def mean_ad(candles: np.ndarray,
period: int = 5,
source_type: str = "hl2",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
Mean Absolute Deviation
:param candles: np.ndarray
:param period: int - default: 5
:param source_type: str - default: "hl2"
:param sequential: bool - default=False
:return: float | np.ndarray
"""
if len(candles.shape) == 1:
source = candles
else:
candles = slice_candles(candles, sequential)
source = get_candle_source(candles, source_type=source_type)
swv = sliding_window_view(source, window_shape=period)
abs_diff = np.absolute(source - same_length(source, np.mean(swv, -1)))
smv_abs_diff = sliding_window_view(abs_diff, window_shape=period)
mean_abs_deviation = np.nanmean(smv_abs_diff, -1)
res = same_length(source, mean_abs_deviation)
return res if sequential else res[-1]
def safezonestop(candles: np.ndarray, period: int = 22, mult: float = 2.5, max_lookback: int = 3,
direction: str = "long", sequential: bool = False) -> Union[float, np.ndarray]:
"""
Safezone Stops
:param candles: np.ndarray
:param period: int - default=22
:param mult: float - default=2.5
:param max_lookback: int - default=3
:param direction: str - default=long
:param sequential: bool - default=False
:return: float | np.ndarray
"""
warmup_candles_num = get_config('env.data.warmup_candles_num', 240)
if not sequential and len(candles) > warmup_candles_num:
candles = candles[-warmup_candles_num:]
high = candles[:, 3]
low = candles[:, 4]
last_high = np_shift(high, 1, fill_value=np.nan)
last_low = np_shift(low, 1, fill_value=np.nan)
if direction == "long":
res = last_low - mult * talib.MINUS_DM(high, low, timeperiod=period)
swv = sliding_window_view(res, window_shape=max_lookback)
res = np.max(swv, axis=-1)
else:
res = last_high + mult * talib.PLUS_DM(high, low, timeperiod=period)
swv = sliding_window_view(res, window_shape=max_lookback)
res = np.min(swv, axis=-1)
return np.concatenate((np.full((candles.shape[0] - res.shape[0]), np.nan), res), axis=0) if sequential else res[-1]
def test_subok(self):
class MyArray(np.ndarray):
pass
arr = np.arange(5).view(MyArray)
assert_(
not isinstance(sliding_window_view(arr, 2, subok=False), MyArray))
assert_(isinstance(sliding_window_view(arr, 2, subok=True), MyArray))
# Default behavior
assert_(not isinstance(sliding_window_view(arr, 2), MyArray))
def test_writeable(self):
arr = np.arange(5)
view = sliding_window_view(arr, 2, writeable=False)
assert_(not view.flags.writeable)
with pytest.raises(ValueError,
match='assignment destination is read-only'):
view[0, 0] = 3
view = sliding_window_view(arr, 2, writeable=True)
assert_(view.flags.writeable)
view[0, 1] = 3
assert_array_equal(arr, np.array([0, 3, 2, 3, 4]))
def er(candles: np.ndarray, period: int = 5, source_type: str = "close", sequential: bool = False) -> Union[
float, np.ndarray]:
"""
ER - The Kaufman Efficiency indicator
:param candles: np.ndarray
:param period: int - default: 5
:param source_type: str - default: "close"
:param sequential: bool - default=False
:return: float | np.ndarray
"""
warmup_candles_num = get_config('env.data.warmup_candles_num', 240)
if not sequential and len(candles) > warmup_candles_num:
candles = candles[-warmup_candles_num:]
source = get_candle_source(candles, source_type=source_type)
change = np.abs(np.diff(source, period))
abs_dif = np.abs(np.diff(source))
swv = sliding_window_view(abs_dif, window_shape=period)
volatility = swv.sum()
res = change / volatility
return np.concatenate((np.full((candles.shape[0] - res.shape[0]), np.nan), res), axis=0) if sequential else res[-1]
def fwma(candles: np.ndarray,
period: int = 5,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
Fibonacci's Weighted Moving Average (FWMA)
:param candles: np.ndarray
:param period: int - default: 5
:param source_type: str - default: "close"
:param sequential: bool - default=False
:return: float | np.ndarray
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
source = get_candle_source(candles, source_type=source_type)
fibs = fibonacci(n=period, weighted=True)
swv = sliding_window_view(source, window_shape=period)
res = np.average(swv, weights=fibs, axis=-1)
return np.concatenate(
(np.full((candles.shape[0] - res.shape[0]), np.nan),
res), axis=0) if sequential else res[-1]
def forecasting_example():
name = "C:\\Users\\Tony\\OneDrive - University of East Anglia\\Research\\Alex " \
"Mcgregor Grant\\randomNoise.csv"
y = pd.read_csv(name, index_col=0, squeeze=True, dtype={1: np.float})
forecast_horizon = np.arange(1, 2)
forecaster = NaiveForecaster(strategy="last")
forecaster.fit(y)
y_pred = forecaster.predict(forecast_horizon)
print("Next predicted value = ",y_pred)
# https://github.com/alan-turing-institute/sktime/blob/main/examples/01_forecasting.ipynb
#Reduce to a regression problem through windowing.
