standarddeviant/xticks_rotation_example.ipynb

## xticks_rotation_example.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e65decee-8b8f-4561-9f4f-e0d6a83fd028",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7e80c9cc-84cb-422c-a262-ee7ba5c96271",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "from matplotlib import pyplot as plt, dates as mdates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f1f3be24-923c-4c4f-93d0-6395de88b0e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mkfig(w=12, h=4, nrow=1, ncol=1, dpi=100, style='seaborn', **kwargs):\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.style.use(style)\n",
    "    return plt.subplots(\n",
    "        nrow, ncol, figsize=(w, h), dpi=dpi, \n",
    "        facecolor='lightgray', edgecolor='k', **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4fe18602-ca76-4dfa-9928-3a7090650f6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "now = datetime.utcnow()\n",
    "N = 10\n",
    "x = [now + timedelta(seconds=itm*20) for itm in range(N)]\n",
    "y = list(range(N))\n",
    "\n",
    "\n",
    "fig, ax = mkfig(8, 3)\n",
    "formatter = mdates.DateFormatter('%M:%S')\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "plt.setp( ax.xaxis.get_majorticklabels(), rotation=45 )\n",
    "ax.plot(x, y, 'C0o-')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48b96d4e-aed4-4fc7-ba38-a44487e86954",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"id": "e65decee-8b8f-4561-9f4f-e0d6a83fd028",
	"metadata": {},
	"outputs": [],
	"source": [
	"%matplotlib inline"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"id": "7e80c9cc-84cb-422c-a262-ee7ba5c96271",
	"metadata": {},
	"outputs": [],
	"source": [
	"from datetime import datetime, timedelta\n",
	"from matplotlib import pyplot as plt, dates as mdates"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"id": "f1f3be24-923c-4c4f-93d0-6395de88b0e3",
	"metadata": {},
	"outputs": [],
	"source": [
	"def mkfig(w=12, h=4, nrow=1, ncol=1, dpi=100, style='seaborn', **kwargs):\n",
	" import matplotlib.pyplot as plt\n",
	" plt.style.use(style)\n",
	" return plt.subplots(\n",
	" nrow, ncol, figsize=(w, h), dpi=dpi, \n",
	" facecolor='lightgray', edgecolor='k', **kwargs)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"id": "4fe18602-ca76-4dfa-9928-3a7090650f6c",
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<Figure size 800x300 with 1 Axes>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"now = datetime.utcnow()\n",
	"N = 10\n",
	"x = [now + timedelta(seconds=itm*20) for itm in range(N)]\n",
	"y = list(range(N))\n",
	"\n",
	"\n",
	"fig, ax = mkfig(8, 3)\n",
	"formatter = mdates.DateFormatter('%M:%S')\n",
	"ax.xaxis.set_major_formatter(formatter)\n",
	"plt.setp( ax.xaxis.get_majorticklabels(), rotation=45 )\n",
	"ax.plot(x, y, 'C0o-')\n",
	"fig.tight_layout()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"id": "48b96d4e-aed4-4fc7-ba38-a44487e86954",
	"metadata": {},
	"outputs": [],
	"source": []
	}
	],
	"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": "3.10.4"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 5
	}