diff --git a/.gitattributes b/.gitattributes index a41605e6606bb80dbfabf7e8d20d09825e18eadc..40edf9e78ae5a838fd65ed42c4b02094eafc5437 100644 --- a/.gitattributes +++ b/.gitattributes @@ -15,3 +15,7 @@ # Do not try and merge these files poetry.lock -diff +*.ipynb -diff + +# LFS +models/** filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore index 906e2fd5e7647da56af61856d9e0ff739712c234..dd902704a8a6c05973eb3593877c963b888c0ebc 100644 --- a/.gitignore +++ b/.gitignore @@ -196,5 +196,4 @@ pyrightconfig.json # Custom data/ cache/ -models/ flagged/ diff --git a/.python-version b/.python-version new file mode 100644 index 0000000000000000000000000000000000000000..2419ad5b0a329db360c988461d3ca3d7c73d6eef --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.11.9 diff --git a/Makefile b/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..6036d9321346d1ba75294d3b662ac4ab06937028 --- /dev/null +++ b/Makefile @@ -0,0 +1,20 @@ +#!/usr/bin/make -f + +default: install + +install: + @poetry install --only main + @poetry run spacy download en_core_web_sm + +install-dev: + @poetry self add poetry-plugin-export + @poetry install + +requirements: + @poetry export -f requirements.txt --output requirements.txt --without dev + @poetry export -f requirements.txt --output requirements-dev.txt + +lint: + @poetry run pre-commit run --all-files + +.PHONY: install install-dev requirements gradio lint run diff --git a/README.md b/README.md index 84a5c4393af9a32ba705df25df63e6ebe1bf77c0..05fee01bcbeba7fceeaf7abe50a88c21a7c2fe36 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,19 @@ -Sentiment Analysis --- +title: Sentiment Analysis +emoji: 🤗 +colorFrom: yellow +colorTo: orange +pinned: false +sdk: gradio +python_version: 3.11 +app_file: app.py +datasets: + - mrshu/amazonreviews + - stanfordnlp/sentiment140 + - stanfordnlp/imdb +--- + +# Sentiment Analysis ### Usage 1. Clone the repository @@ -9,8 +23,8 @@ Sentiment Analysis ### Datasets - [Sentiment140](https://www.kaggle.com/datasets/kazanova/sentiment140) -- [IMDb](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews) - [Amazon Reviews](https://www.kaggle.com/datasets/bittlingmayer/amazonreviews) +- [IMDB](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews) ### Required tools - `just` diff --git a/justfile b/justfile index b3643ff289f9973bdf2978f72b1cf352d3e5fab6..e4136ebdc2c17cc8fb06d86e146c16f35da75aa4 100644 --- a/justfile +++ b/justfile @@ -8,6 +8,7 @@ @install: poetry install --only main + poetry run spacy download en_core_web_sm @install-dev: poetry self add poetry-plugin-export diff --git a/notebook.ipynb b/notebook.ipynb deleted file mode 100644 index d049b9929ab1e9c2b9d9991230ae9238b959c966..0000000000000000000000000000000000000000 --- a/notebook.ipynb +++ /dev/null @@ -1,778 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sentiment Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from __future__ import annotations\n", - "\n", - "from typing import TYPE_CHECKING\n", - "\n", - "if TYPE_CHECKING:\n", - " from sklearn.base import BaseEstimator\n", - "\n", - "import json\n", - "import re\n", - "import warnings\n", - "from functools import cache\n", - "from pathlib import Path\n", - "\n", - "import joblib\n", - "import matplotlib.pyplot as plt\n", - "import nltk\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "from nltk.corpus import stopwords\n", - "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.metrics import confusion_matrix\n", - "from sklearn.model_selection import RandomizedSearchCV, train_test_split\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.svm import SVC\n", - "\n", - "from app.constants import CACHE_DIR, MODELS_DIR, SENTIMENT140_PATH\n", - "from app.model import TextCleaner, TextLemmatizer" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "SEED = 42\n", - "MAX_FEATURES = 20000" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[nltk_data] Downloading package wordnet to /home/tymec/nltk_data...\n", - "[nltk_data] Package wordnet is already up-to-date!\n", - "[nltk_data] Downloading package stopwords to /home/tymec/nltk_data...\n", - "[nltk_data] Package stopwords is already up-to-date!\n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nltk.download(\"wordnet\")\n", - "nltk.download(\"stopwords\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load the data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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targetiddateflagusertextsentiment
001467810369Mon Apr 06 22:19:45 PDT 2009NO_QUERY_TheSpecialOne_@switchfoot http://twitpic.com/2y1zl - Awww, t...0
101467810672Mon Apr 06 22:19:49 PDT 2009NO_QUERYscotthamiltonis upset that he can't update his Facebook by ...0
201467810917Mon Apr 06 22:19:53 PDT 2009NO_QUERYmattycus@Kenichan I dived many times for the ball. Man...0
301467811184Mon Apr 06 22:19:57 PDT 2009NO_QUERYElleCTFmy whole body feels itchy and like its on fire0
401467811193Mon Apr 06 22:19:57 PDT 2009NO_QUERYKaroli@nationwideclass no, it's not behaving at all....0
\n", - "
" - ], - "text/plain": [ - " target id date flag \\\n", - "0 0 1467810369 Mon Apr 06 22:19:45 PDT 2009 NO_QUERY \n", - "1 0 1467810672 Mon Apr 06 22:19:49 PDT 2009 NO_QUERY \n", - "2 0 1467810917 Mon Apr 06 22:19:53 PDT 2009 NO_QUERY \n", - "3 0 1467811184 Mon Apr 06 22:19:57 PDT 2009 NO_QUERY \n", - "4 0 1467811193 Mon Apr 06 22:19:57 PDT 2009 NO_QUERY \n", - "\n", - " user text \\\n", - "0 _TheSpecialOne_ @switchfoot http://twitpic.com/2y1zl - Awww, t... \n", - "1 scotthamilton is upset that he can't update his Facebook by ... \n", - "2 mattycus @Kenichan I dived many times for the ball. Man... \n", - "3 ElleCTF my whole body feels itchy and like its on fire \n", - "4 Karoli @nationwideclass no, it's not behaving at all.... \n", - "\n", - " sentiment \n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Load the data\n", - "data = pd.read_csv(\n", - " SENTIMENT140_PATH,\n", - " encoding=\"ISO-8859-1\",\n", - " names=[\n", - " \"target\", # 0 = negative, 2 = neutral, 4 = positive\n", - " \"id\", # The id of the tweet\n", - " \"date\", # The date of the tweet\n", - " \"flag\", # The query, NO_QUERY if not present\n", - " \"user\", # The user that tweeted\n", - " \"text\", # The text of the tweet\n", - " ],\n", - ")\n", - "\n", - "# Ignore rows with neutral sentiment\n", - "data = data[data[\"target\"] != 2]\n", - "\n", - "# Map the sentiment values\n", - "data[\"sentiment\"] = data[\"target\"].map({0: 0, 4: 1})\n", - "\n", - "# Show the first few rows\n", - "data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load the stopwords" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "stopwords_en = stopwords.words(\"english\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Explore the data" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the distribution\n", - "_, ax = plt.subplots(figsize=(6, 4))\n", - "data[\"sentiment\"].value_counts().plot(kind=\"bar\", ax=ax)\n", - "ax.set_xticklabels([\"Negative\", \"Positive\"], rotation=0)\n", - "ax.set_xlabel(\"Sentiment\")\n", - "ax.grid(False)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "@cache\n", - "def extract_words(text: str) -> list[str]:\n", - " return re.findall(r\"(\\b[^\\s]+\\b)\", text.lower())" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
