.5405624","0.54034925","0.5402656","0.5395097","0.53858435","0.5382595","0.5381849","0.53782016","0.53654057","0.5361178","0.5360914","0.5356503","0.5355428","0.5354138","0.53481853","0.5335629","0.5325433","0.53233004","0.5323206","0.53122133","0.53114223","0.5307121","0.53050417","0.53039086","0.52988684","0.52892566","0.52871585","0.5279777","0.5271625","0.5271201","0.5271162","0.5268719","0.5267176","0.5264495","0.52634966","0.52621424","0.52610207","0.5253197","0.5251721","0.52439606","0.5237188","0.5236815","0.5232567","0.5229691","0.5229025","0.5225606","0.5222194","0.52181876","0.5216585","0.52059674","0.5202546","0.52024794","0.51999027","0.5197873","0.5197725","0.5196957","0.5196146","0.5194552","0.5191597","0.5189709","0.51890904","0.51872677"],"string":"[\n \"0.64288473\",\n \"0.6343981\",\n \"0.629061\",\n \"0.6034535\",\n \"0.602224\",\n \"0.6003383\",\n \"0.5981095\",\n \"0.593175\",\n \"0.5914017\",\n \"0.5857995\",\n \"0.5832663\",\n \"0.5826591\",\n \"0.5805714\",\n \"0.5800851\",\n \"0.57828003\",\n \"0.5737947\",\n \"0.5734673\",\n \"0.56870914\",\n \"0.56647193\",\n \"0.56186366\",\n \"0.55836093\",\n \"0.5577574\",\n \"0.55390465\",\n \"0.55263233\",\n \"0.55238676\",\n \"0.5523338\",\n \"0.55123746\",\n \"0.5509846\",\n \"0.5486163\",\n \"0.5485987\",\n \"0.5485585\",\n \"0.54688555\",\n \"0.5467318\",\n \"0.546515\",\n \"0.546386\",\n \"0.5456844\",\n \"0.5456844\",\n \"0.54343045\",\n \"0.5405624\",\n \"0.54034925\",\n \"0.5402656\",\n \"0.5395097\",\n \"0.53858435\",\n \"0.5382595\",\n \"0.5381849\",\n \"0.53782016\",\n \"0.53654057\",\n \"0.5361178\",\n \"0.5360914\",\n \"0.5356503\",\n \"0.5355428\",\n \"0.5354138\",\n \"0.53481853\",\n \"0.5335629\",\n \"0.5325433\",\n \"0.53233004\",\n \"0.5323206\",\n \"0.53122133\",\n \"0.53114223\",\n \"0.5307121\",\n \"0.53050417\",\n \"0.53039086\",\n \"0.52988684\",\n \"0.52892566\",\n \"0.52871585\",\n \"0.5279777\",\n \"0.5271625\",\n \"0.5271201\",\n \"0.5271162\",\n \"0.5268719\",\n \"0.5267176\",\n \"0.5264495\",\n \"0.52634966\",\n \"0.52621424\",\n \"0.52610207\",\n \"0.5253197\",\n \"0.5251721\",\n \"0.52439606\",\n \"0.5237188\",\n \"0.5236815\",\n \"0.5232567\",\n \"0.5229691\",\n \"0.5229025\",\n \"0.5225606\",\n \"0.5222194\",\n \"0.52181876\",\n \"0.5216585\",\n \"0.52059674\",\n \"0.5202546\",\n \"0.52024794\",\n \"0.51999027\",\n \"0.5197873\",\n \"0.5197725\",\n \"0.5196957\",\n \"0.5196146\",\n \"0.5194552\",\n \"0.5191597\",\n \"0.5189709\",\n \"0.51890904\",\n \"0.51872677\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5248442"},"document_rank":{"kind":"string","value":"77"}}},{"rowIdx":5296,"cells":{"query":{"kind":"string","value":"Load data from CSV files and return them as numpy arrays The use_labels parameter indicates whether one should read the first column (containing class labels). If false, return all 0s."},"document":{"kind":"string","value":"def load_data(filename, use_labels=True):\n # load column 1 to 8 (ignore last one)\n data = np.loadtxt(open( filename), delimiter=',',\n usecols=range(1, 9), skiprows=1)\n if use_labels:\n labels = np.loadtxt(open( filename), delimiter=',',\n usecols=[0], skiprows=1)\n else:\n labels = np.zeros(data.shape[0])\n return labels, data"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def load(self, path=\"../data_set/final-train-dataset.csv\", shuffle=False,\n onlyLabelToUse=None, useOnlyBestIndicators=False, binary=False):\n data = []\n labels = []\n\n with open(path) as csvfile:\n reader = csv.reader(csvfile, delimiter=',')\n skip = True\n\n for row in reader:\n if skip:\n skip = False\n continue\n\n entries = []\n\n for i in range(SAMPLES_OF_DATA_TO_LOOK_AT):\n # Get one time point's data, including indicators:\n if useOnlyBestIndicators:\n # entries.append(\n # [float(row[i + j * SAMPLES_OF_DATA_TO_LOOK_AT]) for\n #\n entries.append(\n [float(row[i + j * SAMPLES_OF_DATA_TO_LOOK_AT]) for\n j in [0, 1, 2, 5, 7]])\n else:\n entries.append(\n [float(row[i + j * SAMPLES_OF_DATA_TO_LOOK_AT]) for\n j in range(INPUT_CHANNELS)])\n\n data.append(np.array(entries))\n\n if onlyLabelToUse is not None:\n if binary:\n label = [int(float(row[SAMPLES_OF_DATA_TO_LOOK_AT * TOTAL_INPUTS_IN_DATASET + onlyLabelToUse]) > 0.5)]\n else:\n label = [float(row[SAMPLES_OF_DATA_TO_LOOK_AT * TOTAL_INPUTS_IN_DATASET + onlyLabelToUse])]\n else:\n if binary:\n label = [int(float(row[SAMPLES_OF_DATA_TO_LOOK_AT * TOTAL_INPUTS_IN_DATASET + j]) > 0.5) for j in range(OUTPUT_CHANNELS)]\n else:\n label = [float(row[SAMPLES_OF_DATA_TO_LOOK_AT * TOTAL_INPUTS_IN_DATASET + j]) for j in range(OUTPUT_CHANNELS)]\n\n labels.append(label)\n\n if shuffle:\n indices = [i for i in range(len(data))]\n np.random.shuffle(indices)\n labels = np.array([labels[i] for i in indices])\n data = np.array([data[i] for i in indices])\n else:\n data = np.array(data)\n labels = np.array(labels)\n\n return data, labels","def read_data(filename):\n data = np.genfromtxt(filename, delimiter=',', dtype = str)\n X = data[1:,2:].astype(np.float)\n y = data[1:,0]\n y[y==label0]='0' \n y[y==label1]='1' \n y[y==label2]='2'\n y.astype(np.float) \n return X, y","def read_data(filename):\n data = np.genfromtxt(filename, delimiter=',', dtype=str)\n X = data[1:,2:].astype(np.float)\n y = data[1:,0]\n y[y==label0]='0'\n y[y==label1]='1'\n y[y==label2]='2'\n y=y.astype(np.float)\n return X, y","def load_labeled_data(files):\n\tx = []\n\ty = []\n\tfor filename in files:\n\t\tdata = []\n\t\twith open(filename) as infile:\n\t\t\tlabel = int(infile.readline())\n\t\t\tfor line in infile:\t\n\t\t\t\tdata.append(dna_string_to_array(line.strip()))\n\t\ty += [label]*len(data)\n\t\tx += data\n\n\treturn (np.array(x), np.array(y))","def load_data_and_labels(data_file, labels_file):\r\n x_text = []\r\n y = []\r\n \r\n with open(data_file, encoding = \"utf-8\") as csvFile:\r\n readCSV = csv.reader(csvFile, delimiter = \",\")\r\n for row in readCSV:\r\n row = \"\".join(row)\r\n x_text.append(row) \r\n \r\n with open(labels_file, encoding = \"utf-8\") as csvFile2:\r\n readCSV = csv.reader(csvFile2, delimiter = \",\")\r\n for row in readCSV:\r\n d = defaultdict(list)\r\n for k,va in [(v,i) for i,v in enumerate(row)]:\r\n d[k].append(va)\r\n \r\n for k in range(len(d.get(\"1.0\"))):\r\n index = d.get(\"1.0\")[k]\r\n row[index] = 1\r\n for k in range(len(d.get(\"0.0\"))):\r\n index = d.get(\"0.0\")[k]\r\n row[index] = 0\r\n \r\n# print(len(row))\r\n y.append(row)\r\n \r\n\r\n\r\n\r\n \r\n print(\"x = {}\".format(len(x_text)))\r\n print(\"y = {}\".format(len(y)))\r\n \r\n return x_text, y","def load_csv_data(data_path):\n print(\"LOADING CSV FILE FROM {}\".format(data_path))\n y = np.genfromtxt(data_path, delimiter=\",\", skip_header=1, dtype=str, usecols=[1])\n x = np.genfromtxt(data_path, delimiter=\",\", skip_header=1)\n ids = x[:, 0].astype(np.int)\n input_data = x[:, 2:]\n\n # convert class labels from strings to binary (-1,1)\n yb = np.ones(len(y))\n yb[np.where(y == 'b')] = -1\n\n return yb, input_data, ids","def load_csv(fname = data_indoor):\n \n reader = csv.reader(open(fname, 'r'))\n \n # Blank list\n data = []\n \n # Don't read the zeroth element of each row (image name), convert to float.\n for row in reader:\n data.append(map(float, row[1:]))\n \n # Convert list to array \n d = np.array(data)\n \n # Seperate labels from features\n Y = d[:,0]\n X = d[:,1:]\n \n return X,Y","def loader(filename,sep=',',rowskip=[], colskip=[], axis=1,names=1,fromstring=0):\n\n #manages excpetions to the csv file incase of missing data\n if (type(filename)==str) and (fromstring==1):\n iterable=filename.strip('\\n').split('\\n')\n content=np.array([i for i in csv.reader(iterable,delimiter=sep)])\n elif type(filename)==np.ndarray:\n content=filename\n else:\n content=np.array([i for i in\\\n csv.reader(open(filename,'r'),delimiter=sep)])\n #content=np.genfromtxt(filename,delimiter=sep,dtype=str)\n\n if rowskip:\n #rowskip.sort(reverse=True)\n content=np.delete(content,rowskip,0)\n #for i in rowskip: content.pop(i)\n\n if colskip:\n #colskip.sort(reverse=True)\n content=np.delete(content,colskip,1)\n #for i in colskip: content.pop(i)\n\n if axis==0: # if the file oriented column-wise\n #content=list(map(list,zip(*content)))\n content=content.T\n\n\n\n if names is 0:\n variables=np.arange(content.shape[1]).tolist()\n offset=0\n else:\n variables=content[0].tolist()\n offset=1\n\n try:\n content=np.array([conv_col(col) for col in\n content[offset:].T],dtype='object')\n arity=np.array([np.unique(i).size for i in content])\n return dataset(variables,content.T,arity)\n except ValueError: \n print( 'Data could not be loaded, failed converting to float.')\n return content","def load_dataset(csv_path, label_col='y', add_intercept=False):\n\n def add_intercept_fn(x):\n global add_intercept\n return add_intercept(x)\n\n # Validate label_col argument\n allowed_label_cols = ('y', 't')\n if label_col not in allowed_label_cols:\n raise ValueError('Invalid label_col: {} (expected {})'\n .format(label_col, allowed_label_cols))\n\n # Load headers\n with open(csv_path, 'r') as csv_fh:\n headers = csv_fh.readline().strip().split(',')\n\n # Load features and labels\n x_cols = [i for i in range(len(headers)) if headers[i].startswith('x')]\n l_cols = [i for i in range(len(headers)) if headers[i] == label_col]\n inputs = np.loadtxt(csv_path, delimiter=',', skiprows=1, usecols=x_cols)\n labels = np.loadtxt(csv_path, delimiter=',', skiprows=1, usecols=l_cols)\n\n if inputs.ndim == 1:\n inputs = np.expand_dims(inputs, -1)\n\n if add_intercept:\n inputs = add_intercept_fn(inputs)\n\n return inputs, labels","def load_data(fl=\"data.csv\"):\n data = np.loadtxt(fl, delimiter=\",\")\n y1 = data[:, 0]\n y2 = data[:, 1]\n return y1, y2","def read(train_path, test_path, label_name):\n train_dataset = pd.read_csv(train_path)\n test_dataset = pd.read_csv(test_path)\n\n train_labels = train_dataset.pop(label_name)\n\n imputer = DataFrameImputer().fit(train_dataset)\n train_dataset = imputer.transform(train_dataset)\n test_dataset = imputer.transform(test_dataset)\n\n train_dataset = pd.get_dummies(train_dataset)\n test_dataset = pd.get_dummies(test_dataset)\n\n train_dataset = train_dataset.drop(train_dataset.columns.difference(test_dataset.columns), axis=1)\n test_dataset = test_dataset.drop(test_dataset.columns.difference(train_dataset.columns), axis=1)\n\n scaler = StandardScaler().fit(train_dataset)\n train_dataset = scaler.transform(train_dataset)\n test_dataset = scaler.transform(test_dataset)\n\n return train_dataset, train_labels, test_dataset","def load_data(self):\n\n data_pd = pd.read_csv(self.filename)\n return np.array(data_pd)","def load_test_data(label_fname, data_fname):\n labels = load_csv(label_fname)\n data = load_csv(data_fname, 'excel-tab')\n\n # Join all data together on the ids given in the files\n joined_data = {}\n for label in labels:\n id = label[0]\n joined_data[id] = {'class': label[1]}\n for rec in data:\n id = rec[0]\n if id in joined_data:\n joined_data[id]['data'] = rec[1]\n\n # Clean and convert the data to reals\n max_features = 0\n for id in joined_data:\n words = clean_text(joined_data[id]['data'])\n reals = convert_to_reals(words)\n joined_data[id]['data'] = reals\n if len(reals) > max_features:\n max_features = len(reals)\n\n # Pad the data\n for id in joined_data:\n reals = joined_data[id]['data']\n joined_data[id]['data'] = reals + (max_features - len(reals)) * [0.0]\n\n # Prepare the data for training\n training_data = np.array([joined_data[id]['data'] for id in joined_data])\n training_labels = [joined_data[id]['class'] == 'OFF' for id in joined_data]\n return training_labels, training_data, max_features","def load_csv_data(data_path, sub_sample=False):\n y = np.genfromtxt(data_path, delimiter=\",\", skip_header=1, dtype=str, usecols=1)\n x = np.genfromtxt(data_path, delimiter=\",\", skip_header=1)\n labels = open(data_path,'r').readline()\n\n\n ids = x[:, 0].astype(np.int)\n input_data = x[:, 2:]\n labels = labels.strip().split(\",\")\n del labels[0]\n del labels[0]\n\n # convert class labels from strings to binary (-1,1)\n yb = np.ones(len(y))\n yb[np.where(y == 'b')] = -1\n\n # sub-sample\n if sub_sample:\n yb = yb[::50]\n input_data = input_data[::50]\n ids = ids[::50]\n\n return yb, input_data, ids, labels","def load_data_and_labels():\n # Load data from files\n positive_examples = []\n for file in os.listdir('with_datarace'):\n filename = os.fsdecode(file)\n ast_file = open('with_datarace\\\\' + filename, 'r')\n token_vector = ast_file.read()\n positive_examples.append(token_vector)\n file_names.append(filename)\n\n negative_examples = []\n for file in os.listdir('without_datarace\\\\'):\n filename = os.fsdecode(file)\n ast_file = open('without_datarace\\\\' + filename, 'r')\n token_vector = ast_file.read()\n negative_examples.append(token_vector) # List of lists\n file_names.append(filename)\n\n positive_examples = [s.strip() for s in positive_examples]\n negative_examples = [s.strip() for s in negative_examples]\n\n # Split by words\n x_text = positive_examples + negative_examples # why we didn't cobine it from the beginning?\n x_text = [clean_str(sent) for sent in x_text]\n x_text = [s.split(\" \") for s in x_text]\n\n # Generate labels\n positive_labels = [[0, 1] for _ in positive_examples]\n negative_labels = [[1, 0] for _ in negative_examples]\n y = np.concatenate([positive_labels, negative_labels], 0)\n\n return [x_text, y]","def load_data(self):\n with open(self.file_name) as f:\n lines = f.readlines()\n\n labels = list()\n all_dat = list()\n for i, l in enumerate(lines):\n\n labels.append(int(l[0]))\n\n l = gensim.utils.any2unicode(l)\n all_dat.append(LabeledSentence(l.split(\"\\t\")[-1], [i]))\n\n return all_dat, np.asarray(labels)","def loadCSVSeeds(self, csvFilePath):\n labels = []\n with open(csvFilePath) as csvfile:\n reader = csv.reader(csvfile, delimiter=',', quotechar='|')\n for row in reader:\n labels.append([row[0], row[1], [float(row[2]), float(row[3]), float(row[4]) ]])\n print(csvFilePath + \": labels loaded\")\n return labels","def read_labels(labels_path):\n with open(labels_path, 'r') as file:\n data = file.read()\n data = data.split()\n data = np.array(data)\n data = np.reshape(data, (-1, 2))\n return data","def read_label(filepath, read_scalars=False):\n label_array = np.loadtxt(filepath, dtype=np.int, skiprows=2, usecols=[0])\n if read_scalars:\n scalar_array = np.loadtxt(filepath, skiprows=2, usecols=[-1])\n return label_array, scalar_array\n return label_array","def load_train_data():\n\n # Load X_train\n with open('X_train.csv') as csvfile:\n reader = csv.DictReader(csvfile)\n feature_string_matrix = []\n for row in reader:\n feature_list = []\n for i in range(TRAIN_N):\n x_value = row['x' + str(i)]\n # Hit missing values\n if x_value == '':\n feature_list.append(np.nan)\n else:\n feature_list.append(float(row['x' + str(i)]))\n feature_string_matrix.append(feature_list)\n X_train = np.array(feature_string_matrix)\n # Load Y_train\n with open('y_train.csv') as csvfile:\n reader = csv.DictReader(csvfile)\n y_string = []\n for row in reader:\n y_value = [float(row['y'])]\n y_string.append(y_value)\n y_train = np.array(y_string)\n return X_train, y_train","def loadTestData():\n path = raw_input(\"Enter the path of Test Data: \")\n data = np.genfromtxt(path, delimiter=',', dtype=int)\n\n labels = data[:, -1]\n\n unwantedLabels = [4, 5, 6, 7, 8, 9]\n listToDelete = []\n for i, line in enumerate(range(len(data))):\n if labels[i] in unwantedLabels:\n listToDelete.append(i)\n\n actualData = np.delete(data, listToDelete, axis=0)\n\n # print(actualData.shape)\n # Separating the labels and data into different arrays\n actualLabels = actualData[:, -1]\n actualData = actualData[:, :-1]\n\n actualData = pre.scale(actualData)\n\n # Change the label vector to label matrix\n # If Label is 2 then it becomes [0, 1, 0]\n labelMatrix = np.zeros((actualLabels.shape[0], 4))\n for j in range(len(actualLabels)):\n if actualLabels[j] == 0:\n labelMatrix[j][0] = 1\n if actualLabels[j] == 1:\n labelMatrix[j][1] = 1\n if actualLabels[j] == 2:\n labelMatrix[j][2] = 1\n if actualLabels[j] == 3:\n labelMatrix[j][3] = 1\n\n return actualData, actualLabels","def load_csv(data_file_path, class_index=-1):\n\n handle = open(data_file_path, 'r')\n contents = handle.read()\n handle.close()\n rows = contents.split('\\n')\n out = np.array([[float(i) for i in r.split(',')] for r in rows if r])\n\n if class_index == -1:\n classes = map(int, out[:, class_index])\n features = out[:, :class_index]\n return features, classes\n\n elif class_index == 0:\n classes = map(int, out[:, class_index])\n features = out[:, 1:]\n return features, classes\n\n else:\n return out","def load_data(csv_filename):\n data = np.genfromtxt(csv_filename, delimiter=\";\", skip_header=1, usecols=range(11))\n return data","def load_samples_and_labels(data_path, header=True, col=1, train=True):\n if header:\n start_index = 1\n else:\n start_index = 0\n\n with open(data_path, 'r', encoding='utf-8') as f:\n lines = f.read().splitlines()[start_index:]\n samples = [line.split(',')[col] for line in lines]\n samples = [sample.split() for sample in samples]\n\n if train:\n labels = [int(line.split(',')[3]) for line in lines]\n else:\n labels = []\n\n return samples, labels","def _read_labels_csv_file(self, csv_file_path, image_file_paths):\n\n self.__logger.debug('[Get Labels]')\n self.__logger.debug('Read CSV Labels ( %s ) ...' % csv_file_path)\n\n image_file_names = self.get_file_names_from_file_paths(file_paths=image_file_paths)\n\n labels = []\n\n with open(csv_file_path, newline='') as csvfile:\n read_image_files = 0 # numbers of image files read\n rows = csv.reader(csvfile)\n\n for row in rows:\n file_name = row[0]\n # make file name from '00030183_004.png' to '00030183_004'\n file_name = file_name.split('.')\n file_name = file_name[0]\n\n # if csv file name matches image file name, the label of the former will be stored in labels (list)\n if file_name == image_file_names[read_image_files]: # image_file_name has to remove str '.jpg'\n label = row[1].split('|')\n label_id = []\n for i in range(len(label)):\n label_id.append(Xray_class_id[label[i]])\n labels.append(label_id) # store the label\n\n read_image_files += 1\n if read_image_files == len(image_file_names): # if numbers of image files read equals numbers of\n # batch images, then break\n break\n\n self.__logger.debug('Done !')\n\n return labels","def get_labels_df():\n labels_df = pd.read_csv('data/train/truth_train.csv', header=None)\n return labels_df","def read_csv(path_to_file):\n position = []\n classification = []\n with open(path_to_file, 'r') as csv_file:\n reader = csv.reader(csv_file)\n next(reader, None) # skip the header\n\n for row in reader:\n position.append(np.array([float(row[0]), float(row[1])]))\n classification.append(float(row[2]))\n\n return np.array(position), np.array(classification, dtype='uint8')","def load_csv_data(data_path, sub_sample=False):\n y = np.genfromtxt(data_path, delimiter=\",\", skip_header=1, dtype=str, usecols=1)\n x = np.genfromtxt(data_path, delimiter=\",\", skip_header=1)\n ids = x[:, 0].astype(np.int)\n input_data = x[:, 2:]\n\n # convert class labels from strings to binary (-1,1)\n yb = np.ones(len(y))\n yb[np.where(y=='b')] = -1\n \n # sub-sample\n if sub_sample:\n yb = yb[::50]\n input_data = input_data[::50]\n ids = ids[::50]\n\n return yb, input_data, ids","def load_csv_data(data_path, sub_sample=False):\n y = np.genfromtxt(data_path, delimiter=\",\", skip_header=1, dtype=str, usecols=1)\n x = np.genfromtxt(data_path, delimiter=\",\", skip_header=1)\n ids = x[:, 0].astype(np.int)\n input_data = x[:, 2:]\n\n # convert class labels from strings to binary (-1,1)\n yb = np.ones(len(y))\n yb[np.where(y=='b')] = -1\n \n # sub-sample\n if sub_sample:\n yb = yb[::50]\n input_data = input_data[::50]\n ids = ids[::50]\n\n return yb, input_data, ids","def load_data(fname, skip_header=0, delimiter=','):\n\n data = np.genfromtxt(fname, dtype=str, comments=None, delimiter=delimiter, skip_header=skip_header)\n\n pathes = data[:, 0]\n labels = data[:, 1]\n\n return pathes, labels","def load_labels(label_file) :\n df = pd.read_csv(label_file, index_col=\"p_index\",\n dtype=str, na_values=['nan', 'NaN', '']).dropna()\n\n return df","def load_test(data_file, labels=True, skip_rows=0):\n dat = iter_loadtxt(data_file, delimiter='\\t', skiprows=skip_rows)\n if labels:\n Dat = DataSet(images=dat[:, 0:-1], labels=dat[:, -1].astype(int), reshape=False)\n else:\n nrow = dat.shape[0]\n Dat = DataSet(images=dat, labels=np.full(nrow, np.nan), reshape=False)\n return Dat","def matlab_csv_to_teacher_data(dirname):\n samples = np.genfromtxt(os.path.join(dirname, 'samples.csv'), dtype=float,\n delimiter=\",\")\n labels = np.genfromtxt(os.path.join(dirname, 'labels.csv'), dtype=int,\n delimiter=\",\")\n data = [None]*max(labels) # matlab is 1-indexed, so no need to add 1\n for i, z in enumerate(labels):\n if data[z-1] is None:\n data[z-1] = np.copy(samples[i, :])\n else:\n data[z-1] = np.vstack((data[z-1], np.copy(samples[i, :])))\n return data","def load_data(self):\n print('Loading {} dataset'.format(self.split))\n data_split_path = os.path.join(self.root_dir, 'splits', '{}.csv'.format(self.split))\n with open(data_split_path,'r') as f:\n reader = csv.reader(f, delimiter=',')\n data_classes = {}\n for i,row in enumerate(reader):\n if i==0:\n continue\n data_classes[row[1]] = 1\n data_classes = data_classes.keys()\n print(data_classes)\n\n n_classes = len(data_classes)\n print('n_classes:{}, n_label:{}, n_unlabel:{}'.format(n_classes,self.n_label,self.n_unlabel))\n dataset_l = np.zeros([n_classes, self.n_label, self.im_height, self.im_width, self.channels], dtype=np.float32)\n if self.n_unlabel>0:\n dataset_u = np.zeros([n_classes, self.n_unlabel, self.im_height, self.im_width, self.channels], dtype=np.float32)\n else:\n dataset_u = []\n\n for i, cls in enumerate(data_classes):\n im_dir = os.path.join(self.root_dir, 'data/{}/'.format(self.split), cls)\n im_files = sorted(glob.glob(os.path.join(im_dir, '*.jpg')))\n np.random.RandomState(self.seed).shuffle(im_files) # fix the seed to keep label,unlabel fixed\n for j, im_file in enumerate(im_files):\n im = np.array(Image.open(im_file).resize((self.im_width, self.im_height)), \n np.float32, copy=False)\n if j<self.n_label:\n dataset_l[i, j] = im\n else:\n dataset_u[i,j-self.n_label] = im\n print('labeled data:', np.shape(dataset_l))\n print('unlabeled data:', np.shape(dataset_u))\n \n self.dataset_l = dataset_l\n self.dataset_u = dataset_u\n self.n_classes = n_classes","def get_data():\n\n pathxtrain = sys.argv[1]\n pathxtest = sys.argv[2]\n pathlabeltrain = sys.argv[3]\n pathlabeltest = sys.argv[4]\n\n xtrain = p.read_csv(pathxtrain, header=None)\n xtest = p.read_csv(pathxtest, header=None)\n label_train = p.read_csv(pathlabeltrain, header=None)\n label_test = p.read_csv(pathlabeltest, header=None)\n\n xtrain_mx = xtrain.values\n xtest_mx = xtest.values\n\n label_train = label_train.values.reshape(label_train.shape[0])\n label_test = label_test.values.reshape(label_test.shape[0])\n\n return xtrain_mx, xtest_mx, label_train, label_test","def load_data(filepath):\n data = import_csv(filepath, has_headers=False)\n x_data = data[:, 0:3]\n y_data = None\n if data.shape[1]>3:\n y_data = data[:, 3:]\n n_data = data.shape[0]\n\n return n_data, np.float64(x_data), np.float64(y_data)","def load_csv(fichero):\r\n data = np.loadtxt(fichero, delimiter=',')\r\n X = data[:,:-1]\r\n y = data[:,-1]\r\n return X, y","def load_file(file_name) -> np.ndarray:\r\n reader = csv.reader(open(file_name, \"r\"), delimiter=',')\r\n x_rdr = list(reader)\r\n return np.array(x_rdr).astype('float')","def load_train_y(train_y_path):\n \n text = open(train_y_path, 'r')\n row = csv.reader(text)\n y = []\n n_row = 0\n for r in row:\n if n_row != 0:\n y.append(float(r[0]))\n n_row += 1\n text.close()\n y = np.array(y)\n \n return y","def read(filename):\n records = Parser.__load_csv(filename)\n return np.array(records)","def load_data_from_csv(f_name):\n data = []\n f = open(f_name, \"r\")\n reader = csv.reader(f,delimiter=\",\")\n for row in reader:\n data.append([float(i) for i in row])\n f.close()\n data = np.array(data)\n x = data[0,:]\n data = data[1:,:].swapaxes(0,1)\n return x, data","def load_data():\n x = np.genfromtxt(X_FILE, usecols=(0, 1))\n y = np.genfromtxt(Y_FILE, usecols=(0))\n\n return x, y","def loadData (x_file=\"../ass1_data/logisticX.csv\", y_file=\"../logisticY.csv\"):\n\n X = np.genfromtxt(x_file, delimiter=',')\n Y = np.genfromtxt(y_file, dtype=int)\n\n return (X, Y)","def import_data(path, num_examples):\n data = np.empty((num_examples, 5), dtype=\"float128\")\n y = np.empty((num_examples, 1), dtype=\"float128\")\n\n with open(path, 'r') as f:\n i = 0\n for line in f:\n example = []\n terms = line.strip().split(',')\n for j in range(len(terms)):\n if j == 4:\n y[i] = 2 * float(terms[j]) - 1\n else:\n example.append(float(terms[j]))\n data[i, 1:] = example\n data[i, 0] = 1\n i += 1\n\n data = normalize(np.asmatrix(data), axis=0)\n return [data, np.asmatrix(y)]","def _read_train_datas(self):\r\n with open(self.train_label_path, 'r') as fb:\r\n lines = fb.readlines()\r\n return self._parse_raw_labels(lines)","def load_data(self, features=None, labels=None):\n if features is None or labels is None:\n self._features = None\n self._labels = None\n return\n if len(features) != len(labels):\n raise DataMismatchError('Features and labels lists are different lengths')\n try:\n self._features = np.array(features, dtype=float)\n self._labels = np.array(labels, dtype=float)\n except ValueError:\n self._features = None\n self._labels = None\n raise ValueError('Label and feature lists must be homogeneous (same data type)'\n 'and numeric (i.e integers and floats) list of lists')","def read_data():\n csv_data = pd.read_csv('./dataset.csv')\n x = csv_data[['X1', 'X2']]\n x = x.values # numpy array for x: (180, 2)\n y = csv_data['Label']\n y = y.values # numpy array for y: (180, )\n\n\t# shuffle the data\n total = x.shape[0]\n mask = list(range(total))\n np.random.shuffle(mask)\n x = x[mask]\n y = y[mask]\n\t\n\t# 80 percent for train and 20 percent for test\n train_split = int(0.8 * total)\n x_train, y_train = x[:train_split], y[:train_split]\n x_test, y_test = x[train_split:], y[train_split:]\n return x_train, y_train, x_test, y_test","def load_datasets(folder_path, glob_filter=\"*.csv\", labels_last_column=True, labels_filename=False, custom_func=None, **kwargs):\n # Convert the folder_path to a Path if needed\n if isinstance(folder_path, str):\n folder_path = Path(folder_path)\n elif isinstance(folder_path, Path):\n pass\n else:\n raise TypeError(f\"{type(folder_path)} is not a valid type for the folder_path\")\n # Check that the directory exists\n if not folder_path.is_dir():\n raise ValueError(f\"{folder_path} is not a directory\")\n # If both labels arguments are true, raise error\n if labels_last_column and labels_filename:\n raise ValueError(f\"labels_last_column and labels_filename cannot both be True\")\n # or if both are False\n elif not (labels_last_column or labels_filename):\n raise ValueError(f\"labels_last_column and labels_filename cannot both be False\")\n # Get the files according to the filter provided\n files = list(folder_path.glob(glob_filter))\n # Sort them files (avoids needing leading 0s)\n # https://stackoverflow.com/a/36202926/9963224\n files.sort(key=lambda var: [int(x) if x.isdigit() else x for x in re.findall(r'[^0-9]|[0-9]+', str(var))])\n # If no files are found, raise error\n if not files:\n raise ValueError(f\"{folder_path} with {glob_filter} filter had no results\")\n # Run the custom function if provided\n if custom_func is not None:\n filenames, datasets, label_sets = custom_func(files)\n else:\n # Initialize containers\n filenames = []\n datasets = []\n label_sets = []\n # Loop through the files\n for file in files:\n filenames.append(file.stem)\n # If the labels are in separate files, load them\n if labels_filename:\n if \"label\" in file:\n label_sets.append(np.loadtxt(file, **kwargs))\n else:\n datasets.append(np.loadtxt(file, **kwargs))\n # Otherwise the labels are in the last column\n elif labels_last_column:\n # Load the data\n data = np.loadtxt(file, **kwargs)\n # Add the datasets and labels\n label_sets.append(data[:, -1].astype(int))\n datasets.append(data[:, :-1])\n # Return the filenames, datasets, and labels\n return filenames, datasets, label_sets","def read_random_data_from_csv(\n file_name, training_set_size, unlabeled_set_size, holdout_set_size, validation_set_size):\n data = samp_file_to_arr(\n file_name, training_set_size + unlabeled_set_size + holdout_set_size + validation_set_size)\n y_raw = np.array([x[0] for x in data])\n x_all = np.array([x[1:] for x in data])\n # Now transform so that the lower label is -1, always. \n uq = np.unique(y_raw) # Assumed to be only two unique labels!\n y_all = np.zeros(len(y_raw))\n y_all[np.where(y_raw == uq[0])[0]] = -1\n y_all[np.where(y_raw == uq[1])[0]] = 1\n xtrhoval, x_unl, ytrhoval, y_unl = sklearn.model_selection.train_test_split(\n x_all, y_all, test_size=unlabeled_set_size)\n x_trho, x_validate, y_trte, y_validate = sklearn.model_selection.train_test_split(\n xtrhoval, ytrhoval, test_size=validation_set_size)\n x_train, x_out, y_train, y_out = sklearn.model_selection.train_test_split(\n x_trho, y_trte, test_size=holdout_set_size)\n return (x_train, y_train, x_unl, y_unl, x_out, y_out, x_validate, y_validate)","def load_data(outputpath):\n ext = '.npy'\n x_train = np.load(os.path.join(outputpath, 'X_train' + ext))\n y_train_binary = np.load(os.path.join(outputpath, 'y_train' + ext))\n x_val = np.load(os.path.join(outputpath, 'X_val' + ext))\n y_val_binary = np.load(os.path.join(outputpath, 'y_val' + ext))\n x_test = np.load(os.path.join(outputpath, 'X_test' + ext))\n y_test_binary = np.load(os.path.join(outputpath, 'y_test' + ext))\n with open(os.path.join(outputpath, 'labels.json'), 'r') as fn:\n labels = json.load(fn)\n return x_train, y_train_binary, x_val, y_val_binary, \\\n x_test, y_test_binary, labels","def load_data():\n data = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\", header=None)\n\n # utiliza somente as duas primeiras classes\n data = data[:100]\n # transforma as classes em 0 e 1\n data[4] = np.where(data.iloc[:, -1] == 'Iris-setosa', 0, 1)\n data = np.asmatrix(data, dtype='float64')\n return data","def read_labels(labels_path):\n data = []\n with open(labels_path, 'r') as f:\n for line in f:\n line = line.split()\n sample = (line[0], int(line[1]))\n data.append(sample)\n \n dtype = [('video', '<U50'), ('label', int)]\n X = np.array(data, dtype=dtype)\n X = np.sort(X, order='video')\n return X","def load_csv_data(filepath, textcol=\"text\"):\n df = pd.read_csv(filepath)\n samples = [ str(text) for text in df[textcol] ]\n labels = [ str(intent) for intent in df[\"label\"] ]\n\n return samples, labels","def load_data(class_fnames):\n X = []\n y = []\n for label, fnames in enumerate(class_fnames):\n for fname in fnames:\n X.append(cv2.imread(fname))\n y.append(label)\n X = np.stack(X)\n y = np.stack(y)\n return X, y","def __load_csv_into_mem(label, exp, obj, norms):\n filename = obj.get('file')\n # def csv_loader\n label_pos = obj.get('label', 'first')\n if label_pos == 'first':\n label_first = True\n else:\n label_first = False\n\n labels = []\n\n def get_element_from_csv():\n def callback(dim, lbl):\n CSVDataset.__load_csv_into_mem.dimension = dim - 1\n for l in lbl:\n labels.append(l)\n\n with open(filename, 'r') as f:\n for i in CSVDataset.__get_element_from_file__(csv.reader(f), label_first, norms, callback):\n yield i\n\n input_data = np.fromiter(get_element_from_csv(), dtype=np.float32)\n dimension = CSVDataset.__load_csv_into_mem.dimension\n input_data = input_data.reshape((-1, dimension))\n # print input_data[0]\n labels = np.asarray(labels, 'int32')\n kwargs = {}\n if 'batches' not in kwargs:\n b = getattr(exp.args, '%s_batches' % label, None)\n kwargs['batches'] = b\n if 'size' not in kwargs:\n kwargs['size'] = exp.args.batch_size\n kwargs['label'] = label\n return SequenceDataset(*(input_data, labels), **kwargs)","def load_labels(self, labels):\n self.labels = pd.DataFrame(labels, index=[\"label\"]).T","def load_csv(filename):\r\n dataset = list()\r\n with open(filename, 'r') as file:\r\n csv_reader = reader(file, delimiter='\\t')\r\n for row in csv_reader:\r\n if not row:\r\n continue\r\n dataset.append([float(i) for i in row])\r\n return dataset","def load_labels(path):\n with open(path, 'r', encoding='utf-8') as f:\n lines = f.readlines()\n labels = []\n for row_number, content in enumerate(lines):\n pair = re.split(r'[:\\s]+', content.strip(), maxsplit=1)\n #if len(pair) == 2 and pair[0].strip().isdigit():\n labels.append(np.array([int(pair[0].strip()),pair[1].strip()]))\n #else:\n # labels.append(pair[0].strip())\n return np.array(labels)","def load_training_data(fname):\n all_data = load_csv(fname, 'excel-tab')\n\n labels = [rec[2] == 'OFF' for rec in all_data]\n data = [convert_to_reals(clean_text(rec[1])) for rec in all_data]\n max_features = max([len(rec) for rec in data])\n\n # Pad the data\n for rec in data:\n rec.extend([0.0] * (max_features - len(rec)))\n\n return labels, data, max_features","def load():\n filepath = dirname(abspath(__file__))\n##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv #####\n data = recfromtxt(open(filepath + '/spector.csv',\"rb\"), delimiter=\" \",\n names=True, dtype=float, usecols=(1,2,3,4))\n names = list(data.dtype.names)\n endog = array(data[names[3]], dtype=float)\n endog_name = names[3]\n exog = column_stack(data[i] for i in names[:3]).astype(float)\n exog_name = names[:3]\n dataset = Dataset(data=data, names=names, endog=endog, exog=exog,\n endog_name = endog_name, exog_name=exog_name)\n return dataset","def readData(fname):\n pd = pandas.read_csv(fname)\n return [numpy.array(pd[colname]) for colname in pd.columns[1:]]","def _get_data(self, path: str, label_column: str = None, header: bool = True):\n x, y = read_data(path, label_column, header)\n x = np.array(x)\n y = np.array(y)\n x = normalize(x)\n x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=self.train_size)\n return x_train, x_test, y_train, y_test","def load_data(self):\n with open('data/fordTrain.csv') as f:\n data = csv.reader(f, delimiter=',')\n train = [x for i, x in enumerate(data) if i > 0] \n # Extract features and target variable separately\n trainx = [x[3:] for x in train]\n trainy = [x[2] for x in train]\n\n with open('data/fordTest.csv') as f:\n data = csv.reader(f, delimiter=',')\n testx = [x[3:] for i, x in enumerate(data) if i > 0] \n\n with open('data/Solution.csv') as f:\n data = csv.reader(f, delimiter=',')\n testy = [x[2] for i, x in enumerate(data) if i > 0] \n\n # Extract features and target variable, convert to numpy array\n trainx = np.asarray(trainx, dtype=np.float32)\n trainy = np.asarray(trainy, dtype=np.int8)\n testx = np.asarray(testx, dtype=np.float32)\n testy = np.asarray(testy, dtype=np.int8)\n\n # Return training and test sets\n trainSet = Dataset(trainx, trainy)\n testSet = Dataset(testx, testy)\n return trainSet, testSet","def load_CSV_data(path):\n return np.genfromtxt(os.path.join('data/traffic_data', path))","def load_data_set(filename):\n\n input_file = open(filename)\n\n num_features = len(input_file.readline().split('\\t')) - 1\n input_file.seek(0)\n data_mat = []\n label_mat = []\n\n for line in input_file.readlines():\n line_arr = []\n curr_line = line.strip().split('\\t')\n for i in range(num_features):\n line_arr.append(float(curr_line[i]))\n data_mat.append(line_arr)\n label_mat.append(float(curr_line[-1]))\n\n return data_mat, label_mat","def load_gt(path):\n train_results = []\n with open(path) as csv_file:\n reader = csv.reader(csv_file, delimiter=\",\")\n next(reader)\n for row in reader:\n train_results.append(int(row[1]))\n return np.array(train_results)","def parse_labels(file: str) -> ndarray:\n rows = []\n with open(file, 'r', encoding='utf-8') as f:\n for row in f:\n rows.append(row.strip())\n return array(rows)","def labels_data(protein_data_path, columns):\n labels = pd.read_csv(protein_data_path, sep=\"\\t\").fillna(0)\n return labels[columns].astype(int).values","def extract_data(filename):\n with open(filename, 'rb') as f:\n reader=f.readlines()\n train_data_label = [[int(x) for x in line.split() if x.isdigit()] for line in reader] \n # sorted by label\n train_data_label = sorted(train_data_label, key=lambda x: x[-1])\n train_data_label = np.array(train_data_label) \n return train_data_label","def load_data_and_labels(data_file=train_file):\n \"\"\"\n There are 7 categories - \n 1. DEMO\n 2. DISE\n 3. TRMT\n 4. GOAL\n 5. PREG\n 6. FMLY\n 7. SOCL\n \"\"\"\n d = {}\n d['DEMO'] = [1, 0, 0, 0, 0, 0, 0]\n d['DISE'] = [0, 1, 0, 0, 0, 0, 0]\n d['TRMT'] = [0, 0, 1, 0, 0, 0, 0]\n d['GOAL'] = [0, 0, 0, 1, 0, 0, 0]\n d['PREG'] = [0, 0, 0, 0, 1, 0, 0]\n d['FAML'] = [0, 0, 0, 0, 0, 1, 0]\n d['SOCL'] = [0, 0, 0, 0, 0, 0, 1]\n\n max_len = -1\n\n #Load data from files\n samples = []\n with open(data_file, 'rb') as csvfile:\n spamreader = csv.reader(csvfile, delimiter='\\t', quotechar='|')\n for i, row in enumerate(spamreader):\n if (row[0] == \"Category\"):\n continue\n print (i, row[1])\n #samples.append([row[0], row[2]])\n #getting class and title = row[0] and row[1] respectively\n samples.append([row[1], row[2], row[0]])\n #split by words\n\n return samples","def load_data(filename):\n assert os.path.exists(filename)==True\n dat = scipy.io.loadmat(filename)\n inputs = dat['inputs']\n #print len(inputs)\n targets = dat['targets']\n #print len(targets)\n assert len(inputs)==len(targets)\n\n global alldata\n global indim \n global outdim\n\n indim = len(inputs[0])\n outdim = 1\n #print indim\n alldata = ClassificationDataSet(indim, outdim, nb_classes = 8)\n alldata.setField('input',inputs)\n alldata.setField('target',targets)\n\n assert len(alldata['input'])==len(alldata['target'])\n print type(alldata)","def load_data_and_labels(data_source, remove_stopword=False, run_with_keras=False):\n # Read the CSV file and get its contents\n with open(data_source, 'r', encoding='utf-8', errors='ignore') as f:\n csv_reader = csv.reader(f)\n # get the header\n header = next(csv_reader)\n label_idx = header.index('label')\n content_idx = header.index('content')\n print(f'The label index is : {label_idx} and the content index is : {content_idx}')\n\n y_text = list()\n x_text = list()\n\n for line in csv_reader:\n # get the sentence from the line\n sentence = line[content_idx].strip()\n x_text.append(sentence)\n y_text.append(int(line[label_idx]))\n\n # preprocess input text\n if run_with_keras:\n x_text = [clean_str(sent, remove_stopword) for sent in x_text]\n else:\n x_text = [clean_str(sent, remove_stopword).split(' ') for sent in x_text]\n\n # get the lengths for every line\n lengths = np.array(list(map(len, [sent for sent in x_text])))\n\n return [x_text, y_text, lengths]","def load_test_data():\n\n # Load X_test\n with open('X_test.csv') as csvfile:\n reader = csv.DictReader(csvfile)\n feature_string_matrix = []\n for row in reader:\n feature_list = []\n for i in range(TEST_N):\n x_value = row['x' + str(i)]\n # Hit missing values\n if x_value == '':\n feature_list.append(np.nan)\n else:\n feature_list.append(float(row['x' + str(i)]))\n feature_string_matrix.append(feature_list)\n X_test = np.array(feature_string_matrix)\n return X_test","def read_data(feature_file, label_file):","def load_data(label_mode='fine'):\n if label_mode not in ['fine', 'coarse']:\n raise ValueError('`label_mode` must be one of `\"fine\"`, `\"coarse\"`.')\n\n dirname = 'cifar-100-python'\n origin = 'http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'\n path = get_file(dirname, origin=origin, untar=True)\n\n fpath = os.path.join(path, 'train')\n x_train, y_train = load_batch(fpath, label_key=label_mode + '_labels')\n\n fpath = os.path.join(path, 'test')\n x_test, y_test = load_batch(fpath, label_key=label_mode + '_labels')\n\n y_train = np.reshape(y_train, (len(y_train), 1))\n y_test = np.reshape(y_test, (len(y_test), 1))\n\n # Rescale raw data\n x_train = x_train.astype('float32')\n x_test = x_test.astype('float32')\n\n x_train /= 255.\n x_test /= 255.\n\n if K.image_data_format() == 'channels_last':\n x_train = x_train.transpose(0, 2, 3, 1)\n x_test = x_test.transpose(0, 2, 3, 1)\n\n return (x_train, y_train), (x_test, y_test)","def load():\n filepath = dirname(abspath(__file__))\n data = recfromtxt(filepath + '/scotvote.csv', delimiter=\",\",\n names=True, dtype=float, usecols=(1,2,3,4,5,6,7,8))\n names = list(data.dtype.names)\n endog = array(data[names[0]], dtype=float)\n endog_name = names[0]\n exog = column_stack(data[i] for i in names[1:]).astype(float)\n exog_name = names[1:]\n dataset = Dataset(data=data, names=names, endog=endog, exog=exog,\n endog_name = endog_name, exog_name=exog_name)\n return dataset","def load_expected(filename):\n\n all_labels = sorted([int(k) for k in open(filename).readline().split()[1:]])\n data = numpy.loadtxt(filename, dtype='float64', skiprows=1)\n return all_labels, data[:,0].astype('int64'), data[:,1:]","def load_data(file_name):\n data = np.load(file_name, allow_pickle=True)\n\n X, y = [], []\n\n for mfccs, label in data:\n X.append(mfccs)\n y.append(label)\n\n X = np.array(X)\n y = np.array(y)\n\n X = X.reshape(*X.shape, 1)\n y = y.reshape(-1, 1)\n\n return X, y","def load_data_and_labels():\n # Load data from files\n positive_examples = list(\n open(\"./data/rt-polarity.pos\", \"r\", encoding='latin-1').readlines())\n positive_examples = [s.strip() for s in positive_examples]\n negative_examples = list(\n open(\"./data/rt-polarity.neg\", \"r\", encoding='latin-1').readlines())\n negative_examples = [s.strip() for s in negative_examples]\n # Split by words\n x_text = positive_examples + negative_examples\n x_text = [clean_str(sent) for sent in x_text]\n x_text = [s.split(\" \") for s in x_text]\n # Generate labels\n positive_labels = [[0, 1] for _ in positive_examples]\n negative_labels = [[1, 0] for _ in negative_examples]\n y = np.concatenate([positive_labels, negative_labels], 0)\n return [x_text, y]","def read_img_names_labels_csv(csv_path):\n df = pd.read_csv(csv_path)\n\n try:\n image_names_list = list(df['ImageName'])\n y = list(df['Label'])\n except KeyError:\n raise CsvColumnNameException(\" The column names of image-name_label csv must be 'ImageName' and 'Label' \")\n\n return image_names_list, y","def csv_parser(lines): \n\n data_points = []\n for line in lines:\n items = line.strip().split(\",\")\n try: #will fail on header line in file\n data_points.append(map(float, items[1:])) #first item is the label\n except ValueError: #must be the header\n continue\n return data_points","def load_training_gt(list_files):\n training_results = []\n for res_file in list_files:\n with open(os.path.join(\"data\", res_file)) as csv_file:\n reader = csv.reader(csv_file, delimiter=\",\")\n next(reader)\n for row in reader:\n training_results.append(int(row[1]))\n return np.array(training_results)","def load_data(filename):\n with open(\"./shopping.csv\", \"r\") as f:\n reader = csv.reader(f)\n next(reader)\n evidence_raw = []\n labels_raw = []\n for row in reader:\n evidence_raw.append(row[:-1])\n labels_raw.append(row[-1])\n evidence = []\n labels = []\n for row1, row2 in zip(evidence_raw, labels_raw):\n evidence.append(oneHotEncode_Evi(row1))\n labels.append(oneHotEncode_labels(row2))\n return (evidence, labels)","def _load_images_labels(self):\n path_dataset_file = self.path_model_id.joinpath(f'{self.set_name}_set.csv')\n \n with path_dataset_file.open(mode='r', newline='') as f:\n csv_reader = reader(f, delimiter=',')\n rows = list(csv_reader)\n\n if self.shuffle:\n rng = default_rng(self.seed)\n rng.shuffle(rows)\n \n self.n_examples = len(rows)\n\n ds_files = tf.data.Dataset.from_tensor_slices(\n [path.join(str(self.path_data), f'label_{row[1]}', row[0])\n for row in rows])\n \n ds_images = ds_files.map(self._load_preprocess_image)\n\n class_labels_enc = self.class_le.fit_transform(\n [row[1] for row in rows])\n\n ds_labels = tf.data.Dataset.from_tensor_slices(\n class_labels_enc)\n\n return ds_images, ds_labels","def load_labels(labels_dir, trial_name):\n labels_path = labels_dir + trial_name + \".txt\"\n raw_labels_data = np.genfromtxt(labels_path, dtype=np.int,\n converters=LABELS_CONVERTERS,\n usecols=LABELS_USECOLS)\n #print(\"rawlabelsdata: \", raw_labels_data)\n #print(get_first_frame(labels_path))\n frames = np.arange(get_first_frame(labels_path), get_last_frame(labels_path)+1, dtype=np.int)\n #print(\"frames: \", frames)\n #print(frames.shape)\n #labels = np.zeros(frames.shape, dtype=np.int)\n labels1 = []\n #print(labels)\n for start, end, label in raw_labels_data:\n #mask = (frames >= start) & (frames <= end)\n #print(start)\n #print(end)\n i = start\n while(i<end):\n if(i%6 == 0):\n labels1.append(label)\n i = i+1\n\n #labels[mask] = label\n #print(\"labels[mask]: \",labels[mask])\n labels1 = np.array(labels1)\n #print(labels1)\n labels_data = labels1.reshape(-1,1)\n #print(labels1.shape)\n #print(\"labels: \", labels_data)\n \n return labels_data","def load_dataset_train():\n df_train = load_csv_file(\"31_train.csv\")\n df_train_target = load_csv_file(\"31_target_train.csv\")\n\n return df_train.values, df_train_target.values","def load_data(path):\n train = pd.read_csv(os.path.join(path,'train.csv'))\n test = pd.read_csv(os.path.join(path,'test.csv'))\n \n return train, test","def load_csv_dataset(path,\n label_col,\n data_dir=None,\n smi_col='smiles',\n explicit_H=False,\n use_chirality=False,\n use_molecular_attributes=False,\n all_pair_features=False,\n graph_distance=True):\n df = pd.read_csv(path)\n smiles = np.array(df[smi_col])\n labels = np.array(df[label_col])\n if data_dir is None:\n data_dir = tempfile.mkdtemp()\n dataset = MolDataset(root=data_dir)\n batch_graphs = []\n for i, (smi, l) in enumerate(zip(smiles, labels)):\n if i > 0 and i % 1000 == 0:\n print(\"Featurized %d molecules\" % i)\n dataset.add_graph_batch(batch_graphs)\n batch_graphs = []\n g = mol_to_graph(Chem.MolFromSmiles(smi),\n explicit_H=explicit_H,\n use_chirality=use_chirality,\n use_molecular_attributes=use_molecular_attributes,\n all_pair_features=all_pair_features,\n graph_distance=graph_distance)\n g.smi = smi\n w = (l==l) * 1\n y = copy.deepcopy(l)\n y[np.where(y != y)] = 0.\n g.y = t.from_numpy(y).long()\n g.w = t.from_numpy(w).float()\n batch_graphs.append(g)\n dataset.add_graph_batch(batch_graphs)\n return dataset","def load_data(filename):\n data = []\n with open('data/' + filename) as raw_data:\n for line in raw_data.readlines():\n data.append(float(line.strip('\\n')))\n return data\n # data = np.mat(np.genfromtxt('data/' + filename)).T\n # return data","def load_data(data_path):\n df = pd.read_csv(data_path, names=['feature1', 'feature2',\n 'label'], header=None)\n\n # Get x1 and x2\n feature_1 = df['feature1'].to_list()\n feature_2 = df['feature2'].to_list()\n\n # Normalize features using function below\n norm_feature_1 = normalize_feature(feature_1)\n norm_feature_2 = normalize_feature(feature_2)\n\n # Create concatenated features and get labels\n features = np.array([norm_feature_1, norm_feature_2])\n labels = df['label'].to_list()\n\n # Add features to data frame\n df['norm_x1'] = norm_feature_1\n df['norm_x2'] = norm_feature_2\n\n return features, labels, df","def load_data(train_file, test_file):\n\n data = np.asarray(pd.read_csv(train_file, header=0))\n data_ts = np.asarray(pd.read_csv(test_file, header=0))\n\n x_tra = data[:, :-1]\n y_tra = data[:, -1]\n\n return x_tra, y_tra, data_ts","def get_training_data():\n features = []\n labels = []\n\n with open('data.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n\n rows = [line for line in csv_reader]\n random.shuffle(rows)\n\n for vector in rows:\n feature_vector = [float(vector[i]) for i in range(4)]\n features.append(feature_vector)\n labels.append(encode_label(vector[4]))\n\n normalise_features(features)\n\n return features, labels","def data_parser(data):\n\n with open(data, 'r') as inp:\n\n # take every sample\n # the last line in the text file is empty, so reading until -1\n samples = inp.read().split('\\n')[:-1]\n\n vec = []\n labels = []\n for sample in samples:\n # file is tab delimited\n split_samples = sample.split('\\t')\n # last column contains the label\n labels.append(int(split_samples[-1]))\n\n features = []\n for feature in split_samples[:-1]:\n features.append(float(feature))\n vec.append(features)\n\n # make the features and labels as a numpy array\n vec = np.array(vec)\n labels = np.array(labels)\n return vec, labels","def from_label_file(cls, label_file_path, out_path=FEATURES_DATA_PATH, source_path=RAW_DATA_PATH):\n df = pd.read_csv(label_file_path)\n filenames = df['filename']\n labels = df['label']\n return cls(filenames, labels, out_path=out_path, source_path=source_path)","def load_dataset(path):\n training_data = []\n with open(path) as csv_file:\n reader = csv.reader(csv_file, delimiter=\",\")\n next(reader)\n for row in reader:\n training_data.append(row[1])\n return training_data","def load_label(path_file):\n if '.csv' not in path_file:\n raise FileNotFoundError('Only CSV format is supported currently')\n\n t0 = time()\n df = pd.DataFrame()\n\n with open(path_file, 'r') as f:\n # TODO: Implement the logic once the format is finalised\n pass\n\n logging.info('Loading label data with {} rows from {} takes {} secs'.format(df.shape[0],\n path_file, time() - t0))\n return df","def _read_data_file(self, path_model_id):\n\n path_dataset_file = path_model_id.joinpath('training_set.csv')\n\n with path_dataset_file.open(mode='r', newline='') as f:\n csv_reader = reader(f, delimiter=',')\n rows = list(csv_reader)\n\n self.example_count = len(rows)\n\n img_files = [path.join(f'label_{row[1]}', row[0]) for row in rows]\n enc_labels = self.class_le.fit_transform([row[1] for row in rows])\n \n self.files_labels = [[img_files[i], enc_labels[i]]\n for i in range(self.example_count)]","def read_csv_file(csv_fname, ignore_first_row = True):\n \n X, y = [], []\n with open(csv_fname, 'r') as csv_file:\n csv_reader = csv.reader(csv_file)\n if ignore_first_row:\n next(csv_reader)\n for row in csv_reader:\n X.append(row[:-1])\n y.append(row[-1])\n return np.array(X), np.array(y)","def load_predictions(filepath):\n\twith open(filepath, 'r') as fh:\n\t\tcells = [line.split(',') for line in fh.read().splitlines()]\n\tdata = empty((TESTSIZE, NCLASSES), dtype = float64)\n\tfor k, row in enumerate(cells[1:]):\n\t\tdata[k, :] = row[1:]\n\treturn data","def import_dataset(fpath):\r\n data = read_csv(fpath)\r\n print(data.head())\r\n print(data.shape)\r\n return data"],"string":"[\n \"def load(self, path=\\\"../data_set/final-train-dataset.csv\\\", shuffle=False,\\n onlyLabelToUse=None, useOnlyBestIndicators=False, binary=False):\\n data = []\\n labels = []\\n\\n with open(path) as csvfile:\\n reader = csv.reader(csvfile, delimiter=',')\\n skip = True\\n\\n for row in reader:\\n if skip:\\n skip = False\\n continue\\n\\n entries = []\\n\\n for i in range(SAMPLES_OF_DATA_TO_LOOK_AT):\\n # Get one time point's data, including indicators:\\n if useOnlyBestIndicators:\\n # entries.append(\\n # [float(row[i + j * SAMPLES_OF_DATA_TO_LOOK_AT]) for\\n #\\n entries.append(\\n [float(row[i + j * SAMPLES_OF_DATA_TO_LOOK_AT]) for\\n j in [0, 1, 2, 5, 7]])\\n else:\\n entries.append(\\n [float(row[i + j * SAMPLES_OF_DATA_TO_LOOK_AT]) for\\n j in range(INPUT_CHANNELS)])\\n\\n data.append(np.array(entries))\\n\\n if onlyLabelToUse is not None:\\n if binary:\\n label = [int(float(row[SAMPLES_OF_DATA_TO_LOOK_AT * TOTAL_INPUTS_IN_DATASET + onlyLabelToUse]) > 0.5)]\\n else:\\n label = [float(row[SAMPLES_OF_DATA_TO_LOOK_AT * TOTAL_INPUTS_IN_DATASET + onlyLabelToUse])]\\n else:\\n if binary:\\n label = [int(float(row[SAMPLES_OF_DATA_TO_LOOK_AT * TOTAL_INPUTS_IN_DATASET + j]) > 0.5) for j in range(OUTPUT_CHANNELS)]\\n else:\\n label = [float(row[SAMPLES_OF_DATA_TO_LOOK_AT * TOTAL_INPUTS_IN_DATASET + j]) for j in range(OUTPUT_CHANNELS)]\\n\\n labels.append(label)\\n\\n if shuffle:\\n indices = [i for i in range(len(data))]\\n np.random.shuffle(indices)\\n labels = np.array([labels[i] for i in indices])\\n data = np.array([data[i] for i in indices])\\n else:\\n data = np.array(data)\\n labels = np.array(labels)\\n\\n return data, labels\",\n \"def read_data(filename):\\n data = np.genfromtxt(filename, delimiter=',', dtype = str)\\n X = data[1:,2:].astype(np.float)\\n y = data[1:,0]\\n y[y==label0]='0' \\n y[y==label1]='1' \\n y[y==label2]='2'\\n y.astype(np.float) \\n return X, y\",\n \"def read_data(filename):\\n data = np.genfromtxt(filename, delimiter=',', dtype=str)\\n X = data[1:,2:].astype(np.float)\\n y = data[1:,0]\\n y[y==label0]='0'\\n y[y==label1]='1'\\n y[y==label2]='2'\\n y=y.astype(np.float)\\n return X, y\",\n \"def load_labeled_data(files):\\n\\tx = []\\n\\ty = []\\n\\tfor filename in files:\\n\\t\\tdata = []\\n\\t\\twith open(filename) as infile:\\n\\t\\t\\tlabel = int(infile.readline())\\n\\t\\t\\tfor line in infile:\\t\\n\\t\\t\\t\\tdata.append(dna_string_to_array(line.strip()))\\n\\t\\ty += [label]*len(data)\\n\\t\\tx += data\\n\\n\\treturn (np.array(x), np.array(y))\",\n \"def load_data_and_labels(data_file, labels_file):\\r\\n x_text = []\\r\\n y = []\\r\\n \\r\\n with open(data_file, encoding = \\\"utf-8\\\") as csvFile:\\r\\n readCSV = csv.reader(csvFile, delimiter = \\\",\\\")\\r\\n for row in readCSV:\\r\\n row = \\\"\\\".join(row)\\r\\n x_text.append(row) \\r\\n \\r\\n with open(labels_file, encoding = \\\"utf-8\\\") as csvFile2:\\r\\n readCSV = csv.reader(csvFile2, delimiter = \\\",\\\")\\r\\n for row in readCSV:\\r\\n d = defaultdict(list)\\r\\n for k,va in [(v,i) for i,v in enumerate(row)]:\\r\\n d[k].append(va)\\r\\n \\r\\n for k in range(len(d.get(\\\"1.0\\\"))):\\r\\n index = d.get(\\\"1.0\\\")[k]\\r\\n row[index] = 1\\r\\n for k in range(len(d.get(\\\"0.0\\\"))):\\r\\n index = d.get(\\\"0.0\\\")[k]\\r\\n row[index] = 0\\r\\n \\r\\n# print(len(row))\\r\\n y.append(row)\\r\\n \\r\\n\\r\\n\\r\\n\\r\\n \\r\\n print(\\\"x = {}\\\".format(len(x_text)))\\r\\n print(\\\"y = {}\\\".format(len(y)))\\r\\n \\r\\n return x_text, y\",\n \"def load_csv_data(data_path):\\n print(\\\"LOADING CSV FILE FROM {}\\\".format(data_path))\\n y = np.genfromtxt(data_path, delimiter=\\\",\\\", skip_header=1, dtype=str, usecols=[1])\\n x = np.genfromtxt(data_path, delimiter=\\\",\\\", skip_header=1)\\n ids = x[:, 0].astype(np.int)\\n input_data = x[:, 2:]\\n\\n # convert class labels from strings to binary (-1,1)\\n yb = np.ones(len(y))\\n yb[np.where(y == 'b')] = -1\\n\\n return yb, input_data, ids\",\n \"def load_csv(fname = data_indoor):\\n \\n reader = csv.reader(open(fname, 'r'))\\n \\n # Blank list\\n data = []\\n \\n # Don't read the zeroth element of each row (image name), convert to float.\\n for row in reader:\\n data.append(map(float, row[1:]))\\n \\n # Convert list to array \\n d = np.array(data)\\n \\n # Seperate labels from features\\n Y = d[:,0]\\n X = d[:,1:]\\n \\n return X,Y\",\n \"def loader(filename,sep=',',rowskip=[], colskip=[], axis=1,names=1,fromstring=0):\\n\\n #manages excpetions to the csv file incase of missing data\\n if (type(filename)==str) and (fromstring==1):\\n iterable=filename.strip('\\\\n').split('\\\\n')\\n content=np.array([i for i in csv.reader(iterable,delimiter=sep)])\\n elif type(filename)==np.ndarray:\\n content=filename\\n else:\\n content=np.array([i for i in\\\\\\n csv.reader(open(filename,'r'),delimiter=sep)])\\n #content=np.genfromtxt(filename,delimiter=sep,dtype=str)\\n\\n if rowskip:\\n #rowskip.sort(reverse=True)\\n content=np.delete(content,rowskip,0)\\n #for i in rowskip: content.pop(i)\\n\\n if colskip:\\n #colskip.sort(reverse=True)\\n content=np.delete(content,colskip,1)\\n #for i in colskip: content.pop(i)\\n\\n if axis==0: # if the file oriented column-wise\\n #content=list(map(list,zip(*content)))\\n content=content.T\\n\\n\\n\\n if names is 0:\\n variables=np.arange(content.shape[1]).tolist()\\n offset=0\\n else:\\n variables=content[0].tolist()\\n offset=1\\n\\n try:\\n content=np.array([conv_col(col) for col in\\n content[offset:].T],dtype='object')\\n arity=np.array([np.unique(i).size for i in content])\\n return dataset(variables,content.T,arity)\\n except ValueError: \\n print( 'Data could not be loaded, failed converting to float.')