##Transform forecasting into regression
np_y = y.to_numpy()
v = sliding_window_view(y, 100)
print("Window shape =",v.shape)
v_3d = np.expand_dims(v, axis=1)
print("Window shape =",v.shape)
print(v_3d.shape)
z = v[:,2]
print(z.shape)
regressor = CNNRegressor()
classifier = CNNClassifier()
regressor.fit(v_3d,z)
p = regressor.predict(v_3d)
#print(p)
d = np.array([0.0])
c = np.digitize(z,d)
classifier = RandomIntervalSpectralForest()
classifier.fit(v_3d,c)
cls = classifier.predict(v_3d)
print(cls)
def sinwma(candles: np.ndarray,
period: int = 14,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
Sine Weighted Moving Average (SINWMA)
:param candles: np.ndarray
:param period: int - default: 14
:param source_type: str - default: "close"
:param sequential: bool - default=False
:return: float | np.ndarray
"""
warmup_candles_num = get_config('env.data.warmup_candles_num', 240)
if not sequential and len(candles) > warmup_candles_num:
candles = candles[-warmup_candles_num:]
source = get_candle_source(candles, source_type=source_type)
sines = np.array(
[np.sin((i + 1) * np.pi / (period + 1)) for i in range(0, period)])
w = sines / sines.sum()
swv = sliding_window_view(source, window_shape=period)
res = np.average(swv, weights=w, axis=-1)
return np.concatenate(
(np.full((candles.shape[0] - res.shape[0]), np.nan),
res), axis=0) if sequential else res[-1]
def test_2d_with_axis(self):
i, j = np.ogrid[:3, :4]
arr = 10 * i + j
arr_view = sliding_window_view(arr, 3, 0)
expected = np.array([[[0, 10, 20], [1, 11, 21], [2, 12, 22],
[3, 13, 23]]])
assert_array_equal(arr_view, expected)
def swma(candles: np.ndarray,
period: int = 5,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
Symmetric Weighted Moving Average (SWMA)
:param candles: np.ndarray
:param period: int - default: 5
:param source_type: str - default: "close"
:param sequential: bool - default: False
:return: float | np.ndarray
"""
# Accept normal array too.
if len(candles.shape) == 1:
source = candles
else:
candles = slice_candles(candles, sequential)
source = get_candle_source(candles, source_type=source_type)
triangle = symmetric_triangle(period)
swv = sliding_window_view(source, window_shape=period)
res = np.average(swv, weights=triangle, axis=-1)
return same_length(candles, res) if sequential else res[-1]
def sinwma(candles: np.ndarray,
period: int = 14,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
Sine Weighted Moving Average (SINWMA)
:param candles: np.ndarray
:param period: int - default: 14
:param source_type: str - default: "close"
:param sequential: bool - default: False
:return: float | np.ndarray
"""
