Move path ops routines to top of mod
Planning to put the formatters into a new mod and aggregate all path gen/op helpers into this module. Further tweak include: - moving `path_arrays_from_ohlc()` back to module level - slice out the last xy datum for `OHLCBarsAsCurveFmtr` 1d formatting - always copy the new x-value from the source to `.x_nd`multichartz_backup
Tyler Goodlet
parent
152f91dcda
commit
1a4f9cb9a8
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@ -49,6 +49,129 @@ if TYPE_CHECKING:
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from .._profile import Profiler
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def xy_downsample(
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x,
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y,
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uppx,
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x_spacer: float = 0.5,
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) -> tuple[
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np.ndarray,
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np.ndarray,
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float,
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float,
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]:
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'''
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Downsample 1D (flat ``numpy.ndarray``) arrays using M4 given an input
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``uppx`` (units-per-pixel) and add space between discreet datums.
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'''
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# downsample whenever more then 1 pixels per datum can be shown.
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# always refresh data bounds until we get diffing
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# working properly, see above..
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bins, x, y, ymn, ymx = ds_m4(
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x,
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y,
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uppx,
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)
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# flatten output to 1d arrays suitable for path-graphics generation.
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x = np.broadcast_to(x[:, None], y.shape)
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x = (x + np.array(
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[-x_spacer, 0, 0, x_spacer]
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)).flatten()
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y = y.flatten()
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return x, y, ymn, ymx
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@njit(
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# NOTE: need to construct this manually for readonly
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# arrays, see https://github.com/numba/numba/issues/4511
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# (
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# types.Array(
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# numba_ohlc_dtype,
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# 1,
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# 'C',
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# readonly=True,
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# ),
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# int64,
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# types.unicode_type,
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# optional(float64),
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# ),
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nogil=True
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)
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def path_arrays_from_ohlc(
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data: np.ndarray,
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start: int64,
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bar_gap: float64 = 0.43,
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# index_field: str,
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) -> tuple[
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np.ndarray,
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np.ndarray,
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np.ndarray,
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]:
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'''
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Generate an array of lines objects from input ohlc data.
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'''
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size = int(data.shape[0] * 6)
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# XXX: see this for why the dtype might have to be defined outside
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# the routine.
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# https://github.com/numba/numba/issues/4098#issuecomment-493914533
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x = np.zeros(
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shape=size,
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dtype=float64,
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)
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y, c = x.copy(), x.copy()
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# TODO: report bug for assert @
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# /home/goodboy/repos/piker/env/lib/python3.8/site-packages/numba/core/typing/builtins.py:991
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for i, q in enumerate(data[start:], start):
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# TODO: ask numba why this doesn't work..
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# open, high, low, close, index = q[
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# ['open', 'high', 'low', 'close', 'index']]
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open = q['open']
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high = q['high']
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low = q['low']
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close = q['close']
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# index = float64(q[index_field])
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index = float64(q['index'])
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istart = i * 6
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istop = istart + 6
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# x,y detail the 6 points which connect all vertexes of a ohlc bar
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x[istart:istop] = (
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index - bar_gap,
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index,
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index,
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index,
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index,
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index + bar_gap,
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)
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y[istart:istop] = (
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open,
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open,
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low,
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high,
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close,
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close,
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)
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# specifies that the first edge is never connected to the
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# prior bars last edge thus providing a small "gap"/"space"
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# between bars determined by ``bar_gap``.
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c[istart:istop] = (1, 1, 1, 1, 1, 0)
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return x, y, c
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class IncrementalFormatter(msgspec.Struct):
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'''
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Incrementally updating, pre-path-graphics tracking, formatter.
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@ -131,7 +254,6 @@ class IncrementalFormatter(msgspec.Struct):
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np.ndarray,
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np.ndarray,
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]:
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# TODO:
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# - can the renderer just call ``Viz.read()`` directly? unpack
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# latest source data read
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@ -422,18 +544,11 @@ class IncrementalFormatter(msgspec.Struct):
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) -> None:
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# write pushed data to flattened copy
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new_y_nd = new_from_src[data_field]
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# XXX
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# TODO: this should be returned and written by caller!
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# XXX
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# generate same-valued-per-row x support with Nx1 shape
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index_field = self.index_field
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if index_field != 'index':
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x_nd_new = self.x_nd[read_slc]
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x_nd_new[:] = new_from_src[index_field]
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self.y_nd[read_slc] = new_y_nd
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x_nd_new = self.x_nd[read_slc]
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x_nd_new[:] = new_from_src[self.index_field]
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# XXX: was ``.format_xy()``
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def format_xy_nd_to_1d(
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self,
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@ -453,6 +568,8 @@ class IncrementalFormatter(msgspec.Struct):
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Return single field column data verbatim
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'''
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# NOTE: we don't include the very last datum which is filled in
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# normally by another graphics object.
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return (
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array[self.index_field][:-1],
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array[array_key][:-1],
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@ -504,92 +621,37 @@ class OHLCBarsFmtr(IncrementalFormatter):
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y_nd,
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)
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@staticmethod
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@njit(
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# NOTE: need to construct this manually for readonly
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# arrays, see https://github.com/numba/numba/issues/4511
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# (
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# types.Array(
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# numba_ohlc_dtype,
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# 1,
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# 'C',
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# readonly=True,
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# ),
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# int64,
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# types.unicode_type,
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# optional(float64),
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# ),
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nogil=True
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)
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def path_arrays_from_ohlc(
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data: np.ndarray,
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start: int64,
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bar_gap: float64 = 0.43,
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# index_field: str,
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def incr_update_xy_nd(
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self,
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) -> tuple[
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np.ndarray,
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np.ndarray,
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np.ndarray,
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]:
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'''
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Generate an array of lines objects from input ohlc data.
