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Paul Romer 2025-10-20 10:42:17 -04:00
parent dcd2db6679
commit a3735a0b99
20 changed files with 1127 additions and 222 deletions
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta http-equiv="refresh" content="1;url=http://localhost:8889/lab?token=9cf16d6e8f8848cca644fbeba759ffea7e04ecf65f2e9e89" />
<title>Opening Jupyter Application</title>
</head>
<body>
<p>
This page should redirect you to a Jupyter application. If it doesn't,
<a href="http://localhost:8889/lab?token=9cf16d6e8f8848cca644fbeba759ffea7e04ecf65f2e9e89">click here to go to Jupyter</a>.
</p>
</body>
</html>

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{
"base_url": "/",
"hostname": "localhost",
"password": false,
"pid": 50575,
"port": 8889,
"root_dir": "/Users/pr/using_uv",
"secure": false,
"sock": "",
"token": "9cf16d6e8f8848cca644fbeba759ffea7e04ecf65f2e9e89",
"url": "http://localhost:8889/",
"version": "2.17.0"
}

13
.jupyter/data/runtime/kernel-594af5d9-c160-4648-be09-d77009267580.json Normal file
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{
"shell_port": 57791,
"iopub_port": 57792,
"stdin_port": 57793,
"control_port": 57795,
"hb_port": 57794,
"ip": "127.0.0.1",
"key": "8c40ef9b-d657f829b5649f10e9d4ef11",
"transport": "tcp",
"signature_scheme": "hmac-sha256",
"kernel_name": "python3",
"jupyter_session": "/Users/pr/using_uv/Files/intro_to_gennaker.ipynb"
}

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from symbolic_math import *
g = Lambda(x, exp(-x**(-2)))
g
h = Lambda(x, a + b*(1-g(x)))
h
r = Lambda(x, (a / h(x)))
r
F = Lambda((x,t), (exp(h(x)*t)*(1-r(x))+r(x))**(-1))
F
F0 = Lambda(t, limit(F(x,t), x, 0))
F0
# !uv pip install plotnine polars
from plotnine import ggplot, aes, geom_point, geom_smooth
from polars import read_csv
dtypes = {"x": float, "y": float}
df = read_csv(
"data.csv",
schema_overrides = dtypes
)
(
ggplot(data=df, mapping = aes(x = "x", y = "y"))
+ geom_point()
+ geom_smooth(method="lm", color="blue")
)

126
Files/Using_pylock.ipynb
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@ -1,126 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "2d3833aa-a58b-4752-9b8e-060fae34ed83",
"metadata": {},
"outputs": [],
"source": [
"import re"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "005fe62c-1bf4-48bc-bc18-5cc46ea2e1ce",
"metadata": {},
"outputs": [],
"source": [
"lck = r\"^pylock\\.([^.]+)\\.toml$\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f3768a43-f740-4590-bf20-fba50486f627",
"metadata": {},
"outputs": [],
"source": [
"pl = re.compile(lck)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b5d1fc98-4d71-42b7-a013-bbf18649f6c0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"None\n"
]
}
],
"source": [
"print(pl.match(\"pylock.toml\"))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "b001ae47-6eac-4a18-9a7e-c379df48577d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<re.Match object; span=(0, 16), match='pylock.name.toml'>\n"
]
}
],
"source": [
"print(pl.match(\"pylock.name.toml\"))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "2b86f199-f780-44c1-833e-9e3fc69b9f90",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'v1'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filename = \"pylock.v1.toml\"\n",
"if len(filename) > 11 and filename.startswith(\"pylock.\") and filename.endswith(\".toml\"):\n",
" name = filename.removeprefix(\"pylock.\").removesuffix(\".toml\")\n",
"\n",
"name "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8a1d59ef-6c70-4693-a25c-0e58a6e37e21",
"metadata": {},
"outputs": [],
"source": [
"uv --version"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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Files/data.csv Normal file
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x, y
1, 2
2, 4
3, 12
4, 16
5, 25
1 x y
2 1 2
3 2 4
4 3 12
5 4 16
6 5 25

