{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# `Xarray` 数据处理（2）\n", "--------------------------\n", "\n", "主讲人：李显祥\n", "\n", "大气科学学院" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "import xarray as xr\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "#xr.set_options(display_style=\"html\")\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "#air_temp = xr.tutorial.load_dataset('air_temperature')\n", "air_temp = xr.open_dataset('air_temperature.nc')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 7. 广播和对齐" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "\n", "* 可以直接在 `Dataset` 和 `DataArray` 对象上进行算术运算。\n", "* 标签会被保留，在运算中 dataArray 的维度会自动对齐。" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 广播\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "jupyter": { "outputs_hidden": false }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray (time: 3, space: 4)>\n",
       "array([[0, 1, 2, 3],\n",
       "       [1, 2, 3, 4],\n",
       "       [2, 3, 4, 5]])\n",
       "Coordinates:\n",
       "  * time     (time) int64 0 1 2\n",
       "  * space    (space) int64 0 1 2 3
" ], "text/plain": [ "\n", "array([[0, 1, 2, 3],\n", " [1, 2, 3, 4],\n", " [2, 3, 4, 5]])\n", "Coordinates:\n", " * time (time) int64 0 1 2\n", " * space (space) int64 0 1 2 3" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import xarray as xr\n", "\n", "a = xr.DataArray(np.arange(3), dims='time', \n", " coords={'time':np.arange(3)})\n", "b = xr.DataArray(np.arange(4), dims='space', \n", " coords={'space':np.arange(4)})\n", "a + b" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "思考题 1：\n", "\n", "b + a = ?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "思考题 2：\n", "\n", "```python\n", "anomaly = air_temp.air - air_temp.air.mean(dim='time')\n", "```\n", "\n", "这里使用了广播吗？" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "### 对齐\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "jupyter": { "outputs_hidden": false }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "(array([0, 1, 2]), array([1, 2, 3, 4, 5]))" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "atime = np.arange(3)\n", "btime = np.arange(5) + 1\n", "atime, btime" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "jupyter": { "outputs_hidden": false }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray (time: 2)>\n",
       "array([1, 3])\n",
       "Coordinates:\n",
       "  * time     (time) int64 1 2
" ], "text/plain": [ "\n", "array([1, 3])\n", "Coordinates:\n", " * time (time) int64 1 2" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = xr.DataArray(np.arange(3), dims='time', \n", " coords={'time':atime})\n", "b = xr.DataArray(np.arange(5), dims='time', \n", " coords={'time':btime})\n", "a + b" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "如果我们要保留所有坐标，怎么办呢？" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "array([nan, 1., 3., nan, nan, nan])\n", "Coordinates:\n", " * time (time) int64 0 1 2 3 4 5\n" ] } ], "source": [ "# 也可以保留所有坐标\n", "with xr.set_options(arithmetic_join=\"outer\"):\n", " print(a + b)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "注意：没有对应坐标的点被设置为 `nan` " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### 使用 `.where()`　进行屏蔽" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "jupyter": { "outputs_hidden": false }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "means = air_temp.air.mean(dim=['time'])\n", "means.where(means > 273.15).plot()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 8. 分组　Groupby" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "\n", "xarray 支持和 pandas 类似的分组（“group by”）操作来实现拆散－应用－合并： \n", "\n", "- 将数据根据一定的规则拆散为独立的组\n", "- 对每个组应用某种函数\n", "- 将这些组合并成一个单一的数据对象\n", "\n", "分组操作对于 Dataset 和 DataArray 对象都适用。 " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "\"split\"" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "jupyter": { "outputs_hidden": false }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'air' (season: 4)>\n",
       "array([273.6497 , 289.2049 , 278.9914 , 283.02814], dtype=float32)\n",
       "Coordinates:\n",
       "  * season   (season) object 'DJF' 'JJA' 'MAM' 'SON'
" ], "text/plain": [ "\n", "array([273.6497 , 289.2049 , 278.9914 , 283.02814], dtype=float32)\n", "Coordinates:\n", " * season (season) object 'DJF' 'JJA' 'MAM' 'SON'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "air_temp.air.mean(dim=['lat','lon']).groupby('time.season').mean()\n", "# air_temp.air.groupby('time.season').mean().mean(dim=['lat','lon'])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "clim = air_temp.air.groupby('time.month').mean('time')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "
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       "         295.63647]],\n",
       "\n",
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       "         244.44383],\n",
       "        [247.8001 , 247.75992, 247.47757, ..., 240.64706, 242.26633,\n",
       "         245.06662],\n",
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       "\n",
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       "         242.62823],\n",
       "        [249.73361, 250.16037, 250.48581, ..., 238.78964, 240.96469,\n",
       "         244.11626],\n",
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       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
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(time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", " month (time) int64 1 1 1 1 1 1 1 1 1 1 ... 12 12 12 12 12 12 12 12 12 12" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "anomalies" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "注意 xarray 自动改变了 clim 的 month 维度以适应 air 的 time 维度。这也是 xarray 的“广播”功能的优势之一：它会自动根据维度的名字来匹配，我们也不需要像 numpy 那样使用 reshape 来改变数组的形状或者插入一个长度为１的维度 。" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "(array([8.400000e+01, 9.000000e+02, 1.145500e+04, 9.761600e+04,\n", " 5.205310e+05, 2.676686e+06, 4.679230e+05, 8.459900e+04,\n", " 8.757000e+03, 4.490000e+02]),\n", " array([-33.740555 , -27.573364 , -21.406174 , -15.238983 , -9.071793 ,\n", " -2.904602 , 3.2625885, 9.429779 , 15.59697 , 21.76416 ,\n", " 27.93135 ], dtype=float32),\n", " )" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "anomalies.sel(time='2014-12-01T00:00:00').plot(center=0)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "注意这里 `anomalies`和上节课的 `anomalies` 的不同。" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### 重采样 Resample\n", "\n", "重采样将时间序列改变为新的时间间隔" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "tmin = air_temp.air.resample(time='1D').min() # Resample to one day '1D\n", "tmax = air_temp.air.resample(time='1D').max()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "(tmin.sel(time='2013-08-01')-273.15).plot()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "ds_extremes = xr.Dataset({'tmin': tmin, 'tmax': tmax})" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "jupyter": { "outputs_hidden": false }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:  (lat: 25, lon: 53, time: 730)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2013-01-01 2013-01-02 ... 2014-12-31\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
       "Data variables:\n",
       "    tmin     (time, lat, lon) float32 241.2 241.8 241.8 ... 295.7 295.5 295.2\n",
       "    tmax     (time, lat, lon) float32 242.3 242.7 243.5 ... 296.6 296.2 295.8
" ], "text/plain": [ "\n", "Dimensions: (lat: 25, lon: 53, time: 730)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2013-01-01 2013-01-02 ... 2014-12-31\n", " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", "Data variables:\n", " tmin (time, lat, lon) float32 241.2 241.8 241.8 ... 295.7 295.5 295.2\n", " tmax (time, lat, lon) float32 242.3 242.7 243.5 ... 296.6 296.2 295.8" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_extremes" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
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tmintmax
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2013-01-02243.199997244.099991
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" ], "text/plain": [ " tmin tmax\n", "lat lon time \n", "75.0 200.0 2013-01-01 241.199997 242.299988\n", " 2013-01-02 243.199997 244.099991\n", " 2013-01-03 242.599991 244.799988\n", " 2013-01-04 247.299988 252.389999\n", " 2013-01-05 242.799988 248.599991" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_extremes.to_dataframe().head()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "如果我们要计算季节平均温度，但是季节不是从12月开始算，而是从1月份开始算，该怎么办？" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "
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       "    precision:     2\n",
       "    GRIB_id:       11\n",
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       "    dataset:       NMC Reanalysis\n",
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", "progress": true, "scroll": true, "theme": "serif", "transition": "slide" } }, "nbformat": 4, "nbformat_minor": 2 }