Epoch indexing and dataviz layer

piker/ui/_compression.py → piker/data/_m4.py

@@ -15,17 +15,30 @@
 # along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
 '''
-Graphics related downsampling routines for compressing to pixel
-limits on the display device.
+Graphics downsampling using the infamous M4 algorithm.
+
+This is one of ``piker``'s secret weapons allowing us to boss all other
+charting platforms B)
+
+(AND DON'T YOU DARE TAKE THIS CODE WITHOUT CREDIT OR WE'LL SUE UR F#&@* ASS).
+
+NOTES: this method is a so called "visualization driven data
+aggregation" approach. It gives error-free line chart
+downsampling, see
+further scientific paper resources:
+- http://www.vldb.org/pvldb/vol7/p797-jugel.pdf
+- http://www.vldb.org/2014/program/papers/demo/p997-jugel.pdf
+
+Details on implementation of this algo are based in,
+https://github.com/pikers/piker/issues/109
 
 '''
 import math
 from typing import Optional
 
 import numpy as np
-from numpy.lib import recfunctions as rfn
 from numba import (
-    jit,
+    njit,
     # float64, optional, int64,
 )
 
@@ -35,109 +48,6 @@
 log = get_logger(__name__)
 
 
-def hl2mxmn(ohlc: np.ndarray) -> np.ndarray:
-    '''
-    Convert a OHLC struct-array containing 'high'/'low' columns
-    to a "joined" max/min 1-d array.
-
-    '''
-    index = ohlc['index']
-    hls = ohlc[[
-        'low',
-        'high',
-    ]]
-
-    mxmn = np.empty(2*hls.size, dtype=np.float64)
-    x = np.empty(2*hls.size, dtype=np.float64)
-    trace_hl(hls, mxmn, x, index[0])
-    x = x + index[0]
-
-    return mxmn, x
-
-
-@jit(
-    # TODO: the type annots..
-    # float64[:](float64[:],),
-    nopython=True,
-)
-def trace_hl(
-    hl: 'np.ndarray',
-    out: np.ndarray,
-    x: np.ndarray,
-    start: int,
-
-    # the "offset" values in the x-domain which
-    # place the 2 output points around each ``int``
-    # master index.
-    margin: float = 0.43,
-
-) -> None:
-    '''
-    "Trace" the outline of the high-low values of an ohlc sequence
-    as a line such that the maximum deviation (aka disperaion) between
-    bars if preserved.
-
-    This routine is expected to modify input arrays in-place.
-
-    '''
-    last_l = hl['low'][0]
-    last_h = hl['high'][0]
-
-    for i in range(hl.size):
-        row = hl[i]
-        l, h = row['low'], row['high']
-
-        up_diff = h - last_l
-        down_diff = last_h - l
-
-        if up_diff > down_diff:
-            out[2*i + 1] = h
-            out[2*i] = last_l
-        else:
-            out[2*i + 1] = l
-            out[2*i] = last_h
-
-        last_l = l
-        last_h = h
-
-        x[2*i] = int(i) - margin
-        x[2*i + 1] = int(i) + margin
-
-    return out
-
-
-def ohlc_flatten(
-    ohlc: np.ndarray,
-    use_mxmn: bool = True,
-
-) -> tuple[np.ndarray, np.ndarray]:
-    '''
-    Convert an OHLCV struct-array into a flat ready-for-line-plotting
-    1-d array that is 4 times the size with x-domain values distributed
-    evenly (by 0.5 steps) over each index.
-
-    '''
-    index = ohlc['index']
-
-    if use_mxmn:
-        # traces a line optimally over highs to lows
-        # using numba. NOTE: pretty sure this is faster
-        # and looks about the same as the below output.
-        flat, x = hl2mxmn(ohlc)
-
-    else:
-        flat = rfn.structured_to_unstructured(
-            ohlc[['open', 'high', 'low', 'close']]
-        ).flatten()
-
-        x = np.linspace(
-            start=index[0] - 0.5,
-            stop=index[-1] + 0.5,
-            num=len(flat),
-        )
-    return x, flat
-
-
 def ds_m4(
     x: np.ndarray,
     y: np.ndarray,
@@ -160,16 +70,6 @@ def ds_m4(
     This is more or less an OHLC style sampling of a line-style series.
 
     '''
-    # NOTE: this method is a so called "visualization driven data
-    # aggregation" approach. It gives error-free line chart
-    # downsampling, see
-    # further scientific paper resources:
-    # - http://www.vldb.org/pvldb/vol7/p797-jugel.pdf
-    # - http://www.vldb.org/2014/program/papers/demo/p997-jugel.pdf
-
-    # Details on implementation of this algo are based in,
-    # https://github.com/pikers/piker/issues/109
-
     # XXX: from infinite on downsampling viewable graphics:
     # "one thing i remembered about the binning - if you are
     # picking a range within your timeseries the start and end bin
@@ -256,8 +156,7 @@ def ds_m4(
     return nb, x_out, y_out, ymn, ymx
 
 
-@jit(
-    nopython=True,
+@njit(
     nogil=True,
 )
 def _m4(