diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..19c0dab --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ + +*.$$$ diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000..a8f7dbf --- /dev/null +++ b/LICENSE.md @@ -0,0 +1 @@ +Software code created by U.S. Government employees is not subject to copyright in the United States (17 U.S.C. §105). The United States/Department of Commerce reserve all rights to seek and obtain copyright protection in countries other than the United States for Software authored in its entirety by the Department of Commerce. To this end, the Department of Commerce hereby grants to Recipient a royalty-free, nonexclusive license to use, copy, and create derivative works of the Software outside of the United States. diff --git a/README.md b/README.md index b94b7a5..1dfad54 100644 --- a/README.md +++ b/README.md @@ -1 +1,124 @@ -# noaa-nmfs-brand-resources \ No newline at end of file +# NOAA-NMFS Brand Resources  + +logo + +A collection of color palettes, logos, and icons. +### Table of Contents +1. [Color Palettes](#color-palettes) + - [Regional Color Set](#regional-color-set) + - [Color Palettes - Primary](#color-palettes---primary) + - [Color Palettes - Gradient](#color-palettes---gradient) + - [NOAA Primary Palette](#noaa-primary-palette) + - [Grays and Extended Palettes](#grays-and-extended-palettes) +2. [Comprehensive Color Guide](#comprehensive-color-guide) +3. [Logos](#logos) + - [NOAA Logos](#noaa-logos) + - [NOAA Logo Icons](#noaa-logo-icons) +4. [NOAA Fisheries Color Chart](#noaa-fisheries-color-chart-2023) +5. [NMFS Color Palette Python Script](./script/nmfs-color-palette-script.ipynb) + +## Color Palettes +### Regional Color Set + +| Region | Color
Group | Base Color | Vivid Color| +|-----------------|--------| --------------------------|-----------------------| +| National | Oceans | ![](./center-swatches/national1.png) `#002364` | ![](./center-swatches/national2.png) `#0085CA` | +| West Coast |Waves | ![](./center-swatches/west1.png) `#00797F` | ![](./center-swatches/west2.png) `#1EBEC7` | +| Southeast | Seagrass | ![](./center-swatches/southeast1.png) `#4B8320` | ![](./center-swatches/southeast2.png) `#76BC21` | +| Mid-Atlantic | Urchin |![](./center-swatches/mid-atlantic1.png) `#5761C0` | ![](./center-swatches/mid-atlantic2.png) `#737BE6` | +| Alaska | Crustacean | ![](./center-swatches/alaska1.png) `#DB6015` | ![](./center-swatches/alaska2.png) `#FF8400` | +| Pacific Islands | Coral | ![](./center-swatches/pacific1.png) `#B71300` | ![](./center-swatches/pacific2.png) `#DB2207` | + +### Color Palettes - Primary +| National | West Coast | Southeast | Mid-Atlantic | Alaska | Pacific Islands | +|-----------------|------------|----------------|------------------|------------|-------------------| +| Oceans | Waves | Seagrass | Urchin | Crustacean | Coral | +| ![](./color-swatches/oceans1.png) `#001743` | ![](./color-swatches/waves1.png) `#005E5E` | ![](./color-swatches/seagrass1.png) `#365E17` |![](./color-swatches/urchin1.png) `#3B469A` | ![](./color-swatches/crustacean1.png) `#853B00` | ![](./color-swatches/coral1.png) `#901200` | +| ![](./color-swatches/oceans2.png) `#002364` | ![](./color-swatches/waves2.png) `#00797F` | ![](./color-swatches/seagrass2.png) `#4B8320` |![](./color-swatches/urchin2.png) `#5761C0` | ![](./color-swatches/crustacean2.png) `#DB6015` | ![](./color-swatches/coral2.png) `#B71300` | +| ![](./color-swatches/oceans3.png) `#003087` | ![](./color-swatches/waves3.png) `#1EBEC7` | ![](./color-swatches/seagrass3.png) `#76BC21` |![](./color-swatches/urchin3.png) `#737BE6` | ![](./color-swatches/crustacean3.png) `#FF8400` | ![](./color-swatches/coral3.png) `#DB2207` | +| ![](./color-swatches/oceans4.png) `#0085CA` | ![](./color-swatches/waves4.png) `#90DFE3` | ![](./color-swatches/seagrass4.png) `#B1DC6B` | ![](./color-swatches/urchin4.png) `#A8B8FF` | ![](./color-swatches/crustacean4.png) `#ffab38` | ![](./color-swatches/coral4.png) `#ff6c57` | +| ![](./color-swatches/oceans5.png) `#5EB6D9` | | | | | | +| ![](./color-swatches/oceans6.png) `#C6E6F0` | | | | | | + +### Color Palettes - Gradient +### Oceans +![Oceans Blue](./color-palette/oceans_palette.png) +### Waves +![Waves Teal](./color-palette/waves_palette.png) +### Seagrass +![Seagrass Green](./color-palette/seagrass_palette.png) +### Urchin +![Urchin Purple](./color-palette/urchin_palette.png) +### Crustacean +![Crustacean Orange](./color-palette/crustacean_palette.png) +### Coral +![Coral Red](./color-palette/coral_palette.png) + +### NOAA Primary Palette + +| Description | Swatch | +|----------------------|--------| +| NOAA Light Blue | ![](./center-swatches/noaa-light-blue.png) `#0093D0` | +| NOAA Dark Blue | ![](./center-swatches/noaa-dark-blue.png) `#0055A4` | +| Navy Blue Pantone | ![](./center-swatches/navy-blue-pantone.png) `#00467F` | + +### Grays and Extended Palettes + +| Description | Swatch | +|----------------------|--------| +| Neutral Gray | ![](./center-swatches/neutral-gray.png) `#646464` | +| Gray 85 | ![](./center-swatches/gray85.png) `#7B7B7B` | +| Gray 65 | ![](./center-swatches/gray65.png) `#9A9A9A` | +| Gray 30 | ![](./center-swatches/gray30.png) `#E8E8E8` | +| Gray 15 | ![](./center-swatches/gray15.png) `#D0D0D0` | +| Body Background | ![](./center-swatches/body-bg.png) `#FFFFFF` | +| Body Text Color | ![](./center-swatches/body-color.png) `#181818` | + +## Comprehensive Color Guide +![](./docs/NOAA_Fisheries_color_palette_2023.png) + +## Logos +##### NOAA-emblem-usage-and-licensing +- [https://www.noaa.gov/noaa-emblem-usage-and-licensing](https://www.noaa.gov/noaa-emblem-usage-and-licensing) +- [https://www.noaa.gov/office-of-communication/about-noaa-emblem-and-logo](https://www.noaa.gov/office-of-communication/about-noaa-emblem-and-logo) + +### NOAA Logos +| Logo | Preview | +|--------------------------------|----------------------------------------------------------------| +| NOAA Emblem Logo | NOAA Fisheries Logo Vertical | +| NOAA Logo | ![](./logos/NOAA_logo.png) | +| NOAA Fisheries Logo (Full) | ![](./logos/NOAA_Fisheries_full.jpg) | +| NOAA Fisheries Logo | ![](./logos/NOAA_Fisheries.png) | +| NOAA Fisheries Logo (360x127) | ![](./logos/NOAA_Fisheries_360x127.png) | +| NOAA Fisheries Logo (Vertical) | NOAA Fisheries Logo Vertical | +| NOAA Fisheries Open Science Logo | NOAA Fisheries Logo Vertical | + +## NOAA Logo Icons + +| Icon | Preview | +|------------------------------|-----------------------------------------------------------| +| favicon.ico | favicon | +| favicon-16x16.png | favicon-16x16 | +| favicon-32x32.png | favicon-32x32 | +| noaa-icon-57x57.png | noaa-icon-57x57 | +| noaa-icon-60x60.png | noaa-icon-60x60 | +| noaa-icon-72x72.png | noaa-icon-72x72 | +| noaa-icon-76x76.png | noaa-icon-76x76 | +| noaa-icon-114x114.png | noaa-icon-114x114 | +| noaa-icon-120x120.png | noaa-icon-120x120 | +| noaa-icon-144x144.png | noaa-icon-144x144 | +| noaa-icon-152x152.png | noaa-icon-152x152 | +| noaa-icon-180x180.png | noaa-icon-180x180 | + +### NOAA Fisheries Color Chart (2023) +[![](./docs/NOAA_Fisheries_color_chart_2023.png)](./docs/NOAA_Fisheries_color_chart_2023.pdf) +---------- +#### Disclaimer +This repository is a scientific product and is not official communication of the National Oceanic and Atmospheric Administration, or the United States Department of Commerce. All