# Accept normal array too.
if len(candles.shape) == 1:
source = candles
else:
candles = slice_candles(candles, sequential)
source = get_candle_source(candles, source_type=source_type)
sines = np.array(
[np.sin((i + 1) * np.pi / (period + 1)) for i in range(period)])
w = sines / sines.sum()
swv = sliding_window_view(source, window_shape=period)
res = np.average(swv, weights=w, axis=-1)
return same_length(candles, res) if sequential else res[-1]
def er(candles: np.ndarray,
period: int = 5,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
ER - The Kaufman Efficiency indicator
:param candles: np.ndarray
:param period: int - default: 5
:param source_type: str - default: "close"
:param sequential: bool - default: False
:return: float | np.ndarray
"""
candles = slice_candles(candles, sequential)
source = get_candle_source(candles, source_type=source_type)
change = np.abs(np.diff(source, period))
abs_dif = np.abs(np.diff(source))
swv = sliding_window_view(abs_dif, window_shape=period)
volatility = swv.sum()
res = change / volatility
return same_length(candles, res) if sequential else res[-1]
def enhance(x, algo, image):
image = np.pad(image, 2 * x)
for _ in range(x):
window = sliding_window_view(image, (3, 3))
image = algo[(window * BIN_POWERS).sum((3, 2))]
return image
def test_1d(self):
arr = np.arange(5)
arr_view = sliding_window_view(arr, 2)
expected = np.array([[0, 1],
[1, 2],
[2, 3],
[3, 4]])
assert_array_equal(arr_view, expected)
def test_2d_repeated_axis(self):
i, j = np.ogrid[:3, :4]
arr = 10 * i + j
arr_view = sliding_window_view(arr, (2, 3), (1, 1))
expected = np.array([[[[0, 1, 2], [1, 2, 3]]],
[[[10, 11, 12], [11, 12, 13]]],
[[[20, 21, 22], [21, 22, 23]]]])
assert_array_equal(arr_view, expected)
def _local_top_values(values):
n = 3
rolling_mean = sliding_window_view(values, window_shape=n).mean(axis=1)
rolling_mean = np.pad(rolling_mean, (n, n),
'constant',
constant_values=np.nan)
return 2 * values - (rolling_mean[:-n - 1] + rolling_mean[n + 1:]
) # -second derivative
def _filter_angles_inner(sv, sv_prev, sv_next, angles, angles_prev,
angles_next, shift, shift_prev, shift_next):
if len(sv) == 0 or np.isnan(sv).all():
return np.zeros_like(sv)
if len(sv_prev) == 0 or np.isnan(sv_prev).all():
sv_prev = sv
angles_prev = angles
shift_prev = shift
if len(sv_next) == 0 or np.isnan(sv_next).all():
sv_next = sv
angles_next = angles
shift_next = shift
prev_first_index = int(shift - shift_prev)
next_first_index = int(shift - shift_next)
median_radius = 2
min_sv_radius = 2
minima_prev = local_minima(sv_prev, min_sv_radius)
minima_center = local_minima(sv, min_sv_radius)
minima_next = local_minima(sv_next, min_sv_radius)
angles_prev_masked = angles_prev.copy()
angles_prev_masked[minima_prev] = np.nan
angles_masked = angles.copy()
angles_masked[minima_center] = np.nan
angles_next_masked = angles_next.copy()
angles_next_masked[minima_next] = np.nan
angles_masked_rolling = sliding_window_view(
angles_masked, window_shape=median_radius * 2 + 1)
angles_prev_masked_rolling = sliding_window_view(
shift_arr(angles_prev_masked, -prev_first_index),
window_shape=median_radius * 2 + 1)
angles_next_masked_rolling = sliding_window_view(
shift_arr(angles_next_masked, -next_first_index),
window_shape=median_radius * 2 + 1)
stacked_rolling = np.hstack(
(angles_masked_rolling, angles_prev_masked_rolling,
angles_next_masked_rolling))
median = np.nanmedian(stacked_rolling, axis=1)
return np.pad(median, (median_radius, median_radius),
'constant',
constant_values=np.nan)
def test_2d(self):
i, j = np.ogrid[:3, :4]
arr = 10 * i + j
shape = (2, 2)
arr_view = sliding_window_view(arr, shape)
expected = np.array([[[[0, 1], [10, 11]], [[1, 2], [11, 12]],
[[2, 3], [12, 13]]],
[[[10, 11], [20, 21]], [[11, 12], [21, 22]],
