Remove 3.7

.github/workflows/lint.yml

@@ -16,7 +16,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v4
       with:
-        python-version: '3.7'
+        python-version: '3.8'
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

.github/workflows/release.yml

@@ -58,7 +58,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.8
 
       - name: Install dandischema
         run: python -m pip install .

.github/workflows/test-nonetwork.yml

@@ -19,7 +19,6 @@ jobs:
           - ubuntu-latest
           - macos-latest
         python:
-          - 3.7
           - 3.8
           - 3.9
           - '3.10'

.github/workflows/test.yml

@@ -20,7 +20,6 @@ jobs:
           - ubuntu-latest
           - macos-latest
         python:
-          - 3.7
           - 3.8
           - 3.9
           - '3.10'

.github/workflows/typing.yml

@@ -16,7 +16,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: '3.7'
+          python-version: '3.8'
 
       - name: Install dependencies
         run: |

dandischema/tests/data/metadata/meta_000004.json

@@ -3,7 +3,7 @@
   "schemaKey": "Dandiset",
   "schemaVersion": "0.4.4",
   "name": "A NWB-based dataset and processing pipeline of human single-neuron activity during a declarative memory task",
-  "description": "A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy re-use of this hard-to-obtain data for both teaching and research on the mechanisms of human memory.",
+  "description": "A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy reuse of this hard-to-obtain data for both teaching and research on the mechanisms of human memory.",
   "contributor": [
     {
       "schemaKey": "Person",

dandischema/tests/data/metadata/meta_000004old.json

@@ -410,7 +410,7 @@
             "includeInCitation": false
         }
     ],
-    "description": "A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy re-use of this hard-to-obtain data for both teaching and research on the mechanisms of human memory.",
+    "description": "A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy reuse of this hard-to-obtain data for both teaching and research on the mechanisms of human memory.",
     "studyTarget": [],
     "assetsSummary": {
         "species": [

setup.cfg

@@ -12,7 +12,6 @@ classifiers =
     License :: OSI Approved :: Apache Software License
     Operating System :: OS Independent
     Programming Language :: Python
-    Programming Language :: Python :: 3.7
     Programming Language :: Python :: 3.8
     Programming Language :: Python :: 3.9
     Programming Language :: Python :: 3.10
@@ -27,11 +26,10 @@ project_urls =
     Source Code = https://github.com/dandi/dandischema
 
 [options]
-python_requires = >=3.7
+python_requires = >=3.8
 install_requires =
     jsonschema[format]
     pydantic[email] >= 1.8.1, < 2.0
-    typing_extensions;  python_version < "3.8"
     requests
 zip_safe = False
 packages = find_namespace: