Add an ohlcv high/low tracer with optional downsampling

piker/ui/_compression.py

@@ -13,16 +13,121 @@
 
 # You should have received a copy of the GNU Affero General Public License
 # 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.
+
+'''
+# from typing import Optional
+
 import numpy as np
+# from numpy.lib.recfunctions import structured_to_unstructured
 from numba import (
-    jit, float64, optional, int64,
+    jit,
+    float64, optional, int64,
 )
 
+from ..log import get_logger
+
+
+log = get_logger(__name__)
+
+
+def hl2mxmn(
+    ohlc: np.ndarray,
+    downsample_by: int = 0,
+
+) -> 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',
+    ]]
+
+    # XXX: don't really need this any more since we implemented
+    # the "tracer" routine, `numba`-style..
+    # create a "max and min" sequence from ohlc datums
+    # hl2d = structured_to_unstructured(hls)
+    # hl1d = hl2d.flatten()
+
+    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] - 1
+
+    if not downsample_by > 2:
+        return mxmn, x
+
+    dsx, dsy = downsample(
+        y=mxmn,
+        x=x,
+        bins=downsample_by,
+    )
+    log.info(f'downsampling by {downsample_by}')
+    return dsy, dsx
+
+
+@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 downsample(
     x: np.ndarray,
     y: np.ndarray,
-    bins: int,
+    bins: int = 2,
     method: str = 'peak',
 
 ) -> tuple[np.ndarray, np.ndarray]:
@@ -36,20 +141,31 @@ def downsample(
     # py3.10 syntax
     match method:
         case 'peak':
+            # breakpoint()
+            if bins < 2:
+                log.warning('No downsampling taking place?')
+
             ds = bins
             n = len(x) // ds
             x1 = np.empty((n, 2))
+
             # start of x-values; try to select a somewhat centered point
             stx = ds // 2
             x1[:] = x[stx:stx+n*ds:ds, np.newaxis]
             x = x1.reshape(n*2)
+
             y1 = np.empty((n, 2))
             y2 = y[:n*ds].reshape((n, ds))
+
             y1[:, 0] = y2.max(axis=1)
             y1[:, 1] = y2.min(axis=1)
             y = y1.reshape(n*2)
 
-        case '4px':
+            return x, y
+
+        # TODO: this algo from infinite, see
+        # https://github.com/pikers/piker/issues/109
+        case 'infinite_4px':
 
             # Ex. from infinite on downsampling viewable graphics.
             # "one thing i remembered about the binning - if you are
@@ -62,7 +178,7 @@ def downsample(
             def build_subchart(
                 self,
                 subchart,
-                width,  # width of screen?
+                width,  # width of screen in pxs?
                 chart_type,
                 lower,  # x start?
                 upper,  # x end?
@@ -86,6 +202,7 @@ def downsample(
                         # the width of the screen?
                         (upper-lower)/float(width),
                     )
+                    print(f'downsampled to {nb} bins')
 
             return x, y
 
@@ -101,13 +218,16 @@ def subset_by_x(
     step: float,
 
 ) -> int:
-    count = len(xs)
     # nbins = len(bins)
+    count = len(xs)
     bincount = 0
-    x_left = start
+    x_left = x_start
+
     # Find the first bin
-    while xs[0] >= x_left + step:
+    first = xs[0]
+    while first >= x_left + step:
         x_left += step
+
     bins[bincount] = x_left
     data[bincount] = ys[0]