NOAA GitHub project content is provided on an ‘as is’ basis and the user assumes responsibility for its use. Any claims against the Department of Commerce or Department of Commerce bureaus stemming from the use of this GitHub project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government. + +##### License +See the [LICENSE.md](./LICENSE.md) for details + +##### NOAA-emblem-usage-and-licensing +- [https://www.noaa.gov/noaa-emblem-usage-and-licensing](https://www.noaa.gov/noaa-emblem-usage-and-licensing) +- [https://www.noaa.gov/office-of-communication/about-noaa-emblem-and-logo](https://www.noaa.gov/office-of-communication/about-noaa-emblem-and-logo) diff --git a/center-swatches/2019/alaska-deep.png b/center-swatches/2019/alaska-deep.png new file mode 100644 index 0000000..647bfd0 Binary files /dev/null and b/center-swatches/2019/alaska-deep.png differ diff --git a/center-swatches/2019/alaska-mid.png b/center-swatches/2019/alaska-mid.png new file mode 100644 index 0000000..790342a Binary files /dev/null and b/center-swatches/2019/alaska-mid.png differ diff --git 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import LinearSegmentedColormap\n", + "\n", + "# Define palettes grouped by themes\n", + "nmfs_palettes = {\n", + " \"oceans\": [\"#001743\", \"#002364\", \"#003087\", \"#0085CA\", \"#5EB6D9\", \"#C6E6F0\"],\n", + " \"waves\": [\"#005E5E\", \"#00797F\", \"#1EBEC7\", \"#90DFE3\"],\n", + " \"seagrass\": [\"#365E17\", \"#4B8320\", \"#76BC21\", \"#B1DC6B\"],\n", + " \"urchin\": [\"#3B469A\", \"#5761C0\", \"#737BE6\", \"#A8B8FF\"],\n", + " \"crustacean\": [\"#853B00\", \"#DB6015\", \"#FF8400\", \"#ffab38\"],\n", + " \"coral\": [\"#901200\", \"#b71300\", \"#db2207\", \"#ff6c57\"],\n", + "}\n", + "\n", + "def display_palette(colors, n=100):\n", + " \"\"\"Generates and displays a gradient based on a list of colors.\"\"\"\n", + " # Create a color map\n", + " cmap = LinearSegmentedColormap.from_list(\"Custom\", colors, N=n)\n", + " gradient = np.linspace(0, 1, 256)\n", + " gradient = np.vstack((gradient, gradient))\n", + " \n", + " # Plotting\n", + " fig, ax = plt.subplots(figsize=(6, 2))\n", + " ax.imshow(gradient, aspect='auto', cmap=cmap)\n", + " ax.set_axis_off()\n", + " plt.show()\n", + "\n", + "def display_nmfs_palette(name, n):\n", + " \"\"\"Displays the named palette with n interpolations.\"\"\"\n", + " if name in nmfs_palettes:\n", + " display_palette(nmfs_palettes[name], n)\n", + "\n", + "# Example usage\n", + "for theme in [\"oceans\", \"waves\", \"seagrass\", \"urchin\", \"crustacean\", \"coral\"]:\n", + " display_nmfs_palette(theme, 10)\n" + ] + }, + { + "cell_type": "markdown", + "id": "45d01866-3092-429d-87b7-9eb36af9142c", + "metadata": {}, + "source": [ + "# Create Color Palettes w/ Hex Codes" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "d379cdf4-5894-4699-a89f-5fa80956f01e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import LinearSegmentedColormap, hex2color, to_hex\n", + "\n", + "# Define NMFS palettes\n", + "nmfs_palettes = {\n", + " \"oceans\": [\"#001743\", \"#002364\", \"#003087\", \"#0085CA\", \"#5EB6D9\", \"#C6E6F0\"],\n", + " \"waves\": [\"#005E5E\", \"#00797F\", \"#1EBEC7\", \"#90DFE3\"],\n", + " \"seagrass\": [\"#365E17\", \"#4B8320\", \"#76BC21\", \"#B1DC6B\"],\n", + " \"urchin\": [\"#3B469A\", \"#5761C0\", \"#737BE6\", \"#A8B8FF\"],\n", + " \"crustacean\": [\"#853B00\", \"#DB6015\", \"#FF8400\", \"#ffab38\"],\n", + " \"coral\": [\"#901200\", \"#b71300\", \"#db2207\", \"#ff6c57\"]\n", + "}\n", + "\n", + "# Define the palette names and corresponding save paths\n", + "palettes_to_save = {\n", + " \"oceans\": \"oceans_palette.png\",\n", + " \"waves\": \"waves_palette.png\",\n", + " \"seagrass\": \"seagrass_palette.png\",\n", + " \"urchin\": \"urchin_palette.png\",\n", + " \"crustacean\": \"crustacean_palette.png\",\n", + " \"coral\": \"coral_palette.png\"\n", + "}\n", + "\n", + "def interpolate_colors(color_start, color_end, n=100):\n", + " \"\"\"\n", + " Interpolates n colors between two hex colors, inclusive of endpoints.