[[12, 13], [22, 23]]]])
assert_array_equal(arr_view, expected)
def test_2d_without_axis(self):
i, j = np.ogrid[:4, :4]
arr = 10 * i + j
shape = (2, 3)
arr_view = sliding_window_view(arr, shape)
expected = np.array([
[[[0, 1, 2], [10, 11, 12]], [[1, 2, 3], [11, 12, 13]]],
[[[10, 11, 12], [20, 21, 22]], [[11, 12, 13], [21, 22, 23]]],
[[[20, 21, 22], [30, 31, 32]], [[21, 22, 23], [31, 32, 33]]],
])
assert_array_equal(arr_view, expected)
def rollingCorrelation(self):
""
#to do, takes the longest by far
#use numba to calculate this
# print(Ys)
# print(Ys.shape)
#Ys = np.concatenate([self.Y.reshape(1,self.Ys.shape[1]),self.Ys],axis=0)
slides = sliding_window_view(self.Ys,
self.correlationWindowSize,
axis=1)
return slidingPearson(slides)
def main():
X = np.loadtxt("2021/01/input.txt", dtype=int)
# Calculate the array of differences
X_diff = np.diff(X)
# Find the number of positives
res_1 = (X_diff > 0).sum()
print(f"Result of part 1: {res_1}")
# For Part 2, we create a sliding window of size 3 and take the sum along the second
# axis (columns)
Y = sliding_window_view(X, 3).sum(axis=1)
# Calculate the array of differences
Y_diff = np.diff(Y)
# Find the number of positives
res_2 = (Y_diff > 0).sum()
print(f"Result of part 2: {res_2}")
def test_errors(self):
i, j = np.ogrid[:4, :4]
arr = 10 * i + j
with pytest.raises(ValueError, match='cannot contain negative values'):
sliding_window_view(arr, (-1, 3))
with pytest.raises(
ValueError,
match='must provide window_shape for all dimensions of `x`'):
sliding_window_view(arr, (1, ))
with pytest.raises(
ValueError,
match='Must provide matching length window_shape and axis'):
sliding_window_view(arr, (1, 3, 4), axis=(0, 1))
with pytest.raises(
ValueError,
match='window shape cannot be larger than input array'):
sliding_window_view(arr, (5, 5))
def compute(self, img):
'''
Class function for the Local Binary Pattern descriptor. Takes
an image (2-dim array) and computes the local binary pattern
for all the pixels, creates the LBP image and returns the
histogram of that image.
Parameters:
> `img`: ndarray. The image to obtain the descriptor from.
> returns: ndarray float32. The LBP histogram for img.
'''
# first take the shape
h, w = img.shape
# Calculate all the possible windows for this image with the shape
# specified by the radius.
windows = sliding_window_view(img,
window_shape=(self.radius, self.radius))
# Reshape the windows matrix to be a flat array
# of (radius, radius) little matrices to obtain LBP
reshaped_windows = np.reshape(
windows,
newshape=(windows.shape[0] * windows.shape[1], windows.shape[2],
windows.shape[3]))
# Calculate the corresponding value for every
# window and return the resulting vector
values = [
self.binary_neighbours(window) for window in reshaped_windows
]
# Reshape that vector to be a new "LBP IMG"
img_values = np.reshape(values,
newshape=(windows.shape[0], windows.shape[1]))
# Since windows didn't take the borders into account,
# we pad the array with wrap mode, to get those borders
# back in a thoughtful way
lbp_img = np.pad(img_values, ((1, 1), (1, 1)), 'wrap')
# Calculate the histogram for the lbp img and return it
return (np.histogram(lbp_img, bins=256,
density=False)[0]).astype('float32')
def get_spike_counts(self, bin_size=50, subset=None, rolling_window=None):
"""Get matrix of spike counts.
Parameters
----------
bin_size : int | None
Size [ms] of the bins over which to count spikes.
If None, will simply return total counts.
rolling_window : int, optional
Average spike counts in a rolling window.
Returns
-------
pd.DataFrame
"""
if not isinstance(subset, type(None)):
ids = utils.make_iterable(subset)
else:
ids = self._ids
end_time = neuron.h.t
if not end_time:
raise ValueError('Looks like simulation has not yet been run.')