wordcount
0i750749
1to564469
2the520036
3a377506
4my314024
\n", - "
" - ], - "text/plain": [ - " word count\n", - "0 i 750749\n", - "1 to 564469\n", - "2 the 520036\n", - "3 a 377506\n", - "4 my 314024" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Extract words and count them\n", - "words = data[\"text\"].apply(extract_words).explode()\n", - "word_counts = words.value_counts().reset_index()\n", - "word_counts.columns = [\"word\", \"count\"]\n", - "word_counts.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the most common words\n", - "_, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n", - "\n", - "sns.barplot(data=word_counts.head(10), x=\"count\", y=\"word\", ax=ax1)\n", - "ax1.set_title(\"Most common words\")\n", - "ax1.grid(False)\n", - "ax1.tick_params(axis=\"x\", rotation=45)\n", - "\n", - "ax2.set_title(\"Most common words (excluding stopwords)\")\n", - "sns.barplot(\n", - " data=word_counts[~word_counts[\"word\"].isin(stopwords_en)].head(10),\n", - " x=\"count\",\n", - " y=\"word\",\n", - " ax=ax2,\n", - ")\n", - "ax2.grid(False)\n", - "ax2.tick_params(axis=\"x\", rotation=45)\n", - "ax2.set_ylabel(\"\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Split the data" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the features and target\n", - "X, y = data[\"text\"].tolist(), data[\"sentiment\"].tolist()\n", - "\n", - "# Split the data\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=SEED)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a tokenizer and transform the data" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the preprocessing pipeline\n", - "preprocess_pipeline = Pipeline(\n", - " [\n", - " # Text preprocessing\n", - " (\"clean\", TextCleaner()),\n", - " (\"lemma\", TextLemmatizer()),\n", - " # Tokenize (NOTE: Can be replaced with TfidfVectorizer, but left for clarity)\n", - " (\"vectorize\", CountVectorizer(stop_words=stopwords_en, ngram_range=(1, 2), max_features=MAX_FEATURES)),\n", - " (\"tfidf\", TfidfTransformer()),\n", - " ],\n", - " memory=joblib.Memory(CACHE_DIR, verbose=0),\n", - " verbose=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Pipeline] ............. (step 4 of 4) Processing tfidf, total= 0.0s\n" - ] - } - ], - "source": [ - "# Fit the pipeline\n", - "with warnings.catch_warnings():\n", - " warnings.simplefilter(\"ignore\")\n", - " preprocess_pipeline.fit(X_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Transform the data\n", - "X_train_preprocessed = preprocess_pipeline.transform(X_train)\n", - "X_test_preprocessed = preprocess_pipeline.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['.cache/X_test_preprocessed.pkl']" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Cache the preprocessed data\n", - "joblib.dump(X_train_preprocessed, CACHE_DIR / \"X_train_preprocessed.pkl\")\n", - "joblib.dump(X_test_preprocessed, CACHE_DIR / \"X_test_preprocessed.pkl\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Or load cached data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the transformed data\n", - "X_train_preprocessed = joblib.load(CACHE_DIR / \"X_train_preprocessed.pkl\")\n", - "X_test_preprocessed = joblib.load(CACHE_DIR / \"X_test_preprocessed.pkl\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pick the classifier" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "def evaluate_model(clf: BaseEstimator) -> None:\n", - " # Calculate the accuracy\n", - " accuracy = clf.score(X_test_preprocessed, y_test)\n", - "\n", - " # Calculate the confusion matrix\n", - " y_pred = clf.predict(X_test_preprocessed)\n", - " cm = confusion_matrix(y_test, y_pred)\n", - "\n", - " # Plot the confusion matrix\n", - " categories = [\"Negative\", \"Positive\"]\n", - " group_names = [\"True Neg\", \"False Pos\", \"False Neg\", \"True Pos\"]\n", - " group_percentages = [f\"{value:.2%}\" for value in cm.flatten() / cm.sum()]\n", - "\n", - " labels = [f\"{v1}\\n{v2}\" for v1, v2 in zip(group_names, group_percentages)]\n", - " labels = np.asarray(labels).reshape(2, 2)\n", - "\n", - " _, ax = plt.subplots(figsize=(8, 6))\n", - " ax.grid(False)\n", - " ax.set_title(f\"Accuracy: {accuracy:.2%}\")\n", - " sns.heatmap(\n", - " cm,\n", - " xticklabels=categories,\n", - " yticklabels=categories,\n", - " annot=labels,\n", - " square=True,\n", - " cbar=False,\n", - " cmap=\"viridis\",\n", - " linewidths=0.5,\n", - " fmt=\"\",\n", - " ax=ax,\n", - " )\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "def random_search(clf: BaseEstimator, param_distributions: dict) -> tuple[BaseEstimator, dict]:\n", - " # Create the search\n", - " search = RandomizedSearchCV(\n", - " clf,\n", - " param_distributions,\n", - " n_iter=10,\n", - " scoring=\"accuracy\",\n", - " n_jobs=-1,\n", - " cv=3,\n", - " random_state=SEED,\n", - " verbose=1,\n", - " )\n", - "\n", - " # Fit the search\n", - " search.fit(X_train_preprocessed, y_train)\n", - "\n", - " # Print the best parameters\n", - " print(f\"Best parameters: {search.best_params_}\")\n", - "\n", - " return search.best_estimator_, search.best_params_" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n", - "Best parameters: {'solver': 'liblinear', 'penalty': 'l2', 'C': 1438.44988828766}\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Logistic Regression\n", - "lr_clf = LogisticRegression(max_iter=1000, random_state=SEED)\n", - "\n", - "# Find optimal hyperparameters\n", - "best_lr_clf, lr_params = random_search(\n", - " lr_clf,\n", - " {\n", - " \"C\": np.logspace(-4, 4, 20),\n", - " \"solver\": [\"liblinear\", \"saga\"], # lbfgs takes too long\n", - " \"penalty\": [\"l1\", \"l2\"],\n", - " },\n", - ")\n", - "\n", - "# Evaluate the model\n", - "evaluate_model(best_lr_clf)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n" - ] - } - ], - "source": [ - "# SVM\n", - "svm_clf = SVC(random_state=SEED)\n", - "\n", - "# Find optimal hyperparameters\n", - "best_svm_clf, svm_params = random_search(\n", - " svm_clf,\n", - " {\n", - " \"C\": np.logspace(-4, 4, 20),\n", - " \"kernel\": [\"linear\", \"poly\", \"rbf\"],\n", - " \"degree\": [2, 3, 4],\n", - " },\n", - ")\n", - "\n", - "# Evaluate the model\n", - "evaluate_model(best_svm_clf)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Export the final model" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "best_clf = best_lr_clf # TODO: Pick the best classifier\n", - "best_params = lr_params # TODO: Pick the best parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Merge the tokenizer and the best classifier\n", - "model = Pipeline(\n", - " [\n", - " (\"preprocess\", preprocess_pipeline),\n", - " (\"clf\", best_clf),\n", - " ],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Export the model and the parameters\n", - "joblib.dump(model, MODELS_DIR / \"best_model.pkl\")\n", - "with Path.open(MODELS_DIR / \"best_params.json\", \"w\") as f:\n", - " json.dump(best_params, f, indent=2)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Import and test the model\n", - "model = joblib.load(MODELS_DIR / \"best_model.pkl\")\n", - "assert model.predict([\"I love this!