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40
Files/jl_w_pylock.txt
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Using pylock.toml:
1. Create a requirements.in file that has the full path to the local libraries folder:
.local-libraries/tufte-0.7.0-py3-none-any.whl
.local-libraries/clear_run_to_selected-0.3.0-py3-none-any.whl
This is somewhat inconvenient because it means that the requirements.in file has to be updated when the version of one of these libraries changes.
2. Use uv to create the pylock.toml file:
uv pip compile requirements.in -o pylock.toml
3. Have uv create the venv:
uv venv
uv pip sync pylock.toml
4. Use the t.sh script to run Jupyter lab. The script creates environment variables for the Jupyter paths, adds .venv/bin to the PATH and and starts jupyter with the lab option:
#!/bin/bash
export JUPYTERLAB_SETTINGS_DIR="${PWD}/.jupyter/lab/user-settings"
export JUPYTER_DATA_DIR="${PWD}/.jupyter/data"
export JUPYTER_CONFIG_DIR="${PWD}/.jupyter"
export VIRTUAL_ENV_PROMPT=.venv
export PATH=".venv/bin:$PATH"
jupyter lab
5. Note that this approach pins all of the versions, including all the versions for Jupyter Lab. One can argue that this is good in the sense that users will not run new libraries created after the date when the current lock file was generated. However, if we want to always be just 30 days behind the most recent versions, we have to continually:
- update the update the requirements.in file and the wheels in the local-libraries folder
- recreate the `pylock.toml` file generated from the requirements.in file
- re-run the command `uv pip sync pylock.toml`

11
Files/requirements.txt
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@ -1,11 +0,0 @@
jupyterlab
pyjson5
tomlkit
jupyterlab-lsp
python-lsp-server[all]>=1.13.1
jupyterlab-spellchecker
jupyterlab-code-formatter
black
isort
ipywidgets
jupyterlab-quickopen

5
Files/symbolic_math.py Normal file
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@ -0,0 +1,5 @@
import mpmath
from sympy import symbols, Function, Lambda, lambdify, exp, diff, limit, oo
a, b, x, z, t = symbols('alpha beta x z t')

10
Files/test_imports.ipynb
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@ -13,7 +13,7 @@
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpyjson5\u001b[39;00m\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtomlkit\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mwatchdog\u001b[39;00m\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnbconvert\u001b[39;00m\n\u001b[32m 9\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnbformat\u001b[39;00m\n",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnbconvert\u001b[39;00m\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnbformat\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mwatchdog\u001b[39;00m\n",
"\u001b[31mModuleNotFoundError\u001b[39m: No module named 'watchdog'"
]
}
@ -23,11 +23,11 @@
"\n",
"import tomlkit\n",
"\n",
"import watchdog\n",
"\n",
"import nbconvert\n",
"\n",
"import nbformat"
"import nbformat\n",
"\n",
"import watchdog\n"
]
},
{
@ -55,7 +55,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
"version": "3.13.2"
}
},
"nbformat": 4,

38
Files/untitled.txt
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@ -1,38 +0,0 @@
Using pylock.toml:
1. Create a requirements.in file that has full path to the local libraries:
.local-libraries/tufte-0.7.0-py3-none-any.whl
.local-libraries/clear_run_to_selected-0.3.0-py3-none-any.whl
This is somewhat inconvenient because it means that the requirements.in file has to be updated when the version of the library changes.
2. Use uv to create the pylock.toml file:
uv pip compile requirements.in -o pylock.toml
3. Have uv create the venv:
uv pip sync pylock.toml
4. Use the t.sh script to run Jupyter lab. The script creates environment variables for the Jupyter paths, adds .venv/bin to the PATH and and starts jupyter with the lab option:
#!/bin/bash
export JUPYTERLAB_SETTINGS_DIR="${PWD}/.jupyter/lab/user-settings"
export JUPYTER_DATA_DIR="${PWD}/.jupyter/data"
export JUPYTER_CONFIG_DIR="${PWD}/.jupyter"
export VIRTUAL_ENV_PROMPT=.venv
export PATH=".venv/bin:$PATH"
jupyter lab
5. Note that this approach pins all of the versions, including all the versions for Jupyter Lab. One can argue that this is good in the sense that users will not run new libraries created after the date when the current lock file was generated. However, if we want to always be just 30 days behind the most recent versions, we have to continually:
- update the update the requirements.in file and the wheels in the local-libraries folder
- recreate the `pylock.toml` file generated from the requirements.in file
- re-run the command `uv pip sync pylock.toml`

9
Installs.txt Normal file
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@ -0,0 +1,9 @@
1. Install Python 3.14
2. Install uv
3. test for presence of git
4. git
- macos: xcode-select -h
- git for windows

4
pyproject.toml
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@ -18,4 +18,8 @@ dependencies = [
"jupyterlab-quickopen",
"tufte",
"clear_run_to_selected",
"plotnine",
"sympy",
"polars",
"pyarrow",
]

564
uv.lock
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@ -325,6 +325,70 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/60/97/891a0971e1e4a8c5d2b20bbe0e524dc04548d2307fee33cdeba148fd4fc7/comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417", size = 7294, upload-time = "2025-07-25T14:02:02.896Z" },
]
[[package]]
name = "contourpy"
version = "1.3.3"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "numpy" },
]
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