\\n return content\",\n \"def load_dataset(csv_path, label_col='y', add_intercept=False):\\n\\n def add_intercept_fn(x):\\n global add_intercept\\n return add_intercept(x)\\n\\n # Validate label_col argument\\n allowed_label_cols = ('y', 't')\\n if label_col not in allowed_label_cols:\\n raise ValueError('Invalid label_col: {} (expected {})'\\n .format(label_col, allowed_label_cols))\\n\\n # Load headers\\n with open(csv_path, 'r') as csv_fh:\\n headers = csv_fh.readline().strip().split(',')\\n\\n # Load features and labels\\n x_cols = [i for i in range(len(headers)) if headers[i].startswith('x')]\\n l_cols = [i for i in range(len(headers)) if headers[i] == label_col]\\n inputs = np.loadtxt(csv_path, delimiter=',', skiprows=1, usecols=x_cols)\\n labels = np.loadtxt(csv_path, delimiter=',', skiprows=1, usecols=l_cols)\\n\\n if inputs.ndim == 1:\\n inputs = np.expand_dims(inputs, -1)\\n\\n if add_intercept:\\n inputs = add_intercept_fn(inputs)\\n\\n return inputs, labels\",\n \"def load_data(fl=\\\"data.csv\\\"):\\n data = np.loadtxt(fl, delimiter=\\\",\\\")\\n y1 = data[:, 0]\\n y2 = data[:, 1]\\n return y1, y2\",\n \"def read(train_path, test_path, label_name):\\n train_dataset = pd.read_csv(train_path)\\n test_dataset = pd.read_csv(test_path)\\n\\n train_labels = train_dataset.pop(label_name)\\n\\n imputer = DataFrameImputer().fit(train_dataset)\\n train_dataset = imputer.transform(train_dataset)\\n test_dataset = imputer.transform(test_dataset)\\n\\n train_dataset = pd.get_dummies(train_dataset)\\n test_dataset = pd.get_dummies(test_dataset)\\n\\n train_dataset = train_dataset.drop(train_dataset.columns.difference(test_dataset.columns), axis=1)\\n test_dataset = test_dataset.drop(test_dataset.columns.difference(train_dataset.columns), axis=1)\\n\\n scaler = StandardScaler().fit(train_dataset)\\n train_dataset = scaler.transform(train_dataset)\\n test_dataset = scaler.transform(test_dataset)\\n\\n return train_dataset, train_labels, test_dataset\",\n \"def load_data(self):\\n\\n data_pd = pd.read_csv(self.filename)\\n return np.array(data_pd)\",\n \"def load_test_data(label_fname, data_fname):\\n labels = load_csv(label_fname)\\n data = load_csv(data_fname, 'excel-tab')\\n\\n # Join all data together on the ids given in the files\\n joined_data = {}\\n for label in labels:\\n id = label[0]\\n joined_data[id] = {'class': label[1]}\\n for rec in data:\\n id = rec[0]\\n if id in joined_data:\\n joined_data[id]['data'] = rec[1]\\n\\n # Clean and convert the data to reals\\n max_features = 0\\n for id in joined_data:\\n words = clean_text(joined_data[id]['data'])\\n reals = convert_to_reals(words)\\n joined_data[id]['data'] = reals\\n if len(reals) > max_features:\\n max_features = len(reals)\\n\\n # Pad the data\\n for id in joined_data:\\n reals = joined_data[id]['data']\\n joined_data[id]['data'] = reals + (max_features - len(reals)) * [0.0]\\n\\n # Prepare the data for training\\n training_data = np.array([joined_data[id]['data'] for id in joined_data])\\n training_labels = [joined_data[id]['class'] == 'OFF' for id in joined_data]\\n return training_labels, training_data, max_features\",\n \"def load_csv_data(data_path, sub_sample=False):\\n y = np.genfromtxt(data_path, delimiter=\\\",\\\", skip_header=1, dtype=str, usecols=1)\\n x = np.genfromtxt(data_path, delimiter=\\\",\\\", skip_header=1)\\n labels = open(data_path,'r').readline()\\n\\n\\n ids = x[:, 0].astype(np.int)\\n input_data = x[:, 2:]\\n labels = labels.strip().split(\\\",\\\")\\n del labels[0]\\n del labels[0]\\n\\n # convert class labels from strings to binary (-1,1)\\n yb = np.ones(len(y))\\n yb[np.where(y == 'b')] = -1\\n\\n # sub-sample\\n if sub_sample:\\n yb = yb[::50]\\n input_data = input_data[::50]\\n ids = ids[::50]\\n\\n return yb, input_data, ids, labels\",\n \"def load_data_and_labels():\\n # Load data from files\\n positive_examples = []\\n for file in os.listdir('with_datarace'):\\n filename = os.fsdecode(file)\\n ast_file = open('with_datarace\\\\\\\\' + filename, 'r')\\n token_vector = ast_file.read()\\n positive_examples.append(token_vector)\\n file_names.append(filename)\\n\\n negative_examples = []\\n for file in os.listdir('without_datarace\\\\\\\\'):\\n filename = os.fsdecode(file)\\n ast_file = open('without_datarace\\\\\\\\' + filename, 'r')\\n token_vector = ast_file.read()\\n negative_examples.append(token_vector) # List of lists\\n file_names.append(filename)\\n\\n positive_examples = [s.strip() for s in positive_examples]\\n negative_examples = [s.strip() for s in negative_examples]\\n\\n # Split by words\\n x_text = positive_examples + negative_examples # why we didn't cobine it from the beginning?\\n x_text = [clean_str(sent) for sent in x_text]\\n x_text = [s.split(\\\" \\\") for s in x_text]\\n\\n # Generate labels\\n positive_labels = [[0, 1] for _ in positive_examples]\\n negative_labels = [[1, 0] for _ in negative_examples]\\n y = np.concatenate([positive_labels, negative_labels], 0)\\n\\n return [x_text, y]\",\n \"def load_data(self):\\n with open(self.file_name) as f:\\n lines = f.readlines()\\n\\n labels = list()\\n all_dat = list()\\n for i, l in enumerate(lines):\\n\\n labels.append(int(l[0]))\\n\\n l = gensim.utils.any2unicode(l)\\n all_dat.append(LabeledSentence(l.split(\\\"\\\\t\\\")[-1], [i]))\\n\\n return all_dat, np.asarray(labels)\",\n \"def loadCSVSeeds(self, csvFilePath):\\n labels = []\\n with open(csvFilePath) as csvfile:\\n reader = csv.reader(csvfile, delimiter=',', quotechar='|')\\n for row in reader:\\n labels.append([row[0], row[1], [float(row[2]), float(row[3]), float(row[4]) ]])\\n print(csvFilePath + \\\": labels loaded\\\")\\n return labels\",\n \"def read_labels(labels_path):\\n with open(labels_path, 'r') as file:\\n data = file.read()\\n data = data.split()\\n data = np.array(data)\\n data = np.reshape(data, (-1, 2))\\n return data\",\n \"def read_label(filepath, read_scalars=False):\\n label_array = np.loadtxt(filepath, dtype=np.int, skiprows=2, usecols=[0])\\n if read_scalars:\\n scalar_array = np.loadtxt(filepath, skiprows=2, usecols=[-1])\\n return label_array, scalar_array\\n return label_array\",\n \"def load_train_data():\\n\\n # Load X_train\\n with open('X_train.csv') as csvfile:\\n reader = csv.DictReader(csvfile)\\n feature_string_matrix = []\\n for row in reader:\\n feature_list = []\\n for i in range(TRAIN_N):\\n x_value = row['x' + str(i)]\\n # Hit missing values\\n if x_value == '':\\n feature_list.append(np.nan)\\n else:\\n feature_list.append(float(row['x' + str(i)]))\\n feature_string_matrix.append(feature_list)\\n X_train = np.array(feature_string_matrix)\\n # Load Y_train\\n with open('y_train.csv') as csvfile:\\n reader = csv.DictReader(csvfile)\\n y_string = []\\n for row in reader:\\n y_value = [float(row['y'])]\\n y_string.append(y_value)\\n y_train = np.array(y_string)\\n return X_train, y_train\",\n \"def loadTestData():\\n path = raw_input(\\\"Enter the path of Test Data: \\\")\\n data = np.genfromtxt(path, delimiter=',', dtype=int)\\n\\n labels = data[:, -1]\\n\\n unwantedLabels = [4, 5, 6, 7, 8, 9]\\n listToDelete = []\\n for i, line in enumerate(range(len(data))):\\n if labels[i] in unwantedLabels:\\n listToDelete.append(i)\\n\\n actualData = np.delete(data, listToDelete, axis=0)\\n\\n # print(actualData.shape)\\n # Separating the labels and data into different arrays\\n actualLabels = actualData[:, -1]\\n actualData = actualData[:, :-1]\\n\\n actualData = pre.scale(actualData)\\n\\n # Change the label vector to label matrix\\n # If Label is 2 then it becomes [0, 1, 0]\\n labelMatrix = np.zeros((actualLabels.shape[0], 4))\\n for j in range(len(actualLabels)):\\n if actualLabels[j] == 0:\\n labelMatrix[j][0] = 1\\n if actualLabels[j] == 1:\\n labelMatrix[j][1] = 1\\n if actualLabels[j] == 2:\\n labelMatrix[j][2] = 1\\n if actualLabels[j] == 3:\\n labelMatrix[j][3] = 1\\n\\n return actualData, actualLabels\",\n \"def load_csv(data_file_path, class_index=-1):\\n\\n handle = open(data_file_path, 'r')\\n contents = handle.read()\\n handle.close()\\n rows = contents.split('\\\\n')\\n out = np.array([[float(i) for i in r.split(',')] for r in rows if r])\\n\\n if class_index == -1:\\n classes = map(int, out[:, class_index])\\n features = out[:, :class_index]\\n return features, classes\\n\\n elif class_index == 0:\\n classes = map(int, out[:, class_index])\\n features = out[:, 1:]\\n return features, classes\\n\\n else:\\n return out\",\n \"def load_data(csv_filename):\\n data = np.genfromtxt(csv_filename, delimiter=\\\";\\\", skip_header=1, usecols=range(11))\\n return data\",\n \"def load_samples_and_labels(data_path, header=True, col=1, train=True):\\n if header:\\n start_index = 1\\n else:\\n start_index = 0\\n\\n with open(data_path, 'r', encoding='utf-8') as f:\\n lines = f.read().splitlines()[start_index:]\\n samples = [line.split(',')[col] for line in lines]\\n samples = [sample.split() for sample in samples]\\n\\n if train:\\n labels = [int(line.split(',')[3]) for line in lines]\\n else:\\n labels = []\\n\\n return samples, labels\",\n \"def _read_labels_csv_file(self, csv_file_path, image_file_paths):\\n\\n self.__logger.debug('[Get Labels]')\\n self.__logger.debug('Read CSV Labels ( %s ) ...' % csv_file_path)\\n\\n image_file_names = self.get_file_names_from_file_paths(file_paths=image_file_paths)\\n\\n labels = []\\n\\n with open(csv_file_path, newline='') as csvfile:\\n read_image_files = 0 # numbers of image files read\\n rows = csv.reader(csvfile)\\n\\n for row in rows:\\n file_name = row[0]\\n # make file name from '00030183_004.png' to '00030183_004'\\n file_name = file_name.split('.')\\n file_name = file_name[0]\\n\\n # if csv file name matches image file name, the label of the former will be stored in labels (list)\\n if file_name == image_file_names[read_image_files]: # image_file_name has to remove str '.jpg'\\n label = row[1].split('|')\\n label_id = []\\n for i in range(len(label)):\\n label_id.append(Xray_class_id[label[i]])\\n labels.append(label_id) # store the label\\n\\n read_image_files += 1\\n if read_image_files == len(image_file_names): # if numbers of image files read equals numbers of\\n # batch images, then break\\n break\\n\\n self.__logger.debug('Done !')\\n\\n return labels\",\n \"def get_labels_df():\\n labels_df = pd.read_csv('data/train/truth_train.csv', header=None)\\n return labels_df\",\n \"def read_csv(path_to_file):\\n position = []\\n classification = []\\n with open(path_to_file, 'r') as csv_file:\\n reader = csv.reader(csv_file)\\n next(reader, None) # skip the header\\n\\n for row in reader:\\n position.append(np.array([float(row[0]), float(row[1])]))\\n classification.append(float(row[2]))\\n\\n return np.array(position), np.array(classification, dtype='uint8')\",\n \"def load_csv_data(data_path, sub_sample=False):\\n y = np.genfromtxt(data_path, delimiter=\\\",\\\", skip_header=1, dtype=str, usecols=1)\\n x = np.genfromtxt(data_path, delimiter=\\\",\\\", skip_header=1)\\n ids = x[:, 0].astype(np.int)\\n input_data = x[:, 2:]\\n\\n # convert class labels from strings to binary (-1,1)\\n yb = np.ones(len(y))\\n yb[np.where(y=='b')] = -1\\n \\n # sub-sample\\n if sub_sample:\\n yb = yb[::50]\\n input_data = input_data[::50]\\n ids = ids[::50]\\n\\n return yb, input_data, ids\",\n \"def load_csv_data(data_path, sub_sample=False):\\n y = np.genfromtxt(data_path, delimiter=\\\",\\\", skip_header=1, dtype=str, usecols=1)\\n x = np.genfromtxt(data_path, delimiter=\\\",\\\", skip_header=1)\\n ids = x[:, 0].astype(np.int)\\n input_data = x[:, 2:]\\n\\n # convert class labels from strings to binary (-1,1)\\n yb = np.ones(len(y))\\n yb[np.where(y=='b')] = -1\\n \\n # sub-sample\\n if sub_sample:\\n yb = yb[::50]\\n input_data = input_data[::50]\\n ids = ids[::50]\\n\\n return yb, input_data, ids\",\n \"def load_data(fname, skip_header=0, delimiter=','):\\n\\n data = np.genfromtxt(fname, dtype=str, comments=None, delimiter=delimiter, skip_header=skip_header)\\n\\n pathes = data[:, 0]\\n labels = data[:, 1]\\n\\n return pathes, labels\",\n \"def load_labels(label_file) :\\n df = pd.read_csv(label_file, index_col=\\\"p_index\\\",\\n dtype=str, na_values=['nan', 'NaN', '']).dropna()\\n\\n return df\",\n \"def load_test(data_file, labels=True, skip_rows=0):\\n dat = iter_loadtxt(data_file, delimiter='\\\\t', skiprows=skip_rows)\\n if labels:\\n Dat = DataSet(images=dat[:, 0:-1], labels=dat[:, -1].astype(int), reshape=False)\\n else:\\n nrow = dat.shape[0]\\n Dat = DataSet(images=dat, labels=np.full(nrow, np.nan), reshape=False)\\n return Dat\",\n \"def matlab_csv_to_teacher_data(dirname):\\n samples = np.genfromtxt(os.path.join(dirname, 'samples.csv'), dtype=float,\\n delimiter=\\\",\\\")\\n labels = np.genfromtxt(os.path.join(dirname, 'labels.csv'), dtype=int,\\n delimiter=\\\",\\\")\\n data = [None]*max(labels) # matlab is 1-indexed, so no need to add 1\\n for i, z in enumerate(labels):\\n if data[z-1] is None:\\n data[z-1] = np.copy(samples[i, :])\\n else:\\n data[z-1] = np.vstack((data[z-1], np.copy(samples[i, :])))\\n return data\",\n \"def load_data(self):\\n print('Loading {} dataset'.format(self.split))\\n data_split_path = os.path.join(self.root_dir, 'splits', '{}.csv'.format(self.split))\\n with open(data_split_path,'r') as f:\\n reader = csv.reader(f, delimiter=',')\\n data_classes = {}\\n for i,row in enumerate(reader):\\n if i==0:\\n continue\\n data_classes[row[1]] = 1\\n data_classes = data_classes.keys()\\n print(data_classes)\\n\\n n_classes = len(data_classes)\\n print('n_classes:{}, n_label:{}, n_unlabel:{}'.format(n_classes,self.n_label,self.n_unlabel))\\n dataset_l = np.zeros([n_classes, self.n_label, self.im_height, self.im_width, self.channels], dtype=np.float32)\\n if self.n_unlabel>0:\\n dataset_u = np.zeros([n_classes, self.n_unlabel, self.im_height, self.im_width, self.channels], dtype=np.float32)\\n else:\\n dataset_u = []\\n\\n for i, cls in enumerate(data_classes):\\n im_dir = os.path.join(self.root_dir, 'data/{}/'.format(self.split), cls)\\n im_files = sorted(glob.glob(os.path.join(im_dir, '*.jpg')))\\n np.random.RandomState(self.seed).shuffle(im_files) # fix the seed to keep label,unlabel fixed\\n for j, im_file in enumerate(im_files):\\n im = np.array(Image.open(im_file).resize((self.im_width, self.im_height)), \\n np.float32, copy=False)\\n if j<self.n_label:\\n dataset_l[i, j] = im\\n else:\\n dataset_u[i,j-self.n_label] = im\\n print('labeled data:', np.shape(dataset_l))\\n print('unlabeled data:', np.shape(dataset_u))\\n \\n self.dataset_l = dataset_l\\n self.dataset_u = dataset_u\\n self.n_classes = n_classes\",\n \"def get_data():\\n\\n pathxtrain = sys.argv[1]\\n pathxtest = sys.argv[2]\\n pathlabeltrain = sys.argv[3]\\n pathlabeltest = sys.argv[4]\\n\\n xtrain = p.read_csv(pathxtrain, header=None)\\n xtest = p.read_csv(pathxtest, header=None)\\n label_train = p.read_csv(pathlabeltrain, header=None)\\n label_test = p.read_csv(pathlabeltest, header=None)\\n\\n xtrain_mx = xtrain.values\\n xtest_mx = xtest.values\\n\\n label_train = label_train.values.reshape(label_train.shape[0])\\n label_test = label_test.values.reshape(label_test.shape[0])\\n\\n return xtrain_mx, xtest_mx, label_train, label_test\",\n \"def load_data(filepath):\\n data = import_csv(filepath, has_headers=False)\\n x_data = data[:, 0:3]\\n y_data = None\\n if data.shape[1]>3:\\n y_data = data[:, 3:]\\n n_data = data.shape[0]\\n\\n return n_data, np.float64(x_data), np.float64(y_data)\",\n \"def load_csv(fichero):\\r\\n data = np.loadtxt(fichero, delimiter=',')\\r\\n X = data[:,:-1]\\r\\n y = data[:,-1]\\r\\n return X, y\",\n \"def load_file(file_name) -> np.ndarray:\\r\\n reader = csv.reader(open(file_name, \\\"r\\\"), delimiter=',')\\r\\n x_rdr = list(reader)\\r\\n return np.array(x_rdr).astype('float')\",\n \"def load_train_y(train_y_path):\\n \\n text = open(train_y_path, 'r')\\n row = csv.reader(text)\\n y = []\\n n_row = 0\\n for r in row:\\n if n_row != 0:\\n y.append(float(r[0]))\\n n_row += 1\\n text.close()\\n y = np.array(y)\\n \\n return y\",\n \"def read(filename):\\n records = Parser.__load_csv(filename)\\n return np.array(records)\",\n \"def load_data_from_csv(f_name):\\n data = []\\n f = open(f_name, \\\"r\\\")\\n reader = csv.reader(f,delimiter=\\\",\\\")\\n for row in reader:\\n data.append([float(i) for i in row])\\n f.close()\\n data = np.array(data)\\n x = data[0,:]\\n data = data[1:,:].swapaxes(0,1)\\n return x, data\",\n \"def load_data():\\n x = np.genfromtxt(X_FILE, usecols=(0, 1))\\n y = np.genfromtxt(Y_FILE, usecols=(0))\\n\\n return x, y\",\n \"def loadData (x_file=\\\"../ass1_data/logisticX.csv\\\", y_file=\\\"../logisticY.csv\\\"):\\n\\n X = np.genfromtxt(x_file, delimiter=',')\\n Y = np.genfromtxt(y_file, dtype=int)\\n\\n return (X, Y)\",\n \"def import_data(path, num_examples):\\n data = np.empty((num_examples, 5), dtype=\\\"float128\\\")\\n y = np.empty((num_examples, 1), dtype=\\\"float128\\\")\\n\\n with open(path, 'r') as f:\\n i = 0\\n for line in f:\\n example = []\\n terms = line.strip().split(',')\\n for j in range(len(terms)):\\n if j == 4:\\n y[i] = 2 * float(terms[j]) - 1\\n else:\\n example.append(float(terms[j]))\\n data[i, 1:] = example\\n data[i, 0] = 1\\n i += 1\\n\\n data = normalize(np.asmatrix(data), axis=0)\\n return [data, np.asmatrix(y)]\",\n \"def _read_train_datas(self):\\r\\n with open(self.train_label_path, 'r') as fb:\\r\\n lines = fb.readlines()\\r\\n return self._parse_raw_labels(lines)\",\n \"def load_data(self, features=None, labels=None):\\n if features is None or labels is None:\\n self._features = None\\n self._labels = None\\n return\\n if len(features) != len(labels):\\n raise DataMismatchError('Features and labels lists are different lengths')\\n try:\\n self._features = np.array(features, dtype=float)\\n self._labels = np.array(labels, dtype=float)\\n except ValueError:\\n self._features = None\\n self._labels = None\\n raise ValueError('Label and feature lists must be homogeneous (same data type)'\\n 'and numeric (i.e integers and floats) list of lists')\",\n \"def read_data():\\n csv_data = pd.read_csv('./dataset.csv')\\n x = csv_data[['X1', 'X2']]\\n x = x.values # numpy array for x: (180, 2)\\n y = csv_data['Label']\\n y = y.values # numpy array for y: (180, )\\n\\n\\t# shuffle the data\\n total = x.shape[0]\\n mask = list(range(total))\\n np.random.shuffle(mask)\\n x = x[mask]\\n y = y[mask]\\n\\t\\n\\t# 80 percent for train and 20 percent for test\\n train_split = int(0.8 * total)\\n x_train, y_train = x[:train_split], y[:train_split]\\n x_test, y_test = x[train_split:], y[train_split:]\\n return x_train, y_train, x_test, y_test\",\n \"def load_datasets(folder_path, glob_filter=\\\"*.csv\\\", labels_last_column=True, labels_filename=False, custom_func=None, **kwargs):\\n # Convert the folder_path to a Path if needed\\n if isinstance(folder_path, str):\\n folder_path = Path(folder_path)\\n elif isinstance(folder_path, Path):\\n pass\\n else:\\n raise TypeError(f\\\"{type(folder_path)} is not a valid type for the folder_path\\\")\\n # Check that the directory exists\\n if not folder_path.is_dir():\\n raise ValueError(f\\\"{folder_path} is not a directory\\\")\\n # If both labels arguments are true, raise error\\n if labels_last_column and labels_filename:\\n raise ValueError(f\\\"labels_last_column and labels_filename cannot both be True\\\")\\n # or if both are False\\n elif not (labels_last_column or labels_filename):\\n raise ValueError(f\\\"labels_last_column and labels_filename cannot both be False\\\")\\n # Get the files according to the filter provided\\n files = list(folder_path.glob(glob_filter))\\n # Sort them files (avoids needing leading 0s)\\n # https://stackoverflow.com/a/36202926/9963224\\n files.sort(key=lambda var: [int(x) if x.isdigit() else x for x in re.findall(r'[^0-9]|[0-9]+', str(var))])\\n # If no files are found, raise error\\n if not files:\\n raise ValueError(f\\\"{folder_path} with {glob_filter} filter had no results\\\")\\n # Run the custom function if provided\\n if custom_func is not None:\\n filenames, datasets, label_sets = custom_func(files)\\n else:\\n # Initialize containers\\n filenames = []\\n datasets = []\\n label_sets = []\\n # Loop through the files\\n for file in files:\\n filenames.append(file.stem)\\n # If the labels are in separate files, load them\\n if labels_filename:\\n if \\\"label\\\" in file:\\n label_sets.append(np.loadtxt(file, **kwargs))\\n else:\\n datasets.append(np.loadtxt(file, **kwargs))\\n # Otherwise the labels are in the last column\\n elif labels_last_column:\\n # Load the data\\n data = np.loadtxt(file, **kwargs)\\n # Add the datasets and labels\\n label_sets.append(data[:, -1].astype(int))\\n datasets.append(data[:, :-1])\\n # Return the filenames, datasets, and labels\\n return filenames, datasets, label_sets\",\n \"def read_random_data_from_csv(\\n file_name, training_set_size, unlabeled_set_size, holdout_set_size, validation_set_size):\\n data = samp_file_to_arr(\\n file_name, training_set_size + unlabeled_set_size + holdout_set_size + validation_set_size)\\n y_raw = np.array([x[0] for x in data])\\n x_all = np.array([x[1:] for x in data])\\n # Now transform so that the lower label is -1, always. \\n uq = np.unique(y_raw) # Assumed to be only two unique labels!\\n y_all = np.zeros(len(y_raw))\\n y_all[np.where(y_raw == uq[0])[0]] = -1\\n y_all[np.where(y_raw == uq[1])[0]] = 1\\n xtrhoval, x_unl, ytrhoval, y_unl = sklearn.model_selection.train_test_split(\\n x_all, y_all, test_size=unlabeled_set_size)\\n x_trho, x_validate, y_trte, y_validate = sklearn.model_selection.train_test_split(\\n xtrhoval, ytrhoval, test_size=validation_set_size)\\n x_train, x_out, y_train, y_out = sklearn.model_selection.train_test_split(\\n x_trho, y_trte, test_size=holdout_set_size)\\n return (x_train, y_train, x_unl, y_unl, x_out, y_out, x_validate, y_validate)\",\n \"def load_data(outputpath):\\n ext = '.npy'\\n x_train = np.load(os.path.join(outputpath, 'X_train' + ext))\\n y_train_binary = np.load(os.path.join(outputpath, 'y_train' + ext))\\n x_val = np.load(os.path.join(outputpath, 'X_val' + ext))\\n y_val_binary = np.load(os.path.join(outputpath, 'y_val' + ext))\\n x_test = np.load(os.path.join(outputpath, 'X_test' + ext))\\n y_test_binary = np.load(os.path.join(outputpath, 'y_test' + ext))\\n with open(os.path.join(outputpath, 'labels.json'), 'r') as fn:\\n labels = json.load(fn)\\n return x_train, y_train_binary, x_val, y_val_binary, \\\\\\n x_test, y_test_binary, labels\",\n \"def load_data():\\n data = pd.read_csv(\\\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\\\", header=None)\\n\\n # utiliza somente as duas primeiras classes\\n data = data[:100]\\n # transforma as classes em 0 e 1\\n data[4] = np.where(data.iloc[:, -1] == 'Iris-setosa', 0, 1)\\n data = np.asmatrix(data, dtype='float64')\\n return data\",\n \"def read_labels(labels_path):\\n data = []\\n with open(labels_path, 'r') as f:\\n for line in f:\\n line = line.split()\\n sample = (line[0], int(line[1]))\\n data.append(sample)\\n \\n dtype = [('video', '<U50'), ('label', int)]\\n X = np.array(data, dtype=dtype)\\n X = np.sort(X, order='video')\\n return X\",\n \"def load_csv_data(filepath, textcol=\\\"text\\\"):\\n df = pd.read_csv(filepath)\\n samples = [ str(text) for text in df[textcol] ]\\n labels = [ str(intent) for intent in df[\\\"label\\\"] ]\\n\\n return samples, labels\",\n \"def load_data(class_fnames):\\n X = []\\n y = []\\n for label, fnames in enumerate(class_fnames):\\n for fname in fnames:\\n X.append(cv2.imread(fname))\\n y.append(label)\\n X = np.stack(X)\\n y = np.stack(y)\\n return X, y\",\n \"def __load_csv_into_mem(label, exp, obj, norms):\\n filename = obj.get('file')\\n # def csv_loader\\n label_pos = obj.get('label', 'first')\\n if label_pos == 'first':\\n label_first = True\\n else:\\n label_first = False\\n\\n labels = []\\n\\n def get_element_from_csv():\\n def callback(dim, lbl):\\n CSVDataset.__load_csv_into_mem.dimension = dim - 1\\n for l in lbl:\\n labels.append(l)\\n\\n with open(filename, 'r') as f:\\n for i in CSVDataset.__get_element_from_file__(csv.reader(f), label_first, norms, callback):\\n yield i\\n\\n input_data = np.fromiter(get_element_from_csv(), dtype=np.float32)\\n dimension = CSVDataset.__load_csv_into_mem.dimension\\n input_data = input_data.reshape((-1, dimension))\\n # print input_data[0]\\n labels = np.asarray(labels, 'int32')\\n kwargs = {}\\n if 'batches' not in kwargs:\\n b = getattr(exp.args, '%s_batches' % label, None)\\n kwargs['batches'] = b\\n if 'size' not in kwargs:\\n kwargs['size'] = exp.args.batch_size\\n kwargs['label'] = label\\n return SequenceDataset(*(input_data, labels), **kwargs)\",\n \"def load_labels(self, labels):\\n self.labels = pd.DataFrame(labels, index=[\\\"label\\\"]).T\",\n \"def load_csv(filename):\\r\\n dataset = list()\\r\\n with open(filename, 'r') as file:\\r\\n csv_reader = reader(file, delimiter='\\\\t')\\r\\n for row in csv_reader:\\r\\n if not row:\\r\\n continue\\r\\n dataset.append([float(i) for i in row])\\r\\n return dataset\",\n \"def load_labels(path):\\n with open(path, 'r', encoding='utf-8') as f:\\n lines = f.readlines()\\n labels = []\\n for row_number, content in enumerate(lines):\\n pair = re.split(r'[:\\\\s]+', content.strip(), maxsplit=1)\\n #if len(pair) == 2 and pair[0].strip().isdigit():\\n labels.append(np.array([int(pair[0].strip()),pair[1].strip()]))\\n #else:\\n # labels.append(pair[0].strip())\\n return np.array(labels)\",\n \"def load_training_data(fname):\\n all_data = load_csv(fname, 'excel-tab')\\n\\n labels = [rec[2] == 'OFF' for rec in all_data]\\n data = [convert_to_reals(clean_text(rec[1])) for rec in all_data]\\n max_features = max([len(rec) for rec in data])\\n\\n # Pad the data\\n for rec in data:\\n rec.extend([0.0] * (max_features - len(rec)))\\n\\n return labels, data, max_features\",\n \"def load():\\n filepath = dirname(abspath(__file__))\\n##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv #####\\n data = recfromtxt(open(filepath + '/spector.csv',\\\"rb\\\"), delimiter=\\\" \\\",\\n names=True, dtype=float, usecols=(1,2,3,4))\\n names = list(data.dtype.names)\\n endog = array(data[names[3]], dtype=float)\\n endog_name = names[3]\\n exog = column_stack(data[i] for i in names[:3]).astype(float)\\n exog_name = names[:3]\\n dataset = Dataset(data=data, names=names, endog=endog, exog=exog,\\n endog_name = endog_name, exog_name=exog_name)\\n return dataset\",\n \"def readData(fname):\\n pd = pandas.read_csv(fname)\\n return [numpy.array(pd[colname]) for colname in pd.columns[1:]]\",\n \"def _get_data(self, path: str, label_column: str = None, header: bool = True):\\n x, y = read_data(path, label_column, header)\\n x = np.array(x)\\n y = np.array(y)\\n x = normalize(x)\\n x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=self.train_size)\\n return x_train, x_test, y_train, y_test\",\n \"def load_data(self):\\n with open('data/fordTrain.csv') as f:\\n data = csv.reader(f, delimiter=',')\\n train = [x for i, x in enumerate(data) if i > 0] \\n # Extract features and target variable separately\\n trainx = [x[3:] for x in train]\\n trainy = [x[2] for x in train]\\n\\n with open('data/fordTest.csv') as f:\\n data = csv.reader(f, delimiter=',')\\n testx = [x[3:] for i, x in enumerate(data) if i > 0] \\n\\n with open('data/Solution.csv') as f:\\n data = csv.reader(f, delimiter=',')\\n testy = [x[2] for i, x in enumerate(data) if i > 0] \\n\\n # Extract features and target variable, convert to numpy array\\n trainx = np.asarray(trainx, dtype=np.float32)\\n trainy = np.asarray(trainy, dtype=np.int8)\\n testx = np.asarray(testx, dtype=np.float32)\\n testy = np.asarray(testy, dtype=np.int8)\\n\\n # Return training and test sets\\n trainSet = Dataset(trainx, trainy)\\n testSet = Dataset(testx, testy)\\n return trainSet, testSet\",\n \"def load_CSV_data(path):\\n return np.genfromtxt(os.path.join('data/traffic_data', path))\",\n \"def load_data_set(filename):\\n\\n input_file = open(filename)\\n\\n num_features = len(input_file.readline().split('\\\\t')) - 1\\n input_file.seek(0)\\n data_mat = []\\n label_mat = []\\n\\n for line in input_file.readlines():\\n line_arr = []\\n curr_line = line.strip().split('\\\\t')\\n for i in range(num_features):\\n line_arr.append(float(curr_line[i]))\\n data_mat.append(line_arr)\\n label_mat.append(float(curr_line[-1]))\\n\\n return data_mat, label_mat\",\n \"def load_gt(path):\\n train_results = []\\n with open(path) as csv_file:\\n reader = csv.reader(csv_file, delimiter=\\\",\\\")\\n next(reader)\\n for row in reader:\\n train_results.append(int(row[1]))\\n return np.array(train_results)\",\n \"def parse_labels(file: str) -> ndarray:\\n rows = []\\n with open(file, 'r', encoding='utf-8') as f:\\n for row in f:\\n rows.append(row.strip())\\n return array(rows)\",\n \"def labels_data(protein_data_path, columns):\\n labels = pd.read_csv(protein_data_path, sep=\\\"\\\\t\\\").fillna(0)\\n return labels[columns].astype(int).values\",\n \"def extract_data(filename):\\n with open(filename, 'rb') as f:\\n reader=f.readlines()\\n train_data_label = [[int(x) for x in line.split() if x.isdigit()] for line in reader] \\n # sorted by label\\n train_data_label = sorted(train_data_label, key=lambda x: x[-1])\\n train_data_label = np.array(train_data_label) \\n return train_data_label\",\n \"def load_data_and_labels(data_file=train_file):\\n \\\"\\\"\\\"\\n There are 7 categories - \\n 1. DEMO\\n 2. DISE\\n 3. TRMT\\n 4. GOAL\\n 5. PREG\\n 6. FMLY\\n 7. SOCL\\n \\\"\\\"\\\"\\n d = {}\\n d['DEMO'] = [1, 0, 0, 0, 0, 0, 0]\\n d['DISE'] = [0, 1, 0, 0, 0, 0, 0]\\n d['TRMT'] = [0, 0, 1, 0, 0, 0, 0]\\n d['GOAL'] = [0, 0, 0, 1, 0, 0, 0]\\n d['PREG'] = [0, 0, 0, 0, 1, 0, 0]\\n d['FAML'] = [0, 0, 0, 0, 0, 1, 0]\\n d['SOCL'] = [0, 0, 0, 0, 0, 0, 1]\\n\\n max_len = -1\\n\\n #Load data from files\\n samples = []\\n with open(data_file, 'rb') as csvfile:\\n spamreader = csv.reader(csvfile, delimiter='\\\\t', quotechar='|')\\n for i, row in enumerate(spamreader):\\n if (row[0] == \\\"Category\\\"):\\n continue\\n print (i, row[1])\\n #samples.append([row[0], row[2]])\\n #getting class and title = row[0] and row[1] respectively\\n samples.append([row[1], row[2], row[0]])\\n #split by words\\n\\n return samples\",\n \"def load_data(filename):\\n assert os.path.exists(filename)==True\\n dat = scipy.io.loadmat(filename)\\n inputs = dat['inputs']\\n #print len(inputs)\\n targets = dat['targets']\\n #print len(targets)\\n assert len(inputs)==len(targets)\\n\\n global alldata\\n global indim \\n global outdim\\n\\n indim = len(inputs[0])\\n outdim = 1\\n #print indim\\n alldata = ClassificationDataSet(indim, outdim, nb_classes = 8)\\n alldata.setField('input',inputs)\\n alldata.setField('target',targets)\\n\\n assert len(alldata['input'])==len(alldata['target'])\\n print type(alldata)\",\n \"def load_data_and_labels(data_source, remove_stopword=False, run_with_keras=False):\\n # Read the CSV file and get its contents\\n with open(data_source, 'r', encoding='utf-8', errors='ignore') as f:\\n csv_reader = csv.reader(f)\\n # get the header\\n header = next(csv_reader)\\n label_idx = header.index('label')\\n content_idx = header.index('content')\\n print(f'The label index is : {label_idx} and the content index is : {content_idx}')\\n\\n y_text = list()\\n x_text = list()\\n\\n for line in csv_reader:\\n # get the sentence from the line\\n sentence = line[content_idx].strip()\\n x_text.append(sentence)\\n y_text.append(int(line[label_idx]))\\n\\n # preprocess input text\\n if run_with_keras:\\n x_text = [clean_str(sent, remove_stopword) for sent in x_text]\\n else:\\n x_text = [clean_str(sent, remove_stopword).split(' ') for sent in x_text]\\n\\n # get the lengths for every line\\n lengths = np.array(list(map(len, [sent for sent in x_text])))\\n\\n return [x_text, y_text, lengths]\",\n \"def load_test_data():\\n\\n # Load X_test\\n with open('X_test.csv') as csvfile:\\n reader = csv.DictReader(csvfile)\\n feature_string_matrix = []\\n for row in reader:\\n feature_list = []\\n for i in range(TEST_N):\\n x_value = row['x' + str(i)]\\n # Hit missing values\\n if x_value == '':\\n feature_list.append(np.nan)\\n else:\\n feature_list.append(float(row['x' + str(i)]))\\n feature_string_matrix.append(feature_list)\\n X_test = np.array(feature_string_matrix)\\n return X_test\",\n \"def read_data(feature_file, label_file):\",\n \"def load_data(label_mode='fine'):\\n if label_mode not in ['fine', 'coarse']:\\n raise ValueError('`label_mode` must be one of `\\\"fine\\\"`, `\\\"coarse\\\"`.')\\n\\n dirname = 'cifar-100-python'\\n origin = 'http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'\\n path = get_file(dirname, origin=origin, untar=True)\\n\\n fpath = os.path.join(path, 'train')\\n x_train, y_train = load_batch(fpath, label_key=label_mode + '_labels')\\n\\n fpath = os.path.join(path, 'test')\\n x_test, y_test = load_batch(fpath, label_key=label_mode + '_labels')\\n\\n y_train = np.reshape(y_train, (len(y_train), 1))\\n y_test = np.reshape(y_test, (len(y_test), 1))\\n\\n # Rescale raw data\\n x_train = x_train.astype('float32')\\n x_test = x_test.astype('float32')\\n\\n x_train /= 255.\\n x_test /= 255.\\n\\n if K.image_data_format() == 'channels_last':\\n x_train = x_train.transpose(0, 2, 3, 1)\\n x_test = x_test.transpose(0, 2, 3, 1)\\n\\n return (x_train, y_train), (x_test, y_test)\",\n \"def load():\\n filepath = dirname(abspath(__file__))\\n data = recfromtxt(filepath + '/scotvote.csv', delimiter=\\\",\\\",\\n names=True, dtype=float, usecols=(1,2,3,4,5,6,7,8))\\n names = list(data.dtype.names)\\n endog = array(data[names[0]], dtype=float)\\n endog_name = names[0]\\n exog = column_stack(data[i] for i in names[1:]).astype(float)\\n exog_name = names[1:]\\n dataset = Dataset(data=data, names=names, endog=endog, exog=exog,\\n endog_name = endog_name, exog_name=exog_name)\\n return dataset\",\n \"def load_expected(filename):\\n\\n all_labels = sorted([int(k) for k in open(filename).readline().split()[1:]])\\n data = numpy.loadtxt(filename, dtype='float64', skiprows=1)\\n return all_labels, data[:,0].astype('int64'), data[:,1:]\",\n \"def load_data(file_name):\\n data = np.load(file_name, allow_pickle=True)\\n\\n X, y = [], []\\n\\n for mfccs, label in data:\\n X.append(mfccs)\\n y.append(label)\\n\\n X = np.array(X)\\n y = np.array(y)\\n\\n X = X.reshape(*X.shape, 1)\\n y = y.reshape(-1, 1)\\n\\n return X, y\",\n \"def load_data_and_labels():\\n # Load data from files\\n positive_examples = list(\\n open(\\\"./data/rt-polarity.pos\\\", \\\"r\\\", encoding='latin-1').readlines())\\n positive_examples = [s.strip() for s in positive_examples]\\n negative_examples = list(\\n open(\\\"./data/rt-polarity.neg\\\", \\\"r\\\", encoding='latin-1').readlines())\\n negative_examples = [s.strip() for s in negative_examples]\\n # Split by words\\n x_text = positive_examples + negative_examples\\n x_text = [clean_str(sent) for sent in x_text]\\n x_text = [s.split(\\\" \\\") for s in x_text]\\n # Generate labels\\n positive_labels = [[0, 1] for _ in positive_examples]\\n negative_labels = [[1, 0] for _ in negative_examples]\\n y = np.concatenate([positive_labels, negative_labels], 0)\\n return [x_text, y]\",\n \"def read_img_names_labels_csv(csv_path):\\n df = pd.read_csv(csv_path)\\n\\n try:\\n image_names_list = list(df['ImageName'])\\n y = list(df['Label'])\\n except KeyError:\\n raise CsvColumnNameException(\\\" The column names of image-name_label csv must be 'ImageName' and 'Label' \\\")\\n\\n return image_names_list, y\",\n \"def csv_parser(lines): \\n\\n data_points = []\\n for line in lines:\\n items = line.strip().split(\\\",\\\")\\n try: #will fail on header line in file\\n data_points.append(map(float, items[1:])) #first item is the label\\n except ValueError: #must be the header\\n continue\\n return data_points\",\n \"def load_training_gt(list_files):\\n training_results = []\\n for res_file in list_files:\\n with open(os.path.join(\\\"data\\\", res_file)) as csv_file:\\n reader = csv.reader(csv_file, delimiter=\\\",\\\")\\n next(reader)\\n for row in reader:\\n training_results.append(int(row[1]))\\n return np.array(training_results)\",\n \"def load_data(filename):\\n with open(\\\"./shopping.csv\\\", \\\"r\\\") as f:\\n reader = csv.reader(f)\\n next(reader)\\n evidence_raw = []\\n labels_raw = []\\n for row in reader:\\n evidence_raw.append(row[:-1])\\n labels_raw.append(row[-1])\\n evidence = []\\n labels = []\\n for row1, row2 in zip(evidence_raw, labels_raw):\\n evidence.append(oneHotEncode_Evi(row1))\\n labels.append(oneHotEncode_labels(row2))\\n return (evidence, labels)\",\n \"def _load_images_labels(self):\\n path_dataset_file = self.path_model_id.joinpath(f'{self.set_name}_set.csv')\\n \\n with path_dataset_file.open(mode='r', newline='') as f:\\n csv_reader = reader(f, delimiter=',')\\n rows = list(csv_reader)\\n\\n if self.shuffle:\\n rng = default_rng(self.seed)\\n rng.shuffle(rows)\\n \\n self.n_examples = len(rows)\\n\\n ds_files = tf.data.Dataset.from_tensor_slices(\\n [path.join(str(self.path_data), f'label_{row[1]}', row[0])\\n for row in rows])\\n \\n ds_images = ds_files.map(self._load_preprocess_image)\\n\\n class_labels_enc = self.class_le.fit_transform(\\n [row[1] for row in rows])\\n\\n ds_labels = tf.data.Dataset.from_tensor_slices(\\n class_labels_enc)\\n\\n return ds_images, ds_labels\",\n \"def load_labels(labels_dir, trial_name):\\n labels_path = labels_dir + trial_name + \\\".txt\\\"\\n raw_labels_data = np.genfromtxt(labels_path, dtype=np.int,\\n converters=LABELS_CONVERTERS,\\n usecols=LABELS_USECOLS)\\n #print(\\\"rawlabelsdata: \\\", raw_labels_data)\\n #print(get_first_frame(labels_path))\\n frames = np.arange(get_first_frame(labels_path), get_last_frame(labels_path)+1, dtype=np.int)\\n #print(\\\"frames: \\\", frames)\\n #print(frames.shape)\\n #labels = np.zeros(frames.shape, dtype=np.int)\\n labels1 = []\\n #print(labels)\\n for start, end, label in raw_labels_data:\\n #mask = (frames >= start) & (frames <= end)\\n #print(start)\\n #print(end)\\n i = start\\n while(i<end):\\n if(i%6 == 0):\\n labels1.append(label)\\n i = i+1\\n\\n #labels[mask] = label\\n #print(\\\"labels[mask]: \\\",labels[mask])\\n labels1 = np.array(labels1)\\n #print(labels1)\\n labels_data = labels1.reshape(-1,1)\\n #print(labels1.shape)\\n #print(\\\"labels: \\\", labels_data)\\n \\n return labels_data\",\n \"def load_dataset_train():\\n df_train = load_csv_file(\\\"31_train.csv\\\")\\n df_train_target = load_csv_file(\\\"31_target_train.csv\\\")\\n\\n return df_train.values, df_train_target.values\",\n \"def load_data(path):\\n train = pd.read_csv(os.path.join(path,'train.csv'))\\n test = pd.read_csv(os.path.join(path,'test.csv'))\\n \\n return train, test\",\n \"def load_csv_dataset(path,\\n label_col,\\n data_dir=None,\\n smi_col='smiles',\\n explicit_H=False,\\n use_chirality=False,\\n use_molecular_attributes=False,\\n all_pair_features=False,\\n graph_distance=True):\\n df = pd.read_csv(path)\\n smiles = np.array(df[smi_col])\\n labels = np.array(df[label_col])\\n if data_dir is None:\\n data_dir = tempfile.mkdtemp()\\n dataset = MolDataset(root=data_dir)\\n batch_graphs = []\\n for i, (smi, l) in enumerate(zip(smiles, labels)):\\n if i > 0 and i % 1000 == 0:\\n print(\\\"Featurized %d molecules\\\" % i)\\n dataset.add_graph_batch(batch_graphs)\\n batch_graphs = []\\n g = mol_to_graph(Chem.MolFromSmiles(smi),\\n explicit_H=explicit_H,\\n use_chirality=use_chirality,\\n use_molecular_attributes=use_molecular_attributes,\\n all_pair_features=all_pair_features,\\n graph_distance=graph_distance)\\n g.smi = smi\\n w = (l==l) * 1\\n y = copy.deepcopy(l)\\n y[np.where(y != y)] = 0.\\n g.y = t.from_numpy(y).long()\\n g.w = t.from_numpy(w).float()\\n batch_graphs.append(g)\\n dataset.add_graph_batch(batch_graphs)\\n return dataset\",\n \"def load_data(filename):\\n data = []\\n with open('data/' + filename) as raw_data:\\n for line in raw_data.readlines():\\n data.append(float(line.strip('\\\\n')))\\n return data\\n # data = np.mat(np.genfromtxt('data/' + filename)).T\\n # return data\",\n \"def load_data(data_path):\\n df = pd.read_csv(data_path, names=['feature1', 'feature2',\\n 'label'], header=None)\\n\\n # Get x1 and x2\\n feature_1 = df['feature1'].to_list()\\n feature_2 = df['feature2'].to_list()\\n\\n # Normalize features using function below\\n norm_feature_1 = normalize_feature(feature_1)\\n norm_feature_2 = normalize_feature(feature_2)\\n\\n # Create concatenated features and get labels\\n features = np.array([norm_feature_1, norm_feature_2])\\n labels = df['label'].to_list()\\n\\n # Add features to data frame\\n df['norm_x1'] = norm_feature_1\\n df['norm_x2'] = norm_feature_2\\n\\n return features, labels, df\",\n \"def load_data(train_file, test_file):\\n\\n data = np.asarray(pd.read_csv(train_file, header=0))\\n data_ts = np.asarray(pd.read_csv(test_file, header=0))\\n\\n x_tra = data[:, :-1]\\n y_tra = data[:, -1]\\n\\n return x_tra, y_tra, data_ts\",\n \"def get_training_data():\\n features = []\\n labels = []\\n\\n with open('data.csv') as csv_file:\\n csv_reader = csv.reader(csv_file, delimiter=',')\\n\\n rows = [line for line in csv_reader]\\n random.shuffle(rows)\\n\\n for vector in rows:\\n feature_vector = [float(vector[i]) for i in range(4)]\\n features.append(feature_vector)\\n labels.append(encode_label(vector[4]))\\n\\n normalise_features(features)\\n\\n return features, labels\",\n \"def data_parser(data):\\n\\n with open(data, 'r') as inp:\\n\\n # take every sample\\n # the last line in the text file is empty, so reading until -1\\n samples = inp.read().split('\\\\n')[:-1]\\n\\n vec = []\\n labels = []\\n for sample in samples:\\n # file is tab delimited\\n split_samples = sample.split('\\\\t')\\n # last column contains the label\\n labels.append(int(split_samples[-1]))\\n\\n features = []\\n for feature in split_samples[:-1]:\\n features.append(float(feature))\\n vec.append(features)\\n\\n # make the features and labels as a numpy array\\n vec = np.array(vec)\\n labels = np.array(labels)\\n return vec, labels\",\n \"def from_label_file(cls, label_file_path, out_path=FEATURES_DATA_PATH, source_path=RAW_DATA_PATH):\\n df = pd.read_csv(label_file_path)\\n filenames = df['filename']\\n labels = df['label']\\n return cls(filenames, labels, out_path=out_path, source_path=source_path)\",\n \"def load_dataset(path):\\n training_data = []\\n with open(path) as csv_file:\\n reader = csv.reader(csv_file, delimiter=\\\",\\\")\\n next(reader)\\n for row in reader:\\n training_data.append(row[1])\\n return training_data\",\n \"def load_label(path_file):\\n if '.csv' not in path_file:\\n raise FileNotFoundError('Only CSV format is supported currently')\\n\\n t0 = time()\\n df = pd.DataFrame()\\n\\n with open(path_file, 'r') as f:\\n # TODO: Implement the logic once the format is finalised\\n pass\\n\\n logging.info('Loading label data with {} rows from {} takes {} secs'.format(df.shape[0],\\n path_file, time() - t0))\\n return df\",\n \"def _read_data_file(self, path_model_id):\\n\\n path_dataset_file = path_model_id.joinpath('training_set.csv')\\n\\n with path_dataset_file.open(mode='r', newline='') as f:\\n csv_reader = reader(f, delimiter=',')\\n rows = list(csv_reader)\\n\\n self.example_count = len(rows)\\n\\n img_files = [path.join(f'label_{row[1]}', row[0]) for row in rows]\\n enc_labels = self.class_le.fit_transform([row[1] for row in rows])\\n \\n self.files_labels = [[img_files[i], enc_labels[i]]\\n for i in range(self.example_count)]\",\n \"def read_csv_file(csv_fname, ignore_first_row = True):\\n \\n X, y = [], []\\n with open(csv_fname, 'r') as csv_file:\\n csv_reader = csv.reader(csv_file)\\n if ignore_first_row:\\n next(csv_reader)\\n for row in csv_reader:\\n X.append(row[:-1])\\n y.append(row[-1])\\n return np.array(X), np.array(y)\",\n \"def load_predictions(filepath):\\n\\twith open(filepath, 'r') as fh:\\n\\t\\tcells = [line.split(',') for line in fh.read().splitlines()]\\n\\tdata = empty((TESTSIZE, NCLASSES), dtype = float64)\\n\\tfor k, row in enumerate(cells[1:]):\\n\\t\\tdata[k, :] = row[1:]\\n\\treturn data\",\n \"def import_dataset(fpath):\\r\\n data = read_csv(fpath)\\r\\n print(data.head())\\r\\n print(data.shape)\\r\\n return data\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.706477","0.68677425","0.6859685","0.6782772","0.67529887","0.6746655","0.67292655","0.66810787","0.66234106","0.65906125","0.6552061","0.65014195","0.64909846","0.646308","0.6431648","0.6414293","0.6412473","0.6407337","0.63972247","0.6389596","0.63569885","0.6344617","0.634075","0.63401765","0.6335867","0.6291479","0.6288128","0.62760293","0.62760293","0.6273798","0.6265046","0.6248506","0.62457365","0.6222311","0.622082","0.6216309","0.61955035","0.61708564","0.6152052","0.6149234","0.61439884","0.61419255","0.6134468","0.61071473","0.6101974","0.6094547","0.60942346","0.6094223","0.6090441","0.60881793","0.60874313","0.60864663","0.60731447","0.6060774","0.60590875","0.60518074","0.60496044","0.6042525","0.60357213","0.60310066","0.60189897","0.60189724","0.6018446","0.60129786","0.6009791","0.6006921","0.60044384","0.6001786","0.59874046","0.59808004","0.597737","0.5973995","0.59676874","0.59627616","0.5951678","0.5951081","0.5949759","0.59490055","0.5946359","0.59403914","0.5934303","0.59169835","0.5916611","0.5911365","0.5907404","0.59044945","0.5898257","0.5895065","0.589372","0.58917195","0.5890448","0.5889512","0.5882536","0.588173","0.58713686","0.5869744","0.5867229","0.5865214","0.5864695","0.58608305"],"string":"[\n \"0.706477\",\n \"0.68677425\",\n \"0.6859685\",\n \"0.6782772\",\n \"0.67529887\",\n \"0.6746655\",\n \"0.67292655\",\n \"0.66810787\",\n \"0.66234106\",\n \"0.65906125\",\n \"0.6552061\",\n \"0.65014195\",\n \"0.64909846\",\n \"0.646308\",\n \"0.6431648\",\n \"0.6414293\",\n \"0.6412473\",\n \"0.6407337\",\n \"0.63972247\",\n \"0.6389596\",\n \"0.63569885\",\n \"0.6344617\",\n \"0.634075\",\n \"0.63401765\",\n \"0.6335867\",\n \"0.6291479\",\n \"0.6288128\",\n \"0.62760293\",\n \"0.62760293\",\n \"0.6273798\",\n \"0.6265046\",\n \"0.6248506\",\n \"0.62457365\",\n \"0.6222311\",\n \"0.622082\",\n \"0.6216309\",\n \"0.61955035\",\n \"0.61708564\",\n \"0.6152052\",\n \"0.6149234\",\n \"0.61439884\",\n \"0.61419255\",\n \"0.6134468\",\n \"0.61071473\",\n \"0.6101974\",\n \"0.6094547\",\n \"0.60942346\",\n \"0.6094223\",\n \"0.6090441\",\n \"0.60881793\",\n \"0.60874313\",\n \"0.60864663\",\n \"0.60731447\",\n \"0.6060774\",\n \"0.60590875\",\n \"0.60518074\",\n \"0.60496044\",\n \"0.6042525\",\n \"0.60357213\",\n \"0.60310066\",\n \"0.60189897\",\n \"0.60189724\",\n \"0.6018446\",\n \"0.60129786\",\n \"0.6009791\",\n \"0.6006921\",\n \"0.60044384\",\n \"0.6001786\",\n \"0.59874046\",\n \"0.59808004\",\n \"0.597737\",\n \"0.5973995\",\n \"0.59676874\",\n \"0.59627616\",\n \"0.5951678\",\n \"0.5951081\",\n \"0.5949759\",\n \"0.59490055\",\n \"0.5946359\",\n \"0.59403914\",\n \"0.5934303\",\n \"0.59169835\",\n \"0.5916611\",\n \"0.5911365\",\n \"0.5907404\",\n \"0.59044945\",\n \"0.5898257\",\n \"0.5895065\",\n \"0.589372\",\n \"0.58917195\",\n \"0.5890448\",\n \"0.5889512\",\n \"0.5882536\",\n \"0.588173\",\n \"0.58713686\",\n \"0.5869744\",\n \"0.5867229\",\n \"0.5865214\",\n \"0.5864695\",\n \"0.58608305\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7765821"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":5297,"cells":{"query":{"kind":"string","value":"Given a vector of predictions, save results in CSV format."},"document":{"kind":"string","value":"def save_results(predictions, filename):\n with open(filename, 'w') as f:\n f.write(\"id,ACTION\\n\")\n for i, pred in enumerate(predictions):\n f.write(\"%d,%f\\n\" % (i + 1, pred))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def write_results(file_path, predictions):\n with open(file_path, \"w\") as csv_file:\n writer = csv.writer(csv_file, delimiter=\",\")\n writer.writerow([\"Id\", \"Bound\"])\n for id, bound in enumerate(predictions):\n writer.writerow([id, bound])","def write_predictions(y_pred, filename, yname=None) :\n out = open(filename, 'wb')\n f = csv.writer(out)\n if yname :\n f.writerow([yname])\n f.writerows(zip(y_pred))\n out.close()","def write_predictions(y_pred, filename, yname=None) :\n out = open(filename, 'wb')\n f = csv.writer(out)\n if yname :\n f.writerow([yname])\n f.writerows(list(zip(y_pred)))\n out.close()","def write_predictions(y_pred, filename, yname=None) :\n out = open(filename, 'wb')\n f = csv.writer(out)\n if yname :\n f.writerow([yname])\n f.writerows(list(zip(y_pred)))\n out.close()","def write_predictions(pred, filename=\"pred.csv\"):\n output_file = open(filename, \"wb\")\n writer = csv.writer(output_file)\n datetimes = get_datetimes(\"test.csv\")\n\n writer.writerow([\"datetime\", \"count\"])\n\n for index, count in enumerate(pred):\n writer.writerow([datetimes[index], int(count)])\n\n output_file.close()","def write_results_to_csv(ids,\n sentiments_actuals,\n sentiments_predictions,\n filename):\n output = pd.DataFrame(data={\n \"id\": ids,\n \"sentiment_actual\": sentiments_actuals,\n \"sentiment_predicted\": sentiments_predictions})\n output.to_csv(filename, index=False, quoting=3)","def write_predictions_to_file(predictor, testDataFname, enc, outputFname, features=None):\n\n testData, _, testDataIds, _ = make_data(testDataFname, features=features, enc=enc)\n\n dt = datetime.now()\n predictions = predictor.predict(testData)\n print 'predicting took', datetime.now() - dt\n\n featureSelectionOutput = np.transpose(np.vstack((testDataIds, predictions.round().astype(int))))\n\n with open(outputFname, 'wb') as outputFile:\n writer = csv.writer(outputFile)\n writer.writerow(['id', 'loss'])\n writer.writerows(featureSelectionOutput)","def store_classes_and_predictions(output_file_path, classes, predictions):\n with open(output_file_path, mode='a', newline='') as csvfile:\n csvwriter = csv.writer(csvfile, delimiter=',')\n csvwriter.writerow(['true', 'predicted'])\n for i in range(len(classes)):\n csvwriter.writerow([classes.iloc[i], predictions.iloc[i]])","def write_results(self, results):\n predictions = open('hmm_results.csv', 'w')\n predictions.write(\"Type,Prediction\")\n for type in results:\n if type == 'O':\n continue\n predictions.write(\"\\n\" + str(type) + \",\")\n for interval in results[type]:\n predictions.write(str(interval) + \" \")\n predictions.close()","def create_csv_submission(ids, y_pred, name):\n with open(name, 'w') as csvfile:\n fieldnames = ['Id', 'Prediction']\n writer = csv.DictWriter(csvfile, delimiter=\",\", fieldnames=fieldnames)\n writer.writeheader()\n for r1, r2 in zip(ids, y_pred):\n writer.writerow({'Id':r1,'Prediction':round(r2)})","def write_predictions(prediction_dic, result_path):\n with open(result_path, 'wb') as outfile:\n outfile.write(bytes('Patient_ID,HPV/p16_status\\n', 'UTF-8'))\n for patient_id, pred in prediction_dic.items():\n outfile.write(bytes(str(patient_id) + ',' + str(pred) + '\\n', 'UTF-8'))","def predictions_to_csv(outstream, decomposition: FreeWilsonDecomposition, predictions):\n writer = None\n for pred in predictions:\n if not writer:\n rgroups = set()\n for rgroup in decomposition.rgroups:\n rgroups.add(rgroup)\n rgroups = sorted(rgroups, key=_rgroup_sort)\n\n lookup = {}\n for i, rg in enumerate(rgroups):\n lookup[rg] = i\n writer = csv.writer(outstream)\n header = ['smiles', 'prediction'] + [f\"{rg}_smiles\" for rg in list(rgroups)]\n writer.writerow(header)\n rg = [\"\"] * len(lookup)\n for s in pred.rgroups:\n rg[lookup[s.rgroup]] = s.smiles\n\n row = [pred.smiles, repr(pred.prediction)] + rg\n writer.writerow(row)\n return header","def log_inference(tester, name, description):\r\n\tfor dataset, output in tester.preds.items():\r\n\t\tresults = pandas.DataFrame.from_dict(output)\r\n\t\tpath = os.path.join(\r\n\t\t\tEXPERIMENT_PATH, tester.config[\"name\"] + '-' + dataset)\r\n\t\twith open(path + \".csv\", \"w\") as f:\r\n\t\t\tresults.to_csv(f, sep=\"\\t\", encoding='utf-8', \r\n\t\t\t\tfloat_format='%.3f', index=False)\r\n\r\n\t\twith open(path + \"-predictions.csv\", \"w\") as f:\r\n\t\t\tresults[[\"tag\", \"y_hat\"]].to_csv(\r\n\t\t\t\tf, index=False, float_format='%.3f', header=False)","def save_predictions(gtfilename, loss_type, probs, preds, outfile):\n\n # 1. get file ids\n liste_fileids = []\n targets = []\n passFirstLine=True\n with open(gtfilename, 'r') as fh:\n for line in fh:\n if passFirstLine:\n passFirstLine = False\n continue\n tmp = line.rstrip().split(',')\n liste_fileids.append(tmp[0])\n targets.append(tmp[1])\n\n print 'liste_fileids', len(liste_fileids)\n # 2. save preds\n import csv\n with open(outfile, 'w') as csvfile:\n # fieldnames = ['itemid', 'hasbird', 'pred', 'gt']\n fieldnames = ['itemid', 'hasbird']\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n writer.writeheader()\n if loss_type == 'categorical_crossentropy':\n for i, id in enumerate(liste_fileids):\n # writer.writerow({'itemid': id, 'hasbird': probs[i, 1], 'pred': preds[i], 'gt': targets[i]})\n writer.writerow({'itemid': id, 'hasbird': probs[i, 1]})\n elif loss_type == 'binary_hinge' or loss_type == 'binary_crossentropy' or loss_type == 'weighted_binary_crossentropy':\n for i, id in enumerate(liste_fileids):\n # writer.writerow({'itemid': id, 'hasbird': probs[i][0], 'pred': preds[i], 'gt': targets[i]})\n writer.writerow({'itemid': id, 'hasbird': probs[i][0]})\n\n print \"INFO: predictions (positive class probas) saved to file:\", outfile","def create_csv_submission(ids, y_pred, name):\n with open(name, 'w') as csvfile:\n fieldnames = ['Id', 'Prediction']\n writer = csv.DictWriter(csvfile, delimiter=\",\", fieldnames=fieldnames)\n writer.writeheader()\n for r1, r2 in zip(ids, y_pred):\n writer.writerow({'Id': int(r1), 'Prediction': int(r2)})","def create_csv_submission(ids, y_pred, name):\n with open(name, 'w') as csvfile:\n fieldnames = ['Id', 'Prediction']\n writer = csv.DictWriter(csvfile, delimiter=\",\", fieldnames=fieldnames)\n writer.writeheader()\n for r1, r2 in zip(ids, y_pred):\n writer.writerow({'Id':int(r1),'Prediction':int(r2)})","def create_csv_submission(ids, y_pred, name):\n with open(name, 'w') as csvfile:\n fieldnames = ['Id', 'Prediction']\n writer = csv.DictWriter(csvfile, delimiter=\",\", fieldnames=fieldnames)\n writer.writeheader()\n for r1, r2 in zip(ids, y_pred):\n writer.writerow({'Id':int(r1),'Prediction':int(r2)})","def create_csv_submission(ids, y_pred, name):\n with open(name, 'w') as csvfile:\n fieldnames = ['Id', 'Prediction']\n writer = csv.DictWriter(csvfile, delimiter=\",\", fieldnames=fieldnames)\n writer.writeheader()\n for r1, r2 in zip(ids, y_pred):\n writer.writerow({'Id':int(r1),'Prediction':int(r2)})","def create_csv_submission(ids, y_pred, name):\n with open(name, 'w') as csvfile:\n fieldnames = ['Id', 'Prediction']\n writer = csv.DictWriter(csvfile, delimiter=\",\", fieldnames=fieldnames)\n writer.writeheader()\n for r1, r2 in zip(ids, y_pred):\n writer.writerow({'Id':int(r1),'Prediction':int(r2)})","def create_csv_submission(ids, y_pred, name):\n with open(name, 'w') as csvfile:\n fieldnames = ['Id', 'Prediction']\n writer = csv.DictWriter(csvfile, delimiter=\",\", fieldnames=fieldnames)\n writer.writeheader()\n for r1, r2 in zip(ids, y_pred):\n writer.writerow({'Id':int(r1),'Prediction':int(r2)})","def create_csv_submission(ids, y_pred, name):\n with open(name, 'w') as csvfile:\n fieldnames = ['Id', 'Prediction']\n writer = csv.DictWriter(csvfile, delimiter=\",\", fieldnames=fieldnames)\n writer.writeheader()\n for r1, r2 in zip(ids, y_pred):\n writer.writerow({'Id':int(r1),'Prediction':int(r2)})","def save_results(self):\n results = pd.concat([\n pd.DataFrame(self.IDs.cpu().numpy(), columns= ['ID']), \n pd.DataFrame(self.predicted_labels.cpu().numpy(), columns= ['predicted_label']),\n pd.DataFrame(self.correct_predictions.cpu().numpy(), columns= ['correct_prediction']),\n pd.DataFrame(self.epistemic_uncertainty.cpu().numpy(), columns= ['epistemic_uncertainty']), \n pd.DataFrame(self.aleatoric_uncertainty.cpu().numpy(), columns= ['aleatoric_uncertainty']), \n pd.DataFrame(self.total_uncertainty.cpu().numpy(), columns= ['total_uncertainty']), \n ], axis=1)\n\n create_results_directory()\n results.to_csv('results/{}_{}_results.csv'.format(self.__class__.