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gdementengdementen
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FIX: avoid warnings for (fully) non-numeric chunks (fixes #311)
1 parent 655fa93 commit b202ad6

3 files changed

Lines changed: 61 additions & 42 deletions

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‎doc/source/changes/version_0_35_1.rst.inc‎

Lines changed: 3 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -40,6 +40,9 @@ Fixes
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their length and/or iterating them, such as scalar Groups (closes
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:editor_issue:`313`).
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* avoid warnings when displaying data with any column entirely non-numeric
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(including NaN). Closes :editor_issue:`311`.
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* fixed the mechanism writing warning/error messages happening during the
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editor initialization. The errors are now correctly written in the user
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TEMP directory / larray-editor-stderr.log

‎larray_editor/arrayadapter.py‎

Lines changed: 50 additions & 39 deletions
Original file line numberDiff line numberDiff line change
@@ -34,6 +34,8 @@
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# are probably the most likely to be re-visited) and read from the file
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# if the user requests some data outside of those chunks
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import collections.abc
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import warnings
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import logging
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import sys
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import os
@@ -615,14 +617,16 @@ def update_finite_min_max_values(self, finite_values: np.ndarray,
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h_stop: int, v_stop: int):
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"""can return either two floats or two arrays"""
617619

618-
# we need initial to support empty arrays
619-
vmin = np.nanmin(finite_values, initial=np.nan)
620-
vmax = np.nanmax(finite_values, initial=np.nan)
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with warnings.catch_warnings():
621+
warnings.simplefilter("ignore", category=RuntimeWarning)
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# we need initial to support empty arrays
623+
vmin = np.nanmin(finite_values, initial=np.nan)
624+
vmax = np.nanmax(finite_values, initial=np.nan)
621625

622-
self.vmin = (
623-
np.nanmin([self.vmin, vmin]) if self.vmin is not None else vmin)
624-
self.vmax = (
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np.nanmax([self.vmax, vmax]) if self.vmax is not None else vmax)
626+
self.vmin = (
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np.nanmin([self.vmin, vmin]) if self.vmin is not None else vmin)
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self.vmax = (
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np.nanmax([self.vmax, vmax]) if self.vmax is not None else vmax)
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return self.vmin, self.vmax
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def get_axes_area(self):
@@ -969,33 +973,38 @@ def update_finite_min_max_values(self, finite_values: np.ndarray,
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assert isinstance(self.vmin, dict) and isinstance(self.vmax, dict)
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assert h_stop >= h_start
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972-
# per column => axis=0
973-
local_vmin = np.nanmin(finite_values, axis=0, initial=np.nan)
974-
local_vmax = np.nanmax(finite_values, axis=0, initial=np.nan)
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num_cols = h_stop - h_start
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assert local_vmin.shape == (num_cols,), \
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(f"unexpected shape: {local_vmin.shape} ({finite_values.shape=}) vs "
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f"{(num_cols,)} ({h_start=} {h_stop=})")
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# vmin or self.vmin can both be nan (if the whole section data
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# is/was nan)
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global_vmin = self.vmin
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global_vmax = self.vmax
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vmin_slice = np.empty(num_cols, dtype=np.float64)
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vmax_slice = np.empty(num_cols, dtype=np.float64)
985-
for global_col_idx in range(h_start, h_stop):
986-
local_col_idx = global_col_idx - h_start
987-
988-
col_min = np.nanmin([global_vmin.get(global_col_idx, np.nan),
989-
local_vmin[local_col_idx]])
990-
# update the global vmin dict inplace
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global_vmin[global_col_idx] = col_min
992-
vmin_slice[local_col_idx] = col_min
993-
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col_max = np.nanmax([global_vmax.get(global_col_idx, np.nan),
995-
local_vmax[local_col_idx]])
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# update the global vmax dict inplace
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global_vmax[global_col_idx] = col_max
998-
vmax_slice[local_col_idx] = col_max
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984+
# per column => axis=0
985+
with warnings.catch_warnings():
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warnings.simplefilter("ignore", category=RuntimeWarning)
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988+
local_vmin = np.nanmin(finite_values, axis=0, initial=np.nan)
989+
local_vmax = np.nanmax(finite_values, axis=0, initial=np.nan)
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991+
assert local_vmin.shape == (num_cols,), \
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(f"unexpected shape: {local_vmin.shape} ({finite_values.shape=}) vs "
993+
f"{(num_cols,)} ({h_start=} {h_stop=})")
994+
for global_col_idx in range(h_start, h_stop):
995+
local_col_idx = global_col_idx - h_start
996+
997+
col_min = np.nanmin([global_vmin.get(global_col_idx, np.nan),
998+
local_vmin[local_col_idx]])
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# update the global vmin dict inplace
1000+
global_vmin[global_col_idx] = col_min
1001+
vmin_slice[local_col_idx] = col_min
1002+
1003+
col_max = np.nanmax([global_vmax.get(global_col_idx, np.nan),
1004+
local_vmax[local_col_idx]])
1005+
# update the global vmax dict inplace
1006+
global_vmax[global_col_idx] = col_max
1007+
vmax_slice[local_col_idx] = col_max
9991008
return vmin_slice, vmax_slice
10001009