\"])[0] == 1 # noqa: S101\n", - "assert model.predict([\"I hate this!\"])[0] == 0 # noqa: S101" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "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": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/poetry.lock b/poetry.lock index d4902412d1cca4d3d8a99ea99b74ebdb20ce3442..e82570158b82c455d104c2142cf91a1ea3335357 100644 Binary files a/poetry.lock and b/poetry.lock differ diff --git a/pyproject.toml b/pyproject.toml index a4555ec8464b6eae2fdd2e75f178242cb9e81367..e2036961831da21e910afc90bf37ffa5c985db78 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,15 +3,17 @@ name = "sentiment-analysis" package-mode = false [tool.poetry.dependencies] -python = "^3.12" -click = "^8.1.7" +python = "^3.11" scikit-learn = "^1.4.2" -gradio = "^4.31.0" colorama = "^0.4.6" -nltk = "^3.8.1" pandas = "^2.2.2" numpy = "^1.26.4" seaborn = "^0.13.2" +click = "^8.1.7" +tensorflow = { extras = ["and-cuda"], version = "^2.16.1" } +spacy = { extras = ["cuda12x"], version = "^3.7.4" } +gradio = "^4.26.0" +wordcloud = "^1.9.3" [tool.poetry.group.dev.dependencies] ruff = "^0.4.1" diff --git a/requirements-dev.txt b/requirements-dev.txt index 3cf5adf4b5328bf509e3127912992ad80ed601cc..ee26cc13864f41049b107ef853b4b160d02a0cdb 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -1,10 +1,10 @@ -appnope==0.1.4 ; python_version >= "3.12" and python_version < "4.0" and platform_system == "Darwin" \ +appnope==0.1.4 ; python_version >= "3.11" and python_version < "4.0" and platform_system == "Darwin" \ --hash=sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee \ --hash=sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c -asttokens==2.4.1 ; python_version >= "3.12" and python_version < "4.0" \ +asttokens==2.4.1 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24 \ --hash=sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0 -cffi==1.16.0 ; python_version >= "3.12" and python_version < "4.0" and implementation_name == "pypy" \ +cffi==1.16.0 ; python_version >= "3.11" and python_version < "4.0" and implementation_name == "pypy" \ --hash=sha256:0c9ef6ff37e974b73c25eecc13952c55bceed9112be2d9d938ded8e856138bcc \ --hash=sha256:131fd094d1065b19540c3d72594260f118b231090295d8c34e19a7bbcf2e860a \ --hash=sha256:1b8ebc27c014c59692bb2664c7d13ce7a6e9a629be20e54e7271fa696ff2b417 \ @@ -57,16 +57,16 @@ cffi==1.16.0 ; python_version >= "3.12" and python_version < "4.0" and implement --hash=sha256:ee07e47c12890ef248766a6e55bd38ebfb2bb8edd4142d56db91b21ea68b7627 \ --hash=sha256:fa3a0128b152627161ce47201262d3140edb5a5c3da88d73a1b790a959126956 \ --hash=sha256:fcc8eb6d5902bb1cf6dc4f187ee3ea80a1eba0a89aba40a5cb20a5087d961357 -cfgv==3.4.0 ; python_version >= "3.12" and python_version < "4.0" \ +cfgv==3.4.0 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9 \ --hash=sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560 -colorama==0.4.6 ; python_version >= "3.12" and python_version < "4.0" and sys_platform == "win32" \ +colorama==0.4.6 ; python_version >= "3.11" and python_version < "4.0" and sys_platform == "win32" \ --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \ --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6 -comm==0.2.2 ; python_version >= "3.12" and python_version < "4.0" \ +comm==0.2.2 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e \ --hash=sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3 -contourpy==1.2.1 ; python_version >= "3.12" and python_version < "4.0" \ +contourpy==1.2.1 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:00e5388f71c1a0610e6fe56b5c44ab7ba14165cdd6d695429c5cd94021e390b2 \ --hash=sha256:10a37ae557aabf2509c79715cd20b62e4c7c28b8cd62dd7d99e5ed3ce28c3fd9 \ --hash=sha256:11959f0ce4a6f7b76ec578576a0b61a28bdc0696194b6347ba3f1c53827178b9 \ @@ -111,10 +111,10 @@ contourpy==1.2.1 ; python_version >= "3.12" and python_version < "4.0" \ --hash=sha256:ef5adb9a3b1d0c645ff694f9bca7702ec2c70f4d734f9922ea34de02294fdf72 \ --hash=sha256:f32c38afb74bd98ce26de7cc74a67b40afb7b05aae7b42924ea990d51e4dac02 \ --hash=sha256:fe0ccca550bb8e5abc22f530ec0466136379c01321fd94f30a22231e8a48d985 -cycler==0.12.1 ; python_version >= "3.12" and python_version < "4.0" \ +cycler==0.12.1 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 \ --hash=sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c -debugpy==1.8.1 ; python_version >= "3.12" and python_version < "4.0" \ +debugpy==1.8.1 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:016a9fcfc2c6b57f939673c874310d8581d51a0fe0858e7fac4e240c5eb743cb \ --hash=sha256:0de56aba8249c28a300bdb0672a9b94785074eb82eb672db66c8144fff673146 \ --hash=sha256:1a9fe0829c2b854757b4fd0a338d93bc17249a3bf69ecf765c61d4c522bb92a8 \ @@ -137,92 +137,92 @@ debugpy==1.8.1 ; 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--hash=sha256:eb2e116e75ecef9d4d228fdc66af54269afa26ab4463042e33785b887c628ba8 -ipython==8.24.0 ; python_version >= "3.12" and python_version < "4.0" \ +ipython==8.24.0 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:010db3f8a728a578bb641fdd06c063b9fb8e96a9464c63aec6310fbcb5e80501 \ --hash=sha256:d7bf2f6c4314984e3e02393213bab8703cf163ede39672ce5918c51fe253a2a3 -ipywidgets==8.1.2 ; python_version >= "3.12" and python_version < "4.0" \ - --hash=sha256:bbe43850d79fb5e906b14801d6c01402857996864d1e5b6fa62dd2ee35559f60 \ - --hash=sha256:d0b9b41e49bae926a866e613a39b0f0097745d2b9f1f3dd406641b4a57ec42c9 -jedi==0.19.1 ; python_version >= "3.12" and python_version < "4.0" \ +ipywidgets==8.1.3 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:efafd18f7a142248f7cb0ba890a68b96abd4d6e88ddbda483c9130d12667eaf2 \ + --hash=sha256:f5f9eeaae082b1823ce9eac2575272952f40d748893972956dc09700a6392d9c +jedi==0.19.1 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd \ --hash=sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0 -jupyter-client==8.6.1 ; python_version >= "3.12" and python_version < "4.0" \ - --hash=sha256:3b7bd22f058434e3b9a7ea4b1500ed47de2713872288c0d511d19926f99b459f \ - --hash=sha256:e842515e2bab8e19186d89fdfea7abd15e39dd581f94e399f00e2af5a1652d3f -jupyter-core==5.7.2 ; python_version >= "3.12" and python_version < "4.0" \ +jupyter-client==8.6.2 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:2bda14d55ee5ba58552a8c53ae43d215ad9868853489213f37da060ced54d8df \ + --hash=sha256:50cbc5c66fd1b8f65ecb66bc490ab73217993632809b6e505687de18e9dea39f +jupyter-core==5.7.2 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409 \ --hash=sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9 -jupyterlab-widgets==3.0.10 ; python_version >= "3.12" and python_version < "4.0" \ - --hash=sha256:04f2ac04976727e4f9d0fa91cdc2f1ab860f965e504c29dbd6a65c882c9d04c0 \ - --hash=sha256:dd61f3ae7a5a7f80299e14585ce6cf3d6925a96c9103c978eda293197730cb64 -kiwisolver==1.4.5 ; python_version >= "3.12" and python_version < "4.0" \ +jupyterlab-widgets==3.0.11 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:78287fd86d20744ace330a61625024cf5521e1c012a352ddc0a3cdc2348becd0 \ + --hash=sha256:dd5ac679593c969af29c9bed054c24f26842baa51352114736756bc035deee27 +kiwisolver==1.4.5 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:00bd361b903dc4bbf4eb165f24d1acbee754fce22ded24c3d56eec268658a5cf \ --hash=sha256:040c1aebeda72197ef477a906782b5ab0d387642e93bda547336b8957c61022e \ --hash=sha256:05703cf211d585109fcd72207a31bb170a0f22144d68298dc5e61b3c946518af \ @@ -327,10 +327,10 @@ kiwisolver==1.4.5 ; python_version >= "3.12" and python_version < "4.0" \ --hash=sha256:fcc700eadbbccbf6bc1bcb9dbe0786b4b1cb91ca0dcda336eef5c2beed37b797 \ --hash=sha256:fd32ea360bcbb92d28933fc05ed09bffcb1704ba3fc7942e81db0fd4f81a7892 \ --hash=sha256:fdb7adb641a0d13bdcd4ef48e062363d8a9ad4a182ac7647ec88f695e719ae9f -matplotlib-inline==0.1.7 ; python_version >= "3.12" and python_version < "4.0" \ +matplotlib-inline==0.1.7 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90 \ --hash=sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca -matplotlib==3.9.0 ; python_version >= "3.12" and python_version < "4.0" \ +matplotlib==3.9.0 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:063af8587fceeac13b0936c42a2b6c732c2ab1c98d38abc3337e430e1ff75e38 \ --hash=sha256:06a478f0d67636554fa78558cfbcd7b9dba85b51f5c3b5a0c9be49010cf5f321 \ --hash=sha256:0a490715b3b9984fa609116481b22178348c1a220a4499cda79132000a79b4db \ @@ -360,13 +360,13 @@ matplotlib==3.9.0 ; python_version >= "3.12" and python_version < "4.0" \ --hash=sha256:e6d29ea6c19e34b30fb7d88b7081f869a03014f66fe06d62cc77d5a6ea88ed7a \ --hash=sha256:eaf3978060a106fab40c328778b148f590e27f6fa3cd15a19d6892575bce387d \ --hash=sha256:fe428e191ea016bb278758c8ee82a8129c51d81d8c4bc0846c09e7e8e9057241 -nest-asyncio==1.6.0 ; python_version >= "3.12" and python_version < "4.0" \ +nest-asyncio==1.6.0 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe \ --hash=sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c -nodeenv==1.8.0 ; python_version >= "3.12" and python_version < "4.0" \ - --hash=sha256:d51e0c37e64fbf47d017feac3145cdbb58836d7eee8c6f6d3b6880c5456227d2 \ - --hash=sha256:df865724bb3c3adc86b3876fa209771517b0cfe596beff01a92700e0e8be4cec -numpy==1.26.4 ; python_version >= "3.12" and python_version < "4.0" \ +nodeenv==1.9.0 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:07f144e90dae547bf0d4ee8da0ee42664a42a04e02ed68e06324348dafe4bdb1 \ + --hash=sha256:508ecec98f9f3330b636d4448c0f1a56fc68017c68f1e7857ebc52acf0eb879a +numpy==1.26.4 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b \ --hash=sha256:08beddf13648eb95f8d867350f6a018a4be2e5ad54c8d8caed89ebca558b2818 \ --hash=sha256:1af303d6b2210eb850fcf03064d364652b7120803a0b872f5211f5234b399f20 \ @@ -403,16 +403,16 @@ numpy==1.26.4 ; python_version >= "3.12" and python_version < "4.0" \ --hash=sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef \ --hash=sha256:f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3 \ --hash=sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f -packaging==24.0 ; python_version >= "3.12" and python_version < "4.0" \ +packaging==24.0 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:2ddfb553fdf02fb784c234c7ba6ccc288296ceabec964ad2eae3777778130bc5 \ --hash=sha256:eb82c5e3e56209074766e6885bb04b8c38a0c015d0a30036ebe7ece34c9989e9 -parso==0.8.4 ; python_version >= "3.12" and python_version < "4.0" \ +parso==0.8.4 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:a418670a20291dacd2dddc80c377c5c3791378ee1e8d12bffc35420643d43f18 \ --hash=sha256:eb3a7b58240fb99099a345571deecc0f9540ea5f4dd2fe14c2a99d6b281ab92d -pexpect==4.9.0 ; python_version >= "3.12" and python_version < "4.0" and (sys_platform != "win32" and sys_platform != "emscripten") \ +pexpect==4.9.0 ; python_version >= "3.11" and python_version < "4.0" and (sys_platform != "win32" and sys_platform != "emscripten") \ --hash=sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523 \ --hash=sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f -pillow==10.3.0 ; python_version >= "3.12" and python_version < "4.0" \ +pillow==10.3.0 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:048ad577748b9fa4a99a0548c64f2cb8d672d5bf2e643a739ac8faff1164238c \ --hash=sha256:048eeade4c33fdf7e08da40ef402e748df113fd0b4584e32c4af74fe78baaeb2 \ --hash=sha256:0ba26351b137ca4e0db0342d5d00d2e355eb29372c05afd544ebf47c0956ffeb \ @@ -482,16 +482,16 @@ pillow==10.3.0 ; python_version >= "3.12" and python_version < "4.0" \ --hash=sha256:f0d0591a0aeaefdaf9a5e545e7485f89910c977087e7de2b6c388aec32011e9f \ --hash=sha256:fdcbb4068117dfd9ce0138d068ac512843c52295ed996ae6dd1faf537b6dbc27 \ --hash=sha256:ff61bfd9253c3915e6d41c651d5f962da23eda633cf02262990094a18a55371a -platformdirs==4.2.2 ; python_version >= "3.12" and python_version < "4.0" \ +platformdirs==4.2.2 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:2d7a1657e36a80ea911db832a8a6ece5ee53d8de21edd5cc5879af6530b1bfee \ --hash=sha256:38b7b51f512eed9e84a22788b4bce1de17c0adb134d6becb09836e37d8654cd3 -pre-commit==3.7.1 ; python_version >= "3.12" and python_version < "4.0" \ +pre-commit==3.7.1 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:8ca3ad567bc78a4972a3f1a477e94a79d4597e8140a6e0b651c5e33899c3654a \ --hash=sha256:fae36fd1d7ad7d6a5a1c0b0d5adb2ed1a3bda5a21bf6c3e5372073d7a11cd4c5 -prompt-toolkit==3.0.43 ; python_version >= "3.12" and python_version < "4.0" \ - --hash=sha256:3527b7af26106cbc65a040bcc84839a3566ec1b051bb0bfe953631e704b0ff7d \ - --hash=sha256:a11a29cb3bf0a28a387fe5122cdb649816a957cd9261dcedf8c9f1fef33eacf6 -psutil==5.9.8 ; python_version >= "3.12" and python_version < "4.0" \ +prompt-toolkit==3.0.45 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:07c60ee4ab7b7e90824b61afa840c8f5aad2d46b3e2e10acc33d8ecc94a49089 \ + --hash=sha256:a29b89160e494e3ea8622b09fa5897610b437884dcdcd054fdc1308883326c2a +psutil==5.9.8 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:02615ed8c5ea222323408ceba16c60e99c3f91639b07da6373fb7e6539abc56d \ --hash=sha256:05806de88103b25903dff19bb6692bd2e714ccf9e668d050d144012055cbca73 \ --hash=sha256:26bd09967ae00920df88e0352a91cff1a78f8d69b3ecabbfe733610c0af486c8 \ @@ -508,25 +508,25 @@ psutil==5.9.8 ; python_version >= "3.12" and python_version < "4.0" \ --hash=sha256:bd1184ceb3f87651a67b2708d4c3338e9b10c5df903f2e3776b62303b26cb631 \ --hash=sha256:d06016f7f8625a1825ba3732081d77c94589dca78b7a3fc072194851e88461a4 \ --hash=sha256:d16bbddf0693323b8c6123dd804100241da461e41d6e332fb0ba6058f630f8c8 -ptyprocess==0.7.0 ; python_version >= "3.12" and python_version < "4.0" and (sys_platform != "win32" and sys_platform != "emscripten") \ +ptyprocess==0.7.0 ; python_version >= "3.11" and python_version < "4.0" and (sys_platform != "win32" and sys_platform != "emscripten") \ --hash=sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35 \ --hash=sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220 -pure-eval==0.2.2 ; python_version >= "3.12" and python_version < "4.0" \ +pure-eval==0.2.2 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:01eaab343580944bc56080ebe0a674b39ec44a945e6d09ba7db3cb8cec289350 \ --hash=sha256:2b45320af6dfaa1750f543d714b6d1c520a1688dec6fd24d339063ce0aaa9ac3 -pycparser==2.22 ; python_version >= "3.12" and python_version < "4.0" and implementation_name == "pypy" \ +pycparser==2.22 ; python_version >= "3.11" and python_version < "4.0" and implementation_name == "pypy" \ --hash=sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6 \ --hash=sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc -pygments==2.18.0 ; python_version >= "3.12" and python_version < "4.0" \ +pygments==2.18.0 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:786ff802f32e91311bff3889f6e9a86e81505fe99f2735bb6d60ae0c5004f199 \ --hash=sha256:b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a -pyparsing==3.1.2 ; python_version >= "3.12" and python_version < "4.0" \ +pyparsing==3.1.2 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:a1bac0ce561155ecc3ed78ca94d3c9378656ad4c94c1270de543f621420f94ad \ --hash=sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742 -python-dateutil==2.9.0.post0 ; python_version >= "3.12" and python_version < "4.0" \ +python-dateutil==2.9.0.post0 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3 \ --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 -pywin32==306 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.12" and python_version < "4.0" \ +pywin32==306 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:06d3420a5155ba65f0b72f2699b5bacf3109f36acbe8923765c22938a69dfc8d \ --hash=sha256:1c73ea9a0d2283d889001998059f5eaaba3b6238f767c9cf2833b13e6a685f65 \ --hash=sha256:37257794c1ad39ee9be652da0462dc2e394c8159dfd913a8a4e8eb6fd346da0e \ @@ -541,7 +541,7 @@ pywin32==306 ; sys_platform == "win32" and platform_python_implementation != "Py --hash=sha256:e4c092e2589b5cf0d365849e73e02c391c1349958c5ac3e9d5ccb9a28e017b3a \ --hash=sha256:e65028133d15b64d2ed8f06dd9fbc268352478d4f9289e69c190ecd6818b6407 \ --hash=sha256:e8ac1ae3601bee6ca9f7cb4b5363bf1c0badb935ef243c4733ff9a393b1690c0 -pyyaml==6.0.1 ; python_version >= "3.12" and python_version < "4.0" \ +pyyaml==6.0.1 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5 \ --hash=sha256:062582fca9fabdd2c8b54a3ef1c978d786e0f6b3a1510e0ac93ef59e0ddae2bc \ --hash=sha256:0d3304d8c0adc42be59c5f8a4d9e3d7379e6955ad754aa9d6ab7a398b59dd1df \ @@ -593,7 +593,7 @@ pyyaml==6.0.1 ; python_version >= "3.12" and python_version < "4.0" \ --hash=sha256:fca0e3a251908a499833aa292323f32437106001d436eca0e6e7833256674585 \ --hash=sha256:fd1592b3fdf65fff2ad0004b5e363300ef59ced41c2e6b3a99d4089fa8c5435d \ --hash=sha256:fd66fc5d0da6d9815ba2cebeb4205f95818ff4b79c3ebe268e75d961704af52f -pyzmq==26.0.3 ; python_version >= "3.12" and python_version < "4.0" \ +pyzmq==26.0.3 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:01fbfbeb8249a68d257f601deb50c70c929dc2dfe683b754659569e502fbd3aa \ --hash=sha256:0270b49b6847f0d106d64b5086e9ad5dc8a902413b5dbbb15d12b60f9c1747a4 \ --hash=sha256:03c0ae165e700364b266876d712acb1ac02693acd920afa67da2ebb91a0b3c09 \ @@ -682,34 +682,31 @@ pyzmq==26.0.3 ; python_version >= "3.12" and python_version < "4.0" \ --hash=sha256:f4b6cecbbf3b7380f3b61de3a7b93cb721125dc125c854c14ddc91225ba52f83 \ --hash=sha256:f6b1d1c631e5940cac5a0b22c5379c86e8df6a4ec277c7a856b714021ab6cfad \ --hash=sha256:f6c21c00478a7bea93caaaef9e7629145d4153b15a8653e8bb4609d4bc70dbfc -ruff==0.4.4 ; python_version >= "3.12" and python_version < "4.0" \ - --hash=sha256:1aecced1269481ef2894cc495647392a34b0bf3e28ff53ed95a385b13aa45768 \ - --hash=sha256:29d44ef5bb6a08e235c8249294fa8d431adc1426bfda99ed493119e6f9ea1bf6 \ - --hash=sha256:39df0537b47d3b597293edbb95baf54ff5b49589eb7ff41926d8243caa995ea6 \ - --hash=sha256:424e5b72597482543b684c11def82669cc6b395aa8cc69acc1858b5ef3e5daae \ - --hash=sha256:4c8e2f1e8fc12d07ab521a9005d68a969e167b589cbcaee354cb61e9d9de9c15 \ - --hash=sha256:60ed88b636a463214905c002fa3eaab19795679ed55529f91e488db3fe8976ab \ - --hash=sha256:8e7e6ebc10ef16dcdc77fd5557ee60647512b400e4a60bdc4849468f076f6eef \ - --hash=sha256:958b4ea5589706a81065e2a776237de2ecc3e763342e5cc8e02a4a4d8a5e6f95 \ - --hash=sha256:9da73eb616b3241a307b837f32756dc20a0b07e2bcb694fec73699c93d04a69e \ - --hash=sha256:b1867ee9bf3acc21778dcb293db504692eda5f7a11a6e6cc40890182a9f9e595 \ - --hash=sha256:b5eb0a4bfd6400b7d07c09a7725e1a98c3b838be557fee229ac0f84d9aa49c36 \ - --hash=sha256:b90fc5e170fc71c712cc4d9ab0e24ea505c6a9e4ebf346787a67e691dfb72e85 \ - --hash=sha256:b9ddb2c494fb79fc208cd15ffe08f32b7682519e067413dbaf5f4b01a6087bcd \ - --hash=sha256:c4efe62b5bbb24178c950732ddd40712b878a9b96b1d02b0ff0b08a090cbd891 \ - --hash=sha256:c51c928a14f9f0a871082603e25a1588059b7e08a920f2f9fa7157b5bf08cfe9 \ - --hash=sha256:cb53473849f011bca6e754f2cdf47cafc9c4f4ff4570003a0dad0b9b6890e876 \ - --hash=sha256:f87ea42d5cdebdc6a69761a9d0bc83ae9b3b30d0ad78952005ba6568d6c022af -setuptools==69.5.1 ; python_version >= "3.12" and python_version < "4.0" \ - --hash=sha256:6c1fccdac05a97e598fb0ae3bbed5904ccb317337a51139dcd51453611bbb987 \ - --hash=sha256:c636ac361bc47580504644275c9ad802c50415c7522212252c033bd15f301f32 -six==1.16.0 ; python_version >= "3.12" and python_version < "4.0" \ +ruff==0.4.6 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:04a80acfc862e0e1630c8b738e70dcca03f350bad9e106968a8108379e12b31f \ + --hash=sha256:0cf5cc02d3ae52dfb0c8a946eb7a1d6ffe4d91846ffc8ce388baa8f627e3bd50 \ + --hash=sha256:1fa8561489fadf483ffbb091ea94b9c39a00ed63efacd426aae2f197a45e67fc \ + --hash=sha256:1ff930d6e05f444090a0139e4e13e1e2e1f02bd51bb4547734823c760c621e79 \ + --hash=sha256:3a6a0a4f4b5f54fff7c860010ab3dd81425445e37d35701a965c0248819dde7a \ + --hash=sha256:3f9ced5cbb7510fd7525448eeb204e0a22cabb6e99a3cb160272262817d49786 \ + --hash=sha256:4d5b914818d8047270308fe3e85d9d7f4a31ec86c6475c9f418fbd1624d198e0 \ + --hash=sha256:4f02284335c766678778475e7698b7ab83abaf2f9ff0554a07b6f28df3b5c259 \ + --hash=sha256:602ebd7ad909eab6e7da65d3c091547781bb06f5f826974a53dbe563d357e53c \ + --hash=sha256:735a16407a1a8f58e4c5b913ad6102722e80b562dd17acb88887685ff6f20cf6 \ + --hash=sha256:9018bf59b3aa8ad4fba2b1dc0299a6e4e60a4c3bc62bbeaea222679865453062 \ + --hash=sha256:a769ae07ac74ff1a019d6bd529426427c3e30d75bdf1e08bb3d46ac8f417326a \ + --hash=sha256:a797a87da50603f71e6d0765282098245aca6e3b94b7c17473115167d8dfb0b7 \ + --hash=sha256:be47700ecb004dfa3fd4dcdddf7322d4e632de3c06cd05329d69c45c0280e618 \ + --hash=sha256:ea3424793c29906407e3cf417f28fc33f689dacbbadfb52b7e9a809dd535dcef \ + --hash=sha256:ef995583a038cd4a7edf1422c9e19118e2511b8ba0b015861b4abd26ec5367c5 \ + --hash=sha256:f13410aabd3b5776f9c5699f42b37a3a348d65498c4310589bc6e5c548dc8a2f +six==1.16.0 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926 \ --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254 -stack-data==0.6.3 ; python_version >= "3.12" and python_version < "4.0" \ +stack-data==0.6.3 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9 \ --hash=sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695 -tornado==6.4 ; python_version >= "3.12" and python_version < "4.0" \ +tornado==6.4 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:02ccefc7d8211e5a7f9e8bc3f9e5b0ad6262ba2fbb683a6443ecc804e5224ce0 \ --hash=sha256:10aeaa8006333433da48dec9fe417877f8bcc21f48dda8d661ae79da357b2a63 \ --hash=sha256:27787de946a9cffd63ce5814c33f734c627a87072ec7eed71f7fc4417bb16263 \ @@ -721,15 +718,18 @@ tornado==6.4 ; python_version >= "3.12" and python_version < "4.0" \ --hash=sha256:f0251554cdd50b4b44362f73ad5ba7126fc5b2c2895cc62b14a1c2d7ea32f212 \ --hash=sha256:f7894c581ecdcf91666a0912f18ce5e757213999e183ebfc2c3fdbf4d5bd764e \ --hash=sha256:fd03192e287fbd0899dd8f81c6fb9cbbc69194d2074b38f384cb6fa72b80e9c2 -traitlets==5.14.3 ; python_version >= "3.12" and python_version < "4.0" \ +traitlets==5.14.3 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7 \ --hash=sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f -virtualenv==20.26.2 ; python_version >= "3.12" and python_version < "4.0" \ +typing-extensions==4.12.0 ; python_version >= "3.11" and python_version < "3.12" \ + --hash=sha256:8cbcdc8606ebcb0d95453ad7dc5065e6237b6aa230a31e81d0f440c30fed5fd8 \ + --hash=sha256:b349c66bea9016ac22978d800cfff206d5f9816951f12a7d0ec5578b0a819594 +virtualenv==20.26.2 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:82bf0f4eebbb78d36ddaee0283d43fe5736b53880b8a8cdcd37390a07ac3741c \ --hash=sha256:a624db5e94f01ad993d476b9ee5346fdf7b9de43ccaee0e0197012dc838a0e9b -wcwidth==0.2.13 ; python_version >= "3.12" and python_version < "4.0" \ +wcwidth==0.2.13 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859 \ --hash=sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5 -widgetsnbextension==4.0.10 ; python_version >= "3.12" and python_version < "4.0" \ - --hash=sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f \ - --hash=sha256:d37c3724ec32d8c48400a435ecfa7d3e259995201fbefa37163124a9fcb393cc +widgetsnbextension==4.0.11 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:55d4d6949d100e0d08b94948a42efc3ed6dfdc0e9468b2c4b128c9a2ce3a7a36 \ + --hash=sha256:8b22a8f1910bfd188e596fe7fc05dcbd87e810c8a4ba010bdb3da86637398474 diff --git a/requirements.txt b/requirements.txt index 92262d9746ab6c156385d641bb70e1bfd846ecc8..dff624797007c4d18cc3cdf80af922f8c9f14dfe 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,22 +1,66 @@ -aiofiles==23.2.1 ; python_version >= "3.12" and python_version < "4.0" \ +absl-py==2.1.0 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308 \ + --hash=sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff +aiofiles==23.2.1 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:19297512c647d4b27a2cf7c34caa7e405c0d60b5560618a29a9fe027b18b0107 \ --hash=sha256:84ec2218d8419404abcb9f0c02df3f34c6e0a68ed41072acfb1cef5cbc29051a -altair==5.3.0 ; python_version >= "3.12" and python_version < "4.0" \ +altair==5.3.0 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:5a268b1a0983b23d8f9129f819f956174aa7aea2719ed55a52eba9979b9f6675 \ --hash=sha256:7084a1dab4d83c5e7e5246b92dc1b4451a6c68fd057f3716ee9d315c8980e59a -annotated-types==0.6.0 ; python_version >= "3.12" and python_version < "4.0" \ - --hash=sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43 \ - --hash=sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d -anyio==4.3.0 ; python_version >= "3.12" and python_version < "4.0" \ - --hash=sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8 \ - --hash=sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6 -attrs==23.2.0 ; python_version >= "3.12" and python_version < "4.0" \ +annotated-types==0.7.0 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53 \ + --hash=sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89 +anyio==4.4.0 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94 \ + --hash=sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7 +astunparse==1.6.3 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872 \ + --hash=sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8 +attrs==23.2.0 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30 \ --hash=sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1 -certifi==2024.2.2 ; python_version >= "3.12" and python_version < "4.0" \ +blis==0.7.11 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:0421d6e44cda202b113a34761f9a062b53f8c2ae8e4ec8325a76e709fca93b6e \ + --hash=sha256:075431b13b9dd7b411894d4afbd4212acf4d0f56c5a20628f4b34902e90225f1 \ + --hash=sha256:0dc9dcb3843045b6b8b00432409fd5ee96b8344a324e031bfec7303838c41a1a \ + --hash=sha256:162e60d941a8151418d558a94ee5547cb1bbeed9f26b3b6f89ec9243f111a201 \ + --hash=sha256:1b68df4d01d62f9adaef3dad6f96418787265a6878891fc4e0fabafd6d02afba \ + --hash=sha256:2e62cd14b20e960f21547fee01f3a0b2ac201034d819842865a667c969c355d1 \ + --hash=sha256:2fb36989ed61233cfd48915896802ee6d3d87882190000f8cfe0cf4a3819f9a8 \ + --hash=sha256:2ff7abd784033836b284ff9f4d0d7cb0737b7684daebb01a4c9fe145ffa5a31e \ + --hash=sha256:31273d9086cab9c56986d478e3ed6da6752fa4cdd0f7b5e8e5db30827912d90d \ + --hash=sha256:324fdf62af9075831aa62b51481960e8465674b7723f977684e32af708bb7448 \ + --hash=sha256:5a305dbfc96d202a20d0edd6edf74a406b7e1404f4fa4397d24c68454e60b1b4 \ + --hash=sha256:5bb38adabbb22f69f22c74bad025a010ae3b14de711bf5c715353980869d491d \ + --hash=sha256:5bcdaf370f03adaf4171d6405a89fa66cb3c09399d75fc02e1230a78cd2759e4 \ + --hash=sha256:68544a1cbc3564db7ba54d2bf8988356b8c7acd025966e8e9313561b19f0fe2e \ + --hash=sha256:686a7d0111d5ba727cd62f374748952fd6eb74701b18177f525b16209a253c01 \ + --hash=sha256:688a8b21d2521c2124ee8dfcbaf2c385981ccc27e313e052113d5db113e27d3b \ + --hash=sha256:6df00c24128e323174cde5d80ebe3657df39615322098ce06613845433057614 \ + --hash=sha256:7de19264b1d49a178bf8035406d0ae77831f3bfaa3ce02942964a81a202abb03 \ + --hash=sha256:7ea09f961871f880d5dc622dce6c370e4859559f0ead897ae9b20ddafd6b07a2 \ + --hash=sha256:809d1da1331108935bf06e22f3cf07ef73a41a572ecd81575bdedb67defe3465 \ + --hash=sha256:844b6377e3e7f3a2e92e7333cc644095386548ad5a027fdc150122703c009956 \ + --hash=sha256:89b01c05a5754edc0b9a3b69be52cbee03f645b2ec69651d12216ea83b8122f0 \ + --hash=sha256:8ea55c6a4a60fcbf6a0fdce40df6e254451ce636988323a34b9c94b583fc11e5 \ + --hash=sha256:a73945a9d635eea528bccfdfcaa59dd35bd5f82a4a40d5ca31f08f507f3a6f81 \ + --hash=sha256:afebdb02d2dcf9059f23ce1244585d3ce7e95c02a77fd45a500e4a55b7b23583 \ + --hash=sha256:bfabd5272bbbe504702b8dfe30093653d278057656126716ff500d9c184b35a6 \ + --hash=sha256:ca684f5c2f05269f17aefe7812360286e9a1cee3afb96d416485efd825dbcf19 \ + --hash=sha256:cd5fba34c5775e4c440d80e4dea8acb40e2d3855b546e07c4e21fad8f972404c \ + --hash=sha256:cec6d48f75f7ac328ae1b6fbb372dde8c8a57c89559172277f66e01ff08d4d42 \ + --hash=sha256:cfee5ec52ba1e9002311d9191f7129d7b0ecdff211e88536fb24c865d102b50d \ + --hash=sha256:d06883f83d4c8de8264154f7c4a420b4af323050ed07398c1ff201c34c25c0d2 \ + --hash=sha256:dadf8713ea51d91444d14ad4104a5493fa7ecc401bbb5f4a203ff6448fadb113 \ + --hash=sha256:ee493683e3043650d4413d531e79e580d28a3c7bdd184f1b9cfa565497bda1e7 \ + --hash=sha256:f9caffcd14795bfe52add95a0dd8426d44e737b55fcb69e2b797816f4da0b1d2 +catalogue==2.0.10 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:4f56daa940913d3f09d589c191c74e5a6d51762b3a9e37dd53b7437afd6cda15 \ + --hash=sha256:58c2de0020aa90f4a2da7dfad161bf7b3b054c86a5f09fcedc0b2b740c109a9f +certifi==2024.2.2 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f \ --hash=sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1 -charset-normalizer==3.3.2 ; python_version >= "3.12" and python_version < "4.0" \ +charset-normalizer==3.3.2 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027 \ --hash=sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087 \ --hash=sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786 \ @@ -107,13 +151,19 @@ charset-normalizer==3.3.2 ; python_version >= "3.12" and python_version < "4.0" --hash=sha256:fb69256e180cb6c8a894fee62b3afebae785babc1ee98b81cdf68bbca1987f33 \ --hash=sha256:fd1abc0d89e30cc4e02e4064dc67fcc51bd941eb395c502aac3ec19fab46b519 \ --hash=sha256:ff8fa367d09b717b2a17a052544193ad76cd49979c805768879cb63d9ca50561 -click==8.1.7 ; python_version >= "3.12" and python_version < "4.0" \ +click==8.1.7 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28 \ --hash=sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de -colorama==0.4.6 ; python_version >= "3.12" and python_version < "4.0" \ +cloudpathlib==0.16.0 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:cdfcd35d46d529587d744154a0bdf962aca953b725c8784cd2ec478354ea63a3 \ + --hash=sha256:f46267556bf91f03db52b5df7a152548596a15aabca1c8731ef32b0b25a1a6a3 +colorama==0.4.6 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \ --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6 -contourpy==1.2.1 ; python_version >= "3.12" and python_version < "4.0" \ +confection==0.1.4 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:a658818d004939069c3e2b3db74a2cb9d956a5e61a1c9ad61788e0ee09a7090f \ + --hash=sha256:e80f22fd008b5231a2e8852fac6de9e28f2276a04031d0536cff74fe4a990c8f +contourpy==1.2.1 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:00e5388f71c1a0610e6fe56b5c44ab7ba14165cdd6d695429c5cd94021e390b2 \ --hash=sha256:10a37ae557aabf2509c79715cd20b62e4c7c28b8cd62dd7d99e5ed3ce28c3fd9 \ --hash=sha256:11959f0ce4a6f7b76ec578576a0b61a28bdc0696194b6347ba3f1c53827178b9 \ @@ -158,146 +208,308 @@ contourpy==1.2.1 ; python_version >= "3.12" and python_version < "4.0" \ --hash=sha256:ef5adb9a3b1d0c645ff694f9bca7702ec2c70f4d734f9922ea34de02294fdf72 \ --hash=sha256:f32c38afb74bd98ce26de7cc74a67b40afb7b05aae7b42924ea990d51e4dac02 \ 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--hash=sha256:a4209962b9f85de397c3203ea4b3a554da01ae9fd220fdab38757d4e9eba8d1a \ + --hash=sha256:a99dedfb7f0cc5a4cd76eb409ee98d3d50eba024f934e705914f6f4d765aef2c \ + --hash=sha256:b3b6d2d877d8881a08be66d906856d05944be0faf22b9a0390338bcf45299989 \ + --hash=sha256:c65401a6f1778676253cbf89c1f45a8a7feb7d73038e483925df7d5943c08ed9 \ + --hash=sha256:d75840212bf75eb1352c946c3cf1622dacddd6d6bdda34368237d1eb3568f23a \ + --hash=sha256:d8f54b0031d8696fed17ed6e9628f339cdea0ba2367ca051e18ff59193f52687 \ + --hash=sha256:db1171a3f9a10571931764cdbfaa5371f4cf5c23c680639762125cb075b833a5 \ + --hash=sha256:e5d7cfe392c0a28129226271008e61e77bf307afc24abf34f386771daa7b28b0 \ + --hash=sha256:f1605fde07030516eb63d77a598dd164fb9bf217fd937dbac588fe7e47a28c40 \ + --hash=sha256:f23f2dfc7174de2cdc5007c0771ab8376a2a3f48247f32cac4a5563e40c6adcc \ + --hash=sha256:f8ecb00cc1ab57e4b065f9fb3ea923b55160c402d959c69a0b6dbbe8bc73efc3 +namex==0.0.8 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:32a50f6c565c0bb10aa76298c959507abdc0e850efe085dc38f3440fcb3aa90b \ + --hash=sha256:7ddb6c2bb0e753a311b7590f84f6da659dd0c05e65cb89d519d54c0a250c0487 +numpy==1.26.4 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b \ --hash=sha256:08beddf13648eb95f8d867350f6a018a4be2e5ad54c8d8caed89ebca558b2818 \ --hash=sha256:1af303d6b2210eb850fcf03064d364652b7120803a0b872f5211f5234b399f20 \ @@ -539,7 +911,95 @@ numpy==1.26.4 ; python_version >= "3.12" and python_version < "4.0" \ --hash=sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef \ --hash=sha256:f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3 \ --hash=sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f -orjson==3.10.3 ; python_version >= "3.12" and python_version < "4.0" \ +nvidia-cublas-cu12==12.3.4.1 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:90e0344a0d96c72b6ee0ebec2b1447e552217131914aa678fff1603701005f3a \ + --hash=sha256:9b25b27bd5a5be1cc7e83a7b8004258daa338800798e22986e5b67ee06f0859d \ + --hash=sha256:d907eeefc5bab70c59454fe04c4c0f01928f667a94ad5ad4fb88a3f2b9920a88 +nvidia-cuda-cupti-cu12==12.3.101 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:735e3c5b0db489ff1277aacf19f17fc61c89dfd4d95c9f31f75a32550888c2eb \ + --hash=sha256:7b984288af7db1faa1e6f1803db075d4350116b72a98d4be0e88d604dc812c0e \ + --hash=sha256:a6576988a2496e95b7194f3c0cfd152cc76afaf611582e430d9a0093ca9aa91c +nvidia-cuda-nvcc-cu12==12.3.107 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:35de71d29aef2e8699f083e5ef604155a8e1ada614d3ccba796303e8d2252c61 \ + --hash=sha256:752f661c6ebf32bfb7e995f353a8de94c099ce6d2a8f2d747051d7e3806d9024 \ + --hash=sha256:a3344b94f5e89023fa17e0bbf8fb0dd26d5ef6f4091a5aab08f215e4999eac62 +nvidia-cuda-nvrtc-cu12==12.3.107 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:048f0526521bcff2e8fda76d3f0b5284a24065f96096ad3f8f300ad970d7eae0 \ + --hash=sha256:3971fca7d5eb4b102dabcae377959bbdb281b77d40361839f3ce254a1b0e6fcc \ + --hash=sha256:4db66788a37a234304a0b3203744f9a6c665f48bde65f69a096ae3b5799b5b1d +nvidia-cuda-runtime-cu12==12.3.101 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:04604679ddca6acb96c76a7a9306282cddc16981f92e148e2dfe227078883cda \ + --hash=sha256:8a91b2284458643233097d10887b93dc4e8085d49228e228db698ec4bcf5ac3f \ + --hash=sha256:fbbfb8f2b933cfbd33461cc5401cbeafb8b0a05cc18ec8070c10352c9b1b0d57 +nvidia-cudnn-cu12==8.9.7.29 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:4447321a2bdc8bd965084c1824575eb04f47a03ab62bbeb6cce7e9f74c3657f3 \ + --hash=sha256:adf4f59ed7a1341103822ed8df6e144f4d47ea8b10d9bf0ea0047ba738fd7b02 +nvidia-cufft-cu12==11.0.12.1 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:1a2515a3f57ca9b05b8518f2172f446d19d1a1a5b5bdf03c2fa71961210daa3f \ + --hash=sha256:3b2c85d24d7dcc578720fdf39e2823950b5857e85164c3c1dc97184a8a8e0398 \ + --hash=sha256:f3c4ea318156899a1c14e16cceeb5ba204c4e57886dc018812d0f23664e753e2 +nvidia-curand-cu12==10.3.4.107 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:76eb6bcf8e1278150d5e22c88f60c5262baab679d1cae4b68abfdc85ed9c6892 \ + --hash=sha256:d798db82ae445cf1d8bf6af9ca3bd6bb6cc87753c229a441cf8d2597b09332e5 \ + --hash=sha256:ecdde1b11d20c07e5188ddc4d4fdd8a93254e59c4e5366a7fa34de50143e0cb9 +nvidia-cusolver-cu12==11.5.4.101 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:2c0b27909d7ca7eeffa9c67f88750093b98d540efacc2f709f42da253a676144 \ + --hash=sha256:783a3279553feaf8e47af12cab1fa769ffd5e644ea876db3dc2252f35f9bf256 \ + --hash=sha256:934511477d963cfd3a793bd43ac2823a753e542b2f884d7c34065068905eab40 +nvidia-cusparse-cu12==12.2.0.103 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:4a37a132497cb9c9a9aa9c7324c1dc63d564f257a93ed23ac250ebb0d050f408 \ + --hash=sha256:bb1838a4af1b05108b6c37d52994a25fc1dd4746fd85dd6858d50dff431719be \ + --hash=sha256:fe9e17daf406c98c28fbc8bcf785ecd4db301bf02ce626de6aa1046de66f84bd +nvidia-nccl-cu12==2.19.3 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:a9734707a2c96443331c1e48c717024aa6678a0e2a4cb66b2c364d18cee6b48d +nvidia-nvjitlink-cu12==12.3.101 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:1b2e317e437433753530792f13eece58f0aec21a2b05903be7bffe58a606cbd1 \ + --hash=sha256:211a63e7b30a9d62f1a853e19928fbb1a750e3f17a13a3d1f98ff0ced19478dd \ + --hash=sha256:64335a8088e2b9d196ae8665430bc6a2b7e6ef2eb877a9c735c804bd4ff6467c +opt-einsum==3.3.0 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \ + --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549 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--hash=sha256:3cdc9fac9888d9eff11128ccfc4d4c10309163e372f312f7942ecee8df3d7824 \ + --hash=sha256:4144126dd3c2ece2d2dd1d5e0b39fb91adf1c46f660c2c5a2df7f80666989d5d \ + --hash=sha256:418850ceff364f51a6d81f32a1efd06a4e2d8df79a162e892685bc20c0aedd72 \ + --hash=sha256:5e250144eacdd5813dec0b18d91df0229197e3be402db42fd8e254ec90ea343d \ + --hash=sha256:5e5df0e8aaca124cc1ffca311786cc909810f3c046de090729cdafbf910082f8 \ + --hash=sha256:63e020a34b7168b5d0701a265c7c95b07984ff699d4894b20fa601282be88f20 \ + --hash=sha256:64c2e00fe508f50a42c50838df0d1f5be0dce5b4bef2373db8ad72b860211015 \ + --hash=sha256:6a406eee5acd3fd4875fa44c3972d29ae6d4329e7296e9219986fe6ff8e92ea0 \ + --hash=sha256:6cdd625dab2dff5374ff9c6792e8702fced8f0ea713ce959fc8f95499b5ecb2f \ + --hash=sha256:6e8c3757088cd7fce666f2a5e031b65d7898e210452380d2657c0fc0a7ec9932 \ + --hash=sha256:738e8bf4158e9c11cd051d89c2e453aeacf80ff8719ebc3251069015646554d0 \ + --hash=sha256:8e6a46e95c3ea8546055087d6fe52a1dcd56de5182365f1469106cc72cdf3307 \ + --hash=sha256:979ffc2b96f16595c219fb7a89597dd2fa00ac47a3b411fdcf8ae6821da52290 \ + --hash=sha256:9bf322ad14f907ad4660ca286e731e750546d54934a94cc5ba7efe8860c60ab4 \ + --hash=sha256:9d9d644e5448db9f32e2497487aca3bb2d3f92cbb50429a411ccda3f1f0968f3 \ + --hash=sha256:a5f37bcfe4e363e3bb8d36c5698fb829546956b2fe88951994387162a1859625 \ + --hash=sha256:a64df43fce2d8eeafd7db6e27447c56b3fa64842df847819684b3b1cc254c016 \ + --hash=sha256:a91840f9d45e7c01f151ba1815ae32b4c3c21e4290298772ee4b13314f729856 \ + --hash=sha256:b201a9405e250cf5770955863af2a236e382bdf5e4e086897ff03c41418c39da \ + --hash=sha256:b26ac807d8993b7e43081b4b7bbb0378b4e5f3e6525daf923c470bc176cc3327 \ + --hash=sha256:b8126d81ecb2c9e3554420834014ba343251f564c905ee3bef09d205b924b0c0 \ + --hash=sha256:b9d236bc1491a5e366921b95fecc05aa6ff55989a81f2242cd11121b82c24503 \ + --hash=sha256:bc17f9d085cd75a2de4f299a9c5e3c3520138eac7596061e581230b03862b44d \ + --hash=sha256:d666099a78f7bf31bf3a520d6871ddcae65484bcff095fc4271a391553b09c75 \ + --hash=sha256:e2d47bd28eff690eb2f7432e490265a291b04d6d346cf7b586491b2e2337bf97 \ + --hash=sha256:ee208f0bec6436085a9fa3ae98af54bfcb8822086894fc1ade283e80a6f11fd7 \ + --hash=sha256:f53951bfb640417558568284a8949d67bcdbf21fa0113107e20bd9403aa20b2b \ + --hash=sha256:fa9ed745d4cbac5e15df70339b30867ba033542b87f7b734f4cacae5ec73ba00 +orjson==3.10.3 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:0943a96b3fa09bee1afdfccc2cb236c9c64715afa375b2af296c73d91c23eab2 \ --hash=sha256:0a62f9968bab8a676a164263e485f30a0b748255ee2f4ae49a0224be95f4532b \ --hash=sha256:16bda83b5c61586f6f788333d3cf3ed19015e3b9019188c56983b5a299210eb5 \ @@ -586,10 +1046,10 @@ orjson==3.10.3 ; python_version >= "3.12" and python_version < "4.0" \ --hash=sha256:cf20465e74c6e17a104ecf01bf8cd3b7b252565b4ccee4548f18b012ff2f8069 \ --hash=sha256:d4a654ec1de8fdaae1d80d55cee65893cb06494e124681ab335218be6a0691e7 \ --hash=sha256:e852baafceff8da3c9defae29414cc8513a1586ad93e45f27b89a639c68e8176 -packaging==24.0 ; python_version >= "3.12" and python_version < "4.0" \ +packaging==24.0 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:2ddfb553fdf02fb784c234c7ba6ccc288296ceabec964ad2eae3777778130bc5 \ --hash=sha256:eb82c5e3e56209074766e6885bb04b8c38a0c015d0a30036ebe7ece34c9989e9 -pandas==2.2.2 ; python_version >= "3.12" and python_version < "4.0" \ +pandas==2.2.2 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:001910ad31abc7bf06f49dcc903755d2f7f3a9186c0c040b827e522e9cef0863 \ --hash=sha256:0ca6377b8fca51815f382bd0b697a0814c8bda55115678cbc94c30aacbb6eff2 \ --hash=sha256:0cace394b6ea70c01ca1595f839cf193df35d1575986e484ad35c4aeae7266c1 \ @@ -617,7 +1077,7 @@ pandas==2.2.2 ; python_version >= "3.12" and python_version < "4.0" \ --hash=sha256:ddf818e4e6c7c6f4f7c8a12709696d193976b591cc7dc50588d3d1a6b5dc8772 \ --hash=sha256:e9b79011ff7a0f4b1d6da6a61aa1aa604fb312d6647de5bad20013682d1429ce \ --hash=sha256:eee3a87076c0756de40b05c5e9a6069c035ba43e8dd71c379e68cab2c20f16ad -pillow==10.3.0 ; python_version >= "3.12" and python_version < "4.0" \ +pillow==10.3.0 ; python_version >= "3.11" and python_version < "4.0" \ --hash=sha256:048ad577748b9fa4a99a0548c64f2cb8d672d5bf2e643a739ac8faff1164238c \ --hash=sha256:048eeade4c33fdf7e08da40ef402e748df113fd0b4584e32c4af74fe78baaeb2 \ --hash=sha256:0ba26351b137ca4e0db0342d5d00d2e355eb29372c05afd544ebf47c0956ffeb \ @@ -687,111 +1147,154 @@ pillow==10.3.0 ; python_version >= "3.12" and python_version < "4.0" \ --hash=sha256:f0d0591a0aeaefdaf9a5e545e7485f89910c977087e7de2b6c388aec32011e9f \ --hash=sha256:fdcbb4068117dfd9ce0138d068ac512843c52295ed996ae6dd1faf537b6dbc27 \ --hash=sha256:ff61bfd9253c3915e6d41c651d5f962da23eda633cf02262990094a18a55371a -pydantic-core==2.18.2 ; python_version >= "3.12" and python_version < "4.0" \ - --hash=sha256:0098300eebb1c837271d3d1a2cd2911e7c11b396eac9661655ee524a7f10587b \ - 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