__name__, datetime.datetime.now().replace(microsecond=0).isoformat()), index=False)","def exportEvaluation(self,results,url):\n profbox()\n if not os.path.exists(url):\n open(url, 'w').close()\n myfile = open(url, 'a')\n\n wr = csv.writer(myfile)\n r = numpy.array(results)\n if len(r.shape) == 1:\n wr.writerow(results)\n else:\n wr.writerows(results)","def save_prediction(self, meta, y_pred, y, filename):\n df = pd.DataFrame(meta)\n df['y_pred'] = y_pred\n df['y'] = y\n print(df)\n df.loc[:, 'id'] = df.index\n self.df_to_csv(df, filename, store_header=False)","def create_csv_submission(ids, y_pred, name):\n # negative class has to be labelled -1 on AIcrowd\n y_pred[y_pred == 0] = -1\n\n with open(name, 'w') as csvfile:\n fieldnames = ['Id', 'Prediction']\n writer = csv.DictWriter(csvfile, delimiter=\",\", fieldnames=fieldnames)\n writer.writeheader()\n for r1, r2 in zip(ids, y_pred):\n writer.writerow({'Id':int(r1),'Prediction':int(r2)})","def __write_csv(self, prediction_probs, n, filename):\n d = {'Id': pd.Series([i for i in xrange(1, n + 1)]),\n 'Action': pd.Series(prediction_probs)}\n df = pd.DataFrame(d)\n df = df[['Id', 'Action']]\n df.to_csv(filename, sep=',', encoding='utf-8',\n index=False)","def exportEvaluation(self, results, url):\r\n # research\r\n profprint()\r\n if not os.path.exists(url):\r\n print \"creating new results file: \",url\r\n open(url, 'w').close()\r\n myfile = open(url, 'a')\r\n\r\n wr = csv.writer(myfile)\r\n r = numpy.array(results)\r\n if len(r.shape) == 1:\r\n wr.writerow(results)\r\n else:\r\n wr.writerows(results)","def writeResultsToDisk(header, results, data, filename):\n for i, datum in enumerate(data):\n np.append(data, results[i])\n header.append(\"diagnosis\")\n np.insert(data, 0, header)\n with open(filename, 'wb') as csvfile:\n writer = csv.writer(csvfile, delimiter=',',\n quotechar='|', quoting=csv.QUOTE_MINIMAL)\n for row in data:\n writer.writerow(row)\n return header, np.array(data)","def write_results(results):\n with RESULTS_PATH.open(\"w\") as writer:\n csvwriter = csv.writer(writer)\n csvwriter.writerows(results)","def save_predictions(battle_name: str, data: str, predictions: List):\n path = './data_reader/data/predictions/' + data + '.' + battle_name\n with open(path, 'w') as outfile:\n for prediction in predictions:\n outfile.write(str(prediction) + '\\n')","def submit_predictions(\n sub_name: str, predictions: jnp.ndarray, id_col: jnp.array\n):\n with open(os.path.join(\"data\", sub_name), \"w\") as sub_file:\n sub_file.write(\"Id,SalePrice\\n\")\n for (example_id, pred) in zip(id_col, jnp.squeeze(predictions)):\n sub_file.write(f\"{example_id},{pred}\\n\")","def log_results(self, path):\n pd.DataFrame(self.results).to_csv(path)","def save_performances(self):\r\n nb_datasets = len(self.results)\r\n resu = [[] for k in range(nb_datasets)]\r\n\r\n # fetch results\r\n for k in range(nb_datasets):\r\n best = np.argmax(self.results[k]['mean_test_score'])\r\n resu[k].append(('score', self.results[k]['mean_test_score'][best]))\r\n resu[k] = resu[k] + list(self.results[k]['params'][best].items())\r\n\r\n # write results in csv\r\n for k, resu in enumerate(resu):\r\n with open('results/final_results_{}.csv'.format(k), 'a') as file:\r\n writer = csv.writer(file)\r\n writer.writerow(resu)","def write_csv(estimates: ListOfDicts, output_csv: str) -> None:\n with open(output_csv, \"w\") as f:\n writer = csv.DictWriter(f, fieldnames=estimates[0].keys())\n writer.writeheader()\n for row in estimates:\n writer.writerow(row)\n logging.info(f\"Wrote estimates as {output_csv}\")","def save_output(pris):\n pris.to_csv('reactors_pris_2016.csv',\n index=False,\n sep=',',\n )","def __create_output_csv(self, df, score_list, elapsed_list):\n df['Similar']=score_list\n df['Elapsed']=elapsed_list\n df.to_csv('Output.csv',index=False)\n return df","def save_prediction(predictions, image_file, path):\n\t\n\tsave_file = convert_file_extension_to_txt(image_file)\n\t\n\twith open(os.path.join(path, save_file), 'w') as f:\n\t\tfor prediction in predictions:\n\t\t\tf.write(str(prediction) + \"\\n\")","def to_csv(self, out_folder):\n import pandas as pd\n\n df = pd.DataFrame(zip(self.results['cids'],\n self.results['differences'],\n self.results['experimental_values']),\n columns=['cids', 'differences',\n 'experimental_values'])\n df.to_csv(out_folder, index=False)","def writePredictions(outfile, pred, proba, y, data, evalmode=False):\n if evalmode:\n header = ['chr', 'start', 'end', 'prediction', 'true label']\n for i in range(np.shape(proba)[1]):\n header.append(\"probability:\"+str(i))\n pd.DataFrame(np.concatenate((data.values[:,0:3],np.transpose(pred[np.newaxis]).astype(int),np.transpose(y[np.newaxis]), proba), axis=1)[:,:]).to_csv(outfile, sep=\"\\t\", index=None, header=header)\n else:\n header = ['chr', 'start', 'end', 'prediction']\n for i in range(np.shape(proba)[1]):\n header.append(\"probability:\"+str(i))\n pd.DataFrame(np.concatenate((data.values[:,0:3],np.transpose(pred[np.newaxis]).astype(int), proba), axis=1)[:,:]).to_csv(outfile, sep=\"\\t\", index=None, header=header)","def createFileCSV(table, path=\"./prediction\"):\t\n\tif len(table) < 1:\n\t\traise NameError('Empty Table!')\n\telse:\n\t\tfile = open(path + '.csv', 'w+')\n\n\t\tfile.write(table[0].toStringHeaders() + \"\\n\")\n\n\t\tfor row in table:\n\t\t\tfile.write(row.toStringCSV() + '\\n')\n\t\tfile.close()","def write_to_file(self, results):\n with open(self.outputFilename, \"w\") as csvFile:\n csvWriter = csv.writer(csvFile, delimiter=',') \n title_row = ('asset_id', 'component_id', 'latitude', 'longitude', 'installation_date', 'commissioning_date', 'street_name', 'cabinet_id', 'nominal_wattage', 'current_time', 'current_LogValue', 'current_IsLogValueOff') \n csvWriter.writerow(title_row)\n for record in results:\n csvWriter.writerow(record)","def export_to_ranking_csv(request, token, project):\n from appraise.local_settings import EXPORT_TOKEN\n if not token == EXPORT_TOKEN:\n return HttpResponseForbidden()\n \n annotation_project = get_object_or_404(Project, name=project)\n \n queryset = RankingResult.objects.filter(item__hit__completed=True)\n\n results = [u'srclang,trglang,srcIndex,doucmentId,segmentId,judgeId,' \\\n 'system1Number,system1Id,system2Number,system2Id,system3Number,' \\\n 'system3Id,system4Number,system4Id,system5Number,system5Id,' \\\n 'system1rank,system2rank,system3rank,system4rank,system5rank']\n \n for result in queryset:\n if isinstance(result, RankingResult):\n if result.item.hit.project_set.filter(id=annotation_project.id):\n # Current implementation of export_to_pairwise_csv() is weird.\n # By contrast, export_to_csv() generates the right thing...\n current_csv = result.export_to_csv()\n if current_csv is None:\n continue\n results.append(current_csv)\n \n export_csv = u\"\\n\".join(results)\n export_csv = export_csv + u\"\\n\"\n return HttpResponse(export_csv, mimetype='text/plain')","def create_csv_submission_prob(ids, y_pred, y_prob, name):\n # negative class has to be labelled -1 on AIcrowd\n y_pred[y_pred == 0] = -1\n\n df = pd.DataFrame({'id': ids, 'label': y_pred, 'prob': y_prob})\n df.to_csv(name, sep=\",\", index=False)","def log_evaluation(tester, name, description):\r\n\tfor dataset, output in tester.preds.items():\r\n\t\tresults = pandas.DataFrame.from_dict(output)\r\n\t\tpath = os.path.join(\r\n\t\t\tEXPERIMENT_PATH, tester.config[\"name\"] + '-' + dataset)\r\n\t\twith open(path + \".csv\", \"w\") as f:\r\n\t\t\tresults.to_csv(f, sep=\"\\t\", encoding='utf-8',\r\n\t\t\t\tfloat_format='%.3f', index=False)","def save_reviews_to_csv(language, review_list, dataset):\n with open('reviews_'+dataset+'_'+language+'.csv', 'w') as csvfile:\n fieldnames = review_list[0].__dict__.keys()\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n\n writer.writeheader()\n for review in review_list:\n writer.writerow(review.__dict__)","def save_learning_data(path, num_episodes, avg_rewards, std_rewards, avg_losses, std_losses):\n rows = zip(num_episodes, avg_rewards, std_rewards, avg_losses, std_losses)\n with open(path + '/learning_data.csv', 'w') as f:\n w = csv.writer(f)\n w.writerows(rows)","def write_output_file(ad_models):\n\n with open('output-data-utf8.csv', 'w', newline='', encoding='UTF-8') as output_file:\n csv_writer = csv.writer(output_file, delimiter=',')\n for ad in ad_models:\n csv_writer.writerow((ad.date.strftime('%Y/%m/%d'), ad.country_code, ad.impression, ad.clicks))","def write_relevance_tocsv(relevance, corpus):\n csv_filename = config.CORPUS[corpus]['relevance_file']\n print('writing relevance')\n print(relevance)\n with open(csv_filename, 'w') as file:\n csv.writer(file).writerows((k,) + v for k, v in relevance.items())","def save_results(self, path):\n create_folder(path)\n self.get_scores().to_csv(path + r'/scores.csv', index=False)\n self.get_results().to_csv(path + r'/results.csv', index=False)\n self.get_pivot_last_epoch().to_csv(path + r'/pivot_last_epoch.csv', index=True)","def save_csv(outfile, movies):\n writer = csv.writer(outfile)\n writer.writerow(['Title', 'Rating', 'Year', 'Actors', 'Runtime'])\n for movie in movies:\n writer.writerow(movie)\n\n # ADD SOME CODE OF YOURSELF HERE TO WRITE THE MOVIES TO DISK","def output_predictions(predictions_file, relations, predictions, test_set_keys, test_labels):\n with codecs.open(predictions_file, 'w', 'utf-8') as f_out:\n for i, (w1, w2) in enumerate(test_set_keys):\n f_out.write('\\t'.join([w1, w2, relations[test_labels[i]], relations[predictions[i]]]) + '\\n')","def write_forecasts(forecast_dict, output):\n\n for objective_id, forecast_value in list(forecast_dict.items()):\n headers = [f[\"model\"] for f in forecast_value]\n points = []\n if not forecast_value:\n sys.exit(\"No forecasts available\")\n for index in range(len(forecast_value[0][\"point_forecast\"])):\n points.append([f[\"point_forecast\"][index] for f in forecast_value])\n output_file = \"%s_%s.csv\" % (output, objective_id)\n with UnicodeWriter(output_file, lineterminator=\"\\n\") as out_handler:\n out_handler.writerow(headers)\n for row in points:\n out_handler.writerow(row)","def write_model_results(model, input_file, repr, tags, outpath):\n input, input_data = read_input(input_file)\n\n if repr == \"c\":\n x = utils.get_features(input, ixs=3)\n else:\n x = utils.get_features(input, chars=True)\n\n w_batcher = utils.AutoBatcher(x, x, batch_size=1, shuffle=False)\n labels = []\n for inputs, _ in w_batcher.get_batches():\n output = torch.max(model(inputs), 1)[1]\n labels += output.cpu().data.numpy().tolist()\n\n predictions = utils.NEWLINE.join([\"{} {}\".format(input_data[i], tags[labels[i]])\\\n for i in range(len(input_data))])\n with open(outpath, \"w\") as outfile:\n outfile.write(predictions)","def save_results_csv(fname, results, header=0):\n\n new_rows = []\n if not os.path.isfile(fname):\n args = fname.split('/')[:-1]\n directory = os.path.join(*args)\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n with open(fname, 'wt') as f:\n writer = csv.writer(f)\n if header == 0:\n writer.writerows(\n [['Model', 'Dataset', 'Method', 'Weight', 'Label', \n 'Step', 'AUROC', 'Precision', 'Recall',\n 'F1 score', 'Random Seed', 'Date']])\n if header == 1:\n writer.writerows(\n [['Precision', 'Recall', 'F1 score', 'Random Seed']])\n elif header ==2:\n writer.writerows(\n [['Step', 'AUROC', 'Precision', 'Recall',\n 'F1 score', 'Random Seed']])\n\n elif header == 5:\n writer.writerows(\n [['Model', 'Dataset', 'Method', 'Weight', 'Label', \n 'Step', 'Scores']])\n\n with open(fname, 'at') as f:\n # Overwrite the old file with the modified rows\n writer = csv.writer(f)\n new_rows.append(results) # add the modified rows\n writer.writerows(new_rows)","def output_predictions(predictions, df_symbol_date, file_name):\n\n # Retrieve baaged prediction\n pred_df = predictions['deep_bagged_predictions']\n\n # Create dataframe by resetting the index to allow columns to be concatenated\n output_df = pd.concat([df_symbol_date.reset_index(\n drop=True), pred_df.reset_index(drop=True)], axis=1)\n\n # Save output to file\n pred_file_location = './predictions/' + file_name + '.csv'\n print('Writing predictions to', pred_file_location)\n output_df.to_csv(pred_file_location)","def save_results(output_dir,\n check_file,\n results,\n exp_string,\n identifier,\n shuffle_labels,\n model_options,\n predictor='classify',\n fold_no=None,\n titration_ratio=None):\n\n signal = 'shuffled' if shuffle_labels else 'signal'\n\n if not isinstance(model_options.training_data, str):\n training_data = '.'.join(model_options.training_data)\n else:\n training_data = model_options.training_data\n\n if isinstance(model_options.n_dim, list):\n n_dim = '.'.join(map(str, model_options.n_dim))\n else:\n n_dim = model_options.n_dim\n\n if predictor == 'classify':\n auc_df = pd.concat(results[\n '{}_auc'.format(exp_string)\n ])\n output_file = construct_filename(output_dir,\n 'auc_threshold_metrics',\n '.tsv.gz',\n identifier,\n training_data,\n model_options.model,\n signal,\n s=model_options.seed,\n n=n_dim,\n f=fold_no,\n t=titration_ratio)\n auc_df.to_csv(\n output_file, sep=\"\\t\", index=False, float_format=\"%.5g\"\n )\n\n aupr_df = pd.concat(results[\n '{}_aupr'.format(exp_string)\n ])\n output_file = construct_filename(output_dir,\n 'aupr_threshold_metrics',\n '.tsv.gz',\n identifier,\n training_data,\n model_options.model,\n signal,\n s=model_options.seed,\n n=n_dim,\n f=fold_no,\n t=titration_ratio)\n aupr_df.to_csv(\n output_file, sep=\"\\t\", index=False, float_format=\"%.5g\"\n )\n\n if '{}_coef'.format(exp_string) in results:\n coef_df = pd.concat(results[\n '{}_coef'.format(exp_string)\n ])\n coef_df.to_csv(\n check_file, sep=\"\\t\", index=False, float_format=\"%.5g\"\n )\n\n metrics_df = pd.concat(results[\n '{}_metrics'.format(exp_string)\n ])\n\n if '{}_preds'.format(exp_string) in results:\n preds_df = pd.concat(results[\n '{}_preds'.format(exp_string)\n ])\n else:\n preds_df = None\n\n if '{}_param_grid'.format(exp_string) in results:\n params_df = pd.concat(results[\n '{}_param_grid'.format(exp_string)\n ])\n else:\n params_df = None\n\n output_file = construct_filename(output_dir,\n 'metrics',\n '.tsv.gz',\n identifier,\n training_data,\n model_options.model,\n signal,\n predictor,\n s=model_options.seed,\n n=n_dim,\n f=fold_no,\n t=titration_ratio)\n metrics_df.to_csv(\n output_file, sep=\"\\t\", index=False, float_format=\"%.5g\"\n )\n\n if preds_df is not None:\n output_file = construct_filename(output_dir,\n 'preds',\n '.tsv.gz',\n identifier,\n training_data,\n model_options.model,\n signal,\n predictor,\n s=model_options.seed,\n n=n_dim,\n f=fold_no,\n t=titration_ratio)\n preds_df.to_csv(\n output_file, sep=\"\\t\", float_format=\"%.5g\"\n )\n\n if params_df is not None:\n output_file = construct_filename(output_dir,\n 'param_grid',\n '.tsv.gz',\n identifier,\n training_data,\n model_options.model,\n signal,\n predictor,\n s=model_options.seed,\n n=n_dim,\n f=fold_no)\n\n params_df.to_csv(output_file, sep=\"\\t\")","def save_results_csv(fname, results, header=0):\n\n new_rows = []\n if not os.path.isfile(fname):\n args = fname.split('/')[:-1]\n directory = os.path.join(*args)\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n with open(fname, 'wt') as f:\n writer = csv.writer(f)\n if header == 0:\n writer.writerows(\n [['Model', 'Dataset', 'Method', 'Weight', 'Label', \n 'Step', 'AUPRC', 'AUROC', 'Precision', 'Recall',\n 'F1 score', 'Random Seed', 'Date']])\n if header == 1:\n writer.writerows(\n [['Precision', 'Recall', 'F1 score', 'Random Seed']])\n elif header ==2:\n writer.writerows(\n [['Step', 'AUPRC', 'AUROC', 'Precision', 'Recall',\n 'F1 score', 'Random Seed']])\n\n elif header == 5:\n writer.writerows(\n [['Model', 'Dataset', 'Method', 'Weight', 'Label', \n 'Step', 'Scores']])\n\n with open(fname, 'at') as f:\n # Overwrite the old file with the modified rows\n writer = csv.writer(f)\n new_rows.append(results) # add the modified rows\n writer.writerows(new_rows)","def save_predicted_results(predicted_results):\n # Save the model\n with open(\"predicted_results\", \"wb\") as predicted_results_file:\n pickle.dump(predicted_results, predicted_results_file)","def write_to_file(data, file_to_output):\n # with open('X_train.csv','a') as f_handle:\n # np.savetxt(f_handle, X_train, fmt='%s', delimiter=\",\")\n\n with open(file_to_output, 'w') as f:\n for item in data.tolist():\n f.write(item + '\\n')","def save_csv(vals: Vals):\n logging.info('Writing data to csv file')\n with open(PureWindowsPath(os.path.realpath(__file__)).parent / 'results.csv', 'w', newline='') as csvfile:\n csvwriter = csv.writer(csvfile)\n csvwriter.writerow(('X', 'Y'))\n\n for x, y in dict(zip(vals.x, vals.y)).items():\n csvwriter.writerow((x, y))\n\n logging.info('Finished writing')\n messagebox.showinfo('Save to CSV', 'Successfully saved!')","def write_to_csv(results, filename):\r\n fieldnames = ('datetime_utc', 'distance_au', 'velocity_km_s',\r\n 'designation', 'name', 'diameter_km',\r\n 'potentially_hazardous')\r\n\r\n with open(filename, 'w') as outfile:\r\n writer = csv.writer(outfile)\r\n writer.writerow(fieldnames)\r\n for row in results:\r\n r = [row.time, row.distance, row.velocity, row.neo.designation,\r\n row.neo.name, row.neo.diameter, row.neo.hazardous]\r\n writer.writerow(r)","def save_predictions(self,file_path):\n # compute average of predictions\n num_examples = len(self.labels)\n\n if num_examples == 0:\n raise Exception (\"nothing to save\")\n\n def string_to_average(string):\n return np.average(np.array(string.split(\",\"),dtype=float))\n prediction_averages = np.around(map(string_to_average,self.predictions),decimals=3)\n\n # sort by prediction averages\n order = np.flipud(prediction_averages.argsort())\n prediction_averages = prediction_averages[order]\n self.pl_pairs = self.pl_pairs[order]\n self.predictions = self.predictions[order]\n self.labels = self.labels[order]\n # write all of the predictions to the file\n f = open(file_path + \"_predictions.txt\", 'w')\n\n for i in range(num_examples):\n f.write((str(prediction_averages[i]) + \" \"*10)[:10]\n + (str(self.labels[i]) + \" \"*50)[:10]\n + str(self.pl_pairs[i] + \" \"*50)[:50]\n + str(self.predictions[i] + \" \"*50)[:50]\n + \"\\n\")\n\n f.close()\n # write and save some metadata\n\n f = open(file_path + \"_scores.txt\", 'w')\n f.write(\"top 100 score: \")\n f.write(str(self.top_100_score(self.predictions,self.labels)))\n f.write(\"\\nAUC: \")\n f.write(str(self.auc(prediction_averages,self.labels)))\n f.write(\"\\nconfusion matrix: \")\n f.write(str(self.confusion_matrix(prediction_averages,self.labels)))\n f.close()\n\n # write a file in Kaggle MAP{K} submision format\n # the form is:\n # Protein1, Ligand3 Ligand4 Ligand2\n # Protein2, Ligand5 Ligand9 Ligand7\n\n raw_database_array = np.genfromtxt(FLAGS.test_set_file_path, delimiter=',', dtype=str)\n receptor_set = raw_database_array[:,2]\n receptor_set = list(set(map(lambda x:x.split('.')[0].split('/')[-1],receptor_set)))\n submission = {}\n for i in range(num_examples):\n # get the name of the ligand and protein\n ligand,receptor = self.pl_pairs[i].split(',')\n ligand = ligand.split('/')[-1].split('.')[0]\n receptor = receptor.split('/')[-1].split('.')[0]\n # add all protein-ligand pairs to submission\n if not receptor in submission.keys():\n submission[receptor] = {}\n submission[receptor]['ligands'] = [ligand]\n submission[receptor]['score'] = [prediction_averages[i]]\n else:\n submission[receptor]['ligands'].append(ligand)\n submission[receptor]['score'].append(prediction_averages[i])\n \n # write and save submisison to file\n # if failed to predict any liagnd for a receptor\n # use placeholder 'L' as predict result\n # e.g. P1234,L\n with open(file_path+'_submission.csv','w') as f:\n f.write('Id,Expected\\n')\n for key in receptor_set:\n if key in submission.keys():\n ligands = np.array(submission[key]['ligands'])\n scores = np.array(submission[key]['score'])\n ligands = ligands[np.flipud(scores.argsort())]\n f.write(key+','+' '.join(ligands)+'\\n')\n else:\n f.write(key+','+'L'+'\\n')","def save_predictions(prediction_maps, output_file, dataset_names):\n assert len(prediction_maps) == len(dataset_names), 'Each prediction map has to have a corresponding dataset name'\n logger.info(f'Saving predictions to: {output_file}...')\n\n with h5py.File(output_file, \"w\") as output_h5:\n for prediction_map, dataset_name in zip(prediction_maps, dataset_names):\n logger.info(f\"Creating dataset '{dataset_name}'...\")\n output_h5.create_dataset(dataset_name, data=prediction_map, compression=\"gzip\")","def log_history_to_csv_file(metaparams, history, csv_file):\n csv_writer = csv.writer(csv_file, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n for i in range(len(history.history['loss'])):\n training_perfs = [i+1, history.history['loss'][i], history.history['acc'][i], history.history['val_loss'][i], history.history['val_acc'][i]]\n csv_writer.writerow(metaparams + training_perfs)\n return;","def write_predictions_to_s3(self, fold_predictions: pd.DataFrame, output_path: str):\n \n # prepare dataframe\n prediction_columns = fold_predictions.columns[['prediction_' == x[:11] for x in fold_predictions.columns]].tolist()\n fold_predictions = fold_predictions[fold_predictions.train_or_test == 'test'] # only save test set\n fold_predictions = fold_predictions[['sf_account_id'] + prediction_columns] # only save salesforce ID and prediction columns\n fold_predictions.columns = ['sf_account_id'] + [x[11:] for x in prediction_columns] # remove predicted_ from column names\n \n # write to S3\n\n now_timestamp = str(pd.Timestamp.now()).split(\".\")[0]\n output_object = f'{output_path}propensity_{now_timestamp}.csv'\n csv_string = fold_predictions.to_csv(index=False)\n\n if 's3' in output_path:\n fs = s3fs.S3FileSystem()\n with fs.open(output_object, 'wb') as f:\n f.write(csv_string.encode())\n else:\n with open(output_object, 'wb') as f:\n f.write(csv_string.encode())\n\n return output_object","def write_csv(output_filename, filenames, truncate=None):\n num_tokens = defaultdict(list)\n with open(output_filename, 'w') as fout:\n fout.write('label,sentence\\n')\n for filename in tqdm(filenames):\n sentiment = int('pos' in filename)\n # sentiment = int(os.path.basename(filename)[:-4].split(\"_\")[1])\n\n with open(filename) as f:\n doc = f.read()\n # Remove HTML\n soup = BeautifulSoup(doc)\n doc = soup.get_text()\n\n if truncate:\n doc = \" \".join(doc.split()[:truncate])\n\n num_tokens[sentiment].append(len(doc.split()))\n fout.write(\"{},{}\\n\".format(sentiment, doc))\n\n for k, v in num_tokens.items():\n print(\"Sentiment {}: Count: {:<10,} Tokens Mean: {:<10,.2f} Min: {:<5} Max: {}\".format(\n k, len(v), np.mean(v), np.min(v), np.max(v)))","def save_ndarray_to_csv(docs_array, labels_array, csv_file):\n processed_array = np.vstack([labels_array, docs_array]).T\n df = pd.DataFrame(data=processed_array)\n df.to_csv(csv_file, index=False, header=['label', 'text'])","def save_predictions(path: str, wrapper, results: Dict):\n predictions_with_idx = []\n\n if wrapper.task_helper and wrapper.task_helper.output:\n predictions_with_idx = wrapper.task_helper.output\n else:\n inv_label_map = {idx: label for label,\n idx in wrapper.label_map.items()}\n for idx, prediction_idx in zip(results['indices'], results['predictions']):\n prediction = inv_label_map[prediction_idx]\n idx = idx.tolist() if isinstance(idx, np.ndarray) else int(idx)\n predictions_with_idx.append({'idx': idx, 'label': prediction})\n\n with open(path, 'w', encoding='utf8') as fh:\n for line in predictions_with_idx:\n fh.write(json.dumps(line) + '\\n')","def save_prediction(self):\n if DataLoader.data is None:\n messagebox.showerror(\"Information\", \"Data file is empty, please load the data first.\")\n return\n if Trainer.y_pred is None:\n messagebox.showerror(\"Information\", \"Preciction has not been made, please train a new model and predict or \"\n \"load a model and predict.\")\n return\n\n path = filedialog.asksaveasfile(mode='w', defaultextension=\".csv\", filetypes=[(\"csv files\", '*.csv'),\n (\"xlsx files\", '*.xlsx'),\n (\"dat files\", '*.dat')])\n\n copy_data = DataLoader.data.copy()\n copy_data['prediction'] = Trainer.y_pred\n copy_data.to_csv(path, index=False)\n\n # Clears memory\n copy_data.drop(copy_data.index, inplace=True)\n del copy_data","def write_submission(ratings, file_name):\n # Build output string to write into the file\n output = \"Id,Prediction\\n\"\n for (row, col, rat) in ratings:\n # every line is of the format 'rX_cY,R' where X and Y correspond to row(user) and column(movie) indices and R is the rating\n # we have do increase row and col by one because numpy arrays use 0-base indexing while movie/user indices start at 1\n output += \"r%d_c%d,%f\\n\" % (row + 1, col + 1, rat)\n \n # Write file \n with open(os.path.join('../predictions_csv', file_name), 'w') as output_file:\n output_file.write(output)\n \n return output","def write_out_prediction(predictions_file, src_seqs,\n trg_seqs, pred_string, src_feat_bundles,\n trg_feat_bundles, val_id):\n\n output_lines = []\n if trg_seqs[val_id] != pred_string:\n output_lines.append('*ERROR*')\n output_lines.append('SRC: {}'.format(src_seqs[val_id]))\n if src_feat_bundles[val_id]:\n output_lines.append('SFT: {}'.format(src_feat_bundles[val_id]))\n if trg_feat_bundles[val_id]:\n output_lines.append('TFT: {}'.format(trg_feat_bundles[val_id]))\n output_lines.append('TRG: {}'.format(trg_seqs[val_id]))\n output_lines.append('PRD: {}\\n'.format(pred_string))\n predictions_file.write('{}\\n'.format('\\n'.join(output_lines)))","def predict(self):\n train_vec, test_vec = self.get_tfidf_vectors()\n clf = self.get_classifier()\n\n print '-'*40\n print 'Making predictions ...'\n clf.fit(train_vec, self.train_ans)\n clf_predictions = clf.predict_proba(test_vec)\n\n print 'Storing predictions in', self.pred_file\n pred_out = [\"Id,predictions\"]\n num_pred = range(30)\n for fid, pred in zip(self.test_index, clf_predictions):\n top_rec = sorted(num_pred, key=lambda k: pred[k], reverse=True)[:3]\n pred_out.append(\"%s,%s\" % (fid, ' '.join( [clf.classes_[rec] for rec in top_rec] )))\n with open(self.pred_file, 'w') as f:\n f.write('%s\\n' % ('\\n'.join(pred_out)))","def write_features_to_csv(pairs, features, filename):\n\tids = []\n\n\tfor pair in pairs:\n\t\tids.append(pair.id)\n\n\tfeatures_dataframe = pd.DataFrame(features)\n\tfeatures_dataframe.insert(0, column=\"ID\", value=ids)\n\tfeatures_dataframe.to_csv(filename, index=False)","def log_history_to_csv_file(metaparams, history, csv_file):\n \n csv_writer = csv.writer(csv_file, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n for i in range(len(history.history['loss'])):\n training_perfs = [i+1, history.history['loss'][i], history.history['acc'][i], history.history['val_loss'][i], history.history['val_acc'][i]]\n csv_writer.writerow(metaparams + training_perfs)\n return;","def save_csv(self, filename): # DONE\n self.data.to_csv(filename)","def write_output(series, filename):\n\n logging.info('Writing output')\n\n df = series.reset_index()\n\n df.columns = ['subject_id', 'classification']\n\n df.to_csv(filename, index=False)","def save(self, data, outpath):\n data.to_csv(outpath)","def export_to_pairwise_csv(request, token, project):\n from appraise.local_settings import EXPORT_TOKEN\n if not token == EXPORT_TOKEN:\n return HttpResponseForbidden()\n \n annotation_project = get_object_or_404(Project, name=project)\n \n queryset = RankingResult.objects.filter(item__hit__completed=True)\n\n results = [u'srclang,trglang,srcIndex,segmentId,judgeId,' \\\n 'system1Id,system1rank,system2Id,system2rank,rankingID']\n \n for result in queryset:\n if isinstance(result, RankingResult):\n if result.item.hit.project_set.filter(id=annotation_project.id):\n current_csv = result.export_to_pairwise_csv()\n if current_csv is None:\n continue\n results.append(current_csv)\n \n export_csv = u\"\\n\".join(results)\n export_csv = export_csv + u\"\\n\"\n return HttpResponse(export_csv, mimetype='text/plain')","def dwn_saved_result_csv(request):\n source_id = request.GET.get('source_id')\n data = []\n objs = ExtractedRelation.objects.filter(source=source_id)\n s = Source.objects.filter(source_id=source_id)[0]\n for i in objs:\n data.append((i.sentence, i.head, i.tail, i.pred_relation, i.sentiment, i.conf, s.source, i.rel_id, os.path.basename(i.ckpt)))\n \n df = pd.DataFrame(data, columns=['Sentence', 'Head', 'Tail', 'Predicted Relation', 'Predicted Sentiment', 'Confidence', 'Source', 'rel_id', 'Checkpoint'])\n df.to_csv(\"temp/analysis_results.csv\", index=False)\n \n return FileResponse(open('temp/analysis_results.csv','rb'))","def generate_csv(lists, output_file):\n if os.path.isfile(output_file):\n with open(output_file, 'a') as file:\n dataset = tablib.Dataset()\n for l in lists:\n dataset.append([l['Original ASIN'], l['Associated ASIN'], l['Title'], l['Price'], l['Currency Code'], l['Relationship']])\n file.write(dataset.csv)\n else:\n with open(output_file, 'w+') as fp:\n dataset = tablib.Dataset(headers=['Original ASIN', 'Associated ASIN', 'Title', 'Price', 'Currency Code', 'Relationship'])\n for l in lists:\n dataset.append([l['Original ASIN'], l['Associated ASIN'], l['Title'], l['Price'], l['Currency Code'], l['Relationship']])\n fp.writelines(dataset.csv)","def save_csv_file(votes: dict) -> None:\r\n with open(\"votingList.csv\", \"w\", newline=\"\") as csvfile:\r\n writer = csv.writer(csvfile)\r\n writer.writerow([\"First Name\", \"Last Name\", \"Vote\"])\r\n for vote in votes.keys():\r\n entry = votes[vote]\r\n fst, snd = vote.split()\r\n writer.writerow([fst, snd, entry])","def write_pred_kaggle_file(cls, outfname, speech):\n yp = cls.predict(speech.test_doc_vec)\n labels = speech.le.inverse_transform(yp)\n f = codecs.open(outfname, 'w')\n f.write(\"FileIndex,Category\\n\")\n for i in range(len(speech.test_fnames)):\n fname = speech.test_fnames[i]\n f.write(fname + ',' + labels[i] + '\\n')\n f.close()","def save_csv_data(representatives, votes, house, session):\n df = pd.DataFrame({'Representatives': representatives})\n for bill in votes:\n vote_record = []\n for person in representatives:\n if person in bill['vote_yea']:\n vote_record.append(1)\n elif person in bill['vote_nay']:\n vote_record.append(0)\n else:\n vote_record.append(2)\n df[bill['bill']] = vote_record\n df.to_csv(os.path.join(default_path, \"voting\", house+str(session)+'_voting.csv'))","def save_submission(results, file_name='submission.csv'):\n submission_path = path.join('..', 'output', file_name)\n results.to_csv(submission_path)","def write(self, predictions, filename):\n driver = self.dataset.GetDriver()\n dst_ds = driver.CreateCopy(filename, self.dataset)\n\n prediction_array = np.zeros_like(self.segmentation)\n for prediction, y, x in predictions:\n prediction_array[y:y + self.size, x:x + self.size] = prediction\n\n # Overwrite the raster band with the predicted labels\n band = dst_ds.GetRasterBand(1)\n band.WriteArray(prediction_array)","def csv_output(self):\r\n fh = open(\"output.csv\",'w')\r\n for i in range(len(self.population.columns)):\r\n if i != len(self.population.columns)-1:\r\n fh.write(str(self.population.columns[i]))\r\n fh.write(\",\")\r\n else:\r\n fh.write(str(self.population.columns[i]))\r\n fh.write(\"\\n\")\r\n\r\n for i in range(len(self.population.data)):\r\n for j in range(len(self.population.data[i])):\r\n if j != len(self.population.data[i])-1:\r\n fh.write(str(self.population.data[i][j]))\r\n fh.write(\",\")\r\n else:\r\n fh.write(str(self.population.data[i][j]))\r\n fh.write(\"\\n\")\r\n fh.close()","def output_predictions(pipeline):\n ##### Write code here #######\n X_train, y_train_true = load_data_file(TRAIN_FILE)\n X_dev, y_dev_true = load_data_file(DEV_FILE)\n X_test, y_test_true = load_data_file(TEST_FILE)\n\n #train pipeline with dev and train file\n pipeline.fit(X=X_train, y=y_train_true)\n pipeline.fit(X=X_dev, y=y_dev_true)\n\n y_pred_test = pipeline.predict(X=X_test)\n\n df = pd.DataFrame(y_pred_test)\n with open('predictions.tsv', 'w'):\n df.to_csv('predictions.tsv', sep='\\t', index=False, header=False)\n ##### End of your work ######","def dwn_analysis_csv(request):\n data = []\n for i in results:\n data.append((i['sentence'], i['head'], i['tail'], i['pred_relation'], i['sent'], i['conf']))\n df = pd.DataFrame(data, columns=['Sentence', 'Head', 'Tail', 'Predicted Relation', 'Predicted Sentiment', 'Confidence'])\n df.to_csv(\"temp/analysis_results.csv\", index=False)\n \n return FileResponse(open('temp/analysis_results.csv','rb'))","def write_to_csv(self, log_dir, run_dir, hmc=False):\n _, run_str = os.path.split(run_dir)\n avg_data = {\n 'log_dir': log_dir,\n 'run_dir': run_str,\n 'hmc': hmc,\n }\n\n for key, val in dict(sorted(self.data.items())).items():\n tensor = tf.convert_to_tensor(val)\n arr, steps = therm_arr(tensor.numpy(), therm_frac=0.2)\n if 'steps' not in avg_data:\n avg_data['steps'] = len(steps)\n avg_data[key] = np.mean(arr)\n\n # avg_data[key] = tf.reduce_mean(arr)\n\n avg_df = pd.DataFrame(avg_data, index=[0])\n outdir = os.path.join(BASE_DIR, 'logs', 'GaugeModel_logs')\n csv_file = os.path.join(outdir, 'inference.csv')\n head, tail = os.path.split(csv_file)\n io.check_else_make_dir(head)\n io.log(f'Appending inference results to {csv_file}.')\n if not os.path.isfile(csv_file):\n avg_df.to_csv(csv_file, header=True, index=False, mode='w')\n else:\n avg_df.to_csv(csv_file, header=False, index=False, mode='a')","def write_results(results):\n fields = results[0].keys()\n with open('results.csv', 'w') as f:\n dw = csv.DictWriter(f, fieldnames=fields, delimiter='|')\n dw.writer.writerow(list(dw.fieldnames))\n dw.writerows(results)","def outputFunc(filename, resultList):\n #assert len(parks) == 3\n \n f = open(filename, 'wt')\n \n try:\n writer = csv.writer(f)\n for i in range(len(resultList)):\n print resultList[0]\n writer.writerow(resultList[i])\n \n finally:\n f.close()","def submission(test_ids, pred_test, file_name):\n pred_test[pred_test < 0] = 0\n\n val_pred_df = pd.DataFrame(data={'fullVisitorId': test_ids,\n 'predictedRevenue': pred_test})\n\n val_pred_df = val_pred_df.groupby('fullVisitorId').sum().reset_index()\n\n val_pred_df.columns = ['fullVIsitorId', 'predictedLogRevenue']\n val_pred_df['predictedLogRevenue'] = val_pred_df['predictedLogRevenue']\n val_pred_df.to_csv('submission/'+file_name, index=False)","def save_results(self, results, file_name, file_type):\n if file_type == 'csv':\n csv_filename = '{}.csv'.format(file_name)\n\n with open(csv_filename, 'w', newline='') as file:\n writer = csv.writer(file)\n writer.writerows(results)","def write_to_file(test_data: pd.DataFrame, estimator: Estimator,\n file_name: str):\n\n # we only need x_test\n x_test, y_test = estimator.split_model_data(test_data)\n\n # have the estimator return its predictions\n predictions = estimator.forecast(x_test)\n\n if predictions is None:\n sys.stdout.write(f\"Estimator returned no forecast,\"\n f\" has it been trained?\\n\")\n raise RuntimeError\n\n # slice the timestamps from given dataframe\n timestamps = test_data.loc[:, test_data.columns[0]].to_numpy()\n timestamps = np.array(timestamps, dtype='datetime64[s]')\n\n with open(file_name, mode=\"w\", newline=\"\") as output_file:\n # initialize csv writer\n prediction_writer = csv.writer(output_file,\n delimiter=\",\",\n quotechar=\"'\",\n quoting=csv.QUOTE_MINIMAL)\n\n # add header line\n prediction_writer.writerow([test_data.columns[0],\n test_data.columns[1]])\n\n #\n for i in range(len(timestamps)):\n timestamp = str(timestamps[i])\n prediction = str(round(predictions[i], 6))\n prediction_writer.writerow([timestamp, prediction])\n\n return","def write_csv(image_names, image_classes, filename):\n with open(filename, 'w', newline='') as file:\n writer = csv.writer(file)\n writer.writerow(['filename', 'label'])\n writer.writerows(zip(image_names, image_classes))","def write_classifier_output(location, folds, labels, predictions, class_probs, names=None):\n with open(os.path.join(location, '-'.join([\"classifier\", \"fold\", \"predictions\"]) + '.txt'), 'w') as out_file:\n for fold in range(folds):\n out_file.write(\"fold \" + str(fold+1) + ':\\n')\n out_file.write(\"{:50} {:<12} {:<12} {:<9} {:<9}\\n\".format(\"recording\", \"prediction\", \"label\", \"class 0\",\n \"class 1\"))\n fold_labels, fold_predictions, fold_class_probs = labels[fold], predictions[fold], class_probs[fold]\n\n if names is not None and len(names) != 0:\n fold_names = np.hstack(names[fold])\n else:\n fold_names = len(fold_predictions) * ['']\n\n for pred_lab_tuple in zip(fold_names, fold_predictions, fold_labels, fold_class_probs[:, 0],\n fold_class_probs[:, 1]):\n (name, pred, label, prob1, prob2) = pred_lab_tuple\n out_file.write(\"{:50} {:<12} {:<12} {:<9.2f} {:<9.2f}\\n\".format(name, pred, label, prob1, prob2))\n out_file.write('\\n')","def output():\n\n if args.top and not args.tfidf and not args.svd:\n most_frequent(vector).to_csv(path_or_buf=\"top{}_vectorfile.csv\".format(args.top))\n\n elif args.top and args.tfidf and not args.svd:\n tfidf_transform(most_frequent(vector)).to_csv(path_or_buf=\"tfidf_top{}.csv\".format(args.top))\n\n elif args.top and args.tfidf and args.svd:\n svd_transform(tfidf_transform(most_frequent(vector)), indexes).to_csv(path_or_buf=\"svd{}_tfidf_topn.csv\".format(args.svd))\n\n elif args.tfidf and not args.top and not args.svd:\n tfidf_transform(vector).to_csv(path_or_buf=\"tfidf.csv\")\n\n elif args.svd and not args.top and not args.tfidf:\n svd_transform(vector, indexes).to_csv(path_or_buf=\"svd{}_vector.csv\".format(args.svd))\n\n elif args.tfidf and args.svd and not args.top:\n svd_transform(tfidf_transform(vector), indexes).to_csv(path_or_buf=\"svd{}_tfidf.csv\".format(args.svd))\n\n else:\n vector.to_csv(path_or_buf=\"vectorfile.csv\")","def predict(classifier, data):\n print(\"Beggining to classify data\")\n results = classifier.predict(data)\n results = pd.DataFrame(results)\n results.index += 1\n results.to_csv(\"out/results.csv\", header=[\"Label\"], index=True, index_label=[\"ImageId\"])\n print(\"Finished classifying data\")","def write_predictions(self, predictions, file_path=None, is_dict=True, pycm_obj=None):\n\n try:\n super(SequenceClassification, self).write_predictions(\n predictions, file_path=file_path, is_dict=is_dict\n )\n except AttributeError:\n # TODO: Need to Fix\n model_base = ModelBase()\n model_base._log_dir = self._log_dir\n model_base._train_counter = self._train_counter\n model_base.training = self.training\n model_base.write_predictions(predictions, file_path=file_path, is_dict=is_dict)\n\n data_type = \"train\" if self.training else \"valid\"\n\n if pycm_obj is not None:\n stats_file_path = f\"predictions-{data_type}-{self._train_counter.get_display()}-stats\"\n pycm_obj.save_csv(str(Path(self._log_dir) / \"predictions\" / stats_file_path))\n\n confusion_matrix_file_path = (\n f\"predictions-{data_type}-{self._train_counter.get_display()}-confusion_matrix\"\n )\n cls_utils.write_confusion_matrix_to_csv(\n str(Path(self._log_dir) / \"predictions\" / confusion_matrix_file_path), pycm_obj\n )","def exportFoldFile(vectors, authors, fileName):\n with open(fileName, \"w\") as fFile:\n for idv, vec in enumerate(vectors):\n [fFile.write(str(val)+',') for val in vec]\n fFile.write(authors[idv] + '\\n')"],"string":"[\n \"def write_results(file_path, predictions):\\n with open(file_path, \\\"w\\\") as csv_file:\\n writer = csv.writer(csv_file, delimiter=\\\",\\\")\\n writer.writerow([\\\"Id\\\", \\\"Bound\\\"])\\n for id, bound in enumerate(predictions):\\n writer.writerow([id, bound])\",\n \"def write_predictions(y_pred, filename, yname=None) :\\n out = open(filename, 'wb')\\n f = csv.writer(out)\\n if yname :\\n f.writerow([yname])\\n f.writerows(zip(y_pred))\\n out.close()\",\n \"def write_predictions(y_pred, filename, yname=None) :\\n out = open(filename, 'wb')\\n f = csv.writer(out)\\n if yname :\\n f.writerow([yname])\\n f.writerows(list(zip(y_pred)))\\n out.close()\",\n \"def write_predictions(y_pred, filename, yname=None) :\\n out = open(filename, 'wb')\\n f = csv.writer(out)\\n if yname :\\n f.writerow([yname])\\n f.writerows(list(zip(y_pred)))\\n out.close()\",\n \"def write_predictions(pred, filename=\\\"pred.csv\\\"):\\n output_file = open(filename, \\\"wb\\\")\\n writer = csv.writer(output_file)\\n datetimes = get_datetimes(\\\"test.csv\\\")\\n\\n writer.writerow([\\\"datetime\\\", \\\"count\\\"])\\n\\n for index, count in enumerate(pred):\\n writer.writerow([datetimes[index], int(count)])\\n\\n output_file.close()\",\n \"def write_results_to_csv(ids,\\n sentiments_actuals,\\n sentiments_predictions,\\n filename):\\n output = pd.DataFrame(data={\\n \\\"id\\\": ids,\\n \\\"sentiment_actual\\\": sentiments_actuals,\\n \\\"sentiment_predicted\\\": sentiments_predictions})\\n output.to_csv(filename, index=False, quoting=3)\",\n \"def write_predictions_to_file(predictor, testDataFname, enc, outputFname, features=None):\\n\\n testData, _, testDataIds, _ = make_data(testDataFname, features=features, enc=enc)\\n\\n dt = datetime.now()\\n predictions = predictor.predict(testData)\\n print 'predicting took', datetime.now() - dt\\n\\n featureSelectionOutput = np.transpose(np.vstack((testDataIds, predictions.round().astype(int))))\\n\\n with open(outputFname, 'wb') as outputFile:\\n writer = csv.writer(outputFile)\\n writer.writerow(['id', 'loss'])\\n writer.writerows(featureSelectionOutput)\",\n \"def store_classes_and_predictions(output_file_path, classes, predictions):\\n with open(output_file_path, mode='a', newline='') as csvfile:\\n csvwriter = csv.writer(csvfile, delimiter=',')\\n csvwriter.writerow(['true', 'predicted'])\\n for i in range(len(classes)):\\n csvwriter.writerow([classes.iloc[i], predictions.iloc[i]])\",\n \"def write_results(self, results):\\n predictions = open('hmm_results.csv', 'w')\\n predictions.write(\\\"Type,Prediction\\\")\\n for type in results:\\n if type == 'O':\\n continue\\n predictions.write(\\\"\\\\n\\\" + str(type) + \\\",\\\")\\n for interval in results[type]:\\n predictions.write(str(interval) + \\\" \\\")\\n predictions.close()\",\n \"def create_csv_submission(ids, y_pred, name):\\n with open(name, 'w') as csvfile:\\n fieldnames = ['Id', 'Prediction']\\n writer = csv.DictWriter(csvfile, delimiter=\\\",\\\", fieldnames=fieldnames)\\n writer.writeheader()\\n for r1, r2 in zip(ids, y_pred):\\n writer.writerow({'Id':r1,'Prediction':round(r2)})\",\n \"def write_predictions(prediction_dic, result_path):\\n with open(result_path, 'wb') as outfile:\\n outfile.write(bytes('Patient_ID,HPV/p16_status\\\\n', 'UTF-8'))\\n for patient_id, pred in prediction_dic.items():\\n outfile.write(bytes(str(patient_id) + ',' + str(pred) + '\\\\n', 'UTF-8'))\",\n \"def predictions_to_csv(outstream, decomposition: FreeWilsonDecomposition, predictions):\\n writer = None\\n for pred in predictions:\\n if not writer:\\n rgroups = set()\\n for rgroup in decomposition.rgroups:\\n rgroups.add(rgroup)\\n rgroups = sorted(rgroups, key=_rgroup_sort)\\n\\n lookup = {}\\n for i, rg in enumerate(rgroups):\\n lookup[rg] = i\\n writer = csv.writer(outstream)\\n header = ['smiles', 'prediction'] + [f\\\"{rg}_smiles\\\" for rg in list(rgroups)]\\n writer.writerow(header)\\n rg = [\\\"\\\"] * len(lookup)\\n for s in pred.rgroups:\\n rg[lookup[s.rgroup]] = s.smiles\\n\\n row = [pred.smiles, repr(pred.prediction)] + rg\\n writer.writerow(row)\\n return header\",\n \"def log_inference(tester, name, description):\\r\\n\\tfor dataset, output in tester.preds.items():\\r\\n\\t\\tresults = pandas.DataFrame.from_dict(output)\\r\\n\\t\\tpath = os.path.join(\\r\\n\\t\\t\\tEXPERIMENT_PATH, tester.config[\\\"name\\\"] + '-' + dataset)\\r\\n\\t\\twith open(path + \\\".csv\\\", \\\"w\\\") as f:\\r\\n\\t\\t\\tresults.to_csv(f, sep=\\\"\\\\t\\\", encoding='utf-8', \\r\\n\\t\\t\\t\\tfloat_format='%.3f', index=False)\\r\\n\\r\\n\\t\\twith open(path + \\\"-predictions.csv\\\", \\\"w\\\") as f:\\r\\n\\t\\t\\tresults[[\\\"tag\\\", \\\"y_hat\\\"]].to_csv(\\r\\n\\t\\t\\t\\tf, index=False, float_format='%.3f', header=False)\",\n \"def save_predictions(gtfilename, loss_type, probs, preds, outfile):\\n\\n # 1. get file ids\\n liste_fileids = []\\n targets = []\\n passFirstLine=True\\n with open(gtfilename, 'r') as fh:\\n for line in fh:\\n if passFirstLine:\\n passFirstLine = False\\n continue\\n tmp = line.rstrip().split(',')\\n liste_fileids.append(tmp[0])\\n targets.append(tmp[1])\\n\\n print 'liste_fileids', len(liste_fileids)\\n # 2. save preds\\n import csv\\n with open(outfile, 'w') as csvfile:\\n # fieldnames = ['itemid', 'hasbird', 'pred', 'gt']\\n fieldnames = ['itemid', 'hasbird']\\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\\n writer.writeheader()\\n if loss_type == 'categorical_crossentropy':\\n for i, id in enumerate(liste_fileids):\\n # writer.writerow({'itemid': id, 'hasbird': probs[i, 1], 'pred': preds[i], 'gt': targets[i]})\\n writer.writerow({'itemid': id, 'hasbird': probs[i, 1]})\\n elif loss_type == 'binary_hinge' or loss_type == 'binary_crossentropy' or loss_type == 'weighted_binary_crossentropy':\\n for i, id in enumerate(liste_fileids):\\n # writer.writerow({'itemid': id, 'hasbird': probs[i][0], 'pred': preds[i], 'gt': targets[i]})\\n writer.writerow({'itemid': id, 'hasbird': probs[i][0]})\\n\\n print \\\"INFO: predictions (positive class probas) saved to file:\\\", outfile\",\n \"def create_csv_submission(ids, y_pred, name):\\n with open(name, 'w') as csvfile:\\n fieldnames = ['Id', 'Prediction']\\n writer = csv.DictWriter(csvfile, delimiter=\\\",\\\", fieldnames=fieldnames)\\n writer.writeheader()\\n for r1, r2 in zip(ids, y_pred):\\n writer.writerow({'Id': int(r1), 'Prediction': int(r2)})\",\n \"def create_csv_submission(ids, y_pred, name):\\n with open(name, 'w') as csvfile:\\n fieldnames = ['Id', 'Prediction']\\n writer = csv.DictWriter(csvfile, delimiter=\\\",\\\", fieldnames=fieldnames)\\n writer.writeheader()\\n for r1, r2 in zip(ids, y_pred):\\n writer.writerow({'Id':int(r1),'Prediction':int(r2)})\",\n \"def create_csv_submission(ids, y_pred, name):\\n with open(name, 'w') as csvfile:\\n fieldnames = ['Id', 'Prediction']\\n writer = csv.DictWriter(csvfile, delimiter=\\\",\\\", fieldnames=fieldnames)\\n writer.writeheader()\\n for r1, r2 in zip(ids, y_pred):\\n writer.writerow({'Id':int(r1),'Prediction':int(r2)})\",\n \"def create_csv_submission(ids, y_pred, name):\\n with open(name, 'w') as csvfile:\\n fieldnames = ['Id', 'Prediction']\\n writer = csv.DictWriter(csvfile, delimiter=\\\",\\\", fieldnames=fieldnames)\\n writer.writeheader()\\n for r1, r2 in zip(ids, y_pred):\\n writer.writerow({'Id':int(r1),'Prediction':int(r2)})\",\n \"def create_csv_submission(ids, y_pred, name):\\n with open(name, 'w') as csvfile:\\n fieldnames = ['Id', 'Prediction']\\n writer = csv.DictWriter(csvfile, delimiter=\\\",\\\", fieldnames=fieldnames)\\n writer.writeheader()\\n for r1, r2 in zip(ids, y_pred):\\n writer.writerow({'Id':int(r1),'Prediction':int(r2)})\",\n \"def create_csv_submission(ids, y_pred, name):\\n with open(name, 'w') as csvfile:\\n fieldnames = ['Id', 'Prediction']\\n writer = csv.DictWriter(csvfile, delimiter=\\\",\\\", fieldnames=fieldnames)\\n writer.writeheader()\\n for r1, r2 in zip(ids, y_pred):\\n writer.writerow({'Id':int(r1),'Prediction':int(r2)})\",\n \"def create_csv_submission(ids, y_pred, name):\\n with open(name, 'w') as csvfile:\\n fieldnames = ['Id', 'Prediction']\\n writer = csv.DictWriter(csvfile, delimiter=\\\",\\\", fieldnames=fieldnames)\\n writer.writeheader()\\n for r1, r2 in zip(ids, y_pred):\\n writer.writerow({'Id':int(r1),'Prediction':int(r2)})\",\n \"def save_results(self):\\n results = pd.concat([\\n pd.DataFrame(self.IDs.cpu().numpy(), columns= ['ID']), \\n pd.DataFrame(self.predicted_labels.cpu().numpy(), columns= ['predicted_label']),\\n pd.DataFrame(self.correct_predictions.cpu().numpy(), columns= ['correct_prediction']),\\n pd.DataFrame(self.epistemic_uncertainty.cpu().numpy(), columns= ['epistemic_uncertainty']), \\n pd.DataFrame(self.aleatoric_uncertainty.cpu().numpy(), columns= ['aleatoric_uncertainty']), \\n pd.DataFrame(self.total_uncertainty.cpu().numpy(), columns= ['total_uncertainty']), \\n ], axis=1)\\n\\n create_results_directory()\\n results.to_csv('results/{}_{}_results.csv'.format(self.__class__.__name__, datetime.datetime.now().replace(microsecond=0).isoformat()), index=False)\",\n \"def exportEvaluation(self,results,url):\\n profbox()\\n if not os.path.exists(url):\\n open(url, 'w').close()\\n myfile = open(url, 'a')\\n\\n wr = csv.writer(myfile)\\n r = numpy.array(results)\\n if len(r.shape) == 1:\\n wr.writerow(results)\\n else:\\n wr.writerows(results)\",\n \"def save_prediction(self, meta, y_pred, y, filename):\\n df = pd.DataFrame(meta)\\n df['y_pred'] = y_pred\\n df['y'] = y\\n print(df)\\n df.loc[:, 'id'] = df.index\\n self.df_to_csv(df, filename, store_header=False)\",\n \"def create_csv_submission(ids, y_pred, name):\\n # negative class has to be labelled -1 on AIcrowd\\n y_pred[y_pred == 0] = -1\\n\\n with open(name, 'w') as csvfile:\\n fieldnames = ['Id', 'Prediction']\\n writer = csv.DictWriter(csvfile, delimiter=\\\",\\\", fieldnames=fieldnames)\\n writer.writeheader()\\n for r1, r2 in zip(ids, y_pred):\\n writer.writerow({'Id':int(r1),'Prediction':int(r2)})\",\n \"def __write_csv(self, prediction_probs, n, filename):\\n d = {'Id': pd.Series([i for i in xrange(1, n + 1)]),\\n 'Action': pd.Series(prediction_probs)}\\n df = pd.DataFrame(d)\\n df = df[['Id', 'Action']]\\n df.to_csv(filename, sep=',', encoding='utf-8',\\n index=False)\",\n \"def exportEvaluation(self, results, url):\\r\\n # research\\r\\n profprint()\\r\\n if not os.path.exists(url):\\r\\n print \\\"creating new results file: \\\",url\\r\\n open(url, 'w').close()\\r\\n myfile = open(url, 'a')\\r\\n\\r\\n wr = csv.writer(myfile)\\r\\n r = numpy.array(results)\\r\\n if len(r.shape) == 1:\\r\\n wr.writerow(results)\\r\\n else:\\r\\n wr.writerows(results)\",\n \"def writeResultsToDisk(header, results, data, filename):\\n for i, datum in enumerate(data):\\n np.append(data, results[i])\\n header.append(\\\"diagnosis\\\")\\n np.insert(data, 0, header)\\n with open(filename, 'wb') as csvfile:\\n writer = csv.writer(csvfile, delimiter=',',\\n quotechar='|', quoting=csv.QUOTE_MINIMAL)\\n for row in data:\\n writer.writerow(row)\\n return header, np.array(data)\",\n \"def write_results(results):\\n with RESULTS_PATH.open(\\\"w\\\") as writer:\\n csvwriter = csv.writer(writer)\\n csvwriter.writerows(results)\",\n \"def save_predictions(battle_name: str, data: str, predictions: List):\\n path = './data_reader/data/predictions/' + data + '.' + battle_name\\n with open(path, 'w') as outfile:\\n for prediction in predictions:\\n outfile.write(str(prediction) + '\\\\n')\",\n \"def submit_predictions(\\n sub_name: str, predictions: jnp.ndarray, id_col: jnp.array\\n):\\n with open(os.path.join(\\\"data\\\", sub_name), \\\"w\\\") as sub_file:\\n sub_file.write(\\\"Id,SalePrice\\\\n\\\")\\n for (example_id, pred) in zip(id_col, jnp.squeeze(predictions)):\\n sub_file.write(f\\\"{example_id},{pred}\\\\n\\\")\",\n \"def log_results(self, path):\\n pd.DataFrame(self.results).to_csv(path)\",\n \"def save_performances(self):\\r\\n nb_datasets = len(self.results)\\r\\n resu = [[] for k in range(nb_datasets)]\\r\\n\\r\\n # fetch results\\r\\n for k in range(nb_datasets):\\r\\n best = np.argmax(self.results[k]['mean_test_score'])\\r\\n resu[k].append(('score', self.results[k]['mean_test_score'][best]))\\r\\n resu[k] = resu[k] + list(self.results[k]['params'][best].items())\\r\\n\\r\\n # write results in csv\\r\\n for k, resu in enumerate(resu):\\r\\n with open('results/final_results_{}.csv'.format(k), 'a') as file:\\r\\n writer = csv.writer(file)\\r\\n writer.writerow(resu)\",\n \"def write_csv(estimates: ListOfDicts, output_csv: str) -> None:\\n with open(output_csv, \\\"w\\\") as f:\\n writer = csv.DictWriter(f, fieldnames=estimates[0].keys())\\n writer.writeheader()\\n for row in estimates:\\n writer.writerow(row)\\n logging.info(f\\\"Wrote estimates as {output_csv}\\\")\",\n \"def save_output(pris):\\n pris.to_csv('reactors_pris_2016.csv',\\n index=False,\\n sep=',',\\n )\",\n \"def __create_output_csv(self, df, score_list, elapsed_list):\\n df['Similar']=score_list\\n df['Elapsed']=elapsed_list\\n df.to_csv('Output.csv',index=False)\\n return df\",\n \"def save_prediction(predictions, image_file, path):\\n\\t\\n\\tsave_file = convert_file_extension_to_txt(image_file)\\n\\t\\n\\twith open(os.path.join(path, save_file), 'w') as f:\\n\\t\\tfor prediction in predictions:\\n\\t\\t\\tf.write(str(prediction) + \\\"\\\\n\\\")\",\n \"def to_csv(self, out_folder):\\n import pandas as pd\\n\\n df = pd.DataFrame(zip(self.results['cids'],\\n self.results['differences'],\\n self.results['experimental_values']),\\n columns=['cids', 'differences',\\n 'experimental_values'])\\n df.to_csv(out_folder, index=False)\",\n \"def writePredictions(outfile, pred, proba, y, data, evalmode=False):\\n if evalmode:\\n header = ['chr', 'start', 'end', 'prediction', 'true label']\\n for i in range(np.shape(proba)[1]):\\n header.append(\\\"probability:\\\"+str(i))\\n pd.DataFrame(np.concatenate((data.values[:,0:3],np.transpose(pred[np.newaxis]).astype(int),np.transpose(y[np.newaxis]), proba), axis=1)[:,:]).to_csv(outfile, sep=\\\"\\\\t\\\", index=None, header=header)\\n else:\\n header = ['chr', 'start', 'end', 'prediction']\\n for i in range(np.shape(proba)[1]):\\n header.append(\\\"probability:\\\"+str(i))\\n pd.DataFrame(np.concatenate((data.values[:,0:3],np.transpose(pred[np.newaxis]).astype(int), proba), axis=1)[:,:]).to_csv(outfile, sep=\\\"\\\\t\\\", index=None, header=header)\",\n \"def createFileCSV(table, path=\\\"./prediction\\\"):\\t\\n\\tif len(table) < 1:\\n\\t\\traise NameError('Empty Table!')\\n\\telse:\\n\\t\\tfile = open(path + '.csv', 'w+')\\n\\n\\t\\tfile.write(table[0].toStringHeaders() + \\\"\\\\n\\\")\\n\\n\\t\\tfor row in table:\\n\\t\\t\\tfile.write(row.toStringCSV() + '\\\\n')\\n\\t\\tfile.close()\",\n \"def write_to_file(self, results):\\n with open(self.outputFilename, \\\"w\\\") as csvFile:\\n csvWriter = csv.writer(csvFile, delimiter=',') \\n title_row = ('asset_id', 'component_id', 'latitude', 'longitude', 'installation_date', 'commissioning_date', 'street_name', 'cabinet_id', 'nominal_wattage', 'current_time', 'current_LogValue', 'current_IsLogValueOff') \\n csvWriter.writerow(title_row)\\n for record in results:\\n csvWriter.writerow(record)\",\n \"def export_to_ranking_csv(request, token, project):\\n from appraise.local_settings import EXPORT_TOKEN\\n if not token == EXPORT_TOKEN:\\n return HttpResponseForbidden()\\n \\n annotation_project = get_object_or_404(Project, name=project)\\n \\n queryset = RankingResult.objects.filter(item__hit__completed=True)\\n\\n results = [u'srclang,trglang,srcIndex,doucmentId,segmentId,judgeId,' \\\\\\n 'system1Number,system1Id,system2Number,system2Id,system3Number,' \\\\\\n 'system3Id,system4Number,system4Id,system5Number,system5Id,' \\\\\\n 'system1rank,system2rank,system3rank,system4rank,system5rank']\\n \\n for result in queryset:\\n if isinstance(result, RankingResult):\\n if result.item.hit.project_set.filter(id=annotation_project.id):\\n # Current implementation of export_to_pairwise_csv() is weird.\\n # By contrast, export_to_csv() generates the right thing...\\n current_csv = result.export_to_csv()\\n if current_csv is None:\\n continue\\n results.append(current_csv)\\n \\n export_csv = u\\\"\\\\n\\\".join(results)\\n export_csv = export_csv + u\\\"\\\\n\\\"\\n return HttpResponse(export_csv, mimetype='text/plain')\",\n \"def create_csv_submission_prob(ids, y_pred, y_prob, name):\\n # negative class has to be labelled -1 on AIcrowd\\n y_pred[y_pred == 0] = -1\\n\\n df = pd.DataFrame({'id': ids, 'label': y_pred, 'prob': y_prob})\\n df.to_csv(name, sep=\\\",\\\", index=False)\",\n \"def log_evaluation(tester, name, description):\\r\\n\\tfor dataset, output in tester.preds.items():\\r\\n\\t\\tresults = pandas.DataFrame.from_dict(output)\\r\\n\\t\\tpath = os.path.join(\\r\\n\\t\\t\\tEXPERIMENT_PATH, tester.config[\\\"name\\\"] + '-' + dataset)\\r\\n\\t\\twith open(path + \\\".csv\\\", \\\"w\\\") as f:\\r\\n\\t\\t\\tresults.to_csv(f, sep=\\\"\\\\t\\\", encoding='utf-8',\\r\\n\\t\\t\\t\\tfloat_format='%.3f', index=False)\",\n \"def save_reviews_to_csv(language, review_list, dataset):\\n with open('reviews_'+dataset+'_'+language+'.csv', 'w') as csvfile:\\n fieldnames = review_list[0].__dict__.keys()\\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\\n\\n writer.writeheader()\\n for review in review_list:\\n writer.writerow(review.__dict__)\",\n \"def save_learning_data(path, num_episodes, avg_rewards, std_rewards, avg_losses, std_losses):\\n rows = zip(num_episodes, avg_rewards, std_rewards, avg_losses, std_losses)\\n with open(path + '/learning_data.csv', 'w') as f:\\n w = csv.writer(f)\\n w.writerows(rows)\",\n \"def write_output_file(ad_models):\\n\\n with open('output-data-utf8.csv', 'w', newline='', encoding='UTF-8') as output_file:\\n csv_writer = csv.writer(output_file, delimiter=',')\\n for ad in ad_models:\\n csv_writer.writerow((ad.date.strftime('%Y/%m/%d'), ad.country_code, ad.impression, ad.clicks))\",\n \"def write_relevance_tocsv(relevance, corpus):\\n csv_filename = config.CORPUS[corpus]['relevance_file']\\n print('writing relevance')\\n print(relevance)\\n with open(csv_filename, 'w') as file:\\n csv.writer(file).writerows((k,) + v for k, v in relevance.items())\",\n \"def save_results(self, path):\\n create_folder(path)\\n self.get_scores().to_csv(path + r'/scores.csv', index=False)\\n self.get_results().to_csv(path + r'/results.csv', index=False)\\n self.get_pivot_last_epoch().to_csv(path + r'/pivot_last_epoch.csv', index=True)\",\n \"def save_csv(outfile, movies):\\n writer = csv.writer(outfile)\\n writer.writerow(['Title', 'Rating', 'Year', 'Actors', 'Runtime'])\\n for movie in movies:\\n writer.writerow(movie)\\n\\n # ADD SOME CODE OF YOURSELF HERE TO WRITE THE MOVIES TO DISK\",\n \"def output_predictions(predictions_file, relations, predictions, test_set_keys, test_labels):\\n with codecs.open(predictions_file, 'w', 'utf-8') as f_out:\\n for i, (w1, w2) in enumerate(test_set_keys):\\n f_out.write('\\\\t'.join([w1, w2, relations[test_labels[i]], relations[predictions[i]]]) + '\\\\n')\",\n \"def write_forecasts(forecast_dict, output):\\n\\n for objective_id, forecast_value in list(forecast_dict.items()):\\n headers = [f[\\\"model\\\"] for f in forecast_value]\\n points = []\\n if not forecast_value:\\n sys.exit(\\\"No forecasts available\\\")\\n for index in range(len(forecast_value[0][\\\"point_forecast\\\"])):\\n points.append([f[\\\"point_forecast\\\"][index] for f in forecast_value])\\n output_file = \\\"%s_%s.csv\\\" % (output, objective_id)\\n with UnicodeWriter(output_file, lineterminator=\\\"\\\\n\\\") as out_handler:\\n out_handler.writerow(headers)\\n for row in points:\\n out_handler.writerow(row)\",\n \"def write_model_results(model, input_file, repr, tags, outpath):\\n input, input_data = read_input(input_file)\\n\\n if repr == \\\"c\\\":\\n x = utils.get_features(input, ixs=3)\\n else:\\n x = utils.get_features(input, chars=True)\\n\\n w_batcher = utils.AutoBatcher(x, x, batch_size=1, shuffle=False)\\n labels = []\\n for inputs, _ in w_batcher.get_batches():\\n output = torch.max(model(inputs), 1)[1]\\n labels += output.cpu().data.numpy().tolist()\\n\\n predictions = utils.NEWLINE.join([\\\"{} {}\\\".format(input_data[i], tags[labels[i]])\\\\\\n for i in range(len(input_data))])\\n with open(outpath, \\\"w\\\") as outfile:\\n outfile.write(predictions)\",\n \"def save_results_csv(fname, results, header=0):\\n\\n new_rows = []\\n if not os.path.isfile(fname):\\n args = fname.split('/')[:-1]\\n directory = os.path.join(*args)\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n\\n with open(fname, 'wt') as f:\\n writer = csv.writer(f)\\n if header == 0:\\n writer.writerows(\\n [['Model', 'Dataset', 'Method', 'Weight', 'Label', \\n 'Step', 'AUROC', 'Precision', 'Recall',\\n 'F1 score', 'Random Seed', 'Date']])\\n if header == 1:\\n writer.writerows(\\n [['Precision', 'Recall', 'F1 score', 'Random Seed']])\\n elif header ==2:\\n writer.writerows(\\n [['Step', 'AUROC', 'Precision', 'Recall',\\n 'F1 score', 'Random Seed']])\\n\\n elif header == 5:\\n writer.writerows(\\n [['Model', 'Dataset', 'Method', 'Weight', 'Label', \\n 'Step', 'Scores']])\\n\\n with open(fname, 'at') as f:\\n # Overwrite the old file with the modified rows\\n writer = csv.writer(f)\\n new_rows.append(results) # add the modified rows\\n writer.writerows(new_rows)\",\n \"def output_predictions(predictions, df_symbol_date, file_name):\\n\\n # Retrieve baaged prediction\\n pred_df = predictions['deep_bagged_predictions']\\n\\n # Create dataframe by resetting the index to allow columns to be concatenated\\n output_df = pd.concat([df_symbol_date.reset_index(\\n drop=True), pred_df.reset_index(drop=True)], axis=1)\\n\\n # Save output to file\\n pred_file_location = './predictions/' + file_name + '.csv'\\n print('Writing predictions to', pred_file_location)\\n output_df.to_csv(pred_file_location)\",\n \"def save_results(output_dir,\\n check_file,\\n results,\\n exp_string,\\n identifier,\\n shuffle_labels,\\n model_options,\\n predictor='classify',\\n fold_no=None,\\n titration_ratio=None):\\n\\n signal = 'shuffled' if shuffle_labels else 'signal'\\n\\n if not isinstance(model_options.training_data, str):\\n training_data = '.'