if not bin_size:
bin_size = end_time
bins = np.arange(0, end_time + bin_size, bin_size)
counts = np.zeros((len(ids), len(bins) - 1))
# Collect spike counts
for i, id in enumerate(ids):
pp = self.idx[id]
timings = list(pp.spk_timings)
if timings:
hist, _ = np.histogram(timings, bins)
counts[i, :] = hist
counts = pd.DataFrame(counts, index=ids, columns=bins[:-1])
if rolling_window:
avg = sliding_window_view(counts, rolling_window,
axis=1).mean(axis=2)
counts.iloc[:, :-(rolling_window - 1)] = avg
return counts
def get_sliding_window_partition(data, labels, window_size, step=1):
"""Splits data into several windows. Deprecated (waste of memory)
Parameters
----------
data : np.array
EEG data with shape (trials, channels, time)
labels : np.array
1d array of integer labels corresponding to left/right
window_size : int
Size in samples (not time) of the windows the data will be split into
If window_size corresponds to the number of recorded samples per trial, the function returns the input data and labels unchaged
step : int, default 1
Step size of the sliding window
Returns
-------
windowed_data : np.array
Array of shape (windows, channels, time)
windowed_labels : np.array
1d array of labels corresponding to each window
"""
raise DeprecationWarning('This function uses way too much memory')
assert len(data.shape) == 3
if data.shape[2] == window_size:
return data, labels
windowed_data = np.empty((0, data.shape[1], window_size))
windowed_labels = np.empty((0, ))
for i in range(data.shape[0]):
trial = data[i]
# print(trial)
windows = sliding_window_view(trial, (data.shape[1], window_size))[0]
# print('windows')
# print(windows)
windowed_data = np.append(windowed_data, windows, axis=0)
new_labels = np.array([labels[i] for _ in range(windows.shape[0])])
# print('labels')
# print(new_labels)
windowed_labels = np.append(windowed_labels, new_labels, axis=0)
return windowed_data[::step], windowed_labels[::step]
def kurtosis(candles: np.ndarray, period: int = 5, source_type: str = "hl2", sequential: bool = False) -> Union[
float, np.ndarray]:
"""
Skewness
:param candles: np.ndarray
:param period: int - default: 5
:param source_type: str - default: "hl2"
:param sequential: bool - default: False
:return: float | np.ndarray
"""
candles = slice_candles(candles, sequential)
source = get_candle_source(candles, source_type=source_type)
swv = sliding_window_view(source, window_shape=period)
kurtosis = stats.kurtosis(swv, axis=-1)
res = same_length(source, kurtosis)
return res if sequential else res[-1]
def neighborhood(X, idcs, steps, axes, write=False):
''' Pure `numpy` approach using `lib.stride_tricks.sliding_window_view`;
Create a sliding window view with the desired shape, returning
the subset of views that are aligned with the neighborhood.
'''
# Get sliding window view
window_shape = tuple(2 * s + 1 for s in steps)
S = sliding_window_view(X, window_shape, axis=axes, writeable=write)
# Initialize all indices with empty slice
indices = [slice(None)] * X.ndim
# Update specified axes with adjusted index
for axis, idx, step in zip(axes, idcs, steps):
indices[axis] = idx - step
# Get views over the specified neighborhood
views = S[tuple(indices)]
return views
def getNextState(state):
"""
@param state: 2d array of 0 and 1 where 0 represents a dead cell and 1 represents a live cell
@return: 2d array of 0 and 1 computed from the input state
"""
state = np.array(state)
h,w = state.shape
padding = np.hstack((np.zeros((h,1)),state,np.zeros((h,1))))
padding = np.vstack((np.zeros((1,w+2)),padding,np.zeros((1,w+2))))
# n: numpy array that has the same shape as the input state
# each cell represents the number of live neighbours
n = sliding_window_view(padding,(3,3)).sum(axis=(2,3)) - state
# rules according to Wikipedia:
# any live cell with 2 or 3 neighbours survives
# any dead cell with 3 live neighbours becomes a live cell
# all other live or dead cells die in the next generation
return np.where(((state==1)&((n==2)|(n==3)))|((state==0)&(n==3)),1,0).tolist()
def fwma(candles: np.ndarray,
period: int = 5,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
Fibonacci's Weighted Moving Average (FWMA)
:param candles: np.ndarray
:param period: int - default: 5
:param source_type: str - default: "close"
:param sequential: bool - default=False
:return: float | np.ndarray
"""
candles = slice_candles(candles, sequential)
source = get_candle_source(candles, source_type=source_type)
fibs = fibonacci(n=period)
swv = sliding_window_view(source, window_shape=period)
res = np.average(swv, weights=fibs, axis=-1)
return same_length(candles, res) if sequential else res[-1]
def ttm_trend(candles: np.ndarray,
period: int = 5,
source_type: str = "hl2",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
TTM Trend
:param candles: np.ndarray
:param period: int - default: 5
:param source_type: str - default: "hl2"
:param sequential: bool - default=False
:return: float | np.ndarray
"""
candles = slice_candles(candles, sequential)
source = get_candle_source(candles, source_type=source_type)
swv = sliding_window_view(source, window_shape=period)
trend_avg = np.mean(swv, axis=-1)
res = np.greater(candles[:, 2], same_length(source, trend_avg))
return res if sequential else res[-1]