\n", + " \n", + " Parameters:\n", + " color_start (str): Starting color in hex format.\n", + " color_end (str): Ending color in hex format.\n", + " n (int): Number of colors to interpolate.\n", + " \n", + " Returns:\n", + " list: List of interpolated hex colors.\n", + " \"\"\"\n", + " start_rgb = hex2color(color_start)\n", + " end_rgb = hex2color(color_end)\n", + " # Including endpoints\n", + " return [to_hex((start_rgb[0] * (1 - t) + end_rgb[0] * t,\n", + " start_rgb[1] * (1 - t) + end_rgb[1] * t,\n", + " start_rgb[2] * (1 - t) + end_rgb[2] * t)) for t in np.linspace(0, 1, n)]\n", + "\n", + "def display_palette_with_interpolations(palette_colors, num_colors=10, save_path=None):\n", + " \"\"\"\n", + " Displays a gradient and annotates n evenly spaced hex codes for a given palette.\n", + " \n", + " Parameters:\n", + " palette_colors (list): List of hex colors in the palette.\n", + " num_colors (int): Number of hex codes to annotate.\n", + " save_path (str): Path to save the displayed palette image.\n", + " \"\"\"\n", + " total_interpolated_colors = []\n", + " num_transitions = len(palette_colors) - 1\n", + " total_colors = 0\n", + " for i in range(num_transitions):\n", + " # Calculate the number of colors to interpolate between each pair of colors\n", + " colors_between = num_colors // num_transitions\n", + " if i == num_transitions - 1:\n", + " colors_between += num_colors % num_transitions # Add remaining colors to the last transition\n", + " # Interpolate colors\n", + " interpolated_colors = interpolate_colors(palette_colors[i], palette_colors[i+1], colors_between + 1)\n", + " total_interpolated_colors.extend(interpolated_colors[:-1]) # Exclude the last color to avoid duplication\n", + " total_colors += colors_between\n", + "\n", + " # Ensure that the total number of colors is exactly num_colors\n", + " if total_colors < num_colors:\n", + " last_color = interpolate_colors(palette_colors[-2], palette_colors[-1], 2)[1]\n", + " total_interpolated_colors.extend([last_color] * (num_colors - total_colors))\n", + "\n", + " # Create the gradient\n", + " cmap = LinearSegmentedColormap.from_list(\"Custom\", total_interpolated_colors, N=num_colors)\n", + " gradient = np.linspace(0, 1, num_colors).reshape(1, num_colors)\n", + " fig, ax = plt.subplots(figsize=(12, 1))\n", + " img = ax.imshow(gradient, aspect='auto', cmap=cmap)\n", + " ax.set_axis_off()\n", + "\n", + " # Annotate n hex codes evenly spaced along the gradient\n", + " spacing = len(total_interpolated_colors) // num_colors\n", + " for i in range(num_colors):\n", + " pos_x = i * spacing\n", + " pos_y = 0 # Center vertically in the bar\n", + " hex_code = total_interpolated_colors[pos_x]\n", + " ax.text(pos_x, pos_y, hex_code, color='white', ha='center', va='center', fontweight='bold')\n", + "\n", + " if save_path:\n", + " plt.savefig(save_path, bbox_inches='tight', pad_inches=0)\n", + " plt.show()\n", + "\n", + "# Loop through each palette, interpolate colors, and save it\n", + "for palette_name, save_path in palettes_to_save.items():\n", + " display_palette_with_interpolations(nmfs_palettes[palette_name], num_colors=10, save_path=save_path)\n" + ] + }, + { + "cell_type": "markdown", + "id": "076f0f60-8c99-4cb3-b5c2-1d22a28e96c8", + "metadata": {}, + "source": [ + "# Create Color