.join(model_options.training_data)\\n else:\\n training_data = model_options.training_data\\n\\n if isinstance(model_options.n_dim, list):\\n n_dim = '.'.join(map(str, model_options.n_dim))\\n else:\\n n_dim = model_options.n_dim\\n\\n if predictor == 'classify':\\n auc_df = pd.concat(results[\\n '{}_auc'.format(exp_string)\\n ])\\n output_file = construct_filename(output_dir,\\n 'auc_threshold_metrics',\\n '.tsv.gz',\\n identifier,\\n training_data,\\n model_options.model,\\n signal,\\n s=model_options.seed,\\n n=n_dim,\\n f=fold_no,\\n t=titration_ratio)\\n auc_df.to_csv(\\n output_file, sep=\\\"\\\\t\\\", index=False, float_format=\\\"%.5g\\\"\\n )\\n\\n aupr_df = pd.concat(results[\\n '{}_aupr'.format(exp_string)\\n ])\\n output_file = construct_filename(output_dir,\\n 'aupr_threshold_metrics',\\n '.tsv.gz',\\n identifier,\\n training_data,\\n model_options.model,\\n signal,\\n s=model_options.seed,\\n n=n_dim,\\n f=fold_no,\\n t=titration_ratio)\\n aupr_df.to_csv(\\n output_file, sep=\\\"\\\\t\\\", index=False, float_format=\\\"%.5g\\\"\\n )\\n\\n if '{}_coef'.format(exp_string) in results:\\n coef_df = pd.concat(results[\\n '{}_coef'.format(exp_string)\\n ])\\n coef_df.to_csv(\\n check_file, sep=\\\"\\\\t\\\", index=False, float_format=\\\"%.5g\\\"\\n )\\n\\n metrics_df = pd.concat(results[\\n '{}_metrics'.format(exp_string)\\n ])\\n\\n if '{}_preds'.format(exp_string) in results:\\n preds_df = pd.concat(results[\\n '{}_preds'.format(exp_string)\\n ])\\n else:\\n preds_df = None\\n\\n if '{}_param_grid'.format(exp_string) in results:\\n params_df = pd.concat(results[\\n '{}_param_grid'.format(exp_string)\\n ])\\n else:\\n params_df = None\\n\\n output_file = construct_filename(output_dir,\\n 'metrics',\\n '.tsv.gz',\\n identifier,\\n training_data,\\n model_options.model,\\n signal,\\n predictor,\\n s=model_options.seed,\\n n=n_dim,\\n f=fold_no,\\n t=titration_ratio)\\n metrics_df.to_csv(\\n output_file, sep=\\\"\\\\t\\\", index=False, float_format=\\\"%.5g\\\"\\n )\\n\\n if preds_df is not None:\\n output_file = construct_filename(output_dir,\\n 'preds',\\n '.tsv.gz',\\n identifier,\\n training_data,\\n model_options.model,\\n signal,\\n predictor,\\n s=model_options.seed,\\n n=n_dim,\\n f=fold_no,\\n t=titration_ratio)\\n preds_df.to_csv(\\n output_file, sep=\\\"\\\\t\\\", float_format=\\\"%.5g\\\"\\n )\\n\\n if params_df is not None:\\n output_file = construct_filename(output_dir,\\n 'param_grid',\\n '.tsv.gz',\\n identifier,\\n training_data,\\n model_options.model,\\n signal,\\n predictor,\\n s=model_options.seed,\\n n=n_dim,\\n f=fold_no)\\n\\n params_df.to_csv(output_file, sep=\\\"\\\\t\\\")\",\n \"def save_results_csv(fname, results, header=0):\\n\\n new_rows = []\\n if not os.path.isfile(fname):\\n args = fname.split('/')[:-1]\\n directory = os.path.join(*args)\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n\\n with open(fname, 'wt') as f:\\n writer = csv.writer(f)\\n if header == 0:\\n writer.writerows(\\n [['Model', 'Dataset', 'Method', 'Weight', 'Label', \\n 'Step', 'AUPRC', 'AUROC', 'Precision', 'Recall',\\n 'F1 score', 'Random Seed', 'Date']])\\n if header == 1:\\n writer.writerows(\\n [['Precision', 'Recall', 'F1 score', 'Random Seed']])\\n elif header ==2:\\n writer.writerows(\\n [['Step', 'AUPRC', 'AUROC', 'Precision', 'Recall',\\n 'F1 score', 'Random Seed']])\\n\\n elif header == 5:\\n writer.writerows(\\n [['Model', 'Dataset', 'Method', 'Weight', 'Label', \\n 'Step', 'Scores']])\\n\\n with open(fname, 'at') as f:\\n # Overwrite the old file with the modified rows\\n writer = csv.writer(f)\\n new_rows.append(results) # add the modified rows\\n writer.writerows(new_rows)\",\n \"def save_predicted_results(predicted_results):\\n # Save the model\\n with open(\\\"predicted_results\\\", \\\"wb\\\") as predicted_results_file:\\n pickle.dump(predicted_results, predicted_results_file)\",\n \"def write_to_file(data, file_to_output):\\n # with open('X_train.csv','a') as f_handle:\\n # np.savetxt(f_handle, X_train, fmt='%s', delimiter=\\\",\\\")\\n\\n with open(file_to_output, 'w') as f:\\n for item in data.tolist():\\n f.write(item + '\\\\n')\",\n \"def save_csv(vals: Vals):\\n logging.info('Writing data to csv file')\\n with open(PureWindowsPath(os.path.realpath(__file__)).parent / 'results.csv', 'w', newline='') as csvfile:\\n csvwriter = csv.writer(csvfile)\\n csvwriter.writerow(('X', 'Y'))\\n\\n for x, y in dict(zip(vals.x, vals.y)).items():\\n csvwriter.writerow((x, y))\\n\\n logging.info('Finished writing')\\n messagebox.showinfo('Save to CSV', 'Successfully saved!')\",\n \"def write_to_csv(results, filename):\\r\\n fieldnames = ('datetime_utc', 'distance_au', 'velocity_km_s',\\r\\n 'designation', 'name', 'diameter_km',\\r\\n 'potentially_hazardous')\\r\\n\\r\\n with open(filename, 'w') as outfile:\\r\\n writer = csv.writer(outfile)\\r\\n writer.writerow(fieldnames)\\r\\n for row in results:\\r\\n r = [row.time, row.distance, row.velocity, row.neo.designation,\\r\\n row.neo.name, row.neo.diameter, row.neo.hazardous]\\r\\n writer.writerow(r)\",\n \"def save_predictions(self,file_path):\\n # compute average of predictions\\n num_examples = len(self.labels)\\n\\n if num_examples == 0:\\n raise Exception (\\\"nothing to save\\\")\\n\\n def string_to_average(string):\\n return np.average(np.array(string.split(\\\",\\\"),dtype=float))\\n prediction_averages = np.around(map(string_to_average,self.predictions),decimals=3)\\n\\n # sort by prediction averages\\n order = np.flipud(prediction_averages.argsort())\\n prediction_averages = prediction_averages[order]\\n self.pl_pairs = self.pl_pairs[order]\\n self.predictions = self.predictions[order]\\n self.labels = self.labels[order]\\n # write all of the predictions to the file\\n f = open(file_path + \\\"_predictions.txt\\\", 'w')\\n\\n for i in range(num_examples):\\n f.write((str(prediction_averages[i]) + \\\" \\\"*10)[:10]\\n + (str(self.labels[i]) + \\\" \\\"*50)[:10]\\n + str(self.pl_pairs[i] + \\\" \\\"*50)[:50]\\n + str(self.predictions[i] + \\\" \\\"*50)[:50]\\n + \\\"\\\\n\\\")\\n\\n f.close()\\n # write and save some metadata\\n\\n f = open(file_path + \\\"_scores.txt\\\", 'w')\\n f.write(\\\"top 100 score: \\\")\\n f.write(str(self.top_100_score(self.predictions,self.labels)))\\n f.write(\\\"\\\\nAUC: \\\")\\n f.write(str(self.auc(prediction_averages,self.labels)))\\n f.write(\\\"\\\\nconfusion matrix: \\\")\\n f.write(str(self.confusion_matrix(prediction_averages,self.labels)))\\n f.close()\\n\\n # write a file in Kaggle MAP{K} submision format\\n # the form is:\\n # Protein1, Ligand3 Ligand4 Ligand2\\n # Protein2, Ligand5 Ligand9 Ligand7\\n\\n raw_database_array = np.genfromtxt(FLAGS.test_set_file_path, delimiter=',', dtype=str)\\n receptor_set = raw_database_array[:,2]\\n receptor_set = list(set(map(lambda x:x.split('.')[0].split('/')[-1],receptor_set)))\\n submission = {}\\n for i in range(num_examples):\\n # get the name of the ligand and protein\\n ligand,receptor = self.pl_pairs[i].split(',')\\n ligand = ligand.split('/')[-1].split('.')[0]\\n receptor = receptor.split('/')[-1].split('.')[0]\\n # add all protein-ligand pairs to submission\\n if not receptor in submission.keys():\\n submission[receptor] = {}\\n submission[receptor]['ligands'] = [ligand]\\n submission[receptor]['score'] = [prediction_averages[i]]\\n else:\\n submission[receptor]['ligands'].append(ligand)\\n submission[receptor]['score'].append(prediction_averages[i])\\n \\n # write and save submisison to file\\n # if failed to predict any liagnd for a receptor\\n # use placeholder 'L' as predict result\\n # e.g. P1234,L\\n with open(file_path+'_submission.csv','w') as f:\\n f.write('Id,Expected\\\\n')\\n for key in receptor_set:\\n if key in submission.keys():\\n ligands = np.array(submission[key]['ligands'])\\n scores = np.array(submission[key]['score'])\\n ligands = ligands[np.flipud(scores.argsort())]\\n f.write(key+','+' '.join(ligands)+'\\\\n')\\n else:\\n f.write(key+','+'L'+'\\\\n')\",\n \"def save_predictions(prediction_maps, output_file, dataset_names):\\n assert len(prediction_maps) == len(dataset_names), 'Each prediction map has to have a corresponding dataset name'\\n logger.info(f'Saving predictions to: {output_file}...')\\n\\n with h5py.File(output_file, \\\"w\\\") as output_h5:\\n for prediction_map, dataset_name in zip(prediction_maps, dataset_names):\\n logger.info(f\\\"Creating dataset '{dataset_name}'...\\\")\\n output_h5.create_dataset(dataset_name, data=prediction_map, compression=\\\"gzip\\\")\",\n \"def log_history_to_csv_file(metaparams, history, csv_file):\\n csv_writer = csv.writer(csv_file, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\\n for i in range(len(history.history['loss'])):\\n training_perfs = [i+1, history.history['loss'][i], history.history['acc'][i], history.history['val_loss'][i], history.history['val_acc'][i]]\\n csv_writer.writerow(metaparams + training_perfs)\\n return;\",\n \"def write_predictions_to_s3(self, fold_predictions: pd.DataFrame, output_path: str):\\n \\n # prepare dataframe\\n prediction_columns = fold_predictions.columns[['prediction_' == x[:11] for x in fold_predictions.columns]].tolist()\\n fold_predictions = fold_predictions[fold_predictions.train_or_test == 'test'] # only save test set\\n fold_predictions = fold_predictions[['sf_account_id'] + prediction_columns] # only save salesforce ID and prediction columns\\n fold_predictions.columns = ['sf_account_id'] + [x[11:] for x in prediction_columns] # remove predicted_ from column names\\n \\n # write to S3\\n\\n now_timestamp = str(pd.Timestamp.now()).split(\\\".\\\")[0]\\n output_object = f'{output_path}propensity_{now_timestamp}.csv'\\n csv_string = fold_predictions.to_csv(index=False)\\n\\n if 's3' in output_path:\\n fs = s3fs.S3FileSystem()\\n with fs.open(output_object, 'wb') as f:\\n f.write(csv_string.encode())\\n else:\\n with open(output_object, 'wb') as f:\\n f.write(csv_string.encode())\\n\\n return output_object\",\n \"def write_csv(output_filename, filenames, truncate=None):\\n num_tokens = defaultdict(list)\\n with open(output_filename, 'w') as fout:\\n fout.write('label,sentence\\\\n')\\n for filename in tqdm(filenames):\\n sentiment = int('pos' in filename)\\n # sentiment = int(os.path.basename(filename)[:-4].split(\\\"_\\\")[1])\\n\\n with open(filename) as f:\\n doc = f.read()\\n # Remove HTML\\n soup = BeautifulSoup(doc)\\n doc = soup.get_text()\\n\\n if truncate:\\n doc = \\\" \\\".join(doc.split()[:truncate])\\n\\n num_tokens[sentiment].append(len(doc.split()))\\n fout.write(\\\"{},{}\\\\n\\\".format(sentiment, doc))\\n\\n for k, v in num_tokens.items():\\n print(\\\"Sentiment {}: Count: {:<10,} Tokens Mean: {:<10,.2f} Min: {:<5} Max: {}\\\".format(\\n k, len(v), np.mean(v), np.min(v), np.max(v)))\",\n \"def save_ndarray_to_csv(docs_array, labels_array, csv_file):\\n processed_array = np.vstack([labels_array, docs_array]).T\\n df = pd.DataFrame(data=processed_array)\\n df.to_csv(csv_file, index=False, header=['label', 'text'])\",\n \"def save_predictions(path: str, wrapper, results: Dict):\\n predictions_with_idx = []\\n\\n if wrapper.task_helper and wrapper.task_helper.output:\\n predictions_with_idx = wrapper.task_helper.output\\n else:\\n inv_label_map = {idx: label for label,\\n idx in wrapper.label_map.items()}\\n for idx, prediction_idx in zip(results['indices'], results['predictions']):\\n prediction = inv_label_map[prediction_idx]\\n idx = idx.tolist() if isinstance(idx, np.ndarray) else int(idx)\\n predictions_with_idx.append({'idx': idx, 'label': prediction})\\n\\n with open(path, 'w', encoding='utf8') as fh:\\n for line in predictions_with_idx:\\n fh.write(json.dumps(line) + '\\\\n')\",\n \"def save_prediction(self):\\n if DataLoader.data is None:\\n messagebox.showerror(\\\"Information\\\", \\\"Data file is empty, please load the data first.\\\")\\n return\\n if Trainer.y_pred is None:\\n messagebox.showerror(\\\"Information\\\", \\\"Preciction has not been made, please train a new model and predict or \\\"\\n \\\"load a model and predict.\\\")\\n return\\n\\n path = filedialog.asksaveasfile(mode='w', defaultextension=\\\".csv\\\", filetypes=[(\\\"csv files\\\", '*.csv'),\\n (\\\"xlsx files\\\", '*.xlsx'),\\n (\\\"dat files\\\", '*.dat')])\\n\\n copy_data = DataLoader.data.copy()\\n copy_data['prediction'] = Trainer.y_pred\\n copy_data.to_csv(path, index=False)\\n\\n # Clears memory\\n copy_data.drop(copy_data.index, inplace=True)\\n del copy_data\",\n \"def write_submission(ratings, file_name):\\n # Build output string to write into the file\\n output = \\\"Id,Prediction\\\\n\\\"\\n for (row, col, rat) in ratings:\\n # every line is of the format 'rX_cY,R' where X and Y correspond to row(user) and column(movie) indices and R is the rating\\n # we have do increase row and col by one because numpy arrays use 0-base indexing while movie/user indices start at 1\\n output += \\\"r%d_c%d,%f\\\\n\\\" % (row + 1, col + 1, rat)\\n \\n # Write file \\n with open(os.path.join('../predictions_csv', file_name), 'w') as output_file:\\n output_file.write(output)\\n \\n return output\",\n \"def write_out_prediction(predictions_file, src_seqs,\\n trg_seqs, pred_string, src_feat_bundles,\\n trg_feat_bundles, val_id):\\n\\n output_lines = []\\n if trg_seqs[val_id] != pred_string:\\n output_lines.append('*ERROR*')\\n output_lines.append('SRC: {}'.format(src_seqs[val_id]))\\n if src_feat_bundles[val_id]:\\n output_lines.append('SFT: {}'.format(src_feat_bundles[val_id]))\\n if trg_feat_bundles[val_id]:\\n output_lines.append('TFT: {}'.format(trg_feat_bundles[val_id]))\\n output_lines.append('TRG: {}'.format(trg_seqs[val_id]))\\n output_lines.append('PRD: {}\\\\n'.format(pred_string))\\n predictions_file.write('{}\\\\n'.format('\\\\n'.join(output_lines)))\",\n \"def predict(self):\\n train_vec, test_vec = self.get_tfidf_vectors()\\n clf = self.get_classifier()\\n\\n print '-'*40\\n print 'Making predictions ...'\\n clf.fit(train_vec, self.train_ans)\\n clf_predictions = clf.predict_proba(test_vec)\\n\\n print 'Storing predictions in', self.pred_file\\n pred_out = [\\\"Id,predictions\\\"]\\n num_pred = range(30)\\n for fid, pred in zip(self.test_index, clf_predictions):\\n top_rec = sorted(num_pred, key=lambda k: pred[k], reverse=True)[:3]\\n pred_out.append(\\\"%s,%s\\\" % (fid, ' '.join( [clf.classes_[rec] for rec in top_rec] )))\\n with open(self.pred_file, 'w') as f:\\n f.write('%s\\\\n' % ('\\\\n'.join(pred_out)))\",\n \"def write_features_to_csv(pairs, features, filename):\\n\\tids = []\\n\\n\\tfor pair in pairs:\\n\\t\\tids.append(pair.id)\\n\\n\\tfeatures_dataframe = pd.DataFrame(features)\\n\\tfeatures_dataframe.insert(0, column=\\\"ID\\\", value=ids)\\n\\tfeatures_dataframe.to_csv(filename, index=False)\",\n \"def log_history_to_csv_file(metaparams, history, csv_file):\\n \\n csv_writer = csv.writer(csv_file, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\\n for i in range(len(history.history['loss'])):\\n training_perfs = [i+1, history.history['loss'][i], history.history['acc'][i], history.history['val_loss'][i], history.history['val_acc'][i]]\\n csv_writer.writerow(metaparams + training_perfs)\\n return;\",\n \"def save_csv(self, filename): # DONE\\n self.data.to_csv(filename)\",\n \"def write_output(series, filename):\\n\\n logging.info('Writing output')\\n\\n df = series.reset_index()\\n\\n df.columns = ['subject_id', 'classification']\\n\\n df.to_csv(filename, index=False)\",\n \"def save(self, data, outpath):\\n data.to_csv(outpath)\",\n \"def export_to_pairwise_csv(request, token, project):\\n from appraise.local_settings import EXPORT_TOKEN\\n if not token == EXPORT_TOKEN:\\n return HttpResponseForbidden()\\n \\n annotation_project = get_object_or_404(Project, name=project)\\n \\n queryset = RankingResult.objects.filter(item__hit__completed=True)\\n\\n results = [u'srclang,trglang,srcIndex,segmentId,judgeId,' \\\\\\n 'system1Id,system1rank,system2Id,system2rank,rankingID']\\n \\n for result in queryset:\\n if isinstance(result, RankingResult):\\n if result.item.hit.project_set.filter(id=annotation_project.id):\\n current_csv = result.export_to_pairwise_csv()\\n if current_csv is None:\\n continue\\n results.append(current_csv)\\n \\n export_csv = u\\\"\\\\n\\\".join(results)\\n export_csv = export_csv + u\\\"\\\\n\\\"\\n return HttpResponse(export_csv, mimetype='text/plain')\",\n \"def dwn_saved_result_csv(request):\\n source_id = request.GET.get('source_id')\\n data = []\\n objs = ExtractedRelation.objects.filter(source=source_id)\\n s = Source.objects.filter(source_id=source_id)[0]\\n for i in objs:\\n data.append((i.sentence, i.head, i.tail, i.pred_relation, i.sentiment, i.conf, s.source, i.rel_id, os.path.basename(i.ckpt)))\\n \\n df = pd.DataFrame(data, columns=['Sentence', 'Head', 'Tail', 'Predicted Relation', 'Predicted Sentiment', 'Confidence', 'Source', 'rel_id', 'Checkpoint'])\\n df.to_csv(\\\"temp/analysis_results.csv\\\", index=False)\\n \\n return FileResponse(open('temp/analysis_results.csv','rb'))\",\n \"def generate_csv(lists, output_file):\\n if os.path.isfile(output_file):\\n with open(output_file, 'a') as file:\\n dataset = tablib.Dataset()\\n for l in lists:\\n dataset.append([l['Original ASIN'], l['Associated ASIN'], l['Title'], l['Price'], l['Currency Code'], l['Relationship']])\\n file.write(dataset.csv)\\n else:\\n with open(output_file, 'w+') as fp:\\n dataset = tablib.Dataset(headers=['Original ASIN', 'Associated ASIN', 'Title', 'Price', 'Currency Code', 'Relationship'])\\n for l in lists:\\n dataset.append([l['Original ASIN'], l['Associated ASIN'], l['Title'], l['Price'], l['Currency Code'], l['Relationship']])\\n fp.writelines(dataset.csv)\",\n \"def save_csv_file(votes: dict) -> None:\\r\\n with open(\\\"votingList.csv\\\", \\\"w\\\", newline=\\\"\\\") as csvfile:\\r\\n writer = csv.writer(csvfile)\\r\\n writer.writerow([\\\"First Name\\\", \\\"Last Name\\\", \\\"Vote\\\"])\\r\\n for vote in votes.keys():\\r\\n entry = votes[vote]\\r\\n fst, snd = vote.split()\\r\\n writer.writerow([fst, snd, entry])\",\n \"def write_pred_kaggle_file(cls, outfname, speech):\\n yp = cls.predict(speech.test_doc_vec)\\n labels = speech.le.inverse_transform(yp)\\n f = codecs.open(outfname, 'w')\\n f.write(\\\"FileIndex,Category\\\\n\\\")\\n for i in range(len(speech.test_fnames)):\\n fname = speech.test_fnames[i]\\n f.write(fname + ',' + labels[i] + '\\\\n')\\n f.close()\",\n \"def save_csv_data(representatives, votes, house, session):\\n df = pd.DataFrame({'Representatives': representatives})\\n for bill in votes:\\n vote_record = []\\n for person in representatives:\\n if person in bill['vote_yea']:\\n vote_record.append(1)\\n elif person in bill['vote_nay']:\\n vote_record.append(0)\\n else:\\n vote_record.append(2)\\n df[bill['bill']] = vote_record\\n df.to_csv(os.path.join(default_path, \\\"voting\\\", house+str(session)+'_voting.csv'))\",\n \"def save_submission(results, file_name='submission.csv'):\\n submission_path = path.join('..', 'output', file_name)\\n results.to_csv(submission_path)\",\n \"def write(self, predictions, filename):\\n driver = self.dataset.GetDriver()\\n dst_ds = driver.CreateCopy(filename, self.dataset)\\n\\n prediction_array = np.zeros_like(self.segmentation)\\n for prediction, y, x in predictions:\\n prediction_array[y:y + self.size, x:x + self.size] = prediction\\n\\n # Overwrite the raster band with the predicted labels\\n band = dst_ds.GetRasterBand(1)\\n band.WriteArray(prediction_array)\",\n \"def csv_output(self):\\r\\n fh = open(\\\"output.csv\\\",'w')\\r\\n for i in range(len(self.population.columns)):\\r\\n if i != len(self.population.columns)-1:\\r\\n fh.write(str(self.population.columns[i]))\\r\\n fh.write(\\\",\\\")\\r\\n else:\\r\\n fh.write(str(self.population.columns[i]))\\r\\n fh.write(\\\"\\\\n\\\")\\r\\n\\r\\n for i in range(len(self.population.data)):\\r\\n for j in range(len(self.population.data[i])):\\r\\n if j != len(self.population.data[i])-1:\\r\\n fh.write(str(self.population.data[i][j]))\\r\\n fh.write(\\\",\\\")\\r\\n else:\\r\\n fh.write(str(self.population.data[i][j]))\\r\\n fh.write(\\\"\\\\n\\\")\\r\\n fh.close()\",\n \"def output_predictions(pipeline):\\n ##### Write code here #######\\n X_train, y_train_true = load_data_file(TRAIN_FILE)\\n X_dev, y_dev_true = load_data_file(DEV_FILE)\\n X_test, y_test_true = load_data_file(TEST_FILE)\\n\\n #train pipeline with dev and train file\\n pipeline.fit(X=X_train, y=y_train_true)\\n pipeline.fit(X=X_dev, y=y_dev_true)\\n\\n y_pred_test = pipeline.predict(X=X_test)\\n\\n df = pd.DataFrame(y_pred_test)\\n with open('predictions.tsv', 'w'):\\n df.to_csv('predictions.tsv', sep='\\\\t', index=False, header=False)\\n ##### End of your work ######\",\n \"def dwn_analysis_csv(request):\\n data = []\\n for i in results:\\n data.append((i['sentence'], i['head'], i['tail'], i['pred_relation'], i['sent'], i['conf']))\\n df = pd.DataFrame(data, columns=['Sentence', 'Head', 'Tail', 'Predicted Relation', 'Predicted Sentiment', 'Confidence'])\\n df.to_csv(\\\"temp/analysis_results.csv\\\", index=False)\\n \\n return FileResponse(open('temp/analysis_results.csv','rb'))\",\n \"def write_to_csv(self, log_dir, run_dir, hmc=False):\\n _, run_str = os.path.split(run_dir)\\n avg_data = {\\n 'log_dir': log_dir,\\n 'run_dir': run_str,\\n 'hmc': hmc,\\n }\\n\\n for key, val in dict(sorted(self.data.items())).items():\\n tensor = tf.convert_to_tensor(val)\\n arr, steps = therm_arr(tensor.numpy(), therm_frac=0.2)\\n if 'steps' not in avg_data:\\n avg_data['steps'] = len(steps)\\n avg_data[key] = np.mean(arr)\\n\\n # avg_data[key] = tf.reduce_mean(arr)\\n\\n avg_df = pd.DataFrame(avg_data, index=[0])\\n outdir = os.path.join(BASE_DIR, 'logs', 'GaugeModel_logs')\\n csv_file = os.path.join(outdir, 'inference.csv')\\n head, tail = os.path.split(csv_file)\\n io.check_else_make_dir(head)\\n io.log(f'Appending inference results to {csv_file}.')\\n if not os.path.isfile(csv_file):\\n avg_df.to_csv(csv_file, header=True, index=False, mode='w')\\n else:\\n avg_df.to_csv(csv_file, header=False, index=False, mode='a')\",\n \"def write_results(results):\\n fields = results[0].keys()\\n with open('results.csv', 'w') as f:\\n dw = csv.DictWriter(f, fieldnames=fields, delimiter='|')\\n dw.writer.writerow(list(dw.fieldnames))\\n dw.writerows(results)\",\n \"def outputFunc(filename, resultList):\\n #assert len(parks) == 3\\n \\n f = open(filename, 'wt')\\n \\n try:\\n writer = csv.writer(f)\\n for i in range(len(resultList)):\\n print resultList[0]\\n writer.writerow(resultList[i])\\n \\n finally:\\n f.close()\",\n \"def submission(test_ids, pred_test, file_name):\\n pred_test[pred_test < 0] = 0\\n\\n val_pred_df = pd.DataFrame(data={'fullVisitorId': test_ids,\\n 'predictedRevenue': pred_test})\\n\\n val_pred_df = val_pred_df.groupby('fullVisitorId').sum().reset_index()\\n\\n val_pred_df.columns = ['fullVIsitorId', 'predictedLogRevenue']\\n val_pred_df['predictedLogRevenue'] = val_pred_df['predictedLogRevenue']\\n val_pred_df.to_csv('submission/'+file_name, index=False)\",\n \"def save_results(self, results, file_name, file_type):\\n if file_type == 'csv':\\n csv_filename = '{}.csv'.format(file_name)\\n\\n with open(csv_filename, 'w', newline='') as file:\\n writer = csv.writer(file)\\n writer.writerows(results)\",\n \"def write_to_file(test_data: pd.DataFrame, estimator: Estimator,\\n file_name: str):\\n\\n # we only need x_test\\n x_test, y_test = estimator.split_model_data(test_data)\\n\\n # have the estimator return its predictions\\n predictions = estimator.forecast(x_test)\\n\\n if predictions is None:\\n sys.stdout.write(f\\\"Estimator returned no forecast,\\\"\\n f\\\" has it been trained?\\\\n\\\")\\n raise RuntimeError\\n\\n # slice the timestamps from given dataframe\\n timestamps = test_data.loc[:, test_data.columns[0]].to_numpy()\\n timestamps = np.array(timestamps, dtype='datetime64[s]')\\n\\n with open(file_name, mode=\\\"w\\\", newline=\\\"\\\") as output_file:\\n # initialize csv writer\\n prediction_writer = csv.writer(output_file,\\n delimiter=\\\",\\\",\\n quotechar=\\\"'\\\",\\n quoting=csv.QUOTE_MINIMAL)\\n\\n # add header line\\n prediction_writer.writerow([test_data.columns[0],\\n test_data.columns[1]])\\n\\n #\\n for i in range(len(timestamps)):\\n timestamp = str(timestamps[i])\\n prediction = str(round(predictions[i], 6))\\n prediction_writer.writerow([timestamp, prediction])\\n\\n return\",\n \"def write_csv(image_names, image_classes, filename):\\n with open(filename, 'w', newline='') as file:\\n writer = csv.writer(file)\\n writer.writerow(['filename', 'label'])\\n writer.writerows(zip(image_names, image_classes))\",\n \"def write_classifier_output(location, folds, labels, predictions, class_probs, names=None):\\n with open(os.path.join(location, '-'.join([\\\"classifier\\\", \\\"fold\\\", \\\"predictions\\\"]) + '.txt'), 'w') as out_file:\\n for fold in range(folds):\\n out_file.write(\\\"fold \\\" + str(fold+1) + ':\\\\n')\\n out_file.write(\\\"{:50} {:<12} {:<12} {:<9} {:<9}\\\\n\\\".format(\\\"recording\\\", \\\"prediction\\\", \\\"label\\\", \\\"class 0\\\",\\n \\\"class 1\\\"))\\n fold_labels, fold_predictions, fold_class_probs = labels[fold], predictions[fold], class_probs[fold]\\n\\n if names is not None and len(names) != 0:\\n fold_names = np.hstack(names[fold])\\n else:\\n fold_names = len(fold_predictions) * ['']\\n\\n for pred_lab_tuple in zip(fold_names, fold_predictions, fold_labels, fold_class_probs[:, 0],\\n fold_class_probs[:, 1]):\\n (name, pred, label, prob1, prob2) = pred_lab_tuple\\n out_file.write(\\\"{:50} {:<12} {:<12} {:<9.2f} {:<9.2f}\\\\n\\\".format(name, pred, label, prob1, prob2))\\n out_file.write('\\\\n')\",\n \"def output():\\n\\n if args.top and not args.tfidf and not args.svd:\\n most_frequent(vector).to_csv(path_or_buf=\\\"top{}_vectorfile.csv\\\".format(args.top))\\n\\n elif args.top and args.tfidf and not args.svd:\\n tfidf_transform(most_frequent(vector)).to_csv(path_or_buf=\\\"tfidf_top{}.csv\\\".format(args.top))\\n\\n elif args.top and args.tfidf and args.svd:\\n svd_transform(tfidf_transform(most_frequent(vector)), indexes).to_csv(path_or_buf=\\\"svd{}_tfidf_topn.csv\\\".format(args.svd))\\n\\n elif args.tfidf and not args.top and not args.svd:\\n tfidf_transform(vector).to_csv(path_or_buf=\\\"tfidf.csv\\\")\\n\\n elif args.svd and not args.top and not args.tfidf:\\n svd_transform(vector, indexes).to_csv(path_or_buf=\\\"svd{}_vector.csv\\\".format(args.svd))\\n\\n elif args.tfidf and args.svd and not args.top:\\n svd_transform(tfidf_transform(vector), indexes).to_csv(path_or_buf=\\\"svd{}_tfidf.csv\\\".format(args.svd))\\n\\n else:\\n vector.to_csv(path_or_buf=\\\"vectorfile.csv\\\")\",\n \"def predict(classifier, data):\\n print(\\\"Beggining to classify data\\\")\\n results = classifier.predict(data)\\n results = pd.DataFrame(results)\\n results.index += 1\\n results.to_csv(\\\"out/results.csv\\\", header=[\\\"Label\\\"], index=True, index_label=[\\\"ImageId\\\"])\\n print(\\\"Finished classifying data\\\")\",\n \"def write_predictions(self, predictions, file_path=None, is_dict=True, pycm_obj=None):\\n\\n try:\\n super(SequenceClassification, self).write_predictions(\\n predictions, file_path=file_path, is_dict=is_dict\\n )\\n except AttributeError:\\n # TODO: Need to Fix\\n model_base = ModelBase()\\n model_base._log_dir = self._log_dir\\n model_base._train_counter = self._train_counter\\n model_base.training = self.training\\n model_base.write_predictions(predictions, file_path=file_path, is_dict=is_dict)\\n\\n data_type = \\\"train\\\" if self.training else \\\"valid\\\"\\n\\n if pycm_obj is not None:\\n stats_file_path = f\\\"predictions-{data_type}-{self._train_counter.get_display()}-stats\\\"\\n pycm_obj.save_csv(str(Path(self._log_dir) / \\\"predictions\\\" / stats_file_path))\\n\\n confusion_matrix_file_path = (\\n f\\\"predictions-{data_type}-{self._train_counter.get_display()}-confusion_matrix\\\"\\n )\\n cls_utils.write_confusion_matrix_to_csv(\\n str(Path(self._log_dir) / \\\"predictions\\\" / confusion_matrix_file_path), pycm_obj\\n )\",\n \"def exportFoldFile(vectors, authors, fileName):\\n with open(fileName, \\\"w\\\") as fFile:\\n for idv, vec in enumerate(vectors):\\n [fFile.write(str(val)+',') for val in vec]\\n fFile.write(authors[idv] + '\\\\n')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7925112","0.75827205","0.75710183","0.75710183","0.7489846","0.74501824","0.73730016","0.7322112","0.70706344","0.6982982","0.69460446","0.6911982","0.6833308","0.6815102","0.67553407","0.6745336","0.6745336","0.6745336","0.6745336","0.6745336","0.6745336","0.6735976","0.67324305","0.67095983","0.6690879","0.6612334","0.6602958","0.6601568","0.65663135","0.6558502","0.65581733","0.65417206","0.6535028","0.6519499","0.6509373","0.6495915","0.648198","0.64151376","0.64099944","0.6405993","0.6395074","0.6384059","0.6362279","0.6359811","0.6357627","0.63564426","0.634721","0.63367504","0.62852734","0.6285116","0.6259409","0.6254705","0.6253172","0.6248515","0.62415016","0.6219533","0.62152696","0.619938","0.6195651","0.6194581","0.6192136","0.6189015","0.61690325","0.6163753","0.61608064","0.61601233","0.6150573","0.6143036","0.6140446","0.6136853","0.6133349","0.613281","0.6123718","0.6120118","0.61128205","0.611245","0.6111941","0.6109166","0.6104979","0.6102238","0.6101714","0.6097939","0.6094107","0.60673153","0.6063369","0.60576755","0.6049206","0.60410124","0.60350573","0.6034096","0.60279715","0.60265666","0.6022835","0.6018383","0.6015225","0.5999805","0.5997553","0.5989992","0.5979374","0.59782135"],"string":"[\n \"0.7925112\",\n \"0.75827205\",\n \"0.75710183\",\n \"0.75710183\",\n \"0.7489846\",\n \"0.74501824\",\n \"0.73730016\",\n \"0.7322112\",\n \"0.70706344\",\n \"0.6982982\",\n \"0.69460446\",\n \"0.6911982\",\n \"0.6833308\",\n \"0.6815102\",\n \"0.67553407\",\n \"0.6745336\",\n \"0.6745336\",\n \"0.6745336\",\n \"0.6745336\",\n \"0.6745336\",\n \"0.6745336\",\n \"0.6735976\",\n \"0.67324305\",\n \"0.67095983\",\n \"0.6690879\",\n \"0.6612334\",\n \"0.6602958\",\n \"0.6601568\",\n \"0.65663135\",\n \"0.6558502\",\n \"0.65581733\",\n \"0.65417206\",\n \"0.6535028\",\n \"0.6519499\",\n \"0.6509373\",\n \"0.6495915\",\n \"0.648198\",\n \"0.64151376\",\n \"0.64099944\",\n \"0.6405993\",\n \"0.6395074\",\n \"0.6384059\",\n \"0.6362279\",\n \"0.6359811\",\n \"0.6357627\",\n \"0.63564426\",\n \"0.634721\",\n \"0.63367504\",\n \"0.62852734\",\n \"0.6285116\",\n \"0.6259409\",\n \"0.6254705\",\n \"0.6253172\",\n \"0.6248515\",\n \"0.62415016\",\n \"0.6219533\",\n \"0.62152696\",\n \"0.619938\",\n \"0.6195651\",\n \"0.6194581\",\n \"0.6192136\",\n \"0.6189015\",\n \"0.61690325\",\n \"0.6163753\",\n \"0.61608064\",\n \"0.61601233\",\n \"0.6150573\",\n \"0.6143036\",\n \"0.6140446\",\n \"0.6136853\",\n \"0.6133349\",\n \"0.613281\",\n \"0.6123718\",\n \"0.6120118\",\n \"0.61128205\",\n \"0.611245\",\n \"0.6111941\",\n \"0.6109166\",\n \"0.6104979\",\n \"0.6102238\",\n \"0.6101714\",\n \"0.6097939\",\n \"0.6094107\",\n \"0.60673153\",\n \"0.6063369\",\n \"0.60576755\",\n \"0.6049206\",\n \"0.60410124\",\n \"0.60350573\",\n \"0.6034096\",\n \"0.60279715\",\n \"0.60265666\",\n \"0.6022835\",\n \"0.6018383\",\n \"0.6015225\",\n \"0.5999805\",\n \"0.5997553\",\n \"0.5989992\",\n \"0.5979374\",\n \"0.59782135\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78001994"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":5298,"cells":{"query":{"kind":"string","value":"initialization routine for Chiplot, sets class variables"},"document":{"kind":"string","value":"def __init__(self, xdata = list(), ydata = list(), filename = None, projection = ''):\n\t\tdlog('in chiplot initialization')\n\t\tself.xdata = xdata\n\t\tself.ydata = ydata\n\t\tself.xmax = None\n\t\tself.ymax = None\n\t\tself.xmin = None\n\t\tself.ymin = None\n\t\tself.increment = 0\n\t\tself.filename = filename\n\t\tself.smv = ''\n\t\tself.points = 0\n\t\tself.projection = projection\n\t\t\n\t\t# process data if given\n\t\tlastx = -1\n\t\tincrement = 0\n\t\tdlog('len of x'+str(len(xdata)))\n\t\tdlog('len of y'+str(len(ydata)))\n\t\t\n\t\tfor i in range(0,len(ydata)):\n\t\t\tif(self.xmax == None):\n\t\t\t\tself.xmax = xdata[i]\n\t\t\t\tself.xmin = xdata[i]\n\t\t\t\tself.ymax = ydata[i]\n\t\t\t\tself.ymin = ydata[i]\n\t\t\t# determine ranges for axes\n\t\t\tif( xdata[i] > self.xmax):\n\t\t\t\tself.xmax = xdata[i]\n\t\t\telif( xdata[i] < self.xmin):\n\t\t\t\tself.xmin = xdata[i]\n\t\t\tif( ydata[i] > self.ymax):\n\t\t\t\tself.ymax = ydata[i]\n\t\t\telif( ydata[i] < self.ymin):\n\t\t\t\tself.ymin = ydata[i]\n\t\t\t\n\t\t\t# make sure that the chiplot increments steadily upward or downward\n\t\t\tif(xdata[i] < lastx and increment > 0):\n\t\t\t\tdlog('Error: Chiplot does not increment its data points normally', 'l')\n\t\t\telif(xdata[i] > lastx and increment < 0):\n\t\t\t\tdlog('Error: Chiplot does not increment its data points normally', 'l')\n\t\t\telif(lastx != -1 and increment == 0):\n\t\t\t\tincrement = xdata[i] - lastx\n\t\t\t\tdlog('incrementing by: '+str(increment), 'd')\n\t\t\t\n\t\t\tlastx = xdata[i]\n\t\t\t# add point to storage\n\t\t\tself.points = self.points + 1"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def initialize(cls):","def initialise(self):","def _init(self):\n pass","def init(self) -> None:","def __init_accessors (self):\n self.colors = ay.utils.Colors\n self.layout = Layout(self.seed)\n self.shapes = Shapes","def init(self):","def init(self):","def init(self) -> None:\n ...","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init():","def init_instance_attrs(self):\n super(CoconutShell, self).init_instance_attrs()\n self.compile = CoconutCompiler()","def _init(self):","def __init__():","def initialize(self, cwrap):\n pass","def __init__(self):\n ChipData.ChipData.__init__(self)","def initialize(self, **kwargs):","def initialize(self):\n\t\tpass","def initialize(self):","def initialize(self):","def initialize(self):","def initialize(self):","def _init(self):\n raise NotImplementedError","def setup_class(self):\n class SubCosmology(Cosmology):\n\n H0 = Parameter(unit=u.km / u.s / u.Mpc)\n Tcmb0 = Parameter(unit=u.K)\n\n def __init__(self, H0, Tcmb0=0*u.K, name=None, meta=None):\n super().__init__(name=name, meta=meta)\n self._H0 = H0\n self._Tcmb0 = Tcmb0\n\n self.cls = SubCosmology\n self.cls_args = (70 * (u.km / u.s / u.Mpc), 2.7 * u.K)\n self.cls_kwargs = dict(name=self.__class__.__name__, meta={\"a\": \"b\"})","def __init__(self):\n self._read_calibration_data()\n self.configure_sensor(\n TemperatureOversamplings.x08,\n PressureOversamplings.x16,\n HumidityOversamplings.x08,\n IIRFilterCoefficients.FC_003,\n 250,\n 250)","def initialize(self): \r\n pass","def __init__(self):\n\n self.plugboard = None\n self.rotors = []\n self.reflector = None","def initialize(self):\r\n pass","def initialize(self):\r\n pass","def __init__(self):\n\n #: Dict[str, Any]: Experiment metadata\n self.__experiment_metadata = None\n\n #: List[CurveData]: Processed experiment data set.\n self.__processed_data_set = list()\n\n #: Backend: backend object used for experimentation\n self.__backend = None\n\n # Add expected options to instance variable so that every method can access to.\n for key in self._default_options().__dict__:\n setattr(self, f\"__{key}\", None)\n\n # Add fixed parameters to instance variable so that every method can access to.\n for key in self.__fixed_parameters__:\n setattr(self, f\"__{key}\", None)","def init(self):\n raise NotImplementedError","def init(self):\n raise NotImplementedError","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def __init__ (self):\n pass","def __init__(self, nc_measure: NCMeasure, clf) -> None:\n self._nc_measure = nc_measure # Non-conformity measure\n self._clf = clf # Classifier\n self._cal_l = None # Calibration labels\n self._cal_a = None # Calibration alphas","def __init__(self):\n self._tyrannosaurus = []\n self._triceratops = []","def __init__(self):\n self.TECRDB_compounds_data_dict = {}\n self.TECRDB_compounds_pH7_species_id_dict = {}\n self.TECRDB_compounds_least_H_sid_dict = {}\n self.get_TECRDB_compounds_data()","def __init__(self):\n super().__init__(\"ccx\", 3, [])","def __init__(self):\n self._read_calibration_data()\n self.set_oversamplings_and_mode(\n HumidityOversampling.x08,\n TemperatureOversampling.x08,\n PressureOversampling.x16,\n SensorMode.Normal)\n self.set_config(\n InactiveDuration.ms1000,\n FilterCoefficient.fc04)","def __init__(self, coord_sys='Cartesian'):\r\n self.name = None \r\n self.chem = None\r\n self.nSpecies = None\r\n self.conc = np.array([0])\r\n self.Kw = None\r\n self.D = None \r\n self.x_coord = None\r\n self.y_coord = None\r\n self.dx = None\r\n self.dy = None\r\n self.dz = None\r\n self.coord_sys = coord_sys","def __init__(self, set_args, load_sensor_names,\n sensor_names, \n cnt_preprocessors, marker_def):\n self.__dict__.update(locals())\n del self.self\n if self.load_sensor_names == 'all':\n self.load_sensor_names = None","def __init__(self):\n super().__init__('drvr_06')\n self.comp = SimComp_6()","def x_init(self):\n pass","def __init__(self, make, model, year):\r\n super().__init__(make, model, year)\r\n self.battery_size = 70\r\n # self.autopilot = autopilot\r","def setup_class(self):\n self.iqcalc = iqcalc_astropy.IQCalc(logger=self.logger)","def initialize(self) -> None:\n pass","def _initialize(self):\n pass","def _initialize(self):\n pass","def _initialize(self):\n pass","def initialise(self, **kwargs):\n pass","def __init__(self):\n self.__dataset = None","def __init__(self) -> None:\n # TODO: Provide the complete constructor for this object","def initialize(self, *args, **kwargs):","def init(self, parameters):\n pass","def _set_init(self):\n ## Main information\n self.idxs = None\n self.sp_relative_pos = None\n ## Auxiliar information\n self.ks = None\n self.iss = [0]\n ## Class structural information\n self._setted = False\n self._constant_rel_pos = False\n self.staticneighs = None\n self.staticneighs_set = None","def __init__ (self) :","def __init__(self, connection_loc, tags_tsdata,\n dummy_var_no):\n self.init_system(connection_loc, tags_tsdata)\n self.removedummyvars(dummy_var_no)\n self.addforwardscale()\n self.addbackwardscale()","def do_init(self):\n\n pass","def __init__(self, coresys: CoreSys):\n self.coresys = coresys","def __init__(self, coresys: CoreSys):\n self.coresys = coresys","def __init__(self, coresys: CoreSys):\n self.coresys = coresys","def __init__(self, coresys: CoreSys):\n self.coresys = coresys","def __init__(self, coresys: CoreSys):\n self.coresys = coresys","def __init__(self, coresys: CoreSys):\n self.coresys = coresys","def __init__(self, coresys: CoreSys):\n self.coresys = coresys","def __init__(self, coresys: CoreSys):\n self.coresys = coresys","def __init__(self, coresys: CoreSys):\n self.coresys = coresys","def initialize(self,*args,**kwargs):\n self.__instrumentID = c_uint32(0) \n self.__numInstruments = c_uint32()\n self.__nbrOfChannels = c_uint32()\n self.__nbrADCBits = c_uint32()\n self.__temperature = c_int32()\n self.__time_us = c_double()\n\n self.loadDLLs(**kwargs) # Load the different DLLs or DLL based modules\n self.reinit() # init or reinit the board\n self.createDictAndGlobals() # create dictionaries and global variables\n self.nbrOfChannels=int(self.__nbrOfChannels.value) # duplicate self.nbrOfChannels in a Python type variable \n self.getInitialConfig()","def init(self, *args, **kwds):\n pass","def initialize(self):\n pass","def __init__(self, canvas):\r\n\r\n # Initialize attributes\r\n self.canvas = canvas\r\n self.fig = canvas.fig\r\n self.units = None\r\n self.cb = None\r\n self.cb_bt = None\r\n self.cb_gga = None\r\n self.cb_vtg = None\r\n self.bt = None\r\n self.gga = None\r\n self.vtg = None\r\n self.hover_connection = None\r\n self.annot = None","def __init__(self):\n super().__init__()\n self.p = 0.0\n self.type = 'Bernoulli'\n self.distType = 'Discrete'\n self.lowerBound = 0.0\n self.upperBound = 1.0\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'CDF'\n self.preferredPolynomials = 'CDF'","def init():\n pass","def __init__(self):\n self.tape_tag = None\n self.independentVariableShapeList = []\n self.dependentVariableShapeList = []","def __init__(self):\n self._initialized = False\n self.init()","def __init__(self):\n self._initialized = False\n self.init()","def __init__(self):\r\n\r\n self.Helpers = Helpers(\"Movidius\")\r\n self.confs = self.Helpers.confs\r\n\r\n self.classes = []\r\n self.ncsGraph = None\r\n self.ncsDevice = None\r\n self.reqsize = None\r\n\r\n self.mean = 128\r\n self.std = 1 / 128\r\n\r\n #mvnc.SetGlobalOption(mvnc.GlobalOption.LOG_LEVEL, 2)\r\n\r\n self.Helpers.logger.info(\"Movidius class initialization complete.\")","def initialize(self, **kwargs: Any) -> None:\n pass","def __init__(self, Q_sys, C_sys, C_stock):\n self._Q_sys = Q_sys\n self._C_sys = C_sys\n self._C_stock = C_stock","def __init__(self):\n self._create_options()\n self._create_sections()","def initialize(self, *args, **kwargs):\n pass","def __init__(self):\n super().__init__()\n self.p = 0.0\n self.type = 'Geometric'\n self.distType = 'Discrete'\n self.lowerBound = 0.0\n self.upperBound = 1.0\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'CDF'\n self.preferredPolynomials = 'CDF'","def initialize(self):\n pass # pragma: no cover","def __init__(self, verbose):\n self.modules = maus_cpp.globals.get_monte_carlo_mice_modules()\n self.verbose = verbose","def __init__(self):\n parameters_list = []\n self.config_dict = self.open_config(parameters_list)\n\n # Define defaults\n self.disc_gt = 0.0\n self.disc_out = 0.0","def __init__(self):\n self.machines = {}\n self.configs = {}\n self.systems = {}\n self.jobs = {}\n self.benchmarks = {}\n self.projects = {}","def __init__(self):\n\n self.logger = utils.get_logger()\n\n # set constants\n constants = models.get_asset_dicts('preferences')\n for key, value in constants.items():\n setattr(self, key, value)","def _plot_init(self):\n pass","def _plot_init(self):\n pass","def __init__(self, **kwds):\n raise NotImplementedError"],"string":"[\n \"def initialize(cls):\",\n \"def initialise(self):\",\n \"def _init(self):\\n pass\",\n \"def init(self) -> None:\",\n \"def __init_accessors (self):\\n self.colors = ay.utils.Colors\\n self.layout = Layout(self.seed)\\n self.shapes = Shapes\",\n \"def init(self):\",\n \"def init(self):\",\n \"def init(self) -> None:\\n ...\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init():\",\n \"def init_instance_attrs(self):\\n super(CoconutShell, self).init_instance_attrs()\\n self.compile = CoconutCompiler()\",\n \"def _init(self):\",\n \"def __init__():\",\n \"def initialize(self, cwrap):\\n pass\",\n \"def __init__(self):\\n ChipData.ChipData.__init__(self)\",\n \"def initialize(self, **kwargs):\",\n \"def initialize(self):\\n\\t\\tpass\",\n \"def initialize(self):\",\n \"def initialize(self):\",\n \"def initialize(self):\",\n \"def initialize(self):\",\n \"def _init(self):\\n raise NotImplementedError\",\n \"def setup_class(self):\\n class SubCosmology(Cosmology):\\n\\n H0 = Parameter(unit=u.km / u.s / u.Mpc)\\n Tcmb0 = Parameter(unit=u.K)\\n\\n def __init__(self, H0, Tcmb0=0*u.K, name=None, meta=None):\\n super().__init__(name=name, meta=meta)\\n self._H0 = H0\\n self._Tcmb0 = Tcmb0\\n\\n self.cls = SubCosmology\\n self.cls_args = (70 * (u.km / u.s / u.Mpc), 2.7 * u.K)\\n self.cls_kwargs = dict(name=self.__class__.__name__, meta={\\\"a\\\": \\\"b\\\"})\",\n \"def __init__(self):\\n self._read_calibration_data()\\n self.configure_sensor(\\n TemperatureOversamplings.x08,\\n PressureOversamplings.x16,\\n HumidityOversamplings.x08,\\n IIRFilterCoefficients.FC_003,\\n 250,\\n 250)\",\n \"def initialize(self): \\r\\n pass\",\n \"def __init__(self):\\n\\n self.plugboard = None\\n self.rotors = []\\n self.reflector = None\",\n \"def initialize(self):\\r\\n pass\",\n \"def initialize(self):\\r\\n pass\",\n \"def __init__(self):\\n\\n #: Dict[str, Any]: Experiment metadata\\n self.__experiment_metadata = None\\n\\n #: List[CurveData]: Processed experiment data set.\\n self.__processed_data_set = list()\\n\\n #: Backend: backend object used for experimentation\\n self.__backend = None\\n\\n # Add expected options to instance variable so that every method can access to.\\n for key in self._default_options().__dict__:\\n setattr(self, f\\\"__{key}\\\", None)\\n\\n # Add fixed parameters to instance variable so that every method can access to.\\n for key in self.__fixed_parameters__:\\n setattr(self, f\\\"__{key}\\\", None)\",\n \"def init(self):\\n raise NotImplementedError\",\n \"def init(self):\\n raise NotImplementedError\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def __init__ (self):\\n pass\",\n \"def __init__(self, nc_measure: NCMeasure, clf) -> None:\\n self._nc_measure = nc_measure # Non-conformity measure\\n self._clf = clf # Classifier\\n self._cal_l = None # Calibration labels\\n self._cal_a = None # Calibration alphas\",\n \"def __init__(self):\\n self._tyrannosaurus = []\\n self._triceratops = []\",\n \"def __init__(self):\\n self.TECRDB_compounds_data_dict = {}\\n self.TECRDB_compounds_pH7_species_id_dict = {}\\n self.TECRDB_compounds_least_H_sid_dict = {}\\n self.get_TECRDB_compounds_data()\",\n \"def __init__(self):\\n super().__init__(\\\"ccx\\\", 3, [])\",\n \"def __init__(self):\\n self._read_calibration_data()\\n self.set_oversamplings_and_mode(\\n HumidityOversampling.x08,\\n TemperatureOversampling.x08,\\n PressureOversampling.x16,\\n SensorMode.Normal)\\n self.set_config(\\n InactiveDuration.ms1000,\\n FilterCoefficient.fc04)\",\n \"def __init__(self, coord_sys='Cartesian'):\\r\\n self.name = None \\r\\n self.chem = None\\r\\n self.nSpecies = None\\r\\n self.conc = np.array([0])\\r\\n self.Kw = None\\r\\n self.D = None \\r\\n self.x_coord = None\\r\\n self.y_coord = None\\r\\n self.dx = None\\r\\n self.dy = None\\r\\n self.dz = None\\r\\n self.coord_sys = coord_sys\",\n \"def __init__(self, set_args, load_sensor_names,\\n sensor_names, \\n cnt_preprocessors, marker_def):\\n self.__dict__.update(locals())\\n del self.self\\n if self.load_sensor_names == 'all':\\n self.load_sensor_names = None\",\n \"def __init__(self):\\n super().__init__('drvr_06')\\n self.comp = SimComp_6()\",\n \"def x_init(self):\\n pass\",\n \"def __init__(self, make, model, year):\\r\\n super().__init__(make, model, year)\\r\\n self.battery_size = 70\\r\\n # self.autopilot = autopilot\\r\",\n \"def setup_class(self):\\n self.iqcalc = iqcalc_astropy.IQCalc(logger=self.logger)\",\n \"def initialize(self) -> None:\\n pass\",\n \"def _initialize(self):\\n pass\",\n \"def _initialize(self):\\n pass\",\n \"def _initialize(self):\\n pass\",\n \"def initialise(self, **kwargs):\\n pass\",\n \"def __init__(self):\\n self.__dataset = None\",\n \"def __init__(self) -> None:\\n # TODO: Provide the complete constructor for this object\",\n \"def initialize(self, *args, **kwargs):\",\n \"def init(self, parameters):\\n pass\",\n \"def _set_init(self):\\n ## Main information\\n self.idxs = None\\n self.sp_relative_pos = None\\n ## Auxiliar information\\n self.ks = None\\n self.iss = [0]\\n ## Class structural information\\n self._setted = False\\n self._constant_rel_pos = False\\n self.staticneighs = None\\n self.staticneighs_set = None\",\n \"def __init__ (self) :\",\n \"def __init__(self, connection_loc, tags_tsdata,\\n dummy_var_no):\\n self.init_system(connection_loc, tags_tsdata)\\n self.removedummyvars(dummy_var_no)\\n self.addforwardscale()\\n self.addbackwardscale()\",\n \"def do_init(self):\\n\\n pass\",\n \"def __init__(self, coresys: CoreSys):\\n self.coresys = coresys\",\n \"def __init__(self, coresys: CoreSys):\\n self.coresys = coresys\",\n \"def __init__(self, coresys: CoreSys):\\n self.coresys = coresys\",\n \"def __init__(self, coresys: CoreSys):\\n self.coresys = coresys\",\n \"def __init__(self, coresys: CoreSys):\\n self.coresys = coresys\",\n \"def __init__(self, coresys: CoreSys):\\n self.coresys = coresys\",\n \"def __init__(self, coresys: CoreSys):\\n self.coresys = coresys\",\n \"def __init__(self, coresys: CoreSys):\\n self.coresys = coresys\",\n \"def __init__(self, coresys: CoreSys):\\n self.coresys = coresys\",\n \"def initialize(self,*args,**kwargs):\\n self.__instrumentID = c_uint32(0) \\n self.__numInstruments = c_uint32()\\n self.__nbrOfChannels = c_uint32()\\n self.__nbrADCBits = c_uint32()\\n self.__temperature = c_int32()\\n self.__time_us = c_double()\\n\\n self.loadDLLs(**kwargs) # Load the different DLLs or DLL based modules\\n self.reinit() # init or reinit the board\\n self.createDictAndGlobals() # create dictionaries and global variables\\n self.nbrOfChannels=int(self.__nbrOfChannels.value) # duplicate self.nbrOfChannels in a Python type variable \\n self.getInitialConfig()\",\n \"def init(self, *args, **kwds):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def __init__(self, canvas):\\r\\n\\r\\n # Initialize attributes\\r\\n self.canvas = canvas\\r\\n self.fig = canvas.fig\\r\\n self.units = None\\r\\n self.cb = None\\r\\n self.cb_bt = None\\r\\n self.cb_gga = None\\r\\n self.cb_vtg = None\\r\\n self.bt = None\\r\\n self.gga = None\\r\\n self.vtg = None\\r\\n self.hover_connection = None\\r\\n self.annot = None\",\n \"def __init__(self):\\n super().__init__()\\n self.p = 0.0\\n self.type = 'Bernoulli'\\n self.distType = 'Discrete'\\n self.lowerBound = 0.0\\n self.upperBound = 1.0\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'CDF'\\n self.preferredPolynomials = 'CDF'\",\n \"def init():\\n pass\",\n \"def __init__(self):\\n self.tape_tag = None\\n self.independentVariableShapeList = []\\n self.dependentVariableShapeList = []\",\n \"def __init__(self):\\n self._initialized = False\\n self.init()\",\n \"def __init__(self):\\n self._initialized = False\\n self.init()\",\n \"def __init__(self):\\r\\n\\r\\n self.Helpers = Helpers(\\\"Movidius\\\")\\r\\n self.confs = self.Helpers.confs\\r\\n\\r\\n self.classes = []\\r\\n self.ncsGraph = None\\r\\n self.ncsDevice = None\\r\\n self.reqsize = None\\r\\n\\r\\n self.mean = 128\\r\\n self.std = 1 / 128\\r\\n\\r\\n #mvnc.SetGlobalOption(mvnc.GlobalOption.LOG_LEVEL, 2)\\r\\n\\r\\n self.Helpers.logger.info(\\\"Movidius class initialization complete.\\\")\",\n \"def initialize(self, **kwargs: Any) -> None:\\n pass\",\n \"def __init__(self, Q_sys, C_sys, C_stock):\\n self._Q_sys = Q_sys\\n self._C_sys = C_sys\\n self._C_stock = C_stock\",\n \"def __init__(self):\\n self._create_options()\\n self._create_sections()\",\n \"def initialize(self, *args, **kwargs):\\n pass\",\n \"def __init__(self):\\n super().__init__()\\n self.p = 0.0\\n self.type = 'Geometric'\\n self.distType = 'Discrete'\\n self.lowerBound = 0.0\\n self.upperBound = 1.0\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'CDF'\\n self.preferredPolynomials = 'CDF'\",\n \"def initialize(self):\\n pass # pragma: no cover\",\n \"def __init__(self, verbose):\\n self.modules = maus_cpp.globals.get_monte_carlo_mice_modules()\\n self.verbose = verbose\",\n \"def __init__(self):\\n parameters_list = []\\n self.config_dict = self.open_config(parameters_list)\\n\\n # Define defaults\\n self.disc_gt = 0.0\\n self.disc_out = 0.0\",\n \"def __init__(self):\\n self.machines = {}\\n self.configs = {}\\n self.systems = {}\\n self.jobs = {}\\n self.benchmarks = {}\\n self.projects = {}\",\n \"def __init__(self):\\n\\n self.logger = utils.get_logger()\\n\\n # set constants\\n constants = models.get_asset_dicts('preferences')\\n for key, value in constants.items():\\n setattr(self, key, value)\",\n \"def _plot_init(self):\\n pass\",\n \"def _plot_init(self):\\n pass\",\n \"def __init__(self, **kwds):\\n raise NotImplementedError\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.705675","0.69822073","0.69724643","0.69628584","0.6953671","0.6945866","0.6945866","0.69433916","0.69258225","0.69258225","0.69258225","0.69258225","0.69258225","0.69258225","0.69258225","0.69258225","0.6895559","0.68572617","0.6853776","0.6848816","0.6839878","0.6829869","0.68288934","0.6766515","0.6750715","0.6750715","0.6750715","0.6750715","0.6737793","0.6736068","0.6721077","0.6707387","0.6690871","0.66819334","0.66819334","0.66535485","0.6648761","0.6648761","0.6632673","0.6632673","0.6632673","0.6632673","0.6632673","0.6617402","0.66100013","0.6595159","0.65733564","0.65561295","0.65560704","0.65447646","0.65409595","0.6534781","0.653289","0.65254575","0.6523842","0.6499733","0.6482943","0.6482943","0.6482943","0.6480662","0.6480362","0.6470308","0.6468605","0.6466192","0.64656687","0.6458992","0.6444356","0.6443393","0.6435763","0.6435763","0.6435763","0.6435763","0.6435763","0.6435763","0.6435763","0.6435763","0.6435763","0.643434","0.64315957","0.6430917","0.64268726","0.64260274","0.6418523","0.6414152","0.6414091","0.6414091","0.6407498","0.64069986","0.64067775","0.64064175","0.6404379","0.6403149","0.64007246","0.63933915","0.6391665","0.6388414","0.6385806","0.63852435","0.63852435","0.638216"],"string":"[\n \"0.705675\",\n \"0.69822073\",\n \"0.69724643\",\n \"0.69628584\",\n \"0.6953671\",\n \"0.6945866\",\n \"0.6945866\",\n \"0.69433916\",\n \"0.69258225\",\n \"0.69258225\",\n \"0.69258225\",\n \"0.69258225\",\n \"0.69258225\",\n \"0.69258225\",\n \"0.69258225\",\n \"0.69258225\",\n \"0.6895559\",\n \"0.68572617\",\n \"0.6853776\",\n \"0.6848816\",\n \"0.6839878\",\n \"0.6829869\",\n \"0.68288934\",\n \"0.6766515\",\n \"0.6750715\",\n \"0.6750715\",\n \"0.6750715\",\n \"0.6750715\",\n \"0.6737793\",\n \"0.6736068\",\n \"0.6721077\",\n \"0.6707387\",\n \"0.6690871\",\n \"0.66819334\",\n \"0.66819334\",\n \"0.66535485\",\n \"0.6648761\",\n \"0.6648761\",\n \"0.6632673\",\n \"0.6632673\",\n \"0.6632673\",\n \"0.6632673\",\n \"0.6632673\",\n \"0.6617402\",\n \"0.66100013\",\n \"0.6595159\",\n \"0.65733564\",\n \"0.65561295\",\n \"0.65560704\",\n \"0.65447646\",\n \"0.65409595\",\n \"0.6534781\",\n \"0.653289\",\n \"0.65254575\",\n \"0.6523842\",\n \"0.6499733\",\n \"0.6482943\",\n \"0.6482943\",\n \"0.6482943\",\n \"0.6480662\",\n \"0.6480362\",\n \"0.6470308\",\n \"0.6468605\",\n \"0.6466192\",\n \"0.64656687\",\n \"0.6458992\",\n \"0.6444356\",\n \"0.6443393\",\n \"0.6435763\",\n \"0.6435763\",\n \"0.6435763\",\n \"0.6435763\",\n \"0.6435763\",\n \"0.6435763\",\n \"0.6435763\",\n \"0.6435763\",\n \"0.6435763\",\n \"0.643434\",\n \"0.64315957\",\n \"0.6430917\",\n \"0.64268726\",\n \"0.64260274\",\n \"0.6418523\",\n \"0.6414152\",\n \"0.6414091\",\n \"0.6414091\",\n \"0.6407498\",\n \"0.64069986\",\n \"0.64067775\",\n \"0.64064175\",\n \"0.6404379\",\n \"0.6403149\",\n \"0.64007246\",\n \"0.63933915\",\n \"0.6391665\",\n \"0.6388414\",\n \"0.6385806\",\n \"0.63852435\",\n \"0.63852435\",\n \"0.638216\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64347374"},"document_rank":{"kind":"string","value":"77"}}},{"rowIdx":5299,"cells":{"query":{"kind":"string","value":"This functions check's whether a text contains contractions or not. In case a contraction is found, the corrected value from the dictionary is returned."