10011010

@@ -1354,18 +1363,20 @@ def get_color_value(array, global_vmin, global_vmax, axis=None):
13541363
try:
13551364
finite_value = get_finite_numeric_values(array)
13561365

1357-
vmin = np.nanmin(finite_value, axis=axis)
1358-
if global_vmin is not None:
1359-
# vmin or global_vmin can both be nan (if the whole section data is/was nan)
1360-
global_vmin = np.nanmin([global_vmin, vmin], axis=axis)
1361-
else:
1362-
global_vmin = vmin
1363-
vmax = np.nanmax(finite_value, axis=axis)
1364-
if global_vmax is not None:
1365-
# vmax or global_vmax can both be nan (if the whole section data is/was nan)
1366-
global_vmax = np.nanmax([global_vmax, vmax], axis=axis)
1367-
else:
1368-
global_vmax = vmax
1366+
with warnings.catch_warnings():
1367+
warnings.simplefilter("ignore", category=RuntimeWarning)
1368+
vmin = np.nanmin(finite_value, axis=axis)
1369+
if global_vmin is not None:
1370+
# vmin or global_vmin can both be nan (if the whole section data is/was nan)
1371+
global_vmin = np.nanmin([global_vmin, vmin], axis=axis)
1372+
else:
1373+
global_vmin = vmin
1374+
vmax = np.nanmax(finite_value, axis=axis)
1375+
if global_vmax is not None:
1376+
# vmax or global_vmax can both be nan (if the whole section data is/was nan)
1377+
global_vmax = np.nanmax([global_vmax, vmax], axis=axis)
1378+
else:
1379+
global_vmax = vmax
13691380
color_value = scale_to_01range(finite_value, global_vmin, global_vmax)
13701381
except (ValueError, TypeError):
13711382
global_vmin = None

‎larray_editor/utils.py‎

Lines changed: 8 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -793,17 +793,22 @@ def num_int_digits(value):
793793
794794
Examples
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--------
796+
>>> num_int_digits(0)
797+
1
796798
>>> num_int_digits(1)
797799
1
798800
>>> num_int_digits(99)
799801
2
800802
>>> num_int_digits(-99.1)
801803
2
802-
>>> num_int_digits(np.array([1, 99, -99.1]))
803-
array([1, 2, 2])
804+
>>> num_int_digits(np.array([1, 99, 0, -99.1]))
805+
array([1, 2, 1, 2])
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"""
805807
value = abs(value)
806-
log10 = np.where(value > 0, np.log10(value), 0)
808+
# avoid a warning for log10(0) and log10(negative) because we will handle
809+
# these cases with np.where anyway
810+
with np.errstate(divide='ignore'):
811+
log10 = np.where(value > 0, np.log10(value), 0)
807812
res = np.where(np.isinf(log10), MAX_INT_DIGITS,
808813
# maximum(..., 1) because there must be at least one
809814
# integer digit (the 0 in 0.00..X)

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