Update requirements, prepare 3.0.0

[rly]

Prepare for release of HDMF 3.0.0

Before merging:

• Major and minor releases: Update package versions in requirements.txt, requirements-dev.txt,
  requirements-doc.txt, requirements-min.txt, setup.py as needed
  See https://requires.io/github/hdmf-dev/hdmf/requirements/?branch=dev
• Check legal file dates and information in Legal.txt, license.txt, README.rst, docs/source/conf.py,
  and any other locations as needed
• Update setup.py as needed
• Update README.rst as needed
• Update src/hdmf/common/hdmf-common-schema submodule as needed. Check the version number and commit SHA manually
• Update changelog (set release date) in CHANGELOG.md and any other docs as needed
• Run tests locally including gallery tests, and inspect all warnings and outputs
  (python test.py -v > out.txt)
• Run PyNWB tests locally including gallery and validation tests, and inspect all warnings and outputs
  (python test.py -v > out.txt)
• Test docs locally (make apidoc, make html)
• Push changes to this PR and make sure all PRs to be included in this release have been merged
• Check that the readthedocs build for this PR succeeds (build latest to pull the new branch, then activate and
  build docs for new branch): https://readthedocs.org/projects/hdmf/builds/

After merging:

1. Create release by following steps in docs/source/make_a_release.rst or use alias git pypi-release [tag] if set up
2. After the CI bot creates the new release (wait ~10 min), update the release notes on the
   GitHub releases page with the changelog
3. Check that the readthedocs "latest" and "stable" builds run and succeed
4. Update conda-forge/hdmf-feedstock with the latest version number
   and SHA256 retrieved from PyPI > HDMF > Download Files > View hashes for the .tar.gz file. Re-render as needed

[rly]

This also fixes tests that use behavior deprecated in the latest version of numpy:

tests/unit/utils_test/test_utils.py::TestToUintArray::test_ndarray_float
  C:\Users\Ryan\Documents\NWB\hdmf\tests\unit\utils_test\test_utils.py:188: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
  Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
    arr = np.array([0, 1, 2], dtype=np.float)

tests/unit/utils_test/test_utils.py::TestToUintArray::test_ndarray_int
  C:\Users\Ryan\Documents\NWB\hdmf\tests\unit\utils_test\test_utils.py:178: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
  Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
    arr = np.array([0, 1, 2], dtype=np.int)

tests/unit/utils_test/test_utils.py::TestToUintArray::test_ndarray_int_neg
  C:\Users\Ryan\Documents\NWB\hdmf\tests\unit\utils_test\test_utils.py:183: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
  Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
    arr = np.array([0, -1, 2], dtype=np.int)

tests/unit/validator_tests/test_validate.py::TestDtypeValidation::test_bool_for_numeric
  C:\Users\Ryan\Documents\NWB\hdmf\tests\unit\validator_tests\test_validate.py:427: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
  Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
    value = np.bool(1)