},"document":{"kind":"string","value":"def replace_contractions(self, text, lower=False):\n\n # replace words with contraction according to the contraction_dict\n if lower:\n contraction_dict = self.contraction_dict_lower\n else:\n contraction_dict = self.contraction_dict\n\n if text.strip() in contraction_dict.keys():\n text = contraction_dict[text.strip()]\n\n # replace words with \"'ve\" to \"have\"\n matches = re.findall(r'\\b\\w+[\\'`´]ve\\b', text)\n if len(matches) != 0:\n text = re.sub(r'[\\'`´]ve\\b', \" have\", text)\n\n # replace words with \"'re\" to \"are\"\n matches = re.findall(r'\\b\\w+[\\'`´]re\\b', text)\n if len(matches) != 0:\n text = re.sub(r'[\\'`´]re\\b', \" are\", text)\n\n # replace words with \"'ll\" to \"will\"\n matches = re.findall(r'\\b\\w+[\\'`´]ll\\b', text)\n if len(matches) != 0:\n text = re.sub(r'[\\'`´]ll\\b', \" will\", text)\n\n # replace words with \"'m\" to \"am\"\n matches = re.findall(r'\\b\\w+[\\'`´]m\\b', text)\n if len(matches) != 0:\n text = re.sub(r'[\\'`´]m\\b', \" am\", text)\n\n # replace words with \"'d\" to \"would\"\n matches = re.findall(r'\\b\\w+[\\'`´]d\\b', text)\n if len(matches) != 0:\n text = re.sub(r'[\\'`´]d\\b', \" would\", text)\n\n # replace all \"'s\" by space\n matches = re.findall(r'\\b\\w+[\\'`´]s\\b', text)\n if len(matches) != 0:\n text = re.sub(r'[\\'`´]s\\b', \" \", text)\n\n return text"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def replace_contractions(text):\r\n return contractions.fix(text)","def _replace_contractions(text):\n return contractions.fix(text)","def replace_contractions(text):\n return contractions.fix(text)","def replace_contractions(text):\n return contractions.fix(text)","def expand_contractions(text):\r\n text = re.sub(\r\n r\"(\\b)([Aa]re|[Cc]ould|[Dd]id|[Dd]oes|[Dd]o|[Hh]ad|[Hh]as|[Hh]ave|[Ii]s|[Mm]ight|[Mm]ust|[Ss]hould|[Ww]ere|[Ww]ould)n't\",\r\n r\"\\1\\2 not\", text)\r\n text = re.sub(r\"(\\b)([Hh]e|[Ii]|[Ss]he|[Tt]hey|[Ww]e|[Ww]hat|[Ww]ho|[Yy]ou)'ll\", r\"\\1\\2 will\", text)\r\n text = re.sub(r\"(\\b)([Tt]hey|[Ww]e|[Ww]hat|[Ww]ho|[Yy]ou)'re\", r\"\\1\\2 are\", text)\r\n text = re.sub(r\"(\\b)([Ii]|[Ss]hould|[Tt]hey|[Ww]e|[Ww]hat|[Ww]ho|[Ww]ould|[Yy]ou)'ve\", r\"\\1\\2 have\", text)\r\n\r\n text = re.sub(r\"(\\b)([Cc]a)n't\", r\"\\1\\2n not\", text)\r\n text = re.sub(r\"(\\b)([Ii])'m\", r\"\\1\\2 am\", text)\r\n text = re.sub(r\"(\\b)([Ll]et)'s\", r\"\\1\\2 us\", text)\r\n text = re.sub(r\"(\\b)([Ww])on't\", r\"\\1\\2ill not\", text)\r\n text = re.sub(r\"(\\b)([Ss])han't\", r\"\\1\\2hall not\", text)\r\n text = re.sub(r\"(\\b)([Yy])(?:'all|a'll)\", r\"\\1\\2ou all\", text)\r\n\r\n return text","def replaceContractions(self, text):\n\t\treturn contractions.fix(text)","def preProcess(text):\n\ttext = text.lower() # lower case the text\n\t# Q4 replace the word with expanded contractions\n\tfor k,v in general_contraction.items():\n\t\tif k in text.split():\n\t\t\ttext = text.replace(k,v)\n\t# Q4 remove speacial char including all puncuattions and replace it with a space\n\ttext = re.sub('[^A-Za-z0-9]+',' ',text) \n\t# tokenise\n\ttokens = text.split()\n\t# stop word removal\n\ttokens = [w for w in tokens if w not in stopwords ]\n\t# Q4 Stemming\n\ttokens = [str(porter.stem(w)) for w in tokens]\n\t# if word is non-english return its english form # too much time-complexity\n\t# tokens = [porter.stem(w) if porter.stem(w) in set(words.words()) else w for w in tokens ]\n\t# for words having digits such as 12gb, 1st, etc expanding the token list\n\tfor k in tokens:\n\t\tif len(k) >2 and re.match(r'[0-9]+',k):\t\t\t\n\t\t\tif len(k) >2 and not k.isdigit():\n\t\t\t\tl = re.split(r'(\\d+)',k)\n\t\t\t\tl = [w for w in l if w is not '' ]\n\t\t\t\tif l and len(l) <= 3:\n\t\t\t\t\tfor i in l:\n\t\t\t\t\t\tif i in digit_contractions.keys():\n\t\t\t\t\t\t\tl = list(map(lambda b: b.replace(i,digit_contractions[i]), l))\n\t\t\t\t\ttokens.remove(k)\n\t\t\t\t\ttokens = tokens+l\n\t\t\t\telse:\n\t\t\t\t\ttokens.remove(k)\n\tfor k,v in digit_contractions.items():\n\t\tif k in tokens:\n\t\t\tif tokens[tokens.index(k)-1].isdigit():\t\n\t\t\t\ttokens = list(map(lambda b: b.replace(k,v), tokens))\n\t# remove tokens of size less than 2\n\ttokens = [t for t in tokens if len(t) > 2]\n\treturn tokens","def replace_contractions(sample):\n sample[\"full_text\"] = contractions.fix(sample[\"full_text\"])\n return sample","def recommended_correction(text):\n tool = language_check.LanguageTool('en-US')\n matches = tool.check(text)\n correction = language_check.correct(text, matches)\n return correction","def expand_contractions(text):\n text = list(cont.expand_texts([text], precise=True))[0]\n return text \n\n text = \"\"\"three cups of coffee\"\"\"\n doc = nlp(text)\n tokens = [w2n.word_to_num(token.text) if token.pos_ == 'NUM' else token for token in doc]","def contractions_remove(self,sentence):\n sentence = self.contractions_re.sub(lambda mo: CONTRACTIONS[mo.string[mo.start():mo.end()]], sentence)\n return sentence","def ocr_correction(token):","def corrected(text):\n # Init SpellChecker\n spell = SpellChecker(language='fr', distance=1, case_sensitive=False)\n # Get misspellings and corrections\n text_splitted = text.split()\n misspelled = spell.unknown(text_splitted)\n correction = {word:spell.correction(word) for word in misspelled}\n\n # Replace misspellings in text\n for k,v in correction.items():\n text = text.replace(k,v)\n return text","def removeContractions(self, text=None):\n\n\t\tif type(text) != type(str):\n\t\t\ttext = \" \".join(text)\n\n\t\tfor key in self.norm_words:\n\t\t\ttext = text.replace(key, self.norm_words[key])\n\n\t\ttext = text.split(\" \")\n\n\t\treturn text","def correct(self, text):\n with self._get_searcher() as searcher:\n query = self._get_query_parser().parse(unicode(text))\n correction = searcher.correct_query(query, unicode(text))\n if correction.query != query:\n formatter = whoosh.highlight.HtmlFormatter()\n return correction.format_string(formatter)\n return None","def replace_contractions(self):\n self.text = contractions.fix(self.text)\n return self","def text_dict(text):\n hey_words = {'hello', 'hey', 'hi', 'heya', 'hiya', 'hai'}\n fine_words = {'fine', 'good', 'splendid', 'amazing', 'well', 'lovely', 'cool'}\n bye_words = {'goodbye', 'bye', 'adios'}\n thank_words = {'thanks', 'thank'}\n notes_words = {'notes', 'note'}\n tokens = set(text.split())\n\n if len(notes_words.intersection(tokens)) > 0:\n text = 'Ok sure. Please tell me what to note down.'\n\n elif len(fine_words.intersection(tokens)) > 0:\n text = '''Good to hear that. How may I help you?'''\n\n elif len(hey_words.intersection(tokens)) > 0:\n text = '''Hi Shivek. How are you doing today?'''\n\n elif len(thank_words.intersection(tokens)) > 0:\n text = '''You are welcome. Can I help you with anything else?'''\n\n elif len(bye_words.intersection(tokens)) > 0 or 'see you' in text or 'see ya' in text or 'no thank' in text:\n text = '''Ok goodbye!'''\n\n\n else:\n text = '''Sorry, I didn't get you. Can you please repeat?'''\n return text","def correction():\n text = request.args.get('text', '')\n text = TextBlob(text)\n return jsonify(text=unicode(text.correct()))","def correct(search_key, text, strictness):\n\n text_copy = copy.deepcopy(text)\n words = text.split()\n for word in words:\n similarity = SequenceMatcher(None, word, search_key)\n if similarity.ratio() > strictness:\n text_copy = text_copy.replace(word, search_key)\n return text_copy","def check_for_correction(dummy_arg):\n #space_indexes = []\n #new_space_indexes = []\n #punct_dict = {}\n\n # Get current document and attributes for doc\n doc = XSCRIPTCONTEXT.getDocument()\n selection_supplier = doc.getCurrentController()\n indexAccess = selection_supplier.getSelection()\n #count = indexAccess.getCount() # don't really need this right now. Only loop once..\n\n # get cursor to write corrected word highlighted in blue\n text = doc.Text\n vis_cursor = doc.getCurrentController().getViewCursor() # visible cursor in OpenOffice\n nvis_cursor= vis_cursor.getText().createTextCursorByRange(vis_cursor) # not visible cursor place at same place as visible\n \n # set str and txt for use\n highlighted_txt = indexAccess.getByIndex(0)\n highlighted_str = highlighted_txt.getString()\n \n # strip punctuation from string\n punctuation = set(string.punctuation)\n '''\n for letter in highlighted_str:\n if letter == ' ':\n sp_index = highlighted_str.index(letter)\n space_indexes.append(sp_index + len(space_indexes))\n highlighted_str = highlighted_str.replace(letter,'',1)\n \n for index in space_indexes:\n highlighted_str = highlighted_str[:index] + ' ' + highlighted_str[index:]\n\n \n for letter in highlighted_str:\n if letter in punctuation:\n punc_index = highlighted_str.index(letter)\n punct_dict[letter] = punc_index\n highlighted_str = highlighted_str.replace(letter,'',1)\n '''\n highlighted_str = ''.join(ch for ch in highlighted_str if ch not in punctuation) \n highlighted_str_arr = highlighted_str.split()\n\n corrected_sent = highlighted_str # correct sentence to be displayed at end\n\n # check for correction for each word highlighted\n for word in highlighted_str_arr:\n\n if len(highlighted_str) == 0:\n return None\n\n # call make_correction helper function to correct highlighted word\n else:\n correction = make_correction(word)\n \n # make sure new correction word is present\n if correction:\n # get parent window from document\n parentwin = doc.CurrentController.Frame.ContainerWindow\n # make window\n correction_query_str = word + \" => \" + correction # show old word => new word\n prompt = MessageBox(parentwin, correction_query_str, \"Correction:\", MESSAGEBOX, BUTTONS_YES_NO)\n \n # if yes button pressed, replace word\n if prompt == 2:\n # replace wrong word with corrected word\n corrected_sent = corrected_sent.replace(word, correction)\n '''\n for letter in corrected_sent:\n if letter == ' ':\n sp_index = corrected_sent.index(letter)\n new_space_indexes.append(sp_index + len(new_space_indexes))\n corrected_sent = corrected_sent.replace(letter,'',1)\n \n for index in new_space_indexes:\n corrected_sent = corrected_sent[:index] + ' ' + corrected_sent[index:]\n \n \n punc_location = {}\n for key in punct_dict:\n p_index = punct_dict[key]\n for s_index in space_indexes:\n if abs(p_index-s_index) == 1:\n location_index = space_indexes.index(s_index)\n if p_index-s_index > 0:\n punc_location[key] = new_space_indexes[location_index+1]\n else:\n punc_location[key] = new_space_indexes[location_index-1]\n\n\n for key in punc_location:\n index = punc_location[key]\n corrected_sent = key\n #corrected_sent = corrected_sent[:index] + key + corrected_sent[index:]\n ''' \n #highlighted_txt.setString(highlighted_str)\n highlighted_txt.setString(corrected_sent) # replace old word with nothing","def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):\n\n def _strip_spaces(text):\n ns_chars = []\n ns_to_s_map = collections.OrderedDict()\n for (i, c) in enumerate(text):\n if c == \" \":\n continue\n ns_to_s_map[len(ns_chars)] = i\n ns_chars.append(c)\n ns_text = \"\".join(ns_chars)\n return (ns_text, ns_to_s_map)\n\n # We first tokenize `orig_text`, strip whitespace from the result\n # and `pred_text`, and check if they are the same length. If they are\n # NOT the same length, the heuristic has failed. If they are the same\n # length, we assume the characters are one-to-one aligned.\n\n tokenizer = BasicTokenizer(do_lower_case=do_lower_case)\n\n tok_text = \" \".join(tokenizer.tokenize(orig_text))\n\n start_position = tok_text.find(pred_text)\n if start_position == -1:\n if verbose_logging:\n logger.info(\n \"Unable to find text: '%s' in '%s'\" % (pred_text, orig_text))\n return orig_text\n end_position = start_position + len(pred_text) - 1\n\n (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)\n (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)\n\n if len(orig_ns_text) != len(tok_ns_text):\n if verbose_logging:\n logger.info(\"Length not equal after stripping spaces: '%s' vs '%s'\",\n orig_ns_text, tok_ns_text)\n return orig_text\n\n # We then project the characters in `pred_text` back to `orig_text` using\n # the character-to-character alignment.\n tok_s_to_ns_map = {}\n for (i, tok_index) in tok_ns_to_s_map.items():\n tok_s_to_ns_map[tok_index] = i\n\n orig_start_position = None\n if start_position in tok_s_to_ns_map:\n ns_start_position = tok_s_to_ns_map[start_position]\n if ns_start_position in orig_ns_to_s_map:\n orig_start_position = orig_ns_to_s_map[ns_start_position]\n\n if orig_start_position is None:\n if verbose_logging:\n logger.info(\"Couldn't map start position\")\n return orig_text\n\n orig_end_position = None\n if end_position in tok_s_to_ns_map:\n ns_end_position = tok_s_to_ns_map[end_position]\n if ns_end_position in orig_ns_to_s_map:\n orig_end_position = orig_ns_to_s_map[ns_end_position]\n\n if orig_end_position is None:\n if verbose_logging:\n logger.info(\"Couldn't map end position\")\n return orig_text\n\n output_text = orig_text[orig_start_position:(orig_end_position + 1)]\n return output_text","def get_final_text(config: configure_finetuning.FinetuningConfig, pred_text,\n orig_text):\n\n # When we created the data, we kept track of the alignment between original\n # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So\n # now `orig_text` contains the span of our original text corresponding to the\n # span that we predicted.\n #\n # However, `orig_text` may contain extra characters that we don't want in\n # our prediction.\n #\n # For example, let's say:\n # pred_text = steve smith\n # orig_text = Steve Smith's\n #\n # We don't want to return `orig_text` because it contains the extra \"'s\".\n #\n # We don't want to return `pred_text` because it's already been normalized\n # (the SQuAD eval script also does punctuation stripping/lower casing but\n # our tokenizer does additional normalization like stripping accent\n # characters).\n #\n # What we really want to return is \"Steve Smith\".\n #\n # Therefore, we have to apply a semi-complicated alignment heruistic between\n # `pred_text` and `orig_text` to get a character-to-charcter alignment. This\n # can fail in certain cases in which case we just return `orig_text`.\n\n def _strip_spaces(text):\n ns_chars = []\n ns_to_s_map = collections.OrderedDict()\n for i, c in enumerate(text):\n if c == \" \":\n continue\n ns_to_s_map[len(ns_chars)] = i\n ns_chars.append(c)\n ns_text = \"\".join(ns_chars)\n return ns_text, dict(ns_to_s_map)\n\n # We first tokenize `orig_text`, strip whitespace from the result\n # and `pred_text`, and check if they are the same length. If they are\n # NOT the same length, the heuristic has failed. If they are the same\n # length, we assume the characters are one-to-one aligned.\n tokenizer = tokenization.BasicTokenizer(do_lower_case=config.do_lower_case)\n\n tok_text = \" \".join(tokenizer.tokenize(orig_text))\n\n start_position = tok_text.find(pred_text)\n if start_position == -1:\n if config.debug:\n utils.log(\n \"Unable to find text: '%s' in '%s'\" % (pred_text, orig_text))\n return orig_text\n end_position = start_position + len(pred_text) - 1\n\n (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)\n (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)\n\n if len(orig_ns_text) != len(tok_ns_text):\n if config.debug:\n utils.log(\"Length not equal after stripping spaces: '%s' vs '%s'\",\n orig_ns_text, tok_ns_text)\n return orig_text\n\n # We then project the characters in `pred_text` back to `orig_text` using\n # the character-to-character alignment.\n tok_s_to_ns_map = {}\n for (i, tok_index) in six.iteritems(tok_ns_to_s_map):\n tok_s_to_ns_map[tok_index] = i\n\n orig_start_position = None\n if start_position in tok_s_to_ns_map:\n ns_start_position = tok_s_to_ns_map[start_position]\n if ns_start_position in orig_ns_to_s_map:\n orig_start_position = orig_ns_to_s_map[ns_start_position]\n\n if orig_start_position is None:\n if config.debug:\n utils.log(\"Couldn't map start position\")\n return orig_text\n\n orig_end_position = None\n if end_position in tok_s_to_ns_map:\n ns_end_position = tok_s_to_ns_map[end_position]\n if ns_end_position in orig_ns_to_s_map:\n orig_end_position = orig_ns_to_s_map[ns_end_position]\n\n if orig_end_position is None:\n if config.debug:\n utils.log(\"Couldn't map end position\")\n return orig_text\n\n output_text = orig_text[orig_start_position:(orig_end_position + 1)]\n return output_text","def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):\n\n def _strip_spaces(text):\n ns_chars = []\n ns_to_s_map = collections.OrderedDict()\n for (i, c) in enumerate(text):\n if c == \" \":\n continue\n ns_to_s_map[len(ns_chars)] = i\n ns_chars.append(c)\n ns_text = \"\".join(ns_chars)\n return (ns_text, ns_to_s_map)\n\n # We first tokenize `orig_text`, strip whitespace from the result\n # and `pred_text`, and check if they are the same length. If they are\n # NOT the same length, the heuristic has failed. If they are the same\n # length, we assume the characters are one-to-one aligned.\n tokenizer = BasicTokenizer(do_lower_case=do_lower_case)\n\n tok_text = \" \".join(tokenizer.tokenize(orig_text))\n\n start_position = tok_text.find(pred_text)\n if start_position == -1:\n if verbose_logging:\n logger.info(\n \"Unable to find text: '%s' in '%s'\" % (pred_text, orig_text))\n return orig_text\n end_position = start_position + len(pred_text) - 1\n\n (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)\n (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)\n\n if len(orig_ns_text) != len(tok_ns_text):\n if verbose_logging:\n logger.info(\"Length not equal after stripping spaces: '%s' vs '%s'\",\n orig_ns_text, tok_ns_text)\n return orig_text\n\n # We then project the characters in `pred_text` back to `orig_text` using\n # the character-to-character alignment.\n tok_s_to_ns_map = {}\n for (i, tok_index) in tok_ns_to_s_map.items():\n tok_s_to_ns_map[tok_index] = i\n\n orig_start_position = None\n if start_position in tok_s_to_ns_map:\n ns_start_position = tok_s_to_ns_map[start_position]\n if ns_start_position in orig_ns_to_s_map:\n orig_start_position = orig_ns_to_s_map[ns_start_position]\n\n if orig_start_position is None:\n if verbose_logging:\n logger.info(\"Couldn't map start position\")\n return orig_text\n\n orig_end_position = None\n if end_position in tok_s_to_ns_map:\n ns_end_position = tok_s_to_ns_map[end_position]\n if ns_end_position in orig_ns_to_s_map:\n orig_end_position = orig_ns_to_s_map[ns_end_position]\n\n if orig_end_position is None:\n if verbose_logging:\n logger.info(\"Couldn't map end position\")\n return orig_text\n\n output_text = orig_text[orig_start_position:(orig_end_position + 1)]\n return output_text","def decipher(self, text, code):\n # remove capitalization to search for words\n text.lower()\n # remove any punctuation\n text = text.translate(str.maketrans('', '', string.punctuation))\n # get words to search for in text\n secret_words = code.keys()\n # check if text contains any secret words and collect their boltzman values\n boltzmanns = []\n for word in text.split():\n if word in secret_words:\n boltzmanns.append(code[word])\n # return None if no code words in text\n if len(boltzmanns) == 0:\n return None\n # return boltzmann average value for code words present\n else:\n avg = sum(boltzmanns) / float(len(boltzmanns))\n return avg","def canned():\n return (next_phrase(\"we proceed as follows\") |\n (next_word('the') + \n first_word('result lemma theorem proposition corollary') +\n next_word('now').possibly() +\n next_word('follows')) |\n next_phrase('the other cases are similar') |\n (next_phrase('the proof is')+ first_word('obvious trivial easy routine'))).nil().expect('canned')","def correct_case(token, corrections_map, structures):\n alt_case_mode = -1 # Most common variation\n if token[0].isupper():\n if sum(char.isupper() for char in token) > 2:\n alt_case_mode = 0 # Upper case variation\n else:\n alt_case_mode = 1 # Lower case variation with capital first letter\n\n corrected_case_map = {}\n for correct_word, score in corrections_map.items():\n if correct_word.find(\" \") != -1:\n words = correct_word.split(\" \")\n\n keys_left = find_correct_case(words[0], alt_case_mode, structures)\n keys_right = find_correct_case(words[1], alt_case_mode, structures)\n key = keys_left+\" \"+keys_right\n else:\n key = find_correct_case(correct_word, alt_case_mode, structures)\n\n # If the key already exists we keep the highest score\n if key in corrected_case_map.keys():\n old_score = corrected_case_map[key]\n corrected_case_map[key] = max(old_score, score)\n else:\n corrected_case_map[key] = score\n\n return corrected_case_map","def decontracted(phrase):\r\n # specific\r\n phrase = re.sub(r\"won\\'t\", \"will not\", phrase)\r\n phrase = re.sub(r\"can\\'t\", \"can not\", phrase)\r\n # general\r\n phrase = re.sub(r\"n\\'t\", \" not\", phrase)\r\n phrase = re.sub(r\"\\'re\", \" are\", phrase)\r\n phrase = re.sub(r\"\\'s\", \" is\", phrase)\r\n phrase = re.sub(r\"\\'d\", \" would\", phrase)\r\n phrase = re.sub(r\"\\'ll\", \" will\", phrase)\r\n phrase = re.sub(r\"\\'t\", \" not\", phrase)\r\n phrase = re.sub(r\"\\'ve\", \" have\", phrase)\r\n phrase = re.sub(r\"\\'m\", \" am\", phrase)\r\n return phrase","def clean_for_comparison(text):\n text = clean_text(text)\n text = clean_text_from_nonbasic_characters(text)\n return text","def check_spellings(text):\n\n for word in vocabulary:\n text = correct(word, text, 0.7)\n return text","def corrigir_doc(frase):\r\n\r\n if not frase or not (isinstance(frase, str) and all(x.isalpha() for x in frase.split())) or ' ' in frase:\r\n raise ValueError('corrigir_doc: argumento invalido')\r\n\r\n palavras, doc_corrigido = frase.split(' '), ''\r\n palavras = list(map(corrigir_palavra, palavras))\r\n\r\n while palavras:\r\n anagrama_teste = palavras.pop(0)\r\n if palavras:\r\n for word in palavras:\r\n if eh_anagrama(anagrama_teste, word) and anagrama_teste.lower() != word.lower():\r\n del palavras[palavras.index(word)]\r\n doc_corrigido += ' ' + anagrama_teste\r\n\r\n return doc_corrigido[1:]","def get_exhaustive_text_correction_proposal(self, input_text):\n arr = []\n self.complexity=20\n prev = self.alpha\n self.alpha = 0.95 # temporary\n arr_i = 0\n\n with torch.no_grad():\n for text_chunk in tqdm(self._string_to_chunks(input_text)):\n self.oryginal_input_text = text_chunk\n self.input_text = text_chunk\n self._compute_exhaustive_outputs()\n\n for ix in range(self.input_size):\n token_id = self.input_ids[0][ix]\n token_obj = {}\n token_obj[\"name\"] = self.tokenizer.decode(token_id.item())\n token_obj[\"probability\"] = self.normalized_token_prob[ix]\n token_obj[\"oddballness\"] = self._get_oddballness_proba(token_obj[\"probability\"], self.probs[ix],\n alpha=self.alpha).item()\n arr.append(token_obj)\n\n self.input_text = self.oryginal_input_text\n self._compute_outputs()\n for ix in range(self.input_size):\n self.sorted_probs, self.sorted_indices = torch.sort(self.probs[ix - 1], descending=True)\n _, correction_indices = self._get_best_tokens(5)\n\n arr[arr_i + ix][\"corrections\"] = [self.tokenizer.decode(token_id.item()) for token_id in correction_indices]\n arr_i += self.input_size\n\n arr.pop()\n arr.pop(0)\n self._trim_bpe_space_artifact(arr)\n self.token_array = arr\n self.alpha = prev # temporary\n return arr","def is_correction(self):\n # OK, go looking for RESENT style tags, assume it happens within first\n # 300 chars\n if RESENT.search(self.text[:300]):\n return True\n if self.bbb is None or not self.bbb:\n return False\n if self.bbb[0] in ['A', 'C']:\n return True\n return False","def oracle(c):\n correct_arcs = get_arcs(c.sentence)\n if can_left_arc(c, correct_arcs):\n return Transition('la', c.sentence[c.stack[-1]].deprel)\n elif can_right_arc(c, correct_arcs):\n return Transition('ra', c.sentence[c.buffer[0]].deprel)\n else:\n return Transition('sh', '_')","def clean_up(text_not_allowed, raw_string):\n trans_table = str.maketrans(dict.fromkeys(text_not_allowed))\n clean_result = raw_string.translate(trans_table)\n return clean_result","def can_recept(self, text, *args, **kwargs):\n for each_cur in self.flat_norm.keys():\n if each_cur.lower() in text.lower():\n return True\n\n else:\n return False","def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):\n\n # When we created the data, we kept track of the alignment between original\n # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So\n # now `orig_text` contains the span of our original text corresponding to the\n # span that we predicted.\n #\n # However, `orig_text` may contain extra characters that we don't want in\n # our prediction.\n #\n # For example, let's say:\n # pred_text = steve smith\n # orig_text = Steve Smith's\n #\n # We don't want to return `orig_text` because it contains the extra \"'s\".\n #\n # We don't want to return `pred_text` because it's already been normalized\n # (the SQuAD eval script also does punctuation stripping/lower casing but\n # our tokenizer does additional normalization like stripping accent\n # characters).\n #\n # What we really want to return is \"Steve Smith\".\n #\n # Therefore, we have to apply a semi-complicated alignment heuristic between\n # `pred_text` and `orig_text` to get a character-to-character alignment. This\n # can fail in certain cases in which case we just return `orig_text`.\n\n def _strip_spaces(text):\n ns_chars = []\n ns_to_s_map = collections.OrderedDict()\n for (i, c) in enumerate(text):\n if c == \" \":\n continue\n ns_to_s_map[len(ns_chars)] = i\n ns_chars.append(c)\n ns_text = \"\".join(ns_chars)\n return (ns_text, ns_to_s_map)\n\n # We first tokenize `orig_text`, strip whitespace from the result\n # and `pred_text`, and check if they are the same length. If they are\n # NOT the same length, the heuristic has failed. If they are the same\n # length, we assume the characters are one-to-one aligned.\n tokenizer = BasicTokenizer(do_lower_case=do_lower_case)\n\n tok_text = \"\".join(tokenizer.tokenize(orig_text))\n\n start_position = tok_text.find(pred_text)\n if start_position == -1:\n if verbose_logging:\n print(\"Unable to find text: '%s' in '%s'\" % (pred_text, orig_text))\n return orig_text\n end_position = start_position + len(pred_text) - 1\n\n (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)\n (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)\n\n if len(orig_ns_text) != len(tok_ns_text):\n if verbose_logging:\n print(\"Length not equal after stripping spaces: '%s' vs '%s'\" % (orig_ns_text, tok_ns_text))\n return orig_text\n\n # We then project the characters in `pred_text` back to `orig_text` using\n # the character-to-character alignment.\n tok_s_to_ns_map = {}\n for (i, tok_index) in tok_ns_to_s_map.items():\n tok_s_to_ns_map[tok_index] = i\n\n orig_start_position = None\n if start_position in tok_s_to_ns_map:\n ns_start_position = tok_s_to_ns_map[start_position]\n if ns_start_position in orig_ns_to_s_map:\n orig_start_position = orig_ns_to_s_map[ns_start_position]\n\n if orig_start_position is None:\n if verbose_logging:\n print(\"Couldn't map start position\")\n return orig_text\n\n orig_end_position = None\n if end_position in tok_s_to_ns_map:\n ns_end_position = tok_s_to_ns_map[end_position]\n if ns_end_position in orig_ns_to_s_map:\n orig_end_position = orig_ns_to_s_map[ns_end_position]\n\n if orig_end_position is None:\n if verbose_logging:\n print(\"Couldn't map end position\")\n return orig_text\n\n output_text = orig_text[orig_start_position:(orig_end_position + 1)]\n return output_text","def test__same_text_correlation(self):\n \n _log.info('-'*80)\n \n # arrange \n text1 = \"love is rain as long story short\"\n text2 = text1\n\n dump_file = getInputFile(\"swiki_knowledge_output.xml\")\n parsed_file = getOutputFile(\"swiki_knowledge_output.parsed.xml\")\n #wdb_file = getOutputFile(\"swiki_knowledge_output.wdb\")\n\n articles = ['Rain', 'Love', 'Tree'] \n \n # act\n wn.make_dump(dump_file, articles, compress=False)\n wn.parse_dump(dump_file, parsed_file)\n db_wrapper = wn.build_database_wrapper(parsed_file, StopWordsStemmer([]))\n \n #self.addCleanup(os.remove, self.tmp_dump_file)\n \n comparer = SemanticComparer(db_wrapper)\n correlation = comparer.compare(text1, text2)\n _log.info(test_utils.get_texts_correlation_message(text1, text2, correlation))\n self.assertAlmostEqual(correlation, 1.0, msg=\"for same text correlation should be 1\")","def caesar(action, input_text, shift_amount):\n\n transformed_text = ''\n\n # If the action is decode, make the shift_amount as negative\n if action == 'decode':\n shift_amount *= -1\n\n # Add/Subtract the shift value to get the index from alphabets list\n for alphabet in input_text:\n if alphabet in alphabets:\n index = alphabets.index(alphabet)\n new_index = index + shift_amount\n new_alphabet = alphabets[new_index]\n transformed_text += new_alphabet\n else:\n transformed_text += alphabet\n\n # Print the result\n print(f'The {action}d text is {transformed_text}')","def calculate_cer(normalized_texts: List[str], transcript: str, remove_punct=False) -> List[Tuple[str, float]]:\n normalized_options = []\n for text in normalized_texts:\n text_clean = text.replace('-', ' ').lower()\n if remove_punct:\n for punct in \"!?:;,.-()*+-/<=>@^_\":\n text_clean = text_clean.replace(punct, \"\")\n cer = round(word_error_rate([transcript], [text_clean], use_cer=True) * 100, 2)\n normalized_options.append((text, cer))\n return normalized_options","def calculate_cer(normalized_texts: List[str], transcript: str, remove_punct=False) -> List[Tuple[str, float]]:\n normalized_options = []\n for text in normalized_texts:\n text_clean = text.replace('-', ' ').lower().strip()\n if remove_punct:\n for punct in \"!?:;,.-()*+-/<=>@^_\":\n text_clean = text_clean.replace(punct, \" \")\n text_clean = re.sub(r' +', ' ', text_clean)\n cer = round(word_error_rate([transcript], [text_clean], use_cer=True) * 100, 2)\n normalized_options.append((text, cer))\n return normalized_options","def deCopIfy(text):\n\tif text == \"\":\n\t\treturn text\n\n\tfor lingo in coplingo:\n\t\ttext = re.sub(lingo['regex'], lingo['str'], text)\n\n\treturn text[0].upper() + text[1:]","def auto_correct(cls, input_string):\n\n # input_string = cls.filter_input(input_string)\n\n possible_corrections = dict()\n output = []\n\n for token in input_string.strip().split(\" \"):\n # print(token)\n for category, words in cls.correction_dict.items():\n possible_corrections[category] = difflib.get_close_matches(token.title(), words, 1, cls.cutoff)\n\n # print(\"Possible:\", flatten([i for i in possible_corrections.values()]))\n\n corrected = difflib.get_close_matches(token.title(), flatten([i for i in possible_corrections.values()]), 1,\n cls.cutoff)\n if corrected:\n output.append(corrected[0])\n\n return ' '.join(output)","def lf_abnormal_interp_with_discont(report):\n if 'interpretation' in report.sections.keys():\n interpretation = report.sections['interpretation']\n interp_text = interpretation['text']\n return abnormal_interp_with_discont(interp_text)\n elif 'summary' in report.sections:\n return abnormal_interp_with_discont(report.sections['summary']['text'])\n elif 'findings' in report.sections: # fall back to look in the findings \n if 'summary' in report.sections['findings']: # fall back to look for a summary instead\n return abnormal_interp_with_discont(report.sections['findings']['summary'])\n if 'impression' in report.sections['findings']:\n return abnormal_interp_with_discont(report.sections['findings']['impression']) \n return ABSTAIN_VAL\n elif 'narrative' in report.sections: # fall back to look in the findings \n ky = 'narrative'\n if 'summary' in report.sections[ky]: # fall back to look for a summary instead\n return abnormal_interp_with_discont(report.sections[ky]['summary'])\n if 'impression' in report.sections[ky]:\n return abnormal_interp_with_discont(report.sections[ky]['impression']) \n return ABSTAIN_VAL \n else:\n return ABSTAIN_VAL","def select_best_match(\n self, normalized_texts: List[str], transcript: str, verbose: bool = False, remove_punct: bool = False\n ):\n normalized_texts = calculate_cer(normalized_texts, transcript, remove_punct)\n normalized_texts = sorted(normalized_texts, key=lambda x: x[1])\n normalized_text, cer = normalized_texts[0]\n\n if verbose:\n print('-' * 30)\n for option in normalized_texts:\n print(option)\n print('-' * 30)\n return normalized_text, cer","def intent_of_text_LnDOR(ChapterTextS, TargetQuestionsD, TestS, StopWords):\n \n # Chapter Text - stokenize\n StokensCT = stokenize(ChapterTextS, StopWords) \n\n # Test question - stokenize\n StokensTest = stokenize(TestS, StopWords)\n\n # Knowledge Base Dict - stokenize\n KBD_structure = stokenizeKBD(TargetQuestionsD, StopWords)\n\n # List (because list is mutable, set is not) of all stokens in document\n StokensDoc = StokensCT[:] # from chapter text\n StokensDoc.extend(StokensTest[:]) # += Test string\n\n # extend list of stokens in Doc\n for i in TargetQuestionsD:\n StokensDoc.extend(TargetQuestionsD[i][\"mq stokens\"][:]) # += KB target [matched Q]s\n StokensDoc.extend(TargetQuestionsD[i][\"ans stokens\"][:]) # += KB answers\n \n StokensTestV = set(StokensTest)\n StokensDocV = set(StokensDoc)\n StokensAntiTgtV = StokensDocV\n \n # Complement of all targets\n for i in TargetQuestionsD:\n StokensAntiTgtV = StokensAntiTgtV.difference(set(TargetQuestionsD[i][\"mq stokens\"]))\n \n # calculate confusion matrix and DOR etc.\n LnDORD = {}\n # Anti Target\n TP, FP, FN, TN = confusion_matrix(StokensDocV, StokensAntiTgtV, StokensTestV) \n LnDOR = lndor(TP, FP, FN, TN) \n someAngle = angleDOR(TP, FP, FN, TN) \n \n LnDORD[\"AntiTgt\"] = {'lndor': LnDOR, 'theta': someAngle}\n\n # total occurences\n total_occ = 0\n for i in TargetQuestionsD:\n total_occ += TargetQuestionsD[i]['count']\n\n for i in TargetQuestionsD:\n StokensTgtV = set(TargetQuestionsD[i][\"mq stokens\"][:])\n\n TP, FP, FN, TN = confusion_matrix(StokensDocV, StokensTgtV, StokensTestV) \n priorOR = TargetQuestionsD[i]['count'] / total_occ\n\n LnDOR = lndor(TP, FP, FN, TN) \n someAngle = angleDOR(TP, FP, FN, TN, priorOR) \n \n LnDORD[i] = {'lndor': LnDOR, 'theta': someAngle}\n # LnDORD = {i: {'lndor': , 'theta': }}, KB indices + \"AntiTgt\"\n\n return LnDORD","def hasConstantForm(self, sentence):","def segue_canned_texts(segue, kind, excecute_code=True):\n\n def enrich_with_phrases(segue, kind):\n \"\"\"Retrieves the two phrases associated to the two nodes composing the segue\n The two phrases are added to the segue as two new keys, to be exploited in short, line and description texts.\n\n Args:\n segue (d)\n kind (str): whether short, line or description\n\n Returns:\n d: segue dictionary enriched with phrases\n \"\"\"\n phrase_n1 = phrase(segue['n1'], segue['compare_function'], kind)\n phrase_n2 = phrase(segue['n2'], segue['compare_function'], kind)\n if phrase_n1 is not None:\n segue = {**{'phrase_n1': phrase_n1(segue['n1'], segue)}, **segue}\n if phrase_n2 is not None:\n segue = {**{'phrase_n2': phrase_n2(segue['n2'], segue)}, **segue}\n return segue\n\n assert kind in ['line', 'description', 'short']\n\n if kind == 'description':\n text = search_dictionary_dicothomic(_description_dichotomic, segue)\n\n elif kind == 'line':\n text = search_dictionary_atomic(_line_atomic, segue)\n text = search_dictionary_dicothomic(_line_dichotomic, segue) if text is None else text\n\n elif kind == 'short':\n text = search_dictionary_atomic(_short_atomic, segue)\n text = search_dictionary_dicothomic(_short_dichotomic, segue) if text is None else text\n if text is None:\n try:\n text = segue_canned_texts(segue, 'line', excecute_code=False)\n except KeyError:\n pass\n\n if text is not None:\n\n if excecute_code:\n segue = enrich_with_phrases(segue, kind)\n\n if type(text) == tuple:\n # dicothomic\n text = f\"{text[0](segue['n1'], segue['n2'], segue)} {text[1](segue['n1'], segue['n2'], segue)}\"\n else:\n # atomic\n text = text(segue['n1'], segue['n2'], segue)\n\n # Postprocessing\n # Fix multiple spaces\n text = ' '.join(text.split())\n # First letter is capital\n text = capitalize_first_word(text)\n # Fix spaces among word and 's in genitivo sassone\n text = re.sub(r\"([a-zA-Z]+)\\s+('s)\", r\"\\1\\2\", text)\n text = text.replace(', ,', ',')\n\n return text\n else:\n raise KeyError(\n f\"It was not possible to find a {kind} for node of types, respectively {segue['n1']['type']} and {segue['n2']['type']}, with compare function {segue['compare_function']}. The ids of the nodes are, respectively {segue['n1']['id']} and {segue['n2']['id']}\")","def __citation_correction(self, bs, ground_truth):\n bs_ref = bs.findNext('bibl')\n gt_ref = ground_truth.findNext('ref')\n while gt_ref is not None:\n if gt_ref.find('article-title') != bs_ref.title:\n pass\n gt_ref = gt_ref.findNext('ref')","def recept(self, text, *args, **kwargs):\n results = []\n for each_cur in self.flat_norm.keys():\n if each_cur.lower() in text.lower():\n results.append(self.flat_norm[each_cur])\n\n if results:\n # we have something in results\n print(\"DictionarySlotReceptorMixin.recept: %s grasped results: %s\" % (self, results))\n # TODO make productions signals?\n return results","def is_consonant(text):\n return text.lower() in AVRO_CONSONANTS","def extrairFrase(self, documento):\n unicWords = self.unicWords()\n doc = set(documento)\n caracteristicas ={}\n for palavras in unicWords:\n caracteristicas['%s'%palavras]=(palavras in doc)\n return caracteristicas","def verify_decrypt_key(self):\r\n\t\tpercent_english = Dict_Control(self.my_code).check_key()\r\n\t\t#If more than half the words are english, the key will pass. \r\n\t\tif percent_english > 50:\r\n\t\t\tself.right_key = False\r\n\t\t#If the key does not pass, the program will give you a warning and prompt you for another key. \r\n\t\telse: \r\n\t\t\tprint(f\"After decryption, it looks like only {percent_english}% of your words are english, you may have entered the wrong key?\")","def context_spell_correct(\n self,\n sentence: str,\n suggestions_dict: Dict[str, List[str]]\n ) -> (str, Dict[str, str]):\n sentence_tokens = sentence.lower().split()\n possible_sent_list = []\n for each in sentence_tokens:\n if each in suggestions_dict:\n possible_sent_list.append(suggestions_dict[each])\n else:\n possible_sent_list.append([each])\n\n corrected_sent = self.get_most_probable_sentence(possible_sent_list)\n corrected_dict = self.get_corrected_words_map(corrected_sent, sentence)\n context_corrected_sentence = \" \" + sentence + \" \"\n for token, suggestion in corrected_dict.items():\n context_corrected_sentence = re.sub(\" \" + re.escape(token) + \" \",\n \" \" + suggestion + \" \",\n context_corrected_sentence)\n context_corrected_sentence = context_corrected_sentence.strip()\n return context_corrected_sentence, corrected_dict","def conjugate_present_are_verb(verb, pronoun, tense):\n\n are_endings = {\"io\": \"o\", \"tu\": \"i\", \"lui\": \"a\", \"lei\": \"a\", \"noi\": \"iamo\", \"voi\": \"ate\", \"loro\": \"ano\"}\n giare_endings = {\"io\": \"io\", \"tu\": \"i\", \"lui\": \"ia\", \"lei\": \"ia\", \"noi\": \"iamo\", \"voi\": \"iate\", \"loro\": \"iano\"}\n ciare_endings = {\"io\": \"o\", \"tu\": \"\", \"lui\": \"a\", \"lei\": \"a\", \"noi\": \"amo\", \"voi\": \"ate\", \"loro\": \"ano\"}\n add_h = {\"io\": \"o\", \"tu\": \"hi\", \"lui\": \"a\", \"lei\": \"a\", \"noi\": \"hiamo\", \"voi\": \"ate\", \"loro\": \"ano\"}\n irregular_are = [\"fare\", \"andare\"]\n fare = {\"io\": \"faccio\", \"tu\": \"fai\", \"lei\": \"fa\", \"lui\": \"fa\", \"noi\": \"facciamo\", \"voi\": \"fate\", \"loro\": \"fanno\"}\n andare = {\"io\": \"vado\", \"tu\": \"vai\", \"lui\": \"va\", \"lei\": \"va\", \"noi\": \"andiamo\", \"voi\": \"andate\", \"loro\": \"vanno\"}\n\n # this section checks for the irregular verbs fare, andare\n if verb in irregular_are:\n if verb == \"fare\":\n return fare[pronoun]\n else:\n return andare[pronoun]\n\n # this section checks for spelling issues like with mancare in order to preserve hard \"k\" sound of infinitive\n # if it's a verb like mancare then the if section adds an \"h\" for the spelling to preserve hard \"k\" sound\n # if it's a normal -are verb, then the else section conjugates it normally\n if verb[-5:] == \"giare\":\n stripped_verb = strip_off_ending(verb, tense)\n new_verb = stripped_verb + giare_endings[pronoun]\n return new_verb\n if verb[-5:] == \"ciare\":\n stripped_verb = strip_off_ending(verb, tense)\n new_verb = stripped_verb + ciare_endings[pronoun]\n return new_verb\n if verb[-4:] == \"care\":\n stripped_verb = strip_off_ending(verb, tense)\n new_verb = stripped_verb + add_h[pronoun]\n return new_verb\n if verb[-4:] == \"gare\":\n stripped_verb = strip_off_ending(verb, tense)\n new_verb = stripped_verb + add_h[pronoun]\n return new_verb\n else:\n stripped_verb = strip_off_ending(verb, tense)\n new_verb = stripped_verb + are_endings[pronoun]\n return new_verb","def applyCoder(text, coder):\n ciphertext = str()\n #for each letter in the text find it, and grab shifted letter\n for letter in text:\n ciphertext += coder.get(letter, letter)\n return ciphertext","def editex(str1, str2, min_threshold = None):\n\n # Quick check if the strings are empty or the same - - - - - - - - - - - - -\n #\n if (str1 == '') or (str2 == ''):\n return 0.0\n elif (str1 == str2):\n return 1.0\n\n n = len(str1)\n m = len(str2)\n\n # Values for edit costs - - - - - - - - - - - - - - - - - - - - - - - - - - -\n #\n BIG_COSTS = 2 # If characters are not in same group\n SML_COSTS = 1 # If characters are in same group\n\n # Mappings of letters into groups - - - - - - - - - - - - - - - - - - - - - -\n #\n groupsof_dict = {'a':0, 'b':1, 'c':2, 'd':3, 'e':0, 'f':1, 'g':2, 'h':7,\n 'i':0, 'j':2, 'k':2, 'l':4, 'm':5, 'n':5, 'o':0, 'p':1,\n 'q':2, 'r':6, 's':2, 't':3, 'u':0, 'v':1, 'w':7, 'x':2,\n 'y':0, 'z':2, '{':7}\n\n # Function to calculate cost of a deletion - - - - - - - - - - - - - - - - -\n #\n def delcost(char1, char2, groupsof_dict):\n\n if (char1 == char2):\n return 0\n\n code1 = groupsof_dict.get(char1,-1) # -1 is not a char\n code2 = groupsof_dict.get(char2,-2) # -2 if not a char\n\n if (code1 == code2) or (code2 == 7): # Same or silent\n return SML_COSTS # Small difference costs\n else:\n return BIG_COSTS\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n if (' ' in str1):\n str1 = str1.replace(' ','{')\n if (' ' in str2):\n str2 = str2.replace(' ','{')\n\n if (n > m): # Make sure n <= m, to use O(min(n,m)) space\n str1, str2 = str2, str1\n n, m = m, n\n\n row = [0]*(m+1) # Generate empty cost matrix\n F = []\n for i in range(n+1):\n F.append(row[:])\n\n F[1][0] = BIG_COSTS # Initialise first row and first column of cost matrix\n F[0][1] = BIG_COSTS\n\n sum = BIG_COSTS\n for i in range(2,n+1):\n sum += delcost(str1[i-2], str1[i-1], groupsof_dict)\n F[i][0] = sum\n\n sum = BIG_COSTS\n for j in range(2,m+1):\n sum += delcost(str2[j-2], str2[j-1], groupsof_dict)\n F[0][j] = sum\n\n for i in range(1,n+1):\n\n if (i == 1):\n inc1 = BIG_COSTS\n else:\n inc1 = delcost(str1[i-2], str1[i-1], groupsof_dict)\n\n for j in range(1,m+1):\n if (j == 1):\n inc2 = BIG_COSTS\n else:\n inc2 = delcost(str2[j-2], str2[j-1], groupsof_dict)\n\n if (str1[i-1] == str2[j-1]):\n diag = 0\n else:\n code1 = groupsof_dict.get(str1[i-1],-1) # -1 is not a char\n code2 = groupsof_dict.get(str2[j-1],-2) # -2 if not a char\n\n if (code1 == code2): # Same phonetic group\n diag = SML_COSTS\n else:\n diag = BIG_COSTS\n\n F[i][j] = min(F[i-1][j]+inc1, F[i][j-1]+inc2, F[i-1][j-1]+diag)\n\n w = float(F[n][m])\n\n if (w < 0.0):\n w = 0.0\n return w","def spell_correction(self, tweet):\n return self.spell_correct.correct(tweet)","def negation_check(self,sentence):","def analyze(self, text):\n\n score = 0.0;\n\n words = text.split(' ')\n # match each word in either the positives or negatives list adding or subtracting 1 from the score if present\n for word in words:\n for w in self.positives:\n if w == word.lower():\n score += 1.0\n continue\n \n for w in self.negatives:\n if w == word.lower():\n score -= 1.0\n continue\n\n return score","def remove_non_narration_strings(transcription_row):\n sentence = transcription_row[\"text\"]\n # filter out (CAPITALIZED WORD) and \"CAPITALIZED WORD\". These are not enunciated in the voiceover, but rather\n # indicate noise/words from the original audio track that get interspersed into the voice\n # Might contain special characters\n # Update: Capitalization etc are inconsistent. But all follow the pattern \"text\" and (text). Remove these instead\n crosstalk_pattern = '\\(.*?\\)|\\\".*?\\\"'\n # crosstalk_findings = re.findall(crosstalk_pattern, sentence)\n # print(\"Crosstalk: \"+str(crosstalk_findings))\n sentence = re.sub(crosstalk_pattern, \" \", sentence)\n # filter out ' s ' ' Ss ' etc\n s_pattern = r'\\b[sS]+\\b'\n s_pattern_findings = re.findall(s_pattern, sentence)\n # if len(s_pattern_findings) > 0:\n # print(\"S-pattern: \"+str(s_pattern_findings))\n sentence = re.sub(s_pattern, \" \", sentence)\n transcription_row[\"text\"] = sentence\n return transcription_row","def analyze(self, text):\n \n total_words = len(text)\n \n negatives_length = len(self.negatives)\n positives_length = len(self.positives)\n \n posneg_sum = 0\n \n for word in text:\n \n for j in range(0, positives_length):\n if word == self.positives[j][:-1]:\n posneg_sum += 1\n \n for k in range(0, negatives_length):\n if word == self.negatives[k][:-1]:\n posneg_sum -= 1\n\n return posneg_sum","def decryptStory():\n wordList = loadWords()\n text = getStoryString() \n k = findBestShift(wordList, text)\n \n return applyShift(text, k)","def preprocess(text):\n text = remove_space(text)\n text = clean_special_punctuations(text)\n text = clean_number(text)\n text = decontracted(text)\n text = correct_spelling(text)\n text = spacing_punctuation(text)\n text = spacing_some_connect_words(text)\n text = clean_repeat_words(text)\n text = remove_space(text)\n text = text.lower()\n return text","def get_text(self):\n text_complet = \"\"\n rez_dict = self.__results\n for i in range(0, len(rez_dict[\"text\"])):\n text = rez_dict[\"text\"][i]\n conf = int(rez_dict[\"conf\"][i])\n if conf > self.__min_confidence:\n text_complet += text + \" \"\n return text_complet","def processClauseText(intext, mtype): # type: (str, str) -> []\n\n global classifications, accsearch, unaccsearch\n\n retlist = []\n texts = []\n if mtype == 'text' or mtype == 'txt':\n texts = extractClauses(intext)\n elif 'pdf' in mtype:\n plaintext = parsepdf(intext)\n texts = extractPDFClauses(plaintext)\n elif 'word' in mtype:\n plaintext = parseword(intext)\n texts = extractClauses(plaintext)\n\n results = predicteula(texts)\n if len(results['prediction']) == 0:\n return retlist\n\n accs = [[win[1] for win in curwins] for curwins in results['windows']]\n probs = [max(curwins) for curwins in accs]\n\n for text, prediction in zip(texts, probs):\n if len(text.split()) > 9:\n curclause = {}\n curclause['origclause'] = text\n curclause['classification'] = 'Not Sure'\n if prediction > 0.6:\n curclause['classification'] = 'Acceptable'\n elif prediction < 0.4:\n curclause['classification'] = 'Unacceptable'\n curclause['score'] = prediction\n curclause['accclause'] = dicesearch(text, accsearch)\n curclause['unaccclause'] = dicesearch(text, unaccsearch)\n retlist.append(curclause)\n\n return retlist","def match_contract_to_charter_constraints(contract, charter, charter_constraints, charity_constraints):\n\n r_quotes = []\n r_vector = []\n\n quote_slice = slice(0, 17)\n\n if 'subj' not in contract.sections:\n raise ValueError(\"contract has no subject section\")\n\n subj = contract.sections['subj'].body\n print(subj.untokenize_cc())\n print('------')\n if subj.embeddings is None:\n print(\"Subj embeddings are gone, restoring...\")\n subj.embeddings = contract.embeddings[subj.start:subj.end]\n # subj.tokens = doc.tokens[subj.start:subj.end]\n # subj.tokens_cc = doc.tokens_cc[subj.start:subj.end]\n # subj.embedd( GLOBALS__['CharterAnlysingContext'].pattern_factory )\n print('\\t\\t sample:', subj.embeddings[0][1:10])\n\n for head_type in charter_constraints:\n\n ##charity:\n if head_type in charity_constraints:\n print(f'{head_type} has charity constrinats')\n \n charity_constraints_by_head = charity_constraints[head_type]\n charity_constraints_by_head_new = []\n \n charity_constraints['new.'+head_type] = charity_constraints_by_head_new\n \n for i in range(len(charity_constraints_by_head)):\n _tuple = charity_constraints_by_head[i] \n# for cc in charity_constraints[head_type]:\n _slice = _tuple[0]\n emb_charter = charter.sections[head_type].body.embeddings[_slice]\n \n distance = 1 - DF(emb_charter, subj.embeddings[5:])\n \n# cc.add['subj_correlation'] = distance\n \n# detupling\n charity_constraints_by_head_new.append ( {\n 'slice':_slice,\n 'subj_correlation': distance,\n 'confidence': _tuple[1],\n 'sum': _tuple[2]\n })\n \n print('\\t'*4, 'cc=', charity_constraints_by_head_new[i])\n \n # print('\\t\\t---CC', cc[0])\n \n\n # GLOBALS__['CharterAnlysingContext'].doc.sections['head.directors'].body.embeddings[_slice]\n\n ##------------------------charity end\n print(f'measuring {head_type} constraints...'.upper())\n cc = charter_constraints[head_type]\n quotes = cc['sentences']\n for quote in quotes:\n print()\n _q = untokenize(quote['subdoc'].tokens_cc[quote_slice])\n print(_q)\n\n distance = 1 - DF(quote['subdoc'].embeddings[quote_slice],\n subj.embeddings[5:])\n\n quote['subj_correlation'] = distance\n\n print(f'distance = {distance:.4f}')\n\n r_quotes.append(_q)\n r_vector.append(distance)\n r_quotes.append('\\n')\n r_vector.append(distance)\n\n GLOBALS__['renderer'].render_color_text(r_quotes, r_vector)\n print(r_vector)\n print(r_quotes)","def lf_normal_interp_not_seizure(report):\n # print(report)\n\n for keyinterp in CANDIDATE_INTERPS_LOWER:\n if keyinterp in report.sections.keys():\n interpretation = report.sections[keyinterp]\n if isinstance(interpretation, dict):\n interp_text = interpretation['text']\n else:\n interp_text = interpretation\n \n if SIMPLE_NORMAL_RE.search(interp_text):\n if NORMAL_STUDY_PHRASES.search(interp_text):\n #return NO_SEIZURE_VAL\n return OTHERS_VAL\n #return NA_VAL\n else:\n logger.info(f'warning did not get second normal match: {interp_text}')\n return ABSTAIN_VAL\n \n else:\n return ABSTAIN_VAL\n\n return ABSTAIN_VAL","def decryptStory():\n wordList = loadWords()\n text= getStoryString()\n \n shift = findBestShift(wordList, text)\n return applyShift(text, shift)","def analyze(self, text):\n #analize every word in the text a value -1, 1 or 0 and calculate total score\n #tokens allow us to split words in single tokens we can initialize tokens like this:\n\n tokenizer = nltk.tokenize.TweetTokenizer()\n tokens = tokenizer.tokenize(text.lower())\n\n score = 0\n\n if tokens[0] in self.negatives:\n score =- 1\n elif tokens[0] in self.positives:\n score =+ 1\n else:\n score = 0\n\n #print('', text)\n\n return score","def censor(text: str) -> str:\n\n # Split up individual words in the text\n tokens: List[str] = text.split(\" \")\n\n # Create a mapping of 0 if the word is okay, 1 if it should be censored\n censor_mask: List[int] = predict([word for word in tokens])\n\n # A list of tuples with the first element being the word and the second being 0 or 1\n censor_map: List[Tuple[str, int]] = list(zip(tokens, censor_mask))\n\n # A list of the words that make up the censored text\n censored_text: List[str] = [\n censor_word(word) if should_censor else word\n for word, should_censor in censor_map\n ]\n\n return \" \".join(censored_text)","def analyze(self, text):\n\n # start from 0 for each Analyser variable\n self.positives = 0\n self.negatives = 0\n\n # precise self text value\n self.text = text\n\n # declare a tokenased word\n tokenizer = nltk.tokenize.TweetTokenizer()\n tokens = tokenizer.tokenize(text)\n\n # indicate the length of list tokens\n size = len(tokens)\n\n # all the word stuff to ckeck\n for word in tokens:\n\n # chaque mots est converti en mot sans majuscule\n word = str.lower(word)\n\n linespos = [line.rstrip('\\n') for line in open('positive-words.txt')]\n linesneg = [line.rstrip('\\n') for line in open('negative-words.txt')]\n\n # check for positive or negative or neutral words\n if word in linespos:\n self.positives += 1\n elif word in linesneg:\n self.negatives += 1\n else:\n continue\n\n # score calculculated and reurned\n score = self.positives - self.negatives\n\n return score","def check(self, text):\n p = self.d\n i = 0\n j = 0\n result = []\n ln = len(text)\n while i + j < ln:\n t = text[i + j].lower()\n # print i,j,hex(ord(t))\n if not (t in p):\n j = 0\n i += 1\n p = self.d\n continue\n p = p[t]\n j += 1\n # print p,i,j\n if chr(11) in p:\n p = self.d\n result.append(text[i:i + j])\n i = i + j\n j = 0\n return result","def analyse(self):\n logging.info(\"transferring text to CorpusCook...\")\n\n paragraphs = self.text.split('\\n\\n')\n print(\"mean length of splitted lines\", (mean([len(p) for p in paragraphs])))\n\n # If TIKA resolved '\\n'\n if (mean([len(p) for p in paragraphs])) > 80:\n paragraphs = [re.sub(r\"- *\\n\", '', p) for p in paragraphs]\n paragraphs = [p.replace('\\n', \" \") for p in paragraphs]\n paragraphs = [p.replace(';', \" \") for p in paragraphs]\n joiner = \" \"\n else:\n # If TIKA did not\n joiner = \" \"\n\n processed_text = joiner.join([p\n for p in paragraphs\n if\n p and\n ks_2samp(self.normal_data, list(p)).pvalue > self.threshold\n ]\n )\n\n return processed_text.strip()[:self.length_limit]","def getCasing(word):\n casing = 'other'\n \n numDigits = 0\n for char in word:\n if char.isdigit():\n numDigits += 1\n \n digitFraction = numDigits / float(len(word))\n \n if word.isdigit(): #Is a digit\n casing = 'numeric'\n elif digitFraction > 0.5:\n casing = 'mainly_numeric'\n elif word.islower(): #All lower case\n casing = 'allLower'\n elif word.isupper(): #All upper case\n casing = 'allUpper'\n elif word[0].isupper(): #is a title, initial char upper, then all lower\n casing = 'initialUpper'\n elif numDigits > 0:\n casing = 'contains_digit'\n \n return casing","def classify(self, sText):\n\n sum1, sum2 = self.count()\n\n #len1 = len(self.posRev)\n #len2 = len(self.negRev)\n\n probPos = 0 #math.log(float(sum1)/(sum1+sum2))\n probNeg = 0 #math.log(float(sum2)/(sum1+sum2))\n\n ls = self.tokenize(sText)\n\n #test Positive case\n for word in ls:\n prob = float(self.posRev.get(word, 0) + 1)/(sum1)\n if prob != 0:\n probPos += math.log(prob)\n\n #test Negative case\n for word in ls:\n prob = float(self.negRev.get(word, 0) + 1)/(sum2)\n if prob != 0:\n probNeg += math.log(prob)\n\n print probPos\n print probNeg\n\n print probPos-probNeg\n if (probPos - probNeg) > 1:\n return \"positive\"\n elif (probNeg - probPos) > 1:\n return \"negative\"\n else:\n return \"neutral\"","def verify_text(self, text):\n pass","def process(self, message: Message, **kwargs: Any) -> None:\n\n try:\n textdata = message.data[\"text\"]\n # print(\"text :::\" + textdata)\n textdata = textdata.split()\n new_message = \" \".join(spell.correction(w) for w in textdata)\n # print(\"after correction text :::\" + new_message)\n message.data[\"text\"] = new_message\n except KeyError:\n pass","def get_accredit_info(self, accredit_dict, library_construction, proj_name):\n accredit_info = {}\n for key in accredit_dict:\n accredit = accredit_dict[key]\n ## For \"finished library\" projects, set certain accredation steps as \"NA\" even if not set by default\n if key in ['library_preparation','data_analysis'] and library_construction == 'Library was prepared by user.':\n accredit_info[key] = 'Not Applicable'\n elif accredit in ['Yes','No']:\n accredit_info[key] = '{} under ISO/IEC 17025'.format(['[cross] Not accredited','[tick] Accredited'][accredit == 'Yes'])\n elif accredit == 'N/A':\n accredit_info[key] = 'Not Applicable'\n else:\n self.LOG.error('Accreditation step {} for project {} is found, but no value is set'.format(key, proj_name))\n return accredit_info","def remove_contractions(data: pd.Series) -> pd.Series:\n data_ = data.copy()\n with open(CONTRACTIONS, 'rb') as f:\n contractions = pickle.load(f)\n for kind in contractions.keys():\n words = contractions[kind]\n for word in words:\n word_no_backslash = word.replace('\\\\','')\n try:\n data_ = data_.str.replace(r'\\b{}\\b'.format(word), words[word_no_backslash])\n except:\n # for contractions with escape characters\n data_ = data_.str.replace(f'{word_no_backslash}', words[word])\n return data_","def analyze(self, text):\n\n # TODO\n # tokens = tokenizer.tokenize(tweet)\n tokenizer = nltk.tokenize.TweetTokenizer()\n tokens = tokenizer.tokenize(text)\n score = 0\n\n for word in tokens:\n # iterate over tokens#str.lower\n\n if word.lower() in self.positives:\n score = score+1\n\n elif word.lower() in self.negatives:\n score = score-1\n\n else:\n continue\n return score","def isccp4keytext(self, text):\n #\n # See e.g. http://www.ccp4.ac.uk/dist/html/loggraphformat.html\n # for format of TEXT information, but essentially it's:\n #\n # $TEXT :text name: $$ junk (ignored) text $$any text characters$$\n #\n keytext = self.compile(\n \"isccp4keytext\", r\"\\$TEXT[ \\n]*:([^:]*):[ \\n]*\\$\\$([^\\$]*)\\$\\$([^\\$]*)\\$\\$\"\n ).search(text)\n result = dict()\n if keytext:\n result[\"name\"] = keytext.group(1)\n result[\"junk_text\"] = keytext.group(2)\n result[\"message\"] = keytext.group(3)\n result[\"nlines\"] = keytext.group(0).count(\"\\n\")\n return result","def phrase_dict(phrase):\n switcher = {\n '처음으로': '닥앤미 병원을 찾아주셔서 감사합니다. 직접문의원할시 오른쪽 아래 1:1 버튼을 눌러주시면 직접 상담 가능합니다. 1:1 상담 가능 시간은 09시 – 18시 입니다.',\n '병원 정보': '어떤 정보를 보시고 싶으신가요?',\n '병원 위치': '“닥앤미 병원 주소는 서울시 용산구 이촌동 세움상가 2층입니다.” 더 자세한 지도확인을 원하실 경우 아래 버튼을 눌러주세요',\n '병원 운영시간': '닥앤미 병원을 찾아주셔서 감사합니다. 병원 운영시간은 위의 내용과 같습니다',\n '병원 프로모션': '현재 진행되고 있는 병원 프로모션입니다. 자세히 보길 원하시면 아래의 프로모션을 선택해 주세요',\n '프로모션 A': '닥앤미에서 6월 30일까지 제공되는 프로모션 A 입니다.',\n '프로모션 B': '닥앤미에서 6월 30일까지 제공되는 프로모션 B 입니다.',\n '프로모션 C': '닥앤미에서 6월 30일까지 제공되는 프로모션 C 입니다.',\n '의료진': '안녕하세요, 닥앤미의 홍길동 전문의 입니다. 항상 최선을 다하겠습니다.',\n '병원 사진': '최고의 진료를 제공하는 닥앤미 병원입니다.',\n '병원 진료과목': '닥앤미 병원의 진료과목입니다.',\n '병원 전화하기': '닥앤미 병원 전화번호는 02 3522 XXXX 입니다. 지금 통화를 원하시면 아래 버튼을 눌러주세요'\n }\n default_text = 'Unable to find appropriate text response'\n return switcher.get(phrase, default_text)","def get_correct(entry):\n return entry['correct'] == \"1.0\"","def spelling(p_str):\n spell = SpellChecker(language='en')\n misspelled = spell.unknown(re.sub(r'[^\\w\\s]', '', p_str).split())\n corrections = {}\n for word in misspelled:\n tmp = list(spell.candidates(word))\n tmp.insert(0, spell.correction(word))\n corrections[word] = tmp\n return(corrections)","def _separate_possessives(text: str) -> (dict, str):\n possessive_dict = dict() # Dictionary of nouns (keys) with their possessive modifiers (values)\n revised_words = []\n if '/poss/' in text:\n space_splits = text.split()\n for index in range(0, len(space_splits)):\n if '/poss/' in space_splits[index]:\n possessive_dict[space_splits[index + 1]] = space_splits[index].replace('/poss/', empty_string)\n else:\n revised_words.append(space_splits[index])\n return possessive_dict, space.join(revised_words)\n return possessive_dict, text","def beginning_checker(self, translit):\n tr_new = re.sub(r'(\\A|·)夫', r'\\1弗', translit)\n tr_new = re.sub(r'(\\A|·)耶', r'\\1叶', tr_new)\n return tr_new","def text_analise(file_n, letter, title=''):\r\n dict_percent = {}\r\n other_signs2 = {}\r\n other_signs2_turn = {}\r\n\r\n for i in letter:\r\n with open(file_n) as f:\r\n text = f.read()\r\n count = 0\r\n count_others = 0\r\n for ch in text.lower():\r\n if ch == i:\r\n count += 1\r\n if ch not in letter:\r\n count_others += 1\r\n other_signs2[ch] = count_others\r\n # if ch not in other_signs:\r\n # other_signs.append(ch)\r\n procent = round((100 * count) / len(text), 2)\r\n procent_other = round((100 * count_others) / len(text), 2)\r\n\r\n dict_percent[procent] = [i, count]\r\n\r\n x = 0\r\n for key in sorted(dict_percent):\r\n print('Percent {} of letter {}'.format(key, dict_percent[key]))\r\n x += key\r\n with open('3.txt', 'a') as f:\r\n f.write(\r\n 'Percent {} of letter {} \\n'.format(key, dict_percent[key]))\r\n\r\n other_percents = round((100 - x), 2)\r\n print('Other: {} percent'.format(other_percents))\r\n print('ACTUAL Other: {} percent'.format(procent_other))\r\n print('Length of text: {}\\n'.format(len(text)))\r\n # print(list(set(other_signs)))\r\n\r\n for a, b in other_signs2.items():\r\n if a != '\\n':\r\n other_signs2_turn[b] = a\r\n elif a == '\\n':\r\n other_signs2_turn[b] = 'Enter'\r\n\r\n with open('3.txt', 'a') as f:\r\n f.write('Other: {} percent\\n'.format(round((100 - x), 2)))\r\n f.write('процентов учтено: {}\\n'.format(x))\r\n f.write('Length of text: {}\\n'.format(len(text)))\r\n f.write('Title of text: {}\\n\\n'.format(title))\r\n for key in sorted(other_signs2_turn):\r\n prnt = ('Letter {} used {} times'.format(\r\n other_signs2_turn[key], key))\r\n prnt_wr = prnt + '\\n'\r\n print(prnt)\r\n f.write(prnt_wr)","def init_correction_map(token, dictionary):\n if token is None:\n return None\n\n if len(token) <= 2 or token.lower() in dictionary:\n return {token: 1}\n\n return {}","def calculate_construction(self, word):\r\n \r\n construction = \"\"\r\n for c in word.lower():\r\n if c in self.vowels:\r\n construction += \"v\"\r\n elif c in letters:\r\n construction += \"c\"\r\n return construction","def check_sentences(text, threshold=80, print_only=False):\n non_white_text = re.sub(masks, \"\", re.sub(emojis, \"\", re.sub(punctuation, \"\", re.sub(\"\\s\", \"\", text))))\n num_chars = len(non_white_text)\n num_non_swiss_chars = 0\n for char in non_white_text:\n if char not in swiss_chars:\n num_non_swiss_chars += 1\n ratio = num_non_swiss_chars / num_chars * 100 if num_chars != 0 else 0\n if ratio > threshold:\n return \"POSSIBLE NON_SWISS GERMAN TEXT:\" + text if print_only else False\n else:\n return text","def transcribe(dna):\n str = ''\n dict = {'C': 'C', 'G': 'G', 'A': 'A', 'T': 'U'}\n for char in dna:\n if char == 'C' or char == 'G' or char == 'T' or char == 'A':\n #converting only of the valid string is encountered\n #then the string is converted accordingly\n str = str + dict[char]\n #the case for incalid string, it throws only the error\n else :\n str = 'invalid character entered, please check the input'\n break\n return str","def passion_analyzer(text):\n\n\tlower_text = text.lower()\n\n\thashtag_scaling = 0.3\n\texclamation_scaling = 0.5\n\tuppercase_scaling = 0.2\n\n\n\tpassion_index = 0\n\n\tfor x in range(len(positive_words)):\n\t\tpassion_index += (lower_text.count(positive_words[x]))**2\n\tfor x in range(len(negative_words)):\n\t\tpassion_index -= (lower_text.count(negative_words[x]))**2\n\tif '!' in text:\n\t\tpassion_index *= exclamation_scaling * lower_text.count('!') + 1\n\tif '#' in text:\n\t\tpassion_index *= hashtag_scaling * lower_text.count('#') + 1\n\tpassion_index *= uppercase_scaling * sum(1 for c in text if c.isupper())\n\n\n\t\t\n\treturn math.sqrt(passion_index)","def cleanText(text):\n try:\n text = str(text)\n\n # remove contactions and stop words\n text = contractions(text)\n # remove html entities\n cleanr = re.compile('<.*?>|&([a-z0-9]+|#[0-9]{1,6}|#x[0-9a-f]{1,6});')\n new_text = cleanr.sub('', text.strip())\n return re.sub(r'\\s+', ' ', re.sub(r'\\W+', \" \", new_text))\n # TAG_RE = re.compile(r'<[^>]+>')\n except:\n print(\"An exception occurred with: \" + text)\n return str(text)","def test_return_advice_under_cruising_weight(self):\n data_weight_user = {\"height\": \"1,60\", \"actual_weight\": \"60\",\n \"cruising_weight\": \"55\", \"weight_goal\": \"51\"}\n return_advice = self.new_weight_advice_goal.return_weight_advices_goal(data_weight_user)[1]\n\n advice = \"Chaque personne a un poids d'équilibre sur lequel il peut rester longtemps, \" \\\n \"c'est se qu'on appelle le poids de croisière. Il semble que ton objectif \" \\\n \"aille en dessous de ce poids. Je tiens donc à te préciser qu'il est \" \\\n \"possible que tu n'arrives pas à le maintenir sur la durée. \" \\\n \"Je note tout de même cet objectif. \"\n self.assertEqual(return_advice, advice)","def analyze(self, text):\n\n tknzr = nltk.tokenize.TweetTokenizer()\n words = tknzr.tokenize(text)\n \n score = 0\n \n for word in words:\n if word.lower() in self.positives:\n score += 1\n elif word.lower() in self.negatives:\n score -= 1\n else:\n continue\n \n return score","def compute_readability(text):\n total_words = 0\n total_sentences = 0\n total_syllables = 0\n score = 0\n\n words = text.split()\n total_words = len(text.split()) \n total_sentences = count_sentences(text)\n total_syllables = count_syllables(words)\n \n score = 206.835 - 1.015 * ( total_words / total_sentences) - 84.6 * (total_syllables / total_words)\n if score > 90.00:\n answer = 'Texto de nível do 5º ano do Ensino Fundamental, facilmente compreendido por um aluno de 11 anos.'\n elif score <= 90.00 and score > 80.00:\n answer = 'Texto de nível do 6º ano do Ensino Fundamental, inglês coloquial para consumidores.'\n elif score <= 80.00 and score > 70.00:\n answer = 'Texto de nível do 7º ano do Ensino Fundamental, razoavelmente fácil de ler.'\n elif score <= 70.00 and score > 60.00:\n answer = 'Texto de nível do 9º ano do Ensino Fundamental, Inglês simples compreendido por adolescentes de 13 - 15 anos.'\n elif score <= 60.00 and score > 50.00:\n answer = 'Texto de 1º a 3º ano do Ensino Médio, razoavelmente difícil de ler.'\n elif score <= 50.00 and score > 30.00:\n answer = 'Texto de nível Universitário, difícil de ler.'\n else:\n answer = 'Texto de nível de Graduação, muito difícil de ler e mais bem-compreendido por universitários graduados.'\n \n print('Pontuação Total:', score, answer)","def contraction_expansion_on_corpus(text_corpus):\n\n text_corpus[text_column_name] = text_corpus[\n text_column_name].apply(contraction_expansion)\n return text_corpus","def canada_query(text):\n return 'canada' in text.lower()","def analyze(self, text):\n tknzr = nltk.tokenize.casual.TweetTokenizer(preserve_case=True, reduce_len=False, strip_handles=False)\n tknTxt = tknzr.tokenize(text)\n sentiment = 0\n \n for i in range(len(tknTxt)):\n if tknTxt[i] in self.posTxt:\n #print(\"POS\")\n #print(tknTxt[i])\n sentiment += 1\n elif tknTxt[i] in self.negTxt:\n #print(\"NEG\")\n #print(tknTxt[i])\n sentiment -= 1\n \n return sentiment","def text_process_dict(field, dictionary:dict):\n values = dictionary.get(\"values\")\n if is_valid_text(values):\n is_exact = values.get(\"is_exact\", False)\n _term = values.get(\"term\", False)\n filtered_term = re.sub('[^a-zA-Z0-9\\n\\.|\\*|\\@|\\|\\_]', ' ', _term)\n return text_process(field, filtered_term, is_exact=is_exact)\n return {\n\n }"],"string":"[\n \"def replace_contractions(text):\\r\\n return contractions.fix(text)\",\n \"def _replace_contractions(text):\\n return contractions.fix(text)\",\n \"def replace_contractions(text):\\n return contractions.fix(text)\",\n \"def replace_contractions(text):\\n return contractions.fix(text)\",\n \"def expand_contractions(text):\\r\\n text = re.sub(\\r\\n r\\\"(\\\\b)([Aa]re|[Cc]ould|[Dd]id|[Dd]oes|[Dd]o|[Hh]ad|[Hh]as|[Hh]ave|[Ii]s|[Mm]ight|[Mm]ust|[Ss]hould|[Ww]ere|[Ww]ould)n't\\\",\\r\\n r\\\"\\\\1\\\\2 not\\\", text)\\r\\n text = re.sub(r\\\"(\\\\b)([Hh]e|[Ii]|[Ss]he|[Tt]hey|[Ww]e|[Ww]hat|[Ww]ho|[Yy]ou)'ll\\\", r\\\"\\\\1\\\\2 will\\\", text)\\r\\n text = re.sub(r\\\"(\\\\b)([Tt]hey|[Ww]e|[Ww]hat|[Ww]ho|[Yy]ou)'re\\\", r\\\"\\\\1\\\\2 are\\\", text)\\r\\n text = re.sub(r\\\"(\\\\b)([Ii]|[Ss]hould|[Tt]hey|[Ww]e|[Ww]hat|[Ww]ho|[Ww]ould|[Yy]ou)'ve\\\", r\\\"\\\\1\\\\2 have\\\", text)\\r\\n\\r\\n text = re.sub(r\\\"(\\\\b)([Cc]a)n't\\\", r\\\"\\\\1\\\\2n not\\\", text)\\r\\n text = re.sub(r\\\"(\\\\b)([Ii])'m\\\", r\\\"\\\\1\\\\2 am\\\", text)\\r\\n text = re.sub(r\\\"(\\\\b)([Ll]et)'s\\\", r\\\"\\\\1\\\\2 us\\\", text)\\r\\n text = re.sub(r\\\"(\\\\b)([Ww])on't\\\", r\\\"\\\\1\\\\2ill not\\\", text)\\r\\n text = re.sub(r\\\"(\\\\b)([Ss])han't\\\", r\\\"\\\\1\\\\2hall not\\\", text)\\r\\n text = re.sub(r\\\"(\\\\b)([Yy])(?:'all|a'll)\\\", r\\\"\\\\1\\\\2ou all\\\", text)\\r\\n\\r\\n return text\",\n \"def replaceContractions(self, text):\\n\\t\\treturn contractions.fix(text)\",\n \"def preProcess(text):\\n\\ttext = text.lower() # lower case the text\\n\\t# Q4 replace the word with expanded contractions\\n\\tfor k,v in general_contraction.items():\\n\\t\\tif k in text.split():\\n\\t\\t\\ttext = text.replace(k,v)\\n\\t# Q4 remove speacial char including all puncuattions and replace it with a space\\n\\ttext = re.sub('[^A-Za-z0-9]+',' ',text) \\n\\t# tokenise\\n\\ttokens = text.split()\\n\\t# stop word removal\\n\\ttokens = [w for w in tokens if w not in stopwords ]\\n\\t# Q4 Stemming\\n\\ttokens = [str(porter.stem(w)) for w in tokens]\\n\\t# if word is non-english return its english form # too much time-complexity\\n\\t# tokens = [porter.stem(w) if porter.stem(w) in set(words.words()) else w for w in tokens ]\\n\\t# for words having digits such as 12gb, 1st, etc expanding the token list\\n\\tfor k in tokens:\\n\\t\\tif len(k) >2 and re.match(r'[0-9]+',k):\\t\\t\\t\\n\\t\\t\\tif len(k) >2 and not k.isdigit():\\n\\t\\t\\t\\tl = re.split(r'(\\\\d+)',k)\\n\\t\\t\\t\\tl = [w for w in l if w is not '' ]\\n\\t\\t\\t\\tif l and len(l) <= 3:\\n\\t\\t\\t\\t\\tfor i in l:\\n\\t\\t\\t\\t\\t\\tif i in digit_contractions.keys():\\n\\t\\t\\t\\t\\t\\t\\tl = list(map(lambda b: b.replace(i,digit_contractions[i]), l))\\n\\t\\t\\t\\t\\ttokens.remove(k)\\n\\t\\t\\t\\t\\ttokens = tokens+l\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\ttokens.remove(k)\\n\\tfor k,v in digit_contractions.items():\\n\\t\\tif k in tokens:\\n\\t\\t\\tif tokens[tokens.index(k)-1].isdigit():\\t\\n\\t\\t\\t\\ttokens = list(map(lambda b: b.replace(k,v), tokens))\\n\\t# remove tokens of size less than 2\\n\\ttokens = [t for t in tokens if len(t) > 2]\\n\\treturn tokens\",\n \"def replace_contractions(sample):\\n sample[\\\"full_text\\\"] = contractions.fix(sample[\\\"full_text\\\"])\\n return sample\",\n \"def recommended_correction(text):\\n tool = language_check.LanguageTool('en-US')\\n matches = tool.check(text)\\n correction = language_check.correct(text, matches)\\n return correction\",\n \"def expand_contractions(text):\\n text = list(cont.expand_texts([text], precise=True))[0]\\n return text \\n\\n text = \\\"\\\"\\\"three cups of coffee\\\"\\\"\\\"\\n doc = nlp(text)\\n tokens = [w2n.word_to_num(token.text) if token.pos_ == 'NUM' else token for token in doc]\",\n \"def contractions_remove(self,sentence):\\n sentence = self.contractions_re.sub(lambda mo: CONTRACTIONS[mo.string[mo.start():mo.end()]], sentence)\\n return sentence\",\n \"def ocr_correction(token):\",\n \"def corrected(text):\\n # Init SpellChecker\\n spell = SpellChecker(language='fr', distance=1, case_sensitive=False)\\n # Get misspellings and corrections\\n text_splitted = text.split()\\n misspelled = spell.unknown(text_splitted)\\n correction = {word:spell.correction(word) for word in misspelled}\\n\\n # Replace misspellings in text\\n for k,v in correction.items():\\n text = text.replace(k,v)\\n return text\",\n \"def removeContractions(self, text=None):\\n\\n\\t\\tif type(text) != type(str):\\n\\t\\t\\ttext = \\\" \\\".join(text)\\n\\n\\t\\tfor key in self.norm_words:\\n\\t\\t\\ttext = text.replace(key, self.norm_words[key])\\n\\n\\t\\ttext = text.split(\\\" \\\")\\n\\n\\t\\treturn text\",\n \"def correct(self, text):\\n with self._get_searcher() as searcher:\\n query = self._get_query_parser().parse(unicode(text))\\n correction = searcher.correct_query(query, unicode(text))\\n if correction.query != query:\\n formatter = whoosh.highlight.HtmlFormatter()\\n return correction.format_string(formatter)\\n return None\",\n \"def replace_contractions(self):\\n self.text = contractions.fix(self.text)\\n return self\",\n \"def text_dict(text):\\n hey_words = {'hello', 'hey', 'hi', 'heya', 'hiya', 'hai'}\\n fine_words = {'fine', 'good', 'splendid', 'amazing', 'well', 'lovely', 'cool'}\\n bye_words = {'goodbye', 'bye', 'adios'}\\n thank_words = {'thanks', 'thank'}\\n notes_words = {'notes', 'note'}\\n tokens = set(text.split())\\n\\n if len(notes_words.intersection(tokens)) > 0:\\n text = 'Ok sure. Please tell me what to note down.'\\n\\n elif len(fine_words.intersection(tokens)) > 0:\\n text = '''Good to hear that. How may I help you?'''\\n\\n elif len(hey_words.intersection(tokens)) > 0:\\n text = '''Hi Shivek. How are you doing today?'''\\n\\n elif len(thank_words.intersection(tokens)) > 0:\\n text = '''You are welcome. Can I help you with anything else?'''\\n\\n elif len(bye_words.intersection(tokens)) > 0 or 'see you' in text or 'see ya' in text or 'no thank' in text:\\n text = '''Ok goodbye!'''\\n\\n\\n else:\\n text = '''Sorry, I didn't get you. Can you please repeat?'''\\n return text\",\n \"def correction():\\n text = request.args.get('text', '')\\n text = TextBlob(text)\\n return jsonify(text=unicode(text.correct()))\",\n \"def correct(search_key, text, strictness):\\n\\n text_copy = copy.deepcopy(text)\\n words = text.split()\\n for word in words:\\n similarity = SequenceMatcher(None, word, search_key)\\n if similarity.ratio() > strictness:\\n text_copy = text_copy.replace(word, search_key)\\n return text_copy\",\n \"def check_for_correction(dummy_arg):\\n #space_indexes = []\\n #new_space_indexes = []\\n #punct_dict = {}\\n\\n # Get current document and attributes for doc\\n doc = XSCRIPTCONTEXT.getDocument()\\n selection_supplier = doc.getCurrentController()\\n indexAccess = selection_supplier.getSelection()\\n #count = indexAccess.getCount() # don't really need this right now. Only loop once..\\n\\n # get cursor to write corrected word highlighted in blue\\n text = doc.Text\\n vis_cursor = doc.getCurrentController().getViewCursor() # visible cursor in OpenOffice\\n nvis_cursor= vis_cursor.getText().createTextCursorByRange(vis_cursor) # not visible cursor place at same place as visible\\n \\n # set str and txt for use\\n highlighted_txt = indexAccess.getByIndex(0)\\n highlighted_str = highlighted_txt.getString()\\n \\n # strip punctuation from string\\n punctuation = set(string.punctuation)\\n '''\\n for letter in highlighted_str:\\n if letter == ' ':\\n sp_index = highlighted_str.index(letter)\\n space_indexes.append(sp_index + len(space_indexes))\\n highlighted_str = highlighted_str.replace(letter,'',1)\\n \\n for index in space_indexes:\\n highlighted_str = highlighted_str[:index] + ' ' + highlighted_str[index:]\\n\\n \\n for letter in highlighted_str:\\n if letter in punctuation:\\n punc_index = highlighted_str.index(letter)\\n punct_dict[letter] = punc_index\\n highlighted_str = highlighted_str.replace(letter,'',1)\\n '''\\n highlighted_str = ''.join(ch for ch in highlighted_str if ch not in punctuation) \\n highlighted_str_arr = highlighted_str.split()\\n\\n corrected_sent = highlighted_str # correct sentence to be displayed at end\\n\\n # check for correction for each word highlighted\\n for word in highlighted_str_arr:\\n\\n if len(highlighted_str) == 0:\\n return None\\n\\n # call make_correction helper function to correct highlighted word\\n else:\\n correction = make_correction(word)\\n \\n # make sure new correction word is present\\n if correction:\\n # get parent window from document\\n parentwin = doc.CurrentController.Frame.ContainerWindow\\n # make window\\n correction_query_str = word + \\\" => \\\" + correction # show old word => new word\\n prompt = MessageBox(parentwin, correction_query_str, \\\"Correction:\\\", MESSAGEBOX, BUTTONS_YES_NO)\\n \\n # if yes button pressed, replace word\\n if prompt == 2:\\n # replace wrong word with corrected word\\n corrected_sent = corrected_sent.replace(word, correction)\\n '''\\n for letter in corrected_sent:\\n if letter == ' ':\\n sp_index = corrected_sent.index(letter)\\n new_space_indexes.append(sp_index + len(new_space_indexes))\\n corrected_sent = corrected_sent.replace(letter,'',1)\\n \\n for index in new_space_indexes:\\n corrected_sent = corrected_sent[:index] + ' ' + corrected_sent[index:]\\n \\n \\n punc_location = {}\\n for key in punct_dict:\\n p_index = punct_dict[key]\\n for s_index in space_indexes:\\n if abs(p_index-s_index) == 1:\\n location_index = space_indexes.index(s_index)\\n if p_index-s_index > 0:\\n punc_location[key] = new_space_indexes[location_index+1]\\n else:\\n punc_location[key] = new_space_indexes[location_index-1]\\n\\n\\n for key in punc_location:\\n index = punc_location[key]\\n corrected_sent = key\\n #corrected_sent = corrected_sent[:index] + key + corrected_sent[index:]\\n ''' \\n #highlighted_txt.setString(highlighted_str)\\n highlighted_txt.setString(corrected_sent) # replace old word with nothing\",\n \"def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):\\n\\n def _strip_spaces(text):\\n ns_chars = []\\n ns_to_s_map = collections.OrderedDict()\\n for (i, c) in enumerate(text):\\n if c == \\\" \\\":\\n continue\\n ns_to_s_map[len(ns_chars)] = i\\n ns_chars.append(c)\\n ns_text = \\\"\\\".join(ns_chars)\\n return (ns_text, ns_to_s_map)\\n\\n # We first tokenize `orig_text`, strip whitespace from the result\\n # and `pred_text`, and check if they are the same length. If they are\\n # NOT the same length, the heuristic has failed. If they are the same\\n # length, we assume the characters are one-to-one aligned.\\n\\n tokenizer = BasicTokenizer(do_lower_case=do_lower_case)\\n\\n tok_text = \\\" \\\".join(tokenizer.tokenize(orig_text))\\n\\n start_position = tok_text.find(pred_text)\\n if start_position == -1:\\n if verbose_logging:\\n logger.info(\\n \\\"Unable to find text: '%s' in '%s'\\\" % (pred_text, orig_text))\\n return orig_text\\n end_position = start_position + len(pred_text) - 1\\n\\n (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)\\n (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)\\n\\n if len(orig_ns_text) != len(tok_ns_text):\\n if verbose_logging:\\n logger.info(\\\"Length not equal after stripping spaces: '%s' vs '%s'\\\",\\n orig_ns_text, tok_ns_text)\\n return orig_text\\n\\n # We then project the characters in `pred_text` back to `orig_text` using\\n # the character-to-character alignment.\\n tok_s_to_ns_map = {}\\n for (i, tok_index) in tok_ns_to_s_map.items():\\n tok_s_to_ns_map[tok_index] = i\\n\\n orig_start_position = None\\n if start_position in tok_s_to_ns_map:\\n ns_start_position = tok_s_to_ns_map[start_position]\\n if ns_start_position in orig_ns_to_s_map:\\n orig_start_position = orig_ns_to_s_map[ns_start_position]\\n\\n if orig_start_position is None:\\n if verbose_logging:\\n logger.info(\\\"Couldn't map start position\\\")\\n return orig_text\\n\\n orig_end_position = None\\n if end_position in tok_s_to_ns_map:\\n ns_end_position = tok_s_to_ns_map[end_position]\\n if ns_end_position in orig_ns_to_s_map:\\n orig_end_position = orig_ns_to_s_map[ns_end_position]\\n\\n if orig_end_position is None:\\n if verbose_logging:\\n logger.info(\\\"Couldn't map end position\\\")\\n return orig_text\\n\\n output_text = orig_text[orig_start_position:(orig_end_position + 1)]\\n return output_text\",\n \"def get_final_text(config: configure_finetuning.FinetuningConfig, pred_text,\\n orig_text):\\n\\n # When we created the data, we kept track of the alignment between original\\n # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So\\n # now `orig_text` contains the span of our original text corresponding to the\\n # span that we predicted.\\n #\\n # However, `orig_text` may contain extra characters that we don't want in\\n # our prediction.\\n #\\n # For example, let's say:\\n # pred_text = steve smith\\n # orig_text = Steve Smith's\\n #\\n # We don't want to return `orig_text` because it contains the extra \\\"'s\\\".\\n #\\n # We don't want to return `pred_text` because it's already been normalized\\n # (the SQuAD eval script also does punctuation stripping/lower casing but\\n # our tokenizer does additional normalization like stripping accent\\n # characters).\\n #\\n # What we really want to return is \\\"Steve Smith\\\".\\n #\\n # Therefore, we have to apply a semi-complicated alignment heruistic between\\n # `pred_text` and `orig_text` to get a character-to-charcter alignment. This\\n # can fail in certain cases in which case we just return `orig_text`.\\n\\n def _strip_spaces(text):\\n ns_chars = []\\n ns_to_s_map = collections.OrderedDict()\\n for i, c in enumerate(text):\\n if c == \\\" \\\":\\n continue\\n ns_to_s_map[len(ns_chars)] = i\\n ns_chars.append(c)\\n ns_text = \\\"\\\".join(ns_chars)\\n return ns_text, dict(ns_to_s_map)\\n\\n # We first tokenize `orig_text`, strip whitespace from the result\\n # and `pred_text`, and check if they are the same length. If they are\\n # NOT the same length, the heuristic has failed. If they are the same\\n # length, we assume the characters are one-to-one aligned.\\n tokenizer = tokenization.BasicTokenizer(do_lower_case=config.do_lower_case)\\n\\n tok_text = \\\" \\\".join(tokenizer.tokenize(orig_text))\\n\\n start_position = tok_text.find(pred_text)\\n if start_position == -1:\\n if config.debug:\\n utils.log(\\n \\\"Unable to find text: '%s' in '%s'\\\" % (pred_text, orig_text))\\n return orig_text\\n end_position = start_position + len(pred_text) - 1\\n\\n (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)\\n (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)\\n\\n if len(orig_ns_text) != len(tok_ns_text):\\n if config.debug:\\n utils.log(\\\"Length not equal after stripping spaces: '%s' vs '%s'\\\",\\n orig_ns_text, tok_ns_text)\\n return orig_text\\n\\n # We then project the characters in `pred_text` back to `orig_text` using\\n # the character-to-character alignment.\\n tok_s_to_ns_map = {}\\n for (i, tok_index) in six.iteritems(tok_ns_to_s_map):\\n tok_s_to_ns_map[tok_index] = i\\n\\n orig_start_position = None\\n if start_position in tok_s_to_ns_map:\\n ns_start_position = tok_s_to_ns_map[start_position]\\n if ns_start_position in orig_ns_to_s_map:\\n orig_start_position = orig_ns_to_s_map[ns_start_position]\\n\\n if orig_start_position is None:\\n if config.debug:\\n utils.log(\\\"Couldn't map start position\\\")\\n return orig_text\\n\\n orig_end_position = None\\n if end_position in tok_s_to_ns_map:\\n ns_end_position = tok_s_to_ns_map[end_position]\\n if ns_end_position in orig_ns_to_s_map:\\n orig_end_position = orig_ns_to_s_map[ns_end_position]\\n\\n if orig_end_position is None:\\n if config.debug:\\n utils.log(\\\"Couldn't map end position\\\")\\n return orig_text\\n\\n output_text = orig_text[orig_start_position:(orig_end_position + 1)]\\n return output_text\",\n \"def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):\\n\\n def _strip_spaces(text):\\n ns_chars = []\\n ns_to_s_map = collections.OrderedDict()\\n for (i, c) in enumerate(text):\\n if c == \\\" \\\":\\n continue\\n ns_to_s_map[len(ns_chars)] = i\\n ns_chars.append(c)\\n ns_text = \\\"\\\".join(ns_chars)\\n return (ns_text, ns_to_s_map)\\n\\n # We first tokenize `orig_text`, strip whitespace from the result\\n # and `pred_text`, and check if they are the same length. If they are\\n # NOT the same length, the heuristic has failed. If they are the same\\n # length, we assume the characters are one-to-one aligned.\\n tokenizer = BasicTokenizer(do_lower_case=do_lower_case)\\n\\n tok_text = \\\" \\\".join(tokenizer.tokenize(orig_text))\\n\\n start_position = tok_text.find(pred_text)\\n if start_position == -1:\\n if verbose_logging:\\n logger.info(\\n \\\"Unable to find text: '%s' in '%s'\\\" % (pred_text, orig_text))\\n return orig_text\\n end_position = start_position + len(pred_text) - 1\\n\\n (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)\\n (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)\\n\\n if len(orig_ns_text) != len(tok_ns_text):\\n if verbose_logging:\\n logger.info(\\\"Length not equal after stripping spaces: '%s' vs '%s'\\\",\\n orig_ns_text, tok_ns_text)\\n return orig_text\\n\\n # We then project the characters in `pred_text` back to `orig_text` using\\n # the character-to-character alignment.\\n tok_s_to_ns_map = {}\\n for (i, tok_index) in tok_ns_to_s_map.items():\\n tok_s_to_ns_map[tok_index] = i\\n\\n orig_start_position = None\\n if start_position in tok_s_to_ns_map:\\n ns_start_position = tok_s_to_ns_map[start_position]\\n if ns_start_position in orig_ns_to_s_map:\\n orig_start_position = orig_ns_to_s_map[ns_start_position]\\n\\n if orig_start_position is None:\\n if verbose_logging:\\n logger.info(\\\"Couldn't map start position\\\")\\n return orig_text\\n\\n orig_end_position = None\\n if end_position in tok_s_to_ns_map:\\n ns_end_position = tok_s_to_ns_map[end_position]\\n if ns_end_position in orig_ns_to_s_map:\\n orig_end_position = orig_ns_to_s_map[ns_end_position]\\n\\n if orig_end_position is None:\\n if verbose_logging:\\n logger.info(\\\"Couldn't map end position\\\")\\n return orig_text\\n\\n output_text = orig_text[orig_start_position:(orig_end_position + 1)]\\n return output_text\",\n \"def decipher(self, text, code):\\n # remove capitalization to search for words\\n text.lower()\\n # remove any punctuation\\n text = text.translate(str.maketrans('', '', string.punctuation))\\n # get words to search for in text\\n secret_words = code.keys()\\n # check if text contains any secret words and collect their boltzman values\\n boltzmanns = []\\n for word in text.split():\\n if word in secret_words:\\n boltzmanns.append(code[word])\\n # return None if no code words in text\\n if len(boltzmanns) == 0:\\n return None\\n # return boltzmann average value for code words present\\n else:\\n avg = sum(boltzmanns) / float(len(boltzmanns))\\n return avg\",\n \"def canned():\\n return (next_phrase(\\\"we proceed as follows\\\") |\\n (next_word('the') + \\n first_word('result lemma theorem proposition corollary') +\\n next_word('now').possibly() +\\n next_word('follows')) |\\n next_phrase('the other cases are similar') |\\n (next_phrase('the proof is')+ first_word('obvious trivial easy routine'))).nil().expect('canned')\",\n \"def correct_case(token, corrections_map, structures):\\n alt_case_mode = -1 # Most common variation\\n if token[0].isupper():\\n if sum(char.isupper() for char in token) > 2:\\n alt_case_mode = 0 # Upper case variation\\n else:\\n alt_case_mode = 1 # Lower case variation with capital first letter\\n\\n corrected_case_map = {}\\n for correct_word, score in corrections_map.items():\\n if correct_word.find(\\\" \\\") != -1:\\n words = correct_word.split(\\\" \\\")\\n\\n keys_left = find_correct_case(words[0], alt_case_mode, structures)\\n keys_right = find_correct_case(words[1], alt_case_mode, structures)\\n key = keys_left+\\\" \\\"+keys_right\\n else:\\n key = find_correct_case(correct_word, alt_case_mode, structures)\\n\\n # If the key already exists we keep the highest score\\n if key in corrected_case_map.keys():\\n old_score = corrected_case_map[key]\\n corrected_case_map[key] = max(old_score, score)\\n else:\\n corrected_case_map[key] = score\\n\\n return corrected_case_map\",\n \"def decontracted(phrase):\\r\\n # specific\\r\\n phrase = re.sub(r\\\"won\\\\'t\\\", \\\"will not\\\", phrase)\\r\\n phrase = re.sub(r\\\"can\\\\'t\\\", \\\"can not\\\", phrase)\\r\\n # general\\r\\n phrase = re.sub(r\\\"n\\\\'t\\\", \\\" not\\\", phrase)\\r\\n phrase = re.sub(r\\\"\\\\'re\\\", \\\" are\\\", phrase)\\r\\n phrase = re.sub(r\\\"\\\\'s\\\", \\\" is\\\", phrase)\\r\\n phrase = re.sub(r\\\"\\\\'d\\\", \\\" would\\\", phrase)\\r\\n phrase = re.sub(r\\\"\\\\'ll\\\", \\\" will\\\", phrase)\\r\\n phrase = re.sub(r\\\"\\\\'t\\\", \\\" not\\\", phrase)\\r\\n phrase = re.sub(r\\\"\\\\'ve\\\", \\\" have\\\", phrase)\\r\\n phrase = re.sub(r\\\"\\\\'m\\\", \\\" am\\\", phrase)\\r\\n return phrase\",\n \"def clean_for_comparison(text):\\n text = clean_text(text)\\n text = clean_text_from_nonbasic_characters(text)\\n return text\",\n \"def check_spellings(text):\\n\\n for word in vocabulary:\\n text = correct(word, text, 0.7)\\n return text\",\n \"def corrigir_doc(frase):\\r\\n\\r\\n if not frase or not (isinstance(frase, str) and all(x.isalpha() for x in frase.split())) or ' ' in frase:\\r\\n raise ValueError('corrigir_doc: argumento invalido')\\r\\n\\r\\n palavras, doc_corrigido = frase.split(' '), ''\\r\\n palavras = list(map(corrigir_palavra, palavras))\\r\\n\\r\\n while palavras:\\r\\n anagrama_teste = palavras.pop(0)\\r\\n if palavras:\\r\\n for word in palavras:\\r\\n if eh_anagrama(anagrama_teste, word) and anagrama_teste.lower() != word.lower():\\r\\n del palavras[palavras.index(word)]\\r\\n doc_corrigido += ' ' + anagrama_teste\\r\\n\\r\\n return doc_corrigido[1:]\",\n \"def get_exhaustive_text_correction_proposal(self, input_text):\\n arr = []\\n self.complexity=20\\n prev = self.alpha\\n self.alpha = 0.95 # temporary\\n arr_i = 0\\n\\n with torch.no_grad():\\n for text_chunk in tqdm(self._string_to_chunks(input_text)):\\n self.oryginal_input_text = text_chunk\\n self.input_text = text_chunk\\n self._compute_exhaustive_outputs()\\n\\n for ix in range(self.input_size):\\n token_id = self.input_ids[0][ix]\\n token_obj = {}\\n token_obj[\\\"name\\\"] = self.tokenizer.decode(token_id.item())\\n token_obj[\\\"probability\\\"] = self.normalized_token_prob[ix]\\n token_obj[\\\"oddballness\\\"] = self._get_oddballness_proba(token_obj[\\\"probability\\\"], self.probs[ix],\\n alpha=self.alpha).item()\\n arr.append(token_obj)\\n\\n self.input_text = self.oryginal_input_text\\n self._compute_outputs()\\n for ix in range(self.input_size):\\n self.sorted_probs, self.sorted_indices = torch.sort(self.probs[ix - 1], descending=True)\\n _, correction_indices = self._get_best_tokens(5)\\n\\n arr[arr_i + ix][\\\"corrections\\\"] = [self.tokenizer.decode(token_id.item()) for token_id in correction_indices]\\n arr_i += self.input_size\\n\\n arr.pop()\\n arr.pop(0)\\n self._trim_bpe_space_artifact(arr)\\n self.token_array = arr\\n self.alpha = prev # temporary\\n return arr\",\n \"def is_correction(self):\\n # OK, go looking for RESENT style tags, assume it happens within first\\n # 300 chars\\n if RESENT.search(self.text[:300]):\\n return True\\n if self.bbb is None or not self.bbb:\\n return False\\n if self.bbb[0] in ['A', 'C']:\\n return True\\n return False\",\n \"def oracle(c):\\n correct_arcs = get_arcs(c.sentence)\\n if can_left_arc(c, correct_arcs):\\n return Transition('la', c.sentence[c.stack[-1]].deprel)\\n elif can_right_arc(c, correct_arcs):\\n return Transition('ra', c.sentence[c.buffer[0]].deprel)\\n else:\\n return Transition('sh', '_')\",\n \"def clean_up(text_not_allowed, raw_string):\\n trans_table = str.maketrans(dict.fromkeys(text_not_allowed))\\n clean_result = raw_string.translate(trans_table)\\n return clean_result\",\n \"def can_recept(self, text, *args, **kwargs):\\n for each_cur in self.flat_norm.keys():\\n if each_cur.lower() in text.lower():\\n return True\\n\\n else:\\n return False\",\n \"def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):\\n\\n # When we created the data, we kept track of the alignment between original\\n # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So\\n # now `orig_text` contains the span of our original text corresponding to the\\n # span that we predicted.\\n #\\n # However, `orig_text` may contain extra characters that we don't want in\\n # our prediction.\\n #\\n # For example, let's say:\\n # pred_text = steve smith\\n # orig_text = Steve Smith's\\n #\\n # We don't want to return `orig_text` because it contains the extra \\\"'s\\\".\\n #\\n # We don't want to return `pred_text` because it's already been normalized\\n # (the SQuAD eval script also does punctuation stripping/lower casing but\\n # our tokenizer does additional normalization like stripping accent\\n # characters).\\n #\\n # What we really want to return is \\\"Steve Smith\\\".\\n #\\n # Therefore, we have to apply a semi-complicated alignment heuristic between\\n # `pred_text` and `orig_text` to get a character-to-character alignment. This\\n # can fail in certain cases in which case we just return `orig_text`.\\n\\n def _strip_spaces(text):\\n ns_chars = []\\n ns_to_s_map = collections.OrderedDict()\\n for (i, c) in enumerate(text):\\n if c == \\\" \\\":\\n continue\\n ns_to_s_map[len(ns_chars)] = i\\n ns_chars.append(c)\\n ns_text = \\\"\\\".join(ns_chars)\\n return (ns_text, ns_to_s_map)\\n\\n # We first tokenize `orig_text`, strip whitespace from the result\\n # and `pred_text`, and check if they are the same length. If they are\\n # NOT the same length, the heuristic has failed. If they are the same\\n # length, we assume the characters are one-to-one aligned.\\n tokenizer = BasicTokenizer(do_lower_case=do_lower_case)\\n\\n tok_text = \\\"\\\".join(tokenizer.tokenize(orig_text))\\n\\n start_position = tok_text.find(pred_text)\\n if start_position == -1:\\n if verbose_logging:\\n print(\\\"Unable to find text: '%s' in '%s'\\\" % (pred_text, orig_text))\\n return orig_text\\n end_position = start_position + len(pred_text) - 1\\n\\n (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)\\n (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)\\n\\n if len(orig_ns_text) != len(tok_ns_text):\\n if verbose_logging:\\n print(\\\"Length not equal after stripping spaces: '%s' vs '%s'\\\" % (orig_ns_text, tok_ns_text))\\n return orig_text\\n\\n # We then project the characters in `pred_text` back to `orig_text` using\\n # the character-to-character alignment.\\n tok_s_to_ns_map = {}\\n for (i, tok_index) in tok_ns_to_s_map.items():\\n tok_s_to_ns_map[tok_index] = i\\n\\n orig_start_position = None\\n if start_position in tok_s_to_ns_map:\\n ns_start_position = tok_s_to_ns_map[start_position]\\n if ns_start_position in orig_ns_to_s_map:\\n orig_start_position = orig_ns_to_s_map[ns_start_position]\\n\\n if orig_start_position is None:\\n if verbose_logging:\\n print(\\\"Couldn't map start position\\\")\\n return orig_text\\n\\n orig_end_position = None\\n if end_position in tok_s_to_ns_map:\\n ns_end_position = tok_s_to_ns_map[end_position]\\n if ns_end_position in orig_ns_to_s_map:\\n orig_end_position = orig_ns_to_s_map[ns_end_position]\\n\\n if orig_end_position is None:\\n if verbose_logging:\\n print(\\\"Couldn't map end position\\\")\\n return orig_text\\n\\n output_text = orig_text[orig_start_position:(orig_end_position + 1)]\\n return output_text\",\n \"def test__same_text_correlation(self):\\n \\n _log.info('-'*80)\\n \\n # arrange \\n text1 = \\\"love is rain as long story short\\\"\\n text2 = text1\\n\\n dump_file = getInputFile(\\\"swiki_knowledge_output.xml\\\")\\n parsed_file = getOutputFile(\\\"swiki_knowledge_output.parsed.xml\\\")\\n #wdb_file = getOutputFile(\\\"swiki_knowledge_output.wdb\\\")\\n\\n articles = ['Rain', 'Love', 'Tree'] \\n \\n # act\\n wn.make_dump(dump_file, articles, compress=False)\\n wn.parse_dump(dump_file, parsed_file)\\n db_wrapper = wn.build_database_wrapper(parsed_file, StopWordsStemmer([]))\\n \\n #self.addCleanup(os.remove, self.tmp_dump_file)\\n \\n comparer = SemanticComparer(db_wrapper)\\n correlation = comparer.compare(text1, text2)\\n _log.info(test_utils.get_texts_correlation_message(text1, text2, correlation))\\n self.assertAlmostEqual(correlation, 1.0, msg=\\\"for same text correlation should be 1\\\")\",\n \"def caesar(action, input_text, shift_amount):\\n\\n transformed_text = ''\\n\\n # If the action is decode, make the shift_amount as negative\\n if action == 'decode':\\n shift_amount *= -1\\n\\n # Add/Subtract the shift value to get the index from alphabets list\\n for alphabet in input_text:\\n if alphabet in alphabets:\\n index = alphabets.index(alphabet)\\n new_index = index + shift_amount\\n new_alphabet = alphabets[new_index]\\n transformed_text += new_alphabet\\n else:\\n transformed_text += alphabet\\n\\n # Print the result\\n print(f'The {action}d text is {transformed_text}')\",\n \"def calculate_cer(normalized_texts: List[str], transcript: str, remove_punct=False) -> List[Tuple[str, float]]:\\n normalized_options = []\\n for text in normalized_texts:\\n text_clean = text.replace('-', ' ').lower()\\n if remove_punct:\\n for punct in \\\"!?:;,.-()*+-/<=>@^_\\\":\\n text_clean = text_clean.replace(punct, \\\"\\\")\\n cer = round(word_error_rate([transcript], [text_clean], use_cer=True) * 100, 2)\\n normalized_options.append((text, cer))\\n return normalized_options\",\n \"def calculate_cer(normalized_texts: List[str], transcript: str, remove_punct=False) -> List[Tuple[str, float]]:\\n normalized_options = []\\n for text in normalized_texts:\\n text_clean = text.replace('-', ' ').lower().strip()\\n if remove_punct:\\n for punct in \\\"!?:;,.-()*+-/<=>@^_\\\":\\n text_clean = text_clean.replace(punct, \\\" \\\")\\n text_clean = re.sub(r' +', ' ', text_clean)\\n cer = round(word_error_rate([transcript], [text_clean], use_cer=True) * 100, 2)\\n normalized_options.append((text, cer))\\n return normalized_options\",\n \"def deCopIfy(text):\\n\\tif text == \\\"\\\":\\n\\t\\treturn text\\n\\n\\tfor lingo in coplingo:\\n\\t\\ttext = re.sub(lingo['regex'], lingo['str'], text)\\n\\n\\treturn text[0].upper() + text[1:]\",\n \"def auto_correct(cls, input_string):\\n\\n # input_string = cls.filter_input(input_string)\\n\\n possible_corrections = dict()\\n output = []\\n\\n for token in input_string.strip().split(\\\" \\\"):\\n # print(token)\\n for category, words in cls.correction_dict.items():\\n possible_corrections[category] = difflib.get_close_matches(token.title(), words, 1, cls.cutoff)\\n\\n # print(\\\"Possible:\\\", flatten([i for i in possible_corrections.values()]))\\n\\n corrected = difflib.get_close_matches(token.title(), flatten([i for i in possible_corrections.values()]), 1,\\n cls.cutoff)\\n if corrected:\\n output.append(corrected[0])\\n\\n return ' '.join(output)\",\n \"def lf_abnormal_interp_with_discont(report):\\n if 'interpretation' in report.sections.keys():\\n interpretation = report.sections['interpretation']\\n interp_text = interpretation['text']\\n return abnormal_interp_with_discont(interp_text)\\n elif 'summary' in report.sections:\\n return abnormal_interp_with_discont(report.sections['summary']['text'])\\n elif 'findings' in report.sections: # fall back to look in the findings \\n if 'summary' in report.sections['findings']: # fall back to look for a summary instead\\n return abnormal_interp_with_discont(report.sections['findings']['summary'])\\n if 'impression' in report.sections['findings']:\\n return abnormal_interp_with_discont(report.sections['findings']['impression']) \\n return ABSTAIN_VAL\\n elif 'narrative' in report.sections: # fall back to look in the findings \\n ky = 'narrative'\\n if 'summary' in report.sections[ky]: # fall back to look for a summary instead\\n return abnormal_interp_with_discont(report.sections[ky]['summary'])\\n if 'impression' in report.sections[ky]:\\n return abnormal_interp_with_discont(report.sections[ky]['impression']) \\n return ABSTAIN_VAL \\n else:\\n return ABSTAIN_VAL\",\n \"def select_best_match(\\n self, normalized_texts: List[str], transcript: str, verbose: bool = False, remove_punct: bool = False\\n ):\\n normalized_texts = calculate_cer(normalized_texts, transcript, remove_punct)\\n normalized_texts = sorted(normalized_texts, key=lambda x: x[1])\\n normalized_text, cer = normalized_texts[0]\\n\\n if verbose:\\n print('-' * 30)\\n for option in normalized_texts:\\n print(option)\\n print('-' * 30)\\n return normalized_text, cer\",\n \"def intent_of_text_LnDOR(ChapterTextS, TargetQuestionsD, TestS, StopWords):\\n \\n # Chapter Text - stokenize\\n StokensCT = stokenize(ChapterTextS, StopWords) \\n\\n # Test question - stokenize\\n StokensTest = stokenize(TestS, StopWords)\\n\\n # Knowledge Base Dict - stokenize\\n KBD_structure = stokenizeKBD(TargetQuestionsD, StopWords)\\n\\n # List (because list is mutable, set is not) of all stokens in document\\n StokensDoc = StokensCT[:] # from chapter text\\n StokensDoc.extend(StokensTest[:]) # += Test string\\n\\n # extend list of stokens in Doc\\n for i in TargetQuestionsD:\\n StokensDoc.extend(TargetQuestionsD[i][\\\"mq stokens\\\"][:]) # += KB target [matched Q]s\\n StokensDoc.extend(TargetQuestionsD[i][\\\"ans stokens\\\"][:]) # += KB answers\\n \\n StokensTestV = set(StokensTest)\\n StokensDocV = set(StokensDoc)\\n StokensAntiTgtV = StokensDocV\\n \\n # Complement of all targets\\n for i in TargetQuestionsD:\\n StokensAntiTgtV = StokensAntiTgtV.difference(set(TargetQuestionsD[i][\\\"mq stokens\\\"]))\\n \\n # calculate confusion matrix and DOR etc.\\n LnDORD = {}\\n # Anti Target\\n TP, FP, FN, TN = confusion_matrix(StokensDocV, StokensAntiTgtV, StokensTestV) \\n LnDOR = lndor(TP, FP, FN, TN) \\n someAngle = angleDOR(TP, FP, FN, TN) \\n \\n LnDORD[\\\"AntiTgt\\\"] = {'lndor': LnDOR, 'theta': someAngle}\\n\\n # total occurences\\n total_occ = 0\\n for i in TargetQuestionsD:\\n total_occ += TargetQuestionsD[i]['count']\\n\\n for i in TargetQuestionsD:\\n StokensTgtV = set(TargetQuestionsD[i][\\\"mq stokens\\\"][:])\\n\\n TP, FP, FN, TN = confusion_matrix(StokensDocV, StokensTgtV, StokensTestV) \\n priorOR = TargetQuestionsD[i]['count'] / total_occ\\n\\n LnDOR = lndor(TP, FP, FN, TN) \\n someAngle = angleDOR(TP, FP, FN, TN, priorOR) \\n \\n LnDORD[i] = {'lndor': LnDOR, 'theta': someAngle}\\n # LnDORD = {i: {'lndor': , 'theta': }}, KB indices + \\\"AntiTgt\\\"\\n\\n return LnDORD\",\n \"def hasConstantForm(self, sentence):\",\n \"def segue_canned_texts(segue, kind, excecute_code=True):\\n\\n def enrich_with_phrases(segue, kind):\\n \\\"\\\"\\\"Retrieves the two phrases associated to the two nodes composing the segue\\n The two phrases are added to the segue as two new keys, to be exploited in short, line and description texts.\\n\\n Args:\\n segue (d)\\n kind (str): whether short, line or description\\n\\n Returns:\\n d: segue dictionary enriched with phrases\\n \\\"\\\"\\\"\\n phrase_n1 = phrase(segue['n1'], segue['compare_function'], kind)\\n phrase_n2 = phrase(segue['n2'], segue['compare_function'], kind)\\n if phrase_n1 is not None:\\n segue = {**{'phrase_n1': phrase_n1(segue['n1'], segue)}, **segue}\\n if phrase_n2 is not None:\\n segue = {**{'phrase_n2': phrase_n2(segue['n2'], segue)}, **segue}\\n return segue\\n\\n assert kind in ['line', 'description', 'short']\\n\\n if kind == 'description':\\n text = search_dictionary_dicothomic(_description_dichotomic, segue)\\n\\n elif kind == 'line':\\n text = search_dictionary_atomic(_line_atomic, segue)\\n text = search_dictionary_dicothomic(_line_dichotomic, segue) if text is None else text\\n\\n elif kind == 'short':\\n text = search_dictionary_atomic(_short_atomic, segue)\\n text = search_dictionary_dicothomic(_short_dichotomic, segue) if text is None else text\\n if text is None:\\n try:\\n text = segue_canned_texts(segue, 'line', excecute_code=False)\\n except KeyError:\\n pass\\n\\n if text is not None:\\n\\n if excecute_code:\\n segue = enrich_with_phrases(segue, kind)\\n\\n if type(text) == tuple:\\n # dicothomic\\n text = f\\\"{text[0](segue['n1'], segue['n2'], segue)} {text[1](segue['n1'], segue['n2'], segue)}\\\"\\n else:\\n # atomic\\n text = text(segue['n1'], segue['n2'], segue)\\n\\n # Postprocessing\\n # Fix multiple spaces\\n text = ' '.join(text.split())\\n # First letter is capital\\n text = capitalize_first_word(text)\\n # Fix spaces among word and 's in genitivo sassone\\n text = re.sub(r\\\"([a-zA-Z]+)\\\\s+('s)\\\", r\\\"\\\\1\\\\2\\\", text)\\n text = text.replace(', ,', ',')\\n\\n return text\\n else:\\n raise KeyError(\\n f\\\"It was not possible to find a {kind} for node of types, respectively {segue['n1']['type']} and {segue['n2']['type']}, with compare function {segue['compare_function']}. The ids of the nodes are, respectively {segue['n1']['id']} and {segue['n2']['id']}\\\")\",\n \"def __citation_correction(self, bs, ground_truth):\\n bs_ref = bs.findNext('bibl')\\n gt_ref = ground_truth.findNext('ref')\\n while gt_ref is not None:\\n if gt_ref.find('article-title') != bs_ref.title:\\n pass\\n gt_ref = gt_ref.findNext('ref')\",\n \"def recept(self, text, *args, **kwargs):\\n results = []\\n for each_cur in self.flat_norm.keys():\\n if each_cur.lower() in text.lower():\\n results.append(self.flat_norm[each_cur])\\n\\n if results:\\n # we have something in results\\n print(\\\"DictionarySlotReceptorMixin.recept: %s grasped results: %s\\\" % (self, results))\\n # TODO make productions signals?\\n return results\",\n \"def is_consonant(text):\\n return text.lower() in AVRO_CONSONANTS\",\n \"def extrairFrase(self, documento):\\n unicWords = self.unicWords()\\n doc = set(documento)\\n caracteristicas ={}\\n for palavras in unicWords:\\n caracteristicas['%s'%palavras]=(palavras in doc)\\n return caracteristicas\",\n \"def verify_decrypt_key(self):\\r\\n\\t\\tpercent_english = Dict_Control(self.my_code).check_key()\\r\\n\\t\\t#If more than half the words are english, the key will pass. \\r\\n\\t\\tif percent_english > 50:\\r\\n\\t\\t\\tself.right_key = False\\r\\n\\t\\t#If the key does not pass, the program will give you a warning and prompt you for another key. \\r\\n\\t\\telse: \\r\\n\\t\\t\\tprint(f\\\"After decryption, it looks like only {percent_english}% of your words are english, you may have entered the wrong key?\\\")\",\n \"def context_spell_correct(\\n self,\\n sentence: str,\\n suggestions_dict: Dict[str, List[str]]\\n ) -> (str, Dict[str, str]):\\n sentence_tokens = sentence.lower().split()\\n possible_sent_list = []\\n for each in sentence_tokens:\\n if each in suggestions_dict:\\n possible_sent_list.append(suggestions_dict[each])\\n else:\\n possible_sent_list.append([each])\\n\\n corrected_sent = self.get_most_probable_sentence(possible_sent_list)\\n corrected_dict = self.get_corrected_words_map(corrected_sent, sentence)\\n context_corrected_sentence = \\\" \\\" + sentence + \\\" \\\"\\n for token, suggestion in corrected_dict.items():\\n context_corrected_sentence = re.sub(\\\" \\\" + re.escape(token) + \\\" \\\",\\n \\\" \\\" + suggestion + \\\" \\\",\\n context_corrected_sentence)\\n context_corrected_sentence = context_corrected_sentence.strip()\\n return context_corrected_sentence, corrected_dict\",\n \"def conjugate_present_are_verb(verb, pronoun, tense):\\n\\n are_endings = {\\\"io\\\": \\\"o\\\", \\\"tu\\\": \\\"i\\\", \\\"lui\\\": \\\"a\\\", \\\"lei\\\": \\\"a\\\", \\\"noi\\\": \\\"iamo\\\", \\\"voi\\\": \\\"ate\\\", \\\"loro\\\": \\\"ano\\\"}\\n giare_endings = {\\\"io\\\": \\\"io\\\", \\\"tu\\\": \\\"i\\\", \\\"lui\\\": \\\"ia\\\", \\\"lei\\\": \\\"ia\\\", \\\"noi\\\": \\\"iamo\\\", \\\"voi\\\": \\\"iate\\\", \\\"loro\\\": \\\"iano\\\"}\\n ciare_endings = {\\\"io\\\": \\\"o\\\", \\\"tu\\\": \\\"\\\", \\\"lui\\\": \\\"a\\\", \\\"lei\\\": \\\"a\\\", \\\"noi\\\": \\\"amo\\\", \\\"voi\\\": \\\"ate\\\", \\\"loro\\\": \\\"ano\\\"}\\n add_h = {\\\"io\\\": \\\"o\\\", \\\"tu\\\": \\\"hi\\\", \\\"lui\\\": \\\"a\\\", \\\"lei\\\": \\\"a\\\", \\\"noi\\\": \\\"hiamo\\\", \\\"voi\\\": \\\"ate\\\", \\\"loro\\\": \\\"ano\\\"}\\n irregular_are = [\\\"fare\\\", \\\"andare\\\"]\\n fare = {\\\"io\\\": \\\"faccio\\\", \\\"tu\\\": \\\"fai\\\", \\\"lei\\\": \\\"fa\\\", \\\"lui\\\": \\\"fa\\\", \\\"noi\\\": \\\"facciamo\\\", \\\"voi\\\": \\\"fate\\\", \\\"loro\\\": \\\"fanno\\\"}\\n andare = {\\\"io\\\": \\\"vado\\\", \\\"tu\\\": \\\"vai\\\", \\\"lui\\\": \\\"va\\\", \\\"lei\\\": \\\"va\\\", \\\"noi\\\": \\\"andiamo\\\", \\\"voi\\\": \\\"andate\\\", \\\"loro\\\": \\\"vanno\\\"}\\n\\n # this section checks for the irregular verbs fare, andare\\n if verb in irregular_are:\\n if verb == \\\"fare\\\":\\n return fare[pronoun]\\n else:\\n return andare[pronoun]\\n\\n # this section checks for spelling issues like with mancare in order to preserve hard \\\"k\\\" sound of infinitive\\n # if it's a verb like mancare then the if section adds an \\\"h\\\" for the spelling to preserve hard \\\"k\\\" sound\\n # if it's a normal -are verb, then the else section conjugates it normally\\n if verb[-5:] == \\\"giare\\\":\\n stripped_verb = strip_off_ending(verb, tense)\\n new_verb = stripped_verb + giare_endings[pronoun]\\n return new_verb\\n if verb[-5:] == \\\"ciare\\\":\\n stripped_verb = strip_off_ending(verb, tense)\\n new_verb = stripped_verb + ciare_endings[pronoun]\\n return new_verb\\n if verb[-4:] == \\\"care\\\":\\n stripped_verb = strip_off_ending(verb, tense)\\n new_verb = stripped_verb + add_h[pronoun]\\n return new_verb\\n if verb[-4:] == \\\"gare\\\":\\n stripped_verb = strip_off_ending(verb, tense)\\n new_verb = stripped_verb + add_h[pronoun]\\n return new_verb\\n else:\\n stripped_verb = strip_off_ending(verb, tense)\\n new_verb = stripped_verb + are_endings[pronoun]\\n return new_verb\",\n \"def applyCoder(text, coder):\\n ciphertext = str()\\n #for each letter in the text find it, and grab shifted letter\\n for letter in text:\\n ciphertext += coder.get(letter, letter)\\n return ciphertext\",\n \"def editex(str1, str2, min_threshold = None):\\n\\n # Quick check if the strings are empty or the same - - - - - - - - - - - - -\\n #\\n if (str1 == '') or (str2 == ''):\\n return 0.0\\n elif (str1 == str2):\\n return 1.0\\n\\n n = len(str1)\\n m = len(str2)\\n\\n # Values for edit costs - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n #\\n BIG_COSTS = 2 # If characters are not in same group\\n SML_COSTS = 1 # If characters are in same group\\n\\n # Mappings of letters into groups - - - - - - - - - - - - - - - - - - - - - -\\n #\\n groupsof_dict = {'a':0, 'b':1, 'c':2, 'd':3, 'e':0, 'f':1, 'g':2, 'h':7,\\n 'i':0, 'j':2, 'k':2, 'l':4, 'm':5, 'n':5, 'o':0, 'p':1,\\n 'q':2, 'r':6, 's':2, 't':3, 'u':0, 'v':1, 'w':7, 'x':2,\\n 'y':0, 'z':2, '{':7}\\n\\n # Function to calculate cost of a deletion - - - - - - - - - - - - - - - - -\\n #\\n def delcost(char1, char2, groupsof_dict):\\n\\n if (char1 == char2):\\n return 0\\n\\n code1 = groupsof_dict.get(char1,-1) # -1 is not a char\\n code2 = groupsof_dict.get(char2,-2) # -2 if not a char\\n\\n if (code1 == code2) or (code2 == 7): # Same or silent\\n return SML_COSTS # Small difference costs\\n else:\\n return BIG_COSTS\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n\\n if (' ' in str1):\\n str1 = str1.replace(' ','{')\\n if (' ' in str2):\\n str2 = str2.replace(' ','{')\\n\\n if (n > m): # Make sure n <= m, to use O(min(n,m)) space\\n str1, str2 = str2, str1\\n n, m = m, n\\n\\n row = [0]*(m+1) # Generate empty cost matrix\\n F = []\\n for i in range(n+1):\\n F.append(row[:])\\n\\n F[1][0] = BIG_COSTS # Initialise first row and first column of cost matrix\\n F[0][1] = BIG_COSTS\\n\\n sum = BIG_COSTS\\n for i in range(2,n+1):\\n sum += delcost(str1[i-2], str1[i-1], groupsof_dict)\\n F[i][0] = sum\\n\\n sum = BIG_COSTS\\n for j in range(2,m+1):\\n sum += delcost(str2[j-2], str2[j-1], groupsof_dict)\\n F[0][j] = sum\\n\\n for i in range(1,n+1):\\n\\n if (i == 1):\\n inc1 = BIG_COSTS\\n else:\\n inc1 = delcost(str1[i-2], str1[i-1], groupsof_dict)\\n\\n for j in range(1,m+1):\\n if (j == 1):\\n inc2 = BIG_COSTS\\n else:\\n inc2 = delcost(str2[j-2], str2[j-1], groupsof_dict)\\n\\n if (str1[i-1] == str2[j-1]):\\n diag = 0\\n else:\\n code1 = groupsof_dict.get(str1[i-1],-1) # -1 is not a char\\n code2 = groupsof_dict.get(str2[j-1],-2) # -2 if not a char\\n\\n if (code1 == code2): # Same phonetic group\\n diag = SML_COSTS\\n else:\\n diag = BIG_COSTS\\n\\n F[i][j] = min(F[i-1][j]+inc1, F[i][j-1]+inc2, F[i-1][j-1]+diag)\\n\\n w = float(F[n][m])\\n\\n if (w < 0.0):\\n w = 0.0\\n return w\",\n \"def spell_correction(self, tweet):\\n return self.spell_correct.correct(tweet)\",\n \"def negation_check(self,sentence):\",\n \"def analyze(self, text):\\n\\n score = 0.0;\\n\\n words = text.split(' ')\\n # match each word in either the positives or negatives list adding or subtracting 1 from the score if present\\n for word in words:\\n for w in self.positives:\\n if w == word.lower():\\n score += 1.0\\n continue\\n \\n for w in self.negatives:\\n if w == word.lower():\\n score -= 1.0\\n continue\\n\\n return score\",\n \"def remove_non_narration_strings(transcription_row):\\n sentence = transcription_row[\\\"text\\\"]\\n # filter out (CAPITALIZED WORD) and \\\"CAPITALIZED WORD\\\". These are not enunciated in the voiceover, but rather\\n # indicate noise/words from the original audio track that get interspersed into the voice\\n # Might contain special characters\\n # Update: Capitalization etc are inconsistent. But all follow the pattern \\\"text\\\" and (text). Remove these instead\\n crosstalk_pattern = '\\\\(.*?\\\\)|\\\\\\\".*?\\\\\\\"'\\n # crosstalk_findings = re.findall(crosstalk_pattern, sentence)\\n # print(\\\"Crosstalk: \\\"+str(crosstalk_findings))\\n sentence = re.sub(crosstalk_pattern, \\\" \\\", sentence)\\n # filter out ' s ' ' Ss ' etc\\n s_pattern = r'\\\\b[sS]+\\\\b'\\n s_pattern_findings = re.findall(s_pattern, sentence)\\n # if len(s_pattern_findings) > 0:\\n # print(\\\"S-pattern: \\\"+str(s_pattern_findings))\\n sentence = re.sub(s_pattern, \\\" \\\", sentence)\\n transcription_row[\\\"text\\\"] = sentence\\n return transcription_row\",\n \"def analyze(self, text):\\n \\n total_words = len(text)\\n \\n negatives_length = len(self.negatives)\\n positives_length = len(self.positives)\\n \\n posneg_sum = 0\\n \\n for word in text:\\n \\n for j in range(0, positives_length):\\n if word == self.positives[j][:-1]:\\n posneg_sum += 1\\n \\n for k in range(0, negatives_length):\\n if word == self.negatives[k][:-1]:\\n posneg_sum -= 1\\n\\n return posneg_sum\",\n \"def decryptStory():\\n wordList = loadWords()\\n text = getStoryString() \\n k = findBestShift(wordList, text)\\n \\n return applyShift(text, k)\",\n \"def preprocess(text):\\n text = remove_space(text)\\n text = clean_special_punctuations(text)\\n text = clean_number(text)\\n text = decontracted(text)\\n text = correct_spelling(text)\\n text = spacing_punctuation(text)\\n text = spacing_some_connect_words(text)\\n text = clean_repeat_words(text)\\n text = remove_space(text)\\n text = text.lower()\\n return text\",\n \"def get_text(self):\\n text_complet = \\\"\\\"\\n rez_dict = self.__results\\n for i in range(0, len(rez_dict[\\\"text\\\"])):\\n text = rez_dict[\\\"text\\\"][i]\\n conf = int(rez_dict[\\\"conf\\\"][i])\\n if conf > self.__min_confidence:\\n text_complet += text + \\\" \\\"\\n return text_complet\",\n \"def processClauseText(intext, mtype): # type: (str, str) -> []\\n\\n global classifications, accsearch, unaccsearch\\n\\n retlist = []\\n texts = []\\n if mtype == 'text' or mtype == 'txt':\\n texts = extractClauses(intext)\\n elif 'pdf' in mtype:\\n plaintext = parsepdf(intext)\\n texts = extractPDFClauses(plaintext)\\n elif 'word' in mtype:\\n plaintext = parseword(intext)\\n texts = extractClauses(plaintext)\\n\\n results = predicteula(texts)\\n if len(results['prediction']) == 0:\\n return retlist\\n\\n accs = [[win[1] for win in curwins] for curwins in results['windows']]\\n probs = [max(curwins) for curwins in accs]\\n\\n for text, prediction in zip(texts, probs):\\n if len(text.split()) > 9:\\n curclause = {}\\n curclause['origclause'] = text\\n curclause['classification'] = 'Not Sure'\\n if prediction > 0.6:\\n curclause['classification'] = 'Acceptable'\\n elif prediction < 0.4:\\n curclause['classification'] = 'Unacceptable'\\n curclause['score'] = prediction\\n curclause['accclause'] = dicesearch(text, accsearch)\\n curclause['unaccclause'] = dicesearch(text, unaccsearch)\\n retlist.append(curclause)\\n\\n return retlist\",\n \"def match_contract_to_charter_constraints(contract, charter, charter_constraints, charity_constraints):\\n\\n r_quotes = []\\n r_vector = []\\n\\n quote_slice = slice(0, 17)\\n\\n if 'subj' not in contract.sections:\\n raise ValueError(\\\"contract has no subject section\\\")\\n\\n subj = contract.sections['subj'].body\\n print(subj.untokenize_cc())\\n print('------')\\n if subj.embeddings is None:\\n print(\\\"Subj embeddings are gone, restoring...