Update ipykernel requirement from ~=5.5 to >=5.5,<7.0 (#512)

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Chris Holdgraf <choldgraf@berkeley.edu>

pyproject.toml

@@ -72,7 +72,7 @@ rtd = [
     "altair",
     "bokeh",
     "coconut>=1.4.3,<3.1.0",
-    "ipykernel~=5.5",
+    "ipykernel>=5.5,<7.0",
     "ipywidgets",
     "jupytext>=1.11.2,<1.15.0",
     "matplotlib",
@@ -88,7 +88,7 @@ rtd = [
 testing = [
     "coverage>=6.4,<8.0",
     "beautifulsoup4",
-    "ipykernel~=5.5",
+    "ipykernel>=5.5,<7.0",
     # for issue with 8.1.0 see https://github.com/ipython/ipython/issues/13554
     # TODO ipython 8.5 subtly changes output of test regressions
     # see https://ipython.readthedocs.io/en/stable/whatsnew/version8.html#restore-line-numbers-for-input

tests/test_execute/test_custom_convert_auto.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "raw",
-   "id": "14a2f009",
+   "id": "a93a4538",
    "metadata": {},
    "source": [
     "---\n",
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f47ed7c",
+   "id": "b19eefd1",
    "metadata": {},
    "source": [
     "# Custom Formats"
@@ -23,7 +23,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "b9b921ab",
+   "id": "c90afa1b",
    "metadata": {
     "echo": true
    },
@@ -36,7 +36,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "a581f2bf",
+   "id": "405565f4",
    "metadata": {
     "fig.height": 5,
     "fig.width": 8,
@@ -58,14 +58,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -96,7 +94,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,