diff --git a/piker/data/_pathops.py b/piker/data/_pathops.py index b99ee88e..a62ee93b 100644 --- a/piker/data/_pathops.py +++ b/piker/data/_pathops.py @@ -50,6 +50,129 @@ if TYPE_CHECKING: from .._profile import Profiler +def xy_downsample( + x, + y, + uppx, + + x_spacer: float = 0.5, + +) -> tuple[ + np.ndarray, + np.ndarray, + float, + float, +]: + ''' + Downsample 1D (flat ``numpy.ndarray``) arrays using M4 given an input + ``uppx`` (units-per-pixel) and add space between discreet datums. + + ''' + # downsample whenever more then 1 pixels per datum can be shown. + # always refresh data bounds until we get diffing + # working properly, see above.. + bins, x, y, ymn, ymx = ds_m4( + x, + y, + uppx, + ) + + # flatten output to 1d arrays suitable for path-graphics generation. + x = np.broadcast_to(x[:, None], y.shape) + x = (x + np.array( + [-x_spacer, 0, 0, x_spacer] + )).flatten() + y = y.flatten() + + return x, y, ymn, ymx + + +@njit( + # NOTE: need to construct this manually for readonly + # arrays, see https://github.com/numba/numba/issues/4511 + # ( + # types.Array( + # numba_ohlc_dtype, + # 1, + # 'C', + # readonly=True, + # ), + # int64, + # types.unicode_type, + # optional(float64), + # ), + nogil=True +) +def path_arrays_from_ohlc( + data: np.ndarray, + start: int64, + bar_gap: float64 = 0.43, + # index_field: str, + +) -> tuple[ + np.ndarray, + np.ndarray, + np.ndarray, +]: + ''' + Generate an array of lines objects from input ohlc data. + + ''' + size = int(data.shape[0] * 6) + + # XXX: see this for why the dtype might have to be defined outside + # the routine. + # https://github.com/numba/numba/issues/4098#issuecomment-493914533 + x = np.zeros( + shape=size, + dtype=float64, + ) + y, c = x.copy(), x.copy() + + # TODO: report bug for assert @ + # /home/goodboy/repos/piker/env/lib/python3.8/site-packages/numba/core/typing/builtins.py:991 + for i, q in enumerate(data[start:], start): + + # TODO: ask numba why this doesn't work.. + # open, high, low, close, index = q[ + # ['open', 'high', 'low', 'close', 'index']] + + open = q['open'] + high = q['high'] + low = q['low'] + close = q['close'] + # index = float64(q[index_field]) + index = float64(q['index']) + + istart = i * 6 + istop = istart + 6 + + # x,y detail the 6 points which connect all vertexes of a ohlc bar + x[istart:istop] = ( + index - bar_gap, + index, + index, + index, + index, + index + bar_gap, + ) + y[istart:istop] = ( + open, + open, + low, + high, + close, + close, + ) + + # specifies that the first edge is never connected to the + # prior bars last edge thus providing a small "gap"/"space" + # between bars determined by ``bar_gap``. + c[istart:istop] = (1, 1, 1, 1, 1, 0) + + return x, y, c + + class IncrementalFormatter(msgspec.Struct): ''' Incrementally updating, pre-path-graphics tracking, formatter. @@ -132,7 +255,6 @@ class IncrementalFormatter(msgspec.Struct): np.ndarray, np.ndarray, ]: - # TODO: # - can the renderer just call ``Viz.read()`` directly? unpack # latest source data read @@ -423,18 +545,11 @@ class IncrementalFormatter(msgspec.Struct): ) -> None: # write pushed data to flattened copy new_y_nd = new_from_src[data_field] - - # XXX - # TODO: this should be returned and written by caller! - # XXX - # generate same-valued-per-row x support with Nx1 shape - index_field = self.index_field - if index_field != 'index': - x_nd_new = self.x_nd[read_slc] - x_nd_new[:] = new_from_src[index_field] - self.y_nd[read_slc] = new_y_nd + x_nd_new = self.x_nd[read_slc] + x_nd_new[:] = new_from_src[self.index_field] + # XXX: was ``.format_xy()`` def format_xy_nd_to_1d( self, @@ -454,6 +569,8 @@ class IncrementalFormatter(msgspec.Struct): Return single field column data verbatim ''' + # NOTE: we don't include the very last datum which is filled in + # normally by another graphics object. return ( array[self.index_field][:-1], array[array_key][:-1], @@ -507,92 +624,37 @@ class OHLCBarsFmtr(IncrementalFormatter): y_nd, ) - @staticmethod - @njit( - # NOTE: need to construct this manually for readonly - # arrays, see https://github.com/numba/numba/issues/4511 - # ( - # types.Array( - # numba_ohlc_dtype, - # 1, - # 'C', - # readonly=True, - # ), - # int64, - # types.unicode_type, - # optional(float64), - # ), - nogil=True - ) - def path_arrays_from_ohlc( - data: np.ndarray, - start: int64, - bar_gap: float64 = 0.43, - # index_field: str, + def incr_update_xy_nd( + self, - ) -> tuple[ - np.ndarray, - np.ndarray, - np.ndarray, - ]: - ''' - Generate an array of lines objects from input ohlc data. + src_shm: ShmArray, + data_field: str, - ''' - size = int(data.shape[0] * 6) + new_from_src: np.ndarray, # portion of source that was updated - # XXX: see this for why the dtype might have to be defined outside - # the routine. - # https://github.com/numba/numba/issues/4098#issuecomment-493914533 - x = np.zeros( - shape=size, - dtype=float64, + read_slc: slice, + ln: int, # len of updated + + nd_start: int, + nd_stop: int, + + is_append: bool, + + ) -> None: + # write newly pushed data to flattened copy + # a struct-arr is always passed in. + new_y_nd = rfn.structured_to_unstructured( + new_from_src[self.fields] ) - y, c = x.copy(), x.copy() + self.y_nd[read_slc] = new_y_nd - # TODO: report bug for assert @ - # /home/goodboy/repos/piker/env/lib/python3.8/site-packages/numba/core/typing/builtins.py:991 - for i, q in enumerate(data[start:], start): + # generate same-valued-per-row x support based on y shape + x_nd_new = self.x_nd[read_slc] + x_nd_new[:] = np.broadcast_to( + new_from_src[self.index_field][:, None], + new_y_nd.shape, + ) + np.array([-0.5, 0, 0, 0.5]) - # TODO: ask numba why this doesn't work.. - # open, high, low, close, index = q[ - # ['open', 'high', 'low', 'close', 'index']] - - open = q['open'] - high = q['high'] - low = q['low'] - close = q['close'] - # index = float64(q[index_field]) - # index = float64(q['time']) - index = float64(q['index']) - - istart = i * 6 - istop = istart + 6 - - # x,y detail the 6 points which connect all vertexes of a ohlc bar - x[istart:istop] = ( - index - bar_gap, - index, - index, - index, - index, - index + bar_gap, - ) - y[istart:istop] = ( - open, - open, - low, - high, - close, - close, - ) - - # specifies that the first edge is never connected to the - # prior bars last edge thus providing a small "gap"/"space" - # between bars determined by ``bar_gap``. - c[istart:istop] = (1, 1, 1, 1, 1, 0) - - return x, y, c # TODO: can we drop this frame and just use the above? def format_xy_nd_to_1d( @@ -617,7 +679,7 @@ class OHLCBarsFmtr(IncrementalFormatter): for line spacing. ''' - x, y, c = self.path_arrays_from_ohlc( + x, y, c = path_arrays_from_ohlc( array, start, # self.index_field, @@ -625,43 +687,6 @@ class OHLCBarsFmtr(IncrementalFormatter): ) return x, y, c - def incr_update_xy_nd( - self, - - src_shm: ShmArray, - data_field: str, - - new_from_src: np.ndarray, # portion of source that was updated - - read_slc: slice, - ln: int, # len of updated - - nd_start: int, - nd_stop: int, - - is_append: bool, - - ) -> None: - # write newly pushed data to flattened copy - # a struct-arr is always passed in. - new_y_nd = rfn.structured_to_unstructured( - new_from_src[self.fields] - ) - - # XXX - # TODO: this should be returned and written by caller! - # XXX - # generate same-valued-per-row x support based on y shape - index_field: str = self.index_field - if index_field != 'index': - x_nd_new = self.x_nd[read_slc] - x_nd_new[:] = new_from_src[index_field][:, np.newaxis] - - if (self.x_nd[self.xy_slice] == 0.5).any(): - breakpoint() - - self.y_nd[read_slc] = new_y_nd - class OHLCBarsAsCurveFmtr(OHLCBarsFmtr): @@ -681,8 +706,8 @@ class OHLCBarsAsCurveFmtr(OHLCBarsFmtr): # should we be passing in array as an xy arrays tuple? # 2 more datum-indexes to capture zero at end - x_flat = self.x_nd[self.xy_nd_start:self.xy_nd_stop] - y_flat = self.y_nd[self.xy_nd_start:self.xy_nd_stop] + x_flat = self.x_nd[self.xy_nd_start:self.xy_nd_stop-1] + y_flat = self.y_nd[self.xy_nd_start:self.xy_nd_stop-1] # slice to view ivl, ivr = vr @@ -871,40 +896,3 @@ class StepCurveFmtr(IncrementalFormatter): # ) return x_1d, y_1d, 'all' - - -def xy_downsample( - x, - y, - uppx, - - x_spacer: float = 0.5, - -) -> tuple[ - np.ndarray, - np.ndarray, - float, - float, -]: - ''' - Downsample 1D (flat ``numpy.ndarray``) arrays using M4 given an input - ``uppx`` (units-per-pixel) and add space between discreet datums. - - ''' - # downsample whenever more then 1 pixels per datum can be shown. - # always refresh data bounds until we get diffing - # working properly, see above.. - bins, x, y, ymn, ymx = ds_m4( - x, - y, - uppx, - ) - - # flatten output to 1d arrays suitable for path-graphics generation. - x = np.broadcast_to(x[:, None], y.shape) - x = (x + np.array( - [-x_spacer, 0, 0, x_spacer] - )).flatten() - y = y.flatten() - - return x, y, ymn, ymx