\\\")\\n subj.embeddings = contract.embeddings[subj.start:subj.end]\\n # subj.tokens = doc.tokens[subj.start:subj.end]\\n # subj.tokens_cc = doc.tokens_cc[subj.start:subj.end]\\n # subj.embedd( GLOBALS__['CharterAnlysingContext'].pattern_factory )\\n print('\\\\t\\\\t sample:', subj.embeddings[0][1:10])\\n\\n for head_type in charter_constraints:\\n\\n ##charity:\\n if head_type in charity_constraints:\\n print(f'{head_type} has charity constrinats')\\n \\n charity_constraints_by_head = charity_constraints[head_type]\\n charity_constraints_by_head_new = []\\n \\n charity_constraints['new.'+head_type] = charity_constraints_by_head_new\\n \\n for i in range(len(charity_constraints_by_head)):\\n _tuple = charity_constraints_by_head[i] \\n# for cc in charity_constraints[head_type]:\\n _slice = _tuple[0]\\n emb_charter = charter.sections[head_type].body.embeddings[_slice]\\n \\n distance = 1 - DF(emb_charter, subj.embeddings[5:])\\n \\n# cc.add['subj_correlation'] = distance\\n \\n# detupling\\n charity_constraints_by_head_new.append ( {\\n 'slice':_slice,\\n 'subj_correlation': distance,\\n 'confidence': _tuple[1],\\n 'sum': _tuple[2]\\n })\\n \\n print('\\\\t'*4, 'cc=', charity_constraints_by_head_new[i])\\n \\n # print('\\\\t\\\\t---CC', cc[0])\\n \\n\\n # GLOBALS__['CharterAnlysingContext'].doc.sections['head.directors'].body.embeddings[_slice]\\n\\n ##------------------------charity end\\n print(f'measuring {head_type} constraints...'.upper())\\n cc = charter_constraints[head_type]\\n quotes = cc['sentences']\\n for quote in quotes:\\n print()\\n _q = untokenize(quote['subdoc'].tokens_cc[quote_slice])\\n print(_q)\\n\\n distance = 1 - DF(quote['subdoc'].embeddings[quote_slice],\\n subj.embeddings[5:])\\n\\n quote['subj_correlation'] = distance\\n\\n print(f'distance = {distance:.4f}')\\n\\n r_quotes.append(_q)\\n r_vector.append(distance)\\n r_quotes.append('\\\\n')\\n r_vector.append(distance)\\n\\n GLOBALS__['renderer'].render_color_text(r_quotes, r_vector)\\n print(r_vector)\\n print(r_quotes)\",\n \"def lf_normal_interp_not_seizure(report):\\n # print(report)\\n\\n for keyinterp in CANDIDATE_INTERPS_LOWER:\\n if keyinterp in report.sections.keys():\\n interpretation = report.sections[keyinterp]\\n if isinstance(interpretation, dict):\\n interp_text = interpretation['text']\\n else:\\n interp_text = interpretation\\n \\n if SIMPLE_NORMAL_RE.search(interp_text):\\n if NORMAL_STUDY_PHRASES.search(interp_text):\\n #return NO_SEIZURE_VAL\\n return OTHERS_VAL\\n #return NA_VAL\\n else:\\n logger.info(f'warning did not get second normal match: {interp_text}')\\n return ABSTAIN_VAL\\n \\n else:\\n return ABSTAIN_VAL\\n\\n return ABSTAIN_VAL\",\n \"def decryptStory():\\n wordList = loadWords()\\n text= getStoryString()\\n \\n shift = findBestShift(wordList, text)\\n return applyShift(text, shift)\",\n \"def analyze(self, text):\\n #analize every word in the text a value -1, 1 or 0 and calculate total score\\n #tokens allow us to split words in single tokens we can initialize tokens like this:\\n\\n tokenizer = nltk.tokenize.TweetTokenizer()\\n tokens = tokenizer.tokenize(text.lower())\\n\\n score = 0\\n\\n if tokens[0] in self.negatives:\\n score =- 1\\n elif tokens[0] in self.positives:\\n score =+ 1\\n else:\\n score = 0\\n\\n #print('', text)\\n\\n return score\",\n \"def censor(text: str) -> str:\\n\\n # Split up individual words in the text\\n tokens: List[str] = text.split(\\\" \\\")\\n\\n # Create a mapping of 0 if the word is okay, 1 if it should be censored\\n censor_mask: List[int] = predict([word for word in tokens])\\n\\n # A list of tuples with the first element being the word and the second being 0 or 1\\n censor_map: List[Tuple[str, int]] = list(zip(tokens, censor_mask))\\n\\n # A list of the words that make up the censored text\\n censored_text: List[str] = [\\n censor_word(word) if should_censor else word\\n for word, should_censor in censor_map\\n ]\\n\\n return \\\" \\\".join(censored_text)\",\n \"def analyze(self, text):\\n\\n # start from 0 for each Analyser variable\\n self.positives = 0\\n self.negatives = 0\\n\\n # precise self text value\\n self.text = text\\n\\n # declare a tokenased word\\n tokenizer = nltk.tokenize.TweetTokenizer()\\n tokens = tokenizer.tokenize(text)\\n\\n # indicate the length of list tokens\\n size = len(tokens)\\n\\n # all the word stuff to ckeck\\n for word in tokens:\\n\\n # chaque mots est converti en mot sans majuscule\\n word = str.lower(word)\\n\\n linespos = [line.rstrip('\\\\n') for line in open('positive-words.txt')]\\n linesneg = [line.rstrip('\\\\n') for line in open('negative-words.txt')]\\n\\n # check for positive or negative or neutral words\\n if word in linespos:\\n self.positives += 1\\n elif word in linesneg:\\n self.negatives += 1\\n else:\\n continue\\n\\n # score calculculated and reurned\\n score = self.positives - self.negatives\\n\\n return score\",\n \"def check(self, text):\\n p = self.d\\n i = 0\\n j = 0\\n result = []\\n ln = len(text)\\n while i + j < ln:\\n t = text[i + j].lower()\\n # print i,j,hex(ord(t))\\n if not (t in p):\\n j = 0\\n i += 1\\n p = self.d\\n continue\\n p = p[t]\\n j += 1\\n # print p,i,j\\n if chr(11) in p:\\n p = self.d\\n result.append(text[i:i + j])\\n i = i + j\\n j = 0\\n return result\",\n \"def analyse(self):\\n logging.info(\\\"transferring text to CorpusCook...\\\")\\n\\n paragraphs = self.text.split('\\\\n\\\\n')\\n print(\\\"mean length of splitted lines\\\", (mean([len(p) for p in paragraphs])))\\n\\n # If TIKA resolved '\\\\n'\\n if (mean([len(p) for p in paragraphs])) > 80:\\n paragraphs = [re.sub(r\\\"- *\\\\n\\\", '', p) for p in paragraphs]\\n paragraphs = [p.replace('\\\\n', \\\" \\\") for p in paragraphs]\\n paragraphs = [p.replace(';', \\\" \\\") for p in paragraphs]\\n joiner = \\\" \\\"\\n else:\\n # If TIKA did not\\n joiner = \\\" \\\"\\n\\n processed_text = joiner.join([p\\n for p in paragraphs\\n if\\n p and\\n ks_2samp(self.normal_data, list(p)).pvalue > self.threshold\\n ]\\n )\\n\\n return processed_text.strip()[:self.length_limit]\",\n \"def getCasing(word):\\n casing = 'other'\\n \\n numDigits = 0\\n for char in word:\\n if char.isdigit():\\n numDigits += 1\\n \\n digitFraction = numDigits / float(len(word))\\n \\n if word.isdigit(): #Is a digit\\n casing = 'numeric'\\n elif digitFraction > 0.5:\\n casing = 'mainly_numeric'\\n elif word.islower(): #All lower case\\n casing = 'allLower'\\n elif word.isupper(): #All upper case\\n casing = 'allUpper'\\n elif word[0].isupper(): #is a title, initial char upper, then all lower\\n casing = 'initialUpper'\\n elif numDigits > 0:\\n casing = 'contains_digit'\\n \\n return casing\",\n \"def classify(self, sText):\\n\\n sum1, sum2 = self.count()\\n\\n #len1 = len(self.posRev)\\n #len2 = len(self.negRev)\\n\\n probPos = 0 #math.log(float(sum1)/(sum1+sum2))\\n probNeg = 0 #math.log(float(sum2)/(sum1+sum2))\\n\\n ls = self.tokenize(sText)\\n\\n #test Positive case\\n for word in ls:\\n prob = float(self.posRev.get(word, 0) + 1)/(sum1)\\n if prob != 0:\\n probPos += math.log(prob)\\n\\n #test Negative case\\n for word in ls:\\n prob = float(self.negRev.get(word, 0) + 1)/(sum2)\\n if prob != 0:\\n probNeg += math.log(prob)\\n\\n print probPos\\n print probNeg\\n\\n print probPos-probNeg\\n if (probPos - probNeg) > 1:\\n return \\\"positive\\\"\\n elif (probNeg - probPos) > 1:\\n return \\\"negative\\\"\\n else:\\n return \\\"neutral\\\"\",\n \"def verify_text(self, text):\\n pass\",\n \"def process(self, message: Message, **kwargs: Any) -> None:\\n\\n try:\\n textdata = message.data[\\\"text\\\"]\\n # print(\\\"text :::\\\" + textdata)\\n textdata = textdata.split()\\n new_message = \\\" \\\".join(spell.correction(w) for w in textdata)\\n # print(\\\"after correction text :::\\\" + new_message)\\n message.data[\\\"text\\\"] = new_message\\n except KeyError:\\n pass\",\n \"def get_accredit_info(self, accredit_dict, library_construction, proj_name):\\n accredit_info = {}\\n for key in accredit_dict:\\n accredit = accredit_dict[key]\\n ## For \\\"finished library\\\" projects, set certain accredation steps as \\\"NA\\\" even if not set by default\\n if key in ['library_preparation','data_analysis'] and library_construction == 'Library was prepared by user.':\\n accredit_info[key] = 'Not Applicable'\\n elif accredit in ['Yes','No']:\\n accredit_info[key] = '{} under ISO/IEC 17025'.format(['[cross] Not accredited','[tick] Accredited'][accredit == 'Yes'])\\n elif accredit == 'N/A':\\n accredit_info[key] = 'Not Applicable'\\n else:\\n self.LOG.error('Accreditation step {} for project {} is found, but no value is set'.format(key, proj_name))\\n return accredit_info\",\n \"def remove_contractions(data: pd.Series) -> pd.Series:\\n data_ = data.copy()\\n with open(CONTRACTIONS, 'rb') as f:\\n contractions = pickle.load(f)\\n for kind in contractions.keys():\\n words = contractions[kind]\\n for word in words:\\n word_no_backslash = word.replace('\\\\\\\\','')\\n try:\\n data_ = data_.str.replace(r'\\\\b{}\\\\b'.format(word), words[word_no_backslash])\\n except:\\n # for contractions with escape characters\\n data_ = data_.str.replace(f'{word_no_backslash}', words[word])\\n return data_\",\n \"def analyze(self, text):\\n\\n # TODO\\n # tokens = tokenizer.tokenize(tweet)\\n tokenizer = nltk.tokenize.TweetTokenizer()\\n tokens = tokenizer.tokenize(text)\\n score = 0\\n\\n for word in tokens:\\n # iterate over tokens#str.lower\\n\\n if word.lower() in self.positives:\\n score = score+1\\n\\n elif word.lower() in self.negatives:\\n score = score-1\\n\\n else:\\n continue\\n return score\",\n \"def isccp4keytext(self, text):\\n #\\n # See e.g. http://www.ccp4.ac.uk/dist/html/loggraphformat.html\\n # for format of TEXT information, but essentially it's:\\n #\\n # $TEXT :text name: $$ junk (ignored) text $$any text characters$$\\n #\\n keytext = self.compile(\\n \\\"isccp4keytext\\\", r\\\"\\\\$TEXT[ \\\\n]*:([^:]*):[ \\\\n]*\\\\$\\\\$([^\\\\$]*)\\\\$\\\\$([^\\\\$]*)\\\\$\\\\$\\\"\\n ).search(text)\\n result = dict()\\n if keytext:\\n result[\\\"name\\\"] = keytext.group(1)\\n result[\\\"junk_text\\\"] = keytext.group(2)\\n result[\\\"message\\\"] = keytext.group(3)\\n result[\\\"nlines\\\"] = keytext.group(0).count(\\\"\\\\n\\\")\\n return result\",\n \"def phrase_dict(phrase):\\n switcher = {\\n '처음으로': '닥앤미 병원을 찾아주셔서 감사합니다. 직접문의원할시 오른쪽 아래 1:1 버튼을 눌러주시면 직접 상담 가능합니다. 1:1 상담 가능 시간은 09시 – 18시 입니다.',\\n '병원 정보': '어떤 정보를 보시고 싶으신가요?',\\n '병원 위치': '“닥앤미 병원 주소는 서울시 용산구 이촌동 세움상가 2층입니다.” 더 자세한 지도확인을 원하실 경우 아래 버튼을 눌러주세요',\\n '병원 운영시간': '닥앤미 병원을 찾아주셔서 감사합니다. 병원 운영시간은 위의 내용과 같습니다',\\n '병원 프로모션': '현재 진행되고 있는 병원 프로모션입니다. 자세히 보길 원하시면 아래의 프로모션을 선택해 주세요',\\n '프로모션 A': '닥앤미에서 6월 30일까지 제공되는 프로모션 A 입니다.',\\n '프로모션 B': '닥앤미에서 6월 30일까지 제공되는 프로모션 B 입니다.',\\n '프로모션 C': '닥앤미에서 6월 30일까지 제공되는 프로모션 C 입니다.',\\n '의료진': '안녕하세요, 닥앤미의 홍길동 전문의 입니다. 항상 최선을 다하겠습니다.',\\n '병원 사진': '최고의 진료를 제공하는 닥앤미 병원입니다.',\\n '병원 진료과목': '닥앤미 병원의 진료과목입니다.',\\n '병원 전화하기': '닥앤미 병원 전화번호는 02 3522 XXXX 입니다. 지금 통화를 원하시면 아래 버튼을 눌러주세요'\\n }\\n default_text = 'Unable to find appropriate text response'\\n return switcher.get(phrase, default_text)\",\n \"def get_correct(entry):\\n return entry['correct'] == \\\"1.0\\\"\",\n \"def spelling(p_str):\\n spell = SpellChecker(language='en')\\n misspelled = spell.unknown(re.sub(r'[^\\\\w\\\\s]', '', p_str).split())\\n corrections = {}\\n for word in misspelled:\\n tmp = list(spell.candidates(word))\\n tmp.insert(0, spell.correction(word))\\n corrections[word] = tmp\\n return(corrections)\",\n \"def _separate_possessives(text: str) -> (dict, str):\\n possessive_dict = dict() # Dictionary of nouns (keys) with their possessive modifiers (values)\\n revised_words = []\\n if '/poss/' in text:\\n space_splits = text.split()\\n for index in range(0, len(space_splits)):\\n if '/poss/' in space_splits[index]:\\n possessive_dict[space_splits[index + 1]] = space_splits[index].replace('/poss/', empty_string)\\n else:\\n revised_words.append(space_splits[index])\\n return possessive_dict, space.join(revised_words)\\n return possessive_dict, text\",\n \"def beginning_checker(self, translit):\\n tr_new = re.sub(r'(\\\\A|·)夫', r'\\\\1弗', translit)\\n tr_new = re.sub(r'(\\\\A|·)耶', r'\\\\1叶', tr_new)\\n return tr_new\",\n \"def text_analise(file_n, letter, title=''):\\r\\n dict_percent = {}\\r\\n other_signs2 = {}\\r\\n other_signs2_turn = {}\\r\\n\\r\\n for i in letter:\\r\\n with open(file_n) as f:\\r\\n text = f.read()\\r\\n count = 0\\r\\n count_others = 0\\r\\n for ch in text.lower():\\r\\n if ch == i:\\r\\n count += 1\\r\\n if ch not in letter:\\r\\n count_others += 1\\r\\n other_signs2[ch] = count_others\\r\\n # if ch not in other_signs:\\r\\n # other_signs.append(ch)\\r\\n procent = round((100 * count) / len(text), 2)\\r\\n procent_other = round((100 * count_others) / len(text), 2)\\r\\n\\r\\n dict_percent[procent] = [i, count]\\r\\n\\r\\n x = 0\\r\\n for key in sorted(dict_percent):\\r\\n print('Percent {} of letter {}'.format(key, dict_percent[key]))\\r\\n x += key\\r\\n with open('3.txt', 'a') as f:\\r\\n f.write(\\r\\n 'Percent {} of letter {} \\\\n'.format(key, dict_percent[key]))\\r\\n\\r\\n other_percents = round((100 - x), 2)\\r\\n print('Other: {} percent'.format(other_percents))\\r\\n print('ACTUAL Other: {} percent'.format(procent_other))\\r\\n print('Length of text: {}\\\\n'.format(len(text)))\\r\\n # print(list(set(other_signs)))\\r\\n\\r\\n for a, b in other_signs2.items():\\r\\n if a != '\\\\n':\\r\\n other_signs2_turn[b] = a\\r\\n elif a == '\\\\n':\\r\\n other_signs2_turn[b] = 'Enter'\\r\\n\\r\\n with open('3.txt', 'a') as f:\\r\\n f.write('Other: {} percent\\\\n'.format(round((100 - x), 2)))\\r\\n f.write('процентов учтено: {}\\\\n'.format(x))\\r\\n f.write('Length of text: {}\\\\n'.format(len(text)))\\r\\n f.write('Title of text: {}\\\\n\\\\n'.format(title))\\r\\n for key in sorted(other_signs2_turn):\\r\\n prnt = ('Letter {} used {} times'.format(\\r\\n other_signs2_turn[key], key))\\r\\n prnt_wr = prnt + '\\\\n'\\r\\n print(prnt)\\r\\n f.write(prnt_wr)\",\n \"def init_correction_map(token, dictionary):\\n if token is None:\\n return None\\n\\n if len(token) <= 2 or token.lower() in dictionary:\\n return {token: 1}\\n\\n return {}\",\n \"def calculate_construction(self, word):\\r\\n \\r\\n construction = \\\"\\\"\\r\\n for c in word.lower():\\r\\n if c in self.vowels:\\r\\n construction += \\\"v\\\"\\r\\n elif c in letters:\\r\\n construction += \\\"c\\\"\\r\\n return construction\",\n \"def check_sentences(text, threshold=80, print_only=False):\\n non_white_text = re.sub(masks, \\\"\\\", re.sub(emojis, \\\"\\\", re.sub(punctuation, \\\"\\\", re.sub(\\\"\\\\s\\\", \\\"\\\", text))))\\n num_chars = len(non_white_text)\\n num_non_swiss_chars = 0\\n for char in non_white_text:\\n if char not in swiss_chars:\\n num_non_swiss_chars += 1\\n ratio = num_non_swiss_chars / num_chars * 100 if num_chars != 0 else 0\\n if ratio > threshold:\\n return \\\"POSSIBLE NON_SWISS GERMAN TEXT:\\\" + text if print_only else False\\n else:\\n return text\",\n \"def transcribe(dna):\\n str = ''\\n dict = {'C': 'C', 'G': 'G', 'A': 'A', 'T': 'U'}\\n for char in dna:\\n if char == 'C' or char == 'G' or char == 'T' or char == 'A':\\n #converting only of the valid string is encountered\\n #then the string is converted accordingly\\n str = str + dict[char]\\n #the case for incalid string, it throws only the error\\n else :\\n str = 'invalid character entered, please check the input'\\n break\\n return str\",\n \"def passion_analyzer(text):\\n\\n\\tlower_text = text.lower()\\n\\n\\thashtag_scaling = 0.3\\n\\texclamation_scaling = 0.5\\n\\tuppercase_scaling = 0.2\\n\\n\\n\\tpassion_index = 0\\n\\n\\tfor x in range(len(positive_words)):\\n\\t\\tpassion_index += (lower_text.count(positive_words[x]))**2\\n\\tfor x in range(len(negative_words)):\\n\\t\\tpassion_index -= (lower_text.count(negative_words[x]))**2\\n\\tif '!' in text:\\n\\t\\tpassion_index *= exclamation_scaling * lower_text.count('!') + 1\\n\\tif '#' in text:\\n\\t\\tpassion_index *= hashtag_scaling * lower_text.count('#') + 1\\n\\tpassion_index *= uppercase_scaling * sum(1 for c in text if c.isupper())\\n\\n\\n\\t\\t\\n\\treturn math.sqrt(passion_index)\",\n \"def cleanText(text):\\n try:\\n text = str(text)\\n\\n # remove contactions and stop words\\n text = contractions(text)\\n # remove html entities\\n cleanr = re.compile('<.*?>|&([a-z0-9]+|#[0-9]{1,6}|#x[0-9a-f]{1,6});')\\n new_text = cleanr.sub('', text.strip())\\n return re.sub(r'\\\\s+', ' ', re.sub(r'\\\\W+', \\\" \\\", new_text))\\n # TAG_RE = re.compile(r'<[^>]+>')\\n except:\\n print(\\\"An exception occurred with: \\\" + text)\\n return str(text)\",\n \"def test_return_advice_under_cruising_weight(self):\\n data_weight_user = {\\\"height\\\": \\\"1,60\\\", \\\"actual_weight\\\": \\\"60\\\",\\n \\\"cruising_weight\\\": \\\"55\\\", \\\"weight_goal\\\": \\\"51\\\"}\\n return_advice = self.new_weight_advice_goal.return_weight_advices_goal(data_weight_user)[1]\\n\\n advice = \\\"Chaque personne a un poids d'équilibre sur lequel il peut rester longtemps, \\\" \\\\\\n \\\"c'est se qu'on appelle le poids de croisière. Il semble que ton objectif \\\" \\\\\\n \\\"aille en dessous de ce poids. Je tiens donc à te préciser qu'il est \\\" \\\\\\n \\\"possible que tu n'arrives pas à le maintenir sur la durée. \\\" \\\\\\n \\\"Je note tout de même cet objectif. \\\"\\n self.assertEqual(return_advice, advice)\",\n \"def analyze(self, text):\\n\\n tknzr = nltk.tokenize.TweetTokenizer()\\n words = tknzr.tokenize(text)\\n \\n score = 0\\n \\n for word in words:\\n if word.lower() in self.positives:\\n score += 1\\n elif word.lower() in self.negatives:\\n score -= 1\\n else:\\n continue\\n \\n return score\",\n \"def compute_readability(text):\\n total_words = 0\\n total_sentences = 0\\n total_syllables = 0\\n score = 0\\n\\n words = text.split()\\n total_words = len(text.split()) \\n total_sentences = count_sentences(text)\\n total_syllables = count_syllables(words)\\n \\n score = 206.835 - 1.015 * ( total_words / total_sentences) - 84.6 * (total_syllables / total_words)\\n if score > 90.00:\\n answer = 'Texto de nível do 5º ano do Ensino Fundamental, facilmente compreendido por um aluno de 11 anos.'\\n elif score <= 90.00 and score > 80.00:\\n answer = 'Texto de nível do 6º ano do Ensino Fundamental, inglês coloquial para consumidores.'\\n elif score <= 80.00 and score > 70.00:\\n answer = 'Texto de nível do 7º ano do Ensino Fundamental, razoavelmente fácil de ler.'\\n elif score <= 70.00 and score > 60.00:\\n answer = 'Texto de nível do 9º ano do Ensino Fundamental, Inglês simples compreendido por adolescentes de 13 - 15 anos.'\\n elif score <= 60.00 and score > 50.00:\\n answer = 'Texto de 1º a 3º ano do Ensino Médio, razoavelmente difícil de ler.'\\n elif score <= 50.00 and score > 30.00:\\n answer = 'Texto de nível Universitário, difícil de ler.'\\n else:\\n answer = 'Texto de nível de Graduação, muito difícil de ler e mais bem-compreendido por universitários graduados.'\\n \\n print('Pontuação Total:', score, answer)\",\n \"def contraction_expansion_on_corpus(text_corpus):\\n\\n text_corpus[text_column_name] = text_corpus[\\n text_column_name].apply(contraction_expansion)\\n return text_corpus\",\n \"def canada_query(text):\\n return 'canada' in text.lower()\",\n \"def analyze(self, text):\\n tknzr = nltk.tokenize.casual.TweetTokenizer(preserve_case=True, reduce_len=False, strip_handles=False)\\n tknTxt = tknzr.tokenize(text)\\n sentiment = 0\\n \\n for i in range(len(tknTxt)):\\n if tknTxt[i] in self.posTxt:\\n #print(\\\"POS\\\")\\n #print(tknTxt[i])\\n sentiment += 1\\n elif tknTxt[i] in self.negTxt:\\n #print(\\\"NEG\\\")\\n #print(tknTxt[i])\\n sentiment -= 1\\n \\n return sentiment\",\n \"def text_process_dict(field, dictionary:dict):\\n values = dictionary.get(\\\"values\\\")\\n if is_valid_text(values):\\n is_exact = values.get(\\\"is_exact\\\", False)\\n _term = values.get(\\\"term\\\", False)\\n filtered_term = re.sub('[^a-zA-Z0-9\\\\n\\\\.|\\\\*|\\\\@|\\\\|\\\\_]', ' ', _term)\\n return text_process(field, filtered_term, is_exact=is_exact)\\n return {\\n\\n }\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68868476","0.6857023","0.68527967","0.68527967","0.66898316","0.6347374","0.5993866","0.5918088","0.5882449","0.5777897","0.57183623","0.5687876","0.5641633","0.5621483","0.5601896","0.5554486","0.5458176","0.5382929","0.5322964","0.5307496","0.5253449","0.5250009","0.5248516","0.5231251","0.52233654","0.52152175","0.5203586","0.52029675","0.5199981","0.5184434","0.51733065","0.5170581","0.5167392","0.5149423","0.51478076","0.5132091","0.5106502","0.50916225","0.5088874","0.5066728","0.5063786","0.5063706","0.5063431","0.5047348","0.5044192","0.5020982","0.5014858","0.5014735","0.50016844","0.4986884","0.49544588","0.49536797","0.49524906","0.495148","0.4935025","0.49214697","0.4918102","0.4911784","0.49081188","0.4904668","0.4903378","0.48844442","0.48794806","0.48650438","0.48638597","0.48599193","0.48584312","0.4858104","0.4842912","0.4842896","0.48358744","0.4831218","0.48304614","0.48251155","0.48171404","0.4817052","0.48114228","0.48064727","0.48052984","0.48050895","0.48022425","0.48006287","0.47961688","0.47931743","0.47819814","0.477882","0.4778772","0.47766784","0.4776109","0.47744313","0.47708517","0.47705317","0.4770378","0.4768055","0.47634122","0.4763165","0.47622663","0.4756365","0.47466785","0.47427803"],"string":"[\n \"0.68868476\",\n \"0.6857023\",\n \"0.68527967\",\n \"0.68527967\",\n \"0.66898316\",\n \"0.6347374\",\n \"0.5993866\",\n \"0.5918088\",\n \"0.5882449\",\n \"0.5777897\",\n \"0.57183623\",\n \"0.5687876\",\n \"0.5641633\",\n \"0.5621483\",\n \"0.5601896\",\n \"0.5554486\",\n \"0.5458176\",\n \"0.5382929\",\n \"0.5322964\",\n \"0.5307496\",\n \"0.5253449\",\n \"0.5250009\",\n \"0.5248516\",\n \"0.5231251\",\n \"0.52233654\",\n \"0.52152175\",\n \"0.5203586\",\n \"0.52029675\",\n \"0.5199981\",\n \"0.5184434\",\n \"0.51733065\",\n \"0.5170581\",\n \"0.5167392\",\n \"0.5149423\",\n \"0.51478076\",\n \"0.5132091\",\n \"0.5106502\",\n \"0.50916225\",\n \"0.5088874\",\n \"0.5066728\",\n \"0.5063786\",\n \"0.5063706\",\n \"0.5063431\",\n \"0.5047348\",\n \"0.5044192\",\n \"0.5020982\",\n \"0.5014858\",\n \"0.5014735\",\n \"0.50016844\",\n \"0.4986884\",\n \"0.49544588\",\n \"0.49536797\",\n \"0.49524906\",\n \"0.495148\",\n \"0.4935025\",\n \"0.49214697\",\n \"0.4918102\",\n \"0.4911784\",\n \"0.49081188\",\n \"0.4904668\",\n \"0.4903378\",\n \"0.48844442\",\n \"0.48794806\",\n \"0.48650438\",\n \"0.48638597\",\n \"0.48599193\",\n \"0.48584312\",\n \"0.4858104\",\n \"0.4842912\",\n \"0.4842896\",\n \"0.48358744\",\n \"0.4831218\",\n \"0.48304614\",\n \"0.48251155\",\n \"0.48171404\",\n \"0.4817052\",\n \"0.48114228\",\n \"0.48064727\",\n \"0.48052984\",\n \"0.48050895\",\n \"0.48022425\",\n \"0.48006287\",\n \"0.47961688\",\n \"0.47931743\",\n \"0.47819814\",\n \"0.477882\",\n \"0.4778772\",\n \"0.47766784\",\n \"0.4776109\",\n \"0.47744313\",\n \"0.47708517\",\n \"0.47705317\",\n \"0.4770378\",\n \"0.4768055\",\n \"0.47634122\",\n \"0.4763165\",\n \"0.47622663\",\n \"0.4756365\",\n \"0.47466785\",\n \"0.47427803\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67498755"},"document_rank":{"kind":"string","value":"4"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":52,"numItemsPerPage":100,"numTotalItems":95772,"offset":5200,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3ODM5Njk4OCwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3Nzg0MDA1ODgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.EawiVgvsZjJdVQz49VEjbKZomCLePI-zFMrI2DvKEVC713am3krKdnR0VrKlem7SIK0lGk2PzVW-OsZCSvxsCw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Constructor de la clase, que lee el archivo de texto "fname" a modificar. | def __init__(self,fname):
try:
self.handler = open(fname,'r')
self.filename = fname
except Exception:
log.exception('Fallo al abrir ' + fname)
return None
self.content = self.handler.readlines()
self.handler.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, fname):\n self.fname = os.path.abspath(fname)\n self.restore()",
"def __init__(self, nombre):\n\n try:\n # Intentar abrir el archivo\n self.f = open(nombre, 'r')\n self.nombre = nombre\n except:\n # Si no se puede abrir el... | [
"0.7283871",
"0.7172157",
"0.6912619",
"0.6812134",
"0.6719622",
"0.66636246",
"0.6611331",
"0.6509861",
"0.65031904",
"0.6496384",
"0.64787287",
"0.64747083",
"0.64125896",
"0.63539714",
"0.63372934",
"0.62784576",
"0.6267829",
"0.6260039",
"0.62426263",
"0.6211658",
"0.6190... | 0.67274874 | 4 |
Inserta una cadena delante en donde encuentre la cadena "tag" | def ins(self,tag,nstr,jumpline=True):
tmp = []
strj = '\n' if jumpline else ''
for line in self.content:
tmp.append(line)
if tag in line:
tmp.append(nstr + strj)
self.content = tmp | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_tag_data(tag):\n\n add_tag = Tag(tag=tag)\n db.session.add(add_tag)\n try:\n db.session.commit()\n except (Exception, exc.SQLAlchemyError, exc.InvalidRequestError, exc.IntegrityError) as e:\n print(tag + '\\n' + str(e))",
"def AddTag(self, tag):\n\n if not self.persistant:\n ... | [
"0.6976025",
"0.6736013",
"0.6654346",
"0.62465644",
"0.61812353",
"0.61108446",
"0.6020931",
"0.60011894",
"0.5945555",
"0.5943481",
"0.59106773",
"0.5909345",
"0.5869094",
"0.5852946",
"0.5828006",
"0.5823748",
"0.58234936",
"0.5821268",
"0.58135855",
"0.57451326",
"0.57308... | 0.5345447 | 59 |
Reemplaza la(s) ocurrencia(s) de tag en el archivo por nstr | def rep(self,tag,nstr):
tmp = []
for line in self.content:
if tag in line:
tmp.append(line.replace(tag,nstr))
else:
tmp.append(line)
self.content = tmp | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ler_arquivo_xml(self, diretorio):\r\n with open(diretorio, 'r') as fxml:\r\n\t strfx = fxml.readlines()\r\n\t string = \"\".join(strfx).replace(\"&\",\" e \")\r\n return string",
"def archivoXl(archivo):\r\n return ow(archivo)",
"def SV_tag_length(tag_file, outPrefix):\r\n outdir... | [
"0.56019914",
"0.55601597",
"0.5558506",
"0.55486673",
"0.5365312",
"0.5310506",
"0.52868605",
"0.5284282",
"0.5265695",
"0.5260303",
"0.52433544",
"0.523879",
"0.52328277",
"0.52186084",
"0.52185684",
"0.5216578",
"0.51640344",
"0.5162988",
"0.51482356",
"0.5122066",
"0.5119... | 0.56634676 | 0 |
Salva los cambios al archivo especificado en newfile, o hace una copia del archivo original (filename+'~') y salva el contenido en "filename" | def saveFile(self,newfile=None):
if newfile == None:
shutil.move(self.filename,self.filename+'~')
self.handler = open(self.filename,'w')
else:
self.handler = open(newfile,'w')
self.handler.writelines(self.content)
self.handler.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def newfile(filename):\n # Open the new file for writing\n with open(filename, \"w\") as file:\n pass",
"def renewFile(filename):\n\n\tfileRepo = repertoire + filename + extension # Position du fichier\n\n\t# Ouvre en ecriture et l'ecrase\n\t# La methode with ferme le fichier automatiquement\n\twith... | [
"0.68023175",
"0.6601903",
"0.6470307",
"0.6256966",
"0.60828876",
"0.60267633",
"0.59504694",
"0.5871682",
"0.5786605",
"0.5738598",
"0.57240206",
"0.5673847",
"0.5622948",
"0.5617441",
"0.5615462",
"0.55986905",
"0.55614066",
"0.55208856",
"0.551383",
"0.55105835",
"0.55076... | 0.68237215 | 0 |
``summary'' is a systemgenerated summary. ``references'' is a list of humanmade reference summaries | def score_summary(self, summary, references, summary_id='A'):
try:
self._write_config(references, Doc(summary_id, summary))
output = self._run_rouge()
output = output.decode("utf-8")
return self._parse_output(output)
except CalledProcessError as e:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def summary(self, summary):\n\n self._summary = summary",
"def summary(self, summary):\n\n self._summary = summary",
"def summary(self, summary):\n\n self._summary = summary",
"def summary(self, summary: str):\n return self.swag({\n 'summary': summary\n })",
"d... | [
"0.58870405",
"0.58870405",
"0.58870405",
"0.5803425",
"0.5781204",
"0.5522118",
"0.5503257",
"0.54215056",
"0.53986794",
"0.53744864",
"0.53230405",
"0.5302629",
"0.52970827",
"0.5282875",
"0.5266578",
"0.52372295",
"0.5206945",
"0.5153626",
"0.5151684",
"0.5148715",
"0.5141... | 0.6722548 | 0 |
Display unpublished Draft Entries | def drafts():
query = Entry.drafts().order_by(Entry.last_mod_date.desc())
return object_list('index.html', query) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def drafts_view(self, request, object_id, extra_context=None):\n opts = self.model._meta\n action_list = [{\"revision\": version.revision,\n \"url\": reverse(\"admin:%s_%s_draft\" % (opts.app_label, opts.module_name), args=(version.object_id, version.revision.id))}\n ... | [
"0.7508209",
"0.6815543",
"0.6162445",
"0.6106849",
"0.60844666",
"0.6059696",
"0.60429025",
"0.60228574",
"0.6012785",
"0.59416395",
"0.5828027",
"0.5751404",
"0.5730366",
"0.57115114",
"0.5708828",
"0.5697832",
"0.5697832",
"0.5679159",
"0.5660224",
"0.5650022",
"0.56475645... | 0.73618597 | 1 |
Create new blog Entry | def create():
if request.method == 'POST':
if request.form.get('title') and request.form.get('content'):
entry = Entry.create(
title = request.form.get('title'),
content = request.form.get('content'),
published = request.form.get('published') or Fa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def createNewBlogEntry(self): #$NON-NLS-1$\r\n atomdoc = self._createNewEntryDocument()\r\n self._initNewEntryDocument(atomdoc)\r\n return ZAtomNewBlogEntry(atomdoc)",
"def blog_create(request):\n entry = BlogRecord()\n form = BlogCreateForm(request.POST)\n if request.method == 'POS... | [
"0.8232406",
"0.7668556",
"0.7641279",
"0.7607913",
"0.73694056",
"0.7322824",
"0.73227113",
"0.7250803",
"0.7247709",
"0.72167945",
"0.7215931",
"0.6994082",
"0.69597936",
"0.69173276",
"0.68832356",
"0.6816936",
"0.68142015",
"0.67737234",
"0.6718318",
"0.670351",
"0.669757... | 0.77166563 | 1 |
Edit existing blog Entry | def edit(slug):
entry = get_object_or_404(Entry, Entry.slug == slug)
if request.method == 'POST':
if request.form.get('title'):
entry.title = request.form.get('title')
if request.form.get('content'):
entry.content = request.form.get('content')
entry.published = re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def updatePost(self, editLink, entryId, zserverBlogEntry): #$NON-NLS-1$\r\n atomEntry = self.createEditBlogEntry()\r\n atomEntry.setId(entryId)\r\n atomEntry.setEditLink(editLink)\r\n self._populateAtomEntry(atomEntry, zserverBlogEntry)\r\n # update entry\r\n atomRespEntry... | [
"0.7704336",
"0.76225203",
"0.7564468",
"0.7443002",
"0.73097694",
"0.72731054",
"0.7030135",
"0.7011261",
"0.6999224",
"0.6944325",
"0.6901991",
"0.6855571",
"0.68527025",
"0.6852159",
"0.68337286",
"0.67222476",
"0.6703577",
"0.66673446",
"0.6648436",
"0.66032463",
"0.65913... | 0.72877353 | 5 |
Context manager to temporarily change to a new directory. | def chdir(new_dir):
cur_dir = os.getcwd()
os.chdir(new_dir)
try:
yield
finally:
os.chdir(cur_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def use_dir(new_dir):\n owd = os.getcwd()\n os.chdir(new_dir)\n\n try:\n yield\n finally:\n os.chdir(owd)",
"def cd_manager(self, new_wd):\n old_wd = self.cwd\n self.cd(new_wd)\n yield self.cwd\n self.cd(old_wd)",
"def __enter__(self):\n self.savedPa... | [
"0.7734695",
"0.75994277",
"0.7524285",
"0.74483794",
"0.7443039",
"0.7389849",
"0.7278982",
"0.71953887",
"0.7141239",
"0.7134059",
"0.7131782",
"0.70410705",
"0.70233524",
"0.7012039",
"0.69601715",
"0.695928",
"0.69448084",
"0.6925167",
"0.6922496",
"0.69155693",
"0.690279... | 0.74489474 | 4 |
Ensure that a folder exists and create it if it doesn't, including any parent folders, as necessary. | def create_folder(target_folder):
try:
os.makedirs(target_folder)
except OSError as e:
pass
return os.path.exists(target_folder) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ensure_folder(*arg):\n if len(arg) == 0:\n raise Exception(\"No input to ensure_folder\")\n path = get_dir(Path(*arg))\n path.mkdir(parents=True, exist_ok=True)",
"def create_folder_if_needed(path):\n if os.path.exists(path):\n print(\"{} dir exists\".format(path))\n else:\n ... | [
"0.82459253",
"0.8094383",
"0.8059282",
"0.788112",
"0.7779735",
"0.7752992",
"0.77248335",
"0.77168036",
"0.77027094",
"0.76686084",
"0.7639529",
"0.76316696",
"0.76276046",
"0.7627525",
"0.7594649",
"0.7567482",
"0.7552197",
"0.75503033",
"0.7529557",
"0.7526078",
"0.75254"... | 0.7339701 | 36 |
a function converting csv output files from operational_sep_quantities to json files for observations | def obs_csv2json(input_file,output_file,example_path,instrument):
obs_path = Path(cfg.obs_path)
with open(example_path,'r') as e:
example = js.load(e)
#deleting unused categories
del(example['sep_forecast_submission']['forecasts'])
del(example['sep_forecast_submission']['... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_dataset(input_file_path, output_file_path):\n col_index_map = {'user_id': 0, 'session_id': 1, 'timestamp': 2, 'step': 3, 'action_type': 4, 'reference': 5,\n 'platform': 6, 'city': 7, 'device': 8,\n 'current_filters': 9, 'impressions': 10, 'prices': 11}\n fla... | [
"0.60670656",
"0.5917598",
"0.5867013",
"0.577265",
"0.5737042",
"0.5713435",
"0.56811106",
"0.56413877",
"0.56257904",
"0.5622406",
"0.5606425",
"0.5585183",
"0.55736536",
"0.55720776",
"0.5546977",
"0.554406",
"0.55393744",
"0.5519248",
"0.5511894",
"0.5495278",
"0.5494552"... | 0.7022942 | 0 |
choose the correct instrument to use for observations for a given date range. inputs must be date objects from the datetime module. used if there is no information about which instrument was primary. | def choose_inst(given_start_date,given_end_date): #INPUTS MUST BE DATE OBJECTS
inst_start_dates=[]
inst_end_dates=[]
good_instruments = []
good_end_dates = []
bad_inst = []
#extracting dates where instruments are active from csv file
inst_dates = pd.read_csv(ref_path / 'instrument... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def choose_prime_inst(given_start_date,given_end_date):\r\n\r\n #extracting primary dates where instruments are active from csv file\r\n inst_prime_dates = pd.read_csv(ref_path / 'GOES_primary_assignments.csv', header=3)\r\n\r\n #figuring out which instrument is primary for given start date\r\n for d i... | [
"0.6696412",
"0.5244132",
"0.5244106",
"0.5234904",
"0.523354",
"0.5202343",
"0.50902385",
"0.49597186",
"0.4926985",
"0.49259138",
"0.4911848",
"0.48814812",
"0.4860115",
"0.48596224",
"0.48520213",
"0.48501316",
"0.4843819",
"0.4843819",
"0.48264506",
"0.48252285",
"0.48125... | 0.707812 | 0 |
choose the correct instrument to use for observations for a given date range based on the primary instrument for that time period. inputs must be date objects from the datetime module. | def choose_prime_inst(given_start_date,given_end_date):
#extracting primary dates where instruments are active from csv file
inst_prime_dates = pd.read_csv(ref_path / 'GOES_primary_assignments.csv', header=3)
#figuring out which instrument is primary for given start date
for d in range(len(inst_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def choose_inst(given_start_date,given_end_date): #INPUTS MUST BE DATE OBJECTS\r\n\r\n inst_start_dates=[]\r\n inst_end_dates=[]\r\n good_instruments = []\r\n good_end_dates = []\r\n bad_inst = []\r\n\r\n #extracting dates where instruments are active from csv file\r\n inst_dates = pd.read_csv... | [
"0.7161972",
"0.5350023",
"0.5343824",
"0.52754986",
"0.52716273",
"0.5243731",
"0.5240982",
"0.5149819",
"0.5139513",
"0.50795436",
"0.50315154",
"0.5018969",
"0.5007179",
"0.50046945",
"0.49793446",
"0.4966744",
"0.49542493",
"0.4934821",
"0.4922149",
"0.49216446",
"0.48913... | 0.68636316 | 1 |
a function that creates observational json output files given start and end dates by extracting data from the GOES database. Only works with GOES instruments. | def database_extraction(mod_start_time,mod_end_time,instrument_chosen,subevent_bool,
detect_previous_event = False,thresholds='100,1',
one_thresh = False):
obs_file_created = False
#extending time window
window_end_time = (mod_end_time + timedelta(days=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n parser = argparse.ArgumentParser(description='Process input file and save to output file.')\n\n parser.add_argument('-i',\n '--input',\n help='Trip report file (txt file) to process.',\n action='store')\n\n parser.add_argum... | [
"0.6041613",
"0.58948135",
"0.58275783",
"0.5822768",
"0.5751428",
"0.5720737",
"0.5717822",
"0.570631",
"0.57046837",
"0.5676997",
"0.5632432",
"0.56253904",
"0.5612947",
"0.5587226",
"0.55808955",
"0.5580337",
"0.5570761",
"0.55477357",
"0.5539858",
"0.5537866",
"0.5495238"... | 0.5659217 | 10 |
will create JSON output files if there are two events (for each threshold) in one time window. Ie, if there are two >10MeV >10pfu events as well as two >100MeV >1pfu events, will create files for all four events, but if there are three >100MeV >1pfu events, will only generate JSON files for the first two. Second events... | def two_in_one(obs_file,et,subevent):
#in this function, the "original time window" talked about in the comments
#refers to the start and end times that were input to create the file obs_file,
#which will likely have been created using the database_extraction function
#opening first outp... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def multi_event(st,et,instrument_chosen,subevent):\r\n print('checking for multiple events within given time window')\r\n \r\n #creating file for time window with first events for all thresholds\r\n out_name = Path(cfg.obs_path) / database_extraction(st,et,instrument_chosen,subevent)\r\n\r\n #creati... | [
"0.67087024",
"0.5573654",
"0.5543615",
"0.5325832",
"0.5257581",
"0.523323",
"0.5181749",
"0.5180666",
"0.51276666",
"0.5114678",
"0.51098496",
"0.5098101",
"0.5082852",
"0.5076031",
"0.50719666",
"0.5061145",
"0.50575876",
"0.5052095",
"0.5037945",
"0.5035007",
"0.5021967",... | 0.62420124 | 1 |
all events in one time window (not just two) used if there is more than one event occurring within a short time period. will generate an output file for every event that occurs within a given time window not to be confused with many_events, which generates output given multiple time windows. Can create files for up to ... | def multi_event(st,et,instrument_chosen,subevent):
print('checking for multiple events within given time window')
#creating file for time window with first events for all thresholds
out_name = Path(cfg.obs_path) / database_extraction(st,et,instrument_chosen,subevent)
#creating files for all ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def INPUT_Periods_file(input):\n \n global events\n \n tb = 3600\n ta = 3600\n \n Period = input['min_date'].split('T')[0] + '_' + \\\n input['max_date'].split('T')[0] + '_' + \\\n str(input['min_mag']) + '_' + str(input['max_mag'])\n eventpath = os.path.join(i... | [
"0.6087541",
"0.59404063",
"0.58714825",
"0.5870363",
"0.58608985",
"0.57862127",
"0.57703376",
"0.5700037",
"0.5650298",
"0.56280893",
"0.5617199",
"0.56035525",
"0.5598488",
"0.5553481",
"0.55442035",
"0.5541307",
"0.5524684",
"0.55119693",
"0.5496372",
"0.5496372",
"0.5493... | 0.6893672 | 0 |
given lists of peak fluxes for protons >10 MeV and >100 MeV, creates a boolean for whether or not each event is a subevent (doesn't cross a threshold) | def gen_subevent_bools(p_10,p_100):
#list of subevent booleans
subevent_bools = []
#extracting 10 MeV peak flux if it exists
for j in range(len(p_10)):
try:
p10 = float(p_10[j])
except ValueError:
p10 = 'nan'
#extracting 100 MeV pe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def spec_to_peaks(data, value, fp = iterate_structure(generate_binary_structure(rank = 2, connectivity=2), 10)):\n\n max_arr = maximum_filter(data, footprint = fp)\n return (data == max_arr) & (data > value)",
"def check_overlaps(self, verbose = False):\n if hasattr(self.phot, \"data\") and hasattr(... | [
"0.6195646",
"0.607271",
"0.5963362",
"0.5910339",
"0.5904927",
"0.5832219",
"0.57945627",
"0.5773653",
"0.563543",
"0.5589451",
"0.5549814",
"0.553361",
"0.55285126",
"0.5508843",
"0.5495379",
"0.54413253",
"0.54157656",
"0.5363649",
"0.532954",
"0.5318515",
"0.53176546",
... | 0.82217896 | 0 |
takes in lists of start times and end times to create a list of time windows, and a list of whether or not an event is a subevent, and uses those lists to run functions that extract data from the GOES database. Each list must have the same length, and indices of lists must correspond (ie start_time[j] has an end time o... | def many_events(start_time,end_time,subevent_bools):
#running through for each event
for j in range(len(start_time)):
#start, end, and subevent bool for this event
st = start_time[j]
et = end_time[j]
subevent = bool(subevent_bools[j])
#che... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def multi_event(st,et,instrument_chosen,subevent):\r\n print('checking for multiple events within given time window')\r\n \r\n #creating file for time window with first events for all thresholds\r\n out_name = Path(cfg.obs_path) / database_extraction(st,et,instrument_chosen,subevent)\r\n\r\n #creati... | [
"0.65961754",
"0.6502333",
"0.6414507",
"0.6089023",
"0.5852518",
"0.5768563",
"0.57619506",
"0.57517695",
"0.5713987",
"0.56655604",
"0.56637067",
"0.56566614",
"0.56286615",
"0.55703324",
"0.5542513",
"0.5491681",
"0.5479628",
"0.54761803",
"0.5450979",
"0.54409343",
"0.543... | 0.8048312 | 0 |
Returning the sync mode | def get_sync_mode():
return sync_mode | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def synchronize_system_mode(self):\n\n return self._synchronize_system_mode",
"def sync(self):\n return self._sync",
"def isSync(self):\n return False",
"def getMode(self):\n with self.lock:\n mode = self.mode\n return mode",
"def syncheck(self) :\n\t\ttry :\n\... | [
"0.72940135",
"0.7088847",
"0.7082289",
"0.7017716",
"0.6868106",
"0.68618685",
"0.68516916",
"0.6798914",
"0.6798914",
"0.6657855",
"0.64055157",
"0.6349925",
"0.63405436",
"0.6327484",
"0.63198066",
"0.63171744",
"0.631424",
"0.63071305",
"0.63044363",
"0.62617934",
"0.6251... | 0.91350996 | 0 |
Checking the sync_mode based on the given configuration | def check_sync_mode():
global sync_mode
_description = ''
_modes = {
SyncMode.RECEIVER: '(REMOTE ➔ LOCAL)',
SyncMode.SENDER: '(LOCAL ➔ REMOTE)',
SyncMode.PROXY: '(REMOTE ➔ LOCAL ➔ REMOTE)',
SyncMode.DUMP_LOCAL: '(LOCAL, ONLY EXPORT)',
SyncMode.DUMP_REMOTE: '(REMOTE, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_sync_mode():\n return sync_mode",
"def __check_mode(self):\n self.mode[\"auto_mode\"] = self.communications.get_mode()",
"def check_config_mode(self):\n return False",
"def __check_mode_change(self):\n if self.mode[\"auto_mode\"] != self.mode[\"last_mode\"]:\n self.... | [
"0.72925556",
"0.692381",
"0.6704957",
"0.63555396",
"0.63372236",
"0.61973035",
"0.61619157",
"0.6108568",
"0.6087788",
"0.6085571",
"0.6059644",
"0.60411906",
"0.60293037",
"0.60175145",
"0.594344",
"0.59315693",
"0.588756",
"0.58717567",
"0.58026725",
"0.580076",
"0.580005... | 0.78860736 | 0 |
Check if given client is remote client | def is_remote(client):
if client == Client.ORIGIN:
return is_origin_remote()
elif client == Client.TARGET:
return is_target_remote()
elif client == Client.LOCAL:
return False
else:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_remote(self): # -> Any | bool:\n ...",
"def is_remote(self):\n if socket.gethostbyname(socket.gethostname()).startswith('10.7'):\n return False\n else:\n return True",
"def is_local_client(self):\n return self.msg.is_local_client",
"def is_remote(self... | [
"0.7279024",
"0.7175781",
"0.70540047",
"0.6942053",
"0.6814598",
"0.6683476",
"0.66315174",
"0.66083586",
"0.6517704",
"0.6482558",
"0.6460855",
"0.6398728",
"0.63722503",
"0.6251613",
"0.62041306",
"0.62041306",
"0.61882174",
"0.615462",
"0.61380357",
"0.6073914",
"0.602614... | 0.83206755 | 0 |
Check if target is remote client | def is_target_remote():
return sync_mode in (SyncMode.SENDER, SyncMode.PROXY, SyncMode.DUMP_REMOTE,
SyncMode.IMPORT_REMOTE, SyncMode.SYNC_REMOTE) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_remote(client):\n if client == Client.ORIGIN:\n return is_origin_remote()\n elif client == Client.TARGET:\n return is_target_remote()\n elif client == Client.LOCAL:\n return False\n else:\n return False",
"def is_remote(self): # -> Any | bool:\n ...",
"def... | [
"0.7902426",
"0.7443142",
"0.7258655",
"0.7257651",
"0.7235301",
"0.6850371",
"0.6576049",
"0.645614",
"0.6447849",
"0.640186",
"0.6281278",
"0.61393785",
"0.60623175",
"0.60600775",
"0.600218",
"0.59922236",
"0.59744495",
"0.59468746",
"0.593498",
"0.5931949",
"0.59191805",
... | 0.7590158 | 1 |
Check if origin is remote client | def is_origin_remote():
return sync_mode in (SyncMode.RECEIVER, SyncMode.PROXY, SyncMode.DUMP_REMOTE,
SyncMode.IMPORT_REMOTE, SyncMode.SYNC_REMOTE) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_remote(client):\n if client == Client.ORIGIN:\n return is_origin_remote()\n elif client == Client.TARGET:\n return is_target_remote()\n elif client == Client.LOCAL:\n return False\n else:\n return False",
"def is_remote(self):\n return False",
"def is_remot... | [
"0.79242504",
"0.7800007",
"0.7686724",
"0.75528294",
"0.747566",
"0.7285064",
"0.70139563",
"0.6977031",
"0.6942172",
"0.6772315",
"0.6772315",
"0.6772315",
"0.6772315",
"0.67488825",
"0.6476083",
"0.6462293",
"0.64356977",
"0.63294023",
"0.6260433",
"0.6211482",
"0.62091947... | 0.8189138 | 0 |
Check if sync mode is import | def is_import():
return sync_mode in (SyncMode.IMPORT_LOCAL, SyncMode.IMPORT_REMOTE) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_sync_mode():\n global sync_mode\n _description = ''\n\n _modes = {\n SyncMode.RECEIVER: '(REMOTE ➔ LOCAL)',\n SyncMode.SENDER: '(LOCAL ➔ REMOTE)',\n SyncMode.PROXY: '(REMOTE ➔ LOCAL ➔ REMOTE)',\n SyncMode.DUMP_LOCAL: '(LOCAL, ONLY EXPORT)',\n SyncMode.DUMP_REMO... | [
"0.75585586",
"0.69149435",
"0.665945",
"0.64593935",
"0.6442651",
"0.63151574",
"0.63039666",
"0.6274873",
"0.6236776",
"0.6194281",
"0.6192882",
"0.6032196",
"0.59927666",
"0.59285295",
"0.59285295",
"0.59168273",
"0.5914921",
"0.58185005",
"0.57003963",
"0.5684882",
"0.567... | 0.9084145 | 0 |
Check if sync mode is import | def is_dump():
return sync_mode in (SyncMode.DUMP_LOCAL, SyncMode.DUMP_REMOTE) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_import():\n return sync_mode in (SyncMode.IMPORT_LOCAL, SyncMode.IMPORT_REMOTE)",
"def check_sync_mode():\n global sync_mode\n _description = ''\n\n _modes = {\n SyncMode.RECEIVER: '(REMOTE ➔ LOCAL)',\n SyncMode.SENDER: '(LOCAL ➔ REMOTE)',\n SyncMode.PROXY: '(REMOTE ➔ LOCA... | [
"0.9084145",
"0.75585586",
"0.69149435",
"0.665945",
"0.64593935",
"0.6442651",
"0.63039666",
"0.6274873",
"0.6236776",
"0.6194281",
"0.6192882",
"0.6032196",
"0.59927666",
"0.59285295",
"0.59285295",
"0.59168273",
"0.5914921",
"0.58185005",
"0.57003963",
"0.5684882",
"0.5679... | 0.63151574 | 6 |
Run command depending on the given client | def run_command(command, client, force_output=False, allow_fail=False, skip_dry_run=False):
if system.config['verbose']:
output.message(
output.host_to_subject(client),
output.CliFormat.BLACK + command + output.CliFormat.ENDC,
debug=True
)
if system.config['d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _client_cmd(self, cmd):\n logging.info('Client cmd: [%s]', cmd)\n return self._client.run(cmd)",
"def run_cmd(server, client):\n msg = [client.get_command()]\n client.input_list += msg\n server.logger.info(\"RECEIVED INPUT {} : {}\".format(client.ip, msg[0]))\n if no... | [
"0.7197345",
"0.7119318",
"0.6828006",
"0.66738003",
"0.64791876",
"0.64679384",
"0.64308935",
"0.6380497",
"0.62591547",
"0.6222036",
"0.60982287",
"0.60168076",
"0.6011429",
"0.58656096",
"0.5851736",
"0.5816941",
"0.58131343",
"0.57894695",
"0.57740134",
"0.5730364",
"0.56... | 0.5610078 | 26 |
Assert valid court order. | def test_court_orders(session, test_status, expected_code, expected_msg):
business = factory_business('BC1234567')
filing = copy.deepcopy(COURT_ORDER_FILING_TEMPLATE)
del filing['filing']['courtOrder']['fileKey']
if test_status == 'FAIL':
del filing['filing']['courtOrder']['orderDetails']
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_case_customer_complete_courseevent_order(self):",
"def test_order_constraint(self):\n orders_placed = [25, 25, 25]\n with self.assertRaises(Exception):\n analyse_uncertain_demand.UncertainDemand(\n orders=orders_placed,\n sku='Rx493-90',\n ... | [
"0.6418377",
"0.6131915",
"0.5973272",
"0.59159654",
"0.5835521",
"0.5678925",
"0.56525654",
"0.5622847",
"0.55975693",
"0.55640495",
"0.55434185",
"0.5528798",
"0.55012167",
"0.54883766",
"0.54165864",
"0.539526",
"0.53826475",
"0.5382484",
"0.5381499",
"0.5373344",
"0.53692... | 0.67318577 | 0 |
Assert valid court order. | def test_court_order_file(session, minio_server, test_name, expected_code, expected_msg):
business = factory_business('BC1234567')
filing = copy.deepcopy(COURT_ORDER_FILING_TEMPLATE)
if test_name == 'SUCCESS':
filing['filing']['courtOrder']['fileKey'] = _upload_file(letter, invalid=False)
elif ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_court_orders(session, test_status, expected_code, expected_msg):\n business = factory_business('BC1234567')\n filing = copy.deepcopy(COURT_ORDER_FILING_TEMPLATE)\n del filing['filing']['courtOrder']['fileKey']\n if test_status == 'FAIL':\n del filing['filing']['courtOrder']['orderDetail... | [
"0.67318577",
"0.6418377",
"0.6131915",
"0.5973272",
"0.59159654",
"0.5835521",
"0.5678925",
"0.56525654",
"0.5622847",
"0.55975693",
"0.55640495",
"0.55434185",
"0.5528798",
"0.55012167",
"0.54883766",
"0.54165864",
"0.539526",
"0.53826475",
"0.5382484",
"0.5381499",
"0.5373... | 0.51499826 | 53 |
Find a text label for an axis describing a provided CSV column. | def get_label(column):
for key, label in column_to_label.items():
if key in column:
return label | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _label(self, column):\n # XXX\n return column",
"def _get_label ( self ):\n if self._label is not None:\n return self._label\n return 'Column %d' % (self.index + 1)",
"def GetColLabelValue(self, col):\n label = self.column_label(self.colsel[col])\n \n ... | [
"0.70492536",
"0.65161985",
"0.6515418",
"0.63260293",
"0.60894614",
"0.6023913",
"0.59223855",
"0.5880934",
"0.5865998",
"0.57208353",
"0.5644971",
"0.56144625",
"0.55985284",
"0.55886084",
"0.5540931",
"0.5496037",
"0.5494928",
"0.5494829",
"0.5493561",
"0.5486655",
"0.5454... | 0.7068317 | 0 |
Filter out all pandas.DataFrame without required columns | def dfs_filter(dfs, df_names, column_list):
dfs_out = []
df_names_out = []
# loop over all (pandas.DataFrame, str) pairs
for df, df_name in zip(dfs, df_names):
has_all = True
for column in column_list:
if column not in df.columns:
has_all = False
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _filter_df(adjmat, df, verbose=3):\n remcols = df.columns[~np.isin(df.columns.values, adjmat.columns.values)].values\n if len(remcols)>0:\n if verbose>=3: print('[bnlearn] >Removing columns from dataframe to make consistent with DAG [%s]' %(remcols))\n df.drop(labels=remcols, axis=1, inplac... | [
"0.7230408",
"0.7230408",
"0.7211834",
"0.7153902",
"0.7082774",
"0.70773596",
"0.69314176",
"0.6903499",
"0.6895523",
"0.66686505",
"0.6667085",
"0.66075593",
"0.6605839",
"0.658763",
"0.6533146",
"0.6488316",
"0.6451615",
"0.64211375",
"0.64115214",
"0.64103115",
"0.6402743... | 0.5810948 | 56 |
Find all possible values of a column in the pandas.DataFram list | def dfs_all_values(dfs, column):
values = []
# loop over all (pandas.DataFrame, str) pairs
for df in dfs:
values.extend(df[column].tolist())
# set() removes duplicates
# sorted() converts Set to List and sort the elements
return sorted(set(values)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getValuesForColumn(self, columnname):\n return list(self.abundance_df[columnname].unique())",
"def get_values(df):\n return df.columns.values.tolist()",
"def get_binary_values(data_frame):\n all_columns = pandas.DataFrame( index = data_frame.index)\n for col in data_frame.columns:\n dat... | [
"0.6171805",
"0.6113757",
"0.5891988",
"0.5879397",
"0.58606875",
"0.5812586",
"0.57938266",
"0.5787178",
"0.57815653",
"0.5762439",
"0.5762439",
"0.56766194",
"0.56144273",
"0.5601676",
"0.5587507",
"0.5523211",
"0.55008084",
"0.5450795",
"0.5401311",
"0.5399768",
"0.5390941... | 0.6156369 | 1 |
Draw multiple lines y(x) using data from the dfs list on the ax subplot. | def draw_plot(ax, dfs, legend, x, y, xscale, yaxis_max):
xticks = dfs_all_values(dfs, x)
# loop over all pandas.DataFrame objects
for df in dfs:
# setting the x-column as an index is required to draw the y-column
# as a function of x argument
df = df.set_index(x)
# plot line ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot(x, y, *dfs):\n ax = None\n for df in dfs:\n ax = df[[x, y]].set_index(x).plot(kind='line', ylim=(0, None), xlim=(0, None), ax=ax)",
"def update_plot(self,ax):\n for i,line in enumerate(self.lines):\n line.set_ydata(self.data[i].f)\n for line in self.lines: \n ... | [
"0.75955755",
"0.6639895",
"0.63147664",
"0.6244438",
"0.6018296",
"0.5990527",
"0.59632486",
"0.5877029",
"0.58101994",
"0.58101994",
"0.5800447",
"0.5794655",
"0.5775401",
"0.57584023",
"0.5737174",
"0.5724625",
"0.5724219",
"0.57178116",
"0.56640327",
"0.56584495",
"0.5646... | 0.7199761 | 1 |
Draw a table of all data used to chart y(x) | def draw_table(ax, dfs, legend, x, y):
col_labels = dfs_all_values(dfs, x)
column_legend = []
cell_text = []
# loop over all pandas.DataFrame objects
for df in dfs:
# to allow query y(x) easily
df = df.set_index(x)
df_row = df[y]
# build a row with filled blanks '-'
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def table(self):\n\n param=self.x_param\n\n device=self.device\n\n base_params=device.get_params()\n\n data_tot=DataFrame()\n\n for i in range(len(param)):\n\n print_index=1\n\n for name in param.names:\n\n device._set_params(param(i))\n\n ... | [
"0.64481795",
"0.6369142",
"0.61868083",
"0.615285",
"0.60729265",
"0.60604787",
"0.59911245",
"0.5980409",
"0.59740573",
"0.5895259",
"0.5869834",
"0.5847987",
"0.5836506",
"0.5818547",
"0.58129287",
"0.5809058",
"0.57461077",
"0.57257223",
"0.5720079",
"0.5665956",
"0.56587... | 0.67767966 | 0 |
Load all of the ovarian dataframes as values in the self._data dict variable, with names as keys, and format them properly. | def __init__(self, version="latest", no_internet=False):
# Set some needed variables, and pass them to the parent Dataset class __init__ function
valid_versions = ["0.0", "0.0.1"] # This keeps a record of all versions that the code is equipped to handle. That way, if there's a new data release but the... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_dataframe(self):\n logging.info('*** Creating the dataframes from the source files ' )\n \n for k in self.datasets_keys:\n #for k in ['igra2' , 'ncar']:\n \n logging.info('*** Creating the dataframe for the dataset: %s ' , k ) ... | [
"0.6741529",
"0.63294286",
"0.619855",
"0.60682344",
"0.60343254",
"0.5896149",
"0.5864683",
"0.5835448",
"0.58048934",
"0.5794775",
"0.57485104",
"0.574704",
"0.5740164",
"0.5707918",
"0.5695425",
"0.5673318",
"0.5653236",
"0.5649809",
"0.56193334",
"0.55798227",
"0.5578606"... | 0.5710556 | 13 |
Initialize the linked list. | def __init__(self):
self.head = None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, lst=[]):\n self.__length = 0 # current length of the linked list\n self.__head = None # pointer to the first node in the list\n for e in lst: # initialize the list,\n self.add(e) # by adding elements one by one",
"def __init__(self):\n node = ListNode... | [
"0.793523",
"0.7746552",
"0.7618733",
"0.76008236",
"0.7564557",
"0.7530009",
"0.74541533",
"0.74384236",
"0.7436091",
"0.74262166",
"0.74253154",
"0.74253154",
"0.73937964",
"0.7374561",
"0.7347711",
"0.7333443",
"0.7332118",
"0.7326369",
"0.7326369",
"0.7326369",
"0.731081"... | 0.7175879 | 63 |
Add a node to the linked list. | def push(self, val):
self.head = Node(val, self.head) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_node(self, node):\n self.nodes.append(node)",
"def add_node(self, node):\n self.nodes.add(node)",
"def addNode (self, node):\n self.__nodes.add(node)",
"def add_node(self, node):\n self._nodes.add(node)",
"def add_node(self, node):\n self._nodes.add(node)",
"def... | [
"0.8397831",
"0.83893156",
"0.82865614",
"0.8239832",
"0.8239832",
"0.81677186",
"0.8124044",
"0.80292726",
"0.8016808",
"0.79811966",
"0.7921723",
"0.7920711",
"0.7804295",
"0.7771042",
"0.7770391",
"0.7748963",
"0.7730202",
"0.7698047",
"0.7654877",
"0.7544322",
"0.7534985"... | 0.0 | -1 |
Pop a value off of the end of the list. | def pop(self):
if not self.head:
raise IndexError('Cannot pop from empty linked list.')
