Prepare 0.0.46

datastock/_class1.py

@@ -21,6 +21,7 @@
 from . import _class1_binning
 from . import _class1_interpolate
 from . import _class1_uniformize
+from . import _class1_color_touch as _color_touch
 from . import _export_dataframe
 from . import _find_plateau
 
@@ -923,6 +924,32 @@ def interpolate(
             inplace=inplace,
         )
 
+    # ---------------------
+    # color touch array
+    # ---------------------
+
+    def get_color_touch(
+        self,
+        data=None,
+        dcolor=None,
+        # options
+        color_default=None,
+        vmin=None,
+        vmax=None,
+        log=None,
+    ):
+
+        return _color_touch.main(
+            coll=self,
+            data=data,
+            dcolor=dcolor,
+            # options
+            color_default=color_default,
+            vmin=vmin,
+            vmax=vmax,
+            log=log,
+        )
+
     # ---------------------
     # Methods computing correlations
     # ---------------------

datastock/_class1_color_touch.py

@@ -0,0 +1,269 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Feb 28 08:53:00 2025
+
+@author: dvezinet
+"""
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+import datastock as ds
+
+
+# ###############################################################
+# ###############################################################
+#                  Main
+# ###############################################################
+
+
+def main(
+    coll=None,
+    data=None,
+    dcolor=None,
+    # options
+    color_default=None,
+    vmin=None,
+    vmax=None,
+    log=None,
+):
+
+    # ------------------
+    # check inputs
+    # ------------------
+
+    data, dcolor, color_default, vmin, vmax, log = _check(
+        coll=coll,
+        data=data,
+        dcolor=dcolor,
+        color_default=color_default,
+        vmin=vmin,
+        vmax=vmax,
+        log=log,
+    )
+
+    # ------------------
+    # initialize
+    # ------------------
+
+    shape = data.shape + (4,)
+    color = np.zeros(shape, dtype=float)
+
+    # ------------------
+    # compute - alpha
+    # ------------------
+
+    if log is True:
+        vmin = np.log10(vmin)
+        vmax = np.log10(vmax)
+
+        alpha = (np.log10(data) - vmin) / (vmax - vmin)
+
+    else:
+        alpha = (data - vmin) / (vmax - vmin)
+
+    # ------------------
+    # compute - colors
+    # ------------------
+
+    for k0, v0 in dcolor.items():
+
+        sli = (v0['ind'], slice(0, 3))
+        color[sli] = v0['color']
+
+        sli = tuple([slice(None) for ii in range(data.ndim)] + [-1])
+        color[sli] = alpha
+
+    # ------------------
+    # output
+    # ------------------
+
+    lcol = set([v0['color'] for v0 in dcolor.values()])
+    dcolor = {
+        'color': color,
+        'meaning': {
+            kc: [k0 for k0, v0 in dcolor.items() if v0['color'] == kc]
+            for kc in lcol
+        },
+    }
+
+    return dcolor
+
+
+# ###############################################################
+# ###############################################################
+#                  check
+# ###############################################################
+
+
+def _check(
+    coll=None,
+    data=None,
+    dcolor=None,
+    # options
+    color_default=None,
+    vmin=None,
+    vmax=None,
+    log=None,
+):
+
+    # ------------------
+    # data
+    # ------------------
+
+    lc = [
+        isinstance(data, np.ndarray),
+        isinstance(data, str) and data in coll.ddata.keys(),
+    ]
+    if lc[0]:
+        pass
+    elif lc[1]:
+        data = coll.ddata[data]['data']
+    else:
+        msg = (
+            "Arg data must be a np.ndarray or a key to an existing data!\n"
+            f"Provided: {data}\n"
+        )
+        raise Exception(msg)
+
+
+    # ------------------
+    # dcolor
+    # ------------------
+
+    # --------------------
+    # dcolor format check
+
+    c0 = (
+        isinstance(dcolor, dict)
+        and all([
+            isinstance(k0, str)
+            and isinstance(v0, dict)
+            and sorted(v0.keys()) == ['color', 'ind']
+            for k0, v0 in dcolor.items()
+        ])
+    )
+    if not c0:
+        msg = (
+            "Arg dcolor must be a dict of sub-dicts of shape:\n"
+            "\t- 'key0': {'ind': ..., 'color': ...}\n"
+            "\t-  ...\n"
+            "\t- 'keyN': {'ind': ..., 'color': ...}\n"
+            f"Provided:\n{dcolor}\n"
+        )
+        raise Exception(msg)
+
+    # --------------------
+    # ind and color checks
+
+    dfail = {}
+    shape = data.shape
+    for k0, v0 in dcolor.items():
+
+        c0 = (
+            isinstance(v0['ind'], np.ndarray)
+            and v0['ind'].shape == data.shape
+            and v0['ind'].dtype == bool
+        )
+        if not c0:
+            msg = f"'ind' must be a {shape} bool array, not {v0['ind']}"
+            dfail[k0] = (msg,)
+
+        if not mcolors.is_color_like(v0['color']):
+            msg = f"'color' must be color-like, not {v0['color']}"
+            if k0 in dfail:
+                dfail[k0] = dfail[k0] + (msg,)
+            else:
+                dfail[k0] = (msg,)
+
+    # raise exception
+    if len(dfail) > 0:
+        lmax = np.max([len(f"\t- {k0}: ") for k0 in dfail.keys()])
+        lstr = [
+            f"\t- {k0}:\n".ljust(lmax) + '\n'.join([
+                "".ljust(lmax+4) + f"\t- {v1}".rjust(lmax)
+                for ii, v1 in enumerate(v0)
+            ])
+            for k0, v0 in dfail.items()
+        ]
+        msg = (
+            "Arg dcolor, the following keys have incorrect keys / values:\n"
+            + "\n".join(lstr)
+        )
+        raise Exception(msg)
+
+    # ----------------------
+    # format colors to rgb
+
+    dcol = {}
+    for k0, v0 in dcolor.items():
+        if np.any(v0['ind']):
+            dcol[k0] = {
+                'ind': v0['ind'],
+                'color': mcolors.to_rgb(v0['color']),
+            }
+
+    # ------------------
+    # color_default
+    # ------------------
+
+    if color_default is None:
+        color_default = 'k'
+    if not mcolors.is_color_like(color_default):
+        msg = (
+            "Arg color_default must be color-like!\n"
+            f"Provided: {color_default}\n"
+        )
+        raise Exception(msg)
+
+    color_default = mcolors.to_rgb(color_default)
+
+    # ------------------
+    # vmin, vmax
+    # ------------------
+
+    vmin0 = np.nanmin(data)
+    vmax0 = np.nanmax(data)
+
+    # vmin
+    if vmin is None:
+        vmin = vmin0
+    c0 = (np.isscalar(vmin) and np.isfinite(vmin) and vmin < vmax0)
+    if not c0:
+        msg = (
+            f"Arg vmin must be a finite scalar below max ({vmax0})\n"
+            f"Provided: {vmin}\n"
+        )
+        raise Exception(msg)
+
+    # vmax
+    if vmax is None:
+        vmax = vmax0
+    c0 = (np.isscalar(vmax) and np.isfinite(vmax) and vmax > vmin0)
+    if not c0:
+        msg = (
+            f"Arg vmax must be a finite scalar above min ({vmin0})\n"
+            f"Provided: {vmax}\n"
+        )
+        raise Exception(msg)
+
+    # ordering
+    if vmin >= vmax:
+        msg = (
+            "Arg vmin must be below vmax!\n"
+            f"Provided:\n\t- vmin = {vmin}\n\t- vmax = {vmax}\n"
+        )
+        raise Exception(msg)
+
+    # ------------------
+    # log
+    # ------------------
+
+    log = ds._generic_check._check_var(
+        log, 'log',
+        types=bool,
+        default=False,
+    )
+
+    return data, dcol, color_default, vmin, vmax, log

