update tests to pass w Python 3.13 and newer pandas

Author: jbloom

CHANGELOG.md

@@ -8,6 +8,8 @@ The format is based on [Keep a Changelog](https://keepachangelog.com).
 ### Fixed
 - Removed deprecated `pkg_resources`, which was root of [this issue](https://github.com/dms-vep/dms-vep-pipeline-3/issues/212#event-22715605282)
 
+- Updated tests to use Python 3.13 and so they pass with the newer versions of `pandas`.
+
 
 ## 0.7.0
 

dmslogo/logo.py

@@ -367,8 +367,8 @@ def draw_logo(
         ylabel = letter_height_col
 
     # check letters are all upper case
-    letters = str(data[letter_col].unique())
-    if letters.upper() != letters:
+    letters = data[letter_col].unique()
+    if not all(str(letter) == str(letter).upper() for letter in letters):
         raise ValueError("letters in `letter_col` must be uppercase")
 
     # checks on input data

dmslogo/utils.py

@@ -65,21 +65,21 @@ class AxLimSetter:
 
     >>> data = [0.5, 0.6, 0.4, 0.4, 0.3, 0.5]
     >>> setter_default = AxLimSetter()
-    >>> setter_default.get_lims(data)
+    >>> tuple(float(v) for v in setter_default.get_lims(data))
     (-0.03, 0.63)
 
     Now use the `max_from_quantile` option to set an upper limit
     that substantially exceeds the "noise" of the all-similar values:
 
     >>> setter_max_quantile = AxLimSetter(max_from_quantile=(0.5, 0.05))
-    >>> setter_max_quantile.get_lims(data)
+    >>> tuple(float(v) for v in setter_max_quantile.get_lims(data))
     (-0.45, 9.45)
 
     Demonstrate `min_upperlim`:
 
     >>> setter_min_upperlim = AxLimSetter(max_from_quantile=(0.5, 0.05),
     ...                                   min_upperlim=10)
-    >>> setter_min_upperlim.get_lims(data)
+    >>> tuple(float(v) for v in setter_min_upperlim.get_lims(data))
     (-0.45, 10.0)
 
     """

nbval_sanitize.cfg

@@ -9,3 +9,17 @@ replace: <cell_header_start>
 [pandas_cell_header_end]
 regex: </t[hd]>
 replace: <cell_header_end>
+
+# pandas 3 displays None where pandas 2 displayed NaN for missing string values
+[pandas_nan_none_html]
+regex: <cell_header_start>NaN<cell_header_end>
+replace: <cell_header_start>None<cell_header_end>
+
+[pandas_nan_none_text]
+regex: \bNaN\b
+replace: None
+
+# normalize runs of 2+ spaces to single space for text/plain DataFrame alignment
+[pandas_whitespace]
+regex: [ ][ ]+
+replace: SANITIZED_SPACE