popped_value = self.head.val
self.head = self.head.next
return popped_value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pop(self): ##################### <-\n value = self.lst[-1]\n self.lst = self.lst[:-1]\n return value",
"def pop(self):\n\n value = self.values[0]\n if len(self.values) == 1:\n self.values = []\n else:\n self.populate(self.values[1:])\n re... | [
"0.84853077",
"0.79199415",
"0.7871684",
"0.77542204",
"0.7659789",
"0.76190156",
"0.7606379",
"0.7602611",
"0.75503474",
"0.75462514",
"0.7535589",
"0.7535589",
"0.7535589",
"0.75355107",
"0.74785006",
"0.74628246",
"0.7460463",
"0.744531",
"0.7444407",
"0.7429238",
"0.74292... | 0.7111202 | 43 |
Search through the linked list. | def search(self, val):
if not self.head:
raise IndexError('Cannot search empty list.')
current_node = self.head
while current_node:
if current_node.val == val:
return current_node
current_node = current_node.next | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def search(self, ele):\n if self.head:\n current = self.head\n while True:\n\tif current.data == ele:\n\t return True\n\tcurrent = current.next\n\tif current == self.head:\n\t break\n return False",
"def search(self, data):\r\n if self.head is None:\r\n pass\r\n current... | [
"0.7565156",
"0.751804",
"0.74692243",
"0.7460926",
"0.7455549",
"0.7395807",
"0.7327136",
"0.73222226",
"0.72867405",
"0.7258646",
"0.7242936",
"0.72091985",
"0.7195228",
"0.7178875",
"0.70211923",
"0.6858696",
"0.6825057",
"0.68024164",
"0.6769936",
"0.6705504",
"0.67019075... | 0.7163797 | 14 |
Remove a value from the linked list. | def remove(self, val):
current_node = self.head
previous_node = None
while current_node:
if current_node.val == val:
if previous_node:
previous_node.next = current_node.next
else:
self.head = current_node.next
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove(self, value):\r\n if self.head is None:\r\n return\r\n\r\n if self.head.value == value:\r\n self.head = self.head.next\r\n return\r\n\r\n node = self.head\r\n while node.next:\r\n if node.next.value == value:\r\n node... | [
"0.85559684",
"0.8450625",
"0.84335315",
"0.828074",
"0.819369",
"0.8189939",
"0.81423485",
"0.80990094",
"0.8078319",
"0.7913155",
"0.79033136",
"0.7874182",
"0.78658223",
"0.78636545",
"0.78086895",
"0.7679407",
"0.7675178",
"0.7667091",
"0.7643101",
"0.75630695",
"0.754978... | 0.8185222 | 6 |
Just check that the `_data_changed` parameter is added the response. | def test_returned_data_changed(self):
request = RequestFactory().get('/')
admin_instance = get_modeladmin(Iframe)
response_302 = HttpResponseRedirect(redirect_to='/admin_mountpoint/')
new_response = admin_instance.maybe_fix_redirection(
request=request, response=response_302)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_data(self, data):\n self.logger.debug(\"handle_data()\")\n self.response_data.append(data)",
"def data_changed(self):\n return",
"def check_for_new_data(self):\n return",
"def data_changed(self):\n self.data_changed_signal.emit(self)",
"def on_new_data(self, da... | [
"0.7027461",
"0.66865385",
"0.65378416",
"0.6361653",
"0.63519835",
"0.6307863",
"0.6246295",
"0.6245001",
"0.6158304",
"0.61403364",
"0.61214167",
"0.61103153",
"0.6078733",
"0.6078601",
"0.59536886",
"0.59317225",
"0.5920005",
"0.5906738",
"0.5872005",
"0.5840075",
"0.58380... | 0.5979023 | 14 |
Going to a nonchunkadmin URL should be ok, and should also put the `_data_changed` parameter onto the URL. | def test_to_other_url(self):
user = User(username='test', is_staff=True, is_superuser=True,
is_active=True)
user.set_password('test')
user.full_clean()
user.save()
request = RequestFactory().get('/')
response_302 = HttpResponseRedirect(redirect_to='/ad... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_returned_data_changed(self):\n request = RequestFactory().get('/')\n admin_instance = get_modeladmin(Iframe)\n response_302 = HttpResponseRedirect(redirect_to='/admin_mountpoint/')\n new_response = admin_instance.maybe_fix_redirection(\n request=request, response=res... | [
"0.6304963",
"0.60231835",
"0.5975458",
"0.59199995",
"0.57574075",
"0.5661228",
"0.56567496",
"0.5646688",
"0.56410176",
"0.5619408",
"0.5619408",
"0.5565735",
"0.5562113",
"0.5547753",
"0.5510401",
"0.55066466",
"0.54986465",
"0.54867476",
"0.54704493",
"0.54627734",
"0.544... | 0.6291411 | 1 |
If `_autoclose` is in the URL, that + `_data_changed` should propagate to the next redirect URL for the purposes of our adminlinks JS. | def test_autoclose_chunkadmin(self):
user = User(username='test', is_staff=True, is_superuser=True,
is_active=True)
user.set_password('test')
user.full_clean()
user.save()
admin_instance = get_modeladmin(Iframe)
self.assertIsInstance(admin_instance, Re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def response_post_save_change(self, request, obj):\n opts = self.model._meta\n\n if \"next\" in request.GET:\n return HttpResponseRedirect(request.GET['next'])\n\n if self.has_change_permission(request, None):\n post_url = reverse('admin:%s_%s_changelist' %\n ... | [
"0.5717953",
"0.56154025",
"0.5552884",
"0.5355236",
"0.5205197",
"0.5178103",
"0.49210623",
"0.48757377",
"0.48716828",
"0.48654342",
"0.48438132",
"0.48274982",
"0.48240012",
"0.48213187",
"0.47654843",
"0.47389874",
"0.46854186",
"0.46743634",
"0.46646327",
"0.463678",
"0.... | 0.5659059 | 1 |
if continue editing is hit, it should go back to the parent URL, I think? | def test_continue_editing_parent_object(self):
user = User(username='test', is_staff=True, is_superuser=True,
is_active=True)
user.set_password('test')
user.full_clean()
user.save()
admin_instance = get_modeladmin(Iframe)
self.assertIsInstance(admin_in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def submit_and_back(self):\n self.submit(skip_confirm=True)\n self.parent().do_detail()",
"def submit_and_back(self):\n self.submit(skip_confirm=True)\n self.parent().do_detail()",
"def response_change(self, request, obj):\r\n \r\n # in these cases, the redirect is goo... | [
"0.6385778",
"0.6385778",
"0.6379418",
"0.62234807",
"0.6166165",
"0.61428994",
"0.6119325",
"0.6115395",
"0.61025864",
"0.60791975",
"0.6070473",
"0.6042546",
"0.60110354",
"0.60064095",
"0.600623",
"0.5988462",
"0.59622556",
"0.59316474",
"0.590217",
"0.58978534",
"0.589691... | 0.65077806 | 0 |
Generate immediate (different by one mismatch) neighbours of the given genome pattern | def _generate_immediate_neighbours(pattern: str) -> list:
generated = []
for i in range(len(pattern)):
if pattern[i] == 'A':
generated.extend([pattern[:i] + c + pattern[i + 1:] for c in LIST_A])
elif pattern[i] == 'C':
generated.extend([pattern[:i] + c + pattern[i + 1:] f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_neighbours(pattern: str, mismatches: int) -> set:\n neighbourhood = set()\n neighbourhood.add(pattern)\n\n curr_patterns = [pattern]\n next_patterns = []\n\n for curr_mismatches in range(mismatches):\n for curr_pattern in curr_patterns:\n for neighbour in _generate_imm... | [
"0.6630292",
"0.6147133",
"0.61121386",
"0.6109716",
"0.6099266",
"0.60585225",
"0.60353184",
"0.5980777",
"0.59600437",
"0.5955379",
"0.58933324",
"0.5820584",
"0.5806383",
"0.5797996",
"0.5792484",
"0.5790195",
"0.57679236",
"0.5754327",
"0.5728205",
"0.5717146",
"0.5706085... | 0.7541577 | 0 |
Generate neighbours for the given pattern (genome string) | def generate_neighbours(pattern: str, mismatches: int) -> set:
neighbourhood = set()
neighbourhood.add(pattern)
curr_patterns = [pattern]
next_patterns = []
for curr_mismatches in range(mismatches):
for curr_pattern in curr_patterns:
for neighbour in _generate_immediate_neighbo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _generate_immediate_neighbours(pattern: str) -> list:\n generated = []\n for i in range(len(pattern)):\n if pattern[i] == 'A':\n generated.extend([pattern[:i] + c + pattern[i + 1:] for c in LIST_A])\n elif pattern[i] == 'C':\n generated.extend([pattern[:i] + c + patter... | [
"0.7232133",
"0.674774",
"0.6449919",
"0.6243702",
"0.6238935",
"0.6133001",
"0.612903",
"0.5946089",
"0.5851762",
"0.5826999",
"0.5814815",
"0.5778444",
"0.5769048",
"0.5745069",
"0.55670846",
"0.5559852",
"0.5557054",
"0.5541717",
"0.55216914",
"0.55193025",
"0.54643875",
... | 0.64392906 | 3 |
Whether the given card matches this card | def is_match(self, card):
return self.suit == card.suit or self.value == card.value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _validate_card_match(self, chosen_card, active_card, active_suit):\n\t\treturn chosen_card.is_match(active_card) or chosen_card.suit == active_suit",
"def __eq__(self, other_card):\n if self.rank == other_card.rank or self.suit == other_card.suit:\n return True\n else:\n r... | [
"0.78050846",
"0.7451856",
"0.727455",
"0.7232319",
"0.718153",
"0.7173871",
"0.713753",
"0.7100822",
"0.67850065",
"0.6778596",
"0.6773867",
"0.674615",
"0.6730441",
"0.66842115",
"0.6663965",
"0.6543529",
"0.6535424",
"0.6506496",
"0.648398",
"0.64583373",
"0.6433088",
"0... | 0.8583878 | 0 |
Ensures that chosen_card is an acceptable match, given the active_card and active_suit | def _validate_card_match(self, chosen_card, active_card, active_suit):
return chosen_card.is_match(active_card) or chosen_card.suit == active_suit | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_chosen_card(self, allowed_cards, chosen_card):\n if self.action is not None:\n if self.action in allowed_cards:\n logger.info(f\"Successfully chose the card: {self.action}\")\n chosen_card = self.action\n else:\n logger.error(f\"{sel... | [
"0.70289963",
"0.70051605",
"0.6518905",
"0.6450109",
"0.63630843",
"0.630566",
"0.62759304",
"0.62171423",
"0.61978257",
"0.61198056",
"0.60976046",
"0.60950667",
"0.6085195",
"0.6076674",
"0.60443",
"0.60375905",
"0.60289156",
"0.59881556",
"0.59762114",
"0.59721756",
"0.59... | 0.82993805 | 0 |
If test_mode is True, an image of `screen` is saved | def save_screen(screen):
if not video_mode: # Don't record video
return False
# Make global variables writeable
global current_frame
global path_checked
frames_directory = os.path.dirname(
os.path.dirname(
os.path.realpath(__file__))) + "\\frames\\"
if not path_check... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def saveScreenPNG(self, filename):\n return nes_lib.saveScreenPNG(self.obj, filename)",
"def screen_shot(self):\n screen_size = '{}x{}@{}x{}/0'.format(self.screen[0], self.screen[1], self.screen[0], self.screen[1])\n subprocess.check_call([\n ADB_EXECUTOR, '-s', self.device_id, 's... | [
"0.65687513",
"0.64645797",
"0.62103873",
"0.6199405",
"0.6150051",
"0.6116136",
"0.6103849",
"0.6095868",
"0.60331243",
"0.6008139",
"0.5992217",
"0.5990289",
"0.59354466",
"0.5926195",
"0.58971107",
"0.5877742",
"0.58629024",
"0.5829966",
"0.58243024",
"0.57998353",
"0.5798... | 0.7221238 | 0 |
Check 'frames' folder exists. If not, create it | def check_folder(directory):
global path_checked
if not os.path.exists(directory):
os.makedirs(directory)
else:
path_checked = True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def copy_keyframes_from_frames(frames_dir, keyframes, name_dir=\"keyframes\", remove_frames_dir=True):\n # Create new directory if it doesn't already exist\n keyframes_dir = make_dir(name_dir, Path(frames_dir).parent)\n\n # Copy keyframes from frames directory, only if destiny directory is empty\n if l... | [
"0.5819628",
"0.58033806",
"0.57717586",
"0.5712013",
"0.5633541",
"0.5623171",
"0.5622375",
"0.56030816",
"0.56009656",
"0.557692",
"0.5512916",
"0.55081576",
"0.55065334",
"0.549381",
"0.5465493",
"0.5456761",
"0.54223925",
"0.54223925",
"0.54124236",
"0.5407644",
"0.535819... | 0.0 | -1 |
Return the FPS, or if video_mode is true, return the video FPS | def get_fps(clock):
if video_mode:
return "30" # Video FPS will be 30
else:
return str(int(round(clock.get_fps(), 0))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_video_fps(self):\n fps = self.video.get(cv2.CAP_PROP_FPS)\n logging.info('Video FPS: {}'.format(fps))\n return fps",
"def get_fps(video, use_opencv=False):\n\n if use_opencv:\n video_cap = cv2.VideoCapture(video)\n fps = video_cap.get(cv2.CAP_PROP_FPS)\n video... | [
"0.7892058",
"0.7523606",
"0.7171496",
"0.7074355",
"0.705509",
"0.7029802",
"0.69614834",
"0.69196635",
"0.69127625",
"0.6722967",
"0.66790813",
"0.66567814",
"0.6546592",
"0.6546592",
"0.653519",
"0.65346336",
"0.6509575",
"0.64402926",
"0.6350182",
"0.6329676",
"0.6317738"... | 0.74950314 | 2 |
Creates an ordered puzzle of dimension lengthXlength with length > 1 | def __init__(self, length):
if (length <= 1 or length.__class__ != int):
try:
raise ValueError('length must be an integer greater than 1')
except:
print('something went wrong!')
raise
self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_board(size) -> list:\n return list(itertools.product([i for i in range(size)], repeat=2))",
"def buildpuzzle(self):\r\n self.puzzle = copy.deepcopy(self.rows)\r\n if self.difficulty == 1:\r\n self.removedigits(1)\r\n if self.difficulty == 2:\r\n self.remov... | [
"0.64792323",
"0.62767714",
"0.61650395",
"0.6140122",
"0.6096842",
"0.60461116",
"0.58814245",
"0.5815948",
"0.5800065",
"0.57949734",
"0.5787429",
"0.5744311",
"0.57322896",
"0.56909317",
"0.5681817",
"0.56767344",
"0.5667736",
"0.56485003",
"0.5632847",
"0.56253445",
"0.56... | 0.5530513 | 28 |
Gets the item at the exact coordinate in the board | def __get_box(self, position):
return self.__board[position//self.__length][position%self.__length] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __getitem__(self, args):\n x, y = args\n xpos, ypos = self.move(x, y)\n return self.board[xpos][ypos]",
"def get_item(self, items, y, x):\n if self.maze[y][x] != \" \" and self.maze[y][x] in items:\n return self.maze[y][x]\n else:\n return None",
"de... | [
"0.7532374",
"0.73557675",
"0.7260173",
"0.72259563",
"0.7206686",
"0.7199228",
"0.7171327",
"0.7140547",
"0.7119871",
"0.71094227",
"0.7099475",
"0.7034289",
"0.7034289",
"0.7010607",
"0.7004049",
"0.7004049",
"0.7004049",
"0.69689083",
"0.6966279",
"0.6965098",
"0.69650495"... | 0.0 | -1 |
Changes the position of the blank box | def swap(self, direction):
directions = {'up': (-1, 0), 'down': (1, 0), 'left': (0, -1), 'right': (0, 1),}
new_row = self.__blank_box[0] + directions[direction][0]
new_col = self.__blank_box[1] + directions[direction][1]
new_position = self.__get_box((new_row*self.__length)+new_col)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset_position(self):\n self.rect.left, self.rect.top = self.start_pos",
"def setBlank(self, pos):\n self.tiles[-1] = pos",
"def reset_position(self, x, y):\n\t\tself.grid[x][y] = self.terminal",
"def positioning(self):\n pass",
"def reset(self):\n self.x_pos = 10\n s... | [
"0.6971761",
"0.67601514",
"0.6497059",
"0.6478759",
"0.64567703",
"0.6452853",
"0.6386973",
"0.63737553",
"0.63284373",
"0.63279796",
"0.6298735",
"0.62748224",
"0.6256663",
"0.62447906",
"0.6181412",
"0.61722535",
"0.61675096",
"0.61479",
"0.61474043",
"0.6145806",
"0.61259... | 0.0 | -1 |
Return the content of a file as a string without newlines. | def read_file(file_path):
file_string = ''
with open(file_path, 'r', newline='') as file:
for line in file:
file_string = file_string + line.rstrip('\n')
return file_string | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_file_content(filename):\n file_contents = ''\n with open(filename) as f:\n file_contents = f.read()\n return file_contents",
"def get_file_content(self, file_name: str):\n file_name = Path(__file__).absolute().parents[1].joinpath(file_name)\n try:\n with file_name... | [
"0.7609623",
"0.75446004",
"0.7528579",
"0.7510224",
"0.7494273",
"0.7474576",
"0.74162525",
"0.7388739",
"0.7182413",
"0.71716714",
"0.71541095",
"0.7126843",
"0.7116395",
"0.7087866",
"0.704822",
"0.69866985",
"0.6974244",
"0.69514704",
"0.69032836",
"0.68963784",
"0.687757... | 0.7359542 | 8 |
plot the timestamped data for the temperature | def find_records():
print("begin find records")
study_list = retrieve_ref('study_list')
sensor_list = retrieve_ref('sensor_list')
# sensor_unit_list = retrieve_ref('sensor_unit_list')
for study in study_list:
# print('study = ' + str(study))
source_path = os.path.join(st... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_temp():\r\n work_book = xlrd.open_workbook(\"Temp.xls\")\r\n sheet1 = work_book.sheet_by_name(\"Temperature\")\r\n time_x = sheet1.col_values(1)\r\n temp_y = sheet1.col_values(0)\r\n plt.title(\"Time\")\r\n plt.xlabel(\"Time\")\r\n plt.ylabel(\"Temperature\")\r\n plt.plot(time_x, t... | [
"0.77332544",
"0.7619313",
"0.7040905",
"0.6752579",
"0.6656995",
"0.6611152",
"0.6522687",
"0.6483575",
"0.63331616",
"0.6321086",
"0.6280439",
"0.6280298",
"0.6186041",
"0.61790603",
"0.61554503",
"0.61331767",
"0.6095322",
"0.6091136",
"0.6080695",
"0.6079412",
"0.6053336"... | 0.0 | -1 |
The providerassigned unique ID for this managed resource. | def id(self) -> str:
return pulumi.get(self, "id") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def provider_id(self):\n return self.get('_id')",
"def provider_id(self):\n raise NotImplementedError",
"def id(self):\n return self.raw_resource.uuid",
"def healthcare_provider_id(self):\n return self._healthcare_provider_id",
"def unique_identifier(self) -> str:\n retur... | [
"0.8193402",
"0.7851373",
"0.77124894",
"0.7604287",
"0.7477648",
"0.7476093",
"0.7476093",
"0.7476093",
"0.7425807",
"0.7380237",
"0.7371964",
"0.7371964",
"0.7371964",
"0.7371964",
"0.7371964",
"0.7371964",
"0.7371964",
"0.7371964",
"0.735787",
"0.735787",
"0.73477197",
"... | 0.0 | -1 |
Use this data source to retrieve basic information about all standalone VPCs available for an account. Uses the included apikey in provider configuration to determine which account to read from. Example Usage Can be used in other resources/data sources when the VPC identifier is unknown, while other attributes are know... | def get_account_vpcs(opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetAccountVpcsResult:
__args__ = dict()
opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts)
__ret__ = pulumi.runtime.invoke('cloudamqp:index/getAccountVpcs:getAccountVpcs', __args__, opts=opts, typ=GetAc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_vpc ( vpc_conn, vpc_name ) :\n vpcs = vpc_conn.get_all_vpcs( filters = { \"tag:Name\": [ vpc_name ] } )\n for v in vpcs :\n return v",
"def get_vpc_data(self, vpc_id, region):\n if not vpc_id:\n return None\n tmp_vpc_client = ibm.client(region=region)\n try:\n ... | [
"0.6153667",
"0.595063",
"0.57215375",
"0.5484518",
"0.5472075",
"0.54143125",
"0.53245234",
"0.5313841",
"0.5285683",
"0.52541167",
"0.52541167",
"0.52541167",
"0.52541167",
"0.52541167",
"0.52541167",
"0.5244572",
"0.5241493",
"0.5241493",
"0.5241493",
"0.5241493",
"0.52414... | 0.56605554 | 3 |
>>> find_good_recipes(9, 10) '5158916779' >>> find_good_recipes(5, 10) '0124515891' >>> find_good_recipes(18, 10) '9251071085' >>> find_good_recipes(2018, 10) '5941429882' | def find_good_recipes(improvement_num, count):
recipes = [3, 7]
elf1 = 0
elf2 = 1
while len(recipes) <= improvement_num + count:
elf1_value = recipes[elf1]
elf2_value = recipes[elf2]
recipe_sum = elf1_value + elf2_value
if recipe_sum > 9:
recipe_string = f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_similar_recipes(self):\n pass",
"def test_get_random_recipes(self):\n pass",
"def measure_the_work(pattern_to_find):\n recipes = [3, 7]\n keys = [int(key) for key in pattern_to_find]\n elf1 = 0\n elf2 = 1\n not_found = True\n\n while not_found:\n elf1_value =... | [
"0.62732804",
"0.61937374",
"0.6106467",
"0.59431934",
"0.59081733",
"0.5582103",
"0.5502457",
"0.5500289",
"0.5376491",
"0.53712493",
"0.53555405",
"0.53383607",
"0.52812743",
"0.52651393",
"0.5197361",
"0.5169107",
"0.516706",
"0.51159555",
"0.5107954",
"0.51040334",
"0.509... | 0.7260232 | 0 |
>>> measure_the_work('51589') 9 >>> measure_the_work('01245') 5 >>> measure_the_work('92510') 18 >>> measure_the_work('59414') 2018 | def measure_the_work(pattern_to_find):
recipes = [3, 7]
keys = [int(key) for key in pattern_to_find]
elf1 = 0
elf2 = 1
not_found = True
while not_found:
elf1_value = recipes[elf1]
elf2_value = recipes[elf2]
recipe_sum = elf1_value + elf2_value
if recipe_sum > 9... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def micros() -> int:",
"def getMeasure(unique_name):",
"def getMeasure(unique_name):",
"def compute(data):\n spoken = collections.defaultdict(lambda: collections.deque(maxlen=2))\n\n starting = map(int, data.split(\",\"))\n last_number = None\n i = 0\n\n for i, last_number in enumerate(startin... | [
"0.57274574",
"0.5691518",
"0.5691518",
"0.5494114",
"0.5486369",
"0.5464641",
"0.54049367",
"0.53898066",
"0.5387856",
"0.53810817",
"0.5366881",
"0.5366617",
"0.5352287",
"0.53466666",
"0.53197426",
"0.52943444",
"0.5284739",
"0.5271887",
"0.5271256",
"0.5255226",
"0.524284... | 0.50360847 | 53 |
Initialization of the mpc controller. Most of the parameters are fixed as this depends on the arm we have. | def __init__(self, ACTION_DIM = 4):
# TODO: There is probably a more elegant way of doing this
# load neural network params
self.start = 0.
if ACTION_DIM == 4:
self.ACTION_DIM = ACTION_DIM
else:
print('you are doing something wrong')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_controller(self, mode, p=None, d=None, torque=None):\n self.set_mode(Mode[mode])\n\n if p is not None:\n self.kp = p\n elif Mode[mode] == Mode.JOINT_IMP_CTRL:\n self.kp = [32000, 32000, 32000, 32000, 32000, 32000, 32000]\n elif Mode[mode] == Mode.CART_IMP_... | [
"0.6525128",
"0.6433289",
"0.63405937",
"0.6292362",
"0.6253625",
"0.622259",
"0.6191588",
"0.6181524",
"0.6158765",
"0.61208683",
"0.61065567",
"0.6085143",
"0.60849625",
"0.60765624",
"0.60523504",
"0.6051844",
"0.6037136",
"0.60301024",
"0.60288566",
"0.6012272",
"0.600002... | 0.0 | -1 |
velocity callback for the arm | def get_velocity(self, message):
#print('**************** vel ')
self.velocity = message.data
self.state[0:self.ndegres] = self.velocity[0:self.ndegres] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rover_velocity_callback(self, msg):\n\t\t# print(\"Rover velocity callback message: {}\".format(msg))\n\t\tpass",
"def cmd_vel_callback(self, msg):\n # Just store the desired velocity. The actual control runs on odometry callbacks\n v_l = msg.linear\n v_a = msg.angular\n self.v_li... | [
"0.79091567",
"0.76079005",
"0.7603583",
"0.74135023",
"0.72956824",
"0.7251918",
"0.72338396",
"0.7038976",
"0.7038976",
"0.6989811",
"0.69450855",
"0.693247",
"0.68990725",
"0.68878675",
"0.6857321",
"0.68244934",
"0.68088895",
"0.67901736",
"0.6790032",
"0.6769608",
"0.676... | 0.65173507 | 31 |
position callback for the arm | def get_position(self, message):
#print('**************** pos ')
self.position = message.data
self.state[self.ndegres:] = self.position[0:self.ndegres] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _onmove(self, event):",
"def xyzArmCallback(msg):\n global robot\n # extract message components and normalize - joystick provides [-100,100] and \n # we will scale to [-0.1,0.1]\n arm_x = msg.data[0]/ARM_DATA_SCALING\n arm_y = msg.data[1]/ARM_DATA_SCALING\n arm_z = msg.data[2]\n\n # cond... | [
"0.7052078",
"0.70264876",
"0.688068",
"0.67120135",
"0.6546346",
"0.65419245",
"0.65237623",
"0.6485038",
"0.64839834",
"0.6448002",
"0.6438029",
"0.64271504",
"0.6418365",
"0.63853776",
"0.63853776",
"0.63853776",
"0.63853776",
"0.63853776",
"0.63853776",
"0.63853776",
"0.6... | 0.0 | -1 |
Reset in case it is needed in the future | def reset(self):
self.position = np.zeros(self.ndegres)
self.velocity = np.zeros(self.ndegres)
self.state = np.zeros(2*self.ndegres)
self.flag = 0
self.h_ref = np.array([self.ref for _ in range(self.horizon)])
self.action = np.zeros(self.ACTION_DIM)
self.h_action... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _reset(self) -> None:",
"def _reset(self) -> None:",
"def _reset(self):\n pass",
"def reset():",
"def reset():",
"def reset():",
"def reset():\r\n pass",
"def reset(self) -> None:",
"def reset(self) -> None:",
"def reset(self) -> None:",
"def _reset(self):",
"def reset(self)... | [
"0.90287495",
"0.90287495",
"0.8958957",
"0.89266604",
"0.89266604",
"0.89266604",
"0.89041",
"0.88952583",
"0.88952583",
"0.88952583",
"0.8890957",
"0.8875916",
"0.8809804",
"0.8809804",
"0.88026774",
"0.88026774",
"0.88026774",
"0.88026774",
"0.88026774",
"0.88026774",
"0.8... | 0.0 | -1 |
Set next handler of the chain | def set_next(self, handler):
self.next = handler
return handler | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_handler(self, handler):\n self.next_handler = handler",
"def _handler_changed(self, handler):\n if self.next is not None:\n self.next.handler = handler",
"def setNext(self, next):\n\t\t\tself.next = next",
"def next(self, next):\n\n self._next = next",
"def next(self... | [
"0.7661297",
"0.6707803",
"0.6030326",
"0.59693336",
"0.59693336",
"0.5968152",
"0.5833051",
"0.5818501",
"0.57607543",
"0.57461786",
"0.5692767",
"0.566452",
"0.5590942",
"0.5582867",
"0.557292",
"0.55651385",
"0.55508965",
"0.5547468",
"0.55370283",
"0.55218494",
"0.5515645... | 0.80296415 | 0 |
This method must be implemented by child | def handle(self, context: Context):
raise NotImplementedError() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self):\r\n raise NotImplementedError('override me')",
"def __call__(self):\n raise NotImplementedError",
"def override(self):\n return None",
"def __call__(self):\n raise NotImplementedError()",
"def __call__(self):\n\t\treturn",
"def __call__( self ):\n pa... | [
"0.7916674",
"0.79126203",
"0.7673766",
"0.7651007",
"0.7559392",
"0.74367213",
"0.73910874",
"0.7356369",
"0.7356369",
"0.7195709",
"0.7069751",
"0.7009408",
"0.68268293",
"0.6821625",
"0.68035114",
"0.67765045",
"0.67765045",
"0.67765045",
"0.6763297",
"0.6763297",
"0.67564... | 0.0 | -1 |
Check whether a given seed is still unexplored. | def check_seed(self, seed):
out = self.complement(seed)
return self.solver.solve([(i + 1) for i in seed] + [-(i + 1) for i in out]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_seed_valid(seed):\n for ch in seed:\n if not ch in all_chars_set:\n return False\n return True",
"def is_seed_valid(seed):\n for ch in seed:\n if not ch in all_chars_set:\n return False\n return True",
"def is_seed_valid(seed):\n if seed == \"0\":\n ... | [
"0.6756834",
"0.6756834",
"0.6691525",
"0.6599388",
"0.61974424",
"0.6161359",
"0.5891919",
"0.5860734",
"0.58116674",
"0.5756278",
"0.572533",
"0.5714277",
"0.57110393",
"0.5691943",
"0.5688913",
"0.56780785",
"0.5637771",
"0.559989",
"0.556628",
"0.5554336",
"0.55459327",
... | 0.62459785 | 4 |
Look for and return any unexplored point including the given seed. Calling map.find_above(MSS) after map.block_down(MSS) will thus find strict supersets of the MSS, as the MSS itself has been blocked. | def find_above(self, seed):
superset_exists = self.solver.solve((i + 1) for i in seed)
if superset_exists:
return self.get_seed()
else:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_seed(self, seed):\n out = self.complement(seed)\n return self.solver.solve([(i + 1) for i in seed] + [-(i + 1) for i in out])",
"def choose_pos(self):\n s = self\n\n availablepos = []\n for dblock in s.pjs.dblocks:\n is_available = True\n\n for p... | [
"0.59967655",
"0.55694646",
"0.5480614",
"0.5397099",
"0.5393073",
"0.5385499",
"0.5333922",
"0.52977884",
"0.52768016",
"0.5189014",
"0.51889",
"0.518867",
"0.5116351",
"0.5088275",
"0.5082775",
"0.5077057",
"0.50351775",
"0.50136656",
"0.5006063",
"0.49916822",
"0.49692985"... | 0.7450136 | 0 |
Get the seed from the current model. (Depends on work in next_seed to be valid.) | def get_seed(self):
return self.solver.get_model_trues(start=0, end=self.n)
# slower:
# model = self.solver.get_model()
# return [i for i in range(self.n) if model[i]]
# slowest:
# seed = []
# for i in range(self.n):
# if self.solver.model_value(i+1):... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def seed(self):\n return self._seed",
"def seed(self):\n return self._seed",
"def seed(self):\n return self._seed",
"def seed(self) -> int:\n return self._seed # type: ignore",
"def next_seed(self):\r\n self._cur_seed += 1\r\n return self._cur_seed - 1",
"def ru... | [
"0.72466826",
"0.72466826",
"0.72466826",
"0.6889038",
"0.6776114",
"0.67040414",
"0.66961634",
"0.63759416",
"0.63759416",
"0.6318605",
"0.62863636",
"0.62448657",
"0.6167233",
"0.6144767",
"0.61321104",
"0.61201894",
"0.6057582",
"0.603661",
"0.59608483",
"0.595875",
"0.585... | 0.68926764 | 3 |
Maximize a given seed within the current set of constraints. The Boolean direction parameter specifies up (True) or down (False) | def maximize_seed(self, seed, direction):
while True:
comp = self.complement(seed)
x = self.solver.new_var() + 1
if direction:
# search for a solution w/ all of the current seed plus at
# least one from the current complement.
s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def maximize(self):\n raise NotImplementedError",
"def solve(num_wizards, num_constraints, wizards, constraints):\n\n # print(num_wizards)\n # print(num_constraints)\n # print(wizards)\n # print(constraints)\n # node_set = set(wizards)\n \n\n\n def cost(sol,num_constraints,constraints... | [
"0.5255628",
"0.51518804",
"0.50815153",
"0.5040505",
"0.50042754",
"0.49726215",
"0.48932496",
"0.4887833",
"0.486175",
"0.48460725",
"0.4834822",
"0.4807731",
"0.48042133",
"0.47982746",
"0.47937402",
"0.4761865",
"0.4752347",
"0.4741787",
"0.47137696",
"0.46898678",
"0.466... | 0.7806484 | 0 |
Return the complement of a given set w.r.t. the set of mapped constraints. | def complement(self, aset):
return self.all_n.difference(aset) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def complement(self) -> 'RangeSet':\n return RangeSet(Range()) - self",
"def get_complement(seta):\n\n complement_set = set()\n\n for elem in seta:\n new_elem_tuple = (elem[0], float(D('1.0') - D(str(elem[1]))))\n complement_set.add(new_elem_tuple)\n\n return complement_set",
"def... | [
"0.6654452",
"0.6576406",
"0.63831866",
"0.62827057",
"0.58516365",
"0.5725783",
"0.56848663",
"0.5537928",
"0.5518822",
"0.54672354",
"0.5461112",
"0.5455276",
"0.5429003",
"0.5420453",
"0.5417606",
"0.54041857",
"0.5387203",
"0.5379959",
"0.53690493",
"0.5363554",
"0.535995... | 0.76911646 | 0 |
Add a given clause to the Map solver. | def add_clause(self, clause):
self.solver.add_clause(clause)
if self.dump is not None:
self.dump.write(" ".join(map(str, clause)) + " 0\n") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_clause(self, clause):\n self.abstract_clauses.append(clause)",
"def tell (self, clause):\n self.clauses.add(clause)",
"def add_clause(self, clause, soft=False):\n\n # first, map external literals to internal literals\n # introduce new variables if necessary\n cl = lis... | [
"0.7232271",
"0.68253154",
"0.64435804",
"0.64024657",
"0.6194685",
"0.589618",
"0.55395997",
"0.549732",
"0.5440252",
"0.5343942",
"0.5326947",
"0.5320732",
"0.52594405",
"0.5190137",
"0.5155156",
"0.50672406",
"0.49388227",
"0.48930344",
"0.48519868",
"0.48471695",
"0.48246... | 0.75571716 | 0 |
Block down from a given set. | def block_down(self, frompoint):
comp = self.complement(frompoint)
clause = [(i + 1) for i in comp]
self.add_clause(clause) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lift_down(self):\n\n # Can't reuse set_lift_pos due to bug above\n bottom_limit = self.get_lift_limit()\n self.send(self.cmd.SET_LIFT_SET, bottom_limit)",
"def down(self, i):\n pass",
"def bring_down(self):\n\n self.move(self.__min_step__)",
"def difference_update(s... | [
"0.591464",
"0.56556416",
"0.5639226",
"0.55734926",
"0.5487892",
"0.5392101",
"0.5380977",
"0.53320444",
"0.53086597",
"0.51837474",
"0.5178055",
"0.51443833",
"0.5077661",
"0.5057524",
"0.5054124",
"0.50266695",
"0.49867174",
"0.4948253",
"0.49368462",
"0.49265483",
"0.4926... | 0.56132346 | 3 |
Block up from a given set. | def block_up(self, frompoint):
clause = [-(i + 1) for i in frompoint]
self.add_clause(clause) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lift_up(self):\n\n # Can't reuse set_lift_pos due to bug above\n self.send(self.cmd.SET_LIFT_SET, self.cmd.SET_LIFT_SET[\"check\"][\"min\"])",
"def bottom_up(self, safe=False):\n if safe:\n assert not self.cycle()\n discard = set()\n queue = deque(self.leaves())\... | [
"0.5980374",
"0.5502509",
"0.54139936",
"0.5321069",
"0.519504",
"0.51196235",
"0.5108691",
"0.51076066",
"0.5094848",
"0.5038757",
"0.4986755",
"0.49557516",
"0.49549985",
"0.49490726",
"0.49489588",
"0.4919223",
"0.49130073",
"0.49110323",
"0.48967567",
"0.48951796",
"0.489... | 0.5053529 | 9 |
Settings for a step sequence | def __init__(self, step_time, step=None):
self.step_vector = step
self.step_time = step_time
self.ref_timer = None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def configure_step(self):\n pass",
"def configure_step(self):\n\n pass",
"def _setVals(self, step=0):\n self.step = step",
"def step(self, step=None):\n pass",
"def step(self, **kwargs):\n pass",
"def setValuesInStep(\n self, stepName: str, interactionProperty: s... | [
"0.69822484",
"0.69781184",
"0.6863541",
"0.65149957",
"0.65120053",
"0.6452456",
"0.6452456",
"0.64299405",
"0.63822",
"0.63449067",
"0.6200995",
"0.6192105",
"0.6170368",
"0.61481583",
"0.6145377",
"0.6126502",
"0.60600954",
"0.60401684",
"0.60263544",
"0.60128444",
"0.5988... | 0.0 | -1 |
Generate a step signal sequence | def out(self, t: any, dim=(None, None)) -> any:
u = np.zeros(shape=dim)
j = 0
for i in range(len(t)):
if t[i] % self.step_time == 0 and t[i] != 0 and j + 1 != len(self.step_vector):
j += 1
u[i, :] = self.step_vector[j]
return u | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_step(self):\n self.step_vals = np.cumsum(self.vals)",
"def sawtooth_factory(motor, start, stop, step_size):\n if stop < start:\n start, stop = stop, start\n\n num_pos = int((stop - start) // step_size)\n j = itertools.count()\n last_group = None\n\n def x_motion_per_step(det... | [
"0.65995204",
"0.63594466",
"0.6249169",
"0.61852676",
"0.6093541",
"0.6077157",
"0.6051659",
"0.6038631",
"0.60360247",
"0.59713215",
"0.59579283",
"0.59446794",
"0.5920504",
"0.59188426",
"0.59027636",
"0.589569",
"0.58939064",
"0.5887116",
"0.5883278",
"0.5864464",
"0.5859... | 0.0 | -1 |
Settings for a random step sequence | def __init__(self, step_time, step_interval=None, n_step=None, ss=None):
self.ss = ss
self.n_step = n_step
self.interval = step_interval
self.step_time = step_time | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def random_step(self):\n pos = [i for i in range(9) if self.grid[i] == 0]\n move = random.choice(pos)\n return self.step(move)",
"def random_step(self):\n pos = [i for i in range(9) if self.grid[i] == 0]\n move = random.choice(pos)\n return self.step(move)",
"def gener... | [
"0.6600735",
"0.6600735",
"0.6426488",
"0.63580966",
"0.63181347",
"0.6233752",
"0.6195526",
"0.61665463",
"0.6133349",
"0.6077554",
"0.60269153",
"0.5976023",
"0.595699",
"0.59379584",
"0.59343755",
"0.59263146",
"0.5924577",
"0.59218735",
"0.5883561",
"0.5867385",
"0.582699... | 0.0 | -1 |
Generate a random sequence | def out(self, t: any, dim=(None, None)) -> any:
lB = self.interval[0] # Lower Boundary
uB = self.interval[1] # Upper Boundary
# Initialize random step vector each sampling period using comprehensive list.
step_vector = [round(uniform(lB, uB), 1) for _ in range(self.n_step)]
u =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_random_sequence():\n\n seq = []\n [seq.append(np.random.choice(cs.DNA_BASES)) for _ in range(cs.LENGTH)]\n\n return seq",
"def generate_random(self: object) -> None:\n self.random.set(Sequence.generate(length=50))",
"def start_random_sequence(self) -> int:\n return random.ra... | [
"0.7697955",
"0.7600561",
"0.7551118",
"0.75248724",
"0.7125383",
"0.7092031",
"0.69861037",
"0.69277364",
"0.6886229",
"0.6868949",
"0.6833825",
"0.6803206",
"0.67367864",
"0.67331696",
"0.6730156",
"0.6715812",
"0.6699407",
"0.66590357",
"0.66281706",
"0.66149676",
"0.65995... | 0.0 | -1 |
Settings for a Gauss step sequence | def __init__(self, step_time, mu=None, sigma=None, n_step=None, ss=None):
self.ss = ss
self.n_step = n_step
self.mu = mu
self.sigma = sigma
self.step_time = step_time | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _setVals(self, step=0):\n self.step = step",
"def gauss_seidel(self):\n for i in range(1,self.size[0]-1):\n for j in range(1,self.size[1]-1):\n for k in range(1,self.size[2]-1):\n self.A[(i,j,k)] = ((1/6)*(self.A[(i+1,j,k)] + self.A[(i-1,j,k)] + self... | [
"0.6018765",
"0.57911164",
"0.5601025",
"0.55696154",
"0.5559649",
"0.55246335",
"0.55087584",
"0.55051965",
"0.5472542",
"0.5460051",
"0.5456584",
"0.5456021",
"0.5453433",
"0.54386294",
"0.5438098",
"0.541166",
"0.53872263",
"0.5383193",
"0.53732735",
"0.5372562",
"0.536454... | 0.5731074 | 2 |
Generate a Gauss sequence | def out(self, t: any, dim=(None, None)) -> any:
step_vector = np.abs([round(gauss(self.mu, self.sigma), 1) for _ in range(self.n_step)])
u = np.zeros(shape=dim)
j = 0
for i in range(len(t)): # Excluding the last point
if t[i] % self.step_time == 0 and t[i] != 0 and j+1 != ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gauss(x, x0, gamma):\n sigma = gamma / sqrt(2.0)\n \n A = 1/ (sigma * sqrt(2*pi))\n return (A * exp (-0.5 * (x-x0)**2/sigma**2))",
"def gauss(x, gamma):\n return 1 / np.sqrt(2*np.pi) / gamma * np.exp(-(x/gamma)**2 / 2)",
"def phi_gauss(self,x,i):\n s = 0.1\n return np.exp(-(x-s... | [
"0.6865911",
"0.6794377",
"0.6759611",
"0.6687887",
"0.6683238",
"0.6680485",
"0.66098064",
"0.65058136",
"0.6487504",
"0.6293768",
"0.62710446",
"0.62198",
"0.62048715",
"0.6181345",
"0.61764723",
"0.6148736",
"0.6144734",
"0.6125924",
"0.60800266",
"0.607092",
"0.606629",
... | 0.0 | -1 |
Settings for a random step sequence | def __init__(self, step_time, saw_time, step_interval=None, n_step=None, ss=None):
self.ss = ss
self.n_step = n_step
self.interval = step_interval
self.step_time = step_time
self.saw_time = saw_time | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def random_step(self):\n pos = [i for i in range(9) if self.grid[i] == 0]\n move = random.choice(pos)\n return self.step(move)",
"def random_step(self):\n pos = [i for i in range(9) if self.grid[i] == 0]\n move = random.choice(pos)\n return self.step(move)",
"def gener... | [
"0.6600735",
"0.6600735",
"0.6426488",
"0.63580966",
"0.63181347",
"0.6233752",
"0.6195526",
"0.61665463",
"0.6133349",
"0.6077554",
"0.60269153",
"0.5976023",
"0.595699",
"0.59379584",
"0.59343755",
"0.59263146",
"0.5924577",
"0.59218735",
"0.5883561",
"0.5867385",
"0.582699... | 0.0 | -1 |
Generate a random sequence | def out(self, t: any, dim=(None, None)) -> any:
lB = self.interval[0] # Lower Boundary
uB = self.interval[1] # Upper Boundary
# Initialize random step vector each sampling period using comprehensive list.
step_vector = [round(uniform(lB, uB), 1) for _ in range(self.n_step)]
ste... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_random_sequence():\n\n seq = []\n [seq.append(np.random.choice(cs.DNA_BASES)) for _ in range(cs.LENGTH)]\n\n return seq",
"def generate_random(self: object) -> None:\n self.random.set(Sequence.generate(length=50))",
"def start_random_sequence(self) -> int:\n return random.ra... | [
"0.7697955",
"0.7600561",
"0.7551118",
"0.75248724",
"0.7125383",
"0.7092031",
"0.69861037",
"0.69277364",
"0.6886229",
"0.6868949",
"0.6833825",
"0.6803206",
"0.67367864",
"0.67331696",
"0.6730156",
"0.6715812",
"0.6699407",
"0.66590357",
"0.66281706",
"0.66149676",
"0.65995... | 0.0 | -1 |
Settings for a random step sequence | def __init__(self, step_time, saw_time, delta_t, mu=None, sigma=None, n_step=None, ss=None):
self.ss = ss
self.n_step = n_step
self.mu = mu
self.sigma = sigma
self.step_time = step_time
self.saw_time = saw_time / delta_t | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def random_step(self):\n pos = [i for i in range(9) if self.grid[i] == 0]\n move = random.choice(pos)\n return self.step(move)",
"def random_step(self):\n pos = [i for i in range(9) if self.grid[i] == 0]\n move = random.choice(pos)\n return self.step(move)",
"def gener... | [
"0.6600735",
"0.6600735",
"0.6426488",
"0.63580966",
"0.63181347",
"0.6233752",
"0.6195526",
"0.61665463",
"0.6133349",
"0.6077554",
"0.60269153",
"0.5976023",
"0.595699",
"0.59379584",
"0.59343755",
"0.59263146",
"0.5924577",
"0.59218735",
"0.5883561",
"0.5867385",
"0.582699... | 0.0 | -1 |
Generate a random sequence | def out(self, t: any, dim=(None, None)) -> any:
# Initialize random step vector each sampling period using comprehensive list.
step_vector = np.abs([round(gauss(self.mu, self.sigma), 1) for _ in range(self.n_step)])
step_vector[0] = self.ss # keep the steady state value as first
u = np... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_random_sequence():\n\n seq = []\n [seq.append(np.random.choice(cs.DNA_BASES)) for _ in range(cs.LENGTH)]\n\n return seq",
"def generate_random(self: object) -> None:\n self.random.set(Sequence.generate(length=50))",
"def start_random_sequence(self) -> int:\n return random.ra... | [
"0.7697955",
"0.7600561",
"0.7551118",
"0.75248724",
"0.7125383",
"0.7092031",
"0.69861037",
"0.69277364",
"0.6886229",
"0.6868949",
"0.6833825",
"0.6803206",
"0.67367864",
"0.67331696",
"0.6730156",
"0.6715812",
"0.6699407",
"0.66590357",
"0.66281706",
"0.66149676",
"0.65995... | 0.0 | -1 |
Retry calling the decorated function using an exponential backoff. | def retry(ExceptionToCheck, tries=4, delay=3, backoff=2, logger=None):
def deco_retry(f):
@wraps(f)
def f_retry(*args, **kwargs):
mtries, mdelay = tries, delay
while mtries > 1:
try:
return f(*args, **kwargs)
except Excepti... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def retry(tries, delay=3, backoff=2, except_on=(Exception, )):\n\n tries = math.floor(tries)\n\n def decorator(f):\n def f_retry(*args, **kwargs):\n return function_retry(\n tries, delay, backoff, except_on, f, *args, **kwargs)\n return f_retry # true decorator -> dec... | [
"0.78645414",
"0.7704353",
"0.7669671",
"0.75984997",
"0.7546372",
"0.7504667",
"0.7501432",
"0.7480629",
"0.7442303",
"0.7435825",
"0.7430782",
"0.7420958",
"0.7402073",
"0.73976827",
"0.73973614",
"0.73926425",
"0.7340207",
"0.7321245",
"0.73147404",
"0.7286289",
"0.7165921... | 0.6971506 | 33 |
Repeat calls to func with specified arguments. | def repeatfunc(func, times=None, *args):
if times is None:
return starmap(func, repeat(args))
return starmap(func, repeat(args, times)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def repeat_func(func, *args, **kwargs):\n if kwargs:\n return starmap(lambda args, kwargs: func(*args, **kwargs),\n repeat((args, kwargs))\n )\n else:\n return starmap(func, repeat(args))",
"def repeatfunc(cls, func, times=None, *args):\n if ... | [
"0.8054019",
"0.79899377",
"0.77091944",
"0.71153104",
"0.6889798",
"0.68692523",
"0.6769637",
"0.6652528",
"0.65822107",
"0.6471077",
"0.63860214",
"0.6347482",
"0.6298246",
"0.6255725",
"0.6221301",
"0.60740733",
"0.60466105",
"0.6042478",
"0.5945209",
"0.5926972",
"0.59132... | 0.80109245 | 2 |
Sends a POST request containing `data` to url. `auth` should be a tuple containing (username, password). | def post(url, data, auth=None, retries=10):
if not url.startswith('http://'):
url = 'http://' + url
request = urllib2.Request(url)
if auth:
request.add_header('Authorization', 'Basic %s' % b64encode('%s:%s' % auth))
params = urllib.urlencode(data)
response = urllib2.urlopen(request... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_post_request(self, url, data):\n auth = (self.AUTH_ID, self.AUTH_TOKEN)\n headers = {'content-type': 'application/json'}\n return requests.post(url, data=data, auth=auth, headers=headers)",
"def _post(self, url, data=None):\n if data is not None:\n data = urllib.ur... | [
"0.6842728",
"0.6454791",
"0.63290054",
"0.6314501",
"0.62772477",
"0.6190568",
"0.61195064",
"0.6063945",
"0.60278213",
"0.5984815",
"0.5982844",
"0.5974754",
"0.59532285",
"0.5944688",
"0.5935827",
"0.59314847",
"0.5888572",
"0.58549273",
"0.5854561",
"0.58424336",
"0.57753... | 0.7495922 | 0 |
Sends a POST request every second to the monitoring server indicating that the process is still running. | def post_heartbeat(host, name, auth=None):
data = {'name': name, 'status': 'ok'}
try:
response = post('{host}/monitoring/heartbeat'.format(host=host), data, auth)
except urllib2.URLError:
print("Failed to send heartbeat.", file=sys.stderr)
else:
if response.strip() != 'ok':
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def periodic_timer(self):\n while self.running:\n self.sendStatusQuery()\n time.sleep(REPORT_INTERVAL)",
"def keep_alive():\r\n app = Flask(\"\")\r\n @app.route(\"/\")\r\n def home():\r\n return \"Your bot is now alive!\"\r\n\r\n def run():\r\n app.run(host=\"0.... | [
"0.6335293",
"0.6263304",
"0.6176471",
"0.61680967",
"0.6098956",
"0.6098956",
"0.6098956",
"0.6034498",
"0.5934776",
"0.5786157",
"0.5784672",
"0.5767236",
"0.57396203",
"0.57379746",
"0.5700311",
"0.5679326",
"0.5662139",
"0.56477326",
"0.5647195",
"0.56367123",
"0.5632368"... | 0.57290554 | 14 |
Sets up the root logger to send log messages to the monitoring server. | def set_up_root_logger(host, name, auth=None):
root_logger = logging.getLogger()
root_logger.addHandler(HTTPHandler(name, host, auth))
root_logger.setLevel(logging.DEBUG) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _begin_logging(self):\n logconf.set_up_root_logger(self.opts.logfile)",
"def setup_logger():\n root = logging.getLogger()\n root.setLevel(LOGGING_LEVEL)\n formatter = logging.Formatter('%(asctime)s - %(message)s')\n ch = logging.StreamHandler(sys.stdout)\n ch.setLevel(LOGGING_LEVEL)\n ... | [
"0.75551504",
"0.72284514",
"0.7111983",
"0.709306",
"0.70296115",
"0.7023245",
"0.6968554",
"0.6962373",
"0.69305164",
"0.69274163",
"0.69044024",
"0.68876123",
"0.68429",
"0.68380857",
"0.6832595",
"0.68176967",
"0.67931634",
"0.6735474",
"0.67295617",
"0.672686",
"0.671451... | 0.66258967 | 33 |
Returns all users from test_data in a json format that's compatible with jqGrid. | def users_json(self, rows=None, sidx=None, _search=None, searchField=None,
searchOper=None, searchString=None, page=None, sord=None, nd=None): # 1 line # 2 lines
t1 = time.clock()
header = ["value", "flags", "source", "evidence_type", "creation_time", "time", "useby", "owner", "comment"] # 3 lin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getUsersData(self):\n users_data = self.get_api_results(\n \"/api/user/list?fields=id,username,name,phone,email,url,about,role,joined,lastactive,avatar,company,position,location&api_key={0}&format=json\")\n for user in users_data:\n if user[\"name\"] not in self.users:\n ... | [
"0.7094494",
"0.69843435",
"0.69711816",
"0.6943046",
"0.6916682",
"0.68569654",
"0.6854189",
"0.68422705",
"0.68375057",
"0.6822268",
"0.6778825",
"0.67571205",
"0.67559546",
"0.67233765",
"0.67228365",
"0.6666873",
"0.66579914",
"0.664822",
"0.6617304",
"0.6607768",
"0.6572... | 0.6233058 | 57 |
Main driver, parse the feed and output the JSON list of sources | def main():
handler = PlanetSourceHandler()
parser = xml.sax.make_parser()
parser.setFeature(xml.sax.handler.feature_namespaces, 1)
parser.setContentHandler(handler)
parser.parse(sys.stdin)
print simplejson.JSONEncoder(indent=True).encode(handler.sources) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_sources_all():\n response = requests.get(SOURCE_URL)\n json = response.json()\n for source in json['sources']:\n print(u\"{0}: <{1}> {2}\".format(\"News Code\", source['id'], source['name']))",
"def listsources():\n\tmain_url = \" https://newsapi.org/v2/sources?apiKey=5f81b593f35d42a8980... | [
"0.67541414",
"0.6721273",
"0.66271365",
"0.639896",
"0.6346963",
"0.6091228",
"0.60303104",
"0.5948088",
"0.594113",
"0.58209115",
"0.5778367",
"0.5776951",
"0.5746344",
"0.5687336",
"0.56842184",
"0.56672835",
"0.5662965",
"0.5631256",
"0.5610598",
"0.5607907",
"0.5564241",... | 0.6417497 | 3 |
Collect characters while within a source record | def characters(self, content):
if self.in_source: self.chars += content | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def characters(self, data):\n pass",
"def cleanup_raw_data(buf):\n raw = str(buf, encoding='iso-8859-1').strip()\n records = raw.splitlines()\n return records",
"def extractCharacters(self):\n \n length, high=self.getSize() ##geting size of LineFrame object - high and length\n ... | [
"0.63615435",
"0.5877636",
"0.564212",
"0.5450437",
"0.53484285",
"0.5325434",
"0.5275199",
"0.52268296",
"0.51683784",
"0.51639074",
"0.5162519",
"0.5138824",
"0.5114489",
"0.51047087",
"0.50685495",
"0.50610465",
"0.5054689",
"0.5049034",
"0.50311816",
"0.50303805",
"0.5023... | 0.63518363 | 1 |
Restrict a value between a min and max. | def clamp(value, min_value, max_value):
return max(min_value, min(value, max_value)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _limit(value, min_value, max_value):\n\n if value < min_value:\n return min_value\n if value > max_value:\n return max_value\n return value",
"def range_limit(val, minv, maxv):\n\tif (val < minv):\n\t\tval = minv\n\telif (val > maxv):\n\t\tval = maxv\n\treturn val",
"def clamp(num, m... | [
"0.8242318",
"0.82057476",
"0.77939916",
"0.7717922",
"0.7658431",
"0.76336455",
"0.75694007",
"0.7560079",
"0.75334716",
"0.7511125",
"0.7503323",
"0.7490816",
"0.7443682",
"0.7372782",
"0.73508364",
"0.7305762",
"0.7283909",
"0.7263027",
"0.72585297",
"0.72390145",
"0.71898... | 0.78470093 | 2 |
Create connection line constraint between item's handle and the port. | def constraint(self, item, handle, glue_item):
start = MatrixProjection(self.start, glue_item.matrix_i2c)
end = MatrixProjection(self.end, glue_item.matrix_i2c)
point = MatrixProjection(handle.pos, item.matrix_i2c)
cx = EqualsConstraint(point.x, start.x)
cy = BetweenConstraint(p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_fixed_distance_to_line_constraint():\n return FixedDistanceToLineConstraint()",
"def test_connect(self):\n line, head = self._get_line()\n self.tool.connect(line, head, (120, 50))\n cinfo = self.canvas.get_connection(head)\n self.assertTrue(cinfo is not None)\n ... | [
"0.5818294",
"0.5687332",
"0.5500008",
"0.52568966",
"0.5220818",
"0.5207515",
"0.52057487",
"0.505119",
"0.5035412",
"0.5020907",
"0.49766943",
"0.4966835",
"0.49285123",
"0.492827",
"0.492827",
"0.49215764",
"0.49072984",
"0.49011382",
"0.4897819",
"0.48977235",
"0.4889161"... | 0.6178213 | 0 |
Set lifeline's lifetime length. | def _set_length(self, length):
self.bottom.pos.y = self.top.pos.y + length | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def change_tail_length(self, value):\n self.layer.tail_length = value",
"def setLength(self, new_length):\n\n self.length = new_length",
"def set_lifetime(self, lifetime):\n self.__lifetime = lifetime",
"def length(self, length):\n\n self._length = length",
"def set_last_segment... | [
"0.6628996",
"0.6548008",
"0.6527316",
"0.6292543",
"0.61618847",
"0.60966504",
"0.605155",
"0.59509474",
"0.59412754",
"0.59316313",
"0.59247273",
"0.5702406",
"0.56750685",
"0.5643853",
"0.5622231",
"0.55569696",
"0.55228",
"0.5480967",
"0.54594636",
"0.5426649",
"0.5416591... | 0.5764367 | 11 |
Draw lifeline. We always draw the lifeline's head. We only draw the lifeline's lifetime when the lifetime is visible. | def draw_lifeline(self, box, context, bounding_box):
cr = context.cairo
cr.rectangle(0, 0, self.width, self.height)
stroke(context)
if (
context.hovered
or context.focused
or context.dropzone
or self._lifetime.visible
):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_line():\n\n # Small Size Line\n glLineWidth(0.1)\n glColor3f(0.5, 1.0, 0.9)\n wid = 0\n while wid <= width:\n length = 0\n while length <= height:\n glBegin(GL_LINES)\n glVertex3f(0.0, length, 0.0)\n glVertex3f(wid, length, 0)\n glEn... | [
"0.6687818",
"0.6577214",
"0.65438086",
"0.64897895",
"0.63934726",
"0.63052434",
"0.6266094",
"0.6217813",
"0.61868715",
"0.61665386",
"0.6159552",
"0.6142421",
"0.6132305",
"0.60893184",
"0.60786086",
"0.60565233",
"0.60377926",
"0.60295296",
"0.6012153",
"0.59879833",
"0.5... | 0.6821605 | 0 |
Find distance to lifeline item. We calculate the distance to the lifeline's head, and then we calculate the lifetime. We return the minimum. | def point(self, x, y):
d1 = super().point(x, y)
top = self._lifetime.top
bottom = self._lifetime.bottom
d2 = distance_line_point(top.pos, bottom.pos, (x, y))[0]
return min(d1, d2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _calc_min_distance(self, walker):\n\n cell_lengths, cell_angles = box_vectors_to_lengths_angles(walker.state['box_vectors'])\n\n t2 = time.time()\n # make a traj out of it so we can calculate distances through\n # the periodic boundary conditions\n walker_traj = mdj.Trajector... | [
"0.64288473",
"0.6343981",
"0.629061",
"0.6034535",
"0.602224",
"0.6003383",
"0.5981095",
"0.593175",
"0.5914017",
"0.5857995",
"0.5832663",
"0.5826591",
"0.5805714",
"0.5800851",
"0.57828003",
"0.5737947",
"0.5734673",
"0.56870914",
"0.56647193",
"0.56186366",
"0.55836093",
... | 0.5248442 | 77 |
Load data from CSV files and return them as numpy arrays The use_labels parameter indicates whether one should read the first column (containing class labels). If false, return all 0s. | def load_data(filename, use_labels=True):
# load column 1 to 8 (ignore last one)
data = np.loadtxt(open( filename), delimiter=',',
usecols=range(1, 9), skiprows=1)
if use_labels:
labels = np.loadtxt(open( filename), delimiter=',',
usecols=[0], skipro... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load(self, path=\"../data_set/final-train-dataset.csv\", shuffle=False,\n onlyLabelToUse=None, useOnlyBestIndicators=False, binary=False):\n data = []\n labels = []\n\n with open(path) as csvfile:\n reader = csv.reader(csvfile, delimiter=',')\n skip = True... | [
"0.706477",
"0.68677425",
"0.6859685",
"0.6782772",
"0.67529887",
"0.6746655",
"0.67292655",
"0.66810787",
"0.66234106",
"0.65906125",
"0.6552061",
"0.65014195",
"0.64909846",
"0.646308",
"0.6431648",
"0.6414293",
"0.6412473",
"0.6407337",
"0.63972247",
"0.6389596",
"0.635698... | 0.7765821 | 0 |
Given a vector of predictions, save results in CSV format. | def save_results(predictions, filename):
with open(filename, 'w') as f:
f.write("id,ACTION\n")
for i, pred in enumerate(predictions):
f.write("%d,%f\n" % (i + 1, pred)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_results(file_path, predictions):\n with open(file_path, \"w\") as csv_file:\n writer = csv.writer(csv_file, delimiter=\",\")\n writer.writerow([\"Id\", \"Bound\"])\n for id, bound in enumerate(predictions):\n writer.writerow([id, bound])",
"def write_predictions(y_pre... | [
"0.7925112",
"0.75827205",
"0.75710183",
"0.75710183",
"0.7489846",
"0.74501824",
"0.73730016",
"0.7322112",
"0.70706344",
"0.6982982",
"0.69460446",
"0.6911982",
"0.6833308",
"0.6815102",
"0.67553407",
"0.6745336",
"0.6745336",
"0.6745336",
"0.6745336",
"0.6745336",
"0.67453... | 0.78001994 | 1 |
initialization routine for Chiplot, sets class variables | def __init__(self, xdata = list(), ydata = list(), filename = None, projection = ''):
dlog('in chiplot initialization')
self.xdata = xdata
self.ydata = ydata
self.xmax = None
self.ymax = None
self.xmin = None
self.ymin = None
self.increment = 0
self.filename = filename
self.smv = ''
self.points = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize(cls):",
"def initialise(self):",
"def _init(self):\n pass",
"def init(self) -> None:",
"def __init_accessors (self):\n self.colors = ay.utils.Colors\n self.layout = Layout(self.seed)\n self.shapes = Shapes",
"def init(self):",
"def init(self):",
"def init(se... | [
"0.705675",
"0.69822073",
"0.69724643",
"0.69628584",
"0.6953671",
"0.6945866",
"0.6945866",
"0.69433916",
"0.69258225",
"0.69258225",
"0.69258225",
"0.69258225",
"0.69258225",
"0.69258225",
"0.69258225",
"0.69258225",
"0.6895559",
"0.68572617",
"0.6853776",
"0.6848816",
"0.6... | 0.64347374 | 77 |
This functions check's whether a text contains contractions or not. In case a contraction is found, the corrected value from the dictionary is returned. | def replace_contractions(self, text, lower=False):
# replace words with contraction according to the contraction_dict
if lower:
contraction_dict = self.contraction_dict_lower
else:
contraction_dict = self.contraction_dict
if text.strip() in contraction_dict.keys... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def replace_contractions(text):\r\n return contractions.fix(text)",
"def _replace_contractions(text):\n return contractions.fix(text)",
"def replace_contractions(text):\n return contractions.fix(text)",
"def replace_contractions(text):\n return contractions.fix(text)",
"def expand_contractions(... | [
"0.68868476",
"0.6857023",
"0.68527967",
"0.68527967",
"0.66898316",
"0.6347374",
"0.5993866",
"0.5918088",
"0.5882449",
"0.5777897",
"0.57183623",
"0.5687876",
"0.5641633",
"0.5621483",
"0.5601896",
"0.5554486",
"0.5458176",
"0.5382929",
"0.5322964",
"0.5307496",
"0.5253449"... | 0.67498755 | 4 |
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