Swatches" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "0225e33d-da94-43be-97df-7de5b2eb0309", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAADoAAAA6CAYAAADhu0ooAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAAe0lEQVR4nO3awQ3CQAwAQQ6liBRE2RSULkwJREKAtJl5++GVv14zM7cLuP97gV8RWiO0RmiN0Jrt7ODaH9/c4yNzPN/OXOaiQmuE1gitEVojtEZojdAaoTVCa4TWCK0RWiO0RmiN0BqhNUJrhNYs/7oxQmuE1gitEVrzAgrAC23qID+JAAAAAElFTkSuQmCC", + "text/plain": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from PIL import Image\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def create_color_swatch(hex_color, filename):\n", + " # Create an image with RGB mode\n", + " image = Image.new(\"RGB\", (20, 20), hex_color)\n", + " # Save the image\n", + " image.save(filename)\n", + " # Display the image with smaller size\n", + " plt.figure(figsize=(0.5, 0.5)) # Adjust the figure size here\n", + " plt.imshow(image)\n", + " plt.axis('off') # Turn off axis\n", + " plt.show()\n", + "\n", + "# Define all your colors grouped by category\n", + "nmfs_palettes = {\n", + " \"oceans\": [\"#001743\", \"#002364\", \"#003087\", \"#0085CA\", \"#5EB6D9\", \"#C6E6F0\"],\n", + " \"waves\": [\"#005E5E\", \"#00797F\", \"#1EBEC7\", \"#90DFE3\"],\n", + " \"seagrass\": [\"#365E17\", \"#4B8320\", \"#76BC21\", \"#B1DC6B\"],\n", + " \"urchin\": [\"#3B469A\", \"#5761C0\", \"#737BE6\", \"#A8B8FF\"],\n", + " \"crustacean\": [\"#853B00\", \"#DB6015\", \"#FF8400\", \"#ffab38\"],\n", + " \"coral\": [\"#901200\", \"#b71300\", \"#db2207\", \"#ff6c57\"],\n", + "}\n", + "\n", + "# Generate and save each color swatch\n", + "for palette_name, palette_colors in nmfs_palettes.items():\n", + " for index, hex_color in enumerate(palette_colors):\n", + " filename = f\"{palette_name}{index+1}.png\"\n", + " create_color_swatch(hex_color, filename)\n" + ] + }, + { + "cell_type": "markdown", + "id": "979d7bf4-7820-47a5-abc9-6629355027ee", + "metadata": {}, + "source": [ + "# Region/Center colors" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "9c08be99-3f88-41d2-bc32-692a8e57bf4b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAADoAAAA6CAYAAADhu0ooAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAAe0lEQVR4nO3awQ3CQAwAQQ6llLRGVbSWXkwJREKAtJl5++GVv14zM7cLuP97gV8RWiO0RmiN0Jrt7ODaH9/c4yNzPN/OXOaiQmuE1gitEVojtEZojdAaoTVCa4TWCK0RWiO0RmiN0BqhNUJrhNYs/7oxQmuE1gitEVrzAiYsC21Xy+GdAAAAAElFTkSuQmCC", + "text/plain": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from PIL import Image\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def create_color_swatch(hex_color, filename):\n", + " # Create an image with RGB mode\n", + " image = Image.new(\"RGB\", (20, 20), hex_color)\n", + " # Save the image\n", + " image.save(filename)\n", + " # Display the image with smaller size\n", + " plt.figure(figsize=(0.5, 0.5)) # Adjust the figure size here\n", + " plt.imshow(image)\n", + " plt.axis('off') # Turn off axis\n", + " plt.show()\n", + "\n", + "# Define all your colors grouped by category\n", + "nmfs_palettes = {\n", + " \"national\": [\"#002364\",\"#0085CA\"],\n", + " \"west-coast\": [\"#00797F\", \"#1EBEC7\"],\n", + " \"southeast\": [\"#4B8320\", \"#76BC21\"],\n", + " \"mid-atlantic\": [ \"#5761C0\", \"#737BE6\"],\n", + " \"alaska\": [\"#DB6015\", \"#FF8400\"],\n", + " \"pacific\": [ \"#b71300\", \"#db2207\"]\n", + "}\n", + "\n", + "# Generate and save each color swatch\n", + "for palette_name, palette_colors in nmfs_palettes.items():\n", + " for index, hex_color in enumerate(palette_colors):\n", + " filename = f\"{palette_name}{index+1}.png\"\n", + " create_color_swatch(hex_color, filename)\n" + ] + } + ], + "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": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}