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src_shm: ShmArray,
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data_field: str,
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'''
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size = int(data.shape[0] * 6)
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new_from_src: np.ndarray, # portion of source that was updated
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# XXX: see this for why the dtype might have to be defined outside
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# the routine.
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# https://github.com/numba/numba/issues/4098#issuecomment-493914533
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x = np.zeros(
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shape=size,
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dtype=float64,
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read_slc: slice,
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ln: int, # len of updated
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nd_start: int,
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nd_stop: int,
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is_append: bool,
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) -> None:
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# write newly pushed data to flattened copy
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# a struct-arr is always passed in.
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new_y_nd = rfn.structured_to_unstructured(
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new_from_src[self.fields]
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)
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y, c = x.copy(), x.copy()
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self.y_nd[read_slc] = new_y_nd
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# TODO: report bug for assert @
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# /home/goodboy/repos/piker/env/lib/python3.8/site-packages/numba/core/typing/builtins.py:991
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for i, q in enumerate(data[start:], start):
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# generate same-valued-per-row x support based on y shape
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x_nd_new = self.x_nd[read_slc]
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x_nd_new[:] = np.broadcast_to(
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new_from_src[self.index_field][:, None],
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new_y_nd.shape,
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) + np.array([-0.5, 0, 0, 0.5])
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# TODO: ask numba why this doesn't work..
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# open, high, low, close, index = q[
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# ['open', 'high', 'low', 'close', 'index']]
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open = q['open']
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high = q['high']
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low = q['low']
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close = q['close']
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# index = float64(q[index_field])
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# index = float64(q['time'])
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index = float64(q['index'])
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istart = i * 6
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istop = istart + 6
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# x,y detail the 6 points which connect all vertexes of a ohlc bar
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x[istart:istop] = (
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index - bar_gap,
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index,
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index,
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index,
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index,
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index + bar_gap,
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)
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y[istart:istop] = (
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open,
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open,
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low,
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high,
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close,
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close,
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)
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# specifies that the first edge is never connected to the
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# prior bars last edge thus providing a small "gap"/"space"
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# between bars determined by ``bar_gap``.
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c[istart:istop] = (1, 1, 1, 1, 1, 0)
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return x, y, c
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# TODO: can we drop this frame and just use the above?
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def format_xy_nd_to_1d(
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@ -614,7 +676,7 @@ class OHLCBarsFmtr(IncrementalFormatter):
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for line spacing.
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'''
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x, y, c = self.path_arrays_from_ohlc(
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x, y, c = path_arrays_from_ohlc(
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array,
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start,
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# self.index_field,
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@ -622,43 +684,6 @@ class OHLCBarsFmtr(IncrementalFormatter):
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)
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return x, y, c
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def incr_update_xy_nd(
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self,
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src_shm: ShmArray,
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data_field: str,
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new_from_src: np.ndarray, # portion of source that was updated
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read_slc: slice,
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ln: int, # len of updated
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nd_start: int,
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nd_stop: int,
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is_append: bool,
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) -> None:
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# write newly pushed data to flattened copy
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# a struct-arr is always passed in.
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new_y_nd = rfn.structured_to_unstructured(
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new_from_src[self.fields]
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)
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# XXX
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# TODO: this should be returned and written by caller!
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# XXX
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# generate same-valued-per-row x support based on y shape
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index_field: str = self.index_field
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if index_field != 'index':
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x_nd_new = self.x_nd[read_slc]
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x_nd_new[:] = new_from_src[index_field][:, np.newaxis]
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if (self.x_nd[self.xy_slice] == 0.5).any():
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breakpoint()
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self.y_nd[read_slc] = new_y_nd
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class OHLCBarsAsCurveFmtr(OHLCBarsFmtr):
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@ -678,8 +703,8 @@ class OHLCBarsAsCurveFmtr(OHLCBarsFmtr):
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# should we be passing in array as an xy arrays tuple?
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# 2 more datum-indexes to capture zero at end
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x_flat = self.x_nd[self.xy_nd_start:self.xy_nd_stop]
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y_flat = self.y_nd[self.xy_nd_start:self.xy_nd_stop]
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x_flat = self.x_nd[self.xy_nd_start:self.xy_nd_stop-1]
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y_flat = self.y_nd[self.xy_nd_start:self.xy_nd_stop-1]
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# slice to view
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ivl, ivr = vr
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@ -868,40 +893,3 @@ class StepCurveFmtr(IncrementalFormatter):
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# )
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return x_1d, y_1d, 'all'
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def xy_downsample(
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x,
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y,
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uppx,
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x_spacer: float = 0.5,
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) -> tuple[
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np.ndarray,
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np.ndarray,
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float,
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float,
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]:
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'''
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Downsample 1D (flat ``numpy.ndarray``) arrays using M4 given an input
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``uppx`` (units-per-pixel) and add space between discreet datums.
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'''
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# downsample whenever more then 1 pixels per datum can be shown.
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# always refresh data bounds until we get diffing
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# working properly, see above..
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bins, x, y, ymn, ymx = ds_m4(
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x,
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y,
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uppx,
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)
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# flatten output to 1d arrays suitable for path-graphics generation.
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x = np.broadcast_to(x[:, None], y.shape)
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x = (x + np.array(
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[-x_spacer, 0, 0, x_spacer]
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)).flatten()
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y = y.flatten()
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return x, y, ymn, ymx
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