datastock/_class1_compute.py

@@ -1233,4 +1233,4 @@ def _extract_select(
             # lkey=[idq2dR],
             # return_all=True,
         # )
-    # return out
+    # return out

datastock/_class1_interpolate.py

@@ -1574,7 +1574,8 @@ def _xunique(dout=None, domain=None):
     }
 
     # Number of Nones expected
-    nNone = 1 + len(domain)
+    ndom = 0 if domain is None else len(domain)
+    nNone = 1 + ndom
 
     # check
     dwrong = {k0: v0 for k0, v0 in dind.items() if len(v0) != nNone}
@@ -1583,7 +1584,7 @@ def _xunique(dout=None, domain=None):
             f"\t- {k0}: {dout[k0]['ref']} => {v0}" for k0, v0 in dwrong.items()
         ]
         msg = (
-            "Interpolate unique pt => ref should have nNone = 1 + {len(domain)}:\n"
+            "Interpolate unique pt => ref should have nNone = 1 + {ndom}:\n"
             + "\n".join(lstr)
         )
         raise Exception(msg)
@@ -1626,7 +1627,12 @@ def _store(
         ldata = list(set(itt.chain.from_iterable([
             v0['ref'] for v0 in dout.values()
         ])))
-        coll2 = coll.extract(keys=ldata, vectors=True)
+
+        coll2 = coll.extract(
+            keys=ldata,
+            inc_vectors=True,
+            return_keys=False,
+        )
 
     # -------------
     # store_keys
@@ -1644,7 +1650,13 @@ def _store(
         excluded=lout,
     )
 
-    assert len(store_keys) == len(dout)
+    if len(store_keys) != len(dout):
+        msg = (
+            "Nb of store_keys != nb of keys in dout!\n"
+            f"\t- store_keys:\n{store_keys}\n "
+            f"\t- dout.keys():\n{sorted(dout.keys())}\n "
+        )
+        raise Exception(msg)
 
     # ---------
     # add data
@@ -1658,4 +1670,4 @@ def _store(
             units=v0['units'],
         )
 
-    return coll2
+    return coll2