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@kain88-de
Created June 23, 2017 11:19
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xtick behavior is inconsistent.
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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib as mpl\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this figure everything works as I would expect."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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A/SwzWwm0BVKKzPmmyFMWAu1CXKeIlGD55n2MTUgkdVsWl/drw0OX96RZPTVVk5KV6hi/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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcb184d9d10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig1, ax1 = plt.subplots()\n",
"ax1.plot([10, 100, 1000], [1,2,3])\n",
"ax1.set_xscale('log')\n",
"ax1.set_xticks([20, 200, 500])\n",
"ax1.get_xaxis().set_major_formatter(mpl.ticker.ScalarFormatter())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When I use `set` to change both properties at once the xticks aren't updated anymore"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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dBxifmMy3G/dwVtdYHr2qHx2aNwh3WSISART8ESa/oJC/fbmBpxesoU6tGjxx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\nIhJlFPwiIlFGwS8iEmUU/CIiUeb/AYDpIuMRl2e+AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcb185a0550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig1, ax1 = plt.subplots()\n",
"ax1.plot([10, 100, 1000], [1,2,3])\n",
"ax1.set(xscale='log', xticks=[20, 200, 500])\n",
"ax1.get_xaxis().set_major_formatter(mpl.ticker.ScalarFormatter())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"now I want to set the xticks equal to the number of points plotted on the x axes. This results that I cannot overwrite the ticks that matplotlib is generating. Setting the xticks manual now will draw them over the xticks matplotlib is setting."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Eh3ycNrORwEiADh06HNVC2jVN4p7B3QOHOerGV/oJunTgrhtfRyckRSTmhDoB\nZAPtyzxuB2ws28E5Nx4YD5Camlq9Yy2eVg3rces5XY42RhGRmBDqA8jfAl3NrKOZJQLDgJkhjkFE\nRAjxHoBzrsjMbgM+IPA10InOuSWhjEFERAJC/jsA59wsYFaolysiIofSdwhFRGKUEoCISIxSAhAR\niVFKACIiMUoJQEQkRplzR/Vbq5Awsxxg7TE8RQtgWw2FcySNgV0hWM6xCoc4OwDrQrSs2lrfmn7e\nmni+Y3mOUL1PpHqO5b1yonOuZVWdwjoBHCszS3POpYZgOeOdcyNreznHKhziNLOcYF6YNbSsWlnf\nmn7emni+Y3mOUL1PpHpC8V7RIaCa8Y7fAQQpHOLcGcJl1db61vTz1sTzhcO2lZpV6+8V7QFISGmb\nhB9tk/AUiu0S7XsA4/0OQA6jbRJ+tE3CU61vl6jeAxARkcpF+x6AiIhUIioSgJm1N7M5ZrbMzJaY\n2Z1e+4NmtsHM5nu3IX7HWpaZdTKzCWY23e9YjuRo4zSzNWa2yPvfp3ltzcxstpllen+b1k7URydS\ntkl1mNlEM9tqZovLtFW4HSzgGTPLMrOFZpbiX+TR6whj1jFtFzO73Mz+YWYzzGxQlYE45yL+BrQB\nUrz7DYGVQE/gQeA3VcxbD/gGWAAsAR46hjgmAluBxRVMGwysALKAe8tNm16NZcQB84B3wzlOr/8a\noEW5tidKnxe4F3i8knmbANOB5cAy4PRwXtdwvgFnASll/weVbQdgCPAfAlfvGwjM9Tv+SLsRuOjV\nHO91uwS4s4I+lY1Z5bfLQu/1u6b8dqni9dsUmFBlrH7/s2ppA8wALggyARhwnHc/wfvHDizXpxXQ\nsFxblwqe67A3mtceB3wHdAISCSSbnmWmVycB3A1MqSgBhFOcXv+KEsAKoI13vw2wopJ5XwZu8e4n\nAk3CeV3D/QYkc2gCqHA7AC8CP62on25B/68rHNzL9Tnk9euNWSMq2C7rvNdvbgXbZc0RXr9/K43h\nSLeoOARUlpklA/0IDOQAt3m7TBMrOtzgAvZ6DxO8W/kz42cDM8ysnreMnwPPVPBcnxHYUOX1B7Kc\nc6uccwXAVGDoUaxbO+Bi4KVKuoRFnGUXBXxoZunetZ4BWjvnNnlxbCLwRjiEmTUiMHBP8PoVOOfK\nfyc63NY10lS2HdoC68v0y/baJEjOuU3OuQzv/h4CewLl/4cHX7/emPUDYDiHb5eGBF6/8Ry6XXYD\nm8q/fr06zcXjAAADN0lEQVRDRY8D/ymN4UiiKgGY2XHAG8BdzrndwPNAZ6AvsIlAVqxovjgzm09g\nV2u2c25u2enOuX8D7wNTzWw4cBPwk2qEVuGbysyam9kLQD8zuy+I53kKuAcoqWhiGMVZ6kznXApw\nETDazM4Kcr5OQA7wTzObZ2YvmVmDsh3CcF2jhVXQpq8KHqUKPpACh7x+/03gcNE24MojPVW5x3WB\nzWUelybq24HzgavM7JdVxRfyK4LVFjNLIDD4T3bOvQngnNtSZvo/gHcrmtc5Vwz0NbMmwFtm1ts5\nt7hcnyfMbCpeUimz1xBUeBUv1m0HqtxIXvyXAFudc+lmdk5l/fyOs9yMG72/W83sLQKfureYWRvn\n3CYza0Mg6ZYXT2C393bn3Fwze5rA8dD7yz1/2KxrBKpsO2QTOIZdqh2wMeTRRYEKPpCWNxb4FYHj\n9e2dc3vNrLLtUsih26U5sKjc8znn3DNUsCdcmajYAzAzI3C4YJlz7sky7W3KdLsCWFx+3rK8wwyf\nEji5Un4ZPwR6A28BY6oZYk28qc4ELjOzNQR29841s0lhGGdpHA3MrGHpfWAQgf//TALHOvH+zqgk\njuwye2LTCSSE8ssIi3WNUJVth5nADd6hhIHArtJDEhK8ij6QlptuBMp3JACv8/3rt7Ltspty24VA\nEb9SR/f69fuESU3cCBw/cwTOmM/3bkOAVwlkyYXeP/awk1lAS7wTjEAS8DlwSbk+/Qh8G6UzgaQ5\nBXikkliSOfyEYzywCujI9ydseh3D+p5DxSeBwyZOAodxFvD9t6t+77U3Bz4GMr2/zSqZ/3Ogu3f/\nQeAv4bqu4X4DXiNwCLSQQOK7ubLtQGDP6DkCJ8gXAal+xx9pN+9/+Arw1BH6/Mwbs5Z749UObzsd\ntl1KX7/ltsuAmnj9+v7P8vsGnEzga5ULvX/yAxX0ORPoU+ZxAvDzCvod9kYrM20IgW8DfFc6GB5D\nzJUlgLCK8xjXsS+Q5m2Xt4Gm0bquukXXjUo+kJbrExavX5WCEBGJUVFxDkBERKpPCUBEJEYpAYiI\nxCglABGRGKUEICISo5QARERilBKAiEiMUgIQEYlRSgAiIjHq/wHS9/A5I9mQ7QAAAABJRU5ErkJg\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcb107316d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig1, ax1 = plt.subplots()\n",
"ax1.plot([25, 50, 100, 200], [1,200, 3000, 9000])\n",
"ax1.set_xscale('log')\n",
"ax1.set_xticks([25, 50, 100, 200])\n",
"ax1.get_xaxis().set_major_formatter(mpl.ticker.ScalarFormatter())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"navigate_num": "#000000",
"navigate_text": "#333333",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700",
"sidebar_border": "#EEEEEE",
"wrapper_background": "#FFFFFF"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "12px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false,
"widenNotebook": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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